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Model selection and assessment for multi-species occupancy models.

Kristin M Broms1, Mevin B Hooten1,2,3, Ryan M Fitzpatrick4

  • 1Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, 80523, USA.

Ecology
|November 19, 2016
PubMed
Summary
This summary is machine-generated.

Formal validation methods for multi-species occupancy models (MSOMs) are crucial. This study found cross-validation techniques to be the most effective for predicting species distributions in ecological datasets.

Keywords:
Bayesian hierarchical modelsSouth Platte River Basinbiodiversitycross-validationplains fishspecies distribution maps

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Area of Science:

  • Ecology
  • Bayesian Statistics
  • Biodiversity Research

Background:

  • Multi-species occupancy models (MSOMs) are increasingly used for biodiversity analysis.
  • Formal validation methods for MSOMs have not kept pace with their application.
  • Bayesian statistics are seeing a resurgence in predictive model comparison approaches.

Purpose of the Study:

  • To examine contemporary Bayesian hierarchical approaches for checking and validating MSOMs.
  • To apply these validation methods to a real-world ecological dataset.
  • To identify the most effective model selection strategies for MSOMs.

Main Methods:

  • Evaluation of various Bayesian hierarchical validation techniques.
  • Application to a freshwater aquatic study system in Colorado, USA.
  • Analysis of plains fishes diversity and distributions.

Main Results:

  • Distinct differences were observed among the model selection approaches tested.
  • Cross-validation techniques demonstrated superior predictive performance.
  • Findings highlight the importance of appropriate validation for MSOMs.

Conclusions:

  • Cross-validation is recommended for validating MSOMs due to its predictive accuracy.
  • The study provides guidance for ecologists using MSOMs in biodiversity assessments.
  • Robust validation is essential for reliable ecological inferences from complex models.