All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices

By D. Borcard, Pierre Legendre. 2002

1,925 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e58574e79ac30989fdc8866893a3f87558c4e46d

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Citations in the corpus

Stochastic Community Assembly: Does It Matter in Microbial Ecology? (2017, background): “In addition, the interaction between environmental variables and spatial distance can be quantified, and the remaining variation is termed unexplained variation (143).”
Variation partitioning in ecological communities: Do the numbers add up? (2010, methodology): “This is similar to other transformations of the environmental gradient, but we felt it may better model unimodal trends than other approaches, such as quadratic transformations, based on results reported in Borcard & Legendre (2002).”
On the selection of phylogenetic eigenvectors for ecological analyses (2012, methodology): “Th e problem of selecting eigenvectors generated by eigenfunction spatial analysis (i.e. principal coordinates of neighbor matrices PCNM, Borcard and Legendre 2002, Borcard et al. 2004; Moran ’ s eigenvector maps – MEM, Dray et al. 2006; spatial fi ltering, Griffi th 2003) has been recently…”
Environmental and spatial controls of biotic assemblages in a discrete semi‐terrestrial habitat: comparison of organisms with different dispersal abilities sampled in the same plots (2011, methodology): “We measured or collected data on three groups of explanatory variables (see Appendix S2): water chemistry (variables varying at a fine spatial scale), climate (climatic variables extracted from coarse-scale climatic data), and space (spatial variables covering all relevant scales generated by the an”
Disentangling relationships between habitat conditions, disturbance history, plant diversity, and American black cherry (Prunus serotina Ehrh.) invasion in a European temperate forest (2007, methodology): “…analyses with variation partitioning (RDA-VP): (1) P, E, and I as explanatory variables and D as response variable; (2) P, D, and I as explanatory variables and E as response variable; and (3) P, E, and D as explanatory variables and I as response variable (Borcard & Legendre, 2002; Økland, 2003).”
The spatial scale of plant-animal interactions: effects of resource availability and habitat structure (2011, methodology): “In analytical terms, such a hierarchical patchiness corresponds to the well-defined allocation of the spatial variance of bird abundance or seed predation at several scales along the gradient (Borcard and Legendre 2002).”
Biodiversity patterns in a macroinvertebrate community of a temporary pond network (2014, methodology): “…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).”
Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions (2018, background): “…quality of the single-species SDMs is much lower than SDMs for the same or related species in other studies; and (c) if substitute variables for environmental predictors, such as a spatial field or spatial eigenvector (Borcard & Legendre, 2002), can remove the estimate of the biotic interaction.”
Effective Geographic Sample Size in the Presence of Spatial Autocorrelation (2005, neutral): “Spatial filtering techniques (Getis 1990, 1995; Griffith 2000, 2003; Borcard and Legendre 2002; Getis and Griffith 2002) allow spatial analysts to employ traditional regression techniques while insuring that regression residuals behave according to the traditional model assumption of no spatial…”
Seeing the forest for the trees: partitioning ecological and phylogenetic components of Bergmann's rule in European Carnivora (2007, neutral): “These vectors can then be used as predictors of Y in any form of linear or non-linear modeling (see also Borcard and Legendre 2002, DinizFilho and Bini 2005 and Griffith and Peres-Neto 2006, for analogous applications in a spatial context).”
Accounting for multi‐scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM) (2012, methodology): “SEVM analysis was performed using a combination of SpaceMaker 2 (Borcard & Legendre, 2002) and Spatial Analysis for Macroecology (sam) 4.0 software (Rangel et al., 2010).”
Metacommunity organisation, spatial extent and dispersal in aquatic systems: patterns, processes and prospects (2015, neutral): “For example, in such an approach, the spatial eigenvectors from principal coordinates of neighbour matrices (PCNM; Borcard & Legendre, 2002) or Moran’s eigenvector maps (MEM; Dray, Legendre & Peres-Neto, 2006) are related to various spatial scales.”