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


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.

Download full-text


Available from: Debra P C Peters
    • "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). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Managing multiple, interacting disturbances is a key challenge to biodiversity conservation, and one that will only increase as global change drivers continue to alter disturbance regimes. Theoretical studies have highlighted the importance of a mechanistic understanding of stressor interactions for improving the prediction and management of interactive effects. However, many conservation studies are not designed or interpreted in the context of theory, and instead focus on case-specific management questions. This is a problem as it means that few studies test the relationships highlighted in theoretical models as being important for ecological management. We explore the extent of this problem among studies of interacting disturbances by reviewing recent experimental studies of the interaction between fire and grazing in terrestrial ecosystems. Interactions between fire and grazing can occur via a number of pathways; one disturbance can modify the other's likelihood, intensity or spatial distribution, or one disturbance can alter the other's impacts on individual organisms. The strength of such interactions will vary depending on disturbance attributes (e.g. size or intensity), and this variation is likely to be non-linear. We show that few experiments testing fire-grazing interactions are able to identify the mechanistic pathway driving an observed interaction, and most are unable to detect non-linear effects. We demonstrate how these limitations compromise the ability of experimental studies to effectively inform ecological management. We propose a series of adjustments to the design of disturbance interaction experiments that would enable tests of key theoretical pathways, and provide the deeper ecological understanding necessary for effective management. Such considerations are relevant to studies of a broad range of ecological interactions, and are critical to informing the management of disturbance regimes in the context of accelerating global change. This article is protected by copyright. All rights reserved.
    No preview · Article · Nov 2015 · Global Change Biology
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Aim: Current interest in forecasting changes to species ranges has resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. Location: Eastern North America (as an example). Methods: Our framework builds a metamodel that is constrained by the results of multiple sub-models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presence-absence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America. Results: For both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. Main conclusions: We conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off-the-shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi-source and multi-scale data into ecological decision-making.
    Full-text · Article · Nov 2015
  • Source
    • "Heavy grazing in drought periods is believed to have caused an initial fragmentation of continuous B. eriopoda stands associated with a reduced patch size and increased bare ground. These changes, in turn, triggered accelerating soil degradation, shrub proliferation, and additional grass loss (Schlesinger and others 1990; Peters and others 2004). A long-term study was initiated in 1995 within a remnant grassdominated area to examine the effects of acute, heavy grazing pressure on B. eriopoda loss and recovery (Bestelmeyer and others 2013). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Theoretical models suggest that scale-dependent feedbacks between plant reproductive success and plant patch size govern transitions from highly to sparsely vegetated states in drylands, yet there is scant empirical evidence for these mechanisms. Scale-dependent feedback models suggest that an optimal patch size exists for growth and reproduction of plants and that a threshold patch organization exists below which positive feedbacks between vegetation and resources can break down, leading to critical transitions. We examined the relationship between patch size and plant reproduction using an experiment in a Chihuahuan Desert grassland. We tested the hypothesis that reproductive effort and success of a dominant grass (Bouteloua eriopoda) would vary predictably with patch size. We found that focal plants in medium-sized patches featured higher rates of grass reproductive success than when plants occupied either large patch interiors or small patches. These patterns support the existence of scale-dependent feedbacks in Chihuahuan Desert grasslands and indicate an optimal patch size for reproductive effort and success in B. eriopoda. We discuss the implications of these results for detecting ecological thresholds in desert grasslands.
    Full-text · Article · Oct 2015 · Ecosystems
Show more