Monitoring MonitoringMonitoring Trends in Bat Populations of rends in Bat Populations of rends in Bat Populations of

the United S the United Sthe United States and tates andtates and tates and Territories: erritories: erritories:erritories: erritories:

Problems and Prospects Problems and Prospects Problems and Prospects

Information and Technology Report 

USGS/BRD/ITR–2003-0003 

U.S. Department of the Interior 

U.S. Geological Survey 
To purchase this report, contact the National Technical Information Service, 5285 Port Royal 

Road, Springfield, VA 22161 (call toll free 1-800-553-6847), or the Defense Technical Information 

Center, 8725 Kingman Rd., Suite 0944, Fort Belvoir, VA 22060-6218. 

Cover photograph by Thomas J. O’Shea 

U.S. Geological Survey 
Monitoring MonitoringMonitoring Trends in Bat Populations rends in Bat Populations rends in Bat Populations

of the United S of the United States and tates andtates and Territories: erritories:erritories:

Problems and Prospects Problems and Prospects Problems and Prospects

Information and Technology Report 

USGS/BRD/ITR–2003-0003 

By 

T.J. O’Shea 

M.A. Bogan 

Editors 

U.S. Department of the Interior 

U.S. Geological Survey 
Suggested citation: 

O’Shea, T.J. and Bogan, M.A., eds., 2003, Monitoring trends in bat populations of the United States and territories: 

problems and prospects: U.S. Geological Survey, Biological Resources Discipline, Information and Technology 

Report, USGS/BRD/ITR--2003–0003, 274 p. 

ii 
Contents ContentsContents
 

Page 

Introduction Introduction (T.J. O’Shea and M.A. Bogan) ............................................................................................ 1
 

Bats of the United States and Territories .......................................................................................................................2
 

Problems and Prospects for Monitoring Trends in Bat Populations .............................................................................3
 

Acknowledgments ..........................................................................................................................................................5
 

References Cited .............................................................................................................................................................5
 

Part I. Part I. Assessing Status and tatus andtatus and Trends in Populations of Bats: rends in Populations of Bats: rends in Populations of Bats: An Overview An Overview

Censusing Bats: Challenges, Solutions, and Sampling Biases Censusing Bats: Challenges, Solutions, and Sampling Biases (T.H. Kunz) .................................. 9
 

Introduction ....................................................................................................................................................................9
 

Visual Counts of Roosting Bats ................................................................................................................................... 10
 

Evening Emergence Counts .......................................................................................................................................... 12
 

Evening Dispersal or “Flyout” Counts......................................................................................................................... 12
 

Disturbance Counts ...................................................................................................................................................... 13
 

Estimates Based on Mark-Recapture ............................................................................................................................ 13
 

Challenges and Recent Advances in Censusing Bats ................................................................................................. 13
 

Conclusions .................................................................................................................................................................. 16
 

Acknowledgments ........................................................................................................................................................ 17
 

References Cited ........................................................................................................................................................... 17
 

Estimates of Population Sizes in Summer Colonies of Brazilian Free-Tailed Bats ailed Batsailed Bats

 (Tadarida brasiliensis) (G.F. McCracken) .......................................................................................... 21
 

Introduction .................................................................................................................................................................. 21
 

Life-History Attributes ................................................................................................................................................. 22
 

Techniques Used for Assessing Abundance ............................................................................................................... 24
 

Counts at Exits ....................................................................................................................................................... 25
 

Combined Still and Motion Picture Photography ................................................................................................. 26
 

Extrapolation from Densities within Roosts .......................................................................................................... 26
 

Mark-Recapture ..................................................................................................................................................... 27
 

Indices of Abundance–Guano Deposition, and Bat Trapping ............................................................................. 27
 

Trends in Abundance ................................................................................................................................................... 27
 

Challenges and Prospects for the Future ..................................................................................................................... 28
 

Challenges and Prospects for Counting................................................................................................................ 28
 

Challenges and Prospects for Monitoring ............................................................................................................ 29
 

References Cited ........................................................................................................................................................... 29
 

Estimating Population Sizes of Hibernating Bats in Caves and Mines Estimating Population Sizes of Hibernating Bats in Caves and Mines (M.D. Tuttle) ............... 31
 

Introduction .................................................................................................................................................................. 31
 

Natural History of Bat Hibernation ............................................................................................................................... 32
 

Use of Hibernation Surveys in Status Monitoring ....................................................................................................... 33
 

Precautions for Underground Surveys ......................................................................................................................... 34
 

Procedures and Biases in Counting Hibernating Bats ................................................................................................. 34
 

Substrate Temperature ........................................................................................................................................... 35
 

Cave and Mine Complexity .................................................................................................................................... 37
 

Sampling Consistency ........................................................................................................................................... 37
 

Management Applications of Population Estimates During Hibernation ............................................................. 38
 

Conclusions .................................................................................................................................................................. 38
 

References Cited ........................................................................................................................................................... 39
 

iii 
 

Contents (continued) Contents (continued)
 Page 

Population PopulationPopulation Trends of Solitary Foliage-Roosting Bats ( rends of Solitary Foliage-Roosting Bats T.C. Carter, M.A. Menzel,

 and D.A. Saugey) ..................................................................................................................................... 41
 

Introduction .................................................................................................................................................................. 42
 

Historical Information ................................................................................................................................................... 42
 

Habitat Analysis ........................................................................................................................................................... 43
 

Historical Changes ................................................................................................................................................ 43
 

Potential Population Responses ........................................................................................................................... 44
 

Health Department Submissions .................................................................................................................................. 45
 

Lasiurines and Fire ....................................................................................................................................................... 45
 

Conclusions .................................................................................................................................................................. 45
 

References Cited ........................................................................................................................................................... 46
 

Count Methods and Population Count Methods and Population Trends in Pacific Island Flying Foxes rends in Pacific Island Flying Foxes (R.C.B. Utzurrum,

 G.J. Wiles, A.P. Brooke, and D.J. Worthington) ........................................................................................ 49
 

Introduction .................................................................................................................................................................. 50
 

Study Areas .................................................................................................................................................................. 50
 

American Samoa .................................................................................................................................................... 50
 

The Mariana Islands.............................................................................................................................................. 51
 

Monitoring Considerations .......................................................................................................................................... 51
 

Species Characteristics .......................................................................................................................................... 52
 

Island Characteristics ............................................................................................................................................ 52
 

Count Techniques ........................................................................................................................................................ 52
 

Direct Counts at Colonies ..................................................................................................................................... 53
 

Counts of Bats Dispersing from Colonies ............................................................................................................. 53
 

Station Counts of Non-Colonial Bats .................................................................................................................... 53
 

Variable Circular Plot Technique ............................................................................................................................ 54
 

Population Trends ........................................................................................................................................................ 54
 

American Samoa .................................................................................................................................................... 55
 

Commonwealth of the Northern Mariana Islands ................................................................................................. 57
 

Guam ...................................................................................................................................................................... 58
 

Conclusions .................................................................................................................................................................. 58
 

Acknowledgments ........................................................................................................................................................ 58
 

References Cited ........................................................................................................................................................... 58
 

Current S Current SCurrent Status of Pollinating Bats in Southwestern North tatus of Pollinating Bats in Southwestern North America ( America T.H. Fleming, 

T. Tibbitts, Y.Petryszyn, and V. Dalton) .................................................................................................... 63
 

Introduction .................................................................................................................................................................. 63
 

Methods of Population Assessment ............................................................................................................................ 64
 

Population Trends in the Three Species of Plant-Visiting Bats ................................................................................... 64
 

The Lesser Long-Nosed Bat ................................................................................................................................. 64
 

The Greater Long-Nosed Bat ................................................................................................................................ 65
 

The Mexican Long-Tongued Bat .......................................................................................................................... 66
 

Conclusions .................................................................................................................................................................. 67
 

Acknowledgments ........................................................................................................................................................ 67
 

References Cited ........................................................................................................................................................... 67
 

Western Crevice and Cavity-Roosting Bats estern Crevice and Cavity-Roosting Bats (M.A. Bogan, P.M. Cryan, E.W. Valdez,

 L.E. Ellison, and T.J. O’Shea)............................................................................................................................... 69
 

Introduction .................................................................................................................................................................. 70
 

Methods ....................................................................................................................................................................... 71
 

Results and Discussion ................................................................................................................................................ 71
 

Basic Life History of Crevice-Dwelling Bats ......................................................................................................... 71
 

iv 
Contents (continued) Contents (continued)
 Page 

Roosting Behavior of Crevice-Dwelling Bats ....................................................................................................... 73
 

Monitoring Crevice-Roosting Bats: Challenges and Opportunities ..................................................................... 73
 

Techniques Used for Assessing Abundance ........................................................................................................ 74
 

Summary and Recommendations.................................................................................................................................. 75
 

References Cited ........................................................................................................................................................... 75
 

Survey and Monitoring of Rare Bats in Bottomland Hardwood Forests Survey and Monitoring of Rare Bats in Bottomland Hardwood Forests (M.K. Clark) ............ 79
 

Introduction .................................................................................................................................................. 79
 

Background .................................................................................................................................................................. 79
 

Surveys: State-by-State Review ................................................................................................................................... 81
 

Virginia ................................................................................................................................................................... 81
 

North Carolina ....................................................................................................................................................... 82
 

South Carolina ....................................................................................................................................................... 82
 

Florida .................................................................................................................................................................... 83
 

Louisiana ............................................................................................................................................................... 84
 

Arkansas................................................................................................................................................................ 84
 

Texas ...................................................................................................................................................................... 84
 

Conclusions from the State-by-State Review ....................................................................................................... 84
 

Factors Affecting Survey and Monitoring Success ..................................................................................................... 85
 

Recommendations and Conclusions ............................................................................................................................ 87
 

Acknowledgments ........................................................................................................................................................ 89
 

References Cited ........................................................................................................................................................... 90
 

Bat Colonies in Buildings ( Bat Colonies in Buildings T.H. Kunz and D.S. Reynolds) .................................................................. 91
 

Introduction .................................................................................................................................................................. 91
 

Impact of Human Attitudes and Activities ................................................................................................................... 92
 

Factors Affecting Roost Preferences in Buildings ....................................................................................................... 92
 

Building Roosts in North America ................................................................................................................................ 96
 

Case Studies in North America .............................................................................................................................. 96
 

Colony Persistence ................................................................................................................................................ 97
 

Censusing and Inventorying Bats in Buildings ........................................................................................................... 98
 

Roosts for Research and Conservation ........................................................................................................................ 98
 

Acknowledgments ........................................................................................................................................................ 99
 

References Cited ........................................................................................................................................................... 99
 

The United Kingdom National Bat Monitoring Programme: The United Kingdom National Bat Monitoring Programme: The United Kingdom National Bat Monitoring Programme: Turning Conservation Goals

 into Tangible Results angible Results (A.L. Walsh, C.M.C. Catto, T.M. Hutson, S. Langton, and P.A. Racey) ..... 103
 

Introduction ................................................................................................................................................................ 104
 

Bat Populations in the U.K: Status and Trends .................................................................................................. 104
 

Bat Populations in the U.K.: Policy Background ................................................................................................ 105
 

Program Development ................................................................................................................................................ 106
 

National Bat Monitoring Programme Goals ......................................................................................................... 106
 

Scope, Target Species, and Principal Methods ................................................................................................... 106
 

Volunteer Network ............................................................................................................................................... 107
 

Statistical Design ................................................................................................................................................. 107
 

Program Methods ....................................................................................................................................................... 107
 

Counts at Maternity Colonies ............................................................................................................................. 107
 

Counts at Winter Hibernation Sites .................................................................................................................... 108
 

Summer Bat Detector Surveys ............................................................................................................................. 108
 

Power Analyses ................................................................................................................................................... 109
 

v 
 

Contents (continued) Contents (continued)
 

Page 

Population Decline Alert Levels .......................................................................................................................... 110
 

Program Results .......................................................................................................................................................... 110
 

Volunteers ............................................................................................................................................................ 110
 

Baseline Data ....................................................................................................................................................... 110
 

Power and Monitoring Targets ........................................................................................................................... 111
 

Discussion .................................................................................................................................................................. 112
 

Methodological Considerations.......................................................................................................................... 112
 

Statistical Monitoring Targets............................................................................................................................. 114
 

Program Sustainability ......................................................................................................................................... 115
 

Outlook for the Future ......................................................................................................................................... 116
 

Acknowledgments ...................................................................................................................................................... 116
 

References Cited ......................................................................................................................................................... 116
 

A Critical Look at National Monitoring Programs for Birds and Other Critical Look at National Monitoring Programs for Birds and Other Critical Look at National Monitoring Programs for Birds and Other Critical Look at National Monitoring Programs for Birds and Other Wildlife ildlife

 Species Species ( Species J.R. Sauer) ............................................................................................................................. 119
 

Introduction ................................................................................................................................................................ 119
 

Why Monitor? ............................................................................................................................................................ 120
 

Design Issues for Wildlife Surveys ............................................................................................................................ 120
 

Common Problems with Bird Surveys ........................................................................................................................ 121
 

Analysis of Survey Data ............................................................................................................................................ 122
 

Analysis of 2-Stage Surveys ............................................................................................................................... 122
 

Analysis of Index Surveys .................................................................................................................................. 122
 

What Can Be Done to Develop Monitoring Programs for Species that are Difficult to Survey? .............................. 123
 

Developing Reasonable Population Estimates Within Sample Units ................................................................. 123
 

Sampling Over Space ........................................................................................................................................... 124
 

Conclusions ................................................................................................................................................................ 125
 

A Final Comment ........................................................................................................................................................ 125
 

References Cited ......................................................................................................................................................... 125
 

Existing Data on Colonies of Bats in the United States: Summary and tates: Summary and Analysis of the U.S. Analysis of the U.S.

 Geological Survey’ Geological Survey’ Geological Survey’s Bat Population Database ( s Bat Population Database L.E. Elllison, T.J. O’Shea, M.A. Bogan, 

A.L. Everette, and D.M. Schneider) ................................................................................................................. 127
 

Introduction ................................................................................................................................................................ 128
 

Methods ..................................................................................................................................................................... 128
 

Database Design ................................................................................................................................................. 128
 

Data Acquisition.................................................................................................................................................. 129
 

Data Summaries ................................................................................................................................................... 129
 

Trend Analyses ................................................................................................................................................... 129
 

Terminology and Definitions ............................................................................................................................... 130
 

Results and Discussion .............................................................................................................................................. 130
 

Data Summaries ................................................................................................................................................... 130
 

Trend Analyses ................................................................................................................................................... 135
 

Data Summaries for Bats in the Pacific Island Territories ................................................................................... 138
 

Data Summaries for Bats in the Caribbean Territories ......................................................................................... 139
 

Data Summaries for Bats in the United States ..................................................................................................... 141
 

Conclusions ................................................................................................................................................................ 157
 

Acknowledgments ...................................................................................................................................................... 159
 

References Cited ......................................................................................................................................................... 160
 

Appendices 1–21: Results of analyses for trends in counts of bats at colony sites ................................................. 171
 

vi 
 

 

 

Contents (continued) Contents (continued)
 Page 

Part II. Part II. Report of the Report of the Workshop orkshop

Workshop Format ....................................................................................................................................................... 240
 

Monitoring Bat Populations on a Broad Scale Will Require Strong Commitment and Well-Planned

Working Group A. Analytical and Methodological Problems in Assessing Bat Numbers and Trends,
 

Colonial Bat Species Subgroup Issue 4. Developing a National Monitoring Program (See Also Working

Over-Dispersed Bats Subgroup Issue 2. Use of Echolocation-Monitoring to Determine Trends in Habitat

Over-Dispersed Bats Subgroup Issue 3. Use of Mist Netting Surveys to Evaluate Trends of Over-Dispersed
 

Southwestern Pollinator Subgroup Issue 1. Relative Value of Current Efforts to Monitor Leptonycteris

Southwestern Pollinator Subgroup Issue 2. Standardizing Monitoring Techniques for
 

Working Group B. Categorizing U.S. Bat Species or Species Groups, and Regions in Terms of Priorities

 for Establishing Population-Trend Monitoring Programs Based on Conservation Concerns, Roosting Habits,

Principal Conclusions and Recommendations ........................................................................................................... 240
 

The Natural History of Bats Poses Many Challenges to Population Monitoring .............................................. 240
 

Major Improvements are Needed in Methods of Estimating Numbers of Bats ................................................. 241
 

Objectives and Priorities of Bat Population Monitoring Need Careful Consideration ....................................... 242
 

Sampling Designs ............................................................................................................................................... 242
 

Information Exchange Among Bat Specialists Should be Enhanced .................................................................. 242
 

Their Basis, and Needed Research and Improvements in Techniques .................................................................... 243
 

Panel Discussion ........................................................................................................................................................ 243
 

Seminar ....................................................................................................................................................................... 244
 

Definitions and Monitoring Requirements ................................................................................................................. 245
 

Subgroup Report: Colonial Species ............................................................................................................................ 245
 

Colonial Bat Species Subgroup Issue 1. Timing of Monitoring Surveys ........................................................... 245
 

Colonial Bat Species Subgroup Issue 2. Estimation of Colony Size and Population Trends ............................. 246
 

Colonial Bat Species Subgroup Issue 3. Roost-Switching Between Colonies ................................................... 248
 

Group C Report) ................................................................................................................................................. 248
 

Subgroup Report: Over-Dispersed Bats: Foliage, Cavity, and Crevice Roosting Bats .............................................. 249
 

Over-Dispersed Bats Subgroup Issue 1. Estimation of Population Parameters of Over-Dispersed Bats ........... 249
 

Use by Over-Dispersed Bats ............................................................................................................................. 250
 

Bats ................................................................................................................................................................... 251
 

Over-Dispersed Bats Subgroup Issue 4. Spatial Scale Considerations in Monitoring Over-Dispersed Bats .... 252
 

Over-Dispersed Bats Subgroup Issue 5. Alternatives to Monitoring ................................................................. 252
 

Subgroup Report: Assessment of Population Size and Trends in Pacific Island Fruit Bats ...................................... 253
 

Pacific Island Fruit Bat Subgroup Issue 1. Difficulties in Censusing Pacific Island Fruit Bats .......................... 253
 

Subgroup Report: Improving Assessment of Numbers and Trends in Southwestern Pollinators ............................ 254
 

curasoae ............................................................................................................................................................ 254
 

Leptonycteris curasoae ..................................................................................................................................... 255
 

Southwestern Pollinator Subgroup Issue 3. Monitoring of Leptonycteris nivalis ............................................. 255
 

Southwestern Pollinator Subgroup Issue 4. Monitoring of Choeronycteris mexicana ....................................... 256
 

Southwestern Pollinator Subgroup Issue 5. Continuation of Baseline Monitoring Efforts................................ 257
 

Southwestern Pollinator Subgroup Issue 6. Sharing of Baseline and Monitoring Data for the Three Species . 257
 

Southwestern Pollinator Subgroup Issue 7. Funding for Monitoring and Research ......................................... 257
 

Southwestern Pollinator Subgroup Issue 8. Associated Research Activities .................................................... 258
 

Distribution, Threats, and Other Factors ............................................................................................................... 258
 

Distribution ......................................................................................................................................................... 258
 

Feeding Strategy ................................................................................................................................................. 259
 

Roosting Habits .................................................................................................................................................. 259
 

Population Status ................................................................................................................................................ 260
 

Threats ................................................................................................................................................................. 260
 

vii 
 

Contents (concluded) Contents (concluded)
 Page 

Reality .................................................................................................................................................................. 261
 

Working Group C. Existing Information and Programs to Monitor Bat Population Trends: Utility and Coverage

Working Group C Issue 3. Lack of a Unifying Mandate or Legislative Foundation for a National Bat
 

Concluding Comments ........................................................................................................................................ 261
 

of Current Efforts and Potential Expansion in Scale ............................................................................................ 261
 

Overview .............................................................................................................................................................. 261
 

Working Group C Issue 1. Lack of Organization of Existing Programs and Information .................................... 262
 

Working Group C Issue 2. Analytical Considerations for a National Bat Monitoring Program.......................... 263
 

Conservation Program ...................................................................................................................................... 266
 

Working Group C Issue 4. National Bat Awareness Week .................................................................................. 267
 

Working Group C Issue 5. Optimizing Information Obtained from Marked Bats ................................................ 268
 

References Cited in Working Group Reports ............................................................................................................. 269
 

Workshop Participants ............................................................................................................................................... 272
 

viii 
 

Introduction Introduction 
 Introduction

By 

Thomas J. O’Shea 

U.S. Geological Survey
 

Fort Collins Science Center
 

2150 Centre Avenue, Building C
 

Fort Collins, CO 80526-8118
 

and 

Michael A. Bogan 

U.S. Geological Survey
 

Fort Collins Science Center
 

Aridlands Field Station
 

Museum of Southwestern Biology
 

University of New Mexico
 

Albuquerque, NM 87131
 

Abstract. Bats are ecologically and economically important mammals. The life histories of bats (particularly their low 

reproductive rates and the need for some species to gather in large aggregations at limited numbers of roosting sites) make their 

populations vulnerable to declines. Many of the species of bats in the United States (U.S.) and territories are categorized as 

endangered or threatened, have been candidates for such categories, or are considered species of concern. The importance and 

vulnerability of bat populations makes monitoring trends in their populations a goal for their future management. However, 

scientifically rigorous monitoring of bat populations requires well-planned, statistically defensible efforts. This volume reports 

findings of an expert workshop held to examine the topic of monitoring populations of bats. The workshop participants included 

leading experts in sampling and analysis of wildlife populations, as well as experts in the biology and conservation of bats. Findings 

are reported in this volume under two sections. Part I of the report presents contributed papers that provide overviews of past and 

current efforts at monitoring trends in populations of bats in the U.S. and territories. These papers consider current techniques and 

problems, and summarize what is known about the status and trends in populations of selected groups of bats. The contributed 

papers in Part I also include a description of the monitoring program developed for bat populations in the United Kingdom, a 

critique of monitoring programs in wildlife in general with recommendations for survey and sampling strategies, and a compilation 

and analysis of existing data on trends in bats of the U.S. and territories. Efforts directed at monitoring bat populations are 

piecemeal and have shortcomings. In Part II of the report, the workshop participants provide critical analyses of these problems 

and develop recommendations for improving methods, defining objectives and priorities, gaining mandates, and enhancing information exchange to facilitate future efforts for monitoring trends in U.S. bat populations. 

Abstract AbstractAbstract Abstract

KeyWords: Bats, endangered species, population estimation, species of concern, status and trends. 

1 
2 INFORMATION AND TECHNOLOGY REPORT–2003-0003 

Bats of the United States Bats of the United States Bats of the United States

and Territories erritories erritorieserritories erritories

The bat (Order Chiroptera) fauna of the United States 

(U.S.) and territories includes about 60 species. There is 

growing concern about the population status of many 

species in this diverse group of mammals. There is also 

growing interest in the science underlying management 

and conservation of bats. In terms of biodiversity, there 

are about 45 species of bats in the U.S., including Hawaii 

(Pierson, 1998; but also see Kunz and Reynolds, 2003), 13 

species in Puerto Rico and the U.S. Virgin Islands [including at least 2 species in common with the mainland; 

Koopman (1989)], and 4 species in the Pacific island territories (Flannery, 1995). In addition to their contribution 

to biodiversity, bats can play critical roles in ecosystems 

and provide important economic benefits as consumers 

of agricultural and forest pest insects. Bats serve as pollinators and seed dispersers in deserts of the southwestern U.S. (see Fleming and others, 2003) and in tropical 

ecosystems in the territories [see, for example, Banack 

(1998); Gannon and Willig (1992)] where these functions 

can be of economic importance (Wiles and Fujita, 1992). 

In the mainland U.S., insectivorous bats consume large 

numbers of insect pests that could otherwise cost agriculture and forestry millions of dollars for control with 

insecticides (Whitaker, 1995; Pierson, 1998; McCracken 

and Westbrook, 2002). 

Bats have life history traits that make their populations vulnerable to factors that can result in population 

declines. Unlike many other small mammals, most species 

of bats give birth once annually, typically have a single 

young per birth, and usually do not reproduce until at 

least one year of age (Racey and Entwistle, 2000). Bats 

can have high maximum longevities (25 or more years, 

with up to 34 years recorded in one U.S. species; Barclay 

and Harder, 2003). Populations require high adult survival rates to offset low reproductive rates and prevent 

declines (Tuttle and Stevenson, 1982). Many U.S. bats 

gather in large aggregations or colonies to raise young in 

summer or to hibernate in winter, and seek roosts that 

provide critical microclimates for these purposes. Such 

specialized sites may not be in abundance (bats that require caves, for example, may find suitable conditions 

only at a small subset of caves in a given region), and 

large segments of regional populations of bats may be 

restricted to a few specific roosts during critical times of 

the year. Under such conditions, bats can be very vulnerable to disturbance and disruption by human activities, 

as well as to physical destruction of the roosts. Numerous instances of vandalism and killing of bats have been 

reported from underground bat roosts in the U.S., and 

loss of caves as roosting habitat has occurred as human 

populations and activities have grown with time [see, for 

example, Tuttle (1979)]. Bats in forested areas have also 

suffered from loss of old growth trees that historically 

provided large basal hollows used as roosts (Gellman 

and Zielinski, 1996) as well as a greater array of other 

roosting possibilities (Pierson, 1998). Transformation of 

various habitats across the landscape have likely also 

negatively impacted bat populations, not only through 

loss of roosts, but through changes in vegetation structure and availability of prey and water (Pierson, 1998; 

Hayes, 2003). In addition to deliberate killing and loss of 

habitat, insecticides and other environmental contaminants have impacted bat populations [for reviews see 

Clark (1981) and Clark and Shore (2001)]. Direct mortality 

of both young and adult bats through exposure to persistent pesticides in the food chain has been well documented in U.S. bats, including endangered species 

(Geluso and others, 1976; Clark, 2001; Clark and others, 

1978; O’Shea and Clark, 2002). 

Six species or subspecies of bats in the continental 

U.S. have been declared endangered under the U.S. Endangered Species Act of 1973 (ESA), as has the sole species of bat on Hawaii (Table 1). The Florida mastiff bat 

(Eumops glaucinus floridanus), found in the continental 

U.S. only in southern Florida, was categorized as a Category 1 candidate for listing as endangered in 1994 (U.S. 

Fish and Wildlife Service, 1994), but was subsequently 

judged not to warrant this status until additional information becomes available (U.S. Fish and Wildlife Service, 

1996a). 

Populations of bats of the U.S. territories have also 

suffered negative impacts that have resulted in federal 

protection or designation as candidates for protection. 

One species of flying fox (Pteropus tokudae) endemic to 

Guam was last observed in 1967 and is now extinct (Wiles, 

1987). The remaining species of flying fox on Guam (P. 

mariannus) is legally protected as endangered on that 

island (Table 1) and has been proposed for a legal status 

of threatened under the ESA in the neighboring Commonwealth of the Northern Mariana Islands (CNMI; U.S. 

Fish and Wildlife Service, 1998, 2001). The Pacific or 

Polynesian sheath-tailed bat (Emballonura semicaudata) 

is the only insectivorous bat in the Pacific island territories, but is now extinct on Guam and parts of the CNMI. 

OnAmerican Samoa and parts of the CNMI, the Polynesian 

sheath-tailed bat is a candidate species for which listing 

as endangered or threatened under ESA is deemed warranted but precluded due to other priorities (U.S. Fish 

and Wildlife Service, 2001). 

In addition to the species or subspecies noted above 

that are currently listed or proposed for listing under ESA, 

many of the other species of bats in the U.S. and territories 

were previously designated as Category 2 candidates for 

listing under the ESA, including 19 mainland taxa, 4 Pacific 
 

O’SHEA AND BOGAN 3 

Table 1. Species or subspecies of bats in the U.S. and territories designated as endangered under the U.S. Endangered 

Species Act (U.S. Fish and Wildlife Service, 1999).

 Species or subspecies of bat General distribution in the U.S. 

Corynorhinus townsendii ingens, Ozark big-eared bat Arkansas, Missouri, Oklahoma 

Corynorhinus townsendii virginianus, Virginia big-eared bat Kentucky, North Carolina, Virginia, West Virginia 

Lasiurus cinereus semotus, Hawaiian Hoary bat Hawaii 

Leptonycteris curasoae, Lesser long-nosed bat Arizona, New Mexico 

Leptonycteris nivalis, Greater long-nosed bat New Mexico, Texas 

Myotis grisescens, Gray bat Midwestern and southeastern states 

Myotis sodalis, Indiana bat Eastern and midwestern states 

Pteropus mariannus mariannus, Mariana fruit bat Guam (proposed threatened Aguijan, Tinian,

 Saipan) 

Pteropus tokudae, Little Mariana fruit bat Guam (extinct) 

island taxa, and 1 Caribbean species (Table 2; U.S. Fish 

and Wildlife Service, 1994). This designation raised 

interest on the part of natural resource agencies about 

the population status of these bats in areas under their 

management. Category 2 candidates were defined as “taxa 

for which information ...indicates that proposing to list as 

endangered or threatened is possibly appropriate, but 

for which persuasive data on biological vulnerability and 

threat are not currently available to support proposed 

rules” (U.S. Fish and Wildlife Service, 1994: 58984). 

Although none of these species received official 

protection under the ESA, the U.S. Fish and Wildlife 

Service published its intent “to monitor the status of all 

listing candidates to the fullest extent possible” (U.S. 

Fish and Wildlife Service, 1994: 58983). In 1996, the U.S. 

Fish and Wildlife Service discontinued the use of Category 

2 (U.S. Fish and Wildlife Service, 1996a,b), but instead 

noted that “the Service remains concerned about these 

species, but further biological research and field study 

are needed to resolve the conservation status of these 

taxa. Many species of concern will be found not to warrant 

listing...Others may be found to be in greater danger of 

extinction than some present candidate taxa” (U.S. Fish 

and Wildlife Service, 1996a: 7597). This prompted many 

resource managers to consider the former Category 2 bats 

as “species of concern”. Use of the former Category 2 list 

to designate such species was further clarified in a second 

notice (U.S. Fish and Wildlife Service, 1996b), which 

pointed out that some of the sensitive species 

classifications of other agencies and conservation 

organizations (which include many taxa of bats) are more 

inclusive of species deserving research and management 

attention than the earlier Category 2 list. 

Problems and Prospects for Problems and Prospects for 
 Problems and Prospects for

Monitoring MonitoringMonitoring Monitoring Trends in rends in 
 rends inrends in rends in

Bat Populations Bat Populations Bat Populations
 

Monitoring of trends in U.S. bat populations is a 

worthwhile objective given the prior stated intent to monitor the status of candidate taxa, the need to monitor populations of endangered species of bats to define and reach 

recovery goals, and the widespread interest in managing 

for bat conservation. Although the general objective is 

worthwhile, the means are uncertain. The scientific validity of past and current efforts directed at monitoring U.S. 

bat populations has not been critically examined, nor have 

there been any efforts to synthesize and summarize these 

efforts. As a step in this direction, a scientific workshop 

was convened in Estes Park, Colorado in September 1999. 

The workshop participants included experts in the biology of major groups of bats in the U.S. and territories, 

biologists experienced in monitoring populations of other 

organisms, and specialists in statistical aspects of wildlife population estimation. The workshop was sponsored 

by the National Fish and Wildlife Foundation, Bat Conservation International, the U.S. Forest Service, the Bureau of Land Management, and the U.S. Geological Survey 

(the Fort Collins Science Center, formerly Midcontinent 

Ecological Science Center; the Colorado Cooperative Fish 

and Wildlife Research Unit; and the Biological Resources 

Division’s Status and Trends program office). 

Four objectives were enumerated by the workshop 

steering committee: (1) to review knowledge about the 

status of populations of selected groups of bats in the 

U.S. and territories, including descriptions of how these 
 

 

 

4 INFORMATION AND TECHNOLOGY REPORT–2003-0003 

Table 2. Species or subspecies of bats in the U.S. and territories designated as Category 2 candidates for listing under 

the Endangered Species Act in 1994 (U.S. Fish and Wildlife Service, 1994). In 1996 the U.S. Fish and Wildlife Service 

eliminated Category 2 but considered all species of plants and animals formerly categorized as such to be species 

of concern, and noted that the number of such species would be greater than just those previously designated 

under Category 2 (U.S. Fish and Wildlife Service, 1996a, 1996b). Recognition of many taxa of bats as species of 

concern or in other sensitive species categories employed by federal and state agencies and conservation 

organizations has increased interest in monitoring bat populations. CNMI = Commonwealth of the Northern Mariana 

Islands. 

Species or subspecies of bat General distribution in U.S. 

Choeronycteris mexicana, Mexican long-tongued bat Arizona, New Mexico 

Corynorhinus rafinesquii, Rafinesque’s big-eared bat Southeastern and south-central U.S. 

Corynohinus townsendii pallescens, Pale Townsend’s big-eared bat Western U.S. (inland populations) 

Corynorhinus townsendii townsendii, Pacific Townsend’s big-eared bat Western U.S. coast 

Emballonura semicaudata, Polynesian sheath-tailed bat Pacific islands (several island groups) 

Euderma maculatum, Spotted bat Western U.S. 

Eumops perotis californicus, Greater western mastiff bat West coast and southwestern U.S. 

Eumops underwoodi, Underwood’s mastiff bat Arizona 

Idionycteris phyllotis, Allen’s big-eared bat Southwestern U.S. 

Macrotus californicus, California leaf-nosed bat Southwestern U.S. 

Myotis austroriparius, Southeastern myotis Southeastern and south-central U.S. 

Myotis ciliolabrum, Western small-footed myotis Western U.S. 

Myotis evotis, Long-eared myotis Western U.S. 

Myotis leibii, Eastern small-footed myotis Central and eastern U.S. 

Myotis lucifugus occultus, Occult little brown bat Southwestern U.S. 

Myotis thysanodes, Fringed myotis Western U.S. 

Myotis velifer, Cave myotis Southwestern U.S. 

Myotis volans, Long-legged myotis Western U.S. 

Myotis yumanensis, Yuma myotis Western U.S. 

Nyctinomops macrotis, Big free-tailed bat Southwestern U.S. 

Pteropus mariannus mariannus, Mariana fruit bat CNMI 

Pteropus mariannus paganensis, Pagan Mariana fruit bat CNMI (Pagan population) 

Pteropus samoensis samoensis, Samoan flying fox American Samoa 

Stenoderma rufum, Red fig-eating bat Puerto Rico, U.S. Virgin Islands 

trends were quantified; (2) to provide an overview of current methods and challenges involved in estimating population size and trends for major ecological groupings of 

U.S. bats; (3) to identify critical gaps in knowledge concerning bat population trends in the U.S. and territories; 

and (4) to determine, describe, and recommend scientific 

goals for future monitoring programs, including possible 

new and innovative approaches. The first two objectives 

were approached through a series of plenary presentations. The written contributions in Part I of this report are 

the subsequent, peer-reviewed outgrowths of these presentations. The second two objectives were met largely 

by discussions in working group break-out sessions that 

identified and dissected the problems associated with 

current monitoring efforts, and assessed the prospects 

for improving the monitoring of trends in bat populations. The written reports of these working groups appear as Part II of this report, which also summarizes the 

principal findings and conclusions, and describes the 

format employed in the workshop process. This part of 

the report has been available in electronic format since 

shortly after the workshop (O’Shea and Bogan, 2000). 

The summary information in Part I reflects the current 

state of the science in monitoring bat populations. The 

papers here and the working group reports in Part II reveal 

many shortcomings. Bats present numerous difficulties 

in assessing and monitoring trends in their populations. 

They are a heterogeneous group of mammals in terms of 

natural history and require the application of multiple 

approaches to monitoring. They are highly mobile, 
 5 

predominantly nocturnal, and generally roost in 

inaccessible or concealed situations. Basic natural history, 

distribution, roosting preferences, and colony locations 

are poorly known for many species. Major improvements 

are also needed in methods for estimating numbers of 

bats. Most attempts have relied heavily on use of indices 

at local sites. The use of such sampling approaches to 

estimate population size and trends in animals in general 

is inferior to more statistically defensible methods and 

can lead to incorrect inferences (Thompson and others, 

1998; Anderson, 2001). 

New techniques must be explored and modern 

statistical designs applied to improve the scientific basis 

for future conclusions about bat population trends. Major 

declines in some bat populations are supported by 

dramatic evidence linked to various causal factors, and 

bat conservation efforts are well founded. However, 

greater sophistication in monitoring is needed in the 

future to detect declining trends before they become 

catastrophic, or to quantify increasing trends as positive 

responses to management. Some suggestions regarding 

new technologies and sampling designs that should be 

explored to improve monitoring efforts are provided in 

Part II of this report and in some of the papers in Part I 

[see, for example, Kunz (2003)]. Similar deficiencies and 

shortcomings can be found in attempts to monitor 

populations of many other groups of wildlife. Sauer (2003) 

calls attention to some of the problems that continue to 

complicate the ability to make inferences about trends in 

well-known monitoring programs for other species, and 

offers a blueprint of considerations for developing 

statistically sound sampling schemes for monitoring 

wildlife populations. 

As detailed in Part II, advances in monitoring bat 

populations will also benefit from careful consideration 

of objectives and priorities. Implementation of monitoring programs may be possible for certain species and 

populations, but a more widely encompassing vision for 

monitoring U.S. bat populations will require a stronger 

underlying mandate and greater efforts at information 

exchange. Nonetheless, it is our hope that the recommendations contained in this report will improve the scientific bases of future efforts at monitoring U.S. bat 

populations, and that the assessments of existing data 

on the status of our nation’s bat populations will help 

encourage greater efforts towards their conservation and 

more effective monitoring. 

Acknowledgments Acknowledgments Acknowledgments

Numerous individuals and organizations were involved in this effort. Fellow members of the steering committee (Mary K. Clark, Laura E. Ellison, Joseph A. Kath, 

O’SHEA AND BOGAN

Thomas H. Kunz, Lyle Lewis, Kirk W. Navo, William E. 

Rainey, and Merlin D. Tuttle) provided critical input to 

planning the direction and focus of the workshop. 

David R. Anderson, Kenneth P. Burnham, John R. Sauer, 

and Gary C. White provided insightful discussions of 

issues and principles in sampling, estimation, and survey 

design and their applications in monitoring wildlife population trends. In addition to the above individuals, significant contributions to the scientific discussions and 

written reports of the working groups were made by Robert D. Berry, Anne P. Brooke, Patricia E. Brown, Timothy 

C. Carter, Norita A. Chaney,Alice L. Chung-MacCoubrey, 

Richard L. Clawson, Paul M. Cryan, Virginia M. Dalton, 

Steven G. Fancy, Theodore H. Fleming, Jeffrey A. Gore, 

John P. Hayes, Michael J. Herder, Joseph A. Kath, Allen 

Kurta, Gary F. McCracken, Rodrigo A. Medellin, Michael 

A. Menzel, Michael J. Rabe, Paul A. Racey, David A. 

Saugey, Ruth C. B. Utzurrum, Ernest W. Valdez, Allyson 

L. Walsh, Gary J. Wiles, Don E. Wilson, and Michael B. 

Wunder. Staff of the U.S. Geological Survey, the Colorado Cooperative Fish and Wildlife Research Unit, and 

the National Fish and Wildlife Foundation who provided 

important logistical support included Karen J. Adleman, 

Michele M. Banowetz, Gabriella Chavarria, Beverly Klein, 

and Pamela H. Leinweber. Marc Bosch (U.S. Forest Service), Fred Stabler (U.S. Bureau of Land Management), 

and Steven M. Walker (Bat Conservation International) 

facilitated attendance of some participants. Manuscript 

reviews were provided by J. Scott Altenbach, Robert M. 

R. Barclay, Brian S. Cade, Mary K. Clark, Richard L. 

Clawson, E. Lendell Cockrum, Michael J. Conroy, Denny 

G. Constantine, Paul M. Cryan, Robert R. Currie, Laura E. 

Ellison, Keith N. Geluso, Jeffrey A. Gore, Shauna 

Haymond, Thomas E. Morrell, Sara J. Oyler-McCance, 

Mark F. Robinson, Armando Rodriguez-Duran, David A. 

Saugey, Richard E. Sherwin, Ronnie M. Sidner, John O. 

Whitaker, Jr., Gary J. Wiles, Kenneth T. Wilkins, and Don 

E. Wilson. Production of the final report was facilitated 

by the technical support of Dora Medellin, Jennifer Shoemaker, Dale Crawford, and Delia Story. 

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Part I. Part I. Assessing S Assessing SAssessing Status and tatus andtatus and tatus and Trends in rends in 
 rends inrends in rends in

Populations of Bats: Populations of Bats: Populations of Bats: Populations of Bats: An Overview An Overview 
 An Overview
Censusing Bats: Challenges, Solutions, and Sampling Biases Censusing Bats: Challenges, Solutions, and Sampling Biases 
 Censusing Bats: Challenges, Solutions, and Sampling Biases

By 

Thomas H. Kunz 

Center for Ecology and Conservation Biology
 

Department of Biology
 

Boston University
 

Boston, MA 02215
 

Abstract. Historically, four methods have been used for censusing bats: roost counts, evening emergence counts, evening 

dispersal counts, and disturbance counts. Accurate and reliable estimates of the number of bats present in roosting situations are 

seldom feasible except for relatively small, gregarious species. In other situations, estimates of relative abundance may be the most 

appropriate data that can be obtained using a reasonable amount of time and effort. Mark-recapture methods can be used only if 

certain assumptions are met, including: (1) no differences in mortality between marked and unmarked animals; (2) marked and 

unmarked individuals have the same probability of being recaptured; (3) marks are not lost or overlooked; and (4) marked animals 

mix freely and randomly with the study population. Questions have been raised about the validity of this technique when applied 

to most bat species. There are numerous challenges associated with censusing bats, due largely to the wide range of roosting habits. 

Species that form large aggregations or that roost solitarily in cavities and crevices will be difficult to census. Censuses of 

hibernating bats must be designed to reduce disturbance and minimize the incidence of arousals. Recent technological advances offer 

promise for improving our ability to census bats reliably. 

Abstract Abstract

ords: Commuting bats, disturbance counts, emergence counts, foraging, hibernacula, mark-recapture, maternity roosts, 

roost counts. 

Key Words

Introduction IntroductionIntroduction

Methods suitable for censusing bats vary depending on the size and mobility of the species, the relative 

numbers of individuals present, access of investigators 

to roosting sites, and the availability and applicability of 

devices used for censusing (Mitchell-Jones, 1987; Kunz 

and Kurta, 1988; Thomas and LaVal, 1988; Frantz, 1989; 

Sabol and Hudson, 1995; Kunz and others, 1996a,b). A 

basic knowledge of the species to be censused is important before selecting one or more methods. This knowledge should include a general understanding of roosting 

habits, foraging behavior, seasonal movements, and how 

environmental factors may affect local abundance and 

distribution. Knowledge of temporal and spatial patterns 

associated with a particular species or population is also 

important. If devices such as binoculars, video cameras, 

night-vision devices, or ultrasonic detectors are used to 

extend the sensory capabilities of an observer while 

censusing, researchers must be thoroughly familiar with 

their operation, limitations, and potential biases (Kunz 

and others, 1996b). 

Roost sites that are relatively easy to locate and 

house relatively small to moderately sized colonies of 

bats (<1,000) offer the greatest potential for conducting a 

reliable census (e.g., Kunz and Anthony, 1996; Hoying 

and Kunz, 1998; O’Donnell, 2000). Species that roost alone 

or in small groups in foliage, rock crevices and tree cavities, and species that form large colonies pose the greatest challenges for censusing (Constantine, 1966; 

Humphrey, 1971; Sabol and Hudson, 1995). 

9 
10 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

Historically, four methods have been used for 

censusing bats (Kunz and others, 1996b). These include 

roost counts, evening emergence counts, evening dispersal counts, and disturbance counts. Accurate and reliable estimates of the number of bats present in roosting 

situations are seldom feasible except for relatively small, 

gregarious species. Many solitary bats are cryptic and 

thus difficult to locate. Highly gregarious species often 

require the coordinated efforts of several individuals or 

use of sophisticated imaging devices. Some species are 

highly susceptible to disturbance in roosting situations, 

and may abandon these sites in response to census efforts (Tuttle, 1979). In other situations, lack of observer 

access to a roost or low visibility may preclude making 

reliable estimates during evening emergences. 

In situations where direct access to the interior of a 

roost area is precluded or inadvisable (based on safety 

risks to observers), evening emergence counts offer the 

best alternative for censusing (Kunz and Anthony, 1996; 

Hoying and Kunz, 1998). In other situations, estimates of 

relative abundance may be the most appropriate data that 

can be obtained using a reasonable amount of time and 

effort. Disturbance counts may be of value in some limited situations (Racey, 1979), but in general they are not 

reliable and may increase mortality, especially of nonvolant young. 

Visual Counts of Roosting Bats isual Counts of Roosting Bats isual Counts of Roosting Bats 

In some roosting situations, where a species forms 

small, compact, clusters, direct visual counts can provide 

reliable estimates of colony size (Tuttle, 1979; Hoying 

and Kunz, 1998; Fig. 1A). In other situations, where the 

probability of disturbing adults in maternity roosts is high, 

the number of lactating females can be estimated by counting the number of non-volant young in the roost after 

adults have departed to feed (Kunz, 1974; Tuttle, 1979; 

Fig. 1B). This method requires knowledge of litter size 

and an assumption that all females have given birth. 

Direct visual counts of some gregarious 

megachiropterans may be possible in situations where 

the colonies are relatively small or where roost trees have 

been fully or partially defoliated, making it possible to 

see all or most of the bats (Fig. 2A). However, because 

colonies (camps) of many gregarious species are so large 

and diffuse or obscured by surrounding vegetation 

(Fig. 2B), a roost census may only yield estimates in orders of magnitude. For example, in very large colonies of 

pteropodids, incremental counts (e.g., 1–100, 100–1,000, 

1,000–10,000, and 10,000 plus) have been used for extrapolating to larger areas occupied by the colony (Vardon 

and Tidemann, 1997, 1999). If numbers of roosting bats 

cannot be assessed reliably, “flyout” or dispersal counts 

(described below) may be more appropriate. 

As with highly gregarious, tree-roosting 

megachiropterans, reliable visual censuses of large, active 

colonies of cave-roosting bats pose several challenges. 

Estimates of cluster density averaged from capture or 

Fig. 1. (A) Fig. 1 Small maternity colony of eastern pipistrelles 

(Pipistrellus subflavus) during late pregnancy, roosting 

near the ridgepole of a barn. The number of adult bats 

present in a colony can be censused by direct observation, 

assuming that all bats are visible. (B) Young cave myotis 

(Myotis velifer) roosting on the beam of a barn. The 

number of lactating females in a colony may be estimated 

by counting the number of non-volant young present in 

the roost after adults depart to feed. If the litter size is 

known for a given species being censused, and all females 

have produced young, the number of lactating females 

can be estimated. Photographs by T.H. Kunz. 
 

 KUNZ 11 

Fig. 2. (A) A Fig. 2 colony of giant flying foxes (Pteropus 

giganteus) roosting in a partly defoliated tree near Pune, 

India (Photograph by T.H. Kunz.). Bats may be censused 

from ground level, assuming that all bats can be observed. 

(B) A colony of gray-headed flying foxes (P. 

poliocephalus), roosting in the crown of a tree in eastern 

Australia that is relatively densely foliated (photograph 

by P. Birt, from Hall and Richards, 2000; copyrighted by 

Krieger Publishing Company, used with permission). 

Dense foliage and sensitivity of bats to disturbance may 

preclude direct censusing from ground level. Evening 

dispersal or exit counts of large colonies of Pteropus 

spp. are sometimes possible if observers position 

themselves with an unobstructed view of dispersing bats 

silhouetted against a clear sky. 

photographic methods (Fig. 3A) have been used to 

extrapolate to the total area occupied by roosting bats 

(Tuttle, 1979). However, this approach may cause 

Fig. 3. (A) Adult Brazilian free-tailed bats (Tadarida 

brasiliensis) roosting on the ceiling of a cave in southcentral Texas. The numbers of bats present in large cave 

colonies sometimes can be estimated by determining the 

roosting density of bats at representative sites throughout 

the cave, calculating an average roosting density, and 

then extrapolating this average density to the total cave 

substrate occupied by roosting bats. (B) Roosting 

densities of T. brasiliensis and other gregarious species 

often are not uniform. Irregular roost substrates and 

variable cluster densities of highly gregarious species, 

however, make it very difficult to make reliable estimates 

based on the extrapolation of cluster density to occupied 

areas of the cave substrate. Photographs by T.H. Kunz. 

considerable disturbance to the roosting bats, especially 

during maternity periods. Moreover, irregularities in roost 

substrates, variations in cluster density, and dispersion 
12 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

(Fig. 3B) will lead to biased estimates when cluster 

densities are extrapolated to the areas occupied by bats 

that are not uniformly distributed on the cave substrate. 

At best, the latter method will yield estimates of colony 

size in orders of magnitude. Estimates of colony size based 

on amounts and distribution of guano beneath roosting 

areas or stains deposited on roost substrates left by bats 

have been determined by extrapolating estimates of cluster 

density of roosting bats to the entire colonies (Tuttle, 

1979). However, this method has not been validated and 

promises to be highly unreliable. At best, stained areas 

on ceilings and areas covered by guano may be useful 

for evaluating areas of caves that were previously 

occupied by bats. 

Evening Emergence Counts Evening Emergence Counts Evening Emergence Counts

Evening emergence counts are the most effective for 

censusing bats that depart from buildings, caves, mines, 

and tree cavities (Speakman and others, 1992; Kunz and 

Anthony, 1996; Rydell and others, 1996; Jones and Rydell, 

1998; O’Donnell and Sedgeley, 1999). An emergence 

count may be the only suitable method for censusing 

bats that roost in physically hazardous or inaccessible 

places. In situations where roosts are unknown, a census 

can be accomplished by capturing bats while they are 

feeding or commuting, fitting selected individuals with 

radio transmitters, and tracking the bats to their roosts 

(Kurta and others, 1993; Vonhof, 1996; O’Donnell and 

Sedgeley, 1999). After roosts have been located it may be 

possible to conduct evening emergence counts. 

The number of observers needed to conduct an emergence count at caves, buildings, and tree cavities will 

depend on the size, configuration, and spatial distribution of the roost openings, the number of openings from 

which bats depart, and the relative numbers of bats 

present (Kunz and others, 1996b). Observers should be 

assigned specific exits or fields of view for which they are 

responsible, and should be present at their stations before the onset of emergence to ensure that the earliest 

departing bats are counted. 

Ideally, evening emergence counts should be made 

repeatedly to establish intra-colony variation in the number of bats present (Kunz and Anthony, 1996; Hoying 

and Kunz, 1998; Fig. 4). If time is limited, evening emergence counts should be conducted for at least three consecutive nights during periods of maximum adult colony 

size (late pregnancy and early lactation). For maternity 

colonies, evening emergence counts should be made 

when all adults are present but before young have become volant. More frequent censusing is advisable if time 

and personnel are available, and if there is interest in 

assessing seasonal changes in colony size associated 

Fig. 4. Little brown bats (Myotis lucifugus) and other 

species that form relatively small colonies can sometimes 

be counted as individuals emerge at dusk by silhouetting 

individuals against a clear sky. Nightly censuses of M. 

lucifugus at a small colony in southern New Hampshire 

show seasonal trends in numbers present (after Kunz 

and Anthony, 1996). Seasonal changes in numbers of bats 

present are indicated on the vertical (y) axis. If bats are 

counted at 5-min intervals, it may also be possible to 

establish seasonal patterns in nightly emergence. 

with the reproductive phenology of the colony. If a census is made after young begin to fly, it is important to 

acknowledge that newly volant individuals may depart 

later in the evening than adults (Kunz, 1974; Kunz and 

Anthony, 1996), thus making it necessary to extend the 

census period past the time when the emergence of adults 

has ceased. 

Evening Dispersal or Evening Dispersal or Evening Dispersal or

“Flyout” Counts “Flyout” Counts “Flyout” Counts

Evening dispersal or “flyout” counts are commonly 

used to estimate numbers of megachiropterans that roost 

in trees (Thomas and LaVal, 1988; Kunz and others, 1996b; 

Eby and others, 1999; Garnett and others, 1999; Vardon 

and others, 2001). As bats disperse from their diurnal 

roosts, they can be counted by observing their silhouettes 

against the sky. However, visibility of bats at the time of 

nightly dispersal and the experience of observers can 

greatly influence the reliability of the census. In general, 

reliability decreases with increasing numbers of bats, the 

distance of the observer from bats, and the light 

conditions at the time of emergence (Richards, 1990; Kunz 

and others, 1996b). Evening dispersal counts may be 

underestimated if some individuals delay departure from 

the roost (e.g., lactating females), depart after dark 
(young-of-the-year), or observers cannot adequately see 

individuals due to the density of surrounding foliage 

(Kunz and others, 1996b). 

Several observers should be positioned at least half 

an hour before nightfall at designated stations near a 

colony that is to be censused. Individuals or teams of 

individuals should be assigned to count bats as they 

depart within a pre-assigned arc surrounding the roost. 

Because decreasing light levels can reduce the ability of 

observers to see, use of light-gathering binoculars or lowlight level cameras may facilitate censusing in some situations. The size of nomadic colonies of megachiropterans 

can be assessed by making simultaneous censuses over 

large areas. To be successful, this approach requires large 

numbers of observers and strong coordination among 

teams of observers. 

Disturbance Counts Disturbance Counts Disturbance Counts

Disturbance counts have been used with limited success to census some large megachiropterans (Racey, 1979). 

Typically, this method requires one or more persons to 

enter a roost area (causing bats to take flight during the 

day) and make loud noises while other individuals count 

the bats. Assuming that all individuals in the colony take 

flight, individuals may be counted directly, photographed, 

or videotaped. The success of disturbance counts, however, depends on several factors, including the sensitivity of bats to the type of disturbance, the skill of the 

individuals causing the disturbance, whether all bats simultaneously take flight, and the position of the observers or photographers relative to the flying bats (Racey, 

1979). Because some megachiropterans habituate to extraneous noises, the reliability of this method is highly 

questionable. More importantly, because abandonment 

of adults and deaths of dependent young have been reported following such disturbances at roosts (Garnett 

and others, 1999), this method is not recommended. 

Estimates Based on Estimates Based on 


Mark-Recapture Mark-Recapture 
 Mark-Recapture

Mark-recapture methods can be used successfully 

only if certain assumptions are met. A major assumption 

of the mark-recapture method is that the population or 

colony to be censused is “closed”. A colony of adults 

may be considered “closed” only during a brief period in 

late pregnancy and early lactation when females show 

the strongest fidelity to their roosts and before young 

become volant. In principal, a population is considered 

closed when recruitment, mortality, emigration, or 

KUNZ 13 

immigration are non-existent during the census period. 

Some recent models have relaxed the latter assumption, 

but other assumptions of this method, including: (1) no 

differences in mortality between marked and unmarked 

animals; (2) marked and unmarked individuals have the 

same probability of being recaptured; (3) marks are not 

lost or overlooked; and (4) marked animals mix freely and 

randomly with the study population, raise questions about 

the validity of this technique when applied to most bat 

species. A detailed review of mark-recapture methods is 

beyond the scope of this chapter, but relevant discussion 

and evaluation of mark-recapture models can be found in 

White and others (1982) and Thompson and others 

(1998). For a review of published mark-recapture studies 

on bats, the reader is referred to Thomas and LaVal (1988). 

Unbiased capture and marking methods are essential for successful mark-recapture studies. Many species 

require different capture and marking methods (Barclay 

and Bell, 1988; Kunz and Kurta, 1988; Kunz and others, 

1996a). Some species fail to tolerate traditional marking 

methods, whereas other species cannot be captured repeatedly without causing severe disturbance to colonies. 

Use of passive integrated transponders (PIT tags) for 

marking bats holds considerable promise for mark-recapture studies. To date, PIT tagging has been used successfully in a handful of studies on bats with minimal 

injury or loss of tags (Kerth and König, 1996, 1999; Brooke, 

1997; Horn, 1998). Once animals are marked, potential biases associated with recapture, such as trap happiness 

or trap shyness can be ignored. Mark-recapture studies 

of bats that use PIT tags, however, do not obviate the 

need to satisfy other assumptions. 

Challenges and Recent Challenges and Recent Challenges and Recent Advances Advances 
 AdvancesAdvances Advances

in Censusing Bats in Censusing Bats 
 in Censusing Bats

There are numerous challenges associated with 

censusing bats, due largely to the wide range of roosting 

habits, including foliage, tree cavities, caves (and mines), 

rock crevices, and an assortment of human-made structures. Species that form large roosting aggregations in 

caves, mines, buildings, or similar structures, pose special challenges for censusing. It is usually impractical to 

visually count large numbers of bats as they emerge 

nightly from caves (Fig. 5). Solitary bats and small groups 

that roost in dense foliage, rock crevices, and tree cavities also pose challenges for conducting a reliable census (see also Carter and others, 2003). In the final analysis, 

methods used to census bats should be designed to minimize disturbance and sample biases. 

One of the greatest challenges for censusing bats is 

that nightly emergence periods may extend beyond the 
14 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

Fig. 5. Nightly emergence flight of Brazilian free-tailed 

bats (Tadarida brasiliensis) from a cave in south-central 

Texas. Large colonies are impossible to census during 

nightly emergences using direct, unaided observation. 

Photograph by T.H. Kunz. 

time that visible light can be relied on when using conventional methods. Moreover, some colonies are so large 

(estimated in the thousands and millions) that traditional 

methods of censusing are impractical. Infrared thermal 

imaging offers considerable promise for censusing bats 

at colonies that range from a few hundred to millions 

(Sabol and Hudson, 1995; Frank and others, 2003). An 

important advantage of infrared thermal imaging is that 

individual bats can be detected and counted independent of ambient (visible) light, because this technology 

detects heat given off by the bats. However, for this 

method to be successful, a clear sky or uniform artificial 

background is required. Emerging bats are detected in 

the field of view as digital “hot spots” (Fig. 6A). Subsequently, the uniform background is digitally subtracted 

from the field of view to highlight the bats for analysis. 

Rates of emergence and the numbers of bats emerging 

per unit time can then be computed electronically (Fig. 6B). 

An important advantage of infrared thermal imaging relative to other methods available for censusing bats is that 

it can yield reliable and consistent records independent 

of ambient light. In addition to the high cost, a principal 

limitation of this technology is that the camera and associated computer acquisition and analysis systems require 

an uninterrupted, stable, filtered source of electrical power 

(generator or battery) to obtain reliable results. 

Methods for censusing foliage, crevice and cavityroosting species (Fig. 7) are often limited to random 

searches or are confined to habitats based on previously 

established search images. In general, these approaches 

are labor intensive, biased, and unproductive. However, 

radiotelemetry is an invaluable technique for locating bats 

that roost in foliage and tree cavities (Barclay and others, 

Fig. 6. Infrared thermal imaging, based on computerized 

data processing, offers a powerful approach for remotely 

censusing large colonies of bats that emerge nightly. 

(A) This schematic diagram illustrates the camera position 

and field of view needed to reliably census bats as they 

emerge nightly from roosts. (B) The infrared images of 

bats can be distinguished against a uniform background. 

Images by T.H. Kunz and J.D. Frank. 

1988; Kurta and others, 1993; Betts, 1996; Kalcounis and 

Hecker, 1996; Sasse and Pekins, 1996; Vonhof, 1996; 

Menzel and others, 1998; O’Donnell, 2000). Once roost 

sites are located, a census based on emergence counts 

can be accomplished. 

Censuses of hibernating bats should be designed to 

reduce disturbance and minimize the incidence of arousals. 

Ideally, a hibernaculum should not be censused more often 

than once every 2 years. Species that roost in small, 

discrete clusters can often be counted individually as 

they are encountered (Fig. 8). However, for species that 
 KUNZ 15 

Fig. 7. (A) Small Fig. 7 family group of red bats (Lasiurus 

borealis) roosting in the understory of a deciduous tree. 

(B) Harem group of short-nosed fruit bats (Cynopterus 

brachyotis) roosting beneath a palm leaf that was modified 

into a tent. Some foliage roosting bats can be observed 

and counted directly, although roost locations usually 

must first be located using radiotelemetry, intensive visual 

searches (based on established search images of roosts), 

or by listening to echolocation calls. Photographs by 

T.H. Kunz. 

form large aggregations, numbers are best censused by 

estimating the cluster density at selected sites and 

extrapolating this value to the total area of the roost 

substrate covered by bats (Tuttle, 1979, 2003). Species 

Fig. 8. Small hibernatin Fig. 8. g cluster of cave myotis (Myotis 

velifer). Small clusters can be counted directly and large 

colonies sometimes can be estimated by extrapolating 

cluster density (assuming some average value) to areas 

of the roost substrate occupied by hibernating bats. To 

minimize disturbance, hibernating bats should not be 

censused more than once every two years. Photograph 

by T.H. Kunz. 

identifications based on visual assessment, rather than 

handling, are preferred in order to reduce disturbance. 

Personnel engaged in censusing hibernating bats 

should have experience with all types of caving techniques and knowledge of appropriate safety and rescue 

procedures. Considerations of size and complexity of the 

hibernaculum will dictate the number of personnel needed 

to conduct a census in caves and mines. Census teams 

should make every effort to minimize the amount of time 

conducting a census in order to reduce disturbance to 

the bats. 

Relative numbers of flying bats may be estimated in 

some habitats by deploying mist nets, harp traps, night 

vision devices, infrared cameras (Fig. 9), and ultrasonic 

detectors (for some echolocating species). In regions 

where echolocating bats commute and forage (and where 

trapping is impractical or impossible), ultrasonic bat detectors have proven useful (in some situations) for identifying bats to species (or genera), and for estimating 

their relative abundance (Hayes, 1999, but see also Working Group reports, this volume). 

Users of ultrasonic detectors should have a basic 

understanding of electronics, a thorough knowledge of 

echolocation and bioacoustics, experience in using 

modern methods of sound analysis (Kunz and others, 

1996a; Fenton, 2000), and an understanding of the 

limitations of these devices for monitoring bat 

populations. Quantitative methods for identifying 
16 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

Fig. 9. Infrared Fig. 9 thermal imaging can be used to assess 

the relative abundance and flight trajectories of foraging 

bats. Here, Brazilian free-tailed bats (Tadarida brasiliensis) 

are depicted as contrasting images against a uniform sky 

(image size of individual bats depends on distance from 

the camera). Flight trajectories are shown as a series of 

“wing prints” in the camera’s field of view. Image by T.H. 

Kunz and J.D. Frank. 

echolocating species in the field are preferable to 

qualitative methods (Hayes, 1999, 2000). The ability of 

bat detectors and associated analysis software to 

discriminate between closely related taxa, however, varies 

with the type and quality of the instruments and the 

experience and skill of the observer (Fenton, 2000; Jones 

and others, 2000). 

In general, learning to distinguish different bat species by their echolocation calls requires practice, good 

acoustic memory, and lots of patience (Hayes, 1999). 

Unique characteristics of echolocation calls, including 

frequency, changes in frequency with time, and pulse 

repetition rate may allow an observer to identify bats flying (feeding and commuting) in a given area [O’Farrell 

and Gannon, 1999; O’Farrell and others, 1999a, but see 

critique of Barclay (1999) and reply by O’Farrell and others (1999b)]. The most important attributes of a successful user of bat detectors are training and patience. 

Aided with spotting lights, night vision devices, and 

flash photography, species that have distinct wing shapes 

and flight patterns can be visually identified with some 

degree of confidence (Ahlen, 1980, 1981). With exception 

of a few diurnal species (Speakman, 1995; Thomson and 

others, 1998), it is very difficult to identify bats by sight 

while they are flying. Capture and recordings of 

echolocation calls should confirm species that are 

provisionally identified by sight. 

Conclusions ConclusionsConclusions

A combination of traditional census methods (roost 

counts and evening emergence counts) and recently 

developed remote censusing techniques offer the 

greatest promise for estimating colony sizes of most 

species. Where a given species forms relatively small 

colonies and roosts in open areas on walls and ceilings 

of caves, mines, and buildings, a direct count may be the 

most appropriate method as long as disturbance to 

roosting bats can be avoided or minimized. Disturbance 

to roosting bats can be minimized by using low lightlevel video cameras, night vision devices, or infrared 

thermal cameras and by reducing the number of visits to 

roost areas during the day. 

Traditional methods used to census bats include visual counts within roosts and counts made during 

evening emergences and dispersals. While these methods remain as standards for censusing bats, improved 

capture and marking methods and the use of remote detection devices have increased our ability to more accurately and reliably census both roosting and flying bats. 

Mark-recapture methods have generally proven unsuccessful for censusing bat colonies, largely because colonies (and bat populations as a whole) are not “closed”, 

and because other assumptions often cannot be met. 

Moreover, application of the latter method may be compromised by the fact that some bat colonies often fragment into smaller groups and some individuals may shift 

to alternate roost sites. 

For many bat species, evening emergence counts 

provide the most reliable method for estimating colony 

size, especially when observers cannot gain access to or 

choose not to enter roost areas. Emergence counts are 

most effective at small colonies, and where the emergence 

routes are known and can be monitored with an appropriate number of personnel. Limitations of conducting successful emergence counts include inadequate light and 

poor visibility. 

Infrared thermal imaging holds considerable promise 

for censusing bats as individuals emerge from roosts. 

One of the advantages of infrared thermal imaging is that 

individuals can be censused independent of the ambient 

light at the time of emergence. However, successful application of infrared thermal imaging requires a uniform 

background (clear sky or artificial backdrop) behind the 

emerging bats so that this background can be digitally 

subtracted from the images of emerging bats. 

Censusing hibernating bats is best achieved by 

counting each individual bat or group of bats as they are 

encountered, or by estimating the mean density of bats 
in several representative clusters, and extrapolating this 

density to the total area of the cave wall or ceiling that is 

covered by bats. Censuses of hibernating bats should be 

limited to one census period every other year. 

Methods used for censusing foraging and commuting bats are more problematic and generally limited to 

making relative estimates based on captures or remote 

sensing. Devices suitable for capture include mist nets 

and harp traps, whereas photography and videography 

using supplemental light sources, ultrasonic detectors, 

and infrared thermal cameras are valuable remote sensing 

devices for assessing relative abundance. 

Acknowledgments Acknowledgments Acknowledgments

I would like to thank T. O’Shea and M. Bogan for 

inviting me to contribute this paper, and to L. Ellison for 

help in organizing the Bat Monitoring Workshop held in 

Estes Park, Colorado, where this paper was presented. 

Many students and colleagues have worked closely with 

me over the years to develop and refine census methods 

for bats. In particular, I would like to thank E. Anthony, 

P. August, B. Fenton, J. Frank, A. Kurta, G. McCracken, 

and M. Tuttle, each of whom freely shared their ideas 

and expertise. The Kansas Biological Survey, Boston University, the Lubee Foundation, Inc., the National Geographic Society, and the National Science Foundation 

have supported my research on bats. 

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2001, Seasonal habitat use by flying foxes, Pteropus 

alecto and P. scapulatus (Megachiroptera) in monsoonal Australia: Journal of Zoology (London), 

vol. 253, p. 523–535. 

Vonhof, M.J., 1996, Roost-site preferences of big brown 

bats (Eptesicus fuscus) and silver-haired bats 

(Lasionycteris noctivagans) in the Pend d’Oreille Valley in southern British Columbia, in Barclay, R.M.R. 

and Brigham, R.M., eds., Bats and forests symposium, 

October 19–21, 1995, Victoria, British Columbia, 

Canada: Research Branch, British Columbia Ministry 

of Forests, Working Paper No. 23, p. 62–80. 

White, G.C., Anderson, D.R., Burnham, K.P., and Otis, D.L., 

1982, Capture-recapture and removal methods for 

sampling closed populations: Los Alamos National 

Laboratory, New Mexico, 235 p. 
Estimates of Population Sizes in Summer Colonies of Estimates of Population Sizes in Summer Colonies of
 Estimates of Population Sizes in Summer Colonies of

Brazilian Free-Tailed Bats ( ailed Bats (Tadarida brasiliensis

adarida brasiliensis) 


By 

Gary F. McCracken 

Department of Ecology and Evolutionary Biology
 

University of Tennessee
 

Knoxville, TN 37996-0810
 

Abstract. As recently as the 1950’s and early 1960’s, mid-summer colonies of adult Brazilian free-tailed bats in 17 caves in 

the southwestern United States (U.S.) were estimated to total about 150 million individuals. These estimates were made by several 

workers using different techniques that included exit counts, extrapolations from roosting densities, mark-recapture, and several 

indices of abundance. With notable exceptions, the procedures were poorly described, and the estimates were suspect at the time 

they were made. These estimates may have no bearing on current colony sizes, but numbers from the 1950’s (e.g., 20 million bats 

in Bracken Cave) continue to be quoted because they are the only numbers available. Of the various techniques, exit counts have 

met with greatest success. Exit counts using photography, videography, or thermal imaging offer the best promise for the future. 

Heat sensing to estimate numbers within roosts may have promise. Large-scale banding of bats should be eschewed. Due to the 

bats’ seasonal migration and movements between roosts, the temporal window of opportunity for counting and monitoring is from 

late June to mid-late July, when females nurse their pups and return daily to a single roost site. Prospects for monitoring are 

enhanced because a large proportion of the population aggregates at a limited number of known sites. The huge North American 

population of these bats appears to be in serious decline, but the magnitude of their decline is uncertain due to the absence of 

monitoring. 

Abstract Abstract

KeyWords: Exit counts, maternity colonies, migration, photography, roosting densities, Tadarida brasiliensis, thermal imaging. 

Introduction IntroductionIntroduction

The Brazilian free-tailed bat (Tadarida brasiliensis; 

Fig. 1) is one of the most abundant and conspicuous 

species of bats in North America. Two subspecies are 

recognized in the United States (U.S.). T. b. mexicana (the 

Mexican free-tailed bat, or guano bat) occupies regions 

south of southern Oregon, northern Nevada, Utah, Colorado, and southern Nebraska to the eastern limits of Oklahoma and Texas. T. b. cynocephala (LeConte’s free-tailed 

bat) ranges from eastern Texas and Oklahoma throughout the southeastern U.S., south of northern Arkansas, 

southern Tennessee, and North Carolina (Hall, 1981; 

Wilkins, 1989). In the southwestern U.S. and northern 

Mexico, the Mexican free-tailed bat forms the largest colonies that have been reported for any mammal, with the 

colony in Bracken Cave, Texas, estimated at 20 million 

individuals (Davis and others, 1962; Fig. 2). The historic 

warm season populations in each of over a dozen caves 

in the region have been reputed to number a million or 

more bats (Table 1; Fig. 3). 

Other than state wildlife laws, Brazilian free-tailed 

bats are under no government protection. However, since 

1985 they have been the only bat listed on Appendix I 

(Endangered Migratory Species) of The Convention on 

the Conservation of Migratory Species of Wild Animals 

(also known as the Bonn Convention or CMS (UNEP/ 

CMS, 1994). Brazilian free-tailed bats were given this listing because it was felt that they are a declining, migratory 

species of bat that would benefit from an international 

agreement for its conservation (A.M. Hutson, oral 

commun., 1999). 

In response to observations that several large 

colonies in both the U.S. and Mexico have suffered major 

declines (Cockrum, 1970; Altenbach and others, 1979; 

McCracken, 1986, 1989), the Programa para la 

Conservacion de los Murcielagos Migratorios de Mexico 

y Estados Unidos (PCMM) was established in 1994 by 

 21 
22 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Fig. 1. Brazilian free-tailed bat (Tadarida brasiliensis) in 

flight feeding on a corn earworm moth (Helicoverpa zea). 

Photograph courtesy of M.D.Tuttle. 

Bat Conservation International (BCI) and American and 

Mexican biologists (Walker, 1995).Although Brazilian freetailed bats are still abundant, their long life-span, low rate 

of reproduction, and habit of aggregating in a limited 

number of large colonies for reproduction raise serious 

concerns that populations of these bats may be in 

jeopardy (McCracken, 1986, 1989; Walker, 1995). The 

general lack of information on the status of Mexican freetailed bat colonies in both the U.S. and Mexico, and the 

need to monitor their population sizes are major concerns 

of the PCMM. 

Life-History Life-History Life-History Attributes Attributes AttributesAttributes Attributes

Brazilian free-tailed bats show substantial diversity 

in behavior. Populations of T. b. mexicana in the central 

and southwestern U.S. are typically migratory. They 

spend winter months in central and southern Mexico 

where they roost primarily in caves and man-made structures in colonies of a few hundred to many thousands 

(Davis and others, 1962; Villa-R. and Cockrum, 1962; 

Cockrum, 1969; Glass, 1982). Northward migration of up 

to 1,300 km occurs between February and April, and the 

largest colonies are found between May and October in 

caves in northern Mexico and the southwestern U.S. These 

Fig. 2. Brazilian free-tailed bats ( Fig. 2 Tadarida brasiliensis) roosting in Bracken Cave, Texas. Photograph taken in June 1982 

(courtesy of M.D.Tuttle). 
 

 

 

 

 MCCRACKEN 23 

Table 1. Historical able 1 estimates of colony sizes of Brazilian free-tailed bats in Texas, New Mexico, Arizona, and Oklahoma.

Colony Estimated size Year 	 Source 

Texas

Bracken Cave 20x106

 1957 Davis and others (1962) 

Goodrich Cave 14–18 x 106 1957 Davis and others (1962) 

Rucker Cave 12–14 x 106 1957 Davis and others (1962) 

Frio Cave 10x 106 1957 Davis and others (1962) 

Ney Cave 10 x106 1957 Davis and others (1962) 

Fern Cave 8–12 x 106 1957 Davis and others (1962) 

Devil’s Sink Hole 6–10 x 106 1957 Davis and others (1962) 

James River Cave 6x106 1957 Davis and others (1962) 

Davis Cave 4x106 1957 Davis and others (1962) 

Valdina Sink 4 x106 1957 Davis and others (1962) 

Abandoned 1987 Wahl (1993) 

Quarry Colony 4x 106 1989 Wahl (1993) 

Webb Cave <0.6 x 106 1957 Davis and others (1962) 

Wilson Cave <0.6 x 106 1957 Davis and others (1962) 

Y-O Ranch Cave <0.6 x 106 1957 Davis and others (1962) 

New Mexico New Mexico 

Carlsbad Caverns	 8.7 x 106 1936 Allison (1937) 

4 x 106	 1957 Constantine (1967) 

218,000	 1973 Altenbach and others (1979) 

Arizona Arizona Arizona 

Eagle Creek Cave	 25–50 x 106 1963 Cockrum (1969) 

30,000 1969 Cockrum (1970) 

Oklahoma Oklahoma OklahomaOklahoma

Vickery Cave 1 x 106 1969 Humphrey (1971) 

Vickery, Selman, 

Merrihew, and 

Connor Caves >3x106 1952 Glass (1982) 

Read Cave 0.5–1 x 106 1993 Elliott (1994) 

warm season colonies consist mostly of reproductive females and their offspring (Fig. 4). Other populations of T. 

b. mexicana in California and southern Oregon, and populations of T. b. cynocephala in the southeastern U.S., are 

year-round, non-migratory residents of those regions. 

Brazilian free-tailed bats in these populations hibernate 

during cold weather and roost in much smaller colonies, 

mostly in man-made structures. Most information regarding the ecology, behavior, and natural history of Brazilian 

free-tailed bats concerns the migratory populations of T. 

b. mexicana (e.g., Davis and others, 1962; Constantine, 

1967; Cockrum, 1969; Wilkins, 1989; McCracken and 

Gustin, 1991). This review focuses on published reports 

on the size of populations of T. b. mexicana in large caves 

in summer. 

Brazilian free-tailed bats are adapted to fly at high 

speed and to feed in habitats that are relatively 

uncluttered by vegetation. During a single night, 

individuals can fly 50 km or more from their roosts, often 

at altitudes of up to 3,000 m above ground (Williams and 
B 

24 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Fig. 3. Locations Fig. 3 of the major cave colonies of Brazilian 

free-tailed bats (Tadarida brasiliensis) in the United States 

that are referred to in the text. 

others, 1973). Their high energetic demands and huge 

numbers make them major predators of insects (Kunz and 

others, 1995). Foraging at high altitudes allows the bats 

to prey on migrating populations of insects, many of which 

are major agricultural pests (McCracken, 1996; Lee, 1999; 

Fig. 1). The large populations of these bats provide 

valuable ecosystem services, and this is an additional 

motivation for their conservation. 

Techniques Used for echniques Used for 
 echniques Used for

Assessing AssessingAssessing Assessing Abundance Abundance 
 AbundanceAbundance Abundance

Attempts to estimate the size of large colonies of 

Brazilian free-tailed bats have relied on: (1) counting bats 

as they exit from roosts (Fig. 5); (2) extrapolating colony 

size from roosting densities (Figs. 2 and 4); (3) mark and 

recapture of banded bats; and (4) various combinations 

of these techniques (Table 2). Counts at exits have been 

made from visual estimates, still photography, and a combination of still and motion picture photography (Table 2). 

Workers have also used the durations of exit flights and 

rates of fecal pellet deposition or guano production as 

indices of relative abundance (Table 2). 

None of these attempts to estimate the size of freetailed bat colonies should be called “monitoring.” In many 

cases, descriptions of the techniques used are not adequate to allow replicated counts and monitoring. In most 

cases where techniques have been described in detail, 

there have been no published accounts of efforts by subsequent researchers to replicate the counts of previous 

workers. Although there are multiple estimates from a few 

A 

B 

C 

Fig. 4. Brazilian free-tailed bats ( Fig. 4 Tadarida brasiliensis) 

roosting in Eckert James River Cave. (A) Close-up of pups 

on creche. (B) Pups on creche showing the creche at an 

intermediate distance. (C) Bats at a greater distance. Photographs taken in June 1983 (by G.F. McCracken). 
 

Fig. 5. Brazilian free-tailed bats ( Fig. 5 Tadarida brasiliensis) 

emerging from Frio Cave, Texas. Photograph taken in the 

early 1980’s. Photograph by G.F. McCracken. 

Table 2. Techniques used able 2 to estimate abundance of 

Brazilian free-tailed bats (Tadarida brasiliensis). 

Techniques Source 

Estimates of Number of Bats Estimates of Number of Bats

Exit counts - visual Allison (1937); Davis

 and others (1962) 

Exist counts - still Humphrey (1971)

 photography 

Exit counts - still and motion Altenbach and others 

motion picture photography (1979) 

Extrapolation from roost densities 

Davis and others

 (1962) 

Constantine (1967) 

Cockrum (1969) 

Mark - recapture (Lincoln Index) 

Constantine (1967) 

Exit flight durations 

Davis and others

 (1962) 

Constantine (1967) 

Rates of guano/fecal pellet accumulations 

Cagle (1950) 

Constantine (1967) 

MCCRACKEN 25 

of the same caves, the different estimates were obtained 

by different researchers using different techniques. Thus, 

although numbers obtained in different studies have been 

compared, there is no reason to suspect that the numbers 

are comparable. 

Counts at Exits 

Visual Estimates 

Allison’s (1937) count of 8,741,760 bats emerging 

from Carlsbad Caverns, New Mexico (Fig. 3), on June 16, 

1936, is the earliest published estimate of a colony’s size, 

and is the source of the number (8.7 million bats) that is 

widely cited as the historic population size of Carlsbad 

Caverns. Allison (1937) visually estimated the average 

flight speed in the column of bats that emerged from the 

Caverns at 20 mph, or 29 ft/sec. He also estimated the 

cross-sectional diameter of the column at 20 ft, and the 

density of bats in the column at 1 bat/ft3. From these 

numbers, Allison (1937) calculated a flow rate past a stationary observer of 9,106 bats/sec. He then multiplied 

this flow rate by 14 min (or 840 sec), the duration of the 

“full-force” exodus on the night of his study, and added 

an additional 3 min (or 180 sec) x 50% of this flow rate to 

account for the bats that left the cave before and after the 

full-force exodus. Allison (1937) described his measurements, assumptions, and calculations in detail, and thus, 

his procedures can be replicated. Allison (1937) also reported the suggestion of Bailey (1928) that still photography and motion pictures could be used to more 

accurately estimate the number of emerging bats. 

Still Photography 

Humphrey (1971) used still photography to estimate 

the numbers of bats emerging from Vickery Cave, Oklahoma (Fig. 3), on 12 evenings between May and September 1969. Taking advantage of a situation in which the 

emerging column of bats funneled through a narrow and 

confined canyon, Humphrey (1971) took 1/60 sec crosssectional photographs of the column each minute during 

the emergences. Flight speed was measured by the rate 

of passage of gaps in the column that were created by the 

minor disturbances of an assistant at the cave entrance. 

The numbers of bats on each photograph were counted 

using a microscope, each frame total was multiplied by 

the “number of frame columns per min” to give 1 min 

estimates and totaled for the duration of each emergence. 

“Frame columns per min” was not otherwise defined. 

Humphrey’s (1971) estimates ranged from less than 100,000 

bats in early May to a peak of 1.1 million in late August 

and September (Fig. 6). 
26 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Fig 6. Photographic exit count Fig 6 estimates of colony size of 

Brazilian free-tailed bats (Tadarida brasiliensis) at Vickery 

Cave, Oklahoma, May to October 1969 [after Humphrey 

(1971)]. 

Combined Still and Motion
 

Picture Photography
 

Emerging Mexican free-tailed bats do not pass an 

observer in unidirectional, uniform flow. Rather, the bats 

frequently change direction, and even reverse direction 

so that some bats return to the cave as others are leaving. 

As a consequence, some bats may be counted more than 

once. Also, different bats in the column pass a fixed point 

at different angles and velocities, complicating attempts 

to quantify flow rates. In the most sophisticated exit count 

reported to date, Altenbach and others (1979) attempted 

to account for the varying passage rates and flight directions of bats by combining high-speed motion picture 

photography with still photography. Working at Carlsbad 

Caverns in the summer of 1973, Altenbach and others 

(1979) observed the exodus over several weeks and identified a single, restricted space at the cave entrance 

through which bats exited. On September 1, 1973, still 

(flash) photographs were taken of the exit space every 30 

sec, for the first 44.5 min of the exodus, and still photos 

were taken at 60 sec intervals for the following 15 min, 

until all bats had left the cave. Simultaneously during the 

first 45 min of the exodus, 5 sec high-speed motion picture runs (200 frames/sec) were taken at 5 min intervals. 

Glossy, 8 x 10 in prints of the still photos were used to 

count bats and record their direction of flight. The motion picture runs were used to calibrate and correct for 

bats flying into versus out of the cave, and to compute 

the average replacement time that it took for a group of 

bats photographed at one instant to be replaced by a 

next group of bats. The numbers of bats passing through 

the exit space during each 30 sec (or 1 min) interval were 

then computed and summed for the full exodus. Using 

these procedures, Altenbach and others (1979) calculated 

that 218,153 bats exited from Carlsbad Caverns on September 1, 1973, about 5% of Allison’s (1937) estimate from 

June 1936. 

In a non-technical report, Geluso and others (1987) 

state that the population of bats at Carlsbad Caverns 

fluctuated between about 250,000 and 1 million bats in 

the decade following 1973. Geluso and others (1987) do 

not detail the estimation procedures or give dates. 

Extrapolation From Densities Within Roosts 

In the summer of 1957, Constantine (1967) estimated 

the size of the Carlsbad Caverns bat colony by extrapolating the density of bats roosting on the cave surface to 

the total cave surface area occupied by bats. Constantine 

(1967) counted an average roosting density of 300 adult 

bats/ft2 of cave surface area. He measured the total roosting surface area in the cave as units of “discs of light.” 

Cave ceiling height was measured from the length of a 

string attached to a helium-filled balloon, and the actual 

areas of the “discs” were measured over a range of ceiling heights. Extrapolating 300 bats/ft2

 x the measures of 

the cave surface occupied by the bats, Constantine (1967) 

estimated the numbers of bats occupying Carlsbad at 28-

day intervals between April and October 1957. These estimates showed an increasing population from the arrival 

of the bats in April to a peak estimate of approximately 4 

million bats in September (Fig. 7). Constantine (1967) recognized that irregularities in the cave surface were a source 

of measurement error. 

Many of the largest and most frequently cited 

estimates of sizes of colonies of Mexican free-tailed bats 

were obtained from extrapolations of roosting densities 

but, with the exception of Constantine (1967), descriptions 

of techniques are lacking. In 1957, Davis and others (1962) 

estimated that the mid-summer populations of free-tailed 

bats in 13 large caves in central Texas contained a 

combined total of over 100 million individuals (Table 1; 

Fig. 3). These estimates are the source of some of the 

best known and often quoted colony sizes: 20 million 

bats in Bracken Cave, 6 million in Eckert James River Cave, 

10 million in Frio Cave, and 10 million in Ney Cave (Table 

1). Davis and others (1962, p. 319) provide little detail on 

their procedures; “...Recorded figures are based on a 

combination of estimates -- density inside cave, capture 

rates in the trap, and density and duration of exodus 

flights”. Almost never cited with these numbers is Davis 

and others’ (1962) clearly stated circumspection with 

regard to the accuracy of these estimates, “....The 

precision of our estimates of abundance of guano bats is 

low as attested by the experiences of ourselves and others 

in trying to measure the number of bats present in a guano 

bat cave. Population figures we report are useful at most 

for comparing relative orders of magnitude.” 
Fig. 7. The esti Fig. 7 mated population size of Brazilian freetailed bats (Tadarida brasiliensis) at Carlsbad Caverns, 

New Mexico, April to October 1957. Solid line = estimates 

from extrapolation of densities within the roost; dashed 

line = Lincoln Index estimates from banding and recapture 

of bats [after Constantine (1967)]. 

The largest bat colony ever reported is the 25–50 

million individuals that were thought to have occupied 

Eagle Creek Cave, Arizona (Cockrum, 1969; Fig. 3). In a 

subsequent paper, Cockrum (1970) reported that as late 

as 1963, the bat population at Eagle Creek Cave exceeded 

25 million, but in June 1969, the population totaled only 

30,000 bats, resulting in the conclusion of a nearly 99.9% 

reduction (Cockrum, 1970). In his description of how the 

numbers were obtained, Cockrum (1969, p. 307) states only 

that “... Estimates are based on computation of area covered by roosting bats and numbers hanging in a number 

of sampled places”. 

Mark-Recapture 

Constantine (1967) used data from the capture and 

recapture of banded bats to obtain independent estimates 

of the size of the Carlsbad Caverns bat population. As 

part of his studies at Carlsbad Caverns, Constantine (1967) 

captured and recaptured bats at the entrance to the cave 

using an automatic bat-collecting device (harp trap). 

Captured bats were marked for individual recognition 

using numbered metal bands. During seven capture/ 

release efforts between April and October 1957, about 

1% of the bats that were captured and banded at the cave 

entrance were recaptured on one or more occasion (3,342 

males banded, 36 recaptured; 9,407 females banded, 102 

recaptured). From the numbers banded and recaptured 

between capture intervals, Constantine (1967) used the 

Lincoln Index to estimate the size of the bat population. 

These estimates showed a seasonal pattern that was 

similar to that obtained from extrapolating the densities 

of bats within roosts (Fig. 7); however, the Lincoln Index 

MCCRACKEN 27 

estimates of the largest, mid- to late-season population 

sizes were about 1 million bats less than the estimates 

obtained using the extrapolation technique (Fig. 7). 

Standard errors of the Lincoln Index estimates were not 

reported. 

Indices of Abundance – Guano
 

Deposition, and Bat Trapping
 

Similar seasonal trends in the size of the Carlsbad 

bat population were suggested by indices of bat abundance, i.e., the numbers of bats caught in the harp trap 

[measured in 10,000’s of bats/night; Constantine (1967)], 

and the rate of deposition of fecal pellets in trays that 

were set below the roosting bats (measured as 1,000’s of 

pellets/night). As mentioned earlier, Davis and others 

(1962) also cited capture rates in traps, as well as the 

density and duration of exodus flights as providing information on population sizes. However, Davis and others (1962) provided no other details. 

Trends in rends inrends in rends in Abundance AbundanceAbundance Abundance 

Cagle (1950) appears to be the first author to note a 

declining trend in abundance at a large colony of Mexican 

free-tailed bats. Ney Cave in Texas (Fig. 3) has been mined 

for guano since the Civil War. Cagle (1950) reported that 

20 to 30 tons of guano were still taken annually from Ney 

Cave in 1950. However, the guano miners were concerned 

at that time because the amount of guano available each 

year was decreasing, and, it appeared, so were the numbers 

of bats. Although numbers are not actually known, all 

evidence agrees with Cagle that Mexican free-tailed bat 

populations have been declining at Ney Cave and at other 

sites since the 1950’s, if not before. 

The downward trend of the Carlsbad Caverns population is the best documented of all colonies. Although 

there is little question of a major decline in the numbers of 

bats at Eagle Creek Cave, we cannot be certain that the 

decline was as dramatic as had been portrayed. The bestdocumented case of total colony abandonment in the 

U.S. is at Valdina Sinkhole in Texas (Wahl, 1993; Table 1; 

Fig. 3). Valdina Sinkhole was estimated to house 4 million 

bats in 1957, but was abandoned by the bats after the 

sinkhole was modified to increase the recharge of surface 

water to the Edwards Aquifer (Wahl, 1993). McCracken 

(1986) also reported the absence of free-tailed bats in 

July 1985 from U-Bar Cave in New Mexico (Fig. 3), a large 

cave that had supported a major guano mining operation 

at least into the 1960’s. 

These declines in the U.S. are mirrored, if not 

magnified, in Mexico. Five of nine reportedly large historic 
28 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

roost sites in Mexico that were surveyed in January and 

February 1985 contained no bats. These colonies 

appeared to have been abandoned because of disturbance 

caused by cave commercialization, quarrying, and 

vandalism (McCracken, 1986). In 1991, a subsequent 

survey of 10 major historic roost sites in Mexico (including 

4 of the roosts surveyed in 1985) revealed that two roosts 

had been abandoned and populations at six other roosts 

had declined (Walker, 1995; A. Moreno, oral commun., 

1999). One of the abandoned sites had contained a large 

population of free-tailed bats only 6 years earlier 

(McCracken, 1986), and showed obvious signs of recent 

vandalism (A. Moreno, oral commun., 1999). Although 

we lack any accurate estimate of the numbers of bats that 

have been lost from these roosts, the outcome of the 

declining trend is established for several roosts. Zero is 

an easy number to approximate. 

The decline of several colonies is documented, but it 

is not documented whether, and to what degree, these 

declines translate into an overall decline of the population of Mexican free-tailed bats in North America. The 

population size estimates from the 1950’s were largely 

suspect at the time they were made. Even if accurate estimates of current colony sizes were available for comparison, most of the older estimates did not provide the 

baseline to assess overall trends in abundance. Not knowing, we may fail to respond to the possibility that Carlsbad 

Caverns and Eagle Creek Cave are more the norm than 

the exception. 

Challenges and Prospects Challenges and Prospects Challenges and Prospects

for the Future for the Future for the Future 

Our first challenge is to obtain accurate, baseline 

counts of the numbers of Mexican free-tailed bats in the 

large colonies. This is essential to our second challenge, 

which is to establish a long-term program to monitor 

changes in the size of the North American population. 

Our prospects for the future are improved if we learn 

from the past, and a primary lesson from the past is the 

need to carefully document the procedures and 

assumptions in any counting effort. There are at least 

two reasons why this is essential. The first is so that 

replication and monitoring are possible. Although the 

counts of Allison (1937), Constantine (1967), Humphrey 

(1971), and Altenbach and others (1979) may be 

inaccurate, the techniques, measurements, and 

assumptions are described, and the counts could be 

replicated. In contrast, replication of the counts of Davis 

and others (1962) and Cockrum (1969) are impossible. The 

second reason is to allow for improvements on past 

techniques. Allison’s (1937), Humphrey’s (1971), and 

Altenbach and others (1979) techniques have not been 

replicated, but each subsequent effort was obviously built 

in part upon the previous efforts. 

Challenges and Prospects for Counting 

Both published efforts that used photography to 

count bats as they exited from a roost met with some 

success, and it is obvious that the potential of photography or videography has not been fully explored. Counts 

at exits using photography, videography, or more advanced imaging techniques appear to offer the best opportunity for accurately estimating the size of large 

colonies. In 1995, infrared (IR) video techniques that had 

been successful in counting exits of colonies of gray bats 

(Myotis grisescens) that numbered in the 1,000’s (Sabol 

and Hudson, 1995) were unsuccessful when applied to 

the much larger colonies of Mexican free-tailed bats at 

the Bracken and Eckert-James River Cave colonies (Bruce 

Sabol, oral commun., 1999). Currently, a new generation 

of high resolution IR videography is being tested to obtain counts of the numbers of individuals at the Bracken, 

Eckert James River, and Davis Cave colonies (T.H. Kunz, 

oral commun., 1999). Estimation of colony sizes using the 

new generation of IR videography may ultimately allow 

calibration and monitoring of colony sizes using the U.S. 

Weather Service’s NEXRAD WRS 88 Doppler radar facilities (T.H. Kunz, oral commun., 1999). The possible use 

of NEXRAD as a monitoring tool is exciting because information is collected daily as part of the NEXRAD’s 

normal operations. 

Other approaches appear to offer less promise. 

Counts based on extrapolation of roosting densities suffer from variable densities of roosting bats and irregularities in the cave roosting surfaces. The disturbance caused 

by observers who must go into roosts is an added problem. The use of heat sensing technology to calibrate numbers of bats on the cave surface might circumvent these 

problems, but to my knowledge these techniques have 

not been explored. Counts based on extrapolation of the 

density of pups in creches and the size of creches has 

not been reported (Fig. 4). Such counts of pups in creches 

may be useful for monitoring population trends. 

The use of conventional bat banding is a routine 

technique to monitor populations using mark-recapture 

estimators. Because of their rapid flight, injuries due to 

bands are likely in Mexican free-tailed bats. It is difficult 

to imagine any justification for large-scale banding efforts involving these bats. In the 1950’s and 1960’s a combined total of more than 430,000 Mexican free-tailed bats 

were banded at roosts in Texas, Oklahoma, New Mexico, 
 

Arizona, and Mexico. Researchers working over several 

years at these locations recaptured only about 1,300 

banded bats (McCracken and others, 1994). These banding efforts of the past would be difficult to duplicate or 

improve upon, and given the likely injury to large numbers of bats, there should be no attempts to do so. However, the ability to obtain reliable and accurate estimates 

of aspects of animal population dynamics using a new 

generation of mark-recapture statistical theory has advanced tremendously since Constantine’s (1967) use of 

the simple Lincoln Index. Development of non-harmful 

methods of marking bats could have promise for taking 

advantage of such advances. Simulations of sample size 

requirements are needed to determine if the level of effort 

necessary to mark a sufficient number of individuals is feasible for these large colonies of Brazilian free-tailed bats. 

Indices of abundance, such as rates of guano deposition (Cagle, 1950; Constantine, 1967) and the duration 

of exit flights from roosts (Davis and others, 1962) have 

the advantages of being non-invasive to the bats, simple, 

inexpensive, and repeatable. Indices could have value in 

monitoring population trends but do not inform us on 

numbers and are not a substitute for counts (see Working Group reports, this volume). Thus, indices are a poor 

substitute and last resort to be used only if counting is 

impossible. Because it should be possible to accurately 

estimate the numbers of Mexican free-tailed bats in colonies, efforts should be directed toward obtaining actual 

counts. 

In a recent effort, Bat Conservation International has 

established a program to monitor numbers of bats at fixed 

photopoints within key roosts (B. Keeley, oral commun., 

1999) as an index to track population trends. Photos taken 

annually at fixed points at about the same time of the year 

might provide an index of the relative numbers of bats 

within a roost. However, because of the extreme mobility 

of these migratory bats, the day-to-day variation in colony 

size can be enormous as large numbers of bats arrive, 

mingle in the roost, and depart. Because of these movements, S. Altenbach (oral commun., 1999) has noted up to 

5-fold, day-to-day increases and decreases in the sizes of 

the Brazilian free-tailed bat colonies in Carlsbad Caverns 

and Jornada Cave in New Mexico (Fig. 3). 

Challenges and Prospects for Monitoring 

Because Mexican free-tailed bats in the U.S. are migratory and seasonal in abundance, there are spatial and 

temporal components to their population dynamics that 

complicate monitoring efforts. Colony sizes fluctuate over 

the spring, summer, and autumn (Figs. 6 and 7) as bats 

MCCRACKEN 29 

arrive, give birth, depart, and move among roosts. The 

temporal window of opportunity for estimating and monitoring the size of the large maternity colonies occurs during the approximately 6-week period between parturition 

and weaning, when females return to the roost for the 

daily care of their pups (McCracken and Gustin, 1991). In 

Texas, over 90% of all females give birth during the first 

two weeks in June, pups begin to wean and fly in late July 

and early August, and females do not move between 

roosts at this time. Therefore, the window of opportunity 

for colony size estimates that can be compared from year 

to year is between late June and mid to late July. 

Apart from their seasonal movements, the banding 

studies of the 1950’s and 1960’s show that individuals 

can roost at different sites in different years (Cockrum, 

1969; Constantine, 1967; Glass, 1982). These movements, 

as well as studies of their population genetic structure 

(McCracken and others, 1994; McCracken and Gassel, 

1997), suggest that colonies from throughout North 

America belong to the same large population. Thus, from 

a monitoring perspective, estimating the size of a single 

colony may tell us little about the status of the total population. If the bats are less abundant or absent at one site, 

is it because the population has declined, or is it because 

those bats are someplace else? 

The issue of “what is a colony?” pertains to most, 

and perhaps all, species of bats. However, the situation 

with Mexican free-tailed bats is probably simpler than the 

situation with most other species of bats because a large 

proportion of their population is found at a very limited 

number of sites. Assuming that we know the locations of 

the major roost sites (Table 1), the status of the warm 

season colonies of Mexican free-tailed bats in the U.S. 

could be monitored by estimating colony sizes at only 

about a dozen major roost sites each year between late 

June and mid to late July. Given that adults typically survive 8 to 10 years, placing these 12 major roosts on a 2 or 

3 year rotation for counting might be adequate. 

References Cited References Cited References Cited

Allison, V.C., 1937, Evening bat flight from Carlsbad Caverns: Journal of Mammalogy, vol. 18, p. 80–82. 

Altenbach, J.S., Geluso, K.N., and Wilson, D.E., 1979, Population size of Tadarida brasiliensis at Carlsbad Caverns in 1973, in Genoways, H.H. and Baker, R.J., eds.: 

Biological investigations in the Guadalupe Mountains 

National Park, Texas, National Park Service Proceedings and Transactions, Series Number 4, p. 341–348. 

Bailey, V., 1928,Animal life of the Carlsbad Caverns:American Society of Mammalogists, Monograph No. 3, 195 p. 
 

30 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Cagle, F.R., 1950, A Texas colony of bats, Tadarida 

mexicana: Journal of Mammalogy, vol. 31, p. 400–402. 

Cockrum, E.L., 1969, Migration in the guano bat Tadarida 

brasiliensis: University of Kansas Museum of Natural 

History, Miscellaneous Publication, vol. 51, p. 303–336. 

Cockrum, E.L., 1970, Insecticides and guano bats: Ecology, 

vol. 51, p. 761–762. 

Constantine, D.G., 1967, Activity patterns of the Mexican 

free-tailed bat: University of New Mexico Publications 

in Biology, vol. 7, p. 1–79. 

Davis, R.B., Herreid, II, C.F., and Short, H.L., 1962, Mexican free-tailed bats in Texas: Ecological Monographs, 

vol. 32, p. 311–346. 

Elliott, W.R., 1994, Conservation of western Oklahoma 

bat caves: Oklahoma Underground, vol. 17, p. 44–53. 

Geluso, K.N., Altenbach, J.S., and Kerbo, R.C., 1987, Bats 

of Carlsbad Caverns National Park: Carlsbad Caverns 

Natural History Association, Carlsbad, NM, 33 p. 

Glass, B.P., 1982, Seasonal movements of Mexican freetailed bats Tadarida brasiliensis mexicana banded in 

the Great Plains: Southwestern Naturalist, vol. 27, 

p. 127–133. 

Hall, E.R., 1981, The Mammals of North America, (2nd 

ed.): New York, John Wiley & Sons, vol. 1, p. 1– 

600 + 90. 

Humphrey, S.R., 1971, Photographic estimation of population size of the Mexican free-tailed bat, Tadarida 

brasiliensis: American Midland Naturalist, vol. 86, 

p. 220–223. 

Kunz, T.H., Whitaker, J.O., Jr., and Wadanoli, M.D., 1995. 

Dietary energetics of the insectivorous Mexican freetailed bat (Tadarida brasiliensis) during pregnancy and 

lactation: Oecologia, 101, p. 407–415. 

Lee, Y.F., 1999, Resource use and foraging activity of Mexican free-tailed bats, Tadarida brasiliensis mexicana 

(Molossidae): University of Tennessee, Ph.D. dissertation, 130 p. 

McCracken, G.F., 1986, Why are we losing our Mexican 

free-tailed bats? Bats: Newsletter of Bat Conservation 

International vol. 3, p. 1–4, (Reprinted, 1987, Endangered Species Technical Bulletin, vol. 4, p. 1–4). 

McCracken, G.F., 1989, Cave conservation: Special 

problems with bats: The National Speleological Society 

Bulletin, vol. 51, p. 47–51. 

McCracken, G.F., 1996, Bats aloft: A study of high-altitude 

feeding: Bats, Newsletter of Bat Conservation 

International, vol. 14, p. 7–10. 

McCracken, G.F., and Gustin, M.F., 1991, Nursing behavior in Mexican free-tailed bat maternity colonies: Ethology vol. 89, p. 305–321. 

McCracken, G.F., and Gassel, M.F., 1997, Genetic structure of 

migratory and nonmigratory populations of Brazilian freetailed bats: Journal of Mammalogy, vol. 78, p. 348–357. 

McCracken, G.F., McCracken, M.K., and Vawter, A.T., 1994. 

Genetic structure in migratory populations of the bat 

Tadarida brasiliensis mexicana: Journal of Mammalogy, vol. 75, p. 500–514. 

Sabol, B.M., and Hudson, M.K., 1995, Technique using 

thermal infrared imaging for estimating populations of 

gray bats: Journal of Mammalogy, vol. 76, p. 1242–1248. 

Tuttle, M.D., 1994, The lives of Mexican free-tailed bats: 

Bats, Newsletter of Bat Conservation International, 

vol. 12, p. 6–14. 

UNEP/CMS, 1994, Appendices I and II of the Convention 

on the Conservation of Migratory Species of Wild 

Animals (CMS): UNEP/CMS Secretariat, Bonn, 7 p. 

Villa-R., B., and Cockrum, E.L., 1962, Migration in the 

guano bat Tadarida brasiliensis mexicana (Saussure): 

Journal of Mammalogy, vol. 43, p. 43–64. 

Wahl, R., 1993, Important Mexican free-tailed bat colonies in 

Texas, in Jordan, J. and Obele, R., eds., 1989 National 

Cave Management–Proceedings of the Symposium, Texas 

Parks and Wildlife Department, Austin, p. 47–50 

Walker, S, 1995, Mexico-U.S. partnership makes gains for 

migratory bats: Bats, Newsletter of Bat Conservation 

International, vol. 13, p. 3–5. 

Wilkins, K.T., 1989, Tadarida brasiliensis: Mammalian Species No. 331, p. 1–10. 

Williams, T.C., Ireland, L.C., and Williams, J.M., 1973, High 

altitude flights of the free-tailed bat, Tadarida 

brasiliensis, observed with radar: Journal of 

Mammalogy, vol. 54, p. 807–821. 
 

Estimating Population Sizes of Estimating Population Sizes of
 Estimating Population Sizes of

Hibernating Bats in Caves and Mines Hibernating Bats in Caves and Mines 
 Hibernating Bats in Caves and Mines

By 

Merlin D. Tuttle 

Bat Conservation International
 

P.O. Box 162603
 

Austin, TX 78716
 

Abstract Abstract. Many temperate-zone bats form their largest, most consistent aggregations during hibernation in caves or mines. 

Thus, these sites are of extraordinary importance to management and are focal points for estimating populations. Monitoring at 

hibernation sites has contributed greatly to monitoring trends in status and to determining protection priorities. Abundance can be 

measured directly by counting and identifying individual bats where small populations live in simple caves or mines. However, the 

only technique currently available for estimating large populations involves calculations of densities and areas covered by roosting 

clusters. Accurate estimates are difficult, and sometimes impossible, because bats: (1) vary clustering density according to surface 

roughness and temperature; (2) frequently roost in crevices or high above floors on extremely irregular surface contours; (3) sometimes learn to avoid roosts disturbed by scientists by moving to inaccessible areas; and (4) in some instances have access to large 

sections of caves or mines that are not reachable by scientists. Knowledge of temperature requirements of bats, combined with an 

understanding of cave and mine contours that produce desired temperature gradients, provides a powerful tool for predicting the 

locations bats will select. Where populations cannot be fully measured, estimates of numbers using ideal roosts can be indicative 

of overall trends in status for the location. Consistent visitation schedules, measuring procedures, and assumptions must be well 

documented, and at least two observers should make estimates independently. Appropriate gear and an understanding of risks are 

essential, and disturbance of bats must be minimized. 

KeyWords:Bats, caves, hibernation, population trends and status, mines.

Introduction IntroductionIntroduction

Many North American bats hibernate in winter, 

typically in dense aggregations that form in caves or 

mines, to which they exhibit extreme loyalty (Barbour and 

Davis, 1969; Tuttle, 1976). Because the largest, most 

predictable aggregations occur in these sites, status 

determination for threatened and endangered species of 

bats has relied extensively on midwinter monitoring (e.g., 

Brady and others, 1983). Numbers of bats at hibernation 

sites have been estimated based on counts of individuals 

(Rehak and Gaisler, 1999); calculations based on roosting 

density and area covered (Brady and others, 1983); and 

mark and recapture (Tinkle and Milstead, 1960; Dwyer, 

1966). Counts of individuals can be a reliable means of 

monitoring trends in status for relatively small groups 

roosting on the walls or ceilings of small caves or mines 

(Rehak and Gaisler, 1999). However this becomes 

impossible where bats roost in crevices, form large or 

dense clusters, or occupy sites too complex to fully 

explore during each visit. Crevice-roosting bats require 

individual extraction, or at least prior knowledge of the 

capacity of each occupied crevice. Large or dense clusters 

require calculations of density multiplied by the area 

covered. Although widely relied upon, this technique 

suffers from biases associated with highly variable cluster 

densities (Fig. 1) and varied wall and ceiling textures and 

contours (Tuttle, 1975; Thomas and LaVal, 1988). 

Nevertheless, calculations of cluster density and area 

remain the most reliable for large populations and are 

widely used for monitoring endangered species of bats. 

Approaches that rely on mark and recapture require that 

marked individuals roost randomly and that they remain 

equally “catchable.” Because these criteria are rarely, if 

 31 
32 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Fig 1. Gray bats ( Fig 1 Myotis grisescens) hibernating at 

approximately 9.5oC. Note the sparse, highly variable 

clustering density and extremely uneven roost surfaces 

typical of caves this species uses in hibernation. 

Photograph by Merlin D. Tuttle, Bat Conservation 

International. 

ever, met at bat hibernation sites (Stevenson and Tuttle, 

1981), mark and recapture has rarely been attempted since 

the 1960’s (Thomas and LaVal, 1988). 

Techniques for estimating populations and 

monitoring trends in status of bats have been summarized 

by Thomas and LaVal (1988), who stress the need to 

include variances and confidence limits with all population 

estimates. My paper discusses the natural history of bat 

hibernation, use of hibernation surveys in status 

monitoring, precautions for underground surveys, 

procedures and biases in counting hibernating bats, and 

management applications for population estimates from 

hibernacula. 

Natural History of Bat Natural History of Bat 
 Natural History of Bat

Hibernation Hibernation 
 HibernationHibernation

Many bats are true hibernators. Although some 

migrate south for winter like birds, most bats of the U.S. 

hibernate in caves, mines, or deep rock crevices, some 

occupying hollow trees in mild climates. To reach these 

locations, especially in caves and mines, bats often migrate 

distances exceeding 500 km, although typical distances 

are less than 300 km. During hibernation, each species 

has specific needs for temperature and humidity, most 

preferring roosts where wall temperatures are 1–10o

 C 

and relative humidity is above 75%. Body temperatures 

fall to that of the rock substrate while hibernating, and all 

metabolic processes are dramatically reduced. Arousals 

to drink, defecate, and adjust for changes in roost 

temperature often occur at intervals of 12–19 days, 

although uninterrupted bouts of hibernation can last for 

over 80 days. Even during exceptionally warm weather, 

most U.S. species do not leave their roosts to feed until 

they depart in spring, making conservation of limited fat 

reserves critically important (Tuttle, 1991). 

The little brown bat (Myotis lucifugus) illustrates 

the energy problems facing most hibernating species. 

Where it hibernates an average of 193 days, a typical 

individual arouses naturally about 15 times, staying awake 

for 56 hours at a cost of 1,618.5 mg of fat, accounting for 

84% of its total winter fat supply. In sharp contrast, while 

in deep hibernation, it requires only 308 mg of fat for an 

entire winter. Given that each arousal costs sufficient fat 

to otherwise last for 67 days of hibernation, forced disturbances from human visitation at roosts can threaten 

survival (Thomas and others, 1990). For this reason, it is 

important to minimize human disturbance in winter (Thomas and LaVal, 1988; Kunz and others, 1996). 

Although at least 20 species of North American bats 

at least occasionally hibernate in caves or mines, only 

three, Townsend’s big-eared bats (Corynorhinus 

townsendii), gray bats (Myotis grisescens), and Indiana 

bats (Myotis sodalis), appear to rely exclusively on them. 

Five more, the Rafinesque’s big-eared bat (Corynorhinus 

rafinesquii), the cave bat (Myotis velifer), the little brown 

bat (M. lucifugus), the southeastern bat (M. 

austroriparius), and the eastern pipistrelle (Pipistrellus 

subflavus) rely heavily (perhaps exclusively) on caves or 

mines in some geographic regions, but seldom in others. 

Most members of the genus Myotis use caves or mines 

as important overwintering sites in some areas, although 

large segments of their populations remain unaccounted 

for in winter (Barbour and Davis, 1969). 

Species with the narrowest requirements for unique 

cave environments are the most vulnerable to extinction 

and, not surprisingly, are the most endangered. Gray and 

Indiana bats provide excellent examples. They are extremely loyal to specific caves or mines (or to small groups 

of caves or mines located in close proximity) to which 

they return each winter. Traditionally, they have concentrated over 95% of their total species populations in fewer 

than a dozen sites each winter (Tuttle, 1976; Brady and 

others, 1983). The most important of these included from 

hundreds of thousands to millions of individuals each. 

These endangered species formerly ranked among the 

continent’s most numerous animals (Silliman and others, 

1851; Tuttle, 1997), but they became endangered when 

many of their caves were commercialized or otherwise 

disturbed or destroyed. 
 TUTTLE 33 

Use of Hibernation Surveys Use of Hibernation Surveys Use of Hibernation Surveys

in Status Monitoring in Status Monitoring 

Hibernating populations of bats that exhibit lifelong 

loyalty to specific hibernation sites provide unusual opportunities for population monitoring. Small populations, 

occupying simple roosts, can be counted quite accurately. 

However, population estimates become increasingly difficult when numbers of bats exceed a few thousand individuals, or when they roost in crevices, on high ceilings, 

or in complex caves or mines where some sections may 

be undiscovered or are impenetrable by humans (Thomas and LaVal, 1988). 

Of the three obligate cave and mine hibernators, population monitoring is easier for the endangered gray bat, 

because it typically concentrates in relatively conspicuous groups of tens to hundreds of thousands of individuals each that live in caves along waterways 

year-round. Although estimating their numbers remains 

difficult, they predictably aggregate at specific nursery 

roosts in summer, where they stain cave ceilings and leave 

large guano deposits that enable relatively consistent 

population estimates, upon which recovery planning is 

largely based (Tuttle, 1979; Brady and others, 1982). 

Townsend’s big-eared bat is more difficult, because it 

divides into smaller, less detectable summer colonies in a 

wider range of roost types (Barbour and Davis, 1969). 

The western subspecies of Townsend’s big-eared bat also 

hibernates in largest numbers in mines that are too complex or dangerous to fully survey. 

Although the endangered Indiana bat is an obligate 

user of caves and mines for hibernation, it forms summer 

nursery colonies that are small, inconspicuous, and scattered over large areas. Consequently, all population monitoring, status determination, and recovery planning is 

based exclusively on winter surveys (Brady and others, 

1983). Total population estimates for the species are nearly 

impossible to determine with a high degree of reliability, 

due to the complex nature of the species’ most important 

hibernation sites. Unknown, but potentially large numbers escape detection. This is compounded by difficulties of estimating cluster densities and areas covered on 

highly irregular surfaces. 

Unfortunately, the problems faced in estimating populations of Townsend’s big-eared bats and Indiana bats 

are widespread for other species as well, because the 

most important hibernation caves and mines are often 

exceedingly complex. For example Fern Cave, Alabama, is 

an important hibernation site for more than a million bats 

of several species, including thousands of Indiana bats 

(Tuttle, unpub. data, 1999) and probably more than half 

of the entire species population of gray bats (Tuttle, 1976; 

Brady and others, 1982). Yet its bat roosts are spread 

over kilometers of extremely complex passages and deep 

pits that are exceedingly difficult to traverse (Myrick, 

1972). It is impossible to survey more than a small fraction of potential, or even known, roosts in a single day, 

and some important bat roosts in this cave have never 

been visited by a biologist. 

Many species of U.S. bats that hibernate in caves 

also appear to utilize other locations, or at least are finding caves, or parts of caves, unknown to humans. For 

example, although the little brown bat appears to be an 

obligate cave/mine hibernator (Fig. 2) over much of its 

range in the eastern United States and Canada, it uses as 

yet undiscovered winter roosts in the West, leaving much 

uncertainty range-wide about what proportion of the species population is represented in currently known hibernation sites. Similarly, summer populations of the eastern 

pipistrelle are much larger than suggested by populations known to hibernate in caves and mines (Barbour 

and Davis, 1969). 

Another complication for use of winter surveys to 

determine overall species populations or trends in status 

is that estimates of the largest bat populations rarely have 

been made in a manner that permits calculation of 

confidence limits (Thomas and LaVal, 1988). This is an 

area that can and must be improved, especially in the 

case of the Indiana bat, an endangered species for which 

Fig. 2. Little brown bats ( Fig. 2. Myotis lucifugus) hibernating 

in a mine. Clustering has no constant density, and there 

are more than 50,000 bats in this mine, including many in 

crevices, which precludes counting individual bats. 

Photograph by Merlin D. Tuttle, Bat Conservation 

International. 
34 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

alternative measurements of overall population size or 

status do not exist. 

Precautions for Precautions for
 Precautions for

Underground Surveys Underground Surveys Underground Surveys
 

Because the largest populations of hibernating bats 

are typically found in caves and mines that are large and 

complex, often with deep vertical pits and unstable 

entrances or passages, advance planning is essential to 

ensure personal safety, as well as to avoid unnecessary 

disturbance to bats. Appropriate experience, equipment, 

and precautions are required (Kunz and others, 1996; 

Tuttle and Taylor, 1998). When possible, maps and advice 

should be obtained from local caving groups or mining 

authorities, and pre-surveys should be conducted in 

summer when bats are absent. Potential risks, such as 

toxic gases, instability, deep pits, and other hazards should 

be investigated and allowed for before the winter survey 

(Tuttle and Taylor, 1998). Advance mapping of all 

locations where bat droppings or roosts stained by bats 

are found will help ensure rapid and consistent surveys. 

In thousands of hours spent underground, I have 

had remarkably few mishaps, but a few have nearly cost 

me my life, including two hospitalizations. Because most 

bats have been forced to retreat into especially 

inaccessible locations to avoid human disturbance, the 

largest remaining populations are now often found 

beyond hazardous obstacles. For example, the bat 

hibernation areas of Fern Cave, Alabama, cannot be 

entered without roping down successive vertical drops 

of 25 m, 32 m, and 20 m, and a primary hibernation area in 

Hubbards Cave, Tennessee cannot be reached without 

crawling through unstable breakdown rocks. In a Texas 

cave, I was ready to descend into a pit when a caver’s 

carbide light suddenly quit, warning us of an oxygen 

shortage, and in Arizona, we were nearly overcome by 

poison gas in a mine. Advance preparation, and 

knowledge of risks, will minimize such hazards. 

Because disturbance causes costly forced arousals 

that threaten survival of bats, surveys should not exceed 

one per winter and ideally should not be repeated more 

than once every second or third year. They also should 

be conducted as rapidly as possible and by a minimum 

number of observers (Tuttle, 1979), usually not less than 

two nor more than three (Kunz and others, 1996). The 

more frequently bats are disturbed, the more likely they 

will relocate within a cave or mine to less suitable, or less 

accessible roosts. This may cause declines or falsely indicate declines of stable populations (see below). 

Procedures and Biases in Procedures and Biases in
 Procedures and Biases in

Counting Hibernating Bats Counting Hibernating Bats Counting Hibernating Bats
 

Where bats roost singly (Fig. 3), or in small groups 

in easily viewed locations, they can be accurately 

identified and counted individually by an experienced 

person with minimal or no handling. However, problems 

frequently arise because bats form clusters of varied 

density, often high above the floor, forcing observers to 

estimate numbers based on knowledge of normal 

clustering behavior and densities for each species. 

Clusters appear smaller at greater distances, and 

clustering density can be highly variable. Indiana myotis 

vary from approximately 3,228 to 5,208 bats/m2 (Fig. 4; 

Clawson and others, 2000), whereas gray myotis range 

from 538 to 2,695 bats/m2 (Tuttle, 1975, 1976). Many bats 

also pack into crevices where they may be impossible to 

count without removing each one (Thomas and LaVal, 

Fig. 3. Eastern pipistrelle ( Fig. 3. Pipistrellus subflavus) 

hibernating solitarily in a cave. Bats of this species rarely 

enter crevices or group together, making them easy to 

count. The striking contrast between forearms and wing 

membranes also make identification at a distance easy. 

Photograph by Merlin D. Tuttle, Bat Conservation 

International. 
Fig. 4. Fig. 4. Indiana bats (Myotis sodalis) hibernating in a 

densely packed cluster. Note how easily individuals could 

be missed even in close-up photographs. Photograph by 

Merlin D. Tuttle, Bat Conservation International. 

1988). Finally, roost surface contours (Fig. 5) and 

roughness are additional complicating factors, as is the 

fact that some clusters are shared by more than one 

species (Kunz and others, 1996). Greatest roosting 

densities are typically encountered on the roughest and 

most irregular surfaces, and at the lowest temperatures 

(Fig. 5). 

The most reliable means of determining roosting density is to construct a sturdy frame that encloses a specific 

area within which all bats can be counted (Tuttle, 1975; 

Thomas and LaVal, 1988). When that area involves dense 

clustering, one must compare surface counts versus those 

in which each individual bat is removed and counted, in 

order to ensure accuracy of the former. Where surface 

counts are sufficient, it may prove helpful to compare 

them with photographs that encompass the frame and all 

enclosed bats. If photographs prove adequate, they may 

enable detailed counts of cluster density at a later time. 

This minimizes disturbance during the survey. Photographs that do not show a measured frame with the bats 

may suffer from biases caused by wall contours, camera 

angle, and lens magnification, and must be carefully considered in advance (Kunz and others, 1996). 

In my work on gray bats, I sampled the densest 

clusters (typically those in the coldest, roughest surfaced 

locations) and average density clusters, as well as those 

that were least dense (normally located in the warmest 

TUTTLE 35 

locations used for hibernation). This was rarely repeated 

due to the substantial disturbance caused. In subsequent 

population estimates, I simply kept the range and average 

clustering densities in mind and mentally extrapolated 

where I felt densities were between these numbers. Any 

errors tend to be repeated as constants through time, so 

they should not bias calculations of trends in status. 

It is important that, during winter population surveys, all assumptions made regarding clustering densities and areas covered by bats be recorded for each 

roosting area. In addition, wherever assumptions or estimations are made without actual measurements, they 

should be made and recorded independently by at least 

two individuals. Estimates of large populations, for which 

confidence limits cannot be calculated, can be misleading and counterproductive (Thomas and LaVal, 1988). 

Substrate Temperature 

Density of bats in clusters tends to be inversely proportional to substrate temperature, but not consistently 

enough to enable calculations based on temperature 

alone. Rough or uneven surfaces also tend to increase 

density. Wall temperatures should be carefully recorded 

at consistent locations as near as possible to roosting 

bats early in each survey. It should be noted if temperatures are not recorded at the same height as the bats, 

because readings made closer to the floor might be several degrees cooler than those at the ceiling. To facilitate 

rapid and accurate readings of wall temperatures, I have 

found it convenient to force an approximately 2–3 cm (diameter) chunk of modeling clay into an adjacent wall crevice or other irregularity. Temperature probes are inserted 

into the clay (after it has equilibrated with the wall) during surveys. The clay is left in place for as long as surveys are anticipated. 

Temperature readings are of little value unless recorded with quick reading, digital thermometers that are 

inserted into the wall (preferably into attached or natural 

clay), and calibrated daily. Many thermometers are not 

designed to be used under conditions where the instrument body drops below 21o

 C. Comparing the unit when 

its body is at room temperature versus refrigerated before 

calibration can test this. Submerge the probe in a large 

bowl of crushed ice, and move it back and forth until a 

constant reading is obtained. Tap water typically tests at 

-0.17o C, rather than the expected 0.00o C for distilled water, due to the impact of impurities. Some digital thermometers (e.g., Portable Digital Thermometer 2300-PNC5, IMC 

Instruments, Inc., Menomonee Falls, Wisconsin) can be 

very precise, accurate, reliable, and convenient. 
 

36 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Fig. 5. Gray bats ( Fig. 5. Myotis grisescens) hibernating at approximately 1.1o

C. Note the extremely dense clustering and 

irregular cluster shapes, which make area estimation difficult. Some are also hidden in crevices behind the exposed 

cluster surfaces. Photograph by Merlin D. Tuttle, Bat Conservation International. 

When numbers of bats appear to decline in years of 

exceptionally low temperature, possible errors based on 

increased clustering density should be considered. Also, 

when temperatures change markedly, searches for bats 

may need to include new locations where temperatures 

more closely approximate their original choices (Tuttle 

and Stevenson, 1978; Tuttle, 1979). 

Because temperature is a key element in evaluating 

roost suitability for bats, it should be monitored at each 

visit regardless of other considerations. Most important 

hibernation roosts of Indiana bats are now monitored 

year-round using Hobo Pro Temp/RH data loggers (Onset 

Computer Corporation, Pocasset, Massachusetts). 

Instruments are downloaded in summer. Because roost 

temperatures vary daily throughout the hibernation 

season, this is the only means of fully understanding bat 

needs and choices. Improved knowledge of bat 

temperature requirements and their impact on roost choice 

and trends in status is essential, both in estimating 

populations and understanding management options and 

needs. 

Most bats prefer to hibernate at temperatures in the 

1–10o C range. Thus, areas within this range should be 

checked carefully. The more one knows about a specific 

species’ needs, the closer its use patterns can be predicted. For example, big brown (Eptesicus fuscus) and 

small-footed (Myotis leibii) bats prefer areas that provide 

midwinter temperatures that are near freezing, and thus 
tend to be found near entrances where cold winter air 

enters. Gray and Indiana bats like more stable, intermediate temperatures which in midwinter are typically 4–10o C 

and 3–7o C, respectively. Many species prefer the lowest 

available temperatures that are safe from freezing, but 

often must settle for warmer locations to avoid this risk or 

human disturbance. Thus, in caves that provide exceptional stability, bats tend to be found at cooler roosts. 

Detailed descriptions of cave (Tuttle and Stevenson, 

1978) and mine (Tuttle and Taylor, 1998) contours that 

best meet bat needs are available, and combined with 

knowledge of bat requirements, provide a powerful predictive tool for locating hibernating bats. For example, at 

latitudes and elevations where mean annual surface temperatures are above 10o C, all underground roosts require 

cooling from outside winter air in order to meet needs of 

gray or Indiana bats. This normally requires “chimneyeffect” flow between two or more entrances, meaning that 

hibernating bat populations are restricted to relatively 

small and predictable portions of total cave or mine systems. Sections that are too warm for hibernation need not 

be checked. However, any time that cool air is detected 

moving into an area that could be reached by bats, every 

possible effort should be made to follow it, at which times 

a quick-reading digital thermometer is extremely helpful. 

Such air flow “tracking” is most easily accomplished when 

the fastest airflow is occurring on extra cold days of late 

fall or early winter. This is exactly how I followed the flow 

of cool air through a large pile of breakdown rocks in 

Hubbards Cave, Tennessee, to discover a new roost occupied by 200,000 gray bats. This has worked well on 

other occasions. 

Cave and Mine Complexity 

Because bats are extremely loyal to specific hibernation caves and mines and prefer to use the same roosting 

sites year after year (Hall, 1962; Tuttle, 1976), it is tempting to believe that local populations can be reliably monitored. Nevertheless, major roost switching within caves 

or mines may occur in response to changes in either temperature or human disturbance. Critically important gray 

and Indiana bat hibernacula often include large and complex areas of multilevel passages in which it can be exceedingly difficult to find even the largest aggregations 

of bats. 

Roost switching within complex caves or mines frequently causes serious errors in year-to-year population 

estimates. Over a 14-year study involving the most important gray bat hibernation caves, I continually discovered new roosts, despite thorough previous searches of 

these sites (Tuttle, 1976). Pearson Cave, in Tennessee, 

was my best-studied, simplest hibernation site. Yet, following 16 years of band recovery efforts there, I found 

TUTTLE 37 

yet another important roosting area into which a large 

proportion of my oldest banded bats had moved in an 

apparent attempt to avoid recapture. This had significantly reduced estimates of survivorship (Stevenson and 

Tuttle, 1981). At Hubbards Cave, Tennessee, another of 

the U.S.’s most important bat hibernation sites, only 50,000 

gray bats were known for the first 8 years of my visits. 

However, in 1968 I discovered a new section of the cave 

that contained approximately 200,000 more bats. I also 

discovered a section too dangerous to enter that also 

contained a large number of bats but which to this day 

has never been reached by a biologist. 

These are not isolated examples. On one of my final 

visits to Fern Cave, Alabama, the world’s largest known 

bat hibernation site, I discovered a previously unvisited 

room containing over 250,000 gray bats and an uncounted 

number of Indiana bats. At Tobbaccoport Cave, 

Tennessee, I discovered a new section in 1969 that 

contained 50,000 gray bats that could be reached by 

humans only by tunneling through 3 m of clay, which I 

subsequently replaced for their protection. At James Cave, 

Kentucky, another critical gray bat hibernation cave, is a 

narrow passage, filled mostly with water, that leads to a 

room where I have seen approximately 100,000 gray bats. 

Humans can reach this roost only by first siphoning water 

out of the passage, and I and the Gray and Indiana Bat 

Recovery Team Leader, Richard Clawson, are the only 

biologists to have reached it. Because no one is willing to 

return, any estimate made in that cave can be incorrect by 

at least 100,000 bats. Such experiences have led me to 

focus nearly all of my efforts to report and monitor status 

of gray bats in caves used in summer, where nursery 

groups are far easier to detect and measure (Tuttle, 1979). 

Based on currently existing technology, I know of no 

practical means of gaining more than a ball park estimate 

of numbers in major gray bat hibernation caves, although 

periodic monitoring is essential to detect problems and 

ensure continued protection. 

Where other species, including Indiana bats, occupy 

similarly complex caves, many of the problems I discovered in estimating populations of gray bats are similar. 

This should not be interpreted as reason to ignore the 

results of many such estimates of the past. They are the 

best we have. However, it should sound a cautionary 

note that serious efforts are needed to improve our understanding of key sites and the unique biases inherent 

in determining bat numbers and status. 

Sampling Consistency 

Bat population monitoring often has been seriously 

compromised by a lack of consistent sampling techniques 

and assumptions over time, especially those involving 

estimates of clustering density and areas covered. Sam­
38 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

pling intervals, dates, and procedures need to be rigorously adhered to at each location, and any new assumptions must be clearly recorded. 

All areas of caves and mines where bats hibernate 

should be mapped in summer, and as early as possible, 

each roosting site should be numbered, measured, and 

described, including wall temperatures at each one. This 

is especially important in complex systems. Later, when 

population estimates are made, there should be a consistent order of visitation that assures equal coverage during each visit. Knowledge of temperature will help predict 

new locations of bats that may be forced to move during 

extreme weather. For example, when bats are absent from 

a traditional roost following a drop in temperature, they 

should be searched for in warmer rather than in cooler 

areas of the system. 

Measuring roost areas and attaching removable reflective markers to delineate scale on subsequent visits, 

especially where surfaces are uneven or high above floors, 

can substantially increase the accuracy of surface area 

estimates. This enables consistent estimates, including 

use of photographs, and is especially important where 

highly irregular wall contours, high ceilings, and other 

factors confuse observers regarding true distances and 

areas covered. This alone can dramatically improve yearto-year consistency. 

When surveys must be conducted by new individuals, 

such persons always should have at least one or two 

prior opportunities to accompany and compare their 

results with those of their predecessors to familiarize 

themselves with roost locations, counting techniques and 

assumptions made at each roost. At such times, accurate 

maps and records of all assumptions can be extremely 

helpful. In all cases, estimates of cluster area and density 

should be simultaneously and independently conducted 

by at least two people who average results and report 

error values. 

ManagementApplications of Population 

Estimates During Hibernation 

Population estimates made at hibernation sites can 

be extremely useful indicators of the importance of a given 

site and of the trends in status of its bats. In numerous 

cases, such estimates are invaluable in gaining protection 

of specific cave or mine roosts. They also are essential to 

early detection of adverse changes at a particular location. 

For example, at Pearson Cave, Tennessee, an entrance 

that is key to maintaining the bats’ required roost 

temperatures is gradually closing. Over a 30-year period, 

I have observed it decrease to less than a quarter of its 

original size. Complete closure could lead to the loss of 

approximately 200,000 gray bats and smaller numbers of 

five other species. Entrance blockage also poses a serious 

threat to Indiana bats (Tuttle and Kennedy, 2002) and 

other species. Routine population estimates, combined 

with temperature monitoring, enable early detection and 

avoidance of such threats. 

In addition, even at the largest and most complex 

caves and mines, knowledge of bat temperature 

requirements is highly predictive of where they should 

be found during hibernation (see above). When large 

numbers of bats are not found occupying these areas, 

which are almost invariably near air intake entrances, it 

should be assumed that the population in question is at 

sufficient risk to require additional protection. For example, 

Fern Cave, Alabama, cannot be fully surveyed to estimate 

the size of its very large gray bat population. However, 

estimates at roosts nearest the main cold air intake 

entrance can be used to indicate population status for 

the cave. Full occupancy of these roosts implies a healthy 

population, whereas a drop in numbers, despite the 

continued availability of ideal temperatures, would 

suggest a need for increased protection from human 

disturbance. Alternatively, a decline associated with a 

change in temperature beyond gray bat requirements 

should be considered indicative of a very serious 

problem, perhaps involving natural or unnatural alteration 

of one or more entrances. Because this cave supports a 

majority of the entire species population each winter, such 

findings would impact status consideration for the 

species, despite the lack of hard data on absolute numbers. 

Conclusions ConclusionsConclusions

Though biases often preclude estimates of total population size, even for a given cave or mine, absolute numbers are not required to document population trends which 

provide a basis for management planning and status determination. Also, many of the biases I have discussed 

tend to cause consistent errors in the same direction from 

year to year within a given site, greatly reducing their 

impact on calculation of trends in status. Problems aside, 

population monitoring at roosts used for hibernation is 

an essential tool that continues to play a critical role in 

prioritizing and gaining protective actions for bats. 

Nevertheless, many improvements can and should 

be made. Inferences about trends in status can be biased 

if estimates are not based on consistent techniques and 

assumptions that permit calculation of confidence limits. 

Where caves or mines provide only small areas of 

appropriate temperature, there is little likelihood of missing 

an important segment of the population that gradually 

learns to avoid detection. Nevertheless, where sufficiently 

low temperatures are likely to exist in areas potentially 
reachable by bats, but not humans, this should be 

documented as a possible explanation for apparent 

decline. Improved knowledge of bat temperature 

requirements and their impact on roost choice and trends 

in status is essential, both in estimating populations and 

in understanding management options and needs. To this 

end, the recent availability of temperature data loggers 

provides an important tool for improving the interpretation 

of population data. 

References Cited References Cited

Barbour, R.W., and Davis, W.H., 1969, Bats of America: 

Lexington, University Press of Kentucky, 286 p. 

Brady, J.T., Kunz, T.H., Tuttle, M.D., and Wilson, D.E., 

1982, Gray bat recovery plan: U.S. Department of the 

Interior, Fish and Wildlife Service, Denver, Colo, 21 p. 

Brady, J.T., LaVal, R.K., Kunz, T.H., Tuttle, M.D., Wilson, 

D.E., and Clawson, R.L., 1983, Recovery plan for the 

Indiana bat: U.S. Department of the Interior, Fish and 

Wildlife Service, Rockville, Md, 21 p. 

Clawson, R.L., Brack, V., Currie, R., Gardner, J.E., Harvey, 

M.J., Johnson, S., Kurta, A., MacGregor, J., Stihler, C., 

and Tuttle, M.D., 2000, Indiana bat revised recovery 

plan: U.S. Fish and Wildlife Service, Region 3, Minneapolis, 53 p. 

Dwyer, P.D. 1966, The population pattern of Miniopterus 

schreibersii (Chiroptera) in northeastern New South 

Wales:Australian Journal of Zoology, vol. 14, p. 1073– 

1137. 

Hall, J.S., 1962, A life history and taxonomic study of the 

Indiana bat, Myotis sodalis: Scientific Publication, Reading Public Museum andArt Gallery, vol. 12, p. 1–68. 

Kunz, T.H., Thomas, D.W., Richards, G.C., Tidemann, C.R., 

Pierson, E.D., and Racey, P.A., 1996, Observational techniques for bats, in Wilson, D.E., Cole, F.R., Nichols, 

J.D., Rudran, R., and Foster, M.S., eds., Measuring 

and monitoring biological diversity, standard methods for mammals: Smithsonian Institution Press, Washington, D.C., p. 105–114. 

Myrick, D.R., 1972, Fern Cave, Donal R. Myrick, Huntsville, Al, 106 p. 

Rehak, Z., and Gaisler, J., 1999, Long-term changes in the 

number of bats in the largest man-made hibernaculum 

of the Czech Republic: Acta Chiropterologica, vol. 1, 

series 1, p. 113–123. 

TUTTLE 39 

Silliman, B.S., Jr., Dana, J.D., and Gibbs, W., 1851, On the 

Mammoth Cave of Kentucky: American Journal of 

Science and Arts, vol. 11, p. 332–339. 

Stevenson, D.E., and Tuttle, M.D., 1981, Survivorship in 

the endangered gray bat (Myotis grisescens): Journal 

of Mammalogy, vol. 62, p. 244–257. 

Thomas, D.W., and LaVal, R.K., 1988, Survey and census 

methods, in Kunz, T.H., ed., Ecological and behavioral 

methods for the study of bats: Smithsonian Institution Press, Washington, D.C., p. 77–89. 

Thomas, D.W., Dorais, M., and Bergeron, J.M., 1990, Winter energy budgets and cost of arousals for hibernating little brown bats, Myotis lucifugus: Journal of 

Mammalogy, vol. 7, p. 475–479. 

Tinkle, D.W., and Milstead, W.W., 1960, Sex ratios and 

population density in hibernating Myotis: American 

Midland Naturalist, vol. 63, p. 327–334. 

Tuttle, M.D., 1975, Population ecology of the gray bat 

(Myotis grisescens), Factors influencing early growth 

and development: University of Kansas Occasional 

Papers Museum of Natural History, vol. 36, p. 1–24. 

Tuttle, M.D., 1976, Population ecology of the gray bat 

(Myotis grisescens), Philopatry, timing and patterns 

of movement, weight loss during migration, and seasonal adaptive strategies: University of Kansas Occasional Papers Museum of Natural History, vol. 54, 

p. 1–38. 

Tuttle, M.D., 1979, Status, causes of decline, and management of endangered gray bats: Journal of Wildlife 

Management, vol. 43, p. 1–17. 

Tuttle, M.D., 1991, How North America’s bats survive the 

winter: Bats, vol. 9, p. 7–12. 

Tuttle, M.D., 1997, A mammoth discovery: Bats, vol. 15, 

p. 3–5. 

Tuttle, M.D., and Kennedy, J., 2002, Thermal requirements 

during hibernation, in Kurta, A., and Kennedy, J., eds., 

the Indiana bat: biology and management of an endangered species: Bat Conservation International, 

Austin, Tex, p. 68–78. 

Tuttle, M.D., and Stevenson, D.E., 1978, Variation in the 

cave environment and its biological implications, in 

Zuber, R., Chester, J., Gilbertt, S., and Rhoads, D., eds, 

National Cave Management Symposium Proceedings, 

1977: Adobe Press, Albuquerque, NM, p. 108–121. 

Tuttle, M.D., and Taylor, D.R., 1998, Bats and mines: Bat 

Conservation International Resource Publication 

Number 3, 50 p. 

Population PopulationPopulation Trends of Solitary Foliage-Roosting Bats rends of Solitary Foliage-Roosting Bats 
 rends of Solitary Foliage-Roosting Bats

By 

Timothy C. Carter 

Southern Illinois University
 

Department of Zoology
 

Carbondale, IL 62901-6501
 

Michael A. Menzel 

West Virginia University
 

Division of Forestry
 

Wildlife and Fisheries Resources
 

Box 6125
 

Morgantown, WV 26506-6125
 

and 

David A. Saugey 

U.S. Forest Service
 

Ouachita National Forest
 

P.O. Box 189
 

Jessieville, AR 71949
 

Abstract. There are seven species of foliage-roosting bats in the United States (U.S.), all in the genus Lasiurus. Little is 

known about historical or recent population trends in these bats. Anecdotal accounts suggest higher abundances of some lasiurines 

in the past. However, quantitative analysis of long-term population trends of solitary foliage-roosting bats is not possible because 

of constraints to existing data. We review historical changes in the dynamics of North American forests since European settlement 

as a possible index to the availability of roosting habitat, a potential limiting factor for some bat populations. Greatest rates of 

forest clearing occurred in the late 1800’s, and areas in forest cover stabilized by the 1920’s. However, the resulting increase in 

forest edges may have had a compensatory effect on lasiurines by increasing foraging habitat. As of 1992, 70% of the area originally 

forested in the U.S. remains in forest. We speculate about how changes in forest management and associated human activities may 

have impacted populations of different species of lasiurines in the U.S. We also examine a case study of declining trends in 

submissions of eastern red bats (Lasiurus borealis) for rabies examinations in Arkansas as a possible index of abundance. Eastern 

red bats have recently been documented to hibernate in the leaf litter on the forest floor in some areas, a habit that may render them 

susceptible to fire and negatively impact their abundance. 

Abstract Abstract

KeyWords: Lasiurus, monitoring systems, population trends, roosting, tree bats. 

41 
42 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Introduction IntroductionIntroduction

Solitary foliage-roosting bats include species which 

typically roost alone or in small family groups, and which 

roost in foliage throughout the year. Members of this 

group usually do not roost in caves, mines, rock crevices, wooded cavities, or beneath exfoliating bark. All 

solitary foliage-roosting bats in the U.S. belong to the 

genus Lasiurus. There are seven species of lasiurines in 

the United States (U.S.; Nowak, 1994). The distributions 

of these species vary greatly. The hoary bat (Lasiurus 

cinereus; Fig. 1) can be found throughout the continental U.S. (Shump and Shump, 1982a). The eastern red bat 

(L. borealis; Fig. 2) is found throughout the U.S. east of 

the Rocky Mountains (Shump and Shump, 1982b). The 

western red bat (L. blossevillii) is found west of the Rocky 

Mountains (Nowak, 1994). The Seminole bat (L. 

seminolus), is found in eastern coastal states from Texas 

to Virginia (Wilkins, 1987). The northern yellow bat (L. 

intermedius) is found in coastal areas from Texas to South 

Carolina (Webster and others, 1980). The southern yellow bat (L. ega) is found in southern Texas (Kurta and 

Lehr, 1995). The western yellow bat (L. xanthinus) is found 

in Arizona and southern California into southwestern New 

Mexico (Barbour and Davis, 1969; Kurta and Lehr, 1995). 

Fig. 1. The hoary bat ( Fig. 1 Lasiurus cinereus). Photograph 

by T.C. Carter. 

Fig. 2. The eastern red bat (Lasiurus borealis). Photograph 

by T.C. Carter. 

Some biologists consider the western yellow bat a subspecies of L. ega. Because of the paucity of information 

on this species, we include discussions of the population trends of this species with those of the southern 

yellow bat (Barbour and Davis, 1969; Nowak, 1994; Kurta 

and Lehr, 1995). 

Historical Information Historical Information Historical Information

Little is known concerning historical population 

trends of solitary foliage-roosting bats. The lack of 

information may be due to the apparent absence of 

colonial behavior that is common among the 

cavernicolous bats. Colonial behavior allows easier 

monitoring and research. Most information concerning 

the size of populations of solitary foliage-roosting bats is 

based on anecdotal accounts of observations of mass 

migrations, swarming events, or inferences drawn from 

historical capture/collection records. Allen (1939) reviews 

the topic of mass migrations in lasiurines. In two of the 

accounts discussed, large groups of bats took refuge 

and rested on ships off the eastern coast of the U.S. 

(Thomas, 1921; Allen, 1939). Allen (1939) also discusses 

two separate accounts where hundreds of bats were 

observed during migration. Mearns (1898) observed great 
 

flights of red bats “during the whole day,” which went on 

for at least four days. In Washington, D.C., Howell (1908) 

observed over 100 bats migrating overhead during one 

hour in September. Additionally, Miller (1897) reported 

captures of red and hoary bats from Cape Cod, 

Massachusetts during the fall migration. Miller’s 

observations suggest large numbers of bats were 

migrating through the area. In 1932, a large group of hoary 

bats (200–300) was observed flying among cottonwood 

trees at a site in Nevada (Hall, 1946). Because these 

observations were made in late August and were 

accompanied by the capture of two males fighting on the 

ground, this may have been a mating swarm. Jennings 

(1958) reported observing large mixed-species feeding 

aggregations of bats in Florida, primarily composed of 

lasiurines, eastern pipistrelles (Pipistrellus subflavus), and 

evening bats (Nycticeius humeralis). These aggregations 

appeared to remain constant in size regardless of the 

removal of over 100 bats by shooting. LaVal and LaVal 

(1979) provide one of the best examples of capture data 

for lasiurines. They report capture rates in excess of 13.0 

bats per night in Louisiana (summer of 1966), 5.6 of which 

were eastern red bats. In Missouri (summer of 1976), they 

reported capture rates of more than 11.0 eastern red bats 

per night. Barbour and Davis (1969) reported capturing 

almost 60 hoary bats in one night. Vaughan (1953) 

captured 22 hoary bats in one night using a trip line over 

a pond. However, these capture rates cannot be compared 

to other records without knowing information including 

the capture technique used, the number of traps/nets set 

each night, the habitat types sampled, the sizes of the 

traps/nets, and the amount of time each trap/net was 

deployed. Capture rates are also subject to variable 

trapping proficiency, which is difficult if not impossible 

to account for. 

No quantitative information concerning long-term 

population trends of solitary foliage roosting bats can be 

drawn from existing data. Lack of standardized reporting 

and the inability to determine the proportion of total populations sampled (detection probabilities) for each of the 

observation and capture methods employed renders all 

capture data incomparable. 

Habitat Habitat Analysis Analysis AnalysisAnalysis Analysis

Historical Changes 

Because historical data concerning population trends 

of lasiurines is limited to anecdotal accounts, we can only 

speculate about population trends of solitary foliageroosting bats. Appropriate roosting habitat may be the 

most limiting habitat component for many species of bats 

CARTER AND OTHERS 43 

(Kunz, 1982). Temporal changes in the abundance of 

forestland habitats may influence the availability of 

roosting habitat for this group of bats. Thus, historic 

trends in the availability of roosting habitat of these 

species may reflect their population trends. 

Humans have influenced North American forests for 

over 8,000 years (MacCleery, 1992). Because of the 

dynamic history of North American forests, it is difficult 

to discern the characteristics of these forests in their 

pristine state.Although there is an abundance of evidence 

that Native Americans manipulated and managed forest 

habitats, we speculate that their efforts produced a 

relatively consistent effect on bats between the reestablishment of forests following the most recent ice 

age and the arrival of European settlers. Therefore, for 

the purposes of this overview, we define the 

characteristics of forests before European settlement (pre1500) as those of pristine forests. 

Between 1500 and 1800, European settlers impacted 

North American forests by clearing small plots of land for 

farming and fuel. Farmers suppressed naturally occurring fire, and allowed some naturally occurring 

fire-maintained communities (e.g., prairies) to become 

dense forests. Although the floral community composition of North American forests changed, total forest area 

did not (Williams, 1989; MacCleery, 1992). Therefore, before 1800, humans probably had a negligible effect on the 

availability of roosts for solitary foliage-roosting bats in 

North America. 

In 1800, the U.S. population reached approximately 3 

million. Twenty million acres of cropland (5% of the area 

used today) were required to meet the agricultural demand of the population (Williams, 1989; MacCleery, 1992). 

The increase in human population and associated clearing of forests for agriculture may have diminished roost 

resources to an extent that populations of solitary foliage-roosting bats were impacted. However, the greatest 

rates of forest clearing occurred during the late 1800’s. 

By 1850, the U.S. population reached 23.3 million and 76 

million acres of cropland had been cleared (20% of today’s 

cropland area; Williams, 1989; MacCleery, 1992). 

Although the loss of forests reduced the potential 

roosting area for lasiurines, the gain of edge habitat 

created between cultivated areas and adjacent forests 

may have compensated for some of the negative effects 

of forest clearing by creating or enhancing foraging 

habitat (Menzel, 1998). For example, Ohio was 96% 

forested in 1800; by 1850, it was only 60% forested. By 

1900, only 25% of the state remained forested. The forested 

area in the fertile western side of the state decreased to 

4% (MacCleery, 1992). By 1900, the area of cropland across 

the country had increased to 319 million acres (MacCleery, 

1992). The rate of forest clearing for agriculture was slowed 

in 1920 with the arrival of the boll weevil (Anthonomus 
44 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

grandis), which severely impacted the cotton industry in 

the southeastern U.S. In addition, the advent of motorized 

farm equipment freed millions of acres used to graze work 

animals. This freed land typically was put into agricultural 

production (Williams, 1989). Motorized equipment also 

allowed farmers to increase productivity of existing 

cropland. By 1920, the total area converted to cropland 

stabilized at approximately 400 million acres (Williams, 

1989; MacCleery, 1992). Prior to the 1920’s, the average 

rate of clearing was 3–4 acres per-year per-person added 

to the U.S. population (MacCleery, 1992). Had this rate of 

forest clearing continued, all U.S. forests would have been 

cleared by 1990 (MacCleery, 1992). The end of the cotton 

era also shifted the center of agriculture to the Midwest. 

This gave way to the re-establishment of many forests in 

the eastern U.S. (Williams, 1989; MacCleery, 1992). This 

increase in forestlands in the eastern U.S. probably led to 

a general increase in potential roosting habitat of all 

eastern foliage-roosting bats. As of 1992, approximately 

70% of the areas that were originally forested in the U.S. 

remain forested (MacCleery, 1992). It is unclear how the 

loss of 30% of the forest area affected populations of 

solitary foliage-roosting bats. Open areas created by 

deforestation may have created beneficial foraging habitat 

while destroying available roost sites. The costs and 

benefits associated with deforestation are unclear and 

the ultimate effect on solitary foliage-roosting bat 

populations is uncertain. 

Many factors other than roost availability may have 

influenced past bat populations. Pesticides and other 

contaminants are known to have detrimentally affected 

populations of other species of bats in the past (Geluso 

and others, 1976; Clark and others, 1978; Clark, 1981; 

Clawson and Clark, 1989). 

Potential Population Responses 

Although general trends in forest abundance and 

spatial distribution may influence populations of 

lasiurines that are habitat generalists with large geographic 

ranges, habitat specialists with limited ranges may be more 

sensitive to altered forest composition and increased urbanization. Eastern red and hoary bats have large ranges 

and are habitat generalists (Shump and Shump, 1982a,b). 

Based on roost availability, the beginning of this century 

may have been a low point for red and hoary bat populations. Numbers may have increased following the reforestation of the 1930’s and 1940’s (Shump and Shump, 

1982a,b). This resurgence in roost availability is most 

pronounced in the southeastern U.S. 

Negative impacts of forest clearing in the 

southeastern U.S. may have less impact on species that 

often roost in conifers, such as Seminole bats. Increases 

in pine plantations throughout the southeastern U.S. have 

probably greatly increased availability of suitable roosting 

habitat for these species (Wilkins, 1987; Menzel and 

others, 1998). Important breeding habitat is currently being 

replenished at a rate equal to or greater than the rate of 

removal (Williams, 1989). 

Species most commonly found in the southern coastal 

states, such as northern yellow and Seminole bats, may 

also be affected by the recent increase in urbanization of 

maritime forests (Constantine, 1958; Jennings, 1958; 

Menzel and others, 1995). These coastal areas are among 

the most rapidly developing areas in the country (U.S. 

Census Bureau, 1998). In addition, the growth of the timber industry in the southeastern U.S. has led to the conversion of deciduous forests to pine plantations. Northern 

yellow bats roost in Spanish moss (Tillandsia usneoides) 

throughout the year. Seminole bats roost in Spanish moss 

during the autumn, winter, and spring. Spanish moss is 

found in maritime forests along the southeastern coast 

and may affect the distributions of these species. In addition to urbanization, collecting Spanish moss for padding in car seats and mattresses during the mid-1900’s 

may have impacted populations of both northern yellow 

and Seminole bats by reducing suitable roosting habitat 

for these species. Moss collection may also directly result in bat mortality or interfere with reproduction 

(Constantine, 1958; Jennings, 1958; Adams, 1998). Although large-scale commercial collection of Spanish moss 

stopped after the evolution of economically manufacturable synthetic fibers, Spanish moss is still commercially 

collected for use in the craft industry. In addition, it is 

likely that the reduction of maritime forests caused by 

urbanization in coastal areas will continue to reduce available roosting habitat for species that use coastal forests. 

The paucity of information about the western red bat 

makes it difficult to interpret how historic land use patterns may have affected this species. Populations of this 

species may have mirrored the increase and decrease in 

populations of its eastern counterpart. Regardless, both 

western red and western yellow bats probably have benefited from the proportionally greater amount of commercial and national forest lands in the western part of the 

country. With more land protected and a less dense human population, these species have an advantage over 

eastern lasiurines. Western yellow bats also may have 

benefited from increased roosting habitat provided by 

introduced ornamental palms and fruit trees (Kurta and 

Lehr, 1995). 

The roosting habits of some lasiurines are flexible. 

Eastern red bats have been documented roosting on 

sunflower leaves as well as in a variety of tree species 

(Downes, 1964; Menzel, 1998). Western red bats have 

been found in exotic citrus and fruit trees (Constantine, 

1959). The ability to adapt to new roosting substrate may 
 

give this group an advantage during times of forest 

reduction. However, there will be fewer roost sites for 

tree roosting bats if the amount of vertical structure (forest) 

is reduced across the landscape because of development, 

forestry practices, and conversion of forests to croplands. 

Despite their flexible roosting behavior, the number of 

available roosts probably has declined since 

presettlement times. It is likely that the growing human 

population negatively impacts all species of lasiurines. 

Recent work has shown conflicting effects of 

fragmentation on lasiurine populations (Menzel, 1998). 

Lasiurines are fast-flying insectivores, foraging mostly 

along edge habitats (Farney and Fleharty, 1969; Shump 

and Shump, 1982a,b). However, some of these species 

may prefer interior forests for roosting (Hutchinson, 1998). 

If lasiurines prefer to roost in forest interiors, only limited 

fragmentation would benefit them. Much forest 

fragmentation currently exists; forest management 

decisions tailored to increase forest fragmentation may 

not be necessary. 

Additionally, at least one lasiurine (the hoary bat) 

has been shown to migrate across international borders. 

Recent work with neotropical migratory birds has 

demonstrated that factors on wintering grounds can affect 

populations (Sillett and others, 2000). Little is known 

concerning the migratory patterns of lasiurines, and 

nothing is known concerning how changes in their 

wintering habitats across international borders have 

affected these species. 

Health Department Submissions Health Department Submissions Health Department Submissions

Records of the number of bats submitted to public 

health authorities for rabies testing are a potential index 

of trends in the abundance of foliage-roosting bats. For 

nearly two decades, one of the U.S. authors (David 

A. Saugey) has identified bats submitted to the Arkansas 

Health Department Rabies Lab. From 1983 to 1998, 546 

eastern red bats were submitted for rabies testing. Since 

the beginning of the monitoring program in the early 

1980’s, there has been a significant negative trend in the 

number of submissions (total number submitted = 

5853.41–02.92X, R2 = 0.58, F = 19.27, P = 0.0006; number 

males submitted = 1761.23–0.878X, R2 = 0.46, F = 12.09, 

P = 0.0037; number of females submitted = 4092.18–2.05X, 

R2 = 0.58, F = 18.98, P = 0.0007; where X = number of years). 

During the early 1980’s there were approximately 65 

eastern red bats submitted each year. Rates of submission 

decreased in the late 1980’s to between 25 and 30 

submissions/year (Fig. 3). We would expect heightened 

public health awareness concerning rabies and increased 

human population density to result in an increasing 

CARTER AND OTHERS 45 

detection and submission rate. However, the number of 

eastern red bats submitted each year has declined 

significantly. Although limited conclusions can be drawn 

from these data, declining submission rates suggest the 

size of the red bat population is declining in the sample 

area. The spike and valley nature of the data also suggests 

that the population may be fluctuating over time, perhaps 

reflecting good and poor years of reproduction. Although 

these data are not directly representative of the eastern 

red bat population, they may reflect population trends of 

this species in one area of its range. 

Lasiurines and Fire Lasiurines and Fire Lasiurines and Fire

Recent studies have provided interesting information 

concerning hibernation-roosting habitat used by one 

species of lasiurine. Eastern red bats often hibernate on 

the forest floor (Fig. 4) among dead leaves (D. Saugey, 

unpub. data, 1999; Moorman and others, 1999). This raises 

the question of the effects of both historic wildfires and 

prescribed fires on populations. The amount of forests 

burned by wildfires during the latter half of this century 

is only a fraction of the amount of forested land burned 

during the beginning of this century (Williams, 1989; 

MacCleery, 1992). This trend suggests wildfires currently 

pose less risk to bats hibernating in leaf litter on the forest 

floor than they did during the early part of this century 

(Williams, 1989; MacCleery, 1992). However, prescribed 

fire is used more widely now to reduce fuel loads and 

promote growth and seedling establishment than in the 

past. Because the majority of prescribed fires are 

conducted during the winter months, the time of bat 

hibernation, prescribed fires may affect bats hibernating 

on the ground more seriously. However, most groundroosting bats have been located in the leaf litter of 

deciduous trees, whereas most prescribed fires are 

conducted in coniferous forests. Only a small portion of 

hardwood forests are burned each year, suggesting most 

prescribed fires probably do not affect bats that hibernate 

in the leaf litter.Although these preliminary data suggest 

winter-prescribed burns probably have little impact on 

the population trends of lasiurines, more research should 

be done before discounting this potential problem. 

Conclusions Conclusions ConclusionsConclusions Conclusions

Population trends of solitary foliage-roosting bats 

are unknown. Historic information is anecdotal and does 

not permit quantitative comparisons among observations. 

Because detection probability cannot be determined for 

common current sampling methods (see other sections of 
46 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Fig. 3. Numbers of red bats (Lasiurus borealis) submitted to the Arkansas Health Department from 1983 to 1998. 

Fig. 4. An eastern red bat ( Fig. 4 Lasiurus borealis) in 

hibernation in leaf litter on the forest floor. Photograph 

by D.A. Saugey. 

this report), data currently being collected cannot be used 

to quantitatively estimate trends in population sizes. 

Current capture information may suggest trends in 

population sizes of solitary foliage-roosting bats. We have 

summarized data from existing anecdotal accounts 

including data on resource availability trends (i.e., 

roosting habitat) and historic capture records and 

observations. We also performed cursory analyses on 

the number of eastern red bats submitted for rabies testing 

in Arkansas and detected significant decreasing trends 

in submission rates. Although two of these methods 

(examination of historic capture and observation records, 

analysis of rabies submission data) suggest population 

declines, no methods currently exist capable of 

documenting the magnitude of increases or declines in 

the population trends of solitary foliage-roosting bats. 

Methods for quantitatively determining both the direction 

and magnitude of population trends of solitary foliageroosting bats are needed. Until such methods are 

developed, the population trends of foliage-roosting bats 

will remain unknown. 

References Cited References Cited References Cited

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in Florida: University of Florida, Gainesville, Ph.D. dissertation, 126 p. 

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425 p. 

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Species, vol. 515, p. 1–7. 

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borealis: Journal of Mammalogy, vol. 60, p. 209–212. 

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resiliency and recovery: U.S. Department of Agriculture, Forest Service, FS-450 and Forest History Society, Durham, North Carolina. 58 p. 

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Hudson Highlands, with observations on the Mollusca, 

Crustacea, Lepidoptera, and the flora of the region: 

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Bulletin of the American Museum of Natural History, 

vol. 10, p. 303–352. 

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harvest in a southeastern bottomland hardwood 

community on the roosting and foraging behavior of 

tree-roosting bats: The University of Georgia, Athens, 

M.S. thesis, 160 p. 

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Roosting, foraging, and habitat use by bats of Sapelo 

Island, Georgia: Georgia Department of Natural 

Resources Technical Report, 75 p. 

Menzel, M.A., Carter, T.C. , Chapman, B.R., and Laerm, J., 

1998, Quantitative comparison of tree roosts used by 

red bats (Lasiurus borealis) and Seminole bats (L. 

seminolus): Canadian Journal of Zoology, vol. 76, 

p. 630–634. 

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Moorman, C.E., Russell, K.R., Menzel, M.A., Lohr, S.M., 

Ellenberger, J.E., and Van Lear, D.H., 1999, Bats roosting in deciduous leaf litter: Bat Research News, vol. 40, 

p. 74–75. 

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Md, John Hopkins University Press, 287 p. 

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cinereus: Mammalian Species, vol. 185, p. 1–5. 

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global climate cycle on population dynamics of a migratory songbird: Science, vol. 288, p. 2040–2042. 

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599 p. 

Count Methods and Population Count Methods and Population Count Methods and Population Trends

in Pacific Island Flying Foxes in Pacific Island Flying Foxes in Pacific Island Flying Foxes 

By 

Ruth C.B. Utzurrum 

Department of Marine and Wildlife Resources
 

P.O. Box 3730
 

Pago Pago, American Samoa 96799
 

Gary J. Wiles1 

Division of Aquatic and Wildlife Resources
 

192 Dairy Road
 

Mangilao, Guam 96923
 

Anne P. Brooke2 

Department of Marine and Wildlife Resources
 

P.O. Box 3730
 

Pago Pago, American Samoa 96799
 

and 

David J. Worthington3 

Division of Fish and Wildlife
 

Department of Lands and Natural Resources
 

Rota, CNMI 96951 U.S.A.
 

Abstract. Three species of flying foxes occur in the U.S. Pacific island territories: P. samoensis and Pteropus tonganus in 

American Samoa, and P. mariannus in the Commonwealth of the Northern Mariana Islands (CNMI) and Guam. Population 

assessments for these species have been underway for the last 20–25 years, although early efforts often resulted in erroneous 

estimates. Population abundances of colonial species have been determined primarily through direct counts at colonies or counts 

of individuals dispersing from colonies. Largely solitary species or populations were sampled primarily diurnally, and indices of 

abundance were derived from counts. Survey approaches and protocols have undergone historical revisions, precluding long-term 

statistical analyses of population trends. However, the data have yielded a descriptive profile of temporal trajectories in population sizes. Currently, populations of P. samoensis and P. tonganus in American Samoa are stable after recovering from hunting and 

successive hurricanes in 1990 and 1991. Populations of P. mariannus in the CNMI (primarily Sarigan Island) and Guam are likewise 

stable, albeit at levels lower than historically recorded. Although flying foxes in Guam are under federal protection, those in the 

CNMI are still threatened by hunting. At present, methodological options for monitoring are logistically limited by the unique 

topographic and geographic properties of island territories. Moreover, behavioral and ecological characteristics of the species do 

not lend themselves to application of standard population estimation techniques. We summarize the approaches used for monitoring the three species and discuss the relative virtues of each approach. 

Abstract Abstract

KeyWords:Population estimates, Pteropus, survey methods, U.S. territories.

1

Current address: 1692 Sunflower Lane, Apt. 19202, Tumwater, WA 98512 

2

Current address: Guam National Wildlife Refuge, P.O. Box 8134, MOU-3, Dededo, Guam 96929 

3

Current address: NPS, Capitol Reef National Park, HC 70 Box 15, Torrey, UT 84775 

49 
50 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

Introduction IntroductionIntroduction

The three species of pteropodid bats (genus 

Pteropus) found in the U.S. Pacific territories of American 

Samoa, the Commonwealth of the Northern Mariana Islands (CNMI), and Guam historically have been subjected 

to both natural (e.g., hurricanes and predators) and anthropogenic (e.g., hunting) pressures (Wiles, 1987a; Wilson and Graham, 1992; Craig and others, 1994; Grant and 

Banack, 1995; Pierson and others, 1996; Rainey, 1998). 

The geographic isolation and relatively depauperate fauna 

of these islands enhance the ecological importance of 

flying fox populations to island ecosystems (Cox and others, 1992; Rainey and others, 1995; Webb and Fa’aumu, 

1999; Webb and others, 1999). Moreover, this isolation 

implies limits to inter-island movements as a means of 

naturally reconstituting severely depressed populations 

of bats. Continuous regular monitoring of these species 

of Pacific flying foxes is, therefore, crucial for documenting population trajectories and detecting variables that 

may be affecting numbers and population trends [see 

Utzurrum and Seamon (2001) for a recent discussion]. In 

turn, such information may be useful for developing measures to aid in the recovery of declining populations. 

We present recent trends in the populations of the 

Samoan fruit bat (Pteropus samoensis) and the whitenaped fruit bat (P. tonganus) on American Samoa (Fig. 1), 

and of the Mariana fruit bat (P. mariannus) in the CNMI 

and Guam. We also review the various methods for surveying the different populations, especially addressing 

attendant methodological problems and logistical difficulties. Flying fox surveys have been conducted elsewhere in the Pacific (Engbring, 1984: Yap; Wiles and others, 

1991, 1997: Ulithi, Palau; Bowen-Jones and others, 1997: 

Solomon Islands; Grant, 1998: Tonga), but none of these 

constitute a monitoring program. 

Study Areas

American Samoa 

The U.S. Territory of American Samoa is comprised 

of five volcanic islands (Aunu’u, Ofu, Olosega, Ta’u, and 

Tutuila) located from 170o 50' to 169o 25' W and 14o 23' to 

14o 10' S and two remote atolls (Rose, centered at 168o W, 

15o S, and Swains, at 171o W, 11o S) (Fig. 2). The climate in 

the region is tropical and the islands are subject to periodic hurricanes and tropical storms (Elmqvist and others, 

1994). 

Resident populations of both P. samoensis and P. 

tonganus occur on four of the islands (i.e., Ofu, Olosega, 

Tutuila, and Ta’u; Fig. 2). Tutuila, the largest of these 

Fig. 1. Fruit bats of American Samoa: (top) the Samoan 

fruit bat, Pteropus samoensis, and (bottom) the whitenaped fruit bat, Pteropus tonganus. 

islands, sustains about 96% of the estimated total human 

population of 61,000. The terrain is characteristically bisected and steep, with slopes ranging from 15% to >100% 

(Nakamura, 1984; Webb and others, 1999). A significant 

portion of the island is forested (an estimated 53% as of 

1985: Cole and others, 1988), and largely inaccessible by 
 UTZURRUM AND OTHERS 51

Fig. 2. The Fig. 2. southwestern Pacific islands, with emphasis on the U.S. territories of Guam, the Commonwealth of the 

Northern Mariana Islands, and American Samoa. 

road or even from sea. The three other islands (Ofu, 

Olosega, and Ta’u) are similarly rugged and difficult to 

access. 

The Mariana Islands 

The Mariana Islands, which include the United States 

territories of the CNMI and Guam, extend 750 km from 13o 

14' N, 144o 45' E to 20o 3' N, 144o 54' E and are approximately 

1,500 km east of the Philippines Islands (Fig. 2). The 10 

northernmost islands are volcanic in origin, whereas the 

remaining five islands are largely uplifted coralline 

plateaus. Mariana fruit bats have been known to occur 

on all of these islands at one time or another. The largest 

southern islands [Guam, Rota, Tinian, and Saipan; 

(Fig. 2)] are inhabited by approximately 225,000 people. 

Islands north of Saipan are either unoccupied or support 

just a few families. The climate is tropical, with daily mean 

temperatures of 24o to 32 oC, high humidity, and an average 

annual rainfall of 200 to 260 cm. 

Monitoring Considerations Monitoring Considerations Monitoring Considerations

Monitoring Pacific island flying foxes requires methodologies that differ significantly from those used for 

North American microchiropteran bats. Surveys must be 

designed to count both colonial and spatially dispersed 

or solitary components of Pteropus populations. Variation in the degree of coloniality in a species, as well as 

temporal variation in activity patterns among populations 

on different islands, require that biologists be familiar 

with the specific characteristics of each population and 

island that will be surveyed. 

Studies are needed to determine factors influencing 

behavioral variation (e.g., degree of sociality, diel activity 
52 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

patterns) in the three species found in the territories. 

Changes in population size, reproductive activities, predator pressure, disturbance regimes, and spatio-temporal 

patterns in food availability within and among islands or 

localities are among suggested correlates of such variation (e.g., Pierson and Rainey, 1992; Speakman, 1995; Grant 

and others, 1997). Monitoring protocols must, therefore, 

account for and adjust for the variability that such intrinsic and extrinsic influences contribute to survey results. 

Conditions such as wind speed, cloud cover, and 

observation distance vary in time and space, thus possibly 

affecting count accuracy. Logistical challenges are also 

often immense, and transportation and personnel 

requirements can make surveys expensive and difficult 

to conduct on some islands. Species characteristics and 

island traits, which affect survey efforts, are described 

below. 

Species Characteristics 

All three species are large in size (wingspans of 90– 

120 cm), making them visible in flight at distances of up to 

1 km. Pteropus mariannus and P. tonganus are primarily 

nocturnal, but can also be active in the daytime, especially in the early morning and late afternoon (Wilson and 

Engbring, 1992; Banack, 1996; A.P. Brooke, R.C.B. 

Utzurrum, and G.J. Wiles, unpub. data, 1999). Both species are highly colonial, with smaller portions of populations living solitarily, but this can vary greatly among 

islands. For example, P. mariannus are generally colonial, 

as on Guam. On Ulithi (Caroline Islands), however, a substantial portion of the population occurs as individuals 

(Wiles and others, 1991). Pteropus samoensis are primarily active in the late afternoon and night, but can be seen 

throughout the day, and are generally solitary (Cox, 1983; 

Thomson and others, 1998; Brooke, 2001). Difficulties in 

conducting counts of this species are compounded by 

overlaps in size, morphology, and activity pattern with P. 

tonganus (Banack, 1996, 1998; but see Wilson and 

Engbring, 1992). The colonies or individuals of all three 

species roost in treetops or within forest canopies. Colonies vary in size from a few individuals to rarely up to 100 

animals in P. samoensis, 2,000 animals in P. mariannus, 

and 4,000 or more animals in P. tonganus. 

Flying foxes are strong fliers and have the potential 

to cover an entire island in a single night, as well as move 

distances of up to 100 km between islands (Wiles and 

Glass, 1990; Banack, 1996; Richmond and others, 1998). 

Colonies may shift locations over short periods of time in 

response to changing food availability and human and 

natural disturbances (Banack, 1996; Grant and others, 

1997; Richmond and others, 1998; Brooke and others, 

2000). Bats are hunted on all islands; such disturbance 

can force them into using the roughest terrain and 

possibly into becoming more nocturnal (Brooke and 

others, 2000). 

Island Characteristics 

Aside from Guam, which is 540 km2, most of the 

Pacific islands in the U.S. territories with populations of 

flying foxes range in size from about 5 km2 to 142 km2. 

Severe topography, rugged shorelines, and relatively 

undeveloped road or trail systems can make access to 

count sites difficult on some islands, such as in the remote 

northern Marianas (Fig. 3) and the northern coast of 

Tutuila on American Samoa. In such cases, surveys are 

conducted from a boat or areas accessed from a helicopter 

or boat. Rough seas, heat and humidity, high rainfall, and 

the annual typhoon season can result in harsh and 

unpredictable field conditions that often hamper efforts 

to conduct regularly scheduled surveys. 

CountTechniques echniquesechniques echniques 

Several methods have been employed to count the 

three species, with most surveys to date using a combination of the techniques described below. 

Fig. 3. The southwest coast of the island of Pagan in the Fig. 3.

Commonwealth of the Northern Mariana Islands. Steep 

hillsides (elevation is about 550 m on the ridge tops), 

deep ravines, and thick swordgrass complicate attempts 

to survey and monitor fruit bat populations. 
 

Direct Counts at Colonies 

Flying foxes in aggregations are best counted when 

their roosting trees can be viewed at relatively close distances (100–300 m) from suitable overlooks or vantage 

points. Observers use binoculars or spotting scopes to 

enumerate visible animals. In locations where bats are 

sensitive to human presence, viewpoints are placed downwind of colonies and set back at least 150 m. At densely 

populated roosts, observers may use visual reference 

points (e.g., individual trees) to break aggregates into 

smaller and more manageable counting units. 

Under anything other than ideal viewing conditions, 

direct colony counts do not represent complete censuses. 

For example, under very good viewing conditions, sample 

counts of a P. tonganus colony from a distance of 50 m 

differed by 10–40% depending on whether a Questar spotting scope or high quality 10x binoculars were used. For 

this reason, count totals have been increased by 5–10% 

in several studies (Wiles, 1987a; Stinson and others, 1992; 

Worthington and others, 2001) to account for animals hidden from sight by foliage or roost mates. When applied, 

the magnitude of the correction factor was site-specific 

depending on roosting patterns, foliage density of roost 

trees, and the distance of the observer from the colony. 

However, the accuracy of such correction factors has not 

been tested by any study. 

Counts from boats are more problematic. Counts from 

boats are subject to most of the problems of land-based 

surveys, as well as the effects of boat motion. Given these 

circumstances, surveys typically involve conducting an 

“ample” count (e.g., by trees or portions of trees) or 

categorical scoring (i.e., enumerating clumps or trees by 

estimated group size categories) from which an overall 

estimate can be generated. Often, a single experienced 

observer conducted the counts. There are advantages, 

however, to simultaneous independent counts (by 2–4 

observers) of the same colonies. First, multiple 

independent counts of a colony constitute a form of 

sampling that lends robustness to the resulting estimate. 

Second, it reduces the likelihood of missing individuals, 

especially when counting large colonies. Observer fatigue, 

especially when conducting a series of counts during one 

day or when count conditions are marginal (e.g., counting 

from a boat in rough seas), can compound counting 

problems. This situation can be remedied by having 

several experienced observers alternate among counts of 

successive colonies. 

Counts of Bats Dispersing from Colonies 

Exit or departure counts (described below) are used 

to estimate the sizes of remote colonies when accurate 

UTZURRUM AND OTHERS 53 

direct counts are not possible. These may include colonies of known general location that are obscured from 

view, or when direct count conditions are marginal (e.g., 

when seas are very rough during counts from boats). 

Observers typically position themselves at vantage 

points where bats departing colonies are silhouetted 

against the sky (Fig. 4). Ideally, counts begin at the first 

indication of individuals leaving the colony, possibly 

shortly before dusk, and continue until darkness. Although night vision equipment can extend hours of observation, most currently available models have limited 

ranges and are of limited suitability for long-distance use. 

Nightly differences in emergence patterns of the bats 

and viewing conditions (e.g., changes in cloud cover, or 

seasonal changes in day length) can create considerable 

variability in count results. Because some individuals 

depart unseen or remain in the roost until nightfall, counts 

of bats dispersing from colonies represent a subset of 

the total colony size. Thus, some researchers have multiplied their count results by some factor to arrive at an 

estimate of colony size (Wiles and others, 1989; Stinson 

and others, 1992; Worthington and others, 2001). However, such corrections were generally determined arbitrarily. Clearly, validation, through comparison with direct 

counts of colonies, needs to be done if correction factors are to be used. 

Station Counts of Non-Colonial Bats 

To assess the abundance of solitary flying foxes, 

researchers have relied on daytime (i.e., early morning or 

late afternoon) station counts conducted from vantage 

points with clear views of the nearby landscape. Observers typically scanned the horizon and intervening terrain 

with binoculars to count the numbers of bats (usually 

flying) seen. Count areas typically covered 15–100 ha. 

Fig. 4. View of Nuusetoga Fig. 4. Island from Tutuila, American 

Samoa. This vantage point is used during surveys to 

count fruit bats as they fly to the mainland. 
54 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

Since 1993, counts in the morning in American Samoa 

were standardized to start at dawn and continue for 2 

hours thereafter (Craig and others, 1994; Brooke, 2001). 

Late afternoon counts, on the other hand, usually lasted 

2 hours and extended until darkness or until dispersing 

colonial animals began to intermingle with solitary individuals. Results were based on the total number of bats 

active per unit area per unit time. Numbers obtained were 

used to index abundance, for example, as the number of 

bats per km2

 per 10 min. Some converted these counts to 

density estimates for an area or island (e.g., Craig and 

others, 1994; Brooke, 1997). The estimates were derived 

on the assumption that bat activity at a counting station 

was representative of the total number of solitary animals 

in all similar habitats on an island. The use of indices for 

population monitoring has been criticized (e.g., Lancia 

and others, 1994; Link and Sauer, 1998), as has the practice of converting indices to population size estimates 

(Nichols and Conroy, 1996). However, problems attendant to index methods for detecting trends in abundances 

are not entirely intractable (Bart and others, 1998; Conroy, 

1996). 

The diurnal station count (described above) suffers 

from methodological problems. The difficulty in distinguishing between sympatric P. tonganus and P. samoensis 

has consistently been a problem in American Samoa, even 

for experienced observers (Craig, 1992). Additionally, the 

presence of large numbers of bats increases the likelihood of double-counting the same individuals. Because 

difficulty in tracking individual bats can increase with 

count duration, determining an appropriate interval length 

is important. Finally, some animals may not be active during count periods and can go unrecorded (Brooke, 2001). 

Substantial variation in diurnal station counts has 

been noted in American Samoa. In the past, this problem 

arose in part from the use of multiple observers of varying degree of expertise spread across multiple (>10) counting sites. Morrell and Craig (1995) conducted a series of 

randomized counts and concluded that 10 replicated 

counts (i.e., visits) were needed per site to stabilize mean 

estimates. No surveys in the Marianas have incorporated 

such replications. 

In American Samoa, changes to the counting protocol 

for P. samoensis have been made to address some of the 

conceptual and practical problems discussed above. 

These include: (1) reducing the number of monitoring sites 

from >20 to 6; (2) limiting the number of observers to 1–3 

competent individuals, often working in tandem; 

(3) shortening individual count periods from 30 to 10 

minutes; (4) increasing the number of count replicates 

within a survey from a single 30-minute to eight 10-minute 

counts; and (5) increasing the frequency of surveys from 

annually to monthly. Because of these changes, statistical 

analysis of long-term trends in indices compiled since 

1987 is impossible. However, we believe the measures 

were necessary to reduce variance in counts among 

observers (changes 1 and 2 above) and within counts (4), 

to minimize errors in identification (2), to avoid double 

counting of individuals across space (1), and in time (4), 

and to account for inter-habitat and intra-annual variation 

in numbers (1 and 5). 

Opposition to the use of indices for monitoring of population changes remains strong (see Workshop Group A 

report, this volume). Presently, however, these counts 

constitute the only practical option for monitoring solitary pteropodids in the U.S. Pacific island territories [see 

Working Group A, Pacific Islands Fruit Bat Subgroup 

Report in Part II of this volume; Conroy and Nichols (1996) 

discuss practical limitations in estimating populations in 

mammals]. The number of survey sites (7), their geographic representation (along an east-west continuum), 

frequency of sampling (monthly), and intensity of counts 

(eight 10-minute counts per visit per site) currently employed in P. samoensis surveys suffice for examining 

population changes across various spatial and temporal 

scales [see DeSante and Rosenberg (1998) for criteria and 

a discussion on sampling design and scale]. 

Variable Circular PlotTechnique 

Flying foxes have been counted on one island in the 

Marianas using the variable circular plot (VCP) technique 

(Fancy and others, 1999), a method widely used for forest 

birds. An observer records all bats seen and estimates 

distances during a standardized time period (usually 8 

minutes) at multiple stations along a series of transects. 

A density estimate is then computed for each habitat using count and distance values. Flying foxes violate several important assumptions of the technique because: 

(1) animals clumped in colonies are not evenly distributed across the landscape, (2) roosting individuals may 

frequently go undetected because they rarely vocalize 

and are less active during the daytime when counts are 

conducted, and (3) flying individuals may be recorded 

more than once as they move back and forth through a 

count area. 

Population PopulationPopulation Trends

Following is a synopsis of trends in populations of 

P. mariannus, P. samoensis, and P. tonganus. Accounts 

are descriptive because changes in survey protocol over 

the years preclude statistical detection of long-term 

changes. 
 

American Samoa 

Most survey work has been done on the largest island 

of Tutuila (142 km2), with minimal effort spent in the three 

islands of the Manu’a group (5–45 km2). Amerson and 

others (1982) made the first estimates of bat populations 

in 1975–1976 by converting counts of bats in 0.3 ha survey 

plots to absolute numbers as follows: total estimated 

numbers = mean number of bats per 0.3 ha of a specific 

vegetation type x estimated total area occupied by 

vegetation type on island. Amerson and others, (1982) 

did not specify the duration of the counts, and observers 

did not distinguish between P. tonganus and P. 

samoensis. Their combined estimates for both species 

were 75,000 bats on Tutuila and 65,000 bats in the Manu’a 

Islands, but these were undoubtedly overestimates. 

Pteropus samoensis 

Projecting a trend in numbers of P. samoensis in 

American Samoa is impossible because methods used for 

its survey have undergone numerous changes since 

counts were conducted in the 1980’s. In most cases, the 

surveys generate an index of abundance (bats/unit time 

or bats/unit time/unit area). However, there have been 

instances when these indices were converted to population estimates as discussed in preceding sections. The 

following is our attempt to summarize the data available 

from records at the Department of Marine and Wildlife 

Resources (DMWR) and from various publications. 

In the early 1980’s, Cox (1983) reported extremely low 

numbers of P. samoensis in American Samoa following 

limited sightings of bats on Tutuila (a breeding pair) and 

Ta’u (one individual). Cox and Tuttle (1986) estimated 

that 300 individuals remained on Tutuila and petitioned 

the U.S. Fish and Wildlife Service (USFWS) for endangered status. This petition did not receive much local 

support, but it did result in a memorandum of agreement 

between the Office of Marine and Wildlife Resources and 

the U.S. Fish and Wildlife Service to commission systematic surveys. Multiple non-replicated 20- to 30-minute 

counts were subsequently conducted between 1986 and 

1989 by Wilson and Engbring (1992) and by staff of the 

DMWR of American Samoa. Although no estimates of 

population size were generated, the survey data were statistically compared among years and results indicated 

that populations were stable on both Tutuila and Manu’a 

during this period (Wilson and Engbring, 1992). 

The population of P. samoensis on Tutuila declined 

in the aftermath of two hurricanes in the early 1990’s. 

Prior to Hurricane Ofa in 1990, the population was 

estimated at 700 individuals (Pierson and others, 1992). 

UTZURRUM AND OTHERS 55 

Surveys in 1992 (shortly after Hurricane Val in December 

1991) placed the population at 200–400 bats. The decrease 

in estimated numbers was attributed largely to 

opportunistic and extensive take of weakened and exposed 

(due to habitat damage) individuals by hunters (Craig 

and others, 1994). Since 1995, the estimated number of P. 

samoensis based on dawn (station) counts on Tutuila 

has remained roughly the same at about 900 animals 

(Brooke, 1997). The Manu’a Islands’ collective population 

was estimated at 100 bats in 1996 (Brooke, 1997). Although 

station counts using the survey protocol instituted in 

1995 have been conducted since 1996 on Tutuila and all 

three Manu’a islands, the practice of converting the 

resulting indices to estimates was discontinued. Results 

from the 1997 to 2000 surveys indicate that: (1) the Tutuila 

population, based on relative indices (i.e., number of bats 

sighted per 10 minutes per km2), appears stable at levels 

found since 1995; and (2) the Manu’a populations remain 

low, with counts generally averaging less than one bat 

per 10 minutes at a station (Department of Marine and 

Wildlife Resources annual reports: 1997–2000). 

Pteropus tonganus 

Results of direct and indirect counts of colonies of P. 

tonganus since 1987 on Tutuila are summarized from data 

compiled in the DMWR and as published in Craig and 

others (1994), Brooke (1997), and Utzurrum and Seamon 

(2001) (Table 1). Between 1987–1989, surveys yielded 

estimates of 12,750–28,000 bats island-wide. An export 

ban and a seasonal hunting program instituted in 1986 

were apparently ineffective and the population appeared 

to be in slow decline (Craig and others, 1994; Utzurrum 

and Seamon, 2001). The population declined dramatically 

in the wake of Hurricane Ofa in 1990 to about 4,500 bats 

(Craig and others, 1994). It dropped further to about 1,700 

bats in early 1992 after Hurricane Val hit the island in 

December 1991 (Brooke, 1997). An executive order instituting a total hunting ban was enacted shortly thereafter. 

Two to four island-wide roost surveys of P. tonganus 

on Tutuila have been conducted annually since 1992. 

Counts increased to about 5,000 bats in 1996 (Brooke and 

others, 2000). Although estimates were lower in the two 

subsequent years (i.e., 3,265–4,000 bats in 1997 and 1998), 

the average estimate from surveys in 1999 suggests a 

population of approximately 6,000 bats (DMWR 1999 annual report). 

Single annual surveys of the Manu’a islands (i.e., 

Ofu, Olosega, and Ta’u) in 1990–1994 gave estimates of 

33–390 bats (Department of Marine and Wildlife Resources annual reports). In 1996, two colonial roosts were 

located and numbers estimated at 1,770 bats (Brooke, 
 

 

 

56 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Table 1. A able 1 nnual estimates of Pteropus tonganus population on Tutuila Island, American Samoa. Estimates 

are based on a combination of direct counts and exit 

(dispersal) counts of colonies. [Sources: Brooke (1997) 

for 1987 to 1995, except 1989; Utzurrum and Seamon 

(2001) for 1997–1998; Department of Marine and Wildlife Resources records for 1989, 1996, and 1999–2000.] 

Estimated Number of 

Year total colonies surveyed1 

1987 12,750 11 

1988 13,000 14 

1989 9,300 11 

1990 4,300 8 

1991 4,400 11 

1992 1,700 13 

1993 3,330 5 

1994 4,150 8 

1995 4,300 6 

1996 4,770 7–10 

1997 3,264 7–9 

1998 3,541 7–12 

1999 5,941 8–14 

2000 6,366 10–11 

1

Ranges are provided when estimate represents the mean 

of 2–4 surveys within a year. The total number of colonies 

located and counted varied among surveys, although the 

area covered (i.e., island-wide) was the same among 

surveys. 

1997). Combined estimates for 1998 from all three islands 

put the number at approximately 1,500 individuals that 

were largely concentrated in three colonies, one on each 

of the islands. 

Assessment of Current Status 

Two main legislative measures to protect populations 

of both P. samoensis and P. tonganus in American Samoa 

have been instituted. The first measure was passed in 

1986. It completely banned exportation and commercial 

hunting and restricted subsistence hunting by limiting 

the period of hunting, imposing bag limits, banning 

hunting at roosts, prohibiting daytime hunting, and 

rendering local sale and barter of bats illegal (Craig and 

Syron, 1992). An executive order calling for a total ban on 

hunting was subsequently passed in 1992 and amended 

in 1995 to aid in the recovery of populations decimated 

by Hurricanes Ofa and Val (American Samoa Code 

Annotated, 1995). This order made the capture, 

harassment, and possession of bats punishable by law, 

rendered illegal all forms of trade in bats, and provided 

for permitting of collections for scientific purposes. 

Survey results indicate that the total ban on hunting 

may have been instrumental in the recovery of the bat 

populations on Tutuila (Brooke, 2001; Utzurrum and 

Seamon, 2001). Manu’a populations of P. tonganus also 

appear stable since the ban. However, the rarity of 

sightings of P. samoensis in the Manu’a Islands in recent 

years indicate poor recovery or even a possible decline 

in local numbers. 

The institution of protective measures (i.e., the hunting ban) and concomitant recovery of the fruit bat populations (on Tutuila) through the 1990’s have put into focus 

the need to re-examine the objectives of and approaches 

to population monitoring. First, the difference in predicted 

and observed trajectory of populations of fruit bats on 

Tutuila since the 1990–1991 hurricanes demonstrate, in 

part, the need to go beyond tracking numbers for conservation and management purposes. In this instance, surveys indicate that populations of both species of fruit 

bats on Tutuila have recovered faster than was predicted 

by the theoretical models [see Pierson and Rainey (1992), 

and Craig and others (1994) for model simulations, and 

Brooke (1998) for comparisons]. The lack of congruence 

between observed and theoretical changes in population 

size may be due to differences between actual and assumed values of parameters used in the models, particularly survivorship and years to sexual maturity. For 

example, simulations by Pierson and Rainey (1992) used 

2 years time to sexual maturity as a constant parameter. 

However, females of other pteropodid species have been 

found to be reproductively active within a year of birth 

(e.g., Heideman, 1987: free-ranging Philippine fruit bats; 

Tidemann, 1992: Pteropus melanotis in Australia; Center 

for Tropical Studies [Silliman University, Dumaguete City, 

Philippines]: captive Pteropus leucopterus and P. pumilus). 

It is apparent that demographic studies are needed if 

management programs are to maximize the benefits of 

modeling [see Levins and Puccia (1988) for a discussion 

on the need to shift the emphasis of studies from population abundance to parameters influencing population 

growth]. 

Second, although history shows that hunting has been 

a legitimate threat to populations of fruit bats in the Pacific, 

managed take of animals may actually open opportunities 

for devising improved population estimation protocols 

for detecting trends and may provide realistic 

demographic information needed for management (Conroy 

and Nichols, 1996; Pacific Islands Fruit Bat Subgroup 

Report, this volume). The largely successful application 

of regulatory measures (e.g., the hunting ban) for 

managing fruit bat populations in American Samoa 

suggests that regulated hunting should be given a 

second look as an aid to monitoring. 
 

 

 

 

 

 

 

 

 UTZURRUM AND OTHERS 57 

Table 2. Recent population estimates of Mariana fruit bats in the Mariana Islands. An x denotes that bats were present 

but not counted; dashes indicate that the respective islands were not surveyed. Numerical supercripts indicate 

count methods; letter superscripts indicate sources of information.

 Size 

Island (km2

) 1983–1984 1987 1990 1995 1997–1999 

Guam 540 5001,a 5502,b 4502,c 3252,d 2252,e 

Rota 85 2,000?3,f 2,6004,g,h 1,0674,h 1,0003,i -

Aguiguan 7 <105,f 40–602,g,5 02,h 100–1252,d -

Tinian 102 <255,6,f,j <505,g <255,h <256,k -

Saipan 123 <506,f 100–2005,g <405,h - 100–2005,l 

F. de Medinilla 1 05,f - - - <55,m 

Anatahan 32 3,0003,f - ­ 2,0001,n -

Sarigan 5 1253,f - ­ - 150–2001,7,e,o 

Guguan 4 4003,f 4003,g - - -

Alamagan 11 06,f - x5,p - -

Pagan 48 2,5003,f - - - -

Agrihan 48 1,0003,f - - - -

Asuncion 7 4003,f 5003,g - - -

Maug 2 <253,f 25–503,g - - -

Uracus 2 05,f 05,g - - -

Total 1,017 10,000z - - - -

Methods for deriving estimates: 1direct counts at colonies and station counts; 2direct counts at colonies and miscellaneous sightings; 3

departure and station counts; 4direct counts at colonies, departure and station counts; 5miscellaneous sightings; 6

station counts; and 7variable circular plot survey. 

Sources of information: aWiles (1987a), bWiles (1987b), cWiles (1990), dWiles (1995), eWiles (1999), fWiles et al. (1989), 

g

Glass and Taisacan (1988), hStinson and others (1992), iWorthington and Taisacan (1995), jWiles and others, (1990), 

k

Krueger and O’Daniel (1999), lWorthington (unpubl. data, 1999), mM. Lusk (U.S. Fish and Wildlife Service, Cabeza 

Prieta National Wildlife Refuge, Ajo, Arizona, oral commun., 1999), nWorthington and others (2001), oFancy and others 

(1999), pJ.D. Reichel (CNMI Division of Fish and Wildlife, Department of Lands and Natural Resources, Saipan, oral 

commun., 1999). 

Commonwealth of the Krueger and O’Daniel, 1999), although there is evidence 

Northern Mariana Islands that numbers on Saipan have increased since 1995 

(Table 2). The nine uninhabited islands north of Saipan 

The CNMI is comprised of 14 islands ranging in size have been surveyed as a group only once, with a total 

from 1–123 km2. The first counts of P. mariannus on each minimum estimate of 7,450 bats made in 1983 (Wiles and 

of these islands occurred in the late 1970’s or early 1980’s. others, 1989). Only two islands have been resurveyed 

All surveys conducted since 1987 were incomplete (i.e., since then. Anatahan’s (32 km2

) population decreased 

did not encompass all 14 islands). Results are, therefore, from an estimated 2,500–3,000 bats in 1983 to about 1,900– 

summarized by island (Table 2). 2,150 in 1995 (Worthington and others, 2001). Three surRota’s (85 km2

) population held about 2,400 animals veys of Sarigan (5 km2

) from 1983–1999 have found bat 

from 1986–1988, but declined to about 1,000 animals soon abundance to be fairly stable at about 125–200 animals 

after Typhoon Roy in 1988 (Stinson and others, 1992). (Wiles and others, 1989; Fancy and others, 1999) (Table 2). 

Numbers have been relatively stable since then (Table 2). Hunting for local consumption and export (principally 

Counts on Aguiguan (7 km2), Tinian (102 km2), and Saipan to Guam) has historically been the major threat to 

(123 km2) have each numbered only 25–125 bats since the populations of P. mariannus in CNMI (Lemke, 1992). Local 

late 1970’s (Wheeler, 1980; Wiles and others, 1989, 1990; efforts to curtail hunting (e.g., observing hunting seasons) 
58 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

were instituted independently by some islands in the early 

1970’s but enforcement of regulations was poor. Anominal 

territory-wide hunting moratorium (1 year for islands north 

of Saipan and 2 years for the remaining southern islands) 

enacted in 1977 has since been regularly reauthorized 

(Lemke, 1992), but illegal hunting continues to be the 

most serious threat to local bat populations. Commercial 

trade of bats declined when the P. mariannus population 

on Guam gained endangered status in the 1980’s (see 

following section). However, illegal exportation to Guam 

is believed to continue. Local (CNMI) and regional (e.g., 

Guam) statutes constitute the only protective measures 

presently in effect. It is uncertain whether these measures 

are sufficient to stave off further decline and/or stimulate 

recovery of decimated populations. A formal proposal to 

list fruit bat populations on CNMI as threatened (U.S. 

Fish and Wildlife Service, 1998) needs to progress beyond 

the “proposed” stage. Official listing of this species could 

have a salutary effect on populations of P. mariannus by 

enabling additional funding and creating a more favorable 

climate for protection and conservation enforcement. 

Guam 

Woodside (1958) estimated that a maximum of 3,000 

P. mariannus remained on Guam (540 km2) in the late 1950’s. 

Although it was unclear how this estimate was derived, it 

was assumed that it was based in part on direct counts at 

colonies. Bat abundance declined greatly through the 

late 1970’s, when less than 50 bats were estimated for the 

entire island and no colonies were known (Wheeler and 

Aguon, 1978). A colony of 200–300 bats reappeared in 

northern Guam in 1980 and increased to about 800 bats 

by 1982 (Wiles, 1987a). Since the late 1980’s, it has typically held 150–350 bats during most months of the year, 

with numbers increasing by 100–600 bats during the winter months due to apparent migration from Rota 60 km to 

the north (Wiles and others, 1995). Guam’s population 

also contains small numbers (50–75) of solitary animals 

scattered throughout the island. 

Hunting was the primary cause of historical declines 

in the numbers of P. mariannus on Guam. Hence, this 

local population was placed on the endangered species 

list, first under local statutes in 1981 and then under the 

U.S. Endangered Species Act in 1984 (Lemke, 1992). Recent surveys indicate that the population remains small 

(Table 2). Extreme predation on juvenile bats by introduced brown tree snakes (Boiga irregularis) is believed 

to be the major problem preventing recovery of the population (Wiles, 1987a). 

Conclusions ConclusionsConclusions

Effective conservation and management of 

populations of flying foxes in the U.S. territories depend 

in part on the availability of reliable estimates or indices 

of population sizes.Analysis of much of the data collected 

has been confounded by methods that fail to account for 

temporal and spatial factors that influence population 

sizes both seasonally and circ-annually. There are inherent 

difficulties posed by surveying species that are primarily 

nocturnal, behaviorally and ecologically complex, and 

occur in unpredictable and rugged environments. These 

multi-faceted constraints on surveys have resulted (albeit 

primarily out of necessity) in the use of sundry counting 

and survey methods, thus hampering accurate assessment 

of population trends. However, it is possible to design 

efficacious protocols that can generate data that are 

comparable over time and that permit statistical analysis. 

Recommendations for achieving more standardized 

protocols for counts and for the field evaluation of the 

applicability of true estimation techniques (e.g., distance 

sampling) are discussed in greater detail in the Pacific 

islands fruit bat subgroup report of Working Group A in 

Part II of this volume. 

Acknowledgments Acknowledgments Acknowledgments

Studies on flying foxes inAmerican Samoa, the CNMI, 

and Guam are funded by the Federal Aid to Wildlife Restoration Act through the U.S. Fish and Wildlife Service. 

A.P. Brooke and R.C.B. Utzurrum recognize the effort of 

various visiting researchers (P.A. Cox, J. Engbring, E.D. 

Pierson, W.E. Rainey, and D.E. Wilson among others) and 

of staff at the Department of Marine and Wildlife Resources (including P. Craig, S. Fa’aumu, G. Grant, T. 

Morrell, W. Syron, P. Trail, and A. Tualaulelei) for compiling counts of P. samoensis and P. tonganus in American 

Samoa prior to 1995. In the Mariana Islands, we acknowledge the previous efforts of E. Taisacan, P. Glass, T. Lemke, 

D. Stinson, and M. Wheeler. 

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Current Status of Pollinating Bats Current Status of Pollinating Bats
 Current Status of Pollinating Bats

in Southwestern North in Southwestern North in Southwestern North America America 
 America America

By 

Theodore H. Fleming1
 

Tim Tibbitts2
 

Yar Petryszyn3
 

and
 

Virginia Dalton4
 

Abstract. Three species of phyllostomid bats, Leptonycteris curasoae, L. nivalis, and Choeronycteris mexicana, are important pollinators of columnar cacti and paniculate agaves in parts of the arid Southwest. Presumed population declines in both 

species of Leptonycteris during the 1960’s and 1970’s resulted in their being declared “endangered” in 1988. Since then, considerable effort has gone into documenting population trends in L. curasoae in Arizona and Sonora, Mexico. We conducted annual exit 

censuses at one cave and two mines in southern Arizona and at two caves in Sonora. Census data indicate that although roost sizes 

vary from year to year, there is no evidence of a secular population decline in this species in the northern part of its range. Data 

further indicate that the size of northern populations of L. curasoae is much larger (by at least two orders of magnitude) than 

indicated by U.S. Fish and Wildlife Service (USFWS) surveys in the early 1980’s. Far less information is available for the other two 

species. We recommend that systematic surveys of sites known to harbor L. nivalis and C. mexicana in the United States (U.S.) be 

conducted annually. 

Abstract Abstract

KeyWords: Choeronycteris, columnar cacti, Leptonycteris, paniculate agaves. 

Introduction IntroductionIntroduction

Except for three species of nectar-feeding bats 

whose evolutionary affinities are clearly tropical, the 

bat fauna of the United States (U.S.) is dominated by 

insectivores. The three plant visitors include the lesser 

long-nosed bat, Leptonycteris curasoae; the greater 

long-nosed bat, L. nivalis; and the Mexican longtongued bat, Choeronycteris mexicana (Phyllostomidae, 

Glossophaginae). These species are seasonal migrants 

into southern Arizona (L. curasoae, C. mexicana); 

southwestern New Mexico (L. curasoae, L. nivalis, C. 

mexicana); and southwestern Texas (L. nivalis) from 

Mexico. While in the northern parts of their ranges, 

they visit and pollinate flowers of columnar cacti in the 

Sonoran Desert (L. curasoae) and flowers of paniculate agaves at higher elevations in Arizona (L. curasoae, 

C. mexicana); New Mexico (all three species); and Texas 

(L. nivalis). Leptonycteris curasoae also eats fruit pulp 

and ingests seeds of columnar cacti. Two species, L. 

curasoae and C. mexicana, form maternity roosts in 

Arizona in the spring; maternity roosts of L. nivalis are 

unknown in the U.S. 

Except for L. curasoae in Arizona, none of these 

species appears to be common in the U.S. Furthermore, 

because roost surveys in the 1970’s and 1980’s suggested 

that population sizes of the two species of Leptonycteris 

in Mexico and the U.S. were smaller than in previous 

1

Department of Biology, University of Miami, Coral Gables, FL 33124 

2

U.S. National Park Service, Organ Pipe Cactus National Monument, Route 1, Box 100, Ajo, AZ 85321 

3

Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85121 

4

Pima Community College, Tucson, AZ 85709 

63 
64 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

decades, some biologists urged that these species receive 

“endangered” status in the U.S. Subsequently, both 

species were added to the Endangered Species List (Shull, 

1988). After reviewing all relevant information, however, 

Cockrum and Petryszyn (1991) questioned whether L. 

curasoae truly deserved to be classified as “endangered”. 

Inspired by the federal listing, bat biologists have 

expended considerable effort since 1988 assessing the 

population status of L. curasoae in Arizona and Sonora, 

Mexico. Less effort has been directed at determining the 

status of L. nivalis and C. mexicana anywhere in their 

ranges. Moreno (1999), however, recently completed an 

intensive study of L. nivalis in northeastern Mexico, and 

in 1999, Cryan and Bogan (2003) surveyed sites where C. 

mexicana had previously been reported in Arizona and 

New Mexico. 

The purpose of this paper is to summarize our current 

knowledge about the population status of the three plantvisiting species. We first describe methods that have been 

used to estimate colony sizes before summarizing 

estimates of year-to-year variation in roost populations. 

Major emphasis is placed on populations in the U.S., but 

additional data from Mexico are included. 

Methods of Population 
 Methods of Population

Assessment AssessmentAssessment Assessment
 

Assessment of population trends for any species 

requires accurate census techniques. Highly gregarious 

bats, such as L. curasoae, which lives with other gregarious bats (e.g., species of Mormoopidae in Mexican 

roosts), can be difficult to census. Three different census 

techniques have been used to estimate the size of lesser 

long-nosed bat colonies: direct exit counts, counts from 

videotape images of exiting bats, and visual censuses 

within day roosts. 

At certain roosts (e.g., the maternity roost in Organ 

Pipe Cactus National Monument (ORPI), Arizona, and 

Pinacate Cave in Sonora, Mexico), reasonably accurate 

exit counts are possible because L. curasoae is the sole 

inhabitant, bats fly straight out of the roost without excessive “swirling around” at the entrance, and they depart at rates slow enough for accurate counting. 

Depending on the time of year, however, such exit counts 

are likely to underestimate the total number of bats in a 

roost because not all individuals leave with the first wave 

of departures. This is especially true during the nursing 

period (approximately mid-May through June; Fleming 

and others, 1998). 

The second census technique involves videotaping 

the exit flight and counting the net number of departing 

bats (i.e., number of bats flying back into the roost are 

subtracted from the number flying out) at 1-minute intervals directly from the videotape (Dalton and Dalton, 1994). 

It is critical to choose a census period when most or all 

bats are leaving the roost to feed. Comparison of simultaneous direct exit counts and video counts at the Organ 

Pipe roost indicate that substantial discrepancies (e.g., 

up to 40%) can sometimes occur between the two methods (Dalton and Dalton, 1994). 

The third and most commonly used census technique for all three species has been to quietly enter a 

roost during the day to obtain a visual count of the resting bats. The two of us using this technique (Petryszyn 

and Fleming) attempt to quickly note the areal coverage 

of Leptonycteris bats (in ft2

) before many bats take flight 

and then multiply that number by an estimate of the number of bats/ft2

. Petryszyn usually uses an estimate of 50 

bats/ft2. This is a conservative value because 

Leptonycteris bats are contact-loving and often roost by 

day in very dense masses of more than 50/ft2. Depending 

on the density of bats, Fleming has used values of 50 or 

100 bats/ft2 in his calculations. With all census techniques, 

we have tried to be conservative in estimating the size of 

Leptonycteris colonies. 

Population PopulationPopulation Trends in the rends in the rends in the Three

Species of Plant-Visiting Bats isiting Batsisiting Bats isiting Bats 

The Lesser Long-Nosed Bat 

Based on surveys conducted in Mexico and Arizona 

in 1983–1984, Wilson (1985) reported finding a total of 

about 15,500 individuals of L. curasoae in two roosts: 

15,000 in a sea cave near Chamela, Jalisco, and 500 in a 

cave near Patagonia, Arizona. Since those surveys, annual (or more frequent) censuses of L. curasoae have 

been conducted at three roosts in Arizona (a mine in ORPI, 

Patagonia Bat Cave, and State of Texas Mine) and two 

roosts in Sonora, Mexico (Pinacate Bat Cave and Sierra 

Kino Cave). Results of these plus other censuses (e.g., 

Wilkinson and Fleming, 1996; Ceballos and others, 1997) 

indicate that the total population size of this species is 

orders of magnitude greater than Wilson’s (1985) estimate. Moreover, based on an analysis of genetic diversity of mitochondrial DNA haplotypes, Wilkinson and 

Fleming (1996) estimated that the genetically effective 

population size of this species in western Mexico is 

50,000–100,000 adults. 

In Arizona, maternity roosts are located in the southwestern corner of the state in desertscrub containing large 

populations of columnar cacti whose flowers and fruit 

are major food sources for this species. An abandoned 

mine in ORPI apparently represents the largest maternity 
 

 

roost of this species in the U.S. For the past decade, the 

number of females in this roost has ranged from about 

8,000 to over 19,000 (Fig. 1). About 75% of the females in 

this roost are pregnant each year; the other 25% probably are yearling adults (Fleming and others, 1998, and 

unpublished observations). Additional maternity roosts 

in Arizona include a mine on the Cabeza Prieta National 

Wildlife Refuge and a mine on the Tohono O’odham Reservation. The few times they have been censused, these 

roosts have each contained <5,000 bats. 

The largest known maternity colony of this species 

in Mexico is located in a lava tube on the Pinacate 

Biosphere Reserve, about 50 km south of ORPI, in Sonora. 

Detailed estimates of the size of this colony will be 

published elsewhere (Petryszyn and others, unpub. data, 

1999), but the number of adults is on the order of 80,000– 

100,000 each year (Table 1). Another maternity colony 

occurs in one (or possibly two) caves on Isla Tiburon, 

farther south in Sonora (see Fig. 1 in Wilkinson and 

Fleming, 1996; Horner and others, 1998). Because of its 

inaccessibility, no estimates have yet been made of the 

size of this colony. Based on the size of other maternity 

colonies in Sonora (Wilkinson and Fleming, 1996; Fleming 

and Molina, unpub. data, 1999), however, it is likely that 

this roost contains at least 10,000 adults. A “transient” 

roost (i.e., one that is occupied for variable periods of 

time before and after the maternity period) on the mainland 

near Isla Tiburon (the Sierra Kino Cave) contains from 

2,000 to over 7,000 females in late March and early April 

(Table 1). Most of these bats either move to Isla Tiburon 

or continue migrating north. By the end of April, this 

cave contains very few L. curasoae (Horner and others, 

1998). 

Fig. 1. Estimates of maximum number of adults of 

Leptonycteris curasoae exiting from an abandoned mine 

in Organ Pipe Cactus National Monument,Arizona, 1989– 

1999. 

FLEMING AND OTHERS 65 

Lesser long-nosed bats also occupy “transient” 

roosts in south-central and southeastern Arizona and 

southwestern New Mexico in the late summer (Cockrum 

and Petryszyn, 1991; Hoyt and others, 1994). Two of those 

roosts, Patagonia Bat Cave and the State of Texas Mine, 

were censused annually in the 1990’s. Detailed accounts 

of these roosts will be published elsewhere (Petryszyn 

and Peachey, Petryszyn and Alberti, unpub. data, 1999). 

Numbers of adults and juveniles in these roosts vary 

annually and range from about 10,000 to nearly 60,000 

each year (Table 1). Factors responsible for this variation 

are currently unknown but deserve study (see the account 

of L. nivalis, below). Simultaneous counts at both of these 

roosts indicate that they jointly contain tens of thousands 

of bats. Such a count in mid-August 1999, for example, 

indicated that over 70,000 lesser long-nosed bats were 

present in these roosts (B. Alberti, Coronado National 

Monument, Arizona, unpub. data, 1999). These counts 

do not necessarily represent many of the same individuals 

from the southwestern Arizona maternity roosts. Genetic 

analysis indicates that bats in the Patagonia Bat Cave, 

for instance, do not have the same mtDNA haplotypes as 

those in the ORPI and Cabeza Prieta roosts (Wilkinson 

and Fleming, 1996). 

In summary, tens of thousands of lesser long-nosed 

bats are known to occupy roosts seasonally in southern 

Arizona. Genetic evidence suggests that bats migrate into 

Arizona via two different routes: (1) a spring route along 

the coastal lowlands of western Mexico which brings females to the southwestern maternity caves; and (2) a summer route along the western flanks of the Sierra Madre 

Occidental which brings bats (including some adult males) 

to the transient roosts farther east in southern Arizona 

(Wilkinson and Fleming, 1996). Census data indicate that 

colony sizes tend to vary somewhat from year-to-year 

and that the timing of occupation of transient roosts varies annually. Our data do not indicate that this species is 

uncommon or is experiencing a secular decline in numbers in the U.S., as implied by its “endangered” status. 

The Greater Long-Nosed Bat 

Far less is known about the population status of L. 

nivalis than its congener. Available data, including the 

number of specimens in museums, indicates that L. nivalis 

is less common in Mexico than L. curasoae (Arita, 1991). 

It is less common than the lesser long-nosed bat in the 

U.S., where it is known from only two locations: Mt. Emory 

Cave in Big Bend National Park, Texas; and Guadalupe 

Canyon and the Animas Mountains in southwestern New 

Mexico (Easterla, 1972, 1973; Hoyt and others, 1994). The 

U.S. Fish and Wildlife Service (1994) summarized visual 

estimates of L. nivalis at Mt. Emory, which apparently 
 

 

66 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Table 1. Range of able 1 between-year variation in estimates of roost sizes of Leptonycteris curasoae in Arizona and Mexico.

 Site Roost type Years censused Method of estimation Range of variationa 

Arizona Arizona

Mine in Organ Pipe Cactus Maternity 1989–1999 Exit counts 8,000–18,700 

National Monument 

Patagonia Bat Cave Transient 1989–1999 Visual estimates 15,000–58,000 

Exit counts 

State of Texas Mine Transient 1993–1999 Exit counts 9,300–31,000 

Mexico

Pinacate Cave, Sonora Maternity 1989–1999 Exit counts 80,000–100,000 

Sierra Kino Cave, Sonora Transient 1989–1999 Exit counts ca. 2,000–7,600 

a

Data from maternity roosts indicate maximum number of adults recorded each year. Data from transient roosts include 

maximum numbers of adults and juveniles. 

serves as a “transient” roost in late summer, between the 

years 1967 and 1993. In some years (e.g., 1970, 1992), no 

greater long-nosed bats were found in this cave. In other 

years, numbers ranged from 250 (1990) to 10,650 (1967). 

In recent years, numbers have ranged from >5,000 (in 

1991) to 2,859 (in 1993). 

Hoyt and others (1994) reported capturing 150–200 

Leptonycteris bats at a cattle tank in the Animas Mountains in late August 1992. The ratio of L. curasoae to L. 

nivalis in their captures was about 2:1. Subsequent work 

at that site revealed that these bats roost in a cave in a 

canyon near the tank. The main food for these bats both 

in Texas and New Mexico appears to be nectar and pollen 

from flowers of Agave. 

Judging from the high year-to-year variation in the 

size of the Mt. Emory roost, the two U.S. localities 

probably represent marginal sites for this species. Factors 

responsible for annual variation in the abundance of this 

bat at the northern limits of its geographic range are 

currently unknown. Of particular interest is the 

relationship between bat numbers and Agave flower 

abundance in the U.S. and Mexico. Moreno (1999) has 

documented a positive correlation between these two 

variables at a maternity roost in Nuevo Leon. Do greater 

long-nosed bats move into the U.S. in years of low Agave 

flower abundance in Mexico? 

The Mexican Long-Tongued Bat 

Choeronycteris mexicana is perhaps the least common of the three species of plant-visiting bats in the U.S. 

Unlike Leptonycteris bats, which range from moderately 

(L. nivalis) to highly gregarious (L. curasoae), C. mexicana 

is a non-gregarious bat that appears to live in very small 

colonies (i.e., <50 individuals) throughout its range. In 

the U.S., it has been reported from southern California 

(probably an extralimital record), southern Arizona, and 

southwestern New Mexico (Petryszyn and Cockrum, 

unpub. data, 1999) where it always occurs in very low 

numbers. Most records from Arizona and New Mexico 

come from montane sites >1,500 m in elevation. Only adult 

females and young of both sexes have been reported 

from these two states. 

In the summer of 1999, Cryan and Bogan (2003) visited 

24 historical sites from an initial list of 39 sites from which 

this species has been reported in Arizona and New Mexico. 

They found C. mexicana at 18 (75%) of the sites. Colony 

size averaged 4.5 individuals (range: 1–17), and youngof-year represented 23% of the 104 bats that were 

encountered. Nearly all roosts were located in or near 

riparian habitats and near substantial populations of 

Agave, a known food plant of this species (e.g., Howell 

and Roth, 1981). Based on the number of individuals 
 

encountered and the relatively high rate of recurrence at 

historic sites, Cryan and Bogan (2003) stated that they 

did not have sufficient evidence to conclude that 

populations of this species have declined dramatically in 

recent years. 

Conclusions ConclusionsConclusions

Arid regions of the southwestern U.S. are geographically marginal habitats for migratory, nectar-feeding 

phyllostomid bats. The three species discussed here probably moved into the U.S. with their major food plants, 

columnar cacti and paniculate species of Agave, as aridity increased in the southwest. Certain columnar cacti 

(e.g., Carnegiea gigantea and Stenocereus thurberi) appear to have moved into the U.S. within the last 3,500– 

10,500 years (Van Devender, 1987; Van Devender and 

others, 1990), well after the last glacial maximum.As is the 

case for many marginal populations, year-to-year fluctuations are to be expected in the abundance of these 

species. Judging from the size and stability of maternity 

roosts near the northern edge of its distribution, L. 

curasoae appears to be the most successful of the three 

species. Despite its small colony sizes, which reflect the 

non-gregarious nature of this species, C. mexicana also 

appears to be well-integrated into arid land ecosystems. 

Only L. nivalis, which appears to be an “irruptive” species that roosts in numbers in the U.S. only under certain 

conditions, seems to be a problematic species in the arid 

Southwest. 

Current evidence suggests that at least two of these 

species (L. curasoae and C. mexicana) are not undergoing 

population declines, although both species of 

Leptonycteris will always be vulnerable to population 

losses because of their gregarious roosting behavior. 

Furthermore, none of these species are likely to have been 

particularly common in the Southwestern U.S. in the past. 

Biotic evidence for this hypothesis comes from studies 

of the pollination biology of their major food species, 

columnar cacti and paniculate species of Agave. Neither 

saguaros and organ pipe cacti nor century plants such as 

Agave palmeri are solely dependent on bats for fruit and 

seed set (McGregor and others, 1962; Fleming and others, 

1996; Slauson, 1996), despite having flowers that conform 

to the classic chiropterophilous “syndrome.” At the 

northern edges of their geographic ranges, these species 

are effectively pollinated by diurnal animals such as birds 

and insects, in addition to bats. Chronic scarcity or 

unreliability of bat visitation in the arid southwestern 

U.S. has apparently favored the evolution of subtle 

changes in flowering phenology, including nectar 

FLEMING AND OTHERS 67 

secretion patterns and time of flower closing (Fleming 

and others, 1996). These changes increase the diversity 

of animals that can pollinate their flowers. Tropical nectarfeeding bats may seasonally inhabit the southwestern 

U.S., but their food plants tell us that these bats are not to 

be trusted as their exclusive pollinators. 

Acknowledgments Acknowledgments Acknowledgments

Many people have helped us gather the data summarized in this report. Special thanks go to B. Alberti, T. 

Beathard, D. Dalton, J. Holland, M. Horner, A. Pate, and 

W. Peachey for their field assistance and contribution of 

data. Support for our research has come from the National Geographic Society, National Fish and Wildlife 

Foundation, U.S. National Science Foundation, U.S. National Park Service, and the Arizona Department of Game 

and Fish. 

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vol. 43, p. 259–267. 

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Mexico: College Station, Texas, Texas A&M University, Ph.D. dissertation. 

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long-nosed bats: Federal Register, vol. 53 , no. 190, 

p. 38456–38460. 

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palmeri Engelm, (Agavaceae): Tempe, Arizona State 

University, Ph.D. dissertation. 

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and Wildlife Service, Albuquerque, New Mexico, 100 p. 

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Arizona: Quaternary Research, vol. 27, p. 51–72. 

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Devender, T.R., and Martin, P.S., eds., Packrat middens: 

The last 40,000 years of biotic change: Tucson, University of Arizona Press, p. 326–349. 

Wilkinson, G.S., and Fleming, T.H., 1996, Migration and 

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Washington, D.C. 
Western Crevice and Cavity-Roosting Bats estern Crevice and Cavity-Roosting Bats estern Crevice and Cavity-Roosting Bats

By
 

Michael A. Bogan
 

Paul M. Cryan
 

Ernest W. Valdez 


U.S. Geological Survey
 

Fort Collins Science Center
 

Museum of Southwestern Biology
 

University of New Mexico
 

Albuquerque, NM 87131
 

and
 

Laura E. Ellison 


Thomas J. O’Shea 


U.S. Geological Survey
 

Fort Collins Science Center
 

2150 Centre Avenue, Building C
 

Fort Collins, CO 80526
 

Abstract. Among the 45 species of bats that occur in the United States (U.S.), 34 species regularly occur in western regions 

of the country. Many of these “western” species choose roost sites in crevices or cavities. Herein we provide an introduction to 

the biology of bats that roost in cavities and crevices and assess the challenges and opportunities associated with monitoring their 

populations. We reviewed recent studies and examined the U.S. Geological Survey Bat Population Database (BPD) for records of 

western bats using crevice and cavity roosts. We found records of 25 species of western bats that use crevice or cavity roosts for 

at least part of their annual cycle. There were relatively few (n = 92) observations or counts for these species in the BPD, 

representing only 6% of the observations in the database. This paucity of records likely reflects the difficulty of observing bats in 

such situations rather than actual use. We found no long-term data adequate for population trend analysis among this group of bats. 

Since the development of miniaturized radio transmitters, our knowledge about bats that roost in cavities and crevices has 

increased. Future challenges associated with monitoring these species will include understanding variability in the types of roosts 

used as well as the roost-switching behavior exhibited by many species. 

Abstract Abstract

Key Words: Bat Population Database, BPD, cavity-counting bats, crevice roosting bats, roost selection, roost-switching, 

western U.S. bats. 

69 
70 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Introduction IntroductionIntroduction

“Our bats may be placed for convenience in two 

arbitrary groups—those which roost singly or a 

very few together in trees, high cliffs, or similar 

locations; and those which are in the habit of 

gathering in numbers in caves, hollow trees, and 

old buildings. In the case of the former In the case of the former In the case of the former 

class, few of us are qualified to talk at class, few of us are qualified to talk at class, few of us are qualified to talk at 

great length.” (Howell, 1919:169; emphasis 

ours) 

There are 45 species of bats of 19 genera and four 

families in the United States (U.S.) (Hall, 1981; Harvey 

and others, 1999). Twenty-seven of these have distributions mostly confined to the western U.S. and six species 

occur in both eastern and western North America. Myotis 

septentrionalis occurs westward to British Columbia in 

Canada and eastern Wyoming in the U.S. (Bogan and 

Cryan, 2000) and we include it as a western species. Thus, 

we tabulate a minimum of 34 species of bats that can be 

said to be “western” bats. Some eastern species of bats 

are extending their range westward, likely as a result of 

habitat change (e.g., Pipistrellus subflavus; Yancey and 

others, 1995), so we may expect this number to change 

over time. A considerable proportion of the 34 western 

species of bats use crevices and cavities as roost sites, at 

least seasonally. 

The relatively high diversity of bats in the western 

U.S. undoubtedly results from a greater variety of potential roosts than are found in other regions. Roosts play 

an important role in the lives of bats and the availability 

of suitable roosts likely influences species diversity and 

abundance (Kunz 1982). In particular, Humphrey (1975) 

found that bat diversity and evenness were highest in 

areas with a variety of potential roost structures (e.g., 

cliffs, caves, forests) and that species diversity was generally low in areas where roost structures were lacking 

(e.g., grasslands). 

Roosts are critically important for bats because they 

provide a haven from the elements and predators as well 

as places to mate, raise young, hibernate, rest, digest 

food, and interact socially.Although specific requirements 

vary among species, in general, roosts must meet rather 

specific microclimatic conditions, restrict access to competitors and predators, and be within commuting distance 

of food and water. The diversity of roosting adaptations 

shown by bats was examined by Kunz (1982) and although 

he avoided a rigid classification of roost types, he noted 

that day roosts of bats included sites such as caves, 

crevices in rocks and narrow spaces beneath exfoliating 

tree bark, tree cavities, and foliage and other “external” 

roosts. He stated that crevice-dwelling was a prevalent 

feature of vespertilionid and molossid bats, especially in 

arid and semiarid regions, and commented (p. 5) that “little 

is known about the roosting ecology of crevice-dwelling 

bats, because they are difficult to find and often located 

in inaccessible places.” Kunz (1982) found numerous examples of the use of tree cavities by bats but interestingly, gave no examples of use of cavities by bats of the 

western U.S. He restricted his comments on New World 

bats to Neotropical species, although he gave examples 

of several Palearctic vespertilionids that roost in trees. 

Our ability to obtain information on the roosting habits of bats has improved markedly since Kunz (1982) made 

his comments. In particular, the availability of miniaturized radio-transmitters and their application to bats has 

truly revolutionized field studies of bats, especially in the 

western U.S. In the past decade, a plethora of studies 

using transmitters has greatly expanded our knowledge 

of bat roosting ecology (e.g., Barclay and Brigham, 1996, 

and papers therein). 

Nonetheless, a variety of factors still continue to 

confound our attempts to better understand the roosting 

ecology of bats. One of these factors is the extent to 

which bats use multiple roosts, even during a single life 

history event such as lactation. Data on roost fidelity 

among 43 species of bats (not all North American) was 

summarized by Lewis (1995), who found that 25 species 

frequently change roosts, 14 rarely change, and 4 show 

intraspecific variability in roost switching. Her analysis 

showed that fidelity is directly related to roost permanency and inversely related to roost availability. Thus, 

she predicted that bats would demonstrate low fidelity 

for ephemeral sites that are abundant on the landscape, 

whereas they would show increased fidelity to relatively 

rare sites of high permanence. Since the publication of 

Lewis’paper, multiple papers have appeared that support 

her assertions. In particular, bats that roost in crevices 

and cavities in forest trees, sites that are presumably abundant and ephemeral, seem prone to use multiple roosts 

and to switch among them on a frequent basis (e.g., 

Kalcounis and Brigham, 1998; Weller and Zabel, 2001; 

Menzel and others, 2002). Data also support the existence of the same behavior in some species that use crevices and cavities in cliffs and rocks (e.g., Lewis, 1993; 

Cryan and others, 2001; Lausen and Barclay, 2002; 

S. Haymond and others, written commun., 2003). 

The purpose of this chapter is to provide an 

introduction, but not an extensive review (such as Hayes, 

2003), to the biology of bats that roost in cavities, 

crevices, and similar structures. In addition, we 

characterized features of bats that use such structures, 

assessed challenges and opportunities to monitor these 

species, and attempted to discern the extent to which 

long-term data on their populations exist and might be 
 

used to determine trends in abundance. We were primarily 

interested in bats that use crevices or cavities in cliffs, 

rocks, or trees and similar human-made structures. It was 

primarily these species that were described as “overdispersed” in the Working Group reports in this volume. 

Methods Methods

To obtain information on western bat species that 

roost in crevices and cavities in rocks and trees we examined the U.S. Geological Survey’s Bat Population Database (BPD; Ellison and others, 2003) for records of bats 

using such roosts. In addition, we reviewed an array of 

recent studies that provide new data on species known 

to roost in crevices or cavities in trees and rocks. Our 

search criteria for species that use cavities, crevices, or 

rock shelters were defined as follows: 

• 	 Cavity. A hollow space, typically of small size 

(e.g., < 1 m3), and occurring in trees, rocks, or 

cliffs. These do not include caves. 

• 	 Crevice. A crack forming an opening in a substrate, such as a cliff or tree. 

• 	 Rock shelter. Shallow caves of small size (e.g., < 

5–10 m3), usually moderately well-lighted and 

distinguished from larger caves by lack of 

complexity. 

Results and Discussion Results and Discussion 

We tabulated 25 species of bats in the western U.S. 

that use crevices or cavities as roosts (Table 1). Whereas 

some of these species may only use such sites opportunistically or at certain times of the year, cavities and crevices likely play an important role in the lives of most of 

these species, especially during reproductive periods. 

Variation in the type of roost used within a species is 

likely influenced by such factors as sex, season, and roost 

availability. One species, Eumops underwoodi, is likely 

to roost in crevices in cliffs, but no roosts of this species 

in the U.S. have been described in the literature. Records 

of counts in crevices for some species, such as M. keenii 

and M. velifer, did not exist in the database, although 

these species are known to use crevice roosts (e.g., Kunz, 

1974; Nagorsen and Brigham, 1993) and occur in the U.S. 

(e.g., Parker and Cook, 1996). 

The BPD reveals only 92 observations of western 

bats using crevices or cavities, representing only 6% of 

the 1,513 observations for western bats in the database 

(Table 2). Observations of bats roosting in caves (45%) 

BOGAN AND OTHERS 71 

and mines (23%) accounted for most records. The low 

count for crevices and cavities reflects both the paucity 

of recorded observations at such sites as well as the historic emphasis on counting or studying bats in large aggregations in caves and mines. For at least some of the 

species that we now know use crevices or cavities, information on roosting habits came from opportunistic encounters at, or in, caves, mines, bridges, tunnels, and 

buildings. Many of these roosting sites are likely surrogates for crevices or cavities, at least seasonally. Information from the BPD also reveals that, among the species 

known to use cavities and crevices, use of other more 

spacious structures (i.e., caves, mines, and buildings) 

may occur during hibernation, although not exclusively 

so. Some of the largest roosting groups observed for 

several of these species come from observations in mines 

and caves during winter. For example, in many areas of 

the U.S., Myotis lucifugus typically roosts in crevices 

within buildings, trees, or rocks during the warmer months, 

but then aggregates in large clusters on the ceilings of 

caves and mines during the hibernation season. Nonetheless, there are very few descriptions of winter roosts 

for many species of western bats that roost in crevices 

and cavities during the warmer months. It is likely that 

many species over-winter in cavities and crevices, but 

the difficulty of detecting bats wintering in well-hidden 

sites has led to limited documentation of such behavior. 

As a result of their cryptic roosting habits, there are few 

observations of crevice and cavity dwelling bats that extend over years or even months (Ellison and others, 2003). 

Basic Life History of Crevice-Dwelling Bats 

Much of the basic life history information we have 

for bats that roost in cavities and crevices has come, at 

least since the late 1950’s, from captures of these species 

in mist nets set over water. Such efforts have provided 

considerable information on reproduction, diet, foraging 

areas, activity times, associates, and other aspects of 

natural history. Western landscapes, in particular, promote this activity due to the isolation of one waterhole 

from another, a circumstance that may concentrate bats. 

This “concentration effect” likely depends on seasonal 

precipitation, with wet summers that produce more and 

closer waterholes tending to disperse bats over the landscape with consequently lower capture rates (Findley, 

1993; K.N. Geluso, oral commun., 2000). Most investigators agree that captures of bats in mist nets, although 

they provide considerable “hands-on” data, are fraught 

with a variety of biases and may offer few opportunities, 

beyond monitoring for presence and relative abundance, 

for long-term population monitoring (see Working Group 
 

 

 

 

 

 

 

 

 

72 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Table 1. Species in the Table 1.Table 1. western United States known to use crevices, cavities, or “rock shelters” during at least part of 

their annual cycle. A single citation is included as an entry into the literature. 

Scientific name Common name Citation 

Antrozous pallidus PallidBat O’Shea and Vaughan (1999) 

Choeronycteris mexicana Mexican Long-tongued Bat Cryan and Bogan (2003) 

Corynorhinus townsendii Townsend’s Big-eared Bat Bogan and others (1998) 

Eptesicus fuscus Big Brown Bat Lausen and Barclay (2002) 

Euderma maculatum Spotted Bat Pierson and Rainey (1998a) 

Eumops perotis Greater Mastiff Bat Cockrum (1960) 

Idionycteris phyllotis Allen’s Big-eared Bat Haymond and others (written commun., 2003) 

Lasionycteris noctivagans Silver-hairedBat Mattson and others (1996) 

Leptonycteris curasoae Lesser Long-nosed Bat Cockrum and Petryszyn (1991) 

L. nivalis Greater Long-nosed Bat Hensley and Wilkins (1988) 

Myotis auriculus Southwestern Bat Bernardos and others (2000) 

M. californicus CaliforniaBat Brigham and others (1997) 

M. ciliolabrum Western Small-footed Bat Bogan and Cryan (2000) 

M. evotis Long-eared Bat Chruszcz and Barclay (2002) 

M. keenii Keen’s Bat Nagorsen and Brigham (1993) 

M. lucifugus Little Brown Bat Barbour and Davis (1969) 

M. occultus Occult Bat Stager (1943) 

M. septentrionalis Northern Long-eared Bat Menzel and others (2002) 

M. thysanodes Fringed Bat Cryan and others (2001) 

M. volans Long-legged Bat Cryan and others (2001) 

M. yumanensis YumaBat Gellman and Zielinski (1996) 

Nyctinomops femorosaccus Pocketed Free-tailed Bat Pierson and Rainey (1998b) 

N. macrotis Big Free-tailed Bat Pierson and Rainey (1998b) 

Pipistrellus hesperus Western Pipistrelle Bat Barbour and Davis (1969) 

Tadarida brasiliensis Mexican Free-tailed Bat Krutzsch (1955) 

reports, this volume). Roost sites in crevices and cavities 

with no obvious outward indication of bats are vastly 

under-represented in abundance estimates. 

Categorizing species as crevice and cavity users is 

an artificial classification and there is the possibility that 

in combining them, unique and differing aspects of their 

life histories may be obscured. Although this is undoubtedly true, there are several unifying features of these 

bats. Like nearly all bats north of Mexico, most species 

are insectivorous (there are three nectarivorous forms), 

have low reproductive rates [0.5–1.5 young/female/yr 

(Geisler, 1979); notably excluding Lasiurus spp.], hibernate in the winter (but at least a dozen species migrate 

considerable distances and probably do not hibernate), 

exhibit delayed fertilization (sperm storage during hibernation), have long infant dependency for a small mammal 

(weeks to months), suffer high juvenile mortality but are 

relatively long-lived (average, 5–15 years; extreme, 30 

years; survival rates, 50–70%; Findley, 1993), and may 

have low rates of predation (but see Tuttle and Stevenson, 

1982). 

Conversely, bats using cavities and crevices also 

represent a very diverse assemblage, taxonomically and 

otherwise. For the U.S., the group includes both the 

Table 2. able 2 Frequency of use of roost structures by 25 

species of western bats listed in Table 1, as shown by 

the U.S. Geological Survey Bat Population Database 

(BPD). The roost type “cavity/crevice” includes roosts 

categorized as crevice, cliff, and rock shelter in the 

BPD. 

Roost type Counts Percent (%) 

Cavity/Crevice 92 6 

Bridge 119 8 

Building 249 16 

Cave 678 45 

Mine 350 23 

Other 25 2 

Total 1,513 100 
 

smallest (P. hesperus) and largest (E. perotis) species 

(Barbour and Davis, 1969), slow- (P. hesperus, several 

Myotis) and fast- (lasiurines and molossids) flying species 

(Hayward and Davis, 1964), relatively well- (M. lucifugus) 

and poorly- (Eumops, Nyctinomops) known species, 

slow- (A. pallidus) and faster- (M. lucifugus; Kunz and 

Stern, 1995) developing species, those that escape (sensu 

Findley, 1993) food shortage in north temperate winters 

in time (hibernators) versus those that escape in space 

(migrators), those with protein-rich diets (insectivores) 

and those with low protein diets (nectarivores), and those 

with large (Tadarida brasiliensis; Constantine, 1967) and 

small (Myotis evotis; Chruszcz and Barclay, 2002) group 

sizes. 

Roosting Behavior of Crevice-Dwelling Bats 

Since the emergence of miniaturized radio-transmitters in the mid-1980’s, bats have been shown to roost in a 

variety of structures and situations that were previously 

undocumented. Radio-tracking studies in forested areas 

during the summer months reveal that bats frequently 

form maternity colonies in trees (Barclay and others, 1988; 

Sasse, 1995; Barclay and Brigham, 1996, 2001; Campbell 

and others, 1996; Mattson and others, 1996; Vonhof and 

Barclay, 1996; Brigham and others, 1997; Callahan and 

others, 1997; Betts, 1998; Kalcounis and Brigham, 1998; 

Ormsbee and McComb, 1998; Rabe and others, 1998; 

Waldien and others, 2000; Cryan and others, 2001; Lacki 

and Schwierjohann, 2001; Menzel and others, 2002; Weller 

and Zabel, 2001; Parsons and others, 2003). Although the 

use of trees by bats had been documented previous to 

the advent of radio-transmitters (Barbour and Davis, 1969), 

there were no practical means by which to find and examine such roosts. Similar disclosure of rock crevices used 

as roosts by bats also has been possible with radio-transmitters (e.g., Lewis, 1993; Bogan and others, 1998; Cryan 

and others, 2001; Lausen and Barclay, 2002). However, 

most work to date has involved simply characterizing such 

roosts, following movements of radio-tracked bats (often 

among a network of roosts), obtaining information on 

foraging behavior, and making counts of emerging bats. 

Monitoring of trends in these species has not been a 

focus. 

Monitoring Crevice-Roosting Bats:
 

Challenges and Opportunities
 

Western bat species that use crevices and cavities 

are variable and flexible in their roosting behaviors. For 

example, M. septentrionalis uses buildings and caves, as 

well as several species of trees (Mumford and Cope, 1964; 

Foster, 1993; Sasse, 1995; Foster and Kurta, 1999; Cryan 

BOGAN AND OTHERS 73 

and others, 2001; Lacki and Schwierjohann, 2001; Menzel 

and others, 2002); Myotis thysanodes is known to inhabit 

buildings (Dalquest, 1947; Musser and Durrant, 1960; 

Studier, 1968), rock crevices (Bogan and others, 1998; 

Cryan and others, 2001), trees (Rabe and others, 1998; 

Cryan and others, 2001; Chung-MacCoubrey, 2003), mines 

(J.S. Altenbach, oral commun., 2000), and caves (Baker, 

1962); M. volans uses buildings (Dalquest and Ramage, 

1946), several species of trees (Baker and Phillips, 1965; 

Vonhof and Barclay, 1996; Ormsbee and McComb, 1998; 

Rabe and others, 1998; Chung-MacCoubrey, 2003), and 

rock crevices (Quay, 1948; Cryan and others, 2001); and 

E. fuscus is known to use buildings (Barbour and Davis, 

1969; Barclay, 1991), several species of trees (Brigham, 

1991; Vonhof and Barclay, 1996; Bogan and others, 1998; 

Kalcounis and Brigham, 1998; Cryan and others, 2001); 

cactus (Cross and Huibregtse, 1964); and caves and rock 

crevices (Barbour and Davis, 1969; Barclay, 1991; Lausen 

and Barclay, 2002). The considerable variation in the type 

of roost structures occupied by these species cannot be 

entirely attributed to regional differences in roost 

availability or roosting behavior. In the Black Hills of South 

Dakota, Cryan and others (2001) documented individuals 

of both M. volans and M. thysanodes moving from 

crevices in rocks to crevices in trees, showing that local 

populations are not limited to using crevices and cavities 

in a single type of roost structure. Furthermore, for many 

species there is still not sufficient information to draw 

definitive conclusions. 

Another source of variation in roosting habits of 

bats is that in most species, males and females exhibit 

contrasting roosting behaviors during the summer. 

Differences in roost selection between sexes of bats stem 

from increased energy and water demands placed on 

pregnant and lactating females. In brief, males are able to 

use periodic (usually daily) periods of torpor to lower 

their body temperature and, hence, their energy 

expenditure (Grinevitch and others, 1995). Females, 

however, usually maintain a constant body temperature 

during pregnancy and lactation. This promotes rapid and 

timely growth of the fetus and young, thus enabling 

young-of-the-year to acquire and store energy to meet 

the demands of either hibernation or migration (Racey 

and Entwistle, 2000). In general, males are frequently 

encountered roosting alone in caves, mines, under tree 

bark, or in buildings. Females typically choose sites that 

retain heat (e.g., cavities in large trees and snags or 

crevices in exposed cliff faces) and where both they and 

their young can maintain the constant body temperatures 

that promote rapid growth. During the summer months, 

maternity groups must find larger spaces in which to 

aggregate than solitary males, likely influencing the type 

of structure selected. In addition to sex differences in 

summer roost use by species that use cavities and 
74 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

crevices, there are often differences in roost selection 

among reproductive stages. For example, Chruszcz and 

Barclay (2002) found that the type of rock crevices used 

by female M. evotis differed with their reproductive state; 

pregnant females typically roosted in horizontally oriented 

crevices, whereas lactating females frequently used 

vertically oriented crevices. Differential roost selection 

among female reproductive groups was also observed 

among pregnant, lactating, and postlactating E. fuscus 

using rock crevices (Lausen and Barclay, 2002). Variability 

in roost use that results from different energy needs 

between the sexes during the warmer months likely 

diminishes with the annual cessation of reproductive 

activity. Therefore, both sexes probably exhibit less 

dichotomous roosting behaviors during the colder months 

and may be more likely to occupy the same roosts during 

winter. 

Given the incidental nature of many observations 

and lack of data on specific locations of overwintering 

sites, it is not yet clear that cavity- and crevice-dwelling 

bats can be monitored in a systematic fashion at their 

winter roosts. Certainly this will be difficult for many of 

the migratory species that travel great distances (e.g., 

Cryan, 2003). Even for species that only migrate very short 

distances to their winter quarters, we must be able to 

track them to such sites. The development of smaller and 

longer-lasting transmitters and the application of new 

tracking techniques (e.g., stable isotopes; Cryan and others, in press) may enhance our ability to follow some 

species from summer to winter quarters. Likewise, development of remote-monitoring methods may allow censuses of some species in roosts that cannot or should 

not be entered in the winter. Once roost locations are 

known, it will be feasible to contemplate the establishment of a long-term monitoring program, assuming funding for such activities is available. In the meantime some 

level of continued inventory for new roost locations may 

be required. 

A major obstacle confounding any attempt to assess 

the abundance of crevice or cavity roosting bats, at least 

during the summer months, is the fact that many species 

change roosts frequently. As Lewis (1995) noted, costs 

of short-term movement (of bats among roosts) should 

be balanced by benefits associated with moving. The 

presumed benefits of fidelity include greater site familiarity, 

maintenance of social relationships, and retention of 

roosts suited for raising offspring. Conversely, the 

benefits of lability include decreased commuting costs to 

foraging areas, familiarity with roosts that may differ in 

microclimate, and possible lower probability of predation 

and parasitism. In relation to caves and mines, cavities 

and crevices in trees and rock are generally less 

permanent, likely influencing bats roosting in such 

structures to move frequently. There are many problems 

associated with monitoring roosts used by creviceroosting bats that switch roosts frequently. For example, 

Lausen and Barclay (2002) studied a maternity group of 

E. fuscus that roosted in a series of rock crevices in western 

Canada. After following approximately 32 members of this 

colony for two seasons, they documented the use of 72 

different roost crevices within the study area. With so 

many potential roosts, current methods of monitoring (e.g., 

visual emergence counts) would be inadequate in such a 

situation. However, there is increasing evidence that roostswitching bats that roost in both rock and tree crevices/ 

cavities typically move within relatively small areas 

(Vonhof and Barclay, 1996; Callahan and others, 1997; 

Kalcounis and Brigham, 1998; Cryan and others, 2001; 

Lausen and Barclay, 2002). Unfortunately, most studies 

of roost switching in bats are limited in time (~2–3 years; 

Miller and others, 2003) and have not adequately 

determined the level of fidelity that roost-switching bats 

exhibit toward their roosting areas. Currently there are 

too many unanswered questions regarding the basic 

natural history of roost-switching bats to competently 

proceed with monitoring of such populations. Important 

topics to address with future research include determining 

the seasonal movements and dispersal patterns of roostchanging bats in a given area and how such factors vary 

with locality (Cryan and others, 2001). In addition, 

determination of underlying roost characteristics (e.g., 

microclimate, internal dimensions) that are common to 

the various structures occupied by these bats might help 

explain their roost-switching behavior and aid in future 

attempts at monitoring (i.e., help predict “suitable” 

roosts). 

Techniques Used for Assessing Abundance 

Many relatively standardized techniques potentially 

can be used for monitoring bats using crevices and 

cavities, including netting, banding, exit counts (both 

unassisted and assisted), use of passive integratedtransponder tags, thermal imaging, and bat detectors (see 

also Kunz, 1988, 2003). Because bats roosting in cavities 

or crevices are typically not visible from the outside, 

abundance estimates must be based on counts of the 

bats leaving the roost or by somehow looking into the 

roost. The former method is the most commonly used 

technique and typically involves capturing or visually 

observing bats as they exit the roost. Capture methods 

allow positive species identification and determination 

of colony demographics, but are invasive and may bias 

future monitoring. Visual emergence counts are minimally 

invasive, but the drawbacks to visual counts include 

limited light levels by the time the bats emerge, distance 

from roost (e.g. crevice high on cliff wall, cavity high in 
 

tree), difficulty in counting multiple bats leaving at once, 

and not being able to confirm species identification. Such 

obstacles can be minimized by using night-vision scopes, 

infrared or thermal-imaging cameras, automated counting 

devices, and ultrasonic bat detectors (for species 

identification in some cases). Actually looking into a bat 

roost may seem to be a less practical way of counting 

bats, but miniaturized camera probes, if used in a manner 

that does not unduly disturb bats, may allow such efforts 

in the future. 

Summary and Recommendations Summary and Recommendations Summary and Recommendations

In spite of the proliferation of new data on roosting 

behavior of western bats, we are not aware of any current 

long-term monitoring efforts of bats that roost in cavities 

or crevices in the western U.S. Nonetheless, follow-up 

surveys of historically occupied bat roosts indicate the 

utility and importance of monitoring crevices and shelters to assess long-term population trends (e.g., Pierson 

and Rainey, 1998a; O’Shea and Vaughan, 1999; Cryan 

and Bogan, 2003). In light of the lack of long-term studies 

as well as our limited understanding of colony dynamics 

in those species of bats that roost in cavities and crevices, we recommend that efforts be made to establish research projects which investigate colonies of these 

species over longer periods of time (> 5–10 years). Only 

by studying the movements and levels of site fidelity 

exhibited by these species at larger landscape scales and 

for longer periods, will we be able to make progress toward better understanding them and effectively monitoring their populations. 

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Survey and Monitoring of Rare Bats in Survey and Monitoring of Rare Bats in 
 Survey and Monitoring of Rare Bats in

Bottomland Hardwood Forests Bottomland Hardwood Forests 
 Bottomland Hardwood Forests

By 

Mary Kay Clark 

North Carolina State Museum of Natural Sciences 

11 W. Jones Street 

Raleigh, NC 27603 

Abstract. Abstract.Abstract. Abstract. Survey and monitoring efforts for Rafinesque’s big-eared bat (Corynorhinus rafinesquii) and the southeastern 

myotis (Myotis austroriparius) are needed in bottomland hardwood forests. These forests occur in a large part of the ranges of 

these two species, but little is known about the status of populations of these bats in this habitat. The possible rare status of these 

two species, combined with the documented decline of bottomland hardwood forests in the United States, indicate that survey and 

monitoring in these areas should be a high priority. Surveys for these bats in seven states that contain large areas of bottomland 

hardwood forests demonstrate that new records for these species are not difficult to obtain. However, estimation of colony and 

population sizes has not been feasible for these species. Exploration of alternative methods to determine population status should 

include evaluation of geospatial technology to develop predictive models. 

ords: Distribution, geospatial technology, habitat specificity, population status, Rafinesque’s big-eared bat, southeastern 

myotis, tree cavity roost. 

Key Words

“Not everything that can be counted counts, and not everything that counts can be counted.” Albert Einstein 

Introduction IntroductionIntroduction

The above quote sums up one of the conundrums 

that all biologists face when the need arises to enumerate 

a population—what can be counted and what should be 

counted? 

Ideally, for bats what should be counted are the 

numbers of individuals in a given area, but aspects of the 

life histories of bats make it difficult to make the counts or 

estimates that are needed for population monitoring. 

Species of bats that are widely dispersed over large 

forested areas and that roost alone, or in low densities, 

probably pose the greatest challenges for survey and 

monitoring. Natural roosts for many of these species 

include a variety of structural components of trees such 

as foliage, large and small cavities, and various types of 

crevices (e.g., loose bark, lightning scars). The broad 

dispersal patterns of these species, combined with their 

preference for roosting in inconspicuous structures that 

are located within vast forested landscapes, makes it 

difficult to find individuals and colonies. But many of the 

bats in the United States (U.S.) roost in some type of 

structural component of trees for at least part of the year 

(Pierson, 1998). Forest loss and degradation are of 

concern throughout the U.S. (Noss and others, 1995), 

resulting in a need to address survey and monitoring 

efforts for forest-dwelling bats. 

This paper discusses the survey and monitoring challenges and needs for two forest bats, Rafinesque’s bigeared bat (Corynorhinus rafinesquii) (Fig. 1A) and the 

southeastern myotis (Myotis austroriparius) (Fig. 1B), in 

the parts of their ranges where they use bottomland hardwood forests (Fig. 2). The objectives of this paper are to 

review surveys and summarize other available information relevant to population status and the survey and 

monitoring needs for these two species; describe factors 

that may affect survey and monitoring design for these 

species in bottomland hardwood forests; and provide 

recommendations for study to improve our knowledge of 

the status of these two species. 

Background BackgroundBackground

The conservation status of bottomland hardwood 

forests has been of concern to natural resource managers 

79 
80 INFORMATION AND TECHNOLOGY REPORT–2003-0003 

A B 


Fig. 1. Fig. 1 (A) Rafinesque’s big-eared bat (Corynorhinus rafinesquii; photograph by James F. Parnell, after Webster and 

others, 1985). (B) The southeastern myotis (Myotis austroriparius; photograph by David A. Saugey). 

Fig. 2. Aerial view of a bottomland hardwood forest in 

the lower Roanoke River basin in northeastern North 

Carolina. This portion of the basin is representative of 

sites that were surveyed for bats between 1996 and 1998 

(M. K. Clark, unpub. data, 1998). Seven species of bats 

were captured, including the southeastern myotis (Myotis 

austroriparius) and Rafinesque’s big-eared bat 

(Corynorhinus rafinesquii). Photograph by Mary K. Clark. 

for a number of years. Harris (1984) estimated a 78% 

decline of pre-settlement bottomland hardwood forests 

in the southeastern U.S. Noss and others (1995) reviewed 

ecosystem status in the U.S. and categorized these 

wetland forests as threatened due to widespread losses 

and degradation. Recent investigations have shown that 

the annual change in bottomland hardwood area is 

diminishing and the frequency of large (>2,023 ha) forest 

fragments is declining in the Mississippi Alluvial Valley 

(Rudis, 2001). These changes have the potential to 

significantly affect populations of bats in the 

southeastern U.S. 

A high percentage, 61% (11 of 18 species; Table 1), 

of the species of bats that occur in the southeastern U.S. 

have been documented from bottomland hardwood 

forests, indicating that these habitats are rich in resources 

for bats. These species were captured or otherwise 

observed from these forests by a number of investigators 

in different states. These include, for example: Louisiana 

(Lowery, 1974; Lance and Garrett, 1997; Lance and others, 

2001); North Carolina (M.K. Clark, written commun., 1999); 
CLARK 81 

Table 1. E able 1 leven species of bats that have been documented from bottomland hardwood forests in the 

southeastern United States. This list was compiled 

from Cochran (1999); Hoffman (1999); Lance (1997); 

Lance and others (2001); Lowery (1974); and agency 

reports of M.K. Clark of the North Carolina State Museum of Natural Sciences (unpub. data, 1994, 1996, 

1997, 1998) and S. Lambiase of North Carolina State 

Parks, Raleigh (unpub. data, 2001). 

Species Common name 

Corynorhinus rafinesquii Rafinesque’s big-eared

 bat 

Eptesicus fuscus Big brown bat 

Lasiurus borealis Red bat 

Lasiurus cinereus Hoary bat 

Lasiurus seminolus Seminole bat 

Lasionycteris noctivagans Silver-haired bat 

Myotis austroriparius Southeastern myotis 

Myotis lucifigus Little brown bat 

Nycticieus humeralis Evening bat 

Pipistrellus subflavus Eastern pipistrelle 

Tadarida brasiliensis Brazilian free-tailed bat 

and Arkansas (Cochran, unpub. data, 1999; Hoffman, 

unpub. data, 1999). Most of the species found in 

bottomland forests (Table 1) are widespread, occurring 

throughout much of North America (such as Eptesicus 

fuscus and Lasionycteris noctivagans; Barbour and 

Davis, 1969) or over large portions of the eastern U.S. 

(such as Pipistrellus subflavus and Nycticeus humeralis; 

Barbour and Davis, 1969). Rafinesque’s big-eared bat and 

the southeastern myotis, however, are only found in 16 

southeastern and south-central states and have 

distributions that are nearly identical (Jones, 1977; Jones 

and Manning, 1989). More than half of the states in their 

range (nine) are Gulf Coast and Atlantic Coast states that 

have large areas that are currently, or were historically, 

covered by bottomland hardwood forests. These areas 

are significant for these two species of bats. 

There are two subspecies of Rafinesque’s big-eared 

bat: C. r. rafinesquii and C. r. macrotis (Jones, 1977). The 

subspecies rafinesquii occurs in the more western and 

northern parts of the range where there are karst features; the subspecies macrotis is distributed along the 

Atlantic and Gulf coast states (Jones, 1977) where forested wetlands are prevalent. It is generally accepted that 

the southeastern myotis is a monotypic species, although 

in the past at least three different subspecies were recognized (Jones and Manning, 1989). Both species have been 

considered rare or difficult to find (Barbour and Davis, 

1969; Lowery, 1974), and currently most states in the 

ranges of these two species list them in some category of 

concern (Laerm and others, 2000).

 Surveys: State-by-State Review Surveys: State-by-State Review Surveys: State-by-State Review 

Surveys and other studies in 7 of the 16 states in 

which these two species of bats occur have generated 

new data on distribution, life history characteristics, and 

other information. Because the results of most of the efforts in these seven states appear in agency reports that 

are not widely available, a summary of their major findings is provided below. 

Virginia 

In 1994, a multi-county survey of buildings was conducted in the southeastern coastal plain of Virginia (M.K. 

Clark and S. Williams, written commun., 1994) to obtain 

new records for Rafinesque’s big-eared bat. The results 

were encouraging in that over 50 new roosts were located in buildings. However, the numbers of bats found 

were quite low. Less than one-fifth of these sites contained colonies of this species, and most of these colonies were composed of less than a dozen adult bats. All 

other observations were of single bats. 

In 1998 mist-net surveys were conducted in natural 

areas in the vicinity of the resort town of Virginia Beach 

(M.K. Clark, unpub. data, 1998) to survey for Rafinesque’s 

big-eared bat. These surveys took place in wetlands that 

are contained within two adjoining properties, Fort Story 

military installation and First Landing State Park. Through 

these efforts Rafinesque’s big-eared bat was documented 

on both sites. The first record of the southeastern myotis 

from Virginia was reported from the lower southeastern 

corner of the state in 1996 (Hobson, 1998). Subsequently, 

this species was also captured at First Landing State Park 

during surveys conducted for Rafinesque’s big-eared bat. 

In the summer of 2000 the mist-net surveys were followed 

by a radio-telemetry study conducted to locate roosts of 

Rafinesque’s big-eared bat on both of these properties 

(M.K. Clark, unpub. data, 2000). A total of five roosts 

were located for Rafinesque’s big-eared bat. The first bat 

tagged with a radio-transmitter in 2000 was caught in a 

mist net placed across an opening in a flooded forest. 

The bat was later tracked to its day roost in a building 

where it was observed roosting with others in a small 

maternity cluster. Individuals from this maternity roost 

were radio-tracked in late summer of 2000 to four trees in 

the wetlands on Fort Story. 

Monitoring efforts for both species in Virginia sites 

are irregular and opportunistic. Bats have been counted 

annually for at least 3 years at the building roost on Fort 
 

 

 

82 INFORMATION AND TECHNOLOGY REPORT–2003-0003 

Story. No significant changes in numbers have been 

noted, but the colony size fluctuated over the summer of 

2000, ranging from no bats to a high of approximately 20. 

Alternate roosts are known to be used by this colony so 

it is not possible to assess the significance of these 

changes in colony size (M.K. Clark, unpub. data, 2000). 

North Carolina 

In the 1980’s, a bat survey of rural buildings in selected counties in the southeastern and northeastern 

Coastal Plain of North Carolina yielded many new records 

of Rafinesque’s big-eared bat (M.K. Clark, unpub. data, 

2000). Prior to this effort there was speculation that the 

species was no longer present in the state (Lee and others, 1982). A selected number of these sites were monitored for presence-absence over a 14-year period 

(beginning in 1986). Significant reductions in numbers 

were noted, as well as roost deterioration and total roost 

destruction for some sites (M.K. Clark, unpub. data, 1998). 

The decline in the numbers of bats seen in individual 

sites over this period, in combination with the loss of 

natural and human-made roosting habitat, prompted the 

state’s committee on nongame mammals to recommend 

that status for this species be upgraded from “Special 

Concern” to “State Threatened” (Clark ,1987; M.K. Clark, 

written commun., 1998). 

In the 1990’s, more surveys for bats in the Coastal 

Plain generated new records for both species in a variety 

of anthropogenic structures as well as roosts in trees. 

During the summers of 1996 and 1997, surveys using mist 

nets were conducted in the lower Roanoke River basin 

(Fig. 2), an extensive forested tract of approximately 

19,600 ha (49,000 acres) that includes broad expanses of 

bottomland hardwood forests. Both Rafinesque’s bigeared bat and the southeastern myotis were captured in 

these surveys, although very few records were obtained 

for Rafinesque’s big-eared bat in this study (M.K. Clark, 

unpub. data, 1999). The southeastern myotis was one of 

the most frequently captured species in the survey and 

was found in 5 of the 10 vegetation communities that 

were sampled. Rafinesque’s big-eared bat was found in 

only three of the vegetation communities surveyed. Surveys of bridges were conducted in the Coastal Plain in 

1997 and 1998, and both species were documented using 

bridges for day roosting (McDonnell, 2001). Surveys of 

North Carolina state parks using mist nets, conducted in 

the summer of 2000, also yielded new records of both 

species (M.K. Clark, unpub. data, 2000). In one of these 

parks 45 trees were identified as being used by these 

species after park staff conducted visual inspections of 

tree cavities in baldcypress-water tupelo (Taxodium 

distichum-Nyssa aquatica) communities within the park 

(S. Lambiase, written commun., 2003). Roosts that were 

located in state parks include trees and a variety humanmade structures. Coordinates for each roost were documented in a database, and all of the trees with roosts 

found in Merchants Millpond State Park were marked 

with permanent numbered tags so that bat use of individual trees could be monitored over time. 

Opportunistic monitoring efforts span more than a 

decade for a limited number of summer day roosts for 

Rafinesque’s big-eared bat in human-made structures in 

Chowan County, North Carolina. Declines in numbers of 

bats have been noted at all of these sites (M.K. Clark, 

unpub. data, 1998). Biologists with the U.S. Fish and Wildlife Service survey two mines in North Carolina for 

Rafinesque’s big-eared bat on a biannual basis. These 

sites are protected by fencing and counts at these sites 

are stable (R. Currie, U.S. Fish and Wildlife Service, written commun., 1999). 

South Carolina 

In 1994 bat surveys were conducted in the Francis 

Beidler Forest, a National Audubon Sanctuary in Berkeley and Dorchester counties in the Lower Coastal Plain 

region of the state (M.K. Clark, unpub. data, 1994). This 

sanctuary protects over 6,000 ha (15,000 acres) of riverine swamplands and associated uplands, including 520 ha 

(1,300 acres) of virgin cypress-gum swamp forest. The 

two most frequently captured species in that survey were 

the southeastern myotis and Rafinesque’s big-eared bat 

(however, netting sites were selected to favor captures of 

these species). Radio-telemetry was used in the Francis 

Beidler Forest to study the roosting and foraging ecology of Rafinesque’s big-eared bat and the southeastern 

myotis in the summers of 1996 and 1997 (M.K. Clark, 

unpub. data, 1997). Forty roost trees were located for 

these two species and foraging data were obtained for 13 

bats. Cavities in the trees were used as roosts by 

Rafinesque’s big-eared bat (Fig.3A) and southeastern 

myotis (Fig. 3B), as determined by radio-tracking bats to 

day roosts and visually inspecting cavities. In September 2001, opportunistic surveys were made of roost trees 

that were found in 1996 and 1997 in Francis Beidler Forest 

in which a limited number of tree cavities were visually 

inspected for presence-absence (M.K. Clark, unpub. data, 

2001). No bats were found in these trees during the September 2001 surveys. The area was in a severe drought 

and bats may have moved in response to the drier conditions. Bat surveys also have been conducted in the Upper Coastal Plain region of the state at the U.S. Department 

of Energy’s Savannah River Site (Menzel and others, 

2003a,b). Between 1996 and 2000, both species were captured on this 78,000 ha site, but numbers were low (two 

captures of southeastern myotis and nine captures of 

Rafinesque’s big-eared bat). 
 

 

 

 

CLARK 83 

A B 


Fig. 3. Fig. 3 Day roosts for Rafinesque’s big-eared bat (Corynorhinus rafinesquii) (A) and the southeastern myotis (Myotis 

austroriparius) (B) found in Francis Beidler Forest (Harleyville, South Carolina) in 1996. Trees in (A) and (B) are both 

water tupelos (Nyssa aquatica) and both have extensive interior cavities, however, the tree in (A) is part of an evenaged stand of water tupelos that grows near a creek within the swamp, whereas the one in (B) is an isolated tree farther 

from a major water body. Research is needed to determine whether this is an artifact of sampling or an important roost 

selection factor for these species, and to identify other factors that may affect roost selection by each species in 

bottomland hardwood forests. 

Florida 

In 1993, a Rafinesque’s big-eared bat colony was 

found in an abandoned mobile home adjacent to a large 

wetland mitigation site, the Disney Wilderness Preserve, 

in central Florida. Year-round observations have been 

made at this site since 1994 (L.S. Finn, written commun., 

1995, 1999). Numbers of bats in the mobile home fluctuate 

throughout the year, with the largest estimates occurring 

in mid-winter (e.g., about 60 on 21 January 1995). In the 

spring, just before young are born, the numbers are about 

half of those observed in winter months (e.g., 31 counted 

on 29 May 1995). Young have been successfully raised 

each year that this site has been monitored, but colony 

size has not grown appreciably over the years, suggesting that significant numbers of bats may be dispersing to 

unknown sites. Observations of extreme fluctuations (e.g., 

30 bats decreasing to one or two individuals, then increasing to 30 or more) within the course of a week suggest that alternate day roosts are used by this colony. 

Individuals from this colony were radio-tracked and found 

to use night roosts in cavities in cypress trees (L.S. Finn, 

unpub. data, 1999). 

Roosts of southeastern myotis in caves have been 

surveyed and monitored in Florida (Gore and Hovis, 1998), 

but otherwise there is no information on sizes of 

populations for this species. As reported by J. Gore 

(written commun.,1999) presence-absence data are 

obtained for southeastern myotis every one to two years. 

Other sites monitored for presence-absence of this species 

include bridges, culverts, and a single tree cavity.A winter 

colony site containing both gray bats and southeastern 

myotis has been checked annually, and numbers of 

southeastern myotis have been relatively stable in that 
84 INFORMATION AND TECHNOLOGY REPORT–2003-0003 

cave over time. Two Rafinesque’s big-eared bat colonies 

are also monitored every one to two years. 

Louisiana 

New records of both Rafinesque’s big-eared bat and 

the southeastern myotis were obtained in surveys in central Louisiana in the late 1990s (Lance and Garrett, 1997; 

Lance and others, 2001). Roosts located during these 

surveys were in human-made structures and tree cavities. The southeastern myotis was the most frequently 

captured species in these investigations.A stand of water tupelos on the Darbonne National Wildlife Refuge in 

northern Louisiana was the site of surveys for 

Rafinesque’s big-eared bat (G. Langford, written commun., 

2000). During this survey, 44 day roosts were found in 

cavities in water tupelos (Fig. 4). Most were roosts for 

Rafinesque’s big-eared bat, but one was the day roost of 

a large colony of southeastern myotis. These trees were 

marked with permanent numbered tags so that monitoring could be done in the future. No monitoring programs 

are in place for either of these species in Louisiana. 

Arkansas 

Since 1988 investigators in the Gulf Coastal Plain of 

Arkansas have studied Rafinesque’s big-eared bat 

Fig. 4. A large summer colony of Rafinesque’s big-eared 

bats (Corynorhinus rafinesquii) day-roosting inside the 

cavity of a water tupelo (Nyssa aquatica) on the Darbonne 

National Wildlife Refuge in Louisiana. Photograph by 

Gypsy Langford, courtesy of U.S. Fish and Wildlife 

Service. 

colonies that were found in buildings, cisterns, water wells, 

and tree cavities (D. England and D.A. Saugey, unpub. 

data, 1998; D.A. Saugey, unpub. data, 2000). Colony size 

at individual sites appears to have remained stable, but 

many building sites have undergone significant changes 

that resulted in either loss or serious and irreversible 

deterioration of the sites. Bat surveys in bottomland 

hardwood forests in the Delta region were conducted by 

students from Arkansas State University (Cochran, 1999; 

Hoffman, 1999). Five roosts of Rafinesque’s big-eared 

bat and one roost of a male southeastern bat were found. 

Monitoring efforts for bats using cisterns and water wells 

are opportunistic (D.A. Saugey, oral commun., 2000) and 

there is no information on monitoring roosts in trees. 

Texas 

Texas Parks and Wildlife Department staff began the 

Texas Rare Bat Survey in 1994, focusing on surveys and 

studies of southeastern myotis and Rafinesque’s bigeared bats. The objective the first year was to reaffirm the 

presence of these two bats in eastern Texas. Survey efforts for the first year yielded records of one or both of 

the target species at four of eight locations that were 

surveyed in the southeastern portion of the state 

(P. Horner, unpub. data, 1995). In subsequent years, the 

objectives of the Texas Rare Bat Survey were to document the distribution of southeastern myotis and 

Rafinesque’s big-eared bat throughout their historic range 

[as delineated by Schmidly (1991)], locate and characterize roosts, and investigate the roosting and foraging ecology of these bats (K. Mirowsky and P. Horner, unpub. 

data, 1996). Annika Keeley, coordinator for the Texas Rare 

Bat Survey in 1998 and 1999, provided a review of progress 

through October 1999 (A. Keeley, written commun., 1999). 

Between 1994 and 1996 the Texas Rare Bat Survey efforts 

resulted in a significant change in the number of counties 

in Texas with occurrences for both species. Two maternity roosts for southeastern myotis were discovered in 

1995, and were the first ever documented for the state. 

Between 1994 and 1999, the number of sites of occurrence for southeastern myotis in Texas increased from 9 

to 20, including the discovery of southeastern bats wintering in a culvert. The number of counties in eastern 

Texas with documented occurrences of Rafinesque’s bigeared bat increased from 7 to 17. As of October 1999, the 

Texas Rare Bat Survey has been regularly monitoring eight 

roosts of Rafinesque’s big-eared bat and nine roosts of 

southeastern myotis. 

Conclusions from the State-by-State Review 

Activities in each state primarily targeted the most 

basic need: to determine where these species occur. This 
 

CLARK 85 

is not surprising because the lack of data range wide for 

Rafinesque’s big-eared bat and the southeastern myotis 

is often cited as a reason that these species are listed in 

some category of concern (Clark, 1987; U.S. Fish and 

Wildlife Service, 1994). New records were obtained in all 

states where surveys were conducted for one or both of 

these species. The findings from all of these states support Lowery’s (1974) contention that with some effort, 

many new records for southeastern myotis can be obtained. Results from these states also indicate that this is 

also the case for Rafinesque’s big-eared bat. Other than 

the two mines monitored for Rafinesque’s big-eared bat 

in North Carolina, and the caves that are monitored in 

Florida for the southeastern myotis, there are no regular 

monitoring efforts for these two species. 

Surveys in most of these states included efforts to 

locate natural roosts as well as those in anthropogenic 

structures. Cavities used as night roosts were identified 

in two states (Florida and South Carolina) and there are 

now numerous trees identified as day-roosts for these 

species. Although six of the states (Virginia, North Carolina, South Carolina, Louisiana, Texas, and Arkansas) reported finding one or both species day-roosting in tree 

cavities, most of these types of roosts were found in 

three states (South Carolina, North Carolina, and Louisiana each identified 40 or more tree cavities that were used 

by these two species). The large numbers of trees found 

in those states can be attributed to intentional concentration of field efforts in continuous tracts of bottomland 

hardwood forests (Francis Beidler Forest, South Carolina; Darbonne National Wildlife Refuge, Louisiana; and 

Merchants Millpond State Park, North Carolina) where 

the goals were to learn more about the natural roosts 

used by these bats. None of these sites contained anthropogenic structures used as roosts. Each of these sites 

is managed to conserve natural resources by public or 

private entities, and were known to contain large stands 

of mature baldcypress-water tupelo swamp forest (Francis 

Beidler Forest, Merchants Millpond State Park) or a nearly 

pure stand of mature water tupelos (Darbonne National 

Wildlife Refuge). 

Survey methods used most often were mist-netting 

and visual inspections of both anthropogenic structures 

and basal cavities in trees. Radio-telemetry was used successfully to locate roosting sites and foraging areas in six 

states (Virginia, North Carolina, South Carolina, Florida, 

Louisiana, and Texas). Visual inspections of basal cavities in trees proved to be an effective means of finding 

new roost sites for both species in four states (Virginia, 

North Carolina, South Carolina, and Texas). Two examples 

from state survey efforts illustrate the value of this method 

for survey: (1) Hobson (1998) documented the first record 

of the southeastern myotis in Virginia after he found a 

roost of this species by visually inspecting a tree cavity; 

and (2) this was the only method used in surveys conducted in 2002 and 2003 in Merchants Millpond State 

Park in North Carolina, where 45 tree cavities were found 

to be used by both Rafinesque’s big-eared bats and southeastern myotis. Investigations that used radio-telemetry 

as a means to locate roosts also supplemented those efforts by using the visual inspection method, often finding roosts that were used by bats that were not 

radio-tagged. 

Factors Factors Affecting Survey and fecting Survey and fecting Survey and

Monitoring Success Monitoring Success Monitoring Success 

Bottomland hardwood forests are challenging environments in which to work. Gaining access to study sites 

requires a considerable amount of planning and resources. 

These forests are characterized by variable hydrology, 

ranging from some relatively dry soils on ridges to saturated soils and areas that are flooded temporarily, permanently, semipermanently, intermittently, and seasonally. 

These hydrologic conditions have largely prevented development and widespread road-building in these areas, 

resulting in the preservation of some large tracts of 

unfragmented forested wetlands (Fig. 2). This is good for 

wildlife, but challenging for the biologist. 

A combination of travel methods may be needed in 

order to transport equipment and personnel to selected 

sites. This includes transport over land by four-wheel 

drive vehicles, boating to sites in various types of watercraft, and significant foot travel. Initially, it is essential to 

consult maps, aerial photographs, and all other materials 

that aid in the identification of desired study site characteristics and access points, and to work with knowledgeable people in the area, including local residents. It is also 

helpful to conduct an aerial reconnaissance of the area to 

gain a landscape perspective and assist in the identification of access points. All of these factors make studies in 

bottomland forests equipment- and labor-intensive. 

Key to any bat survey is knowledge of the roosting 

ecology of the target species. Roost availability may limit 

the distribution of bats (Kunz, 1982). Rafinesque’s bigeared bat and the southeastern myotis both roost in a 

variety of human-made and natural structures including 

buildings, mines, and caves (Jones, 1977; Jones and 

Manning, 1989). Trees that are used by these species for 

day roosts are found only where certain conditions occur, 

may not be abundant on the landscape for a number of 

reasons, may not be as stable as other kinds of roosts 

(caves, mines, buildings) and may occur in patches. 

Significant differences in roosting ecology may occur 

between bats using roosts that are distributed more 

randomly than tree roosts and bats occurring in areas 
86 INFORMATION AND TECHNOLOGY REPORT–2003-0003 

where roost structures may be more stable than trees. For 

this reason, some conclusions about roosting ecology 

for these species in some areas may not apply to 

bottomland hardwood forests, and researchers should 

be cautious about making assumptions based on such 

data. 

Additionally, tree roosts provide less space for bats 

to aggregate, so for some species, colonies in trees may 

be smaller than those found in larger structures such as 

bridges, buildings, mines, and caves. Southeastern myotis 

and Rafinesque’s big-eared bats are both colonial species, 

but the big-eared bats form much smaller colonies (often 

<50 bats; Jones, 1977) than southeastern myotis. Several 

thousand southeastern myotis have been observed in 

some caves (Jones and Manning, 1989). In tree cavities, 

colony size of southeastern myotis may range up to about 

200 individuals (K. Mirowsky and P. Horner, unpub. data, 

1997). Approximately 80 Rafinesque’s big-eared bats were 

seen in a tree cavity in Darbonne National Wildlife Refuge 

in Louisiana (Fig. 4 ; G. Langford, written commun., 2000). 

For these reasons, it is likely not feasible to use population 

size data derived from other areas to estimate population 

size for southeastern myotis and Rafinesque’s big-eared 

bats in bottomland hardwood forests. 

Researchers studying these bats in bottomland 

hardwood forests have most often found them roosting 

in basal cavities in water tupelos (Fig. 3). Water tupelos 

grow at the lowest elevation sites in bottomland hardwood 

forests and are often found in association with 

baldcypress. Both tree species will develop large 

buttressed trunks that make them distinctive in the forest 

landscape. Water tupelos have a propensity to develop 

hollows at the bases and the resulting interior cavity can 

be extensive (Fig. 5). Tree cavities used by Rafinesque’s 

big-eared bats and southeastern myotis have large 

diameters (>30 cm; M.K. Clark, unpub. data, 1997; G. 

Langford, written commun., 2000). Additionally, these 

trees are often clumped in distribution rather than being 

randomly dispersed in the landscape (Fig. 6). These 

characteristics make it relatively easy to locate potential 

roost trees for these two species and to survey a number 

of them in a small area. 

Roost fidelity and roost switching are important 

facets of roosting ecology to consider in survey and 

monitoring programs. Based on radio-telemetry studies 

conducted in 1996 in Francis Beidler Forest in South 

Carolina (M.K. Clark, unpub. data, 1997). Rafinesque’s 

big-eared bats roosted in two to six trees over a two to 

three week period. All roosts used were in close proximity 

to each other, suggesting that although this species has 

low roost fidelity, a colony of Rafinesque’s big-eared bat 

may be loyal to a cluster of trees. This makes it easy to 

locate alternate roosts for this species. Roost-switching 

can otherwise be problematic for the development of 

effective survey and monitoring programs, because when 

observers find reduced numbers at a site it may not be 

possible to know whether the bats have gone elsewhere 

or if they are absent due to mortality. 

In general, roosts are the sites where bats can be 

most easily counted or where their numbers can be 

estimated by other techniques, such as exit counts. Direct 

observational methods have been used to gather colony 

size statistics for bats, but these methods are likely not 

possible for bats residing in bottomland hardwood 

forests. It is not possible to visually inspect each cavity 

to count bats in all tree roosts in a given area for several 

Fig. 5. A group of water tupelos ( Fig. 5 Nyssa aquatica), in the 

Francis Beidler Forest (Harleyville, South Carolina), 

showing large cavities that were used as day roosts in 

1996 by Rafinesque’s big-eared bats (Corynorhinus 

rafinesquii). Groupings of such trees are frequented by 

Rafinesque’s big-eared bats and are found throughout 

the Forest, occurring where hydrology and other 

conditions are conducive to the growth of almost pure 

stands of this species. Photograph by Mary K. Clark. 
CLARK 87 

Fig. 6. Locations of water tupelo roosts for southeastern myotis (Myotis austroriparius) and Rafinesque’s big-eared 

bats (Corynorhinus rafinesquii) found in the summer of 1996 by radio-tracking bats to their day roosts in the Francis 

Beidler Forest (M.K. Clark, unpub. data, 1996). Numbered trees in groupings, as follows, were used by Rafinesque’s 

big-eared bats: group 1, trees 1-4; group 2, trees 24-26; group 3, trees 21, 23 and XX. Tree 36 was not part of a cluster, 

but was an isolated water tupelo that was used as a day roost by a colony of southeastern myotis. 

reasons. Variability in cavity size and configuration makes 

it difficult or impossible to see and count bats while they 

are roosting during the day (Fig. 7A–D). The interior of 

the tree may have features that obscure parts of the cavity, 

and the trunk may be twisted or bent, making it impossible 

to view the entire inner chamber. Nightly emergence 

counts would need to be conducted simultaneously at a 

number of roosts within the sampling plot. This would be 

costly in that it would require multiple sets of equipment 

and a large number of personnel. Forests are cluttered 

environments; it may not be possible to find an 

unobstructed view of the cavity to view the emergence. 

Additionally, bats may exit from more than one cavity in 

the tree and some may be missed if observers are not 

placed to view all possible exit points. 

Recommendations and Recommendations and 
 Recommendations and

Conclusions Conclusions 
 ConclusionsConclusions Conclusions

At the most basic level there is a great need to gather 

distribution and life history data for both Rafinesque’s 

big-eared bat and the southeastern myotis throughout 
88 INFORMATION AND TECHNOLOGY REPORT–2003-0003 

A 

C D 

B 

Fig. 7. Examples of the variation in the cavity opening and clutter around the cavities in four water tupelo trees (Nyssa 

aquatica) that were used as day roosts in 1996 by Rafinesque’s big-eared bats (Corynorhinus rafinesquii) in the Francis 

Beidler Forest, Harleyville, SC (M. K. Clark, unpub. data, 1996). The configuration and size of the smaller opening shown in 

illustration (A) prevented direct observation of the interior of this tree to confirm the presence of bats other than the radiotagged individual. Size and configuration of cavities shown in (B), (C) and (D) allowed visual inspection of the cavity 

interiors, where radio-tagged bats were observed roosting with others. Clutter in and around openings may affect cavity use 

by these two species: Rafinesque’s big-eared bat is a slower, more agile flyer than the southeastern myotis (Myotis 

austroriparius, and may be able to negotiate more cluttered environments. Illustrations by Renaldo Kuhler. 
the geographic ranges of these species. Survey and 

monitoring efforts for these two species in bottomland 

hardwood forests should be given high priority. These 

forests constitute a large portion of the regions used by 

these two species (occurring in over half the states in 

their ranges), but these ecosystems have experienced 

significant loss and degradation (Noss and others, 1995). 

These bats also show some degree of habitat specificity 

to a limited habitat type (cypress-gum swamp forest) that 

occurs within bottomland hardwood forests. 

Bottomland hardwood forests are highly variable in 

terms of their quality and potential to provide adequate 

roosting habitat for Rafinesque’s big-eared bats and 

southeastern myotis. Information on habitat quality and 

its effects on distribution and population size of these 

bats is needed. Optimal and suboptimal roosting habitat 

should be identified in bottomland hardwood forests. 

Results from the most pristine situations should be considered the baseline for comparison to other situations. 

Conditions in high quality (undisturbed) mature bottomland forests may provide population size and trend information that is most representative of natural conditions 

before European settlement. 

The use of infrared technology for locating and 

counting bats in tree cavities warrants some consideration as a survey method in bottomland hardwood forests. Two locations where a high number of roost trees 

have been found (Darbonne National Wildlife in Louisiana and Merchants Millpond State Park in North Carolina) would make good test sites for this technique 

because they are on public lands, some baseline data are 

available at each of these sites, roost trees are permanently marked with unique numbers to permit future monitoring, and both sites have high density bat use in a 

discrete area (vs. clusters of trees spread throughout a 

larger landscape). 

There may be enough data available on the natural 

history of these species and their use of bottomland 

hardwood forests to develop predictive habitat models 

for each species. Predictive habitat models aim to simulate 

the geographic distribution of organisms using geospatial 

technology, a set of explanatory variables, and statistical 

models. Once a statistical model has been formulated and 

the explanatory variables are mapped, the distribution 

and abundance of species or habitats in space can be 

predicted. Although it may not be possible to make 

abundance predictions for the target species, this 

technique should allow for better assessments of their 

status based on the distribution and size of available 

habitat. Additionally, a historical review of the land-use 

practices that affect bottomland hardwood forests in the 

southeastern U.S. may provide some insight into the 

historical range and distribution of these two species, 

CLARK 89 

and help to identify the factors that may have affected 

their distribution and population status over time. 

The success of survey efforts in Arkansas, the Carolinas, Florida, Louisiana, Texas, and Virginia should encourage and inspire others to devote resources to learn 

more about the distribution and habitat preferences of 

these two species. This information is needed to provide 

baseline data for monitoring populations. Bottomland 

hardwood forests likely contain some of the best remaining continuous habitat for Rafinesque’s big-eared bat and 

southeastern myotis, but in order to understand their 

range-wide status it will be important to study populations in other systems as well. It is clear that anthropogenic structures (such as bridges and cisterns) are 

important roosting sites for these two species and the 

role that these types of roosts play in population status 

should be assessed. The loss of more permanent types 

of human-made roosts, such as the water wells and cisterns used by wintering Rafinesque’s big-eared bats in 

Arkansas, should be further investigated. Loss of these 

structures for wintering aggregations may render this 

species unable to maintain viable population levels in its 

current range in southern Arkansas (D. Saugey, written 

commun., 2000). 

Acknowledgments Acknowledgments Acknowledgments

I am grateful to the following colleagues for sharing 

their unpublished data with me: Shawn Cochran, Laura 

Finn, Jeff Gore, Vernon Hoffman, Peggy Horner, Annika 

Keeley, Richard Lance, Gypsy Langford, Alex Menzel, 

Katy Mirowsky, and David Saugey. This paper was vastly 

improved by review comments provided by Jeff Gore and 

David Saugey.A number of federal and state agencies, as 

well as nonprofit groups, supported the work described 

in this paper, including The North Carolina State Museum of Natural Sciences, North Carolina Natural Heritage Trust, North Carolina State Parks, North Carolina 

Wildlife Resources Commission-Nongame Division, the 

U.S. Geological Survey Biological Resources Division, 

U.S. Department of Defense, U.S. Forest Service, U.S. 

Fish and Wildlife Service, The Audubon Society, Bat Conservation International, Disney Wilderness Preserve and 

various chapters of the Nature Conservancy. I am especially grateful for the invitation to participate in the Bat 

Population Monitoring Workshop, which provided the 

incentive to organize thoughts on this subject, and for 

the opportunity to assist in the process of advancing 

surveying and monitoring efforts for bats. 
90 INFORMATION AND TECHNOLOGY REPORT–2003-0003 

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distributional survey of North Carolina mammals: 

Occasional Papers of the North Carolina Biological 

Survey, no. 10, 70 p. 

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adjacent waters: Baton Rouge, Louisiana State University Press, 565 p. 

McDonnell, J.E., 2001, Use of bridges as day roosts by 

bats in the North Carolina Coastal Plain: North Carolina State University, Raleigh, M.S. thesis, 74 p. 

Menzel, J.M., Menzel, M.A., Ford, W.M., Edwards, J.W., 

Sheffield, S.R., Kilgo, J.C., and Bunch, M.S., 2003a, 

The distribution of the bats of South Carolina: Southeastern Naturalist, vol. 2, p. 121–152. 

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T.C., and Edwards, J.W., 2003b, Bats of the Savannah 

River site and vicinity: General Technical Report SRS68, Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 69 p. 

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ecosystems of the United States: A preliminary assessment of loss and degradation: Biological Report 

28, U.S. Department of the Interior, National Biological 

Service, Washington D.C. 

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landscapes: Conservation biology of North American 

bats, in Kunz, T.H., and Racey, P.A., eds., Bat biology 

and conservation: Smithsonian Institution Press, 

Washington, D.C., p. 309–325. 

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bottomland hardwoods: a regional assessment of historic change, in Hamel, P.B. and Foti, T.L., eds., Bottomland hardwoods of the Mississippi Alluvial Valley: 

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Asheville, NC: U.S. Department of Agriculture, Forest 

Service, Southern Research Station, p. 28–48. 

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Texas A&M University Press, 185 p. 

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species: Federal Register, vol. 59(219), p. 58982–59028. 

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Chapel Hill, University of North Carolina Press, 255 p. 
Bat Colonies in Buildings Bat Colonies in Buildings 
 Bat Colonies in Buildings

By
 

Thomas H. Kunz 


and
 

D. Scott Reynolds 


Department of Biology 

Boston University 

Boston, MA 02215 

Abstract. Abstract.Abstract. Abstract. Bats use buildings as maternity roosts, night roosts, bachelor roosts, transient roosts, and occasionally as 

hibernacula. Of the 46 species of bats known from North America north of Mexico, over half are known to use buildings as roosts 

at least for part of the year. Use of human-made structures is a consequence of the loss of natural shelters that no longer exist and 

occurs wherever bats and humans co-exist. Nonetheless, the few available data suggest that the number of colonies in buildings is 

declining and that persistence is limited by deterioration of structures and attempts by residents to exclude bats. In North America, 

big brown bats (Eptesicus fuscus), little brown bats (Myotis lucifugus), eastern pipistrelles (Pipistrellus subflavus), and Brazilian 

free-tailed bats (Tadarida brasiliensis) are the best-known species that roost in buildings. All form maternity colonies in buildings 

during the summer. Efforts to census bats in buildings pose several challenges. Evening emergence counts provide the most reliable 

estimates, especially where colonies consist of less than 1,000 individuals. Such counts should be made on at least three consecutive evenings in the period of late pregnancy to mid-lactation, which generally corresponds to the maximum adult population. With 

continued loss of natural habitats, bat houses offer opportunities for bat conservation as well as platforms for research on aspects 

of bat biology that are difficult or impossible to study in natural roosts. 

KeyWords: Buildings, hibernacula, maternity roosts, night roosts, transient roosts. 

Introduction IntroductionIntroduction

Roosts and food are the two most important resources 

known to influence the distribution and abundance of 

bats (Humphrey, 1975; Kunz, 1982; Kunz and Lumsden, 

2003). Bats seek shelter in a number of natural structures, 

including caves, foliage, rock crevices, and tree cavities, 

but they also exploit various human-made structures, 

such as mines, tombs, houses, barns, bridges, culverts, 

and bat houses (Kunz, 1982; Tuttle and Hensley, 1993; 

Keeley and Tuttle, 1999; Kunz and Lumsden, 2003). As a 

consequence of increased urbanization, conversion of 

natural landscapes to agriculture and management of 

forests, bats use human-made structures as alternatives 

to many natural shelters that no longer exist. 

Buildings, mostly of European-style architecture, 

offer a range of internal and external habitats for roosting 

bats (Gaisler, 1979; Greenhall, 1982; Kunz, 1982; Entwistle 

and others, 1997; Jenkins and others, 1998). Interior spaces 

in houses, churches, barns, schools, and similar structures 

have, in effect, become substitutes for tree cavities and 

exfoliating bark (Figs. 1–4). Spaces beneath tile, 

corrugated metal and fiberglass roofs, wood shingles, 

and areas behind shutters offer physical characteristics 

similar to natural roosts. The widespread use of buildings 

by bats in both temperate and tropical regions clearly 

indicates that these structures are important roosting 

habitats for bats. Bats use buildings as maternity roosts, 

night roosts, bachelor roosts, transient roosts, and 

occasionally as hibernacula. Of the 46 species of bats 

known from North America (north of Mexico), over half 

91 
92 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

are known to use buildings as roosts at least for part of 

the year (Barbour and Davis, 1969; Wilson and Ruff, 1999; 

Table 1). At present, the use of buildings by bats ranges 

from the occasional to the obligatory. 

In North America, bats that most commonly roost in 

buildings include the big brown bat (Eptesicus fuscus), 

Brazilian free-tailed bat (Tadarida brasiliensis), eastern 

pipistrelle (Pipistrellus subflavus), evening bat (Nycticeius 

humeralis), little brown myotis (Myotis lucifugus), cave 

myotis (M. velifer), southeastern myotis (M. 

austroriparius), Yuma myotis (M. yumanensis), and pallid bat (Antrozous pallidus) (Wilson and Ruff, 1999). 

Three species (Eptesicus fuscus, M. lucifugus, and M. 

yumanensis) have become so completely associated with 

buildings in warm months that there are few records of 

their occurrence in natural roosts (Barbour and Davis, 

1969). Exceptions include populations in western North 

America where these three species are also known to 

roost in tree cavities (Barclay and Brigham, 1996). 

Since the construction of European-style buildings 

in North America, some bat species have probably increased in number and distribution. For example, by forming maternity colonies in buildings, Myotis velifer 

(Fig. 1A) and T. brasiliensis (Fig. 1B) have extended their 

summer ranges beyond the limits of historical distributions (Kunz, 1974; Genoways and others, 2000). In Texas, 

populations of T. brasiliensis have increased as much as 

15% above numbers recorded before modern building 

construction (Schmidly, 1999). Similarly, the use of buildings by E. fuscus (Fig. 2) and M. lucifugus (Fig. 3) has 

also made it possible for these two species to extend their 

summer ranges into previously uninhabitable regions of 

NorthAmerica (Fenton and Barclay, 1980; Kurta and Baker, 

1990; Whitaker and Gummer, 2000). 

In Europe, at least 11 species of bats are associated 

with buildings. The most common of these are the 

pipistrelle (Pipistrellus pipistrellus), noctule (Nyctalus 

noctula), greater horseshoe bat (Rhinolophus 

ferrumequinum), common long-eared bat (Plecotus 

auritus), serotine (Eptesicus serotinus), northern bat (E. 

nilssoni), Natterer’s bat (M. nattereri), and greater mouseeared bat (Myotis myotis) (Entwistle and others, 1997; 

Schober and Grimmberger, 1997; Jenkins and others, 1998; 

Racey, 1998). Several species that commonly roost in 

buildings are listed as vulnerable or are considered at 

severe risk (Schober and Grimmberger, 1997; Hutson and 

others, 2001) largely due to a decrease in natural roosts 

(Racey, 1998), contamination of human-made roosts with 

pesticides and wood preservatives (Voûte, 1980–1981), 

and loss of foraging habitats (Limpens and Kapteyn, 

1991). 

The exploitation of buildings by bats in tropical 

regions also appears to have contributed to expanded 

distributions and increased local abundance. For example, 

in tropical Africa, several crevice-dwelling species 

regularly roost in buildings, such as Mops midas, Nycteris 

grandis, Chaerephon pumila, Pipistrellus nanus, and 

Scotophilus spp. (Kingdon, 1974; O’Shea, 1980; Fenton 

and Rautenbach, 1998). Several members of the genus 

Eptesicus, including E. tenuipinnis, E. capensis, and E. 

redalli, show strong affinities for buildings (Verschuren, 

1957; Rosevear, 1965). In the Indian subcontinent, 

Taphozous melanopogan, T. perforatus, and Megaderma 

lyra almost exclusively roost in buildings (Bates and 

Harrison, 1997). 

Several neotropical species use buildings as roosts, 

including Saccopteryx bilineata, Desmodus rotundus, 

Artibeus jamaicensis, Phyllostomus hastatus, and Carollia 

perspicillata (Nowak, 1994), although they rarely do so 

exclusively. Two widely distributed insectivorous species, Myotis nigricans (Wilson, 1971) and Molossus 

molossus (Greenhall and Stell, 1960; Rodriguez-Duran and 

Kunz, 2001), commonly roost in buildings in the 

Neotropics. 

Impact of Human Impact of Human Impact of Human Attitudes AttitudesAttitudes Attitudes 

and Activities ActivitiesActivities Activities 

Although the relatively recent availability of buildings as roosting sites may have contributed to expanded 

ranges and increased numbers in some species, other 

human activities such as overuse of non-target pesticides, contamination of water, and misguided forest management have had detrimental effects on their roosting 

and foraging activities. Extensive deforestation and habitat deterioration has had a marked effect on the availability of roosting and foraging habitats for many species 

(Barclay and Brigham, 1996; Racey, 1998). Fear of rabies 

(as well as fear from the mere presence of bats in human 

dwellings), indifference, and misunderstanding have also 

led to the extermination of bats from some buildings 

(Tuttle, 1987). Building restorations have led to the elimination of some bat roosts. In addition, the direct application of toxic chemicals and repellants has contributed to 

the reduction and/or extirpation of some bat colonies in 

buildings (Kunz and others, 1977; Daan, 1980; Hurley 

and Fenton, 1980; Tuttle, 1987; Clark, 1981). 

Factors Factors Factors Affecting Roost fecting Roost fecting Roost

Preferences in Buildings Preferences in Buildings Preferences in Buildings

Few studies have been conducted to assess 

preferences of bats for roosting in buildings. Entwistle 

and others (1997) compared the characteristics of 
Table 1. Primary roosting habits of North American bats north of Mexico, summarized from Keeley and Tuttle (1999), Whitaker and Hamilton (1998), and Wilson 

and Ruff (1999).a

 Tree cavities/


Family and Species Buildings Caves/mines bark crevices Foliage Rock crevices Bridges Other 


Family Mormoopidae

Mormoops megalophylla X 

Family Phyllostomidae

Artibeus jamaicensis X X Choeronycteris mexicana X X Diphylla ecaudata X Leptonycteris curasoae X Leptonycteris nivalis X Macrotus californicus X X X 

FamilyVespertilionidae

Antrozous pallidus X X X X Corynorhinus rafinesquii X X X X Corynorhinus townsendii X X X Eptesicus fuscus X X X X Euderma maculatum X X X X Idionycteris phyllotis X X X Lasionycteris noctivagans X X Lasiurus borealis X Lasiurus blossevillii X X Lasiurus cinereus X Lasiurus ega X Lasiurus intermedius X Lasiurus seminolus X Lasiurus xanthinus X Myotis auriculus X X Myotis austroriparius X X X X Myotis californicus X X X Myotis ciliolabrum X X X X X Myotis evotis X X X X X X Myotis grisescens X X 

KUNZ AND REYNOLDS 93 
 

Table 1. able 1 Concluded. 

Tree cavities/


Family and Species Buildings Caves/mines bark crevices Foliage Rock crevices Bridges Other 


Family

Vespertilionidae (concluded) espertilionidae (concluded)

Myotis keenii X X Myotis leibii X X X X Myotis lucifugus X X X X X Myotis septentrionalis X X X X Myotis sodalis X X X X Myotis thysanodes X X X Myotis velifer X X X Myotis volans X X X X X Myotis yumanensis X X X X X Nycticeius humeralis X X Pipistrellus hesperus X X X Pipistrellus subflavus X X X 

Family Molossidae

Eumops glaucinus X X X Eumops perotis X X Eumops underwoodi X X X Molossus molossus X Nyctinomops femorosaccus X X X Nyctinomops macrotis X X X X Tadarida brasiliensis X X X 

aSome species may have roosting habits in other parts of their range that differ from what has been observed in North America. Bat houses are not included. 

94 INFORMATION AND TECHNOLOGY REPORT–2003--0003 
 KUNZ AND REYNOLDS 95 

Fig. 1. (A) Fig. 1 Maternity colony of Myotis velifer roosting 

in a barn in south-central Kansas near the northern limits 

of its breeding range. (B) Maternity colony of Tadarida 

brasiliensis roosting in the attic of an abandoned building 

in south-central Kansas near the northern limits of its 

breeding range. Photographs by T.H. Kunz. 

buildings selected by Plecotus auritus with a random 

sample of buildings in the United Kingdom. This species 

preferred older buildings with attics divided into several 

compartments constructed from rough-cut wood. 

Buildings that were located near forested areas and bodies 

of water were also preferred, suggesting that feeding 

habitat near the roost was an important factor affecting 

roost selection. In contrast, Pipistrellus pipistrellus did 

not select roosts with specific structural attributes 

(Jenkins and others, 1998), but instead roosted in buildings 

that were surrounded by trees and had associated linear 

landscapes, often near a major river. When compared to a 

random sample of buildings, maternity colonies of 

Eptesicus fuscus in North America were often found in 

older, taller, and more accessible structures, often having 

tin roofs (Williams and Brittingham, 1997). 

Fig. 2. (A) Matern Fig. 2. ity colony of Eptesicus fuscus roosting 

on the ridgepole of a barn in central Massachusetts. Some 

individuals are marked with colored, plastic split-ring 

bands for identification. (B) Exterior view of an attic vent 

of a house in southern New Hampshire that provides an 

alternative roosting space for a small maternity colony of 

E. fuscus. (C) This colony roosted in the partially enclosed 

space between the exterior louvers and interior screening, 

although sometimes individuals shifted to a roost on the 

ridgepole in an adjacent barn. Photographs by T.H. Kunz. 
96 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

Fig. 3. (A) Sm Fig. 3. all maternity cluster of Myotis lucifugus 

roosting in the crevice of a barn in southern New 

Hampshire. (B) Solitary male M. lucifugus roosting in the 

attic of a building in southern New Hampshire. 

Photographs by T.H. Kunz. 

Building Roosts in North Building Roosts in North America America America America

Most North American bats use buildings on a 

seasonal basis as maternity roosts, night roosts, and 

transient shelters during migration. Many species of bats 

use buildings, such as houses, barns, sheds, porches, 

breezeways, and garages as night roosts (Kunz, 1982). 

Buildings are most commonly used during maternity 

periods, especially when they provide appropriate thermal 

conditions for rearing young (Tuttle and Stevenson, 1982; 

Kunz and Hood, 2000). Darkness, shelter from the wind 

and rain, proximity to feeding areas, and reduced predation 

risks are important factors that govern the selection of 

these shelters (Kunz, 1982). Only rarely do bats use 

buildings as hibernacula. 

Buildings offer bats a wide range of roost microhabitats including spaces along the ridgepole, in mortises, 

beneath floor boards, in spaces between bricks and wood, 

inside insulation, beneath burlap bags, under hanging 

pictures, and behind curtains and drapes (Licht and 

Leitner, 1967; Barbour and Davis, 1969; Kunz, 1974; Anthony and others, 1981; Williams and Brittingham, 1997). 

Structures located on the exterior of buildings also provide roosting sites for bats, including crevices between 

bricks and stones, between screened and louvered vents 

(Fig. 3B), behind windows and screens, spaces in boxed 

cornices, behind shutters, and spaces beneath weathered clapboards, facia boards, and shingles (Barbour and 

Davis, 1969). 

Case Studies in NorthAmerica 

In North America, Eptesicus fuscus, M. lucifugus, T. 

brasiliensis, and P. subflavus are perhaps the best-known 

species that roost in buildings (Davis and others, 1962; 

Humphrey and Cope, 1976; Fenton and Barclay, 1980; 

Fujita and Kunz, 1984; Wilkins, 1989; Kurta and Baker, 

1990; Whitaker and Gummer, 1992, 2000; Winchell and 

Kunz, 1996; Williams and Brittingham, 1997; Hoying and 

Kunz, 1998; Whitaker, 1998a). 

Eptesicus fuscus usually forms maternity colonies 

in buildings ranging from a few dozen upward to several 

hundred individuals (Williams and Brittingham, 1997; 

Kurta and Baker, 1990; Whitaker and Gummer, 2000). Females typically roost along open ridgepoles (Fig. 2A), 

although others occupy enclosed or partly enclosed roost 

spaces in walls, boxed cornices, and between louvered 

vents and screens (Fig. 2B and 2C). Males are typically 

solitary and occupy spaces in buildings separate from 

females during the summer, often roosting beneath shutters and weathered shingles (Kurta and Baker, 1990), or in 

crevices in cooler parts of the interior of buildings 

(Whitaker and Gummer, 2000). 

Eptesicus fuscus is one of the few North American 

species that hibernates in buildings (Mills and others, 

1975; Whitaker and Gummer, 1992, 2000). Buildings used 

as hibernacula are invariably heated in winter and thus 

provide roost temperatures that are usually above freezing. E. fuscus commonly roosts in buildings during warm 

months, although fewer individuals occupy buildings in 

winter (Whitaker and Gummer, 2000). 

Myotis lucifucus invariably hibernates in caves and 

mines in winter months. During warm months, this species typically forms maternity colonies in buildings 

(Fig. 3A), although tree cavities also serve as maternity 

roosts. Maternity colonies range from a few hundred to 

several thousand individuals (Fenton and Barclay, 1980; 

Burnett and August, 1981; Kunz and Anthony, 1996). 

Maternity colonies of M. lucifugus seldom form one single 

aggregation, but instead roost in several small clusters. 

Males are generally solitary in summer (Barbour and 

Davis, 1969; Fenton, 1970; Humphrey and Cope, 1976; 

Fenton and Barclay, 1980), where they usually roost in 

small crevices, behind shutters, and similar structures 
(Fig. 3B). This species has twice been reported to hibernate in buildings during winter months, but in both instances they were solitary males (Whitaker, 1998b). 

Tadarida brasiliensis is one of the most abundant bat 

species in North America. Migratory populations typically form enormous maternity colonies in caves in the 

southwestern United States and northern Mexico during 

warm months and spend the winter months in central and 

southern Mexico (Davis and others, 1962; Wilkins, 1989). 

Smaller colonies are known to occupy buildings (Fig. 1B) 

or roost beneath bridges. Thus, they have contributed to 

range extensions beyond the historic distribution of this 

species that traditionally roosts in caves (Keeley and 

Tuttle, 1999; Schmidly, 1999; Genoways and others, 2000). 

In contrast, non-migratory populations from the southeastern United States, California, and southern Oregon 

are year-round residents. In these areas, they typically 

roost in buildings, forming maternity colonies in warm 

months and winter colonies during cooler months (Wilkins, 

1989). 

Pipistrellus subflavus typically hibernates in caves 

and mines during cold months, and during warm months 

seeks shelter in buildings (Fujita and Kunz, 1984; Hoying 

and Kunz, 1998; Whitaker, 1998a,b; Fig. 4), tree cavities 

(Menzel and others, 1996) and foliage (Winchell, 1990; 

Veilleux, 2001). Maternity colonies in buildings range from 

a few up to 40 adults and their pups (Hoying and Kunz, 

1998; Whitaker, 1998b), although colonies in foliage are 

considerably smaller (Veilleux, 2001). Females that roost 

in buildings often select cavities and crevices along the 

ridgepole of barns, houses, and similar structures (Fujita 

and Kunz, 1984). During warm months, entire colonies 

may shift roost sites within buildings (Hoying and Kunz, 

1998; Whitaker, 1998a). This bat has also been observed 

roosting on the exterior walls of buildings (Whitaker, 

1998a). 

Colony Persistence 

Few data are available on the persistence of bat colonies in buildings. Because most buildings are temporary, 

knowledge of colony persistence in these structures can 

be valuable for assessing the viability of populations. 

Buildings eventually deteriorate with time and are either 

abandoned, renovated, or replaced with new structures. 

Thus, bat colonies that roost in buildings are eventually 

displaced or, at worst, exterminated. 

A survey in Indiana in 1959 revealed 190 bat colonies 

in buildings; 128 of these colonies were present at these 

sites in 1989 (Cope and others, 1991). Among the buildings that were surveyed in 1989, 95 were occupied by E. 

fuscus, 27 by M. lucifugus, 5 by N. humeralis, and 1 by P. 

subflavus. Only eight (29.6%) of the M. lucifugus colonies and 21 (22.1%) of the E. fuscus colonies identified in 

KUNZ AND REYNOLDS 97 

Fig. 4. (A) Sm Fig. 4. all maternity colony of Pipistrellus subflavus 

clustered on the ridgepole of an abandoned barn in 

eastern Massachusetts. Some individuals were marked 

with colored plastic, split-ring wing bands for 

identification. Thermocouples and wires attached to 

recording devices were used to measure roost 

temperatures. (B) Behavioral responses of P. subflavus 

to a warm roost in mid-summer. In response to high roost 

temperatures, bats are widely dispersed on a wall of the 

barn instead of being tightly clustered. Photographs by 

T.H. Kunz. 

1959 were still active in 1989. Among the colonies of N. 

humeralis and P. subflavus observed in 1959, none were 

found in 1989. From these observations, Cope and others 

(1991) concluded that an average of 3.3% of the colonies 

disappeared each year over a 30-year period. 

A survey of buildings in New England during the 

1990’s (D.S. Reynolds and T.H. Kunz, unpub. data, 1999) 

identified 638 bat colonies, including 172 of M. lucifugus, 

108 of E. fuscus, 9 of M. septentrionalis, 2 of P. subflavus, 

and 347 colonies from undetermined species. Although 

some of these colonies appeared to be of relatively recent origin, most were initially recorded over 10 years 

ago, and some were recorded 40 years earlier (based on 

field notes of H.B. Hitchcock and D.R. Griffin). Although 

many of these colonies have not yet been verified, the 
98 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

trends from those that have been evaluated are alarming. 

For example, at least 21% of the historic colonies (median 

record date of 1962) are known to have been extirpated. 

More recent colonies (recorded by T.H. Kunz, with a median record date of 1981) had a known extirpation rate of 

20%. Lastly, a data set with a median record date of 1994 

(primarily from Massachusetts Fish and Wildlife records) 

was found to have a known extirpation rate of 36%. 

Interviews with building owners have revealed that 

some type of exclusion was attempted at 160 of these 

colonies; in 15 cases, multiple methods were employed to 

remove the bats. Physical exclusion was the most common method (47%), particularly in the most recently controlled colonies. However, chemical control (including 

napthalene, DDT, rodenticides, insect sprays, and sulfur 

candles) accounted for 38% of all exclusion attempts, followed by electronic control (10%: lights or ultrasonic 

devices) and killing or relocation of individuals (10%). 

Clearly, these data suggest that more effort is needed to 

adequately census commensal bats and determine the 

full extent of exclusion and harassment that such colonies are experiencing. 

Censusing and Inventorying Censusing and Inventorying

Bats in Buildings Bats in Buildings Bats in Buildings 

Efforts to census bats that roost in buildings pose 

several challenges. Some homeowners do not permit researchers to enter buildings for the purpose of inventorying and censusing bats, and even if access is allowed, 

many bats that occupy crevices and cavities cannot be 

observed and counted directly. Mark-recapture studies 

seldom yield reliable estimates because the assumptions 

inherent in using this method cannot be met (see Kunz, 

2003). Evening emergence counts provide the most reliable estimates and are most successful when colonies 

are relatively small (<1,000 individuals) (Kunz and others, 

1996). Depending on the number of observers, it may be 

possible to count all or most bats that emerge from buildings at dusk by observing their silhouettes against the 

sky (Kunz and others, 1996; Hoying and Kunz, 1998), or 

by recording (and counting) them using infrared video 

cameras (Frantz, 1989). Notwithstanding, colony censuses based on nightly emergence counts can be biased 

when bats shift to alternate roost sites (Brigham and 

Fenton, 1986; Brigham, 1991; Lewis, 1995; Barclay and 

Brigham, 1996; Whitaker, 1998a). Roost-shifting behavior highlights the need for researchers to explore all possible exit routes and alternate roosts before conducting a 

colony inventory or census (Thomas and LaVal, 1988; 

Kunz and others, 1996). 

Whenever emergence counts are used to assess longterm trends in colony size, they should be made on at 

least three consecutive evenings in the period from late 

pregnancy to mid-lactation. This period generally corresponds to the maximum adult population [Thomas and 

LaVal (1988); Kunz and Anthony (1996); Kunz and others 

(1996); also see Kunz (2003)]. If additional time is available for censusing, emergence counts should be repeated 

after young-of-the-year have become volant, but before 

adults have emigrated for a given year. When assessing 

annual or seasonal changes in colony size, emergence 

counts should be made at weekly intervals to insure that 

seasonal patterns of reproductive phenology can be detected (Hoying and Kunz, 1988; Kunz and Anthony, 1996). 

Guano accumulation can also be used as a crude 

method of inventory to estimate the relative size of a 

colony. Once the species has been verified by direct observation and all pre-existing guano has been removed, 

an analysis of fresh guano accumulation can be used as a 

rough estimate of colony size. This method is useful for 

extensive, long-term surveys where regular emergence 

counts are unrealistic, but the quality of the estimates is 

limited to broad classes that can be delineated by successive orders of magnitude (one or few, 10–20, around 

100, and over 1,000). 

Estimates of colony size based on guano accumulation are more reliable in colonies where bats roost in the 

open (e.g., on the ridge pole of a barn that is too high to 

reliably count) or where the bats roost in a crevice that 

opens below (such as bats roosting under fascia boards, 

flashing, or between the wood structure and the chimney 

of a house). In situations where roosts are not known, or 

no clear accumulation of guano occurs, this method is 

not appropriate. To validate the guano estimation method, 

an emergence count or visual count should be performed 

periodically and compared to estimates derived from 

guano accumulation. 

Roosts for Research Roosts for Research Roosts for Research

and Conservation and Conservation 

Buildings offer ideal opportunities for investigating 

aspects of bat biology that are difficult or impossible to 

study in natural roosts (e.g., Kunz, 1974; Burnett and 

August, 1981; Burnett and Kunz, 1982; Kunz andAnthony, 

1982; Kurta and others, 1989; Wilkinson, 1992; Winchell 

and Kunz, 1996; Hoying and Kunz, 1998). With continued 

loss of natural habitats, structures (bat houses) 

specifically designed to mimic the physical and thermal 

conditions of tree cavities have been increasingly used 

in Europe and North America for conservation purposes 
(Stebbings and Walsh, 1985; Tuttle and Hensley, 1993; 

Fig. 5). In addition to their conservation value, bat houses 

offer excellent opportunities for research on topics 

ranging from social and mating behavior, population 

structure and dynamics, and energetics (but see Gerell 

and Lundberg, 1985; Lundberg and Gerell, 1986; 

Wilkinson, 1992; Kerth and König, 1996, 1999; Kerth and 

others, 2000). If properly designed, located, and 

maintained (Tuttle and Hensley, 1993), bat houses of 

varying design and size can serve both research and 

conservation interests. 

Acknowledgments Acknowledgments Acknowledgments

We would like to thank T. O’Shea and M. Bogan for 

inviting us to contribute this paper, and L. Ellison who 

Fig. 5. (A) Small Fig. 5. bat house used to attract Tadarida 

brasiliensis for insect control in south-central Texas. 

(B) This large bat house, designed for T. brasiliensis, is 

occupied by thousands of individuals that were displaced 

from buildings and other human-made structures on the 

University of Florida campus, Gainesville, Florida. 

Photographs by T.H. Kunz. 

KUNZ AND REYNOLDS 99 

helped organized the Bat Monitoring Workshop held in 

Estes Park, Colorado, where this paper was presented. 

Our research on colonies of bats in buildings has been 

supported by the Kansas Biological Survey, Boston University, the National Science Foundation (Kunz), and Bat 

Conservation International, American Society of Mammalogists, the Theodore Roosevelt Fund, and the American Museum of Natural History (Reynolds). 

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The United Kingdom National Bat Monitoring Programme: The United Kingdom National Bat Monitoring Programme: 
 The United Kingdom National Bat Monitoring Programme:

Turning Conservation Goals Into urning Conservation Goals Into urning Conservation Goals Into Tangible Results angible Results 
 angible Results

By 

Allyson L. Walsh1
 

Colin M.C. Catto 


Tony M. Hutson
 

Steve Langton 


and
 

Paul A. Racey 


The Bat Conservation Trust
 

15 Cloisters House
 

8 Battersea Park Road
 

London, SW8 4 BG
 

Abstract. Effective bat conservation relies on gathering information to identify changes in populations that are of conservation concern, and to measure the population response to management. From 1996 to 2000, the Bat Conservation Trust was 

commissioned by the United Kingdom (U.K.) government’s Department of the Environment, Transport and Regions to develop 

and implement monitoring procedures for eight target species of bats and to assess how these techniques could be applied to all 16 

resident U.K. species. The resultant “National Bat Monitoring Programme” (NBMP) is designed to provide accurate information 

about bat population trends based on data gathered by a volunteer network covering large numbers of sites. The application of 

formal sampling strategies and standardized counting techniques enables meaningful estimations of bat population trends. By 

1999, the NBMP had approximately 807 volunteers active annually in bat surveys (returning data) and a total membership of 

1,447 people. The NBMP site network currently includes a total of 796 maternity colony sites monitored using evening exit 

counts, 952 field sites monitored using bat detector transect survey counts, and 255 underground hibernation sites monitored using 

visual counts of hibernating bats. Power analyses based on counts from these schemes indicate that after approximately 10 to 20 

years of monitoring, all NBMP schemes will detect small annual declines (1– 2%) at powers of over 90% and satisfy monitoring 

targets. Although there are obvious difficulties in monitoring bats, and elements of the NBMP are likely to be improved over time, 

it is essential to establish sustainable monitoring programs for bats within a time frame and on a scale that will contribute to 

conservation interests. 

Abstract Abstract

KeyWords: Bats, population trends, power analysis, survey design. 

1

Current address: The Lubee Foundation, Inc., 1309 NW 192nd Avenue, Gainesville, FL 32609 

103 
104 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

Introduction IntroductionIntroduction

Bat Populations in the U.K:
 

Status and Trends
 

Bats are the most important contributors to mammalian biodiversity in the United Kingdom (U.K.). The 16 

recorded breeding species form one-third of our land mammal fauna. The present distribution of those species of 

bats resident in the U.K. appears to be strongly influenced by climatic and habitat gradients. Many species of 

bats occur in the U.K. at the northern edge of their predominantly southern distribution within Europe, and so 

are absent from a significant part of the country (Corbet 

and Harris, 1991). Such a distribution suggests that although the balance and status of U.K. bat populations 

are undoubtedly influenced by factors which are specific 

to the U.K., they are probably also linked to factors such 

as climate change affecting European bat populations as 

a whole. 

Observations of bats disappearing from censused 

hibernation sites have demonstrated considerable declines in European bat populations from the 1950’s to 

early 1980’s (summarized in Daan and others, 1980; 

Stebbings, 1988; Stebbings and Griffith, 1986). In the U.K., 

the two horseshoe bats (greater, Rhinolphus 

ferrumequinum and lessser, Rhinolophus hipposideros) 

(Fig. 1) have become very rare or extinct over significant 

areas of their former range (Stebbings, 1988), and the 

greater mouse-eared bat (Myotis myotis) was recorded 

as extinct in 1990. Current estimates of population trends 

of bats of the U.K. are provided by Harris and others 

(1995), who identify seven species in decline (eight species when the pipistrelle is separated into two species), 

and suggest that for the remaining eight species, populations either appear to be stable or are unknown (Table 1). 

Harris and others (1995) highlight the lack of published 

quantitative data available, either historically or currently, 

on which to base estimates of population size and trend. 

Historically, efforts to quantify changes in 

populations of bats in the U.K. have been geographically 

fragmented and concentrated on just a few species. For 

three species (lesser horseshoe bat, greater horseshoe 

bat, and pipistrelle), reasonable quantitative data have 

been collected. The recent reclassification of “pipistrelle” 

bats into two distinct species: Pipistrellus pipistrellus and 

Pipistrellus pygmaeus (Barratt and others, 1997; Jones 

and Barratt, 1999), means that historical data are not 

species-specific. The greater horseshoe bat is the beststudied chiropteran in the U.K. Counts of this species 

have been made using banded animals in capture/mark/ 

recapture studies at hibernacula since the 1940’s, and 

counts have been made at summer roosts since the 1960’s 

Fig. 1. The Fig. 1 lesser horseshoe bat (Rhinolopus 

hipposideros) in flight, one of the species monitored by 

the United Kingdom National Bat Monitoring Programme 

(photograph by Frank Greenaway, Natural History 

Museum, London). 

(Hooper, 1983; Stebbings and Arnold, 1987; Ransome, 

1989). However, no single standard counting protocol 

has been followed over time. Individuals who have studied 

populations in different parts of the species range hold 

historical data independently. Whether declines in 

numbers of greater horseshoe bats were identified across 

its range between 1950 and 1980, counts over the past 20 

years show small declines or stable populations in some 

areas and increasing populations in others (Harris and 

others, 1995). A compilation of these data to examine 

historical trends in the entire population across its range 

has not been published to date. 

The lesser horseshoe bat has also been counted in 

both winter and summer sites. Population trends are 

variable among regions, although whether this reflects 

real differences or differences in counting methods is 

unclear (Harris and others, 1995). In order to determine 

how populations of lesser horseshoe bats are changing, 

the Countryside Council for Wales (CCW) established a 

project to monitor maternity colonies in Wales in 1993. 

This project was revised and extended to England in 1995. 

In a recent evaluation of the monitoring data, Witter (1998) 

found that lesser horseshoe bat populations in Wales 

appeared to have remained stable over the 1993–1997 

period. The same methodology is currently being used 

by NBMP in monitoring maternity colonies of this and 

other species. Collaboration with CCW has resulted in 
 

 

 

 WALSH AND OTHERS 105 

Table 1. Population status and trends of the 16 resident species of bats in the United Kindgom. Data are for Great 

Britain. Species in bold are those targeted by the NBMP 1996–2000. 

Population Distribution/ 

Common name Species name estimatea statusb Estimated trenda 

Greater horseshoe Greater horseshoe Rhinolophus ferrumequinum 4,000(4) Restricted/rare Decline 

Lesser horseshoe Lesser horseshoe Rhinolophus hipposideros 14,000(4) Restricted/rare O 

Daubenton’ Daubenton’s Myotis daubentoni 150,000(2) Widespread/common O 

Natterer NattererNatterer’s Myotis nattereri 100,000(2) Widespread/frequent O 

Serotine SerotineSerotine Serotine Eptesicus serotinus 15,000(2) Widespread/frequent O 

Noctule Noctule Noctule Nyctalus noctule 50,000(3) Widespread/frequent Decline 

Pipistrelle PipistrellePipistrellec Pipistrellus pipistrellus 

Pipistrellus pygmaeus 200,000(3) Widespread/common Decline 

Bechstein’s Myotis bechsteini 150,000(2) Restricted/rare O 

Brandt’s Myotis brandti 30,000(1) Widespread/scarce Decline 

Whiskered Myotis mystacinus 40,000(2) Widespread/scarce Decline 

Nathusius’ pipistrelled Pipistrellus nathusii Unknown Widespread/rare O 

Leisler’s Nyctalus leisleri 10,000(2) Widespread/rare O 

Barbastelle Barbastella barbastellus 5,000(1) Widespread/rare Decline 

Brown long-eared Plecotus auritus 200,000(2) Widespread/common Decline 

Grey long-eared Plecotus austriacus 1,000(3) Restricted/rare O 

a

After Hutson (1993). Population estimate: the reliability of the estimate is given in parentheses on a scale of 1 to 5 (5
 

being the most credible estimate based on scientific evaluation of the data available for the species).
 

b

After Harris and others (1995). Estimated trend: O = stable/unknown, Decline = declining. Estimates for Northern
 

Ireland have not been made due to a lack of information on the distribution and status of bats in Northern Ireland.
 

c

This species is now considered to comprise two species and their relative status has not yet been assessed.
 

d

This species has only recently been ascribed breeding status in Britain and only a few breeding colonies have been
 

recorded. 


the application of consistent methods in Wales and 

England, and data from the Welsh project are made 

available to the NBMP. 

The only U.K.-wide bat population surveillance program instigated prior to the NBMP is the National Bat 

Colony Survey (NBCS). This program was initially funded 

by the Institute of Terrestrial Ecology and is now privately run by the Robert Stebbings Consultancy, Ltd. 

(Mitchell-Jones, 1999). The NBCS began collecting data 

in 1978 and relies on standardized counts of bats emerging from summer maternity colonies (mainly house-dwelling pipistrelle bats). In an examination of these data, 

Stebbings (1988) estimated a 62% decline in populations 

of pipistrelle bats between 1978 and 1987. However, in a 

reassessment of the data, a 43.5% decline was estimated 

to have occurred between 1980 and 1992 (Harris and others, 1995). 

Despite the best efforts of many committed naturalists 

and biologists to provide data on local populations of 

bats in the past, there has been no structured framework 

for monitoring bat populations at a national level. The 

NBMP was intended to fill this gap and provide the 

information on populations so urgently needed for 

conservation and management. 

Bat Populations in the U.K.:
 

Policy Background
 

Information needs for monitoring bats are firmly anchored in national legislation and a number of international conventions, directives and agreements, which 

specifically target bats or indirectly target the protection 

of bats and their habitats. Comprehensive wildlife legislation protects all species of bats recognized in the U.K., 

and their roosts, from disturbance (Wildlife and Countryside Act 1981, Wildlife Order 1985 – Northern Ireland). It 

is an offense to kill, injure, or capture bats, or to disturb 
106 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

them at their roosts, and roost sites themselves are protected. This legislation has led to an increase in public 

concern about bats and to the formation of a network of 

groups working to promote bat conservation across the 

U.K. (Mitchell-Jones and others, 1993). It has also led to 

the inclusion of bats as species of community interest in 

international treaties protecting flora and fauna. Interpretation of monitoring information will allow the U.K. to 

report against targets and objectives within the framework of these treaties, and therefore they have been a 

major stimulus to develop and adopt a national monitoring strategy for bat populations (Racey, 2000). There are 

three main treaties that are of particular relevance to monitoring bat populations. 

Convention on Biological Diversity 1992 (CBD) 

Over 150 countries have acceded to the CBD, which 

requires inter alia signatories to prepare national 

biodiversity strategies to monitor key elements of 

biodiversity. The U.K. government has produced a 

Biodiversity Action Plan, which includes action plans for 

six species of bats. The NBMP will help fulfil statutory 

requirements for the CBD by providing a monitoring 

mechanism at a national, regional, and local scale. 

European Union Council Directive on the Conservation of Natural Habitats and of Wild Fauna and 

Flora 1992 (EEC Habitat Directive) 

The Directive lists all bats as protected species, with 

commitments to maintain and restore their populations to 

a “favourable conservation status”, and to carry out 

particular conservation measures (including the 

designation of Special Areas for Conservation and 

surveillance of the conservation status of species) for 

five of the species of bats occurring in the U.K. To 

implement the Directive effectively, population 

monitoring procedures for listed species need to be in 

place. 

Convention on the Conservation of Migratory 

Species of Wild Animals 1979 (Bonn Convention) 

This Convention covers migratory species and those 

that regularly cross political boundaries. It allows for the 

conclusion of formal Agreements to protect species, and 

The Agreement on the Conservation of Bats in Europe 

(Eurobats) is one such Agreement that came into force in 

1994. Obligations to the Agreement include cooperation 

towards developing consistent bat-monitoring strategies 

across Europe. Implementation of this commitment led to 

Fig. 2. A United Kingdom National Bat Monitoring 

Programme field officer trains a group of volunteers in 

bat identification skills in Northern Ireland (photograph 

by Shirley Thompson, Bat Conservation Trust). 

the U.K. Government’s decision to fund a National Bat 

Monitoring Programme. The Eurobats Agreement in particular provides a model for other countries to develop 

international collaboration on important bat conservation issues. 

Program Development Program Development Program Development

National Bat Monitoring Programme Goals 

The long-term goal of establishing a national scheme 

for monitoring bat populations is to provide government 

and non-government organizations with accurate monitoring data on which to base advice relevant to the conservation needs of the U.K.’s 16 species of bats. Specific 

objectives for the initial 5-year phase of the NBMP project 

(1996–2000) were to develop and implement protocols 

to: monitor the relative abundance of selected species of 

bats, establish quantitative baseline data for each selected species, and produce improved distribution information for all bats in the U.K. This paper addresses the 

first two objectives. 

Scope, Target Species, and Principal Methods 

Two professional staff are responsible for the design 

of monitoring concepts, all organization and coordination, 

assessment and analysis of the monitoring data, and 

interpretation and presentation of the results. A network 

of skilled amateurs carries out the majority of NBMP 

fieldwork across the country. The decision to use a 

volunteer force was based primarily on the practical need 
 

to achieve representative coverage of a large geographical 

area. The monitoring program encompasses the whole of 

the U.K.: England, Wales, Scotland, and Northern Ireland. 

At the start of the program, it was clear that not all 

species could be monitored. Eight target species were 

selected: lesser horseshoe, greater horseshoe, serotine 

(Eptesicus serotinus), noctule (Nyctalus noctule), 

Natterer’s (Myotis nattereri) and Daubenton’s (Myotis 

daubentoni) bats, as well as Pipistrellus pipistrellus and 

Pipistrellus pygmaeus. Target species were chosen according to conservation concern and because they constitute a representative range of different roosting, feeding 

and habitat requirements with populations of a sufficient 

size to provide adequate data for countrywide surveys. 

Estimating bat population trends at a large scale demands 

simple, repeatable methods that balance disturbance to 

animals, survey effort, cost, and geographical coverage. 

Three broad monitoring methods were selected: summer 

maternity colony counts, hibernacula counts, and surveys of foraging areas. All three methods have potential 

biases, either through the nature of the bats themselves, 

through exogenous factors that influence bat behavior, 

or through skill levels of those undertaking the monitoring. Therefore, to evaluate methods, a double-sampling 

approach is being taken whereby each target species is 

being monitored using at least two of these methods. 

Volunteer Network 

To implement the NBMP, it has been necessary to 

develop and maintain a volunteer force covering all regions of the U.K. and provide training for volunteers to 

ensure the collection of sufficiently high quality data. 

Recruitment of a volunteer force was based primarily on 

recruiting volunteers from an existing network of bat 

groups in the U.K. Volunteers from these groups already 

have considerable expertise on bats and provide longterm continuity and commitment. Recruitment of these 

and other volunteers (such as people with bats roosting 

in their homes, members of other nature groups, and the 

wider public) is targeted in areas of low coverage, and 

includes talks, workshops, leaflet distribution, popular 

articles, and web-based information. Training of volunteers to improve skill levels is carried out through an 

annual series of bat identification workshops: introductory day or weekend workshops for beginners and workshops introducing time expansion techniques for 

echolocation surveys to more advanced volunteers 

(Fig. 2). Efforts to improve training techniques have included the development of an “electronic bat” which enables indoor training of volunteers during the winter 

season, and the publication of a species identification 

WALSH AND OTHERS 107 

training manual and accompanying compact disc of bat 

echolocation calls. 

Statistical Design 

The primary task in establishing the NBMP has been 

to develop the technical capacity to carry out standardized surveys of bats on a wide geographic scale. The first 

5-year phase (1996–2000) has concentrated on the development of clearly defined, repeatable methods and their 

practical application using a volunteer workforce. Although a strong emphasis has been placed on the statistical design of monitoring schemes, the NBMP has sought 

solutions that balance statistical aspirations with the practical demands of field-based schemes. Early on in the 

development of the program, a working group was set up 

to assess available methods and sampling strategies. Input was sought from population statisticians and researchers involved in monitoring other species (birds and 

mammals). Power analyses have been carried out to aid 

the design of monitoring schemes. Wherever possible, 

three fundamental principles of sampling and survey design have been applied: sampling methods should minimize bias and maximize precision of counts, sampling 

should be as representative of the whole population as 

possible, and sampling should provide data that are adequate to detect the presence of biologically important 

trends. 

Program Methods Program Methods Program Methods

Counts at Maternity Colonies 

Many studies surveying or monitoring bat populations have focused on stable summer roosting aggregations of female bats, termed maternity colonies. Although 

visual counts may be made inside the roost (Tuttle, 1979), 

a less disruptive method is to make visual counts of adult 

female bats exiting the roost in the evening (e.g., Dwyer, 

1966; Swift, 1980). In the U.K., maternity colonies are 

established in April/May. Numbers at the colony rise and 

reach a peak when the young are born in mid June to late 

July. Birth dates vary annually and are dependent on 

weather conditions (Ransome and McOwat, 1994). Some 

species are more mobile than others and switch roosts at 

intervals through the summer and show sporadic annual 

site fidelity. Species selected for this method are species 

that show relatively high roost fidelity and whose roosts 

are known and accessible. 

Maternity roosts (generally in buildings) are chosen 

from a sample of sites known to exist locally by bat groups 
 

 

108 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

or reported by roost-owners to the Bat Conservation Trust 

(BCT). Volunteers are encouraged to count sites with small 

numbers of bats as well as large sites, to search for new 

sites, and to initiate counts at new sites, as they become 

known. Two counts of bats are made as they emerge from 

roost sites during a 20-day period in May/June (29 May– 

7 June, 8–17 June for lesser horseshoe bats, 6–15 June, 

16–25 June for all other species). This is just prior to 

average parturition dates when the numbers of bats in 

the roosts are more stable and provide a repeatable estimate of colony size. On each survey evening observers 

record the net number of bats emerging, ambient weather 

conditions, and supplementary information, such as 

whether a bat detector was used to aid counting. A full 

emergence count is defined as the net number of bats 

leaving a roost, starting with the first bat to be observed 

and ending when there is no further activity, activity has 

ceased for 10 minutes, or when darkness or bat-exiting 

behavior results in bats not being seen clearly. For all 

new roosts entering the scheme, site and habitat details 

around the site are recorded. For several species, too few 

maternity roost sites are currently known to permit a countrywide scheme, and so exercises to stimulate the location of new colonies are being encouraged. Schemes are 

implemented annually. There is no overlap of species monitoring using this method, with a single species monitored 

at each roost site. Sometimes colonies are mixed, but differences in size, behavior, and emergence time allow species to be distinguished. 

Counts at Winter Hibernation Sites 

Traditionally, assessment of populations in hibernation sites during winter has been the most consistently 

and widely employed technique for population monitoring throughout Europe. Although there are constraints 

on the reliability of such data, it has been successful in 

highlighting declines and local extinctions (e.g., Daan 

and others, 1980; Kowalski and Lesinski, 1991). As a multispecies approach it provides a valuable comparison between species and has provided data on species not 

currently targeted by the NBMP. However, because some 

species are not as reliant on underground sites as others, 

this method is not appropriate for all species (Hutson, 

1993). In the U.K., hibernation site surveys can only be 

carried out under the guidance of a licensee with an appropriate endorsement, which ensures data quality but 

restricts the number of people who can participate in surveys. 

Hibernation sites (generally underground) are chosen 

from a sample of known sites. Volunteers are encouraged 

to incorporate smaller sites as well as larger sites, and to 

search for new sites and initiate counts at new sites as 

they become known. Surveyors make two counts of 

hibernating bats at each site over a 2-month period: one 

in January and one in February. This is when temperatures 

in the U.K. are generally at their coolest and most stable. 

Supplementary data collected include information on the 

structure and type of site, habitat types present at the 

site, and for each survey conducted, ambient air 

temperature and the coolest and warmest internal 

temperatures at the site. An NBMP hibernationmonitoring scheme has been implemented annually from 

1997 to 2000. 

Summer Bat Detector Surveys 

The availability of heterodyne bat detectors at an 

affordable price has increased the number of volunteers 

able to identify and record free-flying bats. This was demonstrated by the large number of sites surveyed by volunteers in the U.K. National Bats and Habitats Survey 

(Walsh and others, 1993; Walsh and Harris, 1996a,b), and 

also by the Dutch national bat survey (Limpens, 1993a,b). 

Although field surveys are labor intensive, they provide 

an opportunity for monitoring species simultaneously and 

validating count data at roosts. Surveyors require a minimum amount of training to differentiate between the species monitored by the NBMP; this training is being carried 

out through bat detector workshops organized by the 

NBMB. As expertise and equipment develop, the use of 

bat detectors is likely to become an increasingly important monitoring technique. 

Monitoring foraging areas can be carried out using 

two basic techniques, continuous counts of bat passes 

along randomly placed transect lines of fixed or variable 

length, or counts of bat passes for a discrete time period 

at a fixed number of spots spaced systematically along 

randomly placed transect lines. The NBMP employs both 

methods. A1-km2

 area is the basic sampling unit for NBMP 

field surveys. This is because 1-km2 areas are easily surveyed within a single evening, and they integrate with a 

land classification scheme developed by the Institute of 

Terrestrial Ecology (Bunce and others, 1996; Firbank and 

others, 2003). This system assigns every 1-km2 in Britain 

to one of 40 land classes (grouped into six major environmental zones). Land classes are defined through multivariate analysis of climate, geology, and morphology and 

are used to target surveys of vegetation and land use. In 

a previous national bat survey (Walsh and Harris, 1996a,b), 

land class was found to be a significant factor influencing abundance; therefore, it was selected as a stratification system for field surveys. Field sites are selected 

randomly from each land class following an optimal allocation scheme. This allocation scheme is based on the 

relative proportions of each land class in the U.K. and 

estimated variation in bat abundance within each land 

class. In allocating sites to volunteers, skilled observers 
 

are contracted to cover sites in rare and under-represented 

land classes. In the case of roost and hibernation-site 

monitoring schemes, stratification by land class is posthoc (see Cochran, 1977). 

Two main surveys of flying bats are operated 

annually by the NBMP, the noctule, serotine, and 

pipistrelle survey, and the Daubenton’s bat waterway 

survey. 

Noctule, Serotine, and Pipistrelle Survey 

This is a multi-species survey of noctule, serotine, 

Pipistrellus pipistrellus, and Pipistrellus pygmaeus. Surveyors walk a predetermined triangular transect route 

across an allocated 1-km2 area on two evenings during a 

30 day period in July (1–15 July and 16–30 July). Noctule 

and serotine bat passes are recorded while walking with a 

bat detector tuned to 25 kHz, and pipistrelle 45/55 kHz 

bat passes are recorded at 12 predetermined stopping 

points along the route (totalling 24 mins), with the detector retuned to 50 kHz. (Fig. 3). Supplementary data collected includes habitats at each site and weather 

conditions on each survey evening. 

Daubenton’s Bat Waterway Survey 

This is a single species survey of Myotis 

daubentonii, which focuses on linear waterways. This is 

because Daubenton’s bats are mainly found in riparian 

habitats and rarely identified correctly away from riparian 

habitats. Surveyors walk a 1 km transect route along an 

allocated waterway site on two evenings during August 

(1–15 August, 16–30 August). Using a mini bat detector 

tuned to 35 kHz and a flashlight to observe bats simultaWALSH AND OTHERS 109 

neously, they record Daubenton’s bat passes at 10 equally 

spaced stopping points along their route for a total of 30 

minutes (Fig. 4). Supplementary data collected includes 

habitats at each site and weather conditions on each survey evening. Waterway sites, in addition to being stratified by land class, are sites that have previously been 

surveyed for habitat and water quality by the Environment Agency, which has statutory responsibility for 

England’s rivers, and has conducted surveys throughout the U.K. This collaborative approach will enable a 

more detailed analysis of distribution patterns of 

Daubenton’s bat. 

Power Analyses 

Each of the described monitoring schemes aims to 

minimize the possibility of wrong conclusions about 

trends. Such errors are particularly costly for 

conservation managers. If a significant decline in a 

threatened species is not identified, the population may 

decline to a point where extinction is inevitable. 

Conversely, if managers respond to a perceived decline 

that is not real, then resources may be wasted when there 

is no threat to the persistence of the species. Power is a 

statistical measure of the risk of not detecting a trend in a 

population when one actually exists, and is a measure of 

the adequacy of a monitoring program. Assessments of 

power given a specified sampling regimen, and the 

manipulation of sampling regimes to assess changes in 

power can help identify appropriately balanced 

monitoring designs. 

Power depends on interactions between sample size 

(number of sites at which counts are made), the duration 

(years of monitoring) for which the population is studied, 

Fig. 3. A typical 1-km Fig. 3 2 field survey site for noctules, 

serotines, and pipistrelle bats in the United Kingdom 

National Bat Monitoring Programme. 

Fig. 4. A typical Fig. 4 Daubenton’s bat (Myotis daubentoni) 

waterway field survey site bordering a river, United 

Kingdom National Bat Monitoring Programme. A transect 

route and 10 stopping points walked by a surveyor are 

marked. 
110 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

the frequency of surveys (within years and between 

years), the magnitude of change (trend) in the population 

over time, and variability in counts due to other factors 

(e.g., weather, bat behavior, observer variation). A power 

analysis examines the interactive effects of these factors 

on the overall power of a design to detect population 

trends of varying magnitude. 

Power Analysis Technique 

Raw bat counts gathered during the first 3 years of 

the NBMP were log-transformed and analyzed using a 

Residual Maximum Likelihood (REML) (Robinson, 1987; 

Verboom, 1998) to estimate different sources of variability in the data. Power was calculated using a simple form 

of route regression, considering only linear trends. The 

variance components from REML were used to calculate 

the expected variation in the estimate of linear trend using the standard rules for calculating the variance of a 

linear combination of random variables (e.g., Bulmer, 1979). 

The probability of detecting a trend (the power) was assessed using the t-distribution function. All sites were 

weighted equally and two-sided tests (to examine either 

upward or downward trends) were used, with a more liberal alpha level of 10% (P <0.10) (see Macdonald and 

others, 1998). This method is similar to the route regression used in the program MONITOR (Gibbs, 1995), but 

has an improved ability to examine the influence of different sources of variation in the counts. Because real data 

on bat abundance are not a perfect fit to the log-normal 

distribution, particularly for low counts, the power figures produced will be an approximation, but are accurate 

enough to make informed choices about the best design 

to adopt. All analyses were carried out in Genstat 5 (1993). 

Population DeclineAlert Levels 

To apply monitoring information to conservation 

objectives, conservation managers must decide on 

meaningful alert levels (levels of population decline that 

are of biological significance) that they wish to detect. In 

our analyses, we chose to examine magnitudes of 

population decline identified as alert levels for U.K. birds 

by Wilson and others (1998). These levels were based on 

criteria used by the IUCN to identify alert levels for 

threatened species of animals in general. Thus, we 

examined annual declines of 1.14% (= 25% decline over 

25 years), 2.73% (= 50% decline over 25 years), and more 

rapid declines of 5% (= 72% decline over 25 years). 

Program Results Program Results Program Results

Volunteers 

By 1999, the NBMP had approximately 807 volunteers active annually in bat surveys (returning data) and 

a total membership of 1,447 people. Rising recruitment 

rates have shown no sign of fatigue and balance or exceed the rate of loss of volunteers for all surveys (Fig. 5). 

From 1996–1999, 62 bat identification workshops and 

a further 57 talks were given by NBMP staff and key 

volunteers throughout the U.K. During early 1999 alone, 

more than 214 people attended training workshops to 

improve their bat identification skills. Although most volunteers participate in just one of the monitoring projects, 

200 people have participated in two or more projects. An 

estimated 30,000 person-hours have been spent on surveys. If each volunteer had been paid for his or her contribution, the estimated value of the data collected to 

date would be about £0.5 million. 

Baseline Data 

The network of sites surveyed within the umbrella of 

NBMP monitoring schemes has risen annually or remained 

stable for all schemes (Fig. 6). The monitoring network of 

maternity colonies now includes a total of 157 colonies of 

lesser horseshoe bats, 586 colonies of pipistrelles, and 54 

colonies of serotines. The difference in the total number 

of sites monitored for each species reflects differences in 

the restricted distribution of species and differences in 

the number of known sites, rather than survey effort. 

Taking the pipistrelle-monitoring scheme as an example, 

Fig. 5. Rising volunte Fig. 5 er recruitment rate for the United 

Kingdom National Bat Monitoring Programme, 1996–1999. 
 

 

 WALSH AND OTHERS 111 

duration of the monitoring program, affect our ability to 

detect different levels of population change. The minimum 

number of sites required in the lesser horseshoe bat 

colony monitoring scheme to achieve 90% power in 

detecting annual trends of 1.14%, 2.73%, and 5% over 

periods of 5 to 25 years, based on a sampling frequency 

Pip – Pipistrelle maternity colony counts 

Lesser – Lesser horseshoe maternity colony counts 

Sero –Serotine maternity colony counts 

Hib – Hibernation site counts (all species) 

Daub – Daubenton’s bat detector field survey 

NSP – Noctule, serotine, pipistrelle bat detector field survey 

Fig. 6. Total number of sites surveyed for each United 

Kingdom National Bat Monitoring Programme monitoring 

scheme, 1996–1999. 

whereas the total number of sites included in the scheme 

is large, the number of sites counted consistently in all 

years the scheme has been in operation is much lower 

(n = 88 sites surveyed in 1997, 1998, and 1999). The 

monitoring network of field sites surveyed using bat 

detectors now includes a total of 716 Daubenton’s bat 

survey sites and 367 noctule, serotine, and pipistrelle 

survey sites. Because different sites have been surveyed 

(except for a selected subsample of sites) over the current 

operation of these schemes, consistency in annual site 

coverage is unknown at present. The monitoring network 

of hibernation sites now includes a total of 255 sites, with 

a high annual consistency of site coverage (approximately 

150 sites repeated annually in 1997, 1998, and 1999). 

Power and Monitoring Targets 

Power estimates presented are based solely on the 

numbers of years for which we have repeat data. It should 

be noted that at this stage year-to-year variability is estimated with relatively poor precision for field surveys due 

to the limited number of years of repeated sites currently 

available. 

We have selected two examples (Figs. 7 and 8) to 

illustrate the types of analyses undertaken. Both illustrate 

principles common to all schemes. In graphing the results, 

we have set adequate power at 90% and illustrated how 

changes in the sampling intensity and frequency, and 

Fig. 7. Minimum Fig. 7 number of sites needed to obtain at 

least 90% power to detect existing declines of 1.14%, 

2.73%, and 5% per year, based on length of monitoring 

periods in years and two counts per site per year. Power 

was calculated using route regression, P <0.10. 

Fig. 8. Minimum number of Fig. 8 sites needed to obtain at 

least 90% power to detect an existing decline of 2.73% 

per year (red alert), based on length of monitoring periods 

in years and one or two counts per site per year. Power 

was calculated using route regression and variances 

obtained from pilot data. 
112 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

of two counts made annually, is shown in Fig. 7. After 7 

or more years, a sample size of approximately 100 sites 

appears to be adequate to detect a 2.73% decline (= red 

alert). At this stage, detecting smaller changes is not 

feasible because the required sample size is too large to 

be practically achievable (over 500 roosts to detect a 1.14% 

decline). However, after a series of 13 years of data has 

been collected, a sample of just fewer than 100 sites will 

be adequate to detect a 1.14% annual rate of decline 

(= amber alert). In all schemes, the longer monitoring is 

carried out, the greater our ability to detect smaller and 

smaller population changes, and the required sample size 

is reduced. As a general guideline, to monitor annual 

trends of over 1% to 3%, a sample of approximately 50 to 

100 sites, surveyed twice per year, should be operated 

over a period of more than 10 years in order to achieve 

adequate power. 

The minimum number of sites required in the pipistrelle 

colony monitoring scheme to achieve 90% power in detecting an annual 2.73% decline over periods of 5 to 25 

years, based on sampling frequencies of two and a single 

count made annually, is shown in Fig. 8. After 10 years of 

monitoring with a single annual count, an additional 72 

sites are required to reach the same level of power when 

two counts are taken each year. After 15 years, this difference is reduced to 21 sites, and after 20 years the difference is 9 sites. The reduction in power when a single visit 

is made is more pronounced when smaller declines are to 

be detected, when sample sizes are smaller, and when 

shorter monitoring time periods (<10 years) are considered. As a general guideline, a reduction to a single count 

may be recommended once the monitoring scheme has 

been in operation for a period of more than 20 years. 

Discussion DiscussionDiscussion

Methodological Considerations 

Bats are difficult to count, and even using the best 

available sampling methods there will be uncertainties 

inherent in population estimates and estimates of trend. 

Knowledge of the behavior and ecology of bats suggests that for all available counting methods, not all animals will be detected equally, introducing bias to 

population estimates. If the counts are constantly wrong 

for any reason, then changes from year-to-year can still 

be measured accurately using repeatable methods to 

achieve high precision. An example might be in maternity 

colony monitoring schemes: not all bats exit a roost each 

survey night, but the proportion of bats not exiting is 

roughly the same each year. In this case, population estimates will always be lower than the actual population 

size, but they will be directly comparable from year-toyear, and measured trends will reflect true trends. When 

dealing with small populations however, accuracy with 

regards to the true population size becomes more critical. 

If counts are wrong in an inconsistent way or in a way 

that follows a trend over time, bias resulting from unknown and unequal detectabilities remains a problem. An 

example might be in field monitoring schemes if new bat 

detector technology with increased sensitivity is introduced over time, resulting in more bats being detected 

over time.An upward trend might then be identified which 

is false. Although the ability to count bats as accurately 

as methods permit and with the same detectability each 

year remains an essential attribute of a successful bat 

population monitoring scheme, it is important to understand the magnitude of bias that will lead to incorrect 

conclusions. Often the effects of small sources of bias 

are overemphasized in comparison with the effects of a 

lack of precision (see Toms and others, 1999). 

Factors Affecting Counts at Maternity Colonies 

Main sources of variability in the exit counting procedure include the emergence behavior of the bats, contribution of observers, and survey dates. Although it is 

recognized that not all bats leave the roost site every 

night, internal validation counts conducted post emergence have demonstrated that the majority of lesser horseshoe bats leave on nights with good weather (Smith, 1993). 

Counts are therefore only made in good weather conditions, avoiding nights of heavy rain, wind, or cold when 

a higher proportion of bats might remain within the roost. 

In an analysis of pilot data, the additional use of a bat 

detector, or a tally counter did not significantly increase 

counts of lesser horseshoe bats, whereas validation and 

a qualitative measure of observer experience did increase 

counts (Witter, 1998). Large variation due to inexperience 

was also reported in counts of lesser horseshoe bats by 

Smith (1993), suggesting training of new volunteers is 

advisable for this species. Validation of counts at roosts 

by a simultaneous independent count is encouraged as 

part of the NBMP procedure. Lesser horseshoe bats are 

late-emerging species and exhibit light-sampling behavior on emergence, making them one of the more difficult 

species to observe. Counts are likely to be more accurate 

for other NBMP species, and validation using infrared 

counting equipment is being carried out. 

To monitor trends in numbers, it is not critical that a 

colony is counted at its peak size. There is little to gain 

from repeated visits, other than to cover for the possibility of a particularly low count on one visit. Standardization across years and colonies is more important. Thus, 

two visits per year within a relatively narrow window of 
 WALSH AND OTHERS 113

dates each year, carried out at a high percentage of surveyed roosts, will allow more reliable and precise quantification of trends in the population that the roosts 

represent than does a scheme that aims for three or four 

visits per year but only delivers one or even no counts at 

a significant proportion of ‘surveyed’ roosts. However, 

there should be no relationship between date and roost 

size over time. Although there is a likely cline in birth 

dates with latitude, and annual fluctuations of birth date 

will occur due to prevailing weather conditions (Ransome 

and McOwat, 1994), a radical shift in phenology over the 

longer-term seems unlikely unless climate changes are 

severe. Predictive modeling of birth dates may help identify any such shift in response to climate change. 

The policy of monitoring known colonies probably 

overestimates negative changes in abundance. Colony 

extinctions will be monitored, but colony formation will 

be unobserved, so that in species that readily establish 

new colonies, estimated trends will be subject to 

considerable bias. There is also potential for bias in the 

other direction. Larger colonies are more likely to be 

discovered, and surveys of larger colonies are more likely 

to be maintained over time. Thus, if a species is in decline 

in a density-dependent way, so that small colonies become 

smaller or extinct while large colonies maintain their size, 

roost counts may fail to quantify the extent of the decline. 

Another possibility is that as a population declines, the 

proportion of non-breeding females decreases as a 

density-dependent response. In that case, the decline 

observed in breeding colonies would be smaller than the 

true decline in the population. Thus, counts at colonies 

are likely to be effective for monitoring change only when 

nearly all colonies are known and monitored; or when it is 

rare for new colonies to be established, and a 

representative sample of colonies is monitored. Whereas 

there are few data available to assess whether the NBMP 

species readily establish new colonies or not, only species 

for which roost mobility is considered relatively low have 

been selected for monitoring using this method. Highly 

mobile tree-dwelling noctule bats for example, are not 

monitored using colony counts. The most likely scheme 

to be affected by such bias is the pipistrelle colonymonitoring scheme. However, the sample of colonies 

monitored in this scheme is large, and new sites are 

continually being added to the scheme: thus, some element 

of new colony formation/colony turnover is encompassed. 

In addition, to verify pipistrelle colony counts, a fieldmonitoring scheme using bat detectors to monitor 

pipistrelles is being run in parallel. Effort needs to be 

concentrated on exploring methods to validate roost 

counts and on carrying out pertinent autecological 

research to aid the interpretation of data on population 

trends. 

Factors Affecting Counts at Hibernacula 

A number of human-induced factors may influence 

counts at particular sites. These may range from disturbance of the site (causing bats to vacate at a critical time) 

to the efforts of conservationists to protect or improve 

sites specifically for bats (resulting in increased use by 

bats). Other factors which influence the numbers of bats 

and the ability to monitor the populations of bats using 

the sites relate to the nature of the site, the weather at or 

near the time of the count, and the nature of the bats 

themselves. For the most part these are fairly constant 

factors that will represent background fluctuation over 

time. 

The size of a site, and number and size of entrances 

will influence the number of bats using it. Larger sites 

usually offer a wider range of environmental conditions 

and roosting opportunities. However, a large site with a 

single small entrance will offer more uniform conditions 

than a site with many entrances and so may be less attractive to a range of species or to larger numbers of bats. 

Whereas small sites may not provide for large numbers 

of bats, they are used by almost all species and are of 

considerable value for distribution monitoring. Their importance to bats may be underestimated because of the 

small number of bats found in each site. Small sites may 

also be important at other times of the year (e.g., as male 

mating territories in the autumn). The rate of loss of such 

sites is high in some areas and monitoring the loss of the 

sites themselves should also be considered. 

The surface structure will also influence use by bats. 

A smooth well-mortared brick tunnel or even a smoothwalled natural passage will provide a poor substrate for 

roosting bats. Weather may significantly affect the 

occurrence of bats in underground sites. Particular 

species, such as Natterer’s, long-eared bats (Plecotus 

auritus and Plecotus austriacus), and barbastelle bats 

(Barbastella barbastellus), are more likely to occur in 

increased numbers in sites which remain frost-free during 

periods of prolonged cold weather. The NBMP survey 

forms require data describing the nature of the site and 

weather at the time of survey so that these factors may be 

included as co-variables when modeling trends. 

Identification difficulties will also affect counts. The 

separation of Brandt’s (Myotis brandti) and whiskered 

(Myotis mystacinus) bats can rarely be made with confidence without handling the animal. Because the general 

policy in hibernation site monitoring is to discourage the 

handling of bats, these two species are usually combined 

in survey results. Species of Myotis as a whole may 

present difficulties in identification if the key features of 

the bat cannot be seen clearly or if observers have limited 

experience. Even greater and lesser horseshoe bats may 
114 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

be difficult to distinguish on the roof of a high cavern. 

However, most surveys at hibernation sites are carried 

out by groups of people where the range of experience 

can achieve accurate identification for most bats. The 

number of unidentified or questionably identified bats 

can be accounted for in the survey results, and are unlikely to affect the general trend over time. 

The above factors can be accommodated in the longterm monitoring of underground sites to give reliable data 

on population changes, but the ability of bats to conceal 

themselves in spaces that cannot be inspected (such as 

gaps behind brickwork, natural rock or boulder formations, within rock scree on the floor) means the number 

counted may be an unknown proportion of the number of 

bats present. An assumption is made that even where the 

majority of bats may be hidden from view [as was shown 

for one site by Baagoe and others (1988)], the bats that 

are visible are representative and this representativeness 

remains constant from year to year. Movement of bats 

between sites, as identified in greater horseshoe bats by 

Park and others (1999), may also affect counts, although 

the magnitude of this is likely to be small. 

Factors Affecting Field Surveys Using Bat Detectors 

Randomized sample survey methods avoid many of 

the difficulties associated with roost and hibernation site 

counts. In principle, trends over time within a species can 

be estimated purely from the index of number of passes 

detected; precisely how many animals are detected is not 

needed. Over time however, several requirements need to 

be met. 

Transect lines should be placed according to a randomized design. Failing that, they might be placed in the 

same, nonrandom locations each year, in which case trend 

estimates will apply to the locations covered, and not 

necessarily to a wider area of interest. There should be 

no trend over time in the sensitivity of the equipment. 

Advances in bat detector technology are inevitable, and 

as technology improves it is not logical to justify retaining inferior equipment. To introduce new detectors to 

monitoring schemes, calibration against the old detectors will be required for each species (see Waters and 

Walsh, 1994). If a measure of the effectiveness of a detector can be recorded, the analyst can adjust for it, although 

such sequences of data are notoriously difficult to model 

reliably. 

There should be no trend in detectability of bats 

over time. For monitoring relative abundance, it does not 

matter if it is impossible to determine whether a count of, 

say, five bat passes corresponds to five different animals, 

or to just one animal passing five times. Provided the 

average number of passes per bat does not show a trend 

over time, number of passes can be taken as an index of 

number of bats: if the number of passes halves in 5 years, 

and other factors are unchanged, we estimate that the 

number of bats has halved. If bats vary in their 

detectability between habitats, then habitat successional 

changes might cause bias in estimated trends. However, 

this must be examined on a species by species basis. 

Noctule bats prefer open habitats, and will rarely be found 

close to edge habitats, and never within cluttered habitats. 

Thus, their detectability remains relatively constant due 

to habitat specificity. Serotines most frequently forage in 

edge/open habitat, Daubenton’s bats most frequently 

forage over water, and pipistrelles favor edge 

(occasionally more enclosed areas) and tend to avoid 

open or very cluttered habitats. Thus, differential 

detectability between habitats is unlikely to be a large 

bias. However, in areas of high bat activity, it can be 

difficult to count the number of bat passes. If observers 

cannot reliably estimate the number, there is the potential 

for bias in estimated trends. It does not matter if the counts 

are subject to error, provided that observers do not 

consistently estimate high or low. If, for example, there 

were a tendency to underestimate the number of passes 

at high density, then any decline in numbers of bats would 

also be underestimated. 

Detected passes should be reliably identifiable by 

species. Alternatively, a proportion of passes should be 

identifiable, and there should be no trend over time in 

this proportion. For example, if 80% of bat passes are 

correctly identified in the waterway-monitoring scheme 

for Daubenton’s bats, then this must remain at 80% for 

the duration of the monitoring scheme. If observers improve in their ability to identify bats over time, then a 

false increasing trend might be identified. In NBMP 

schemes, bats are recorded as bat passes of the species 

under study or, when the observer is uncertain of identification, as “unsure” bat passes. The ratio of identified to 

unsure bat passes may therefore be calculated and trends 

in this ratio examined. If a measure of the effectiveness of 

classes of observers can be made, the analyst can adjust 

for it. 

Statistical Monitoring Targets 

At the outset, the major point to consider when planning monitoring programs is the dominating effect of time 

over most survey variables. A key question to answer is 

how much time managers are willing or able to wait for 

conclusive results. Testing for trends is complicated because long-term declines may take the form of slow gradual 

declines or sudden crashes; trends are set against a backdrop of natural fluctuations in size of bat populations 

due to stochastic factors, such as the effects of weather 
 

 

 

on reproduction and survival, and potentially complicated 

by density-dependent feedback (Ransome, 1989; 

Ransome and McOwat, 1994). In addition, estimates of 

population size/trends will fluctuate with biases associated with sampling regimens, such as biased site selection (maternity roost/hibernacula) and unequal detection 

probabilities among observers, equipment, and habitats. 

Unequal detection probabilities between species of bats 

are not a problem, because trends are only assessed for 

each species separately and absolute estimates of population sizes are not required. Incorporating these factors 

into models when testing for trends would help to remove efforts of some ephemeral fluctuations in the data 

and improve power analyses. Whereas some factors can 

be measured and estimated for inclusion in models as 

covariables, for many issues there is insufficient information at present to make quantitative assessments. 

Because data from bat monitoring do not perfectly fit 

the log-normal distribution, particularly for low counts, 

the power estimates produced will be an approximation, 

but are accurate enough to make informed choices about 

the best design to adopt. Mace and Lande (1991) propose that negative population trends of a magnitude of 

1–2%/year equate to unacceptable probabilities of extinction in many animals. Based on our results, to monitor annual trends of over 1% to 3%, a sample of 

approximately 50 to 100 sites, surveyed twice per year, 

should be operated over a period of more than 10 years in 

order to achieve adequate power. Reductions in the number of counts made per year and in the frequency of monitoring to biennial or once every 5 years decreases the 

power of monitoring schemes greatly during the early 

stages of the schemes, but has a more negligible effect 

after longer periods of time (>20 years). Thus, maintaining high survey effort over the first 10 years of a monitoring scheme may be advisable, with a view that 

implementing a reduction in survey effort in the longerterm may decrease costs. 

Program Sustainability 

Volunteers represent a valuable resource to the monitoring program (Fig. 9), and in order to maintain consistent coverage of sites and the sustainability of the 

monitoring program, it is vital that turnover of volunteers 

and sites is minimized. There is a community value in 

people actively participating in conservation projects on 

a voluntary basis. To maintain this spirit, the NBMP ensures that adequate feedback is provided to volunteers 

through personal correspondence, regular progress talks 

delivered regionally and nationally, and a dedicated annual newsletter “Bat Monitoring Post”. In addition, the 

BCT’s quarterly newsletter “Bat News”, reports survey 

WALSH AND OTHERS 115 

Fig. 9. A volunteer for the United Kingdom National Bat 

Monitoring Programme records bats along a river as part 

of the Daubenton’s bat (Myotis daubentoni) monitoring 

scheme (photograph by Julie Agate, Bat Conservation 

Trust). 

progress to BCT members, and information about the 

NBMP is available on BCT’s web site (www.bats.org.uk). 

There are few examples of similar bat monitoring 

projects in Europe on which to base judgements on the 

long-term sustainability of volunteer-based monitoring 

schemes. Most countrywide biodiversity monitoring initiatives do not include bats because of the operational 

difficulties of bat monitoring [see for example, Hintermann 

and others (2000)]. An exception is the Dutch Mammal 

Monitoring Project (Zoogdiermonitoring) which is government funded. The Dutch Mammal Society organizes 

the project, which monitors selected species of mammals, 

including bats. A mix of volunteers and professionals (the 

mix is weighted towards professionals) carry out bat monitoring activities which include: counts of hibernating bats 

in winter, counts of maternity colonies, counts of advertising male bats on transects, and counts of passing bats 

(mixed species) on transects. However, no formal sampling strategies are in place. A setback occurred several 

years ago, when the rising costs of maintaining coverage 

in bat-detector based field surveys (due to a lack of volunteers) could no longer be met. Government funding 

was withdrawn from these surveys, alternative sponsors 

could not be found, and the surveys were discontinued. 

Roost and hibernation site monitoring continue with government support. This demonstrates the cost-benefit advantage of utilizing a volunteer network in preference to 

professional surveyors, but emphasizes the need to nurture the network to maintain monitoring. 

The U.K. government’s Department of the Environment, Transport and Regions funded the BCT to establish the NBMP over a 5-year period (1996–2000). Examples 
116 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

of such significant investment in non-governmental organizations to undertake conservation research work are 

rare. Building on the success of the program, the BCT 

has secured substantial financial support from the 

government’s conservation agency (Joint Nature Conservation Committee) to maintain and develop the NBMP 

through 2005. Over the long-term, BCT is seeking to form 

a series of partnerships among government conservation 

agencies, the devolved statutory nature conservation organizations, and other government and non-government 

organizations, all of whom are important users of the 

monitoring results. Ultimately, funding from a number of 

diverse sponsors and the synergy between the amateur 

and professional sectors will provide a more stable support system than reliance on a single sponsor or single 

sector. 

Outlook for the Future 

This paper has focused on the early development 

and structure of the program. Publication of the results of 

the initial 5-year development phase of the monitoring 

program will be forthcoming. Future activities of the 

NBMP are likely to fall into two key areas: maintenance 

and revision of the core set of monitoring schemes, and 

identification and implementation of techniques to monitor the remaining (eight) nontarget species, particularly 

species that are rare or of international concern. 

The monitoring techniques developed by the NBMP 

have already been widely recognized internationally and 

have provided a model for developing standard 

transboundary monitoring techniques for bats in Europe 

(accepted by Parties of the European Bats Agreement in 

1998). Our success, alongside the long-standing success 

of the British Trust for Ornithology, in recruiting and 

coordinating volunteer work forces, has recently 

prompted the Department of the Environment, Transport 

and Regions to undertake a scoping study to assess how 

volunteers might be involved in a national mammal 

monitoring program within the U.K. In the future, it is 

likely that bat monitoring will become an integral part of a 

wider mammal monitoring network. 

There is no doubt that considerable improvement of 

our knowledge of bat populations through supporting 

research projects is needed to assess and improve monitoring methods. However, conservation decisions must 

be based on the best available evidence at the time. The 

NBMP has taken the best scientific knowledge and techniques available, and put them directly into practice. 

Acknowledgments Acknowledgments Acknowledgments

We thank the many people who have dedicated their 

spare time to help with the collection of field data under 

arduous nighttime conditions. We also extend thanks to 

the many volunteers, office and BCT staff (in particular 

Julie Agate, Julia Baker,Andy Jones, Meesha Patel, James 

Bell, Clare Bowen, Nicky Belbeck, James Farrell, Conrad 

Ellam, and Mari Monii) who have worked to keep the 

NBMP office running smoothly. The support of English 

Nature, Countryside Council for Wales, Scottish Natural 

Heritage, the Joint Nature Conservation Committee, and 

the Environment Agency are gratefully acknowledged. 

The Department of the Environment, Transport and Regions funded the program from 1996 to 2000. 

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A Critical Look at National Monitoring Programs A Critical Look at National Monitoring Programs 
 A Critical Look at National Monitoring Programs

for Birds and Other for Birds and Other for Birds and Other Wildlife Species ildlife Species 
 ildlife Species

By 

John R. Sauer 

U.S. Geological Survey
 

Patuxent Wildlife Research Center
 

11510 American Holly Drive
 

Laurel, MD 20708
 

Abstrac Abstract. Concerns about declines in numerous taxa have created a great deal of interest in survey development. Because birds 

have traditionally been monitored by a variety of methods, bird surveys form natural models for development of surveys for other 

taxa. Here I suggest that most bird surveys are not appropriate models for survey design. Most lack important design components 

associated with estimation of population parameters at sample sites or with sampling over space, leading to estimates that may be 

biased. I discuss the limitations of national bird monitoring programs designed to monitor population size. Although these surveys 

are often analyzed, careful consideration must be given to factors that may bias estimates but that cannot be evaluated within the 

survey. Bird surveys with appropriate designs have generally been developed as part of management programs that have specific 

information needs. Experiences gained from bird surveys provide important information for development of surveys for other taxa, 

and statistical developments in estimation of population sizes from counts provide new approaches to overcoming the limitations 

evident in many bird surveys. Design of surveys is a collaborative effort, requiring input from biologists, statisticians, and the 

managers who will use the information from the surveys. 

KeyWords: Bats, bias, capture-recapture, estimation, index, monitoring, sample frame, surveys. 

Introduction IntroductionIntroduction

Birds are a highly visible and charismatic component 

of the natural world, and are often viewed as indexes to 

quality of nature. Most are protected by international 

treaties that create a legal mandate to monitor their 

populations, and hunted species are particularly well 

monitored by Federal and state agencies. Volunteers have 

proven to be enthusiastic counters of birds in large-scale 

projects such as the North American Breeding Bird Survey 

(BBS) and the Christmas Bird Count (CBC). Consequently, 

large amounts of information are available regarding 

counts of birds in NorthAmerica, and a remarkable number 

of projects exist that purport to function to provide 

population information on birds to assist in conservation. 

These activities include breeding and wintering bird 

atlases, roost counts, constant-effort mist netting, 

acoustic sampling, radar imaging of migrating birds, 

roadside survey counts, nest-box monitoring, aerial 

surveys, point counts, play-back counts, and many other 

methods of encountering birds (e.g., Sauer and Droege, 

1990). Surveys for other taxa are often modeled after 

bird surveys, including roadside surveys of calling 

amphibians (e.g., Mossman and others, 1998) and 4th of 

July butterfly counts that collect information analogous 

to that collected during CBCs. 

Even though all of these programs provide 

information about the targeted populations, there is still 

a great deal of controversy regarding whether these 

surveys provide useful results for population 

management. Much of this controversy is based on 

statistical concerns that the design of the surveys does 

not permit unbiased estimation, and in part reflects recent 

advances in understanding of monitoring methods. Our 

knowledge of what constitutes a reasonable survey is 

much more sophisticated now than it was several decades 

ago. We have a much clearer view of how surveys should 

119 
120 I NFORMATION AND TECHNOLOGY REPORT–2003-0003 

be designed to provide precise estimates of trend or 

habitat-specific abundance to assist in achieving 

management goals, and technical tools for analysis and 

integration of data have undergone remarkable changes 

over the past few years. However, many new programs 

for surveying birds duplicate all the deficiencies of earlier 

programs. In general, they either are not sensitive to the 

management need that motivates them or they fail to 

appropriately sample the population of interest. Surveys 

must reflect collaboration between management, 

biological, and statistical expertise. Unfortunately, the 

interdisciplinary nature of survey design and 

implementation is often ignored in survey development, 

leading to surveys that are limited with regard to at least 

one critical component. In this paper, I review some ideas 

of what constitutes a reasonable survey, and review 

whether selected bird surveys provide reliable 

information about populations. 

Why Monitor? Why Monitor? Why Monitor?

Many bird surveys are developed with only vague 

notions about the uses of the survey results. For example, surveys on federal lands sometimes result from 

legislative mandates to monitor, some surveys are established to provide birding activities for the public, and 

other programs develop simply from the perception that 

useful information can be gathered from a new technological tool such as weather radar or sound recording 

equipment. Vagueness associated with goals and uses 

of survey information often makes it impossible to design a relevant survey. Unless goals are precisely defined, it is impossible to define a population to be 

sampled, develop a survey design to meet the goals, or 

judge the relative merits of alternative procedures. 

Most relevant surveys are tied directly to management and research needs for population management. 

Migratory bird managers use estimates of change in population size from waterfowl surveys to evaluate the consequences of harvest regulations; land managers use 

estimates of population change to judge the effectiveness of land management activities. Occasionally, estimates of movement rates among colony sites or refuges 

are needed for management, or demographic information 

such as survival and productivity is needed to assess 

the viability of local populations. 

The information collected in a survey must be relevant to the goals of the management or research. Traditional management of migratory birds has relied primarily 

on time series of estimates of population size to assess 

population status. Often, these data are counts of observed numbers of birds, although occasionally banding 

studies are used to estimate population size for populations that cannot be observed for counting. Although 

population size information has obvious relevance, it is 

often difficult to understand the causes of population 

change from population size data. The observation of 

change in numbers has little utility if it provides no insight into why change is occurring. Consequently, several bird monitoring programs focus on estimation of 

primary demographic parameters such as survival, productivity, and movement rates (e.g., DeSante, 1992) in an 

attempt to estimate parameters that are more likely to be 

associated with causal factors. Nonetheless, many biologists view estimation of population size (or change in 

population size) as a primary goal of surveys, and I will 

emphasize surveys that address this goal. 

Waterfowl biologists have recently initiated adaptive harvest management of selected species (Williams 

and Johnson, 1995). In adaptive management, managers make a decision based on best predictions of the population responses to alternative management options. 

Monitoring is used to evaluate the quality of the predictions and to update the models used to make future management decisions. This use of monitoring provides 

insight into the causes of population change because it 

allows managers to determine which model will provide 

the best predictions for consequences of management, 

and is perhaps the most effective use of monitoring in a 

management context. When management goals exist, it is 

important to consider the role of monitoring information 

in assessing the consequences of management. 

Design Issues for Design Issues for Design Issues for Wildlife ildlife 
 ildlife ildlife

Surveys Surveys 
 Surveys Surveys

Survey design has a large literature, both in wildlife 

and statistics journals. In particular, Thompson and others (1998) and Skalski and Robson (1992) provide general 

reviews of many components of the design of wildlife 

surveys. Surveys are generally based on probability sampling, in which the population is divided into a series of 

sample units, each of which has a known probability of 

appearing in a sample. The actual samples chosen in the 

survey are selected randomly based on associated probabilities of selection, allowing development of sampling 

theory and estimates of population attributes. In almost 

all wildlife surveys, an additional complication exists in 

that we generally cannot census sample units, and we 

have to estimate total numbers of animals (our attribute 

of interest) in each sample unit. Skalski (1994) refers to 

this as 2-stage sampling, where probability sampling over 

spatial sampling units is the first stage, and the estimation of animal density within sample units is the second 
stage. This is an extremely useful distinction, as both 

components are critical in wildlife survey design. Note 

that the second stage requires estimation of population 

size for a known area. 

Cochran (1977) outlines components that should be 

considered when planning and implementing a sample 

survey (Table 1). This very general outline should be 

consulted before any survey is designed, as it contains 

several logistical and conceptual components often omitted from wildlife surveys. For example, notions of goals, 

target populations, pilot studies, and planning for quality control all need additional emphasis in most wildlife 

studies. Also imbedded in this outline are the particular 

constraints of wildlife surveys, as Skalski’s (1994) first 

stage particularly relates to definition of the target population and development and sampling from the frame, 

whereas the second stage relates to methods of measurement and collection of relevant data (Table 1). 

Common Problems with 
 Common Problems with

Bird Surveys Bird Surveys Bird Surveys
 

In my view, most bird monitoring programs are 

missing several of the components suggested by Cochran 

(1977). They often lack clear statements of objectives, 

and sometimes have vaguely defined target populations, 

incomplete sampling frames, and poorly thought-out 

methods of measurement. Even the most well-known bird 

surveys, such as the BBS or CBC, provide incomplete 

lists of species and numbers of individuals present at a 

particular time and place. The CBC, which was started to 

provide a recreational activity for birdwatchers, is often 

considered “the largest wildlife survey in the world” 

SAUER 121 

(Butcher, 1990, p. 5). The BBS was developed specifically 

to monitor landbirds (Robbins and others, 1986). 

Unfortunately, both surveys are deficient in two critical 

components: 

Deficiency 1. The counts are not censuses. Instead, 

varying numbers of groups of counters record 

birds from areas within the 15-mile diameter 

“circles” that form the sample units of the CBC. 

Clearly, numbers of birds counted varies with the 

amount of effort in counting and the competence 

of the observers, and no attempt is made to estimate the number of birds actually present. In the 

BBS, the 50 point-counts that comprise each survey route are also not censuses, but count an 

unknown proportion of the birds present in an 

area. It is well known that the detectability of birds 

varies between routes and observers in the BBS 

(Sauer and others, 1994). 

Deficiency 2. The sample units are not randomly selected. Instead, in the CBC they are generally centered in places likely to be of interest to birders. 

In the BBS, although there is an element of random route selection, the routes are restricted to 

roadsides, and any site >0.25 mile from a roadside is not in the sampling frame. 

The consequences of these deficiencies are obvious. 

For Deficiency 1, it is clear that counts from the surveys 

always underestimate the population size. Thus, any use 

of the data requires that we assume that either the counts 

accurately index the population (i.e., the counts are a 

constant proportion of the population size), or that the 

variation in the proportion counted can be controlled by 

use of effort (for the CBC) or observer (for the BBS) 

covariates. Unless obviously incorrect assumptions are 

Table 1. List of ess able 1 ential elements for development of a sample survey, as defined by Cochran (1977). 

• Development of objectives is needed to provide structure for the project. 

• The target population must be defined to ensure that it coincides with the sampled population. 

• Data to be collected must be relevant to the objectives. 

• Needed degree of precision must be specified. 

• Methods of measurement must be chosen. 

• A sampling frame (listing of all possible sample units) must be developed that covers the entire population. 

• Methods of selecting a sample from the frame must be defined. 

• Small-scale trials of design (pretests, pilot studies) are useful to evaluate efficiency. 

• Organization of fieldwork must incorporate planning for quality control and quality assurance. 

• Summary and analysis of data should be considered during survey design. 

• All surveys must be viewed as providing information to be used in designing future surveys. 
122 I NFORMATION AND TECHNOLOGY REPORT–2003-0003 

made, CBC and BBS count data cannot be considered a 

census. For Deficiency 2, it is clear that any information 

from the sample units cannot be used to extrapolate to 

areas not sampled unless we assume either that they 

constitute a random sample from the population, or that 

the lack of representativeness can be controlled by use 

of covariates that reflect differences among the actual 

sample sites and the rest of the area. 

Statisticians refer to Deficiency 1 as visibility bias in 

estimation, and Deficiency 2 as an incomplete sample 

frame. Surveys containing these deficiencies are often 

called “index” surveys because they explicitly only count 

parts of the actual population of interest. Note that in the 

context of surveys, an index is often implicitly defined as 

a count that is related in some unknown (but assumed to 

be consistent) way to an underlying parameter. Most biologists tend to consider indexes in the context of the 

second stage of sampling because a count collected at a 

sample unit is often considered to index population size 

at a site. However, it is also useful to consider indexes in 

the spatial sampling context. 

Almost all bird surveys have some deficiencies associated with use of indexes. Every survey discussed in 

Sauer and Droege (1990) as providing information on 

population trends could be categorized as an index survey. Popular bird survey methods, such as point counts 

(Ralph and others, 1995), only index population size at 

sample sites. The only example of a long-term, geographically extensive survey designed with explicit consideration of both stages of sampling is the Spring Breeding 

Ground Survey for waterfowl (Smith, 1995, p. 29). 

Analysis of Survey Data Analysis of Survey Data Analysis of Survey Data

Analysis of index surveys has proven to be very 

controversial, and the statistical literature contains many 

cautions about their limitations. As examples, it has been 

stated that: 

“Using just the count of birds detected (per unit 

effort) as an index (to) abundance is neither scientifically sound nor reliable” (Burnham, 1981, 

p. 325), and “It is imperative in designing the 

preliminary survey to build in the capability of 

the sampling program the ability of testing homogeneity of the proportionality factor values...” (Skalski and Robson, 1992). 

Naive analysts of index surveys treat them as single 

stage sample surveys. That is, they assume that withinsite indexes are censuses reflecting area-specific abundances, then ignore possible sample frame problems and 

calculate estimates using standard sample survey theory. 

Estimating a total population size of a species from CBC 

data or using mean counts from BBS routes are examples 

of the naive approach to survey analysis. Although most 

analysts recognize that naive analyses of index surveys 

are likely to lead to biased estimates (e.g., James and 

others, 1990; Lancia and others, 1994), many examples of 

inappropriate analyses of index surveys exist. Generally, 

appropriate analysis of index surveys tend to be much 

more complicated (and problematic) than analysis of 2­

stage surveys. 

Analysis of 2-Stage Surveys 

The 2-stage nature of wildlife surveys always introduces some complications into analysis, in that withinsample unit abundances must be estimated. Two-stage 

surveys require some statistical modeling for estimation 

in the second stage, but then are design-based, in that 

the probabilistic design of the sampling in the first stage 

is model-free. This means that some statistical procedure 

such as capture-recapture is used to estimate visibility 

rates of animals within sample units, but once they are 

estimated the first stage can be treated using standard 

sample survey theory. 

Analysis of Index Surveys 

Index surveys often cannot be assumed to provide 

censuses with sites or even fixed areas of sampling. Appropriate analysis of data from surveys such as the CBC 

or the BBS requires that deficiencies of the surveys be 

acknowledged and accommodated. Generally, these accommodations involve additional statistical modeling that 

seeks to minimize bias in estimation at each stage of the 

survey. For the second stage, this involves identifying 

factors that might influence the visibility rates of birds 

(such as effort in the CBC), and modeling the effects of 

effort on counts as part of the analysis. For the first stage, 

factors such as habitat areas within regions form possible covariates. For either stage, resulting estimates are 

model-based, in that it must be assumed that the covariate 

adjustments adequately accommodate the deficiencies 

of the original sample. Care must be taken, however, to 

distinguish covariates influencing the proportion counted 

from covariates related to actual population sizes; the 

former should be included in analyses and the latter 

should not. Covariates influencing both population size 

and proportion counted introduce confounding (e.g., 

Bennetts and others, 1999). 

Often, index surveys are used to estimate change 

over time in population size, rather than actual population 

size. Because it is acknowledged that sample units are 
vaguely defined in index surveys, covariate adjustments 

that attempt to control for visibility differences over time 

within sites often have more credibility than adjustments 

that control for visibility differences among sites. This 

approach is used to estimate population change in the 

BBS, in which observer differences are controlled using 

covariates in a log-linear model (e.g., Link and Sauer, 1998). 

Model-based approaches to analysis of index surveys 

still have assumptions, and the validity of the overall 

results depends on how well the model accommodates 

differences in visibility. Of course, many factors that 

influence visibility are not observed and cannot be 

modeled (Lancia and others, 1994). Nevertheless, this 

model-based approach to survey analysis provides the 

only means to enhance the credibility of most bird surveys. 

What Can Be Done to Develop
 What Can Be Done to Develop

Monitoring Programs for Species Monitoring Programs for Species Monitoring Programs for Species
 

That Are Dif Are Difficult to Survey? ficult to Survey? 
 ficult to Survey?

Because of widespread interest in monitoring, a 

variety of groups have been attempting to develop 

surveys for taxa that have never been effectively 

monitored. For example, regional surveys are under 

development for marsh-breeding birds, amphibians, and 

invertebrates. Unfortunately, many of these projects are 

at risk of duplicating the mistakes of earlier programs. In 

particular, the BBS is often presented as a model for these 

developing programs, and readily available results from 

the BBS (e.g., Sauer and others, 1997) tend to reinforce 

the notion that the large amounts of information available 

from the survey overwhelm potential deficiencies. In my 

view the BBS can provide reasonable results in many 

cases. However, the untestable assumptions implicit in 

analysis must always be considered when interpreting 

results from the survey (Link and Sauer, 1998) and 

corroborative evidence is often critical for confirmation 

of results when BBS data are used in management. 

Incorporating tests for visibility differences and correcting 

sampling frame deficiencies in the BBS would greatly 

enhance the credibility of the results. 

Developing Reasonable Population
 

EstimatesWithin Sample Units
 

Any experimental study involving inference about 

change in animal abundance over time and space requires 

a measure of abundance. For most taxa, indexes to 

abundances are routinely used in inference, but are often 

inappropriately treated as censuses. Although flawed, 

these indexes often have a basis in the biology of the 

SAUER 123 

species, and occasionally large historical databases of 

index information have been accumulated. Consequently, 

the indexes form a starting point in development of 

appropriate estimates of abundance. 

Unfortunately, most indexes such as point counts 

and netting counts for birds and calling, pond, and coverboard counts for amphibians not only count an unknown 

proportion of the individuals present, but also do not 

provide a definable area of counting. To define appropriate abundance estimates in the context of such indexes 

requires: 

1.	 Determining whether the population sampled by 

the index is the target population. For example, in 

bird point counts, the sampled population is often birds that are visible to the observer (such as 

singing males), while the implicit target population is all individuals. 

2.	 Developing methods of estimation of detectability in the context of the index. Often, modification 

of survey methods allows estimation of detectability of individuals. For example, with bird point 

counts, distance methods (Buckland and others, 

1993) or double-observer methods (Nichols and 

others, 2000) can be used to estimate detectability. For other situations such as mist netting of 

birds or cover board studies of salamanders, more 

intensive methods such as capture-recapture can 

be used to estimate population size (e.g., Otis 

and others, 1978). By introducing these methods, 

credibility of survey results are greatly enhanced 

because investigators can directly test for detectability differences over time and space. 

3.	 Considering the area covered by the abundance 

index at a sample site. Often, the area covered by 

an index is only vaguely defined, and density of 

animals cannot be accurately estimated. Skalski 

(1994) emphasizes that understanding of the area 

associated with abundance estimates is required 

for estimation of population density. If areas cannot be specified, a different conceptual framework that explicitly defines the abundance 

estimate in the context of a model of spatial population change is needed for analysis (e.g., Link 

and Sauer, 1998). 

Methods That Can Be Used to Estimate Detectability in the Context of Count Indexes 

Distance methods. Distance methods include line and 

point transects and involve collection of counts of 

animals, but with a covariate (distance from observer to 

animal when first observed). The covariate information 
 

 

124 INFORMATION AND TECHNOLOGY REPORT–2003-0003 

allows modeling of detection rate as a function of distance 

from the observer. In line transect approaches, the 

observer walks along a transect and records individuals 

observed at varying distances from the transect line, 

whereas in point transects (also called variable circular 

plots) the observer stands at a point and records 

distances. From these data, decline in detection rate is 

modeled as a function of distance from the transect (or 

point), and by assuming the detection rate at the transect 

or point (distance 0) is 1, the density of animals can be 

estimated. Buckland and others (2001) provide a 

comprehensive review of these methods and describe 

the computer program DISTANCE that is used to fit 

models to the detection-rate distance relationship and 

estimate density. 

Double-observer method. The double-observer 

method is also based on count data, but permits estimation of proportion of animals detected by statistical modeling of numbers of animals counted by dependent 

observers at multiple sites. In this design, two observers 

count at each sample site (or transect). One observer is 

designated a primary observer, the other a secondary 

observer. The primary observer notes all animals he or 

she sees at the site, and the secondary observer notes 

any animals missed by the primary observer. At the next 

sample site, they switch roles, and repeat the sampling 

procedure. These data provide sufficient information to 

allow estimation of detection rates for each observer 

(Nichols and others, 2000). Although density is not directly estimated using this method, restriction of counts 

to a fixed area or additional statistical modeling allows 

conversion of the abundance estimate to a density estimate (Nichols and others, 2000). 

Capture-recapture methods. Although more effortintensive, populations of many cryptic animals can only 

be estimated using capture-recapture methods, in which 

animals are captured, marked, and released at one time, 

then recaptured (or resighted) at a later time. Original 

uses of capture-recapture were to estimate population 

size during a short time period when the population was 

closed (not changing in size due to birth, death, or migration), or for estimation of population size and survival 

over longer periods during which the population could 

change in the between-trapping intervals (open population models, such as the Jolly-Seber model). In recent 

years, capture-recapture methods have been greatly extended through statistical developments that allow for 

better estimation of: detectability; movement among sites; 

influence of covariates on survival; and population 

change. Statistical software that allows users to implement these methods is now available (White, 1999; White 

and Burnham 1999). Capture-recapture estimates of population size generally only provide abundance estimates 

for an unknown area, unless modeling is used to define 

effective areas (e.g., Otis and others, 1978) or captures 

are conducted in a restricted area (such as marsh habitat 

or a cave) that provides a natural unit for sampling. 

Emerging methods. Recent research has provided a 

variety of new statistical methods for population 

estimation. Noteworthy new methods that refine existing 

methods or apply new approaches for estimation of 

detection rates from count data include: (1) a temporal 

removal method for analysis of point count data that 

provides an alternative to double-observer and distance 

approaches (Farnsworth and others, 2002); (2) a procedure 

for estimation of site occupancy rates from repeated visits 

to sites (MacKenzie and others, 2002); and (3) a method 

of estimating abundance from repeated counts at sites 

(Royle and Nichols, 2003). 

Sampling Over Space 

Unless sample units are selected at random from a 

sampling frame, standard statistical methods cannot be 

used to estimate population attributes. For example, a 

sampling frame for marsh birds would include a list of all 

marsh areas in a region, and sample units would be randomly selected from the list. When all sampling sites cannot be listed, area is often used as a sample frame, with 

the region of interest divided into area-based sample units 

that are then randomly selected and sampled. Unfortunately, logistical constraints often prevent biologists from 

selecting or accessing sample sites from the entire area of 

interest, leading to areas that are not covered. One important example of this is the roadside sampling frame of the 

BBS that prevents coverage of off-road sites. Frequently, 

biologists make these choices of areas to be sampled without consideration of the limitations that they will impose 

on the estimation. However, statisticians have considered a number of approaches that allow efficient sampling in the context of logistical and physical constraints. 

Some of these approaches, such as stratification to allow 

differing sample intensity over space, are well known to 

biologists. However, approaches such as dual-frame sampling (Haines and Pollock, 1998) and adaptive sampling 

(Thompson and Seber, 1996) also exist, and hold great 

potential for increasing efficiency of surveys. 

Dual-frame sampling (Haines and Pollock, 1998) allows 

for efficient sampling in the case where traditional sites 

(such as colonies of birds, or nesting sites) are known to 

be used by animals. These traditional sites are known as 

the list frame, while all possible sites in the area of interest 

form an area frame. Random sampling is conducted in 

both frames, but generally the list frame is sampled at a 

relatively high intensity, while the area frame has a less 
intensive sample. For analysis, the overlap among the 

samples is identified, and the overlapping samples are 

eliminated from the area sample. Frames are then treated 

as separate estimations and the population totals from 

the adjusted frames are summed to derive a total 

population estimate. See Haines and Pollock (1998) for an 

application of this method for estimation of active eagle 

nesting sites. 

Adaptive sampling (Thompson and Seber, 1996) is a 

procedure for sampling rare attributes that tend to be 

clustered. In adaptive sampling, the sample selection 

procedure is modified as a consequence of information 

obtained during the survey. For example, one common 

application of adaptive cluster sampling is based on a 

simple random sample. For each sample unit in which an 

animal is found, adjacent units are sampled. The process 

is repeated with newly selected sample units until no new 

units with animals are found in the adjacent sample. Then, 

a variable-probability sampling procedure is used to 

estimate the total population. See Thompson and Seber 

(1996) for examples of adaptive sampling applications, 

and Smith and others (1995) for an example based on 

waterfowl surveying. 

Conclusions ConclusionsConclusions

The Skalski (1994) formulation of biological sampling 

as a 2-stage process provides a reasonable and productive starting point for development (and improvement) of 

any monitoring program. All surveys must be judged in 

terms of their ability to adequately sample within sites 

and over space. For many taxa presently considered for 

survey development, indexes to abundance exist but little 

work has been conducted on development of efficient 

methods for estimation of visibility rates in the context of 

these indexes. Development of these methods, and incorporation of visibility rate estimation into routine sampling, are critical components of any monitoring program. 

Fortunately, many tools now exist for survey development that can be very effectively applied in new programs. Recent years have seen an enormous amount of 

development of statistical theory and methods for visibility rate estimation, and Geographic Information Systems (GIS) provide a unique opportunity to develop and 

test alternative sampling frames. The challenge is for biologists to remain sensitive to the need for statistical 

rigor in survey design, and for statisticians to remain 

sensitive to biological concerns. 

It is also important to recognize the implicit connection 

to management in all surveys, and to design surveys 

whenever possible to provide information that can 

explicitly be used in management. Monitoring provides 

SAUER 125 

the only means for managers to evaluate the population 

response to management, and if the survey is designed 

appropriately it can be a component of an adaptive 

management procedure (e.g., Conroy and Noon, 1996). 

A Final Comment A Final Comment A Final Comment

One important limitation of operational survey programs is the inertia associated with historical data. Many 

managers are reluctant to modify surveys because of 

concerns of continuity of information and fears of undermining the credibility of the program. However, all surveys need to be amenable to constant revision as our 

understanding of populations and methods changes. In 

this context, it is productive to evaluate existing surveys, 

determine where model-based assumptions must be applied for analysis, and devote effort to development of 

modified sampling methods that will allow for direct estimation of population parameters. 

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Existing Data on Colonies of Bats in the Existing Data on Colonies of Bats in the
 Existing Data on Colonies of Bats in the

United S United SUnited States: Summary and tates: Summary and tates: Summary and Analysis of the Analysis of the 
 Analysis of the

U.S. Geological Survey’ U.S. Geological Survey’s Bat Population Database s Bat Population Database s Bat Population Database
 

By
 

Laura E. Ellison
 

Thomas J. O’Shea 


U.S. Geological Survey
 

Fort Collins Science Center
 

2150 Centre Avenue, Building C
 

Fort Collins, CO 80526-8118
 

Michael A. Bogan 

U.S. Geological Survey
 

Fort Collins Science Center
 

Museum of Southwestern Biology
 

University of New Mexico
 

Albuquerque, NM 87131
 

and 

A. Lance Everette
 

Diane M. Schneider
 

U.S. Geological Survey
 

Fort Collins Science Center
 

2150 Centre Avenue, Building C
 

Fort Collins, CO 80526-8118
 

Abstract Abstract. There has been increasing concern about the status of bat populations in the United States (U.S.) and territories. Abstract

However, there have been few efforts to compile and evaluate the fragmented information available on this topic. In this paper, we 

summarize and review existing information on the status of bat colonies in the U.S. and territories. We compiled a central database to 

store estimates of colony sizes made by others. We used these data to investigate colony trends and evaluate the potential of existing 

information to form the basis of monitoring programs. The U.S. Geological Survey Bat Population Database is available to the public 

on the Internet (http//www.fort.usgs.gov/products/data/bpd/bpd.asp). The database organizes estimates of colony size or counts of 

bats found in the scientific literature and in various recent efforts at inventorying and monitoring by others. Currently, the database 

includes more than 26,600 records for 43 species and three subspecies of bats in the U.S. and seven species in the territories. Although 

estimates date as early as 1855, two-thirds of the observations were made after 1980. We used nonparametric rank analysis to analyze 

counts in the database that were conducted in time series of >4 years at 179 summer and 294 winter roosts of 22 species of bats. 

Trends were not detectable at most of these roosts, and most time series had high coefficients of variation. In addition, we summarized 

reports by others pertinent to the status of populations, and provide comments on the sources of data, kinds of roosts occupied, and 

information on the trends for each species of bat. We discuss shortcomings of existing data that must be overcome in the design of 

future monitoring programs. These include the need to develop statistically valid sampling designs to meet monitoring objectives; to 

apply population estimation techniques such that both sampling and process-based variance can be determined; to develop and employ 

standards for surveys; to understand the basis for fluctuations in colony sizes at target roosts and to use this information to develop 

standards for timing of surveys; and to monitor greater numbers of species at more locations over longer spans of time. 

KeyWords: Bats, ords colonies, counts, emergence, hibernacula, maternity colonies, monitoring, roosts, territories, trends, United States.

127 
 

 

128 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Introduction IntroductionIntroduction

There are approximately 45 species of bats known 

from the United States (U.S.) and 15 additional species 

in the Pacific and Caribbean territories. Colonies at roosts 

of some of these species have declined or even disappeared in recent decades (e.g., Tuttle, 1979; Rabinowitz 

and Tuttle, 1980; U.S. Fish and Wildlife Service, 1982, 

1992; Grant and others, 1994; Clark, 2001), causing 

attention to be drawn to the need to develop inventory 

and monitoring programs for bats. The U.S. Fish and 

Wildlife Service lists eight species of bats in the U.S. 

and territories as endangered or threatened; an additional 

25 species or subspecies of bats were formerly considered as candidates for listing under the Endangered Species Act (U.S. Fish and Wildlife Service, 1994). Despite 

increasing concern for many species of bats, efforts to 

determine population status and trends have been fragmented among agencies and organizations. In late 1995, 

we began a project to compile existing population information for bats in the U.S. and territories. Our objectives were to: (1) develop a database which incorporated 

as much of the available information on counts at bat 

colonies in the U.S. and territories as possible; (2) evaluate the suitability of these data for statistical analysis of 

trends; (3) evaluate applicability of existing data to design future monitoring programs; and (4) serve the database on the Internet (with restrictions on accessibility 

to sensitive location information) for use by those who 

may have an interest in using the information for monitoring or conservation purposes. Our original intent was 

to examine population trends of bats, but we found that 

defining what constitutes a “population,” or even a 

“colony” in this group of animals can be difficult. Thus, 

we focus this paper on counts at roosts. We summarize 

and evaluate the available information on counts and 

trends in counts at roosts compiled by species and species groups. We discuss issues surrounding use of previously existing information in designing and conducting 

monitoring programs for bats. We also review the literature pertinent to the population status of each species. This literature is largely anecdotal for most species 

because of a lack of consistent effort aimed at monitoring, particularly prior to the last decade. 

Methods Methods Methods Methods

Database Design 

We designed a relational database to collect and store 

data on sizes of bat colonies (see definitions below). The 

database will hereafter be called the Bat Population 

Database (BPD). We created 14 different tables of information with seven linking tables (Fig. 1). A table is database terminology for a collection of data about a specific 

topic, and is organized into columns, also called fields, 

and rows, or records. By using a separate table for each 

topic, the data are stored only once, which makes a database more efficient and reduces data-entry errors. One 

record in the BPD consists of an observation for a species on a unique date at a unique location linked to a 

bibliographic citation (publication, unpublished report, 

thesis/dissertation) or contributor (e.g., state Natural 

Heritage programs, game and fish departments, or federal agencies such as the U.S. Forest Service). An observation can be information such as an emergence count, 

a collection of specimens, a capture with mist nets or 

harp traps, a survey of a cave/mine, or other information. Sensitive location information (e.g., latitudinal and 

longitudinal coordinates) was not included in our database. Multiple data types can be linked to the same date 

for those observations that involve multiple methods 

(such as emergence counts conducted at a cave entrance, 

while also netting or trapping at the entrance). With this 

relational database design, information can be easily 

extracted and sorted by species, location, state, county, 

type of colony (i.e., hibernating, maternity, bachelor) or 

structure (i.e., cave, mine, tree, building), colony size 

estimation methods, types of observations (colony, mist 

net, trap, acoustic), data source, land management authority, and other attributes. The BPD is currently being 

served on the Internet with the capability to search by 

site, species, and state with associated literature citations or links to other databases with the original contact information (http://www.fort.usgs.gov/products/ 

data/bpd/bpd.asp). No sensitive location information is 

provided on this website. 

Fig. 1. The 14 different tables of information in the 


USGS Bat Population Database and how they are linked. 

Data Acquisition 

We began data acquisition by reviewing the scientific literature, starting with peer-reviewed journals (e.g., 

Journal of Mammalogy, Mammalian Species). We conducted literature searches in a number of databases, libraries, and the Internet. We also reviewed books specific 

to the mammal faunas of each state. Bibliographic citations were cross-examined for further references. We 

contacted 48 state Natural Heritage Programs for information in their databases. We also contacted researchers involved in ongoing bat surveys in several states (e.g., 

Colorado Division of Wildlife Bats and Mines project, 

New Hampshire Fish and Game Department, the New 

York Division of Wildlife Winter Bat Survey, the Pennsylvania Game Commission Winter Bat Hibernacula 

Survey, and the Wyoming Game and Fish Department). 

Other states and individual researchers conducting longterm monitoring programs for bats were also contacted. 

We focused our data acquisition and entry on counts 

at roosts of colonial species. Geographic distribution 

records or lists of bat specimens in museum collections 

were not actively sought. Similarly, records of bats captured in mist-nets, traps or by other collection methods 

(such as acoustic surveys) at foraging locations or other 

sites away from roosts were not a focus of our search, 

except when those types of data were associated with a 

colony location and estimate of colony size. However, 

the BPD has the capacity to include such information in 

the future. 

We reviewed data sources for mention of a roost 

location and colony size for each species of bat. Very 

few publications included monitoring of bat populations 

over time, and many were one-time observations. 

Location information (site name, county, state), date of 

the observation, and number of bats found at that location 

on that date were entered in the BPD. We also 

incorporated more detailed habitat descriptions, methods 

used to count individuals and other miscellaneous 

information when relevant. Each observation was linked 

to the literature citation or contributor and to individual 

species. 

Each observation in the BPD was checked for accuracy and errors by at least one independent observer. 

The independent observer reviewed entered data for 

spelling errors, accuracy of counts, and any relevant information from articles that might have been missed in 

the review process. 

Data Summaries 

We used SAS software to summarize the records 

collected in the BPD (Version 8.02 of the SAS System 

ELLISON AND OTHERS 129 

for Windows, SAS Institute Inc., 2001). SAS procedures 

were used to compute frequency and summary statistics 

of observations by species, location, source of information, and types of data collection. 

Trend Analyses 

We summarized trends for those species with time 

series of four or more distinct annual surveys at a particular location, conducted in the same season of year, 

and using similar methods. A time series of counts did 

not necessarily consist of counts made in consecutive 

years, but could include surveys spanning several decades at irregular intervals greater than one year. If a 

range of counts was reported, we used the midpoint between the upper and lower bounds (i.e., if a survey reported 100–200 individuals, we used a value of 150 for 

the colony size estimate). Most counts were reported from 

different sources and almost none had sampling variances associated with them. Therefore, we used a MannKendall nonparametric test for trend (Kendall and 

Gibbons, 1990) as recommended for analysis of count 

data with such attributes by Thompson and others (1998), 

who also noted that this technique has an advantage in 

that exact estimates of population size are not necessary. The Mann-Kendall nonparametric test is a rank 

correlation technique that takes the magnitudes of the 

counts and ranks their differences as pluses and minuses. 

We calculated an S-statistic to test for trend when time 

series were <10 distinct years. If the S-statistic was positive and large, counts taken later in time tended to be 

larger than those taken earlier and conversely, if the value 

for S was a large negative number, counts taken later in 

time tended to be smaller (Thompson and others, 1998). 

To test for an upward trend, we rejected the null hypothesis of no trend if S was positive and the probability 

value associated with the calculated S was less than the 

a priori level of 0.05. Similarly, to test for a downward 

trend in counts, we rejected the null hypothesis of no 

trend if S was negative and the probability value was 

less than 0.05. We calculated the Kendall tau coefficient, 

tau, for time series >10 (Kendall and Gibbons, 1990). 

The tau-statistic ranged from -1 to +1. We conducted 

one-tailed tests for downward or upward trends. If the 

null hypothesis was not rejected using either the MannKendall S-statistic or Kendall’s tau-statistic, we concluded that no trend was detectable for the time series 

analyzed. Where counts at roosts through time had tied 

ranks, a modified tau was calculated per Kendall and 

Gibbons (1990). More rigorous regression techniques 

to analyze for trends were not considered valid because 

of the differing sources, methods, and quality of the 

data. 
130 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

For each time series analyzed, we calculated a mean, 

standard deviation, and coefficient of variation (Zar, 

1984). The coefficient of variation (CV), expressed as a 

percentage, is the ratio of a standard deviation of a parameter estimate to the parameter estimate, and is a 

measure of relative precision when comparing degree 

of variation between or among sets of data (Thompson 

and others, 1998). Large CVs indicated high variability 

in counts at roosts over time, and small CVs indicated 

low variability. We provide CVs to allow the reader to 

make a judgment regarding the basis for failure to reject 

a null hypothesis of no trend detectable. In cases where 

CVs are relatively high, failure to reject the null hypothesis may be due to high variability in counts. In 

cases where CVs are low, the trend may be stable. 

Terminology and Definitions 

Terms Used Throughout the Report 

Census. A complete count of bats in a survey area, 

but usually made without estimating and correcting for 

sampling and observation probabilities. 

Colony. A group of bats of a single species, which 

occupy a definable boundary at a particular time interval where population parameters can be defined (Working Group A Report, this volume). See also definition of 

colony size estimate below. 

Colony size estimate. A count or estimate of the size 

of a group of individuals of the same species living in a 

particular area at a particular time. We make the assumption that most counts of bats at roosts are estimates 

of colony size. However, in many cases bats may exist 

in fusion-fission social groupings wherein fractions of 

such groups can be at different roosts at the same time. 

In such cases, counts at single roosts may not represent 

the entire social group. Because such situations are usually unknown at the time of counting, a more conservative definition of the data on counts of colony size can 

be reduced to simply “counts at a roost.” 

Count. A generic term for how many bats were found 

in a particular location on a unique date. Methods used 

to obtain a “count” varied (e.g., counts of bats exiting at 

evening emergence, counts of bats in clusters within 

roosts, capturing bats at the entrance to roosts). Sometimes a count is a survey, or “best guess” of the original 

investigator and is not a census. 

Day roost. Any place a bat settles down to rest during the daylight hours, but sources do not specify roost 

function (e.g., roost could be for a maternity, bachelor, 

or hibernating colony). 

Hibernacula. Any site where bats roost for 

hibernation in winter. 

Location. A unique site where bats were found. 

Maternity colony. A group of bats where most of the 

individuals in the colony are pregnant females or lactating females with their young. 

Night roost. Any site used by bats at night to rest 

and digest food, usually on a temporary basis between 

foraging bouts and usually at a different location than 

their day roosts. 

Observation. A documented bat occurrence on a 

unique date at a unique location. An observation can be 

a count or any other method of estimating a colony size 

for a particular species of bat on a unique date at a unique 

location. 

Population. A group of individuals of the same species living in a particular area (Working Group A Report, this volume). A population can consist of multiple 

colonies with spatial boundaries that vary within and 

among years. 

Record. One row of information or data in a table 

in the BPD. 

Roost. Any discrete location a bat settles down to rest. 

Summer colony. A colony of bats of unspecified function found in the summer (could be a maternity, transient, or bachelor colony, but the function and 

composition were not documented in the original source). 

Transient roost. Any roosting site used by bats on 

an irregular, short-term basis as defined by the original 

source (e.g., a roost used during migration). 

Unspecified roost. Any site of unspecified function 

used by bats. 

Results and Discussion Results and Discussion Results and Discussion

Data Summaries 

The BPD contains 26,643 observations for 43 species and subspecies in the U.S., and seven species from 

the territories. Eighty-nine percent of these observations 

(23,716) consist of surveys, visits, or counts made at 

roosts. Fourteen percent of the observations (3,730) are 

from mist-netting records [8% (298) of these mist-netting records also included a count at a roost], and 3% 

(799) are from trapping, acoustic, and miscellaneous data 

types. The remainder of the summaries and analyses of 

this paper focuses on counts at roosts. Counts from mist 

netting, trapping and acoustic methods are biased due 

to different protocols and unknown factors, and were 

usually conducted where bats were dispersing and foraging, not concentrating at a roost. 

There were seven different categories of data sources 

for observations of counts at roosts: Federal sources, 

unpublished or technical reports, individual researchers, 
ELLISON AND OTHERS 131 

theses or dissertations, Natural Heritage programs, state 

wildlife agencies, and other publications (consisting of 

mostly journals and books). We reviewed more than 

3,000 bibliographic citations (unpublished or technical 

reports, theses or dissertations, scientific journals, and 

books). The majority of these citations were from peerreviewed journals (over 80%). Journal of Mammalogy 

was the most frequently cited source we reviewed (40%). 

We found colony observations from 1,450 of these 

bibliographic citations. Ten state Natural Heritage 

programs contributed information on bat colonies 

(Alabama, Arizona, Florida, Indiana, Maine, Missouri, 

Montana, North Carolina, North Dakota, and Oregon). 

Fifty-two percent of the colony observations (12,400) 

were from the literature [(36% publications, 12% theses 

or dissertations, and 5% unpublished or technical reports; Fig. 2)]. Twenty-seven percent of the observations 

(6,486) were from state wildlife agencies including Arizona, Colorado, Kentucky, New Hampshire, New York, 

Pennsylvania, and Wyoming. Natural Heritage databases 

provided 12% (2,772), individual researchers, 6% 

(1,459), and federal databases including the U.S. Forest 

Service and National Park Service, 2.5% (599). 

Counts at roosts were compiled from 6,044 unique 

locations. Only 2,614 of these documented a management authority; 33.9% (886 locations) were federally 

owned (i.e., U.S. Forest Service, National Park Service, 

Bureau of Land Management), 60.6% (1,584 locations) 

were located on private property, and 4.9% (128 locations) are owned by states (Fig. 3). Counties or municipalities owned the remaining 1% (26 locations). 

Number of colony observations varied by state and 

species. The largest number of these observations was 

collected from Pennsylvania totaling 3,923 (16%), followed by Kentucky at 2,886 (12%), Indiana at 2,207 

(9%), Arizona at 1,654 (7%), Missouri at 1,387 (6%), 

and New York at 1,168 (5%) (Fig. 4). These states have 

established monitoring efforts. Indiana bats (Myotis 

sodalis) were the most frequently counted species with 

2,867 observations (12.1%), followed by big brown bats 

with 2,835 [(Eptesicus fuscus; 11.9%)], Eastern 

pipistrelles, 2,136 [(Pipistrellus subflavus; 9%)], little 

brown bats, 2,117 [(Myotis lucifugus; 8.9%)], gray bats, 

1,874 [(M. grisescens; 7.9%)], and Townsend’s big-eared 

bats, 1,575 [(Corynorhinus townsendii townsendii and 

C. t. pallescens; 6.6%)] (Fig. 5). 

Counts of bats were made at a variety of roost 

structures. Caves were the most frequent roost structure 

from which counts were available, with 2,081 distinct 

caves representing 34% of all locations. We also 

compiled data with counts from 1,667 buildings (27% 

of total), 1,031 mines (17%), 408 bridges (7%), 309 trees 

(5%), 69 crevices/cliffs (1%), and 87 tunnels (1%). We 

also located accounts of bats roosting in bat houses, bird 

boxes, bird nests, bushes, cacti, dams, drill holes, fences, 

kilns, rocks, sewers, sedges, and woodpiles. 

Fig. 2. Sources for bat Fig. 2. colony counts in the USGS Bat Population Database. Sources included two federal agencies 

(U.S. Forest Service and National Park Service ), unpublished and technical reports, individual researchers, unpublished theses and dissertations, Natural Heritage Programs, state wildlife agencies, and publications. There were a 

total of 23,716 counts of bats at colonies. 
132 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

Fig. 3. Fig. 3. Management authorities for bat colonies represented in the USGS Bat Population Database (a total of 

2,164 locations of bat colonies recorded an associated 

management authority). 

The earliest record included in the BPD is from 1855 

in Dona Ana, New Mexico for a collection of hoary bats 

(Lasiurus cinereus, a normally solitary roosting species) 

at a roost (Bailey, 1931). The most recent records included in the database were for winter counts of gray 

bats in Arkansas in 2001 (M. Harvey, written commun., 

2003). The majority of colony locations in the BPD were 

represented by single surveys (Fig. 6). Of the 6,044 roost 

locations, 72% (4,368) were visited just once. Only 14% 

of roost locations (831) had more than two distinct annual surveys during the same season of year and even 

fewer were visited for more than three years (562). The 

longest time series available was 33 years of visits (from 

1937 to 1999) to the hibernating colony of Indiana bats 

at Bat Cave, Carter Caves State Park, Kentucky (Welter 

and Sollberger, 1939; Hall, 1962; Hardin, 1967; Hardin 

Fig. 4. Number of Fig. 4 observations at bat colony locations by state for the USGS Bat Population Database. This figure 

does not include states with less than 20 observations (Alaska, Connecticut, Hawaii, Maine, Mississippi, Nebraska, 

North Dakota, Rhode Island, South Carolina, or Wisconsin). Territories were also not included in this figure. 
 

 

ELLISON AND OTHERS 133 

Fig. 5. Number of observations per species in the USGS Bat Population Database. Species with less than 20 observations were not included in this figure. 

Fig. 6. Number of distinct counts made annually by colony location in the USGS Bat Population Database. 
134 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

and Hassell, 1970; T. Wethington, written commun., 

1999, Kentucky Department of Fish and Wildlife Resources). The purpose for most of these visits was to 

study Indiana bats, but big brown bats, eastern 

pipistrelles, little brown bats, Rafinesque’s big-eared bats 

(Corynorhinus rafinesquii), and northern myotis (Myotis 

septentrionalis) were also counted in this cave system. 

The numbers of visits to Bat Cave were not made in 

consecutive years, nor were the same methods consistently used to count individuals. One cave in Oklahoma, 

coded AD-013, was visited on 25 distinct years for counts 

of the endangered Ozark big-eared bat (Corynorhinus 

townsendii ingens). 

A major shortcoming of the existing data we reviewed was that methods used to estimate or count individual bats in their roosts were usually unspecified, or 

simply designated as a “count” with no elaboration on 

how the count was made. Methods described simply as 

a “count” accounted for 66% (15,653) of all methods 

reported for roost observations. Unspecified methods 

composed 18% (4,268) of all observations. The remaining 16% (3,795) of methods reported included capture, 

trapping, estimates based on guano or staining, markrecapture (Lincoln Indices, Schnabel Estimates, banding), mist netting or harp trapping, photographic or 

videotaped estimates, total area estimates, and visual 

timed estimates. Total area estimates were frequently 

used in cases where bats were roosting over large areas 

and in large clusters. The size of the cluster was measured and the total number of bats was extrapolated using an average number of bats per square area. The average number of bats per square area can vary by species, 

season, or surface characteristics (Tuttle, 2003). For 

example, hibernating Indiana bats have been estimated 

to include 3,229 bats/m2 (Brack and others, 1984), 

whereas a colony of the Mexican long-nosed bat 

(Leptonycteris nivalis) was estimated to include 1,614 

bats/m2 (Easterla, 1972), and a maternity colony of the 

southeastern bat (Myotis austroriparius) was estimated 

to include 2,000 bats/m2 (Gore and Hovis, 1994). Considerable variation in cluster densities can occur within 

a species as well. Tuttle (2003) notes that gray bats can 

range from 538 to 2,695 bats/m2 and Indiana bats from 

3,228 to 5,208 bats/m2. 

Another major shortcoming of the existing data for 

detecting trends in sizes of colonies was that sampling 

variances or standard errors were rarely documented. In 

the entire BPD, only 15 estimates of sampling variance 

were reported (Brenner, 1968; Mitchell, 1970; McManus 

and Esher, 1971; McManus, 1974; Clem, 1992; Mattson, 

1994; Mattson and others, 1996; Adam and Hayes, 

2000). This represented less than 0.06% of all reported 

counts. 

Counts or estimates of colony sizes in the literature 

and major databases maintained by states and Natural 

Heritage programs are a recent phenomenon (Fig. 7). 

Nearly 40% (9,486) of colony observations in the BPD 

were made from 1991 to 2000, which may reflect an 

Fig. 7. Number of colony observations per decade in the USGS Bat Population Database. 
 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 135 

increased interest in the conservation status of bat populations. Sixty percent (14,229) of the observations were 

made in the past two decades. 

We do not claim that the BPD is completely exhaustive in including all information available on counts 

of bats in the U.S. and territories. However, it is an extensive consolidation of information that we think is 

representative of most efforts at counting bats. 

Trend Analyses 

We analyzed time series for counts at colonies at 

473 locations for trends (locations with >4 years in a 

time series). More than half of these locations were winter hibernacula [(294 colonies; Table 1)]. Seventeen species were involved in analyses for trends at hibernacula. 

Counts at the majority of these hibernacula (198; 67.3%) 

showed no significant trend over the limited periods of 

time analyzed. Fifty-six (19.0%) of the series of counts 

indicated an upward trend over time while 40 (13.6%) 

suggested declines over the period of time analyzed. 

Colonies of hibernating Indiana bats were the most frequently analyzed (97 winter locations; 33.0%). The species was listed as endangered in 1967, with full legal 

protection provided with passage of the Endangered 

Species Act of 1973, and has been the focus of considerable monitoring ever since (U.S. Fish and Wildlife Service, 1999). 

We encountered a much lower number of summer 

locations to analyze for trends (Table 2). Summer colonies included maternity, transient, and bachelor groups. 

We analyzed data from 179 of these locations for trends, 

encompassing 20 species. Upward or downward trends 

were not detectable in the majority of these colonies (145; 

81.0%) whereas 17 (9.5%) indicated an upward trend 

and 17 (9.5%) a downward trend. Maternity colonies of 

gray bats were the most frequently analyzed (103 summer roosts; 57.5%). 

Coefficients of variation (CVs) ranged from a low 

of 0% to a high of 369.2%. An example of a CV of 0 

was illustrated by Rafinesque’s big-eared bat in a cabin 

in Illinois where the number of individuals reported did 

not vary from year to year, but were reported to remain 

at 30 for six consecutive years (Appendix 5; Hoffmeister, 

1989). Another example of a CV of 0 was for gray bats 

in Cave Spring Cave, Illinois where five years of counts 

Table 1 Table 1. Summary of trend analyses by species for winter hibernacula in the U.S. Geological Survey Bat Population 

Database. Trends were analyzed using the Mann-Kendall Nonparametric Test for Trend. (A P-value of 0.05 was 

used for all significance tests.) Species are displayed in descending order by number of hibernacula analyzed. See 

Appendices 1–21 for details for trend analyses by species. 

Number of

 hibernating colonies Number Number with no Number with

 analyzed for trends with increasing trend detected declining 

Species (n >4 distinct years) trend (%) (%) trend (%) 

Myotis sodalis 97 18(18.6) 49(50.5) 30(30.9) 

Pipistrellus subflavus 44 11(25.0) 33(75.0) 0 

Myotis lucifugus 42 13(30.9) 27(64.3) 2(4.8) 

Eptesicus fuscus 31 4(12.9) 27(87.1) 0 

Corynorhinus townsendii 15 1(6.7) 12(80.0) 2(13.3) 

Myotis grisescens 12 3(35.0) 7(58.3) 2(16.7) 

M. septentrionalis 12 3(25.0) 9(75.0) 0 

M. leibii 10 2(20.0) 8(80.0) 0 

Corynorhinus townsendii ingens 7 0 7(100.0) 0 

C. t. virginianus 5 1(20.0) 3(60.0) 1(20.0) 

Myotis velifer 5 0 3(60.0) 2(40.0) 

Corynorhinus rafinesquii 4 0 4(100.0) 0 

Macrotus californicus 3 0 3(100.0) 0 

Myotis volans 2 0 2(100.0) 0 

M. austroriparius 2 0 2(100.0) 0 

M. ciliolabrum 2 0 2(100.0) 0 

M. thysanodes 1 0 0 1(100.0) 

Totals 294 56(19.0) 198(67.3) 40(13.6) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

136 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Table 2. Summary Table 2 of trend analyses by species for summer colonies in the U.S. Geological Survey Bat Population 

Database with number of colonies analyzed for trends, number of colonies showing an increasing trend, number of 

colonies where no trend was detected, and number of colonies showing a decreasing trend. Summer colonies 

included maternity, bachelor, transient, and colonies of unspecified function. Trends were analyzed using the 

Mann-Kendall Nonparametric Test for Trend. (A P-value of 0.05 was used for all significance tests.) Species are 

displayed in descending order by number of colonies analyzed. See Appendices 1–21 for details for trend analyses 

by species.

 Number of 

summer colonies Number Number with no Number 

analyzed for trends with increasing trend detected with declining 

Species (n >4 distinct years) trend (%) (%) trend (%) 

Myotis grisescens 103 9(8.7) 88(85.4) 6(5.8) 

Pteropus tonganus 16 4(25.0) 8(50.0) 4(25.0) 

Pteropus mariannus 9 0 8(88.9) 1(11.1) 

Tadarida brasiliensis 8 2(25.0) 6(75.0) 0 

Corynorhinus townsendii ingens 7 1(14.0) 5(71.0) 1(14.0) 

Leptonycteris curasoae 7 0 6(85.7) 1(14.3) 

Corynorhinus townsendii 6 0 5(83.3) 1(16.7) 

Myotis austroriparius 4 0 3(75.0) 1(25.0) 

M. lucifugus 3 1(33.3) 2(66.7) 0 

Antrozous pallidus 2 0 1(50.0) 1(50.0) 

Corynorhinus townsendii virginianus 2 0 2(100.0) 0 

Macrotus californicus 2 0 2(100.0) 0 

M. thysanodes 2 0 2(100.0) 0 

Pipistrellus subflavus 2 0 2(100.0) 0 

Corynorhinus rafinesquii 1 0 1(100.0) 0 

Eptesicus fuscus 1 0 0 1(100.0) 

Leptonycteris nivalis 1 0 1(100.0) 0 

Myotis velifer 1 0 1(100.0) 0 

M. volans 1 0 1(100.0) 0 

Nycticeius humeralis 1 0 1(100.0) 0 

Totals 179 17(9.5) 145(81.0) 17(9.5) 

from 1958 to 1963 remained at 10,000 (Appendix 12; 

Hall and Wilson, 1966; Whitaker and Winter, 1977). A 

high CV of 369.2% was for a hibernating colony of 

Indiana bats in Aitkin Cave, Pennsylvania. Five hundred 

individuals were counted in 1930, two were found in 

1960, 12 in 1964, but for the period of 1986–1996, none 

were found each year, and again in 1997, nine were 

counted (Appendix 16). A CV of 257% was noted for a 

maternity colony of gray bats in Missouri, where counts 

ranged from 2,000 in 1964 to seven in 1998 and varied 

dramatically among years between (Appendix 12). The 

great majority of CVs ranged above 50% and below 

200% (340 locations; 71.9% of counts), but with many 

exceeding 100% (152 locations; 32.1% of counts). 

Colonies counted in summer (e.g., maternity, bachelor, 

and transient colonies) tended to show more temporal 

variability from year to year than colonies counted in 

winter. We arbitrarily considered CVs below 50% as 

relatively stable, 50–100% as variable, 100–200% highly 

variable, and above 200, extremely variable. Forty 

percent of all summer colonies (73 locations) of all 

species combined had CVs in excess of 100% whereas 

CVs of only 26.5% of all winter colonies (79 locations) 

exceeded 100%. Only 35 of the 179 (19.6%) summer 

colonies analyzed had CVs below 50%, compared to 86 

of the 294 winter colonies (29.2%). This pattern of higher 

CVs for summer roosts over winter roosts was difficult 

to mirror within a species, however, due to the low 

number of species for which time series of both winter 

and summer counts at colonies were available. Smaller 

CVs for winter colonies could be due to many factors 

such as a higher incidence of roost-switching in summer, 

and differences in methods used to count bats in summer 

vs. winter. High variability in counts or estimates over 
time confounds results of trend analyses, making it 

difficult to determine whether a colony at a particular 

site declined or increased in size. 

We next illustrate two significant downward trends 

and two significant upward trends. The first example is 

the Indiana bat in two different hibernacula in Missouri 

(Figs. 8 and 9). Both of these colonies declined over the 

time period analyzed, but the variability in counts was 

substantially different. Cave location 6189 showed a 

dramatic decline from 21,000 individuals in the winter 

of 1975 to 155 in 1999, and had a CV of 130.8% due to 

the large difference in the range of counts (Fig. 8). The 

Fig. 8. Counts of hibernating Indiana bats (Myotis 

sodalis) from a cave in Missouri (Location 6189) illustrating a significant decline from 1975 to 1999 

(t = -0.843, P < 0.05), but with a high coefficient of variation (130.8%; Appendix 16). 

Fig. 9. Counts of hibernating Fig. 9. Indiana bats from a cave 

in Missouri (Location 6194) illustrating a significant 

decline from 1979 to 1999 (t = -0.436, P < 0.05), but 

with a lower coefficient of variation than the time series 

in Fig. 8 (55.2%; Appendix 16). 

ELLISON AND OTHERS 137 

hibernacula in cave location 6194 declined from 8,100 

in the winter of 1979 to 2,700 in 1999, but had a CV of 

55.2% (Fig. 9). Two substantial upward trends are 

illustrated by big brown bats hibernating in a storm sewer 

in Minnesota and by little brown bats hibernating in 

Lemon Hole, Pennsylvania (Figs. 10 and 11). The big 

brown bats in the storm sewer increased from 35 

individuals in the winter of 1951 to 293 in 1970, with a 

CV of 65.9% in counts (Fig. 10). The little brown bats 

wintering in Lemon Hole increased from 909 individuals 

in 1985 to 1,472 in 1997, with a CV of only 20.1% 

(Fig. 11). 

Fig. 10. Counts of Fig. 10.Fig. 10. hibernating big brown bats (Eptesicus 

fuscus) in a storm sewer in Minnesota illustrating a significant upward trend from 1951 to 1970 (t = 0.642, P < 

0.05) with 65.9% variation in counts (Appendix 9). 

Fig. 11. Counts of hibernating little brown bats ( 1. Myotis 

lucifugus) from Lemon Hole, Pennsylvania, illustrating 

a substantial upward trend and low variability of counts 

(S = +29, P <0.05, CV = 20.1%; Appendix 14). 
138 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

Below we summarize information in the BPD by 

species for the U.S. and territories. We also review pertinent and sometimes anecdotal information from the 

literature regarding trends for each species. We begin 

by summarizing information on bats in the territories, 

and then summarize information on bats in the U.S. 

Within each of these geographic areas, species are listed 

by family in systematic order following Jones and others (1997), and then alphabetically within families. Common names of species also follow Jones and others 

(1997). Detailed information on counts at individual 

colony sites, types of colonies, results of trend analyses, 

summary statistics, and sources of information are provided in Appendices 1–21. We report trend statistics in 

the text only for those species not included in the Appendices. 

Data Summaries for Bats in the
 

Pacific Island Territories
 

We compiled information on the following species 

of bats for the Pacific Island territories: the Mariana flying fox (Pteropus mariannus), the Samoan flying fox (P. 

samoensis), the Tonga flying fox (P. tonganus), and the 

Pacific sheath-tailed bat (Emballonura semicaudata). The 

Pacific Island territories include American Samoa, 

Guam, and the Commonwealth of the Northern Mariana 

Islands (CNMI). 

Pteropodidae 

Pteropus mariannus (Mariana flying fox). The 

Mariana flying fox has been listed as endangered on 

Guam under the U.S. Endangered Species Act since 1984 

[see Utzurrum and others (2003) for a review]. The population on Guam is thought to be maintained only by 

immigration from islands to the north (Wiles and others, 1995), due to a complete failure of reproduction from 

exhaustive predation on young by the exotic brown tree 

snake (Boiga irregularis; Wiles, 1987). Presence of these 

bats on Guam fluctuates seasonally (with peaks from 

November to February and lowest counts from June to 

September) due to movements between Guam and Rota 

in the CNMI (Wiles and others, 1995). Counts made in 

1983–1984 on 14 islands of the CNMI showed that densities of fruit bats were lowest on islands where hunting 

was common, and highest on islands where hunting was 

low (Wiles and others, 1989). The Mariana flying fox 

has been proposed for listing as threatened in the CNMI 

(U.S. Fish and Wildlife Service, 2001). 

We located 105 observations at 20 different locations for the Mariana flying fox. Trend data were obtained for four islands of the CNMI (Aguiguan, Rota, 

Saipan, and Tinian) and for the island of Guam (Appendix 1). These observations were all gathered from 

publications (e.g., Wheeler, 1980; Wiles, 1987; Glass 

and Taisacan, 1988; Wiles and others, 1989; Lemke, 

1992; Stinson and others, 1992; Wiles, 1995; Krueger 

and O’Daniel, 1999; Worthington and others, 2001; 

Utzurrum and others, 2003). Colonies of this species 

were found roosting on branches in trees. Estimates of 

population size were for the entire islands, except for 

Rota where Stinson and others (1992) reported population estimates in four different areas of the island. No 

significant trend was detected for the population estimates given for the entire island of Rota using our rank 

analysis, but counts changed from 2,450 individuals in 

1987 to 773 in 1990. Only one site showed a significant 

decline over five years of estimates [(1,356 individuals 

in 1986 to 590 in 1990; Stinson and others, 1992)]. No 

trends were detectable for the remaining islands of the 

CNMI. We analyzed 12 years of counts for Guam. No 

significant trend was detected using our analysis, although counts were lowest in most recent years (Appendix 1). Worthington and others (2001) counted Mariana 

fruit bats on the island of Anatahan in 1983–1984 (approximately 3,500 individuals) and again in 1995 (approximately 1,902–2,136 individuals). They suggested 

this apparent decline was due to chronic illegal hunting 

and declining food resources due to overgrazing by feral goats and pigs. Only 5% of the available observations in the BPD on the Mariana flying fox were made 

after 1990. 

Pteropus samoensis (Samoan flying fox). We compiled 100 observations from 38 locations for the Samoan 

flying fox. All observations were gathered from publications (e.g., Wilson and Engbring, 1992; Pierson and 

others, 1996; Brooke and others, 2000; Utzurrum and 

others, 2003). Diurnal roosts for this species were located in trees on various islands in American Samoa. 

Although time series exceeding four years were available for this species, we did not analyze them because 

estimation methods varied over time and this species 

was often difficult to detect due to its solitary and cryptic roosting behavior. Utzurrum and others (2003) review current status, counting methods, and resulting 

indices of abundance for this species. Utzurrum and others (2003) describe how methods used to survey the Samoan flying fox have undergone numerous changes since 

the 1980’s, making it statistically invalid to project a 

trend in numbers for this species. For the entire population on Tutuila (all roosting sites combined), counts 

ranged from 55 to 900 individuals over the period from 

1986 to 1995 (Craig and Syron, 1992; Wilson and 

Engbring, 1992; Brooke and others, 2000). Population 

declines were noted on Tutuila in the early 1990’s due 

to two hurricanes and subsequent taking of weakened 
and exposed bats by hunters (Craig and others, 1994; 

Pierson and others, 1996). The population size for Tutuila 

since 1995 has been thought to remain at about 900 

(Brooke and others, 2000). Counts on other islands in 

American Samoa were considerably smaller, ranging 

from one to eight individuals. Data for the Samoan flying fox compiled in the BPD were mostly before 1990 

(85% of the observations). 

Pteropus tonganus (Tonga flying fox). Data available about the Tonga flying fox were more comprehensive than those for other Pacific Island species of bats. 

We compiled 716 observations from 90 locations. We 

were able to analyze more trends at colonies of this species than any other species in the Pacific Islands. Most 

of the observations we obtained for the Tonga flying fox 

were collected after 1990 (459; 64.1%), possibly reflecting the increased conservation interest in this species 

within the last decade. All observations were obtained 

from publications (e.g., Wilson and Engbring, 1992; 

Pierson and others, 1996; Brooke and others, 2000; 

Utzurrum and others, 2003). The data were from colonies roosting in branches and foliage of trees located on 

Tutuila Island, American Samoa. Tutuila is the largest 

of the four islands of American Samoa with resident flying foxes. We analyzed 16 time series for this species: 

one for the entire island from 1987 to 2000, and 15 from 

different roosting locations around the island (Appendix 2). There were no significant trends for these bats 

on the entire island from 1987 to 2000, although a high 

of 12,750 was counted in 1987, a minimum of 1,700 in 

1992, and 6,366 in 2000 (Utzurrum and others, 2003). 

The minimum in 1992 was attributed to mortality from 

two hurricanes, Cyclones Ofa in 1990 and Val in 1991, 

and overhunting (Craig and others, 1994; Pierson and 

others, 1996; Grant and others, 1997). Trend analyses 

for the separate locations around the island support the 

findings of the island-wide analysis: no trend was found 

in six, four showed an upward trend, and five exhibited 

a downward trend over the time periods reported (Appendix 2). These isolated locations around the island of 

Tutuila showed more instability in population estimates 

(CVs exceeded 100% for all sites except at Puaneva 

Point). This large variation reflects both the difficulty 

in counting this species and frequent movements of bats 

among sites. 

Emballonuridae 

Emballonura semicaudata (Pacific or Polynesian 

sheath-tailed bat). Insufficient count data were available 

from colonies of the Pacific sheath-tailed bat to conduct 

trend analyses. This is the only insectivorous bat known 

from Guam, the CNMI, and American Samoa. Colonies 

are typically found in caves. There were no time series 

ELLISON AND OTHERS 139 

of counts available for analysis, but extensive searches 

have suggested that it has been extinct on Guam since 

1972 (Lemke, 1986; Wiles and others, 1995). It is also 

extinct on Rota in the CNMI (U.S. Fish and Wildlife 

Service, 2001). Roosting bats were detected at six of 78 

caves on Aguiguan in 1995 and colonies ranged in size 

from 2–64 individuals, but at that time these bats were 

considered extinct elsewhere in the CNMI (Worthington 

and Taisacan, 1996; Wiles and Worthington, 2002). The 

number on Aguiguan may have been reduced to about 

only 10 bats by 2001 (U.S. Fish and Wildlife Service, 

2001). Amerson and others (1982) estimated that some 

11,000 sheath-tailed bats were in American Samoa in 

1975–1976, but the methods used to obtain this estimate are unknown (Grant and others, 1994). Knowles 

(1988) documented seeing 100 bats in 1988 and hearing another 100. By 1993, populations on American 

Samoa may have been reduced to as few as four individuals due to habitat damage from three cyclones (Grant 

and others, 1994; U.S. Fish and Wildlife Service, 2001). 

Data Summaries for Bats 

in the Caribbean Territories 

The U.S. territories in the Caribbean Islands include 

Puerto Rico and the U.S. Virgin Islands. There are 13 

species of bats from these islands: the Jamaican fruiteating bat (Artibeus jamaicensis), the Antillean fruiteating bat (Brachyphylla cavernarum), big brown bat, 

the buffy flower bat (Erophylla sezekorni = bombifrons), 

red bat (Lasiurus borealis), Pallas’ free-tailed bat 

(Molossus molossus), Puerto Rican long-tongued bat 

(Monophyllus redmani), Blainville’s ghost-faced bat 

(Mormoops blainvillii), greater bulldog bat (Noctilio 

leporinus), Parnell’s moustached bat (Pteronotus 

parnellii), sooty moustached bat (P. quadridens), red 

fig-eating bat (Stenoderma rufum), and Brazilian freetailed bat (Tadarida brasiliensis) (Koopman, 1989). We 

summarize information gathered for the following nine 

species: the Jamaican fruit-eating bat, the Antillean fruiteating bat, the buffy flower bat, the Puerto Rican longtongued bat, Blainville’s ghost-faced bat, Parnell’s 

moustached bat, the sooty moustached bat, the red figeating bat, and the Brazilian free-tailed bat. We were 

unable to obtain adequate data on the remaining four 

species found in the U.S. Caribbean Islands. 

Mormoophidae 

Mormoops blainvillii (Blainville’s ghost-faced bat). 

Insufficient data were available to conduct trend analyses for Blainville’s ghost-faced bat. Information was 

available for this species from only two caves in Puerto 
140 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

Rico. Jones and others (2001) captured 60 individuals 

at Culebrones Cave before Hurricane Georges in September of 1998 and 182 individuals after the disturbance. 

Rodriguez-Duran and Lewis (1987) used photographic 

techniques to estimate 43,400 Blainville’s ghost-faced 

bats roosting in Cucaracha Cave. This species was also 

found roosting in seven other caves in Puerto Rico by 

Rodriguez-Duran (1998). 

Pteronotus parnellii (Parnell’s moustached bat). Insufficient data were available to investigate trends of 

Parnell’s moustached bat, but some information exists 

from a few caves in Puerto Rico. Jones and others (2001) 

found no bats of this species using Culebrones Cave before Hurricane Georges, but found one individual after 

the disturbance. Rodriguez-Duran (1998) found this 

species roosting in five other caves in Puerto Rico, but 

no estimates of population size were available. 

Pteronotus quadridens (sooty moustached bat). Insufficient data were available to investigate trends for 

the sooty moustached bat. Jones and others (2001) captured 31 individuals at Culebrones Cave in Puerto Rico 

before Hurricane Georges in September 1998, and 109 

individuals after the hurricane. Rodriguez-Duran and 

Lewis (1985) used photographic techniques to estimate 

123,900 + 21,800 individuals roosting in Cucaracha 

Cave on Puerto Rico in October 1981. In 1987, these 

same authors reported 141,000 bats at this cave 

(Rodriguez-Duran and Lewis, 1987). Rodriguez-Duran 

(1998) also found this species roosting in four other caves 

in Puerto Rico, but no estimates of population size were 

available. 

Phyllostomidae 

Artibeus jamaicensis (Jamaican fruit-eating bat). 

The Jamaican fruit-eating bat has a wide geographic 

distribution in tropical and subtropical America and 

comprises at least 60% of the total bat fauna of Puerto 

Rico (Willig and Bauman, 1984). Rodriguez-Duran 

(1998) found Jamaican fruit-eating bats roosting in 18 

of the 27 caves he surveyed in Puerto Rico, but no 

estimation of colony sizes were available to analyze for 

trends. Information was collected using mist net captures 

per net-hour for Jamaican fruit-eating bats on Puerto 

Rico for three years prior to Hurricane Hugo, September 

1989, and three years after (Gannon and Willig, 1994). 

Although no colony size estimates were available, 

captures using mist nets, which may or may not reflect 

population changes, declined to near zero immediately 

following the hurricane, remained low for almost two 

years, and recovered to the pre-hurricane levels in the 

third year. Rodriguez-Duran and Vazquez (2001) studied 

a colony of the Jamaican fruit-eating bat roosting in 

Convento Cave on Puerto Rico before and after Hurricane 

Georges, which occurred in September 1998. There was 

a reduction in the relative number of bats netted after 

the hurricane, although no population estimates were 

made. 

Brachyphylla cavernarum (Antillean fruit-eating 

bat). There were no time series of counts available to 

analyze for the Antillean fruit-eating bat in either Puerto 

Rico or the Virgin Islands. This species was found roosting in seven caves in Puerto Rico by Rodriguez-Duran 

(1998), but no estimates of colony sizes were made. Nellis 

and Ehle (1977) mentioned the existence of several roosts 

of this species on the island of St. Croix, Virgin Islands. 

A colony of about 5,000 was found roosting in a well; a 

colony of about 50–100 individuals was found roosting 

in a sea cliff; and another small colony was found in a 

warehouse. No dates were associated with these colony 

size estimates. Although no trend data were available 

for this species, past information suggests that excessive mortality due to intentional gassing occurred at some 

locations (Bond and Seaman, 1958). 

Erophylla sezekorni (buffy flower bat). We compiled 

colony size information on the buffy flower bat gathered 

by others from several caves in Puerto Rico. There were 

not enough data to conduct trend analyses for this species. There is little other information available from the 

literature that relates to trends in populations of this 

species in the Caribbean territories. Jones and others 

(2001) compared the number of bats captured in mist 

nets at Culebrones Cave on Puerto Rico 10 months after 

Hurricane Georges in September 1998 to numbers captured 35 months prior to the disturbance. Before the 

hurricane, 3,643 buffy flower bats were captured, representing 94.6% of the captures of all species roosting in 

the cave. After the hurricane, there was only one individual present (Jones and others, 2001). RodriguezDuran (1998) found the buffy flower bat roosting in four 

other caves in Puerto Rico, but these caves were visited 

only to determine presence of species, not to estimate 

colony sizes. 

Monophyllus redmani (Puerto Rican long-tongued 

bat). We have only three records in the BPD for the Puerto 

Rican long-tongued bat. This is insufficient for analysis 

of trends. In related studies, Rodriguez-Duran and Lewis 

(1987) visited a colony in Cucaracha Cave in Puerto 

Rico in April 1983. They estimated 544,000 individuals 

roosting in this cave using photographic techniques. 

Jones and others (2001) captured 114 individuals at 

Culebrones Cave before Hurricane Georges in 1998, but 

captured only seven after the hurricane. This species was 

also found roosting in 12 other caves in Puerto Rico by 

Rodriguez-Duran (1998), but no estimates of colony sizes 

were made. Information was collected using mist net 

captures per net-hour for the Puerto Rican long-tongued 

bat for three years prior to Hurricane Hugo, September 
1989, and three years after (Gannon and Willig, 1994). 

Capture rates for this species remained relatively stable 

before and after Hurricane Hugo, with a slight increase 

soon after the hurricane. 

Stenoderma rufum (red fig-eating bat). The red figeating bat roosts in foliage in the forest canopy and does 

not form social groups or show fidelity to roost locations (M.R. Gannon, 1991; Gannon and Willig, 1994). 

Thus, there were no time series or counts available to 

analyze for this species. Information was collected using mist net captures per net-hour for red fig-eating bats 

on Puerto Rico for three years prior to and after Hurricane Hugo in September 1989 (Gannon and Willig, 

1994). Capture rates of this species declined gradually 

after the impact of the hurricane, reaching the lowest 

level in 1991, and have remained at levels far below 

those prior to the disturbance from the hurricane. 

Molossidae 

Tadarida brasiliensis (Brazilian free-tailed bat). No 

trend data were available to analyze for the Brazilian 

free-tailed bat in the Caribbean Islands. Whitaker and 

Rodriguez-Duran (1999) reported a colony of from 200– 

300 Brazilian free-tailed bats roosting in an abandoned 

train tunnel in northwestern Puerto Rico. They report 

that this colony has been roosting in this tunnel since its 

abandonment by the railroad some 40 years ago. 

Data Summaries for Bats 

in the United States 

Mormoopidae 

The ghost-faced bat (Mormoops megalophylla) is 

the only species of the family Mormoopidae found in 

the continental U.S. We compiled a total of 18 observations from nine distinct locations for the ghost-faced bat, 

all from Texas prior to 1990. The majority of observations (16; 89%) were from cave roosts (Constantine, 

1958b; Raun and Baker, 1958; Reddell, 1967), one from 

a house (Mearns, 1900), and one from a railroad tunnel 

(Davis, 1960). These low numbers of available observations may reflect the marginal range of this species in 

the U.S. and the infrequency of encountering this species. There were no trend data available for this species. 

Phyllostomidae 

The BPD includes counts for the following members 

of the family Phyllostomidae, or leaf-nosed bats, from the 

U.S.: Mexican long-tongued bat (Choeronycteris 

mexicana); hairy-legged vampire (Diphylla ecaudata); 

ELLISON AND OTHERS 141 

southern long-nosed bat (Leptonycteris curasoae); Mexican long-nosed bat (L. nivalis); and California leaf-nosed 

bat (Macrotus californicus). 

Choeronycteris mexicana (Mexican long-tongued 

bat). We compiled 82 observations at 42 locations for 

the Mexican long-tongued bat. Twenty-nine percent of 

these observations (24) were made after 1990. Observations were collected from Arizona and New Mexico, with 

the majority (61; 74%) from Arizona. One specimen was 

collected from a garage in Texas in 1970 (Chapman and 

Chapman, 1990), and roosts of this species were reported 

from San Diego, California, but these represent marginal occurrences (Olson, 1947; Huey, 1954). This species’ northern range is southernmost Arizona and New 

Mexico, where it is only a summer resident (ArroyoCabrales and others, 1987). Mexican long-tongued bats 

were reported to roost in a number of structures including bridges, buildings, caves, crevices, mines, rock shelters, and tunnels. Almost 37% of the records (30) are 

from small colonies in caves or rock shelters and 25% 

(20) from colonies in mines. Counts ranged from a minimum of one to a maximum of 176. Many roosts were 

described as “day roosts” (20; 25%), or as unspecified 

(52; 63%). Arizona Game and Fish Department’s Heritage Database provided 57% of all observations (47) in 

the BPD for this species (S. Schwartz, written commun., 

2000); the remaining 43% (35) were obtained from publications. More than 70% of the observations (58) were 

made before 1990. 

There were no data available for colonies of Mexican long-tongued bats with sufficient time series to analyze for trends. Cryan and Bogan (2003) visited 23 of 

the 48 localities from which this species had been reported in the past in Arizona and New Mexico. They 

found this species present at 17 of these historically 

known sites. 

Diphylla ecaudata (hairy-legged vampire). No 

colony size data were available for the hairy-legged vampire bat. This species is not thought to be resident in the 

U.S., as it is known only by a single female specimen 

collected in 1967 in an abandoned railroad tunnel in 

southern Texas (Reddell, 1968). The hairy-legged vampire is solitary and does not aggregate in large groups. 

Leptonycteris curasoae (southern long-nosed bat). 

We compiled records of 237 observations at 44 locations for the southern long-nosed bat. These observations were from Arizona and New Mexico, with more 

than 98% (232) of the counts from colonies in Arizona. 

The northern range of this species is southernmost Arizona and New Mexico (Fleming and others, 2003). This 

species was reported roosting in a variety of structures 

including bridges, buildings, caves, crevices, and mines. 

More than 40% (103) of all counts were from cave roosts 

and approximately 48% (114) were from mines. Counts 
142 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

of colonies ranged from a minimum of one to a maximum of 15,700 at Copper Mountain Mine, Arizona 

(Cockrum and Petryszyn, 1991; Dalton and Dalton, 

1994). Most records were of roosts occupied by maternity colonies. This species is a seasonal resident of the 

U.S., arriving in the northern part of its range to give 

birth and rear young during the spring and summer 

(Fleming and others, 2003). The Arizona Game and Fish 

Department’s Heritage Database provided 45% (107) of 

all observations in the BPD for this species (S. Schwartz, 

written commun., 2000). Other observations were obtained from publications (78; 33%), theses/dissertations 

(31; 13%), and unpublished reports (21; 9%). Most of 

the data we found were from 1990 or earlier (216; 91%). 

We analyzed trends of colonies at seven locations, 

all in Arizona (Appendix 3). Three of these colonies 

were in mines, three in caves, and one in a large crevice. No trends were detected except at one colony. The 

maternity colony at Colossal Cave was surveyed in 11 

different years and declined from 2,000 in 1954 to 0 in 

1985 (Appendix 3; Beatty, 1955; Reidinger, 1972; Sidner 

and Davis, 1988; Cockrum and Petryszyn, 1991; Arizona Game and Fish Department’s Heritage Database). 

The U.S. Fish and Wildlife Service categorized this species as endangered throughout its range in 1988, but 

little evidence actually documented a widespread longterm decline, and the ruling may have been influenced 

by the abandonment of Colossal Cave (Cockrum and 

Petryszyn, 1991). 

Leptonycteris nivalis (Mexican long-nosed bat). We 

compiled data from 16 observations of a colony of the 

Mexican long-nosed bat from one location in Texas (Mt. 

Emory Cave, Big Bend National Park). This species has 

a limited range in the U.S., with large colonies historically found only in Texas. Counts at Mt. Emory Cave 

ranged from a minimum of 0 to a maximum of 10,650 

(Easterla, 1972, 1973; Fleming and others, 2003). Although 16 observations were compiled in the BPD, only 

nine years of counts satisfied our a priori assumptions 

for trend analysis. The colony at Mt. Emory Cave 

changed from 10,650 in 1967 to 0 in 1970, then from 

8,025 in 1971 to 2,859 in 1993, but this trend was not 

significant (n = 9, S = -13, P > 0.05). The mean count 

for the nine years was 3,965, the standard deviation 

3,704.5, and the CV was relatively high at 93.4%. This 

reflects the fact that use of this roost is transient. No 

bats were found when this site was visited in 1970 and 

1992. 

Macrotus californicus (California leaf-nosed bat). 

We compiled 344 observations at 143 locations for the 

California leaf-nosed bat. These observations were from 

colonies in Arizona, California, and Nevada; 90% (310) 

of the counts at colonies were from Arizona. This species 

was found roosting in a variety of structures including 

bridges, buildings, caves, mines, and tunnels. However, 

more than 80% (275) of all available counts were at 

mines. Counts ranged from a minimum of one to a 

maximum of 2,000 at Boomerang Mine, Arizona, in July 

of 1957 (Arizona Game and Fish Department Heritage 

Database). Data were compiled mostly from the Arizona 

Game and Fish Department (248; 72%), with the 

remainder of observations obtained from publications, 

theses, dissertations, and unpublished reports. Forty-five 

percent (155) of the observations were made after 1990. 

We analyzed counts at five colonies for trends (Appendix 4). All of these colonies were in abandoned mines 

in Arizona and none showed detectable trends. Three 

were considered winter colonies, one was a maternity 

colony (Boomerang Mine), and one was a colony of unspecified function counted in the summer (Blue Bird 

Mine). Data collected at the Fortuna Mine illustrate the 

substantial variation in colony size that can occur in 

colonies of the California leaf-nosed bat. Bradshaw 

(1961) and Davis (1966) visited this mine from 2 February 1958 through 12 November 1960 and conducted 

34 counts during all seasons of the year. These counts 

varied dramatically by date (Fig. 12). This time series 

illustrated the importance of timing when conducting 

surveys; there was extreme temporal fluctuation in numbers of bats both within and among seasons. The California leaf-nosed bat is a former Category 2 Candidate 

for listing under the Endangered Species Act (U.S. Fish 

and Wildlife Service, 1994). 

Vespertilionidae 

The BPD includes counts for the following members of the family Vespertilionidae: pallid bat (Antrozous 

Fig. 12. Fig. 12.Fig. 12. Counts of the California leaf-nosed bat at the 

Fortuna Mine, California, from 7 February 1958 through 

7 February 1960, illustrating dramatic fluctuations over 

one year of surveys [S = -6, P > 0.05, CV = 119.0%; 

Bradshaw (1961) and Davis (1966)]. 
pallidus); Rafinesque’s big-eared bat; Townsend’s bigeared bat; Ozark big-eared bat; Virginia big-eared bat 

(Corynorhinus townsendii virginianus); big brown bat; 

spotted bat (Euderma maculatum); Allen’s big-eared bat 

(Idionycteris phyllotis); silver-haired bat (Lasionycteris 

noctivagans); Lasiurus spp.; southwestern myotis 

(Myotis auriculus); southeastern myotis; California 

myotis (M. californicus); western small-footed myotis 

(M. ciliolabrum); long-eared myotis (M. evotis); gray 

bat; Keen’s myotis (M. keenii); eastern small-footed bat; 

little brown bat; northern myotis; Indiana bat; fringed 

myotis (M. thysanodes); cave myotis (M. velifer); longlegged myotis (M. volans); Yuma myotis (M. 

yumanensis); evening bat (Nycticeius humeralis); western pipistrelle (Pipistrellus hesperus); and eastern 

pipistrelle. 

Antrozous pallidus (pallid bat). We compiled 292 

observations from 133 pallid bat roosts. These observations were collected from 11 western states: 34% (99) 

from Arizona, 18% (52) from Oregon, 12% (35) from 

California, and 10% (29) from New Mexico. The remaining data were from colonies in Colorado, Kansas, 

Nevada, Oklahoma, Texas, Utah, and Wyoming. This 

species roosted in a variety of structures including bridges 

(99; 34%), buildings (73; 25%), caves (38; 13%), crevices (20; 7%), mines (23; 8%), cliffs (18; 6%), and trees 

(9; 3%). Most colonies reported were of an unspecified 

type (175; 60%), but maternity colonies were defined in 

26% (76) of the cases and night roosts in 14% (41). 

Data were compiled mostly from publications, theses or 

dissertations, and unpublished reports [(245; 84%; e.g., 

Beck and Rudd, 1960; Herreid, 1961; Davis, 1966; 

Reidinger, 1972; Vaughan and O’Shea, 1976; Ellinwood, 

1978)]. Additional data were provided by the Arizona 

Game and Fish Department (S. Schwartz, written 

commun., 2000), Bats in American Bridges Program (B. 

Keeley, written commun., 1999, Bat Conservation International), Colorado Division of Wildlife (K. Navo, 

written commun., 2000), National Park Service (C. 

Baldino, written commun., 1999), Oregon Natural Heritage Program (T. Campos, written commun., 1999), and 

Wyoming Game and Fish Department (B. Luce, written 

commun., 1999). Most of these data (228; 78%) were 

collected before 1990. 

Only two summer colonies provided time series of 

sufficient length to analyze for trends. A bridge roost in 

Arizona declined significantly from 80 individuals in 

1957 to 0 in 1970 [(n = 5, S = -9, P < 0.05, CV = 176.5%; 

Reidinger, 1972)]. O’Shea and Vaughan (1999) reported 

an apparent decline in a colony of pallid bats using crevices in cliffs in the Verde Valley of Arizona concurrent 

with an increase in human activity at the site. They reported 63 on 29 June 1972, 64 on 24 May 1976, 40 on 3 

June 1977, and 0 on 1 July 1997, but this change was 

ELLISON AND OTHERS 143 

not statistically significant using the nonparametric trend 

analysis (n = 4, S = -4, P > 0.05, CV = 71.8%). 

Corynorhinus rafinesquii (Rafinesque’s big-eared 

bat). We compiled 290 observations from 148 locations 

for Rafinesque’s big-eared bat. These observations were 

from 14 southeastern states. The majority of records were 

from Kentucky (159; 55%), North Carolina (20; 7%), 

Florida (14; 5%), and Arkansas (12; 4%). Most counts 

were made at caves (165; 57%), but this species was 

also found roosting in mines (35; 12%), buildings (46; 

16%), bridges (12; 4%), cisterns (6; 2%), tunnels (6; 

2%), and trees (2; <1%). More than half of all counts 

for this species were from colonies in hibernacula (150; 

52%). Maternity colonies constituted 12% (35) of the 

observations. Data in publications accounted for 54% 

(157) of the count information (e.g., Hoffmeister, 1989; 

Meade, 1992; Hurst, 1997; Hurst and Lacki, 1999), and 

the Kentucky Department of Fish and Wildlife Resources 

provided 31% (90 observations; T. Wethington, written 

commun., 1999). Nearly half of the observations we compiled were made after 1990 (140; 48%). 

We analyzed counts from four hibernacula in Kentucky and one summer colony of unspecified function in 

Illinois (Appendix 5). None of these colonies showed 

statistically significant increases or decreases. Counts 

at a cabin in Illinois remained at 30 individuals from 

1977 to 1982 (Hoffmeister, 1989). The largest hibernating colony analyzed for trends was the Donahue 

Rockshelter in Kentucky. There were 11 years of counts 

available and the colony ranged in size from 34 individuals in 1987 to a high of 134 in 1984, with a CV of 

38.1%. Rafinesque’s big-eared bat is a former Category 

2 Candidate for listing under the Endangered Species 

Act (U.S. Fish and Wildlife Service, 1994). The largest 

colony of this species is in a hibernaculum in North 

Carolina (R. Currie, written commun., 2003). Up to 

1,700 individuals roost at this site and it is monitored 

every 2–3 years, but counts through time were not available for our analysis. Recent research suggests that this 

species roosts in hollow trees in bottomland hardwood 

forests more frequently than previously realized (Clark, 

2003). 

Corynorhinus townsendii (Townsend’s big-eared 

bat). There are four subspecies of Townsend’s big-eared 

bat in the U.S.: C. t. townsendii and C. t. pallescens in 

the western U.S., and the Ozark big-eared bat and the 

Virginia big-eared bat in the central and eastern U.S. 

(Handley, 1959). Information on the latter two subspecies 

is provided separately. The two western subspecies are 

usually not distinguished during field observations and 

we refer to them in this summary analysis simply as 

Townsend’s big-eared bat. We compiled 1,575 counts of 

colonies at 615 unique locations, 21 of which had time 

series >4 years. Three locations had time series exceeding 
144 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

10 years. More than half of the observations in the 

database for Townsend’s big-eared bat were made after 

1990 (850; 54%), which may reflect the increase in 

conservation interest for this species. Observations were 

from 20 western states, and the majority of counts were 

from Arizona (162; 10%), California (199; 13%), 

Colorado (106; 7%), Idaho (80; 5%), Kansas (84; 5%), 

Oregon (206; 13%), South Dakota (201; 13%), and 

Washington (176; 11%). This species was found roosting 

mostly in caves (850; 54%) and mines (582; 37%), but 

was also reported using buildings, bridges, cliff 

dwellings, crevices, tunnels, rocks, and trees as roosting 

habitat. Forty-one percent of all observations (646) were 

collected at hibernacula, 13% (205) at day roosts, and 

7% (110) at maternity colonies. Nearly 50% (780 

observations) of the data we collected were provided by 

publications (e.g., Jones and Genoways, 1967; Turner 

and Jones, 1968; Turner and Davis, 1970; Easterla, 1972, 

1973; Martin and Hawks, 1972; Reidinger, 1972; Genter, 

1986; Safford, 1989; Wackenhut, 1990; Stihler and 

Brack, 1992; Doering, 1996; Choate and Anderson, 

1997; Jagnow, 1998). 

Despite the large number of records for this species, 

only counts made at 15 hibernacula and six summer 

colonies had >4 years of records available for analysis 

of trends. These were in Arizona, California, Colorado, 

Idaho, New Mexico, Oregon, South Dakota, Texas, and 

Washington (Appendix 6). Statistically significant trends 

could not be detected for most of the hibernacula (12; 

80%); one increased and two had declined (Table 1). 

Trends could not be detected for most summer colonies 

(5; 83.3%) and one had declined (Table 2). The two 

hibernating colonies that showed a downward trend were 

at Jewel Cave, South Dakota, and Spider Cave, 

Washington. There were 14 distinct annual surveys for 

the colony at Jewel Cave, where the colony declined from 

3,750 in 1959 to 853 in the winter of 2000 (Fig. 13). 

The colony in Spider Cave declined from 268 in 1968 to 

27 in 1983 (Fig. 14). Declines at Jewel Cave and Spider 

Cave may reflect the effects of past disturbance by 

researchers during the critical hibernation period. There 

was a marked decline in numbers from 1959 to 1967 at 

Jewel Cave when extensive banding was conducted 

(Choate and Anderson, 1997). Whether the bats switched 

roosts or died as a direct result of banding is unknown. 

Spider Cave in Washington showed a similar dramatic 

decline in numbers, but within a shorter time period. 

From 1965 to 1967 numbers dropped from 268 to less 

than 50; banding was conducted at this location during 

this time (C. Senger, written commun., 1996). Senger 

found a similar pattern for Bat Cave in Washington 

(numbers dropped from 218 in 1966 to 56 in 1967), but 

this trend was not found to be significant. 

Available data are insufficient for making statistically based inferences about trends in counts of 

Townsend’s big-eared bat across its geographic range 

in the western U.S. Pierson and others (1999), however, 

document a substantial number of anecdotal cases that 

have been interpreted to be evidence of declines. In these 

cases numbers at roosts re-visited after long periods between attempts at counting were low or zero, and evidence of roost destruction or killing of bats was 

sometimes unequivocal. Such cases often do not include 

sufficient time series for statistical analyses of trends. 

However, Townsend’s big-eared bats can frequently shift 

roosts and monitoring their numbers can be extremely 

challenging, and this renders it difficult to make inferences about true population status based on absence or 

reduced numbers even at local scales (Sherwin and others, 2003). Nonetheless most organizations concerned 

with bat management and conservation have taken a 

Fig. 13. Counts of hibernating Townsend’s big-eared Fig. 13.Fig. 13.

bats (Corynorhinus townsendii) at Jewel Cave, South 

Dakota (t = -0.319, P < 0.05, CV = 75.0%; Appendix 6). 

Fig. 14. Counts of hibernating Townsend’s big-eared bats Fig. 14.Fig. 14.

(Corynorhinus townsendii) at Spider Cave, Washington 

(t = -0.409, P < 0.05, CV = 152.9%; Appendix 6). 
precautionary approach based on the accumulation of 

anecdotal case accounts and are concerned about the status of this species. We summarize some of these accounts 

below, bearing in mind that documentation of declines 

may be more likely to appear in the literature than reports noting stable or even increasing trends. Townsend’s 

big-eared bat was considered a Category 2 Candidate 

for listing under the Endangered Species Act prior to 

elimination of this category (U.S. Fish and Wildlife Service, 1994). The Bureau of Land Management and U.S. 

Forest Service also categorized this bat as Sensitive or a 

Species of Concern in most areas, and it was also given 

special status by wildlife management agencies in most 

western states (Pierson and others, 1999). 

California provides a number of examples of anecdotal accounts indicating likely declines in Townsend’s 

big-eared bat. Pierson and Rainey (1998a) accumulated 

case histories that indicated a 52% loss in the total number of maternity colonies, a 45% decline in the number 

of available roosts, a 54% decline in the total number of 

individual bats, and a 33% decrease in the average size 

of remaining colonies over the past 40 years (Pierson 

and others, 1999). Among specific cases from California, Pearson and others (1952) studied a maternity colony 

of 140 females and a hibernaculum of 65 in 1949–1950 

in northern California. In 1987–1988, these two colonies numbered about 70 and 26, respectively. Another 

maternity colony numbering about 200 bats in the 1960’s 

in a separate area in the same region consisted of about 

150 in 1987 (Pierson and others, 1991). Four hibernation sites in California studied by Pearson and others 

(1952) that housed a total of 470 bats held just 59 individuals in the late 1980’s and early 1990’s (Pierson and 

Rainey, 1998a). In coastal California only seven small 

colonies were known for Townsend’s big-eared bat in 

1989 (Pierson, 1989). 

Similar contrasts between past and present abundance of Townsend’s big-eared bat were documented 

from specific sites in other western states (Pierson and 

others, 1999). Major downward shifts were noted at sites 

in Oregon and Washington. Intensive surveys over large 

areas in Nevada revealed only two sites with small maternity groups. In Colorado, a hibernaculum with over 

500 in 1968 was reduced to just a few bats, and only 

four maternity sites were known to be active in the state 

in recent years, with the largest numbering about 80 females. Four colonies in hibernacula in Idaho also had 

lower numbers since 1987. One recently discovered hibernaculum in New Mexico that housed more than 

10,000 individuals in 1992 had been vandalized by fire 

the same winter, with hundreds of carcasses evident and 

thousands presumed dead. In Arizona, two historically 

known colonies in caves had disappeared, and another 

ELLISON AND OTHERS 145 

with historical estimates of several hundred adult females dropped to less than 100, although the species is 

currently known at numerous sites in the state (Pierson 

and others, 1999). O’Shea and Vaughan (1999) reported 

an increase in abundance from 1972 to 1997 at one site 

in Arizona occupied by a small colony of breeding individuals, but suggested that the 1997 numbers remained 

below those presumed present in 1931. Although results 

of most of these visits to sites resurveyed after long intervals have led to conclusions of widespread declines 

in Townsend’s big-eared bat in the west, recent research 

cautions that apparent absence can be an artifact of survey effort (Sherwin and others, 2003). In the Great Basin of Nevada and Utah, this species may show high 

fidelity to roosts in caves or mines where these roosting 

situations are few in numbers. However, in other areas 

within this region where potential roosts are more abundant, individuals and colonies may frequently switch 

roost sites. Nine or more visits by researchers may be 

required before surveys have a 90% probability of revealing roosting bats, depending on the area and type of 

colony (Sherwin and others, 2003). 

Corynorhinus townsendii ingens (Ozark big-eared 

bat). The Ozark big-eared bat is categorized as endangered under the U.S. Endangered Species Act. Its distribution is restricted to limestone areas in Arkansas, 

Oklahoma, and Missouri. This isolated distribution is 

thought to be a relict of post-Pleistocene climates 

(Humphrey and Kunz, 1976). We compiled 354 observations at 56 distinct localities from three states, with 

72% (255) of the observations from Oklahoma; 16% (57) 

from Arkansas; and 12% (42) from Missouri. All records 

were from caves. Most records were compiled from publications and unpublished agency reports (329; 93%; e.g., 

Harvey and others, 1981; Grigsby and Puckette, 1982; 

Harvey, 1989; Clark and others, 1997a,b). The Missouri 

Natural Heritage Program provided 7% (25) of the 

records (J. Sternburg, written commun., 1999). Thirtyseven percent of the observations (131) were made after 

1990. 

We analyzed data from seven hibernating colonies 

and seven summer (five maternity and two bachelor) 

colonies for trends, in Arkansas, Oklahoma, and Missouri (Appendix 7). No significant trends were detectable for counts at hibernating colonies. One summer 

colony decreased significantly, whereas one increased. 

The colony that showed evidence of a decline was a bachelor colony roosting in Marble Falls Cave, Arkansas. 

This colony declined from 100 individuals in 1978 to 0 

in 1988. Marble Falls Cave also serves as a hibernaculum during the winter. The counts from 1978 to 1987 

ranged from 145 individuals to 420, with no detectable 

trend over time. The colony that showed evidence of an 
146 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

increasing trend was a maternity colony in a cave coded 

AD-010 in Oklahoma. This colony increased from a 

count of 15 in 1981 to 314 in 1995 during 15 consecutive years of surveys (Clark and others, 1997a,b). This 

cave also serves as a hibernaculum in the winter, but in 

1994 only one individual was counted. 

The largest reported winter aggregation of Ozark 

big-eared bats was 485 counted in November 1989 at a 

cave coded AD-003 in Oklahoma (Clark and others, 

1997b). Earlier, Sealander and Heidt (1990) suggested 

that the population size of this subspecies was about 500, 

with about half of these found in Arkansas where they 

were known from one maternity cave and one hibernaculum cave. More recently, however, it has been suggested 

that there may be 1,600 bats in Oklahoma, and 260– 

700 in Arkansas, but none in Missouri (Harvey, 1992; 

U.S. Fish and Wildlife Service, 1992; Clark and others, 

1997a). The number of adult females in Oklahoma using maternity sites from 1987 to 1995 fluctuated from 

852 to 515, with lower numbers in the most recent half 

of this period (Clark and others, 1997a). Counts in winter at the four known hibernacula in eastern Oklahoma 

were about 40% of the counts during summer, suggesting local movements to hibernacula in Arkansas. This 

species can be difficult to count during winter surveys 

because of frequent movements of these bats among hibernacula (Clark and others, 1997a). A revised recovery 

plan was created for this species in 1995 (U.S. Fish and 

Wildlife Service, 1995). 

Corynorhinus townsendii virginianus (Virginia bigeared bat). We compiled 117 observations at 31 localities 

for the Virginia big-eared bat. These observations were 

from four southeastern states. This subspecies occurs in 

West Virginia, Virginia, Kentucky, and North Carolina, 

a distribution that is also considered a relict of postPleistocene climates (Humphrey and Kunz, 1976). The 

majority of observations were gathered from Kentucky 

(91; 78%). North Carolina provided 16% (19) of the 

observations, 5% (6) were from West Virginia, and one 

(<1%) was from Virginia. The vast majority of counts 

were from caves (105; 90%), but counts were also 

available from roosts in mines, rocks, and tunnels. Half 

of the colonies counted were in hibernacula (58; 50%), 

20% (23) were maternity colonies, and 15% (18) were 

bachelor colonies. The Kentucky Department of Fish and 

Wildlife Resources provided half of all observations [(58; 

50%); T. Wethington, written commun., 1999], the North 

Carolina Natural Heritage Program provided 16% (19; 

H. LeGrand, written commun., 1999), and 35% (41) 

were extracted from publications and theses or 

dissertations (e.g., Rippy and Harvey, 1965; Adam, 

1992; Meade, 1992; Lacki and others, 1993, 1994). 

More than half of the surveys we compiled for C. 

t. virginianus (75; 64.1%) were conducted after 

1990, which may reflect increased concern about 

the population status for this subspecies. 

We analyzed counts from five hibernating colonies 

(three in Kentucky and two in North Carolina) and two 

summer colonies, both in Kentucky (Appendix 8). An 

upward trend was detected at the Stillhouse Cave hibernaculum in Kentucky. The number of individuals at this 

site increased from 1,487 in 1980 to 5,105 in 1999 (T. 

Wethington, written commun., 1999). This cave also 

harbors a maternity colony in the summer that ranges 

in size from 810 to 3,068 females. No trends were detected in the other two hibernating colonies in Kentucky. 

Cranberry Iron Mine in North Carolina declined significantly from 10 individuals in 1992 to two in 1997 

(H. LeGrand, written commun., 1999). 

Virginia big-eared bats were designated endangered 

under the U.S. Endangered Species Act in 1979 due to 

their small population size, limited distribution, and 

vulnerability to human disturbance (U.S. Fish and Wildlife Service, 1979). A recovery plan has been completed 

(Bagley, 1984). The Virginia big-eared bat population 

was thought to have numbered about 13,500 bats 10 years 

ago (U.S. Fish and Wildlife Service, 1992). 

Eptesicus fuscus (big brown bat). We compiled 2,838 

observations at 1,745 localities for colonies of the big 

brown bat. Observations were found for 44 states, more 

than any other species with records in the BPD. Almost 

35% (993) of the records were established after 1990. 

Big brown bats also showed the most variety in roosting 

structures (25 different kinds of structures, including 

buildings, caves, mines, trees, storm sewers, dams, 

bridges, and tunnels). Forty-two percent of all records 

(1,192) were from caves, 35% (993) from buildings, and 

13% (369) from mines. Although this species is found 

throughout the U.S., more than half of the observations 

were collected in Indiana, Kentucky, and Pennsylvania 

(1,891 observations). More than 50% of all counts 

(1,459) were conducted at hibernating colonies and 21% 

at maternity colonies (593). Data for this species were 

found in almost every kind of source pursued. Data summarized from publications, theses or dissertations, and 

unpublished reports represented more than 33% of the 

observations (1,206) for this widely distributed species 

(e.g., Mohr, 1932b; Goehring, 1954, 1958, 1972; Hall 

and Brenner, 1968; Reidinger, 1972; Brack, 1983; Brack 

and others, 1984,1991). 

We analyzed data from 31 hibernating colonies and 

one summer colony of unspecified function for trends. 

These sites were in Arizona, Indiana, Kentucky, 

Minnesota, and Pennsylvania (Appendix 9). The 

majority of counts at hibernating colonies (27; 87.1%) 

showed no detectable trends; four (12.9%) indicated an 

upward trend (Table 1). A storm sewer in Minnesota, 

which served as a hibernaculum for big brown bats, 
yielded 20 distinct years of counts for analysis (see 

Fig. 10). In 1951, 35 individuals were found wintering 

in the sewer and by 1970 there were 293 individuals 

(Goehring, 1954, 1958, 1972). This upward trend was 

significant (Appendix 9). The one summer colony 

analyzed for trends was from a bridge in Arizona where 

the colony declined from 60 individuals in 1962 to 0 in 

1969. Although the big brown bat is one of the most 

common building-dwelling bats, there were no maternity 

or bachelor colonies roosting in buildings with sufficient 

time series available in the literature to analyze for trends. 

Euderma maculatum (spotted bat). We compiled 15 

observations from 14 different localities for the spotted 

bat. Most of these observations were collections of single 

individuals, thus, there were no time series of counts at 

colonies of this species available to analyze for trends. 

The data compiled were mostly before 1990 (12; 80%). 

The largest number of individuals roosting together was 

found at Crocodile Cave, Utah, where Hardy (1941) reported collecting four hibernating individuals in 1930. 

Spotted bats were reported roosting in buildings, caves, 

rock crevices, and cliffs. This species tends to be highly 

labile in use of roosts, making trends difficult to determine (Watkins, 1977). Spotted bats were once considered rare because from 1891 to 1965, only 35 specimens 

were reported in the literature (Watkins, 1977). As of 

1985, 73 specimens were reported (Best, 1988). The increased use of acoustic surveys as a field method for 

determining spotted bat presence has provided evidence 

that this species is more widespread and abundant than 

previously thought (Pierson and Rainey, 1998c). The 

spotted bat is a former Category 2 candidate for listing 

under the Endangered Species Act (U.S. Fish and Wildlife Service, 1994). 

Idionycteris phyllotis (Allen’s big-eared bat). We 

compiled 22 observations from 11 localities in Arizona 

for Allen’s big-eared bat. This species was found roosting 

in buildings, caves, mines, and trees. Group sizes ranged 

from single individuals up to 97 from a maternity colony 

in a mine tunnel (Cockrum, 1964). Rabe and others 

(1998) found reproductive females of this species 

roosting in ponderosa pine snags in the Coconino 

National Forest, northern Arizona. Half of the 

observations in the BPD were obtained from publications 

(e.g., Commissaris, 1961; Cockrum, 1964) and the other 

half from the Arizona Game and Fish Department’s 

Heritage Database (S. Schwartz, written commun., 

2000). There were no time series of counts available to 

analyze for trends. The Allen’s big-eared bat is a former 

Category 2 candidate for listing under the Endangered 

Species Act (U.S. Fish and Wildlife Service, 1994). 

Lasionycteris noctivagans (silver-haired bat). We 

compiled 68 observations at 61 localities for the silverhaired bat. There were no counts at colonies for this 

ELLISON AND OTHERS 147 

species available to analyze for trends. Thirty-two percent of these observations (22) were made after 1990. 

Silver-haired bats are migratory, roost in trees, and little 

is known of their population status (Mattson, 1994; 

Mattson and others, 1996). Frequent switching among 

roosts in trees, their migratory movements, and lack of 

research contribute to this absence of information 

(Campbell and others, 1996; Cryan, 2003). 

Lasiurus. We compiled records for the following 

eight species of lasiurines: western red bat (Lasiurus 

blossevillii), eastern red bat, hoary bat, Hawaiian hoary 

bat (L. cinereus semotus), southern yellow bat (L. ega), 

northern yellow bat (L. intermedius), Seminole bat (L. 

seminolus), and western yellow bat (L. xanthinus). No 

time series were available to analyze for this group of 

bats, most of the existing information was from before 

1990, and most observations were of single individuals. 

The majority of observations were for the eastern red 

bat, with 66 total records and 27 roost sites. We compiled 

21 observations at 16 roost locations for the western red 

bat. This species was found roosting in foliage in trees 

(71.4% of the observations), a mine, cave, a log cabin, 

and an abandoned house. We compiled 61 observations 

at 49 different locations for the hoary bat. This species 

was also found roosting mostly in trees, but incidental 

collections were made of this bat in buildings, caves, 

bridges, and mines. There were only three observations 

collected for the Hawaiian hoary bat, and all were bats 

using foliage in trees. The Hawaiian hoary bat is the 

only native terrestrial mammal known from the Hawaiian 

Archipelago. It was listed as endangered in 1970 (U.S. 

Fish and Wildlife Service, 1970). Historic and current 

data on abundance of this subspecies are not available 

(U.S. Fish and Wildlife Service, 1998). Three 

observations were gathered for the southern yellow bat, 

and all three observations were of individuals roosting 

in trees in Texas (Spencer and others, 1988). We 

compiled nine observations from eight locations for the 

northern yellow bat, two observations at two tree 

locations for the Seminole bat, and six observations at 

three tree roosts for the western yellow bat. These low 

numbers of observations illustrated the lack of 

information in the literature on monitoring of tree and 

foliage-roosting species. Carter and others (2003) 

provide an overview of information related to historical 

abundance of bats in this group. Past observations of 

large numbers of migrating red bats visible in flight 

during daylight hours and notable concentrations of 

hoary bats in migration suggest possible reductions in 

abundance (Carter and others, 2003). 

Myotis auriculus (southwestern myotis). We compiled information from seven colonies of the southwestern myotis in Arizona and New Mexico. Six of the 

colonies were located in Arizona: three in mines and 
148 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

one each in a tunnel, cave, and snag. The only record 

from New Mexico in the BPD was from a cave. Most 

colonies were not specified as to type (6; 86%), the roost 

in the snag housed a maternity colony and was located 

on the Coconino National Forest in northern Arizona 

(Arizona Game and Fish Department Heritage Database). The Arizona Game and Fish Department provided 

most of the limited number of observations for this species (S. Schwartz, written commun., 2000). There were 

insufficient time series available to analyze for trends in 

this species. 

Myotis austroriparius (southeastern myotis). We 

compiled 344 observations at 108 locations for the southeastern myotis. These observations were made in 13 

southeastern states. The majority was from Florida (239; 

70%), and more than 73% of all observations were from 

caves (253 observations), 16% (55) from buildings, 4% 

(15) from bridges, 4% (15) from culverts, and about 1% 

(5) from mines. One-third of the counts (115) were at 

maternity colonies, 14% (51) at hibernacula, 11% (36) 

at unspecified day roosts, 2% (7) at bachelor colonies, 

and the remaining 38% (132) were of unspecified colonies. The majority of colony data for this species was 

obtained from publications (274; 80%), with 9% (30) 

from the Florida Natural Areas Inventory. Only 25% (86) 

of the total observations for this species were made after 

1990. 

We analyzed counts from six colonies for trends 

(Appendix 10). Two of these colonies were unspecified 

summer colonies, two were maternity colonies in Florida, 

and two were from hibernacula (one in Indiana and one 

in Kentucky). All of these colonies were located in caves 

and counts in the time series ranged in size from 0 to 

170,000 (Sweet Gum Cave, Florida). Counts at the two 

hibernacula and the three summer colonies showed no 

detectable trend. One maternity colony indicated a downward trend. Sweet Gum Cave, Florida declined from 

170,000 in 1936 (using an unspecified method of counting individuals) to 0 in 1991. The longest time series 

was for Old Indian Cave, Florida, with nine distinct years 

of counts in the summer. Counts at this cave varied dramatically from year to year ranging from two in 1981 to 

10,437 in 1989 (CV of 120.7%). 

Although we analyzed only six colonies for trends 

and most of these colonies showed no consistent trend, 

there has been an accumulation of anecdotal accounts 

that have suggested cause for concern about the status 

of the southeastern myotis. Barbour and Davis (1969) 

suggested that the population in the lower Ohio River 

Valley was much more rare than in the past and possibly was close to extinction. This bat is considered uncommon or rare in the northernmost states within its 

range (Barbour and Davis, 1974; Hoffmeister, 1989; 

Sealander and Heidt, 1990). Mumford and Whitaker 

(1982) reported an apparent decline in wintering colonies in Indiana since 1949. In the Ouachita Mountains 

in Arkansas, one colony in a mine drift was inundated 

by an impoundment, and a second colony in an abandoned mine containing 150 hibernating individuals declined to just a few individuals by 1986, probably due to 

disturbance (Saugey and others, 1988). 

This species is considered to be most abundant in 

Florida, where colonies occur in the panhandle and the 

north-central peninsular regions of the state (Gore and 

Hovis, 1994). The accuracy of population estimates for 

this species in Florida is uncertain and little is known of 

seasonal movements among caves, which Humphrey and 

Gore (1992) cautioned precludes evaluation of trends 

from the scanty data available. Despite this lack of 

knowledge and uncertainty in estimates, there have been 

several published accounts suggesting declines in this 

species in Florida. For instance, one colony of 2,500 

reported in a cave by Rice (1957) was gone in the early 

1990’s, a second of 90,000 remained at about the same 

number, and a third consisting of 30,000 bats was on a 

site scheduled for development of a housing project 

(Humphrey and Gore, 1992). Three caves in the Florida 

panhandle that had previously supported bats, including 

a colony of 11,000 at one site in the 1950’s, were 

completely devoid of bats by the early 1990’s (Humphrey 

and Gore, 1992). Another cave in the Florida panhandle 

with a maternity colony documented to be 15,000 in 1970 

had fewer than 200 in 1981 (Wenner, 1984). These 

downward shifts prompted an intensive statewide survey 

for maternity colonies in 1991–1992 (Gore and Hovis, 

1994). Caves with maximum colony size estimates in 

the past (adults prior to parturition only) noted at various 

times from 1936–1982 totaled 377,000 bats; in 1991– 

1992 a maximum of about 165,000 were estimated at 

these same sites (Gore and Hovis, 1994). These numbers 

suggest lower colony sizes, but are not directly 

comparable because it is unknown how many of the 

earlier sites were continuously or simultaneously 

occupied, how many undiscovered populations existed 

in the recent past, how much movement occurred among 

sites, and how methods of estimation may have differed. 

Most of the maternity colonies visited in 1991 or 1992 

showed evidence of successful production of young, 

particularly in the panhandle, but just three of six caves 

in the peninsula occupied by females in spring 1992 had 

evidence of volant young by summer. The other three 

showed signs of disturbance and abandonment. The only 

known maternity colony in Alabama, reported to contain 

about 8,000 bats in 1990, was reported as being 

“extremely vulnerable to destruction” because of high 

use of the cave by people, disturbance, and vandalism 
(Best and others, 1992). Another summer colony at a 

different cave was previously described as the largest in 

Alabama, but had been extirpated by the mid-1980s due 

to disturbance and vandalism. The southeastern myotis 

was previously a Category 2 Candidate for listing under 

the Endangered Species Act (U.S. Fish and Wildlife 

Service, 1994). 

In contrast to the situation described above for 

populations of the southeastern myotis that roost in caves, 

Clark (2003) provided evidence that this species also 

commonly roosts in hollow trees in bottomland swamps 

and forests. This suggests that colonies in these habitats 

may be more abundant than previously realized. 

Myotis californicus (California myotis). We compiled 105 observations from 88 locations for the California myotis. These counts were compiled from 10 

western states. The majority of observations were from 

Arizona (26; 25%), California (29; 28%), Colorado (10; 

9%), and Nevada (19; 18%). This species was reported 

from mines (48; 45%), buildings (25; 25%), caves (10; 

10%), and bridges (4; 4%). The remainder was reported 

from crevices, shrubs, the ground, cacti, rocks, signs, 

trees, and tunnels. Most colonies were not specified as 

to type (53; 51%). Unspecified day roosts (14; 14%), 

hibernacula (15; 14%), maternity (6; 6%), and night 

roosts (14; 14%) were also reported. Sixty-seven percent of the colony data (70 observations) we analyzed 

were from publications, theses or dissertations, and unpublished reports (e.g., Dalquest and Ramage, 1946; 

Krutzsch, 1954; Cockrum, 1964; Easterla, 1973; 

Hasenyager, 1980; Perkins and others, 1990). We obtained 17% (18) of the total observations for this species from the Arizona Game and Fish Department (S. 

Schwartz, written commun., 2000), 10% (10) from the 

Colorado Division of Wildlife (K. Navo, written 

commun., 2000), and 4% (4) from the National Park 

Service (C. Baldino, written commun., 1999). Thirtyseven percent of the observations (39) were made after 

1990. There were insufficient time series available to 

analyze for trends in this species. The California myotis 

is a former Category 2 Candidate for listing under the 

Endangered Species Act (U.S. Fish and Wildlife Service, 1994). 

Myotis ciliolabrum (western small-footed myotis). 

We compiled 401 observations from 182 locations and 

16 western states for the western small-footed myotis. 

Thirty percent of available observations (120) were from 

South Dakota, 26% (103) from Wyoming, 17% (69) from 

Colorado, and 8% (34) from Idaho. Half the observations 

(201) were from mines and 41% (167) from caves. 

Bridges, buildings, crevices, rocks, and tunnels were also 

reported as roosts of this species. Counts for hibernating 

colonies comprised nearly half of all the observations 

ELLISON AND OTHERS 149 

(182; 45%), with the remaining observations from 

bachelor groups, unspecified day roosts, maternity 

colonies, and night roosts. Fifty-three percent of counts 

at colonies (215) of the western small-footed myotis were 

obtained from publications (e.g., Turner and Jones, 1968; 

Martin and Hawks, 1972; Turner, 1974; Worthington, 

1992; Choate and Anderson, 1997; Jagnow, 1998), 21% 

(83) from the Black Hills National Forest Database 

(B. Phillips, written commun., 1999), 15% (59) from 

the Colorado Division of Wildlife (K. Navo, written 

commun., 2000), and 7% (27) from the Wyoming Game 

and Fish Department (B. Luce, written commun., 1999). 

Nearly 70% (280) of the observations were made after 

1990. 

We analyzed data from two hibernating colonies for 

trends (Appendix 11). The two colonies were Torgac 

Cave, New Mexico, and Jewel Cave, South Dakota. Data 

from both of these colonies demonstrated no trend. 

Torgac Cave’s counts ranged from 0 to 111 individuals 

and Jewel Cave ranged from four to 20 individuals. 

Jagnow (1998) reported an increase in the number of 

western small-footed bats found hibernating in Torgac 

Cave, but the number of years in the time series was too 

small for our analysis. We are unaware of other published information pertinent to the status of this species. The western small-footed myotis is a former 

Category 2 candidate for listing under the Endangered 

Species Act (U.S. Fish and Wildlife Service, 1994). 

Myotis evotis (long-eared myotis). We compiled 137 

observations at 110 colony locations and 12 western 

states for the long-eared myotis, with the majority of 

observations obtained from Colorado (41; 30%), Montana (14; 10%), and Oregon (24; 18%). The species 

roosted in several different types of structures including 

40% (55) in caves, 37% (51) in mines, 8% (11) in buildings, 7% (9) in bridges, 1% (2) in rocks, and the remainder in snags, stumps, and trees. Most colonies were 

of an unspecified type (79; 58%). Unspecified day roosts 

(19; 14%), hibernacula (19; 14%), maternity (8; 6%), 

and night roosts (11; 8%) were also reported. Thirtytwo percent of the colony data (47 observations) we gathered were from publications and unpublished reports 

(e.g., Senger and others, 1974; Swenson and Shanks, 

1979; Marcot, 1984; Perkins and others, 1990; 

Worthington and Ross, 1990; Priday and Luce, 1996). 

We obtained 28% of our observations (39) from the Colorado Division of Wildlife, 7% (10) from private individuals (C. Senger, written commun., 1996), 9% (13) 

from the Oregon Natural Heritage Program, and 7% (10) 

from the Wyoming Game and Fish Department. There 

were insufficient data available to analyze for trends in 

counts for this species. This species had 67% (92) of its 

observations collected after 1990. The long-eared myotis 
150 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

is a former Category 2 candidate for listing under the 

Endangered Species Act (U.S. Fish and Wildlife Service, 1994). 

Myotis grisescens (gray bat). We compiled 1,879 

observations of gray bats gathered from 334 roost locations in 14 south-central and southeastern states. The 

majority of observations were collected from Missouri 

(735; 39%), Arkansas (377; 20%), Alabama (273; 15%), 

and Kentucky (194; 10%). Gray bat colonies were found 

in a variety of structures including bridges, buildings, 

caves, culverts, dams, mines, and sewers. However, more 

than 96% (1,807) of all counts were conducted at caves. 

Forty-six percent of these were maternity colonies (866), 

16% (301) transient roosts, 10% (196) hibernacula, and 

5% (101) bachelor colonies. Thirty percent of the observations (564) were made after 1990. The Alabama Natural Heritage Program provided 11% (207) of the 

observations (T. Manasco, written commun., 1999), the 

Missouri Natural Heritage Program 31% (587 observations; J. Sternburg, written commun., 1999), and the 

Indiana Natural Heritage Program 1% (9 observations; 

R. Hellmich, written commun., 1999). Publications, theses or dissertations, unpublished reports (937; 50%), and 

the Kentucky Department of Fish and Wildlife Resources 

[(122; 6%); T. Wethington, written commun., 1999)] 

provided the remainder of our information. 

Gray bats form large aggregations of females and 

young in the summer. Counts have reached nearly half 

a million in single caves (485,400 for Sauta Cave, 

Alabama, Alabama Natural Heritage Program). We 

analyzed information from counts at 103 summer 

colonies and from 12 hibernacula. These colonies were 

in Alabama, Arkansas, Florida, Illinois, Kansas, 

Kentucky, Missouri, and Tennessee (Appendix 12). The 

vast majority of these colonies were in caves (105; 99%) 

with the notable exception of a maternity colony using a 

storm sewer in Kansas. The majority of the data from 

summer colonies showed no trend (88; 85.4%), nine 

indicated an upward trend, and six indicated a downward 

trend (Table 2). The six summer colonies that showed 

significant declining trends were Hollyberry Cave, 

Alabama; Big Creek Cave, Optimus Cave, and Shirley 

Bat Cave, Arkansas; and two caves in Missouri 

(Locations 6102 and 6067). Counts at Hollyberry Cave 

declined from 20,000 in 1986 to 0 in 1997. Counts at 

Big Creek Cave declined from 18,000 individuals in 

1980 to 1,680 in 1988. Optimus Cave, a transient roost 

surveyed for 10 distinct summers, declined from 7,000 

in 1977 to 0 in 1988. Shirley Bat Cave, a bachelor colony, 

had nine years of counts, and declined from 10,200 in 

1977 to 2,020 in 1988. An unnamed cave (coded 6102) 

was home to a maternity colony that had seven years of 

counts and declined from 2,000 in 1964 to 0 in 1998. 

Cave location 6067 housed a maternity colony of 50,000 

in 1964, but only 400 were counted in 1989. The longest 

time series available for this species was 19 years of 

counts at Cave Springs Cave in Alabama. This colony 

increased from 20,000 in the summer of 1978 to 47,500 

in 1997 (Alabama Natural Heritage Program; Harvey, 

1989; Harvey and others, 1981). 

No trends were detected for 7 of the 12 hibernating 

colonies of gray bats (58.3%); three showed an upward 

trend (25.0%), and two a downward trend (16.7%; 

Table 1). Few data are available for gray bat hibernation sites because of their sensitivity to disturbance 

(R. Currie, written commun., 2003). The two hibernacula 

that declined were for Bonanza Cave, Arkansas and 

Marvel Cave, Missouri. Bonanza Cave declined from 

250,000 in 1979 to 55,000 in 2001 and this decline was 

attributed to disturbance (M. Harvey, written commun., 

2003). The number counted at Marvel Cave declined 

from 14,500 in 1935 to 2,527 in 1976. 

The gray bat was listed as endangered in 1976 (U.S. 

Fish and Wildlife Service, 1976), and a recovery plan 

was created in 1982 (U.S. Fish and Wildlife Service, 

1982). Tuttle (2003) reviews the problems and unique 

issues associated with estimating population size of hibernating bats, including the gray bat. Gray bats have 

been contaminated and killed by pesticide poisoning 

through the food chain (Clark and others, 1978b, 

1983a,b, 1988). However, their populations have been 

most affected by disturbance and vandalism to colonies 

in caves. They are reported to be highly selective in their 

use of particular caves, utilizing only very small proportions of available caves and a limited number of sites 

(Tuttle, 1979, 1986). This makes them very vulnerable 

to human activities because they are highly aggregated. 

Reduced numbers of gray bats at 20 caves in Kentucky 

from an estimated past summer abundance of over 

500,000 to just 61,000 by 1979 was attributed to frequency and intensity of disturbance (Rabinowitz and 

Tuttle, 1980), as was a reduction in gray bats at summer 

roosts in Alabama and Tennessee from a likely 1.2 million in the recent past to 294,000 by 1976 (Tuttle, 1979). 

Analysis of survival based on banding studies was consistent with declines in counts at some sites (Stevenson 

and Tuttle, 1981). Deliberate destruction of entire colonies from misguided fears about the degree of threat from 

rabies has also occurred (Tuttle, 1979). 

Gray bat numbers are thought to have rebounded in 

recent years because of intensive recovery efforts initiated by the U.S. Fish and Wildlife Service and many 

others (R. Currie, written commun., 2003). At the time 

the Recovery Plan was written, the gray bat population 
was thought to be about 1,575,000 across its range. In 

2002, the population was thought to be 2,678,137, up 

61.5% from the time the plan was written. 

Myotis keenii (Keen’s myotis). We found no information for colonies of Keen’s myotis, a species with a 

very limited range in the Pacific Northwest. Until 1979, 

the northern long-eared myotis was considered a subspecies of Keen’s myotis, but discovery of differences in 

distribution and morphology were used to justify recognizing the two taxa as distinct species (van Zyll de Jong, 

1979). 

Myotis leibii (eastern small-footed myotis). We compiled 785 observations from 502 locations from 16 states 

for the eastern small-footed myotis. More than 71% of 

all observations (561) were collected from Pennsylvania, 17% (133) from New York, and 3% (19) from Arkansas. This species was found roosting in seven different 

types of structures (boulders, buildings, caves, culverts, 

mines, and tunnels). Seventy-one percent of all observations were made in caves (558), 22% (176) in mines, 

and 4% (33) in tunnels. More than 90% (710) of all 

counts were conducted at hibernacula. Forty-four percent of the observations (345) were made after 1990. 

The majority of counts for this species were obtained 

from the Pennsylvania Game Commission’s Winter Bat 

Hibernacula Survey [(526; 67%); J. Hart, written 

commun., 2000], 14% (107) from publications (e.g., 

Mohr, 1932a,b, 1933a; Tuttle, 1964; Krutzsch, 1966; 

Martin and others, 1966; McDaniel and others, 1982;), 

and 16% (123) from New York’s Division of Wildlife 

Winter Bat Survey (A. Hicks, written commun., 2000). 

Ten colonies of hibernating eastern small-footed 

myotis in Pennsylvania were analyzed for trends (Appendix 13). Two of these colonies were in mines and 

eight in caves. Colonies of this species tended to be small, 

with time series ranging in size from 0 to a maximum of 

46. Trends could not be detected for the majority of these 

colonies (8; 80%), and two (20%) were found to have 

increased (Table 1). The eastern small-footed myotis is 

a former Category 2 candidate for listing under the Endangered Species Act (U.S. Fish and Wildlife Service, 

1994). 

Myotis lucifugus (little brown bat). We compiled 

2,117 observations from 1,244 colony locations from 42 

states for the widely distributed little brown bat. Thirty 

percent of all observations were collected from 

Pennsylvania (615), 17% (369) from Indiana, 10% (209) 

from Kentucky, 5% (104) from Massachusetts, and 9% 

(185) from New York. This species was found roosting 

in 18 different kinds of roosting structures, with 55% 

(1,169) of all observations from caves. Little brown bats 

also used mines (326; 15%) and buildings (448; 21%). 

ELLISON AND OTHERS 151 

More than 60% (1,280) of all counts were conducted at 

hibernacula and 12% (255) at maternity colonies. Thirtynine percent of the observations (826) were made after 

1990. Forty-two percent of colony data (877 

observations) for this species were obtained from 

publications, theses or dissertations, and unpublished 

reports (e.g., Bailey, 1933; Welter and Sollberger, 1939; 

Hall and others, 1957; Humphrey and Cope, 1963; 

Brack, 1983; Brack and others, 1984, 1991; Gates and 

others, 1984; Whitaker and Rissler, 1992a,b); 25% (529) 

from the Pennsylvania Game Commission’s Winter Bat 

Hibernacula Survey (J. Hart, written commun., 2000); 

7% (154) from the Kentucky Department of Fish and 

Wildlife Resources (T. Wethington, written commun., 

1999); 7% (151) from D. Scott Reynold’s survey of 

building-roosting little brown bats (1999); and 6% (123) 

from the New York Division of Wildlife Winter Bat 

Survey (A. Hicks, written commun., 1999). 

We analyzed counts from 45 colonies for trends 

(Appendix 14). These colonies were in Indiana, Kentucky, Massachusetts, Maryland, New Hampshire, Pennsylvania, South Dakota, Vermont, and West Virginia. 

Thirty-eight of these colonies were hibernacula in caves; 

three were hibernacula in mines, three were maternity 

colonies in buildings, and one was a maternity colony 

in an unspecified roost structure, most likely a building 

(Humphrey and Cope, 1963). The majority of counts 

made at hibernacula showed no detectable trend (27; 

64.3%), 13 (30.9%) had increased, and two (4.8%) had 

declined (Table 1). The two colonies that declined were 

Ray’s Cave, Indiana, and Haine’s Gap, Pennsylvania. 

Ray’s Cave’s colony size declined from 3,380 in 1987 to 

351 in 1993 (R. Hellmich, written commun., 1999; 

Brack, 1983; Brack and others, 1984, 1991). Haine’s 

Gap had a colony size of 87 in 1985, but dropped to 52 

in 1993 (J. Hart, written commun., 2000). One summer 

colony in a building in Massachusetts showed an upward trend from 350 in 1994 to 520 in 1997 (D. 

Reynolds, written commun., 1999). The other summer 

colonies showed no trends. The longest time series available for the little brown bat was from Aitkin Cave, Pennsylvania. Thirteen years of counts were available at 

Aitkin Cave, beginning in 1932 when 406 bats were 

counted by Mohr (1932b) and ending in 1997 when 1,653 

were counted during the Pennsylvania Game 

Commission’s Winter Bat Hibernacula Survey (J. Hart, 

written commun., 2000). 

Myotis septentrionalis (northern myotis). We 

compiled 1,077 observations for the northern myotis 

from 736 locations from 31 states. More than 51% of all 

observations (553) were collected from Pennsylvania; 

12% (129) from New York; 11% (115) from Indiana; 
152 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

6% (62) from Kentucky; and 5% (50) from South Dakota. 

Colonies of this species were found roosting in seven 

different types of structures (buildings, caves, culverts, 

mines, sewers, trees, and tunnels) with 70% (757) of all 

observations in caves and 24% (254) in mines. More 

than 80% (862) of all counts were conducted at 

hibernacula. Forty-two percent of the observations (452) 

were made after 1990. Information on counts of colonies 

was obtained from the Pennsylvania Game Commission’s 

Winter Bat Hibernacula Survey [(529; 49%); J. Hart, 

written commun., 2000)]; 27% (290) from publications, 

theses or dissertations, and unpublished reports (Bures, 

1948; Hall and Brenner, 1968; Cope and Humphrey, 

1972; Brack, 1983; Brack and others, 1984; Whitaker 

and Rissler, 1992a; Cryan and others, 2001); 11% (123) 

from New York’s Division of Wildlife Winter Bat Survey 

(A. Hicks, written commun., 2000); 5% (50) from the 

Kentucky Department of Fish and Wildlife Resources 

(T. Wethington, written commun., 1999); and 3% (33) 

from the Black Hills National Forest Database (B. 

Phillips, written commun., 1999). 

We analyzed data from 12 colonies for trends (Appendix 15). These were all wintering colonies in hibernacula in Maryland and Pennsylvania. Four of these 

colonies were in mines and eight in caves. No trend was 

detectable in the majority of these colonies (9; 75%). 

Three colonies (25%) increased over the time period 

analyzed (Table 1). The three colonies found to have 

increased were Lemon Hole, Ruth Cave, and Sharer 

Cave, all in Pennsylvania. Counts at these hibernacula 

were all low, however, ranging from 0 to a maximum 

count of 93 (Sharer Cave, Pennsylvania). Lemon Hole 

increased from one individual in 1985 to six in 1997. 

Numbers at Ruth Cave increased from two in 1985 to 

52 in 1995. Sharer Cave increased from 0 in 1985 to 28 

in 1997, but the coefficient of variation was relatively 

high (134.5%). Aitkin Cave provided the longest series 

of counts for this species, with 13 years of counts beginning in 1964 with a count of 10 individuals (Hall and 

Brenner, 1968) and ending in 1997 with 36 bats (J. Hart, 

written commun., 2000). However, no trend could be 

detected at this site. 

The northern long-eared myotis was considered a 

subspecies of Keen’s myotis until 1979, but differences 

in distribution and morphology were used to justify recognizing the two taxa as distinct species (van Zyll de 

Jong, 1979). 

Myotis sodalis (Indiana bat). We compiled 2,867 

observations for the Indiana bat at 920 colony locations 

from 24 eastern states. Oklahoma was the western-most 

state with observations of this species. Most observations were from Indiana (418; 15%), Kentucky (960; 

33%), New York (186; 6%), and Pennsylvania (557; 

19%). This species was found roosting in a variety of 

structures including bridges, buildings, caves, culverts, 

mines, trees, and tunnels. More than 86% of all counts 

(2,480) were conducted at caves and 90% (2,600) were 

from hibernacula. Indiana bats roost in the winter in 

large aggregations, with colonies often on the order of 

magnitude of 100,000 individuals. Twenty-eight percent 

of the observations (803) were made after 1990. Natural 

Heritage Programs (Alabama, Indiana, Kentucky, Missouri, and North Carolina) provided 28% (803) of all 

observations. Twenty-five percent of the observations 

were obtained from the New York Division of Wildlife 

Winter Bat Survey [(180; 6%); A. Hicks, written 

commun., 2000] and the Pennsylvania Game 

Commission’s Winter Bat Hibernacula Survey [(528; 

19%); J. Hart, written commun., 2000]. The remaining 

observations were obtained from publications (559; 

19%), theses or dissertations (668; 23%), and unpublished reports (109; 4%). We compiled more observations for the Indiana bat than for any other species of 

bat. The Indiana bat also had the most colonies with 

>10 years of surveys of any species (30 sites had >10 

years of surveys). 

We analyzed 97 wintering colonies in hibernacula 

for trends (Appendix 16). These were at sites in Alabama, Arkansas, Illinois, Indiana, Kentucky, New York, 

Pennsylvania, and West Virginia. All of these colonies 

roosted in caves or mines. Trends were not detectable 

for about half (49; 50.5%), 18 increased (18.6%), and 

30 (30.9%) declined (Table 1). Notable colonies that indicated significant declines using nonparametric trend 

analyses included Bat Wing Cave and Twin Domes Cave, 

Indiana; Bat Cave and Coach Cave, Kentucky; and Bat 

Cave, Missouri. Bat Wing Cave’s colony size declined 

from 50,000 in 1977 to 7,400 in 1997. Twin Domes 

Cave’s colony size declined from 100,000 individuals 

in 1975 to 67,100 in 1997. Bat Cave in Carter County, 

Kentucky, had a colony size of Indiana bats estimated at 

90,000 in 1937 and 100,000 in 1956, but declined to 

25,100 in 1999. Coach Cave’s colony declined from 

100,000 in 1957 to 33 in 1999, and Bat Cave, Missouri, 

declined from 100,000 in 1958 to 4,275 in 1987. 

The two longest time series available to analyze for 

trends for the Indiana bat was at Ray’s Cave in Indiana 

and Bat Cave, Carter County, Kentucky, both with 23 

distinct years of surveys. Numbers of Indiana bats at 

Ray’s Cave increased from 1,500 in 1956 to more than 

51,000 in 1997. This was a significant increase (Appendix 16). The winter colony roosting in Bat Cave, Kentucky declined significantly from 90,000 in 1937 to just 

over 25,000 in 1999. Another example of a notable, significant increase in counts was at Wyandotte Cave, which 

can be attributed to changes in cave gating which enhanced temperature conditions in the hibernaculum 

(Richter and others, 1993). 
ELLISON AND OTHERS 153 

The Indiana bat was listed as endangered in 1967, 

with full legal protection provided with passage of the 

Endangered Species Act of 1973 (U.S. Fish and Wildlife 

Service, 1999). Based on censuses conducted at 

hibernacula, the total population size of the Indiana bat 

across its entire range was thought to be about 353,000 

bats during the 1995–1997 survey years. This is less than 

half of the total population size of 808,505 thought to 

exist in 1960 (U.S. Fish and Wildlife Service, 1999). In 

2001, the total known population size for this species 

was thought to be 380,000 (Clawson, 2002). Clawson 

(2002) reviews the history and current status of the 

Indiana bat. A number of papers in the recent symposium 

volume edited by Kurta and Kennedy (2002) summarize 

current issues in research and management for this 

species. 

Myotis thysanodes (fringed myotis). We obtained 

235 observations from 127 colony locations of the fringed 

myotis. These observations were from 10 western states, 

with 26% (61) in Arizona, 24% (56) in South Dakota, 

15% (35) in New Mexico, 9% (22) in Oregon, 8% (20) 

in Colorado, 7% (16) in Wyoming, and 6% (14) in California. The remaining few observations were in Nevada, 

Texas, and Utah. Almost half of the observations were 

from caves (120; 46%), with an additional 23% (55) 

from mines, 14% (34) from buildings, 6% (14) from 

bridges, and 3% (6) from trees. Twenty-one percent of 

counts (50) were at unspecified day roosts, 14% (33) at 

maternity roosts, 10% (24) at night roosts, 1% (3) at 

bachelor roosts, and 38% (102) of unspecified colony 

type. More than half of the observations were made after 1990 (120; 51%). Forty-two percent of the observations (109) were obtained from publications, theses or 

dissertations, and unpublished reports (e.g., Cockrum 

and Ordway, 1959; Davis, 1966; Martin and Hawks, 

1972; Worthington, 1992; Choate and Anderson, 1997; 

Cryan and others, 2001), 20% (47) from the Arizona 

Game and Fish Department’s Heritage Database (S. 

Schwartz, written commun., 2000), 13% (31) from the 

Black Hills National Forest Database (B. Phillips, written commun., 1999), 6% (13) from the Colorado Division of Wildlife (K. Navo, written commun., 2000), 8% 

(18) from the Oregon Natural Heritage Program (T. 

Campos, written commun., 1999), and 5% (11) from P. 

Cryan (written commun., 1998). 

We analyzed three colonies for trends 

(Appendix 17). These three colonies were all located in 

caves and ranged in size from a minimum of two 

individuals to a maximum of 121 at Christopher 

Mountain Cave, Arizona. The colonies in Arizona were 

summer roosts of unspecified function and showed no 

trend, whereas numbers counted at the hibernaculum at 

Jewel Cave, South Dakota decreased significantly from 

10 individuals in 1969 to two in 1992. We are unaware 

of any published literature pertinent to the status of this 

species, although it was considered a Category 2 

Candidate for listing under the Endangered Species Act 

prior to elimination of this category (U.S. Fish and 

Wildlife Service, 1994). 

Myotis velifer (cave myotis). We obtained 585 observations from 195 colony locations for the cave myotis. 

Observations were from seven western states with 32% 

(186) in Arizona, 29% (171) in Kansas, 28% (166) in 

Texas, 5% (31) in Oklahoma, and the remaining few 

observations in California, Nevada, and New Mexico. 

More than 50% of all observations (297) were from 

caves, 24% (152) from mines, 12% (67) from bridges, 

and 9% (52) from buildings. Bird nests, crevices, shrubs, 

and tunnels were also reported as roosts by this species. 

Twenty-seven percent of the observations (158) were 

made after 1990. Nearly a quarter of the observations 

were from hibernacula (140; 24%). Maternity colonies 

and unspecified day roosts were the remaining colony 

types reported. Sixty percent of the data for this species 

were obtained from publications (344; e.g., Blair, 1954; 

Tinkle and Milstead, 1960; Tinkle and Patterson, 1965; 

Dunnigan and Fitch, 1967; Adams, 1995; Jagnowm, 

1998). Seventeen percent of the observations (98) were 

from theses or dissertations, 16% (93) from the Arizona 

Game and Fish Department’s Heritage Database (S. 

Schwartz, written commun., 2000), and 8% (48) from 

the Bats in American Bridges Project (B. Keeley, written commun., 1999, Bat Conservation International). 

Only one summer colony and five wintering colonies in hibernacula met criteria for analysis of trends 

(Appendix 18). All of these colonies were located in caves 

and time series for these locations ranged in size from 0 

to 3,778. The single summer colony (Colossal Cave, 

Arizona) and three of the hibernacula showed no significant trend (Tables 1 and 2). Counts at two of the 

wintering colonies in hibernacula declined in the late 

1950’s to early 1960’s. Panther Cave, Texas, declined 

from 1,190 in 1958 to 37 in 1961. Walkup Cave in Texas 

declined significantly from 3,798 in 1958 to 174 in 1962. 

We found little information in the literature relevant to 

trends in colony size in this species. O’Shea and Vaughan 

(1999) reported abandonment of a roost by a colony of 

about 5,000 in central Arizona. The cave myotis is a 

former Category 2 Candidate for listing under the Endangered Species Act (U.S. Fish and Wildlife Service, 

1994). 

Myotis volans (long-legged myotis). We compiled 

290 observations from 186 locations for colonies of the 

long-legged myotis. Observations were compiled from 

13 western states. Most observations were from Colorado (66; 23%), Oregon (33; 11%), South Dakota (62; 

21%), Washington (42; 14%), and Wyoming (39; 14%). 

More than 50% of all observations (153) were from caves 
154 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

with 25% (73) from mines, 11% (33) from buildings, 

and 8% (23) from bridges. Crevices in cliffs, rocks, and 

trees were also documented as roosts used by this species. The majority of colonies (211; 78%) were unspecified as to whether they were maternity, bachelor, or 

hibernating groups. Nearly 50% of observations (144) 

were obtained from publications (e.g., Martin and 

Hawks, 1972; Senger and others, 1974; Turner 1974; 

Choate and Anderson, 1977; Cryan and others, 2001). 

The Colorado Division of Wildlife provided 20% of the 

observations (59 observations; K. Navo, written 

commun., 2000); the Black Hills National Forest Database provided 12% (35 observations; B. Phillips, written commun., 1999); and the remaining observations 

were from the Arizona Game and Fish Department [(5; 

2%); S. Schwartz, written commun., 2000]; C. Senger 

[(10; 3%); written comm., 1997)]; the Oregon Natural 

Heritage Program [(16; 5%); T. Campos, written 

commun., 1999]; P. Cryan [(11; 4%); written commun., 

1998]; and the Wyoming Game and Fish Department 

[(10; 3%); B. Luce, written commun., 1999]. Nearly 60% 

of all observations (170) were collected after 1990. 

We analyzed counts at one summer colony and two 

hibernating colonies for trends (Appendix 19). All of 

these colonies were located in caves and ranged in size 

from a minimum of one individual to a maximum of 50 

at Jewel Cave, South Dakota in the winter of 1969. We 

found no significant trends for any of these colonies. 

We are unaware of any published literature pertinent to 

the status of this bat, although it was considered a Category 2 Candidate for listing under the Endangered Species Act prior to elimination of this category (U.S. Fish 

and Wildlife Service, 1994). 

Myotis yumanensis (Yuma myotis). We compiled 

213 observations from 123 locations for colonies of the 

Yuma myotis. These observations were obtained from 

12 western states, with 13% (29) from Arizona, 27% 

(57) from California, and 35% (74) from Oregon. Colonies of this species occupied several different roosting 

structures, with almost 50% of reported locations (97) 

in buildings, 29% (62) in bridges, 8% (18) in caves, 2% 

(4) in crevices, 7% (14) in mines, 2% (5) in trees, and 

less than 1% in dams (1) and tunnels (2). Most colonies 

were unspecified day roosts (84; 40%), maternity colonies (50; 24%), or of unspecified type (58; 27%), with 

some classified as hibernacula (11; 5%), and night roosts 

(9; 4%). More than 70% of the counts (151) we obtained 

were from publications and theses or dissertations (e.g., 

Dice, 1919; Dalquest, 1947; Commissaris, 1959; 

Constantine, 1961; Easterla, 1966; Reidinger, 1972; 

Senger and others, 1974). We obtained 14% of the observations (29) from the Oregon Natural Heritage Program (T. Campos, written commun., 1999), 7% (14) 

from the Arizona Game and Fish Department’s Heritage Database (S. Schwartz, written commun., 2000), 

4% (8) from the Bats in American Bridges Program 

(B. Keeley, written commun., 1999, Bat Conservation 

International), and C. Senger [(10; 5%); written 

commun., 1996]. There were no colonies at which >4 

years of counts were available to analyze for trends for 

this species. We are unaware of any published literature 

pertinent to the status of this species, although it was 

considered a Category 2 Candidate for listing under the 

Endangered Species Act prior to elimination of this category (U.S. Fish and Wildlife Service, 1994). 

Nycticeius humeralis (evening bat). We compiled 

193 observations from 94 locations for colonies of the 

evening bat. Observations were compiled for 15 states, 

with 29% (56) from Missouri, 24% (47) from Indiana, 

19% (36) from Iowa, 13% (25) from Florida, and the 

remainder from Alabama, Arkansas, Georgia, Illinois, 

Kentucky, Louisiana, Michigan, Mississippi, North 

Carolina, Oklahoma, and Texas. The majority of observations we obtained were from roosts in buildings (130; 

67%), but reports included counts at roosts in trees (39; 

20%), bridges (10; 5%), and caves (3; 2%). Most colonies counted were maternity groups (158; 82%). Data 

were assembled primarily from publications, theses or 

dissertations, and unpublished reports (181; 94%), but 

information was also provided by the Bats in American 

Bridges Project [(6; 3%); B. Keeley, written commun., 

1999, Bat Conservation International], the Indiana Natural Heritage Program [(3; 2%); R. Hellmich, written 

commun., 1999], and J.O. Whitaker [(1; <1%); written 

comm., 1998]. Only one maternity colony had a time 

series of sufficient length to analyze for trends (Whitaker 

and Gummer, 1988; Clem, 1992, 1993; Whitaker and 

Clem, 1992). This colony was located in a church in 

Indiana. The colony showed no detectable trend (n = 5, 

S = -6, P > 0.05) over five years from 1987 to 1992, and 

averaged 295 + 135 bats (CV = 51.0%). 

Pipistrellus hesperus (western pipistrelle). We compiled 56 observations from 48 locations for the western 

pipistrelle. Observations were from Arizona (10; 18%), 

California (8; 15%), Colorado (2; 4%), Nevada (12; 

22%), New Mexico (14; 26%), Texas (3; 6%) and Utah 

(4; 7%). This species was found roosting in a variety of 

structures including bridges, buildings, caves, crevices, 

desert shrubs, garages, mines, rocks, and tunnels. Colonies of this species were usually small with maxima of 

11–12 found roosting together in summer (Stager, 1943; 

Koford and Koford, 1948; Cross 1965). Only 14% of 

the total observations (8) were made after 1990. About 

90% of our observations (49) were gathered from publications (e.g., von Bloeker, 1932; Hardy, 1949; Cross, 

1965; Hirshfeld and others, 1977), but single observations were provided by the Arizona Game and Fish 

Department’s Heritage Database Management System 
(S. Schwartz, written commun., 2000), the Bats and 

American Bridges Project (B. Keeley, written commun., 

1999, Bat Conservation International), the Colorado 

Division of Wildlife (K. Navo, written commun., 2000), 

and the National Park Service (C. Baldino, written 

commun., 1999). There were no time series available to 

analyze for this species. 

Pipistrellus subflavus (eastern pipistrelle). We compiled 2,136 observations from 1,044 locations of colonies of the eastern pipistrelle. Observations were 

compiled from 33 eastern states. Thirty-four percent of 

all counts (723) were from Kentucky, 26% (557) from 

Pennsylvania, and 12% (246) from Indiana. More than 

83% of all counts (1,793) were made at hibernacula. 

Counts for this species were mostly from caves (1,688; 

80%), 13% (289) were from mines, 4% (77) were in 

buildings, and 2% (52) were in tunnels. Fifty-five percent of the counts (1,194) were obtained from publications, theses or dissertations, and unpublished reports 

(e.g., Mohr, 1932a, 1945; Davis, 1957, 1959, 1966; 

Brack, 1983; Brack and others, 1984, 1991; Gates and 

others, 1984; Saugey and others, 1988; Whitaker, 1998; 

Best and others, 1992; Whitaker and Rissler, 1992a,b); 

25% (529) from the Pennsylvania Game Commission’s 

Winter Bat Hibernacula Survey (J. Hart, written 

commun., 2000); 6% (123) from the New York Division of Wildlife Winter Bat Survey (A. Hicks, written 

commun., 2000); and 10% (221) from the Kentucky 

Department of Fish and Wildlife Resources 

(T. Wethington, written commun., 1999). 

We conducted trend analyses on counts from 44 hibernacula and two summer colonies in Alabama, Arkansas, Indiana, Kentucky, Maryland, New York, 

Pennsylvania, and West Virginia (Appendix 20). Most 

of the counts in hibernacula showed no detectable trend 

over the time period analyzed (33; 75%), 11 (25%) 

showed an upward trend, and none showed a declining 

trend. The two summer colonies also showed no detectable trend over the time period analyzed. The largest 

hibernating numbers were in two caves in West Virginia. 

Each of these caves housed an average of 1,000 individuals over the five years surveyed. 

Molossidae 

The BPD includes counts for the following members of the family Molossidae: Wagner’s mastiff bat 

(Eumops glaucinus); greater western mastiff bat (E. 

perotis); Underwood’s mastiff bat (E. underwoodi); velvety free-tailed bat; pocketed free-tailed bat 

(Nyctinomops femorosaccus); big free-tailed bat (N. 

macrotis); and Brazilian free-tailed bat. 

Eumops glaucinus (Wagner’s mastiff bat). In the 

U.S., Wagner’s mastiff bat is found only in southern 

ELLISON AND OTHERS 155 

Florida, where it roosts in hollow trees and in tile roofs 

(Belwood, 1992). It was designated a Category 1 

candidate for listing under the Endangered Species Act 

in 1994 (U.S. Fish and Wildlife Service, 1994), but was 

removed in 1996 (U.S. Fish and Wildlife Service, 1996). 

We compiled data from three counts at three different 

localities for this species, none of which were suitable 

for analysis of trends. A maternity colony of eight 

individuals was found roosting in a pine tree, which was 

subsequently felled (K. Marois, written commun., 1999, 

Florida Natural Areas Inventory; Belwood, 1981). The 

other two observations were of single individuals found 

roosting in buildings, but those individuals were 

subsequently collected (Belwood, 1981; Schwartz, 1952). 

Eumops perotis (greater western mastiff bat). We 

compiled 49 counts at 28 different localities for the 

greater western mastiff bat. Observations we gathered 

were from Arizona (13; 26.5%), California (25; 51.0%), 

and Texas (11; 22.5%). This species was found roosting 

in buildings (17; 34.7%), caves (11; 22.4%), and crevices 

(21; 42.9%). Eighty-eight percent of the observations 

(43) were obtained from publications (e.g., Howell, 1920; 

Dalquest, 1946; Vaughan, 1959; Cockrum, 1960; Cox, 

1965; Ohlendorf, 1972), and the Arizona Game and Fish 

Department provided 12% (6) of the observations. 

There were no series of counts available for analysis of trends in this species. However, in the early 1990’s 

Pierson and Rainey (1998b) visited historically known 

roosting areas and likely sites throughout California and 

confirmed that this species still occurs in many regions 

in California. They also added additional distributional 

records. Few colonies were observed directly, but all 

colonies counted were small (less than 100 individuals). Possible switching among alternate roost sites and 

the capability of individuals to forage over great distances make estimation of colony sizes difficult. These 

bats were confirmed to occur near a site in the Coast 

Range in San Benito County, California, where a colony 

was also known to exist in 1940 (Dalquest, 1946), but 

the crevice utilized at that time had since eroded away 

(Pierson and Rainey, 1998b). A roost on the Kern River 

in the Sierra Nevada occupied by about 100 bats in August 1948 was occupied by up to 75 bats in 1992. About 

seven new roosts with colonies of up to 60 bats were 

also located near Fresno and Jamestown. Greater mastiff bats were also detected in the central Sierra Nevada, 

where two roosts with evidence of breeding colonies were 

found. Despite recent concern for populations in southern California, Pierson and Rainey (1998b) reported that 

greater western mastiff bats still occur in western Riverside and San Diego counties. The locations of three small 

colonies (10–12 bats), one of which was active in the 

1940’s, were rediscovered in the 1990’s. A fourth site 

where Vaughan (1959) had described an active colony 
156 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

no longer had evidence of bats because it was in an area 

that had since become a housing subdivision. The greater 

western mastiff bat is a former Category 2 Candidate 

for listing under the Endangered Species Act (U.S. Fish 

and Wildlife Service, 1994). 

Eumops underwoodi (Underwood’s mastiff bat). We 

have no information in the database for Underwood’s 

mastiff bat, and to our knowledge no breeding colonies 

of this bat have been discovered in the U.S. This species 

is only known from capture records in extreme southern 

Arizona (Hoffmeister, 1986; Petryszyn and others, 1997). 

It is a former Category 2 Candidate for listing under the 

Endangered Species Act (U.S. Fish and Wildlife Service, 1994). 

Molossus molossus (velvety free-tailed bat). We 

compiled data from four observations for the velvety freetailed bat. In 1994, three colonies of this species were 

found roosting in buildings on three separate islands in 

the Florida Keys (Frank, 1997). This was the first documented occurrence of colonies of the velvety free-tailed 

bat in the U.S. Colony sizes for these three roosts in 

buildings ranged from 70 to 268 individuals based on 

emergence counts. There were no time series of colony 

sizes available for this species. 

Nyctinomops femorosaccus (pocketed free-tailed 

bat). We compiled five observations of colonies of the 

pocketed free-tailed bat from the literature. These colonies were located in California and Arizona (Gould, 

1959; Krutzsch, 1944a,b,c). This species was found 

roosting in crevices in southern California by Krutzsch 

(1944a,b,c), and in a building on the campus of the 

University of Arizona, Tucson by Gould (1959). Only 

two of the five observations reported a population size 

estimate for the colonies. A crevice roost in southern 

California contained 55 bats in March 1940 (Krutzsch, 

1944a). The building roost at the University of Arizona 

was estimated to have 60 individuals (Gould, 1959). The 

pocketed free-tailed bat has a limited range in the U.S. 

and its current population status is unknown. There were 

no time series available to analyze for trends in counts 

for this species. 

Nyctinomops macrotis (big free-tailed bat). We compiled 75 observations of the big free-tailed bat, 14 of 

which were observations of colonies. The remaining 61 

observations were gathered from mist-netting records. 

This species was found roosting in buildings, caves, and 

crevices in California, Kansas, New Mexico, and Texas. 

There were no time series available to analyze for this 

species. 

Big free-tailed bats are colonial and presumably 

migratory. They aggregate into maternity colonies of 

moderate numbers, but locations of breeding colonies 

in the U.S. are poorly known. One colony of an estimated 

150 females was discovered in a horizontal crevice in a 

cliff in Big Bend National Park in 1937 (Borell, 1939). 

A colony of unknown size was reported to still be present 

at the site in 1958, thought by Davis and Schmidly (1994) 

to be the only known nursery colony of this species in 

the U.S. However, this colony was not located again in 

attempts after 1958 (Schmidly, 1991). A nursery colony 

was also suspected to exist in Guadalupe Mountains 

National Park in Texas based on the presence of nine 

reproductive females netted over water in 1968 and 1970 

(LaVal, 1973), but subsequent surveys could not confirm 

the existence of a resident colony (Genoways and others, 

1979). Constantine (1961) described the existence of two 

small colonies in New Mexico. Recent research has 

revealed several breeding colonies numbering from about 

40 to several hundred each in crevices in steep cliff faces 

in the Jemez Mountains of New Mexico (Bogan and 

others, 1997). Based on records of occurrence of 

reproductive females, breeding colonies are also likely 

to occur in parts of Arizona, California, Nevada, and 

Utah. The big free-tailed bat is a former Category 2 

Candidate for listing under the Endangered Species Act 

(U.S. Fish and Wildlife Service, 1994). 

Tadarida brasiliensis (Brazilian free-tailed bat). We 

compiled 1,530 counts from 228 locations of colonies of 

the Brazilian free-tailed bat. These records were collected 

from 18 states. Most records were from Arizona (289; 

19%), New Mexico (454; 30%), Oklahoma (166; 11%), 

and Texas (343; 23%). This species was reported roosting in several different types of structures, including 

bridges (324; 21%), buildings (218; 14%), caves (792; 

52%), and mines (141; 9%). Brazilian free-tailed bats 

have also been documented roosting in crevices, dams, 

sedges, shrubs, trees, and tunnels. Most colonies counted 

were either maternity (598; 40%) or unspecified day 

roosts (850; 57%). Ninety-two percent of the data (1,398 

observations) were obtained from publications (e.g., 

Bailey, 1931; Allison, 1937; Constantine, 1957, 1958; 

Cockrum, 1969, 1970; Reidinger, 1972; Meacham, 1974; 

Altenbach and others, 1975; Reidinger and Cockrum, 

1978; Svoboda and Choate, 1987; Freeman and Wunder, 

1988; Thies and Gregory, 1994; Thies and others, 1996); 

2% (34) from the Arizona Game and Fish Department 

(S. Schwartz, written commun., 2000), 4% (70) from 

the Bats in American Bridges Project (B. Keeley, written commun., 1999, Bat Conservation International); 

and <1% (9) from the Colorado Division of Wildlife (K. 

Navo, written commun., 2000). Estimates were made 

using different methods ranging from exit counts, extrapolations from roosting densities, mark-recapture, and 

other indices of abundance (see review by McCracken, 

2003). 

We analyzed counts from eight summer colonies of 

this species for trends (Appendix 21). Of these eight 

colonies, the largest was Eagle Creek Cave, Arizona. 
Gross estimates of colony size ranged from about 75 

million individuals in the summer of 1964 to 30,000 in 

1969 (Cockrum, 1970; Reidinger, 1972). None of the 

colonies analyzed for trends showed significant declines 

using our rank analysis, despite such well-known examples of major losses in bats at Eagle Creek Cave in 

Arizona and Carlsbad Caverns, New Mexico (see 

McCracken, 2003). Two of the colonies analyzed showed 

an increasing trend: the Orient Mine, Colorado, and a 

bat house in Florida. The Orient Mine, Colorado, home 

to a bachelor colony of Brazilian free-tailed bats, increased in size from 9,000 individuals in 1967 to 107,240 

individuals in 1983. The University of Florida, 

Gainesville, built a large bat house in 1991 and then 

excluded Brazilian free-tailed bats (T. b. cynocephala) 

from buildings around campus. Bats began to use the 

bat house in 1993 and from September 1995 to September 2001, the colony increased from 8,000 to about 

100,000 individuals (K. Glover, written commun., 2002). 

Brazilian free-tailed bats have not been considered 

for special federal conservation status, although concern 

exists that a large population be maintained because of 

their agricultural and ecological importance (see review 

by McCracken, 2003). The International Convention on 

Migratory Species of Wild Animals (the Bonn Convention of 1979) lists this species in its Appendix 1. However, the U.S. and Mexico (which share a migratory 

population of the subspecies T. brasiliensis mexicana) 

are not parties to this agreement. The Programa para la 

Conservacion de los Murcielagos Migratorios de Mexico 

and Estados Unidos (PCMM) was established in 1994 

by Bat Conservation International and American and 

Mexican biologists and authorities in response to observations of declines in several large colonies in both the 

U.S. and Mexico (Walker, 1995). In some areas declines 

or loss of colonies were linked to food-chain poisoning 

by pesticides (Geluso and others, 1976; Clark, 2001), 

vandalism and disturbance (McCracken, 2003). The 

Brazilian free-tailed bat can be very adaptable in roosting habits, however, and large colonies have formed in 

buildings, bridges, and other artificial structures that 

have become commonplace on the landscape with advancing human settlement. 

Conclusions ConclusionsConclusions

This compilation and analysis of the available data 

on counts of bats revealed several important issues that 

need to be considered when estimating population sizes 

of bats and designing long-term monitoring programs. 

We believe our synthesis reinforces other reports in this 

volume by underscoring how imperative it is to improve 

methods for counting bats. The information we compiled 

ELLISON AND OTHERS 157 

reflects enormous levels of effort by biologists throughout 

the nation (sometimes with significant potential for loss 

of human life) that have been aimed at enumerating bats. 

Many of the counts extracted from the less recent 

literature were made incidental to other purposes. 

However, the intention behind some of the more recent 

efforts was to detect trends in population sizes of bats, 

so that management interventions can be made before 

dramatic declines occur. Nonetheless, most of the data 

that are available are not suitable for the parametric 

approaches, such as regression, that are more suitably 

used to detect trends. To be useful, such techniques 

require knowledge of variance in the size estimates (see 

below; Thompson and others, 1998). The nature of the 

available data on bat populations [much of which can 

be considered index data gathered through convenience 

sampling, see Anderson (2001)] required us to rely on 

nonparametric analyses that do not require exact 

estimates of colony size but simply direction of change 

between successive estimates (Thompson and others, 

1998). Our analyses also focused on colonies of bats at 

unique roost sites, not necessarily populations. Trends 

at specific roosts may or may not reflect population 

trends. In most cases it is unknown what the potential 

sampling frame is that such sites may be drawn from, 

and over what spatial scales inferences about trends at 

single roosts can be extended (see also Working Group 

Reports, this volume; Sauer, 2003). Furthermore, because 

interest in monitoring bat populations is primarily a 

recent phenomenon, very few sites have multiple time 

series of counts over long periods and thus many of the 

nonparametric tests for trends we carried out had as few 

as four years of counts. Colonies at many sites exhibited 

wide differences in counts within a time series, with high 

CVs across years (the great majority exceeding 50% and 

many over 100%; see Appendices). The resulting lack 

of statistical power to detect trends in population size is 

disconcerting, particularly in light of the known cases 

of biologically significant losses in bat populations (see 

other reports in this volume). Nonparametric methods, 

for example, might not detect exponential declines that 

also include frequent random variation. This may be the 

case with certain large colonies of bats (e.g., some 

Mexican free-tailed bat colonies in the southwestern 

U.S.; Appendix 21) where very large early counts seem 

to have dropped precipitously but then exhibited more 

random-like variation thereafter. 

Elsewhere in this volume, working group reports 

and case studies by others make numerous 

recommendations for improving estimates of sizes of bat 

colonies. In addition to improving counts by attempting 

to follow their specific recommendations for sampling, 

estimation, and enumeration, our examination of the 

available data pointed out a general need for basic 
158 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

improvements in several related areas. Almost none of 

the counts included estimates of sampling-based 

variation (such as standard errors or other estimates of 

variance for counts within years using formal methods 

such as capture-recapture procedures) or replicate 

counting. Less than 0.06% of all counts had an associated 

standard error of the estimate, and less than half 

documented even a simple range of colony sizes. Processbased variation in counts (true fluctuations in numbers 

present such as changes due to environmental factors, 

switching among roosts, variations in activity patterns, 

or changes in vital rates) is typically not estimated. 

Development and employment of standards for survey methods and techniques are needed for monitoring 

sizes of bat populations. Methods of estimating population sizes employed by the sources of the data we compiled varied dramatically, often depending on type of 

colony. For instance, most summer or maternity colonies were “exit counts” whereas most surveys conducted 

at hibernacula were “counts” or “censuses” within a roost 

[but without strong documentation that these met criteria for true censuses; see Tuttle (2003) for descriptions 

of techniques]. Many variations of these generalized 

methods appear in the literature. 

In our review, we found several examples that illustrate the importance of decisions regarding timing of 

surveys for monitoring. The California leaf-nosed bat at 

the Fortuna Mine was one example of the fluctuation in 

counts that can occur within a single year (Fig. 12). 

Without an understanding of variance in counts, single 

surveys conducted at such a site could lead to widely 

divergent conclusions depending on conditions on the 

date selected for sampling. Data collected on the southern long-nosed bat illustrated another example of the 

importance of survey timing. Reports of disappearances 

of this species appeared to be the result of not “looking 

in the right places at the right times” (Cockrum and 

Petryszyn, 1991). Many species of bats differ considerably in fidelity to roosts, and some switch roosts frequently depending on the time of year (Lewis 1995). 

Low fidelity to roosts can also contribute to the high 

variability in counts over time evident in some of the 

data we have compiled. Alternative approaches, such as 

developing means to estimate density over meaningful 

areas of suitable habitat, may be more useful for monitoring populations of bats that consist of colonies that 

frequently move among roosts. 

Other issues that came to light in our examination 

of available data about bat populations include length of 

available time series, incomplete documentation of 

efforts, and lack of adequate data for many species of 

bats. Future monitoring programs must aim to be longterm. Most available data on colonies of bats do not yet 

provide enough data in a time series to attempt to derive 

information on population trends. The majority of reports 

(Fig. 6) were one-time visits and many of the colonies 

we analyzed for trends had counts for only four years of 

surveys. It is unlikely that definitive conclusions can be 

made regarding population trends with small numbers 

of data points, especially in colonies where large 

fluctuations may occur in numbers among years. Many 

of the reports we reviewed lacked careful and consistent 

documentation of methods of counting, dates of counting, 

locations, kinds of colonies, and other critical details of 

surveys. Incomplete documentation in the literature 

sometimes hampered our ability to make accurate 

assessments of the available data. We recommend that 

authors should be more precise in documenting roost or 

colony functions (“summer colony” or “day roost” is 

much less useful than “maternity colony” or “bachelor 

roost”). We also recommend providing more accurate 

dates in methods sections of publications (e.g., “23 

August 1972” is much more informative than “late 

summer”), and including more detail on methods used 

to estimate the size of a colony of bats (e.g., “we counted 

49 bats emerging” is much more useful than “a colony 

of about 40–60 bats was present”). More detailed 

descriptions of roost locations would also be helpful (by 

perhaps designating a management authority as a 

repository for precise details of sensitive locations). 

Consistent application of site names, or identifying 

alternate names for the same sites is also important in 

documentation of surveys for long-term monitoring. The 

ability to determine trends is compromised in cases where 

this is not available. Editors of publications and reports 

of importance for monitoring populations of bats should 

allow authors to be more detailed in their descriptions 

of survey methods and thus allow future replication and 

interpretation. 

There are notable exceptions where survey efforts 

for bats in the U.S. follow standard protocols and are 

well documented. These include some of the regular 

surveys of endangered bats in caves [e.g., efforts directed 

at gray bats and Indiana bats; but see details in Tuttle 

(2003); Clawson (2002)]. One of the most extensive 

databases is the Pennsylvania Game Commission’s 

Winter Bat Hibernacula Survey (J. Hart, written 

commun., 2000). This project, in effect since 1985, 

specifically searches for seven species in about 200 

different caves, mines, and sinkholes in Pennsylvania 

every winter or every other winter. The Pennsylvania 

project uses consistent methods and conducts surveys 

for bats during the same time of year, and probably has 

a greater likelihood of detecting trends. However, 

sampling error for these assumed “censuses” is usually 

not provided. Specific suggestions for improving 
methods of counting bats will differ by the species and 

specific location (see Working Group reports in this 

volume). 

Most of the available data with time series of counts 

>4 years are restricted to a few species of bats, particularly those that are accessible in winter hibernacula. 

There were only eight out of about 60 species of bats in 

the U.S. and territories for which 10 or more time series 

of > 4 counts in hibernacula were available, and only 

two species for which more than 10 such time series 

were available for counts during the summer season 

(Tables 1 and 2). Although two endangered species top 

the lists of these efforts, much less information is available for other endangered species of bats, and the efforts 

aimed at monitoring those species of bats that are not 

accessible in caves or mines in winter are very inadequate. There are also special problems even among species that can be found in hibernacula. For example, 

counts ranged from 1–111 (with CV’s up to 270%) for 

the western small-footed myotis and the eastern smallfooted myotis, species that are scattered in small numbers in hibernacula where other species may gather in 

large aggregations (Appendices 11 and 13). The dispersed pattern and low numbers make such species susceptible to errors in sampling. Levels of effort need to 

be increased for monitoring these and other species that 

roost in very small numbers or are more dispersed across 

the landscape (see also Working Group reports and case 

studies in this volume).

 Despite the limitations of existing information revealed by this synthesis, the resulting database (http:// 

www.fort.usgs.gov/products/data/bpd/bpd.asp) is a potentially useful resource. The BPD may provide a basic 

framework for planning future surveys, particularly at 

local or regional levels or for selected species, and is a 

consolidated source of historical information and bibliographic records. Our compilation and analysis of the 

data should encourage greater focus on improving methods and documentation for future efforts. We also hope 

that the BPD can be used for additional purposes, such 

as analyses designed to test hypotheses about the 

macroecology, life history, and biogeographical patterns 

of colonial bats. 

This compilation and synthesis of existing data 

revealed just how little is known about recent trends in 

populations of bats of the U.S. and territories. The quality 

of data we compiled precludes the ability to make any 

blanket statements about the status of U.S. bat 

populations in general. Although we documented 

locations of colonies where significant declines had 

occurred for particular species, there often were 

significant upward trends for that species in other 

locations. Fundamentally, sampling and estimation 

ELLISON AND OTHERS 159 

designs and data collection methods need to be improved, 

and more species need to be monitored for longer time 

periods at greater numbers of well-chosen locations in 

order to be able to determine significant declines or 

upward trends on large scales. The paper by Sauer (2003) 

and the Working Group reports in this volume discuss 

the need for rigor in designing surveys for monitoring, 

including issues regarding sampling frames. The 

inability to determine population trends in many species 

and colonies of bats based on available data should 

certainly not be used as justification to avoid active 

management for conservation. Precipitous changes and 

unfavorable conditions will be apparent at local scales, 

and will continue to require swift attention. However, if 

the goals of monitoring programs are to detect more 

subtle changes in populations on large scales before the 

catastrophic losses of the past are repeated, or to 

demonstrate incremental improvements in response to 

management actions, major improvements to estimating 

and monitoring population sizes of bats are needed. 

Acknowledgments Acknowledgments Acknowledgments

We thank the following people for their invaluable 

help gathering bat population information from 

publications and incorporating it into the Bat Population 

Database: J. Crosby, P. Cryan, S. Jojola-Elverum, L. 

Stone, and K. Castle. Numerous individuals and 

organizations contributed their hard-earned data to this 

project. We wish to thank the following people who 

provided information to us: C. Baldino (National Park 

Service, California); T. Campos (Oregon Natural 

Heritage Program, Oregon); P. Cryan (USGS, New 

Mexico); M. Curtin (National Park Service, South 

Dakota); K. Duttenhefner (North Dakota Natural 

Heritage Program, North Dakota); K. Glover (University 

of Florida, Florida); J. Hart (Pennsylvania); R. Hellmich 

(Indiana Natural Heritage Data Center, Indiana); A. 

Hicks (New York Fish and Wildlife Department, New 

York); D. Ingram (National Park Service, Maryland); J. 

Kanter (New Hampshire Fish and Game Department, 

New Hampshire); B. Keeley (Bat Conservation 

International, Texas); A.J. Kuenzi (Montana Tech 

University, Montana, provided data from Nevada); H. 

LeGrand (North Carolina Natural Heritage Program, 

North Carolina); B. Luce (Wyoming Game and Fish 

Department, Wyoming); M. Ludlow (Florida Caverns 

State Park, Florida); T. Manasco (Alabama Natural 

Heritage Program, Alabama); K. Marois (Florida Natural 

Areas Inventory, Florida); M. Miller (Montana Natural 

Heritage Program, Montana); K. Morris (Maine Natural 

Heritage Program, Maine); K. Navo (Colorado Division 
160 I NFORMATION AND TECHNOLOGY REPORT–2003--0003 

of Wildlife, Colorado); B. Phillips (USFS, Black Hills 

National Forest, South Dakota); D. Reynolds (Northeast 

Ecological Services); S. Schwartz (Arizona Game and 

Fish Department, Arizona); C. Senger (Washington); J. 

Sternburg (Missouri Department of Conservation, 

Missouri); T. Wethington (Kentucky Department of Fish 

and Wildlife Resources, Kentucky); and J. Whitaker 

(Indiana State University, Indiana). We would also like 

to thank P. Cryan and J. Crosby for checking the entire 

Bat Population Database for errors made during data 

compilation and entry. A special thanks also goes to B. 

Cade and P. Mielke, Jr. for their nonparametric statistical 

advice. R. Clawson, R. Currie, A. Rodriguez-Duran, R. 

Sherwin, and G. Wiles provided useful comments on a 

previous draft of this paper. 

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White, D.H., and Seginak, J.T., 1987, Cave gate design 

for use in protecting endangered bats: Wildlife Society Bulletin, vol. 15, p. 445–449. 

Wiles, G.J., 1987, The status of fruit bats on Guam: Pacific Science, vol. 41, p. 148–157. 

Wiles, G.J., 1995, Natural history, biology, and habitat 

protection for the Mariana fruit bat, in Division of 

Aquatic and Wildlife Resources annual report (Fiscal Year 1995): Guam Department of Agriculture, 

Mangilao, p. 91–97. 

Wiles, G.J., and Worthington, D.J., 2002, A population 

assessment of Pacific sheat-tailed bats (Emballonura 

semicaudata) on Aguiguan, Mariana Islands, unpublished report prepared for the U.S. Fish and Wildlife 

Service Pacific Islands Office, Ecological Services, 

Honolulu, Hawaii. 

Wiles, G.J., Lemke, T.O., and Payne, N.H., 1989, Population estimates of fruit bats (Pteropus mariannus) in 

the Mariana Islands: Conservation Biology, vol. 3, 

p. 66–76. 

Wiles, G.J., Aguon, C.F., Davis, G.W., 1995, The status 

and distribution of endangered animals and plants in 

northern Guam: Micronesia, vol. 28, p. 31–49. 

Willig, M.R., and Bauman, A., 1984, Notes on bats from 

the Luquillo Mountains of Puerto Rico: CEER-T194:1-12, Center for Energy and Environmental Research, University of Puerto Rico. 

Wilson, D.E., and Engbring, J., 1992, The flying foxes 

Pteropus samoensis and Pteropus tonganus, status in 

Fiji and Samoa, in Wilson, D.E., and Graham, G.L., 

eds., Pacific flying foxes: Proceedings of an International Conservation Conference, U.S. Fish and Wildlife Biological Report 90, p. 74–101. 

Worthington, D.W., 1992, Methods and results of a census of bats in Jewel Cave on December 16, 1992, 

unpublished report to Jewel Cave National Monument: U.S. Fish and Wildlife Service, National Ecology Research Center, Fort Collins, Colorado, 3 p. 

Worthington, D.W., and Ross, H.N., 1990, Abundance 

and distribution of bats in the Pryor Mountains of 

south-central Montana, unpublished report from the 

Montana Natural Heritage Program: Bureau of Land 

Management, Billings Resource Area, 20 p. 

Worthington, D.W., and Taisacan, E.M., 1996, Fruit bat 

research, in Annual report Fiscal Year 1995, Federal 

Aid in Wildlife Restoration Program, Commonwealth 

of the Northern Mariana Islands: Division of Fish 

and Wildlife, Report W-1-R-1-11, p. 6–16. 

Worthington, D.W., Marshall, A.P., Wiles, G.J., and 

Kessler, C.C., 2001, Abundance and management of 

Mariana fruit bats and feral ungulates on Anatahan, 

Mariana Islands: Pacific Conservation Biology, vol. 7, 

p. 134–142. 

Zar, J.H., 1984, Biostatistical analysis (2nd ed.), 

Englewood Cliffs, New Jersey, Prentice Hall, 718 p. 
Appendices 1 Appendices 1 –21.Results of analyses for trends in counts of bats at colony sites. For each table in these 

appendices, colonies are ordered alphabetically by state or territory and then by site name. S, an approximation of 

Kendall’s tau, is reported for colonies with <10 distinct years of counts and a t is reported for trends with >10 years 

of counts (Kendall and Gibbons, 1990; Thompson and others, 1998). For the “Trend” column, a “ND” indicates no 

trend detected, a “+” indicates an upward trend was detected, and a “-” indicates a downward trend was detected. 

SD is the standard deviation of the counts and CV is the coefficient of variation expressed as a percentage. 

171 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

172 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 1. Appendix 1Appendix 1 Results of trend analyses at colony sites for the Mariana flying fox (Pteropus mariannus) in the Pacific 

Trust Territories. CNMI is the Commonwealth of the Northern Mariana Islands. 

Mean, standard 

deviation, and 

Island Territory 

Type of 

colony N Date:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Aguiguan CNMI Day roost 4 1983–1984:<10 

1987:40–50 

1990:0 

1995:100–125 

S = +2 

P > 0.05 

ND Mean = 42 

SD = 50.7 

CV = 120.7% 

Glass and Taisacan (1988); 

Wiles and others (1989); 

Utzurrum and others (this 

volume); Stinson and others 

(1992); Wiles (1995) 

Rota, entire 

island 

CNMI Day roost 5 1986:2,050 

1987:2,450 

1988:1,427 

1989:657 

1990:773 

S = -6 

P > 0.05 

ND Mean = 1,471 

SD = 781.9 

CV = 53.1% 

Wheeler (1980); Wiles and 

others (1989); Lemke 

(1992); Stinson and others 

(1992) 

Rota (Site 1) CNMI Day roost 5 1986:1,365 

1987:1,199 

1988:640 

1989:398 

1990:590 

S = -8 

P < 0.05 -

Mean = 838.4 

SD = 419.0 

CV = 50.0% 

Stinson and others (1992) 

Rota (Site 2) CNMI Day roost 5 1986:350 

1987:836 

1988:460 

1989:163 

1990:25 

S = -6 

P > 0.05 

ND Mean = 367 

SD = 311.2 

CV = 84.8% 

Stinson and others (1992) 

Rota (Site 3) CNMI Day roost 5 1986:100 

1987:150 

1988:53 

1989:0 

1990:22 

S = -6 

P > 0.05 

ND Mean = 65 

SD = 60.6 

CV = 93.2% 

Stinson and others (1992) 

Rota (Site 4) CNMI Day roost 5 1986:10 

1987:25 

1988:229 

1989:35 

1990:45 

S = +6 

P > 0.05 

ND Mean = 69 

SD = 90.5 

CV = 131.6% 

Stinson and others (1992) 

Saipan CNMI Day roost 4 1983–1984:<50 

1987:100–200 

1990:<40 

1997–1999:100– 

200 

S = +1 

P > 0.05 

ND Mean = 98 

SD = 60.8 

CV = 62.0% 

Glass and Taisacan (1988); 

Wiles and others (1989); 

Stinson and others (1992); 

Utzurrum and others (this 

volume); D. Worthington 

unpubl. data 

Tinian CNMI Day roost 4 1983–1984:<25 

1987:<50 

1990:<25 

1995:<25 

S = -1 

P > 0.05 

ND Mean = 31 

SD = 12.5 

CV = 40.3% 

Glass and Taisacan (1988); 

Wiles and others (1989, 

1990); Stinson and others 

(1992); Krueger and 

O’Daniel (1999); Utzurrum 

and others (this volume) 

Guam Guam Day roost 12 1972:<1,000 

1974–1977:<50 

1978:<50 

1981:650–750 

1982:850–1,000 

1983:600–775 

1984:475–550 

1983–1984:500 

1987:550 

1990:450 

1995:325 

1997–1999:225 

tau = -0.351 

P > 0.05 

ND Mean = 498 

SD = 304.8 

CV = 61.2% 

Wiles (1987); Wiles and 

others (1989); Utzurrum and 

others (this volume) 
 

 

 

 

 

 

ELLISON AND OTHERS 173 

Appendix 2. Appendix 2Appendix 2 Results of trend analyses for the Tonga flying fox (Pteropus tonganus). All colonies are day roosts in trees 

on Tutuila Island, American Samoa. The estimates for the entire island from 1987 to 2000 are presented first, then each 

of 15 different roost sites around the island are presented alphabetically. 

Site name N Year:Count 

Mann-Kendall Test 

results Trend 

Mean, standard deviation, and 

coefficient of variation (%) Source 

Entire island, all known 14 1987:12,750 tau = -0.187 ND Mean = 5,794 Utzurrum and others 

roost trees 1988:13,000 

1989:9,300 

P > 0.05 SD = 3,479.8 

CV = 60.0% 

(2003) 

1990:4,300 

1991:4,400 

1992:1,700 

1993:3,330 

1994:4,150 

1995:4,300 

1996:4,770 

1997:3,264 

1998:3,541 

1999:5,941 

2000:6,366 

Amalau Valley 9 1987:colony present 

1988:0 

1989:0 

1990:0 

1991:0 

1992:10 

1993:400 

1994:0 

1995:400 

1996:200 

S = +17 

P < 0.05 

+ Mean = 112 

SD = 175.6 

CV = 156.8% 

Brooke and others (2000) 

Asili 11 1986:17 

1987:0 

1988:0 

1989:0 

1990:110 

1991:0 

1992:20 

1993:0 

1994:0 

1995:0 

1996:0 

tau = -0.234 

P > 0.05 

ND Mean = 13 

SD = 32.9 

CV = 253.1% 

Wilson and Engbring 

(1992); Brooke and others 

(2000) 

Fagatele Bay 11 1986:5,000 

1987:4,000 

1988:3,000 

1989:300 

1990:130 

1991:750 

1992:280 

1993:0 

1994:10 

1995:1,230 

1996:1,730 

tau = -0.382 

P > 0.05 

ND Mean = 1,494 

SD = 1,752.5 

CV = 117.3% 

Pierson and others (1996); 

Brooke and others (2000) 

Leelee Point 10 1987:450 

1988:500 

1989:0 

1990:110 

1991:50 

1992:30 

1993:0 

1994:0 

1995:0 

1996:0 

S = -27 

P < 0.05 - Mean = 114 

SD = 193.8 

CV = 170.0% 

Brooke and others (2000) 

Nu’uomanu Rock 10 1987:0 

1988:0 

1989:0 

1990:0 

1991:25 

1992:30 

1993:375 

1994:1,025 

S = +33 

P < 0.05 

+ Mean = 334 

SD = 454.0 

CV= 135.9% 

Brooke and others (2000) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

174 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 2. Appendix 2 Continued. 

Site name N Year:Count 

Mann-Kendall Test 

results Trend 

Mean, standard deviation, and 

coefficient of variation (%) Source 

1995:1,000 

1996:880 

Oa 10 1987:0 

1988:500 

1989:0 

1990:840 

1991:100 

1992:50 

1993:0 

1994:340 

1995:270 

1996:300 

S = +4 

P > 0.05 

ND Mean = 240 

SD = 273.0 

CV= 113.8% 

Brooke and others (2000) 

Ogetu Ridge 10 1987:300 

1988:700 

1989:0 

1990:840 

1991:50 

1992:30 

1993:0 

1994:0 

1995:0 

1996:0 

S = -23 

P < 0.05 - Mean = 192 

SD = 319.8 

CV = 166.6% 

Brooke and others (2000) 

Olavalu Crater 10 1987:0 

1988:1,000 

1989:0 

1990:860 

1991:395 

1992:150 

1993:875 

1994:1,220 

1995:0 

1996:0 

S = -1 

P > 0.05 

ND Mean = 450 

SD = 488.4 

CV = 108.5% 

Pierson and others (1996); 

Brooke and others (2000) 

Olomoana Mountain 10 1987:4,000 

1988:3,000 

1989:3,000 

1990:200 

1991:185 

1992:30 

1993:300 

1994:120 

1995:140 

1996:320 

S = -19 

P > 0.05 

ND Mean = 1,130 

SD = 1,547.2 

CV = 136.9% 

Brooke and others (2000) 

Polauta Ridge, West 9 1987:1,000 

1988:1,000 

1989:colony present 

1990:0 

1991:0 

1992:30 

1993:15 

1994:130 

1995:250 

1996:100 

S = 0 

P > 0.05 

ND Mean = 280 

SD = 415.6 

CV = 148.4% 

Brooke and others (2000) 

Puaneva Point 10 1987:0 

1988:0 

1989:0 

1990:350 

1991:210 

1992:120 

1993:500 

1994:325 

1995:300 

1996:475 

S = +23 

P < 0.05 

+ Mean = 228 

SD = 192.2 

CV = 84.3% 

Brooke and others (2000) 

Siliaga Point 10 1987:0 

1988:0 

S=+20 

P <0.05 

+ Mean = 468 

SD = 597.1 

Brooke and others (2000) 
 

 

 

 

 

 

 

 

ELLISON AND OTHERS 175 

Appendix 2. Appendix 2 Concluded. 

Site name N Year:Count 

Mann-Kendall Test 

results Trend 

Mean, standard deviation, and 

coefficient of variation (%) Source 

1989:2,000 

1990:275 

1991:245 

1992:100 

1993:370 

1994:560 

1995:275 

1996:850 

Siufaga 10 1987:600 

1988:500 

1989:2,000 

1990:190 

1991:0 

1992:85 

1993:0 

1994:0 

1995:0 

1996:0 

S = -29 

P < 0.05 - Mean = 338 

SD = 624.9 

CV = 184.9% 

Brooke and others (2000) 

Taputapu 8 1987:300 

1988:colony present 

1989:colony present 

1990:15 

1991:25 

1992:20 

1993:10 

1994:0 

1995:0 

1996:0 

S = -21 

P < 0.05 - Mean = 46 

SD = 103.0 

CV = 223.9% 

Brooke and others (2000) 

Tolotolooleoti Point 10 1987:0 

1988:0 

1989:0 

1990:200 

1991:1,175 

1992:200 

1993:975 

1994:0 

1995:600 

1996:250 

S = +16 

P > 0.05 

ND Mean = 340 

SD = 431.8 

CV = 127.0% 

Brooke and others (2000) 
 

 

 

 

 

 

 

 

176 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 3. Appendix 3Appendix 3 Results of trend analyses for the southern long-nosed bat (Leptonycteris curasoae). All colonies analyzed are located in Arizona and are ordered alphabetically by site name. 

Mean, standard 

Mann- deviation, and 

Site name 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

Blue Bird Mine Maternity 7 1970:250 

1980:50 

1987:50 

1989:3,000 

1990:1,500 

1991:650 

1992:300 

S = +4 

P > 0.05 

ND Mean = 829 

SD = 1,082.0 

CV = 130.5% 

Cockrum and Petryszyn (1991); 

S. Schwartz (written commun., 

2000, Arizona Game and Fish 

Department) 

Box Canyon Crevice Maternity 4 1960:250 

1966:211 

1985:0 

1986:50 

S = -4 

P > 0.05 

ND Mean = 128 

SD = 121.4 

CV = 94.8% 

Cockrum (1969); Sidner and Davis 

(1988); Cockrum and Petryszyn 

(1991); S. Schwartz (written 

commun., 2000, Arizona Game 

and Fish Department) 

Buckalew Cave Maternity 4 1954:1,000 

1955:1,500 

1956:4 

1958:20 

S = -2 

P > 0.05 

ND Mean = 631 

SD = 743.4 

CV = 117.8% 

Cockrum and Petryszyn (1991); 

S. Schwartz (written commun., 

2000, Arizona Game and Fish 

Department) 

Cave Transient 4 1976:200 

1985:500 

1988:300 

1989:14,000 

S = +4 

P > 0.05 

ND Mean = 3,750 

SD = 6,834.5 

CV = 182.2% 

Cockrum and Petryszyn (1991) 

Colossal Cave Maternity 11 1954:2,000 

1956:1,000 

1958:102 

1959:35 

1960:1,000 

1964:300 

1968:200 

1969:0 

1970:0 

1972:0 

1985:0 

tau = -0.782 

P < 0.05 - Mean = 422 

SD = 647.1 

CV = 153.3% 

Beatty (1955), Reidinger (1972); 

Sidner and Davis (1988); Cockrum 

and Petryszyn (1991); S. Schwartz 

(written commun., 2000, Arizona 

Game and Fish Department) 

Copper Mountain Mine Maternity 10 1989:11,634 

1990:15,700 

1991:14,480 

1992:10,800 

1993:12,774 

1995:11,000 

1996:11,000 

1997:14,500 

1998:19,000 

1999:15,000 

S = 8 

P > 0.05 

ND Mean = 13,621 

SD = 2,660.0 

CV = 19.5% 

Cockrum and Petryszyn (1991); 

Dalton and Dalton (1994); Fleming 

and others (2003) 

Mine tunnels Summer 5 1955:150 

1958:200 

1959:9 

1968:4 

1986:13 

S = -4 

P > 0.05 

ND Mean = 75 

SD = 92.8 

CV = 123.7% 

S. Schwartz (written commun., 

2000, Arizona Game and Fish 

Department) 
 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 177 

Appendix 4. Appendix 4Appendix 4 Results of trend analyses for the California leaf-nosed bat (Macrotus californicus). All colonies are 

located in Arizona and are ordered alphabetically by site name. 

Mean, standard deviation, 

Type of Mann-Kendall and coefficient of 

Site name colony N Year:Count Test rsults Trend variation (%) Source 

Blue Bird Mine Summer 6 1970:150 

1975:150 

1989:200 

1990:52 

1991:650 

1992:350 

S = +6 

P > 0.05 

ND Mean = 259 

SD = 215.0 

CV = 83.0% 

S. Schwartz (written 

commun., 2000, Arizona 

Game and Fish Department) 

Boomerang Mine Maternity 4 1957:2,000 

1958:250 

1970:2,000 

1983:100 

S = -3 

P > 0.05 

ND Mean = 1088 

SD = 1055.4 

CV = 97.0% 

S. Schwartz (written 

commun., 2000, Arizona 

Game and Fish Department) 

Fortuna Mine Winter 5 1941:1,100 

1958:250 

1959:100 

1960:275 

1988:62 

S = -6 

P > 0.05 

ND Mean = 357 

SD = 425.2 

CV = 119.0% 

Bradshaw (1961), S. 

Schwartz (written commun., 

2000, Arizona Game and 

Fish Department) 

Great Central Mine #8 Winter 6 1972:489 

1977:2 

1992:153 

1993:5 

1995:300 

1996:400 

S = +3 

P > 0.05 

ND Mean = 225 

SD = 204.6 

CV = 90.9% 

S. Schwartz (written 

commun., 2000, Arizona 

Game and Fish Department) 

War Eagle Mine Winter 4 1993:726 

1994:16 

1995:535 

1996:278 

S = -2 

P > 0.05 

ND Mean = 389 

SD = 308.9 

CV = 79.4% 

S. Schwartz (written 

commun., 2000, Arizona 

Game and Fish Department) 
 

 

 

 

 

 

 

 

 

 

 

 

 

178 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 5. Appendix 5Appendix 5 Results of trend analyses for the Rafinesque’s big-eared bat (Corynorhinus rafinesquii). 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of variation 

(%) Source 

Cabin IL Summer 6 1977:30 

1978:30 

1979:30 

1980:30 

1981:30 

1982:30 

S = 0 

P > 0.05 

ND Mean = 30 

SD = 0 

CV = 0% 

Hoffmeister (1989) 

Cave KY Hibernating 4 1993:14 

1995:21 

1997:17 

1998:49 

S = +4 

P > 0.05 

ND Mean = 25 

SD = 16.1 

CV = 64.4% 

Hurst (1997); Hurst and 

Lacki (1999) 

Clack Mountain 

Railroad Tunnel 

KY Hibernating 5 1982:15 

1984:8 

1987:13 

1991:8 

1992:7 

S = -7 

P > 0.05 

ND Mean = 10 

SD = 3.6 

CV = 36.0% 

Meade (1992); 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Donahue 

Rockshelter 

KY Hibernating 11 1982:61 

1984:134 

1986:118 

1987:34 

1988:95 

1989:86 

1990:77 

1991:49 

1992:53 

1995:70 

1999:94 

tau = -0.2 

P > 0.05 

ND Mean = 79 

SD = 30.1 

CV = 38.1% 

Meade (1992); 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

War Fork Cave KY Hibernating 4 1990:2 

1996:55 

1998:11 

1999:57 

S = +4 

P > 0.05 

ND Mean = 31 

SD =28.8 

CV = 92.9% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 179 

Appendix 6. Appendix 6Appendix 6 Results of trend analyses for the Townsend’s big-eared bat (Corynorhinus townsendii). 

Site name State 

Type of 

colony N Year:Count 

MannKendall Test 

results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

Agua Caliente Caves AZ Summer 5 1988:80 

1989:6 

1991:40 

1992:1 

1993:4 

S = -6 

P > 0.05 

ND M ean = 26 

SD = 34.0 

CV = 130.8% 

S. Schwartz (written commun., 

2000, Arizona Game and Fish 

Department) 

Colossal Cave AZ Summer 5 1953:20 

1954:39 

1955:40 

1957:11 

1970:0 

S = -4 

P > 0.05 

ND Mean = 22 

SD = 17.5 

CV = 79.5% 

Reidinger (1972) 

Mines AZ Summer 6 1992:125 

1993:294 

1994:247 

1995:86 

1996:46 

1997:61 

S = -9 

P < 0.05 - Mean = 143 

SD = 103.3 

CV = 72.2% 

S. Schwartz (written commun., 

2000, Arizona Game and Fish 

Department) 

Eureka Mine #1 CA Hibernating 4 1992:16 

1993:54 

1994:57 

1998:27 

S = 0 

P > 0.05 

ND M ean = 37 

SD =17.9 

CV = 48.4% 

C. Baldino (written commun., 

1998, National Park Service) 

Peacock Mine West CO Summer 4 1991:4 

1992:1 

1993:5 

1994:1 

S = -1 

P > 0.05 

ND Mean = 3 

SD = 2.1 

CV = 70.0% 

K. Navo (written commun., 

Colorado Division of Wildlife) 

Middle Butte Cave ID Hibernating 5 1984:15 

1987:16 

1988:21 

1989:38 

1992:91 

S = +10 

P < 0.05 

+ Mean = 36 

SD = 32.0 

CV = 88.9% 

Doering (1996), Genter 

(1986), Wackenhut (1990) 

Fort Stanton Cave NM Hibernating 9 1977:400 

1978:680 

1979:350 

1980:500 

1981:500 

1982:700 

1985:500 

1986:600 

1987:700 

S = +16 

P > 0.05 

ND Mean = 548 

SD = 129.6 

CV = 23.6% 

Safford (1989) 

Torgac Cave NM Hibernating 7 1966:100 

1987:141 

1988:46 

1989:68 

1990:147 

1994:87 

1995:148 

S = +7 

P > 0.05 

ND Mean = 105 

SD = 41.0 

CV = 39.0% 

Jagnow (1998) 

Cave OR Summer 4 1974:3 

1984:0 

1989:75 

1995:0 

S = -1 

P > 0.05 

ND Mean = 20 

SD = 37.0 

CV = 185.0% 

T. Campos (written commun., 

1999, Oregon Natural 

Heritage Program) 

Cinnebar Mine OR Hibernating 5 1983:21 

1985:10 

1986:19 

1987:8 

1988:13 

S = -4 

P > 0.05 

ND M ean = 14 

SD = 5.6 

CV = 40.0% 

T. Campos (written commun., 

1999, Oregon Natural 

Heritage Program) 

Mine OR Hibernating 4 1983:21 

1984:3 

1989:36 

1994:10 

S = 0 

P > 0.05 

ND Mean = 18 

SD = 14.4 

CV = 80.0% 

T. Campos (written commun., 

1999, Oregon Natural 

Heritage Program) 

Jewel Cave SD Hibernating 14 1959:3,750 

1967:2,000 

1969:1,000 

1986:728 

1989:614 

1990:831 

1992:1,187 

1993:791 

1994:895 

1995:721 

1996:730 

1997:593 

1998:901 

2000:853 

tau = -0.319 

P < 0.05 - Mean = 1,114 

SD = 835.3 

CV = 75.0% 

Jones and Genoways (1967) 

Turner and Jones (1968) 

Turner and Davis (1970) 

Martin and Hawks (1972) 

Choate and Anderson (1997) 

M. Curtin (written commun., 

2000, National Park Service, 

Jewel Cave National 

Monument) 
 

 

 

 

 

 

 

 

 

 

 

180 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 6. Appendix 6 Concluded. 

Site name State 

Type of 

colony N Year:Count 

MannKendall Test 

results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

R-A12 Mine SD Hibernating 4 1991:2 

1992:16 

1993:8 

1994:7 

S = -4 

P > 0.05 

ND Mean = 12 

SD = 4.6 

CV = 38.3% 

B. Phillips (written commun., 

1999, Black Hills National 

Forest Database) 

Mt. Emory Cave TX Maternity 5 1967:1 

1968:100 

1969:75 

1970:150 

1971:13 

S = +2 

P > 0.05 

ND Mean = 68 

SD = 61.9 

CV = 91.0% 

Easterla (1972, 1973) 

Ape Cave WA Hibernating 4 1971:1 

1974:0 

1975:2 

1983:4 

S = +4 

P > 0.05 

ND Mean = 2 

SD = 1.7 

CV = 85.0% 

C. Senger (written commun., 

1996) 

Bat Cave WA Hibernating 15 1966:218 

1967:56 

1969:77 

1970:41 

1971:34 

1972:30 

1973:56 

1974:61 

1975:73 

1976:67 

1977:82 

1978:70 

1979:72 

1983:78 

1985:4 

tau = +0.067 

P > 0.05 

ND Mean = 68 

SD = 46.8 

CV = 68.8% 

C. Senger (written commun., 

1996) 

Blanchard Cave WA Hibernating 7 1973:9 

1974:11 

1975:13 

1976:12 

1977:18 

1979:7 

1981:9 

S = 0 

P > 0.05 

ND Mean = 11 

SD = 3.6 

CV = 32.7% 

C. Senger (written commun., 

1996) 

Flow Cave WA Hibernating 5 1971:3 

1972:4 

1974:0 

1975:0 

1978:1 

S = -3 

P > 0.05 

ND Mean = 2 

SD = 1.8 

CV = 90.0% 

C. Senger (written commun., 

1996) 

Prince Albert Cave WA Hibernating 6 1971:7 

1973:2 

1974:0 

1976:6 

1978:3 

1983:2 

S = -4 

P > 0.05 

ND Mean = 3 

SD = 2.6 

CV = 86.7% 

C. Senger (written commun., 

1996) 

Spider Cave WA Hibernating 15 1965:268 

1966:118 

1967:39 

1968:19 

1969:35 

1970:23 

1971:10 

1972:14 

1974:23 

1975:14 

1976:31 

1977:19 

1978:7 

1979:29 

1983:27 

tau = -0.409 

P < 0.05 - Mean = 44 

SD = 67.3 

CV = 152.9% 

C. Senger (written commun., 

1996) 

Hellhole Cave WV Hibernating 4 1965:500 

1986:500 

1988:500 

1991:6,188 

S = +3 

P > 0.05 

ND Mean = 1,922 

SD = 2,844 

CV = 148.0% 

Stihler and Brack (1992) 
 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 181 

Appendix 7. Appendix 7Appendix 7 Results of trend analyses for the Ozark’s big-eared bat (Corynorhinus townsendii ingens). 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

Coefficient of 

variation (%) Source 

Blue Heaven Cave AR Maternity 8 1978:120 

1979:170 

1983:170 

1984:79 

1985:64 

1986:46 

1987:60 

1988:82 

S = -13 

P < 0.05 

ND Mean = 99 

SD = 48.9 

CV = 49.4% 

Harvey (1989); Harvey and 

others (1981) 

Devil’s Den Crevice 

Caves 

AR Hibernating 10 1975:60 

1978:35 

1979:0 

1980:2 

1983:60 

1984:23 

1985:4 

1986:45 

1987:60 

1988:5 

S = +2 

P > 0.05 

ND Mean = 29 

SD = 25.8 

CV = 89.0% 

Harvey (1989); Harvey and 

others (1981) 

Gourd Cave AR Hibernating 4 1985:14 

1986:0 

1987:0 

1988:0 

S = -3 

P > 0.05 

ND Mean = 4 

SD = 7.0 

CV = 175.0% 

Harvey (1989); Harvey and 

others (1981) 

Marble Falls Cave AR 

Hibernating 7 1978:257 

1979:420 

1980:156 

1983:420 

1984:177 

1986:145 

1987:200 

S = -6 

P > 0.05 

ND Mean = 254 

SD = 119.3 

CV = 47.0% 

Harvey (1989); Harvey and 

others (1981) 

Bachelor 5 1983:100 

1984:35 

1985:7 

1987:1 

1988:0 

S = -10 

P < 0.05 - Mean = 29 

SD = 42.4 

CV = 146.2% 

Harvey (1989); Harvey and 

others (1981) 

Reed Cave AR Bachelor 4 1985:35 

1986:0 

1987:0 

1988:0 

S = +3 

P > 0.05 

ND Mean = 9 

SD = 17.5 

CV = 194.4% 

Harvey (1989); Harvey and 

others (1981) 

AD-003 OK Hibernating 10 1981:75 

1986:242 

1987:268 

1988:235 

1989:485 

1990:343 

1991:182 

1992:316 

1993:323 

1994:230 

S = +7 

P > 0.05 

ND Mean = 270 

SD = 108.4 

CV = 40.1% 

Clark and others (1997a,b); 

Grigsby and Puckette (1982) 

AD-010 OK Hibernating 8 1986:12 

1987:68 

1989:83 

1990:118 

1991:0 

1992:2 

1993:0 

1994:1 

S = -9 

P > 0.05 

ND Mean = 36 

SD = 47.1 

CV = 130.8% 

Clark and others (1997a,b) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

182 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 7. Appendix 7 Concluded. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

Coefficient of 

variation (%) Source 

AD-010 OK Maternity 15 1981:15 

1982:97 

1983:152 

1984:165 

1985:153 

1986:262 

1987:220 

1988:226 

1989:239 

1990:274 

1991:220 

1992:231 

1993:190 

1994:275 

1995:314 

tau = +0.638 

P < 0.05 

+ Mean = 202 

SD = 76.8 

CV = 38.0% 

Clark and others (1997a,b) 

AD-013 OK Maternity 11 1984:81 

1985:66 

1986:103 

1987:109 

1988:110 

1989:148 

1990:137 

1991:65 

1992:50 

1993:44 

1994:50 

tau = -0.273 

P > 0.05 

ND Mean = 88 

SD = 36.1 

CV = 41.0% 

Clark and others (1997a,b) 

AD-017/018 OK Maternity 13 1983:63 

1984:49 

1985:64 

1986:76 

1987:125 

1988:75 

1989:175 

1990:132 

1991:107 

1992:119 

1993:105 

1994:71 

1995:96 

tau = +0.256 

P > 0.05 

ND Mean = 97 

SD = 35.2 

CV = 36.3% 

Clark and others (1997a,b) 

AD-125 OK 

Maternity 9 1987:260 

1988:169 

1989:276 

1990:309 

1991:262 

1992:127 

1993:42 

1994:157 

1995:75 

S = -16 

P > 0.05 

ND Mean = 186 

SD = 95.0 

CV = 51.1% 

Clark and others (1997a,b) 

Hibernating 4 1987:247 

1991:1 

1993:12 

1994:0 

S = -4 

P > 0.05 

ND Mean = 65 

SD = 121.4 

CV = 186.8% 

Clark and others (1997a,b) 

Cave MO Hibernating 5 1957:4 

1981:0 

1987:0 

1988:0 

1999:0 

S = -4 

P > 0.05 

ND Mean = 1 

SD = 1.8 

CV = 180.0% 

J. Sternburg (written 

commun., 1999, Missouri 

Natural Heritage Database) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 183 

Appendix 8. Appendix 8Appendix 8 Results of trend analyses for the Virginia big-eared bat (Corynorhinus townsendii virginianus). 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Cave KY Summer 5 1963:300 

1964:850 

1990:1,153 

1991:1,535 

1992:295 

S = +2 

P > 0.05 

ND Mean = 827 

SD = 540.6 

CV = 65.4% 

Rippy and Harvey (1965); 

Adam (1992); Lacki and 

others (1993, 1994) 

Donahue 

Rockshelter 

KY Hibernating 5 1984:1 

1986:2 

1988:2 

1989:1 

1990:1 

S = -2 

P > 0.05 

ND Mean = 1 

SD = 0.5 

CV = 50.0% 

Meade (1992); 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Murder Branch Cave KY Hibernating 4 1982:4 

1983:0 

1984:1 

1988:1 

S = -1 

P > 0.05 

ND Mean = 2 

SD =1.7 

CV = 85.0% 

Meade (1992); 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Stillhouse Cave KY 

Hibernating 9 1980:1,487 

1985:2,703 

1987:3,664 

1989:3,420 

1991:3,706 

1994:4,700 

1995:3,894 

1997:4,963 

1999:5,105 

S = +32 

P < 0.05 

+ Mean = 3,738 

SD = 1,149.2 

CV = 30.7% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Maternity 5 1981:306 

1984:800 

1989:745 

1990:810 

1991:500 

S = +2 

P > 0.05 

ND Mean = 632 

SD = 221.6 

CV = 35.1% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Black Rock Cliffs 

Cave 

NC Hibernating 5 1984:33 

1991:118 

1992:137 

1994:31 

2000:350 

S = +4 

P > 0.05 

ND Mean = 76 

SD = 59.6 

CV = 78.4% 

H. LeGrand (written 

commun., 1999, North 

Carolina Natural Heritage 

Program); R. Currie (written 

commun., 2003) 

Cranberry Iron Mine NC Hibernating 4 1992:10 

1003:8 

1995:6 

1997:2 

S = -6 

P < 0.05 - Mean = 6 

SD = 3.4 

CV = 56.7% 

H. LeGrand (written 

commun., 1999, North 

Carolina Natural Heritage 

Program) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

184 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 9. Appendix 9Appendix 9 Results of trend analyses for the big brown bat (Eptesicus fuscus). 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Bridge AZ Summer 5 1962:60 

1964:30 

1965:30 

1968:6 

1969:0 

S = -9 

P < 0.05 - Mean = 25 

SD = 23.8 

CV = 94.2% 

Reidinger (1972) 

Buckner’s Cave IN Hibernating 5 1982:2 

1985:9 

1987:0 

1989:0 

1991:0 

S = -5 

P > 0.05 

ND Mean = 2 

SD = 3.9 

CV = 195.0% 

Brack (1983); Brack and 

others (1984, 1991) 

Clifty Cave IN Hibernating 5 1982:10 

1987:17 

1989:9 

1991:15 

1993:1 

S = -4 

P > 0.05 

ND Mean = 10 

SD = 6.2 

CV = 62.0% 

Brack (1983); Brack and 

others (1984, 1991) 

Coon’s Cave IN Hibernating 7 1981:0 

1982:1 

1985:2 

1987:3 

1989:5 

1991:4 

1993:7 

S = +19 

P < 0.05 

+ Mean = 3 

SD = 2.4 

CV = 80.0% 

Brack (1983); Brack and 

others (1984, 1991) 

Endless Cave IN Hibernating 4 1982:17 

1987:11 

1991:9 

1993:9 

S = -5 

P > 0.05 

ND Mean = 11 

SD = 3.8 

CV = 34.5% 

Brack (1983); Brack and 

others (1984, 1991) 

Jug Hole Cave IN Hibernating 4 1987:0 

1989:13 

1991:16 

1993:10 

S = +2 

P > 0.05 

ND Mean = 10 

SD = 6.9 

CV = 69.0% 

Brack and others (1991) 

Parker’s Pit Cave IN Hibernating 4 1987:10 

1989:5 

1991:9 

1993:4 

S = -4 

P > 0.05 

ND Mean = 7 

SD = 2.9 

CV = 41.4% 

Brack (1983); Brack and 

others (1984, 1991) 

Ray’s Cave IN Hibernating 8 1981:60 

1982:95 

1983:85 

1985:59 

1987:74 

1989:53 

1991:88 

1993:118 

S = +4 

P > 0.05 

ND Mean = 79 

SD = 21.9 

CV = 27.7% 

Brack (1983); Brack and 

others (1984, 1991) 

Saltpeter Cave IN Hibernating 5 1982:8 

1987:7 

1989:0 

1991:12 

1993:7 

S = -1 

P > 0.05 

ND Mean = 7 

SD = 4.3 

CV = 61.4% 

Brack (1983); Brack and 

others (1984, 1991) 

Saltpeter Cave IN Hibernating 4 1982:46 

1987:33 

1991:14 

1993:16 

S = -4 

P > 0.05 

ND Mean = 27 

SD = 15.1 

CV = 55.9% 

Brack (1983); Brack and 

others (1984, 1991) 

Wyandotte Cave IN Hibernating 6 1981:11 

1985:2 

1987:12 

1989:32 

1991:11 

1993:38 

S = +8 

P > 0.05 

ND Mean = 18 

SD = 14.0 

CV = 77.8% 

Brack (1983); Brack and 

others (1984, 1991) 

Bowman Saltpeter 

Cave 

KY Hibernating 4 1990:2 

1991:5 

1996:2 

1998:7 

S = +4 

P > 0.05 

ND Mean = 4 

SD = 2.4 

CV = 60.0% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Clack Mountain 

Railroad Tunnel 

KY Hibernating 4 1982:1 

1987:13 

1991:9 

1992:13 

S = +3 

P > 0.05 

ND Mean = 9 

SD = 5.6 

CV = 62.2% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Goochland Cave KY Hibernating 4 1990:12 

1991:5 

S = 0 

P > 0.05 

ND Mean = 12 

SD = 5.8 

T. Wethington (written 

commun., 1999, Kentucky 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 185 

Appendix 9. Continued. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

1996:19 

1998:10 

Mine Branch Cave KY Hibernating 5 1983:3 

1987:3 

1988:5 

1991:7 

1996:6 

S = +3 

P > 0.05 

ND Mean = 5 

SD = 1.8 

CV = 36.0% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Murder Branch Cave KY Hibernating 7 1982:5 

1988:1 

1991:5 

1992:3 

1995:1 

1996:3 

1998:2 

S = -6 

P > 0.05 

ND Mean = 3 

SD = 1.7 

CV = 56.7% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Shaw Hill Bat Cave KY Hibernating 5 1988:1 

1989:1 

1990:9 

1991:2 

1996:1 

S = +1 

P > 0.05 

ND Mean = 3 

SD = 3.5 

CV = 116.7% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Waterfall Cave KY Hibernating 4 1990:1 

1991:3 

1996:5 

1998:1 

S = +1 

P > 0.05 

ND Mean = 2 

SD = 1.9 

CV = 95.0% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Well Cave KY Hibernating 4 1995:3 

1996:2 

1997:2 

1999:2 

S = -3 

P > 0.05 

ND Mean = 2 

SD = 0.5 

CV = 25.0% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Storm sewer MN Hibernating 20 1951:35 

1952:36 

1953:51 

1954:51 

1955:75 

1956:94 

1957:92 

1958:74 

1959:93 

1960:59 

1961:49 

1962:64 

1963:56 

1964:79 

1965:115 

1966:143 

1967:164 

1968:173 

1969:206 

1970:293 

tau = +0.649 

P < 0.05 

+ Mean = 100 

SD = 65.9 

CV = 65.9% 

Goehring (1954, 1958, 

1972) 

Aitkin Cave PA Hibernating 12 1986:8 

1987:28 

1988:6 

1989:9 

1990:32 

1991:46 

1992:47 

1993:27 

1994:22 

1995:36 

1996:4 

1997:9 

tau = +0.030 

P > 0.05 

ND Mean = 23 

SD = 15.6 

CV = 67.8% 

Hall and Brenner (1968); 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Barton Cave PA Hibernating 4 1986:2 

1989:4 

1993:6 

1996:5 

S = +4 

P > 0.05 

ND Mean = 4 

SD = 1.7 

CV = 42.5% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Canoe Creek Mine PA Hibernating 6 1987:20 

1989:34 

1991:32 

1993:22 

S = -3 

P > 0.05 

ND Mean = 24 

SD = 7.8 

CV = 32.5% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 
 

 

 

 

 

 

 

 

 

 

 

 

186 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 9. Appendix 9 Concluded. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

1997:25 

Copperhead Cave PA Hibernating 8 1985:0 

1986:0 

1987:0 

1988:9 

1989:0 

1990:10 

1991:0 

1992:0 

S = +3 

P > 0.05 

ND Mean = 2 

SD = 4.4 

CV = 220.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Eiswert Cave PA Hibernating 9 1987:0 

1988:0 

1989:0 

1990:1 

1991:0 

1992:0 

1994:0 

1995:1 

1996:5 

S = +14 

P > 0.05 

ND Mean = 1 

SD = 1.6 

CV = 160.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Petersburg Cave PA Hibernating 5 1990:31 

1991:69 

1992:36 

1993:37 

1995:19 

S = -2 

P > 0.05 

ND Mean = 38 

SD = 18.5 

CV = 48.7% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Ruth Cave PA Hibernating 10 1985:19 

1986:30 

1987:35 

1988:21 

1989:26 

1990:21 

1991:41 

1992:26 

1993:35 

1995:30 

S = +15 

P > 0.05 

ND Mean = 28 

SD = 7.2 

CV = 25.7% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Salisbury Mine PA Hibernating 11 1986:68 

1987:171 

1988:186 

1989:155 

1990:96 

1991:155 

1992:230 

1993:224 

1995:269 

1996:307 

1997:233 

tau = 0.600 

P < 0.05 

+ Mean = 190 

SD = 71.5 

CV = 37.6% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Seawra Cave PA Hibernating 5 1986:7 

1991:34 

1993:48 

1996:24 

1997:39 

S = +4 

P > 0.05 

ND Mean = 30 

SD = 15.7 

CV = 52.3% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Stover Cave PA Hibernating 6 1985:1 

1987:3 

1990:0 

1993:17 

1994:8 

1996:20 

S = +7 

P > 0.05 

ND Mean = 8 

SD = 9.1 

CV = 113.8% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

U.S. Steel Mine PA Hibernating 5 1987:3 

1989:0 

1993:0 

1995:0 

1997:2 

S = -1 

P > 0.05 

ND Mean = 1 

SD = 1.4 

CV =140.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Woodward Cave PA Hibernating 7 1985:0 

1988:0 

1990:14 

1991:9 

1992:15 

1994:8 

1996:20 

S = +12 

P < 0.05 

+ Mean = 9 

SD = 7.6 

CV = 84.4% 

Mohr (1932a); J. Hart 

(written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 
 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 187 

Appendix 10. Appendix 10 Results of trend analyses for the southeastern myotis (Myotis austropriparius). 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Sander’s Cave AL Summer 5 1970:4,000 S = -2 ND Mean = 5,940 Best and others 

1990:8,000 

1991:16,000 

1995:200 

P > 0.05 SD = 6,361.4 

CV = 107.1% 

(1992); T. Manasco 

(written commun., 

1999, Alabama 

1996:1,500 Natural Heritage 

Program) 

Old Indian Cave FL Summer 9 1954:1,500 

1955:800 

1969:3,000 

1975:25 

1981:2 

1987:1,284 

1988:2,171 

1989:10,437 

1990:6,002 

S = +12 

P > 0.05 

ND Mean = 2,813 

SD = 3,395.5 

CV = 120.7% 

Rice (1955a,b); 

Jennings and Layne 

(1957); Wenner 

(1984); M. Ludlow 

(written commun., 

1999, Florida Natural 

Areas Inventory) 

Robert’s Cave FL Maternity 4 1954:6,000 

1978:21,600 

1991:27,400 

1992:23,100 

S = +4 

P > 0.05 

ND Mean = 19,525 

SD = 9,345.7 

CV = 47.9% 

Rice (1955a); Gore 

and Hovis (1994) 

Sweet Gum Cave FL Maternity 5 1936:170,000 

1954:15,000 

1955:4,500 

1990:0 

1991:0 

S = -9 

P < 0.05 - Mean = 37,900 

SD = 74,099.9 

CV = 195.5% 

Rice (1955a); Gore 

and Hovis (1994) 

Donnehue’s Cave IN Hibernating 7 1954:9 

1955:19 

1956:28 

1959:1 

1970:8 

1971:1 

1973:1 

S = -10 

P > 0.05 

ND Mean = 10 

SD = 10.4 

CV = 104.0% 

Mumford and 

Whitaker (1975); 

Whitaker and 

Gammon (1988) 

ShawHill Bat Cave KY Hibernating 5 1988:460 

1989:21 

1990:189 

1991:1 

1996:312 

S = -2 

P > 0.05 

ND Mean = 197 

SD = 194.8 

CV = 98.9% 

T. Wethington (written 

commun., 1999, 

Kentucky Department 

of Fish and Wildlife 

Resources) 
 

 

 

 

 

 

 

188 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 11. Appendix 11 Results of trend analyses for the western small-footed myotis (Myotis ciliolabrum). 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Torgac Cave NM Hibernating 7 1966:10 

1987:30 

1988:7 

1989:0 

1990:26 

1994:111 

1995:108 

S = +7 

P > 0.05 

ND Mean = 42 

SD= 47.5 

CV = 113.1% 

Jagnow(1998) 

Jewel Cave SD Hibernating 5 1967:4 

1969:20 

1986:6 

1990:17 

1992:4 

S = -1 

P > 0.05 

ND Mean = 10 

SD = 7.7 

CV = 77.0% 

Turner and Jones (1968); Martin 

and Hawks (1972);Turner (1974); 

Worthington (1992); Choate and 

Anderson (1997); M. Curtin (written 

commun., 2000, National Park 

Service, Jewel Cave National 

Monument) 
 

 

 

 

 

 

 

 

ELLISON AND OTHERS 189 

Appendix 12. Appendix 12 Results of trend analyses for the gray bat (Myotis grisescens). HP = gross estimate of historical 

population size. 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

Bishop Cave AL Summer 5 1991:54 

1992:58 

1993:11 

1996:10 

1997:12 

S = -4 

P > 0.05 

ND Mean = 29 

SD = 24.7 

CV = 85.2% 

T. Manasco (written commun., 

1999, Alabama Natural Heritage 

Program) 

Blowing Spring 

Cave 

AL Bachelor 6 1993:10,948 

1994:9,000 

1995:0 

1996:9,800 

1997:7,450 

S = -7 

P > 0.05 

ND Mean = 7,150 

SD = 3,954.4 

CV = 55.3% 

T. Manasco (written commun., 

1999, Alabama Natural Heritage 

Program) 

Cave Spring Cave AL Maternity 19 1978:20,000 

1979:23,000 

1980:12,240 

1982:10,000 

1983:8,700 

1984:20,000 

1985:58,000 

1986:28,000 

1987:22,400 

1988:30,000 

1990:48,600 

1991:79,400 

1992:45,080 

1993:49,000 

1994:8,500 

1995:63,400 

1996:11,500 

1997:47,500 

tau = +0.399 

P < 0.05 

+ Mean = 30,854 

SD = 21,982.1 

CV = 71.2% 

Harvey and others (1981); Harvey 

(1989); T. Manasco (written 

commun., 1999, Alabama Natural 

Heritage Program) 

Collier Cave AL Maternity 12 1986:3,000 

1987:7,457 

1988:5,040 

1990:0 

1991:10,309 

1992:8 

1993:21 

1994:2 

1995:0 

1996:0 

1997:14 

1998:30 

tau = -0.294 

P > 0.05 

ND Mean = 2,157 

SD = 3,573.5 

CV = 165.7% 

Henry (1998); T. Manasco (written 

commun., 1999, Alabama Natural 

Heritage Program) 

Davis Bat Cave AL Maternity 9 1985:7,167 

1986:9,000 

1987:2,900 

1992:1,698 

1993:7,250 

1994:6,130 

1995:1,700 

1996:1,750 

1997:1,750 

S = -12 

P > 0.05 

ND Mean = 4,372 

SD = 2,975.1 

CV = 68.0% 

T. Manasco (written commun., 

1999, Alabama Natural Heritage 

Program) 

Hambrick Cave AL Maternity 14 1976:10,000 

1979:20,000 

1981:100,000 

1985:151,020 

1987:322,200 

1990:250,000 

1991:105,570 

1992:17,075 

1993:67,000 

1994:32,680 

1995:55,790 

1996:32,400 

1997:20,754 

1998:27,480 

tau = -0.165 

P > 0.05 

ND Mean = 86,569 

SD = 94,885.5 

CV = 109.6% 

Henry (1998), T. Manasco (written 

commun., 1999, Alabama Natural 

Heritage Program) 

Hollyberry Cave AL Summer 7 1986:20,000 

1987:38,340 

1991:7 

1992:5,580 

1994:3,700 

S = -13 

P < 0.05 - Mean = 9,768 

SD = 14,418.9 

CV = 147.6% 

T. Manasco (written commun., 

1999, Alabama Natural Heritage 

Program) 
 

 

 

 

 

 

 

 

 

 

 

190 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 12. Appendix 12. Continued. 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

1995:750 

1997:0 

Indian Cave AL Maternity 11 1976:6,500 

1979:4,568 

1985:5,430 

1987:3,070 

1991:4,076 

1992:4,838 

1993:5,578 

1994:4,072 

1995:13,590 

1996:12,500 

1997:1,415 

tau = -0.020 

P > 0.05 

ND Mean = 5,967 

SD = 3,755.3 

CV = 62.9% 

T. Manasco (written commun., 

1999, Alabama Natural Heritage 

Program) 

King’s School Cave AL Bachelor 7 1991:1,600 

1992:0 

1993:34 

1994:200 

1995:189 

1996:784 

1997:93 

S = +1 

P > 0.05 

ND Mean = 414 

SD = 585.8 

CV = 141.5% 

T. Manasco (written commun., 

1999, Alabama Natural Heritage 

Program) 

McKinney Cave AL Summer 4 1993:25 

1994:11 

1995:13 

1997:3 

S = -4 

P > 0.05 

ND Mean = 13 

SD = 9.1 

CV = 70.0% 

T. Manasco (written commun., 

1999, Alabama Natural Heritage 

Program) 

Old Blowing Cave AL Summer 4 1992:1,750 

1993:4,214 

1996:1,850 

1997:1,190 

S = -2 

P > 0.05 

ND Mean = 2,251 

SD = 1,340.5 

CV = 59.6% 

T. Manasco (written commun., 

1999, Alabama Natural Heritage 

Program) 

Sauta Cave AL Maternity 17 1976:126,000 

1979:285,000 

1980:268,500 

1981:256,080 

1982:360,000 

1983:274,000 

1984:360,000 

1985:485,400 

1989:350,000 

1990:324,600 

1991:173,288 

1992:105,370 

1993:174,500 

1994:116,600 

1995:126,500 

1996:220,000 

1997:187,500 

tau = -0.235 

P > 0.05 

ND Mean = 246,667 

SD = 106,917.8 

CV = 43.3% 

White and Seginak (1987); T. 

Manasco (written commun., 1999, 

Alabama Natural Heritage

 Program) 

Bennett Cave AR Transient 6 1979:2,500 

1983:2,500 

1984:0 

1985:8 

1986:170 

1987:0 

S = -7 

P > 0.05 

ND Mean = 863 

SD = 1,269.7 

CV = 147.1% 

Harvey and others (1981); Harvey 

(1989) 

Big Creek Cave AR Maternity 8 1980:18,000 

1981:18,000 

1983:18,000 

1984:5,500 

1985:0 

1986:15,460 

1987:2,250 

1988:1,680 

S = -17 

P < 0.05 - Mean = 9,895 

SD = 8,169.2 

CV = 82.6% 

Harvey and others (1981); Harvey 

(1989) 

Blagg Cave AR Maternity 8 1975:3,000 

1977:3,600 

1979:3,000 

1983:13,000 

1984:1,000 

1985:3,360 

1986:1,350 

1988:2,520 

S = -7 

P > 0.05 

ND Mean = 3,854 

SD = 3,809.9 

CV = 98.9% 

Saugey (1978); Harvey and others 

(1981); Harvey (1989) 
 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 191 

Appendix 12. Appendix 12. Continued. 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

Blanchard Springs 

Caverns 

AR Hibernating 18 1979:150 

1983:7,000 

1985:33 

1986:55 

1987:188 

1988:520 

1989:6,200 

1990:8,000 

1991:10,000 

1992:18,000 

1993:20,000 

1994:58,600 

1996:65,000 

1997:71,000 

1998:65,000 

1999:85,000 

2000:81,900 

2001:147,850 

tau = +0.869 

P < 0.05 

+ Mean = 35,805 

SD = 42,437.9 

CV = 118.5% 

Harvey and others (1981); Harvey 

(1989); M. Harvey (written 

commun., 2003) 

AR Bachelor 13 1978:18,000 

1983:18,000 

1984:10,000 

1985:1,000 

1986:8,000 

1987:7,000 

1988:7,000 

1996:4,250 

1997:20,400 

1998:3,060 

1999:6,500 

2000:20,600 

2001:17,000 

tau = -0.103 

P > 0.05 

ND Mean = 10,831 

SD = 6,982.2 

CV = 64.5% 

Harvey and others (1981); Harvey 

(1989); M. Harvey (written 

commun., 2003) 

Bonanza Cave AR Hibernating 7 1979:250,000 

1983:250,000 

1985:250,000 

1988:250,000 

1996:243,000 

2000:150,000 

2001:55,000 

S = -15 

P < 0.05 - Mean = 206,857 

SD = 76,425.2 

CV = 36.9% 

Henry (1998); M. Harvey (written 

commun., 2003) 

Bone Cave AR Maternity 10 1975:15,000 

1979:17,000 

1980:36,000 

1981:18,000 

1983:52,000 

1984:15,000 

1985:5,000 

1986:156,000 

1987:37,220 

1988:46,500 

S = +14 

P > 0.05 

ND Mean = 39,772 

SD = 43,657.1 

CV = 109.8% 

Sealander and Young (1955); 

Harvey and others (1981); Harvey 

(1989) 

Brewer Cave AR Transient 5 1979:2,200 

1983:2,200 

1984:0 

1985:670 

1986:80 

S = -5 

P > 0.05 

ND Mean = 1,030 

SD = 1,099.0 

CV = 106.7% 

Harvey and others (1981); Harvey 

(1989) 

Cave Mountain 

Cave 

AR Hibernating 13 1976:300 

1979:40 

1980:700 

1983:700 

1984:125 

1986:240 

1988:205 

1996:108,000 

1997:54,500 

1998:70,000 

1999:200,000 

2000:172,500 

2001:234,850 

S = +0.632 

P < 0.05 

+ Mean = 64,782 

SD = 86,549.9 

CV = 133.6% 

Harvey and others (1981); Harvey 

(1989); M. Harvey (written 

commun., 2003) 

Cave River Cave AR Maternity 9 1977:10,200 

1979:7,700 

S = -11 

P > 0.05 

ND Mean = 13,730 

SD = 9,407.6 

Harvey and others (1981); Harvey 

(1989) 
 

 

 

 

 

 

 

 

 

 

 

 

 

192 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 12. Appendix 12. Continued. 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

1981:27,000 

1983:27,000 

1984:12,000 

1985:21,000 

1986:13,440 

1987:4,030 

1988:1,200 

Cave Springs Cave AR Maternity 7 1979:6,000 

1983:10,600 

1984:3,800 

1985:6,000 

1986:10,390 

1987:5,350 

1988:22,000 

S = +4 

P > 0.05 

ND Mean = 9,163 

SD = 6,213.8 

CV = 67.8% 

Harvey and others (1981); Harvey 

(1989) 

Crane Cave AR Bachelor 7 1977:7,700 

1978:200 

1983:7,700 

1984:0 

1985:0 

1986:0 

1987:86 

S = -9 

P > 0.05 

ND Mean = 2,241 

SD = 3,730.0 

CV = 166.4% 

Harvey and others (1981); Harvey 

(1989) 

Crystal Cave AR Transient 9 1977:28,600 

1979:1,700 

1980:12,000 

1983:28,600 

1984:0 

1985:1,000 

1986:4,030 

1987:6,720 

1988:10,420 

S = -3 

P > 0.05 

ND Mean = 10,341 

SD = 11,131.8 

CV = 107.6% 

Dellinger and Black (1940); 

Sealander and Young (1955); 

Harvey and others (1981); Harvey 

(1989) 

Dodd Cave AR Transient 8 1975:1,500 

1977:24,000 

1980:2,500 

1983:24,000 

1984:2 

1985:1 

1986:1,010 

1987:40 

S = -11 

P > 0.05 

ND Mean = 6,632 

SD = 10,755.0 

CV = 162.2% 

Saugey (1978); Harvey and others 

(1981); Harvey (1989) 

Fallout Cave AR Bachelor 7 1979:6,000 

1980:9,300 

1983:12,000 

1984:8,400 

1986:10,920 

1987:4,030 

1988:0 

S = -7 

P > 0.05 

ND Mean = 7,236 

SD = 4,204.1 

CV = 58.1% 

Harvey and others (1981); Harvey 

(1989) 

Flea Cave AR Transient 5 1980:75 

1983:500 

1984:4 

1985:0 

1986:0 

S = -7 

P > 0.05 

ND Mean = 116 

SD = 217.1 

CV = 187.2% 

Harvey and others (1981); Harvey 

(1989) 

Hankins Cave AR Hibernating 9 1976:300 

1979:15 

1980:50 

1983:50 

1984:0 

1985:0 

1986:130 

1987:1,030 

1988:200 

S = +6 

P > 0.05 

ND Mean = 197 

SD = 328.4 

CV = 166.7% 

Saugey (1978); Harvey and others 

(1981); Harvey (1989) 

Horseshoe Cave AR Bachelor 8 1977:2,000 

1980:250 

1983:3,000 

1984:5,500 

1985:6,720 

1986:10,080 

1987:1,180 

1988:3,360 

S = +10 

P > 0.05 

ND Mean = 4,011 

SD = 3,252.2 

CV = 81.1% 

Harvey and others (1981); Harvey 

(1989) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 193 

Appendix 12. Appendix 12. Continued. 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

John Eddings Cave AR Bachelor 8 1978:1,200 

1979:1,200 

1983:10,000 

1984:8,400 

1985:3,360 

1986:5,040 

1987:1,050 

1988:1,350 

S = -3 

P > 0.05 

ND Mean = 3,950 

SD = 3,550.4 

CV = 89.9% 

Harvey and others (1981); Harvey 

(1989) 

Jones Cave AR Transient 6 1978:2,000 

1983:4,000 

1984:0 

1985:420 

1986:340 

1987:1,340 

S = -3 

P > 0.05 

ND Mean = 1,353 

SD = 1,489.9 

CV = 110.1% 

Harvey and others (1981); Harvey 

(1989) 

Logan Cave AR Maternity 8 1979:16,300 

1980:24,500 

1983:14,500 

1984:8,000 

1985:0 

1986:19,780 

1987:20,300 

1988:25,000 

S = +5 

P > 0.05 

ND Mean = 17,298 

SD = 8,983.3 

CV = 51.9% 

Harvey and others (1981); Harvey 

(1989) 

Old Joe Cave AR Maternity 11 1977:54,700 

1978:3,000 

1979:8,000 

1980:19,000 

1981:40,000 

1983:54,700 

1984:4,000 

1985:20,160 

1986:26,880 

1987:6,720 

1988:9,500 

tau = -0.054 

P > 0.05 

ND Mean = 22,424 

SD = 19,410.1 

CV = 86.6% 

Harvey and others (1981); Harvey 

(1989) 

Optimus Cave AR Transient 10 1977:7,000 

1979:2,500 

1980:2,500 

1981:2,500 

1983:7,000 

1984:2,000 

1985:0 

1986:2,690 

1987:0 

1988:0 

S = -22 

P < 0.05 - Mean = 2,619 

SD = 2,568.9 

CV = 98.1% 

Harvey and others (1981); Harvey 

(1989) 

Peter Cave AR Bachelor 8 1979:2,500 

1980:4,000 

1983:21,000 

1984:340 

1985:5,380 

1986:3,360 

1987:5,580 

1988:6,220 

S = +10 

P > 0.05 

ND Mean = 6,048 

SD = 6,334.1 

CV = 104.7% 

Harvey and others (1981); Harvey 

(1989) 

Rory Cave AR Transient 6 1979:2,500 

1983:9,000 

1984:7,600 

1985:10,080 

1986:3 

1987:210 

S = -3 

P > 0.05 

ND Mean = 4,899 

SD = 4,531.4 

CV = 92.5% 

Harvey and others (1981); Harvey 

(1989) 

Shirley Bat Cave AR Bachelor 9 1977:10,200 

1980:3,000 

1981:8,000 

1983:10,200 

1984:5,200 

1985:4,200 

1986:3,360 

1987:2,520 

1988:2,020 

S = -23 

P < 0.05 - Mean = 5,411 

SD = 3,239.6 

CV = 59.9% 

Harvey and others (1981); Harvey 

(1989) 

Summer Cave AR Maternity 6 1983:12,000 

1984:4,000 

S = -5 

P > 0.05 

ND Mean = 6,430 

SD = 3,717.5 

Harvey and others (1981); Harvey 

(1989) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

194 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 12. Appendix 12. Continued. 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

1985:5,040 

1986:9,740 

1987:2,100 

1988:5,700 

Wet Cave AR Bachelor 8 1980:9,000 

1981:0 

1983:9,000 

1984:7,600 

1985:2,520 

1986:37,800 

1987:7,560 

1988:5,880 

S = -3 

P > 0.05 

ND Mean = 9,920 

SD = 11,707.3 

CV = 118.0% 

Harvey and others (1981); Harvey 

(1989) 

Key Cave FL Maternity 12 1979:33,564 

1985:36,000 

1987:36,700 

1988:7,400 

1991:34,252 

1992:4,200 

1993:59,464 

1994:28,766 

1995:2,500 

1996:32,858 

1997:43,042 

1998:19,417 

tau = -0.121 

P > 0.10 

ND Mean = 28,180 

SD = 16,961.2 

CV = 60.2% 

Henry (1998) 

Cave Spring Cave IL Maternity 5 1958:10,000 

1959:10,000 

1960:10,000 

1961:10,000 

1963:10,000 

S = 0 

P > 0.05 

ND Mean = 10,000 

SD = 0 

CV = 0% 

Hall and Wilson (1966); Whitaker 

and Winter (1977) 

Storm sewer KS Maternity 4 1962:5,500 

1971:8,000 

1982:3,058 

1988:1,500 

S = -4 

P > 0.05 

ND Mean = 4,514 

SD = 2,847.7 

CV = 63.1% 

Hays and Bingman (1964); 

Ubelaker (1966); Elder and Gunier 

(1981); Hays and others (1983); 

Choate and Decher (1996) 

Big Sulphur 

Springs Cave 

KY Maternity 5 1979:1,900 

1989:2,100 

1990:117 

1997:292 

1999:1,450 

S = -2 

P > 0.05 

ND Mean = 1,172 

SD = 915.9 

CV = 78.1% 

Rabinowitz and Tuttle (1980); 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Boone’s Cave KY Maternity 9 1958:1,000 

1959:1,000 

1960:1,000 

1961:1,000 

1963:1,000 

1989:24,900 

1996:20,597 

1998:8,940 

S = +16 

P > 0.05 

ND Mean = 7,780 

SD = 9,330.3 

CV = 119.9% 

Hall and Wilson (1966); 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Bryant Edmunds 

Cave 

KY Maternity 5 1989:1,730 

1990:6 

1994:3,376 

1997:114 

1999:91 

S = -2 

P > 0.05 

ND Mean = 1,063 

SD = 1,479.6 

CV = 139.2% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Burgess Cave KY Summer 6 1979:3,600 

1989:900 

1990:19 

1994:333 

1997:4,546 

1999:526 

S = -1 

P > 0.05 

ND Mean = 1,654 

SD = 1,918.8 

CV = 116.0% 

Rabinowitz and Tuttle (1980); 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Carpenter Cave KY Maternity 5 1989:800 

1990:68 

1994:1,858 

1997:4,118 

1999:10,511 

S = +8 

P < 0.05 

+ Mean = 3,471 

SD = 4,221.9 

CV = 121.6% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Cool Springs Cave KY Maternity 5 1979:8,200 

1989:1,400 

1990:287 

1997:1,031 

1999:3,663 

S = -2 

P > 0.05 

ND Mean = 2,916 

SD = 3,211.0 

CV = 110.1% 

Rabinowitz and Tuttle (1980); 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 195 

Appendix 12. Appendix 12. Continued. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Glass Farm Cave KY Maternity 4 1989:331 

1990:172 

1997:199 

1999:1 

S = -4 

P > 0.05 

ND Mean = 176 

SD = 135.6 

CV = 77.0% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Ison’s Cave KY Maternity 7 1958:1,000 

1959:1,000 

1960:1,000 

1961:1,000 

1963:1,000 

1989:1,700 

1994:3 

S = -1 

P > 0.05 

ND Mean = 958 

SD = 495.2 

CV = 51.7% 

Hall and Wilson (1966); 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Jones’ Cave KY Maternity 11 1958:7,500 

1959:7,500 

1960:7,500 

1961:7,500 

1963:7,500 

1989:14,200 

1990:4,200 

1993:13,000 

1994:12,200 

1996:16,741 

1998:16,344 

tau = +0.502 

P < 0.05 

ND Mean = 10,380 

SD = 4,248.1 

CV = 40.9% 

Hall and Wilson (1966); 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Overstreet Cave KY Maternity 8 1979:20,100 

1981:400 

1989:8,300 

1990:2,000 

1993:7,900 

1994:10,000 

1996:5,775 

1998:20,124 

S = +6 

P > 0.05 

ND Mean = 9,325 

SD = 7,388.9 

CV = 79.2% 

Rabinowitz and Tuttle (1980); 

MacGregor and Westerman 

(1982); Lacki (1994); 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Payne Saltpeter 

Cave 

KY Maternity 5 1979:0 

1990:2,173 

1994:3,570 

1997:13,210 

1999:6,615 

S = +8 

P < 0.05 

+ Mean = 5,114 

SD = 5,123.1 

CV = 100.2% 

Rabinowitz and Tuttle (1980); 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Phil Goodrum Cave KY Maternity 5 1989:15,700 

1990:23,117 

1994:5,315 

1996:20,147 

1998:14,269 

S = -2 

P > 0.05 

ND Mean = 15,710 

SD = 6,794.9 

CV = 43.2% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Riders Mill Cave KY Maternity 5 1979:9,200 

1989:22,300 

1990:14,485 

1996:12,095 

1998:18,851 

S = +2 

P > 0.05 

ND Mean = 15,386 

SD = 5,237.3 

CV = 34.0% 

Rabinowitz and Tuttle (1980), 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Smoky Cave KY Maternity 4 1989:15,298 

1990:22,400 

1996:20,010 

1998:14,260 

S = -2 

P > 0.05 

ND Mean = 18,017 

SD = 3,836.5 

CV = 21.3% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Son of Finney Cave KY Maternity 4 1989:1,400 

1990:573 

1997:7,274 

1999:1,411 

S = +2 

P > 0.05 

ND Mean = 2,664 

SD = 3,098.0 

CV = 116.3% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Sulphur Creek 

Cave 

KY Maternity 5 1989:800 

1990:0 

1994:2,330 

1997:20 

1999:227 

S = 0 

P > 0.05 

ND Mean = 675 

SD = 979.8 

CV = 145.2% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Location 6021 

Cave 

MO Maternity 5 HP:26,500 

1989:6,125 

1991:8,225 

1994:13,600 

1997:8,200 

S = -2 

P > 0.05 

ND Mean = 12,530 

SD = 8,285.7 

CV = 66.1% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6084 

Cave 

MO Maternity 5 HP:3,000 

1978:2,200 

1983:1,500 

1990:3,650 

1994:1,375 

S = -4 

P > 0.05 

ND Mean = 2,345 

SD = 975.7 

CV = 41.6% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

196 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 12. Appendix 12. Continued. 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

Location 6023 

Cave 

MO Maternity 7 HP:2,000 

1979:5,000 

1987:2,300 

1988:4,000 

1989:9,350 

1991:11,900 

1998:13,875 

S = +13 

P < 0.05 

+ Mean = 6,918 

SD = 4,775.6 

CV = 69.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6024 

Cave 

MO Maternity 6 1979:25,000 

1988:385 

1992:0 

1994:2,040 

1996:10,000 

1997:20,000 

S = +3 

P > 0.05 

ND Mean = 9,571 

SD = 10,767.6 

CV = 112.5% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6086 

Cave 

MO Maternity 4 1978:3,700 

1988:2,350 

1989:2,875 

1994:3,425 

S = 0 

P > 0.05 

ND Mean = 3,088 

SD = 599.5 

CV = 19.4% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6087 

Cave 

MO Transient 6 1964:3,500 

1979:2,000 

1980:2,700 

1994:1,025 

1996:2,720 

1998:6,800 

S = +3 

P > 0.05 

ND Mean = 3,124 

SD = 1,983.2 

CV = 63.5% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6088 

Cave 

MO Maternity 7 1978:10,950 

1983:22,900 

1988:39,800 

1990:33,150 

1992:33,150 

1994:36,725 

1998:30,260 

S = +6 

P > 0.05 

ND Mean = 29,562 

SD = 9,773.7 

CV = 33.1% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6095 

Cave 

MO Maternity 4 1964:8,000 

1978:75 

1985:15,650 

1990:18,350 

S = +4 

P > 0.05 

ND Mean = 10,519 

SD = 8,227.5 

CV = 78.2% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6096 

Cave 

MO Maternity 10 1977:40,000 

1978:100,000 

1979:2,000 

1980:300 

1983:60,000 

1988:54,800 

1990:71,400 

1992:51,000 

1994:73,450 

1998:81,600 

S = +15 

P > 0.05 

ND Mean = 53,455 

SD = 32,292.4 

CV = 60.4% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6097 

Cave 

MO Transient 6 HP:23,000 

1979:0 

1983:0 

1990:22,950 

1992:30,600 

1994:21,425 

S = +2 

P > 0.05 

ND Mean = 16,329 

SD = 13,047.9 

CV = 79.9% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6098 

Cave 

MO Maternity 6 1978:7,300 

1985:4,000 

1988:10,200 

1990:11,500 

1994:11,900 

1998:9,575 

S = +7 

P > 0.05 

ND Mean = 9,079 

SD = 2,976.0 

CV = 32.8% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6102 

Cave 

MO Maternity 7 1964:2,000 

1976:375 

1977:6 

1979:0 

1989:1 

1994:0 

1998:0 

S = -16 

P < 0.05 - Mean = 340 

SD = 745.0 

CV = 219.1% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6103 

Cave 

MO Hibernating 8 1976:2,000 

1987:3 

1988:90 

1989:5 

1990:4 

S = -4 

P > 0.05 

ND Mean = 272 

SD = 699.1 

CV = 257.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 
 

 

 

 

 

 

ELLISON AND OTHERS 197 

Appendix 12. Appendix 12. Continued. 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

1992:47 

1993:16 

1998:7 

Location 6104 

Cave 

MO Maternity 5 1976:5,400 

1983:6,800 

1989:7,650 

1991:15,300 

1993:16,150 

S = +10 

P > 0.05 

ND Mean = 10,260 

SD = 5,062.0 

CV = 49.3% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6106 

Cave 

MO Maternity 5 1977:18,000 

1978:5,500 

1983:7,200 

1989:5,000 

1994:8,150 

S = -2 

P > 0.05 

ND Mean = 8,770 

SD = 5,313.8 

CV = 60.6% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6108 

Cave 

MO Maternity 4 1978:2,000 

1983:170 

1984:0 

1992:0 

S = -5 

P > 0.05 

ND Mean = 542 

SD = 975.0 

CV = 179.7% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6111 

Cave 

MO Maternity 6 1976:18,000 

1983:27,700 

1987:15,625 

1989:22,450 

1991:15,425 

1994:23,800 

S = -1 

P > 0.05 

ND Mean = 20,500 

SD = 4,945.8 

CV = 24.1% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6112 

Cave 

MO Maternity 4 1976:91,800 

1990:0 

1992:0 

1996:0 

S = -3 

P > 0.05 

ND Mean = 22,950 

SD = 45,900.0 

CV = 200.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6113 

Cave 

MO Maternity 5 1976:3,600 

1980:0 

1983:0 

1989:5,775 

1991:12,800 

S = +5 

P > 0.05 

ND Mean = 5,503 

SD = 4,209.6 

CV = 76.5% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6114 

Cave 

MO Maternity 4 1983:2,000 

1988:6,100 

1989:11,775 

1994:8,225 

S = +4 

P > 0.05 

ND Mean = 7,025 

SD = 4,086.9 

CV = 58.2% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database);, R. Clawson (written 

commun., 2003) 

Location 6117 

Cave 

MO Maternity 6 HP:14,000 

1983:16,950 

1987:14,600 

1989:20,650 

1991:19,500 

1994:15,475 

S = +5 

P > 0.05 

ND Mean = 16,862 

SD = 2,703.6 

CV = 16.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6032 

Cave 

MO Maternity 4 1968:2,000 

1978:25 

1992:12,750 

1994:2,200 

S = +2 

P > 0.05 

ND Mean = 4,244 

SD = 5,755.2 

CV = 135.6% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6056 

Cave 

MO Maternity 9 1964:5,000 

1977:27,000 

1979:0 

1980:0 

1983:5,400 

1985:9,500 

1987:9,900 

1990:12,250 

1994:12,250 

S = +16 

P > 0.05 

ND Mean = 9,033 

SD = 8,194.0 

CV = 90.7% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6079 

Cave 

MO Maternity 4 1983:4,700 

1989:6,300 

1991:8,225 

1994:5,350 

S = +2 

P > 0.05 

ND Mean = 6,144 

SD = 1,535.2 

CV = 25.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6031 

Cave 

MO Maternity 5 1964:5,000 

1977:27,000 

1994:0 

1997:9,000 

1998:125 

S = -2 

P > 0.05 

ND Mean = 8,225 

SD = 11,144.1 

CV = 135.5% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6034 

Cave 

MO Maternity 4 1964:4,000 

1988:30,600 

1990:36,700 

1992:42,850 

S = +6 

P < 0.05 

+ Mean = 28,538 

SD = 17,105.7 

CV = 59.9% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

198 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 12. Appendix 12. Continued. 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

Location 6081 

Cave 

MO Hibernating 4 1964:150,000 

1979:250,000 

1981:316,300 

1983:355,500 

S = +6 

P < 0.05 

+ Mean = 267,950 

SD = 89,883.5 

CV = 33.5% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6129 

Cave 

MO Maternity 4 1985:6,000 

1988:23,000 

1991:1,900 

1994:2,050 

S = -2 

P > 0.05 

ND Mean = 8,238 

SD = 10,023.1 

CV = 121.7% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6036 

Cave 

MO Maternity 4 1980:4,500 

1983:8,800 

1989:6,125 

1994:4,750 

S = 0 

P > 0.05 

ND Mean = 6,044 

SD = 1,971.5 

CV = 32.6% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6042 

Cave 

MO Transient 5 1978:5,500 

1979:9,000 

1987:1,100 

1991:1,500 

1994:3,400 

S = -2 

P > 0.05 

ND Mean = 4,100 

SD = 3,248.8 

CV = 79.2% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6040 

Cave 

MO Maternity 6 1964:2,500 

1978:7,300 

1985:4,000 

1990:4,250 

1994:1,825 

1998:45,900 

S = +3 

P > 0.05 

ND Mean = 10,962 

SD = 17,220.2 

CV = 157.1% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6119 

Cave 

MO Maternity 6 1980:1,400 

1983:0 

1984:0 

1985:0 

1986:0 

1990:4,250 

S = +1 

P > 0.05 

ND Mean = 942 

SD = 1,714.8 

CV = 182.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6128 

Cave 

MO Maternity 6 1981:7,500 

1985:8,100 

1988:9,450 

1990:7,750 

1994:3,400 

1998:2,750 

S = -7 

P > 0.05 

ND Mean = 6,492 

SD = 2,738.5 

CV = 42.2% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6045 

Cave 

MO Maternity 6 1964:5,000 

1978:12,800 

1983:33,300 

1989:19,200 

1991:16,450 

1994:27,200 

S = +7 

P > 0.05 

ND Mean = 18,992 

SD = 10,126.2 

CV = 53.3% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6122 

Cave 

MO Transient 4 1964:6,500 

1977:0 

1992:0 

1994:3,910 

S = -1 

P > 0.05 

ND Mean = 2,602 

SD = 3,185.7 

CV = 122.4% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6046 

Cave 

MO Maternity 4 1964:6,000 

1977:50,000 

1994:9,000 

1998:8,940 

S = 0 

P > 0.05 

ND Mean = 18,485 

SD = 21,056.6 

CV = 113.9% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6048 

Cave 

MO Maternity 7 1964:2,000 

1983:34,200 

1987:32,300 

1989:27,550 

1991:33,650 

1994:41,050 

1998:35,200 

S = +11 

P < 0.05 

+ Mean = 29,421 

SD = 12,734.8 

CV = 43.3% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6052 

Cave 

MO Maternity 8 1983:24,750 

1985:11,600 

1987:25,800 

1989:0 

1990:10,200 

1992:20,400 

1994:12,250 

1998:40,800 

S = +4 

P > 0.05 

ND Mean = 18,255 

SD = 12,481.2 

CV = 68.5% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6053 

Cave 

MO Maternity 5 HP:36,000 

1964:7,000 

1977:8,000 

1986:7,300 

S = 0 

P > 0.05 

ND Mean = 15,360 

SD = 12,498.9 

CV = 81.4% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 
 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 199 

Appendix 12. Appendix 12. Continued. 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

1989:18,500 

Location 6054 

Cave 

MO Maternity 4 1964:6,000 

1977:250 

1987:0 

1994:0 

S = -5 

P > 0.05 

ND Mean = 1,562 

SD = 2,960.7 

CV = 189.5% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6142 

Cave 

MO Hibernating 4 1983:300 

1985:11 

1989:1 

1993:1 

S = -5 

P > 0.05 

ND Mean = 78 

SD = 147.9 

CV = 189.6% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6153 

Cave 

MO Maternity 4 1985:100 

1994:3 

1996:32 

1997:1 

S = -4 

P > 0.05 

ND Mean = 34 

SD = 46.2 

CV = 135.9% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6027 

Cave 

MO Maternity 5 1978:7,000 

1983:13,000 

1987:6,600 

1989:6,850 

1991:4,800 

S = -6 

P > 0.05 

ND Mean = 7,650 

SD = 3,118.9 

CV = 40.8% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6057 

Cave 

MO Maternity 14 1964:3,000 

1976:9,000 

1978:11,500 

1979:11,000 

1980:11,500 

1981:24,000 

1983:24,400 

1985:30,450 

1987:26,050 

1991:46,300 

1993:17,030 

1995:37,950 

1997:36,400 

tau = +0.714 

P < 0.05 

+ Mean = 22,665 

SD = 12,664.5 

CV = 55.9% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6058 

Cave 

MO Hibernating 6 1950:175,000 

1976:54,000 

1981:89,500 

1983:112,200 

1985:89,500 

1989:87,300 

S = -4 

P > 0.05 

ND Mean = 101,250 

SD = 40,650.4 

CV = 40.1% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6029 

Cave 

MO Hibernating 6 1964:130,000 

1979:3,800 

1980:34,200 

1983:8,900 

1988:1,300 

1991:4,800 

S = -11 

P >0.05 

ND Mean = 30,500 

SD = 50,212.6 

CV = 164.6% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6067 

Cave 

MO Maternity 4 1964:50,000 

1976:40,000 

1988:7,480 

1989:400 

S = -6 

P < 0.05 - Mean = 24,470 

SD = 24,228.0 

CV = 99.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6030 

Cave 

MO Hibernating 6 1983:4,850 

1987:3,900 

1988:0 

1989:2,750 

1991:0 

1997:400 

S = -8 

P > 0.05 

ND Mean = 1,983 

SD = 2,137.9 

CV = 107.8% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6068 

Cave 

MO Maternity 7 1967:9,000 

1983:3,450 

1989:1,825 

1991:0 

1992:0 

1994:3,400 

1997:3,400 

S = -7 

P > 0.05 

ND Mean = 3,011 

SD = 3,052.8 

CV = 101.4% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6069 

Cave 

MO Hibernating 5 1976:5,000 

1983:1,000 

1987:7 

1989:750 

1993:725 

S = -6 

P > 0.05 

ND Mean = 1,496 

SD = 1,993.2 

CV = 133.2% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Location 6070 

Cave 

MO Transient 6 1978:2,000 

1983:22,200 

1988:22,850 

S = +13 

P < 0.05 

+ Mean = 26,386 

SD = 19,887.2 

CV = 75.4% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 
 

 

 

 

 

 

 

200 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 12. Appendix 12. Concluded. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

1989:30,150 

1991:51,775 

1994:51,175 

Marvel Cave MO Hibernating 10 1935:14,500 

1948:20,000 

1968:6,077 

1969:12,550 

1970:141 

1972:2,437 

1973:1,930 

1974:1,188 

1975:1,997 

1976:2,527 

S = -19 

P < 0.05 - Mean = 6,335 

SD = 6,870.8 

CV = 108.5% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Blythe Ferry Cave TN Summer 5 1992:65 

1995:50 

1996:46 

1997:110 

1998:38 

S = -4 

P > 0.05 

ND Mean = 62 

SD = 28.7 

CV = 46.3% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Gallatin Fossil 

Plant Cave 

TN Maternity 5 1988:5,000 

1994:8,670 

1996:14,644 

1997:4,096 

1998:6,890 

S = 0 

P > 0.05 

ND Mean = 7,860 

SD = 4,182.3 

CV = 53.2% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Nickajack Cave TN Maternity 9 1976:35,000 

1981:110,000 

1991:20,500 

1992:72,370 

1994:66,500 

1995:117,540 

1996:81,568 

1997:63,440 

1998:34,215 

S = -3 

P > 0.05 

ND Mean = 66,792 

SD = 33,387.9 

CV = 50.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Norris Dam Cave TN Summer 9 1976:4,000 

1981:140 

1989:50 

1991:266 

1992:162 

1994:330 

1995:388 

1997:342 

1998:54 

S = +1 

P > 0.05 

ND Mean = 637 

SD = 1,267.3 

CV = 199.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database), R. Clawson (written 

commun., 2003) 
 

 

 

 

 

 

 

ELLISON AND OTHERS 201 

Appendix 13. Appendix 13 Results of trend analyses for the eastern small-footed myotis (Myotis leibii). 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

Aitkin Cave PA Hibernating 12 1986:10 

1987:9 

1988:11 

1989:12 

1990:15 

1991:16 

1992:22 

1993:18 

1994:22 

1995:31 

1996:6 

1997:19 

tau = 0.485 

P < 0.05 

+ Mean = 16 

SD = 17.0 

CV = 106.2% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Canoe Creek Mine PA Hibernating 6 1987:12 

1989:21 

1991:37 

1993:17 

1995:14 

1997:9 

S = -5 

P > 0.05 

ND Mean = 18 

SD = 10.0 

CV = 55.6% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Eiswert Cave PA Hibernating 9 1987:29 

1988:8 

1989:16 

1990:12 

1991:10 

1992:10 

1994:14 

1995:15 

1996:20 

S = +5 

P > 0.05 

ND Mean = 15 

SD = 6.4 

CV = 42.7% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Petersburg Cave PA Hibernating 5 1990:17 

1991:46 

1992:20 

1993:46 

1995:18 

S = +1 

P < 0.05 

ND Mean = 29 

SD = 15.2 

CV = 52.4% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Ruth Cave PA Hibernating 10 1985:0 

1986:1 

1987:1 

1988:3 

1989:1 

1990:0 

1991:4 

1992:0 

1993:2 

1995:5 

S = +14 

P > 0.05 

ND Mean = 2 

SD = 1.8 

CV = 90.0% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Salisbury Mine PA Hibernating 11 1986:3 

1987:4 

1988:4 

1989:7 

1990:0 

1991:2 

1992:6 

1993:7 

tau = +0.366 

P > 0.05 

ND Mean = 4 

SD = 2.4 

CV = 60.0% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

202 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 13. Concluded. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1995:3 

1996:5 

1997:8 

Seawra Cave PA Hibernating 5 1986:0 

1991:1 

1993:0 

1996:1 

1997:3 

S = +6 

P > 0.05 

ND Mean = 1 

SD = 1.2 

CV = 120.0% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Sharer Cave PA Hibernating 11 1985:0 

1986:0 

1987:1 

1988:0 

1989:0 

1990:0 

1991:0 

1992:0 

1993:9 

1995:0 

1997:0 

tau = +0.031 

P > 0.05 

ND Mean = 1 

SD = 2.7 

CV = 270.0% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Stover Cave PA Hibernating 8 1932:6 

1933:12 

1985:1 

1987:0 

1990:0 

1993:3 

1994:19 

1997:12 

S = +4 

P > 0.05 

ND Mean = 7 

SD = 7.0 

CV = 100.0% 

Mohr (1933a); J. Hart (written 

commun., 2000, Pennsylvania 

Game Commission Winter Bat 

Hibernacula Survey) 

Woodward Cave PA Hibernating 7 1985:0 

1988:0 

1990:1 

1991:4 

1992:6 

1994:5 

1996:10 

S = +18 

P < 0.05 

+ Mean = 4 

SD = 3.7 

CV = 92.5% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 203 

Appendix 14. Appendix 14 Results of trend analyses for the little brown bat (Myotis lucifugus). 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Buckner’s Cave IN Hibernating 6 1982:32 

1985:21 

1987:29 

1989:16 

1991:16 

1993:23 

S = -6 

P > 0.05 

ND Mean = 23 

SD = 6.6 

CV = 28.7% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, 

Indiana Natural Heritage 

Program) 

Clifty Cave IN Hibernating 5 1982:298 

1987:295 

1989:233 

1991:334 

1993:176 

S = -4 

P > 0.05 

ND Mean = 267 

SD = 62.6 

CV = 23.4% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, 

Indiana Natural Heritage 

Program) 

Colony IN Maternity 5 1958:467 

1959:485 

1960:450 

1961:467 

1963:450 

S = -4 

P > 0.05 

ND Mean = 464 

SD = 14.6 

CV = 3.1% 

Humphrey and Cope (1963) 

Coon’s Cave IN Hibernating 7 1981:31 

1982:12 

1985:20 

1987:152 

1989:176 

1991:394 

1993:392 

S = +15 

P < 0.05 

+ Mean = 168 

SD = 166.6 

CV = 99.2% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, 

Indiana Natural Heritage 

Program) 

Copperhead Cave IN Hibernating 4 1986:82 

1988:111 

1989:133 

1991:314 

S = +6 

P < 0.05 

+ Mean = 160 

SD = 104.8 

CV = 65.5% 

Whitaker and Rissler (1992a,b); 

J.O. Whitaker, Jr. (written 

commun., 1998) 

Endless Cave IN Hibernating 4 1982:163 

1987:330 

1991:460 

1993:602 

S = +6 

P < 0.05 

+ Mean = 389 

SD = 187.0 

CV = 48.1% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, 

Indiana Natural Heritage 

Program) 

Grotto Cave IN Hibernating 7 1981:589 

1982:1,090 

1985:291 

1987:311 

1989:213 

1991:178 

1993:338 

S = -9 

P > 0.05 

ND Mean = 430 

SD = 319.7 

CV = 74.3% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, 

Indiana Natural Heritage 

Program) 

Jug Hole Cave IN Hibernating 4 1987:9 

1989:5 

1991:15 

1993:9 

S = +1 

P > 0.05 

ND Mean = 10 

SD = 4.1 

CV = 41.0% 

Brack and others (1991); R. 

Hellmich (written commun., 

1999, Indiana Natural Heritage 

Program) 

Parker’s Pit Cave IN Hibernating 4 1987:101 

1989:141 

1991:110 

1993:209 

S = +4 

P > 0.05 

ND Mean = 140 

SD = 48.9 

CV = 34.9% 

Brack and others (1991) 

Ray’s Cave IN Hibernating 8 1981:3,380 

1982:779 

S = -18 

P < 0.05 - Mean = 1,382 

SD = 1,061.0 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 
 

 

 

 

 

 

 

 

 

 

 

 

 

204 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 14. Continued. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

1983:1,834 

1985:1,044 

1987:2,395 

1989:671 

1991:600 

1993:351 

Saltpeter Cave IN Hibernating 5 1982:114 

1987:198 

1989:28 

1991:154 

1993:76 

S = -2 

P > 0.05 

ND Mean = 114 

SD = 66.1 

CV = 58.0% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, 

Indiana Natural Heritage 

Program) 

Saltpeter Cave IN Hibernating 4 1982:19 

1987:0 

1991:68 

1993:79 

S = +4 

P > 0.05 

ND Mean = 42 

SD = 38.0 

CV = 90.5% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, 

Indiana Natural Heritage 

Program) 

Wildcat Cave IN Hibernating 4 1982:332 

1987:520 

1991:310 

1993:314 

S = -2 

P > 0.05 

ND Mean = 369 

SD = 101.1 

CV = 27.4% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, 

Indiana Natural Heritage 

Program) 

Wyandotte Cave IN Hibernating 6 1981:6 

1985:21 

1987:272 

1989:8 

1991:15 

1993:12 

S = +1 

P > 0.05 

ND Mean = 56 

SD = 106.1 

CV = 189.5% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, 

Indiana Natural Heritage 

Program) 

Bat Cave KY Hibernating 4 1937:5,000 

1991:300 

1997:121 

1999:145 

S = -4 

P > 0.05 

ND Mean = 1,392 

SD = 2,407.0 

CV = 172.9% 

Welter and Sollberger (1939); 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Bowman Saltpeter KY Hibernating 4 1990:119 S = -3 ND Mean = 114 T. Wethington (written 

Cave 1991:119 P > 0.05 SD = 9.3 commun., 1999, Kentucky 

1996:100 

1998:118 

CV = 8.2% Department of Fish and 

Wildlife Resources) 

Dixon Cave KY Hibernating 4 1929:500 

1991:50 

1997:30 

1999:85 

S = -2 

P > 0.05 

ND Mean = 166 

SD = 223.6 

CV = 134.7% 

Bailey (1933); T. Wethington 

(written commun., 1999, 

Kentucky Department of Fish 

and Wildlife Resources) 

Donahue Rockshelter KY Hibernating 6 1984:2 

1986:1 

1987:1 

1988:1 

1989:1 

1991:1 

S = -1 

P > 0.05 

ND Mean = 1 

SD = 0.4 

CV = 40.0% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 
 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 205 

Appendix 14. Continued. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Murder Branch Cave KY Hibernating 8 1982:40 

1988:64 

1990:50 

1991:85 

1992:97 

1995:43 

1996:50 

1998:64 

S = +6 

P > 0.05 

ND Mean = 62 

SD = 20.3 

CV = 32.7% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Shaw Hill Bat Cave KY Hibernating 5 1988:91 

1989:64 

1990:102 

1991:81 

1996:20 

S = -4 

P > 0.05 

ND Mean = 72 

SD = 32.0 

CV = 44.4% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

War Fork Cave KY Hibernating 4 1990:17 

1996:30 

1998:25 

1999:38 

S = +4 

P > 0.05 

ND Mean = 28 

SD = 8.8 

CV = 31.4% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Waterfall Cave KY Hibernating 4 1990:61 

1991:101 

1996:100 

1998:92 

S = 0 

P > 0.05 

ND Mean = 88 

SD = 18.8 

CV = 21.4% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Building MA Maternity 4 1994:200 

1995:350 

1996:450 

1997:520 

S = +6 

P < 0.05 

+ Mean = 380 

SD = 138.8 

CV = 36.5% 

D. Reynolds (written commun., 

1999) 

Colony MA Hibernating 4 1934:350 

1935:350 

1936:350 

1937:350 

S = 0 

P > 0.05 

ND Mean = 350 

SD = 0 

CV = 0% 

Hall and others (1957) 

John Friend Cave MD Hibernating 4 1977:19 

1978:26 

1979:5 

1980:24 

S = 0 

P > 0.05 

ND Mean = 18 

SD = 9.5 

CV = 52.8% 

Gates and others (1984) 

Turpin Barn NH Maternity 4 1974:150 

1975:110 

1978:110 

1979:110 

S = -3 

P > 0.05 

ND Mean = 120 

SD = 20.0 

CV = 16.7% 

Anthony and Kunz (1977); 

Anthony and others (1981); 

Kunz and Anthony (1996) 

Aitkin Cave PA Hibernating 13 1932:406 

1986:306 

1987:574 

1988:538 

tau = +0.615 

P < 0.05 

+ Mean = 1,139 

SD = 788.7 

CV = 69.2% 

Mohr (1932b,1945); Hall and 

Brenner (1968); J. Hart (written 

commun., 2000, Pennsylvania 

Game Commission Winter Bat 

1989:849 

1990:980 

Hibernacula Survey) 

1991:1,109 

1992:1,768 

1993:1,443 

1994:1,510 

1995:3,173 

1996:494 

1997:1,653 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

206 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 14. Continued. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Barton Cave PA Hibernating 5 1986:28 

1989:84 

1993:115 

1996:157 

S = +6 

P < 0.05 

+ Mean = 96 

SD = 54.3 

CV = 56.6% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Canoe Creek Mine PA Hibernating 6 1987:3,256 

1989:6,155 

1991:10,875 

1993:13,502 

1995:12,839 

1997:13,180 

S = +11 

P < 0.05 

+ Mean = 9,968 

SD = 4,277.0 

CV = 42.9% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Copperhead Cave PA Hibernating 8 1985:1,585 

1986:802 

1987:647 

1988:654 

1989:1,007 

1990:1,084 

1991:1,244 

1992:1,395 

S = +10 

P > 0.05 

ND Mean = 1,052 

SD = 343.6 

CV = 32.7% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Eiswert Cave PA Hibernating 9 1987:96 

1988:59 

1989:112 

1990:104 

1991:160 

1992:174 

1994:147 

1995:182 

1996:187 

S = +28 

P < 0.05 

+ Mean = 136 

SD = 44.7 

CV = 32.9% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Haine’s Gap PA Hibernating 4 1985:87 

1986:80 

1990:59 

S = -6 

P < 0.05 - Mean = 70 SD = 16.7 

CV = 23.8% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

1993:52 Hibernacula Survey) 

Lemon Hole PA Hibernating 10 1985:909 

1986:1,038 

1987:937 

1988:1,160 

1989:889 

1991:1,101 

1992:1,111 

1993:1,298 

1995:1,558 

1997:1,472 

S = +29 

P < 0.05 

+ Mean = 1,147 

SD = 231.0 

CV = 20.1% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Petersburg Cave PA Hibernating 5 1990:0 

1991:2 

1992:0 

1993:1 

1995:1 

S = +2 

P > 0.05 

ND Mean = 1 

SD = 0.8 

CV = 80.0% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Ruth Cave PA Hibernating 10 1985:48 

1986:131 

1987:157 

1988:204 

S = +41 

P < 0.05 

+ Mean = 238 

SD = 120.6 

CV = 50.7% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 
 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 207 

Appendix 14. Continued. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

1989:197 

1990:256 

1991:248 

1992:308 

1993:365 

1995:467 

Salisbury Mine PA Hibernating 11 1986:206 

1987:431 

1988:426 

1989:518 

1990:487 

1991:659 

1992:735 

1993:1,096 

1995:1,758 

1996:973 

1997:950 

tau = 0.745 

P < 0.05 

+ Mean = 706 

SD = 432.8 

CV = 61.3% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Seawra Cave PA Hibernating 5 1986:102 

1991:747 

1993:1,262 

1996:1,903 

1997:1,544 

S = +8 

P < 0.05 

+ Mean = 1,112 

SD = 705.0 

CV = 63.4% 

Hall and Brenner (1968); J. 

Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Sharer Cave PA Hibernating 11 1985:234 

1986:184 

1987:215 

1988:457 

1989:767 

1990:729 

1991:645 

1992:756 

1993:196 

1995:863 

1997:477 

tau = 0.345 

P > 0.05 

ND Mean = 502 

SD = 262.4 

CV = 52.3% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Stover Cave PA Hibernating 6 1985:0 

1987:1 

1990:0 

1993:0 

1994:0 

1997:1 

S = +2 

P > 0.05 

ND Mean = 0.3 

SD = 0.5 

CV = 166.7% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

U.S. Steel Mine PA Hibernating 5 1987:1,024 

1989:2,008 

1993:2,234 

1995:5,074 

1997:5,963 

S = +10 

P < 0.05 

+ Mean = 3,261 

SD = 2,134.0 

CV = 65.4% 

J. Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Woodward Cave PA Hibernating 13 1931:100 

1938:238 

1939:57 

1940:39 

1941:12 

1948:10 

tau = +0.564 

P < 0.05 

+ Mean = 905 

SD = 833.9 

CV = 92.1% 

Mohr (1932b); J. Hart (written 

commun., 2000, Pennsylvania 

Game Commission Winter Bat 

Hibernacula Survey) 
 

 

 

 

 

 

 

 

 

208 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 14. Concluded. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

1985:1,232 

1988:1,264 

1990:1,630 

1991:1,764 

1992:1,454 

1994:2,164 

1996:1,799 

Woodward Cave PA Hibernating 4 1932:113 

1938:236 

1939:119 

1964:715 

S = +4 

P > 0.05 

ND Mean = 296 

SD = 285.2 

CV = 96.4% 

Mohr (1945); Hall and Brenner 

(1968) 

Jewel Cave SD Hibernating 4 1969:200 

1986:432 

1990:39 

1992:162 

S = -2 

P > 0.05 

ND Mean = 208 

SD = 164.2 

CV = 78.9% 

Martin and Hawks (1972); 

Worthington (1992); Choate 

and Anderson (1997) 

Plymouth Union Cave VT Hibernating 4 1934:14 

1935:40 

1939:31 

1955:100 

S = +4 

P > 0.05 

ND Mean = 46 

SD = 37.4 

CV = 81.3% 

Griffin (1940); Gifford and 

Griffin (1960) 

Hellhole Cave WV Hibernating 4 1962:20,000 

1986:20,000 

1988:20,000 

1991:49,707 

S = +3 

P > 0.05 

ND Mean = 27,427 

SD = 14,853.5 

CV = 54.2% 

Stihler and Brack (1992) 
 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 209 

Appendix 15. Appendix 15 Results of trend analyses for the northern myotis (Myotis septentrionalis). 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Chrome mine #1 MD Hibernating 6 1941:30 

1942:12 

1943:22 

1944:14 

1945:20 

1946:16 

S = -3 

P > 0.05 

ND Mean = 19 

SD = 6.5 

CV = 34.2% 

Bures (1948) 

Aitkin Cave PA Hibernating 13 1964:10 

1986:1 

1987:10 

1988:8 

1989:6 

1990:29 

1991:23 

1992:7 

1993:1 

1994:8 

1995:13 

1996:0 

1997:36 

tau = +0.051 

P > 0.05 

ND Mean = 12 

SD = 11.1 

CV = 92.5% 

Hall and Brenner (1968); J. 

Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Canoe Creek Mine PA Hibernating 6 1987:1 

1989:20 

1991:8 

1993:6 

1995:32 

1997:13 

S = +5 

P > 0.05 

ND Mean = 13 

SD = 11.2 

CV = 86.2% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Eiswert Cave No. 2 PA Hibernating 9 1987:2 

1988:3 

1989:7 

1990:12 

1991:6 

1992:4 

1994:18 

1995:11 

1996:5 

S = +12 

P > 0.05 

ND Mean = 8 

SD = 5.2 

CV = 65.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Lemon Hole PA Hibernating 10 1985:1 

1986:2 

1987:0 

1988:2 

1989:4 

1991:3 

1992:9 

1993:6 

1995:6 

1997:6 

S = +29 

P < 0.05 

+ Mean = 4 

SD = 2.8 

CV = 70.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Ruth Cave PA Hibernating 10 1985:2 

1986:11 

1987:5 

1988:0 

1989:10 

1990:25 

S = +27 

P < 0.05 

+ Mean = 18 

SD = 16.1 

CV = 89.4% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 
 

 

 

 

 

 

 

 

 

 

210 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 15. Concluded. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

1991:32 

1992:26 

1993:19 

1995:52 

Salisbury Mine PA Hibernating 11 1986:7 

1987:9 

1988:11 

1989:5 

1990:2 

1991:19 

1992:38 

1993:12 

1995:4 

1996:10 

1997:7 

tau = +0.037 

P > 0.05 

ND Mean = 11 

SD = 10.0 

CV = 90.9% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Seawra Cave PA Hibernating 5 1986:5 

1991:12 

1993:31 

1996:16 

1997:6 

S = +2 

P > 0.05 

ND Mean = 14 

SD = 10.5 

CV = 75.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Sharer Cave PA Hibernating 11 1985:0 

1986:0 

1987:1 

1988:14 

1989:93 

1990:18 

1991:17 

1992:9 

1993:4 

1995:36 

1997:28 

tau = 0.440 

P < 0.05 

+ Mean = 20 

SD = 26.9 

CV = 134.5% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Stover Cave PA Hibernating 6 1985:0 

1987:0 

1990:0 

1993:1 

1993:4 

1997:1 

S = +9 

P > 0.05 

ND Mean = 1 

SD = 1.5 

CV = 150.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

U.S. Steel Mine PA Hibernating 5 1987:1 

1989:6 

1993:3 

1995:2 

1997:69 

S = +4 

P > 0.05 

ND Mean = 16 

SD = 29.6 

CV = 185.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Woodward Cave PA Hibernating 7 1985:6 

1988:15 

1990:21 

1991:50 

1992:28 

1994:14 

1996:46 

S = +9 

P > 0.05 

ND Mean = 26 

SD = 16.7 

CV = 64.2% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 
 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 211 

Appendix 16. Appendix 16 Results of trend analyses for the Indiana bat (Myotis sodalis). 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

Sauta Cave AL Hibernating 4 1977:300 

1995:192 

1996:307 

1997:197 

S = 0 

P > 0.05 

ND Mean = 249 

SD = 63.0 

CV = 25.3% 

T. Manasco (written commun., 

1999, Alabama Natural Heritage 

Program) 

Amphitheater Cave AR Hibernating 10 1975:400 

1978:224 

1979:225 

1980:225 

1983:400 

1984:300 

1985:300 

1986:300 

1987:400 

1988:425 

S = +18 

P > 0.05 

ND Mean = 320 

SD = 80.6 

CV = 25.2% 

Harvey and others (1981); Harvey 

(1989) 

Barkshed Saltpeter 

Cave 

AR Hibernating 7 1978:35 

1983:100 

1984:33 

1985:21 

1986:26 

1987:18 

1988:17 

S = -17 

P < 0.05 - Mean = 36 

SD = 29.2 

CV = 81.1% 

Harvey and others (1981); Harvey 

(1989) 

Biology Cave AR Hibernating 4 1978:100 

1983:130 

1984:0 

1987:0 

S = -3 

P > 0.05 

ND Mean = 58 

SD = 67.5 

CV = 116.4% 

Harvey and others (1981); Harvey 

(1989) 

Cave Mountain Cave AR Hibernating 7 1978:1,200 

1979:400 

1980:200 

1983:7,000 

1984:100 

1986:400 

1988:420 

S = -2 

P > 0.05 

ND Mean = 1,388 

SD = 2,499.6 

CV = 180.0% 

Harvey (1979, 1989); Harvey and 

others (1981) 

Corkscrew Cave AR Hibernating 5 1979:30 

1980:0 

1983:30 

1984:0 

1985:0 

S = -4 

P > 0.05 

ND Mean = 12 

SD = 16.4 

CV = 136.7% 

Harvey and others (1981); Harvey 

(1989) 

Edgeman Cave AR Hibernating 5 1981:3,000 

1983:5,000 

1984:1,850 

1986:1,660 

1988:1,400 

S = -9 

P < 0.05 - Mean = 2,582 

SD = 1,483.6 

CV = 57.5% 

Harvey and others (1981); Harvey 

(1989) 

Fitton Cave AR Hibernating 5 1984:110 

1985:25 

1986:31 

1987:0 

1988:73 

S = -2 

P > 0.05 

ND Mean = 48 

SD = 43.6 

CV = 90.8% 

Harvey and others (1981); Harvey 

(1989) 

Gustafsen Cave AR Hibernating 8 1979:130 

1980:100 

1983:130 

S = +23 

P < 0.05 

+ Mean = 239 

SD = 128.3 

CV = 53.7% 

Harvey and others (1981); Harvey 

(1989) 
 

 

 

 

 

 

 

 

 

212 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1984:200 

1985:200 

1986:350 

1987:350 

1988:450 

Hankin’s Cave AR Hibernating 8 1979:46 

1980:50 

1983:130 

1984:117 

1985:158 

1986:0 

1987:150 

1988:90 

S = +6 

P > 0.05 

ND Mean = 93 

SD = 56.2 

CV = 60.4% 

Harvey and others (1981); Harvey 

(1989) 

Hidden Spring Cave AR Hibernating 10 1975:130 

1978:0 

1979:0 

1980:0 

1983:135 

1984:2 

1985:0 

1986:0 

1987:0 

1988:0 

S = -10 

P > 0.05 

ND Mean = 27 

SD = 55.8 

CV = 206.7% 

Harvey and others (1981); Harvey 

(1989) 

Horseshoe Cave AR Hibernating 6 1983:50 

1984:0 

1985:450 

1986:70 

1987:300 

1988:0 

S = 0 

P > 0.05 

ND Mean = 145 

SD = 186.4 

CV = 128.6% 

Harvey (1989) 

Rowland Cave AR Hibernating 10 1975:50 

1978:0 

1979:0 

1980:0 

1983:150 

1984:0 

1985:0 

1986:50 

1987:100 

1988:30 

S = +8 

P > 0.05 

ND Mean = 38 

SD = 51.6 

CV = 135.8% 

Harvey and others (1981); Harvey 

(1989) 

Blackball Mine IL Hibernating 11 1953:600 

1956:337 

1957:257 

1958:120 

1959:120 

1960:337 

1975:192 

1983:20 

1985:200 

1987:290 

1989:460 

tau = -0.093 

P > 0.05 

ND Mean = 267 

SD = 165.2 

CV = 61.9% 

Hall (1962); Humphrey (1978); 

Hoffmeister (1989); Gardner and 

others (1990) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 213 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

Cave Spring Cave IL Hibernating 7 1953:83 

1954:8 

1957:0 

1958:2 

1960:2 

1974:0 

1975:0 

S = -13 

P < 0.05 - Mean = 13 

SD = 30.7 

CV = 236.2% 

Hall (1962); Humphrey (1978); 

Hoffmeister (1989) 

Fogelpole Cave IL Hibernating 5 1982:70 

1985:180 

1986:410 

1987:400 

1989:336 

S = +4 

P > 0.05 

ND Mean = 279 

SD = 148.8 

CV = 53.3% 

Gardner and others (1990) 

Bat Wing Cave IN Hibernating 10 1977:50,000 

1981:29,960 

1983:26,650 

1985:14,750 

1987:17,450 

1989:14,500 

1991:13,150 

1993:9,350 

1995:9,300 

1997:7,400 

S = -43 

P < 0.05 - Mean = 19,251 

SD = 13,062.8 

CV = 67.8% 

Richter and others (1978); Brack 

(1983); Brack and others (1984); 

R. Hellmich (written commun., 

1999, Indiana Natural Heritage 

Program) 

Buckner’s Cave IN Hibernating 13 1952:500 

1953:300 

1960:63 

1962:160 

1974:300 

1975:345 

1982:488 

1985:301 

1987:336 

1989:24 

1991:51 

1993:25 

1997:15 

tau = -0.410 

P < 0.05 - Mean = 224 

SD = 176.5 

CV = 78.8% 

Humphrey (1978); Brack (1983); 

Brack and others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural Heritage 

Program) 

Clifty Cave IN Hibernating 7 1954:9 

1982:66 

1987:198 

1989:412 

1991:357 

1993:307 

1997:369 

S = +13 

P < 0.05 

+ Mean = 245 

SD = 157.9 

CV = 64.4% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, Indiana 

Natural Heritage Program) 

Coon’s Cave IN Hibernating 15 1953:150 

1957:9 

1958:0 

1960:9 

1974:70 

1975:24 

1981:1,190 

1982:550 

tau = +0.798 

P < 0.05 

+ Mean = 1,681 

SD = 1,876.2 

CV = 111.6% 

Hall (1962); Humphrey (1978); 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, Indiana 

Natural Heritage Program) 
 

 

 

 

 

 

 

 

 

 

 

 

214 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

Cave 

Location 6188 

MO Hibernating 11 1980:3,900 

1981:1,800 

1983:1,600 

1985:500 

1987:40 

1989:35 

1991:450 

1993:625 

1995:450 

1997:195 

1999:175 

tau = -0.550 

P < 0.05 - Mean = 888 

SD = 1,159.7 

CV = 130.6% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6192 

MO Hibernating 13 1978:19,500 

1979:19,500 

1981:12,000 

1983:11,150 

1985:5,500 

1987:4,900 

1989:3,050 

1991:2,700 

1993:1,550 

1995:750 

1996:535 

1997:600 

1999:400 

tau = -0.968 

P < 0.05 - Mean = 6,318 

SD = 6,979.2 

CV = 110.5% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6193 

MO Hibernating 13 1975:6,000 

1978:10,000 

1979:10,500 

1981:5,800 

1983:4,950 

1985:2,000 

1987:700 

1989:475 

1991:160 

1993:80 

1995:40 

1997:15 

1999:14 

tau = -0.923 

P < 0.05 - Mean = 3,133 

SD = 3,889.2 

CV = 124.1% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6211 

MO Hibernating 4 1985:225 

1994:95 

1995:95 

1996:37 

S = -5 

P > 0.05 

ND Mean = 113 

SD = 79.5 

CV = 70.4% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6194 

MO Hibernating 13 1979:8,100 

1980:4,000 

1981:2,500 

1983:5,350 

1985:3,550 

1987:4,900 

1989:2,600 

1991:2,975 

1993:2,250 

t = -0.436 

P < 0.05 - Mean = 3,888 

SD = 2,145.9 

CV = 55.2% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 215 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1963:960 

1965:3,000 

1968:600 

1971:2,760 

1973:2,500 

1975:2,700 

1980:1,920 

1981:12,500 

1982:11,822 

1983:13,475 

1985:16,200 

1987:22,990 

1989:28,851 

1991:41,854 

1993:38,386 

1995:41,158 

1997:51,365 

Robinson Ladder 

Cave 

IN Hibernating 4 1989:95 

1991:388 

1993:376 

1997:326 

S = 0 

P > 0.05 

ND Mean = 296 

SD = 136.8 

CV = 46.2% 

Brack and others (1991); R. 

Hellmich (written commun., 

1999, Indiana Natural Heritage 

Program) 

Saltpeter Cave 

(Crawford County) 

IN Hibernating 8 1953:22 

1974:95 

1982:352 

1987:516 

1989:295 

1991:508 

1993:375 

1997:577 

S = +18 

P < 0.05 

+ Mean = 342 

SD = 200.0 

CV = 58.5% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, Indiana 

Natural Heritage Program) 

Saltpeter Cave 

(Monroe County) 

IN Hibernating 7 1952:13 

1954:18 

1982:83 

1987:19 

1991:221 

1993:245 

1997:136 

S = +15 

P < 0.05 

+ Mean = 105 

SD = 98.2 

CV = 93.5% 

Brack (1983); Brack and others 

(1984, 1991); R. Hellmich 

(written commun., 1999, Indiana 

Natural Heritage Program) 

Twin Domes Cave IN Hibernating 12 1975:100,000 

1976:100,000 

1977:100,000 

1981:98,250 

1983:70,750 

1985:56,650 

1987:79,650 

1989:70,800 

1991:78,500 

1993:87,350 

1995:78,875 

1997:67,100 

tau = -0.450 

P < 0.05 - Mean = 82,327 

SD = 14,794.3 

CV = 17.8% 

Humphrey (1978); Richter and 

others (1978); Brack (1983); 

Brack and others (1984, 1991); 

Richter and others (1993); R. 

Hellmich (written commun., 

1999, Indiana Natural Heritage 

Program) 

Wallier Cave Site IN Hibernating 4 1991:36 

1993:72 

S = +4 

P > 0.05 

ND Mean = 264 

SD = 247.9 

Brack and others (1991); R. 

Hellmich (written commun., 
 

 

 

 

 

 

 

 

 

 

216 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 16. Continued. 

Site name State 

Type of 

colony N Year:Count 

MannKendall Test 

results Trend 

Mean, standard 

deviation, and 

coefficient of 

variation (%) Source 

1995:537 

1997:409 

Wildcat Cave IN Hibernating 6 1950:6 S = +9 ND Mean = 29 Brack (1983); Brack and others 

1982:29 P > 0.05 SD = 23.5 (1984, 1991); R. Hellmich 

1987:0 CV = 81.0% (written commun., 1999, Indiana 

1991:31 Natural Heritage Program) 

1993:61 

1997:48 

Wyandotte Cave IN Hibernating 19 1952:15,000 tau = +0.563 + Mean = 7,363 Kirkpatrick and Conaway (1948); 

1956:2,000 P < 0.05 SD = 7,864.3 Hall (1962); Mumford (1969); 

1960:1,944 CV = 106.8% Humphrey (1978); Brack (1983); 

1962:2,000 Brack and others (1984, 1991); 

1965:3,000 Whitaker and Gammon (1988); 

1968:1,140 Richter and others (1993); R. 

1970:1,000 Hellmich (written commun., 

1974:1,900 1999, Indiana Natural Heritage 

1975:1,460 Program) 

1977:2,500 

1981:2,152 

1983:4,550 

1985:4,627 

1987:6,681 

1989:10,344 

1991:13,000 

1993:17,304 

1995:23,878 

1997:25,424 

Armine Branch Cave KY Hibernating 4 1980:225 S = -2 ND Mean = 230 T. Wethington (written commun., 

1983:275 P > 0.05 SD = 41.7 1999, Kentucky Department of 

1988:246 CV = 18.1% Fish and Wildlife Resources) 

1989:176 

Ash Cave KY Hibernating 6 1984:132 S = -15 - Mean = 77 T. Wethington (written commun., 

1988:104 P < 0.05 SD = 38.1 1999, Kentucky Department of 

1990:78 CV = 49.5% Fish and Wildlife Resources) 

1991:73 

1997:47 

1999:26 

Bat Cave (Carter KY Hibernating 23 1937:90,000 tau = -0.499 - Mean = 57,913 Welter and Sollberger (1939); 

County) 1956:100,000 P < 0.05 SD = 27,316.5 Hall (1962), Hassell (1963); 

1959:100,000 CV = 47.2% Hardin (1967); Hardin and 

1960:100,000 Hassell (1970); T. Wethington 

1961:100,000 (written commun., 1999, 

1962:100,000 Kentucky Department of Fish and 

1965:90,000 Wildlife Resources) 

1974:40,000 

1975:40,000 

1976:40,000 

1981:51,500 

1983:43,500 

1984:45,300 
 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 217 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1985:36,450 

1986:36,450 

1987:37,600 

1988:37,600 

1989:45,280 

1990:45,275 

1991:49,575 

1992:49,575 

1997:28,788 

1999:25,100 

Bat Cave 

(Edmonson County) 

KY Hibernating 8 1959:6 

1960:14 

1982:212 

1985:66 

1987:70 

1990:57 

1996:39 

1998:31 

S = 0 

P > 0.05 

ND Mean = 62 

SD = 64.9 

CV = 104.7% 

Hall (1962); T. Wethington 

(written commun., 1999, 

Kentucky Department of Fish and 

Wildlife Resources) 

Big Bat Cave KY Hibernating 4 1990:80 

1991:60 

1996:100 

1998:1 

S = -2 

P > 0.05 

ND Mean = 60 

SD = 42.7 

CV = 71.2% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Big Sulphur Springs 

Cave 

KY Hibernating 4 1988:47 

1989:37 

1996:34 

1998:10 

S = -6 

P < 0.05 - Mean = 32 

SD = 15.7 

CV = 49.1% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Bowman Saltpeter 

Cave 

KY Hibernating 7 1980:100 

1981:34 

1983:26 

1990:22 

1991:44 

1996:45 

1998:37 

S = -1 

P > 0.05 

ND Mean = 44 

SD = 26.1 

CV = 59.3% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Bus Stop Cave KY Hibernating 4 1987:75 

1989:300 

1990:80 

1991:56 

S = -2 

P > 0.05 

ND Mean = 128 

SD = 115.3 

CV = 90.1% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Cave Branch Cave KY Hibernating 7 1983:176 

1985:282 

1988:354 

1989:366 

1990:418 

1997:790 

1999:752 

S = +19 

P < 0.05 

+ Mean = 448 

SD = 233.6 

CV = 52.1% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Cave Hollow Cave KY Hibernating 15 1978:1,000 

1979:1,530 

1980:2,150 

1982:2,000 

1983:2,603 

1984:2,250 

tau = +0.695 

P < 0.05 

+ Mean = 2,462 

SD = 772.0 

CV = 31.4% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

218 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1985:1,812 

1986:2,167 

1987:2,609 

1988:2,947 

1989:3,485 

1990:2,312 

1991:2,753 

1997:3,969 

1998:3,340 

Cave Hollow Pit KY Hibernating 6 1980:1 

1982:1 

1987:1 

1988:3 

1991:17 

1997:3 

S = +9 

P > 0.05 

ND Mean = 4 

SD = 6.3 

CV = 157.5% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Cedar Post Cave KY Hibernating 6 1983:56 

1990:113 

1994:184 

1997:132 

1998:103 

1999:95 

S = -1 

P > 0.05 

ND Mean = 114 

SD = 42.6 

CV = 37.4% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Coach Cave KY Hibernating 21 1957:100,000 

1958:100,000 

1959:100,000 

1960:100,000 

1961:100,000 

1975:4,500 

1976:4,500 

1982:550 

1983:600 

1984:600 

1985:424 

1986:425 

1987:250 

1988:250 

1989:50 

1990:50 

1991:48 

1992:50 

1993:27 

1997:27 

1999:33 

tau = -0.899 

P < 0.05 - Mean = 24,399 

SD = 43,324.5 

CV = 177.6% 

Hall (1962); Humphrey (1978); 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Colossal Cave KY Hibernating 14 1953:6,000 

1956:1,000 

1957:1,000 

1958:2,000 

1959:2,000 

1960:3,000 

1975:14 

1982:349 

tau = -0.411 

P < 0.05 - Mean = 1,296 

SD = 1,592.3 

CV = 122.9% 

Hall (1962); Humphrey (1978); 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 219 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1985:445 

1987:498 

1989:614 

1991:556 

1997:284 

1999:387 

Cool Springs Cave KY Hibernating 8 1981:400 

1983:126 

1984:241 

1985:78 

1988:346 

1990:308 

1996:189 

1998:221 

S = -4 

P > 0.05 

ND Mean = 239 

SD = 109.3 

CV = 45.7% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Dixon Cave KY Hibernating 15 1956:2,500 

1957:2,500 

1958:2,500 

1959:2,500 

1960:2,500 

1969:4,000 

1975:3,600 

1982:30,000 

1983:30,000 

1985:26,850 

1987:16,550 

1989:10,700 

1991:9,150 

1997:7,050 

1999:5,575 

tau = +0.382 

P < 0.05 

+ Mean = 10,398 

SD = 10,392.8 

CV = 99.9% 

Bailey (1933); Mohr (1933b); 

Hall (1962); Humphrey (1978); T. 

Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Goochland Cave KY Hibernating 9 1976:50 

1981:136 

1983:160 

1987:65 

1989:121 

1990:134 

1991:226 

1996:253 

1998:356 

S = +24 

P < 0.05 

+ Mean = 167 

SD = 96.8 

CV = 58.0% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Great Saltpeter Cave KY Hibernating 4 1964:10 

1978:10 

1981:0 

1990:0 

S = -4 

P > 0.05 

ND Mean = 5 

SD = 5.8 

CV = 116.0% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Indian Cave KY Hibernating 6 1973:100 

1986:21 

1987:19 

1988:19 

1989:16 

1990:17 

S = -12 

P < 0.05 - Mean = 32 

SD = 33.4 

CV = 104.4% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Jesse James Cave KY Hibernating 8 1980:1,293 

1983:700 

S = -26 

P < 0.05 - Mean = 308 

SD = 461.1 

T. Wethington (written commun., 

1999, Kentucky Department of 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

220 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1985:230 

1987:160 

1989:75 

1991:1 

1997:3 

1999:0 

Line Fork Cave KY Hibernating 5 1963:10,000 

1982:8,379 

1988:5,016 

1991:3,297 

1999:1,308 

S = -10 

P < 0.05 - Mean = 5,600 

SD = 3,575.9 

CV = 63.8% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Little Amos Cave KY Hibernating 7 1983:1,160 

1986:188 

1988:440 

1989:380 

1995:1,972 

1997:1,835 

1999:114 

S = -1 

P > 0.05 

ND Mean = 870 

SD = 784.4 

CV = 90.2% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Long’s Cave KY Hibernating 14 1947:50,000 

1956:1,200 

1957:3,000 

1958:2,000 

1959:1,500 

1960:1,500 

1962:2,000 

1975:7,600 

1982:7,527 

1985:3,717 

1987:2,801 

1988:2,646 

1989:2,669 

1991:1,249 

tau = -0.056 

P > 0.05 

ND Mean = 6,386 

SD = 12,721.0 

CV = 199.2% 

Hall (1962), Humphrey (1978) 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Minton Hollow Cave KY Hibernating 4 1986:131 

1987:26 

1988:46 

1990:54 

S = 0 

P > 0.05 

ND Mean = 64 

SD = 46.0 

CV = 71.9% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Murder Branch Cave KY Hibernating 5 1983:1 

1988:2 

1991:2 

1992:4 

1995:3 

S = +7 

P > 0.05 

ND Mean = 2 

SD = 1.1 

CV = 55.0% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Shaw Hill Bat Cave KY Hibernating 5 1988:183 

1989:35 

1990:25 

1991:53 

1996:34 

S = -4 

P > 0.05 

ND Mean = 66 

SD = 66.2 

CV = 100.3% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Smokehole Cave KY Hibernating 8 1976:1,000 

1981:1,702 

1983:1,882 

1987:2,609 

S = +6 

P > 0.05 

ND Mean = 1,788 

SD = 519.8 

CV = 29.1% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 221 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1989:1,468 

1990:1,858 

1996:1,417 

1998:2,367 

Stillhouse Cave KY Hibernating 11 1979:2,400 

1980:1,488 

1982:1,545 

1983:1,864 

1985:1,204 

1987:1,047 

1988:1,213 

1991:1,238 

1995:1,223 

1997:679 

1999:711 

tau = -0.564 

P < 0.05 - Mean = 1,328 

SD = 493.7 

CV = 37.2% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Thornhill Cave KY Hibernating 4 1963:3,680 

1986:82 

1987:5 

1998:1 

S = -6 

P < 0.05 - Mean = 942 

SD = 1,825.7 

CV = 193.8% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

War Fork Cave KY Hibernating 8 1971:300 

1981:1,000 

1983:446 

1990:946 

1994:809 

1996:743 

1998:662 

1999:595 

S = -4 

P > 0.05 

ND Mean = 688 

SD = 239.0 

CV = 34.7% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Waterfall Cave KY Hibernating 7 1976:1,000 

1981:980 

1982:600 

1990:1,138 

1991:891 

1996:963 

1998:760 

S = -7 

P > 0.05 

ND Mean = 904 

SD = 176.3 

CV = 19.5% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Well Cave KY Hibernating 4 1995:699 

1996:696 

1997:596 

1999:540 

S = -6 

P < 0.05 - Mean = 633 

SD = 78.2 

CV = 12.4% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Wind Cave KY Hibernating 8 1981:251 

1983:312 

1986:245 

1989:56 

1990:94 

1994:288 

1996:491 

1998:432 

S = +8 

P > 0.05 

ND Mean = 271 

SD = 148.8 

CV = 54.9% 

T. Wethington (written commun., 

1999, Kentucky Department of 

Fish and Wildlife Resources) 

Bat Cave (Shannon 

County) 

MO Hibernating 9 1958:100,000 

1959:100,000 

1960:30,000 

1975:40,000 

S = -26 

P < 0.05 - Mean = 38,225 

SD = 37,545.4 

CV = 98.2% 

Hall (1962); Hall and Blewett 

(1964); Myers (1964a,b); J. 

Sternburg (written commun., 

1999, Missouri Natural Heritage 
 

 

 

 

 

 

 

 

 

222 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1983:30,750 

1985:30,450 

1987:4,275 

1989:4,275 

1991:4,275 

Cave 

Location 6177 

MO Hibernating 4 1990:350 

1992:250 

1994:500 

1996:650 

S = +4 

P > 0.05 

ND Mean = 438 

SD = 175.0 

CV = 40.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6190 

MO Hibernating 21 1955:600 

1958:100 

1960:600 

1962:80 

1981:5,350 

1982:4,350 

1983:3,250 

1984:2,500 

1985:2,250 

1987:2,050 

1988:2,500 

1989:1,575 

1991:1,257 

1992:700 

1993:700 

1994:525 

1995:325 

1996:380 

1997:260 

1998:270 

1999:155 

tau = -0.933 

P < 0.05 - Mean = 1,170 

SD = 1,485.2 

CV = 104.7% 

Hall (1962); Humphrey (1978); 

Myers (1964a,b); J. Sternburg 

(written commun., 1999, Missouri 

Natural Heritage Database); R. 

Clawson (written commun., 2003) 

Cave 

Location 6182 

MO Hibernating 14 1982:1,100 

1983:1,100 

1984:750 

1985:650 

1987:525 

1988:400 

1989:400 

1990:350 

1991:300 

1992:275 

1993:225 

1995:190 

1997:95 

1998:90 

tau = -0.989 

P < 0.05 - Mean = 461 

SD = 330.6 

CV = 71.7% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6189 

MO Hibernating 18 1975:21,000 

1976:12,000 

1977:9,050 

1978:12,050 

1979:8,850 

1980:9,300 

tau = -0.843 

P < 0.05 - Mean = 4,645 

SD = 6,074.3 

CV = 130.8% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 
 

 

 

 

 

 

 

ELLISON AND OTHERS 223 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1981:5,200 

1983:3,150 

1985:1,050 

1987:600 

1989:250 

1990:200 

1991:160 

1992:150 

1993:125 

1995:140 

1997:175 

1999:155 

Cave 

Location 6208 

MO Hibernating 4 1988:63 

1990:1 

1992:175 

1998:79 

S = +2 

P > 0.05 

ND Mean = 80 

SD = 72.0 

CV = 90.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6199 

MO Hibernating 9 1957:250 

1964:250 

1978:60 

1988:700 

1990:0 

1993:625 

1995:400 

1997:570 

1999:500 

S = +6 

P > 0.05 

ND Mean = 373 

SD = 248.4 

CV = 66.6% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6203 

MO Hibernating 7 1984:400 

1988:1,000 

1991:900 

1993:750 

1995:775 

1997:510 

1999:450 

S = -7 

P > 0.05 

ND Mean = 684 

SD = 232.7 

CV = 34.0% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6187 

MO Hibernating 8 1954:600 

1958:100 

1960:600 

1962:30 

1987:575 

1989:375 

1993:100 

1997:0 

S = -14 

P < 0.05 - Mean = 298 

SD = 268.4 

CV = 90.2% 

Hall (1962); Humphrey (1978); 

Myers (1964a,b); J. Sternburg 

(written commun., 1999, Missouri 

Natural Heritage Database); R. 

Clawson (written commun., 2003) 
 

 

 

 

 

 

 

 

 

 

 

 

 

224 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

Cave 

Location 6188 

MO Hibernating 11 1980:3,900 

1981:1,800 

1983:1,600 

1985:500 

1987:40 

1989:35 

1991:450 

1993:625 

1995:450 

1997:195 

1999:175 

tau = -0.550 

P < 0.05 - Mean = 888 

SD = 1,159.7 

CV = 130.6% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6192 

MO Hibernating 13 1978:19,500 

1979:19,500 

1981:12,000 

1983:11,150 

1985:5,500 

1987:4,900 

1989:3,050 

1991:2,700 

1993:1,550 

1995:750 

1996:535 

1997:600 

1999:400 

tau = -0.968 

P < 0.05 - Mean = 6,318 

SD = 6,979.2 

CV = 110.5% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6193 

MO Hibernating 13 1975:6,000 

1978:10,000 

1979:10,500 

1981:5,800 

1983:4,950 

1985:2,000 

1987:700 

1989:475 

1991:160 

1993:80 

1995:40 

1997:15 

1999:14 

tau = -0.923 

P < 0.05 - Mean = 3,133 

SD = 3,889.2 

CV = 124.1% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6211 

MO Hibernating 4 1985:225 

1994:95 

1995:95 

1996:37 

S = -5 

P > 0.05 

ND Mean = 113 

SD = 79.5 

CV = 70.4% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6194 

MO Hibernating 13 1979:8,100 

1980:4,000 

1981:2,500 

1983:5,350 

1985:3,550 

1987:4,900 

1989:2,600 

1991:2,975 

1993:2,250 

t = -0.436 

P < 0.05 - Mean = 3,888 

SD = 2,145.9 

CV = 55.2% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 
 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 225 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1994:8,000 

1995:2,125 

1997:1,500 

1999:2,700 

Cave 

Location 6202 

MO Hibernating 4 1962:150 

1987:50 

1997:975 

1999:1,660 

S = +4 

P > 0.05 

ND Mean = 709 

SD = 757.6 

CV = 106.9% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6173 

MO Hibernating 6 1981:2,250 

1987:400 

1988:250 

1991:20 

1992:0 

1997:0 

S = -13 

P < 0.05 - Mean = 487 

SD = 879.1 

CV = 180.5% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6191 

MO Hibernating 14 1979:2,950 

1980:2,750 

1981:2,800 

1983:4,550 

1985:3,400 

1987:5,300 

1989:5,150 

1990:6,000 

1991:6,225 

1993:4,550 

1995:3,600 

1997:1,615 

1998:1,400 

1999:975 

tau = -0.121 

P > 0.05 

ND Mean = 3,662 

SD = 1,691.2 

CV = 46.2% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6196 

MO Hibernating 11 1975:81,800 

1981:72,500 

1983:85,700 

1985:77,950 

1987:60,650 

1989:38,875 

1991:32,125 

1993:22,750 

1995:13,850 

1997:11,875 

1999:9,100 

tau = -0.891 

P < 0.05 - Mean = 46,107 

SD = 30,230.6 

CV = 65.6% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6198 

MO Hibernating 8 1975:125 

1978:113 

1986:12 

1988:75 

1993:6 

1996:90 

1997:45 

1999:1 

S = -16 

P < 0.05 - Mean = 58 

SD = 49.4 

CV = 85.2% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 

commun., 2003) 

Cave 

Location 6174 

MO Hibernating 4 1978:500 

1987:1 

1988:0 

S = -5 

P > 0.05 

ND Mean = 125 

SD = 249.8 

CV = 199.8% 

J. Sternburg (written commun., 

1999, Missouri Natural Heritage 

Database); R. Clawson (written 
 

 

 

 

 

 

 

 

 

 

 

 

226 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 16. Continued. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1989:0 

Barton Hill Mine NY Hibernating 8 1985:518 

1986:1,025 

1987:1,337 

1988:2,183 

1989:3,042 

1990:3,019 

1993:4,079 

1994:3,229 

S = +24 

P < 0.05 

+ Mean = 2,304 

SD = 1,244.3 

CV = 54.0% 

A. Hicks (written commun., 2000, 

NewYork Division of Wildlife 

Winter Bat Survey) 

Bennett Hill 

Hitchcock Mine 

NY Hibernating 6 1983:0 

1988:50 

1989:60 

1992:51 

1993:23 

1994:0 

S = -2 

P > 0.05 

ND Mean = 31 

SD = 26.8 

CV = 86.4% 

A. Hicks (written commun., 2000, 

NewYork Division of Wildlife 

Winter Bat Survey) 

Dente’s Third Lake 

Mine 

NY Hibernating 7 1984:3,430 

1986:4,426 

1987:4,672 

1988:5,631 

1989:5,926 

1990:5,887 

1994:6,889 

S = +19 

P < 0.05 

+ Mean = 5,266 

SD = 1,156.0 

CV = 21.9% 

A. Hicks (written commun., 2000, 

NewYork Division of Wildlife 

Winter Bat Survey) 

Glen Park Caves NY Hibernating 11 1982:631 

1983:1,228 

1984:522 

1985:1,313 

1986:1,582 

1987:1,579 

1988:1,499 

1989:1,777 

1990:2,138 

1991:2,614 

1994:2,371 

tau = 0.782 

P < 0.05 

+ Mean = 1,568 

SD = 653.1 

CV = 41.6% 

A. Hicks (written commun., 2000, 

NewYork Division of Wildlife 

Winter Bat Survey) 

Glen Park 

Commercial Cave 

NY Hibernating 6 1988:3 

1989:0 

1990:1 

1992:2 

1993:4 

1994:1 

S = +2 

P > 0.05 

ND Mean = 2 

SD = 1.5 

CV = 75.0% 

A. Hicks (written commun., 2000, 

NewYork Division of Wildlife 

Winter Bat Survey) 

Haile’s Cave NY Hibernating 9 1983:99 

1984:88 

1985:637 

1986:147 

1987:167 

1988:290 

1990:563 

1993:749 

1994:700 

S = +24 

P < 0.05 

+ Mean = 382 

SD = 276.1 

CV = 72.3% 

A. Hicks (written commun., 2000, 

NewYork Division of Wildlife 

Winter Bat Survey) 

Jamesville Quarry 

Cave 

NY Hibernating 11 1982:2,340 

1983:3,508 

tau = -0.016 

P > 0.05 

ND Mean = 2,569 

SD = 568.2 

A. Hicks (written commun., 2000, 

NewYork Division of Wildlife 
 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 227 

Appendix 16. Concluded. 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

1984:3,035 

1985:1,740 

1986:3,056 

1988:3,235 

1989:2,344 

1990:2,016 

1991:2,015 

1993:2,614 

1994:2,360 

Main Graphite Mine NY Hibernating 4 1988:86 

1991:100 

1992:63 

1994:135 

S = +2 

P > 0.05 

ND Mean = 96 

SD = 30.1 

CV = 31.4% 

A. Hicks (written commun., 2000, 

NewYork Division of Wildlife 

Winter Bat Survey) 

Aitkin Cave PA Hibernating 15 1930:500 

1960:2 

1964:12 

1986—96:0 

1997:9 

tau = -0.331 

P < 0.05 - Mean = 35 

SD = 128.7 

CV = 369.2% 

Mohr (1932b); Hall and Brenner 

(1968); Humphrey (1978); J. Hart 

(written commun., 2000, 

Pennsylvania Game Commission 

Winter Bat Hibernacula Survey) 

Canoe Creek Mine PA Hibernating 6 1987:297 

1989:127 

1991:262 

1993:148 

1995:353 

1997:158 

S = +1 

P > 0.05 

ND Mean = 224 

SD = 92.7 

CV = 41.3% 

J. Hart (written commun., 2000, 

Pennsylvania Game Commission 

Winter Bat Hibernacula Survey) 

Hellhole Cave WV Hibernating 6 1962:500 

1965:1,500 

1975:1,500 

1986:1,500 

1988:1,500 

1991:5,470 

S = +9 

P > 0.05 

ND Mean = 1,995 

SD = 1,748.8 

CV = 87.6% 

Humphrey (1978); Stihler and 

Brack (1992) 
 

 

 

 

 

 

228 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 17. Appendix 17 Results of trend analyses for the fringed myotis (Myotis thysanodes). 

Mann- Mean, standard deviation, 

Type of Kendall Test and coefficient of variation 

Site name (county) State colony N Year:Count results Trend (%) Source 

Christopher 

Mountain Cave 

AZ Summer 6 1992:4 

1993:121 

1994:25 

1995:9 

1996:2 

1997:50 

S = -1 

P > 0.05 

ND Mean = 35 

SD= 45.7 

CV= 130.6% 

S. Schwartz (written 

commun., 2000, Arizona 

Game and Fish 

Department) 

Redman Cave AZ Summer 4 1994:59 

1995:71 

1996:19 

1997:39 

S = -2 

P > 0.05 

ND Mean = 47 

SD= 22.9 

CV= 48.7% 

S. Schwartz (written 

commun., 2000, Arizona 

Game and Fish 

Department) 

Jewel Cave SD Hibernating 4 1969:10 

1986:9 

1990:4 

1992:2 

S = -6 

P < 0.05 - Mean = 6 

SD= 3.8 

CV= 63.3% 

Martin and Hawks (1972); 

Worthington (1992); 

Choate and Anderson 

(1997) 
 

 

 

 

 

 

 

ELLISON AND OTHERS 229 

Appendix 18. Appendix 18 Results of trend analyses for the cave myotis (Myotis velifer). 

Mean, standard 

deviation, and 

Site name (county) 

State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Colossal Cave AZ Summer 5 1954:70 

1956:94 

1957:1 

1960:15 

1970:0 

S = -6 

P > 0.05 

ND Mean = 36 

SD = 43.2 

CV = 120.0% 

Reidinger (1972) 

Triple Arch Cave KS Hibernating 4 1933:200 

1963:500 

1964:400 

1993:100 

S = -2 

P > 0.05 

ND Mean = 300 

SD = 182.6 

CV = 60.9% 

Dunnigan and Fitch (1967); 

Adams (1995) 

Torgac Cave NM Hibernating 7 1966:560 

1987:282 

1988:655 

1989:2,039 

1990:3,778 

1994:450 

1995:711 

S = +7 

P > 0.05 

ND Mean = 1,211 

SD = 1,271.5 

CV = 105.0% 

Jagnow (1998) 

Panther Cave TX Hibernating 4 1958:1,190 

1959:736 

1960:69 

1961:37 

S = -6 

P < 0.05 - Mean = 508 

SD = 557.3 

CV = 109.7% 

Blair (1954); Tinkle and 

Milstead (1960); Tinkle and 

Patterson (1965) 

Sinkhole Cave TX Hibernating 4 1958:1,718 

1959:1,839 

1960:658 

1961:106 

S = -4 

P > 0.05 

ND Mean = 1,080 

SD = 838.6 

CV = 77.6% 

Tinkle and Milstead (1960); 

Tinkle and Patterson (1965) 

Walkup Cave TX Hibernating 5 1958:3,798 

1959:1,886 

1960:233 

1961:171 

1962:74 

S = -8 

P < 0.05 - Mean = 1,252 

SD = 1,601.0 

CV = 127.9% 

Tinkle and Milstead (1960); 

Tinkle and Patterson (1965) 
 

 

 

 

 

 

 

 

 

 

 

230 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 19. Appendix 19 Results of trend analyses for the long-legged myotis (Myotis volans). 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of variation 

(%) Source 

Davenport Cave SD Summer 4 1992:6 

1993:2 

1994:1 

1995:5 

S = -2 

P > 0.05 

ND Mean = 4 

SD = 2.4 

CV= 60.0% 

Turner (1974); B. Phillips 

(written commun., 1999, 

Black Hills National 

Forest Database) 

Jewel Cave SD Hibernating 4 1969:50 

1986:1 

1989:14 

1990:13 

S = -2 

P > 0.05 

ND Mean = 20 

SD = 21.2 

CV = 106.0% 

Martin and Hawks (1972); 

Choate and Anderson 

(1997); P. Cryan (written 

commun., 2000) 

Bat Cave WA Hibernating 4 1971:12 

1973:3 

1974:1 

1983:1 

S = -5 

P > 0.05 

ND Mean = 4 

SD = 5.2 

CV = 130.0% 

Senger and others (1974); 

C. Senger (written 

commun., 1996) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 231 

Appendix 20. Results of trend analyses for the eastern pipistrelle (Pipistrellus subflavus). 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Buzzard’s Den 

Cave 

AL Hibernating 4 1988:12 

1989:20 

1990:100 

1991:175 

S = +6 

P < 0.05 

+ Mean = 77 

SD = 76.6 

CV = 99.5% 

Best and others (1992) 

Pipistrelle Mine AR Hibernating 4 1982:700 

1986:700 

1987–1988:700 

S = 0 

P > 0.05 

ND Mean = 700 

SD = 0 

CV = 0% 

Saugey and others (1988) 

Bat Wing Cave IN Hibernating 4 1981:11 

1991:1 

1993:2 

1995:21 

S = +2 

P > 0.05 

ND Mean = 9 

SD = 9.3 

CV = 103.3% 

Brack (1983); Brack and 

others (1984); R. Hellmich 

(written commun., 1999, 

Indiana Natural Heritage 

Program) 

Beardsley-Trout 

House 

IN Maternity 4 1989:15 

1990:26 

1991:29 

1992:28 

S = +4 

P > 0.05 

ND Mean = 24 

SD = 6.4 

CV = 26.7% 

Whitaker (1998) 

Buckner’s Cave IN Hibernating 6 1982:57 

1985:0 

1987:12 

1989:9 

1991:9 

1993:3 

S = -6 

P > 0.05 

ND Mean = 15 

SD = 21.0 

CV = 140.0% 

Brack (1983); Brack and 

others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 

Clifty Cave IN Hibernating 5 1982:46 

1987:124 

1989:73 

1991:106 

1993:53 

S = 0 

P > 0.05 

ND Mean = 80 

SD = 33.7 

CV = 42.1% 

Brack (1983); Brack and 

others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 

Coon’s Cave IN Hibernating 7 1981:6 

1982:5 

1985:5 

1987:166 

1989:103 

1991:278 

1993:208 

S = +12 

P < 0.05 

+ Mean = 110 

SD = 110.9 

CV = 100.8% 

Brack (1983); Brack and 

others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 

Copperhead Cave IN Hibernating 5 1986:201 

1988:201 

1989:113 

1990:99 

1991:170 

S = -5 

P > 0.05 

ND Mean = 157 

SD = 48.3 

CV = 30.8% 

Whitaker and Rissler 

(1992a,b); J. Whitaker 

(written commun., 1998) 

Endless Cave IN Hibernating 4 1982:26 

1987:29 

1991:55 

1993:74 

S = +6 

P < 0.05 

+ Mean = 46 

SD = 22.8 

CV = 49.6% 

Brack (1983); Brack and 

others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 

Grotto Cave IN Hibernating 7 1981:2 

1982:44 

1985:8 

1987:1 

1989:0 

S = -2 

P > 0.05 

ND Mean = 10 

SD = 15.4 

CV = 154.0% 

Brack (1983); Brack and 

others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

232 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 20. Continued. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

1991:5 

1993:8 

Jug Hole Cave IN Hibernating 4 1987:6 

1989:9 

1991:12 

1993:3 

S = 0 

P > 0.05 

ND Mean = 8 

SD = 3.9 

CV = 48.8% 

Brack and others (1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 

Parker’s Pit Cave IN Hibernating 4 1987:18 

1989:6 

1991:14 

1993:7 

S = -2 

P > 0.05 

ND Mean = 11 

SD = 5.7 

CV = 51.8% 

Brack (1983); Brack and 

others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 

Ray’s Cave IN Hibernating 8 1981:14 

1982:10 

1983:14 

1985:15 

1987:38 

1989:10 

1991:94 

1999:33 

S = +12 

P > 0.05 

ND Mean = 28 

SD = 28.5 

CV = 101.8% 

Brack (1983); Brack and 

others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 

Saltpeter Cave 

(Crawford County) 

IN Hibernating 5 1982:7 

1987:25 

1989:7 

1991:60 

1993:15 

S = +3 

P > 0.05 

ND Mean = 23 

SD = 22.1 

CV = 96.1% 

Brack (1983); Brack and 

others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 

Saltpeter Cave 

(Monroe County) 

IN Hibernating 4 1982:0 

1987:1 

1991:12 

1993:20 

S = +6 

P < 0.05 

+ Mean = 8 

SD = 9.5 

CV = 118.8% 

Brack (1983); Brack and 

others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 

Schrader Pavilion IN Maternity 4 1989:12 

1990:13 

1991:13 

1992:20 

S = +5 

P > 0.05 

ND Mean = 14 

SD = 3.7 

CV = 26.4% 

Whitaker (1998) 

Twin Domes Cave IN Hibernating 4 1976:1 

1981:0 

1991:8 

1995:10 

S = +4 

P > 0.05 

ND Mean = 5 

SD = 4.9 

CV = 98.0% 

Brack (1983); Brack and 

others (1984); R. Hellmich 

(written commun., 1999, 

Indiana Natural Heritage 

Program) 

Wildcat Cave IN Hibernating 4 1982:30 

1987:63 

1991:33 

1993:19 

S = -2 

P > 0.05 

ND Mean = 36 

SD = 18.8 

CV = 52.2% 

Brack (1983); Brack and 

others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 

Wyandotte Cave IN Hibernating 6 1981:2 

1985:1 

1987:2 

1989:14 

1991:21 

1993:4 

S = +8 

P > 0.05 

ND Mean = 7 

SD = 8.2 

CV = 117.1% 

Brack (1983); Brack and 

others (1984, 1991); R. 

Hellmich (written commun., 

1999, Indiana Natural 

Heritage Program) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 233 

Appendix 20. Continued. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

Bowman Saltpeter 

Cave 

KY Hibernating 4 1990:108 

1991:104 

1996:42 

1998:108 

S = -1 

P > 0.05 

ND Mean = 90 

SD = 32.4 

CV = 36.0% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Donahue 

Rockshelter 

KY Hibernating 8 1984:4 

1986:1 

1988:2 

1989:6 

1990:7 

1991:5 

1992:6 

1999:3 

S = +7 

P > 0.05 

ND Mean = 4 

SD = 2.1 

CV = 52.5% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Mine Branch Cave KY Hibernating 6 1983:18 

1986:1 

1987:34 

1988:25 

1991:51 

1996:32 

S = +7 

P > 0.05 

ND Mean = 27 

SD = 16.8 

CV = 62.2% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Murder Branch 

Cave 

KY Hibernating 7 1988:134 

1990:100 

1991:163 

1992:150 

1995:129 

1996:153 

1998:136 

S = +3 

P > 0.05 

ND Mean = 138 

SD = 20.6 

CV = 14.9% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

ShawHill Bat Cave KY Hibernating 5 1988:4 

1989:4 

1990:24 

1991:18 

1996:5 

S = +3 

P > 0.05 

ND Mean = 11 

SD = 9.4 

CV = 85.4% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

War Fork Cave KY Hibernating 4 1990:17 

1996:15 

1998:29 

1999:21 

S = +2 

P > 0.05 

ND Mean = 20 

SD = 6.2 

CV = 31.0% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Waterfall Cave KY Hibernating 4 1990:22 

1991:35 

1996:41 

998:73 

S = +6 

P < 0.05 

+ Mean = 43 

SD = 21.7 

CV = 50.5% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

Well Cave KY Hibernating 4 1995:17 

1996:9 

1997:12 

1999:13 

S = 0 

P > 0.05 

ND Mean = 13 

SD = 3.3 

CV = 25.4% 

T. Wethington (written 

commun., 1999, Kentucky 

Department of Fish and 

Wildlife Resources) 

John Friend Cave MD Hibernating 4 1977:38 

1978:31 

1979:18 

1980:29 

S = -4 

P > 0.05 

ND Mean = 29 

SD = 8.3 

CV = 28.6% 

Gates and others (1984) 

Aitkin Cave PA Hibernating 11 1986:39 

1987:76 

1988:51 

1989:24 

tau = +0.164 

P > 0.05 

ND Mean = 72 

SD = 31.6 

CV = 43.9% 

Hall and Brenner (1968); J. 

Hart (written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

234 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 20. Continued. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

1990:96 

1991:103 

1992:120 

1993:104 

1995:81 

1996:39 

1997:63 

Barton Cave PA Hibernating 4 1986:0 

1989:28 

1993:60 

1996:113 

S = +6 

P < 0.05 

+ Mean = 50 

SD = 48.5 

CV = 97.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Canoe Creek Mine PA Hibernating 6 1987:70 

1989:4 

1991:6 

1993:3 

1995:22 

1997:4 

S = -4 

P > 0.05 

ND Mean = 18 

SD = 26.4 

CV = 146.7% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Copperhead Cave PA Hibernating 8 1985:0 

1986:8 

1987:8 

1988:3 

1989:11 

1990:0 

1991:22 

1992:25 

S = +14 

P > 0.05 

ND Mean = 10 

SD = 9.5 

CV = 95.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Eiswert Cave PA Hibernating 9 1987:11 

1988:6 

1989:3 

1990:5 

1991:24 

1992:12 

1994:20 

1995:32 

1996:21 

S = +18 

P < 0.05 

+ Mean = 15 

SD = 9.9 

CV = 66.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Haine’s Gap PA Hibernating 4 1985:29 

1986:25 

1990:29 

1993:25 

S = -2 

P > 0.05 

ND Mean = 27 

SD = 2.3 

CV = 8.5% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Lemon Hole PA Hibernating 10 1985:13 

1986:11 

1987:18 

1988:27 

1989:32 

1991:30 

1992:27 

1993:8 

1995:49 

1997:29 

S = +16 

P > 0.05 

ND Mean = 24 

SD = 12.2 

CV = 50.8% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Petersburg Cave PA Hibernating 5 1990:1 

1991:1 

S = -6 

P > 0.05 

ND Mean = 0.4 

SD = 0.5 

J. Hart (written commun., 

2000, Pennsylvania Game 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 235 

Appendix 20. Continued. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

1992:0 

1993:0 

1995:0 

Ruth Cave PA Hibernating 10 1985:40 

1986:49 

1987:62 

1988:79 

1989:131 

1990:161 

1991:171 

1992:172 

1993:160 

1995:225 

S = +39 

P < 0.05 

+ Mean = 125 

SD = 63.2 

CV = 50.6% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Salisbury Mine PA Hibernating 11 1986:31 

1987:141 

1988:117 

1989:166 

1990:199 

1991:159 

1992:194 

1993:286 

1995:280 

1996:393 

1997:404 

tau = 0.818 

P < 0.05 

+ Mean = 215 

SD = 114.8 

CV = 53.4% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Schofer’s Cave PA Hibernating 4 1987:0 

1990:0 

1995:3 

1996:1 

S = +3 

P > 0.05 

ND Mean = 1 

SD = 1.4 

CV = 140.0% 

Mohr (1945); J. Hart (written 

commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Seawra Cave PA Hibernating 5 1986:44 

1991:62 

1993:122 

1996:108 

1997:88 

S = +4 

P > 0.05 

ND Mean = 85 

SD = 32.1 

CV = 37.8% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Sharer Cave PA Hibernating 11 1985:32 

1986:27 

1987:12 

1988:44 

1989:99 

1990:101 

1991:124 

1992:69 

1993:24 

1995:168 

1997:51 

tau = 0.345 

P > 0.05 

ND Mean = 68 

SD = 49.1 

CV = 72.2% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Stover Cave PA Hibernating 6 1985:1 

1987:1 

1990:2 

1993:2 

S = +11 

P < 0.05 

+ Mean = 2 

SD = 1.5 

CV = 75.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 
 

 

 

 

 

 

 

 

 

 

 

236 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Appendix 20. Concluded. 

Mean, standard 

deviation, and 

Site name State 

Type of 

colony N Year:Count 

Mann-Kendall 

Test results Trend 

coefficient of 

variation (%) Source 

1994:5 

1997:3 

U.S. Steel Mine PA Hibernating 5 1987:0 

1989:0 

1993:0 

1995:1 

1997:2 

S = +7 

P > 0.05 

ND Mean = 1 

SD = 0.9 

CV = 90.0% 

J. Hart (written commun., 

2000, Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Woodward Cave PA Hibernating 7 1985:8 

1988:24 

1990:36 

1991:53 

1992:39 

1994:63 

1996:66 

S = +19 

P < 0.05 

+ Mean = 41 

SD = 21.0 

CV = 51.2% 

Mohr (1932a); J. Hart 

(written commun., 2000, 

Pennsylvania Game 

Commission Winter Bat 

Hibernacula Survey) 

Greenville Saltpeter 

Cave 

WV Hibernating 5 1952:1,000 

1953:1,000 

1954:1,000 

1955:1,000 

1956:1,000 

S = 0 

P > 0.05 

ND Mean = 1,000 

SD = 0 

CV = 0% 

Davis (1957, 1959, 1966) 

Thorn Mountain 

Cave 

WV Hibernating 5 1952– 

1956:1,000 

S = 0 

P > 0.05 

ND Mean = 1,000 

SD = 0 

CV = 0% 

Davis (1957, 1959, 1966) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELLISON AND OTHERS 237 

Appendix 21. Appendix 21 Results of trend analyses for the Brazilian free-tailed bat (Tadarida brasiliensis). (All sites are summer 

colonies.) 

Mean, standard 

Mann- deviation, and 

Site name State 

Type of 

colony N Year:Count 

Kendall Test 

results Trend 

coefficient of 

variation (%) Source 

Bridge AZ Summer 4 1962:5,000 

1963:1,000 

1964:5,000 

1969:0 

S = -3 

P > 0.05 

ND Mean = 2,750 

SD = 2,630.0 

CV = 95.6% 

Reidinger (1972) 

Eagle Creek Cave AZ Maternity 9 1948:1,000,000 

1952:1,000,000 

1958:2,000,000 

1959:3,000,000 

1960:1,500,000 

1963:25,000,000 

1964:75,000,000 

1969:30,000 

1970:600,00 

S = +3 

P > 0.05 

ND Mean = 12,125,556 

SD = 24,860,483.0 

CV = 205.0% 

Constantine (1958a,b); 

Cockrum (1970); 

Reidinger (1972); Reidinger 

and Cockrum (1978); S. 

Schwartz (written commun., 

2000, Arizona Game and 

Fish Department) 

Hale Mine AZ Summer 4 1959:300 

1962:200 

1963:10,000 

1964:1,000 

S = +2 

P > 0.05 

ND Mean = 2,875 

SD = 4,763.3 

CV = 165.7% 

S. Schwartz (written 

commun., 2000, Arizona 

Game and Fish Department) 

Railroad Bridge AZ Maternity 4 1962:5,000 

1963:500 

1964:0 

1965:300 

S = -4 

P > 0.05 

ND Mean = 1,450 

SD = 2,375.6 

CV = 163.8% 

Cockrum (1969) 

Silverbell Mine AZ Summer 4 1958:300 

1962:200 

1963:20,000 

1964:1,000 

S = +2 

P > 0.05 

ND Mean = 5,375 

SD = 9,756.5 

CV = 181.5% 

Cockrum (1969) 

Orient Mine CO Bachelor 7 1967:9,000 

1978:50,000 

1979:75,000 

1980:100,000 

1981:86,000 

1982:88,771 

1983:107,240 

S = +17 

P < 0.05 

+ Mean = 73,716 

SD = 34,020.9 

CV = 46.2% 

Meacham (1974); Svoboda 

(1984); Svoboda and Choate 

(1987); Freeman and Wunder 

(1988); K. Navo (written 

commun., 2000, Colorado 

Division of Wildlife) 

Bat House FL Maternity 6 1995:8,000 

1996:10,000 

1997:60,000 

1998:70,000 

2000:80,000 

2001:100,000 

S = +15 

P < 0.05 

+ Mean = 54,667 

SD = 37,771.2 

CV = 69.1% 

K. Glover (written commun., 

2002) 

Carlsbad Caverns NM Maternity 5 1923:2,000,000 

1936:8,741,760 

1957:2,813,866 

1973:218,153 

1991:700,000 

S = -4 

P > 0.05 

ND Mean = 2,894,756 

SD = 3,426,943.0 

CV = 118.4% 

Bailey (1931); Allison 

(1937); Constantine (1967); 

Altenbach and others (1975); 

Thies and Gregory (1994); 

Thies and others (1996) 

Part II. Part II.Part II. Report of the Report of the Report of the Workshop orkshop 
 orkshop orkshop
240 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Workshop Format orkshop Format orkshop Format 

Prior to the workshop, participants submitted lists of 

important unresolved issues pertinent to monitoring bat 

populations in the United States (U.S.) and territories. 

Three main topic areas were defined and the issues listed 

within these topic areas. Participants also ranked their 

preferences for joining Working Groups corresponding 

to these topic areas. The topic areas were: 

A.	 Analytical and methodological problems in assessing bat numbers and trends, their basis, and 

needed research and improvements in techniques 

B.	 Categorizing U.S. bat species or species groups, 

and regions in terms of priorities for establishing 

population-trend monitoring programs based on 

conservation concerns, roosting habits, distributions, threats, and other factors 

C.	 Existing information and programs to monitor bat 

population trends: utility and coverage of current efforts, and potential expansion in scale 

At Estes Park each of the main Working Groups (A– 

C) met following the presentations, a panel discussion, 

and a seminar on capture-recapture models. The groups 

identified specific issues to discuss in greater detail, and 

subsequently developed recommendations and written 

statements on these issues. The issue statements were 

intended to provide: a succinct definition of the issue; a 

short description of what is known about the issue and 

what critical uncertainties surround the issue; and recommendations on how research, monitoring, or programmatic frameworks might best be designed to resolve these 

uncertainties. (Critical uncertainties were considered to 

be the facts, scientifically reliable data, research approaches, or programmatic means that need to be established in order to resolve specific issues related to 

monitoring bat populations in the U.S. and territories.) 

Participants were encouraged to follow a format in 

Working Group reports that included the following 

sections: Issue Title, Issue Description and Rationale, 

Means to Resolve the Critical Uncertainties Surrounding 

the Issue, and Suggestions Regarding Existing 

Monitoring and Research Programs. The “Issue 

Description and Rationale” section explains why the issue 

is important, what is generally known about the issue, 

what in general needs to be determined to resolve the 

critical uncertainties surrounding the issue, and what the 

consequences will be if the issue is not addressed (e.g., 

how it will delay progress in science and policy, what the 

implications are for bat populations in the U.S. and 

territories). The section “Means to Resolve the Critical 

Uncertainties Surrounding the Issue” recommends the 

kinds of observations, studies, experiments, or monitoring 

programs that are needed. The strengths, weaknesses, 

and feasibility of various approaches are identified as 

appropriate. A final section “Suggestions Regarding 

Existing Monitoring and Research Programs” is included 

when appropriate. This section provides recommendations 

for improvements to ongoing efforts that attempt to 

address the issue of monitoring U.S. bat populations. 

(Not all issue statements follow this format, depending 

on the judgment of the participants at the time the 

statements were initially developed.) Literature citations 

are combined in a single reference list after the Working 

Group C report. In the weeks following the workshop, 

drafts of the written statements were circulated among all 

workshop participants for final review and comment prior 

to posting on the worldwide web as an interim report. 

Principal Conclusions and Principal Conclusions and
 Principal Conclusions and

Recommendations Recommendations Recommendations
 

A number of conclusions and recommendations regarding monitoring of U.S. bat populations emerged at 

the workshop as a result of the presentations, panel discussions, and Working Group reports. In this section, 

the editors have attempted to highlight major aspects of 

these findings under five general headings. Greater detail 

on these topics is found in each Working Group report. 

This summary was circulated to each workshop participant for review and comment with the draft interim report. 

Conclusions and recommendations are not listed in any 

order of priority, because the workshop participants did 

not attempt to rank every issue considered. In general, 

the focus and objectives of this workshop (see above) 

emphasized providing general overviews of the state of 

the science in monitoring U.S. bat populations and 

stressed identification of critical gaps and important directions for future research and monitoring. Excellent 

descriptions of techniques currently employed widely in 

the study of bat populations are available in the volumes 

edited by Kunz (1988) and Wilson and others (1996). 

The Natural History of Bats Poses Many 

Challenges to Population Monitoring 

Bats pose many logistic challenges to population 

monitoring. They are a very heterogeneous group of 

mammals in terms of natural history and require the 

application of multiple approaches to monitoring. Some 

species are essentially solitary and roost cryptically in 

foliage, whereas others aggregate in the millions at 
predictable locations. Many others occur in a range of 

intermediate situations. Bats are highly mobile, almost all 

are nocturnal, and they generally roost in inaccessible or 

concealed situations. Their annual cycles can include 

seasonal long-distance migrations, and some species form 

colonies of different size, sex and age compositions at 

different times of the year. They are also very susceptible 

to disturbance, which can reduce survival (particularly in 

hibernation). Some colonies switch roost locations every 

few days or less during warm months, and basic natural 

history, distribution, roosting preferences and colony 

locations are poorly known for many species. 

Despite these problems, the Working Group reports 

provide a number of recommendations aimed at improving monitoring of populations of bats in four specific 

categories: colonial species; over-dispersed species (i.e., 

foliage-, cavity-, and crevice-roosting bats); Pacific Island fruit bats; and southwestern pollinators. Monitoring of colonial species can be improved by timing surveys 

to coincide with periods in the annual cycle when colony 

size is most stable and at a seasonal peak, as for example, 

conducting exit counts at maternity colonies during the 

week prior to parturition. Guidelines for making such exit 

counts are provided, including using multiple observers 

to assess observer variation, and using standard forms 

for recording data and ancillary information. Bats that 

roost in foliage, tree cavities, and rock crevices tend to 

roost in low densities or solitarily, and present additional 

monitoring challenges. Current estimates of relative abundance of over-dispersed species come primarily from mist 

net and echolocation detector index measures. However, 

these methods have no means for estimating detectability and thus provide data of limited value for assessing 

abundance. Surmounting problems in estimating numbers of these bats will require improvements in methodology. The three species of Pacific Island fruit bats pose 

very difficult challenges to population monitoring because 

of patterns of dispersion, rarity, and inaccessibility. The 

most pressing need for monitoring populations of these 

fruit bats is to improve methods of estimating detectability. This might best be developed by improving abilities 

to capture, mark, and resight these bats. Developing artificial lures through use of sound, scent, or food-based 

baits and experimenting with means of inducing self-marking merits exploration, as does using controlled hunts of 

fruit bats to recover marked individuals [other than those 

protected by the U.S. Endangered Species Act (ESA)]. In 

the interim, current methods should be continued, standardized, and include measures of logical covariates to 

abundance. Current monitoring of southwestern pollinators should also be continued, as methods under use are 

likely to reveal major trends or catastrophic changes. 

However, techniques for monitoring pollinators should 

PART II 241 

be standardized and improved with infrared videotaping 

and use of additional observers. 

Major Improvements Are Needed in 

Methods of Estimating Numbers of Bats 

With the possible exception of certain small colonies 

in which individual bats can be completely counted, 

attempts to estimate bat population trends in the U.S. 

and territories have relied heavily on the use of indices at 

local sites. The use of indices to estimate population size 

and trends in animals in general is inferior to more 

statistically defensible methods and can lead to incorrect 

inferences. New techniques must be explored and modern 

statistical designs applied in order to improve the 

scientific basis for conclusions about future bat 

population trends. Although the bat research community 

should strive to improve scientific methods of population 

estimation for future applications, we agree that changes 

in bat abundance documented by less direct methods, 

when accompanied by clear-cut causes, have provided 

strong evidence of past declines. Bat conservation efforts 

are well founded, and current monitoring approaches, 

although providing scientifically less rigorous 

information than desirable, have some merit for 

conservation if applied cautiously and conservatively. 

However, shortcomings of current methods must be fully 

acknowledged. The use of indices has serious flaws 

because most indices, including those using echolocation 

detectors, are affected by a host of variables other than 

actual trends in bat populations. These include 

environmental variables, observer variables, and variables 

related to the bats themselves, all of which can affect 

counts by altering detection probabilities in complex and 

largely unknown ways. Furthermore, these variables may 

also change with time, obscuring the ability to assess 

and understand the true trends in bat populations. 

Developing uniform standards for collecting index data 

can be useful, but aspects of many important variables 

affecting detection probabilities are unknown and cannot 

be standardized. This weakens the reliability of index 

values even when controllable factors are accounted for 

using standardized approaches. 

New research is needed to develop means to replace 

currently used indices, particularly if bat population 

monitoring objectives include detecting declines before 

they become catastrophic. The Working Group reports 

provide a number of recommendations for improving 

techniques for estimating population trend and 

population parameters (e.g., survival, reproduction, 

dispersal, and movements). These include 

recommendations to assess the feasibility of applying 
242 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

new theory in mark-recapture statistics to sampling 

designs, to develop new marking and resighting 

technology [such as Passive Integrated Transponder 

(PIT) tags and microtaggants], to incorporate doublesampling techniques and other means to calibrate indices, 

and to introduce replication and multiple observers in 

order to incorporate estimates of variance in exit counts 

or other counting situations. Developing applications of 

new technical equipment to assist in estimating numbers 

is also recommended. Such equipment may include video 

cameras with low light recording capability, infrared video 

cameras (reflectance-based imagery), computer methods 

for counting bats in these images, infrared cameras, and 

other remote sensing techniques. Attempts to use infrared 

or other new technology and multiple observers to 

calibrate indices based on detection of echolocation calls 

should be explored for estimating abundance of overdispersed bats. 

Objectives and Priorities of Bat Population 

Monitoring Need Careful Consideration 

Model species for population monitoring programs 

should be carefully selected based on specified objectives and relevant spatial scales. Monitoring should be 

carried out using proven methodology that provides reliable information on population trends. Poorly designed 

or flawed monitoring programs could lead to unreliable 

results at the cost of disturbance or other potential harm 

to bat survival. Priority setting should consider species 

distributions, feeding strategies, roosting habits, population status, threats to the species, and feasibility of 

obtaining reliable data. Species with specialized roosting 

requirements and very limited numbers of suitable roosts 

are of high importance for monitoring for conservation of 

biodiversity. Species with feeding strategies of great economic or ecosystem importance may also be of high priority for monitoring. Although most monitoring has been 

limited to bats that are legally classified as endangered, 

monitoring programs may better benefit unlisted species 

by providing data needed to prevent such taxa from becoming listed in the future. Species with localized distributions may be more amenable and important for 

monitoring than species that occur across the continent, 

particularly considering sampling logistics, likely smaller 

population sizes, and greater ability of managers to recognize specific human activities with potential to impact 

populations. Conversely, a monitoring program for species that roost in moderate-to-large colonies may be quite 

successful because of the relative ease in detecting such 

roosts and the fewer sites that need to be monitored. 

Monitoring Bat Populations on a Broad Scale
 

Will Require Strong Commitment and Well-


Planned Sampling Designs
 

Changes in bat populations have ramifications for 

agricultural and forestry segments of the U.S. economy, 

ecosystem function, and conservation of national biological diversity. There is a need for status information 

on a wide range of U.S. species, and bat population monitoring programs on a national or other broad scale are 

clearly desirable. However, there is no unifying mandate 

or legislative foundation for a national bat conservation 

program. Bats in the U.S. cross international and state 

boundaries, and models for bat conservation exist in international agreements in Europe (Agreement on the Conservation of Bats in Europe, London, 1991), and in 

protective national legislation for other species in the 

U.S. (the Migratory Bird Treaty Act, and the Marine Mammal Protection Act). As in these other examples, population monitoring should be an important component of 

such mandates. Firmer foundations for bat conservation 

and monitoring are needed, including heightening public 

support through efforts such as a National Bat Awareness Week. Any resulting expansion in population monitoring efforts, however, must recognize the need for 

application of the most appropriate statistical sampling 

and hypothesis-testing approaches in order to provide 

scientifically meaningful results. This will require research 

on basic ecology and life history of some species of bats, 

breakthroughs in developing detectability functions for 

population estimation, and development of appropriate 

spatial sampling frames. 

Information Exchange Among Bat
 

Specialists Should be Enhanced
 

Existing efforts to monitor bat populations are not 

well linked. Methods and protocols may lack 

comparability, and the information gathered may not be 

used as effectively as possible in signaling the extent 

and magnitude of bat population problems needing 

conservation attention. A web-based clearinghouse 

should be developed to enhance information exchange 

about bat population monitoring. A voluntary 

clearinghouse could provide useful information directly, 

and also provide electronic links to existing sites 

maintained by others. As examples, information or links 

could include a directory of organizations and individuals, 

descriptions of sampling protocols, a simple metadata 

description of ongoing studies, a bibliography, the bat 
population database under development by the U.S. 

Geological Survey, and echolocation call libraries. Given 

the potential value of renewed efforts at marking bats for 

population studies, a web-based clearinghouse that 

includes information on bat marking techniques, statistical 

approaches to marked animal sampling designs and data 

analysis, pertinent bibliographic references, directories 

of individuals and organizations marking bats, and 

metadata on tagging projects would also be of value. 

Working Group orking Group A. Analytical Analytical 
 AnalyticalAnalytical Analytical

and Methodological Problems in and Methodological Problems in 


Assessing Bat Numbers and Assessing Bat Numbers and
 

Trends, Their Basis, and Needed Their Basis, and Needed 


Research and Improvements Research and Improvements 
 Research and Improvements

in Techniques echniquesechniques echniques
 

Working Group Members: Bob Berry, Mike Bogan, 

Anne Brooke, Tim Carter, Paul Cryan, Virginia Dalton, 

Ted Fleming, Jeff Gore, Michael Herder, John Hayes 

(Leader), Tom Kunz, Gary McCracken, Rodrigo Medellin, 

Alex Menzel, Mike Rabe (Rapporteur), Paul Racey, Ruth 

Utzurrum, Allyson Walsh, Gary Wiles, and Don Wilson 

This Working Group divided into four subgroups to 

deal with the numerous issues under consideration. The 

four subgroup topics were: colonial bat species, overdispersed bats, Pacific Island fruit bats, and southwestern 

pollinators. In addition, many of the issues that were 

considered by this group were directly related to topics 

that emerged from the panel discussion and a seminar on 

mark-recapture statistical procedures. This report also 

provides a summary of pertinent aspects of the panel 

discussion and seminar as a background to the subgroup 

reports, and a brief discussion of definitions and general 

monitoring requirements before presenting subgroup 

findings. 

Panel Discussion Panel Discussion 

The Working Group acknowledged that the Monday 

afternoon Panel Discussion was directly relevant to the 

charge of the group. Panel members were: Don Wilson, 

moderator; David Anderson; Kenneth Burnham; Thomas 

Kunz; John Sauer; Allyson Walsh; and Gary C. White. 

Summarizing the entire discussion is beyond the scope 

of this report as there were many issues raised by 

participants and panel members. However, much of the 

discussion centered on the statistical reliability of current 

PART II 243 

bat research and monitoring programs. In that regard, two 

exchanges, paraphrased below, were deemed especially 

relevant although unanimity of opinion on these issues 

varied. 

Question I: A considerable amount of historical 

data on bat populations is available. Are these data 

useful or do we need to establish new monitoring designs? 

Response: Most historical data are indices of population parameters and not direct measures of the parameters of interest. For example, mist net captures are 

indices of abundance, but do not measure abundance 

directly. An index is a convolution of several things, 

and we are almost always unable to determine what the 

index means in terms of the parameter. An index is a 

combination of: (1) true abundance (this is what we are 

typically interested in); (2) observer effect; (3) environmental effects; and (4) animal behavior cues, i.e., 

cues that cause us to detect (or catch) one animal and 

not another. The last three effects interfere with our 

ability to provide the most scientifically defensible 

population estimates. By using an index, we assume 

that there is a direct, linear relationship between our 

index and the parameter of interest (e.g., population 

size). With an index, we assume that this relationship is 

invariant over time (which is not reasonable) and thus 

our index provides some kind of “relative abundance” 

information. Such indices may only be “numbers” rather 

than data that lend themselves to good science; we 

should not be using indices when other methods are 

available. We need to strive to upgrade to more robust 

techniques than are currently being used to monitor 

bat abundance. 

Question II: It seems as if “robust techniques” are 

currently not applicable to monitoring studies of most, 

if not all, bats. Does this mean we shouldn’t even try to 

monitor bats? 

Response: It may be necessary to shrink our current goals, and be careful to limit studies to those where 

we can be sure of collecting meaningful data. Clearly 

some species and problems may be beyond our reach 

at this time. However, there are new technologies that 

we should explore. It might be useful to start with the 

easier problems and species and build to the more complex problems and problematic species as we grow accustomed to new methods. For example, PIT tags and 

readers may offer alternative ways to mark bats. These 

marks allow unique identification of individuals. It may 

be possible to deploy an array of antennae on a number of portals (e.g., a bat gate) and it might then be 

possible to identify individual bats as they enter and 

exit the gate. These technologies are expensive now, 

but the price is likely to decrease in the future. 
244 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Seminar Seminar

The Working Group’s activities on Tuesday morning 

began with a seminar on capture-recapture methodology 

given by David Anderson, Ken Burnham, and Gary White. 

Highlights of that seminar are presented here. Currently 

available capture-recapture models are far more powerful 

than the simple Lincoln-Petersen index more familiar to 

bat researchers. The purpose of this presentation was to 

point out some of the strengths and flexibility of modern 

capture-recapture methods. These provide true 

population parameter estimation techniques. 

The term capture-recapture can be misleading. 

Programs NOREMARK and MARK (written and 

maintained by Gary White and available at http:// 

www.cnr.colostate.edu/~gwhite/ without cost) include a 

number of models for examining these types of studies. 

Mark-resight approaches (where marked animals are 

resighted or re-detected rather than recaptured) are 

equivalent from a statistical point of view, as long as 

certain assumptions can be met: 

(1) marked and unmarked animals have the same 

resighting probability; 

(2) researchers must be able to correctly distinguish 

marked from unmarked animals; and 

(3) depending on the statistical estimator, the researcher must be able to correctly identify individual marked animals. 

The power of these methods is that they not only 

allow the researcher to enumerate populations with known 

precision, but they also enable the estimation of other 

population parameters. Depending on the particular model 

selected, these parameters include: differential mortality 

among individuals, differential mortality between sex and 

age classes, and differential detection probability among 

individual animals. All these are important attributes that 

we can and should attempt to estimate for bats. 

If we can incorporate radio-tagged animals into the 

design, we can estimate how many animals are available 

for resighting. The addition of radio-tagged animals then 

provides a solution to the immigration- emigration problem. A typical field scenario would include the following 

steps: (1) mark animals; (2) resight population and distinguish marked from unmarked individuals; and (3) conduct multiple resightings. 

An important point regarding marking and resighting 

is that the method used to capture animals and mark them 

should be different than the method used to resight them. 

With trap-shy animals, captured individuals will avoid 

being resighted if the same method is used. For example, 

if mist nets are used to capture bats and attach marks, 

mist nets are not appropriate for resighting animals 

because previously caught animals will avoid nets and 

violate the assumptions of the model. Similarly, if bats are 

marked at a roost, the roost may not be the appropriate 

location for resightings. 

Multi-strata models provide extensions of the above 

and allow the estimation of parameters at several locations 

as well as the interactions between the locations (for 

examples, see Hestbeck and others, 1991; Brownie and 

others, 1993). Multi-strata models also allow the 

incorporation of environmental covariates (e.g., 

temperature). If we consider strata to be separate roost 

locations in proximity to each other, then these models 

may be especially useful for bat populations where roost 

switching occurs and roost environments differ. These 

models allow independent estimates of survival and other 

parameters. We can also use multi-strata models to 

estimate probabilities of detection within each of the strata 

as well as the probabilities of detection for individuals in 

transition among strata. It seems reasonable to think that 

different bat colony locations might have different 

survival rates and detection probabilities. Multi-strata 

models may allow us to estimate important population 

parameters in these types of complex systems. 

Following the presentation, there were a number of questions and statements from those attending regarding capture-recapture techniques and programs NOREMARK and 

MARK. Some of the more relevant questions and responses 

are paraphrased here; they are not direct quotes. 

Question (Kunz): Can we use these methods to separate dispersal from mortality? In bats, we often do not 

know whether a marked bat has emigrated or died. 

Response (White): A robust design model (a specific 

model of program MARK) can separate these two events. 

In order to do so however, the model requires population 

closure. To achieve this, short mark-resight times are necessary. 

Question (Kunz): We have seen several models that 

were derived for use in other taxa (such as deer and elk). 

Do the unique life histories of bats suggest that other 

models could be specifically developed for them? 

Response (White): Yes. New capture-recapture 

models are under development now. There is a list-server 

for program MARK and we give workshops every summer 

in June. We also teach a graduate-level course at Colorado 

State University for those who really want to understand 

capture-recapture models. Clearly not all of the data that 

are typically collected for bats will be useful for these 

models. However, some overlap between the data 

collected for bats and the data useful for parameter 

estimation does exist. 

Question (Hayes): How can these techniques be applied to larger scales than single locations? 

Response (White): This is mostly a sampling issue. 

First, select the sampling frame you are interested in (the 
particular part of the landscape) and then select random 

samples (of roost sites) from within that frame. 

Question (Kunz and Hayes): How precise do these 

estimates have to be? I expect we would obtain some 

pretty imprecise estimates from bats. What precision 

would be needed for long-term monitoring tools? 

Response (Burnham): If the goal was to be able to 

detect a 5% change in a population over a 10-year period, 

the estimates would not have to be as precise as you 

might think. A SE of 20% of the mean measured over a 10­

year period would probably be able to show that degree 

of population change. 

Question (Tuttle): We don’t know the long-term effects of PIT tags on bats. We need to test these effects 

before we embark on any massive pit-tagging projects. 

Response (Kunz): I have used PIT tags in 7.1 g 

Myotis lucifugus without noticing any ill effects. The 

tags only weigh about 0.1 g and I have even injected 

them into pups with no problems so far (3 years). There is 

a small amount of migration of the tags from the injection 

site, but not much. 

Definitions and Monitoring Definitions and Monitoring

Requirements Requirements Requirements

The Working Group agreed to use standard definitions for “colony” and “population” during subsequent 

discussions to avoid ambiguity and clearly define sampling units. These definitions are: 

Colony: A stable group of single species, which occupy a definable boundary at a particular time interval 

where population parameters can be defined. 

Population: A group of individuals of the same species living in a particular area at a particular time. 

Additionally, we agreed that objectives for any monitoring activity should include: (1) the estimation of population parameters through time that are adequate to detect 

trends significant to the long-term persistence of the species, subspecies, or population unit; and (2) monitoring 

should be able to determine changes in species distributions or population numbers. 

In any bat monitoring plan, efforts should be made 

first to census the population (completely enumerate the 

population), and if that is not possible, estimate the population numbers using a robust, defensible technique. If 

neither a census nor an estimate is possible, an index to 

population size may have to be developed. 

Recognizing that bats are a diverse group of 

organisms and that there are no overall solutions to the 

unique problems some groups present for population 

monitoring, the group divided into four smaller 

subgroups. These subgroups were comprised of members 

with particular expertise or interest in the bat categories 

PART II 245 

they considered. David Anderson, Gary White, John 

Sauer, or Ken Burnham assisted all groups in their 

deliberations. The four categories were: colonial species, 

solitary or “over-dispersed” species, Pacific Island fruit 

bats, and southwestern pollinators. 

Subgroup Report: Colonial Subgroup Report: Colonial 
 Subgroup Report: Colonial

Species Species Species
 

Subgroup Members: Bob Berry, Jeff Gore, Michael 

Herder, Tom Kunz, Mike Rabe, and Paul Racey 

Because some bats aggregate in colonies, various 

methods have been used to estimate the number of bats 

in a particular colony and develop estimates of the total 

population size. However, because bats are highly mobile, 

inhabit a variety of sites, and display a range of social 

structures, it is important that a colony be defined and 

that monitoring times be standardized to ensure that 

estimates are comparable. As defined, the term colony (a 

stable, single-species group of bats that occupies a 

definable area over a particular time interval and for which 

population parameters can be defined) is most readily 

applicable to large groups of bats at stable roost sites. 

However, colonies may also include small aggregations 

of bats that might use crevices, snags, trees, buildings, 

mines, or caves as roost habitat. We further suggest 

colonies be classified into three size classes: small = <200 

individuals; medium = 200–9,999; or large = >10,000 

individuals. This classification system, although 

somewhat arbitrary, was incorporated because colonies 

of different sizes pose unique challenges in developing 

suitable monitoring protocols. 

Colonial Bat Species Subgroup Issue 1. 

Timing of Monitoring Surveys 

Issue Description and Rationale 

There is considerable variability in the opinions 

among researchers as to the best time for conducting 

colony monitoring. Ideally, colonies should be monitored 

when they are most stable in terms of numbers. While 

this is sometimes dictated by the physical attributes of 

the roost, moon phase, or sampling strategy, too often 

monitoring is scheduled mostly for convenience of the 

researcher or to maximize the number of counts within a 

particular season. Monitoring during particular life history 

events, such as parturition, lactation, or hibernation can 

cause disturbance or even mortality among the bat species 

being studied if not approached cautiously. Transient or 

roost-switching (Lewis, 1995) bats complicate the 
246 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

estimation process by introducing an unknown rate of 

immigration or emigration. Fluctuation in the number of 

individuals causes great problems in gaining an accurate 

estimate of colony size. Monitoring during lactation may 

lead to erroneous assumptions, such as all bats exited 

the roost, or all bats counted at emergence were lactating 

females with non-volant young. As young become volant, 

adults may move to new roosts and form breeding 

aggregations. Counts made in hibernacula pose 

considerable disturbance to the bats being monitored and 

may reduce individual fitness or lead to mortality of the 

animals. Mortality caused by the monitoring technique 

compromises the reliability of the count and introduces 

dilemmas for the researchers. Additionally, as with other 

aspects of bat population monitoring, lack of consistency 

in timing between researchers in neighboring areas 

minimizes the reliability of intercolony comparisons. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Monitoring for a particular species should be standardized with regard to the timing, location, methodology, and data collected. In order to minimize the effect on 

the counts of transient or roost-switching bats, monitoring should be conducted at a time when the colony size is 

most stable and most or all of the bats within the colony 

are exiting the roost. Monitoring at the roost eliminates 

the problems associated with attempting to assess population trends based on counts of commuting or foraging 

bats. Maternity roosts are typically stable and should be 

the highest priority for monitoring. We recommend that 

maternity colonies be surveyed in the first week before 

parturition in order to estimate colony size at its most 

stable point and greatest size. During this period, most of 

the bats within the colony should be exiting to forage 

and transient animals should have moved to other roosts. 

Counting at this time may require carefully conducted, 

pre-survey captures to determine the reproductive state 

of the females of the species, particularly in years where 

aberrant environmental conditions may alter the timing 

of reproductive events. Timing of these events may vary 

due to latitude, climate, or other factors and we encourage the building of predictive models (e.g., those from 

the U.K., A. Walsh, oral commun., 1999) that would help 

refine our understanding of the best time to survey. 

Monitoring at hibernacula is generally not 

recommended due to the potential for disturbance to the 

animals. However, for some species, monitoring within 

hibernation sites may be the best or only reasonable 

alternative in obtaining an accurate count with minimal 

bias. Where this is the case, we recommend monitoring 

each site once every 3 years.1 Hibernation counts are 

sometimes conducted more frequently in the U.K., but 

opinions vary on the degree of disturbance involved. A 

rotational system also may allow more sites to be 

surveyed. Care should be taken to complete the count as 

quickly and with as little disturbance as possible. 

Colonial Bat Species Subgroup Issue 2. 

Estimation of Colony Size and Population Trends 

Issue Description and Rationale 

Determining trends in populations requires accurate 

assessments of colony sizes. Where the population 

comprises colonies dispersed over a wide area, a 

randomized sampling of colonies should be performed. 

Unfortunately, different species present different 

challenges for making accurate assessments. Even within 

a single species, colonies of different sizes or those in 

different locations may require different techniques or 

levels of effort. Biologists often select a survey method 

based more on what appears to be practical rather than 

on what would provide the most useful and accurate 

results. This can lead to estimates of colony size that are 

unreliable or have no estimate of error. Furthermore, these 

colony size estimates can prove useless or even harmful 

when used to detect population trends. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Census. The preferred method for estimating colony 

size is a complete count or census and the best census 

method is to count bats as they exit the roost at night. 

Observers should arrive at least one hour before the normal exit time for the resident species. Noise and movement by observers should be minimized. Observers should 

be positioned where they can see the bats but are not 

likely to be detected by the bats, particularly not directly 

1

Editors note: Recommendations to conduct counts in hibernacula less often than every year are precautionary and intended to reduce possible disturbance effects from surveys on 

survival or reproduction of bats. Other sources recommend 

conducting counts in hibernacula every two years (Sheffield 

and others, 1992; U.S. Fish and Wildlife Service, 1999). 
in the outflight path. At small to medium-sized colonies, 

bats should be counted until no individuals are seen for 

15 minutes at any exit. Larger colonies may have a few 

bats exiting over a long period, yet staying at the roost 

may not be efficient. In these cases, it may be helpful to 

develop and test a depletion count technique that would 

allow observers to stop counts when less than a designated proportion of the colony is observed exiting over a 

15-minute period (e.g., Tuttle and Taylor, 1994; Altenbach, 

1995; Navo, 1995). 

We recommend two or three separate counts if 

variation among nights is expected. Double-blind counts 

conducted by two independent observers would improve 

the reliability of the count and aid in assessing variation 

between observers. Following completion of the exit 

count, observers should refrain from entering the roost 

to count the number of bats remaining to minimize 

disturbance to remaining animals. We recommend that a 

standard form be developed and used by all monitoring 

crews. Forms should include information such as colony 

location (including coordinates determined by global 

positioning systems where useful), number and species 

of bats counted, number of entrances, moon phase, wind, 

date, humidity, number and names of observers, sunset, 

moonrise, noise level, identification technique, counting 

technique, how multiple exits were accounted for, and a 

drawing of roost exits if possible. Photographs should be 

taken outside the colony site if possible. 

A variety of equipment can be used to census 

colonies (Rainey, 1995). Infrared thermal imaging, nightvision equipment, and infrared cameras (reflectance-based 

imagery) may be the only means of counting large colonies. 

A computer program that counts bats from the infrared 

imaging is being developed and testing of this program 

should be encouraged. For smaller colonies, the above 

equipment may be useful along with infrared counters, 

acoustic sensors (as a count starter, camera starter), 

clickers (tally counters), cameras, and lights with red filters. 

Estimation. If direct counts of emerging bats are not 

practical, it may be possible to estimate colony size with 

capture-recapture techniques. Statistical models are 

available for determining population parameters and these 

should be carefully evaluated to determine which are most 

appropriate for each situation. The capture-recapture 

models make several assumptions that are often not easily 

met when working with colonial bats. Most models 

assume that marked and unmarked animals have the same 

resighting probability; this may be violated with any 

capture technique because bats quickly learn to avoid 

capture. Because of their small size and reliance on flight, 

PART II 247 

bats are also sensitive to many marking techniques and 

care must be taken that the marking technique does not 

cause increased mortality (including predation), 

significant behavioral changes, or abandonment of 

habitually used areas. All models also assume that marked 

animals can be correctly distinguished from unmarked 

animals. The small size, high mobility, and cryptic nature 

of bats means that marked animals are often difficult to 

detect. Conversely, wing bands can be so distinct that 

marked animals are more likely to be detected. Another 

problem is that bats can remove or deface bands and 

other external marks. Finally, depending on the estimation 

model used, it may be necessary to correctly identify 

individually marked animals and this can be a serious 

problem with bats. New techniques such as PIT tags and 

microtaggants should be explored for marking bats. 

All marking techniques present special concerns and 

these concerns should be considered along with the 

advice of a biologist experienced with the species before 

a marking program is begun. In all cases, the need for and 

expected benefits of a marking program should be carefully 

considered relative to the potential harm to the bats (see 

also Working Group C Report, Issue 5, “Optimizing 

Information Obtained from Marked Bats”). Some concerns 

and problems are as follows: 

• 	 wing bands: can cause serious injury to some 

species, some species will not tolerate bands; 

• 	 necklaces: crevice or foliage roosting bats may 

snag necklace on projections; 

• 	 radios: short-lived, expensive, and due to weight 

and antenna they may cause behavioral changes; 

• 	 dyes, wing punches, freeze branding: potential 

for toxicity, short-lived, unknown long-term effect 

to bat health, research needed; 

• 	 PIT tags: need to focus bat flight through a relatively small space; unknown long-term effects to 

the bat, research needed; and 

• 	 microtaggants: short-lived, unknown toxicity, research needed. 

Indices. Indices of colony size are inferior to census 

or estimation techniques. Therefore, they should be used 

only as a last resort and their limitations should be recognized. When possible, indices should be calibrated to 

population size as measured by a census. Indices are 

most likely to be useful in detecting dramatic changes in 

population size over long periods of time. 

Widely dispersed colonies. It is important to note 

that censusing known colonies may give biased results, 

depending on the extent to which there are unknown 
248 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

or undiscovered colonies. Monitoring of known 

colonies will allow colony extinctions to be recorded, 

but the formation of new colonies may go unrecorded 

if attempts are not made to find other significant roosts. 

Investigators will have to determine the extent to which 

this phenomenon may occur in their species and adjust 

sampling designs accordingly. 

Colonial Bat Species Subgroup Issue 3.
 

Roost-Switching Between Colonies
 

Issue Description and Rationale 

Colonial bat species are known to switch from one roost 

to another. Roost switching may be for the purpose of 

predator avoidance, a response to predator encounters or 

disturbance by the researcher, or changes in internal roost 

conditions (e.g., temperature or parasite infestations). There 

has been growing information on roost switching in bats 

since the review by Lewis (1995), but more research is needed 

to improve understanding of this phenomenon and to 

properly account for it in population monitoring. Some 

species or individuals within a colony apparently engage in 

regular roost switching, although genetic and other studies 

in the U.K. and elsewhere indicate that females in maternity 

colonies are highly philopatric (A. Walsh, oral commun., 

1999; Tuttle, 1976; Palmeirim and Rodrigues, 1995). Nonreproductive individuals within the colony may move to 

separate roosting sites, remain with the colony, or move 

between the two sites. As females complete lactation and 

prepare for breeding, they may move from maternity roosts 

to breeding sites. Migrating bats may join an existing stable 

colony for a brief period. Fluctuations in the number of 

individuals introduce substantial variation into counts and 

violate the assumption that the colony is a closed population. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Transient and roost-switching animals are not considered in the definition of colony as it is applied here. 

While several of the program MARK capture-recapture 

models can be used to estimate colony size in an open 

system, it is preferable to use a census technique rather 

than an estimate. The preferred method for minimizing 

the effect of roost switching on colony counts is to conduct the monitoring survey when the colony is most stable. 

In many species this may occur during hibernation or 

approximately one week before or during parturition. (See 

also Issue 1 above.) 

Colonial Bat Species Subgroup Issue 4. 

Developing a National Monitoring Program 

(See Also Working Group C Report) 

Issue Description and Rationale 

Some researchers have proposed the development 

of a nationwide or continent-wide monitoring program to 

detect large-scale population trends in bats over time. A 

national program has been employed with relative success in the U.K. However, many North American bat species are widely distributed across the entire country. The 

scale of nationwide programs in the U.S. could be too 

large to be feasible if the purpose was to monitor all bat 

species throughout their ranges (see also Working 

Group B and C reports). Also, the life history characteristics of some species are either unknown or do not allow 

for any population census or population estimation to be 

made in any meaningful way. Nevertheless, some bats in 

the U.S. have relatively restricted distributions and have 

life history characteristics that make them likely candidates for a large scale, multi-year monitoring effort. Poorly 

designed or flawed monitoring programs should not be 

conducted. It is preferable to miss years or observations 

rather than conduct widespread, unreliable monitoring of 

bat roosts. Surveys may pose a possible disturbance to 

bat colonies. If the information from a survey is likely to 

be imprecise, then it would be better to not conduct surveys of that colony or perhaps to limit data to presenceabsence information. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Target bat species could be selected that have relatively small distributions in the U.S. and whose roosting 

habits and life histories suggest that such a monitoring 

plan would be possible (see also Working Group B report, “Prioritizing Monitoring Needs”). After selecting 

model species, the monitoring strategy could be designed 

using the following guidelines: 

Stratification. All known roosts should be stratified 

by geographic region, land type, estimated colony size, 

and proximity to urban areas (see also Working Group C 

report, Issue 2, “Analytical Considerations for a National 

Bat Monitoring Program”). This stratification not only 

reduces the variation among roosts and allows for more 

precise estimates, but would also allow researchers to 

examine changes in population sizes among the strata. 

Roosts would then be selected from these known roosts 
in a random fashion. Randomizing the sample could pose 

serious logistical problems but would strengthen the statistical inferences that could be made from any population changes. If random samples pose insurmountable 

problems, then a nonrandom selection could be chosen 

and still be useful. However, the inference from a nonrandom sample would be restricted to the sample that was 

being surveyed. 

Sample size. A sample size of 25–30 roosts would 

likely be sufficient to document substantial changes in 

many populations over time but may depend on size of 

the sampling frame. Estimation of sample size requirements 

and power analysis should be integral to planning efforts 

(Gibbs, 1995; Eagle and others, 1999). 

Timing of surveys. All roosts could be sampled once 

every 2 or 3 years rather than every year. Although there 

is a logistical advantage to yearly surveys (experienced 

crews remain intact), surveys could be staggered without 

serious loss of inferential power. 

Subgroup Report: Over- Subgroup Report: Over-
 Subgroup Report: OverDispersed Bats: Foliage, Cavity, 


and Crevice Roosting Bats and Crevice Roosting Bats 
 and Crevice Roosting Bats

Subgroup Members: Tim Carter, John Hayes, Alex 

Menzel, and Allyson Walsh 

Over-dispersed bats roost solitarily or in low 

densities, generally in foliage, cavities, or crevices. 

Characteristics of over-dispersed bats present unique 

problems with respect to monitoring and estimating 

population parameters. The roosting ecology of these 

species limits applicability of methods described for 

colonial species. Furthermore, the high vagility, low 

detectability, and low probability of recapture make it 

difficult to apply mark and recapture or resight methods 

for estimation of population parameters. 

Over-Dispersed Bats Subgroup Issue 1.
 

Estimation of Population Parameters
 

of Over-Dispersed Bats
 

Issue Description and Rationale 

Estimating the density or survival of over-dispersed 

bats is necessary to monitor trends of these species. 

PART II 249 

Trends in densities could be used to monitor the effects 

of factors such as habitat manipulations and changes in 

climatic patterns on the health or spatial distribution of 

populations of over-dispersed bats. Currently, two 

methods (use of bat detectors and mist nets) are used to 

determine indices of abundance for these species in limited 

geographic areas. We currently have no understanding 

of detection probabilities (i.e., the probability of detecting 

an individual with a given technique under specified 

conditions) associated with each of these methods, and 

it may be impossible to standardize detection probabilities 

among researchers or studies and over time. Thus, it is 

not possible to determine the precision or accuracy of 

these indices. Without an understanding of accuracy and 

precision, it is difficult to determine if trends based on 

these indices reflect actual changes in population 

densities or changes in the detection probabilities. The 

inability to estimate detection probability greatly limits 

the usefulness of data collected using uncalibrated indices 

produced either by mist netting or bat detector surveys. 

To calibrate these indices, appropriate population 

parameters must be estimated. Currently, these population 

parameters can only be estimated using mark-resight 

techniques. To date, mark-resight techniques have not 

been developed or applied to estimate population 

parameters for any species of bat in this group. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

The uncertainty and problems associated with this 

issue are substantial and daunting. The problems revolve 

around uniquely marking and resighting animals. No 

methodologies have yet been developed or applied for 

marking and resighting or recapture of over-dispersed 

bats in an economical or logistically feasible manner. 

Problems associated with recapturing members of this 

group make utilization of unique marking techniques, like 

forearm-banding, inappropriate. Other techniques used 

to individually mark animals include radio-transmitters 

and PIT tags. Because of the high cost associated with 

radio-transmitters, their use for marking animals to 

estimate population parameters may not be economically 

feasible. The short distance required between the PIT tag 

scanner and the bat to detect the PIT tag limits their use 

for over-dispersed bats. Technological advances may 

alleviate many of these problems. Technological 

advances, including transponders and diode lights, may 

make marking and resighting large numbers of overdispersed bats economically and logistically feasible. 
250 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Until problems surrounding estimation of population parameters are resolved, alteration of current methods to increase statistical rigor is desirable. Current 

limitations of indices may be reduced through the use of 

double sampling procedures (Thompson and others, 1998, 

p. 115), in which an inexpensive index is gathered in a 

large sample followed by expensive but more reliable 

measures on a smaller sample, results of which are used 

to calibrate the index. For bats, perhaps mark-resight or 

other enumeration techniques can be used to calibrate 

more expensively measured parameters (e.g., density) to 

more easily measured indices (e.g., habitat type, mist net 

captures, bat detector data). 

We suggest two initiatives regarding existing monitoring and research programs. First and most importantly, 

it is essential that methodologies be developed to determine unbiased estimates of population parameters such 

as abundance, density, and survival of over-dispersed 

bats. Without such methodologies, it will never be possible to reliably monitor trends in populations of these 

species. These methodologies will likely involve new approaches for marking and resighting bats. 

Second, once methodologies for mark-resight studies 

are established, evaluation and calibration of widely used 

methodologies and indices, such as catch per unit effort 

from mist netting or number of bat passes in echolocation 

monitoring studies, is necessary. Current methods 

employed for surveying or monitoring over-dispersed 

bats are primarily limited to mist net and bat detector 

surveys. Because detection probabilities associated with 

these methods are unknown, data currently collected 

using these techniques are of limited value. The precision 

of data currently collected should be evaluated. Provided 

data collected by these indices are positively and 

significantly correlated with the population parameters 

they are intended to estimate, their usefulness will be 

greatly increased through calibration. 

Because of the expense and logistical difficulties 

currently associated with estimating the population 

parameters of over-dispersed bats, it is unlikely that 

indices currently used can be calibrated adequately by a 

single study or research team. Because data used in 

calibration will probably be collected in multiple studies 

by many individuals, the manner in which the mist netting 

and bat detector data are collected should be standardized 

to the degree possible. Following calibration of these 

indices, the usefulness of index data collected in the future 

will depend on the collection methods paralleling those 

used in the calibration studies. 

Over-Dispersed Bats Subgroup Issue 2.
 

Use of Echolocation-Monitoring to
 

Determine Trends in Habitat Use
 

by Over-Dispersed Bats
 

Issue Description and Rationale 

Bat detectors have become increasingly available over 

the past decade, and are used for long-term monitoring of 

bats. For example, nationwide monitoring programs in the 

U.K. (Walsh and Catto, 1999) and the Netherlands have 

incorporated use of bat detectors as one tool for monitoring 

bats. In the U.K., surveys using heterodyne bat detectors 

are conducted during the summer to complement counts at 

maternity colonies or hibernacula for five species of bats. 

Because of the difficulties in capturing over-dispersed bats 

in many environments, use of bat detectors to evaluate trends 

in bat populations would be a cost-efficient, non-invasive 

technology if crude indices based on echolocation detectors could be calibrated against actual numbers of bats. 

A problem in using bat detectors is the inability to 

use echolocation-monitoring data to assess number of 

individuals using a site and hence measure absolute 

abundance. For example, it is not possible to distinguish 

between a single individual flying over a given site on 10 

occasions, and 10 individuals each flying over the site 

once. Hayes (2000) identified and discussed assumptions 

inherent to use of bat detectors in echolocationmonitoring studies. Hayes concluded that it is unlikely 

that echolocation-monitoring data can be an effective tool 

for assessing population trends of bats because such 

data do not assess abundance directly. However, Hayes 

noted that under some situations, bat detectors might be 

appropriate for monitoring use of different habitats 

through time if care is taken to assure adequate spatial 

and temporal replication. Bat detectors also may play a 

valuable role in monitoring changes in species 

distributions for taxa that can be identified unambiguously 

based on echolocation calls. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

It is recommended that use of bat detectors in monitoring programs for over-dispersed bats be used only 

with recognition of the limitations restricting inference to 

changes in species distributions and use of habitats rather 

than changes in abundance. Studies using infrared video 
recorders and at least two observers may be valuable in 

quantifying the relationship between numbers of bats 

and bat passes in different habitats. 

Over-Dispersed Bats Subgroup Issue 3.
 

Use of Mist Netting Surveys to Evaluate Trends
 

of Over-Dispersed Bats
 

Issue Description and Rationale 

A large number of inventories and studies of bats 

using mist nets are conducted each year across the U.S. 

Some of these efforts, including surveys conducted on 

public and private lands, are specifically targeted at determining status of species. However, these surveys generally only provide meaningful information on the 

presence and distribution of species, and rarely if ever 

provide reliable information on abundance or density of 

populations. Many other mist netting efforts are targeted 

to achieve a variety of objectives such as capture of individuals for radio-telemetry or collection of fecal pellets 

for dietary analysis. Information on number of individuals captured and presence of species is incidental to the 

primary objective. A key problem with these data is a lack 

of consistency in approaches used to collect the data. 

Furthermore, there have been minimal efforts to date to 

evaluate large-scale patterns in numbers of captures of 

bats using these data. 

Because of the inability to assess population parameters using mist netting data in the absence of recapture 

or resighting information (see Issue 1, this subgroup report), meaningful estimates of changes in population density based on data currently collected in mist netting 

surveys and studies are not possible. Changes in numbers of captures over time can result from changes in 

capture probabilities or from changes in abundance. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Uncertainties concerning interpretation of mist netting data and the extent to which changes in numbers of 

captures reflect changes in abundance or changes in capture probabilities preclude use of these data for unbiased 

estimation of population trends. However, in the absence 

of improvements to current approaches, we suggest that 

methods for collecting and compiling data collected in 

mist netting studies and surveys might provide a valuPART II 251 

able “early warning system” to monitor major trends in 

populations of over-dispersed bats. An early warning 

system using mist netting data would enable identification of probable changes in the distribution of species 

through time, and would provide evidence of potential 

dramatic changes in abundance of species. The rationale 

for the application of mist netting data as an early warning system relies on the principle that capture probabilities are not likely to change beyond certain bounds 

through time (assuming no significant changes in capture techniques). If capture success for a species changed 

through time, and if the magnitude of change exceeded 

the maximum rate expected given changes in capture probability, this would suggest a significant change in abundance. For example, if one assumed that a change in 

capture probability by a factor of 10 was highly unlikely, 

then any 10-fold change in number of bats captured 

would be unlikely to result from changes in capture probabilities alone, and would likely be the result of changes 

in abundance. In addition, mist netting data could be 

used directly to assess distribution of species and 

changes over time. If apparent changes in distribution or 

abundance of species were noted that were substantial 

enough to be of potential conservation concern, additional, more rigorous studies could be pursued. 

Implementation of this approach would require two 

changes. First, standardization of mist netting methodologies is essential to provide data that are reasonably 

comparable among studies. Capture probabilities are a 

function of a variety of factors, some of which are under 

the control of surveyors, others are not. Controlling for 

as many of the factors known to influence capture probability as possible may increase the probability that 

changes in capture success reflect changes in abundance. 

Standardization of factors such as time nets are deployed, 

duration of deployment, and weather conditions during 

which netting is conducted will help control for some of 

this variation. [However, standardizing counting protocols alone does not satisfy constant proportionality assumptions inherent in use of indices (Thompson and 

others, 1998, p. 77).] In addition, recording data concerning the size of nets used, location of sites, habitat characteristics of the area, and ambient conditions (e.g., 

temperature) may provide useful covariates for future 

analyses. However, because all factors related to capture 

probabilities cannot be controlled or taken into account 

in future analyses (indeed some of the factors responsible for differences in capture success will probably not 

even be known), use of these data will only be valuable 
252 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

to address coarse-scale changes of relatively large magnitude. Second, data collected from mist netting studies 

would need to be archived in an accessible format so that 

trends could be evaluated. 

While we advocate the use of this approach as an 

early warning system, we offer three caveats. First, the 

lack of statistical rigor inherent to this approach should 

be recognized and managers should not misinterpret potential trends identified with this approach as actual 

trends. Second, only substantial trends will be apparent 

using this approach; important, but smaller trends will 

not be identifiable using this approach. Finally, use of 

this approach should not divert resources from development of more rigorous procedures for evaluation of actual trends. 

Over-Dispersed Bats Subgroup Issue 4.
 

Spatial Scale Considerations in
 

Monitoring Over-Dispersed Bats
 

Issue Description and Rationale 

Determining the appropriate spatial scale for monitoring is a critical issue (see also Working Group C Report, Issue 2, “Analytical Considerations for a National 

Bat Monitoring Program”). Monitoring programs can be 

established to evaluate population trends at a number of 

spatial scales, from very small, localized populations (e.g., 

at a scale of several hectares) to regional trends (e.g., 

within states or regions of the country) or at very expansive spatial scales (e.g., nationally or across the entire 

distribution of the species). Real or apparent trends at 

very restrictive spatial scales could be an artifact of localized conditions or stochastic variation that is offset by 

counter-trends within other small populations. As a consequence, monitoring at very fine spatial resolutions is 

likely to be of value to managers only under limited situations. For over-dispersed bats, the appropriate scale to 

provide meaningful information for conservation or management of bats will generally be at the regional or higher 

spatial scales. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Pending development of techniques to better 

estimate population parameters for this group of bats (see 

Issue 1, this subgroup), progress may be limited. However, 

methods to determine sampling protocols at different 

spatial scales are well developed in the statistical and 

sampling literature (e.g., Goodwin and Fahrig, 1998). If 

feasible, sampling protocols based on stratified random 

sampling of large areas probably would be most 

appropriate for this group. 

The resources required to implement even modest efforts for a well-developed, statistically rigorous, large-scale 

monitoring program for over-dispersed bats would be considerable. It is unlikely that technological advances in approaches to monitor bats will alter this in the foreseeable 

future. Compilation of data from existing mist netting, trapping, or bat detector studies may be an alternative to development of rigorous large-scale sampling for these species. 

However, the previously mentioned caveats concerning 

these methods should not be overlooked. 

Over-Dispersed Bats Subgroup Issue 5. 

Alternatives to Monitoring 

Issue Description and Rationale 

Because of the difficulties noted above in monitoring populations of over-dispersed bats, current evaluation of population trends in these bats may require use of 

alternatives to monitoring. One valuable alternative approach is based on the premise that causal factors related 

to abundance, survival, or recruitment of bats could be 

identified. The extent to which those causal factors are 

expressed in some geographic area would reflect status 

and changes in population parameters through time. Studies of the response of bats to habitat structure or environmental perturbation conducted at appropriate spatial 

scales could serve as surrogates for monitoring. Applicability of data collected from these studies beyond the 

area studied should be tested to determine the limits of 

their applicability (e.g., spatial and temporal scale). 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Double sampling is a method that can be used to 

statistically calibrate surrogates (e.g., Thompson and 

others, 1998). This method uses mark-resight or other 

reliable enumeration techniques to calibrate expensively 

measured parameters (e.g., density) to those more easily 

measured (e.g., habitat type). Following development of 

appropriate methodologies, studies should involve the 

use of mark-resight techniques to obtain population 

densities in limited areas. Causal factors that may 

influence density should be identified and evaluated. Then 

extrapolation can be done across a limited area where 

similar factors occur. Although initial studies correlating 
potential causal variables and population parameters will 

be costly and time consuming, measurement of the 

surrogate variable across the inference area should be 

relatively easy. 

There are many examples of studies of relative use of 

different areas by bats. Because most of the methods do 

not account for detection probability, many of these approaches lack statistical rigor. We recommend that future 

studies attempt to evaluate population density, rather than 

an index of abundance, wherever possible. Furthermore, 

these programs should include double sampling methods to extrapolate results to wider spatial scales. 

Subgroup Report: Subgroup Report: Subgroup Report: Assessment of Assessment of 
 Assessment of

Population Size and Population Size and Population Size and Trends in rends in 
 rends inrends in

Pacific Island Fruit Bats Pacific Island Fruit Bats 
 Pacific Island Fruit Bats

Subgroup Members: Anne Brooke, Ruth Utzurrum, 

Gary Wiles, and Don Wilson 

In the geographic areas under consideration, American Samoa, Guam, and the Commonwealth of the Northern Marianas, there are three species of fruit bats: 

Pteropus mariannus, P. samoensis, and P. tonganus. A 

review of census methodology and population trends for 

these three species appears in Utzurrum and others (2003). 

In general, these three species fit into two basic lifestyles: 

colonial and solitary. 

Pteropus samoensis is solitary, with individual bats 

roosting alone in the canopy of the forest. Most animals 

spend at least part of their time foraging actively during 

the day, and their tendency to soar and ride thermals 

makes them visible to properly situated observers. For 

the past decade or so, relatively standardized counts of 

flying bats over given periods of time have been made at 

permanently located stations. The numbers generated by 

these counts are used as an index to the health of the 

population on the largest island in American Samoa. 

Pteropus tonganus occurs in colonies ranging from 

dozens to thousands of bats. The colonies are relatively 

easy to detect, although hunting pressure in years past 

in American Samoa has driven the colonies to the most 

inaccessible parts of the islands. Once colonies are located, it is possible to census them by direct counts using binoculars and spotting scopes, but there is 

considerable variation in the counts, due to differential 

detectability of animals within a colony. It is also possible 

in some cases to make dispersal counts on colonies. These 

counts are also subject to some unknown amount of variaPART II 253 

tion due to potential differential dispersal routes for the 

colonies. Some unknown (although probably small) percentage of the population also roosts solitarily and is 

well dispersed with regard to known colonies. 

Pteropus mariannus has a lifestyle similar to that of P. 

tonganus. Most animals live in colonies that are relatively 

easy to detect. However, an unknown percentage 

(possibly somewhat higher than in P. tonganus) also lives 

solitarily at any given time. On Guam, a single remaining 

colony has been censused monthly by direct counts by 

the same individual for the past 15 years. These counts 

are reasonably reliable, and the population estimates for 

Guam are probably the most sound of all three species 

and all other areas. Counts on other islands in the Northern 

Marianas are less reliable, and have been conducted 

regularly only on a single island (Rota). Counts on these 

islands are done with combinations of direct colony 

counts, indirect departure counts, and counts of flying 

bats at widely dispersed observation stations. Some 

unknown colonies likely remain to be detected. 

Pacific Island Fruit Bat Subgroup Issue 1.
 

Difficulties in Censusing Pacific Island
 

Fruit Bats
 

Issue Description and Rationale 

P. samoensis presents the most intractable problems 

among the three species. Its solitary roosting habits and 

dispersion through inaccessible forest in extremely rugged terrain makes censusing difficult. Different observers have performed the station counts over time and the 

techniques themselves have been modified slightly at 

different times. This makes even relative comparisons 

somewhat difficult to make. There is a need for a means to 

measure detectability, and for a means to extrapolate the 

findings from the areas surveyed to the entire population. 

P. tonganus presents a different, but related set of 

problems. Probably not all roosts are currently known. 

Improved means to detect all roosts on a given island are 

needed. Counting individuals within a known roost is also 

difficult. There is a need for better methods of standardizing these counts, and of getting some measure of interobserver differences. These problems apply equally to 

dispersal counts conducted at P. tonganus roosts. 

The problems with P. mariannus are similar to those 

outlined for P. tonganus. We need to locate all of the 

colonies on a given island, especially in the Northern 

Marianas. Once located, the colonies need to be censused 

in a more standardized manner, allowing some indication 

of individual observer differences. In addition, some 
254 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

improved technique for estimating the size of the 

population that occurs as solitary individuals is needed. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

We met with David Anderson and discussed a number 

of methodological approaches to censusing these species. 

Use of mark-recapture methods appears stymied at 

present by our current inability to reliably capture the 

animals for marking. In turn, distance techniques that rely 

on some measure of detectability seem precluded by 

logistical difficulties. 

For all three species, the most pressing need is for a 

measure of detectability that would allow more accurate 

estimation of the total population from current counting 

techniques. We believe that research directed towards 

improving the census methodology in that direction 

should be pursued. Probably the most promising area is 

to devise a method of capturing the animals that would 

allow marking. If we had a marked proportion of animals 

in any of our study areas, it would allow us to begin the 

process of injecting more rigor into the statistical analysis of our count data. 

Additionally, research into attracting animals using 

artificial lures might be profitable. Recordings of calls, or 

artificially generated call simulations, might allow bats to 

be attracted to sites where they could be counted or 

marked. Similarly, research directed at using scent stations based on actual food sources, or chemically enhanced stimuli, might be useful. If the bats could be 

attracted to some sort of bait station, it would greatly 

increase the chances of capturing and marking them. If 

bats can be attracted to chosen sites, we would also need 

additional research on methods of netting or trapping 

them. Methods of self-marking at such bait stations 

should also be explored. 

We also recommend additional study into the 

possibility of controlled hunts in some areas or some 

islands. This might be especially useful if some method 

of marking animals is developed. Such hunts might 

increase involvement of the local people in conservation 

activities by allowing their participation in a worthwhile 

scientific endeavor, while at the same time enjoying 

traditional hunting activities that are currently denied. 

Additional research into the feasibility of using aerial 

surveys and remote sensing information to detect colonies 

of both P. tonganus and P. mariannus would be useful. In 

the interim, the currently used census methods should 

be continued and every possible effort should be made 

to standardize them as much as possible. In addition, 

logical covariates of bat population densities also should 

be measured regularly, with a view towards explaining 

future trends. 

Subgroup Report: Improving Subgroup Report: Improving
 Subgroup Report: Improving

Assessment of Numbers and Assessment of Numbers and 
 Assessment of Numbers and

Trends in Southwestern rends in Southwestern rends in Southwestern
 

Pollinators Pollinators 
 PollinatorsPollinators Pollinators

Subgroup members: Mike Bogan, Paul Cryan, Virginia 

Dalton, Ted Fleming, and Rodrigo Medellin 

Three species of nectarivorous bats seasonally occur 

in the southwestern U.S. (primarily Arizona and New 

Mexico); the greater part of their geographic range is in 

Mexico. During the spring and summer they migrate northward into the U.S. as flowering plants (columnar cacti 

and agaves), on which they depend for sustenance, begin to bloom. These three species play an important, but 

not clearly understood, role in southwestern ecosystems, 

primarily by providing pollination and seed-dispersal services. The three species are: 

•	 Leptonycteris curasoae, Lesser Long-nosed Bat. 

Most of the major roosts are in Mexico. The species occurs seasonally in several large maternity 

roosts in southwestern Arizona and in smaller 

numbers in southeastern Arizona and southwestern New Mexico. The species is listed as endangered by the U.S. Fish and Wildlife Service (FWS). 

•	 Leptonycteris nivalis, Greater Long-nosed Bat. 

Little is known of this species although it occurs 

in some large roosts in Mexico. In the U.S., it is 

known only from southwestern New Mexico in 

late summer and from one cave roost in Big Bend 

National Park in Texas. The species is listed as 

endangered by the FWS. 

•	 Choeronycteris mexicana, Mexican Long-tongued 

Bat. This species ranges from Honduras northward into southern Arizona and New Mexico in 

the spring and summer. The species is a former 

FWS Category 2 Candidate Species and is now 

considered a “Species of Concern.” 

Southwestern Pollinator Subgroup Issue 1.
 

Relative Value of Current Efforts to Monitor
 

Leptonycteris curasoae
 

Issue Description and Rationale 

Leptonycteris curasoae is listed as endangered in 

the U.S. and is of special concern in Mexico. Monitoring 

programs are currently in place in Mexico and are conducted by the Program for the Conservation of Migratory Bats (PCMM). Roost sites are visited once a month 

or every other month. During each visit, census data are 
collected in a standardized fashion (data also are recorded 

for L. nivalis). The program hopes to detect both longterm declines and catastrophic events (e.g., vandalism, 

etc.). Despite the endangered status of L. curasoae, there 

is no coordinated monitoring program in the U.S. Efforts 

to monitor the species in the U.S. have been conducted 

by several individuals in a non-standardized fashion; 

monitoring in the U.S. is not coordinated with Mexican 

efforts. Current techniques involve counting bats in, or 

as they exit, their roosts. 

Current efforts are based on two major assumptions. 

The first assumption is that there is an equal likelihood 

that bats will return to the same site year after year. The 

second assumption is that there is minimal movement of 

bats between roosts during the monitoring period. Based 

on our current knowledge of these species, we are confident that these assumptions are not seriously violated in 

current monitoring efforts and that such efforts are producing useful information on population trends in roosts. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

The subgroup agreed that current census efforts provide sufficient resolution to monitor major population 

trends and catastrophic events and should be continued. Additionally, the PCMM is a valuable conservation 

and education effort that should continue in Mexico. 

Nonetheless, current efforts are low resolution and should 

be improved. Deficiencies of the current system and ways 

to improve these efforts, including using a standardized 

monitoring approach throughout the range of the species, are discussed in the context of Issue 2. 

Southwestern Pollinator Subgroup Issue 2.
 

Standardizing Monitoring Techniques for
 

Leptonycteris curasoae
 

Issue Description and Rationale 

An important problem is the absence of a standardized approach to counts of bats of this species over time 

and space. The following issues and possible solutions 

are important in attempting to develop a standardized 

counting protocol for L. curasoae and may also be useful 

for the other two species of pollinating bats in the U.S. 

Means to Resolve Critical Uncertainties Surrounding the Issue 

a. 	 Methods of counting emerging bats. Comparisons 

of counts made from videotapes to real-time visual 

PART II 255 

observations suggest that videotaping the 

emergence provides the most reliable way to 

count (Dalton and Dalton, 1994). Two individuals 

should make all counts of videotaped emergences 

until counts converge. Video also has archival 

properties and digital images may be quantified 

with computer methodology that is in 

development. The subgroup recommended that 

a cascade of approaches be used with infrared 

videotaping preferred where and when equipment 

is available. In the absence of that equipment, 

internal or exit counts should be made by at least 

two or more observers. Using only a single 

observer is not recommended, as then no error 

estimate is possible. 

b.	 Types of illumination used during exit counts. It 

is likely that both white light and red-filtered lights 

modify bat behavior. We recommend the following light types, in order of preference: (1) infrared, (2) red-filtered, and (3) white. 

c. 	 Length of emergence counts. Current efforts generally count through a period that is believed to 

approximate the major portion of the emergence, 

about two hours, and this seems adequate. It 

might be useful to obtain more precise data on 

length of emergences. 

d.	 Covariates that should be recorded during exit 

counts. We recommend that the following 

covariates be recorded: time of day, length of time 

for emergence, presence and relative amount of 

nearby water, wind speed, temperature, other climatological factors, phenology of flowering 

plants important to bats, and other noteworthy 

items, including evidence of disturbance. These 

factors may be used as covariates to help explain 

variation in colony numbers. 

e. 	 Counting target species in roosts with multiple 

species. Multispecies roosts confound exit 

counts at many of the significant roosts of L. 

curasoae in Mexico. Suggested solutions include 

conducting an internal count first to determine 

the proportion of each species in the roost, then 

conducting the emergence count, and adjusting 

the number by proportion present (this should 

be tested for reliability and, ideally, two observers 

should estimate proportions and numbers). 

Videotaping and still photographs also may 

provide estimates of proportions of other species 

in the roost. Additional work is needed to further 

address this problem. 

f.	 Minimum number of observers needed to make 

counts. This varies by site to some extent but as 

noted earlier, at least two individuals should count 

bats, whether on tape or during emergence. Those 
256 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

in charge of monitoring roosts should attempt to 

get additional help when needed. In the U.S., this 

may be less of a problem because there are fewer 

roosts and a shorter season in which roosts must 

be monitored. 

g.	 Standardized descriptions of roosting sites 

(caves, mines). We recommend that attempts be 

initiated to develop standardized descriptions for 

roosts of this species. Most important are 

descriptions of roost configuration (e.g., location, 

shape and size of main exit, number of exits, 

passages, length, etc.). A standardized protocol 

to describe these and related aspects of roosts 

may be useful. In Mexico, PCMM uses a 

speleologist to go to each cave that is monitored 

and provide cave maps with entrances and other 

details. In addition, qualitative descriptions of 

nearby vegetation, nearest available water, and 

selected microclimate variables should be 

included. 

h.	 Ranking of roost sites in terms of biological or 

conservation importance. In Mexico, due to the 

number of roost sites and the fact that they cannot all be monitored in 1 year, roosts are ranked 

for monitoring purposes. Rankings are based on 

the number of bats present, status of species 

occupying the cave, species richness, proximity 

of the roost to threats (e.g., urban areas), and 

location of the roost in relation to migratory 

routes. 

i. 	 Standardized schedule for exit counts. Ken 

Burnham noted that if we are only trying to monitor long-term changes due to environmental degradation we do not need to monitor every year. If 

there is a need to check sites for catastrophic 

changes or vandalism this can still be done without conducting exit counts on every visit. This 

may allow more roosts to be covered in a given 

period (e.g., every 2 years). 

j. 	 Standardizing counts of bats inside caves or 

mines. In Mexico, the configuration of some caves 

limits the feasibility of emergence counts as observers or video equipment cannot be usefully 

located. Thus, internal counts are the only possible means of counting. We recommend that in 

such situations the counts be conducted by two 

observers (see also Altenbach, 1995), so that error estimates can be made. 

k.	 Importance of transient roosts for monitoring. 

There are potentially important transient roosts 

in southeastern Arizona in early and late summer 

that are likely dependent on a localized food 

resource. These bats may represent a presently 

unknown maternity colony in northeastern 

Sonora. Even though we are uncertain of the 

importance of some transient roosts, there was a 

consensus that exit counts should be conducted 

at these sites as well. 

l. 	 Disturbance of bats during monitoring activities. There was general agreement that bats may 

move due to disturbance but that such moves 

are temporary. Nonetheless, counts and other 

activities should be conducted with the least 

possible disturbance to the bats. 

Southwestern Pollinator Subgroup Issue 3. 

Monitoring of Leptonycteris nivalis 

Issue Description and Rationale 

In Mexico, PCMM is trying to identify gaps in information pertaining to L. nivalis and will initiate further 

work in the future. In the U.S., the only known roost of L. 

nivalis is at Mount Emory Cave, Big Bend National Park, 

Texas; occasionally individuals have been captured in 

New Mexico. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

We recommend that the U.S. National Park Service 

initiate or allow routine monitoring of Mount Emory Cave, 

as well as searching the area around Mount Emory Cave, 

and perhaps adjacent areas, for additional caves that may 

be used by L. nivalis. Researchers in New Mexico and 

southeastern Arizona should be alert to the possibility 

that they may capture L. nivalis at times. Such instances 

should be recorded and forwarded to a central clearinghouse for information on the species. 

Southwestern Pollinator Subgroup Issue 4. 

Monitoring of Choeronycteris mexicana 

Issue Description and Rationale 

We discussed monitoring needs of C. mexicana as a 

part of our activities. The U.S. Geological Survey (USGS) 

conducted a search for all historic roosts of this species 

in Arizona and New Mexico during summer 1999 (Cryan 

and Bogan, 2003). Site fidelity was high, occupancy rates 

were consistent with historic numbers, and young 
PART II 257 

frequently accompanied females. This species may be an 

example of an “over-dispersed” species, and comments 

elsewhere in this report may pertain as well (see Working 

Group A subgroup report, “Over-Dispersed Bats”). 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Given the generally favorable nature of the 1999 

survey results (Cryan and Bogan, 2003) along with 

comments by K. Burnham on needed frequency of actual 

counts, we recommend that the survey be repeated every 

2 to 3 years. Choeronycteris appears to be amenable to a 

recruitment and survivorship marking study because 

individuals are visible from outside the roost, they are 

found in manageable groups, and are relatively limited in 

distribution (patchy). There was a consensus that this 

would be worthwhile only as part of an in-depth, longterm research study of the biology of the species. Given 

the ability to make actual counts, marking of individuals 

is not needed for monitoring efforts. 

Southwestern Pollinator Subgroup Issue 5. 

Continuation of Baseline Monitoring Efforts 

Issue Description and Rationale 

The subgroup agreed that efforts to establish 

baseline monitoring information and data for these three 

species should be continued. There was further consensus that this probably has to be done on a species-byspecies basis. There is not enough monitoring directed 

at L. nivalis, and the first attempt at a range-wide survey 

for C. mexicana in the U.S. was just completed (Cryan 

and Bogan, 2003). In addition, efforts should be continued to find new roosts, particularly in areas where there 

are gaps in the known current range. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

As noted earlier, with relatively long-lived species, 

such as bats, it is not necessary to monitor every year to 

pick up long-term trends in population. Given current 

budgets and resources available for monitoring, 

monitoring every 2 years could increase the number of 

roosts monitored over time, particularly in Mexico. 

However, annual counts are useful for picking up shortterm changes, catastrophic events, and gathering data 

on covariate influence on population numbers. 

Southwestern Pollinator Subgroup Issue 6. 

Sharing of Baseline and Monitoring Data 

for the Three Species 

Issue Description and Rationale 

In the case of L. curasoae, we have two types of 

data: roost locality/characteristics and counts of bats at 

roosts. We agreed that precise locality data must be controlled and released only to qualified individuals. We also 

reached a consensus that we need a central repository 

for all data, but at this time could not agree on where that 

would be. In Mexico, the Comision Nacional Para El 

Conocimiento y Uso de la Biodiversidad (CONABIO)2 

will fund projects to gather data. The data are the 

collector’s for 5 years after collection, but then become 

available to others, unless the collector specifically requests controlled access to data. Then the collector becomes the gatekeeper to data. PCMM posts metadata 

rather than specific data. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Efforts should be continued to identify a central clearinghouse for information on the three species as well as 

to resolve differences about exactly what data should be 

stored and what should be released to various parties 

interested in the data. 

Southwestern Pollinator Subgroup Issue 7. 

Funding for Monitoring and Research 

Issue Description and Rationale 

Funding for this group of unique pollinators seems 

relatively difficult to obtain, other than for specific research 

studies. Recovery plans have been written for the two 

endangered species of pollinating bats, but we were 

uncertain whether the plans are being implemented. Both 

plans contain fairly complete synopses of useful and 

important research and management activities that should 

be conducted as a part of the recovery of the two species. 

2

Editor’s note: CONABIO is an interministerial Mexican government commission established by Presidential decree 

March 16, 1992. The mission of CONABIO is to coordinate 

conservation and research efforts designed to preserve Mexico’s 

biological resources. For additional information see: 

http://www.conabio.gob.mx. 
258 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Efforts should be initiated at federal and state levels 

to obtain funding for collecting baseline information on 

these species and for long-term population monitoring. 

Current interest in pollinators may provide a useful springboard for efforts to obtain such funding. Discussions on 

the status of recovery plans and the need to initiate greater 

levels of activity should be held with Department of the 

Interior agencies that have lands on which these species 

occur or that have mandated responsibilities under the 

ESA. 

Southwestern Pollinator Subgroup Issue 8. 

Associated ResearchActivities 

We discussed the potential of more sophisticated 

monitoring regimes (e.g., mark and recapture studies) for 

estimating population parameters. Ken Burnham noted 

that such approaches should best be reserved only for 

research purposes and should not be used for long-term 

monitoring given the geographic distribution of roosts 

and logistical difficulties of moving among roosts. Banding studies would help identify movement between colonies, provide information on site fidelity, and allow some 

inferences on natality and mortality. However, such studies would require thousands of marked individuals and 

intensive follow-up monitoring. 

Several factors confound our ability to monitor these 

species. Migration, and our relative ignorance of it, makes 

decisions on sampling and sampling frames difficult. It 

might be possible to use a particular season of the year 

when the bats are most concentrated within their range 

and those sites could be sampled; however, this 

information is not currently available. If winter is the time 

of greatest concentration of L. curasoae, then it may be 

possible to count all 30 known wintering sites (estimated). 

If all sites cannot be visited within a short period, sampling 

priorities could be established (e.g., by using numbers of 

bats present), and then a sample of caves/roosts could 

be selected. 

Indirect methods, such as monitoring bat visitation 

at flowers and feeders may offer promise in identifying 

areas of new or unknown roosts and times of arrival and 

departure. In addition, there may be some use for 

molecular tools in assessing historical, long-term 

population numbers but only for research purposes. 

Finally, there may be a potential role for non-specialists 

in these efforts, in Mexico to help define migration 

corridors, and in the U.S. to monitor bat use of 

hummingbird feeders. 

Working Group B. Categorizing orking Group B. Categorizing 


U.S. Bat Species or Species U.S. Bat Species or Species 
 U.S. Bat Species or Species

Groups, and Regions in Groups, and Regions in Terms of erms of erms of
 

Priorities for Establishing Priorities for Establishing 


Population-Trend Monitoring rend Monitoring 
 rend Monitoring

Programs Based on Conservation Programs Based on Conservation 


Concerns, Roosting Habits, Concerns, Roosting Habits, 
 Concerns, Roosting Habits,

Distribution, Threats, Distribution, Threats, 
 Distribution, Threats,

and Other Factors and Other Factors and Other Factors
 

Working Group Members: Pat Brown, Mary Kay 

Clark, Joe Kath (Leader), Allen Kurta (Rapporteur), Kirk 

Navo, David Saugey, Merlin Tuttle, Ernest Valdez, and 

Mike Wunder 

Monitoring any population of animals generates a 

wealth of biological information, including increased 

knowledge of natural history, ecology, and behavior. Such 

information is potentially useful to wildlife managers and 

research biologists and can be of interest to the general 

public. In addition, data obtained by monitoring are essential for demonstrating demographic trends that are 

important to conservation. 

Although it may be intrinsically desirable to monitor 

all species, such an undertaking may not be necessary or 

practical. Before beginning a monitoring program, one 

must establish the: 

• 	 goal of the monitoring program, 

• 	 feasibility of the monitoring program, and 

• 	 criteria to be used when deciding which species 

or population to monitor. 

In this paper, we focus on the latter two issues and 

examine various biological and non-biological factors to 

consider when deciding which group of bats to monitor. 

Our discussion touches on six broad categories of factors that are not mutually exclusive. These categories 

are: (1) distribution, (2) feeding strategy, (3) roosting habits, 

(4) population status, (5) threats, and (6) reality. 

Distribution 

Bats display an array of geographic distributions. 

Some, such as the hoary bat (Lasiurus cinereus), occur 

across the North American continent, whereas others, 

such as Wagner’s mastiff bat (Eumops glaucinus), are 

found only in small portions of a single state. Other species with limited distribution are restricted to oceanic islands (e.g., Samoan flying fox, Pteropus samoensis) or to 
islands of uncommon habitat (e.g., Mexican long-tongued 

bat, Choeronycteris mexicana, in the Sonoran Desert). In 

general, taxa with localized distributions are more amenable to monitoring because of logistic considerations, 

and often are those species more in need of monitoring 

because of their presumed smaller population sizes. A 

related concern is the disjunct distribution of some taxa, 

such as the Virginia big-eared bat (Corynorhinus 

townsendii virginianus). Although the entire range may 

appear large, the individual, isolated populations may be 

highly vulnerable and, thus, more in need of monitoring. 

The size of a species range is one consideration, but 

location of that range in relation to human activity may 

be equally important. Humans are capable of drastically 

altering the landscape, and bat populations occurring 

within areas undergoing rapid change are of particular 

concern. Large-scale changes, such as urban sprawl, rural 

development, habitat fragmentation, and artificial 

conversion of forest types may negatively impact bat 

populations by altering roosting and foraging habitat 

(Carter and others, 2003). For example, in the southeastern 

U.S., a rapidly expanding human population coupled with 

fragmentation and loss of bottomland hardwood forests 

(Carter and others, 2003; Clark, 2003) may signal a need 

for monitoring activities in that region. 

Feeding Strategy 

Bats in the U.S. and its territories have three broad 

feeding strategies: insectivory, nectarivory, and frugivory. 

Most species are insectivorous, but available data on 

specific dietary items vary considerably across species 

and season (e.g., Ross, 1961; Black, 1974; Whitaker, 1972, 

1988, 1995). Even for those taxa that have been studied in 

greatest detail, dietary components generally have been 

identified only to the level of order and, occasionally, 

family. To better understand the role of bats in their ecosystems or their economic value to forestry or agriculture will require identification of prey to the level of genus 

and species. Detailed studies have shown the economic 

importance of at least two species of North American 

bats that prey on crop pests. The Mexican free-tailed bat 

(Tadarida brasiliensis) preys on corn earworm moths 

[Helicoverpa zea (McCracken and others, 1997)], and the 

big brown bat (Eptesicus fuscus) consumes large numbers of cucumber beetles (Diabrotica spp.), the larvae of 

which are the destructive corn rootworm (Whitaker, 1995). 

Because most bat communities in the U.S. are insectivorous and the diet of most species is so poorly understood, prioritizing monitoring needs based on diet does 

not seem reasonable for most parts of the country. 

There are only three nectarivorous species 

(Leptonycteris curasoe, Leptonycteris nivalis, and 

PART II 259 

Choeronycteris mexicana) and one frugivorous species 

(Artibeus jamaicensis) that occur in the U.S., although 

several others are found in various Pacific and Caribbean 

territories (see also Working Group A, “Pacific Island Fruit 

Bats” and “Southwestern Pollinators: subgroup reports). 

Nectarivorous species are functionally important in their 

ecosystems because of their role in pollinating various 

plants. For example, the three species found in the U.S. 

are important pollinators of columnar cacti and paniculate 

agaves, even though they spend only a portion of the 

year in the southwestern part of the country (Fleming 

and others, 2003). Nectarivorous species often eat fruit 

and function as seed dispersers, in addition to their role 

as pollinators. Similarly, frugivores are functionally 

important, acting as seed dispersers and occasionally as 

pollinators for a variety of tropical plants. On some Pacific 

Islands, pteropodid bats are responsible for dispersing 

the seeds or pollinating the flowers of more than 50% of 

the species of native woody plants (Fujita and Tuttle, 

1991; Banack, 1998). In areas where the ecological or 

economic importance of bats has been demonstrated, 

feeding strategy is one factor that might be considered 

when prioritizing monitoring needs. 

Roosting Habits 

Roosting habits of bats are highly varied, but in general, roosting sites can be categorized as either “natural” 

or “anthropogenic” (Pierson, 1998). Natural roosts include 

caves, rock crevices, and trees. Trees, in turn, provide 

roosting sites underneath loose bark, in cavities or crevices, or in the foliage. Anthropogenic roosts include buildings, bridges, and mines, among others. Some species of 

bats are roost specialists and are restricted to only one or 

few types of roosts; for example, gray bats (Myotis 

grisescens) roost only in caves throughout the year. Other 

species, in contrast, are generalists, using different roost 

types at any one time of the year (e.g., big brown bats use 

trees, bridges, and buildings in summer and caves, mines, 

and buildings in winter). 

In the past, most monitoring efforts focused on 

roosts, and today, roosting habits are still factors to 

consider when deciding which species or population to 

monitor. A species that uses only one type of uncommon 

roost is predictable in time and space, potentially 

simplifying the monitoring task (e.g., California leaf-nosed 

bat, Macrotus californicus, in geothermally heated mines). 

In addition, dependency on an uncommon type of roost 

makes an extreme specialist more susceptible to 

population declines, thus making monitoring more critical. 

Species that rely on roosting sites that are common in the 

environment may be difficult to monitor, even if they 
260 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

“specialize.” For example, hoary bats only roost in the 

foliage of trees, but potential roost trees often are 

abundant and widely dispersed across the landscape, 

making it difficult to locate, let alone monitor, populations 

of such a species (see also Working Group A, “OverDispersed Bats” subgroup report). 

At least three aspects of roosting behavior--social 

grouping, movement among roosts, and intersexual 

differences--must also be considered when developing 

monitoring priorities. Some species (e.g., the lasiurines) 

are solitary, some form small colonies containing a few 

hundred individuals or less (e.g., Rafinesque’s big-eared 

bat, Corynorhinus rafinesquii), and others aggregate in 

the millions (e.g., Mexican free-tailed bat). A monitoring 

program may be more successful if based on a species 

that roosts in moderate-to-large colonies because of the 

relative ease in detecting such roosts and the fewer sites 

that need to be monitored. (See also Working Group A, 

“Colonial Bats” subgroup report.) 

Some bats, particularly species that live in trees, tend 

to change roosts frequently (Lewis, 1995). Female Indiana bats (Myotis sodalis), for example, change roosts 

about every 3 days. A group of these bats may use more 

than 17 different trees in a single maternity season (Kurta 

and others, 1996). Such roost-switching behavior makes 

the monitoring task extremely difficult because of the 

unpredictability of the bats in space and time. 

To complicate matters even further, males and females 

of many species often exhibit different roosting behaviors. Adult female little brown bats (Myotis lucifugus) 

typically roost in summer maternity colonies that contain 

more than 95% females, whereas adult males generally 

are solitary (Barbour and Davis, 1969). If the goal of the 

monitoring program is to analyze long-term trends for an 

entire population, then a monitoring procedure that focuses on only one sex may not yield the desired results. 

Population Status 

Bats as a group may rank as the most endangered 

land mammals in the U.S. (Tuttle, 1995), with eight species 

or subspecies classified as endangered and others 

classified as candidates for listing or considered species 

of concern. Today, population status (i.e., endangered, 

threatened, etc.) is often the first, and occasionally the 

only, consideration in prioritizing monitoring and 

conservation needs. Although convenient, the practice 

of solely relying on government-designated status to 

prioritize species for monitoring may not be justified. For 

example, the gray bat is classified as endangered by the 

federal government, but it is well on its way to recovery 

(M.D. Tuttle, oral commun., 1999). Establishing a new 

monitoring program for this species, simply because it is 

endangered, may not be warranted. Other species, such 

as the Indiana bat, may be so imperiled (U.S. Fish and 

Wildlife Service, 1999) that immediate, direct measures 

are more likely to benefit the species than a long-term 

monitoring program that may not produce results for 

years. Finally, a monitoring program may better benefit 

unlisted species (e.g., small-footed bat, Myotis leibii, or 

red bat, Lasiurus borealis), providing data needed to 

prevent such taxa from being listed in the future. 

Threats 

More important than a government-designated status 

may be the actual threats to continued survival of a species 

or population. Potential threats to bats may be direct or 

indirect (Tuttle and Stevenson, 1982; Pierson, 1998). Direct 

destruction includes, among other things, hunting for 

food (Rainey, 1998; Utzurrum and others, 2003), 

extermination from building roosts (Cope and Hendricks, 

1970), and wanton killing (Tuttle, 1995). Indirect 

destruction may not be as obvious as direct killing, but 

for many species, indirect threats potentially have greater 

impact. Many indirect threats are ecological in nature and 

relate to water, food, and roosts. 

Mining operations indirectly kill bats that drink from 

leaching ponds containing cyanide (Clark, 1991; Clark 

and Hothem, 1991). Changes in water quality impact the 

prey of bats (Vaughan and others, 1996) and may partly 

explain decreased species diversity of bats in urban areas 

(Kurta and Teramino, 1992). Pesticides that enter the food 

chain result indirectly in death or decreased reproductive 

success (Clark, 1981, 1988), and many other chemicals, 

such as environmental estrogens (MacLachlan and 

Arnold, 1996), may have deleterious, but currently 

undiscovered, effects on bats. Food chains may be 

disrupted if foraging habitat is destroyed or modified, 

leading to a decline in bat populations (Brown and others, 

1993, 1995). Reproductive success decreases after 

maternity colonies are excluded from buildings (Brigham 

and Fenton, 1986), and closure of abandoned mines 

indirectly causes decreased survival or reproductive 

success by eliminating maternity and hibernation sites 

(Tuttle and Taylor, 1994). Our purpose is not to list every 

possible source of mortality (Tuttle and Stevenson, 1982; 

Pierson, 1998) but to illustrate the different ways in which 

bats are affected by human activity. Species or populations 

with clearly defined threats may be more in need of 

monitoring programs than other groups. 
Reality 

The feasibility and eventual success of batmonitoring programs depend on making sound biological 

choices, having appropriate statistical techniques (see 

Working Group A report; Sauer, 2003), and securing 

appropriate resources, such as personnel, equipment, and 

funds. Any monitoring program requires workers in the 

field and a program demanding a large number of highly 

skilled workers may be more difficult to implement than 

one designed to use volunteers with minimal training 

(Walsh and others, 2003). Similarly, technologically simple 

programs may be less expensive and easier to implement. 

On the other hand, some projects may have to wait 

development of technological innovations or new 

statistical methodology. 

Most personnel and equipment problems may be 

overcome (at least in theory) by increased levels of 

funding, but in reality, budgets are rarely adequate. 

Funding for any monitoring program is influenced by 

economic factors, legal considerations, and public 

opinions. Projects with demonstrated effects on 

agriculture or forestry are more likely to be funded. Legal 

mandates, such as the Endangered Species Act and the 

National Environmental Policy Act, can bias which 

species is monitored and where. Public opinion can 

influence whether or not private organizations or 

government agencies will fund a particular program. A 

positive public attitude also may lead to a greater number 

of volunteers for a monitoring program, as well as increased 

donations to private or government agencies that 

ultimately may sponsor bat-monitoring programs (see 

Working Group C report, this volume). In contrast, 

negative attitudes, such as those fostered by some public 

health agencies (Tuttle, 1999), may affect the ability to 

obtain funds or volunteers for any monitoring program 

dealing with bats. Although, in a perfect world, science 

should direct priorities, practical considerations (funding, 

equipment, personnel) are unavoidable. 

Concluding Comments 

The decision as to which species or population to 

monitor is complex, and one must consider a range of 

biological and practical considerations. Unfortunately, 

there is no single set of guidelines that can be used with 

every bat community in every part of the country. Specific 

criteria used to prioritize species for monitoring will 

depend on the goals of the program, the species involved, 

and the scale of the program (national vs. local). 

PART II 261 

Monitoring programs are essential for effective 

conservation and management of bat populations, but 

the details of any program, including selection of species, 

must be tailored for each situation. 

Working Group C. Existing orking Group C. Existing 


Information and Programs Information and Programs 
 Information and Programs

to Monitor Bat Population to Monitor Bat Population 
 to Monitor Bat Population

Trends: Utility and Coverage of rends: Utility and Coverage of 
 rends: Utility and Coverage of

Current Efforts and Potential Current Efforts and Potential Current Efforts and Potential
 

Expansion in Scale Expansion in Scale 
 Expansion in Scale

Working Group Members: Norita Chaney, Alice 

Chung-MacCoubrey, Rick Clawson, Laura Ellison (Rapporteur), Steve Fancy, Tom O’Shea (Leader), Paul Racey, 

John Sauer, and Allyson Walsh 

Overview 

Participants submitted a number of issues for consideration under this topic in advance of the workshop. 

These issues generally fell into four broad categories: 

organizational and implementation issues, design and 

analysis issues, programmatic and policy issues, and data 

management issues. Based on the presentations at the 

overall meeting and results of the panel discussion, we 

concluded that expanding use of existing information to 

estimate bat population trends on a broad scale presents 

difficult sampling and design challenges that could not 

be fully explored in the available time. The group instead 

focused on making recommendations on five issues that 

are important precursors to consideration of future expanded-scale bat monitoring programs. These issues include: (1) the current lack of organization of existing 

programs and information on monitoring bat populations 

in the U.S.; (2) necessary analytical considerations for 

monitoring bats on an expanded or national scale; (3) lack 

of a unifying mandate or legislative foundation for bat 

conservation; (4) promoting public awareness and gaining support for such a mandate (e.g., a National Bat Awareness Week); and (5) optimizing information obtained from 

marked bats (including existing efforts as well as future 

studies). 

The Working Group recognized the importance of the 

limited existing information on bat population status, and 

the value of compiling and synthesizing this information 

on a national scale in efforts such as the U.S. Geological 
262 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Survey’s Bat Population Database. The group also 

recognized that although well-designed frameworks for 

using existing information to measure bat population 

trends with statistical accuracy and precision have been 

lacking, there are qualitative historical comparisons, indexbased studies, and anecdotal but reliable accounts of 

declines that provide a strong imperative for bat 

conservation. Nonetheless, development of more 

objective and scientifically reliable methods of monitoring 

trends in bat populations remains an important goal for 

providing a national perspective on bat conservation 

needs and successes. The Working Group also 

recognized, however, that further advances in technology, 

statistical design, and funding support would be 

necessary to create an expanded or national bat 

monitoring program that can meet this goal. 

A network of information flow will be important for 

stimulating and recognizing such necessary advances, 

and for communicating information that may be useful in 

identifying situations needing conservation attention. 

Thus, our first recommendation is the development of a 

web-based clearinghouse of information on bat 

conservation-related research. Because bat populations 

are of significance to agriculture and related segments of 

the U.S. economy and national biodiversity, monitoring 

bat populations is clearly desirable. Therefore, our second 

set of recommendations points out three areas of 

consideration necessary to establish a scientifically 

defensible bat population monitoring program: increasing 

basic ecological information on bats (especially rare 

species), developing means to estimate detectability at 

sample sites, and developing appropriate spatial sampling 

designs (see also Working Group A report, this volume). 

Monitoring bat population trends, however, has no 

specific national mandate. In a third issue statement, 

therefore, we call attention to the importance of bat 

populations in the U.S., the movements of bats across 

state and international boundaries, and the desirability 

of establishing formal provisions for bat conservation 

that can include population monitoring. We highlight legal 

steps already completed in this regard by other nations, 

and provide some initial suggestions regarding the U.S. 

One such step would be to establish a National Bat 

Awareness Week to help increase public support for bat 

conservation, as described in our fourth issue statement. 

Finally, because much valuable population information 

can be obtained through properly designed mark and 

recapture studies (see also Working Group A report, this 

volume), we provide specific recommendations on 

developing a clearinghouse approach to making technical 

information on this topic available, and on additional 

considerations for the design of needed research on 

marked bats. Our Working Group did not explore data 

management issues, one of the four broad categories of 

issues submitted in advance by participants, because we 

felt it would be premature to do so pending further 

advances in the other areas we considered. 

Working Group C Issue 1. Lack of 

Organization of Existing Programs 

and Information 

Issue Description and Rationale 

Why is this issue important? Although the 

importance of bats to healthy ecosystems is not as well 

recognized by the general public as it is to scientists, 

declines in bat populations have been an important 

concern for resource managers and researchers. However, 

the breadth of the problem of declining bat populations 

is not scientifically well understood because current 

efforts to track declines include different methods and 

protocols that may lack compatibility and comparability. 

Considerable information already exists that can assist in 

identifying data gaps and conservation needs, but this 

information is stored in various locations. It is important 

that researchers and resource managers be aware of 

existing information and expertise on bat research and 

monitoring in order to use knowledge that has already 

been obtained. New funding is difficult to secure, and 

given that there is no legislative or other mandate for any 

group or agency to coordinate and fund a nationwide 

bat-monitoring program, it is important to make the most 

of existing information and to be effective in the use of 

available funds. 

What is generally known about this issue? Considerable information related to abundance and distribution 

of bats exists. This information is scattered among numerous organizations in the form of databases and reports, as well as in scientific publications. This and related 

information such as directories of expertise and sources 

of local knowledge could be brought together through a 

clearinghouse (a central source for the organization and 

distribution of information related to bat populations). 

What in general needs to be determined to resolve 

the critical uncertainties surrounding the issue? A 

clearinghouse should be developed that solicits and 

provides information from bat researchers, land 

management agencies, conservation organizations, and 

others. The information should provide a clear picture of 

what is known, who is doing the research, and where 

gaps exist. It should allow users the opportunity to interact 

and facilitate greater cooperation and collaboration among 

research scientists and resource managers. 

What are the consequences if this issue is not 

addressed? General problems with declining bat 

populations at a landscape or regional scale may not be 
identified and declines may occur from which it will take 

bats many years to recover, with consequent ecological 

and economic costs. Important data gaps may not be 

identified if this issue is not addressed, and there will be 

fewer opportunities for comparing data and adding spatial 

dimensions to monitoring programs. Interpretation of data 

(putting site-specific data into context) will be difficult 

with a lack of communication and information-sharing 

among various agencies and scientists. Funds may be 

expended needlessly in duplicating information or 

repeating mistakes made by others. Management 

agencies may not direct funding optimally if they are 

unaware of who the subject experts are and the level of 

existing information. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

A web-based clearinghouse should be developed to 

provide a mechanism for identifying existing information 

and key individuals and organizations involved in bat 

conservation and research. Provisions should be made 

to regularly update the information. The clearinghouse 

could include the following components: 

Directory of organizations and individuals in bat 

conservation and research. This directory would include 

names, addresses, phone numbers, e-mail addresses, and 

a short description of the role or interest of various 

organizations and individuals, such as the regional bat 

working groups, bat recovery team members, and 

scientists involved in bat research. This directory would 

explain the purpose of each of the groups. 

Metadata database. The clearinghouse would not 

contain raw data from various studies, but would give a 

description of data sets and various studies and management efforts that could be searched using keywords. For 

a particular data set (e.g., exit counts at a particular cave 

over a 9-year period), the entry in the database would 

include how the data were collected, the format of the 

data, where it is stored, and who to contact. The database could also describe current pertinent research 

projects by summarizing the study objectives, name, and 

contact information for the investigator, scheduled 

completion dates, and expected products. 

Protocol database. The clearinghouse could provide 

electronic copies of existing sampling protocols being 

used for bats, including example data collection forms 

and recommendations for analyzing and presenting the 

data. Descriptions of state-of-the-art sampling and analytical methods could also be provided here. 

Bat population database (BPD). The BPD that is being 

developed by the U.S. Geological Survey should be part 

of the clearinghouse. 

PART II 263 

Searchable bibliography. References on bats could be 

added to the database. The clearinghouse could also point 

to internet resources such as Cambridge Abstract 

Services, the Institute for Scientific Information, and 

several other indexing sources. 

Band or PIT tag database. There is no centralized organization for assigning band numbers or PIT tag numbers used on bats, such as the service provided by the 

U.S. Fish and Wildlife Service for bird banding. The clearinghouse could be used to inform others about ongoing 

tagging projects and to facilitate exchange of information 

on marked bats (see Issue 5, this Working Group Report). 

Bat sound recording database. A database linked to 

the clearinghouse could identify where reference collections and archived records of bat calls are stored. 

Other links. Links to other databases and web sites 

that contain information pertinent to bat conservation 

and research (e.g., other agency monitoring programs, 

weather data, threatened and endangered species databases, Integrated Taxonomic Information System). 

Suggestions Regarding Existing Monitoring and Research Programs 

Existing monitoring and research programs should 

strive to identify their activities by participating in an 

informally linked, web-based clearinghouse. It may be 

possible to develop and fund portions of the 

clearinghouse through the U.S. Geological Survey’s 

National Biological Information Infrastructure (NBII). This 

program already serves similar databases for other natural 

resources, and the objectives of the clearinghouse fall 

within the mission of the NBII. Temporarily, the group at 

the Fort Collins Science Center (fomerly the Midcontinent 

Ecological Science Center) may be able to develop a simple 

prototype to start the clearinghouse on a limited scale. 

The Integrated Taxonomic Information System (ITIS), an 

interagency database that provides taxonomic standards 

for sharing information on species, may help with 

problems of nomenclature. 

Working Group C Issue 2. Analytical
 

Considerations for a National Bat
 

Monitoring Program
 

Issue Description and Rationale 

Changes in bat populations have ramifications for 

agricultural and forestry segments of the U.S. economy, 

ecosystem function (including pollination of important 

vegetation in the American Southwest), and conservation 

of national biological diversity. Currently, attempts to 
264 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

monitor bat populations are very fragmented, concentrate 

on just a few species that are endangered or threatened, 

or involve very local independent efforts. There is need 

for status information on a wider range of U.S. bat species. 

For example, in 1994 the U.S. Fish and Wildlife Service 

named 24 species or subspecies of bats as Category 2 

Candidates for listing under the U.S. Endangered Species 

Act, based largely on an absence of population status 

and trend information (U.S. Fish and Wildlife Service, 

1994). These taxa have subsequently been considered 

“species of concern” since the elimination of Category 2 

classifications (U.S. Fish and Wildlife Service, 1996). 

A need clearly exists for bat monitoring programs on 

a national scale. National level monitoring of bat populations could provide broader perspectives for conservation priorities, prevent duplication of effort, and promote 

standardized collection of data. Monitoring bat populations on a national scale would help identify bat population changes that may not be detected by scattered and 

uncoordinated local efforts. Conservation actions in response to local monitoring efforts may not otherwise 

occur fast enough to prevent significant widespread 

losses, whereas establishing that stability or growth in 

populations is occurring over broad areas may help 

change priorities when small, local declines are observed. 

However, any such program must be properly designed to provide reliable, scientifically defensible information that is more spatially encompassing than results 

that have been obtained thus far (see also Working 

Group A, “Colonial Bat Species” subgroup report, Issue 4, 

this volume). There are three major considerations for 

developing surveys for monitoring bat population trends 

on a national scale: 

• 	 Needs for basic information on ecology and life 

history of rare species, and criteria for selecting 

species to be monitored (see also Working Group 

B report, this volume). 

• 	 Estimation of detectability at sample sites. In general, bat studies have not included estimation of 

detectability when estimating population attributes, but instead have used indices of abundance (see also Working Group A report, this 

volume). Indices do not provide the most reliable 

data because their accuracy in reflecting the underlying population trends is usually unknown. 

• 	 Spatial sampling. Studies of U.S. bats, in general, 

have not adequately sampled the entire 

population of a species. Instead, surveys typically 

occur at single (or few) sites and the results cannot 

be extrapolated to entire populations across a 

species range. 

Why are these sampling issues important? Although 

a number of indices to bat abundance have been 

proposed, few provide truly reliable information by 

incorporating methods of estimating detectability. Similar 

to initial reports of amphibian population changes several 

years ago, much of the bat population status information 

is anecdotal or based on counts or indices that may not 

reliably reflect the underlying populations. Much of the 

bat population data are also local, reflecting populations 

at individual sites without indications of how well these 

represent regional populations. Consequently, patterns 

of population change estimated from indices at local sites 

may not reflect what is truly occurring with the regional 

population. Because bats migrate, generally have 

widespread geographic distributions, and pose unique 

problems for population estimation, a statistically 

defensible survey must be developed before monitoring 

can be implemented on a national scale. These programs 

would have to provide information at geographic scales 

relevant to managers, such as individual sites, regions, 

and states. 

What is generally known about these issues? In 

recent years, a variety of statistical methods have been 

developed for estimating wildlife abundance, density, 

survival, and other population parameters. Most of these 

developments have not yet been applied to bats. Capturerecapture methods in particular provide opportunities for 

estimation of colony-specific population size, survival, 

and other demographic parameters (see also Working 

Group A report, this volume). A number of existing 

techniques developed for abundance estimation such as 

distance or multiple observer methods might also allow 

estimation of bat detectability rates. Large scale surveys 

of other wildlife, such as the North American Breeding 

Bird Survey (BBS), provide an enormous amount of 

information regarding the virtues and flaws of nationwide 

programs. Documented deficiencies of these surveys 

should be avoided in implementation of new monitoring 

programs (Sauer, 2003). In particular, detectability should 

be estimated during the survey, sampling frame issues 

(such as potential biases in estimation associated with 

roadside counts) can be avoided, and statistical designs 

such as variable probability sampling or dual-frame 

sampling can be used to develop cost-effective sampling. 

What needs to be determined to resolve the critical 

uncertainties surrounding the issues? Spatial sampling 

schemes need to be developed by exploring alternative 

designs, including dual-frame sampling and variable probability sampling. Often, these designs will allow complete 

coverage of important sites, but also provide unbiased 

estimates from the sampling of less important sites at 

lesser intensities. Development of appropriate designs 

will require elaboration of geographic information on sampling frames such as caves or other habitats that can be 

used to develop strata. 
PART II 265 

Appropriate population estimation methods are still 

poorly defined for bats. Development of these methods 

will require pilot studies over limited numbers of sites 

and areas to determine feasibility and obtain pilot data 

for design of regional scale surveys. Often, collection of 

ancillary data as covariates will be critical to allow assessment of correlates of changes in survival and population size. These covariates may be at the geographic 

scale (such as land-use data), or the local scale (such as 

roost temperature changes). 

Surveys will require considerable planning and design based on an understanding of species life histories 

and other factors. GIS can be used in designing sampling 

frames and displaying results such as distribution data. 

Whenever possible, simplicity should be encouraged to 

allow maximum acceptance of results, and clarity of presentation should be encouraged while maintaining the 

ability to answer management questions in a statistically 

defensible manner. 

What are the consequences if the issue is not addressed? Without development of these surveys, it will 

be impossible to estimate trends for populations of bats 

on a regional or national scale. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Before a national-scale bat monitoring program can 

be developed, advances must be made in methods of 

enumerating population estimates of bats, beginning at 

local and colony scales, and these methods need to be 

applied in an appropriate sampling design. Working Group 

A has a number of recommendations involving research 

needs for improving estimation of population size and 

trend of bats. In addition, for many species of bats in the 

U.S. and territories, additional basic natural history and 

distribution information may be necessary for developing adequate monitoring designs and interpreting results 

of sampling. 

Suggestions Regarding Existing Monitoring and Research Programs 

Recognizing the absence of a structured national 

scheme, the group recommends that ongoing efforts 

should improve communication and coordination in order to detect broader scale conservation problems. Development of a worldwide web-based clearinghouse (as 

recommended under Issue 1 by this Working Group) 

should help in this regard, as should efforts to maintain 

and improve communication among endangered species 

coordinators and existing networks of informal state and 

regional bat Working Groups. 

The following suggestions should also be explored 

to help resolve analytical and sampling issues involved 

with monitoring bat populations. 

•	 Ongoing surveys/monitoring programs for bats 

should be evaluated to determine whether they 

can provide pilot data for regional surveys. 

•	 A number of surveys exist that provide information 

on population change for bats. For example, 

Indiana bats are monitored every 2 years at certain 

key hibernacula in Missouri, Indiana, Kentucky, 

and Illinois. These surveys should be analyzed 

and critically evaluated. Methods that provide 

reliable information can be used as models for 

future survey development for similar species in 

similar regions. Coordinators of the surveys 

should be encouraged to publish results in peerreviewed journals. Information from other 

programs that have developed well-planned 

sampling designs and protocols, such as those 

developed in the U.K. and The Netherlands, 

should also be evaluated. 

•	 Detectability issues should be reviewed. 

Development of regional surveys that provide 

reliable data requires that new methods be 

developed and implemented to estimate 

detectability at sample sites. New technological 

tools (including electronic devices in 

developmental phases and bat detectors which 

are currently used only for obtaining index 

information) should be evaluated as sources of 

reliable population information. Infrared video 

recorders should be experimented with to 

visualize bats recorded by bat detectors. 

However, pending further developments in 

acoustic sampling, new sampling efforts should 

focus on direct estimation of numbers of bats 

rather than counting bat echolocation calls. Mist 

netting should also be evaluated as a source of 

reliable information on bat populations. Finally, 

although population estimation may not be 

feasible using count or index data such as these, 

species richness may be a useful parameter of 

interest that can be estimated using count 

statistics and modern sampling designs (Nichols 

and Conroy, 1996). 

•	 Sampling frames that allow variable probability 

sampling of sites known to be of importance to 

bat populations of monitoring concern should 

be developed. GIS is useful in summarizing existing 

information (allowing display of maps of survey 

points) and should be used in designing sampling 

frames. 
266 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Working Group C Issue 3. Lack of a Unifying
 

Mandate or Legislative Foundation for a
 

National Bat Conservation Program
 

Issue Description and Rationale 

Why is the issue important? Bats are of tremendous 

economic importance to U.S. agriculture and forestry. 

They play important functional roles in ecosystems and 

are important components of our national biological diversity. Bats migrate across U.S. state and international 

boundaries. A national program and transboundary agreements among nations neighboring the U.S. are needed to 

appropriately manage for many U.S. species of bats. 

What is generally known about the issue? Currently 

there is no formal legal mandate for bat conservation in 

the U.S. However, there are examples of conservation 

mandates in Europe and the U.S. that may be used as 

models and can provide lessons on which to draw. The 

European Bats Agreement (Agreement on the 

Conservation of Bats in Europe, London, 1991) under the 

auspices of the Convention on Migratory Species of Wild 

Animals, Bonn, 1979, has fostered monitoring of bat 

populations by some countries. (Although Appendix I to 

the Bonn Convention identifies the common U.S. migrant 

Tadarida brasiliensis among migratory mammals, the U.S., 

Mexico, and Canada are not among the 65 parties to this 

international agreement.) The European Union Habitats 

and Species Directive addresses both sites and species 

and also applies to bats. The European Bats Agreement 

was developed because bats whose ranges and 

migrations crossed national boundaries were known to 

be under threat. It was signed in 1999 and put in force to 

various degrees by 13 nations. The agreement raises 

consciousness regarding bat conservation and stipulates 

protection for bats, their roosts, and important feeding 

areas, but it does not mandate or fund population 

monitoring of bats. The various parties to the agreement 

instead carry out monitoring independently. As a result, 

there are different levels of activity in different countries. 

The U.K. has the most intense program, and has allocated 

£500,000 to their bat monitoring program over a 5-year 

period. This program uses volunteers to gather data (see 

Walsh and others, 2003). The existence of a cadre of 

volunteers was a significant factor in the decision of the 

Department of Environment, Transport, and the Regions 

to allocate this funding. After the initial 5-year funding 

period is concluded, the Statutory Nature Conservation 

Organizations (England, Scotland, Wales, and Northern 

Ireland) will continue partial funding; partners are being 

sought to augment these funds. The Netherlands also 

has an active bat monitoring program that started with an 

atlas approach. Other European countries have small 

numbers of personnel devoted to bat monitoring. 

The Convention on Biological Diversity (under the 

Rio Convention) provides that signatory countries obligate themselves to maintain biological diversity. With time 

this could provide some foundation for bat conservation 

in the U.S. The U.K., for example, has drafted species 

action plans under the auspices of this Convention and 

is seeking corporate sponsorship to underwrite the costs 

of the plans. The U.S. signed the Convention in 1993 but 

has not ratified it. Mexico and Canada have both signed 

and ratified the Convention. 

In the U.S., there are two models of long-term wildlife 

monitoring at a national scale: the Breeding Bird Survey 

sponsored by the federal government, and the Christmas 

Bird Count conducted by the Audubon Society. In the 

U.K., the British Trust for Ornithology also has a volunteer network that carries out annual bird counts. In some 

schemes, the volunteers pay the Trust an annual fee and, 

in return, receive newsletters and reports. The British 

Mammal Society, consisting of both professionals and 

amateurs, also sponsors surveys. 

What in general needs to be done to resolve the 

critical uncertainties? Greater consideration should be 

given to strengthening bat conservation efforts in the 

U.S. through formal legislation and treaties. Proposals 

for international conservation of some bat species as 

transboundary migrants should be supported through 

the joint U.S.-Mexico-Canada Commission on 

Environmental Cooperation. Programs should include a 

component earmarked for in-depth consideration of 

design and implementation of bat population monitoring. 

Several domestic legislative acts and international 

agreements have elements that could be used as examples 

or models for drafting national bat conservation legislation. The U.S. Marine Mammal Protection Act of 1972 

currently protects pinnipeds, cetaceans, sirenians, sea 

otters, marine otters, polar bears, and the ecosystems in 

which these species occur (Baur and others, 1999). Over 

the years, funding through this mandate has stimulated 

considerable research in the design and implementation 

of population monitoring methods for marine mammals. 

The Migratory Bird Treaty Act also could serve as a 

model. In the U.K., the Wildlife and Countryside Act protects all species of bats as well as their roosts. No other 

group receives this level of protection in the U.K. An 

important benefit of this Act was that it focused attention 

on two species of the horseshoe bat and resulted in 

censusing of their populations. 

Two U.S. initiatives may indirectly provide initial steps 

towards a national bat monitoring program. Recent 

legislation and funding for the National Park Service is 

mandating a monitoring program for biological resources 

(which can include bats) on National Park Service 
properties. The Environmental Protection Agency has 

“Star Grants” that can fund regional monitoring programs. 

These may be sources that could support design and 

development of pilot bat monitoring projects. 

What are the consequences of not addressing the 

issue? Reductions in abundance of common species of 

bats will have economic consequences to agriculture, 

forestry, and perhaps public health (declines in bats as 

consumers of insect vectors of disease). Under the current 

lack of unified efforts and firm mandates, there is also a 

higher probability of losing rare species of bats before 

critical knowledge on basic ecology and population status 

can be gained, particularly in comparison to more common 

species. Rare species will likely need greater resources to 

monitor adequately, and thus are at greater risk of being 

lost before adequate population data can be acquired, 

given the existing level of resources available to devote 

to bat conservation. Loss of species or significant 

populations of bats on the public lands, or of those 

designated as having special conservation status by 

resource management agencies, will signal a failure in 

stewardship. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

The Working Group recommends that nongovernment organizations and other interested parties 

consider proposing bat conservation programs at a 

national level, either through support for new legislation 

and budget initiatives, or through new provisions in 

existing legislation. Support should also be given for 

international agreements and ratification of treaties that 

would include measures for bat conservation. Advantages 

of formal legislation would include recognition of the 

importance of bats as part of our national fauna and 

authorization of funding for bat conservation, aspects of 

which can involve well-designed programs to monitor 

bat populations. Professional and scientific societies 

should be encouraged to support such initiatives. The 

American Society of Mammalogists should be asked to 

consider a resolution calling for the development of 

legislation that would support national bat conservation 

and monitoring programs. Other professional societies 

(e.g., The Wildlife Society, the Society for Conservation 

Biology), museums, conservation groups, and similar 

organizations and institutions should also be invited to 

support such initiatives. 

Suggestions Regarding Existing Monitoring and Research Programs 

Current efforts to monitor bat populations and improve 

techniques for estimating bat population trends should 

PART II 267 

be continued and expanded. Ecological monitoring and 

research programs now concentrating on other biological resources should expand their focus to include bats. 

As examples: bat conservation on public lands should be 

a priority for public land management agencies at all levels; the National Science Foundation’s Long-Term Ecological Research sites should include components related 

to bat diversity, distribution, and abundance. Because 

the existence and distribution of many species of bats are 

closely tied to ambient temperatures, monitoring of bat 

populations and modeling bat population and distribution responses to temperature shifts should be proposed 

under various global change research programs. 

Working Group C Issue 4. National
 

Bat Awareness Week
 

Issue Description and Rationale 

Suggestions have been made by workshop 

participants and others (e.g., Western Bat Working Group) 

about designing and implementing a National Bat Survey 

Week, and there are some ongoing local efforts in this 

regard. Considering the underlying unresolved analytical 

issues in measuring bat population trends, the results of 

such an effort may not at this time provide reliable 

information. The public and resource managers could 

easily misunderstand the intent of such activities with 

raised expectations that reliable bat population monitoring 

was taking place. However, the idea of a National Bat 

Awareness Week for conservation education is an 

excellent concept that would meet part of the underlying 

motivation for a National Bat Survey Week. 

Means to Resolve the Critical Uncertainties 

A National Bat Awareness Week could be designed 

as a period in which press releases about bats are issued, 

public education programs and lectures are scheduled, 

and groups are taken to the field by knowledgeable bat 

biologists. Events could range from group observations 

of colony emergences at well known sites where 

disturbance by observers is not of concern (e.g., Carlsbad 

Caverns National Park, the Congress Avenue Bridge in 

Austin, the University of Florida Bat House) to 

echolocation detector demonstrations at evening 

programs in parks and refuges, and lay groups 

accompanying bat biologists on netting trips. Such 

activities and the favorable media attention they would 

engender could help counter negative images of bats 

currently being portrayed through the media, and might 

promote public support for broader mandates for bat 

conservation. 
268 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Suggestions Regarding Existing Monitoring and Research Programs 

A National Bat Awareness Week can be promoted as 

an informal collaboration among many groups, including 

conservation agencies, non-government organizations 

and many local groups, schools, libraries, museums, and 

volunteers. With media attention the amount of activity 

will likely increase substantially over the first few years. 

Successful examples elsewhere already exist, including 

European Bat Night and National Bat Week in England, 

coordinated by the Bat Conservation Trust. The North 

American Bat Conservation Partnership (a consortium of 

interested agencies, non-government organizations, and 

regional Working Groups) would be an appropriate umbrella under which such an effort could be initiated. 

Working Group C Issue 5. Optimizing 

Information Obtained from Marked Bats 

Issue Description and Rationale 

In the past, U.S. bat banding efforts, many of which 

were large scale and involved many thousands of bats 

nationwide, were largely uncoordinated and occurred with 

minimal communication among bat researchers. Negative 

effects of bands and their application were also unknown 

at the onset of early bat banding activities. Although 

these studies obtained new and important natural history 

information about U.S. bats, including gross movement 

patterns and longevity estimates, they sometimes lacked 

specific objectives and sampling designs (in some cases, 

mass banding was conducted at certain sites without any 

subsequent sampling of the area for recaptures.) However, there is now a major subdiscipline in quantitative 

ecology that allows the more sophisticated estimation 

and modeling of animal population parameters based on 

well-designed mark-recapture statistical principles (e.g., 

Thompson and others, 1998; Burnham and Anderson, 

1999). These new mark-recapture models have yet to be 

applied thoroughly in bat studies, but their implementation could lead to important new information critical to 

monitoring bat population trends (e.g., Entwistle and others, 2000). 

Discretion and proper technique in the application 

of bands or tags must be used when designing and implementing mark-recapture studies of bats. Greater communication between bat researchers is also necessary 

because bats are highly mobile and likely to move in and 

out of any given study area. Improving the ability of researchers to identify marked bats and relay recapture information to the original marker can increase the potential for gaining information from marked bats. The degree 

to which such information has been gained from past 

banding efforts has been limited. For instance, the FWS 

served as a clearinghouse for bat banders for several 

decades. Although hundreds of thousands of bat bands 

were distributed to researchers over many years, minimal 

recaptures or recoveries were reported to the FWS (less 

than or about 1%). In addition, a moratorium was placed 

on the use of these aluminum bands on bats in the mid1970’s. Researchers had noticed alarming adverse effects 

of the bands on some bats and suspected that local population declines were caused by poorly timed banding efforts and band-related injuries. The potentially negative 

consequences of bands on survivorship and fecundity 

are reasons to promote discretion in marking bats and to 

stress proper technique in their application. With indiscriminate marking and lack of communication, the risk of 

harming individuals and populations is incurred without 

obtaining the full benefits of mark-recapture efforts based 

on new statistical theory (e.g., estimates of rates of movements, longevity, survival, effects of management practices and environmental covariates, etc.). Because of the 

tremendous scientific value of well-designed marked animal studies, we also recommend experimentation with alternative marking techniques, such as PIT tags, that may 

provide advantages over bands in their application. 

Means to Resolve the Critical Uncertainties Surrounding the Issue 

Web site clearinghouse on marking techniques and 

existing marked bat studies. A web site clearinghouse 

could serve as a centralized resource, providing information and references on proper bat marking techniques 

and a means for exchange of marking information. Potential information provided by this web site could include a 

list of contacts (researchers, manufacturers, etc.), a bibliography of related references (e.g., statistical analyses of 

mark-recapture data, application techniques, and relevant 

references from other taxa), and a review of mark-recapture practice and theory as they pertain to bats. This 

review would include information on mark-recapture principles, types of information that can be obtained, proper 

marking techniques, and the potentially negative impacts 

of tag/band misuse and poor project planning. A book in 

preparation tentatively titled, “A practical guide to marking bats” (edited by T.H. Kunz) is an example of the kind 

of reference that could be highlighted at such a site. This 

web site might also provide a forum for exchange of information on product performance, methods, recent advances 
in statistical techniques, and other mark-recapture related 

issues. 

A second function of this web site would be to serve 

as a repository for “metadata” on marking projects. From 

this site, researchers could access information on who 

has applied marks; where, when, how many, and what 

types of bands or tags were applied; and what species of 

bats were marked. (Primary data such as individual tag 

numbers and attributes of the tagged animals would not 

be included.) The material provided by this site would be 

based on the voluntary submission of information by 

researchers directly to the web site, and would perhaps 

include existing information in the U.S. Geological 

Survey’s Bat Population Database. Creating a centralized 

reference site for bat marking projects maximizes the exchange of information that can be gained from band and 

tag application. This may be particularly useful when different investigators make recoveries over long distances 

or time periods, and when different manufacturers of PIT 

tags or readers may be involved. The web site could assist bat biologists in avoiding use of duplicate band numbers (or colors) and PIT tag numbers and suggest ways 

of creating unique identifiers. 

Needed research on mark-recapture of bats. A critical look at the effects of different banding and marking 

techniques is needed (see also Working Group A report, 

this volume). A study or multiple studies should be designed to investigate the specific effects of different marking techniques, such as PIT tags versus bands or other 

techniques, and how they impact traits critical to bat population dynamics such as survival and reproduction. This 

might first be conducted on species that are not as sensitive to disturbance as others and are more common and 

abundant (i.e., Myotis lucifugus or Eptesicus fuscus), 

and might be carried out in a local geographic area with a 

large network of roosts (i.e., caves, mines, or buildings). 

This mark-recapture study could also be designed to answer questions about movements, dispersal, environmental effects, management strategies, survival, population 

size and trend, etc., depending on the study area and 

other objectives. Determination of the applicability of 

current mark-recapture techniques to bats should be made 

in a scientific and repeatable manner. 

Additional considerations. Other issues and questions remain regarding permanent marking of bats in the 

U.S. Should state and federal agencies be involved in 

acquiring marking information? Should the use and application of marks to bats be controlled or monitored? If 

so, by whom? Can useful information still be obtained 

from past bat banding records? Is this information worth 

the expense and effort required to track down or enter 

historic data (e.g., former USFWS bat banding files)? 

PART II 269 

Should efforts be made to standardize equipment (e.g., 

PIT tag readers)? 

Suggestions Regarding Existing Monitoring and Research Programs 

In summary, regarding the management of existing 

information and the implementation of programs involving marking of bats, we suggest: (1) a web site clearinghouse for mark-recapture information, and (2) further 

research focusing on the effects of marking techniques 

on bat populations. These would help enhance the understanding of bat population biology, thereby improving the ability to monitor bat populations and reduce 

ecological and economic costs associated with declines 

that might otherwise be poorly detected. 

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Canadian Journal of Zoology, vol. 50, p. 877–883. 

Whitaker, J.O., Jr., 1988, Food habits analysis of 

insectivorous bats, in Kunz, T.H. ed., Ecological and 

behavioral methods for the study of bats: Washington, 

D.C., Smithsonian Institution Press, p. 171–189. 

Whitaker, J.O., Jr., 1995, Food of the big brown bat 

Eptesicus fuscus from maternity colonies in Indiana 

and Illinois: American Midland Naturalist, vol. 134, 

p. 346–360. 

Wilson, D.E., Cole, F.R., Nichols, J.D., Rudran, R., and Foster, 

M.S., 1996, Measuring and monitoring biological 

diversity: Standard methods for mammals: Washington, 

D.C., Smithsonian Institution Press, 409 p. 
272 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Workshop Participants orkshop Participants orkshop Participants
 

David R. Anderson 

Cooperative Fish & Wildlife Research Unit 

Colorado State University 

201 Wagar Building 

Fort Collins, CO 80523-1484 

Michele M. Banowetz 

U.S. Geological Survey 

Fort Collins Science Center 

2150 Centre Ave., Bldg C 

Fort Collins, CO 80526-8118 

Robert D. Berry 

Brown-Berry Biological Consulting 

134 Wilkes Crest Road 

Bishop, CA 93514 

Michael A. Bogan 

U.S. Geological Survey 

Fort Collins Science Center 

Biology Department 

University of New Mexico 

Albuquerque, NM 87131-0001 

Anne P. Brooke 

Box 102 

Newfields, NH 08356 

(Present address: 

Guam National Wildlife Refuge, 

P.O. Box 8134, MOU-3 

Dededo, GU 96929) 

Patricia E. Brown 

UCLA/Brown-Berry Biological Consulting 

134 Wilkes Crest Road 

Bishop, CA 93514 

Kenneth P. Burnham 

Cooperative Fish & Wildlife Research Unit 

Colorado State University 

201 Wagar Building 

Fort Collins, CO 80523-1484 

Timothy C. Carter 

Department of Zoology 

Southern Illinois University 

Carbondale, IL 62901-6501 

Norita A. Chaney 

U.S. Geological Survey 

Biological Resources Division 

300 National Center, Room 1C215 

12201 Sunrise Valley Drive 

Reston, VA20192 

Alice L. Chung-MacCoubrey 

U.S. Forest Service 

Rocky Mountain Research Station 

2205 Columbia SE 

Albuquerque, NM 87106-3222 

Mary Kay Clark 

Curator of Mammals 

NC State Museum of Natural Science 

P.O.Box 29555 

Raleigh, NC 27626 

Richard L. Clawson 

Missouri Department of Conservation 

1110 S. College Avenue 

Columbia, MO 65201 

Paul M. Cryan 

U.S. Geological Survey 

Biology Department 

University of New Mexico 

Albuquerque, NM 87131-0001 

( Present address: 

U.S. Geological Survey 

Fort Collins Science Center 

2150 Centre Ave., Bldg C 

Fort Collins, CO 80526-8118) 

Virginia M. Dalton 

Department of Wildlife and Fisheries Science 

University of Arizona 

Tucson, AZ 85721 

Laura E. Ellison 

U.S. Geological Survey 

Fort Collins Science Center 

2150 Centre Ave., Bldg C 

Fort Collins, CO 80526-8118 
PART II 273 

Steven G. Fancy 

National Park Service 

National Inventory & Monitoring Program 

1201 Oakridge Drive 

Fort Collins, CO 80525-4489 

Theodore H. Fleming 

Department of Biology 

University of Miami 

Coral Gables, FL 33124 

Jeffrey A. Gore 

Florida Fish and Wildlife 

Conservation Commission 

3911 Highway 2321 

Panama City, FL 32409 

Leanne Hansen 

U.S. Geological Survey 

Biological Resources Division 

PO Box 25046 - MS 300 

Denver CO 80225 

(Present address: 

U.S. Geological Survey 

Fort Collins Science Center 

2150 Centre Ave., Bldg C 

Fort Collins, CO 80526-8118 

John P. Hayes 

Department of Forest Science 

Richardson Hall 

Corvallis, OR 97331-7501 

Michael J. Herder 

USDI Bureau of Land Management 

345 E. Riverside Drive 

St. George, UT 84790 

Joseph A. Kath 

Illinois Department of Natural Resources 

Division of Natural Heritage 

524 South Second St. 

Springfield, IL 62701-1787 

Thomas H. Kunz 

Department of Biology 

Boston University 

5 Cummington Street 

Boston, MA 02215 

Allen Kurta 

Eastern Michigan University 

Department of Biology 

Ypsilanti, MI 48197 

Gary F. McCracken 

University of Tennessee 

Dept. of Ecology & Evolutionary Biology 

569 Dabney Hall 

Knoxville, TN 37996 

Rodrigo A. Medellin 

Instituto de Ecologia 

UNAM 

Ap. Postal 70-275 

04510 Mexico, D.F. 

Michael A. Menzel 

Division of Forestry 

West Virginia University 

Morgantown WV 26506 

Kirk W. Navo 

Colorado Division of Wildlife 

0722 S. 1 E. 

Monte Vista, CO 81144 

Thomas J. O’Shea 

U.S. Geological Survey 

Fort Collins Science Center 

2150 Centre Ave., Bldg C 

Fort Collins, CO 80526-8118 

Michael J. Rabe 

Arizona Game and Fish Department 

Game Branch 

221 W. Greenway 

Phoenix AZ 85023 

Paul A. Racey 

Department of Zoology 

University of Aberdeen 

Tillydrone Avenue 

Aberdeen AB24 2TZ 

David A. Saugey 

U.S. Forest Service 

Jessieville Ranger Dist. 

PO Box 189 

Jessieville, AR 71949 

John R. Sauer 

U.S. Geological Survey 

Patuxent Wildlife Research Center 

11510 American Holly Drive 

Laurel, MD 20708-4017 
 

274 INFORMATION AND TECHNOLOGY REPORT–2003--0003 

Merlin D. Tuttle 

Bat Conservation International 

P.O.Box 162603 

Austin, TX 78716 

Ruth C. B. Utzurrum 

Dept. of Marine and Wildlife Resources 

PO Box 3730 

Pago Pago, AS 96799 

Ernest W. Valdez 

U.S. Geological Survey 

Museum of Southwestern Biology 

University of New Mexico 

Albuquerque, NM 87131 

Allyson L. Walsh 

The Bat Conservation Trust 

15 Cloisters House 

8 Battersea Park Road 

London SW8 4BG 

(Present address: The Lubee Foundation, Inc. 

1309 N.W. 192nd Avenue 

Gainesville, FL 32609) 

Gary C. White 

Department of Fishery and Wildlife Biology 

Colorado State University 

211B Wagar Building 

Fort Collins, CO 80523 

Gary J. Wiles 

1692 Sunflower Lane, Apt. 19202 

Tumwater, WA98512 

Don E. Wilson 

Smithsonian Institution 

Division of Mammals, MRC-108 

Washington, DC 20560 

Michael B. Wunder 

Colorado Natural Heritage Program 

254 General Services Building 

Colorado State University 

Fort Collins, CO 80523 
 

 

 

 

 

 

April 2004 

REPORT DOCUMENTATION PAGE 

2. REPORT DATE 3. REPORT TYP 

Public reporting burden for this collection is estimated to average 1 hour per response, including tim 

gathering and maintaining the data needed, and completing and reviewing the collection of information. Se 

aspect of this collection of information, including suggestions for reducing this burden, to Washington Headq 

Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Manage 

Washington, DC 20503. 

1. AGENCY USE ONLY (Leave 

Blank) 

4. TITLE AND SUBTITLE 

6. AUTHOR(S) 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESSES 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESSES 

11. SUPPLEMENTARY NOTES 

12a. DISTRIBUTION/AVAILABILITY STATEMENT 

13. ABSTRACT (Maximum 200 words) 

Information and Technology Report (Final) 

O’Shea, T.A. and Bogan, M.A., editors 

Available from the National Technical Information Service, 5285 Port 

Royal Road, Springfield, VA 22161 (1-800-553-6847 or 703-487-4650). 

Available to registered users from the Defense Technical Information Center, 

Attn: Help Desk, 8725 Kingman Road, Suite 0944, Fort Belvoir, VA 

22060-6218 (1-800-225-3842 or 703-767-9050). 

Monitoring Trends in Bat Populations of the United States and Territories: 

Problems and Prospects 

N/A 

Prepared in cooperation with National Fish and Wildlife Foundation 

Bats are ecologically and economically important mammals, but their populations are vulnerable to declines. 

USGS/BRD 

Fort Collins Science Center 

2150 Centre Ave., Bldg. C 

Fort Collins, CO 80526-8118 

8327-STOPO; 

8327-SMB20; 

8327-SMBF5;

 8327-SMBF3 

USGS/BRD/ITR– 

2003--0003 

N/A 

Many species of bats in the United States and territories are endangered or threatened, have been candidates 

for such categories, or are species of concern. The importance and vulnerability of bat populations makes 

monitoring trends in their populations a goal for their management. However, scientifically rigorous monitoring of bat populations requires well-planned, statistically defensible efforts. This volume reports findings of 

an expert workshop held to examine this topic. Part I includes overviews of efforts at monitoring populations 

of bats in the U.S. and territories. These papers consider techniques and problems, and summarize what is 

known about the status and trends in selected groups of bats. Part I also includes a description of the bat 

monitoring program in the United Kingdom, a critique of monitoring programs in wildlife with recommendations for survey and sampling strategies, and an analysis of existing data on trends in bats in the U.S. and 

territories. In Part II, workshop participants critically analyze problems and make recommendations for 

improving methods, defining objectives and priorities, gaining mandates, and enhancing information exchange to facilitate future efforts for monitoring trends in U.S. bat populations. 

274 Bats, endangered species, population estimation, 

species of concern, status and trends 

Unclassified UL Unclassified Unclassified 
 

Fort Collins Science Center Fort Collins Science Center Fort Collins Science Center

Production Staff 

Leader, Leader, Information InformationInformation Management Services Management Services Jennifer Shoemaker 

Desktop Publishing Specialist Desktop Publishing Specialist Desktop Publishing Specialist Dora E. Medellin 

NOTE: Mention NOTE: of trade names or commercial products does not constitute endorsement or recommendation for use by 

the U.S. Government. 
U.S. Department of the Interior U.S. Department of the Interior U.S. Department of the Interior

U.S. Geological Survey U.S. Geological Survey 

As the Nation’s principal conservation agency, the Department of the Interior has responsibility for most of our nationally owned 

public lands and natural resources. This responsibility includes fostering the sound use of our lands and water resources; protecting 

our fish, wildlife, and biological diversity; preserving the environmental and cultural values of our national parks and historical 

places; and providing for the enjoyment of life through outdoor recreation. The Department assesses our energy and mineral 

resources and works to ensure that their development is in the best interests of all our people by encouraging stewardship and 

citizen participation in their care. The Department also has a major responsibility for American Indian reservation communities.