Suchergebnisse

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

Abstract
By using the latest available data, we provide estimates of wild boar (Sus scrofa) distribution and abundance pre‐African Swine Fever (ASF) based on occurrence data in Europe. Secondly, as a basis for the calibration model output into densities, we used the predictions of relative abundance, and hunting yield‐based model (hunted individuals per km2), at 2x2 km for wild boar (by ENETWILD Consortium) and local wild boar densities (individuals per km2) considered reliable and obtained in the framework of the European Observatory of Wildlife (EOW), as well as some from recent literature (2015 onwards). Hunting yield predictions were considered at different spatial scales namely 5, 10 and 15 km radii buffer around localities with density estimations. The calibration of hunting yield‐based model into densities are a better fit for 15 km radius buffer and a significant relationship between model predictions of hunting yield and reliable density values at European level. This calibration of wild boar hunting yield‐based model into densities will offer the possibility to predict density values of wild boar. This will be useful to incorporate into risk factor analyses for African Swine Fever at the selected spatial range. This is the first time that absolute density estimates have been made using these two approaches for Europe, which demonstrates the added value of the observatory approach (a number of study areas where reliable density values are obtained, such as from the EOW) to generate novel information of high value for epidemiological assessment. During an ASF outbreak hunting effort will change dramatically and will take a few years to return to similar pre‐ASF levels, so post‐ASF estimates of density would be limited to areas where ASF has been present for a while. However, there will be relatively limited effect on sighting data as these rely on a number of different actors, many of whom may be expected to return to normal activities relatively soon after ASF arrives. Thus, relative post‐ASF wild boar density may be more reliable in the short term. These relative post‐ASF densities were calculated but with the limited sighting data available at the chosen locations the uncertainty was high. We advocate for the developing this nework of wildlife monitoring across Europe, and in general, harmioized wildlife monitoring programs, ensuring standardisation and consistency in the data generated and collected, which is essential for assessing management and risks related not only to ASF but other wildlife diseases.