REPORTS & DOCS

  • Here, we will link to reports published by EFSA journal associated to ENETWILD
  • We also include links to other reports associated to African Swine Fever
  • Users will be able to access to outputs (predictions) from modelling in the map section

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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 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
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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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

Reports by EFSA about African Swine Fever