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Quantifying the expected value of uncertain management choices for over-abundant Greylag Geese

Tulloch, Ayesha; Nicol, Sam; Bunnefeld, Nils

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In many parts of the world, conservation successes or global anthropogenic changes have led to increasing native species populations that then compete with human resource use. In the Orkney Islands, Scotland, a 60-fold increase in Greylag Goose Anser anser numbers over 24 years has led to agricultural damages and culling attempts that have failed to prevent population increase. To address uncertainty about why populations have increased, we combined empirical modelling of possible drivers of...[Show more]

dc.contributor.authorTulloch, Ayesha
dc.contributor.authorNicol, Sam
dc.contributor.authorBunnefeld, Nils
dc.date.accessioned2021-09-06T04:05:19Z
dc.identifier.issn0006-3207
dc.identifier.urihttp://hdl.handle.net/1885/247375
dc.description.abstractIn many parts of the world, conservation successes or global anthropogenic changes have led to increasing native species populations that then compete with human resource use. In the Orkney Islands, Scotland, a 60-fold increase in Greylag Goose Anser anser numbers over 24 years has led to agricultural damages and culling attempts that have failed to prevent population increase. To address uncertainty about why populations have increased, we combined empirical modelling of possible drivers of Greylag Goose population change with expert-elicited benefits of alternative management actions to identify whether to learn versus act immediately to reduce damages by geese. We built linear mixed-effects models relating annual goose densities on farms to landuse and environmental covariates and estimated AICc model weights to indicate relative support for six hypotheses of change. We elicited from experts the expected likelihood that one of six actions would achieve an objective of halting goose population growth, given each hypothesis for population change. Model weights and expected effects of actions were combined in Value of Information analysis (VoI) to quantify the utility of resolving uncertainty in each hypothesis through adaptive management and monitoring. The action with the highest expected value under existing uncertainty was to increase the extent of low quality habitats, whereas assuming equal hypothesis weights changed the best action to culling. VoI analysis showed that the value of learning to resolve uncertainty in any individual hypothesis for goose population change was low, due to high support for a single hypothesis of change. Our study demonstrates a two-step framework that learns about the most likely drivers of change for an over-abundant species, and uses this knowledge to weight the utility of alternative management actions. Our approach helps inform which strategies might best be implemented to resolve uncertainty when there are competing hypotheses for change and competing management choices.
dc.description.sponsorshipAT was supported by the Australian Research Council Centre of Excellence in Environmental Decisions (CEED) Early Career Researcher Travel Grant. N.B. has received funding from the European Research Council under the European Union's H2020/ERC grant agreement no 679651 (ConFooBio)
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherElsevier
dc.sourceBiological Conservation
dc.subjectHuman-wildlife conflict
dc.subjectValue of information
dc.subjectAdaptive management
dc.subjectUncertainty
dc.subjectOver-abundant native species
dc.subjectExpected utility
dc.subjectExpected value of partial information
dc.subjectGreylag Geese Anser anser
dc.titleQuantifying the expected value of uncertain management choices for over-abundant Greylag Geese
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume214
dc.date.issued2017
local.identifier.absfor050202 - Conservation and Biodiversity
local.identifier.ariespublicationa383154xPUB8347
local.publisher.urlhttp://www.elsevier.com/
local.type.statusPublished Version
local.contributor.affiliationTulloch, Ayesha, College of Science, ANU
local.contributor.affiliationNicol, Sam, CSIRO
local.contributor.affiliationBunnefeld, Nils, University of Stirling
local.description.embargo2099-12-31
local.bibliographicCitation.startpage147
local.bibliographicCitation.lastpage155
local.identifier.doi10.1016/j.biocon.2017.08.013
local.identifier.absseo960800 - FLORA, FAUNA AND BIODIVERSITY
dc.date.updated2020-11-23T11:00:01Z
local.identifier.scopusID2-s2.0-85027522413
CollectionsANU Research Publications

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