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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
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Spatial capture--recapture models for jointly estimating population density and landscape connectivity.

J Andrew Royle1, Richard B Chandler, Kimberly D Gazenski

  • 1USGS Patuxent Wildlife Research Center, 12100 Beech Forest Road, Laurel, Maryland 20708, USA. aroyle@usgs.gov

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|May 23, 2013
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Summary

New spatial capture-recapture (SCR) models estimate animal density and landscape connectivity simultaneously. This approach uses ecological distance, improving upon traditional methods that ignore landscape structure and potentially bias density estimates.

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Published on: October 11, 2016

Area of Science:

  • Ecology
  • Population Biology
  • Conservation Science

Background:

  • Population viability depends on size and landscape connectivity, but simultaneous estimation is challenging.
  • Traditional spatial capture-recapture (SCR) models estimate density but do not incorporate landscape connectivity.
  • Existing SCR models assume Euclidean distance, implying stationary home ranges unaffected by landscape resistance.

Purpose of the Study:

  • To develop novel spatial capture-recapture (SCR) models for simultaneously estimating population density and connectivity parameters.
  • To integrate ecological distance, specifically least-cost path analysis, into SCR models to account for landscape resistance.
  • To enable explicit inferences on animal density, distribution, and landscape connectivity from capture-recapture data.

Main Methods:

  • Developed encounter probability models based on ecological distance (least-cost path) between traps and animal activity centers.
  • Integrated least-cost path models into a likelihood-based estimation framework for SCR models.
  • Conducted a simulation study to evaluate the performance of the new models and compare them to naive SCR models.

Main Results:

  • The integrated SCR models successfully estimate population density and parameters of the least-cost encounter probability model.
  • Demonstrated that ignoring landscape connectivity in SCR models can lead to negatively biased density estimators.
  • The new approach allows for direct inference on landscape connectivity relevant to animal movement.

Conclusions:

  • Novel SCR models incorporating ecological distance provide a robust framework for estimating both density and connectivity.
  • Accounting for landscape connectivity is crucial for accurate animal density estimation using capture-recapture data.
  • This methodology advances our ability to understand and manage wildlife populations in complex landscapes.