The goal of this report is i) to model the occurrence and hunting yield (HY) density of wild ungulates not only for widely distributed species in Europe, but also for those ones which have a constrained distribution and ii) to compare the output of occurrence with observed HY. Random Forest function was used for modelling occurrence of species. We used occurrence data available from the past 30 years, and HY data (period 2015‐2020) from records collected by ENETWILD. Like previous models based on HY, the response variable was the maximum number of wild ruminants annually hunted in 2015‐2020 hunting seasons divided by the area (km2) of the corresponding administrative unit (HY density). Models based on HY were statistically downscaled to make predictions to 10x10km squares. Occurrence data models indicated a good predictive performance for most species, showing that the model framework proposed have improved results in comparison to previous models. The transferability of models into new regions was limited by the exposure of species to environmental conditions. As for HY models, the calibration plots showed a good and linear predictive performance for widely distributed species, as well as constrained distributed species. Overall, our results were consistent with the expected abundance distribution of widely distributed species. The removal of zeros on the validation datasets affected the calibration plots of all regions, showing a better predictive performance when zeros were removed for widely distribution species, but the opposite was evidenced for species with limited distributions. We conclude that (i) the importance of co‐correlation variables when variable importance is inferenced from random forest model results, (ii) manipulation presence and absence locations could yield further improvement in occurrence model outputs, and (iii) HY model projections displayed good abundance patterns for most of species, showing that the three frameworks proposed were a good approximation for modelling the distribution of wild ungulates HY, although it should be explored how to improve the results when distribution is patchy.

African swine fever (ASF) is a highly contagious disease affecting all suids and wild boar (Sus scrofa). Since 2007, ASF has spread to more than 30 countries in Europe and Asian regions, and the most recent outbreak has been in mainland Italy (reported on January 2022). When the genotype II of the ASF virus infects a population, a mortality rate close to 90% is usually reported. This drop in wild boar abundance produces a cascade effect in the entire ecosystem. In this context, effective monitoring tools for deriving management parameters are a priority aspect, and the utility of camera trapping could have been overlooked. Here, sampling the infected area in north Italy, we showed the utility of camera traps in the context of ASF infection. Specifically, we used 43 camera traps randomly distributed to (i) estimate movement parameters and population density of wild boar, roe deer (Capreolus capreolus), and wolf (Canis lupus); (ii) quantify wild boar recruitment; and (iii) assess whether the human restriction rules are being met. On the first spring after the outbreak detection, our results for wild boar indicated a density of 0.27 ind·km−2 ± 0.11 (standard error, SE), a daily activity level of 0.49 ± 0.07 (i.e., 11.76 h·day−1), a daily distance travelled of 9.07 ± 1.80 km·day−1, a litter size of 1.72 piglets·group−1, and a 72% of pregnant females. Despite human outdoor activities being restricted in the infected zone, we recorded human presence in 19 camera traps. The wide range of parameters estimated from the camera trap data, together with some intrinsic and practical advantages of this tool, allows us to conclude that camera traps are well positioned to be a reference approach to monitor populations affected by ASF. The population-specific parameters are of prime importance for optimizing ASF control efforts.

The definition of the most relevant parameters that describe the wild boar (WB) population dynamics is essential to guide African swine fever (ASF) control policies. These parameters should be framed considering different contexts, such as geographic, ecological and management contexts, and gaps of data useful for the parameter definition should be identified. This information would allow better harmonized monitoring of WB populations and higher impact of ASF management actions, as well as better parametrizing population dynamics and epidemiological models, which is key to develop more efficient cost‐benefit strategies. This report presents a comprehensive compilation and description of parameters of WB population dynamics, including general drivers, population demography, mortality, reproduction, and spatial behaviour. Beyond the collection of current available data, we provided an open data model to allow academics and wildlife professionals to continuously update new and otherwise hardly accessible data, e.g. those from grey literature which is often not publicly available or only in local languages. This data model, conceived as an open resource and collaborative approach, will be incorporated in the European Observatory of Wildlife (EOW) platform, and include all drivers and population parameters that should be specified in studies on wild boar, and wildlife in general, ecology and epidemiology at the most suitable spatio‐temporal resolution. This harmonized approach should be extended to other taxa in the future as an essential tool to improve European capacities to monitor, to produce risk assessment and to manage wildlife under an international perspective.

A science‐based participatory process guided by EFSA identified 10 priority zoonotic pathogens for future One Health surveillance in Europe: highly pathogenic avian influenza, swine influenza, West Nile disease, tick‐borne‐encephalitis, echinococcosis, Crimean Congo Haemorrhagic Fever, hepatitis E, Lyme disease, Q‐fever, Rift Valley fever. The main aim of this report is to formulate recommendations and technical specifications for sustainable coordinated One health surveillance for early detection of these zoonotic pathogens where wildlife is implicated. For this purpose: (i) first, we reviewed the cornerstones of integrated wildlife monitoring that are applicable to zoonotic disease surveillance in wildlife under OH surveillance in the EU; (ii) we analysed the characteristics of the main wildlife groups and the selected pathogens relevant to surveillance aimed at early detection, and integrated with other health compartments; (iii) we proposed general recommendations for the first steps of sustainable wildlife zoonotic disease surveillance in the EU, and (iv) specific recommendations of surveillance aimed at risk based early detection of pathogens in the main wild species groups. We finally proposed (iv) a framework for integrating animal disease surveillance components (wildlife, domestic, environment) for early detection under OH approach.

The American mink Neogale vison is an invasive alien species in Europe that threatens endemic biodiversity and can transmit zoonotic diseases, including the SARS‐CoV‐2 virus. The last attempt to map the geographic range of this species in Europe, at continental scale, dates back to 2007. We aimed to update the distribution map of the feral American mink and assess its temporal trends. The information we collected was critically analysed with the aim of improving future monitoring protocols and data collection. We gathered and standardised data from 34 databases, covering 32 countries. Through 3 five‐year periods from 2007 to 2021, changes in range size, hunting bags and capture statistics were analysed. We also reviewed the current situation of mink farming in the different European countries and recorded population control schemes. The American mink is now widespread in the Baltic States, France, Germany, Iceland, Ireland, Poland, Scandinavia, Spain and the UK. The species is reported to be absent in some areas (e.g. parts of the UK, Iceland and Norway). Data are deficient for several countries, mainly in south‐eastern Europe. These findings indicate that, during the last 15 years, the species has continued to spread across the continent, increasing its potential extent of occurrence in most countries. Our effort to collect and harmonise data across international borders highlighted information gaps and heterogeneity in data quality. Updated distribution data on the species provided here will aid risk assessment and risk management policies. These actions require a coordinated effort for population monitoring at continental level. Monitoring effort and data collection should be intensified in south‐eastern Europe to improve data on the current distribution of this invasive species.