Skip to main content
ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #350794

Research Project: Management Technologies for Conservation of Western Rangelands

Location: Range Management Research

Title: An integrated view of complex landscapes: A big data-model integration approach to transdisciplinary science

Author
item Peters, Debra - Deb
item Burruss, N. Dylan - New Mexico State University
item Rodriguez, Luis
item Mcvey, D Scott - Scott
item Elias, Emile
item Pelzel-mccluskey, Angela - Animal And Plant Health Inspection Service (APHIS)
item Derner, Justin
item Schrader, Theodore
item Yao, Jin - Non ARS Employee
item Pauszek, Steven
item Lombard, Jason - Animal And Plant Health Inspection Service (APHIS)
item Archer, Steven - University Of Arizona
item Bestelmeyer, Brandon
item Browning, Dawn
item Brungard, Colby - New Mexico State University
item Hatfield, Jerry
item Hanan, Niall - New Mexico State University
item Herrick, Jeffrey - Jeff
item Okin, Gregory - University Of California
item Sala, Osvaldo - Arizona State University
item Savoy, Heather - New Mexico State University
item Vivoni, Enrique - Arizona State University

Submitted to: Bioscience
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/16/2018
Publication Date: 9/1/2018
Citation: Peters, D.C., Burruss, N., Rodriguez, L.L., McVey, D.S., Elias, E.H., Pelzel-McCluskey, A.M., Derner, J.D., Schrader, T.S., Yao, J., Pauszek, S.J., Lombard, J., Archer, S.R., Bestelmeyer, B.T., Browning, D.M., Brungard, C., Hatfield, J.L., Hanan, N.P., Herrick, J.E., Okin, G.S., Sala, O.E., Savoy, H., Vivoni, E.R. 2018. An integrated view of complex landscapes: A big data-model integration approach to transdisciplinary science. Bioscience. 68:653-669. https://doi.org/10.1093/biosci/biy069.
DOI: https://doi.org/10.1093/biosci/biy069

Interpretive Summary: We developed a Trans-Disciplinary Data-Model Integration (TDMI) approach that focuses on spatio-temporal modeling and cross-scale interactions, and employs human-centered machine learning strategies in order to assist in understanding, predicting, and managing for complex dynamics in agricultural systems. Our approach integrates knowledge and data on: (1) biological processes, (2) spatial heterogeneity in the land surface template, and (3) variability in environmental drivers using data and knowledge drawn from multiple lines of evidence (i.e., observations, experimental manipulations, analytical and numerical models, products from imagery, conceptual model reasoning, theory). We apply this approach to a suite of increasingly complex agricultural and ecologically-relevant problems. We then provide a framework linked with a Data Science Integration System (DSIS) to allow other questions to be addressed in the future.

Technical Abstract: The Earth is a complex system comprised of many interacting spatial and temporal scales. Understanding, predicting, and managing for these dynamics requires a trans-disciplinary integrated approach. Although there have been calls for this integration, a general approach is needed. We developed a Trans-Disciplinary Data-Model Integration (TDMI) approach that focuses on spatio-temporal modeling and cross-scale interactions, and employs human-centered machine learning strategies. Applied to ecological problems, our approach integrates knowledge and data on: (1) biological processes, (2) spatial heterogeneity in the land surface template, and (3) variability in environmental drivers using data and knowledge drawn from multiple lines of evidence (i.e., observations, experimental manipulations, analytical and numerical models, products from imagery, conceptual model reasoning, theory). We apply this approach to a suite of increasingly complex ecologically-relevant problems. We then provide a framework linked with a Data Science Integration System (DSIS) to allow other questions to be addressed in the future.