Spatial autocorrelation and statistical tests in ecology

By M. Dale, Marie-Josée Fortin. 2002

258 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/de8f8815fc224a8922ed33e569761b632dc1075b

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Citations in the corpus

Consequences of spatial autocorrelation for niche‐based models (2006, methodology): “Such spatial dependency between values is termed spatial autocorrelation (SA; Cliff & Ord 1973) and its causes and consequences have been the focus of much research (Legendre 1993; Koening & Knops 1998; Lennon 2000; Dale & Fortin 2002; Fortin & Payette 2002; Legendre et al. 2002).”
Influence of landscape structure and stand age on species density and biomass of a tropical dry forest across spatial scales (2011, background): “Colonia Chuburná de Hidalgo, C.P. 97200 Mérida, Yucatán, México e-mail: jl_stefanoni@cicy.mx identification and explanation of spatial variation is one of the main current issues in ecology (Dale and Fortin 2002).”
How to test the significance of the relation between spatially autocorrelated data at the landscape scale: A case study using fire and forest maps (2002, methodology): “Several procedures have been proposed to address this issue and are reviewed by Dale and Fortin (2002).”
Altitudinal patterns of seed plant richness in the Gaoligong Mountains, south‐east Tibet, China (2007, methodology): “Consequently, a bootstrap method was used to eliminate effects of autocorrelation in significance testing (Dale & Fortin, 2002; Oommen & Shanker, 2005).”
Effects of incorporating spatial autocorrelation into the analysis of species distribution data (2007, background): “Dale and Fortin (2002) argue that autologistic regression corrects the coefficient estimates for the presence of SAC, but does not correct the degrees of freedom employed.”
Influence of shoreline features on fish distribution in the Laurentian Great Lakes (2004, methodology): “To correct the effect of spatial autocorrelation, we applied Dutilleul’s (1993) correction when carrying out the analyses (Fortin and Payette 2002; Legendre et al.”
Economic prosperity, biodiversity conservation, and the environmental Kuznets curve (2009, methodology): “Next, we consider three full models, incorporating several additional predictor variables used by Dietz and Adger (2003) (though other potentially important explanatory variables should be considered in future work (see, for example, Brown and Pearce, 1994 for detailed exploration of various factors”
Understanding what controls the spatial distribution of fish populations using a multi-model approach (2011, background): “…population distribution (in the sense of statistical predictions), rather than models restricted to strict inference, (i.e. models designed to investigate the relevance of the different hypotheses but that can not be used for 355 statistical predictions, e.g. Mantel test, Dale and Fortin, 2002).”
Non‐stationarity and local approaches to modelling the distributions of wildlife (2007, methodology): “Maps of local Moran coefficients identify clusters of residuals that deviate from the mean and provide a valuable tool for locating hotspots on maps (Boots, 2002; Dale & Fortin, 2002).”
Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. (2006, background): “The problem arises in these situations because statistical models and tests overestimate the effects of environmental factors driving species distribution if solely based on environmental predictors without accounting for spatial variation (Legendre and Legendre 1998, Dale and Fortin 2002).”