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Assessing the fit of site-occupancy models

Assessing the fit of site-occupancy models Few species are likely to be so evident that they will always be detected at a site when present. Recently a model has been developed that enables estimation of the proportion of area occupied, when the target species is not detected with certainty. Here we apply this modeling approach to data collected on terrestrial salamanders in the Plethodon glutinosus complex in the Great Smoky Mountains National Park, USA, and wish to address the question “how accurately does the fitted model represent the data?” The goodness-of-fit of the model needs to be assessed in order to make accurate inferences. This article presents a method where a simple Pearson chi-square statistic is calculated and a parametric bootstrap procedure is used to determine whether the observed statistic is unusually large. We found evidence that the most global model considered provides a poor fit to the data, hence estimated an overdispersion factor to adjust model selection procedures and inflate standard errors. Two hypothetical datasets with known assumption violations are also analyzed, illustrating that the method may be used to guide researchers to making appropriate inferences. The results of a simulation study are presented to provide a broader view of the methods properties. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Agricultural, Biological, and Environmental Statistics Springer Journals

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References (36)

Publisher
Springer Journals
Copyright
Copyright © 2004 by International Biometric Society
Subject
Statistics; Statistics for Life Sciences, Medicine, Health Sciences; Agriculture; Environmental Monitoring/Analysis; Biostatistics
ISSN
1085-7117
eISSN
1537-2693
DOI
10.1198/108571104X3361
Publisher site
See Article on Publisher Site

Abstract

Few species are likely to be so evident that they will always be detected at a site when present. Recently a model has been developed that enables estimation of the proportion of area occupied, when the target species is not detected with certainty. Here we apply this modeling approach to data collected on terrestrial salamanders in the Plethodon glutinosus complex in the Great Smoky Mountains National Park, USA, and wish to address the question “how accurately does the fitted model represent the data?” The goodness-of-fit of the model needs to be assessed in order to make accurate inferences. This article presents a method where a simple Pearson chi-square statistic is calculated and a parametric bootstrap procedure is used to determine whether the observed statistic is unusually large. We found evidence that the most global model considered provides a poor fit to the data, hence estimated an overdispersion factor to adjust model selection procedures and inflate standard errors. Two hypothetical datasets with known assumption violations are also analyzed, illustrating that the method may be used to guide researchers to making appropriate inferences. The results of a simulation study are presented to provide a broader view of the methods properties.

Journal

Journal of Agricultural, Biological, and Environmental StatisticsSpringer Journals

Published: Nov 28, 2009

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