气候变化领域集成服务门户

Aim Predicting species distributional shifts in future climate scenarios representing conditions that do not exist in the current world is a challenge. Species distribution models may result in misrepresented projections for species living in extreme conditions if based on truncated response functions. Model extrapolation may not detect declines that could occur if future environment conditions exceeded the physiological tolerance of the species. We developed a novel method aimed to overcome this constrain by incorporating the physiological response function of a tropical hydrocoral to temperature as a predictor variable in a Hybrid SDM. Location Atlantic Ocean. Methods We conducted ecophysiological experiments simulating heat and cold stress to determine the maximum photochemical efficiency of the hydrocoral's symbiont along a thermal gradient to identify sublethal and lethal conditions. The response curve obtained was then applied to a temperature raster to create a new physio-climatic variable, which was integrated into the Hybrid SDM as a predictor. Simple Physiological and Correlative SDMs were compared with the Hybrid model. Results The Hybrid SDM outperformed the Correlative SDM allowing predictions without extrapolations in the physio-climatic predictor. It suggested habitat contractions in tropical regions with forecasted temperatures above the coral's physiological tolerance, which were underrepresented by the Correlative SDM. It also incorporated habitat suitability restrictions by other predictors of unknown physiological response by incorporating correlative information (as limitations in river mouths by low salinity). In this way, by integrating mechanistic and correlative knowledge, the Hybrid SDM also predicted a potential expansion to higher latitudes, which agreed with the recent evidence of its expansion into the subtropical Canary Islands. Main conclusion Integrating physiological knowledge into Hybrid SDMs by adding a physio-climatic predictor improves model transferability resulting in predictions of decline in future climates, which may be misrepresented by SDMs trained at present-day conditions, and therefore are advisable for early warning in conservation management.