Wild ungulate density data generated by camera trapping in 37 European areas: first output of the European Observatory of Wildlife (EOW). Issue 3 (27th March 2023) - ,; Guerrasio, Tancredi; Pelayo Acevedo, P; Apollonio, Marco; Arnon, Amir; Barroqueiro, Carlos; Belova, Olgirda; Berdión, Oskar; Blanco‐Aguiar, José Antonio; Bijl, Hanna; Bleier, Norbert; Bučko, Josef; et al.
Abstract: The European Observatory of Wildlife (EOW) as part of the ENETWILD project, aims to improve the European capacity for monitoring wildlife populations, implementing international standards for data collection, providing guidance on wildlife density estimation, and finally, to promote collaborative, open data networks to develop wildlife monitoring, initially focusing on terrestrial wild mammals. This report presents density estimates for species that are widely distributed (wild boar ( Sus scrofa ), European roe deer ( Capreolus capreolus ), red deer ( Cervus elaphus )) by following a standardised camera trapping (CT) protocol, in 48 areas from 28 different countries in Europe, during 2022. Density values are provided for 37 areas from 20 countries, while an additional 9 locations from 8 countries are currently completing the data analysis. The EOW involved different stakeholders over most European countries, which resulted for the first time in a number of reliable (known precision) wild ungulate density estimates, from areas representing different European bioregions. These estimates are the result of a collaborative effort from the network to apply practical systematic and rigorous protocols. The results presented from the first pilot campaign of the EOW cannot be used to accurately describe wildlife population gradients and trends at European level but can be used as first baseline data for future trend analyses. Our results show data gaps, but also provide relevant insights into some of the main drivers of demographic evolution of wild ungulate populations in Europe. We will expand and improve the EOW in the future to include more representative sites. The Agouti app, including photogrammetry methods to estimate CT detection zone size and animal speed of movement using a computer vision process proved useful to reduce the workload and to improve objectivity of measurements for REM method. We discuss the results obtained by the 2022 campaign in relation to the specific objectives of the EOW and propose the next steps.