Performance of spatial capture-recapture models with repurposed data: Assessing estimator robustness for retrospective applications.

Autor: Smith JB; Department of Fisheries and Wildlife, Boone and Crockett Quantitative Wildlife Center, Michigan State University, East Lansing, Michigan, United States of America., Stevens BS; Department of Fish and Wildlife Sciences, Idaho Cooperative Fish and Wildlife Research Unit, University of Idaho, Moscow, Idaho, United States of America., Etter DR; Wildlife Division, Michigan Department of Natural Resources, Lansing, Michigan, United States of America., Williams DM; Department of Fisheries and Wildlife, Boone and Crockett Quantitative Wildlife Center, Michigan State University, East Lansing, Michigan, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2020 Aug 14; Vol. 15 (8), pp. e0236978. Date of Electronic Publication: 2020 Aug 14 (Print Publication: 2020).
DOI: 10.1371/journal.pone.0236978
Abstrakt: Advancements in statistical ecology offer the opportunity to gain further inferences from existing data with minimal financial cost. Spatial capture-recapture (SCR) models extend traditional capture-recapture models to incorporate spatial position of capture and enable direct estimation of animal densities across a region of interest. The additional inferences provided are both ecologically interesting and valuable for decision making, which has resulted in traditional capture-recapture data being repurposed using SCR. Yet, many capture-recapture studies were not designed for SCR and the limitations of repurposing data from such studies are rarely assessed in practice. We used simulation to evaluate the robustness of SCR for retrospectively estimating large mammal densities over a variety of scenarios using repurposed capture-recapture data collected by an asymmetrical sampling grid and covering a broad spatial extent in a heterogenous landscape. We found performance of SCR models fit using repurposed data simulated from the existing grid was not robust, but instead bias and precision of density estimates varied considerably among simulations scenarios. For example, while the smallest relatives bias of density estimates was 3%, it ranged by 14 orders of magnitude among scenarios and was most strongly influenced by detection parameters. Our results caution against the casual repurposing of non-spatial capture-recapture data using SCR and demonstrate the importance of using simulation to assessing model performance during retrospective applications.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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