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Nondetection sampling bias in marked presence-only data
1. Species distribution models (SDM) are tools used to determine environmental features that influence the geographic distribution of species' abundance and have been used to analyze presence-only records. Analysis of presence-only records may require correction for nondetection sampling bias t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Blackwell Publishing Ltd
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892331/ https://www.ncbi.nlm.nih.gov/pubmed/24455151 http://dx.doi.org/10.1002/ece3.887 |
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author | Hefley, Trevor J Tyre, Andrew J Baasch, David M Blankenship, Erin E |
author_facet | Hefley, Trevor J Tyre, Andrew J Baasch, David M Blankenship, Erin E |
author_sort | Hefley, Trevor J |
collection | PubMed |
description | 1. Species distribution models (SDM) are tools used to determine environmental features that influence the geographic distribution of species' abundance and have been used to analyze presence-only records. Analysis of presence-only records may require correction for nondetection sampling bias to yield reliable conclusions. In addition, individuals of some species of animals may be highly aggregated and standard SDMs ignore environmental features that may influence aggregation behavior. 2. We contend that nondetection sampling bias can be treated as missing data. Statistical theory and corrective methods are well developed for missing data, but have been ignored in the literature on SDMs. We developed a marked inhomogeneous Poisson point process model that accounted for nondetection and aggregation behavior in animals and tested our methods on simulated data. 3. Correcting for nondetection sampling bias requires estimates of the probability of detection which must be obtained from auxiliary data, as presence-only data do not contain information about the detection mechanism. Weighted likelihood methods can be used to correct for nondetection if estimates of the probability of detection are available. We used an inhomogeneous Poisson point process model to model group abundance, a zero-truncated generalized linear model to model group size, and combined these two models to describe the distribution of abundance. Our methods performed well on simulated data when nondetection was accounted for and poorly when detection was ignored. 4. We recommend researchers consider the effects of nondetection sampling bias when modeling species distributions using presence-only data. If information about the detection process is available, we recommend researchers explore the effects of nondetection and, when warranted, correct the bias using our methods. We developed our methods to analyze opportunistic presence-only records of whooping cranes (Grus americana), but expect that our methods will be useful to ecologists analyzing opportunistic presence-only records of other species of animals. |
format | Online Article Text |
id | pubmed-3892331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Blackwell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-38923312014-01-21 Nondetection sampling bias in marked presence-only data Hefley, Trevor J Tyre, Andrew J Baasch, David M Blankenship, Erin E Ecol Evol Original Research 1. Species distribution models (SDM) are tools used to determine environmental features that influence the geographic distribution of species' abundance and have been used to analyze presence-only records. Analysis of presence-only records may require correction for nondetection sampling bias to yield reliable conclusions. In addition, individuals of some species of animals may be highly aggregated and standard SDMs ignore environmental features that may influence aggregation behavior. 2. We contend that nondetection sampling bias can be treated as missing data. Statistical theory and corrective methods are well developed for missing data, but have been ignored in the literature on SDMs. We developed a marked inhomogeneous Poisson point process model that accounted for nondetection and aggregation behavior in animals and tested our methods on simulated data. 3. Correcting for nondetection sampling bias requires estimates of the probability of detection which must be obtained from auxiliary data, as presence-only data do not contain information about the detection mechanism. Weighted likelihood methods can be used to correct for nondetection if estimates of the probability of detection are available. We used an inhomogeneous Poisson point process model to model group abundance, a zero-truncated generalized linear model to model group size, and combined these two models to describe the distribution of abundance. Our methods performed well on simulated data when nondetection was accounted for and poorly when detection was ignored. 4. We recommend researchers consider the effects of nondetection sampling bias when modeling species distributions using presence-only data. If information about the detection process is available, we recommend researchers explore the effects of nondetection and, when warranted, correct the bias using our methods. We developed our methods to analyze opportunistic presence-only records of whooping cranes (Grus americana), but expect that our methods will be useful to ecologists analyzing opportunistic presence-only records of other species of animals. Blackwell Publishing Ltd 2013-12 2013-12-02 /pmc/articles/PMC3892331/ /pubmed/24455151 http://dx.doi.org/10.1002/ece3.887 Text en © 2013 Published by John Wiley & Sons Ltd. http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation. |
spellingShingle | Original Research Hefley, Trevor J Tyre, Andrew J Baasch, David M Blankenship, Erin E Nondetection sampling bias in marked presence-only data |
title | Nondetection sampling bias in marked presence-only data |
title_full | Nondetection sampling bias in marked presence-only data |
title_fullStr | Nondetection sampling bias in marked presence-only data |
title_full_unstemmed | Nondetection sampling bias in marked presence-only data |
title_short | Nondetection sampling bias in marked presence-only data |
title_sort | nondetection sampling bias in marked presence-only data |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892331/ https://www.ncbi.nlm.nih.gov/pubmed/24455151 http://dx.doi.org/10.1002/ece3.887 |
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