Big data, local science: Not an oxymoron

TitleBig data, local science: Not an oxymoron
Publication TypeConference Paper
Year of Publication2020
AuthorsBestelmeyer B, McCord S, Webb N, Brown J, Herrick J, Peters D
Conference NameEcological Society of America
Date Published08/2020
Conference LocationVirtual Conference
ARIS Log Number378846

Collaborative natural resource management (CNRM) projects are increasingly a common approach to link science to decision-making in rangelands, forests, and fisheries. Such approaches emphasize interactions among scientists from a variety of disciplines with stakeholders to manage adaptively. The availability of human and data resources, however, limit the initiation of CNRM projects and their benefits to stakeholders and ecosystems. We propose a “big data” framework for catalyzing and supporting CNRM projects based on standardized monitoring, open access databases, gridded spatial data products, data-model integration, and mobile applications.
We provide examples of information products used to support local decision-making from desert grassland areas of the Southwestern U.S. used for livestock production and that are experiencing widespread shrub encroachment. Products include 1) long-term evaluations of vegetation and wind erosion responses to shrub control using standard monitoring methods coupled to models and gridded spatial data and 2) remote sensing-based analyses of long-term trends in vegetation composition and forage production at 30m and 4 km resolutions, respectively, used to provide region-scale environmental context for a collaborative landscape management effort. A unified big data framework has yet to be fully developed, but its creation could help scale up CNRM activities that will be essential to promoting human well-being and conserving biodiversity. Poster #81832.