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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mapping Absolute DNA Density in Cell Nuclei using Single-molecule Localization Microscopy
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Hierarchical models for estimating density from DNA mark-recapture studies.

Beth Gardner1, J Andrew Royle, Michael T Wegan

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

Ecology
|May 20, 2009
PubMed
Summary

Wildlife density estimation using genetic sampling is improved by a new spatial capture-recapture model. This method accounts for animal movement, providing more accurate population density estimates for species like black bears.

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Area of Science:

  • Wildlife ecology
  • Population genetics
  • Spatial statistics

Background:

  • Genetic sampling and DNA analysis are vital tools for wildlife biologists estimating species abundance and density.
  • Standard closed population methods struggle with accurate density estimation due to unknown individual movement patterns and trapping exposure areas.

Purpose of the Study:

  • To develop and apply a novel hierarchical spatial capture-recapture model for improved wildlife density estimation.
  • To explicitly model individual movement and trap detection probability as a function of distance.

Main Methods:

  • A hierarchical spatial capture-recapture model was developed with explicit spatial point process and detection probability functions.
  • The model incorporates individual movement on and off the trapping array and detection probability based on distance to traps.
  • Bayesian analysis was performed using the WinBUGS software.

Main Results:

  • The model was applied to a 2006 black bear (Ursus americanus) study in the Adirondack region, New York.
  • Estimated black bear density was 0.159 bears/km2.
  • This estimate is lower than the 0.410 bears/km2 derived from standard closed population techniques.

Conclusions:

  • The proposed hierarchical spatial capture-recapture model offers a more accurate approach to density estimation in wildlife populations.
  • Accounting for animal movement and distance-dependent detection improves the reliability of genetic sampling data for population management.
  • The model provides a valuable advancement for wildlife biologists and managers utilizing genetic data.