Kristina Vogt, Tobias Roth, Sven Signer, Christian Simon Willisch, Valentin Amrhein
{"title":"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","authors":"Kristina Vogt, Tobias Roth, Sven Signer, Christian Simon Willisch, Valentin Amrhein","doi":"10.1007/s10344-024-01804-4","DOIUrl":null,"url":null,"abstract":"<p>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 (<i>Lynx lynx</i>) on Alpine chamois (<i>Rupicapra rupicapra</i>) 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.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating kill intervals for a specific prey species using location clusters from GPS-collared Eurasian lynx (Lynx lynx)\",\"authors\":\"Kristina Vogt, Tobias Roth, Sven Signer, Christian Simon Willisch, Valentin Amrhein\",\"doi\":\"10.1007/s10344-024-01804-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 (<i>Lynx lynx</i>) on Alpine chamois (<i>Rupicapra rupicapra</i>) 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.</p>\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1007/s10344-024-01804-4\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1007/s10344-024-01804-4","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Estimating kill intervals for a specific prey species using location clusters from GPS-collared Eurasian lynx (Lynx lynx)
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.
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.