Decision science for effective management of populations subject to stochasticity and imperfect knowledge

Yokomizo, Hiroyuki, Coutts, Shaun R. and Possingham, Hugh P. (2014) Decision science for effective management of populations subject to stochasticity and imperfect knowledge. Population Ecology, 56 (1). pp. 41-53. ISSN 1438-3896

Full content URL: http://doi.org/10.1007/s10144-013-0421-2

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Abstract

Many species are threatened by human activity through processes such as habitat modification, water management, hunting, and introduction of invasive species.These anthropogenic threats must be mitigated as efficiently as possible because both time and money available for mitigation are limited. For example, it is essential to address the type and degree of uncertainties present to derive effective management strategies for managed populations. Decision science provides the tools required to produce effective management strategies that can maximize or minimize the desired objective(s) based on imperfect knowledge, taking into account stochasticity. Of particular importance are questions such as how much of available budgets should be invested in reducing uncertainty and which uncertainties should be reduced. In such instances, decision science can help select efficient environmental management actions that may be subject to stochasticity and imperfect knowledge.Here, we review the use of decision science in environmental management to demonstrate the utility of the decision science framework. Our points are illustrated using examples from the literature. We conclude that collaboration between theoreticians and practitioners is crucial to maximize the benefits of decision science’s rational approach to dealing with uncertainty.

Keywords:Adaptive management, Information-gap decision theory, Monitoring, Stochasticdynamic programming, Uncertainty, Value ofinformation analysis
Subjects:C Biological Sciences > C180 Ecology
C Biological Sciences > C181 Biodiversity
Divisions:College of Science > National Centre for Food Manufacturing > Lincoln Institute for Agri-Food Technology
ID Code:39016
Deposited On:02 Dec 2019 09:46

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