Detection and Assessment of Ecosystem Regime Shifts from Fisher Information

Karunanithi, Arunprakash T.; Cabezas, Heriberto; Frieden, B. Roy; Pawlowski, Christopher W.

doi:10.5751/es-02318-130122

Cited by 62 publications

(112 citation statements)

References 35 publications

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“…In order to evaluate real systems, Equation 5 was adapted to derive methods for computing FI for discrete data. Details of the FI derivation and calculation methodology can be found in the literature [41,64]. One of the foundational elements of employing FI for assessing changes in system condition is that different states of a system exhibit different degrees of dynamic order.…”

Section: Fisher Information Methodsmentioning

confidence: 99%

“…One of the foundational elements of employing FI for assessing changes in system condition is that different states of a system exhibit different degrees of dynamic order. Hence, (1) an orderly regime is defined by a non-zero FI that does change over time (i.e., /0 d FI dt  ); (2) progressive decrease in FI indicates increasing variation in system variables, thereby signifying loss of dynamic order and movement away from stability and sustainability; (3) steadily increasing FI indicates the system is changing at a slower rate and becoming more organized; however this increase does not ensure there is a shift unless the system actually settles into a new regime ( /0 d FI dt  ); and (4) a sharp decrease in FI between two stable dynamic regimes denotes a regime shift [63][64][65] . Although increasing FI is indicative of higher dynamic order, it does not automatically represent a transition to a preferred state (e.g., oligotrophic vs. eutrophic lake or an economically viable business vs. one nearing economic collapse).…”

Section: Fisher Information Methodsmentioning

confidence: 99%

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Integrated Metrics for Improving the Life Cycle Approach to Assessing Product System Sustainability

et al. 2014

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Life cycle approaches are critical for identifying and reducing environmental burdens of products. While these methods can indicate potential environmental impacts of a product, current Life Cycle Assessment (LCA) methods fail to integrate the multiple impacts of a system into unified measures of social, economic or environmental performance related to sustainability. Integrated metrics that combine multiple aspects of system performance based on a common scientific or economic principle have proven to be valuable for sustainability evaluation. In this work, we propose methods of adapting four integrated metrics for use with LCAs of product systems: ecological footprint, emergy, green net value added, and Fisher information. These metrics provide information on the full product system in land, energy, monetary equivalents, and as a unitless information index; each bundled with one or more indicators for reporting. When used together and for relative comparison, integrated metrics provide a broader coverage of sustainability aspects from multiple theoretical perspectives that is more likely to illuminate potential issues than individual impact indicators. These integrated metrics are recommended for use in combination with traditional indicators used in LCA. Future work will test and demonstrate the value of using these integrated metrics and combinations to assess product system sustainability.

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Section: Fisher Information Methodsmentioning

confidence: 99%

Section: Fisher Information Methodsmentioning

confidence: 99%

Integrated Metrics for Improving the Life Cycle Approach to Assessing Product System Sustainability

et al. 2014

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“…The actual mathematical procedure for computing the Fisher Information Index from time series data is well documented elsewhere (Karunanithi, Cabezas, Frieden, & Pawlowski, 2008;Pawlowski et al, 2005). We outline it here for continuity and clarity.…”

Section: The Fisher Information Indexmentioning

confidence: 99%

A new framework for urban sustainability assessments: Linking complexity, information and policy

Zellner

Theis

Karunanithi

et al. 2008

Computers, Environment and Urban Systems

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a b s t r a c tUrban systems emerge as distinct entities from the complex interactions among social, economic and cultural attributes, and information, energy and material stocks and flows that operate on different temporal and spatial scales. Such complexity poses a challenge to identify the causes of urban environmental problems and how to address them without causing greater deterioration. Planning has traditionally focused on regulating the location and intensity of urban activities to avoid environmental degradation, often based on assumptions that are rarely revisited and producing ambiguous effects. The key intellectual challenge for urban policy-makers is a fuller understanding of the complexity of urban systems and their environment. We address this challenge by developing an assessment framework with two main components: (1) a simple agent-based model of a hypothetical urbanizing area that integrates data on spatial economic and policy decisions, energy and fuel use, air pollution emissions and assimilation, to test how residential and policy decisions affect urban form, consumption and pollution; (2) an information index to define the degree of order and sustainability of the hypothetical urban system in the different scenarios, to determine whether specific policy and individual decisions contribute to the sustainability of the entire urban system or to its collapse.

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“…Moreover, empirical efforts to predict or forecast management outcomes, which involve formulating dynamic models of resilience and accounting for key uncertainties, are still in their infancy , Scheffer and Carpenter 2003, Scheffer 2009). Monitoring in terms of early warning signs for critical regime shifts is receiving increased attention (Karunanithi et al 2008, Biggs 2009), but learning about regime shifts in time to avert them remains a challenge. Finally, a resilience-based approach to valuing decision outcomes has barely been explored , Peterson et al 2003a, despite the need to define utility in a way that avoids a focus on a narrow range of ecosystem goods or services that, if optimized, could erode resilience.…”

Section: A Management-based Critique Of Resilience Thinkingmentioning

confidence: 99%

Resilience Thinking and a Decision-Analytic Approach to Conservation: Strange Bedfellows or Essential Partners?

Johnson¹,

Williams²,

Nichols³

2013

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ABSTRACT. There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is held to reduce spatial, temporal, or organizational heterogeneity that would otherwise limit efficiency, leading to homogenization of a system and making it less able to cope with unexpected changes or disturbances. For their part, decision analysts have been critical of resilience proponents for not providing much practical advice to decision makers. We believe a key source of tension between resilience thinking and application of decision science is the pursuit of efficiency in the latter (i.e., choosing the "best" management action or strategy option to maximize productivity of one or few resource components), vs. a desire in the former to keep options open (i.e., maintaining and enhancing diversity). It seems obvious, however, that with managed natural systems, there must be a principle by which to guide decision making, which at a minimum allows for a comparison of projected outcomes associated with decision alternatives. This is true even if the primary concern of decision making is the preservation of system resilience. We describe how a careful framing of conservation problems, especially in terms of management objectives and predictive models, can help reduce the purported tension between resilience thinking and decision analysis. In particular, objective setting in conservation problems needs to be more attuned to the dynamics of ecological systems and to the possibility of deep uncertainties that underlie the risk of unintended, if not irreversible, outcomes.Resilience thinking also leads to the suggestion that model development should focus more on process rather than pattern, on multiple scales of influence, and on phenomena that can create alternative stability regimes. Although we acknowledge the inherent difficulties in modeling ecological processes, we stress that formulation of useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner.

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Detection and Assessment of Ecosystem Regime Shifts from Fisher Information

Cited by 62 publications

References 35 publications

Integrated Metrics for Improving the Life Cycle Approach to Assessing Product System Sustainability

Integrated Metrics for Improving the Life Cycle Approach to Assessing Product System Sustainability

A new framework for urban sustainability assessments: Linking complexity, information and policy

Resilience Thinking and a Decision-Analytic Approach to Conservation: Strange Bedfellows or Essential Partners?

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