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Generalized spatial mark-resight models with incomplete identification: An application to red fox density estimates.

Jose Jimenez1, Richard Chandler2, Jorge Tobajas1

  • 1Instituto de Investigación en Recursos Cinegéticos (IREC, CSIC-UCLM-JCCM) Ciudad Real Spain.

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Summary

This study introduces an improved spatial mark-resight model for wildlife abundance estimation, even when marks are not always identifiable. The new method enhances accuracy with more marked individuals, proving valuable for ecological monitoring.

Keywords:
camera trappinggeneralized spatial mark–resightincomplete identificationmarkred foxtelemetry

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Area of Science:

  • Ecology
  • Wildlife population dynamics
  • Statistical modeling

Background:

  • Accurate wildlife population estimation is crucial for ecological research and conservation.
  • Spatially explicit capture-recapture (SCR) models are standard tools, often relying on individual identification.
  • Existing Generalized spatial mark-resight (Gen-SMR) models require full mark identification, limiting their application.

Purpose of the Study:

  • To develop and validate an extension of the Gen-SMR model for situations with incomplete identification of marked individuals.
  • To assess the influence of marked individuals and identification rates on model performance.
  • To estimate red fox density using the novel model with empirical data.

Main Methods:

  • Developed a new extension of the Generalized spatial mark-resight (Gen-SMR) model to handle incomplete mark identification.
  • Utilized simulation studies to evaluate the impact of the number of marked individuals and identification rates on bias and precision.
  • Applied the model to two red fox (Vulpes vulpes) datasets with varying densities and identification rates.

Main Results:

  • Simulation results indicated that model accuracy improves with a higher number of marked individuals (m) and is less sensitive to the individual identification rate (δ).
  • Empirical application to red fox populations yielded density estimates of 1.60 individuals/km² (high density) and 0.28 individuals/km² (low density).
  • The model successfully estimated densities despite differing identification rates (0.21 and 0.91) in the case studies.

Conclusions:

  • The extended Gen-SMR model effectively addresses the challenge of incomplete identification in mark-resight surveys.
  • This methodological advancement broadens the applicability of SCR models in wildlife research and monitoring.
  • The study provides reliable density estimates for red foxes, demonstrating the model's practical utility.