# Host life-history traits and movements across scales as a driver of parasite spread in bats
Clara Castex

Castex Clara, 2025, Host life-history traits and movements across scales as a driver of parasite spread in bats

Originally published at : Thesis, University of Lausanne

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UNIL | Université de Lausanne

Faculté de biologie
et de médecine

Département d'Ecologie et d'Evolution

# Host life-history traits and movements across scales as a driver of parasite spread in bats

Thèse de doctorat ès sciences de la vie (PhD)

présentée à la

Faculté de biologie et de médecine
de l'Université de Lausanne

par

Clara CASTEX

Master en Ecologie et Evolution de l'Université de Toulouse

Jury

Prof. Philippe Reymond, Président

Prof. Philippe Christe, Directeur de thèse

Prof. Jérôme Goudet, Co-directeur de thèse

Prof. Luca Fumagalli, Expert

Dre Nathalie Charbonnel, Experte

Lausanne
(2025)
<figure><img src="image_2.png" /></figure>

UNIL | Université de Lausanne

Faculté de biologie
et de médecine

Ecole Doctorale

Doctorat ès sciences de la vie

Imprimatur

Vu le rapport présenté par le jury d'examen, composé de

<table><tr><td>Président-e</td><td>Monsieur Prof. Philippe Reymond</td></tr><tr><td>Directeur-trice de thèse</td><td>Monsieur Prof. Philippe Christe</td></tr><tr><td>Codirecteur-trice</td><td>Monsieur Prof. Jérôme Goudet</td></tr><tr><td rowspan="2">Expert-e-s</td><td>Monsieur Prof. Luca Fumagalli</td></tr><tr><td>Madame Dre Nathalie Charbonnel</td></tr></table>

le Conseil de Faculté autorise le dépôt de la thèse finale de

Clara Lucie Joanne Castex

Master en Biologie, Université Paul Sabatier / Toulouse III, France

intitulée

Host life-history traits and movements across scales as a driver of parasite spread in bats

Lausanne, le 28 novembre 2025

pour le Doyen
de la Faculté de biologie et de médecine

<figure><img src="image_3.png" /></figure>

Prof. Philippe Reymond
Table of contents

<table><tr><td>Remerciements</td><td>4</td></tr><tr><td>Summary</td><td>6</td></tr><tr><td>Résumé</td><td>7</td></tr><tr><td>General introduction</td><td>9</td></tr><tr><td>Chapter 1</td><td>31</td></tr><tr><td>Chapter 2</td><td>45</td></tr><tr><td>Abstract</td><td>46</td></tr><tr><td>Introduction</td><td>47</td></tr><tr><td>Materials & Methods</td><td>49</td></tr><tr><td>Results</td><td>56</td></tr><tr><td>Discussion</td><td>62</td></tr><tr><td>Supplementary Materials</td><td>75</td></tr><tr><td>Chapter 3</td><td>89</td></tr><tr><td>Abstract</td><td>90</td></tr><tr><td>Introduction</td><td>91</td></tr><tr><td>Materials & Methods</td><td>94</td></tr><tr><td>Results</td><td>98</td></tr><tr><td>Discussion</td><td>101</td></tr><tr><td>Supplementary Materials</td><td>112</td></tr><tr><td>Chapter 4</td><td>129</td></tr><tr><td>Abstract</td><td>130</td></tr><tr><td>Introduction</td><td>131</td></tr><tr><td>Materials & Methods</td><td>133</td></tr><tr><td>Results</td><td>136</td></tr><tr><td>Discussion</td><td>140</td></tr><tr><td>General discussion</td><td>149</td></tr></table>
Remerciements

Premièrement, je tiens à remercier les **1500 chauves-souris** qui sont passées entre mes mains et qui ont bien voulu que je les identifie, les mesure et les échantillonne. Merci aux **2746 Nycteribiidae** et aux **3249 Spinturnicidae** qui se sont plus ou moins gentiment sacrifiés afin d'éclaircir un peu plus les connaissances sur leur dispersion.

Merci, **Philippe** et **Jérôme**, mes co-directeurs de thèse, pour votre supervision et vos conseils avisés. Merci à Philippe pour ton expertise sur la biologie des chauves-souris et de ses parasites et merci à toi de m'avoir éveillée à la biodiversité suisse. Merci à Jérôme pour ton expertise et tes conseils sur les méthodes d'analyses de génétique des populations et surtout merci d'être arrivé en cours de route et d'avoir su redresser le cap. Merci à vous deux pour cette thèse qui a su rester stimulante. Merci aussi à **mon jury de thèse**, pour le temps consacré à la lecture de ma thèse.

Je voudrais remercier tout spécialement **Muriel Dietrich**, ma superviseuse de Master à PIMIT, La Réunion. Un jour on m'a dit : « une fois que tu découvres les chauves-souris, tu ne peux plus jamais retourner en arrière ». C'est grâce à toi que je les ai découvertes et je ne te remercierai jamais assez de m'avoir ouverte à ce monde merveilleux qu'est la chiroptérologie. Merci aussi à **Sam** pour nos moments passés là-bas. Sans vous, cette thèse n'aurait pas eu lieu.

Je tiens à remercier mes deux groupes de recherches : **le groupe Christe** et **le groupe Goudet** et **leurs anciens membres** pour les discussions stimulantes et leurs conseils tout au long de ma thèse. Merci d'avoir participé à cette bonne ambiance de groupe qui a su faire passer les moments plus difficiles. Merci en particulier à **Molly**, **Antoine** et **Tristan**, pour votre collaboration. Merci de m'avoir laissé vous embêter à n'importe quelle heure de la journée.

Je souhaiterai aussi remercier **mes stagiaires** (Luca, Elsa, Morgane, David, Anouk, Miles et Alyzée) et toutes les personnes qui ont pu m'aider de près ou de loin sur le terrain et dans la capture des chauves-souris. Merci à vous, **Luca** et **Laura**, de m'avoir appris les techniques de manipulation de chauves-souris. Merci à **David**, **Anouk** et **Marc** du CCO pour le grand effort d'échantillonnage en 2023 et merci Marc pour les photos. Merci à vous tous d'avoir mis une si bonne ambiance sur le terrain. Je voudrais particulièrement remercier **Alyzée**. Merci pour ton investissement pendant tout ton projet de master et ton intérêt particulier pour la biologie des chauves-souris et le parasitisme. Merci à toi d'avoir su prendre en main ce grand projet qui m'a permis de faire mon chapitre 2.

Merci à **la team 11h30**, qui a changé de membres au cours des années mais qui a toujours su rester cohérente : pause à 9h, manger à 11h30 et pause à 15h. Merci pour les guerres, les perudos, les rois, les binchs et les soirées pétanque. Merci aux **PLS Doctors** : vous avez été une safe place pour me plaindre et rendre mes objectifs réalisables. Merci à **Kim** qui a toujours su me faire rire, même pendant les moments difficiles. Je n'oublierai pas ces assistanats en Valais avec toi.

Un merci tout particulier à **Isa** et **Eléonore**, mes collègues du groupe Goudet mais surtout mes amies. Merci à vous deux pour votre soutien, votre absence de jugement et votre compréhension. Merci pour votre joie de vivre et votre gentillesse innée.
Il est enfin temps que je remercie les personnes sans qui cette thèse n'aurait pas pu voir
le jour. **Molly, Julie** (lulie), **Julie** (Gulie), **Camille**, **Romain**, **Eddie** et **Manon**. Pour moi qui
parle beaucoup, aucun mot n'est assez fort sur cette planète pour représenter toute la
gratitud que j'ai envers vous. Merci d'avoir été des épaules sur lesquelles je pouvais me
reposer. Merci pour les rires, pour la joie, pour les délires et surtout pour votre
bienveillance. Merci de m'avoir toujours écoutée (ou d'avoir fait semblant) pendant que
je parlais (ou piaillais) dans toutes les occasions. Merci de m'avoir toujours tirée vers le
haut. **Eddie**, merci de toujours glousser à mes vannes, toi et moi on est la même personne
de toute façon, merci pour les cartes, les photos et les conseils en statistiques. **Molly,**
**lulie** et **Gulie**, j'ai commencé ma thèse avec vous, merci d'avoir été mes piliers. Un grand
merci à toi, **Molly**, pour toute ton aide au labo ou autre. Merci pour les rigolades, merci
d'avoir partagé mon bureau pendant 5 ans et d'avoir chanté à tue-tête du Disney ou du
ACDC. **lulie**, merci pour ton humanité et merci de m'avoir fait sentir que peu importe ce
que je fais, c'est ok. Merci pour les différentes relectures de l'introduction. **Gulie**, merci
pour ton énergie et ton authenticité. **Manon**, merci pour ton humour et ta jovialité.
**Romain**, merci pour ta douceur et ta générosité. Et **Camille**, merci pour ta luminosité et
ta bonté et merci pour les relectures de dernière minute de mes chapitres. Je ne vous
remercierai jamais tous assez, les moments passés à vos côtés sont uniques et précieux.
Vous êtes tous des ami.es de rêve, ma famille choisie. Je vous aime, merci d'avoir rendu
ma vie plus douce.

Mes copains de Toulouse : **Tristan**, **Laurine**, **Paul** et **Sophie**. Merci à vous pour votre présence et votre soutien depuis le début. Merci de ne pas m'avoir tenu rigueur d'avoir quasi disparu pendant 5 ans. Vous aussi savez ce que c'est. Special shoutout à toi **Laurine** pour tes talents de graphiste.

Je tiens aussi à remercier **ma famille** pour leur soutien durant toute ma vie. Merci à mes parents d'avoir toujours su encourager mes passions et à ma sœur de s'être toujours intéressée à ce que je faisais. **Maman, Kime**, merci d'avoir toujours été là pour m'écouter me plaindre et me conseiller. Merci d'avoir été compréhensives quand ça n'allait pas. Merci à **ma grand-mère** de m'avoir initiée à l'ornithologie. Grâce à toi je me suis ouverte au monde qui nous entoure et ma vie n'est plus la même depuis. Merci aussi à la **famille Delmas** de m'avoir accueillie et de m'avoir fait sentir des leurs. Je suis reconnaissante d'avoir deux familles aimantes.

**Cyprien**, merci à toi d'avoir toujours été là. Merci d'avoir été mon pilier tout au long de cette thèse et dans ma vie en général. Merci de m'écouter, de me conseiller, de ne pas me juger même quand tu ne comprends pas et de me donner des solutions même quand je ne t'en demande pas. Merci de me changer les idées.

**Maoni**, ma star, mon fils, mon chat. Merci d'être entré dans ma vie et désolé pour toi.

La thèse est une période difficile et je suis particulièrement reconnaissante d'avoir eu un entourage présent, aimant et bienveillant. Je vous dois tout. Merci à vous.

Et enfin merci à la musique qui calme, qui fait danser, qui fait planer, qui fait voyager.
**Summary**

Host-parasite interactions are a fundamental component of natural ecosystems, shaped by coevolutionary dynamics. Variation in parasite load among individuals reflects a combination of biological traits, ecological conditions, and human-induced factors. While most studies focus on single host-parasite relationships, considering multiple parasites provides a more comprehensive understanding of their dynamics. Bats provide an excellent model in this regard, their high susceptibility, seasonal life cycle and diverse social structures make them particularly relevant for studying parasite transmission. This thesis investigates the mechanisms underlying parasite spread in European bats, both across host populations and individuals. First, I examined host specialization in the common bed bug (Cimex lectularius), revealing clear genetic differentiation at the local scale between human- and bat-associated lineages, with stronger structuring in human-associated bugs. Using Daubenton's bat (Myotis daubentonii) as a focal species, I then studied how host life history traits and population genetics influence the transmission of three parasites: the bat fly (Nycteribia kolenatii), the wing mite (Spinturnix andegavinus), and the haemosporidian (Polychromophilus murinus). Bat populations in the Vaud canton showed high genetic connectivity and parasite abundance was more strongly associated with host traits than with genetic diversity. Despite contrasting life histories, bat flies and wing mites also exhibited high genetic connectivity, reflecting bat social behaviour and movements. Finally, infection dynamics of the three parasites varied with season and host traits, highlighting their distinct ecologies. By integrating population genetics, epidemiological data, and molecular quantification, this thesis demonstrates that parasite transmission is governed by a complex interplay of host biology and environmental context. Bat dispersal, social structure, and reproductive behaviour emerged as key drivers, emphasizing the importance of conserving host populations for both biodiversity and disease control.

**Keywords:** host-parasite interactions, bat parasites, transmission, genetic connectivity, infection dynamics
Résumé

Les interactions hôte-parasite constituent un élément fondamental des écosystèmes naturels, façonnées par des dynamiques de co évolution. La variation de la charge parasitaire entre les individus reflète une combinaison de facteurs biologiques, écologiques et anthropiques. Alors que la plupart des études se concentrent sur des relations hôte-parasite uniques, la prise en compte de multiples parasites permet une compréhension plus globale des dynamiques parasitaires. Les chauves-souris offrent un excellent modèle à cet égard : leur forte susceptibilité, leur cycle de vie saisonnier et leurs structures sociales variées les rendent particulièrement pertinentes pour l'étude de la transmission parasitaire. Cette thèse explore les mécanismes sous-jacents à la propagation des parasites chez les chauves-souris européennes, tant au niveau des populations hôtes que des individus. Dans un premier temps, j'ai examiné la spécialisation chez la punaise de lit (Cimex lectularius), révélant une différenciation génétique nette à l'échelle locale entre les lignées associées à l'humain et celles associées aux chauves-souris, avec une structuration plus marquée chez les punaises liées à l'humain. En utilisant la chauve-souris de Daubenton (Myotis daubentonii) comme espèce modèle, j'ai ensuite étudié comment les traits d'histoire de vie de l'hôte et la génétique des populations influencent la transmission de trois parasites : la mouche Nycteribia kolenatii, l'acarien Spinturnix andegavinus et l'hémosporidie Polychromophilus murinus. Les populations de chauves-souris dans le canton de Vaud ont montré une forte connectivité génétique, et l'abondance parasitaire était davantage liée aux traits de l'hôte qu'à la diversité génétique. Malgré des histoires de vie contrastées, les mouches et les acariens ont également présenté une forte connectivité génétique, reflétant le comportement social et les déplacements des chauves-souris. Enfin, la dynamique d'infection des trois parasites variait selon la saison et les traits de l'hôte, mettant en évidence leurs écologies distinctes. En intégrant la génétique des populations, les données épidémiologiques et la quantification moléculaire, cette thèse démontre que la transmission parasitaire est régie par une interaction complexe entre la biologie de l'hôte et le contexte environnemental. La dispersion, la structure sociale et le comportement reproducteur des chauves-souris se révèlent être des moteurs clés, soulignant l'importance de la conservation des populations hôtes pour la biodiversité et le contrôle des maladies.

Mots-clefs : interactions hôtes-parasites, parasites de chauves-souris, transmission, connectivité génétique, dynamiques d'infection
# General introduction

## Biotic interaction and parasitism

Biotic interactions refer to the diverse relationships among living organisms within ecosystems, playing a fundamental role in their origin, maintenance and functionality (Andresen et al., 2018). These interactions can be either beneficial or detrimental to the species involved. Positive interactions, such as mutualism or commensalism, facilitate access to resources like nutrients, protection or dispersal mechanisms, thereby enhancing survival (Dimijian, 2000). For example, sea anemones protect clown fishes from predators through their toxic tentacles, while clown fishes provide nutrients via excretions (Mebs, 2009; Delgado et al., 2022). Conversely, antagonistic interactions, such as predation, herbivory, competition or parasitism, can reduce the fitness of one or both parties (Chase et al., 2002).

Parasitism is a particularly widespread antagonistic strategy that has evolved independently multiple times (Meeûs and Renaud, 2002; Meeûs et al., 2003). It involves a relationship in which one organism, the parasite, exploits another, the host, for resources and reproduction (Rózsa and Garay, 2023). This dynamic imposes reciprocal selective pressures where the parasite must adapt to the host environment to survive and reproduce, while the host evolve mechanisms to mitigate the impact of infection (Olsen, 1974; Rózsa and Garay, 2023). Over time, this coevolution drives phenotypic optimization in both species (Combes, 2004; Rózsa and Garay, 2023). The specificity of host-parasite associations varies widely. Generalist parasites are capable of infecting and reproducing across multiple host species, whereas specialist parasites exhibit high host specificity, often restricted to a single or few closely related species.

## Heterogeneity in the distribution of parasites

In natural systems, infection levels are rarely uniform. Parasite distribution tends to be highly heterogeneous across individual hosts (Poulin, 1993; Shaw et al., 1998). This uneven distribution has important implications for parasite dynamics, as a small proportion of hosts often contributes disproportionately to parasite transmission and
persistence (Poulin, 2013). Understanding these patterns is crucial to public health and
essential for predicting and controlling the emergence and spread of parasites
(Penczykowski et al., 2015), as it provides valuable insights into the dynamics of disease
spread within and among host populations. Differences in parasite load among hosts are
frequently attributed to variation in susceptibility or exposure, which may be influenced
by behavioural, physiological, or immunological traits (Poulin, 2013). In addition to host-
level variation, infection patterns can fluctuate across spatial and temporal scales
(Poulin, 2013; Penczykowski et al., 2015). Environmental heterogeneity, defined by
variation in both biotic and abiotic factors, plays a central role in shaping host-parasite
interactions. Factors such as interspecific competition among parasites, differences in
resource availability, and ecological variation among host populations contribute to the
complexity of these dynamics. Although many studies investigate parasite evolution
within single host-parasite systems, broader approaches are needed to capture the full
scope of parasite ecology (Rigaud et al., 2010; Johnson et al., 2015a; Merrill et al., 2022).

Investigating parasite dynamics across spatiotemporal scales, while accounting
for interactions among diverse parasite species and host populations, enables a more
accurate application of ecological and evolutionary theory. Such integrative frameworks
are essential for generating accurate predictions regarding the emergence, transmission,
and persistence of parasites.

What can affect parasite spread?

Several factors contribute to explain the infection and the maintenance of a parasite
in a host, and among them: the host acting as a habitat for the parasite, the influence of
the parasite on the host mortality or reproductive success, the ability of the host to
develop immunity, and the requirement for transmission between hosts (Anderson and
May, 1979). Host-parasite association and therefore parasite spread depend on
ecological, biological and anthropogenic factors (Fig. 1 - Kołodziej-Sobocińska, 2019).
Globally, these factors influence parasite spread by shaping contacts between the
parasite and potential hosts (“encounter filter” such as behaviour or biodiversity) and by
determining whether the parasite and host can coexist ("compatibility filter" such as
resource or defence - Combes, 2004).

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Factors influencing host-parasites associations. Adapted from Kołodziej-Sobocińska, 2019.</figcaption></figure>

Among the biological factors shaping these filters, host sex plays an important role
in determining parasite prevalence and intensity. Male-biased parasitism is common in
vertebrates and has been linked with the ecological, behavioural and physiological
differences between sexes (Folstad and Karter, 1992; Zuk and McKean, 1996; Klein, 2004).
However, in many species, female-biased patterns can also occur (Christe et al., 2000;
2007; Klein, 2004) and have been linked to the reproductive status, as pregnant or
lactating females are often more immunocompromised and therefore more susceptible
to infection (Lloyd, 1983; Plowright et al., 2008).
Age of the host is another biological factor that impact the prevalence and intensity of parasite in a host. In most system, juveniles, usually immunologically naïve individuals, are more susceptible to parasitism than adults (Kołodziej-Sobocińska, 2019). This increased vulnerability may be partially mitigated by maternal antibodies, which can provide temporary immunological protection during early development (Jackson and Nazar, 2006; Hurley and Theil, 2011). However, the relationship between age and infection risk is complex and subject to conflicting hypotheses. On one hand, cumulative parasite exposure over time can enhance immune defences, leading to reduced susceptibility in older individuals (Bruns, 2019). On the other hand, infection can increase with age due to immunosenescence (Ginaldi et al., 2001).

Body condition of the host has often been linked with parasitism (Sánchez et al., 2018; Kołodziej-Sobocińska, 2019). In some species, a positive relationship has been observed, meaning that host in better condition tolerate higher parasite infections (Sánchez et al., 2018; Kołodziej-Sobocińska, 2019). Negative effects have also been reported but the direction of these negative effects remains unclear; it is not always evident whether poor body condition results from parasitism or whether individuals in poor body condition are more susceptible to parasite infections. Indeed, the modification of the host body condition through induced stress (for example decrease in resource availability or immunodepression) can lead to an increase in the susceptibility to parasitism (Beldomenico et al., 2008). Conversely, the presence of parasites can also impair the host body condition (Holmstad et al., 2005). In practice, the underlying mechanisms are more complex, and retroactive loops may occur. In the field voles, individuals with higher condition were more infected but this pattern was generally followed by reduced condition (Beldomenico et al., 2008).

Host population size is critical for parasite transmission (Anderson and May, 1979; Nunn et al., 2003) because it influences contacts between individuals. Smaller host populations also often exhibit a lower connectivity driving genetic drift to increase within populations and gene flow to be reduced between populations resulting in lower genetic diversity within populations and higher genetic differentiation between populations (Keyghobadi, 2007). Host population with a weak genetic diversity are often more susceptible to parasitism (Pearman and Garner, 2005; Altermatt and Ebert, 2008;
Charpentier et al., 2008; Whitehorn et al., 2010; Ruiz-López et al., 2012; Ekroth et al., 2019; Gibson and Nguyen, 2020). However, an increase in the host density would results in an increase in parasite prevalence and diversity (Morand and Poulin, 1998). In large host populations, with more connectivity and lower genetic drift, genetic diversity within populations increases and genetic differentiation between populations decreases. In such populations, susceptibility to parasitism can also increase with genetic diversity (Westerdahl et al., 2005a; Goüy De Bellocq et al., 2008). This is particularly true when studies investigate the MHC polymorphism supporting the hypothesis that parasites and especially specialist parasites could be a driving factor in the maintenance of the host MHC polymorphism (Šimková et al., 2006). This correlation between the host genetic diversity and parasitism is context- and host-dependent (Gibson and Nguyen, 2020).

Yet, few studies have explicitly examined how these traits intersect to shape parasite dynamics, especially in a multi-parasite system. This gap limits our ability to fully understand host susceptibility and the evolutionary pressures acting on host-parasite relationships. Addressing this interaction is crucial for disentangling the multifactorial nature of parasitism and for refining predictive models of disease spread in wild vertebrate populations.

## Population genetics: a tool to understand parasite spread

Population genetics analyses offer power tools to investigate the connectivity of populations and, in the context of parasitism, to understand the mechanisms underlying parasite transmission and spread. By examining genetic variation across space and time, these approaches help identify how parasites move between and within host populations, and what ecological or evolutionary factors facilitate or constrain this movement. For instance, phylogenetic analyses have revealed host switching events in several parasite lineages of vertebrates, expanding their host range and potentially altering transmission dynamics (Witsenburg et al., 2012; Duval et al., 2012; Borner et al., 2016; Martinů et al., 2018; Roth et al., 2018). Spatial genetic structure analyses often reveal host-dependent genetic differentiation in parasites. A notable example is the seabird tick, whose genetic structure reflects two independent colonisation routes,
resulting in host-associated lineages (McCoy et al., 2003). Finally, comparative genetic studies between hosts and parasites provide additional insights. They highlight how host life history traits, such as mobility, social structure and reproductive behaviour, shape parasite spread (van Schaik et al., 2015; Booth et al., 2015). Moreover, mismatches between host and parasite genetic patterns can reveal cryptic ecological interactions, such as the presence of locally extinct hosts, non-reproductive contacts between hosts (Bruyndonckx et al., 2010), or the influence of vectors on parasite transmission (Witsenburg et al., 2015b).

Advances in high-throughput sequencing have significantly improved the resolution and accessibility of genetic data in both model and non-model species (Garvin et al., 2010; Davey et al., 2011). Among these markers, single nucleotide polymorphisms (SNPs) have been valuable tools in parasitology, notably for studying mechanisms underlying parasite transmission among hosts. SNPs have been applied to characterize parasite strains (Kanai et al., 2022), to assess genome-wide diversity and structure (Hupalo et al., 2015; Fola et al., 2023), to detect signals of divergence and natural selection (Neafsey et al., 2008) and to monitor the emergence of drug resistance (Coulibaly et al., 2022). In host populations, genome-wide association studies (GWAS) have facilitated the identification of loci associated with parasite resistance (Twomey et al., 2019) and have provided insights into the genomic consequences of host-parasite coevolution (Märkle et al., 2021). Together, these approaches underscore the value of integrating host and parasite population genetics to better understand the ecological and evolutionary dynamics of parasite spread. However, gaps remain in linking genetic patterns to transmission processes, especially in systems with complex host-parasite interactions or limited genomic resources.

## Bats: a good model to study parasite spread

Appeared over 52.5 million years ago (Simmons et al., 2008; Fenton and Simmons, 2020), bats have the second highest specific richness in mammals after rodents with more than 1400 species (~20% of all living mammals – Teeling et al., 2018). They are largely distributed across the globe, except the polar regions, and exhibit a variety of
habitat (Simmons, 2005). Bats are also known to play an important role in the transmission of parasites. They carry a wide variety of pathogens such as viruses (i.e. Rhabdovididae, Coronaviridae or Paramyxoviridae – Calisher et al., 2006; Chen et al., 2014; Krishnamoorthy et al., 2020), bacteria (Mühldorfer, 2013; Szentiványi et al., 2022), nematods (Lord and Brooks, 2014; Santos and Gibson, 2015), fungus (Fischer et al., 2025) or even blood parasites (i.e. trypanosomes or haemosporidians – Hamilton et al., 2012; Schaer et al., 2013; Lord and Brooks, 2014). Such pathogens can affect bat health and, in some cases, pose risks to other wildlife and humans (Brook and Dobson, 2015). Understanding the transmission dynamics of these pathogens in bat populations is not only critical for anticipating potential spillover events, but also essential for conserving bat species that provide key ecological services. Through their diverse morphologies and diets, they play major roles in ecosystem services such as pollination, seed dispersal, and pest regulation. In addition to hosting internal pathogens, bats carry a variety of ectoparasites (Frank et al., 2014; Szentiványi et al., 2016), some of which may act as vectors transmitting the above-mentioned pathogens (Lučan et al., 2016; Szentiványi et al., 2019, 2022). Studying these mutli-level host-parasite interactions offers valuable insights into the factors driving differential susceptibility among individuals and the mechanisms underlying parasite spread across bat populations. Such knowledge contributes to both wildlife health monitoring and broader goal of maintaining resilient bat populations in changing environments.

The high susceptibility of bats to parasites has first been hypothesised to be due to their flight adaptation. Flight may have promoted the emergence of tissue repair mechanisms, limiting the development of inflammation and increasing bat tolerance to higher parasitic and viral loads than other mammals (O'Shea et al., 2014; Fenton and Simmons, 2020). Moreover, immune system of bats allows them to adapt to stress and to better respond to parasitism through changes in the baseline expression and function of the innate immune response, combined with the development of multiple additional host defence mechanisms (Irving et al., 2021). Recently, a comparison of 115 mammalian genomes allowed to detect an excess of immune genes adaptations in bats and signatures of selection in immune genes in the ancestral branch of bats confirming the theory that the evolution of flight is linked to parasite susceptibility in bats (Morales
et al., 2025). Moreover, their ability to fly has led to the evolution of many migratory species, facilitating the rapid invasion and spread of parasites and pathogens across large geographical scales (Messenger et al., 2003).

Bat social system is one of the most complex across mammals. Most species are social and gregarious. They form heterogeneous colonies in terms of size, sex, age or even reproductive status. They use different roosts according to their biological state or activity, such as hibernation, pregnancy, or mating (Kunz and Fenton, 2003). With exception for species inhabiting regions where food supply is constant (such as tropical species or vampire bats), most bats follow the same seasonal pattern of reproduction (Fig. 2 – Racey and Entwistle, 2000). The fission-fusion social system of bats and the spatiotemporal heterogeneity of contacts between individuals is most likely to influence the transmission of parasites and pathogens between bats (van Schaik et al., 2014, 2015; Wilder et al., 2015). It is therefore critical to study these infection dynamics in natural bat populations to understand mechanisms of transmission between bats but also to other species.

<figure><img src="image_5.png" /><figcaption>Fig. 2. Life cycle of temperate bats.</figcaption></figure>
# Species description for chapters 2, 3 and 4

## Host

To study parasite spread, a good model is the Daubenton's bat (Myotis daubentonii), recognisable by its large feet, which allow it to catch aquatic insects (Fig. 3A). This vespertilionid bat is present in almost all countries in Europe and its habitat ranged up to central Asia (Fig. 3B – Krättli and Moeschler, 2021). Its habitat is strictly connected with the presence of forest and water streams (Fig. 3C; Langton et al., 2010). Daubenton's bat have a strong site fidelity (Kapfer et al., 2008) but follow the life cycle of most temperate bats. During winter, its hibernacula are mostly caves and during summer, this species prefers to roost in tree holes but can also be found in bridge cracks or in housings, highlighting its/their adaptability. In spring, bats leave their hibernacula and gestating females gather in maternity colonies for the parturition in summer. As other bat species, females tend to be philopatric to their natal maternity colonies whereas males disperse in smaller colonies or are solitary (Moussy et al., 2013). In general, maternity colonies host about fifty individuals but can be hundreds or even thousands (Fig. 3D) and roosts are not further than 4 km from the foraging sites (Arthur and Lemaire, 2009; Dietz et al., 2009). Usually, there is an altitudinal sexual segregation where males roost and hunt in higher altitude than females (Leuzinger and Brossard, 1994). Males are more likely to be found upstream of the river whereas females are roosting more downstream (Russo, 2002; Encarnação et al., 2005; Dietz et al., 2009). This behaviour has been hypothesized to be linked with resource competition or physiological constraints between sexes (Russo, 2002; Encarnação et al., 2005). From August to November, males and females from different roosts and maternity colonies gather near caves, underground galleries or chasms for the mating (Johnson et al., 2015b). Mating systems in bats are various but the Daubenton's bat is hypothesized to do swarming behaviour with males defending a female group (Dorrestein et al., 2024). Mating mostly occurs in swarming sites during late summer and autumn, but the increased presence of males in female-dominated roosts during late summer also suggest it happen in maternity roosts (Encarnação et al., 2004; Encarnação, 2012a; Angell et al., 2013). This was confirmed by a genetic analysis that found multiple paternities for males in maternity roosts (Encarnação, 2012b).
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Description of the biology of the Daubenton's bat (*Myotis daubentonii*). (A) Picture of a Daubenton's bat taken during swarming. (B) Habitat range of the Daubenton's bat. (C) Capture night across a river, the mist net has more than 30 Daubenton's bats entangled. (D) Picture of a cluster of Daubenton's bat in a maternity colony, juveniles are recognisable with their grey fur.</figcaption></figure>

During swarming, the habitat of the Daubenton's bat ranged from 254 km² to a potential catchment area of 2240 km² (Parsons and Jones, 2003), using river corridors. Daubenton's bat is considered as a facultative migrant since it covers middle range distances between summer and winter roosts (from 50 to 150 km – Kunz and Fenton, 2003; Hutterer et al., 2005) even if the max distance found was 304 km (Hutterer et al., 2005). Consequently, low genetic differentiation was found in the bat in Europe or at more local scale (Mayer and Helversen, 2001; Ngamprasertwong et al., 2008; Atterby et al., 2010; Laine et al., 2013). However, ringing studies showed that Daubenton's bat are faithful to their swarming sites within and between years (Parsons and Jones, 2003; Glover and Altringham, 2008). These findings suggest that further population genetic studies are needed in the Daubenton's bat, using higher resolution approaches.

The dynamic life cycle of the Daubenton's bat can create a lot of heterogeneity of contacts between individuals from different age and sex but also among colonies, which
may impact the spread of the Daubenton's bat parasites. Indeed, the Daubenton's bat is also known to have various species of parasites, such as wing mites (Spinturnix andegavinus) and bat flies (e.g. Nycteribia kolenati) and is the principal host of a malaria-like haemosporidian, Polychromophilus murinus (Megali et al., 2011). These three parasites rely heavily on the Daubenton's bats for the majority of their life cycle and can be impacted by the bat social structure and movements within and across colonies.

## Wing mite

Spinturnix andegavinus (Spinturnicidae, Mesostigmata) is a highly specialized hematophagous ectoparasites that primarily infests the Daubenton's bat. This wing mite exhibits a strict host specificity and is exclusively located on the patagium, the wing and tail membranes of the bat (Fig 4 - Dowling, 2006). Despite their ecological significance, the reproductive biology of wing mites remains poorly understood. They are nymphiparous, with eggs, larvae and nymphal stages developing internally within the female (Rudnick, 1960; Evans, 1969), decreasing the physiological pressures associated with the ectoparasitic lifestyle.

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Description of the bat mite, *Spinturnix andegavinus*. (A) Two adult bat mites on the patagium of a Daubenton's bat, the one on the left is most likely a gravid female and the one on the right is either a non gravid female or a male. (B) Female *S. andegavinus*, dorsal view. (C) Female *S. andegavinus*, ventral view. (D) Male *S. andegavinus*, dorsal view. (E) Male *S. andegavinus*, ventral view.</figcaption></figure>
Phylogenetic analyses have revealed strong cophylogenetic patterns between wing mites and their bat hosts (Bruyndonckx et al., 2009a). Experimental evidence further supported this coevolution with *S. andegavinus* having higher survival rates on the Daubenton's bat and being unable to persist for more than a few hours in the absence of its host (Giorgi et al., 2004). This intimate host-parasite relationship suggests that bat life-history traits considerably influence wing mite population dynamics.

Seasonal patterns of *S. andegavinus* infestation in the Daubenton's bats are similar to those observed in other wing mites, with a peak of intensity during the nursing period of the bat, decreasing during swarming season (van Schaik and Kerth, 2017). Additionally, higher intensities were observed on pregnant adult females and juvenile individuals (Lučan, 2006), highlighting the influence of the host physiology and behaviour in shaping the wing mite dynamics.

## Bat fly

The Daubenton's bat host a variety of bat flies, with *Nycteribia kolenatii* (*Nycteribiidae*, Diptera) identified as the principal species (Fig. 5 – Szentivanyi et al., 2016). Bat flies are obligate, hematophagous ectoparasites exclusively associated with bats. They are taxonomically divided into two families: Streblidae, which are more prevalent in the Western Hemisphere and Nycteribiidae, which dominate in the Eastern Hemisphere (Porter et al., 2022).

Unlike wing mites, bat flies do not reside continuously on the bat, within the fur. Although the reproductive biology of *N. kolenatii* remains poorly characterized, insights have been generalized from study based on limited number of species (Dick and Patterson, 2006). Marshall (1970) well described the life cycle of *Basilia hispida*, which is presumed to be representative of other nycteribiids. The eggs and the larval stages develop internally within the female, and the terminal larva stage (prepupa) is deposited by the female onto the bat roost substrate. Upon emergence, the adult fly must locate and colonize a new host (Ching and Marshall, 1968).
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Description of the bat fly, *Nycteribia kolenatii*. (A) Bat mite being sampled with forceps during capture night. (B) Female *N. kolenatii*, dorsal view. (C) Female *N. kolenatii*, ventral view. (D) Male *N. kolenatii*, dorsal view. (E) Male *N. kolenatii*, ventral view.</figcaption></figure>

Bat flies are strongly dependent of bats and are unable to survive extended periods once removed from their bat host (Marshall, 1970; Witsenburg *et al.*, 2015a). While the infection dynamics of *N. kolenatii* have not been directly studied, experimental evidence indicates that its abundance positively correlated with the Daubenton's bat body condition. Additionally, frequent host switches among individuals Daubenton's bats suggests high mobility and interaction within bat populations (Witsenburg *et al.*, 2015a). These findings are consistent with population genetics study demonstrating substantial gene flow among bat fly metapopulations (van Schaik *et al.*, 2015).

In other bat fly species, reproductive cycle appears to be synchronized with those of their host, with increased numbers of gravid bat flies observed during bat pregnancy and nursing periods (Lourenço and Palmeirim, 2008). Furthermore, pregnant and juvenile bats tend to harbour higher bat fly loads than other bats (Lourenço and Palmeirim, 2008) and parasite abundance was positively correlated with the host body condition (Tai *et al.*, 2022).

**Blood parasite**

Haemosporidians are eukaryotic protozoan parasites, vectored by arthropods, that infect the blood and tissues of different vertebrate hosts. This order comprise several
genera, among which *Plasmodium* is the most extensively studied due to its role in human malaria, particularly the virulent strain *P. falciparum*, a leading cause of infectious disease mortality worldwide (WHO, 2024).

Bats host nine different genera of haemosporidian parasites, seven of which are exclusive to bats, highlighting the significant role bats plays in the study of malaria parasites (Schaer et al., 2013; Perkins and Schaer, 2016; Pacheco and Escalante, 2023). Among these, *Polychromophilus* spp. is the most extensively documented genus and has a global distribution (Perkins and Schaer, 2016). Although five morphospecies of *Polychromophilus* spp. have been described, molecular studies suggest a greater diversity (Schaer et al., 2013; Chumnandee et al., 2021; Sándor et al., 2021; Poofery et al., 2023; Timm et al., 2025). Phylogenetic analyses reveal that *Polychromophilus* species are more closely related to haemosporidians infecting lizards and birds than to those infecting other mammal, positioning *Polychromophilus* spp. as a sister group to *Plasmodium* (Witsenburg et al., 2012; Borner et al., 2016; Galen et al., 2022).

*Polychromophilus* species share a similar life cycle with *Plasmodium* (Fig. 6 – Garnham, 1966). Sexual development occurs within the dipteran vector, while asexual development takes place in their vertebrate hosts. In the case of *P. murinus*, sporozoites residing in the salivary glands of bat flies are transmitted to bats during feeding. Contrary to *Plasmodium*, the parasite initiates exoerythrocytic schizogony in various organs of the bat (reticular cells, lung, kidney, liver and spleen) before localizing in the liver. Then, gametocytes infect red blood cells, where sexual differentiation occurs (Gardner and Molyneux, 1988; Witsenburg et al., 2014).

*Polychromophilus murinus* represents the most documented haemosporidian species, alongside *P. melanipherus* (Megali et al., 2011; Duval et al., 2012). Both species are transmitted to bats via nycteribid bat fly, which serve as their dipteran vectors (Gardner and Molyneux, 1988). *P. murinus* mainly infects vespertilionid, with the Daubenton's bat exhibiting the highest prevalence rates, ranging from 51% to 74% (Megali et al., 2011; Witsenburg et al., 2014). In this host species, *P. murinus* infection fluctuates seasonally, with elevated red blood cells parasitaemia observed during summer and autumn (Witsenburg et al., 2014). Moreover, intensity of infection is negatively correlated with adult body condition and is highest in juvenile bats (Witsenburg et al., 2014).
Notably, *P. murinus* also impacts its vector, as infected bat flies exhibit reduced survival rates (Witsenburg *et al.*, 2015a), suggesting fitness costs associated with parasite transmission.

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Description of the haemosporidian, *Polychromophilus murinus*. (A) Blood smear showing a mature female gametocyte (arrow), 1000x magnification. (B) Life cycle of *P. murinus*.</figcaption></figure>
## PhD outline

In this PhD dissertation, I was interested in understanding how parasites from a European bat spread at local scale. First, I wondered how a generalist parasite could spread between hosts. To do so, I characterized the population genetics of the common bed bug, *Cimex lectularius*, parasitizing both humans and bats (**Chapter 1**). Two bat lineages were previously discovered at the European scale (Booth *et al.*, 2015), but no study ever compared the genetic differentiation of the two host-associated bugs at local scale. After confirming that this pattern of genetic differentiation was also present at the scale of Western Switzerland, I investigated the genetic structure of the common bed bugs between host-specific sampling sites. I also looked at the distribution of the resistance haplotypes between the two lineages.

Then, I wondered how bat-specific parasites could spread between individuals from the same species of host (**Chapter 2, 3 and 4**). For these parts, I focused on the Daubenton's bat (*Myotis daubentonii*), two of its ectoparasites (a bat fly, *Nycteribia kolenatii* and a wing mite, *Spinturnix andegavinus*) and one blood parasite (*Polychromophilus murinus*), described in the previous section. I first explored the population genetic diversity and structure of the Daubenton's bat (**Chapter 2**). This allowed to better understand the distribution of the Daubenton's bat across the Vaud canton in Switzerland, providing valuable insights into the transmission of its parasites among bats in the following chapters. Then, I investigated the link between host genetic diversity and parasite abundance (**Chapter 2**). This relationship has been previously studied in various species using different measures of genetic diversity, but results are inconsistent, with no consensus emerging. Instead, the association appears to be both context- and species-dependent. In this chapter, I explored the relationship between parasite abundances and different measures of the Daubenton's bat genetic diversity using RADseq data.

In **Chapter 3**, I compared the population genetic structure of the bat fly *Nycteribia kolenatii* and the wing mite *Spinturnix andegavinus*. Wing mites and bat flies have different life history. They both live and feed on the bat but the bat fly pupae on the roost's wall, creating more movement in this species. The population genetic structure of two ectoparasites form the same families has already been assessed with microsatellites loci
in other species of bats (van Schaik *et al.*, 2014, 2015). Using comparative genomics (based on RADseq), I characterized the population genetic structure of the Daubenton's bat specific wing mite (*Spinturnix andegavinus*) and bat fly (*Nycteribia kolenatii*) to better understand how different life histories can play a role in their spread.

Finally, I was interested in the infection dynamics of the Daubenton's bats to better understand how the spatiotemporal heterogeneity of the bat life cycle shaped the spread of their parasites among individuals. **(Chapter 4)**. Infection dynamics in bat parasites have already been described (Lourenço and Palmeirim, 2007, 2008; Witsenburg *et al.*, 2014). Yet, there is a lack of information regarding their temporal variation and the interaction between coinfecting parasites, in particular, between *P. murinus* and its vector, the bat fly. In this chapter, I first explored the seasonal dynamic of wing mites, bat flies and *P. murinus*. Then, I looked at the susceptibility to infection of different classes of bats. Finally, I investigated the importance of the wing mite and bat fly abundances in the prevalence and intensity of *P. murinus* in Daubenton's bats. I thus, provided a clarification of how parasite infections are maintained in the bat population at different scales and how parasite interact together in bats.

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# Chapter 1

## Genetic characterization of the bat and human lineages of the common bed bug (Cimex lectularius) at local scale

**Clara Castex**<fnref n="1" />, Antoine Perrin<fnref n="1" />, Laura Clément<fnref n="1" />, Pierre Perréaz<fnref n="1" />, Jérôme Goudet<fnref n="1" />, <fnref n="2" />* and
Philippe Christe<fnref n="1" />*

Corresponding author: clara.castex@unil.ch

**Authors contributions:** LC and PC conceived and planned the study. CC, AP and PP ran the analyses, and all authors contributed to the interpretation of results. CC and PP wrote the first draft of the manuscript, and all authors provided critical feedback and approved the final manuscript. JG and PC are the co-senior authors.

**Status:** This chapter has been published in *Parasitology*.

<footnote n="1">¹Department of Ecology and Evolution, University of Lausanne, Switzerland</footnote>

<footnote n="2">²Swiss Institute of Bioinformatics, Lausanne, Switzerland</footnote>

<footnote>*Co-senior authors</footnote>
# Genetic characterization of the bat and human lineages of the common bed bug (*Cimex lectularius*) at a local scale

## Research Article

*These authors contributed equally to this work.

**Cite this article:** Castex C, Perrin A, Clément L, Perréaz P, Goudet J, Christe P (2025) Genetic characterization of the bat and human lineages of the common bed bug (*Cimex lectularius*) at a local scale. *Parasitology* **152**, 510–521. https://doi.org/10.1017/S0031182025000575

Received: 4 November 2024

Revised: 25 March 2025

Accepted: 12 April 2025

First published online: 21 April 2025

**Keywords:**

*Cimex lectularius*; genetic differentiation; host races; insecticide resistance; local scale

Corresponding author: Clara Castex;

Email: clara.castex@unil.ch

Clara Castex ID, Antoine Perrin, Laura Clément, Pierre Perréaz, Jérôme Goudet* and Philippe Christe*

Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland

## Abstract

After its near eradication in the 1940s, the common bed bug (*Cimex lectularius*) experienced a global resurgence. Within a few years after, some populations displayed insecticide resistance. Two distinct lineages of bed bugs were identified, each associated with humans and bats, respectively. A strong genetic differentiation was identified between bugs from human and bat sites across Europe. This raises the question of whether the same pattern is found at a local scale. Moreover, because long-distance dispersal of bed bugs is essentially human-mediated, we investigated the spread of bed bugs within and among sites. Using mitochondrial (cytochrome oxidase unit I (COI) and 16S rRNA genes) and nuclear (10 microsatellite loci) markers, we compared the genetic composition of human- and bat-associated bed bugs from western Switzerland. We first conducted a median-joining analysis and compared it to European sequences to detect local-scale host-specific separation of haplotypes. We estimated levels of genetic diversity and structure between and within the two host-associated bed bugs. Our results reveal two genetic clusters associated with bats and humans and a strong structure among human sites ($F_{SC} = 0.579$). An analysis of knock-down insecticide resistance gene variants (V419L, L925I, I936F) shows that bed bugs infecting humans in western Switzerland carry insecticide resistance (99%) whereas bed bugs infecting bats do not (0%). Our results show that at the scale of western Switzerland, bed bugs are structured by host association, thus supporting the hypothesis of host specialization in the common bed bugs. Moreover, human-associated bugs might have settled from multiple colonization events and/or undergone bottlenecks.

## Introduction

Parasites depend on their hosts to survive and reproduce and have developed a diverse array of life cycle complexities, host ranges and host switches for this reason (Poulin, 1995; Morand, 2015; Hay et al., 2020). One major driver of parasite diversification is host specialization, defined as the degree of association between a parasite and different host species (Dick and Patterson, 2007). This degree is variable between parasites with some generalist and more specialist species. Different measures have been described to assess host specialization such as the number of hosts exploited, the phylogenetic distance and the functional diversity of hosts (Poulin et al., 2011; Medina and Langmore, 2016). Such measures help understand how host specialization can lead to speciation or the formation of host races. Drès and Mallet (2002) defined host races as the genetic differentiation of lineages specialized on different hosts living in the same area. For example, the study of seabird ticks, *Ixodes uriae*, hosted by the black-legged kittiwake (*Rissa tridactyla*) and the Atlantic puffin (*Fratercula arctica*), has revealed a host-related diversification in the genetic structure of the ticks (Mccoy et al., 2003).

Host specialization is a phenomenon that is particularly visible in the *Cimicidae* bug family. *Cimicidae* are temporary ectoparasites that feed directly on their host and spend most of their time concealed within the host's shelter (Usinger, 1966). These obligate blood-feeding parasites are widespread among different host species. Out of 22 genera of *Cimicidae*, 12 are bat-only and 9 are bird-only associated parasites (Roth et al., 2019). The *Cimex* genus contains 10 species and is recognized to be associated with several groups of hosts. Most of the *Cimex* species (e.g. *C. pipistrelli*, *C. adjunctus* or *C. pilosellus*) are specialized on bats, which are thought to be the ancestral host of the genus (Usinger, 1966). However, some of the *Cimex* species can be found on multiple hosts including bats, birds or even humans (e.g. *C. lectularius* and *C. hemipterus*). The common bed bug, *C. lectularius*, is predominant in temperate regions (Usinger, 1966; Zorrilla-Vaca et al., 2015).

Human-associated *C. lectularius* were almost eradicated between 1940 and 1950 in many areas due to intensive use of dichlorodiphenyltrichloroethane (DDT) insecticides (Barnes, 1946;

© The Author(s), 2025. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.

<figure><img src="image_10.png" /></figure>

CAMBRIDGE
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Seong et al., 2010; Davies et al., 2012). However, nowadays bed bugs seem to spread again around the world (Levy Bencheton et al., 2011; Davies et al., 2012; Delaunay, 2012; Dang et al., 2015). One of the main reasons for this resurgence is an increase in insecticide tolerance (Romero et al., (2007); Zhu et al., 2010; Lewis et al., 2023). In addition, the intensification of human interactions, such as better access to long-distance transport (Delaunay, 2012), may contribute to this recent resurgence. It increases the risk of introducing resistant individuals into existing populations, making pest control methods less efficient. As a result, bed bug populations may continue to grow, leading to their wider spread as an urban pest species and further increasing their already significant public health and economic impact (Doggett et al., 2018; Hwang et al., 2018; Perron et al., 2018).

Ecological differences between bats and humans can result in different selective pressures, which may lead to host race formation in bed bugs. Common bed bugs *C. lectularius* found in human shelters and bat roosts were shown to be genetically differentiated (Balvín et al., 2012; Booth et al., 2015; Balvín and Booth, 2018). Additionally, based on the differences in feeding behaviour, survival and development, the two host-associated groups were defined as two distinct ecotypes according to Wawrocka and Bartonička (2013). This supports the morphological differentiation in sensory, feeding and dispersal organs (Balvín et al., 2012). However, except for Wawrocka et al. (2015), recent hybridization experiments did not find any evidence for post-mating barriers and reproductive isolation (Devries et al., 2020; Sasínková et al., 2023). Factors other than ecological could have shaped genetic differentiation between bat- and human-associated bed bugs. Indeed, as mentioned above, the intensive use of insecticide by humans has induced insecticide resistance around the world due to mutations in the voltage-gated sodium channel (Doggett et al., 2004; Zhu et al., 2010; Durand et al., 2012; Dang et al., 2015, 2017; Lilly et al., 2015; Palenchar et al., 2015; Raab et al., 2016; Cho et al., 2020; Vander Pan et al., 2020; Akhoundi et al., 2021; Lewis et al., 2023). If individuals were resistant, then we would expect this mutation to be present in bed bugs from human shelters and not in bugs from bat roosts that have not been exposed to insecticides, as pointed out by previous studies in Europe (Booth et al., 2015; Balvín and Booth, 2018).

The genetic differentiation of the two lineages of bed bugs was mostly investigated at large scale in Europe (Balvín et al., 2012; Booth et al., 2015; Balvín and Booth, 2018). This raises the question of whether the same pattern can occur between bat and human sites at the local scale. There are few records of local-scale genetic differentiation of bed bugs within hosts (Djouaher et al., (2024); Booth et al., 2012). Because the long-distance dispersal of the common bed bug between human sites is mediated by humans (Doggett et al., 2004; Reinhardt and Siva-Jothy, 2007; Delaunay, 2012), it would be interesting to explore if bed bugs are able to spread within and among sites at a more local scale.

Here, we performed genetic analyses to understand bed bug transmission between and within hosts at a local scale, in Western Switzerland. We examined genetic differentiation and gene flow both between and within bat- and human-associated bed bugs and compared it to previous results of differentiation across Europe (Booth et al., 2015). Genetic diversity and structure provided information on the extent of the differentiation of the two host-associated lineages of bed bugs at a local scale. We also investigated bed bug movements within hosts and disentangled whether gene flow occurred between human-associated sites and whether the same patterns were observed within bat roosts. Additionally, we

tested insecticide resistance mutation as a potential genetic marker to differentiate bed bugs from bats and humans. We first estimated the proportion of the insecticide-tolerant bugs from the two hosts and then assigned the prevalence of each of those two tested mutations.

## Materials and methods

### Bed bugs collection and DNA extraction

We collected 417 *C. lectularius* (345 – on humans, 72 – on bats) from 30 human dwellings and 2 bat roosts (*Myotis myotis*), respectively (Figure S1, Table S1). The human-associated bugs were collected by various pest management professionals before any treatment was applied. All bugs were stored at –20°C in tubes filled with 90% ethanol. The bed bugs were rehydrated in distilled water for 30 min, and DNA was extracted using the DNeasy Blood and Tissue kit (Qiagen) following the manufacturer's protocol.

### mtDNA analysis

A 658 bp fragment of the cytochrome oxidase unit I (COI) gene was amplified using the modified primers LepF (5'-ATTCAACCAATCATAAAGATATTGG-3') and LepR (5'-TAAACTTCTGGATGTCCAAAAAATCA-3') from Hajibabaei et al. (2006) according to Booth et al. (2015). In addition, a 382 bp fragment of the 16S rRNA gene was amplified using the primers LR-J-13007 (5'-TTACGCTGTTATCCCTAA-3') (Kambhampati and Smith, 1995) and LR-N-13398 (5'-CGCCTGTTTATCAAAAACAT-3') (Simon et al., 1994). Polymerase chain reaction (PCR) protocol from Rosli et al. (2011) was improved using 0.3 µM of primers, 3.0 mM of MgCl₂ and 35 cycles. Amplifications were tested on a 1.5% agarose gel, and amplified fragments were sent for sequencing (Microsynth AG: Sanger method). Sequences were aligned in MEGA11 (Tamura et al., 2021). To compare the local haplotypes with the European ones, the reverse complement of 266 COI and the 16S sequences (214 human-associated bed bugs from 24 sites and 52 bat-associated bed bugs from 2 sites) were concatenated into a 948 bp sequence and aligned to 214 sequences of Booth et al. (2015), available on Dryad. A median-joining network (Bandelt et al., 1999) was built in PopArt v1.7 (Leigh and Bryant (2015)) using the default parameters. One hundred and forty-six individuals out of 417 were discarded because fragments failed to be sequenced, and 5 individuals were disregarded because of signal of heteroplasmy, frequent in the common bed bugs (Booth et al., 2015; Robison et al., 2015).

### Microsatellite analysis

An analysis of polymorphic sites was performed using 12 species-specific microsatellite loci (BB15B, BB21B, BB28B, BB29B, BB31B, BB38B, BB42B, Clec6, Clec11, Clec37, Clec48, Clec99) developed by Booth et al. (2012). The PCR were run according to Booth et al. (2012) with 0.3 µl of primer for each locus and an increase of 40 cycles. Taq amount was doubled for BB15B, BB21B, BB28B, BB29B and BB31B to increase the success of genotyping. PCR success was tested on 1.5% agarose gel. Sequencing was performed on a 3100 Genetic Analyzer (Applied Biosystems). Scoring of the various allele sizes was done by hand using GeneMapper 4.0 (Applied Biosystems). Micro-checker v2.2.3 (Van Oosterhout et al., 2004) was used to assess the presence of
scoring errors, null alleles or large-allele dropout in each locus. Linkage disequilibrium was analysed between loci on 1000 per-mutations with the FSTAT software v2.9.4 (Table S2; Goudet, 2002). Because we lack a common panel, our microsatellite data could not be compared to the European microsatellite dataset from Booth et al. (2015). Among the 32 sites sampled in this study, 27 sites sampled in 2016 were genotyped to carry out the microsatellite analysis (Table S1). Out of the 12 tested loci, BB15B and BB28B showed null alleles and missing data, and thus 10 loci were kept in the analysis (BB21B, BB29B, BB31B, BB38B, BB42B, Clec6, Clec11, Clec37, Clec48 and Clec99). In total for the microsatellite analysis, 370 individuals from 25 human shelters (N = 311) and 2 bat roosts (N = 59) were analysed.

To assess the genetic diversity between and within sampling sites, we kept the 17 sites with 5 or more individuals. The following summary statistics were obtained with the 'hierfstat' R package 0.5.11 (Goudet et al., 2022; R Core Team, 2022): allelic richness per host (Ar), observed (Ho) and expected heterozygosity (He) and population inbreeding coefficient ($F_{IS}$). The confidence intervals for $F_{IS}$ per site were obtained by bootstrapping over loci with 1000 bootstraps. Paired Wilcoxon tests were performed to compare summary statistics between human and bat sites. Pairwise $F_{ST}$ values were calculated to assess population structure between the host-associated sites using the method of Weir and Cockerham (1984). To test for population structure, we used the test.g function from the 'hierfstat' R package with 2720 randomizations to adjust for multiple testing. To estimate the genetic variance components between hosts and among sampling sites, a hierarchical analysis of variance was carried out using the varcomp.glob function of the 'hierfstat' R package and significance was tested with 1000 permutations. The 95% confidence interval of the variance components was also calculated over 1000 bootstraps using the boot.vc function of the same package.

To test for isolation by distance considering the structure of our dataset, the correlation between the pairwise $F_{ST}$, the geographical distance between sites and two geographical structure matrices was estimated. The first structural matrix accounted for the fact that 10 of the 17 human sites were tightly clustered around Geneva and thus took a value of 1 if the pairwise sites both belonged to Geneva or did not belong to Geneva, and a value of 0 if one of the sites was from the Geneva cluster while the other was not. The second matrix accounted for bat roosts that were not sampled in the same places as human shelters (i.e. 1 for pairwise sites from the same host (bat or human) and 0 for pairwise sites from different hosts). Since the structural matrices were correlated with geographic distances (Table S3), and because of the non-independence of pairwise distances, a generalized least squares (GLS) model with a maximum-likelihood population effects correlation structure was conducted (MLPE, Clarke et al., 2002). The gls function from the 'corMLPE' package (Pope, 2024) was used for the entire dataset using only the structural matrices. Finally, the effect of geographical distances on genetic distances was tested on pairs of bed bugs from Geneva and on pairs of bed bugs from outside Geneva on human-associated bugs only, using a Mantel test.

Finally, using all 27 sites, we calculated the pairwise kinship between individuals with the beta.dosage function in 'hierfstat' (Goudet et al., 2022). Because of the high levels of relatedness (see Results section), a single specimen per site was randomly chosen, and the analysis was randomized 100 times. To examine the amount of substructure among the various bed

bug sites collected on the two hosts, principal component analysis (PCA) between host-associated bed bugs was run using the indpca function implemented in 'hierfstat' (Goudet et al., 2022).

## Pyrethroid resistance: knock-down resistance mutation

Knock-down resistance ($kdr$) genotypes at the amino acids 419, 925 and 936 in the voltage-gated sodium channel were determined to assess the resistance of *C. lectularius* to pyrethroid insecticide. PCR was done to identify the three mutations in the voltage-gated sodium channel. The allele independent primers BBParaF1 (5'-AACCTGGATATACATGCCTTCAAGG-3') and BBParaR1 (5'-TGATGGAGATTTTGCCACTGATG-3') were used to amplify a fragment containing the amino acid 419, and BBParaF3 (5'-GGAATTGAAGCTGCCATGAAGTTG-3') and BBParaR3 (5'-TGCCTATTCTGTCGAAAGCCTCAG-3') were used to amplify the fragment containing the amino acids 925 and 936 (Zhu et al., 2010). PCR was run in total volume of 25 µl with the minimal amount of 20 ng DNA template, 0.3 µM of each primer, 0.5 U of Taq polymerase (Promega), 0.3 mM of dNTPs, 1x Taq buffer (Promega), and 0.5 mM MgCl₂. PCR mix was completed to 25 µl with H₂O MilliQ. The PCR conditions were 94°C for 3 min, followed by 35 cycles of 94°C for 30 s, 55°C for 30 s and 72°C for 1 min with a final extension step at 72°C for 10 min. Successfully, amplified products were sent for forward sequencing (Microsynth), with a successful sequencing of 342 individuals (with 55 and 287 individuals for bats and human-associated bed bugs, respectively). Sequences were then checked for the mutated base pairs for both locations known to be associated with $kdr$ resistance: V419L (wild-type: valine = GTC; mutated: leucine = CTC), L925I (wild-type: leucine = CTT; mutated: isoleucine = ATT) and I936F (wild-type: isoleucine = ATT; mutated: phenylalanine = TTT) using MEGA11 software. Individuals were identified as heterozygotes for mutation where overlapping peaks occurred in the chromatograms (Dang et al., 2015). The annotation of Lewis et al. (2023) was used to designate all the $kdr$ genotypes.

## Results

### mtDNA analysis: comparison with European haplotypes

In the common bed bugs of western Switzerland only, the COI and 16S concatenated genes revealed 11 haplotypes. The haplotype median-joining network was consistent with a clear separation between the two hosts (Figure 1A). Among the 11 haplotypes, 9 were human-associated and 2 were bat-associated. No haplotype was shared between the two hosts (Figure S2; Table S4). In general, we observed the same pattern of host differentiation at the local scale and at the European scale. Indeed, with the addition of the European sequences, the analysis revealed a total of 27 haplotypes, 11 of which were associated with humans, 14 with bats and 2 with both host-associated sequences. Haplotypes were shared between sites. In total, 6 haplotypes were shared between the local and the European datasets (Figure 1B). Of these 6 haplotypes, 1 haplotype was strictly associated with bats, 3 haplotypes were associated with humans and 2 haplotypes shared both host-associated sequences. We identified 5 new haplotypes (H2, H3, H8, H9 and H10 in Figure 1B). Four of these were associated with humans, while H10 was exclusively associated with bats. (Figure S2).
A

<figure><img src="image_11.png" /></figure>

B

<figure><img src="image_12.png" /></figure>

**Figure 1.** Median joining network based on the concatenated 16S and COI mitochondrial genes with (A) Swiss individuals only and (B) European individuals from Booth et al. (2015). Human-associated haplotypes are represented in orange, and bat-associated haplotypes are purple. The number of individuals sharing the same haplotype is highlighted by the size of the circles. The black dots represent ancestral or unsampled haplotypes. Traits denote the number of nucleotide differences between haplotypes. In (B), circles without outlines represent haplotypes from Booth et al. (2015), black circles highlight new haplotypes whereas black dotted circles represent shared haplotypes between the Swiss and the European dataset.

## Microsatellite analysis

### Genetic diversity

For all 10 microsatellite markers, our analyses with micro-checker (Van Oosterhout et al., 2004) found no evidence of scoring error, null allele presence or large allele dropout in the final dataset. After Bonferroni correction, there was no significant linkage disequilibrium. The gene diversity per population over loci was significantly different between hosts (paired Wilcoxon test: $V = 50, P = 0.020$), with bat-associated bugs more genetically diverse than human-associated lineages, despite a much lower sample size (Tables 1 and 2; Table S5). Similarly, we found higher allelic richness in the bat-associated bugs (paired Wilcoxon test: $V = 50, P = 0.020$). However, more private alleles were found in the human-associated bed bugs than the bat-associated ones (paired Wilcoxon test: $V = 2.5, P = 0.019$).

### Genetic structure

Over the 10 microsatellite loci, pairwise $F_{ST}$ values were estimated to detect genetic structure between and within bat- and human-associated sites. Pairwise $F_{ST}$ analyses were consistent with a genetic differentiation among sites (Figure 2 and Table S6). The estimates of the variance components and hierarchical *F*-statistics over all loci were large and significant among sites within host-associated lineages ($F_{SC} = 0.579, P = 0.001$); the differentiation between host-associated lineages was also large ($F_{CT} = 0.254$), but not significant when tested using permutations of sites between hosts ($P = 0.179$). However, the 95% confidence interval for $F_{CT}$ obtained by bootstrapping over loci did not overlap with 0 ($F_{CT} = 0$) 95% CI = [0.008; 0.455]).

The GLS model highlighted a significant relationship between the host structural matrix and the genetic distances of the bed

bugs (GLS model, $t = -2.987, p = 0.003$). The bed bugs from the same host showed less genetic differentiation than the samples from the other host. There was also a significant relationship between genetic distances and the structure associated with the Geneva sites (GLS model: $t = -4.499, p < 0.0001$). The effect of geographical distances on genetic distances was tested on pairs of bed bugs that belong to Geneva and on pairs of bed bugs from outside Geneva on human-associated bugs only. Both Mantel tests showed non-significant relationships between the geographical and genetic distances (Geneva: $r = -0.115, p = 0.612$; outside Geneva: $r = 0.087, p = 0.289$). The gene flow within Geneva or outside Geneva did not depend on the geographical distances (Figure 3).

The PCA analysis revealed a substructure among the different sites and split the individuals by host along the first and second axes (Figure 4). Pairwise kinship between individuals was higher between bugs from the same host and particularly, from the same site for human-associated bugs (Figure 5). The human-associated bugs were closer with bed bugs from the same site whereas the bat-associated bugs showed no substructure.

## Knock-down resistance in the sodium channel

In total, *kdr* genotypes were available for 342 common bed bugs from 25 locations for the V419L, L925I and I936F sites. All 55 bat-associated bugs (from 2 sites) exhibited the wild-type form of the amino acids 419, 925 and 936. In contrast, among the human-associated bed bugs, 1 resistant variant (925I) was present in all but 3 individuals (among 283 mutants out of 286 individuals, 258 were homozygous for the resistant variant and 25 heterozygous), V419 was absent, and I936 was present in a heterozygous state in one individual (Table S7).
<table><caption>**Table 1.** Genetic diversity of the common bed bugs over loci</caption><thead><tr><th rowspan="2">Locus</th><th colspan="3">Ar</th><th colspan="3">Ho</th><th colspan="3">He</th></tr><tr><th>Overall</th><th>Bat</th><th>Human</th><th>Overall</th><th>Bat</th><th>Human</th><th>Overall</th><th>Bat</th><th>Human</th></tr></thead><tbody><tr><td>BB21B</td><td>1.268</td><td>1.696</td><td>1.211</td><td>0.123</td><td>0.085</td><td>0.128</td><td>0.279</td><td>0.709</td><td>0.217</td></tr><tr><td>BB29B</td><td>1.305</td><td>1.522</td><td>1.276</td><td>0.141</td><td>0.000</td><td>0.160</td><td>0.349</td><td>0.534</td><td>0.317</td></tr><tr><td>BB31B</td><td>1.288</td><td>1.483</td><td>1.260</td><td>0.182</td><td>0.000</td><td>0.208</td><td>0.291</td><td>0.511</td><td>0.259</td></tr><tr><td>BB38B</td><td>1.360</td><td>1.605</td><td>1.327</td><td>0.320</td><td>0.633</td><td>0.278</td><td>0.367</td><td>0.604</td><td>0.335</td></tr><tr><td>BB42B</td><td>1.236</td><td>1.763</td><td>1.161</td><td>0.198</td><td>0.746</td><td>0.119</td><td>0.243</td><td>0.763</td><td>0.168</td></tr><tr><td>Clec6</td><td>1.101</td><td>1</td><td>1.114</td><td>0.067</td><td>0.000</td><td>0.076</td><td>0.104</td><td>0.000</td><td>0.119</td></tr><tr><td>Clec11</td><td>1.221</td><td>1.436</td><td>1.193</td><td>0.204</td><td>0.342</td><td>0.185</td><td>0.227</td><td>0.437</td><td>0.198</td></tr><tr><td>Clec37</td><td>1.252</td><td>1.339</td><td>1.240</td><td>0.199</td><td>0.250</td><td>0.192</td><td>0.255</td><td>0.341</td><td>0.243</td></tr><tr><td>Clec48</td><td>1.022</td><td>1.186</td><td>1</td><td>0.019</td><td>0.164</td><td>0.000</td><td>0.023</td><td>0.185</td><td>0.000</td></tr><tr><td>Clec99</td><td>1.268</td><td>1.112</td><td>1.289</td><td>0.267</td><td>0.025</td><td>0.300</td><td>0.273</td><td>0.113</td><td>0.295</td></tr><tr><td>Mean</td><td>1.232</td><td>1.414</td><td>1.207</td><td>0.172</td><td>0.224</td><td>0.164</td><td>0.241</td><td>0.420</td><td>0.215</td></tr></tbody></table>

The allelic richness (Ar), the observed and the expected heterozygosity (Ho and He) are represented between hosts and overall.

<table><caption>**Table 2.** Genetic diversity of the common bed bug over sites with more than 5 individuals</caption><thead><tr><th>Site</th><th>Total sample size</th><th>Ar</th><th>Ho</th><th>He</th></tr></thead><tbody><tr><td>1</td><td>39</td><td>1.366</td><td>0.211</td><td>0.372</td></tr><tr><td>2</td><td>20</td><td>1.462</td><td>0.238</td><td>0.469</td></tr><tr><td>3</td><td>18</td><td>1.304</td><td>0.314</td><td>0.304</td></tr><tr><td>4</td><td>11</td><td>1.046</td><td>0.064</td><td>0.045</td></tr><tr><td>5</td><td>11</td><td>1.065</td><td>0.071</td><td>0.065</td></tr><tr><td>8</td><td>6</td><td>1.000</td><td>0</td><td>0</td></tr><tr><td>9</td><td>30</td><td>1.172</td><td>0.178</td><td>0.172</td></tr><tr><td>15</td><td>11</td><td>1.218</td><td>0.104</td><td>0.225</td></tr><tr><td>19</td><td>14</td><td>1.444</td><td>0.385</td><td>0.447</td></tr><tr><td>20</td><td>21</td><td>1.471</td><td>0.424</td><td>0.472</td></tr><tr><td>21</td><td>27</td><td>1.341</td><td>0.298</td><td>0.343</td></tr><tr><td>23</td><td>5</td><td>1.044</td><td>0</td><td>0.057</td></tr><tr><td>24</td><td>30</td><td>1.035</td><td>0.035</td><td>0.035</td></tr><tr><td>25</td><td>7</td><td>1.277</td><td>0.283</td><td>0.308</td></tr><tr><td>27</td><td>39</td><td>1.194</td><td>0.085</td><td>0.196</td></tr><tr><td>28</td><td>34</td><td>1.297</td><td>0.113</td><td>0.300</td></tr><tr><td>30</td><td>14</td><td>1.164</td><td>0.080</td><td>0.175</td></tr></tbody></table>

The allelic richness (Ar), the observed and the expected heterozygosity (Ho and He) are represented between hosts and overall.

Discussion

Using three types of markers, we showed that bed bugs display the same pattern of genetic differentiation at a very regional scale as at the European scale with two main lineages that are specialized on their respective hosts. The results revealed different structures between human-associated and bat-associated populations. Our results are consistent with previous studies investigating the genetic structure of the two lineages in Europe using mitochondrial, microsatellite markers and *kdr* profiles (Balvín et al., 2012; Booth

et al., 2015; Balvín and Booth, 2018). We also showed strong differences between frequencies of resistance alleles as they are only present in the human lineage. Due to the resurgence in human shelters, human-associated bed bugs are heavily exposed to insecticides. This exposure has led to the emergence and increased frequency of a *knock-down resistance* mutation in the voltage-gated sodium channel (Zhu et al., 2010; Dang et al., 2015; Holleman et al., 2019; Lewis et al., 2023). These different selective pressures between bat- and human-associated bugs have led to genetic divergence between the two lineages.

Host specialization of *C. lectularius*

Mitochondrial DNA analysis of the COI and 16S concatenated sequences showed a separation between bat- and human-associated bugs (Figure 1A). Unlike previous studies that have found shared haplotypes (Balvín et al., 2012; Booth et al., 2015), the 9 haplotypes associated with humans were not found in the bat-associated bugs. Thus, in this study, bed bugs from the same hosts clustered together. However, when European sequences are included in the analyses, two haplotypes were shared between hosts (Figure 1B; H4 and H5). As it was underlined by Booth et al. (2015), this can be a result of recent introgression, shared alleles, or incorrect assignment of the host. Nevertheless, the comparison between European and local bugs revealed the same pattern of genetic differentiation between larger and smaller scales. Indeed, haplotypes were separated between hosts but not between localities. The bugs from Switzerland displayed the same pattern of genetic differentiation between hosts as the European ones.

The analysis of host differentiation using 10 microsatellite loci was consistent with the mitochondrial haplotype network. Despite the small number of loci, we showed genetic differentiation between the host-associated bugs. PCA revealed two host-associated genetic clusters (Figure 4) indicating the presence of two hosts-specialized lineages suggesting reproductive isolation between bat- and human-associated *C. lectularius*, as it was also demonstrated by Booth et al. (2015). The low level of gene flow between bed bugs from human and bat host species revealed by
<figure><img src="image_13.png" /><figcaption>**Figure 2.** Heatmap of pairwise *F*<sub>ST</sub> over all pairs of sites based on the microsatellites data.</figcaption></figure>

the pairwise *F*<sub>ST</sub> analysis is highly consistent with previous popu-
lation genetics studies in Europe with high overall *F*<sub>ST</sub> (*F*<sub>ST</sub> = 0.683,
Booth et al., 2015). Despite a large *F*<sub>CT</sub> (0.254) and a bootstrap CI
not overlapping 0, the permutation test for the hierarchical analy-
sis of variance did not show a significant differentiation between
the two host-associated lineages. This is most likely due to the
lack of power of the permutation test because of the low number
of bat roosts (N = 2). Despite our sampling and low number of
markers, the significant effect of the host structural matrix revealed
the strong genetic differentiation between the two host-associated
lineages.

Although Wawrocka et al. (2015) have found reproductive iso-
lation between the two lineages from their hybridization exper-
iments, recent studies of cross-experiments suggest otherwise
(Devries et al., 2020; Sasínková et al., 2023). The successful repro-
duction of the two host-associated bed bugs and the presence of
reproductive offsprings are revealing the lack of post-copulatory
reproductive barriers in the common bed bugs (Devries et al.,
2020; Sasínková et al., 2023). Explanations about the maintenance
of these genetic differences need further investigations. It is still
unclear to what extent bed bugs are able to disperse between
their different hosts. Opportunity for host switch depends on the

dispersal mechanisms of individuals (Combes, 2004). Contacts
between the two hosts are likely to occur allowing the transmission
of bed bugs from one host to another. An experimental study has
provided evidence that human-associated bed bugs are activated
and attracted by human odour and its components (Suchy and
Lewis, 2011; Harraca et al., 2012; Liu and Liu, 2015; Devries et al.,
2019). Moreover, morphological mechanisms of host detection
may be different between host-associated bed bugs (Balvín et al.,
2012), but the cues that control these differences are unclear. Heat,
exhaled CO₂ and kairomones are used by other haematophagous
insects to detect their host (Lehane, 2005) and could help the
bugs to detect and locate their hosts (Rivnay, 1932; Reinhardt
and Siva-Jothy, 2007; Suchy and Lewis, 2011). However, there is
a lack of information on the host-seeking behaviour of *C. lectu-
larius*, in particular the comparison of host attraction between
humans and bats. Balvín et al. (2017) tested pre-mating barri-
ers between the bat- and human-associated bugs by investigating
lineage-specific aggregation after blood meals. They found no clear
behavioural differences, which means that shelter fidelity does
not explain the genetic differentiation between the two lineages.
Human-associated bed bugs are more active during the night
(Mellanby, 1939; Romero et al., 2010). It may be assumed that the
<figure><img src="image_14.png" /><figcaption>**Figure 3.** Isolation by distance for human-associated bed bugs. Orange dots highlight pairwise human shelters from the Geneva canton (sites 19–28; Mantel test: *P* = 0·612). Red dots represent pairwise human shelters from outside the Geneva canton (sites 3–15 and 30; Mantel test: *P* = 0·289). Yellow dots show pairwise human shelters from and outside the Geneva canton.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Figure 4.** Principal component analysis (PCA) based on the entire microsatellite dataset. After subsampling one individual per sampling site for 100 replicates, 70% of the analysis differentiated the two host-associated bugs into two genetic clusters.</figcaption></figure>

activity period of bat-associated bugs in bat roosts is switched, where bugs would be more active during the day when bats are present, but we found no record of studies investigating this pattern. Understanding the host choice behaviour of the different lineages of the common bed bug could help to understand how it spreads between host species and how the genetic differences between the two lineages are maintained. Moreover, genome-wide association studies could also provide further understanding of the lineages maintenance by identifying specific loci associated

with host specialization. Whole genomes of the common bed bugs were previously published (Benoit et al., 2016; Rosenfeld et al., 2016; Miles et al., 2024), providing a valuable resource for future investigations.

## Host-specific population dynamics

Bat-associated bugs showed higher genetic diversity but fewer private alleles than human-associated bugs, possibly due to the low
<figure><img src="image_16.png" /><figcaption>**Figure 5.** Heatmap of the pairwise kinship over all pairs of individuals based on the microsatellites data.</figcaption></figure>

number of bat roosts sampled. The low pairwise $F_{ST}$ between the two bat roosts ($F_{ST}$ = 0.0769) and the kinship between bat-associated bugs are consistent with a strong gene flow between bugs from these two sites (Figures 2 and 5; Table S6). The two *Myotis myotis* roosts sampled in this study are separated by approximately 17 km, and the estimated population sizes are around 380 and 450 individuals (respectively, sites 1 and 2). *M. myotis* have a low level of genetic differentiation across distant sites in the Alps (separated from 14 to 296 km, Castella et al., (2001)). Therefore, distance between our sampling bat roosts should not act as a barrier to gene flow for both bats and their associated bed bugs. This would be consistent with the low genetic differentiation between the two bat roosts sampled in this study. These results contrast with the dynamic of human-associated bug populations but are consistent with previous studies showing higher nucleotide diversity and allelic richness for bat bugs (Balvín et al., 2012; Booth et al., 2015). Comparison with the European sequences from Booth et al. (2015) allowed to increase the number of sampling sites, along with the number of bat-associated haplotypes. Bat-associated bugs from different sites shared haplotypes, emphasizing the lack of barrier to bugs transmission between bat roosts. Bat- and human-associated sites of bed bugs in Europe could have been colonized independently from foreign sources. Indeed, our results showed a very different picture for human-associated bed bugs, with very limited gene flow. Pairwise $F_{ST}$ values were high (Figure 2 and Table S6), and kinships revealed that bugs from the same human shelters were more related than bugs from different sites (Figure 5). Along with a lower genetic diversity, our results show a strong substructure in the human-associated bugs. As small populations tend to be eradicated as quickly as possible, human-associated populations have not had time to grow. Consequently, the low number of multiple infestations (one site exhibiting different haplotypes;

Figure S2) and the lower genetic diversity in human-associated bugs suggest that populations were established by founder effects (Figure 1 and Table S5). This supports the theory of a small propagule infestation (Booth et al., 2012, 2018; Saenz et al., 2012). Furthermore, considering that the re-emergence of bed bugs is mainly explained by the increase in transport (Davies et al., 2012; Delaunay, 2012), it is likely that these small founder effects originated from different human sources. To test this hypothesis, it may be of interest to infer the historical demography of the common bed bug in Switzerland. For example, the host-dependent genetic structure of the seabird tick *Ixodes uriae* was explained by the colonization of the northern hemisphere via two independent routes, resulting in two lineages associated with two species of seabirds (Mccoy et al., 2003; Dietrich et al., 2014). In our results, the absence of a relationship between genetic and geographic distances in the human-associated samples, from Geneva and outside Geneva, could be an indication (1) of an absence of gene flow between sites or (2) that the amount of gene flow is not lower between two distant sites compared to two geographically close sites (Booth, 2024). The resolution of the markers could also play a role in the absence of isolation by distance. This result was consistent with other studies (reviewed in Akhoundi et al., 2015; Booth et al., 2018). Significant isolation by distance was found in human-associated bugs in the USA despite a weak relationship that suggests it is more likely that the source populations may come from more distant sites (Saenz et al., 2012; Lewis 2023). Therefore, stepwise colonization does not explain the observed genetic patterns. It is most likely that bed bugs have colonized human shelters through multiple independent events through human-mediated dispersal. This hypothesis explains the substructure between human sites that are close together. It would be interesting to investigate patterns of isolation by distance between bed bugs on a European scale.
## The resurgence of C. lectularius in human shelters

The resurgence of bed bugs in human shelters has been attributed to insecticide resistance (Romero et al., 2007). Investigation of insecticide resistance in bugs revealed that 99% of the human-associated bed bugs tested had a mutation conferring pyrethroid tolerance (L925I), confirming high exposure to insecticides. This contrasts with the results from bugs collected from bat roosts, where none of the bed bugs sampled have this mutation (all the bat-associated bugs display a wild-type form for the three *kdr* mutations). This resistance to insecticide compounds has already been confirmed in *C. lectularius* with varying intensity depending on the study area (Yoon et al., 2008; Zhu et al., 2010; Booth et al., 2015; Dang et al., 2015; Balvín and Booth, 2018; Lewis et al., 2023). Among the resistant individuals in this study, 8% were heterozygotes (Table S7). Heterozygotes have also been reported in the USA (Lewis et al., 2023). Genotype distribution alone does not provide sufficient information to determine the dominance of the resistance mutation. Further investigations are needed to determine whether heterozygotes exhibit the same level of survival as resistant homozygotes. An experimental survival study exposing the three genotypes (homozygote wild-type, heterozygote, and homozygote mutant) to pyrethroid insecticides would be necessary to assess potential differences in resistance levels. Our results show a high prevalence of the *kdr*-associated resistant human-associated bed bugs in western Switzerland, in agreement with the results of studies in Europe (Durand et al., 2012; Booth et al., 2015; Balvín and Booth, 2018; Vander Pan et al., 2020; Akhoundi et al., 2021). Only one other study investigated the role of the I936F mutation in the pyrethroid resistance of human-associated European bed bugs, and all bat-associated bugs presented the wild-type form of this allele (Balvín and Booth, 2018). Their study found 9 human-associated populations with this mutation including 1 in Switzerland. In agreement with our results and previous studies around the world, the I936F mutation is present at low levels in the human-associated bed bugs (Dang et al., 2015; Palenchar et al., 2015; Lewis et al., 2023). This mutation contributes less to the resistance of pyrethroid insecticides in the bed bugs (Dang et al., 2015).

Additional markers beyond those used in our study have been investigated for detecting insecticide resistance in the common bed bug, from targeted sequencing to transcriptomic analysis and quantitative trait locus mapping (see Dang et al., 2017 for a review). Recently, Haberkorn et al. (2023) have identified a 6 Mb genomic region involved in the pyrethroid resistance in bed bugs. However, it should be acknowledged that this analysis might ignore resistant mutations that are unique to a bed bug population. To further expand the understanding of insecticide resistance, genome-wide association studies could be valuable in identifying mutated genes beyond voltage-gated sodium channel mutations. This approach would provide deeper insight into the extent and underlying mechanisms of resistance in common bed bugs.

Knock-down resistance (*kdr*) mutations have been studied to induce pyrethroid tolerance in many animal species (Williamson et al., 1993; Schuler et al., 1998), particularly in other household pests such as several susceptible cockroach species (Liu et al., 2000; Rahimian et al., 2019) or in cat fleas (Bass et al., 2004; Erkunt Alak et al., 2020), highlighting the high frequency of resistance due to the intensive use of pyrethroids. For effective pest control, alternative strategies beyond pyrethroid insecticides must be explored, such as integrated pest management

strategies that combine both chemical and non-chemical methods (Kara хағаç, 2012).

## Conclusion

Our results are consistent with previous studies that have found host specialization in human and bat bed bugs, and strong genetic structure within human shelters (Balvín et al., 2012; Booth et al., 2012, 2015; Balvín and Booth, 2018). The genetic differences observed among European populations were also present on a smaller, local scale. This is particularly true in this study, where the genetic variations observed locally between the two lineages accurately represented the patterns seen globally. Despite the absence of reproductive barriers (Devries et al., 2020; Sasínková et al., 2023), the limited gene flow between the lineages, explained by the morphological differences (Balvín et al., 2012) and the potential different host seeking behaviour, has resulted in increased host fidelity and specialization. Furthermore, the introduction of small propagules from multiple sources and the quick eradication of bugs in human shelters have resulted in a strong genetic structure and insecticide resistance in the human lineage.

**Supplementary material.** The supplementary material for this article can be found at https://doi.org/10.1017/S0031182025000575.

**Data availability statement.** All data used in the analysis will be deposited in Mendeley Data.

**Acknowledgements.** We would like to thank the numerous disinfestation companies (A+A Désinfection, A-Team Désinfection, AlchiTech Sàrl, Anticimex, AntiraSA, Burri Désinfection, COMAS Désinfection, D.tec Punaise, Dexterm SA, Renon Désinfection, SCANBUG and Raphaël Heimo), especially to Gérard Cuendet for his help in contacting them. We thank Sabrina Joye for granting us access to the bat colonies. We are grateful to Céline Simon for her support in the lab and Consolée Aletti for the sequencing of the microsatellite data. Finally, we would like to thank Tristan Cumer for his invaluable comments on the manuscript and Eléonore Lavanchy and Tristan Cumer for their useful advice on the analyses conducted.

**Author contributions.** LC and PC conceived and planned the study. CC, AP and PP ran the analyses, and all authors contributed to the interpretation of results. CC and PP wrote the first draft of the manuscript, and all authors provided critical feedback and approved the final manuscript. JG and PC are the co-senior authors.

**Financial support.** Department of Ecology and Evolution, University of Lausanne.

**Competing interests.** The authors declare there are no conflicts of interest.

**Ethical standards.** Not applicable.

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**Chapter 2**

**Host population genetics and parasite dynamics in the**
**Daubenton's bat at local scale**

**Clara Castex**<fnref n="1" />*, Alyzée Tassel<fnref n="1" />*, Tristan Cumer<fnref n="1" />,<fnref n="2" />, Antoine Perrin<fnref n="1" />, Molly Baur<fnref n="1" />, Monica Zanutta<fnref n="1" />, Marc Bastardot<fnref n="1" />, Jérôme Goudet<fnref n="1" />,<fnref n="2" />† and Philippe Christe<fnref n="1" />†

Corresponding author: clara.castex@unil.ch

**Authors contributions:** CC, JG and PC conceived and planned the study. CC and MBas did the field work. CC did the RADseq library preparation while MBau and MZ developed and ran the qPCR in the lab. CC and AT ran the analyses with the help of TC and AP. All authors contributed to the interpretation of the results. CC and AT wrote the first draft of the manuscript with valuable inputs of TC and AP, and all authors provided critical feedback and approved the final manuscript.

**Status:** This chapter has not been submitted to a journal archived as a preprint yet.

<footnote n="1">¹Department of Ecology and Evolution, University of Lausanne, Switzerland</footnote>

<footnote n="2">²Swiss Institute of Bioinformatics, Lausanne, Switzerland</footnote>

<footnote>*Co-first authors</footnote>

<footnote>†Co-senior authors</footnote>
**Abstract**

While host genetic diversity has been widely hypothesized to influence parasite dynamics, empirical findings from natural populations remain inconsistent. In this study, we examined whether genetic variation in Daubenton's bat (*Myotis daubentonii*) affected the abundance of three associated parasites, wing mites (*Spinturnix andegavinus*), bat flies (*Nycteribia kolenatii*), and the haemosporidian *Polychromophilus murinus*, at a local scale in western Switzerland. Using double-digest RAD sequencing (ddRADseq), we genotyped 359 individuals across 18 sites, generating 35,919 SNPs. First, population genetic analyses revealed high connectivity among colonies and low inbreeding coefficients, consistent with Daubenton's bats mating at swarming sites. Then, generalized linear mixed models were used to test the link between the host genetic diversity metrics (relative heterozygosity, inbreeding coefficients) as well as bat age and sex and parasite abundances. No significant relationship was found between genetic diversity and parasite abundance, either over all SNPs or within the Major Histocompatibility Complex (MHC) region. In contrast, host sex and age were consistent predictors of parasite abundance, with females and juveniles exhibiting higher infestation rates. These findings suggest that life-history traits, rather than genetic variation, are the primary drivers of parasite dynamics in *M. daubentonii* at local scale.

**Keywords:** *Myotis daubentonii*, high connectivity, genetic diversity, parasite dynamics
## Introduction

Genetic diversity within individuals can influence their survival and reproductive success. This relationship is captured by the Heterozygosity-Fitness Correlation (HFC), which describes the association between individual genetic diversity and fitness-related traits (Hansson and Westerberg, 2002; Chapman et al., 2009; Forstmeier et al., 2012; Rodríguez-Quilón et al., 2015). Thus, genetic diversity influences the ability of hosts to survive, reproduce and adapt to changing environments (Frankham, 1995a; Reed and Frankham, 2003; Willoughby et al., 2017a; Harrisson et al., 2019). In particular, genetic diversity can provide a buffer against environmental stressors, such as parasitism (Fox and Reed, 2011). Numerous studies have explored HFC using parasite resistance as a proxy for fitness, but the strength and direction of this relationship differ markedly among studies (Acevedo-Whitehouse et al., 2005; Westerdahl et al., 2005b; Ilmonen et al., 2007; Ruiz-López et al., 2012; Brambilla et al., 2015; Portanier et al., 2019; Biedrzycka et al., 2023). This inconsistent pattern observed may arise because the link between genetic diversity and parasite infection is highly context-dependent, influenced by differences among host species, parasite taxa, and the ecological settings in which interactions occur (Kołodziej-Sobocińska, 2019). Exploring HFC across different ecological contexts and species will help determine how genetic diversity influences host-parasite dynamics in natural populations.

In addition to genetic diversity, host life-history traits such as age or sex can significantly influence parasite prevalence and intensity (Kołodziej-Sobocińska, 2019). Juveniles are often more susceptible to infection than adult due to their naïve immune system and/or poor developed anti-parasite behaviours (Christe et al., 2000). The relationship between age and infection risk is complex and varies across systems. While cumulative pathogen exposure can enhance immunity over time (Bruns, 2019), some studies report increased susceptibility in older individuals due to immunosenescence (Ginaldi et al., 2001). Similarly, male-biased parasitism is widespread in vertebrates because of ecological, behavioural and physiological differences between sexes (Zuk and McKean, 1996; Kołodziej-Sobocińska, 2019; Wesołowska, 2022), although exceptions to this pattern are common. These life-history traits may also interact with genetic diversity in shaping infection outcomes. Indeed, while heterozygosity may influence immune
function and parasite resistance, these effects could be modulated by age- or sex-
specific physiological and behavioural factors (French et al., 2009; Charbonnel et al.,
2010; Budischak et al., 2022). For instance, the adaptive benefits of genetic diversity
might differ between juveniles and adults, or between males and females, due to variation
in immune system maturity, hormonal profiles, or exposure risk (Brock et al., 2015;
Portanier et al., 2019). Yet, few studies have explicitly examined how these traits intersect
to shape parasite dynamics.

Hosts with lower genetic diversity are often more vulnerable to parasitism (Ekroth
et al., 2019). However, this increased susceptibility may not be a direct consequence of
parasitism itself, but rather a result of limited gene flow between isolated host
populations. Such genetic isolation can lead to low diversity, which in turn compromises
the host ability to resist parasites (Frankham, 1995b; Spurgin and Richardson, 2010).
Connectivity among hosts can facilitate the spread of parasites, although the benefits of
gene flow for population persistence often outweigh the costs of increased parasite
transmission (Collinge, 2009). Understanding the host genetic diversity and structure is
key to predicting infection dynamics in wildlife, providing a basis to explore how genetic
variation influence individual fitness and susceptibility to parasitism. Thus, addressing
the interaction between biological and ecological factors influencing parasite dynamics
is crucial for disentangling the multifactorial nature of parasitism and for refining
predictive models of disease spread in wild vertebrate populations.

Bats (Chiroptera) are an especially interesting model for investigating the
interaction between parasitism, host genetic diversity and life history traits. They are
mostly gregarious and exhibit a wide range of life-history traits, social organizations, or
mating systems (McCracken and Wilkinson, 2000; Racey and Entwistle, 2000; Dorrestein
et al., 2024). Bats have high dispersal ability with many species showing philopatry, sex-
biased dispersal, and mating patterns decoupled from breeding sites (Moussy et al.,
2013; Dufresnes et al., 2023). These behavioural, geographical, and ecological barriers
influence gene flow and contribute to genetic structuring among populations (Moussy et
al., 2013; Dorrestein et al., 2024). Their susceptibility to parasitism is shaped by various
intrinsic factors, including body condition, immune function, age, reproductive status
and sex (Christe et al., 2000, 2007; Reckardt and Kerth, 2009; Witsenburg et al., 2014;
Dietrich *et al.*, 2015, 2018; Irving *et al.*, 2021). Specific behaviours, such as roosting behaviour, can also facilitate parasite transmission due to close proximity among bats, while dispersal behaviours tend to reduce the exposure of individuals to parasites (Zahn and Rupp, 2004; van Schaik *et al.*, 2014). However, the role of the host genetic diversity in mediating parasite tolerance remains largely unexplored in bats.

In this study, we assessed the relationship between the genetic diversity and structure of the Daubenton's bat (*Myotis daubentonii*) in Western Switzerland and the abundance of its parasites. Genetic studies using mitochondrial DNA or microsatellite loci have revealed low level of genetic differentiation across Daubenton's bat populations at both regional and continental scales (Mayer and Helversen, 2001; Ngamprasertwong *et al.*, 2008; Atterby *et al.*, 2010; Laine *et al.*, 2013). However, a recent study demonstrated in another bat species (*Miniopterus schreibersii*) that Single Nucleotide Polymorphism (SNP) data offered more accurate estimates of genomic variation compared to microsatellites and enhanced resolution of local population structure (Dufresnes *et al.*, 2023). Finally, Daubenton's bats host a diverse parasite community (Encarnação and Becker, 2023). Previous studies have shown heterogeneity in parasitism, shaped by different life-history traits (Zahn and Rupp, 2004; Lučan, 2006; Encarnação *et al.*, 2012; Witsenburg *et al.*, 2014; van Schaik and Kerth, 2017), including host body condition (Lourenço and Palmeirim, 2007; Witsenburg *et al.*, 2014; Tai *et al.*, 2022), empirically linked with individual fitness (Schulte-Hostedde *et al.*, 2005). These features make the species an effective model for studying bat tolerance to parasites. Using Restriction site Associated DNA sequencing (RADseq), we investigated patterns of genetic differentiation and tested whether individual genetic diversity influenced parasite abundance. We focused on a local scale to identify how the gene flow among colonies may shape parasite transmission and distribution within bat populations.

# Materials & Methods

## Data collection

Between June to October 2023, 395 Daubenton's bat (*Myotis daubentonii*) were captured at 18 sampling sites across the Vaud canton in Switzerland. These sites included
maternity colonies (N = 10), foraging (N = 5) and three putative swarming sites (Fig. 1).
Females tend to be philopatric (Linton and Macdonald, 2019) whereas males are more
dispersal and occupy smaller colonies in higher altitudes (Russo, 2002). Daubenton's bat
is considered as a facultative migratory bat as their summer roosts are situated within 3
km from their foraging areas, near waterbodies (Arthur and Lemaire, 2009; Dietz et al.,
2009; Langton et al., 2010) and distances between their summer and winter roosts range
from 50 to 150 km (Kunz and Fenton, 2003; Hutterer et al., 2005). Sampling was
conducted under the license authorization VD3679. Bats were captured using a harp trap

<figure><img src="image_17.png" /><figcaption>**Fig. 1.** Map of the 18 sampling sites of the study. Numbers depict the sampling site ID. Sampling sites are: **1** – Aigle Grande Eau, **2** – Boiron de Nyon, **3** – Bois de la Râpe, **4** – Clarens, **5** – Cully, **6** – Dorigny, **7** – Embouchure Aubonne, **8** – La Cascade, **9** – Lac Morat, **10** – Le Brassus, **11** – Le Pont, **12** – Les Clées, **13** – Maison de la Rivière, **14** – Moudon, **15** – Papiboum, **16** – Rossinière, **17** – Vufflens la Ville and **18** – Yvonand.</figcaption></figure>

or mist nets at dusk across water streams. For each bat, a wing tissue biopsy was
obtained with a 2 mm gauge punch (Stiefel) and sex and the age was assessed. Age was
determined according to morphological criteria based on the presence or the absence of
a dark chinspot and a fusion of the phalangeal epiphyses (Richardson, 1994; Haarsma, 2008): adults (AD) exhibited a light chinspot with fused epiphyses, subadults (SAD) had a dark chinspot and fused epiphyses and juveniles (JUV) showed a dark chinspot without epiphyseal fusion. Ectoparasite abundance was quantified for two species: the wing mite *Spinturnix andegavinus* and the bat fly *Nycteribia kolenatii*. Additionally, blood samples were collected from the uropatagium vein using a 0.5 mm gauge needle (HSW, Germany) to estimate infection by the haemosporidian *Polychromophilus murinus*. Blood was collected with microcapillary tubes (VWR International), transferred into 60 µL PBS (Phosphate-Buffered Saline) and stored at -80°C. Subsequently, Giesma-stained blood smears were done for 45 random-selected samples.

## Wet lab

### DNA extraction

DNA was extracted from the wing biopsies (N = 395) to assess genetic diversity and structure among Daubenton's bat across sampling sites. In addition, DNA was extracted from blood samples (N = 373) to quantify the haemosporidian *P. murinus*. All extractions were performed using the DNeasy Blood & Tissue kit (Qiagen, Hilden, Germany), following the instruction of the manufacturer. Extracted DNA samples were stored at -20°C until library preparation or quantitative PCRs.

### ddRADseq library preparation

Double-digest RAD (ddRAD) libraries were done in plates comprising individuals from different sampling sites in each plates, following the protocol of Brelsford et al. (2016), adapted from Parchman et al. (2012) and modified for the Aviti sequencer. DNA was digested using restriction enzymes EcoRI-HF and MseI (New England Biolab, Ipswich, MA, USA), followed by ligation of enzymes-specific adapters to the fragments. EcoRI adaptors included a unique barcode for library identification. After purification, amplification of the fragments was done using i5- and i7-indexed adaptors (Unique Dual Indexes) for individual tagging. Libraries were pooled and fragments of 300-500 base pair
(bp) were selected using BluePippin 2% DF Marker V2 cassette (Sage Science, MA, USA).
Sequencing was conducted with 150 bp single-end reads on the Aviti sequencer
(Genomic Technologies Facility, Lausanne, Switzerland). A total of 1.3 billion reads were
generated and used for SNP calling (Fig. S1).

Quantitative PCRs (qPCR)

The abundance of *P. murinus* in the bat blood was estimated using quantitative
PCRs (qPCR), with primers specifically developed for this study. Primers targeted a
fragment of the cytochrome b gene (Poly_167F: 5'-TTA CCT TGG GGT CAA ATG AG-3' and
Poly_232R: 5'-CTA ACA AGA TAT CCA CCG CA-3'). The master mix (10 µL) was composed
of 2 µL of DNA template, 1X of BioRad Universal SYBRGreen Mix and 0.4 µL of each primer.
The qPCR of *P. murinus* was conducted as followed: denaturation step (95°C, 5mn), 40
cycles of amplification (denaturation at 95°C for 30 sec, hybridization at 60°C for 30sec
and elongation at 72°C for 45 sec). Finally, the melting curve was constructed following
steps of 95°C for 4 sec, 60°C for 30 sec and then a gradual increase in temperature by
0.5°C every 5 sec until reaching 95°C.

Due to variability in the concentrations of the blood samples, a second qPCR
targeting the bat 16S rRNA mitochondrial gene (16Smam1: 5'-CGG TTG GGG TGA CCT
CGG A-3' and 16Smam2: 5'-GCT GTT ATC CCT AGG GTA ACT-3') was performed following
the protocol of Hübner (2013), to quantify the abundance of *P. murinus* relative to the
quantity of the bat DNA. All reactions were run in triplicate (QuantStudio Q6). Quantities
of *P. murinus* and bat DNA were estimated from the standard curve and the relative
abundance of *P. murinus* was calculated as the ratio of the quantity of *P. murinus* to the
total DNA (sum of the quantity of *P. murinus* and bat DNA).

To validate qPCR efficiency, gametocytes of *P. murinus* were counted in blood
smears with 2960-5100 under light microscope at 1000x magnification (Garnham, 1966;
Landau *et al.*, 1980; Witsenburg *et al.*, 2014). Repeated counts were conducted on
selected smears (N = 5) to assess measurement repeatability using the *rptR* function from
the *rptR* package in R (Nakagawa and Schielzeth, 2010; **Appendix S1**). The correlation
between the relative abundances and the gametocyte counts was evaluated using a
Spearman correlation with the cor.test function in R. A McNemar's test was performed to detect any differences in detection between the two methods, using the *mcnemar.test* function in R. According to the results (**Appendix S1**), only the qPCR-based relative abundances were retained for the rest of the analyses.

## ddRAD genotyping and SNPs calling

To remove Illumina adapters sequences and low-quality reads, raw reads were trimmed using the *bbduk* function from *bbmap* v39.01 (Bushnell, 2014). Individual genotyping was conducting using the reference-based pipeline of Stacks v2.53 (Catchen *et al.*, 2013). Reads were demultiplexed per individual using *process_radtags* and mapped against the Daubenton's reference genome (Ruedi *et al.*, 2024) with the *mem* function from *bwa* v07.7.17 (Li and Durbin, 2009). SNP calling was carried out with *gstacks* avec 1'070'589 genotyped loci, representing 0.05% of the genome. Filters were applied with *populations*. SNPs present in 80% of the individuals within (-r 0.8) and across sampling sites (-R 0.8) were kept for the analyses, as well as SNPs present in all sampling sites (-p 18). A maximum observed heterozygosity threshold of 0.5 was applied (--max-obs-het 0.5). After quality control of each VCF file, individuals exhibiting more than 30% missing genotype data were removed and the correlation between the proportion of polymorphic sites and coverage was checked.

All following filtering were performed using *vcftools* v0.1.16 (Danecek *et al.*, 2011a) unless stated otherwise. Genotypes were filtered with a minimum depth per SNPs of 5X (--minDP 5). Following Hemstrom *et al.* (2024), all SNPs with at least 15% of missing genotypes (--max-missing 0.85) were excluded. Low quality and duplicated SNPs were filtered with a mean minimum coverage of 10X (--min-meanDP 10) and a mean maximum coverage of 26X (--mean-maxDP 26). All SNPs under Hardy-Winberg equilibrium (--hwe 0.05) and present at least in 5 individuals (--mac 5) were retained.

After filtering, the final dataset comprised 359 individuals and 35'919 SNPs with a mean coverage of 13.88X.
## Data analyses

### Population genetic analyses

Individual and population-level relative heterozygosity in Daubenton's bat were calculated as the proportion of polymorphic sites over the total number of genotyped loci per individual. Two different inbreeding coefficients were estimated. The first one was based on the correlation between united gametes ($F_{UNI}$), according to the calculation of Lavanchy et al. (2024). The second one was based on the expected allele-sharing between individuals ($F_{AS}$, Zhang et al., 2022) using the matrix diagonal from the *beta.dosage* function from the hierfstat package (Goudet et al., 2022). Pairwise kinships were estimated among individuals using the *beta.dosage* function. Pairs of individuals with a kinship exceeding 0.2 (indicative of either a parent-offspring relationship, full siblings, monozygotic twins or same individuals) were further analysed by calculating their identity-by-descent (IBD) coefficients using the *snpgdsIBDMoM* function from the SNPrelate package (Zheng et al., 2012), to discriminate the different types of relationships. Parent-offspring pairs typically exhibit values of $k0 = 0$ and $k1 = 1$, whereas full siblings are expected to show $k0 = 0.25$ and $k1 = 0.5$. Null values of $k0$ and $k1$ may indicate that samples originated from the same individual or from monozygotic twins. Intermediate values generally correspond to half-siblings or other types of second-degree relationships (Stevens et al., 2011; Galván-Femenía et al., 2017).

Population genetic structure of the bats was investigated using the hierfstat v0.5.11 package unless indicated otherwise (Goudet et al., 2022). Mean relative heterozygosity proportions per sampling sites and overall were computed. A Kruskal-Wallis test was done to assess significant variation across sampling sites. To avoid bias from related individuals in the following analyses, those with a kinship higher than 0.125 were pruned, resulting in a dataset of 328 individuals. Principal Component Analyses (PCA) was performed to examine the degree of genetic structuring between individuals, and a parallel coordinate plot was done to visualize the genetic differences between individuals, using the *indpca* function. Ancestry coefficients were estimated using the *snmf* function from the package LEA v3.15 (Frichot and François, 2015). The optimal number of ancestral population (K) was estimated with the lowest cross-entropy criterion, testing K values from 1 to 10 with 10 replicates for each K. Genetic differentiation
among sampling sites was further characterized using the mean kinship (FsM matrix) and
the pairwise F<sub>ST</sub> (Fst2x2 matrix) with the *fs.dosage* function. Using the same function,
population-specific F<sub>IS</sub> and F<sub>ST</sub> were bootstrapped 100 times across loci. Loci were
considered independent after evaluating linkage disequilibrium (**Fig. S2**) using the
*snpgdsLDMat* function from the *SNPRelate* package (Zheng *et al.*, 2012). Since
populations can exhibit spatial structure in rare variants despite low overall genetic
differentiation (Mathieson and McVean, 2012; De la Cruz and Raska, 2014), we analysed
the genetic structure on a down-sampled dataset keeping only the rare alleles (mac ≤ 10),
applying the *fs.dosage* function. To detect isolation by distance, the correlation between
pairwise F<sub>ST</sub> and geographical distances between sampling sites was tested using using
the *mantel* function from the *vegan* package (Dixon, 2003) with 1000 permutations.

To better understand the Daubenton's bat dispersal dynamics, sex-biased
dispersal between adult and subadult males and females was assessed using the mAlc
statistic from the *sexbias.test* function (Goudet *et al.*, 2002). This assignment index
measures, for each individual, the probability that a genotype appears in a population,
corrected by the average probability across all individuals. Tajima's D estimates were
computed using the *--Tajima* function from vcftools v0.1.16 prior to minor allele filtering
(Danecek *et al.*, 2011a). Contemporary effective population size (Ne) was estimated
using the linkage disequilibrium method from the NeEstimator v2.1 software (Do *et al.*,
2014) and implemented in the *gl.LDNe* function from the dartR package (Gruber *et al.*,
2018), assuming non-overlapping generations. Juveniles were excluded as they were
non-reproductive during the sampling. A minor allele frequency threshold of 0.5 was
applied. Random mating was assumed, as it is known to be the principal mating system
in Daubenton's bats (Bogdanowicz, 1994; Encarnação, 2012b).

Statistical analyses

To assess the influence of bat traits on parasite abundances, generalized linear
models were fitted using the *glmmTMB* function from the *glmmTMB* package (Brooks *et al.*, 2017). Models were constructed separately for three response variables: the count of *S. andegavinus* (log-transformed), the count of *N. kolenatii* (log-transformed) and relative
abundance of *P. murinus*. Fixed effects included the bat genetic diversity metrics derived from the full SNP dataset ($F_{UNI}$, $F_{AS}$ and relative heterozygosity), as well as age, sex and the interaction between both. Sampling sites were included as random factor to account for any spatial variation in the parasite distribution. Model assumptions were evaluated using the *check_model* function from the performance package (Lüdecke *et al.*, 2025). Model selection was performed via stepwise comparison based on the lowest Akaike information criterion (Burnham and Anderson, 2003) and statistical significance was evaluated using the *anova* function. To further explore differences among predictors, post-hoc pairwise comparisons were conducted using the *emmeans* function from the *emmeans* package (Lenth, 2021).

The SNP dataset was then filtered to retain only loci located within the Major Histocompatibility Complex (MHC) region, resulting in 244 SNPs. To assess whether the MHC region exhibited elevated levels of heterozygosity compared to the genome-wide background, relative heterozygosity was calculated and compared to that of randomly selected sets of 244 SNPs from the full dataset, bootstrapped 100 times. Generalized mixed-effects models, similar to those applied to the complete SNP dataset, were then fitted to evaluate the influence of MHC-specific relative heterozygosity on wing mite and bat fly counts as well as the relative abundance of *P. murinus*.

## Results

We first measured infection rates of 359 Daubenton's bat for three parasites (namely the wing mite, the bat files and the blood parasite *P. murinus*), captured across 18 sites in western Switzerland. Daubenton's bats exhibited high infestation rates with 94.2% and 86.4% of parasitized individuals by bat flies and wing mites, respectively. Among infected individuals, the mean parasite abundances were 4.7 bat flies and 7 wing mites per bat. Additionally, qPCR analysis revealed that 51.8% of Daubenton's bats were infected by *P. murinus*.
## Genetic diversity within and between individuals

Using ddRAD sequencing, we obtained 35'919 SNPs with a mean coverage of 13.88X and assessed the diversity present within individual bats. The mean relative heterozygosity ranged from 0.138 to 0.195 with a mean of 0.187. Inbreeding coefficients ($F_{UNI}$) were generally low with an average of 0.005 and ranged from -0.025 to 0.249. Pairwise kinship among individuals varied from -0.031 to 0.510 (Fig. 2A). Most pairs exhibited a kinship below 0.2, with the exception of 27 pairs (Table S1). Among these, three pairs of males from the same site displayed kinship values near 0.5 with $k_0$ and $k_1$ values close to 0 (Fig. 2B). The comparison of the forearm lengths and ages estimated during capture allowed to confirm that each pair corresponded to the same individual caught twice, indicating three recaptures. The remaining 24 pairs showed kinship values ranging from 0.24 to 0.29, indicative of close genetic relationships (Table S1). Of these, 22 were sampled within the same site (representing 12 out of 18 sampling sites) and the other two involved individuals from different sites. Fraternity coefficient

A

<figure><img src="image_18.png" /><figcaption>**Fig. 2. Genetic relatedness and identity-by-descent analysis.** (A) Distribution of the pairwise kinship among individuals. (B) Identity-by-descent (IBD) coefficients. Each point represents a pair of individuals with estimated $k_0$ and $k_1$ values. Expected IBD coefficients are indicated for parent-offspring (PO, purple: $k_0$ = 0, $k_1$ = 1), full-sibling (FS, orange: $k_0$ =0.25, $k_1$ = 0.5), and same individual or monozygotic twins (R, yellow: $k_0$ = 0, $k_1$ = 0). Other types of relationships are depicted in grey.</figcaption></figure>

B

<figure><img src="image_19.png" /></figure>

analysis allowed to discriminate between parent-offspring and full-sibling relationships. Twenty pairs were identified as parent-offsprings based on $k_0$ values lower than 0.05 and $k_1$ values higher than 0.9 (Fig. 2B). Sex and age of bats allowed to determine the parent
for 14 pairs of these pairs (Table S1). Four additional pairs exhibited k0 values between 0.2 and 0.3 and k1 values between 0.4 and 0.5, consistent with full-sibling relationship (Fig. 2B; Table S1). Among the between-site pairs, two pairs showed a kinship of 0.25, consistent with a first-degree relationship. Interestingly, one individual from the first pair was also genetically linked with one individual from the second pair, with a kinship coefficient of 0.12, corresponding to a second-degree relationship, such as half-siblings or grandparent-grandchild (Fig. 2B; Table S1; Appendix S2).

To assess the relatedness and the connectivity between populations of bats, we measured their genetic differentiation. Individual relative heterozygosity was similar across sampling sites, ranging from 0.182 to 0.189 (Kruskal-Wallis test: $\chi^2 = 19.103$, $p = 0.323$). The overall inbreeding coefficient ($F_{IS}$) was significantly different from 0, but small ($F_{IS} = 0.0036$; 95% CI [0.003;0.0043]). Population-specific $F_{IS}$ ranged from -0.0103 to 0.026, with confidence intervals from 7 of the 18 sampling sites not overlapping 0 (Fig. S3A, Table S2). After excluding closely related individuals, population genetic analyses indicated a strong genetic connectivity among sampling sites. The overall fixation index ($F_{ST}$) was low but significantly different from 0 ($F_{ST} = 0.0033$; 95% CI [0.0031;0.0035]; Table S2), indicating little genetic differentiation. Consistently, principal component analysis (PCA) and individual ancestry coefficients showed no evidence of genetic clustering by site (Fig. 3A; Fig. S3B), although some individuals from site 14 appeared more genetically distinct (Fig. S3C). Supporting this high connectivity among sampling sites, pairwise $F_{ST}$ between sites were extremely low with mostly positive values (ranging from -0.0009 to 0.009; Fig. 3B; Table S3), as well as the mean kinship between pairs of sampling sites (from -0.007 to 0.005; Fig. 3B; Table S3). No significant correlation between the genetic and geographical distances was detected (mantel test: $r = 0.236$; $p = 0.084$), indicating an absence of isolation by distance (Fig. 3C). The same pattern of high genetic connectivity was observed when analyses were restricted to rare alleles ($mac \le 10$), further confirming extensive gene flow across sampling sites (Fig. S4). The dynamic of the bat population was further assessed by looking at the probability that a genotype appears in the population and revealed an absence of significant sex-biased dispersal (test mAlc: $t = -1.789$; $p = 0.075$). Tajima's D statistics were significantly negative (mean Tajima's D = -0.081; Wilcoxon test: $V = 1014198358$, $p < 2.2e-16$), indicating an excess of rare alleles. Along
with the allele frequency distribution this result suggested a population expansion (Fig. 3D). Finally, the estimated effective bat population size was Ne = 1599.5 (Jackknife 95% CI [969.6;4192.0]).

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /><figcaption>**Fig. 3.** Population genetic analyses based on the RAD sequences of the bats. (A) Principal Component Analysis (PCA). (B) Heatmap of the mean kinship and pairwise FST over pairs of sampling sites. The upper matrix is the mean kinship between sites (FsM), high values represent pairs of populations where individuals are more related than that with other populations. The diagonal represents the intra-populational mean kinship (population-specific FST) and the and lower matrix represent the pairwise FST. (C) Correlation between the genetic distances (FST) and the geographical distances between pairs of sampling sites. (D) Allele frequency distribution.</figcaption></figure>
Infection dynamics in the Daubenton's bat

To evaluate which factors influence the most the parasitism in the Daubenton's bats, we modelled parasite abundance according to the overall and MHC-specific genetic diversity, sex and age of the bats. The results revealed species-specific patterns, with best models only including age and/or sex, while the different genetic diversity measures were not significant.

Sex was the only significant predictor of bat fly abundance, with female bats exhibiting higher infestation levels than males (Fig. 4A, glmmTMB: estimate = 0.157; *t* = 2.328; *p* = 0.021). Age categories did not significantly influence the abundance of bat flies (anova: χ² = 4.416; *df* = 2; *p* = 0.110). Neither the inbreeding coefficient (*F*<sub>UNI</sub>, Fig. 5A, Fig. S5, anova: χ² = 1.160; *df* = 1; *p* = 0.281) nor the overall relative heterozygosity (Fig. 5B, anova: χ² = 0.534; *df* = 1; *p* = 0.465) were significant predictors. Similarly, MHC-specific relative heterozygosity did not explain the variation in bat fly abundance (Fig. S6, anova: χ² = 0.428, *df* = 1, *p* = 0.513).

For the wing mites, the interaction between sex and age was significant and retained in the final model (anova: χ² = 17.617; *df* = 2; *p* = 1.495e-04). Across all age classes, males were generally less parasitized than females (Fig. 4B, glmmTMB: estimate = -0.757; *t* = -6.923; *p* = 4.42e-12). Within males, juveniles showed higher wing mite abundances than adult and subadults (Fig. 4B, glmmTMB: AD vs JUV; estimate = -0.944; *t* = -6.679; *p* < 0.0001, SAD vs JUV, estimate = -0.745; *t* = -4.673; *p* = 0.0001). Genetic predictors were not significant: inbreeding coefficient (*F*<sub>UNI</sub>, Fig. 5A, Fig. S5, anova: χ² = 0; *df* = 1; *p* = 0.997), overall relative heterozygosity (Fig. 5B, anova: χ² = 0.131; *df* = 1; *p* = 0.717) and MHC-specific relative heterozygosity (Fig. S6, anova: χ² = 0.003, *df* = 1, *p* = 0.955).

Both sex and age were significant predictors of the relative abundance of *P. murinus* (anova: sex; χ² = 8.736; *df* = 1; *p* = 0.003, age; χ² = 36.597; *df* = 2; *p* = 1.13e-08). Females were more infected than males (Fig. 4C, glmmTMB: estimate = 0.776; *z* = 2.836; *p* = 0.005). Juveniles had the highest *P. murinus* relative abundance, with significantly lower infestation observed in adults and subadults (Fig. 4D, glmmTMB: AD vs JUV; estimate = -2.007; *z* = -6.225; *p* < 0.0001, SAD vs JUV; estimate = -1.173; *Z* = -3.260; *p* = 0.003). Adults were also less infected than subadults (Fig. 4D, glmmTMB: estimate = -
<figure><img src="image_24.png" /><figcaption>**Fig. 4.** Effect of the sex and the age of the bat on its three associated parasite abundances. (A) Effect of the age on the bat fly abundance. (B) Effect of the interaction between the sex and the age on the wing mite abundance. (C) Effect of the sex on *P. murinus* relative abundance. (D) Effect of the age on *P. murinus* relative abundance. Significance levels: \*p < 0.05; \*\*p < 0.01; \*\*\*p < 0.001; \*\*\*\*p < 0.0001.</figcaption></figure>
0.834; *z* = -2.550; *p* = 0.029). Similarly to the two ectoparasites, neither the inbreeding coefficients of the Daubenton's bat (*F*<sub>UNI</sub>, **Fig. 5A, Fig. S5**), anova: χ<sup>2</sup> = 1.504; *df* = 1; *p* = 0.220) nor its overall relative heterozygosity, (**Fig. 5B**, anova: χ<sup>2</sup> = 2.624; *df* = 1; *p* = 0.105) or its MHC-specific relative heterozygosity (**Fig. S6**, anova: χ<sup>2</sup> = 0.263, *df* = 1, *p* = 0.608) were significant predictors.

<figure><img src="image_25.png" /><figcaption>**Fig. 5.** Effect of the parasite abundance (A) on the bat inbreeding coefficient FUNI and (B) on the bat overall relative heterozygosity. The bat fly abundance is in orange, the wing mite abundance in blue and *P. murinus* relative abundance in green.</figcaption></figure>

<figure><img src="image_26.png" /></figure>

Discussion

In this study, we examined the relationship between the genetic diversity and parasite dynamics in Daubenton's bats in western Switzerland. Using genome-wide restriction site associated DNA sequencing (RADseq data), we showed that individual genetic diversity, measured over all SNPs or in the MHC region, did not strongly vary among individuals and populations and was not associated with parasite abundance. Instead, parasite dynamics were more strongly shaped by host life-history traits, with both sex and age significantly influencing infection patterns across bat flies, wing mites, and the haemosporidian *Polychromophilus murinus*.
## Host population dynamics

The genetic analyses of the Daubenton's bats allowed us to characterize the dynamic of its populations in western Switzerland. Unexpectedly, full-sibling relationships and special family structure were detected in the dataset (Fig. 2B, Table S1, Appendix S2), suggesting repeated mating involving the same individuals between different years. This is particularly surprising given Daubenton's bats are supposed to mate at swarming sites, generally promoting outbreeding. No mate choice has been documented in the Daubenton's bat. However, it has been suggested that males could visit females maternity roosts to mate (Encarnação, 2012b). This behaviour would be advantageous to maximize their mating success by decreasing the sperm competition that could occur at mating roosts (Racey and Entwistle, 2000; Senior et al., 2005). As female are generally philopatric for their maternity colony, the high summer site fidelity for males could lead to repeated mating and producing related offsprings. Additionally, our results revealed that related females tended to be caught in the same sites (Table S1), consistent with female philopatry. Four pairs of males-females were also caught on the same site. Three of them corresponded to juvenile males, born during the year of sampling, with their mother, but the relationship of the remaining pair could not be established because both individuals belonged to the same age class. Catching adult or subadult males in summer sites is more surprising considering the dispersal behaviour of males after summer (Senior et al., 2005; Dietz et al., 2009; Angell et al., 2013). The sampling sites in our study are mostly foraging sites, expected to be within a 3 km perimeter around the maternity colony (Dietz et al., 2009). Daubenton's males and females segregate along an altitudinal gradient, with males at higher altitudes than females, along the same river (Russo, 2002; Nardone et al., 2015). Maternity roosts and male roosts are also found in close proximity within the same habitat range and during the same period (Linton and Macdonald, 2019). Therefore, the presence of an adult male in the same site as an adult female may reflect overlapping foraging area between the sexes or, as previously suggested, may indicate that the male is visiting maternity roost to mate prior to the swarming period (Encarnação, 2012b).

The significant negative Tajima's D observed were consistent with an excess of rare alleles and indicative of ongoing population expansion (Tajima, 1989; Aris-Brosou and
Excoffier, 1996). The Daubenton's bat is listed as Least Concerned by the IUCN Red List (Kruskop et al., 2020) and the population tend to stabilize or expand across Europe (Uhrin et al., 2010; Van der Meij et al., 2015; Bernard et al., 2019; Verhees et al., 2022), consistent with the high gene flow across populations and the estimated effective population size (Ne) of the Daubenton's bat found in this study. Indeed, our contemporary Ne estimate, based on individuals sampled in a single year, likely reflects the number of breeders (Nb) rather than the total census size (Waples and Do, 2010; Waples, 2025). Given that Ne represents only a fraction of the total census size, the Daubenton's bat population in western Switzerland likely exceed 1599 individuals. The absence of genetic structure locally and across European (Atterby et al., 2010; Laine et al., 2013) suggest our sample includes both migrant and resident bats, indicating that Ne reflects the metapopulation. Previous studies on highly mobile bat species have reported infinite Ne estimates (Korstian et al., 2015; Chipps et al., 2020; Cornman et al., 2021; Nagel et al., 2024). Ne estimates are sensitive to migration (Waples and Do, 2010), missing data (Marandel et al., 2020), and demographic factors such as sex ratio, reproductive success, longevity, and litter size (Waples, 2025). The presence of full siblings in our sampling may reflect variance in reproductive success in the Daubenton's bat.

## High connectivity between sites

The main objective of this study was to examine the relationship between the Daubenton's bat genetic diversity and the abundance of three associated parasites. Given that genetic isolation can reduce diversity and potentially compromise the host ability to resist parasites (Frankham, 1995b; Spurgin and Richardson, 2010), we assessed the population genetic structure of the Daubenton's bat to evaluate its influence on individual genetic variation. The individual's inbreeding coefficients along with the genetic structure analyses, suggested that the bat population is genetically diverse and experiences low levels of inbreeding (Fig. 5). Our study was conducted on a small geographical region, focusing on the Daubenton's bat, known to exhibit high levels of dispersal and weak population structure at broad scales. Indeed, previous studies using mitochondrial markers and a limited number of microsatellites similarly reported little to no genetic structuring across local and continental scales (Ngamprasertwong et al.,
2008; Atterby et al., 2010; Laine et al., 2013). In Scotland, mitochondrial markers revealed two distinct lineages, suggesting different recolonisation events of this region (Ngamprasertwong et al., 2008). Genome-wide SNP datasets such as RADseq provide a much higher resolution for detecting subtle patterns of differentiation, especially in species with high mobility and consistent gene flow (Sunde et al., 2020; Dufresnes et al., 2023). By applying RADseq, we were able to assess fine-scale population genetic structure in this species.

Across the study area, Daubenton's bats showed no evidence of genetic subdivision. PCA and ancestry analyses revealed no clustering, pairwise and overall $F_{ST}$ values were low, and kinship between sampling sites was limited (Fig. 3A and 3B). These results support extensive gene flow across colonies. High connectivity was also reflected in the lack of isolation by distance (Fig. 3C). Such widespread genetic mixing is consistent with the species mating at swarming sites, which promotes outbreeding and reduces the likelihood of mating between close relatives (Rivers et al., 2005; Glover and Altringham, 2008). Although overall genetic structure was weak, the PCA and parallel coordinate plot suggested a small group of outlier individuals, notably seven bats from one site (site 14; Fig. S3C). These individuals were genetically more similar to each other than to bats from nearby colonies, despite having comparable levels of heterozygosity and inbreeding. One possible explanation is that they may have mated at different sites, promoting gene flow with colonies outside our study area. Nevertheless, our results as a whole reinforce the picture of high connectivity and lack of barriers to gene flow among Daubenton's bats in western Switzerland, providing a robust framework to minimize potential confounding effects of population structure on parasitism analyses.

## Infection dynamics

Following the analysis of the bat genetic connectivity, we then examined whether individual genetic diversity was associated with variation in parasite abundance. Our models revealed no association between bat overall genetic diversity and ectoparasite abundance, suggesting that individuals with lower overall genetic diversity are not necessarily more susceptible to ectoparasite infestation than those with higher genetic
diversity. Bats exhibited little variation of genetic diversity across individuals (Fig. 5), which may have limited the detection of any potential associations. Previous studies that identified significant associations between genetic diversity and parasitism generally reported higher levels of variation in genetic diversity measures (Pearman and Garner, 2005; Whitehorn et al., 2010; Ruiz-López et al., 2012; Gutiérrez et al., 2024). To better assess the potential link between host genetic diversity and parasite load, future studies should consider including individuals from a broader geographic range or from more genetically isolated populations to capture a wider spectrum of genetic diversity.

These three associated bat parasites appear to have no direct impact on host
survival or overall fitness (Christe et al., 2000; Giorgi et al., 2001; Lučan, 2006; Patterson
et al., 2008b; Witsenburg et al., 2014; Tai et al., 2022). This lack of strong selective
pressure could account for the absence of a relationship between host genetic diversity
and parasite abundance. However, higher intensities of wing mites and bat flies have been
found on pregnant female bats (Lučan, 2006; Lourenço and Palmeirim, 2008; Encarnação
et al., 2012; Tai et al., 2022). Experimental studies assessing the survival and reproductive
success of the Daubenton's bat in relation to its parasite abundance could help
determine whether the apparent low pathogenicity of these parasites has limited the
selective pressure for stronger immune responses and may help explain the lack of a
detectable association between parasite load and genetic diversity in this species. Our
results suggest that the dynamics of bat flies, wing mites and *P. murinus* are more likely
influenced by the bat life-history traits such as its sex and age rather than by individual
genetic diversity. In line with previous studies, female and juvenile Daubenton's bats tend
to harbour higher parasite loads compared to other individuals. (Christe et al., 2000,
2007; Patterson et al., 2008b; Lourenço and Palmeirim, 2008; Encarnação et al., 2012;
Witsenburg et al., 2014; van Schaik and Kerth, 2017; Szentiványi et al., 2017; Tai et al.,
2022). These results may be attributed to increased susceptibility to parasitism during the
bat reproductive period, when gravid females experience immunosuppression and
juveniles possess naïve immune systems (Christe et al., 2000). Additionally, unlike
reproductive females, males do not participate in nursing, and their grooming activity
does not decline during this period (McLean and Speakman, 1997).
While RADseq provides a broad overview of genome-wide variation, it offers
limited resolution at specific genomic regions. RADseq may overlooked subtle but
functionally important variation in key genomic regions associated with parasitism, such
as immune-related genes such as the Major Histocompatibility Complex (MHC).
However, we detected 244 SNPs in the MHC region and did not find any link between the
MHC genetic diversity of the bat and its parasite abundance. Previous studies on other
host species have shown that parasite load can influence the composition of MHC class
I and II alleles, usually associated with an increased polymorphism in these regions
(Westerdahl et al., 2005b; Šimková et al., 2006; Goüy De Bellocq et al., 2008; Schad et
al., 2012). Our findings were surprising as bat flies, wing mites and *P. murinus*
abundances have been known to be directly or indirectly associated with the bat
immunity. In *Myotis myotis*, the greater mouse-eared bat, the increase in T-cell response
was associated with an increase in the bat body condition and a decrease in the load of
its associated wing mite, *Spinturnix myoti* (Christe et al., 2000). Both bat flies and *P.
murinus* were associated with the bat body condition, with bat flies occurring more
frequently on individuals exhibiting higher body condition (Szentiványi et al., 2017; Tai et
al., 2022), while *P. murinus* infections were more prevalent among bats with lower body
condition (Witsenburg et al., 2014). In Daubenton's bat, bat flies and wing mites have
been linked with the bat oxidative stress response, with an increase in the antioxidant
enzyme activity (Lilley et al., 2014). Targeted sequencing of such regions could provide
deeper insight into the relationship between the Daubenton's bat genetic variation and its
associated parasite abundance.

Conclusion

This study explored the population genetic structure and parasite dynamics of the
Daubenton's bat in western Switzerland using RADseq. Surprisingly, the kinship analyses
revealed the presence of full-sibling relationships among individuals sampled from
different locations. This suggested the unexpected dynamics in the Daubenton's bat
movements, potentially indicating social structure or natal dispersal patterns that should
be further explored. Our results confirmed extensive gene flow between sampling sites
and low levels of inbreeding, consistent with the species mating behaviour and dispersal