Linking modelling, monitoring and management: an integrated approach to controlling overabundant wildlife
Linking modelling, monitoring and management: an integrated approach to controlling overabundant wildlife
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DOI：
10.1111/j.1365-2664.2010.01877.x
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发表时间：
2010-12-01
影响因子：
5.7
通讯作者：
Wintle, Brendan A.
中科院分区：
文献类型：
--
作者：
Chee, Yung En;Wintle, Brendan A.
1.过多的野生动物会造成经济和生态破坏。因此，种群控制通常寻求将物种的丰度维持在期望的控制限度内。有效的控制需要目标、控制前后估计种群规模的方法以及预测种群规模的可靠手段。人口随机性、环境变异性和模型不确定性使这些任务复杂化。监测在控制过程中提供关键反馈，但综合监测和管理的例子很少。我们开发了一个集成的贝叶斯人口建模和监测算法，以协助动态淘汰控制过剩人口。我们描述了组件的控制算法和它们的组合，以产生一个结构化的，顺序的处方实施控制的袋鼠种群。我们展示了它的应用在一个单一的管理年，并评估其性能在一系列的情况下，反映人口动态的不确定性，在多年的地平线。对该算法的仿真测试表明，它为人口控制管理提供了一个连贯、灵活、高效和鲁棒的基础。它的连贯性在于管理目标、模式和操作规则之间的联系是明确的，并具有逻辑上的整合。它的灵活性在于管理目标可以自由变化。这是成本和操作效率，因为：（一）它避免了一个昂贵的，专门的抽样过程，以估计人口规模之前，剔除;（二）相对较少的剔除产生合理的人口规模估计和（三）估计清除过程中能够直接评估是否已实现控制。最后，它是鲁棒的，因为即使当系统状态和动态存在很大的不确定性时，该算法也能在管理期内保持种群处于控制之下。合成与应用。当目标是将物种的丰度保持在控制范围内时，我们为综合监测和淘汰提供了一个通用和灵活的框架。我们的框架明确处理人口随机性，生态复杂性和缺乏知识所产生的不确定性，并提供了基础，最大限度地提高效率和成本效益的控制操作。我们的方法可以应用于任何情况下，控制是通过剔除。
1. Overabundant wildlife can cause economic and ecological damage. Therefore population control typically seeks to maintain species' abundance within desired control limits. Efficient control requires targets, methods for estimating population size before and after control, and reliable means of forecasting population size. Demographic stochasticity, environmental variability and model uncertainty complicate these tasks. Monitoring provides critical feedback in the control process, yet examples of integrated monitoring and management are scarce.2. We developed an integrated Bayesian population modelling and monitoring algorithm to assist with dynamic cull control of an overabundant population. We describe components of the control algorithm and their combination to produce a structured, sequential prescription for implementing control of a kangaroo population. We demonstrate its application within a single management year and evaluate its performance over a multi-year horizon under a range of scenarios reflecting uncertainties about population dynamics.3. Simulation testing of the algorithm demonstrates that it provides a coherent, flexible, efficient and robust basis for managing population control. It is coherent in that connections between management objectives, models and operating rules are explicit and logically integrated. It is flexible in that the management objectives can be freely varied. It is both cost and operationally efficient because: (i) it avoids the need for an expensive, dedicated sampling process to estimate population size prior to culling; (ii) a relatively small number of culls produces reasonable population size estimates and (iii) the estimation by removal process enables direct assessment of whether control has been achieved. Lastly, it is robust because even when there is substantial uncertainty about system state and dynamics, the algorithm performs well at keeping the population under control over the duration of the management horizon.4. Synthesis and applications. We provide a general and flexible framework for integrated monitoring and culling when the objective is to keep a species' abundance within control limits. Our framework explicitly deals with uncertainty arising from demographic stochasticity, ecological complexity and lack of knowledge, and provides the foundation for maximizing efficiency and cost-effectiveness of control operations. Our approach could be applied in any instances where control is effected via culling.