Drivers of species composition across beaches and levels
We first partitioned the variance in species composition due to
geographical distances and to the environmental differences included in
the previous models as predictors (Borcard et al., 1992; Peres-Neto et
al., 2006), using the R package ade4 v. 1.7-19 (Dray & Dufour, 2007,
Bougeard & Dray, 2018). Second, we investigated the relationship
between species community composition and geographical distances using
Mantel test for each separate beach level. Third, we quantified the
effect of environmental variables on the community of proseriates using
the function ‘manyglm’ in the R package mvabund v. 4.2.1 (Wang et al.,
2022). Finally, to specifically address this hypothesis, we used an
environment-by-trait fourth-corner interaction by analyses of deviance
using the function ‘anova.traitglm’ included in the R package mvabund.
Fourth-corner analyses fit a predictive model for species occurrence as
a function of the environmental variables and the species traits. Since
most of the functional traits were categorical, we applied a Gower
dissimilarity transformation to the complete trait matrix and extracted
orthogonal morphological axes through principal component analyses,
which we then used to fit the model (Mammola & Cardoso, 2020). The
selected axes accounted for 79% of the cumulative variance.