Predictions of beta diversity for reef macroalgae across southeastern Australia
We analyzed and predicted spatial patterns of turnover in macroalgal community composition (beta diversity) that accounted for broad-scale environmental gradients using two contrasting community modelling methods, Generalised Dissimilarity Modelling (GDM) and Gradient Forest Modelling (GFM). Percentage cover data from underwater macroalgal surveys of subtidal rocky reefs along the southeastern coastline of continental Australia and northern coastline of Tasmania were combined with 0.01°-resolution gridded environmental variables, to develop statistical models of beta diversity. GDM, a statistical approach based on a matrix regression, and GFM, a machine learning approach based on ensemble tree based methods, were used to fit models and generate predictions of beta diversity within unsurveyed areas across the region of interest. Patterns of macroalgal beta diversity predicted by both methods were remarkably congruent and showed a similar and striking change in community composition from eastern South Australia to western Victoria and northern Tasmania. Macroalgal communities differed markedly in predicted composition between the open coast and inshore locations. A distinct algal community was predicted for the enclosed Port Philip Bay in Victoria. Sea surface temperature standard deviation and average contributed most to changes in beta diversity for both the GDM and GFM models; changes in wave exposure and oxygen also influenced beta diversity in the GDM model, while salinity and exposure contributed substantially to the GFM model. The GDM and GFM analyses allowed us to model and predict spatial patterns of beta diversity in macroalgal communities comprising >180 species over 6600 km of coastline. These outputs advance regional-scale conservation management by allowing planners to interpolate from point source ecological data to assess the distribution of biodiversity across their full domain of interest. The congruence between methods suggests that strong environmental gradients related to temperature and exposure are the common drivers of community change in this region.