Multi-scalar strategy for connecting science to policy and resource management

Background/Question/Methods

Over the decade, there have been repeated calls for public policy to be better informed by the best possible science.  However, various policies have been criticized as being “one size fits all”, or the equivalent of taking a sledgehammer to a problem when a surgical approach is required.  How can we better inform policy with scale-relevant science informed by the best available data? 

Results/Conclusions

In recent years, “data intensive science” has been heralded as enabling the era where scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets.  However, it can be argued that same vision can be applied to enable a better understanding of complex coupled human-natural landscapes using scale-relevant environmental and socio-economic data.  At the interface of scientifically informed public policy and data intensive science therefore lies the potential for producers of credible, integrated, multi-scalar environmental data like the National Ecological Observatory Network (NEON) and its partners to capitalize on (1) data and informatics interoperability initiatives that enable the vision of data intensive science, and (2) programs like the Department of Interior’s Climate Science Centers (CSC) and Landscape Conservation Cooperatives (LCC) and NOAA’s Regional Integrated Sciences and Assessments (RISA) that make scale-relevant science accessible to various stakeholders.  ESA’s public policy section is a means for fostering distributed groups of science-policy professionals who help connect science to policy at that level.  These existing structures make it possible to envision public policies to be formulated using the best possible science informed by data and information at the relevant geographical and temporal scale. 

NEON has been working closely with national-scale environmental observatories (EOs) to co-evolve a common vision for leveraging existing EO infrastructure.  The vision, captured in a concept called an “infrastructure stack”, calls for shared critical measurements in the variable space that includes major drivers of large-scale change.  Partner EOs will focus on ecosystem response variables aligned with their respective research and mission priorities.  We foresee that this approach, if successfully executed, will yield data that can be used to advance models for resource management, socio-economic analyses, environmental risk management, and decision support.   We also foresee an ecosystem of government, NGO, and academic data sources that will serve as credible producers of linked data, promote data sharing, and champion responsible data life cycle management.