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Spatially explicit inference for open populations: estimating demographic parameters from camera-trap studies.

Beth Gardner1, Juan Reppucci, Mauro Lucherini

  • 1U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland 20708, USA. bgardner@usgs.gov

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Summary

We created a new spatial capture-recapture model for open populations using camera trap data. This method estimates density and vital rates, ideal for rare species with limited data.

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Area of Science:

  • Ecology
  • Population Biology
  • Wildlife Management

Background:

  • Spatial capture-recapture (SCR) methods are crucial for estimating wildlife population density and demographics.
  • Traditional SCR models often assume closed populations or lack integration of detailed spatial capture information.
  • Auxiliary spatial data from camera traps or DNA sampling provide valuable individual encounter information.

Purpose of the Study:

  • To develop a hierarchical spatial capture-recapture model for demographically open populations.
  • To integrate individual-based Jolly-Seber models with spatially explicit capture-recapture frameworks.
  • To estimate population density and demographic parameters (survival, recruitment) using spatial data.

Main Methods:

  • Developed a hierarchical capture-recapture model incorporating spatial location data.
  • Integrated individual-based Jolly-Seber models with spatially explicit capture-recapture models.
  • Employed a Bayesian framework with data augmentation in WinBUGS for inference.
  • Applied the model to camera trapping data of Pampas cats (Leopardus colocolo) in Argentina.

Main Results:

  • Estimated population density and vital rates for Pampas cats in the High Andes.
  • Demonstrated the model's applicability to camera trapping studies.
  • Acknowledged poor precision in estimates due to sparse data.

Conclusions:

  • The developed model effectively estimates density and demographic parameters in open populations using spatial capture data.
  • Bayesian inference is suitable for small or sparse datasets, common in studies of rare or endangered species.
  • This approach offers a robust framework for wildlife population monitoring and conservation.