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Mixture regression models for closed population capture-recapture data.

Fodé Tounkara1, Louis-Paul Rivest1

  • 1Département de mathématiques et de statistique, Université Laval, 1045 av. de la Médecine, Québec, Canada G1V 0A6.

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Summary
This summary is machine-generated.

New capture-recapture models improve population size estimation by accounting for unobserved heterogeneity using random effects alongside covariates. This approach offers more stable and accurate population size (N) estimates, crucial for ecological and conservation studies.

Keywords:
Archimedean copulasClosed populationMaximum likelihoodRandom unit effectResidual heterogeneityUnit level covariates

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Area of Science:

  • Ecology
  • Statistics
  • Population Dynamics

Background:

  • Capture-recapture studies are vital for estimating population size (N).
  • Individual covariates help stabilize estimates, but unobserved heterogeneity can still lead to underestimation of N.
  • Existing models may not fully address residual heterogeneity, impacting population size accuracy.

Purpose of the Study:

  • To develop and evaluate two novel capture-recapture models incorporating unobserved random effects and covariates.
  • To improve the accuracy and stability of population size (N) estimates by accounting for residual heterogeneity.
  • To provide robust inference techniques for population size estimation.

Main Methods:

  • Extension of Darroch's random effect model to include unit-level covariates.
  • Generalization of the zero-truncated binomial model with random effects for unobserved heterogeneity.
  • Development of Horvitz-Thompson estimators and confidence intervals for N.
  • Model selection using Akaike information criterion (AIC).

Main Results:

  • The proposed models offer improved population size (N) estimation by addressing unobserved heterogeneity.
  • Closed-form expressions are derived for key inferential quantities, simplifying analysis.
  • Simulation studies investigate the sensitivity of inference to model specification.
  • Comparison with existing methods (e.g., Huggins' model Mh) demonstrates performance.

Conclusions:

  • The novel models provide a more reliable approach to estimating population size (N) in the presence of unobserved heterogeneity.
  • Accounting for both covariates and random effects leads to more stable and accurate population estimates.
  • These methods offer valuable tools for ecological research and wildlife management.