Abstract

Wild boar is a host of a number of arthropod-vectored diseases and its numbers are on the rise in mainland Europe. The species potentially impacts ecosystems, humans and farming practices and so its distribution is of interest to policy makers in a number of fields beyond that of the primarily epidemiological goal of this study.Three statistical model outputs describing the distribution and abundance of the species Sus scrofa (Wild boar) are included in this data package. The extent of this dataset covers continental Europe. These data were presented as a poster [1] at the conference Genes, Ecosystems and Risk of Infection (GERI 2015).The first of the three models provide a European map presenting the probability of presence of Sus scrofa, which can be used to describe the likely geographical distribution of the species. The second and third models provide indices to help describe the likely abundance across the continent. The two indices include “the proportion of suitable habitat where presence is estimated” and a simple classification of boar abundance across Europe using quantiles of existing abundance data and proxies.

Highlights

  • The species potentially impacts ecosystems, humans and farming practices and so its distribution is of interest to policy makers in a number of fields beyond that of the primarily epidemiological goal of this study

  • The first of the three models provide a European map presenting the probability of presence of Sus scrofa, which can be used to describe the likely geographical distribution of the species

  • The second and third models provide indices to help describe the likely abundance across the continent

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Summary

Context Spatial coverage Description

Areas such as national parks or in some cases up to country level. These were recorded by different methods and across different time periods and has a spatial coverage across Europe which was far from regular. A notable exception was a recent review of wild boar population trends in 18 countries in Europe, based on hunting statistics [2]. To complement these abundance data, hunting figures were identified for a number of countries at both national level and sub-national level [34,35,36,37,38].

Methods
46. R-Project randomForest package 2012 randomForest
Full Text
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