Environmental variables are often used as indirect surrogates for mapping biodiversity because species survey data are scant at regional scales, especially in the marine realm. However, environmental variables are measured on arbitrary scales unlikely to have simple, direct relationships with biological patterns. Instead, biodiversity may respond nonlinearly and to interactions between environmental variables.

To investigate the role of the environment in driving patterns of biodiversity composition in large marine regions, we collated multiple biological survey and environmental data sets from tropical NE Australia, the deep Gulf of Mexico and the temperate Gulf of Maine. We then quantified the shape and magnitude of multispecies responses along >30 environmental gradients and the extent to which these variables predicted regional distributions. To do this, we applied a new statistical approach, Gradient Forest, an extension of Random Forest, capable of modelling nonlinear and threshold responses.

The regional-scale environmental variables predicted an average of 13–35% (up to 50–85% for individual species) of the variation in species abundance distributions. Important predictors differed among regions and biota and included depth, salinity, temperature, sediment composition and current stress. The shapes of responses along gradients also differed and were nonlinear, often with thresholds indicative of step changes in composition. These differing regional responses were partly due to differing environmental indicators of bioregional boundaries and, given the results to date, may indicate limited scope for extrapolating bio-physical relationships beyond the region of source data sets.

Synthesis and applications. Gradient Forest offers a new capability for exploring relationships between biodiversity and environmental gradients, generating new information on multispecies responses at a detail not available previously. Importantly, given the scarcity of data, Gradient Forest enables the combined use of information from disparate data sets. The gradient response curves provide biologically informed transformations of environmental layers to predict and map expected patterns of biodiversity composition that represent sampled composition better than uninformed variables. The approach can be applied to support marine spatial planning and management and has similar applicability in terrestrial realms.