Diarra et al. Parasites & Vectors (2018) 11:341
https://doi.org/10.1186/s13071-018-2920-7
RESEARCH
Open Access
Spatial distribution modelling of Culicoides
(Diptera: Ceratopogonidae) biting midges,
potential vectors of African horse sickness
and bluetongue viruses in Senegal
Maryam Diarra1,2,3*, Moussa Fall1, Assane Gueye Fall1, Aliou Diop2, Renaud Lancelot4,5, Momar Talla Seck1,
Ignace Rakotoarivony4,5, Xavier Allène4,5, Jérémy Bouyer1,4,5 and Hélène Guis4,5,6,7,8
Abstract
Background: In Senegal, the last epidemic of African horse sickness (AHS) occurred in 2007. The western part of
the country (the Niayes area) concentrates modern farms with exotic horses of high value and was highly affected
during the 2007 outbreak that has started in the area. Several studies were initiated in the Niayes area in order to
better characterize Culicoides diversity, ecology and the impact of environmental and climatic data on dynamics of
proven and suspected vectors. The aims of this study are to better understand the spatial distribution and diversity
of Culicoides in Senegal and to map their abundance throughout the country.
Methods: Culicoides data were obtained through a nationwide trapping campaign organized in 2012. Two
successive collection nights were carried out in 96 sites in 12 (of 14) regions of Senegal at the end of the rainy
season (between September and October) using OVI (Onderstepoort Veterinary Institute) light traps. Three different
modeling approaches were compared: the first consists in a spatial interpolation by ordinary kriging of Culicoides
abundance data. The two others consist in analyzing the relation between Culicoides abundance and environmental
and climatic data to model abundance and investigate the environmental suitability; and were carried out by
implementing generalized linear models and random forest models.
Results: A total of 1,373,929 specimens of the genus Culicoides belonging to at least 32 different species were
collected in 96 sites during the survey. According to the RF (random forest) models which provided better
estimates of abundances than Generalized Linear Models (GLM) models, environmental and climatic variables that
influence species abundance were identified. Culicoides imicola, C. enderleini and C. miombo were mostly driven by
average rainfall and minimum and maximum normalized difference vegetation index. Abundance of C. oxystoma
was mostly determined by average rainfall and day temperature. Culicoides bolitinos had a particular trend; the
environmental and climatic variables above had a lesser impact on its abundance. RF model prediction maps for
the first four species showed high abundance in southern Senegal and in the groundnut basin area, whereas C.
bolitinos was present in southern Senegal, but in much lower abundance.
(Continued on next page)
* Correspondence: myriem85@yahoo.fr
1
InstitutSénégalais de Recherches Agricoles, Laboratoire National de l’Elevage
et de Recherches Vétérinaires, Dakar, Sénégal
2
Université Gaston Berger, Laboratoire d’Etudes et de Recherches en
Statistiques et Développement, Saint-Louis, Sénégal
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Diarra et al. Parasites & Vectors (2018) 11:341
Page 2 of 15
(Continued from previous page)
Conclusions: Environmental and climatic variables of importance that influence the spatial distribution of species
abundance were identified. It is now crucial to evaluate the vector competence of major species and then combine
the vector densities with densities of horses to quantify the risk of transmission of AHS virus across the country.
Keywords: Senegal, African horse sickness, Culicoides vectors, Environmental and climatic data, Random forest
models, Generalized Linear Models, Spatial distribution, Mapping
Background
African horse sickness (AHS) and Bluetongue (BT) are
vector-borne viral diseases affecting equids and ruminants
(mainly cattle and small ruminants), respectively. These two
viruses are biologically transmitted by females of several
species of Culicoides (Diptera: Ceratopogonidae) biting
midges. Bluetongue is distributed worldwide, affecting almost all continents (except Antarctica) while AHS occurs
in sub-Saharan Africa, with rare incursions into Europe and
Asia [1–3]. The importance of these two arboviral diseases
derives from their potential for rapid spread and their major
economic impact due to direct mortalities, restriction of
animal movements, surveillance and vaccination costs, as
shown by the recent outbreaks of BT in Europe [4–6] and
AHS in Africa [7, 8]. Their importance requires immediate
notification to the World Animal Health Organization [9].
Following the important AHS outbreak in Senegal in
2007 [7, 8], several studies were initiated in the Niayes area
(in the western part of the country) in order to better
characterize Culicoides diversity and ecology [10–14], and
the impact of environmental and climatic variables on the
dynamics of major vector species (proven and suspected
vectors) [15]. These studies recorded for the first time the
presence of Culicoides oxystoma and showed that this species was extremely abundant in the Niayes, both in
black-light and in horse-baited traps. Because of its abundance, close contact with horses and its known or suspected
role in the transmission of other arboviruses (Akabane, epizootic haemorrhagic disease and bluetongue viruses), these
studies concluded that it should be considered as a potential
vector of AHS virus. These studies also showed that Culicoides population peak of abundance occurred at end of the
rainy season in September and October.
In order to anticipate future events, prevent and better
control Culicoides-borne disease outbreaks, it is essential
to identify areas with higher risk of Culicoides-borne pathogens transmission at a national or regional level. This allows characterizing vector species distribution in Senegal.
This is essential to determine if vectors are present
throughout the country, if their abundance is highly variable or not and thus identify high-risk transmission areas.
Combined with horse population distribution, this enables
to target vaccination in these high-risk areas in the event
of an outbreak. The first step towards this aim implies improving our knowledge on the distribution of vectors in
Senegal. Thus, a nation-wide entomological trapping campaign was carried out to model the abundance of proven
and potential vectors of AHS virus (AHSV) and/or BT
virus (BTV) in Senegal and map their distribution.
Several studies demonstrated the ability to predict the
presence and/or abundance of Culicoides midges using
meteorological and environmental variables mainly derived from satellite imagery. These models were established using statistical techniques such as discriminant
analysis [16–20], generalized linear models such as logistic regression [21–25] and more recently, data-mining
techniques such as random forests [17, 26, 27].
The aim of this study is to model the spatial distribution
of Culicoides species which are known or suspected to be
vectors of AHSV and/or which are the most abundant in
Senegal. The models could help stakeholders to make decisions for the control of these diseases. For this, entomological data were obtained through a nationwide trapping
campaign and models for each species were developed to
map their abundance. Three different modeling approaches
were compared in order to find a simple yet robust method
for each species. The first method consists in a spatial
interpolation by ordinary kriging of Culicoides abundance
data and does not necessitate environmental data. The
other two consist of analyzing the relationship between
Culicoides abundance, environmental and climatic data to
model abundance in unsampled areas and were carried out
by implementing generalized linear models and random
forest models.
Methods
Entomological data
In 2012, a nationwide Culicoides trapping campaign was
organized to collect information on the spatial distribution
of Culicoides species in Senegal. Overall, with the help of
the veterinary services, 108 sites holding equids were initially selected as follows: 3 sites per department and 3 departments per region in 12 (of the 14) regions of Senegal.
Both the Institut Sénégalais de Recherches Agricoles
(ISRA) team and veterinary services’ officers carried out
trapping over two consecutive collection nights in each
site at the end of the rainy season (between September
and October). This timeframe was chosen because it corresponds to the peak of abundance of Culicoides in the
Niayes region [13–15, 28]. Geographical coordinates of
Diarra et al. Parasites & Vectors (2018) 11:341
sampling sites (Fig. 1) were recorded using Garmin© global positioning system receivers (accurate to within 10
meters). Culicoides species were collected using Onderstepoort black-light suction traps (Onderstepoort Veterinary
Institute South Africa) positioned close to equids (for further information on trap set up see [13, 14]). Identification
of Culicoides species was carried out as explained by [13].
Maximum abundance of the two nights was considered
as the best estimate of the population present as abundance can decrease rapidly in sub-optimal trapping conditions [29]. If the mean abundance between two nights is
used instead of the maximum abundance, then the abundance recorded will be decreased in the sites. Mean is
typically meaningful when data are normally distributed.
Page 3 of 15
Maximum is much more meaningful here as we are sure
that the site holds at least this number of Culicoides corrected by an unknown trap efficiency factor. Entomological data are provided in Additional file 1: Table S1.
Climatic and environmental data
Climatic and environmental variables characterizing
favorable habitats for Culicoides were selected based on literature review of presence and abundance models [15–18,
20, 26, 30–37]. A total of 21 variables were selected belonging to 5 categories (temperature, vegetation index, precipitation, land cover and livestock density). Climatic variables
included day-time and night-time land surface temperature
(DLST and NLST) and rainfall. Environmental variables
Fig. 1 Maximum abundance maps (2 nights of captures) for the five Culicoides species of veterinary interest in Senegal. Abbreviations: DK, Dakar;
TH, Thies; DB, Diourbel; FT, Fatick; KF, Kaffrine; KL, Kaolack; LG, Louga; SL, Saint-Louis; MT, Matam; TB, Tambacounda; KD, Kedougou; KL, Kolda; SD,
Sedhiou; ZG, Ziguinchor
Diarra et al. Parasites & Vectors (2018) 11:341
included the normalized difference vegetation index (NDVI),
percentages of 3 land-cover classes (water, savannah and forest), and ruminant host densities.
Temperature, NDVI and rainfall were extracted from
satellite images from September 1, 2011 to October 31,
2012. The DLST and NLST images were obtained from
MODIS sensor (http://modis.gsfc.nasa.gov) and NDVI images were derived from Spot-Vegetation (https://rs.vito.be/
africa/en/data/Pages/vegetation.aspx). For DLST, NLST
and NDVI, the maxima, minima and means were
calculated for this 14 months period. Precipitation data
were derived from National Oceanic and Atmospheric
Administration
(http://iridl.ldeo.columbia.edu/expert/
SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/
.ARC2/.daily/.est_prcp/). The maximum, mean and cumulative rainfalls were calculated. Land cover data was derived from the Senegal Land Cover Database produced by
the Food and Agriculture Organization of the United Nations (FAO) in the framework of Global Land Cover Network activities (http://www.fao.org/geonetwork/srv/en/
main.home?uuid=545be438-bc87-480b-83ec-ba3f4e486daf). Percentage of surface covered by water, particularly
favorable for Culicoides [15, 26] savannah and forest in the
1 km2 pixels comprising the trap were extracted. Ruminant (cattle, sheep and goats) livestock density data was
obtained from the global distribution of livestock maps
produced by the FAO (http://www.fao.org/ag/againfo/resources/en/glw/GLW_dens.html).
All data layers were clipped on the Senegalese territory
and projected in the same projection system with a spatial
resolution of 1 km2 using R2.10.1 statistical language environment [38] using of the R-package: maps [39], mapdata
[40], maptools [41], raster [42], rgdal [43] and sp [44]. Climatic data are provided in Additional file 2: Figure S1.
Statistical analysis
Three methods were used to map the abundances of targeted species of interest: ordinary kriging and two regression
techniques: generalized linear models and random forests.
Spatial interpolation by ordinary kriging
Ordinary kriging is an interpolation technique that aims
to predict the value of a variable in an unsampled site [45]
taking into account the spatial dependence of the data. It
enables converting point data (data measured in specific
sample sites) into raster data (images). In spatial statistics,
the empirical semivariance is described as follows:
1 1 X
γ ð hÞ ¼ :
ððzðxi þ hÞ−zðxi ÞÞ2
2 N ðhÞ i¼1
Page 4 of 15
First experimental semi-variograms (a graph of semivariances versus distances between ordered data) are plotted to
visualize statistical dependence values. Then a model is adjusted according to semivariogram's points to assess the
statistical dependencies between sites and thus determine
the maximal interpolation distance (over this distance interpolations can no longer be carried out).
Generalized linear model
One of the distributions commonly used to model count
data is the Poisson distribution. However, this distribution
assumes equidispersion of count data. Culicoides data generally show strong overdispersion [15]. Overdispersion was
tested according to [46]. The statistical approach was
planned as follows: in case of significant overdispersion of
the residuals of the Poisson model, a negative binomial
model was tested, and in case again of significant overdispersion of residuals of the negative binomial model, a hurdle model was tested [47, 48].
Random Forest model
Random Forest (RF) method is a robust ensemble learning
technique for data analysis which consists of a set of
classifications and regressions trees constructed from
sub-samples of the complete data set [49]. This approach
can be applied to model either the presence probability by
performing classifications for qualitative variables (Random
Forest classification) [17, 26, 27] or abundances by performing regressions for quantitative variables (Random Regression Forest) [26]. For more information on RF models and
their application to model Culicoides presence and abundance, see [26]. By bootstrapping the data and by randomly
changing the predictive variable sets over the different tree,
RF increase diversity among regression trees. Each of the k
regression trees is grown using another bootstrap subset of
the original data set and the nodes are split using the best
split variable among a subset of m randomly selected predictive variables [50]. RF parameters, which are the number
of trees (k) and the number of predictive variables used to
split the nodes (m), are user-defined. In this study, to allow
for error convergence, k was set to 500 and m to 4.
The Root means squared error (RMSE) was calculated
for internal validation of general linear models (GLM)
and RF models. Statistical analysis and modeling were
performed with the R2.10.1 statistical language environment [38] using of the R-package: rgdal [43], splancs
[51], gstat [52], maptools [41] and randomForest [50].
N ðh Þ
where, z (xi) are data at a particular location, h is a distance between ordered data, N(h) the number of paired
data at a distance of h.
Results
Between September, 17th and October, 14th 2012, among
the 108 sites initially selected, 98 sites were sampled and 10
sites were not prospected because of logistical issues. In
two of the 98 sampled sites the trapping results were not
Diarra et al. Parasites & Vectors (2018) 11:341
Page 5 of 15
exploitable and thus were not considered (one conservation
issue and one battery failure).
A total of 1,373,929 specimens of the genus Culicoides belonging to 32 different species were collected
in 96 sites during the survey (Table 1). At least 32
species were collected; there may be other species
among the 1.8% of captured Culicoides that were not
identified. Among those specimens, some belong to
the Milnei and Similis groups but could not be
identified more precisely as one sex was missing.
Some individuals belonging to the Imicola group were
shown to world experts of Culicoides but could not
be identified; at this stage it is unclear whether they
are variants of known species or new species. Two
species were recorded for the first time in Senegal:
Culicoides murtalai [53] and Culicoides ochrothorax
[54], increasing the number of described Culicoides
species found in Senegal from 53 [14] to 55.
Table 1 Abundance and frequency of Culicoides species captured during the nationwide survey in Senegal
Species
Total no. collecteda
Mean abundance per site/per night
% of catches
% of females
No. of positive sites (%)
C. oxystomab
369,618
1925.04
26.9
85.46
93 (96.88)
C. enderleinib
328,339
1710.09
23.89
86.79
92 (95.83)
C. imicolab
197,573
1029.02
14.38
86.95
92 (95.83)
C. miombo
176,917
921.44
12.87
87.80
79 (82.29)
C. trifasciellus
147,917
770.40
10.76
97.76
54 (56.25)
C. milnei
30,784
160.33
2.24
94.55
38 (39.58)
C. neavei
29,958
156.03
2.18
96.91
27 (28.13)
C. kingi
16,706
87.01
1.21
79.82
41 (42.71)
C. moreli
14,978
78.01
1.09
85.69
54 (56.25)
C. leucostictus
7277
37.90
0.53
74.71
53 (55.21)
C. nevilli
6345
33.04
0.46
49.16
33 (34.38)
C. distinctipennis
5131
26.72
0.37
70.10
25 (26.04)
C. quinquelineatus
4783
24.91
0.34
91.26
21 (21.88)
C. bolitinos
4460
23.23
0.13
83.97
55 (57.29)
C. nivosus
1894
9.86
0.09
83.54
34 (35.42)
C. fulvithorax
1366
7.11
0.08
97.91
17 (17.71)
C. pseudopallidipennis
1198
6.24
0.04
93.29
13 (13.54)
C. translucens
614
3.19
0.03
34.56
14 (14.58)
C. accraensis
504
2.62
0.03
58.86
19 (19.79)
C. hortensis
481
2.50
0.03
96.40
6 (6.25)
C. murtalai
481
2.50
0.01
100
2 (2.08)
C. similis
260
1.35
0.01
65.21
11 (11.46)
C. pycnostictus
228
1.18
< 0.01
94.60
4 (4.17)
C. africanus
91
0.47
< 0.01
100
4 (4.17)
C. austeni
78
0.40
< 0.01
100
2 (2.08)
C. azerbajdzhanicus/C. ravus
57
0.29
< 0.01
98.25
2 (2.08)
C. punctithorax
45
0.23
< 0.01
4.48
2 (2.08)
C. ochrothorax
42
0.21
< 0.01
100
1 (1.04)
C. yankari
32
0.16
< 0.01
100
1 (1.04)
C. exspectator
26
0.13
< 0.01
100
1 (1.04)
C. vomensis
18
0.09
< 0.01
100
2 (2.08)
C. robini
1
0.005
< 0.01
100
1 (1.04)
Culicoides sp.
25,729
134.00
1.81
73.27
–
Total
1,373,929
7155.88
100
87.90
–
b
b
a
2 nights, 96 sites
b
The five species of veterinary interest selected
Diarra et al. Parasites & Vectors (2018) 11:341
Each of the following five species represented more than
10% of the catch: Culicoides oxystoma, Culicoides enderleini, Culicoides imicola, Culicoides miombo and Culicoides trifasciellus. As found previously in the Niayes
region, C. oxystoma (26.9% of catches) was the most abundant species and was present in 93 out of the 96 sites sampled (97% of sites). Percentages of individuals caught
compared to the total (and frequencies of sites where the
species was present) catches were 23.89% (95.83%) for C.
enderleini; 14.38% (95.83%) for C. imicola; 12.87%
(82.29%) for C. miombo and 10.76% (56.25%) for C. trifasciellus, respectively (Table 1).
We chose to model the distribution of five species: the
first four most abundant (C. oxystoma, C. enderleini, C.
imicola and C. miombo) and also Culicoides bolitinos as
it is a proven vector of AHSV in South Africa [55]. Culicoides bolitinos was collected in low numbers (0.32% of
catches) and in 57.29% of the prospected sites.
For the five Culicoides species of veterinary interest, observed abundance data (maximum abundance of the two
consecutive nights) are shown in Fig. 1. Overall, three species
Page 6 of 15
had similar (yet not identical) patterns: C. oxystoma, C. imicola and C. enderleini. They were abundant in the west
(groundnut basin), in the south-east and in some sites in the
north-east (river delta). The distribution of C. miombo differed mainly in that it was very rare in the north-eastern sites
while the distribution of C. bolitinos was much sparser.
Regarding C. oxystoma, the sites where abundances were
the highest are located in west central (Fatick, Kaolack and
Diourbel regions), in the far west (Dakar region) and south
of Senegal (Kolda region) (Fig. 2). For C. enderleini, the
highest abundances were observed in the south (Kolda and
Kedougou), in the groundnut basin (specifically in Fatick
and Kaolack) and northwest of the Saint-Louis. For C. imicola, the highest abundances were recorded in the south
and southeast (Kolda, Tambacounda and Kedougou) and in
the center-west (Thies, Diourbel, Fatick and Kaolack). Culicoides miombo and C. bolitinos were very rare in northern
Senegal. The highest abundances of C. miombo were obtained in Kolda, Tambacounda and in the groundnut basin
(Fig. 2). Culicoides bolitinos abundances were very low.
Culicoides bolitinos highest densities were noticed in
Fig. 2 Representation of isohyets (1961–1990) and delimitation of agroecological zones of Senegal. Source: Adapted from Centre de Suivi
Ecologique du Sénégal (CSE)
Diarra et al. Parasites & Vectors (2018) 11:341
Kedougou region and in the groundnut basin (particularly
in Fatick, Kaffrine and Kaolack).
The variogram results determined the maximal
interpolation distances for each of the five species. These
distances were: 20 km for C. oxystoma, 48 km for C. imicola, 13 km for C. enderleini, 25 km for C. miombo and 10
km for C. bolitinos (Fig. 3). Different models were fitted to
the variograms: spherical models for C. oxystoma, C. imicola and C. bolitinos, a circular model for C. miombo and
an exponential model for C. enderleini. Abundance maps
by ordinary kriging were developed respecting the maximal
interpolation distance for each species (Fig. 4). For C. oxystoma, C. enderleini, C. imicola and C. miombo, abundances
were predicted to be very high in west-central and southern
Senegal and decrease gradually towards the north. However, high abundances of C. enderleini were predicted in
the northwest of Senegal and, to a lesser extent, for C. oxystoma. For C. bolitinos, the highest abundances were
Page 7 of 15
modelled in the extreme south of Senegal and south of the
groundnut basin (specifically in Kaffrine region).
To produce abundance maps for the entire Senegalese
territory, GLM and RF models were developed. Table 2
presents the 10 most important predictors for each species
according to GLM models. Maximum NDVI was among
the three most important variables for all species. Average
rainfall was also among the 3 most important variables for
3 of the 5 species (C. oxystoma, C. enderleini and C. imicola). Other environmental variables that influence Culicoides abundances were day and night temperatures and
percentage cover of water bodies for C. oxystoma and C.
imicola; night temperatures for C. enderleini; day and
night temperatures for C. miombo; livestock density and
percentage cover of savannah for C. bolitinos. Abundance
maps based on GLM modeling showed that for all five
species of veterinary interest, predicted abundances are
very low along the Senegal River Valley and very high in
Fig. 3 Variogram models for each of the five Culicoides species of veterinary interest in Senegal
Diarra et al. Parasites & Vectors (2018) 11:341
Page 8 of 15
Fig. 4 Abundance maps obtained by ordinary kriging for the five Culicoides species in Senegal. Abbreviations: DK, Dakar; TH, Thies; DB, Diourbel;
FT, Fatick; KF, Kaffrine; KL, Kaolack; LG, Louga; SL, Saint-Louis; MT, Matam; TB, Tambacounda; KD, Kedougou; KL, Kolda; SD, Sedhiou; ZG, Ziguinchor
the south (Fig. 5). High abundances of C. imicola and C.
oxystoma are predicted on the entire country although for
C. imicola areas of high and low abundance are strongly
interlinked (areas of high abundance are often close to
areas of low abundance). Culicoides imicola and C. enderleini predicted abundances are particularly strong in the
southern third of the country and medium in the middle
third. The distribution of C. miombo in the south is even
more pronounced: abundant in the southern third, it is
less abundant in the central and almost absent in the
northern third of the country. Not surprisingly, C. bolitinos predicted abundances are lower than those of the
other species. The area where it is most abundant is the
delta in Casamance in southeast Senegal and in southwest
Senegal (Fig. 5).
Table 3 lists the 10 most important predictor variables
from RF model of the different Culicoides species. Rainfall and/or NDVI were the most important variables influencing the abundance of the five Culicoides species.
This is particularly true for C. imicola, C. enderleini and
C. miombo, since the 3 most important variables are
average rainfall and minimum and maximum NDVI.
Abundance of C. oxystoma was mostly determined by
average rainfall and day temperature and less so by
NDVI (only ranked 7th by order of importance). Abundance of C. enderleini was mostly determined by average
precipitation, NDVI (minimum and maximum) and
average day temperature; that of C. imicola by average
precipitation and maximum NDVI. Abundance of C.
miombo was mostly driven by maximum NDVI followed
Diarra et al. Parasites & Vectors (2018) 11:341
Page 9 of 15
Table 2 The 10 most important predictors according to GLM model, indicated by the variable importance (VarImp) for the five
Culicoides species of veterinary interest
C. oxystoma
C. enderleini
C. imicola
C. miombo
C. bolitinos
Predictors
VarImp
Predictor
VarImp
Predictor
VarImp
Predictor
VarImp
Predictor
VarImp
AvRaina
83
MaxNDVIa
100
AvRaina
74
MaxNDVIa
100
MaxNDVIa
72
MaxNDVIa
74
AvRaina
64
MinNDVIa
58
MinNDVIa
98
AvHosta
64
a
a
MinDlst
62
MinNDVI
58
MaxNDVI
MaxNlsta
57
MaxNlst
39
MaxNlst
34
a
a
MaxNlst
87
lcSavanha
63
32
MinDlsta
68
MaxDlst
47
MinNlst
37
MinNlst
33
MinDlst
32
AvHost
37
MaxNlst
36
lcWater
35
MaxRain
28
lcWater
28
AvRain
36
avDlst
30
MinNDVI
22
lcWater
17
avNlst
27
AvDlst
21
lcForest
16
MaxRain
18
AvDlst
13
avDlst
27
MaxRain
15
lcWater
14
AvDlst
14
lcForest
11
AvHost
25
AvRain
12
AvRain
10
AvHost
12
MinDlst
8
MaxRain
23
lcWater
8
MinDlst
6
Abbreviations: AV average, Min minimum, Max maximum, Dlst day land surface temperature, Nlst night land surface temperature, lc landcover, NDVI normalized
difference vegetation index, Host livestock density
Predictors for which VarImp are greater than 50%
a
by average precipitation, minimum NDVI and average
night temperature. Culicoides bolitinos had a particular
trend; the above environmental and climatic variables
had a lesser impact on its abundance.
Predicted Culicoides abundance maps provided by RF
model are shown in Fig. 6. For the five species, an
increasing abundance gradient from north to south is
predicted. For C. oxystoma, C. enderleini and C.
imicola, predicted abundances are very high in the
southern third of the country, and for C. miombo abundances are high to very high in the southern and middle thirds. For all the species, predicted abundances are
very low in the north of the country, particularly in the
north of Ferlo and in the Senegal River valley. Culicoides oxystoma is predicted to be particularly abundant along the western coast from Dakar to Ziguinchor
and in The Gambia along the river. This species also
has very high predicted abundances in the south,
southeast and center-west of Senegal (Fig. 6). Culicoides enderleini and C. imicola exhibit a similar
pattern with very high abundances predicted in Casamance (Ziguinchor, Sedhiou and Kolda), eastern
Senegal (Tambacounda and Kedougou) and the
center-west of the groundnut basin (Fatick and surrounding). Regarding C. miombo and C. bolitinos, very
low abundances were predicted in the Niayes’ area.
Culicoides bolitinos predicted abundances are lower
than those of other species; the area where its abundance is predicted to be the highest is the south and
southeast, particularly in Kedougou region.
Prediction errors measured by RMSE (on log10(n+1)
scale) between observed and predicted values are lower
for the RF model than for the GLM model for all five
species (Table 4). Among RF models, predictions errors
are lowest for C. imicola (RMSE = 0.31).
Discussion
The identification of suitable areas for five targeted Culicoides species in Senegal was performed with three different modelling techniques: a spatial interpolation method,
kriging and two statistical modelling methods using meteorological and environmental variables, GLM and RF
models. Interpolation by kriging yielded incomplete prediction maps. To obtain complete maps, more intensive
entomological survey would be necessary, especially for C.
bolitinos and C. enderleini. Given the logistical efforts required to conduct such survey and identify Culicoides species, this option seems unrealistic.
The comparison of predicted abundance maps from
each model showed consistent spatial structures. Although the predictions of maximum abundances provided by kriging were limited by extrapolation distances,
abundance maps by kriging and RF models identified
similar structures whereas GLM models provided abundance maps slightly different from those resulting from
kriging and RF models. GLM outcomes differed for C.
oxystoma particularly in Tambacounda and Kedougou,
for C. enderleini in Fatick, Kaffrine, Kaolack and western
Saint-Louis, for C. imicola in Thies, Fatick, Kaolack, for
C. miombo in Dakar, Kaffrine and Tambacounda and for
C. bolitinos in Kaolack, Kaffrine and Fatick.
Prediction errors between observed and predicted
abundances were lower for the RF model than for the
GLM model. Recent studies have also shown that RF approaches provide good modelling results both for regression [26] and classification [17, 26, 27] trees. The effect
of climatic and environmental variables will be discussed
for each species based on the results from RF models
since they provided better estimates of abundances.
Regarding C. oxystoma, this is, to our knowledge, the
first spatial distribution model published. Studies on
Diarra et al. Parasites & Vectors (2018) 11:341
Page 10 of 15
Fig. 5 Predicted abundance maps according GLM model for five Culicoides species of veterinary interest in Senegal. Abbreviations: DK, Dakar; TH,
Thies; DB, Diourbel; FT, Fatick; KF, Kaffrine; KL, Kaolack; LG, Louga; SL, Saint-Louis; MT, Matam; TB, Tambacounda; KD, Kedougou; KL, Kolda; SD,
Sedhiou; ZG, Ziguinchor
Culicoides dynamics in Senegal show that this species is
very frequent and abundant in the Niayes area [13, 14].
Our study shows that this species is widespread and abundant over the entire country, with an increasing gradient
from north to south, and high predicted abundance in the
west, near the coast and around river deltas.
Average rainfall was the most important variable influencing C. oxystoma, C. enderleini and C. imicola abundance,
and the second most important variable for C. miombo.
This variable seems to limit the abundance of these species
in the north, more arid (see isohyets representation of
Senegal in Fig. 2). In addition to rainfall, day and night
temperatures strongly influenced C. oxystoma abundance.
Its highest abundance was observed in the groundnut basin
and southern Senegal. Although ecological drivers of C.
oxystoma remain poorly described, this species is known to
be abundant in South Korea [56] and in Kagoshima, southern Japan [57]. These two areas are characterized by a
humid subtropical climate. Culicoides oxystoma was collected from May to October in the south of Korea [56] and
from May to November in southern Japan [57], i.e. in the
warmer and rainy period of the year, showing that rainfall
and temperature appear to influence the distribution of this
species in both Japan and in Senegal.
Diarra et al. Parasites & Vectors (2018) 11:341
Page 11 of 15
Table 3 The 10 most important predictors according to the RF model, indicated by the variable importance (VarImp) for the five
Culicoides species of veterinary interest
C. oxystoma
C. enderleini
C. imicola
C. miombo
C. bolitinos
Predictor
VarImp
Predictor
VarImp
Predictor
VarImp
Predictor
VarImp
Predictor
VarImp
AvRainb
13.54
AvRainb
13.40
AvRainb
18.82
MaxNDVIa
47.42
MaxNDVIc
6.31
AvDlstb
11.41
MinNDVIb
13.16
MaxNDVIb
10.37
AvRainb
20.64
AvHostc
5.91
c
c
MaxDlst
6.07
MaxNDVI
11.87
MinNDVI
8.42
MinNDVI
11.15
MinDlst
5.66
MinNlstc
5.96
AvDlstb
11.08
MaxRainc
6.19
AvNlstb
10.51
MaxDlstc
5.65
c
b
c
c
b
c
c
AvNlst
5.47
MaxDlst
8.13
AvNlst
5.66
MaxNlst
9.50
AvDlst
MinDlstc
5.32
MaxRainc
6.35
AvDlstc
5.25
MaxDlstc
8.88
MaxNlstc
c
c
c
5.64
5.30
c
MaxNDVI
5.04
MinNlst
5.85
MinNlst
4.62
MinNlst
8.33
MinNDVI
5.21
MaxRain
4.93
MinDlstc
5.31
MaxNlst
4.37
MaxRainc
7.25
lcSavanhac
5.20
c
c
c
MaxNlst
4.63
AvNlst
5.16
MinDlst
4.09
MinDlst
6.91
lcForest
5.13
AvHost
4.08
AvHost
4.43
MaxDlst
3.45
AvDlstc
6.81
lcWater
4.72
a
Predictors for which VarImp are greater than 30%
Predictors for which VarImp are lower than 30% and greater than 10%
Predictors for which VarImp are lower than 10% and greater than 5%
b
c
Livestock density seems to have a weak influence on C.
oxystoma abundances. Generally, livestock density was not
identified as a variable with a high impact on Culicoides
abundance except for C. bolitinos. This is surprising because for many other Culicoides species, especially those
vectors of pathogens to animals, the presence of hosts and
their density are key factors known to affect their catch
[58–60]. Several reasons might explain this outcome. First,
host densities data was available at a low spatial resolution
(100 ×100 km). Secondly, these consisted in 2005 predictions, and densities could have changed (increased or decreased) since then. Thirdly, host densities included only
three ruminant species (cattle, sheep and goats) but did not
include horse densities. To our knowledge there is no map
of horse densities available for Senegal. The lack of data on
horse densities could be an important hindrance since in
the field, in Senegal, Fall et al. [61] found high abundances
of C. oxystoma on horses. Finally, some Culicoides species
are able to feed on other hosts, including birds and wildlife,
and it is possible that the densities of these other host species impact the distribution of that species.
As for C. oxystoma, little information in the literature is
available on climatic and environmental variables that could
impact C. enderleini. In this study, C. enderleini was very
abundant in the southern third of the country. RF models
showed that its abundance is significantly associated with
rainfall, vegetation index, day and night temperatures and,
to a lesser extent, livestock density. This is coherent since
in the south of Senegal, rainfall is important, average NDVI,
temperatures and livestock density are high.
The distribution of C. imicola and C. enderleini followed
a similar pattern with an increasing north-south gradient.
Again, average rainfall could be a limiting factor in the
north, explaining higher abundances in the south
(Ziguinchor, Kolda and Kedougou) and center-west (in the
groundnut basin) regions that record the highest rainfall in
Senegal. The importance of rainfall on the distribution and
abundance of C. imicola was highlighted in Morocco [33]
and Europe [22, 26, 37, 62]. The study of Culicoides dynamics in the Niayes area also showed that peaks of abundance
of C. imicola occurred during the end of the rainy season
(September-October) [13].
Maximum and minimum NDVI were the second and
third most important variables associated with C. imicola
abundance. The link between the distribution of C. imicola and NDVI is consistent with previous studies showing that high NDVI values were associated with increased
C. imicola abundance in the Niayes area in Senegal [15],
in north Africa and in Europe [16, 24, 30–33]. In Senegal,
areas where the vegetation index is the highest are also
those where rainfall is highest, as both are linked [63].
Studies have also highlighted the link between NDVI and
soil moisture [64]. Soil moisture could be an important
factor influencing larval development of this species. Temperatures (day and night) were also found associated with
C. imicola abundance. This finding has been confirmed by
several other studies [16, 18, 19, 25, 29, 32, 35].
Concerning C. miombo, maximum NDVI was by
far the most important variable influencing its abundance. Its highest abundances were recorded in areas
where the vegetation index is the highest, i.e. in the
groundnut basin and in southern Senegal. In the
north of the country where the vegetation index is
the lowest, C. miombo is almost absent. In the Niayes
area (particularly in Dakar and Thies), the abundance
of C. miombo was low, confirming the results of a
previous study [13]. This is probably due to the low
vegetation index in this area. In this study, rainfall
and temperatures were found associated with the
abundance of C. miombo, in coherence with
Diarra et al. Parasites & Vectors (2018) 11:341
Page 12 of 15
Fig. 6 Predicted abundance maps according RF model for five Culicoides species of veterinary interest in Senegal. Abbreviations: DK, Dakar; TH,
Thies; DB, Diourbel; FT, Fatick; KF, Kaffrine; KL, Kaolack; LG, Louga; SL, Saint-Louis; MT, Matam; TB, Tambacounda; KD, Kedougou; KL, Kolda; SD,
Sedhiou; ZG, Ziguinchor
Table 4 Internal validation (root means squared error (RMSE) on
log10(n+1) scale)of GLM and RF models for each of the five
species of veterinary interest
Species
RMSE
GLM
RF
C. oxystoma
1.72
0.33
C. enderleini
1.9
0.37
C. imicola
1.75
0.31
C. miombo
1.36
0.37
C. bolitinos
1.42
0.38
Meiswinkel’s study, which revealed that this species
is sensitive to rainfall and high temperatures [65].
Regarding C. bolitinos, no single variable was a dominant
driver of its abundance. Indeed, the contribution of the best
environmental predictor (maximum NDVI) on its abundance remained very low (variable importance = 6.31). The
most important variables impacting its abundance were the
NDVI, livestock density and temperature. These three types
of variables have also been linked to C. bolitinos abundance
in South Africa [66]. Livestock density was an important
variable which greatly impacted model accuracy. Studies
conducted by Fall et al. [61] showed that C. bolitinos is
Diarra et al. Parasites & Vectors (2018) 11:341
particularly aggressive on horses with a blood-feeding rate
of over 75%. This species is also known to feed on large
mammals [67]. Other studies describe the larval habitats of
C. bolitinos as being the dung of wild and domestic Bovidae
in Africa [68]. The ecology of this species is therefore closely
linked to its hosts (through feeding and through its breeding
media) and it is thus coherent to find a strong link to livestock densities and weaker environmental drivers of its
abundance.
Overall, the highest abundances of these five species,
proven or suspected vectors of AHS and BT viruses, were
recorded in southern Senegal. If C. oxystoma seems present
rather in the southwest of the country, C. enderleini, C.
miombo and C. imicola have very high abundances in the
center-south (Kolda), in the south-east (Kedougou) and in
the groundnut basin. Culicoides abundance maps from RF
models were compared with those of AHS outbreaks in
2007 in Senegal [8], which show significant mortality of
horses in the groundnut basin. According to Diouf et al. [8],
the greatest risk of introduction of AHS virus in Senegal is
through the northeastern border because of important commercial trade movements and numerous markets to and
from which carters transport goods. These authors suggested focusing surveillance of AHS virus introduction in
that area in order to prevent the virus from reaching the
groundnut basin where the dense network of markets could
substantially amplify disease transmission and diffusion.
Diouf et al. [8] considered the south and southeastern part
of the country to be less at risk of AHS because of lower
horse densities due to the presence of tsetse flies. Because
low densities of Culicoides were found in the north-eastern
part of Senegal, this study strengthens the hypothesis that
the main driver of AHS introduction and spread in 2007
was horse movements. It confirms that the groundnut basin
is an area at high risk of AHS transmission, because it combines important vector, host and market densities, which
could lead to important epidemics. The very important
densities of Culicoides in the south suggest that if the virus
was present in the neighboring countries (Mali, the Republic
of Guinea and Guinea Bissau), it could be introduced
through the spread of infected Culicoides and then be amplified in donkeys.
The two main knowledge gaps to better assess AHS
transmission risk in Senegal are (i) vector competence of
suspected vectors and of very abundant species; and (ii) a
map of equid (horse and donkey) densities. Assessing the
vector competence for ASHV of species such as C. oxystoma, C. enderleini and C. miombo is essential to evaluate
their roles in disease transmission. Mapping not only
equid densities but also equid movement networks in
Senegal would be of great help for decision makers, since
combining vectors abundance maps with maps of equid
densities and movements of equids would enable evaluation of AHS transmission risk. In a second step,
Page 13 of 15
combining transmission risk with AHS virus introduction
risk would enable making recommendations in terms of
early warning systems and vaccination policies.
Conclusions
To our knowledge, this study is the first to map the distribution of five major species of Culicoides of veterinary
interest in Senegal. The highest abundances of Culicoides
were observed in the south and in the groundnut basin,
whereas abundances were the lowest in northern Senegal.
Abundance maps were produced using three different
modelling approaches. RF models provided better estimates
of abundances than GLM models and were not limited by
interpolation distances contrary to kriging. Environmental
and climatic variables of importance that influence the
spatial distribution of species abundance were identified. It
is now crucial to evaluate the vector competence of major
species and then combine the vector densities with densities of horses and ruminants to quantify the risk of transmission of AHS and BT virus across the country.
Additional files
Additional file 1: Table S1. Culicoides data from Senegal throughout a
nation-wide trapping campaign in 2012. (XLSX 15 kb)
Additional file 2: Figure S1. Climatic and environmental data on
Senegalese territory with a spatial resolution on 1 km2. Abbreviations: Av,
Average; Min, Minimum; Max, Maximum; Dlst, Day land surface temperature;
Nlst, Night land surface temperature; lc, landcover; NDVI, Normalized
difference vegetation index; Host, livestock density. (PDF 308 kb)
Abbreviations
AHSV: African horse sickness virus; BTV: Bluetongue virus; DLST: Day-time
land surface temperature; GLM: Generalized Linear Models; NDVI: Normalized
difference vegetation index; NLST: Night-time land surface temperature;
RF: Random Forest
Acknowledgements
We thank the veterinary services of Senegal for helping us organize and
carry out the trapping campaign. We thank the horse owners for allowing us
access to their holdings. We thank Dr Thomas Balenghien and Dr Claire
Garros for the Culicoides identification training session they organized at
ISRA. We also thank Dr Claire Garros for her constructive comments on the
manuscript.
Funding
This study was funded by EU grant FP7-261504 EDENext and is catalogued
by the EDENext Steering Committee as EDENext389 (http://www.edenext.eu).
The contents of this publication are the sole responsibility of the authors
and do not necessarily reflect the views of the European Commission.
Availability of data and materials
Data supporting the conclusions of this article are included within the article
and its additional files. Raw data are available from the corresponding author
upon request.
Authors’ contributions
HG, MD, AGF, MTS, AD and JB designed and supervised the study. MF, AGF,
MD and MTS performed sampling and global management of the
entomological material. RL extracted climatic and environmental data. MF
performed the species identification with the help of XA and IR. MD and HG
analyzed the data and wrote the first draft of the manuscript. All authors
read and approved the final manuscript.
Diarra et al. Parasites & Vectors (2018) 11:341
Ethics approval and consent to participate
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
InstitutSénégalais de Recherches Agricoles, Laboratoire National de l’Elevage
et de Recherches Vétérinaires, Dakar, Sénégal. 2Université Gaston Berger,
Laboratoire d’Etudes et de Recherches en Statistiques et Développement,
Saint-Louis, Sénégal. 3Institut Pasteur de Dakar, G4 Biostatistique,
Bioinformatique et Modélisation, Dakar, Sénégal. 4CIRAD, ASTRE, Montpellier,
France. 5ASTRE, INRA, CIRAD, Univ Montpellier, Montpellier, France. 6Cirad,
ASTRE, Antananarivo, Madagascar. 7Institut Pasteur, Epidemiology Unit,
Antananarivo, Madagascar. 8FOFIFA, DRZVP, Antananarivo, Madagascar.
Received: 12 October 2017 Accepted: 27 May 2018
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