Seroprevalence and factors associated with Toxoplasma gondii infection in wild boar ( Sus scrofa) in a Mediterranean island.
ABSTRACT Knowledge of the factors affecting the presence of Toxoplasma gondii in wildlife is limited. Here we analyse which local landscape characteristics are associated with the presence of toxoplasmosis in wild boar, Sus scrofa, on the island of Corsica, France. Meat juice samples from 1399 wild boars collected during two hunting seasons were tested for T. gondii antibodies using the modified agglutination test (titre 1:4). The overall seroprevalence was 0.55 (95% CI 0.50-0.59) for the first year and 0.33 (95% CI 0.29-0.35) for the second year. Seroprevalence varied according to age and county. At the county level, seropositivity in adults was related to farm density during year 1, and to habitat fragmentation, farm density and altitude during year 2. The exposure of wild boar to T. gondii is thus variable according to landscape characteristics and probably results in a variable risk of transmission of toxoplasmosis to humans.
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ABSTRACT: Wild and farmed game meat consumption has been highlighted as an emerging risk factor for Toxoplasma gondii infection in humans. In Central Italy wild boar is widely distributed and is also one of the most popular game species. The main goal of the present study was to estimate the seroprevalence of T. gondii antibodies through a serological survey conducted on 400 hunted wild boars (250 males and 150 females) during three subsequent hunting seasons (2009-2011), using an Immunofluorescence Antibody Assay. The animals were sorted by age, determined on the evaluation of the dental table; 101 were <1 year old, 175 from 1 to 3 years, and 124 > 3 years. Antibodies against T. gondii were detected in 56 (14%) serum samples with titers ranging from 40 to ≥160; a significant association (p < 0.05) was found between seropositivity and age, but not gender, hunting districts, or year of sampling.Parasite 01/2013; 20:48. · 0.82 Impact Factor
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ABSTRACT: Toxoplasmosis is a major zoonosis, and its prevention requires multiple approaches due to the complex life-cycle of its causative agent, Toxoplasma gondii. Environmental contamination by oocysts is a key factor in the transmission of T. gondii to both humans and meat-producing animals; however, its spatial and temporal variations are poorly understood. We analysed the distribution of T. gondii seropositivity in a sample of 210 cats, including the European wildcat (Felis silvestris silvestris), the domestic cat (Felis silvestris catus) and their hybrids that were collected in Central and Eastern France between 1996 and 2006. We searched for spatial variability among communes and temporal variations among years to relate this variability to landscape and meteorological conditions, which can affect the population dynamics of rodent hosts and the survival of oocysts. The overall seroprevalence was 65.2% (95% CI: 58.6–71.4). As expected, adults were more often infected than young individuals, while the occurrence of infection was not related to cat genotypes. Seroprevalence correlated significantly with farm density and the North-Atlantic Oscillation index, which describes temporal variations of meteorological conditions at the continental scale. The highest seroprevalence values were obtained in areas with high farm densities and during years with cool and moist winters. These results suggest that both farming areas and years with cool and wet winters are associated with increased T. gondii seroprevalence in cats. As cat infection determines the environmental contamination by oocysts, climate and landscape characteristics should be taken into account to improve the risk analysis and prevention of T. gondii.International Journal for Parasitology: Parasites and Wildlife. 01/2013; 2:278–285.
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ABSTRACT: Toxoplasma gondii is a protozoan parasite infecting humans and animals. Wild boars Sus scrofa are a potential source of human infection and an appropriate biological model for analyzing T. gondii dynamics in the environment. Here, we aimed to identify environmental factors explaining the seroprevalence of toxoplasmosis in French wild boar populations. Considering 938 individuals sampled from 377 'communes', overall seroprevalence was 23% (95% confidence interval: [22-24]). Using a Poisson regression, we found that the number of seropositive wild boars detected per 'commune' was positively associated with the presence of European wildcats (Felis silvestris) and moderate winter temperatures.EcoHealth 07/2012; 9(3):303-9. · 2.20 Impact Factor
Seroprevalence and factors associated with Toxoplasma gondii
infection in wild boar (Sus scrofa) in a Mediterranean island
C. RICHOMME1,2, E. AFONSO3, V. TOLON4,5, C. DUCROT1, L. HALOS6,
A. ALLIOT6, C. PERRET6, M. THOMAS6, P. BOIREAU6
AND E. GILOT-FROMONT4,7*
1INRA, UR 346 Epide´miologie Animale, Theix, France
2INRA, UR 45 L.R.DE., Corte, France
3Laboratoire Chrono-environnement, Universite´ de Franche-Comte´, UMR CNRS 6249 usc INRA, Besanc ¸on,
4Universite´ de Lyon, Universite´ Lyon 1, Laboratoire de Biome´trie et Biologie Evolutive UMR 5558, Villeurbanne,
5Laboratoire d’Ecologie Alpine CNRS UMR 5553, Universite´ de Savoie, Le Bourget-du-Lac, France
6ENVA/AFSSA/INRA UMR BIPAR, National Reference Laboratory for Foodborne Parasites,
7Vet Agro Sup, Marcy l’Etoile, France
(Accepted 16 December 2009; first published online 25 January 2010)
Knowledge of the factors affecting the presence of Toxoplasma gondii in wildlife is limited. Here
we analyse which local landscape characteristics are associated with the presence of toxoplasmosis
in wild boar, Sus scrofa, on the island of Corsica, France. Meat juice samples from 1399 wild
boars collected during two hunting seasons were tested for T. gondii antibodies using the modified
agglutination test (titre 1:4). The overall seroprevalence was 0.55 (95% CI 0.50–0.59) for the first
year and 0.33 (95% CI 0.29–0.35) for the second year. Seroprevalence varied according to age
and county. At the county level, seropositivity in adults was related to farm density during year 1,
and to habitat fragmentation, farm density and altitude during year 2. The exposure of wild boar
to T. gondii is thus variable according to landscape characteristics and probably results in a
variable risk of transmission of toxoplasmosis to humans.
Key words: Landscape, Sus scrofa, Toxoplasma gondii, wildlife, zoonoses.
Toxoplasmosis is a worldwide zoonosis due to the
protozoan parasite Toxoplasma gondii. T. gondii can
infect a wide range of animal species and has a com-
plex life-cycle which involves felids as definitive hosts,
many mammals and bird species as intermediate hosts
and an environmental stage (oocysts). Approximately
one in four people are infected with T. gondii , and
although most infections are asymptomatic, the social
and economic impact of toxoplasmosis is estimated
to be comparable to that of major foodborne diseases
. Severe clinical symptoms occur mainly after
compromised patients when T. gondii infection, or
reactivation of a latent infection, occurs .
* Author for correspondence: Dr E. Gilot-Fromont, Universite ´ de
Lyon, Vet Agro Sup, 1 avenue Bourgelat, 69280 Marcy l’Etoile,
Epidemiol. Infect. (2010), 138, 1257–1266.
f Cambridge University Press 2010
Humans may become infected by ingesting either
oocysts of T. gondii, after contact with contaminated
soil, water or raw vegetable, or infective tissue cysts
containing bradyzoites, in raw or undercooked in-
fected meat . The relative importance of these two
routes of contamination may vary in populations,
depending on food habits and geographical location
. Therefore, knowledge of sources of infection for a
given population is a prerequisite for toxoplasmosis
risk prevention. Because the parasite may infect any
mammalian or bird species, any meat is a potential
source of human infection. In particular, the con-
sumption of game has been found to be a risk factor
of infection for pregnant women . The wild boar
(Sus scrofa) is a species that carries a particular
risk, being one of the leading species consumed, and
because the wild boar are frequently infected [6–10].
Moreover, populations of wild boar have dra-
matically increased during the three last decades in
In this paper we study the presence of toxo-
plasmosis in wild boar in the French Mediterranean
island of Corsica, where an increasing wild boar
population is also observed . The consumption
of hunted wild boar meat is traditionally developed
on the island . Moreover, cooking practices are
changing (F. Casabianca, personal communication),
with an increase in barbecue cooking where the meat
is not fully cooked. However, no data on toxo-
plasmosis in animals exist for this insular region. In a
perspective of risk analysis, the first aim of the current
study was to estimate the prevalence of toxoplasmosis
in the wild boar population of Corsica.
We also aimed to analyse the local variations of
prevalence, since a better knowledge of the conditions
affecting the presence of the parasite in wildlife is
necessary in order to better estimate the risk of toxo-
plasmosis in humans in relation to game meat con-
sumption. Factors associated with variations of
seroprevalence have not been studied in the wild boar.
by rooting and feeding from soil contaminated with
oocysts excreted by cats, as shown for other species
with similar behaviour, e.g. poultry , or by inci-
dentally ingesting infected rodents. We thus expected
seroprevalence in wild boar to vary locally according
to the concentration of oocysts in the soil used by wild
boar, which itself should depend on cat density, the
presence of areas where wild boar may be in contact
with cats’ habitat, and oocyst survival. We used farm
density as an estimator of cat density, because farms
constitute areas where cat groups may grow up due to
the presence of prey and shelters . To estimate
the probability that a wild boar comes in contact with
soil or a rodent contaminated by oocysts from cat
faeces, we estimated the density of edges between cat
habitat and wild boar habitat using information on
land cover. Finally, because oocyst survival depends
on local meteorological conditions (precipitation and
temperature) , we used meteorological data to
characterize local physical conditions. We then used
these three variables as potential explanatory factors
of seropositivity in wild boar.
MATERIALS AND METHODS
Study area and sampling
The study involved the entire island of Corsica
(8680 km2) (42x 9k N, 9x 5k E), i.e. a mountainous
island mainly constituted of parallel valleys oriented
north-east/south-west. The mean altitude of the
island is 568 m, with a culminate point at 2710 m
(Monte Cintu). It is constituted of 360 administrative
counties (communes in French), with areas varying
from 0.8 to 203 km2and mean altitude ranging from
19 to 1554 m.
On the island, the wild boar’s habitat is mainly
constituted by sclerophyllous vegetation (maquis),
non-deciduous (conifers) and mixed mountain forest.
More than 20000 wild boars are hunted each year on
the island .
Samples were collected throughout island with the
help of volunteer hunters during two consecutive
and August 2007 to January 2008 (year 2). At carving,
hunters removed the diaphragm muscle and placed it
into plastic bags. The samples were kept frozen and
transported to the National Reference Laboratory for
Foodborne Parasites in Maisons-Alfort (Northern
France). For each animal sampled, hunters recorded
the county of hunting, gender and age. Age was
judged on the basis of coat colour and estimated body
Muscle fluid was obtained from 25 g of diaphragm cut
into small pieces and frozen overnight at x20 xC in a
plastic bag. After thawing at room temperature, the
meat juice was collected with a pipette into a micro-
tube as previously described . The modified
1258 C. Richomme and others
agglutination test (MAT) for the detection of
T. gondii-specific immunoglobulin (IgG) antibodies
was performed as previously described .
Muscle fluid from year 1 samples were analysed
using antigen from a commercial kit Toxoscreen1
(bioMe ´ rieux, France). Fluids were diluted at titres of
1:4, 1:20 and 1:400. The manufacturer’s instructions
were followed except the threshold dilution, which
was 1:4 and not 1:40 due to the lower concentrations
of antibodies in muscle fluids compared to sera
[18, 20, 21]. Year 2 samples from were analysed using
formalin-fixed whole RH tachyzoites as antigen, and
fluids were serially diluted twofold from titre 1:2 up
to titre 1:128. Formalin-fixed whole RH tachyzoites
were provided by the Biological Resource Centre for
Toxoplasmosis, Laboratoire de Parasitologie, Reims,
France. Muscle fluid samples reactive at a titre o1:4
were considered indicative of T. gondii infection.
A cross-validation test of 120 randomly selected
samples was performed in order to determine the
agreement between the two MAT tests. The level of
agreement was determined using the kappa statistic
interpreted according to the usual scale: <0.4. poor
agreement; 0.4–0.74, fair agreement; >0.74, good to
excellent agreement .
Design of the statistical analysis
We estimated seroprevalence (percentage of sero-
positive individuals) in wild boar and then searched
for factors influencing the probability of carrying
antibodies. The statistical unit was the wild boar.
Since the antigens used in MATs for the two con-
secutive hunting seasons were different, we analysed
the results of years 1 and 2 separately.
We also separated data according to the age class:
we defined two age classes, corresponding to juveniles
(<1 year) and adults (o1 year), and tested if sero-
prevalence depended on age using a Pearson x2test.
Data were analysed separately for juveniles and adults
because information on each age class brings distinct
information. In wild boar, births mostly occur in
March–May and July–August , while hunting
information on the risk of T. gondii infections during
the year of capture, while infections in adults cannot
be attributed to any particular period. We thus built
four models: juveniles/year 1 (n=64), juveniles/year 2
(n=171), adults/year 1 (n=425) and adults/year 2
For each of the four models, we analysed the
relationship between potential explanatory variables
and antibody carriage, considered as a binary out-
come. Since boars were located at the county level,
values of environmental variables were estimated at
the same level. Thus, all individuals hunted in a given
county had the same value for environmental vari-
ables (see below for description of the environmental
We first tested if seroprevalence varied between
counties, using a log-likelihood ratio (G test) test
of independence . As we grouped together in-
dividuals from the same county, we also checked that
data were not over-dispersed by estimating the dis-
persion (or scale) parameter of the null model, com-
puted as the residual deviance divided by the degrees
of freedom. Then each of the four models was built
using a binomial-logistic regression, including all
variables and first-order interaction terms between
altitude and the two other environmental factors (see
below). To select significant variables, we simplified
the model using a backward approach based on
Akaike’s Information Criterion (AIC) . When
models had similar AIC values (DAIC<2), we re-
tained the most parsimonious model, i.e. the one with
fewest parameters. The overall fit of the final logistic
equation was assessed using a Pearson goodness-of-fit
test . Odds ratios (ORs) and 95% confidence
intervals (CIs) were computed to quantify the associ-
ation between each variable and serological T. gondii
status. For each final model we calculated the part of
inter-county variability explained by the final model
using R2deviance , computed as the ratio of the
null model deviance minus the final model deviance
on the null model deviance minus the deviance of the
model with county as an explanatory factor.
Statistical tests were considered significant if the
P value was <0.05. All statistical analyses were per-
formed using R software .
Variables describing landscape at the county level
We obtained estimates at the county level for the three
factors that may be involved in the risk of toxo-
plasmosis in wild boar: farm density, edge density
between cat and wild boar habitats, and meteoro-
Farm density. We used farm density as an estimate of
cat density because farms are a privileged area for the
constitution of large groups of cats. Moreover, farms
may serve as reservoirs of toxoplasmosis infection
Toxoplasmosis and landscape in wild boar1259
because of the frequent presence of rodents and cats
, and the high resistance of oocysts in moist and
shaded environments of farm sites [30, 31]. We ob-
tained the number of farms per county from the
French Ministry of Agriculture (online AGRESTE
sidered all farms regardless of the precise production
involved, and we calculated farm density as the ratio
of the number of farms in the overall area of the
county. We expected seroprevalence to increase with
to calculate an index corresponding to the density
of ecotone – or edge – between wild boar and cat
habitats, we used the Corine Land Cover (CLC) map
(IFEN1data) with a resolution of 0.25 km2. The
CLC classifies land cover and use into 44 classes. We
grouped these 44 CLC classes into five categories to
characterize the openness of the landscape and
urbanization: forests; mid-open areas (maquis, moors
and heathland, transitional woodland, fruit plan-
tations and olive groves); open areas (arable and culti-
vated land, vineyards, pastures, natural grasslands);
urban areas (artificial surfaces); and others (beach,
bare rocks, burnt areas, salines, water bodies). As
populations of the European wildcat, Felis silvestris,
are considered as residual in Corsica , we only
considered domestic cats to which we attributed
patches of urban and open areas, both being their
areas of residence or hunting . We attributed
patches of forests and mid-open areas to wild boar
habitat [34, 35]. Within each county, the edge index
was calculated as a ratio of the sum of lengths of
interface between wild boar areas and cat areas on
the overall area of the county. We expected sero-
prevalence to increase with edge index, i.e. with in-
creasing density of ecotone between wild boar and cat
habitats. Analyses of the landscape composition were
performed using ArcView 3.2a (ESRI Inc., USA).
Calculation of the edge index was performed using the
Shapefile package of R software.
Meteorological conditions. Meteorological conditions
may affect oocyst survival, especially through desic-
cation when conditions are dry and hot [16, 36].
We obtained meteorological data on rain (mean pre-
cipitation in summer) and temperature (mean tem-
perature in summer) in 2006 and 2007 from
MeteoFrance1. However, these data were collected
from 20 field stations scattered over the island, and
not at the county level. We thus used the relationship
between altitude and meteorological conditions: in
Corsica, due to the strong altitudinal gradient, both
rain and temperature are correlated with altitude. We
first verified these relationships for summers 2006 and
2007. We then used altitude of the centroid of the
county as an indicator of precipitation and tempera-
ture conditions. We expected seroprevalence to in-
crease with altitude.
In order not to impose a linear relationship between
the seropositive response (logit scale) and each in-
dependent continuous environmental covariate (edge
index, farm density, altitude), we tested them as dis-
crete factors, after transformation into three mod-
alities of equal sample size. In each model we included
first-order interaction terms between altitude and
the two other factors; because altitude describes
meteorological conditions whereas farm density and
edge index deal with the local landscape we hypothe-
sized that both aspects could interact.
We collected 1399 muscle samples, 489 during year 1
and 910 during year 2 (Fig. 1). Samples came from
129/360 counties in Corsica, with 1–251 samples per
county (24 counties with one sample and 74 with a
maximum of five samples). Fifty-three counties were
sampled during the 2 years.
The agreement between the two MATs was good
to excellent (kappa=0.87) (Table 1). The overall
seroprevalence during year 1 was 0.55 (95% CI
0.50–0.59) and was significantly higher than during
year 2 (0.33, 95% CI 0.29–0.35) (Pearson x2=64.415,
P<0.001) (Table 2).
Epidemiological analysis and explanatory factors
Seroprevalence was lower in juveniles (0.45 and 0.25
for years 1 and 2, respectively) than in adults (0.56
and 0.35), but the difference was statistically signifi-
cant for year 2 only (Pearson x2=2.365 and 5.802,
respectively, P=0.124 and P=0.016). For both years,
there was a significant variability in the spatial distri-
bution of seropositivity at the county level (G test
statistic=163.498 and 197.416, respectively, both
1260C. Richomme and others
to altitude (Fig. 2). Altitude was correlated positively
with mean rainfall in summer (r=0.881 and 0.453 for
years 1 and 2, respectively, P<0.001 and P=0.045)
and negatively with mean temperature over the period
from June to August (r=x0.756 and x0.659, both
The results of the four models were different
(juveniles/year 1, juveniles/year 2, adults/year 1 and
adults/year 2). For both modelson juveniles, nofactor
was significantly related to seroprevalence. In year 1
adults, edge index had no effect, but seroprevalence
was related to farm density (Table 3): the probability
of being seropositive for an adult increased with the
farm density in the county of hunting. The odds ratio
for being seropositive was 2.7 (95% CI 1.65–4.43) for
areas with the highest farm density (o0.45 farms/
km2) compared to the areas with low farm density
(f0.22 farms/km2). The Pearson goodness-of-fit test
indicated that the final model fit was adequate
(P=0.449). The model including farm density as
unique risk factor explained 9.4% of the spatial
heterogeneity at the county level. In year 2 adults, we
found an effect of farm density but in a farm densityr
altitude interaction a nonlinear effect for farm
density was found (Table 3). The probability of being
seropositive was highest when both farm density and
altitude were high (farm density o0.47 farms/km2
and altitude o573 m), and lowest at high altitudes
but low farm density. The odds ratio for counties with
Fig. 1. Spatial distribution of toxoplasmosis in wild boar collected in Corsica from 2006 to 2008: (a) year 1 (hunting season
2006–2007), (b) year 2 (2007–2008). Circles are positioned at the centroid of the corresponding sampled county. The diameter
of the circles is proportional to sample size in the county (from 1 to 98 samples) and the level of shading to seroprevalence
(0% white; 100% black).
Table 1. Agreement between modified agglutination
test (MAT) using commercial antigen (Ag)
Toxoscreen1(used for year 1) and MAT using
antigen from the French Biological Resource Centre
for Toxoplasmosis (BRC) (used for year 2)
Ag Toxoscreen/Ag BRC
Wild boar573 5551200.87
Toxoplasmosis and landscape in wild boar1261
high farm density and high altitude was estimated as
25 compared to counties with low farm density and
high altitude. Moreover, we detected an effect of edge
index, which was also estimated to be nonlinear: wild
boars were most often seropositive when edge index
was low (OR 2.45 for low edge index). The selected
model fitted observed values (Pearson goodness-of-
fit test, P=0.361) and explained 19% of the spatial
heterogeneity at the county level.
To our knowledge the current study is the first survey
to associate T. gondii seroprevalence in a game species
with environmental risk factors. We observed a high
overall seroprevalence, ranging from 33% to 55%,
depending on the year of sampling, and a high level of
spatial heterogeneity that could be partially explained
by environmental features, especially farm density,
altitude and edge index.
Seroprevalences are in agreement with data from
previous studies using MAT to detect T. gondii in-
fection in sera of wild boar (cut-off titre 1:25): in the
USA, 18–37% [6, 7] of wild boar were found with
antibodies to T. gondii, and 38.4% in Spain .
Several other studies also showed that toxoplasmosis
is common in wild boar in Europe, even if results are
less comparable since distinct methods or thresholds
areused: 19% in Austria
fluorescence test) , 15% in the Czech Republic
(Dye test, threshold 1:4)  or 26.2% (indirect flu-
orescence antibody test)  in the Czech Republic and
Table 2. Sample size and seroprevalence for Toxoplasma gondii in wild boars collected during year 1 (2006–2007)
and year 2 (2007–2008) in Corsica (n=1399). Results are presented per age class and gender, and overall
seroprevalence is given with 95% confidence interval (CI)
0200 400 600800 1000
Precipitation (summer 2006)
0 200400600800 1000
Precipitation (summer 2007)
Temperature (summer 2006)
0200 400600800 1000
Temperature (summer 2007)
Fig. 2. Relationship between altitude and average temperature or precipitation for summers 2006 and 2007 in 20 meteoro-
logical stations from Corsica (MeteoFrance1data).
1262C. Richomme and others
8.1% in the Slovak Republic (commercial ELISA)
The MAT with a cut-off titre 1:25 has been vali-
dated to detect IgG antibodies to T. gondii in sera of
domestic pigs . In order to detect antibodies to
T. gondii in muscle fluid in the current study, we chose
the threshold 1:4 because previous studies indicated
that antibodies are diluted at least tenfold in muscle
fluid compared to serum [18, 20, 21]. The use of this
threshold may have led to an over-estimation of
the seroprevalence in the current study. Nevertheless
the results of the analysis of explanatory factors were
stable using a lower threshold (data not shown).
Seroprevalence was significantly different between
the two study years. This interesting feature may
be linked to climatic variations between the two con-
secutive years. Afonso et al.  showed that sero-
prevalence in cats varied among years, cats being
most often seropositive when temperature and pre-
cipitation prior to capture were high. High tempera-
ture and dryness decrease the survival of oocysts
[16, 41]. In Corsica, precipitation during summer 2007
was lower than during summer 2006 (975 mm vs.
1572 mm of water cumulated for the 20 main
meteorological stations of Corsica; MeteoFrance
data). The difference in prevalence may be related to
drier climatic conditions during the second year of
sampling. Moreover, domestic cats roam further
away from farms when temperatures are high .
The dissemination of T. gondii in the environment
may be higher in these conditions. Variations of con-
tamination of the environment and variations in sur-
vival conditions of parasite infective stages in external
environment could explain the significant difference
of seroprevalence we detected in wild boar in the
sampling years. More data on the temporal variability
of toxoplasmosis are required to further study the
temporal variability of toxoplasmosis risk.
We investigated environmental risk factors at the
county level, whereas, for a given individual host,
risk factors are acting at the level of its home range,
which is generally smaller than a county, but can be
variable and encompass parts of several counties .
However, this scale is appropriate to describe local
landscape management, especially via agricultural
practices. Under the same assumption, Afonso 
demonstrated that antibody prevalence in European
wildcats was related to the number of farms per
county. In agreement with the hypothesis that farm
density is an indicator of the presence of favourable
areas for cats and toxoplasmosis, we showed that
antibody prevalence in adult wild boar is related to
farm density in the county of capture. Moreover, we
found a synergic interaction between high altitude
Table 3. Coefficients of the multivariable logistic regression models selected to explain Toxoplasma gondii
seropositivity in adult wild boar from Corsica [for each modality, parameter estimate with its standard error
(S.E.), P value of the Wald test and the adjusted odd ratio (OR) with 95% confidence interval (CI)]
estimate (S.E.)P (Wald test) OR 95% CI
Year 1 Intercept
Year 2 Intercept
Farm densityraltitude* f0.25 farms/km2
0.135 1.77 0.84–3.76
0.25–0.47 farms/km2f235 m1.129 (0.565)
* No county had low farm density and low altitude.
Non-significant ORs are in italics.
Toxoplasmosis and landscape in wild boar1263
and high farm density. In Corsica, the number of
outdoor pig-breeding farms is highest at high altitudes
(Fig. 3). Swine farms are reservoirs of T. gondii in-
fection [30, 31] and, at high altitudes, high precipi-
tation levels are observed, which favours the survival
of oocysts in the environment . In Corsica, agri-
cultural areas located at high altitude meet all con-
ditions for the intense propagation of the parasite,
and wild boar hunted in these areas are particularly at
risk of transmitting infection to consumers.
Contrary to our prediction, the probability of an
adult wild boar being infected by T. gondii is highest
at low edge index, i.e. in counties with a low level of
landscape fragmentation. The edge index was ex-
pected to quantify the presence of places where in-
fective contacts may occur between wild boar and
areas frequented by cats. However, patches smaller
than 0.25 km2are not considered in the CLC data. We
thus underestimated the level of fragmentation in
counties with patches below this size. Moreover, ur-
banized areas can be smaller than 0.25 km2, as it is the
case for small villages where the agricultural land-
scape is favourable to high cat density . In our
dataset, a main part of the counties with a low edge
index corresponds to counties with small villages
where toxoplasmosis might be favoured. Counties
with a low edge index also tend to be situated at high
altitude in Corsica (r=x0.28 between edge index and
altitude), where swine farms are frequent and
meteorological conditions favour oocyst survival
(Fig. 3). Finally, the confusion between edge index,
patch size and altitude may explain the counter-in-
tuitive result found, and this confusion could not be
disentangled at this level. This result underlines the
need for investigation at a fine scale to better under-
stand how landscape composition could model the
wild boar–cat interface and influence the inter-specific
transmission of T. gondii.
The datasets concerning juveniles did not enable us
to detect any risk factor. One explanation may be the
lack of statistical power due to too few animals being
tested. Moreover, this population may be homo-
geneous regarding the risk of infection, and differ-
ences due to the environmental heterogeneity may
appear only in adults. This would be in accord with
the results found in adult populations where 9% and
19% of the variability in counties is explained by
the variables tested. An important part of variability
thus remains to be explained, possibly by other factors
that we could not investigate here, e.g. direct esti-
mation of cat density and infestation, local meteoro-
logical variables at the county level, or soil burden of
The consumption of raw or undercooked meat is
the main route of infection in humans in Europe,
representing 30–63% of infections depending on the
country considered . Specifically, pork meat is
considered as one of the major sources . However,
over the last two decades, infection in pigs decreased
dramatically with changes in pig production and
management , which raises the question of the role
of other species, including game, as a source of human
infection. Traditionally, wild boar meat is consumed
after lengthy cooking, which kills tissue cysts ;
however, consumption of raw or undercooked meat is
increasing. In Corsica, raw and salted pork meat
products are traditionally prepared, and may contain
wild boar meat . The curing of meat does not af-
fect the parasite immediately and the survival time of
tissue cysts varies with the concentration of the salt
solution and the storage temperature . Finally,
salting does not kill all tissue cysts in home-made pork
sausages . Although the origin of most human in-
fections cannot be documented precisely, acute toxo-
plasmosis has been reported in hunters following
consumption of undercooked meat from wild pigs
. Thus, while prevalence of T. gondii in pork meat
decreases, game meat should not be neglected as a
significant and possibly increasing source of toxo-
plasmosis. However, the assessment of the risk re-
lated to wildlife is only possible through a better
understanding of the complex life-cycle of T. gondii in
the natural environment. This step can be achieved by
combining information on toxoplasmosis, land use by
intermediate and definitive hosts as well as physical
characteristics of the environment, at different spatial
and temporal scales.
Number of farms
Fig. 3. Relationship between altitude and number of farms
with pigs (–2–), cattle (–&–), sheep (–m–) and goats (–*–)
in Corsica (data from French Ministry of Agriculture and
Fishing). We defined the same three altitude classes
(19–235 m, 236–572 m and 573–1554 m) as used in the
logistic regression analysis, then each class was separated in
two parts with an identical number of counties.
1264C. Richomme and others
We thank all the volunteer hunters, especially Oscar
Maestrini, for collecting field samples, the two Vet-
erinarian Departmental Laboratories of Corsica for
their technical help, and the Biological Resource
Centre for Toxoplasmosis in Reims for formalin-fixed
whole RH tachyzoites used in MAT. We thank Re ´ mi
Bouche and Franc ¸ ois Casabianca from INRA LRDE
in Corte for their material and scientific support. This
work was financially supported by Programme Bio-
scope (ANR 05 SEST 048-02) from the French
of Agriculture and Fishing.
DECLARATION OF INTEREST
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