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凑合着用吧:数据稀疏是否会妨碍制定欧洲水鸟的明智收获策略?

Making do with less: must sparse data preclude informed harvest strategies for European waterbirds?

机构信息

U.S. Geological Survey, Wetland and Aquatic Research Center, 7920 NW 71 Street, Gainesville, Florida, 32653, USA.

Finnish Wildlife Agency, Sompiontie 1, 00730, Helsinki, Finland.

出版信息

Ecol Appl. 2018 Mar;28(2):427-441. doi: 10.1002/eap.1659. Epub 2018 Jan 29.

Abstract

The demography of many European waterbirds is not well understood because most countries have conducted little monitoring and assessment, and coordination among countries on waterbird management has little precedent. Yet intergovernmental treaties now mandate the use of sustainable, adaptive harvest strategies, whose development is challenged by a paucity of demographic information. In this study, we explore how a combination of allometric relationships, fragmentary monitoring and research information, and expert judgment can be used to estimate the parameters of a theta-logistic population model, which in turn can be used in a Markov decision process to derive optimal harvesting strategies. We show how to account for considerable parametric uncertainty, as well as for different management objectives. We illustrate our methodology with a poorly understood population of Taiga Bean Geese (Anser fabalis fabalis), which is a popular game bird in Fennoscandia. Our results for Taiga Bean Geese suggest that they may have demographic rates similar to other, well-studied species of geese, and our model-based predictions of population size are consistent with the limited monitoring information available. Importantly, we found that by using a Markov decision process, a simple scalar population model may be sufficient to guide harvest management of this species, even if its demography is age structured. Finally, we demonstrated how two different management objectives can lead to very different optimal harvesting strategies, and how conflicting objectives may be traded off with each other. This approach will have broad application for European waterbirds by providing preliminary estimates of key demographic parameters, by providing insights into the monitoring and research activities needed to corroborate those estimates, and by producing harvest management strategies that are optimal with respect to the managers' objectives, options, and available demographic information.

摘要

许多欧洲水鸟的种群动态情况并不为人所了解,因为大多数国家几乎没有进行监测和评估,而且各国之间在水鸟管理方面也几乎没有协调。然而,政府间条约现在要求采用可持续的、适应性的收获策略,而这些策略的制定受到缺乏人口统计信息的挑战。在本研究中,我们探讨了如何结合多种方法,包括比例关系、零碎的监测和研究信息以及专家判断,来估计 theta-logistic 种群模型的参数,然后可以在马尔可夫决策过程中使用这些参数来推导出最佳的收获策略。我们展示了如何考虑相当大的参数不确定性,以及不同的管理目标。我们使用一个了解甚少的泰加 Bean 鹅(Anser fabalis fabalis)种群来举例说明我们的方法,泰加 Bean 鹅在芬诺斯堪的亚是一种受欢迎的猎鸟。我们对泰加 Bean 鹅的研究结果表明,它们的种群增长率可能与其他研究充分的鹅类相似,而且我们基于模型的预测结果与现有的有限监测信息相一致。重要的是,我们发现,通过使用马尔可夫决策过程,即使其种群具有年龄结构,简单的标量种群模型也可能足以指导该物种的收获管理。最后,我们展示了两种不同的管理目标如何导致非常不同的最佳收获策略,以及如何相互权衡相互冲突的目标。这种方法将广泛适用于欧洲水鸟,为关键人口统计参数提供初步估计,为证实这些估计所需的监测和研究活动提供深入了解,并为管理者的目标、选项和可用人口统计信息制定最佳的收获管理策略。

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