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Title: CROSS-SCALE INTERACTIONS, NONLINEARITIES, AND FORECASTING CATASTROPHIC EVENTS

Author
item Peters, Debra
item PIELKE, ROGER - COLORADO STATE UNIVERSITY
item Bestelmeyer, Brandon
item ALLEN, CRAIG - USGS JEMEZ MT FIELD STA
item MUNSON-MCGEE, STUART - NEW MEXICO STATE UNIV
item Havstad, Kris

Submitted to: Proceedings of the National Academy of Sciences (PNAS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/13/2004
Publication Date: 10/19/2004
Citation: Peters, D.C., Pielke, R.A., Bestelmeyer, B.T., Allen, C.D., Munson-Mcgee, S., Havstad, K.M. 2004. Cross-scale interactions, nonlinearities, and forecasting catastrophic events. Proceedings of the National Academy of Sciences. 101(42):15130-15135.

Interpretive Summary: The spread of catastrophic events, such as wildfire, invasive species, infectious diseases, and engineering failures, often result in 'surprises' with severe consequences for the environment and human welfare. These events are difficult to predict because information is frequently provided at a single spatial and temporal scale. In this paper, we introduce a general framework for understanding the occurrence and consequences of catastrophic events in order to minimize the impacts of these events on ecosystem services, atmospheric conditions, and human welfare. We show that decisions minimizing the likelihood of catastrophic events must be based on cross-scale interactions and such decisions will often be counter-intuitive. Given the continuing challenges associated with global change, approaches that cross disciplinary boundaries to include interactions and feedbacks at multiple scales are needed to increase our ability to predict catastrophic events and to develop strategies for minimizing their occurrence and impacts. Our framework is an important step in developing predictive tools and designing new experiments to examine cross-scale interactions.

Technical Abstract: Catastrophic events share characteristic nonlinear behaviors that are often generated by cross-scale interactions and feedbacks among system elements. These events result in surprises that cannot be easily predicted based on information obtained at a single scale. Progress to date on catastrophic events has focused on one of two areas: (1) nonlinear dynamics through time without an explicit consideration of spatial connectivity, and (2) spatial connectivity and the spread of contagious processes without a consideration of cross-scale interactions and feedbacks. These approaches have rarely ventured beyond traditional disciplinary boundaries. We provide an interdisciplinary, conceptual, and general mathematical framework for understanding and forecasting nonlinear dynamics through time and across space. We illustrate the generality and utility of our approach using new data and a recasting of published data from ecology (wildfires, desertification), epidemiology (infectious diseases) and engineering (structural failures). We show that decisions minimizing the likelihood of catastrophic events must be based on cross-scale interactions and such decisions will often be counter-intuitive. Given the continuing challenges associated with global change, approaches that cross disciplinary boundaries, to include interactions and feedbacks at multiple scales, are needed to increase our ability to predict catastrophic events and to develop strategies for minimizing their occurrence and impacts. Our framework is an important step in developing predictive tools and designing new experiments to examine cross-scale interactions.