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Cited by 3,433Spatial and Panel Data AnalysisSpecies Distribution and Climate ChangeEcology and Vegetation Dynamics Studies

Methods to account for spatial autocorrelation in the analysis of species distributional data: a review.

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F. Dormann, Carsten, M. McPherson, Jana, B. Araújo, Miguel, Bivand, Roger, Bolliger, Janine, Carl, Gudrun, G. Davies, Richard, Hirzel, Alexandre +9 more·2007·Ecography
DOI 10.1111/j.2007.0906-7590.05171.x
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Abstract

Species distributional or trait data based on range map (extent‐of‐occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species’ distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix.

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  • An autocovariate was added to the fitted GLM to account for spatial autocorrelation, following the methodology for non-normally distributed data reported in the appendix to Dormann et al.

    “To account for this spatial autocorrelation we added an “autocovariate” [25] to the fitted GLM, which is a covariate representing spatial autocorrelation, following the methodology for non-normally distributed data reported in the appendix to Dormann et al. [40].”

    From Land Management Practices Associated with House Loss in Wildfires (2012) · cites this paper as [40]

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  1. Legendre, P. and Legendre, L. 1998. Numerical ecology. Á Elsevier.

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Authors
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., Kühn, I., Ohlemüller, R., R. Peres‐Neto, P., Reineking, B., Schröder, B., M. Schurr, F., & Wilson, R.
ORCID
Venue
Ecography
Publisher
Wiley
Published
2007-09-27
Volume
30
Issue
5
Pages
609-628
ISSN
  • 0906-7590 (ISSN-L)
  • 1600-0587
DOI
10.1111/j.2007.0906-7590.05171.x
OpenAlex
W2170565777
Cited by
3,433
References
147
Citations by year
2026: 212 · 2025: 196 · 2024: 186 · 2023: 179 · 2022: 182 · 2021: 210 · 2020: 216 · 2019: 207 · 2018: 218 · 2017: 231 · 2016: 208 · 2015: 221 · 2014: 223 · 2013: 171 · 2012: 151
FWCI
203.64
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Ecography (publishedVersion)
  • IRIS (submittedVersion)
  • DORA WSL (Swiss Federal Institute for Forest, Snow and Landscape Research) (submittedVersion)
  • ERef Bayreuth (University of Bayreuth) (submittedVersion)
  • publish.UP (University of Potsdam) (submittedVersion, cc-by)
  • SERVAL (Université de Lausanne) (submittedVersion, other-oa)
  • UEA Digital Repository (University of East Anglia) (submittedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
  • Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
  • Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
Keywords
spatial autocorrelation, species distribution modeling, generalized least squares, autoregressive models, generalized estimating equations, type I error, presence-absence data, species abundance data, range map

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