Fitting and interpreting occupancy models.

We show that occupancy models are more difficult to fit than is generally appreciated because the estimating equations often have multiple solutions, including boundary estimates which produce fitted probabilities of zero or one. The estimates are unstable when the data are sparse, making them diffi...

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Autores principales:	Alan H Welsh, David B Lindenmayer, Christine F Donnelly
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c689ca523b9443fa87959a6593d99afe
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spelling	oai:doaj.org-article:c689ca523b9443fa87959a6593d99afe2021-11-18T08:02:04ZFitting and interpreting occupancy models.1932-620310.1371/journal.pone.0052015https://doaj.org/article/c689ca523b9443fa87959a6593d99afe2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23326323/?tool=EBIhttps://doaj.org/toc/1932-6203We show that occupancy models are more difficult to fit than is generally appreciated because the estimating equations often have multiple solutions, including boundary estimates which produce fitted probabilities of zero or one. The estimates are unstable when the data are sparse, making them difficult to interpret, and, even in ideal situations, highly variable. As a consequence, making accurate inference is difficult. When abundance varies over sites (which is the general rule in ecology because we expect spatial variance in abundance) and detection depends on abundance, the standard analysis suffers bias (attenuation in detection, biased estimates of occupancy and potentially finding misleading relationships between occupancy and other covariates), asymmetric sampling distributions, and slow convergence of the sampling distributions to normality. The key result of this paper is that the biases are of similar magnitude to those obtained when we ignore non-detection entirely. The fact that abundance is subject to detection error and hence is not directly observable, means that we cannot tell when bias is present (or, equivalently, how large it is) and we cannot adjust for it. This implies that we cannot tell which fit is better: the fit from the occupancy model or the fit ignoring the possibility of detection error. Therefore trying to adjust occupancy models for non-detection can be as misleading as ignoring non-detection completely. Ignoring non-detection can actually be better than trying to adjust for it.Alan H WelshDavid B LindenmayerChristine F DonnellyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 1, p e52015 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alan H Welsh David B Lindenmayer Christine F Donnelly Fitting and interpreting occupancy models.
description	We show that occupancy models are more difficult to fit than is generally appreciated because the estimating equations often have multiple solutions, including boundary estimates which produce fitted probabilities of zero or one. The estimates are unstable when the data are sparse, making them difficult to interpret, and, even in ideal situations, highly variable. As a consequence, making accurate inference is difficult. When abundance varies over sites (which is the general rule in ecology because we expect spatial variance in abundance) and detection depends on abundance, the standard analysis suffers bias (attenuation in detection, biased estimates of occupancy and potentially finding misleading relationships between occupancy and other covariates), asymmetric sampling distributions, and slow convergence of the sampling distributions to normality. The key result of this paper is that the biases are of similar magnitude to those obtained when we ignore non-detection entirely. The fact that abundance is subject to detection error and hence is not directly observable, means that we cannot tell when bias is present (or, equivalently, how large it is) and we cannot adjust for it. This implies that we cannot tell which fit is better: the fit from the occupancy model or the fit ignoring the possibility of detection error. Therefore trying to adjust occupancy models for non-detection can be as misleading as ignoring non-detection completely. Ignoring non-detection can actually be better than trying to adjust for it.
format	article
author	Alan H Welsh David B Lindenmayer Christine F Donnelly
author_facet	Alan H Welsh David B Lindenmayer Christine F Donnelly
author_sort	Alan H Welsh
title	Fitting and interpreting occupancy models.
title_short	Fitting and interpreting occupancy models.
title_full	Fitting and interpreting occupancy models.
title_fullStr	Fitting and interpreting occupancy models.
title_full_unstemmed	Fitting and interpreting occupancy models.
title_sort	fitting and interpreting occupancy models.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/c689ca523b9443fa87959a6593d99afe
work_keys_str_mv	AT alanhwelsh fittingandinterpretingoccupancymodels AT davidblindenmayer fittingandinterpretingoccupancymodels AT christinefdonnelly fittingandinterpretingoccupancymodels
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Fitting and interpreting occupancy models.

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