Harvesting large carnivores can be a management tool for meeting politically set goals for their desired abundance. However, harvesting carnivores creates its own set of conflicts in both society and among conservation professionals, where one consequence is a need to demonstrate that management is sustainable, evidence-based, and guided by science. Furthermore, because large carnivores often also have high degrees of legal protection, harvest quotas have to be carefully justified and constantly adjusted to avoid damaging their conservation status. We developed a Bayesian state-space model to support adaptive management of Eurasian lynx harvesting in Scandinavia. The model uses data from the annual monitoring of lynx abundance and results from long-term field research on lynx biology, which has provided detailed estimates of key demographic parameters. We used the model to predict the probability that the forecasted population size will be below or above the management objectives when subjected to different harvest quotas. The model presented here informs decision makers about the policy risks of alternative harvest levels. Earlier versions of the model have been available for wildlife managers in both Sweden and Norway to guide lynx harvest quotas and the model predictions showed good agreement with observations. We combined monitoring data with data on vital rates and were able to estimate unobserved additional mortality rates, which are most probably due to poaching. In both countries, the past quota setting strategy suggests that there has been a de facto threshold strategy with increasing proportion, which means that there is no harvest below a certain population size, but above this threshold there is an increasing proportion of the population harvested as the population size increases. The annual assessment of the monitoring results, the use of forecasting models, and a threshold harvest approach to quota setting will all reduce the risk of lynx population sizes moving outside the desired goals. The approach we illustrate could be adapted to other populations of mammals worldwide.

中文翻译：

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使用贝叶斯预测的大型食肉动物小种群的收获模型。

捕捞大型食肉动物可以成为一种管理工具，用于实现政治上设定的目标，以实现其所需的丰富程度。然而，捕猎食肉动物在社会和保护专业人员之间造成了一系列冲突，其中一个后果是需要证明管理是可持续的、基于证据的并以科学为指导。此外，由于大型食肉动物通常也受到高度的法律保护，因此必须仔细论证捕捞配额并不断调整，以避免损害其保护状况。我们开发了贝叶斯状态空间模型来支持斯堪的纳维亚半岛欧亚山猫捕捞的适应性管理。该模型使用了对山猫丰度的年度监测数据以及对山猫生物学的长期实地研究的结果，该研究提供了关键人口统计参数的详细估计。我们使用该模型来预测在不同收获配额下预测种群规模低于或高于管理目标的概率。这里提出的模型让决策者了解替代收获水平的政策风险。该模型的早期版本已可供瑞典和挪威的野生动物管理者使用，以指导山猫的收获配额，并且模型预测与观察结果吻合良好。我们将监测数据与生命率数据结合起来，并能够估计出未观察到的额外死亡率，这很可能是由于偷猎造成的。在这两个国家，过去的配额设定策略表明，存在着事实上的比例递增的阈值策略，这意味着低于一定人口规模的情况下不会有收获，但超过这个阈值时，收获的人口比例会增加人口规模增加。监测结果的年度评估、预测模型的使用以及配额设定的阈值收获方法都将降低山猫种群规模超出预期目标的风险。我们展示的方法可以适用于世界各地的其他哺乳动物种群。