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The influence of spatial errors in species occurrence data used in distribution models

Graham, Catherine H.; Elith, J; Hijmans, Robert J.; Guisan, Antoine; Townsend Peterson, A.; Loiselle, Bette A.; Anderson, Robert P.; Dudik, Miroslav; Ferrier, Simon; Huettmann, Falk; Leathwick, John; Hijmans, R.J.; Moritz, Craig

Description

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the...[Show more]

dc.contributor.authorGraham, Catherine H.
dc.contributor.authorElith, J
dc.contributor.authorHijmans, Robert J.
dc.contributor.authorGuisan, Antoine
dc.contributor.authorTownsend Peterson, A.
dc.contributor.authorLoiselle, Bette A.
dc.contributor.authorAnderson, Robert P.
dc.contributor.authorDudik, Miroslav
dc.contributor.authorFerrier, Simon
dc.contributor.authorHuettmann, Falk
dc.contributor.authorLeathwick, John
dc.contributor.authorHijmans, R.J.
dc.contributor.authorMoritz, Craig
dc.date.accessioned2015-12-13T22:53:37Z
dc.identifier.issn0021-8901
dc.identifier.urihttp://hdl.handle.net/1885/81892
dc.description.abstract1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.
dc.publisherBritish Ecological Society
dc.sourceJournal of Applied Ecology
dc.subjectKeywords: algorithm; error analysis; geographical distribution; geographical region; modeling; population distribution; prediction; spatial distribution; species occurrence; uncertainty analysis Error; Geo-referencing; Locality points; Predictive modelling algorithms; Species distribution model; Uncertainty
dc.titleThe influence of spatial errors in species occurrence data used in distribution models
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume45
dc.date.issued2008
local.identifier.absfor060399 - Evolutionary Biology not elsewhere classified
local.identifier.ariespublicationf5625xPUB10200
local.type.statusPublished Version
local.contributor.affiliationGraham, Catherine H., Stony Brook University
local.contributor.affiliationElith, J, University of Melbourne
local.contributor.affiliationHijmans, Robert J., University of California
local.contributor.affiliationGuisan, Antoine, University of Lausanne
local.contributor.affiliationTownsend Peterson, A., University of Kansas
local.contributor.affiliationLoiselle, Bette A., University of Missouri
local.contributor.affiliationAnderson, Robert P., Unknown
local.contributor.affiliationDudik, Miroslav, Unknown
local.contributor.affiliationFerrier, Simon, CSIRO Ecosystem Sciences
local.contributor.affiliationHuettmann, Falk, Unknown
local.contributor.affiliationLeathwick, John, Unknown
local.contributor.affiliationMoritz, Craig, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationHijmans, R.J., International Rice Research Institute
local.description.embargo2037-12-31
local.bibliographicCitation.issue1
local.bibliographicCitation.startpage239
local.bibliographicCitation.lastpage247
local.identifier.doi10.1111/j.1365-2664.2007.01408.x
local.identifier.absseo970106 - Expanding Knowledge in the Biological Sciences
dc.date.updated2015-12-11T10:57:35Z
local.identifier.scopusID2-s2.0-38349178606
local.identifier.thomsonID000252558400026
CollectionsANU Research Publications

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