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

By Carsten F. Dormann, J. McPherson, Miguel B. Araújo, Roger S. Bivand, J. Bolliger, G. Carl, R. G. Davies, A. Hirzel, Walter Jetz, Werner Kissling and 7 more. 2007

3,368 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/d21d2a615c7b92f1b1796e0cb357a4529b9209c4

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Potential range of the invasive fish rotan (Perccottus glenii) in the Holarctic (2011, methodology): “In SEVM, eigenvectors reducing the spatial autocorrelation of residuals are computed and then included as spatial predictors into generalized linear models; we performed SEVM as implemented by Dormann et al. (2007).”
The outcome of shared pollination services is affected by the density and spatial pattern of an attractive neighbour (2014, methodology): “To test whether the overall plant species distribution in the field may have affected pollinator choices, we calculated correlograms based on Moran’s I for the total number of visits to each plot in each of the observation days (Dormann et al. 2007).”
Identifying appropriate spatial scales of predictors in species distribution models with the random forest algorithm (2013, methodology): “Accounting for spatial autocorrelation [with techniques such as simultaneous autoregression (see e.g. Dormann et al. 2007)] may not remove the parameter estimate bias originating from the misspecification of spatial scales of predictor variables (de Knegt et al. 2010).”
So Many Variables: Joint Modeling in Community Ecology. (2015, background): “This problem also arises in spatial modeling, where a missing predictor can induce spatial autocorrelation [67].”
blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models (2018, background): “If it does not, structure might not be properly accounted for in the model or the model might be over-fitted to the training data (Dormann et al., 2007; Roberts et al., 2017).”
The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling (2012, background): “It is however, also possible to find evidence supporting interactions among species by considering the spatial structure of the residuals in a single species’ model, although this by itself is not enough to indicate the presence of biotic interactions (Dormann et al., 2007).”
A comparison of 3 types of green roof as habitats for arthropods (2013, methodology): “When SAC was detected, an autocovariate was computed and integrated in the model, as proposed by Dormann et al. (2007).”