Numerical Ecology with R

By D. Borcard, F. Gillet, Pierre Legendre. 2011

3,279 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/628846750a9629ef6104322a185996ec97d361d7

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Citations in the corpus

Disentangling the correlates of species and site contributions to beta diversity in dung beetle assemblages (2018, background): “However, the test of significance of Moran’s I is not a reliable criterion to eliminate PCNMs from the analysis and, therefore, we retained all PCNMs showing positive spatial correlation (Borcard et al., 2011).”
Changes in Barents Sea ecosystem state, 1970 -2009: climate fluctuations, human impact, and trophic interactions (2012, methodology): “The R package “mvpart” was used to perform chronological clustering, as described in Borcard et al. (2011), to cluster the years from 1970 to 2009 into periods according to the changes in the biotic timeseries.”
Community ecology in the age of multivariate multiscale spatial analysis (2012, methodology): “M6 variation partitioning Borcard et al. (1992), Borcard and Legendre (1994) yes ordination method”
Quantifying community assembly processes and identifying features that impose them (2013, background): “Spatial eigenvectors describe spatial relationships among communities across a range of spatial scales; the first eigenvector breaks sampling locations into broadly distributed clusters, and subsequent eigenvectors characterize spatial relationships at increasingly fine scales (Borcard and Legendre,”
Comparative analysis of diversity and environmental niches of soil bacterial, archaeal, fungal and protist communities reveal niche divergences along environmental gradients in the Alps (2021, methodology): “This method creates a series of variables that correspond to spatial structures at all spatial scales contained within a given sampling design (Borcard et al., 2018).”
Estimating and mapping ecological processes influencing microbial community assembly (2015, methodology): “The spatial positions of sampled locations were used with Principal Coordinates of Neighbor Matrices’ (PCNM, now referred to as ‘Moran’s Eigenvector Maps’) to describe spatial eigenvectors (function ‘pcnm’ in R package ‘vegan’; Borcard and Legendre, 2002; Borcard et al., 2011).”
High mammal species turnover in forest patches immersed in biofuel plantations (2017, methodology): “We performed a redundancy analysis (RDA) to evaluate how the landscape structure affected the species composition of medium and large-sized mammals, using the ‘vegan’ package (Oksanen et al., 2013) in R, and the rda function (Borcard et al., 2011; Legendre and Legendre, 2012).”
Estimating patterns of reptile biodiversity in remote regions (2013, methodology): “For SEVM, values of R2D were adjusted to take into account the number of predictors in the model (Borcard et al., 2011).”
Hydrological stability drives both local and regional diversity patterns in rock pool metacommunities (2015, methodology): “Single linkage (nearest neighbor) was used as an amalgamation rule since this is a more eff ective way to visualize gradients (chains) compared to complete linkage or average agglomerative clustering methods (Borcard et al. 2011).”