Data synthesis for environmental management: A case study of Chesapeake Bay

Abstract

Synthesizing large, complex data sets to inform resource managers towards effective environmental stewardship is a universal challenge. In Chesapeake Bay, a well-studied and intensively monitored estuary in North America, the challenge of synthesizing data on water quality and land use as factors related to a key habitat, submerged aquatic vegetation, was tackled by a team of scientists and resource managers operating at multiple levels of governance (state, federal). The synthesis effort took place over a two-year period (2016–2018), and the results were communicated widely to a) scientists via peer review publications and conference presentations; b) resource managers via web materials and workshop presentations; and c) the public through newspaper articles, radio interviews, and podcasts. The synthesis effort was initiated by resource managers at the United States Environmental Protection Agencys’ Chesapeake Bay Program and 16 scientist participants were recruited from a diversity of organizations. Multiple short, immersive workshops were conducted regularly to conceptualize the problem, followed by data analysis and interpretation that supported the preparation of the synthetic products that were communicated widely. Reflections on the process indicate that there are a variety of structural and functional requirements, as well as enabling conditions, that need to be considered to achieve successful outcomes from synthesis efforts.