Spatial autocorrelation and red herrings in geographical ecology

By J. Diniz-Filho, Luis Mauricio Bini, B. Hawkins. 2003

1,066 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/7097a1adc459d4b53bd021cfce7fa61716c6b1ea

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Citations in the corpus

Post‐Eocene climate change, niche conservatism, and the latitudinal diversity gradient of New World birds (2006, methodology): “…coefficients, calculated for 15 geographic distance classes established at irregular distance intervals in order to keep the number of connections among cells similar in each class, so that errors of Moran’s I are comparable (see Legendre & Legendre, 1998; Diniz-Filho et al., 2003 for details).”
High tropical net diversification drives the New World latitudinal gradient in palm (Arecaceae) species richness (2008, methodology): “Adding spatial filters to the regression models allowed unbiased estimation of regression coefficients and their significance levels (Diniz-Filho et al., 2003; Diniz-Filho & Bini, 2005).”
Accounting for multi‐scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM) (2012, background): “…tested spatial models have used simulated datasets with simplistic inclusion of SAC through error terms (e.g. Dormann et al., 2007; de Knegt et al., 2010), although a few studies have used spatially coarse biogeographical data for native taxa (e.g. Diniz-Filho et al., 2003; Segurado et al., 2006).”
Global analysis of bird elevational diversity (2009, background): “Lastly, spatial autocorrelation exists both in the bird elevational diversity and environmental data, which may influence the results of these analyses, although not necessarily (Diniz-Filho et al., 2003; Hawkins et al., 2007b).”
Energy, water and large-scale patterns of reptile and amphibian species richness in Europe (2005, methodology): “Finally, we used the technique described by Diniz-Filho et al. (2003) to assess the sources of spatial autocorrelation in the data.”
Environment–richness relationships for mammals, birds, reptiles, and amphibians at global and regional scales (2010, background): “Broad-scale species richness patterns are spatially autocorrelated, which may inflate the rate of type I error (Diniz-Filho et al. 2003) and bias regression coefficients (Dormann 2007; Kühn 2007; but see Hawkins et al. 2007b) in hypothesis testing.”
Temperature, but not productivity or geometry, predicts elevational diversity gradients in ants across spatial grains (2007, methodology): “If no spatial autocorrelation is found in the residuals of the model including the environmental factors, then there is no statistical bias introduced by spatial autocorrelation in the original regression (Legendre et al. , 2002; Diniz-Filho et al. , 2003).”
Sampling Hubbell's neutral theory of biodiversity (2004, neutral): “It was therefore preferable not to use techniques that mask those 2004 Blackwell Publishing Ltd/CNRS variations (Diniz-Filho et al. 2002).”
Spatial autocorrelation analysis in plant population: An overview (2015, background): “Diniz-Filho et al. (2003) and Kuhn (2007) have argued that the presenc of residual spatial autocorrelation should always be tested for in spatial ecology and appropriate methods should be used if there is shown to be significant spatial autocorrelation Spatial autocorrelation is a widespread…”
Bird community responses to habitat fragmentation: how consistent are they across landscapes? (2005, methodology): “As patterns of spatial autocorrelation in data can influence results in these types of regression analysis, we conducted a spatial autocorrelation analysis in all three landscapes using SAAP 4.3 software (Wartenberg, 1989; Diniz-Filho et al., 2003; Hawkins & Pausas, 2004).”
Understanding global patterns in amphibian geographic range size: does Rapoport rule? (2012, background): “This can inflate degrees of freedom and violate the assumption of independently distributed errors in non-spatial analyses (Diniz-Filho et al., 2003).”
Dismantling the Mantel tests (2011, background): “eem to rely on any parametric assumption. It is well known that data displaying some form of structure or auto-correlation are ubiquitous in ecology. Their analysis brings up some statistical issues (Diniz-Filho et al., 2003) because structure or auto-correlation violates many assumptions made by st”
Future climate‐driven shifts in distribution of Calanus finmarchicus (2011, background): “Nevertheless, the impact of SAC remains uncertain for some authors in the case of ENM (Diniz-Filho et al., 2003).”