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dc.contributor.authorPopov, Valentin
dc.contributor.authorShah, Payal
dc.contributor.authorRunting, Rebecca K.
dc.contributor.authorRhodes, Jonathan R.
dc.date.accessioned2021-10-04T12:30:10Z
dc.date.available2021-10-04T12:30:10Z
dc.date.issued2021-10-01
dc.identifier.citationPopov , V , Shah , P , Runting , R K & Rhodes , J R 2021 , ' Managing risk and uncertainty in systematic conservation planning with insufficient information ' , Methods in Ecology and Evolution , vol. Early View , 13725 . https://doi.org/10.1111/2041-210X.13725en
dc.identifier.issn2041-210X
dc.identifier.otherPURE: 275984343
dc.identifier.otherPURE UUID: 86e82f6b-bf47-4ca7-be78-4ed1a81df0ce
dc.identifier.otherRIS: urn:5A8F955D8BB570B51C12E6D9E20B3B33
dc.identifier.urihttp://hdl.handle.net/10023/24078
dc.descriptionThis research was supported by Japan Society for the Promotion of Science and by the Okinawa Institute of Science and Technology Graduate University. RKR was supported by an Australian Research Council Discovery Early Career Research Award (DE210100492). JRR was supported by an Australian Research Council Future Fellowship (FT200100096) .en
dc.description.abstract1. Recent advances in systematic conservation planning make use of modern portfolio theory - a framework to construct and select optimal allocation of assets - to address the challenges posed by climate change uncertainty. However, these methods are difficult to implement for fine scale conservation planning when the information on future climate scenarios is insufficient. Insufficient information makes the estimators of the key inputs in the optimisation procedure unreliable leading to technical problems for the construction of optimal asset allocation. 2. We identify three statistical methods - Constant Correlation Model, the Ledoit-Wolf approach and the weighted non-negative least-squares approach - that can overcome the lack of sufficient information and enable the use of modern portfolio theory for fine scale conservation planning. 3. We illustrate the use of the three methods for identifying efficient portfolio allocation strategies, i.e. strategies that give minimum amount of risk for a chosen level of return or maximum return for a chosen level of risk, using case studies of wetland conservation planning in North America and coastal conservation planning in Australia. We compare conservation planning strategies with complete information using standard portfolio theory and with insufficient information using the three methods to highlight their advantages and disadvantages. We find the Ledoit-Wolf and weighted non-negative least-squares approaches perform well and can identify risk-return out-comes that are close to those identified with complete information. 4. The methods presented in this study broaden the range of cases where the application of modern portfolio theory is possible in conservation planning to enhance its uptake and lead to more efficient allocation of conservation resources.
dc.format.extent13
dc.language.isoeng
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.rightsCopyright © 2021 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.en
dc.subjectClimate risken
dc.subjectDiversificationen
dc.subjectInsufficient informationen
dc.subjectNatural resource managementen
dc.subjectPortfolio theoryen
dc.subjectSystematic conservation planningen
dc.subjectUncertaintyen
dc.subjectGE Environmental Sciencesen
dc.subjectQA Mathematicsen
dc.subjectDASen
dc.subject.lccGEen
dc.subject.lccQAen
dc.titleManaging risk and uncertainty in systematic conservation planning with insufficient informationen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews.Statisticsen
dc.identifier.doihttps://doi.org/10.1111/2041-210X.13725
dc.description.statusPeer revieweden


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