Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses.

By Daniel A. Griffith, P. Peres‐Neto. Ecology, 2006

629 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/1d7753bfffa0a1aa3e5ca3dec6613c7fdcb997b5

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Citations in the corpus

Spatial statistics, spatial regression, and graph theory in ecology (2012, methodology): “…these spatial scale(s) can be achieved using a hierarchical decomposition approach based on Moran’s eigenvector maps (MEM; Dray et al., 2006; Griffith and Peres-Neto, 2006) or nested kriging (Wackernagel, 2003; Bellier et al., 2007) for sampled data, and a wavelet multi-scale decomposition…”
Spatial-Filtering-Based Contributions to a Critique of Geographically Weighted Regression (GWR) (2008, methodology): “than a topological matrix (a binary geographic weights matrix C). A comparison of their method with the spatial filtering methodology employed here appears in Griffith and Peres-Neto (2007) .”
Integrating dispersal proxies in ecological and environmental research in the freshwater realm (2017, methodology): “…flexibility and 434 usefulness of eigenfunction spatial analysis and other similar methods in spatial modelling 435 have been stressed elsewhere (Griffith and Peres-Neto 2006; Dray et al. 2006; Dray et al. 436 2012), and we briefly emphasize that they deserve their place in community…”
Quantifying and disentangling dispersal in metacommunities: how close have we come? How far is there to go? (2010, background): “The predictors range from simple geographical coordinates, latitude/longitude, to more sophisticated such as geographical polynomials and orthonormal transformations such as eigenvector maps and splines (Griffith and Peres-Neto 2006).”
Less than eight (and a half) misconceptions of spatial analysis (2012, background): “Single CARTs overfit (Hastie et al., 2009), therefore their extended versions (boosted regression trees: Ridgeway, 1999; Elith et al., 2006; random forests: Breiman, 2001) are nowadays more commonly used in ecology.”
Community ecology in the age of multivariate multiscale spatial analysis (2012, background): “…which makes the species residuals in R not i.i.d., has long been seen as a statistical nuisance for the environmental control model (Legendre 1993, Griffith and Peres-Neto 2006), often generating inflated type I error rates in the environmental model and spurious (apparent) species– environment…”
Economic prosperity, biodiversity conservation, and the environmental Kuznets curve (2009, background): “the question of environmental degradation by Grossman and Krueger (1991), has been applied to a variety of environmental indicators.”
Support for major hypotheses in invasion biology is uneven and declining (2012, methodology): “The selection of the eigenvectors was implemented with a forward method that focused on minimising residual autocorrelation (Griffith and Peres-Neto 2006).”
Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate (2013, methodology): “These methods that include spatial eigenfunction analyses, such as principle coordinates of neighbor matrices, Moran's eigenvector maps (MEM) and asymmetric eigenvector maps (Blanchet et al. 2008; Borcard and Legendre 2002; Borcard et al. 2004; Dray et al. 2006; Griffith and Peres-Neto 2006) have re”
Landscape size affects the relative importance of habitat amount, habitat fragmentation, and matrix quality on forest birds (2011, methodology): “We controlled for the effects of spatial autocorrelation by using spatial eigenvector mapping to remove all significant autocorrelation from the residuals of global models (Griffith and Peres-Neto 2006).”