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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #378846

Research Project: Science and Technologies for the Sustainable Management of Western Rangeland Systems

Location: Range Management Research

Title: Big data, local science: Not an oxymoron

Author
item Bestelmeyer, Brandon
item McCord, Sarah
item WEBB, NICHOLAS - NEW MEXICO STATE UNIVERSITY
item BROWN, JOEL - NATURAL RESOURCES CONSERVATION SERVICE (NRCS, USDA)
item Herrick, Jeffrey - Jeff
item Peters, Debra - Deb

Submitted to: Ecological Society of America Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 4/21/2020
Publication Date: 8/3/2020
Citation: Bestelmeyer, B.T., McCord, S.E., Webb, N., Brown, J., Herrick, J.E., Peters, D.C. 2020. Big data, local science: Not an oxymoron [abstract]. Ecological Society of America Meeting. August 3-6, 2020, Virtual. Poster #81832.

Interpretive Summary:

Technical Abstract: 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. Results/Conclusions 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.