Modelling geographical patterns in species richness using eigenvector-based spatial filters

By J. Diniz-Filho, Luis Mauricio Bini. 2005

326 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/41735f8f8434ed244a65f17409e198ed2c61ebe7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Spatial autocorrelation analysis allows disentangling the balance between neutral and niche processes in metacommunities (2012, background): “According to Fortin et al. (1989), random or systematic-cluster designs ‘can bring out more information about the spatial structure than a systematic design’.”
Hot, dry and different: Australian lizard richness is unlike that of mammals, amphibians and birds (2010, methodology): “We employed spatial eigenvector mapping (SEVM) with corrected Akaike information criterion (AICc)weight model averaging to account for spatial covariation at multiple scales (Diniz-Filho & Bini, 2005; Dormann et al., 2007).”
Less than eight (and a half) misconceptions of spatial analysis (2012, background): “Mapping the spatial distribution of spatial autocorrelation (Diniz-Filho & Bini, 2005; Kühn et al., 2006, 2009) can facilitate the identification of important spatial processes not included in the analysis.”
Disentangling good from bad practices in the selection of spatial or phylogenetic eigenvectors (2018, background): “These paradigms of ‘nuisance’ and ‘legacy’ also apply for temporal and phylogenetic patterns (Diniz-Filho and Bini 2005, Griffith and Peres-Neto 2006, Diniz-Filho et al.”
Accounting for Spatial Autocorrelation in Linear Regression Models Using Spatial Filtering with Eigenvectors (2013, background): “Another intriguing aspect of SFE is that each of the eigenvectors can capture spatial autocorrelation at different scales (Diniz-Filho and Bini 2005 ; Dormann et al.”
Squamate richness in the Brazilian Cerrado and its environmental–climatic associations (2007, methodology): “In addition, some studies supporting the species–energy hypothesis have also found a significant effect of topography or landscape heterogeneity as in South American birds (Diniz-Filho & Bini, 2005) and plants (Lavers & Field, 2006).”
Contrasting patterns in elevational diversity between microorganisms and macroorganisms (2011, methodology): “Spatial autocorrelation was taken into account by including eigenvector-based spatial filters derived from geographic distances in all the models (Diniz-Filho & Bini, 2005).”
Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression (2009, methodology): “…‘‘variants’’ of SEVM in terms of using different criteria to select eigenvectors to be added to the model (Borcard and Legendre 2002, Borcard et al. 2004, Diniz-Filho and Bini 2005, Griffith and Peres-Neto 2006), and this can have a substantial impact on the regression coefficients associated…”
Biodiversity patterns in a macroinvertebrate community of a temporary pond network (2014, background): “…Society, Insect Conservation and Diversity, 7, 4–21 brate b-diversity, we only obtained spatial descriptors with high eigenvalues, a result that, in natural systems, signifies the involvement of broad spatial scales (Borcard & Legendre, 2002; Diniz-Filho & Bini, 2005; Griffith & Peres-Neto, 2006).”
Conserving the Brazilian semiarid (Caatinga) biome under climate change (2012, methodology): “Eigenvectors extracted from this matrix represent the spatial structure at multiple scales, the first of which refers to broad and the last of which to fine scales (Diniz-Filho and Bini 2005).”
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).”
Priority areas for the conservation of Atlantic forest large mammals (2009, methodology): “We accounted for significant autocorrelations in the residuals, whenever at least one was detected, by applying eigenvector filtering analysis (Diniz-Filho and Bini, 2005; Rangel et al., 2006) and including selected spatial filters as explanatory variables in the linear models.”
Spatial autocorrelation and the selection of simultaneous autoregressive models (2007, unknown): “A number of methods exist to deal with spatial autocorrelation in ecological data (Cressie, 1993; Haining, 2003; Diniz-Filho & Bini, 2005; Fortin & Dale, 2005; Rangel et al. , 2006).”