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Model‐aided learning for adaptive management of natural resources: an evolutionary design perspective
Author(s) -
Groot Jeroen C. J.,
Rossing Walter A. H.
Publication year - 2011
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/j.2041-210x.2011.00114.x
Subject(s) - adaptive management , computer science , natural resource management , ecosystem management , ecosystem services , diversification (marketing strategy) , stakeholder , process (computing) , social learning , natural resource , resource management (computing) , environmental resource management , knowledge management , management science , ecology , business , engineering , ecosystem , computer network , environmental science , public relations , marketing , political science , biology , operating system
Summary 1. Researchers using the adaptive management paradigm consider learning about the behaviour of social‐ecological systems as an inherent element of endeavours to improve the provision of ecosystem services. Learning‐by‐experience about social‐ecological systems is a slow process attributable to system complexity. We review recent developments in systems modelling which support learning by creating a salient diversity of management alternatives and by translating science‐based results into stakeholder perspectives. 2. Design‐oriented learning cycles aimed at developing ecosystem services could be improved using systematic model‐based diversification and selection of natural resource management alternatives. 3. Recent advances in spatially explicit computer‐based ecological modelling and in visualization of results can effectively support repeated learning cycles. 4. Prioritization and weighing of conservation objectives and ecosystem services should be postponed until after the exploration of the synergies and trade‐offs among objectives. 5. Investigating whether this evolutionary design approach can increase adoption of management adjustments and help to avoid lock‐in onto unsustainable development trajectories should be part of efforts to understand the way we learn.