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Optimal sampling design for spatial capture-recapture.

Gates Dupont1,2, J Andrew Royle3, Muhammad Ali Nawaz4,5,6

  • 1Department of Environmental Conservation, University of Massachusetts, 160 Holdsworth Way, Amherst, Massachusetts, 01003, USA.

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Summary
This summary is machine-generated.

Optimizing spatial capture-recapture (SCR) sampling designs with a genetic algorithm improves population density estimates. This new method enhances accuracy and precision for wildlife monitoring, outperforming traditional recommendations.

Keywords:
SCRcamera trapsdensitygenetic algorithmoptimal designsampling designspatial capture-recapturespatial samplingspatially explicit capture-recapturetrap spacing

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Area of Science:

  • Ecology
  • Wildlife Biology
  • Conservation Science

Background:

  • Spatial capture-recapture (SCR) is standard for estimating wildlife population density.
  • Trap number and spatial arrangement critically influence SCR precision.
  • Current sampling design advice is heuristic and lacks empirical validation.

Purpose of the Study:

  • To develop a genetic algorithm for optimizing SCR sampling designs.
  • To create near-optimal designs minimizing objective functions for density estimation.
  • To improve the accuracy and precision of population size estimates.

Main Methods:

  • A genetic algorithm was employed to optimize sampling designs.
  • Optimization criteria were based on model-based capture probabilities.
  • Simulations were used to compare optimized designs against existing recommendations.

Main Results:

  • Optimized designs demonstrated reduced bias and improved precision and accuracy in population size estimation.
  • The genetic algorithm approach significantly outperformed designs based on current recommendations.
  • Simulations confirmed the superiority of the proposed optimization method.

Conclusions:

  • The proposed genetic algorithm offers a robust method for generating customized, near-optimal SCR sampling designs.
  • This approach enhances the reliability of wildlife density estimates for conservation and research.
  • The method is accessible to practitioners via the R package oSCR.