Tulloch Ayesha I T, Nicol Sam, Bunnefeld Nils
School of Earth and Environmental Sciences, University of Queensland, Brisbane, QLD 4072, Australia.
ARC Centre of Excellence for Environmental Decisions, Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2602, Australia.
Biol Conserv. 2017 Oct;214:147-155. doi: 10.1016/j.biocon.2017.08.013.
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 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.
在世界许多地方，保护工作的成功或全球人为变化导致本地物种数量增加，进而与人类资源利用产生竞争。在苏格兰的奥克尼群岛，灰雁数量在24年间增长了60倍，导致了农业损失以及为控制数量而进行的捕杀，但未能阻止其数量增长。为了解决数量增长原因的不确定性，我们将灰雁数量变化可能驱动因素的实证模型与专家提出的替代管理行动的效益相结合，以确定是立即采取行动还是先进行研究，以减少灰雁造成的损失。我们建立了线性混合效应模型，将农场年度灰雁密度与土地利用和环境协变量联系起来，并估计AICc模型权重，以表明对六种变化假设的相对支持程度。我们向专家询问了在每种数量变化假设下，六种行动之一实现阻止灰雁数量增长目标的预期可能性。在信息价值分析（VoI）中，将模型权重和行动的预期效果相结合，以量化通过适应性管理和监测解决每种假设中不确定性的效用。在现有不确定性下，预期价值最高的行动是增加低质量栖息地的面积，而假设各假设权重相等则会使最佳行动变为捕杀。VoI分析表明，由于对单一变化假设的高度支持，了解灰雁数量变化任何单个假设中不确定性的价值很低。我们的研究展示了一个两步框架，即了解过剩物种变化的最可能驱动因素，并利用这些知识权衡替代管理行动的效用。我们的方法有助于在存在变化的竞争假设和管理选择竞争时，为解决不确定性而实施的最佳策略提供参考。
