Evaluating the efficacy of adaptive management approaches: Is there a formula for success?

McFadden, Jamie E.; Hiller, Tim L.; Tyre, Andrew J.

doi:10.1016/j.jenvman.2010.10.038

Cited by 108 publications

(82 citation statements)

References 32 publications

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“…This field of study has matured through two primary schools of thought: the Resilience-Experimentalist School (with high emphasis on stakeholder involvement, resilience, and highly complex models) and the Decision-Theoretic School (which results in relatively simple models by emphasizing stakeholder involvement for identifying management objectives) (McFadden et al, 2011). Application of adaptive management is now common to a variety of complex resource management issues, and while practitioners and scientists have developed adaptive management and structured decision making techniques, and mathematicians have developed methods to reduce the uncertainties encountered in resource management, there continues to be misapplication of the method, and misunderstanding of its purpose.…”

Section: Introductionmentioning

confidence: 99%

Adaptive management for a turbulent future

Allen

Fontaine

Pope

et al. 2011

Journal of Environmental Management

400

238

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a b s t r a c tThe challenges that face humanity today differ from the past because as the scale of human influence has increased, our biggest challenges have become global in nature, and formerly local problems that could be addressed by shifting populations or switching resources, now aggregate (i.e., "scale up") limiting potential management options. Adaptive management is an approach to natural resource management that emphasizes learning through management based on the philosophy that knowledge is incomplete and much of what we think we know is actually wrong. Adaptive management has explicit structure, including careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. It is evident that adaptive management has matured, but it has also reached a crossroads. Practitioners and scientists have developed adaptive management and structured decision making techniques, and mathematicians have developed methods to reduce the uncertainties encountered in resource management, yet there continues to be misapplication of the method and misunderstanding of its purpose. Ironically, the confusion over the term "adaptive management" may stem from the flexibility inherent in the approach, which has resulted in multiple interpretations of "adaptive management" that fall along a continuum of complexity and a priori design. Adaptive management is not a panacea for the navigation of 'wicked problems' as it does not produce easy answers, and is only appropriate in a subset of natural resource management problems where both uncertainty and controllability are high. Nonetheless, the conceptual underpinnings of adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex social-ecological systems, but management decisions must still be made, and whenever possible, we should incorporate learning into management.Published by Elsevier Ltd.

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Section: Introductionmentioning

confidence: 99%

Adaptive management for a turbulent future

Allen

Fontaine

Pope

et al. 2011

Journal of Environmental Management

400

238

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“…The use of adaptive management has been increasing over the last decade (McFadden et al, 2011). Given the changing paradigms in ecological research, that is, the increasing prevalence of multimodel inference, we sought to document the use of multimodel inference in two top management and conservation journals, and the pervasiveness of weak inference resulting from its use.…”

Section: Introductionmentioning

confidence: 99%

Multimodel inference and adaptive management

Rehme

Powell

Allen

2011

Journal of Environmental Management

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a b s t r a c tEcology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study's inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.

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“…McFadden et al (2011) distinguish between the experimental resilience school and the decision theoretic school. The experimental resilience school (ER school) makes a strong normative assumption that building resilience is a key goal of adaptive management of SES (Gunderson et al, 1995).…”

Section: Resultsmentioning

confidence: 99%