Institut für Statistik und Ökonometrie

1. A Wildlife Simulation Package WWalter Zucchini1, David Borchers2, Stefan Kirchfeld1, Martin Erdelmeier1 Jon Bishop2 Institut für Statistik und Ökonometrie Research Unit for Wildlife Population Assessment 1University of Göttingen 2University of St. Andrews Contributors: Shirley Pledger, Alexey Botvinnik, Günter Kratz, Oliver Kellenberger, Jürgen Meinecke

2. 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 • Survey design • Describes 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. • Observation model • Describes 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

3. Abundance estimation process in WiSP generate.region generate.density 1.) Define survey region & population density region density functions objects

4. Generating a survey region Survey region = An area of given height and width survey region

5. Generating a population density (1) Simple density with linear trend: high density low density The population density defines the spatial distribution of animals in the survey region.

6. Generating a population density (2) Increase complexity by adding hotspots and coldspots: hotspot coldspot

7. Generating a population density (3) Add more complexity: Strips of constant density no animals here

8. Abundance estimation process generate.region generate.density 1.) Define survey region & population density region density setpars.population 2.) Generate population generate.population population functions objects

9. Generating a population The population specifies the positions and characteristics of groups and individuals • Population parameters • region • density • probability distributions of group and individual characteristics (group sizes and exposures) • number of groups

10. Generating a population Generate population group-size distribution Population exposure distribution population parameters Region, density and N

11. A population with 500 groups large group low exposure small group high exposure

12. Abundance estimation process inWiSP generate.region generate.density 1.) Define survey region & population density region density setpars.design setpars.population 3.) Generatesurveydesign 2.) Generate population generate.design generate.population design population functions objects

13. Survey designs for closed populations • plot sampling Count all animals in a selected sub-region • distance sampling line transect Count along a transect – record the distances point transect Count within a circle – record the distances • removal methodsRepeatedly remove some • mark-recapture various Repeatedly capture, mark, return • nearest object single nearest object Measure distance to nearest object detected

14. plot sampling (random) line transect point transect Survey designs with Incomplete Coverage

15. Abundance estimation process inWiSP generate.region generate.density 1.) Define survey region & population density region density setpars.design setpars.population 3.) Generate survey design 2.) Generate population generate.design generate.population design population setpars.survey 4.) Survey population generate.sample sample functions objects

16. Specify survey pars Do survey to generate … … a sample A line-transect survey Specify design pars Generate design (random)

17. Abundance estimation process inWiSP generate.region generate.density 1.) Define survey region & population density region density setpars.design setpars.population 3.) Generate survey design 2.) Generate population generate.design generate.population design population setpars.survey 4.) Survey population generate.sample sample point.sim.est point.est int.est simulation est pointest interval est State Model Design Observation Model 5.) Compute estimates functions objects

18. Assumed model vs reality generate.region generate.density 1.) Define survey region & population density region density setpars.design setpars.population 3.) Generate survey design 2.) Generate population generate.design generate.population design population setpars.survey 4.) Define generating observation modeland survey population generate.sample sample 5.) Compute estimates using assumed observation model point.est int.est point est interval est functions objects

19. Assessing sensitivity to assumptions Mark-recapture method Homogeneityequal capture probabilites Heterogeneity trap-shyness, unequal exposure Estimate Generate Generating observation model Assumedobservation model Assumption violated Generating observation modeldefines the true detection/capture probabilities for all animals Assumed observation modelis what we use to carry out the estimation Enables sensitivity analysis

20. Function and object names Suffix (method extension) .cr1capture-recapture .dp double-platform .lt line-transect .no nearest object .pl plot sampling .pt point-transect .rm2removal Prefix* (action) setpars.survey generate.design plot.design generate.sample summary.sample point.est interval.est point.sim etc. + 1Estimation functions for .cr extension are .crM0, .crMt, crMb, crMh 2 Estimation functions for .rm extension are .rm, .ce and .cir *incomplete list Example:setpars.survey.pt Set survey parameters for a point transect Also summary() and plot() functions for most objects