Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM)

By Stéphane Dray, Pierre Legendre, P. Peres‐Neto. 2006

1,944 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/abe869cc4448a28a84b7a2414f14c3325aca6a0f

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Citations in the corpus

Integrating dispersal proxies in ecological and environmental research in the freshwater realm (2017, methodology): “…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 ecologists’ 437 toolbox.”
Forward selection of explanatory variables. (2008, methodology): “Key words: forward selection; Moran’s eigenvector maps (MEM); non-orthogonal explanatory variables; orthogonal explanatory variables; principal coordinates of neighbor matrices (PCNM); simulation study; Type I error.”
Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. (2006, methodology): “The distance-based eigenvector procedure (after Dray et al. 2006) can be summarized with the following steps: 1) Compute a pairwise Euclidean (geographic) dis- tance matrix D among sampling units (D ¼ [dij]).”
A Moran coefficient-based mixed effects approach to investigate spatially varying relationships (2016, methodology): “This distance-based ESF often is called 358 Moran’s eigenvector maps, a popular approach in ecology (see, Dray et al., 2006; Griffith 359 and Peres-Neto, 2006; Legendre and Legendre, 2012).”
Less than eight (and a half) misconceptions of spatial analysis (2012, methodology): “Later, Hawkins praises eigenvector filtering (a spatial modelling technique: Diniz-Filho et al., 2003; Dray et al., 2006; Griffith & PeresNeto, 2006) and describes hierarchical partitioning based on trend-surface regression, a method which uses geographical location as a predictor (Legendre, 1990,…”
A practical guide to environmental association analysis in landscape genomics (2015, methodology): “(2010b) included Moran’s eigenvector maps (MEMs, based on coordinates of the sampling locations, Borcard & Legendre 2002; Dray et al. 2006).”