Analysis
Spatial autoregression was used to investigate the relationship between
bivalve diversity and environmental variables (R package spatialreg:
Bivand et al. , 2013). For each model, logged diversity was
compared with the eight environmental variables as predictors. This was
repeated for each dataset and diversity metric. Finally, to identify the
best predictor variables for each diversity metric and dataset, an
optimal submodel was chosen using the function RegBest in the R package
FactoMineR (LĂȘ et al. , 2008). This analysis was repeated using
the taxonomic composition data to test for the underlying causes of any
biogeographic gradient.
Results