Community trends: RLQ analysis
Trait and community responses to environmental gradients were ordinated
using an RLQ framework, a multivariate technique that summarizes joint
structure among matrices (Dray and Legendre 2008; Dray et al. 2014). In
our case, these three matrix tables L (species distribution across river
reaches surveyed as 15 species abundances*68 sites), R (environmental
characteristics of samples: 68 sites*12 environmental variables) and Q
(species traits: 15 species*8 traits) were analysed separately using
different ordination methods in “ade4” package in R (Dray and Dufor
2007). The L-species table was analysed using Correspondence Analysis
(CA), while the R-environmental variables table and Q-trait table were
analysed by a Hill-Smith PCA combining quantitative and qualitative
variables using CA species scores as a column weight to couple Q and L
(Brown et al. 2014). In trait analysis, the RLQ approach crosses traits
and environmental variables weighted by species abundances with
significant effects tested using a two-step permutation procedure (25000
permutations). Model 2 permutes the rows of the L matrix to test the
null hypothesis that no relationship exists between species abundance
data with fixed traits and their environment; model 4 permutes the
columns of dataset L to test the null hypothesis that species
composition is not influenced by species traits given fixed
environmental characteristics (Dray et al. 2014).