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Model selection and assessment for multi‐species occupancy models

While multi‐species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring i... Full description

Journal Title: Ecology July 2016, Vol.97(7), pp.1759-1770
Main Author: Broms, Kristin M.
Other Authors: Hooten, Mevin B. , Fitzpatrick, Ryan M.
Format: Electronic Article Electronic Article
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ID: ISSN: 0012-9658 ; E-ISSN: 1939-9170 ; DOI: 10.1890/15-1471.1
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recordid: wj10.1890/15-1471.1
title: Model selection and assessment for multi‐species occupancy models
format: Article
creator:
  • Broms, Kristin M.
  • Hooten, Mevin B.
  • Fitzpatrick, Ryan M.
subjects:
  • Bayesian Hierarchical Models
  • Biodiversity
  • Cross‐Validation
  • Plains Fish
  • South Platte River Basin
  • Species Distribution Maps
ispartof: Ecology, July 2016, Vol.97(7), pp.1759-1770
description: While multi‐species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single‐species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross‐validation techniques performing the best in terms of prediction.
language:
source:
identifier: ISSN: 0012-9658 ; E-ISSN: 1939-9170 ; DOI: 10.1890/15-1471.1
fulltext: fulltext
issn:
  • 0012-9658
  • 00129658
  • 1939-9170
  • 19399170
url: Link


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subjectBayesian Hierarchical Models ; Biodiversity ; Cross‐Validation ; Plains Fish ; South Platte River Basin ; Species Distribution Maps
descriptionWhile multi‐species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single‐species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross‐validation techniques performing the best in terms of prediction.
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abstractWhile multi‐species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single‐species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross‐validation techniques performing the best in terms of prediction.
doi10.1890/15-1471.1
pages1759-1770
date2016-07