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Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models.

Cyril Milleret1, Soumen Dey1, Pierre Dupont1

  • 1Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway.

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Summary
This summary is machine-generated.

Open-population spatial capture-recapture (OPSCR) models can now estimate spatial survival variations. Accounting for spatial heterogeneity in survival is crucial, preventing up to 10% bias in abundance estimates.

Keywords:
mortalitynimbleSCRpopulation dynamicspopulation-level inferenceswolverines (Gulo gulo)

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Area of Science:

  • Ecology
  • Population Biology
  • Spatial Statistics

Background:

  • Open-population spatial capture-recapture (OPSCR) models are valuable for estimating population density and demographic parameters using spatial detection data.
  • While OPSCR models can estimate spatial variation in vital rates, their application and testing for such complex scenarios remain limited.

Purpose of the Study:

  • To develop and test a Bayesian OPSCR model that integrates spatial covariates to analyze spatial variation in survival.
  • To investigate density-dependent effects on survival within a unified spatial framework.
  • To assess the impact of ignoring spatial heterogeneity in survival on abundance estimates.

Main Methods:

  • Developed a Bayesian open-population spatial capture-recapture (OPSCR) model incorporating spatial covariates to analyze survival.
  • Utilized simulations to evaluate the model's performance in estimating the effects of spatial covariates on survival and density-dependent survival.
  • Applied the model to empirical data on female wolverines (Gulo gulo) in Sweden and Norway to estimate cause-specific mortality.

Main Results:

  • The developed OPSCR model accurately infers the effects of spatial covariates on survival, even with multiple mortality sources.
  • Estimating local density-dependent survival was feasible but demanded more extensive data due to model complexity.
  • Failure to account for spatial heterogeneity in survival resulted in a positive bias of up to 10% in abundance estimates.

Conclusions:

  • The Bayesian OPSCR model provides a robust framework for analyzing spatial variation in survival and its drivers.
  • Incorporating spatial covariates into OPSCR models is essential for accurate population estimates and understanding survival dynamics.
  • This research is a significant step towards fully spatially explicit OPSCR models for disentangling complex spatial influences on population dynamics.