Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/25892
Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: Quantifying the expected value of uncertain management choices for over-abundant Greylag Geese
Authors: Tulloch, Ayesha I T
Nicol, Sam
Bunnefeld, Nils
Contact Email: nils.bunnefeld@stir.ac.uk
Keywords: Human-wildlife conflict
Value of information
Adaptive management
Uncertainty
Over-abundant native species
Expected utility
Expected value of partial information
Greylag Geese Anser anser
Issue Date: Oct-2017
Citation: Tulloch AIT, Nicol S & Bunnefeld N (2017) Quantifying the expected value of uncertain management choices for over-abundant Greylag Geese, Biological Conservation, 214, pp. 147-155.
Abstract: 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 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 land-use 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.
DOI Link: http://dx.doi.org/10.1016/j.biocon.2017.08.013
Rights: © 2017, The Authors. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

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