Monitoring in the presence of species misidentification: the case of the<scp>E</scp>urasian lynx in the<scp>A</scp>lps

Abstract

Abstract Inferring the distribution and abundance of a species from field records must deal with false‐negative and false‐positive errors. False‐negative errors occur if a species present goes undetected, while false‐positive errors are typically a consequence of species misidentification. False‐positive observations in studies of rare species may cause an overestimation of the distribution or abundance of the species and distort trend indices. We illustrate this issue with the monitoring of the E urasian lynx in the A lps. We developed a three‐level classification of field records according to their reliability as inferred from whether they were validated or not. The first category ( C 1) represents ‘hard fact’ data (e.g. dead lynx); the second category ( C 2) includes confirmed data (e.g. tracks verified by an expert); and the third category ( C 3) are unconfirmed data (e.g. any kind of direct visual observation). For lynx, which is a comparatively well‐known species in the A lps, we use site‐occupancy modelling to estimate its distribution and show that the inferred lynx distribution is highly sensitive to presence sign category: it is larger if based on C 3 records compared with the more reliable C 1 and C 2 records. We believe that the reason for this is a fairly high frequency of false‐positive errors among C 3 records. This suggests that distribution records for many lesser‐known species may be similarly unreliable, because they are mostly or exclusively based on unconfirmed and thus soft data. Nevertheless, such soft data form a considerable part of species assessments as presented, for example in the I nternational U nion for C onservation of N ature R ed L ist. However, C 3 records can often not be discarded because they may be the only information available. When inferring the distribution of rare carnivores, especially for species with an expanding or shrinking range, we recommend a rigorous discrimination between fully reliable and un‐ or only partly reliable data, in order to identify possible methodological problems in the distribution maps related to false‐positive records.