SeeingShore: Understanting and predicting the impact of climate change on coastal habitats
SeeingShore is an ambitious research proposal which combines several approaches to better understand and predict the impact of climate changes on the structure of coastal ecosystems. The proposal embraces large-scale observational studies, experimental approaches and modelling activities. The project first aim is to gather new evidence on the impact of climate change on our coastal areas. Hence, we propose a new survey of the areas already studied by Lima and colleagues more than one decade ago and previously surveyed by a French researcher in the past century. Once the initial assessment of potential climate change impacts on rocky shores is done, the next aim of the project is building causal links between climate drivers and the response of biological systems. The project will take advantage of the large experience by the team in manipulative experimental approaches. Manipulative experiments will examine individual direct functional responses to climate drivers, allowing to understand how close organisms are to their thermal limits in nature and also helping to determine the maximum rate of environmental change that populations can cope with. In natural systems, differences among species in the functional responses to environmental drivers may shift their relative contribution to the community, changing the balance of species interactions and generating indirect climate effects which may amplify or attenuate the direct ones. Experiments on species interactions and climate change will provide novel information on how global change drivers can secondarily affect species by modifying their interactions. Finally, ecological research on climate change requires predictive approaches able to assess the vulnerability of the communities and to provide assistance in the management and mitigation of the impacts. Hence SeeingShore proposal third aim is to implement new operative management tools like community shifts metrics and vulnerability maps. Furthermore, the proposal aims to develop new predictive tools that better incorporate relevant biological processes, moving from correlative species distribution models to mechanistic approaches. All these modelling efforts will benefit largely from the high-quality biodiversity data collected at the initial task of the project.