Article

Cross-Scale Interactions, Nonlinearities, and Forecasting Catastrophic Events

U.S. Department of Agriculture Agricultural Research Service, Jornada Experimental Range, New Mexico State University, Las Cruces, NM 88003, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 10/2004; 101(42):15130-5. DOI: 10.1073/pnas.0403822101
Source: PubMed

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.

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    • "Further, when chain and modification interactions both occur, their net effects may become non-linear through cross-scale interactions (Peters et al., 2007). When the effects of disturbances and their interactions are nonlinear , knowledge of key points, such as when a small change in disturbance intensity has a large effect on biodiversity (or vice versa), are potentially invaluable for informing targeted and effective management (Peters et al., 2004; Huggett, 2005; Groffman et al., 2006; Didham et al., 2007). For example, the effect of fire on the susceptibility of forests to pine-beetle attacks is non-linear: low severity fire can increase the susceptibility of forests to pine beetle outbreaks (Kulakowski & Jarvis, 2013), while high-severity, stand-replacing fires can reduce forest susceptibility to the same pests (Kulakowski et al., 2003). "
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    • "This approach provides a number of advantages, including: (1) the ability to incorporate multiple modes of inference (e.g. mechanistic, correlative models) (Van Oijen et al., 2005; Clark & Gelfand, 2006; Hobbs & Ogle, 2011; Hartig et al., 2012), (2) an easy mechanism to include multiple data sources at various scales (Levin, 1992; Peters et al., 2004), and (3) an intuitive and comprehensive reporting of uncertainty in model predictions that reflects variation at all levels of organization (Cressie et al., 2009; Hobbs & Ogle, 2011). Unlike hybrid methods, the aim is not to link different sub-models into a single model, but to condition the predictions of a metamodel at the target scale (e.g. an entire species' range) with information from independent sub-models at a variety of spatial scales, allowing for more flexibility regarding the type of information included. "
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