los abajo firmantes, como directores de esta tesis
TRANSCRIPT
Los abajo firmantes, como directores de esta tesis, hacemos constar que la tesis
titulada “Factores que modulan las tendencias temporales de las enfermedades
compartidas con la fauna silvestre”, y realizada por Mariana Boadella Caminal,
licenciada en veterinaria, reúne los requisitos necesarios para su defensa y
aprobación, y por tanto, para optar al grado de Doctor.
Vº Bº de los Directores
Dr. Christian Gortázar Schmidt Dr. Pelayo Acevedo Lavandera
Dr. Joaquín Vicente Baños
Universidad de Castilla-La Mancha
Instituto de Investigación en Recursos Cinegéticos
(CSIC-UCLM-JCCM)
A mis estrellas polares, Dolors y Albert, por no dejar de brillar ni una sola noche
A Roberto, sin motivo y por todos
Un pollo de buitre negro en la portada y esta tesis no trata sobre los buitres. No es
un sinsentido, los buitres para mí son grandes gestores de la sanidad animal. Pero
por paradojas del enorme desequilibrio que el hombre ha creado, el afán por
erradicar una enfermedad les perjudicó.
Y ésta es la forma que tengo de recordarme que aunque nuestra ciencia sea esa
verdad en la que creemos, quizás debemos humildemente medir el alcance de lo
que hacemos en su nombre, porque al fin, ella no es más que nuestro incansable
intento para explicarnos lo que pasa de verdad.
Este trabajo de Tesis Doctoral se realizó gracias a los siguientes proyectos y convenios:
Proyecto FISCAM PI-2007/56, Junta de Comunidades de Castilla-La Mancha
Proyecto PPIC10-0226-0243, Junta de Comunidades de Castilla-La Mancha
Proyecto POII09-0141-8176, Junta de Comunidades de Castilla-La Mancha
Proyecto AGL2005-07401, Plan Nacional I+D+i, MICINN y FEDER
Proyecto AGL2008-03875, Plan Nacional I+D+i, MICINN y FEDER
Proyecto FAU2008-00004-C03 INIA
Proyecto TB-STEP FP 7212414, Unión Europea
Convenio Grupo Santander - Fundación Marcelino Botín
Convenio MARM, Organismo Autónomo de Parques Nacionales (OAPN) y CSIC
Convenio entre el CSIC y el Principado de Asturias
Agradecimientos
Phoenix, noviembre de 2011
Cuesta empezar a escribir sobre algo tan grande en un sitio tan pequeño. Dar las
gracias por lo vivido en estos cuatro años es de lo que intento escribir, pero es
también todo lo que no puedo escribir, lo que no cabe y lo que no tiene palabras,
todo eso que espero haberos dicho ya sin necesidad de ellas. Lo compartido, lo
aprendido y lo que admiro de cada uno de vosotros.
Tres directores, Gortázar, Acevedo y Vicente. Pero ante todo, Christian, Pelayo y
Joaquín. Es cierto, todos lo saben, lleváis la ciencia dentro, pero también la
generosidad. Hay mucho para dar las gracias, porque para mí ha sido ésta una gran
suerte. A Christian, por ser el Coronel de este gran batallón y el motor detrás de
esta tesis, por el entusiasmo en lo que hace, y esa capacidad para contagiarlo. Por la
confianza que depositó en mí. Por los cafés a cambio de libreta. Por romper los
aburridos silencios con una gran risa. A Pelayo por esa actitud positiva, por el rigor
en hacer las cosas, por las buenas ideas, por los ratos de Skype. A Joaquín por esa
mente tan prodigiosa y los entrañables despistes que le hacen humano, por esos
momentos de mirar al vacío que tanto me han hecho temblar, y por las pocas
palabras que han hecho falta para saber dónde había una amistad.
A este gran grupo de gente mal llamado submundo, un batallón que aguanta
muchas guerras, en el que me sentí como en casa desde el primer día y con el que
ha sido un placer trabajar. A Paqui por los cinco mil favores sin que faltara una
sonrisa en ninguno de ellos, por querernos tanto. A Encarni por tener tanta
paciencia. A Mauricio, Bea y Valeria por la buena voluntad siempre. A Jose Ángel
por ser tan trabajador pero también por cuidar de mí y dejarme ser tan payasa. A
Vidal, por las pequeñas agradables palabras en su camino a la impresora. A las
Virginias, los Joaos, Nacho, Isa. A Fran, por ser tan atento y preocuparse de mis
dudas. A Álvaro y Óscar, por todo el trabajazo en el frente del norte. Y a la que se
fue al norte y se llevó allí sus rizos y su alegría, pero echamos de menos en el sur. A
Mari Paz y los cien motes con la que la llamamos como pequeña muestra del gran
Tesis doctoral
cariño que se ha ganado, por ser un ejemplo de fortaleza y no dejar de sonreírle a la
vida.
A los nuevos, Iratxe, Nelson, David, por aguantar a este ser rancio sin apenas
conocerle. A los que dejaron el relevo y también buenos recuerdos de días de
campo, Rafita, Ricardo, Gamarra, Jesús, Tamara, Elisa, Lucca, Tania, Caterina. A
Vane, por ser la mejor anfitriona del B3 que uno pueda tener, y ser tan única, y por
haber ampliado nuestro vocabulario de forma ya irreversible.
A Alfredo por esa amistad rara pero divertida, por los ratos de coche comiendo
pipas camino de San Quirce.
A los guardas de Lugarnuevo, por hacer de los días de muestreo algo tan parecido a
una película cómica.
A Jorge Jordi y la gente del CReSA por hacer tan agradable el eje IREC-Barcelona.
A Susana, Paulo Célio y la gente de CTM. A Pp, Nacho, Lina, Konstantin, y a
todos con los que he tenido el placer de trabajar.
A Montse por cuidar siempre tan bien de nosotros, en los días de tesis en el Llorà,
y a esa otra familia que se llama Joglars.
A Salva por estar siempre ahí, por los paseos en bici en medio de los agobios. A
Jesús por esa capacidad de hacernos reír a todos sin mover un músculo. A Marisa,
por los divertidos momentos de grullas gritonas en los despachos. A Loren, por ese
humor cínico, inigualable, por ser más listo que el hambre. A Eli por su visión
crítica y tan cabal de las cosas, por sus risas de sorpresa. A Elo, por ser tan atenta,
por cuidar y confiar en mí. A Alba por tener claro lo que la amistad significa. A
Elenis por ese optimismo recalcitrante, esas ganas de todo. A Raspa por ser la
personalidad más auténtica que he conocido. A Tachu por esa risa auténtica, como
él. A Carlos por ese espíritu joven, a los dos, por amenizar viajes y veladas de
colores y formas distintas. A Luis y Conchi, por estar siempre dispuestos a pasarlo
bien. A Sandra por convertirse en una persona tan especial en mi vida, con la que
me va a costar mucho no vivir. A vosotros, por las mil historias tan geniales que
hemos generado juntos, por mantenerme a flote en los momentos que fueron
Agradecimientos
bajos, por cambiar algo y tanto en mí, por hacerme sentir una persona tan
afortunada.
A esa institución llamada Akelarre, y sus maravillosos miércoles de desahogos y
risas.
A todos los que hacéis del IREC un magnífico lugar para trabajar, incluso a Harry y
los sucios, que con sus correos nos dan el gusto de saber que no somos como ellos.
A los que habéis pasado pero no desaparecido, Ainhoa, Julien, María.
A Roberto, por haberme empujado a esto con tanta decisión, por su apoyo y por
ese riñón que espero no tener que usar. A Damià, por la confianza que tuvo en mis
principios, por haber luchado en un nido de víboras. A Noé por no dudar para que
empezara esto, a pesar de todo.
Al apoyo que siempre he encontrado en mi familia.
A Mari, Vane, Loli y Martona por los principios en el camino de los silvestres.
Y también a Alatriste, por lo que fuiste antes de desaparecer en los tercios, y por
esa fuerza con la que me envolviste, que pareciera poderlo todo.
A todos los que no por no estar en estas líneas no estáis en mi vida.
En este mismo lugar, a nueve mil kilómetros, estaba antes de empezar estos cuatro
años de tesis, y aquí vuelvo a estar ahora, quizás cerrando esta maravillosa etapa.
Pero para la siguiente me llevo el equipaje de todo lo que habéis llenado, tanto que
no desearía ir a ninguna otra parte sin él.
Simplemente, gracias.
Índice
ÍNDICE GENERAL
OBJETIVOS .........................................................................................................................i
ORGANIZACIÓN DE LA TESIS................................................................................iii
CAPÍTULO 1. INTRODUCCIÓN.................................................................................1
Tendencias temporales de las infecciones en fauna silvestre: revisión....................1
CAPÍTULO 2. METODOLOGÍA............................................................................... 17
2.1. Metodología para la monitorización de las enfermedades de la fauna silvestre ........................................................................................................................... 21
2.2. Efecto de la hemólisis y la congelación-descongelación repetida en el análisis de anticuerpos mediante ELISA................................................................... 33
CAPÍTULO 3. VIGILANCIA SANITARIA DE ZOONOSIS.............................. 43
3.1. Incremento en el contacto con el virus de la hepatitis E en el ciervo Ibérico............................................................................................................................. 47
3.2. ¿Permiten los ungulados silvestres mejorar la vigilancia de flavivirus? ......... 55
3.3. Una tendencia decreciente: la triquinelosis del jabalí ....................................... 67
CAPÍTULO 4. RIESGOS SANITARIOS ASOCIADOS AL MANEJO CINEGÉTICO INTENSIVO DE LOS UNGULADOS SILVESTRES ............. 83
4.1. El jabalí: ¿un riesgo para el control de la enfermedad de Aujeszky en el cerdo?.......................................................................................................................... 89
4.2. Evolución temporal de la seroprevalencia de cuatro patógenos relevantes en el jabalí .................................................................................................. 105
4.3. Distribución espacial y factores de riesgo de la brucelosis en ungulados de la Península Ibérica ............................................................................ 123
4.4. Expansión de la tuberculosis en el jabalí ......................................................... 147
CAPÍTULO 5. APORTACIONES AL CONTROL DE LAS ENFERMEDADES COMPARTIDAS..................................................................... 159
5.1. Persistencia de lesiones compatibles con tuberculosis en poblaciones de ciervo Ibérico bajo distintas condiciones de manejo ....................................... 163
Tesis doctoral
5.2. Efectos del control poblacional no selectivo del jabalí sobre la prevalencia de contacto con bovis y el virus de la enfermedad de Aujeszky .................................................................................................................. 177
Mycobacterium
CAPÍTULO 6. DISCUSIÓN ....................................................................................... 197
Seis recomendaciones para la mejora de la monitorización de las enfermedades compartidas ........................................................................................ 197
CAPÍTULO 7. SÍNTESIS Y CONCLUSIONES .................................................... 217
SÍNTESIS .................................................................................................................... 219
CONCLUSIONES..................................................................................................... 225
BIBILIOGRAFÍA.......................................................................................................... 227
Objetivos
OBJETIVOS
En esta tesis se estudian las tendencias en el tiempo del contacto de ungulados
silvestres de la Península Ibérica con diversos patógenos compartidos entre la fauna
silvestre, los animales domésticos y las personas. El primer objetivo perseguido es
utilizar la vigilancia sanitaria para describir la variación espaciotemporal de las tasas
de contacto de los ungulados silvestres con varios agentes zoonóticos. El segundo
objetivo propone identificar factores de riesgo para las enfermedades y poblaciones
estudiadas, a fin de poder inferir herramientas de gestión con las que mejorar la
sanidad global y la conservación de la fauna silvestre. El tercer objetivo es analizar
el efecto de la reducción no selectiva de la población de jabalí sobre su situación
sanitaria y la de otros ungulados en simpatría. Finalmente, con el conjunto del
trabajo se pretende aportar mejoras para la vigilancia sanitaria de la fauna silvestre.
i
Organización
iii
ORGANIZACIÓN DE LA TESIS
En la introducción de esta tesis, Capítulo 1, se revisan estudios sobre
tendencias temporales en enfermedades de mamíferos silvestres terrestres
tomados de la bibliografía internacional. Se describen las características de las
tendencias temporales recogidas y se identifica la información que debería ser
registrada en este tipo de trabajos para poder optimizar su análisis.
En el Capítulo 2 se describen brevemente las peculiaridades de la
metodología empleada para el seguimiento de las enfermedades en los
ungulados silvestres.
Los siguientes capítulos siguen un orden paralelo al curso de la investigación de las
enfermedades de la fauna silvestre, desde su descubrimiento, pasando por los
análisis de riesgos, hasta la puesta en marcha de medidas para su control.
En el Capítulo 3 se aborda la vigilancia sanitaria y el seguimiento temporal del
contacto de los ungulados silvestres de la Península Ibérica con tres agentes
zoonóticos: el virus de la hepatitis E, el género Flavivirus, y Trichinella sp.
En el Capítulo 4 se analizan factores de riesgo que determinan las variaciones
temporales en el contacto de ungulados silvestres con siete patógenos
relevantes, así como su posible asociación al manejo cinegético intensivo.
El Capítulo 5 describe el efecto del control poblacional no selectivo de
jabalíes sobre la prevalencia de contacto con el virus de la enfermedad de
Aujeszky y con Mycobacterium bovis en el jabalí, sobre la incidencia de reactores
bovinos a la prueba de tuberculina, y sobre la prevalencia de infección por M.
bovis en el ciervo.
La discusión, Capítulo 6, propone cómo mejorar la monitorización sanitaria
en fauna silvestre, haciendo especial hincapié en las enfermedades causadas
por micobacterias.
Capítulo 1
CAPÍTULO 1. INTRODUCCIÓN
Tendencias temporales de las infecciones en fauna
silvestre: revisión
Boadella, M., Acevedo, P., Gortázar, C. Time trends in wildlife infections: a review. En preparación.
Capítulo 1
Resumen
La repetición de estimas de prevalencia y de otros indicadores de frecuencia de
enfermedades permite el seguimiento de su evolución en el espacio y tiempo y
resulta imprescindible para evaluar el resultado de eventuales estrategias de
intervención. Sin embargo, datos sobre tendencias temporales de las enfermedades
de la fauna silvestre son aún escasos, y en ocasiones los resultados no están
correctamente descritos en la literatura científica. En este trabajo se realizó una
búsqueda bibliográfica en las principales bases de datos de información científica
(Scopus, PubMed, e ISI Web of Knowledge) para identificar estudios que
describieran tendencias en las prevalencias de enfermedades de la fauna. Se
incluyeron aquellos estudios que describían tendencias de prevalencia en un
período de tiempo superior a 5 años o cuando dos o más estudios transversales
describían prevalencias para el mismo hospedador y enfermedad con una diferencia
de al menos 5 años. En este último caso, los estudios se combinaron y se calculó la
tendencia de la prevalencia entre ellos. En total se identificaron 101 artículos que
mostraban datos de tendencias temporales de prevalencia en distintas
enfermedades de mamíferos terrestres. Estos trabajos resultaron en un total de 191
series temporales de contacto de 42 especies hospedadoras con 51 patógenos
distintos. Las series descritas en ungulados y carnívoros fueron las más frecuentes
en cuanto a hospedadores, los virus fueron estudiados con mayor frecuencia que
las bacterias o los parásitos y el 41% de los patógenos estudiados eran zoonóticos.
De los 101 estudios que permitían el análisis estadístico de las tendencias
temporales, 39 usaron tests no paramétricos, 21 usaron modelos multivariantes, y
en 41 de las publicaciones no se describía el método usado o no se llegaron a
analizar las tendencias. De las 72 series para las que se evidenció una tendencia, ésta
resultó negativa en el 30% y positiva en el 70%. El número de muestreos, la
prevalencia, el estatus del hospedador (reservorio o accidental) y su densidad,
resultaron ser factores asociados con la probabilidad de identificar tendencias
temporales. Finalmente, la literatura revisada permitió enumerar los datos más
1
Tesis doctoral
2
importantes que se deberían registrar y reportar a fin de permitir un metanálisis
adecuado de las tendencias temporales de las prevalencias en fauna silvestre.
Capítulo 1
Abstract
Repeated estimation of prevalence or other indicators of disease frequency
allows monitoring its evolution in space and time and assessing the outcome of
intervention strategies. However, time trend data on wildlife diseases are often
missing or not properly described in the scientific literature. A bibliographical
search was launched on scientific web databases (Scopus, PubMed, and ISI Web of
Knowledge) to search for studies that described prevalence trends on wildlife
diseases. Studies included where those that described prevalence trends with a time
span greater than 5 years or when two or more cross-sectional studies described
prevalences for the same host and disease with a difference of at least 5 years
between them. In the latter case, studies were combined and the trend was
calculated. We identified 101 papers dealing with time trends in disease prevalence
in different wild terrestrial mammals. These papers comprised a total of 191 time
series on contact of 42 different host species with 51 different pathogens. Series on
ungulates and carnivores dominated regarding the host species, and viral agents
were more often studied than bacteria or parasites. Of the pathogens studied, 41%
were zoonotic, including the four most often studied ones. Of 101 studies
describing time trends, 39 used nonparametric homogeneity tests, 21 used models,
and 41 did not state the test or used no test at all. Of the 72 series where a time
trend was evidenced, 30% were declining trends and 70% were increasing. Factors
linked with the identification of time trends included number of samplings,
prevalence, presumed host status (maintenance or spillover) and host density.
Finally, the reviewed literature allowed listing the most important data that need to
be recorded and reported in order to allow a proper analysis of prevalence time
trends in wildlife. Comprehensive studies are scarce but needed to get the most
information of a given scenario.
3
Tesis doctoral
Introduction
Repeated estimation of prevalence or other indicators of disease frequency
allows monitoring its evolution in space and time. This in turn allows identifying
changes in disease occurrence and assessing the outcome of intervention strategies.
Hence, disease monitoring in wildlife is promoted in order to obtain information to
compare with the distribution and prevalence trends in humans or in livestock, as a
basis for decision making regarding wildlife disease control, and as a means for
assessing the effects of any disease management action (Boadella et al., 2011a). The
last point is particularly relevant because the effectiveness of most procedures
currently used for management in diseases in wild animals remains largely untested
and unknown. Therefore, every disease management program should include
means to measure its effectiveness (Wobeser, 1994). However, time trend data on
wildlife diseases are often missing or not properly described in the scientific
literature.
True time series data are characterized by an outcome measured at equal time
intervals over a reasonably long time period and stratified spatial sampling. In
human and in domestic animal medicine such time series often originate from data
records on e.g. disease incidence rates, hospital admissions or production records
(Dohoo et al., 2009). For instance, progress in (human) tuberculosis (TB) control is
monitored worldwide by counting TB cases (incidence) accurately through routine
surveillance. By contrast, disease prevalence surveys are costly and laborious, but
give unbiased measures of TB burden and trends, and are justified in high-burden
countries where many cases and deaths are missed by surveillance systems (Dye et
al., 2008). In livestock, too, monitoring of infectious diseases can be achieved
through routine data on apparent prevalence gathered for instance during meat
inspection at slaughter (Enoe et al., 2003), or by monitoring prevalence through
regularly repeated cross-sectional surveys (e.g. cattle skin testing for bovine TB or
Aujeszky’s disease virus detection by ELISA).
4
Capítulo 1
In wildlife, infection monitoring is even more difficult due to the low
accessibility of most species, the lack of suitable diagnostic tools and the limited
resources available, among other factors (Wobeser, 1994). Thus, long and data-rich
time series on wildlife diseases are an exception that is usually limited to zoonoses
such as rabies (Müller et al., 2005) and trichinellosis (Pannwitz et al., 2010). A few
long term studies on wildlife host-parasite systems also provide valuable data for
trend analysis and epidemiological modelling (e.g. the Soay sheep – gastrointestinal
nematodes system, (Coltman et al., 1999). In most wildlife settings however, time
trend data are limited to a few years or need to be inferred from two or more cross-
sectional studies on the same host-pathogen system and study area repeated at
different times.
For instance, two separate studies described the Mycobacterium bovis infection
prevalence among wild ungulates in Doñana National Park (DNP) in southern
Spain. From 1998 to 2003, a sample of 214 wild boar (Sus scrofa), 168 red deer
(Cervus elaphus) and 134 fallow deer (Dama dama) yielded M. bovis infection
prevalences of 28%, 15% and 13%, respectively (Romero et al., 2008). A new
cross-sectional survey carried out between 2006 and 2007 on 124 DNP wild boar,
95 red deer and 97 fallow deer yielded infection prevalences of 52%, 27% and 18%,
respectively (Gortázar et al., 2008). It is most likely that these huge increases in
infection prevalence represented true increasing time trends. However, differences
in sampling and necropsy procedures, in sample stratification by sex and age, in
sample distribution within the study area or in laboratory protocols could also have
had an effect on the apparent prevalences recorded (Gortázar et al., 2008; Martín-
Hernando et al., 2010; Figure 1).
One classical example of a wildlife disease time trend is the surveillance on red
fox (Vulpes vulpes) rabies in Europe. Data recording on annual fox rabies cases (the
outcome variable) started as early as 1954 and is still continued. Since rabies is an
important zoonotic disease, exceptional measures for its control in foxes have been
implemented. In Germany, fox oral vaccination against rabies with baits containing
5
Tesis doctoral
a recombinant vaccinia virus started in 1985. This action was successful in reducing
the annual number of fox rabies cases from peaks of over 10,000 in the late 1970s
and early 1980s, to almost zero nowadays (Müller et al., 2005). This long data series
provided and extremely valuable insight into the rabies virus – red fox system, and
allowed assessing the efficacy of the disease control action (vaccination).
Unfortunately, this example is an exception, as information on the time trends of
most wildlife infections is usually either fragmentary or completely lacking.
Another relevant aspect regarding disease trend assessment is the need for
quality background data on population size and distribution. While this is usually
granted in humans and regarding most domestic animals, wildlife population data
are not always available. In Europe, many wildlife species are changing their range
and population size in the last decades. Examples include deer species (Acevedo et
al., 2005), wild bovids (Acevedo et al., 2007a), Eurasian wild boar (Sáez-Royuela
and Telleria, 1986), carnivores such as red fox (Vos, 1995) and lagomorphs such as
the European wild rabbit (Williams et al., 2007). In some of these cases, diseases
have been suspected to drive a species’ decline (rabbits, myxomatosis and rabbit
haemorrhagic disease, Delibes-Mateos et al., 2008; foxes and sarcoptic mange,
Lindstrom et al., 1994). In other occasions disease control measures have been
pointed as causes for wildlife population increase (red fox and rabies control,
König et al., 2005). In other cases, changes in distribution and increases in
abundance of a wildlife host have been linked with increases in the prevalence of
density-dependent infections such as Aujeszky’s disease virus (Pannwitz et al.,
2011) and M. bovis (Gortázar et al., in press).
Since wildlife populations are usually not stable in time, we hypothesized that
contact with pathogens would also vary in time, and this trend (or the likelihood of
detecting it) would be linked to host population characteristics, pathogen
characteristics and study design. Herein, we reviewed scientific literature for time
trend data regarding wildlife diseases in order to infer recommendations for future
studies.
6
Capítulo 1
Figure 1.- Examples of annual time series and combined cross-sectional surveys. Upper panel: number of fox-mediated rabies cases in Germany from 1955 to 2010. Large-scale fox oral vaccination programmes began in 1985 (modified from (Müller et al., 2005). Lower panel: Mycobacterium bovis infection prevalence (in %, ± 95% CI) in European wild boar (diamonds), red deer (squares) and fallow deer (triangles) from Doñana National Park in two different cross-sectional surveys (Gortázar et al., 2008; Romero et al., 2008).
Material and methods
Sources of wildlife infection time series
A bibliographical search was launched on scientific web databases (Scopus,
PubMed, and ISI Web of Knowledge) combining different key words to search for
studies that described prevalence trends on wildlife diseases. The first search was
done by combining the words [time] or [trend], [disease] and [wildlife]. Secondly, a
7
Tesis doctoral
search on 36 diseases that are considered relevant at the interface with livestock
(Gortázar et al., 2007; EU Wildtech project list, http://www.wildtechproject.com),
and that witnessed recent progress regarding the involvement of wildlife hosts was
done by entering the [name of pathogen] or [name of the disease] with [wildlife] or
[known wildlife host species for the disease].
Studies included where those that described prevalence trends with a time span
greater than 5 years or when two or more cross-sectional studies described
prevalences for the same host and disease with a difference of at least 5 years
between them. In the latter case, studies were combined and the trend was
calculated. Additionally, we included some studies retrieved from the citations of
the former ones, and 6 own surveys that are under review.
Variables analyzed and statistics
For each selected study, different sampling, host and disease characteristics
were recorded as variables for analysis (Table 1). The response variable was the
presence or absence of a disease trend as defined by the authors of each study.
Trends between combined cross-sectional studies were calculated with Chi-
square tests. Associations between categorical variables were analyzed by means of
homogeneity tests (Chi square or Fischer’s test when appropriate). Correlations
between continuous variables were analyzed by means of Spearman’s rank test.
Results were considered significant when p<0.05. Data was analyzed using the IBM
SPSS statistical package, version 19.0 (IBM Corporation, Somers, NY, USA).
8
Capítulo 1
Table 1.- Sampling, host and pathogen characteristics recorded as variables for the analysis, their description, type and number of cases (n).
Variable Description Type of variable
(categories) n
Sampling method
Study area Geographical location of the study site
Nominal 101
Country
Country where the study took place
Nominal 101
Continent
Continent where the study took place
Nominal 101
Area size
Relative size of the study area, e.g. local, regional, nationwide
Categorical (small, medium, large)
101
Sample size (n)
Total number of animals or samples analyzed
Continuous 175
Test used
Diagnostic test used to determine prevalence or incidence
Nominal 182
Hr/Ag
Diagnostic test directed to determine the host response (Hr) or the agent (Ag)
Categorical 190
Mean prevalence
Overall prevalence for the whole study
Continuous 168
Prevalence category
Categorized relative prevalence (%)
Categorical (Low: 0-5%, Medium: 5.1-10%, High: 10.1-50%, Very high: >50%)
171
Initial prevalence
Prevalence in the first period/year of the study
Continuous 105
Final prevalence
Prevalence in the last period/year of the study
Continuous 106
Difference in prevalence
Final prevalence minus the initial prevalence
Continuous 104
Sampling events
Number of samplings during the study period or in different cross-sectional studies
Categorical (2, 3 to 5, > 5) 143
Nr of years Duration of the study in years Continuous 191
Time span Categorized duration of the study Categorical (5 to 10, 11 to 20, >20)
191
Test used for trend
Statistical test used for trend analysis
Categorical (nonparametric, model, no test)
61
Trend
The trend identified by the authors or calculated when combining different studies
Categorical (increasing, decreasing, stable) 191
Trend Y/N
Host trend categorized into presence of absence of disease trend
Response variable, categorical 191
Host characteristics Host Host species Nominal 42 Family or
taxonomic group Hosts species grouped into taxonomic groups
Nominal 17
9
Tesis doctoral
Table 1.- Continued
Variable Description Type of variable
(categories) n
Host characteristics
Stated host status The authors state in the article if the host is considered a reservoir or a spillover of the disease.
Categorical (spillover, maintenance) 126
Presumed host status
If authors or current knowledge on the field allow classification of the host into spillover or maintenance.
Categorical (spillover, maintenance)
160
Gregariousness
Level of gregariousness of the host species in the study site
Categorical (low, medium, high)
122
Density
The density of the host in the study site
Categorical (low, medium, high)
63
Host trend
Host trend as identified by the authors
Categorical (increasing, decreasing, stable)
77
Host trend Categorized host trend Categorical (presence,
absence) 77
Pathogen characteristics
Endemic/epidemicThe pathogen is endemic or epidemic in the study site and host
Categorical (Endemic, epidemic)
185
Type Type of pathogen Categorical (prion, virus,
bacteria, parasite) 51
Mm
Pathogen categorized into macroparasite or microparasite
Categorical (macroparasite, microparasite)
51
Transmission Type of transmission Categorical (direct, indirect,
both, vector) 51
Vector
The pathogen is transmitted by vectors
Categorical (yes, no) 51
Environmental stages
The pathogen has environmental stages
Categorical (yes, no) 50
Acute/chronic
The pathogen causes an acute or a chronic disease in the studied host species
Categorical (chronic, acute) 120
Seasonal or not
The pathogen is more frequent during a season or throughout the year
Categorical (seasonal, not seasonal) 51
Zoonosis
The pathogen causes a zoonotic disease
Categorical (yes, no) 51
10
Capítulo 1
Results and discussion
We identified 101 papers dealing with time trends in disease prevalence in
different wild terrestrial mammals. These papers comprise a total of 191 time series
on contact of 42 different host species with 51 different pathogens. In the
following sections we first describe the characteristics of these 191 time series and
later analyze some of the factors linked with the presence or absence of time trends
(see complete table of time series and their references in:
https://www.dropbox.com/home/Material%20suplementario%20Tesis%20M.%2
0Boadella#:::72822462).
Numerical data on these time series are summarized in Table 2. A total of 110
of 191 series described prevalence trends in ungulates (58%), and 71 described
trends in carnivores (37%). Other orders including rodents and lagomorphs
comprised only 5% of the time series. The most often studied hosts were red
deer/elk (43/191, 22%) and wild boar (33/191, 17%) among the ungulates, and
two canids, the wolf (Canis lupus; 13/191, 6.8%) and the red fox (11/191, 5.7%)
among the carnivores. Viral agents were more often studied (32 viruses; 91 of 191
time series, 48%) than bacteria (11 bacteria; 68/191, 36%) or parasites (7 parasites;
31/191, 16%). Of the 51 different pathogens studied, 21 (41%) were zoonotic,
including the four most often studied ones. These included the members of the
Mycobacterium tuberculosis complex (mainly M. bovis; 23/191, 12%), followed by
Brucella spp. (20/191, 10%), Trichinella spp. (13/191, 7%) and rabies virus (12/191,
6%). Thus, microparasites dominated among the studied time trends (172/191,
90%). One reason for identifying so many time trend studies on viruses in contrast
to so few ones on parasites is that our literature search concentrated on selected
relevant diseases, which include more viruses and bacteria than parasites (Gortázar
et al., 2007). Another explanation is that for most relevant pathogens for the
livestock industry (many viruses among them), there are serological tests available
that allow testing large numbers of samples at a low cost, while such techniques are
11
Tesis doctoral
often either not commercially available or have a low specificity in the case of many
bacteria and parasites.
Table 2.- Mean, median, range and number of cases for the four continuous variables recorded.
Variable Mean Median Range n
Total sample size 20,452 343 36-2,469,996 175
Time span (years) 11.2 10 5-46 191
Samplings 6.5 6 2-42 143
Mean prevalence 21.6 11.45 0-100 168
Most time series described settings with a mean prevalence lower than 30%
(119/167, 71%), while only 13/167 (7.8%) described settings with a mean
prevalence higher than 70% (Figure 2). Study area sizes were generally medium
(44/101, 44%) or large (42/101, 42%). Studies from Europe (58%) and North
America (28%) dominated, followed by African studies (10%). In contrast, studies
from other regions were scarce (Asia 3%, Oceania 1%) or were lacking, as was the
case for South America. Hence, long term surveys on less known host species from
regions with limited available information such as South America are needed,
especially when considering that areas in the continent have been identified as likely
to change the temporal and geographical distribution of infectious diseases (such as
bluetongue or West Nile fever) due to climate change (Pinto et al., 2008).
Also, most time series described host response (generally antibody prevalence)
and fewer ones described antigen prevalence (133/191, 70% vs. 57/191, 30%,
respectively). Fifty percent of the studies stating the number of sampling events
had more than five, while 17% reported only two sampling events or were based
on repeated cross-sectional studies on the same host/pathogen/site. Of 101 studies
describing time trends, 39 used nonparametric homogeneity tests, 21 used models,
and 41 did not state the test or used no test at all. Most time series did not allow
identifying an increasing or decreasing time trend (119/191, 62%). Of the 72 series
where a time trend was evidenced (38%), 22 (30%) were declining trends and 50
12
Capítulo 1
(70%) were increasing. This difference was statistically significant (Chi2=21.7, 1 d.f.,
p<0.001). This finding is rather surprising and could be explained by two not
mutually excluding ideas: first, since there is a bias towards ungulates regarding the
hosts studied, and these are generally expanding their distribution and increasing
their abundance, increasing trends of density dependent infections or multi-host
infections would be expected. Second, increasing trends might be more attractive
to report and thus be more likely to get published than negative trends or trend
absence. In a similar way, positive results of gamebird restocking tend to be more
often reported than negative ones (Duarte et al., 2011).
Figure 2.- Proportion of studies (total numbers in columns) that detect a trend (dark grey) or do not detect it (light grey) plotted against 10% prevalence intervals.
The total sample size was negatively correlated with the mean prevalence (rs=-
0.32, p<0.001) and positively correlated with the number of samplings (rs=0.27,
p<0.01). There was no correlation between sample size or number of samplings
and the time span. Regarding the factors linked with the identification of time
trends, these included number of samplings, prevalence, presumed host status and
host density.
Counter intuitively, trend presence was associated with a lower number of
sampling events (Chi2=23.8, 3 d.f., p<0.01). Fewer sampling events (or grouping
the initial and the final parts of a time series) is a simplification of reality. However,
13
Tesis doctoral
it can be debatable if this simplification is or not advantageous, since it is clear that
budget limitations drive sample size and the number of sampling events, and that
grouping such samples allows inferring at least a gross idea on the evolution of
selected infections through time. For instance, by grouping the initial and final
years of a ten year data series on red deer contact with hepatitis E virus, (Boadella
et al., 2010) managed to identify an increasing trend of contact with this zoonosis in
Spain. However, time trends based on continuous monitoring (e.g. Figure 1, upper
panel) offer more information than just disease situations at time 1 and time 2 (e.g.
Figure 1, lower panel). In fact, if significant changes in disease prevalence occur
during the time series, these might go unnoted if only two periods are defined.
Regarding prevalence, medium and high prevalences were more associated to
trend detection than low or very high ones (Chi2=8.5, 3 d.f., p<0.05; Figure 2). In
fact, detecting a significant change in prevalence in situations of very low or very
high prevalence requires high sample sizes, and makes monitoring more difficult
and costly (Boadella et al., 2011a; Hoye et al., 2010).
Figure 3.- Proportion of studies detecting a trend (dark grey) or not (light grey) in host species classified as maintenance or spillover.
Regarding the host, a trend was detected in 45% of those hosts classified as
maintenance but only in 21% of hosts classified as spillover (Chi2=7.1, 1 d.f.,
14
Capítulo 1
p<0.01; Figure 3). This might reflect a difference in prevalence, with larger ones in
maintenance than in spillover hosts. Higher host densities were more often linked
to presence of a trend (Chi2=8.1, 2 d.f., p<0.05), possibly because higher host
densities enable obtaining larger sample sizes (Boadella et al., 2011a). In any case,
wildlife disease monitoring will only make sense if population monitoring is carried
out at the same time, allowing to link changes in abundance or management with
changes in disease indicators (Boadella et al., 2011a).
Host density trends and prevalence trends were linked (Chi2=15, 6 d.f., p<0.05;
Figure 4). We found however no link between host density and mean prevalence
(Chi2=8.2, 6 d.f., p>0.05). This can be due to the fact that diseases with different
behaviours were analyzed together. For example, increasing fox abundance has
been linked with increasing prevalence of the cestode Echinococcus multilocularis
(König et al., 2005), while an increase of myxomatosis caused a declining wild
rabbit density by direct mortality (Myers, 1962).
Figure 4.- Relation between host density trend and increasing (dark grey) or decreasing (light grey) prevalence trend.
Finally, the reviewed literature allows listing the most important data that need
to be recorded and reported in order to allow a proper analysis of prevalence time
trends in wildlife. Regarding the pathogen, these are the type of outcome measured
and eventually the type of diagnosis; test used and test interpretation (e.g. cut off).
15
Tesis doctoral
16
Regarding the host, sex and age are key factors affecting contact likelihood with
many pathogens. Regarding the sampling methods, it is important to state how
sampling was stratified in space and time (sampling season), and if the analyzed
individuals were included randomly or not. Importantly, most surveys do not
define the precise sites sampled each year within large study areas and some do
even fail to report the stratification by age and sex (e.g. Romero et al., 2008 vs.
Gortázar et al., 2008). Thus, the presence or absence of apparent time trends can
be due to differences in the yearly sample distribution and stratification. To avoid
this, all long term studies and also all cross-sectional surveys should include the
listed minimal information in order to allow proper investigation of time trends.
There is a lack of interdisciplinary studies on wildlife disease and wildlife
population trends. Factors regulating disease trends are often a network of
ecological, anthropological, and epidemiological aspects that normally are not
treated as a whole. Studies carried out by biologists normally lack of
epidemiological data while those done by veterinarians often miss basic population
data. Comprehensive studies are scarce but needed to get the most information of a
given scenario.
Capítulo 2
CAPÍTULO 2. METODOLOGÍA
2.1. Metodología para la monitorización de las enfermedades de la fauna silvestre
2.2. Efecto de la hemólisis y la congelación-descongelación repetida en el análisis de anticuerpos mediante ELISA
Capítulo 2
19
Resumen
La vigilancia sanitaria de fauna silvestre que se lleva a cabo en el IREC desde
1999 se basa en realizar investigaciones continuas sobre poblaciones determinadas
con vistas a detectar la aparición de enfermedades o la variación de su prevalencia a
lo largo del tiempo. Esa labor se divide en tres fases principales. Una primera fase
de trabajo de campo en los sitios de estudio que incluye la caracterización del
hábitat y manejo cinegético, y de las poblaciones objeto de estudio, así como la
toma de muestras durante la temporada de caza. Posteriormente, en la segunda fase
se procesan las muestras tomadas y éstas se almacenan en serotecas e histotecas. En
una tercera fase se realiza el análisis de muestras (patología, serología,
microbiología, diagnóstico molecular) y datos (GIS, dinámica de poblaciones,
análisis de factores de riesgo, etc.).
Por otro lado, las muestras de suero de especies silvestres son valiosas para la
detección de contacto con patógenos, pero a menudo están hemolizadas y se usan
varias veces para distintos análisis, por lo que se las somete a repetidos ciclos de
congelación-descongelación (C-D). Con el objetivo de estudiar los efectos de la
hemólisis y de los ciclos de C-D sobre el resultado de un ELISA comercial para la
detección de anticuerpos frente al virus de la Enfermedad de Aujeszky, se usaron
sueros limpios y hemolizados de jabalí (Sus scrofa) y se sometieron a cinco ciclos
repetidos de C-D. La hemólisis no redujo la prevalencia media observada, y a pesar
de que el 27% de las muestras cambiaron su clasificación en alguno de los ciclos,
ninguna de las muestras inicialmente positivas cambió su estado. En vista de los
resultados obtenidos, recomendamos (1) establecer puntos de corte más restrictivos
cuando se analicen sueros de especies silvestres, (2) registrar la calidad del suero
antes de almacenarlo, (3) registrar el número de C-D y (4) almacenar el suero en
varias alícuotas. Los sueros muy hemolizados deberían descartarse para estudios de
monitorización de anticuerpos y ningún suero debería someterse a más de 5 ciclos
de C-D.
Capítulo 2.1
Metodología para la monitorización de las
enfermedades de la fauna silvestre
Capítulo 2.1
Introducción al método de vigilancia sanitaria de fauna silvestre del IREC
Los animales, y muy particularmente la fauna silvestre, se consideran la fuente
de más del 70% de todas las enfermedades emergentes. En consecuencia, la
vigilancia sanitaria de la fauna silvestre es crítica para el control de esas
enfermedades (Kuiken et al., 2005). Las enfermedades de la fauna silvestre tienen
relevancia por varios motivos:
Por incluir zoonosis
Por afectar a la sanidad de la cabaña ganadera
Por comprometer la producción cinegética
Por sus efectos en la conservación de la fauna silvestre
Dentro de la fauna silvestre, la importancia para la sanidad animal de las
especies susceptibles de aprovechamiento cinegético se debe sobre todo a su
abundancia y distribución, y a su proximidad filogenética con el ganado. En
general, las especies cinegéticas son muy accesibles para la toma de muestras, y esto
hace que estas especies sean buenas candidatas para la vigilancia sanitaria.
La vigilancia sanitaria de fauna silvestre que se lleva a cabo en el IREC desde
1999 se basa en la realización de investigaciones continuas sobre poblaciones
determinadas con vistas a detectar la aparición de enfermedades o la variación de su
prevalencia a lo largo del tiempo. En ese sentido, el estudio continuado de las
enfermedades se puede sintetizar en tres fases (Figura 1):
Fase 1: trabajo de campo en los sitios de estudio
Caracterización del hábitat y del manejo cinegético al que
están sometidas las poblaciones
Caracterización de las poblaciones en términos de
abundancia y distribución
Toma de muestras durante la temporada de caza
Fase 2: Procesado de las muestras tomadas y su almacenaje en serotecas
e histotecas
23
Tesis doctoral
Fase 3: Análisis de muestras (patología, serología, microbiología,
diagnóstico molecular) y datos (GIS, dinámica de poblaciones, análisis de
factores de riesgo)
Figura 1.- Esquema del sistema de vigilancia sanitaria del IREC, basado en tres fases.
Fase 1.- Trabajo de campo
La caracterización y monitorización de las variables de hábitat (p.ej. el estado de
la vegetación o los usos del suelo, etc.) y de las variables de manejo (presencia de
vallado, alimentación suplementaria, puntos de agua, etc.) es una parte fundamental
para la monitorización del estado sanitario de las especies cinegéticas. Las
poblaciones objeto de aprovechamiento cinegético suelen estar sujetas a estrategias
de manejo para mantener densidades elevadas. Esto entre las medidas más
habituales incluye el aporte de agua y alimento (todo el año o sólo en periodos
críticos), los vallados (caza mayor), el control de predadores (caza menor) o los
traslados y repoblaciones. Estas poblaciones manejadas son más susceptibles de
mantener enfermedades debido al mayor riesgo de contacto entre animales. Así, las
variables asociadas al manejo de estas especies son de especial importancia ya que
24
Capítulo 2.1
varias de ellas se han identificado como factores de riesgo de enfermedades tan
relevantes como la enfermedad de Aujeszky o la tuberculosis bovina (Vicente et al.,
2005; Acevedo et al., 2007b).
El conocimiento de la ecología y comportamiento de las especies silvestres es
esencial para poder realizar una valoración completa del estatus de sus poblaciones,
incluyendo el aspecto sanitario (Delahay et al., 2009). Existe una gran variación en
cuanto a la distribución y densidad de las diferentes especies de ungulados silvestres
en la Península Ibérica. Pero además, éstas se ven afectadas por factores antrópicos
(manejo para obtener mayores rendimientos cinegéticos, aprovechamientos
ganaderos, etc.). Todo ello genera una situación en la que una multitud de factores
relacionados con las especies estudiadas influyen en la adquisición, mantenimiento
y diseminación de distintas enfermedades (Tabla 1).
Tabla 1.- Algunos factores relacionados con las especies estudiadas más relevantes (jabalí y ciervo) según el nivel en el que podrían influir en la epidemiología de sus enfermedades (negro=elevado, gris=medio, blanco=bajo).
FACTOR ECOLÓGICO Jabalí Ciervo
Distribución y abundancia
Organización social
Capacidad reproductiva
Área de campeo
Distancias de movimiento y dispersión
Ineficacia de las barreras frente al movimiento
Interacción con animales domésticos
Interacción con otros animales silvestres
La fauna silvestre es difícil de gestionar y las campañas de control pueden verse
abocadas al fracaso debido a la dificultad de detección de estas especies silvestres o
la imposibilidad de establecer barreras (Figura 2).
25
Tesis doctoral
Figura 2.- Los cambios en el manejo de la fauna silvestre (hacia modelos más intensivos) y en la producción ganadera (hacia modelos más extensivos) complican la epidemiología y el control de las enfermedades compartidas (extraído de Gortázar et al., 2007).
Por todo ello, una parte importante de la vigilancia sanitaria es la
caracterización de las poblaciones en los sitios de estudio, en las que uno de los
aspectos de mayor relevancia en la estimación de la abundancia de las poblaciones
silvestres de forma periódica. A lo largo de los últimos años dentro del grupo de
sanidad animal del IREC se han desarrollado y validado diferentes metodologías
con el objetivo de optimizar la estima de abundancias de los ungulados silvestres de
mayor interés epidemiológico (Tabla 2).
Tabla 2.- Metodologías recomendadas en ambientes mediterráneos para la realización de estimas de abundancia en jabalí y ciervo.
Especie Método Resumen Referencia
Jabalí Índices de abundancia
Frecuencia de aparición de heces en 40x10 tramos de 10m a lo largo de un transecto lineal de 4 Km. La distribución espacial de los tramos con heces sirve para estimar además un índice de agregación.
(Acevedo et al., 2007b)
Ciervo Observación
directa
Recorridos nocturnos con faro 100w desde todo-terreno a lo largo de transectos lineales. Se anotan distancias y ángulos para aplicar el muestreo de distancias.
(Acevedo et al., 2008)
26
Capítulo 2.1
Por otro lado, durante la temporada de caza (de octubre a febrero) se realiza la
toma de muestras biológicas de los animales cazados ya que la caza mediante
monterías o batidas se considera un método de muestreo aleatorio de la población
(Fernández-Llario and Mateos-Quesada, 2003). En cada finca de estudio el objetivo
es realizar un muestreo representativo, fijado en alrededor de 20 animales por
especie y año, y estratificado por sexo y edades. Esto permite calcular prevalencias
con intervalos de confianza aceptables de enfermedades con prevalencias medias o
altas (ver Tabla 1 del Capítulo 6) y compararlas en el espacio (entre localidades o
regiones) y en el tiempo.
Protocolo de toma de muestras
Después de las cacerías se selecciona aleatoriamente una muestra estratificada y
representativa de los animales abatidos. A los animales seleccionados se les realiza
una inspección general y se les toman biometrías, tales como longitud total (desde
la punta del hocico del animal hasta donde se articula la cola), perímetro torácico y
longitud del pie posterior (desde la punta de la pezuña hasta el corvejón), y al
mismo tiempo se registra el sexo y la edad del animal (Sáenz de Buruaga et al.,
1991). Se realiza una inspección externa del animal para registrar la presencia de
ectoparásitos y en el caso de que los haya, se estima su abundancia y se recoge una
muestra representativa siempre que no sea posible recoger la totalidad de los
mismos.
Una vez registrados los datos de cada animal, se procede a la toma de muestras
de tejidos internos (Tabla 3). Se recogen una serie de tejidos de forma sistemática
de cada animal, pero además es importante recolectar una muestra de todos
aquellos órganos o tejidos en los que se detecte algún tipo de lesión que pueda ser
de interés. Se toma sangre del corazón o de la cavidad torácica. En el caso de que
no sea posible obtener sangre, se toma un trozo de pulmón como muestra
alternativa para poder obtener exudado pulmonar por centrifugación (Ferroglio et
al., 2000). Así, las muestras que sistemáticamente se recogen son:
27
Tesis doctoral
Cabeza. En los cérvidos se recogen los linfonodos (LN) retrofaríngeos
mediales y en el jabalí los LN mandibulares. En ambos se colectan las
tonsilas.
Órganos de la cavidad torácica. En todas las especies se toma el LN
bronquial izquierdo, el LN mediastínico y se inspecciona detalladamente el
pulmón para detectar lesiones macroscópicas. Del pulmón se recogen 3
muestras correspondientes a los lóbulos apical, intermedio y diafragmático,
además de las partes donde se hayan detectado lesiones.
Órganos de la cavidad abdominal. De igual modo que en la cavidad torácica,
hay que examinar macroscópicamente todas las vísceras a fin de observar
posibles lesiones. Se colecta el bazo, el riñón derecho con la grasa peri renal
para calcular el índice de engrasamiento renal (KFI) y los LN mesentéricos e
ileocecales. Se recogen también los ovarios para determinar el estatus
reproductivo de las hembras.
Tabla 3.- Muestras de tejidos más relevantes para la vigilancia sanitaria en jabalí y rumiantes silvestres.
JABALÍ
Tipo de análisis Muestra Importancia PCR o
cultivo Patología Otros
Ejemplos
Encéfalo, ganglio trigémino
Baja Sí Sí E de Aujeszky (ganglio
trigémino)
LN mandibulares
Alta Sí Sí Tuberculosis
Tonsilas Alta Sí Sí E de Aujeszky, Mal rojo, Tuberculosis
Pulmón Baja Sí Sí Parasitología Varias enfermedades
Bazo Alta Sí Sí Pestes porcinas, Mal rojo, Salmonelosis
LN mesentérico
Media Sí Sí Varias enfermedades
Hígado, vesícula biliar
Media Sí ELISA (bilis) Hepatitis E
Riñón Baja Sí Leptospirosis
28
Capítulo 2.1
RUMIANTES SILVESTRES
Tipo de análisis Muestra Importancia PCR o
cultivoPatología Otros
Ejemplos
Tronco de encéfalo
Media Sí ELISA Encefalopatías ET
LN retrofaríngeos mediales
Alta Sí Sí Tuberculosis
Tonsilas Alta Sí Sí Tuberculosis Pulmón Baja Sí Sí Varias enfermedades Bazo Alta Sí Sí BVD LN mesentérico
Media Sí Sí Tuberculosis y Paratuberculosis
Riñón Baja Sí Leptospirosis
Fase 2.- Procesado y almacenamiento en serotecas e histotecas
El procesado de las muestras recogidas en campo tiene lugar en los laboratorios
del IREC. En la sala de necropsias se registra información sobre aspectos como la
condición física o el estado reproductivo de los animales muestreados, a partir por
ejemplo del engrasamiento renal o del análisis de úteros y ovarios. Los órganos y
tejidos linfoides más importantes se inspeccionan de forma sistemática, se procesan
y se conservan debidamente identificados tanto en formol como mediante
congelación. Igualmente, en el laboratorio de patología, se etiquetan y conservan
los ectoparásitos y se separa suero sanguíneo para futuras analíticas.
Dado que las enfermedades a investigar varían en función de la situación
epidemiológica y que los avances en epidemiología y diagnóstico pueden mejorar su
detección, en el IREC se conservan desde el año 1999 copias de las muestras más
relevantes (suero y tejidos linfoides) en serotecas e histotecas. Este sistema de
almacenaje de muestras permite la realización de estudios epidemiológicos
retrospectivos como los que configuran esta tesis.
29
Tesis doctoral
Logística de los bancos de tejidos
Una vez inspeccionadas en detalle (fileteado fino de todos los tejidos linfoides)
y procesadas las muestras de forma sistemática, se registran en una base de datos y
se almacenan en congeladores de -20º C, salvo cuando son muestras destinadas a
estudios de virología que se conservan a -80º C. Varias copias del suero obtenido
de cada animal se conservan en una seroteca donde se almacenan anualmente unos
3.000 sueros de ungulados silvestres. Del mismo modo, cada tejido colectado se
almacena por duplicado en una histoteca.
En la segunda parte de este capítulo metodológico se aborda uno de los
problemas más habituales que aparece a la hora de trabajar con material de los
bancos de suero de fauna silvestre y que está relacionado con la calidad de los
mismos y su efecto sobre los resultados de las analíticas. El segundo problema de
trabajar con bancos de muestras, aún no resuelto, es el equilibrio entre la
conservación de un número creciente de muestras valiosas y el espacio físico
disponible para ello, que cada vez es más limitante.
Fase 3.- Análisis de muestras y datos
Las analíticas destinadas a la detección del antígeno en fauna silvestre no tienen
particularidades más allá de lo referente al muestreo. En cambio, las analíticas
serológicas para la detección de anticuerpos sí pueden requerir una puesta a punto
especial cuando las inmunoglobulinas del taxón a investigar no presenten suficiente
reacción cruzada con las anti-inmunoglobulinas utilizadas en los protocolos de
diagnóstico para animales domésticos. Otra particularidad de las analíticas
serológicas en fauna silvestre es la calidad de las muestras, que, como anteriormente
se ha comentado, se discute ampliamente en el Capítulo 2.2.
30
Capítulo 2.1
Analíticas en IREC y en colaboración
En general, todas las analíticas serológicas y por PCR se realizan en el IREC.
Los cultivos que requieran laboratorios P3 o ciertas técnicas especializadas se
remiten a laboratorios colaboradores como VISAVET en Madrid y NEIKER en
Derio (micobacterias) o CITA en Zaragoza (Brucella).
Para los trabajos presentados en esta tesis, la técnica serológica IPMA (ensayo
de inmunoperoxidasa en monocapa) utilizada para la detección de anticuerpos
frente al Circovirus Porcino 2 (PCV2; Capítulo 4.2) se realizó en el CReSA, en
Bellaterra. El CITA desarrolló el ELISA para la detección de anticuerpos frente a
Brucella descrito en el Capítulo 4.3, y analizó todas las muestras, tanto por cultivo
como por ELISA.
Tabla 4.- Ejemplos de algunas particularidades del diagnóstico en fauna silvestre en cuanto a las muestras a tomar y el análisis.
Enfermedad Muestras clave Análisis
Tuberculosis (JABALÍ)
Linfonodos (LN) mandibulares, suero
Cultivo como en domésticos. En estudios a gran escala es de utilidad el ELISA (Boadella et al., 2011b), registrar la proporción de jabalíes que presentan lesiones macroscópicas compatibles (Vicente et al., 2006) y realizar tinciones específicas (Santos et al., 2010).
Tuberculosis (RUMIANTES)
LN retrofar. mediales, bronquial izq., mesentéricos
Cultivo como en domésticos. En estudios a gran escala es de utilidad registrar la proporción de individuos que presentan lesiones macroscópicas compatibles y realizar tinciones específicas (Vicente et al., 2006).
Brucelosis (UNGULADOS)
LN inguinales, bazo, suero
Para análisis serológicos de brucelosis en ungulados silvestres puede servir de orientación la técnica de aglutinación. Idealmente, aplicar la técnica ELISA empleando proteína G como conjugado (Muñoz et al., 2010). Los LN inguinales y el bazo de los seropositivos deberán cultivarse.
Tratamiento de la información
Toda la información recopilada se registra en bases de datos. Existe una base de
datos de los individuos muestreados en la que se integra toda la información
31
Tesis doctoral
32
recopilada a nivel de cada individuo (datos biométricos, finca de procedencia,
tejidos disponibles, lesiones detectadas, etc.). Estos datos individuales se pueden
cruzar con todas las variables disponibles para su población de procedencia (datos
GIS, datos de manejo, etc.), y de este modo se realiza la integración de la
información sobre hábitat y manejo con la información biológica y de sanidad de
cada animal para su análisis.
Capítulo 2.2
Efecto de la hemólisis y la congelación-
descongelación repetida en el análisis de anticuerpos
mediante ELISA
Boadella, M. & Gortázar, C. Effect of haemolysis and repeated freeze-thawing cycles on wild boar serum antibody testing by ELISA. Aceptado, BMC Research Notes.
Capítulo 2.2
Abstract
Monitoring wildlife diseases is needed to identify changes in disease occurrence.
Wildlife blood samples are valuable for this purpose but are often gathered
haemolysed. To maximise information, sera often go through repeated analysis and
freeze-thaw cycles. Herein, we used samples of clean and haemolysed Eurasian wild
boar (Sus scrofa) serum stored at -20ºC and thawed up to five times to study the
effects of both treatments on the outcome of a commercial ELISA test for the
detection of antibodies against Suid Herpesvirus 1 (ADV). The estimated
prevalence of antibodies against ADV was 50-53% for clean and haemolysed sera.
Hence, haemolysis did not reduce the mean observed serum antibody prevalence.
However, 10 samples changed their classification after repeated freeze-thawing.
This included 3 (15%) of the clean sera and 7 (41%) of the haemolysed sera. None
of the 19 initially positive samples changed their classification. We recommend (1)
establishing more restrictive cut-off values when testing wildlife sera, (2) recording
serum quality prior to sample banking, (3) recording the number of freezing-
thawing cycles and (4) store sera in various aliquots to reduce repeated usage. For
instance, sera with more than 3 freeze-thaw cycles and a haemolysis of over 3 on a
scale of 4 should better be discarded for serum antibody monitoring. Even clean
(almost not haemolysed) sera should not go through more than 5 freeze-thaw
cycles.
Background
Monitoring wildlife diseases is needed to identify changes in disease occurrence
and to measure the impact of intervention. However, obtaining samples from wild
animals is difficult as compared to pets or livestock, due to the limited accessibility
of the former (Cotilla et al., 2010; Boadella et al., 2011a). Wildlife blood samples are
often gathered post-mortem from shot (hunter-harvested) animals, and then
centrifuged to obtain serum. Occasionally, whole blood samples are obtained from
gamekeepers and sent frozen to the laboratory. Sera are stored frozen and often re-
35
Tesis doctoral
used several times in order to maximise the information obtained. In consequence,
wildlife sera are often haemolysed and/or go through repeated freeze-thaw cycles
(e.g. Muñoz et al., 2010). However, both haemolysis and freeze-thawing may affect
the performance of tests based on serum antibody detection, such as the popular
enzyme-linked immunosorbent assay (ELISA).
Recently, a study on the effect of swine blood sample handling on Erysipelothrix
rhusiopathiae antibody detection by indirect ELISA revealed that serum
immunoglobulin G antibodies were stable in the face of several sample mishandling
events, including repeated freeze-thawing and minimal to severe haemolysis. Only
samples simulating extreme haemolysis (100% haemolysed whole blood) had
significantly lower optical density (OD) readings. However, haemolysis and freeze-
thawing were not studied in combination, and the effect of such treatments on
antibodies against other disease agents is unknown (Neumann and Bonistalli, 2009).
Herein, we used samples of clean and haemolysed Eurasian wild boar (Sus
scrofa) serum stored at -20ºC and thawed up to five times to study the effects of
both treatments on the outcome of an ELISA test for the detection of antibodies
against Suid Herpesvirus 1 (ADV), the aetiological agent of Aujeszky’s disease.
Based on the abovementioned results for E. rhusiopathiae in pigs, we expected no
strong effect of haemolysis and freeze-thawing on test performance.
Methods
Samples
Blood samples were collected from 20 stalking-harvested wild boar. Blood was
drawn from the thoracic major veins by exsanguination immediately after death.
Two blood collection tubes were filled per animal and stored at 4ºC during
transport until the laboratory; although for three animals only one tube could be
filled. Serum was obtained by centrifugation from one of the tubes and stored at
4ºC. The second tube was used to obtain haemolysed serum after freezing the
whole blood simulating the treatment that occurs when blood samples are sent
36
Capítulo 2.2
frozen. In a subjective haemolysis-scale from 1 (almost nil) to 4 (severe), the
haemolysed obtained sera were classified as level 3. After obtaining all fresh and
haemolysed sera, the first ELISA test was performed. Sera were stored at -20ºC for
one week and thawed at 4ºC for one night. One hour before performing the
ELISA analysis, sera were removed from 4ºC storage and brought to room
temperature. After analysis, sera were frozen again to -20ºC. This process was
repeated 5 times.
All animal samples used for this study came from opportunistic sampling
during legal hunting. No live animal was handled and no special permits were
required.
ELISA test
A commercially available blocking ELISA was used for detection of antibodies
to the gpI antigen of Suid Herpesvirus 1 (IDEXX HerdCheck Anti-ADV gpI,
IDEXX, Inc., USA). This ELISA technique has been broadly used for testing
antibodies to ADV in different wild boar populations (Vicente et al., 2005; Ruiz-
Fons et al., 2006; Pannwitz et al., 2011).
The ELISA was performed following the manufacturer’s instructions, in an
ADV antigen-coated microwell plate, using a 1:2 serum dilution. One hundred
microliters of diluted sera were added in each microwell and incubated for 1 hour
at room temperature (RT). Samples, positive and negative controls were tested in
duplicate in each plate. Subsequent to a wash step, 100μl of anti-ADVgpI
monoclonal antibody conjugate was added and incubated at RT for 20 minutes. If
no gpI antibodies were present in the tested serum, the conjugated gpI antibodies
were free to react with the gpI antigen. Conversely, if gpI antibodies were present
in the tested serum, the enzyme-conjugated monoclonal antibodies were blocked
from reacting with the antigen. Following the incubation, the unreacted conjugate
was washed out and the reaction was revealed by adding 100 μl of
substrate/chromogen solution. In the presence of substrate enzyme, reaction
37
Tesis doctoral
generated blue colour. After 15 minutes of revealing, the reaction was stopped with
50µl/well of Stop solution and optical density (OD) was measured in a
spectrophotometer at 650 nm.
Results were expressed as a percentage of inhibition (%IN) value using the
following formula: [%IN = (mean negative control OD - mean sample OD / mean
negative control OD) x 100]. The quantity of antibodies to ADV-gpI was inversely
proportional to the OD and directly proportional to the %IN. According to the
manufacturer’s instructions, only samples with %IN values equal to or greater than
40% were considered positive. Samples with a %IN value between 30-40% were
considered doubtful and sera with %IN values <30% were classified as negative.
Figure 1.- Mean optical densities (OD) for positive (black diamonds) and negative (black squares) clean sera, and for positive (grey diamonds) and negative (grey squares) haemolysed sera ( standard error, SE) through the five freeze-thaw cycles (Freezing 1 to 5).
Results
A total of 20 sera were analyzed clean and from these, 17 could also be tested
haemolysed (Table 1). The ELISA results coincided in 14 cases (8 positive, 6
negative; 82%). Two negative clean sera tested positive and doubtful, respectively,
38
Capítulo 2.2
with haemolysis, and one positive clean sera tested negative with haemolysis. The
estimated prevalence of antibodies against ADV was 10 of 20 (50%; 29-70 95% CI)
and 9 of 17 (53%; 29-75 95% CI) for clean and haemolysed sera, respectively.
Hence, haemolysis did not reduce the observed serum antibody prevalence
(χ2=0.032, 1df, p>0.05).
Table 1.- Classification of the 20 wild boar sera samples (20 clean; 17 haemolysed) after 1 to 5 freeze-thaw cycles into positive (1), negative (0) or doubtful (2) according to a commercial ELISA for the detection of serum immunoglobulin G antibodies against Aujeszky’s disease virus.
Freeze/thaw cycle 1 2 3 4 5 1 2 3 4 5 Sample number
1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 3 0 0 0 0 0 0 0 0 0 2 4 0 0 0 0 2 0 0 0 0 2 5 0 0 0 0 0 0 0 0 0 0 6 1 1 1 1 1 1 1 1 1 1 7 1 1 1 1 1 1 1 1 1 1 8 0 0 0 0 0 0 0 2 0 2 9 1 1 1 1 1 0 0 0 2 1 10 1 1 1 1 1 1 1 1 1 1 11 0 0 0 0 0 1 1 1 1 1 12 1 1 1 1 1 1 1 1 1 1 13 0 2 0 0 0 2 2 0 0 0 14 1 1 1 1 1 1 1 1 1 1 15 0 0 0 0 0 0 0 0 0 2 16 1 1 1 1 1 1 1 1 1 1 17 0 0 0 0 0 0 0 0 2 2 18 0 0 0 0 2 19 0 0 0 0 0 20 1 1 1 1 1
Nº positive samples 10 10 10 10 10 9 9 9 9 10 Nº negative samples 10 9 10 10 8 7 7 7 6 2 Nº doubtful samples 0 1 0 0 2 1 1 1 2 5 Nº changing state 1 0 0 2 0 2 3 7 % changing state 5% 0% 0% 10% 0% 12% 18% 41%
Table 1 shows the outcome of the experimental manipulation of 37 wild boar
sera in terms of ELISA test results. Only 3 (15%) of the clean sera changed their
result after repeated freeze-thawing, changing from negative to doubtful (1 case at
39
Tesis doctoral
the 2nd cycle and 2 cases at the 5th). In contrast, 7 (41%) of the haemolysed sera
changed their result (2 cases at the 3rd cycle, 3 at the 4th and 7 at the 5th). These
changes occurred between negative and doubtful, except for one case. This one
consisted of a serum testing first three times negative, then doubtful and finally
positive. All 10 sera with changes at any cycle (27% of 37) tested negative (9) or
doubtful (1) in the first time, while all 19 sera that tested positive at the first cycle
(10 clean sera and 9 haemolysed sera) maintained their positivity through the five
freeze-thaw cycles. Figure 1 shows the mean optical density readings for clean and
haemolysed sera during the five freeze-thaw cycles.
Discussion
Based on the results obtained in this experiment, we rejected our initial
hypothesis: Haemolysis alone and the combination of haemolysis and freeze
thawing affected the results of the ADV ELISA test. These observations have
implications for wildlife disease monitoring based on serum antibody detection.
The effect of the described sample mishandling events was mostly a higher rate
of doubtful results. Based on our observations, we recommend (1) establishing
more restrictive cut-off values when testing wildlife sera, (2) recording serum
quality prior to sample banking (3) recording the number of freezing-thawing cycles
and (4) store sera in various aliquots to reduce repeated usage. Regarding the cut-
off, we observed a trend towards lower ODs with increasing number of freeze-
thawing cycles. So, if the cut off was maintained as the one defining a positive
sample (i.e. considering any doubtful samples as negative), the result in terms of
prevalence (number of positive samples divided by total number of tested samples)
would not change except for one single case at one single cycle. Regarding the
possibility to classify sera based on their apparent quality, a subjective haemolysis-
scale from 1 (almost nil) to 4 (severe), could be defined. Additionally, the number
of freeze-thaw cycles can be recorded for each sample aliquot. For instance, sera
with more than 3 freeze-thaw cycles and a haemolysis of over 3 on a scale of 4
40
Capítulo 2.2
41
should better be discarded for serum antibody monitoring. Even clean (almost not
haemolysed) sera should not go through more than 5 freeze-thaw cycles.
In studies on domestic animals, samples are normally ELISA tested in duplicate
to reduce within-laboratory variability (e.g. Charlier et al., 2009). This is not always
the case in wildlife, particularly if the available serum volume is limited and several
different tests are run (e.g. Falconi et al., 2010; Holzwarth et al., 2011). Running
each serum in duplicate also means duplicating the cost per individual test,
particularly when using commercial ELISA kits. However, efforts should be done
to test all wildlife sera in duplicate in order to reduce variability (e.g. Curry et al.,
2011).
As in Neumann and Bonistalli’s (Neumann and Bonistalli, 2009) study on E.
rhusiopathiae antibody detection in pigs, our results on ADV antibody detection in
wild boar are not directly applicable to other host-pathogen binomia. It is however
advisable to take care in the interpretation of ELISA results obtained from poor
quality samples. Efforts should be made to evaluate the specific effects of thawing
and haemolysis on the results of other antibody detection tests.
Acknowledgments
Authors thank Alfredo & Tomás for their help in obtaining the serum samples in the funniest
way nobody can imagine.
Capítulo 3
CAPÍTULO 3. VIGILANCIA SANITARIA DE ZOONOSIS
3.1. Incremento en el contacto con el virus de la hepatitis E en el ciervo Ibérico
3.2. ¿Permiten los ungulados silvestres mejorar la vigilancia de flavivirus?
3.3. Una tendencia decreciente: la triquinelosis del jabalí
Capítulo 3
Resumen
Un punto clave de la vigilancia sanitaria es el seguimiento a lo largo del tiempo
del contacto de las especies silvestres susceptibles con agentes zoonóticos. La fauna
silvestre se ha identificado como reservorio potencial para los humanos del virus de
la hepatitis E (VHE), de ciertos virus del género Flavivirus y de Trichinella spp. En
este capítulo se describen las tendencias temporales de su contacto con poblaciones
de ciervo (Cervus elaphus) y jabalí (Sus scrofa) de la Península Ibérica.
3.1. Con el objetivo de describir la tendencia temporal del contacto en distintas
poblaciones de ciervo con el VHE, se analizaron 968 sueros mediante ELISA y 81
mediante PCR para la detección de ARN vírico. La seroprevalencia global para
todo el periodo de estudio fue del 10%, pero la detectada durante el período de
2006-2009 fue significativamente mayor (12%) que la detectada durante el período
de 2000-2005 (8%). La detección de un 13% de muestras positivas a ARN de VHE
confirmó que el virus circula activamente en las poblaciones de ciervo estudiadas.
El incremento de la seroprevalencia detectado podría suponer un incremento en el
riesgo zoonótico potencial de infección por consumo.
3.2. Como posible herramienta para la vigilancia de la circulación de los
flavivirus, se analizaron sueros de 887 ciervos y 742 jabalíes juveniles del período
2000-2011 procedentes de poblaciones silvestres, y de 327 ciervos de granja
muestreados durante tres años consecutivos en ese mismo periodo. La
seroprevalencia global detectada en las poblaciones de ciervo silvestres fue del
0,2%. En cambio, la prevalencia de contacto con flavivirus en las poblaciones de
jabalí fue de un 4% y se mantuvo estable durante el periodo de estudio. La
seropositividad del ELISA en los ciervos de granja aumentó diez veces tras los
brotes de flavivirus (West Nile y Bagaza) detectados en la zona durante el verano y
otoño de 2010. El estudio evidenció la utilidad de los ungulados silvestres jóvenes,
particularmente del jabalí, como buenos centinelas del contacto con flavivirus en la
Península Ibérica. El almacenaje sistemático de muestras procedentes de animales
45
Tesis doctoral
46
cazados o de granja proporciona un valioso material para estudios epidemiológicos
retrospectivos y para futuras monitorizaciones del contacto con patógenos.
3.3. Finalmente, para analizar la tendencia de la prevalencia y distribución de
Trichinella spp. en poblaciones de jabalí de la provincia de Ciudad Real durante las
temporadas cinegéticas 1998-99 y 2009-10, se generó una base de datos por finca y
año de los resultados de detección de Trichinella spp. por digestión realizados por
los Servicios Veterinarios Oficiales. De los 93182 jabalíes analizados durante los 12
años de estudio, 47 resultaron positivos (0,05%). Según el modelo logístico aplicado
para el análisis de factores de riesgo, la presencia de Trichinella spp. tuvo una
tendencia decreciente durante el periodo de estudio y el factor temporal explicó la
mayor proporción de la varianza, seguido de las características ambientales y los
factores de manejo en la finca. Paralelamente, se analizaron mediante ELISA 1432
sueros de jabalíes cazados en la misma área. La prevalencia de anticuerpos fue del
7%, pero sólo un 3% de los positivos se confirmó por Western blot. Dada su falta
de especificidad, el ELISA no parece una herramienta útil para la monitorización
del contacto del jabalí con la Trichinella spp.
Capítulo 3.1
Incremento en el contacto con el virus de la hepatitis
E en el ciervo Ibérico
Boadella, M., Casas, M., Martín, M., Vicente, J., Segalés, J., de la Fuente, J., Gortázar, C. 2010. Increasing contact with hepatitis E virus in red deer, Spain. Emerging Infectious Diseases 16, 1994-1996.
Capítulo 3.1
Abstract
We tested Iberian red deer for Hepatitis E virus (HEV) RNA and antibodies.
Overall, 101 of 968 sera (10.4%) were ELISA positive and 11 of 81 (13.6%) were
RT-PCR positive. Sequencing evidenced genotype 3 infection. The increasing
prevalence trend in Iberian red deer suggests a potential exposure risk for humans.
Introduction
Hepatitis E (HE) is caused by hepatitis E virus, the only member of the
Hepeviridae family (Panda et al., 2007). Four major genotypes of HEV have been
recognized: genotypes 1 and 2 are restricted to humans and associated with
epidemics in developing countries, whereas genotypes 3 and 4 are zoonotic in both
developing and industrialized countries. Wild and domestic animals are being
identified as potential HEV reservoirs for humans (Panda et al., 2007; Tei et al.,
2003; Teo, 2010).
Studies on wild Sika deer (Cervus nippon) have found low detection rates
suggesting that Sika deer are accidental hosts to HEV (Matsuura et al., 2007; Yu et
al., 2007) despite the transmission link discovered between them and hepatitis E in
Japan (Tei et al., 2003) that raised awareness on the potential that game animals
have to transmit HEV (Teo, 2010). In Europe, information about HEV infection
in wild ruminants is limited to reports suggesting that the roe deer (Capreolus
capreolus) and red deer (Cervus elaphus) can act as HEV hosts (Forgách et al., 2009;
Reuter et al., 2009; Rutjes et al., 2010). Apart from these limited studies, there are
no large-scale surveys on HEV epidemiology in wild cervids. In Spain, the relatively
high HEV seroprevalence detected in domestic pigs and wild boar suggests that
HEV infection is probably widespread (de Deus et al., 2008).
Red deer density, distribution, and hunting harvest are increasing throughout
Europe in recent years (Milner et al., 2006). In Spain, high densities are recorded
(Acevedo et al., 2008) and hence, the red deer is an important game meat source.
49
Tesis doctoral
This scenario stresses the need of a better understanding of the epidemiology of
this pathogen in the Iberian game populations.
Our goals were to describe the epidemiology and time trends of HEV in
Iberian red deer by means of serology and PCR. Based on previous results on wild
boar (de Deus et al., 2008), we hypothesised that red deer would show a
widespread contact with HEV in the Iberian Peninsula.
The study
Sera from 968 Iberian red deer were collected between 2000 and 2009. These
samples came from hunter-harvested red deer in 21 wild or semi-free ranging
populations (n=892) and from 2 farms (n=76). This includes a variety of habitats
and climates, which can be simplified into 5 different bioregions in the mainland
(Figure 1; Muñoz et al., 2010). Sampling sites were grouped into seven areas and
two red deer farms (Table 1; Figure 1). Sex and age were recorded. Management
conditions of red deer were classified as open (no fencing and no management, 9
sites), fenced (fencing and artificial feeding, 12 sites) and farmed deer (livestock-like
management, 2 farms). In order to analyse time trends, samples collected between
2000 and 2005 were classified as “time 1” and those collected between 2006 and
2009, as “time 2”. Only sites where sampling occurred in both periods and with
comparable sampling sizes were included in the time trend analysis.
Serum samples were tested for anti-HEV IgG antibodies by means of ELISA
described previously (Matsuura et al., 2007; Peralta et al., 2009) but using protein G
horseradish peroxidase (Sigma Chemical, S. Louis, MO, USA) as a conjugate, as
previously used in red deer (Muñoz et al., 2010).
Anti-HEV positive sera were obtained from ELISA and RT-PCR positive
domestic swine. Anti-HEV negative sera were obtained from previous studies (de
Deus et al., 2007) and negative controls were obtained from HEV negative cattle
(Peralta et al., 2009). Results were expressed as the percentage of optical density
(%OD) using the formula [% OD =100 X sample OD / sum of negative controls
50
Capítulo 3.1
OD]. Serum samples with values of %OD greater than 100% were considered
positive.
For the RT-PCR, 81 sera were randomly selected and analysed. Viral RNA was
extracted from 150 ml of serum with Nucleospin® RNA virus kit (Macherey-Nagel
Gmbh & Co., Düren, Germany), following manufacturer’s instructions. HEV
detection was done by means of a semi-nested RT-PCR as previously described (de
Deus et al., 2007). In each run, negative and positive controls were added.
Eight HEV RT-PCR positive samples were sequenced. HEV sequences were
identified by using the Blast algorithm at www.ncbi.org against HEV sequences
available in the GenBank (on 25th January 2010). Sequences were deposited in the
GenBank database under accession numbers HM113373 and HM113374.
Sterne's exact method was used to estimate apparent prevalence confidence
intervals. The association of age, sex, sampling site and management with
serological and RT-PCR results was analysed by means of chi-square tests.
Relationship between seropositivity and presence of HEV RNA in the serum was
also analyzed by means of a Pearson’s chi-square test. Differences were considered
statistically significant when p < 0.05.
Table 1.- IgG serology to HEV and RT-PCR results in different Iberian regions and two red deer farms.
Region Sites SamplesNumber
seropositive Prevalence (%)
(95 CI) RT-PCR*
Cantábrico Occidental 3 122 21 17.2 (11.4-24.9) 2/14 Cantábrico Oriental 1 29 0 0.0 (0.0-11.5) 0
Sistema Central 1 16 0 0.0 (0.0-20.8) 0 Montes de Toledo 7 366 19 5.2 (3.2-8.0) 2/18 Valle del Guadiana 2 86 22 25.6 (17.3-35.9) 5/13
Sierra Morena 4 203 15 7.4 (4.3-11.9) 1/14 Doñana 3 70 22 31.4 (21.3-43.5) 1/21 Cádiz † 1 50 1 2.0 (0.1-10.6) 0
Navarra † 1 26 1 3.8 (0.2-18.8) 0 TOTAL 23 968 101 10.4 (8.62-12.53) 11/81
*Number positive/number tested. † Red deer farms
51
Tesis doctoral
Overall, 101 sera (10.43%, 95% confidence interval [CI]=8.62-12.53) were
positive for IgG (Table 1). No significant differences in HEV seroprevalence were
observed between sex (Chi2=0.894, 1 d.f., p>0.05) and age classes (Chi2=12.436, 3
d.f., p>0.05). When analysing prevalences in time, seroprevalence in time 2
(12.15%, 95% CI=9.75-15) was significantly higher than seroprevalence in time 1
(7.52%, 95% CI=5.11-10.82; Chi2=5.181, 1 d.f., p<0.05). Local IgG
seroprevalences ranged from 0% (95% CI=0-20.8) to 31.4% (95% CI =21.3-43.5;
Figure 1). Statistically significant differences were found for IgG seroprevalence
considering management types (Chi2=6.876, 2 d.f., p<0.05), with higher values in
open (14.89%, 95% CI=11.29-19.38) than in fenced (9.09%, 95% CI=6.95-11.72)
and farmed (2.63%, 95% CI=0.47-9.02) areas.
Figure 1.- Map of the Iberian Peninsula showing the five peninsular bioregions (numbers 1 to 5) and the 21 sampling sites. Numbers indicate positive animals/sampled animals. Numbers in brackets indicate fenced estates. The two red deer farms are marked by asterisks.
52
Capítulo 3.1
Eleven out of 81 samples (13.6%, 95% CI=7.35-22.73) were RT-PCR positive.
Local viral RNA prevalence ranged from 4.5% (95% CI=2.4-22.21) to 38.5% (95%
CI =16.57-65.84; Table 1). No statistically significant differences in HEV
prevalence were observed among geographic areas and management types.
Sequence analysis revealed that all deer sequences from this study belonged to
genotype 3. Seven samples, belonging to sequence HM113374, shared 99%
nucleotide identity with Spanish domestic swine strains. One sample, sequence
HM113373, showed similarity (91%) with a strain from an acute human case of HE
in Marseille, France, according to GenBank.
Conclusions
This is the first demonstration of HEV infection in Iberian red deer,
confirming that HEV circulates actively among deer populations in Spain, as
described before for the wild boar (de Deus et al., 2008). Although it has been
previously shown that red deer can be infected with HEV (Forgách et al., 2009;
Rutjes et al., 2010), this is the first large serosurvey in this species in Europe.
Moreover, the results show an increasing prevalence trend in the last decade.
De Deus et al., (2008) showed that higher IgG seroprevalences were found in
estates with higher wild boar densities. However, in the present study, the lowest
mean seroprevalences were found in red deer farms, where densities were the
highest and red deer had no contact with wild boar or domestic swine. In contrast,
the highest seroprevalences were reported in open areas where contact with suids
may occur. However, wild boar densities are also high in fenced hunting estates
(Acevedo et al., 2007b), and deer from these sites had intermediate HEV antibody
prevalences. These differences could indicate that Iberian red deer may need a
source of infection and thus, are acting as spillovers more than true reservoirs.
Presence of HEV RNA in 13% of deer sera implies that deer carcasses
represent a risk for zoonotic transmission and, consequently, handling of live
animals and carcasses, a risk activity. Red deer are infected with HEV at lower rates
53
Tesis doctoral
54
than wild boar and domestic pigs, but may act as a potential source of HEV
infection in humans. Further studies are needed to fully elucidate the epidemiology
of HEV in wildlife and the food-borne zoonotic transmission risks.
Acknowledgements
We thank Bibiana Peralta (CReSA) for providing the HEV antigen protein for ELISA, ELISA
and RT-PCR positive controls. Many colleagues at IREC helped in field and laboratory work.
Capítulo 3.2
¿Permiten los ungulados silvestres mejorar la
vigilancia de flavivirus?
Boadella, M., Díez-Delgado, I., Gutiérrez-Guzmán, AV., Höfle, U., Gortázar, C. Do wild ungulates allow improved monitoring of Flavivirus circulation in Spain? En prensa, Vector-Borne and Zoonotic Diseases.
Capítulo 3.2
Abstract
As a response for the need of improved and cost-efficient West Nile virus
(WNV) and other flavivirus surveillance tools, we tested 887 juvenile free living red
deer, 742 free living wild boar and 327 farmed deer to detect temporal variability in
exposure to these viruses. Thirty of 742 juvenile wild boar samples (4%; 95% CI:
2.8-5.7) yielded a positive ELISA result. Antibody positive individuals had been
sampled between 2003 and 2011 in localities from central and southern Spain. No
wild boar from the northern half of Spain (n=120) tested positive. Regarding
juvenile wild red deer, only two out of 887 samples yielded a positive ELISA result
(0.2%; 95% CI: 0.1-0.8). These two samples came from the same site and sampling
year. The likeliness of detecting contact with WNV or cross reacting flaviviruses
was 18 times higher among juvenile wild boar than among juvenile red deer. ELISA
positivity among farmed deer increased tenfold after local flavivirus outbreaks
recorded in summer and autumn 2010. This survey evidenced the potential
usefulness of juvenile wild ungulates, particularly wild boar, as suitable flavivirus
sentinels in southwestern Europe, and that systematic serum banking of samples
from hunter harvested wildlife or from individual farmed ungulates provides
valuable material for retrospective epidemiological surveys and future disease
monitoring.
Introduction
The importance of the genus Flavivirus in terms of global health is mainly based
on the zoonotic nature of some of its members such as West Nile virus (WNV),
dengue virus, tick-borne encephalitis virus, yellow fever virus, and several other
viruses which can cause fatal disease in humans (Gould and Gritsun, 2006). Four
different members of the genus Flavivirus have been identified in the Iberian
Peninsula: Spanish sheep encephalomyelitis virus (Marin et al., 1995), WNV
(Jiménez-Clavero et al., 2008), Usutu virus (USUV; Busquets et al., 2008; Vázquez
González et al., 2011), and more recently, Bagaza virus (BagV; Agüero et al., 2011).
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Spanish sheep encephalomyelitis virus is a member of the tick borne encephalitis
serocomplex and is restricted to sheep in Atlantic habitats of northern Spain. The
remaining three members of the genus are mosquito borne. All three are
enveloped, single-stranded positive-sense RNA viruses antigenically related to
Japanese encephalitis virus.
Antibodies against WNV have been reported in several bird species, with
seroprevalences ranging from 2% to 43% (Figuerola et al., 2007; Figuerola et al.,
2008), thus WNV is probably endemic and widespread in its usual bird-mosquito
cycle in Mediterranean Iberia (Hayes et al., 2005). However, few human cases of
WN fever have been recorded (Bofill et al., 2006; Kaptoul et al., 2007;
http://web.oie.int/wahis/public.php?page=event_summary&this_country_code=
ESP&reportid=9695), although human serosurveys suggested that WNV or closely
related flaviviruses circulated at least since the 1970s in the Ebro delta and other
areas in Spain (Garea Gonzalez and Filipe, 1977; Lozano and Filipe, 1998; Bofill et
al., 2006). In contrast to the high mortality recorded in native North American
birds, WNV only recently and in parallel with the increase of human and equine
disease outbreaks, causes sporadic disease and immune suppression in European
birds, including endangered raptors in Spain (Höfle et al., 2008). USUV has been
isolated from mosquitos in north-eastern and southern Spain (Busquets et al., 2008;
Vázquez González et al., 2011). USUV also affects birds (Steinmetz et al., 2011)
and may cause human fatal neuroinvasive disease (Cavrini et al., 2009; Pecorari et
al., 2009). Finally, an outbreak due to BagV with high mortality in wild red-legged
partridges (Alectoris rufa), moderate mortality among pheasants (Phasianus colchicus)
and other bird species, occurred in late summer and early autumn 2010 in Cádiz,
southern Spain, being the first record of BagV in Europe (Agüero et al., 2011).
Hunter-harvested mammals often are more accessible than wild birds and allow
sampling larger amounts of serum. Thus, it has often been suggested to use
mammals as substitute flavivirus sentinels because contact detection would indicate
transmission outside the enzootic bird cycle (Root et al., 2005; Teehee et al., 2005;
58
Capítulo 3.2
Gómez et al., 2008; Blitvich et al., 2009). However, medium sized and large
mammals have generally a long life span, and antibodies against WNV may
eventually persist more than one year (Geevarghese et al., 1994). Hence, the annual
cohorts of juvenile mammals (after loosing their maternal antibodies) are
potentially the best indicators of current flavivirus circulation. Studies in white-
tailed deer (Odocoileus virginianus) demonstrated that it is unlikely that they are an
important amplifying host for WNV (Farajollahi et al., 2004) but that clinical
disease and mortality are possible (one fatal case described by Miller et al., 2005).
Several authors evidenced seroconversion with enzyme-linked immunosorbent
assays (ELISA) and by plaque-reduction neutralization tests (PNRT) and
prevalences obtained ranged from 0.9 % to 12.7% (Farajollahi et al., 2004; Santaella
et al., 2005). Regarding the Eurasian wild boar (Sus scrofa), we assume an analogous
response as domestic pigs (Sus scrofa). Pigs develop low viremias of short duration
and it is unlikely that they are amplifying hosts, but due to their serological reaction,
pigs can be useful as sentinels (Teehee et al., 2005). Also, flavivirus antibody
prevalences have been reported in wild boar from the Czech Republic (6.5%;
Halouzka et al., 2008) and feral swine in the USA (22.5%; Gibbs et al., 2006). A
preliminary explorative study on Eurasian wild boar from Spain revealed exposure
of the species to WNV in areas with high reported WNV activity in birds
(Gutiérrez-Guzmán et al., submitted). Cattle would be easier to sample than wild
ungulates. In fact, a 4% seropositivity was recorded in Turkey (Ozkul et al., 2006).
However, one study in Spain found no seropositive cattle in a wetland where WNV
was known to circulate among birds and equids (Jiménez-Clavero et al., 2007).
We hypothesized that in the Iberian Peninsula, red deer (Cervus elaphus) and
Eurasian wild boar would contact with members of the Flaviviridae family at
similar rates as sympatric wild birds, and that subsequent seroconversion would be
easier to monitor since these abundant and widespread game species exhibit several
advantages when compared to birds: (1) relatively long life-span, (2) limited home
range and less mobile than their avian counterparts, (3) easy and cost-effective
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Tesis doctoral
sampling of hunter harvested animals and in game farms, and (4) larger quantity of
serum available. With the aim of adding a useful tool to the WNV and flavivirus
surveillance, we tested a large number of juvenile red deer and wild boar to detect
temporal variability in exposure to WNV or cross reacting members of the genus
Flavivirus.
Material and methods
Sampling
Serum samples were collected between 2000 and 2011 from hunter-harvested
Eurasian wild boar (n=742) and red deer (n=862). Samples were stored frozen at -
20ºC until analyzed. Only wild boar between 4 and 12 months of age were studied.
Regarding deer, animals included in this part of the study were between 4 and 18
months old.
In addition, we had access to individual serum samples obtained from 327
farmed red deer of all ages except calves in Cádiz, southern Spain. This farm
(indicated by an asterisk in Figure 1) is placed in the region where a WNV
(http://ec.europa.eu/food/committees/regulatory/scfcah/animal_health/presenta
tions/1112102010_wnf_spain.pdf) and a BagV outbreak occurred during summer
and autumn 2010 (Agüero et al., 2011). Three serum samples from each individual
deer were obtained by jugular vein puncture during routine health check protocols
in December 2008/January 2009, January 2010, and January 2011, respectively.
ELISA test
A WNV blocking ELISA was used to screen for flavivirus-specific
immunoglobulin G (IgG) antibodies (INGENZIM WN Compac® INGENASA,
Madrid, Spain), (Sotelo et al., 2011). Following manufacturer’s instructions, samples
with % of inhibition greater than 40% were considered positive. Although the assay
uses the protein E of WNV, this method may detect cross-reactive antibodies
60
Capítulo 3.2
against antigenically related flaviviruses of the Japanese Encephalitis complex. Due
to logistic limitations positive sera could not be confirmed by seroneutralization.
Statistics
Comparison of seroprevalences between species and sampling periods was
performed by means of Chi-square tests. Only wild boar from sites with n>10 in
both time periods were considered for the time analysis. Generalized linear models
(GLM) with binomial distribution and logit link function were used for calculating
the effect of sampling year (continuous), the sampling area (categorical, n=6) and
their interaction on the probability of testing positive to the ELISA (binomial, 1
positive, 0 negative). Areas without stratified sampling per year were excluded from
the analysis. We used a backward stepwise strategy to obtain the final model,
selected by best p value. Data was analyzed using the IBM SPSS statistical package,
version 19.0 (IBM Corporation, Somers, NY, USA).
Table 1.- Sampling effort and WNV/Flavivirus seroprevalences in each of the five peninsular Bio-regions (BR).
BR Nº sites Time span Wild boar sampled
Prev. wild boar (%)
Red deer sampled
Prev. red deer (%)
1 1 2002-2010 47 0 23 0
2 3 2000-2010 13 0 53 0
3 13 2000-2011 622 4.82 769 0.26
4 1 2002-2010 0 - 17 0
5 1 2007 60 0 0* -
TOTAL 19 2000-2011 742 4.04 862 0.23 * The 327 farmed red deer samples are from Bio-region 5.
Results
As shown in Table 1, only 30 of 742 juvenile wild boar samples (4%; 95% CI:
2.8-5.7) yielded a positive ELISA result. These antibody positive individuals had
been sampled between 2003 and 2011 in localities from central and southern Spain
(Bio-region 3), where sample sizes were larger (Table 1; Figure 1). No wild boar
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from the northern half of Spain (n=120) tested positive. When analyzing over time
periods (n=612), wild boar sampled between 2000 and 2005 showed a
seroprevalence of 7%, and in those sampled between 2006 and 2011, the
seroprevalence remained stable at 3.4% (χ2=3.2, 1 d.f., p>0.05). The best model
was explained by the area of sampling (Wald χ2=12.2, 2 d.f., p<0.05), while year
was not a significant factor in the model. Modelling revealed no significant time
trend (β=0.067, p>0.05).
Figure 1.- Map of the Iberian Peninsula showing the 19 sample sites and their flavivirus antibody seroprevalence. Different shapes represent the sampled species, red deer (Cervus elaphus) and wild boar (Sus scrofa) and their sizes are proportional to the sample (big sizes are for sample sizes greater than 50 animals while small ones are for sample sizes below 50 individuals). Grey shapes indicate negative results and black shapes positivity. Mean seroprevalences are indicated in percentage. The years when seropositive animals were detected are the numbers in the squares. The asterisk marks the location of the red deer farm in Cádiz. Grey numbers indicate the five peninsular Spanish Bio-regions.
62
Capítulo 3.2
Regarding juvenile wild red deer, only two out of 887 samples yielded a positive
ELISA result (0.2%; 95% CI: 0.1-0.8; Table 1). These two samples came from the
same site (a private hunting estate in south-central Spain) and had been collected in
January and December 2006, respectively (Figure 1). No wild boar samples were
available for 2006 in this site. The likelihood of detecting contact with WNV or
cross reacting flaviviruses was 18 times higher among juvenile wild boar than
among juvenile red deer (χ2=30, 1 d.f., p<0.001).
Figure 2.- Individual serum antibody levels (expressed as % inhibition) against West Nile virus or cross reacting flavivirus detected by ELISA in 327 farmed red deer from Cádiz, southern Spain. Data are presented for December 2008/January 2009 (Panel a) and January 2011 (Panel b).The dashed line indicates the cut off.
Figure 2 presents the results obtained from the individually surveyed farmed
deer. ELISA positivity among deer increased tenfold after the lineage 1 WNV
outbreak that affected horses and the parallel BagV outbreak that affected game
birds in the same locality. Overall seroprevalences in winter 2008-2009 and winter
2009-2010 were 1.2% and 3%, respectively. Samples collected in January 2011 from
the same individuals, gave 38.8% of seropositivity. Among these 327 red deer sera,
two tested positive in 2009, negative in 2010 and positive again in 2011 and 6 tested
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Tesis doctoral
negative in 2009, positive in 2010 and negative again in 2011. Two deer consistently
tested positive in the three years. All other sera either tested consistently negative
(58.7%) or tested positive only in January 2011 (36%).
Discussion
This survey evidenced the potential usefulness of wild boar, and to a lower
extent of red deer, as sentinels for WNV or cross reacting members of the genus
Flavivirus. It also showed the value of routine serum banking for retrospective
epidemiology for providing an easy and cost-effective way to monitor the current
year activity of a given pathogen (flavivirus) in a region (Boadella et al., 2011a).
Based on the literature, we expected similar seropositivity rates in deer and wild
boar (Santaella et al., 2005; Halouzka et al., 2008). However, free living juvenile
wild boar appeared to be far more suitable as sentinels than free living juvenile red
deer. Several not mutually excluding hypotheses might explain the differences in
antibody prevalence observed between free living juvenile wild boar and red deer.
First, the low hair density, the low thickness of the epidermis and other peculiarities
of the wild boar skin (Meyer et al., 2011), making them eventually more susceptible
to mosquito bites than deer; second, its scavenging behaviour since WNV (and
possibly other flaviviruses) can occasionally be transmitted orally, via consumption
of infected prey or carrion as previously demonstrated in mammals (Austgen et al.,
2004); third, that wild boar (and pigs) might be more likely to produce an antibody
response than ruminants, as is observed in tuberculosis for instance (Boadella et al.,
2011b); and fourth, since host preferences are common factors modulating vector-
borne diseases (Zwiebel and Takken, 2004), we suggest that flavivirus vectors may
display a preference for suids.
The only two seropositive wild red deer were recorded in the same site and
during the same year. This suggests an epidemiologic link between both events,
rather than coincidence, and shows that using juveniles for surveillance purposes
can be effective. Unfortunately, no wild boar were sampled in this site the same
64
Capítulo 3.2
year, precluding any comparisons. However, the higher apparent sensitivity of wild
boar as flavivirus sentinels allowed detection of antibodies in all years since 2003
(sample sizes were low before 2003), and the analysis along time showed a stable or
even declining seroprevalence. This would confirm that WNV (or cross-reacting
flaviviruses) have been circulating for years in the Iberian Peninsula, as reported for
birds of prey (Höfle et al., 2008), aquatic birds (Figuerola et al., 2007) and for
humans (Garea Gonzalez and Filipe, 1977; Lozano and Filipe, 1998). A very
important aspect of WNV surveillance is early detection (Rockx et al., 2006).
Results reported herein prove that antibodies can be detected in wild ungulates
even before cases are detected in horses or humans, for instance in central Spain.
The fact that two of the farmed deer consistently tested positive in 2009-2011
could suggest that antibodies against flavivirus may persist for periods over one
year, or alternatively it could suggest re-exposure (Geevarghese et al., 1994). This
confirms the choice of juveniles for flavivirus surveillance, since antibodies in these
would indicate a recent exposure to the agent. This study also evidenced that
farmed deer offer an easily accessible sample that can eventually be used to detect
seroconversion. Finally, the fact that neither deer nor wild boar from the northern
third of the Peninsula had a positive ELISA result may be due to two facts. First,
contact with flavivirus is not a frequent event among these wild ungulates or
second, lower sample sizes compared to Bioregion 3, and thus a reduced detection
probability.
In addition to wild and domestic birds, horses would seem the most
straightforward sentinel species for WNV and related flaviviruses (Jiménez-Clavero
et al., 2007). However, well maintained horses are often vaccinated, impeding their
use as sentinels, and access to semi-free ranging unvaccinated horses would seem as
complex as access to hunter-harvested wild ungulates. Regarding wild ungulates,
sampling could eventually be further facilitated by using soft tissue extracts (e.g.
lung extracts) as an alternative to serum (Ferroglio et al., 2000).
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Tesis doctoral
66
In conclusion, we confirmed that juvenile wild ungulates, particularly wild boar,
are suitable flavivirus sentinels in southwestern Europe, and that systematic serum
banking of samples from hunter harvested wildlife or from individual farmed
ungulates provides valuable material for retrospective epidemiological surveys and
future disease monitoring.
Acknowledgements
Authors acknowledge the support of the Ministerio de Medio Ambiente, Rural y Marino
(MARM). This study was achieved thanks to the colleagues at IREC that participated in the
fieldwork during the whole study period, to J. Queirós for help with the sera database and to the
commitment of J.A. Ortiz to research collaborations.
Capítulo 3.3
Una tendencia decreciente: la triquinelosis del jabalí
Boadella, M., Barasona, JA,. Pozio, E., Montoro, V., Gortázar, C., Acevedo, P. Declining trends of Trichinella spp. infection in wild boar (Sus scrofa) of central Spain. En preparación.
Capítulo 3.3
Abstract
In south-central Spain, the Eurasian wild boar (Sus scrofa) harvest has heavily
increased in the last decade in association with more intensive management actions
such as fencing and supplementary feeding. We investigated the relationship
between the increasing wild boar hunting bag and Trichinella spp. prevalence. Data
on artificial digestion of muscle-tissue sample from 93,182 wild boar hunted during
the period 1998-2010 was obtained from the Official Veterinary Services. Sera from
1,432 hunter-harvested wild boar were collected between 2000 and 2011 in 25
hunting estates and analyzed by ELISA. The spatial-temporal trend and the risk
factors related to the hunting management on the detection of Trichinella spp.
infected wild boar were assessed using logistic regression. From the hunting
seasons 1998-99 to 2009-10, 47 out of 93,182 wild boar (0.05%; 0.04-0.06 95% CI)
tested positive for Trichinella spp. infection. According to the final model, the
presence of Trichinella spp. infection in wild boar had a decreasing trend during the
study period, and it was found to be negatively related with fenced populations and
non cultivated areas. Variation partitioning showed that the temporal factor,
independently of the rest of considered factors, explained the largest proportion of
the variation (67.26%). A total of 102 of 1,432 wild boar analyzed by ELISA tested
positive (7%; 5.9-8.5 95% CI). However, only one positive sample out of 30 was
confirmed by Western blot. We conclude that ELISA is not a suitable tool for
Trichinella contact monitoring among wild boar and discuss why Trichinella spp.
infection is declining in central Spain, which contrasts with epidemiological data on
other European wild boar populations.
Introduction
Nematodes of the genus Trichinella, the causal agents of trichinellosis, are
among the most widespread zoonotic pathogens (Gibbs, 1997; Murrell et al., 2000;
Pozio, 2007). Worldwide, the most important source of the human infection is the
domestic pig and, in industrialized areas of the European Union, the efforts
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Tesis doctoral
focused to remove Trichinella spp. from the pig food chain were almost a success
(EFSA, 2011).
Nonetheless, trichinellosis in Europe is still a problem, due to the sylvatic cycle
where the red fox (Vulpes vulpes) and the Eurasian wild boar (Sus scrofa) play
important roles as reservoir (Pozio et al., 2009), making it impossible to eradicate
this zoonosis (Rafter et al., 2005).
Pork and pork derived products from wild boar represent the second most
important source of trichinellosis for humans (Murrell and Pozio, 2011). In the
European Union, all game animals susceptible to Trichinella spp. infections should
be tested for these pathogens by digestion if the meat is intended for the market
(European Commission Regulation, 2005). However, outbreaks of trichinellosis
associated to the consumption of pork from wild boar consumed by hunters, their
households and friends, are continuously documented (Arevalo et al., 2009; Garcia-
Sanchez et al., 2009).
Given that the wild boar is an important Trichinella reservoir, understanding the
spatial-temporal distribution of this parasite-host system is quite important for
disease control and policy (e.g., Boadella et al., 2011a). Some studies have described
temporal trends of trichinellosis in European wildlife (Ramisz et al., 2001;
Kurdova-Mintcheva et al., 2009). Increasing time trends of Trichinella spp.
prevalence have been recorded in wild boar from Slovakia (Hurnikova and
Dubinsky, 2009), north-eastern Germany (Pannwitz et al., 2010), and Poland
(Ramisz et al., 2011).
In south-central Spain, the wild boar harvest has heavily increased in the last
decade in association with more intensive management actions on wild boar
populations (e.g., supplementary feeding, translocations of animals, etc.) in order to
increase hunting harvest (Acevedo et al., 2007b). The aim of this work was to
investigate the relationship between the increasing wild boar hunting bag and
Trichinella spp. prevalence in a province of Spain.
70
Capítulo 3.3
Material and methods
Study area and sampling
The study area was Ciudad Real, a 19,813 km2 province in central Spain (Figure
1). This area is characterized by a continental Mediterranean climate and moderate
to high wild boar densities (up to 90 ind/km2; Acevedo et al., 2007b). Wild boar is
widely distributed in this region where it locally cohabits with domestic pig (see e.g.,
Ruiz-Fons et al., 2008c).
On the one hand, sera from 1,432 legally hunter-harvested wild boar were
collected between 2000 and 2011 in 25 hunting estates located along the study area.
Blood was drawn from the heart or the thoracic cavity during field sampling, and
serum was collected and stored frozen at -20°C until analyzed. On the other hand,
data on artificial digestion of muscle-tissue sample from 93,182 wild boar hunted
during the period 1998-2010 was obtained from the Official Veterinary Services
(see below).
ELISA test
Wild boar sera were analyzed by means of a commercial ELISA (ID Screen
Trichinella Indirect, ID Vet), based on the excretory/secretory antigen (E/S),
allowing the detection of antibodies directed against Trichinella spp. Following
manufacturer’s instructions, cut-off was calculated as the S/P ratio: 100 x [(OD
sample - OD negative control) / (OD positive control – OD negative control)].
Sera with a S/P ratio > 60% were considered positive. A selection of 30 positive
sera was sent to the European Union Reference Laboratory for Parasites of Rome
to confirm the ELISA positivity by Western blot.
Spatio-temporal trends
All hunted wild boar were tested for Trichinella spp. infection by artificial
digestion according to the Commission Regulation 2075/2005. Number and origin
of animals tested from 1998-1999 to 2009-2010 hunting seasons (n=12,787 hunting
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activities) was provided by the regional sanitary authorities and digitalized into a
database. Since no precise information for each hunting activity was required for
the analyses, the original database was simplified and total values per hunting estate
and hunting season were considered (n=5,186 records). Consequently, the available
information was: the number of hunted wild boar and the number of those which
tested positive yearly for Trichinella spp. per hunting estate (the response variable
for modelling purposes).
Figure 1. Study area including the hunting estates where Trichinella spp. infections were detected (in dark) in wild boar (Sus scrofa) by the Official Veterinary Services in each hunting season.
The spatial-temporal trend and the risk factors related to the hunting
management on the detection of Trichinella spp. infected wild boar were assessed
using logistic regression (Hosmer and Lemeshow, 2000). We extracted information
to be used as predictors from 3 main sources. First, information related to the
hunting estate that can be potentially used as predictors in the models was gathered
from the database on veterinary inspections. So, we recorded the hunting season
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Capítulo 3.3
and determined the presence of Trichinella spp. infection in previous hunting
seasons in the same hunting estate in order to account for the time trend of
Trichinella spp. infection in the study area (see e.g., Pannwitz et al., 2010). From the
same data source, the relative abundance of wild boar was estimated through the
hunting yields as the mean number of animals hunted per hunting season in each
hunting area (for details about catch-based abundances indices see e.g., Boitani et
al., 1995; Acevedo et al., 2007b). Secondly, hunting estates have a technical plan for
hunting in which information on game management for each four-year period is
detailed. This database was available from the regional administration and the
information on the presence/absence of fences in the perimeter of the hunting
estate was used in the models. The presence of fences, linked to intensively
managed populations, was raised as one of the most relevant factors explaining the
occurrence of parasites (see e.g., Vicente et al., 2004; Acevedo et al., 2007b). We
also characterized each hunting estate with eco-geographical variables by using
geographic information systems. The geographic coordinates of the centroid of
each hunting estate were taken into account in order to explore the spatial trends in
the response variable (Borcard et al., 1992; Legendre and Legendre, 1998). In
addition, the land uses in each hunting estate were obtained from CORINE
database (Bossard et al., 2000). For this purpose, original classes of the CORINE
were re-coded in order to obtain a simplified legend more useful for the spatial
scale used in this study. Finally, we grouped all obtained predictors in four
explanatory factors (see Table 1): spatial (2 variables), temporal (2 variables),
hunting management (3 variables) and estate-related characteristics (18 variables).
A two-stage statistical analysis was carried out. First, as statistical theory
predicts that type I error rate increases with the number of predictors because of
repeated testing, we independently analyzed the relationship between each
predictor and the response variable by using a logistic regression in order to
minimize the number of predictors in the final model. The effect of hunting estate
was controlled for all models. At this first stage, all predictors for p<0.1 were
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Tesis doctoral
selected for their inclusion in the final model (see Table 1). At the second stage, a
forward-backward stepwise procedure following the corrected Akaike Information
Criteria (AICc) was used to select the most parsimonious model (Akaike, 1974;
Burnham and Anderson, 2002).
Table 1. Epidemiological and geographical predictors considered in the study. Those marked with (*) were selected on the basis of the results of the first-stage statistical analysis (see text for details).
Spatial Longitude and latitude of the hunting areas (continuous variable) Temporal Hunting season (ordinal variable)* and the presence of Trichinella spp. infections in previous hunting seasons (binomial variable)* Hunting management Presence/absence of fences (binomial)*, the relative abundance of wild boar (continuous)*. Estate-related characteristics Hunting estate code (nominal) and its surfaces covered by: urban area, road and railway, sandpit, non-irrigated culture and vineyard*, irrigated culture, irrigated fruit orchard, pasture, agricultural area, forest, natural grassland, moor, sclerophyll vegetation, forest-scrubland ecotone, riparian habitat, burned area, marshes, river and dam*, was also characterized (continuous variables).
Probability values yielded by the logistic regression were included in the
favourability function (see Real et al., 2006) to represent the spatial-time trends and
to estimate a risk map for Trichinella spp. infection in wild boar. The concept of
favourability, even if it has not been widely used in epidemiology, has a high
potential in this field. The favourability values are a measure of the degree to which
local conditions lead to a local probability higher or lower than that expected at
random (F=0.5, i.e., neutral favourability), being this random probability defined by
the overall prevalence of the event. Thus, favourability may be used to detect, for
example, conditions that really enhance the presence of a given disease or that
favour in the same degree the occurrence of a rare disease and a common seasonal
flu, even when the probability of suffering them differs due to their different
74
Capítulo 3.3
prevalence. Favourabilities (F) may be directly derived from probabilities (P)
yielded by a logistic regression according to the following equation:
1
0
(1 )
(1 )
PP
Fn Pn P
Being n1 and n0 the number of presence and absence in the global dataset,
respectively.
One inherent characteristic of this function is that favourability values can be
regarded as the degree of membership of the localities to the fuzzy set of sites with
conditions favourable for the event’s presence which enables the easy application
of fuzzy logic operations for modelling (e.g., Robertson et al., 2004). Fuzzy logic
operations expand the possibilities of the favourability function for comparison
between situations such as the different hunting seasons considered in this study
(Acevedo et al., 2010a; Real et al., 2010; Acevedo et al., 2011). In this study, at
hunting estate level, we combined the favourability values obtained for each
hunting season in order to obtain two proxies of the risk for Trichinella spp.
infection (e.g., Rochlin et al., 2011). One index is defined to display areas where
Trichinella spp. infection was present during the study period (endemic areas for the
parasite), and it is estimated by the minimum value of the seasonal favourabilities
(Zadeh, 1965). Another was designed to determine the global distribution of these
parasites during the study period and can be estimated by the maximum value of
the seasonal favourabilities (ibid.)
Finally, the selected model was partitioned in order to enhance its explanatory
capacity and improve its reliability and interpretation in the presence of
multicollinearity between predictors (Graham, 2003). Variation partitioning
procedures (see Borcard et al., 1992) are used to estimate the variation of the final
model explained independently by each factor (pure effects) and the variation
explained simultaneously by two or more factors (overlaid effects) following
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subtraction techniques. For details about the subtraction techniques see (Acevedo
et al., 2010a).
The statistical analyses were performed using SPSS 18.0 (SPSS Inc., Chicago,
IL, USA) statistical software.
Figure 2.- Predicted favourability for Trichinella spp. in wild boar for each hunting estate and studied season according to the final model (Table 2). Favourability values higher than 0.5 (blue colours) indicate conditions that foster the presence of the parasite.
Results
From the hunting seasons 1998-99 to 2009-10, 47 wild boar (0.05%; 0.04-0.06
95% CI) tested positive for Trichinella spp. infection out of 93,182 animals analyzed
by the Official Veterinary Services. After the first stage of the statistical analysis,
only 6 independent variables were considered for the final model; namely, hunting
season, the presence of Trichinella spp. infections in previous hunting seasons,
presence/absence of fences, the relative abundance of wild boar, the surface of the
hunting estate occupied by non-irrigated culture and vineyards, and the surface
occupied by rivers and dams (asterisked in Table 1). Four variables, related to
76
Capítulo 3.3
spatio-temporal (hunting season and Trichinella spp. infections in previous hunting
seasons), environmental (surface of the hunting estate occupied by vineyards and
non-irrigated lands) and hunting management factors (presence of fences), were
retained in the final model (Tables 2 and 3; explained deviance 11.5%). According
to the model, the presence of Trichinella spp. infection in wild boar had a decreasing
trend during the study period (Figure 2), and it was found to be negatively related
with fenced populations and non cultivated areas. The predicted favourability for
Trichinella spp. infection for each hunting season and hunting estate is shown in
Figure 2; favourable conditions (F>0.5) disappeared completely after the 2006-07
season. Risk maps generated from the seasonal favourabilities (Figure 3) show that
even when endemic areas for these parasites are really scarce in the study area
(Figure 3A), Trichinella spp. infection was widely distributed since favourable
conditions for these parasites have been appearing in different hunting seasons
along the study area (Figure 3B).
Table 2.- Summary of the stepwise model selection procedure based on the corrected Akaike Information Criteria AICc (Akaike 1974). ∆AIC represents differences in AICc in relation to the best model (lowest AICc value).
∆AIC AICc Model 44.69 530.93 Null model (including hunting estate) 15.23 501.47 Hunting season (V1) 5.74 491.98 V1+ Vineyards and non-irrigated lands (V2) 2.28 488.52 V1 + V2 + Presence of fences (V3)
0 486.24 V1 + V2 + V3 + Trichinella spp. infections in wild boar in previous hunting seasons
Table 3.- Variables included in the final model, their coefficients (β), Wald test values and significance (p-value). (*) Coefficients in relation to absence of fences and absence of Trichinella spp. infection in wild boars in previous hunting seasons.
Variable β Wald χ2 p-values
Constant -5.145 93.628 <0.001 Hunting season -0.275 27.492 <0.001 Vineyards and non-irrigated lands -0.004 6.285 0.012 Presence of fences -0.709* 5.866 0.015 Trichinella spp. infection in wild boar in previous hunting seasons
1.007* 5.074 0.024
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Variation partitioning results of the final model are shown in Figure 4. The
temporal factor, independently of the rest of considered factors, explained the
largest proportion of the variation (67.26%). In terms of pure effects, this was
followed in relevance by the environment (24.39%) and the hunting management
(10.76%). The low percentages explained by the combined effect of two or more
factors, showed a high independence among predictors.
Figure 3.- A) Favourability value for Trichinella spp. in wild boar maintained during the study period and considered as a proxy of the local favourability to be an endemic area, and B) maximum favourability values per hunting season which is considered as a proxy of the spread of this parasite in the study area.
ELISA results
Out of the 1,432 wild boar of 25 hunting estates analyzed by ELISA, 102 wild
boar tested positive (7%; 5.9-8.5 95% CI). However, only one positive sample out
of 30 was confirmed by Western blot. To compare both methods (digestion and
ELISA) from the Official Veterinary Services database we selected the same 25
hunting estates tested by ELISA. During the study period 6207 wild boar were
tested and 5 positive animals were identified in these 25 hunting estates (0.08%;
0.03-0.2 95% CI). At this level, we found no significant correlation between
prevalences obtained by the two methods (rs=0.128, p=0.542, n=25).
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Capítulo 3.3
Figure 4.- Results of variation partitioning of the final model in temporal trend (T), hunting management (M) and environmental characteristics (E). Values shown in diagrams are the percentages of variation explained.
Discussion
In contrast to the epidemiological data on Trichinella spp. infection in European
wild boar populations (Pannwitz et al., 2010; Ramisz et al., 2011), we observed a
decreasing prevalence in the last 12 years in central Spain. This finding appears to
be in contrast to the increasing wild boar populations and hunting management.
Why is central Spain of particular interest regarding the sylvatic cycle of
Trichinella? In south-western and central Spain, wild boar are often raised under
intense conditions for hunting purposes in commercial hunting estates that tend to
maintain overabundant wild boar populations in order to maximize returns. These
high densities are maintained through fencing, artificial watering in summer and
supplementary feeding throughout the year (Gortázar et al., 2006; Acevedo et al.,
2007b). Studies in these areas reported Trichinella spp. prevalences of 0.3%-0.8%
(Pérez-Martín et al., 2000; Garcia-Sanchez et al., 2009), while the mean prevalence
for Spain in the last years (2007-2009) was 0.2% (EFSA, 2011). Thus, intensive
management of wild boar populations could be one of the factors determining the
re-emergent character of this parasite.
The predictive model for Trichinella spp. infection in wild boar showed that, in
addition to the temporal trend, other factors related to environmental
characteristics and hunting management explained a part of the variation of the
prevalence of infection at the hunting estate level. So, bearing in mind that the
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Tesis doctoral
deviance explained by the final model was low, factors related with both land use
and the absence of fences contributed to modulate the prevalence of infection for
these parasites in wild boar. This result is coherent with rejecting the hypothesis by
which a link between game management and high prevalences was established for
other parasites (e.g., Acevedo et al. 2007b; Vicente et al. 2007b). In our study there
was a higher probability to detect Trichinella spp. infection in wild boar from
unfenced hunting estates than in fenced ones. The former are populations under
less-intensive management strategies (e.g., Acevedo et al., 2007b; Ruiz-Fons et al.,
2008c). Three relevant characteristics could differentiate the results obtained for
fenced and unfenced wild boar populations. First, in fenced hunting estates under
an intensive hunting management the effectiveness of the sanitary control (e.g.
veterinary inspection and hunting remain -gutpile- destruction) is maximized since
all wild boar are hunted in a few hunting events. Less available gut piles for wild
boar could lead to a decrease in the intraspecific Trichinella spp. transmission, as
described for polar bears (Larsen and Kjos-Hanssen, 1983). By contrast, some wild
boar may eventually go uninspected in unfenced estates (for instance, road-kills).
Second, the epidemiological interaction between domestic pigs and wild boar is
higher in these ‘unmanaged’ areas, since movements are less restricted. Finally,
fenced estates with overabundant wild boar have most probably a lower
biodiversity than open sites (Gortázar et al., 2006), and this in turn might affect the
likelihood of Trichinella maintenance and transmission.
The application of the favourability function to spatial modelling of risk factors
for a parasite under time-series data allowed us to estimate spatially-contextualized
indices able to determine endemic areas for theses parasites and their spread
throughout the period but also to identify those localities with risk factors
enhancing the presence of these parasites (F>0.5). So, the analysis showed a clear
absence of areas where Trichinella spp. infection could be considered endemic, but
also that these parasites can occur in most of the study area (Figure 3).
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Capítulo 3.3
81
The reference diagnostic method for routine testing of pig and wild boar meat
is the artificial digestion according to regulation (European Commission [EC]) no.
2075/2005.
Alternatively, serological techniques, such as ELISA, have been developed in
order to detect antibodies against Trichinella sp. and have been used in domestic pig
populations (Moller et al., 2005; Nockler et al., 2005; Bień, 2007). This method is
more sensitive than artificial digestion in domestic pig (Gamble et al., 2004), but its
utility in wild boar is under debate due its lack of specificity when compared with
the digestion (Frey et al., 2009a; Frey et al., 2009b; Nockler et al., 2009). In this
survey, only 3.3% of positive samples were confirmed by Western blot, thus
indicating a poor specificity of the ELISA as a diagnostic tool for the contact of
wild boar with Trichinella spp. The lack of specificity in wild boar could be due to
the contact of these wild animals with a vast number of other nematodes
(Fernández-de-Mera et al. 2003). Hence, we do not recommend ELISA as a tool
for Trichinella contact monitoring among wild boar.
Acknowledgements
This study was achieved thanks to the colleagues at IREC that participated in the fieldwork. The
authors also thank the Official Veterinary Services for the information given.
Capítulo 4
CAPÍTULO 4. RIESGOS SANITARIOS ASOCIADOS AL MANEJO CINEGÉTICO INTENSIVO DE LOS
UNGULADOS SILVESTRES
4.1. El jabalí: ¿un riesgo para el control de la Enfermedad de Aujeszky en el cerdo?
4.2. Evolución temporal de la seroprevalencia de cuatro patógenos relevantes en el jabalí
4.3. Distribución espacial y factores de riesgo de la brucelosis en ungulados de la Península Ibérica
4.4. Expansión de la tuberculosis en el jabalí
Capítulo 4
Resumen
La continua expansión de las poblaciones de jabalí (Sus scrofa) y la persistencia y
expansión de prácticas de manejo cinegético intensivo, con escasa presencia de
medidas de control sanitario, genera preocupación en cuanto a la transmisión de
enfermedades.
4.1. El objetivo del primer trabajo fue el de describir la evolución temporal del
contacto con el virus de la enfermedad de Aujeszky (VEA) de varias poblaciones de
jabalí sometidas a distintos sistemas de manejo y con distintas probabilidades de
contacto con cerdo doméstico. Para ello, se testaron mediante ELISA 1659 sueros
de jabalí procedentes de 6 áreas de la Península Ibérica, colectadas en el periodo de
2000 a 2010. La seroprevalencia media fue del 50%, y las prevalencias más altas se
detectaron en áreas con intenso manejo cinegético. La proporción anual de sitios de
muestreo positivos se mantuvo estable durante todo el período de estudio, mientras
que a nivel estatal, la proporción de comarcas positivas al VEA en cerdo doméstico
disminuyó del 70% en 2003 al 1,7% en 2010. Concluimos que el mantenimiento del
VEA en las poblaciones de jabalí puede suponer un riesgo para el éxito del
programa de erradicación en la cabaña porcina.
4.2. Para detectar cambios temporales y posibles factores de riesgo asociados a
ellos en las prevalencias de contacto con cuatro patógenos relevantes (circovirus
porcino tipo 2, CVP2; virus del síndrome respiratorio y reproductivo porcino,
VPRRS; virus de la hepatitis E, VHE y Erysipelothrix rhusiopathiae), se testó la
presencia de anticuerpos en 1279 sueros de jabalí durante el período 2000-2011.
Las seroprevalencias observadas frente a CVP2 y a VHE se mantuvieron estables
durante el período estudiado (con prevalencias medias del 48% y 26%,
respectivamente), mientras que la seroprevalencia frente E. rhusiopathiae disminuyó.
La baja prevalencia de anticuerpos frente al VPRRS (2%) no permitió su análisis en
el tiempo. A nivel de las localidades estudiadas, los incrementos de prevalencia
detectados siempre fueron mayores en fincas cerradas que en poblaciones abiertas.
Este estudio confirmó la persistencia de altas prevalencias de anticuerpos frente a
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Tesis doctoral
CVP2 y VHE en poblaciones de jabalí de la Península Ibérica, hecho que sugiere
que factores de riesgo como la agregación o las altas densidades siguen actuando, y
no sólo para los patógenos estudiados, sino que probablemente favorezcan también
a la transmisión de otros.
4.3. El papel de los ungulados silvestres ibéricos como reservorios de la
brucelosis aún no había sido establecido con claridad. Este trabajo ha permitido
descartar que los rumiantes silvestres tengan un papel en la epidemiología de las
brucelosis bovina u ovina/caprina en España, y en consecuencia no ha evidenciado
tendencias temporales en el contacto de rumiantes silvestres con Brucella sp. Otro
objetivo de este trabajo era determinar la distribución espacio-temporal e identificar
factores de riesgo para el contacto con Brucella suis en varias poblaciones de jabalí
de la Península Ibérica. Para ello, se analizaron entre 1999 y 2009 un total de 4454
jabalíes mediante ELISA. La seroprevalencia aparente detectada varió de un 25% a
un 46% para las distintas áreas estudiadas, pero las prevalencias más altas se
detectaron el las zonas del centro-sur peninsular. Según el modelo aplicado, el año
de muestreo no resultó ser un factor significativo, mientras que sí lo fueron la edad,
el sexo o el mes de muestreo. El estudio demostró que la brucelosis en el jabalí está
extendida y se mantiene estable en el tiempo. Estos resultados sugieren que el jabalí
puede constituir un factor de riesgo para el cerdo doméstico, especialmente en los
sistemas de cría al aire libre.
4.4. Las poblaciones de jabalí del centro-sur peninsular se han identificado
como reservorios de Mycobacterium bovis y otros miembros del complejo
Mycobacterium tuberculosis (CMTB), causantes de la tuberculosis bovina (TB). En este
estudio se determinó la distribución espacial y temporal del contacto del jabalí con
el CMTB en la Península Ibérica mediante serología, y se aplicaron modelos para
tratar de identificar factores de riesgo asociados. Los resultados describieron un
nuevo rango geográfico del contacto del jabalí con el CMTB. La seroprevalencia
media fue del 22% y permaneció estable en la última década, resultado que
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Capítulo 4
87
contrasta con el éxito de la campaña de erradicación de la tuberculosis en el ganado
bovino.
La posibilidad de la expansión de la TB del jabalí hacia áreas no endémicas se
debería gestionar urgentemente para tratar de evitar una futura situación parecida a
la presente en el centro-sur de la Península.
Capítulo 4.1
El jabalí: ¿un riesgo para el control de la enfermedad
de Aujeszky en el cerdo?
Boadella, M., Gortázar, C., Vicente, J., Ruiz-Fons, F. Wild boar: an increasing concern for Aujeszky’s disease control in pigs? Aceptado, BMC Veterinary Research.
Capítulo 4.1
Abstract
The goal of this study was to describe the temporal evolution of Aujeszky’s
disease virus (ADV) contact prevalence among Eurasian wild boar (Sus scrofa)
populations under different management regimes and with different contact
likelihoods with domestic pigs. Given the recent increase in wild boar abundance
throughout Europe, we hypothesized that wild boar contact with ADV would
remain stable in time even after significant reduction of ADV prevalence in
domestic pigs. Sera from 1659 wild boar were collected from 2000 to 2010 within 6
areas from the Iberian Peninsula and tested for the presence of antibodies against
ADV by a commercial ELISA. Wild boar were grouped according to sampling date
into three main time periods. ADV prevalences were compared through period
both globally and by geographic area. Overall seroprevalence for the ten-year study
period was 49.6 ± 2.4%. The highest seroprevalences were recorded in areas where
intense wild boar management was present. The annual proportion of positive wild
boar sampling sites was stable during the ten-year period, while the percentage of
domestic pig AD positive counties decreased from 70% in 2003 to 1.7% in 2010.
Results presented herein confirmed our hypothesis that ADV would remain almost
stable in wild boar populations. This evidences the increasing risk wild boar pose in
the final stages of ADV eradication in pigs and for wildlife conservation.
Background
Aujeszky’s disease (AD), also known as pseudorabies, is one of the most
economically important infectious diseases of swine for which suids are the natural
hosts (Müller et al., 2011). The disease is caused by Suid herpesvirus type I, a
neuroinvasive virus with a wide host range that excludes only higher primates.
Mammals other than suids are considered dead-end hosts because infection is
normally fatal before virus excretion occurs. AD has a high economic impact in pig
husbandry both through direct effects of the disease on the animals and through
movement and trade restrictions of pigs and their products. The direct impact of
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AD in wild boar population dynamics is considered to be low, but AD outbreaks
with associated wild boar mortality have been reported and restrictions to wild boar
movements may also have an impact on wild boar production for hunting (Ruiz-
Fons et al., 2008b; Gortázar et al., 2002).
Implications in conservation are considerable since fatal cases have repeatedly
been described in endangered carnivores after consumption of ADV contaminated
meat (Glass et al., 1994; Zanin et al., 1997). In the Iberian Peninsula, the Iberian
wolf (Canis lupus signatus) uses Eurasian wild boar (Sus scrofa, the ancestor of the
domestic pig) as an important part of the diet (Barja, 2009). From the literature
reviewed, to date ADV infection has not been reported in wolves even though fatal
cases do occur in hunting dogs (Cay and Letellier, 2009). Moreover, other
endangered carnivores such as the brown bear (Ursus arctos) and the Iberian lynx
(Lynx pardinus) do occasionally consume wild boar among their prey or carrion
species (Valverde, 1967; Blanco et al., 2011), and thus may also be at risk of ADV
infection (e.g. fatal ADV reports in brown bears; Zanin et al., 1997; Banks et al.,
1999).
Wildlife can act as reservoirs for pathogens shared with their related domestic
species, being able to transmit and maintain them even without the presence of the
domestic reservoir (Gortázar et al., 2007). The wild boar-domestic pig interface
represents one of the clearest examples of this scenario, as both species have a
mutual transmission risk for their infectious and parasitic diseases (Ruiz-Fons et al.,
2008b; Meng and Lindsay, 2009). As disease eradication programs are implemented
in the domestic species, wildlife reservoirs should be considered for the program
success since they become increasingly important (Müller et al., 2000).
In many parts of the world, efforts are being carried out to control ADV in
domestic pigs. In Europe, most countries (including Spain) have implemented strict
national eradication programs based on initial large scale vaccination of pigs with
attenuated glycoprotein E (gE) – deleted vaccines. In countries that have reached
the AD-free status, vaccination against ADV is forbidden (Pannwitz et al., 2011).
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Capítulo 4.1
But despite the efforts and subsequent success on AD eradication in domestic pigs,
the disease is being continuously reported in wild boar populations. For instance,
Germany achieved the AD-free status in 2003 despite the increasing
seroprevalences (from 0.4% in 1985 to 16.5% in 2008) and widespread AD
distribution in wild boar (Lutz et al., 2003; Pannwitz et al., 2011). In France
occasional outbreaks have been described in outdoor pig farms. Contact with wild
boar was deemed as the origin (Hars and Rossi, 2005; OIE, 2010). ADV contact
prevalence in wild boar has also been recorded in several other European countries,
such as Spain (0.8-44%; Vicente et al., 2005; Closa-Sebastià et al., 2011), France
(3.5%; Albina et al., 2000), Italy (30-51%; Lari et al., 2006; Montagnaro et al., 2010),
Switzerland (2.8%; Köppel et al., 2007), Croatia (55%; Zupancic et al., 2002),
Slovenia (31%; Vengust et al., 2006), Poland (11%; Szweda et al., 1998) and Russia
(32%; Kukushkin et al., 2009); suggesting that ADV may be endemic in most of
these wild boar populations. In contrast, countries with limited wild boar
populations such as Netherlands, or Sweden with recently expanding wild boar
populations, do not record ADV in wild boar (Elbers et al., 2000; Swedish National
Veterinary Institute [SVA], 2010).
In Spain, the national AD eradication scheme started in 1995 (Royal Decree
[RD] 245/1995; MARM, 2011a). The main control measures were compulsory
vaccination with gE negative vaccines, movement restriction and serological
testing. The AD eradication program was reinforced in 2003 (RD 427/2003) and
subsequently in recent years by applying stricter animal movement restrictions and
more intensive serological testing and vaccination (Allepuz et al., 2009). The AD
eradication program has led to a considerable reduction of ADV prevalences in
domestic pigs, although eradication in the whole territory has not yet been achieved
(MARM, 2011a).
The wild boar is the most widespread and generally also the most abundant
wild ungulate in large portions of the Iberian Peninsula. Wild boar populations are
continuously expanding numerically and geographically (Gortázar et al., 2000;
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Acevedo et al., 2006). Furthermore, in some areas of the south-central Iberian
Peninsula, wild boar are part of a growing hunting industry where management
practices, such as high-wire fencing, artificial feeding and restocking are on the rise
(Acevedo et al., 2006). At the same time, sanitary measures for wildlife are not
being implemented to match this development. As a result, high wild boar densities
have already been shown to be a risk factor with negative consequences for the
control of AD and other infectious diseases (Gortázar et al., 2006; Acevedo et al.,
2007b; Vicente et al., 2007b; Ruiz-Fons et al., 2008c).
Although for ADV it has been shown that the prevalence in wild boar
populations was not a significant risk factor for the level of AD prevalence in the
coexisting pig farms (Ruiz-Fons et al., 2008c), there are studies that suggest the
opposite (Corn et al., 2009). Moreover, the experimental infection of domestic pigs
with ADV strains of wild boar origin (Müller et al., 2001) and the excretion of virus
to the environment by wild boar (Müller et al., 1998; Ruiz-Fons et al., 2007),
suggest the possibility of ADV transmission between both suids.
The goal of this study was to describe the temporal evolution of ADV contact
prevalence among wild boar populations under different management regimes and
varying contact with pigs in Spain. Based on the European literature, we
hypothesized that wild boar contact with ADV would remain stable in time even
after significant reduction of ADV prevalence in domestic pigs.
Methods
Wild boar sampling
A total of 1659 serum samples collected between 2000 and 2010 from beating
or Monteria hunter-harvested wild boar, were selected for this retrospective study.
Monteria hunting of wild boar is random and thus, is accepted as a random survey
method for wild boar (Fernández-Llario and Mateos-Quesada, 2003). The selected
sample was stratified by sex and age classes. Sex was known for 1503 animals and
included 687 males and 816 females. Age classes of biological meaning included
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Capítulo 4.1
juveniles (n=316), yearlings (n=464), and adults (n=733), as described in previous
studies (Vicente et al., 2004) and in Sáenz de Buruaga et al. (1991). Sera selected for
this study had gone through less than five freeze-thaw cycles and severely
haemolysed samples were excluded (Capítulo 2.2).
Samples came from 37 sites (range 5 to 111 samples per site) and were grouped
into six geographic areas of biological meaning (Table 1; Figure 1) plus an isolated
fenced estate (not shown in Figure 1). The selected areas are representative of a
gradient of situations from an intense hunting management (involving fencing,
artificial feeding and watering) to a lesser or inexistent hunting management. More
precise descriptions of these areas have been given by Vicente et al. (2004). One
area (SM) is part of the geographical range of Iberian pig production, a traditional
breed that is reared by open air farming or as backyard production (Table 1).
Table 1.- Number of sampled wild boar, categorized wild boar density (low, medium, high), wild boar management (inexistent to intense) and likelihood of contact with open air raised domestic pigs (low, medium, high) generally present in each of the six areas of the study, mainly based on observational data from the authors (unpublished results).
Area Number wild boar sampled
Wild boar density
Wild boar management
Likelihood of contact with open air raised
domestic pigs
Asturias (AS) 133 Medium Low or
inexistent Low
Sistema Central (SC) 127 Medium Low or
inexistent Low
Sistema Ibérico (IBER)
76 Low Low or inexistent
Low
Toledo (TO) 91 Low Low or
inexistent Low
Montes de Toledo (MT) 765 High
Frequently intense
Low (Ruiz-Fons et al., 2008c)
Sierra Morena (SM) 361 High Frequently intense
High (Bech-Nielsen et al., 1995)
Doñana (DN) 46 Medium-high
Inexistent Low
In order to analyze prevalence changes in time, samples were grouped by area
into three periods: years 2000-2003, 2004-2007 and 2008-2010. We also used the
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annual proportion of positive sampling sites to compare with data on positive
counties regarding pigs (Figure 2).
In one private hunting estate outside the described areas, we recorded wild
boar relative abundance (FBII) and aggregation index (Z) in 2002 and 2010, as
described in Vicente et al. (2004) and in Acevedo et al. (2007). ADV seroprevalence
was calculated for wild boar sampled in 2003-2005 (n=12) and in 2008-2010
(n=48). Wild boar management started to change late in 2005 through improved
fencing and supplementary feeding.
ELISA test
A commercially available blocking ELISA was used for screening of antibodies
to ADV in accordance with the manufacturers’ instructions (IDEXX HerdCheck
Anti-ADV gpI, IDEXX, Inc., USA). This ELISA technique has been broadly used
in wild boar (Vicente et al., 2005; Ruiz-Fons et al., 2006; Pannwitz et al., 2011) and
for domestic pigs it has a sensitivity of 95-98% and a specificity of 97–99%
according to the manufacturer.
Data on pig status
ADV seroprevalence data of the control and eradication campaign in Spain at
county level from 2003 to 2010 were available from the Spanish Ministry of the
Environment and Rural and Marine Affairs (MARM, 2011a). With the data
provided, we calculated the annual proportion of positive counties.
Statistics
Standard errors at 95% confidence intervals were calculated for apparent
prevalences. Mean prevalence estimates were adjusted for test sensitivity and the
specificity using Rogan-Gladen corrections (RGC). RGC were calculated using the
lowest values of ELISA sensitivity and specificity given by the manufacturer, 95%
sensitivity - 97% specificity (Rogan and Gladen, 1978). ADV prevalences were
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Capítulo 4.1
compared through period both globally and by geographic area by means of chi-
square tests. The p-value was set at 0.05. Data was analyzed using the IBM SPSS
statistical package, version 19.0 (IBM Corporation, Somers, NY, USA).
WinEpiscope software (WinEpiscope software, 2011) was used to calculate the
level of confidence for negative results.
Results
The overall seroprevalence for the ten-year study period was 49.6 ± 2.4% (S.E.
at 95% CI), (Rogan-Gladen correction [RGC]: 50.7 ± 2.4). Antibody prevalences
were high in all areas except for AS (7.5 ± 4.4% [RGC: 4.9 ± 3.7%]) and TO (11 ±
6.4% [RGC: 8.7 ± 5.8%]). Figure 1 shows the observed prevalences by area in the
three sampling periods. The highest mean seroprevalences were recorded in areas
where intense wild boar management was present: MT (61.4 ± 3.4% [RGC: 63.5 ±
3.4%]) and SM (54.6 ± 5.1% [RGC: 56.1 ± 5.1%]).
Figure 1.- Map of the Iberian Peninsula showing the six sampled areas for the study (right panel). Seroprevalences (and associated 95% standard errors) for each area during the three considered seasons (2000-2003, 2004-2007, 2008-2010) are shown in the left panel. Within each area, significant differences in overall season seroprevalences are marked with an asterisk.
In three areas the observed increase in seroprevalence was statistically
significant (IBER, TO and SM), while in MT the change in seroprevalence had no
clear trend (Chi-square, p<0.005 in all cases). In TO, ADV contact appeared for
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the first time in the period 2004-2007 (12.3 ± 8.5% [RGC: 10.1 ± 7.8%]) and
increased in the following period (Figure 1).
The annual proportion of individual sampling sites with at least one
seropositive wild boar remained stable during the ten-year period, while the
percentage of domestic pig AD positive counties decreased from 70% in 2003 to
1.7% in 2010 (Figure 2).
Figure 2.- Temporal trends on Aujeszky’s disease virus (ADV) seroprevalences in wild boar and pig. Time trend (2000-2010) of the annual proportion of sampling sites with seropositive wild boar (black diamonds) and of the proportion of counties in Spain with ADV in domestic pig (grey squares; based on data from the Spanish Ministry of the Environment and Rural and Marine Affairs, MARM). Numbers on the black line indicate the number of wild boar sampling sites per year. Numbers in grey indicate the number of reported counties per year. The discontinuous grey line is an estimated prevalence of positive counties before 2003 as data were not available before this date. The dotted line represents the hypothetical relative risk of ADV spill-back from wild boar to domestic pig, based on the difference between the pig and wild boar ADV proportions.
In one specific study site in northern Spain, outside the range of the main high
prevalence areas, ADV seropositivity was first detected in 2008 in 27 out of 48
sampled wild boar (56.3 ± 14.0% [RGC: 57.9 ± 14.0%]). The estimated level of
confidence for the negative results in the preceding period 2003-2007 (none out of
12) was 95% for an expected prevalence of 20%. Wild boar censusing confirmed a
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Capítulo 4.1
marked increase in abundance and spatial aggregation between both time periods
(Figure 3).
Figure 3.- Aujeszky’s disease virus (ADV) seroprevalence and wild boar relative abundance and spatial aggregation changes in a private hunting estate. Wild boar relative abundance (FBII; diamonds), aggregation index (Z; squares) and ADV seroprevalence (black triangles, 95% CI) in an estate where wild boar management drastically changed during the study period.
Discussion
Results presented here confirmed our hypothesis that ADV would remain
almost stable in wild boar populations. This occurred in those areas where wild
boar production as a hunting resource is practiced, and ADV seroprevalences are
high. Results also showed increasing seroprevalence rates for some of the studied
areas in spite of the decreasing trend reported in pigs. Time trends in wild boar
contact with ADV were independent of the area’s likelihood of contact with pigs,
adding evidence to the hypothesis of that AD maintenance in wild boar is
independent of the pig situation (Müller et al., 1998; Ruiz-Fons et al., 2008c;
Pannwitz et al., 2011).
Sample sizes per individual site were small. This motivated studying areas
which were representative of wild boar distribution and management characteristics
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in Spain. The limited sample size also means that results, particularly regarding time
trends by area, need to be taken with caution. However, total wild boar ADV
seroprevalence clearly remained stable after ten years, confirming that AD remains
endemic at high prevalences in the south-central Spanish wild boar populations
(Vicente et al., 2005; Ruiz-Fons et al., 2008c). This finding is in compliance with
other studies which also record stable or even increasing trends of ADV contact in
different wild boar and feral pig populations (Albina et al., 2000; Lutz et al., 2003;
Corn et al., 2004; Pannwitz et al., 2011). In our area, wild boar density and spatial
aggregation within fenced hunting areas have been previously identified as risk
factors for wild boar ADV contact prevalence in wild boar populations (Acevedo et
al., 2007b; Ruiz-Fons et al., 2008c). These factors have not changed during the
studied period. Thus, in the absence of any control measure and considering the
ability of ADV to remain latent in infected suids (Alemañ et al., 2001), ADV
prevalences were not expected to decline. Prevalences recorded in areas with
intense management are among the highest of the literature worldwide (Müller et
al., 2011). Thus, the observed time trends in these prevalences (decrease in MT and
increase in SM) may represent cyclic fluctuations around a “steady state” that ADV
seroprevalence may have reached under these particular conditions. Even though
wild boar population characteristics are different, a similar dynamic situation has
also been proposed to be occurring in wild boar ADV high-prevalence areas of
Germany (Müller et al., 2011).
This asymptote seems not to have been reached in other Spanish wild boar
populations. Furthermore, intense hunting management practices are becoming
popular in certain areas outside south-central Spain. This might suggest that higher
prevalences will be reached as the wild boar population increases and the
management becomes more intense.
The specific case illustrated in Figure 3 is an example of the effect of intense
wild boar management for hunting on the temporal trend of ADV seroprevalence.
Fencing and feeding led to a significant increase of wild boar abundance and
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Capítulo 4.1
aggregation (Acevedo et al., 2007b), and to the detection of high contact
prevalences with ADV (56%). It is unlikely that a high ADV prevalence could have
gone undetected in the preceding period. Therefore, based on the current and
previous observations (Vicente et al., 2005), we suggest that the emergence of ADV
seroprevalence could be boosted by intense hunting management practices,
including a possible translocation of wild boar from positive sites. As suggested for
tuberculosis, efforts should be done to control the proliferation of such intense
game management without sanitary control in disease-free areas, since they can
become risk hotspots with negative implications for animal health and for
conservation (Boadella et al., in press).
In contrast and despite of the situation in the studied wild boar populations,
ADV seroprevalence in Spanish domestic pigs experienced a significant reduction
thus showing that the eradication efforts were successful. A comprehensive study
of European ADV isolates of wild boar origin, including Spanish ones,
demonstrated that all except one belonged to genotype I (Müller et al., 2010). Based
on the observation that mainly type II strains were found in domestic pigs in
Central Europe, it has been suggested that infections of wild boar by domestic pigs
did not occur recently (Müller et al., 2011). Thus, spill over between pigs and wild
boar is apparently not a frequent event. However, the pig vaccination campaigns
probably had a main role in this decrease of ADV, but we open the question of
which will be the situation if Spain reaches the ADV-free status and pig vaccination
is no longer permitted? Outdoor pig production is an environmentally friendly and
sustainable productive system that additionally improves animal welfare and
product quality, aspects that are increasingly demanded by the European society.
These added values of outdoor production carry nonetheless a sanitary risk because
of the increased probability of interactions with wild boar and other wildlife of
uncontrolled sanitary status. There are several examples in the literature about the
link between open-air or back-yard pig production and the risk of disease
transmission at the pig-wild boar interface (Classical Swine Fever in Germany,
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Fritzemeier et al., 2000; African swine fever in Sardinia, Laddomada et al., 1994 and
the Caucasus, FAO, 2008; and ADV, Hars and Rossi, 2005). Thus, when pig
biosafety measures are insufficient to avoid contact with wild boar, the wild boar
could become a risk for ADV re-introduction (Köppel et al., 2007). If wild boar are
seen as a source of the disease, a potential conflict on biosafety can arise between
the pig industry and hunting land owners (Ruiz-Fons et al., 2008c; Gortázar et al.,
2010). Because of the huge difficulties in controlling ADV in free-roaming wild
boar, the main recommendation to maintain ADV-free open-air produced
domestic pigs would be not to stop vaccination. Nonetheless, in countries without
vaccination such as Switzerland, it has been advised to include outdoor pigs in
areas at risk in routine wild boar ADV surveillance programs, since transmission
between infected wild boar and outdoor pigs might occur in the future (Köppel et
al., 2007). In parallel, it is important to drive efforts towards improved pig biosafety
(Gortázar et al., 2011b), along with continuous monitoring of the wild boar AD
epidemiological situation (e.g. the recent establishment of the Spanish National
Wildlife Disease Surveillance Scheme, MARM, 2011b). Eventually, research on
means to control ADV in wild boar could be pertinent (Ruiz-Fons et al., 2008a).
In the Iberian Peninsula, the presence of ADV in wild boar also exposes
endangered wild carnivores to the risk of contracting lethal infection (Capua et al.,
1997). ADV contact has been detected in wild boar in protected areas where they
coexist with endangered carnivores (bear and wolf in AS, wolf in IBER, wolf and
lynx in SM, lynx in DN). This adds interest to ADV regarding conservation.
Unfortunately, conservation programs often underestimate the role that wildlife
diseases can play in their success (Leopold, 1933).
Conclusions
With the presented scenario, where wildlife populations represent a potential
sanitary risk for livestock, trans-disciplinary wildlife disease research may provide
an opportunity for stakeholders to reconsider the current approach of disease
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Capítulo 4.1
103
eradication in livestock towards a less severe but more sustainable concept of
disease control, at least for open-air systems.
Acknowledgements
F. Ruiz-Fons is supported by the Spanish National Research Council (CSIC). We thank Paqui
Talavera and all the colleagues at IREC that participated in the field work, collecting and
processing all the samples and making this research possible.
Capítulo 4.2
Evolución temporal de la seroprevalencia de cuatro
patógenos relevantes en el jabalí
Boadella, M., Ruiz-Fons, J.F., Vicente, J., Martín, M., Segalés, J., Gortázar, C. Seroprevalence evolution of selected pathogens in Iberian wild boar. Aceptado, Transboundary and Emerging Diseases.
Capítulo 4.2
Abstract
A total of 1279 Eurasian wild boar (Sus scrofa) sera were collected from 2000 to
2011 in the Iberian Peninsula in order to reveal time changes in serum antibody
prevalences against selected infectious agents (porcine circovirus type 2, PCV2;
porcine reproductive and respiratory syndrome virus, PRRSV; hepatitis E virus,
HEV; and Erysipelothrix rhusiopathiae) and to identify putative individual or
population factors driving such changes. Overall seroprevalences were 48%, 26%,
2% and 15% for PCV2, HEV, PRRSV and E. rhusiopathiae, respectively. The global
observed prevalence of antibodies against PCV2 and HEV remained stable during
the study period, while the global mean antibody seroprevalence against E.
rhusiopathiae declined. The mean increment in prevalence was always lower for open
than for fenced sites. This study evidenced for the first time that wild boar from
the Iberian Peninsula have widespread contact with E. rhusiopathiae, and confirmed
high prevalences of antibodies against PCV2 and HEV. Maintained high
prevalences of transmissible agents in wild boar suggest that epidemiological
drivers such as aggregation and high density are still acting. This will most probably
also affect the transmission rates of other disease agents, and should be taken into
account regarding disease control at the wildlife livestock interface.
Introduction
The Eurasian wild boar (Sus scrofa) is the ancestor of the domestic pig, and
shares most if not all pig pathogens. Wild boar populations have notably increased
worldwide, and can maintain viral and bacterial pathogens without the intervention
of domestic or other wild animals (Naranjo et al., 2008; Ruiz-Fons et al., 2008a;
Muñoz et al., 2010).
Wild boar pathogens, such as Mycobacterium bovis (Gortázar et al., 2011b),
Aujeszky’s disease virus (ADV; Müller et al., 2011) and classical swine fever virus
(Le Potier et al., 2006) are highly relevant not only for the livestock industry but
also for wildlife conservation (Gortázar et al., 2010) and for the hunting industry
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(Vicente et al., 2004; Ruiz-Fons et al., 2008a). In Spain, hunting is a significant
business where landowners often earn higher incomes through hunting permits
than through livestock and forestry. Commercial hunting estates tend to maintain
overabundant wild boar populations in order to maximize returns. These high
densities are maintained through an intense management including fencing,
artificial watering in summer and supplementary feeding throughout the year
(Gortázar et al., 2006; Acevedo et al., 2007b).
Moreover, some wild boar pathogens are zoonotic (Meng and Lindsay, 2009).
Since several million wild boar are harvested and consumed yearly in Europe, wild
boar meat and derivates are a likely source of human infections (Gauss et al., 2005).
In Europe, most attention has been devoted to diseases that are under official
surveillance and control in pigs, wild boar or both, while other infections have
received comparatively less effort.
Porcine circovirus type 2 (PCV2), the essential infectious agent in porcine
circovirus diseases (PCVD, including postweaning multisysemic wasting syndrome,
PMWS), circulates at high rates among domestic pig and wild boar populations
(Reiner et al., 2011). Contact or infection with PCV2 has been reported in wild
boar from many countries in Europe (Sánchez et al., 2001; Knell et al., 2005;
Cságola et al., 2006; Lipej et al., 2007; Sedlak et al., 2008; Sofia et al., 2008; Petrini
et al., 2009; Morandi et al., 2010; Reiner et al., 2010; Turcitu et al., in press),
including Spain, where PCV2 has been linked to juvenile mortality (Vicente et al.,
2004).
Hepatitis E virus (HEV), a small RNA virus belonging to the Hepeviridae
family (Emerson and Purcell, 2003), is a major cause of human viral hepatitis in
tropical and subtropical countries (Smith, 2001). In industrialized countries,
hepatitis E (HE) is an emerging zoonosis that is often acquired by consuming raw
pig or wild boar meat, liver or bile (Kaba et al., 2010; Kim et al., 2011). In Europe,
Anti-HEV antibodies were reported in The Netherlands (Rutjes et al., 2010), and
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Capítulo 4.2
several studies reported HEV RNA detection (Kaci, 2008; Martelli et al., 2008;
Forgách et al., 2009; Kaba et al., 2010; Jemersic et al., 2011; Widén et al., 2011).
Porcine reproductive and respiratory syndrome virus (PRRSV) causes one of the
most economically significant diseases in the pig industry (Meng, 2000). Antibodies
to PRRSV are occasionally detected in wild boar, generally at low prevalences that
do not suggest a reservoir status (Albina et al., 2000; Closa-Sebastià et al., 2011;
Montagnaro et al., 2010). Temporal changes in prevalence have been detected
when comparing different studies in Germany. A first study found no wild boar
contact with PRRSV (Lutz and Wurm, 1996), while a more recent study revealed
3.8% antibody prevalence in sera collected between 2000 and 2005. Later, a PCR
survey carried out in Germany from 2004 to 2007 detected PRRSV RNA in 16%
of the studied wild boar (Reiner et al., 2009). Thus, local wild boar populations can
become infected by PRRSV at high rates, comparable to those occurring in pigs.
Erysipelothrix infection is caused by bacilli of the genus Erysipelothrix, mainly E.
rhusiopathiae (26 serovars, Takahashi et al., 1999). The infection is easily transmitted
to humans by direct contact with infected hosts. Wild boar contact with E.
rhusiopathiae has been described in Spain (Vicente et al., 2002; Closa-Sebastià et al.,
2011).
The Spanish domestic swine population is considered to be enzootically infected by
PCV2, PRRSV and HEV. PCV2 is highly widespread and no serologically negative
farms have been found (López-Soria et al., 2005). Moreover, antibodies against
PCV2 are not just elicited by natural infection but also by vaccination; it is
estimated that around 60% of the domestic pigs are currently vaccinated (data not
shown). PRRSV herd seroprevalence was found to be 89% in a recent study
screening 107 Spanish farms (Fraile et al., 2010). In the same study, around 40% of
the operations vaccinated sows against PRRSV, while piglet vaccination was not
indicated as a common practice. HEV herd seroprevalence was found to be 97.6%
in a study using 41 farms (Seminati et al., 2008). Currently, there is no vaccine
against HEV. Finally, no data on seroprevalence against E. rhusiopathiae have been
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so far published in Spain, although vaccination of sows against this pathogen,
usually in combination with porcine parvovirus, is a systematic practice.
Information on time trends of disease prevalence and distribution is needed for
decisions regarding wildlife management and disease prevention at the wildlife-
livestock interface (Boadella et al., 2011a). However, time trends in the prevalence
of wild boar contact with infectious agents have rarely been investigated, and only
regarding ADV (Pannwitz et al., 2011; Capítulo 4.1), CSFV (Albina et al., 2000),
and Mycobacterium bovis (Gortázar et al., 2011a; Boadella et al., in press). To fill this
gap, we retrospectively studied the epidemiology of four of the less known
pathogens of wild boar, including two viral ones of relevance for the pig industry
(PCV2 and PRRSV), one zoonotic virus (HEV), and one zoonotic bacterium (E.
rhusiopathiae). The aim of this study was to reveal time changes in serum antibody
prevalence and to identify putative individual or population factors determining
such changes.
Material and methods
A total of 1279 wild boar sera were collected from 25 study sites throughout
the Iberian Peninsula. The volume of serum available for a given individual was
limited, particularly for the oldest samples. Thus, only a proportion of the total
1279 sera were tested for each agent. Sera with limited available volume were
randomly assigned to each test controlling for age and sex bias and targeting a
sample size between 20 and 30 for each time period. Blood was drawn from the
heart or the thoracic cavity during field necropsies. Sera were obtained after
centrifugation and frozen at -20ºC until analyzed. Sex and age were recorded. Based
on tooth eruption patterns, animals less than 12 months old were classified as
juveniles, those between 12 and 24 months as yearlings, and those more than 2
years old as adults (Saenz de Buruaga et al., 1991; Table 1).
Hunting harvest sampling is accepted as a random survey method for wild boar
(Fernández-Llario and Mateos-Quesada, 2003). Fenced and open sites were
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Capítulo 4.2
generally located in similar habitats, but wild boar abundances are usually higher in
fenced sites due to supplementary feeding (Acevedo et al., 2007b). Contact with
domestic pigs is generally not likely because the distributions of the pig farms and
wild boar sampling sites did not match (Ruiz-Fons et al., 2008c).
Table 1.- Time span, sample size and antibody seroprevalences against the four studied pathogens.
PCV2 HEV PRRSV E.
rhusiopathiaeObservation period 2000-2008 2000-2011 2000-2009 2000-2009 Total sampled animals 818 942 407 874 Age (juveniles/yearlings/adults/unknown)
(119/232/382/71)
(127/270/465/59)
(54/130/200/18)
(129/244/415/86)
Sex (male/female/unknown) (336/396/86) (409/457/76) (132/251/24) (359/413/102)
Prevalence (%) 47.68 26.33 2.21 15.45
95% IC (44.2-51.2) (23.5-29.2) (1.1-4.13) (13.1-18)
Table 2 describes the tests used for detecting antibodies against each pathogen,
along with the established cut-off and other relevant information. The in-house
ELISA used to test antibodies against HEV was performed using Protein G as
conjugate at a dilution of 1:1500 as described in (Boadella et al., 2010).
Table 2.- Different ELISA tests used for the serological assays of wild boar sera.
Agent Test Positive
threshold Reference
PCV2 Immunoperoxidase
monolayer assay (IPMA) Titre ≥1:320 (Vicente et al., 2004)
HEV Modified In-house ELISA (Protein G as conjugate)
Percentage of the negative control sum
>100%
(Boadella et al., 2010)
PRRSV ELISA; DEXX HerdChek® PRRS 2XR IDEXX, USA
S/P ratio >0.4 (Ruiz-Fons et al., 2006)
E. rhusiopathiae
Ingezim Mal Rojo 11.MR.K1 Ingenasa, Spain.
Mean OD negative controls + 0.1
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Associations of age (categorical; juveniles, yearlings and adults), sex (categorical;
male and female), sampling site, time and management (categorical; open and
fenced) with serological results were analyzed by means of Pearson’s chi-square test
or Fisher’s exact test.
Due to their lower likeliness of contact with infectious disease agents and
possible interference of maternal antibodies, juveniles were only used for describing
overall prevalences (Table 1), but removed when analyzing site, management or sex
associations with seroprevalences. Thus, for these analyses, only a subsample of
948 sera (yearling and adult animals) was used.
For time analysis, only yearlings and adults from sampling sites with stratified
sampling in both periods were considered (n=865). Two sampling periods were
established for Chi-square comparisons with seroprevalences: Period 1, from year
2000 to 2005 and Period 2, from 2006 to 2011. GLM with binomial distribution
were used for calculating the beta of prevalence by sampling year at the sampling
site level. Sterne's exact method was used to estimate 95% apparent prevalence
confidence intervals (95% CI), (Reiczigel, 2003). Differences were considered
statistically significant when p<0.05. For statistical evaluation, SPSS 19.0 software
was used (IBM Corporation, Somers, NY, USA).
Results
Overall antibody prevalences of the 1279 wild boar tested for the four studied
pathogens are presented in Table 1. Mean global and local antibody
seroprevalences for yearling and adult wild boar are presented in Table 3 (Apéndice
4.2).
Antibodies against PRRSV were detected only in 7 of 22 studied sites (Table 3
[Apéndice 4.2]; Figure 1). Moreover, the prevalence of serum antibodies against
PRRSV was low and at a low mean OD (0.63; max: 0.91; cut-off: 0.4). Thus, we
made no further analysis on PRRSV antibody prevalence.
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Capítulo 4.2
Figure 1.- Map of the Iberian Peninsula showing the 25 sampling sites (letters from A to Y) and positivity to the four pathogens tested in each, represented by different shapes. Black shapes indicate seropositivity while white shapes are for negative sites.
Sex-related differences in prevalence were found only for E. rhusiopathiae (males
19.6%, females 11.2%; Chi2= 7.9, p<0.01). Age had a significant effect on antibody
seroprevalence against E. rhusiopathiae (22.5%, 20.9% and 11.1% for juvenile,
yearling and adult wild boar, respectively; Chi2= 15.9, p<0.001) but not against
PCV2 (Chi2= 7.3, p>0.05) and HEV (Chi2= 2.6, p>0.05). Type of management
(open vs. fenced) was a significant factor for antibody prevalence against PCV2
(29.5% in open vs. 56.3% in fenced sites, Chi2= 50, p<0.001) and against HEV
(21.3% in open vs. 28.7% in fenced sites, Chi2= 3.9, p<0.05), but not for E.
rhusiopathiae (Chi2=2.1, p>0.05; Figure 2).
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Figure 2.- Apparent antibody seroprevalences against the four pathogens tested (PCV2, HEV, PRRS and E. rhusiopathiae) by age and type of population (open vs. fenced; +95% CI). Asterisks indicate significant differences.
The overall observed antibody prevalence against PCV2 and HEV stayed stable
from Period 1 to Period 2 (Chi2=3, p>0.05 and Chi2=0.09, p>0.05; respectively),
while the observed antibody prevalence against E. rhusiopathiae declined (Chi2=6.9,
p<0.05; Figure 3). When analyzing time trends by GZLM, the probability of testing
positive to PCV2 and HEV between the years 2000 and 2011 was stable (β=0.05,
p>0.05 and β=0.06, p>0.05; respectively), while the probability of testing positive
to E. rhusiopathiae had a negative trend (β=-0.18, p<0.05).
Results on the trends for the analyzed sampling sites are shown in Table 3
(Apéndice 4.2). Local PCV2 prevalences increased between Period 1 and 2 in three
fenced sites. Regarding HEV, two fenced sites had increasing prevalences and four
fenced sites had decreasing prevalences, while no significant trends were evidenced
in open sites. Regarding E. rhusiopathiae two fenced sites had increasing prevalences
and three fenced sites and one open site had decreasing prevalences (Figure 4
[Apéndice 4.2]). In one site (L), a fenced private hunting estate with an intense
game management, serum antibody prevalences against all three agents (PCV2,
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Capítulo 4.2
HEV and E. rhusiopathiae) increased between Period 1 and 2. In contrast, none of
four open populations had a significant change in PCV2 and HEV prevalence
between time periods. In fact, the mean increase in prevalence was always lower for
open than for fenced sites (5.8 vs. 7.4, 4.4 vs. 7 for PCV2 and HEV respectively),
while the mean decrease for E. rhusiopathiae was higher for open than for fenced
sites (-14 vs. -10.3; Figure 4 [Apéndice 4.2]).
Figure 3.- Mean antibody seroprevalences for PCV2, HEV and E. rhusiopathiae (bars indicate 95% CI) in Period 1 (grey diamonds) and Period 2 (black squares).
Discussion
This study evidenced for the first time that wild boar from the Iberian
Peninsula have widespread contact with E. rhusiopathiae, and confirmed high
prevalences of antibodies against PCV2 and HEV. In the study period,
seropositivity to PCV2 and HEV remained globally stable, while a decreasing
contact rate with E. rhusiopathiae was identified.
Monitoring wildlife diseases faces a number of wildlife-specific constraints,
including sampling difficulties regarding proper sample and site stratification,
consistent sampling of the same sites, and limitations of the diagnostic tests
available for wildlife (see Boadella et al., 2011b for a recent review). In wild boar,
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the proportion of seropositive animals is, in general, significantly higher in
individuals older than one year (Kaden et al., 2009). However, disease can occur
more often in juveniles, as for example in PMWS (Vicente et al., 2004; Morandi et
al., 2010). Thus, in contrast to other studies (e.g. Reiner et al., 2009), testing for
time changes using paired samples from the same sampling sites avoided site bias,
using only yearling and adult wild boar to control for age bias while keeping a
balanced sex-ratio. The same type of sample (serum) was used throughout the
study, a difference with those studies that changed the type of sample analyzed (e.g.
Kaci, 2008) vs. (Schielke et al., 2009). Moreover, techniques used for antibody
detection were also constant throughout this retrospective study (in contrast to
Pannwitz et al., 2011). Thus, time changes in prevalences reported herein are not
considered biased due to sampling or diagnostic method constraints. However, one
specific constraint of our study was the low sample size in most of the studied sites.
Therefore, we performed two different analyses since the sample size was
considered reasonable for a Chi-square comparison between two arbitrary time
periods, but not always enough for a proper regression analysis since annual sample
sizes were too low. Nevertheless, the output of the two analyses was generally
comparable, with differences in significance only at site level (Table 3 [Apéndice
4.2]).
PRRSV evidence of contact was noted in the present study, but at a very low
prevalence. This coincides with low prevalences recorded in France (3.6%; Albina
et al., 2000) and north-eastern Spain (3%; Closa-Sebastià et al., 2011). However, it
contrasts with the high nucleic acid prevalence recently recorded in Germany
(Reiner et al., 2009) and the 38% of antibodies to PRRSV described in Italy
(Montagnaro et al., 2010). Although sensitivity and specificity for the PRRSV
ELISA test used are reported to be high (Seuberlich et al., 2002), some percentage
of false positives can occur with this test. In the present study, the mean OD value
for the positives was low, with a maximum value under 1. Due to this fact, further
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Capítulo 4.2
studies aimed at elucidating if the origin of the detected seropositivity is a true
contact with the virus or the result of false positives will be needed.
Regarding PCV2, prevalences recorded in this study coincide with the known
high prevalences in wild boar in Spain (48%; Vicente et al., 2004). The observed
prevalence remained steady in the last decade although there was significant local
increase in prevalence in several sites that are intensely managed for hunting. Along
with the observed higher prevalences in fenced sites as compared to open ones,
this suggests that PCV2 prevalence could be a good indicator of artificial wild boar
management for commercial hunting purposes.
Regarding HEV, the mean of 26% IgG antibodies was similar to the one
reported previously in Spain (28%; de Deus et al., 2008) and larger than the 12%
recorded in The Netherlands (Rutjes et al., 2010). HEV prevalence was also
affected by the type of management, with higher means in fenced estates. This may
be due to the higher wild boar densities in these estates as compared to open ones,
since high HEV prevalences are also common in pig farms (Seminati et al., 2008).
The observed prevalence increase from Period 1 to Period 2 was not statistically
significant. However, such increase had previously been recorded for sympatric red
deer (Cervus elaphus; Boadella et al., 2010).
Finally regarding E. rhusiopathiae, the 15% serum antibody prevalence recorded
in this study is higher than the 5% described previously in serosurveys carried out
in south-central and north-eastern Spain, respectively (Vicente et al., 2002; Closa-
Sebastià et al., 2011). Contact with the causal agent of SE was affected by host sex
and age, but not by the type of management. In fact, the significant declining trend
recorded in this study contrasts with those observed for PCV2 and HEV, and
suggests that opposite drivers are mediating wild boar contact with these pathogens
in the study region. For instance, PCV2 and HEV may produce more persistent
infections than E. rhusiopathiae, and these viral agents are possibly more
transmissible than the bacterial one. Another possibility could be based on the
multi-host nature of E. rhusiopathiae in contrast to a more host-specific nature of
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Tesis doctoral
PCV2 and HEV. Overabundant wild boar populations within intensively managed
sites may cause a loss of fauna diversity (Gortázar et al., 2006). This may disfavour
the transmission of the former, as suggested for Metastrongylus sp. (e.g. Acevedo et
al., 2007b). However, a clinical outbreak of SE was recently reported in farmed wild
boar (Risco et al., 2011).
High prevalences of transmissible infectious agents in wild boar suggest that
epidemiological drivers such as aggregation and high density are still acting. This
will most probably also affect the transmission rates of other disease agents, and
should be taken into account regarding disease control at the wildlife livestock
interface (Ruiz-Fons et al., 2008b).
Acknowledgements
Ruiz-Fons is supported by the Spanish National Research Council (CSIC). We thank many
colleagues at IREC who participated in the field sampling. We also thank Anna Maria Llorens
from CReSA for their help with the laboratory work.
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Capítulo 4.2
Apéndice 4.2
Table 3.- Number of analyzed sera from yearling and adult animals (n), mean prevalences (in %) and 95% confidence intervals (CI) for the four pathogens analyzed in each of the 25 sampling sites. The time trend GzLM beta (β) and p values, and the Chi-square results are indicated for PCV2, HEV and E. rhusiopathiae (*Significant by Chi-square and GzLM; #Only significant by GzLM; &Only significant by Chi-square).
PRRSV PCV2
n Prevalence CI n Prev. CI β p
A 26 0 (0-12.8) 70 21.4 (11.8-31) 0 1 B 19 5.3 (0.2-25.3) 26 50 (30.8-69.2) -0.184 0.439 C 28 3.6 (0-10.4) 58 51.7 (38.9-64.6) 0.132 0.189 D 11 0 (0-26.4) 55 67.3 (54.9-79.7) 0.274 0.008* E 7 0 (0-37.7) 34 23.5 (9.3-37.8) -0.098 0.507 F 34 0 (0-9.8) 64 68.8 (57.4-80.1) 0.612 0.001* G 24 0 (0-13.9) 27 66.7 (48.9-84.4) -0.341 0.133 H 10 0 (0-29) 18 83.3 (58.6-95.2) -0.139 0.617 I 20 5 (0.2-24.4) 48 50 (35.9-64.1) 0.019 0.845 J 20 0 (0-16.6) 50 46 (32.2-59.8) 0.039 0.656 K 14 7.1 (0.3-31.6) 22 36.4 (16.3-56.5) 0.143 0.517 L 16 6.3 (0.3-30.5) 32 71.9 (56.3-87.5) 0.289 0.042* M 27 0 (0-12.4) 25 52 (32.4-71.6) 0.445 0.104 N 5 0 (0-50) 28 60.7 (42.6-78.8) 0.075 0.658 O 26 34.6 (16.3-52.9) -0.025 0.893 P Q 4 0 (0-52.7) 17 47.1 (23.3-70.8) 0.077 0.893 R 26 3.8 (0.2-18.8) 11 90.9 (59.5-99.5) S 7 0 (0-37.7) T 5 20 (1-65.7) U 5 0 (0-50) V W 1 0 (0-94.9) 3 0 (0-36.5) X 6 0 (0-41.1) Y 14 0 (0-23.8)
TOTAL 329 2.1 (0.6-3.7) 614 51.3 (47.3-55.3)
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Tesis doctoral
Table 3.- Continued.
HEV E. rhusiopathiae n Prev. CI β p n Prev. CI β p
A 71 7 (1.1-13) 0.198 0.396 73 11 (3.8-18.1) -0.064 0.693
B 47 46.8 (32.5-61.1) 0.408 0.040# 30 16.7 (3.3-30) -1.02 0.012#
C 71 23.9 (14-33.9) -0.152 0.096 61 9.8 (2.4-17.3) -0.21 0.225
D 45 46.7 (32.1-61.2) 0.15 0.087 54 18.5 (8.2-28.9) 0.224 0.054&
E 43 16.3 (5.2-27.3) 0.007 0.959 34 2.9 (0.1-15.6) 0.158 0.702
F 37 56.8 (40.8-72.7) 0.188 0.177 48 12.5 (3.1-21.9) -0.101 0.51
G 46 26.1 (13.4-38.8) -0.298 0.046* 41 24.4 (11.2-37.5) -0.369 0.049#
H 22 45.5 (24.6-66.3) 0.328 0.027# 16 0 (0-20.8) -0.183 1
I 66 25.8 (15.2-36.3) -0.205 0.026# 43 11.6 (2-21.2)
J 44 18.2 (6.8-29.6) -0.448 0.010* 47 12.8 (3.2-22.3) -0.249 0.101
K 28 60.7 (42.6-78.8) 0.154 0.392 25 24 (7.3-40.7) -0.533 0.030#
L 31 51.6 (34-69.2) 0.267 0.052& 32 34.4 (17.9-50.8) 0.4 0.035*
M 44 4.5 (0.8-15.5) -0.371 0.238 29 20.7 (5.9-35.4) 0.061 0.836
N 19 5.3 (0.2-25.3) 20 15 (4.2-37.2) -0.162 0.538
O 43 9.3 (0.6-18) 0.651 0.174 31 22.6 (7.9-37.3) -0.339 0.078
P 29 44.8 (26.7-62.9) 29 10.3 (2.8-27.2) -0.91 0.046#
Q 30 3.3 (0.1-17.7) 17 0 (0-19.6) -0.494 0.999
R 14 0 (0-23.8) 14 7.1 (0.3-31.6)
S
T
U
V 10 10 (0.5-44.6)
W 3 0 (0-36.5) 5 40 (7.6-81)
X
Y 2 50 (2.5-97.4)
TOTAL 735 26.5 (23.3-29.7) 659 14.7 (12.2-17.6)
120
Capítulo 4.2
121
Figure 4.- Differences in antibody seroprevalence between Period 1 and Period 2 among sampling sites available for both periods for PCV2 (upper panel), HEV (middle panel) and E. rhusiopathiae (lower panel). Empty diamonds indicate open sites, while black diamonds are for fenced sites. Numbers at the left of the diamonds indicate sample size in Period 1 and those on the right indicate sample size for Period 2. Diamonds above the dotted line signal a relative prevalence increase over time whereas diamonds below the dotted line signal a relative decrease in prevalence.
Capítulo 4.3
Distribución espacial y factores de riesgo de la
brucelosis en ungulados de la Península Ibérica
Muñoz, P., Boadella, M., Arnal, M., de Miguel, M., Revilla, M., Martínez, D., Vicente, J., Acevedo, P., Oleaga, A., Ruiz-Fons, F., Marin, C., Prieto, J., de la Fuente, J., Barral, M., Barberán, M., Fernández de Luco, D., Blasco, J., Gortázar, C. 2010. Spatial distribution and risk factors of Brucellosis in Iberian wild ungulates. BMC Infectious Diseases 10, 46.
Capítulo 4.3
Abstract
The role of wildlife as a brucellosis reservoir for humans and domestic
livestock remains to be properly established. The aim of this work was to
determine the aetiology, apparent prevalence, spatial distribution and risk factors
for brucellosis transmission in several Iberian wild ungulates. A multi-species
indirect immunosorbent assay (iELISA) was developed. In several regions having
brucellosis in livestock, individual serum samples were taken between 1999 and
2009 from 2,579 wild bovids, 6,448 wild cervids and 4,454 Eurasian wild boar (Sus
scrofa), and tested to assess brucellosis apparent prevalence. Strains isolated from
wild boar were characterized to identify the presence of markers shared with the
strains isolated from domestic pigs. Mean apparent prevalence below 0.5% was
identified in chamois (Rupicapra pyrenaica), Iberian wild goat (Capra pyrenaica), and
red deer (Cervus elaphus). Roe deer (Capreolus capreolus), fallow deer (Dama dama),
mouflon (Ovis aries) and Barbary sheep (Ammotragus lervia) tested were seronegative.
Only one red deer and one Iberian wild goat resulted positive in culture, isolating B.
abortus biovar 1 and B. melitensis biovar 1, respectively. Apparent prevalence in wild
boar ranged from 25% to 46% in the different regions studied, with the highest
figures detected in South-Central Spain. The probability of wild boar being positive
in the iELISA was also affected by age, age-by-sex interaction, sampling month,
and the density of outdoor domestic pigs. A total of 104 bacterial isolates were
obtained from wild boar, being all identified as B. suis biovar 2. DNA
polymorphisms were similar to those found in domestic pigs. In conclusion,
brucellosis in wild boar is widespread in the Iberian Peninsula, thus representing an
important threat for domestic pigs. By contrast, wild ruminants were not identified
as a significant brucellosis reservoir for livestock.
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Background
Brucellosis is an infectious disease caused by bacteria of the genus Brucella,
characterized by abortion and infertility in several mammal species, and being
considered one of the most important zoonosis worldwide (Cutler et al., 2005).
Brucella melitensis, followed by Brucella abortus and Brucella suis, are the main species
involved in the infection of human beings, thus being the main target of eradication
campaigns.
With very few exceptions, B. suis infection in both humans and pigs remains an
important problem in most countries. B. suis biovar 2 is the main responsible of
brucellosis in pigs in Europe. Despite having been isolated from human beings
(Teyssou et al., 1989), this biovar 2 seems to be less pathogenic for humans than
the biovars 1 and 3 (Godfroid et al., 2005). Other Brucella species have been isolated
in rodents, terrestrial carnivores, and sea mammals, but the relevance of these
Brucella species for livestock and human beings is quite limited (Tryland et al., 1999;
Godfroid et al., 2005; Scholz et al., 2008).
Wild animals are often at risk as a consequence of contacts with infected
livestock, particularly in extensive breeding systems. In addition to the B. abortus
infection specific problem shared by cattle, bison (Bison bison) and elk (Cervus
elaphus) in limited territories of the USA (see below), some sporadic cases have been
reported in wild bovids, such as ibex (Capra ibex) and chamois (Rupicapra sp.) in the
EU (Garin-Bastuji et al., 1990; Ferroglio et al., 1998). Although wild ruminants
have been suggested to hold brucellosis and eventually originate spillback to
domestic animals or infection in humans, the most extended opinion is that these
wild animals are occasional victims of brucellosis transmitted from infected
livestock, rather than a true reservoir of the disease for domestic animals (Gortázar
et al., 2007). In fact, only limited cases of brucellosis have been reported in these
free-living animals (Garin-Bastuji and Delcueillerie, 2001; Godfroid et al., 2005;
Gaffuri et al., 2006), and only weak evidence for a direct relationship between
brucellosis apparent prevalence and wild ruminant population size/density has
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Capítulo 4.3
been found (e.g., Conner et al., 2008 and references therein). However, the risk can
be high in overabundant wildlife populations in contact with infected livestock and
when artificial management increases aggregation (Gortázar, 2007; Conner et al.,
2008). In the Greater Yellowstone Ecosystem (GYE), USA, winter feeding of elk
and bison contributes to maintain valuable wildlife populations and avoid contacts
between B. abortus infected wildlife and cattle, but significantly increases the intra-
specific transmission risk (Etter and Drew, 2006). Modelling of observational data
has shown that brucellosis prevalence in elk correlates with the timing of the winter
feeding season (Cross et al., 2007). This underlines that human dimension issues
are fundamental to successful management of wildlife diseases (Conner et al.,
2008).
Brucellosis caused by B. suis biovar 2 is frequently reported in the Eurasian wild
boar (Sus scrofa) and the European brown hare (Lepus europaeus), and apparent
prevalence ranging from 8 to 32% has been reported in wild boar in the EU
(Garin-Bastuji and Delcueillerie, 2001; Hubálek et al., 2002; Cvetnic et al., 2003; Al
Dahouk et al., 2005; Koppel et al., 2007; Bergagna et al., 2009). It is accepted that
both species play a relevant role as a brucellosis reservoir for domestic pigs, even
under natural environmental conditions (Cvetnic et al., 2003; Godfroid et al., 2005;
Pikula et al., 2005). In fact, both wildlife species have been directly involved in the
transmission of infection to domestic pigs reared in outdoor farms (Garin-Bastuji
and Delcueillerie, 2001). Outside the EU, feral pigs may maintain B. suis biovars 1
and 3, being a potential source of infection to both domestic pigs and human
beings (Drew et al., 1992).
Only limited information on wildlife brucellosis is available in the Iberian
Peninsula. Regarding wild ruminants, brucellosis has not been detected in limited
studies conducted on Barbary sheep (Ammotragus lervia) (Candela et al., 2008),
Cantabrian chamois (Rupicapra pyrenaica parva) (Falconi et al., 2010) and mouflon
(Ovis aries) (Lopez-Olvera et al., 2009). In contrast, several cases of infections
induced by B. suis biovar 2, have been reported in wild boar (Garcia-Yoldi et al.,
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2007) and European brown hares (Lavín et al., 2006). Wild ungulates are currently
expanding and increasing in density in the whole Iberian Peninsula (Gortázar,
2000), as well as the artificial management of these wild species including fencing,
feeding and translocation, then increasing the risk of infectious disease transmission
(Gortázar et al., 2006).
The availability of accurate and validated diagnostic tests is of paramount
importance to properly assessing the prevalence of brucellosis in wildlife (Van
Houten et al., 2003). In this work we developed a multispecies iELISA to
determine brucellosis apparent prevalence in several Iberian wild ungulate species,
and determined spatial distribution and risk factors associated with brucellosis. We
hypothesised that: (1) free-living wild ruminants would not show significant
infection with Brucella species; (2) wild boar, conversely, would show infection with
B. suis biovar 2, constituting a potential hazard for domestic pigs; and (3) apparent
prevalence would vary with environmental, population and individual risk factors
such as artificial management.
Materials and Methods
Study area
The study area was the Iberian Peninsula in the south-western European
Union. This includes a variety of habitats and climates, which can be simplified into
5 different Bio-regions in the mainland, as defined in the Spanish Wildlife Disease
Surveillance Scheme (http://rasve.mapa.es/Publica/Programas/Normativa.asp).
Table 1 summarises the most relevant characteristics of each Bio-region.
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Capítulo 4.3
Table 1.- Characteristics of the Bio-regions of the Iberian Peninsula included in the study area.
Bio-region Environment Wildlife Sampling site characteristics
1.- Atlantic
Atlantic climate with high precipitation. Pastures and deciduous woodlands. Mountain habitats. Almost no fencing of wildlife habitats.
Wild boar and roe deer abundant. Locally red deer abundant. Chamois at high altitudes (Cantabrian Mts.).
n=76. Woodlands: 62%; Agricultural lands: 33%. Altitude (in m): mean 452 (range 0-2032). Mean annual precipitations (in mm): 1284. Mean annual temperature (in ºC): 12
2.- Northern- Plateau
Continental Mediterranean climate. Dry, hot summers, dry, cold winters. Open, cereal landscapes with pine or oak woodlands, limited to the north by mountains. Little fencing.
Ungulates expanding and locally abundant. Chamois limited to high altitudes in the Pyrenees. Locally ibex and fallow deer.
n=98. Woodlands: 68%; Agricultural lands: 30%. Altitude (in m): mean 987 (range 67-3314). Mean annual precipitations (in mm): 808. Mean annual temperature (in ºC): 10.5
3.- South-Central
Continental Thermo Mediterranean climate. Pastures and crops with interspersed vegetation, sometimes forming savannah-like structures. Low altitude mountains with scrubland. Frequent fencing.
Wild boar and red deer often at high density; feeding and watering. Locally abundant fallow deer and Iberian ibex, and introduced wild bovids.
n=72. Woodlands: 68%; Agricultural lands: 29%. Altitude (in m): mean 705 (range 47-2321). Mean annual precipitations (in mm): 605. Mean annual temperature (in ºC): 14.5
4.-Interior Mountains
Severe Continental Mediterranean climate. Limestone mountain and high-plateau habitats with cereal crops, pastures, and pine and oak woodlands. Little fencing.
Wild boar, roe deer, and ibex widely distributed but usually at moderate abundance. Locally abundant red deer.
n=22. Woodlands: 71%; Agricultural lands: 29%. Altitude (in m): mean 1178 (range 248-1932). Mean annual precipitations (in mm): 568. Mean annual temperature (in ºC): 11.3
5.- South and East Coast
Coastal Thermo Mediterranean climate; arid in the central portion. Few well preserved wildlife habitats (mountains). Little fencing.
Wild boar abundant in the northern and southern ends. Other ungulates locally abundant.
n=7. Woodlands: 48%; Agricultural lands: 23%. Altitude (in m): mean 190 (range 0-1238). Mean annual precipitations (in mm): 720. Mean annual temperature (in ºC): 15.7
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Animal sampling procedures
The number of samples obtained by species and study region is summarised in
Table 2. Sampling was opportunistic and biased towards the hunting season
(October to February in most species, and summer in chamois and roe deer), and
took place from 1999/2000 to 2008/2009. The total number of wild ungulates
sampled was 13,481, including 2,579 bovids (Barbary sheep, chamois, Iberian wild
goat and mouflon), 6,448 cervids (roe deer, red deer and fallow deer) -see Table 2
for the precise numbers in each animal species-, and 4,454 wild boar. Samples were
collected from hunter-harvested animals. Blood was drawn from the heart or the
thoracic cavity during field necropsies, then the serum (usually haemolysed) was
collected after centrifugation and kept frozen at -20°C until analysed. Whenever
possible, cranial and iliac lymph nodes, spleen and sexual organs were collected and
stored at -20°C for microbiological analyses. The number of samples from the
different animal species submitted to microbiological studies is shown in Table 2.
Age-classes of biological meaning were defined. Based on tooth eruption
patterns, wild ruminants were classified as fawns (first year of life), yearlings
(second year of life), juveniles (third to fourth year of life), and adults (fifth year of
life onwards). Wild boar less than 7 months old were classified as piglets, between 7
and 12 months were classified as juveniles, those between 12 and 24 months as
sub-adults, and those over 2 years as adults (Saenz de Buruaga et al., 1991). Sex was
known in 5,683 wild ruminants, and age-classes in 4,065. For wild boar, sex was
known in 2,688 animals and age in 2,419.
Serological studies
A multi-species indirect enzyme immunoassay (iELISA) was developed and
validated to assess brucellosis apparent prevalence. A detailed description of the
technique can be found in Muñoz et al. (2010). Briefly, an extract from B. melitensis
was obtained as described elsewhere (Leong et al., 1970). ELISA plates were coated
with antigen solution in and incubated at 4ºC overnight. Sera were added by
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Capítulo 4.3
duplicate to each well, and the plates incubated. Optimal serum dilutions previously
assessed were added. After washing, a conjugate solution of Protein G/HRP was
added, and the reaction was developed with ABTS solution. Results were expressed
as the percentage of optical density (%OD) using the formula [% OD=100 X mean
OD of duplicated sample / mean OD of duplicate positive control]. Due to the
lack of gold standard sera (i.e., taken from culture positive and brucellosis free
animals) from the different wild ungulate species, the sera used for setting up and
iELISA validation were from Brucella culture positive (CP) and Brucella-free (BF)
phylogenetically related domestic animals. The overall results were then submitted
to ROC analyses (Medcalc. 9.2.1.0 software) and cut-offs resulting in 100%
diagnostic specificity and the maximal diagnostic sensitivity for sheep, goats and
cattle, and pigs, were selected to further assess the apparent prevalence in the
corresponding phylogenetically wild animals tested.
Bacteriological analysis and Brucella typing
Necropsy samples (lymph nodes, spleen and/or sexual organs) from iELISA-
positive animals (see Table 2 for precise numbers in each species) were submitted
to bacteriological analysis. To assess the relative diagnostic specificity of the
iELISA developed, similar necropsy samples taken from iELISA-negative animals
(see Table 2) were also cultured. Briefly, each sample was surface decontaminated
and homogenised in a blender. Each homogenate was smeared onto at least two
plates of both Farrell’s and modified Thayer Martin’s culture media (Marín et al.,
1996). After 5-7 days of incubation at 37ºC in 10% CO2 atmosphere, the resulting
Brucella isolates were identified according to standard procedures (Alton et al.,
1988).
Brucella field isolates were further analysed using both molecular and standard
bacteriological procedures. Bacterial DNA was extracted using QIAamp DNA
minikit (QIAGEN, Hamburg, Germany). For the identification and differentiation
of Brucella species, the Bruce-ladder multiplex PCR was applied as described
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elsewhere (Garcia-Yoldi et al., 2006). To assess the precise biovar a multiplex PCR
was used (Garcia-Yoldi, 2008).
Table 2.- Sample size by host species and Bio-region studied, apparent prevalence obtained, and Brucella culture results in Iberian wild ungulate species.
Serum samples by region Common name Latin name 1 2 3 4 5 Total
Barbary sheep Ammotragus lervia 0 0 8 0 0 8
Mouflon Ovis aries 0 0 75 0 0 75
Iberian wild goat1 Capra pyrenaica 0 41 2 1042 1 1086
Chamois3 Rupicapra pyrenaica 57 1353 0 0 0 1410
Roe deer Capreolus capreolus 77 152 5 9 42 285
Fallow deer Dama dama 92 107 47 32 64 342
Red deer Cervus elaphus 452 1591 2378 932 468 5821
Wild boar Sus scrofa 658 1920 1499 132 245 4454
TOTAL 1336 5164 4014 2147 820 13481
Common name Mean prevalence
(95% CI) Samples submitted for
culture Nr. of isolates
(species and biovar)
Barbary sheep 0 (0-36) 0
Mouflon 0 (0-5) 0
Iberian wild goat1 0.1 (0-0.6) 12 1 (B. melitensis biovar 1)
Chamois3 0.8 (0.4-1.4) 11
Roe deer 0 (0-1) 0
Fallow deer 0 (0-1) 0
Red deer 0.4 (0.3-0.6) 814 1 (B. abortus biovar 1)
Wild boar 33 (31.6-34.4) 5895 104 (B. suis biovar 2)
TOTAL 682 106 1Includes mainly the Mediterranean subspecies Capra pyrenaica hispanica. 2 All animals were sampled randomly during hunting or at game farms but for the ibex tissues submitted for culture, which came from a clinical case with suspected brucellosis. 3 Cantabrian chamois (Rupicapra pyrenaica parva) in Bio-region 1 and Pyrenean chamois (R. p. pyrenaica) in Bio-region 2. 4 Thirty-one out of these 81 samples came from iELISA-positive animals and 50 from iELISA-negative ones. 5A total of 539 out of these 589 samples were from iELISA-positive animals and 50 from iELISA-negative ones.
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Capítulo 4.3
Statistical analyses
We used Sterne's exact method (up to N=1,000), or adjusted Wald method
(N>1,000) to estimate apparent prevalence confidence intervals (Reiczigel, 2003).
Apparent prevalence comparisons among categories were done with homogeneity
tests. The Mantel test was used to assess the spatial association between brucellosis
apparent prevalence in wild boar across different sampling sites. Calculations were
done with the PASSAGE software (Rosenberg, 2001).
Quantitative exploratory analysis of risk factors for brucellosis apparent
prevalence was carried out at two different geographic scales (peninsular and
regional) using two-stage analyses. First, the associations between all the
hypothesized risk factors and apparent prevalence were analyzed using single factor
generalized models. Factors that captured the effect of any set of highly correlated
variables for which P<0.1 were selected for inclusion in the multivariate models
(Table 3). In a second step, the selected variables were then jointly evaluated in a
multiple logistic model. The individual iELISA result (N=3,883) was the response
variable (binomial, i.e. antibody presence or absence). Since sampling across
different populations was not homogeneous in relation to age and sex, statistical
analyses were conducted at the individual level to control for them. Age was
included as a continuous discrete explanatory variable and sex was included as a
categorical binomial explanatory variable. We used a stepwise strategy to obtain the
final model. Statistical significance was assumed wherever P<0.05. We used the
SAS statistical package.
In the Peninsular scale model we controlled for the effect of the Bio-region by
including it as categorical random variable. Factors tested are listed in Table 3.
In the smaller geographical scale model (Ciudad Real province, Bio-region 3),
we restricted our analysis to wild boar sampled on 20 sites, that were well
characterized regarding habitat characteristics (e.g. estate-related environmental
conditions, land cover and habitat structure) and relevant wildlife management
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factors such as fencing, supplemental feeding, watering sites, and estimated
abundance (Vicente et al., 2007b). The variables tested are shown in Table 3.
Hunting season (from 2000-2001 to 2008-2009) and sampling site were
included as random factors in both models.
Table 3.- Factors included in the analysis, indicating those significantly associated (excluding other highly correlated variables) with apparent prevalence of brucellosis at the Peninsular (GLM, P<0.1, N=2416) and the regional (GLM, P<0.1, N=460) scales. Sampling season and sampling site were included as random factors.
Peninsular scale
Factor Estimate N p
Significantly associated with prevalence (selected for the model):
Age class (1-4) 2416 <.0001
Month (1-12) 4394 <.0001
Annual rainfall -0.00013 4079 0.0011
Cultivated lands 0.000629 4079 0.0091
Non-irrigated cultures 0.000908 4079 0.0181
Iberian hare habitat suitability 0.000011 4019 0.0287
Road 0.07015 4079 0.0386
Woodlands (-0.000644) 4079 0.0529
Irrigated cultures 0.001514 4079 0.0709
Urban 0.00572 4079 0.0745
Not associated with prevalence (not selected):
Sex (1-2), wild boar management, European brown hare habitat suitability, irrigated fruit orchards, pastures, annual radiation, slope range, mean slope, maximum slope, mean altitude, min. altitude, max. altitude, altitude range, annual temperature (Jothikumar et al., 2006), annual temp. (min), annual temp. (max)
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Capítulo 4.3
Table 3.- Continued.
Regional scale
Factor Estimate: N p
Selected: Age class 0.0001 Month (1-12) 505 0.0263 Iberian hare abundance (pellet FBII) -177.415 460 0.0457 Mean open-air farm size (number of pigs) 0.000213 500 0.0532 Number of pigs on open-air farms 0.000209 500 0.0625 Number of pigs on open-air farms per square Km 0.1253 500 0.0949
Not selected: Sex, Iberian hare habitat suitability, wild rabbit abundance (pellet FBII), wild boar km abundance, wild boar spatial aggregation index (Z), wild boar abundance (dropping FBII), red deer FBII, red deer density (distance estimates), wild boar FBII by feeding site and ha, wild boar FBII by watering site and ha, annual temperature (Jothikumar et al.), mean slope, annual rainfall, annual radiation, mean altitude, sampling estate surface (Ha), type of population (open, fenced, farm), fencing, % boundary fenced, riparian habitats, irrigated cultures, non-irrigated cultures, cultivated lands, woodlands, irrigated fruit orchards, urban, tree diversity, grass cover, scrubland cover, pine woodlands, pastures, dehesa (savannah-like open oak woodlands), number of Quercus trees/5 m, total woodlands, tree cover, soil cover, total wood + scrublands, Quercus spp.>4 m/5m, cultures (%), scrublands (%), number of waterholes, waterholes per ha, wild boar supplemental feeding, wild boar feeding sites, wild boar feeders per ha, deer feeding sites, goats per ha, cattle per ha, sheep per ha, number of pig farms in municipality, pig farms per Km2, total pigs in municipality, total number of pigs in municipality per Km2, mean farm size (number of pigs), number of pigs on closed farms per Km2, closed pig farms in municipality, closed pig farms per Km2, mean closed farm size, pigs on closed farms, open-air pig farms in municipality, open-air pig farms per Km2.
Results
iELISA validation
As an example of the iELISA validation procedure followed, the distribution of
%OD results obtained with the gold standard populations in domestic goats and its
phylogenetically related Capra pyrenaica counterpart is shown in Figure 1. As seen in
this figure, a relatively wide range of % OD were resulting in 100% sensitivity and
specificity with the gold standard populations tested, and this picture was similar
when using gold standard sera from the cattle, sheep and pig populations used as
reference controls. The corresponding cut-offs for the different wild animal species
tested were 50% OD (for all wild ruminant species) and 40% OD (for wild boar),
considering that the resulting sensitivity and specificity with the corresponding gold
standard populations was always 100%.
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The relative specificity of the iELISA versus the culture results obtained with the
50 iELISA negative wild ruminants tested (Table 2) was adequate since no positive
isolations were obtained in these animals. The relative specificity versus the culture
results was also adequate in wild boar, since only one B. suis biovar 2 strain was
isolated from the cultured specimens of the 50 iELISA negative animals tested.
Figure 1.- Example of the typical distribution of optical density (% OD) results obtained by iELISA when testing the gold standard populations (from domestic goats) and its phylogenetically related Iberian wild goat (Capra pyrenaica) counterpart. The horizontal line represents the cut off selected for assessing the apparent prevalence of brucellosis.
Studies in wild ruminant species
Our results revealed not or only very limited antibody responses to infections
by smooth Brucella species in Iberian wild ruminants (Table 2). Anti-Brucella
antibodies were detected in chamois, red deer, and to a lesser extent, the Iberian
wild goat. The highest apparent prevalence (0.8%) was identified in chamois, being
essentially detected in the animals living in the Pyrenean Mountains, in Bio-region
2.
Altogether, the overall estimated apparent prevalence in wild ruminants was as
low as 0.4% (95% CI range 0.3-0.6%), and no significant inter-species differences
(χ2=10.2, 6 d.f., P>0.05) or spatial aggregation (data not shown) were evidenced.
However, slightly higher apparent prevalence was observed locally. As an example,
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Capítulo 4.3
the percentage of red deer positive reactors reached maximum value of 1.9% (3 out
of 158 animals tested; 95% CI 0.5-5.5) in the Garcipollera reserve (Pyrenees, Bio-
region 2), and 0.8% (16 out of 1,899 animals tested; range 0.5-1.4) in the Montes
Universales reserve (Bio-region 4).
Only two out of the 93 animals submitted to bacteriological analyses (one from
a clinical case, 42 from iELISA-positive animals, and 50 from ELISA-negative
animals, Table 2) resulted in Brucella positive culture. One of the strains identified
(B. melitensis biovar 1) was isolated from the clinical case, a severely ill Iberian wild
goat buck found in Albacete province (Bio-region 4), and that resulted positive in
the iELISA. The other strain isolated (B. abortus biovar 1) came from a hunter-
harvested red deer stag, from Montes Universales reserve in Teruel province (Bio-
region 4), and found also positive in the iELISA.
Studies in wild boar
In strong contrast with results found in wild ruminants, wild boar showed a
high apparent prevalence of brucellosis (33%; 95%CI 31.6-34.4; see also Additional
Material 1), in all Bio-regions tested (Figure 2 panel A). The highest apparent
prevalence (average 46% with some populations reaching over 80%) was found in
Bio-region 3 (Figure 2 panel A). The remaining Bio-regions showed lower but still
high values (average 26%; Figure 2 panel A). No statistically significant spatial
association was found by Mantel test (Pearson r=−0.10, n=68; p=0.99).
A total of 539 necropsy samples from iELISA positive wild boar were
submitted to bacteriological culture (Table 2). One hundred and four isolates
(representing 19.3% of the animals tested) were obtained from these seropositive
animals cultured, while only 1 of the 50 iELISA negative wild boar tested resulted
in positive culture, being this difference statistically significant (P<0.001). All
isolates were identified as Brucella suis, and the multiplex PCR identified patterns
consistent with those characteristic of B. suis biovar 2. Type A strains (n=57) were
found widely distributed throughout Bio-regions 1, 2 and 3, whereas type C (n=46)
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and B (n=1) strains were restricted to Bio-regions 2 and 3, respectively (Figure 2
panel B).
Figure 2.- Panel A: Apparent prevalence of brucellosis in Eurasian wild boar (Sus scrofa) in Bio-regions 1 to 5. Dots are proportional to prevalence. Only data from localities with at least 10 wild boar samples are shown. Panel B: Distribution of the different haplotypes of Brucella suis strains isolated from wild boar. Points represent an infected population cluster rather than individual isolates; the dotted line represents the south-western distribution limit of the European brown hare (Lepus europaeus).
Table 4 panel A shows the variables included in the final large-scale model. The
probability of wild boar testing positive in the iELISA was affected by age
(χ2=42.3, 3 d.f., p<0.001; Figure 3 panel A), age-by-sex interaction, rainfall, Bio-
region and month. By contrast, apparent prevalence was not affected by sex (males
35.8%, 95% CI 33.3-38.5; females 36.5%, 95% CI 34.0-39.0). Apparent prevalence
increased during the hunting season reaching maximum levels in February (Figure 3
panel B). Apparent prevalence in wild boar also varied among Bio-regions (χ2=183,
4 d.f., p<0.001), Bio-region 3 showing almost the double of apparent prevalence
than the other Bio-regions.
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Capítulo 4.3
Figure 3.- Distribution of apparent prevalence in wild boar (Sus scrofa) through age and sex classes (Panel A), and sampling period (Panel B) at the Peninsular scale.
Table 4 panel B shows the 6 variables included in the final regional-scale model.
The probability of testing positive in the iELISA was affected by age-by-sex
interaction, sampling month, and the number of open-air bred pigs per square Km
in the sampling municipality. Fifty eight additional variables resulted not statistically
significant in the first analysis and thus, not selected for the model (Table 3).
Table 4.- Effects on the probability of testing positive to brucellosis at Peninsular (Panel A) and regional (Panel B) scales. DF degrees of freedom; F test statistic; Pr>F probability.
A B
Effect DF F Pr>F Effect DF F Pr>F
Age 3.1947 23.2 <0.001 Month 6.373 2.39 0.0280
Sex by age interaction
4.1886 2.53 0.0390 Open-air pigs per sq km
1.136 3.29 0.0919
Rainfall 1.186 10.7 0.0013 Sex by age interaction
5.48 4.90 0.0002
Bio-region 4.207 10.7 <0.001
Month 4.1557 2.80 0.0247
Discussion
We developed and validated a multi-species immunosorbent assay and applied
it to determine the apparent prevalence and distribution of brucellosis in wild
ungulates from the Iberian Peninsula. Our results showed that wild ruminants do
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not play a relevant role in the maintenance of B. abortus and B. melitensis infections.
In contrast, the wild boar was identified as an important threat for B. suis infection.
The quality of the diagnostic methodology used is of paramount importance to
assess the prevalence of wildlife diseases (Van Houten et al., 2003). Due to the lack
of brucellosis tests validated for wildlife species, the most recommendable
approach for studies to determine brucellosis prevalence in wildlife should be based
in the use of classical serological tests such as the Rose Bengal (RBT), which has
been widely validated in the domestic animal species phylogenetically related with
wild ungulates, and extensively used worldwide (OIE, 2009). These classical tests,
however, require samples of a very high quality to avoid haemolysis problems.
However, gathering high quality serum samples devoid from haemolysis is
frequently impossible in standard wildlife sampling procedures, particularly those
based on hunted specimens. To circumvent this problem, many recent brucellosis
studies in wildlife have been based on immunosorbent assays -ELISA- (Cvetnic et
al., 2004; Zarnke et al., 2006; Koppel et al., 2007). One of the advantages of this
serological test is that the degree of haemolysis of the serum samples does not
affect significantly the ELISA performance (Neumann and Bonistalli, 2009). Due
to the absence of specific conjugates against the immunoglobulin isotypes of the
different wildlife species, indirect ELISAs have not been widely used, and most of
studies have been based on the use of competitive ELISAs, which are potentially
able to identify specific anti-Brucella antibodies in all animal species (Gall et al.,
2000; Nielsen et al., 2001; Van Bressem et al., 2001; Van Houten et al., 2003).
However, due to the absence of adequate gold standard sera, most studies in
wildlife have been performed using the protocols (i.e., serum dilution, antigen
concentration, cut-off, etc.) as recommended by manufacturers in domestic
livestock (Deem et al., 2004; Al Dahouk et al., 2005; Koppel et al., 2007), and
therefore without adequate validation for the corresponding wild species tested.
Moreover, the problem of the false-positive serological reactions induced by gram-
negative bacteria sharing common epitopes with Brucella (Kittelberger et al., 1997;
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Capítulo 4.3
Muñoz et al., 2005) is also an important issue to properly assess brucellosis
prevalence. Hence, recent studies suggest the need for better diagnostic tools to
obtain reliable results in serological studies on brucellosis in wildlife (Koppel et al.,
2007).
The best gold standard known in brucellosis diagnosis is the isolation of the
bacteria. However, individual bacteriology is cumbersome, unpractical and very
expensive to be used as the unique test to determine the prevalence of brucellosis
in animal populations. Thus, the most recommendable approach is a combination
of serological and bacteriological studies, such as those conducted here. We
developed an iELISA using an antigen sharing the major common surface epitopes
present in all smooth Brucella species (Alton, 1990; Cherwonogrodzky et al., 1990),
allowing the diagnosis of infections induced by B. abortus, B. melitensis and B. suis.
The lack of availability of polyclonal or monoclonal antibodies raised to detect
specifically the immunoglobulin isotypes of wildlife species was overcome by using
protein G as a conjugate. This reagent has been reported suitable in wildlife for
detecting antibodies to Brucella (Godfroid et al., 1994; Nielsen et al., 2004) and
other pathogens (Aurtenetxe et al., 2008; Reyes-Garcia et al., 2008). Due to the
absence of gold standard sera from culture positive and brucellosis free wild
animals, we validated our iELISA using gold standard sera from the closest
phylogenetically related domestic species. The adequate relative sensitivity of the
iELISA with respect to the bacteriological status of the animals was confirmed in
wild boar, in which the number of strains isolated from seropositive animals was
relatively high (Table 2), being comparable to those obtained in similar studies
conducted in the EU (Godfroid et al., 1994).
The success for bacteriological isolation depends on the quality of the samples
cultured. Unfortunately, in our study it was not always possible to obtain necropsy
samples of proper quality, which probably decreased the final sensitivity of the
bacteriological methods applied. This can explain the relatively high number of
samples from iELISA positive animals that resulted in negative culture. Moreover,
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the relative specificity of the iELISA versus culture results was also adequate since
only one out of the 50 iELISA negative animals tested yielded a positive culture.
However, this iELISA negative serum from an infected wild boar could also be due
to a recent B. suis infection in which antibodies of the IgG isotypes (the only ones
detected by protein G) had not yet been produced, or simply, as a consequence of a
human error in sampling or identification.
The relative sensitivity of the iELISA developed could not be properly assessed
in wild ruminants due to the low apparent prevalence figures detected and,
accordingly, the low number of iELISA positive samples cultured (Table 2). The
only two animals in which field Brucella strains were isolated resulted positive in the
iELISA. Finally, no brucellae were isolated from the 50 iELISA negative wild
ruminants tested, this result supporting the adequate relative specificity of the
serological test developed. Therefore, this iELISA should be considered as
adequate enough for detecting Brucella antibodies in the wild species studied.
At least for the species with large sampling sizes (Table 2), it can be concluded
that wild ruminants are not a significant potential source of B. abortus and B.
melitensis infections for livestock in the Iberian Peninsula. However, data on species
with a limited sample size, such as Barbary sheep (N=8) and mouflon (N=75), are
not enough to support that general conclusion. The finding of the B. melitensis
infected Iberian wild goat in a locality with no active sampling stresses, however,
the importance of setting up passive wildlife surveillance networks.
The small variations in the geographical distribution of seropositive wild
ruminants can reflect sampling biases rather than real differences in apparent
prevalence. However, the relatively high apparent prevalence found in some areas
could be also related with the high prevalence of brucellosis in domestic species
reared in extensive breeding systems. As an example, the percentage of red deer
and chamois positive reactors reached maximum values in some areas of the
Pyrenees (Bio-region 2), and red deer in the Montes Universales reserve (Bio-region
4), that were coincident with some brucellosis outbreaks taking place in domestic
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Capítulo 4.3
sheep and cattle in these mountain areas during the 2002 and 2004 seasons
(Gobierno de Aragón, Annual Animal Health Report, unpublished data).
Current knowledge on B. abortus epidemiology in the GYE strongly suggests
that artificial management including crowding and supplemental feeding influences
the dynamics of wildlife brucellosis (Cross et al., 2007). The very low apparent
prevalence of brucellosis in Iberian wild ruminants may be explained by a couple of
non-mutually excluding hypotheses. First, the relatively low overall prevalence of
brucellosis in domestic ruminants in Spain makes the transmission to wildlife highly
improbable, despite the existence of important risk factors such as overabundance
(Gortázar et al., 2006). Second, artificial feeding in southern Spain takes place
mostly in summer, once the lambing/calving season is over. Thus, abortions
occurring at winter feeding sites as in elk in the GYE (Cross et al., 2007), are
unlikely. This is consistent with recent results on the effects of management on elk
behaviour and brucellosis transmission (Maichak et al., 2009).
In strong contrast with the situation in wild ruminants, the wild boar
population was found seriously affected by B. suis biovar 2 infection. The general
apparent prevalence figures found herein (Table 2) were similar to those indicated
in other European reports (Dedek et al., 1986; Garin-Bastuji and Delcueillerie,
2001; Hubálek et al., 2002; Cvetnic et al., 2003; Al Dahouk et al., 2005; Koppel et
al., 2007). However, apparent prevalence close to 100% was recorded locally
(Figure 2). Bio-region 3, the area where game is more intensively managed through
fencing, feeding and translocation, was the region with the highest apparent
prevalence (Figure 2 panel A). This Bio-region concentrates practically the whole
Iberian censuses of domestic Iberian pigs reared in fully out door breeding systems.
The absence of sex effects on brucellosis apparent prevalence in wild boar
(Table 3) was not surprising, since similar results have been found also in other
diseases (Vicente et al., 2004, 2007b). However, we found at both geographical
scales a significant effect of the sex-by-age interaction on the apparent prevalence
of brucellosis (Table 3). This effect can be explained by sex and age related
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differences in wild boar behaviour (Ruiz-Fons et al., 2008c). While females live in
matriarchal groups, adult males live solitary and only contact with these matriarchal
groups during the mating season (Rosell and Herrero, 2002). Apparent prevalence
observed among adult wild boar was higher than that found in younger age classes,
as expected by the higher participation in reproduction by adults (Ruiz-Fons et al.,
2007).
In wild boar, positivity to several other infectious agents has been linked with
density, spatial aggregation or artificial management (e.g. Aujeszky’s disease,
Vicente et al., 2005; Ruiz-Fons et al., 2007; Bovine tuberculosis, Vicente et al.,
2007b; Porcine circovirus type 2, Vicente et al., 2004. However, no relationship
between apparent prevalence and wild boar management or density risk factors has
been evidenced in this study. There is no clear explanation for this finding, and
further research is needed to better identify the factors modulating B. suis infection.
Several authors have suggested that spillover from wild boar and European
hares to domestic pigs could be a frequent event, and the explanation of the re-
emergence of brucellosis due to B. suis biovar 2 in outdoor reared pigs in EU
countries (Godfroid and Kasbohrer, 2002; Leuenberger et al., 2007). Historical
contact between free ranging Iberian domestic pigs and wild boar could have
boosted wild boar infection with B. suis biovar 2 in the Iberian Peninsula. As
indicated above, Bio-region 3 is the Spanish region with more open-air bred
domestic pigs, and in which the apparent prevalence figures in wild boar were
maximal (Figure 2). In the small scale study carried out in this Bio-region 3, a
positive relationship between apparent prevalence in wild boar and the density of
open air bred Iberian pigs was evidenced (Table 4). This may contribute to explain
the important prevalence of brucellosis reported in Iberian pig farms in the last
years in Spain (Muñoz et al., 2003; Garcia-Yoldi et al., 2007). Accordingly, having in
consideration the close genetic characteristics of the strains isolated in Spain (Lavín
et al., 2006), our study confirms that domestic Iberian pigs reared outdoor and wild
boar share the same brucellosis infection due to B. suis biovar 2. Three out of the
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Capítulo 4.3
five wild boar estates showing the highest apparent prevalence were fully open and
sharing pastures with free-ranging domestic pigs.
In contrast with the situation reported in France (Garin-Bastuji et al., 2006),
wild boar were capable to maintain B. suis biovar 2 infection independently of the
existence of European brown hares. Interestingly, the unique B. suis biovar 2 strain
isolated from European brown hare in Spain (Lavín et al., 2006) was showing a
molecular pattern different from the three haplotypes identified in this study in
wild boar (J.M Blasco, unpublished results). This hare strain was showing also
different restriction patterns from those identified in the B. suis biovar 2 Thomsen
reference strain and other B. suis biovar 2 strains isolated from hares in France,
which show common patterns with those identified in wild boar (B. Garin-Bastuji,
personal communication). This suggests that at least in Spain, the B. suis biovar 2
haplotypes infecting European brown hares and wild boar may be different.
However, this must be confirmed in further studies using larger numbers of
animals. The possible role of the Iberian hare (Lepus granatensis) in B. suis biovar 2
epidemiology is currently unknown. No isolation of B. suis biovar 2 has been
reported in Iberian hares but no adequate studies are available. Suitability of Iberian
hare habitat, meaning open, flat, sparsely-forested Mediterranean agrosystems, was
selected in the first step of the analysis, but not in the final model. Its weak link
with wild boar apparent prevalence may be due to a correlation between Iberian
hare habitat suitability and Bio-region 3. A similar explanation can be given for the
inclusion of rainfall in the large-scale model, having in consideration that rainfall is
more abundant in the North (e.g. Bio-region 1) than in Bio-region 3 (Table 1).
Data provided herein suggest that B. suis biovar 2 infection can be maintained
in wild boar in an independent epidemiological cycle to that taking place in
domestic pigs. The period of the year (month of sampling) was a significant factor
affecting apparent prevalence (Table 4), suggesting that the reproductive season
may influence brucellosis spreading among wild boar. An alternative explanation
could be related with differences in host-specific behaviour, for example regarding
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146
carrion consumption from gut piles during the hunting season (October to
February).
In summary, we conclude that free-living wild ruminants are not a significant
brucellosis reservoir in the Iberian Peninsula but conversely, wild boar is an
important threat regarding B. suis biovar 2 infection. This represents an important
hazard particularly for the Iberian pig population reared in out door breeding
systems, but the entry of the disease in the highly intensified pig industry should
not be disregarded. This situation could become of great concern if brucellosis
control programs in domestic pigs are envisaged.
Additional Material
Table S1 is provided as supplemental material in .doc format. Table S1 data
shows sample size, number of ELISA positive samples, and serum antibody
prevalence of wild boar from the Iberian Peninsula
(http://www.biomedcentral.com/content/supplementary/1471-2334-10-46-
S1.DOC).
Acknowledgements
Many colleagues at IREC, UNIZAR, NEIKER and SERIDA helped in field and laboratory.
We acknowledge the dedicated assistance of the game wardens of Aragon and Asturias. The
Aragon Government has financed part of this work under the programme “Health status
surveillance on game wildlife in Aragon”. NEIKER thanks the funding of the Department for
Environment, Spatial Planning, Agriculture and Fisheries of the Basque Government and the
collaboration of ACCA and Regional Governments.
Capítulo 4.4
Expansión de la tuberculosis en el jabalí
Boadella, M., Acevedo, P., Vicente, J., Mentaberre, G., Balseiro, A., Arnal, M., Martínez, D., García-Bocanegra, I., Casal, C., Álvarez, J., Oleaga, Á., Lavín, S., Muñoz, M., Sáez-Llorente, J.L., de la Fuente, J., Gortázar, C. Spatio-temporal trends of Iberian wild boar contact with Mycobacterium tuberculosis complex detected by ELISA. En prensa, EcoHealth.
Tesis doctoral
Abstract
The continuing expansion of Eurasian wild boar (Sus scrofa) populations raises
concerns regarding disease transmission. In south-central Spain, overabundant wild
boar are reservoirs of Mycobacterium bovis, and related members of the Mycobacterium
tuberculosis complex (MTBC), the causative agents of bovine tuberculosis (bTB). An
indirect enzyme-linked immunosorbent assay (ELISA) using bovine purified
protein derivative (bPPD) was applied to determine the spatial and temporal
distribution of wild boar contact with MTBC in the Iberian Peninsula and to model
and identify the associated risk factors. Wild boar apparent seroprevalence was
22%. Seropositives were detected in 71% of 81 sites, including 23 sites where
wildlife was thought to be bTB free. The results described a new geographic range
of wild boar contact with MTBC and a stable prevalence in this wildlife reservoir
that contrasts with the success of bTB control in cattle. Inference of which host
(wild boar or cattle) is driving bTB maintenance was not possible with our
correlational results. The possibility of a wild boar bTB emergence in non-endemic
regions should urgently be taken into account to avoid a future scenario resembling
the current situation in south-central Spain.
Introduction
In the last decades, Eurasian wild boar (Sus scrofa) populations have expanded
both geographically and in densities throughout Europe, driven by rural
abandonment, changes in agricultural production and changes in game
management (Kruger, 1998; Gortázar et al., 2000; Hartley and Gill, 2010; Acevedo
et al., 2011). This continuing expansion raises concerns regarding the control of
diseases shared with livestock (Meng and Lindsay, 2009).
In Spain, one of these shared diseases is bovine tuberculosis (bTB), caused by
Mycobacterium bovis and closely related members of the Mycobacterium tuberculosis
complex (MTBC). Infection in cattle has been reduced by test-and-slaughter.
However, in some south-central regions, prevalences are at a standstill (MARM,
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Capítulo 4.4
2011a). There, transmission amongst wild ungulates (wild boar, red deer Cervus
elaphus, and fallow deer Dama dama, where present) and livestock contributes to the
maintenance of the causative agents (Gortázar et al., 2008; Gortázar et al., 2011b).
However, in the wildlife multi host system, the wild boar plays the most important
role (Vicente et al., 2006; Gortázar et al., 2008; Naranjo et al., 2008; Santos et al.,
2009).
In some areas of the south-central Iberian Peninsula, wild boar are part of a
growing hunting industry. In this bio-region (BR3 sensu Muñoz et al., 2010), habitat
factors such as summer droughts, as well as feeding and watering within fenced
hunting areas, have contributed to create a singular epidemiological situation that
has led to high prevalences of bTB in wildlife (Vicente et al., 2007b). Resultant
overabundant wild boar populations have been identified as true wildlife reservoirs
for MTBC (Gortázar et al., 2008; Naranjo et al., 2008). Wild boar management is
less artificial in the remaining parts of peninsular Spain. Nonetheless populations
are increasing (Acevedo et al., 2006). Here, wild boar bTB is either not reported or
present locally (Mentaberre et al., 2010), often at low prevalence as compared to
south-central Spain (Vicente et al., 2006).
A recently developed ELISA with a fair sensitivity and excellent specificity and
proven to correlate with bTB lesions under experimental conditions (Garrido et al.,
2011), opened a new inexpensive and largely observer-independent way for MTBC
contact monitoring in this species (Boadella et al., 2011a). Herein, we apply this
ELISA to [1] determine the current spatial distribution of wild boar contact with
MTBC in the Iberian Peninsula; [2] to identify management and environmental
factors associated with the probability of testing positive; and [3] to detect eventual
time trends in wild boar apparent antibody seroprevalence.
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Materials and methods
Samples
The study area was the Iberian Peninsula, in south-west Europe. Sera from 3007
legally hunter-harvested wild boar were collected between year 2000 and 2010 in 81
sampling sites belonging to the five peninsular Bio-regions (BR) previously
described (Muñoz et al., 2010; Figure 1). Hunting harvest sampling is accepted as a
random survey method for wild boar (Fernández-Llario and Mateos-Quesada,
2003). Sex was known for 2685 animals (1297 males and 1388 females). Age classes
of biological meaning were determined and animals less than 12 months old were
classified as juveniles (n=585), those between 12 and 24 months as yearlings
(n=652), and those more than 2 years old as adults (n=1205), (Saenz de Buruaga et
al., 1991). Blood was drawn from the heart or the thoracic cavity during field
necropsies, and then the serum was collected after centrifugation and kept frozen at
-20°C until analyzed. Sera used in this study had gone through less than five freeze-
thaw cycles and excluded severely haemolyzed samples (Capítulo 2.2). Information
on the presence of bTB compatible lesions (bTBL) was recorded for 1371 animals
in BR3 and for 624 animals from outside BR3 (Non-BR3), as described in Vicente
et al. (2006).
ELISA test
Serum samples were tested by means of an indirect ELISA using bovine purified
protein derivative (bPPD) following the protocol previously described (Boadella et
al., 2011b). Sample results were expressed as an ELISA percentage (E%) that was
calculated using the formula [Sample E% = (sample OD / 2 x mean negative
control OD) x 100]. Serum samples with E% values greater than 100 were
considered positive.
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Capítulo 4.4
Statistical analysis
Quantitative exploratory analysis of factors modulating bTB contact at the
individual level was carried out using two different two-stage analyses (Zuur et al.,
2009), and as done in Muñoz et al. (2010), one for BR3 (n=1619), where bTB is
endemic (Vicente et al., 2006), and one for samples from Non-BR3 (n=1325). The
individual bPPD-ELISA result was the response variable (binomial, i.e.,
thresholded antibody presence or absence). First, the associations between all the
hypothesized factors (Tables 1 and 2) and ELISA positivity were analyzed using
single factor generalized linear models (GLM). Factors that captured the effect of
any set of highly correlated variables for which p<0.1 were selected for inclusion in
the multivariate models. In a second step, the selected independent variables were
evaluated with generalized linear mixed models (GLMMX) using a backward
stepwise strategy to obtain the final model, selected by Akaike Information
Criterion (AIC; Akaike, 1974). For both stages, models where built with a binomial
structure and a logit link function, the year of sampling was included as a
categorical factor (since no linear trend along time was expected) and sampling site
was included as random factor.
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Table 1.- Factors tested in the single factor generalized linear models (GLM). Bold type indicates those associated and selected for inclusion in the multivariate models (excluding other highly correlated variables) with bPPD ELISA positivity at the BR3 (GLM, p<0.1, n=1653) and non-BR3 (GLM, p<0.1, n=1354) scales.
BR 3 Non-BR3
Effect Est. F-
Value p Est. F-
Value p
Year of sampling 5.44 <0.001 1.66 0.105
Type of population (Categorical: open, fenced, farm)
2.25 0.136 18.56 <0.001
Artificial feeding (Cat.: presence, absence) 0.4 0.533 0.25 0.62
Latitude (Mean lat. in the locality [m]) -5.78E-06 10.69 0.003 -4.68E-06 39.01 <0.001
Longitude (Mean long. in the locality [m]) -3.91E-06 3.2 0.084 0.5 0.484
Mean altitude (Mean alt. in the locality [m]) 1.13 0.296 0.9 0.349
Annual mean temperature (ºC)1 0.024 3.29 0.081 0.021 3.3 0.078
Annual mean of maximum temp. (ºC) 1 0.024 3.71 0.065 0.013 4.38 0.044
Annual rainfall (mm) 1 1.49 0.231 0.88 0.353
Annual radiation (10 kJ·m-2·day-1·µm-1) 1 0.11 0.738 0 0.996
Woodlands (per locality [%])2 1.38 0.251 0.39 0.537
Favourability for Sus scrofa 1.09 0.305 1.72 0.196
Favourability for Cervus elaphus 1.16 0.291 1.29 0.263
Number of cattle herds by county 3 0.12 0.737 1.07 0.305
Nº of beef cattle herds by county 3 0.23 0.639 0.39 0.538
Nº of bullfighting cattle herds by county 3 0.14 0.709 0.126 9.3 0.005
Density of cattle herds by county 3 0 0.978 1.09 0.303
Density of beef cattle herds by county 3 0 0.985 0.48 0.493
Density of bullfighting cattle herds by county 3 0.26 0.616 0.19 E5 9.99 0.004
Density of cattle by county 3 1.18 0.288 1.59 0.211
Density of beef cattle by county 3 1.16 0.29 0.15 0.701
Density of bullfighting cattle by county 3 0.24 0.628 83.881 10.24 0.004
Mean cattle herd bTB prev. by county 3 0.033 4.06 0.055 2.88 0.1
Nº of bTB positive cattle herds by county 3 0.015 4 0.056 0.037 4.09 0.052
Mean cattle bTB prevalence (animals) by county 3
2.03 0.167 1.248 7.07 0.012
Number of bTB positive animals (cattle) by county 3
0.69 0.414 0.004 8.99 0.005
Sources: 1 Digital climatic atlas of the Iberian Peninsula at spatial resolution of 200 m.; 2 CORINE land cover database at spatial resolution of 250 m (European Environment Agency, 2000; www.eea.europa.eu); 3 MARM, 2011a.
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Capítulo 4.4
We used the SAS statistical package for statistical analysis. The Mantel test was
used to assess the spatial associations of ELISA seroprevalence across sampling
sites. Calculations were done with the PASSAGE software (Rosenberg, 2001).
Comparisons of mean observed prevalences for Non-BR3 were done with Mann-
Whitney U tests at the sampling site (n=41) and county (n=36) scale since data did
not meet the assumption of normality. Finally, independent GLM (binomial
distribution and logit link function) were used for calculating the beta of bTBL
prevalence by sampling year for wild boar in BR3 and Non-BR3.
Figure 1.- Spatial distribution of wild boar contact with Mycobacterium bovis in the Iberian Peninsula. Apparent prevalence of M. bovis contact in the 81 sampled localities within Bio-regions 1 to 5. Dot sizes are proportional to observed prevalences for localities where more than 10 animals were analyzed. Squares represent localities where less than 10 animals where sampled and none tested positive (white) or at least one was ELISA positive (black).
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Results
Figure 1 shows the spatial distribution of the apparent prevalence of antibodies
against MTBC in Iberian wild boar. The global seroprevalence was 22.2% (IC95%:
20.7-23.7%), ranging from 35.2% in BR3 to 6.9% in Non-BR3. Seropositive wild
boar were detected in 58 out of 81 sites (71.6 %; 97.2% in 36 BR3 sites; 51.1% in
45 Non-BR3 sites). This included 23 sites where wildlife bTB had never been
recorded before, 22 of them outside BR3. Sites with similar observed prevalence
were spatially clustered (Mantel test, n=81; r=0.22; p<0.01).
Figure 2.- Time trend comparative prevalences for bTB in wild boar and cattle. Mean annual bPPD ELISA seroprevalence in wild boar (black squares) and mean annual bTB compatible lesion prevalence (grey squares) compared with mean annual cattle bTB herd prevalence (grey triangles, based on official testing results) in regions belonging to BR3 (upper panel) and Non-BR3 (lower panel).
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Capítulo 4.4
Figure 2 shows the global wild boar ELISA and bTBL seroprevalence trends
for BR3 (β=-0.016 and -0.018, respectively, p>0.05) and Non-BR3 (β=0.105 and -
0.089 respectively, p>0.05). In all cases, observed prevalences were stable with
trendless inter-annual fluctuations. Prevalences in cattle were always lower and
reaching an asymptote during the study period.
Variables tested for association with MTBC contact in BR3 and outside BR3
are listed in Table 1. Table 2 shows the variables included in the final models. In
the BR3 model, the probability of testing positive varied with the sampling season
and was associated with the number of bTB positive cattle herds in the county. For
the Non-BR3 model, the probability of testing positive was statistically associated
with the type of wild boar population (higher if fenced) and by the (higher) density
of bullfighting cattle herds in the county (Table 2; Figure 3).
Table 2.- Selected factors. Factors modulating the probability of Eurasian wild boar testing positive to bPPD ELISA in BR3 and Non-BR3 sites. GLMMX odds ratio (Exp E) and their minimum and maximum values; Estimate of the model; DF degrees of freedom; F-Value test statistic; p probability).
Effect Estimate DF F-Value p Exp E
(min - max)
BR 3
Year - 10 5.5 <0.001
Intercept -0.5848 0.076 0.557 (0.40 – 0.77)
Number of bTB positive cattle herds by county
0.0141 1 3.39 0.077 1.014 (1.00 – 1.02)
Non-BR3
Type of population (fenced vs. open) - 1 7.6 0.014
Intercept -1.9631 0.001 0.140 (0.08 – 0.23)
Density of bullfighting cattle herds by county
1.3043 1 4.71 0.045 3.685 (2.02 – 6.72)
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Discussion
This study describes a new geographic range of bTB contact in wild boar as
well as a stable apparent prevalence in this wildlife reservoir that contrasts with the
success of bTB control in cattle (Figure 2), raising concerns regarding the relative
importance of wildlife for bTB control in Europe (Gortázar et al., in press). We
detected for the first time MTBC contact in a high proportion (50%) of the
sampling sites outside BR3.
Detecting a low apparent prevalence of wild boar contact with MTBC in Non-
BR3 does not imply a significant role of the wild boar in bTB epidemiology.
However, our results are relevant in the current scenario of increasing wild boar
densities (Acevedo et al., 2006) and sympatric alternative MTBC hosts such as the
Eurasian badger (Meles meles) in BR1 (Balseiro et al., in press).
Despite the high specificity of this ELISA (Boadella et al., 2011b), the presence
of false positives can not be ruled out. However, wild boar from Non-BR3
presented bTBL (Figure 2) and existence of MTBC infection was confirmed by M.
bovis culture positive animals. This suggests that a more intensive surveillance is
likely to reveal an even more widespread bTB distribution.
Spatial clusters of wild boar bTB were found in the present study at a
peninsular level, with aggregates of high prevalence in BR3 and of low prevalence
outside BR3. This might indicate that factors that modulate bTB prevalence are
visible at large scales, possibly linked with habitat characteristics, but not at local
ones (Vicente et al., 2007b). Fencing, in turn linked to higher densities, feeding and
translocations (Vicente et al., 2007b) was identified as a key risk factor for wild boar
contact with MTBC outside BR3. This implies that the – relatively few – fenced
hunting estates and wildlife movements from bTB affected areas outside BR3
should become targets for active surveillance, and that efforts should be done to
limit the proliferation of such intense game management.
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Capítulo 4.4
Figure 3.- Non-BR3 mean observed sampling site seroprevalence ( S.E.) by type of wild boar population (open vs. fenced; p=0.01) and mean observed county seroprevalence ( S.E.) by presence of bullfighting cattle herds in the county (absence vs. presence; p=0.464).
Cattle-related variables were selected in both the BR3 and the Non-BR3
models. From these correlational results it is not possible to infer if wild boar
(wildlife reservoir) are driving cattle bTB prevalences, or if the presence of infected
cattle (cattle bTB reservoir) affects both cattle and wild boar. Differences in bTB
prevalence among cattle farming systems (dairy, beef, bullfighting) may be due to
(a) the fact that control programs were fully implemented in beef and bullfighting
herds later than in dairy; (b) that skin testing of beef cattle (and even more of
bullfighting cattle), is more difficult than in dairy and is impaired by their temper,
hence limiting test accuracy; and (c) that changes in the bTB control program
aimed to increase the sensitivity of the testing have concentrated on high
prevalence areas. Moreover, open air ranging beef and bullfighting cattle are more
likely to come in contact with wildlife or other bTB hosts than dairy (MARM,
2011a).
Our results evidenced that wild boar contact with MTBC is more widespread in
Spain than previously thought and that serum antibody prevalences in wild boar
remained stable during the study period. We suggest that BR3 represents a more
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158
advanced situation, where wildlife bTB has reached certain stability at high
prevalence, and can contribute to the difficulty of the already complex bTB control
in cattle. Serology results for this region coincide with local prevalence data based
on culture (Gortázar et al., 2008). At the same time, wildlife bTB emergence in
Non-BR3 regions needs urgently to be taken into account to avoid a future
scenario resembling the current BR3 situation. Moreover, it is advisable that other
countries with large wild boar populations and with a recent or persistent history of
bTB in cattle also carry out targeted bTB surveillance in this wildlife host.
Acknowledgements
Studies on diseases shared between domestic animals and wildlife are also supported by SDG
Recursos Agrarios, Consejería de Medio Ambiente y Ordenación del Territorio de la Comunidad
de Madrid, JCCM, Principado de Asturias, Gobierno de Aragón. PA is currently holding a Juan
de la Cierva research contract awarded by the Ministerio de Ciencia e Innovación—Fondo Social
Europeo. Authors also thank Roger Vila, Gerardo Domínguez and all colleagues that participated
in the sampling.
Capítulo 5
CAPÍTULO 5. APORTACIONES AL CONTROL DE LAS ENFERMEDADES COMPARTIDAS
5.1. Persistencia de lesiones compatibles con tuberculosis en poblaciones de ciervo Ibérico bajo distintas condiciones de manejo
5.2. Efectos del control numérico no selectivo del jabalí sobre la prevalencia de contacto con Mycobacterium bovis y el virus de la
enfermedad de Aujeszky
Capítulo 5
Resumen
En el suroeste europeo existe una preocupación creciente por las enfermedades
relevantes compartidas entre los ungulados silvestres y los domésticos, incluyendo
la tuberculosis (TB) y la enfermedad de Aujeszky (EA). En esas áreas existe un
complejo sistema multi-hospedador donde los ungulados silvestres y el ganado
doméstico (bovino, caprino, porcino) contribuyen al mantenimiento de la TB,
mientras que para el caso de la EA, dado que la cabaña porcina está sometida a
vacunación, la enfermedad se mantiene de forma endémica sólo en las poblaciones
de jabalí (Sus scrofa). A nivel mundial, los fracasos en la erradicación de las
enfermedades en el ganado se han relacionado en ocasiones con reservorios
silvestres de la enfermedad. Por tanto, existe una necesidad de desarrollar
estrategias de control para agentes que causan enfermedades de relevancia como
Mycobacterium bovis o el virus de la enfermedad de Aujeszky (VEA) en reservorios
silvestres. Este capítulo incluye sendos trabajos que describen el efecto de la
reducción de la población de jabalí (una especie considerada reservorio) sobre la
prevalencia de contacto con estas enfermedades en otras especies simpátricas,
domésticas o silvestres.
5.1. Con frecuencia se registran lesiones compatibles con la TB (TBL) e
infección con Mycobacterium bovis en el ciervo (Cervus elaphus). Sin embargo, existe un
gran desconocimiento sobre las tendencias temporales de la TB en esta especie. Se
investigaron las tendencias temporales en la prevalencia de TBL en España usando
3403 ciervos cazados en 20 poblaciones diferentes, desde el año 2000 a 2011. La
prevalencia de TBL para el periodo estudiado se mantuvo estable (β=-0,04,
p>0,05), con variaciones inter-anuales significativas y con un rango del 5,3% al
16,6% (Chi2=25,5, 10 g.l., p<0,05). Sólo un sitio de estudio tuvo una tendencia
(negativa) temporal en la prevalencia de TBL. En ese sitio se produjo una
reducción significativa de la abundancia de jabalí, y de la prevalencia de TBL en el
jabalí. Esto sugiere que una reducción significativa de la densidad de jabalí puede
tener consecuencias no sólo para el control de la TB en esta especie, sino también
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162
para el control de la TB en especies simpátricas como el ciervo. Los resultados
sugieren además que el ciervo juega un papel en la persistencia de la TB en el
sistema multi-hospedador del centro-sur peninsular.
5.2. En el segundo trabajo de este capítulo se describen los efectos de un
control numérico no selectivo de las poblaciones jabalí en el mantenimiento de la
TB y de la EA en un área de alta prevalencia (centro-sur de la Península Ibérica).
Las dos infecciones respondieron de forma distinta al control numérico. En los
sitios control (n=10), la prevalencia de TBL incrementó, mientras que la
prevalencia de positivos a cultivo y la prevalencia de seropositivos a ELISA se
mantuvo estable. De forma contraria, la prevalencia conjunta de contacto con el
VEA se mantuvo estable en los sitios sometidos a tratamiento. En el único sitio
tratamiento con presencia de ganado bovino, el número de jabalíes cazados se
correlacionó negativamente con el número de vacas positivas a la prueba de la
tuberculina. Los resultados sugieren que el control numérico no selectivo redujo la
probabilidad de que los jabalíes no infectados contactaran con M. bovis, y que existe
una conexión entre la TB en el jabalí y en el bovino. La reducción de la prevalencia
de TB se logró a pesar de que ninguna otra especie fue sometida a una caza intensa.
La eficacia de esta estrategia de reducción numérica en términos de prevalencia está
ligada a la epidemiología de cada enfermedad. El control numérico no selectivo
podría integrarse con otras estrategias de control de enfermedad en las que se
incluyen cambios de manejo o vacunación, especialmente si un control numérico
inicial temporal pero sustancial pudiera contribuir a aumentar el éxito de otras
estrategias de control, o a reducir sus costes totales.
Capítulo 5.1
Persistencia de lesiones compatibles con tuberculosis
en poblaciones de ciervo Ibérico bajo distintas
condiciones de manejo
Boadella, M., Vicente, J., Gortázar, C. Persistence of tuberculosis-compatible lesions in Iberian red deer populations under diverse management factors. En preparación.
Capítulo 5.1
Abstract
In south-western Europe tuberculosis (TB) is of increasing concern among
wild ungulates and TB-like lesions (TBL) and Mycobacterium bovis infection have
been often reported in red deer (Cervus elaphus). However, time trends of TB
prevalence in red deer are largely unknown. Herein we investigate time trends in
red deer TBL in Spain by using 3403 hunter-harvested red deer sampled in 20
different populations from hunting season 2000/2001 to hunting season
2010/2011. The TBL prevalence for the studied period had a stable trend (β=-0.04,
p>0.05) with significant inter-annual variation, ranging from 5.3% to 16.6%
(Chi2=25.5, 10 d.f., p<0.05). Only one site had a significant (negative) time trend in
the prevalence of TBL (β=-0.72, p<0.05). In this site, a significant reduction of
wild boar abundances (and wild boar TBL prevalence) has taken place. This
suggests that a significant reduction of wild boar density can have positive
consequences not only for wild boar TB control but also for TB control among
sympatric hosts such as deer. Overall, our findings suggest a role for red deer in the
persistence of TB in a multi-host system along South central Spain, where mainly
wild ungulate species and domestic cattle contribute to maintain the circulation of
M. bovis. Future monitoring in order to detect changes in TB prevalence of red deer
is therefore needed through the country.
Introduction
Tuberculosis (TB), a chronic disease of cattle caused by Mycobacterium bovis and
closely related members of the Mycobacterium tuberculosis complex (MTC), has turned
into a wildlife disease of major concern worldwide (Corner, 2006). TB in wildlife
populations is a relevant issue due to its potential effect on TB control in cattle and
the subsequent economic consequences (Gortázar et al., 2011b), but also because
of conservation concerns regarding endangered species and protected areas
(Gortázar et al., 2008, 2010).
The potential role of wild deer in perpetuating TB in cattle has come under
increasing focus (Ward and Smith, 2011). Red deer and elk (Cervus elaphus) are
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among the wildlife hosts most often involved in TB epidemiology (Gortázar et al.,
in press). Despite the fact that the highest prevalence of infection (above 50%) has
been reported in farmed deer (Griffin et al., 2004), prevalences around 10-20%
have repeatedly been recorded in wild populations (Vicente et al., 2006; Delahay et
al., 2007; Zanella et al., 2008), and in few cases even higher prevalences can occur
(Lugton et al., 1998; Gortázar et al., 2008). Red deer are considered TB
maintenance or spillover hosts depending on the region and on the density
(Corner, 2006). However, modelling suggests that even at densities of up to 50 red
deer per square km, the host status of this species remains uncertain (Ward and
Smith, 2011). One peculiarity of red deer as compared to wild boar or badgers, for
instance, is their longevity. Since TB is a chronic disease and red deer can be long
lived (up to over 20 years), disease eradication is unlikely to become a reality unless
the population is held below the maintenance host threshold, and all other sources
of M. bovis infection are removed, for at least one complete generation (Nugent,
2011; Ward and Smith, 2011).
In south-western Europe TB is of increasing concern among wild ungulates,
particularly where these are managed for hunting purposes by fencing, artificial
watering and feeding (Vicente et al., 2007b). TB-like lesions (TBL) and M. bovis
infection have been often reported in Eurasian wild boar (Sus scrofa), red deer and
locally in fallow deer (Dama dama) of the south-western Iberian Peninsula,
suggesting that the infection is largely endemic (Aranaz et al., 2004; Hermoso de
Mendoza et al., 2006; Vicente et al., 2006; Gortázar et al., 2008; Santos et al., 2009).
Among these wild ungulates, the wild boar has been identified as the main reservoir
host for TB in the Mediterranean Iberia (Naranjo et al., 2008). Prevalence of TBL
in wild boar has been found to correlate with prevalence in red deer, although wild
boar always show higher prevalences, both in Spain (42% and 14% of TBL,
respectively; Vicente et al., 2006) and in Portugal (22% and 12% of TBL,
respectively; Vieira-Pinto et al., in press). TBL risk in red deer has been associated
with the aggregation of wild boar at artificial watering sites and at feeding sites,
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Capítulo 5.1
denoting that management practices that promote host aggregation could increase
the risk of transmission (Vicente et al., 2007b). There is also a clear aggregation of
red deer TB in south-central Spain, as compared to other peninsular regions
(Vicente et al., 2006).
However, time trends of TB prevalence in red deer are largely unknown. A
regional study based on carcass inspection found a steady increase in M. bovis
prevalence in Extremadura (south-western Spain) from 1997 to 2002, reaching a
maximum prevalence of 1.7% (Parra et al., 2006). The prevalence in 2004 was 2.9%
(Hermoso de Mendoza et al., 2006). In the same area, animals sampled between
2007 and 2009 showed an apparent prevalence, as determined by TBL, of 5.1%,
indicating a possible increase of TB linked to an intensification of game
management (Castillo et al., 2011). Even in a protected and unmanaged area of the
south Iberian Peninsula, Doñana National Park (DNP), an increase of M. bovis
culture prevalence was observed since 1998. From 1998 to 2003, 168 red deer
yielded an M. bovis infection prevalence of 15% (Romero et al., 2008). A recent
study in 2007 confirmed infection with M. bovis in 27.4% of 95 sampled red deer
(Gortázar et al., 2008).
The aim of this study was to investigate time trends in red deer TB in Spain by
using a large data set on TB-like lesion (TBL) prevalence. Based on the above cited
precedents, we hypothesized that TBL prevalence would increase in Iberian red
deer populations.
Material and Methods
Sampling
From hunting season 2000/2001 to hunting season 2010/2011, 3403 hunter-
harvested red deer were sampled from 20 different populations throughout
mainland Spain (Figure 1). Based on tooth eruption and tooth wear, animals were
grouped into age classes of biological meaning as follows: young (animals up to 2
years old; n=717), sub-adults (up to 4 years old; n=366) and adults (≥5 years;
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n=2161). Age was unknown for 159 animals. Table 1 shows the detailed numbers
of animals sampled per site.
Table 1.- Sampling site characteristics, mean prevalence and linear regression results between the sampling season and the annual TBL prevalence for positive sites with more than two sampled years.
Linear regressionSite
Open/ fenced
BR Timespan* Sampled animals
Prevalence (IC) Beta p
1 Open 3 2000-2010 (10) 279 6.8 (4-10) 0.19 0.623
2 Open 3 2002-2010 (9) 311 7.1 (4-10) 0.62 0.137
3 Fenced 3 2002-2010 (8) 371 9.4 (6-12) 0.27 0.567
4 Fenced 3 2002-2010 (8) 177 34.5 (27-41) -0.11 0.805
5 Fenced 3 2000-2010 (9) 159 3.1 (0.4-6) 0.03 0.953
6 Fenced 3 2000-2010 (9) 118 1.7 (0.3-6) 0.47 0.247
7 Fenced 3 2000-2010 (11) 779 11.7 (9-14) -0.72 0.012
8 Open 3 2002-2010 (7) 77 5.2 (0.2-10) 0.35 0.570
9 Fenced 3 2002-2008 (5) 82 13.4 (6-21) -0.92 0.080
10 Fenced 3 2000-2010 (9) 178 2.2 (0-4) 0.03 0.951
11 Open 3 2005-2010 (3) 124 29.8 (22-38) 0.85 0.359
12 Fenced 3 2001-2010 (4) 127 11.8 (6-17) 0.42 0.584
13 Fenced 3 2001-2008 (5) 95 23.2 (15-32) 0.69 0.200
14 Fenced 3 2001-2007 (4) 58 32.8 (21-46) -0.59 0.405
15 Fenced 3 2002-2006 (2) 57 8.8 (1-16)
16 Fenced 3 2000-2006 (3) 33 0 (0-10)
17 Open 1 2003-2007 (5) 39 0 (0-9)
18 Open 1 2001-2009 (8) 210 0 (0-2)
19 Fenced 2 2000-2007 (3) 74 0 (0-5)
20 Fenced 4 2002-2010 (3) 55 0 (0-7)
Total 3403 10.3 (9-11) * Number of samplings in brackets.
At site 7, red deer and wild boar relative abundance data were obtained by
maximal counts at observational points (Rodriguez-Hidalgo et al., 2010). Counts
were transformed into deer and wild boar per ha.
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Capítulo 5.1
Presence of tuberculosis-like lesions
Presence of TBL was diagnosed by field necropsy of the deer with detailed
macroscopic inspection of lymph nodes and abdominal and thoracic organs as
described in (Vicente et al., 2006; Martín-Hernando et al., 2010). Lymph nodes
were later dissected, sectioned serially and carefully examined for gross lesions in
the laboratory. Animals were considered to have generalized TBL when lesions
were found in at least two different anatomical regions (head, thorax and
abdomen).
TBL presence was used as a criterion to evaluate disease distribution and in
order to obtain comparable TBL rates between sampling sites (Rodwell et al.,
2001). Although presence of TBL is not a perfect tool for estimating prevalence of
disease, such information has been proven to be a valuable and inexpensive tool
for exploring the magnitude and general distribution of infection in red deer and in
wild boar when large sampling sizes are obtained from extensive areas (Vicente et
al., 2006; Santos et al., 2010). Although isolation of M. bovis is the reference
standard for TB diagnosis, the high costs of culturing make this technique
unsuitable for most large-scale surveys. Besides, (Rohonczy et al., 1996) found that
in the elk (Cervus elaphus), the presence of post-mortem TBL showed good
agreement with the isolation of M. bovis (kappa agreement coefficient: 69%).
One possible interference with the use of lesions as a proxy for TB in red deer
would be paratuberculosis (PTB). PTB, a chronic enteritis of ruminants with a
worldwide distribution, and caused by Mycobacterium avium paratuberculosis (MAP),
can cause lesions resembling those of M. bovis infection (Mackintosh et al., 2004).
However, a recent survey on 332 Iberian red deer, that included culture and
histopathology, found no culture positive animal and no visible PTB lesion (Carta
et al., in press). In the present study, we confirmed at least one animal as M. bovis
infected per site with TBL.
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Statistical analysis
For statistical analysis of the TBL time trends, only a subsample including
animals from areas with macroscopic evidence of TB in at least one individual
(n=2626) and more than three sampled seasons was considered. Negative areas
were excluded from the analysis. The association of sampling season (global and
per sampling site) with the presence of TBL was analyzed by means of Pearson’s
chi-square test. The trend on the yearly prevalence of TBL was analyzed with a
linear regression both globally and per sampling site. We used the Spearman rank
correlation to test for an association between red deer TBL prevalence and red deer
and wild boar abundances in site 7.
Differences or trends were considered statistically significant when p<0.05. For
statistical evaluation, IBM SPSS 19.0 software (IBM Corporation, Somers, NY,
USA) and STATISTICA 7.1 (StatSoft, Inc., www.statsoft.com) were used.
Results
The overall TBL prevalence was 10.3% (95% CI: 9.4-11.4), but local
prevalences ranged from 0% (95% CI: 0-1.8) to 34.5% (95% CI: 21.3-45.6) (Figure
1; Table 1). Figure 2 shows the yearly overall prevalence of TBL and the annual
proportion of generalized lesions in lesion positive Iberian red deer, between 2000
and 2010. The TBL prevalence had a stable trend (β=-0.04, p>0.05) with
significant inter-annual variation, ranging from 5.3% to 16.6% (Chi2=25.5, 10 d.f.,
p<0.05). The proportion of deer with generalized lesions had no significant trend
(β=-0.32, p>0.05) and the inter-annual differences were not significant.
Results on the TBL time trends per sampling site are summarized in Table 1.
Only one site (site 7) had a significant (negative) time trend in the prevalence of
TBL (β=-0.72, p<0.05; Figure 3). In this site, annual red deer density increased
during the study period (β=0.67, p<0.05), while wild boar density decreased (β=-
0.62, p<0.05).
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Capítulo 5.1
Figure 1.- Map of Peninsular Spain showing the 20 sampled sites and their positivity (black squares are for sites with at least one positive animal while white squares represent negative sites).
Figure 2.- Annual overall mean prevalence of TBL (black diamonds) and proportion of individuals with generalized lesions (grey squares) from hunting season 2000/2001 to hunting season 2010/2011. The dashed line indicates the regression for the prevalence of TBL (β=-0.04, p>0.05).
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Five of the 20 sampling sites maintained their negative results during the whole
study period, three of which where the populations from northern Spain that we
included in this study.
Discussion
Although our sampling does not ensure a proportional and representative
sample of the overall deer across Spain, provides insights into the temporal pattern
of red deer TB status, and helps to focus future monitoring programs on the issue,
confirming TB may persist for long in red deer populations, and therefore
contributing to the complex epidemiology of TB in the Iberian Peninsula. This can
be so especially under Mediterranean circumstances, where environment,
management and habitat or resource sharing with other TB hosts concurs. Our
results of overall prevalence are similar to those reported for south-central Spain by
Vicente et al. (2006), and for central Portugal by Vieira-Pinto et al. (2011), and in
the international context, can be considered as medium to high (Corner, 2006). We
should have into account that in the red deer, prevalences may have been
underestimated according to Lugton and others (1998), who affirm that up to one
quarter of infected deer may show no detectable gross lesions.
Although we did not confirm our initial hypothesis suggesting an increasing
trend in the prevalence of TBL in Iberian red deer in the last ten years, the fact is
that, overall, such prevalence at least keep stationary. In contrast to our results, the
few other studies on time trends in red deer TB in Spain showed an increase (e.g.
Parra et al., 2006). However, these increases occurred mostly in regions with
relatively low prevalence, suggesting that the threshold level has not yet been
reached. In our study, prevalences remained stable but at rather high levels,
suggesting that even at high densities and intense management it is unlikely to
expect even higher mean prevalences in red deer. The percentage of red deer with
generalized lesions did not vary significantly during the study suggesting that
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Capítulo 5.1
changes in susceptibility and/or pathogenicity seemed not to occur during the
sampling period.
The single study site where deer TBL prevalence declined in time (coincident
with the best sampled site), is a site where a significant reduction of wild boar
abundances (and wild boar TBL prevalence) has taken place (Figure 3). This
suggests that a significant reduction of wild boar density can have positive
consequences not only for wild boar TB control but also for TB control among
sympatric hosts such as deer. The absence of significant changes in some other
sampling sites can be due to inadequate sampling effort, too few sampled animals
per year and too few years sampled.
Figure 3.- Annual density of wild boar and red deer (grey squares and diamonds, respectively; animals/ha) and prevalence (%) of TBL in red deer (black triangles) in site 7 from hunting season 2000 to hunting season 2010.
Overall, our findings suggests a role for red deer in the persistence of in a
multi-host system along South central Spain, which must be evaluated together
with the changes in the determinants of disease transmission and persistence at
intra but also inter-specific level. Under our epidemiological circumstances the red
deer, usually considered a poor reservoir host, and the wild boar (in mixed
populations or alone (Boadella et al., in press), were both able to maintain TB
infection in the wild in a number of different situations. Molecular studies based on
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part of the animals here reported found that wild boar and red deer share the same
M. tuberculosis complex organisms, and the comparison of the obtained patterns
(Aranaz et al., 2004). Therefore we need (pseudo)experimental designs in the field
considering the main reservoir of TB in Spain TB (see below considering location
7), the wild boar, which is widely spread but impeding situations where deer is
present alone.
It is interesting to note that, even within the core area of high TBL prevalence,
the negative sites remained negative during the study period, mainly in northern
Spain. Game management is uncommon in Northern Spain, and this may
contribute to explain the observed geographical pattern. This contrasts with a study
based on TB serology in the wild boar, where a geographic expansion was detected
towards previously thought TB-free areas (Boadella et al., in press). In the red deer,
we did not detect such expansion even though the sampling sites were the same.
Explanations for this finding include (1) the absence of large scale acceptable
serology tests in the red deer (due to cross-reactions; Carta et al., in press), which
may impede an early detection of M. bovis circulation among wild populations; (2)
that red deer are possibly spillover rather than maintenance hosts, dependent
mainly on transmission from sympatric wild boar; and (3) the possibility that red
deer get infected later than wild boar (or wild boar easier than red deer), so that it
will take more time to detect a TB expansion trend in deer than in wild boar. This
in turn would suggest that wild boar are more suitable for TB monitoring than red
deer (Boadella et al., 2011a). The red deer, in contrast, is spatially more restricted
and colonizes new areas slower than wild boars do. In conclusion, the importance
of red deer resides in that may act as a long-lived reservoir of infection, having the
potential to initiate new outbreaks of infection well outside currently infected areas,
or to reinitiate infection after TB has been eliminated by controlling reservoirs and
other short-lived vectors (Griffin et al., 2004). Information gathered in this study
contributes therefore to the view that red deer contribute to a multi-host system,
where wild ungulate species, domestic cattle, and to a lesser extent (due to their low
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Capítulo 5.1
175
densities) carnivores contribute to maintain the circulation of M. bovis. Future
monitoring in order to detect changes in TB prevalence of red deer is therefore
needed through the country.
Acknowledgements
Authors thank all colleagues at IREC that participated in the fieldwork. We thank Yolanda Fierro
for access to her study site and for sharing valuable data.
Capítulo 5.2
Efectos del control poblacional no selectivo del jabalí
sobre la prevalencia de contacto con Mycobacterium
bovis y el virus de la enfermedad de Aujeszky
Boadella, M., Vicente, J., Ruiz-Fons, F., de la Fuente, J., Gortázar, C. Effects of culling Eurasian wild boar on the prevalence of contact with Mycobacterium bovis and Aujeszky’s disease virus. En evaluación.
Capítulo 5.2
Abstract
Worldwide, failure to eradicate a disease in livestock has sometimes been
related to wildlife reservoirs of infection. Therefore, there is a need for the
development of strategies aimed at controlling relevant infectious disease agents,
such as Mycobacterium bovis and Aujeszky’s disease virus (ADV), in wildlife
reservoirs. Herein we describe the effects of culling Eurasian wild boar (Sus scrofa)
on the maintenance of two chronic infectious diseases: tuberculosis (TB) and
Aujeszky’s disease (AD) in a high prevalence area (South-central Spain). The two
infections studied responded differently to culling. Regarding the control sites
(n=10), the prevalence of TB-compatible lesions increased, while bovine purified
protein derivative (bPPD) ELISA positivity and M. bovis culture prevalence
remained stable. The global ADV contact prevalence decreased in the control sites.
In the treatment sites (n=3), a decrease was detected for TB-compatible lesions,
bPPD ELISA and culture prevalence. Conversely, ADV contact prevalence
remained stable in treatment sites. In the only treatment site with cattle, the annual
wild boar cull was negatively correlated with the annual number of skin test reactor
cattle. We suggest that culling effectively reduced the probability of uninfected wild
boar to contact M. bovis in the treatment sites and that some link between wild boar
and cattle TB may exist. The reduction in wild boar TB was achieved despite no
alternative M. bovis host was included in the culling strategy. The effectiveness of
culling in terms of prevalence reduction is linked to the epidemiology of each
disease. We advocate that culling could become a part of integrated control
strategies including management changes and vaccination, particularly if an initial
short term but substantial reduction of host density and disease prevalence could
contribute to increase the success likelihood of other control tools, or contribute to
reduce the total expenses.
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Introduction
Selective culling of infected animals after systematic testing is, along with
vaccination, a key tool for disease control in livestock. Worldwide, failure to
eradicate a disease in livestock has sometimes been related to wildlife reservoirs of
infection (e.g. Delahay et al., 2002; Corner, 2006). Therefore, there is a need for the
development of strategies aimed at controlling infectious disease agents in wildlife
reservoirs (Gortázar et al., in press). Culling is often considered in situations where
a wildlife reservoir is suspected to interfere with disease control in livestock (shared
diseases; Gortázar et al., 2007). Reducing host density is a straightforward strategy
that should lead to a lower contact rate and incidence until a threshold density is
reached, at which the infection will disappear due to the low probability of
transmission (Ward and Smith, 2011). Herein, we analyse the effects of culling
Eurasian wild boar (Sus scrofa) on the maintenance of two chronic infectious
diseases: tuberculosis (TB) and Aujeszky’s disease (AD).
Bovine TB is a chronic disease of cattle shared with wildlife among many other
hosts and caused by Mycobacterium bovis and closely related members of the
Mycobacterium tuberculosis complex (MTBC; O'Reilly and Daborn, 1995). The disease
causes concern because of its significant impact mainly on economy, but also on
global health and conservation. In Spain, since national test and slaughter
campaigns began in cattle, the percentage of positive cattle herds has experienced a
significant reduction from 11.1% in 1986 to 1.5% in 2010, with a yearly cost (e.g. in
2009) of approximately 30 million € (http://rasve.mapa.es). Despite the TB
reduction in cattle in most parts of the country, in some south-central regions,
cattle TB herd prevalences are at a standstill, being beef and bullfighting cattle the
ones with higher infection rates (Allepuz et al., 2011). In south-central Spain (SCS),
beef and bullfighting cattle are commonly raised free-ranging and thus, share
habitat with infected wildlife (Vicente et al., 2006). MTBC infection prevalence in
wild boar ranges up to 50% in SCS (Gortázar et al., 2008).
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Capítulo 5.2
AD is caused by infection of mammals other than primates with Suid
Herpesvirus 1, the Aujeszky’s disease virus (ADV). Wild and domestic suids are the
only maintenance hosts for ADV. AD causes important losses to the pig industry
and is currently close to be controlled in Spanish livestock after intense efforts
based on combining test and slaughter and vaccination (MARM, 2011a). AD also
creates conservation concerns, since it can infect and kill endangered predators
after contact with infected wild boar or pigs and their remains (Glass et al., 1994;
Zanin et al., 1997). ADV contact prevalence in wild boar in Spain is high (over 40%
in SCS), thus representing a potential threat for the success of the current Spanish
eradication program in pigs. Again, the highest wild boar contact prevalences with
ADV are recorded in SCS (Vicente et al., 2005a).
SCS has the added particularity of having a growing hunting industry in a
Mediterranean ecosystem (Acevedo et al., 2007b). This region is characterized by
intense summer droughts and an intense management of game species (mainly red
deer Cervus elaphus and wild boar) by fencing and feeding. However, high densities
and high disease prevalences also occur in protected areas in absence of
management for hunting, possibly because of the overabundant wild ungulate
populations (Gortázar et al., 2008). These factors promote the aggregation of
animals at watering sites, for example, facilitating contact between individuals and
among different species. This particular situation does also affect the epidemiology
of other wildlife diseases of relevance for human or livestock health (e.g. Ruiz-Fons
et al., 2008c; Boadella et al., 2010). However, problems such as TB in cattle or AD
in pigs should not be oversimplified considering the wildlife reservoirs as the only
obstacle to eradication; their epidemiology is much more complex, including
livestock acting as their own reservoir, particularly regarding TB (Woodroffe et al.,
2006; Gortázar et al., 2008).
Hence, information on means to reduce the high infection prevalences in SCS
wild boar populations is needed. Also, further knowledge on the main drivers of
infection persistence in wildlife populations is necessary for the development of
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Tesis doctoral
holistic and long-term sustainable control schemes, not only regarding livestock
health, but also to protect humans and wildlife (Gavier-Widén et al., 2009; Boadella
et al., 2011a).
Culling is an unpopular strategy and has to be time-flexible optimized to be
efficient and sustainable (Bolzoni and De Leo, 2007). Despite this, culling has been
a common tool to attempt to control infectious diseases in wildlife populations,
including TB. Eurasian badger (Meles meles) culling, for instance, formed a
component of both Ireland and UK TB control policy for many years (Krebs et al.,
1997). In Ireland, badgers are culled when a severe breakdown in cattle is deemed
to have been due to badgers (focal or reactive culling). When sustained over large
areas (proactive culling), removal of badgers led to a significant reduction in the
incidence of tuberculosis in associated cattle populations (e.g. Griffin et al., 2005;
Donnelly et al., 2006), as reviewed in Corner et al. (2011). However, field trials of
reducing badger numbers in the UK had contrasting outcomes. Proactive culling
reduced cattle TB incidence in the culled core areas, but increased incidence in the
adjoining areas (Donnelly et al., 2006). It seems that culling disrupted social and
territorial organization, leading badgers to range more widely and to increase
contact rates between them. This may explain the marked increases in M. bovis
prevalence that have been detected in badger populations subjected to culling
(Woodroffe et al., 2006; Jenkins et al., 2007). In Michigan and in Minnesota, USA,
white-tailed deer culling is part of the strategy to control TB in this wildlife
reservoir. Apparently, intense culling in the low density Minnesota deer herd
resulted effective, while less intense culling in Michigan had only limited effect in
terms of TB control (Carstensen et al., 2011). In New Zealand, 14 species of
domestic and wild animals are M. bovis hosts, with the introduced brushtail possum
(Trichosurus vulpecula) being the single most significant source of infection for cattle.
Culling of infected possum populations gave good results since it was associated
with a decrease in the risk of breakdown in neighbouring cattle herds (Caley et al.,
1999).
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Capítulo 5.2
Culling wild boar for TB or AD control has never been reported. However,
control of another viral disease, classical swine fever (CSF) by shooting wild boar
has been attempted in France, Germany and Italy. There is evidence that this
measure was much less effective than expected or even produced the contrary
effect (Laddomada, 2000). Due to the wild boar ecological elasticity (i.e. increased
turnover due to compensatory reproduction enhances disease persistence), the
population may be quickly re-established and the threshold level for infection die-
out may not be reached after culling (Artois et al., 2002). For example, in Eastern
Sardinia, infection stayed endemic for at least 17 years at low prevalence, even if
45% of the population was culled each year (Guberti et al., 1998). However, CSF
control in Sardinia is also hindered by the large population of backyard pigs. It has
been estimated that between 47 and 72% of the total wild boar population needs to
be culled instantaneously to reach a threshold density of local extinction of CSFV
(EFSA, 2009).
Since wild boar density is a known TB and AD risk factor (Gortázar et al.,
2006; Vicente et al., 2007b), we hypothesized that culling would reduce the
prevalence of both diseases, particularly among the younger age classes. To
challenge this hypothesis, we used information on three instances where SCS wild
boar populations were reduced. The aims of this study were (1) to quantify the
changes of TB and AD prevalence after a significant reduction of wild boar density
in three sites of SCS; (2) To compare TB and AD prevalence between sites with an
intervention and sites without intervention; and (3) to assess the potential effect of
wild boar density reduction on the TB status of sympatric host species such as
cattle, red deer and fallow deer (Dama dama).
Material and Methods
Study sites
The study included 13 public and private sites with different characteristics,
summarized in Table 1. Study sites were chosen on the basis of (1) previous
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knowledge of their management characteristics and (2) exhibiting a representative
range of management practices. Sampled sites include both protected areas and
hunting estates and ranged in size from 723 to 54,252 hectares.
Table 1.- Main characteristics (A) and sample sizes (B) for each of the 13 studied sites.
*RD: Red deer (Cervus elaphus); RoD: Roe deer (Capreolus capreolus); FD: Fallow deer (Dama dama).
A Site characteristics
Site Private/ Public
Main use Open/ fenced
Supp. feeding
Livestock Cervids*
Control 1 Private Big game hunting Fenced Yes No RD 2 Public Protected area Open No Yes RD/RoD 3 Public Protected area Open No No none 5 Public Agricultural Open No Yes RD 6 Private Big game hunting Fenced Yes No RD 7 Private Big game hunting Fenced Yes No RD/FD 10 Public Big game hunting Fenced No No RD 11 Public Big game hunting Fenced No No RD/FD/RoD12 Public Big game hunting Fenced No No RD/RoD 13 Private Big game hunting Fenced Yes No RD
Treatment 4 Public Protected area Open No Yes RD/FD 8 Private Big game hunting Fenced Yes No RD 9 Private Big game hunting Fenced Yes No RD/FD
B Number of samples tested
Site Gross lesions bPPD ELISA M. bovis culture ADV ELISA Total
Control 1 219 92 61 92 245 2 291 124 50 129 324 3 248 99 35 50 262 5 134 62 43 70 145 6 137 42 32 46 139 7 86 56 32 56 99 10 62 50 35 40 70 11 300 81 82 69 307 12 207 94 68 100 231 13 0 70 9 60 76
Treatment 4 197 149 191 46 197 8 132 100 82 98 157 9 154 84 74 101 176
Total 2167 1103 794 957 2428
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Capítulo 5.2
Ten sites were used as controls. All were known to have constant or increasing
hunting bags or frequency of wild boar faecal droppings found on transects (FBII;
Acevedo et al., 2007b). Three sites were defined as treatment sites for having a
substantial wild boar density reduction. Site number 4 (54,252 ha) implemented a
more intense wild boar culling strategy in 2008 to control wild boar populations
due to high TB prevalences reported in this species. Site 8, a private estate of 723
ha, decided to reduce wild boar density since 2005 by hunting all the available wild
boar with the purpose of eliminating the entire wild boar population. Site 9 (2,690
ha) started to move out all female and part of the young wild boar in 2005 to an
adjacent estate in order to raise only big trophy males for hunting. In any of the
sites other coexisting ungulate populations were subject to increased hunting to
have densities reduced.
Both for sites 4 and 8, data on TB-compatible lesions and M. bovis culture
prevalences were available for the coexisting ungulate species. In site 4, data on the
TB skin testing results of coexisting cattle (Gortázar et al., 2008), was available for
the period 1994-2011.
Wild boar data
Hunted wild boar (n=2428) were sampled from 2000 to 2011. Sex was known
for 2289 animals (1192 females, 1097 males). Age-classes of biological meaning
(according to reproductive and social status) were defined. Based on tooth eruption
patterns, wild boar until 12 months were classified as juveniles (n=623), those
between 12 and 24 months as sub-adults (n=627) and those over 2 years as adults
(n=1079), (Saenz de Buruaga et al., 1991).
The presence or absence of TB-compatible lesions was recorded for 2167
individuals. Out of these, 2159 animals were also classified by the following TB
lesion scores: 0 for animals with no visible lesions (n=989); 1 for animals with
lesions smaller than 1 cm of diameter or only in one cavity (head, thorax or
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abdomen; n=686); and 2 for animals with at least one lesion larger than 1 cm or
lesions in more than one cavity (n=484).
A total of 794 individuals had M. bovis culture results, 191 of them from site 4.
Serum samples were tested for M. bovis contact by means of an indirect ELISA
(with a sensitivity of 79% and a specificity of 100%) using bovine purified protein
derivative (bPPD) following the protocol previously described for wild boar
(Boadella et al., 2011b).
Data on ADV seroprevalence was obtained by screening sera for antibodies to
ADV with a commercially available blocking ELISA (95-98% sensitivity and 97–
99% specificity; IDEXX HerdCheck Anti-ADV gpI, IDEXX, Inc., Maine, USA),
previously used in wild boar (Ruiz-Fons et al., 2006).
Statistics
In order to analyse changes over time, for control sites, samples collected
between 2000 and 2005 were classified as “Time 1” (T1) and those collected
between 2006 and 2011, as “Time 2” (T2). For treatment sites, samples collected
before the beginning of the population reduction, were classified as “Time 1” and
those collected after, as “Time 2”.
Prevalences for TB-compatible lesions, bPPD ELISA and ADV ELISA were
compared through period by means of Pearson chi-square or Fischer tests. Time
trends for cattle skin-test positivity were calculated by lineal regression. Differences
were considered statistically significant when p<0.05. Spearman’s rank correlations
were used to assess the relationship between the annual increase (%) of cattle TB
skin reactors (cosine transformed) and the annual increase (%) of the wild boar
hunted (cos. transformed). Data was analyzed using the IBM SPSS statistical
package, version 19.0 (IBM Corporation, Somers, NY, USA) and STATISTICA
(data analysis software system), version 7.1. (StatSoft, Inc., www.statsoft.com).
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Capítulo 5.2
Results
Considering globally the 13 study sites and both time periods, TB-compatible
lesions were recorded in 1174 out of 2178 wild boar (53.9%); the bPPD ELISA
detected antibodies in 376 out of 1103 (34%) animals; and culture yielded 168
isolates out of 794 (21.1%) wild boar. ADV contact prevalence, as estimated by the
presence of specific antibodies, was 59%.
In treatment site 4, annual numbers of culled wild boar from 2000 to 2010
ranged from 46 to 959. The most intense culling occurred in 2008 and 2009 (n=773
and 959, respectively). This cull was highly correlated with the estimated wild boar
abundance (rs=-0.9, p<0.05, n=5). In treatment site 8, wild boar densities
diminished from 0.2 animals/ha in 2000 and 0.19 in 2005 to 0.03 in 2011. No exact
information was available regarding site 9.
Differences in the overall prevalences of TB-compatible lesions, bPPD ELISA
and M. bovis culture between T1 and T2 for each site are presented in Table 2.
Regarding the control sites, TB-compatible lesions remained stable in all but two
sites, where lesion prevalence increased. Bovine PPD ELISA and M. bovis culture
prevalences remained stable in all control sites. In the treatment sites, a significant
decrease of TB-compatible lesions prevalence was detected in one site and a
significant bPPD ELISA and culture prevalence decrease occurred in two sites.
Figure 1 shows the mean prevalences of TB-compatible lesions, bPPD ELISA
positivity and M. bovis infection at T1 and T2 in the control and treatment sites. In
the control sites, the mean prevalence of TB-compatible lesions increased while the
mean prevalence for bPPD ELISA positivity and M. bovis infection remained stable
between T1 and T2. In the treatment sites, all prevalences decreased significantly
between T1 and T2. Figure 2 shows the mean prevalences of TB-compatible
lesions, bPPD ELISA positivity and M. bovis infection by age class in each of the
three treatment sites. The global prevalence of TB-compatible lesions in these
treatment sites, remained stable in juveniles (mean -5.5%, Chi2=0.06, p>0.05), but
decreased in sub-adults (28.6%, Chi2=3.8, p<0.05) and in adults (24.7%, Chi2=5.7,
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p<0.05); bPPD ELISA positivity decreased in all age classes (juveniles: mean
77.67%, Chi2=19.3, p<0.05; sub-adults: 28.7%, Chi2=1.6, p>0.05; and adults:
41.1%, Chi2=9.2, p<0.05; respectively). In turn, M. bovis infection prevalence stayed
stable in juveniles and in sub-adults (37.8%, Chi2=1.9, p>0.05; 34.2%, Chi2=2.5,
p>0.05; respectively) and decreased 78.8% in adults (Chi2=9.1, p<0.05). In
contrast, in the control sites, the only significant change was the increase on the
prevalence of TB-compatible lesions in juveniles (mean -51.7%, Chi2=5.9, p<0.05);
in sub-adults and adults it remained stable. Equally, bPPD ELISA positivity and M.
bovis infection prevalence stayed stable between periods in all age classes
(Chi2=0.01-2.3, p>0.05; Figure 2).
Table 2.- Mean prevalences, prevalence at T1 and T2 and significance of the difference between prevalence at T1 and T2 for TB-compatible lesions, bPPD ELISA positivity and M. bovis culture in the studied sites. Significant p values (P) are underlined.
TB lesions bPPD ELISA Culture
Mean prev
Prev T1
Prev T2
P Mean prev
Prev T1
Prev T2
P* Mean prev
Prev T1
Prev T2
P
Control sites
1 46.6 42.11 48.15 0.52 ª 19.6 21.9 18.3 0.895 ª 11.5 8.3 13.5 0.834 ª
2 62.5 45.6 67.7 0.002 ª 37.1 36.8 37.2 1 ª 16 25 7.7 0.199 ª
3 35.1 22.2 35.6 0.5 b 20 13.5 0.63 b14.1 0 0 0 -
5 37.3 29.7 40.2 0.262 ª 22.6 11.5 30.6 0.144 ª 14 11.5 17.6 0.908 ª
6 50.4 51.6 49.3 0.927 ª 57.1 55 59.1 1 ª 53.1 53.1 - -
7 75.6 85 72.7 0.376 b 54.5 64.4 0.73 b62.5 18.8 12.5 25 0.650 ª
10 58.1 70 55.8 0.499 b 38 40 37.5 1 b 17.1 10 20 0.831 ª
11 78.3 50 81.7 0 ª 46.9 36 51.8 0.283 ª 17.1 12.5 19 0.747 b
12 40.6 35.7 41.3 0.721 ª 5.6 14.5 0.451 b12.8 7.4 10.7 5 0.396 b
13 - - - - 8.6 0 10 0.583 b 0 0 - -
Treatment sites
4 60.4 62.9 56.2 0.433 ª 55 68.3 38.8 0.001 ª 45.5 52.4 32.8 0.014 ª
8 53 67.9 43 0.009 ª 45 57.9 37.1 0.068 ª 9.8 17.0 0 0.023 ª
9 48.7 52.3 43.9 0.389 ª
27.4 45.7 14.3 0.003 ª 5.4 6.1 0 1 ª *P values: ª (Chi2 test p); b (Fisher’s exact test).
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Capítulo 5.2
Figure 1.- Mean prevalences of TB-compatible lesions, bPPD ELISA positivity and M. bovis culture in T1 (light grey) and T2 (dark grey), for the control sites (upper panel) and treatment sites (lower panel). Asterisks indicate significant differences between T1 and T2 at p<0.05.
ADV contact prevalence significantly decreased in the control sites from 64.2%
(58-7095%IC) in T1 to 53.7% (49-5895%IC) in T2 (Chi2=6.9, p<0.01). In the treatment
sites ADV contact prevalence remained stable (from 60.7% [52-6995%IC] in T1 to
67.3% [58-7695%IC] in T2; Chi2=0.8, p>0.05). The observed non significant increase
was due to site 9, where prevalence changed from 66.1% (54-7895%IC) in T1 to
89.7% (80-9995%IC) in T2 (Chi2=5.9, p<0.05). In sites 4 and 8, ADV contact
prevalences remained stable (52.4% in T1, 44% in T2 for Site 4 and 57.7% in T1,
60.9% in T2 for Site 8). No significant differences in time of the mean prevalences
of contact with ADV by age class were observed in any of the three treatment sites
(data not shown).
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Figure 2.- Changes between T1 and T2 on the mean prevalences for TB-compatible lesions, bPPD ELISA and M. bovis culture by age class in each of the three treatment sites (sites 4, 8 and 9), and in the 10 control sites. Asterisks indicate significant differences at p<0.05.
Official data on cattle TB skin test results were available for site 4. In this site,
the annual wild boar cull was negatively correlated with the annual number of skin
test reactor cattle (rs=-0.79, p<0.05; Figure 3). No cattle are present at sites 8 and 9.
TB free cattle had been introduced into site 8 in 1989, but had to be slaughtered
due to an increasing TB prevalence after only 3 years.
Regarding sympatric deer, mean prevalences of TB-like lesions and M. bovis
infection did not differ significantly between T1 and T2 in adult red and fallow deer
from site 4 (Chi2=0.1-0.8, p>0.05 in all cases). However, no data on yearling or
sub-adult deer were available for site 4 at T2. For site 8, the prevalence of TB-like
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Capítulo 5.2
lesions in red deer remained stable (Chi2=0.8, p>0.05), while the infection
prevalence decreased significantly from 10.11% to 1.57% (Chi2=12.6, p<0.01). At
T2, none of 53 fawns, yearlings or sub-adult deer tested in site 8 yielded a positive
culture and all 3 positive deer were adults.
Figure 3.- Spearman’s rank correlation between the annual increase (%) of cattle TB positive skin reactors and the annual increase (%) of the number of culled wild boar in site 4 from 2000 to 2010.
Discussion
This is the first description of the short-term effects of culling on the sanitary
status of wild boar in a high TB and ADV prevalence area. The two diseases
studied responded differently to culling. While TB prevalences decreased, ADV
contact seroprevalences remained largely unaffected in the treatment sites.
Culling effects on wild boar disease status
In directly and indirectly transmitted diseases probably a few severely affected
individuals or “super-spreaders” contribute disproportionately to infection
maintenance (Kramer-Schadt et al., 2009). In the case of M. bovis, these individuals
tend to be juveniles and sub-adults, rather than adults (Vicente et al., 2006; Martin-
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Hernando et al., 2007). The risk of TB in wild boar is age-dependent and correlates
with abundance and spatial aggregation (Acevedo et al., 2007b; Vicente et al.,
2007b). We suggest that culling effectively reduced the probability of uninfected
wild boar to contact M. bovis in the treatment sites. It is worth noting that this
reduction in wild boar TB was achieved despite no alternative M. bovis hosts such as
red deer were included in the culling strategy. This confirms previous reports
suggesting that wild boar are the main drivers of TB epidemiology in SCS (Vicente
et al., 2007b; Naranjo et al., 2008).
Regarding ADV, contact prevalence is also linked to age, abundance and spatial
aggregation (Ruiz-Fons et al., 2008c; Acevedo et al., 2007b), but transmission is
mostly direct and often linked to the reproductive season (Ruiz-Fons et al., 2008b).
The fact that mean contact with ADV did not decrease after culling suggests that
ADV clearance will not occur immediately once the virus becomes endemic. The
particular ADV “carrier” state of some of the infected individuals involves a
continued high viral shedding after infection has been apparently cleared. By such
mechanism, ADV enhances its persistence after most susceptible hosts have been
lost from the population, which is clearly a selective advantage when compared to
MTC. Alternatively, the proportion of ADV infected wild boar excreting ADV may
be larger than the proportion of M. bovis infected wild boar excreting the bacteria,
explaining the differences in the success of culling in reducing disease transmission.
One site (number 9) even witnessed an increase in ADV contact prevalence. This is
explained by the fact that only juvenile and sub-adult individuals were removed,
while the (generally ADV positive) adult males were maintained.
Spatial structuring of the host may allow pathogen persistence (e.g. TB in
badgers): host populations that are structured into sub-populations (or social
groups) promote persistence of microparasites by allowing epidemics to occur
asynchronously in the various sub-populations and avoiding deep global troughs
(Bolker and Grenfell, 1996). However, if the sub-populations are small and
isolated, fadeout will occur (Rohani et al., 1996). Contrary to wild boar biology, this
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is particularly important for infections of wild-mammal populations that are
frequently at low and variable densities, with low reproductive rates. For example,
some badger social groups remain chronically infected for many years, while
neighbouring groups remain uninfected (Vicente et al., 2007a). Studies are needed
in relation to culling disruption of social and territorial organization in the wild
boar (Scillitani et al., 2010).
As discussed in Laddomada (2000), culling was ineffective to control CSF
because of (1) interference with the establishment of herd immunity, as it induced a
quicker turnover of the population; (2) induction of long distance animal
movements and more frequent contacts between different groups of animals; and
(3) virus spread related to evisceration of carcasses and use of meat. By contrast,
our scenario has some dissimilarities with the CSF one. First, natural herd
immunity seems not to be relevant factor regarding TB epidemiology, although it
might be relevant for AD (De Jong and Kimman, 1994). Second, many SCS wild
boar populations are raised with supplementary feeding and watering within fenced
estates. So, a massive increase of long distance movements caused by culling is less
likely (Scillitani et al., 2010). However, consumption of wild boar carcasses or
hunting remains (gralloch or gut-piles) might constitute a relevant factor in the
maintenance of both TB and AD in SCS.
Since data on wild boar densities and total cull were only available for two
treatment sites, it is difficult estimating the threshold required for a significant
reduction of TB prevalence. However, a rough estimate based on these two sites
would be that culling at least 50% of the estimated population would allow
reducing TB prevalence by 23-50% in high prevalence sites. The annual hunting
harvest goal of a wild boar population is approximately 30% (Fernández-Llario and
Mateos-Quesada, 2003). So, to achieve a reduction of TB prevalence a 67%
increase of this cull would be necessary. Moreover, one must take into account that
in addition to the reduced prevalence in wild boar, a 50% reduction of wild boar
numbers would also contribute to a similar reduction in direct or indirect contact
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likelihood with this M. bovis reservoir host. So, effects on the TB status of
sympatric host species such as deer or cattle are indeed expected.
Third species
Despite the data we used in this study were not collected to analyze the effects
of wild boar culling on the TB prevalence in sympatric ruminants, data obtained on
cattle and deer suggest that some positive effect may exist. In site 4, the correlation
between percentage of cattle TB skin test reactors and wild boar culling (Figure 3)
may indicate some link between wild boar and cattle TB. Regarding deer, infection
prevalence decreased significantly in the only site where deer sampling was
representative enough. Moreover, the fact that only adult deer were infected at T2
evidences that red deer infection pressure declined after wild boar culling.
Pros & cons of culling wild boar
Under certain circumstances, culling may prove effective to achieve a rapid and
inexpensive reduction in wild boar TB prevalence. This suggests that increased
hunting could contribute within an integrated TB control strategy. However, even
though the wild boar is a game species, conflict with stakeholders such as hunters,
game producers, conservationists (in protected areas) and even the general public
can arise when culling is considered (White et al., 2011). Moreover, culling alone,
especially in large areas, is likely not a sustainable long term option: in private-
owned hunting estates, high densities are targeted to maximize profit and in public
lands budget constraints and public opinion will limit its use. Wild boar are
extremely able to adapt to different environmental circumstances and will respond
to increased mortality through a compensatory larger reproductive success
(Gethöffer et al., 2007; Fonseca et al., 2011). Finally, even if all key maintenance
hosts were extirpated from a given area, sympatric alternative M. bovis hosts could
eventually maintain the infection. This is particularly a risk in the case of long-lived
hosts such as the red deer (Nugent, 2011).
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Capítulo 5.2
Alternatives (or complements) to culling
Diseases are a natural component of ecosystems. However, the extremely high
TB and AD prevalences recorded in SCS are in part due to human intervention
(Vicente et al., 2007b; Ruiz-Fons et al., 2008c). In SCS, overabundance and spatial
aggregation are key factors modulating infection patterns. So, reducing density or
aggregation would be the most straightforward strategy for disease control.
However, reduced densities may be achieved by two means: habitat management
and population reduction (culling). For instance, limiting feeding would reduce the
carrying capacity of wildlife habitats and reduce wild boar density in a more
sustainable way than culling. Moreover, aggregation at feeders would no longer take
place, even if the risk would persist at waterholes (Gortázar et al., 2008). Since the
management characteristics of each estate are among the determinants of disease
transmission (at least can favour disease persistence that thereafter can disperse to
neighbouring territories), one future direction is to convince hunters and wildlife
managers of the benefits of management for quality instead of quantity (Gortázar
et al., 2006; Vicente et al., 2007b). That is, information campaigns (for example TB
in possum in NZ; http://www.ahb.org.nz/). Since disease can spill back from
cattle to wildlife, farmers need to be part of the strategy. Any action encouraged to
mitigate wildlife-livestock interaction is welcomed, especially when culling would
boost such interaction due to increased wild boar movements and contacts with
cattle.
Alternatively, red deer and wild boar within clusters of intensely managed
fenced hunting estates could be defined as semi-domestic livestock. As such,
specific regulations regarding TB control, segregation from cattle, game meat
inspection and pre-movement testing, could eventually be implemented.
The last but likely most expensive alternative to culling is vaccination. In
principle, it is possible to immunize wild boar against ADV (Ruiz-Fons et al.,
2008a) and against M. bovis (Ballesteros et al., 2009b; Garrido et al., 2011). With this
purpose, specific baits have been designed (Ballesteros et al., 2009a) and
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196
deployment strategies tested (Ballesteros et al., 2010). However, field vaccination
experiments are pending and it is unlikely that vaccination alone will allow
controlling infections at large spatial scales, particularly under funding constraints.
We conclude that the effectiveness of culling in terms of prevalence reduction
is linked to the epidemiology of each disease. We advocate that culling could
become a part of integrated control strategies including management changes and
vaccination, particularly if an initial short term but substantial reduction of host
density and disease prevalence could contribute to increase the success likelihood
of other control tools, or contribute to reduce the total expenses.
Acknowledgements
Authors thank all colleagues at IREC that participated in the fieldwork. We thank Yolanda Fierro
for access to her study site and for sharing valuable data. We also thank the “Equipo de
Seguimiento de Procesos Naturales de la Estación Biológica de Doñana" for sharing the data on
wild boar abundance (http://www-rbd.ebd.csic.es/Seguimiento/mediobiologico.htm) and José
Antonio Muriel and the rest of the colleagues from the Doñana National Park for making the
sampling possible.
Capítulo 6
CAPÍTULO 6. DISCUSIÓN
Seis recomendaciones para la mejora de la
monitorización de las enfermedades compartidas
Boadella, M., Gortázar, C., Acevedo, P., Carta, T., Martín-Hernando, M.P., de la Fuente, J., Vicente, J. 2011. Six recommendations for improving monitoring of diseases shared with wildlife: examples regarding mycobacterial infections in Spain. European Journal of Wildlife Research 57, 697–706.
Capítulo 6
199
Resumen
La monitorización resulta necesaria para identificar cambios de prevalencia de
las enfermedades y para medir el impacto de eventuales intervenciones. Usando
como ejemplo las enfermedades causadas por micobacterias, en este trabajo se
discuten los pros y contras del Plan Nacional de Vigilancia Sanitaria en España,
aportando sugerencias para la monitorización de enfermedades relevantes
compartidas con la fauna silvestre en otras regiones con situaciones similares.
Deberían considerarse seis puntos: (1) asegurar que la enfermedad se monitoriza en
los animales domésticos o incluso en humanos; (2) asegurar que se dispone de
información sobre la ecología de las poblaciones silvestres para así maximizar los
beneficios del esfuerzo de monitorización; (3) seleccionar los hospedadores
adecuados para monitorizar, siendo suficientemente flexibles para incorporar otros
nuevos si surgen resultados que así lo sugieren; (4) seleccionar métodos apropiados
para el diagnóstico y para el análisis de tendencias espacio-temporales; (5) decidir
qué parámetros monitorizar; y finalmente (6) establecer un esfuerzo de muestreo
razonable y suficientemente estratificado para garantizar la detección de cambios en
el tiempo o en respuesta a acciones de manejo. La monitorización sanitaria de la
fauna genera información que beneficia al menos a tres sectores, sanidad animal,
salud pública y conservación. Éstos deberían combinar esfuerzos y recursos para
hacer viable la monitorización. El establecimiento de programas estables,
exhaustivos y precisos a distintas escalas espaciales, debería convertirse en una
prioridad. Los recursos siempre son un factor limitante, pero la experiencia
demuestra que los esfuerzos combinados y en colaboración permiten establecer
programas con un coste suficientemente bajo como para resultar sostenibles en el
tiempo. Estos seis pasos para la monitorización de las enfermedades relevantes
compartidas se pueden adaptar a otras zonas geográficas y a distintas situaciones
epidemiológicas.
Capítulo 6
Abstract
Monitoring is needed to identify changes in disease occurrence and to measure
the impact of intervention. Using mycobacterial diseases as an example, herein we
discuss the pros and cons of the current Spanish Wildlife Disease Surveillance
Scheme providing suggestions for monitoring relevant diseases shared with wildlife
in other regions facing similar challenges. Six points should be considered. This
includes: (1) making sure the disease is properly monitored in the relevant domestic
animals or even in humans; (2) that background information on wildlife population
ecology is available to maximize the benefits of the monitoring effort; (3) selecting
the appropriate wildlife hosts for monitoring, while being flexible enough to
incorporate new ones if research suggests their participation; (4) selecting the
appropriate methods for diagnosis and for time and space trend analysis; (5)
deciding which parameters to target for monitoring; and finally (6) establishing a
reasonable sampling effort and a suitable sampling stratification to ensure detecting
changes over time and changes in response to management actions. Wildlife disease
monitoring produces knowledge that benefits at least three different agencies,
namely animal health, public health and conservation, and these should combine
efforts and resources. Setting up stable, comprehensive and accurate schemes at
different spatial scales should become a priority. Resources are always a limiting
factor, but experience shows that combined, cross-collaborative efforts allow
establishing acceptable schemes with a low enough cost to be sustainable over time.
These 6-steps for monitoring relevant shared diseases can be adapted to many
other geographical settings and different disease situations.
Introduction
The history of wildlife disease surveillance in Europe possibly started with the
first passive surveillance schemes set up in Scandinavian countries in the 1930s
(Mörner et al., 2002). Surveillance of rabies (King et al., 2004) and trichinellosis
(Blancou, 2001) started afterwards. However, the first scientific meetings did not
occur till the early 1990s (Symposium on the health and management of free-
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ranging mammals held in Nancy, France, in 1991; First conference of the European
section of the Wildlife Disease Association EWDA, in Paris, France, in 1994).
These meetings prompted a more widespread interest in wildlife disease
surveillance. In the last decades, classical swine fever in Eurasian wild boar (Sus
scrofa; Rossi et al., 2005) and highly pathogenic avian influenza (Chen et al., 2005),
further contributed to a growing interest on diseases shared with wildlife, such as
zoonotic diseases and diseases that have potential risk for domestic species
(Gortázar et al., 2007). Detection of these relevant diseases in wildlife was
identified as a determinant of the structure and function of European surveillance
schemes (Leighton, 1995). At a worldwide scale, the OIE (World Organization for
Animal Health) working group on wildlife diseases was also established in 1994. It
is now recognized that those countries which conduct disease surveillance of their
wild animal populations are more likely to detect the presence of infectious and
zoonotic diseases and to swiftly adopt counter measures (Mörner et al., 2002).
In Spain, the interest in wildlife diseases started in the 1980s and was boosted
in 1989 with the emergence of rabbit hemorrhagic disease in European wild rabbits
(Oryctolagus cuniculus; Villafuerte et al., 1994). In the last decade however, resources
for studying wildlife diseases increased after the identification of wildlife species as
actors in the epidemiology of important livestock diseases such as Aujeszky’s
disease (Müller et al., 1998), bluetongue (Ruiz-Fons et al., 2008d) and bovine
tuberculosis (bTB; Naranjo et al., 2008), and more recently after realizing the
importance of diseases in Iberian lynx (Lynx pardinus) conservation (Millán et al.,
2009). Risk factors for the appearance of wildlife reservoirs are commonly the
spillover from domestic livestock in combination with anthropogenic activities
such as translocation of wildlife, supplemental feeding of wildlife and wildlife
populations reaching densities beyond normal habitat carrying capacities (Gortázar
et al., 2006; Palmer, 2007). This, along with the size of the Spanish livestock
industry and the significant proportion of free range breeding systems, prompted
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specific calls for wildlife disease research in the national grant scheme in 2006 and
2008 (INIA-FAU, http://sp.inia.es/ucc/contenidos/memo1.pdf).
Using mycobacterial diseases as an example, herein we discuss the pros and
cons of the current Spanish Wildlife Disease Surveillance Scheme (MARM, 2011b)
providing suggestions for wildlife disease monitoring in other regions facing similar
challenges.
Mycobacterial diseases in European wildlife
Tuberculosis in Eurasian badgers (Meles meles) was first diagnosed in
Switzerland (Bouvier, 1963), a country where no further reports on wildlife TB
exist in the scientific literature (Wyss et al., 2000). Later, M. bovis was isolated from
badgers in southwest England in 1971 and Ireland in 1973. Since then, the
infection in badgers has been found throughout dense badger populations of
southwestern England and parts of Wales (Krebs, 1997) and throughout Ireland
(Dolan, 1993). By contrast, there was no published TB case in badgers from the
continent since the first description in Switzerland in the 1960’s, until a recent case-
report from Spain (Sobrino et al., 2008). This is surprising, since many countries in
continental Europe have both TB and badgers. Lower badger densities as
compared to Britain and Ireland may partly explain this absence. However, a lack
of targeted surveillance could also contribute (Artois et al., 2009).
More recently, a growing body of evidence suggests that other wildlife hosts do
also act as M. bovis reservoirs in different parts of Europe (Gortázar et al., in press),
including the Eurasian wild boar in Spain (Naranjo et al., 2008) and Portugal
(Santos et al., 2009) and several cervids in different countries (e.g. Gortázar et al.,
2008). As many countries attempt to eradicate bTB from domestic livestock,
success is impeded by spillback from wildlife reservoirs. It will not be possible to
eradicate M. bovis from livestock until transmission between wildlife and domestic
animals is halted. Such an endeavor will require a collaborative effort between
agricultural, wildlife, environmental and political interests (Palmer, 2007).
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Nowadays TB is among the wildlife diseases receiving more attention by scientists
and government agencies.
Paratuberculosis in wildlife, by contrast, is receiving far less attention in wildlife
than TB. This disease, caused by Mycobacterium avium paratuberculosis (MAP), has been
considered as a major disease of ruminants for more than a century and has
significant economic and welfare effects on livestock in all continents. Recently,
this bacterium has received an increasing interest because of scientific evidence
suggesting that human infection with this micro-organism may be causing some,
and possibly all, cases of Crohn’s disease (Naser et al., 2004; Uzoigwe et al., 2007).
The incidence of paratuberculosis is high in animals kept intensively under
environmental and husbandry conditions which are conducive to the spread of the
infection (Chiodini et al., 1984). Cervids and other wild ruminants have frequently
been identified as MAP hosts, and high prevalence along with clinical disease was
reported in some cases (Balseiro et al., 2008), but not in others (Carta et al., in
press). In Scotland, wild rabbits have been identified as true wildlife MAP
reservoirs, too (Beard et al., 2001), and a similar status may locally apply in Spain
(Maio et al., 2011).
However regular surveillance, other than the annual reporting of TB cases and
far more sporadic reporting of wildlife paratuberculosis to the OIE, is not done at
the (European) country level, or at least not recorded in the scientific literature.
Wildlife disease monitoring
Wildlife disease monitoring can be defined as the systematic recording of
epidemiological data, with the specific purpose of detecting spatial and temporal
trends as well as the presence/absence of the disease. Data and samples gathered
can be used for detecting emerging diseases (Rhyan and Spraker, 2010) and in
retrospective studies (Oleaga et al., 2008; Ruiz-Fons et al., 2008d). Ideally,
monitoring information should integrate data on the risk factors determining the
pathogen epidemiology, such as host abundance and distribution, as they can
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inform us on potential disease spread in a given spatial or temporal frame. The
concept is similar to surveillance, which is done in order to meet the objectives of
controlling the disease (Artois et al., 2009). In contrast to disease surveillance,
which may be passive based on clinical cases, or active based on random sampling,
monitoring is more often active.
Disease control at the human – livestock – wildlife interface should be based
on a thorough knowledge of the "natural history" (ecology) of the disease agent
and its human, domestic and wild hosts (Woodford, 2009). Disease and population
monitoring is a fundamental part of disease ecology. Figure 1 presents a diagram of
how new diseases usually lead first to descriptive epidemiology and eventually to
risk factor analyses and control actions. If humans or domestic animals are
affected, disease monitoring will start early in time. The decision to monitor this
disease in wildlife will depend on the relevance of wildlife hosts as disease
reservoirs for humans or domestic animals or on the effects of the disease on
wildlife population dynamics. Only if at least one of these options is suspected, will
monitoring of the disease among wildlife hosts be considered. As a consequence,
wildlife disease monitoring usually starts much later in time. However, while this is
the case for most regions in developed countries, in areas where wildlife species
provide greater economic returns than livestock, the opposite might be the case.
This has driven wildlife research and monitoring schemes in less-developed
countries where livestock and human health care are poor or non-existent (Kock et
al., 2002).
Disease monitoring in wildlife is promoted in order to obtain information to
compare with the distribution and prevalence trends in livestock; as a basis for
decision making regarding wildlife disease control; and as a means for assessing the
effects of any disease management action. Monitoring, by definition, has no limited
time frame. Monitoring wildlife disease trends requires adequate diagnostic
methods and differential diagnoses; a large scale and long term sampling network;
the logistics linked to the preparation, distribution and conservation of valuable
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Tesis doctoral
wildlife samples; and expertise for data management and analysis. In addition, a
vital need exists to gather data from the ecology and wildlife management field, in
order to combine them with disease information regarding both wildlife and
livestock (Delahay et al., 2009).
Figure 1.- Schematic representation of disease management in humans and domestic animals (upper part) and wildlife (lower part). Dotted boxes indicate decisions and the arrow at the bottom suggests time. Wildlife disease monitoring will mainly occur if wildlife species are identified as significant reservoirs for humans or domestic animals, or if the disease has a significant impact on wildlife populations. This will probably happen later in time than monitoring in humans or domestics.
Recommendations for monitoring diseases in wildlife
To properly monitor a wildlife disease, several points must be considered. This
includes (1) making sure the disease if shared, is properly monitored in the relevant
domestic animals or even in humans; (2) also making sure that background
information on wildlife population ecology is available to maximize the benefits of
the monitoring effort; (3) selecting the appropriate wildlife hosts for monitoring,
while being flexible enough to incorporate new ones if research suggests their
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participation; (4) selecting the appropriate methods for diagnosis and for
time/space trend analysis; (5) deciding which parameters to target for monitoring:
one or more disease agents, or lesions, or contact as revealed by serum antibodies?;
and finally (6) establishing a reasonable sampling effort and a suitable sampling
stratification that can be prolonged over time.
First, if the disease is shared with humans or domestic animals, do appropriate
monitoring programs that allow for instance trend comparisons between these and
wildlife, exist? Regarding bTB, good information on prevalence and incidence in
bovine livestock will be available in most European situations. But, at the same
time, information may be lacking for other relevant – or potentially relevant –
domestic species, such as goats and free-range pigs.
Second, wildlife disease monitoring will only make sense if population
monitoring is carried out at the same time, allowing to link changes in abundance
or management with changes in disease indicators (Acevedo et al., 2007b). This
should not only include the target wildlife hosts, but also other relevant competitor
or prey species (Sobrino et al., 2009).
Third, wildlife disease monitoring should select for the most appropriate
wildlife hosts, considering distribution, abundance, degree of protection, prevalence
and disease susceptibility, but also ease of sample collection and diagnostic
sensitivity and specificity. For instance, in Spain TB has mainly been recorded in
wild boar, red deer (Cervus elaphus) and fallow deer (Dama dama; e.g. Gortázar et al.,
2008), and as previously stated, sporadically in badgers (Sobrino et al., 2008). TB
has also occasionally been described in red fox (Vulpes vulpes; Martín-Atance et al.,
2005) and Iberian lynx (Lynx pardinus; Peña et al., 2006). However, wild boar are
considered the best TB surveillance target because of their wider distribution and
higher abundance, high availability as a game species, and because of their lesion
distribution (Martín-Hernando et al., 2007). The recent design of a specific and
sensitive enough ELISA test (Aurtenetxe et al., 2008; Boadella et al., 2011b) makes
sample harvesting and laboratory analyses relatively easy even if only head lymph
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nodes and blood samples are available. By contrast, the detection of TB compatible
lesions in cervids requires the inspection of the head and neck, thorax and
abdomen (Vicente et al., 2006; Martín-Hernando et al., 2010). Moreover, wild
ruminants are often infected with other mycobacteria such as MAP, further
compromising diagnostic specificity of some tests, particularly those based on
serum antibodies (Reyes-García et al., 2008; Carta et al., in press). In turn, badgers
have a more limited distribution in Spain and are protected by law, making
sampling difficult. However, monitoring schemes should be flexible enough to
allow incorporating new species if research suggests their participation in disease
epidemiology (Delahay et al., 2001).
Fourth, the diagnostic and statistic methods should be defined in a way
assuring repeatability and data quality. Diagnostic methods selected for wildlife
disease monitoring will depend on factors such as the selected host species and
expected sample size, the cost of each test, and its specificity and sensitivity. Tests
suitable for their use in wildlife are not always available, and the difficulties
imposed by field sampling contribute to reduce test sensitivity (Donnelly and
Hone, 2010). Statistical methods will depend on factors such as the expected
prevalence, the geographic scale, the length of the time series and the degree of
change in time of the measured variable, being it prevalence or lesion intensity (Joly
et al., 2009). It is often of use to study the age-specific prevalence rates, particularly
using juvenile prevalence as a proxy for incidence (Wobeser, 1994).
Epidemiological data are peculiar from a statistical perspective. Data with
aggregated distributions are usual in the epidemiological databases so parametric
statistics, which are requiring normal distribution of the data, cannot be generally
used (e.g. Jewell, 2009). So, in risk factor and disease trend assessment, generalized
models – with Poisson, negative binomial, zero-inflated or binomial distributions –
are needed. Information is often generated at different spatial scales – from
individual to population or even to region – and so it is required to use mixed
models in which, by means of random variables, pseudo-replication can be avoided
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(Zuur et al., 2009). Another essential peculiarity is that the epidemiological data of
different host species is rarely available at the same spatial resolution and at a high
enough resolution to allow meaningful inferences to be made. In general terms,
data analyzed should be referred to the same territorial units (municipalities or
provinces, for example), and the lowest resolution will determine the spatial
resolution of the analysis (see Pfeiffer et al., 2008).
Fifth, once the host species are defined, it must be decided what to target for
monitoring. This means defining the agent or agents: M. bovis only, or members of
the M. tuberculosis complex (MTBC), or MTBC and MAP, for instance; and also
defining what data will be needed, be it the antigen by culture or PCR, specific
antibodies or even characteristic lesions (Vicente et al., 2006; Aurtenetxe et al.,
2008; Santos et al., 2010). It is important to choose parameters for which detection
tools of known effectiveness are available (Wobeser, 1994). In addition, it is
important to consider testing expenses and budget limitations. Thus, if funding is
limited it can be wise to combine more expensive techniques such as culture,
applied for confirmation to a subsample, with cheaper techniques such as gross
pathology (e.g. Vicente et al., 2006). In most cases of mycobacterial disease
monitoring, the target will be MTBC, but under certain circumstances monitoring
may need to include MAP because of the relevance of cross-reactivity to the tests
used, or because of the importance of MAP for the regional livestock industry (e.g.
Balseiro et al., 2008). Moreover, prevalence rates have a limited value for
monitoring chronic diseases with a very protracted course (Wobeser, 1994), such as
mycobacterial infections.
Finally, it is of paramount importance defining an adequate and reasonable
sample size as well as number and distribution of sampling localities according to
statistical recommendations (Table 1). This must keep in mind the budget and the
current and future logistic constraints, such as the laboratory analysis throughput
per day, the space available for short and long term sample storage, and the design
of proper databases and sample banking registers. Moreover, sampling must be
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adequately stratified by age and sex (Vicente et al., 2006), management (Vicente et
al., 2007b) and study zone (Muñoz et al., 2010). Epidemiology software can help
identifying suitable sample sizes enabling to detect time trends when a known initial
prevalence and an expected prevalence change are given (e.g. Win Episcope; EFSA,
2010). For instance, sampling requirements will depend on the expected initial
prevalence or the expected degree of change in these prevalences from time 1 to
time 2 (Table 1). In order to spare costs, it may be advisable to pool samples for
analysis (e.g. Tayce et al., 2008) or to accumulate samples gathered during several
years until the required sample size is achieved (see Table 2).
Table 1.- Sample effort needed for the detection of disease according to the expected prevalence (assuming a population size of >10,000) and for the detection of prevalence variations over 50% according to the initial prevalence (with a power of 90% and confidence level of 95%; Win Episcope 2.0).
Detection P>10,000 Expected prevalence 0.1% 1% 5% 10% Required sample size 2,990 300 59 29 Variation >50% Initial prevalence 1% 12% 30% 60% Required sample size 5,098 387 130 44
Table 2.- Example regarding the Spanish Wildlife Disease Surveillance Scheme. Probability of detection: Annual samples by taxon and bio-region (BR 1 to 6). Shadings indicate that sampling is sufficient for the detection of prevalences of 10% (light grey), 5% (medium grey), and 1% (dark grey), with a power of 90% and confidence level of 95%; Win Episcope 2.0. White boxes represent situations where these levels are not achieved in only one year of sampling.
Birds Carnivores Hares Rodents
Wild boar
Red deer
Roe deer
Wild bovids
BR 1 200 60 0 100 400 70 50 20
BR 2 100 60 120 200 570 190 60 40
BR 3 100 60 90 100 510 250 35 30
BR 4 100 60 60 100 245 120 40 60
BR 5 200 60 65 0 345 50 20 75
BR 6 100 0
TOTAL 800 300 335 500 2,070 680 205 225
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Monitoring mycobacterial diseases in Spanish wildlife
Spain is a 504,782 Km2 country in southwestern Europe that includes two
archipelagos, the Canary Islands off the West African coast, the Balearic Islands in
the Mediterranean, and the Autonomous Towns of Ceuta and Melilla in the north
of Africa. Based on habitat and climate features and wildlife population
characteristics, Spain can roughly be divided into 6 Bio-regions (Muñoz et al., 2010;
Figure 2). The compulsory control of bTB in Spanish cattle has been successful, so
that current individual cattle incidence is below 0.5%. However, the distribution of
positive cattle herds is not uniform, with higher prevalence in Mediterranean
habitats of the south and west of the Spanish mainland. Islands with no potential
wildlife reservoirs are almost bTB-free (MARM, 2011a). Of the susceptible
domestic hosts, bTB is only monitored in cattle and in goats living in close contact
to cattle. Some regions have also implemented compulsory or voluntary bTB
control programs in goats. In Spain, paratuberculosis has been diagnosed for over
20 years in all three (cattle, sheep and goat) domestic ruminant species (Aller et al.,
1973; Garrido and León-Vizcaíno, 1979), but is not monitored.
Figure 2.- Map of Spain, with a division into six large Bio-regions for sampling and wildlife disease monitoring, according to the Spanish Wildlife Disease Surveillance Scheme.
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Tesis doctoral
The current situation regarding tuberculosis in Spanish wildlife was recently
reviewed (Gortázar et al., 2011). Paratuberculosis, in turn, has been recorded in
farmed red deer (Fernández-de-Mera et al., 2009), but preliminary data from
nationwide surveys suggest that wildlife is only locally relevant in MAP
epidemiology (Carta et al., in press). This is the case of fallow deer in an intensively
grazed mountain area in northern Spain (Balseiro et al., 2008) and possibly of
European wild rabbits sharing pastures with infected domestic ruminants in
southern Spain (Maio et al., 2011). Sporadic records of MAP are also available for
wild boar (Álvarez et al., 2005).
So, wildlife TB prevalence is two orders of magnitude higher than in cattle, and
it is most likely that certain wildlife reservoirs might locally interfere with the cattle
bTB eradication efforts (Gortázar et al., 2008). In addition, TB has killed several
endangered Iberian lynxes causing conservation concerns (Peña et al., 2006). These
are clear reasons for targeting wild ungulates for TB monitoring, and for taking into
account the possible interference of MAP in certain diagnostic tools and host
species (Boadella et al., 2011b; Carta et al., in press). Table 3 presents an overview
of the application of the six abovementioned recommendations to the current
Spanish circumstances.
Table 3.- Main requisites, current circumstances, and recommendations for tuberculosis monitoring in Spanish wildlife.
Requisite Current circumstances Recommendations (1) Disease is properly monitored in the relevant domestic animals or even in humans.
Excellent monitoring in cattle. No nationwide compulsory monitoring in other domestic animals. Human cases not always differentiated from M. tuberculosis.
Include most goat herds in monitoring. Improve information exchange with medics.
(2) Background information on wildlife population ecology is available to maximize the benefits of the monitoring effort.
Tools for estimating relative abundance and spatial aggregation are available for wild boar (Acevedo et al., 2007b). No easy density estimation methods are available for wild boar. In deer, population density can be estimated (Acevedo et al., 2008; 2010c). Management-related risk factors (feeding, waterholes, fencing) have been identified (Vicente et al., 2007b) and are monitored.
Decide a tool (dung counts and/or hunting yields) and apply to all selected sampling sites. Characterize other risk factors and monitor their changes through time.
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Table 3.- Continued.
Requisite Current circumstances Recommendations
(3) Select the appropriate wildlife hosts for monitoring, while being flexible enough to incorporate new ones.
Wild boar is an accessible and widespread game species; is more able to cross fences and likely to contact cattle than other ungulates; and serosurveillance already exists for other infections. Deer are not as widespread. Badger distribution and abundance is limited. Foxes are poor sentinels for mycobacterial diseases (Carta et al., 2011).
Use wild boar as key indicator species. Collect head lymph nodes and sera, along with data on sex and age. Where available, use red deer, fallow deer and badger, too.
(4) Select appropriate methods for diagnosis and for time trend analysis.
Sensitive and highly specific ELISA available for wild boar (Aurtenetxe et al., 2008, Boadella et al., 2011b), lesions easily detectable in wild boar heads (Martín-Hernando et al., 2007). Cross reactions and low sensitivity limit the use of ELISA in deer, and TB monitoring in deer requires inspecting whole carcass and using expensive and time demanding pathology and culture (Martín-Hernando et al., 2010).
Use ELISA for calculating serum antibody prevalence, pathology for additional lesion scoring, and culture a subsample, for confirmation and molecular epidemiology. Expertise required for data management and statistical analysis.
(5) Decide which parameters to target for monitoring: one or more disease agents, or lesions, or contact as revealed by serum antibodies?
Serum antibodies and TB compatible lesions are time and cost – effective in wild boar (Vicente et al., 2006; Aurtenetxe et al., 2008; Santos et al., 2010; Boadella et al., 2011b).
Use wild boar serum antibody prevalence as main parameter, lesion scoring as additional tool. Pay attention to prevalence in juvenile age classes. Some proportion of culture confirmation is advisable for strain characterization and epidemiology.
(6) Establish a reasonable sampling effort and distribution.
Wildlife sampling bio-regions have been defined (Muñoz et al., 2010) and cattle bTB prevalence and distribution is well described. Sampling effort depends on regional wild boar abundance and the collaboration of hunters and local authorities.
Stratify sampling by bio-region and cattle bTB prevalence. Better sample from permanent sampling sites, which can be monitored for host abundance and management.
Discussion
As our knowledge on wildlife diseases grows, disease control becomes more
often an option. However, monitoring is needed to identify changes in disease
occurrence and to measure the impact of interventions (McDonald et al., 2008).
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Despite this fact, wildlife disease monitoring is largely in its infancy (Artois et al.,
2009), and setting up stable, comprehensive and accurate schemes at different
spatial scales (local, national and global) should become a priority for health
authorities and wildlife managers.
In many countries including New Zealand, the United States, and several ones
in the European Union, wildlife vaccination as a means to contribute to bTB
control in livestock is being seriously considered (e.g. Ballesteros et al., 2009b;
Corner et al., 2009; Tompkins et al., 2009; Chambers et al., 2011). In this context,
the implementation of wildlife TB monitoring schemes is a real need.
One point to consider is who takes charge of the monitoring costs. Wildlife
disease monitoring produces knowledge that benefits at least three different
agencies, namely animal health, public health and conservation. It would be wise to
combine efforts and resources from all three compartments, and to take advantage
of already existing expertise in government agencies and academic institutions.
Government attitudes towards wildlife disease research have changed during the
last decades, for reasons already listed in the introduction. Now it is needed that
other stakeholders, too, such as the livestock industry, the hunting lobby or the
conservationists, and even medics, become convinced of the need to monitor
wildlife diseases if we are thinking about their future control. Successful examples
of collaboration between conservationists and vets (e.g. the detection and
management of feline leukemia in the endangered Iberian lynx, López et al., 2009),
between vets and medics regarding many zoonoses such as trichinellosis (e.g.
Wacker et al., 1999); and between conservationists, medics and vets, for instance in
zoonoses where wildlife are both reservoirs and victims, such as TB (Gortázar et
al., 2005, 2008) should serve as a trigger for future collaborations.
The 6-steps for surveillance of relevant shared diseases can be adapted to many
other geographical settings and different disease situations. Regarding
mycobacterial diseases, these are worldwide distributed, and do frequently affect
multi-host systems at the domestic animal – wildlife interface, as described for
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215
Spain. In any such situation, similar requisites to those outlined in Table 3 do apply.
The same requisites are also valid for other disease systems, if they affect domestic
animals or humans. For instance, deer might be better indicators of bluetongue
virus circulation than vaccinated domestic sheep or cattle (Rodríguez-Sánchez et al.,
2010).
Wildlife disease monitoring programs that are integrated within national animal
health surveillance infrastructures should have the capacity to respond promptly to
the detection of unusual wildlife mortality and to institute epizootiological
researches into new and emerging wildlife diseases (Mörner et al., 2002). Increased
training and preparedness of human and animal health staff and government
agencies, improved communication and continued research will enhance wildlife
monitoring efforts (Belant and Deese, 2010). Resources are always a limiting factor,
but the developments towards the monitoring of TB in Spanish wildlife show that
combined efforts of local and national government agencies, along with the
commitment of trans-disciplinary research can allow setting up acceptable schemes
with a low enough cost to be sustainable in time. Improvements, such as extending
animal TB surveillance to goats and pigs, and establishing improved links and data
exchange with the human health system, are still needed. There exist opportunities
for similar approaches elsewhere, regarding other diseases, hosts, and geographic
circumstances.
Acknowledgements
Tania Carta acknowledges a grant from Regione Sardegna, and Pelayo Acevedo and Maria Paz
Martín-Hernando acknowledge a Juan de la Cierva (Fondo Social Europeo) and an ISCIII
postdoctoral contract from MCINN, respectively. Jose Luis Sáez made valuable comments to the
first draft.
Capítulo 7
CAPÍTULO 7. SÍNTESIS Y CONCLUSIONES
Capítulo 7
SÍNTESIS
Este apartado sintetiza los contenidos más relevantes de la presente tesis,
poniendo especial énfasis en resaltar los resultados sobre tendencias temporales.
En el Capítulo 1, de revisión bibliográfica sobre tendencias temporales, se
pone de manifiesto que la repetición de estimas de prevalencia u otros indicadores
de frecuencia de las enfermedades, permite el seguimiento de su evolución en el
espacio y en el tiempo, y resulta imprescindible para evaluar el resultado de
eventuales estrategias de intervención. El número de muestreos, la prevalencia, el
estatus del hospedador (reservorio o accidental) y su densidad, resultaron ser
factores asociados con la probabilidad de identificar tendencias temporales, por lo
que esa es información que, en la medida de los posible, se debe aportar en los
estudios epidemiológicos.
El Capítulo 2 describe la metodología en vigilancia sanitaria de fauna
silvestre, dedicando atención al problema de la conservación de sueros y, en
concreto, al efecto de las descongelaciones sucesivas y de la hemólisis sobre la
detección de anticuerpos mediante ELISA. Utilizando como ejemplo un test para la
detección de anticuerpos frente al virus de la enfermedad de Aujeszky (VEA) en el
jabalí (Sus scrofa), se sugiere que los sueros con más de tres ciclos de
congelación/descongelación y una hemólisis de más de 3 en una escala de 4
deberían descartarse. Incluso sueros limpios, no hemolizados, nunca deberían pasar
por más de 5 ciclos de congelación/descongelación.
En el Capítulo 3, sobre vigilancia sanitaria de zoonosis, se aborda la
vigilancia sanitaria y el seguimiento temporal del contacto de los ungulados
silvestres de la Península Ibérica con tres agentes zoonóticos: el virus de la hepatitis
E (VHE), miembros del género Flavivirus, y el nematodo Trichinella spp.
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Tesis doctoral
Dados los resultados previos que describían un contacto extendido del jabalí
con el VHE en la Península Ibérica (de Deus et al. 2008), se testaron ciervos
Ibéricos (Cervus elaphus) para la detección de anticuerpos frente el VHE y para
detectar la presencia de ARN vírico por PCR. Un 10% y un 13% de los ciervos
analizados resultaron positivos al ELISA y a la PCR, respectivamente. El análisis
temporal de las prevalencias detectadas por ELISA entre el período 2000-2005 y el
período 2006-2009 reveló un incremento de la prevalencia del contacto con este
virus que junto a la detección de ARN vírico en esta especie, podría significar
también un aumento de la potencial exposición de las personas.
En cuanto a los flavivirus, se testaron jabalíes y ciervos jóvenes de distintas
regiones peninsulares con el objetivo de detectar la existencia de variabilidad
temporal en el contacto con los virus del género Flavivirus. Se observó que la
probabilidad de detectar contacto con flavivirus era 18 veces mayor en jabalíes
jóvenes que en ciervos de la misma edad. La seroprevalencia detectada en las
poblaciones de jabalíes se mantuvo estable durante los 11 años estudiados. Con los
resultados obtenidos se evidenció el uso potencial de los ungulados silvestres
juveniles, particularmente del jabalí, como centinelas de la circulación de flavivirus
en el sureste europeo.
Contrariamente a los dos trabajos anteriores, el ELISA para la detección de
anticuerpos frente a Trichinella spp. en el jabalí no resultó ser una herramienta útil
para su monitorización dada su baja especificidad. En cambio, el análisis temporal
de los resultados de las inspecciones oficiales y detección de Trichinella spp. en carne
de jabalí evidenció una tendencia decreciente de los casos positivos en la provincia
de Ciudad Real para el período 1998-99 a 2009-10.
En el Capítulo 4, sobre riesgos sanitarios asociados al manejo cinegético
intensivo de los ungulados silvestres, se analizan los factores de riesgo que
determinan las variaciones temporales en el contacto de ungulados silvestres con
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Capítulo 7
siete patógenos relevantes y su posible asociación a prácticas de manejo cinegético
intensivo.
En primer lugar, se estudió la evolución temporal del contacto de poblaciones
de jabalí con el VEA durante un período de 10 años. Se observó que la alta
proporción de sitios de estudio positivos permanecía estable mientras que para el
cerdo doméstico, el número de comarcas positivas disminuía del 70% al 1,7%
durante el mismo período. Se constató que las prevalencias más altas de contacto
con el VEA se detectaban en áreas donde existe un frecuente manejo intensivo de
las poblaciones de jabalí. Este hecho pone de relieve el creciente riesgo que puede
suponer el jabalí para la erradicación del VEA en el cerdo doméstico, especialmente
en aquellas situaciones donde haya contacto entre los dos.
Cuando se analizaron las prevalencias de anticuerpos frente al circovirus
porcino tipo 2 (PCV2), al VHE y a Erysipelothrix rhusiopathiae en distintas
poblaciones de jabalí para el período 2000-2011, las prevalencias observadas de
PCV2 y VHE se mantuvieron estables, mientras que la de E. rhusiopathiae
disminuyó. Las altas prevalencias encontradas sugieren que los factores
previamente identificados como de riesgo (Vicente et al., 2004; Acevedo et al.,
2007b), siguen estando presentes y deberían tenerse en cuenta para el control de las
enfermedades en la interfaz domésticos-silvestres.
Las prevalencias más altas de anticuerpos frente a Brucella suis y a los miembros
del complejo Mycobacterium tuberculosis (CMTB), causantes de la tuberculosis (TB), se
encontraron en las poblaciones de jabalí del centro-sur peninsular en comparación
con otras áreas estudiadas de la Península Ibérica. En ambos casos la serología
demostró que el contacto con los dos agentes infecciosos está extendido en las
poblaciones de jabalí y que las altas prevalencias persisten a lo largo del período
estudiado. En el caso de la TB, la aplicación de esta nueva técnica de diagnóstico
permitió detectar que el rango geográfico del contacto con CMTB era más amplio
de lo esperado.
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Tesis doctoral
El Capítulo 5, sobre aportaciones al control de las enfermedades
compartidas, describe la tendencia temporal estable de las lesiones compatibles
con TB (TBL) en poblaciones ibéricas de ciervo y cómo sólo en una de las 20
localidades estudiadas, la prevalencia de TBL disminuyó entre los años 2000 y 2011.
Los resultados obtenidos para esta localidad, sugieren que se trata de un efecto del
control numérico no selectivo de jabalíes en esa localidad.
La reducción de las poblaciones de jabalí tuvo efecto sobre los indicadores de
sus propias prevalencias de TB, pero también sobre la incidencia de reactores
bovinos a la prueba de tuberculina y sobre la prevalencia de infección por M. bovis
en el ciervo. De forma contraria, la prevalencia conjunta de contacto con el VEA
en el jabalí se mantuvo estable en los sitios sometidos a una reducción numérica no
selectiva de jabalíes. Ello sugiere que la eficacia del control numérico como
herramienta de manejo sanitario en fauna silvestre está ligada a la epidemiología de
cada enfermedad. Para el caso de la TB, el control numérico no selectivo de jabalí
podría aplicarse como paso inicial dentro de una estrategia integrada de control de
esta enfermedad.
La discusión de esta tesis, Capítulo 6, se centra en la necesidad de la
monitorización sanitaria de la fauna silvestre como herramienta para detectar
cambios en las enfermedades y para poder medir el impacto de eventuales
intervenciones. Se utilizan las enfermedades causadas por micobacterias como
ejemplo para discutir sobre los pros y los contras del Plan Nacional de Vigilancia
Sanitaria en España. Se proponen seis medidas para mejorar la monitorización
sanitaria en fauna silvestre que pueden ser adaptadas a otras zonas y otras
situaciones de enfermedad. Dichos puntos son los siguientes: (1) asegurar que la
enfermedad se monitoriza en los animales domésticos o incluso en humanos; (2)
asegurar que se dispone de información sobre la ecología de las poblaciones
silvestres para así maximizar los beneficios del esfuerzo de monitorización; (3)
seleccionar los hospedadores adecuados para monitorizar, siendo suficientemente
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Capítulo 7
flexibles para incorporar otros nuevos si surgen resultados que así lo sugieren; (4)
seleccionar métodos apropiados para el diagnóstico y para el análisis de tendencias
espacio-temporales; (5) decidir qué parámetros monitorizar; y finalmente (6)
establecer un esfuerzo de muestreo razonable y suficientemente estratificado para
garantizar la detección de cambios en el tiempo o en respuesta a acciones de
manejo.
La monitorización sanitaria de la fauna genera información que beneficia a la
sanidad animal, la salud pública y la conservación. Los gestores de estos tres
sectores deberían combinar esfuerzos y recursos para establecer programas de
monitorización sostenibles, sólidos, exhaustivos y precisos a distintas escalas
espaciales.
En conjunto, esta tesis no ha identificado tendencias temporales significativas
para el contacto con flavivirus, VEA, PCV2, VHE, B. suis en jabalí, ni para el
contacto con CMTB en ciervo y jabalí. Debe tenerse en cuenta que la tesis abarca
un periodo de poco más de una década, relativamente corto, y que durante esta
década no han tenido lugar cambios drásticos en cuanto a manejo y gestión de los
ungulados silvestres españoles, como por ejemplo ocurrió con las repoblaciones de
los años 1960 y 1970, o con la proliferación de vallados cinegéticos en los años
1980 y 1990. En el caso del contacto con flavivirus esta ausencia de tendencias
temporales se debe por una parte a que con prevalencias tan bajas resulta difícil
identificar variaciones significativas, y por otra a que los ungulados silvestres
simplemente reflejan una dinámica entre virus, vectores y aves, en la que no tienen
participación directa. En la mayoría de los demás casos, la estabilidad temporal
observada sugiere que los citados patógenos ya han alcanzado su máxima difusión
en las poblaciones de hospedadores. En consecuencia, las altas prevalencias y su
estabilidad en el tiempo indican que esos agentes patógenos constituyen un
problema permanente para la salud pública, la sanidad animal y la conservación.
Por otra parte, se observa que tales variaciones sí ocurren a escalas más reducidas,
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Tesis doctoral
224
por ejemplo a nivel de aquéllas localidades de estudio en las que se han producido
cambios significativos de gestión. Esto implica que, como es natural, nuestra
percepción de las variaciones en el tiempo es dependiente de la escala espacial y
temporal que definamos.
El único caso en el que se observa un incremento general en el contacto entre
un patógeno y su hospedador silvestre es el de VHE en el ciervo. Este fenómeno
podría deberse a que el ciervo refleja con retraso la previa difusión de VHE en la
población simpátrica de jabalí, su hospedador silvestre principal. Esta posibilidad se
vería apoyada por la observación de que las menores prevalencias de VHE en
ciervo se registran en granjas donde no hay contacto ciervo/jabalí.
Dos patógenos, Erysipelothrix rhusiopathiae y Trichinella spp., disminuyen en
prevalencia durante el periodo de estudio. Nuevamente, se trata de observaciones
difíciles de explicar. Ambos patógenos tienen la capacidad de aprovechar múltiples
especies hospedadoras. En consecuencia, cabe especular que la progresiva
simplificación de los ecosistemas, consecuencia a su vez de la creciente
intensificación de los sistemas de producción cinegética, afecte negativamente al
éxito de transmisión de los patógenos que dependan de relaciones complejas entre
hospedadores. Por el contrario, tales entornos facilitarían el mantenimiento de
patógenos densodependientes.
En el futuro, convendría aprovechar los contrastes entre los espacios naturales
menos alterados y las explotaciones cinegéticas y ganaderas para explorar en
profundidad la interacción entre la biocenosis y la comunidad de patógenos a fin de
identificar patrones y mecanismos que contribuyan a explicar su evolución en el
tiempo.
Capítulo 7
CONCLUSIONES
1. La monitorización sanitaria de la fauna silvestre es una parte necesaria de la
vigilancia sanitaria, que debe integrarse con la vigilancia en salud pública y en
ganadería. Una monitorización eficaz permite detectar variaciones temporales
y espaciales del contacto de la fauna con agentes patógenos, así como evaluar
críticamente la eficacia de eventuales medidas de control.
Wildlife disease monitoring is a substantial part of disease surveillance that should be
integrated with public and livestock health surveillance. An effective monitoring allows the
detection of spatio-temporal trends of wildlife contact with pathogens. It also allows assessing
critically the efficacy of eventual control measures.
2. En particular, los ungulados silvestres presentan gran variabilidad demográfica,
de gestión, exposición a patógenos e interacción con otra fauna silvestre,
ganado y humanos. Por ello, es importante integrar la monitorización sanitaria
con la biología y gestión de las poblaciones estudiadas.
Wild ungulates present a great variability in demography, management, exposure to
pathogens, and interaction with other wildlife, livestock and humans. Therefore, it is
important to integrate the sanitary aspects with the biology and management of the studied
populations.
3. El creciente manejo cinegético intensivo de la caza mayor en terrenos vallados,
así como la sobreabundancia, se asocian negativamente con la evolución en el
tiempo del contacto de los ungulados cinegéticos con agentes patógenos
compartidos con los animales domésticos y las personas.
The increasing intense management of big game for hunting, along with overabundance,
negatively modulate the temporal trend of the contact between wild ungulates and pathogens
shared with livestock and humans.
4. La permanencia en el tiempo de las situaciones de reservorio silvestre de
enfermedades compartidas, incluyendo enfermedades emergentes y re-
emergentes como la hepatitis E o la tuberculosis, tiene consecuencias para la
salud pública, la sanidad animal, la conservación y la producción cinegética.
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Tesis doctoral
226
The persistence over time of wild reservoirs of shared diseases, including emerging or resurgent
diseases such as hepatitis E or tuberculosis has consequences for public health, animal
health, conservation and game production.
5. Por primera vez se evidencia una asociación entre la reducción no selectiva de
la población de jabalí y un descenso en la prevalencia de tuberculosis en
rumiantes. Es urgente abordar investigaciones experimentales sobre las
distintas opciones para el control de las enfermedades compartidas. Estas
estrategias de control deberían ser integradas y sostenibles.
An association between wild boar culling and a decreasing prevalence of tuberculosis in
ruminants is evidenced for the first time. Experimental research on the different control
options for shared diseases is urgently needed. Control strategies based on this research should
be integrated and sustainable.
6. Todo programa de monitorización debería contar con el conocimiento sobre
cuáles son los hospedadores, los métodos de diagnóstico y de tratamiento de
datos, los parámetros a monitorizar y el tamaño y estratificación del muestreo
más adecuados para optimizar la detección de cambios en el tiempo en el
contacto con patógenos.
Every monitoring program should rely on knowledge about the most appropriate hosts,
diagnostic and data analysis tests, parameters to monitor and size and stratification of the
sample in order to optimize the detection of changes over time of the contact with pathogens.
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