Effects of incorporating spatial autocorrelation into the analysis of species distribution data

By Carsten F. Dormann. 2007

678 citations recorded by the source corpus.

Subfields: Ecological Modeling, Economics and Econometrics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/60b82539b3115013a3ddf116b56e4fa881eb2d69

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In Ecological Modeling

In Economics and Econometrics

In Nature and Landscape Conservation

Citations in the corpus

A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, background): “…is to include spatial predictors or covariance structures that control for the variation in the data that cannot be attributed to the variation in observed abiotic or biotic environmental conditions (Augustin et al. 1996, Dormann 2007, Dormann et al. 2007, Miller 2012; Table 2, Feature E).”
Improving species distribution models: the value of data on abundance (2014, background): “…areas of development range from the incorporation of land cover variables and biotic interactions, to accounting for spatial autocorrelation (Guisan & Thuiller 2005; Ara ujo & Guisan 2006; Dormann 2007a; Bagchi et al. 2013) and incorporating biological traits (Higgins, O’Hara & R€omermann 2012).”
Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression (2009, methodology): “Based on the rSACe metric created by Dormann (2007), LagResp and LagPred generated the strongest shifts in standardized coefficients (Fig.”
Measuring the relative effect of factors affecting species distribution model predictions (2014, background): “Furthermore, we have only investigated the relative effects of the factors in terms of prediction accuracy, but other proper- ties such as model selection, coefficient estimation and uncer- tainty estimation may be of interest (see e.g. Dormann 2007).”
Predicting the distribution of vulnerable marine ecosystems in the deep sea using presence-background models (2014, background): “%) of published SDM studies did not implement methods to account for its effects (Dormann, 2007a), and to date no studies conducted for the deep-sea have explicitly addressed the effects of SA on model predictions.”
Accounting for uncertainty when mapping species distributions: The need for maps of ignorance (2011, methodology): “Dormann CF (2007a) Effects of incorporating spatial autocorrelation into the analysis of species distribution data.”
The state of plant population modelling in light of environmental change (2008, background): “Many vegetation models that do not explicitly include demographic processes focus on broad spatial scales, typically adopting a top-down rather than a bottom-up approach (e.g. Bondeau et al., 2007; Dormann, 2007; Jakob et al., 2007).”
Spatial autocorrelation analysis in plant population: An overview (2015, background): “…cases, however, the presence of spatial autocorrelation is seen as posing a serious shortcoming for hypothesis testing and prediction (Lennon, 2000; Dormann, 2007), because it violates the assumption of independently a d identically distributed (i.e.) errors of most standr statistical procedures…”
Promising the future? Global change projections of species distributions (2007, methodology): “The statistical methods are available, but not even 20% of species distribution studies reviewed use them (Dormann, 2007).”
Incorporating spatial autocorrelation may invert observed patterns (2006, background): “Spatial autocorrelation, however, can be an important issue for analyses at scales ranging from the local to the global (Dormann, 2006).”
Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology (2007, methodology): “…causes workers to abandon the results of standard ordinary least squares (OLS) regression and to interpret instead coefficients generated using one of several spatially explicit modeling procedures (Lennon 2000, Selmi and Boulinier 2001, Tognelli and Kelt 2004, Bahn 2006, Dormann 2007, Kühn 2007).”