Adaptive management of natural systems using fuzzy logic |
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| Authors: | Tony Prato |
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| Affiliation: | 1. Energy, Environment and Water Research Centre (EEWRC), The Cyprus Institute, Nicosia, 2121, Cyprus;2. Computation-based Science and Technology Research Centre (CaSToRC), The Cyprus Institute, Nicosia, 2121, Cyprus;3. Max Planck Institute for Chemistry, Mainz, 55128, Germany;1. Department of Hydraulic Engineering and Environment, Universitat Politècnica de València, València, Spain;2. Research and Technology Food and Agriculture (IRTA), Caldes de Montbui, Spain;3. Center of Biological Sciences and Nature, Federal University of Acre, Rio Branco, Acre, Brazil;4. Department of Hydraulic Engineering and Environment, Research Group in Forest Science and Technology (Re-ForeST), Universitat Politècnica de València, València, Spain;1. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084 PR China;2. Institute for Nuclear and Energy Technologies, Karlsruhe Institute of Technology, Karlsruhe, D-76021, Germany;1. School of Environmental Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 500-712, Republic of Korea;2. Department of Chemistry, Faculty of Science, Royal University of Phnom Penh, Russian Blvd, Phnom Penh, Cambodia;3. Croucher Institute for Environmental Sciences and Department of Biology, Hong Kong Baptist University, Hong Kong SAR, PR China;4. United Nations University-International Institute For Global Health and Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, UKM Medical Centre, 56000 Kuala Lumpur, Malaysia;5. Department of Environmental Engineering, College of Engineering, Dong-A University, Saha-gu, Busan 604-714, Republic of Korea;6. Resource Development International-Cambodia, Kean Svay, Kandal, P.O. Box 494, Phnom Penh, Cambodia |
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| Abstract: | Hypotheses about how management practices influence ecosystem services can be tested using a crisp, probability-based, or fuzzy decision rule. The correct decision rule depends on whether: (1) the observed state of an ecosystem service (x) is non-stochastic or stochastic; (2) the true state of the ecosystem service (y) is non-stochastic or stochastic; and (3) the relationship between x and y is deterministic, stochastic, or uncertain. Crisp and probability-based decision rules are not appropriate when the relationship between y and x is uncertain in the sense that the decision maker is unable or unwilling to specify conditional probabilities of y given x. Under these conditions, a fuzzy decision rule is appropriate. A hypothetical case study is used to illustrate how a fuzzy decision rule is used to test hypotheses about whether selective cutting of timber provides greater or less forest biodiversity than clearcutting of timber. The case study describes how to incorporate the decision rule in an active adaptive management framework to sequentially test the extent to which changes over time in other factors influencing ecosystem services, such as greater spread of invasive species due to global warming, alter the efficacy of timber management practices. The fuzzy adaptive management decision rule can be generalized to account for the effects of management practices on multiple ecosystem services. |
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