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Cross scale interactions, nonlinearities, and forecasting catastrophic events

PNAS

By:
, , , ORCID iD , , and
https://doi.org/10.1073/pnas.0403822101

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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 easily be predicted based on information obtained at a single scale. Progress on catastrophic events has focused on one of the following two areas: nonlinear dynamics through time without an explicit consideration of spatial connectivity [Holling, C. S. (1992) Ecol. Monogr. 62, 447–502] or spatial connectivity and the spread of contagious processes without a consideration of cross-scale interactions and feedbacks [Zeng, N., Neeling, J. D., Lau, L. M. & Tucker, C. J. (1999) Science 286, 1537–1540]. These approaches rarely have 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 usefulness of our approach by using new data and recasting published data from ecology (wildfires and desertification), epidemiology (infectious diseases), and engineering (structural failures). We show that decisions that minimize the likelihood of catastrophic events must be based on cross-scale interactions, and such decisions will often be counterintuitive. 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 develop strategies for minimizing their occurrence and impacts. Our framework is an important step in developing predictive tools and designing experiments to examine cross-scale interactions.

Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Cross scale interactions, nonlinearities, and forecasting catastrophic events
Series title:
PNAS
DOI:
10.1073/pnas.0403822101
Volume:
101
Issue:
42
Year Published:
2004
Language:
English
Publisher:
National Academy of Sciences of the United States of America
Contributing office(s):
Fort Collins Science Center
Description:
6 p.
First page:
15130
Last page:
15135