To support coastal planning through improved understanding of patterns of biotic and abiotic surrogacy at broad scales, we used gradient forest modeling (GFM) to analyze and predict spatial patterns of compositional turnover of demersal fishes, macroinvertebrates, and macroalgae on shallow, temperate Australian reefs. Predictive models were first developed using environmental surrogates with estimates of prediction uncertainty, and then the efficacy of the three assemblages as biosurrogates for each other was assessed. Data from underwater visual surveys of subtidal rocky reefs were collected from the southeastern coastline of continental Australia (including South Australia and Victoria) and the northern coastline of Tasmania. These data were combined with 0.01 degree-resolution gridded environmental variables to develop statistical models of compositional turnover (beta diversity) using GFM. GFM extends the machine learning, ensemble tree-based method of random forests (RF), to allow the simultaneous modeling of multiple taxa. The models were used to generate predictions of compositional turnover for each of the three assemblages within unsurveyed areas across the 6600 km of coastline in the region of interest. The most important predictor for all three assemblages was variability in sea surface temperature (measured as standard deviation from measures taken interannually). Spatial predictions of compositional turnover within unsurveyed areas across the region of interest were remarkably congruent across the three taxa. However, the greatest uncertainty in these predictions varied in location among the different assemblages. Pairwise congruency comparisons of observed and predicted turnover among the three assemblages showed that invertebrate and macroalgal biodiversity were most similar, followed by fishes and macroalgae, and lastly fishes and invertebrate biodiversity, suggesting that of the three assemblages, macroalgae would make the best biosurrogate for both invertebrate and fish compositional turnover.
Thomson RJ, Hill NA, Leaper R, Ellis N, Pitcher CR, Barrett NS, Edgar GJ.