Describing and mapping of the main existing structures and systematic initiatives and academic activities for surveillance in the EU for zoonoses (transboundary, emerging and re-emerging) in domestic animals and wildlife

The present report describes and maps the main existing structures and systematic initiatives and academic activities for surveillance in the EU for transboundary, emerging and re-emerging zoonoses in domestic animals, wildlife, and the environment, developed by the different sectors, namely human, domestic animal, wildlife and environmental, under One Health approach. This is essential to provide scientific and technical advice and improve future schemes of surveillance. A questionnaire was compiled by MSs and the information collected was complemented by literature reviews about (i) the main existing structures and systematic initiatives or activities, and (ii) academic activities for surveillance in the EU for zoonoses in domestic animals and wildlife. We focused on a 50 zoonotic diseases that were pre-selected for the prioritisation exercise by the One Health working group of EFSA. In total, 21 countries returned the questionnaire. The analysis of zoonotic disease surveillance evidenced that high fragmentation of surveillance programmes occurs in Europe and therefore the main challenge to integrate One Health surveillance is to integrate different surveillance programmes and One Health sectors to progress towards multi-host and multi-sector surveillance programmes. When different sectors oversee the coordination of surveillance programmes, the subsequent integration over the different phases of surveillance is enhanced. A structured approach is needed to determine priorities for surveillance and the approach to be used in European surveillance schemes to achieve a higher benefit-cost ratio with existing or reduced resources. The literature review indicated potential relevance of the hunting sector to participate in surveillance programmes and a bias towards research in vector-borne pathogens and vectors by the academia; experience that can be used to build One health surveillance. Recommendations are provided for further implementation of One health surveillance

Improvement of information technology tools to collect, process and analyse data on wildlife population

ENETWILD consortium with the collaboration of the MammalNet project2 has promoted some informatic tools to improve the data collection of wildlife distribution and abundance: iMammalia; MammalWeb and Agouti. Here we update the activities in relation to (i) the new languages implemented; (ii) new functionalities, (iii) and the improvement and testing of the artificial intelligence module to identify species in Agouti. The iMammalia app is now available in 17 languages with at least two more to be added soon. MammalWeb is available in six languages with more to be added soon. Agouti is available in seven languages. iMammalia automates data transfer to the global database GBIF, and MammalWeb will consider a similar approach in the near future. Technical improvements were made to meet the needs of iMammalia as a carcass reporting app for wild boar, which will favour early awareness in case of ASF outbreak. As for density estimation through camera trapping, processing of big number of images by hand is tedious, and to facilitate the annotation process Agouti offers and has continuously improved automatic species recognition using Artificial Intelligence (AI). We summarize several topics for the further development of Agouti

ENETWILD training: "First online course on the use of camera trapping for monitoring wildlife and density estimation in the framework of the European Observatory of Wildlife (5th May 2022)

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 wildlife experts from national hunting and forestry authorities. Detailed explanations, protocols, and examples for applying such protocols were provided.EFSA-Q-2022-00056Peer reviewe

Analysis of wild boar-domestic pig interface in Europe: spatial overlapping and fine resolution approach in several countries

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.Peer reviewe

Update of model for wild boar abundance based on hunting yield and first models based on occurrence for wild ruminants at European scale

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 understand the root cause; whether a result of underlying assumptions regarding the testing data or due to the model performance itself.EFSA-Q-2020-00678Peer reviewe

Data generated by camera trapping in 40 areas in Europe including East and South Europe: report of the field activities (May 2022)

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. This coordinated field trial activity over a range of European countries, involving different experts and professionals, follows the original plan.EFSA-Q-2022-00057Peer reviewe

Wild boar ecology: a review of wild boar ecological and demographic parameters by bioregion all over Europe

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.EFSA-Q-2022-00047Peer reviewe

Update of model for wild ruminantabundance based on occurrence and first models based on hunting yieldat European scale

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 is done at lower resolution scales, and (ii), modelling HY by accounting the fact that certain countries provide most data, to avoid that these areas overinform the model. As for occurrence data model, next steps for data acquisition and occurrence data modelling are: (i) review target group definitions for each species, (ii) revise definitions of “true” absence for model testing for better parity with fitting, and (iii) either replace principal component analysis with variance inflation factor analysis to remove co-correlates and model calibration for variable selection or develop post-model analysis to recover environmental dependencies.EFSA-Q-2020-00679Peer reviewe

Report of the 2nd Annual General Meeting of ENETWILD 5-6th October 2021

External Scientific Report.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 decision-making framework (inter-institutional and inter-sectorial). The development of a European legislation on wildlife management may represent an opportunity for addressing the abovementioned steps, identifying data priorities matching the needs of the various European Directorates, Agencies, and monitoring frameworks.EFSA-Q-2020-00669.Peer reviewe