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1. 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. 2. 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. 3. 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. 4. 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. 5. 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.

In order to monitor wildlife populations in a manner that supports policy makers and natural resource managers, data must be collected using frameworks and methodologies that allow for comparisons between projects and across time. Though hunting statistics may represent a reliable data source for monitoring population trends in game species, a standardised framework for collecting and analysing this data has never been established in Europe, even within countries. Here we describe a case study on the use of hunting statistics in Spain in order to (i) describe the variability in big game statistics collection frameworks across mainland regions of Spain and (ii) propose a minimum common denominator for a standardised approach at the country level. The main differences in methodologies identified are that each region collects different variables, uses different spatial and temporal resolution, and follows different methodologies. We described spatial patterns by grouping regions based on similarities in the hunting data collection system and identified socio-economic factors as a potential driver of differences in methodologies among regions. Hunting effort-related variables and improved temporal resolution (to the event level) must be incorporated in order to achieve country-level standardisation of methodologies. The use of application software to collect information from the field in a standardised way is recommended, which necessitates engaging stakeholders as part of the monitoring process. Applications software should be designed intentionally, and only after clear objectives for the monitoring program have been defined. Making hunting data open access will improve collaboration and information transfer to scientific and professional sectors. Our recommendations can be adapted to other European countries, which would make hunting data more useful for population monitoring and wildlife policy-making at large spatial scales. Initiatives such as the “European Wildli

The goal of this report is modelling the occurrence for carnivores at the European scale and to compare the output of occurrence with observed hunting yield (HY) density models for red fox (Vulpes vulpes) and badger (Meles meles). Random Forest function was used for modelling occurrence of species. Occurrences available from the past 30 years (1990-2020), and HY data (period 2012-2021) from records submitted to ENETWILD were considered for modelling. Like previous models based on HY for ungulates, the response variable was the maximum number of carnivores hunted in that period divided by the area in km2 of the corresponding administrative unit (HY density). Models based on HY were statistically downscaled to make predictions to 10x10 km2. Occurrence data models indicated a good predictive performance for most species, showing that the model framework proposed for ungulates can also be applied for carnivores. Realistic distribution maps of carnivore species were achieved under this framework, except for those ones which are expanding their range, the golden jackal (Canis aureus), or those considered alien species, raccoon (Procyon lotor) and raccoon dog (Nyctereutes procyonoides); or those having a very limited distribution as the Iberian lynx (Lynx pardinus) or the steppe polecat (Mustela eversmanii): in those cases the obtained models were underestimating their suitability in Europe. Suitability has potential to be used as a proxy for abundance of red fox and badger. Validation of suitability on HY suggested the potential to be used as a proxy for abundance of red fox and badger but depending on each species. The calibration plots for HY models showed a good and linear predictive performance for fox and badger as well as an expected pattern of abundance of species, according to the data. However, differences in type of hunting and regulations in game carnivores between countries must be playing an important role in the patterns obtained. We conclude that (i) the fr

The European Observatory of Wildlife (EOW) as part of the ENETWILD project aims progressively developing integrated wildlife monitoring (population abundance and pathogens). The present report shows how to link the wildlife population monitoring (by camera trapping) and wildlife disease surveillance at European scale, by using environmental sampling over 15 study areas of the EOW from 10 Countries (4 study areas in 4 countries will be incorporated next). We specifically focused on multi-host pathogens Mycobacterium tuberculosis Complex (bacteria, MTC), and Hepatitis E virus (HEV). The aims of this trial were, first, to evaluate the harmonized implementation of a simple field sampling protocol for detecting zoonotic pathogens in environmental samples (standing water) through a network of wildlife professionals at European level. Secondly, we got insights for future improved strategies of wildlife integrated monitoring through environmental sampling. This trial prioritized the inclusion of a diverse array of study areas and a simple sampling approach rather than complex protocols and illustrated. We evidenced the importance of supporting such a coordinate network of wildlife professionals to progressively improve strategies, protocols, the general design, sampling, target matrix, selected pathogens, preservation and transport of samples, analytical techniques, and sample and data flow. We discuss specific results on pathogens, remarking the detection of the MTC in certain areas.

It is essential to provide tools to wildlife professionals and researchers in order to facilitate data collection on wildlife density estimation following standardized protocols in the field. This is relevant for efficient harmonized data management systems, from the field to final reporting. Our main objective was to facilitate the collection of information in the field using established density estimation protocols. The specific objectives were (i) to evaluate and use already existing data registration IT tools for collecting and storing the data in the field; (ii) to make these data available in real time (cloud-based solution), and (iii) being flexible enough to incorporate new protocols and species, as methods (such as camera trap-based) and needs continuously evolves. We improved an already existing tool, Spatial Monitoring and Reporting Tool (SMART; https://smartconservationtools.org/). It is an open source software, which allows easily collect, visualize, store, analyze, report and act on a wide range of field data relevant for wildlife monitoring. The integration of SMART tools on EOW was successfully done for (i) distance sampling, (ii) hunting data and (iii) camera trap protocols. ENETWILD, therefore, made now available new IT functionalities to wildlife professionals and researchers to facilitate and harmonize wildlife data collection systems.

Thanks to the Pan-European Common Birds Monitoring Scheme and Breeding Bird Survey, the temporal changes in abundance of the Red-legged Partridge are known in most countries of its area of distribution. However, these are based on abundance indices and no strong inference is possible about potential causes of local trends. Robust estimates of abundance or densities at national scale are still lacking. Information from hunting bags cannot be used owing to the inclusion of released Red-legged Partridges in those numbers. Even in the absence of released individuals in hunting bags, these estimates cannot be used owing to a lack of knowledge of variations in reporting behaviour in Europe and potential age-specific mortality. Numbers at the local scale are obtained using similar (although not equal) methods across countries based on line/point transect methods. Given that some capture-recapture studies have been carried out from time to time in most of the distribution range, their results could help to estimate the demographic parameters required for management. Finally, given that these common methods are applied with, possibly, some avoidable differences, a more integrative strategy of methodological design and spatial sampling may make it possible to extrapolate estimates from regional to national/continental scale, and to provide assessments of abundance as well as spatial and temporal variation in Red-legged Partridge population size. This global approach may increase the current value of monitoring to all stakeholders interested in Red-legged Partridges.

In a previous report, ENETWILD proposed a generic model framework to predict habitat suitability and likely occurrence for wild ruminant species using opportunistic presence data (occurrence records for wild ungulate species from the Global Biodiversity Information Facility). In this report, for the first time, we develop models based on hunting yield data (HY) for the most widely distributed wild ruminant species in Europe: roe deer (Capreolus capreolus) and red deer (Cervus elaphus). We also update models based on occurrence (roe deer, red deer, fallow deer (Dama dama), European moose (Alces alces) and muntjac (Muntiacus reevesi), evaluate the performance of both approaches, and compare outputs. As for HY models, we could not conduct one model per bioregion as there are not enough data for modelling in some bioregions, and therefore, we calibrated a unique model, including eco-geographical variables as predictors. The calibration plots for HY models showed a good predictive performance for red deer in the Eastern bioregion and roe deer at Eastern and Western. The abundance distribution pattern of red deer HY was widely scattered over all Europe, as expected for a widely distributed species which shows high ecological plasticity, and roe deer presented the highest abundance in Atlantic and Eastern Europe, progressively decreasing towards Northern Mediterranean bioregions. Overall, calibration plot did not perform well in the Northern region, which could be due to the low availability of data for both species in this bioregion. As for occurrence data models, performances using our revised approach for most species showed similarly moderate predictive accuracy. To sum, HY model projections showed good patterns where good quality data was provided, while worst predictions are found in neighbouring countries/bioregions. Two approximations to be explored for next models are: (i) modelling HY per bioregion providing more flexibility to the models, even if data projection

The International Symposium on Wild Boar and Other Suids (IWBS 2022), which took place in Montseny Biosphere Reserve (Catalonia, Spain) in September 2022, provided to ENETWILD with the opportunity to meet in-person for the first time after 2.5 years, and meet the international scientific community with expertise on wild suids and other ungulates. Twelve members of ENETWILD consortium representing 6 partners were present. Bringing together international experts, stakeholders and ENETWILD collaborators was a perfect occasion to present the European Observatory of Wildlife (EOW). Two hundred and twenty-five wildlife experts from 25 countries were present at symposium, and at presentation of the EOW. Overall, 3 'Plenary Talks' and 118 presentations (62 oral and 56 posters) were made. The meeting has gone through all the possible topics regarding wild suids, from genetics to monitoring and management. This was the optimal context to introduce the EOW to an ideal target audience, both in terms of interest and in terms of potential new member of the Network. From our presentation, it emerged the importance of comparable data on geographical distribution and abundance of wildlife hosts in Europe, fundamental to develop the best management policies and to perform effective risk assessments for shared emergent diseases. The adoption of a common and effective protocol adopted throughout the continent would ensure such comparability. Moreover, the discussion highlighted the need of extending the network to as many European countries as possible and, when feasible, of having multiple sites within each country. A number of participants manifested their interest to join the EOW during the 2023 campaign. Such a capillary distribution of observation points would provide solid and comparable density estimates as well as effective feedback about the field protocol implemented by the EOW. A number of questions were raised by the audience during the presentation of the EOW.

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 goo

The new-born European Observatory of Wildlife (EOW)2 is a part of the EFSA-funded ENETWILD project, and has the aim of improving the European capacities 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. As a next step, the EOW has engaged and enhanced the existing network of collaborators, and a number of participants are currently preparing field operations to estimate wild mammal density (focused on wild ungulates and other medium to big sized mammals) in certain areas from their respective countries. A field camera trap (CT) based protocol provided by the EOW is going to be applied. An online training course held in May 2022 provided specific training on camera trapping methods and protocols, specifically the random encounter method (REM) and other methods which do not require individual recognition. Here we also present the new field protocol, which is compatible with the subsequent application of artificial intelligence to process and analyze photo trappings using the online app AGOUTI. This strategy aims at promoting a network of professionals/researchers capable of designing, developing field work and analysing data, contributing also to disseminate the experience and train other colleagues in their respective countries. By now, the overall number of countries participating in the EOW is 25. Some participants from 12 countries could already estimate mammal densities during the previous seasons 2019/2020/2021, which will also apply the same methodology in different populations during 2022 in their respective countries. The number of density values finally obtained through this experience by the end of 2022 will exceed 40 different locations in a total of at least 30 countries, since some countries are on the process to confirm their participation. The EOW website is presented.

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.

This report presents the results of field activities in relation to the generation of reliable wild boar density values by camera trapping (CT) in 19 areas in Europe, mainly in East Europe. Random Encounter Model (REM) densities ranged from 0.35±0.24 to 15.25±2.41 (SE) individuals/km2. No statistical differences in density among bioregions were found. The number of contacts was the component of the trapping rate that determined the coefficient of variation (CV) the most. The daily range (DR) significantly varied as a function of management; the higher values were detected in hunting grounds compared to protected areas, indicating that movement parameters are population specific, and confirming the potential role of hunting activities in increasing wild boar movement and contact rates among individual or groups. The results presented in this report illustrate that a harmonized approach to actual wildlife density estimation (namely for terrestrial mammals) is possible at a European scale, sharing the same protocols, collaboratively designing the study, processing, and analysing the data. This report adds reliable wild boar density values that have the potential to be used for wild boar abundance spatial modelling, both directly or to calibrate outputs of model based on abundance (such as hunting bags) or occurrence data. Future REM developments should focus on improving the precision of estimates (probably through increased survey effort). Next steps require an exhaustive and representative design of a monitoring network to estimate reliable trends of wild boar populations as a function of different factors in Europe. In this regard, the newly created European Observatory of Wildlife will be a network of observation points provided by collaborators from all European countries capable to monitor wildlife population at European level.

One of the main objectives of ENETWILD consortium is to collect data on density, hunting statistics and wildlife occurrence in order to model the geographical distribution and abundance of wildlife species across Europe as a tool to support the assessment of risks associated, for example, with disease transmission. Created in the framework of the ENETwild project, the European Wildlife Observatory (EOW2) provides the backbone for an integrated, interdisciplinary, multi-sectoral and multi-institutional approach to wildlife monitoring, initially focusing on terrestrial mammals in Europe. The EOW applies similar camera-trapping-based protocols for population estimation and data collection standards to facilitate harmonization and interoperability. For this purpose, continuous training of the network of wildlife professionals in Europe is a key activity of the EOW. In this context, during the last few years the ENETWILD consortium has organized different online training courses and workshops on the use of camera traps, addressing different approaches from the design and handling of camera traps to the processing of the collected data. Many of the participants in our previous courses are now part of the EOW and require updated information on methodology to process with next steps in the field. The course here reported presented improvements and refinements in the sampling protocols, aimed specially at new collaborators to be incorporated in the network. Therefore, the objectives of this introductory online course held on 5th May 2022 were: (i) to present milestones and achievements of the ENETWILD project and the EOW, and (ii) to review scientific methods for determining wildlife abundance and density, providing specific training on camera trapping methods and protocols, specifically the random encounter method (REM) and other methods which do not require identification of individuals. This course was attended by 46 wildlife biologists, animal health professionals and wi

Reliable estimates of the distribution of species abundance are a key element in wildlife studies, but such information is usually difficult to obtain for large spatial or long temporal scales. Wildlife–vehicle collision (WVC) data is systematically registered in many countries and could be used as a proxy of population abundance if the number of WVC in each territory increase with the population abundance. However, factors such as road density or human population should be controlled to obtain accurate abundance estimations from WVC data. Here, we propose a hierarchical modeling approach using the Royle–Nichols model for detection–non-detection data to obtain population abundance indices from WVC. Relative abundance and individual detectability were modeled for two species, wild boar Sus scrofa and roe deer Capreolus capreolus at 10 × 10 km cells in mainland Spain from WVC data using environmental, anthropological and temporal covariates. For each cell, a detection was annotated if at least one WVC was recorded at each month (used as survey occasion). The predicted abundance indices were compared with raw hunting statistics at region level to assess the performance of the modeling approach. Site specific covariates such as road density or administrative region and the month of the year, affected individual detectability, with higher WVC probability between October and December for wild boar and between April and July for roe deer. Wild boar and roe deer abundance can be explained by both, bioclimatic and land cover covariates. Abundance indices obtained from WVC data were significantly positively correlated with regional raw hunting yields for both species. We presented empirical evidence supporting that accurate wildlife abundance indices at fine spatial resolution can be generated from WVC data when individual detectability is considered in the modeling process.

While reliable estimates of species abundance distribution are required for wildlife management and are greatly needed at broad spatial scales, such information is scarce. In this context, the usefulness of spatial modelling as a tool for predicting game species relative abundance and distribution from hunting yield data was studied. Hunting yield data is affected by several factors related to species management, hunting regulations, and hunting efficacy and some doubts have been raised about the use or reliability of this data for large-scale modelling. Some years ago, Acevedo et al. (2014) calibrated five spatially explicit models (one per bioregion) by using hunting yield data for wild boar Sus scrofa (from hunting seasons 2006 to 2009) for approximately 60% of mainland Spain. After internal validation, the models were extrapolated to produce predictions of species relative abundance for the whole mainland country. Here, we reviewed these previous models to evaluate their predictive performance on new data (from hunting seasons 2014 to 2018) in areas where the models had been calibrated (interpolation areas) and also when projected into new ones (extrapolation areas). Our results showed that the previous models were able to forecast current general patterns of wild boar relative abundance with population growth rates equivalent to those reported by other authors, although differences between bioregions were observed. Performance on interpolation areas was higher than that obtained on extrapolation areas. Accuracy of model predictions decreased when fine resolution assessment at hunting ground level was carried out. Our results suggest that spatial models calibrated on hunting yields could be a good option to predict general wild boar relative abundance distribution patterns, although critical assessment is needed, since models can fail when they are extrapolated to areas for which no information is available and at fine scale resolution. These results represent a

In the previous ENETWILD model, the predicted patterns of wild boar abundance based on hunting yield data reached an acceptable reliability when the model was downscaled to higher spatial resolution. This new approach, based on the modelling of hunting yield densities instead of hunting yield counts and the assessment of spatial autocorrelation, was only applied with simulated data and with data from two regions at hunting ground level, the smallest spatial resolution. In this report, (1) we evaluate whether this approach can correct the overpredictions for high-resolution predicted patterns when raw data are present at a different spatial resolution (i.e. the European region). For this purpose, hunting yield densities were incorporated as response variable (one model per bioregion) and predictions reliability at 10x10km and 2x2km spatial resolution were assessed. Internal validations and comparisons with the previous two-step model carried out at European scale were addressed, as well as an evaluation with external data at the same scale at country level. The model presented certain overprediction (much less than the previous model) of the total hunting bags reported per country, although a good correlation in terms of values and linearity between observed and predicted values was achieved. Secondly (2), a generic model framework to predict habitat suitability and likely occurrence for wildlife species using opportunistic presence data was proposed (occurrence records for wild ungulate species from the past 20 years exclusively from the Global Biodiversity Information Facility extracted on 9/12/2020). Across all wild ungulate species (elk (Alces alces), roe deer (Capreolus capreolus), red deer (Cervus elaphus), dam deer (Dama dama), muntjac (Muntiacus reevesi), wild boar (Sus scrofa)) the model framework performs well. For those species where area under the curve is below 0.7 we note lower accuracy in predicting absences, which requires further investigation to und

The 2nd ENETWILD Annual General Meeting took place on 5-6th October 2021, bringing together experts, stakeholders and ENETWILD collaborators in online workshop discussions. First, workshop discussions contributed to the analysis and proposal of approaches for a harmonized European-wide wildlife monitoring framework able of sustaining coordinated decision-making. Secondly, participants identified the key challenges that managers face in making decisions for wildlife in Europe and data needs for policies. Finally, we illustrated these challenges with the case of wild boar as a model species widely distributed across Europe. Inputs from the participants were collated into a plan of proposed steps and objectives for the mid-term (5-year time frame) to achieve progress on harmonised, coordinated, and integrated wildlife monitoring at the European level, which requires the contribution of experts from the early stages.. Specific proposed actions include the creation of a trans-disciplinary authority at the European level, effective points of reference for data collection and sharing at different administrative levels and countries, a standing committee to coordinate and exchange experience and capacities on data collection between countries, and expert groups for problem solving, with proper EU financial support, establishing regular policy meetings. . To provide useful results, wildlife monitoring must ensure proper design and data analysis for subsequent science-based management and best allocation of management resources. The 'Observatory' approach (a representative network of intensively monitored sites) can provide long-term systematic and representative insights, normally more feasible for comparative studies, providing less biases and support for decision-making. For international decision-making by wildlife managers and politicians based on scientific knowledge and interdisciplinary research, experts should define the foundations of a common European wildlife deci

In order to define the spatial interface between wild boar and domestic pigs in Europe, the ENETWILD consortium (www.enetwild.com) described in a preliminary report the different sources of data for domestic pigs at European scale, and developed a preliminary risk map of possible spatial interaction between both groups. This modelexplored and assessed the use of pig distribution data from Gridded Livestock of the Worlddatabase (GLW), FAO. However, in some specific countries used as cases, the GLW predictions did not reliably represent the pig abundance distribution within countries. The currently available census data of livestock at the European Union level (Eurostat) is limited to the spatial resolution at NUTS2. While Eurostat ensures that data can be potentially comparable,there is still needed to resolve definition issues regarding better spatial resolution (level of aggregation of information) and the pig production systems. In this context, the objectives of this report are (i) assessing the spatial interface between pigs and wild boar over Europe using the best quality data available (Eurostat data and ENETWILD spatial models). We(ii) secondly assessed the interface at higher spatial resolution, distinguishing pig production types in countries where data was available. Based on comparisons at different scales and quality of data, we propose future steps in both data collection and modelling approach.Precisespatial resolution of pig data is not available at European level yet, and the discrimination of extensive vs. intensive farms, backyards vs. commercial; outdoor vs. indoor, is essential to quantify and perform risk analyses separatelyfor each production system and/or considering this relevant source of variation in risk at the interface. The development of a framework to collect harmonised and standardised data at European scale athigher resolution is needed.

Besides being a threat to conservation, the American mink (Neovison vison) also has the potential to play a crucial role in the ongoing pandemic, as it was demonstrated to be a susceptible host for a mutant version of the SARS-CoV-2 and capable of transmitting it back to humans. This virus can potentially be present also in feral specimens. Despite control and eradication plans, the American mink is still quite widespread in the continent and there is lack of information about its distribution, with the last mapping in Europe dated 2006. The aim of the project was to gather information about the presence and distribution of this species, creating a harmonized map of Europe, pointing out the trends over the years and the issues concerning harmonization of population data. In the frame of the ENETWILD project, we managed to collect information about the presence of American mink in all the western European countries and most of the eastern ones. Data were harmonized from different sources and spatial resolutions, grouped by year and transformed in 10x10km grid of presence and absence. Maps were compared to outline changes over time. We took Spain as a model for a more detailed work, where data of captures were related to capture effort: allowing a density index to be created that gave a better picture of the situation. We also reviewed the European current mink farming situation and the existence of control schemes for feral populations. This information will be extremely useful for risk assessment at a European level: it could represent the basis for coordinating the efforts to control a still spreading species with a relevant health interest. Moreover, the issue of a harmonized data collection for wildlife species clearly emerges as key element to implement to obtain better quality and more useful data.

The ENETWILD consortium (www.enetwild.com) aims at defining the spatial interface between wild boar and domestic pigs in Europe, which is essential to evaluate the risk for ASF spread between wild and domestic. This report describes the different sources of data for domestic pigs in Europe and develops a preliminary risk map of possible spatial interaction between both groups. Specific cases from Romania and Spain where reliable data were available were assessed. This model of the interface was based on the data from Gridded Livestock of the World (GLW) database of FAO, which provides predictions on a 1x1 km scale globally and the wild boar abundance distribution model recently elaborated by ENETWILD consortium (2020). The present available census data of livestock at the European Union level (Eurostat) is restricted to a maximum spatial resolution of NUTS2, remarking the need of developing a framework to collect harmonised data with higher resolution. This will ensure that data can be comparable, validated and used. There is need also to resolve definition issues regarding the pig production systems. Our prediction model of the interface between pig and wild boar at European level indicated that the maximum risk is scattered over Central Europe, large parts of Spain, north-east France and Romania. Hungary, so as Serbia and Croatia in the Balkans are at the highest risk in that area. In the specific case of Romania, no statistically significant association between census data of pigs collected from national authorities and predicted values was found when assessing the GLW model, evidencing that GLW predictions do not reliably represents the pig abundance distribution within countries. When assessing the interface model in Spain, certain areas of interaction were lacked, e.g. where extensive farming is relevant. The current discrimination of extensive vs. intensive farms of predictive models (GLW) is not reliable to perform analyses separately for each production sys