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Avian influenza (AI) is an infectious viral disease of birds, including domestic poultry, which has been causing outbreaks worldwide, leading to several millions of dead wild birds and culled poultry. AI is mainly found in birds, but recently, there was an increase in reported infections in mammals, ranging from no symptoms to mass mortality events and some human cases. Epidemiologically of great concern, evidence of mammalian adaptations have been found, but the transmission routes and pathogenesis in mammals are still to be defined. Hence, it is paramount to address all facets of AI viruses epidemiology, including investigating taxa not customarily thought to be involved in the transmission and/or trafficking of AI, such as wild mammals. The scope of this report was to assess the role of mammals in AI epidemiology, virology and pathology, i.e. AI maintenance, reservoir role, immunity, role of mammals in a potential pandemic. To do so, we performed an all-encompassing review of the literature on the topic with a two-fold approach: a systematic review of the published AI cases in wild mammals and a narrative approach to provide an expert opinion on the role of mammals in AI spread. The final number of peer-reviewed papers included in the systematic literature review was 76, resulting in 120 unique infection records with AI in wild mammal species. The most represented taxa were included in the order Carnivora. The risk of infection was identified mainly as predation (or feeding) upon infected birds or contact with avian species. Evidence of mammal-to-mammal transmission in the wild is only circumstantial and yet to be confirmed. Cases of AI from the systematic review of experimental findings were discussed concerning epidemiology, pathology and virology. Knowledge gaps and potential pandemic drivers were identified. In summary, although a greater number of infections in wild mammals have been reported, there is no hard evidence for sustained mammal-to-mammal transmis

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.

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 coord

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.

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

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.

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.

The ubiquitous wild boar is causing diverse and growing conflicts of socio-ecological and economic relevance worldwide. For that reason, knowledge of its spatial ecology is crucial to designing effective management programmes. But this knowledge is scarce in Mediterranean areas with mixed land uses. We describe the spatial ecology and habitat selection of 41 adult wild boar monitored using GPS collars and analyse the effects of sex and the period (food shortage period, hunting season and food abundance period) under different land uses (protected areas, mixed farms and fenced hunting estates). The spatial ecology of wild boar was characterised by marked temporality, mediated by sex and the land uses in the area. The activity (ACT), daily range (DR) and home range (HR) were higher for males than females, and in mixed farms versus fenced hunting estates, while the lowest values were obtained in protected areas. These effects were more marked for ACT and DR (movement) than HR. The selection of scrublands and avoidance of woodlands was observed where drive hunt events occur (mixed farms and fenced estates), but not in the protected areas. The differences in the requirements, reproductive behaviour and, interestingly, response to disturbance according to sex may explain this dissimilar behaviour. Disturbance originated higher movement rates and the selection of sheltering land cover as a refuge during the hunting season. This information is useful for designing species monitoring and management programmes; including both preventive and reactive actions in response to events such as outbreaks of African swine fever and agricultural damage produced by wild boar in Europe.

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.