Research Article
Tuberculosis Epidemiology and Spatial Ecology at the Cattle-Wild
Boar Interface in Northern Spain

Gloria Herrero-Garcı́a ,1 Pelayo Acevedo ,2 Pablo Quirós ,3 Miguel Prieto,4

Beatriz Romero ,5 Javier Amado,3 Manuel Antonio Queipo,3 Christian Gortázar ,2

and Ana Balseiro 1,6

1Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad de León, León 24071, Spain
2SaBio-IREC (CSIC-UCLM), Ciudad Real 13071, Spain
3Gobierno del Principado de Asturias, Oviedo 33005, Spain
4Servicio Regional de Investigación y Desarrollo Agroalimentaria del Principado de Asturias-SERIDA, Gijón 33394, Spain
5Centro de Vigilancia Sanitaria Veterinaria and Departamento de Sanidad Animal, Facultad de Veterinaria,
Universidad Complutense de Madrid, Madrid 28040, Spain
6Departamento de Sanidad Animal, Instituto de Ganadeŕıa de Montaña (CSIC-Universidad de León), Finca Marzanas,
Grulleros 24346, León, Spain

Correspondence should be addressed to Ana Balseiro; abalm@unileon.es

Received 2 November 2022; Revised 2 February 2023; Accepted 9 February 2023; Published 23 February 2023

Academic Editor: Pedro Esteves

Copyright © 2023 Gloria Herrero-Garcı́a et al.Tis is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.

Tuberculosis (TB) is a contagious chronic disease due to infection withMycobacterium tuberculosis complex (MTC) bacteria. Monitoring
of wildlife, especially potential reservoirs, is important for detecting changes in disease occurrence and assessing the impact of in-
terventions. Here, we examined whether wild boar (Sus scrofa) may contribute to the re-emergence of TB in Asturias (10,604km2),
northern Spain. Although this province was declared free of TB in cattle in November 2021, MTC bacteria remain prevalent in several
“hotspots,” with the European badger (Melesmeles) suggested as a TB potential wild reservoir. Drawing on data from the SpanishNational
Bovine Tuberculosis Eradication Program and theGovernment of the Principality of Asturias covering the period 2014–2020, we analyzed
the prevalence of TB in cattle andwild boar in this region. In hotspots (592km2), we also investigated the ranging behavior and habitat use
of fve cows that belonged to farmswith a history of TB and six trapped sympatric wild boar. During the observation period, TB prevalence
was 0.14% among cattle overall and 0.13–0.41% in hotspots, which was much lower than the prevalence in wild boar, which was 3.15%
overall and 5.23–5.96% in hotspots. Infected cattle and infectedwild boar in hotspots shared the same strains ofM. bovis, andGPS tracking
showed spatiotemporal overlap between the species, mainly around pastures during sunrise (06:00–07:00h) and sunset (19:00–20:00h).
Our results suggest that in addition to cattle and badgers, wild boar possibly help maintain TB in northern Spain, increasing the host
richness that infuences TB transmission risk in the area, which should be taken into account in monitoring and eradication eforts.

1. Introduction

Tuberculosis (TB) is a contagious chronic disease due to in-
fection with Mycobacterium tuberculosis complex (MTC)
bacteria, principally M. bovis and M. caprae [1]. Te disease is
a major social, economic, and public health challenge, afecting
domestic and wild animals [2]. Reservoirs of the disease among
wildlife can reduce the efcacy of eforts to eradicate it from

cattle [3]. TB in Europe afects multiple species, such that most
infected animals are not bovine [4], implying a wide range of
potential reservoirs. For example, the native Eurasian wild boar
(Sus scrofa), whose populations are growing in Europe [5, 6],
can contribute substantially to TB epidemiology in south-
western Spain and France [7–9]. On the other hand, the
European badger (Meles meles) is the reservoir of TB in the
British Islands [10]. Te infuence of these and other wild TB

Hindawi
Transboundary and Emerging Diseases
Volume 2023, Article ID 2147191, 11 pages
https://doi.org/10.1155/2023/2147191

https://orcid.org/0000-0002-2268-8163
https://orcid.org/0000-0002-3509-7696
https://orcid.org/0000-0002-1497-2631
https://orcid.org/0000-0001-5263-1806
https://orcid.org/0000-0003-0012-4006
https://orcid.org/0000-0002-5121-7264
mailto:abalm@unileon.es
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https://doi.org/10.1155/2023/2147191


reservoirs depends on numerous factors, including the prev-
alence of TB among cattle, characteristics and density of res-
ervoir population, land use, as well as cattle and wildlife
management practices [11–13].

Te abundance of potential TB reservoirs in the wild
highlights the need for integrated wildlife monitoring in
order to identify changes in disease occurrence and assess
the impact of interventions [14–16]. Tis is particularly true
in “hotspots” where MTC bacteria have become endemic
and remain prevalent in cattle [17]. Such monitoring should
examine the epidemiology and temporal dynamics of TB
[18, 19] as well as the spatial ecology of wildlife [11, 20]. A
particularly useful tool in these eforts is the global posi-
tioning system (GPS) technology, which can monitor in-
dividuals and characterize the network of interactions within
and between potential hosts to clarify how pathogens persist
and spread between livestock and wildlife [21–24].

Asturias (10,604 km2), in northern Spain, was declared free
of TB in cattle in November 2021, Implementing Regulation
(UE) 2021/1911 [25], but several hotspots persist, and the
region appears to contain potential disease reservoirs, i.e.,
badger [26]. Disease transmission between badgers and cattle
has been documented and supported by, for example, GPS
studies showing badgers’ presence in cattle paddocks [20, 26].
In this way, Asturias is an excellent example of a region where
a comprehensive understanding of TB persistence and trans-
mission inmultiple wild hosts is essential to control the disease.

To clarify to what extent the interaction between wild
boar and cattle helps maintain TB in this area, we analyzed
disease prevalence in these species in hotspots in Asturias
during the period 2014–2020. We also used GPS collars to
track the ranging behavior and habitat use of wild boar in
relation to cattle. Our results may help clarify how wild hosts
can maintain TB in endemic hotspots within an area de-
clared free of the disease in cattle.

2. Materials and Methods

2.1. Ethical Statement. All methods were carried out in
accordance with relevant guidelines and regulations [27]. All
experimental protocols were approved by ethical commit-
tees from the Government of Principality of Asturias, license
reference number: PROAE 47/2018.

2.2. StudyArea. Te study was carried out in three TB hotspots
in Piloña (A) and Caso (B, C) in Asturias (Figure 1), which are
located in northwestern Spain (43°21′N; 5°57′W).Tis region is
characterized by an Atlantic climate, abundant precipitations
(>1000mm/year), soft temperatures, and small thermal am-
plitude [28]. Te land varies from 0m to 2650m above sea level
(“Torrecerredo” peak) and is covered by 1.8% of cultures, 29.4%
of pastures and 57.5%of forested land, approximately [29].Here,
large-scale fencing for cattle does not exist, and wild boar, whose
abundance has increased in the last years, moves freely over the
territory [30].

2.3. Tuberculosis Prevalence in Cattle and Wild Boar.
Cattle TB prevalence data from 2014 to 2020 for Asturias
and Caso and Piloña (see Figure 1) were obtained from the

2022 National Bovine Tuberculosis Eradication Program
[25], whilst wild boar TB prevalence data were obtained
from the Government of the Principality of Asturias. Data
were obtained by the ofcial single intradermal tuberculin
test and culture in cattle and by culture in wild boar
(n � 1341).

In this study, cattle (n= 12) and wild boar (n= 29) MTC
isolates from Caso and Piloña during this period were
submitted for MTC species identifcation by PCR and
subsequent mycobacterial interspersed repetitive units-
variable number of tandem repeats (MIRU-VNTR) typ-
ing. A quantitative PCR was performed on culture isolates,
in which the MTC forward-primer 5′-TAGTGCATGCAC
CGAATTAGAACGT-3′ and the MTC reverse primer 5′-
CGAGTAGGTCATGGCTCCTCC-3′ were used, in addi-
tion to the TaqMan probe YY/BHQ 5′- AATCGCGTCGCC
GGGAGC-3′, which amplifes a 184-bp fragment [31]. MTC
isolates were characterized using DVR-spoligotyping
(VISAVET, Madrid, Spain) and coded according to the
M. bovis spoligotype database website [32]. To confrm
similarity between the isolates from both species, MIR-
U-VNTR typing was performed using the following nine
VNTR markers: ETR-A, ETR-B, ETR-D, ETR-E, MIRU26,
QUB11a, QUB11b, QUB26, and QUB3232, as described
previously [33].

Diferences in prevalence between species were assessed
using Mann–Whitney U tests. Te statistical test was carried
out using SPSS Statistics 25 (IBM, New York, USA), and the
signifcance level was set at p< 0.05.

2.4. Animal Trapping and Monitoring. Diferent cattle and
wild boar were monitored in relation to their ranging be-
havior in Caso and Piloña (approximately an area of
592 km2) (Figure 1).

2.4.1. Cattle. Five adult cows (C1, C2, C3, C4, and C5) were
monitored in the three hotspots (see Figure 1).We randomly
selected cows that belonged to farms with a history of TB.
Tey were tracked using Digitanimal GPS radio-collars
(Digitanimal®, Madrid, Spain), programmed to acquire
one location per 30min, including time, date, geographic
coordinates, and temperature (Table 1).

2.4.2. Wild Boar. Six adult free-ranging wild boar were
monitored in the three hotspots (W1,W2,W3,W4,W5, and
W6) (see Figure 1). Wild boar showing good body condition
were randomly selected. Homologated cages (Jauteco®,Spain) were used to trap the animals, after which they were
anaesthetized with tiletamine-zolazepam (0.06mL/kg) and
ketamine (0.02mL/kg), administered by means of in-
tramuscular injection [34].

Tey were monitored in diferent years and months by
using Microsensory GPS radio-collars (Microsensory Sys-
tem, Córdoba, Spain) and Digitanimal GPS radio-collars,
programmed to provide the location of the animal in a de-
termined frequency, time, date, geographic coordinates, and
temperature (Table 1).

2 Transboundary and Emerging Diseases



GPS radio-collars in cattle were used for further studies,
and they had a shorter programmed acquisition time and
a larger monitoring period; consequently, the number of
cattle locations was higher than that of wild boar.

2.5.DataAnalyses. For the 11 monitored animals (cattle and
wild boar), activity patterns, home ranges (HRs), and habitat
selection patterns were completed. Spatial overlap, envi-
ronmental overlap, and temporal overlap between species
were analyzed to describe the potential of species
interactions.

2.5.1. Activity Pattern. To calculate the animal’s activity pat-
tern, a straight line was obtained between consecutive GPS
locations separated by intervals of 2 hours (h), as locations of

wild boar were programmed to acquire information every 2h
(W4 and W5 were not considered due to the lack of locations,
see Table 1). Tis distance was then divided by the time elapsed
between them (km/h) and was used to infer activity patterns.

2.5.2. Temporal Overlap. Overlap analysis for cattle and wild
boar activity patterns acquired was undertaken using the
“overlap” R package [35]. Overlap coefcients (Δ) went from
0 (no overlap) to 1 (full overlap), and the 95% confdence
intervals (CI) were obtained through 1000 bootstrap sam-
ples. As all samples were >50 records, the coefcient Δ4 was
considered [36].

2.5.3. Home Range. Annual and seasonal HRs were esti-
mated using the fxed-kernel function in the “adehabitat” R

Table 1: Information provided by the GPS of the monitored animals. Identifcation given to each animal, months and years in which the
GPS provided information, acquisition programmed time (frequency of locations), number of days with data, and number of locations
given. Note that the number of cattle locations is higher than the number of wild boar locations due to shorter programmed acquisition time
and larger monitoring period.

ID
C1 C2 C3 C4 C5 W1 W2 W3 W4 W5 W6

Month May–Dec Jan–Dec Jan–Oct Jul–Sep May–Sep Jun–Jul May-Jun May–Jul Jul–Sep Jun–Sep May–Sep
Year 2020 2020 2020 2020 2020 2020 2020 2020 2019 2018 2019
Progr 30min 30min 30min 30min 30min 2 h 30min 2 h 4 h 4 h 2 h
Days 177 348 234 158 136 19 14 49 50 39 138
Loca 5868 11880 6664 2251 3167 71 246 260 186 167 705
ID: Identifcation; C: Cow;W:Wild boar; Progr: Programmed time, Loca: Locations, Jan: January, Jun: June, Jul: July, Sep: September, Oct: October, and Dec:
December.

Asturias councils
Prevalence of TB in bovine herds by counties

Hot-spots

C4, C5,
W5, W6 

C2, C3,
W4

C1, W1,
W2, W3 

B.

C.

A.

0 %
0.01 – 1.00 %
1.01 – 3.00 %

Scale 1:800,000
0.5 m0.25

Spain

France

Figure 1: Study area. Location of the study area (Asturias) in northwestern Spain, delimitation of counties, situation of the hotspots of
Piloña (n� 1; A) and Caso (n� 2; B and C), monitored animals in each hotspot [n� 5 cows (C1, C2, C3, C4, C5); n� 6 wild boar (W1, W2,
W3, W4, W5, W6)], and 2020 TB prevalence situation in bovine herds in Asturias (information obtained from [25]).

Transboundary and Emerging Diseases 3



package, R version 3.6.1 in [35] program [37]. Kernel 95%
was used to indicate the home range (HR), and kernel 50%
the core range (CR) [38]. Te least-squarescross-validation
method failed to converge in animals with large sample
sizes; therefore, kernels were estimated using the reference
bandwidth method [39, 40]. According to previous studies
and to estimate HRs [20, 40], the minimum number of
relocations per individual was established at 25.

Diferences in HR and CR among seasons and species
were assessed using Kruskal–Wallis H tests and Man-
n–Whitney U tests, respectively.

2.5.4. Spatial Overlap. Home range and CR were used to
estimate spatial overlap between wild boar and cattle
within the study area. Spatial overlap was calculated
using the overlap function in “rgeos” R package [35]. Tis
gave information on the area intersected (HR and CR)
between cattle and wild boar relative to cattle when the
area was divided by the HR or CR of the wild boar and
vice versa when it was divided by the HR or CR of the
cattle [41].

2.5.5. Environmental Overlap. Land uses of Caso and Piloña
were obtained from a combination of the Corine Land
Cover [42] and the National Center for Geographic In-
formation [43], with scale cartography of 1:250,000 and
considering the following land uses: cultures, pastures,
woodland, shrubland, water, no vegetation areas, and ur-
ban areas, as seen in previous studies [20, 44, 45], in view of
biological relevance. In this study, urban areas were villages
with low population density (<27 inhab/km2), with eco-
nomical activities mainly based on agricultural and live-
stock production.

GPS locations were bufered to assess interactions be-
tween cattle and wild boar in diferent land uses. Tese
considered GPS positional error and missing locations to
avoid misclassifcation of habitat use, possibly given by
landscape heterogeneity and lack of GPS locations [46]. Te
proportional cover of each land use was calculated within
each bufer. Latent selection diference functions (LSDs)
were estimated using logistic regression and the “rms” R
package [35], as described in Barasona et al. [11]. In these
analyses, locations of cattle were coded as 1 and locations of
wild boar as 0, so cattle resource selection or avoidance
relative to wild boar could be evaluated. Variables with
signifcant positive coefcients showed preferred land uses
by cattle relative to wild boar, and those with signifcant
negative coefcients indicated avoided land uses. However,
variables with no signifcant coefcients indicated areas with
no diference of use and, therefore, with a high potential for
interaction.

Te Huber–White sandwich estimator was used to es-
timate standard errors, grouping data by the individual,
which considered an unbalanced sampling design and
nonindependence of observations belonging to the same
individual [47]. Te best model was considered by means of
a forwards-backwards stepwise procedure based on Akaike
information criteria (AIC) [48].

3. Results

3.1. Tuberculosis Prevalence in Cattle and Wild Boar. Te
prevalence of TB in cattle in Asturias decreased between
2014–2018 [0.21% (2014), 0.28% (2015), 0.17% (2016), 0.08%
(2017), and 0.05% (2018)], and showed a slight increase in
2018–2020 [0.05% (2018), 0.09% (2019), and 0.09% (2020)]
(Figure 2). Wild boar TB prevalence, however, oscillated in
the same period observing the highest prevalence in 2018
[2.14% (95% CI� 0.26%–4.54%; 3/140) in 2014, 3.48% (95%
CI� 1.24%–5.72%; 9/258) in 2015, 3.59% (95% CI� 1.59%–
5.59%; 12/334) in 2016, 5.72% (95% CI� 2.44%–9.0%; 11/
192) in 2017, 6.35% (95% CI� 2.72%–9.98%; 11/173) in
2018, 0.0% in 2019, and 0.78% (95% CI� 0.75%–2.31%; 1/
127) in 2020] (Figure 2). Signifcant diferences were ob-
served between species (p � 0.024).

In Caso, cattle TB prevalence decreased between
2014–2018 and had an upturn in 2019: 2.24% (2014), 0%
(2015, 2016, 2017, and 2018), 0.63% (2019), and 0% (2020).
In this area, wild boar TB prevalence was higher than that
observed for the species in overall Asturias: 5.0% (95%
CI = 4.55%–14.55%; 1/20) in 2014, 5.88% (95% CI = 2.03%–
13.79%; 2/34) in 2015, 7.14% (95% CI = 0.40%–13.88%; 4/56)
in 2016, 10.87% (95% CI = 1.87%–19.85%; 5/46) in 2017,
5.13% (95% CI = 1.80%–12.04%; 2/39) in 2018, 0.0% (0/44)
in 2019, and 2.63% (95% CI = 2.46%–7.72%; 1/38) in 2020
(Figure 2). Signifcant diferences were observed between
species (p � 0.008).

In Piloña, the TB prevalence of cattle was as follows:
0.14% (2014), 0.19% (2015), 0.11% (2016), 0.08% (2017),
0.05% (2018), 0.09% (2019), and 0.20% (2020). In that re-
gion, wild boar TB prevalence by years also showed higher
percentages than for Asturias: 8.33% (95% CI� 2.73%–
19.39%; 2/24) in 2014, 5.41% (95% CI� 1.83%–12.93%; 2/37)
in 2015, 8.33% (95% CI� 2.73%–19.39%; 2/24) in 2016,
4.49% (95% CI� 0.19%–8.79%; 4/89) in 2017, 15.0% (95%
CI� 3.93%–26.07%; 6/40) in 2018, 0% (0/14) in 2019, and 0%
(0/25) in 2020 (Figure 2). No signifcant diferences were
observed between species in Piloña (p � 0.195).

Both in Caso and Piloña, the sameM. bovis isolates were
characterized in cattle and wild boar from the same region.
Te identifed 4 spoligotypes and VNTR profles are shown
in Table 2.

3.2. Cattle and Wild Boar TB Epidemiology

3.2.1. Activity Pattern and Temporal Overlap. To compare
activity patterns of cattle and wild boar, and due to the
absence of information on wild boar GPS monitoring in
autumn, winter, and spring, activity patterns were only
obtained for summer. Cattle manifested one peak at 06:00 h
and a similar movement until 20:00 h, when activity started
to decrease. Wild boar, on the other side, exhibited two
diferent peaks in their activity: frst at around 05:00 h and
second at around 21:00 h, coinciding with sunrise and sunset
approximately (Figure 3). However, considering both spe-
cies, two diferent periods could be established: (1) from 08:
00 h to 20:00 h and (2) from 20:00 h to 08:00 h. Te

4 Transboundary and Emerging Diseases



coefcient of overlap (Δ4) resulted in 0.63 (confdence in-
tervals 0.54–0.73) (Figure 3).

3.2.2. Home Range. Although it seems that wild boar used
larger areas than cattle (Figure 4), no signifcant diferences
were observed among cattle summer HR and CR sizes
(average± SE, HR= 93.87± 42; CR = 20.25± 8) and wild
boar HR and CR sizes (HR = 185.23± 113; CR = 40.50± 25)
(Mann–Whitney U, p � 0.178; p � 0.170, respectively). No
signifcant diferences were found among the three
hotspots.

Te largest cattle HR and CR sizes were observed in
autumn (HR= 135.73± 118; CR= 30.85± 29), followed by
summer (HR= 93.87± 42; CR= 20.25± 8), spring
(HR= 16.93± 29; CR= 3.44± 6), and winter
(HR= 11.69± 15; CR= 2.61± 3) (Figure 4). Signifcant dif-
ferences were found in cattle HR and CR among seasons
(Kruskal–Wallis χ2 = 9.817, p � 0.020; χ2 = 9.307, p � 0.025,
respectively).

3.2.3. Spatial Overlap. Spatial overlaps between cattle and
wild boar in HR and CR difered among areas during
summer, observing the highest overlap in the hotspot of
Piloña (A), followed by hotspot “B” and hotspot “C” in Caso
(Figure 5). Overall, >32% of the cattle HR overlapped wild
boar HR, whereas around 17% of the wild boar HR over-
lapped cattle HR.When comparing summer wild boar HR to
the annual HR sizes of cattle, overlaps increased in most of
them, either HR or CR. In this case, overall, >40% of the
cattle HR overlapped with wild boar HR, whereas 28% of the
wild boar HR overlapped with cattle HR (Table 3).

3.2.4. Environmental Overlap. Models selected woodland
and pastures as areas that better segregated cattle and wild
boar (coefcients resulted signifcant or marginally signif-
cant), whereas cultures, shrubland, and urban areas as land
use which worst segregated both species (not signifcant
coefcients). Cattle showed avoidance of areas with a higher
proportion of woodland, relative to wild boar, and preferred
areas with a higher proportion of pastures, also relative to
wild boar (Table 4).

Te model selected, which better explained the habitat
selection of cattle relative to wild boar, chose as infuential
variables woodland, urban areas, pastures and shrubland.
Te resulting model was signifcant (p � < 0.0001) and with
an R2 = 0.671.

4. Discussion

Our 7-year analysis of Asturias indicates that the prevalence
of TB among wild boar, particularly in hotspots, has
gradually increased, and we have provided evidence that the
disease moves between wild boar and cattle. Tese fndings
establish wild boar, together with badger [26], as wild res-
ervoirs of the disease in hotspots, which helps clarify how TB
remains a threat to cattle in these areas.

Te same M. bovis strains and VNTR profles were
identifed from infected wild boar and infected cattle in our
study. Given that TB prevalence among cattle in Asturias
increased from 2014 to 2015 and then increased among wild
boar from 2016–2018, we speculate that TB might have
travelled from cattle to wild boar during the study period.
Indeed, an initial increase in disease prevalence among cattle
followed by an increase among wild boar was observed in the
hotspots of Caso and Piloña. Te slight increase in TB
prevalence among cattle from 2018 (0.05%) to 2019-2020
(0.09%), in turn, suggests that either other infected cattle or
wildlife (i.e., wild boar and badger) might be the source of
infection in TB-free farms. Wild boar in Europe have been

0

1

2

3

4

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pr
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Figure 2: Cattle and wild boar tuberculosis (TB) prevalence trends.
Information is given for the region of Asturias and the regions of
Caso and Piloña (where the hotspots remain) during 2014–2020.

Transboundary and Emerging Diseases 5



shown to transmit TB efciently to cattle [49]. In addition,
the population density and distribution of wild boar have
increased in northern Spain since the 1990s [30, 50]. Te
wild boar may continue to grow as a TB threat to cattle in
this area, given their ability to thrive in a wide range of
habitats [51].

Te cattle in our study were active mainly in the middle of
the day, whereas wild boar were active mainly in the early
morning and late afternoon. Nevertheless, we identifed two
periods when both species were highly active: 06:00–07:00 h
and 19:00–20:00 h. At these times, the two species likely came
into contact, given the spatial overlap in their distribution.
Tus, restricting cattle movements at sunrise and sunset may
help protect them from infected wild boar. Such measures
may be particularly important in the hotspot of Piloña, where
the two species showed the greatest spatial overlap and the
highest TB prevalence among wild boar (2018).

Te HR and CR of cattle in our study were signifcantly
larger in autumn and summer than in spring and winter,
consistent with the extensive production systems common
in Asturias [52]. In such systems, animals are kept in
communal pastures and open meadows in spring and
summer, with short periods of stabling during winter. In
summer, the HR and CR of wild boar were even larger than
those of cattle. As cattle and wild boar share habitat use with
no or little restrictions in these traditional communal pas-
tures, diferent herds, as well as wildlife species, can circulate
among the same pastures, increasing the risk of disease

transmission. Our analysis, therefore, strengthens the case
for biosecurity measures in this region [24, 53].

We analyzed the HR of wild boar only during summer,
neglecting the hunting season from September to February.
During the summer, this HR substantially overlapped with
the HR of cattle, implying that the two HRs overlapped to at
least some extent during other seasons, including when
cattle were in paddocks. Indeed, the HR of wild boar in
Asturias remained large throughout the year, though it
increased signifcantly during the hunting season [54]. We
speculate that wild boar in Asturias may reach even larger
numbers of cattle in the autumn than in the summer. On the
other hand, hunting can substantially reduce the number of
wild boar [30, 55, 56]. In fact, an efcient hunting season
may explain the lower TB prevalence among wild boar in
2018-2019, although infuence from diferences in feld
sampling and diagnostic testing cannot be excluded [57].
Future work should consider the efects of hunting season on
the TB risk that wild boar poses to cattle. In addition to
hunting by humans, predation by gray wolves (Canis lupus)
can reduce wild boar populations, especially the numbers of
TB sickly animals, which are more likely to shed pathogen
into the environment, and the numbers of piglets, which are
more susceptible to infection. Such predation of wildlife
reservoirs has been proposed as a major form of natural
infection control [58, 59].

While wildlife reservoirs are a major source of infection
for cattle [60, 61], other factors also contribute to infection
risk, including the size of the cattle herd, the number of
incoming animals in recent years, pasture lease agreements
and transhumance to areas with high TB prevalence [62–64].
Future work should explore the full range of factors driving
TB prevalence among cattle in Asturias, which may help
clarify disease maintenance in other TB-free areas. Such
work should carefully consider additional factors that may
infuence the risk of disease transmission directly or in-
directly. For example, although our study refects that cattle
prefer pastures and wild boar prefer woodlands because they
provide shelter [45], the cattle dung on pastures favors the
presence of earthworms [65], which are known to attract
wild boar [66, 67] and also badgers [20], potentially in-
creasing interactions and infection transmission. At the
same time, wild boar is expanding into more humanized
landscapes, usually when their nutritional needs are not met
[68]. Aside from landscape type and land use, environmental
contamination may be important for maintaining TB in an
area, given that M. bovis can persist in favorable environ-
ments for long periods [69–71].

Table 2:Mycobacterium bovis isolates characterized for cattle and wild boar in the tuberculosis hotspots from Caso and Piloña. Spoligotype
(SB) number, VNTR profle, number (n) of cattle and wild boar, and total of animals characterized are indicated.

Spoligotype VNTR profle Cattle (n) Wild boar
(n) Total (n)

Caso
SB0134 6-3-3-3-5-9-4-5-5 6 21 27
SB0121 4-4-3-3-5-10-2-5-8 1 4 5
SB1658 6-2-3-3-4-9-3-5-5 0 2 2

Piloña SB0828 5-5-3-4-5-9-3-3-6 5 2 7

Time (hours)
00:00 06:00 12:00 18:00 24:00

0.02

0.04

0.06

0.08

0.00

D
en

sit
y

Wild boar
Cattle

Figure 3: Cattle and wild boar activity patterns and overlap among
them. Animals were monitored in the three hotspots, and activities
were measured for summer. Overlap (gray shade) between cattle’s
activity pattern (blue dotted line) and wild boar’s activity pattern
(black line) among the diferent hours of the day is indicated.

6 Transboundary and Emerging Diseases



Cattle
Wild boar

Cultures
Pastures
Woodland
Shrubland
Water
No vegetation
Urban areas

Land uses

(a)

(b)

(c)

Figure 5: Cattle and wild boar home range (HR). Representation of spatial overlap between cattle HR (in red) and wild boar HR (in green) in
the three hotspots: (a) Piloña, (b) and (c) Caso. Information is given for summer. Land uses are included, as well as identifcation of each of
the eleven monitored animals (cattle: C1, C2, C3, C4, and C5, and wild boar: W1, W2, W3, W4, W5, and W6).

Summer Autumn Winter Spring
0

50

100

150

200

250

H
R 

(h
a)

(a)

Cattle Wild boar
0

50

100

150

200

250

300

H
R 

(h
a)

(b)

Figure 4: Cattle and wild boar home range (HR) and core range (CR). (a) HR (red) and CR (gray) of cattle in summer, autumn, winter, and
spring. (b) HR of cattle (red) and wild boar (dark blue) and CR (gray) for both species in summer. Error bars indicate standard deviations.

Transboundary and Emerging Diseases 7



Our work underscores that TB in some regions of
Europe is a truly multi-host disease that involves cattle as
well as domestic and wild nonbovine species [4]. Host
richness is an important factor infuencing the transmission
risk of infectious diseases, including TB [72]. TB in northern
Spain is known to be maintained jointly by cattle and badger
[26], to which the present work adds wild boar. Te dy-
namics of infection among all these epidemiologically rel-
evant hosts should be considered if TB control programs are
to be efective [4, 72].

Our fndings in this study, however, should be inter-
preted with caution as the results in activity pattern, and
temporal, spatial, and environmental overlap may be biased
due to insufcient animals, lack of locations and short pe-
riods of monitoring, thus representing a conservative fgure
of the epidemiology and spatial ecology in the area.

5. Conclusions

Our work shows evidence of spatiotemporal overlap be-
tween cattle and wild boar in areas with high TB preva-
lence, mainly around pastures during sunrise and sunset.
We also indicate a possible interspecies TB transmission,
likely from cattle to wild boar, although TB prevalence
trends suggest that TB-free cattle could also be infected by
wild boar. Terefore, in addition to cattle and badger, wild
boar can help maintain TB in northern Spain, increasing
the host richness that infuences TB transmission risk in
the area.

Data Availability

Te data that support the fndings of this study are available
by the corresponding author upon reasonable request.

Conflicts of Interest

Te authors declare that they have no confict of interest.

Authors’ Contributions

GHG, PA, BR, JA, MAQ, CG, and AB performed the lab-
oratory analysis. PQ and MP performed the feldwork. GHG
and PA performed the statistical analysis. AB conceptualized
the study and obtained the funding. All the authors par-
ticipated in the writing of the manuscript and contributed,
importantly, to the intellectual content.

Acknowledgments

Te authors would like to thank our colleagues from
SERIDA, the Government of Asturias, SaBio-IREC, VISA-
VET, and the University of León for their help and support.
Tis work is a result of the I+D+i research project RTI2018-
096010-B-C21, funded by the Spanish MCIN/AEI/10.13039/
501100011033/Ministry of Science, Innovation and the
European Regional Development Funds (FEDER Una
manera de hacer Europa), and of PCTI 2021–2023 (GRU-
PIN: IDI2021-000102) funded by Principado de Asturias and
FEDER. Tis work was partially fnanced by the Ministerio
de Agricultura, Pesca y Alimentación. Gloria Herrero-
Garćıa is funded by Junta de Castilla y León and FSE (grant
no. LE036-20).

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