The Crimean‐Congo haemorrhagic fever tick vector Hyalomma marginatum in the south of France: Modelling its distribution and determination of factors influencing its establishment in a newly invaded area

Abstract We developed a correlative model at high resolution for predicting the distribution of one of the main vectors of Crimean‐Congo haemorrhagic fever virus (CCHFV), Hyalomma marginatum, in a recently colonised area, namely southern France. About 931 H. marginatum adult ticks were sampled on horses from 2016 to 2019 and 2021 in 14 southern French departments, which resulted in the first H. marginatum detection map on a large portion of the national territory. Such updated presence/absence data, as well as the mean number of H. marginatum per examined animal (mean parasitic load) as a proxy of tick abundance, were correlated to multiple parameters describing the climate and habitats characterising each collection site, as well as movements of horses as possible factors influencing tick exposure. In southern France, H. marginatum was likely detected in areas characterised by year‐long warm temperatures and low precipitation, especially in summer and mostly concentrated in autumn, as well as moderate annual humidity, compared to other sampled areas. It confirms that even in newly invaded areas this tick remains exclusively Mediterranean and cannot expand outside this climatic range. Regarding the environment, a predominance of open natural habitats, such as sclerophyllous vegetated and sparsely vegetated areas, were also identified as a favourable factor, in opposition to urban or peri‐urban and humid habitats, such as continuous urban areas and inland marshes, respectively, which were revealed to be unsuitable. Based on this model, we predicted the areas currently suitable for the establishment of the tick H. marginatum in the South of France, with relatively good accuracy using internal (AUC = 0.66) and external validation methods (AUC = 0.76 and 0.83). Concerning tick abundance, some correlative relationships were similar to the occurrence model, as well as the type of horse movements being highlighted as an important factor explaining mean parasitic load. However, the limitations of estimating and modelling H. marginatum abundance in a correlative model are discussed.


INTRODUCTION
Zoonotic tick-borne diseases transmitted by ticks of the Ixodidae family are an increasing health threat in Europe. Among tick-borne zoonoses in Southern Europe, Crimean-Congo haemorrhagic fever (CCHF) is one of the most concerning because of the severity of its clinical signs and high fatality rate in humans, as well as its potential to cause secondary nosocomial cases and even outbreaks. For these reasons, it is becoming a public health problem in Europe. While cases have been reported throughout the Balkans and the Anatolian Peninsula since the 1950s (Bakir et al., 2005;Christova et al., 2009;Papa et al., 2008), the south-western regions of Russia recorded the disease only in 1999 after 27 years without any human cases (Onishchenko & Efremenko, 2004), and more recently, 9 autochthonous cases have occurred in Spain in 2013, 2018, 2020(Negredo et al, 2017; ECDC Cases of Crimean-Congo haemorrhagic fever in the EU/EEA, 2013present). In these regions, the tick Hyalomma marginatum is considered to be one of the main vectors of the CCHF virus, as its wide geographical range is closely related to CCHF case distribution (Ergönul, 2006), except in Spain where it seems likely to be Hyalomma lusitanicum. Indeed, the CCHF virus was much more frequently detected in H. lusitanicum, whereas the infection rate of H. marginatum remained very low in the same region (Anabel Negredo et al., 2017;Ana Negredo et al., 2019;Estrada-Pena et al., 2012;Moraga-Fernández et al., 2021).
Since climate, especially temperature and humidity, is one of the main factors impacting hard ticks' species distribution range (Cumming, 2002), there is mounting evidence that the emergence of tickborne diseases in Europe is linked to climate change (Andreassen et al., 2012;Daniel et al., 2003;El-Sayed & Kamel, 2020;Tokarevich et al., 2011). To some extent, warmer conditions may prolong the active season of ticks and enhance the survival of non-parasitic stages (i.e., eggs, questing stages, detached moulting stages, ovipositing females) in the environment. In some cases, this allows the establishment of tick colonies in new areas that are becoming more suitable for tick survival and development (Gray et al., 2009). Assessing the geographical extent of viable populations of the main tick vectors of pathogens is essential to assess the risk of emergence or re-emergence of tick-borne diseases. This can be determined using Hutchinson's ecological niche concept (1957), defined as a hypervolume shaped by the environmental conditions under which a species can persist indefinitely. Modelling approaches combining the characterisation of the ecological niche of a species with georeferenced information on easily measurable environmental variables allow drawing inferences on the distribution range of that species without systematic and updated sampling, thereby facilitating the identification of priority areas for surveillance. This can be done through process-based models (Dormann et al., 2012;Kearney & Porter, 2009;Peterson et al., 2015), which use physiological information about the species in question, obtained from life-history trait monitoring in controlled conditions that determine the range of environmental conditions in which the species can thrive. Another approach relies on correlative models (Guisan & Zimmermann, 2000) that directly relate environmental variables and species occurrence or abundance data, to identify major reliable parameters for species distribution and general equations for predictions. For the latter, species distribution models (SDMs) usually rely on presence-only data obtained from museums, personal collections, published literature or citizen records, since both presence and absence data are hard to collect, update and validate. Presence-only data are known to be prone to sampling biases because sighting distribution may only reflect highly visited regions, whereas presenceabsence data sampled via systematic surveys are collected in both favourable and unfavourable regions without distinction (Elith et al., 2006;Fithian et al., 2015;Newbold, 2010).
Several modelling studies conducted at the European level to explain and predict the distribution of H. marginatum have emphasised that its most suitable habitats are characterised by high temperatures with low precipitation and low relative humidity (Estrada-Peña & Venzal, 2007;Estrada-Peña et al., 2011. Apart from climatic conditions influencing the development rate and activity of non-parasitic stages, H. marginatum also needs the presence of vertebrate hosts to complete the blood meals required to moult from one development stage to the next. Hyalomma marginatum is a ditropic tick meaning that it is a two-host species. The larvae and nymph stages feed on the same host species, which are small vertebrates such as lagomorphs, birds, hedgehogs and rodents, whereas adult stages usually feed on large ungulates such as horses, cattle, sheep, goat, deer or wild boar, and occasionally humans (Apanaskevich, 2004). As H. marginatum seems to be a generalist species, establishment may not be limited by host presence, as seems to be the case for other tick species. Although some host preference has been reported amongst its wide range of hosts (Grech-Angelini et al., 2016), they should be considered with caution as patterns can differ from one region to another (Mccoy et al., 2013).
Likewise, preferred local host density may likely influence the dynamics and abundance of tick populations rather than their establishment.
In addition, incorporating host availability in predictive models is challenging because host density data are often unavailable or not detailed at a local geographical scale, especially for wild hosts and even more so for small vertebrates. However, for some wild species such as wild boar or rodents, it has been demonstrated that vegetation could be a relevant proxy for predicting abundance since it reflects the availability of food resources (Pittiglio et al., 2018). Vegetation can also directly influence the survival of non-parasitic tick stages, especially by reduc- In France, H. marginatum has been reported on the island of Corsica as early as 1946 (Delpy, 1946;Morel, 1959). This tick is now abundant and widely distributed all over the island (Grech-Angelini et al., 2016). On the mainland, rare historical records date from 1940 to the 1970s (Morel, 1959;Rageau, 1972). Most of these records were not able to confirm the presence of H. marginatum due to doubtful identification, absence of information concerning specimen's development stage, or because they seemed to likely correspond to a punctual introduction by migratory birds of immature stages that successfully moult as adults without any subsequent tick population establishment. The presence of viable and abundant H. marginatum populations in continental France was only recently reported (Vial et al., 2016)

Tick sampling and identification
We aimed to sample ticks on at least eight horses per structure and if possible, from horses using different pastures to have a good representation of infestation in the structure. If the structures had fewer than eight horses, we sampled all available individuals and considered their infestation status as representative of the visited structure if they have access to the whole environment around the structure.
Tick collection consisted of a thorough full body search for any ticks on the animal. When feeding on a host, H. marginatum is usually attached around the genitals and anus, between the thighs, between the udders, and on the abdomen, in rare instances. While exploring its host, prior to attachment at the definitive engorgement site, the tick usually waits behind the pasterns or near the hooves (Apanaskevich, 2004). On horses, other tick species can be found attached on dorsal side of the host, or near the mane or tail. All ticks were removed manually and placed in plastic tubes. Each evening, ticks were sorted by sex and developmental stage, and the species was determined according to its morphological characteristics using relevant species descriptions and identification keys (Apanaskevich & Horak, 2008;Estrada-Peña et al, 2004;Claudine, 2007). After identification, ticks were frozen in liquid nitrogen. Once returning from the field, ticks were stored at −80 • C in the laboratory for later use.

Equestrian structures selection
We included structures that used as little acaricide treatment as possible and which provided horses with access to vegetated areas (Tirosh-Levy et al., 2018) to favour horses' exposure to the environment and therefore to ticks. A structure was included in our sample once we obtained owner consent. Furthermore, before our visit, we specifically asked owners not to apply any parasite treatment and, upon visiting, we verified the absence of such treatment (acaricides, but also anthelminthic ivermectin that may have an effect on ticks We reported in each structure the mean number of ticks found on examined horses. We assumed that the tick detection error on an infested horse was negligible since the tick attachment location on horses is well known for H. marginatum. In addition, most of the examinations were conducted jointly by two observers. We considered that H. marginatum was absent from a structure when no tick was found on a sufficient or representative number of horses, and after ensuring the absence of acaricide treatments; if treatments were conducted within the last 14 days, absence was validated if some specimens from other tick species were found on horses. We considered that the tick was established as a permanent population in a structure when at least two ticks were found on one or two horses, provided that they had not recently moved. For H. marginatum, a single tick found on a horse may be due to an engorged nymph having been introduced by a migratory bird (Capek et al., 2014;Černỳ & Balát, 1957;Jameson et al., 2012),  Lesiczka et al., 2022). Therefore, when a single H. marginatum was found in the whole structure, it was labelled as a "single presence" and was not considered to be sufficient reliable evidence of H. marginatum establishment (i.e., the existence of a permanent population in the area) to be used in the analysis. As female ticks engorge for 7-12 days and males can remain attached for at least a month, an observed infestation could also result from a recent horse movement from alreadyinfested areas. Collected information regarding recent horse movements helped to detect such doubtful detection cases, and only tick count data from horses that did not move from another department or country during the month prior to sampling were considered.

Environmental data collection and treatment
We used a combination of CORINE Land Cover ( BDF contains information on a finer scale than CLC. For these reasons, we were much more confident in using BDF despite being limited to forested areas. Therefore, we first used CLC and then transferred BDF values to CLC in overlapping pixels that were classified as forests using the packages "raster" (Hijmans, 2020) and "rgdal" . This resulted in a composite raster with a pixel resolution of 100 * 100 m. From this composite raster and for each data point, we extracted land cover proportions within a radius of 5 km around the GPS coordinates to have information on the environment around the equestrian structures, which likely reflects the environment where horses can become infested by H. marginatum. Among the several kilometre radii that we tested within the 1 to 10 km range, we chose 5 km as a sensible trade-off between bias and variance, which produced the best area under the receiver operating characteristic curve (AUC) using the leave-one-out method. AUC is a threshold-independent measure of a model's ability to discriminate between sites where a species is present, versus sites where it is absent (Hanley & McNeil, 1982 (Dray & Dufour, 2007), "sf" (Pebesma, 2018) and "factoextra" (Kassambara & Mundt, 2020). Then, for each tick sampling point, PC coordinates were extracted from the cell where the point was located, using the package "rgeos" (Bivand & Rundel, 2020).

Statistical analysis
Out of the 169 sampling points used to develop the model (data from 2021 excluded), 113 had no ticks and 13 were single observations, corresponding to about two-thirds of count observations being zeros.
Thus, a zero-inflated Poisson mixture (ZIP) model (Lambert, 1992) was used to estimate the probability of occurrence and abundance of H.
marginatum. The ZIP model assumes that the data are generated by two underlying processes, which results in two distinct layers in the model: The "zero layer" and the "count layer". The first process is mod- Model selection was based on Akaike's information criteria (AIC) and likelihood ratio tests (LRTs). The first model included all predictors in both the Binomial and Poisson components; we then conducted a backward stepwise elimination process to select the most predictive variables in the final model (Akaike, 1974;Woolf, 1957

Correlations between climatic variables
The first three axes of PCA accounted for 87.7% of the variability of climatic data. They were therefore all included in the model. can thus be interpreted as a humidity index.
The third principal component (PC3) explained 8.9% of the variability and was mostly defined by precipitation during autumn (September to November), with scores strongly and positively correlated with PC3 (scores ≥ 0.73; PC3, Figure 2B). PC3 can thus be interpreted as an autumn precipitation index.

Habitats sampling effort
We measured H. marginatum abundance on structures in seven different habitats. Their distribution in our sample according to various criteria is presented in Table 1.

Effects of climate and habitat on H. marginatum distribution
The results of the ZIP model are presented in Table 2 Figure 3D). This means that H. marginatum is likely present under fairly dry conditions (Figure 4 and 5 in supplementary data) until a certain threshold after which conditions seem to become too dry.
Conversely, increasing humidity is favourable to tick abundance. Nevertheless, for tick abundance, the range of PC2 values on the observed presences is exclusively positive, corresponding to dry conditions only.
Maximal probability of presence is predicted for 71% mean annual relative humidity and mean monthly winter precipitation of 51 mm. An effective maximal parasitic load of 12 ticks/horse is obtained under a relative humidity of 73% and a mean monthly winter precipitation of 57 mm.
PC3 had a positive effect on the probability of tick presence. On tick parasitic load, in the observed presence range of PC3's values, the relationship is slightly convex. (Figures 3E and 3F). However, the abundance. Horses are more likely to be exposed to a larger amount of ticks when strolling around or trekking for a few days within the sampling zone (level-2) than horses staying in their pastures and paddocks (level-1), but also more than horses that are used to traveling far away for competitions, breeding or trade (level-3).

DISCUSSION
In this study, we built a correlative model for predicting the distribution of H. marginatum, namely in the South of France, which has recently been colonised by this tick species, using updated presence and absence data collected from the field. We also measured the mean parasitic load of horses as a proxy of tick relative abundance. We associated these field data with climatic and habitat variables to better understand the environmental factors that limit or facilitate the establishment and the proliferation of this invasive tick. Based on this model, we were able to predict suitable areas for the current establishment of H. marginatum on a larger zone than sampled in the south of France.
Our sampling data showed that H. marginatum is already established in several departments in southern France, from Pyrénées-Orientales in the west to Var in the east, and Ardèche in the north. The fact that tick presence is spatially aggregated in three clusters centred on locations with particularly high tick abundances, and that several single-presence recordings have been documented around these clusters suggest a possible ongoing colonisation process of contiguousfree favourable habitats stemming from presence clusters of alreadyestablished tick populations due to host movements (Lockwood et al., 2005 -Peña et al., 2011;Hoogstraal, 1979). We support the hypothesis that temperature has a direct effect on critical development processes of certain stages, namely egg incubation and nymph moulting in spring and summer, as ticks need to moult into adults (i.e., an overwintering stage that sufficiently resists to low temperature) before winter (Estrada-Peña et al., 2011;Gray et al., 2009;Kotti et al., 2001; ECDC Hyalomma marginatum -Factsheet for experts). Conversely, we assume that the observed positive correlation with low summer but high autumn precipitations likely reflects the dependency of H.
marginatum on the Mediterranean and transitionary Mediterranean climates, which are both characterised by specific rainfall patterns in addition to high annual temperatures (Joly et al., 2010 (2008), such a crucial aspect of phenotypic adaptation that is rarely explored in ticks should be further investigated.
Regarding the effects of habitat on tick presence, in agreement with previous observations (Akimov & Nebogatkin, 2011;Hoogstraal, 1979;Uspensky, 2002), we demonstrated a positive relationship of H.
marginatum presence with open, natural habitats such as scrubland, a xerophytic biotope commonly found in a non-uniform fashion in south-  (Buczek, 2000;Morel, 2003 (Lobo et al., 2008). Nevertheless, such bias does not exist in the 2021 sampling survey for which we obtained the best AUC scores, as we decided to target areas where the tick was assumed to be absent to better assess its northern margin of distribution. This reinforces the ability of our model to predict absences and that the pseudoabsences sampled outside of the Mediterranean sampling zone were likely to be true absences. However, the existence of other unmeasured explanatory variables may also explain discrepancies and should be further investigated.
Measured mean parasitic loads as proxies of tick abundances used for the "count layer" of the model are disputable and need to be considered with caution. Indeed, if H. marginatum is still in a colonizing phase, populations may not be in equilibrium. A sampling point with higher abundance does not necessarily mean that the climate and the habitat are much more suitable; this can also be due to a longer period of establishment of the population, allowing for more individuals to be generated. Indeed, as well described for some invasive tick species such as Rhipicephalus microplus (De Clercq et al., 2013), we assumed that secondary colonisation by H. marginatum from one or several original established populations is a homogeneous process where the tick will colonise the surrounding favourable habitats in a spreading pattern via the movement of wild animals, particularly ungulates and resident birds. In addition, in this study, we estimated the abundance of H. marginatum by measuring the mean number of ticks per examined horse. This was based on the hypothesis that, at the peak of the adult activity, most ticks are searching for large ungulates to engorge, and that examined horses can sample a large part of the targeted area.
However, horses' parasitic loads also vary according to husbandry practices in each visited structure, as it was clearly shown in our model through the significant effect of horse movements on tick abundance.
Many more H. marginatum were found on horses that were trekking or staying in a pasture for a few days, which corresponds to the best occasion for horses to be exposed to ticks, rather than horses that stay in their paddock or go outside of the study zone for competitions, breeding or trade. Interestingly, in a previous study, pastures were reported as the main factor influencing exposure to H. marginatum (Tirosh-Levy et al., 2018). Although horses cover long distances when going to competitions, they do not necessarily travel into infested areas; they are instead housed in stables or sparse paddocks during their displacement and are often treated against parasites. Rotation in pastures is also an important practice that may modify horse exposure to ticks. In one of the sampling zones included in the study where the infestation is regularly monitored, this rate was shown to re-increase in summer as soon as a new pasture was offered to horses (Stachursky, personal communication); However, for the rest of our sampling points, which were only visited once, it was difficult to assess such possible bias. Another potential source of bias is caused by undisclosed grooming practices, which can lead to an underestimation of tick abundance. Indeed, as H.
marginatum adults remain attached to hosts for an average of 10 days (Morel, 2003), measures of mean parasitic load reflect the number of ticks attached during this period, unless horse owners are used to regularly detaching ticks. This was not always mentioned during visits with owners, especially in pensions where horses may belong to different owners. Nevertheless, we believe that it did not affect our results on presence detection since a total absence of ticks (all species included) was observed in only 9 of the sampling points, which lacked information regarding treatment or grooming practices.
Considering all these limitations on the "count layer" of the model,