|Holifield Collins, Chandra|
|HURST, ZACH - University Of Idaho|
|PONCE-CAMPOS, GUILLERMO - University Of Arizona|
Submitted to: US-International Association for Landscape Ecology
Publication Type: Abstract Only
Publication Acceptance Date: 2/21/2022
Publication Date: 4/13/2022
Citation: Goslee, S.C., Baffaut, C., Coffin, A.W., Holifield Collins, C.D., Hurst, Z., Pisarello, K., Ponce-Campos, G., Witthaus, L.M. 2022. Agroecosystem conceptual frameworks and methods [abstract]. US-International Association for Landscape Ecology. P.1.
Interpretive Summary: No Interpretive Summary is required for this Abstract Only. JLB.
Technical Abstract: Maps of regions have long been recognized as useful tools for representing large spatial extents divided into manageable numbers of internally similar areas, and for highlighting spatial patterns. Of these regionalizations, biomes are possibly the best-known example, but there are many different implementations. Given that abundance, why add more? It is crucial to recognize that there is no “one true regionalization.” Instead, each is the product of the implicit and explicit assumptions inherent in choices of data and methods used to divide a continuous surface into discrete areas, and both of those aspects deserve careful consideration. Given the USDA Long-Term Agroecosystem Research (LTAR) Network’s need for tools to facilitate scaling of research findings to the appropriate spatial extent, and the necessity to represent US agriculture for the entire nation and across the three domains of environment, production, and human dimensions, how can the most useful regionalization be developed? Such a regionalization must be quantitative, repeatable, able to adapt to changing conditions such as climate change, and most importantly, it must answer the question it is intended to address. The LTAR Regionalization Project team has developed a framework to address both conceptual and practical aspects of selecting input data and analysis methods that meet LTAR needs and are grounded in ecological theory. The analysis workflow is fully supported by an R package and documentation. Outcomes of this regionalization process include: a conceptually sound set of input data; identification of data gaps; a decision matrix for spatial and temporal data harmonization; a decision matrix for clustering method selection; variable importance measures; cluster assessment indices and maps; and the regionalization itself. Both workflow and outcomes are broadly applicable to many sorts of ecological studies.