Estimating kill intervals for a specific prey species using location clusters from GPS-collared Eurasian lynx (Lynx lynx)

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Kristina Vogt, Tobias Roth, Sven Signer, Christian Simon Willisch, Valentin Amrhein
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引用次数: 0

Abstract

An increasing number of GPS telemetry studies have helped to gain important insights into predator-prey relationships in recent years. However, considerable time and effort is needed to evaluate whether GPS location clusters (GLCs) reflect predation events. To reduce field effort, predictive models are being developed to calculate predator kill intervals, but few studies have attempted to do this for a specific species of prey. Between 2013 and 2018, we studied predation by 13 GPS-collared Eurasian lynx (Lynx lynx) on Alpine chamois (Rupicapra rupicapra) in the northwestern Swiss Alps. Our objectives were to predict the total number of killed chamois, including potential kills in unchecked GLCs, and to evaluate if model predictions were sufficiently accurate. We built a set of generalized linear models (GLM) predicting the occurrence of GLCs containing lynx-killed chamois (1) versus GLCs containing other prey types or no prey (0) and compared their predictive performance by means of k-fold cross-validation. We found that model performance was very similar for all candidate models, with the full model yielding the best cross-validation result (accuracy = 0.83, sensitivity = 0.43, specificity = 0.94). Female lynx killed on average one chamois every 11.9 days (10.6–13.0 days, 95% CI); male lynx killed one chamois every 7.2 days (6.7–7.6 days, 95% CI). Our model showed high specificity for detecting non-chamois GLCs, but sensitivity for detection of GLCs with actual chamois kills was low. We conclude that the sensitivity of the models should be further improved, but the results can be sufficient for practical application. Predictive modelling approaches do not replace extensive fieldwork but require large sets of field data, high individual variability and thorough knowledge of a predator’s ecology and prey community. Our approach may provide useful results for binary classifications in rather simple predator-prey systems, but extrapolations from one study system to another might be difficult.

Abstract Image

利用 GPS 标示的欧亚猞猁(Lynx lynx)位置群估计特定猎物的捕杀间隔时间
近年来,越来越多的 GPS 遥测研究有助于深入了解捕食者与被捕食者之间的关系。然而,要评估 GPS 定位集群(GLC)是否反映了捕食事件,还需要大量的时间和精力。为了减少野外工作,人们正在开发预测模型来计算捕食者的捕杀间隔,但很少有研究尝试针对特定种类的猎物这样做。2013 年至 2018 年间,我们研究了瑞士阿尔卑斯山西北部 13 只佩戴 GPS 颈圈的欧亚猞猁(Lynx lynx)对高山麂(Rupicapra rupicapra)的捕食情况。我们的目标是预测被猎杀的羚羊总数,包括在未受控制的全球濒危物种保护区内可能发生的猎杀,并评估模型预测是否足够准确。我们建立了一组广义线性模型(GLM),预测含有被猞猁杀死的麂子(1)与含有其他猎物类型或无猎物(0)的 GLC 的发生率,并通过 k 倍交叉验证比较了它们的预测性能。我们发现,所有候选模型的性能都非常相似,其中完整模型的交叉验证结果最好(准确率 = 0.83,灵敏度 = 0.43,特异性 = 0.94)。雌性猞猁平均每 11.9 天杀死一只羚羊(10.6-13.0 天,95% CI);雄性猞猁平均每 7.2 天杀死一只羚羊(6.7-7.6 天,95% CI)。我们的模型在检测非羚羊的 GLC 方面显示出较高的特异性,但在检测实际捕杀羚羊的 GLC 方面灵敏度较低。我们的结论是,模型的灵敏度有待进一步提高,但其结果足以满足实际应用的需要。预测建模方法不能取代大量的野外工作,但需要大量的野外数据、较高的个体变异性以及对捕食者生态学和猎物群落的透彻了解。我们的方法可以为相当简单的捕食者-猎物系统中的二元分类提供有用的结果,但从一个研究系统推断到另一个研究系统可能比较困难。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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