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2022

Abstract

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.

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Abstract

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.

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Abstract

Citizen science (CS) has been increasingly used in wildlife disease surveillance since it can facilitate the detection and recovery of carcasses of target species used as sentinels of infection. This report updates on the improvement of an app adapted from iMammalia (MammalNet project) for early reporting carcasses of wild boar with a warning system. This app has a great potential for improving wildlife disease surveillance (carcass reporting), such as for ASF. The app has been expanded in the context of ENETWILD project during the second part of 2021 to several countries in their respective languages: Italy, Greece, Serbia, Montenegro, Kosovo, Bosnia-Herzegovina, North Macedonia, Albania, and the latest to incorporate, Portugal. The ENETWILD project has generated alliance with FAO to promote the use of iMammalia in the Balkans to collect data in gap areas about the distribution of wild boar, to document the presence of carcasses of dead animals. The technical improvements that have been made to meet the needs of iMammalia to report the presence of wild boar carcasses correspond to the possibility of recording different parameters, such as sex, age, whether the dead animal has been hunted or not, or the degree of decomposition observed. The possibility of generating warnings to notify these records in real time to wildlife and/or sanitary services will be implemented in 2022. To date, iMammalia collected 14,393 mammal observations all over Europe, 797 of which corresponds to wild boar. A total of 1,270 observations correspond to animals found dead (5.53%), 30 out of which are wild boar, which, in some cases, were later diagnosed as ASF positive (e.g. in Serbia). We finally summarize the next step of ENETWILD in relation to iMammalia promotion, including an online workshop to provide an overview of disease surveillance systems in Europe, and to propose changes, including the promotion of CS.

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Abstract

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.

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2021

Abstract

ENETWILD consortium has developed methodologies for modelling wild boar abundance distribution based on hunting yield (HY) data. Although the methodologies reached an acceptable reliability, when models were downscaled to higher spatial resolution the predictions of absolute numbers of hunted animals tended to overprediction. Some important issues such as HY‐surface relationship and the spatial autocorrelation of HY data or the accuracy of downscaled predictions were not fully addressed yet due to the complexity of dealing with huge datasets at a European scale. In this report we (i) explored the use of hunted wild boar densities (numbers of hunted wild boar relative to surface) instead of raw counts (numbers of hunted animals) as response variable, and (ii) introduced intrinsic Conditional Auto‐Regressive models (iCAR) taking into account spatial autocorrelation. Using simulations and actual wild boar data, these new actions were aimed to produce high resolution predictions (2×2 km grid) with higher accuracy. We assessed model fitting in two different regions in Europe with high quality resolution HY data: Aragón autonomous region (North East Spain, belonging to South Bioregion as defined by ENETWILD) and the whole country of Slovenia (East Bioregion). We found that the marked overprediction, as observed in previous reports when models were downscaled, was now controlled by using hunted wild boar densities as response variable. Additionally, higher accuracy in model predictions was reached when iCAR approach was used to control for spatial autocorrelation. This high accuracy was maintained even when high resolution predictions were aggregated and compared to actual wild boar HY. These approaches should be considered in future models and represent an important step forward to model the distribution of wild boar abundance and other wildlife at high resolution over Europe.

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Script

Abstract

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 model explored 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 separately for 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 at higher resolution is needed.

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Abstract

The ecological plasticity of wild boar and their growing populations can generate conflicts with human activities and can be a threat to livestock and public health. Particularly, the emergence of African Swine Fever in Europe is of major importance. However, there are gaps in knowledge about wild boar ecology, population monitoring, management and population control that prevent the design and application of the best science-based ASF control policies, and/or adaptive evaluation of the actions taken. The effectiveness of wildlife policies is known to be directly proportional to their acceptance by stakeholders. However, it is unknown how the acceptance of these policies and different management scenarios vary among stakeholder groups, in different socio-economic and cultural contexts. Acceptance by stakeholders in different contexts determines the success of management strategies. Finally, factors that influence wild boar abundance and disease spread are not bound by national borders. Thus, there is need to coordinate national and international decision-making. In this context, this report presents research protocols to address a number of knowledge gaps previously identified by EFSA, and aims to improve the strategy to control ASF in the short-term. Twelve research objectives grouped into six categories address aspects of: (i) wild boar ecology, i.e. studies on basic aspects of wild boar population dynamics and assessment of the factors that determine the presence of wild boar near outdoor pig farms; (ii) wild boar monitoring, i.e. implementation of practical methods to estimate wild boar density and strategies to promote their application; (iii) wild boar management and population control, i.e. effect of feed availability, role and efficacy of recreational hunting and professional culling, efficacy of wild boar trapping and different fencing methods and the use of trained dogs in ASF affected areas; (iv) social acceptance by the stakeholders; (v) assessment and management of risk factors (biosecurity awareness and implementation among backyard pig farmers, evaluation of passive surveillance and carcass removal); and (vi) national and international decision-taking. We propose protocols for each specific research objective, their study design, implementation methodology, required time frames and budget limitations. We comparatively summarize the protocols and discuss them in terms of solving overlaps and interactions among protocols that address different research objectives, which eventually can be combined to optimize the use of resources and budgets and to reduce the required time needed to achieve objectives.

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Abstract

The activities related to the generation of reliable wild boar density values in at least 15 areas in Europe by camera trapping, mainly in East Europe are presented. The ENETWILD consortium has offered training to collaborators in order to generate harmonized wild boar abundance data (following standards) and has enhanced the network of wildlife professionals in Europe. As a next step, ENETWILD has engaged and enhanced this network, and a number of participants are currently developing field operations to estimate wild boar density in certain areas from their respective countries or confirmed that will do during summer 2021. A protocol for field camera trap (CT) provided by ENETWILD is being applied. By now, 12 field studies are ongoing in 10 countries and in most of them data has been already collected. In addition, there are 3 countries (Albania, North Macedonia and Turkey) in which, due to logistic problems during transport of the CTs at customs, the field work will be developed during 2021. In April 2021, ENETWILD is offering a second online training course to participants, so they will be trained specifically in data analysis. This strategy aims promoting a network of professionals/researchers capable of designing, developing field work and analysing data on their own, contributing also to disseminate the experience and train other colleagues in their respective countries. Most participants, who were already estimating wild boar densities during the season 2019/2020, will also apply the same methodology in different areas during 2021 in their respective countries.

Therefore, the number of wild boar density values finally obtained through this experience by the end of 2021 will exceed 15 different locations in a total of 12 countries.

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Abstract

The main objective of ENETWILD is to collect data on wildlife density, hunting and occurrence and to model geographical distribution and abundance of wildlife species throughout Europe. This subject is of particular concern in the case of wild boar due to the continued advance of African swine fever (ASF). Training, generation of harmonized wildlife abundance data following defined standards and enhancing the network of wildlife professionals in Europe is a key activity of the project that, especially in previously identified gap areas (eastern Europe). In this context, the ENETWILD consortium previously organised an online training course on camera trapping in September 2020. An outcome of this previous course was the need to organizing specific and more intensive training focused exclusively on camera trap data analysis. Therefore, the objectives of a second online course carried out in April 2021 were: (i) to be able to prepare a datasheet (using their own data or those provided by ENETWILD), and (ii) to analyse the data to estimate the density and error intervals. This course was attended by 53 game biologists, animal health professional and wildlife experts from national hunting and forest authorities. Detailed explanations, protocols and examples to implement such analyses were provided. This course on the use of camera trapping for monitoring wildlife and density was useful to complete training of a network of collaborators which are estimating wild boar densities over gap regions of Europe and are funded and supported by ENETWILD. They are now self-sufficient to apply field protocols and to analyse data. In addition, several participants manifested their interest to join this initiative by using their own means, contributing to this pioneer network of harmonized wildlife monitoring over Europe

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Abstract

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.

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Abstract

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.

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Abstract

The ENETWILD consortium (www.enetwild.com) aims at progressively defining the spatial interface between wild ungulates and livestock in Europe, which is essential to evaluate the risk for shared diseases. This is to provide preliminary risk maps of possible wild-domestic interfaces at European scale using relatively similar sized regions by compiling, for the first time, comprehensive data for both groups, wild and domestic ungulates in the continent. We spatially represented (i) the richness of species (livestock and wild ungulates), (ii) their specific occupancy and abundance (the latter for livestock), and finally, (iii) their spatial overlapping over Europe. Species richness in animal communities, including wildlife and domestic hosts, may moderate pathogen transmission and disease outcome.. As a first step, we should characterize the diverse assemblages of animal communities at large scale to better understand possible scenarios for further assessment of shared infection dynamics. About 90% of Europe land area hosts from one to five species of wild native ungulates. Therefore, the interface between livestock and wildlife is wide spread over the European continent. Native wild boar, roe deer and reed deer are widely distributed species, present in most possible assemblages of wild/domestic communities over Europe. The richness of ungulate species is high in Central Europe, from West to East, from the Alps (where the presence of mountain ungulates adds richness), extending to countries with important big game tradition and presence of introduced species, and finally, to Eastern Europe (where also typically northern species such as bisons appear)… To sum, we described by pair of species a wide diversity of potential interfaces, which had variable distribution areas.. While the analysis presented herein is purely spatial and at administrative level, the interface between wild and domestic ungulates is influenced by livestock husbandry (e.g., enclosed, herded or free-ranging, level of biosecurity), landscape and land uses, and wildlife management practices, among other factors, operating locally. Therefore, there is need for a more detailed picture of the interface at European scale.

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Abstract

The activities related to the generation of reliable wild boar density values in at least 15 areas in Europe by camera trapping, mainly in East Europe are presented. The ENETWILD consortium has offered training to collaborators in order to generate harmonized wild boar abundance data (following standards) and has enhanced the network of wildlife professionals in Europe. As a next step, ENETWILD has engaged and enhanced this network, and a number of participants are currently developing field operations to estimate wild boar density in certain areas from their respective countries or confirmed that will do during summer 2021. A protocol for field camera trap (CT) provided by ENETWILD is being applied. By now, 12 field studies are ongoing in 10 countries and in most of them data has been already collected. In addition, there are 3 countries (Albania, North Macedonia and Turkey) in which, due to logistic problems during transport of the CTs at customs, the field work will be developed during 2021. In April 2021, ENETWILD is offering a second online training course to participants, so they will be trained specifically in data analysis. This strategy aims promoting a network of professionals/researchers capable of designing, developing field work and analysing data on their own, contributing also to disseminate the experience and train other colleagues in their respective countries. Most participants, who were already estimating wild boar densities during the season 2019/2020, will also apply the same methodology in different areas during 2021 in their respective countries. Therefore, the number of wild boar density values finally obtained through this experience by the end of 2021 will exceed 15 different locations in a total of 12 countries.

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2020

 

Abstract

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 1×1 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.

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Abstract

Enetwild consortium aims at aggregating data on occurrence, abundance and hunting bag of wildlife in Europe, either as raw data or as results of statistical estimation. These data come from a large community of researchers, hunters and wildlife managers. A flexible and robust data standard is therefore necessary to present the large diversity of data and collection method. We evaluated the possibilities offered by the Darwin Core Standard. The Event core, the occurrence extension and the extended measurement or fact extension proved their utility for our purpose. However, these were not able to record statistical estimation values. We proposed to extend the measurement or fact extension to allow them to be nested among themselves. Any confidence interval or precision measure is indeed a measurement about the punctual estimate, another measurement. We proposed controlled vocabularies adapted to wildlife survey in data and metadata. This will be aligned with the EFSA data model harmonisation under the SIGMA project.

 

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Abstract

The ENETWILD consortium updated in August 2019 suitability maps of wild boar occurrence and relative abundance based on hunting statistics, providing predictions at 10×10 km. External validation of this relative abundance model and a new downscaling on a 2×2 km grid was addressed in the ENETWILD report in January 2020. In this report, we update and incorporate additional data to provide new maps of wild boar suitability with a resolution of 2×2 km, obtained from a presence‐only algorithm, and a new model based on hunting yield data for MS and neighbouring countries. Hunting yield‐based modelling provides further novelties in two ways: new predictor variables and new (two) modelling approaches, i.e. smooth bioregion modelling and two‐step independent bioregion modelling). Internal validations and comparisons among previous and new suitability and hunting yield models were also addressed, as well as external evaluation of the best new approach at European scale (at country level). The suitability map showed a good agreement with the expert‐derived species range published by the International Union for Conservation of Nature. New models of relative abundance performed in general better than the previous one according to internal validations, concretely the two‐step independent bioregion approach gives the best validation scores. This approach solved the abrupt changes in predictions between bioregion boundaries. As with the previous model, the external evaluation of the new model based on hunting yield presented certain over‐prediction of the total hunting bags reported per country, although a high linearity between observed and predicted values was achieved. Previous and new hunting yield model predictions showed disagreements particularly in North and East regions, and other scattered areas in South and West, being areas in which the new model provides more reliable predictions. Hunting yield model outputs showed a relevant improvement in smoothing transitions between bioregions due to the flexibility provided by the new approach. Our analysis showed only partial agreement between suitability and relative abundance models, and reasons for these differences are discussed in this report.

 

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Abstract

The methods for estimating relative abundance and density in wild ruminant species are reviewed and insights on how to obtain reliable estimations by using those methods are provided. Eighteen methods used in nineteen wild ruminant species widely distributed across Europe are reviewed. In accordance with the ENETWILD consortium objectives, we evaluate if different types of data can be used to generate harmonisedand comparable database at large scale and for calibration of hunting data into abundanceindices or population density. In addition, recommendations to select the methods to estimate the abundance or density and its implementations for ungulate populations are provided. How to increase the output quality provided by certain methods recognised as reliable (good accuracy and precision)and with the potential to be used for the validation and calibration ofother direct (i.e. based on observation of animals) or indirect (i.e. based on signs of animal activity) methods was recommended. Largely, the “counting” of large herbivores on a regional scale is often unfeasible, it can only be possible to accurately assess population status at local scale. We show that the habitat type plays a key role in the selection of the best method to determine density or relative abundance and that this is partially irrespective to species characteristics. A method that gives a density estimate rather than relative abundance, if possible, should be used. High‐quality hunting data statistics (collected at fine spatial resolution) have the highest availability and comparability potential across Europe, to give long‐term and large‐scale trends and should be used in predictive spatial modelling of wild ruminant relative abundance and density. Therefore, their standardized and harmonised collection is strongly recommended. On a local scale (e.g. management units), camera trapping is a method that can be conducted in different environmental conditions and at any time to collect robust data. In open areas, where camera trapping may require an excessive effort, we suggest using methods involving the direct detection of animals (vantage points, linear transects, block counts, random points). This should be carried out by correctly defining the study areas (for instance by means of distance sampling) and by estimating the repeatability of the results.

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Abstract

This guidance reviews the methods for estimating relative abundance and density in nine large European wild carnivore species, some representing relevant health concerns and provides insights on how to obtain reliable estimations by using those methods. On a local scale, the appropriate method should take into accountthe characteristics of the study area, the estimated survey efforts, the expected results (i.e. a measure of true density or just an index of abundance to monitor the trend in space and time) the level of accuracy and precision, and a proper design so to obtain a correct interpretation of the data. Among all methods, the camera trapping (CT) methods, especially those recently developed, are the most promising for the collection of robust data and can be conducted in a wide range of species, habitats, seasons and densities with minimal adjustments. Some recently developed CT methods do not require individual recognition of the animals and are a good compromise of cost, effort and accuracy. Linear transects, particularly Kilometric Abundance Index (KAI) is applicable for monitoring large regions. A large challenge is compiling and validating abundance data at different spatial scales. Based on ENETWILD initiative, we recommend developing a permanent network and a data platform to collect and share local density estimates, so as abundance in the EU, which would enable to validate predictions for larger areas by modelling. It would allow to identify gaps in the data on wild carnivores (including the species not assessed in the present report) and to focus on these areas for improving predictions. This platform must facilitate the reporting by wildlife policy makers and relevant stakeholders, but also citizen science initiatives. Also, there is need to improve the reliability of local density estimations by developing practical research on methods able to derive densities in untested species and situations, making the application of methods easier for local teams.

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2019

Abstract
By reviewing the different types of data targeted by the ENETWILD Wild Boar Data Collection Model (occurrence, hunting bag, abundance data) that have become available, an initial model could be built with occurrence data. A preliminary model analysis was performed to estimate the likely distribution of wild boar comparing the performance of a presence‐only model (bioclim) and presence‐background model (MaxEnt). Based on the results of this modelling, locations were identified, notably in Eastern Europe, where more data are required in order to produce more robust model projections of occurrence. This report also outlines the current state of available data collected by ENETWILD (occurrence, hunting bag and density) on wild boar and the development of a model framework that can be used to produce outputs on the yearly density distributions at high resolution of wild boar at a European scale.

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Abstract

After presenting preliminary models to estimate the habitat suitability for wild boar in MSs and neighbouring countries as a proxy for its relative abundance (i.e. the relative representation of a species in a particular ecosystem, a kind of proxy of the density) the ENETWILD consortium has developed further models for the estimation of wild boar abundance across this extent based on hunting yields (HY). This report therefore presents: i) updated maps of habitat suitability at 10×10 km resolution based on newly available data of wild boar occurrence together with new analysis to test the feasibility of performing such analysis at higher resolution (2×2 km); and, ii) a new model for predicting wild boar relative abundance, also at 10×10 km resolution, using hunting yields. The results of the occurrence model show that more occurrence data are required for specific locations in Eastern Europe in order to ensure robust model prediction of habitat suitability and consequently wild boar distribution. We used the hunting yields model to identify the environmental drivers of species abundance at European scale and fitted separate models for three regions (Southern, Western and Eastern Europe) to predict the distribution of wild boar at 10×10 km resolution. Our initial results highlighted some methodological issues relating to the statistical downscaling that should be taken into account to improve the reliability of the predictions. Whilst the spatial pattern in some areas was similar when comparing the predictions from both the occurrence and abundance models, in other regions there were marked discrepancies. To improve the models it is recommended to i) collect more occurrence data in the North‐Eastern region of Europe, in particular on survey effort; ii) combine regional and local hunting records to validate hunting yield predictions to higher spatial resolutions; and, iii) incorporate new environmental variables, especially those closely associated with wild boar abundance and distribution.

Abstract

In October 2018 the ENETWILD consortium created suitability maps based on available data on wild boar occurrence at 10 km square resolution and initial version of abundance models based on hunting statistics at NUTS3 and NUTS2 resolution, that were statistically downscaled for MSs to 10×10 km grid squares. This report presents updated suitability map for wild boar presence based on additional occurrence data and new algorithms, and new models based on high‐resolution hunting yield data for MSs and neighbouring countries. New environmental variables closely associated with wild boar abundance and distribution were also included. Our results showed no consensus for a single best occurrence model: out of those tested, both Maxent and random forest could be considered the best options depending on the choice of assessment metric. Predictions from these models notably disagreed in eastern Europe where data on wild boar occurrence are limited. Despite agreement among models, predictions in the south appeared over‐predicted, most likely due to a lack of contrasting absence data. Whilst there remain some methodological adjustments which could be tested, substantial improvement in the prediction from occurrence models relies on further collection of wild boar occurrence data in the east and complimentary data on survey effort in the south.The predictive performance of the hunting yield model was high. Although the incorporation of new data at higher spatial resolution markedly improved predictions, such data is still needed in some regions, ideally coupled with hunting effort, which would allow such estimates to be transformed into reliable densities. Comparison between predictions from the occurrence and hunting yield models showed they were statistically associated, but the strength of that relationship was dependent on the type of occurrence model and the bioregion. These findings are compatible with previous interpretations of the occurrence model, and highlight the relevance of obtaining more accurate data, especially from northern and eastern bioregions in Europe.

In October 2018 the ENETWILD consortium created suitability maps based on available data on wild boar occurrence at 10 km square resolution and initial version of abundance models based on hunting statistics at NUTS3 and NUTS2 resolution, that were statistically downscaled for MSs to 10×10 km grid squares. This report presents updated suitability map for wild boar presence based on additional occurrence data and new algorithms, and new models based on high‐resolution hunting yield data for MSs and neighbouring countries. New environmental variables closely associated with wild boar abundance and distribution were also included. Our results showed no consensus for a single best occurrence model: out of those tested, both Maxent and random forest could be considered the best options depending on the choice of assessment metric. Predictions from these models notably disagreed in eastern Europe where data on wild boar occurrence are limited. Despite agreement among models, predictions in the south appeared over‐predicted, most likely due to a lack of contrasting absence data. Whilst there remain some methodological adjustments which could be tested, substantial improvement in the prediction from occurrence models relies on further collection of wild boar occurrence data in the east and complimentary data on survey effort in the south.The predictive performance of the hunting yield model was high. Although the incorporation of new data at higher spatial resolution markedly improved predictions, such data is still needed in some regions, ideally coupled with hunting effort, which would allow such estimates to be transformed into reliable densities. Comparison between predictions from the occurrence and hunting yield models showed they were statistically associated, but the strength of that relationship was dependent on the type of occurrence model and the bioregion. These findings are compatible with previous interpretations of the occurrence model, and highlight the relevance of obtaining more accurate data, especially from northern and eastern bioregions in Europe.

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Abstract

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.

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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

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.

2018

Abstract
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.

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Abstract
The aim of this guidance is to assess the accuracy and reliability of the methods for estimation of density (i.e. population size per area unit) and relative abundance (i.e. relative representation of a species in a particular ecosystem, a kind of proxy of the density) of wild boar and to provide indications for calculating reliable and accurate estimates of those parameters using comparable methods. For this purpose eighteen methods were reviewed and evaluated. Since counting wild boar on a large regional scale is unfeasible, estimations of density and abundance are reliable only at local scale in specific habitats. Three methods (camera trapping, drive counts, and distance sampling with thermography) were recommended to estimate wild boar density on a local scale, and guidelines for their implementation was provided. In particular camera trapping is a method that can be conducted everywhere, irrespective of the habitat specificities and at any time to generate comparable data. Wild boar demographic data obtained by different methods cannot directly be combined by simple equations but spatial models are needed to determine abundance and predicted densities that are reliable at larger scales. On a large spatial scale and to describe long‐term trends, high quality hunting data statistics (collected on a fine spatial scale) have the highest availability and potential comparability potential across Europe, and these can be used in predictive spatial modelling of wild boar relative abundance and density. There is need for compiling and validating wild boar abundance data at different spatial scales: hunting bag data alone are not sufficient because a calibration with more accurate density estimation methods conducted at local scale is required. The latter are also required for evaluating predictive models for large areas and converting predicted relative abundances into densities.

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Abstract
This report provides a review of existing models for predicting the spatial distribution and abundance of wild boar at various scales (global, continental, national and regional) in order to inform the development of a new model to produce estimates of wild boar abundance at European level. The review identifies and discusses a range of models based on a wide variety of data types, corresponding to those targeted by the data collection model set by ENETwild, such as occurrence data (presence‐only, presence‐background and presence‐absence), hunting bag data and density data. The reviewed models are categorised in two main groups, the first based on occurrence data to predict a distribution of wild boar, and the second based on hunting bag, census and/or density data to directly model abundance. Owing to the diversity of methodologies, an ensemble modelling approach is here proposed for combining the outputs from a range of complimentary models and generating density estimates of wild boar at European scale. This would retain the flexibility necessary to utilise all available data whilst maintaining a robust output. An initial model has been outlined which uses occurrence data to generate wild boar distribution across Europe. The resulting suitability scores are related to available density estimates to establish a relationship, so the suitability map can be converted into a map of absolute density. In order to further utilize other types of data in this framework, the produced outputs of prediction of habitat suitability or presence/absence are used to underpin models based on available abundance data (hunting bag, census or density).

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Abstract
Heterogeneities in the wild boar data collection frameworks across Europe were analysed using questionnaires to explore comparability of hunting data in the short term and propose a common framework for future collection. Fifty‐seven respondents representing 32 countries covering more than 95% of European territory participated to the questionnaire. The most frequently recorded information in the official statistics included the quantity of animals shot per hunting ground and season (24 countries) and the size of the hunting (management) ground (21 countries). Georeferenced maps for the hunting grounds were collected (total or partial) for 20 countries. The least frequently recorded information was at the level of hunting events. We conclude that (i) sources of hunting statistics providing quantitative information on wild boar (and by extension, for other big game species) are lacking or are not harmonised across Europe, as well as incomplete, dispersed and difficult to compare; (ii) a feasible effort is needed to achieve harmonisation of data in a short time for the most basic statistics at the hunting ground level, and (iii) the coordination of the collection of hunting statistics must be achieved first at national and then at European level. The following is recommended: (i) countries should collect data at hunting ground level; (ii) efforts should be focused on data‐poor countries (e.g. Eastern Europe), and (iii) the data should be collected at the finest spatial and temporal resolution, i.e. at hunting event level. ENETWILD proposes the development of a robust and well‐informed data collection model as the basis for a common data collection framework. The present report identified some countries where, though the potential to share good quality data is present, the data collection promoted by ENETWILD has not succeeded so far (i. e. Eastern Europe). This highlights the need of further strategies to be developed so to encourage and support these countries to share hunting data.

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Reports by EFSA about African Swine Fever

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