Research Overview

Coastal ecosystems exist at a dynamic interface between continents, oceans and the atmosphere.  The increasing human use of coastal watersheds, coupled with climate change and sea-level rise, alters the ways in which materials and energy are transformed and transported within these systems. Changes in the functioning of coastal ecosystems will have important consequences for the people who value them for food, recreation, storm protection, and other ecosystem services.

Since its inception in 1998, the Plum Island Ecosystems LTER has been working toward a predictive understanding of the long-term response of coupled land-marsh-estuary-ocean ecosystems to changes in three key drivers: climate, sea level, and human activities.  As rates of change in these drivers have accelerated in the PIE region,  there is critical need to understand the mechanisms that underlie these responses, and to provide information necessary for effective and timely management.




Our current research examines the dynamics of coastal ecosystems in a region of rapid climate change , sea level rise, and human impacts.    By focusing in on sediment dynamics, species interactions, and the role that warmer water plays on species changes, we are building on our previous work  that showed how geomorphic connectivity between  landscapes, riverscapes, and seascapes are altered by those external drivers, while maintaining the systems-level approach that has characterized PIE research.   This approach led to key findings about the importance of scale in evaluating interactions between humans and the landscape, for example, or about the importance of understanding fish movement between habitats as indicators of ecosystem function.    Other research highlights have shown the importance of the hydrologic drivers at PIE, and have connected those drivers to biogeochemical cycles and thence to system productivity and food webs.    Experimental approaches are underpinned by long-term monitoring of fundamental ecosystem processes and physical drivers, and are guided by modeling efforts that aim to synthesize the knowledge we gain.