Present address: International Rice Research Institute, Los Baños, Laguna, Philippines.
The influence of spatial errors in species occurrence data used in distribution models
Article first published online: 2 NOV 2007
© 2007 The Authors
Journal of Applied Ecology
Volume 45, Issue 1, pages 239–247, February 2008
How to Cite
Graham, C. H., Elith, J., Hijmans, R. J., Guisan, A., Townsend Peterson, A., Loiselle, B. A. and The Nceas Predicting Species Distributions Working Group (2008), The influence of spatial errors in species occurrence data used in distribution models. Journal of Applied Ecology, 45: 239–247. doi: 10.1111/j.1365-2664.2007.01408.x
- Issue published online: 2 NOV 2007
- Article first published online: 2 NOV 2007
- Received 30 October 2006; accepted 14 August 2007; Handling Editor: Robert Freckleton
- locality points;
- predictive modelling algorithms;
- species distribution model;
- 1Species 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.
- 2We 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.
- 3Locational 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.
- 4Synthesis 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.