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: 41-53. doi:10.1007/s10144-013-0421-2


Author Yokomizo, Hiroyuki
Coutts, Shaun R.
Possingham, Hugh P.
Title Decision science for effective management of populations subject to stochasticity and imperfect knowledge
Journal name Population Ecology   Check publisher's open access policy
ISSN 1438-3896
1438-390X
Publication date 2014-01-01
Year available 2013
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1007/s10144-013-0421-2
Open Access Status Not Open Access
Volume 56
Issue 1
Start page 41
End page 53
Total pages 13
Place of publication Tokyo, Japan
Publisher Springer
Language eng
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.
Keyword Adaptive management
Information-gap decision theory
Monitoring
Stochastic dynamic programming
Uncertainty
Value of information analysis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 5 December 2013.

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: Official 2014 Collection
School of Biological Sciences Publications
 
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