Modeling the effects of climate change on the habitat suitability of Mediterranean gorgonians

Abstract

Multiple stressors including global warming increasingly threaten the distribution and abundance of gorgonian forests. We built species distribution models (SDM) combined with machine learning algorithms, to compare the ecological niche of three Mediterranean gorgonian species (Paramuricea clavata, Eunicella cavolinii and Eunicella singularis) and distribution response to climate change under the worst IPCC scenario RCP8.5. Three Machine Learning models, XGBoost, Random Forest and the Knearest neighbour, 23 physico-chemical and 4 geophysical environmental variables were used to obtain the potential habitat suitability and future projections (2040-2050) of their distribution in the Mediterranean Sea. The global sensitivity and uncertainty analysis was used to identify the most important environmental variables shaping the habitat suitability of the species and to disentangle the interaction terms among different environmental variables. For all species, bathymetry was the main variable in uencing habitat suitability, with higher interactions with silicate, salinity and concavity. In future climatic conditions, P. clavata was expected to shift its habitat suitability from lower to higher latitudes, mainly in the Adriatic Sea. For both E. cavolinii and E. singularis, a general habitat reduction was predicted. In particular, E. cavolinii was expected to reduce the occupancy area of 49% suggesting that the sensitivity of the symbiotic algae (zooxanthellae) may not be the main responsible of the corresponding susceptibility of the holobiont to thermal stresses and climate change.