Ecological Optimization and Adaptive Management. Walters, C. J. & Hilborn, R. Annual Review of Ecology and Systematics, 9(1):157–188, 1978. doi abstract bibtex Over the last two decades a large body of literature on control and optimization of dynamic systems has developed. There have been attempts to apply some of the concepts and techniques to problems in resource ecology, particularlyin relation to harvesting policies for exploited populations. This review seeks to provide a non-mathematical overview with emphasis on the anatomy of optimization formulations, the technical problems of obtaining solutions, and the prospects for good decision making in the face of uncertainty. Uncertainty is a pervasive feature of ecological management problems. Rarely is it possible to predict even the short-term effects of major interventions. Given complete biological understanding,we would still be faced with the unpredictability of various environmental agents. Usually our perceptions are further clouded by statistical problems of measurement and aggregation. The practice in fields such as fisheries management has often been to develop deterministic prediction models based on the best available estimates of dynamic parameters, then to hedge against uncertainty by adopting somewhat more conservative behavior than the models predict to be optimal. While the pretense is scientific management, mistakes and failures are seldom treated as useful adaptive experiments or tests of understanding; we bury our mistakes instead of learning from them. In this paper we explore the consequences of uncertainty by examining various optimization analyses for managed populations, beginning with deterministic optimal control models that presume full knowledge and ending with adaptive control models that presume almost complete ignorance. No real population has been managed for a sustained period by consistently applying any of the analyses we will discuss; indeed actual practice always involves a richer set of objectives, constraints, and hedging activities than any simple model can reflect. Thus each model should be viewed as a simplification that has something to teach us if we can avoid its pitfalls. We do not view any of the models as inherently good or bad, because we will not presume to set meaningful absolute standards of performance; rather we hope that the models can be judged against one another and against existing management practice to point toward ways of improving that practice. This review cannot be considered exhaustive. Due to our own inexperience we did not consider related fields, such as water resources. Likewise, space limitations made it impossible to provide more examples on the topics covered, and interested readers are referred to the original literature.
@article{waltersEcologicalOptimizationAdaptive1978,
title = {Ecological Optimization and Adaptive Management},
author = {Walters, C. J. and Hilborn, R.},
year = {1978},
volume = {9},
pages = {157--188},
issn = {1545-2069},
doi = {10.1146/annurev.es.09.110178.001105},
abstract = {Over the last two decades a large body of literature on control and optimization of dynamic systems has developed. There have been attempts to apply some of the concepts and techniques to problems in resource ecology, particularlyin relation to harvesting policies for exploited populations. This review seeks to provide a non-mathematical overview with emphasis on the anatomy of optimization formulations, the technical problems of obtaining solutions, and the prospects for good decision making in the face of uncertainty. Uncertainty is a pervasive feature of ecological management problems. Rarely is it possible to predict even the short-term effects of major interventions. Given complete biological understanding,we would still be faced with the unpredictability of various environmental agents. Usually our perceptions are further clouded by statistical problems of measurement and aggregation. The practice in fields such as fisheries management has often been to develop deterministic prediction models based on the best available estimates of dynamic parameters, then to hedge against uncertainty by adopting somewhat more conservative behavior than the models predict to be optimal. While the pretense is scientific management, mistakes and failures are seldom treated as useful adaptive experiments or tests of understanding; we bury our mistakes instead of learning from them. In this paper we explore the consequences of uncertainty by examining various optimization analyses for managed populations, beginning with deterministic optimal control models that presume full knowledge and ending with adaptive control models that presume almost complete ignorance. No real population has been managed for a sustained period by consistently applying any of the analyses we will discuss; indeed actual practice always involves a richer set of objectives, constraints, and hedging activities than any simple model can reflect. Thus each model should be viewed as a simplification that has something to teach us if we can avoid its pitfalls. We do not view any of the models as inherently good or bad, because we will not presume to set meaningful absolute standards of performance; rather we hope that the models can be judged against one another and against existing management practice to point toward ways of improving that practice. This review cannot be considered exhaustive. Due to our own inexperience we did not consider related fields, such as water resources. Likewise, space limitations made it impossible to provide more examples on the topics covered, and interested readers are referred to the original literature.},
journal = {Annual Review of Ecology and Systematics},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13378320,~to-add-doi-URL,adaptive-control,control-problem,curse-of-dimensionality,dynamic-programming,ecology,feedback,forest-pests,forest-resources,optimisation},
lccn = {INRMM-MiD:c-13378320},
number = {1}
}
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