부산대학교 도서관

Byline: Julie Louvrier, Anja Molinari-Jobin, Marc Kery, Thierry Chambert, David Miller, Fridolin Zimmermann, Eric Marboutin, Paolo Molinari, Oliver Mueller, Rok Aerne, Olivier Gimenez Keywords: false positives; large carnivores; lynx; occupancy models; species imperfect detection; carnivoros mayores; deteccion imperfecta de especies; lince; modelos de ocupacion; positivos falsos; ae[sup.3]aes.; a[currency]s.ae[pounds sterling]ea[umlaut]c[c]; cc; a ae[R]ae[umlaut] a; c[c]cs.a[cedilla]aaae[pounds sterling]ae[micro] Abstract As large carnivores recover throughout Europe, their distribution needs to be studied to determine their conservation status and assess the potential for human-carnivore conflicts. However, efficient monitoring of many large carnivore species is challenging due to their rarity, elusive behavior, and large home ranges. Their monitoring can include opportunistic sightings from citizens in addition to designed surveys. Two types of detection errors may occur in such monitoring schemes: false negatives and false positives. False-negative detections can be accounted for in species distribution models (SDMs) that deal with imperfect detection. False-positive detections, due to species misidentification, have rarely been accounted for in SDMs. Generally, researchers use ad hoc data-filtering methods to discard ambiguous observations prior to analysis. These practices may discard valuable ecological information on the distribution of a species. We investigated the costs and benefits of including data types that may include false positives rather than discarding them for SDMs of large carnivores. We used a dynamic occupancy model that simultaneously accounts for false negatives and positives to jointly analyze data that included both unambiguous detections and ambiguous detections. We used simulations to compare the performances of our model with a model fitted on unambiguous data only. We tested the 2 models in 4 scenarios in which parameters that control false-positive detections and true detections varied. We applied our model to data from the monitoring of the Eurasian lynx (Lynx lynx) in the European Alps. The addition of ambiguous detections increased the precision of parameter estimates. For the Eurasian lynx, incorporating ambiguous detections produced more precise estimates of the ecological parameters and revealed additional occupied sites in areas where the species is likely expanding. Overall, we found that ambiguous data should be considered when studying the distribution of large carnivores through the use of dynamic occupancy models that account for misidentification. Article Note: Article impact statement: Use of ambiguous detections can improve understanding of large-carnivore distribution dynamics. CAPTION(S): The scripts used for running the simulations (Appendix S1) and to fit our model to the lynx data (Appendix S2), the resulting biases and MSE from 4 scenarios of the simulation study (Appendix S3), and the estimates of ecological parameters in the MU and MUA models (Appendix 4) are available online. The authors are solely responsible for the content and functionality of these materials. Queries (other than absence of the material) should be directed to the corresponding author. Supporting information Appendix S3 Appendix S4