Optimal sampling design for spatial capture-recapture

Gates Dupont, J. Andrew Royle, Muhammad Ali Nawaz, Chris Sutherland

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)
18 Downloads (Pure)


Spatial 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 out-perform 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.
Original languageEnglish
Article numbere03262
Number of pages9
Issue number3
Early online date2 Feb 2021
Publication statusPublished - Mar 2021


  • SCR
  • Spatial capture-recapture
  • Spatially-explicit capture-recapture
  • Camera traps
  • Density
  • Optimal design
  • Sampling design
  • Spatial sampling
  • Trap spacing
  • Genetic algorithm


Dive into the research topics of 'Optimal sampling design for spatial capture-recapture'. Together they form a unique fingerprint.

Cite this