RT Journal Article SR Electronic T1 Towards optimal sampling design for spatial capture-recapture JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.04.16.045740 DO 10.1101/2020.04.16.045740 A1 Gates Dupont A1 J. Andrew Royle A1 Muhammad Ali Nawaz A1 Chris Sutherland YR 2020 UL http://biorxiv.org/content/early/2020/04/18/2020.04.16.045740.1.abstract AB Spatial capture-recapture (SCR) has emerged as the industry standard for analyzing observational data to estimate population size by leveraging information from spatial locations of repeat encounters of individuals. The resulting 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 - i.e., spatially-structured and logistically challenging landscapes. 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 two model-based criteria related to the probability of capture. We use simulation to show that these designs out-perform those based on existing recommendations in terms of bias, precision, and accuracy in the estimation of population size. Our approach allows conservation practitioners and researchers to generate customized sampling designs that can improve monitoring of wildlife populations.Competing Interest StatementThe authors have declared no competing interest.