douconca
Double Constrained Correspondence Analysis for Trait-Environment Analysis in Ecology
Double constrained correspondence analysis (dc-CA) analyzes
(multi-)trait (multi-)environment ecological data by using the 'vegan'
package and native R code. Throughout the two step algorithm of ter Braak
et al. (2018) is used. This algorithm combines and extends community-
(sample-) and species-level analyses, i.e. the usual community weighted
means (CWM)-based regression analysis and the species-level analysis of
species-niche centroids (SNC)-based regression analysis. The two steps use
canonical correspondence analysis to regress the abundance data on to the
traits and (weighted) redundancy analysis to regress the CWM of the
orthonormalized traits on to the environmental predictors. The function
dc_CA() has an option to divide the abundance data of a site by the site
total, giving equal site weights. This division has the advantage that the
multivariate analysis corresponds with an unweighted (multi-trait)
community-level analysis, instead of being weighted. The first step of
the algorithm uses vegan::cca(). The second step uses wrda() but
vegan::rda() if the site weights are equal. This version has a predict()
function. For details see ter Braak et al. 2018
douconca
R library douconca analyzes multi-trait multi-environment ecological
data by double constrained correspondence analysis (ter Braak & van
Rossum, 2025) using vegan and native R code. It has a formula
interface for the trait- (column-) and environment- (row-) models, which
allows to assess, for example, the importance of trait interactions in
shaping ecological communities. Throughout the two step algorithm of ter
Braak et al. (2018) is used. This algorithm combines and extends
community- (sample-) and species-level analyses, i.e. the usual
community weighted means (CWM)-based regression analysis and the
species-level analysis of species-niche centroids (SNC)-based regression
analysis. The CWM regressions are specified with an environmental
formula and the SNC regressions are specified with a trait formula. dcCA
finds the environmental and trait gradients that optimize these
regressions. The first step uses
cca (Oksanen et al. 2022) to regress the transposed
abundance data on to the traits and (weighted) redundancy analysis to
regress the community-weighted means (CWMs) of the orthonormalized
traits, obtained from the first step, on to the environmental
predictors. The sample total of the abundance data are used as weights.
The redundancy analysis is carried out using
rda if sites have equal weights (after division of the
rows by their total) or, in the general weighted case, using wrda.
Division by the sample total has the advantage that the multivariate
analysis corresponds with an unweighted (multi-trait) community-level
analysis, instead of being weighted, which may give a puzzling
difference between common univariate and this multivariate analysis.
References: ter Braak, CJF, Šmilauer P, and Dray S. 2018. Algorithms and biplots for double constrained correspondence analysis. Environmental and Ecological Statistics, 25(2), 171-197. https://doi.org/10.1007/s10651-017-0395-x
ter Braak, C.J.F. and van Rossum, B. (2025). Linking Multivariate Trait Variation to the Environment: Advantages of Double Constrained Correspondence Analysis with the R Package Douconca. Ecological Informatics, 88. https://doi.org/10.1016/j.ecoinf.2025.103143
Installation
You can install the CRAN version of douconca by:
install.packages("douconca")
You can install the development version of douconca by:
install.packages("remotes")
remotes::install_github("Biometris/douconca", ref = "develop", dependencies = TRUE)