PS 23-71 - Community science in support of ecosystem-based management: A case study from the Damariscotta River Estuary, Maine, USA

Abstract: Coastal marine ecosystems are dynamic social-ecological systems (SESs) that support diverse ecosystem services and human activities. The complexity of these SESs – including the multitude of goals that people have for these places and the services they provide; the spatially and temporally variable interactions that occur within them among humans and non-humans, and the context dependency of outcomes – mean that focusing on a single service or sector in developing management strategies is insufficient. For these reasons, ecosystem-based approaches are increasingly used to generate and apply knowledge to support coastal marine ecosystem stewardship. We report how a community science program in Maine, USA has begun to create robust social and ecological data to support ecosystem-based management at the local scale. The Damariscotta River Estuary in Maine, USA is experiencing growing social-ecological challenges, but local managers have limited data and capacity to monitor change. In the estuary, we are engaging local students, shellfish harvesters, and other citizens in data collection, interpretation and application that contributes to municipal shellfish management as well as a broader vision for sustaining this SES. By developing and testing social and ecological data collection protocols that generate management-relevant understanding and reporting on the resulting data, we highlight the importance of local knowledge and history as well as the value of monitoring spatial and temporal variability in both the social and ecological domains. In keeping with Ostrom’s 2009 SES framework, we demonstrate how social science and ecological approaches can be integrated to generate fine-scale information; fill historical data gaps; support community-led management; and contribute to field methods that people with varied lived experiences and training can implement in a sustainable fashion. Our approach can be adapted to other social-ecological contexts, particularly in data-limited SESs that are experiencing observable social and ecological change.