DISSECTING THE SPATIAL STRUCTURE OF ECOLOGICAL DATA AT MULTIPLE SCALES

By D. Borcard, Pierre Legendre, C. Avois-Jacquet, Hanna Tuomisto. 2004

940 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/aa80e52cc9f375685f887b5ba71ae6d6dc1472b4

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In Environmental Engineering

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Citations in the corpus

Dissecting a biodiversity hotspot: The importance of environmentally marginal habitats in the Atlantic Forest Domain of South America (2017, background): “…neighbour matrices (PCNMs; Borcard, Legendre, Avois- Jacquet, & Tuomisto, 2004), which represent the spatial structure of the sampling units at multiple spatial scales without considering any environmental variation (Borcard, Legendre, & Drapeau, 1992; Borcard et al., 2004; Legendre et al., 2002).”
Seed dispersal by a frugivorous marsupial shapes the spatial scale of a mistletoe population (2009, methodology): “…scales by, first, identifying significant spatial structures in the ecological descriptors along the gradient of these scales and, second, relating the form of these scale-specific spatial structures to environmental factors (Borcard et al. 2004, see also application in Ramette & Tiedje 2007).”
Spatially explicit structural equation modeling (2014, methodology): “…the joint spatial patterns of more than one variable (Fortin and Dale 2005), multiscale ordination and spectral decomposition methods can be used to describe spatial patterns in community composition (ver Hoef and Glenn-Lewin 1989, Borcard and Legendre 2002, Wagner 2003, 2004, Borcard et al. 2004).”
Community ecology in the age of multivariate multiscale spatial analysis (2012, methodology): “The predicted values, generated for each submodel, can be then reanalyzed by canonical analysis against environmental variables in order to identify the environmental variables linked to the species distributions at the scale represented by each submodel (Borcard et al. 2004; M5 in Table 1).”
Variation partitioning in ecological communities: Do the numbers add up? (2010, methodology): “Despite the problems encountered with the eigenvector techniques, they have a number of desirable aspects (Borcard et al. 2004), including decomposition of spatial scales for analysing scale-dependent community structuring.”
Determinants of community structure in the global plankton interactome (2015, methodology): “Environmental variableswere z score–transformed; spatial variables (MEM eigenvectors) were calculated based on latitude and longitude (75).”
Forward selection of explanatory variables. (2008, methodology): “…of spatial analyses using principal coordinates of neighbor matrices (PCNM; Borcard and Legendre 2002, Borcard et al. 2004) and Moran’s eigenvector maps (MEM; Dray et al. 2006), which, by construction, generate a large number of variables modeling the spatial relationships among the sampling sites.”
From communities to continents: beta diversity of herbivorous insects (2005, methodology): “Beta diversity can be also quantified using multivariate methods partitioning changes in species abundance into spatial and environmental components (Leps & Smilauer 2003, Borcard et al. 2004).”
Influence of landscape structure and stand age on species density and biomass of a tropical dry forest across spatial scales (2011, methodology): “Variation partitioning Following Borcard et al. (2004), we used regression analysis to partition the variation in species density and biomass into independent variance components, but we expanded the method to three groups of variables (stand age, landscape structure, and spatial dependence)…”
Disturbance alters beta‐diversity but not the relative importance of community assembly mechanisms (2015, methodology): “autocorrelation) obtained from principal coordinates of neighbour matrices (Borcard et al. 2004; Legendre et al. 2009).”
Testing the significance of canonical axes in redundancy analysis (2011, background): “…response data: Griffith’s (2000) spatial eigenfunctions from a connection matrix of neighbouring regions or sites; Moran’s eigenvector maps (MEM, Borcard & Legendre 2002; Borcard et al. 2004; Dray, Legendre, & Peres-Neto 2006); asymmetric eigenvector maps (AEM, Blanchet, Legendre, & Borcard 2008).”
Environmental filtering affects soil fungal community composition more than dispersal limitation at regional scales (2014, background): “However, the use of latitude and longitude onlymay hinder our ability to determine dispersal limitation at multiple scales (i.e. Borcard et al., 2004).”
Studying beta diversity: ecological variation partitioning by multiple regression and canonical analysis (2008, methodology): “In Borcard and Legendre (2002) and Borcard et al. (2004) , PCNM spatial base functions were used instead, providing a much better explanation of the spatial variation in species composition among the cores (beta diversity).”
The spatial scale of plant-animal interactions: effects of resource availability and habitat structure (2011, methodology): “Each causal scheme was checked at different spatial scales, by repeatedly running a given path model with the values of a given response variable (frugivorous bird abundance, seed predation rate) predicted by the different PCNM submodels (Borcard et al. 2004).”