Variation partitioning of species data matrices: estimation and comparison of fractions.

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

2,233 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/f4c555085fba52d744480328ce6446c1e05016da

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. (2006, methodology): “…the issues of normality here may appear as a concern, and if so can be addressed as in Griffith (2002) and Griffith (2004), the Hellinger-transformation provides reliable estimates in variance partitioning (Peres-Neto et al. 2006) and representation in canonical space (Legendre and Gallagher 2001).”
Spatial ordination of vegetation data using a generalization of Wartenberg's multivariate spatial correlation (2008, positive): “Methods of variation partitioning (Peres-Neto et al. 2006) can then be used to evaluate the part of the variation in species composition which is due to space, environment or both.”
Reconstructing community relationships: the impact of sampling error, ordination approach, and gradient length (2007, methodology): “…have standardizations or transformations implicit in the measure, but in various instances the performance of several of the methods may be improved through the incorporation of additional transformations and standardizations (e.g. see Legendre & Gallagher, 2001; Peres-Neto et al., 2006).”
Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate (2013, methodology): “New methods have recently been proposed to learn about the multi-scale spatial structure of the data, and derived methods such as multi-scale ordination (Wagner 2003;Wagner 2004) and multi-scale variation partitioning (Peres-Neto et al. 2006).”
Contrasts between habitat generalists and specialists: an empirical extension to the basic metacommunity framework. (2009, methodology): “The species data sets were transformed using Hellinger transformations prior to ordination analysis (see Legendre and Gallagher 2001, Peres-Neto et al. 2006).”
The variation of tree beta diversity across a global network of forest plots (2012, methodology): “Applications of the method usually compare the adjusted R2 values (Peres-Neto et al., 2006).”
Machine learning for digital soil mapping: Applications, challenges and suggested solutions (2020, methodology): “…advantage is to enable subsequent interpretation of the role of environ-551 mental covariates, spatial covariates (most often in the form of Moran’s eigenvector552 maps) and unexplained (uncorrelated) residuals (the “ignorance”) using variation553 partitioning techniques (Peres-Neto et al., 2006).”
Variation partitioning in ecological communities: Do the numbers add up? (2010, methodology): “To determine the variation explained by each variable, we used the standard multivariate analysis (eqn 1 in Peres-Neto et al. 2006) with the programmed components (weighted values from eqns 2 and 3) used as the predicted Y values.”
Should the Mantel test be used in spatial analysis? (2015, methodology): “Secondly, dbMEM analysis is a method for modelling the spatial structure in univariate or multivariate response data at different scales; the fitted values of the regression or RDA models can be mapped, providing a visual representation of the structure at different spatial scales; the R(2)adj of un”
Soil pH and biome are both key determinants of soil archaeal community structure (2015, methodology): “We performed a redundancy analysis (RDA)-based variation partitioning analyses (Peres-Neto et al., 2006) to assess the relative effects of environmental, climate and spatial variables on species composition of both 16S rRNA and amoA genes OTUs.”
Quantifying and disentangling dispersal in metacommunities: how close have we come? How far is there to go? (2010, methodology): “…in community distribution (composition or abundance) into variation explained by components of space [S] and environment [E] (Borcard et al. 1992; Peres-Neto et al. 2006); that explained by pure environmental variables [E|S], pure spatial variables [S|E], a common component, spatially structured…”