Wild 9 rig license#

311931 (ASFORCE – Targeted research effort on African swine fever).Ĭompeting interests: The authors have declared that no competing interests exist.

Wild 9 rig license#

This is an open access article distributed under the terms of the Creative Commons Attribution IGO License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.ĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: Funded by European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. Received: MaAccepted: FebruPublished: May 16, 2018Ĭopyright: © 2018 Food and Agriculture Organization of the United Nations (FAO).

We discuss advantages and limits of the method and its potential application in animal health.Ĭitation: Pittiglio C, Khomenko S, Beltran-Alcrudo D (2018) Wild boar mapping using population-density statistics: From polygons to high resolution raster maps. 4) Model accuracy was assessed at each different step using input as well as independent data.

3) These maps were then mosaicked to produce a final output map of predicted wild boar densities across most of Eurasia. The trend and residual components obtained at 5 km resolution were finally added to generate fine scale wild boar estimates for each bioclimatic region. Next, the residual components (i.e., the differences between the trend components and the original wild boar data at administrative units) were downscaled at 5 km resolution using area-to-point kriging. Quantitative relationships between wild boar data and environmental variables were estimated through multiple regression and used to derive trend components at 5 km spatial resolution. subnational areas, were decomposed into trend and residual components for each bioclimatic region. 1) First a stratification method was used to define homogeneous bioclimatic regions for the analysis 2) Under a geostatistical framework, the wild boar densities at administrative units, i.e. In this paper, a four-step geostatistical downscaling approach is presented and used to disaggregate wild boar population density statistics from administrative units of different shape and size (polygons) to 5 km resolution raster maps by incorporating auxiliary fine scale environmental variables. Data are commonly available at aggregated administrative units with little or no information about the distribution of the species within the unit. Its management and control would strongly benefit from accurate and detailed spatial information on species distribution and abundance, which are often available only for small areas.

Due to increasing densities and expanding ranges worldwide, the related economic losses in livestock and agricultural sectors are significant and on the rise. The wild boar is an important crop raider as well as a reservoir and agent of spread of swine diseases.