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# Risk factors for tick infestation and equine Piroplasmosis infection among draught horses in France

Clémence Nadal, Claire Chanet, Clarisse Delaunay, Pierre-Hugues Pitel, M.

Marsot, S.I. Bonnet

► To cite this version:

Clémence Nadal, Claire Chanet, Clarisse Delaunay, Pierre-Hugues Pitel, M. Marsot, et al. Risk factors for tick infestation and equine Piroplasmosis infection among draught horses in France. Ticks and Tick-borne Diseases, 2025, 16 (3), pp.102468. 10.1016/j.ttbdis.2025.102468. hal-04994083

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# Risk factors for tick infestation and equine Piroplasmosis infection among draught horses in France

C. Nadal<sup>a,b</sup> <small>D</small>, C. Chanet<sup>a</sup>, C. Delaunay<sup>a</sup>, Pierre-Hugues Pitel<sup>c</sup>, M. Marsot<sup>a,1,*</sup> <small>D</small>, S.I. Bonnet<sup>b,1,*2</sup> <small>D</small>

## ARTICLE INFO

Keywords:

Babesia caballi

Theileria equi

Equine piroplasmosis

Tick

Prevalence

Risk factor

## ABSTRACT

Equine piroplasmosis, caused by the protozoan parasites *Babesia caballi* and *Theileria equi*, is endemic in Europe's Mediterranean basin, creating significant health and economic challenges for the equine sector. With no available vaccine, an eco-epidemiological approach is essential in order to identify and implement effective preventive measures. With this aim in view, we identified risk factors associated with *B. caballi* and *T. equi* infections and tick infestation for draught horses in the Auvergne-Rhône-Alpes region of France, known for its high piroplasmosis seroprevalence. During the spring of 2021, blood samples from 146 horses and ticks from both hosts and pastures were collected across 38 farms, then analysed using PCR to determine parasite prevalence. Environmental and meteorological data, along with information on management practices and individual horse characteristics, were collected on-site and from national databases. The results showed significant spatial variability in *T. equi* and *B. caballi* prevalence in horses, ranging from 25.7% [95% CI: 13.1 – 43.6] to 83.7% [95% CI: 69.8 – 92.2] and from 0% [95% CI: 0.0 – 26.8] to 26.5% [95% CI: 15.4 – 41.3], respectively. Among 1046 collected ticks, 3% carried either a species of *Babesia* or *Theileria* and were identified as *Dermacentor marginatus*, *Dermacentor reticulatus* or *Ixodes ricinus*. We showed a stronger link between the environment and tick exposure for *T. equi* infections, while *B. caballi* infections were more closely associated with individual horse characteristics and management practices. Key risk factors for tick infestation included environmental conditions (temperature, rainfall, vegetation cover, altitude, etc.) and the presence of wild animals in pastures. These findings provide a valuable foundation on which to develop targeted prevention and control strategies to protect horses in enzootic regions.

## 1. Introduction

Equine piroplasmosis is a globally significant tick-borne disease affecting all equids, including horses, donkeys, mules and zebras. This disease is caused by two obligatory intraerythrocytic protozoan parasites, *Babesia caballi* and *Theileria equi* (de Waal, 1992; Mehlhorn and Schein, 1998). Recently, *Theileria haneyi* was identified as a third aetiological agent, though it has only been described in America (United States and Brazil) and Africa (Bhoora et al., 2020; Knowles et al., 2018; Mshelia et al., 2020; Vieira et al., 2018). Infections by *B. caballi* and

*T. equi* can cause non-specific symptoms in equids such as fever, anaemia, limb oedema and severe jaundice, which can eventually lead to the animal's death (Onyiche et al., 2019; Tamzali, 2013). While the chronic form of equine piroplasmosis is rarer in horses, it is frequently observed in donkeys, leading to decreased appetite and weight loss (Laus et al., 2015; Waal and van Heerden, 2004). Additionally, abortions and neonatal deaths have been reported in both horses and donkeys. Case fatality for equine piroplasmosis varies between 10 and 50% (OIE, 2019). In addition, asymptomatic infections, which are common in endemic areas, also generate substantial economic losses due to trade

<footnote><sup>a</sup> Epidemiology Unit, Laboratory for Animal Health, ANSES, University Paris Est, Maisons-Alfort, France</footnote>

<footnote><sup>b</sup> ANSES, INRAE, Ecole Nationale Vétérinaire d'Alfort, Laboratoire de Santé Animale, UMR BIPAR, Maisons-Alfort, France</footnote>

<footnote><sup>c</sup> LABEO, 1 route de Rosel, Saint Contest, 14053 Caen Cedex 4, France</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: maud.marsot@anses.fr (M. Marsot), Sarah.Bonnet@inrae.fr (S.I. Bonnet).</footnote>

<footnote n="1">¹ The two authors contributed equally to the manuscript.</footnote>

<footnote n="2">² Present address: Ecology and Emergence of Arthropod-borne pathogens unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, INRAE USC 1510, Paris, France.</footnote>
restrictions imposed by piroplasmosis-free countries (Camino et al., 2020a; Onyiche et al., 2019).

In Europe, and particularly in France, this disease is endemic and of significant concern (Nadal et al., 2022). The transmission of *B. caballi* and *T. equi* is primarily attributed to ticks from the *Hyalomma*, *Dermacentor*, and *Rhipicephalus* genera, with *Hyalomma marginatum*, *Dermacentor reticulatus*, *Dermacentor marginatus*, and *Rhipicephalus bursa* confirmed as vectors through experimental studies (Onyiche et al., 2019; Scoles and Ueti, 2015; Wise et al., 2013). Additional species, including *Rhipicephalus annulatus*, *Ixodes ricinus*, and *Haemaphysalis punctata*, and some belonging to the *Rhipicephalus sanguineus* group, are suspected vectors based on parasite DNA detection in field-collected unfed ticks, suggesting potential trans-stadial transmission (Nadal et al., 2021). A meta-analysis has estimated that the average seroprevalence in equids of *T. equi* in Europe is approximately 30 %, with a prevalence of 24.5 %, while *B. caballi* has a seroprevalence in equids of 7.8 % and a prevalence of 2.4 % (Nadal et al., 2021). Due to the significant sanitary and economic impacts on the equine industry, it is crucial to identify the risk factors leading to horse infestation by ticks and infection with *B. caballi* and *T. equi* in order to develop effective prevention and control strategies. These risk factors are multifaceted and interconnected, encompassing biological, environmental and meteorological aspects in addition to human behaviour related to management practices (Onyiche et al., 2019). In Europe, most studies on equine piroplasmosis risk factors are based on serological analyses for *B. caballi* and *T. equi* (Nadal et al., 2021). These studies predominantly rely on univariate analyses, which risk introducing confounding biases and often overlook the environmental conditions to which the animals are exposed, potentially missing critical factors essential for understanding vector-borne diseases. Despite the lack of consensus on herd management and environmental risk factors, age and tick exposure have emerged as common risk factors, underscoring the need for further studies on disease management and environmental impacts on equine piroplasmosis (Nadal et al., 2021). While some publications describe tick species that feed on horses, studies specifically addressing the risk factors for equid infestation by ticks in Europe are rare (see review by Nadal et al., 2021).

Building on these insights and the disease episystem proposed by Nadal et al. (2021), our study aimed to identify both individual and environmental risk factors for *B. caballi* and *T. equi* infections, as well as

for infestation of horses by ticks; the ultimate goal being to issue recommendations for farmers to mitigate such risks. To achieve this, a field survey was performed on draught horse stud farms during the period of tick activity in Auvergne-Rhône-Alpes (AuRA) region in France.

# 2. Materials and methods

## 2.1. Study areas

The survey was conducted from 12 April to 14 May 2021 in the AuRA region of France. The choice of this region was justified by the presence of a diverse range of draught horse breeds (Breton, Comtois, Percheron and Ardennais) and its high circulation of *B. caballi* and *T. equi* (Nadal et al., 2022). Four study areas were selected based on 1) census data from the French Society of Working Equids (SFET) to focus on zones with a high concentration of draught horse stud farms; 2) differences in altitude; 3) differences in climate (Fig. 1). Areas 1 (A1) and 3 (A3) were located in low-altitude regions, while areas 2 (A2) and 4 (A4) were in high-altitude mountainous regions. A2 and A4 have a mountainous climate, A1 has a predominantly mountainous climate with some modified oceanic influences, and A3 is characterised by a mix of semi-continental and modified oceanic climates (Joly et al., 2010). Oral/written consent was obtained from equid owners for the collection of both ticks and horse blood samples.

## 2.2. Horse selection and blood sample collection

Draught horses were ideal for this study because they are kept in pastures almost year-round, making them highly exposed to tick bites and associated diseases. Draught horses included in the study were randomly selected from farms affiliated with the SFET using a two-stage random sampling method. As draught horse stud farms in this region are generally small, our sampling calculation was based on an average of four horses per farm. This calculation applied the method presented by Toma et al. (2018) for conducting a two-stage random survey (stage 1: farm; stage 2: horse). With an expected prevalence of 40 % for *T. equi* (Le Métayer, 2007) and an intraclass correlation coefficient of 4.5 % (Costa et al., 2019), the objectives were set at 22 % for relative precision and 5

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Location of the four study areas selected for the field survey in the Auvergne-Rhône-Alpes region, France. The number of draught horse stud farms is shown for each municipality. Climate and median [minimum - maximum] altitudes of the sampled pastures where the horses were located are indicated. The climate classifications are as follows: *M* = *Mountainous*, *OA* = *Modified Oceanic*, *SC* = *Semi-Continental*.</figcaption></figure>
‰ for alpha. It was then concluded that the minimum total number of 140 horses and 35 farms would be required. The number of horses selected from each farm depended on the farm's size: all animals were included for farms with one to four horses, five horses were included randomly for farms with five to nine horses, and seven horses were included randomly for farms with 10 or more horses.

Blood samples of 2-3 ml each were collected in EDTA tubes from the jugular vein of the selected horses to screen for infections by *B. caballi* and *T. equi*. The protocol was approved by the ANSES/ENVA/UPEC Animal Experimentation Ethics Committee and registered under number 24,386-2,020,022,715,532,389 v4.

## 2.3. Tick collection and species identification

To characterise horse infestation, ticks were looked for through visual inspection and palpation during a full examination of the animal, and removed using a tick remover. Questing ticks were collected in horse pastures using the dragging method by dragging a 1 m² square cotton sheet over the vegetation along 10 m transects (Gray and Lohan, 2008; Vassallo et al., 2000) positioned 1 m from the edge of the pastures as described by Agoulon et al. (2012). The number of transects varied according to the pasture size: four transects for a pasture of 4 hectares (ha) or less; six for a pasture between 4 and 12 ha; and eight for a pasture over 12 ha. Ticks were collected from the cotton sheet in the middle and at the end of each transect to minimise the risk of tick detachment.

All specimens were preserved in 70 % ethanol then identified to the species level using taxonomic keys and categorised by sex and life stage (Estrada-Peña et al., 2017; Pérez-Eid, 2007). For molecular identification confirmation, ticks were first individually crushed in a bead beater (Precellys®24, Bertin, France) as previously described (Reis et al., 2011). PCR amplification was then done using a TaqMan real-time PCR on a LightCycler® 480 (Roche Applied Science, Germany) as described by Michelet et al. (2014) with the exception of DNA extraction, which was carried out using the Nucleospin Tissue kit (Macherey Nagel, Düren, Germany) according to the manufacturer's instructions. Tick DNA was extracted individually and eluted in a final volume of 50 µl for nymphs and 100 µl for adults, then stored at -20 °C before use. Two microlitres of the eluted DNA were then used for PCR amplification with the primers shown in Appendix S1.

## 2.4. Parasite DNA detection in horse blood samples and tick samples

Babesia caballi and *T. equi* DNA in horse blood samples was detected by the LABEO Frank Duncombe laboratory (Saint Contest, France). Following DNA extraction, RT-PCR was performed according to Bhoora et al. (2010) for *B. caballi*, with modified cycle durations: a denaturation phase of 30 s at 95 °C, followed by 40 cycles of 5 s at 60 °C and 34 s at 60 °C. For *T. equi*, RT-PCR was conducted following the protocol described by Kim et al. (2008) with the same cycle durations as mentioned above for *B. caballi*.

Parasite DNA was detected in the samples from ticks collected in both pastures and on horses using PCR amplification, with specific primers targeting the 18S RNA gene to detect all species of the genera *Babesia* and *Theileria* (Bonnet et al. 2007). PCR amplification used 5 µl of DNA extract obtained as described above in a total volume of 20 µl containing 10 mM dNTP mix, 10X buffer, 0.2 µL of Taq Polymerase TaKaRa Ex Taq® (Takara Biomedical Group, Shiga, Japan), and 1 µl of each 10 µM primer: BabGF2 (5'-GYYTTGTAATTGGAATGATGG-3') and BabGR2 (5'-CCAAAGACTTTGATTTCTCTC-3'). A MyCycler thermocycler (Bio-Rad, Strasbourg, France) was used for amplification with the following programme: a denaturation phase of 8 min at 94 °C, 35 cycles of 1 min at 94 °C, 1 min at 60 °C, and 1 min at 72 °C, with a final elongation step of 10 min at 72 °C. Positive and negative controls were included as previously described (Reis et al., 2011). The resulting amplicons were analysed on 2 % agarose gels stained with ethidium bromide. Successful DNA extraction was confirmed in all samples by

PCR amplification of the 16S rRNA mitochondrial gene using tick-specific primers TQ16S+1F (5'-CTG CTC AAT GAT TTT TTA AAT TGC TGT GG-3') and TQ16S-2R (5'-ACG CTG TTA TCC CTA GAG-3') as previously described (Black and Piesman, 1994). All positive PCR products were subject to direct sequencing by the Eurofins Scientific medical analysis laboratory (France). Contiguous sequences were compared with known sequences listed in the GenBank nucleotide sequence database using the Blast search option of the US National Center for Biotechnology Information (www.ncbi.nlm.nih.gov/BLAST).

## 2.5. Study of risk factors

To study the risk factors for horse infection by *B. caballi* and *T. equi* and infestation by ticks, four types of variables were defined: individual horse characteristics, management practices, environmental and meteorological variables (Table 1).

A questionnaire was given to equid holders that helped to collect data such as host-related information (age, sex, breed, coat colour, date of the last foaling or expected foaling for mares), herd management (use of antiparasitics/insecticides in the past two months, animal's vaccination status, mixed grazing practices with other animal species, number of equines grazing together in each pasture), and eco-geography of each pasture corresponding to the studied horses.

Regarding environmental variables, the GPS location, as well as data such as the presence/absence of a water body (attractive for wildlife), and the pasture's direct proximity to agricultural land or forest, were collected directly in the field for each pasture. The pasture outlines were drawn (creating polygons) in QGIS 3.26.3, based on the recorded GPS locations and satellite images. To characterise the environment and weather conditions, the altitude of the pasture centroids was first extracted from the BD ALTI® database. Using BD TOPO® 2021, we then calculated the length of the forest edge and the vegetation area within the pasture, as well as the forest area (attractive to wildlife and providing habitats for tick hosts) and the built-up area (used as an indicator of urbanisation) within an 800 m radius. The fragmentation of the habitat surrounding the pastures was characterised by analysing the landscape within a 20 m, 300 m, and 800 m radius around the pastures. The choice of these radiuses was justified by considering the home ranges of potential tick hosts, such as small mammals, lagomorphs and cervids (GBIF, 2023a, 2023b, 2023c). Using the Corine Land Cover® (CLC) 2018 database ("CORINE Land Cover 2018 (CLC 2018), 2018), we calculated habitat fragmentation indicators deemed relevant for the study of ticks and tick-borne diseases (Diuk-Wasser et al., 2020). These indicators included (i) the Simpson Diversity Index (SIDI) at the landscape scale, which is a measure of diversity that considers both the number of species present (richness) and the relative abundance of each species (evenness) within a habitat, (ii) the average size of forest and agricultural land plots at the plot scale, (iii) the number of forest plots at the plot scale, and (iv) contiguity between forest plots. To this end, the 43 CLC classes were grouped into five classes: meadows/pastures, crops, permanent plantations (orchards/vineyards), shrubland, and forests (Appendix S2).

To characterise the meteorological conditions to which the horses were exposed when they were bitten by ticks, we calculated the minimum and maximum daily average temperatures and the cumulative rainfall over the past 7 days, considering that the blood meal can last between 3 and 10 days (Lees, 1948; Matuschka et al., 1990). Additionally, we calculated the number of frost days (i.e. the number of days when the temperature dropped below 0 °C) during the previous winter (Paul et al., 2016). These variables were calculated using data from the ERA5 hourly database (Hersbach et al., 2021). Finally, the climate affecting each pasture was recorded based on data from Joly et al. (2010).

All variable calculations were performed using R v.4.1.2. The habitat fragmentation indicators were calculated using the *landscapemetrics* package in R.
<table><caption>**Table 1**
List of individual, environmental, meteorological and farming practice-related variables that were either collected or calculated.</caption><thead><tr><th>Type of variable</th><th>Variable name</th><th>Variable type</th><th>Database used</th></tr></thead><tbody><tr><td rowspan="5">INDIVIDUAL CHARACTERISTICS OF HORSES</td><td>Age</td><td>Numerical</td><td>Questionnaire</td></tr><tr><td>Breed</td><td>Factor (Ardennais/Breton/Comtois/Percheron/Crossbreed)</td><td>Questionnaire</td></tr><tr><td>Sex</td><td>Factor (Male/Female)</td><td>Questionnaire</td></tr><tr><td>Coat colour</td><td>Factor (Light/Dark)</td><td>Questionnaire</td></tr><tr><td>Foaling during the past month or expected in the next month</td><td>Factor (Yes/No)</td><td>Questionnaire</td></tr><tr><td rowspan="3">FARMING PRACTICES</td><td>Antiparasitic treatment</td><td>Factor (Yes/No)</td><td>Questionnaire</td></tr><tr><td>Ivermectin</td><td></td><td></td></tr><tr><td>Doramectin</td><td></td><td></td></tr><tr><td rowspan="7">ENVIRONMENTAL VARIABLES</td><td>Moxidectin</td><td></td><td></td></tr><tr><td>Insecticide (Deltamethrin)</td><td>Factor (Yes/No)</td><td>Questionnaire</td></tr><tr><td>Mixed grazing (alternating, simultaneous, no mixed grazing)</td><td>Factor (Yes/No)</td><td>Questionnaire</td></tr><tr><td>Number of horses grazing together</td><td>Numerical</td><td>Questionnaire</td></tr><tr><td>Pasture area</td><td>Numerical</td><td>Questionnaire</td></tr><tr><td>Water body in the pasture</td><td>Factor (Yes/No)</td><td>Questionnaire</td></tr><tr><td>Pasture altitude</td><td>Numerical</td><td>BD ALTI®</td></tr><tr><td rowspan="10">METEOROLOGICAL AND CLIMATIC VARIABLES</td><td>Area of vegetation (medium and high) inside the pasture</td><td>Numerical</td><td>BD TOPO®</td></tr><tr><td>Length of forest edge around the pasture</td><td>Numerical</td><td></td></tr><tr><td>Forest area within an 800-m radius</td><td>Numerical</td><td></td></tr><tr><td>Built-up area within an 800-m radius</td><td>Numerical</td><td></td></tr><tr><td>Simpson Diversity Index (SIDI) within 800-m, 300-m and 20-m radius</td><td>Numerical</td><td>Corine Land Cover®</td></tr><tr><td>Average forest plot size within an 800-m radius</td><td>Numerical</td><td></td></tr><tr><td>Average agricultural plot size within an 800-m radius</td><td>Numerical</td><td></td></tr><tr><td>Number of forest plots within an 800-m radius</td><td>Numerical</td><td></td></tr><tr><td>Contiguity between forest plots</td><td>Numerical</td><td></td></tr><tr><td>Daily minimum and maximum average temperatures during the past week</td><td>Numerical</td><td>ERA5®</td></tr><tr><td>Amount of rainfall during the past week</td><td>Numerical</td><td></td></tr><tr><td>Number of frosty days during winter</td><td>Numerical</td><td></td></tr><tr><td>Climate type</td><td>Factor (Mountain/Semi-Continental/Modified Oceanic)</td><td>Joly et al. (2010)</td></tr></tbody></table>

## 2.6. Statistical analyses

Chi-square tests were conducted to compare prevalence of infections by *B. caballi* and *T. equi* in horses and their associated 95 % confidence intervals between areas. To identify risk factors associated with horse infection, a multivariable logistic regression was performed for each parasite species, the dependent variable being the horse's infection status (PCR-positive or -negative). The explanatory variables included (i) age, breed and sex of the horse, (ii) use of antiparasitics and/or insecticides, number of horses in the pasture, type of grazing (unmixed, alternating or simultaneous with cattle); (iii) built-up area and forest area within an 800-m radius around the pasture; Shannon diversity index SIDI 800 m and 300 m all around the pasture; altitude; tick burden of the horse by tick species; presence of ticks per species in the horse's environment (detected in the pasture directly or on at least one horse in the pasture); and (iv) the climate affecting the pasture (Table 1). Since a positive PCR could indicate either a very recent infection (a few days) or an older infection (up to several years), the variables studied represented relatively stable exposure conditions over time to *B. caballi* and *T. equi* infections. Multicollinearity between explanatory variables was assessed by examining the generalised variance inflation factor (GVIF), and variables with GVIF(1/2Df) > 10 were removed from the model (Fox and Monette, 1992). The final models were chosen using a stepwise backward selection method, the model with the lowest Akaike Information Criterion (AIC) being retained.

A Kruskal-Wallis test was used to compare the median tick burden on horses (i.e. the number of ticks collected from each horse, whatever the species) for each area. Generalised additive models (GAMs) were used to study the risk factors for horse infestation by ticks by considering non-linear relationships between infestation and explanatory variables. All the parameters listed in Table 1 as explanatory variables were included in the model. For individual horse characteristics, we considered age,

breed, sex and coat colour, and for mares only, whether the last foaling was in the previous month or the next foaling was expected in the following month. For management practices, we included the use of antiparasitics and/or insecticides, the number of horses grazing together, and the practice of mixed grazing with cattle (alternating or simultaneous). Environmental parameters included in the model were the pasture's surface area and altitude, the presence of a water body within the pasture, the vegetation area (average and height) within the pasture, the length of the forest edge around the pasture, and the forest surface area or a built-up area within an 800-m radius of the pasture. Additionally, we considered the SIDI at 20 m, 300 m and 800 m, the average size of forest plots and agricultural plots within an 800 m radius, the number of forest plots within an 800-m radius, and contiguity between these plots. Meteorological parameters included the minimum and maximum daily temperatures, the cumulative precipitation recorded in the seven days preceding the collection, and the number of frosty days during the previous winter. Unlike the analyses concerning horse infections by *B. caballi* and *T. equi*, the number of ticks observed on the horse reflects recent exposure (a few days).

The intensity of horse infestation by ticks was analysed using the predominant tick species identified in our study, *D. reticulatus*. The dependent variable was the number of *D. reticulatus* specimens observed on horses infested by at least one *D. reticulatus* tick. The explanatory variables included in the model were the same as those mentioned for the tick burden model.

For both models concerning horse infestation by ticks, explanatory variables with a concurvity (the non-parametric equivalent of collinearity) greater than 0.9 were excluded from the models. The variables considered to be significantly associated with tick infestation in horses were those with a p-value below to alpha level of 5 % in the GAMs.

Eight horses were excluded from the statistical analyses on risk factors: two grazed in an inaccessible pasture (A3), and six (one in A3 and
five in A4) were housed indoors during data collection due to the mountainous climate and low temperatures at the time of sampling.

R v.4.1.2 was used for all the statistical analyses, and the GAMs were run using the *mgcv* package.

# 3. Results

## 3.1. Characteristics of the study population and areas

Out of the 150 draught horses initially selected, blood samples were obtained from 146, with the remaining four horses not sampled due to insufficient docility for safe handling during sampling. The sampled population included 134 females and 16 males (of which three were geldings) from 38 farms, with an average number of four horses per farm. The breeds represented included 84 Comtois, 13 Ardennais, 37 Bretons and 4 Percherons with 12 horses of mixed breed. In total, 73 pastures were sampled at altitudes ranging from 176 m to 1288 m, giving a median altitude of 561 m. Overall, A2 and A4 had larger forested areas within an 800 m radius around the sampled pastures than A1 and A3. Additionally, A3 and A4 had larger built-up areas within an 800 m radius around the pastures, coupled with smaller average pasture surface areas.

## 3.2. Tick collection

From 12 April to 14 May 2021, a total of 1049 ticks (140 on pastures and 909 on horses) were collected from the four study areas, of which 608 were identified as *D. reticulatus*, 243 as *D. marginatus* and 195 as *I. ricinus*, while three ticks were too damaged to be identified and excluded from the study. The tick collection was comprised of 938 adults, 108 nymphs and no larvae. The number, life stage and species of ticks collected per area on horses and pastures are presented in Fig. 2. Most of the ticks (nearly 800) were collected in area 3 from both horses and pastures. The median tick burden on horses varied significantly between areas, with horses in A3 having a significantly higher burden (p < 0.001) than those in other areas (Fig. 3). Sixty horses, including 52 females and eight males, were infested by at least one *D. reticulatus* tick.

## 3.3. Risk factors of tick infestation of horses

The model concerning total tick burden on horses and explanatory variables explained 82.2 % of the variance. Individual parameters such

as breed and age of the horses were significantly associated with tick burden (Table 4). Specifically, Ardennais and Percheron breeds showed significantly lower infestation levels than Comtois horses, while older horses were more heavily infested than younger ones (Fig. 4 and Table 4). Among broodmares, recent or upcoming foaling within the month was associated with lower tick burdens (Table 4). The use of doramectin was significantly linked to lower tick burdens, whereas alternating grazing was associated with higher tick burdens than unmixed grazing (Table 4). The minimum daily temperature recorded over the past 7 days, vegetation cover within the pasture, and average size of forest plots were all positively associated with tick burdens on horses (Table 4 and Fig. 4). Conversely, cumulative rainfall over the past 7 days and length of forest edge were negatively associated with total tick burdens. Pasture altitude and the SIDI value within a 300-m radius around the pasture were also significantly related to tick burdens (Table 4). The relationship between tick burden and SIDI followed a positive trend for lower values, becoming negative after a SIDI threshold of 0.3 (Fig. 4). The altitude followed a bell-shaped curve: there was a positive slope up to an altitude of 500 m, a negative slope between 500 and 1000 m of altitude, and then a positive slope again for higher altitude values (Fig. 4).

The model on the infestation intensity by *D. reticulatus* showed an explained deviance of 82.4 %. Infestation intensity was lower in Percheron and Breton breeds than in Comtois horses, lower in males than in females, and lower when foaling had occurred within the last month or was expected in the following month for mares (Table 5). On the contrary, the number of *D. reticulatus* ticks collected from horses was significantly higher when ivermectin had been administered in the past 2 months, and when there was a water body in the pasture (Table 5). Positive relationships linked cumulative rainfall over the last 7 days and SIDI within 300 m around the pasture with horse infestation intensity by *D. reticulatus* at lower values. These relationships turned negative beyond a certain threshold (50 mm for rainfall and 0.35 for SIDI, Fig. 5, and Table 5). Finally, the number of forest plots within an 800-m radius of the pastures was negatively associated with the intensity of horse infestation by *D. reticulatus* (Table 5 and Fig. 5).

## 3.4. Prevalence of *B. caballi* and *T. equi* in horse blood samples and risk factor analysis

The overall prevalence in horses of the study was estimated at 50.0 % [95 % CI: 41.9 – 58.1] for *T. equi* and 14.8 % [95 % CI: 9.6 – 22.0] for

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Prevalence of *T. equi* and *B. caballi* among sampled draught horses with associated 95 % confidence interval in each of the four study areas in the Auvergne-Rhône-Alpes region, France. Horses in A3 were significantly more infected with both *B. caballi* (χ²*Fisher*; *p* = 0.005) and *T. equi* (χ²*Pearson*; *p* = 1.2e-08) than horses in other areas.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Number of ticks per species (*D. marginatus*, *D. reticulatus* and *I. ricinus*) and life stage (adult and nymph) collected on horses and in pastures in each of the four study areas in the Auvergne-Rhône-Alpes region, France.</figcaption></figure>

*B. caballi*. Prevalence varied between 25.7 % [95 % CI: 13.1 – 43.6] in A1 and 83.7 % [95 % CI: 69.8 – 92.2] in A3 for *T. equi*, and between 0.0 % [95 % CI: 0.0 – 26.8] in A4 and 26.5 % [95 % CI: 15.4 – 41.3] in A3 for *B. caballi* (Fig. 6). According to $\chi^2$ test results, horses in A3 were significantly more infected with both *B. caballi* and *T. equi* than horses in other areas.

The best model selected to identify risk factors for *T. equi* infection in horses related to individual horse characteristics, management practices and environmental variables (Table 2). Breed was significantly associated with *T. equi* infection, fewer Breton horses being infected than Comtois ones (OR = 0.16 [95 % CI: 0.04–0.6]). The administration of ivermectin was significantly associated with *T. equi* infections, with treated horses less likely to test positive than untreated horses (OR = 0.07 [95 % CI: 0.01–0.3]). A larger forested area within an 800 m radius around the pasture was positively associated with *T. equi* infections (OR = 1.01 [95 % CI: 1.0–1.01]), while altitude was negatively associated with infections (OR = 0.99 [95 % CI: 0.99–0.99]). Lastly, regarding tick exposure, the number of *D. reticulatus* ticks both on the horse and in the pasture was identified as a risk factor for *T. equi* infection (with OR = 1.47 [95 % CI: 1.14–2.07] and OR = 6.37 [95 % CI: 1.97–23.21],

respectively).

Regarding *B. caballi* infection in horses, the best model was linked to individual horse characteristics and management practices (Table 3). Age was negatively associated with *B. caballi* infection in horses (OR = 0.86 [95 % CI: 0.75–0.97]), whereas more males were infected than females (OR = 9.25 [95 % CI: 1.69–54.93]). The risk of *B. caballi* infection was higher in simultaneous grazing than unmixed pastures (OR = 8.72 [95 % CI: 2.02–43.18]).

### 3.5. Prevalence of *B. caballi* and *T. equi* DNA in ticks

The results of parasite DNA detection in ticks are presented in Table 6. All the ticks detected positive for the presence of *Theileria* or *Babesia* DNA were collected from horses except one questing *D. reticulatus* female that carried the DNA of two different parasite species, preventing species identification by sequencing. Among the 1046 ticks analysed individually by PCR, 31 (2.96 % [95 % CI: 2.10 – 4.18]) were positive for at least one species of the two parasite genera: 20 males, seven females and one nymph. Four infected ticks were collected in area 2: one *I. ricinus* nymph infected with *Babesia venatorum*,
<figure><img src="image_7.png" /><figcaption>Fig. 4. Distribution of tick burden observed on horses in each of the four study areas in the Auvergne-Rhône-Alpes region, France: the bold line in the centre of the box plot represents the median. The median tick burden in A3 is significantly different from that in the other areas (Kruskal-Wallis tests, *p* < 0.01).</figcaption></figure>

one female *D. reticulatus* infected with *T. equi* and two male *D. reticulatus* ticks infected with two different parasite species. All the remaining infected ticks (n = 27) were collected in area 3. *Theileria equi* was detected in 2.13 % [95 % CI: 1.25 – 3.62] and 2.47 % [95 % CI: 1.14 – 5.28], and *B. caballi* in 0.82 % [95 % CI: 0.35 – 1.91] and 0.41 % [95 % CI: 0.07 – 2.29] of collected *D. reticulatus* and *D. marginatus* ticks, respectively. None of the parasites responsible for equine piroplasmosis were detected in *I. ricinus*, whereas *Babesia microti* and *B. venatorum* were detected.

## 4. Discussion

Based on a field study performed in a French area heavily impacted by equine piroplasmosis, we integrated horse individual, environmental and human factors in order to identify risk factors for horses, for both tick infestation and parasitic infection. Our results provide then valuable insights into the “eco-epidemiology” of equine piroplasmosis, which are

essential for developing targeted prevention and control strategies in enzootic regions.

Three different tick species were collected on horses in the AuRA region: *D. reticulatus*, *D. marginatus*, and *I. ricinus*. This revealed a lower tick diversity compared to another study performed in further south region in France also highly affected by equine piroplasmosis. In fact, in addition to *Dermacentor* sp. *R. bursa*, *R. sanguineus* s.l., *H. marginatum* and *H. punctata* have been recently identified on horses in Camargue area (Rocafort-Ferrer et al., 2022). This difference may reflect the more favourable climatic conditions for the *Rhipicephalus* and *Hyalomma* genera in Camargue, but may be also due to the fact that their study has been performed within a two-year period (2015–2016), potentially allowing ticks of different species having different seasons of activity during the year to be collected, while we only collected ticks during the spring. The predominance of *Dermacentor* sp. in our study (81 % of all ticks collected) reflects their feeding preferences, with adults often parasitizing horses (Földvári et al., 2016; Rubel et al., 2016), while
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Relationships between the quantitative variables significantly associated with the total tick burden on horses, based on the generalised additive model. The GAM considered individual characteristics, farming practices and environmental variables.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Relationships between quantitative variables significantly associated with *D. reticulatus* infestation intensity among horses infested at least with one *D. reticulatus* tick, based on the generalised additive model. The GAM considered individual characteristics, farming practices and environmental variables.</figcaption></figure>

*I. ricinus* is a generalist tick feeding more sporadically on horses (Kahl and Gray, 2023). As well, the assumed endophilic behaviour of *Derma-centor* larvae and nymphs (Földvári et al., 2016), explains that almost all the nymphs collected are *I. ricinus*, an exophilic tick at all life stages (Kahl and Gray, 2023). Furthermore, the scarcity of ticks collected from vegetation compared to those collected on horses could be attributed to the unusually cool spring in 2021.

Our study identified key risk factors for horse tick infestation, with models explaining over 80 % of the variance and highlighting the pre-dominance of environmental conditions over individual horse characteristics and management practices. Tick burden was positively associated with minimum daily average temperature in the week preceding collection, consistent with findings for *D. reticulatus*, which is active from 1 °C and become inactive when average temperature exceeds 15 °C (Sands et al., 2021). This pattern aligns with studies showing *I. ricinus* nymph activity increases with increasing monthly minimum temperature (Paul et al., 2016). Interestingly, a negative relationship was observed between tick burden and cumulative precipitation over the previous seven days. As other studies have suggested no direct link between precipitations and questing activity of *D. reticulatus* and *I. ricinus* (Paul et al., 2016; Sands et al., 2021), we hypothesised that reduced horse movement during rain could limit exposure to ticks. Additionally, the bell-shaped relationship *D. reticulatus* infestation
<caption>**Table 2**</caption>

Variables included in the final logistic regression model for *T. equi* infections in horses and individual characteristics, management practices and environmental variables considered, Odds ratio and associated 95 % Confidence Interval (CI). P-values of significant variables are in bold.

<table><caption>**Table 3**</caption><thead><tr><th>Type of variable</th><th>Variable</th><th>Level</th><th>Odds ratio</th><th>95 % CI</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="5">INDIVIDUAL CHARACTERISTICS</td><td rowspan="5">Breed</td><td>Comtois</td><td>reference</td><td></td><td></td></tr><tr><td>Ardennais</td><td>0.12</td><td>0.007-1.1</td><td>0.08</td></tr><tr><td>Percheron</td><td>0.11</td><td>0.001-3.5</td><td>0.27</td></tr><tr><td>Breton</td><td>0.16</td><td>0.04-0.6</td><td><strong>0.01</strong></td></tr><tr><td>Mixed</td><td>9.20</td><td>0.9-124.7</td><td>0.07</td></tr><tr><td rowspan="3">MANAGEMENT PRACTICES</td><td rowspan="2">Ivermectin</td><td>No</td><td>reference</td><td></td><td></td></tr><tr><td>Yes</td><td>0.07</td><td>0.01-0.3</td><td><strong>0.001</strong></td></tr><tr><td>Moxidectin</td><td>No</td><td>reference</td><td></td><td></td></tr><tr><td rowspan="3">ENVIRONMENTAL DATA</td><td>Forested area within 800-m radius</td><td>Yes</td><td>0.12</td><td>0.01-1.04</td><td>0.07</td></tr><tr><td>Altitude</td><td>Numerical</td><td>1.01</td><td>1.0-1.01</td><td><strong>0.03</strong></td></tr><tr><td>SIDI within 300-m radius</td><td>Numerical</td><td>0.99</td><td>0.992-0.997</td><td><strong>&lt;0.001</strong></td></tr><tr><td rowspan="4">VECTOR EXPOSURE</td><td>Number of <i>D. marginatus</i> ticks on horses</td><td>Numerical</td><td>14.4</td><td>0.67-453.65</td><td>0.10</td></tr><tr><td>Number of <i>D. reticulatus</i> ticks on horses</td><td>Numerical</td><td>1.43</td><td>0.98-3.14</td><td>0.19</td></tr><tr><td>Presence of <i>D. reticulatus</i> in the horses' direct environment</td><td>No</td><td>reference</td><td>1.14-2.07</td><td><strong>0.01</strong></td></tr><tr><td>Presence of <i>D. marginatus</i> in the horses' direct environment</td><td>Yes</td><td>6.37</td><td>1.97-23.21</td><td><strong>0.003</strong></td></tr></tbody></table>

Estimates and p-values for each variable (individual characteristics, management practices and environment) considered in the logistic regression model of *B. caballi* infections in horses. P-values of significant variables are in bold.

<table><thead><tr><th>Type of variable</th><th>Variable</th><th>Level</th><th>Odds ratio</th><th>95 % CI</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="2">INDIVIDUAL CHARACTERISTICS</td><td>Age</td><td>Numerical</td><td>0.86</td><td>0.75-0.97</td><td><strong>0.03</strong></td></tr><tr><td>Sex</td><td>Female</td><td>reference</td><td></td><td></td></tr><tr><td rowspan="5">MANAGEMENT PRACTICES</td><td rowspan="2">Deltamethrin</td><td>Male</td><td>9.25</td><td>1.69-54.93</td><td><strong>0.01</strong></td></tr><tr><td>No</td><td>reference</td><td></td><td></td></tr><tr><td rowspan="4">Type of grazing</td><td>Yes</td><td>5.46</td><td>0.87-34.61</td><td>0.07</td></tr><tr><td>Unmixed</td><td>reference</td><td></td><td></td></tr><tr><td>Alternating</td><td>4.71</td><td>0.96-24.39</td><td>0.06</td></tr><tr><td>Simultaneous</td><td>8.72</td><td>2.02-43.18</td><td><strong>0.005</strong></td></tr><tr><td rowspan="4">ENVIRONMENTAL VARIABLES</td><td>Forested area within an 800-m radius</td><td>Numerical</td><td>1.01</td><td>0.99-1.01</td><td>0.13</td></tr><tr><td>Altitude</td><td>Numerical</td><td>0.99</td><td>0.99-1.00</td><td>0.10</td></tr><tr><td rowspan="3">Climate</td><td>Mountainous</td><td>reference</td><td></td><td></td></tr><tr><td>Modified oceanic</td><td>0.20</td><td>0.01-3.01</td><td>0.25</td></tr><tr><td>Semi-continental</td><td>1.60</td><td>0.27-10.73</td><td>0.61</td></tr><tr><td>VECTOR EXPOSURE</td><td>Number of <i>I. ricinus</i> ticks on horses</td><td>Numerical</td><td>0.57</td><td>0.19-1.02</td><td>0.20</td></tr></tbody></table>

intensity suggested favourable abiotic conditions at low precipitation levels, with behavioural changes in horses or adverse conditions for ticks at higher rainfall levels.

Our results confirmed that the AuRA region is endemic for equine piroplasmosis, with high levels of *B. caballi* and *T. equi* detection both in draught horses (15 % and 50 %, respectively), and in *Dermacentor* species, proven vectors of both parasites (1 % and 2 %, respectively). These results place therefore our study region in the high average of European countries with reported *T. equi* prevalence ranges from 1 % in Poland to 49 % in Hungary and *B. caballi* prevalence ranges from 0 % in the Netherlands to 11 % in Sicily in equids (horses, donkeys, and mules) (see review by Nadal et al., 2021). This may be explained by both extensive exposure to tick bites (59 % of horses were infested) and the piroplasm infection rate of 3 %, which is among the highest reported in Europe (Butler et al., 2016; Garcia-Sanmartín et al., 2008; Iori et al., 2010; Jongejan et al., 2015). It is also notable that these results are consistent with those obtained during the study carried out in the Camargue which reported 69 % and 6 % of horses infected by *T. equi* and *B. caballi*, respectively (Rocafort-Ferrer et al., 2022). Interestingly, two *I. ricinus* females were infected with *B. microti*, and one nymph with *B. venatorum*, two zoonotic parasites for which *I. ricinus* is a proven vector (Bonnet et al., 2007; Gray et al., 2002). Although *B. caballi* and *T. equi* DNA has been detected in questing *I. ricinus* ticks in other studies (Butler et al., 2016; Garcia-Sanmartín et al., 2008; Iori et al., 2010), vector competence for these pathogens remains unconfirmed and requires experimental validation. Overall, *T. equi* was more prevalent than *B. caballi*, reflecting its ability to establish long-term infections in equids, while

*B. caballi* is often cleared through sterilising immunity (Ruegg et al., 2008, 2007), which is consistent with previous European studies (Axt et al., 2024; Camino et al., 2020a; Laus et al., 2013; Rocafort-Ferrer et al., 2022; Zanet et al., 2017).

We reported a spatial variation within the AuRA region, highlighting the influence of environmental conditions. Area A3, with its low altitude and a mild climate, showed the highest prevalence of *T. equi* and *B. caballi* in both horses and ticks, consistent with more favourable conditions for *Dermacentor* sp. activity (Joly et al., 2010). For example, *B. caballi* was absent in A4, potentially due to colder winters limiting transovarian transmission in ticks (Ouhelli and Schein, 1988), while *T. equi* persisted due to its reservoir dynamics in horses. However, our analysis of risk factors also clearly demonstrated that environmental factors were strongly linked to *T. equi* infections. Horses in low-altitude pastures harboured higher *T. equi* prevalence, likely due to favourable conditions for tick survival and activity, consistent with findings from Villa et al. (2022). In the same way, forested area within an 800-m radius, medium to high vegetation cover and water body presence in pastures providing ideal habitats and attractiveness for wildlife hosts and then tick maintenance, were positively associated with both *D. reticulatus* presence and *T. equi* infection (Bhosale et al., 2023). Landscape fragmentation also influenced tick presence; a bell-shaped relationship between Simpson's diversity index and *D. reticulatus* intensity suggested that moderate habitat fragmentation facilitates wildlife movements through ecological corridors, whereas excessive fragmentation disrupts connectivity. Additionally, pastures with more numerous but smaller forest patches within an 800-m radius appeared to
<caption>**Table 4**</caption>

Estimates and p-values for each variable (individual characteristics, management practices and environment) considered in the generalised additive model for the total tick burden observed among horses. P-values of significant variables are in bold.

<table><thead><tr><th>Type of variable</th><th>Variable</th><th>Level</th><th>Estimate</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="10">INDIVIDUAL CHARACTERISTICS</td><td rowspan="5">Breed</td><td>Comtois</td><td>reference</td><td></td></tr><tr><td>Ardennais</td><td>-7.70</td><td><strong>0.049</strong></td></tr><tr><td>Percheron</td><td>-1.65</td><td><strong>0.049</strong></td></tr><tr><td>Breton</td><td>-1.59</td><td>0.08</td></tr><tr><td>Mixed breed</td><td>-0.17</td><td>0.84</td></tr><tr><td rowspan="3">Sex</td><td>Female</td><td>reference</td><td></td></tr><tr><td>Male</td><td>-1.07</td><td>0.11</td></tr><tr><td>Light</td><td>reference</td><td></td></tr><tr><td rowspan="2">Coat colour</td><td>Dark</td><td>-0.16</td><td>0.59</td></tr><tr><td>Numerical</td><td>-</td><td><strong>0.04</strong></td></tr><tr><td rowspan="10">MANAGEMENT PRACTICES</td><td rowspan="2">Age</td><td>No</td><td>reference</td><td></td></tr><tr><td>Yes</td><td>-0.90</td><td><strong>0.02</strong></td></tr><tr><td rowspan="2">Foaling in the last month or expected in the next month</td><td>No</td><td>reference</td><td></td></tr><tr><td>Yes</td><td>-0.79</td><td>0.11</td></tr><tr><td rowspan="2">Ivermectin</td><td>No</td><td>reference</td><td></td></tr><tr><td>Yes</td><td>0.93</td><td>0.25</td></tr><tr><td rowspan="2">Moxidectin</td><td>No</td><td>reference</td><td></td></tr><tr><td>Yes</td><td>-3.38</td><td><strong>&lt; 0.001</strong></td></tr><tr><td rowspan="2">Doramectin</td><td>No</td><td>reference</td><td></td></tr><tr><td>Yes</td><td>-0.36</td><td>0.60</td></tr><tr><td rowspan="10">ENVIRONMENTAL DATA</td><td rowspan="2">Type of grazing</td><td>Unmixed</td><td>reference</td><td></td></tr><tr><td>Alternating</td><td>2.23</td><td><strong>0.02</strong></td></tr><tr><td rowspan="2">Number of equids in pasture</td><td>Simultaneous</td><td>0.67</td><td>0.39</td></tr><tr><td>Numerical</td><td>-</td><td>0.56</td></tr><tr><td rowspan="2">Water body in pasture</td><td>No</td><td>reference</td><td></td></tr><tr><td>Yes</td><td>-0.29</td><td>0.58</td></tr><tr><td>Maximum daily temperature in last 7 days</td><td>Numerical</td><td>-</td><td>0.78</td></tr><tr><td>Minimum daily temperature in last 7 days</td><td>Numerical</td><td>-</td><td><strong>0.03</strong></td></tr><tr><td>Cumulative rainfall in last 7 days</td><td>Numerical</td><td>-</td><td><strong>0.01</strong></td></tr><tr><td>Vegetation cover (average and high) in pasture</td><td>Numerical</td><td>-</td><td><strong>0.02</strong></td></tr><tr><td>Built-up area within 800 m radius</td><td>Numerical</td><td>-</td><td>0.07</td></tr><tr><td>Length of forest edge</td><td>Numerical</td><td>-</td><td><strong>0.006</strong></td></tr><tr><td>Altitude</td><td>Numerical</td><td>-</td><td><strong>0.01</strong></td></tr><tr><td>Average forest plot size within 300 m radius</td><td>Numerical</td><td>-</td><td><strong>0.008</strong></td></tr><tr><td>Average agricultural plot size within 300 m radius</td><td>Numerical</td><td>-</td><td>0.06</td></tr><tr><td>Number of forest plots within 800 m radius</td><td>Numerical</td><td>-</td><td>0.11</td></tr><tr><td>SIDI within 20 m radius</td><td>Numerical</td><td>-</td><td>0.07</td></tr><tr><td>SIDI within 300 m radius</td><td>Numerical</td><td>-</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>SIDI within 800 m radius</td><td>Numerical</td><td>-</td><td>0.44</td></tr></tbody></table>

be less suitable for wildlife, reducing then tick abundance.

Our study also revealed some horse individual risk factors influencing *T. equi* and *B. caballi* infections, as well as tick burdens in draught horses, and underscore the complex interplay of breed, age, and sex with tick infestations and pathogen susceptibility. Breton draught horses appeared to be less susceptible to *T. equi* infection than Comtois horses, indicating a breed-specific effect likely due to genetic predispositions, as suggested in other studies on equine and bovine susceptibility (Bartolomé Del Pino et al., 2016; Bock et al., 1999; Camino et al., 2020a; Guidi et al., 2015; Parker et al., 1985). No such breed effect was detected for *B. caballi*, possibly due to its lower prevalence. Similarly, breed influenced tick burden, with Percheron and Ardennais horses being less infested than Comtois horses. However, sample size for these breeds and their geographical clustering across areas (e.g. most Percherons in A3) complicated disentangling breed-related predisposition from geographical exposure. These findings align with research in cattle, where resistance to ticks has been linked to immune response and dermal integrity (Barnard, 1990; Hewetson, 2008; Kongsuwan et al., 2008; Maryam et al., 2012). We also showed that younger horses had higher susceptibility to *B. caballi*, likely reflecting the development of protective immunity with age, while the prolonged persistence of *T. equi* infections in enzootic areas explained the lack of age correlation for this parasite (Axt et al., 2024; Ruegg et al., 2008, 2007; Tirosh-Levy et al., 2021). Interestingly, older horses carried a higher tick burden, potentially due to less frequent grooming or immune system differences, a pattern noted in other studies on horses, and also on donkeys and mules (Ali et al., 2021; DeNotta and McFarlane, 2023; Mooring et al., 2000).

Males were also more frequently infected with *B. caballi* than mares, though this finding warranted caution due to the smaller sample size of males in the present study. Sex-related immune differences, as observed in mice with *B. microti*, may play a role (Sasaki et al., 2013), but the geographical clustering of males in areas with high *D. reticulatus* abundance limits conclusions here. Mares close to foaling showed lower tick burdens and *D. reticulatus* infestations, possibly due to increased care and monitoring by breeders. However, no association was found between foaling status and infection rates.

Regarding risk factors link to management, we showed that the administration of ivermectin emerged as a protective factor against *T. equi*, consistent with findings from Guidi et al. (2015), who reported that horses treated with internal antiparasitics were more often seronegative for *T. equi*. This effect may reflect improved management practices, such as better monitoring and reduced tick exposure, or a direct impact of ivermectin on tick populations, as macrocyclic lactones, including ivermectin, have been shown to affect certain tick species, like *R. microplus* (Pereira, 2009). Supporting this, our model on the total tick burden revealed a protective effect of doramectin, another macrocyclic lactone. However, ivermectin was identified as a risk factor for *D. reticulatus* infestation intensity, potentially reflecting its more frequent use on farms with high tick burdens. Mixed grazing with cattle was a significant risk factor for *B. caballi* infection, because both animal species are potential hosts for ticks and therefore increase both tick abundance and infection risk (Camino et al., 2020b; Guidi et al., 2015). Conversely, *T. equi* infections were unaffected, likely because horse themselves serve as the primary reservoir for this pathogen.
<caption>**Table 5**</caption>

Estimates and p-values for each variable (individual characteristics, management practices and environment) considered in the generalised additive model for the infestation intensity of horses by *D. reticulatus* among horses presenting at least one *D. reticulatus* tick. P-values of significant variables are in bold.

<table><caption>**Table 6**</caption><thead><tr><th>Type of variable</th><th>Variable</th><th>Level</th><th>Estimate</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="10">INDIVIDUAL CHARACTERISTICS</td><td rowspan="5">Breed</td><td>Comtois</td><td>reference</td><td></td></tr><tr><td>Ardennais</td><td>-0.56</td><td>0.76</td></tr><tr><td>Percheron</td><td>-1.94</td><td><strong>0.006</strong></td></tr><tr><td>Breton</td><td>-1.55</td><td><strong>0.016</strong></td></tr><tr><td>Mixed Breed</td><td>-0.67</td><td>0.41</td></tr><tr><td rowspan="2">Sex</td><td>Female</td><td>reference</td><td></td></tr><tr><td>Male</td><td>-2.77</td><td><strong>&lt; 0.001</strong></td></tr><tr><td rowspan="2">Coat colour</td><td>Light</td><td>reference</td><td></td></tr><tr><td>Dark</td><td>0.37</td><td>0.20</td></tr><tr><td rowspan="10">MANAGEMENT PRACTICES</td><td rowspan="2">Age</td><td>Numerical</td><td>reference</td><td></td></tr><tr><td>No</td><td>-0.87</td><td><strong>0.04</strong></td></tr><tr><td rowspan="2">Foaling in the last month or expected in the next month</td><td>Yes</td><td>reference</td><td></td></tr><tr><td>No</td><td>1.33</td><td><strong>0.01</strong></td></tr><tr><td rowspan="2">Ivermectin</td><td>Yes</td><td>reference</td><td></td></tr><tr><td>No</td><td>-0.70</td><td>0.47</td></tr><tr><td rowspan="2">Moxidectin</td><td>Yes</td><td>reference</td><td></td></tr><tr><td>No</td><td>-0.38</td><td>0.71</td></tr><tr><td rowspan="2">Doramectin</td><td>Yes</td><td>reference</td><td></td></tr><tr><td>No</td><td>-0.21</td><td>0.68</td></tr><tr><td rowspan="10">ENVIRONMENTAL DATA</td><td rowspan="2">Type of grazing</td><td>Unmixed</td><td>reference</td><td></td></tr><tr><td>Alternate</td><td>0.23</td><td>0.79</td></tr><tr><td rowspan="2">Number of equids in the pasture</td><td>Numerical</td><td>-1.05</td><td>0.21</td></tr><tr><td>Numeric</td><td>0.07</td><td>0.07</td></tr><tr><td rowspan="2">Water body in pasture</td><td>No</td><td>reference</td><td></td></tr><tr><td>Yes</td><td>1.01</td><td><strong>0.04</strong></td></tr><tr><td rowspan="2">Pasture surface area</td><td>Numerical</td><td>reference</td><td></td></tr><tr><td>Numerical</td><td>reference</td><td></td></tr><tr><td rowspan="2">Minimum daily temperature in last 7 days</td><td>Numerical</td><td>reference</td><td></td></tr><tr><td>Numerical</td><td>reference</td><td></td></tr><tr><td rowspan="2">Cumulative rainfall in last 7 days</td><td>Numerical</td><td>reference</td><td></td></tr><tr><td>Numerical</td><td>reference</td><td></td></tr><tr><td rowspan="2">Built-up area within 800-m radius</td><td>Numerical</td><td>reference</td><td></td></tr><tr><td>Numerical</td><td>reference</td><td></td></tr><tr><td rowspan="2">Length of forest edge</td><td>Numerical</td><td>reference</td><td></td></tr><tr><td>Numerical</td><td>reference</td><td></td></tr><tr><td rowspan="4">Babesia venatorum</td><td rowspan="2">Number of forest plots within 800-m radius</td><td>Numerical</td><td>reference</td><td></td></tr><tr><td>Numerical</td><td>reference</td><td></td></tr><tr><td rowspan="2">Average size of agricultural plots within 300-m radius</td><td>Numerical</td><td>reference</td><td></td></tr><tr><td>Numerical</td><td>reference</td><td></td></tr><tr><td rowspan="2">SIDI within 300-m radius</td><td>Numerical</td><td>reference</td><td></td></tr><tr><td>Numerical</td><td>reference</td><td></td></tr></tbody></table>

Species and number of ticks (adults and nymphs) collected from both horses and pastures in the Auvergne-Rhône-Alpes region (France) that harbour the DNA of *Babesia* and *Theileria* parasites.

<table><thead><tr><th></th><th>Dermacentor reticulatus</th><th>Dermacentor marginatus</th><th>Ixodes ricinus</th><th>Total</th></tr></thead><tbody><tr><td>Theileria equi</td><td>13 (10M; 3F)</td><td>6 (6M)</td><td>0</td><td>19 (1.8 %)</td></tr><tr><td>Babesia caballi</td><td>5 (3M;2F)</td><td>1 (1M)</td><td>0</td><td>6 (0.6 %)</td></tr><tr><td>Babesia microti</td><td>0</td><td>0</td><td>2 (2F)</td><td>2 (0.2 %)</td></tr><tr><td>Babesia venatorum</td><td>0</td><td>0</td><td>1 (1N)</td><td>1 (0.1 %)</td></tr><tr><td>Co-infection*</td><td>3 (2M;1F)</td><td>0</td><td>0</td><td>3 (0.3 %)</td></tr><tr><td>Total ticks collected</td><td>608</td><td>243</td><td>195</td><td>1046</td></tr></tbody></table>

M: male; F: female; N: nymph.

Our study had several limitations that should be acknowledged. First due to convergence issues in fitting a mixed-effects logistic regression model with a random effect for farm - likely caused by limited variability in infection prevalence at the farm level and insufficient group-level data - we opted for a simple logistic regression model. While this approach allowed us to analyze the data, it does not account for potential clustering of observations within farms. Second, collinearity issues led to exclusion of horse location from our models. This is particularly relevant since Breton draught horses were located in area A1, while Comtois horses were predominantly in areas A2 and A3. This overlap complicated the ability to distinguish between breed-related

susceptibility and geographical exposure, a challenge also highlighted in previous studies (Camino et al., 2020a; Steinman et al., 2012). Addressing these limitations in future research will require larger, more geographically balanced datasets and more robust modelling approaches to account for clustering effects.

Our findings highlighted the complex interplay of individual, environmental, and management factors in shaping tick-borne pathogen dynamics in horses and the distribution of equine piroplasmosis in enzootic regions. In the context of global changes, identifying factors influencing tick-borne diseases is critical to anticipate and mitigate their impacts, particularly as environmental and wildlife dynamics evolve (Stachurski et al., 2021). Obtained results on risk factors emphasize here the importance of tailoring prevention strategies to local ecological conditions, and allow to develop concrete recommendations to combat equine piroplasmosis and tick infestations. For horses grazing year-round in enzootic areas, measures to reduce tick burden include manual tick removal (with appropriate tools preventing secondary infections), avoiding mixed grazing with cattle, and installing fences to limit wildlife movement. In high risk-zones, pasture rotation during peak tick activity and regular tick monitoring, particularly on vulnerable areas (mane, axillary folds and chest), are essential. Young horses require special attention, as they are more frequently infected by *B. caballi* than older animals. These proactive strategies can help protect equine health and reduce the burden of tick-borne diseases in enzootic regions.

### CRediT authorship contribution statement

C. Nadal: Writing – review & editing, Writing – original draft, Visualization, Software, Investigation, Formal analysis, Data curation.

C. Chanet: Investigation. C. Delaunay: Investigation. Pierre-Hugues

Pitel: Investigation. M. Marsot: Writing – review & editing, Validation,

<footnote>* No species can be identified in the case of co-infection due to overlapping of DNA sequences</footnote>
Supervision, Resources, Project administration, Methodology, Funding acquisition, Data curation, Conceptualization. **S.I. Bonnet:** Writing – review & editing, Validation, Supervision, Investigation, Funding acquisition, Conceptualization.

## Declaration of interests

The authors declare no conflict of interest.

## Acknowledgements

This work was funded by the Institut Français du Cheval et de l'Equitation (IFCE) and by the Fonds Eperon.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ttbdis.2025.102468.

## Data availability

Data will be made available on request.

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