Conservation in the face of climate change: The roles of alternative models, monitoring, and adaptation in confronting and reducing uncertainty

Conroy, Michael J.; Runge, Michael C.; Nichols, James D.; Stodola, Kirk W.; Cooper, Robert J.

doi:10.1016/j.biocon.2010.10.019

Cited by 128 publications

(88 citation statements)

References 28 publications

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“…In turn, this will promote more holistic and effective modeling, management, and monitoring of general resilience. This can overcome common management shortcomings, including delayed action in the face of uncertainty (Conroy et al 2011), failed optimization of financial resources (Stewart-Koster et al 2015), and the lack of coordinated effort and scale mismatches across space, time, and institutional boundaries (Crowl et al 2008, Cumming et al 2013. Using this approach, uncertainty cannot be eliminated immediately or entirely.…”

Section: Methodsmentioning

confidence: 99%

A quantitative framework for assessing ecological resilience

Baho¹,

Allen²,

Garmestani³

et al. 2017

E&S

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ABSTRACT. Quantitative approaches to measure and assess resilience are needed to bridge gaps between science, policy, and management. In this paper, we suggest a quantitative framework for assessing ecological resilience. Ecological resilience as an emergent ecosystem phenomenon can be decomposed into complementary attributes (scales, adaptive capacity, thresholds, and alternative regimes) that embrace the complexity inherent to ecosystems. Quantifying these attributes simultaneously provides opportunities to move from the assessment of specific resilience within an ecosystem toward a broader measurement of its general resilience. We provide a framework that is based on reiterative testing and recalibration of hypotheses that assess complementary attributes of ecological resilience. By implementing the framework in adaptive approaches to management, inference, and modeling, key uncertainties can be reduced incrementally over time and learning about the general resilience of dynamic ecosystems maximized. Such improvements are needed because uncertainty about global environmental change impacts and their effects on resilience is high. Improved resilience assessments will ultimately facilitate an optimized use of limited resources for management.

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Section: Methodsmentioning

confidence: 99%

A quantitative framework for assessing ecological resilience

Baho¹,

Allen²,

Garmestani³

et al. 2017

E&S

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“…This has been named 'epistemic uncertainty' (Walker et al 2003) or http://www.ecologyandsociety.org/vol18/iss4/art63/ Gunderson 2011, Duncan and Wintle 2008, Lee 1999, Walters 1997 A focus on planning and discussion with laissez faire treated as an option (action procrastination) Keith et al 2011, Walters 1997 Insufficient attention to building shared understanding and joint decisionmaking among diverse interest groups Gregory et al 2006, Feldman 2008, Allen and Gunderson 2011, Keith et al 2011 Tendency of scientists to overstate ability to measure complex functional relationships experimentally Gregory et al 2006 Conflict and self-serving behavior impede leadership, communication and joint action Conroy et al 2011, Keith et al 2011 Evaluation and reflection Scientists fail to recognize the full range of management options, belief by management agencies that a single best policy confers credibility Gregory et al 2006, Walters 1997 Surprises are suppressed Keith et al 2011 Management goals become subordinate to research interests, valuing action more than learning Lee 1999, Walters 1997 'incomplete knowledge' (Brugnach et al 2008); we use the label ecological uncertainty. Ecological uncertainty can exist due to lack of data, unreliability of data, lack of theoretical understanding, or general ignorance.…”

Section: Stage 1: Is Adaptive Management Appropriate?mentioning

confidence: 99%

A New Paradigm for Adaptive Management

Rist¹,

Felton²,

Samuelsson³

et al. 2013

E&S

110

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ABSTRACT. Uncertainty is a pervasive feature in natural resource management. Adaptive management, an approach that focuses on identifying critical uncertainties to be reduced via diagnostic management experiments, is one favored approach for tackling this reality. While adaptive management is identified as a key method in the environmental management toolbox, there remains a lack of clarity over when its use is appropriate or feasible. Its implementation is often viewed as suitable only in a limited set of circumstances. Here we restructure some of the ideas supporting this view, and show why much of the pessimism around AM may be unwarranted. We present a new framework for deciding when AM is appropriate, feasible, and subsequently successful. We thus present a new paradigm for adaptive management that shows that there are no categorical limitations to its appropriate use, the boundaries of application being defined by problem conception and the resources available to managers. In doing so we also separate adaptive management as a management tool, from the burden of failures that result from the complex policy, social, and institutional environment within which management occurs.

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“…However, a systematic approach to formulating useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner (Conroy et al 2011. The critical point is that informed decisions require predictions about the outcomes of potential actions, and these predictions must come from some sort of model of system dynamics.…”

Section: Predicting and Monitoring Outcomesmentioning

confidence: 99%

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

Johnson¹,

Williams²,

Nichols³

2013

E&S

Self Cite

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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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Conservation in the face of climate change: The roles of alternative models, monitoring, and adaptation in confronting and reducing uncertainty

Cited by 128 publications

References 28 publications

A quantitative framework for assessing ecological resilience

A quantitative framework for assessing ecological resilience

A New Paradigm for Adaptive Management

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

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