Abstract
As large carnivores recover throughout Europe, there is a need to study their distribution to determine their conservation status and assess the potential for conflicts with human activities. However, efficient monitoring of many large carnivore species is challenging due to their rarity, elusive behavior and large home range size. In Europe, most current monitoring protocols rely on multiple detection methods, which 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. When not accounted for, both can bias estimates from species distribution models (SDMs). False negative detections can be accounted for in SDMs that deal with imperfect detection. In contrast, false positive detections, due to species misidentification, have only rarely been accounted for in SDMs. Generally, researchers use ad hoc methods to avoid false positives through data filtering to discard ambiguous observations prior to analysis. These practices may discard valuable ecological information on the distribution of a species. Here, we investigated the costs and benefits of including data types that might include false positives rather than discard them for SDMs of large carnivores. We showcase a dynamic occupancy model that simultaneously accounts for false negatives and positives to jointly analyze data that include both unambiguous detections and ambiguous detections. Using simulations, we show that the addition of ambiguous detections increases the precision of parameter estimates. The analysis of data on the Eurasian lynx (Lynx lynx) suggested that 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, our work shows that ambiguous data should be considered when studying the distribution of large carnivores, through the use of dynamic occupancy models accounting for misidentification.