Development of Protocols 

To Inventory or Monitor 

Wildlife, Fish, or Rare Plants

United States 

Department 

of Agriculture

Forest Service

Gen. Tech.

Report WO-72

June 2006
Development of Protocols 

To Inventory or Monitor 

Wildlife, Fish, or Rare Plants

By

David Vesely

Brenda C. McComb

Christina D. Vojta

Lowell H. Suring

Juraj Halaj

Richard S. Holthausen

Benjamin Zuckerberg

Patricia M. Manley

United States 

Department 

of Agriculture

Forest Service

Gen. Tech.

Report WO-72

June 2006
The proper citation for this document is as follows:

Vesely, D.; McComb, B.C.; Vojta, C.D.; Suring, L.H.; Halaj, J.; Holthausen, R.S.; 

Zuckerberg, B.; Manley, P.M. 2006. Development of Protocols To Inventory or Monitor 

Wildlife, Fish, or Rare Plants. Gen. Tech. Rep. WO-72. Washington, DC: U.S. Department 

of Agriculture, Forest Service. 100 p.

Cover photos. Monitoring wildlife, rare plants, and their habitats is conveyed through 

three photos: (1) Peromyscus maniculatus, on Pesola scale (photo credit: Dean E. 

Pearson); (2) western prairie fringed orchid (Platanthera praeclara) (photo credit: 

Carolyn Hull Sieg); and (3) measuring a ponderosa pine (Pinus ponderosa) log in Idaho 

(photo credit: Victoria A. Saab).

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(TDD). USDA is an equal opportunity provider and employer.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants iii

Acknowledgments

We sincerely thank Jeff Beck, Curt Flather, and Michael Schwartz for helping to write 

portions of this technical guide and Rudy King for providing an extensive review of the 

statistical principles addressed in the guide. We also thank the following colleagues for 

reviewing earlier drafts of the technical guide: Jeff Beck, Brad Compton, Margaret Griep,

Tim Hardin, Greg Hayward, Kim Mellen, Sylvia Mori, Maile Neel, Rob Pabst, and Fred 

Samson.
iv Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants v

Authors

David Vesely

Pacific Wildlife Research

P.O. Box 1061

Corvallis, OR 97330

Brenda C. McComb

Department of Natural 

Resources Conservation

216 Holdsworth Hall

University of Massachusetts

Amherst, MA 01003 

Christina D. Vojta

USDA Forest Service

Terrestrial Wildlife Ecology Unit

Forestry Sciences Laboratory

2500 S. Pine Knoll

Flagstaff, AZ 86004

Lowell H. Suring

USDA Forest Service

Terrestrial Wildlife Ecology Unit

Aquatic Sciences Laboratory

322 East Front Street, Suite 401

Boise, ID 83702

Juraj Halaj

Cascadien, Inc.

1903 NW Lantana Drive

Corvallis, OR 97330–1016

Richard S. Holthausen

USDA Forest Service

Terrestrial Wildlife Ecology Unit

Forestry Sciences Laboratory

2500 S. Pine Knoll

Flagstaff, AZ 86004

Benjamin Zuckerberg

Department of Environmental 

and Forest Biology

College of Environmental 

Science and Forestry

State University of New York

244A Illick Hall

1 Forestry Drive

Syracuse, NY 13210

Patricia M. Manley

USDA Forest Service

Wildlife, Fish, and 

Watershed Research

Rosslyn Plaza, Building C 

1601 N. Kent Street 

Arlington, VA 22209
vi Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants vii

Contents

Acknowledgments......................................................................................iii

Authors ...........................................................................................................v

Contents ...................................................................................................... vii

Chapter 1. Overview.................................................................................1-1

1.0 Overview and Purpose....................................................................................... 1-1

1.1 Background and Business Needs....................................................................... 1-2

1.2 Key Concepts..................................................................................................... 1-3

1.3 Roles and Responsibilities................................................................................. 1-5

1.3.1 National Responsibilities..................................................................... 1-5

1.3.2 Regional Responsibilities.................................................................... 1-5

1.3.3 Forest and Grassland Responsibilities................................................. 1-6

1.4 Relationship to Other Federal Inventory and Monitoring Programs................. 1-6

1.4.1 Forest Service Programs...................................................................... 1-7

1.4.2 Programs in Other Federal Agencies................................................... 1-7

1.5 Quality Control and Assurance .......................................................................... 1-7

1.6 Change Management.......................................................................................... 1-8

Chapter 2. Specific Inventory and Monitoring Strategies..............2-1

2.0 Objective ............................................................................................................ 2-1

2.1 Planning and Design .......................................................................................... 2-2

2.1.1 Species’ Life History and Conceptual Model...................................... 2-2

2.1.2 Selected Measures of Population and Habitat..................................... 2-3

2.1.3 Sampling Design ................................................................................. 2-4

2.1.4 Pilot Studies......................................................................................... 2-5

2.1.5 Prospective Power Analysis................................................................. 2-6

2.2 Data Collection .................................................................................................. 2-6

2.2.1 Data Collection Methods..................................................................... 2-6

2.2.2 Personnel Qualifications and Training................................................. 2-8

2.2.3 Quality Control and Assurance............................................................ 2-8

2.2.4 Data Forms .......................................................................................... 2-9

2.2.5 Logistics .............................................................................................. 2-9

2.3 Data Storage.....................................................................................................2-10

2.3.1 Data Cleaning Methods.....................................................................2-10

2.3.2 Database Structure.............................................................................2-11

2.3.3 Metadata Requirements.....................................................................2-11
viii Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

2.4 Data Analysis ...................................................................................................2-11

2.4.1 Analysis, Synthesis, and Interpretation .............................................2-11

2.4.2 Analysis Tools ...................................................................................2-12

2.5 Reporting..........................................................................................................2-12

2.5.1 Expected Reports...............................................................................2-12

2.5.2 Reporting Schedule ...........................................................................2-13

2.6 List of Preparers...............................................................................................2-13

2.7 Literature Cited ................................................................................................2-13

2.8 Appendixes.......................................................................................................2-13

Chapter 3. Further Considerations in Developing 

Inventory and Monitoring Protocols ....................................................3-1

3.0 Stating the Objective .......................................................................................... 3-1

3.1 Considerations for Planning and Design............................................................ 3-2

3.1.1 Conceptual Model ............................................................................... 3-2

3.1.2 Selected Measures of Population and Habitat..................................... 3-3

3.1.3 Developing a Sampling Design........................................................... 3-9

3.2 Data Collection: Biological Study Ethics ........................................................3-16

3.3 Data Storage: Metadata Purpose and Standards ..............................................3-17

3.4 Analytical Methods..........................................................................................3-18

3.4.1 Data Visualization and Exploratory Data Analysis...........................3-19

3.4.2 Basic Assumptions of Parametric Models.........................................3-22

3.4.3 Possible Remedies if Parametric Assumptions Are Violated.............3-24

3.4.4 Statistical Distributions of Plant and Animal Population Data .........3-27

3.4.5 Analysis Models and Methods..........................................................3-28

3.4.6 Interpreting the Analysis ...................................................................3-43

3.4.7 Assessment of Meeting Management Goals.....................................3-43

3.5 Reporting..........................................................................................................3-44

Appendix A. Literature Cited.................................................................A-1

Appendix B. Glossary..............................................................................B-1

Appendix C. References.........................................................................C-1

Aquatic Habitat Monitoring.....................................................................................C-1

Fish and Aquatic Amphibian Populations................................................................C-2

Rare Plant Populations.............................................................................................C-2

Terrestrial Habitat Monitoring .................................................................................C-3

Wildlife Populations.................................................................................................C-4

Biological Research Ethics ......................................................................................C-6

Statistical Guidance and Software Tools..................................................................C-7
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 1-1

Chapter 1. Overview

Suggested Outline for 

WFRP I&M Protocols

Chapter 1. Overview

1.0 Overview and Purpose

1.1 Background and Business 

Needs

1.2 Key Concepts

1.3 Roles and Responsibilities 

1.4 Relationships to Other 

Federal Inventory and 

Monitoring Programs

1.5 Quality Control and 

Assurance

1.6 Change Management

Chapter 2. Specific Inventory 

and Monitoring Strategies

2.0 Objective

2.1 Planning and Design

2.2 Data Collection

2.3 Data Storage

2.4 Data Analysis

2.5 Reporting

2.6 List of Preparers

Appendixes

Literature Cited

Glossary

References

Chapters 1 and 2 use the Forest Service technical 

guide format to describe the expected content of 

WFRP inventory or monitoring technical guides (see 

the box entitled Suggested Outline for WFRP I&M 

Protocols). Chapter 3 provides additional supporting 

material relevant to the design of I&M projects and 

describes various data analysis approaches to give 

protocol developers a starting point for their own 

literature reviews for designing I&M protocols. The 

information in chapter 3 is also intended to provide 

a basis for early consultations with statisticians 

familiar with the design of biological investigations 

and subsequent analysis of data. We urge all 

protocol developers to enlist the assistance of such 

statisticians early in the process and to keep them 

involved throughout the process. This publication is 

not intended to be a comprehensive guide to these 

topics. Numerous other resources exist to serve those 

roles (e.g., Nielsen and Johnson 1983, Bibby et al. 

1992, Heyer et al. 1994, Wilson et al. 1996, Elzinga 

et al. 2001, Thompson et al. 1998).

1

 Terms indicated in bold typeface are defined in the glossary in appendix B.

1.0 Overview and Purpose

The purpose of this technical guide (hereafter referred to as the Species Protocol 

Technical Guide) is to provide guidelines for developing inventory1 and monitoring 

(I&M) protocols for wildlife, fish, and rare plants (WFRP) using the U.S. Department of 

Agriculture (USDA) Forest Service technical guide format. In particular, this publication 

will accomplish the following:

•	 Facilitate the development of WFRP I&M protocols for species and groups of 

species at national and regional levels.

•	 Provide expectations for presenting the protocols in Forest Service technical 

guide format.

•	 Provide technical information on sampling

designs, measure selection, and analysis tools

that will aid in designing specific I&M protocols.
1-2 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

The major headings of chapters 1 and 2 provide the format for subsequent WFRP 

I&M technical guides and are mandatory for development of any Forest Service I&M 

technical guide. Some major headings include recommended subheadings. As we present 

each subheading, we describe why it is recommended and give suggestions for content. 

For example, the Planning and Design section addresses the importance of clearly 

articulating the inventory or monitoring questions and gives examples of the types of 

questions likely to be addressed in subsequent protocols. It is intended that Forest Service 

sponsored teams will use this guide for preparing inventory and/or monitoring plans at 

national, regional, and local scales for species and their habitats.

1.1 Background and Business Needs

This section of each WFRP I&M technical guide will provide the ecological and/or 

social history that created an interest or need to conduct an inventory or to develop 

a monitoring program for the subject species or species group. This section should 

begin with a description of the species or species group targeted by the technical guide, 

including scientific names and, if appropriate, subspecies names. If the technical guide is 

for a species group, this section will identify each of the species in the group and provide 

a rationale for treating the group as an assemblage. It should include brief information 

about the known or suspected impacts from management actions, the current legal or 

conservation status (Federal, State, and Forest Service), and the history of petitions to 

list the species under the Federal Endangered Species Act (ESA) or other actions. More 

specifics about the effects of management actions should be included in section 2.2.1., 

Species Life History and Conceptual Model.

As an example of a background section, we present the background for the Species 

Protocol Technical Guide as follows. The Forest Service, through the Ecosystem 

Management Coordination staff, has undertaken a multiyear effort to improve the 

consistency of inventory and monitoring throughout the agency. All resource areas have 

participated in this effort. The tasks for improving I&M are outlined in the National 

Inventory and Monitoring Action Plan (Inventory and Monitoring Issue Team 2000). Task 

8 of the action plan is to “ensure [that] scientifically credible sampling, data collection, 

and analysis protocols are used in all inventory and monitoring activities” (Inventory 

and Monitoring Issue Team 2000). To achieve this task, various resource areas within the 

Forest Service are establishing protocols for how data are to be collected, stored, analyzed, 

and reported. Protocols with national or regional application will generally be written as 

technical guides within the Forest Service directives system.

To inventory and monitor the diverse WFRP resources found in national forests and 

grasslands, many protocols will need to be developed. As a result, several technical guides 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 1-3

will be prepared. Each guide will be designed for a species or a group of similar species. 

National protocols will be developed for species that occur across several administrative 

regions. In most cases, however, protocols will be developed by or at the request of Forest 

Service administrative regions to meet needs unique to each region. In some situations, 

protocols may be developed for use in a single forest or adjacent forests.

“Business needs” are the motivating reasons for undertaking an activity. Many WFRP 

I&M activities are prompted by existing laws and policies (table 1.1). For example, if the 

I&M technical guide is for a federally listed species under the ESA, the Business Needs 

section should provide the ecological and/or social reasons explaining why the species 

is federally listed and should reference the recovery plan for that species. In addition 

to addressing legal business requirements, the Business Needs section might address a 

variety of other business needs that require inventory or monitoring information, such 

as whether the distribution of the species is well established, a current petition to list the 

species under the ESA exists or is expected, an interagency agreement to monitor the 

species is in place, or the public has expressed high interest in the status of the species.

In the case of this Species Protocol Technical Guide, the Forest Service identified 

a need for guidelines to help develop I&M technical guides similar in format, content, 

and level of detail. Also, the Forest Service recognized the need for specific information 

on setting objectives, choosing a sampling design, and conducting data analysis so I&M 

protocol development teams would meet standards required under the Data Quality Act. 

This Species Protocol Technical Guide was created to meet these needs.

1.2 Key Concepts

The introductory chapter of each I&M technical guide will include a section describing 

the key concepts related to I&M of the targeted species or species group. For example, if 

a species is migratory and if monitoring will occur only during the breeding season, a key 

concept is the limited nature of the data, because it provides information on population 

status only during the breeding season. Other key concepts might relate to specific aspects 

of the species’ life history that affect the inventory or monitoring design, such as colonial 

nesting, the use of leks, or territoriality.

A key concept of this Species Protocol Technical Guide is the term “protocol,” which 

is often used to refer to standards for collecting field data. The Forest Service Inventory 

and Monitoring Issue Team has recommended a broader interpretation of the term 

protocol to include all aspects of an inventory or a monitoring plan: the sampling design, 

data collection methods, data analysis methods, and reporting structure. We have followed 

this recommendation and have included these topics in our descriptions of inventory and 

monitoring protocols in the following chapters.
1-4 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

Table 1.1. Forest Service inventory and monitoring business needs pertaining to wildlife, fish, and rare plants.

Business need Target group Type of information Analysis scale Type of report needed

To provide 

information on MIS 

for forest planning 

(NFMA 1982 reg. & 

Dept. reg. 9500-004)

To provide 

information for Forest 

Plan revision

To aid in the recovery 

of species listed under 

the ESA

To avoid Federal 

listing of plant and 

animal species (FSM 

2670)

To aid in the 

conservation of birds 

protected by the 

MBTA

To provide 

information for 

the environmental 

analysis of proposed 

projects (NEPA)

To gather subsistence 

harvest data, in 

compliance with 

the Alaska National 

Interest Lands 

Conservation Act

To work cooperatively 

with States in the 

conservation of 

selected species

MIS (may be any 

taxa)

Any species, as 

needed 

Federal threatened or 

endangered species

Plant and animal 

species designated as 

Sensitive by the Forest 

Service 

All bird species 

protected by the 

MBTA

Primarily TES and 

MIS species, but may 

be species without 

formal status

Species harvested for 

subsistence uses

Any species identified 

for conservation 

through a MOU 

between a State and 

the Forest Service

Population trends in 

relation to habitat

Data and information 

needs to be identified 

through scoping

Population trends, 

habitat trends, trends 

in affecters/stressors 

Distribution, status, 

and trend of species 

and their habitats

Not specified; information, as needed, to 

be shared with other 

agencies

Availability of 

suitable habitat and 

species’ presence in 

project area and larger 

landscape context

Population trends

Information specified 

in the MOU; States 

usually collect 

population data and 

the Forest Service 

usually collects 

habitat data

The planning area: 

usually national 

forest or multi-forest/

grassland

The planning area

Species range or a 

significant portion of 

their range

Not specified

Not specified

Usually the project 

area and larger landscape context

Subsistence harvest 

units in Alaska

A State or the range 

of a species within a 

State

Forest plan; annual 

monitoring and 

evaluation reports

Forest plan and 

associated EIS

Annual reports of 

recovery plans or in 

biological opinions

Conservation 

agreements and 

progress reports

MOUs with 

USF&WS and 

associated progress 

reports

Project EA and postactivity monitoring 

reports as specified 

in EA

Regulations for 

subsistence harvest 

Progress reports as 

specified by the MOU

EA = environmental assessment.

EIS = environmental impact statement.

ESA = Endangered Species Act.

FSM = Forest Service Manual.

GPRA = Government Performance Review Act.

MBTA = Migratory Bird Treaty Act.

MIS = management indicator species.

MOU = memorandum of understanding.

NEPA = National Environmental Policy Act.

NFMA = National Forest Management Act.

TES = threatened, endangered, and sensitive species.

USF&WS = U.S. Fish & Wildlife Service.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 1-5

1.3 Roles and Responsibilities

Each WFRP I&M technical guide will contain a section on each administrative level’s 

roles and responsibilities in carrying out the specific inventory or monitoring plan. The 

following lists of roles and responsibilities apply to all aspects of WFRP I&M protocol 

development and implementation.

1.3.1 National Responsibilities

• Develop a Species Protocol Technical Guide and provide a review of the guide at 

5-year intervals to ensure it contains timely and relevant information.

•	 Develop I&M protocols for species and species groups with inventory and/

or monitoring needs that are shared by two or more regions and that require 

consistency in the regions’ inventory and/or monitoring approaches.

•	 Provide criteria for administrative regions to evaluate existing protocols’ comprehensiveness and capability of meeting Forest Service I&M protocol requirements.

•	 Facilitate information sharing and collaboration across administrative regions, 

with other Federal and State agencies, and with Forest Service Research and 

Development efforts to avoid development of duplicate protocols.

•	 Provide adequate funding for protocol development at regional levels and for 

collaboration with other agencies.

•	 Obtain technical and administrative review of protocols developed at a national 

level. Provide timely technical and administrative review of protocols developed 

for multiregional use.

1.3.2 Regional Responsibilities

•	 Ensure the use of the Species Protocol Technical Guide during the development 

of inventory and/or monitoring technical guides at regional and local scales.

•	 Develop technical guides for species and species groups with inventory and 

monitoring needs shared by several forests and grasslands.

•	 Use nationally developed criteria to evaluate existing protocols’ 

comprehensiveness and capability of meeting Forest Service I&M protocol 

requirements.

•	 Facilitate information sharing and collaboration within the region, with adjacent 

regions, with other Federal and State agencies, and with Forest Service Research 

and Development efforts to avoid the development of duplicate protocols for the 

same species.

•	 Include protocol development in regional I&M program plans.

•	 Obtain technical and administrative review of protocols developed by the region.

•	 Provide timely technical and administrative review of protocols developed for use 

within the region.
1-6 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

1.3.3 Forest and Grassland Responsibilities

•	 Obtain a list of protocols applicable to species on the forest or grassland with 

inventory and/or monitoring needs.

•	 Participate in regional or bioregional monitoring efforts as described in 

applicable protocols.

•	 Ensure the use of established protocols for I&M of species occurring on the 

forest or grassland.

•	 Develop protocols for species and species groups with I&M needs that are local 

in nature and for which regional or national protocols are not available.

•	 Use nationally developed criteria to evaluate existing local protocols’ 

comprehensiveness and capability of meeting Forest Service I&M protocol 

requirements.

•	 Use the Species Protocol Technical Guide during the development of I&M 

protocols at the local scale.

•	 Facilitate sharing of information with adjacent forests/grasslands and regions to 

avoid developing duplicate protocols.

•	 Obtain technical and administrative review of locally developed protocols.

1.4 Relationship to Other Federal Inventory and Monitoring 

Programs

Each WFRP I&M technical guide should explain how the technical guide fits in with 

other Federal I&M programs developed by the Forest Service and other Federal agencies. 

For example, an I&M technical guide for a bird species should describe how the 

monitoring program complements existing Forest Service regional land bird monitoring 

programs. Such a guide also should explain the monitoring protocol’s relationship to the 

U.S. Geological Survey (USGS) Breeding Bird Survey program.

WFRP I&M technical guides should also describe the relationship between the 

protocol and the Forest Service Natural Resource Information System (NRIS). The NRIS 

is a set of corporate databases and computer applications designed to fulfill many of 

field-level users’ information needs (NRIS 2005). NRIS databases contain basic natural 

resource data in standard formats designed for application within the Forest Service 

computing environment. This unified system is organized into seven NRIS modules; six 

focus on different resource information areas and one develops applications and analysis 

tools for the other six modules. It is anticipated that WFRP inventory or monitoring 

efforts will be entered into the NRIS FAUNA Module as basic survey data and as basic 

observation data.

The remainder of this section describes the relationship of this Species Protocol 

Technical Guide to other Federal I&M programs.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 1-7

1.4.1 Forest Service Programs

The Forest Service has been developing several other I&M technical guides concurrently 

with this technical guide: Terrestrial Ecological Unit Inventory (Winthers et al. 2005), 

Aquatic Ecological Unit Inventory, Existing Vegetation Classification and Mapping 

(Brohman and Bryant 2005), Multiple Species Inventory and Monitoring (Manley et 

al., in press), Northern Goshawk Inventory and Monitoring (Woodbridge and Hargis, 

in press), and Social and Economic Profiles. We anticipate that WFRP I&M technical 

guides could use the Terrestrial Ecological Unit Inventory, the Aquatic Ecological Unit 

Inventory, and the Existing Vegetation Classification and Mapping protocols to classify 

and map habitat. We also anticipate a relationship between monitoring designs targeted for 

individual species and the monitoring design described in the Multiple Species Inventory 

and Monitoring Technical Guide.

1.4.2 Programs in Other Federal Agencies

The Species Protocol Technical Guide does not currently have an equivalent in other 

Federal agencies. Several agencies, however, have developed I&M Web sites that contain 

information about protocol development. Comprehensive Web sites are maintained by the 

National Park Service (DOI NPS 2005) and the USGS Patuxent Wildlife Research Center 

(USGS PWRC 2005). In Canada, the British Columbia Ministry of Sustainable Resource 

Management has published a Species Inventory Fundamentals guide that contains 

information that is similar to the content of this technical guide (Ministry of Environment, 

Lands and Parks 1998). The Web sites that describe these efforts may be located through a 

search engine.

1.5 Quality Control and Assurance

This section should briefly describe processes that have been used to ensure the technical 

guide meets Data Quality Act standards. It does not need to describe quality control and 

assurance for specific field protocols; this topic is addressed under each specific I&M 

chapter. Instead, this section should describe the technical guide peer review process, list 

the credentials of those who prepared the technical guide, and reference the use of peerreviewed protocols that served as the basis for the specific I&M protocols described in 

subsequent chapters.

To ensure the quality of every WFRP I&M technical guide, all aspects of each 

inventory or monitoring strategy (including setting the objective, selecting population and 

habitat measures, selecting a sampling design, and selecting analytical tools) should be 

done in consultation with a statistician as well as those experts who are knowledgeable 

about the targeted species or species group. The draft strategy should then be reviewed by 

statisticians and other experts on the organism’s biology. The design must be statistically 
1-8 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

sound and biologically meaningful. External review of the design can help identify 

sampling design features that may limit usefulness of the data in the monitoring program’s 

analytical phase. It is much better to be thorough when developing the design phase and 

minimize the risk of making errors at that point than to find out after a year or more of 

data collection that the results are biased, not independent, or are too variable to make 

inferences.

Biologists, research scientists, and statisticians involved in developing a specific 

inventory or monitoring strategy will be listed in section 2.6 of the chapter in which the 

strategy is presented. If the list of preparers is the same for all chapters, the list will follow 

the title page of the technical guide. Reviewers will be acknowledged on an introductory 

page before the Contents page.

The quality control and assurance for the Species Protocol Technical Guide is as 

follows. The concepts for this technical guide were developed by a Forest Service team 

consisting of wildlife ecologists at the national and regional levels with assistance from 

Forest Service research scientists. A Request for Proposal to develop this technical guide 

was advertised in November 2002. After reviewing potential developers’ credentials, the 

Forest Service selected Pacific Wildlife Research, Inc., a consulting firm with expertise 

in ecological principles and biostatistics, to develop this technical guide in cooperation 

with Forest Service personnel. Credentials of the Pacific Wildlife Research, Inc., staff and 

associates are posted on the company’s Web site. 

The content of this technical guide is based on more than 150 published references 

from ecological, statistical, and biometric literature and on the authors’ expertise. The 

draft technical guide was internally reviewed by the initial team of Forest Service wildlife 

ecologists and research scientists who developed the concept and outline. Two Forest 

Service research statisticians then reviewed the draft to ensure statistical concepts were 

accurately portrayed.

1.6 Change Management

This section describes how the technical guide will be kept current and what 

circumstances will trigger the decision to update the document. The potential for regional 

supplementation of the technical guide, if appropriate, also would be addressed here.

Anticipated actions that may require changes in an I&M technical guide include 

the publication of a new Federal regulation to guide planning on national forests and 

grasslands; the regulation will likely change monitoring requirements in support of 

forest planning. Roles and responsibilities for protocol development may also change 

as WFRP I&M technical guides are completed and implemented. Aspects of data 

collection, storage, analysis, and reporting may need to be updated to accommodate 

changes in technology or new information. Each WFRP I&M protocol will describe 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 1-9

anticipated changes in protocols and provide a timeframe for incorporating these changes. 

At a minimum, each WFRP I&M technical guide should state that it is a draft until the 

protocols have been field tested for at least one season. The Change Management section 

should state approximately when field tests are planned and when final publication of 

the technical guide is expected. The Change Management section might also describe 

monitoring techniques, vegetation mapping, or analytical tools that are under development 

and that could require subsequent changes to the protocol.

Change management for the Species Protocol Technical Guide is as follows: 

Because this technical guide provides guidelines for developing subsequent WFRP I&M 

technical guides, the general format is not expected to change for many years. New 

developments in biostatistics and new tools for I&M, however, would trigger the need to 

update the technical guide. For example, this technical guide mentions genetics as a tool 

for determining species distribution and estimating minimum population size. Because 

the use of genetics is advancing rapidly, future guide revisions would certainly include 

recent applications of genetic data to I&M. This technical guide will be reviewed 5 

years after publication by Forest Service wildlife ecologists and research scientists; their 

recommendations for changes will be incorporated in an updated revision.
1-10 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 2-1

Each WFRP I&M technical guide will likely contain two or more chapters following 

the introductory chapter. Each chapter will address a specific inventory or monitoring 

objective for the species or species group. The title of each subsequent chapter will be the 

title of the specific objective. Suggested chapters are:

•	 Chapter 2. Bioregional or Other Broad-Scale Monitoring Objective

•	 Chapter 3. Forest or Multiforest Monitoring Objective

•	 Chapter 4. Protocols for Project Surveys

The subheadings of each chapter will be Objective, Planning and Design, Data Collection, Data Storage, Data Analysis, and Reporting. Using this format, we describe the 

expected content of each section.

2.0 Objective

This section should contain a clear, concise statement of the current chapter’s specific 

inventory or monitoring objective. Here are some examples of objectives:

•	 To conduct a single or multiple species inventory of a specific area.

•	 To estimate the distribution of a species in a specific area.

•	 To monitor the status and trend of a species in a specific area.

•	 To monitor the effects of management activities on a species in a specific area.

The objective section should also include the following:

•	 The desired levels of precision. What confidence level is desired or necessary to 

provide managers with useful information?

•	 The desired (or anticipated) power to detect change (if the objective is monitoring). How much sensitivity to change is necessary to determine whether a modification of management practices is appropriate?

•	 The estimated level of change (trigger point) that would result in management 

modifications.

•	 The scope of inference. The spatial and temporal scales over which the inventory 

or monitoring results are to be applied should be identified. In most cases, the 

spatial scope of inference is the area from which a random sample of the selected 

population and habitat attributes was taken. The temporal scope of inference may 

be affected by anticipated rates of change in human influences (e.g., urbanization), 

habitat (e.g., succession), or climate (e.g., drought periodicity) and may affect the 

period of time over which the monitoring or inventory occurs.

Chapter 2. Specific Inventory and Monitoring Strategies
2-2 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

Detail and focus are crucial to a well-designed inventory or monitoring protocol. The 

use of vague or unclear terms, broad questions, or unclear spatial and temporal extents 

will increase the risk that the data collected will not adequately address the key questions 

at meaningful scales. Furthermore, clearly articulated questions ensure that data collected 

are adequate to address specific key knowledge gaps or assumptions. In addition, clearly 

articulated questions provide the basis for identifying response thresholds, or trigger 

points, that indicate management actions that need to be changed.

2.1 Planning and Design

2.1.1 Species’ Life History and Conceptual Model

This section should highlight aspects of life history that influence the choice of inventory 

and monitoring approaches. The life history description should contain sufficient details 

to support the conceptual model.

Relevant material might include the following items:

•	 Description. Diagnostic characteristics and behaviors of the species or species 

group and variation in these characteristics among subpopulations.

•	 Distribution. The species’ geographic range and altitudinal limits; local 

boundaries (if known) of population distribution within the I&M protocol’s 

geographic scope. 

•	 Habitat. Habitats and environmental conditions with which the species is most 

closely associated, including fine-scale habitat elements (e.g., cobble-type 

stream substrates, large-diameter conifer trees) required by the species for 

reproduction or other life requisites.

•	 Reproduction and ontogeny. Mating strategy, growth patterns (in plants), 

reproductive and rearing behavior (in animals), differences among life stages or 

age classes, life span (in animals).

•	 Phenology (in plants) or activity patterns (in animals). Aspects of natural history 

that influence the organism’s temporal and spatial patterns. 

•	 Intra- or inter-specific relationships. Territoriality, colonial behavior, lek behavior, 

avoidance of or co-occurrence with other species.

•	 Stressors. Known or suspected factors that affect population status, both those 

external to Forest Service control and those believed to relate to Forest Service 

management.

A conceptual model represents a hypothesis regarding the expected response of a 

species or species group to changes in environmental conditions and/or management. 

It can help I&M strategy developers identify the states and processes in which we have 

the least confidence and that may be most directly affected by management activities. A 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 2-3

conceptual model predicts how a species might respond to a specific activity. Thus, the 

model suggests ecological elements to monitor.

Links between stressors and biotic responses may be indirect; the conceptual 

model can be a valuable tool to show these pathways (Noon et al. 1999). For example, a 

management activity could reduce the competitive advantage of a target species relative to 

another species, which, in turn, could reduce the targeted species’ reproductive output. In 

this case, the conceptual model would suggest monitoring the target species’ reproductive 

output and the competitor’s presence or relative abundance.

A conceptual model is integral to a monitoring design. The model can also be useful 

for developing an inventory strategy. Models of wildlife and plant habitat relationships 

can help focus on the location where and the time period when a targeted species is likely 

to occur, and can provide rationale for sampling areas where occupancy is likely but 

currently unknown.

No inventory or monitoring program will have all the information needed to 

completely develop a conceptual model for the system under consideration. Available 

information will have to be extracted from literature, other systems, and expert opinion. 

Nonetheless, the conceptual model needs to be developed to identify the key gaps in our 

knowledge, enable clear articulation of the most pertinent questions, provide rationale for 

selecting population and habitat measures, and establish the link from monitoring results 

to management actions.

2.1.2 Selected Measures of Population and Habitat

To attain the inventory or monitoring objective in a quantifiable way, population and 

habitat measures that represent the objective must be selected. If the objective is to 

monitor a population’s status and trend, examples of relevant population measures may 

include frequency of occurrence, relative abundance, density, or total population size 

(a complete census). If the objective is to estimate changes in reproductive success, 

examples of relevant measures may include the number of adults with offspring, the 

number of young (or, for plants, seeds) per reproductive unit, or the number of offspring 

successfully fledged.

Habitat measures should be drawn from the habitat relationships described in the 

conceptual model. If a species is affected by landscape pattern, some possible measures 

may include patch size, patch isolation, edge density, or the number of vegetation types 

and structural stages per unit area. Measures of stand structure include vegetation height, 

diameter, and species composition. Special habitat features (snags, logs) can be measured 

by presence, density, size class, volume, or condition class.
2-4 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

2.1.3 Sampling Design

The sampling design provides the approach for selecting individual sampling units from 

a statistical population for measurement or observation. The primary functions of a 

sampling design include the following:

•	 To ensure that the sample’s attributes (particularly the population or habitat 

indices of interest) accurately represent the attributes of the larger population.

•	 To ensure that sampling is conducted as efficiently as possible. That is, the 

sample will have the best statistical properties (usually the lowest variance) that 

can be achieved within the project’s budget and time constraints.

A sampling design that best meets a monitoring plan’s objectives should be selected 

for each monitoring plan. For an area inventory, the sampling design should provide 

for good spatial dispersion of observations within the inventory area, across all of the 

targeted species’ potential habitats. For cause-and-effect monitoring, the sampling design 

should include replicates of the management action, if possible, and replicates of sites 

without implementation of management practices (i.e., controls). This replication is 

necessary to isolate, as much as possible, the management action as the only difference 

among treatment and control sites. Also, while not absolutely essential, sampling 

pretreatment conditions on all sites is important for analyzing cause and effect. Chapter 

3 addresses several different sampling designs and their possible application to WFRP 

monitoring.

The sampling design must also take into account specific aspects of a species’ life 

history and habitats so data collection can be optimized and results can be properly 

interpreted. Four aspects of life history that might affect sampling design are home range 

size, territoriality (or conversely, social clumping), seasonal use patterns, and natural 

population fluctuations. Home range size could influence plot size or the spacing between 

plots within the sampling frame. Territoriality or social clumping could be deciding 

factors in whether the sampling design is simple random sampling or stratified random 

sampling. Seasonal use patterns could affect the optimal time of year for detecting a 

species and for interpreting fluctuations within a season related to the appearance of 

young of the year. For multiple species monitoring, the sampling design should include 

sampling several times over the potential sampling season so data are not biased toward 

early or late seasonal species. Natural population fluctuations affect the ability to detect 

significant change in abundance, and must be considered when specifying a desired 

effect size. For example, if a 20 percent change in abundance is within the range of 

normal fluctuations, it may not be relevant to detect a 20 percent change in abundance

for management purposes. A larger effect size and, hence, smaller sample size might be 

adequate.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 2-5

The size, shape, and spacing of sampling units can have major effects on population 

or habitat index values. Chapter 3 of this technical guide addresses considerations for 

determining the optimum configuration of sampling units for a particular project. I&M 

protocol development teams, however, should also review more comprehensive texts on 

the subject. Hayek and Buzas (1997), Thompson (1992), and Thompson et al. (1998) are 

three examples among many excellent references on natural resource sampling designs. 

Furthermore, I&M teams are strongly encouraged to consult with a statistician early in the 

design process to help ensure the sampling design matches the scale and objectives of the 

inventory or monitoring questions.

The Sampling Design section of each WFRP I&M technical guide should address the 

following elements:

•	 Definition of the target population.

•	 The sampling frame (i.e., spatial and temporal bounds of sample selection) and 

statistical scope of inference, and how these elements relate to the target population.

•	 Sample selection and stratification methods (e.g., stratified random, systematic) 

and the process for selecting sampling units (e.g., mechanism for random 

selection of a unit).

•	 The size, shape, and spacing of sampling units.

•	 Methods to control or measure observer bias resulting from imperfect observation or species detectability.

•	 An estimation of sample size needed to meet the objectives.

•	 Temporal aspects of the sampling design, annually and over the course of a 

multiyear measurement cycle, if applicable.

2.1.4 Pilot Studies

This section can be used to describe an intended pilot study of the monitoring strategy 

or report a pilot study’s outcome. If the pilot study has not yet occurred, this section will 

describe the study’s objective and state when, where, and how the pilot study will occur. 

If the pilot study has already taken place, this section will describe how the data from 

this effort applies to the inventory or monitoring design. Pilot study data may be valuable 

in estimating optimal sample size, providing estimates of needed parameters (e.g., 

detection probability, sex ratio of detected individuals), or focusing attention on specific 

habitats. Pilot study data also might be helpful in the selection of a more effective index of 

population size (Gibbs et al. 1998). Because early knowledge about data characteristics, 

logistical constraints, and potential sources of bias can pay huge dividends in the long run, 

we recommend that all I&M designs begin with pilot studies.
2-6 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

2.1.5 Prospective Power Analysis

The primary purpose of a prospective power analysis is to choose a sample size that will 

meet the desired levels of precision and power for detecting a biologically significant 

phenomenon. The ability to meet the inventory or monitoring objective largely depends 

on the sampling intensity. An insufficient sample is nearly equivalent to not sampling 

at all, because meaningful inferences are not possible. Statistical power is a function 

of sample size, effect size, and significance level (α) and can be calculated using a 

wide range of statistical software (see Thomas and Krebs 1997 for an excellent review 

of suitable software). Pilot studies can provide the data to use in these calculations; 

therefore, the prospective power analysis can be included in the Pilot Studies section 

instead of appearing as a separate section.

2.2 Data Collection

2.2.1 Data Collection Methods

This section should adequately describe all the methods associated with randomly 

selecting sampling units in the field, observing or trapping target organisms, recording 

and managing data in the field, and handling voucher specimens. Protocol developers 

should consider adapting tested and peer-reviewed methods before developing new 

techniques. A bibliography of selected reference publications for sampling rare plants, 

fish, and wildlife is provided in appendix C of this technical guide.

The Data Collection section might logically be divided into A. Population Data and 

B. Habitat Data, since each type of data will require different data collection methods. 

The subheadings used below may not be necessary if field method descriptions are short, 

but all the topics listed under these subheadings should be addressed.

Locating Sampling Units 

The technical guide should clearly identify field methods necessary for biologists to 

translate the conceptual sampling design into field procedures for locating sampling 

units, even under challenging conditions. Criteria or rules for establishing plot boundaries 

should be described, if required by the design. Consideration should be given to mapping 

sampling sites using a geographic information system (GIS) and then using global positioning sensors to field-locate them.

Layout and Marking

The dimensions of plots, transects, or other sampling units should be described. Efficient 

techniques for positioning and measuring sampling units under field conditions should 

be identified. Providing a diagram or map to indicate the spacing and configuration of 

sampling units would also be useful. Recommendations for marking and establishing 

monuments that are resistant to natural disturbances and vandalism should be provided 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 2-7

for long-term monitoring projects. Elzinga et al. (2001) provide an excellent review of 

such techniques.

Field Methods

A comprehensive description of field methods for sampling the target species or habitat 

element should be provided. This description should include the following elements:

•	 Observational or capture techniques. This element should include a description 

of the equipment used and the rationale for the equipment chosen. The rationale 

should point out the chosen method’s advantages and disadvantages with regard 

to the technique’s precision and repeatability. Subtle details of techniques such 

as guidelines for trap placement, binoculars used, and weather conditions may be 

very useful in reducing interannual variability in estimates.

•	 Temporal sampling period. Explain how the temporal framework for sampling 

interfaces with periods of activity for the species of interest or how it is 

associated with the function of the habitat element of interest. Point out the 

advantages and disadvantages of the proposed timing with regards to the 

precision and repeatability of estimating the index of interest.

•	 Duration of sampling. This element should ensure the sampling effort is adequate 

for developing a precise estimate over a period of time that is meaningful to the 

population of concern.

•	 Data recording. For each variable, document exactly how data are to be collected. 

Include references to the significant digits used to record data. Clearly state the 

taxonomic level expected, the measurement’s degree of precision, and the specific 

techniques used to acquire the data.

•	 Plant or animal marking techniques. These elements must be considered carefully 

because any marking technique that introduces bias relative to survival or 

reproduction can lead to highly unreliable monitoring information. References 

to standard guidelines for marking plants and animals should be provided. In the 

case of radio transmitters, make it clear that transmitter mass should not exceed 

specific guidelines provided in the literature. Bands, ear tags, passive integrated 

transponder (i.e., PIT) tags, and other markers should not unduly modify the 

organism’s mobility, survival, reproductive potential, or other functions that may 

result in an unreliable indication of population function.

•	 Use of equipment and materials. This element should be precisely described. It is 

better to provide too much detail than too little regarding how equipment should 

be used, maintained, and stored.

Voucher Specimens

The methods used to handle, prepare, and store plant or animal specimens collected in the 

field should be described. If laboratory analyses are required for the protocol, the facility 
2-8 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

where the analyses will be conducted should be identified along with appropriate shipping 

methods. The museum or university collection that will ultimately house voucher 

specimens also should be identified.

2.2.2 Personnel Qualifications and Training

One of the most important considerations in planning a biological monitoring program is 

to ensure that trained technicians, working under the supervision of well-qualified biologists, perform data collection and analytical procedures. Advanced, electronic data loggers 

and other technological improvements improve good surveyors’ efficiency, but cannot 

make up for the shortcomings of inexperienced or poorly trained personnel. Moreover, 

different levels of training and experience among survey personnel may be a significant 

source of observer variability. To ensure reliable and efficient data collection, WFRP I&M 

technical guides should specify the minimum qualifications and responsibilities for biologists, crew leaders, and crew members involved in conducting the inventory or monitoring 

study. Establishing written qualifications for personnel is particularly important for multiyear monitoring studies in which a significant amount of turnover among the monitoring 

program participants is likely during the course of the study.

2.2.3 Quality Control and Assurance

The purposes of quality control and assurance include the following:

•	 To ensure consistent implementation of an inventory or monitoring design by 

different Forest Service units or other agencies.

•	 To maintain the scientific credibility of the results by standardizing materials and 

methods used during data collection and analysis, thus facilitating independent 

review and replication of the monitoring design.

•	 To quantify measurement error associated with implementation of the sampling 

design.

Forest Service personnel customarily perform remeasurements to verify stand exam 

data during timber inventories contracted to private surveyors, yet, such data quality 

assurance methods are infrequently used for WFRP inventories. WFRP I&M technical 

guide developers should consider the data collection tasks most vulnerable to error and 

should describe procedures to minimize the likelihood of such errors occurring. For example, the Quality Control and Assurance section might recommend midseason calibration of 

scales or other instruments, midseason calibration of ocular estimates, and weekly examination of forms for potential errors. Developers could also design data verification tests 

and recommend their use when the protocol is implemented. They could also recommend 

that an independent examiner conduct resurveys on a subset of sampling units to measure 

error rates. This section should also establish criteria for acceptable levels of observer 

error and describe remedial measures when measurement error is not acceptable.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 2-9

2.2.4 Data Forms

This section should briefly list all forms needed for data collection, indicate whether 

each form is optional or required, and refer to an appendix containing templates for these 

forms. The appendix should also provide a data format sheet that identifies the data type, 

unit of measurement, and valid range of values for each field of the data collection form. 

The data format sheet should identify all codes and abbreviations used in the form. We 

encourage developing digital forms and providing ways to access digital forms (e.g., on a 

personal digital recorder).

2.2.5 Logistics

This section covers the following logistical considerations for administering and 

conducting field surveys:

•	 Types of permits needed and how to obtain them.

•	 Safety considerations.

•	 Sources of field equipment.

•	 Anticipated work schedules.

The Logistics section should also outline the expected content of an annual operation 

plan that would be prepared by personnel implementing the specific inventory or monitoring 

design. In general, an annual operation plan should address the following items:

•	 The current year’s status of memorandums of understanding (MOUs) and 

agreements with monitoring collaborators.

•	 The current year’s status of any permits needed for access to private lands.

•	 The current year’s status of scientific collecting permits.

•	 Plans for housing field personnel.

•	 Arrangements for vehicles.

•	 Radio communications and frequencies.

•	 The coordination of flagging and marking schemes with other concurrent projects.

•	 A checklist of field equipment.

•	 Safety considerations.

Permits

Most States require surveyors to possess scientific collecting permits for studies involving 

the removal of rare plants or capture of native wildlife. Species listed as endangered or 

threatened under the ESA receive additional, stringent protection. I&M protocols that 

target ESA-listed species may require Forest Service personnel to consult with the U.S. 

Fish and Wildlife Service or National Oceanic and Atmospheric Administration Fisheries 

Service before beginning work. The Logistics section should identify State and Federal 

collecting permits that may be necessary for conducting fieldwork, as well as permits that 
2-10 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

must be obtained to use controlled substances or materials (e.g., immobilizing agents or 

other drugs, syringes, dart guns).

Safety Considerations

The Forest Service requires a job hazard analysis for each task that is carried out by 

Forest Service personnel or contractors. The Logistics section should list all potential 

safety hazards associated with data collection to enable the personnel implementing 

the specific inventory or monitoring design to develop appropriate job hazard analyses. 

Examples of potential safety hazards include the following:

•	 Exposure to animal-borne diseases (e.g., rabies, hantavirus).

•	 Risk of injury from handling wild animals (e.g., capturing large carnivores).

•	 Risk of injury from using special equipment or materials (e.g., an electroshocker).

•	 Hazardous activities (e.g., tree climbing, spelunking).

•	 Risks associated with weather.

•	 Risks associated with driving.

•	 Risks associated with off-trail hiking.

The National Center for Infectious Diseases provides fact sheets for many diseases 

that biological technicians may be at risk of contracting (NCID 2003). The Logistics 

section should also describe training or qualifications necessary for performing hazardous 

procedures.

2.3 Data Storage

Data from WFRP inventory or monitoring programs will be stored in the Forest Service’s 

NRIS (NRIS 2005). Steps in preparing data for entry into NRIS need to be addressed in 

the Data Storage section. Data storage details may not be known until the inventory or 

monitoring design has been tested or even implemented, so the Data Storage section could 

be rudimentary in the first draft of the technical guide. Also, it is expected that NRIS 

will not have capabilities to store specialized data that might be produced by a specific 

inventory or monitoring design. Those who develop and test the I&M designs will need to 

work with NRIS developers to enhance the system to store specific data. They can use the 

following subheadings to elaborate on various aspects of data storage.

2.3.1 Data Cleaning Methods

Data collected in the field must be reviewed for completeness and errors before entry 

into NRIS. Concerns and techniques specific to the protocol being developed should be 

addressed.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 2-11

2.3.2 Database Structure

This section would describe the entire database, both the variables collected in the field 

and any derived variables, including how the derived variables are calculated. For each 

variable, this section would provide the unit of measurement, and the valid range of 

values.

2.3.3 Metadata Requirements

The term “metadata” refers to “data about data.” Metadata is information about the origins 

of a database or a map provided by its developer, changes to the work made by secondary 

users, and quality of the data. WFRP protocol development teams should become familiar 

with the major elements of the Federal Geographic Data Committee (FGDC) Content 

Standard for Digital Geospatial Metadata (CSDGM) Biological Metadata Profile (FGDC 

and USGS 1999), and how these metadata standards are incorporated into NRIS. The 

FGDC metadata standard includes seven major elements; some are mandatory for every 

database and map, and some are applicable only to certain types of data. NRIS automates 

all the mandatory elements of the standards. NRIS developers would work with WFRP 

I&M technical guide authors to ensure that any other necessary metadata are incorporated 

into the guide. These metadata could then be summarized in the Metadata Requirements 

section of the technical guide, as in the following examples:

•	 Complete descriptions and bibliographic citations for taxonomic, population, or 

ecological classification systems used in the guide, including identification of 

keywords consistent with the Biological Metadata Profile, where appropriate.

•	 Sources of maps, geospatial data, and population information that are used to delineate the monitoring program’s geographic boundaries or locate sampling units.

•	 Units of measurement.

•	 Names and qualifications of field personnel and of people who will be 

responsible for maintaining and distributing data (i.e., data stewards).

•	 All data codes, variable names, acronyms, and abbreviations used in the protocol.

•	 An outline or template of the structure of tabular databases.

Additional information about metadata is provided in Section 3.3, Data Storage: 

Metadata Purpose and Standards.

2.4 Data Analysis

2.4.1 Analysis, Synthesis, and Interpretation

This section will describe the general approach to data analysis, specific statistical tests 

that will be used, and why they will be used. The rationale for selecting the statistical tests 

will include the type of data (e.g., continuous, binary), the expected distribution of the 

data, underlying assumptions, and any other relevant factors.
2-12 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

In WFRP I&M technical guides, this section will be divided into subheadings (e.g., 

A, B, C) for each analysis objective. For example, a monitoring strategy designed to 

detect changes in relative abundance in relation to changes in habitat might have three 

objectives:

• A. Single-Year Estimate of Relative Abundance.

• B. Changes in Relative Abundance Between Two Time Periods.

• C. Correlations Between Relative Abundance and Habitat.

The analysis need not always be statistical. For example, a species list, diversity 

index, distribution maps, or other graphical techniques may often be sufficient to 

convey information at a level appropriate for a given objective. Alternative techniques to 

traditional statistical frameworks such as Bayesian inference (Dennis 1996, Ellison 1996, 

Taylor et al. 1996), or testing based on confidence intervals also may be recommended 

(Steidl et al. 1997, Johnson 1999). Analytical Methods, Section 3.4, describes the logic 

and utility behind selected generalized statistical models that are commonly used to 

analyze data from inventory and monitoring designs.

2.4.2 Analysis Tools

Use this section to provide information about analytical software that is available for 

the specific analyses. The vendor information necessary for ordering software could be 

included to facilitate software acquisition. Countless software packages are available for 

application of a wide range of statistical tests in ecological applications. Individual products vary in their sophistication, ease of use, computer requirements, and purchase price 

(see appendix C for references on recommended software tools).

2.5 Reporting

2.5.1 Expected Reports

This section will describe the reports that will result from the inventory or monitoring 

design and may suggest a format for specific reports. The Reporting section encourages 

those who implement the design to report results from the standpoint of the inventory 

or monitoring objective and to recommend how the results might be used to improve 

and/or validate Forest Service resource management. The Reporting section, however, 

should clearly differentiate between data results and management recommendations. The 

discussion of management recommendations should give attention to the Forest Service 

information needs identified in the Background and Business Needs section.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 2-13

2.5.2 Reporting Schedule

This section will list a timetable of expected reports, beginning with the pilot study, then 

the first year of full implementation, followed by annual reports and landmarks at perhaps 

5- and 10-year intervals.

2.6 List of Preparers

Each chapter of the technical guide should list the contributing authors, their titles or 

positions, and their Forest Service units or the organizations where they work. If the list 

of preparers is the same for all chapters, the list will follow the title page of the technical 

guide.

2.7 Literature Cited

The Literature Cited section will appear as appendix A following the numbered chapters 

of the technical guide and will list all publications referenced in the text. The format for 

literature cited will comply with the standard established and used by the Forest Service.

2.8 Appendixes

The titles of all appendixes to the technical guide will be listed on the Contents page. The 

following materials may appear in an appendix:

•	 Glossary.

•	 Examples of field data forms.

•	 Identification keys or guides.

•	 Database structure and data dictionary.

•	 Copies of contracts, MOUs, and other agreements.
2-14 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-1

The purpose of this chapter is to provide technical assistance for developing each section 

of a WFRP I&M technical guide. The chapter is organized around the primary headings 

and subheadings of chapter 2 so protocol developers can locate information relevant to 

each section.

3.0 Stating the Objective

I&M objectives should be applicable throughout most of the range of the targeted 

species or species group and should focus on broad information needs that are relevant 

to management. In addition, those who implement the I&M protocols should be able 

to narrow certain objectives to address local information needs. Examples of broad 

objectives are as follows:

•	 Expand knowledge about the spatial distribution of the targeted species.

•	 Expand knowledge about habitat associations of the targeted species.

•	 Expand knowledge about co-occurrence of the targeted species with other species.

•	 Monitor broad-scale population trends in relation to habitat changes.

•	 Monitor changes in population in relation to specific management actions.

•	 Monitor changes in demographic factors in relation to specific management actions.

As part of the objective statement, protocol developers need to determine the level 

of confidence desired or necessary to provide managers with useful information. For an 

inventory design, the developers will need to decide whether it is important to know with 

very high confidence that a species occurs in a specific habitat or specific area or if some 

lesser level of confidence is acceptable. The level of confidence needed will dictate the 

sampling design and the intensity with which the area is sampled.

The issue of statistical power is particularly pressing in conservation work and other 

applications with direct bearing on critical management decisions. Committing a Type II 

error (β; missed-effect) can have more adverse consequences under these circumstances 

than declaring statistical significance for an effect lacking biological meaning (Taylor and 

Gerrodette 1993, Hayes and Steidl 1997, Steidl et al. 1997, Johnson 1999, Roosenburg 

2000). For example, if results of monitoring failed to statistically detect the presence 

of a true adverse effect of forest thinning on a rare amphibian species, failure to take 

appropriate actions could speed up the species’ demise in commercially managed forests. 

The Data Analysis section addresses retrospective (post hoc) power analysis.

The objective statement also should include an estimated threshold or trigger point 

that would result in changes in management. Frequently a 20 percent change in population 

Chapter 3. Further Considerations in Developing 

Inventory and Monitoring Protocols
3-2 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

level is adopted as a trigger point for change, but this number is often selected arbitrarily, 

without ecological considerations. If the population of interest is very small, it may 

be essential to alter management when only a 10 percent decline has occurred. If the 

population of interest frequently fluctuates 10 to 20 percent, then a value larger than 20 

percent should be selected for a trigger point.

The threshold should apply to a unidirectional change (i.e., declining or increasing, 

but not both), because a smaller sample size is needed for a one-tailed versus a twotailed test. Thresholds are usually set for an undesired decline, but they may be set for an 

increase (e.g., for recovering species at the point when intensive management would no 

longer be necessary; or the point when an overabundant species might be detrimental to 

habitat condition).

3.1 Considerations for Planning and Design

3.1.1 Conceptual Model

The development of a conceptual model is critical to the development of a successful 

monitoring program that is scientifically based and founded in ecological theory. 

Before developing and implementing a monitoring program, it is essential to clearly 

understand why the proposed monitoring is important, determine which characteristics 

of the environments are to be monitored, know what that information indicates about 

environmental quality, and know how to use that information to better manage the 

landscape (Noon et al. 1999). Conceptual models document the ecosystem components 

and processes we believe are relevant to the species’ well-being, document our 

assumptions about how those components and processes are related, and identify gaps in 

what we know about contributing factors (Manley et al. 2000).

Through the development of a conceptual model, the factors that drive ecological 

systems often become apparent, which enables us to determine which attributes may 

be important to system function and suggests ecological elements to monitor. These 

factors can also help us identify the components and processes about which we have 

the least confidence in our understanding but which might be most directly affected 

by management activities. This process leads to the identification of parameters that 

will need to be measured by monitoring. For example, it might be determined through 

the development of a conceptual model that a change in a species’ relative abundance, 

reproductive output, or genetic makeup may result if proposed management actions are 

implemented. The parameter that appears to be most sensitive to management actions and 

to a species’ well-being may then be selected as the monitoring measure (fig. 3.1). The 

overall purpose of the model is to provide a logical sequence to the selection and use of 

monitoring measures.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-3

A useful conceptual model will do the following:

•	 Describe ecological processes of interest and related variables.

•	 Contribute to understanding interactions between ecosystem processes and 

selected variables.

•	 Identify key links between drivers, stressors, and ecosystem response.

•	 Facilitate selection and justification of monitoring measures.

•	 Facilitate evaluation of data from the monitoring program.

•	 Facilitate incorporation of the monitoring program results into management 

activities.

Gross (2003) provided a step-by-step approach to the development of conceptual 

models for monitoring programs.

3.1.2 Selected Measures of Population and Habitat

The following discussion distinguishes between data collected in the field and the 

population or habitat measures derived from the data. For example, counts of individuals 

are a type of data; the resulting measures are relative abundance, absolute abundance, or 

density. In the case of habitat, the counts of individual trees and the measurement of their 

diameters are two types of data used to derive a stand density index.

Figure 3.1. An example of a conceptual model illustrating relationships between natural and human-induced stressors and 

their effects on northern spotted owls (after Lint et al. 1999).

Processes Acting as 

Stressors

Habitat disturbance (natural)

•	 Catastrophic fire

• Insects, pathogens

• Volcanic eruptions

• Flooding and landslides

Habitat disturbance (human 

induced)

• Silviculture-harvest

• Salvage logging

• Fire suppression

• Road construction

• Insects, pathogens

Habitat conversion

• Forest type conversion

• Urbanization

• Human development

Effects on Habitat

• Decline in the size of lateseral patches

• Increased distance 

between late seral 

patches

• Large-scale declines in 

habitat quality

• Fragmentation of 

previously contiguous 

habitat

• Increased “hard” edges 

between habitat patches

• Creation of a hostile 

landscape matrix

• Loss of connectivity 

between late seral forest 

patches

• Increased levels of 

human disturbance

Effects on Spotted Owls

• Decreased rate of pair 

formation

• Declines in reproduction

• Decreased survival of 

juveniles

• Decreased dispersal 

success

• Isolation of local 

populations

• Local populations 

subject to demographic 

stochasticity

•	 Decrease gene flow

• Decline in owl numbers

Potential Measures

Habitats

• Distribution of distance 

among habitat patches

• Distribution of patch 

sizes

• Average perimeter-toarea ratios for habitat 

patches

• Fractal index of the 

landscape

• Patch contagion index

Populations

• Population size

• Population distribution

• Nest initiation

• Reproductive success

• Territory occupancy rate

• Territory turnover rate

• Juvenile survival rate

• Juvenile dispersal rate

• Adult survival rate
3-4 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

Population and Community Measures

To be meaningful, all population and community measures require an estimate of 

detection probability, unless surveyors are confident that all individuals present at the 

sampling unit will be detected each time a survey is conducted. Although detection 

probabilities are not explicitly addressed here, the concept is integral to all the population 

and community measures presented. Methods for estimating detection probabilities are 

presented in Section 3.4, Analytical Methods.

ECOLOGY OF

PENSTEMON LEMHIENSIS

Conceptual Models1

When developing a conceptual model, consider the following points:

• It should represent your current understanding of the system you intend to monitor.

• It should help you understand how the system works. What are the entities that define the 

structure of the system? What are the key processes? This knowledge often yields a narrative 

model—a concise statement of how (you think) the system works.

• It should describe the state variables. What mechanisms and constraints will be included, and 

which will be excluded?

• What assumptions will be made about the system? At what spatial and temporal scales does 

the system operate? These considerations often result in the construction of a schematic model, 

perhaps a Forester diagram (a “box and arrow” model). It should provide a framework for 

generating hypotheses about how the system works. The key states or processes most likely to be 

affected by management actions should be identified for monitoring.

1

 Figure excerpted from Elzinga et al. (2001: 262).
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-5

Population measures are derived from the following types of data:

•	 Detection of unmarked individuals or associated signs (e.g., tracks, scat, hair).

•	 Detection and location of marked individuals.

•	 Counts of marked or unmarked individuals.

•	 Reproductive parameters.

•	 Genetic data.

Community measures are derived from detections or counts of individuals within 

species or species groups. The following is a description of common population and 

community measures and their usefulness in meeting different inventory and monitoring 

objectives (adapted from Holthausen et al. 2005).

Population Measures

Presence/Absence. Presence/absence is derived from detection data by direct 

observation or a reliable sign of the species’ presence (e.g., vocalization, genetic sample, 

scat), and is adjusted for bias of imperfect detection. This measure is useful when the 

objective is to expand knowledge about the habitat associations of a targeted species or to 

expand knowledge about the co-occurrence of a species with other species. It is primarily 

an inventory measure that results in species lists and habitat associations.

Frequency of Occurrence. This measure estimates, for a statistical population of 

survey sites, the proportion of sites with the target species present, based on a random 

sample of the statistical population. Frequency of occurrence is not spatially explicit, so it 

cannot be used to map a species’ spatial distribution. It can be used, however, to estimate 

whether the spatial distribution is changing over time; for instance, whether the number 

of occurrences (as opposed to numbers of individuals) of a rare plant are increasing or 

decreasing. Frequency of occurrence is a useful indicator of relative abundance if the 

relationship between frequency of occurrence and population density has been established 

or estimated (e.g., each survey site represents one breeding pair). If so, then change in 

frequency of occurrence can be used to estimate change in relative abundance over time.

Abundance or Density. These measures are derived from counts of individuals or 

from the presence and location of marked individuals. Abundance is either absolute (a 

complete census or an estimate of total abundance), or relative (number-of-individualsper-unit effort). Relative abundance becomes relative density when the comparative unit 

is area (number of individuals per unit of area). Relative density can be estimated from 

counts of individuals and their detection probabilities. It can also be estimated from 

habitat associations and average home range size derived from the presence and location 

of marked individuals. Abundance or density provides more information than does 

presence/absence about the relative importance of different habitats and about the strength 

of association in co-occurrence of certain species. Also, it provides greater sensitivity to 

detect change over time than frequency of occurrence does.
3-6 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

Indices of Relative Abundance. This suite of measures is based on detections 

or counts of objects, such as pellets, tracks, or vocalizations, which are surrogates for 

individuals. Thompson et al. (1998) state that any unadjusted partial count of individuals 

is an index; therefore, relative density without an adjustment for detectability is 

technically an index of relative abundance. An index can be used for inventory purposes 

such as mapping spatial distribution and co-occurrence with other species. Caughley 

(1977) advocated the use of indices for monitoring change in abundance over time, 

indicating that many studies that used estimates of absolute density could have used 

density indices without losing information. He suggested that use of indices often results 

in a more efficient use of time and resources and produces results with higher precision 

than population estimates do (Caughley 1977, Caughley and Sinclair 1994).

Others have questioned the reliability of index values for monitoring change over 

time, however, because the relationship between the index and true population abundance 

usually is not quantified or known (Thompson et al. 1998; Anderson 2001, 2003), and 

the opportunity for bias associated with indices of abundance is quite high. For instance, 

track counts could be related to animal abundance, animal activity levels, or both. 

Capture rates of animals over area and time may be related to animal abundance or to 

the animals’ vulnerability to capture in areas of differing habitat quality. Consequently, 

although indices of abundance are often used because of logistical constraints, 

considerable caution must be exercised when interpreting these results. Nevertheless, 

Engeman (2003) concluded that an index may be the most efficient means to address 

population monitoring objectives and that the concerns associated with the use of 

indices can be addressed with appropriate and thorough experimental design and data 

analyses. Moreover, McKelvey and Pearson (2001) found that some indices exhibit lower 

variance than do population estimators, particularly with small sample sizes or when the 

estimators’ underlying population attributes are largely unknown. Therefore, the choice of 

an index or an estimator will depend on data quality.

Vital Rates. Vital rates are age-specific birth and death rates or emigration/

immigration rates that are derived from evidence of reproduction, such as the number 

of young per female or the number of seed pods per plant. Vital rates are a cornerstone 

of population viability analysis. An understanding of vital rates provides insight into 

population status. Depending on life history, monitoring of vital rates can provide a 

better measure of population status than do measures of abundance. Furthermore, 

understanding how vital rates change in response to management provides insight into 

potential mediation or mitigation. Demographic sensitivity analysis can help identify the 

appropriate vital rate to monitor.

Range Distribution Measures. Geographic range can be estimated from presence/

absence or counts of individuals, while further differentiation of breeding and nonbreeding range can be determined from evidence of reproduction. Boundaries of 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-7

genetically distinct populations can be estimated from genetic data. Range distribution 

measures are valuable when the monitoring goal is to estimate whether the range of a 

species is expanding, contracting, or remaining relatively constant. This information is 

needed for exotic species and endangered species management, where the goal may be to 

compare the current geographic range to historic distributions. Current research indicates 

a correlation between a species’ abundance and distribution, so range dynamics may 

provide an effective indication of abundance (He and Gaston 2000).

Genetic Measures. DNA-based inventory and monitoring efforts will provide new 

insights into fish, wildlife, and plant population health and trends. There are two distinct 

ways in which DNA and population genetics can be used for inventory and monitoring. 

First, genetics can be used to bolster other monitoring efforts described above. For 

example, to delineate an animal’s range, traditional methods often call for the use of 

historical records or the identification of snow tracks; both delineation techniques are 

highly unreliable and prone to high error rates. Using DNA obtained from hair, urine, 

or scat associated with snow tracks, however, can provide reliable, positive species 

identification. In this sense, DNA can augment traditional methods and provide more 

reliable estimates of abundance, presence/absence, and geographic range. Many examples 

of such DNA usage exist for estimating abundance (Paetkau 2003, Schwartz et al. 2004), 

presence/absence (Taberlet et al. 1997, Schwartz et al. 2004), and geographic range 

(Taberlet et al. 1997, McKelvey et al., in press).

Secondly, data from DNA can be used to abet I&M in a population genetics 

framework. That is, once samples are collected, the genetic data can be tapped for further 

information about the population. In particular, the genetic data can be used for the 

following purposes:

•	 To examine changes in genetic diversity in the population over time or compare 

genetic diversity across space.

•	 To detect recent genetic population bottlenecks.

•	 To estimate effective population size or changes in effective population size over 

time (detailed in following sections).

Some of these techniques are well established; others will require additional development before implementation. Overall, the genetics field is rapidly advancing, with novel 

and more precise techniques available each year. In the not-too-distant future, advances in 

conservation genetics may provide us with unprecedented power to infer change in WFRP 

populations.

Community Measures

Diversity Measures. Species richness, evenness, or diversity can be estimated from 

counts of individuals. Research studies have used diversity measures to examine particular 
3-8 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

questions in community and ecosystem ecology, such as community relationships based 

on trophic, functional, or taxonomic groupings. Diversity measures pose challenges, 

however, because they are not easily interpreted. For example, an increase in species 

diversity could represent restoration of a native community, but it could also indicate loss 

of rare species and gains of invasive species. These challenges have limited the utility of 

these measures in resource management monitoring.

Integrity Measures. Karr and Dudley (1981) define biological integrity as the 

“capability of supporting and maintaining a balanced, integrated, adaptive community 

of organisms having a species composition, diversity, and functional organization 

comparable to that of the natural habitat of the region.” The concept of biological integrity 

has evolved in response to perceived flaws in biological diversity measures. Biological 

integrity is used to compare current conditions on the landscape with conditions present 

at a specific instance in history or to a nearby “natural” baseline condition. Because this 

measure relies on one of the diversity measures described above it is subject to all the 

strengths and weaknesses associated with those measures.

Habitat Measures

The ability to correlate species and habitat data allows for better predictions of species 

occurrence and distribution and the effects of management on populations. Two critical 

elements are needed for an accurate, sound comparison of population and habitat data 

(Jones 1986). First, both species and habitat data must be collected on the same site and 

during the same time period. If habitat data are collected at a later date, correlations 

between species’ presence and seasonal changes in the habitat might be missed. Second, 

the habitat definition must be determined before data collection. If habitat is defined as 

any set of ecological conditions in which the species is present, then presence/absence 

data will suffice. If the objective is to identify breeding habitat or differentiate between 

source and sink habitat, however, then data about the species’ ability to survive and 

reproduce (e.g., mortality, survivorship, predation, parasitism) also must be collected 

(Cody 1985).

Species respond to habitat availability and quality at multiple scales. Most habitat 

assessment techniques are designed for assessing vegetation composition and structure 

within a patch or stand, but many organisms also respond to habitat at landscape scales 

(e.g., McGarigal and McComb 1995). Concepts such as metapopulation dynamics, 

source-sink dynamics, dispersal capabilities, and landscape heterogeneity have become 

an important basis for collecting data that characterize landscapes. Examples of such data 

are mean patch size, patch isolation, and edge density. FRAGSTATS (McGarigal and 

Marks 1995) provides descriptions and algorithms for a suite of landscape measures. It 

also provides software for performing calculations on vector or rastor images.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-9

3.1.3 Developing a Sampling Design

Spatial Patterns of Organisms

The spatial distribution of organisms within a population is an important consideration 

for sampling design selection. The three basic population spatial patterns are random, 

aggregated, and uniform (fig. 3.2). Random dispersion, often approximated by a Poisson 

distribution, is found in populations in which the spacing between individuals is irregular 

and the presence of one individual does not directly affect the location of another 

individual. Aggregated (also known as “clumped”) populations are characterized by 

patches or clusters of individuals; the probability of finding one individual increases with 

the presence of another individual. Count data from populations exhibiting aggregated 

spatial patterns can frequently be modeled with the negative binomial distribution. 

Uniform (also known as “regular”) distributed populations have individuals that are 

distributed more or less evenly throughout an area; the presence of one individual decreases 

the probability of finding another individual closer than the spacing pattern.

Resource distribution and habitat quality may affect the dispersion of individuals 

and populations (McComb 2001). Random distribution is often found in species that 

depend on ephemeral resources; species that depend on temporary or seasonal resources 

may exhibit different types of distribution at different points in their life histories. Social 

behavior and territoriality may affect vertebrates’ distribution. Highly territorial species 

tend to follow a regular distribution, while more gregarious and colonial nesting species 

tend to occur in clumps (Curtis and Barnes 1988, Newton 1998). Spatial patterns of 

organisms, and, consequently, sample distributions resulting from their enumeration, 

often change with the spatial scale of observations. Populations may appear uniformly 

distributed at a fine scale, but may show a more random or aggregated distribution 

throughout their range.

As observed organisms become more numerous and the mean number of individuals 

per sample exceeds 10, the Poisson distribution begins to approach a normal distribution. 

Similarly, if the total number of observed individuals (pooled across samples) exceeds 

100, the normal distribution can be used to calculate confidence intervals for the 

population mean (Krebs 1989). Zar (1999) suggested adjusting the sampling unit (e.g., 

Figure 3.2. Three basic spatial patterns of biological populations (Curtis and Barnes 1988, 

Krebs 1989).
3-10 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

quadrat size, length of the sampling period) to increase the probability of detecting 

more individuals in the habitat. If data counts follow the Poisson or negative binomial 

distributions, and the primary objective of monitoring is to estimate only the parameter’s 

population mean, the protocol may suggest that the appropriate sample size and 

confidence intervals be determined using calculations developed specifically for both 

types of distributions (see Krebs 1989 for examples).

Statistical Population

A statistical population is the entire set of potential sampling units from which a sample 

is drawn and from which inferences can be made. For example, if bird-count stations are 

randomly selected from a grid of potential x,y coordinates superimposed over a watershed, 

the statistical population is the total number of x,y coordinates on the grid. The sample 

might be 30 stations within a population of 3,000 coordinates. There is not necessarily a 

correspondence between a statistical population and a biological population.

Standard Sampling Designs

As noted in section 2.1.3, sampling designs may have multiple objectives. First, the 

design should ensure that the sample selected represents the population being monitored 

or studied. This objective is usually accomplished by incorporating a random selection 

procedure into the design process so all members of the population have an equal, or at 

least known, probability of being selected. Randomization is essential for reducing bias 

and estimating the parameters of a population. Second, the design may seek to maximize 

the statistical efficiency of data collection by achieving the greatest possible precision 

for the least cost. The greatest gains in statistical precision are usually attained with 

increasing sample size, which can be financially impractical for most I&M programs. 

Therefore, we recommend that the design process include criteria to allocate a sample 

size sufficient to answer the primary questions of interest with a desired level of certainty.

The following are five sampling designs that have been found to be useful for natural 

resource I&M projects. This introductory material is meant only to guide protocol 

developers toward more comprehensive references on sampling design.

Simple Random Sampling. Simple random sampling occurs when a random 

subset of units are selected as observations from a population in such a way that every 

unit has an equal chance of being chosen (Krebs 1989). This randomly generated set 

of observations may be collectively analyzed as representative of the population within 

the study area. The strength of simple random sampling is that it makes no assumptions 

about the distribution of features in the landscape being sampled. The weakness of simple 

random sampling is that unless the sample size is large, it may not represent the range 

of conditions that occur on the landscape. Consequently, simple random sampling is 

generally not appropriate for large-scale monitoring because it is not cost-efficient.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-11

Stratified Random Sampling. Stratified random sampling is intended to reduce 

variation in a sample by allocating observations to individual strata and then randomly 

locating sample sites within each stratum. Stratification is based on any factor or set of 

factors that could reduce variability within each stratum, such as habitat types, habitat 

quality, or topography. Stratification can also be designed to increase the cost efficiency 

of a sampling scheme (i.e., units of information collected per unit cost). Cost stratification 

can be placed over any other stratification (Scheaffer et al. 1996). For example, sampling 

costs might differ among sites depending on the distance of sample sites from roads. Near 

and far distances from roads may then be used as two strata, with a greater proportion of 

the total sample allocated to the near stratum to reduce costs. Control of costs via stratification, 

however, will not necessarily achieve an overall estimate with the smallest variance.

The strength of stratified random sampling is that it can increase efficiency by 

reducing the number of observations required to reach a desired precision level in an 

estimate. It is most commonly and effectively used for monitoring species that occur in 

low numbers, or when different habitats have different probabilities of the organism’s 

presence. For example, the bioregional monitoring design for the northern goshawk 

(Accipiter gentilis) (Hargis and Woodbridge 2006) uses a stratified random sampling 

design because goshawks’ occupancy rates are expected to be higher in primary habitat 

than in secondary habitat.

The weakness of stratified sampling is that it requires assumptions about the relative 

spatial and temporal variability of strata and its design is inflexible. Strata should remain 

fixed on the landscape over time and data should not be restratified based on some other 

strata of interest that may arise in the future. Sampling designs based on strata that may 

be ephemeral (e.g., early successional stages for vegetation) may not be useful over long 

periods of time. Habitat boundaries may change over time or the same habitats may not be 

present during all sampling periods. 

Systematic Sampling. Systematic sampling consists of a fixed, regular pattern of 

sampling units after random selection of a starting point for the systematic layout. The 

strength of systematic sampling is that it confers an equal probability of selection for 

all observations within the geographic area of inference, enabling observations to be 

aggregated by various strata (e.g., national forests, vegetation types, species ranges, 

with and without experimental treatments) without having to estimate the probability of 

selection within strata. In addition, it can be readily augmented by increasing sample site 

density in strategic locations to reach species-specific sampling objectives (e.g., increase 

sample site density within specific habitats for species of interest at national forests or 

larger scales to improve estimates).

A weakness of systematic sampling is that it may over sample some strata of interest 

and under sample others; systematic sampling is thereby less efficient than a stratified 

sampling approach for those particular strata (e.g., common, widespread vegetation 
3-12 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

types). Systematically sampled observations may also be correlated, which increases 

analysis complexity. Systematic sampling is recommended for forest- and regionalscale monitoring of multiple species because it does not make assumptions about the 

distribution or abundance of various strata across the landscape and it is flexible in terms 

of analyzing subsets of observations to address various management questions.

The Multiple Species Inventory and Monitoring (MSIM) protocol (Manley et al. 

2004, Manley et al., in press) uses a systematic sampling design based on the sampling 

grid of the Forest Service Forest Inventory and Analysis (FIA) program. The current FIA 

sampling design consists of a systematic hexagonal grid across all ownerships in the 

United States; each hexagon contains approximately 2,403 hectares. One FIA sampling 

unit is randomly located within each hexagon. At each unit, vegetation structure and 

composition are scheduled to be surveyed once every 10 to 15 years (Roesch and Reams 

1999). The MSIM collects information on a variety of terrestrial and aquatic plant and 

animal species at plots located 100 meters from the FIA sampling unit in a random 

direction. In addition to the MSIM design, wildlife habitat information for a variety of 

species has been gleaned from FIA vegetation data (e.g., Rudis 1991).

When establishing a systematic sample, care must be taken to ensure that the spatial 

arrangement of observations is not correlated with any regular environmental parameter. 

For example, in the Midwestern United States, placement of sampling points at 1.6-

kilometer (km) intervals may result in a biased sample because the arrangement of roads 

and agricultural infrastructure in the region is based on a 1.6-km land survey scheme.

Adaptive Cluster Sampling. Many species have a tendency to occur in population 

clusters because of dispersal mechanisms, behavior patterns (e.g., herding, colonialism), 

or habitat associations. Under these conditions, it is predictable that monitoring programs 

conducted according to conventional procedures will expend most of the sampling 

effort at locations where the species is not observed. Adaptive cluster sampling refers to 

procedures in which sample selection depends on the values of counts or other variables 

observed during the course of sampling. Initially, a probability procedure is used to select 

a set of sampling units in the study area. When any of the selected units satisfy some 

predetermined criterion (e.g., detection of the target species), additional units are sampled 

in the neighborhood of the qualifying unit. Sampling is extended until no further units 

satisfy the criterion.

For rare or highly aggregated populations, adaptive cluster sampling may greatly 

increase the precision of population size or density estimates when compared to a simple 

or stratified random design of equal cost (Thompson 1992). Adaptive cluster sampling 

can be used with quadrats, belt transects, variable circular plots, and other types of 

sampling units. Pilot studies are strongly recommended to determine the sampling 

design’s optimal scale. Adaptive cluster sampling is more complex to implement than 

most other sampling designs. Therefore, most teams developing WFRP I&M protocols 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-13

will need to arrange for a statistical consultant to develop analytical methods appropriate 

for adaptive sampling. Teams conducting I&M projects will require similar assistance in 

performing data analysis.

Before-After Control-Impact. The recommended design for monitoring the effects 

of a management treatment is a before-after control-impact (BACI) design (Green 1979, 

Stewart-Oaten et al. 1986), which enables comparison of before-and-after effects as well 

as treatment-control effects. The ideal design consists of before-and-after data on replicates of control and treatment sites, but it is usually not possible to replicate the treatment 

due to the treatment’s large spatial extent or its unique nature (e.g., a single power plant, 

ski area, or dam). The asymmetrical BACI design has a single treatment and one or more 

controls, with before-and-after data collected from each (Stewart-Oaten and Bence 2001).

Other variants to the basic BACI design are possible, depending on the type of 

treatment and the anticipated effects. The gradient design involves placing sites at varying 

distances from the source of the treatment (Ellis and Schneider 1997). The factorial 

design creates several paired sites within the treatment and the control that share similar 

attributes; the distinguishing attributes become the factors (Evans and Coote 1993). For 

example, the factors could be two vegetation cover types and dense versus open stands, 

with paired sites in and out of the treatment that represent each cover type and stand 

density combination. Several BACI design variants are presented in Smith (2002).

Size and Shape of Sampling Units

Count data obtained from plots are affected by the sampling unit’s size and shape. Square 

plots and circular plots have smaller boundary/interior ratios than rectangular shapes of 

equal area. Plots with exaggerated lengths are sometimes referred to as strip transects 

or belt transects. Under some sampling conditions it might be difficult for the surveyor 

to determine whether organisms occurring near a plot boundary are inside or outside 

the plot, resulting in counting errors. In these circumstances, compact plot shapes are 

preferred. Boundary/interior ratios also decrease as plot size increases. As a result larger 

plots seemingly offer another approach for reducing counting errors. The tedious nature 

of counting organisms on a large plot under difficult field conditions, however, may 

cause surveyors to make mistakes. Counting error is not the only factor to consider when 

determining plot size and shape. In heterogeneous habitats, data collected on long plots 

often have been found to have lower statistical variance than data from compact plots of 

the same total area (Krebs 1989).

The optimum size and shape of a plot will differ according to the species, 

environmental conditions, and monitoring program objectives. Typically, the optimum 

plot configuration will be one that provides the greatest statistical precision (i.e., lowest 

standard error) for a given area sampled. Several investigators have developed approaches 

for selecting the most appropriate plot size and shape for a particular population 
3-14 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

monitoring program (e.g., Hendricks 1956, Wiegert 1962). Krebs (1989) provides a useful 

review of standardized plot configurations.

While the usual notion of a plot is that of an area delineated by a frame or flagging, 

other techniques may be used to obtain a sample count of target organisms in a given area. 

For example, cover board surveys have been widely adopted for estimating the relative 

abundance of amphibian and reptile populations in different habitat types (Grant et al. 

1992, Harpole and Haas 1999).

Line-transect and point-transect sampling are specialized plot methods in which 

a search for the target organism is conducted along a narrow strip with a known area. 

Rarely can it be assumed that all animals are detected along a transect. If the probability 

of detection can be predicted from the distance between the animal and the centerline 

of the transect, however, then a detection function can be used to estimate population 

density. The approach can be adapted to monitoring programs conducted by foot, 

snorkeling, and ground or air vehicles. Buckland et al. (2001) provides a complete, 

although highly technical, introduction to line-transect and point-transect distance 

sampling methods. The approach has been widely applied to monitoring of vertebrates, 

including desert tortoises (Gopherus agassizii) (Anderson et al. 2001), marbled murrelets 

(Brachyramphus marmoratus) (Madsen et al. 1999), songbirds in oak-pine woodlands 

(Verner and Ritter 1985), and mule deer (Odocoileus hemionus) (White et al. 1989), 

among many others.

Population abundance can be estimated by a variety of “plotless” monitoring methods 

that use measurements to describe individuals’ spacing in an area. These techniques 

are founded on the assumption that the number of individuals in a population may be 

estimated by measuring the average distance among individuals in the population or 

between individuals and randomly selected observations. One of the most widely used 

techniques is the point-centered quarter method (Cottam and Curtis 1956). Distance 

methods have been commonly used for vegetation surveys and are easily adapted to 

inventories of rare plants or other sessile organisms. The approach may also be useful for 

population studies of more mobile animal species by obtaining abundance estimates of 

their nests, dens, roosting sites, or scat piles.

Plotless methods may have some practical advantages over plots or transects, such as 

the following:

•	 Plotless methods are less susceptible to counting errors that often occur near plot 

boundaries; thus, they may yield more accurate abundance estimates.

•	 The time and effort required to attain an adequate sample of plotless 

measurements in an area often is less than that required to search for every target 

organism on a plot; thus, the efficiency of the monitoring program increases.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-15

Field techniques vary depending on the plotless method selected for the monitoring 

program. All plotless methods use random selection procedures to choose center points 

and/or compass bearings. Equipment requirements are minimal; usually only a compass, 

flagging, and measuring device appropriate to the scale of the population and monitoring 

area are needed. However, data collection protocols may be relatively complicated and 

sample size calculations may need to be performed in the field. Therefore, a rigorous 

training program is recommended for personnel conducting the monitoring program. Two 

useful references for designing inventories based on plotless methods are Seber (1982) 

and Bonham (1989).

Temporal Aspects of the Sampling Design

The description of the sampling design should include temporal aspects within a sampling 

period, such as the optimal time period (month or season) for conducting surveys, any 

daily restrictions in sampling period (e.g., mornings only), and the temporal sequence 

of within-year sampling (e.g., once a month, every 2 weeks). For monitoring strategies, 

the design should also describe the resampling cycle. Any restrictions imposed by the 

sampling design, however, may affect how well the sample represents the adopted target 

population and/or sampling frame.

Monitoring strategies typically call for resampling the same set of units in each 

sampling period, thus building a history of site conditions across a consistent set of sites. 

This approach is effective if the sampling effort is sufficient to represent the range of 

conditions across the area of inference. It is sometimes called a “single panel” design. 

The term “panel” describes the set of units that are sampled every sampling period. From 

the standpoint of statistical analysis of temporal change, data collected for the same units 

every sampling period will have lower variance than data collected for different units 

each year. As a result, the sample size needed to detect a change with the desired level 

of precision and power will be smaller than if the sampling units changed each year. 

A main attribute of the panel approach is the ability to make estimates for individual 

years or other points in time. The ability to estimate change, however, is not necessarily 

improved over a repeated sampling design (see Cochran [1977] for a summary of the 

tradeoffs). One weakness of repeated measurements of the same sites over time is that 

through chance (or selection of which sites are being monitored), sites with changes in the 

monitoring measure may not be included in the sample (i.e., a change may occur during 

the monitoring period, but the change is not detected because no or too few monitored 

sites are affected). A new random sample drawn at each time point has a better chance 

of picking up such a change, but the tradeoff is that the sample size needed each time is 

larger than that needed for the repeated-measures design.

Over the past decade, the use of rotating panels has gained popularity as an approach 

to gaining greater representation of ecological conditions across large landscapes (a rotating-panel design is different from drawing a new random sample each time). A resampling 
3-16 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

design, known as a serial alternating panel approach, involves cycling through a series of 

panels, each of which contains a set of points that spatially represents the landscape and 

which, in sum, is more likely to ecologically represent the area of inference. In a panel approach, a systematic subset of points (a panel) is identified for each sampling period. The 

alternating-panel design generally consists of n sampling units partitioned into m panels. 

Each panel contains np sampling units (np = n/m) and has the same temporal pattern of 

remeasurement. The remeasurement schedule determines the number of panels; if all sites 

are visited every 5 years, then there are five panels, one for every year. As an example, the 

FIA sampling design is based on a serial alternating-panel approach (Lesser and Overton 

1994, Thornton et al. 1994, Roesch and Reams 1999). Most FIA regions use 10 panels 

consisting of 10 percent of the sample, resampled every 10 years.

For cases in which resource conditions vary substantially from year to year and 

influence the abundance of organisms (e.g., the abundance of mice), it is wise to augment 

the panel approach with an additional panel that is sampled every year to track annual 

variation. This additional panel is the augmented serial alternating panel (ASAP) design.

When a series of panels is completed, the strength of an ASAP design is its ability to 

rival or exceed the statistical power to detect a population trend obtained from the singlepanel design while achieving better ecological representation across the landscape. Also, 

it creates larger data sets for exploring habitat relationships and potential causal factors 

for observed changes. A weakness of the ASAP design is that the statistical model is not 

well developed; thus, some uncertainties exist regarding the exact procedure to follow for 

generating trends. Also, the ASAP design requires consistent funding each year to ensure 

that each scheduled panel is fully surveyed. Incomplete or unsurveyed panels would 

further complicate the statistical analysis or could reduce the design to one that simply 

samples different sites every year. Because of the advantages, however, many broad-scale 

monitoring programs are moving toward a panel approach (e.g., Reeves et al. 2003). To 

determine which approach best meets the needs and capabilities of the national forests/

grasslands and regions, it is recommended that panel approaches be considered and 

evaluated against single-panel designs for monitoring population trends.

3.2 Data Collection: Biological Study Ethics

Capture, marking, and observation techniques may cause subject animals to experience 

pain, permanent injuries, and increased mortality rates. Indeed, some animal inventories 

and monitoring studies depend on lethal traps for the collection of voucher specimens 

or population data. The justification for such studies must balance the benefits of newly 

acquired knowledge with the welfare of animals subjected to study methods. Most 

wildlife, fisheries, and zoological professional societies have adopted guidelines to help 

field biologists minimize adverse impacts on individual animals and populations (e.g., 

American Society of Mammalogists 1987; ASIH, AFS, and AIFRB 1988; Gaunt et al. 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-17

1997). In some situations, university faculty collaborating with the Forest Service may 

also be required to maintain the standards prescribed by their universities’ Institutional 

Animal Care and Use Committees. We recommend that protocol developers become 

familiar with the standards for animal usage in field studies and that the methods be 

designed with consideration of these standards.

3.3 Data Storage: Metadata Purpose and Standards

I&M data can benefit scientific research and facilitate species conservation programs 

for many decades. The usefulness of a database is determined not only by the rigor of 

the methods used to conduct the monitoring program, but also by the ability of future 

investigators to decipher the variable codes, measurement units, and other details 

associated with the database. The term “metadata” refers to “data about data.” Metadata 

is information about the origins of a database or a map provided by its developer, changes 

to the work made by secondary users, and quality of the data. Metadata facilitates 

information sharing among current users. It is crucial for maintaining the value of data 

to future investigations. The standardized metadata that will accompany I&M databases 

should be recognized as one of the principle means to improve the transferability 

of biological monitoring information among different Forest Service programs and 

management units.

Since 1995, all Federal agencies have adopted a content standard for geospatial data 

sets. The standard, called Content Standard for Digital Geospatial Metadata (CSDGM), 

was developed by the Federal Geographic Data Committee (FGDC); this group also is 

responsible for reviewing and updating the standard as needed. The currently approved 

FGDC standard is CSDGM Version 2 – FGDC-STD-001-1998. The FGDC metadata 

standard includes seven major elements, some of which are mandatory for every database 

and map; other elements are applicable only to particular types of work (fig. 3.3). Some 

GIS packages include software tools that automate some metadata documentation tasks; 

however, most fields must be manually completed by the originator of the database. 

The process of describing data sources, precision tests, geoprocessing methods, and 

organizational information can be tedious and add many hours to the preparation of a data 

set. The initial cost of the labor, however, will ensure that the data can be used for many 

years, possibly for research or conservation purposes not anticipated by the originator of 

the data set.

The CSDGM was developed to be applicable to all geospatial databases. The CSDGM 

framework identifies the originators of the database, describes data sources, and captures 

spatial reference information. But the generic nature of the CSDGM does not provide 

for standardization of many attributes commonly shared among biological databases. To 

extend the effectiveness of the CSDGM framework, the FGDC’s Biological Data Working 

Group has developed the Biological Metadata Profile (FGDC and USGS 1999) to 
3-18 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

standardize the use of terms and definitions in metadata prepared for biological databases. 

The Biological Metadata Profile addresses topics such as taxonomic classification, 

voucher specimens, environmental attributes, and similar issues not considered in the 

CSDGM. The Biological Metadata Profile is also applicable to nongeospatial data sets.

Figure 3.3. Graphical representation of major metadata elements specified by the approved 

Federal standard for geospatial databases (CSDGM Version 2–FGDC-STD-001-1998). (This 

figure was copied from http://biology.usgs.gov/fgdc.metadata/version2.)

3.4 Analytical Methods

This section should help familiarize the reader with the basic characteristics and 

assumptions of analysis models and the circumstances under which they can be employed 

effectively. The purpose of this information is to help build a well-planned and effective 

data analysis framework. Since this section deals with complex technical issues, only 

key elements and concepts of effective data processing are addressed in this technical 

guide. Protocol developers should consult statisticians and more comprehensive statistical 

resources to establish a rigorous context for data analysis. General references such as Zar 

(1999) provide useful guidance for many aspects of analysis.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-19

3.4.1 Data Visualization and Exploratory Data Analysis

The initial phase of every data analysis should include exploratory data evaluation. 

Graphical display of information is an integral component of every research undertaking. 

As a first step in evaluating the data’s nature, quality, and underlying assumptions, I&M 

technical guides should encourage a visual exploratory data analysis (Tukey 1977) before 

statistical testing. The protocol may direct users to excellent examples of graphical display 

in Spear (1952), Tukey (1977), Tufte (1983), Elzinga et al. (2001), and Ellison (2001). 

Tufte (1983, 1997, 1990) gives a thorough overview of how to design and use images in a 

wide range of applications.

Anscombe (1973) advocates several iterations in the process of examining data to 

reveal unique features. Hilborn and Mangel (1997) recommend plotting data in different 

ways to uncover “plausible relationships.” Numerous types of graphical displays can 

be used to examine data before analysis and to display summary statistics. The most 

commonly used include normal probability plots, density plots (histograms, dit plots), box 

plots, scatter plots, bar charts, and point-and-line charts (Elzinga et al. 2001). The reader 

can find excellent examples of how to construct and use graphical displays in almost any 

introductory text on statistical analysis. 

According to Tufte (1983), effective graphical displays show the essence of the collected data and should do the following:

•	 Show the data.

•	 Induce the viewer to think about the substance rather than methodology, graphic 

design, the technology of graphic production, or something else.

•	 Avoid distorting what the data have to say.

•	 Present many numbers in a small space.

•	 Make large data sets coherent.

•	 Encourage the eye to compare different pieces of data.

•	 Reveal the data at several levels of detail, from a broad overview to the fine structure.

•	 Serve a reasonably clear purpose—description, exploration, tabulation, or decoration.

•	 Be closely integrated with the statistical verbal descriptions of a data set.

In some cases, the pattern of the data will actually guide the selection of the model 

that can be used to describe the relationship in the data (Anscombe 1973, Hilborn and 

Mangel 1997). For example, refer to the four graphs in figure 3.4. They all display 

relationships that produce identical outputs if subjected to a simple linear regression 

analysis (table 3.1).
3-20 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

Figure 3.4. Relationships between the four sets of x,y pairs (after Anscombe 1973).

Table 3.1. Four hypothetical data sets of x,y variable pairs.

A B C D Analysis output

X Y X Y X Y X Y

10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58

8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76

13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71

9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84

11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47

14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04

6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25

4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50

12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56

7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91

5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89

N = 11

Mean of Xs = 9.0

Mean of Ys = 7.5

Regression line: Y = 3 + 0.5X

Regression SS = 27.50

r = 0.82

R2 = 0.67

Source: Modified from Anscombe (1973).
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-21

Yet, whereas a simple linear regression model may describe the trend in Case A 

reasonably well, its use in the remaining three cases may not be appropriate, at least 

not without an adequate examination and transformation of the data. Case B could be 

described best using a logarithmic rather than a linear model. The relationship in Case D 

is spurious, resulting from connecting a single point to the rest of the data cluster. Case C 

also reveals the presence of an apparent outlier (i.e., an extreme value that may have been 

missed without a careful examination of the data). This simple example illustrates the 

value of a visual scrutiny of data before data analysis.

Under some circumstances, visual displays alone can provide an adequate assessment 

of the data. This approach may even be superior to formal data analyses in situations with 

large quantities of data (e.g., detailed measurements of demographics or vegetation cover) 

or if data sets are sparse as a result of inadequate sampling or pilot investigations. For example, maps can be used effectively to present a great volume of information (if a spatial 

context is relevant to the question of interest). Tufte (1983) argues that maps are the only 

means for displaying large quantities of spatial data in a relatively small amount of space 

while still allowing for a meaningful interpretation of the information. In addition, maps 

allow for a visual analysis of data at different levels of temporal and spatial resolution. 

They also allow for the assessment of relationships among variables and can help identify 

causes of the detected pattern.

A simple assessment of the species richness of a community can be accomplished 

by presenting the total number of species found. Additional information can be acquired 

by plotting the cumulative number of species detected against sampling effort to assess 

whether the survey includes all or most of the species in the community. A steep slope 

of the resulting curve would suggest that additional sampling effort might yield more 

species, whereas flattening of the curve would indicate that most of the species in the 

community have been detected (Magurran 1988, Southwood 1992).

Construction of species abundance models such as log-normal distribution, log series, 

McArthur’s broken stick, or geometric series may be used to visually acquire additional 

information about a particular research area. Individual species abundance models 

describe communities with unique characteristics. For example, mature undisturbed 

systems characterized by greater species richness typically display a log-normal 

relationship between the number of species and their respective abundances. On the 

other hand, early successional sites or environmentally stressed communities (e.g., those 

affected by pollution) are characterized by geometric or log series species distribution 

models (Southwood 1992).

The use of confidence intervals presents another attractive approach to exploratory 

data analysis. Some even argue that confidence intervals represent a more meaningful 

and powerful alternative to statistical hypothesis testing since they give an estimate of the 

magnitude of an effect under investigation (Steidl et al. 1997, Johnson 1999). Confidence 
3-22 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

intervals can be placed on estimates of population density, observed effects of population 

change in observations taken over time, or treatment effects in perturbation experiments. 

They are also commonly used in calculations of effect size in power or meta-analysis 

(Hedges and Olkin 1985, Gurevitch et al. 1992).

3.4.2 Basic Assumptions of Parametric Models

Parametric statistical models are based on a set of assumptions that are necessary for 

models to properly fit and describe the data. If assumptions are violated, statistical 

analyses may produce erroneous results (Krebs 1989, Sabin and Stafford 1990, Sokal and 

Rohlf 1995). Thus, developers of I&M protocols should consider whether data will likely 

fit the assumptions of a selected model. Options for dealing with problems of assumption 

violations should always be presented.

Independence of Observations

An essential condition of most statistical tests is the independence of observations in 

space and time (usually obtained using random selection). Observations can be counts of 

individuals or replicates of treatment units in manipulative studies. Krebs (1989) argues 

that if the assumption of independence is violated, the chosen probability for Type I error 

(α) cannot be achieved. Analysis of variance (ANOVA) and linear regression techniques 

are sensitive to this violation (Sabin and Stafford 1990, Sokal and Rohlf 1995). Mixedmodel analysis procedures, which are now available in some statistical software packages, 

allow for some relaxation of the assumption of independence.

Homogeneity of Variances

Parametric models frequently assume that sampled populations have similar variances 

even if their means are different. This assumption becomes critical in studies comparing 

different groups of organisms, treatments, or sampling intervals. If the sample sizes are 

equal, then parametric tests are fairly robust to the departure from homoscedasticity (i.e., 

equal variance of errors across the data) (Day and Quinn 1989, Sokal and Rohlf 1995). 

In fact, equal sample sizes across treatments should be obtained whenever possible since 

most tests are overly sensitive to violations of assumptions in situations with unequal 

sample sizes (Day and Quinn 1989). Plotting the residuals of the analysis against 

predicted values can reveal the nature and severity of the potential problem. This type of 

output is a standard feature in many statistical packages. Visual inspection of the data can 

help determine if transformation of the data is needed and can also indicate the type of 

distribution (fig. 3.5). Although several formal tests exist to determine the heterogeneity 

of variances (e.g., Bartlett’s test, Levine’s test), these techniques assume normal data 

distribution, which reduces their utility in most ecological studies (Sokal and Rohlf 1995). 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-23

Figure 3.5. Three hypothetical residual scatters. In Case A, the variance is proportional to 

predicted values, which suggests a Poisson distribution. In Case B, the variance increases 

with the square of expected values and the data approximate a log-normal distribution. The 

severe funnel shape in Case C indicates that the variance is proportional to the fourth power 

of predicted values (from Sabin and Stafford 1990).

A B C

Normality

Unfortunately, ecological data rarely follow a normal distribution and nonnormality 

appears to be the norm in biology (Potvin and Roff 1993, White and Bennetts 1996, 

Hayek and Buzas 1997, Zar 1999). Moreover, the normal distribution primarily describes 

continuous variables whereas count data, often the type of information gathered during 

I&M studies, are discrete (Krebs 1989). Although parametric statistics are fairly robust 

to violations of normality, highly skewed distributions can significantly affect the results. 

Ideally, the sample size should be equal among groups and sufficiently large (e.g., n > 20). 

The significance of nonnormality can be tested with several techniques, including the 

W-test and the Kolmogorov-Smirnov D-test for larger sample sizes. Sabin and Stafford 

(1990), however, argue that the applicability of both tests is limited because they exhibit 

low power if the sample size is small, and excessive sensitivity when the sample size is 

large. Graphical examinations of the data are actually more appropriate than formal tests 

because they enable one to detect the extent as well as the type of the problem. I&M 

protocols may suggest plotting and scrutinizing data in normal-probability plots (fig. 3.6), 

stem-and-leaf diagrams, or histograms (Day and Quinn 1989, Sabin and Stafford 1990). 

Data that are reasonably symmetric about the mean and that do not have a large number of 

observations in the distribution tails are generally well enough approximated by a normal 

distribution for most standard analyses for which this is an assumption.
3-24 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

Figure 3.6. Plots of four hypothetical distributions (left column) with their respective normal 

probability plots (right column). Solid and broken lines show the observed and normal 

(expected) distributions, respectively (from Sabin and Stafford 1990).

3.4.3 Possible Remedies if Parametric Assumptions Are Violated

Data Transformation

If significant violations of parametric assumptions occur, protocol users should be 

advised to implement an appropriate data transformation to try to resolve the violations. 

During a transformation, data will be converted and analyzed at a different scale than the 

original data. Transformations effectively reweight the data and can result in detecting 

statistical differences when none could be detected otherwise, so it is important to 

consider the effects of transforming dependent variables on the eventual output. Protocol 

users must also be aware of the need to back-transform the results after analysis to present 

parameter values on the original data scale. Table 3.2 gives examples of common types of 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-25

Table 3.2. Some common data transformations in biological studies. Transformations in this table are for dependent (y) 

variables typically transformed to meet assumptions of statistical tests when testing means. Biologists should be aware of 

assumptions for each statistical procedure to assess the need to transform variables before transforming variables.

Transformation

When appropriate to consider using Transformation

Back

type transformation

Square root

Logarithmic

Inverse

Arcsine square root

Box-Cox objective 

approach

Use with count data following a Poisson 

distribution (figure 3.5A); more generally, when variances are proportional to 

means. In some instances, addition of 3/8 

will improve normality.

Use with count data when means are 

proportional to standard deviations (figure 

3.5B). A rule of thumb suggests its use 

when the largest value of the dependent 

variable is at least 10 times the smallest 

value.

Use when data residuals exhibit a severe 

funnel shaped pattern (figure 3.5C), 

which is often the case in data sets with 

many near-zero values.

Appropriate for proportional or binomial 

data. This transformation is beneficial if 

it improves normality for nonbinomial 

proportions. Most efficient when most 

proportions occur at ends of the scale 

(0.0–0.25 and/or 0.75–1.0), and least effective when proportions are distributed 

in the middle (0.25–0.75).

If it is difficult to decide on what transformation to use, this procedure finds 

an optimal model for the data. Box-Cox 

approaches may address skewed residual 

distributions and heterogeneous variance.

y′ = loge(y+c)

where c = 0 if all y > 1

and c = 1 otherwise

y′ = 1/y

Note: Inverse transformations will 

cause very large values to be very 

small and very small values to be 

very large. Thus, one must reverse the 

distribution before transforming by 

multiplying a variable by -1, and then 

adding a constant to the distribution to 

bring the minimum value above 1.0. 

Once the inverse transformation is 

complete, the ordering of values will 

be identical to the original data.

y′ = arcsin(square root[y]), where 

y is a proportion.

where, λ is an estimated parameter

y = y′

2

y = exp(y′)-c

y = 1/y′

y = (sin y′)2

Source: Modified from Sabin and Stafford (1990).
3-26 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

transformations that may be recommended for use. A wisely chosen transformation can 

often improve homogeneity of variances as well as produce an approximation of a normal 

distribution. Sabin and Stafford (1990) and Zar (1999) give good overviews of data 

transformations in ANOVA and regression models.

A primary reason to avoid transformations is that interpreting transformed variables 

is very difficult (e.g., what is the arcsine square root of a proportion?). As a result, it is 

recommended that the data be back-transformed after analysis but back-transformations 

are not always necessarily at the same scale as the original data. Therefore, practitioners 

must be aware of the assumptions of the particular statistical model and how transforming 

will affect their data set. Removing outliers or perhaps using a nonparametric technique 

may be a better approach than trying to normalize the distribution of data and homogenize 

variances to meet the assumptions of a parametric model.

Nonparametric Alternatives

If the data violate basic parametric assumptions and transformations fail to remedy the 

problem, a nonparametric method might be appropriate (Sokal and Rohlf 1995, Conover 

1999). Nonparametric techniques have less stringent assumptions about the data, are less 

sensitive to the presence of outliers, and are often more intuitive and easier to compute 

(Sokal and Rohlf 1995, Hollander and Wolfe 1999). Since nonparametric models are less 

powerful than their parametric counterparts, protocols should not advocate the use of 

nonparametric tests if data meet, or approximate, parametric assumptions (Day and Quinn 

1989, Johnson 1995, Smith 1995).

Randomization Tests

These tests are not alternatives to parametric tests, but rather are a means of estimating 

the statistical significance that relies only on the independence of observations. They 

are extremely versatile and can be used to estimate significance of test statistics for 

a wide range of models. Although randomization tests are computationally difficult 

even with small sample sizes (Edgington 1995), the Forest Service (R. King, personal 

communication) has developed a Microsoft® Excel macro for randomization tests that 

is available to Forest Service employees at http://statistics.fs.fed.us. More information 

on computation-intensive techniques can be found in Crowley (1992), Potvin and Roff 

(1993), and Petraitis et al. (2001).

Other Approaches

Other parametric techniques such as generalized linear models employ a distribution 

appropriate for the data instead of trying to normalize them (Ministry of Environment, 

Lands and Parks 1998). For example, White and Bennetts (1996) give an example of 

fitting the negative binomial distribution to point-count data for orange-crowned warblers 

(Vermivora celata) to compare their relative abundance among forest sites. Zero-inflated 

Poisson (ZIP) models and negative binomial regression models are recommended for 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-27

analysis of count data with frequent 0 values (e.g., rare species studies) in which data 

transformations are not feasible (e.g., Heilbron 1994, Welsh et al. 1996, Ridout et al. 

1998, Agarwal et al. 2002, Hall and Berenhaut 2002).

3.4.4 Statistical Distributions of Plant and Animal Population Data

Poisson Distribution

The Poisson distribution approximates a random spatial distribution common among 

species with low population density, where the probability of detecting an individual in 

any sample is rather low (Southwood 1992). As the mean number of individuals in the 

sample increases, the Poisson distribution begins to approach the normal distribution 

(Krebs 1989, Zar 1999).

During sampling, the key assumption of the Poisson distribution is that the expected 

number of organisms in a sample is the same and that it equals μ, the population 

mean (Krebs 1989). One intriguing property of the Poisson distribution is that it can 

be described by its mean and that the mean equals the variance (s2). The probability 

(frequency) of detecting a given number of individuals (x) in a sample collected from a 

population with mean = μ is:

Px = (e-μ μx)/x!

Whether or not the data follow a Poisson distribution can be tested with a simple Chisquare goodness of fit test:

Chi-square = Σ (observed frequency-expected frequency)2/expected frequency,

or with an index of dispersion (I), which is expected to be 1.0 if the assumption of 

randomness is satisfied:

I = s2 / x,

where x and s2 are the observed sample mean and variance, respectively.

Krebs (1989) and Zar (1999) provide excellent worked examples of tests for goodness 

of fit for Poisson distributions. The presence of a Poisson distribution in data can also be 

assessed visually by examining the scatter pattern of residuals during analysis (fig. 3.5; 

Sabin and Stafford 1990). If we reject the null hypothesis that observations came from a 

Poisson distribution, the sampled organisms are either distributed uniformly or regularly 

(underdispersed) with s2 < x, and s2/ x < 1.0, or they are clumped (overdispersed).

Negative Binomial Distribution

One mathematical distribution that describes clumped or aggregated spatial patterns is 

the negative binomial (Pascal) distribution (Anscombe 1950, Krebs 1989). White and 

Bennetts (1996) suggested that this distribution is frequently a better approximation to 
3-28 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

count data than the Poisson or normal distributions. The negative binomial distribution 

is described by the mean and the dispersion parameter k, which expresses the extent 

of clumping. As a result of aggregation, it always follows that s2 > x and the index 

of dispersion (I) > 1.0. The value of k decreases with an increase in the degree of 

aggregation and vice versa. The value of k can be approximated with:

k = x2 / (s2 –x)

Several techniques exist to evaluate the goodness-of-fit of data to the negative binomial 

distribution. Good descriptions and examples of their use can be found in Krebs (1989), 

Southwood (1992), and Zar (1999). Since the variety of possible clumping patterns in 

nature is practically infinite, neither the Poisson or negative binominal distributions may 

always adequately fit the data at hand (Krebs 1989).

3.4.5 Analysis Models and Methods

Species’ Presence and Frequency of Occurrence

In some cases, simply determining whether a species is present in an area may be a sufficient objective. For example, biologists attempting to conserve a threatened wetland 

orchid may need to monitor the extent of the species’ range and degree of population 

fragmentation on a national forest. One hypothetical approach is to map all wetlands in 

which the orchid is known to be present as well as additional wetlands that may qualify as 

potential habitat. To monitor changes in orchid distribution at a coarse scale, data collection could consist of a semiannual monitoring program conducted along transects at 

each mapped wetland to determine if at least one individual orchid (or some alternative 

criterion to establish occupancy) is present. Using only a list that includes the wetland 

label (i.e., the unique identifier), the monitoring year, and whether the species was present 

or absent, biologists could prepare a time series of maps displaying all the wetlands on 

the forest by monitoring year and distinguish the subset of wetlands that were found to be 

occupied by the orchid (i.e., frequency of occurrence). Such an approach could support 

a qualitative assessment of changes in the species distribution pattern and generate new 

hypotheses regarding the cause of the observed changes.

An analysis of species’ presence usually requires estimation of the probability of 

detection because it is rare that individuals of a species will always be detected during a 

survey. Without estimation of a detection probability, it will not be known whether a lack 

of detection truly represents a species’ absence, or if it represents some probability of the 

species’ presence.

Detection probabilities are estimated from repeated surveys of the same samples, 

either during a pilot study or during the actual survey (MacKenzie et al. 2002). For example, if a site is sampled three times, with X representing presence and O representing 

absence, the possible outcomes of three surveys are 3 X’s, 3 O’s, 1 X and 2 O’s or 2 X’s 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-29

and 1 O, in various patterns of XOX, XXO, etc. An average detection probability for all 

sites sampled can be calculated; the standard error of this average can be estimated using 

a nonparametric bootstrap method.

MacKenzie et al. (2002) estimated the frequency of occurrence (proportion of sites 

with species’ presence), using a maximum likelihood estimation technique in which the 

detection probability and probability of occupancy are both estimated parameters. The 

method is similar to a closed-population, mark-recapture model. In their example, the 

statistical population was the total number of ponds in a prescribed area and the object 

was to estimate the proportion of ponds with frogs present (MacKenzie et al. 2002). In 

a grid-based sample, the total number of grid cells is the statistical population, and the 

object is to estimate the proportion of grid cells that are likely to be occupied by the target 

species, given the outcome from a sample of grid cells. This approach has been adopted 

for estimating goshawk presence over large geographic areas (Hargis and Woodbridge 

2006). Recently, new methods such as noninvasive genetic sampling have bolstered our 

ability to detect presence in grid cells and thus have increased our detection probabilities 

and precision in presence/absence measures (McKelvey et al. 1999, McDaniel et al. 2000).

Recent studies indicate a positive correlation between frequency of occurrence and 

species’ abundance, because species that increase in abundance also show increases in the 

number of sites they occupy (Gaston et al. 2000, He and Gaston 2000). As a consequence, 

presence-absence information is useful for monitoring population trends, (He and Gaston 

2000, MacKenzie et al. 2003, MacKenzie et al. 2005).

Assumptions, Data Interpretation, and Limitations. Presence-absence data are 

binary because each survey site has one of two possible outcomes: presence or absence. 

A major assumption of presence-absence data is that a species’ presence or absence at a 

site does not change during the survey period because the detection probability is based 

on a constant state of presence (or absence) during all the visits to the same site. An 

additional assumption is imposed if the average detection probability for all observations 

is calculated from pilot data: the detection probability for the actual sample period is 

the same as that of the pilot study. The detection probability could change, however, if 

the pilot study took place in a different year, took place at a different time of the same 

year, used different observers, or was in slightly different habitats. Thus, we recommend 

that detection probabilities be calculated for each survey period from the actual survey 

data even though this approach will require multiple visits per site. Survey costs can be 

reduced by obtaining the detection probability from a subsample of sites as long as the 

subsample is fairly representative of other sites in terms of factors (such as vegetation 

density) that could affect detection of the target species.

An analysis of presence or frequency of occurrence works best if the plot size is 

designed to contain only one or two individuals of the target species because density 

can affect the detection probability, with higher probability of detections associated with 
3-30 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

higher densities (Williams and Berkson 2004). When density is consistently high as in a 

colony or cluster, an analysis of presence based on colony presence rather than individual 

presence could be meaningful. If density varies greatly among sampling units, the 

variance around the mean detection probability could be high, thus reducing the precision 

of the estimate of presence. In this case, it might be better to estimate relative or absolute 

abundance rather than reduce the data to an analysis of presence.

Abundance and Density

Any analysis of abundance or density should include distance measurements along with 

counts of individuals whenever imperfect detectability of all individuals is present in 

a sampling unit. Distance sampling is not simply an analysis tool; it is an integrated 

approach that encompasses study design, data collection, and statistical analyses 

(Rosenstock et al. 2002). It is based on research indicating that the probability of 

detecting an individual declines as distance from the observer increases (Buckland et 

al. 2001). It also takes into consideration the differences in detectability between point 

and line transects, because point transects have a larger proportion of observations at 

distances where detection probability is low than do line transects (Buckland et al. 2001, 

Rosenstock et al. 2002).

Statistical analyses of distance sampling data can be accomplished with the software 

package DISTANCE (Thomas et al. 2005), which is available free of charge online from 

the Centre for Research into Ecological and Environmental Modelling, Research Unit for 

Wildlife Population Assessment, St. Andrews University, Scotland (http://www.ruwpa.

st-and.ac.uk/distance).

After exploratory analysis of distance data, the data are modeled and tested with 

goodness-of-fit tests. When binomial distance sampling methods are used, goodness-of-fit 

cannot be tested (Buckland et al. 2001), but Akaike’s Information Criterion (AIC) (see 

the box entitled Akaike’s Information Criterion) can be used to select among competing 

models (Rosenstock et al. 2002).

Mark-recapture models may be used to estimate absolute densities of populations 

and provide additional information on such aspects as animal movement, geographic 

Akaike’s Information 

Criterion

Akaike’s information criterion (AIC), derived from information theory, may be used to select 

the best fitting model among a number of a priori alternatives. AIC can be easily calculated for 

any maximum likelihood-based statistical model, including linear regression, ANOVA, and other 

general linear models. The model hypothesis with the lowest AIC value is generally identified as the 

best model among the identified set of models; models with ΔAIC <5 are viewed as competing, or 

equal, models (Burnham and Anderson 2002). Stephens et al. (2005) provide a general perspective 

on information theory and hypothesis testing. A more in-depth discussion of practical uses of AIC 

may be found in Lebreton et al. (1992), Anderson et al. (1994), Franklin et al. (2001), and Burnham 

and Anderson (2002). 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-31

distribution, and survivorship (Krebs 1989). Open mark-recapture models (e.g., JollySeber) assume natural changes in the population size of the species of interest during 

sampling. In contrast, closed models (e.g., Petersen, Schnabel) assume a constant 

population size (Krebs 1989). Program MARK (White and Burnham 1999) performs 

sophisticated maximum-likelihood-based mark-recapture analyses and can test and 

account for many of the assumptions such as open populations and heterogeneity. The 

selection of appropriate models to analyze mark-recapture data can be enhanced with the 

use of AIC (Lebreton et al. 1992, Anderson et al. 1994, Burnham and Anderson 2002). 

Excellent discussions of absolute abundance techniques can be found in Krebs (1989) 

and Caughley (1977). Recently, due to the popularity of abundance estimates with genetic 

data, new mark-recapture models have been created that consider unique properties of 

genetic tagging (McKelvey and Schwartz 2004, Lukacs and Burnham, in press).

Occurrence data may be used to predict density if the relationship between species 

occurrence and density is known and the model’s predictive power is reasonably high 

(Hayek and Buzas 1997). For example, one can record plant abundance and species 

richness in sampling quadrats. The species’ proportional abundance, or constancy of its 

frequency of occurrence (Po), can then be calculated as:

Po = No. of species occurrences (+ or 0) / sample size (quadrats)

Consequently, the average species density is plotted against its proportional abundance 

to derive a model to predict species abundance in other locations with only occurrence 

data (fig. 3.7). Note, however, that the model may function reasonably well only in similar 

Figure 3.7. A relationship between a species sample frequency (proportion of occurrences) 

and its mean density in habitat (from Hayek and Buzas 1997).
3-32 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

and geographically related types of plant communities (Hayek and Buzas 1997). Other 

examples of the use and analysis of relative density data can be found in James et al. 

(1996), Rotella et al. (1996), Huff et al. (2000), and Knapp and Matthews (2000).

Assumptions, Data Interpretation, and Limitations. Analyses of relative 

abundance data require attention to data distribution. Since the focus is on count data, 

alternative statistical methods can be employed to fit the data distribution. Absolute 

abundance techniques have stringent requirements (Krebs 1989). Given that statistical 

power negatively correlates with the variability of the monitoring index, choosing an 

appropriate measure of abundance and estimates of its confidence interval is crucial 

(Harris 1986, Gerrodette 1987, Gibbs et al. 1999). An excellent overview of a variety of 

groups of animals and plants for which the variability in estimating their population is 

known is given in Gibbs et al. (1999).

Vital Rates

The effects of population and habitat management and other environmental factors often 

directly influence vital rates of populations (DeSante and George 1994). Therefore, 

an understanding of demographic parameters of populations such as productivity and 

survivorship is an important element in efforts to monitor any population (Baillie 1990, 

DeSante and Nott 2000). Information on population density alone may not provide a true 

indication of population status due to source-sink dynamics (Van Horne 1983). Variation 

in population size, reflected by density changes, which may potentially be associated 

with management actions or environmental factors may be confounded by immigration 

and/or emigration from the surrounding area (George et al. 1992). Monitoring a 

variety of demographic factors such as age-specific mortality, reproductive rates, age 

of first reproduction, or number of offspring per female can reveal specific aspects of 

demography that correlate with observed population changes. These correlations can 

then be used to investigate whether certain age groups or life events are particularly 

sensitive to management actions. Furthermore, an understanding of vital rates can 

provide information about when a population has been stressed in a yearly life cycle. 

This information may be particularly important to determine if management actions on 

migratory animals’ breeding or wintering areas are influencing the population (DeSante 

and Nott 2000).

When vital rates are used to monitor population change, efforts must be made 

to ensure that the vital rates represent the entire population and not a subset of the 

population that has greater opportunities for survival and reproduction. For example, 

when estimating reproductive rates or adult survival, the lower rates of senescing adults 

could be an important consideration (Raphael et al. 1996). Reproductive rates should 

also include an estimate of the proportion of nonbreeding females in the population. 

All aspects of population demography must reflect rates associated with low-quality as 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-33

well as high-quality habitat (Raphael et al. 1996). A good sampling design will reduce 

potential bias associated with habitat quality.

The ability to use a finite rate of population change (λ) for monitoring purposes is 

often challenged by a lack of data on juvenile emigration rates. This problem is not trivial, 

as seen in the case of the northern spotted owl (Strix occidentalis), in which original 

estimates of juvenile survival rates for three study areas were adjusted 42 to 137 percent 

when researchers were able to incorporate radio telemetry data of juvenile emigration 

(Franklin et al. 1999, cited in Franklin et al. 2004). In the absence of site-specific 

emigration rates, Pradel (1996) developed analytical techniques that estimate λ directly 

from capture-recapture data.

Monitoring efforts associated with land management practices that potentially 

influence populations should be designed to address the demographic parameters 

responsible for population change (DeSante 1995). This design may have a higher 

likelihood of determining the effect of the management practice on the population 

of interest than relying only on habitat correlations with presence-absence or relative 

abundance data (DeSante and Rosenberg 1998, Villard et al. 1999).

Emphasis in monitoring populations of northern spotted owls shifted from estimating 

numbers and densities to evaluating trends of vital rates (e.g., reproduction, survival) 

(Gutiérrez et al. 1996) because the relationship between land management practices and 

population trends was considered to be better estimated by vital rates (Franklin et al. 

1996). However, a continuing need is recognized for independent estimates of population 

trend to allow comparisons with, and evaluations of, demographic study results (Bart and 

Robson 1995, Lint et al. 1999).

Measures of Geographic Range

The principal measures of a species’ geographic range are its size, shape, orientation, and 

internal structure (Rapoport 1982, Maurer 1994, Brown et al. 1996). Estimates of range 

size at two or more points in time enable analyses of range expansions (Andow et al. 

1990, Hastings 1996) or contractions (e.g., Fisher and Shaffer 1996, Flather et al. 2004, 

Laliberte and Ripple 2004). Range shape is a useful measure for evaluating the influences 

of physical geography (Rapoport 1982, Brown and Maurer 1989), other environmental 

limitations (Brown et al. 1996), and factors that result in contractions or expansions 

(Laliberte and Ripple 2004). Range orientation is less useful for monitoring purposes, but 

reveals basic relationships between orientation and range size (Brown and Maurer 1989). 

The internal structure of a geographic range is a measure of discontinuities caused by the 

number, size, and location of holes and fragments (Brown et al. 1996). Characterizations 

of internal structure can reveal the processes by which range expansions or contractions 

take place. Rapoport (1982) observed that home ranges tend to be less continuous toward 

the periphery, but Channell and Lomolino (2000) found abundant examples of range 

contractions that left holes within the range’s core.
3-34 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

Before conducting an analysis of geographic range, investigators must first decide 

whether their interest is in the full geographic range, including areas not used by the 

species, or in only the specific areas where the species actually occurs. Gaston (1991) 

coined the term “extent of occurrence” for the full range and “area of occupancy” for the 

areas within the range that actually are occupied. Each of these definitions is associated 

with different ecological questions. Range maps typically show the extent of occurrence 

and much of the theory of range size, orientation, and boundary characteristics has 

stemmed from studies of generalized range maps. In contrast, analyses based on area 

of occupancy provide information about how the species is distributed within its range. 

These patterns reveal changes in the internal structure of a geographic range that would 

not be detectable with generalized range maps. This section highlights three approaches 

for evaluating changes in a species’ geographic range: spatial analysis of occurrence 

records, spatial analysis of abundance, and analysis of range maps. The first two 

approaches are based primarily on area of occupancy, whereas the third approach is based 

on extent of occurrence.

A spatial analysis of occurrence is actually a special case of an analysis of a species’ 

frequency of occurrence as described under the first subheading of this section. The 

distinction is that the objective is to evaluate all known occurrences, display them 

spatially, and use them to infer a geographic distribution. For monitoring purposes, a 

comparison is made of historic and recent occurrences, with historic occurrences based 

on museum specimens and accounts and recent occurrences based on databases such 

as the Natural Heritage Programs or on recent field data collected specifically for the 

purpose of the analysis. For statistical comparisons, occurrences are displayed on a grid 

or by administrative units such as counties or States. Fisher and Shaffer (1996) mapped 

historical and current occurrences of amphibians by county in the Central Valley of 

California and documented statistically significant declines in the number of species 

currently found in most counties. When mapped, the current distribution of occurrences 

revealed substantial range contractions for three native species. Flather et al. (2004) used 

State-level occurrence data to evaluate changes in species’ ranges for 1,642 terrestrial 

animal species in the United States that are associated with forest habitats. Their two 

sources of historic and current occurrences were NatureServe’s central databases and 

the historic and current geographic range data for all species listed under the ESA. Their 

analysis showed that the percentage of each taxonomic group now occupying less than 80 

percent of historic range was 5.7 percent for mammals, 2.3 percent for amphibians, 1.4 

percent for birds, and less than 1 percent for reptiles.

A spatial analysis of abundance takes advantage of abundance estimates obtained 

from field data across large geographic extents. An estimate of range shape and its 

internal structure is possible by constructing a probability ellipse based on the likelihood 

of the species’ presence in areas surrounding sites with abundance estimates (Maurer 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-35

1994). Rodríguez (2002) used Breeding Bird Survey data to map areas of high and low 

abundance for 27 species of birds that had undergone significant declines since 1966. He 

observed range contractions in 22 of the 27 declining species and was able to detect how 

these contractions affected the internal structure of the geographic ranges.

An analysis of range maps compares changes in the mapped extent of a species’ 

distribution between two or more time periods. This approach is particularly challenging 

because the mapped image of a species’ range lacks precision and accuracy and is subject 

to considerable interpretation. Current and historic maps might exhibit differences in 

geographic range that are not ecologically meaningful but instead reflect differences 

in map resolution, mapping rules, and survey efforts between the two time periods. A 

historic map might simply display the outline of a species’ range, whereas a current 

map might show holes within a range boundary and islands around the perimeter where 

isolated populations are found (Brown et al. 1996). In spite of these difficulties, a 

comparison of historic and current range maps is highly valuable because it can reveal an 

increased risk of extinction for some species and unwanted expansions for others. 

Channell and Lomolino (2000) used published range maps as well as authorities’ 

opinions as the basis for comparing historic and current range maps for 309 species of 

animals and plants. They developed an “index of centrality” to characterize whether the 

current (remnant) range fell inside or outside the central portion of the historic range. 

Laliberte and Ripple (2004) compared historic and current geographic ranges of 43 

North American carnivores and ungulates using published range maps that they digitized 

into a GIS. Like Channell and Lomolino (2000), they looked at whether the observed 

contraction was toward the center or toward the periphery of the historic range. They 

additionally evaluated whether the pattern of contraction was associated with one or more 

variables of human influence.

Genetic Measures

Genetic measures are a potential useful monitoring tool because sometimes only relatively 

small sample sizes are required to make inferences about the population under consideration. 

(Note: the power to detect a phenomenon can be amplified by increasing the number of 

genetic markers analyzed and the sample size.) In particular, the following three genetic 

assays will provide data using only a single sampling occasion (in a single year):

•	 Changes in genetic diversity.

•	 Detection of genetic bottlenecks.

•	 Estimation of effective population size.

Examining differences in genetic diversity, or, more specifically, measures of allelic 

diversity and heterozygosity, is a powerful approach. It is well documented that in 

contracting populations, “rare alleles” (rare forms of genes) are rapidly lost; over time 
3-36 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

this loss is reflected in low levels of allelic diversity and heterozygosity. By comparing 

allelic diversity or heterozygosity between populations we can assess which populations 

may be at higher risk of population extinction (Saccheri et al. 1998). If no baseline exists, 

comparisons can be made across populations.

Furthermore, even without a reference population at time zero for comparison, 

patterns of allelic diversity (and patterns of allelic diversity combined with 

heterozygosity) can be used to assess genetic bottlenecks. Luikart and Cornuet (1998) 

clearly show the detection of previously unknown bottlenecks in natural populations of 

mountain sheep, wolves, coyotes, brown bears, wombats, and various bird species using 

single-year samples. Research is currently underway to determine if we can “screen a 

landscape” to find “cryptic genetic bottlenecks.”

Effective population size can also be estimated with a single-year sample. Effective 

population size is an estimate of population size discounted by demographic factors such 

as unequal sex ratios in a population and variance in reproductive success. This measure 

may be more pertinent to managers than abundance estimates because it is the measure 

that natural selection and other evolutionary forces act upon. Good estimates of effective 

population size can be obtained with small genetic samples from a population (Schwartz 

et al. 1998). The measure is more precise, however, if multiple genetic samples can 

be obtained spanning several generations. Recently, effective population size has been 

estimated by collecting one sample in a single year and then comparing this sample to 

DNA obtained from museum specimens (Miller and Waits 2003). One cautionary note 

is that while DNA can lead to effective population size estimates, it can be difficult to 

interpret the spatial and temporal scale encompassed by this estimate. The spatial problem 

is no different than what is encountered when trying to traditionally estimate density. 

The temporal problem can be used to the manager’s advantage as it is probably a more 

pertinent piece of information than a single-point estimate.

The advantage of using genetic measures as described above or to help estimate 

geographic distribution, presence-absence, or abundance (see the Population Measures 

section) is that once the DNA is collected, ancillary information can readily be extracted. 

For example, without collecting any additional information we can estimate substructure, 

migration rates between populations, or relatedness between individuals within a 

population (Manel et al. 2003, McKelvey and Schwartz 2004).

Species-Habitat Relationship Models

Presence-absence data, relative abundance, and estimates of absolute density can be used 

to build models of species’ habitat relationships. The presence and location of marked 

individuals are especially valuable because the data provide information about species’ 

home ranges and resource usage (Smith et al. 1982, Otis and White 1999, Compton 

et al. 2002), although such information is usually obtained through specific research 

studies instead of through monitoring. Regardless of the source, the data must enable 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-37

a comparison between habitats that were used versus what was available, or between 

habitats where species were present versus absent, in order to model habitat selection.

Chi-square goodness-of-fit analysis has been widely used to compare observed versus 

expected use of potential habitat types (Neu et al. 1974, Alldredge and Ratti 1986, 1992). 

The significance of different explanatory variables (habitat descriptors) in modeling the 

species’ habitat use may be assessed with multivariate techniques (James and McCulloch 

1990, Block et al. 1998), logistic regressions (North and Reynolds 1996, Block et al. 

1998, Compton et al. 2002), multiple regressions (Orians and Wittenberger 1991, Block 

et al. 1998), and classic ANOVAs (Orians and Wittenberger 1991) or their nonparametric 

equivalents such as the Friedman’s test and Wilcoxon’s signed-ranks test (Alldredge and 

Ratti 1986, 1992). Additional information on the analysis of habitat selection can be found 

in Thomas and Taylor (1990), Arthur et al. (1996), Cherry (1996), Garshelis (2000), and 

McDonald and Manly (2001).

Using a logistic regression, presence-absence data can be used to model the 

relationship between species occurrence and habitat variables (Breslow and Day 1993, 

Trexler and Travis 1993, Hosmer and Lemeshow 2000, Agresti 2002). For example, a set 

of variables to predict the presence of a forest-dwelling salamander species can include 

such attributes as the percentage of vegetation cover, amount of coarse woody debris, or 

presence of snags. The resulting function provides an index of certainty regarding species’ 

presence. Cross-validation functions enable the user to identify the probability value that 

best separates sites where a species was found from where it was not found based on the 

existing data. Ideally, observations are withheld from formal analysis and used to test the 

relationships after the predictive relationships are developed on most of the data. A similar 

model, Poisson regression, is appropriate for data that include counts or the probability 

of occurrence. This approach is similar to multiple regression except that it models count 

data from a Poisson distribution (Zar 1999, Agresti 2002). In contrast to the logistic 

regression, there is no limit on the number of values of the dependent variable in a Poisson 

regression (i.e., 0 or 1 versus 0, 1, 2, 3...). Count data may also be modeled with negative 

binominal regressions (White and Bennetts 1996)

Another model that has some utility in modeling presence or absence of a species is 

discriminant function analysis (DFA), which is a multivariate technique used to determine 

a set of variables that discriminate between two or more groups. An example is the set of 

variables that discriminate between surveyed locations where a species is present from 

locations where a species was absent, with presence and absence being the two groups of 

interest. Whereas a primary goal of regression analyses is to predict the value of a variable 

of interest based on a set of predictor variables, a primary application of discriminant 

analysis is to classify group membership of an observation based on a set of predictor 

variables (Johnson 1998). A fundamental similarity between DFA and logistic regression 

is that dependent variables are categorical, while in linear regression they are continuous 
3-38 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

(James and McCulloch 1990). DFA assumes that predictor variables are distributed 

multivariate normal and that across-group variance-covariance matrices for variables are 

homogeneous (although this assumption may be relaxed), whereas logistic regression 

does not. Overall, DFA is less efficient than logistic regression because the latter can 

consider categorical and continuous predictor variables and has fewer assumptions (James 

and McCulloch 1990, Johnson 1998).

DFA can also be used in an exploratory analysis context, much like principal 

components analysis. When used in exploratory analysis, relationships are assessed by 

creating discriminant functions among a group of habitat predictor variables. Scores 

computed from discriminant functions can be plotted along discriminant axes or 

incorporated into regression models to further understand their influence on presence 

or absence of species. Because discriminant function axes are orthogonal to each 

other, one can compute correlation coefficients between each habitat predictor variable 

and discriminant axis to provide an interpretation of those variables most influencing 

variation in a species’ presence or absence along a particular axis. Logistic regression is 

more interpretable than DFA because it computes odds ratios for categorical predictor 

variables, providing an interpretation of the likelihood of a species being present based 

on one subgroup in a category in relation to the other subgroups in that category. Logistic 

regression models, however, are typically limited to analysis of two groups (extension to 

more than two groups is possible, although more complex).

Assumptions, Data Interpretation, and Limitations. Van Horne (1983) and Block 

et al. (1998) questioned the general assumption that density estimates alone represent 

a sufficient measure of habitat quality and, in describing habitat choice, suggested a 

complementary use of demographic data such as individual survivorship rates and 

expected future reproduction. For example, the need for this approach could arise in 

situations in which the species’ density may reflect largely past habitat conditions rather 

than current or long-term habitat quality (Van Horne 1983, Knick and Rotenberry 2000). 

Additional issues to consider in analyzing habitat selection data include scale-dependency 

(Orians and Wittenberger 1991, North and Reynolds 1996), subjective decisions about 

what habitat components constitute potential habitats (Johnson 1980), and species 

mobility (Rosenberg and McKelvey 1999, Hjermann 2000, Compton et al. 2002).

Species Diversity

The number of species per sample (e.g., 1-m2 plot) can give a simple assessment of local, 

α diversity or these data may be used to compare species composition among several 

locations (β diversity) using simple binary formulas such as Jaccard’s index or the 

Sorensen coefficient (Magurran 1988, Krebs 1989). For example, the Sorensen qualitative 

index may be calculated as:

CS = 2j / (a +b),
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-39

where a and b are numbers of species in locations A and B, respectively, and j is the 

number of species found at both locations. If species abundance is known (number 

individuals/species), species diversity can be analyzed with a greater variety of descriptors 

such as numerical species richness (e.g., number of species/number of individuals), 

quantitative similarity indices (e.g., Sorensen quantitative index, Morisita-Horn index), 

proportional abundance indices (e.g., Shannon index, Brillouin index), or species 

abundance models (Magurran 1988, Krebs 1989, Hayek and Buzas 1997). Dyer (1978) 

describes additional general modeling of species diversity.

Assumptions, Data Interpretation, and Limitations. Species diversity measures 

can yield results that do not lend themselves to facile interpretations. For example, 

replacement of a rare or keystone species by a common or exotic species would not affect 

the community’s species richness and could actually improve diversity metrics. The informative value of qualitative indices is rather low because they disregard species abundance 

and are sensitive to sample size differences (Magurran 1988, Krebs 1989). Rare and common 

species are weighted equally in community comparisons. Often this assumption may be 

erroneous since the effect of a species on the community is expected to be proportional to 

its abundance; keystone species are rare exceptions (Power and Mills 1995).

Trend Data: Change in a Population Measure Over Time

Nearly all types of population measures (e.g., frequency of occurrence, relative density, 

absolute abundance, vital rates, and a variety of genetic measures) can be used to examine 

change in population status over time. In this section we highlight a few of the many 

population measures that can be used to evaluate population trend, and then we provide a 

brief overview of analytical models.

There is high interest in using frequency of occurrence (proportion of sites with 

presence) data to evaluate population trends because the data are often easier to obtain 

than relative abundance or any measure of vital rates. Statistical power simulations, 

however, indicate that power to detect small changes in the frequency of occurrence is 

generally low and that managers must be prepared to obtain sample sizes in the hundreds, 

depending on detection rates, to detect moderate to large changes (Kendall et al. 1992, 

Zielinski and Stauffer 1996, Strayer 1999). Moreover, possible differences in detection rates 

from year to year require multiple visits per site to estimate each year’s detection probability 

(MacKenzie et al. 2003). Nevertheless, for species that occur at low densities there is 

value in pursuing the use of frequency of occurrence data to evaluate trends, at least in the 

context of broad-scale monitoring designs where adequate sample size is possible.

Relative and absolute abundance and relative density tend to provide greater 

sensitivity to change than does frequency of occurrence, thus requiring smaller sample 

sizes to detect change than with frequency of occurrence data. This increased sensitivity 

brings with it a greater expression of variance, however, creating a challenge to separate 

a potentially meaningful change in population size from random noise and normal 
3-40 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

fluctuations in population. Thompson et al. (1998) address the following sources of 

variation that must be considered when trying to detect trends:

•	 Sampling variation (uncertainty in each year’s population estimate).

•	 Spatial variation (differences in population size between sites).

•	 Temporal variation (population fluctuations within a “normal” range).

Generalized linear regression models, in which estimates of population size are 

plotted against time, are commonly used to analyze trend data. A useful exercise is to 

begin with a least-squares estimation model and examine whether the residuals exhibit 

a normal distribution. If not, then a simple linear regression model cannot be used for 

the analysis (Krebs 1989, Zar 1999). Since count data can be approximated by either a 

Poisson or negative binomial distribution, other forms of generalized linear models, such 

as a Poisson regression model, are likely to be more appropriate than a simple linear 

regression model. Thomas (1996) thoroughly reviews four regression models used for 

evaluating trends in bird populations and the assumptions associated with each approach. 

He also addresses factors that complicate analysis of trend data, including observer bias 

and missing data.

If individual measurements in trend data are autocorrelated, regression models 

can give skewed estimates of standard errors and confidence intervals and inflate the 

coefficient of determination (Edwards and Coull 1987, Gerrodette 1987). The DurbinWatson test (Draper and Smith 1981) can be used to test for autocorrelation among leastsquares residuals for an entire analysis. However, computation of a Durbin-Watson test 

within a grouping factor (e.g., for observations within each plot) is not typically available 

in commercial software. Several applications, however, can estimate models with plotlevel temporal correlation. The MIXED procedure in the SAS statistical analysis software 

package is capable of specifying temporal (and many other) correlation structures within 

plots, or more generally within a specified grouping structure, for estimation of models 

assuming normally distributed residuals (SAS Institute Inc. 2004). The GLIMMIX 

procedure in SAS extends this capability to generalized linear model formulations, such 

as Poisson and logistic regression models. The GENMOD procedure in SAS supports a 

more limited set of correlation structures for generalized linear models, but the estimation 

procedure used is more robust to small sample sizes than the estimation procedure 

used in GLIMMIX. S-PLUS and R analysis software packages also provide support for 

specifying correlation structures among observations for linear and generalized linear 

models (Insightful Corporation 2001, Venables and Smith 2005). Edwards and Coull 

(1987) suggested that correct errors in linear regression analysis can be modeled using an 

autoregressive integrated moving-average process model (ARIMA model). Mixed models 

provide a general framework within which correlated data may be modeled (e.g., Littell et 

al. 1996).
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-41

Alternative analytical approaches may be necessary for some data where large sample 

sizes are not possible or where variance structure cannot be estimated reliably. This need 

is particularly true when the risk of concluding that a trend cannot be detected is caused 

by large variance or small sample sizes, the species is rare, and the failure to detect a trend 

could be catastrophic for the species. Wade (2000) provides an excellent overview of the 

use of Bayesian analysis (see the box entitled Bayesian Inference) to address these types 

of problems. Trend analyses provide information on how populations have changed in the 

past but cannot be used for projecting change into the future because each trend analysis 

is limited to the years the data were collected. It is sometimes possible to make shortterm projections, but reliability quickly diminishes as the projection is pushed further 

out. Nevertheless, trend data can be used to estimate future changes in population in the 

context of simulation models where habitat and other environmental factors are varied 

to produce a suite of possible population outcomes. An example of predictive population 

modeling is demonstrated by research done on the northern spotted owl on Washington 

State’s Olympic Peninsula. Fecundity and adult survivorship were varied in relation to 

the amount of nesting, roosting, and foraging habitat, producing four possible population 

outcomes (Raphael and Holthausen 2002). In this manner, the uncertainty of future 

projections can be clearly displayed.

Bayesian Inference

Bayesian inference represents an alternative to traditional statistical methodology. Bayesian methods 

test hypotheses not by rejecting or accepting them, but by calculating their probabilities of being 

true. Thus, P-values, significance levels, and confidence intervals are moot points here (Dennis 

1996, Taylor et al. 1996). Basing their decisions on existing knowledge, investigators assign a priori 

probabilities to alternative hypotheses and then use data to calculate (“verify”) posterior probabilities 

of the hypotheses with a likelihood function (Bayes theorem). The highest probability identifies the 

hypothesis that is most likely to be true given the experimental data at hand (Dennis 1996, Ellison 

1996). Bayesian inference—

• Takes advantage of pre-existing data.

• May be used with small sample sizes.

• Is relatively easy to calculate.

• Yields results that are intuitively easier to understand and are more relevant to management (Dennis 1996, Ellison 1996).

For example, conclusions of an I&M analysis could be framed as: “There is a 65 percent chance that 

clearcutting will negatively affect this species,” or, “The probability that this population is declining 

at a rate of 3 percent per year is 85 percent.” Bayesian inference can be used in a variety of statistical 

tasks, including parameter estimation and hypothesis testing, post hoc multiple comparison tests, 

trend analysis, ANOVA, and sensitivity analysis (Ellison 1996). A more in-depth coverage of the use 

of Bayesian inference in ecology can be found in Dennis (1996), Ellison (1996), Dixon and Ellison 

(1996), Taylor et al. (1996), Wade (2000), Burnham and Anderson (2002), and O’Hara et al. (2002). 

Even though Bayesian inference is easy to grasp and perform, it is still relatively rare in natural 

resources applications, and sufficient support resources for these types of tests may not be readily 

available. It is recommended that it be implemented only with the assistance of a consulting statistician.
3-42 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

Cause-and-Effect Data

The value of trend studies lies in their capacity to detect changes in populations. To 

understand the reason for population fluctuations, however, the causal mechanism behind 

the observed change needs to be determined. Cause-and-effect studies represent the only 

appropriate approach to test cause-and-effect relationships and are often used to assess 

effects of management decisions on populations. Estimation of cause-and-effect requires 

control of presumed causative factors within a study in which manipulation of these factors is possible.

Many of the population measures addressed above can be used as response variables 

in cause-and-effect studies, most notably relative abundance, absolute abundance, 

and measures of vital rates. Additional response variables include characteristics of 

individuals such as weight, physiology, onset of first breeding, home range size, activity 

periods, diet, or social behavior.

Experimental design is the critical component in estimating cause-and-effect. 

Once a design is in place, however, parametric and distribution-free (nonparametric) 

models provide countless alternatives to interpreting cause-and-effect data (Sokal and 

Rohlf 1995, Zar 1999). Excellent introductions to the design and analysis of ecological 

experiments, specifically for ANOVA models, can be found in Underwood (1997) 

and Scheiner and Gurevitch (2001). In situations in which repeated measurements are 

taken from the same sampling units (e.g., quadrats, individuals), the assumption of 

independence among observations is violated and a more appropriate design calls for 

a special type of ANOVA, the repeated-measures model (Gurevitch and Chester 1986). 

Hollander and Wolfe (1999) review application of nonparametric models.

Assumptions, Data Interpretations, and Limitations. When using ANOVA 

models, one must pay attention to parametric assumptions. Alternative means of assessing 

manipulative studies may also be employed. For example, biologically significant effect 

size with confidence intervals may be used in lieu of classic statistical hypothesis testing. 

An excellent overview of arguments in support of this approach with examples can be 

found in Hayes and Steidl (1997), Steidl et al. (1997), Johnson (1999), and Steidl and 

Thomas (2001).

Evaluating the Effects of Management Actions

Analysis of BACI design data depends on the specifics of the design and the properties 

of the response variables. Generally speaking, the analysis will be a form of ANOVA. 

If the treatment and control are replicated and have before-after measurement pairs, the 

analysis will be a repeated measures design (Stewart-Oaten et al. 1986, Smith 2002). If 

the response variable is species abundance, the data might be first log transformed and 

the subsequent analysis is basically an analysis of ratios of abundance in the control 

versus the treatment (Smith 2002). If multiple visits are made to each site (for example, 

three bird point counts per year), the visits would be treated as subsamples and would 
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-43

contribute another error term to the ANOVA. If it is not possible to replicate the treatment, 

the design might call for multiple sites within the treatment to be paired with sites 

outside the treatment that resemble each other in some aspect, such as stand density or 

vegetation type. In this case, the multiple sites would not be considered subsamples but 

would be treated as a factorial design, with each density category or each vegetation 

type representing a factor in the ANOVA (Smith 2002). It should be recognized that an 

unreplicated comparison is essentially a case study in which data apply only to the sites 

that are sampled, not to a larger population (i.e., the population of inference is defined 

only by the sampled sites). There is a cost in treating subsamples as analysis units because 

constraints need to be imposed on the inference from the analysis. Other variations and 

extensions of the basic BACI model and analysis are presented by Evans et al. (1993).

3.4.6 Interpreting the Analysis

When using a statistical hypothesis test, the results should be carefully evaluated for validity. If a statistical test turns out to be non-significant and fails to reject the null hypothesis 

(HO); does this outcome truly indicate that a biological change has not occurred? Or, was 

an effect not detected due to low statistical power, thus committing a Type II (β; missedeffect) error in the process? Confidence intervals can lend insight into test results. If 

the set of values associated with non-significance range widely from the null value, the 

rejected hypothesis may not actually represent a “near-null” state (Hoenig and Heisey 

2001). Confidence intervals not only indicate whether the effect was different from 0; they 

also provide an estimate of the likely magnitude of the true effect size and its biological 

significance (Hayes and Steidl 1997, Steidl et al. 1997, Johnson 1999).

Some researchers advocate the use of retrospective power analysis to interpret test 

results. Once a confidence interval is constructed, however, power calculations provide no 

additional insights and can even be misleading (Hoenig and Heisey 2001). If used, retrospective power analysis should be performed only using an effect size other than the effect 

size observed in the study (Hayes and Steidl 1997, Steidl et al. 1997). In other words, post 

hoc power analyses can answer only whether the performed study, in its original design, 

would have allowed for detection of the newly selected effect size.

3.4.7 Assessment of Meeting Management Goals

Two primary goals of monitoring projects are to evaluate whether management objectives 

are being met and to provide early warnings of unfavorable conditions. The initial step in 

this process involves comparing the estimates of population indices or parameters acquired 

during an I&M program against a priori target (threshold) values (Elzinga et al. 2001). 

The principle of management goal assessment is illustrated in figure 3.8. In this 

scenario, a natural resource team wanted to know whether implemented eradication 

measures (e.g., mechanical plant removal) reduced an exotic plant’s population size to 

a specified level that made further actions (e.g., biocontrol options) feasible. If both the 
3-44 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

observed parameter (e.g., index of relative abundance) and its confidence interval (e.g., 

95 percent) were below or above the target threshold value the results would be easy 

to interpret because the team could be 95 percent confident that the observed value did 

not reach the target threshold value or it completely crossed the designated target point. 

Consequently, the management team could proceed with follow up measures designated 

for either scenario. On the other hand, if the confidence intervals included the threshold 

value, the interpretation of the monitoring outcome would be less straightforward. One 

approach is to decide before monitoring that, if any part of the confidence interval crosses 

the threshold, action will be taken (Elzinga et al. 2001).

3.5 Reporting

The format and content of reports generated from I&M efforts will vary depending 

on the purpose of the report and the time period of the inventory or monitoring effort 

that the report represents. Types of reports include reports of pilot studies, interim 

reports, monitoring and evaluation reports in support of forest plans, final reports at 

the conclusion of the inventory or monitoring effort, and peer-reviewed publications. 

Regardless of the reporting purpose, all reports should contain certain elements. The 

Reporting section provides the opportunity to specify key elements that all reports need 

to include to meet data quality standards and ensure that the reports are sufficiently 

thorough.

The following key elements are recommended for inclusion in all reports:

•	 A statement of the local or regional management problem or policy that 

prompted the inventory and/or monitoring effort.

•	 A statement of the inventory or monitoring objective (which could be taken 

directly from the technical guide that the monitoring effort is based on).

Figure 3.8. A decisionmaking process in which an observed population parameter (± confidence 

interval) is compared against its a priori target (threshold) value (from S. Mori [unpublished 

data] cited in Elzinga et al. 2001: fig. 9.8).
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants 3-45

•	 A description of previous I&M efforts relevant to the targeted species or species 

group in the management area.

•	 A description of the area where work was conducted, including a brief 

characterization of physiography, ecosystems, hydrology, plant communities, and 

current and past management practices.

•	 A brief description of the inventory or monitoring design, with reference to a 

specific technical guide for further details.

•	 A brief description of how the design was implemented locally or regionally, 

including the number of sampling units, time period, and any sampling design 

modifications that were necessary due to local or regional conditions.

•	 A description of actions taken to ensure data quality (e.g., personnel training, 

precision checks).

•	 A detailed description of I&M results in text format and in supporting tables, 

figures, and/or maps.

•	 An interpretation of the results, given the limitations imposed by the time period 

of the inventory or monitoring effort.

•	 Recommendations for further I&M efforts.

If the report is final or represents a major milestone (e.g., 5 years, 10 years), address 

the relationship of the inventory or monitoring effort to any preselected threshold value 

and provide recommendations for how the results may be applied to evaluate or improve 

management strategies.
3-46 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants
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Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants B-1

accuracy. (1) The closeness of computations or estimates to the exact or true value; (2) 

the magnitude of systematic errors or degree of bias associated with an estimation procedure that affects how well the estimated value represents the true value (not synonymous 

with precision).

bias. The difference between the true value of a parameter and its expected value based on 

sampling. Sources of bias include measurement error (e.g., poorly calibrated instruments) 

or use of inappropriate estimators for a given sampling design (e.g., failing to correct for a 

small sample size in estimating the variance of a sample, where n = 9).

biological population. A defined group of organisms of one species living in a particular 

area at a particular time.

biological studies. A wide range of scientific investigations designed to test hypotheses or 

elucidate ecological relationships. The following two general types of studies are particularly relevant to WFRP I&M technical guides:

•	 Cause and effect studies—Experiments and rigorously controlled observational 

studies designed to test whether a change in a specific environmental, ecological, or 

human factor causes a measurable response in a population.

•	 Wildlife-habitat relationship studies—The coincidental measurement of a population parameter (or an index) and ancillary measurements of the site or surrounding 

environment. Qualitative or quantitative analyses are conducted to determine correlative relationships between the population parameter and environmental variables for 

the purpose of determining species-habitat associations.

case study. The collection and presentation of detailed data from an inventory or monitoring effort that did not replicate treatments and/or did not collect observations in a random 

fashion. Conclusions may be drawn only about the area in which data were collected 

and only about the individuals from which observations were made. Case studies do not 

focus on the determination of findings that are generally applicable, nor do they typically 

provide cause-and-effect relationships; instead, emphasis is placed on exploration and 

description.

census. A complete enumeration or count of individuals to determine population size.

Appendix B. Glossary
B-2 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

detectability. The conditional probability that an individual from the target population 

will be observed or captured on a sampling unit, given that the species is present. Only 

in rare situations is it tenable to assume that every individual is detected in a sample or 

that detectability is uniform across the sampling frame. Pilot studies, double sampling, 

and capture-recapture methods may be employed to estimate detectability and improve 

estimates of population size or density.

effect size. The magnitude of a biological effect, often expressed in the original units of 

measurements as a difference between two means divided by their pooled standard deviation. The power of a statistical test depends, in part, on the effect size identified by the 

investigator based on biological (as opposed to statistical) significance.

effective population size. The number of breeding individuals in a population.

element. An individual, object, or item of interest that is directly measured, counted, or 

recorded.

habitat element. Abiotic and biotic features such as rock, soil, elevation, vegetation 

types, snags, ground cover, and litter that may be ecologically important to a species’ 

welfare.

inventory. (1) The process of collecting data to describe the size, status, or distribution of 

a population; (2) A survey designed to develop a list of species in a particular area.

landscape. A spatially heterogeneous area, scaled relative to the organism or process of 

interest. 

management indicator species. Those species whose response to environmental conditions is assumed to index like responses of a larger number of species and whose habitats 

can therefore be managed to benefit a larger set of species; more broadly, species for 

which a set of management guidelines has been written.

metapopulation. Distinct subpopulations linked by the migration of individuals, which 

permits the recolonization of an area after the occurrence of a local extinction.

monitor. To watch, keep track of, or check, usually for a special purpose.

monitoring measure. Quantitative criteria for measuring or assessing the attainment of 

project objectives and/or the effects of project activities. Monitoring measures should be 

explicit, pertinent, and objectively verifiable.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants B-3

parameter. A statistical metric that describes the central tendency (e.g., population 

mean), dispersion (e.g., standard deviation), or other variable of interest for a population. Parameter values are based on a complete set of observations for every member of 

the population, a circumstance that is very rarely attained in natural resource inventories. 

Typically, parameter values are approximated using measurements, called statistics, based 

on data from an incomplete sample of the population.

population monitoring. The process by which a biological population is repeatedly 

sampled over time for the purpose of detecting changes in abundance, distribution, or 

demographic parameters.

precision. The closeness to each other of repeated measurements of the same quantity 

(not synonymous with accuracy).

sampled population. All elements associated with sampling units listed or mapped within 

the sampling frame.

sampling. The process of selecting and observing (or measuring) a portion of a population for the purpose of estimating a population parameter.

sampling frame. The spatial and temporal limits of the sampled population. A list of all 

possible sampling units eligible to be selected for sampling.

sampling unit. The basic component of sampling on which observations or measurements 

are performed. Examples include plots, transects, or individual organisms.

scale, spatial. A measure that is characterized by extent and grain. Extent refers to the 

area across which the population of interest is distributed. Grain refers to the size of the 

sampling unit on which observations are made. From a cartographic perspective, the extent is the area of the landscape encompassed within the boundaries of a map, and grain is 

determined by the size of the minimum mapping unit (e.g., a 25-m pixel).

scale, temporal. A measure of time, usually in years or groups of years.

scope of inference. The scale (of space or time) over which the results can be extrapolated. The scope of inference will depend on the area from which sampling sites were 

randomly chosen, which is the statistical population or sampling frame. If choice of sites 

is not random, then the scope of inference is only to those sites and not to other areas (i.e., 

a case study).
B-4 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

spatial extent. (1) The area over which observations are made (e.g., the boundaries of a 

study area, a species range); (2) the geographic extent of a geographic data set specified 

by the minimum bounding rectangle (i.e., xmin, ymin and xmax, ymax).

statistical population. The entire underlying set of individuals from which samples are 

drawn. The population is defined implicitly by the sampling frame.

stressors. Physical, chemical, or biological perturbations to a system that are either (1) 

foreign to that system or (2) natural to the system but applied at an excessive [or deficient] level (Barrett et al. 1976: 192). Stressors cause significant changes in the ecological 

components, patterns, and processes in natural systems. Examples include water withdrawal, pesticide use, timber harvesting, traffic emissions, stream acidification, trampling, 

poaching, land-use change, and air pollution.

survey. Within the Forest Service, the term commonly refers to inventories performed at a 

small spatial scale, usually for an individual project. Surveys are distinguished from field 

checks, site visits, and other casual inspections of an area or a condition because surveys 

typically have written, systematic protocols for data collection.

target population. All elements representing the species of interest within some defined 

area and time period.

trigger point. A value of the parameter being monitored. When this value is reached or 

exceeded, specific, previously defined mitigation measures are implemented.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants C-1

Appendix C. References

It is beyond the scope of this technical guide to list the thousands of sampling protocols 

and data collection methods developed by botanists and vertebrate biologists to inventory 

and monitor biological diversity. However, we offer a short list of references used 

during the course of our own field work and recommended to us by other plant, wildlife, 

and aquatic ecologists. We have selected these particular references because of their 

applicability to a wide range of I&M applications. Authors of WFRP I&M protocols are 

encouraged to conduct a comprehensive review of existing sampling methods relevant to 

the particular species of interest before developing new protocols.

Aquatic Habitat Monitoring

Armour, C.L.; Burnham, K.P.; Platts, W.S. 1983. Field methods and statistical analyses for 

monitoring small salmonid streams. FWS/OBS-83/33. Washington, DC: U.S. Department 

of the Interior, U.S. Fish & Wildlife Service. 200 p.

Bain, M.B.; Stevenson, N.J., eds. 1999. Aquatic habitat assessment: common methods. 

Bethesda, MD: American Fisheries Society. 224 p.

Barbour, M.T.; Gerritsen, J.; Snyder, B.D.; Stribling, J.B. 1999. Rapid bioassessment 

protocols for use in streams and wadeable rivers: periphyton, benthic macroinvertebrates, 

and fish. 2nd ed. EPA-841-B-99-002. Washington, DC: U.S. Environmental Protection 

Agency. 339 p.

Dolloff, C.A.; Hankin, D.G.; Reeves, G.H. 1993. Basinwide estimation of habitat and fish 

populations in streams. Gen. Tech. Rep. SE-GTR-83. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southeastern Forest Experiment Station. 25 p.

Flosi, G.; Downie, S.; Hopelain, J.; Bird, M.; Coey, R.; Collins, B. 1998. California 

salmonid stream habitat restoration manual. 3rd ed. Rancho Cordova, CA: California 

Department of Fish and Game, Inland Fisheries Division. 497 p.

Murphy, B.R.; Willis, D.W., eds. 1996. Fisheries techniques. 2nd ed. Bethesda, MD: 

American Fisheries Society. 732 p.

Platts, W.S.; Megahan, W.F.; Minshall, W.G. 1983. Methods for evaluating stream, 

riparian, and biotic conditions. Gen. Tech. Rep. INT-GTR-138. Ogden, UT: U.S. 

Department of Agriculture, Forest Service, Intermountain Research Station. 70 p.
C-2 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

Fish and Aquatic Amphibian Populations

Bury, R.B.; Corn, P.S. 1991. Sampling methods for amphibians in streams in the 

Pacific Northwest. Gen. Tech. Rep. PNW-GTR-275. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 29 p.

Dolloff, C.A.; Hankin, D.G.; Reeves, G.H. 1993. Basinwide estimation of habitat and fish 

populations in streams. Gen. Tech. Rep. SE-GTR-83. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southeastern Forest Experiment Station. 25 p.

Murphy, B.R.; Willis, D.W., eds. 1996. Fisheries techniques. 2nd ed. Bethesda, MD: 

American Fisheries Society. 732 p.

Thurow, R.F. 1994. Underwater methods for study of salmonids in the Intermountain 

West. Gen. Tech. Rep. INT-GTR-307. Ogden, UT: U.S. Department of Agriculture, Forest 

Service, Intermountain Research Station. 28 p.

Rare Plant Populations

Baskin, J.M.; Baskin, C.C. 1986. Some considerations in evaluating and monitoring 

populations of rare plants in successional environments. Natural Areas Journal. 6: 26-30.

Borchert, M. 1989. Post-fire demography of Thermopsis macrophylla H.A. var. agina

J.T. Howell (Fabaceae), a rare perennial herb in Chaparral. American Midland Naturalist. 

122: 120-132.

Bowles, M.L.; Apfelbaum, S.I. 1989. Effects of land use and stochastic events on the 

heart-shaped plantain (Plantago cordata Lam.) in an Illinois stream system. Natural Areas 

Journal. 9: 90-101.

Chambers, M.P.; Brown, R.W. 1988. A mapping table for obtaining plant population data. 

Journal of Range Management. 41: 267-268.

Elzinga, C.L.; Salzer, D.W.; Willoughby, J.W.; Gibbs, J.P. 2001. Monitoring plant and 

animal populations. Malden, MA: Blackwell Publishing, Inc. 360 p.

Kennedy, K.A.; Addison, P.A. 1987. Some considerations for the use of visual estimates 

of plant cover in biomonitoring. Journal of Ecology. 75: 151-157.

Laferriere, J.E. 1987. A central location method for selecting random plots for vegetation 

surveys. Vegetation. 71: 75-77.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants C-3

Menges, E.S.; Gordon, D.R. 1996. Three levels of monitoring intensity for rare plant 

species. Natural Areas Journal. 16: 227-237.

Miller, R.I.; Wiegert, R.G. 1989. Documenting completeness, species-area relations, and 

the species-abundance distribution of a regional flora. Ecology. 70: 16-22.

Owens, M.K.; Gardiner, H.G.; Norton, B.E. 1985. A photographic technique for 

repeated mapping of rangeland plant populations in permanent plots. Journal of Range 

Management. 38: 231-232.

Pavlik, B.; Barbour, M.G. 1988. Demographic monitoring of endemic sand dune plants, 

Eureka Valley, California. Biological Conservation. 46: 217-242.

Schwegman, J. 1986. Two types of plots for monitoring individual herbaceous plants over 

time. Natural Areas Journal. 6: 64-66.

Sutter, R.D. 1986. Monitoring rare plant species and natural areas—ensuring the 

protection of our investment. Natural Areas Journal. 6: 3-12.

Whysong, G.L.; Miller, W.H. 1987. An evaluation of random and systematic plot 

placement for estimating frequency. Journal of Range Management. 40: 475-479.

Wiegert, R.G. 1962. The selection of optimum quadrat size for sampling the standing crop 

of grasses and forbs. Ecology. 43: 125-129.

Windas, J.L. 1986. Photo-quadrat and compass-mapping tools. Natural Areas Journal. 6: 

66-67.

Terrestrial Habitat Monitoring

Avery, T.E.; Burkhart, H.E. 2002. Forest measurements. 5th ed. New York: McGraw-Hill. 

456 p.

Awbrey, R.T. 1977. Locating random points in the field. Journal of Range Management. 

30: 157-158.

Bate, L.J.; Garton, E.O.; Wisdom, M.J. 1999. Estimating snag and large tree densities and 

distributions on a landscape for wildlife management. Gen. Tech. Rep. PNW-GTR-425. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station. 76 p.

Bonham, C.D. 1989. Measurements for terrestrial vegetation. New York: John Wiley & 

Sons. 338 p.
C-4 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

Cooperrider, A.Y.; Boyd, R.J.; Stuart, H.R. 1986. Inventory and monitoring of wildlife 

habitat. Denver, CO: U.S. Department of the Interior, Bureau of Land Management, 

Service Center. 858 p.

Frances, J.C.; Shugart, H.H., Jr. 1970. A quantitative method of habitat description. 

Audubon Field Notes. 24: 727-736.

Gove, J.H.; Ducey, M.J.; Stahl, G.; Ringvall, A. 2001. Point relascope sampling: a new 

way to assess downed coarse woody debris. Journal of Forestry. 99: 4-11.

Harmon, M.E.; Sexton, J. 1996. Guidelines for measurements of woody detritus in forest 

ecosystems. US LTER Publication 20. Seattle, WA: US LTER Network Office, University 

of Washington, College of Forest Resources. 73 p.

Hart, R.H.; Laylock, W.A. 1996. Repeat photography of range and forest lands in the 

Western United States. Journal of Range Management. 49: 60-67.

Scott, C.T. 1996. Sampling methods for estimating change in forest resources. Ecological 

Applications. 8: 228-233.

Wildlife Populations

Bibby, C.J.; Burgess, N.D.; Hill, D.A.; Mustoe, S.H. 2000. Bird census techniques. 

London: Academic Press. 302 p.

Braun, C., ed. 2005. Techniques for wildlife investigations and management. 6th ed. 

Bethesda, MD: The Wildlife Society. 974 p.

Burnham, K.P.; Anderson, D.R.; Laake, J.L. 1980. Estimation of density from line transect 

sampling of biological populations. Wildlife Monograph. 72: 1-202.

Corn, P.S.; Bury, B.R. 1990. Sampling methods for terrestrial amphibians and reptiles. 

Gen. Tech. Rep. PNW-GTR-256. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 34 p.

Franklin, A.B.; Anderson, D.R.; Forsman, E.D.; Burnham, K.P.; Wagner, F.W. 1996. 

Methods for collecting and analyzing demographic data on the northern spotted owl. 

Studies in Avian Biology. 17: 12-20.

Heyer, W.R.; Donnelly, M.A; McDiarmid, R.W.; Hayek, L.C.; Foster, M.S., eds. 1994. 

Measuring and monitoring biodiversity: standard methods for amphibians. Washington, 

DC: Smithsonian Institution Press. 364 p.
Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants C-5

Huff, M.H.; Bettinger, K.A.; Ferguson, H.L.; Brown, M.J.; Altman, B. 2000. A habitatbased point-count protocol for terrestrial birds, emphasizing Washington and Oregon. 

Gen. Tech. Rep. PNW-GTR-501. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 39 p.

Jenni, L.; Leuenberger, M. 1996. Capture efficiency of mist nets with comments on their 

role in the assessment of passerine habitat use. Journal of Field Ornithology. 67: 263-274.

Kreeger, T.J. 1996. Handbook of wildlife chemical immobilization. Laramie, WY: 

International Wildlife Veterinary Services Inc. 340 p.

Manuwal, D.A.; Carey, A.B. 1991. Methods for measuring populations of small, 

diurnal forest birds. Gen. Tech. Rep. PNW-GTR-278. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 23 p.

Mills, L.S.; Citta, J.J.; Lair, K.P.; Schwartz, M.K.; Tallmon, D.A. 2000. Estimating 

animal abundance using noninvasive DNA sampling—promise and pitfalls. Ecological 

Applications. 10: 283-294.

Moritz, C. 1994. Applications of mitochondrial DNA analysis in conservation: A critical 

review. Molecular Ecology. 3: 401-411.

Neave, H.M.; Norton, T.W.; Nix, H.A. 1996. Biological inventory for conservation 

evaluation I: Design of a field survey for diurnal, terrestrial birds in southern Australia. 

Forest Ecology and Management. 85: 107-122.

Olson, D.H.; Leonard, W.P.; Bury, R.B. 1997. Sampling amphibians in lentic habitats: 

Methods and approaches for the Pacific Northwest. Northwest Fauna. 4: 1-134.

Ralph, C.J.; Geupel, G.R.; Pyle, P.; Martin, T.E.; DeSante, D.F. 1993. Handbook of field 

methods for monitoring landbirds. Gen. Tech. Rep. PSW-GTR-144. Albany, CA: U.S. 

Department of Agriculture, Forest Service, Pacific Southwest Research Station. 41 p.

Ralph, C.J.; Sauer, J.R.; Droege, S. 1995. Monitoring bird populations by point counts. 

Gen. Tech. Rep. PSW-GTR-149. Albany, CA: U.S. Department of Agriculture, Forest 

Service, Pacific Southwest Research Station. 187 p.

Ralph, C.J.; Scott, J.M., eds. 1981. Estimating numbers of terrestrial birds. Studies in 

Avian Biology. 6: 1-630.

Reynolds, R.T.; Scott, J.M.; Nussbaum, R.A. 1980. A variable circular-plot method for 

estimating bird numbers. Condor. 82: 309-313.
C-6 Development of Protocols To Inventory or Monitor Wildlife, Fish, or Rare Plants

Sauer, J.R.; Droege, S., eds. 1990. Survey designs and statistical methods for the 

estimation of avian population trends. Washington, DC: U.S. Department of the Interior, 

Fish & Wildlife Service Biological Report. 90(1): 1-166.

Sauer, J.R.; Pendleton, G.W.; Peterjohn, B.G. 1996. Evaluating causes of population 

change in North American insectivorous songbirds. Conservation Biology. 10: 465-478.

Scotts, D.J.; Craig, S.A. 1988. Improved hair-sampling tube for the detection of rare 

mammals. Australian Wildlife Research. 15: 469-472.

Suckling, G.C. 1978. A hair sampling tube for the detection of small mammals in trees. 

Australian Wildlife Research. 5: 249-252.

Sullivan, T.P. 1997. Sampling methodology for small mammals. Vancouver, BC: 

University of British Columbia. 31 p.

Taberlet, P.; Waits, L.P. 1998. Non-invasive genetic sampling. Trends in Ecology and 

Evolution. 13: 26-27.

Thomas, D.W.; La Val, R.K. 1988. Survey and census methods. In: Kunz, T.H., ed. 

Ecological and behavioral methods for the study of bats. Washington, DC: Smithsonian 

Institution Press: 77-89.

White, G.C.; Anderson, D.R.; Burnham, K.P.; Otis, D.L. 1982. Capture-recapture and 

removal methods for sampling closed populations. Rep. LA-8787-NERP. Los Alamos, 

NM: Los Alamos National Laboratory. 235 p.

Wilson, D.E.; Cole, F.R.; Nichols, J.D.; Rudran, R.; Foster, M.S., eds. 1996. Measuring 

and monitoring biodiversity: standard methods for mammals. Washington, DC: 

Smithsonian Institution Press. 409 p.

Zielinski, W.J.; Kucera, T.E., tech. eds. 1995. American marten, fisher, lynx, and wolverine: 

survey methods for their detection. Gen. Tech. Rep. PSW-GTR-157. Albany, CA: U.S. 

Department of Agriculture, Forest Service, Pacific Southwest Research Station. 163 p.

Biological Research Ethics

American Society of Ichthyologists and Herpetologists, American Fisheries Society, and 

American Institute of Fisheries Research Biologists (ASIH, AFS, and AIFRB). 1988. 

Guidelines for use of fishes in field research. Fisheries. 13: 16-23.
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American Society of Mammalogists. 1987. Acceptable field methods in mammalogy: 

preliminary guidelines approved by the American Society of Mammalogists. Journal of 

Mammalogy. 68(Suppl.): 1-18.

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Wingfield, J.C. 1997. Guidelines to the use of wild birds in research. Washington, DC: The 

Ornithological Council Special Publication 1997. 59 p.

Statistical Guidance and Software Tools

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Meta-analysis of stream predation experiments. Ecology. 80: 1132-1141.

Fowler, N. 1990. The 10 most common statistical errors. Bulletin of the Ecological 

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The application of biological models in meta-analysis. American Naturalist. 150: 798-812.

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Palmer, A.R. 1999. Detecting publication bias in meta-analyses: A case study of 

fluctuating asymmetry and sexual selection. American Naturalist. 154: 220-233.

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R.; Meslow, E.C.; Noon, B.R. 1996. Use, interpretation, and implications of demographic 

analyses of northern spotted owl populations. In: Forsman, E.D.; DeStefano, S.; Raphael, 

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of habitat management on northern spotted owls. In: Scott, J.M.; Heglund, P.J.; Morrison, 

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biological research. 3rd ed. New York: W. H. Freeman. 887 p.

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A biometrical approach. 3rd ed. New York: McGraw-Hill. 666 p.

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