Wildlife Simulation Package - synergy · Abundance estimation process in WiSP generate.region...

Institut für Statistik und Ökonometrie

Wildlife Simulation Package

Walter Zucchini1, David Borchers2, Stefan Kirchfeld1, Martin Erdelmeier1& Jon Bishop2

Contributors: Shirley Pledger, Alexey Botvinnik, Günter Kratz, Oliver Kellenberger, Jürgen Meinecke

Research Unit for Wildlife Population Assessment

1 University of Göttingen 2 University of St. Andrews

Downloaded fromhttp://www.ruwpa.st-and.ac.uk/estimating.abundance/WiSP/ of 20

Concepts in animal abundance estimation

State model Describes the spatial distribution and characteristics of animals in a region

Who lives here and where are they?

Population: group size, composition (gender, type), position, exposure

Observation modelDescribes the probability that animals with given characteristics are detected.

What we assume we are likely to see.Detection probability: certain, constant, distance dependent, covariate-dependent

Survey designDescribes the covered region, survey units, effort (observers/traps)

Where, how, and how hard we look.

Survey design: plot sampling, removal methods, mark-recapture,distance sampling, etc.


Abundance estimation process in WiSPgenerate.region generate.density

region density1.) Define survey region

& population density

functions objectsDownloaded fromhttp://www.ruwpa.st-and.ac.uk/estimating.abundance/WiSP/ of 20

Generating a survey region

Survey region = An area of given height and width

survey region

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0

10

20

30

40

50


Generating a population density (1)

Simple density with linear trend:

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010

2030

4050

x

y

Density

high density

low density

The population density defines the spatial distribution of animals in the survey region.



Increase complexity by adding hotspots and coldspots:

0 20 40 60 80

010

2030

4050

xy

Density

hotspot

coldspot



Add more complexity: Strips of constant density

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010

2030

4050

xy

Density

no animals here


Abundance estimation processgenerate.region generate.density



setpars.population

generate.population

population

2.) Generate population


Generating a population

The population specifies the positions and characteristics of groups and individuals

Population parameters

• region

• density

• probability distributions of group and individualcharacteristics (group sizes and exposures)

• number of groups


Generating a population

group-size distribution

1 2 3 4 5

0.0

0.3

0.6 exposure distribution

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0.00

0.06

population parameters

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010

2030

4050

Region, density and NGenerate population

Population

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10

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A population with 500 groups

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50large grouplow exposure

small grouphigh exposure


generate.region generate.density



setpars.design

generate.design

design

3.) Generate surveydesign

setpars.population

generate.population

population


Abundance estimation process in WiSP


Survey designs for closed populations

• plot sampling Count all animals in a selected sub-region

• removal methods Repeatedly remove some

• mark-recapturevarious Repeatedly capture, mark, return

• distance samplingline transect Count along a transect – record the distancespoint transect Count within a circle – record the distances

• nearest objectsingle nearest object Measure distance to nearest object detected


Survey designs with Incomplete Coverage

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plot sampling (random)

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point transect

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line transect





setpars.design

generate.design

design

3.) Generate survey design

setpars.survey

generate.sample

sample

4.) Survey population

setpars.population

generate.population

population



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A line-transect survey

Specify design parsGenerate design (random)

… a sample

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Do survey to generate …Specify survey pars


point.sim.est

simulation est




setpars.design

generate.design

design


setpars.survey

generate.sample

sample

4.) Survey population

setpars.population

generate.population

population


5.) Compute estimatespoint.est int.est

point est interval est

functions objects

State Model

Design

Observation Model


setpars.survey

generate.sample

sample

4.) Define generating observation modeland survey population

Assumed model vs realitygenerate.region generate.density



setpars.design

generate.design

design


point.est int.est

point est interval est

5.) Compute estimates using assumed observation model

setpars.population

generate.population

population



Assessing sensitivity to assumptionsGenerating observation model defines the true detection/capture probabilities for all animals

Homogeneityequal capture probabilites

Heterogeneitytrap-shyness, unequal exposure

Mark-recapture method

Assumedobservation model

Generating observation model

GenerateEstimate

Assumption violated

Enables sensitivity analysis

Assumed observation model is what we use to carry out the estimation


Function and object names

setpars.surveygenerate.design plot.design generate.sample summary.sample point.estinterval.estpoint.simetc.

Prefix* (action)

.cr1 capture-recapture

.dp double-platform

.lt line-transect

.no nearest object

.pl plot sampling

.pt point-transect

.rm2 removal

Suffix (method extension)

Example:

setpars.survey.pt Set survey parameters for a point transect

+

1 Estimation functions for .cr extension are .crM0, .crMt, crMb, crMh2 Estimation functions for .rm extensionare .rm, .ce and .cir

* incomplete list

Also summary() and plot() functions for most objectsDownloaded fromhttp://www.ruwpa.st-and.ac.uk/estimating.abundance/WiSP/ of 20

Wildlife Simulation Package - synergy · Abundance estimation process in WiSP generate.region...

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