Guidance on Estimation of Abundance and Density of Wild Carnivore Population:Methods, Challenges, Possibilities

This guidance reviews the methods for estimating relative abundance and density in nine large European wild carnivore species, somerepresenting relevant health concerns andprovides insights on how to obtain reliable estimations by using those methods. On a local scale, the appropriate method should take into accountthe characteristics of the study area, the estimated survey efforts, the expected results (i.e. a measure of true density or just an index of abundance to monitor the trend in space and time) the level of accuracy and precision, and a proper design so to obtain a correct interpretation of the data. Among all methods, the camera trapping (CT) methods, especially those recently developed, are the most promising for the collection of robust data and can be conducted in a wide range of species, habitats, seasons and densities with minimal adjustments. Some recently developed CT methods do not require individual recognition of the animals and are a good compromise of cost, effort and accuracy. Linear transects,particularly Kilometric Abundance Index (KAI) is applicable for monitoring large regions.A large challenge is compiling and validating abundance data at different spatial scales. Based on ENETWILD initiative, we recommend developing a permanent network and a data platform to collect and share local density estimates, so as abundance in the EU, which would enable to validate predictions for larger areas by modelling. It would allow to identify gaps in the data on wild carnivores (including the species not assessed in the present report) and to focus on these areas for improving predictions. This platform must facilitate the reporting by wildlife policy makers and relevant stakeholders, but also citizen science initiatives.Also, there is need to improve the reliability of local density estimations by developing practical research on methods able to derive densities in untested species and situations, making the application of methods easier for local teams.