More than 60% of the worldwide population live on or near a coastline. These areas of considerable socio-cultural and economic importance host extraordinary biodiversity that supports many significant services to humankind (e.g. fisheries, carbon sequestration). However, human activities have deleterious effects (water pollution, introduction of alien species, climate changes etc.) on coastal environments and their biodiversity. These pressures often act simultaneously, yet their cumulative impacts on biodiversity are poorly understood. First, traditional approaches for monitoring coastal biodiversity present multiple constraints that limit observations to a small number of species and restrain the extent of studies. Second, studies often focus on the impact of a single stressor and ignore interacting effects between stressors. To improve the health of coastal environments, we need more integrative approaches for monitoring biodiversity and disentangling the effects of the different stressors. To reach this goal, and thus support adapted management strategies, this project aims at developing a new generation of tools, based on molecular approaches named ‘environmental DNA’ (i.e. the study of genetic material retrieved in environmental samples such as water and sediment). These methods for monitoring biodiversity (aka "eDNA biomonitoring") seek to identify species living in a given area from DNA they release in the environment. They proved to be powerful tools for obtaining comprehensive and standardised biodiversity surveys in a relatively rapid and cost-efficient way. The TEAM-Coast project pursues three main objectives. The first one aimed at developing protocols for the large-scale implementation of eDNA biomonitoring in costal environments. We validated and used efficient and cost-effective sampling strategies and protocols for collecting and analyzing eDNA samples at the landscape scale in several coastal environments. The second one aimed at precisely describing the diversity and distribution of species assemblages, and identifying the main factors affecting these communities. Based on eDNA data, several biodiversity metrics were derived and were used to assess the responses of communities to the multiple anthropogenic pressures observed. For instance, the community composition was shown to differ according to the studied estuary, as a response to the anthropogenic gradient among them. Not all species groups however responded the same way highlighting the need for multi-species approaches to assess biodiversity and the health status of these ecosystems. Ultimately, this project aimed at developing ecological risk assessment models, based on eDNA data, and test management scenarios for striking the balance between human activities and the ecological integrity of coastal environments. Several models for eukaryotes were implemented and validated, and are now in course of complexification to include multiple taxonomic groups and provide useful management scenarios.
