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RESEARCH ARTICLE
Wild boar mapping using population-density
statistics: From polygons to high resolution
raster maps
Claudia Pittiglio, Sergei Khomenko, Daniel Beltran-Alcrudo*
Animal Production and Health Division, Food and Agriculture Organization of the United Nations, Viale delle
Terme di Caracalla, Rome, Italy
*Daniel.BeltranAlcrudo@fao.org
Abstract
The wild boar is an important crop raider as well as a reservoir and agent of spread of swine
diseases. Due to increasing densities and expanding ranges worldwide, the related eco-
nomic losses in livestock and agricultural sectors are significant and on the rise. Its manage-
ment and control would strongly benefit from accurate and detailed spatial information on
species distribution and abundance, which are often available only for small areas. Data are
commonly available at aggregated administrative units with little or no information about the
distribution of the species within the unit. 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. 1) First a stratification method
was used to define homogeneous bioclimatic regions for the analysis; 2) Under a geostatisti-
cal framework, the wild boar densities at administrative units, i.e. subnational areas, were
decomposed into trend and residual components for each bioclimatic region. 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. Next,
the residual components (i.e., the differences between the trend components and the origi-
nal wild boar data at administrative units) were downscaled at 5 km resolution using area-to-
point kriging. The trend and residual components obtained at 5 km resolution were finally
added to generate fine scale wild boar estimates for each bioclimatic region. 3) These maps
were then mosaicked to produce a final output map of predicted wild boar densities across
most of Eurasia. 4) Model accuracy was assessed at each different step using input as well
as independent data. We discuss advantages and limits of the method and its potential
application in animal health.
Introduction
The wild boar (Sus scrofa, L. 1758) is a generalist and opportunistic species found from western
Europe and the Mediterranean Basin to eastern Russian Federation and Japan, throughout
PLOS ONE | https://doi.org/10.1371/journal.pone.0193295 May 16, 2018 1 / 19
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OPEN ACCESS
Citation: Pittiglio C, Khomenko S, Beltran-Alcrudo
D (2018) Wild boar mapping using population-
density statistics: From polygons to high resolution
raster maps. PLoS ONE 13(5): e0193295. https://
doi.org/10.1371/journal.pone.0193295
Editor: Stephanie S. Romanach, U.S. Geological
Survey, UNITED STATES
Received: March 17, 2017
Accepted: February 8, 2018
Published: May 16, 2018
Copyright: ©2018 Food and Agriculture
Organization of the United Nations (FAO). This is an
open access article distributed under the terms of
the Creative Commons Attribution IGO License
(http://creativecommons.org/licenses/by/3.0/igo/
legalcode), which permits unrestricted use,
distribution, and reproduction in any medium,
provided the original work is properly cited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: Funded by European Union’s Seventh
Framework Programme (FP7/2007-2013) under
grant agreement no. 311931 (ASFORCE – Targeted
research effort on African swine fever).
Competing interests: The authors have declared
that no competing interests exist.
southeast Asia. Wild boar occupies one of the largest geographic range among all terrestrial
mammals [1,2], including a variety of habitats, vegetation types and climate. Due to a combi-
nation of biological (i.e., species ecological plasticity, high reproduction rate), environmental
(i.e., climate change, mild winters), and anthropogenic factors (i.e., depopulation of rural
areas, reintroduction, lack of large natural predators, change in agricultural practices, reduced
hunting pressure, supplementary feeding and other husbandry practices), the abundance of
the wild boar has continuously increased over the last decades and its distribution expanded
across the whole geographic range [3,4,5,6]. This expansion poses a threat to the agriculture,
conservation and livestock health sectors, as the wild boar is an invasive and pest species caus-
ing substantial economic loss [7,8]. In recent years, there has been an increase in the likelihood
of disease spread, car accidents, damages to crops and to natural vegetation [4]. In particular,
the species may represent a reservoir or play another type of role in the transmission of many
livestock, wildlife and human diseases such African and classical swine fever, brucellosis,
tuberculosis, salmonellosis, Aujeszky’s disease and foot and mouth disease [9]. Domestic pigs
and wild boar share most diseases. A review of viral diseases of the European wild boar with a
direct effect on wild boar and an economic impact on domestic pig production systems identi-
fied 17 viral agents [9]. The involvement of the species in the recent expansion and persistence
of African swine fever in eastern Europe and the Caucasus, despite control efforts, has
attracted considerable international attention, stressing the difficulties of managing animal
diseases in wild populations [10].
The management and control of wild boar populations require accurate and detailed spatial
information on species distribution and abundance. Wild boar density varies from 0.01 to 43
animals / km
2
, following an east-west biogeographical gradient and exhibiting high inter-
annual fluctuation due to their high reproductive potential [2,11]. However, accurate and
detailed wild boar population statistics, such as census and hunting data, are not available for
large-scale studies [11,12,13,14]. Two recent studies have extrapolated and predicted wild boar
distribution and expansion at global level using wild boar data available across parts of the geo-
graphical range of the species [5,6]. This is probably due to the ecological traits of the species
(e.g. complex social structure, nocturnal activity pattern, preference for dense vegetation, and
high inter- and intra- annual variability in reproduction rate), that make direct observations
over large geographic areas more difficult and expensive than the collection of indirect signs,
such as faecal droppings, tracks, etc. [15]. Species distribution models based on indirect signs
such as presence/absence or presence-only data have been recently applied to predict the wild
boar occurrence from environmental and climatic covariates [6,12,13]. Although habitat suit-
ability models are useful management and conservation tools, their capacity to estimate species
abundance is highly controversial as the occupancy may not reflect species density and stability
[16,17,18,19,20]. In addition, abundance estimates are needed to study the epidemiology of
wildlife diseases, rather than suitability and distribution maps [15]. Wild boar population sta-
tistics are mainly available at aggregated spatial level, i.e. by census, hunting or administrative
units [7,8,13,15], with little or no information about the distribution of the species within these
units. The units can be very heterogeneous in shape, size as well as in terms of land use and
environmental characteristics. Aggregated population data does not account for this variation
among and within the units [21], and they are difficult to model [22]. Specific disaggregation
methods are required to predict population statistics from coarse to fine scale. Recently, multi-
ple linear regression has been used to predict wild boar density at global level [5]. Geostatistics
has been successfully used for mapping wild species distribution, including elusive marine
mammals, from observations acquired at point or transect level [23], as well as for spatial
downscaling and data disaggregation from coarse to fine resolution in land cover mapping
[24], human population mapping estimation [21], image sharpening [25], disease mapping
Wild boar mapping
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[26], and precipitation estimate [27]. Recent studies demonstrated the utility of incorporating
the residuals of the regression models to increase interpolation accuracy. In particular, multi-
ple regression and area-to-point kriging of the regression residuals were applied to estimate
human population density from coarse census blocks to fine resolution land use maps [21], as
well as to downscale precipitation data from coarse to fine resolution rasters [27]. However, to
the best of our knowledge, the method has not yet been applied to disaggregate wild boar pop-
ulation data from polygons/units of different shape and size to high resolution raster maps.
We present a geostatistical method based on regression modelling and area-to-point resid-
ual kriging to disaggregate wild boar statistics, map and predict its abundance from spatially
heterogeneous administrative units (polygons) to high resolution raster maps at 5 km using
climatic and environmental covariates. We discuss advantages and limits of the method and its
potential application in animal health.
Materials and methods
Study area
The wild boar population density was estimated from western Europe to central-northern Asia
(Fig 1). This area encompasses Mediterranean, Temperate, and Boreal bioclimatic zones [28]
with a wide range of vegetation types, climate and elevation [29], which are known to influence
the distribution of the species regionally and locally [11].
Fig 1. Wild boar density by administrative units (original input data) and wild boar range.
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Wild boar population data and geographic extent
The wild boar population density was estimated using a dataset available at FAO, including
hunting and census data available from different sources and at different level of administra-
tive units [30], for a total of 46 countries and 509 sub-national units (Fig 1 and S1 Table). Cen-
suses and population estimates were acquired for 74% of the countries and were available
particularly for eastern Europe, while hunting data were available for 83% of the countries,
and mainly for western Europe. Both types of data were acquired for 57% of the countries.
This subset was used to calculate the proportion of hunted animals and estimate a correction
factor to convert hunting data to densities for those countries presenting hunting data only
(n = 8)(S1 Table) [30]. The data were spatially and temporally heterogeneous. National totals
were acquired for 27 countries, while sub-national data (level 1 or higher, n = 480) were avail-
able for 19 countries. Except for Turkey, Kazakhstan, Turkmenistan, Uzbekistan, the countries
with national totals were similar in size to the sub-national units of surrounding countries,
thus making the dataset regionally homogeneous and regular in terms of the size and shape
of the spatial units. The time frame of the dataset ranged between 1993 and 2011, with 84% of
the data acquired after the year 2007. Data earlier than 2000 were obtained from Azerbaijan
and Spain (4%). Population, hunting data, correction factors and source data are reported in
S1 Table.
The International Union for Conservation of Nature (IUCN) wild boar occurrence map
[31] was updated by digitizing detailed maps for Spain, Italy, Iran, Greece and ex-USSR coun-
tries found through a literature review [32,33,34,35,36,37] and used to exclude unsuitable wild
boar areas inside each administrative unit using a simple GIS overlay operation in ArcGIS (Fig
1). The population density data, expressed as numbers per km
2
of suitable habitat, were nor-
malized to approximate to a normal distribution using the fourth root power (x1
4) transforma-
tion prior to perform the statistical analysis [38].
Predictors
Based on a literature review of environmental and climatic determinants of the wild boar dis-
tribution, 18 bioclimatic variables, 3 continuous vegetation cover (i.e. percentage of tree cover,
herbaceous vegetation and bare ground) and 2 topographic variables (elevation and slope)
were selected as the most significant covariates for predicting wild boar density across the
study area [2,5,7,11,12,39,40]. The bioclimatic variables were obtained from the WorldClim
online database at 30 arc-seconds (~1 km) spatial resolution for the period 1950–2000 [41].
The vegetation cover grids at 500 m resolution were downloaded from the Global Land Cover
Facility [42]. The elevation and slope were derived from the Shuttle Radar Topography Mis-
sion (SRTM), 30 arc-second pixel [43]. Average values of the predictors by suitable habitat by
wild boar administrative unit were normalized and standardized and used in the statistical
analysis. The predictors and properties are listed in Table 1.
All GIS layers were re-projected to the Polar Lambert Azimuthal Equal Area and re-sam-
pled (with the bilinear interpolation method) to 1 ×1 km
2
before performing spatial analysis.
Spatial analysis was performed in ArcGIS 10.0 (ESRI) and statistical analysis in R 3.1.0 [44].
Wild boar modelling approach
A flowchart illustrates the four-step modelling approach developed in this study (Fig 2). First,
we calculated the average value of each predictor by wild boar administrative unit. Given the
large geographical and high bioclimatic heterogeneity of the study area, a given predictor vari-
able may be associated quite differently with the species densities in different bioclimatic
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regions [45]. We therefore applied a stratification method [46] to define regions with similar
environmental conditions for the wild boar occurrence and group the wild boar units accord-
ingly. Second, for each bioclimatic region, we applied a two-step downscaling geostatistical
approach based on multiple regression and area-to-point residual kriging [27] to disaggregate
wild boar densities from coarse (polygons) to fine resolution raster maps (5 km). Within the
geostatistical framework, the wild boar density was decomposed into trend and residual com-
ponents. Quantitative relationships between wild boar data and environmental variables by
administrative unit were estimated through multiple regression analysis and the coefficients
were used to derive the trend components at 1 and 5 km spatial resolution. Then, the residual
components i.e., the differences between the trend components and the original wild boar data
by administrative units were downscaled at 5 km resolution using area-to-point kriging. The
trend and residual components at 5 km resolution were finally added to generate fine scale
wild boar estimates for each bioclimatic region. The trend components for each bioclimatic
region were also extrapolated to the whole study area. Third, we produced three different out-
put maps of predicted wild boar density for the whole study area: (a) a “geostatistical mosaicked
model” by mosaicking (merging) the predicted wild boar densities obtained for each biocli-
matic region; (b) an “averaged trend model” by averaging the trend components extrapolated
to the whole study area and (c) an “averaged geostatistical model” by adding the map to the
kriged residual components. Fourth, we validated the three final output models using the
Table 1. Predictor variables included in the wild boar model-building process.
Variable Name Description Resolution Unit
Bioclimatic
BIO1 Annual Mean Temperature 1 km Degrees Celsius
BIO2 Mean Diurnal Range (Mean of monthly (max temp—min temp)) 1 km Degrees Celsius
BIO3 Isothermality (BIO2/BIO7) (100) 1 km Percent
BIO4 Temperature Seasonality (standard deviation 100) 1 km Percent
BIO6 Min Temperature of Coldest Month 1 km Degrees Celsius
BIO7 Temperature Annual Range (BIO5-BIO6) 1 km Degrees Celsius
BIO8 Mean Temperature of Wettest Quarter 1 km Degrees Celsius
BIO9 Mean Temperature of Driest Quarter 1 km Degrees Celsius
BIO10 Mean Temperature of Warmest Quarter 1 km Degrees Celsius
BIO11 Mean Temperature of Warmest Quarter 1 km Degrees Celsius
BIO12 Annual Precipitation 1 km Millimeter
BIO13 Precipitation of Wettest Month 1 km Millimeter
BIO14 Precipitation of Driest Month 1 km Millimeter
BIO15 Precipitation Seasonality (Coefficient of Variation) 1 km Percent
BIO16 Precipitation of Wettest Quarter 1 km Millimeter
BIO17 Precipitation of Driest Quarter 1 km Millimeter
BIO18 Precipitation of Warmest Quarter 1 km Millimeter
BIO19 Precipitation of Coldest Quarter 1 km Millimeter
Vegetation cover
Percentage Tree cover Modis Vegetation continuous fields 500 m Percent
Percentage herbaceous Modis Vegetation continuous fields 500 m Percent
Percentage bare ground Modis Vegetation continuous fields 500 m Percent
Topography
Elevation Elevation 1 km Meter
Slope Slope 1 km Degree
https://doi.org/10.1371/journal.pone.0193295.t001
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original wild boar input data as well as an independent dataset. Each step of the process is
detailed below. The spatial resolution of 5 km was chosen to match the spatial resolution of
livestock distribution maps produced by FAO, including the domestic pig density map [38] to
facilitate the analysis of diseases’ transmission among wild and domestic species.
Study area stratification: Definition of the wild boar bioclimatic regions. The normal-
ized and standardized average values of the predictors by administrative units were input in a
principal component analysis (PCA) to reduce the dimensionality of the environmental data-
set into a set of linearly uncorrelated and independent components. The first four principal
components (eigenvector >1) were input in the k-means cluster analysis to classify the study
area in homogeneous bioclimatic regions. The elbow method was used to define the number
of bioclimatic regions [47](Fig 2, box 1).
Two-step downscaling approach: Disaggregating and predicting wild boar densities
from polygons to 5 km resolution maps. For each bioclimatic region, we applied a two-step
downscaling geostatistical approach [27] based on multiple regression and area-to-point resid-
ual kriging to disaggregate wild boar population data from coarse scale administrative units
Fig 2. Flowchart illustrating the 4 main steps of the wild boar (WB) density modelling approach.
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(polygons) to fine resolution raster maps (5 km) using auxiliary fine resolution bioclimatic and
environmental information. The method decomposed wild boar densities D(u) in: (a) a deter-
ministic, trend component m(u) (at coarse scale, i.e., polygon level), which indicates the wild
boar density (trend) influenced by the bioclimatic and geographic variables, and (b) a stochas-
tic residual component R(u) that accounts for the spatial correlation information of the input
wild boar residuals (see Eq 1).
DðuÞ ¼ mðuÞ þ RðuÞ ð1Þ
The trend component was estimated from the statistical relation between wild boar density
and auxiliary environmental variables by administrative units through multiple regression.
Under the assumption that attribute values at a coarse scale are linear averages of their constit-
uent fine scale point values, these relationships were applied to the environmental variables in
order to estimate the trend component at a fine scale. The stochastic residual component was
estimated at finer scale by interpolating the regression residuals using the area-to-point kriging
[48]. Then the trend component was added to the residual component to produce a fine resolu-
tion wild boar density map (hereafter ATP-based WB predicted density) for each region using
the Eq 1. This approach presents the mass preservation property, i.e. it can reproduce the origi-
nal wild boar density values when the downscaling results at a fine scale are re-aggregated to
the coarse scale [27] and it accounts for irregular geographic units (shape, size)[26] (Fig 2,
box 2).
Theory on regression-based interpolation and area-to-point residual kriging. Consider
a study area of kwild boar density data by irregular and coarse administrative units {z(v
k
),
k= 1,. . .,K}, where v
k
=v(u
k
) is the kth data with its centroid u
k
, and Mauxiliary fine scale
environmental variables fyk
iðunÞ;i¼1;...;M;n¼1;. . . ;Ngwithin each kth unit. Nis the
number of discretizing points within each unit and its determination depends on the prede-
fined finer scale value. If the environmental variables show linear relationships with the wild
boar density values, the latter at both coarse and fine scales can be expressed in terms of the
environmental variables via multiple linear regression as:
zðvkÞ ¼ aþX
M
i¼1
biyiðvkÞ þ RðvkÞ;ð2Þ
zkðunÞ ¼ aþX
M
i¼1
biyk
iðunÞ þ RkðunÞ;
where aand b
i
are regression coefficients for the intercept and slope of the ith variable, respec-
tively. z
k
(u
n
) is the downscaled wild boar value at a target finer scale within the kth unit. R(v
k
)
and R(u
n
) are the residual components at coarse and fine scales, respectively, which cannot be
accounted for by environmental variables. If the original wild boar data, environmental vari-
ables, and the residual components at a coarse scale can be expressed by the average values of
Nfine scale data within each unit, respectively, Eq (2) can be reformulated as (3)
zðvkÞ ¼ aþX
M
i¼1
bi
1
NX
N
n¼1
yk
iðukÞ
" #þ1
NX
N
n¼1
Rkun
ð Þ ð3Þ
zðvkÞ ¼ 1
NX
N
n¼1
aþX
M
i¼1
biyk
iðunÞ þ RkðunÞ
" #
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zðvkÞ ¼ 1
NX
N
n¼1
zkðunÞ
The trend component at a finer scale was estimated by applying the regression relationships
obtained at course scale to the environmental variables. The residual component at finer scale
was predicted using area-to-point simple kriging [27] of the residual components available at a
coarse scale. Area-to-point simple kriging predicts the residual component values at a fine
scale by a linear combination of neighboring attribute values at a coarse scale [26,27]. Briefly,
variogram deconvolution was applied to the regression residuals for each region to estimate
the unknown point-support variogram of the residuals.
A detailed explanation of the area-to-point kriging can be found elsewhere [21,26,27].
Area-to point residual kriging was implemented using SpaceStat 2.0 (BioMedware).
The normalized and standardized predictors were tested for multicollinearity prior to per-
forming the multiple regression because multicollinearity may violate statistical assumptions
and may alter model predictions [49]. Different methods exist to evaluate multicollinearity in
regression analysis, including correlation matrix, PCA, Tolerance and Variance Inflation fac-
tor [50]. In this study we chose the VIF. Predictors with VIF larger than 2.5 were excluded
from the multiple regression [51,52]. The variables were tested one by one. A (forwards) step-
wise variable selection procedure was applied to select a parsimonious set of predictors for the
multiple regression modelling. The method is explained in detail elsewhere [50,51,52].
Mapping the wild boar predicted densities for the whole study area (5 km). The first
output (“geostatistical mosaicked model”, hereafter mosaicked model) was obtained by
mosaicking the ATP-based wild boar density maps predicted for each bioclimatic region in a
single, seamless map for the study area (see Fig 2, box 3). In order to minimize the abrupt
changes along the boundaries of adjacent regions, a blending image processing technique was
applied to the input maps. Specifically, a buffer of 100 km was built around the boundary of
each region and used as a mask to average inside the wild boar predicted densities obtained
from models of adjacent regions.
Two additional modelling outputs were generated and compared with the mosaicked
model: 1) the averaged trend model (output 2, Fig 2, box 3), which was derived by averaging
the trend components of each bioclimatic region extrapolated at 1 km resolution to the whole
study area and resampled at 5 km resolution; 2) the averaged geostatistical model (output 3,
Fig 2, box 3), obtained by adding the kriged residual component to the averaged trend model.
The residual component was generated from an ordinary kriging of all regression residuals.
Model evaluation and accuracy assessment. The predictive performance of the models
was assessed using the wild boar densities of the original input data as well as an independent
dataset (Fig 2 box 4). Specifically, the overall accuracy of the averaged trend model, averaged
geostatistical model and the mosaicked model were measured by calculating the averaged pre-
dicted densities by administrative unit and then relating these averaged predicted density to
the averaged observed density of the input data. Given the mass preservation of the ATP-based
method [26], a 1:1 relationship was expected. Model performance was also evaluated for each
region. In addition, the accuracy across the whole wild boar study area was independently
assessed using the density data reported by Melis et al. [11] for 54 locations in Eurasia. These
locations were georeferenced and overlaid with the three output models. The predicted wild
boar densities extracted at those locations were therefore related to the densities observed by
Melis et al. [11].
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The Pearson correlation coefficient (r
p
), coefficient of determination (R
2
) and root mean
square error (RMSE) calculated between predicted and observed densities were used to assess
model accuracy [53].
Results
The k-means cluster analysis grouped the wild boar units into four main homogeneous biocli-
matic regions for the wild boar occurrence: 1) Asian, 2) eastern; 3) western, and 4) southern
(Fig 3). The units within each region were similar in size and shape, except for southern,
which included the largest units (Iran, Turkey, Kazakhstan, Uzbekistan and Turkmenistan).
Elevation and slope were highly correlated with most of the bioclimatic variables (Pearson
correlation coefficient, p<0.05) and were therefore excluded from the geostatistical modelling.
The results of multiple regression models performed for each bioclimatic region are reported
in Table 2. The variance explained by the trend components ranged between 49 and 53%, indi-
cating that regression alone is not sufficient to predict the wild boar density in each region.
The predictors were reduced to 2–6 significant variables and differed among regions. In the
Fig 3. Bioclimatic regions for the wild boar as defined by PCA and cluster analysis. The cluster plot of the first and second components is shown in the inset. The
symbols represent the administrative units grouped in the 4 clusters/regions: Asian (red), eastern (pink), western (green) and southern (blue).
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Asian region (pink, Fig 3) wild boar density was positively related to annual mean temperature
(BIO1) and negatively related to both precipitation of coldest quarter (BIO19) and continuous
herbaceous cover. Instead, in the eastern region (blue) wild boar density was negatively related
to both temperature annual range (BIO7) and precipitation of wettest month (BIO13), but
positively associated with mean temperature of wettest quarter (BIO8) and continuous tree
cover. In the southern region (yellow), a significant positive association was found with mean
diurnal range (BIO2), minimum temperature of coldest month (BIO6), and continuous tree
cover. In the western region (orange) wild boar density was positively related to annual mean
temperature (BIO1) and continuous tree cover, but negatively related to mean diurnal range
(BIO2), mean temperature of wettest quarter (BIO8), precipitation seasonality (BIO15) and
precipitation of coldest quarter (BIO19). Given the lower variance explained by the regression
model for the Asian region, as well as the large residuals associated with the largest units in the
southern region (i.e. Iran, Kazakhstan, Uzbekistan and Turkmenistan), the Asian model was
excluded from the geostatistical modelling and the boundary of the southern region was
delimited along by the Ural mountains and the Caspian sea.
The trend components generated by applying the three regression relationships to the envi-
ronmental variables at 1 km resolution and extrapolated for the whole study area at 5 km reso-
lution are displayed in Fig 4A–4C. The results of the variogram deconvolution for each region
are shown in S2 Table, while an example of the area-to-point residual kriging is given for the
eastern region in S1 Fig. The predicted wild boar population-densities by region obtained by
adding trend and residual components are shown in S2 Fig. When the downscaled results at 5
km were re-aggregated at the original administrative unit level, the preservation of the coher-
ence (mass) property was highest for the eastern region (Pearson correlation coefficient r
p
=
0.7), western (r
p
= 0.6) and lowest for the southern region (r
p
= 0.4). The wild boar densities
predicted in the Iberian Peninsula (southern region) were less accurate than those obtained
using the regression coefficient of the western region. Consequently, the western region was
modified to include the Iberian Peninsula and the mosaic mask for generating the final
Table 2. Results of the multiple regression models by bioclimatic region: Standardized coefficients and standard errors (in brackets), adjusted R
2
, sample size (N),
F value and degrees of freedom (dfs), residual standard error (RSE) and p-values.
Predictors Asian Eastern Southern Western
(Intercept) 1.21 7.19 -0.87 2.13
Annual Mean Temperature (BIO1) 0.04 0.08
Mean Diurnal Range (BIO2) 1.45 -1.11
Min Temperature of Coldest Month (BIO6) 0.03
Temperature Annual Range (BIO7) -3.60
Mean Temperature of Wettest Quarter (BIO8) 0.02 -0.01
Precipitation of Wettest Month (BIO13) -0.92
Precipitation Seasonality (BIO15) -0.61
Precipitation of Coldest Quarter (BIO19) -0.05 -0.07
Continuous herbaceous cover -0.04
Continuous tree cover 0.25 0.28 0.80
R
2
adjusted 0.51 0.53 0.50 0.49
N26 176 94 171
F9.67 50.2 31.5 28.5
dfs (3)(22) (4)(171) (3)(90) (6)(164)
RSE 0.07 0.17 0.23 0.22
p<0.05 <0.05 <0.05 <0.05
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prediction map was adjusted accordingly (Fig 4C). The final wild boar population-density
models, i.e. averaged trend, average geostatistical and mosaicked models, are shown in Fig
5A–5C. Since population density cannot be less than 0, a small proportion of negative esti-
mates (1%), mainly located in mountainous areas (i.e., Pyrenees, Alps, Caucasus) was adjusted
to 0.
The model performance assessed using the wild boar input data increased substantially
from the average trend model (t = 17.38, df = 456, p<0.05, Pearson correlation coefficient r
p
=
0.63), to averaged geostatistical model (t = 24.5, df = 456, p<0.05, r
p
= 0.75), and it was highest
for the mosaicked model (t = 32.1, df = 456, p<0.05, r
p
= 0.83), though a 1:1 relation was not
found. We found a significant positive correlation between the mosaicked model wild boar
predicted densities and the densities reported by Melis et al. [11] (t = 9.16, df = 43, p<0.001;
r
p
= 0.81).
Table 3 shows the r, MAE and RMSE of the validation analysis for the three models with
input and independent data.
Fig 4. Trend components derived from the regression relationship obtained for (A) the eastern, (B) western and (C) southern regions and extrapolated to the whole
study area respectively. (D) Redefined bioclimatic regions and blending zones. The Asian region is not shown as it was excluded from the geostatistical analysis.
https://doi.org/10.1371/journal.pone.0193295.g004
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PLOS ONE | https://doi.org/10.1371/journal.pone.0193295 May 16, 2018 11 / 19
Discussion
Wild boar population statistics are mainly available at aggregated level, or as scattered observa-
tions, particularly for large geographical areas [5,7]. However, the management and control of
wild boar populations require accurate and detailed spatial information on species distribution
Fig 5. Model outputs: average trend (A), average geostatistical model (B) and mosaicked model (C).
https://doi.org/10.1371/journal.pone.0193295.g005
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PLOS ONE | https://doi.org/10.1371/journal.pone.0193295 May 16, 2018 12 / 19
and abundance. Two recent studies have extrapolated and predicted wild boar distribution
and expansion at global level using multiple linear regression [5] and Bayesian approaches [6]
based on wild boar data available across parts of geographical range of the species. In our
study, a multi-step geostatistical approach was proposed, tested and validated to disaggregate
wild boar population density statistics from polygons of irregular size and shape to fine spatial
resolution maps. The approach represents a valuable downscaling tool that could be applied to
any population statistics, including other wildlife species, livestock, human, as well as disease
data. Strengths and limitations of the approach are discussed below.
The first strength of the approach is the stratification step. Differently from other studies
[5,6,7,8], our novel method was able to account for the large geographical and high bioclimatic
heterogeneity of the study area and to define four different biogeographic/bioclimatic regions
—Asian, southern, eastern, and western—reflecting different spatiotemporal patterns of food
resources, shelter/cover, available to the species. By analyzing each region separately, a parsi-
monious set of specific predictors and limiting factors for wild boar distribution and density
was identified for each region: winter harshness in the Asian and eastern regions (as indicated
by the negative relation with Precipitation of Wettest Month (BIO13) and Precipitation of
Coldest Quarter (BIO19) respectively); forest productivity and shelter in western, eastern and
southern regions (as indicated by the positive relation with tree vegetation cover); extreme
temperature and precipitation variability (e.g., droughts, floods) in western region (as indi-
cated by the negative relation with Precipitation seasonality (BIO15) and Annual Mean Diur-
nal Range (BIO2)); low habitat heterogeneity and lack of shelter in the Asian region (as
indicated by the negative relation with the herbaceous vegetation cover). These results con-
firmed that winter severity, temperature and precipitation anomalies, as well as vegetation
structure, are main macroecological determinants of the wild boar distribution and abundance
in northern and temperate latitudes, respectively [5,7,11,12,14], as they affect population
dynamics, particularly the survival of newborn piglets. Differently from previous studies
[12,39], slope and elevation were not significant predictors, suggesting that terrain is an impor-
tant species determinant at local scale but not at regional scale.
The main advantage of the approach is the geostatistical framework (step 2), which decom-
posed the wild boar density in trend and residual components. Under this framework, the
quantitative relationship found between wild boar densities and environmental variables at
coarse scale (administrative units) through the regression analysis was used to derive the trend
component at finer scale (1km and 5 km). The low accuracy of the trend models was in agree-
ment with the results obtained by other studies [21,27,54], indicating that regression alone
cannot account for the within-units environmental heterogeneity and its influence on species
distribution and abundance.
The accuracy of the regression-based trend models varied among the regions: the western
and the eastern regions showed the lowest (R
2
= 0.49) and highest (R
2
= 0.53) accuracy respec-
tively. The wild boar density in the western region was mainly estimated from hunting data,
while the southern region was mainly characterized by larger and irregular units, suggesting
that low quality of the input data (hunting vs. census, [4]), differences in years covered by the
data series [11], as well as units variability in size and shape can impact model performance.
Table 3. Validation results.
Model name r
p
adjusted R
2
RMSE p
Output 1: Mosaicked model 0.83 0.69 0.17 <0.001
Output 2: Averaged Trend model 0.63 0.40 0.13 <0.001
Output 3: Averaged Geostatistical model 0.75 0.57 0.21 <0.001
https://doi.org/10.1371/journal.pone.0193295.t003
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As expected, the predictive performance of the models increased with the incorporation of
the regression residuals through the area-to-point kriging. This result was in line with the find-
ing of other studies [21,27] due to the ability of the Area-to-point kriging to account for the
stochastic component and the spatial autocorrelation of the aggregated input data. The accu-
racy was highest for the mosaicked model (r= 0.83), and lowest for the averaged trend model
(r= 0.63), meaning that the statistical relations between wild boar abundance and bioclimatic
variables cannot be extrapolated to areas outside the training regions [55]. This is clearly
shown by the trend maps (Fig 4A–4C), which display different distribution patterns of wild
boar among the bioclimatic regions and low predictability outside the training regions. This
finding suggests that model transferability remains an issue in species distribution models
[56]. This result further corroborates the importance of defining bioclimatic regions based on
statistical stratification methods. However, high environmental variability in certain areas, as
well as anthropogenic factors, may also impact the accuracy of the models [5]. In our study,
the model performance for Spain based on the regression coefficients of the southern region
was low, but improved when we applied the coefficient of the adjacent western region. The
two model outputs were discussed with wild boar experts in Spain, who provided a visual vali-
dation of the two maps, confirming the higher accuracy of the western model. This result was
explained by the high environmental and topographic heterogeneity of the country [12] as well
as the wide use of supplementary feeding in some areas [45], which could not be captured by
the predictors of the southern region. High environmental heterogeneity was a positive predic-
tor for the wild boar distribution and range expansion in recent studies [5,6,8].
Although we found a strong relation between predicted and observed wild boar densities
(r= 0.83), the expected 1:1 relation was not found. As highlighted by Liu et al. [21], the mass-
preserving property of the area-to-point kriging is lost when negative estimates are reset to 0.
The model accuracy was very high even when assessed against independent wild boar data
[11]. The biogeographical West-East gradient in wild boar density observed by Melis et al. [11]
was also found in our model. This longitudinal decline in wild boar densities is mainly related
to milder climatic conditions as well as higher vegetation productivity and biodiversity (i.e.,
tree species with edible seeds for wild boar) in western and southern Europe as compared to
the eastern Eurasian range [11].
In synthesis, the approach optimized the use of available coarse resolution abundance data
for producing high resolution density maps. Although the geostatistical method accounted for
units of different size and shape, the quality of the input data and high variability in size and
shape impacted model performance. In this study the approach was tested using main climatic
and environmental predictors for the wild boar. Additional predictors such as metrics of land-
scape fragmentation [32] could increase model performance [8]. The application of Poisson
regression could be tested to improve the regression-based trend interpolation [57]. In addi-
tion to the mean value, other measures of the variability of the predictors by administrative
unit (e.g., range, coefficient of variation) could be explored to improve model prediction, par-
ticularly for regions with high environmental heterogeneity. Given the availability of new
areas, more recent or more precise wild boar and geographic data, the model can be easily
updated, as well as applied to other geographic areas where the species occurrence is expand-
ing (e.g., China, United States of America).
Wild boar distribution and density maps can become useful tools to assess the growing
threat that these populations pose to agriculture (i.e. crop damage), conservation, road traffic,
and health (livestock, wildlife, and even human). Such maps will allow to first assess the situa-
tion, and then implement management actions accordingly, in an attempt to solve, or at least
minimize, the negative effects. The next section expands on how such assessment and manage-
ment applies specifically to animal health, which is a topic of particular urgency and concern
Wild boar mapping
PLOS ONE | https://doi.org/10.1371/journal.pone.0193295 May 16, 2018 14 / 19
given the current progressive spread of ASF in Europe, which often involves wild boar. An
important objective of the FAO is to disseminate the data. The predicted wild boar densities
based on the mosaicked model are freely available and can be downloaded from S1 Geodataset
of the Supporting Information, provided the original authors and source are credited.
Applications to animal health management
Wildlife and livestock connect through different paths, which allows for disease transmission
in both directions. This has clear implications in health management, as the objective is to
keep livestock and wildlife healthy, by preventing the introduction of diseases from one popu-
lation to another. Sometimes, there is also a public health component/concern. Wild boar dis-
tribution and density maps can be an extremely useful tool for veterinary services, wildlife
managers and epidemiologists to prevent and control animal diseases. When incorporated
into risk analysis or disease risk modelling, such maps allow the identification and assessment
of the specific pathways and risks posed by wild boar in the introduction, spread and mainte-
nance of animal diseases in a certain region, and their potential subsequent spread to livestock
(usually domestic pigs) and vice versa. In this line, spatial models have been recently developed
to simulate the introduction and spread of classic swine fever and foot-and-mouth disease in
wild pigs in Australia, allowing for the testing of the effectiveness of different control measures
and surveillance strategies [58,59,60]. Similar models based on accurate maps of wild boar
densities will allow identifying the potential corridors of introduction, the areas of highest den-
sities, or where wild boar-domestic pig interactions are more likely (e.g. where backyard or
free-ranging pigs, and other low biosecurity production systems exist; unsecured dumping
sites which may contain infected pig products; or through hunters). As a result, early detection
strategies in high-risk areas can be planned and implemented, e.g. strengthening passive sur-
veillance, testing hunted animals or road kills, or through targeted surveillance (non-invasive
sampling or capture and release). Prevention measures can also be applied in high risk areas.
Those applied to the domestic pig sector will be most effective in preventing infection in both
directions, e.g. double-fencing, permanent enclosure of animals, proper disposal of kitchen
and slaughtering waste, and other biosecurity improvements. Although controversial and still
subjected to extensive debate, there are intervention measures to prevent, or at least minimize,
the entry of infected wild boar into certain areas (repellents, fences, hunting pressure, etc.).
There are also management options if a disease becomes established in wild boar: vaccination,
carcass removal, ban of supplementary feeding, or hunting strategies.
The precise set of measures will depend to a certain extent on the mechanism of transmis-
sion of the disease, but in all the cases, knowing the numbers and distribution of wild boar will
greatly help to plan the strategies and estimate the efforts/resources needed.
Supporting information
S1 Fig. ATP residual kriging for the eastern region.
(TIF)
S2 Fig. Predicted wild boar density by region based on ATP-regression models: (A) east-
ern, (B) southern, (C) western, and (D) original input density data by administrative unit. Leg-
end: Low (green)–High (red) density values.
(TIF)
S1 Table. Wild boar source data.
(DOCX)
Wild boar mapping
PLOS ONE | https://doi.org/10.1371/journal.pone.0193295 May 16, 2018 15 / 19
S2 Table. Results of the variogram deconvolution for the three bioclimatic regions and rel-
ative charts.
(DOCX)
S1 Geodataset. Predicted wild boar densities based on the mosaicked model.
(ZIP)
Acknowledgments
We acknowledge the European Union’s Seventh Framework Programme (FP7/2007-2013)
under grant agreement no. 311931 (ASFORCE–Targeted research effort on African swine
fever) for funding. We would also like to acknowledge Vittorio Guberti (ISPRA, Italy), Sophie
Rossi (ONCFS, France) and Tsviatko Alexandrov (BFSA, Bulgaria), who facilitated acquisition
of the most up-to-date sub-national statistical data on some wild boar populations. Timothy
Robinson (FAO) conducted the first experimental modeling trails with the earlier versions of
wild boar population dataset using disaggregation methodology applied to livestock, which
helped to identify ways for developing original downscaling approach presented in this paper.
The views expressed in this information product are those of the author(s) and do not neces-
sarily reflect the views or policies of FAO.
Author Contributions
Conceptualization: Claudia Pittiglio, Sergei Khomenko, Daniel Beltran-Alcrudo.
Data curation: Claudia Pittiglio, Sergei Khomenko.
Formal analysis: Claudia Pittiglio, Sergei Khomenko.
Funding acquisition: Daniel Beltran-Alcrudo.
Investigation: Claudia Pittiglio.
Methodology: Claudia Pittiglio, Sergei Khomenko.
Project administration: Daniel Beltran-Alcrudo.
Resources: Sergei Khomenko.
Software: Claudia Pittiglio.
Supervision: Daniel Beltran-Alcrudo.
Validation: Claudia Pittiglio, Sergei Khomenko, Daniel Beltran-Alcrudo.
Writing – original draft: Claudia Pittiglio, Daniel Beltran-Alcrudo.
Writing – review & editing: Claudia Pittiglio, Daniel Beltran-Alcrudo.
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