Simulation-based validation of spatial capture-recapture models: A case study using mountain lions.

Authors:
Kelly Proffitt
Kelly Proffitt
Montana Department of Fish
Jay Rotella
Jay Rotella
Montana State University
United States

PLoS One 2019 19;14(4):e0215458. Epub 2019 Apr 19.

Department of Ecology, Montana State University, Bozeman, Montana, United States of America.

Spatial capture-recapture (SCR) models have improved the ability to estimate densities of rare and elusive animals. However, SCR models have seldom been validated even as model formulations diversify and expand to incorporate new sampling methods and/or additional sources of information on model parameters. Information on the relationship between encounter probabilities, sources of additional information, and the reliability of density estimates, is rare but crucial to assessing reliability of SCR-based estimates. We used a simulation-based approach that incorporated prior empirical work to assess the accuracy and precision of density estimates from SCR models using spatially unstructured sampling. To assess the consequences of sparse data and potential sources of bias, we simulated data under six scenarios corresponding to three different levels of search effort and two levels of correlation between search effort and animal density. We then estimated density for each scenario using four models that included increasing amounts of information from harvested individuals and telemetry to evaluate the impact of additional sources of information. Model results were sensitive to the quantity of available information: density estimates based on low search effort were biased high and imprecise, whereas estimates based on high search effort were unbiased and precise. A correlation between search effort and animal density resulted in a positive bias in density estimates, though the bias decreased with increasingly informative datasets. Adding information from harvested individuals and telemetered individuals improved density estimates based on low and moderate effort but had negligible impact for datasets resulting from high effort. We demonstrated that density estimates from SCR models using spatially unstructured sampling are reliable when sufficient information is provided. Accurate density estimates can result if empirical-based simulations such as those presented here are used to develop study designs with appropriate amounts of effort and information sources.

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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215458PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6474654PMC
April 2019
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References

(Supplied by CrossRef)
The estimation of animal abundance and related parameters
GAF Seber et al.
The estimation of animal abundance and related parameters 1982
Capture-recapture models
JD Nichols et al.
BioScience 1992
Analysis and management of animal populations
BK Williams et al.
2002
Hierarchical modeling and inference in ecology: the analysis of data from populations, metapopulations and communities
JA Royle et al.
2008
Use of spatial capture-recapture modeling and DNA data to estimate densities of elusive animals
M Kery et al.
Conserv Biol 2011

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