Fatality Estimator User’s Guide

U.S. Department of the Interior

U.S. Geological Survey

Data Series 729

Version 1.1, December 2015

Fatality Estimator User’s Guide

By Manuela Huso, U.S. Geological Survey; and Nicholas Som and Lew Ladd, 

EcoStats, LLC

Data Series 729

Version 1.1, December 2015

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia

First release: 2012

Revised: 2015

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resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1–888–ASK–USGS.

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visit http://www.usgs.gov/pubprod

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Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Huso, Manuela, Som, Nicholas, and Ladd, Lew, 2012, Fatality estimator user’s guide (ver. 1.1, December 2015): U.S. 

Geological Survey Data Series 729, 22 p., http://dx.doi.org/10.3133/ds729.
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Description of Product.........................................................................................................................1

Installation Instructions.......................................................................................................................1

Getting Started ...............................................................................................................................................2

Main Page.......................................................................................................................................................3

Input Files...............................................................................................................................................3

Fatality ...........................................................................................................................................3

Searcher Efficiency (SE).............................................................................................................3

Carcass Persistence (CP)...........................................................................................................3

Path to R ........................................................................................................................................3

Output Files ...........................................................................................................................................3

Results Observed and Bootstrap ..............................................................................................3

Results SE and CP Estimates .....................................................................................................3

Results Fatality Estimates...........................................................................................................3

Parameters.............................................................................................................................................4

Total Number of Turbines at Site...............................................................................................4

Total Number of Turbines in Study............................................................................................4

Number of Bootstrap Resamples .............................................................................................4

Fatality Data..................................................................................................................................4

Summary List .......................................................................................................................4

Required Variables .............................................................................................................4

Searcher-Efficiency Model ........................................................................................................4

Required Variables .............................................................................................................4

Select Variables for Model ...............................................................................................4

Carcass-Persistence Model ......................................................................................................5

Required Variables .............................................................................................................5

Select Variables for Model ...............................................................................................5

Alpha ...........................................................................................................................................5

Tutorial .............................................................................................................................................................5

Input Files...............................................................................................................................................6

Fatality ...........................................................................................................................................6

Searcher Efficiency (SE).............................................................................................................7

Carcass Persistence (CP)...........................................................................................................7

Path to R ........................................................................................................................................8

Output Files ............................................................................................................................................8

Results Observed and Bootstrap ..............................................................................................8

Results SE and CP........................................................................................................................8

Results Fatalities Estimates .......................................................................................................8
iv

Contents—Continued

Tutorial—Continued

Parameters.............................................................................................................................................9

Fatality Data................................................................................................................................10

Searcher-Efficiency Model ......................................................................................................10

Carcass-Persistence Model ....................................................................................................11

Results ..................................................................................................................................................11

SE Model Selection ...................................................................................................................11

CP Model Selection...................................................................................................................12

Final Estimates............................................................................................................................13

Details ...........................................................................................................................................................15

Data Formats—Case Sensitivity, Blanks within Columns, and Preceding Numbers .............15

Missing Values ....................................................................................................................................15

Input File Formats................................................................................................................................15

Example Fatality Input Data .....................................................................................................15

Example SE Trial Input Data.....................................................................................................16

Example CP Trial Input Data.....................................................................................................16

Output Files Contents .........................................................................................................................17

Results Observed and Bootstrap ...........................................................................................17

Results SE and CP Estimates ..................................................................................................17

Results Fatality Estimates.........................................................................................................18

Density-Weighted Proportion Area Searched ..............................................................................19

Model Construction and Assumptions............................................................................................19

SE Models ...................................................................................................................................19

CP Models...................................................................................................................................20

Estimating Turbine-Specific Fatality ................................................................................................20

References Cited..........................................................................................................................................20

Appendix A. Flow Chart of Estimation Process and Bootstrap Variance..........................................21
v

Figures

Figure 1. Fatality estimator interface ............................................................... 2

Figure 2. Completed Input Files window ............................................................ 8

Figure 3. Completed Output Files window ......................................................... 8

Figure 4. Completed Parameters window ......................................................... 9

Figure 5. Completed Fatality Data window ......................................................... 10

Figure 6. Completed Searcher-Efficiency Model window ....................................... 10

Figure 8. AICc for the SE Model based on Visibility Class ....................................... 11

Figure 7. Completed Carcass-Persistence Model window ....................................... 11

Figure 9. AICc results for the CP Model based on Visibility Class and a Weibull 

distribution.................................................................................... 12

Figure 10. AICc results for the CP Model based on Visibility Class and a lognormal 

distribution.................................................................................... 12

Figure 12. Original fatality data and bootstrap sample estimates of SE, CP, r, and adjusted 

fatality ....................................................................................... 13

Figure 11. Kaplan-Meir curve (stair-step) and fitted models for four carcass-persistence 

distributions ................................................................................. 13

Figure 13. Estimated SE and bootstrap confidence intervals based on 2000 bootstrap 

iterations .................................................................................... 14

Figure 14. Estimated CP, r, and bootstrap confidence intervals based on 2000 bootstrap 

iterations .................................................................................... 14

Figure 15. Estimated fatality for all bats and for each species, and bootstrap confidence 

intervals based on 2000 bootstrap iterations............................................. 14

Figure 16. Example of fatality data as they appear in a spreadsheet.............................. 15

Figure 17. Example of searcher-efficiency trial data as they appear in a spreadsheet ......... 16

Figure 18. Example of carcass-persistence trial data as they appear in a spreadsheet ...... 16

Figure 19. Example bootstrap results output ......................................................... 17

Figure 20. Example SE results output.................................................................. 17

Figure 22. Example Fatality results output ............................................................ 18

Figure 21. Example CP results output.................................................................. 18

Tables

Table 1. First few rows of fictitious example of Fatality data in Site1.Fatality.csv ............ 6

Table 2. First few rows of fictitious example of SE Trial data in Site1.Trial SE.csv ............ 7

Table 3. First few rows of fictitious example of CP Trial Data in Site1.Trial CP.csv ............ 7

Table 4. AICc results for SE Models with and without Visibility Class ........................ 11

Table 5. AICc results for CP Models using lognormal distribution with and without 

Visibility Class .............................................................................. 12
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Fatality Estimator User’s Guide

By Manuela Huso, U.S. Geological Survey; and Nicholas Som and Lew Ladd, EcoStats, LLC

Abstract

This publication is the User's Guide for software 

developed to estimate wildlife fatalities at wind-power 

facilities, although the software is applicable to a variety of 

circumstances in which the objective is to estimate the size 

of a superpopulation and the probability of detection of the 

individuals is less than one. Simple counts of carcasses do not 

accurately reflect fatality and do not allow comparison among 

locations because carcasses may be detected at different rates. 

This software uses data collected during carcass searches 

and knowledge of detection rates to accurately estimate the 

number of fatalities and to provide a measure of precision 

associated with the estimate. These estimates are fundamental 

to understanding acute and cumulative effects of wind power 

on wildlife populations. 

Only carcasses judged to have been killed after the 

previous search should be included in the fatality data set 

submitted to this estimator software. This estimator already 

corrects for carcasses missed in previous searches, so 

carcasses judged to have been missed at least once should be 

considered “incidental” and not included in the fatality data 

set used to estimate fatality. Note: When observed carcass 

count is <5 (including 0 for species known to be at risk, but 

not observed), USGS Data Series 881 (http://pubs.usgs.gov/

ds/0881/) is recommended for fatality estimation.

Introduction

Description of Product

This software is developed for use by anyone interested 

in estimating the size of a superpopulation of a group 

of animals based on observations of individuals whose 

probability of detection is less than one. A superpopulation 

is defined as the total number of animals occurring in a 

study area during a fixed period of time. The population is 

not closed, and the probability of detection can vary among 

individuals based either on the physical characteristics of the 

individual, such as size, or on the environmental conditions 

in which the individual exists, such as vegetation. The 

software was developed to estimate the total abundance 

of carcasses at wind-power facilities from the number of 

observed carcasses. Typically at these sites, an area beneath 

each turbine is searched by humans, sometimes with the aid 

of dogs, to discover carcasses of animals that may have been 

killed by the rotating turbine blades. The simple count of 

observed carcasses does not accurately represent the actual 

population of animals killed by turbines because it is practical 

to search only a fraction of any given site, because scavengers 

sometimes remove carcasses before the search is conducted, 

and because observers simply overlook carcasses due to 

obstructing vegetation, topography, and simple bad luck. More 

importantly, the simple count is not linearly related to the 

population, so rarely can it be used as an index of mortality. 

The Fatality Estimator software uses data provided by the 

user to estimate the probability that a searcher will miss a 

carcass and the probability that a carcass will be removed 

before a searcher has the opportunity to observe it. These 

estimates are combined with the observed casualties and an 

estimate of the fraction of the population of killed animals 

expected to be in the search area to form an estimate of total 

abundance of carcasses over a specified period of time. Much 

of the conceptual framework for this approach can be found in 

Huso (2011).

Installation Instructions

• Make sure R software is installed on your machine and 

you know its location.

• R is open-source statistical software available at http://

cran.r-project.org/. The Fatality Estimator software is 

Windows based, so select R for Windows. 

• Make sure the survival package in R is also installed on 

your machine.

• Download the archive file “ds729_EstimatorSetup.zip” 

to your computer. 

• Unzip the contents to a folder on your system. In this 

zip file, you will find the following: 

• Estimator Project User’s Guide.pdf

• EstimatorSetup.msi

• Site1.Fatality.csv

• Site1.Trial.CP.csv

• Site1.Trial.SE.csv

• Site1.Search Schedule.csv
2 Fatality Estimator User’s Guide

The comma-delimited (.csv) files are example files from a 

fictitious site. They show expected file formats and present the 

data used in the tutorial included in this user’s guide.

• Double click on EstimatorSetup.msi and follow 

instructions for installing the software. The 

InputPlaceholder.txt file that will be installed with the 

Fatality Estimator contains the R code for the program, 

and most updates will occur through updates to this 

file.

• You can now safely delete the zip file.

Getting Started

Start the Fatality Estimator by double-clicking on the 

shortcut created during installation. The first time you run 

the program all input windows of the main screen will be 

empty (fig. 1). After you run the program once, the software 

remembers the most recent input. On the main page are three 

blocks: Input Files, Output Files, and Parameters. Each 

block is described below. A schematic of the general process 

used by this software is given in appendix A.

Figure 1. Fatality estimator interface.
Main Page 3

Main Page

All input data should be in comma-delimited file format 

(.csv). The main screen is divided into three sections: Input 

Files, Output Files, and Parameters. Three separate input 

data files in comma-delimited format (.csv) are required. 

Three separate output files in comma-delimited format (.csv) 

are produced. The user will have the opportunity to provide 

information about which columns in the trial data represent 

responses and explanatory variables, if applicable.

Input Files

Fatality

The user must browse to and select the appropriate file 

in her/his system containing the records of carcasses found 

during searches and any covariates that might affect detection 

probability. The file must contain at least four columns: a 

turbine identifier, the date the carcass was discovered, the 

date of the previous search at that turbine, and the proportion 

of carcasses expected to fall within the searched area of the 

turbine (see sections “Parameters” and “Details” for more 

information).

Searcher Efficiency (SE)

The user must browse to and select the appropriate file in 

her/his system containing the records of carcasses used during 

searcher-efficiency (SE) trials. The file must contain at least 

one column—an indicator (numeric or character) indicating 

whether the trial carcass was found by searchers or not (see 

sections “Parameters” and “Details” for more information).

Carcass Persistence (CP)

The user must browse to and select the appropriate file 

in her/his system containing the records of carcasses used 

during carcass-persistence (CP) trials. The file must contain 

at least three columns—one indicating the left censor time, 

one indicating the right censor time, and one representing the 

censoring type (see sections “Parameters” and “Details” for 

more information).

Path to R

Because all systems are uniquely configured, the user is 

asked to navigate to the location of the local R installation on 

the system, that is, the location of R.exe, which typically is in 

C:\Program Files\R.

Output Files 

The Output Files screen is used to navigate to the 

locations and assign names to the three output files where 

(1) bootstrap samples, (2) SE and CP estimates, and 

(3) fatality estimates will be written. All three output files will 

be comma‐delimited (.csv) files. The files cannot be written if 

they are open in other applications.

Results Observed and Bootstrap

The user must browse to a location in her/his system and 

name a file in which s/he wants to save the fatality data plus 

all bootstrap samples of fatality data and bootstrap estimates 

of SE (p ), average CP time ( t

 ), and estimated proportion of 

carcasses persisting through the interval (r

) for each carcass.

Results SE and CP Estimates

The user must browse to a location in her/his system 

and name a file in which she/he wants to save the SE and CP 

estimates. This file will contain a summary of the original 

SE trial data and estimated SE (p ) with (1-α) bootstrap 

confidence intervals, based on a user-defined model of SE; a 

summary of original CP trial data, estimated CP time ( t

 ) and 

proportion of carcasses persisting through the interval (r

) with 

(1-α) bootstrap confidence intervals based on the user-defined 

distribution and model of CP time.

Results Fatality Estimates

The user must browse to a location in her/his system and 

name a file in which she/he wants to save the per-turbine and 

total-site-fatality estimates with (1-α) bootstrap confidence 

intervals, for each group selected by the user. 
4 Fatality Estimator User’s Guide

Parameters

The Parameters screen is used to input site and study 

information and to allow the user to define models of SE and 

CP time.

Total Number of Turbines at Site

The user enters the total number of wind turbines at the 

location of the study—that is, the total number of turbines 

out of which the sample turbines were selected and to which 

the user wants to draw inference. The ratio of this number 

to the number of turbines sampled in the study will be used 

to expand estimated per-turbine fatality to all turbines at the 

study site.

Total Number of Turbines in Study

The user enters the number of turbines at the site where 

searches were conducted for carcasses. This number must be 

less than or equal to the total number of turbines at the site. 

If this number is smaller than the total number of turbines 

located at the study site, these turbines will be assumed to be 

a representative sample of all turbines. It is possible that no 

carcasses were found at some of the searched turbines. The 

software checks to assure that the number of unique turbine 

codes in the fatality data does not exceed the number of 

turbines searched in the study because such a situation would 

indicate that fatalities are included at turbines that are not part 

of the searched set.

Number of Bootstrap Resamples 

The user enters the number of bootstrap iterations 

desired. Reported results should be computed using no 

fewer than 1,000 bootstrap resamples. We recommend using 

5,000 resamples. The amount of time the program takes to 

complete is greatly affected by this number. It is common to 

choose fewer than 1,000 resamples, for example 1, to conduct 

a practice-run of the Fatality Estimator to ensure that all data 

are formatted correctly. This trial run also allows the user 

to determine if the output contains requested information. 

An additional use of a trial run is at the model-selection 

phase when the user only wants information regarding AICc 

for a particular model. If a number smaller than 1,000 is 

placed in this field for the number of bootstrap iterations, the 

output will contain a warning that the results should not be 

considered valid.

Fatality Data

Summary List

A list of variable names in the Input Fatality Data is 

available from the pull-down menu in this field. The user 

selects variable(s) from this menu that contain subgroup 

identifiers for each carcass and for which summary estimates 

of fatality are desired. The selected variables must be 

categorical, not continuous, with no missing values. To 

remove variables to this list, the user highlights the variable 

in the list on the right, and then selects the “Remove 

Selection” button.

Required Variables

Turbine ID: The user selects the column representing a 

unique turbine identifier.

Density-Weighted Proportion Area Searched: The 

user selects the column representing the proportion of 

carcasses (greater than 0 and less than or equal to 1) 

expected to fall within the searched area of the turbine 

(see section “Details” for more information).

Date Carcass Found: The user selects the column 

representing the date on which the carcass was found, in 

mm/dd/yyyy format.

Date of Previous Search: The user selects the column 

representing the date on which the previous search of 

that turbine occurred, in mm/dd/yyyy format. An error 

message is generated if the date of the previous search is 

greater than or equal to the date the carcass was found.

Searcher-Efficiency Model

Required Variables

Response Column: The user selects the column (numeric 

or character) representing an indicator of whether the trial 

carcass was found by searchers or not. The larger value 

will be assumed to indicate that the carcass was found.

Select Variables for Model 

A list of variable names in the Input Searcher-Efficiency 

Trial Data is presented. The user must formulate the model, 

following syntax used by R. All models are additive models, 

that is, additive in the coefficients. All effects are separated by 

“+”, with interactions of two variables indicated by “:”. 
Tutorial 5

The first main effect is included by double-clicking on a 

variable name. If further covariates are to be included, the “+” 

operator must be selected by double-clicking. Then further 

covariates can be selected, separated by “+”. Interactions 

between variables that have previously appeared as main 

effects are indicated by “:”. The user selects the variable(s) 

she/he wants to include in the model, separated by the selected 

operator. Consistent with R syntax, interactions between 

variables are represented by the semicolon operator. The 

selected variables must be categorical, not continuous. The 

Model-Building Help button provides brief instructions for 

building models.

Carcass-Persistence Model

Required Variables

Censoring Type: The user selects the (numeric) column 

representing the censoring type of each observation. 

Possible values are 0, 1, 2, or 3 (see sections “Parameters” 

and “Details” for more information).

Left: The user selects the (numeric) column representing 

the left censor time in days or fractions of days.

Right: The user selects the (numeric) column representing 

the right censor time in days or fractions of days.

Failure Time Distribution: The user selects the 

desired failure time distribution from the pull-down 

menu. Options are exponential, Weibull, loglogistic, 

and lognormal (Bispo and others, 2012).

Select Variables for Model 

A list of variable names in the Input Carcass-Persistence 

Trial Data is presented. The user selects the variable(s) she/

he wants to include in the model, separated by the selected 

operator. Consistent with R syntax, interactions between 

variables are represented by the semicolon operator. The 

selected variables will be treated as categorical not continuous. 

The Model-Building Help button provides brief instructions 

for building models.

Alpha

The user enters a number greater than 0 and less than 1, 

which is used to define the width of the confidence intervals 

computed by the estimator. For example, if 90-percent 

confidence intervals are desired, then alpha = 1 – 0.90 = 0.10.

Tutorial

In this tutorial, we walk you through an analysis of the 

fictitious example data provided for Site 1. At this site, 15 of 

35 turbines at the site were randomly selected to be searched 

weekly for bats and birds from July 28 through September 26, 

2008. A scheduled search of Turbine 11 on August 18 was not 

completed due to turbine maintenance by the owner (see Site1.

Search Schedule.csv). Clean-out searches were conducted the 

week of July 21–25 to remove any existing carcasses. Three 

turbines were searched per day by the same searcher over a 

5-day work week. The data provided reflect only the bat data. 

At least one bat carcass was found at each turbine, except 

at Turbine 22 where none was found. The proportion of bat 

carcasses expected to fall within the searchable area beneath 

each turbine (Hull and Muir, 2010) as estimated and coded 

in a column named DWP (Density-Weighted Proportion). 

In this study, the habitat beneath each turbine was mapped 

into three visibility classes: Easy, Moderate and Difficult. It 

was hypothesized that both average searcher-efficiency and 

carcass-persistence time would differ significantly among 

these three classes. The objectives of the analysis are:

1. Determine whether Visibility Class significantly affects 

searcher efficiency, that is, average SE differs by more 

than 10 percent between classes. We will compare models 

incorporating Visibility Class to those with no covariate 

using AICc, and also compare estimated SE.

2. Determine the best carcass-persistence distribution and 

whether visibility class significantly affects average 

carcass-persistence time, and consequently the proportion 

of carcasses expected to persist through a search interval 

(one week). Visibility class will be considered significant 

if expected proportions differ by more than 10 percent 

between classes. We will compare models incorporating 

Visibility Class to those with no covariates using AICc, 

and also compare estimated proportion expected to persist 

through a search interval (r).

3. Estimate bat fatality (and 95-percent confidence interval) 

at this site from July 28 through September 26, 2008.
6 Fatality Estimator User’s Guide

Input Files

Fatality

Browse to and select the file containing Fatality Data Site1.Fatality.csv. Each row of this dataset corresponds to a single 

observed bat carcass. As this was a study to estimate bat fatality and all records are bats, there is no column distinguishing birds 

and bats (table 1).

Table 1. First few rows of fictitious example of Fatality data in Site1.Fatality.csv.

Turbine DWP Date DateLast Visibility Species Freq Age Sex Condition

2 0.61 08-11-2008 08-04-2008 Easy LACI Low U U LateDecomp

2 0.61 08-18-2008 08-11-2008 Difficult LACI Low U U Desiccated

2 0.61 08-25-2008 08-18-2008 Easy LANO Low A M Fresh

2 0.61 09-15-2008 09-08-2008 Easy LABO High A M Fresh

4 0.44 08-12-2008 08-05-2008 Easy UNKN UNKN A F Fresh

4 0.44 08-26-2008 08-19-2008 Difficult LANO Low A F EarlyDecomp

Column Description Possible values

Turbine Unique identifier for each turbine. 2, 4, 7, 8, 10, 11, 12, 15, 17, 19, 22, 23, 25, 28, 31

DWP The distance-weighted proportion of bat carcasses expected to fall

within the searchable area beneath the turbine.

0.01–1.00

Date Date the carcass was found. 07-28-2008–09-26-2008

DateLast Date of the previous search of this turbine. 07-21-2008–09-19-2008

Visibility Visibility class representing the vegetation density in which the

carcass was found. 

Easy, Moderate, Difficult

Species 4 character code for the bat species. UNKN represents unknown. EPFU, LABO, LACI, LANO, MYLU, PESU, UNKN

Freq Call frequency group for this species of bat. UNKN represents

unknown. 

High, Low, UNKN

Age Single letter code representing reproductive status of the bat.

Possible values are Adult, Juvenile, Unknown.

A, J, U

Sex Single letter code representing sex of the bat. Possible values are

Female, Male, Unknown.

F, M, U

Condition Condition of the carcass when found. Possible values are Fresh,

EarlyDecomp[osition], LateDecomp[osition], Desiccated.

Fresh, EarlyDecomp, LateDecomp, Desiccated
Tutorial 7

Searcher Efficiency (SE)

Browse to and select file containing Searcher-efficiency (SE) Trial Data Site1.Trial.SE.csv. Each row of this dataset 

corresponds to a trial carcass used to estimate searcher efficiency (table 2).

Table 2. First few rows of fictitious example of SE Trial data in Site1.Trial SE.csv.

Turbine Placed Visibility Species Found

22 07-28-2008 Difficult LABO 0

12 07-29-2008 Easy LACI 0

15 07-30-2008 Difficult LACI 0

25 07-30-2008 Easy LABO 1

8 07-31-2008 Moderate LABO 1

10 08-01-2008 Moderate LABO 1

Column Description Possible values

Turbine Unique identifier for each turbine. 2, 4, 7, 8, 10, 11, 12, 15, 17, 19, 22, 23, 25, 28, 31

Placed Date the carcass was placed in the trial. 7-28-2008–9-26-2008

Visibility Visibility class representing the vegetation density in which the

carcass was found. 

Easy, Moderate, Difficult

Species 4 character code for the bat species. UNKN represents unknown. EPFU, LABO, LACI, LANO, MYLU, PESU, UNKN

Found Indicator of whether the searcher found the trial carcass (1) or not (0). 0, 1

Carcass Persistence (CP)

Browse to and select file containing Carcass-persistence (CP) Trial Data Site1.Trial.CP.csv. Each row of this dataset 

corresponds to trial carcass used to estimate carcass-persistence models (table 3). 

Table 3. First few rows of fictitious example of CP Trial Data in Site1.Trial CP.csv.

Turbine Placed Visibility Species Left Right Event

23 07-29-2008 Moderate LACI 0.5 0.5 2

10 08-01-2008 Moderate LABO 19.5 19.5 0

22 07-28-2008 Difficult LABO 0.4 1.5 3

12 07-29-2008 Easy LACI 0.6 1.6 3

15 07-30-2008 Difficult LACI 0.5 1.6 3

11 08-04-2008 Moderate LABO 18.6 18.6 0

25 07-30-2008 Easy LABO 0.6 1.4 3

Column Description Possible values

Species Four-character code for the bat species. UNKN represents unknown.

Turbine Turbine at which the carcass was placed.

Visibility Visibility class representing the vegetation density in which the carcass was placed. Easy, Moderate, and Difficult

Placed Date the carcass was placed in the trial.

Left Time (in days) when carcass was last known to be present.

Right Time (in days) when carcass was first known to be absent or undetectable.

Event Censoring code. This is the response in the survival models used to estimate carcasspersistence time (see section “Details” for code definitions).
8 Fatality Estimator User’s Guide

Path to R

Browse to the location of the local R.exe installation on the system. The Input Files window should now be filled in (fig. 2).

Figure 2. Completed Input Files window.

Figure 3. Completed Output Files window.

Output Files

Results Observed and Bootstrap

Browse to the folder in which you want to save the original data and the bootstrap iteration results and name it S1.Boot.

Results SE and CP

Browse to the folder in which you want to save the estimated values for SE and average carcass-persistence time and 

confidence intervals and name it S1.SECP. 

Results Fatalities Estimates

Browse to the folder in which you want to save the estimated fatality and confidence intervals for the groups selected in the 

Parameters window (below) and name it S1.Fat. The Output Files window should now be filled in (fig. 3).
Tutorial 9

Parameters

• Total Number of Turbines at Site

Enter 35, the number of turbines at Site 1.

• Total Number of Turbines in the Study

Enter 15, the number of turbines randomly selected to be searched. This is critical to being able to accurately calculate 

per-turbine fatality when no carcasses were found at some turbines, and consequently, those turbines do not occur in 

the fatality data.

• Number of Bootstrap Resamples

Enter 1 to execute a test run to calculate AICc for model comparisons before running a full 1000 or more iterations.

• Alpha

Enter a number between 0 and 1, to request (1-alpha) percent confidence limits. The Parameters window should now be 

filled in (fig. 4).

Figure 4. Completed Parameters window.
10 Fatality Estimator User’s Guide

Fatality Data

From the Main Menu Parameter screen, select: 

and select relevant columns.

• Summary Variables

From the pull-down menu select Species as the variable 

indicating the groups for which we would like fatality 

to be estimated. 

• Required Variables

• Turbine ID: Select Turbine from the pull-down 

menu.

• Density Weighted Proportion Searched: Select DWP 

from the pull-down menu.

• Date Carcass Found: Select Date from the pull-down 

menu.

• Date of Previous Search: Select DateLast from the 

pull-down menu.

The Fatality Data window should now be filled in (fig. 5).

Searcher-Efficiency Model

From the Main Menu Parameter screen, select

and select relevant columns.

• Required Variables

• Response Column: Select Found from the pull-down 

menu.

• Select Variable(s) for Model: Double-click on 

Visibility. Visibility will appear in the list of 

Variables in Model.

Click on Close. The SE Model window should now be filled in 

(fig. 6).

Figure 5. Completed Fatality Data window.

Figure 6. Completed Searcher-Efficiency Model window.
Tutorial 11

Carcass-Persistence Model

From the Main Menu Parameter screen, select:

and select relevant columns.

• Required Variables 

• Censoring Type: Select Event from the pull-down 

menu.

• Left: Select Left from the pull-down menu.

• Right: Select Right from the pull-down menu.

• Failure Time Distribution: Select Weibull from the 

pull-down menu.

• Select Variable(s) for Model: Double-click on 

Visibility. Visibility will appear in the list of 

Variables in Model. 

Click on Close. The Carcass-Persistence Model window 

should now be filled in (fig. 7).

Results

All input data are complete, and the program is executed 

by clicking on the GO button. A window with the estimates 

of SE, CP, and Fatality is automatically opened once all 

bootstrap iterations are complete. Because we are initially 

using the software to select the best models of SE and CP, we 

calculate only a single bootstrap iteration, and consequently, 

the reported confidence intervals in this exercise should 

be ignored.

SE Model Selection

Look at the results for the SE Model selection (fig. 8). 

AICc for the SE Model based on Visibility Class is 65.83. 

Make note of this, then edit the SE Model to remove Visibility 

Class from the model by clicking on Clear. Close the SE 

Model window and rerun by clicking on GO.

The AICc for the SE Model with no explanatory variables 

is 76.27 (table 4). The difference in AICc is greater than 

10 units, and estimated SE differs among Visibility Classes by 

more than 10 percent; therefore, we will decide to model SE 

as a function of Visibility Class.

Figure 7. Completed Carcass-Persistence Model window.

Table 4. AICc results for SE Models with 

and without Visibility Class.

AICc results for SE Models

Explanatory variables

Visibility Class None

65.83 76.27

Figure 8. AICc for the SE Model based on Visibility Class.
12 Fatality Estimator User’s Guide

CP Model Selection

Look at the results for the CP Model selection (fig. 9). AICc for the CP Model based on Visibility Class and 

assuming a Weibull distribution is 224.16 (fig. 9). Make note of this, and then edit the CP Model to remove Visibility 

Class from the model by clicking on Clear. Close the CP Model window and rerun by clicking on GO. AICc for the CP 

Model with no explanatory variables and assuming a Weibull distribution is 220.59.

Repeat the process selecting each of the three other 

distributions and models with and without Visibility Class. 

The results (table 5) indicate that the best model is the 

lognormal distribution with no covariates, although the 

loglogistic model is a close second and differs from the 

lognormal model by less than two units.

The model with minimum AICc was the lognormal with 

no covariates. Estimated proportion of carcasses persisting 

through a search interval of 7 days (r) did not differ by 

more than 10 percent among the Visibility classes (fig. 10). 

Therefore, we will decide to model CP as lognormal with no 

explanatory variables. The difference in estimated proportion 

of carcasses persisting among three of the distributions is 

minimal (fig. 11). Only the exponential distribution results in a 

significantly different persistence model that fits the empirical 

distribution poorly, and this outcome is reflected in its high 

AICc value.

Figure 9. AICc results for the CP Model based on Visibility Class and a Weibull distribution.

Table 5. AICc results for CP Models using lognormal distribution 

with and without Visibility Class.

AICc results for carcass-persistence models

Distribution Explanatory variables

Visibility Class None

Weibull 224.16 220.59

exponential 262.07 259.42

loglogistic 220.41 216.52

lognormal 219.24 215.26

Figure 10. AICc results for the CP Model based on Visibility Class and a lognormal distribution.
Tutorial 13

Final Estimates

Return to the main menu, change the bootstrap iterations to 2000 (or even 5000), and click GO to estimate parameters 

and bootstrap confidence intervals. It will take several minutes to run through a large number of bootstrap iterations. A window 

will automatically open showing the three output files designated earlier: the original fatality data with the full set of bootstrap 

iterations appended (fig. 12); the estimates of average SE (fig. 13), average CP time and r, the proportion of carcasses expected 

to persist through the interval based on selected models (fig. 14); estimated Fatality for each requested group (fig. 15). The 

presented confidence intervals represent (1-α) percent empirical confidence intervals, but because the confidence intervals are 

bootstrapped, the ones you calculate may not be exactly the same as those presented here.

Figure 11. Kaplan-Meir curve (stairstep) and fitted models for four carcasspersistence distributions: lognormal ( —— ), 

exponential (••••), Weibull (— • — • —), and 

loglogistic (— — —).

Figure 12. Original fatality data and bootstrap sample estimates of SE, CP, r, and adjusted fatality.
14 Fatality Estimator User’s Guide

Seven columns are appended to the original data. SE_ Cov_Ind and CP_Cov_Ind are columns indicating the level of 

the combination of covariates selected in the model‐selection process. Because our selected CP Model had no explanatory 

variables, this column for CP is simply filled with the number 1. For SE, the values in this column reflect the values in 

the Visibility variable selected as an explanatory variable for the SE Model. SE_pred and CP_pred are the estimated SE 

and average CP time for this bootstrap sample. rhat is the estimated proportion of carcasses persisting through the interval 

(calculated as the difference between DateLast and Date). AdjFat is the adjusted fatality estimate for the carcass, and boot_ID 

is an indicator of the bootstrap sample, with 0 representing the original data.

Figure 13. Estimated SE and bootstrap confidence intervals based on 2000 bootstrap iterations.

Figure 14. Estimated CP, r, and bootstrap confidence intervals based on 2000 bootstrap iterations.

Figure 15. Estimated fatality for all bats and for each species, and bootstrap confidence intervals based on 2000 bootstrap 

iterations. Fatalities are estimated per turbine and extrapolated to the entire study area.
Details 15

Details

Data Formats—Case Sensitivity, Blanks within Columns, and Preceding Numbers 

• All input data must be in comma-delimited file format (.csv). 

• The maximum number of columns is 16,384. The maximum number of rows is 1,048,573.

• All column names are case-sensitive and must begin with a letter, not a number. 

• Do not name the first column in the dataset ‘ID’ because this may cause it to be recognized as a corrupted format. 

• Values within any column should be either numeric or character, but no character value should begin with a 

number.

• Values within any column cannot include spaces or punctuation marks and are case-sensitive. That means values 

with different cases are considered different values, for example, Bat is not the same as bat. 

Missing Values

Missing values are not allowed for any of the required variables in any of the three input files nor for any of the 

potential covariates used in CP or SE models. While missing values could conceptually be acceptable in covariate 

columns for CP or SE data, their presence will result in slightly different datasets being compared in AICc model 

selection, thereby nullifying AICc results.

Turbine DWP Date DateLast Visibility Species Freq Age Sex Condition

2 0.61 8/11/2008 8/4/2008 Easy LACI Low U U LateDecomp

2 0.61 8/18/2008 8/11/2008 Difficult LACI Low U U Desiccated

. . . 

Figure 16. Example of fatality data as they appear in a spreadsheet.

Input File Formats

Example Fatality Input Data

Each row of this dataset corresponds to a single observed fatality.

See Data Formats section for formatting details.
16 Fatality Estimator User’s Guide

See section “Data Formats” for formatting details. Any 

column used to model SE must also occur in the Fatality data, 

and all values must be consistently coded in both files.

Searcher-efficiency trials are generally conducted in this 

way. A study manager places carcasses in conditions [time 

of year, landscape characteristics, and carcass characteristics 

(size)] that will be used to model the probability that a 

searcher observes a carcass. Next, a searcher conducts a 

regularly scheduled search of the plot, unaware that a trial 

carcass has been placed there. If the carcass is found by 

the searcher, the trial is noted as a success (Found=1 in the 

example data). If the carcass is not found, the manger checks 

to see that the placed carcass was not scavenged and was 

potentially observable by the searcher. If so, the trial is noted 

as unsuccessful (Found=0 in the example data). If the carcass 

is missing, the trial should not be included in the SE trial data.

Example SE Trial Input Data

Each row of this dataset corresponds to a single trial 

carcass used to estimate SE. 

Turbine Placed Visibility Species Found

22 7/28/2008 Difficult LABO 0

12 7/29/2008 Easy LACI 0

. . . 

Figure 17. Example of searcher-efficiency trial data as 

they appear in a spreadsheet.

Turbine Placed Visibility Species Left Right Event

23 7/29/2008 Moderate LACI 0.5 0.5 2

10 8/1/2008 Moderate LABO 19.5 19.5 0

. . . 

Figure 18. Example of carcass-persistence trial data as they appear in 

a spreadsheet.

Example CP Trial Input Data

Each row of this dataset corresponds to a single trial 

carcass used to estimate CP. 

See section “Data Formats” for formatting details. Any 

column used to model CP must also occur in the Fatality 

data, and all values must be consistently coded in both files. 

Carcass-persistence trials are generally conducted this way. 

Throughout the relevant study period, carcasses are placed 

in conditions [time of year, landscape characteristics, and 

carcass characteristics (size)] that will be used to model the 

average amount of time a carcass is expected to persist. Next, 

the carcass is visited at pre-determined times (not necessarily 

at equally spaced intervals) to ascertain its continued presence 

and observability. For instance, carcasses could be checked 

1, 2, 3, 4, 5, 6, 7, 10, 14, 21, 28, and 35 days after placement. 

This dataset does not need to contain observations for each 

of the visits. It does need to contain columns for each of the 

explanatory variables, and three additional columns used for 

the statistical models. The columns in the example data are 

called Left, Right, and Event. Left and Right are measures of 

time that is always recorded as number of days or fractions 

of days since the carcass was placed in the study. Below are 

directions for constructing these columns:

	 If the carcass was already gone on the first visit: Both 

the Left and Right values are set to the time of the first 

check. The Event value would be the number 2, reflecting 

left censoring.

If the carcass was still present when the study ends:

Both the Left and Right values are set to the time of the 

last check. The Event value would be the number 0 (zero) 

reflecting right censoring.

If the carcass disappears between checks, the most 

common case: The Left value is set to the time of the 

last check when the carcass was known to be present. 

The Right value is set to the time when the carcass was 

first known to be no longer observable. The Event value 

would be the number 3, reflecting interval censoring. For 

example, if the carcass was present during the 

check conducted on the 7th day after the carcass 

was placed but was gone when the carcass was 

rechecked on the 10th day, the Left value would 

be 7, the Right value would be 10, and the 

Event value would be 3. Similarly, if a particular 

carcass was still present on the 10th day, but that 

carcass was not monitored on the 14th and 21st 

days, and was then found to be gone on the 28th 

day, the Left value would be 10, the Right value 

would be 28, and the Event value would be 3.

If removal of the carcass was actually 

witnessed, so time of removal is known 

exactly: The Left and Right values are set to the 

time of removal, and the Event value would be 

the number 1 reflecting known Event time and 

no censoring.
Details 17

Figure 19. Example bootstrap results output.

SE.Cov.Ind CP.Cov.Ind SE.pred CP.pred rhat AdjFat boot.ID

Difficult 1 0.22 7.18 0.63 11.83 0

Difficult 1 0.22 7.18 0.63 16.4 0

Difficult 1 0.22 7.18 0.63 8.29 0

. . . 

Output Files Contents

Results Observed and Bootstrap 

This output file will contain seven columns appended to the original data:

SE.Cov.Ind Visibility Found Placed SE Lwr Upr

Difficult Difficult 4 18 0.22 0.06 0.44

Easy Easy 15 18 0.83 0.61 1

Moderate Moderate 11 18 0.61 0.39 0.83

Figure 20. Example SE results output.

SE.Cov.Ind and CP.Cov.Ind: Level(s) of the combinations of covariates identified in the SE and CP model selection 

processes, respectively. If there are no covariates in the SE or CP models, the column is simply a column filled with the 

number 

SE.pred and CP.pred: Estimated SE and average CP time for the current bootstrap sample.

rhat: Estimated proportion of carcasses persisting through the interval, calculated from columns in the input Fatality data.

AdjFat: Adjusted fatality estimate based on Huso (2011) for each carcass.

boot.ID: Indicator of the bootstrap iteration, with 0 representing the original data.

Results SE and CP Estimates 

This output file will contain separate sections for SE and CP. 

The SE results will consist of a header describing the model and AICc results followed by six or more columns:

SE.Cov.Ind: Level(s) of the combinations of covariates identified in the model selection process. The column value will be 

simply “mean” if there are no covariates in the SE Model.

Optional Column(s): These can exist with information indicating the level of each covariate in the SE Model individually. 

If there are no covariates in the SE Model, these columns will not occur.

Found: Number of trial carcasses in each covariate combination that was found.

Placed: Total number of trial carcasses in each covariate combination that were placed in the trial.

SE: Estimated SE for each covariate combination.

Lwr: Lower bootstrap (1-alpha) percent confidence interval for SE.

Upr: Upper bootstrap (1-alpha) percent confidence interval for SE.
18 Fatality Estimator User’s Guide

The CP results will consist of a header describing the model and AICc results followed by eight or more columns:

CP.Cov.Ind: Level(s) of the combinations of covariates identified in the model selection process. The column value will be 

simply “mean” if there are no covariates in the CP Model.

Optional column(s): Level of each covariate in the CP Model individually. If there are no covariates in the CP Model, the 

CP columns will not occur.

Placed: Total number of trial carcasses in each covariate combination that were placed in the trial.

CP: Estimated CP for each covariate combination.

Lwr: Lower bootstrap (1-alpha) percent confidence interval for CP.

Upr: Upper bootstrap (1-alpha) percent confidence interval for CP.

r: Estimated proportion of carcasses persisting through the interval for each covariate combination. The interval represented 

is the interval most commonly occurring in the Fatality data, that is, the mode.

r.Lwr: Lower bootstrap (1-alpha) percent confidence interval for CP.

r.Upr: Upper bootstrap (1-alpha) percent confidence interval for CP.

Results Fatality Estimates

This output file will consist of nine columns, and will have one section for each variable selected as a summary variable:

Group: Variable identified in the Fatality Data Summary List (see below) that contains subgroup identifiers for each carcass 

and for which summary estimates of fatality are desired.

Level: Subgroups of the Group variable. 

Number Found: Summary of the total number of carcasses in each subgroup that were found during searches. 

PerTurb.Est: Estimated number of carcasses of each subgroup killed per turbine

PerTurb.Lwr: Lower bootstrap (1-alpha) percent confidence interval for PerTurb.Est

PerTurb.Upr: Upper bootstrap (1-alpha) percent confidence interval for PerTurb.Est

SiteTot.Est: Estimated total number of carcasses of each subgroup killed at the site

SiteTot.Lwr: Lower bootstrap (1-alpha) percent confidence interval for SiteTot.Est

SiteTot.Upr: Upper bootstrap (1-alpha) percent confidence interval for SiteTot.Est

Number PerTurb. PerTurb. PerTurb. SiteTot. SiteTot. SiteTot.

Group Level Found Est Lwr Upr Est Lwr Upr

Overall 91 26.43 17.82 51.04 926 624 1787

Species LABO 46 13.61 8.97 26.98 477 314 945

Species LACI 45 12.82 8 25.67 449 281 899

Figure 22. Example Fatality results output.

CP.Cov.Ind Placed CP Lwr Upr r r.Lwr r.Upr

mean 58 7.18 3.17 20.4 0.63 0.52 0.75

Figure 21. Example CP results output.
Details 19

Density-Weighted Proportion Area Searched 

Even if we search every turbine at a site, our search 

areas vary in size from one site to another and may not 

include all areas in which a carcass might fall. In addition, 

a turbine’s entire search area may not be searchable, for 

example, landscape characteristics might prevent a full-area 

search. Because carcasses are more likely to land close to 

a turbine than far from a turbine, the configuration and size 

of the searched are will affect the proportion of carcasses 

expected to land within it. The fatality data should contain a 

column indicating what proportion of the carcass density was 

estimated to be within searchable areas beneath each turbine 

(Hull and Muir, 2012). This is not the same as the fraction of 

the designated search plot that was searchable. The numbers in 

this column should all be greater than 0 and less than or equal 

to 1 (0.75 would be correct, but 75 percent would NOT be). 

This factor is used expand estimates to the entire search area. 

If this factor is unknown, the default is 1, but selecting the 

default may lead to underestimated fatality.

Model Construction and Assumptions

All variables selected as model covariates will be 

interpreted as factors. Continuous covariates cannot yet be 

incorporated in SE or CP models. A minimum of 10 trial 

carcasses per defined factor combination is required to assure 

adequate samples to calculate bootstrap sampling distributions.

SE Models

The Searcher-Efficiency Trial input file must contain 

at least one column indicating whether each trial carcass 

was found or not. The variable selected by the user as the 

response is assumed to be distributed as a Bernoulli (binary) 

random variable, with found indicated by the highest numeric 

value (or highest alphabetic value if the response variable is 

character-valued). 

NOTE: Not all data in any modeled combination of covariates 

for SE can be 0 or not found. In addition, the number of levels 

in any covariate used to model SE must be equal to or greater 

than the number of observed levels in the fatality data. For 

example, if size is a meaningful covariate of SE, then all levels 

of size occurring in the fatality data also must be represented 

in the SE trial data. 

A generalized linear model of the binary response with a 

logit link is fit to the data. The modeled response is loge(odds 

of observing a carcass) = loge(p/(1-p)). If covariates are 

selected for the SE Model, they are converted to factors, and 

indicator variables are created by the software. An indicator 

variable is created for each level of a factor except the first, 

which is considered the reference level. For example, a 

covariate called season with four possible values (“Fall”,”Spr

ing”,”Summer”,”Winter”) will be converted to three indicator 

variables, S1-S3 as follows:

S1 = 1 if Season=“Spring”, 0 otherwise

S2= 1 if Season=“Summer”, 0 otherwise

S3= 1 if Season=“Winter”, 0 otherwise

“Fall” is the default reference level

A covariate called Size with two possible values 

(“L”,”S”) will be converted as follows:

Z1 = 1 if Size=“Small”, 0 otherwise.

 “Large” is the default reference level.

Examples of three different models involving season 

withand without size:

Model: SEASON

loge(odds) = β0 + β1*S1 + β2*S2 + β3*S3

Model: SEASON + SIZE

loge(odds) = β0 + β1*S1 + β2*S2 + β3*S3+ β4*Z1

Model: SEASON + SIZE + SEASON : SIZE

loge(odds) = β0 + β1*S1 + β2*S2 + β3*S3+ β4*Z1

+β5*S1*Z1 + β6*S2*Z1 + β7*S3*Z1

In the models above, the estimate of β0 represents the 

estimated loge(odds of observing a reference-level carcass), 

where the reference level is a carcass in the Fall in the first 

model and a Large carcass in the Fall in the second two 

models. β1 represents how much the loge(odds of observing a 

reference-level carcass) are estimated to change if the search is 

conducted in the Spring. 

NOTE: None of the parameter estimates is actually visible 

to the user. This information is provided to help the user 

understand how to formulate the model using correct syntax 

and how the difference between the second and third models is 

represented.

With some datasets, a bootstrap sample of SE trial 

carcasses may result in all observations being unsuccessful, 

that is, 0, which would lead to a runtime error when the 

estimator would attempt to divide by 0. In this case, SE is set 

to 0.05, representing a 1 in 20 chance of observing a carcass 

under the specified conditions in order to prevent division 
20 Fatality Estimator User’s Guide

by 0. This may result in slight underestimation of the upper 

confidence limits of fatality but will not affect the estimate 

or the lower confidence limit. Simply dropping samples that 

result in zero detection for any stratum would potentially 

affect both the upper and lower limits. The user is cautioned 

against using trial data for which SE is extremely low. 

CP Models

The Carcass-Persistence Trial input file must contain 

at least three columns: one indicating the left censor time, 

that is, the time in days since being placed in the study when 

carcass was last known to be present; one indicating the right 

censor time, that is, the time in days since being placed in the 

study when carcass was first known to be absent or the study 

reached its end; and one representing the censoring type. 

These are used to estimate carcass-persistence time, and must 

be numeric, with only one of three possible values: 

• 0=right censored, that is, the carcass was still present 

when the study ended. Both right and left censor times 

must have the same value and it is the number of days 

from when the carcass was first placed to when it was 

last observed at the end of the study

• 1=no censoring, that is, time of removal is known 

exactly (this will almost never be the case).

• 2=left censored, that is, the carcass was removed 

sometime between when it was placed and the first 

check. Again, both right and left censor times must 

have the same value and it is the number of days from 

when the carcass was first placed to the time of the first 

check when it was observed to have been removed.

• 3=interval censored, that is, the carcass was removed 

sometime between the previous check when it was last 

known to be present and the current check when it was 

first known to be absent. The value in the left censor 

time must be less than the value in the right censor 

time. 

NOTE: Not all data in any modeled combination of covariates 

can be right censored.

In addition, the number of levels in any covariate used to 

model CP must be equal to or greater than the number of 

observed levels in the fatality data. For example, if size is a 

meaningful covariate of CP, then all levels of size occurring in 

the fatality data also must be represented in the CP trial data. 

Carcass-persistence times are modeled via log-linear 

parametric accelerated failure time models. If we define T 

as a persistence-time random variable, then a general form 

of our model is loge(T) = β0 + σε, where β0 is the mean, σ is 

a scale parameter, and ε is assumed to follow a probability 

distribution (Collett, 2003). When persistence time is not 

known exactly or is censored, the model is appropriately 

modified (see Collette, 2003 for details beyond the scope 

of this user’s guide). Decisions about the distribution of ε 

dictate the distribution of T, and we offer four commonly 

used accelerated failure-time model options: exponential, 

Weibull, log-logistic, and lognormal. If covariates are selected 

for the CP Model, they are converted to factors and indicator 

variables are created by the software as described above in SE 

Models. 

NOTE: None of the parameter estimates is actually visible to 

the user.

Estimating Turbine-Specific Fatality

This software is designed to estimate per-turbine and 

total-site fatality, but is not designed to estimate fatality at 

individual turbines. However, the user can summarize adjusted 

fatality estimates provided in the Bootstrap Results file by 

turbine to generate these estimates if they are of interest and 

if there are adequate observations at each turbine to support 

such estimates.

References Cited

Collett, D., 2003, Modelling survival data in medical research: 

Boca Raton, Florida, Chapman and Hall, 408 p.

Bispo, R., Bernardino, J., Marques, T., and Pestana, D., 

2012, Modeling carcass removal time for avian mortality 

assessment in wind farms using survival analysis: Springer, 

Environmental and Ecological Statistics, doi:10.1007/

s10651-012-0212-5.

Hull, C., Muir, S., 2010, Search areas for monitoring bird and 

bat carcasses at wind farms using a Monte-Carlo model: 

Australian Journal of Environmental Management, v. 17, 

p. 77–87. 

Huso, M.M.P., 2011, An estimator of wildlife fatality from 

observed carcasses: Environmetrics, v. 22, p. 318–329.
Appendix A 21

Data Function Output

Decision

Searcher 

Efficiency (SE) 

Trials Data

Observed 

Fatality Data

Carcass 

Persistence (CP) 

Trials Data

Verify SE and 

CP covariates 

exist in Fatality No

Error 

output

No

Yes

Yes

Verify sufficient SE 

and CP observations 

to estimate 

requested 

parameters

Key to flowchart symbols:

From three input files:

Appendix A. Flow Chart of Estimation Process and Bootstrap Variance
22 Fatality Estimator User’s Guide

Fatality Data

Bootstrap 

SE

Estimate 

SE based 

on model

SE Trials Data

Bootstrap 

Fatality

Bootstrap 

CP

Estimate 

CP based 

on model

CP Trials Data

Merge Data

Output fatality 

estimates and CIs

Summarize 

fatality by 

requested 

variables

Estimate 

fatality using 

SE and CP

Output to 

Bootstrap File

Output SE and CP 

estimates and CIs

Summarize SE 

and CP by 

covariates

Appendix A.—Continued
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For more information concerning the research in this report, contact the

Director, Forest and Rangeland Ecosystem Science Center,

U.S. Geological Survey, 777 NW 9th Street

Corvallis, Oregon 97330

http://fresc.usgs.gov
Printed on recycled paper

Huso and others—Fatality Estimator User’s Guide—Data Series 729, ver. 1.1