TY - JOUR
T1 - The influence of spatial errors in species occurrence data used in distribution models
AU - Graham, Catherine H.
AU - Elith, Jane
AU - Hijmans, Robert J.
AU - Guisan, Antoine
AU - Townsend Peterson, A.
AU - Loiselle, Bette A.
AU - Anderson, Robert P.
AU - Dudk, Miroslav
AU - Ferrier, Simon
AU - Huettmann, Falk
AU - Leathwick, John
AU - Lehmann, Anthony
AU - Li, Jin
AU - Lohmann, Lucia
AU - Loiselle, Bette
AU - Manion, Glenn
AU - Moritz, Craig
AU - Nakamura, Miguel
AU - Nakazawa, Yoshinori
AU - Overton, Jake
AU - Phillips, Steven
AU - Richardson, Karen
AU - Pereira, Ricardo Scachetti
AU - Schapire, Robert
AU - Soberón, Jorge
AU - Williams, Stephen
AU - Wisz, Mary
AU - Zimmermann, Niklaus
PY - 2008/2
Y1 - 2008/2
N2 - 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 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.
AB - 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 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.
KW - Error
KW - Geo-referencing
KW - Locality points
KW - Predictive modelling algorithms
KW - Species distribution model
KW - Uncertainty
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UR - http://www.scopus.com/inward/citedby.url?scp=38349178606&partnerID=8YFLogxK
U2 - 10.1111/j.1365-2664.2007.01408.x
DO - 10.1111/j.1365-2664.2007.01408.x
M3 - Article
AN - SCOPUS:38349178606
SN - 0021-8901
VL - 45
SP - 239
EP - 247
JO - Journal of Applied Ecology
JF - Journal of Applied Ecology
IS - 1
ER -