Author:
Vogt Kristina,Roth Tobias,Signer Sven,Willisch Christian Simon,Amrhein Valentin
Abstract
AbstractAn 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.
Publisher
Springer Science and Business Media LLC
Reference42 articles.
1. Amrhein V, Greenland S (2022) Discuss practical importance of results based on interval estimates and p-value functions, not only on point estimates and null p-values. J Inform Technol 37:316–320
2. BAFU (2016) Konzept Luchs Schweiz – Vollzugshilfe des BAFU zum Luchsmanagement in der Schweiz. Bundesamt für Umwelt, Abteilung Arten, Ökosysteme, Landschaften. Bern, Switzerland
3. Baumann M, Struch M, Jäggi C, Schnidrig-Petrig R (2000) Habitatspezifisches Überleben von Gemsgeissen: Leben Waldgemsen bei sympathrischem Vorkommen des Luchses riskanter als Alpingemsen? In: Bauman M, Struch M (2000) Waldgemsen – Neue Erscheinung der Kulturlandschaft oder alte Variante der Naturlandschaft? WildARK. Bern, Switzerland
4. Blecha KA, Alldredge MW (2015) Improvements on GPS location cluster analysis for the prediction of large Carnivore feeding activities: ground-truth detection probability and inclusion of activity sensor measures. PLoS ONE 10:1–19
5. Breitenmoser U, Ryser A, Molinari-Jobin A, Zimmermann F, Haller H, Molinari P, Breitenmoser-Würsten C (2010) The changing impact of prédation as a source of conflict between hunters and reintroduced lynx in Switzerland. In: MacDonald DW, Loveridge AJ (eds) Biology and Conservation of Wild Felids. Oxford University Press, pp 493–505