Performance of predictive models in marine benthic environments based on predictions of sponge distribution on the Australian continental shelf

This study tested the performance of 15 predictive models in predicting the distribution of sponge assemblages on the Australian continental shelf using a common set of marine environmental variables. The models included traditional regression and more recently developed machine learning models. The results demonstrate that the spatial distribution of sponge assemblages can be successfully predicted, although the effectiveness of predictions varied among models. Overall, machine learning models achieved the best prediction performance. The direct variable of bottom-water temperature and the resource variables that describe bottom-water nutrient status were found to be useful surrogates for the distribution of sponge assemblages at the broad regional scale. A new method of deriving pseudo-absence data (weighted pseudo-absence) was compared with random pseudo-absence data - the new data were able to improve modelling performance for all the models both in terms of statistics (~ 10%) and in the predicted spatial distributions. Results from this study will further refine modelling methods used to predict the spatial distribution of marine biota at broad spatial scales, an outcome especially relevant to managers of marine resources.