Integrating functional traits into correlative species distribution models to investigate the vulnerability of marine human activities to climate change.

Climate change and particularly warming are significantly impacting marine ecosystems and the services they provided. Temperature, as the main factor driving all biological processes, may influence ectotherms metabolism, thermal tolerance limits and distribution species patterns. The joining action of climate change and local stressors (including the increasing human marine use) may facilitate the spread of non-indigenous and native outbreak forming species, leading to associated economic consequences for marine coastal economies. Marine aquaculture is one among the most economic anthropogenic activities threatened by multiple stressors and in turn, by increasing hard artificial substrates at sea would facilitate the expansion of these problematic organisms and face negative consequences regarding facilities management and farmed organisms' welfare. Species Distribution Models (SDMs) are considered powerful tools for forecasting the future occurrences and distributions of problematic species used to preventively aware stakeholders. In the current study, we propose the use of combined correlative SDMs and mechanistic models, based on individual thermal performance curve models calculated through non-linear least squares regression and Bayesian statistics (functional-SDM), as an ecological relevant tool to increase our ability to investigate the potential indirect effect of climate change on the distributions of harmful species for human activities at sea, taking aquaculture as a food productive example and the benthic cnidarian Pennaria disticha (one of the most pernicious fouling species in aquaculture) as model species. Our combined approach was able to improve the prediction ability of both mechanistic and correlative models to get more ecologically informed "whole" niche of the studied species. Incorporating the mechanistic links between the organisms' functional traits and their environments into SDMs through the use of a Bayesian functional-SDM approach would be a useful and reliable tool in early warning ecological systems, risk assessment and management actions focused on important economic activities and natural ecosystems conservation.