National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

Protocol for Monitoring Brown Bears ─ Version 1.0 

Southwest Alaska Inventory and Monitoring Network 

Natural Resource Report NPS/SWAN/NRR—2010/275 
ON THE COVER 

Brown bears in a coastal meadow, Tuxedni Bay, Lake Clark National Park and Preserve. 

Photograph by: Buck Mangipane, National Park Service, Lake Clark National Park and Preserve. 
Protocol for Monitoring Brown Bears ─ Version 1.0 

Southwest Alaska Inventory and Monitoring Network 

Natural Resource Report NPS/SWAN/NRR—2010/275 

William L. Thompson1, Kellie Peirce2, and Buck A. Mangipane3 

1

National Park Service 

Southwest Alaska Network, 

240 West 5th Avenue 

Anchorage, Alaska 99501 

2

P.O. Box 752912 

Fairbanks, Alaska 99775 

3

National Park Service 

Lake Clark National Park and Preserve and Southwest Alaska Network 

1 Park Place 

Port Alsworth, Alaska 99653 

December 2010 

U.S. Department of the Interior 

National Park Service 

Natural Resource Program Center 

Fort Collins, Colorado 
The National Park Service, Natural Resource Program Center publishes a range of reports that 

address natural resource topics of interest and applicability to a broad audience in the National 

Park Service and others in natural resource management, including scientists, conservation and 

environmental constituencies, and the public. 

The Natural Resource Report Series is used to disseminate high-priority, current natural resource 

management information with managerial application. The series targets a general, diverse 

audience, and may contain NPS policy considerations or address sensitive issues of management 

applicability. All manuscripts in the series receive the appropriate level of peer review to ensure 

that the information is scientifically credible, technically accurate, appropriately written for the 

intended audience, and designed and published in a professional manner. This report received 

formal peer review by subject-matter experts who were not directly involved in the collection, 

analysis, or reporting of the data, and whose background and expertise put them on par 

technically and scientifically with the authors of the information. 

Views, statements, findings, conclusions, recommendations, and data in this report do not 

necessarily reflect views and policies of the National Park Service, U.S. Department of the 

Interior. Mention of trade names or commercial products does not constitute endorsement or 

recommendation for use by the U.S. Government. 

This report is available from the Southwest Alaska Inventory & Monitoring Network website 

(http://science.nature.nps.gov/im/units/swan/index.cfm?theme=brown_bear) and the Natural 

Resource Publications Management website (http://www.nature.nps.gov/publications/NRPM). 

Please cite this publication as: 

W. L. Thompson, K. Peirce, and B. A. Mangipane 2010. Protocol for monitoring brown bears ─

Version 1.0: Southwest Alaska Inventory and Monitoring Network. Natural Resource Report 

NPS/SWAN/NRR—2010/275. National Park Service, Fort Collins, Colorado. 

NPS 953/106217, December 2010 

ii 
Revision History Log 

All edits and amendments made to this document since its inception should be recorded in the 

table below. Users of this protocol should promptly notify the project leader of the terrestrial 

wildlife monitoring program of recommended edits or changes. The project leader will review 

and incorporate suggested changes as necessary, record these changes in the revision history log, 

and modify the date and version number on the title page of this document to reflect these 

changes. For more details, please refer to Appendix A, SOP #8. 

Revision History Log: 

Previous Version # Revision Date Author Changes Made Reason for Change New Version # 

Add rows as needed for each change or set of changes associated with each version. 

iii 

Contents 

Page 

Revision History Log.....................................................................................................................iii 

Figures........................................................................................................................................... vii 

Tables............................................................................................................................................. ix 

Appendices..................................................................................................................................... xi 

Executive Summary.....................................................................................................................xiii 

Acknowledgments......................................................................................................................... xv 

1 Background and Objectives ......................................................................................................... 1 

1.1 Introduction........................................................................................................................ 1 

1.2 Rationale for Monitoring Brown Bears ............................................................................. 2 

1.3 Measurable Objective ........................................................................................................ 4 

2 Sampling Design.......................................................................................................................... 5 

2.1 Rationale for Selecting this Sampling Design over Others ............................................... 5 

2.2 Choosing Sampling Units.................................................................................................. 8 

2.3 Recommended Number and Location of Sampling Units................................................. 8 

2.4 Recommended Frequency and Timing of Sampling ......................................................... 9 

2.5 Level of Change that can be Detected ............................................................................. 10 

3 Field Season............................................................................................................................... 11 

3.1 Field Season Preparations and Equipment Set-up........................................................... 11 

3.2 Sequence of Events During Field Season........................................................................ 11 

3.3 Recording Data ................................................................................................................ 11 

3.4 Post-collection Processing of Data .................................................................................. 11 

3.5 End-of-season Procedures ............................................................................................... 12 

4 Data Handling, Analysis and Reporting .................................................................................... 13 

v 
vi 

4.1 Metadata Procedures........................................................................................................ 13 

4.2 Overview of Database Design ......................................................................................... 13 

4.3 Data Download, Verification and Editing ....................................................................... 13 

4.4 Routine Data Summaries and Statistical Analyses.......................................................... 13 

4.5 Report Format.................................................................................................................. 14 

4.6 Methods for Trend Analyses ........................................................................................... 15 

4.7 Data Archival Procedures................................................................................................ 16 

4.8 Reporting ......................................................................................................................... 16 

5 Personnel Requirements and Training....................................................................................... 19 

5.1 Roles and Responsibilities............................................................................................... 19 

5.2 Qualifications................................................................................................................... 19 

5.3 Training Procedures......................................................................................................... 20 

6 Operational Requirements ......................................................................................................... 21 

6.1 Annual Workload and Field Schedule............................................................................. 21 

6.2 Facility and Equipment Needs......................................................................................... 21 

6.3 Start-up Costs and Budget Considerations ...................................................................... 22 

7 Procedures for Revising the Protocol ........................................................................................ 23 

Literature Cited ............................................................................................................................. 25 
Figures 

page 

Figure 1.1. The five national park units within SWAN (figure from Bennett et al. 2006)............ 2 

Figure 2.1. Example of a histogram of detection distances recorded during an aerial line 

transect survey of brown bears in Togiak National Wildlife Refuge and BLM Goodnews 

Block, southwest Alaska (figure from Walsh et al. 2008). Note the peak of the detection 

distances occurs away from the line or zero distance. The top 5% of the recorded distances 

are removed to facilitate modeling of the detection function (see Buckland et al. 2001 for 

details)............................................................................................................................................. 6 

Figure 2.2. Depiction of the uphill strip width w (solid blue) and its area calculation based 

on the union of right angle translates from the transect line (figure from Becker 2001). .............. 7 

Figure 2.3. Twenty-five km transects that were randomly selected from Game Management 

Unit 9C (blue outline; KATM shaded in green) and surveyed for brown bears during 2004-

2005 (figure from Olson and Putera 2007)..................................................................................... 9 

Figure 4.1. Example figure displaying abundance estimates and 90% credible intervals from 

aerial surveys completed once every five years. Surveys requiring more than one year to 

complete assume a closed population of bears during the survey period..................................... 15 

vii 

Tables 

Page 

Table 4.1. An example summary table for brown bears observed during aerial survey efforts 

(G. Collins, USFWS, Sheldon Hart National Wildlife Refuge, Lakeview, OR, personal 

communication). ........................................................................................................................... 14 

Table 4.2. Example summary table for bears observed by percent cover for brown bear 

monitoring data (G. Collins, USFWS, Sheldon Hart National Wildlife Refuge, Lakeview, 

OR, personal communication). ..................................................................................................... 14 

Table 5.1. Work flow and responsibilities for monitoring brown bears. ..................................... 19 

Table 5.2. Personnel qualifications for executing the protocol for monitoring brown bears in 

SWAN park units.......................................................................................................................... 20 

Table 6.1. Estimated workload and schedule for monitoring brown bears in SWAN park 

units............................................................................................................................................... 21 

Table 6.2. Estimated costs for monitoring brown bears in KATM and LACL............................ 22 

ix 

Appendices 

Page 

Appendix A: Standard Operating Procedures.............................................................................. 31 

Appendix B: User's Manual for GammaMRDS ........................................................................ 107 

Appendix C: Using Simulation to Determine Sampling Frequencies Required to Detect a 

Specified Trend in Brown Bear Abundance Estimates in Katmai and Lake Clark National 

Parks and Preserves..................................................................................................................... 137 

Appendix D: Beta Version of R2WinBUGS Code.................................................................... 149 

xi

Executive Summary 

This protocol narrative and associated standard operating procedures (SOPs) provide detailed 

information on monitoring brown bears (Ursus arctos) in four national park units in the 

Southwest Alaska Network (SWAN): Alagnak Wild River, Aniakchak National Monument and 

Preserve, Katmai National Park and Preserve (KATM) and Lake Clark National Park and 

Preserve (LACL). The protocol narrative provides a general overview of the history, 

justification, sampling methods and procedures for performing monitoring. SOPs offer step-bystep instructions for performing the various monitoring tasks and are more detailed in nature than 

the protocol narrative. 

The National Park Service initiated the “vital signs monitoring” program to address the need to 

observe and understand the condition of park natural resources. Vital signs can be physical, 

chemical, and biological elements and processes that represent the condition of a park. Brown 

bears were ranked as “essential” during the SWAN vital signs selection process due to their 

importance to ecosystem functions, usefulness as an indicator, strong linkages to other attributes 

in network ecosystem models and high relevance to park resource management and protection. 

Their ecological and management significance necessitated development of a long-term brown 

bear monitoring strategy for SWAN parks. 

The vast size, remoteness, and mountainous terrain of SWAN parks preclude ground survey 

methods to monitor brown bears because of extreme costs and accessibility issues. Therefore, a 

double-count, aerial line transect survey method modified to be conducted along elevational 

contours (Becker and Quang 2009) will be employed to estimate distribution, abundance, and 

density of brown bears in four SWAN park units. This survey technique has undergone 13 years 

of refinement by its developers since the first model was discussed in Quang and Becker (1996), 

and has been used to estimate brown bear populations in different areas in southcentral and 

southwestern Alaska (Becker 2003), including recent surveys by Alaska Department of Fish 

&Game (ADF&G) and park staffs in the ADF&G Game Management Units containing KATM 

and LACL during 2003-2005. 

A stratified random sampling design will be used to select transects primarily along elevational 

contours. Stratification criteria include physical features related to perceived densities of bears. 

Simulations based on coefficients of variation of 10%-13.5% and sampling every five years 

indicated a 75%-80% chance of detecting about a 1% annual decline with 90% confidence when 

the true annual decline was 3% during a 21-22 year period, which is the target level of detectable 

change. Depending on bear densities, park resources (staffing and availability of pilots), size of 

survey area of interest, and weather conditions, an entire survey will usually take one to three 

spring sessions (years) to complete. Timing of sampling will occur in May after bears have 

emerged from dens, but prior to leaf-out because the probability of detecting bears is highest at 

this time. 

Program GammaMRDS (Appendix B) will be used to estimate abundance and density of brown 

bears for the areas of interest within SWAN parks. This program will be converted to a Bayesian 

hierarchical modeling framework to estimate trends in abundance and density. Estimates of trend 

in brown bear distribution also will derive from the Bayesian hierarchical modeling framework. 

xiii 
xiv 

Estimates of density from surveyed transect strips will be linked to existing landcover data layers 

containing landcover class (vegetation) codes, elevation and slope to produce a bear density 

surface for the area of interest. A composite change surface indicating spatially explicit 

increases, decreases or stable densities will be produced based on previous and current surveys to 

quantify changes in spatial distribution of bear densities across time, which is analogous to 

change detection in a time series of vegetation images. 

All data will be subjected to rigorous quality assurance/quality control procedures. Metadata for 

geospatial data will be fully compliant with the Federal Geographic Data Committee standards. 

These metadata standards also will be applied to non-spatial data to the extent possible and 

relevant. Long-term archiving of brown bear data will occur at the Alaska Regional Curation 

Center located at the National Park Service Alaska Regional Office in Anchorage. 
Acknowledgments 

Development of the brown bear protocol narrative and SOPs involved important contributions 

from numerous individuals. Earl Becker and Becky Strauch of the Alaska Department of Fish 

and Game provided very helpful information on GIS procedures, data management and statistical 

analyses. Their hard work and interagency cooperative spirit have created a robust bear survey 

method for state and federal agencies across Alaska. Tammy Olson (formerly Katmai National 

Park and Preserve) and Judy Putera (Wrangell-St. Elias National Park and Preserve) offered 

much useful information to and review comments on previous drafts. Their report on aerial linetransect surveys of bears in Katmai and Lake Clark National Parks and Preserves served as an 

invaluable starting point for this document. Gail Collins, United States Fish and Wildlife Service 

at Tetlin National Wildlife Refuge, generously shared survey procedure guidelines gleaned from 

associated survey efforts on Refuge lands. Pat Walsh, United States Fish and Wildlife Service at 

Togiak National Wildlife Refuge, provided additional details on associated Refuge land surveys 

and several helpful figures. Dorothy Mortenson, formerly with the Southwest Alaska Network, 

provided helpful suggestions related to data management and comments on an earlier draft. Dave 

Bush and Kirmanie Ravariere of GeoNorth (Anchorage, Alaska) wrote the ArcPad 7.1 data entry 

applet and the ArcGIS 9.3 extension for generating random transects as well as provided the 

draft protocol and training session on their use as part of a contract with the Southwest Alaska 

Network. Thanks to Cuyler Smith of the Southwest Alaska Network for editing this training 

video and converted it to several viewable formats on DVD. Colleen Matt, formerly with Lake 

Clark National Park and Preserve, offered useful assistance at the project inception. Our sincere 

thanks to T. DeBruyn, J. Schmidt, E. Becker, and G. Pendleton for their many helpful 

suggestions that improved this protocol. Finally, thanks to Joel Reynolds and Brook Russell of 

the United States Fish and Wildlife Service, Earl Becker, and Pham Quang (emeritus) of the 

University of Alaska-Fairbanks for allowing the inclusion of their user's guide for the 

GammaMRDS program as an important component of Appendix A, SOP #5 (Data Analysis). 

xv 

1 Background and Objectives 

1.1 Introduction 

The purpose of this document is to describe the protocol used to monitor brown bears (Ursus 

arctos) in four National Park Service (NPS) units in southwest Alaska, namely, 

1. Katmai National Park and Preserve (KATM) managed by the KATM office in 

King Salmon 

2. Alagnak Wild River (ALAG) managed by the KATM office 

3. Aniakchak National Monument and Preserve (ANIA) managed by the KATM 

office 

4. Lake Clark National Park and Preserve (LACL) managed by offices in Port 

Alsworth, Anchorage and Homer. 

These park units fall within the Southwest Alaska Network (SWAN) of the NPS Inventory and 

Monitoring (I&M) Program (Figure 1.1). SWAN identified brown bears as an essential vital sign 

to monitor within the I&M program (Bennett et al. 2006). Brown bears were not selected as a 

vital sign in Kenai Fjords National Park, the fifth park unit within SWAN (Figure 1.1), because 

historic and current populations are too low to warrant monitoring. This protocol narrative, along 

with eight standard operating procedures (SOPs; Appendix A), explain the processes for 

successful execution of the brown bear monitoring program in SWAN park units. 

NPS initiated the “vital signs monitoring” program to address the need to observe and understand 

the condition of park natural resources. A vital sign is defined as “a set of physical, chemical, 

and biological elements and processes of park ecosystems that are selected to represent the 

overall health or condition of park resources, known or hypothesized effects of stressors, and/or 

are of value to humans” (Bennett et al. 2006:45). Vital signs monitoring gathers scientifically 

sound information on the status and long-term trends of park ecosystems and evaluates how well 

current management practices are sustaining those ecosystems (Bennett et al. 2006). 

The ecosystems within SWAN parks are important because they have intact populations of 

wilderness-dependent large mammal species, naturally functioning terrestrial ecosystems, and 

historic levels of biodiversity (Bennett et al. 2006). These characteristics of land systems for 

large carnivores are increasingly rare worldwide. This is particularly true for brown bears. 

SWAN parks support healthy brown bear populations, whereas bear populations are imperiled in 

the southern portions of their ranges worldwide, including in the conterminous United States 

(Kendall et al. 2008). Global bear populations are a fraction of their historic range (Schoen 

1989). 

Brown bears were ranked as “essential” during the SWAN vital signs selection process. This was 

due to their importance to ecosystem function, usefulness as an indicator, strong linkages to other 

attributes in network ecosystem models and high relevance to park resource management and 

protection. Their ecological and management significance necessitated development of a longterm brown bear monitoring strategy for ALAG, ANIA, KATM, and LACL. 

1 
Figure 1.1. The five national park units within SWAN (figure from Bennett et al. 2006). 

Monitoring of brown bears in the four park units of SWAN is dependent upon appropriate 

development and testing of monitoring protocols. This should be based on a well-designed and 

implemented sampling strategy that includes successful execution of pre-season logistics, 

preparation of survey transect routes for sampling, completion of aerial surveys for bear count 

data and quality data management, analysis and reporting. The primary method for monitoring 

brown bears in SWAN parks will be by aerial survey conducted from fixed-wing aircraft. 

Estimates of abundance/density and distribution of brown bears will be obtained from these 

aerial surveys performed at five-year intervals and analyzed for long-term trends. 

1.2 Rationale for Monitoring Brown Bears 

Brown bears are a fundamental component of ALAG, ANIA, KATM, and LACL parks and are 

listed in the enabling legislation of ANIA, KATM, and LACL. They are an important species to 

monitor because they play a key ecological role in their environment (Reimchen 2000). They are 

integral in ecosystem nutrient cycling (Tardiff and Stanford 1998, Robbins et al. 2004) and serve 

as a means of nutrient transfer from spawning salmon (Oncorhynchus sp.) (Gende et al. 2002) 

and marine nitrogen (Hilderbrand et al. 1999a) to the terrestrial system. They play important 

ecological roles as top predators by influencing population dynamics of ecosystem community 

species. This has been documented between brown bears and moose (Alces alces) (Ballard and 

2 
Miller 1990), moose calves (Ballard et al. 1981), caribou (Rangifer tarandus) (Boertje et al. 

1988), other bears (Hessing and Aumiller 1994, Sellers et al. 1998, Ben-David et al. 2004), and 

salmon (Quinn and Kinnison 1999, Ruggerone et al. 2000, Gende et al. 2004). Further, they are 

important indicator species for other valuable wide-ranging mammals such as wolves (Canis

lupus), wolverine (Gulo gulo) and lynx (Lynx canadensis) (Matz 2000). 

From a park management perspective, brown bears are important culturally and recreationally to 

people in Alaska (Matz 2000). Surveys by the Alaska Department of Fish and Game (ADF&G) 

have indicated that brown bears are Alaska’s most valuable species of wildlife from the 

perspective of both wildlife viewers and hunters (Matz 2000). They support the livelihood of 

businesses and have become economic drivers in some communities. Further, they provide 

valuable resources for subsistence users and are culturally valuable to Native Alaskans (Mogart 

et al. 1992, Van Daele et al. 2001). 

High densities of brown bears exist in ALAG, ANIA, KATM, and LACL. KATM has the 

highest documented density of brown bears in North America (Sellers et al. 1999). These high 

bear densities are supported by coastal environments rich in food resources such as marine 

mammals, clams (Siliqua sp., Protothaca staminea, Saxidomus gigantus), sedges (Carex sp.), 

terrestrial prey species, and salmon. Salmon are of particular importance and have been shown to 

influence a variety of demographic and life history parameters of brown bears (Hilderbrand 

1999b, 1999c, 2004). 

These productive coastal regions also attract sport fishers, bear hunters, subsistence users, bear 

viewers and backcountry recreationalists. Understanding potential pressures from human 

activities on bear populations is important for management of their long-term viability (Mattson 

1989). Human activities are increasing in SWAN parks. Bear viewing has increased in the past 

two decades (National Park Service 2004) and the demand for bear viewing along western Cook 

Inlet (including SWAN park units) has exponentially increased in recent years (Matz 2000). 

Human activities such as sport fishing (Smith 2002) and bear viewing (Olson et al. 1997, Rode et 

al. 2006a,b, Rode et al. 2007) have been shown to affect bear behavior, with some activities 

leading to habituation (Smith et al. 2005). Bear hunting and bear take (numbers of bears killed) 

have also been increasing (Matz 2000). Increases in hunting led to the Alaska Board of Game 

approving bear hunting on previously protected state and federal lands adjacent to SWAN parks 

in 2005. Opposition to opening hunting on these lands, led to a reversal in 2007 before the 

hunting season was initiated. Brown bears are a long-lived species with low recruitment potential 

and are prone to inadvertent overharvest (Miller 1990). All of these human activities create 

potential pressures on bears in SWAN parks and understanding more about brown bear 

populations through monitoring will help facilitate park management efforts. 

Long-term monitoring data on brown bears in ALAG, ANIA, KATM, and LACL park units have 

been lacking in the past and therefore, potential impacts to brown bears from a diverse array of 

human users are largely unknown. These unknowns, combined with the vital ecological roles 

brown bears play, creates a pressing need to gain a further understanding of bear distribution and 

abundance/density in SWAN park units. 

3 
1.3 Measurable Objective 

Monitoring brown bears in ALAG, ANIA, KATM and LACL is designed to answer the question: 

are distribution and numbers/densities of brown bears remaining stable in these parks? 

The specific measurable objective is: 

 Estimate long-term trends in distribution and abundance/density of brown bears from a 

random sample of relevant elevations and terrains in ALAG, ANIA, KATM, and LACL. 

4 
2 Sampling Design 

2.1 Rationale for Selecting this Sampling Design over Others 

The vast size, remoteness, and mountainous terrain of SWAN parks preclude ground surveys for 

brown bears because of extreme costs and accessibility issues. Aerial surveys conducted from 

fixed-wing aircraft are the logical option because they are often the quickest and most costeffective way to estimate and monitor the status of wildlife populations (Becker 2001). Aerial 

surveys based on line-transect methods have been used to survey wildlife populations for many 

years (Drummer et al. 1990, Johnson et al. 1991, Quang and Lanctot 1991) and are an 

improvement over simple aerial counts because abundance estimates are adjusted for incomplete 

and variable detection rates of animals, which can be a serious source of bias that confounds both 

population and trend estimators (Moore and Kendall 2004). Mark-recapture/mark-resight 

techniques based on a sample of radiotagged individuals also have been used to obtain rigorous 

estimates of bear abundance in various parts of Alaska (Miller et al. 1997), but are much more 

labor-intensive and expensive to employ in large, remote wilderness areas than aerial surveys 

alone. 

A double-count, aerial line transect survey method modified to be conducted along elevational 

contours (Becker and Quang 2009) will be employed to estimate distribution, abundance, and 

density of brown bears in four SWAN park units. The double-count aerial line-transect survey 

technique has undergone 13 years of refinement by its developers since the first model was 

discussed in Quang and Becker (1996) (see also Quang and Becker 1997, 1999). The approach 

has been used to estimate brown bear populations in different areas in southcentral and 

southwestern Alaska (Becker 2003), including recent surveys by ADF&G and park staffs in the 

ADF&G Game Management Units containing KATM and LACL during 2003-2005 (Olson and 

Putera 2007). 

Conventional line transect sampling uses perpendicular distances recorded from randomly 

chosen lines to detected objects (e.g., bears) to fit a detection function that, in turn, is used to 

estimate the probability of detection ( ) for the objects within the surveyed area (Buckland et al. 

2001). This information is used with the area sampled (a) to estimate density, 

pˆ

pa

n D ˆ ˆ

  , where 

n is the total objects detected and a is twice the width (w) of the survey transects multiplied by 

their total length (L) (i.e., area of the rectangles on either side of a center line [strip or belt 

transect]) (Buckland et al. 2001). This density estimator can be rewritten as pwL

n D

)2( ˆ ˆ  . or in 

terms of the effective strip half-width, ˆ  pwˆ , which yields the general line transect density 

estimator, 2 ˆ ˆ

L

n D  (Buckland et al. 2001). Multiplying both sides of the equation by the area 

sampled (A) generates the corresponding abundance estimator, 2 ˆ ˆ

L

nA N  (Buckland et al. 2004). 

The effective strip half-width is the distance from the line in which an equal number of objects 

are detected beyond it as are undetected within it, so the line transect effectively samples the 

surrounding strip area of size 2Lˆ (Buckland et al. 2004). Three key assumptions of line 

5 
transect sampling include 1) all objects exactly on the transect line are detected, 2) objects are 

detected at their original locations or do not move in response to the observer(s) (i.e., no flight 

response prior to detection), and 3) perpendicular distances to detected objects are accurately 

recorded (Buckland et al. 2001). The conventional line transect estimators of density and 

abundance have been extended to allow for more flexibility, such as incorporating covariates to 

model the detection function or being combined with mark-recapture estimators to relax the 

assumption of complete detection on the transect line (Buckland et al. 2004).

Using aerial line transects to survey brown bears in the mountainous terrain of SWAN parks 

poses challenges for meeting the key assumptions of this approach. First, there is a blind spot 

underneath the plane (transect line at zero distance); even if the detection function is shifted, not 

all animals are detected on or near the new "zero distance" line so there is not complete 

detectability at the peak of the detection function. Second, the peak of a detection function for a 

conventional line transect model occurs at the line (or shifted position of the zero distance) and 

usually decreases slowly closer to the line to form a shoulder before decreasing more steeply 

farther away from the line (e.g., half normal function). Previous aerial line transect surveys of 

brown bears in southwest and southcentral Alaska have shown that the peak of detection 

distances, and hence the detection function, occurs some distance away from the line so there is 

not a shoulder (Figure 2.1). Third, conventional aerial line-transect surveys of wildlife 

populations (e.g., Johnson et al. 1991) are limited to flat terrain because flying linear transects in 

Figure 2.1. Example of a histogram of detection distances recorded during an aerial line transect survey 

of brown bears in Togiak National Wildlife Refuge and BLM Goodnews Block, southwest Alaska (figure 

from Walsh et al. 2008). Note the peak of the detection distances occurs away from the line or zero 

distance. The top 5% of the recorded distances are removed to facilitate modeling of the detection 

function (see Buckland et al. 2001 for details). 

6 
uneven terrain causes the plane’s height above ground to constantly shift. This shift causes 

changes in sightability and eliminates the possibility of using strut marks on the wings as an aid 

to place sightings into distance classes (Becker 2001). 

Becker and Quang (2009) have modified the conventional aerial line transect approach to allow 

for transects to be surveyed along elevational contours, detection distances to be collected by two 

independent observers to relax the assumption that all objects (e.g., bears) are sighted on the line 

or at the peak of the detection function, covariates included to account for additional factors 

affecting detection rates (or sightability), and a Gamma detection function that is flexible enough 

to allow for a peak away from zero distance. These modifications also are based on surveying 

only the uphill side of the transect (Figure 2.2). Thus, the Becker-Quang approach combines the 

advantages of mark-recapture models (Cook and Jacobson 1979), line transect sampling 

(Buckland et al. 2001), and sightability models (Steinhorst and Samuel 1989) to adjust for 

incomplete detection of bears. The assumptions are 1) all bears are potentially observable; 2) 

aircraft do not influence bear location data; 3) distances recorded to located bears are accurate; 4) 

data are independent between the two observers; 5) recorded bear group size is accurate; 6) the 

population of bears being sampled is fixed during the entire survey; and 7) the model reasonably 

fits data. Further details on this survey method are discussed in Becker and Quang (2009), 

Appendix A (SOP #4 and #5), and Appendix B. 

Transect buffer Transect Scale: 1 cm = 1 km

Figure 2.2. Depiction of the uphill strip width w (solid blue) and its area calculation based on the union of 

right angle translates from the transect line (figure from Becker 2001). 

7 
2.2 Choosing Sampling Units 

The sample frames coincide with the boundaries of the SWAN park units of interest, i.e., ANIA, 

KATM/ALAG (combined), and LACL. Potential survey locations are restricted to those areas 

that could possibly contain bears so, for example, high elevation areas (>914 m) and large lakes 

(>4 km2

; Olson and Putera 2007) are excluded from the sampling universe from which sampling 

units (transects) are randomly chosen. Survey transects are equal lengths, typically 20 or 25 km. 

A 500-m buffer will be placed from shorelines into the body of water (e.g., lake, ocean) to ensure 

adequate sample coverage of this areas that are often used as travel routes by bears. Details of 

transect delineation are further described in Appendix A, SOP #2. 

A stratified random sampling design is used to select transects primarily along elevational 

contours. Stratification criteria include physical features related to perceived densities of bears. 

Stratified random sampling allows an unbiased estimator of abundance/density to the larger 

spatial scale of interest (e.g., parkwide). 

2.3 Recommended Number and Location of Sampling Units 

The number of transects should be sufficient to ensure enough bears are counted to yield a 

reasonably precise estimator of abundance or density. Based on previous surveys within Alaska, 

a minimum of 175 bear groups must be observed to derive a reasonably precise estimator (E. 

Becker, ADF&G, Anchorage, AK, personal communication). Surveys performed in KATM and 

LACL during 2003-2005 were part of a larger survey of ADF&G Game Management Units that 

contain them, so only data from surveyed transects that were within the boundaries of each park 

were used to obtain abundance estimates. Four hundred thirteen groups of bears were detected 

along 639, ~25 km-long transects in KATM, which produced an abundance estimate with a 

coefficient of variation (CV) of 17%, whereas 421 groups were detected along 740 transects in 

Game Management Unit 9C, which yielded an abundance estimate with a CV of about 13.5%. 

One hundred thirteen groups of bears were spotted along 365 transects of approximately 20 km 

in LACL, which yielded an abundance estimate with a CV of 50%. 

Simulation results indicated a CV of 13.5% corresponded to about a 75% chance of detecting a 

trend of a reasonable size if sampling once every five years (Appendix C). Based on these 

results, a minimum of 750, 25-km transects or equivalent are initially recommended parkwide to 

achieve a CV of around 13.5% when obtaining an abundance or density estimate every five years 

in KATM. An additional 10 transects in KATM would improve the precision of the abundance 

or density estimator so that the chance of detecting a trend may approach 80%. Note that 

simulation results are approximations only, and the Bayesian hierarchical modeling framework 

discussed in section 4.6 will likely improve the ability to detect a trend due to its shrinkage 

properties versus the generalized linear model used to estimate trend in the simulations 

(Appendix C). This same sampling effort can be used as an initial estimate for LACL, but future 

surveys there will be focused on its coast where bear densities are high, so less sampling effort 

will likely be required to achieve the same precision. 

8 
Figure 2.3. Twenty-five km transects that were randomly selected from Game Management Unit 9C (blue 

outline; KATM shaded in green) and surveyed for brown bears during 2004-2005 (figure from Olson and 

Putera 2007). 

Seventy-five percent of existing survey transects randomly chosen by Olson and Putera (2007) 

within KATM (Figure 2.3), will be used for monitoring brown bears because the custom 

selection tool (ADFG_BearTrans; Strauch 2003, 2004) used to generate these transects 

undersampled valleys. Therefore, 25% of the transects will be randomly re-chosen using a new 

ArcGIS 9.3 (ESRI, Inc., Redlands, CA) software extension (see Appendix A, SOP #2). A 

training DVD is available that illustrates use of this software extension (contact the SWAN 

Terrestrial Wildlife Project Leader at 

http://science.nature.nps.gov/im/units/swan/index.cfm?theme=network_staff). In LACL, new 

transects will be randomly selected within the coastal areas of LACL, which will be their focal 

area for monitoring brown bears because it is the area of greatest management concern. Brown 

bear densities within the interior of LACL are too low to be adequately sampled within current 

funding levels. 

Brown bears are subject to harvest in the Preserve portions of ANIA, KATM and LACL. 

Preserve-specific estimates of bear density or abundance will require more sampling effort 

within those areas, i.e., random selection of additional transects to supplement existing ones that 

fell within those areas. More transects also may be needed in non-Preserve portions of parks 

where bears may be moving outside of park boundaries to areas subject to harvest. 

2.4 Recommended Frequency and Timing of Sampling 

Depending on bear densities, park resources (staffing and availability of pilots), size of survey 

area, and weather conditions, an entire survey will usually take one to three spring sessions 

9 
(years) to complete. The preferred goal is to complete a survey within one year to ensure 

population closure, but budgetary and logistical constraints could make such a survey infeasible. 

A multiyear survey provides a weighted population estimate with the weights proportional to the 

number of transects surveyed each year (E. Becker, ADF&G, Anchorage, AK, personal 

communication). A single-year survey would allow useful estimates of status over the short term 

(e.g., for immediate resource management needs), which may or may not be the case for a 

multiyear survey, depending on how much the bear population is changing over that time period. 

Brown bear populations in SWAN parks likely do not change much on an annual basis, so a 

multiyear survey may suffice for providing useful information on both short-term estimates of 

status and long-term estimates of trend. However, care should be taken if multiyear surveys are 

being conducted in areas where harvest rates of bears are considered high, if meaningful shortterm estimates of status are required by resources managers. 

Bears typically emerge from hibernation dens in April, and by May are available for detection. 

Vegetation leaf-out generally occurs in late May or early June. Therefore, timing of sampling 

occurs in May after bears have emerged from dens, but prior to leaf-out because the probability 

of detecting bears is highest at this time. Timing of emergence of bears from their dens is 

variable from year to year, though, so care should be taken that surveys are not initiated too 

early. Proper timing of a survey can be assessed by plotting the bear encounter rate per 100 

sampled transects, where a random scatter of points indicates good survey timing (E. Becker, 

ADF&G, Anchorage, AK, personal communication). 

Preliminary surveys of LACL and KATM took 10 days each May during 2003-2005 to derive a 

population estimate. Thus, flights will be flown daily, weather dependent, for 10-14 days or until 

the pre-determined number of transects have been surveyed. Transects initially selected at 

random will be retained throughout the monitoring program and will be re-surveyed every five 

years (see section 2.5). 

2.5 Level of Change that can be Detected 

The level of detectable change in a population is dictated by the amount of variability in the 

abundance or density estimates and by the frequency of surveys. This variability can be further 

divided into sampling variation from obtaining estimated, rather than true, abundances or 

densities for each survey and temporal variation from the year-to-year changes (population 

dynamics) in bear numbers and distribution. Bennett et al. (2006) initially suggested a sampling 

frequency of 5-10 years. Simulations based on a coefficient of variation (CV) of 13.5% and 

sampling every five years indicated a 75% chance of detecting about a 1% annual decline with 

90% confidence when the true annual decline was 3% during a 21-22 year period, which is the 

target level of detectable change (see Appendix C for details). Note that surveys of Game 

Management Unit 9C (Figure 2.3) during 2004-2005 produced a CV of about 13.5%. There were 

essentially no situations in which a 10-year sampling interval was adequate for detecting trends 

in abundances, even for a CV of 10%. Future simulations will be conducted based on Bayesian 

hierarchical models, which should provide more precise estimators of trend and hence the ability 

to detect smaller changes. Bayesian modeling approaches also use prior survey data, especially 

for fitting the detection function, so less sampling effort will likely be required for subsequent 

surveys (to a point). 

10 
3 Field Season 

3.1 Field Season Preparations and Equipment Set-up 

Aerial surveys will be conducted in May. Personnel will be trained, field computers will be 

checked for proper working order, and data forms, survey routes and maps will be prepared. 

Details on field season preparation are described in Appendix A, SOP #1 (Preparations for the 

Field Season), and SOP #3 (Training Observers). 

3.2 Sequence of Events During Field Season 

The sequence of events during the field season are as follows. More detailed information is 

included in Appendix A, SOP #s 1-4. 

 Review survey maps, transect lists, and determine the transects to be surveyed by each 

aircraft. 

 Gather field equipment at the office prior to meeting survey aircraft. Equipment to bring 

includes personal gear (e.g., survival gear [vest], NOMEX flight suit, NOMEX or leather 

gloves, leather boots, helmet, safety equipment, lunch, extra clothes, water) and survey 

gear (e.g., on-board laptop computer, Global Positioning System (GPS) unit, notebook 

[data forms], pencils, curtain, light system). 

 Meet the pilot and prepare aircraft for survey. Connect the GPS to the laptop via the data 

cord , ensure the GPS is inputting data into the laptop, and confirm the ArcPad 7.1 (ESRI, 

Inc., Redlands, CA) application is functioning properly. Load up and take off. 

 Data Collection-collect data as detailed in section 3.3 and in Appendix A, SOP #4. 

 Upon landing after flight completion, gather all gear from the plane and report back to 

office. Store gear. 

 Perform data verification, download laptop, copy data forms, make back-ups, and record 

necessary information relevant to the flight. 

3.3 Recording Data 

A high level of effort will be devoted to accurately recording field measurements. Two primary 

tools are used to achieve minimal data errors when collecting brown bear monitoring data: 

properly training observers (Appendix A, SOP #3) and using laptops in conjunction with 

formatted project specific data forms. Use an ArcPad 7.1 (ESRI, Inc., Redlands, CA) applet 

(Appendix A, SOP #4) specifically designed for data entry during aerial survey flights, with hard 

copy data forms along for back up and comments. All data forms should be legible, complete, 

and printed on acid free paper to prevent fading over time. Refer to the data management plan 

(Southwest Alaska Network 2006) if changes to data fields become necessary. Refer to 

Appendix A, SOP #4 for details on conducting aerial surveys and data recording procedures; a 

training DVD is available that illustrates use of the ArcPad 7.1 (ESRI, Inc., Redlands, CA) applet 

(contact the SWAN Terrestrial Wildlife Project Leader at 

http://science.nature.nps.gov/im/units/swan/index.cfm?theme=network_staff). 

3.4 Post-collection Processing of Data 

Processing of data after their collection during surveys will be composed of the following steps. 

 Download all data from the laptop computer, GPS unit and digital camera (if used). 

Complete data verification and validation checks as described in Appendix A, SOP #6 

11 
and the Southwest Alaska Network (2006). Back-up data and store at an offsite location. 

Charge electronics as needed. 

 Check all original data forms for legibility and completeness. Complete data verification 

and validation checks on data. Any changes to data forms should be crossed out with a 

line and the new information written below. No errors should be erased or completely 

crossed out. Make photocopies of the originals. Copies should be archived in a safe 

location off site upon survey completion. 

 Review all ArcPad (ESRI, Inc., Redlands, CA) shapefiles, note data discrepancies 

between shapefiles and data sheets. Discuss and correct with observer. 

 Stow gear and equipment in appropriate locations. 

3.5 End-of-season Procedures 

End-of-field-season procedures include performing final organization of data, equipment, 

budgeting information and preparing data for summaries and analysis. All data forms and data 

file back-ups should be filed in an organized fashion at park headquarters office as well as an off 

site location. Long-term archiving of data will be performed at the Alaska Regional Curation 

Center (ARCC) located at the NPS Alaska Regional Office in Anchorage (see Appendix A, SOP 

#6). Ensure all hours from aerial surveys are logged and personnel time and effort are recorded 

for budgeting purposes. Clean, organize and store survey equipment in appropriate locations. 

12 
4 Data Handling, Analysis and Reporting 

An overview of data handling, analysis and reporting is described in Figure 4.1. Data handling 

involves collecting data via aerial surveys and processing these data into a usable format for 

estimating abundance, density and distribution of brown bears for a given survey and their 

corresponding trends from surveys across time. Findings obtained from these analyses then are 

interpreted and summarized in reports (see Appendix A, SOP #7). 

4.1 Metadata Procedures 

Metadata describe the attributes of a data resource, providing users with the who, what, where, 

why, and how type of information. This documentation also should reference the quality 

assurance procedures applied to the data and results of data quality reviews. Metadata for 

geospatial data should be fully compliant with the Federal Geographic Data Committee (FGDC) 

standards. These metadata standards also will be applied to non-spatial data to the extent possible 

and relevant. Details of the metadata procedures for the brown bear monitoring protocol are in 

Appendix A, SOP# 5, whereas SWAN metadata procedures are described in Southwest Alaska 

Network (2006). 

4.2 Overview of Database Design 

SWAN is using a survey protocol that also is being used by ADF&G and USFWS for monitoring 

bear populations. Thus, the Microsoft Access database for aerial survey data will contain 

variables in common so that they can be linked for a larger analysis, if one occurs. Further, the 

database will include the necessary input variables and variable names required by program 

GammaMRDS (Appendix B) to estimate abundance and density. This database is currently 

under development. 

4.3 Data Download, Verification and Editing 

Data will be downloaded as soon as possible upon returning from the field. Raw data files are 

backed-up and the GIS manager verifies that data from ArcPad (ESRI, Inc., Redlands, CA) 

matches the hardcopy recorded by the observer. The GIS manager edits data to correct 

discrepancies in ArcGIS (ESRI, Inc., Redlands, CA) and prepares data for processing by arc 

macro language (AML) scripts. After processing with the AMLs is completed, data are imported 

into Microsoft Access for combining data from several files by specific queries. The files 

produced in Access are exported to a file format that can be inputted into GammaMRDS 

(Appendix B) for abundance and density estimation. 

4.4 Routine Data Summaries and Statistical Analyses 

Survey-wide estimates of abundance, density and distribution of brown bears will be produced 

after every complete survey (see section 4.6). These analyses will include descriptive summaries 

of observations such as displayed in Tables 4.1 and 4.2. Estimates of spatial distribution will be 

graphically displayed as a density surface, where color shades will denote different densities (see 

section 4.6). Trend estimates of abundance, density and distribution will be produced after the 

third complete survey and updated with data collected from each subsequent survey, which 

should be every five to seven years thereafter (see section 4.6 and Appendix A, SOP #4). 

13 
4.5 Report Format 

Reporting is the culmination of the brown bear monitoring effort. The main objective of brown 

bear monitoring efforts in SWAN parks is to track estimates of abundance, density and 

distribution within parks across time. Therefore, summaries of bear abundance, density, and 

distribution will be included in monitoring reports. Reports will conform to specific guidelines 

set by the Natural Resource Publications Management website 

(http://www.nature.nps.gov/publications/NRPM/index.cfm). Reports will include maps, graphs, 

figures and other visuals to facilitate comprehension of findings. An example summary table is 

shown in Table 4.1. Other summary data may include sex and age class of observed bear groups 

during annual survey efforts and/or percent cover recorded around bear groups (Table 4-2). 

Example figures may include maps detailing bear locations and distribution across the landscape, 

areas surveyed, cover types observed and sex/age groups observed throughout time, and plots of 

abundance or density estimates (Figure 4.1). Additional information relevant to reporting is 

discussed in Appendix A, SOP #7. 

Table 4.1. An example summary table for brown bears observed during aerial survey efforts (G. Collins, 

USFWS, Sheldon Hart National Wildlife Refuge, Lakeview, OR, personal communication). 

Date No. of transects No. of bear groups No. of individual 

bears 

Groups/transect 

5/19/2004 35 6 9 0.26 

5/20/2004 46 11 15 0.33 

5/21/2004 49 12 18 0.37 

5/22/2004 55 18 22 0.40 

5/23/2004 53 14 16 0.30 

5/25/2004 21 5 6 0.29 

5/26/2004 60 20 26 0.43 

5/27/2004 52 19 23 0.44 

5/28/2004 36 8 11 0.31 

Table 4.2. Example summary table for bears observed by percent cover for brown bear monitoring data 

(G. Collins, USFWS, Sheldon Hart National Wildlife Refuge, Lakeview, OR, personal communication). 

Percent Cover Number of bear groups Number of 

individual bears 

Percent bears observed in 

cover type 

0 41 67 35 

10 20 34 18 

20 11 18 10 

30 9 16 8 

40 8 9 5 

50 7 10 5 

60 8 10 5 

70 5 8 4 

80 6 7 4 

90 3 6 3 

100 2 4 2 

14 
0

250

500

750

1000

1250

1500

1750

2000

2250

2500

2750

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Year

Estimated Abundance

22

Figure 4.1. Example figure displaying abundance estimates and 90% credible intervals from aerial 

surveys completed once every five years. 

4.6 Methods for Trend Analyses 

Program GammaMRDS (Appendix B) can be used as the traditional (strictly likelihood-based) 

method to estimate abundance and density of brown bears within SWAN parks. This program is 

written in freely available R code (R Development Core Team 2009). Appendix D contains R 

code that fits the Becker-Quang model within a Bayesian hierarchical modeling framework 

(Royle and Dorazio 2008) to estimate trends in abundance and density based on the coefficient 

of a time (year) variable (and associated measure of precision) included in the hierarchical 

model. Bayesian hierarchical models offer several advantages over traditional models, including 

decomposition of the sources of variation into process and observational components, and trend 

estimators that are often more precise due to "shrinkage" induced by borrowing strength across 

all estimates (random effect model) and use of prior data (when available). The R code in 

Appendix D uses the R2WinBUGS library (Sturtz et al. 2005) so that the Markov Chain Monte 

Carlo (MCMC; Gilks et al. 1996) method required for this approach can be called from within R 

to run in the freeware program WinBUGS (Lunn et al. 2000). However, the Becker-Quang 

model used in GammaMRDS and in the R code in Appendix D is biased due to lack of 

independence in the double-observer component (e.g., see Buckland et al. 2010). A correction is 

under development and will be implemented in the next version of this protocol. 

Estimates of trend in brown bear distribution will derive from the Bayesian hierarchical spatial 

models (e.g., Johnson et al. 2010). The minimum spatial unit for obtaining a measure of 

adjacency will have to be defined, but will be larger than a transect and based on the average 

perceived movements of bears during the survey. Once this spatial unit has been defined, it can 

15 
be linked within ArcGIS 9.3 (ESRI, Inc., Redlands, CA) to existing landcover maps and 

associated data layers containing landcover (vegetation) class codes, elevation and slope for each 

park (data available from the SWAN Data Manager [contact: 

http://science.nature.nps.gov/im/units/swan/index.cfm?theme=network_staff]). Relative 

percentages of slope categories , elevation categories, and landcover classes will be calculated 

for each surveyed unit (collection of transects) and used as potential predictors to generate an 

estimated density surface for the entire sampling universe. The final predictive model will be 

selected based on the DIC criterion (Spiegelhalter et al. 2002) from a set of candidate models 

comprised of various combinations of percent landcover class, percent slope category, and 

percent elevation category. If no candidate model is clearly better supported than others, a 

composite predictive model will be generated by model averaging across all models (Hoeting et 

al. 1999, Burnham and Anderson 2002). Model convergence and goodness of fit will be 

evaluated using the Gelman-Rubin statistic (Gelman and Rubin 1992, Brooks and Gelman 1998) 

and the Bayesian P-value (Gelman et al. 2004). Each complete survey will provide estimated 

densities from each spatial unit comprising this surface. A composite change surface indicating 

spatially explicit increases, decreases or stable densities will be produced based on previous and 

current surveys to quantify changes in spatial distribution of bear densities across time, much 

akin to change detection in a time series of vegetation images (e.g., Reed et al. 2006). Further 

details are provided in Appendix A, SOP #5. 

4.7 Data Archival Procedures 

Long-term archiving of brown bear data will occur at the ARCC. Electronic files related to 

brown bear monitoring efforts will be stored on the SWAN server. All hard copy materials, such 

as log books, copies of data forms and other resource materials are considered “mission critical” 

and will be stored in the long-term archive at the ARCC. Hard copy materials may also be stored 

at the associated park headquarters facility, such as the KATM or LACL offices. In this case, 

copies of all materials will be made and sent to the SWAN archive. See Appendix A, SOP #6 for 

detailed information related to archiving data. 

4.8 Reporting 

Written reports on monitoring of brown bears in SWAN parks will primarily be: 1) survey 

reports, 2) newsletters, field season highlights and mid-winter notes; 3) annual administrative 

reports, 4) resource briefs, and 5) peer-reviewed articles in scientific publications. These reports 

may be written by the SWAN Terrestrial Project Leader, SWAN park staff, or a combination of 

both. Oral reporting may consist of presentations to the SWAN technical committee during 

survey years, either oral or poster presentations at the SWAN Biennial Science Symposium, and 

either oral or poster presentations at scientific conferences. 

Survey reports will be generated by the September following the spring survey; these will be 

final reports for completed surveys and field season updates (see below) for multiyear partial 

surveys that have not been completed. Final survey reports will be formatted based on guidelines 

described on the Natural Resource Publications Management website 

(http://www.nature.nps.gov/publications/NRPM/index.cfm). The target audiences for these 

reports include park staff, and natural resource staff and scientists from other state and federal 

agencies. They will be posted on the SWAN website 

16 
17 

(http://science.nature.nps.gov/im/units/swan/index.cfm?theme=reports_pub) to ensure their 

public accessibility. 

Annual newsletters, field season highlights and mid-winter notes will provide interim summaries 

of activities and accomplishments associated with the SWAN brown bear monitoring program. 

The target audiences for these reports will be SWAN park and Alaska Regional Office staffs, 

collaborators from other state and federal agencies, and the general public. These reports will be 

publicly available on the SWAN website 

(http://science.nature.nps.gov/im/units/swan/index.cfm?theme=reports_pub). 

The annual administrative report provides a summary of a network's fiscal year activities, 

accomplishments, and associated budget expenditures to park superintendents, I&M network 

technical committee members, I&M network staff, I&M regional coordinators, and servicewide 

program managers. Servicewide program managers use this information in their annual Report to 

Congress, to respond to inquiries from the budget office, and for other purposes. The format of this 

report is set by the I&M Washington Office in Fort Collins, CO. Summary information on brown 

bear monitoring surveys, results and budget expenditures will be included in the annual 

administrative report during years of partial or complete surveys. 

Resource briefs provide one-page summaries describing the importance and issues associated 

with each SWAN vital sign. They will be updated as new survey data are collected and analyzed. 

The target audiences for these briefs will be SWAN park staffs, scientists from other state and 

federal agencies, and the general public. The resource brief for brown bears is publicly available 

on the SWAN website 

(http://science.nature.nps.gov/im/units/swan/index.cfm?theme=brown_bear). 

Peer-reviewed journal articles may be published from time to time as relevant and appropriate. 

The target audience for these articles will mainly be the scientific community. 

5 Personnel Requirements and Training 

5.1 Roles and Responsibilities 

The implementation of this protocol requires advanced knowledge of GIS, database applications, 

survey operations, data management and statistical methods (Table 5.1). Pilots experienced in 

wildlife survey operations and, when available, seasoned observers will be needed. Experience 

of observers can vary. Seasoned observers that are experienced in spotting wildlife and 

comfortable in small aircraft typically detect more bears and wildlife versus new observers. 

However, if experienced observers are not available, new observers will be trained to prepare 

them for survey operations (Appendix A, SOP #3). A high level of expertise by a biometrician 

will be required throughout the duration of the protocol to provide assistance with survey design 

as needed, to conduct the statistical analysis and to interpret findings. 

5.2 Qualifications 

A project leader/wildlife biologist, biometrician/quantitative ecologist, GIS specialist and data 

manager are key personnel required to develop and complete the process of monitoring brown 

bears in SWAN park units. The project leader/wildlife biologist executes pre-season logistics, 

conducts aerial surveys to obtain bear count data, manages observer crews, and reporting. The 

GIS specialist/wildlife biologist performs pre-survey computer and spatial data preparations, data 

editing, data management, and tracks survey progress. In previous surveys, these individuals 

Table 5.1. Work flow and responsibilities for monitoring brown bears. The same pilots and observers will 

be used across aerial surveys when feasible. 

Task Personnel 

Data Acquisition: 

 -Aerial surveys 

Project Leader/Wildlife Biologist 

Project Leader/GIS Specialist 

Other Park Wildlife Biologists 

Park Pilots/OAS Contract Pilots 

Other Observers 

Data Management: 

-Copy new files to central data server 

-Manage data formats, directory structure, 

archiving 

-Maintain consistent data characteristics and 

software compatibility with other SWAN data 

sets 

Project Leader/Wildlife Biologist 

Project Leader/GIS Specialist 

Data Manager 

Network Coordinator 

SWAN Project Leader 

Crew Member/ Biological Technician 

Data Analysis and Interpretation: 

 -Manual interpretation of field data 

 -Spatial analyses 

 -Trend analyses 

 -Data summaries 

Biometrician/Quantitative Ecologist 

Project Leader/Wildlife Biologist 

SWAN Project Leader 

Reporting Biometrician/Quantitative Ecologist 

Project Leader/Wildlife Biologist 

SWAN Project Leader 

Back-ups All Users (local) and SWAN data manager (archives). IT 

Specialist. 

Minor Protocol Revisions and Documentation Project Leader/Wildlife Biologist, Network Coordinator, 

SWAN Project Leader 

Major Protocol Revisions Work group-designated 

19 
Table 5.2. Personnel qualifications for executing the protocol for monitoring brown bears in SWAN park 

units. 

Personnel Qualifications/Experience 

Project Leader/Wildlife Biologist Experience or ability to conduct aerial surveys and fly in small aircraft 

for long periods of time 

Ability to synthesize information from multiple data sets to develop 

summary reports 

Experience with wildlife statistical procedures 

Project Leader/GIS Specialist Experience with GIS software to create spatial data 

Experience editing spatial data using GIS software 

Experience creating maps using GIS software 

Biometrician/Quantitative Ecologist Extensive experience in all aspects of sampling designs and data 

analysis associated with monitoring and research, development and 

application of models, especially Bayesian models 

Familiarity with model development, biometrics and statistical 

procedures related to wildlife populations 

Ability to develop methods for data analysis and refinements related to 

bear monitoring 

Data Manager Extensive experience with all aspects of data management 

Ability to work with principal investigators to design appropriate 

databases for data collection and integration of data 

were park wildlife biologists. The SWAN project leader may or may not be the same individual 

as the project leader/wildlife biologist. If the same individual is not in this position, he/she will 

be responsible for overall project supervision of the SWAN terrestrial wildlife monitoring 

program. The biometrician/quantitative ecologist is instrumental in sampling design development 

and testing as well as assistance with estimating abundance/density and distribution from aerial 

survey data. Future changes or refinements to the sampling design or monitoring analysis will be 

performed by the biometrician/quantitative ecologist and designated review group. The data 

manager will conduct the long-term archival processes related to storing brown bear monitoring 

data on the SWAN server. Table 5.2 outlines qualifications for personnel necessary to 

successfully complete bear monitoring efforts. 

5.3 Training Procedures 

Personnel participating in the brown bear survey will review the protocol narrative and SOPs to 

familiarize themselves with the tasks their roles requires. Survey personnel will complete 

aviation safety training as well as training covering the computer data collection equipment. 

They also will undergo in-air training with experienced observers prior to the survey. 

Completion of safety training requirements will help ensure safety during survey operations, 

whereas data collection training will provide observers with experience in collection methods 

that so as to comply with quality assurance protocols. Specific requirements are listed in 

Appendix A, SOP #3. 

20 
6 Operational Requirements 

6.1 Annual Workload and Field Schedule 

Pre-season preparations will occur prior to the May survey, typically between January and April. 

Survey flights will occur in May but the exact timeframe will depend on spring bear emergence 

from dens and leaf out. Task and workload estimates are in Table 6.1. 

Downloading data will occur in the evening after survey flights are conducted. Data analysis and 

reporting will occur during the fall and winter months following surveys. Data management will 

occur throughout the year as needed and as detailed in Appendix A, SOP #6. 

6.2 Facility and Equipment Needs 

Equipment requirements to successfully conduct brown bear monitoring include aircraft for 

survey flights, sufficient staff to observe and assist in field procedures, flight safety equipment, 

laptop computers and GPS units for electronic data recording during flights, home base 

computers (for data management, pre-season transect selection and mapping, and data analysis), 

a printer and plotter, and hand fuel pumps with associated aircraft fueling equipment. Up to five 

aircraft may be needed to complete aerial surveys. The number of GPS units and field laptop 

computers required will coincide with the number of aircraft the project leader deems necessary 

to complete survey transect allotments in the specified timeframe. At least one spare laptop 

computer should be available for survey use in case technical issues arise with primary 

equipment. 

Facility needs include an area to base the computer and printer, post survey maps and track 

transect completion, allow survey crews to meet and coordinate flight plans, storage of 

Table 6.1. Estimated workload and schedule for monitoring brown bears in SWAN park units. 

Task Timeframe Workload 

Pre-season tasks 

-obtaining permits 

-readying equipment 

-determining survey routes 

-printing maps 

-training observers 

-contract pilots 

- reserve housing 

-locate and supply fuel caches 

January-April 

5 hours 

100 hours 

80 hours 

10 hours 

< 1 week 

40 hours 

8 hours 

40 hours 

Conducting aerial surveys May 200 hours of flight time, 10-14 days 

Downloading survey data, quality 

assurance, tracking transects 

completed, creating compiled 

transect maps and lists, and 

computer maintenance 

May 100 hours 

Post-season data processing June 80 hours 

Analyzing survey data Oct-Jan 180 hours 

Data storage/archiving On going 100 hours 

Reporting Dec-Feb 160 hours 

21 
equipment, data and data back-ups. Stand-alone licenses (with dongle) for ArcGIS 9.3 and 

ArcPad 7.1. will be required for the survey; the Project Leader should contact the GIS team at 

the National Park Service, Alaska Regional Office to acquire licenses. Fuel caches for aircraft 

during survey work will be required (Appendix A, SOP #1 for detailed information on facility 

and equipment needs. 

6.3 Start-up Costs and Budget Considerations 

Start-up costs for the preliminary surveys conducted in KATM and LACL during 2003-2005 

(Olson and Putera 2007) are included in Table 6.2. Based on this information, the bulk of the 

budget for future surveys will be associated with aircraft operation and salaries. Expenses for 

ANIA will be more than other SWAN parks because of its greater remoteness and more extreme 

weather. Specifically, more fuel will be needed for surveys and the time necessary to complete 

surveys will also be increased due to longer flying times to reach survey locations. Longer flying 

times will increase the overall survey effort impacting staff time and associated salary cost 

increases. Salary estimates are based on a team of five observers who will typically be park 

biologists and technicians. Some overtime will be required due to the long nature of survey days. 

Start-up equipment costs include light systems, curtains, GPS units, antennas, laptop computers, 

interface cables, port adapters, power converters, light adapters, software, and associated 

licensing needed for aerial surveys. 

Food and housing will be needed for the pilots and staff survey team (8-12 people) for a period 

of 10-14 days in May. Housing will be provided by the host park in KATM or LACL. Surveys in 

ANIA will require making housing arrangements in Port Heiden. 

Table 6.2. Estimated costs for monitoring brown bears in KATM and LACL. 

Estimated Costs KATM LACL 

Food ($35/day for 14 days, 12 people) 5,180 5,180 

Contract aircraft (2) for 10 days (100 hrs @ 165/hr) 16,500 16,500 

DOI agency aircraft (2) for 10 days (100 hrs @ 90/hr) 9,000 9,000 

Fuel 1600 gallons @ $6.00/gal 9,600 9,600 

Travel to/from survey area 2,000 2,000 

Start-up equipment costs 2,000 500 

Salaries including overtime 14,000 14,000 

Total $58,280 $56,780 

22 
23 

7 Procedures for Revising the Protocol 

All edits and amendments made to the protocol narrative and/or SOPs should be recorded in the 

revision history log table at the beginning of this document. Users of this protocol should 

promptly notify the project leader of the terrestrial wildlife monitoring program of recommended 

edits or changes. The project leader will review and incorporate suggested changes as necessary, 

record these changes in the revision history log, and modify the date and version number on the 

title page of this document to reflect these changes. For more details, please refer to Appendix A, 

SOP #8 

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31 

Appendix A: Standard Operating Procedures 

SOP #1 Preparations for the Field Season 

SOP #2 Computer Setup and Survey Transect Development 

SOP #3 Training Observers 

SOP #4 Aerial Survey Flights 

SOP #5 Data Analysis 

SOP #6 Data Management 

SOP #7 Reporting 

SOP #8 Revising the Protocol or SOPs 

Preparations for the Field Season 

Standard Operating Procedure #1 

Version 1.0 (7/12/2009) 

Kellie Peirce 

Fairbanks, Alaska 

Buck A. Mangipane 

NPS – Lake Clark National Park and Preserve and Southwest Alaska Network 

Revision History Log: 

Previous Version # Revision Date Author Changes Made Reason for Change New Version # 

Add rows as needed for each change or set of changes associated with each version. 

33
1.0 Introduction 

This SOP describes how to prepare for the field season. Pre-season field tasks include 1) 

scheduling personnel, 2) coordinating aircraft, 3) preparing equipment, 4) selecting survey 

transect routes (when required), 5) mapping these routes (when required), 6) obtaining permits 

and 7) personnel training (discussed in SOP #3). 

1.1 Scheduling Personnel 

Up to 12 people will need to be scheduled (or hired) to conduct the data collections from aerial 

surveys of brown bears. Prior to the field season, project staff will be hired and/or scheduled for 

a 10-14 day period in May. The exact number of project staff required will depend on survey 

effort and how many aircraft will be used in survey flights. Each aircraft requires one pilot and 

one observer will be needed per plane. One to two extra observers and a standby pilot should be 

available during surveys as a back-up measure in the event other scheduled staff personnel are 

unable to continue surveys. Project staff used in previous surveys included NPS biologists from 

SWAN parks and Alaska Department of Fish and Game (ADF&G) personnel. 

Housing and food will be required for up to 12 people for 10-14 days where the survey is based. 

For scheduling pilots and aircraft, see the ‘coordinating aircraft’ section below. 

1.2 Coordinating Aircraft 

Up to five survey aircraft will be required to conduct survey flights within the narrow sampling 

window. The project leader/wildlife biologist and SWAN biological science technician will 

schedule park and contract biologists during the winter prior to spring survey flights. 

Experienced pilots book up well in advance so scheduling early is strongly recommended. A 

combination of Piper SuperCub, Aviat Husky and/or Cessna Bird Dog aircraft should be used for 

survey operations. 

Caching fuel in remote locations is needed to facilitate efficient survey operations. The project 

leader and SWAN biological science technician will coordinate fuel amounts needed and cache 

locations for surveys flights. Once fuel cache sites are determined, NPS permits need to be 

obtained for storing fuel at these sites. The amount of fuel needed will depend on survey aircraft 

types being used, survey site locations, number of survey aircraft, and location of transects 

relative to fueling facilities. Arrangements for the purchase of the necessary quantity of fuel 

should be made early (January) to allow sufficient time for shipment to the drop-off point where 

pick up and transport to the field will begin. A Beaver or Cessna 206 is required to transport the 

fuel to cache sites, which will be stored in sealed 208 liter (55 gallon) barrel drums. 

Tie down space will be needed for up to five aircraft. Arrangements will be needed to preheat up 

to five aircraft simultaneously when air temperatures are below – 6 o

C (21 oF). Check this 

temperature with the contracted pilots because they may have specific requirements for their 

aircraft engines that vary. Ensure extension cables reach to tie down locations if aircraft are 

based at an airport with power. If remote field camps will be used, portable generators will be 

needed for preheating. 

34 
1.3 Preparing Equipment 

Major categories of equipment preparation include electronic survey equipment preparation, 

other equipment preparation, and data collection equipment organization. More details on each 

of these are listed below.

1.3.1 Electronic Survey Equipment

Electronic survey equipment includes Global Positioning System (GPS) units and laptop 

computers. Each aircraft will have one laptop computer and one GPS unit. Total quantities 

needed for survey operations will depend on the number of aircraft being used. There should be 

one GPS unit(s) per airplane (in addition to the airplane’s own navigation system), as well as two 

backup units available. 

Use a GPS unit to document survey flight paths, bear locations, search distances and other 

survey related location data. A GPS unit should be equipped with a programmable baud rate 

setting function, accuracy to within 15 m (50 ft) and equipped with a back-up lithium battery. 

Use a baud rate setting of 9600. This speed (or faster) is required so the GPS can keep pace with 

the aircraft flight speed. External receiving antennas with adapters are required to ensure quality 

satellite reception and accurate position information. GPS to PC interface cables with appropriate 

ports will be needed so data from the GPS can be transferred into the laptop during flight 

operations. 

The Garmin GPS Model 12 XL was used in 2003/2004/2005 surveys of KATM and LACL, but 

this model has been discontinued. A newer model, such as the Garmin GPSMap76CSx, can be 

used instead if the Model 12 XL is no longer in use (a user manual for the Model 12 XL is 

available at 

http://www8.garmin.com/manuals/GPS12XL_OwnersManual_SoftwareVersion4.0andabove_.pd

f). The GPSMap76CSx offers a much more sensitive receiving antenna and has a removable 

microSD memory card that can be upgraded to 2 Gb. A user's manual for the GPSMap76CSx is 

available at http://www8.garmin.com/manuals/GPSMAP76CSx_OwnersManual.pdf. The 

GPSMAP76CSx will last up to 18 hours on 2 AA batteries, whereas the 12XL will last up to 24 

hours on 4 AA batteries. 

The interface page of the GPS unit allows the user to specify formats for connecting external 

devices. Use the NMEA (National Marine Electronics Association interface and data protocol) 

format for transferring GPS data into the laptop via the GPS to PC interface cable. The baud rate 

on each GPS unit will be checked and set to 9600 if this is not the default setting. This can be 

performed in the interface page of the unit. All GPS units will be individually labeled with a 

unique number. Should additional cables or adapters be needed for future surveys, refer to the 

Garmin website for a complete list of external device accessories. 

Laptop computers loaded with ArcPad software (version 7.1, ESRI, Inc., Redlands, CA) will be 

used to record data during surveys. Operating system requirements include Windows XP. Laptop 

computers are powered during survey flights by using the aircraft power system with fully 

charged, high-capacity laptop batteries installed. Power adapters and power converters will be 

required to access the aircraft power source (types depending on laptop and aircraft 

configurations). Typically a cigarette lighter adapter is used that allows connection to a 12 volt 

DC cigarette lighter plug located on the instrument panel of the aircraft. 

35 
External back-up media, such as USB drives, will be needed for transferring data to the data 

management computer and backing up survey data. Several of the laptop computers used in 

previous surveys had USB ports but several other laptops were older and required external zip 

drives for backing up. 

Digital cameras are an optional piece of equipment and may be used for capturing photographs 

of survey operations for later use in professional presentations or in outreach products. 

1.3.2 Equipment List for Electronics 

 Garmin GPSMAP76CSx GPS unit or equivalent 

 External receiving antennas for the GPS units (e.g., Garmin GA29) 

 GPS to PC interface cables with appropriate port (Garmin PC interface cable) 

 Serial I/O PC card or USB serial adaptor (if needed) 

 Laptop computers for use in aircraft loaded with ArcPad software (7.1) and bear survey 

program installed. 

 PC 12 volt DC power converters 

 Cigarette lighter adapters or telemetry system adapters for power-type depending on 

setup 

 USB drives, external zip drives or other preferred back-up media 

 Digital camera (optional) 

1.3.3 Other Equipment 

Other equipment needs include the curtain and light box systems for the aircraft to separate the 

pilot view from the back-seat observer and vice versa. This system is used to facilitate 

independence of observations for sighting bears (described in the protocol narrative). 

Data forms (Figure A1.1) will need to be printed and photocopied. The exact number of forms 

will depend on survey effort but up to 200 may be needed. Ensure copies are printed on acid-free 

paper for archiving purposes. Miscellaneous equipment needs are listed below. 

 Light systems (one per aircraft =two lights and two switchboxes) 

 Extra fuses for the cigarette plug 

 Curtains (one per aircraft) with Velcro for attachment 

 Clipboards (one per aircraft) 

 Data forms (up to 200 copies printed on acid free paper) 

 Survey instructions sheet 

 Cover classification sheet 

 Transect list with latitude/longitude 

 Main study area overview maps and transect maps (see maps section below) 

 Field gear list (see below) 

Lists of field gear to take on aerial survey flights are shown in Table A1.1. Most equipment was 

already purchased for surveys performed in KATM and LACL during 2003-2005. However, 

some routine equipment upgrades will need to be made over time as gear wears out and the 

36 
Figure A1.1 Data form used to record brown bear sightings during aerial surveys in KATM and LACL 

during 2003-2005. 

Table A1.1. Equipment needs for monitoring brown bears. 

Safety equipment Survey equipment Personnel gear 

1. NOMEX suit 

2. Flight helmet 

3. NOMEX gloves 

4. Flight vest equipped 

with safety items: 

flares, lighter, fire 

starter, energy bar, 

headnet, knife 

5. Satellite Phone 

1. Data forms 

2. Laptop computer 

3. GPS unit 

4. GPS external antenna 

5. PC Interface cable 

6. PC DC 12V power 

converter 

7. USB serial adaptor or serial 

PC card 

8. Extra laptop battery 

9. Extra AA batteries 

10. Pencils 

11. Digital camera 

12. Binoculars 

13. Field notebook 

14. Percent vegetation cover 

sheets 

15. Survey instruction sheet 

16. Duct tape 

1. Food 

2. Water 

3. Leather boots extending 

above the ankle 

4. Wool or NOMEX clothing 

5. Air sickness bag 

6. Motion sickness remedy 

7. Sunglasses 

37 
useful life of survey components decrease. Upgrades in computer equipment will need to be 

made over time as technological advances in electronics are made in future years. 

1.4 Obtaining Permits and other Compliance Procedures 

1.4.1 Research Permit 

An NPS research permit should be obtained before conducting brown bear monitoring efforts on 

NPS lands. Applications can be submitted online through 

http://science.nature.nps.gov/research/ac/ResearchIndex. A special-use permit is required for 

remote fuel caches within the parks. These are obtained through the environmental compliance 

specialist and require the specific cache locations for the evaluation process. Ensure permits are 

obtained well in advance of survey operations to avoid delays in collecting data. Ensure all 

personnel have or are scheduled for appropriate training (see SOP #3). 

1.4.2 Aviation 

Aviation activities will comply with all applicable policies and regulations issued by the 

Department of the Interior (DOI), the Federal Aviation Administration (FAA), and NPS. Alaska 

parks have specific regulations concerning fixed-wing aircraft, published at 36 Code of Federal 

Regulations CFR Part 13 and 43 CFR 36.11(f) (NPS 2006). Additional regulations pertinent to 

brown bear monitoring are discussed below. 

Aerial surveys for brown bear monitoring are considered ‘Special Use’ flights, which are defined 

as any flight operation other than point to point. These flights require special pilot and aircrew 

qualifications (see SOP #3), special equipment, and personal protective equipment (Table A1.1) 

to enhance safe transport of personnel (NPS 2003). Personal protective equipment required for 

Special Use flights includes fire-retardant clothing (NOMEX), aviator’s protective helmet, 

gloves made completely of NOMEX or a combination of NOMEX and leather, and leather boots 

that extend above the ankles. Wearing materials with low temperature melting characteristics, 

such as synthetic material made of fleece, nylon, polyester, are not approved. First aid and 

survival kits are mandatory equipment and are the responsibility of the pilot. Pilots will ensure 

these kits are complete prior to flight operations (the Aviation Life Support Equipment 

Handbook details equipment lists for these kits, NPS 2003). 

 

Aerial surveys for brown bear monitoring will be conducted at 91 m (300 ft) Above Ground 

Level (AGL) and are considered ‘Low-Altitude’ flights (below 152 m [500 ft] AGL). Lowaltitude flight operations are conducted in accordance with Federal Aviation Regulations FAR 

91.119, subpart D of part 135, part 137 or a FAA waiver or grant of exemption, if applicable. 

Specifications related to transport of hazardous materials and fuel handling are described in 

Chapter 10 of the Aviation Management Plan (NPS 2003). If caching fuel for aircraft surveys 

will be necessary, ensure fuel drums are properly stored per DOI regulations. 

Flight following for the survey aircraft is required by Operational Procedures (OPM) 

Memorandum NO. 06-02 (http://amd.nbc.gov/library/opm/06-02.pdf). Following requires 

position report at <1 hour intervals under normal circumstances and parks may require shorter 

durations between position reports. The presence of Automated Flight Following (AFF) system 

allows for changes to the following procedures as described in Interagency Aviation Safety Alert 

38 
39 

06-05 (http://amd.nbc.gov/safety/alerts/IA_Alert_0605.pdf). Procedures indicates that pilots 

make contact with local dispatch center as soon as practical after takeoff to confirm radio 

communication and the AFF status, and upon positive contact, requires no additional radio 

communication. Dispatch must monitor Web Tracker (AFF program) at a minimum of every 15 

minutes for aircraft data. During work hours in May, dispatch will conduct flight following, but 

arrangements must be made for after-hours and weekend flights. SWAN requires aircraft used in 

its field projects to be equipped with AFF. The use of satellite phones is also recommended for 

contracted aircraft. 

Literature Cited 

NPS. 2003. Aviation Management Reference Manual RM 60. National Park Service, U.S. 

Department of Interior. Washington, D.C. Available from 

(http://www.nps.gov/fire/download/fir_avi_RM60.pdf). Accessed 31 July 2009. 

Computer Setup and Survey Transect Development 

Standard Operating Procedure #2 

Version 1.0 (7/13/2009) 

Buck A. Mangipane 

NPS – Lake Clark National Park and Preserve and Southwest Alaska Network 

Revision History Log: 

Previous Version # Revision Date Author Changes Made Reason for Change New Version # 

Add rows as needed for each change or set of changes associated with each version. 

41
2.0 Introduction 

This SOP describes how to set up the computers and create transects to use during the surveys. 

Pre-season tasks include 1) installing necessary software applications and extensions, 2) 

selecting survey transect routes (when required), 3) mapping these routes (when required), 4) 

uploading the transects to the field computers. The ArcMap extension tool for generating random 

transects and the ArcPad applet for entering aerial survey data were designed by Dave Bush and 

Kirmanie Ravariere of GeoNorth LLC, Anchorage, AK. 

2.1 Installation of Transect Generation Tool 

The Transect Generation ArcMap Extension can be found in the ‘Transect Generation ArcMap 

Extension’ folder. The contents of the folder include the following: 

 Transecttool Image file: This image is used as the background image for the Transect 

tool’s ArcMap button. 



 NPSConfig XML file: This file stores application settings. 



 NPS_Transect_Tool MXD file: This MXD is where the ‘Transect Generation ArcMap 

Extension’ actually resides as VBA (Visual Basic for Applications) scripts. 



 NPS geodatabase: The geodatabase referenced by layers in the NPS_Transect_Tool 

MXD file. This geodatabase is where the Transects are stored and eventually where the 

field data will be stored. 



 APLs folder: This folder contains the APL (ArcPad Layer) files which define the 

symbology used to display the Shapefiles in ArcPad. 



The following are the list of steps required to install the Transect Generation Tool. 

1. Copy the ‘Transect Generation ArcMap Extension’ folder to the desired location. 

2. Double click the NPS_Transect_Tool MXD file to start ArcMap. 

3. If you are unable to locate an ArcMap button with a blue plane as the background icon 

then do the following: 



 From ArcMap, navigate to Tools Customize Commands and select [UIControls] in 

the right item list. The button ‘Project.TransectTool’ should now be visible in the 

‘Commands’ item list to the left. If not, make sure the ‘Save in’ field at the bottom left 

has the name of the MXD file and not the word ‘Normal.mxt’. 



 Drag and drop the Transect Tool’s button to any toolbar on the ArcMap window. While 

the Customize window is still open, right-click on the Transect tool button. 



 Select the ‘Image only’ option then select ‘Change Button Image’ to browse to the 

‘Transect Generation ArcMap Extension’ folder and select the Transecttool bitmap image 

file. 

42 
4. Click the Transect tool’s button. If you can see the Transect Generation tool’s form, the 

installation was successful. 

2.2 Transect generation 

Computer/program requirements: ArcGIS 9.3 (ArcMap and ArcCatalog), Transect Generation 

ArcMap Extension 

Survey transects will be selected from relevant habitats within each park across a variety of 

elevations and terrains. Transects will be randomly selected and will be re-sampled on 

subsequent surveys once established within a study area. Additional transects for these areas may 

need to be selected for future surveys if effort requirements change or supplementary transects 

become necessary. 

The Transect Generation ArcMap Extension is used to generate random transect lines drawn at 

random angles and at random locations. The Transect Generation tool is also used for exporting 

geodatabase content to Shapefiles which are then used to store field data collected via the 

Animal Tracking ArcPad Applet. 

2.2.1 Creating Survey Boundaries 

The first step in using the Transect Generation tool is to build a polygon to represent the 

boundary of the study area. The survey boundary polygon must be stored in the 

‘SurveyBoundary’ FeatureClass (‘Survey Boundary’ layer in the NPS_Transect_Tool MXD 

file). 

1. Navigate to the NPS.gdb using ArcCatalog, open the geodatabase ( by double-clicking) to 

view the features contained in the database. 

2. Find the SurveyBoundary feature, select it, and right click the mouse. From the list of 

options, select Load  Load Data... options. 

43 
3. Using the box that opens (Simple Data Loader), browse to the input data file representing 

the study area. Once you find it, select the Add option to add it to the List of source data 

to load. 

44 
4. Select Next, and on the following screen, verify that the correct geodatabase, target 

feature class, and the button for I do not want to load all features into a subtype is 

selected. Once these are confirmed, select Next. 

5. Assess the matches for the Target Field and Matching Source Field. Once these are 

reviewed, select Next. 

45 
6. Check the button for Load all of the source data and select Next. 

7. On the Summary page select Finish. 

46 
The next step is to edit the SurveyID field of the new survey boundary feature. The survey 

boundary feature must be assigned a SurveyID (e.g. ‘1005’) or it will not be selectable from 

within the Transect Generation tool. 

2.2.2 Setting a Digital Elevation Map (DEM) 

Another important step in using the Transect Generation tool is ensuring that the tool knows 

where to find a DEM file. Most of the steps in the Transect Generation tool require a DEM file to 

generate their results. 

To set the tool’s default DEM file, click the ‘Config’ tab in the Transect tool window. Click the 

‘Browse’ button next to the ‘DEM File Path’ field and browse to the location of the DEM file 

you wish to use. Click the DEM file to select it. The path to the DEM file should now be set in 

the ‘DEM File Path’ field. It may become necessary to re-project a raster to that of the ‘Survey 

Boundary’ FeatureClass projection if the raster does not align with your boundary (the error 

message ‘the boundary polygon for SurveyID [SurveyID] is out of range of the Raster extent’ 

will indicate so). 

To re-project the raster, first right click on the ArcToolbox and click Environments. In the 

Environment Settings window, click General Settings and update the Current Workspace and 

Scratch Workspace fields to ‘C:\Temp’ and click OK. This will avoid any security issues that 

may come up while performing necessary processing on the raster. 

Next, from the ArcToolbox, navigate to Data Management ToolsProjections and 

TransformationsRasterProject Raster. From the Project Raster window, select the path to 

your raster and select a path and name for the output raster. Set the output Coordinate System by 

navigating to and selecting the Survey Boundary FeatureClass. Click OK to create your new 

raster. 

2.2.3 Transect Generation Tool Steps 

The four main Transect Generation tool steps - 

‘Generate Excluded Areas’, ‘Generate Flat 

Areas’, ‘Generate Random Points’ and 

‘Generate Transect Lines’ – all generate 

features for a particular SurveyID. At any point in time, a particular SurveyID may or may not 

have features generated by one or more of the tool steps. For example, a certain SurveyID may 

have flat areas generated but no random points while another SurveyID may have flat areas and 

random points but no Transects generated. This is where the progress bars come in. 

Directly above the main tabs (e.g. ‘Step 1’ tab) are the survey progress bars. Notice that each 

progress bar shares the same name as the tab it sits directly above. This is because each progress 

bar indicates the status of the Transect tool step with which it shares its name. The progress bars 

indicate whether any of the four steps were run for a particular SurveyID (green if features exist 

for a particular SurveyID and gray if not). 

Step 1─Generate Excluded Areas: The ‘Generate Excluded Areas’ step uses a DEM to generate 

polygons of areas that are above or below the specified elevation. These polygons will be 

47 
deemed as excluded areas and random points and Transect lines will not be generated within 

these polygons. 

1. Pick the Select Survey ID number of the survey boundary for which you wish to generate 

excluded areas. Excluded area polygons will only be generated within the selected survey 

boundary. 

2. Enter an elevation value. The unit of elevation is determined from the DEM that will 

typically be in meters. 

3. Select whether the areas above or below the specified elevation value will be excluded. If 

Above Entered Elevation Range is selected then all areas/cells in the DEM file with 

elevation values above the specified elevation value will be extracted and converted to 

polygons and the adjacent polygons aggregated. If Below Entered Elevation Range is 

selected then all areas/cells in the DEM file with elevation values below the specified 

elevation value will be extracted and converted to polygons and the adjacent polygons 

aggregated. 

4. Check the Replace All Excluded Areas for This Survey box if you wish to delete existing 

Excluded Area polygons for this SurveyID. 

5. Click the Execute button to run the ‘Generate Excluded Areas’ step. The resulting 

polygons will be stored in the ‘ElvPoly’ FeatureClass (‘Excluded Areas’ layer in the 

NPS_Transect_Tool MXD file). 

Step 2─Generate Flat Areas: The ‘Generate Flat Areas’ step uses a DEM to generate polygons of 

areas that are within a specified slope range. These polygons will be deemed as flat areas and 

only straight Transect lines – not contour lines - will be generated within these polygons. 

1. Pick the Select the SurveyID number of the survey boundary for which you wish to 

generate flat areas. Flat area polygons will only be generated within the selected survey 

boundary. 

48 
2. Enter a slope range by entering slope values in the ‘From’ slope and ‘To’ slope fields. 

The ‘Generate Flat Areas’ step works by running an ESRI tool to calculate a slope value 

for each cell in a DEM. When the observer clicks the ‘Execute’ button, the cells with 

slope values within the specified range will be converted to polygons and the adjacent 

polygons aggregated. 

3. Check the Replace All Flat Areas for This Survey box if you wish to delete existing ‘Flat 

Area’ polygons for this SurveyID. 

4. Click the Execute button to run the ‘Generate Flat Areas’ step. The resulting polygons 

will be stored in the ‘FlatAreas’ FeatureClass (‘Flat Areas’ layer in the 

NPS_Transect_Tool MXD file). 

Step3─Generate Random Points: The ‘Generate Random Points’ step generates points that are 

later used as centers of origin for Transect generation. Before points can be generated for a 

SurveyID, excluded area polygons must already exist for that SurveyID. 

The two methods used to generate points are ‘Random Generation’ and ‘Grid Format’. The 

‘Random Generation’ method creates a specified number of points at random locations which 

fall within the selected SurveyID’s boundary but outside of any excluded area polygons. The 

‘Grid Format’ method generates points in a grid with a specified grid spacing distance. The ‘Grid 

Format’ method may generate grid points with or without a specified starting point. If no starting 

point is specified, the ‘Grid Format’ method will simply generate the grid points using the upperleft corner of the survey boundary’s envelope as a starting point. If a starting point is specified, 

the grid will be generated with the starting point as its center point. 

1. First ‘Select the SurveyID’ of the survey boundary for which you wish to generate 

random points. 

2. To use the ‘Random Generation’ method, select the Random Generation option and enter 

the number of points to be generated. 

49 
3. To use the ‘Grid Format’ method, select the Grid Format option, specify a starting X and 

Y coordinate if desired and set the distance between grid points (point spacing). 

4. Check the Replace All Random Points for This Survey box if you wish to delete existing 

random points for this SurveyID. 

5. Click the Execute button to run the ‘Generate Random Points’ step. The resulting points 

will be stored in the ‘TrnPoints’ FeatureClass (‘Random Points’ layer in the 

NPS_Transect_Tool MXD file). 

Step 4─Generate Transect Lines: After excluded areas, flat areas and random points have been 

generated, it is time to build transect lines. Random points are selected and used as points of 

origin from which transects are drawn. Transect lines may be straight transects or contour 

transect. Straight Transects are ones that completely fit within the flat area polygons and the 

survey boundary but are completely outside of the excluded areas. Contour Transects are ones 

that are completely outside of the flat area polygons and excluded areas polygons but are within 

the survey boundary. 

1. Pick the ‘Select the SurveyID’ number of the survey boundary for which you wish to 

generate transect lines. 

2. Specify the minimum and maximum length of the transects to generate. The ‘Generate 

Transect Lines’ process attempts to draw transects to the maximum length first and if the 

resulting transect is invalid (falls within excluded areas or outside the survey boundary) 

the ‘Generate Transect Lines’ process will then attempt to draw the transect line at the 

minimum length. If the transect cannot be drawn at the minimum length, the random 

point used as the point of origin will then be marked off as abandoned by setting the 

‘HasTransect’ field of the random point feature to ‘N’. 

3. Specify the number of transect lines to be generated. An observer can only create as 

many transect lines as there are random points. If the # of Transects to Generate field is 

50 
left blank, the ‘Generate Transect Lines’ process will attempt to generate transects for 

every random point for the selected SurveyID. 

4. Check the Replace All Transect Lines for This Survey box if you wish to delete existing 

‘Transect Lines’ polygons for this SurveyID. 

5. Click the ‘Execute’ button to run the ‘Generate Transect Lines’ step. The resulting 

polygons will be stored in the ‘TrnOrig’ FeatureClass (‘Generated Transects’ layer in the 

NPS_Transect_Tool MXD file). 

Step 5─Export Data: After successfully generating transect lines for a particular SurveyID, these 

transects may now be exported. The ‘Export/Import Data’ tab is where the transects can be 

exported to Shapefiles and the data collected in the field can be imported into the geodatabase. 

1. Pick the Select the SurveyID number of the survey boundary for which you wish to 

export data. 

2. Click the ‘Browse’ button next to the Export Path field and navigate to the path of the 

storage device you wish to export to. Click the folder to select it. The path to the folder 

should now be set in the Export Path field. 

3. Click the ‘Export’ button to export the data folder to the specified device or location. The 

data folder is named ‘Survey’ and includes the following: 

 ‘TrnOrig’ shapefile: Has all the transects for the SurveyID that was selected. 

 ‘animals’ shapefile: An empty shapefile for storing animal sightings. 

 ‘Track Log’ shapefile: An empty shapefile for storing the actual transect flight path. 

 ‘Horizon’ shapefile: An empty shapefile for storing horizon marks for animal sightings. 

 ‘GPSPointsLog’ shapefile: An empty shapefile for storing all GPS points obtained during 

the entire flight. 

 APL files for each of the shapefiles mentioned above. These APL files contain the 

symbology for displaying the shapefile features. This includes not only the colors but the 

symbols used to represent the shapes. 

51 
2.3 Installation of the Arcpad Survey Applet 

Ensure that ArcPad 7.1 (ESRI, Inc., Redlands, CA) is installed on all laptop computers and that 

the registration number was entered. 

2.3.1 Animal Marking Applet Folder Contents 

The Animal Marking ArcPad applet can be found in the ‘Animal Tracking ArcPad Applet’ 

folder. The contents of the folder include the following: 

 Image Files: These image files are used for the various button icons through the applet. 

 Application Files: The files NPSApplet.vbs, NPSApplet.apa and NPSHelper.vbs contain 

the actual Animal Tracking applet. 

 Survey Folder: This folder contains the output shapefiles (more on these later) from the 

Transect Generation tool. 

 DBF files: The ‘Point Log’ DBF file is used to backup GPS points per transect segment. 

The ‘default values’ DBF stores application data. 

2.3.2 Animal Marking Applet Installation Steps 

There are four steps to installing the Animal Marking applet. 

1. Navigate to ArcPad’s ‘Applets’ folder which is in the ArcPad application folder (e.g. path 

‘C:\Program Files\ArcPad 7.1\Applets’). Please remove any existing Applets and/or other 

content from this folder. 

2. Copy the ‘ArcPad Survey Tool’ folder’s contents to ArcPad’s ‘Applets’ folder. 

3. Start ArcPad and ensure you can now see a toolbar with the text ‘NPS Animal Marking’ 

visible. This indicates that the installation was successfully. 

4. The Animal Marking applet also comes with the well known ArcPad FakeGPS extension. 

To install the extension, simply copy the ‘Fake GPS Extension’ folder’s contents to the 

52 
ArcPad’s ‘Extensions’ folder which is in the ArcPad application folder (e.g. path 

‘C:\Program Files\ArcPad 7.1\Extensions’). 

2.3.4 Laptop Setup 

Laptops used in aerial surveys should be set up in the following manner. 

1. Any screen saver was turned off and power conservation settings are changed so that the 

monitor and hard drive would not turn off due to inactivity. 

2. Test the ArcPad set-up by connecting a GPS receiver running in simulator mode to the 

laptop, then running an ArcPad session to test the custom survey applet (testing can 

included simulated transects and recording of hypothetical sightings). The GPS was 

turned on only after it was connected to the laptop because the computer sometimes 

otherwise misinterpreted the connection as an external mouse, which resulted in erratic 

uncontrollable mouse cursor movements. 

3. Attach summary instructions sheet to laptop (Figure A2.1) 

2.4 Preparing Maps 

Once the transect selection process is completed, the next step is to generate maps of the survey 

routes. Prepare layouts in GIS and print the appropriate number of maps needed for the survey 

effort. This number may vary depending on survey and project leader requirements. Generally, 

two sets of maps will be needed for each transect set per aircraft and a wall map depicting each 

transect set will be needed as well. 

The 11x17 maps of the study area and survey transects are used by observers and pilots to 

navigate and monitor completion of transects. Wall maps are useful for viewing the overall 

study area at base camp, planning daily survey flights, and recording transects completed. The 

11x17 map set subdivides the study area into a workable number of extents with slight overlap 

making them useable in small aircraft. Use the ArcGIS bookmark feature to save view extents 

for repeated use. For each transect set, a plotter-sized wall map layout is created in ArcGIS. Lists 

of transects by transect set are produced from the transect shapefile attribute table and printed for 

survey crews. These provide starting and ending points to use in navigating to and beginning the 

survey of the transects. 

2.5 Data Management 

Prior to the surveys, a Microsoft Access database should be created based upon the compiled 

transects tables, manually added transects, and random points included. This facilitates tracking 

the completion of transects during the surveys. Fields in this database table to related to surveyed 

transects should be reviewed at the end of each survey day. This helps prevent resurveying 

transects and minimizes daily coordination time of the flight crews. 

53 
 Click to end transect

 Click to land and end session

 Return to where you departed the transect and

resume

 Mark horizon

 Click to mark animal   

 Animal sighting occurs, turn on signal light, 

check partner’s, note horizon

 Switch view side as needed

 Click to start transect

 Click on a transect to select it, enter direction 

viewing, weather

 Click to activate the Select Transect feature

 Power PC & Log on as: __________________

 Attach and Turn on GPS; check battery power

 Start Arcpad 

 Click for Take Off/Load Map

 On Take Off tab select “SurveyID number, pilot, 

observer...” 

 On Animals tab select appropriate species

Figure A2.1. Summary ArcPad survey instructions that should be attached to each aerial survey laptop 

computer. 

54 
55 

Training Observers 

Standard Operating Procedure #3 

Version 1.0 (7/14/2009) 

Kellie Peirce 

Fairbanks, Alaska 

Revision History Log: 

Previous Version # Revision Date Author Changes Made Reason for Change New Version # 

Add rows as needed for each change or set of changes associated with each version. 

3.0 Introduction 

This SOP describes training procedures for personnel conducting aerial surveys for monitoring 

brown bears in SWAN park units. Personnel will be familiar with the brown bear protocol 

narrative and SOPs. The use of aircraft within the National Park Service is managed by the 

Department of Interior, National Business Center, Aviation Management Directorate 

(http://amd.nbc.gov/akro/index.htm). Survey personnel will complete safety training as well as 

data collection training to comply with quality assurance protocols. Fulfilling safety training 

requirements will aid in maximizing safety during survey operations; safety is the most 

important aspect of conducting aerial surveys for monitoring brown bears in SWAN parks. 

Aerial wildlife surveys are one of the more dangerous occupations for wildlife biologists (Sasse 

2003). With this in mind, survey operations will be conducted with safety as the top priority in 

decision making processes. 

3.1 Safety Training 

3.1.1 Required Safety Training 

B-3 Basic Airplane and Helicopter Safety (8 hours): This class is for those people who have not 

had previous aviation safety training. Personnel who participate in Special Use flight activities 

(e.g., flights lower than152 m [500 ft] Above Ground Level [AGL]) or function as aircrew 

members (e.g., observe, photographer, radio tracker, survey recorder) must take this class before 

flying. DOI bureau employees, except USGS, are required to take their initial B3 training in the 

classroom. Class schedules and locations can be found at 

https://www.iat.gov/Training/course_list.asp.

B-3 Refresher (8 hours): Employees who have completed the Basic class are required to take a 

refresher class every 3 years. Topics covered will be similar to the Basic class, but in more 

specific detail and includes updated aviation safety and policy information. The B-3 Refresher 

may be completed online at http://www.iat.gov. Five modules are required for the B3 online 

refresher course: 

 A101 Aviation Safety 

 A105 Aviation Life Support Equipment 

 A106 Aviation Mishap Reporting 

 A108 Preflight Checklist & Briefing/Debriefing 

 A113 Crash Survival 

3.1.2 Additional Safety Training 

 DOI Underwater Egress (Dunker) Training 

 Bear and Firearms Safety Training 

 CPR/1st Aid/Wilderness Safety 

The project leader must have a basic understanding of aviation management including the use of 

personal protection equipment (PPE), aircraft contracting, and pilot flight time and duty time 

limitations. PPE should be available for all persons who fly in the aircraft. The project leader will 

review the following Department of Interior Manuals and Handbooks, which can be downloaded 

from http://amd.nbc.gov/library/index.htm. 

57
 Departmental Manual (DM) Parts 350-354- Aviation Management 

 Aviation Life Support Equipment (ALSE) Handbook 

3.1.3 Pilots 

Pilots will be required to possess a current Office of Aircraft Services (OAS) certification. 

Ensure pilots have this certification by checking their OAS card and expiration date. Pilots with 

an OAS certification have taken additional training and flight exam specifications that are more 

restrictive than FAA requirements for commercial pilots and therefore have met all other 

required government certifications and requirements. Local OAS contract aircraft and pilot 

sources can be found at http://amd.nbc.gov under the Aircraft and Pilot Sources link. 

3.2 Data Collection Training 

Typically the project leader will conduct training related to data collection or will designate a 

team member who is qualified to perform the training. All personnel involved in aerial survey 

flights will receive data collection training. The data manager will train the project leader to 

ensure established data quality and verification procedures are followed throughout the survey 

period and beyond. These procedures are detailed in the Southwest Alaska Network (2006) and 

discussed further in SOP #6. 

Personnel involved in pre-season logistics and field preparation will be trained in transect 

selection procedures, map creation, GIS, and data management. This will typically be conducted 

by the project leader or other qualified staff member. 

3.3 Survey Technique and Protocol Review 

Each team member, including pilots, will review the brown bear protocol narrative and SOPs. 

The basic survey protocol will be reviewed by survey teams during a pre-survey briefing. A 

survey methods handout will be provided to crew members and should be available to the 

observer while in the aircraft. This handout lists general pilot and observer instructions, and the 

ArcPad program setup and operation instructions. 

All team members will review the survey aircraft curtain and light box system procedures to 

ensure independent double count data. 

All team members will review collection of bear sighting covariate data. 

 Review proper identification of bear species and sex and age classification. 

 Bear location should be marked where the bear was first observed. 

 Record covariate data (activity, percent cover and percent snow) where the bear was first 

observed. 

 Estimate percent snow and cover within a 9 m (30 ft) radius of bear. To determine what a 

9 m radius looks like from the airplane, place a 189 liter (50 gallon) drum in the center of 

a 9 m. radius circle made with bright orange paint on the edge of a runway or in an open 

field. Fly over the circle several times at 91 m (300 ft) AGL. During surveys, use the 

percent cover diagram to estimate percent cover around the bear as required by the data 

sheet. 

58 
 Marking of the horizon (effective sighting distance) should be explained and discussed 

with all survey members present so that everyone is interpreting this variable in the same 

way. 

Review use of GPS and proper settings. Provide user manual associated with the particular GPS 

unit. 

Review specific operation and use of the ArcPad Animal Marking applet to collect data during 

survey flights. The following laptop computer training will detail common functions used to 

perform data collection procedures during aerial flights (see SOP #2). 

 Run the ArcPad applet in an office setting by connecting a GPS receiver to a laptop 

computer and set the GPS to run in simulator mode. Turn on the computer and use the 

survey methods handout to run through the ArcPad program setup and operation 

instructions using the pre programmed function keys or screen displayed buttons to fly 

hypothetical transects and record hypothetical bear sightings. 

 Following the office training, each survey team member will complete a practice aerial 

survey flight with an experienced pilot to gain familiarity with data collection procedures 

while in the air. The paper data form should also be used during this practice session 

3.4 Use of Paper Data Forms 

Training in use of data forms will detail how and when to use these forms. Most data are entered 

through the pop-up windows associated with the ‘transect set up’ and ‘add animal’ icons or 

function keys while running the ArcPad applet on the laptop computer. Paper data forms will be 

used as a back-up in the case of a laptop failure, or to note that a mistake was made while 

entering data in ArcPad. In situations where many bear sightings occur within a small area (i.e., 

coastal bear aggregation areas) sighting covariate data can be recorded primarily on paper data 

forms to expedite data entry if preferred. Observers must note on the paper data forms when this 

occurs and that the paper data form is the primary record. In previous survey efforts, some 

observers used data forms to record data or used a combination of the laptop and data forms. 

Training will emphasize the use of the laptop computer as the primary data collection tool. Data 

forms should be used when necessary, but minimally, to reduce transcription errors and time it 

takes to verify data at day's end. 

3.5 Pilots 

The project leader will review survey flight compliance procedures (listed in SOP #1) with 

pilots. The review will stress the importance of flight procedures that maneuver the aircraft to 

minimize disturbance to bears and other wildlife. Experienced pilots who perform wildlife 

surveys have advanced training and many hours of flight time and therefore will already be 

familiar with these flight procedures. This is of particular importance to the SWAN brown bear 

survey methodology and statistical model assumptions that require bears to be accurately 

recorded at their initial locations (i.e., locations of detected bears are recorded prior to responsive 

movements to aircraft noise or presence, SOP #5). 

59 
60 

Literature Cited 

Sasse, D. B. 2003. Job-related mortality of wildlife workers in the United States, 1937-2000. 

Wildlife Society Bulletin 31:1015-1020. 
Aerial Survey Flights 

Standard Operating Procedure #4 

Version 1.0 (8/5/2009) 

Buck Mangipane 

NPS – Lake Clark National Park and Preserve and Southwest Alaska Network 

Kirmanie Ravariere 

GeoNorth, LLC, Anchorage, Alaska 

Kellie Peirce 

Fairbanks, Alaska 

Revision History Log: 

Previous Version # Revision Date Author Changes Made Reason for Change New Version # 

Add rows as needed for each change or set of changes associated with each version. 

61

4.0 Introduction 

This SOP explains the processes for collecting and recording data during aerial survey flights for 

monitoring of brown bears in SWAN parks. Computer/program requirements include: Windows 

XP with minimum 128 MB RAM and 100 MB free hard disk space; ArcPad version 7.1; and 

base maps of survey area, study area or park boundaries. 

4.1 Conducting Aerial Surveys 

There are four main steps to completing aerial survey flights (Figure A4.1).A typical aerial 

survey flight starts by navigating to the pre-selected transect. The observer will start the laptop 

computer, the GPS and related software while en route to the survey transect. The pilot will 

navigate to the survey location, which can take between 15 minutes to over an hour to reach 

depending on location of departure relative to survey area. 

When the aircraft reaches the beginning of the survey transect, the pilot will follow the predetermined survey route. The observer and pilot will start looking for bears once the pilot begins 

flying on the survey transect. When a bear (or bear group) is sighted the pilot, observer, or both 

activates their light switch to indicate to the other that a bear has been observed. The aircraft will 

leave the transect and will fly over the bear for the observer to record its location and relevant 

data collection (see below section on Starting and surveying a transect, step 14). The plane will 

then return to the transect departure point and will resume the survey along the designated 

transect. 

Conducting Aerial Surveys

STEP 1:

Prepare 

Equipment 

for Surve

 Gather survey, personal & 

safety equipment 

y

Figure A4.1. Steps in conducting aerial surveys of brown bears. 

STEP 3:

Go Flying & 

Obtain Data

STEP 2:

Meet the 

Plane

 Load up gear 

 Ensure software on laptop/GPS unit 

are working 

 Conduct survey 

 Fly transects 

 Obtain data 

STEP 4:

Return 

Home 

 Unload plane 

 Download 

 Error check 

 Backup 

63
4.1.1 Procedures for Using Laptop Computers 

Data will be collected with laptop computers during aerial surveys. We will use a bear survey 

applet in ArcPad that is loaded onto the laptop computers. Order of surveying transects will be 

determined by logistics, weather conditions, aircraft safety, and perceived movements of bears 

during the survey period. Care will be exercised to avoid confounding of bear movements with 

spatial ordering of surveyed transects. 

4.1.1.1 Getting Started: 

1. Use duct tape to connect the computer to the battery via the DC adaptor. Connect the 

cable from the GPS to the PC via the serial port. Attach the antenna to the GPS. The 

antenna is placed in a location with a clear view of the sky, typically on the crossbar 

overhead. Turn on the computer. 

2. Turn on the GPS (be sure to start the computer first). 

3. Double click on the ArcPad shortcut on the desktop to open the program. 

4. Set up a new session using Take Off/Load Map button. 

a. Select survey number. 

b. The pilot and observer names are entered from the lists. 

c. Select aircraft from pick list. 

d. Select weather from pick list. 

e. Click Next. 

5. Customizing the pick lists. 

a. The second tab, called the ‘Animals’ tab, allows the observer to append list options to 

the various pick lists used in the Animal Mark form and through the applet. 

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b. To add a new list option, select the list to be appended to from the right, enter the text 

for the option in the Add New field and then click the Add New button. 

c. To delete a list option, select the list to be appended to from the right, select the list 

option to the left and click the Delete button. 

d. Click Finish to complete the Load Map step. 

4.1.1.2 Select a Transect: After completing the New Session form, the Select Transect (gray 

background) button will become available. The Observe Direction buttons will also appear, 

which will allow the observer to toggle the observing direction of the pilot and observer(s). The 

button PL will become PR when clicked which indicates the pilot has now switched from 

observing out the left side of the plane to observing out the right side. The other two buttons, 1L 

and 2L, will also change the observing direction (1R and 2R) for observer 1 and, if a second 

observer was set on the New Session form, observer 2. 

To select a Transect, click on the Select Transect (gray background) button. The Select 

Transect (gray background) button’s icon will change to (green background) to indicate it 

65 
is now active. Clicking on a Transect will select that Transect, zoom to it and open the New 

Transect form. 

The New Transect form allows the observer to further edit the pilot and observer(s) observing 

directions as well as set the current weather. Click Finish to complete the Transect setup or 

Cancel to select another Transect. 

4.1.1.3: Starting and Surveying a Transect: 

1. After completing the New Session form, you will be able to select a transect. 

2. To select a transect click on the Select Transect button which will to turn green 

to indicate it is active. 

3. Clicking on a transect in the map will select that transect, zoom to it and open the New Transect 

form. The New Transect form allows the observer to further edit the pilot and observer(s) 

observing directions (select the uphill side) as well as set the current weather. 

4. Click Finish to complete the Transect setup or Cancel to select another Transect 

5. After completing the New Transect form, the Start Transect button will become 

available. 

6. Upon starting a Transect, the Mark Animal and End Transect buttons become 

available while the Start Transect , Take Off /Load Map and Select Transect 

buttons are disabled. 

7. Look out the uphill side of the window for bears. Both pilot and observer look out the 

same sides to ensure obtaining good double- count data. If making a turn in a canyon, 

look out the downhill side. Make sure to click the pilot and observer observing side 

66 
buttons to change sides in this situation. In flat terrain, the viewing side is determined by 

a coin flip, the seconds on a watch (e.g., 00-29 as right, 30-59 as left), or something 

similar. 

8. After sighting a bear (or group), wait for 5 seconds after the group is beyond the front 

wing strut of the aircraft and then turn on the light to indicate a sighting. 

9. Look at the light to see if the other observer has detected the bear. 

10. Announce your bear sighting. 

11. Note which observer saw the bear (or note both if both observers did). 

12. Click the Mark Animal button to move the applet from On-Transect mode to OffTransect mode. The Mark Animal form will automatically open. A green line will appear 

showing the on-Transect flight path from the point the Start Transect button was 

clicked up until the observer went Off-Transect. 

13. The pilot will now fly off the transect over the bear and parallel to the transect line (when 

safety permits). Click the Mark Animal button to mark the location where the bear was 

initially observed in the GPS data. This point will later be used to calculated 

perpendicular distances. 

14. Record the following covariate information in the computer on the Mark Animal form 

and on the paper data forms. 

a. Bear species (brown or black bear in areas where both occur). 

b. Group size (number of bears detected at this location). 

c. Group type (adult-AdultUnk, boar-LrgMale, subadult-AdultSub, female with yearling 

cubs-FemaleWY, female with cubs of the year-FemaleWC, female with 2 year old 

cubs-FemaleWY2, breeding pair-Breeding). 

d. Activity (bedded, sitting, standing, feeding, walking, running). 

67 
e. Percent vegetation cover around the bear or site where it was initially observed (10 m 

radius). First consult the cover diagrams (see section below). 

f. Percent snow cover around the bear or site where it was initially observed (10 m 

radius). 

g. Which observer detected the bear (pilot, backseat observer or both). 

15. Next, an overflight will be made to record the location of the farthest distance that was 

actively searched (termed horizon location, which will yield the ‘effective distance or 

search distance’). Click the Mark Horizon button to mark this location. In flat terrain, if 

the horizon location is beyond the maximum observation distance (as determined by 

analysis of the survey data), then the maximum observation distance will be used. 

16. If the horizon is the same as a previous sighting (as can be the case when many bears are 

concentrated in a salt marsh), click the Use Last Horizon button. 

17. Other features on the Mark Animal form include: 

a. ’Prev’ and ‘Next’ buttons: These allow the observer to select and edit any other 

animal mark created on the current off-transect segment. The newly selected feature 

can be identified by the light blue square around it. The options that were selected for 

that shape will be reloaded and any changes will be automatically saved by either 

68 
creating another animal mark, navigating to another existing one or by simply closing 

or pausing the form. 

b. Update Position’ button: This button will adjust the location of the currently selected 

animal mark to the current position of the GPS device. 

c. ’Update Horizon’ button: This will update the position of the Horizon set for the 

currently selected animal mark to the current position of the GPS device. 

d. ’Hide Form’ button: This button allows the form to be hidden so the map may be 

accessed for zooming and panning. During hidden state, the only button available is 

the ‘Mark Animal’ tool button which is used to restore the form. 

e. ‘Clear Last Selection’ checkbox: When unchecked, the form options will not be 

cleared when ‘Mark Animal’ is clicked to create a new animal mark. This allows the 

information entered for the previous animal mark to be reused for the current animal 

mark. When checked, the form options will clear each time a new animal mark is 

created. 

18. The plane will now return back to the survey transect to a point just prior to its departure 

location. Click the Resume Transect button to continue the transect. A read dashed line 

will now be visible indicating the path flown from the moment the plane went OffTransect up until the observer clicked the Resume Transect button. 

19. Continue along the survey transect recording the above information upon detecting a bear 

(or bear groups). 

20. At the end of the transect, the observer must then click the End Transect button to 

mark the Transect as completed. 

21. The Transect will change to a gray line to indicate that it has been surveyed. The Start 

Transect and Select Transect buttons will be restored to allow the observer to 

select and start flying another Transect. 

22. If the transect is shorter that the pre-determined desired length, after completing the 

segment depicted on the map, select “off transect”, navigate to nearby equal elevation 

habitat, select “resume transect”, and complete the transect. 

69 
23. When the observer is through flying Transects for this flight, the pilot will return to the 

air strip. When the pilot lands, the Land button must be clicked to signal to the Mark 

Animal applet that it should stop tracking the GPS flight path. It is now safe to close 

ArcPad. 

4.1.1.4 Percent Cover Diagrams: Results from previous surveys suggested percent cover near the 

bear influenced its detectability. Percent vegetation cover is estimated by comparing the cover 

around a sighted bear with a digitized cover diagram of bears in 10, 20, 30, 40, up to 80% cover. 

The observer first consults the diagram (Figure A4.2) before classifying the cover to the closest 

10% around the bear (or group). Cover percentage was calculated by a pixel count of vegetation 

in the diagram and all the percentage diagrams are accurate to within 0.5%. 

4.1.1.5 Restoring Segment if ArcPad Fails: When the user begins an Off-Transect or OnTransect segment, the GPS points being collected are not only stored in memory but are being 

backed up in a DBF file called PointLog. In the event ArcPad crashes while a Transect is being 

surveyed, the segment may be generated from the backup points up to the point when ArcPad 

crashed. 

To restore the segment, simple restart ArcPad and click the Take Off /Load Map button. The 

Mark Animal applet will detect backup points in the DBF file and offer to restore them as a 

segment. Choose ‘Yes’ to restore the segment or ‘No’ to clear out the points from the log. To 

resume flying the Transect, fly to the end of the restored segment, select the Transect, and then 

click the Start Transect button. If the crash occurred during an off-Transect segment, clicking 

the Start Transect button will automatically open the Animal Mark form. 

4.1.2 Important Notes 

1. BEFORE you fly the next transect, click on the Select Transect button to allow the 

selection of a new transect. 

2. Before you take a lunch break or at the end of the day, click the Land button to end 

the flight path tracking by the GPS. Shut down ArcPad and turn off the computer and 

GPS to save battery power. 

3. Bears initially detected while off-transect to locate a detected bear or bear group should 

be recorded but should not be included in the estimation of detection. 

70 
Figure A4.2. Diagram used as a guide to estimate percent cover of vegetation during brown bear aerial 

surveys (from E. Becker, ADF&G, Unpublished report). 

71 
4.1.2.1 Pilot Instructions: 

1. Pilots will fly aircraft 91 m (300 ft) AGL. Adjust the plane distance from the mountain 

slope to obtain a sufficient view of the area represented by the line (contour) on the map 

(approximately 75 m [246 ft] from the plane on the horizontal axis). 

2. Pilots who see a bear or bear group will: 

a. Toggle their light button ON. 

b. Wait until the animal is 5 seconds past the trailing edge of the wing. 

c. Check the observer’s light (always before the next step). 

d. Inform the observer of the bear. 

e. Avoid inadvertently alerting the observer to a bear sighting. 

3. Pilots should not adjust the plane once a bear is sighted - this is a test of the observer. 

4. Pilots will inform the observer of upcoming bank turns to provide forewarning that 

observations will soon occur out the opposite window of the plane. 

5. When feasible, pilots will mark the bear location and horizon location with a parallel 

flight pattern relative to the transect for standardization purposes. 

4.1.2.2 Other Things to Remember: 

1. Carry extra AA batteries at all times for the GPS as a back-up precaution. 

2. Test the GPS battery strength by selecting the PAGE button until the satellite page 

appears. Battery power is displayed on the left side of the page. Ensure battery power is 

above one-quarter power. 

3. Check the GPS signal strength being received. Refer to the ‘status field’ in the satellite 

page. A “3D Nav” reading indicates sufficient signal strength for a three dimensional 

read from the satellites. The PDOP (Position Dilution of Precision) value can also be 

checked. PDOP measures the geometrical strength of the GPS satellite configuration. 

PDOP values less than 4 gives the best accuracy (under 1 meter), between 4 and 8 gives 

acceptable accuracy and greater than 8 gives poor accuracy. 

4. Each time menu buttons are depressed (for transect actions or animal/horizon/markspots 

events), the GPS location associated with that depression is marked. The mark is placed 

at the instant the key is depressed. 

5. Use the geometric center of a group to mark a bear group.

4.2 Daily Survey Data Review and Editing Procedures 

Program and User requirements: ArcPad 7.1, ArcGIS 9.3, User should be versed in basic feature 

editing within the ArcGIS environment. 

After Transects have been surveyed, the Shapefiles exported in the Export process should now 

contain data collected in the field. This import data may include: animal sightings, horizon 

markers, flight track log, GPS point log and updated records in the Transect Shapefile. 

1. First ‘Select the SurveyID’ of the survey boundary for which you wish to import data. 

2. First, click the ‘Browse’ button next to the ‘Import Path’ field and navigate to the path of 

the Survey folder. It’s important to note that the Survey folder should not be part of the 

path – the import path should be the path to the Survey folder not the path to the Survey 

folder contents. 

3. Next, click the ‘Import’ button to perform the import process. The import process 

performs the following actions: 

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a. The import process will first ensure that all Shapefiles – ‘animals’, ‘Track Log’, 

‘Horizon’ and ‘GPSPointsLog’ – are in the Survey folder. 

b. The import process will then determine the TransectID of the Transects that were 

surveyed and have data to import. 

c. The import process will determine if survey data already exist for any of the 

Transects and alert the observer. The observer will then have the option to abort the 

import process or override the existing data. If the observer chooses to override the 

existing data, then all existing data will be deleted for the Transects with existing 

data. The import process will then copy all the data from the Shapefiles into their 

corresponding FeatureClasses – the Shapefiles are named after the FeatureClasses 

from which they were generated. 

4. Make a backup copy of the imported files and store by date and field computer. 

5. Complete the initial data verification (cleaning the data) procedures suggested below on a 

daily basis. Typically, one individual (with GIS expertise) will need to be responsible for 

performing these tasks, working with the observers to ensure data accuracy: 

a. Review the data captured in ArcPad (daily) from survey flights. 

b. Review data entries on data forms daily (if they are used). 

c. Review the downloaded data in ArcGIS 9.3. ArcPad outputs shapefiles (‘animals’, 

‘Track Log’, ‘Horizon’ and ‘GPSPointsLog’) that are directly imported for 

manipulation. This must be completed for each ArcPad session. 

d. Check that applicable information on the data forms are in the files. Make these 

checks using a copy of the original raw data file. 

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e. Additional data editing and management tasks listed are from Olson and Putera 

(2007): 

i. Verify that surveyed transects and bear locations recorded on data forms are 

listed in the files. If they are not, enter the information. 

ii. For each transect, select all records from all the survey shapefiles 

(animals.shp, effsite.shp, and transect.shp), then inspect the records in a view 

zoomed to the extent of the selected records: 

 Verify that corresponding on-transect segments, sightings, and horizons 

are numbered correctly ( SurveyID, TransectID, and SegmentID are the 

same) and that this results in a unique combination for identification. 

 Edit survey shapefiles as per any notes written on the survey data forms. 

 Cross-check survey data forms with the shapefiles to verify that all 

sightings and transects listed on data forms are recorded in the shapefiles, 

and also to cross-check covariate information. 

 Review effective sighting distance (Horizon) locations relative to sighting 

locations to evaluate whether they appeared to make sense (an effective 

sighting distance location should not be closer to a transect line than the 

corresponding sighting location), and editing was done as needed. 

 Edit transect lines (displayed as DB Tracklog in ArcGIS) to delete offtransect segments as appropriate. This editing is done methodically for 

each sighting to be sure that editing is done correctly (for example, 

sometimes the recorder may have forgotten to mark “off transect” until 

after the plane had already left the transect line. In this case, the segment 

previous to the off-transect one is edited to delete the length of the 

segment that is actually off transect rather than on). Also, check off/on 

transect breaks in the transects for significant overlaps—generally, 

observers should try to minimize this issue, but sometimes segments 

overlap some, in which case one should edit the segment continuing along 

the survey line to eliminate the overlap. 

 Review sighting locations to verify that they were recorded while on 

transect (occasionally an observer may record a bear observation after 

passing the designated end length of the transect). 

 It is sometimes helpful to print maps of any sightings in question for the 

observer to refer to when discussing editing questions. Ideally, an effort 

should be made to consult observers regarding any editing questions 

before observer left to fly surveys the following day. 

iii. Back up the edited survey data once editing work for a particular survey date 

is completed. This will be by date and field computer. 

iv. While reviewing and editing survey shapefiles for a particular survey laptop, 

maintain a list for each transect recorded. Cross-check this list with the table 

for the Generated Transects shapefile. Resolve any inconsistencies. Once all 

survey laptop data are checked in, print reports as needed of cumulative 

transects completed, completed transects for a particular date, and transects 

remaining. The lists of transects remaining is especially useful for doublechecking that transects marked as completed on wall maps are in fact 

completed, for specifically identifying quickly which transects still need to be 

74 
surveyed, and for determining when new maps depicting remaining are 

needed. 

v. Prepare new maps that depict remaining transects as needed by working from 

the ArcGIS project files originally used to produce survey maps.

6. Check the computer battery level and charge if needed. 

7. Check-off completed transects from wall maps to assist with the survey planning for the 

following day. 

4.3 Exporting Data into ArcPad 

After successfully backing up and editing the data collected during survey flights, the shapefiles 

are exported into ArcPad for data collection the following day. The ‘Export/Import Data’ tab is 

where the data generated is exported to Shapefiles. 

1. First ‘Select the SurveyID’ of the survey boundary for which you wish to export data. 

2. Click the ‘Browse’ button next to the ‘Export Path’ field and navigate to the path of the 

storage device you wish to export to. Click the folder to select it. The path to the folder 

should now be set in the ‘Export Path’ field. 

3. Click the ‘Export’ button to export the data folder to the specified device or location. The 

data folder is named ‘Survey’ and includes the following: 

a. ‘TrnOrig’ Shapefile-Has all the Transects for the SurveyID that was selected. 

b. ‘animals’ Shapefile-An empty Shapefile for storing animal sightings. 

c. ‘Track Log’ Shapefile-An empty Shapefile for storing the actual transect flight path. 

d. ‘Horizon’ Shapefile-An empty Shapefile for storing horizon marks for animal 

sightings. 

e. ‘GPSPointsLog’ Shapefile-An empty Shapefile for storing all GPS points obtained 

during the entire flight. 

75 
f. APL files for each of the Shapefiles mentioned above. These APL files contain the 

symbology for displaying the Shapefile features. This includes not only the colors but 

the symbols used to represent the shapes. 

4. The ‘Survey’ folder is then placed on the field computer as described in SOP #2 section 

2.3, and it will be ready for data collection. 

4.4 Post Survey Data Editing and Preparation for Analysis 

After all survey flights are completed and data edited as described in section 4.2, data will need 

to be compiled, reviewed, processed, and exported for analysis. 

4.4.1 Post Survey Data Editing 

1. The three shapefiles ‘Track Log’, ‘Horizon’, and ‘animals’ from each field computer will 

need to be merged into one complete file for each feature (ie. ‘Complete Track Log’, 

‘Complete Horizon’, and ‘Complete animals’). The resulting files will contain all 

completed transects, animal sightings, and horizons collected during the survey. 

2. Covariate fields are checked for spelling errors and also for any duplicate transect 

numbers. 

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3. The merged files of sightings and effective sighting distance records are compared to 

verify that a corresponding effective sighting distance record existed for each sighting 

record. 

4. The “DIST2TRANS” field in the compiled animals table and the “DistToSeg” field of 

the compiled Horison table are examined to look for any unusual distances (such as 

distances less than the 22-m blind strip and exceptionally far distances; any such records 

are then reviewed/verified). In addition, the “DIST2TRANS” field value for each 

sighting record was checked to verify that it was less than or equal to “DistToSeg” value 

for the corresponding effective sighting distance record. Comparison of corresponding 

distances is most efficiently done in Microsoft Excel (working from the compiled dbase 

files from the shapefiles): 

a. Sort the ‘Horizon’ records by “TransectID”, “SegmentID”, and “HorizonID”. Sort the 

‘animal’ records by “TransectID”, “SegmentID”, and “AnimalID” number, then 

b. Paste the “DistToSeg” and “HorizonID” fields from the ‘Horizon’ table and 

“DIST2TRANS” and “AnimalID” from the ‘animal’ table into a new worksheet in 

Microsoft Excel. 

c. Subtract the “DIST2TRANS” value from the “DistToSeg” value and look for any 

negative values. 

4.4.2 Post-Survey Processing of Final Edited Data 

For data analysis, the biometrician or quantitative ecologist uses data records that include 

sighting records with corresponding effective sighting distance; transect records summarizing 

length flown for each surveyed transect and including corresponding buffer areas based on buffer 

distances specified. The following steps produce the data records formatted per the analyst’s 

specifications. 

1. The ‘Complete TrackLog’ is buffered using the Buffer tool in the ArcToolbox of ArcGIS. 

Specific buffer distances are selected by the analyst (sometimes after initial analysis was 

performed on an initial data set). Buffering always included a 22-m buffer, which was the 

approximate blind strip below the aircraft. Additional buffers are generated at 600, 800, 

and 1,000 m. Buffer coverages follow a standard naming convention of transyybnnnn, 

where yy = last 2 digits of survey year and nnnn = buffer distance in meters. 

77 
2. Buffer coverages are used to determine areas surveyed along the transects by following 

the steps below in ArcGIS: 

a. Start an edit session for the buffer coverage. 

b. Right-click the layer and click Open Attribute Table. You can only perform 

geometric calculations on attribute tables. 

c. Right-click the field heading for which you want to make a calculation and click 

Calculate Geometry. 

d. Click the geometric property you want to calculate. Different properties are available 

depending on the type of layer you're using. 

e. Click to use either the coordinate system of the data source or the coordinate system 

of the data frame. 

f. Click the units of the output calculations. 

3. The tables for the buffer coverages, ‘TrackLog’, ‘Complete Horizon’, ‘and 'Complete 

animals’ are exported for use in preparing files for the data analysis software. The steps 

for this process are listed below: 

a. Click on the Options button on the bottom toolbar of the table and select Export... 

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b. Select the location to store the exported table and the file type (.dbf) and click okay. 

c. Repeat the process above for the other three files. 

79 
80 

Table A4.1. Fields included in dbase files exported for data analysis.

Field name Field definition 

Transect Data 

TransNo Transect identification number. 

Length Length of surveyed transect (m). 

Area Area surveyed along transect (based on various specified buffers). 

Detected Object Data 

GroupID Unique ID of each group of animals detected (note: group could equal 1). 

OBS Numerical ID of observer who detected the group of animals (i.e., 1 for 

Pilot, 2 for Passenger, or 3 for both). 

DistanceOffTransect Perpendicular distance (m) from the transect to the detected animal group. 

TransNo Unique ID of transect from which animal group was detected. 

GroupSize Number of animals within the detected group. 

OtherCovariates Additional covariates considered in fitting the detection function. 

4. The data files for the exported buffer coverages and ‘TrackLog’ are used to make a file 

containing a list of transect numbers, along with corresponding lengths, and buffer area. 

This file represents the ‘Transect Data’ used in the analysis and require specific naming 

for each variable listed in Table A4.1. 

5. The data exported from the ‘animals’ file are used for the ‘Detected Object Data’ file. 

The variables and their naming convention are listed in Table A4.1. 

Literature Cited 

Olson, T. L., and J. A. Putera. 2007. Refining monitoring protocols to survey brown bear 

populations in Katmai National Park and Preserve and Lake Clark National Park and 

Preserve. Alaska Region Natural Resources Technical Report NPS/AR/NRTR-2007-66, 

National Park Service, Anchorage, Alaska. Available from 

(http://www.arlis.org/docs/vol1/227063700.pdf). Accessed 9 July 2009. 
Data Analysis 

Standard Operating Procedure #5 

Version 1.0 (7/29/2009) 

William L. Thompson 

NPS─Southwest Alaska Network 

Revision History Log: 

Previous Version # Revision Date Author Changes Made Reason for Change New Version # 

Add rows as needed for each change or set of changes associated with each version. 

81

5.0 Introduction 

This SOP describes how to use the aerial survey data to estimate abundance and density of 

brown bears, a hierarchical modeling approach to estimate bear distribution and trends, and other 

analyses that will be used to summarize data. A general overview of the survey procedure is 

described in section 2 of the protocol narrative and in Becker and Quang (2009). An introduction 

to the theory and application of line transect sampling is provided by Buckland et al. (2001, 

2004). 

Required freeware programs and libraries: 

 R (R Development Core Team 2009) 

 GammaMRDS (Reynolds et al. 2009; Appendix B) 

 R2WinBUGS (Sturtz et al. 2005) 

 WinBUGS (Lunn et al. 2000). 

5.1 Estimating Bear Abundance, Density and Distribution 

5.1.1 Abundance and Density 

Program GammaMRDS (Reynolds et al. 2009) will be used to convert observations collected 

during aerial surveys (SOP #4) into estimates of brown bear abundance and density for the areas 

of interest within SWAN parks. This program is written in R code (R Development Core Team 

2009). The user's manual for GammaMRDS in Appendix B provides step-by-step instructions 

for installing the program and conducting an analysis. A copy of the program can currently be 

obtained either from Joel Reynolds (USFWS, Anchorage, Alaska; joel_reynolds@fws.gov) or 

from the SWAN Quantitative Ecologist 

(http://science.nature.nps.gov/im/units/swan/index.cfm?theme=network_staff). 

5.1.2 Distribution 

As with abundance and density estimates, a robust estimate of spatial distribution of brown bears 

will require adequate correction for bears present but undetected along surveyed transects. A 

common approach to estimating spatial distribution of animals that adjusts for incomplete 

detectability is occupancy modeling (MacKenzie et al. 2006), but this requires repeated visits to 

the same sampling units (transects) during the survey period, which is not feasible within the 

budget constraints of SWAN's brown bear monitoring program. Instead, program GammaMRDS 

has been converted to a Bayesian hierarchical modeling framework (Royle and Dorazio 2008; 

Appendix D) and, once the minimum spatial unit for measuring adjacency has been defined, will 

be used to generate a surface of estimated bear densities covering the sampling universe. The 

minimum spatial unit will be larger than a transect and based on the average perceived 

movements of bears during the survey. Thus, spatial distribution will be represented as relative 

differences in density rather than in estimated occupancy. Note that occupancy is intrinsically 

linked to abundance and density (Royle and Nichols 2003, Dorazio 2007, Royle and Dorazio 

2008). Bayesian hierarchical spatial models offer several advantages over traditional (strictly 

likelihood-based) models, including decomposition of the sources of variation into process and 

observational components, trend estimators that are often more precise (due to use of prior data 

and "shrinkage" induced by borrowing strength across all models [random effects]), and spatially 

explicit (sub-area) estimates of abundance or density. 

83
Appendix D contains R code that fits the Becker-Quang model within a Bayesian hierarchical 

modeling framework (Royle and Dorazio 2008) to estimate trends in abundance and density 

based on the coefficient of a time (year) variable (and associated measure of precision) included 

in the hierarchical model. This code uses the R2WinBUGS library (Sturtz et al. 2005) so that the 

Markov Chain Monte Carlo (MCMC; Gilks et al. 1996) method required for this approach can be 

called from within R to run in the freeware program WinBUGS (Lunn et al. 2000). However, the 

Becker-Quang model used in GammaMRDS and in the R code in Appendix D is biased due to 

lack of independence in the double-observer component (e.g., see Buckland et al. 2010). A 

correction is under development and will be implemented in the next version of this protocol. 

 

Users will continue to follow the analytical steps provided in the GammaMRDS manual 

(Appendix B), with additional steps describing how to use the Gelman-Rubin statistic (Gelman 

and Rubin 1992, Brooks and Gelman 1998) and visual inspection to assess proper convergence 

in MCMC chains, determining the appropriate "burn-in" for MCMC sequences (i.e., how many 

of the initial iterations to discard before convergence), and assessing goodness of fit of the model 

(in conjunction with the Bayesian P-value; Gelman et al. 2004). DIC (Spiegelhalter et al. 2002) 

will replace AIC or AICc (Burnham and Anderson 2002) as the model selection criterion. An 

option will be added for users to employ a conditional spatial autoregressive model to account 

for spatial dependence among transects (e.g., Webster et al. 2008, Johnson et al. 2010). 

Bear density for each minimum spatial unit will be estimated using the Bayesian hierarchical 

modeling described previously. The following steps describe how this information will be used 

in conjunction with landcover data layers from SWAN parks to generate an estimated density 

surface of bears within the areas of interest. 

 Define the minimum spatial unit of interest, which will be larger than a transect and 

based on the average perceived movements of bears during the survey. 

 Use ArcGIS 9.3 (ESRI, Inc., Redlands, CA) to generate the spatial units throughout the 

area of interest (sampling universe). 

 Use ArcGIS 9.3 to link all spatial units to existing landcover maps and associated data 

layers of SWAN parks. Data layers include landcover (vegetation) class codes, elevation 

and slope for each point in a grid with points spaced 50 m apart and overlaid onto each 

park (data are available from the SWAN Data Manager [contact: 

http://science.nature.nps.gov/im/units/swan/index.cfm?theme=network_staff]). 

 In consultation with park and/or ADF&G biologists, develop two-four categories 

of slope and elevation thought to be correlated with bear distribution. 

 Use the landcover data associated with each point falling within a given spatial 

unit to calculate its average percent landcover class, percent slope category, and 

percent elevation category. 

 Export the resulting percentage data for each spatial unit as a DBF or other file 

format that can be read into R. 

 Develop a set of candidate predictive models comprised of various combinations of 

percent landcover class, percent slope category, and percent elevation category, which 

will be used as potential predictors of brown bear distribution. 

 Import the data into R and, with the R2WinBUGS library, use DIC (Spiegelhalter et al. 

2002) to select the best-fitting model and use it as the final predictive model if it is 

clearly better supported than others. If no candidate model is clearly better supported than 

84 
others, a composite predictive model will be generated by model averaging across all 

models (Hoeting et al. 1999). 

 Use the final predictive model to generate estimated densities of brown bears for 

unsurveyed spatial units. Categorize bear densities as desired and assign these categories 

to each spatial unit. Summarize the percentage of each density category (e.g., low, 

medium, high) across the area of interest. 

5.2 Estimating Trends in Bear Abundance, Density and Distribution 

Converting program GammaMRDS into a Bayesian hierarchical modeling framework for 

estimating spatial distribution of bears also makes seamless the estimation of trends in 

abundance and density. Hierarchical models allow both the estimation of bear abundance or 

density by survey and, by adding a time (year) covariate, an estimation of trend. The same 

approach can be applied to estimating trend in spatial distribution of brown bears, but requires 

the following additional steps. 

 Use a time covariate in the Bayesian hierarchical model from section 5.1 to estimate 

trend in densities for each spatial unit (or, more likely, collection of spatial units). 

 Based on the trend estimate and its precision, characterize each spatial unit (or collection 

of spatial units) as increasing, decreasing, or stable density. Note "stable" could refer to a 

stable density or to a relatively small change in density that is masked by the imprecision 

of the trend estimator. 

 Tally the number of spatial units (or collections of spatial units) with increasing, 

decreasing or stable densities to quantify relative percentages across the area of interest. 

A minimum of three complete surveys will be required to provide an initial estimate of trend, but 

more surveys will likely be needed before reasonably precise estimators of trend can be obtained. 

5.3 Other Analyses 

Other summary analyses include descriptive statistics such as means and standard deviations for 

particular variables measured during aerial surveys (e.g., bear group size, group composition, 

elevation ranges of recorded bear locations). An analysis of bear sighting rate per transect per 

day could indicate changes in timing of den emergence, both within and among years. Example 

summary tables are included in section 4.5 of the protocol narrative. Graphical analyses could 

include use of ArcGIS 9.3 to produce color-coded maps of relative densities of bears or of a 

composite change surface indicating spatially explicit (strip-level) increases, decreases or stable 

densities to quantify changes in spatial distribution of bear densities across time, much akin to 

change detection in a time series of vegetation images (e.g., Reed et al. 2006). Finally, estimates 

of trends in bear density or distribution could be evaluated within the context of visitor use or 

park management policies. 

Literature Cited 

Becker, E. F., and P. X. Quang. 2009. A gamma-shaped detection function for line-transect 

surveys with mark-recapture and covariate data. Journal of Agricultural, Biological, and 

Environmental Statistics 14: 207-223. 

85 
Brooks, S., and A. Gelman. 1998. General methods for monitoring convergence of iterative 

simulations. Journal of Computational and Graphical Statistics 7:434-455. 

Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas. 

2001. Introduction to Distance Sampling: Estimating Abundance of Biological Populations. 

Oxford University Press, Oxford, United Kingdom. 

Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas. 

2004. Advanced Distance Sampling: Estimating Abundance of Biological Populations. 

Oxford University Press, Oxford, United Kingdom. 

Burnham, K. P., and D. R. Anderson. 2002. Model Selection and Multimodel Inference: A 

Practical Information-Theoretic Approach, 2nd Edition. Springer, New York, New York. 

Dorazio, R. M. 2007. On the choice of statistical models for estimating occurrence and extinction 

from animal surveys. Ecology 88:2773-2782. 

Gelman, A. J. B. Carlin, H. S. Stern, and D. B. Rubin. 2004. Bayesian Data Analysis, 2nd edition. 

Chapman Hall/CRC, Boca Raton, Florida. 

Gelman, A., and D. B. Rubin. 1992. Inference from iterative simulation using multiple 

sequences. Statistical Science 7:457-511. 

Gilks, W. R., S. Richardson, and D. J. Spiegelhalter. 1996. Markov Chain Monte Carlo in 

Practice. Chapman and Hall, London, United Kingdom. 

Hoeting, J. A., D. Madigan, A. E. Raftery, and C. T. Volinsky. 1999. Bayesian model averaging: 

A tutorial (with discussion). Statistical Science 14:382-417. 

Johnson, D. S., J. L. Laake, and J. M. Ver Hoef. 2010. A model-based approach to making 

ecological inference from distance sampling data. Biometrics 66:310-318. 

Lunn, D. J., A. Thomas, N. Best, and D. Spiegelhalter. 2000. WinBUGS─A Bayesian modelling 

framework: concepts, structure, and extensibility. Statistics and Computing 10:325-337 

Program available from (http://www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml). Accessed 21 

July 2009. 

MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2006. 

Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species 

Occurrence. Elsevier-Academic Press, New York, New York. 

R Development Core Team. 2009. R: A Language and Environment for Statistical Computing. R 

Foundation for Statistical Computing, Vienna, Austria. Program available from 

(http://www.r-project.org/). Accessed 21 July 2009. 

86 
87 

Reed, B., M. Budde, P. Spencer, and A. Miller. 2006. Satellite-derived measures of landscape 

processes: Draft monitoring protocol for the Southwest Alaska I&M Network. National Park 

Service, Inventory and Monitoring Program, Southwest Alaska Network, Anchorage, Alaska. 

Available from 

(http://science.nature.nps.gov/im/units/swan/vs/lp/Protocols/SWAN_2006_LP_Narrative_v1.

pdf). Accessed 9 July 2009. 

Reynolds, J. H., B. Russell, E. F. Becker, and P. X. Quang. 2009. GammaMRDS: an R package 

for fitting gamma-shaped detection functions to mark-recapture line-transect data. U.S. Fish 

and Wildlife Service, Division of Realty and Natural Resources, Anchorage, Alaska. 

Royle, J. A., and R. M. Dorazio. 2008. Hierarchical Modeling and Inference in Ecology: The 

Analysis of Data from Populations, Metapopulations and Communities. Elsevier-Academic 

Press, New York, New York. 

Royle, J. A., and J. D. Nichols. 2003. Estimating abundance from repeated presence-absence 

data or point counts. Ecology 84:777-790. 

Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. van der Linde. 2002. Bayesian measures of 

model complexity and fit (with discussion). Journal of the Royal Statistical Society, B 

64:583-639. 

Sturtz, S., U. Ligges, and A. Gelman. 2005. R2WinBUGS: A package for running WinBUGS 

from R. Journal of Statistical Software 12:1-16. 

Webster, R. A., K. H. Pollock, and T. R. Simons. 2008. Bayesian spatial modeling of data from 

avian point count surveys. Journal of Agricultural, Biological, and Environmental Statistics 

13:121-139. 

Data Management 

Standard Operating Procedure #6 

Version 1.0 (7/29/2009) 

Kellie Peirce 

Fairbanks, Alaska 

Revision History Log: 

Previous Version # Revision Date Author Changes Made Reason for Change New Version # 

Add rows as needed for each change or set of changes associated with each version. 

89

6.0 Introduction 

This standard operating procedure (SOP) provides instructions for data entry, data validation, 

and administration of the monitoring data collected by the brown bear monitoring protocol for 

the Southwest Alaska Network (SWAN). This SOP has been developed following strategies and 

guidance for data handling and quality assurance/quality control as outlined in the SWAN Data 

Management Plan (Southwest Alaska Network 2006). Additional procedures for data 

management may be found in this plan. 

Currently, data are managed using a variety of programs including MS Access, MS Excel and 

ArcGIS 9.3 software (including ArcPad 7.1; ESRI Inc., Redlands, CA). Of these, GIS software is 

the primary tool used for managing brown bear monitoring data. Some of the current brown bear 

databases will need to be adjusted to conform to these standards, including the development of 

an overall working database that incorporates all data output entities. This will be accomplished 

as part of the brown bear survey in Lake Clark National Park and Preserve (LACL) during 2010. 

6.1 Data Stewardship Roles and Responsibilities 

The purpose of data stewardship is to ensure information is organized, useful, compliant, 

available, and secure. Managing brown bear monitoring data appropriately requires a high level 

of attention to detail for all personnel involved with the monitoring protocol. This includes 

survey crew members, data entry technicians and project leaders in addition to the data manager. 

Personnel will perform specific tasks listed in Table A6.1, as well as communicate with and 

assist others in order to accomplish all required data management activities. 

6.2 Database Design and System Architecture 

The development of an overall working database that incorporates all data for brown bear 

monitoring needs to be developed. Programs used for various aspects of brown bear monitoring 

include: ArcPad 7.1 (data collection), ArcMap/ArcInfo 9.3 (transect development, map creation, 

data verification, spatial analysis), ArcInfo 9.3 with GRID (data editing, preparing files for 

statistician, spatial analysis), R (statistical analysis), MS Excel (various survey related files 

including field data form), MS Word (equipment lists, reporting) and MS Access (transect lists). 

These are described in more detail below. Suggested database design should follow guidelines 

outlined in Southwest Alaska Network Data Management Plan (2006). 

6.2.1 Data Acquisition, Processing, and Workflow 

6.2.1.1 Acquisition: Data are collected according to the SWAN Brown Bear Monitoring Protocol 

and the specifics described in SOP #4 (Aerial Surveys). Emphasis will be placed on obtaining 

legible and accurate data during the acquisition phase. This will be accomplished through 

thorough pre-season training and through data verification and validation processes to be 

described. 

91
Table A6.1. Specific responsibilities related to data management for the brown bear monitoring program 

in SWAN parks (revised from Daley 2006).

Role Primary responsibilities related to brown bear monitoring data 

management 

Project Crew Member This 

is typically an observer on 

aerial flights who operates 

survey equipment, collects 

and enters data. 

 Obtain training in data collection procedures and use of project equipment (on 

board laptop, GPS unit, etc.), data entry and data documentation. 

 Record measurements and observations based on monitoring objectives and 

protocols. 

 Verify recorded values daily. 

 Use field notes to document methods, procedures and anomalies. 

 Enter field data and documentation. 

Project Leader/ Wildlife 

Biologist This is a member 

of the project crew with the 

same duties as other crew 

members as well as 

leadership responsibilities for 

field operations. 

 Supervise and assist project crew members with training, briefings, data 

collection, management, verification and documentation. 

 Coordinate field and office operations including staff training, equipment 

calibration, data collection, and aspects of analysis and reporting. 

 Read and follow the SWAN brown bear monitoring protocols, standard 

operating procedures, and related operational guidelines. 

Project Leader/GIS 

Specialist 

This is the individual 

responsible for reviewing, 

editing, and managing the 

data collected during the 

survey and processing the 

data for analysis post-survey. 

 Enact and supervise data quality assurance measures related to data 

collection, entry, verification, validation, backing up procedures, and 

deviations from SOPs. 

 Maintain communication with all cooperating project staff on issues regarding 

the collection, quality assurance, processing and documentation of monitoring 

data. 

 Ensure the production, storage, and availability of data. 

 Act as the main point of contact for brown bear database content for the park. 

Network Data Manager 

This is the individual who is 

responsible for data 

management oversight of the 

SWAN monitoring programs. 

 Ensure that brown bear monitoring data are organized, useful, compliant, 

available, and secure. 

 Keeps SOP version information current in data dictionaries and data objects. 

 Assist crew personnel, project leaders/wildlife biologists, and other network 

and park staff with data management procedures and assignments to ensure 

that brown bear data collected by NPS staff and cooperators are entered, 

quality-checked, analyzed, reported, archived, documented, cataloged, and 

made available for management decision-making, research, and education 

and conducts related training. 

 Assign and oversee data stewardship responsibilities; ensure regular archival 

of project materials. 

 Ensure project documentation is complete, complies with metadata 

requirements, and enhances the interpretability and longevity of the project 

data. 

Network 

Biometrician/Quantitative 

Ecologist 

This individual is responsible 

for overseeing data analysis 

and project design phases of 

SWAN monitoring.

 Review and analyze data resulting from brown bear monitoring. 

 Assist the project leader/wildlife biologist with information needs, monitoring 

objectives, and reporting requirements. 

 Perform data analysis in cooperation with project leader/wildlife biologist if 

pertinent. 

 Contribute to the reporting of status and trends for the brown bear vital sign. 

SWAN Monitoring Project 

Leader for Terrestrial 

Wildlife 

This individual is responsible 

for overseeing monitoring of 

the brown bear vital sign. 

 This individual works cooperatively with the project leader/wildlife biologist in 

cases where both positions exist. 

 Assist and oversee monitoring activities related to the brown bear vital sign. 

 Review and analyze data resulting from brown bear monitoring projects for 

compliance with stated monitoring objectives, information needs, and 

reporting requirements. 

92 
Data from brown bear monitoring efforts will be generated from the following general 

categories: 

 Pre-season preparation 

 Aerial surveys 

 Post-season analysis 

 Reporting. 

Data types generated from brown bear monitoring (per survey year) include the following. 

 GIS maps and related files for field season preparation (shapefiles) 

 paper data from field data forms (MS Excel spreadsheet file) 

 digital photos documenting general information related to monitoring efforts (jpeg files) 

 data from computers and GPS units collected during aerial surveys (shapefiles) 

 edited data files compiled into coverages for data analyis 

 results from statistical analyses (post-season analyses including abundance estimation, 

modeling results, and associated figures and tables) (MS Excel files, MS Access files, R 

output files, shapefiles) 

 data on bear location and distribution (shapefiles) 

 reports and summaries (MS Word files, PDF files) 

6.2.1.2 Processing: The following steps outline procedures for processing data acquired from 

brown bear surveys. Unless otherwise stated, the GIS specialist typically performs the following. 

1. Collect all data forms from crew members. After completing aerial surveys for the 

day, ensure all crew members have checked their data forms for accuracy, clarity and 

completeness and then collect data forms. 

2. Review the data forms for clarity while on site. Resolve any illegible or questionable 

data forms with the appropriate crew member.

3. Download data collected during aerial surveys. The GIS specialist will download the 

on-board laptop computers (and digital cameras if used) at the end of each survey day. 

Raw files will be backed up on a desktop computer and external storage devices. Back-up 

files will be stored in a safe location at base camp and an off site location when feasible. 

All files will be verified for accuracy and completion after surveys occur. Discrepancies 

or inaccuracies will be resolved with crew members on site. Data processing using AMLs 

will compile data, preparing it for final edits and input into files for data analysis. 

4. Photocopy the original data forms. As soon as possible upon returning from the field, 

make a copy of the original field data forms. Review each copied data form for clarity 

and legibility. The copies will be used for data entry. 

5. Archive the original data forms. Organize the original field data forms by date and 

survey area. Coordinate with the SWAN data manager for long term storage. 

6. Proof the photocopied data forms. Review and proofread all the copied data forms as a 

group to ensure they make sense as a combined unit (e.g., all forms per surveyed transect 

for the day are present). 

7. Mark corrections on copied data forms with red pen. Any corrected errors, or changes 

made by the data “proofer” should be circled and corrected using a red ballpoint pen. 

Notes, in red ink, should be written on the margins or in the comments section whenever 

necessary to document the reason for the corrections. Do not fully cross out or erase data 

on the data forms. 

93 
8. Back-up all files on external media. Once the proofing process has been completed, 

save the files onto back-up files on the SWAN “J” and “X” drives. A suggested back-up 

file name will consist of the project file name with an extra underbar (“_”) and 8 digit 

date (e.g., “SWAN_bear_YYYYMMDD.mdb”). The backend database file (if/when 

developed) will be saved onto a CD-ROM along with any additional pertinent files. The 

CD-ROM should be labeled “SWAN Bear database back-up,” or similar terminology, 

dated and placed in a secure location. 

9. Inform the data manager that data editing and processing for the current year is 

complete. 

6.2.2 Quality Assurance and Quality Control 

Data verification and data validation are two processes used to ensure data quality assurance and 

control. Data verification checks that data entered into a secondary (e.g., electronic) format 

match the source data, whereas data validation checks that data make sense. Although data entry 

and verification can be conducted by personnel with a general familiarity of the data, the 

validation process requires in-depth knowledge about the project and data collected (Southwest 

Alaska Network Data Management Plan 2006). 

6.2.2.1 Data Verification: The project leader is responsible for overseeing data verification of 

brown bear monitoring data (both physical and electronic sources). The purpose of verifying data 

is to reduce collection errors thereby enhancing accuracy of data and subsequent analysis and 

reporting. 

Physical data (e.g., paper field data forms and notes) are verified for accuracy, legibility and 

completeness immediately after collection, first by the particular observer who collected the data, 

and then by the project leader. Verified physical data are entered, typically by crew members, 

into the database as soon after collection as possible (as described above). Project leaders then 

verify these data values entered in the database. 

Field data for brown bear monitoring are collected during a short, but intense time period 

spanning 10-14 days/survey year. These verification requirements are for data generated during 

this period. Regularly scheduled data verification will occur throughout the year as necessary to 

ensure data generated from pre-season, post-season and analysis efforts are accurate and 

complete. The project leader will resolve any errors as soon as possible after these checks are 

performed following established procedures detailed in SOP #8 (Revising the Protocol or SOPs). 

These verification processes will facilitate timely database accuracy and annual reporting. 

6.2.2.2 Data Validation: Prior to distributing brown bear data for any reason, the project 

leader/GIS specialist validates the brown bear database in their park’s master brown bear data 

file. Data validation involves conducting error checks on all data file components to ensure 

overall order and accuracy. Data validation will occur for each new batch of data to ensure 

accuracy for NPS internal uses as well as related area-wide projects. 

Validation of project data by a project leader/wildlife biologist will be ideally completed by midJanuary (or a later scheduled date if workload does not permit this) for data from the previous 

calendar year. If results are reported from data that have not been validated, a statement and 

explanation should be included to caution users regarding data quality. 

94 
Overall quality control procedures are outlined in Southwest Alaska Network (2006). Specifics 

related to brown bear monitoring are listed below and also are detailed in SOP #4. 

6.2.2.3 Data Modifications; Additional editing of processed survey data will be accomplished by 

the project leader/GIS specialist. When possible, all changes to data will be documented in the 

edit log (within the database itself) and accompanied by an explanation that includes pre- and 

post-edit data descriptions. However, time constraints during the field survey period may 

preclude the project leader from entering edits in the electronic log. In these cases, edits should 

be made on hard copy data forms and later transferred to the electronic log. Allowable changes 

include those that improve or update the data formats while maintaining data integrity. 

Computer file organization will be based off of the directory structure implemented in the 

Katmai National Park and Preserve (KATM) office in King Salmon and in accordance with 

guidelines in Southwest Alaska Network Data Management Plan (2006). An example of a 

project directory includes: name of project and year (PROJECTNAMEYR, C:/_lacl9a03 where 

“lacl” represents Lake Clark National Park, “9a” represents GMU 9A surveys, and “03” 

represents the 2003 survey year. Figure A6.1 provides an example image of a file directory 

structure. Before any major changes are made to brown bear monitoring files or a database, a 

copy will be stored with the appropriate version number to allow for tracking of changes over 

time. Versioning of archived data sets is handled by adding an eight digit number to the file 

name that represents the month (2 digits), day (2 digits) and year (4 digits). Frequent users of the 

data are notified of the updates, and provided with a copy of the most recent archived version. 

Changes to a database structure will be recorded within a table of a database itself. 

6.2.3 Data Documentation 

6.2.3.1 Backup Procedures: A master copy of the brown bear data file will be kept on servers at 

the participating park(s) and at the SWAN office in Anchorage. The SWAN project leader will 

coordinate with participating agencies (if relevant) for additional locations. The project leader 

and network data manager will work with park computer support staff to ensure the data file is 

secure, accessible to the appropriate group of users, and backed-up regularly. 

The Alaska Regional Information Technology staff will manage system backups as detailed in 

Southwest Alaska Network Data Management Plan (2006). Additional backups of working 

documents related to brown bear monitoring are performed by the project leader as needed. 

6.2.3.2 A Note on Hardcopy Data: The project leader/wildlife biologist will ensure that all 

hardcopy data and related data documentation items (e.g., log books, field notes, tapes, video, 

data forms) are gathered upon completion of aerial survey operations. This will occur prior to 

95 
Figure A6.1. Example file directory structure used for brown bear survey data in KATM during 2004-

2005. 

crew member departure from the survey effort. Review of hardcopy data for the individual’s 

name, date, project title, and numbered pages will be made prior to archiving. These materials 

are considered “mission critical records” and will be archived permanently. Original materials 

from the monitoring project may be filed and maintained at each park or with the SWAN data 

manager for archival in the Alaska Regional Curation Center (ARCC). If originals are kept at 

individual park locations, copies will be made and archived at the ARCC. Copies of annual 

reports, summary reports, photographs and maps will also be archived at the ARCC. 

6.2.3.3 Data Dissemination and Archiving: The project leader and data manager will work with 

park staff to establish appropriate user access permission (read-only or read-write) for data 

dissemination. User permissions will be allocated in order to maintain the integrity of brown bear 

database files. For example, project staff may have read-write access to the data files, whereas 

other park staff may be assigned read-only permission to these files and folders. 

The SWAN data manager will maintain an archive copy of each park’s master brown bear file on 

the SWAN server. This copy serves as an access point for network staff and as another back-up 

for data. During periods of data collection via aerial surveys, KATM/LACL will have the most 

96 
Figure A6.2. Data flow from collection to archiving for SWAN brown bear monitoring data. 

current data content, some of which may not meet qualification standards depending on the 

timing of verification and validation activities. Access to each park’s most current brown bear 

data will be arranged through the project leader who works with the network data manager and 

park staff to obtain a copy of the master data file and process it to meet the needs of the network 

staff or other requestor. 

Long-term archiving of brown bear data will occur at the ARCC. The project leader will work 

with the SWAN data manager to determine which data components are sensitive and non 

sensitive. Non sensitive data may be posted on the SWAN website for public distribution and 

outreach. 

When data requests are made for the most recent brown bear data, the project leader/wildlife 

biologist will respond to such requests according to the appropriate NPS policy. The network 

data manager may assist in coordinating the data transfer for such requests. As mentioned above, 

an annual submission of all data files and types will be made to the SWAN office. Data file types 

may include the data files, database, images and reference documents. A copy of the submitted 

master brown bear data file will be archived on the SWAN server at the time of submission. An 

overview of data flow procedures from data collection through distribution and archiving are 

detailed in Figure A6.2. 

B. Data Entry 

and Quality 

Assurance 

A. Data 

Collection 

C. Data Distribution 

and Archiving 

Digital camera

Physical Data

Stored at KATM 

in fireproof safe 

& copies sent to 

SWAN at 

Alaska Regional 

Electronic Data

Database & data 

files kept at 

SWAN park units 

(KATM/LACL) & 

SWAN at Alaska 

Regional Office 

Field data form GPS Laptop

1. Download data 5. Backup data 

2. Enter data 6. Copy data forms 

3. Verify data 7. Store data files 

4. Validate data 8. Routine error checks

97 
Literature Cited 

Daley R. 2006. Standard Operating Procedure #8: Data Management Procedures for Water 

Quality Monitoring, Version 1.1. Appendix E in: Regulatory water quality monitoring 

protocol, Version 2.0. National Park Service, Greater Yellowstone Network, Bozeman, 

Montana. 

Southwest Alaska Network. 2006. Data management plan for the Inventory and Monitoring 

Program, Southwest Alaska Network. National Park Service, Anchorage, Alaska. Available 

from (http://science.nature.nps.gov/im/units/swan/index.cfm?theme=reports_pub). Accessed 

9 July 2009. 

98 
99 

Reporting 

Standard Operating Procedure #7 

Version 1.0 (7/29/2009) 

William L. Thompson 

NPS─Southwest Alaska Network 

Revision History Log: 

Previous Version # Revision Date Author Changes Made Reason for Change New Version # 

Add rows as needed for each change or set of changes associated with each version. 

7.0 Introduction 

Written reports on monitoring of brown bears in Southwest Alaska Network (SWAN) parks will 

primarily be: 1) survey reports, 2) newsletters, field season highlights and mid-winter notes; 3) 

annual administrative reports, 4) resource briefs, and 5) peer-reviewed articles in scientific 

journals. These reports may be written by the SWAN Terrestrial Project Leader, SWAN park 

staff, or a combination of both. Oral reports may consist of presentations to the SWAN technical 

committee during survey years, either oral or poster presentations at the SWAN Biennial Science 

Symposium, and either oral or poster presentations at scientific conferences. 

7.1 Written Reports 

7.1.1 Survey Reports 

Survey reports will be generated by the September following the spring survey; these will be 

final reports for completed surveys and field season highlights (see below) for multiyear partial 

surveys that have not been completed. Final survey reports will be formatted based on guidelines 

described on the Natural Resource Publications Management website 

(http://www.nature.nps.gov/publications/NRPM/index.cfm). The target audiences for these 

reports include park staff and natural resource staff and scientists from other state and federal 

agencies. They will be posted on the SWAN website 

(http://science.nature.nps.gov/im/units/swan/index.cfm?theme=reports_pub) to ensure their 

public accessibility. 

7.1.2 Annual Newsletters, Field Season Highlights and Mid-Winter Notes 

Annual newsletters, field season highlights and mid-winter notes will provide interim summaries 

of activities and accomplishments associated with the SWAN brown bear monitoring program. 

The target audiences for these reports will be SWAN park and Alaska Regional Office staffs, 

collaborators from other state and federal agencies, and the general public. These reports will be 

publicly available on the SWAN website 

(http://science.nature.nps.gov/im/units/swan/index.cfm?theme=reports_pub). Examples of each 

can be viewed on the SWAN webpage: annual newsletter 

(http://science.nature.nps.gov/im/units/swan/Libraries/Newsletters/swan_newsletter_20080512_l

owres.pdf); field season highlights 

(http://science.nature.nps.gov/im/units/swan/Libraries/Newsletters/SWAN_field_highlights_200

8_L_20081002.pdf); and mid-winter notes 

(http://science.nature.nps.gov/im/units/swan/Libraries/Newsletters/swan_2008midwinter_notes_l

owres.pdf). 

7.1.3 Annual Administrative Report and Work Plan 

The annual administrative report provides a summary of a network's fiscal year activities, 

accomplishments, and associated budget expenditures to park superintendents, SWAN technical 

committee members, SWAN staff, Inventory and Monitoring (I&M) regional coordinators, and 

servicewide program managers. Servicewide program managers use this information in their annual 

Report to Congress, to respond to inquiries from the budget office, and for other purposes. The 

format of this report is set by the I&M Washington Office in Fort Collins, CO. Summary 

information on brown bear monitoring surveys, results and budget expenditures will be included in 

the annual administrative report during years of partial or complete surveys. 

101
7.1.4 Resource Briefs 

Resource briefs provide one-page summaries describing the importance and issues associated 

with each SWAN vital sign. They will be updated as new survey data are collected and analyzed. 

The target audiences for these briefs will be SWAN park staffs, scientists from other state and 

federal agencies, and the general public. The resource brief for brown bears is publicly available 

on the SWAN website 

(http://science.nature.nps.gov/im/units/swan/index.cfm?theme=brown_bear). 

7.1.5 Peer-Reviewed Articles 

Peer-reviewed journal articles may be published if innovative methods of sampling design or 

data analysis are developed during the course of the brown bear monitoring program. Peerreviewed journal articles lend credibility to the SWAN monitoring program, and the I&M 

Program as a whole, so should be pursued whenever possible. The target audience for these 

articles will mainly be the scientific community, but they also are an important component of the 

Annual Administrative Report that is used to inform Congress of the accomplishments of the 

I&M Program.

7.2 Oral Reports 

Oral reports could be in the form of oral presentations or posters. Program updates will be 

provided to the SWAN Technical Committee during their biannual meetings. Either poster 

(mainly) or oral presentations may be given at the SWAN Biennial Science Symposium (e.g., see 

http://science.nature.nps.gov/im/units/swan/index.cfm?theme=science_symposium_2009), which 

is attended by park staff, cooperating agencies and the general public. Noteworthy results or 

methodological innovations may be presented at scientific conferences, such as the biennial 

meeting of the George Wright Society (http://www.georgewright.org/). Travel to out-of-state 

conferences may largely be dictated by availability of travel funds. 

102 
103 

Revising the Protocol and SOPs 

Standard Operating Procedure #8 

Version 1.0 (7/29/2009) 

Kellie Peirce 

Fairbanks, Alaska 

Revision History Log: 

Previous Version # Revision Date Author Changes Made Reason for Change New Version # 

Add rows as needed for each change or set of changes associated with each version. 

8.0 Introduction 

This SOP outlines the process of documentation for changes to the protocol narrative and 

accompanying SOPs. Personnel editing the protocol narrative or any of the SOPs must follow 

these procedures (Oakley et al. 2003). 

8.1 Revision Procedures 

1. Edits to the protocol narrative or SOPs will require review for clarity and technical 

soundness. Minor changes or additions to brown bear monitoring procedures will be 

reviewed and approved by the project leader (see number 4 below). If major changes in 

methods become necessary, an external review will be required. Regional/national NPS 

staff and outside personnel with expertise in wildlife biology, brown bear research and 

statistical analyses, will be used as reviewers. 

2. Edits to the protocol narrative and SOPs will be tracked in the Revision History Log. The 

protocol narrative and each SOP is accompanied by a revision history log. The purpose of 

the log is to track the date, version number, changes and personnel who made the changes 

to document reason for changes over time. Version numbers assigned to each edited draft 

should reflect the magnitude of changes. For example, major changes will be designated 

by whole number increases (e.g., Version 1.0 to Version 2.0, etc.) and minor changes by 

tenths (e.g., Version 1.1 to Version 1.2, etc.). 

 

 The following information will be recorded in the associated Revision History Log for 

each round of changes to the protocol narrative or SOPs: the previous version number, 

new version number, date of revision, author of the revision, paragraph(s) and page(s) 

where changes appear, and the rationale for each change. 

3. The data manager will be informed regarding changes to the protocol narrative or SOP so 

that the new version number can be incorporated in the metadata of the project database. 

The database may require editing by the data manager to accompany changes in the 

protocol narrative and SOPs. The data manager will post new versions of the protocol on 

the internet and, when possible, notify individuals known to have a previous version. 

4. Significant changes to data collection procedures must be approved by the project leader, 

the SWAN monitoring project leader, the network biometrician, the I&M coordinator and 

the data manager. Upon reviewing the proposed changes, the I&M coordinator will 

decide on whether additional peer review is required before accepting the changes. If the 

changes are considered minor they will be approved by the project leader. Any major 

changes will require a more detailed review as described in number 1 above. 

 Changes to the protocol narrative and SOPs may occur as a result of regularly scheduled 

Program reviews. During the review, data may be analyzed to determine if they are 

satisfying monitoring objectives. If the protocol has not achieved desired results then 

changes should be recommended.. 

Literature Cited 

Oakley, K., L. P. Thomas, and S. G. Fancy. 2003. Guidelines for long-term monitoring 

protocols. Wildlife Society Bulletin 31:1000-1003. 

105

Appendix B: User's Manual for GammaMRDS 

107

User’s Manual for GammaMRDS: An R Package for Fitting 

Gamma-Shaped Detection Functions to Mark-Recapture LineTransect Data 

Version 1.0.1 (7/7/2009) 

Joel H. Reynolds1, Brook Russell1, Earl F. Becker2, and Pham X. Quang3

1

 U.S. Fish and Wildlife Service, National Wildlife Refuges, Division of Natural Resources, 

Region 7, 1011 E. Tudor Rd MS 221, Anchorage, AK 99503 

2

 Alaska Department of Fish and Game, Division of Wildlife Conservation, 

333 Raspberry Rd, Anchorage, AK 99518 

3

 (Emeritus) Dept. of Mathematical Sciences, University of Alaska, Fairbanks, AK 99775 

Revision History Log: 

Previous Version # Revision Date Author Changes Made Reason for Change New Version # 

Add rows as needed for each change or set of changes associated with each version. 

109
How to reference this user's manual: 

Reynolds, J. H., B. Russell, E. F. Becker, and P. X. Quang. 2009. GammaMRDS: an R package 

for fitting gamma-shaped detection functions to mark-recapture line-transect data. U.S. Fish and 

Wildlife Service, Division of Realty and Natural Resources, 1011 E. Tudor Rd, MS 221, 

Anchorage, AK, 99503. 

ACKNOWLEDGEMENTS: 

The package evolved from S-Plus code written by Pham X. Quang and Earl F. Becker 

(earl.becker@alaska.gov) as part of their development of the methodology (Becker and Quang 

1999, Becker and Quang 2009). Aaron Christ ported that earlier code (‘LT 6.0’) to R around 

2004. The current package evolved from the version circa 2006, ‘LT 7.0’. The software reflects 

all of their efforts and, hopefully, allows others to benefit from those efforts. The package would 

not exist in its current form if not for the assistance of Brook Russell and the support of Togiak 

National Wildlife Refuge, Dillingham, AK, and the Division of Realty and Natural Resources, 

U.S. Fish & Wildlife Service, Anchorage, AK. 

110 
111 

1 Introduction 

This manual illustrates the analysis of distance sampling data from a double-observer (‘markrecapture’) line transect survey collected following the methods outlined in Becker and Quang 

(2009). These methods allow the detection function to be non-monotonic (peak occurs off 

transect) and have a maximum detection ≤ 1.0. The analysis code was developed for the 

statistical software package R (R Development Core Team 2008). 

The methods and software were originally developed for using aerial surveys to estimate brown 

bear densities in Alaska (Quang and Becker 1999, Becker and Quang 2009). The software is 

demonstrated estimating the density of brown bears in Togiak National Wildlife Refuge in 

2003/2004 (Walsh et al. 2008). The general method, however, is applicable in any doubleobserver line-transect survey meeting the survey assumptions; there are no inherently Ursuscentric assumptions, though there are some Ursus-centric options. 

This manual assumes that the analyst has a working familiarity with distance sampling 

estimation, generalized linear models, bootstrap methods, and the R software environment. 

References to the literature are provided to guide the analyst toward the technical details. As with 

any tool, familiarity leads to safe use. 

2 Analysis Methods Overview 

The underlying statistical model and estimation process are detailed in Becker and Quang 

(2009). A summary follows. 

2.1 Data Collection and Preparation 

The line transect data are assumed to have been collected by two independent observers sharing 

a common observation platform, e.g., small fixed-wing aircraft. These observers are generically 

referred to as ‘Pilot’ and ‘Passenger’. Survey protocols are detailed in Olson and Putera (2007). 

The main features required are: 

 Surveys are conducted on a random sample of line transects over the study region. 

 The Pilot and Passenger observe, or search, independently. That is, procedures are used 

to ensure that detection by one observer does not cue the other observer to search more 

thoroughly, etc. 

 The perpendicular distance of the detected object from the transect, at the time of 

detection, is measured accurately. Generally, this is achieved by recording the flight line 

using on-board GPS and ‘going off transect’ and using the GPS to mark the location of 

the detected group. The perpendicular distance can then be calculated using GIS. 

 Covariates describing the detected group and surrounding habitat are accurately recorded. 

 Objects can move around within the study region as long as the speed at which the survey 

transect is traversed is at least two to three times faster than the average object movement 

speed (Hilby 1986 quoted in Buckland et al. 2001). 

2.2 Density Estimation 

The density estimate for the study region is obtained by (i) fitting a model describing the 

probability of detecting an animal as a function of distance from the transect and possibly other 

covariates, then (ii) using the model-estimated probabilities of detection and observed object 

group sizes to estimate object density in the searched region. Because the transects were a simple 

random sample from the study region, this is also an estimate of object density in the study 

region. 

All detections made by ‘the pilot’ are used to estimate the pilot’s detection function; all 

detections made by ‘the observer’ are used to estimate the observer’s detection function. The 

combined pilot-observer data are used to estimate the two apex parameters, i.e., maximum 

prob(detection | Pilot) and maximum prob(detection | Observer). 

The uncertainty of the resulting density estimate is driven by two components. Foremost is the 

uncertainty in the fitted detection functions, which is primarily affected by the total number of 

groups detected and the sources of systematic variation in the detection probability. The total 

number of groups detected will be affected by the actual density of groups and the sample effort 

(number of surveyed transects, length of transects, etc.). The other component is variation in 

group size, which mainly affects density estimation and not in the fitting of the detection model. 

Total area searched only affects density estimation. Note that if a particular transect is flown 

twice in the course of the survey, its search area is counted twice. Thus transects overlapping in 

space but not time, repeated observations of the same object at different times (on different 

transects), etc., do not invalidate the method or introduce bias. 

113
2.2.1 Fitting Detection Models 

A detection function is modeled as a gamma-density, which has parameters for shape and scale. 

This allows the detection probability to peak away from the inner edge of the search area; i.e., 

detection probability does not have to decrease monotonically with distance from the transect. 

The scale parameter of the detection function is modeled as a function of covariates, which 

allows the detection function to stretch or contract (see Figure 1). The two observers can have 

different general linear models and different detection functions. 

Specifically, detection functions are modeled as a gamma distribution: 

    1

detect ( ) Pr ( | , , ) exp

j j

j j jj jj

r h y y

jj j rb b b dist y h r     

  (Eqn 1). 

y is the perpendicular distance from the transect to the detected object, j distinguishes between 

the detection parameters of pilot and observer, h is the maximum detection probability at the 

function’s apex, and r is the shape parameter. The scale parameter is set equal to b   , where 

is a general linear model of the observed covariates (Eqn. 2) and b is defined as 

1 1

( )

1 j

j

r rj

r e b

 



       , 

where e is the natural logarithmic base, i.e., ln(e)=1. 

Covariates are incorporated via the log-linear model 

1 1 ln( )j jo j jk k       x  x (Eqn 2), 

where the xi’s are the covariates associated with the ith group detected by observer j, i.e. the pilot 

or the observer. 

Detection functions are fit separately for the pilot and observer positions to allow for varying 

covariate effects and inherent differences in detection. Detection functions are fitted using 

maximum likelihood. Estimates of pilot and observer peak detection probabilities {h1, h2} are 

obtained from the double-count data via an application of the expectation-maximization, or E-M, 

algorithm (McLachlan 1997). 

2.2.2 Model Selection 

After identifying a suite of plausible detection functions for each observer, model selection can 

proceed using AIC-based methods (Burnham and Anderson 2002). 

2.2.3 Model Goodness of Fit 

Assuming that a gamma distribution adequately describes an observer’s detection function, one 

can evaluate goodness of fit by assessing whether the estimated detection probabilities of the 

detected groups differ substantially from a random sample from a gamma distribution. The 

detection models fit by our methods, though, allow the different groups detected by a specific 

observer to arise from different gamma distributions because the gamma scale parameter can 

vary with a group’s covariate settings. However, the fitted detection function and the probability 

integral transformation can be used to transform the detections to observations expected to be 

114 
115 

from a uniform [0,1] distribution. Either a Chi-square test (after binning observations) or a 

Kolmogorov test (Conover 1999) can be used to assess the transformed observations for 

departures from the expected uniform distribution. 

2.2.4 Density Estimation 

Having fitted a detection model, the number of bears in the searched area then is estimated using 

a Horvitz-Thompson estimator (Thompson 1992). Bear density is estimated by dividing the 

estimated number of bears by the total area searched. Each transect's search area is estimated as 

the area perpendicular to the transect out to the effective strip-width distance, less any blind strip 

directly alongside the transect and hence underneath the airplane (Becker and Quang 2009). The 

effective strip width is usually defined as the upper 95th percentile of the observed detection 

distances (Buckland et al. 2001). For curvilinear transects, area lost or gained due to transect 

curvature will be calculated using GIS; macros to do so have been written in Arc Info 9.x by R. 

Strauch (Alaska Department of Fish and Game, Anchorage, Alaska). Total search area is 

estimated as the sum of the transect search areas. Because of the random transect selection 

design, the estimated density in the searched area is also the estimated density for the whole 

study area. 

2.2.5 Standard Errors and Confidence Intervals 

Although the model fitting function, FitGMRDSmodel, provides standard error estimates for the 

total number of animals and animal groups in the surveyed area (Table B4.1), these are underestimates because they treat the fitted detection model as known with complete certainty and thus 

do not account for the uncertainty from estimating the model parameters nor from the model 

selection process. More accurate standard errors and confidence intervals are obtained by 

nonparametric bootstrap resampling of the survey transects and repeating the analysis. For a 

general introduction to bootstrap resampling, see Lunneborg (2000); for a technical introduction 

see Davison and Hinkley (1997). 

3 Software Installation 

The GammaMRDS package is a set of R functions for fitting detection models (‘Gamma [models 

for] Mark-Recapture [line-transect] Distance Sampling’). 

3.1 Required Software 

 R version 2.5 or newer. 

This is available for free download from the Comprehensive R Archive Network 

(‘CRAN’) website, http:/cran.r-project.org/. Follow the installation directions 

appropriate for your computer on the R FAQ document. 

 R libraries / packages: GammaMRDS, boot, nlme, and trust. 

 If your computer is connected to the internet, then installing GammaMRDS will 

automatically install the boot, nlme, and trust R packages required by GammaMRDS if 

they are not already installed. 

If your computer is not connected to the Internet, then you must download these packages 

on another computer, copy their zip files to the desired computer, and install them from 

the copied files. 

 A text editor for editing the user scripts, such as notepad, emacs, or WinEdt1

. 

3.2 Installing R Packages 

Packages only need to be installed on your machine once. 

3.2.1 Internet-connected Computer 

Packages are obtained, after downloading and installing R, by starting R and then using the menu 

Packages => Set CRAN mirror to select the nearest package repository. Then use Packages 

=> Install package(s) to generate an alphabetical list of all available R packages from which 

you will select those you want to install on your machine. Select a package by ‘right-clicking’ on 

its name with the mouse; select multiple packages by holding down the CTRL- key while you 

right-click additional package names. Hitting Enter when you are done selecting packages will 

start the downloading and installation process. 

Non-connected Computer 

Install packages on an Internet-connected computer as described above, but note where the 

package zip files are placed (this will be shown in the R command window during the 

installation process) and do not allow R to delete the zip files after installation. Copy the zip files 

to the non-connected computer. The R menu Packages => Install from (local) zip file

can be used to install packages from a copy of the package zip file. 

 

1

 The R package RWinEdt allows one to link the text editor WinEdt (http://www.winedt.com/) to R, providing many 

useful features for quick copy and pasting of code, etc. 

117
3.3 Loading GammaMRDS into R 

The GammaMRDS package has to be loaded during any session in which you want to conduct 

an analysis. 

Load the package via the R command library(GammaMRDS). This will automatically load the 

other required packages, making available both the user functions (FitGMRDSmodel, 

GoF.GMRDS, BootstrapGMRDSmodel, BootSummary) and the data for the Togiak NWR brown 

bear example (TogiakObjectData, TogiakTransectData, TogiakModel, 

TogiakBootSummary). 

118 
4 Conducting a Simple Analysis 

An analysis is conducted by working through the sequence FitGMRDSmodel => GoF.GMRDS => 

BootstrapGMRDSmodel => BootSummary. Type the R commands shown directly into the R 

command window (followed by Enter key), or copy them from a text editor and paste into the R 

command window. You can change the data object names, but the functions assume that the 

required variables within those data objects are named as shown. 

4.1 Data Preparation 

4.1.1 Required Data and Formatting 

The analysis requires two sets of data with the following minimum components and required 

variable names. The objects can, and likely will, contain additional variables, but these must 

exist with these names for the analysis functions to work. 

 Transect Data (‘TransectData’) 

Each row of this object records information on a surveyed transect: 

‘TransNo’ - its unique transect number or ID, 

‘Length’ - surveyed length, 

 ‘Area’ – the area surveyed along the transect. 

Often the area searched will be calculated later, after the initial analysis of detection 

distances identifies the strip width (Buckland et al. 2001). 

 Detected Object Data (‘ObjectData’) 

Each row of this object records information on a detected group of animals: 

‘GroupID’ - its unique object ID, 

‘OBS’ - who detected the group, numerically coded as 1: Pilot, 2: Passenger or 

 3: Both, 

‘DistanceOffTransect’ - perpendicular distance from the object to the transect, 

‘TransNo’ - the transect the group was detected from, 

‘GroupSize’ - the number of individuals detected in the group, 

and any other covariates for consideration in fitting the detection functions. 

Both TransectData and ObjectData must be data frames. 

4.1.2 Importing Data 

If you are unfamiliar with how to import data into R, review the ‘R Data Import/Export’ help file 

(Help => Manuals (in PDF) => R Data Import/Export). The read.csv command is a 

useful means of importing data from an Excel worksheet (saved as a *.csv file). As described in 

the Import/Export help file, the functions in the rodbc library can be used to import data from 

most common database formats. Check that the resulting objects are data frames via the 

is.data.frame command2 and, if necessary, convert them. 

 

2

 For information on this command use the Help menu on the R toolbar, or at the R command prompt (‘>’) type 

?is.data.frame and hit Enter. 

119 
4.1.3 Data Checking 

The analysis assumes that all detection distances are positive. This can be checked using the 

commands 

# Check for negative distance & display 

# detected objects that have them. 

ObjectData[ObjectData$DistanceOffTransect < 0,]

Any negative distances must be assessed as to their legitimacy and either deleted from the object 

data frame or otherwise modified as appropriate into positive distances. Make sure to discuss all 

negative observations with the person in charge of data collection and pre-processing. Some 

negative distances may be possible. For example, if flying contour transects in mountains, when 

the plane banks into a turn the Pilot and Passenger should look out the other side of the plane 

(the "downhill" side), which could lead to detecting an object with a negative distance. Negative 

distances that come from detecting an object that is running away across the transect and onto the 

'wrong' side, warrant deletion because they violate distance sampling assumptions. 

4.1.4 Variables Specifically Required for Bear Survey Analyses 

The GammaMRDS functions were developed for analyzing bear survey data (Quang and Becker 

1999, Becker and Quang 2009). Portions of the analysis code, therefore, specifically focus on the 

number of detected cubs, yearlings, and ‘Legal’3

 bears. 

These variables can be automatically created by the following commands if your ObjectData 

includes a variable named GROUPTYPE. # These commands may have to be modified if your 'Group Type' 

# variable is named something else or if your encoding of 

# group type differs from that used here. 

ObjectData$Ncub <- ifelse(ObjectData$GROUPTYPE == "FemaleWC", 

 (ObjectData$GroupSize -1),0) 

ObjectData$Nyrlg <- ifelse(ObjectData$GROUPTYPE == "FemaleWY1", 

 (ObjectData$GroupSize -1),0) 

ObjectData$NLegal <- ObjectData$GroupSize - ObjectData$Ncub – 

 ObjectData$Nyrlg 

If you are not analyzing bear survey data and don’t care about these variables, create null values 

so the calculation scripts still work properly: 

ObjectData$Ncub <- 0 

ObjectData$Nyrlg <- 0 

ObjectData$NLegal <-ObjectData$GroupSize 

4.1.5 Setting the Strip Width 

It is common practice in analyzing distance sampling data to eliminate the most extreme 

detection distances by defining a strip width that excludes the 5% (roughly) of detections with 

 

3

 Legally harvestable brown bears under the Alaska Department of Fish and Game regulations, ages 2 years or older. 

120 
the largest distances (Buckland et al. 2001). These observations often represent unique 

conditions, such as objects silhouetted against the horizon, etc., that aid detection even though 

there is no probability of detecting a closer object in that context (i.e., one further down the slope 

of the hill, closer to the transect, rather than on the ridgeline). A variety of data displays can help 

identify the strip width. 

hist(ObjectData$DistanceOffTransect) 

stem(abs(ObjectData$DistanceOffTransect)) 

quantile(abs(ObjectData$DistanceOffTransect), 

 c(.925,.94,.95,.96,.975,.99),na.rm=T) 

plot(density(abs(ObjectData$DistanceOffTransect),na.rm=T), 

 main="Distances of Observed Groups") 

The strip width is used to filter the data set and is passed as an argument to the model fitting and 

bootstrapping functions (below). Note that the example object data frame, ObjectData, has 

already had its upper 5% of detections removed. 

4.1.6 Setting the Blind Strip Width 

Aerial line transect surveys using a backseat observer have a blind strip underneath the plane that 

cannot be viewed by that observer. This blind strip stretches from the flight line (the transect 

line) out to a distance that depends on the type of aircraft, the flight protocol (altitude above 

ground), the aircraft windows (flat or bubble type), etc. This distance must be determined by 

conducting a flight study. All objects detected within this distance of the transect must be 

eliminated since the backseat observer had no chance to detect them (thus they violate the 

‘double-observer’ aspect of the study). The blind strip width is passed as an argument to the 

model fitting and bootstrapping functions (below). 

4.1.7 Filter the Observations 

Eliminate from the ObjectData all observations that were detected within the blind strip or 

beyond the effective strip width. Before doing so, you should save a copy of the full object data 

frame under a different name so that it is available for later use. The example object data in 

ObjectData was filtered using the command: 

ObjectData<- ObjectData[ObjectData$DistanceOffTransect <= 750 & 

 ObjectData$DistanceOffTransect >= 22,] 

4.1.8 Calculating the Search Area 

After the strip width has been determined you will need to calculate the area searched on each 

transect, which gets stored in TransectData$Area. If only linear transects were surveyed this 

will just be the transect’s length multiplied by the strip width. For contour transects, this will 

have to be calculated in GIS4

 to account for the overlap when buffering out to the strip width and 

removing the blind strip area, then the result must be re-imported into R. 

 

4

 Refer to Becky Strauch's (ADF&G) AML scripts, "bufftran.aml." 

121 
Assuming that the relevant values have already been calculated for each transect and stored in 

the TransectData object, then calculate the total effective search area by summing the transect 

search areas. 

area <- sum(TransectData$Area,na.rm=TRUE) 

4.2 Defining the Detection Model 

The FitGMRDSmodel function requires the design matrices for each observer’s general linear 

model. For example 

## Pi1ot scale model design matrix: log(effective search distance) 

## and centered julian calendar date centered 

XdesPilot <- model.matrix(DistanceOffTransect ~ log(EFFDIST) + 

AdjDateCntrd, data=Objectdf) 

dimnames(XdesPilot)[[2]] <- c("Intercept","ln(EFFDIST)","AdjDateCntrd") 

## Passenger's: log(EFFDIST), and survey year as factor. 

XdesPassenger <- model.matrix(DistanceOffTransect ~ log(EFFDIST) + 

I(YR==2004), data=Objectdf) 

dimnames(XdesPassenger)[[2]] <- c("Intercept","ln(EFFDIST)","YR2004") 

These commands will have to be modified for each analysis so that the desired covariates are 

included on the right hand side of the ‘~’ in the model.matrix function for the ‘Pilot’ and 

‘Passenger’ design matrices. The ‘Details’ section of the online help file5

 for the R command 

formula describes the notation and built-in formula functions, such as for automatic creation of 

interaction terms or indicator variables. 

All columns of the design matrices must be named. If a covariate is a categorical variable, the 

design matrix will include an indicator variable for all but one of the observed categories. Take 

care in naming each indicator column; you will likely have to review the design matrix and the 

object data frame to discover which category was selected as the baseline. Alternatively, you 

could control the process by creating the indicator variables directly in the object data frame and 

then simply including all but the baseline category indicator as covariates in the model.matrix

call. 

4.3 Setting the Starting Values for Model Fitting 

Observer-specific detection functions are fitted using the trust numerical optimization program 

to maximize the model likelihood (Geyer 2005). The optimization problem can be rather 

complicated, therefore for best performance you need to provide the optimization routine 

reasonable starting values so it can begin its search in a feasible section of the parameter space. 

Each design matrix has an associated starting value vector that is passed as arguments to the 

model fitting function. The starting value vector must have one more element than the number of 

columns in the design matrix. The components of the starting value vector must be in the same 

 

5

 Use the Help menu on the R toolbar, or at the R command prompt (‘>’) type ?formula and hit Enter. 

122 
order as the columns of the design matrix: e.g., the first element of the Pilot’s starting value 

vector must be the starting guess for the intercept coefficient (the intercept is the first column of 

the Pilot’s design matrix), the second must be the starting guess for the coefficient of the 

covariate in the second column of the Pilot’s design matrix, etc. 

The last element in the starting value vector is the starting guess for the shape parameter of the 

fitted gamma density (‘r’ in Eqn 1). This parameter is constrained to be ≥ 1. When it is 1, peak 

detection occurs on the inner edge of the transect search area and the detection function is 

monotonically decreasing. This is highly unlikely for aerial surveys and thus should be seen as a 

potential warning flag of poor starting values resulting in poor convergence. For Alaskan bear 

surveys using this method, 3 has been a reasonable shape parameter starting value. 

 

## Assign starting values (Intercept and shape param set to 3) 

 startbPilot <- c(3,rep(1,dim(XdesPilot)[2]-1),3) 

 startbPassenger <- c(3,rep(1,dim(XdesPassenger)[2]-1),3) 

4.3.1 Efficient Starting Values 

The optimization uses a trust region algorithm that rescales the parameter space during the search 

using information on the relative scale of the coefficients being estimated (Geyer 2005). The 

analysis estimates the relative scale of the coefficients from the starting values, thus care should 

be taken to use starting values of appropriate relative scale. The default starting values are 

Intercept = 3, gamma shape parameter (r) = 3, and all covariate coefficients = 1. Having the same 

order of magnitude for the covariate coefficient starting values works for the example because all 

of the covariates take values on roughly the same order of magnitude (from 1 – 10): 

ln(EFFDIST) varies from 4 to 7.5, AdjDateCntrd varies from -8 to 6, and YR2004 varies from 0 

to 1. 

Where the covariates vary over different orders of magnitude, their coefficients will likely differ 

in their orders of magnitude. Good starting values should reflect this difference. For example, 

consider two covariates in the design matrix, with the first varying over a range from 1 to 4 and 

the second varying over a range from 1 to 100, thus differing by a factor of 25 in their range of 

variation. If both covariates are expected to have roughly similar magnitude effect on the 

response, then the starting value guesses for their respective coefficients should be scaled on the 

order of roughly 25 to 1 (or 1 and 0.04). 

Alternatively, when faced with a number of potential covariates, one could fit a set of models for 

each observer, each model containing only a single covariate, then use the resulting parameter 

estimates as the starting values. These should both reflect the appropriate relative scales and 

provide a good starting point for the optimization. 

4.4 Fitting the Detection Model 

## Fit the detection model. 

DetectionModel <- FitGMRDSmodel(ObjectData=Objectdf, 

 XdesPilot=XdesPilot,XdesPassenger=XdesPassenger, 

 startbPilot=startbPilot, startbPassenger=startbPassenger, 

 wMax=750, wb=22, modeltitle="Togiak Example") 

123 
4.4.1 ‘Bear Survey’-specific Options 

Cubs can be very difficult to detect, thus it is reasonable to expect that some are missed, resulting 

in some groups that are actually sows with cubs being recorded as single adult bears. If you are 

analyzing bear survey data and wish to use an ad hoc adjustment to account for undercounting 

cub groups, set the UseCubFix argument to TRUE in the model fitting function call (the default is 

FALSE). 

After fitting the detection model, this adjustment calculates a correction factor when the 

estimated total number of bear groups with cubs is less than the estimated total number of bear 

groups with yearlings, as this is counter to general biological expectations under an assumption 

of relatively constant productivity and frist year survival (Earl Becker, Alaska Department of 

Fish and Game, Anchorage, personal communication). The adjustment factor is then used as a 

multiplicative expansion factor to ‘correct’ the estimates of total number of cubs, total number of 

bear groups, and total number of adults (since a cub group is assumed to include a sow). Both the 

unadjusted and adjusted estimates are returned. 

4.4.2 Fitted Model Object, Checking Convergence 

If the numerical optimization of either the Pilot or Passenger general linear models fails to 

converge, a warning will be printed in the command window stating the source of the failure 

(Pilot or Passenger model) and the DetectionModel object will be assigned a value of NULL. If 

this occurs, try different starting values (and double check that all columns of the design matrix 

are named, that all required variables have their required names, etc.). 

If both numerical optimizations converge, the model fitting function will print summary details 

of the model fit in the command window and pass all the results to the DetectionModel object. 

The DetectionModel object is a list with the components described in Table B4.1. 

Even if the model appears to have converged, check the estimates to make sure they are sensible: 

specifically, the last two components of hvec, b1, b2, Ng and Na of the fitted model object 

(Table B4.1). You should also rerun the analysis with a variety of starting value vectors, saving 

each resulting model and checking that they are converging to the same parameter estimates 

(approximately). 

Standard errors and confidence intervals for all quantities of interest are calculated using 

bootstrap resampling (see section 4.9 ‘Assessing Uncertainty’). 

4.5 Model Selection 

Although not illustrated in the example analysis, the model fitting function FitGMRDSmodel

returns the maximum likelihood, AIC, and AICc values of each observer’s scale model (Table 

B4.1), allowing one to conduct AIC-based model selection on the covariate models and/or multimodel inference (Burnham and Anderson 2002). AICc is recommended when the number of 

observations is small relative to the number of parameters, e.g. sample size < 40 * number of 

parameters (ibid). The parameters include the coefficients in the scale model + the gamma shape 

parameter; the sample size for fitting an observer’s model is the number of bear groups detected 

by that observer (recorded in the fitted model object’s hvec component, Table B4.1). 

124 
Table B4.1. Components of the fitted detection model object. 

Component 

Name 

Content 

Ydata Matrix with columns GroupID, OBS, DistanceOffTransect

XdatPilotF The Pilot design matrix with an additional last column containing the fitted detection probability 

for each detected object (named pii for the detection probability vector Пi) 

XdatPassengerF The Passenger design matrix additional last column containing the fitted detection probability 

for each detected object (named pii for the detection probability vector Пi) 

hvec A 5-element vector recording the total number of groups detected just by the Pilot, the total 

number detected just by the Passenger, the total number detected by both observers, and the 

resulting maximum likelihood estimates of the Pilot’s and Passenger’s maximum probabilities of 

detection (the estimates of hj in Eqn 1) 

Ng Horvitz-Thompson estimate of the total number of animal groups within the surveyed area 

Na Horvitz-Thompson estimate of the total number of animals within the surveyed area 

seNgHT Estimated Horvitz-Thompson standard error for the Ng estimate 

seNaHT Estimated Horvitz-Thompson standard error for the Na estimate 

NaLegal Horvitz-Thompson estimate of the total number of ‘Legal’ bears (aged 2 and above) within the 

surveyed area 

CubCF Estimated ‘cub’ correction factor (if UseCubFix = TRUE was used) 

Ng_adj Adjusted estimate of total number of groups in surveyed area (from using the cub correction 

factor) 

Na_adj Adjusted estimate of total number of adults in surveyed area (from using the cub correction 

factor) 

b1 Vector of Pilot’s detection function parameter estimates 

b2 Vector of Passenger’s detection function parameter estimates 

wMax Effective strip width used in the analysis 

wb Blind strip width used in the analysis 

modeltitle Descriptive title of the detection model. 

maxlik1 Maximum likelihood from the Pilot’s scale model 

maxlik2 Maximum likelihood from the Passenger’s scale model 

AIC1 Akaike’s Information Criterion for the Pilot’s scale model 

AIC2 Akaike’s Information Criterion for the Passenger’s scale model 

AICc1 Corrected Akaike’s Information Criterion for the Pilot’s scale model 

AICc2 Corrected Akaike’s Information Criterion for the Passenger’s scale 

4.6 Model Goodness of Fit 

The function GOF.GMRDS provides two tests of the consistency between the transformed 

observations and the Uniform [0,1] distribution. Setting the function’s argument USE.KS = TRUE

conducts a two-sided one-sample Kolmogorov test with an exact p-value calculated for sample 

sizes < 100. This test will be somewhat conservative since the transformed values are based on 

the fitted parameters of the observer’s scale model (Tadikamalla 1990). Setting USE.KS = FALSE

only bins the transformed values into 15 equal-sized bins and calculates a Chi-square goodness 

of fit test. This test ignores information in the ordering of the bins and can be very unstable and 

heavily dependent upon the chosen number of bins. 

4.7 Calculating Final Density Point Estimates 

## Calculate surveyed area 

 area <- sum(TransectData$Area,na.rm=TRUE) 

## [1] 16544.42 

125 
# Estimate density of objects (e.g., brown bears) in study area 

 cat("Overall Animal Density = ", 1000*DetectionModel$Na/area," per 1000 

km^2\n") 

## Overall Animal Density = 40.35072 per 1000 km^2 

4.8 Visualizing the Fitted Detection Model 

The script FinalModelPlots (section 5) illustrates using the lattice commands to effectively 

present the detection model and how its shape changes under different covariate values. In the 

Togiak example, the Pilot’s scale parameter varied with the natural logarithm of the effective 

search distance6

, ‘ESD’, and the search day relative to the full survey season. Because each 

combination of specific covariate values (potentially) results in a different detection function, 

there must be judicious choice of settings for display (Figure B4.1). The example script 

calculates three values for each covariate –the three quartiles (25th, 50th, and 75th percentiles) for 

ESD and equally spaced survey dates, then uses the multi-panel and group/superpose features of 

the lattice package to display the representative detection functions. 

4.9 Assessing Uncertainty 

The function BootstrapGMRDSmodel conducts the nonparametric bootstrap resampling of 

transects and fitting of the detection function. The default is to conduct 2,000 bootstrap replicates 

as recommended by Lunneborg (2000); this may require a long time to compute for complicated 

models, in which case we recommend you let it run overnight. 

4.9.1 Accounting for Survey Stratification 

For valid estimates, the bootstrap resampling must mimic the original survey sampling structure. 

If the original survey included stratification on a factor, then the bootstrap resampling must be 

stratified on that factor. For example, the Togiak brown bear study sampled transects over the 

course of two years and thus was stratified by year, so the bootstrap must also stratify the 

resampling of transects by year. This is accounted for by setting the Strata argument in the 

BootstrapGMRDSmodel function equal to a vector recording the year each transect was sampled. 

The default is to assume all observations are from the same strata (= unstratified). 

## using Togiak NWR example data, but just 100 replicates to illustrate 

## USE MORE REPLICATES IN REAL LIFE 

 boot.out <- BootstrapGMRDSmodel(TogiakTransectdf, TogiakObjectdf, 

 FittedModel=TogiakModel,Strata=TogiakTransectdf$YR, 

 UseCubFix=FALSE) 

 

6

 Effective search distance is the furthest distance being actively searched at the time of the observation. It records 

the instantaneous width of a person’s active search area. It is expected to change with both terrain and habitat; it is 

expected to be shorter on contour transects in high gradient terrain due to visual blocking by the wings, and shorter 

in thicker vegetation due to slower search rates hence narrower search area for a fixed flight speed. Effective search 

distance was recorded for the person who detected the bear group; when both parties detected the group, an agreed 

upon common ESD was recorded (Walsh et al. 2008). 

126 
4.9.2 Convergence Problems during Bootstrapping 

The Pilot and Passenger detection models are fitted to each bootstrap replicate, i.e., 2000 model 

estimations under the default settings. If the numerical optimization routine fails to converge for 

any of those replicates, a warning will be printed to the R command window identifying whether 

the problem was in the Pilot or Passenger models. The bootstrap replication will continue with 

all of the parameter estimates for the failing replicate set to NA. Consider rerunning the bootstrap 

command to see if the failure was just an extreme resample or sign of some more serious 

numerical problem in fitting the models. 

4.9.3 Bootstrap Output 

Bootstrapping creates a ‘bootstrap object’, here named boot.out, with a number of components. 

The component of main interest is boot.out$t, the matrix containing the parameter and density 

estimates from each of the bootstrap resamples (each row is a resample). The distribution of the 

Figure B4.2. Pilot’s detection function model from the Togiak example data set. The Pilot’s scale 

parameter model varies with the natural logarithm of the effective search distance (ESD) and the survey 

date relative to the full survey period. So detection functions are plotted for early, middle, and late survey 

dates (curves in each panel) at the three quartiles of the ESD (panels). In later surveys there was a 

noticeable tendency to search further out from the transect, perhaps due to increasing experience with 

the survey protocol and survey region. 

127 
bootstrap replicate estimates for a given parameter is an estimate of the parameter’s sampling 

distribution, and provides a basis for estimating bias, standard errors, coefficients of variation 

and confidence intervals. 

4.9.4 Bootstrap Summaries 

The BootSummary function calculates a number of summaries for each parameter or statistic in 

the estimation output (Table B4.2). There are many types of bootstrap confidence intervals, the 

summary function calculates bias-corrected accelerated (‘BCa’) and percentile intervals 

(Lunneborg 2000). A bootstrap percentile 95% confidence intervals is estimated as the 2.5% and 

97.5% quantiles of the distribution of bootstrap replicate estimates and can always be calculated. 

BCa intervals are more difficult to calculate but are more accurate7

 (Davison and Hinkley 1997). 

The summary function attempts to calculate both types of intervals – if successful, we 

recommend using the BCa intervals. If the BCa interval calculations fail, you should reset the 

CItype=’perc’ in the command. You can also modify the desired confidence level and 

rounding of results. The function will stop if there are any NAs in the boot.out$t.

ExampleBootSummary <- BootSummary(bootObject=boot.out, 

CItype=c('perc'),CONF=0.95) 

ExampleBootSummary 

## Parameter Est Bias RelativeBias StdError CV LowerPercentile UpperPercentile 

## Ng 378.1178 4.5536 0.0120 44.3266 0.1172 301.2090 472.7558 

## Na 666.2586 8.5613 0.0128 85.2152 0.1279 524.2119 848.4671 

## ... 

Table B4.2. Summary characteristics of each parameter’s sampling distribution estimated from 

bootstrapping. See Lunneborg (2000) for details. 

Summary Formula Comments 

Bias   bootstrap estimate  The average of the bootstrap replicate 

estimates for a parameter  minus the 

parameter’s estimate from the original model 

fitting 

Relative 

Bias ( )/   bootstrap estimate estimate 

Standard 

Error 2

1

() ( ) /( 1)

B

boot bootstrapi bootstrap

i

SE   B    ibootstrap



 is the ith bootstrap replicate 

estimate and B is the number of bootstrap 

replicate samples

Coef. Of 

Variation ( )/ boot estimate SE  

 

7 BCa intervals attempt to “...correct the percentile interval estimates both for any bias in the sampling 

distribution and for any tendency [of] the sampling distribution spread to [change] ... as the value of θ

changes,” Lunneborg (2000).

128 
4.9.5 Bootstrap Diagnostics 

plot(boot.out) creates three diagnostic plots of the bootstrap replicate estimates for each 

parameter or statistic, allowing visual assessment of ‘extreme’ observations (here, transects) and 

their impact on the estimates (Figure B4.2). Generally, the sample sizes required by the survey 

method should be sufficiently large to ameliorate the influence of any extreme observations. The 

plots are a useful means of confirming this assumption. 

A histogram of replicate estimates allows one to visually assess the skewness of the bootstrap 

resampling distribution; it also includes a vertical dashed line marking the estimate from the 

original model fitting, allowing visual assessment of bias (if the ‘center’ of the histogram differs 

widely from the actual estimate). 

A quantile-quantile plot of the replicate estimates against a standard normal distribution allows 

visual assessment of whether the estimates are approximately normally distributed (do they fall 

along a diagonal line?) (see Cleveland 1993). 

A jackknife-after-bootstrap plot (Canty et al. 2006) displays, for each observation, both its 

‘extreme-ness’ (along the horizontal axis) and its impact on the bootstrap distribution 

(vertically). Consider, for example, the jackknife-after-bootstrap plot of the ‘animal density per 

1000 km2’ statistic from the example data set (Figure B4.2, lower figure). 

‘Extreme-ness’ of an observation, more properly denoted influence, is calculated as the 

difference between the mean of all replicate density estimates and the mean of the just those 

estimates from bootstrap samples that did not include the observation. This value is then divided 

by the bootstrap standard error to get the ‘standardized jackknife value’ plotted on the horizontal 

axis (Figure B4.2). In the example, the 742nd observation is shown to have a standardized 

jackknife value of 11 or so (see the small ‘742’ in the lower right corner of the plot), meaning 

that the density estimate is about 11 standard errors smaller in the bootstrap samples that don’t 

include this transect. The 742nd row of the TransectData is transect number 890. There were 

three bear groups with a total of 5 bears detected on this one transect! 

> ObsYdatboot[ObjectTranboot==890,] 

 [,1] [,2] [,3] [,4] [,5] [,6] [,7] 

[1,] 3004 3 51.189 1 1 1 1 

[2,] 3005 1 660.595 1 1 1 1 

[3,] 3006 2 52.629 3 3 3 3 

A group (row 1) with 1 animal (column 4) was detected by both observers (column 2) 51 m off 

the transect (column 3); a group (row 2) with 1 animal detected by the Pilot 660 m off the 

transect; and a group with 3 animals detected by the Passenger 53 m off the transect. 

As a result of the large number of animals detected on this one transect, the distribution of the 

bootstrap replicate estimates for just those replicates without this transect differs somewhat from 

that constructed from all of the replicate estimates. This is displayed in the graph’s main area 

where the solid lines (denoting, according to the y-axis label, the 5%, 10%, 16%, 50% (median), 

84%, 90%, and 95% quantiles of the sampling distribution) of the outer tail quantiles shrink 

slightly toward the median relative to their values from the full set of replicate estimates 

129 
Figure B4.3. Bootstrap diagnostic plots for the density of brown bears per 1000 km2

 from the Togiak 

NWR survey (Walsh et al. 2008).

(denoted by the dashed lines). For example, without this transect the 95% quantile shrinks from 

about 9 animals/1000 km2

 above the median value to about 7.5 animals above the median. 

Setting the argument jack=FALSE in the plot command will eliminate calculation of the 

jackknife-after-bootstrap plot for each. 

130 
5 Example Lattice Script 

##### Plots to illustrate final combined model 

## Pilot: log(ESD) + AdjDate 

## Passenger: log(ESD) + Year 

############################################################# 

### Example Script for Plotting Final Detection Model ##### 

############################################################# 

# PURPOSE: Commands to generate plots depicting a fitted 

# detection function at some strategically chosen covariate 

# combinations. 

# 

# Specifically, these commands depict the detection function 

# models fit to the 2004/2005 brown bear survey data from 

# Togiak National Wildlife Refuge (Walsh et al. 2008). 

############################################### 

# authors: Joel_Reynolds@fws.gov 

# last edit: 3 Oct 2008 

############################################### 

# References: 

# Walsh, P., J. Reynolds, G. Collins, B. Russell, M. Winfree, 

# and J. Denton. 2008. Brown bear population density on 

# Togiak National Wildlife Refuge and BLM Goodnews Block, 

# southwest Alaska. U. S. Fish and Wildlife Service, 

# Dillinham, Alaska. 

# 

############################################### 

library(lattice) 

# set the color theme to color with a white background 

lattice.options(default.theme = "col.whitebg") 

###################################### 

# Pilot Detection Functions 

###################################### 

# /* Percentiles for Search Distance */ 

# Pilot Covariates: 

# log(ESD) log of effective search distance 

# AdjDate centered version of Julian date of survey 

############################### 

# Estimates 

TrashOut<-TogiakModel 

############################################################ 

#### Selecting Representative Covariate Combinations ####### 

############################################################ 

# log(Effective Search Distance) 

# get quartile values of observed distribution of 

# this continuous covariate. Each quartile value will 

# be used in single panel of the plot. 

logESD<-quantile(TrashOut$XdatPilotF[,2],probs=c(.25,.50,.75), 

 na.rm = FALSE) 

# AdjDate 

# a centered version of Julian date ranging from -7 to 6. 

# Select three equally spaced values for depiction. 

# Each value will be used in a single curve per panel. 

AdjDate<-c(-3.5,0,3.5) 

131 
# y - distance of detected object from transect 

# main covariate; will form x-axis of scatterplot. 

trashPredictorDistance<-seq(22,750,5) 

############################################################ 

#### Get Parameter Estimates ####### 

############################################################ 

# shape parameter - last component of parameter vector 

r1 <- TrashOut$b1[4] 

# calculate b component of scale 

bscale <- (1/gamma(r1))*(((r1-1)/exp(1))^(r1-1)) 

# covariate coefficients (& intercept) 

betas <- TrashOut$b1[-4] 

# create data frame 

trashlength<-length(trashPredictorDistance) 

trashPredict<-data.frame(Distance=rep(trashPredictorDistance,9), 

 Inter=rep(1,trashlength*9), 

 LogESD=rep(rep(logESD,rep(trashlength,3)),3), 

 AdjDate=rep(AdjDate,rep(trashlength*3,3))) 

# calculate detection densities 

lambda <- exp(as.matrix(trashPredict[,-1])%*%betas) 

blah<-trashPredict$Distance/(lambda*bscale) 

trashPredict$detect<-TrashOut$hvec[4]*(1/(gamma(r1)*bscale))* 

 (blah^(r1-1))*exp(-1*blah) 

Apex <- bscale*(r1-1)*lambda 

############################# 

# Display Pilot detection functions 

############################# 

xyplot(detect~Distance|as.factor(LogESD),data=trashPredict, 

 # 'groups' argument automatically overlays the different curves for the 

 # different 'AdjDate' values (treating AdjDate like a grouping 

factor) 

 groups=AdjDate,type="l",lwd=2, 

 layout=c(1,3),xlab="Distance from Transect",ylab="P(Detect)", 

 #main="Pilot Detection Function", 

 # add legend at top of plot 

 key=list(space="top", 

 text=list("Early"),lines=list(lty=1,col="darkgreen"), 

 text=list("Mid"),lines=list(lty=2,col="red"), 

 text=list("Late"),lines=list(lty=3,col="blue"),lwd=2), 

 # create specific labels for each panel's strip 

 strip=strip.custom(style=1,factor.levels=c("ESD = 350 m", 

 "ESD = 525 m", "ESD = 758 m"), bg="white")) 

 

###################################### 

# Passenger Detection Functions 

###################################### 

# /* Percentiles for Search Distance */ 

# Passenger Covariates: 

# log(ESD) log of effective search distance 

# Year Factor: 2004 coded as 0, 2005 as 1 

############################### 

132 
133 

############################################################ 

#### Selecting Representative Covariate Combinations ####### 

############################################################ 

# log(Effective Search Distance)- use same as for Pilot 

logESD<-quantile(TrashOut$XdatPilotF[,2],probs=c(.25,.50,.75), 

 na.rm = FALSE) 

# Year runs from 0, 1 

Year<-c(0,1) 

# y - distance from transect 

trashPredictorDistance<-seq(22,750,5) 

# shape 

r1 <- TrashOut$b2[4] 

# b component of scale 

bscale <- (1/gamma(r1))*(((r1-1)/exp(1))^(r1-1)) 

# covariate coefficients (& intercept) 

betas <- TrashOut$b2[-4] 

# make into data frame 

trashlength<-length(trashPredictorDistance) 

trashPredict<-data.frame(Distance=rep(trashPredictorDistance,6), 

 Inter=rep(1,trashlength*6), 

 LogESD=rep(rep(logESD,rep(trashlength,3)),2), 

 Year=rep(Year,rep(trashlength*3,2))) 

# calculate detection densities 

lambda <- exp(as.matrix(trashPredict[,-1])%*%betas) 

blah<-trashPredict$Distance/(lambda*bscale) 

trashPredict$detect<-TrashOut$hvec[4]*(1/(gamma(r1)*bscale))* 

 (blah^(r1-1))*exp(-1*blah) 

Apex <- bscale*(r1-1)*lambda 

############################### 

# Display fitted detection function for Passenger 

############################### 

xyplot(detect~Distance|as.factor(LogESD),data=trashPredict, 

 # 'groups' argument automatically overlays each year's function in each 

panel 

 groups=Year,type="l",lwd=2, 

 layout=c(1,3),xlab="Distance from Transect",ylab="P(Detect)", 

 #main="Passenger Detection Function", 

 # add legend at top of plot 

 key=list(space="top", 

 text=list("2003"),lines=list(lty=1,col="darkgreen"), 

 text=list("2004"),lines=list(lty=2,col="red"),lwd=2), 

 # create specific labels for each panel's strip 

 strip=strip.custom(style=1,factor.levels=c("ESD = 350 m", 

 "ESD = 525 m", "ESD = 758 m"),bg="white")) 

Literature Cited 

Becker, E. F. and P. X. Quang. 2009. A gamma-shaped detection function for line-transect 

surveys with mark-recapture and covariate data. Journal of Agricultural, Biological, and 

Environmental Statistics 14(2): 207-223. 

Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D .L. Borchers, and L. Thomas. 

2001. Introduction to Distance Sampling: Estimating Abundance of Biological Populations. 

Oxford University Press, Oxford, United Kingdom. 

Burnham, K. P., and D. R. Anderson. 2002. Model Selection and Multi-model Inference: A 

Practical Information-theoretic Approach, 2nd edition. Springer, New York, New York. 

Canty, A., A. C. Davison, D. V. Hinkley, and V. Ventura. 2006. Bootstrap diagnostics and 

remedies. Canadian Journal of Statistics 34(1): 5-27. 

Cleveland, W. 1993. Visualizing Data. Hobart Press, Summit, New Jersey. 

Davison, A. C., and D. V. Hinkley. 1997. Bootstrap Methods and Their Application. Cambridge 

University Press, Cambridge, United Kingdom. 

Geyer, C. 2005. trust: Trust Region Optimization. R package version 0.1. Available from 

(http://www.stat.umn.edu/geyer/trust). Accessed 23 July 2009. 

Hilby, A. R. 1986. Results of a hazard rate model relevant to experiments on the 1984/85 IDCR 

minke whale assessment cruise. Report of the International Whaling Commission 36:497-

498. 

Lunneborg, C. 2000. Data Analysis by Resampling: Concepts and Applications. Duxbury Press, 

Pacific Grove, California. 

McLachlan, G. 1997. The EM Algorithm and Extensions. Wiley and Sons, New York, New 

York. 

Olson, T. L., and J. A. Putera. 2007. Refining monitoring protocols to survey brown bear 

populations in Katmai National Park and Preserve and Lake Clark National Park and 

Preserve. National Park Service, Alaska Region Natural Resources Technical Report 

NPS/AR/NRTR-2007-66, Anchorage, Alaska. Available from 

(http://www.arlis.org/docs/vol1/227063700.pdf). Accessed 23 July 2009. 

Quang, P. X., and E. F. Becker. 1999. Aerial survey sampling of contour transects using doublecount and covariate data. Pages 87-97 in G. W. Garner, S. C. Amstrup, J. L. Laake, B. F. L. 

Manley, L. L. McDonald, and D. G. Robertson, editors. Marine mammal survey and 

assessment methods. A. A. Balkema, Rotterdam, Holland. 

135
136 

R Development Core Team. 2008. R: A language and environment for statistical computing. R 

Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL 

http://www.R-project.org. 

Tadikamalla, P. R. 1990. Kolmogorov-Smirnov type test-statistics for the gamma, Erlang-2 and 

the inverse Gaussian distributions when the parameters are unknown. Communications in 

Statistics: simulation and computation 19: 305–314. 

Thompson, S. 1992. Sampling. Wiley and Sons, New York, New York. 

Walsh, P., J. H. Reynolds, G. Collins, B. Russell, M. Winfree, and J. Denton. 2008. Brown bear 

population density on Togiak National Wildlife Refuge and BLM Goodnews Block, 

southwest Alaska. U. S. Fish and Wildlife Service, Dillingham, Alaska. 
Appendix C: Using Simulation to Determine Sampling 

Frequencies Required to Detect a Specified Trend in Brown 

Bear Abundance Estimates in Katmai and Lake Clark 

National Parks and Preserves 

William L. Thompson 

NPS- Southwest Alaska Network 

137

1 Introduction and Methods 

Simulations were run to evaluate the ability of different sampling frequencies to detect specified 

trends in abundances of brown bears in Katmai (KATM) and Lake Clark (LACL) National Parks 

and Preserves. Bennett et al. (2006) suggested a sampling frequency of 5-10 years for monitoring 

brown bears in parks within the Southwest Alaska Network, so these simulations evaluated 

sampling frequencies of 5, 7, and 10 years, with a 1-year (annual) interval added as a benchmark. 

The simulation approach is adapted from Lukacs (In prep.) and is detailed below, with results 

presented in the next section. 

 1. Obtain estimates of abundance ( Nˆ ), standard error ( ] ˆ Sˆ[E N ), and coefficient of 

variation ( ] ˆ C ˆ [V N ) of brown bears for KATM and LACL from Olson and Putera (2007). 



 KATM: 2,183 ± 379 ( ] ˆ Sˆ[E N ); 17 .0) ˆ C ˆ (V N 

 LACL: 466 ± 232 ( ] ˆ Sˆ[E N ); .0) 50 ˆ C ˆ (V N 



 where 

N

N N ˆ

) ˆ Sˆ(E ) ˆ C ˆ (V 



 2. Randomly generate 1000, 22-year time series of true abundances (N) under a specified 

finite rate of population growth (λ = 0.99-0.95, which is a decrease of 1% to 5% per year) 

for each park. The true abundance for a given year i ( Ni ) in a time series is calculated as 

i

i N (Eberhardt 1987), where N0 is the initial true abundance from each park in 

Step 1. 

N  0

 

 a) Randomly choose the observed finite rate of decline value for a given year i

(i.e., i λ , i = 1 to 22) within each simulation run from a Normal distribution with 

mean λ = 0.99 to 0.95 (true rate) and 026 )(SE  .0 (from a 15-year data set of 

brown bears in MT and BC; Hovey and McLellan 1996). This step imposes 

temporal (process) variation into the true abundances within each simulated time 

series. 



 b) Borchers et al. (2002) referred to the results of this step as the state model, 

which basically describes the dynamics of a population. 



 3. Randomly generate an estimated abundance ( Ni

ˆ ) and its estimated standard error 

( ] ˆ Sˆ[E N ) for each year i of each simulated time series produced in Step 2. 



 a) Randomly select an estimated abundance from a Normal distribution with 

mean based on the true abundance for year i (i.e., Ni ) of each simulated time 

series produced in Step 2 and standard error ) ˆ Sˆ(E N from each park in Step 1. 

139
 b) Randomly select the estimated standard error of the estimated abundance in 

year i, ] ˆ Sˆ[E Ni , by multiplying the true abundance for a given year i ( Ni ) by the 

randomly chosen estimated coefficient of variation for that year, ) ˆ C ˆ (V Ni . The 

estimated coefficient of variation for a given year i, ) ˆ C ˆ (V Ni , was randomly 

chosen from a Normal distribution with a mean ) ˆ C ˆ (V N of the relevant park from 

Step 1 and standard error ( ] SE[C ) = 0.009 (from McClintock et al. 2006 for 3 Vˆ

brown bear populations in Alaska; original data from Miller et al. 1997). 



 c) Availability bias is assumed to be relatively constant over time. 



 d) This step applies sampling variation to the true abundances generated in Step 2 

thereby producing abundance estimates for each year in each simulated time 

series. This sampling variation includes variation due to sampling design (amongunit variation; Thompson et al. 1998) and due to incomplete counts within 

sampling units (enumeration variation in Thompson et al. 1998 or the observation 

model in Borchers et al. 2002) 



 4. Estimate the trend for each simulated time series and summarize how often the upper 

90% confidence limit of each trend estimate either is equal to and/or below different 

values of annual rates of change. 



 a) Log-transform the estimates of abundance within each simulated time series 

from Step 3 and use them as response variables in the trend models by park 

(Earnst et al 2005). 



 b) Fit a weighted loglinear model to each change rate/time series combination 

(e.g., Kissling et al. 2007) from Step 3 and record how often in the 1000 

simulated time series, by park, that a trend of a specified rate of change is 

detected based on sampling every year, once every 5 years, once every 7 years, 

and once every 10 years. Estimates of trend are the estimate coefficients of the 

year (predictor) variable in the weighted loglinear model, where annual rate of 

change = (EXP[estimated coefficient of year variable from loglinear model] - 1) 

(or multiply this value by 100% to get percent annual change; Earnst et al. 2005). 

If the specified percent annual change (0%, -0.5%, -1%, etc.) is equal to and/or 

below the upper 90% confidence limit for the estimated coefficient of the year 

variable, then a trend of that magnitude has been detected (Figure C1.1). Note that 

typical statistical power analysis compares the upper confidence limit to 0 (or 

lower limit, if positive change), which only tells you that there was a trend but not 

its magnitude (or its detectable magnitude) unless you assume the cited true 

 change value (model) is correct, i.e., a statistically significant trend equals the true 

trend value used in power calculations. 

 

140 
141 

Figure C1.1. An illustration of how the probability of detecting an annual change is calculated for 

different levels of true change and sampling frequencies. In this example, 90% confidence intervals 

(CIs) of the trend (change) estimate are generated from three different simulation runs from a 20-22 

year time series of abundance estimates upon which a true annual change of 2% (black arrow) was 

applied. Note that these CIs could have been produced under any one of the sampling frequencies, 

which dictate length of the time series and the number of observations used in these calculations (see 

footnote in Table C1.1). The upper limit of the top 90% CI is greater than 0 so a change was not 

detected, but the upper limits of the other 2 CIs were lower than 0 so they did detect a non-zero 

change. Consequently, there is a 67% (2/3) chance of detecting a <0% annual change ("Estimated 

annual change" in Table C1.1) when the true annual change is 2%. The upper limits of both the 

middle and the top CIs exceed -0.5%, whereas the upper limit of the bottom CI does not, so there is a 

33% (1/3) chance of detecting a -0.5% annual change when the true annual change is 2%. Similarly, 

there is a 33% chance of detecting a -1% annual change because the upper limit of the bottom CI is 

less than -1%. The upper limits of all 3 CIs exceed -1.5% annual change so there is a 0% chance of 

detecting an annual change with 90% confidence at this level and lower. 

 5. Repeat Steps 1-4 with other values of ] ˆ C ˆ [V N and ] ˆ Sˆ[E N , as necessary. For instance, 

a 10  .0]C would be derived by adjusting the observed ] ˆ Sˆ[E N in Step 1 so that it 

was 10% of the observed Nˆ for a particular park (i.e., 218] S ˆ  for an  ,2 183 in

KATM). 

ˆ ˆ [V N

ˆ[E N Nˆ

2 Results and Discussion 

An estimated coefficient of variation of 50% (i.e., ), which was based on the 

abundance estimate for LACL, was inadequate to detect even a trivially small change under a 

true 5% annual decline when sampling every year over a 20-year period. The chance of detecting 

a non-zero change under this maximum-change scenario in these simulations was only 38%; in 

most cases, there was less than a 20% chance of detecting a change (decline) of any magnitude 

included in simulations based on (results not shown). The ability to detect trends 

in abundance greatly improved with the lower from KATM (17%), but even this value 

was largely inadequate except for sampling at 1-year intervals (Table C1.1). However, 

simulations based on a and sampling every five years indicated this effort was 

sufficient for detecting a non-zero annual decline when the true rate of annual decline was 2%, 

which translates into about an 80% chance of detecting a declining trend (greater than 0% 

reduction) in a population when the true population was reduced by about 35% over 21 years (1-

0.9821=0.35). In this same scenario, simulation results indicated a 75% chance of detecting at 

least a 1% annual decline when the true annual decline was 3% during a 21-22 year period, 

which is equivalent to detecting a 19% (1-0.9921=0.19) population reduction when the true 

population was reduced by 47% (1-0.9721=0.47) over 21 years. There were essentially no 

situations in which a 10-year sampling interval was adequate for detecting trends in abundances, 

even for a (results not shown). 

C ˆ (V Nˆ  .0) 50

) ˆ (N

C ˆ (V Nˆ  .0) 50

C

.0 135

Vˆ

) ˆ C ˆ (V N 

C ˆ (V Nˆ  .0) 10

143
Table C1.1. Percentage of 1000 simulation runs in which the upper 90% confidence limit (CL) of the 

estimated population trend in brown bear abundance was equal to and/or below a specified annual 

change rate for true rates of population change (λ) of 0.99 to 0.95 (or 1% to 5% decline per year) during a 

20-22 year period. The simulated time series of bear abundances were based on observed estimated 

coefficients of variation ( ) of 0.17 (17%) for Katmai National Park and Preserve and of 0.135 

(13.5%) for Game Management Unit 9C, which contains Katmai National Park and Preserve (Figure 2.3; 

Olson and Putera 2007). Values in bold font indicate at least an 80% chance of detecting an annual rate 

of change of the listed magnitude given the true annual rate of change, sampling frequency, and 

during a 20-22 year period (i.e., analogous to statistical power = 0.8). 

] ˆ C ˆ [V N

] ˆ C ˆ [V N

Percentage of 1000 simulation runs in which the upper 90% CL was 

equal to and/or below the listed estimated annual change (i.e., 

minimum detectable annual rate of change) 

Estimated annual change 

] ˆ C ˆ [V N

Sampling 

interval 

(year)a

True 

annual 

change <0%b ≤-0.5% ≤-1% ≤-1.5% ≤-2% ≤-2.5% 

0.17 1 -1% 41% 18% 6% 2% <1% <1% 

(17%) -2% 79% 59% 36% 16% 6% 2% 

-3% 94% 87% 73% 54% 35% 18% 

-4% 99% 96% 92% 82% 68% 48% 

-5% 100% 99% 97% 93% 84% 72% 

5 -1% 25% 13% 5% 2% 1% 0% 

-2% 51% 38% 23% 14% 7% 4% 

-3% 67% 58% 46% 35% 24% 15% 

-4% 75% 67% 58% 49% 39% 30% 

-5% 81% 74% 67% 60% 48% 40% 

7 -1% 25% 14% 6% 3% 1% <1% 

-2% 46% 33% 22% 13% 7% 3% 

-3% 61% 50% 40% 28% 18% 13% 

-4% 73% 64% 57% 46% 37% 26% 

-5% 75% 69% 63% 56% 48% 40% 

10 -1% 17% 11% 6% 3% 1% <1% 

-2% 30% 24% 16% 10% 6% 4% 

-3% 37% 32% 25% 19% 14% 10% 

-4% 45% 41% 36% 31% 25% 20% 

-5% 49% 45% 40% 35% 30% 25% 

0.135 1 -1% 76% 35% 6% <1% <1% <1% 

(13.5%) -2% 99% 93% 67% 30% 6% 1% 

-3% 99% 99% 99% 91% 63% 27% 

-4% 100% 100% 99% 99% 96% 83% 

-5% 100% 100% 100% 100% 99% 99% 

5 -1% 49% 22% 7% 1% <1% <1% 

-2% 80% 61% 40% 18% 6% 2% 

-3% 93% 86% 75% 58% 37% 19% 

-4% 98% 95% 89% 79% 67% 51% 

-5% 98% 96% 93% 87% 80% 70% 

7 -1% 44% 18% 6% 1% <1% <1% 

-2% 75% 59% 40% 19% 7% 1% 

-3% 86% 77% 67% 50% 36% 16% 

144 
145 

Table C1.1. Percentage of 1000 simulation runs in which the upper 90% confidence limit (CL) of the 

estimated population trend in brown bear abundance was equal to and/or below a specified annual 

change rate for true rates of population change (λ) of 0.99 to 0.95 (or 1% to 5% decline per year) during a 

20-22 year period. The simulated time series of bear abundances were based on observed estimated 

coefficients of variation ( ) of 0.17 (17%) for Katmai National Park and Preserve and of 0.135 

(13.5%) for Game Management Unit 9C, which contains Katmai National Park and Preserve (Figure 2.3; 

Olson and Putera 2007). Values in bold font indicate at least an 80% chance of detecting an annual rate 

of change of the listed magnitude given the true annual rate of change, sampling frequency, and 

during a 20-22 year period (i.e., analogous to statistical power = 0.8) (continued). 

] ˆ C ˆ [V N

] ˆ C ˆ [V N

Percentage of 1000 simulation runs in which the upper 90% CL was 

equal to and/or below the listed estimated annual change (i.e., 

minimum detectable annual rate of change) 

Estimated annual change 

] ˆ C ˆ [V N

Sampling 

interval 

(year)a

True 

annual 

change <0%b ≤-0.5% ≤-1% ≤-1.5% ≤-2% ≤-2.5% 

0.135 7 -4% 95% 89% 83% 74% 62% 47% 

(13.5%) -5% 94% 90% 87% 82% 76% 67% 

10 -1% 28% 16% 7% 2% <1% <1% 

-2% 47% 36% 24% 15% 6% 2% 

-3% 62% 54% 44% 34% 23% 12% 

-4% 69% 63% 56% 48% 39% 29% 

-5% 73% 68% 62% 55% 49% 42% a A 1-year sampling interval yields 20 observations (year 1 - 20) , a 5-year interval produces five 

observations (year 1, 6, 11, 16, and 21) , a 7-year interval generates 4 observations (year 1, 8, 15, and 

22), and a 10-year interval produces three observations (year 1, 11, and 21). b The <0% change category represents the traditional definition of statistical power, i.e., the probability of 

detecting a non-zero change regardless of its magnitude. 

3 Literature Cited 

Bennett, A. J., W. L. Thompson, and D. C. Mortenson. 2006. Vital signs monitoring plan, 

Southwest Alaska Network. National Park Service, Anchorage, Alaska. Available from 

(http://science.nature.nps.gov/im/units/swan/index.cfm?theme=reports_pub). Accessed 9 July 

2009. 

Borchers, D. L., S. T. Buckland, and W. Zucchini. 2002. Estimating Animal abundance: Closed 

Populations. Springer, New York, New York. 

Earnst, S. L., R. A. Stehn, R. M. Platte, W. W. Larned, and E. J. Mallek. 2005. Population size 

and trend of yellow-billed loons in northern Alaska. Condor 107:289-304. 

Eberhardt, L. L. 1987. Population projections from simple models. Journal of Applied Ecology 

24:103-118. 

Hovey, F. W., and B. N. McLellan. 1996. Estimating population growth of grizzly bears from the 

Flathead River drainage using computer simulations of reproduction and survival rates. 

Canadian Journal of Zoology 74:1409-1416. 

Kissling, M. L., M. Reid, P. M. Lukacs, S. M. Gende, and S. B. Lewis. 2007. Understanding 

abundance patterns of a declining seabird: implications for monitoring. Ecological 

Applications 17:2164-2174. 

Lukacs, P. L. In prep. Determining sample size for monitoring studies: beyond simple power 

analyses. Available from 

(http://science.nature.nps.gov/im/monitor/meetings/Austin_05/PLukacs_SampleSize.doc) 

Accessed 26 February 2008. 

McClintock, B. T., G. C. White, and K. P. Burnham. 2006. A robust design mark-resight 

abundance estimator allowing heterogeneity in resighting probabilities. Journal of 

Agricultural, Biological, and Environmental Statistics 11:231-248. 

Miller, S. D., G. C. White, R. A. Sellers, H. V. Reynolds, J. W. Schoen, K. Titus, V. G. Barnes, 

R. B. Smith, R. R. Nelson, W. B. Ballard, and C. C. Schwartz. 1997. Brown and black bear 

density estimation using radiotelemetry and replicated mark-resight techniques. Wildlife 

Monographs 133:1-55. 

 

Olson, T. L., and J. A. Putera. 2007. Refining monitoring protocols to survey brown bear 

populations in Katmai National Park and Preserve and Lake Clark National Park and 

Preserve. Alaska Region Natural Resources Technical Report NPS/AR/NRTR-2007-66, 

National Park Service, Anchorage, Alaska. Available from 

(http://www.arlis.org/docs/vol1/227063700.pdf). Accessed 9 July 2009. 

Thompson, W. L., G. C. White, and C. Gowan. 1998. Monitoring Vertebrate Populations. 

Academic Press, San Diego, California

147

Appendix D: Beta Version of R2WinBUGS Code

149 

# This is a BETA version of code that fits the Becker-Quang (2009) Gamma 

# Mark-Recapture Distance Sampling model to bear groups detected during 

# aerial surveys along randomly selected contour transects. Analysis 

# proceeds using data augmentation in WinBUGS. The individual covariate(s) 

# thought to affect the scale parameter (reparameterized as b x lambda) of 

# the Gamma detection function for each observer are included in a loglinear 

# model. 

# 

# NOTE: This code requires more Beta testing before being put into use. 

# Set the working directory on your computer. 

setwd('C:/Bill/BrBear/Bayesian_Distance_Model') 

# List the R libraries needed for this analysis. 

library(RODBC) 

library(doBy) 

library(xts) 

library("R2WinBUGS") 

library(abind) 

# Import the csv files containing the data for each covariate/variable 

# (separately) and convert each to a matrix. Data are imported with rows 

# corresponding to transects (n = 639), and columns corresponding to actual 

# observations within each transect *plus* data augmented observations 

# (total = 25 observations [columns]). 

# area (m^2) surveyed along transect from 22m to 800m as per Quang's 

# original analysis 

area800<-read.csv("area800.csv",header=TRUE) 

area800<-as.vector(as.matrix(area800)) 

length<-area800/800 

length<-length/1000 

# perpendicular distance (m) from the transect to the detected group 

# (object) 

x<-read.csv("distofftran.csv",header=TRUE) 

x<-as.matrix(x) 

# number of individuals per detected group (assumes complete count of group) 

groupsz<-read.csv("groupsz.csv",header=TRUE) 

groupsz<-as.matrix(groupsz) 

clust<-groupsz 

# estimate of distance (m) to sighting horizon for each detected group (the # 

logarithm of effective distance was the only covariate shown to have an 

# important influence during Quang's original analysis) 

effdist<-read.csv("effectdist.csv",header=TRUE) 

effdist<-as.matrix(effdist) 

# double-observer portion of the input data 

151 
# multinomial representation of detections made by the pilot only 

# (e.g., 10 in encounter history format). 

 

one0<-read.csv("one0.csv",header=TRUE) 

one0<-as.matrix(one0) 

# multinomial representation of detections made by both the pilot 

# and the rear-seat observer (e.g., 11 in encounter history format). 

one1<-read.csv("one1.csv",header=TRUE) 

one1<-as.matrix(one1) 

# multinomial representation of detections made by the rear-seat 

# observer only (e.g., 01 in encounter history format). 

zero1<-read.csv("zero1.csv",header=TRUE) 

zero1<-as.matrix(zero1) 

# multinomial representation of groups missed by both the pilot and 

# the rear-seat observer (e.g., 00 in encounter history format). 

zero0<-read.csv("zero0.csv",header=TRUE) 

zero0<-as.matrix(zero0) 

# generate 3-dimensional array of multinomial 

# representations of detections (e.g., 639 by 25 by 4 array) 

# that is used to compute the "capture" probabilities 

# for each observer later in the program 

ncap<-abind(one0,one1,zero1,zero0,along=3) 

 

# Specify the total number of MCMC samples (ni), the burn-in (nb), the 

# thinning rate (nthin), the number of MCMC chains (nc), the number of 

# detections plus augmented observations for each transect (M), and 

# number of surveyed transects (ntr). The actual MCMC samples selected 

# = (ni-nb)/nthin. 

BQdens.fn=function(ni=100000,nb=20000,nthin=20,nc=2,M=25,ntr=639){ 

# divide perpendicular distances by 1000 to rescale 

x<-x/1000 

# divide effective sighting distances by 1000 to rescale 

 

effdist<-effdist/1000 

leffdist<-log(effdist) 

sink("model.txt") 

cat(" 

model { 

# Assign prior distributions to the various model parameters 

152 
# prior for coefficient of cluster (group) size for relating to distance 

#(correcting for bias) 

betaclust~dnorm(0,0.01) 

cInt~dnorm(0,0.01) # prior for intercept of model relating cluster 

 # size to distance 

#betaeffd~dunif(-10,10) # prior for coefficient of effective sighting 

 # distance 

mu1.p~dunif(-6,6) # avg sightability (p) for pilot 

mu2.p~dunif(-6,6) # avg sightability (p) for observer 

psi~dnorm(0,0.01) # prior for psi, zero-inflation parameter 

rPilot~dunif(1,10) # set prior for Pilot's gamma shape parameter 

rObserver~dunif(1,10) # set prior for Observer's gamma shape 

 # parameter 

hPilot~dunif(0,1) # set prior for Pilot's max (prob(detect)) 

hObserver~dunif(0,1) # set prior for Observer's max (prob(detect)) 

interceptP~dnorm(0,0.01) # set prior for Pilot's intercept of the 

 # log-linear model embedded in the gamma 

 # distn's lambda parameter (scale). 

interceptO~dnorm(0,0.01) # prior for Observer's intercept of the 

 # log-linear model embedded in the gamma 

 # distn's lambda parameter (scale). 

ESDslopeP~dnorm(0,0.01) # prior for Pilot's ESD coeff of the log-linear 

 # model embedded in the gamma dist's lambda 

 # parameter (scale). 

ESDslopeO~dnorm(0,0.01) # set prior for Observer's ESD coeff of the 

 # log-linear model embedded in the gamma 

 # distn's lambda parameter (scale). 

bPP<-loggam(rPilot) 

bOP<-loggam(rObserver) 

bP<-1/exp(bPP)*pow((rPilot - 1)/2.717,rPilot-1) #calculate pilot's b 

bO<-1/exp(bOP)*pow((rObserver - 1)/2.717,rObserver-1) #calculate observer's 

 # b 

lambdaP<-exp(interceptP) #calculate pilot 

 # lambda vector 

lambdaO<-exp(interceptO) ) #calculate observer’s 

 # lambda vector 

tau.e<-1/(sigma.e*sigma.e) # precision in random effects model for psi 

sigma.e~dunif(0,100) # prior for precision in random effects model 

for psi 

 

# for each transect 

for(i in 1:ntr){ 

# e[i]~dnorm(0,tau.e)I(-10,10) # if desired, prior for normal random 

 # effect on psi (habitat/transect 

 # adjustment so don't assume all 

153 
 # transects the same) 

# these two lines use the logit transformation for adding the normal random 

# effect to psi, i.e., constrain values to be between 0 and 1 

lpsi1[i]<-(psi)#+e[i]) 

 psi1[i]<-1/(1+exp(-lpsi1[i])) 

 

# for each object on the transect 

# includes many NAs (i.e., data augmentation) 

 for(j in 1:M){ 

 w[i,j]~dbern(psi1[i]) # prior for presence on a transect 

 x[i,j]~dunif(0, 2.907) # prior for observed perpendicular 

 # distances (max observed distance 

 # (scaled) = 2907/1000=2.907) 

 groupsz[i,j]~dpois(cmean[i,j]) # prior for cluster size 

 log(cmean[i,j])<-cInt+betaclust*x[i,j] # model for cluster size related to 

 # distance (same approach used in 

 # Program DISTANCE) 

 

# pilot and observer detection functions 

# effdist[i,j]<-x[i,j]+stuff[i,j] # prior for effective sighting 

 # distance 

# stuff[i,j]~dgamma(0.01,0.01) 

 

# calculate the thresholds to decide whether pilot, observer, both, or 

# neither saw the group 

#first, extract the following from hvec, b1, and b2 (from the ESD model 

fitted to the Togiak data): 

#create vectors of probabilities for pilot/observer to see groups 

p1[i,j]<-(hPilot/(exp(bPP)*bP))*pow(x[i,j]/(lambdaP*bP),rPilot-1)* 

 exp(-1*x[i,j]/(lambdaP*bP)) 

p2[i,j]<-(hObserver/(exp(bOP)*bO))*pow(x[i,j]/(lambdaO*bO),rObserver-1)* 

 exp(-1*x[i,j]/(lambdaO*bO)) 

# compute capture probabilities for observers 

 

 cp1[i,j]<- (1-p1[i,j])*p2[i,j] 

 cp2[i,j]<- p1[i,j]*(1-p2[i,j]) 

 cp3[i,j]<- p1[i,j]*p2[i,j] 

 cp4[i,j]<- (1-p1[i,j])*(1-p2[i,j]) 

 mu[i,j,1]<-w[i,j]*cp1[i,j] #w is binary indicator 

 mu[i,j,2]<-w[i,j]*cp2[i,j] 

 mu[i,j,3]<-w[i,j]*cp3[i,j] 

 mu[i,j,4]<-w[i,j]*cp4[i,j] + (1-w[i,j]) 

 ncap[i,j,1:4]~dmulti(mu[i,j,1:4],1) 

} 

154 
155 

N[i]<-(sum(w[i,1:M]))*(exp(cInt)) # calculating abundance per 

 # transect, cInt=corrected mean 

 # cluster size 

D[i]<-N[i]/(length[i]*0.8) 

Dint[i]<-D[i]*length[i] 

} 

D1<-sum(Dint[1:ntr])/sum(length[1:ntr]) # density calculation 

TotN<-D1*14031.38 

} 

} 

",fill=TRUE) 

sink() 

data <- list ("x","groupsz","ncap","M","ntr","length") 

e<-rep(0,ntr) 

inits <- function(){ 

 list (psi=0,cInt=1,betaclust=0.1,rPilot=3,rObserver=3.1,hPilot=0.8, 

 hObserver=0.8, interceptP=1.2,interceptO=0.15,ESDslopeP=0.74, 

ESDslopeO=0.89) 

 list (psi=0,cInt=1,betaclust=0.1,rPilot=3,rObserver=3.1,hPilot=0.8, 

hObserver=0.8, 

 interceptP=1.2,interceptO=0.15,ESDslopeP=0.74,ESDslopeO=0.89) 

#sigma.e=2.5 

} 

parameters <- 

c("D1","TotN","bP","bO","cInt","betaclust","psi","lambdaP","lambdaO","rPilot"

,"rObserver", 

"hPilot","hObserver","interceptP","interceptO") #"sigma.e" 

out <- bugs(data, inits, parameters, "model.txt", 

n.thin=nthin,n.chains=nc,n.burnin=nb,n.iter=ni, 

 bugs.directory="/Program Files (x86)/WinBUGS14",debug=TRUE) 

print(out, digits.summary=5) 

} 

BQdens.fn() 
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other information about those resources; and honors its special responsibilities to American Indians, Alaska Natives, and 

affiliated Island Communities. 

NPS 953/106217, December 2010
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