Incorporating spatial autocorrelation may invert observed patterns

By I. Kühn. 2006

278 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/953ba0acd4a42256b0701fc6c56fd8cc9c6b8ea5

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In Economics and Econometrics

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Effects of spatial autocorrelation and imperfect detection on species distribution models (2018, unknown): “However, in some cases, an inversion of the slope estimate of the relationships was observed (Kühn, 2007; Mauricio Bini et al., 2009) and predictions therefore become very misleading.”
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).”
Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression (2009, background): “However, coefficient shifts can be large enough to change interpretation of the ecological and evolutionary mechanisms presumed to be driving the observed patterns in some cases (Kühn 2007).”
Methods to account for spatial autocorrelation in the analysis of species distributional data : a review (2007, methodology): “…of the autocovariate regression approach in our study with regards to parameter estimation contrasts with earlier evaluations of this method (Augustin et al. 1996, Huffer and Wu 1998, Hoeting et al. 2000, He et al. 2003), but is in line with more recent ones (Dormann 2007a, Carl and Kühn 2007a).”
Promising the future? Global change projections of species distributions (2007, background): “In consequence, both the identification of environmental drivers and the estimation of their coefficients – and hence projection by these models – may be severely biased (Kühn, 2007).”
Environment–richness relationships for mammals, birds, reptiles, and amphibians at global and regional scales (2010, background): “2003) and bias regression coefficients (Dormann 2007; Kühn 2007; but see Hawkins et al. 2007b) in hypothesis testing.”
Spatial autocorrelation analysis in plant population: An overview (2015, background): “…2000; Dormann, 2007), because it violates the assumption of independently a d identically distributed (i.e.) errors of most standr statistical procedures (Anselin, 2002) and hence inflates type I errors, occasionally even inverting the slope of relationships from non-spatial analysis (Ku¨hn, 2007).”
Support for major hypotheses in invasion biology is uneven and declining (2012, methodology): “Moreover, the standardised regression coefficients of predictors showed strong differences between the OLS and SEVM models.”
Assessing the validity of autologistic regression (2007, methodology): “A recent study showed that coefficients may be inverted by spatial autocorrelation (Kühn, 2007), demonstrating the need for spatial models.”
Ecology of butterflies in Europe (2009, methodology): “To detect spatial autocorrelation in model residuals which would violate the assumption of independence of sample sites and may lead to misleading interpretations (Kühn, 2007), it is wise to calculate Moran’s I (1950) as a global measure of spatial autocorrelation (as applied in Kerr et al.”