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Wildlife policy makers and managers face challenges in taking decisions and dealing with the complexity of international context, and often operate without informed decision‐making frameworks. This situation evidences the need of a harmonised Europe‐wide wildlife monitoring framework able to sustain coordinated transboundary policy. With a pragmatic approach, here we intend to promote the foundations of a transnational wildlife monitoring framework in Europe, that is not meant to replace but to complement and improve harmonisation of existing monitoring plans. Here we provide a general framework on how to start up national wildlife monitoring programs to obtain comparable, aggregable results at European level. This guidance mainly deals with monitoring of species that are either abundant and managed for hunting or to prevent their impact of whatever nature, or, with species that are rare and protected, though associated with human‐wildlife conflict. In the long term, rather than focusing on one single type of wildlife characteristic or monitoring component, the simultaneous monitoring of multiple components (ecological including populational, epidemiological or sociological) is an appropriate strategy to assess change and deliver integral evidence of the underlying reasons for observed changes (holistic approach). We finally raise a basic proposal indicating the main requirements to set up national wildlife monitoring programs that could be harmonisable at European level, which is based on progressive steps. The essential population and distribution data to be collected in a first instance are (i) hunting statistics, (ii) density data (relative abundance can be used for certain species and habitats) over an observatory network, and (iii) occurrence (presence/absence) data. There are different ways of integrating monitoring programs into a harmonised system. If data collected in the frame of monitoring programs are shared, ad hoc questions could be answered and coordinated wildlife management could be proactively developed, yielding reliable trends that account for factors that disregard international borders. We advocate for an integrated platform for collecting, managing, and sharing wildlife monitoring data across Europe, ensuring standardisation and consistency in the data collected by users while addressing confidentiality and secure data management. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]