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Contrasting climate risks predicted by dynamic vegetation and ecological niche-based models applied to tree species in the Brazilian Atlantic Forest
- Raghunathan, Nima, François, Louis, Dury, Marie, Hambuckers, Alain
- Regional environmental change 2019 v.19 no.1 pp. 219-232
- General Circulation Models, Leontopithecus chrysomelas, biosphere, carbon dioxide, carbon dioxide fixation, climate, climate change, ecosystems, forests, indigenous species, niches, phenology, risk, trees
- Climate change is a threat to natural ecosystems. To evaluate this threat and, where possible, respond, it is useful to understand the potential impacts climate change could have on species’ distributions, phenology, and productivity. Here, we compare future-scenario outcomes between a dynamic vegetation model (DVM; CARbon Assimilation In the Biosphere (CARAIB)) and an ecological niche-based model (ENM; maximum entropy model) to outline the risks to tree species in the Brazilian Atlantic Forest, comprising the habitats of several endemic species, including the endangered primate Leontopithecus chrysomelas (golden-headed lion tamarin; GHLT), our species of interest. Compared to MaxENT, the DVM predicts larger present-day species ranges. Conversely, MaxENT ranges are closer to sampled distributions of the realised niches. MaxENT results for two future scenarios in four general circulation models suggest that up to 75% of the species risk losing more than half of their original distribution. CARAIB simulations are more optimistic in scenarios with and without accounting for potential plant-physiological effects of increased CO₂, with less than 10% of the species losing more than 50% of their range. Potential gains in distribution outside the original area do not necessarily diminish risks to species, as the potential new zones may not be easy to colonise. It will also depend on the tree species’ dispersal ability. So far, within the current range of L. chrysomelas, CARAIB continues to predict persistence of most resource trees, while MaxENT predicts the loss of up to 19 species out of the 59 simulated. This research highlights the importance of choosing the appropriate modelling approach and interpretation of results to understand key processes.