United States Department of Agriculture

A Plan for the North American Bat 

Monitoring Program (NABat)

Susan C. Loeb, Thomas J. Rodhouse, Laura E. Ellison, Cori L. Lausen, Jonathan D. Reichard, 

Kathryn M. Irvine, Thomas E. Ingersoll, Jeremy T. H. Coleman, Wayne E. Thogmartin, John R. Sauer, 

Charles M. Francis, Mylea L. Bayless, Thomas R. Stanley, and Douglas H. Johnson

Forest Service

Research & Development

Southern Research Station

General Technical Report SRS-208
June 2015

Southern Research Station

200 W.T. Weaver Blvd.

Asheville, NC 28804

www.srs.fs.usda.gov

Photo credits

All photos courtesy of Merlin D. Tuttle,

Bat Conservation International, www.batcon.org

unless otherwise noted.

Cover photo 

Millions of Brazilian free-tailed bats emerge from 

Bracken Cave in suburban San Antonio, TX.

Disclaimer

The use of trade or firm names in this publication is for 

reader information and does not imply endorsement by the 

U.S. Department of Agriculture of any product or service.
A Plan for the North American 

Bat Monitoring Program 

(NABat)

Susan C. Loeb, Thomas J. Rodhouse, Laura E. Ellison, 

Cori L. Lausen, Jonathan D. Reichard, Kathryn M. Irvine, 

Thomas E. Ingersoll, Jeremy T. H. Coleman, Wayne E. Thogmartin, 

John R. Sauer, Charles M. Francis, Mylea L. Bayless, 

Thomas R. Stanley, and Douglas H. Johnson

Brazilian free-tailed bats emerge from Bracken Cave in suburban San An

ot n oi , TX.
Abstract

The purpose of the North American Bat Monitoring Program (NABat) is to create a 

continent-wide program to monitor bats at local to rangewide scales that will provide 

reliable data to promote effective conservation decisionmaking and the long-term 

viability of bat populations across the continent. This is an international, multiagency 

program. Four approaches will be used to gather monitoring data to assess changes 

in bat distributions and abundances: winter hibernaculum counts, maternity colony 

counts, mobile acoustic surveys along road transects, and acoustic surveys at 

stationary points. These monitoring approaches are described along with methods 

for identifying species recorded by acoustic detectors. Other chapters describe the 

sampling design, the database management system (Bat Population Database), and 

statistical approaches that can be used to analyze data collected through this program.

Keywords: Acoustic surveys, bat detectors, bats, chiroptera, climate change, 

hibernaculum counts, monitoring, occupancy models, population trends, white-nose 

syndrome.

Mill oi ns

of Brazil ai n free-tailed

bats emerge from Bracken Cave in suburban San Antonio, TX.
Contents iii

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .vi

Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Purpose, Mission, Goals, and Objectives of the 

 North American Bat Monitoring Program . . . . . . . . . . . . . . . . . . . . 1

1.2 Background and Need . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 The Bats of North America and Their Importance . . . . . . . . . . . . . . . 2

1.2.2 Threats to Bats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.3 Need for Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.3 Scope of NABat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3.1 Scope and Species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3.2 The Role of Demographic Studies in NABat . . . . . . . . . . . . . . . . . . 7

1.4 Putting NABat Into Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4.1 General Structure of NABat . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4.2 Products and Expected Outcomes . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4.3 Next Steps and Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.5 Adaptive Monitoring Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Chapter 2. Primary Monitoring Methods of NABat 

and Introduction to Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.1 Introduction and Species-Specific Methods . . . . . . . . . . . . . . . . . . . 10

2.2 Overview of NABat Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.1 Colony Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.2 Acoustic surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3 Focal Demographic Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Chapter 3. The NABat Sampling Design . . . . . . . . . . . . . . . . . . . . .14

3.1 Overview and Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

3.2 The Grid-Based Sampling Frame . . . . . . . . . . . . . . . . . . . . . . . . .14

3.3 The Spatially Balanced Master Sample . . . . . . . . . . . . . . . . . . . . . .16

3.4 Design Criteria and Implementation of the Master Sample . . . . . . . . . . 17

3.5 Response Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

3.6 Temporal Revisit Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Chapter 4. Stationary Point Acoustic Survey Protocols . . . . . . . . . . 20

4.1 Types of Detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.2 Detector Sensitivity and Settings . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.2.1 Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.2.2 File Recording Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.3 Site Selection and the Influence of Clutter on Bat Echolocation . . . . . . . 23

4.4 Equipment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.4.1 Placement Relative to Clutter, Height above Ground, and Orientation . . . . 26

4.4.2 Weatherproofing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.5 Frequency and Timing of Surveys . . . . . . . . . . . . . . . . . . . . . . . . 28

4.6 Collection of Covariates and Ancillary Data . . . . . . . . . . . . . . . . . . 28
iv A Plan for the North American Bat Monitoring Program (NABat)

Chapter 5. Mobile Acoustic Transect Survey Protocols . . . . . . . . . . 29

5.1 Types of Detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.2 Detector Sensitivity and Settings . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.3 Route Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.3.1 Safety Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.3.2 Road Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.3.3 Route Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.3.4 Habitat Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.4 Equipment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.5 Frequency and Timing of Surveys . . . . . . . . . . . . . . . . . . . . . . . . 33

5.6 Marking the Route . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.7 Covariates and Ancillary Data . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Chapter 6. Species Identification of Acoustic Recordings . . . . . . . . . 35

6.1 Responsibility for Species Identification . . . . . . . . . . . . . . . . . . . . . 35

6.2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35

6.3 Process of Species Identification . . . . . . . . . . . . . . . . . . . . . . . . . 36

6.4 Storage of Acoustic Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Chapter 7. Colony Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41

7.2 Multiple Visit and Multiple Observer Methods . . . . . . . . . . . . . . . . . 42

7.3 Colony Count Site Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

7.4 Internal Roost Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

7.4.1 Preferred Methods for Internal Surveys . . . . . . . . . . . . . . . . . . . . 44

7.4.1.1 Visual counts and photographic methods . . . . . . . . . . . . . . . . . 45

7.4.1.2 Estimating cluster density for visual counts . . . . . . . . . . . . . . . . 46

7.4.1.3 Other methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

7.4.2 Preferred Timing for Internal Surveys . . . . . . . . . . . . . . . . . . . . . 47

7.4.2.1 Internal surveys in winter . . . . . . . . . . . . . . . . . . . . . . . . . 47

7.4.2.2 Internal surveys in summer . . . . . . . . . . . . . . . . . . . . . . . . 47

7.4.3 Spatial Variability Among Surveys . . . . . . . . . . . . . . . . . . . . . . 48

7.5 External Roost Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

7.5.1 Preferred Methods for External Surveys . . . . . . . . . . . . . . . . . . . . 49

7.5.1.1 Visual counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

7.5.1.2 Digital video methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

7.5.1.3 Other methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

7.5.1.4 Determination of roost species composition . . . . . . . . . . . . . . . . 51

7.5.2 Preferred Timing of External Surveys . . . . . . . . . . . . . . . . . . . . . .51

7.6 Safety and Environmental Considerations . . . . . . . . . . . . . . . . . . . .51

7.6.1 Minimizing Environmental Impacts . . . . . . . . . . . . . . . . . . . . . . .51

7.6.2 Human Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

7.7 Locating New Colonies and Documenting Absence of Colonies 

 in Winter and Summer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

7.8 Covariates and Ancillary Data . . . . . . . . . . . . . . . . . . . . . . . . . . 53

7.9 Incorporation of New Technologies . . . . . . . . . . . . . . . . . . . . . . . 53
Contents v

Chapter 8. Data Management . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

8.1 Importance of Data Management . . . . . . . . . . . . . . . . . . . . . . . . 54

8.2 History of the Bat Population Database . . . . . . . . . . . . . . . . . . . . . 54

8.3 The Bat Population Data Project . . . . . . . . . . . . . . . . . . . . . . . . . 54

8.4 Database Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

8.5 Additional Support for NABat Provided by the Bat Population Database . . 59

8.5.1 Geographic Information System Support . . . . . . . . . . . . . . . . . . . 59

8.5.2 Datasheets and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . 59

8.5.3 Archiving Acoustic Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

8.6 Data Partnerships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Chapter 9. Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61

9.1 Acoustic Data Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61

9.1.1 Raw Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61

9.1.2 Response Variables for Occupancy Analysis . . . . . . . . . . . . . . . . . 62

9.1.3 Response Variables for Abundance Indices . . . . . . . . . . . . . . . . . . 62

9.1.4 Modeling Occupancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

9.1.5 Estimating Indices of Abundance . . . . . . . . . . . . . . . . . . . . . . . 65

9.2 Colony Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

9.2.2 Modeling Count Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

9.2.3 Dynamic Count Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

9.3 Determining Sampling Sufficiency for Occupancy

 and Hibernacula Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

9.3.1 Occupancy Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

9.3.2 Hibernacula Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

9.4 Incorporation of Legacy and Found Data . . . . . . . . . . . . . . . . . . . . 70

Chapter 10. Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

10.1 Program Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .71

10.2 NABat Staffing and Responsibilities . . . . . . . . . . . . . . . . . . . . . . 72

10.3 Roles and Responsibilities of Participating Organizations . . . . . . . . . . 72

10.4 Potential Role for Nongovernmental Organizations . . . . . . . . . . . . . 73

10.5 Timeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

10.6 Available and Needed Resources . . . . . . . . . . . . . . . . . . . . . . . . 73

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75

Appendix A—Number of full and partial 10- by 10-km grid cells 

 contained in each U.S. State . . . . . . . . . . . . . . . . . . . . . 87

Appendix B—Number of full and partial 10- by 10-km grid cells 

 contained in each Canadian Province . . . . . . . . . . . . . . . . 88

Appendix C—Sample datasheet for stationary point surveys . . . . . . . . . . . . 89

Appendix D—Sample datasheet for mobile transect surveys . . . . . . . . . . . . 90

Appendix E—Species that can be identified by each of the automatic 

 species-identification software programs (as of March 25, 2015) . . .91

Appendix F—Sample datasheet for internal winter hibernaculum surveys . . . . 92

Appendix G—Sample datasheet for internal summer maternity colony surveys . 93

Appendix H—Sample datasheet for emergence count data . . . . . . . . . . . . . 94

Appendix I—The NABat Data Management Plan . . . . . . . . . . . . . . . . . . 95

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Author Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .100
vi A Plan for the North American Bat Monitoring Program (NABat)

Preface

Currently, there is no program, public or private, that conducts standardized 

monitoring of bat species across multiple taxa in North America. The North American 

Bat Monitoring Program (NABat) is a multiagency, multinational effort designed to 

address this need. It grew out of the document “A National Plan for Assisting States, 

Federal Agencies, and Tribes in Managing White-Nose Syndrome in Bats” (hereafter 

National Plan) (U.S. Fish and Wildlife Service 2011). The National Plan established 

seven working groups, including the Conservation and Recovery Working Group. Goal 

1 of the Conservation and Recovery Working Group is to develop and validate rapidassessment monitoring plans to determine differences in susceptibility among species 

and to identify which species are most vulnerable to extinction due to white-nose 

syndrome (WNS). To achieve this goal, the working group was charged with three 

action items: (1) seek consensus on feasible monitoring techniques and protocols that 

would gauge impacts of WNS on bat species, (2) develop and implement monitoring 

plans to establish the degree to which different species of bats are vulnerable to WNS, 

and (3) establish best management practices for population monitoring on a rangewide 

scale for bat species of greatest conservation concern. However, it became evident 

while working on these action items that others in the bat research and management 

community were trying to tackle similar goals to address such issues as the impacts 

of wind energy development on bats. Because bats and the factors that threaten them 

cross international and intranational borders, it became readily apparent that there was 

a need for the development of a comprehensive bat monitoring program for all 

47 North American bat species shared among the United States, Canada, and Mexico. 

This monitoring program is needed not only to provide information about the impacts 

of WNS, but also to inform management and policymakers regarding the impacts of 

wind energy development, climate change, habitat loss, and unanticipated threats that 

may arise in the future. 

Following recommendations of Gitzen and Millspaugh (2012) and Reynolds (2012), 

the development of NABat incorporated the expertise of bat biologists, wildlife 

managers, policymakers, statisticians, and data managers throughout the process. 

The first step in the development of NABat was to build consensus within the 

community of North American bat researchers and biologists on feasible monitoring 

techniques and protocols to assess species responses to WNS (Goal 1, Action Item 

1 of the Conservation and Recovery Working Group). To this end, a workshop 

was held in April 2012 that brought together a wide variety of scientists, including 

bat field biologists, population geneticists, population modelers, statisticians, and 

database experts. The workshop was sponsored by the U.S. Fish and Wildlife 

Service; Forest Service, U.S. Department of Agriculture; Bureau of Land 

Management; U.S. Geological Survey; and Bat Conservation International, and was 

attended by 26 invited participants from 5 Federal agencies, 7 universities, and 1 

nongovernmental organization. The major impetus for the meeting was the need to 

measure the impacts of WNS on bat populations, but measuring the effects of other 

stressors such as climate change and wind energy development was also of concern. 

The intent of the workshop was to build on the outcomes and recommendations of 

a workshop held in 1999 (O’Shea and Bogan 2003) and incorporate new techniques 

and analyses that had been developed since that workshop. Addressing data 

management needs was also a high priority and was considered in all discussions 

of monitoring needs and protocols. After reviewing existing protocols, participants 

made recommendations for monitoring bat populations in maternity and hibernation 

colonies, estimating vital rates, using acoustic detectors and mist nets as monitoring 

techniques, and modeling the spatial distribution and occurrence of bats across large 
Preface vii

spatial scales. Participants also discussed the need for developing a sampling frame 

before a national monitoring program could be established, and briefly outlined such 

a sampling frame. However, time was not available to fully develop this sampling 

frame. Thus, workshop participants recommended that a small group of experts in 

the field of sampling design convene to draft a national sampling frame that could be 

scaled from site to continental scales. 

In summer 2012, funding was obtained from the National Landscape Conservation 

Cooperative network through a national grant opportunity offered by the U.S. Fish 

and Wildlife Service and from the National Institute of Mathematical and Biological 

Synthesis (NIMBioS) to convene three workshops to develop the sampling framework 

for NABat. The objectives of the first workshop, held in February 2013, were to gather 

information on other large-scale monitoring programs and learn from their successes 

and failures. Monitoring program sampling designs for birds, amphibians and reptiles, 

and water quality at national levels, as well as for bats in the United Kingdom, 

Pacific Northwestern United States, and Eastern United States, were presented and 

discussed. Based on these presentations and discussions, it was agreed that some 

type of grid-based finite sampling frame would be the most efficient approach for 

NABat. The second workshop, held in May 2013, was sponsored by NIMBioS 

and held at the NIMBioS laboratory at the University of Tennessee, Knoxville. 

Participants concentrated on fleshing out the details of the grid design, how it would 

be applied to acoustic monitoring and colony counts, and the analytical approaches 

that could be used to analyze resulting data. Additional topics included the appropriate 

covariates that should be collected, the need for standardization of protocols, data 

management, and Geographic Information System support. The third workshop, 

held in November 2013, focused on refining protocols and analyses and preparing the 

current report. Throughout the process, feedback was gathered from potential users 

through presentations in webinars and at scientific meetings (e.g., the International 

Bat Research Conference in August 2013 and the White-Nose Syndrome Workshop in 

September 2013). 

All the authors of this report participated in two or more of the workshops and many 

participated in all four. We thank the following people for their participation in one 

or more workshops and providing guidance and expertise: Sybill Amelon, Kate 

Barlow, Eric Britzke, Brian Cade, Kevin Castle, Matthew Clement, Steve Corn, Paul 

Cryan, Peter Dratch, Winifred Frick, Cris Hein, Kate Jones, William Kendall, Marm 

Kilpatrick, Subash Lele, Kirk Navo, Anthony Olsen, Pat Ormsbee, Sarah OylerMcCance, Luis Viquez Rodriguez, Amy Russell, Robin Russell, Patrick Sullivan, 

Jennifer Szymanski, Maarten Vonhof, Ted Weller, and Craig Willis. Technical reviews 

of this document were obtained from a variety of professionals. We thank the following 

individuals for the time, expertise, and valuable input on one or more chapters: Eric 

Britzke, Matthew Clement, Chris Corben, Gordon Dicus, Allysia Park, Laura Eaton, 

Robert Gitzen, Al Hicks, Vivian Hutchison, Andrew King, David Miller, Robyn Niver, 

Anthon Olsen, Craig Stihler, Patrick Sullivan, Steven Thomas, Janet Tyburec, and 

Michael Whitby.

Finally, we owe special thanks to Patrick Field and Tushar Kansal, Consensus Building 

Institute, who were instrumental in keeping the team on track throughout the process.

Gray myotis, Hubbards Cave survey.

CHAPTER 1 Introduction 1

1. Introduction

1.1 Purpose, Mission, Goals, and Objectives of the 

 North American Bat Monitoring Program

North American bats face unprecedented risks from continuing and emerging threats 

including habitat loss and fragmentation, white-nose syndrome (WNS), wind energy 

development, and climate change. Thus, there is an urgent need to document changes 

in bat populations in response to these threats as well as to assess management actions 

aimed at mitigating these threats. The need to monitor bats in North America has been 

recognized for many years (O’Shea and others 2003), but no coordinated program 

exists to monitor most bat species in North America, although such programs have 

been designed and implemented for birds (Ziolkowski and others 2010) and amphibians 

(Adams and others 2013). The North American Bat Monitoring Program (NABat) is 

being established to fulfill this need. The purpose, mission, goals, and objectives of 

NABat are below.

PURPOSE: The purpose of NABat is to create a continent-wide program to monitor 

bats at local to rangewide scales that will provide reliable data to promote effective 

conservation decisionmaking and the long-term viability of bat populations across the 

continent.

MISSION: The mission of NABat is to provide the biological, administrative, and 

statistical architecture for coordinated bat population monitoring to support regional 

and rangewide inferences about changes in the distributions and abundances of 

bat populations facing current and emerging threats, and to provide guidance for 

monitoring at the local scale.

GOALS: To achieve the purpose and mission as stated above, NABat has established 

two specific goals:

l Develop and maintain a long-term continental program to monitor bat distributions 

and indices of abundance at rangewide, regional, and local scales.

l Provide regular analyses and reporting on the status and trends of bat populations 

to inform managers and policymakers so that they can manage bat populations 

effectively.

OBJECTIVES: The objectives of NABat are to (1) provide the infrastructure needed 

for a coordinated bat monitoring program across national, State, Provincial, tribal/

aboriginal, and private lands boundaries; (2) provide a centralized database to house 

and manage data collected under the NABat program as well as additional data on bats 

of North America; (3) define a statistically robust continent-wide sampling framework 

for the collection of bat monitoring data; (4) provide recommended field protocols 

for colony count and acoustic monitoring data collection; (5) provide statistical 

analyses of status and trends in populations at national and regional scales using the 

most appropriate and robust methods available; (6) provide periodic “State of North 

America’s Bats” reports that assess the status and trends of bats in relation to current 

and emerging threats; and (7) continually assess the monitoring program and adjust 

protocols, sampling designs, and analyses as necessary.

A B

1.1 razilian free-tailed bat with a moth.
2 A Plan for the North American Bat Monitoring Program (NABat)

1.2 Background and Need

1.2.1 The Bats of North America and Their Importance

Bats are the second most diverse order of mammals with approximately 1,240 species 

worldwide (Simmons and Gunnell 2011). Currently, there are 150 species of bats 

recognized in North America, including 138 that are found in Mexico, 47 in the United 

States, and 17 in Canada. NABat will focus on the 47 species that are found in the 

United States and shared with Canada or Mexico (table 1.1) (sec. 1.3.1). In Canada, 

three species are listed as endangered, one species is considered threatened, and 

another species is considered a species of special concern (table 1.1). In the United 

States, eight species or subspecies are listed as endangered and one species is listed 

as threatened. Three species are considered threatened and two species are provided 

special protection in Mexico (Ceballos and others 2002).

Bats are important to the maintenance of healthy ecosystems and provide many other 

benefits to humans. One of the most important services that bats provide is insect 

consumption. Approximately 70 percent of all bat species are obligate or facultative 

insectivores, and these bats consume large numbers of insects throughout the growing 

season (Kunz and others 2011). For example, Brazilian free-tailed bat (Tadarida 

brasiliensis) colonies in Texas often contain over 1 million bats (Betke and others 

2008), and it has been estimated that colonies of this size can eat 8.4 metric tons of 

insects in one night during the peak of lactation (Kunz and others 1995). Several 

studies in the tropics have shown that bats can significantly depress the number of 

insects feeding on important agricultural crops and forest trees (Kalka and others 

2008, Morrison and Lindell 2012, Williams-Guillén and others 2008). While it is 

difficult to measure the economic impact of insect consumption by bats, the mean 

annual value of Brazilian free-tailed bats in an eight-county region of Texas is 

estimated at $12.2 million (range = $4.9 million to $24.0 million) (López-Hoffman and 

others 2014). Another model has estimated that bats across the United States provide 

approximately $4 billion to $53 billion worth of pest control services to agriculture per 

year (Boyles and others 2011).

Some species of bats are also important pollinators of native and commercial plants 

(Kunz and others 2011). For example, bats pollinate Agave tequilana, which is the 

principal component used in the distillation of tequila, an economically important 

product of Mexico. Fruit-eating bats in the tropics and subtropics are important seed 

dispersers and aid in forest and fruit crop regeneration. Bats also redistribute nutrients 

across the landscape. Accumulation of nitrogen from guano piles at the base of 

northern myotis (Myotis septentrionalis) and Indiana myotis (M. sodalis) maternity 

roosts in dead trees fertilize the soil and may be important for forest regeneration 

and gap dynamics (Duchamp and others 2010). Bat guano may also be an important 

source of chitin, a polymer that is used in cosmetics, pharmaceuticals, bioengineering, 

agriculture, textiles, and environmental engineering (Kaya and others 2014).

Bats provide many other benefits to humans, some of which are just becoming known. 

They have served as important models in medical research and aerodynamics, and 

compounds from their bodies are being used in new medical treatments. Because bats 

live much longer than would be expected based on their body size, they provide a good 

model for studies of the aging process (Brunet-Rossinni and Austad 2004), and studies 

of their metabolism have provided insight into some causes of aging (Brunet-Rossinni 

2004). Desmoteplase, a compound found in the saliva of the common vampire bat 

(Desmodus rotundus), is being tested as a treatment for stroke in humans and shows 

particular promise because it provides faster restoration of blood flow with reduced 

1.2

1.2.1

B oi ol g si t counts h bi ernating little brown myotis in Wisconsin’s Bay City Mine. (© Mylea Bayless, Bat Conservat oi n Internat oi nal, www b. a ct no o. gr )
CHAPTER 1 Introduction 

3

risk of bleeding and can be administered later than other stroke treatments (Pugsley 

and others 2006). Bats also provide small but measurable economic stimuli for several 

communities in the United States and elsewhere. For example, bat watching at caves 

such as Carlsbad Caverns or the Congress Avenue Bridge in Austin, TX, brings 

visitors to these areas and may contribute millions of dollars to local communities 

(Bagstad and Wiederholt 2013, Kunz and others 2011). Bats are also considered to be 

good bioindicators for monitoring ecosystem health because of their longevity and 

their sensitivity to stressors that may also affect many other organisms (Jones and 

others 2009).

1.2.2 Threats to Bats

Bats have been experiencing population declines for many decades, and approximately 

24 percent of all bats worldwide are considered critically endangered, endangered, 

or vulnerable (Mickleburgh and others 2002). Because of their unique life history 

strategies, bats are particularly vulnerable to external stressors. North American bats 

have very low reproductive rates compared to other mammals their size, producing 

only one litter of young per year that usually ranges from one to three young per 

litter (Barclay and Harder 2003). They also have long lifespans (up to 38 years in 

some species) (Brunet-Rossinni and Austad 2004) for their body size, associated with 

delayed maturity and the ability to accumulate toxicants over a long period of time. 

Many bat species aggregate in large numbers (thousands to hundreds of thousands of 

individuals) during hibernation and rearing of young, often in relatively few caves or 

mines. For example, 80 percent of the known Indiana myotis population is found in just 

16 hibernacula (Thogmartin and others 2012), and 95 percent of the known gray 

myotis (M. grisescens) population hibernates in 15 caves or mines (Harvey and others 

2011). Thus, disturbance to even one of these hibernacula has the potential to affect a 

large proportion of the population.

Bats have been facing many threats for decades, including habitat loss and 

fragmentation; disturbance and destruction of roost sites, particularly caves and mines; 

pesticides and other contaminants; persecution; and climate change (Jones and others 

2009, Mickleburgh and others 2002, Racey and Entwistle 2003). Cave disturbance and 

destruction have been cited as the major threats contributing to the endangered status 

of the gray myotis, Indiana myotis, and Mexican long-nosed bat (Leptonycteris nivalis) 

(U.S. Fish and Wildlife Service 1982, 1994, 2007), and loss of mature bottomland 

hardwood forest is thought to be the major factor contributing to the putative declines 

of Rafinesque’s big-eared bats (Corynorhinus rafinesquii) and southeastern myotis 

(M. austroriparius) in the coastal plains of the Southeastern United States (Bat 

Conservation International and Southeastern Bat Diversity Network 2013, Miller and 

others 2011). Gating may mitigate the effects of disturbance on some populations, but 

not all (Crimmins and others 2014, Ludlow and Gore 2000). Warming temperatures 

associated with climate change are predicted to result in shifts in the hibernating 

distribution for species such as the little brown myotis (M. lucifugus) (Humphries 

and others 2002) and a reduction and shift in maternity distribution of the Indiana 

myotis (Loeb and Winters 2013). Drier conditions and increased drought associated 

with climate change may significantly impact bat reproductive success (Adams 2010, 

Adams and Hayes 2008).

Since the beginning of the 21st century, two additional threats, WNS and wind energy 

development, have greatly impacted North American bats. WNS is an emerging 

infectious disease caused by the psychrophilic fungus, Pseudogymnoascus destructans

(formerly Geomyces destructans) (Lorch and others 2011, Minnis and Linder 2013). 

The disease was first discovered in North America on bats near Albany, NY, in 2006 

1.2.2
4 A Plan for the North American Bat Monitoring Program (NABat) Table 1.1—The bats of North America that are shared among Canada, the United States, and Mexico and their national conservation/legal status within the respective countries Canada United States Mexico Scientific name Common name Presence Statusa Presence Status Presence Status Antrozous pallidus Pallid bat + SARAThreatened + — + — Artibeus jamaicensis Jamaican fruit-eating bat - — + — + — Choeronycteris mexicana Mexican long-tongued bat - — + — + Threatened Corynorhinus rafinesquii Rafinesque’s big-eared bat - — + — - — Corynorhinus townsendii Townsend’s big-eared bat + CESCC Sensitive + C. t. ingens and C. t. virginianus Endangered + — Eptesicus fuscus Big brown bat + CESCC Secure + — + — Euderma maculatum Spotted bat + SARA Special Concern + — + Special Protection Eumops floridanus Florida bonneted bat - — + Endangered - — Eumops perotis Greater bonneted bat - — + — + — Eumops underwoodii Underwood’s bonneted bat - — + — + — Idionycteris phyllotis Allen’s big-eared bat - — + — + — Lasionycteris noctivagans Silver-haired bat + CESCC Secure + — + Special Concern Lasiurus blossevillii Western red bat -b — + — + — Lasiurus borealis Eastern red bat + CESCC Secure + — + — Lasiurus cinereus Hoary bat + CESCC Secure + L. c. semotus Endangered + — Lasiurus ega Southern yellow bat - — + — + — Lasiurus intermedius Northern yellow bat - — + — + — Lasiurus seminolus Seminole bat - — + — - — Lasiurus xanthinus Western yellow bat - — + — + — Leptonycteris nivalis Mexican long-nosed bat - — + Endangered + Threatened Leptonycteris yerbabuenae Lesser long-nosed bat - — + Endangered + Threatened Macrotus californicus California leaf-nosed bat - — + — + — Molossus molossus Pallas’ mastiff bat - — + — + — Mormoops megalophylla Peter’s ghost-faced bat - — + — + — Myotis auriculus Southwestern myotis - — + — + — Myotis austroriparius Southeastern myotis - — + — - — Myotis californicus California myotis + CESCC Secure + — + — Myotis ciliolabrum Western small-footed myotis + CESCC Secure + — + —Continued
CHAPTER 1 Introduction 

5

Canada United States Mexico

Scientific name Common name Presence Statusa Presence Status Presence StatusMyotis evotis Long-eared myotis + CESCC Secure + — + —Myotis grisescens Gray myotis - — + Endangered - —Myotis keenii Keen’s myotis + CESCC 

May be at Risk

+ — - —

Myotis leibii Eastern small-footed myotis + CESCC 

May be at Risk

+ — - —

Myotis lucifugus Little brown myotis + SARA Endangered + — - —Myotis melanorhinus Dark-nosed small-footed myotis + Not assessed + — + —Myotis occultus Arizona myotis - — + — + —Myotis septentrionalis Northern myotis + SARA Endangered + Threatened - —Myotis sodalis Indiana myotis - — + Endangered - —Myotis thysanodes Fringed myotis + CESCC 

May be at Risk; 

SARA 

Special Concern

+ — + —

Myotis velifer Cave myotis - — + — + —Myotis volans Long-legged myotis + CESCC Secure + — + —Myotis yumanensis Yuma myotis + CESCC Secure + — + —Nycticeius humeralis Evening bat - — + — + —Nyctinomops femorosaccus Pocketed free-tailed bat - — + — + —Nyctinomops macrotis Big free-tailed bat - — + — + —Parastrellus hesperus Canyon bat - — + — + —Perimyotis subflavus Tri-colored bat + SARA Endangered + — + —Tadarida brasiliensis Brazilian free-tailed bat - — + — + —

+ = Species present, - = Species not present, — = No special status. Note: Scientific and common names follow Wilson and Reeder (2005) except for those species whose taxonomy has been revised since publication of that document.a SARA = Species at Risk Act, Schedule 1; CESCC = Canadian Endangered Species Conservation Council assessment of the state of biodiversity nationally, considering provincial status ranks (CESCC 2011). This latter ranking system affords no protection to bats federally, but is indicative of expert assessment of the status of each species. In addition, some species are protected under provincial legislation. b Recent genetic evidence confirms this species has not been found in Canada (Nagorsen and Paterson 2012). 
6 A Plan for the North American Bat Monitoring Program (NABat)

and has been estimated to have killed over 5.7 million bats (U.S. Fish and Wildlife 

Service 2012). As of spring 2015, WNS had spread to 26 States and 

5 Canadian Provinces (U.S. Fish and Wildlife Service 2015). Winter colony counts 

have revealed major declines associated with WNS for four of the seven affected 

species (Turner and others 2011). Although several treatment methods are being tested 

and show promise (e.g., Cornelison and others 2014), it will be many years before these 

may be implemented on a large scale.

The first bat fatalities at wind energy facilities in the United States were reported 

in the late 1990s, but significant numbers of fatalities were not reported until 2003 

when numerous fatalities were observed in the Appalachian Mountains (Cryan and 

Barclay 2009). Since 2003, the widespread occurrence of bat mortality at wind turbine 

facilities across North America has become evident (Arnett and others 2008). Causes 

of mortality include direct impact with the blades as well as barotrauma. Mitigation 

strategies such as deterrents (Arnett and others 2013) or altering the threshold wind 

speed at which the rotors begin to rotate (Arnett and others 2011, Baerwald and others 

2009) can reduce the number of mortalities by approximately 2 to 64 percent and 

50 to 65 percent, respectively. However, deterrents are still in the experimental stage, 

and reducing cut-in speeds as a mitigation strategy is used only at a subset of sites. 

Estimates of mortality at wind energy developments vary widely. Arnett and Baerwald 

(2013) estimated that cumulative bat fatalities in the United States and Canada from 

2000 through 2011 ranged from approximately 840,000 to 1,691,000 bats,1 whereas 

Hayes (2013) and Smallwood (2013) estimated that the number of bats killed at wind 

turbine facilities in the United States during 2012 alone was approximately 684,000 

and 880,000, respectively.

New threats are also likely to emerge. For example, toxicants and chemicals associated 

with new technologies such as hydraulic fracking and produced water ponds (byproducts of oil and gas drilling) also have the potential to impact bats. Additional 

emerging diseases may affect bats in the future, particularly during the winter when 

their immune systems are suppressed (Bouma and others 2010) or as the result of 

pesticides, such as neonicotinoid pesticides, that can suppress animal immune systems 

(Mason and others 2014).

1.2.3 Need for Monitoring

Given the myriad known and unknown threats to bats and our lack of baseline data on 

bat distributions across North America, an effective and efficient monitoring program 

is needed to (1) document the impact of stressors on bat populations, (2) identify 

priority species for conservation actions, and (3) measure the effectiveness of agencies’ 

conservation and management actions to mitigate stressors. Monitoring data are also 

critical for determining species’ risks of extinction, providing an early warning sign 

for species that may be experiencing declines from unknown causes, and managing for 

healthy bat populations. Because bats are wide ranging and can travel over hundreds 

of kilometers annually, an effective monitoring program to document changes in their 

populations and distributions must be extensive in geographic scope (e.g., rangewide 

to continental). Local land managers also need methods to monitor bats on their 

properties to determine the effectiveness of their conservation and management at 

smaller spatial scales (e.g., national park, State forest). Thus, a monitoring program that 

meets the needs of users must be scalable from local to continental scales. NABat will 

help to (1) determine species distributions, (2) focus conservation efforts, (3) provide 

1 Revised data on file with: Erin Baerwald, Department of Biological Sciences, University of Calgary, 2500 

University Drive N.W., Calgary, AB T2N 1N4, Canada.

1.2.3

A pallid bat with a katydid.
CHAPTER 1 Introduction 7

analysis of trends and assessment of impacts, and (4) monitor efficacy of conservation 

and adaptive management efforts.

1.3 Scope of NABat

1.3.1 Scope and Species

One of the main goals of NABat is to establish a long-term monitoring program 

for bats across North America. We define monitoring as assessing one or more 

population state variables (e.g., abundance or distribution) at various points in time 

to draw inferences about changes in those state variables (e.g., trends) (Yoccoz and 

others 2001). Because bats have long life spans and some stressors such as climate 

change may have subtle effects, this effort will require a long-term commitment (i.e., 

multiple decades). For example, Roche and others (2011) estimated that 8 to 38 years of 

monitoring using mobile driving transects would be necessary to document declines 

of 25 to 50 percent in common bat species in Ireland, and Meyer and others (2010) 

estimated that at least 20 years of monitoring are needed to detect a 5-percent change 

in tropical bat species using capture data.

The geographic scope of NABat is the area of North America occupied by bats. 

Although there are 150 species of bats currently recognized in North America, NABat 

will concentrate on the 47 species that are found in the United States and shared with 

either Canada or Mexico (see table 1.1). The bat fauna of Mexico is quite distinct from 

that of the rest of North America, being predominantly made up of Neotropical species 

(Ceballos and others 2002). Some of these Neotropical species may also be monitored 

by the same approaches recommended here, but others may be monitored more 

efficiently with techniques that are outside the scope of NABat.

The focus of NABat will be regional to continental (e.g., State, Province, ecoregion, 

Landscape Conservation Cooperatives, rangewide). However, we intend to provide 

the guidance and infrastructure (e.g., data management) for managers and biologists 

to conduct monitoring projects at the scale appropriate to managing and conserving 

bats on their properties and jurisdictions. It is the intention of NABat to enable data 

collection efforts to be aggregated up to support regional and rangewide syntheses.

1.3.2 The Role of Demographic Studies in NABat

Interpretation of long-term monitoring data will be greatly aided by detailed studies of 

demographic parameters (e.g., reproductive rate, survival rate, population growth rate). 

While these focused studies are not currently included in NABat, the organization of 

NABat will be able to facilitate selecting the location of these studies and provide data 

management support.

1.4 Putting NABat Into Practice

The sampling framework, protocols, analysis methods, and database structure of 

NABat were developed with input from experts in many relevant fields as well as 

preliminary pilot studies and analyses of existing data. Additional pilot studies are 

ongoing. Some changes to the protocols and analytic methods may be necessary 

based on the results of ongoing field studies, input from field biologists implementing 

the program, and results of the first few years of data. However, because of the 

conservation crisis facing North American bats, it is critical that a continental bat 

monitoring program be initiated immediately and that the program be fine-tuned as 

necessary (see sec. 1.5).

1.3.2

1.4

1.3

1.3.1
8 A Plan for the North American Bat Monitoring Program (NABat)

1.4.1 General Structure of NABat

A detailed description of the proposed organizational structure and the responsibilities 

of individual positions within that structure are given in chapter 10. Although 

dedicated staff will be necessary, the success of NABat will rely primarily on the 

participation of biologists, resource managers, and the public. These participants will 

gather the data that will be used to determine changes in bat populations at regional 

and continental scales. We envision that the States and Provinces will organize bat 

monitoring programs within their jurisdictions and work with partners (e.g., Federal 

agencies, tribes, First Nations, nongovernmental organizations, industry, and private 

landowners) to conduct monitoring and submit the data to the Bat Population Database 

(BPD) (see ch. 8). This includes a large role for citizen scientists.

1.4.2 Products and Expected Outcomes

We expect that the outcome of NABat will be statistically robust data that can be 

used by scientists, managers, and policymakers to make informed decisions about 

conservation and management of North America’s bat species. Initially, reports will be 

produced on the status (e.g., distribution and indices of abundance) of bats at State or 

Province, regional, and rangewide scales. Once several years of consecutive data have 

been collected, NABat will begin to produce periodic reports on trends in abundances 

and distributions of bats across North America (e.g., “State of North America’s Bats”). 

Periodic detailed reports focused on individual species of specific concern will also be 

produced. Some of the questions that can be addressed by NABat include:

l What are the summer distributions and indices of abundance of little brown myotis, 

northern myotis, and other hibernating species in areas affected and unaffected by 

WNS, and how do these change over time?

l What are the summer distributions and indices of abundance of migratory bats 

[e.g., hoary bats (Lasiurus cinereus)] and other species affected by wind energy 

development, and how do these change over time?

l How do the sizes of wintering populations of little brown myotis, Indiana myotis, 

northern myotis, and other at-risk species change following the onset of WNS?

l How do populations of summer maternity colonies of species affected by WNS 

change following the onset of the disease?

l What are the trends in bat distributions along gradients of climate and climate 

proxies, including latitude and elevation, relative to patterns of climate change 

across North America?

l What are baseline distributions and indices of abundance patterns of North 

American bats that will allow detection of emerging threats?

Over time, NABat will provide a means to analyze and draw inferences about 

these and other questions and issues, including those related to unanticipated future 

stressors.

1.4.1

1.4.2
CHAPTER 1 Introduction 

9

1.4.3 Next Steps and Implementation

This technical guide represents the first step in the establishment of NABat and 

provides guidance for conducting bat monitoring programs across North America. 

The next steps include providing the infrastructure necessary to support NABat 

(see ch. 10) and initiating bat monitoring programs on the ground. Feedback from 

participants will be gathered to improve the execution of NABat both from a logistical 

and organizational standpoint.

1.5 Adaptive Monitoring Approach

Questions and objectives of NABat may change over time as new threats are 

encountered and more is learned about bats and their ecology. Further, techniques for 

acoustically detecting and identifying bats have advanced greatly over the past several 

decades (Parsons and Szewczak 2009) and are likely to continue to improve, and new 

techniques are being developed to allow more accurate enumeration of bats in caves 

and mines (Azmy and others 2012, Meretsky and others 2010). Thus, even though a 

long-term monitoring program requires consistency across space and time in techniques 

and protocols, NABat will need to adapt to both changes in technology and changes 

in objectives and questions. Lindenmayer and Likens (2009) outlined an adaptive 

monitoring approach that involves an iterative process of defining questions; designing 

the monitoring approach to answer those questions; data collection, management, and 

analysis; and feedback based on a reevaluation of questions or changes in technology or 

analytical methods. However, even though changes in techniques or questions will be 

required, it will be critical to maintain the long-term integrity and utility of the existing 

data, such as by calibrating new methods with the old.

1.4.3

1.5

A hoary bat af tality at a wind energy facility in West

Virgi

ni

a

.

(

©

C

r

i

s

Hein,

Bat Conservation International, www.batcon o. rg)
10 A Plan for the North American Bat Monitoring Program (NABat)

2. Primary Monitoring Methods 

of NABat and Introduction to 

Protocols

2.1 Introduction and Species-Specific Methods

Four approaches will be used to gather monitoring data to assess changes in bat 

populations: winter hibernaculum counts, maternity colony counts, mobile acoustic 

surveys along transects, and acoustic surveys at stationary points. Selection of these 

methods was based on discussions among bat biologists and statisticians at the 2012 bat 

population monitoring workshop (Loeb and others 2012). Because of the diversity of 

North American bat life histories and behavioral characteristics (e.g., colonial roosters 

versus solitary tree bats, migratory species versus year-round residents, low-intensity 

versus high-intensity echolocation calls), method selection will vary by species and 

season (tables 2.1 and 2.2). Further, the most appropriate methods for a species or 

group of species may vary geographically. For example, hibernaculum counts are 

a commonly used method for assessing populations of bats that hibernate in large 

aggregations such as many species of Myotis in eastern North America. However, 

winter hibernaculum counts will not be as useful in areas where hibernacula are not 

known or for species which do not form large aggregations.

2.1

Table 2.1—Preferred methods for monitoring bats in eastern North America

Spring, summer, fall Winter

Species

Acoustic 

point

Mobile 

transect Roost

Acoustic

point

Mobile 

transect

Hibernaculum

or roost

Artibeus jamaicensis — 1 — — 1 —

Corynorhinus rafinesquii 2 — 1 2 — 1

Corynorhinus townsendii — — 1 — — 1

Eptesicus fuscus 3 2 1 — 2 1

Eumops floridanus 2 3 1 2 3 1

Lasionycteris noctivagans 2 1 — 2 1 —

Lasiurus borealis 2 1 — 2 1 —

Lasiurus cinereus 2 1 — 2 1 —

Lasiurus intermedius 2 1 — 2 1 —

Lasiurus seminolus 2 1 — 2 1 —

Molossus molossus 2 3 1 2 3 1

Myotis austroriparius 2 3 1 2 3 1

Myotis grisescens 3 2 1 — — 1

Myotis leibii 1 2 — — — 1

Myotis lucifugus 3 2 1 — — 1

Myotis septentrionalis 1 2 — — — 1

Myotis sodalis 1 2 — — — 1

Nycticeius humeralis 1 2 — 1 2 —

Perimyotis subflavus 2 1 — — 2 1

Tadarida brasiliensis 3 2 1 3 2 1

1 = primary/preferred method, 2 = less preferred, 3 = least preferred, — = method not applicable. 

See table 1.1 for common names. 

Rankings were based on input from bat biologists working in eastern North America.
CHAPTER 2 Primary Monitoring Methods of NABat and Introduction to Protocols 11

Table 2.2—Methods for monitoring bats in western North America

Spring, summer, fall Winter

Species

Acoustic

point

Mobile

transect Roost

Acoustic

pointa

Mobile

transectb

Hibernaculum

 or roost

Antrozous pallidus X X ? — — X

Choeronycteris mexicana — — ? — — ?

Corynorhinus townsendii — — X — — X

Eptesicus fuscus X X — X X X

Euderma maculatum X X — — — X

Eumops perotis X X — — — —

Eumops underwoodii X X — — — —

Idionycteris phyllotis X X — — — X

Lasionycteris noctivagans X X — X X —

Lasiurus blossevillii X X — — — —

Lasiurus borealis X X — — — —

Lasiurus cinereus X X — — — —

Lasiurus xanthinus X X — — — —

Leptonycteris nivalis — — X — — —

Leptonycteris yerbabuenae — — X — — —

Macrotus californicus — — ? — — X

Mormoops megalophylla X X X — — —

Myotis auriculus X X — — — X

Myotis californicus X X — X X Xc

Myotis ciliolabrum X X — X — X

Myotis evotis X X — Xd — X

Myotis keenii X — X — — X

Myotis lucifugus X X ? X — X

Myotis occultus X X — — — X

Myotis septentrionalis X X — — — X

Myotis thysanodes X X — — — X

Myotis velifer X X X — — X

Myotis volans X X — — — X

Myotis yumanensis X X ? X — X

Nyctinomops femorosaccus X X — — — X

Nyctinomops macrotis X X — — — —

Parastrellus hesperus X X — — — X

Tadarida brasiliensis — X X X X X

Note: Due to lack of knowledge for many species, the ranking approach used for eastern bats was not used. 

Methods are designated as: X = appropriate method, ? = possibly appropriate method, and — = not appropriate or not 

known. Designations are based on input from bat biologists working in western North America.

aAcoustic points in winter generally entail passive detectors at a source of open winter water (standing) such as the 

entrance of a creek to a lake, or a detector mounted as close as possible to terrain with rock crevices/mines/caves/

outcrops that remain relatively snow free.

b Road transects in winter should be conducted near open winter water sources in areas of high-density rocky terrain 

during weather conditions that support high bat activity.

cThis species is active at low-elevation mines even though they often do not roost in these mines.

dAcoustic point should be within a few hundred meters of potential or known rock crevice hibernacula.
12 A Plan for the North American Bat Monitoring Program (NABat)

2.2 Overview of NABat Methods

2.2.1 Colony Counts

Many bat species form conspicuous and accessible hibernating colonies that allow 

counts of bats on a periodic basis during the winter season (Kunz and others 2009, 

O’Shea and Bogan 2003). These hibernacula are usually in caves and mines that have 

stable temperatures and relative humidity levels. When conducted at regular intervals, 

population counts allow the estimation of population change (e.g., Ingersoll and 

others 2013, Langwig and others 2012, Thogmartin and others 2012). The relatively 

stable environment of caves and mines allows a surveyor to assume that year-to-year 

variability in winter population counts largely reflects real changes in bat numbers if 

surveys are conducted during the same time of year (Ingersoll and others 2013).

Traditionally, surveyors have either counted individual bats or determined the area of 

the hibernaculum wall that was occupied by a species and multiplied this area by the 

number of bats per unit area (LaVal and LaVal 1980). However, new techniques such 

as digital photography provide more accurate counts and cause fewer disturbances to 

the bats (Meretsky and others 2010). Other methods for counting bats include infrared 

beam-break technology, thermal imaging of bats as they leave the hibernaculum, and 

passive integrated transponder (PIT) tags. Some of these techniques and others are 

discussed in more detail in chapter 7. Long-term datasets from a large number of sites 

are already available for a number of species, particularly endangered species such as 

Indiana myotis (Myotis sodalis) and gray myotis (M. grisescens). These datasets will 

form the basis for much of the hibernaculum count monitoring program. However, 

where hibernaculum locations are not known, emphasis will be placed on locating sites 

for monitoring to be added to the sample frame as they are discovered.

In addition to forming large hibernating colonies, some species such as Townsend’s 

big-eared bats (Corynorhinus townsendii), gray myotis, Mexican long-nosed bats 

(Leptonycteris nivalis), and Brazilian free-tailed bats (Tadarida brasiliensis) form large 

maternity colonies in caves and mines and are highly faithful to these sites, returning 

year after year. Other species form smaller colonies in long-term stable structures such 

as bat boxes, buildings, or other artificial structures (Dobony and others 2011, Frick 

and others 2010). As with hibernaculum counts, regular counts of bats in maternity 

colonies can provide estimates of population trends (e.g., Sasse and others 2007, Stihler 

2011). Methods for surveying bats in maternity colonies are described in chapter 7.

2.2.2 Acoustic Surveys

Many species such as the tree bats [e.g., hoary bats (Lasiurus cinereus) and eastern 

red bats (L. borealis)] do not form summer or winter colonies, and other species such 

as the pallid bat (Antrozous pallidus) and silver-haired bat (Lasionycteris noctivagans) 

form small and inconspicuous colonies that are not easily accessible. For these species, 

colony counts are not feasible, and mist netting and acoustic surveys are the only 

methods available for assessing distributions and occupancy. Mist netting has many 

drawbacks that preclude it from being part of a large-scale long-term monitoring 

program such as NABat. These include: (1) it is costly and labor intensive (Coleman 

2013); (2) it can be carried out only by trained professionals who have received preexposure prophylactic rabies vaccinations; (3) there are many biases associated with 

mist netting related to mist net placement, the ability of some species to successfully 

avoid nets, and environmental conditions at the time of netting (Carroll and others 

2002, Geluso and Geluso 2012, MacCarthy and others 2006); (4) data from mist 

netting cannot be used to estimate abundance, although it is useful for collecting 

2.2

2.2.1

2.2.2
CHAPTER 2 Primary Monitoring Methods of NABat and Introduction to Protocols 13

presence/absence data and some demographic information such as breeding status, 

presence of volant young, and sex ratios; (5) only certain habitats can be sampled (e.g., 

flyways with sufficient canopy cover and small ponds and streams); (6) there is a risk 

of disease transmission among bats, including Pseudogymnoascus destructans spores; 

and (7) there is risk of injury or stress to bats. Although mist-netting data will not be 

used as part of the standard protocols to estimate trends in bat populations, capture 

data can be submitted to the Bat Population Database (BPD) and may be useful in the 

interpretation of acoustic and colony count data to verify species presence in the area.

Two approaches to acoustic surveying will be used in NABat: stationary point surveys 

and mobile transects. Stationary point surveys involve bat detectors placed for multiple 

nights at various features across the landscape (see ch. 4 for information on survey 

site selection). Mobile transects are usually conducted with a bat detector fixed to the 

roof of a vehicle that is driven slowly along a predetermined route (see ch. 5 for more 

details). Resulting call files of sufficient quality will be identified to species or species 

group using at least two methods (see ch. 6). Response variables will be detection/

nondetection of each species for both mobile transects and stationary point surveys 

and the number of bat passes per species along a given transect for mobile transects. In 

addition, indices of activity can be calculated for stationary point surveys that may be 

valuable indicators in future analyses (see ch. 9).

There are several advantages and disadvantages of mobile transects compared to point 

surveys. On mobile transects, each bat pass should represent a different individual, 

as most bats do not fly faster than 32 km/h (Grodzinski and others 2009, Hayward 

and Davis 1964, Kennedy and Best 1972, Patterson and Hardin 1969, Schaub and 

Schnitzler 2007). Thus, the number of passes of each species can be used as an index 

of relative abundance (Roche and others 2011). Road transects are also more cost 

effective than stationary point surveys (Whitby and others 2014) and may be easier 

to implement in areas that are dominated by private land. However, some species 

may be overrepresented whereas others may be underrepresented, depending on their 

affinities for roads and roadside habitat (Roche and others 2011, Whitby and others 

2014). Stationary point surveys remove the road bias associated with mobile transects, 

and it is easier to control for factors that may affect call quality and quantity (see ch. 4). 

However, stationary points also require more time to deploy, and it may be difficult to 

find sites in areas that are primarily in private ownership.

2.3 Focal Demographic Studies

Demographic studies on focal populations are very useful for understanding the 

importance of different parameters, such as survival and reproductive rates, to 

population growth rates and for predicting potential extinction risk of species or 

populations given particular stressors. They require long-term (≥5 years) data on 

marked individuals (O’Donnell 2009). Because of the logistics of capturing and 

recapturing bats (either physically or through dataloggers), only a limited number of 

sites are appropriate for this type of study. Although focal demographic studies are not 

currently a formal part of NABat, data on colony locations and counts gathered within 

NABat can be used to assist researchers in establishing focal demographic studies, 

and data housed in the BPD can provide baseline data on long-term population trends. 

Data gathered from focal demographic studies will be valuable in helping to interpret 

population trends observed through the foundation methods of NABat.

2.3

Profle of a spotted bat. (© Daniel Neubaum, Colorado Parks and Wildlife)
14 A Plan for the North American Bat Monitoring Program (NABat)

3. The NABat Sampling Design

3.1 Overview and Purpose

The use of probability-based sampling is widely recognized as being important 

for environmental monitoring where valid statistical inference from a sample to 

a population of interest is desired (Cochran 1977, Gitzen and others 2012, Green 

1979, Olsen and others 1999, Thompson 2002). This is particularly critical for bat 

monitoring because of the inherent difficulties in describing the status and trends 

of bat populations (Hayes and others 2009, Rodhouse and others 2011) and the 

need for statistically robust status and trend data to inform management and policy 

decisions such as Endangered Species Act petitions for bat species affected by 

white-nose syndrome (e.g., Federal Register 2013). Similar considerations apply to 

other conservation issues such as bat fatalities at industrial wind facilities and longterm population changes in abundance and distribution from climate change and 

habitat loss. Because bats are so difficult to survey, nonprobabilistic approaches have 

typically been favored over more formal probabilistic survey designs (Rodhouse and 

others 2011). However, monitoring to assess the status and trend of bat populations 

will be much more useful and justifiable if undertaken with a coherent statistical 

underpinning. Though nonprobabilistic surveys can contribute information to 

monitoring programs, sampling based solely on nonprobabilistic surveys can 

produce misleading results and conclusions that are not scientifically defensible, and 

findings from nonprobabilistic surveys are more likely to be dismissed by skeptics 

(McDonald 2012). Given the costs and effort associated with implementing bat 

surveys, every attempt should be made to maximize the level of valid inference 

drawn from such efforts. Accordingly, NABat has adopted a grid-based sampling 

frame that can support the probabilistic selection of survey locations across North 

America. Indeed, the establishment of a formal grid-based sampling framework for 

coordinating bat monitoring across major portions of North America will be one of 

the primary contributions of NABat. Similar approaches have been successfully used 

for monitoring bats across broad geographic extents in the United States and United 

Kingdom (Hayes and others 2009, Roche and others 2011, Rodhouse and others 2012).

A central challenge for NABat is to balance the need for “bottom-up” flexibility with 

top-down consistency and quality control. Data collected outside the formal sampling 

frame of NABat will be utilized to encourage the broadest participation possible from 

the bat conservation community at large, but the fundamental emphasis will be on 

promoting implementation of the probabilistic sampling design. Legacy data (i.e., 

nonprobability data provided by contributors following quality protocols prior to the 

development of NABat) and found data (i.e., nonprobability data collected concurrently 

with NABat but not within the sampling framework) (Olsen and others 1999, Overton 

and others 1993) can be weighted to allow incorporation with the probabilistic sample 

data and can also be used to validate models (Olsen and others 1999). It is important 

to emphasize to potential partners and data contributors that data collected within the 

probabilistic sampling framework will be of much greater value and utility.

3.2 The Grid-Based Sampling Frame

The statistical target populations of NABat are the summer populations of the North 

American bat species with geographic ranges that overlap Canada, the United States, 

and Mexico (excluding tropical species that do not range north of the Mexican border) 

and the winter hibernacula and summer maternity colonies of several of these same 

bat species (see table 1.1). Such a large and complex spatial domain of interest requires 

3.1

3.2

Portrait of a Mexican long-tongued bat.
CHAPTER 3 The NABat Sampling Design 15

a flexible, multipurpose sampling frame. The NABat sampling frame is a grid-based 

finite-area frame spanning Canada, the United States, and Mexico consisting of N 

total number of 10- by 10-km (100-km2) grid cell sample units (fig. 3.1). For example, 

there are 133,307 cells in the continental United States. (See appendixes A and B for 

the number of full and partial cells contained in each State and Canadian Province, 

respectively.) These 100-km2 sample units are the focal analytical unit for regional 

and rangewide assessments. This grain size is biologically appropriate given the 

scale of movement of most bat species, which routinely travel many kilometers each 

night between roosts and foraging areas and along foraging routes (e.g., Chambers 

and others 2011, Lacki and others 2007, Norberg 1990, Pierson 1998). The grid 

was developed by the Forest Service, U.S. Department of Agriculture for use in the 

interagency “Bat Grid” monitoring program in the Pacific Northwest (Hayes and 

others 2009, Ormsbee and others 2006, Rodhouse and others 2012) and was expanded 

across Canada, the United States, and Mexico in anticipation of an eventual program 

such as NABat. The 100-km2 grain of the sampling frame is also an appropriate 

resolution for modeling and mapping bat species distributions (e.g., Rodhouse and 

others 2012). Finer grain sizes may be informative for local-scale questions but are 

inefficient for broad, regional syntheses. However, it will be possible to nest finer 

grained secondary and tertiary sampling units within the 100-km2 primary unit 

for local research and management purposes. This should be done in consultation 

with NABat to ensure that these efforts will produce data that can be successfully 

aggregated back up to the 100-km2 unit.

Idaho

Montana

Wyoming

Yellowstone 

National Park

Jurisdiction

National Park Service

Other Federal

Non-Federal

10 km x 10 km sample unit

 ̄

0 10 20 40 60 80 100 Km

Figure 3.1—Part of the finite grid-based sampling frame of the North American Bat Monitoring 

Program. Sample units are 100-km2 grid cells. Northwestern U.S. Federal lands are shown here 

for illustrative purposes only. The frame extends through Canada and Mexico as well. 
16 A Plan for the North American Bat Monitoring Program (NABat)

3.3 The Spatially Balanced Master Sample

NABat will use a master sample approach (Larsen and others 2008) with a grid-based 

frame. Larsen and others (2008) describe several examples of how this has been done 

for other regional and national programs. For NABat, the approach begins by assigning 

a spatially balanced and randomized ordering of all 100-km2 units from the finite 

grid-based sampling frame using the generalized random-tessellation stratified (GRTS) 

survey design algorithm (Stevens and Olsen 2004). Subsamples of 100-km2 units can 

then be made following the GRTS order, ensuring both randomization and spatial 

balance.

The GRTS design provides solutions to several practical challenges faced by bat 

surveyors that are not provided by more familiar designs such as simple random, 

stratified, and systematic sampling. The GRTS design allows for sample site additions 

and deletions, supports unequal-probability selection of survey locations, and provides 

an approximately unbiased neighborhood-weighted variance estimator that takes 

advantage of the spatial structure present in the surveyed population (Stevens and 

Olsen 2003, 2004). These features have made the GRTS design an increasingly popular 

choice for natural resource monitoring programs (Fancy and others 2009, Johnson and 

others 2009, Olsen and others 2009), and it has recently been shown to be useful for bat 

acoustic surveys as well (Rodhouse and others 2011). The hallmark of the GRTS design 

is its flexibility and applicability to the master sample strategy. For example, because 

of changes in financial resources from year to year, it may be possible to increase or, if 

necessary, decrease the number of grid cells surveyed. Other common problems include 

the loss of access to sample units as ownership status changes. The GRTS design 

will accommodate these changes in realized sample size as resources and logistical 

conditions ebb and flow over the life of a monitoring program. By working through the 

spatially balanced ordered list of sample units generated through the GRTS ordering of 

the master sample, sample units not meeting a priori criteria (e.g., access) can be passed 

over (dropped) and subsequent units further down the list can be added (fig. 3.2).

Unit Status

111 Dropped – no access

12 Sample

189 Sample

26 Dropped – mapping error

103 Sample

115 Sample

17 Sample

200 Sample

89 Sample

1 Sample

3 Added to replace 111

54 Added to replace 26

6 Additional oversample

217 Additional oversample

29 Additional oversample

Figure 3.2—An example of a generalized random-tessellation stratified (GRTS) draw. Fifteen 

grid cells were drawn with the intention to sample 10 cells. Two cells were replaced because of 

lack of access and mapping errors. Three other grid cells are available for additional sampling if 

resources allow increased sampling in the future.

3.3
CHAPTER 3 The NABat Sampling Design 17

A draw of sample units from a finite sampling frame using the GRTS design produces 

an ordered list of units such that any ordered subset of that list is also randomized and 

spatially balanced. Therefore, GRTS ordering of all units within the frame (a de facto 

exhaustive sample of all units, the so-called “master sample”) (Larsen and others 2008) 

allows for ordered subsampling by jurisdiction of the entire list of sample units that 

meet specific design criteria. This flexibility provides the crucial capability to integrate 

continental-scale monitoring across multiple partner jurisdictions (e.g., across Federal, 

State, and Provincial boundaries). Each jurisdiction uses the same common master 

sample and a priori criteria to meet common objectives (see ch. 1) but can tailor sample 

sizes in response to their own available resources. The master sample also allows the 

development of an economy of scale, where neighboring partners can collaborate on 

surveys of grid cells that overlap jurisdictions (e.g., a Forest Service team surveys a 

grid cell that also contains a substantial amount of Bureau of Land Management land).

An example of unequal probability subsampling of the master sample is illustrated in 

figure 3.3. In this scenario, a lower sampling intensity was supported on non-Federal 

lands (e.g., perhaps because of fewer resources available by State and nongovernmental 

partners), very high sampling intensity for National Park Service (NPS) lands (e.g., 

under a scenario in which NPS plans to invest a considerable amount of resources into 

bat monitoring), and intermediate intensities for other Federal lands. These varying 

intensities were achieved by attributing the master sample into jurisdictional groups 

Brazilian free-tailed bats emerge at sunset under the Congress Avenue br di ge ni Aust ni , TX. ©(

Ka er Mn r a , sk

r esno Ct aB

av oit n nI

et

nr at oi nal, www.batcon.org)

GRTS Draw

Primary sample

Oversample

Ownership

Federal

NPS

Other

 ̄

Figure 3.3—An example of subsampling of 

the spatially balanced randomized GRTS 

master sample of 100-km2 grid cells (sample 

units) from the Western United States, with 

500 sample units each selected from the 

non-Federal list subset, the National Park 

Service (NPS) subset, and the subset of units 

that overlap other (non-NPS) Federal lands. 

Oversamples of 500 from each subset are also 

included (see sec. 3.3). (GRTS = generalized 

random-tessellation stratified)
18 A Plan for the North American Bat Monitoring Program (NABat)

and then picking n

i

 sites from each jurisdictional subset i following the spatially 

balanced random GRTS order. Remaining sample units not included within the initial ni

 units are retained for future use as “oversamples” in the event that increasing 

resources allow for greater sampling effort. In the case of the master sample approach, 

the oversample list contains all remaining sample units, although figure 3.3 shows only 

the first 2n for illustrative purposes. This strategy allows multiple partners to work 

from a common probability sample rather than from separate disparate samples.

3.4 Design Criteria and Implementation of the Master Sample

In an effort to reduce complexity and maintain as much flexibility as possible (Larsen 

and others 2008, Olsen and others 1999), the NABat master sample assigns an 

inclusion probability of 1 (100 percent) to all sample units across the entire continental 

United States and those portions of Canada and Mexico that are overlapped by 

the range of at least one of the target species. This unstructured (e.g., no a priori 

stratification) master sample allows multiple partners to select units according to a 

priori (e.g., accessibility, jurisdiction) NABat criteria as well as in response to local 

conditions and resource constraints. Also, in the case of frame errors that may result 

in units being inaccessible (e.g., mapping inaccuracies), replacement units can be 

selected, and spatial balance will be maintained. This will result in an unequalprobability realized sample in which inclusion probabilities and design weights 

(inverse inclusion probabilities) vary among jurisdictional domains. Whereas the 

creation of the master sample is straightforward using widely available statistical 

tools (e.g., Kincaid and Olsen 2012), its successful implementation depends on very 

careful tracking of the specific criteria used to establish jurisdictional sample subsets 

and the resulting adjustment of initial design weights to realized design weights that 

will be used in analysis (Larsen and others 2008). This important component of the 

data management system is provided by the Bat Population Database and is described 

further in chapter 8.

Careful attention needs to be given to the sample sizes of individual jurisdictions 

for which robust statistical inference is desired. No formal power analysis will be 

conducted until 2 years of pilot data collection have been completed. During this 

start-up pilot period, we recommend that at least 30 sample units (grid cells) be 

surveyed each year within each jurisdiction until further guidance is available. In 

some instances (e.g., State of Rhode Island), this may require an exhaustive sample 

of all units. This level of sampling may not be necessary when only broader regional 

syntheses and inferences are planned. The Pacific Northwest Bat Grid program 

achieved sample sizes of ~50 to 70 each year for two adjacent large Western States 

(Oregon and Washington) using a combination of trained volunteers and a small 

team of full-time technicians; precision of occupancy model estimates was sufficient 

to detect important spatiotemporal variation with common species over 4 years 

(Rodhouse and others 2012). Precision of temporal trend estimates will improve over 

time, even with modest initial sample sizes (MacKenzie and others 2006, Urquhart 

and others 1998).

3.4
CHAPTER 3 The NABat Sampling Design 19

3.5 Response Designs

Within selected 100-km2 sample units from the master sample, acoustic recording of 

bat calls will be accomplished using stationary deployment of detectors and mobile 

deployment of detectors mounted on the roofs of motor vehicles for 25- to 48-km 

driving transects. Two to four stationary bat detectors should be placed within each 

grid cell, ideally in each 5- by 5-km quadrant, to adequately represent the entire sample 

unit. Stationary detectors should be run for multiple days. Current recommendations 

for NABat participants are to deploy detectors for four nights (e.g., deploy on Monday 

and retrieve on Friday). Note that firmer guidelines about subsampling of grid cells 

will be provided after pilot study results have been evaluated. Driving transects should 

be run twice within the same week as the stationary surveys to permit estimation of 

detection probabilities and integration of data from both methods. Colony count effort 

may also be allocated following the ordered GRTS sample, particularly as a way to 

organize efforts to discover new roosts and hibernacula. Additional details of the 

response designs and sampling procedures are provided in chapters 4, 5, and 7.

3.6 Temporal Revisit Design

The temporal revisit design defines the survey effort of sample units over time. NABat 

will use an “always revisit” design (McDonald 2003) in which the same grid cells 

are surveyed every year for acoustic surveys and external colony counts, and every 

year or every other year for internal roost surveys. This design is considered optimal 

for trend detection (MacKenzie and Royle 2005, Urquhart and others 1998) and has 

been successfully used for temporal trend detection of bat populations in Oregon and 

Washington within a dynamic occupancy modeling framework (Rodhouse and others 

2012). Importantly, some amount of missing data can be accommodated in analyses 

proposed for these data, providing ad hoc flexibility to this design (MacKenzie and 

others 2003, Rodhouse and others 2012, Royle and Dorazio 2008). For example, it is 

acceptable for some number of sample units to be skipped in some years (e.g., as a 

result of funding shortfalls). However, these missing observations must be assumed to 

be random and unbiased; consistent deviation (i.e., the same grid cells are consistently 

not revisited) from the always-revisit strategy should be avoided and should not be 

based on lack of data or species presence/absence. Providing specific guidance on 

how many missing data can be accommodated without unduly compromising the 

efficacy of the program will depend on results of initial pilot efforts and overall sample 

sizes achieved among the primary partners. Note that other revisit strategies are 

also possible, such as a split-panel design where a small subset of units (a panel) are 

revisited annually, while other subsets (i.e., other panels of sample units) are visited 

in alternating years (e.g., see Urquhart and others 1998). However, NABat is pursuing 

an always-revisit design because of statistical power and, importantly, to avoid the 

concomitant added complexity of logistical coordination and analysis that would be 

associated with these more complicated revisit designs.

3.5

3.6

A palid bat captures a scorpion.
20 A Plan for the North American Bat Monitoring Program (NABat)

4. Stationary Point Acoustic 

Survey Protocols

4.1 Types of Detectors

Many types of bat detectors are available for continuous recording of bat echolocation 

calls. Britkze and others (2013) and Parsons and Szewczak (2009) reviewed differences 

among types and present advantages and disadvantages. Detector technology is 

continuously improving over time, and because each detector type has different 

limitations, NABat does not specify a particular type of detector to be used. However, 

whatever detector is used must be capable of recording continuously for the required 

number of nights and detecting species anticipated to be present in the region that can 

be detected acoustically (see tables 2.1 and 2.2). If it becomes necessary to change 

technology or settings, it is important to carry out calibration trials to compare 

detectability with the old and the new technologies or settings. Model types discussed 

below are the most commonly used detectors in North America to date.

Detector types most commonly used for species identification are zero-crossing 

frequency division (e.g., AnabatTM CF–ZCAIM, SD1/SD2, Express; Wildlife Acoustics®

SM2BAT+TM or SM3BATTM on ZC mode), time expansion full-spectrum (e.g., 

Pettersson D240X), and direct recording full-spectrum detectors (e.g., Binary Acoustic 

Technology AR125TM, FR125TM, iFR-IVTM, Pettersson D500x, Wildlife Acoustics®

SM2BAT+TM, SM3BATTM, or EM3+TM). Time expansion systems stretch the signal 

out by a factor of n while transferring the data to the recording device (Britzke and 

others 2013, Parsons and Szewczak 2009), and the system cannot record new sounds 

while this is occurring. Because future analytical approaches may be able to estimate 

abundance by using the number of files or passes recorded (see ch. 9), the use of time 

expansion detectors is not recommended. Thus, any frequency division zero-cross or 

full-spectrum direct recording bat detector that has a time-date stamp for each file can 

be used for stationary point surveys. However, because acoustic technology changes 

rapidly, new detector types may be available in ensuing years that may also be suitable 

for stationary point surveys.

When choosing a detector type, the type of microphone must also be considered. 

Microphones can be classified as omnidirectional or directional depending on how 

they are constructed and how they are deployed. An omnidirectional microphone 

that is not placed into a housing or shield will pick up bat activity in all directions, 

but it may also record more noise because it picks up sound from a greater volume of 

space, particularly from the ground and surrounding vegetation. A more directional 

microphone (either by design or by using a housing, horn, or shield) will mainly 

record bats in front of the detector, with some “side lobes” of detection of lower 

frequencies. Directional microphones often detect bats at a greater distance on the 

central axis of the microphone than omnidirectional microphones. In general, the 

larger the microphone diameter, the more directional it tends to be. For example, the 

AnabatTM SD2 has a much larger diameter microphone than the AnabatTM Express, so 

the Express detector is far more omnidirectional, especially when the microphone is 

mounted on a cable away from the body of the detector. In addition, some detector 

manufacturers offer microphones with different frequency response curves, 

specifically varying sensitivities at low or high frequencies. For example, the AnabatTM

“lo” (white) microphone is more sensitive in the audible range than is the standard 

(black) microphone; the green “hi” microphone has the same frequency response 

as the standard microphone, but should be used when using the microphone off the 

4.1

S

di e v ei w of a long-eared

myotis. (© Daniel Neubaum, Colorado Parks and Wildlife)
CHAPTER 4 Stationary Point Acoustic Survey Protocols 21

detector body (e.g., when mounting it on an extendible pole). The Wildlife Acoustics

®

SMX–UT

TM microphone for the SM2Bat+TM records higher frequencies and is more 

sensitive than the SMX–US

TM; the SMM–U1TM is more sensitive than either of the SMX 

microphones. Some microphones are manufactured within more consistent ranges of 

sensitivities than others. For example, the AnabatTM stainless microphones are factory 

calibrated so that each microphone is within a narrow range of sensitivities. Whatever 

microphone is used, it is critical that they are used consistently at sites over time. Using 

the same type of microphone and periodically checking for microphone performance 

will help to ensure this consistency (see sec. 4.2.1).

4.2 Detector Sensitivity and Settings

Long-term monitoring requires the surveyor to minimize and account for variability 

among detectors and settings. Therefore, it is critical that settings be consistent within 

and between years, and documented in the Bat Population Database (BPD).

4.2.1 Sensitivity

The sensitivity of bat detectors can vary both within and among detector types (Adams 

and others 2012, Larson and Hayes 2000). Full-spectrum recording is inherently 

more sensitive than frequency division zero-cross because full-spectrum detectors 

record signals within the ambient noise, whereas zero-cross detectors record only 

the loudest signal above the noise floor (Corben 2002). However, during deployment 

of full-spectrum detectors, sensitivity is often lowered to reduce the number of 

files recorded with no bats and to reduce recording extraneous noise, thus reducing 

memory requirements (i.e., a louder signal above the noise floor is required to initiate 

recording).

For full-spectrum detectors, gain and signal-to-noise ratio settings affect sensitivity of 

the detector. Sensitivity can be adjusted manually with a dial for all AnabatTM models 

except the AnabatTM Express, but the numbers on this dial are not consistent among 

units or models and thus should not be used as a way of equalizing sensitivities (see 

below). The sensitivity (low, medium, or high) for the AnabatTM Express can be set 

through AnabatTM Toolbox utility software. Some detectors also allow frequency band 

filters to target some frequencies; these filters should be adjusted for the local bat 

community, be documented in the BPD, and be used consistently from year to year at a 

monitoring site.

The signals of bat calls must exceed the noise floor for zero-cross detectors (e.g., 

Wildlife Acoustics

® detectors set on ZC mode or SMZC; AnabatTM II, SD1, and SD2) to 

record them. The ambient level of noise can vary over time (e.g., wind or rain increases 

the noise floor), and detectors that track the ambient noise level and adjust accordingly 

will have varying sensitivity over time (auto-level). For example, Wildlife Acoustics

®

SM2BAT+TM and SM3BAT

TM detectors auto-level when in zero-cross mode, and thus 

their sensitivity can vary daily or even within the night depending on how the detector 

is programmed. AnabatTM detectors use a consistent noise floor as long as the sensitivity 

knob is not changed or the internal digital setting has been applied in the automated 

equalization process (AnabatTM Equalizer, see below).

Calibrating detectors among each other by adjusting their recording sensitivities can 

reduce variation in detection volumes among detectors. Some manufacturers provide 

hardware and software that allow the user to calibrate across and within detectors (e.g., 

AnabatTM Equalizer for AnabatTM SD1 and SD2), while others provide equipment that 

allows the user to test the performance of their equipment against system standards 

4.2

4.2.1
22 A Plan for the North American Bat Monitoring Program (NABat)

(e.g., Wildlife Acoustics® Ultrasonic CalibratorTM). Larson and Hayes (2000) described 

a method for calibrating AnabatTM II detectors against each other. Currently, there is no 

method for testing the performance of AnabatTM Express microphones, but a method is 

expected in the near future.1

Microphone sensitivity can vary over time, making detector calibration and 

performance tests very important, particularly for a long-term monitoring program. 

Testing the performance of microphones and detectors before and during the recording 

season is important to detect any loss in sensitivity. When loss of sensitivity occurs, 

microphones may need to be replaced. Environmental conditions can also affect 

microphone sensitivity. For example, if Wildlife Acoustics® SMX microphones with 

mesh windscreens get soaked with water, their sensitivity diminishes.2

To ensure consistency, it is imperative that the same detector or detector type, 

microphone, and settings are used at a site each year to reduce variation caused by 

equipment differences. However, if equipment needs to be replaced or if new detectors 

are used, they need to be calibrated against the older equipment. Having old and new 

equipment recording side by side for at least one season is the most effective way to 

develop correction factors and allow monitoring to continue with new equipment after 

calibration.

4.2.2 File Recording Settings

A “triggered” bat detector does not record constantly, but starts and stops based on 

input. A triggered detector begins recording when it detects an ultrasonic signal. If a 

trigger window has been set, recording will end after a specified amount of time has 

passed in which no signal is detected. For example, AnabatTM defaults to 5 seconds 

unless the time between calls (Max TBC) setting is changed in CFCRead utility 

software. In contrast, Wildlife Acoustics SM2BAT+TM and SM3BATTM and Binary 

Acoustics AR125TM, FR125TM, and iFR-IVTM allow the user to specify the trigger 

window. To standardize recordings, it is recommended that a 2-second trigger window 

and a maximum file length of 15 seconds be used. Each detector may refer to these 

settings differently. The Binary Acoustics Technology AR125TM and FR125TM refers 

to “Duration” for max file length and “Idle setting” for trigger window. For AnabatTM

detectors, the trigger window is the “Max TBC;” the file size is set at 15 seconds and 

cannot currently be changed.

With full-spectrum recorders, it is also possible to record continuously through the 

night and then use software to scan the recordings to detect bat calls. This approach 

takes advantage of the full sensitivity of the recorder and can lead to substantially 

increased detection probabilities. However, it also has much larger memory 

requirements and requires increased processing time to analyze the data. As with 

other methods, this approach is acceptable provided that the same approach is used 

consistently over time. In this case, not only the recording parameters but also the 

algorithms used to extract calls from the recordings can affect detection probabilities, 

and both need to be standardized. However, if the original recordings are retained, 

it may be possible to reanalyze them in the future if improved detection algorithms 

become available.

1 Personal communication. 2014. K. Livengood, Office Manager, Titley Scientific USA, 601 Business Loop 

70 W, Suite 110, Columbia, MI 65203.

2 Personal communication. 2014. S. Snyder, Product Manager, Wildlife Acoustics, Inc., 3 Clock Tower 

Place, Suite 210, Maynard, MA 01754-2549.

4.2.2

A lesser long-nosed bat pollinates an agave fower.
CHAPTER 4 Stationary Point Acoustic Survey Protocols 23

The division ratios for frequency division zero-cross detectors can be adjusted. 

Because a frequency division detector considers only every nth wave, the lower the 

division ratio (n) the more information is recorded. However, file sizes will also be 

larger. A low division ratio is recommended (e.g., ≤8) in areas with target species that 

have audible, low frequency components to their echolocation calls such as the spotted 

bat (Euderma maculatum). However, a division ratio of 8 or 16 should be used in 

areas where bat species produce high frequency calls [e.g., Californian myotis (Myotis 

californicus)] because of the risk of inaccurate high frequency representation with low 

division ratios.3 Division ratio settings should be consistent between years.

4.3 Site Selection and the Influence of Clutter 

 on Bat Echolocation

With only two to four detectors in a cell, it is not cost effective, practical, or desirable 

to position detectors randomly within the cell. Rather, detectors should be placed in 

areas that maximize the number and quality of recordings. In areas with heterogeneous 

habitats suitable for bats, detectors should be placed to maximize the diversity of 

species likely to be detected. Whenever possible, one detector should be placed in each 

5- by 5-km quadrant of the cell.

Site selection for deployment of detectors can affect the quantity and quality of 

echolocation calls recorded (Britzke and others 2013). It is important that biologists 

consider the bat species in the area and their habitat associations when selecting 

sites for stationary point samples. Because sites will be surveyed each year of the 

monitoring program (see sec. 3.6), it is critical that good sites are selected. Thus, 

knowledgeable biologists should put considerable thought into the site selection process 

and conduct on-the-ground reconnaissance.

Several features in the environment affect the quality and quantity of calls recorded 

by bat detectors (table 4.1). One of the most important features is the amount of clutter, 

defined as the density of obstacles in the flight environment (e.g., tree branches, 

leaves, or water surfaces) (Fenton 1990). For example, transmission of 25 kHz sounds 

is lower in intact forests than in thinned forests (Patriquin and others 2003), and 

detectors oriented away from clutter record more calls than those oriented towards 

clutter (Weller and Zabel 2002) due to multiple reflections off of vegetation. Further, 

bats adjust their echolocation calls while in clutter. Echolocation calls in clutter are 

3 Personal communication. 2014. C. Corben, Acoustic Biologist, Titley Scientific USA, 601 Business Loop 

70 W, Suite 110, Columbia, MI 65203.

4.3

Table 4.1—Factors that may impact the quantity and quality of bat echolocation calls recorded by 

acoustic detectors, the problems associated with each factor, and ways to reduce the effects 

Factor affecting call quality Associated problems Ways to reduce effects

Dense vegetation (classic “clutter”) Poor quality calls (e.g., fragments)

Bats change their call structure

Place detectors in more open areas 

Orient microphone toward more open areas

Other bats Bats change their call structure Place detectors in areas where there are not dense 

concentrations of bats (e.g., avoid recording directly 

at watering holes or close to a roost)

Echoes off hard surfaces Diffuse or spectral echoes Place detectors so that they are not directly over hard 

surfaces such as still water, pavement, or bridges
24 A Plan for the North American Bat Monitoring Program (NABat)

shorter duration, higher frequency, and have greater bandwidths than those produced 

in more open areas (Broders and others 2004, Wund 2006). Thus, identification of 

calls recorded in cluttered environments based on known calls from open areas could 

be erroneous (Britzke and others 2013). Other bats in the area can also represent a 

form of clutter to echolocating bats, and bats will adjust their individual calls for echo 

recognition when many other bats are in their foraging space (Obrist 1995).

Echoes off surfaces can also affect the quality of recorded calls by distorting the 

sound (Parsons and Szewczak 2009). Bat detectors generally record two basic types of 

echoes: diffuse echoes and specular echoes (fig. 4.1). Ultrasound reflected off a rough 

surface, such as a tree trunk, will produce a diffuse echo; smooth surfaces produce 

a specular echo, which is a near-perfect reflection of the original bat pulse. Echoes 

can make species identification more difficult and inflate the number of calls in a 

file. This is especially a concern for automatic species-identification software. Thus, 

when recording occurs in close proximity to flat, reflective surfaces (e.g., still water, 

pavement, or bridges), call quality may be reduced.

Bats use various types of habitats to forage, drink, roost, and commute. Ponds 

or wetlands are often used for drinking and foraging (Seibold and others 2013, 

Stahlschmidt and others 2012), and edge habitats such as along trails and forest roads, 

forest openings, and rock cliffs are often used by bats that are commuting between 

foraging and roosting areas (Jantzen and Fenton 2013, Morris and others 2010, 

Verboom and Huitema 1997). Areas with suitable roosting sites such as mature trees, 

snags, rock crevices, anthropogenic structures (e.g., homes, barns, and log cabins), 

caves, and mines are also potential habitats. Bats are less likely to be found in wideopen places, such as the middle of a cultivated field (Crampton and Barclay 1996, 

Grindal and Brigham 1999). They also avoid very fast flowing streams or creeks that 

produce too much competing noise (Mackey and Barclay 1989, von Frenckell and 

Barclay 1987) or creeks and wetlands with dense vegetation that reduces access to prey 

and water (Ober and Hayes 2008).

Bat species vary in habitat use due to differences in maneuverability, foraging mode, 

ecology, and properties of echolocation call structure (Aldridge and Rautenbach 1987, 

Specular echoes Diffuse echoes

Figure 4.1—Specular and diffuse echoes of Brazilian free-tailed bat (Tadarida brasiliensis) 

echolocation calls recorded with an AnabatTM detector. (recording courtesy of C. Corben, Titley 

Scientific)
CHAPTER 4 Stationary Point Acoustic Survey Protocols 25

Fenton 1990). In general, large fast-flying bats use habitats that are more open, whereas 

smaller, more maneuverable species use more cluttered or edge habitats. Thus, it is 

important to consider the composition of the bat community in the survey area when 

selecting suitable sampling sites.

When placing detectors in the 100-km2 grid cells, it is desirable to monitor bats in 

a variety of habitats. For heterogeneous landscapes, place up to four detectors such 

that they sample different habitat features, preferably one within each quadrant of the 

cell (fig. 4.2). For example, if a cattle watering tank is selected for one sample point, 

consider selecting other habitat types such as larger bodies of water, small roads, or a 

forest opening. Although fewer individual bats may be recorded by deploying detectors 

away from areas where bats may concentrate or other sources of clutter such as water 

tanks or roosts in buildings, a higher percentage of calls may be identifiable (fig. 4.3).

If the habitat within a grid cell is more homogeneous, fewer detectors (minimum of 

two) may be sufficient to capture the potential bat diversity in the cell. However, if 

the surveyor is not familiar with the area or the bat community, we suggest setting up 

three or four detectors to ensure capturing as much of the species diversity in the area 

as possible. Although it is good to place detectors so that a high number of species 

are recorded, sampling should be conducted to maximize detection of all species 

within cells across all detectors. Thus, in some cases it may be desirable to place 

one or two detectors in habitats that may be used only by one or two species if those 

habitats are the most likely area to host those species. For example, northern myotis 

(M. septentrionalis) are more likely to forage in closed canopy forests (Henderson 

and Broders 2008), and placing at least one detector in these habitats may increase the 

probability of detecting them, although doing so may lower the probability of detecting 

other species (Carroll and others 2002).

Low Medium High

Clutter

Identifiable pulses for 

species identification

Figure 4.2—Example of stationary detector placement in a 100-km2 grid cell. Detectors (black 

rectangles) have been placed in four diverse habitat types (clockwise from top left): a forest 

opening, along a stream, along a forest road, and near a pond. 

Figure 4.3—Relationship between clutter and 

probability of successful species identification 

for species that are difficult to acoustically 

differentiate from others. Note: clutter may be 

reduced by increasing horizontal set-back from 

clutter and/or vertical height of microphone. 
26 A Plan for the North American Bat Monitoring Program (NABat)

4.4 Equipment Setup

4.4.1 Placement Relative to Clutter, Height above Ground, and 

Orientation

Bats tend to respond to clutter within a few meters. Thus, detectors should be a 

sufficient distance and oriented away from clutter such as forest edges, buildings, 

cliff faces, and the ground to reduce echoes and the effects of clutter on bat behavior 

and sound quality. Unfortunately, little research has been done on an ideal set-back 

distance of microphones from clutter. In general, 3 to 5 m is likely to be enough to 

reduce the effect of clutter when a directional microphone is used and is oriented 

away from the clutter. A greater set-back distance will be needed for omnidirectional 

microphones unless they are contained in housings that make them more directional 

and are oriented away from the clutter. The set-back distance from clutter depends 

on the sensitivity and range of the microphone being used. It is recommended that 

microphones be elevated as high as possible off the ground; directional microphones 

should be at least 1.4 m above ground (Weller and Zabel 2002), and omnidirectional 

microphones should be placed even higher to reduce background noise and echoes 

from the ground. Detectors or microphones can be mounted on tripods or elevated on 

poles (fig. 4.4). Caution should be used when elevating some microphones on fiberglass 

poles, as they may short out due to static electricity unless the poles are grounded.

The suggested microphone orientation depends on directionality and weatherproofing 

(see sec. 4.4.2). Orientation relative to the horizontal plane may be important for 

detectors with directional microphones. For example, AnabatTM II microphones oriented 

horizontally record fewer calls than those oriented at 45° or greater (Britzke and 

others 2010). Omnidirectional microphones can be angled horizontally or even below 

horizontal without use of a reflector plate.

Figure 4.4—An example of a microphone mounted on (A) a tripod (AnabatTM system), and (B) a 

painter pole (inset: Wildlife Acoustics® SMX-USTM microphone on top of pole). (fig. 4.4A photo 

courtesy Titley Scientific, other photos by C. Lausen)

4.4

4.4.1

(A) (B)
CHAPTER 4 Stationary Point Acoustic Survey Protocols 27

4.4.2 Weatherproofing

Some detector systems have water-resistant microphones (e.g., Wildlife Acoustics®

SM3BATTM, EcoObs Batcorder, Pettersson D500x), and a few have waterproof 

microphones (e.g., AnabatTM Express). For long-term recording under unpredictable 

weather conditions, weatherproofing is necessary for microphones that are not water 

resistant. However, even water-resistant microphones such as the Wildlife Acoustics®

SMX–US/UTTM microphones should not be angled above horizontal in inclement 

weather (see footnote 2) and should be oriented downwards if rain is anticipated. A 

reflector plate can be used with a directional microphone that allows the microphone 

to face down and thus be protected from rain (e.g., the Bat-Hat system). The detector 

records reflected ultrasound, not the original signal, in this case. Generally, reflector 

plates are 45° to horizontal when the microphone is facing directly down (fig. 4.5). 

Alternatively, the detector and microphone can be placed in a waterproof box with the 

microphone at the base of a PVC tube with drain holes and oriented at 45° or 90° to 

horizontal (fig. 4.6).

The type of weatherproofing system (e.g., reflector plate, PVC pipe) can affect 

the number and quantity of calls recorded, although comparisons of the effects of 

weatherproofing have yielded mixed results (Britzke and others 2010, Gruver and 

others 2009). Thus, it is difficult at this time to say which type of weatherproofing is 

best. Although some protocols, such as the Indiana myotis (M. sodalis) summer survey 

guidelines (U.S. Fish and Wildlife Service 2014), suggest using weatherproofing only 

if weather conditions call for it on a particular night, this is not appropriate for longterm monitoring programs such as NABat. The decision to use weatherproofing with 

bat detectors needs to be made at the onset of the monitoring program, and the same 

procedure must be followed throughout to ensure consistency in data collection.

(A)

(B)

Figure 4.5— (A) Schematic of an AnabatTM microphone in a weatherproof housing and oriented 

down towards a reflector plate (figure courtesy of C. Corben and K. Livengood, Titley Scientific); 

(B) the Bat-Hat system from EME Systems (http://www.emesystems.com/bat-hat.htm).

▲

Axis of greatest sensitivity 

is up at 40 degrees

Horizontal base prevents 

water accumulation

x

Figure 4.6—Schematic of an AnabatTM

microphone with a PVC pipe as a weatherproof 

housing. The arrow shows the axis of greatest 

sensitivity. A drain hole exists at the “X.” 

(schematic courtesy of C. Corben and K. 

Livengood, Titley Scientific)

4.4.2
28 A Plan for the North American Bat Monitoring Program (NABat)

4.5 Frequency and Timing of Surveys

Surveys should be conducted during the summer active period prior to the young 

becoming volant. Associated driving transects (see sec. 5.5) should be conducted while 

the stationary points are being surveyed. The weeks when this occurs will vary with 

location (e.g., later in the northern parts of North America and at higher elevations) and 

possibly with species. Detectors should run the entire night, from 15 minutes prior to 

sunset to 15 minutes after sunrise, for a minimum of four nights. Stationary acoustic 

surveys should be conducted at least once per year. When possible, surveys should 

be conducted when weather conditions are optimal for bat activity: (1) seasonally 

warm temperatures, (2) low wind, and (3) little or no rain. Specific limits will change 

depending on the location. For example, in south-central Alaska (lat. 60° N.), bat 

activity declines below 10 °C (Loeb and others 2014), whereas in Massachusetts (lat. 

42° N.), bat activity declines below 15 °C (Brooks 2009). In typically rainy locations, 

bats may fly in light rain.

4.6 Collection of Covariates and Ancillary Data

Bat activity and habitat use can be affected by numerous factors, including habitat 

type, temperature, relative humidity, rainfall, wind, and moonlight. As discussed in 

sections 4.2 through 4.4, many factors related to equipment placement and setup can 

also affect the data. Many of these factors can be controlled during the analysis phase 

if they are known. Thus, it is critical that a number of variables are recorded during the 

surveys and submitted with the data in the BPD. Variables that should be collected are 

listed in tables 8.1 and 8.2, and an example data sheet is provided in appendix C.

4.5

4.6

A silver-haired bat roosts on a tree trunk.
CHAPTER 5 Mobile Acoustic Transect Survey Protocols 29

5. Mobile Acoustic Transect 

Survey Protocols

5.1 Types of Detectors

Many types of bat detectors are available for continuous recording of bat echolocation 

calls. Britkze and others (2013) and Parsons and Szewczak (2009) reviewed differences 

among types and present advantages and disadvantages. Detector technology is 

continuously improving over time, and because each detector type has different 

limitations, NABat does not specify a particular type of detector to be used. However, 

the detectors selected for mobile acoustic transects must be able to detect all of the 

species anticipated to be present in the region that can be detected acoustically (see 

tables 2.1 and 2.2). If it becomes necessary to change technology or settings, it is 

important to carry out calibration trials to compare detectability with the old and the 

new technologies or settings. Model types discussed below are the most commonly 

used detectors in North America to date.

Detector types most commonly used for species identification are zero-crossing 

frequency division (e.g., AnabatTM CF–ZCAIM, SD1/SD2, Express; Wildlife 

Acoustics

® SM2BAT+TM or SM3BATTM on ZC mode), time expansion full-spectrum 

(e.g., Pettersson D240X), and direct recording full-spectrum detectors (e.g., Binary 

Acoustic Technology AR125

TM, FR125TM, iFR-IVTM; Pettersson D500X; Wildlife 

Acoustics

® SM2BAT+TM, SM3BATTM, or EM3+TM). Time expansion recorders cannot 

be used for mobile transects because a time expansion system does not record when 

expanding the signal to the recording device (Britzke and others 2013, Parsons 

and Szewczak 2009). Thus, a considerable amount of recording time (>50 percent) 

could be lost during a 1.5-hour mobile transect. Any zero-cross frequency division 

or full-spectrum direct recording detector that provides a time-date stamp for each 

file is acceptable. Mobile transects can also make use of microphones and digitizers 

connected directly to a tablet, smartphone, or computer. These systems may be less 

expensive than dedicated recorders, especially for volunteers or others who already 

own a suitable device. Options currently available include the Pettersson M500 

and Binary Acoustics AR125

TM, which connect through a USB port, or the Wildlife 

Acoustics

® Echo Meter Touch®, which connects through the Lightning Connector to 

Apple devices such as an iPad or iPhone. However, because the Echo Meter TouchTM

has an omnidirectional microphone, it is not recommended for mobile acoustic 

transects at this time. Other options will likely be available in the near future.

Additional considerations need to be taken into account when choosing a detector or 

recording mode for mobile transects beyond those for stationary detectors (see ch. 4). 

Detectors that auto-level, such as SM2BAT+TM and SM3BAT

TM detectors (see 

sec. 4.2.1), should not be used in zero-cross mode because the sensitivity level could 

vary during the recording session, between nights, and between years. For example, 

ambient noise such as that caused by tires on different surfaces (e.g., gravel, pavement, 

and dirt) may change throughout the transect, and the sensitivity of the detector may 

vary accordingly. Until there is a tested method to compensate for varying sensitivities 

when using these detectors in zero-cross mode, it is recommended that these detectors 

be used in full-spectrum recording mode only. Further, the EM3+TM also should not be 

used in zero-cross mode because it has a low gain setting that does not perform well in 

this mode; this detector should only be used in full-spectrum mode.

1

1 Personal communication. 2014. I. Agranat, President and CEO, Wildlife Acoustics, Inc., 3 Clock Tower 

Place, Suite 210, Maynard, MA 01754-2549.

5.1
30 A Plan for the North American Bat Monitoring Program (NABat)

When choosing a detector for mobile transects, the type of microphone must also be 

considered. Microphones can be classified as omnidirectional or directional depending 

on how they are constructed and how they are deployed. An omnidirectional 

microphone that is not placed into a housing or shield is likely to pick up bat activity 

in all directions, but it will also record more noise. A more directional microphone 

(either by design or by using a housing or shield) mainly records sounds in front of 

the microphone, but often can detect bats at a greater distance than omnidirectional 

microphones. It is recommended that detectors used for mobile transects have a 

directional microphone to minimize road noise and reflections off the top of the 

vehicle. Because the AnabatTM Express has an omnidirectional microphone that 

currently cannot be made directional, it should not be used for driving transects.

Each bat file that is submitted to NABat must be georeferenced (see sec. 5.6), so the 

detector must also be able to record the location of each bat file. Most detectors provide 

global positioning system (GPS) metadata through the attachment of a GPS unit that 

is calibrated to the local time zone (e.g., Binaray Acoustics FR125

TM and iFR-IVTM) 

or manufacturers may provide an optional GPS accessory (e.g., Wildlife Acoustics

®

SM2BAT+TM and EM3+TM, AnabatTM SD1 and SD2). 

5.2 Detector Sensitivity and Settings

Use of relative abundance in the analysis of mobile transects for trend estimates 

requires that the surveyor minimizes and accounts for variability among detectors. It is 

critical that settings are consistent along individual transects within and between years 

and are documented in the Bat Population Database (BPD).

The sensitivity of bat detectors can vary both within and among detector types (Adams 

and others 2012, Larson and Hayes 2000). Full-spectrum recording is inherently 

more sensitive than frequency division zero-cross because full-spectrum detectors 

record signals within the ambient noise, whereas zero-cross detectors record only 

the loudest signal above the noise floor (Corben 2002). However, during deployment 

of full-spectrum detectors, sensitivity is often lowered to reduce the number of 

files recorded with no bats and to reduce recording extraneous noise, thus reducing 

memory requirements (i.e., a louder signal above the noise floor is required to initiate 

recording).

For full-spectrum detectors, gain and signal-to-noise ratio settings affect sensitivity of 

the detector. Sensitivity can be adjusted manually with a dial for all AnabatTM models 

except the AnabatTM Express, but the numbers on this dial are not consistent among 

units or models, and thus should not be used as a way of equalizing sensitivities (see 

below). The sensitivity (low, medium, or high) for the AnabatTM Express can be set 

through AnabatTM Toolbox utility software. Some detectors also allow frequency band 

filters to target some frequencies; these filters should be adjusted for the local bat 

community, be documented in the BPD, and be used consistently from year to year at a 

monitoring site.

Calibrating detectors among each other by adjusting their recording sensitivities can 

reduce variation in detection volumes among detectors. Some manufacturers provide 

hardware and software that allow the user to calibrate across and within detectors (e.g., 

AnabatTM Equalizer for AnabatTM SD1 and SD2) while others provide equipment that 

allows the user to test the performance of their equipment against system standards 

(e.g., Wildlife Acoustics

® Ultrasonic Calibrator). Larson and Hayes (2000) described 

a method for calibrating AnabatTM II detectors against each other. Currently there is no 

5.2
CHAPTER 5 Mobile Acoustic Transect Survey Protocols 31

method for testing the performance of AnabatTM Express microphones, but a method is 

expected in the near future.2

Microphone sensitivity can vary over time, making detector calibration and 

performance tests very important, particularly for a long-term monitoring program. 

Testing the performance of microphones and detectors before and during the recording 

season is important to detect any loss in sensitivity. When loss of sensitivity occurs, 

microphones may need to be replaced. Environmental conditions can also affect 

microphone sensitivity. For example, if Wildlife Acoustics® SMX microphones with 

mesh windscreens get soaked with water, their sensitivity diminishes.3

To ensure consistency, it is imperative that the same detector or detector type, 

microphone, and settings are used on a transect each year to reduce variation caused by 

equipment differences. However, if equipment needs to be replaced or if new detectors 

are used, they need to be calibrated against the older equipment. Having old and new 

equipment recording side by side for at least one season is the most effective way to 

develop correction factors and allow monitoring to continue with new equipment after 

calibration.

Most detectors allow the user to adjust the sensitivity. The sensitivity must be high 

enough that the detector picks up low-intensity bat calls. However, if the sensitivity 

is set too high, the detector will record too much extraneous noise (e.g., insects). The 

appropriate sensitivity setting may vary with the bat community and the environment, 

so detectors should be adjusted accordingly. Division ratios for frequency division 

detectors can also be adjusted. Because a frequency division detector only uses every 

nth wave pass, the lower the division ratio (n) the more information is recorded. While 

file sizes will also be larger, this should not be an issue with current storage abilities 

of most frequency division detectors. A division ratio of 4 or 8 is recommended in 

areas with target species that have low frequency, audible calls such as the spotted bat 

(Euderma maculatum). However, a division ratio of 8 or 16 should be used when bats 

that use high frequency calls are expected to be recorded, as a division ratio of 4 can 

result in inaccurate high frequency representation which can sometimes confound 

species identification.4

5.3 Route Selection

5.3.1 Safety Considerations

One of the first considerations when choosing a route is safety. The route should be safe 

to drive at 32 km/h (20 miles per hour) with minimal stopping. Thus, roads that receive 

heavy traffic during the survey period should not be used, as this will require pulling 

off the road to let other vehicles pass or endanger the surveyor and other drivers as they 

try to pass the survey vehicle. Further, very rough roads where speeds of 32 km/h are 

dangerous should not be used. Drivers should use their hazard lights to warn others of 

their slow speed. Although lights may attract or repel bats (Hickey and Fenton 1990, 

McGuire and Fenton 2010, Polak and others 2011), headlights should always be used 

while driving the transect.

2 Personal communication. 2014. K. Livengood, Office Manager, Titley Scientific USA, 601 Business Loop 

70 W, Suite 110, Columbia, MI 65203.

3 Personal communication. 2014. S. Snyder, Product Manager, Wildlife Acoustics, Inc., 3 Clock Tower 

Place, Suite 210, Maynard, MA 01754-2549.

4 Personal communication. 2014. C. Corben, Acoustic Biologist, Titley Scientific USA, 601 Business Loop 

70 W, Suite 110, Columbia, MI 65203.

5.3

5.3.1

Portrait of a Peter’s

ghost-faced bat.
32 A Plan for the North American Bat Monitoring Program (NABat)

5.3.2 Road Types

In general, roads should be two-lane secondary or tertiary roads with few if any stops. 

Roads with gates that require opening and closing should not be used. Secondary roads 

include State highways and county roads, and tertiary roads include county roads and 

forest roads. Some gravel and dirt roads that are well maintained and allow consistent 

travel at 32 km/h can also be used. The route should be driven prior to the first survey 

to ensure that the road can be safely driven at the appropriate speed and the driver is 

familiar with the route. The test routes should be driven at the proposed start time and 

speed.

5.3.3 Route Configuration

Routes should be approximately 25 to 48 km in length and fit primarily within the 

100-km2 grid cell (e.g., fig. 5.1). If the route extends beyond the grid cell edges due to 

its length, the beginning and end of the transect can be in adjacent grid cells. One of 

the assumptions of the mobile transect method is that individual bats are not counted 

more than once, allowing calculation of an index of relative abundance (Roche and 

others 2011). Thus, the route should not cross back into the likely travel route of a bat, 

as this may result in the same bat being counted more than once. Sections of the route 

should be >100 m from each other if the route contains many curves or switchbacks. 

The surveyor should maintain a speed of ~32 km/h as consistently as possible 

throughout the survey period. If a stop is required, the detector should be paused and 

this should be noted.

5.3.4 Habitat Types

The route should pass through common habitat types of the area as much as possible. 

Depending on the location, this may include agricultural areas, forests, wetlands, and 

residential areas and small towns if traffic is not too congested. Urban areas can be 

important areas for some species of bats but may need to be surveyed with stationary 

point surveys due to the difficulty of maintaining a constant speed. When selecting 

the route, areas with dense forested corridors and a low canopy should be avoided or 

minimized to decrease the chance of recording high-clutter calls. There should be 

at least 3 m between the vehicle and the overhanging canopy (see sec. 4.4.1). Roads 

that parallel waterways (e.g., along a river or lakeshore) are often adequate to pick up 

species associated with water.

5.4 Equipment Setup

Detectors can be mounted on the vehicle in a variety of ways. For example, a unit can 

simply be placed in a low-cost container and strapped firmly to the center of the vehicle 

rooftop (figs. 5.2A and 5.2B). Alternatively, the microphone can be detached from the 

unit, placed in a mount, and attached with a cable to the detector housed in the vehicle 

(figs. 5.2C and 5.2D). It is suggested that the bat detector be oriented straight up. 

Although the microphone can be also pointed down to receive reflected signals off the 

roof of the car (fig. 5.2D), similar to a reflector plate described in section 4.4.2., it may 

be difficult to orient the microphone the same way each time the transect is run. Thus, 

this technique should only be used if the position of the microphone can be exactly 

replicated each survey (e.g., using an AnabatTM suction car mount). Once an orientation 

is selected (i.e., up or down), it should be used consistently throughout the survey 

period (Britzke and others 2010). Because mobile transects should not be conducted 

during poor weather, weatherproofing is not necessary and should not be used, as it can 

reduce microphone sensitivity.

5.3.2

5.3.3

5.3.4

5.4
CHAPTER 5 Mobile Acoustic Transect Survey Protocols 33

5.5 Frequency and Timing of Surveys

Surveys should be conducted twice during the maternity season to allow estimates 

of detection probability. Both surveys should be conducted within a week if possible 

to reduce violations of the closure assumptions in the proposed analytical models. In 

subsequent years, surveys should be conducted within 1 to 2 weeks of the original 

survey to align with similar weather conditions and stages of the life cycle. The most 

appropriate dates for the surveys will vary depending on the phenology of bats in 

the region, but the period from June 1 through July 30 will exclude most migrants. 

It is best to conduct the transect earlier in the survey window rather than later in 

case inclement weather or other issues prevent surveying at the end. Additional runs 

outside of the maternity season may be desirable for local monitoring efforts and can 

be used to focus on other points in the bats’ life cycle such as migration or winter 

activity. These data are not required for NABat, but can be entered into the BPD with 

appropriate documentation.

Driving acoustic transect

10 km x 10 km sample unit

0 1.5 3 km

Figure 5.1—Example of a mobile acoustic transect in a 100-km2 grid cell.

Figure 5.2—Examples of bat 

detector attachments on top of 

vehicles. (fig. 5.2A by the author; 

fig. 5.2B courtesy of Michael 

Whitby, fig. 5.2C courtesy of Carl 

Herzog, and fig. 5.2D courtesy of 

Titley Scientific)

5.5

(A)

(B)

(C)

(D)
34 A Plan for the North American Bat Monitoring Program (NABat)

Surveys should begin 45 minutes after sunset. Driving should commence as soon as 

the detectors are set to “Record,” and detectors should be stopped as soon as the end of 

the transect is reached. The equipment should be tested by rubbing fingers or jangling 

keys in front of the microphone immediately prior to the beginning of the transect 

run and just before the detector is shut off. This allows surveyors to determine if the 

equipment was functional throughout the survey, especially if no bats are recorded at 

the end of the transect. Surveys should occur on nights when there is no rain or fog, 

low wind speed (< ~10 km/h), and, if possible, during a new or quarter moon. Wet 

roads and puddles can affect quality of calls recorded because of increased road noise 

from tires. Thus, these conditions should be avoided if possible, or noted. Because the 

effects of moonlight are equivocal (Ciechanowski and others 2007, Erickson and West 

2002, Hayes 1997), moonlight should be a low-priority criterion when selecting survey 

nights unless it is known that moonlight is a significant factor in the survey area. 

Nights that are exceptionally cool for the area should also be avoided.

5.6 Marking the Route

The route should be recorded with a GPS and submitted as metadata in the BPD 

as an ArcGIS® shapefile. In addition, the latitude/longitude of each acoustic file 

should be recorded and embedded in the file itself, if possible. Detectors such as the 

AnabatTM SD2, Wildlife Acoustics® SM2BAT+TM or SM3BATTM, and Binary Acoustics 

Technology FR/AR125TM accommodate accessory GPS attachments that allow this 

to be done. Other options are available for detectors that cannot record the location 

directly to the file (e.g., Pettersson D500x) such as Myotisoft TransectPro (http://

myotisoft.com/products/transectpro/); in this case, associated shapefiles of tracks and 

waypoints of recorded files should be submitted to the BPD along with the acoustic 

files. The start and end GPS locations should also be recorded and submitted. As with 

all GPS utilities, the time zone needs to be set to ensure the times match those of the 

study area, and GPS and bat detector times should be synchronized each night the 

transect is surveyed.

5.7 Covariates and Ancillary Data

Bat activity and habitat use can be affected by a large variety of factors including 

habitat type, temperature, relative humidity, rainfall, wind, and moonlight. As 

discussed in sections 5.2 and 5.4, many factors related to equipment placement and 

setup can also affect the data. Many of these factors can be controlled for during the 

analysis phase if they are known. Thus, it is critical that a number of variables be 

recorded during the surveys and submitted with the data in the BPD (see ch. 8). Data 

that should be collected are listed in tables 8.1 and 8.2 and an example datasheet is 

provided in appendix D.

5.6

5.7

Portrait of a

pallid bat. (© Daniel Neubaum, Colorado Parks and Wildlife)
CHAPTER 6 Species Identification of Acoustic Recordings 35

6. Species Identification of 

Acoustic Recordings

There are many methods and tools for species identification of acoustic recordings, and 

these methods and tools are continually evolving. Their utility and efficacy may vary 

with equipment type, geographic location, and experience of the user. NABat does not 

recommend specific methods or programs for analyses. However, as explained below, 

at least two methods should be used for verification.

6.1 Responsibility for Species Identification

Individual agencies and biologists will be responsible for identifying acoustic 

recordings to species. Because overall coordination will likely occur at the State 

or Provincial level (see sec. 10.3), ultimately the coordinators at this level will be 

responsible for ensuring that species identification of all acoustic files is conducted 

according to the guidelines given below. Because identification of bats can be 

challenging, it is recommended that whoever conducts the identification has experience 

in bat acoustic species identification. The level of expertise of the person(s) who 

conduct the identification needs to be documented at the time of submission of data 

into the Bat Population Database (BPD).

6.2 Definitions

In North America, a pulse of sound produced by an echolocating bat navigating 

through its environment tends to sweep from a high frequency to a lower frequency, 

although in some cases a pulse of relatively constant frequency may be produced 

(Fenton and Bell 1981). A pulse is sometimes also referred to as a call. A series of calls 

or pulses made by an echolocating bat is referred to as a call sequence or bat pass. 

Much confusion surrounds the use of the phrase bat pass. Fenton (1970) defined a bat 

pass as a series of calls produced by a bat passing by a microphone, and this was later 

made more specific by Hayes (1997) as a sequence of pulses separated from another 

sequence of pulses by ≥1 second. However, the utility of this more specific definition 

has been questioned, particularly as it applies to lower frequency bats that can have 

long periods of silence between their calls. For example, pulses of hoary bats (Lasiurus 

cinereus) can be ≥1 second apart, which would misleadingly equate one pulse to one 

pass. Consequently, a single hoary bat making one pass across the detection area of the 

microphone could be misinterpreted as many bat passes. As such, for NABat a pass is 

defined as sequence of pulses separated by ≥2 seconds. For trigger-based recording, 

setting the trigger window (“Idle Setting or “Max TBC”) to 2 seconds (see sec. 4.2.2) 

allows each file to be considered a separate pass.

Length of a call sequence that is captured in a file produced by a bat detector is 

dependent on the type of detector, its settings, and the recording scenario. For example, 

Wildlife Acoustics SM2BAT+TM; Binary Acoustics AR125

TM, FR125TM, and iFR-IVTM; 

and Pettersson D500x allow a maximum file recording length to be set when recording 

in full-spectrum mode, whereas AnabatTM systems have a fixed maximum recording 

length of 15 seconds before beginning a new file. Recommended settings are presented 

in section 4.2.2. If a different file length or trigger window is used, this should be 

reported in the BPD data submission.

A sequence of calls can consist of different pulse shapes, depending on the behavior of 

the bat (Siemers 2002). Search phase pulses are produced while the bat is navigating its 

6.1

6.2
36 A Plan for the North American Bat Monitoring Program (NABat)

environment (fig. 6.1). A series of search phase pulses is recognizable by the consistent 

shape of calls over time, and it is these types of pulses that are generally used for 

species identification (Fenton and Bell 1981). As a bat approaches an object, such as an 

insect, it changes the shape of its pulses and echolocates more quickly, often sweeping 

through a broader range of frequencies to resolve distance to the insect (approach 

phase); this intensifies in the terminal phase of the approach as the insect is captured 

(feeding buzz) (Fenton and Bell 1979, Griffin and others 1960). In most cases, approach 

and terminal phase pulses are not useful for species identification because of the high 

degree of overlap of pulse shape among species. Pulse shapes of many species of 

bats converge when flying in clutter (e.g., close to or within vegetation), confounding 

species identification (Fenton 1990, Limpens 2002, Siemers 2002). Efforts to reduce 

the likelihood of recording high-clutter calls made during deployment (see secs. 4.3 

and 5.3.4) will aid in species identification.

Differentiation among some species of bats relies on examination of the pattern 

associated with a sequence of pulses (O’Farrell and others 1999). For example, an 

evening bat (Nycticeius humeralis) and an eastern red bat (Lasiurus borealis) can 

have similar pulse shape and frequency, but the minimum frequencies of evening bat 

calls oscillate relatively consistently, whereas the minimum frequencies of eastern red 

bat calls change randomly (fig. 6.2). Similarly, tri-colored bat (Perimyotis subflavus) 

calls can resemble the higher frequency calls of the eastern red bat, but tri-colored bat 

pulses have constant (non-undulating) minimum frequencies (fig. 6.3). Thus, by having 

a sequence of many pulses, it is often possible to differentiate acoustically similar 

species with frequency overlap. Conversely, species identification may be confounded 

if only one or two pulses are recorded. It is thus recommended that a minimum of three 

search phase pulses be present to use a recording for species identification. Files with 

fewer pulses should be assigned to a broader acoustic category (see below).

6.3 Process of Species Identification

The goal of the acoustic analysis is to identify as many files as possible to the species 

level. Spreadsheets from various automated species identification programs or 

personally created spreadsheets that are uploaded to the BPD (see sec. 8.4) should 

use the standardized codes provided in table 6.1. Because of poor quality recordings 

such as short call sequences, incomplete pulse recordings, recording of high-clutter 

pulses, non-search-phase pulses, or overlap in species’ call parameters, identification Frequency

Time

Search phase

Approach phase

Terminal phase (“feeding buzz”)

Figure 6.1—Search, approach, and terminal phases (feeding buzz) of a bat pass.

6.3
CHAPTER 6 Species Identification of Acoustic Recordings 37

to the species level is not always possible. When identification is possible, files should 

be classified as narrowly as possible; for example, files may be classified to one of 

two species [e.g., big brown (Eptesicus fuscus) or silver-haired bat (Lasionycteris 

noctivagans) to EPFU/LANO], or broader categories when necessary [e.g., Low 

frequency bat (LowF)]. Recommendations for clustering of acoustically similar 

species and associated codes are presented in table 6.2. If alternate categories are used, 

definitions and associated codes should be submitted with the data to the BPD.

It is recommended that at least two methods be used to identify files to species. 

Analysis of acoustic recordings can be done in several ways, combining automatic 

species-identification (auto-ID) software and manual identification as appropriate. 

The advantage of using an auto-ID software package is that the data are analyzed 

objectively. Four auto-ID programs are currently available for North American bats 

within the scope of NABat: SonobatTM (www.sonobat.com), Bat Call Identification 

(BCID) (http://www.batcallid.com/Software.html), Kaleidoscope® Pro (http://www.

(A) (B)

Figure 6.2—Example of the relatively consistent pattern of minimum frequency alternation in 

(A) an evening bat (Nycticeius humeralis) versus the more random pattern of alternating higher 

and lower minimum frequencies of (B) an eastern red bat (Lasiurus borealis). (screenshots from 

AnabatTM AnalookW software)

(A) (B)

Figure 6.3—Example of the undulating minimum frequency pattern of (A) the eastern red bat 

(Lasiurus borealis) that sets it apart from the consistent minimum frequency pattern of (B) the 

tri-colored bat (Perimyotis subflavus). (screenshots from AnabatTM AnalookW software)

A roosting Townsend’s big-eared bat. (© Michael Durham/Minden Pictures, Bat Conservat oi n

nI et nr t a noi

oct ab. www,l a

. n r o ) g
38 A Plan for the North American Bat Monitoring Program (NABat)

Table 6.1—Species codes to be used when labeling 

acoustic files and submitting data to the Bat Population 

Database

Common name Scientific name Code

Pallid bat Antrozous pallidus ANPA

Jamaican fruit-eating bat Artibeus jamaicensis ARJA

Mexican long-tongued bat Choeronycteris mexicana CHME

Rafinesque’s big-eared bat Corynorhinus rafinesquii CORA

Townsend’s big-eared bat Corynorhinus townsendii COTO

Big brown bat Eptesicus fuscus EPFU

Spotted bat Euderma maculatum EUMA

Florida bonneted bat Eumops floridanus EUFL

Greater bonneted bat Eumops perotis EUPE

Underwood’s bonneted bat Eumops underwoodii EUUN

Allen’s big-eared bat Idionycteris phyllotis IDPH

Silver-haired bat Lasionycteris noctivagans LANO

Western red bat Lasiurus blossevillii LABL

Eastern red bat Lasiurus borealis LABO

Hoary bat Lasiurus cinereus LACI

Southern yellow bat Lasiurus ega LAEG

Northern yellow bat Lasiurus intermedius LAIN

Seminole bat Lasiurus seminolus LASE

Western yellow bat Lasiurus xanthinus LAXA

Mexican long-nosed bat Leptonycteris nivalis LENI

Lesser long-nosed bat Leptonycteris yerbabuenae LEYE

California leaf-nosed bat Macrotus californicus MACA

Pallas’ mastiff bat Molossus molossus MOMO

Peter’s ghost-faced bat Mormoops megalophylla MOME

Southwestern myotis Myotis auriculus MYAUR

Southeastern myotis Myotis austroriparius MYAUS

California myotis Myotis californicus MYCA

Western small-footed myotis Myotis ciliolabrum MYCI

Long-eared myotis Myotis evotis MYEV

Gray myotis Myotis grisescens MYGR

Keen’s myotis Myotis keenii MYKE

Eastern small-footed myotis Myotis leibii MYLE

Little brown myotis Myotis lucifugus MYLU

Dark-nosed small-footed myotis Myotis melanorhinus MYME

Arizona myotis Myotis occultus MYOC

Northern myotis Myotis septentrionalis MYSE

Indiana myotis Myotis sodalis MYSO

Fringed myotis Myotis thysanodes MYTH

Cave myotis Myotis velifer MYVE

Long-legged myotis Myotis volans MYVO

Yuma myotis Myotis yumanensis MYYU

Evening bat Nycticeius humeralis NYHU

Pocketed free-tailed bat Nyctinomops femorosaccus NYFE

Big free-tailed bat Nyctinomops macrotis NYMA

Canyon bat Parastrellus hesperus PAHE

Tri-colored bat Perimyotis subflavus PESU

Brazilian free-tailed bat Tadarida brasiliensis TABR
CHAPTER 6 Species Identification of Acoustic Recordings 39

Table 6.2—Possible groupings and associated codes 

for species that are acoustically similar and can occur 

sympatrically 

Common name Scientific name Code

Pallid bat

Big brown bat

Antrozous pallidus

Eptesicus fuscus

ANPA/EPFU

Big brown bat

Silver-haired bat

Eptesicus fuscus

Lasionycteris noctivagans

EPFU/LANO

Western red bat

Canyon bat

Lasiurus blossevillii

Parastrellus hesperus

LABL/PAHE

Eastern red bat

Tri-colored bat

Lasiurus borealis

Perimyotis subflavus

LABO/PESU

Eastern red bat

Little brown myotis

Lasiurus borealis

Myotis lucifugus

LABO/MYLU

Eastern red bat

Seminole bat

Lasiurus borealis

Lasiurus seminolus

LABO/LASE

California myotis

Yuma myotis

Myotis californicus

Myotis yumanensis

MYCA/MYYU

Long-eared myotis

Keen’s myotis

Northern myotis

Myotis evotis

Myotis keenii

Myotis septentrionalis

LEMY

(long-eared 

myotis)

User-defined categories

User-defined Various species with pulses that 

have a minimum frequency of 

approximately 25 kHz.

25kHz

User-defined Various species with pulses that 

have a minimum frequency in the 

range of 35-40 kHz.

40kHz

User-defined Various species with pulses having 

a minimum frequency lower than 

~30 kHz.

LowF

User-defined Various species with pulses having 

a minimum frequency higher than 

~30 kHz.

HighF

User-defined Various myotis species with pulses 

having a minimum frequency 

higher than ~30 kHz.

Myotis

Note: For “User-defined” categories, species in these categories will be listed for the 

recording area upon submission to the Bat Population Database.
40 A Plan for the North American Bat Monitoring Program (NABat)

wildlifeacoustics.com/products/kaleidoscope-software), and Echoclass (http://www.

fws.gov/midwest/Endangered/mammals/inba/surveys/inbaAcousticSoftware.html). 

Currently, all four programs can be used to identify species found in the Northeastern 

and Midwestern United States, but only SonobatTM and Kaleidoscope® Pro can be used 

to identify western species (although BCID now includes the canyon bat, Parastrellus 

hesperus). SonobatTM can only be used with full-spectrum calls and Echoclass and 

BCID can only be used with zero-cross calls; Kaleidoscope® Pro can be used with 

either full-spectrum or zero-cross calls. Some species found in the Southeastern United 

States, such as the northern yellow bat (L. intermedius) and Brazilian free-tailed bats 

(Tadarida brasiliensis) are only included in some programs; many western species are 

not included in either Kaleidoscope® Pro or SonobatTM (appendix E). Other methods, 

such as filters in AnabatTM AnalookW call analysis and data management software, also 

provide an objective method for species identification. If using AnalookW filters for 

identification of species, the .abf files should be submitted to the BPD at the time of 

acoustic data submission. Processing of files that contain bats, but are not identified to 

species by an auto-ID program, may proceed using manual identification, use of filters, 

or some combination.

Files that are identified to species from auto-ID programs should be visually reviewed 

and verified by experienced personnel whenever possible, as this has been shown to 

successfully remove false positive identifications and allow the addition of species 

missed by the auto-ID program (Fritsch and Bruckner 2014). Because of variation 

in quality of recordings, the influence of clutter, and the changing performances of 

software packages over time, manual verification is currently recommended for, at 

minimum, unexpected classifications. For example, if a species that is not known 

to reside in the recording area is identified through an auto-ID program, files of this 

unexpected species need to be manually verified. Currently, most auto-ID packages are 

designed to identify only one species of bat per recorded file. Auto-ID software should 

provide some indication of files that may contain more than one species of bat. These 

files should be manually examined to determine species composition and labelled 

appropriately. The general procedure used in analysis of recordings should accompany 

the data when submitted to the BPD (see table 8.1).

How one determines the final identification of a bat recording will be up to the 

experienced biologist conducting the analysis. When depending largely on autoID programs and there is a discrepancy in the identification of a file between the 

programs, it is recommended that the two suggested species both be considered 

possible. For example, if one auto-ID program labels a file as a silver-haired bat 

(LANO) while another determines it to be a big brown bat (EPFU), the final 

identification should be assigned a combined species category of EPFU/LANO unless 

the experienced biologist, upon manual examination, has reason to agree with one of 

the auto-ID programs.

6.4 Storage of Acoustic Files

In the early stages of NABat, both acoustic analysis summary data and files containing 

bat calls will be submitted to the BPD.

6.4

A el sser ol ng-nosed

bat pollinates an agave fower.
CHAPTER 7 Colony Counts 41

7. Colony Counts

7.1 Introduction

Monitoring wildlife populations using direct count data is central to many research 

and management activities. However, conducting direct counts may be particularly 

challenging when populations are open (i.e., there is immigration, emigration, births, 

and deaths), have unknown ranges, or are difficult to observe (Anderson 2001, Royle 

and Nichols 2003, Williams and others 2002). These challenges are highly relevant 

to surveys of roosting bats. Kunz and others (2009: 136) warned: “Efforts to infer 

population trends by comparing current and historical estimates of roosting bats 

are often based on the invalid assumption that availability and quality of roosts is 

static and that bats exhibit high rates of fidelity to roosts through time.” Thus, a 

continental framework for monitoring bat populations with colony counts, whether 

during summer or winter, is recommended only for species or populations having 

ecological and behavioral characteristics conducive to these types of surveys. The 

following characteristics identified by Kunz and others (2009) are important for 

species monitored via colony counts: (1) reasonable geographic limits are placed on the 

area to be monitored, (2) the number of roosts in the area are known, (3) locations of 

roosts within the area are known, (4) potential daily and seasonal movement distances 

of individuals of the species within the area are understood, and (5) information 

about immigration and emigration in the area is available. While colony counts for 

species that do not exhibit these characteristics (e.g., those using ephemeral roost trees 

or forming complex metapopulations) may reveal important biological information 

about them, these methods are not recommended for monitoring population trends for 

NABat.

Counts of bats at hibernacula have been used for decades to monitor abundance 

of some species, such as the Indiana myotis (Myotis sodalis), and have provided 

reasonable estimates of regional population sizes (Thogmartin and others 2012). For 

many colonial hibernating species, these counts provide a robust method for estimating 

population sizes but have limitations associated with incomplete knowledge and 

accessibility of winter sites. In summer, colony counts may be the only feasible method 

for determining presence and estimating abundance for some species. For example, 

whispering bats such as Rafinesque’s big-eared bats (Corynorhinus rafinesquii) and 

Virginia big-eared bats (C. townsendii virginianus) are not readily detected with 

acoustics, so identifying available roosts and conducting colony counts is the best way 

to assess presence and abundance on the summer landscape (Clement and Castleberry 

2011, Stihler 2011). Species such as the Brazilian free-tailed bat (Tadarida brasiliensis) 

pose different challenges for acoustic monitoring. These bats often form aggregations 

of 100,000 or more during the summer, fly at altitudes above the range of ground-based 

bat detectors, and disperse broadly across the landscape, making counts at maternity 

colonies the most reliable estimates of abundance in an area.

Accurate estimates from colony counts depend on both availability (animals are 

present at the time of the survey) and detectability (animals are observed during the 

survey) of individuals in the colony. Both of these factors vary temporally and spatially 

and can also be affected by methodology. Because bats may move readily within and 

among roosts in both winter (Elder and Gunier 1978, Ingersoll and others 2010, Tuttle 

2003) and summer (Johnson and others 2013, Lewis 1995, Watrous and others 2006), 

establishing population-level estimates of abundance can be difficult. Furthermore, it 

is difficult to locate alternate roosts, so colony counts likely represent only a partial 

7.1
42 A Plan for the North American Bat Monitoring Program (NABat)

survey of the population. Nevertheless, colony counts represent the best available 

information on the status and trends of species not well sampled by other methods.

Methods discussed in this chapter standardize surveys of colonies to reduce variation 

in occurrence and abundance estimates caused by availability and to facilitate 

adjustments for variation in detectability. They are designed to allow hierarchical or 

state-space models for monitoring colonies (de Valpine and Hastings 2002) (see ch. 

9) and to allow data collected via different methods or observers or under different 

survey conditions to be combined into a single model. They also allow for adjustments 

between differing survey conditions to estimate absolute abundance or occupancy 

instead of merely providing a proxy of abundance. Use of these models will be possible 

through multiple visit or multiple observer survey methods recommended in this 

chapter. Furthermore, it is important to note that methods discussed in this chapter are 

not intended for focal studies on behavior, demographics, or phylogenetics. There are 

numerous methods for exploring these factors including capture-mark-recapture and 

genotyping. Although understanding these factors may contribute to an understanding 

of abundance and occupancy of a species, these methods have limited application for 

rangewide monitoring programs such as NABat.

7.2 Multiple Visit and Multiple Observer Methods

Imperfect detection (i.e., detection probabilities <1) results in undercounting 

the number of bats present in a colony. Detection probabilities are influenced 

by numerous factors, including time of surveys (e.g., day, week, month, year); 

environmental conditions; roosting and clustering behavior; physical characters of the 

roost; survey method; and experience of surveyors (Ingersoll and others 2013, Tuttle 

2003). Using multiple visit or multiple observer methods allow estimation of species 

detectability, which may then be used for producing reliable estimates of occurrence 

and abundance (Royle and Dorazio 2008).

Hierarchical models can use data from multiple visit or multiple observer methods 

to separate detection probabilities from estimates of the variable of interest, such as 

abundance or occupancy (Royle and Dorazio 2008). To provide unbiased abundance 

estimates, temporally repeated measures must be conducted across a time period 

when abundance is unchanging. Unfortunately, this is not typically practical or 

permissible for hibernating bats that should be surveyed no more than once per winter 

or for summer colonies whose composition may change nightly. However, multiple 

records may be produced simultaneously by more than one observer or method at 

each location, an approach described as multiple independent observers (Williams 

and others 2002). It is critical that data collected in this fashion be independent (i.e., 

on discrete data sheets, no communication between observers, and no changing or 

supplementing data with the observations of others). Furthermore, observers should 

make every effort to minimize additional disturbance to bats that may be caused by 

additional people, noise, and light associated with duplicated effort.

Multiple observer methods produce two or more independent sets of observations 

at each sampling location. For example, two or more individuals may separately 

conduct manual counts of bats roosting in the same hibernaculum, of bats exiting 

at evening emergence, or of bats roosting in a summer colony. This approach can 

be extended to instrument-based survey methods as well. Two or more separate 

sets of photographs of roosting bats or video of emerging bats may be collected, 

ideally by changing the camera angle between sets. The multiple sets of photographs 

or videos should be made and analyzed by different observers. Alternatively, 

different observation methods may be combined, e.g., a manual count paired with 

7.2
CHAPTER 7 Colony Counts 43

a photographic count or video or thermal imaging survey (fig. 7.1). Using multiple 

observer methods allows for continuity and comparability of data through changes in 

surveyors, methods, and environmental conditions and greatly increases the utility 

and longevity of population data.

7.3 Colony Count Site Selection

NABat is an adaptable system of data collection that accommodates variation in 

resources, personnel, methods, and survey locations with careful recording of 

covariates that affect survey effort and bat detection probabilities as well as those 

affecting bat abundance. The selection of which colonies to monitor should not be 

altered solely for the sake of inclusion in the NABat framework if they are providing 

reliable long-term trend information for particular species or regions. NABat will 

integrate these ongoing colony monitoring programs with the continental sampling 

framework to allow inference about trends (including prior to the implementation of 

NABat) while promoting consistency in methodology for future data collection, thus 

allowing for rangewide analyses of relative abundance and distribution. Identifying 

and monitoring summer and winter colonies can be very time and effort consuming, 

and many existing monitoring programs have prioritized sites that maximize benefits 

of these efforts.

The NABat sampling framework, through the generalized random-tessellation 

stratified (GRTS) master sample, provides a mechanism to modify or expand colony 

monitoring programs in a statistically sound manner. If it is feasible and practical 

to monitor all known colonies of a species in a particular region on a regular basis 

(e.g., every 1 to 3 years, though not necessarily all in the same year), it may be most 

appropriate to continue to monitor all colonies. However, if there are more colonies 

than can be sampled at a reasonable sampling interval, the GRTS master sample can 

be used to prioritize sample selection. An appropriate selection method is to start at 

the top of the list and pick the number of grid cells for which colonies can be surveyed 

with available resources. For selected cells, all known colonies within those cells 

should be surveyed. If a particular cell has no known colonies, or if those colonies are 

inaccessible (e.g., due to lack of permission on private land), the cell should be replaced 

with the next cell in the list, continuing until the target number of cells with known 

colonies is reached. If a cell is skipped, it is important to document the reason it was 

rejected. A similar approach could be used to guide searches for new colonies, for 

example, in regions or for species for which colonies are poorly known or which may 

move from one year to the next (e.g., breeding colonies). In this case, an appropriate 

number of cells at the top of the list could be selected for searching, including those 

with and without known colonies. However, cells could still be rejected if they do not 

contain appropriate types of habitat (identified a priori) or if too much of the cell is 

inaccessible. Such searches could potentially be informed by the results of acoustic 

surveys in the same grid cells.

7.4 Internal Roost Counts

Although there is potential to negatively impact roosting bats during internal 

surveys, such surveys are appropriate when (1) acoustic monitoring is insufficient to 

detect a particular species in the area, (2) exit counts cannot be conducted safely or 

effectively, or (3) species identification is unreliable either from acoustics or emergence 

counts. Internal surveys in winter are the preferred method for counting gregarious 

hibernating species when there is reasonable certainty that locations for most 

individuals of a species are known (at least within that region). Furthermore, surveys 

in hibernacula are likely the only method for assessing abundance of bats throughout 

Figure 7.1—Double-observer setup for pairing 

manual exit count at a bat house with infrared 

thermal recording of the same emergence 

activity. (photo by Jonathan Reichard)

7.3

7.4
44 A Plan for the North American Bat Monitoring Program (NABat)

most of North America during winter when bats’ reduced activity makes acoustic 

monitoring impractical in many areas. Internal counts are especially useful when 

there is a need for winter-specific population estimates to assess threats associated 

with hibernacula, such as disease [e.g., white-nose syndrome (WNS)]. In summer, 

internal surveys may be necessary if emergence counts are exceedingly challenging 

or if the colony contains multiple species that cannot be distinguished without direct 

observation. However, because of the likelihood of disturbing bats during pup rearing, 

internal surveys of summer colonies are not recommended if other means of estimating 

the colony size are available.

Roost abandonment, decreased overwinter survival, physiological stress, and mortality 

of nonvolant young have all been linked to disturbance from humans (Barbour and 

Davis 1969; Boyles and Willis 2010; Hicks and Novak 2002; Lacki 2000; Mohr 1972; 

Thomas 1995; Thomas and others 1990; Tuttle 1979, 2003). Thus, limiting sizes of 

survey crews, using red lights, employing infrared lights and night-vision equipment, 

limiting amount of time in roosts, requiring quiet and efficient behavior of surveyors, 

and taking precautions to avoid introducing foreign material or chemicals to a roost 

can reduce the impact of the survey on the bat colony. Surveyors should be prepared to 

work efficiently to minimize time in the roost. Working in pairs helps to ensure safety 

of surveyors while moving efficiently and accurately through the survey. All surveyors 

should be capable of distinguishing species without handling bats if possible, although 

photographic records may be used for later identification or confirmation in some 

situations. New surveyors should conduct multiple surveys under the supervision of 

an experienced observer to ensure consistency among surveyors. Employing doubleobserver methods also helps to validate species identification. Although each roost 

survey is unique, whenever possible, surveyors should follow species-specific methods 

for internal surveys adopted from sources such as the recently published Conservation 

Strategy for Big-Eared Bats and Southeastern Myotis (Bat Conservation International 

and Southeastern Bat Diversity Network 2013) and the Indiana myotis draft recovery 

plan (U.S. Fish and Wildlife Service 2007). Other sources listed in table 7.1 provide 

good models for protocols. Guidance for conducting roost surveys outside of North 

America may also prove useful (e.g., Hundt 2012).

The guidelines recommended here are designed to encourage consistency among 

survey efforts to create comparable datasets throughout a species’ range. However, 

we recognize that circumstances such as availability of equipment, clustering of bats, 

roost configuration, experience of the survey team, and many other factors may require 

deviations from either NABat recommendations or previous survey efforts. We do 

not recommend significant changes to existing roost survey programs unless there 

is reason to believe that reliability would be enhanced by adopting a new method. In 

recent years, developments in winter survey methodologies have improved winter 

survey efficiency and repeatability (Meretsky and others 2010), and we encourage 

surveyors to employ these or similar methods. When transitioning to new methods, 

replicated surveys conducted with both old and new techniques for several sessions 

will enhance the consistency of results over time.

7.4.1 Preferred Methods for Internal Surveys

The following sections do not provide an exhaustive review of internal survey 

methods, but represent the methods generally believed to be most effective. For more 

discussion and details of methods and techniques, we suggest consulting the references 

provided in table 7.1.

7.4.1
CHAPTER 7 Colony Counts 45

7.4.1.1 Visual counts and photographic methods

Direct counts such as visual or photographic counts are effective for estimating colony 

sizes for bats that roost conspicuously in single- or multispecies clusters. Direct 

counts are suitable for hibernating species during winter and gregarious species 

during summer, depending on roosting and clustering behavior. Direct counts are not 

appropriate for species that roost in crevices or otherwise inaccessible parts of a roost 

(Thomas and LaVal 1988). Under most circumstances, visual counts from internal 

surveys accompanied by digital photography offer the most reliable results where 

conditions allow (Meretsky and others 2010). Minimum camera specifications and 

photography methods will vary by roost and target species, but some recommendations 

are discussed in the Indiana myotis survey protocols (U.S. Fish and Wildlife Service 

2007). Detailed discussion of photographic methods can be found in Meretsky and 

others (2010). Commonly used camera systems include a single lens reflex (SLR) 

digital camera with a minimum resolution of eight megapixels, a zoom lens for 

high ceilings, adequate flash and flash extender for high ceilings, and spot metering 

capabilities for survey photographs. For more details on conducting surveys with 

digital SLR cameras see: http://workspace.whitenosesyndrome.org/sites/default/files/

ny_camera_equipment_and_hibernacula_photography_guide.pdf. Less elaborate 

systems such as simple point-and-shoot digital cameras may be adequate for sites 

where surveyors are relatively close to bats, and waterproof point-and-shoot cameras 

facilitate decontamination before use at another site. Laser pointing devices can aid in 

low-light auto-focusing and ensure the correct bat cluster is photographed (Meretsky 

and others 2010) (fig. 7.2).

Figure 7.2—Hibernating Myotis cluster 

photographed for survey purposes. A laser 

pointer (dot on image) was used to aid 

autofocusing in low light conditions. (photo 

courtesy Vesper Environmental, LLC)

7.4.1.1

Table 7.1—Additional resources for conducting internal and external colony counts

Source Description

Kunz and others (2009) General review of bat survey methods

Thomas and LaVal (1988) General review of bat survey methods

U.S. Fish and Wildlife Service (2014) Emergence survey protocols for Indiana myotis 

U.S. Fish and Wildlife Service (2007) Indiana myotis hibernacula survey guidelines 

Bat Conservation International and Southeastern 

Bat Diversity Network (2013)

Roost count guidance for two species

Wisconsin Department of Natural Resources (2013) Roost monitoring Web site for Wisconsin 

Department of Natural Resources

Vermont Fish and Wildlife (2012) Bat monitoring guidance from Vermont Fish and 

Game Department

New Hampshire Fish and Game Department (2014) Bat monitoring guidance for summer maternity 

roosts

Pennsylvania Game Commission (2014) Summer maternity roost monitoring protocols

Alaska Department of Fish and Game (2010) Summer roost emergence count instructions 

from Alaska Fish and Game Department 

New Zealand Department of Conservation (2012) Discussion of internal roost survey methods for 

bats in New Zealand

Hundt (2012) Guidance for bat surveys in United Kingdom
46 A Plan for the North American Bat Monitoring Program (NABat)

It may be necessary to photograph multiple sections of large clusters of bats to achieve 

sufficient resolution for accurate species identification and counting. This approach 

facilitates distinguishing individuals that may be less conspicuous in dense clusters. 

The percentage of a cluster to be targeted in high-resolution digital photographs will 

depend on the overall size of the cluster, the focal length of the camera lens, and the 

resolution of the camera’s sensor. Surveyors should subsample at a level that ensures 

targeted bats are clearly distinguishable in each photograph. It is critical that reference 

points (e.g., a laser pointer held in one position for multiple photos) are included in 

every image so that adjacent fields of view can be spliced together without double 

counting or missing bats.

In addition to producing a permanent record of the survey, high-quality photographs 

allow individual bats to be counted or densities estimated after surveyors have left 

the roost, thus reducing the amount of time surveyors spend in the roost. However, 

photography may not be appropriate for counting bats in hibernacula with complex 

roosting surfaces where the observer must process multiple angles of observation to 

account for bats in the cluster. In these cases, photographic methods should be paired 

with visual counts to estimate detection probabilities with the two methods. Surveyors 

should take special care to identify clusters and record photograph numbers so that 

estimates from the two methods may be directly compared for each cluster rather than 

for the colony as a whole.

7.4.1.2 Estimating cluster density for visual counts

When bats form large clusters, estimates of abundance from visual counts are achieved 

by measuring the dimensions of a roosting cluster and conducting counts on subsets 

of the cluster to estimate the packing density. The estimated packing density is then 

extrapolated over the entire area of the cluster to estimate total colony size. Because 

packing densities often vary across a cluster, this method may produce large errors in 

colony estimates (Thomas and LaVal 1988). If density estimates from visual counts 

are to be used, packing density should be estimated for each cluster and for multiple 

subsets of larger clusters. However, Meretsky and others (2010) have shown that visual 

density estimates produce less consistent results than photographic methods, and thus 

they are not recommended for the purposes of NABat when photographic methods are 

possible.

7.4.1.3 Other methods

Numerous other options are available to estimate colony sizes from internal surveys. 

However, a complete discussion of these methods is beyond the scope of this document 

because these methods are sometimes more narrowly applicable, too labor intensive, 

or too expensive for rangewide monitoring of most species. For example, threedimensional laser scanning has been proposed as an alternative to digital photography 

(Azmy and others 2012). Three-dimensional laser scanners provide a topographical 

map of the cave surface and can be used with very low light, thus reducing disturbance 

to the bats. However, like other methods, bats that are in crevices or covered by 

other bats will not be counted, and this method offers no advantages in species 

identification. Reflectance infrared and thermal infrared imaging have also been 

used inside roosts (Boyles and others 2008), but they are more useful for monitoring 

behavior and physiology than for estimating numbers of roosting bats. Modeling 

abundance using traditional mark-recapture (e.g., O’Shea and others 2004) and related 

molecular methods (e.g., Oyler-McCance and Fike 2011, Puechmaille and Petit 2007) 

are additional tools that may be applied in some situations. Any of these alternative 

methods should be coupled with photographic counts to help validate estimates and 

7.4.1.2

7.4.1.3
CHAPTER 7 Colony Counts 47

scale them appropriately for inclusion in rangewide estimates. We recommend pairing 

historic methods with photography for several surveys to transition to photographic 

counts as the primary method.

7.4.2 Preferred Timing for Internal Surveys

7.4.2.1 Internal surveys in winter

Detection probabilities of hibernating bats in roosts are seasonally heterogeneous, even 

in the best cases when bats are in conspicuous clusters. Cluster densities change with 

date so that some bats are obscured or covered entirely by other bats (Tuttle 2003). 

Fissure-roosting bats such as northern myotis (M. septentrionalis) may periodically 

emerge from a cluster or crevice depending on date or conditions and potentially move 

to another spot, resulting in substantial within-winter variation in counts. Ideal timing 

for surveys likely varies by species and region and has yet to be established except 

in a few special cases. For example, in the Appalachian region, northern myotis are 

more detectable in late February, but counts vary considerably on a day-to-day basis 

(Ingersoll and others 2013). Indiana myotis counts are least variable during January, 

and little brown myotis (M. lucifugus) counts are least variable at the end of January; 

tri-colored bat (Perimyotis subflavus) counts show little variation throughout the winter 

(Ingersoll and others 2013). We recommend that, whenever possible and when specific 

guidance for endangered species does not preclude it, winter surveys be conducted 

between late January and early March, which is also appropriate for identifying field 

signs and collecting samples for WNS surveillance (WNS Surveillance Working 

Group 2010). At hibernacula with multiple species, survey timing might be selected 

based on species of greatest concern or for those for which the most reliable results 

are expected. Until optimal dates are determined at local scales, survey timing should 

be based on consistency with historic records and knowledge of species detectability. 

Although some sites may become inaccessible as ice and snow accumulate, seasonal 

effects on survey data can be modeled and scaled if appropriate covariates are also 

recorded (Ingersoll and others 2013). Hibernaculum surveys should not be conducted 

more frequently than once every year to minimize disturbance (Kunz 2003, Tuttle 

1979, U.S. Fish and Wildlife Service 2007). Weller and others (2014) detected no 

measurable effect of annual surveys on colony sizes of Townsend’s big-eared bats 

(Corynorhinus townsendii). For some species, however, surveys should be no more 

frequent than every other year. Maintaining reasonably regular intervals for bat counts 

reduces the influence of outliers on subsequent modeling and analysis and improves 

the precision of estimates. Use of automated equipment to conduct surveys may allow 

for annual surveys with negligible disturbance to bats, but so far these methods are 

somewhat limited due to cost, supplies, safety of equipment, and other considerations.

7.4.2.2 Internal surveys in summer

Conducting internal roost surveys during the summer maternity season should be 

avoided if emergence counts of bats can be used. If internal surveys are to be used, 

they should be timed to minimize detrimental impacts on bats while maximizing the 

probability that inhabitants represent a relatively stable colony, preferably composed 

mostly of adult bats (Kunz and others 2009). Although exact timing will vary among 

species and geographic region, we recommend that, whenever possible, internal roost 

surveys be conducted during the final 2 weeks of pregnancy. While early lactation 

also represents a period of relatively stable colonies, risk of disturbing neonates may 

be greater at this time. Thus, the preferred window for internal surveys at summer 

roosts is more restrictive than the window for conducting emergence counts. Internal 

roost surveys should be conducted in the afternoon once bats have settled from early 

7.4.2

7.4.2.1

7.4.2.2

Brazilian free-tailed bats emerge from

Bracken Cave ni suburban San An ot n oi , XT .
48 A Plan for the North American Bat Monitoring Program (NABat)

morning foraging activity and prior to increased evening activity in preparation for 

emergence (O’Donnell 2002). As with winter surveys, repeated visits into summer 

roosts may disturb resident bats (Fenton 1997, Kunz and others 2009, Lacki 2000, 

Mann and others 2002). Thus, we recommend using multiple observer methods to 

conduct a single internal survey annually to estimate colony size. Furthermore, use of 

red lights or night vision helps reduce disturbance. Remotely operated cameras may 

also be installed in the roost during the night while bats are absent from the roost and 

operated from outside on subsequent days to obtain internal counts.

7.4.3 Spatial Variability Among Surveys

A persistent problem of cave and mine surveys is inconsistent sampling within sites 

over time. For example, surveyors may cover different or additional portions of a 

site depending on variation in resources or accessibility from year to year. Important 

new bat colonies may be discovered in previously unexplored portions of caves or 

mines (Tuttle 2003). Spatially inconsistent survey routes may render data useless for 

analysis if differences in survey route are not well documented. To accommodate 

route changes, count locations should be topographically referenced or subdivided into 

rooms and passageways to facilitate analysis of location-specific changes from year 

to year. Topographic referencing underground is best achieved with a detailed cave or 

mine map (often available from local National Speleological Society chapters or State 

cave surveys) indicating the survey route and cluster locations. Stihler (2005) provided 

an excellent example of survey effectiveness in a very large roost used by multiple bat 

species. When maps are not available, surveyors should make drawings of the roost 

and indicate all areas surveyed as well as known but unsurveyed areas.

7.5 External Roost Counts

Emergence counts can produce reliable estimates of volant bats at a wide variety of 

roost types (Kunz and others 2009). They also create minimal disturbance and provide 

excellent opportunities for including citizen scientists in monitoring bat populations. 

Suitability of emergence counts varies among species and, for some species, among 

regions. Emergence counts are appropriate when (1) all exits from the roost are known 

and observable; (2) there are sufficient resources to monitor all exits simultaneously; (3) 

emergence trajectories of bats are not obscured by clutter outside the roost; (4) density 

of bats during emergence is low enough that individuals can be distinguished; (5) 

emergence occurs while light is sufficient to observe the duration of the emergence or 

technology is used to facilitate observation; (6) emergence is complete prior to increased 

circling activity at the entrance of the roost and prior to bats returning to the roost after 

foraging; (7) emergence activity is not disturbed by noise, weather, or other factors; and 

(8) species composition and relative abundance of the species in the roost are known.

Accuracy of emergence counts relies in part on the assumption that all bats leave the 

roost on the survey date and that they are visible to observers or recording devices 

as they emerge. In northern latitudes, it is recommended that observers be in place 

and monitoring roosts at least 30 minutes before sunset; however, emergences may 

begin hours before sunset for some species in some regions (e.g., Brazilian free-tailed 

bats in Texas) (Frick and others 2012). We recommend that observers spend at least 

one afternoon and evening monitoring emergence behavior at each site immediately 

prior to the survey to establish timing, flight patterns, optimal observation positions, 

and other factors that may affect the ability to obtain accurate estimates of emerging 

bats. If possible, conducting simultaneous emergence counts at all known roosts in an 

area will reduce error caused by bats moving among nearby roosts. As with internal 

surveys, multiple observer methods or repeated surveys are encouraged.

7.4.3

7.5

A hoary

bat perched in a deciduous oak tree in autumn.
CHAPTER 7 Colony Counts 49

7.5.1 Preferred Methods for External Surveys

7.5.1.1 Visual counts

Counting bats emerging at dusk is a long-established technique for monitoring roosting 

populations of bats that has been well described (Battersby 2010, Elliot and others 

2006, Jones and Rydell 1994, Kunz 2003, Kunz and others 2009, Thomas and La Val 

1988). During midsummer in temperate zones, ambient light is often sufficient to 

observe enough of an evening emergence to account for most bats in a colony. This 

method is inexpensive, but may produce inaccuracies associated with limitations of 

unaided observation or incomplete counts if bats continue to emerge as light fades, 

or double counting if bats exit and return to the roost multiple times during the 

emergence.

When conducting emergence counts, it is important that a sufficient number of 

observers are present to continuously monitor all possible exits from the roost. Where 

multiple exits are used, observers must identify boundaries to the field of view each 

will count to avoid double counting (Kunz and Anthony 1996, Kunz and others 1996). 

If resources are limited and monitoring all exits is not possible, some exits can be 

temporarily obstructed. For example, windows or doors of a house or barn roost can 

be closed or sheets can be draped over openings to deter bats from using openings that 

are not being monitored. To avoid potential harm to bats, primary exits should not be 

obstructed. Modification to a site should be completed well before the expected time 

of emergence and all exits returned to original states as soon as the emergence count is 

complete.

To conduct counts, each observer should use two clickers, one to tally emerging 

bats and one to tally returning bats passing through his or her field of view during 

the emergence period. However, if observers are situated close to flying or roosting 

bats, high frequency sounds from clickers may change bat behavior. Use of audio 

recorders to record recited observations with time stamps may also be appropriate. 

Counting should continue until it is determined that all volant bats have left the 

roost. In many cases, an interval of at least 10 minutes during which no bats emerge 

indicates that the majority of bats have left the roost. However, some larger colonies 

may exhibit multiple emergence pulses, and intervals with no activity may last 

much longer than 10 minutes (Lee and McCracken 2004, Reichard and others 

2009). A bat detector near roost openings can sometimes be used to determine when 

activity inside the roost has ceased. Diminishing light conditions may preclude 

the observers’ abilities to continue counting bats as they emerge. In these cases, a 

clear note about the rate at which bats are emerging (number of bats per minute) as 

observation becomes impossible is informative, but the results of that survey may 

be unreliable. Alternatively, night vision goggles or viewers can be employed with 

infrared light sources to allow the observer to continue tallying emerging bats after 

dark. Spotlights, headlights, or other light sources should not be used to observe 

emerging bats, as they may affect bat behavior.

Under some conditions, an emergence count will not provide an accurate estimate of 

the true colony size. These conditions include extreme temperatures, high wind, heavy 

rain, or pressure changes on the survey night. Extended periods of drought or nearby 

wildfires may also affect emergence timing. Anthropogenic factors such as noise 

near the roost; habitat modification; unnatural light sources; and odors from vehicles, 

foggers, or other sources can also alter bat behavior. Whenever possible, surveys 

should be postponed if any of these conditions occur or if there is any other reason to 

suspect that the emergence behavior would be disturbed in some way.

7.5.1

7.5.1.1
50 A Plan for the North American Bat Monitoring Program (NABat)

7.5.1.2 Digital video methods

Numerous digital video methods for estimating colony size may be applied if resources 

permit. The use of digital video cameras creates a physical record of the emergence 

and enables subsequent manual or automated counting of individuals leaving a roost. 

Combining automated programs (see below) and manual processes to analyze recorded 

footage (i.e., a multiple observer method) can be used to validate estimates. Video 

methods are particularly useful for larger colonies where human error is likely to lead 

to inaccurate estimates, and footage can be slowed to facilitate counting especially 

busy periods of the emergence.

Either thermal infrared (TIR) or near infrared (NIR) cameras can be employed for 

emergence counts (fig. 7.3). Although these technologies are expensive, both allow 

surveyors to “see in the dark” and record activity long after darkness has eliminated 

the ability to observe bats with the unaided eye. There are several trade-offs between 

the two technologies. NIR is less expensive, but it can underestimate colony sizes 

(Elliott and others 2011). Utility of TIR for surveying bat emergences can be limited 

due to a lack of thermal contrast between the target and the background, too large 

a distance between the camera and the subject, and thermal clutter (e.g. clouds, 

ground, and foliage) (Melton and others 2005). NIR imaging is often enhanced with a 

secondary integrated or external infrared light source to illuminate subjects. Infared 

light is unlikely to alter bat behavior, especially if oriented perpendicular to the bats’ 

flight path; however, these light sources require additional power and may have limited 

range and intensity that may diminish their utility for some applications.

Automated methods of counting bats have been developed for TIR (Betke and others 

2008, Frank and others 2003, Hristov and others 2008, Melton and others 2005, Sabol 

and Hudson 1995) and NIR (Elliott 2006, Elliott and others 2006) footage of emerging 

bats. These methods are capable of distinguishing emerging and returning bats to avoid 

recounting most individuals that pass through the field of view more than once. We 

recommend that automated tallies and manual counts of the same footage be used to 

estimate error and detection probabilities. As with direct visual emergence counts, it is 

important that each exit be monitored by at least one camera and that areas of possible 

overlapping fields of view are well defined and accounted for when bats are tallied.

7.5.1.3 Other methods

Numerous other methods have been proposed for conducting external surveys. Beambreak technology (Redell 2005), Doppler radar (Horn and Kunz 2008), and vertical 

profiler radar (Kelly and others 2009) have all been used to estimate colony sizes. 

Although these and other methodologies may develop into convenient survey methods, 

Figure 7.3—(A) Visible light and (B) thermal 

infrared image of Brazilian free-tailed bats 

(Tadarida brasiliensis) emerging from a cave in 

Texas. (photo and image by Nickolay Hristov)

7.5.1.2

7.5.1.3

(A) (B)
CHAPTER 7 Colony Counts 51

they are currently not sufficiently well developed to recommend as general methods for 

assessing colonies to meet the objectives of NABat.

7.5.1.4 Determination of roost species composition

Multispecies roosts can complicate external survey estimates. Several ancillary 

methods are available to determine the species in a roost when species cannot be 

distinguished through the applied survey method. Internal surveys during a nonsensitive time period (e.g., after the young are volant), mist netting or harp trapping 

outside of the roost (e.g., Kunz and Kurta 1988, Whitaker and Rissler 1992), acoustic 

monitoring (e.g., Stihler 2011), and DNA analysis of guano (Zielinski and others 

2007) all provide means of estimating species composition and, to some extent, 

relative abundance of species in a roost. In some cases, body shape or size can be 

used to distinguish among species (e.g., Ammerman and others 2009). In other cases, 

species may segregate themselves temporally or spatially during emergence, allowing 

surveyors to produce discrete species-specific estimates. If relative abundance of 

species in a colony is used to estimate numbers for each species, the associated 

error must be acknowledged, if not quantified. Estimates of associated error or 

acknowledgment that the measure of abundance is deemed relative rather than absolute 

by the surveyors should accompany the data in the BPD for potential use as a covariate 

in subsequent analyses.

7.5.2 Preferred Timing of External Surveys

The timing of external surveys is an important factor to consider when planning 

surveys so that comparable estimates of colony sizes are collected across sites and 

years. The best time to conduct surveys is when the adult colony is relatively stable 

and prior to volancy of the young. Kunz and others (2009) recommend conducting 

emergence counts for two to three nights during the period of maximum adult colony 

size, which often occurs during late pregnancy and early lactation. In general, a 2- to 

3-week period around the summer solstice often corresponds with these reproductive 

stages in many temperate species (e.g., Hristov and others 2008, Humphrey and Cope 

1976, O’Farrell and Studier 1973, Reichard and others 2009, Schowalter and others 

1979). For larger colonies, scheduling counts closer to late pregnancy reduces the 

likelihood of lactating females returning to the roost to nurse young before all bats 

have emerged. In the later stages of the lactation period, increased flight activity 

around the roost may complicate emergence counts as young bats come and go 

throughout the night and adults return periodically to nurse their young (Stihler 2011). 

Because exact dates of parturition vary among species and regions (Bradbury 1979) 

and are further influenced by proximate climate factors (Frick and others 2010, Racey 

and Swift 1981), surveyors should attempt to confirm the reproductive timing at each 

roost to determine the most appropriate survey dates.

7.6 Safety and Environmental Considerations

7.6.1 Minimizing Environmental Impacts

Human activity can disrupt regular ecological conditions and processes in caves 

and mines, and thus care should be taken to minimize these effects (van Beynen 

and others 2012). Surveyors are also subject to restrictions on activity and schedules 

by property owners and State, Provincial, Federal, and tribal agency regulations. 

Alterations at sites (e.g., marking roost surfaces, attaching scientific equipment, 

clearing areas to improve access) should be avoided to leave minimal trace of human 

activity. All personnel should adhere to national decontamination protocols for WNS 

7.5.1.4

7.5.2

7.6

7.6.1

Bats drinking from a water tank.
52 A Plan for the North American Bat Monitoring Program (NABat)

(www.whitenosesyndrome.org) to minimize potential spread of Pseudogymnoascus 

destructans and other microbes.

7.6.2 Human Safety

A complete list of potential hazards that might be encountered by survey crews in 

caves and mines and how to avoid these hazards is beyond the scope of this document. 

In addition to expertise in caving, some sites (e.g., abandoned mines and aqueducts) 

may require specialized certification to gain access. Some common hazards that may 

be encountered in caves and mines include unstable floors and ceilings, dropoffs, 

narrow passages, falling objects, hypothermia, loss of orientation, anoxic conditions, 

flammable materials and gases, and high levels of radon. Risks from these hazards 

can be greatly minimized through training and the use of proper gear. All surveyors 

entering a site should be properly trained according to the site’s health and safety 

regulations and those of the surveyors’ sponsors. Sites should be inspected and deemed 

safe prior to any survey activity, and emergency response plans should be in place. The 

National Speleological Society (www.caves.org) provides many resources on safety, 

training, and appropriate gear to be used in caves, and Altenbach and others (2001) 

provide additional information about conducting mine surveys. Training in vertical 

climbing is critical before entry into many caves and is available through local chapters 

(or “grottoes”) of the National Speleological Society. Surveying bridges, dams, and tree 

roosts can also be hazardous. Other hazards include vehicular traffic, wildlife, debris, 

and even other humans. At a minimum, surveyors should keep a first aid kit on their 

person or in their vehicle and ensure that all members of the survey party know how to 

appropriately respond in case a medical emergency arises (e.g., they know directions to 

the nearest hospital).

7.7 Locating New Colonies and Documenting Absence of

 Colonies in Winter and Summer

Colony counts are limited to known roosts, and identification of new roosts is often 

limited to opportunistic reports or inconsistent search efforts. However, bats may 

respond to disturbance of the roost by moving to new locations (Elder and Gunier 

1978) and occupy new habitat (e.g., mines) as it becomes available (Fath 2002). 

Monitoring species only where they habitually occur will not fully capture this 

dynamic variation because changes in distribution will not be detected. Capturing 

changes in distribution and abundance is especially important given ongoing and 

expected changes to the environment caused by disease, alteration of landscapes, and 

climate change.

The colony count aspect of NABat can capture these changes to distribution through 

a discovery process. The GRTS sampling design of NABat (see ch. 3) provides a 

strategic approach to locating previously unknown roosts and documenting absence 

of bats in potential roosts. Results of the stationary and mobile acoustic monitoring 

program can be used to identify range shifts, and hotspots of acoustic counts may 

be helpful in directing maternity roost survey searches. This additional roost survey 

effort should be guided by the master sample (see ch. 3) or predicted probability 

surfaces derived from the acoustic surveys (see ch. 9). However, acoustic surveys 

alone will provide little (if any) insight into the potential location of winter roost 

locations. For example, even though there are at least 45,000 caves in the contiguous 

United States, with the vast majority of them occurring in the karst regions of the 

Eastern United States (Culver and others 1999), Indiana myotis are known to occur in 

only 0.5 percent of them (Thogmartin and others 2012). Furthermore, the number of 

buildings, trees, culverts, bridges, and other structures potentially inhabited by bats 

7.6.2

7.7
CHAPTER 7 Colony Counts 53

is likely much greater than the number of caves. Thus, to discover new winter roosts 

will require devoting resources to inspection of caves, mines, scree fields, and other 

potentially suitable winter habitat, and elicitation and curation of volunteer-contributed 

information such as reports from cave enthusiasts, surveys conducted in the course 

of project clearance (e.g., wind turbine construction, National Environmental Policy 

Act or Endangered Species Act compliance), local biologists, and the general public. 

Additionally, newly closed mines, newly constructed bridges, and other modified or 

newly created roost-capable locations could be brought into the “formal sample list” to 

prioritize their sampling.

Whether the discovery of additional colonies is necessary for trend analyses is 

dependent in part on whether a sufficient fraction of the species’ population is 

represented in the sample that can be monitored with limited resources (described 

more fully in the paragraph below) and whether the accuracy of trend estimates from 

those data are sufficient for robust inference (see ch. 9). As stressors such as changes 

in climate and land use modify the distribution of species, discovery of new roosting 

locations for establishing the range of species may be necessary, especially for species 

not well monitored by acoustic methods (see ch. 2). Coupling radiotelemetry studies 

with mist netting or spring emergence monitoring can facilitate discovery of summer 

colonies, and fall tracking studies may lead to discovery of unidentified hibernacula 

(Neubaum and others 2006).

In addition to locating new colonies, NABat may help document areas of absence. 

Given limited resources, however, we do not expect considerable effort to be devoted 

to determining a species’ absence, even from areas seemingly suitable to the species. 

Nevertheless, where such data exist, it will be important to understand the loss of 

species from areas where they once occurred and perhaps to identify why they no 

longer occur in those areas, as well as areas occupied only infrequently (for instance, 

when abundance increases because of periodic pulses in resources).

7.8 Covariates and Ancillary Data

A number of covariates are important for providing appropriate context to counts 

collected at a site. These covariates include the date and time of data collection, 

descriptions of the roost structure, information on number and experience of the 

surveyors, the methods used, environmental conditions during the count, and the 

habitat surrounding the structure. Lists of covariates that should be recorded 

(if relevant and possible) during internal and external surveys are given in tables 

8.1, 8.2, and 8.3, and sample data sheets for recording these data are provided in 

appendixes F, G, and H.

7.9 Incorporation of New Technologies

Advances in technology, accessibility of equipment, and intellectual innovations drive 

continuing evolution of survey methods. As new methods are developed and validated, 

it is important that strategic transitions to these new methods are planned so that new 

data can be connected with historic data. Increased effort during the transition to the 

new method will greatly increase the value of monitoring efforts.

7.9

A tri-colored bat from a hibernaculum in

SC. (©

Ben Neece)

7.8
54 A Plan for the North American Bat Monitoring Program (NABat)

8. Data Management

8.1 Importance of Data Management

Data and information are the primary products of any well-designed ecological 

monitoring program. The ability to provide organized and well-documented data and 

analyses to key audiences and ensure their long-term preservation will determine the 

monitoring program’s efficacy and image among critics, peers, and advocates (Fancy 

and Bennetts 2012). Therefore, a critical component of any monitoring program is to 

ensure that data and information are managed so that they can be easily found and 

compiled, are subjected to full quality control before release, and are accompanied 

by complete metadata. Many funding sources are now requiring data management or 

data sharing plans in the proposal process to ensure that data are properly collected, 

stored, and archived (e.g., National Science Foundation, National Institutes of Health). 

A centrally located and easily accessible database or portal that carefully tracks all 

sources of information is also important for projects such as NABat that have multiple 

organizations involved and cover large geographic areas.

8.2 History of the Bat Population Database

In 1994, U.S. Geological Survey (USGS) scientists recognized that despite increasing 

concern for many species of bats known or believed to be declining, data necessary 

to determine population status and trends were fragmented among agencies and 

organizations. The Bat Population Database (BPD) was developed to compile existing 

population information for bats in the United States and territories. The initial goals 

were to (1) synthesize the existing bat population data and publications into a single 

Web-accessible database, (2) test the utility of these data for estimating trends in bat 

populations, and (3) evaluate the applicability of these data for future monitoring 

programs. The relational database was designed using Microsoft Access

® software and 

compiled various components of bat population data gathered between 1855 and 2001. 

The original database included counts of bats at colony locations, location attributes, 

and a bibliography of publications relevant to counts of bats (published literature, 

theses, agency reports, and State agency records of bat observations). These data were 

used to investigate colony trends at 179 summer and 294 winter roosts of 22 species 

of bats (Ellison and others 2003), yet the effort revealed many shortcomings of the 

existing data such as inconsistencies in survey effort and timing. The database was 

available to the public on the Internet for several years; however, the search capabilities 

were limited. Interest in the database languished for years until the combined threats 

to bats from habitat loss and fragmentation, white-nose syndrome, wind energy 

development, and climate change generated renewed interest for data suitable for bat 

population estimation and trend analyses.

8.3 The Bat Population Data Project

The newly named Bat Population Data Project is an effort by the USGS Fort Collins 

Science Center (FORT) to upgrade, update, and extend the capabilities of the original 

BPD to allow for better data management, accessibility, and utility by USGS and its 

data partners. In addition, the Bat Population Data Project provides data stewardship 

and coordination to data partners, in particular the NABat Program Team.

BPD (version 2) is the primary tool of the Bat Population Data Project and in 2012 was 

reconstructed as a Web-based data management application (http://my.usgs.gov/bpd). 

It contains the original 24,000 field observations, most of which are historical colony 

8.1

8.2

8.3
CHAPTER 8 Data Management 55

counts. BPD v. 2 allows users to search the database by date range, author, species, 

and State for public information on historical colony counts of bats from the literature 

and data partners (see sec. 8.6). The BPD now has role-based access control, which is 

a method of regulating access to network resources based on the roles of individual 

users within an enterprise or program. Role-based access specifies the ability of an 

individual user to perform specific tasks such as to view, create, or modify data. The 

roles are defined according to the user’s authority or responsibility within the program. 

Because the BPD is role based, data partners can obtain their own confidential data in 

searches. Additionally, BPD v. 2 now includes fields for capture-based data obtained 

from mist netting and other trapping efforts. Data structures to manage acoustic 

data fields are currently being developed. The data management plan for the BPD is 

provided in appendix I.

In the summer of 2013, scientists at FORT began updating the BPD with colony 

count data collected from 2001 to the present by various agencies’ monitoring and 

endangered species programs and State heritage programs. Further, acoustic data 

collected from mobile transect surveys that were conducted in the Eastern United 

States from 2008 through 2013 will be consolidated into the BPD. The colony count 

and 2008–13 acoustic transect data will be considered “legacy” data. These legacy 

datasets will provide a baseline of information on bat populations for future monitoring 

of population trends under the NABat framework.

8.4 Database Architecture

BPD users will be able to archive and access observation data, site location 

information, acoustic metadata from species identification processing, and colony 

count data that are collected as part of NABat or outside of it. Protocols developed 

in this document determined the fields to be collected and stored in the BPD (see 

tables 8.1, 8.2, and 8.3). Observation information includes dates, times, and the 

specific conditions during the periods surveyed. Other data fields include observers, 

environmental variables (e.g., moon phase, cloud cover, temperature), observation 

methodology (e.g., colony count, acoustic survey), and general habitat descriptions 

(table 8.1). Site location information includes the following fields: site name and 

generalized random-tessellation stratified (GRTS) grid cell identifier, country, State or 

Province, county (if applicable), geographic coordinates, land ownership, and elevation 

(table 8.2). Additional fields related to acoustic data collection are provided in table 

8.1, and include information on how the files were collected (e.g., type and model 

of acoustic detector, microphone type, recording mode, trigger window length, and 

microphone height). Fields related to species identification of acoustic files will be in 

a separate table within the BPD architecture and will allow the automatic upload of 

spreadsheets from software packages that automatically identify bats to species based 

on their echolocation call characteristics (see ch. 6). Spreadsheets created in AnabatTM

AnalookW software using the “Count Labels” tool will also be uploadable to the 

BPD. Colony count data fields include the roost structure (e.g., cave, mine, bridge, or 

bat box), colony type (e.g., maternity, hibernaculum, bachelor, transient, or mixed), 

species, estimation method, population estimate for each species, and lower and upper 

limits of population estimates (table 8.3). Because the BPD is a relational database, 

multiple tables are linked by unique identification numbers. For example, multiple 

Observation IDs are linked to a particular Location ID, multiple Colony Count IDs are 

linked to an Observation ID. A detailed data dictionary will be developed and provided 

to NABat participants and data partners.

8.4

A tri-colored bat from a hibernaculum in

SC. (©

Ben Neece)
56 A Plan for the North American Bat Monitoring Program (NABat)

Table 8.1—Fields to be collected for NABat related to observations

Fields Description Applicable survey method

Site Location Name (e.g., cave name, transect identifier/

name)

All surveys

Grid Cell ID Generalized random-tessellation stratified (GRTS) 

10- by 10-km grid cell number provided by NABat

All surveys

Surveyors Names of all surveyors All surveys

Years of experience Number of years of experience observing (colony 

counts) or identifying species (acoustic surveys)

All surveys

Moon Moon phase All surveys

Observation methodology Colony count, stationary point, or mobile transects All surveys

Observed habitat description Habitat type (urban, agricultural, rangeland, forest 

land, water, wetland, barren land)

All surveys

Survey start/end Beginning and end time of survey All surveys

Other research? Identify other activities conducted simultaneously 

with count surveys

All surveys

Photographic equipment Identify cameras used during the survey All surveys

Photos? Y/N – submit photos that illustrate the detector 

set-up, surrounding area, habitat

All surveys

Observation comments Other comments All surveys

Roost structure Cave, mine, bunker, barn, tree, bridge, etc. Colony counts

Estimate method Survey methods (e.g., internal-photographic, 

external thermal infrared camera, emergence 

count, etc.)

Colony counts

Colony type Maternity, bachelor, hibernaculum, transient, mixed Colony counts

Roost size Dimensions of roost including width, length, and 

height; diameter at breast height and estimated 

height for trees

Colony counts

Proportion of roost surveyed Map or sketch of surveyed area and estimated 

percent of roost surveyed

Colony counts

Vertical distance (m) Height of bats in structure Colony counts

Horizontal distance (m) Distance between observers and bats Colony counts

Roost temp (°C) Air temperature within the roost Colony counts

Roost RH (%) Relative humidity within the roost Colony counts

Presence of water Indicate if there is standing or flowing water in 

the roost

Colony counts

Roost protected? Locks, gates, restricted access Colony counts

Roost accessibility Specific equipment, training, needed to access 

the roost

Colony counts

Signs of disturbance Evidence such as predation, graffiti, flooding, or 

collapsed walls or ceilings

Colony counts

Other roosts? Number and distances of known roosts in the 

vicinity (<10km)

Colony counts

Observed WNS? Y/N (number observed will be entered into the 

colony table)

Colony counts

Specimens collected? Y/N (number collected will be entered into the 

colony table)

Colony counts

RH (%) start/end Relative humidity at beginning and end of survey Colony counts, mobile transects

Date Month, Day, Year Colony counts, mobile transects

Sky start/end Estimate of cloud cover at beginning and end 

of survey

Colony counts, mobile transects

Temp. start/end (°C) Temperature reading at beginning and end of survey Colony counts, mobile transects

Continued
CHAPTER 8 Data Management 57

Fields Description Applicable survey method

Wind start/end (km/h) Calm, light, moderate, high at beginning and end 

of survey

Colony counts, mobile transects

Moonrise/moonset Time of moonrise and moonset Colony counts, mobile transects, 

and stationary points

Sunset/sunrise Time of sunset and sunrise Colony counts, stationary points

Date deployed First night of survey Stationary points

Date ended Date acoustic monitoring ended Stationary points

Time recording started If detector shut down mid-operation, record the date 

and time

Stationary points, mobile transects

Time recording stopped If detector shut down mid-operation, record the date 

and time

Stationary points, mobile transects

Nightly temp (°C) High/low/mean temperatures obtained from local 

weather recording stations or on-site recorders (for 

each detector)

Stationary points

Nightly RH (%) High/low/mean relative humidity obtained from local 

weather recording stations or on-site recorders (for 

each detector)

Stationary points

Nightly wind (km/h) High/low/mean wind obtained from local weather 

recording stations or on-site recorders (for each 

detector)

Stationary points

Significant weather e.g., thunderstorm on a particular night, sudden cold 

snap, windstorm, continuous rain

Stationary points

Distance to clutter Distance between detector and nearest source 

of clutter

Stationary points

Bat detector model Type and model number of bat detector Stationary points, mobile transects

Microphone Type e.g., SMX-USTM, AnabatTM standard, Hi, or stainless 

steel microphone

Stationary points, mobile transects

Recording mode Zero-cross or real-time full spectrum Stationary points, mobile transects

Gain settings Switch settings for SM2BAT+TM, digital gain setting 

for SM3BATTM, input gain for Petterson D500x

Stationary points, mobile transects

Signal-to-noise ratio Trigger level Stationary points, mobile transects

Frequency filters High pass frequency (HPF) for SM2BAT+TM; FrqMax 

and HPF for SM3BATTM; recording setting for 

Petterson D500x

Stationary points, mobile transects

Trigger window e.g., idle setting or Max TBC (time between calls, 

see ch. 4)

Stationary points, mobile transects

Maximum file length e.g., duration (see ch. 4) Stationary points, mobile transects

Microphone height Height of microphone above ground Stationary points 

Waterproofing e.g., Bat-hat, PVC pipe, none Stationary points

Microphone orientation Vertical, horizontal, 45°, facing down Stationary points, mobile transects

Calibration method e.g., AnabatTM Equalizer Stationary points, mobile transects

Method of species 

identification 

e.g., Kaleidoscope® Pro, SonobatTM, BCID, 

EchoClass, other models, filters, visual examination

Stationary points, mobile transects

Version of software Include versions of libraries used if applicable 

(e.g., Kaleidoscope® Pro 1.1.20 with North American 

Classifiers 1.04)

Stationary points, mobile transects

Format of files Format of files used in species identification 

(e.g., input of .wav, output of zero-cross)

Stationary points, mobile transects

Postprocessing Postprocessing of auto-identified files using 

manual verification

Stationary points, mobile transects

WNS = white-nose syndrome

Table 8.1 (Continued)—Fields to be collected for NABat related to observations
58 A Plan for the North American Bat Monitoring Program (NABat)

Table 8.3—Fields in the colony count table of the Bat Population Database to be collected 

for NABat

Fields Description

Present? Y/N

Species Species associated with this particular count (if multiple species of bats are 

observed, a new colony count record will be created for each species)

Population estimate Number of bats counted

Lower limit Lower range of count

Upper limit Upper range of count

Number of clusters Tally of the number of clusters counted

Size of clusters Estimated or measured size of each cluster counted

Dead bats Count or estimate of number of dead bats

Number captured Number of bats captured during colony count survey

Number marked Number of bats banded or marked during colony count survey

Number collected Number of bats collected (e.g., museum, white-nose syndrome diagnostics)

Colony comments Any other notable observations

Table 8.2—Fields in the location table of the Bat Population Database to be collected 

for NABat

Fields Description

Site Name Location name (e.g., cave name, transect identifier, stationary point)

Grid cell ID Generalized random-tessellation stratified (GRTS) cell designated number for 

10- by 10-km grid cell

Country Country

State or Province State name or Province name

County County, if applicable

Federal agency Federal land owner, if applicable

Land unit Name of land unit (e.g., Acadia National Park, Ouray National Wildlife Refuge)

State owned? Y/N

State land name Name of State-owned land unit

Georeference Map datum (WGS84 is recommended)

Latitude/longitude Latitude and longitude of survey in decimal degrees (for mobile transects, this 

will be entered for both start and end points)

Elevation (m) Elevation of site
CHAPTER 8 Data Management 59

8.5 Additional Support for NABat Provided by the Bat

 Population Database

8.5.1 Geographic Information System Support

NABat will provide States, Provinces, and other partners the GRTS-based master 

sample in the form of an ArcGIS® shapefile and attribute table with an ordered list of 

grid cells to survey for their particular area. Each State, Province, and other partner 

organization will be required to track the status of each grid cell sample unit and 

provide that information to NABat for tracking and archiving in the BPD. This will 

enable the sampling design inclusion probabilities to be tracked and used for weighting 

in subsequent analyses. Grid cells that are dropped and replaced with grid cells further 

down on the list must be recorded as such. The criteria used to justify replacing units 

must be recorded as well. Figure 3.2 provides an example of how such a list should be 

managed. BPD developers will be creating an application for tracking the GRTS-based 

master sample so data partners will be able to sign in and easily see which cells are 

already “adopted” for their jurisdiction, which cells have been dropped, and which 

cells are next in line to be surveyed.

8.5.2 Datasheets and Applications

Standardized datasheets for collecting colony count and acoustic data for the NABat 

program will be provided in a Microsoft Excel® spreadsheet format. Applications for 

hand-held field tablets or smartphones that will allow data to be automatically uploaded 

to the BPD will be developed in the future. Automatic species-identification software 

programs produce varying output formats. Spreadsheet uploaders for the following 

automatic species-identification programs will also be developed: EchoClass, BCID, 

Kaleidoscope®, and SonobatTM. Additional uploaders may be developed for new 

software programs if they become widely adopted.

8.5.3 Archiving Acoustic Files

Acoustic files collected during the NABat program will be stored and archived by 

investing in dedicated file storage hardware and eventually through cloud-based 

resources. The acoustic files themselves will be linked to the BPD acoustic table 

by unique file names so that proper quality assurance and control (QA/QC) can be 

periodically conducted. NABat will not be identifying the acoustic files to species; 

however, NABat plans to provide periodic QA/QC on a randomly selected 10 to 20 

percent of the calls submitted by data partners. Automatic species-identification 

programs will continue to improve as more acoustic data are collected, and NABat will 

incorporate these into its QA/QC policies.

8.5

8.5.1

8.5.2

8.5.3

A lesser long-nosed bat pollinates an agave fower.
60 A Plan for the North American Bat Monitoring Program (NABat)

8.6 Data Partnerships

Data partnerships will be developed with organizations collecting data for NABat. Data 

partners are considered owners of the data they contribute and can dictate how those 

data are displayed on the BPD Web site. Data projects are created and maintained by 

the data partners to logically consolidate and organize their field data. For example, 

the NABat program will be a data project within the larger BPD architecture and can 

be managed by the identified NABat team members (fig. 8.1). Partners of NABat will 

agree that the data they are collecting for the program will be used as part of annual 

and multiannual assessments and analyses. The NABat data will not be accessible to 

the public or other partners/users until these annual and multiannual assessments and 

analyses are peer reviewed and published unless partners request that they may be made 

public. Users of the BPD are individuals who have chosen to manage their own data, but 

NABat also has identified the need for dedicated database manager(s) to help facilitate 

data entry and uploading for the success of the program (see ch. 10 and fig. 10.1).

Figure 8.1—A schematic of the U.S. Geological Survey Bat Population Database (BPD) showing 

the relationships among the central database, the NABat data project, data users, and data 

partners (not a complete list). BLM = U.S. Bureau of Land Management; FWS = U.S. Fish and 

Wildlife Service; NPS = U.S. National Park Service; USFS = U.S. Forest Service; USGS = U.S. 

Geological Survey.

8.6
CHAPTER 9 Analysis 61

9. Analysis

The main analytical goal of NABat is to provide robust inferences regarding the 

distributions and abundance of North American bats and how these state variables 

are changing over time. Occupancy models can be used to estimate regional species 

distribution and change over time using data collected following the protocols 

recommended in NABat for acoustic monitoring (see chs. 4 and 5). These approaches 

do not allow for estimation of absolute abundance or density because it is not possible 

to relate the number of calls to the number of bats at stationary point surveys. 

However, counts of species on mobile transect data can be treated as indices of relative 

abundance to estimate changes in populations over space or time if detectability can 

be modeled sufficiently. Colony count data contribute to distribution models and, in 

addition, can be used to estimate population size if the counting method allows for 

accurate within-colony counts and if the sampling frame for selection of colonies is 

well defined.

This chapter describes various analytical methods that can be used with the proposed 

data collection protocols (see chs. 4, 5, and 7). These suggested methods are based on 

the best currently available analytic approaches; however, as the field of statistics and 

environmental monitoring progresses, more suitable approaches may be developed and 

should be applied if the situation warrants. The methods discussed here are appropriate 

and recommended state-of-the-art approaches for analyses of data generated from 

these protocols.

In addition, as highlighted below, the suggested statistical models have assumptions 

that need to be met for valid inferences to be drawn. Initial analyses of the data from 

NABat will include evaluation of data for possible violations of model assumptions. 

Simulation-based studies can be used to assess the repercussions of violated 

assumptions on inferences as data become available. Misidentification is a significant 

concern for acoustical surveys of bats (e.g., Clement and others 2014). Evaluation of 

these errors and incorporation of appropriate ways of estimating or accommodating 

false positives and negatives via analytical or field-based methods will be an ongoing 

analysis component for acoustical bat survey data.

All monitoring programs should be subject to ongoing review, and information from 

the beginning years of data collection from the bat surveys will be used to inform 

and possibly modify the protocols provided here regarding replication and effort 

needed for estimating detectability and occupancy. Also, as monitoring questions 

evolve and scientific understanding progresses, interim assessments on the efficacy 

of the protocols to inform and support the core needs of NABat (see ch. 1) will be 

undertaken. These interim assessments will be useful for maintaining management 

and conservation relevancy of NABat over time.

9.1 Acoustic Data Analyses

9.1.1 Raw Data

Acoustically recorded data are gathered from bat detectors at fixed locations (hereafter, 

stationary points; see ch. 4 for details) and acoustic detectors mounted on vehicles 

traveling prescribed routes (hereafter, mobile transects; see ch. 5 for details). Both of 

these methods record bat ultrasonic calls emitted as bats pass near a detector. Detectors 

at stationary points can record multiple passes from individual bats over the course of 

9.1

9.1.1

Portrait of a spotted bat.
62 A Plan for the North American Bat Monitoring Program (NABat)

any given night, while it is assumed that a detector mounted on a car driving a mobile 

transect records only one pass per individual (Roche and others 2011).

Estimation of occupancy requires replicate sampling at selected sites (grid cells). 

As bat surveys are omnibus (i.e., encounter multiple species for which sampling 

requirements will likely vary), initial design recommendations are based on generic 

recommendations from occupancy sampling (e.g., Mackenzie and Royle 2005) and the 

occupancy analyses conducted by Rodhouse and others (2012).

For stationary points, the temporal observation unit is a night, and each recorder is 

deployed for four nights. The recommended sampling intensity is four detectors per 

100-km2 grid cell placed far enough apart to serve as spatial replicates. Selection of 

sampling locations for the within-grid-cell replicates may vary regionally, and may 

be chosen from habitats that best represent the regional diversity of bats to maximize 

detection probabilities. See chapter 4 for details of the stationary point location 

suggestions and considerations. Date, time, location, and species identification for 

each bat pass, along with relevant covariates, will be available in the Bat Population 

Database (BPD) (see ch. 8).

For mobile transects, the temporal observation unit is also one night, although 

surveying takes place only for 1 to 2 hours each night. Each transect should be 

surveyed on two nights, the minimal replication to permit estimation of detection 

probabilities. Transects may vary in length but should be approximately 25 to 48 

km and may span one or more 100-km2 grid cells. Date, time, location, and species 

identification of each bat pass detected, along with relevant covariates, will be available 

in the BPD.

9.1.2 Response Variables for Occupancy Analysis

For each grid cell, data from both mobile transects and stationary points will be 

summarized as detection (1) or nondetection (0) for each species by detector by night, 

thus creating detection histories for each grid cell (sampling unit). Note that a detection 

corresponds to at least one recorded and identified call for a species. For mobile 

transects, only data from the portion of the transect included within that particular grid 

cell will be included. A grid cell with complete recommended coverage for a season 

would have a detection history composed of 18 zeros and ones for detections of species 

X (2 for the mobile transect run twice and 4 stationary points run for 4 nights each). 

However, the methods described here can accommodate more or fewer observations 

within a season.

9.1.3 Response Variables for Abundance Indices

Data from mobile transects will generally be aggregated to the number of passes per 

species per night on the portion of the transect within each selected grid cell. For 

some analysis purposes, it may be possible to aggregate data from the whole transect, 

including portions outside of the starting grid cell.

9.1.4 Modeling Occupancy

Occupancy models require repeated sampling in time or space within each sampling 

unit for estimating detectability. The recommended sampling protocol for NABat will 

provide both spatial and temporal replication of stationary detectors and temporal 

replication of mobile transects. As mentioned earlier, pilot data will be used to explore 

temporal and spatial variability in detection probabilities to inform allocation of 

9.1.2

9.1.3

9.1.4
CHAPTER 9 Analysis 63

effort within a sampling unit as the program evolves. The analysis method proposed 

below makes use of both stationary and mobile data, accommodating differences in 

detectability between sampling methods. For more detailed information on occupancy 

modeling, see Mackenzie and others (2006) who provide a detailed introduction.

The proposed model for analysis is as follows: let Zi = the partially latent occupancy 

state in grid cell i during the summer prior to the appearance of volant young, with 

distribution:

Zi ~ Bernoulli (φi )

where

φi = the latent occupancy probability

We model φi as a function of covariates Qi at the level of the grid cell (bold denotes 

vector valued quantities), i.e.,

logit(φi ) = αQi

where

α = occupancy-level coefficients for grid cell (sample unit) level covariates.

The observation on night k at detector j within grid cell i is

Yijk = {0 no detection

 1 detection

with distribution:

Yijk ~ Bernoulli (Zi pijk )

where

pijk = the detection probability

Probability of detection can be modeled as a linear function of covariates, including 

length of road for mobile transects, using a logit link as follows:

logit (pijk) = βXij + δWik + θI(MethodType)ijk* LengthOfRoadijk

where

β = detection-level coefficients for detector-level covariates

δ = detection-level coefficients for temporal covariates

θ = proposed parameter to adjust for species detections contributed from using 

mobile transects of varying lengths within a grid cell or sample unit

Xij = detector-level covariates (e.g., habitat type) that vary across detector j within 

grid cell i

Wik = temporal covariates that vary by night k within grid cell i (e.g., hours of 

recording for stationary detectors)

I(MethodType)ijk = the indicator for the type of method used, either mobile (1) or 

stationary detector (0), so the interaction term allows for adjusting by length of 

road within a grid cell

This model assumes that after accounting for appropriate detection-level covariates, 

replicate surveys within a grid cell are independent within a season. A multiscale 

parameterization proposed by Nichols and others (2008) that incorporates multiple 

detection methods could also be explored for this type of combined data.
64 A Plan for the North American Bat Monitoring Program (NABat)

The model above can be fit using Bayesian methods (Kéry and Schaub 2011, Lunn 

and others 2000) or via maximum likelihood methods. The actual covariates used 

to explain occupancy and detection may differ among species and regions. Habitat 

covariates for occupancy analysis must be available for all grid cells and will be 

assembled in a Geographic Information System; covariates for estimation of detection 

probabilities are required for all detectors. See tables 8.1 and 8.2 for a list of covariates 

for stationary points and mobile transects, respectively. This list may change over time 

as more data are collected and the relative importance of different covariates can be 

assessed.

The above model is designed to produce predictive occupancy probabilities for each 

grid cell over each species range, which can be displayed in the form of species 

distribution maps (e.g., Rodhouse and others 2012). It can also be used to quantify 

associations between species occupancy and biologically meaningful covariates. The 

model can also be used to predict the consequences of changes in covariates on species 

occupancy.

The model, as formulated above, describes patterns of occupancy for a single year. 

Multiyear information from repeated monitoring permits the extension of this model 

to estimation of parameters associated with change in occupancy, including local 

extinction (defined as the probability of a site being unoccupied in one year given 

it was occupied the year before) (MacKenzie and others 2006) and colonization 

(MacKenzie and others 2003, McKann and others 2013). Specifically, the occupancy 

parameter can be allowed to vary over time as follows (MacKenzie and others 2003):

ψt = ψt-1(1– εt-1) + (1– ψt-1 )γt-1

where

ψt = the time-specific occupancy probability, dependent on the occupancy 

probability at the previous time step (ψt-1)

εt-1= the probability of local extinction

γt-1 = the probability of colonization 

This is referred to as the multiseason occupancy model (Fiske and Chandler 2011, 

MacKenzie and others 2006). Additional extensions of the model can be used to 

estimate occupancy simultaneously for multiple species and to estimate species 

richness (Royle and Dorazio 2008).

As noted above, occupancy methods assume that individuals are identified correctly 

to species, but one of the challenges with acoustic data is that a percentage of calls are 

unidentified or incompletely identified (i.e., some species are difficult to distinguish). 

Some calls may also be confidently but incorrectly classified. A growing literature 

suggests that misidentifications can bias estimation in occupancy models (e.g., Clement 

and others 2014). Clearly, further work is required to develop methods to incorporate 

and address these additional data and misidentifications (Catelan and others 2010, 

Kéry and Schaub 2011, Miller 2012). Quantifying misidentification rates, developing 

procedures such as collecting ancillary data to correct for misclassification during 

estimation, and implementing composite estimation for groups of species that cannot 

be easily distinguished in acoustical surveys should be a priority for future research.

An additional methodological consideration is incorporation of unequal sampling 

probabilities for grid cells (i.e., design weights) within an occupancy model (Gelman 

2007).

A tri-co ol red bat from

a

hibernaculum in SC. (© Ben Neece)
CHAPTER 9 Analysis 65

9.1.5 Estimating Indices of Abundance

Methods have not yet been developed for estimation of absolute abundance or density 

of bats from acoustic data, but the number of calls per species along the mobile 

transects can be used as an index of abundance. For some analyses (e.g., relating 

indices of abundance to grid-level covariates), only data collected within the grid cell 

can be used, but other analyses may use data from the complete route as the analysis 

unit, even if part of the route extends outside the grid cell.

Single surveys of routes can be analyzed using log-linear models in a similar fashion 

as the North American Breeding Bird Survey (BBS) (Sauer and Link 2011). Log-linear 

models can control for factors that influence detection of bats in the context of the 

analysis. For the BBS, analyses control for observer differences and experience; in 

acoustical surveys, similar covariates could be employed to accommodate changes in 

hardware and software that may be developed over time and among sites that improve 

our ability to record and distinguish bat calls. Road length and time spent driving can 

also be used as an offset or covariate in the analysis to adjust for variable coverage. 

Additional covariates that may influence detection may be identified during initial 

analyses.

Replication of routes within seasons, as recommended by NABat protocols, provides 

additional information that can potentially be used in N-mixture models (Royle 2004) 

(see sec. 9.2.2) to estimate an index of abundance that incorporates covariates of 

detectability. The model proposed by Kéry and others (2005) can be applied to time 

series data to estimate population change.

As in occupancy analyses, abundance estimates are conditioned on confirmed 

identifications (Kéry and Schmidt 2008; MacKenzie and others 2003, 2009; Royle and 

Link 2006). If a significant number of calls are incompletely identified (e.g., Myotis

sp.), analysis can be conducted by modeling the species group rather than individual 

species. New approaches would need to accommodate misclassification as well as 

incorporate both incompletely identified and fully identified records into a single 

model.

Number of calls detected per night at stationary points provides a measure of activity 

at individual locations. Inferences from these data about abundance would require 

strong assumptions about the relationship between the number of calls per night at a 

site and the number of individuals. Auxiliary data on behavioral and calling patterns of 

individual bats would be required to test those assumptions.

9.2 Colony Counts

Counts of bats collected at or in colonies present a number of challenges (Kunz 

and others 2009). If a site can be entered, visual censuses can be conducted; this 

is especially true for species forming small, easily observed clusters of roosting 

individuals (see ch. 7). With increasing species abundance, irregularities in roost 

structure and substrate, and variability in cluster density and dispersion, other 

methods for counting bats are necessary. As a result, most surveys of bats do not result 

in the count of the entire roost population (Kunz and others 2009). Thus, modelbased estimation is necessary for converting raw counts to estimates of population 

abundance.

9.1.5

9.2
66 A Plan for the North American Bat Monitoring Program (NABat)

9.2.1 Raw Data

Colony counts, whether internal or external, represent the number of bats observed at a 

roost. Implicit in many of these counts is the assumption that bats are counted without 

bias and all individuals in a roost are available to be counted. In the presence of 

violations of these two assumptions, counts are necessarily less than the total number 

of individuals. However, it is also assumed that an individual bat is only counted once; 

double counting could inflate the total number of individuals. Methods proposed for 

counting individuals at roosts (see ch. 7) seek to minimize bias and accommodate 

differential availability, but users of these data should remain aware of these issues.

9.2.2 Modeling Count Data

As with acoustic mobile transect surveys, surveys of roosts can be analyzed using 

log-linear models (Thogmartin and others 2012); these approaches currently do not 

explicitly differentiate a detection process from an abundance process. Approaches 

for doing so exist, however. State-space approaches, for instance, parse the count 

process into an observational model and an environmentally determined process model 

(Buckland and others 2004, de Valpine and Hastings 2002, Hinrichsen and Holmes 

2009). For multiple-measure data (i.e., multiple observers or multiple visits within an 

index period), an explicit-process hierarchical model of the following form could be 

considered:

Ni ~ Pois(λi )

where

Ni = the true abundance at location i

λi = the expected value for abundance

The expected value can be modeled using a log-linear model as follows: m

log(λi ) = β0 + Σβk xki

where k = 1

β0 = the abundance intercept

β1:m = the coefficients for abundance covariates x1:m (e.g., presence of WNS, 

proximity to or density of wind energy generation)

The count of bats at location i and visit j, Yij, (or if double-observer the j subscript distinguishes the observer observations) can be modeled using the binomial 

distribution with:

Yij ~ Bin(Ni ,pi )

where

pi = the per-bat detection probability

The detection probability can then be modeled using a logistic model as follows: m

logit(pi ) = α0 + Σαk zki

where k = 1

α0 = the detection intercept

α1:m = the coefficients of detection covariates z1:m (see table 8.1)

9.2.1

9.2.2
CHAPTER 9 Analysis 67

This is the N-mixture model of Royle (2004), which assumes each individual is 

counted only once. For example, for exit counts, we assume that an individual bat 

makes only one exit flight that may or may not be detected by an observer (no multiple 

passes). The N-mixture model may be extended to accommodate estimating spatial 

and temporal dynamics such as changes in population size (Royle and Dorazio 2008) 

and conditions in which individuals are double counted (Ingersoll 2010).

For data such as exit counts, where bats flying into the roost can be distinguished and 

counted separately from bats flying out of the roost, the maximum number of bats 

that leave the roost may be estimated for an observation period. This sum is called the 

maximum emergence. Maximum emergence can be used to correct the count for multiple 

passes into roosts. Assuming the data fit an appropriate distribution, the N-mixture 

model can potentially be used to estimate actual colony size (Ingersoll 2010).

N-mixture models may be difficult to fit to bat roost data due to the need to 

appropriately define underlying distributions and a lack of independence of 

observations. Johnson and others (2014) suggest some approaches for evaluating lack 

of independence among individuals; these approaches can be explored for bat data.

9.2.3 Dynamic Count Models

Statistical models for estimating changes in abundance over time have been proposed 

(e.g., Dail and Madsen 2011, Kéry and others 2009, Royle and Dorazio 2008). For 

example, the models proposed by Kéry and others (2009) could be used as an extension 

to N-mixture models for the case of multiple years of data at the same sites including 

within-year replication for estimating detection.

In the case of count data recorded at the same sites over many years that lack withinyear replication (most legacy or historic data using single-observer and single-visit 

data), the approach in Royle and Dorazio (2008) may be used. For these data, the 

following implicit-process hierarchical model is possible, which can only be used for 

relative abundance estimates (Royle and Dorazio 2008):

E(yit ) = eΣj=1 fj (x(t)it )+Σk=1 βk (xit )+Bi

where

E(yit ) = the expected value for the count of bats at location i and year t

fj(x(t)it ) = nonlinear functions of year such as cubic regression splines

βk(xit ) = linear functions of covariates

Bi = a set of random effects for sites to accommodate the yearly observations 

(similar to a classic repeated measures design with random subject effects)

E(yit) can be scaled to an index of relative abundance by dividing by its maximum, 

giving the fraction of the largest estimated abundance:

E(yit )

max [E(yit )]

The nonlinear functions allow inference regarding the shape of the population 

trajectory, particularly local extrema such as maxima or minima. Terms can be 

eliminated using information-theoretic criteria (e.g., Deviance Information Criterion, 

Akaike’s Information Criterion). If no nonlinear terms are retained by model selection, 

then no extrema may be inferred; if linear terms are reduced to intercept only, no 

annual trend may be inferred (Ingersoll and others 2013). These models can be fit using 

Bayesian methods with WinBUGS, R, or similar software.

9.2.3

Portrait of a hoary bat.

n n
68 A Plan for the North American Bat Monitoring Program (NABat)

The Dail and Madsen (2011) model may also be appropriate for survey data without 

multiple visits within a year. This approach models changes in total abundance over 

time as follows:

N1 = R λ

Nt = ωNt-1 + R γ

where

Nt = the time-specific abundance, dependent on the abundance at the previous time 

step Nt-1

ω = the survival probability

γ = the recruitment rate

R = the number of sample units

λ = the expected value for the initial abundance

This model is an N-mixture model generalized for open populations (Dail and Madsen 

2011) and is implemented in the R library unmarked as the function pcountOpen. As 

with the N-mixture model of Royle (2004), this model can account for heterogeneous 

detection probabilities. The generalized model can accommodate covariate-dependent 

heterogeneity in N, ω, and γ as well, though some inferences are strongly dependent on 

assumptions about the underlying distributions in this approach.

One advantage of these dynamic models is that known stressors, such as proximity to 

wind turbines, climate values, or presence of WNS, can be included as covariates for 

the dynamic or yearly varying effects. Likewise, measurable results of management 

policies, such as installation of gates on caves and mines, can be included. In this 

way, effects of stressors and management practices on local extinction, colonization, 

survival, and recruitment may be estimated.

9.3 Determining Sampling Sufficiency for Occupancy 

 and Hibernacula Counts

9.3.1 Occupancy Estimation

The initial implementation of NABat will include an investigation into survey design 

elements for acoustic methods. Specifically, we will address the following questions:

(1) What are sufficient sample sizes (i.e., number of grid cells) for distribution 

modeling and yearly trend detection?

(2) How many within-season replicates are needed to adequately estimate detection?

(3) Is the planned revisit design (always revisit every site every year) reasonable or will 

it need to be adjusted periodically, resulting in the need for a panel design?

These questions form the central issues for review of the program in the initial 2 to 3 

years of implementation.

McKann and others (2013) analyzed the survey effort necessary to reduce bias in 

estimates derived from multiseason occupancy models. For instance, their work 

indicates that bias in occupancy probability is most dependent upon p, the probability 

of detection. Their work also indicates that for larger values of p, the number of sites 

necessary for unbiased parameter estimation is lower. When p = 0.5, for instance, the 

probability of detecting the species of interest at least once during multiple visits to a 

9.3

9.3.1
CHAPTER 9 Analysis 69

site (psite) is 0.97 after 5 visits. However, if p = 0.1, psite drops to 0.41; to compensate for 

the lower probability of detection, the sample design must increase the number of visits 

from 5 to 40. McKann and others (2013) conclude that if p is low, bias in occupancy 

will be acceptable when psite is ~0.9 and the number of sites exceeds 60; to estimate the 

probability of colonization and extinction with acceptable bias would require >120 sites 

sampled. These results emphasize the need for a sampling design that ensures adequate 

detection of species. Fortunately, detection probability can be increased through 

appropriate study design. McKann and others (2013: 178) wrote:

If efforts can be made to improve detection by, for example, surveying for a 

greater period of time (e.g., 10 min instead of 5 min per visit), surveying during 

ideal weather, choosing an optimal time of day (Williams and Berkson 2004), or 

using experienced observers (Jeffress and others 2011), relative bias of parameter 

estimators will decrease.

Chapters 4 and 5 provide many suggestions on detector deployment that will increase 

species detection and identification for acoustic sampling.

9.3.2 Hibernacula Counts

Other than a few endangered species [e.g., Indiana (Myotis sodalis) and gray (M. 

grisescens) myotis], few roosting species are regularly assessed for population status 

and trends. As such, as more data are contributed to the BPD on a regular basis, it will 

be necessary to determine when enough data have been collected for reliable specieswide inferences.

Leveraging power law relations is one means of determining whether wintering 

populations of hibernating species have been sufficiently surveyed. We expect that 

all hibernating species exhibit a power law relation between the frequency and size 

of colonies for the purposes of thermoregulatory control (Thogmartin and McKann 

2014). Typically, the majority of hibernating bat populations are small, with larger 

populations being relatively rare. Thus, the frequency N and size n of bat populations 

can be characterized by a power law relation. Power laws are often described by a 

cumulative distribution function for N as:

f(n) = bn-α

where

f(n) = in this case, the frequency of hibernacula with n individual bats

b = an unknown constant indicating the intensity of the pattern

α = the rate at which larger populations are progressively less abundant

When logarithms are taken of both sides of the equation, log(n) can be plotted against 

log[f(n)], resulting in a straight line with slope α (fig. 9.1).

Power laws to understand the organization of Indiana myotis populations were 

developed by Thogmartin and McKann (2014). Indiana myotis are currently the best 

surveyed of the eastern North American hibernating species, with populations in 

hibernacula ranging between <400 and ~40,000 bats (more than two orders magnitude 

difference in group size). These populations demonstrate a linear relationship between 

log frequency and log size (fig. 9.1). From this distribution of annual population sizes, 

we see a noticeable dip in the frequency of small populations numbering between 

2 and 32 bats. It is not clear if this is a function of the biology of the species (e.g., 

thermoregulatory benefits of larger groups) or a gap in the survey design (i.e., smaller 

9.3.2

Erecting a gate at the entrance of Hubbards Cave, TN, to

protect bats from

d si ut rbance and van ad msil .
70 A Plan for the North American Bat Monitoring Program (NABat)

hibernacula go unnoticed and are therefore not surveyed). In this example, if the dip is 

attributed to a gap in survey design, it would amount to no more than ~2 percent of the 

total Indiana myotis population being missed by current survey efforts. Whether such 

a small proportion of bats missed from the sampling design warrants increased effort 

needs to be determined. For instance, as WNS passes through populations of this and 

other eastern hibernating species, there is some expectation that bats will demonstrate 

reduced sociality during hibernation (Langwig and others 2012). Thus, there may be 

a need for added effort towards surveying the smaller populations that are currently 

poorly sampled. Decisions of this sort will need to be addressed periodically and 

subsequent sampling adjusted accordingly (see sec. 1.5).

If a power law relation for a species of interest cannot be calculated, it may mean 

that the sample is inadequate to draw robust inferences regarding the species. 

Unfortunately, knowing there are gaps in a survey does not indicate how to alleviate 

those gaps. Formalized exploratory processes would be necessary to amend the current 

set of surveyed populations.

9.4 Incorporation of Legacy and Found Data

NABat will endeavor to include acoustic and count data with appropriate sampling 

methods that were collected outside of the NABat sampling framework (see ch. 3). 

This assumes that field methods compliant with NABat protocols have been used. 

Approaches exist for weighting legacy and found data to incorporate them into the 

master sample structure. For example, existing mobile transects could be clipped to 

the grid to determine sample unit membership and adjust sample weights accordingly. 

However, these weights would then need to be incorporated into the model-based 

analysis, particularly if there are highly variable weights across sample units and these 

weights are related to the latent occupancy state of the associated grid cell. Similarly, 

historical roost counts will be compiled and incorporated where possible into the 

estimation of species status and trends.

Figure 9.1—The relationship between the log frequency of group size and its log size for 

Indiana myotis (Myotis sodalis) in 1996. The slope (alpha) of the relationship varies from year 

to year, but is roughly equal to 3⁄4-power when back transformed. This 3⁄4-power has important 

thermoregulatory implications. Numbers in red are the difference between the predicted and 

observed number of populations. (from Thogmartin and McKann 2014)

9.4
CHAPTER 10 Implementation 71

10. Implementation

10.1 Program Structure

As conceived, NABat is a multinational program requiring broad multiagency support. 

The central coordination and staffing of NABat will be based in the United States, 

with Canadian and Mexican coordinators working with the United States coordinator 

(fig. 10.1). The coordinators will not be responsible for data collection, but instead will 

encourage participation in NABat by States, Provinces, and other agencies; supply the 

States, Provinces, and other agencies with the list of grid cells to be sampled; maintain 

a record of all monitoring programs being conducted as part of NABat; encourage and 

facilitate submission of data to the Bat Population Database (BPD) through interactions 

with participants in NABat; provide guidance to States, Provinces, and other agencies 

on organizing monitoring at various spatial scales; provide guidance to adapt ongoing 

monitoring efforts to conform to NABat protocols and procedures; and provide 

feedback to the U.S. coordinator on the monitoring program.

Figure 10.1—Proposed organizational structure of NABat.

NABat Organizational Structure

US NABat

Coordinator

DB Manager

 Analysts

Admin Support

Training & Tech Support

Canadian NABat

Coordinator

Mexican NABat

Coordinator

Provinces

Federal 

Agencies

Citizen Scientists

NGOs Academics

First 

Nations

States

Federal 

Agencies

Federal 

Agencies

States

Citizen Scientists

Citizen Scientists

NGOs Academics

NGOs Academics

Tribes

US NABat Coordinator

State Coordinator

Federal Agencies State Agencies Tribes NGO’s Academics

10.1
72 A Plan for the North American Bat Monitoring Program (NABat)

10.2 NABat Staffing and Responsibilities

To ensure the success of NABat, several permanent full- and part-time personnel 

are proposed. These include the U.S. coordinator, an administrative assistant, 

a Geographic Information System (GIS) specialist, two statistical analysts, a 

database manager, and a technical and outreach coordinator. The general roles and 

responsibilities of each are described below.

l The U.S. coordinator will be responsible for coordinating NABat across national 

boundaries as well as across State, Federal, tribal, and private boundaries. The U.S. 

coordinator will also be responsible for determining the types of training needed by 

participants and for working with the technical and outreach coordinator to either 

develop that training or find professionals with the required expertise to provide 

the necessary training. The United States coordinator will also be responsible for 

publicizing NABat and encouraging participation in the program by working with 

State and Federal agencies, tribes, nongovernmental organizations (NGOs), and the 

regional bat working groups and assisting the Canadian and Mexican coordinators 

to encourage participation in their respective countries. Interim United States and 

Canadian coordinators were appointed in 2014.

l The database manager will manage the BPD, assist users with data entry and 

retrieval, work with programmers to upgrade the BPD as necessary, work with the 

statistical analyst(s) and GIS specialist to produce reports and other products, and 

create and manage data partnerships.

l The administrative assistant will provide administrative support to the entire 

staff, including maintaining and reconciling budgetary records, arranging travel, 

maintaining a functional office, and maintaining the NABat Web site.

l The GIS specialist will be responsible for providing and updating all GIS layers 

necessary to create the master sample of grid cells and will provide the GIS support 

needed for statistical analysis at various spatial scales. The GIS specialist will also 

create maps for reports and the Web site.

l The statistical analyst(s) will be responsible for analyzing data at various spatial 

and temporal scales and working with the database manager, GIS specialist, and 

coordinator to produce reports and other products. Ideally, there will be two analysts, 

one to work with the colony count data and one to work with the acoustic data.

l The technical and outreach coordinator will provide training on the implementation 

of NABat, including assisting with the master sample of grid cells, providing 

technical guidance and advice on equipment and its use, and conducting training 

via webinars and presentations at meetings. The technical and outreach coordinator 

will also develop training materials specifically designed for distribution to citizen 

scientist participants.

10.3 Roles and Responsibilities of Participating Organizations

Because the master sample of grid cells will be organized by the States and Provinces 

(see ch. 1), it is likely that much of the coordination at the regional level will be 

done by State and Provincial biologists. Thus, the States and Provinces will be 

responsible for working with Federal agencies, tribes and First Nations, NGOs, and 

private landowners to conduct monitoring. Individuals and agencies that participate 

in NABat will be responsible for obtaining the necessary equipment to conduct the 

10.2

10.3

Portrait of a Yuma

myotis.
CHAPTER 10 Implementation 73

monitoring and maintaining the equipment such that it meets performance and data 

quality standards. We anticipate that equipment will be shared among State and 

Federal agencies and other organizations, so there is no expectation that all participants 

will be required to purchase equipment and software. However, participants will be 

responsible for conducting species identification of acoustic files (see ch. 6 for methods 

that can be used to identify bat acoustic files). Due to the large-scale nature of NABat 

and limited resources within agencies, the success of NABat will likely depend on the 

use of citizen scientist volunteers. Thus, State and Provincial coordinators will also 

be responsible for recruiting and training citizen scientists with the assistance of the 

technical and outreach coordinator, providing equipment, ensuring data quality, and 

overseeing data submission to the BPD. NABat will provide standardized datasheets, 

and we strongly encourage their use. Use of these standardized forms will ensure that 

data standards will be met.

Timely and accurate submission of data to the BPD is critical for the success of 

NABat, as these data will be the basis of the status and trend analyses. Thus, State 

and Provincial coordinators will be responsible for ensuring that data collected by all 

participants is submitted to BPD within a timely fashion.

10.4 Potential Role for Nongovernmental Organizations

Nongovernmental organizations such as Bat Conservation International, Organization 

for Bat Conservation, National Speological Society, and Nuisance Wildlife Control 

Operators Association can play an important role in the success of NABat. For 

example, NABat can partner with these organizations to convene workshops and assist 

in providing training on methods used in NABat. These organizations may also be 

a great asset in helping to recruit volunteers from their memberships to participate 

in surveys. Many members of the National Speleological Society already assist with 

hibernaculum surveys and provide valuable information on bat populations within 

caves and mines.

10.5 Timeline

United States and Canadian coordinators were named in 2014 and are performing 

many of their duties. Until NABat can be fully staffed, members of the NABat 

Planning Core Team will continue to act in an advisory role. However, timely 

creation of the administrative assistant, GIS specialist, analyst, database manager, and 

technical and outreach coordinator positions will allow NABat to more readily provide 

necessary support to participating organizations.

Several pilot projects are being initiated in the United States and Canada in 2014 and 

2015, and we expect full on-the-ground implementation of NABat by 2016. Acoustic 

surveys began during summer 2014, and hibernaculum surveys based on NABat 

protocols began in winter 2014–15. Initial data were submitted to the BPD during 

fall 2014. The first status reports of North American bats will be produced in 2016 if 

sufficient staffing is available.

10.6 Available and Needed Resources

NABat will initially be housed at the USGS Fort Collins Science Center (FORT). This 

is the location of the current U.S. coordinator and the BPD. Other U.S. government 

agencies have their monitoring headquarters stationed in Fort Collins (U.S. Fish 

and Wildlife Service Refuge System Inventory and Monitoring Program and 

Environmental Conservation Online System, National Park Service Inventory and 

10.4

10.5

10.6
74 A Plan for the North American Bat Monitoring Program (NABat)

Monitoring Program). The close proximity of other agency monitoring programs may 

encourage collaboration and participation in NABat by these agencies.

The BPD is currently online (https://my.usgs.gov/bpd/) and available for data 

submission. Thus, the infrastructure for this database is in place, and FORT personnel 

are available to maintain and upgrade the system. USGS FORT has committed 

to support these efforts and has agreed to staff the U.S. coordinator position as a 

derivative of the BPD for the foreseeable future. It will be desirable to archive all 

acoustic data collected as part of NABat. However, storage of vast quantities of 

acoustic data (particularly full-spectrum files) will require acquisition of large amounts 

of disk space (many terabytes) or funding for secure data cloud space.

NABat will not provide acoustic detectors, cameras, or other monitoring equipment 

and software. The majority of all such materials will be provided by participating 

agencies and organizations. We recognize that while some organizations may have 

adequate capacity, many will not. NABat staff will help to coordinate equipment 

sharing and, when possible, will help to identify funding opportunities to provide 

States, Provinces, tribes, First Nations, and NGOs with equipment needed to conduct 

surveys.

Gray

myotis hybernating in a cave.
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Appendixes

Appendix A 87

Number of full and partial 10- by 10-km grid cells 

contained in each U.S. State

Appendix B 88

Number of full and partial 10- by 10-km grid cells 

contained in each Canadian Province

Appendix C 89

Sample datasheet for stationary point surveys

Appendix D 90

Sample datasheet for mobile transect surveys

Appendix E 91

Species that can be identified by each of the automatic speciesidentification software programs (as of March 25, 2015)

Appendix F 92

Sample datasheet for internal winter hibernaculum surveys

Appendix G 93

Sample datasheet for internal summer maternity colony surveys

Appendix H 94

Sample datasheet for emergence count data

Appendix I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .95

The NABat Data Management Plan
Appendixes 87

Appendix A

Number of full and partial 10- by 10-km grid cells contained 

in each U.S. State

State Number of full cells Number of partial cells

Alabama 1233 206

Alaska 13318 3550

Arizona 2824 273

Arkansas 1271 207

California 3839 526

Colorado 2583 228

Connecticut 97 64

Delaware 28 52

District of Columbia 0 8

Florida 1201 535

Georgia 1401 236

Idaho 2002 329

Illinois 1342 231

Indiana 846 180

Iowa 1367 193

Kansas 2044 206

Kentucky 930 237

Louisiana 1050 366

Maine 721 256

Maryland 114 260

Massachusetts 146 146

Michigan 1276 501

Minnesota 2046 298

Mississippi 1125 234

Missouri 1673 251

Montana 3637 343

Nebraska 1888 225

Nevada 2712 291

New Hampshire 189 100

New Jersey 146 107

New Mexico 3021 263

New York 1109 316

North Carolina 1085 382

North Dakota 1735 192

Ohio 978 193

Oklahoma 1681 264

Oregon 2362 297

Pennsylvania 1085 180

Rhode Island 11 39

South Carolina 699 194

South Dakota 1882 218

Tennessee 984 218

Texas 6563 655

Utah 2086 225

Vermont 203 100

Virginia 854 358

Washington 1566 342

West Virginia 525 208

Wisconsin 1336 262

Wyoming 2421 224
88 A Plan for the North American Bat Monitoring Program (NABat)

Appendix B

Number of full and partial 10- by 10-km grid cells contained in 

each Canadian Province

Province Number of full cells Number of partial cells

Alberta 6422 437

British Columbia 8703 1444

Manitoba 6287 450

New Brunswick 632 194

Newfoundland & Labrador 3379 1463

Northwest Territories 12670 1748

Nova Scotia 431 281

Nunavut 17108 7081

Ontario 9449 1033

Prince Edward Island 28 67

Quebec 14271 1848

Saskatchewan 6324 397

Yukon Territory 4574 537
Appendixes 89

Appendix C

Sample datasheet for stationary point surveys MethodsNumber of detectors deployed:

Photos submitted? Y N

Mobile Transect Conducted? Y N

Surveyor: Grid cell ID:

Country: State/Province:

County (if applicable): Map datum:

Sunset: Sunrise:

Start date moon phase: End date moon phase:

Variable Detector #1 Detector #2 Detector #3 Detector #4

Location – Latitude

Location – Longitude

Date recording started

Date recording stopped1

Time recording started

Time recording stopped

Bat detector manufacturer and model

Microphone type

Recording mode

Trigger window length

Maximum file length

Microphone height (m)

Weatherproofing type

Calibration method

Habitat Type2

Feature Sampled3

General Description of deployment4

Gains

Frequency Band Filters

High/Low Nightly Temp (°C)5

Night 1

Night 2

Night 3

Night 4

High/Low Nightly RH (%)5

Night 1

Night 2

Night 3

Night 4

High/Low Wind (km/h)5

Night 1

Night 2

Night 3

Night 4

Significant weather events6

Night 1

Night 2

Night 3

Night 4

1 Note: recording may stop early due to equipment failure. Please indicate the day/time a detector stopped if this occurred.

2 Land classes: Urban, agriculture, rangeland, forest, water, wetland, barren

3 Examples: Cattle water tank, pond, stream, forest road, forest trail, mature forest, wildlife opening.

4 Examples: Distance to forest edge, distance to hedgerow, parallel or perpendicular to flyway

5 May be obtained from local weather recording station or on-site recorders.

6 Examples: Strong thunderstorm on a particular night, sudden cold-snap, windstorm

Stationary Point Acoustic Monitoring Datasheet
90 A Plan for the North American Bat Monitoring Program (NABat)

Appendix D

Sample datasheet for mobile transect surveys Location

Surveyor: Grid cell ID:

Country: State/Province:

County (if applicable): Map datum:

Start latitude: End latitude:

Start longitude: End longitude:

Conditions

Sunset: Cloud cover: 

Moon phase: Time moon became visible: 

Start temp. (°C): End temp. (°C):

Start RH (%): End RH (%):

Start wind speed (km/h): End wind speed (km/h):

Methods

Detector Manufacturer and Model:

Microphone type: 

Microphone placement: 

Recording mode: 

Trigger window length: 

File length: 

Digipot sensitivity from AnabatTM Equalizer: 

Gain settings (if applicable): 

Coments

Habitats crossed by transect or general habitat description: 

Additional comments about transect or conditions:

Date: Start time: End time:

Mobile Transect Acoustic Monitoring Datasheet
Appendixes 91

Appendix E

Species that can be identified by each of the automatic 

species-identification software programs (as of March 25, 2015)

Species BCID Echoclass Kaleidoscope® SonobatTM

Antrozous pallidus — — X X

Artibeus jamaicensis — — — —

Choeronycteris mexicana — — — —

Corynorhinus rafinesquii X — — X

Corynorhinus townsendii X — X X

Eptesicus fuscus X X X X

Euderma maculatum — — X X

Eumops floridanus X — X X

Eumops perotis — — X X

Eumops underwoodii — — — —

Idionycteris phyllotis — — — —

Lasionycteris noctivagans X X X X

Lasiurus blossevillii — — X X

Lasiurus borealis X X X X

Lasiurus cinereus X X X X

Lasiurus ega — — — —

Lasiurus intermedius — — — X

Lasiurus seminolus — — — X1

Lasiurus xanthinus — — — —

Leptonycteris nivalis — — — —

Leptonycteris yerbabuenae — — — —

Macrotus californicus — — — —

Molossus molossus — — — —

Mormoops megalphylla — — — —

Myotis auriculus — — — —

Myotis austroriparius X X — —

Myotis californicus — — X X

Myotis ciliolabrum — — X X

Myotis evotis — — X X

Myotis grisescens X X X X

Myotis keenii — — — —

Myotis leibii X X X X

Myotis lucifugus X X X X

Myotis melanorhinus — — — X2

Myotis occultus — — — —

Myotis septentrionalis X X X X

Myotis sodalis X X X X

Myotis thysanodes — — X X

Myotis velifer — — — —

Myotis volans — — X X

Myotis yumanensis — — X X

Nycticeius humeralis X X X X

Nyctinomops femorosaccus — — — —

Nyctinomops macrotis — — — —

Parastrellus hesperus X — X X

Perimyotis subflavus X X X X

Tadarida brasiliensis — — X X

X = Can be identified, — = Cannot be identified.

BCID = Bat Call Identification.

1SonoBat classifiers that overlap the range for Lasiurus seminolus include recordings from these species. However, L. seminolus

and L. borealis produce acoustically indistinguishable calls. Where these species both occur, a file labeled as LASE or LABO 

should be considered as either species (Personal communication. 2015. J.M. Szewczak, Professor, Humboldt State University, 1 

Harpst Street, Arcata, CA 95521).

2 Myotis ciliolabrum and M. melanorhinus produce acoustically indistinguishable calls. SonoBatTM documentation advises users, 

“Consider a classification result for ‘MYCI’ as a classification for MYCI within its defined range and as MYME within its defined range. 

Where these species both occur, irrefutable species confirmation requires capture, and possibly molecular genetics to disambiguate.” 

(Personal communication. 2015. J.M. Szewczak, Professor, Humboldt State University, 1 Harpst Street, Arcata, CA 95521).
92 A Plan for the North American Bat Monitoring Program (NABat) Appendix F Sample datasheet for internal winter hibernaculum surveys BAT SPECIES PRESENT Species Section (name or describe location of bats within site) Estimated number of live/dead bats Upper/ lower range estimates Number of clusters Estimated size of clusters (number of bats or measured size) Colony Type Height of bats in structure Distance between observers and bats Number captured Number banded/ PIT-tagged Number collected TOTAL Other cave life (identify species and nature of observation and sign): Species present but not counted: Comments: WNS status: WNS comments: WNS samples collected: (type/destination/results):

1For example, building, bridge, tree cavity, talus slope, etc.; if tree roost, provide species and status (e.g., live, dead, live-damaged) and bark coverage2For example, gates, locks, fences, etc.

Date:

SurveyorsName Years of experienceMethodsSurvey method(s): Photos submitted? Y N Type of photographic equipment:

Digital storage location:

ConditionsStart time: End time: Cloud cover start: Cloud cover end: Moonrise/moonset: Sunrise/sunset: Moon phase:

Location

Site name: Site owner:Land unit: Grid cell ID: Portion of site surveyedSite type:1Country: State/Province: County (if applicable):Latitude: Longitude: Map datum: Elevation:Habitat type:

Roost info

Roost size (dimensions): Number of other roosts within 10 km:Roost temp. (°C): Roost RH (%): Presence of water? Y N

Outside temp. start (°C): Outside temp. end (°C):Roost protection:2 Signs of disturbance:Special roost survey requirements:

Internal Winter Surveys Datasheet
Appendixes 93

Appendix GSample datasheet for internal summer maternity colony surveys

BAT SPECIES PRESENT

Species

Section 

(name or 

describe 

location of 

bats within 

site)

Estimated 

number 

of adult/

juvenile 

bats

Upper/

lower 

range 

estimates

Number of 

clusters

Estimated 

size of 

clusters 

(number 

of bats or 

measured 

size)

Colony 

Type

Height of 

bats in 

structure

Distance 

between 

observers 

and bats

Number 

captured

Number 

banded/

PIT-tagged

Number 

collected

TOTALOther species in roost (identify species and nature of observation and sign):Species present but not counted:Comments:

1For example, building, bridge, tree cavity, talus slope, etc.; if tree roost, provide species and status (e.g., live, dead, live-damaged) and bark coverage2For example, gates, locks, fences, etc.

Date:

Location

Site name: Site owner:Land unit: Grid cell ID: Portion of site surveyedSite type:1Country: State/Province: County (if applicable):Latitude: Longitude: Map datum: Elevation:Habitat type:

Roost info

Roost size (dimensions): Number of other roosts within 10 km:Roost temp. (°C): Roost RH (%): Presence of water? Y N

Outside temp. start (°C): Outside temp. end (°C):Roost protection:2 Signs of disturbance:Special roost survey requirements:

Internal Summer Surveys Datasheet

SurveyorsName Years of experienceMethodsSurvey method(s): Photos submitted? Y N Type of photographic equipment:

Digital storage location:

ConditionsStart time: End time: Cloud cover start: Cloud cover end: Moonrise/moonset: Sunrise/sunset: Moon phase:
94 A Plan for the North American Bat Monitoring Program (NABat)

Appendix H

Sample datasheet for emergence count data

Date: SurveyorsName Years of experience Methods

Survey method(s):

Photos submitted? Y N

Type of photographic equipment:

Digital storage location:

Conditions

Start time: End time:

Cloud cover start: Cloud cover end:

Moonrise/moonset: Sunrise/sunset:

Moon phase: Time moon visible:

Temp. start (°C): Temp. end (°C):

RH start (%): RH end (%:)

Precipitation start: Precipitation end:

Wind speed start: Wind speed end:

Location

Site name: Site owner:

Land unit: Grid cell ID: Number of entrances monitored/not monitored:

Site type:1

Country: State/Province: County (if applicable):

Latitude: Longitude: Map datum: Elevation:

Habitat type:

Roost info

Roost size (dimensions): Number of other roosts within 10 km:

Roost temp.: Roost RH (%): Outside temp. start: Outside temp. end:

Presence of water? Y N Number of other roosts within 10 km:

Roost protection:2 Signs of disturbance:

Special roost survey requirements:

Species present in roost:

1For example, building, bridge, tree cavity, talus slope, etc.; if tree roost, provide species and status (e.g., live, dead,

live-damaged) and bark coverage.

2For example, gates, locks, fences, etc.

Relative abundance of each species:

Guano: none scattered abundant large mounds

Time first bat emerged: End time:

Corynorhinus Other

TIME OUT IN NET OUT IN NET

2030

2035

2040

2045

TOTAL

Other animals seen:

Comments:

Emergence Count Datasheet
Appendixes 95

Appendix I

The NABat Data Management Plan

1. Types of Data Produced and Products

The data gathered during the NABat program will be collected by partners such 

as States or Provinces, Federal agencies, tribes, academics, nongovernmental 

organizations, and citizen scientists. The data will be counts of bats at hibernacula 

and maternity colonies and acoustic data from mobile transects and stationary point 

surveys. Data collected will be site-specific information such as location, grid cell 

number, date, times, environmental conditions, variables related to bat detectors 

and their settings, species identification information, years of experience identifying 

species or counting bats, and the metadata associated with acoustic surveys (see tables 

8.1. 8.2, and 8.3). The Bat Population Data Project and its primary application, the 

Bat Population Database (BPD), will be the primary repository of these data (http://

my.usgs.gov/bpd). A detailed data dictionary will be provided to the data partners 

and made accessible on the BPD website. Data will be uploaded and entered by data 

partners, and a dedicated database manager(s) will be responsible for assisting data 

partners with their data management. The acoustic files collected during mobile 

transects and stationary point surveys will also be submitted to the NABat database 

manager(s). The database manager(s) will be responsible for ensuring quality 

assurance and control (QA/QC) procedures are completed for all data submitted. 

QA/QC of acoustic species identifications will be conducted on a regular basis by 

randomly selecting 10 to 20 percent of acoustic identifications and verifying the 

identifications using a combination of automatic identification software programs and 

manual qualitative verifications. 

2. Data Storage and Preservation

The NABat Program is intended to be a long-term monitoring program. Therefore, the 

data collected during the program will be archived and preserved for perpetuity using 

several methods. The U.S. Geological Survey (USGS) is committed to providing an 

“Integrated Information Environment” for USGS personnel and their external partners 

to facilitate the production of data that can be shared within the scientific community 

and the public, and USGS has a commitment to storing data for the long term. An 

online resource called myUSGS is a suite of content management and collaboration 

tools for USGS science teams and their partners. The myUSGS platform consists of a 

growing set of public Web sites that share content within this integrated information 

environment as well as a wide variety of online Intranet/extranet communities. U.S. 

Department of the Interior (DOI) users as well as non-DOI users are able to access 

myUSGS. 

Because the BPD uses the myUSGS enterprise hosting services, all data are backed 

up nightly, and permanent data archives are prepared monthly. The database 

manager(s) will be responsible for ensuring that the BPD is regularly backed up. 

Acoustic files collected during mobile transects and stationary point surveys will 

not be stored within the BPD application, but will be stored on a mini-server at the 

USGS Fort Collins Science Center (FORT) in the short term. The use of cloud-based 

services will be investigated in the future to store, archive, and back up the acoustic 

files. External, secondary data repositories will be used to share specific subsets of 

NABat data with other data distribution and storage systems, increasing the data 

backup capability to multiple locations.
96 A Plan for the North American Bat Monitoring Program (NABat)

3. Data Formats and Metadata

Data will be collected using the count-based and acoustic protocols described in 

chapters 4, 5, and 7. Standard datasheets will be provided to the NABat participants 

in Microsoft Excel® spreadsheet format (see appendixes C, D, F, G, and H). FORT 

plans to develop applications for automatically collecting data using handheld devices 

such as tablets and smartphones. 

When a new data project is created in the BPD, the database manager(s) 

simultaneously create a complete project-level metadata record using the Federal 

Geographic Data Committee (FGDC) standards. Data partners will be able to export 

their FGDC-compliant metadata from the BPD data project interface to an XML 

file that they can deliver to their agencies’ preferred or required metadata repository. 

Geographic coordinates will be stored and exported in latitude and longitude and will 

be expressed as decimal fractions of degrees. Metadata for legacy data will also be 

created and accessible to data partners and participants. 

4. Data Dissemination and Policies for Data Sharing and Public Access

Data collected during the NABat Program will always be accessible to NABat team 

members [coordinators, database manager(s), statisticians, etc.]. These data will not 

be accessible to the public until the data are summarized and analyzed. Results from 

analyses will be distributed to managers and policymakers in periodic “The State of 

North America’s Bats” publications. In these reports, the acoustic and colony-count 

data will be analyzed for trends in occupancy and abundance. After this report is peerreviewed and released, data sharing with the public will be determined by the original 

data partnership agreements or data owner. 
Glossary 97

Glossary

Auto-level—The ability of a bat detector to adjust the basement noise level based on 

ambient noise. Thus, a bat detector that auto-levels adjusts its sensitivity according to 

the current noise level.

Availability—Animals are present at the time of the survey and thus available to be 

counted, if detected.

Bat call/pulse—A sound produced by an echolocating bat.

Bat pass—A sequence of echolocation calls separated from other bat calls by 1 to 2 

seconds.

Clutter—Obstacles in the flight environment such as tree branches, leaves, water 

surface, or other bats.

Colony counts—Counts of bats in roosts including both hibernacula and maternity/

bachelor roosts. Counts may be either internal or external.

Data partner—Owners of data that are submitted to the Bat Population Database 

(BPD). Owners agree to allow NABat to use data submitted to the BPD but have 

control over how those data are displayed on the BPD Web site and who has access to 

those data.

Deflector plate—A smooth surface that reflects the signal of a bat into a downwardfacing microphone. A deflector plate is one method of weatherproofing and protects the 

microphone from precipitation. The smooth surface enables a near-perfect reflection of 

the sound; the deflector plate is typically oriented 45° to the microphone.

Detectability—The probability that animals are observed or recorded during the 

survey given that they are present.

Design weight—In statistical sampling, the sample design weight is the inverse of 

the sample inclusion probability. The design weight formally describes the amount of 

inferential information of each sample unit. For example, when sample sizes are small, 

weights become larger because each sample unit represents a larger proportion of the 

sampling frame (i.e., it “carries more weight”). 

Found data—Data provided by contributors collected concurrent to NABat but 

outside of the formal NABat sampling design. Legacy data differs from found data in 

that it predates the implementation of NABat. 

Frequency division—A method of changing ultrasound into audible sound by 

dividing the frequency of the incoming signal by a set ratio (i.e., the division ratio), 

thus lowering its frequency.
98 A Plan for the North American Bat Monitoring Program (NABat)

Full spectrum—A method of recording bat ultrasound that records the full spectral 

composition of a signal, usually through Fourier analysis. This is displayed as a 

spectrogram that displays frequency and amplitude of a signal over time. Fullspectrum recordings store large amounts of data because sampling rates need to be 

twice that of the highest frequency that is to be recorded. Generally, a file generated by 

a full-spectrum detector will be approximately 1000 times as large as a zero-crossing 

file of the same bat pass.

Legacy data—Data provided by contributors following sampling designs and 

protocols developed prior to the development of NABat.

Focal demographic studies—Intensive studies of particular bat colonies to gather 

detailed demographic data such as survival, reproductive rates, and population growth 

rates.

Generalized random-tessellation stratified (GRTS) design—A probabilistic 

sampling design that provides a spatially-balanced ordered list of sample units (10-km 

by 10-km grid cells) with the unique property that any ordered subset of the list will 

also be spatially-balanced. 

Inclusion probability—In statistical sampling, the inclusion probability defines 

the likelihood of selecting a specific sample unit (e.g., a 10-km by 10-km grid cell) 

from a random sampling draw. Inclusion probabilities are very small (~0) when the 

total sample size is very small relative to the size of the sampling frame. Inclusion 

probabilities are 1 (100 percent certainty of being included) when a nonprobability 

sampling scheme (e.g., purposive sampling) is used because only those units chosen for 

survey are included for consideration. Inclusion probabilities or their inverse, design 

weights, are used in analyses to properly scale the relative importance or contributions 

of data from each sample unit. 

Maximum emergence—The maximum number of bats that leave a roost during an 

observation period.

Master sample—The comprehensive list of all sample units within the sampling 

frame that is randomized and spatially-balanced following a GRTS draw. A single 

master sample will be produced for each sampling frame (United States, Canada, and 

Mexico), allowing all contributing partners (e.g., nations, States, and Provinces) to 

sample from their portion of the master sample list.

Mobile acoustic transect survey—An acoustic survey conducted with a bat detector 

or microphone mounted on the roof of a car that is driven at 32 km/h along a predetermined route for 25 to 48 km. Routes are driven twice per summer (preferably 

within one week) and revisited yearly.

Microphone sensitivity—The volume of sound that a microphone can record for a 

given signal; i.e., the amplitude (loudness) of the sound that the microphone can pick 

up.

Multiple observer methods—The use of two or more observers, observer occasions, 

or techniques to estimate the number of bats in a colony.
Glossary 99

Noise floor—The sum of all the noise sources and unwanted signals (i.e., anything 

other than a bat call).

Nonprobability sample—A statistical method of sampling from a definable domain 

that does not use randomization and does not allow for sample units (e.g., 10-km by 

10-km grid cells) to be assigned inclusion probabilities. Nonprobability samples are at 

much greater risk than probability samples of providing data that are not representative 

of the target population. 

Probability sample—A statistical method of sampling from a defined domain, 

typically described by a sampling frame, that uses a randomization process to select 

sample units for survey with each unit having a known selection probability. Simple 

random sampling is a common equal-probability sampling strategy. The GRTS 

algorithm provides for a more complex, but flexible, way to draw equal- or unequalprobability samples from a frame of sample units. 

Pulse shape—The pattern of frequency change over time of a bat call, as visualized in 

a frequency-versus-time sonogram. For example, bat calls described as “steep” sweep 

through a broad range of frequencies in a short time.

Sampling frame—A sampling frame is a statistical tool for defining the population 

from which a survey sample is drawn. Sampling frames are typically finite (e.g., a 

list of lakes for water quality study), but can also be infinite (e.g., the infinite number 

of points within a polygon of interest on a map). The grid-based sampling frame of 

NABat is finite, allowing for the entire set of all 10- by 10-km grid cell sample units to 

be organized into a spatially balanced list and assigned inclusion probabilities. 

Sample unit—The individual members of a statistically defined population that is 

being sampled. For NABat, sample units are the 10 km x 10 km grid cells from the 

grid-based sampling frame. 

Stationary point surveys—Acoustic surveys conducted at a spot on the landscape for 

four nights during a season and revisited every year.

Trigger window—The length of time a bat detector will continue to record after being 

triggered by a bat call if no additional calls are detected. Also referred to as “Idle 

Setting” or “Max TBC” in some bat detector systems.

Zero-cross—A method of recording bat ultrasound that provides information about 

the frequency and timing of bat pulses but does not retain amplitude data of the 

original waveform. The zero-cross system counts the number of times an incoming 

signal crosses the “zero line” and measures the time associated with each complete 

cycle to determine frequencies. Bat recordings stored digitally through this method 

require approximately 1/1000th of the digital memory that a full-spectrum recording of 

the same sound requires.
100 A Plan for the North American Bat Monitoring Program (NABat)

Susan C. Loeb

Research Ecologist

U.S. Department of Agriculture 

Forest Service

Southern Research Station 

237 Lehotsky Hall, Clemson University

Clemson, SC 29634

Thomas J. Rodhouse

Ecologist

National Park Service

Upper Columbia Basin Network

63095 Deschutes Market Road

Bend, OR 97701

Laura E. Ellison

Ecologist

U.S. Geological Survey 

Fort Collins Science Center

2150 Centre Ave., Bldg. C

Fort Collins, CO 80526

Cori L. Lausen

Research Biologist, Bat Specialist

Wildlife Conservation Society Canada

Kaslo, BC V0G 1M0

Jonathan D. Reichard

Wildlife Biologist

U.S. Fish and Wildlife Service

300 Westgate Center Drive

Hadley, MA 01035

Kathryn M. Irvine

Statistician

U.S. Geological Survey

Northern Rocky Mountain Science Center

2327 University Way, Suite 2

Bozeman, MT 59715

Thomas E. Ingersoll

Computational Biologist

National Institute for Mathematical 

and Biological Synthesis

Belcamp, MD 21017

Jeremy T. H. Coleman

Wildlife Biologist

U.S. Fish and Wildlife Service

300 Westgate Center Drive

Hadley, MA 01035

Wayne E. Thogmartin 

Quantitative Ecologist

U.S. Geological Survey 

Upper Midwest Environmental

 Sciences Center

2630 Fanta Reed Road

La Crosse, WI 54603

John R. Sauer

Research Wildlife Biologist

U.S. Geological Survey 

Patuxent Wildlife Research Center

12100 Beech Forest Road

Laurel, MD 20708

Charles M. Francis 

Manager, Bird Population Monitoring

Canadian Wildlife Service

Environment Canada

1125 Colonel By

Ottawa, ON K1A 0H3

Mylea L. Bayless 

Senior Director

United States/Canada Conservation

Bat Conservation International

P.O. Box 162603

Austin, TX 78716

Thomas R. Stanley

Research Wildlife Biologist

U.S. Geological Survey

Fort Collins Science Center

2150 Centre Ave., Bldg. C

Fort Collins, CO 80526

Douglas H. Johnson

Research Statistician and Senior Scientist

U.S. Geological Survey 

Northern Prairie Wildlife Research Center

2003 Upper Buford Circle, Suite 135

University of Minnesota

St. Paul, MN 55108

Author Information
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or go to www.srs.fs.usda.gov/pubeval

Loeb, S.C.; Rodhouse, T.J.; Ellison, L.E.; Lausen, C.L. [and others]. 2015. A 

plan for the North American Bat Monitoring Program (NABat). Gen. Tech. 

Rep. SRS-208. Asheville, NC: U.S. Department of Agriculture Forest Service, 

Southern Research Station. 100 p.

The purpose of the North American Bat Monitoring Program (NABat) is to create 

a continent-wide program to monitor bats at local to rangewide scales that will 

provide reliable data to promote effective conservation decision making and the 

long-term viability of bat populations across the continent. This is an international, 

multiagency program. Four approaches will be used to gather monitoring data to 

assess changes in bat distributions and abundances: winter hibernaculum counts, 

maternity colony counts, mobile acoustic surveys along road transects, and acoustic 

surveys at stationary points. These monitoring approaches are described along 

with methods for identifying species recorded by acoustic detectors. Other chapters 

describe the sampling design, the database management system (Bat Population 

Database), and statistical approaches that can be used to analyze data collected 

through this program. 

Keywords: Acoustic surveys, bat detectors, bats, chiroptera, climate change, 

hibernaculum counts, monitoring, occupancy models, population trends, white-nose 

syndrome.
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