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AuthorDupont, Gates
AuthorRoyle, J. Andrew
AuthorNawaz, Muhammad Ali
AuthorSutherland, Chris
Available date2023-10-05T05:47:55Z
Publication Date2020-11-26
Publication NameEcology
Identifierhttp://dx.doi.org/10.1002/ecy.3262
CitationDupont, G., Royle, J. A., Nawaz, M. A., & Sutherland, C. (2021). Optimal sampling design for spatial capture–recapture. Ecology, 102(3), e03262.
ISSN0012-9658
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85097008638&origin=inward
URIhttp://hdl.handle.net/10576/48263
AbstractSpatial capture–recapture (SCR) has emerged as the industry standard for estimating population density by leveraging information from spatial locations of repeat encounters of individuals. The precision of density estimates depends fundamentally on the number and spatial configuration of traps. Despite this knowledge, existing sampling design recommendations are heuristic and their performance remains untested for most practical applications. To address this issue, we propose a genetic algorithm that minimizes any sensible, criteria-based objective function to produce near-optimal sampling designs. To motivate the idea of optimality, we compare the performance of designs optimized using three model-based criteria related to the probability of capture. We use simulation to show that these designs outperform those based on existing recommendations in terms of bias, precision, and accuracy in the estimation of population size. Our approach, available as a function in the R package oSCR, allows conservation practitioners and researchers to generate customized and improved sampling designs for wildlife monitoring.
Languageen
PublisherWiley-Blackwell
Subjectcamera traps
density
genetic algorithm
optimal design
sampling design
SCR
spatial capture–recapture
spatial sampling
spatially explicit capture–recapture
trap spacing
TitleOptimal sampling design for spatial capture–recapture
TypeArticle
Issue Number3
Volume Number102
dc.accessType Full Text


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