Abstract
Aim: Understanding the drivers of community diversity and composition is a major question in ecology, which is of particular significance for the conservation of narrowly distributed taxa facing habitat alteration. In this study, we examined three ecological processes (environmental and biotic filtering, and dispersal limitation) hypothesized to drive community assembly in the island biodiversity hotspot of New Caledonia, and whether the relative importance of these processes changes along environmental gradients. Methods: First, we assessed environmental filtering by stacking binary presence/absence species distribution models generated for 678 tree species from c. 40,000 occurrences. Second, we acknowledged the influence of dispersal limitation by applying a buffer around each occurrence. Third, we modelled a local hierarchy of performances by ranking species in decreasing order of their predicted habitat suitability, up to the predicted local species richness. Predictions of stacked species distribution models (S-SDMs) accounting for different combinations of the filtering processes were compared with the composition of 12 forest plots. Results The three filters appear to be important driving forces. Accounting for environmental filtering provided fairly accurate assemblage predictions but species richness was overestimated. Considering dispersal limitation increased assemblage specificity (the proportion of correctly predicted absences) while considering differences in local performances increased assemblage sensitivity (the proportion of correctly predicted presences). The predictive ability of all S-SDMs decreased with elevation, which suggests that unpredictable stochastic processes influenced biodiversity dynamics in more productive mountain habitats. Main conclusions: New Caledonian tree communities appear to be similar to those of other tropical regions in their structuring processes (important role of dispersal and biotic filtering, increasing role of neutral processes with productivity). Our findings stress the potential of using S-SDMs to understand and predict current and future biodiversity patterns.