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 current thinking about the relationship between data quality and estimator bias and precision and leverage the encounter process.
Results/Conclusions
We use simulation to show that optimal designs out-perform designs based on existing recommendations, in terms of bias, precision, and accuracy in the estimation of population size. Further, we show that these designs are robust to the geometry of the landscape, deviations from uniform spatial distribution of individuals, and variation in spatial coverage of the trapping array. Deciding on appropriate criterion and employing the genetic algorithm as described here will provide managers with the ability to generate their own sampling designs, which will lead to more accurate estimates of population density and improved monitoring of animal populations.