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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

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Published on: October 11, 2016

An accessible method for implementing hierarchical models with spatio-temporal abundance data.

Beth E Ross1, Mevin B Hooten, David N Koons

  • 1Department of Wildland Resources, Utah State University, Logan, Utah, USA. beth.ross@aggiemail.usu.edu

Plos One
|November 21, 2012
PubMed
Summary
This summary is machine-generated.

Wildlife biologists can now better analyze population dynamics using Integrated Nested Laplace Approximation (INLA). This Bayesian approach addresses complex ecological data, improving population trend inference for species like scaup.

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Area of Science:

  • Ecology
  • Wildlife Management
  • Statistical Modeling

Background:

  • Understanding long-term, large-scale population dynamics is crucial in ecology and wildlife management.
  • Challenges include autocorrelation, excess zeros, and observation error in count data.
  • Bayesian hierarchical models are recommended but often complex to implement.

Purpose of the Study:

  • To demonstrate the application of Integrated Nested Laplace Approximation (INLA) for wildlife population analysis.
  • To showcase how INLA facilitates fitting sophisticated Bayesian hierarchical models.
  • To provide unbiased inference on spatial variation in population trends over time.

Main Methods:

  • Utilized Integrated Nested Laplace Approximation (INLA), a computational tool for Bayesian models.
  • Developed a hierarchical model to decouple observation error from process variation.
  • Accounted for excess zeros and spatio-temporal dependence in count data.

Main Results:

  • Successfully applied INLA to estimate parameters in a complex hierarchical model.
  • Demonstrated INLA's feasibility for wildlife biologists, overcoming quantitative and computational barriers.
  • Enabled unbiased inference on spatial and temporal population dynamics for lesser and greater scaup.

Conclusions:

  • INLA offers a practical solution for fitting advanced Bayesian hierarchical models in ecology.
  • This method improves the analysis of complex ecological count data, including observation error and excess zeros.
  • Facilitates more robust wildlife population trend analysis and management strategies.