P. Dupont

This study identifies landscape-level factors influencing current wolverine density in Scandinavia, showing that historical persecution and modern management practices both shape their distribution.

The paper estimates red deer density in a transboundary forest ecosystem and finds that forest disturbances and management practices influence spatial density patterns.

This paper introduces and evaluates three Bayesian spatial capture-recapture models to account for spatial heterogeneity in detection probabilities, finding that spatially autocorrelated random effects provide the least biased population size estimates.

This paper develops a Bayesian open-population spatial capture-recapture model to estimate spatially variable and density-dependent survival, showing its effectiveness through simulations and providing an R package for implementation.

This study evaluates the consequences of misspecified detection functions in Bayesian spatial capture-recapture models and identifies diagnostic tools to detect such misspecifications.

This study shows that unmodeled spatially autocorrelated detection probability in SCR models can cause severe bias and reduced precision in abundance estimates.

This paper introduces a hierarchical point process model for spatial capture-recapture data using non-invasive genetic sampling, estimating wolverine density without data augmentation.

The paper compares survival rates of GPS-collared and non-collared wolverines, finding collared individuals had lower mortality from non-culling causes.

This study evaluates the reliability of open population spatial capture-recapture models in estimating abundance despite sampling interruptions, finding that interruptions increase uncertainty but not systematic bias.