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.

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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.

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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.

Camera traps (CT) provide an easy and non-invasive way to study wildlife. It is also possible to estimate densities if accurate protocols are followed in a standardised way and additional parameters are estimated. However, the processing and storage of the thousands of images that a typical CT study generates has become a major challenge for CT users. Also, project management can get complex, especially if many CT and multiple people are involved. To facilitate collaborative science among professionals and semi-professionals, the ENETWILD consortium is developing the existing Agouti platform for the management of camera-trapping projects, and the processing and storage of images. Moreover, the consortium is extending Agouti with tools for doing the measurements needed for acquiring the additional estimates and is building an R package to estimate actual density. These developments will significantly further the completion of the Agouti ecosystem. A network of CT-based abundance estimations is consolidated by providing analytical tools and by promoting collaborative science. Specifically, we have worked to (1) harmonize dataset generation by means of Agouti and (2) develop an interface for running CT abundance models (REM, REST, distance sampling). After completion of this work, users should be able to easily export their camera trap records into a format (camtrap-dp) that can subsequently be used to easily run models and determine density using an interface in R, following the methods recommended by ENETWILD. The progress that has been made to date in relation to data generation and analysis is detailed; interactive maps and institutional portals; data recording for abundance estimation; distance and speed estimation, making distances part of camtrap-dp; and finally, R package for distance, speed, and density calculation.

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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 5^th 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.

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