Spatial autocorrelation and the selection of simultaneous autoregressive models

By Werner Kissling, G. Carl. 2007

807 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/ecf9c7a21dfe647a8278aa57339a1e8211e4229e

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Citations in the corpus

Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate. (2009, methodology): “To address the above issues, in addition to the nonspatially explicit linear models (OLS), we also fitted two simultaneous autoregressive models (SAR) including a spatial error term at a “fine scale” and at a “medium scale” (Lichstein et al., 2002; Kissling and Carl, 2008).”
Phylogenetic diversity, trait diversity and niches: species assembly of ferns along a tropical elevational gradient (2011, methodology): “The resulting SAR models are able to reduce the spatial pattern of model residuals with independently distributed errors in regression models (Kissling & Carl, 2008).”
Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology (2007, methodology): “It should also be realized that there are sources of instability in spatial regression models as well, mainly due to the definition of spatial relationships (i.e., weightings) in the model structure or in the residual variance-covariance (see Kissling and Carl 2007).”
The global distribution of frugivory in birds (2009, methodology): “…was calculated by including the four (resolution equivalent to 1 ° ) and two (resolution equivalent to 2 ° ) nearest neighbouring cells, respectively, within each subsample of the data and by using a row-standardized coding scheme to calculate the spatial weights matrix (see Kissling & Carl, 2008).”
Local forest structure, climate and human disturbance determine regional distribution of boreal bird species richness in Alberta, Canada (2013, methodology): “…cover and forest structure and composition as predictors, and bird richness as the response), and then fitted spatial linear models [here ‘spatial simultaneous autoregressive error models (SARs)’; Kissling & Carl, 2008], which allow the inclusion of the residual spatial autocorrelation of the data.”
Improving species distribution models: the value of data on abundance (2014, neutral): “2005; Dormann et al. 2007; Kissling & Carl 2008).”
Refugia within refugia – patterns in endemism and genetic divergence are linked to Late Quaternary climate stability in the Iberian Peninsula (2014, methodology): “Hence, we used simultaneous autoregressive (SAR) models (Kissling & Carl, 2007) to incorporate spatial autocorrelation using the R package spdep.”
Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression (2009, methodology): “2a) and have been shown to generate unstable parameter estimates when tested with simple simulated data (Kissling and Carl 2008).”
Quantitative approaches in climate change ecology (2011, methodology): “Examples include accounting for spatial structure in error terms or response variables by adjusting the variance–covariance matrices in regressions or conducting geographically weighted regressions (Kissling & Carl, 2008).”
Impact of climate change on migratory birds: community reassembly versus adaptation (2007, background): “…and causes of this cross-model variability in model coefficients is still not well understood (Diniz-Filho et al. , 2003; Kissling & Carl, 2007; C.F. Dormann et al. , unpublished), but we note that SAR models are thought to yield relatively reliable parameter estimates (Kissling & Carl, 2007).”