Beta diversity, the variation in species composition among sites within a region, is a key concept for conserving and managing biodiversity.... » More

Beta diversity, the variation in species composition among sites within a region, is a key concept for conserving and managing biodiversity. However, beta-diversity is rarely studied across the large spatial scales relevant for management, particularly in the marine environment. We analyse and predict spatial patterns of turnover in community composition using two relatively new, but contrasting community modelling methods; (a) Generalised Dissimilarity Modelling (GDM), a statistical approach based on regression, and (b) Gradient Forest Modelling (GF), a machine learning approach using an ensemble of decision trees. Macroalgal data is collated from an extensive spatio-temporal survey of subtidal reefs in southern Australia, a region with exceptional diversity and endemism.  Beta diversity patterns are modelled over 5000 km of coastline, for 180+ species, representing the first broad scale quantitative analysis of algal beta diversity for this region. Patterns predicted by both methods are remarkably congruent. Striking changes in algal composition are evident across the region, especially in the South Australian Gulfs, corresponding with changes in sea-surface temperate and nutrients in both methods. Our results suggest that strong environmental gradients are the common drivers of community change in this region. These tools advance conservation assessment in species-rich marine groups, but require further development.

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