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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. [ABSTRACT FROM AUTHOR]

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]

Expanding wild boar populations associate to conflicts with human activities, being a threat to livestock and public health. Particularly, the emergence of African Swine Fever (ASF) in Europe is of major importance. To better understanding the dynamics at the interface between wild boar and domestic pigs in Europe, which is essential to prevent the risk for ASF spread, this report describes (i) the use of extensive pig farm resources by wildlife (wild boar and other mammals) and domestic pigs, and (ii) the factors involved. We studied two regions of Central‐Eastern Europe with different farm management of pigs in Serbia, in low biosecurity farms in forest/bushland habitats, and Hungary, characterized by more industrial and professional farming in fenced pastures. Camera traps (CTs) were placed at a priori risk points for interspecies interactions and in random points in 4 representative outdoor pig farms in different seasons during 2022 (2 farms in Serbia and 2 in Hungary). Also, questionnaires were distributed to 37 farms (17 in Serbia and 20 in Hungary, respectively) to describe the main features and risk factors for wildlife‐pigs interaction on outdoor pig farms. CTs revealed that the wild species that more frequently used the study farms resources were golden jackal (Canis aureus) and wild boar in Serbia, and red fox (Vulpes vulpes) and red deer (Cervus elaphus) in Hungary (at the periphery of fenced farms). The use of extensive farm resources by wild boar was frequent and widespread throughout the study area of Serbia (over 33 % of daily presence per farm, 3.70 visits detected per week) whereas it was rarely detected in our sampled Hungarian farms. Wild boar visit frequency (Serbia) peaked during spring (7.5 visits per CT and day, CT*day), mainly associated with water point use (2.1 visits per CT*day). In Hungary, the greater number of direct interactions occurred between pigs and red deer during summer. Even when a higher average number of risk points were identified inside Hungarian farms, they were less permeable due to effective perimeter fencing, which prevented the entrance of wild boar and other big sized terrestrial wildlife. The study exemplifies contrasted outdoor pig farm managements, and associated risks for interaction with wild boar in ASF infected or at‐risk regions. Management, characterized by almost absence of external biosecurity in the specific type of production in Serbia raise health concerns, and indicates the need to develop efforts to improve biosecurity. Several strategies and specific measures adapted to environmental conditions and farm management could reduce the interactions at the wild boar‐pig interface in Serbia (and similar production systems in Eastern Europe). This should be materialized in farm‐specific biosecurity programs and protocols, which requires the evaluation of their effectiveness, costs, and practical value. The type of farming practiced in marginal agricultural/forest lands in Eastern Europe, often connected to backyards production, is a highly priority for biosecurity issues at European level. However, the focus should not be only on improving technical aspects of biosecurity, but also on socio‐economic and educational determinants. [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]

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 framework developed for modelling ungulates distribution generally well fit to carnivores species, (ii) the predicted suitability were realistic for all carnivores, but alien invasive species, limited distributed species and species expanding its range, and (iii) HY model projections displayed good abundance patterns for red fox and badger, showing that the frameworks proposed for wild ungulates were a good approximation for modelling the distribution of carnivores HY. As a future step, we need to explore how to improve the results when the unavailability of hunting activity for some species limits the extrapolation to other regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 modelof 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 fromnational 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 system.The outputs this model of interface between wild boar and domestic pigs will guide future steps in both data collection and modelling approach. [ABSTRACT FROM AUTHOR]

The ENETWILD consortium (www.enetwild.com) has implemented an EFSA‐funded project whose main objective is to collect information and model the geographical distribution and abundance of wild boar throughout Europe. This is of particular concern owing to the spread of African swine fever from Eastern areas. In January 2018, ENETWILD organised discussion workshops for 70 experts in the field of the ecology, management and epidemiology of wild boar. Three workshops addressed the following questions: (1) what kind of data is needed to develop wild boar abundance maps?; (2) how can estimates of boar abundance be harmonised between regions?; and (3) how can the collection of wild boar distribution and abundance data be improved? In order to collect data on the presence/absence and abundance of wild boar obtained from different sources (administrations, hunters, naturalists and researchers), it is necessary to work on the generation, collection and processing of data in a harmonised manner, thus enabling the information to be comparable and used at a European level. The use of information on hunting statistics (number of animals hunted and hunting effort per surface unit) is particularly essential. The strategy is based on, firstly, collecting existing non‐harmonised wild boar data in the short‐term (occurrence and hunting statistics) by collecting the more accessible data. As a second step, ENETWILD distributed a questionnaire on how and where the data concerning hunting statistics are collected throughout the different Countries or regions in Europe. The objective of this questionnaire was to identify those places in which hunting statistics are still disaggregated (at the highest spatial resolution), with the purpose of standardising the means employed to collect hunting data in Europe. The following step consisted of the appropriate collection of data, using a data model and supported by a data‐sharing agreement. [ABSTRACT FROM AUTHOR]

The ENETWILD consortium implementedtheEFSA‐funded project "Wildlife: collecting and sharing data on wildlife populations, transmitting animal diseases agents", whose main objective is to collect wild boardensity, hunting and occurrence dataand model species geographical distribution and abundance throughout Europe.This subject is of particular concern due to the vastspread of African swine fever (ASF). In September 2019,the ENETWILD consortiumorganised aworkshop in Croatia for 27game biologists, animal health professionals, and experts from national huntingand forest authorities from 14 countriesfrom South East Europe.The overall objectives of the workshop were to present milestones and achievements of the ENETWILD project,to reviewthe framework forwild boar data collection and harmonization (hunting, density and occurrence data) of thedifferent countries,as well as to review scientificmethods for determiningwild boar abundance and density, and to train oncamera trapping and the random encounter method (REM).It was agreed thathunting bag data are currently the main source of information, although not always collected within a harmonized framework and rarely accompanied by a record of the hunting effort. Instead, wild boar abundance and densityestimates available in SouthEast Europe are unreliable because most of them are not based on scientific methods. Hence, there is a need to implement a novel method for determining wild boar abundance and densitythat uses hunting bag statistics including measures of hunting effort and efficiency during collective drive hunts, compared against density values calculated using camera trapping and the random encounter method (REM). Several collaborators have declared their willingness to participate in such pilot studies, and all agreed in improving data collection, including by means of citizen science. [ABSTRACT FROM AUTHOR]

The ENETWILD consortium implementedtheEFSA‐funded project "Wildlife: collecting and sharing data on wildlife populations, transmitting animal diseases agents",whose main objective is to collect wild boardensity, hunting and occurrence dataand model species geographical distribution and abundance throughout Europe.This subject is of particular concern due to the continued advance of African swine fever (ASF). In May 2019,the ENETWILD consortiumorganised aworkshop for 30game biologists, animal health professionals, and experts from national huntingand forest authorities from 14 countries form North East Europe.The overall objectives of the workshop were to present milestones and achievements of the ENETWILD project,review different country frameworks forwild boar data collection and harmonization (hunting, density and occurrence data), as well as to review scientificmethods for determiningwild boar abundance and density, and train oncamera trapping and the random encounter method (REM).It was agreed thatwild boar abundance and densityestimates available in NorthEastern Europe are unreliable because most of them are not based on scientific methods. Hence, there is a need to implement a novel method for determining wild boar abundance and densitythat uses hunting bag statistics including measures of hunting effort and efficiency during collective drive hunts, compared against density values calculated using camera trapping and the random encounter method (REM). Several collaboratorsfrom Poland, Finland, Belarus, Russia, Lithuania have declared their willingness to participate in such pilot studies, and all agreed in improving data collection, including by means of citizen science. [ABSTRACT FROM AUTHOR]

Hunting statistics can be suitable to determine wild boar density estimates if a calibration with an accepted rigorous method is performed. Here, densities calculated from drive counts during collective drive hunting activities are compared against density values calculated by camera trapping using the random encounter method. For this purpose, we selected 10 study sites in Spain, from North to South representing a diversity of habitats, management and hunting traditions without artificial feeding, plus one study site in Czech Republic where artificial feeding was practiced. Density values estimated from both drive counts and camera trapping were strongly positively correlated (R2=0.84 and 0.87 for linear and non‐linear models, respectively) and showed a good agreement. Drive counts data might be therefore used as a density estimate to calibrate models for estimating density in large areas and potentially, to compare densities among areas. For these purposes, there is still the need to harmonise hunting data collection across Europe to make them usable at a large scale. Our results need to be confirmed across a wider number of European populations to provide valid geographical wild boar density predictions across Europe. [ABSTRACT FROM AUTHOR]