We used r (R Development Core Team 2017 ) for statistical analyses, with all recorded fish species included. We used the findCorrelation function from the caret package to identify a set of 17 predictors that were not strongly correlated with each other (based on Spearman’s correlation coefficient <0.7; see Supporting Information Table S2 for list of all variables measured). To determine at what spatial scales fish–habitat associations are the strongest (Question 1), we used the BIOENV procedure (Clarke & Ainsworth, 1993 ), which is a dissimilarity-based method that can be used to identify the subset of explanatory variables whose Euclidean distance matrix has the maximum correlation with community dissimilarities, in our case, based on Bray–Curtis dissimilarity. BIOENV was implemented with functions from the vegan and sinkr packages. We extracted the rho value for the best model at each spatial scale as a measure of the strength of fish–habitat associations, with a higher rho value indicating a stronger association between fish and habitat variables.

We ergo determined the strength of fish–environment relationships that might be questioned oriented strictly towards the height away from replication at each measure regarding the lack of any fish–environment matchmaking, then tested if the our very own BIOENV results was indeed more powerful than that it null presumption

To do this, i at random resampled the first 39 BRUV samples of matching fish–habitat analysis compiled at 100-m level, to produce the full required brand new dataset (we.e., 72 trials). (more…)