The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Effects of incorporating spatial autocorrelation into the analysis of species distribution data
Abstract
Aim Spatial autocorrelation (SAC) in data, i.e. the higher similarity of closer samples, is a common phenomenon in ecology. SAC is starting to be considered in the analysis of species distribution data, and over the last 10 years several studies have incorporated SAC into statistical models (here termed ‘spatial models’). Here, I address the question of whether incorporating SAC affects estimates of model coefficients and inference from statistical models. Methods I review ecological studies that compare spatial and non‐spatial models. Results In all cases coefficient estimates for environmental correlates of species distributions were affected by SAC, leading to a mis‐estimation of on average c. 25%. Model fit was also improved by incorporating SAC. Main conclusions These biased estimates and incorrect model specifications have implications for predicting species occurrences under changing environmental conditions. Spatial models are therefore required to estimate correctly the effects of environmental drivers on species present distributions, for a statistically unbiased identification of the drivers of distribution, and hence for more accurate forecasts of future distributions.
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
Spatial Econometrics: Methods and Models
Anselin, L. (1988) · Studies in Operational Regional Science · doi:10.1007/978-94-015-7799-1 · Cited by 9,274
Novel methods improve prediction of species’ distributions from occurrence data
Elith*, J., H. Graham*, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., et al. (2006) · Ecography · doi:10.1111/j.2006.0906-7590.04596.x · Cited by 9,088
Cressie, N. (1992) · Terra Nova · doi:10.1111/j.1365-3121.1992.tb00605.x · Cited by 8,823
Pseudoreplication and the Design of Ecological Field Experiments
Hurlbert, S. H. (1984) · Ecological Monographs · doi:10.2307/1942661 · Cited by 8,377
Predicting species distribution: offering more than simple habitat models
Guisan, A., & Thuiller, W. (2005) · Ecology Letters · doi:10.1111/j.1461-0248.2005.00792.x · Cited by 6,587
Cressie, N. A. C. (1993) · Wiley Series in Probability and Statistics · doi:10.1002/9781119115151 · Cited by 6,507
Rees, M., & Cressie, N. (1993) · Journal of the Royal Statistical Society. Series A (Statistics in Society) · doi:10.2307/2982871 · Cited by 5,101
Bayesian image restoration, with two applications in spatial statistics
Besag, J., York, J., & Molli�, A. (1991) · Annals of the Institute of Statistical Mathematics · doi:10.1007/bf00116466 · Cited by 4,205
Cited by
F. Dormann, C., M. McPherson, J., B. Araújo, M., Bivand, R., Bolliger, J., Carl, G., G. Davies, R., Hirzel, A., Jetz, W., Daniel Kissling, W., et al. (2007) · Ecography · doi:10.1111/j.2007.0906-7590.05171.x · Cited by 3,433
Joint Species Distribution Modelling: With Applications in R
Ovaskainen, O., & Abrego, N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108591720 · Cited by 394
Birks, H. J. B. (2011) · The Open Ecology Journal · doi:10.2174/1874213001003020068 · Cited by 389
Global mycorrhizal plant distribution linked to terrestrial carbon stocks
Soudzilovskaia, N. A., van Bodegom, P. M., Terrer, C., Zelfde, M. V., McCallum, I., Luke McCormack, M., Fisher, J. B., Brundrett, M. C., de Sá, N. C., & Tedersoo, L. (2019) · Nature Communications · doi:10.1038/s41467-019-13019-2 · Cited by 341
The global distribution and environmental drivers of aboveground versus belowground plant biomass
Ma, H., Mo, L., Crowther, T. W., Maynard, D. S., van den Hoogen, J., Stocker, B. D., Terrer, C., & Zohner, C. M. (2021) · Nature Ecology & Evolution · doi:10.1038/s41559-021-01485-1 · Cited by 315
Metadata
- Authors
- Dormann, C. F.
- Venue
- Global Ecology and Biogeography
- Publisher
- Wiley
- Published
- 2007-02-19
- Volume
- 16
- Issue
- 2
- Pages
- 129-138
- ISSN
- 1466-822X (ISSN-L)
- 1466-8238
- OpenAlex
- W2091308143
- Cited by
- 658
- References
- 75
- FWCI
- 12.82
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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