Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.

MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical spa...

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Autores principales:	Yoan Fourcade, Jan O Engler, Dennis Rödder, Jean Secondi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/91bb1374b9de42a59b3c0cec141b3ed4
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id	oai:doaj.org-article:91bb1374b9de42a59b3c0cec141b3ed4
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spelling	oai:doaj.org-article:91bb1374b9de42a59b3c0cec141b3ed42021-11-18T08:19:50ZMapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.1932-620310.1371/journal.pone.0097122https://doaj.org/article/91bb1374b9de42a59b3c0cec141b3ed42014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24818607/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one "virtual" derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.Yoan FourcadeJan O EnglerDennis RödderJean SecondiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 5, p e97122 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yoan Fourcade Jan O Engler Dennis Rödder Jean Secondi Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.
description	MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one "virtual" derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.
format	article
author	Yoan Fourcade Jan O Engler Dennis Rödder Jean Secondi
author_facet	Yoan Fourcade Jan O Engler Dennis Rödder Jean Secondi
author_sort	Yoan Fourcade
title	Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.
title_short	Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.
title_full	Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.
title_fullStr	Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.
title_full_unstemmed	Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.
title_sort	mapping species distributions with maxent using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/91bb1374b9de42a59b3c0cec141b3ed4
work_keys_str_mv	AT yoanfourcade mappingspeciesdistributionswithmaxentusingageographicallybiasedsampleofpresencedataaperformanceassessmentofmethodsforcorrectingsamplingbias AT janoengler mappingspeciesdistributionswithmaxentusingageographicallybiasedsampleofpresencedataaperformanceassessmentofmethodsforcorrectingsamplingbias AT dennisrodder mappingspeciesdistributionswithmaxentusingageographicallybiasedsampleofpresencedataaperformanceassessmentofmethodsforcorrectingsamplingbias AT jeansecondi mappingspeciesdistributionswithmaxentusingageographicallybiasedsampleofpresencedataaperformanceassessmentofmethodsforcorrectingsamplingbias
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Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.

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