No evidence for a dilution effect of high vertebrate diversity on tick-borne disease hazard in Dutch forests

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However, empirical evidence from Europe is limited at best, while it remains unclear whether dilution effects operate for other tick-borne diseases. Here, we evaluated how often the dilution effect occurs for a wide range of tick-borne pathogens and symbionts in forest areas in the Netherlands. Methods Data on wildlife, tick densities, and tick-borne microorganisms were collected in 19 forest sites. We calculated six different biodiversity indices based on camera trapping and live trapping data to characterize the vertebrate community of each forest site. These indices were correlated with the nymphal infection prevalence (NIP) and density of infected nymphs (DIN) of three Borrelia burgdorferi sensu lato genospecies as well as seven other tick-borne pathogens and symbionts. Results Vertebrate host species diversity, tick densities and infection prevalence varied widely among sites. However, neither the NIP nor the DIN of any of the ten tick-borne pathogens or symbionts was significantly correlated with any of the six indices of vertebrate species diversity or with total host availability. These results were consistent regardless of whether we used the relative abundance of vertebrate species or the proportion of larvae fed by each host species to calculate the diversity indices. Conclusions Our results do not support evidence for a dilution effect in Dutch forests, suggesting that facilitating high species diversity of native wildlife is unlikely to reduce tick-borne disease hazard at the scale of local forest patches. Whether (other) nature conservation strategies in other types of habitats and at other spatial scales can reduce tick-borne disease hazard warrants further investigation. Ixodes ricinus nature restoration biodiversity Lyme borreliosis tick-borne disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Background In several European countries, long-lasting increases in the incidences and geographic expansion of Lyme borreliosis and tick-borne encephalitis have been observed [1,2]. Furthermore, infections and diseases involving other tick-borne pathogens, such as Anaplasma phagocytophilum , Borrelia miyamotoi , Neoehrlichia mikurensis , Rickettsia helvetica , and Spiroplasma ixodetis are emerging, as evidenced by the accumulating cases involving these agents [3–6]. The transmission cycles of these pathogens are driven by poorly understood ecological processes, involving intricate interactions between wildlife, ticks, and environmental and climatic factors [7]. The challenge is to disentangle these interactions and to identify potential management strategies that could interfere with pathogen transmission cycles and reduce the hazard of tick-borne diseases. Although direct evidence is lacking, the increase in tick-borne diseases may be caused in part by the increased population sizes and spread of Ixodes ricinus ticks [8,9]. Tick-suitable areas in Europe have been expanding, largely due to land-use changes such as agricultural abandonment, habitat fragmentation, and reforestation, as well as climatic changes [8–10]. These environmental changes have also impacted the distribution and population sizes of wildlife, including species that are important hosts to ticks [11]. Wild ungulates in particular, such as roe deer ( Capreolus capreolus ), red deer ( Cervus elaphus ) and fallow deer ( Dama dama ), have increased in densities and expanded their ranges in recent decades [12]. Positive relationships between ungulate density and tick abundance is well established: These species act as feeding hosts for all stages of I. ricinus , and principal propagation hosts for adult I. ricinus ticks [13–16]. Fencing out or culling of ungulates has therefore been implemented to reduce tick-borne disease hazard [17]. However, such strategies are costly and more effective on islands, as near-total removal of ungulates is required for sufficient reductions in tick densities to reduce tick-borne disease risk [17–19]. Besides practical and financial limitations, fencing out or culling of ungulates can also cause issues related to public opinion, ethics, land management objectives, economic revenue, and ecological cascades (e.g. impacts on local predator populations and vegetation structure), and may therefore be undesirable [14]. Another potential strategy that is increasingly promoted as an effective measure to control tick-borne diseases, is the conservation of endemic vertebrate diversity. This is often termed as biodiversity conservation in the literature [20–24]. A proposed mechanism is that habitats with high vertebrate species diversity, will have a larger proportion of incompetent reservoir species. This diverts tick bites away from more competent reservoir species, thereby reducing pathogen transmission [25–27]. This “dilution effect” hypothesis was promoted by studies of the Lyme borreliosis system in the northeastern United States [20,23,28–30]. These studies fueled lively debates on the applicability and generality of the dilution effect hypothesis for other geographic regions where Lyme disease is endemic, as well as other zoonotic disease systems [31–38]. Currently, empirical evidence for dilution effects on Lyme disease hazard in Europe is limited at best, while it remains unclear whether dilution effects operate for other tick-borne diseases [39–41]. Yet, understanding whether high host species diversity is indeed associated with reduced tick-borne disease hazard is important, because of its potential societal and management implications. Here, we evaluate whether promoting high vertebrate diversity in forested areas can be an effective strategy to reduce tick-borne disease hazard in the Netherlands, by testing how frequently the dilution effect occurs for a wide range of tick-borne micro-organisms. We considered both human pathogens, including three Borrelia burgdorferi sensu lato genospecies (causing Lyme borreliosis), Anaplasma phagocytophilum (causing human granulocytic anaplasmosis), Neoehrlichia mikurensis (causing neoehrlichiosis), and Borrelia miyamotoi (causing tick-borne relapsing fever) as well as typical I. ricinus symbionts, such as Rickettsiella spp. [42] and Midichloria midichondrii [43]. Although the transmission of symbionts preliminary relies on vertical transmission, most, if not all, are also transmitted horizontally, and thus may be affected by vertebrate community composition [44]. In a previous study, we monitored wildlife communities from 19 forest sites by camera trapping and live-trapping [13,45,46]. Here, we quantified host species diversity for each site, using six different indices as well as total host availability. We then tested whether these indices were significantly correlated with nymphal infection prevalence (NIP) and density of infected nymphs (DIN) for each of the tick-borne micro-organisms. We considered negative correlations of host species diversity with NIP to be consistent with a dilution effect (i.e. reduced transmission) and negative correlations between host species diversity and DIN to reflect reduced disease hazard. Hence, potential outcomes of our study are: 1) dilution of tick-borne pathogens (NIP) and reduction of disease hazard (DIN), 2) dilution of tick-borne pathogens but no reduction of hazard, 3) no or limited dilution of tick-borne pathogens and no reduction of hazard, or 4) no or limited dilution of tick-borne pathogens but a reduction of hazard. Methods Study sites and data collection Data on wildlife hosts, tick density and tick-borne pathogens (TBPs) were collected in 19 forest sites of 1 ha each across the Netherlands (Supplementary Figure S1). Details on study locations and data collection were previously described [13,45,46]. In short, ten sites were sampled in 2013, and nine in 2014. Five sites were dominated by Scots pine ( Pinus sylvestris ), five by European oak ( Quercus robur ), and nine by a mixture of these. All sites had a distance of at least 5 km between them (Supplementary Table S1). Communities of wildlife hosts were sampled by live trapping of small mammals and camera trapping of medium- to large-sized mammals and birds. Live trapping was conducted once per site, either in July or August, by placing 64 longworth live traps (Heslinga traps, Groningen, the Netherlands) in a 8x8 grid with 12m interspacing. Live traps were pre-baited for 3 days with a mixture of grains, mealworms, and carrot, and contained hay as insulation material. After the pre-baiting period, traps were checked during six consecutive trapping sessions at 12h intervals. Captured small mammals were identified to species and their population densities estimated using capture-mark-recapture models for closed populations [47]. Camera trapping followed the protocol of Hofmeester et al. [48]. Briefly, two camera traps (HC500, Reconyx Inc, Holmen, WI) were mounted on trees 40 cm above ground-level without bait or lure at randomly generated locations within each site. Cameras traps were at least 30 m apart and were deployed for 28 days, after which they were rotated to another location within the site for 9 consecutive rounds. Thus, each site had 18 camera trap locations, totaling 504 camera trapping days. Questing nymphal ticks were collected by drag sampling. Each site was sampled six times; once every four weeks from April up to September, the period in which I. ricinus is most active in the Netherlands. Each time, twenty transects of 10m were sampled using a 1m 2 cotton cloth, totaling 1200 m 2 per site. Dragging was only performed when vegetation was dry and temperatures were above 10°C. Collected ticks were stored in Eppendorf tubes and identified to species by an experienced technician using morphological keys as described in [49] and [50]. Pathogens and symbionts were detected using standard qPCR techniques as described previously [43]. In brief, all ticks were hydrolyzed with ammonium hydroxide and analyzed individually for the presence of tick-borne pathogens with different (multiplex) real-time quantitative PCR (qPCR) protocols, based on various target genes. Samples positive for B. burgdorferi s.l. were subjected to conventional PCR and Sanger sequencing of the intergenic spacer region for genospecies identification. All qPCRs were carried out on a LightCycler 480 (Roche Diagnostics Nederland B.V., Almere, the Netherlands) in a final volume of 20 µl with iQ Multiplex Powermix, 3 µl of sample, and 0.2 µM for all primers and different concentrations for probes [43]. Positive controls, highly diluted synthetic plasmids with unique sequence, and negative water controls were used on every plate tested. To minimize contamination and false-positive samples, DNA extraction, PCR mix preparation, sample addition, and qPCR analyses were performed in separate air-locked dedicated labs. Quantification of host species diversity We calculated six different biodiversity indices based on camera trapping and live trapping data to characterize the host community of each forest site: 1) species richness ( S ), calculated as the total number of recorded species, 2) Shannon’s effective number of species, which is the exponentiated form of the Shannon diversity index ( eH ), 3) Shannon’s evenness ( J ), also called Pielou’s evenness, 4) Simpson’s effective number of species, which is the reciprocal of the Simpson diversity index ( 1/D ), 5) Simpson’s evenness ( E ), and 6) Berger-Parker dominance ( BP ) (Table 1 ). Apart from species richness, all of these indices require estimations of each host species’ relative abundance. We quantified this relative abundance by estimating the average passage rate (d -1 ) of 32 vertebrate host species captured by camera trapping and live trapping. These passage rates are directly proportional to encounter rates between hosts and ticks (see [51] for details). Table 1 Diversity indices and their formulas used in this study Diversity index Definition Species Richness S Shannon’s diversity \({H}^{{\prime }}=-\sum _{i=1}^{S}{p}_{i}*{ln}{p}_{i}\) Shannon’s effective number of species \({e}^{{H}^{{\prime }}}\) Shannon’s evenness \(J=\frac{H{\prime }}{{ln}S}\) Simpson’s diversity \(D=\sum _{i=1}^{S}{{p}_{i}}^{2}\) Simpson’s effective number of species \(\frac{1}{D}\) Simpson’s evenness \(E=\frac{1}{DS}\) Berger-Parker dominance \(BP=\frac{{n}_{max}}{N}\) Shannon’s diversity \({H}^{{\prime }}=-\sum _{i=1}^{S}{p}_{i}*{ln}{p}_{i}\) Shannon’s effective number of species \({e}^{{H}^{{\prime }}}\) Shannon’s evenness \(J=\frac{H{\prime }}{{ln}S}\) Simpson’s diversity \(D=\sum _{i=1}^{S}{{p}_{i}}^{2}\) Simpson’s effective number of species \(\frac{1}{D}\) Simpson’s evenness \(E=\frac{1}{DS}\) Berger-Parker dominance \(BP=\frac{{n}_{max}}{N}\) Because different host species do not contribute equally to feeding larval ticks, we also calculated these six biodiversity indices based on the expected number of larvae fed by each host species per plot per day. Following Schmidt and Ostfeld [28], we multiplied the average passage rate of each host species by the average larval burden of that host species in Europe, as obtained from Hofmeester et al. [52] and Fabri et al. [53]. Hence, our first set of diversity indices were based on the proportional abundance of each host species in the community, while the second set was based on the proportion of tick meals provided by each host species, thereby accounting for the differential contribution of hosts to feeding larval ticks. One additional index was calculated for this study: the sum of the passage rates of all 32 vertebrate host species, as a measure of total host availability. Statistical analyses All statistical analyses were carried out using R version 4.1.2 [54] on the RStudio platform [55]. Biodiversity indices were calculated using the ‘fossil’ package [56] and correlation plots were constructed using the ‘lsr’ package [57]. We checked assumptions for normality based on visual inspection of QQ-plots, histograms, and the Shapiro-Wilk test. These assumptions were not all met so we proceeded with Kendall rank correlation tests to evaluate the relationship of the biodiversity indices with two measures of tick-borne disease hazard, namely NIP and DIN. The density of questing, infected nymphs (DIN) was calculated by multiplying the nymphal infection prevalence (NIP) by the density of questing nymphal I. ricinus ticks (DON) for each plot [58]. P-values were adjusted using the Holm method. Results Host species diversity across sites A total of 32 vertebrate host species were identified based on camera trapping and live trapping across the 19 forest sites (Supplementary Table S2). Species diversity and availability differed widely among sites. For diversity indices based on the proportional abundance of host species the following ranges were observed: species richness from 6 to 16, Shannon’s effective number of species from 1.20 to 9.63, Shannon’s evenness from 0.07 to 0.84, Simpson’s effective number of species from 1.05 to 7.57, Simpson’s evenness from 0.09 to 0.52, Berger-Parker dominance from 0.22 to 0.97, and total host availability from 0.05 to 2.72 (Table 2 ). For diversity indices based on the proportion of tick meals provided by each host species, species richness ranged from 6 to 14, Shannon’s effective number of species ranged from 1.13 to 6.39, Shannon’s evenness ranged from 0.07 to 0.80, Simpson’s effective number of species ranged from 1.04 to 5.37, Simpson’s evenness ranged from 0.09 to 0.64, Berger-Parker dominance ranged from 0.27 to 0.98, and the total host availability ranged from 0.05 to 2.72 (Table 2 ). Although each species diversity index had approximately the same range of values when either the proportional abundance of each host species or the proportion of tick meals from each host species was used, they were not correlated with each other. Table 2 Biodiversity indices for 19 forest sites, based on camera and live trapping data More information on the forest sites can be found in Tables S2 to S4 and Figure S1. Species richness Shannon diversity Shannon evenness Simpson diversity Simpson evenness Berger-Parker dominance Availability AW 12 3.21 0.53 2.47 0.27 0.58 2.72 BB 14 1.76 0.21 1.30 0.09 0.87 0.21 BU 11 4.70 0.65 3.79 0.34 0.42 0.14 DK 6 4.18 0.80 3.82 0.64 0.32 0.58 DW 10 4.81 0.68 3.79 0.38 0.41 0.35 EN 13 5.31 0.67 4.29 0.36 0.31 0.37 HD 15 1.54 0.17 1.22 0.10 0.90 0.44 HM 10 3.14 0.55 2.37 0.30 0.61 0.25 KB 12 1.41 0.15 1.19 0.12 0.92 0.28 MH 16 2.91 0.40 2.05 0.15 0.67 0.23 PD 10 3.13 0.52 2.67 0.30 0.44 0.27 PW 9 2.70 0.45 1.92 0.21 0.70 0.21 RB 13 1.48 0.16 1.18 0.10 0.92 0.39 SD 6 1.13 0.07 1.04 0.17 0.98 0.05 ST 14 5.82 0.67 4.55 0.32 0.38 0.31 VA 15 6.39 0.70 5.37 0.38 0.27 0.73 VH 11 3.62 0.54 2.99 0.27 0.43 0.21 VL 16 2.71 0.38 2.01 0.14 0.67 0.66 ZM 11 1.66 0.21 1.28 0.12 0.88 0.27 NIP and DIN across sites A total of 16,568 questing nymphs were collected across all forest sites, of which 16,555 were successfully tested for the presence of pathogens and symbionts. We detected ten different pathogens and symbionts, which varied widely in nymphal infection prevalence (NIP) between sites. These were Anaplasma phagocytophilum (0%-15%), Borrelia afzelii (0.3%-12%), Borrelia garinii (0%-4%), Borrelia valaisiana (0%-2%), Borrelia miyamotoi (0%-5%), Midichloria mitochondrii (47%-77%), Neoehrlichia mikurensis (0%-13%), Rickettsia helvetica (1%-64%), Ricketsiella spp. (20%-96%), and Spiroplasma ixodetis (5%-44%) (Supplementary Table S3). The density of nymphs (DON) among forest sites ranged from 22 to over 2,200 ticks per 1200 m 2 (Supplementary Table S4). Multiplying the DON and NIP resulted in large variations in the density of infected nymphs (DIN) between sites: Anaplasma phagocytophilum (0-113), Borrelia afzelii (1-106), Borrelia garinii (0–25), Borrelia valaisiana (0–15), Borrelia miyamotoi (0–70), Midichloria mitochondrii (17-1436), Neoehrlichia mikurensis (0-294), Rickettsia helvetica (4-522), Ricketsiella spp. (16–839), and Spiroplasma ixodetis (6-591) (Supplementary Table S4). Correlations between biodiversity and tick-borne disease hazard Kendall rank correlation tests indicated that neither the NIP nor the DIN of any of the ten tick-borne micro-organisms was significantly correlated with any of the six indices of host species diversity or with total host availability. This was regardless of whether the proportional abundance of host species or the proportion of tick meals provided by each host species was used to calculate these indices (Figs. 1 – 4 ). Discussion We evaluated whether maintaining high host species diversity could be a potential strategy to reduce tick-borne disease hazard in forested areas in the Netherlands. To this end, we collected questing ticks from 19 forest fragments with distinct wildlife assemblages and tested these for the presence of tick-borne micro-organisms. Using six different indices of host species diversity as well as total host availability, we found no significant correlations with the NIP or DIN for any of the ten tick-borne symbionts that we detected. These results were consistent regardless of whether we used the relative abundance of host species, or the proportion of larvae fed by each host species to calculate the diversity indices. Thus, our results do not support evidence for a dilution effect in Dutch forests, suggesting that maintaining high species diversity of native wildlife is unlikely to reduce tick-borne disease hazard at the scale of local forest patches. Our results are in line with a number of empirical studies that challenge the concept that high host species diversity buffers against tick-borne disease hazard [39,40,59–61]. Initial studies that advocated the dilution effect hypothesis focused on the Lyme disease system and relied heavily on computer simulation models [23,28], with limited empirical data from a single research site [20,29,30]. Moreover, the latter studies focused on NIP rather than DIN, and hence did not address the actual acarological risk [31,33,62]. Other studies have focused on human Lyme disease incidence, reporting either negative or positive correlations with host species richness, depending on the spatial scale [26,63,64]. Besides several concerns regarding the use of human incidence data, these studies were conducted across larger spatial scales and their results are therefore difficult to interpret or apply at the local, within-forest scale. Another issue is that past studies often relied on proxies for host species diversity, such as forest fragment size [58,60,65]. The underlying assumption is that smaller forest fragments have lower host species diversity and higher relative abundances of competent reservoir hosts [58]. This however, is not necessarily true. Fragmentation can sometimes increase overall habitat diversity, edge effects, and landscape complementation, which in turn can increase host species diversity [66]. Moreover, many wildlife species have adapted to anthropogenic environments, including incompetent reservoir hosts such as deer in the case of B. burgdorferi s.l., which may reach high abundances in suburban environments [59,62]. For example, Linske et al. [59] reported significantly higher host species richness and encounter abundance in small, fragmentated residential woodlands than in large, unfragmented woodlands of Connecticut. Although residential habitats had reduced B. burgdorferi infection prevalence in rodents, this finding was driven by host encounter abundance, not host diversity [59]. The complex and variable effects of fragmentation on wildlife communities may explain the discrepancies between studies, with some reporting positive relationships with Lyme disease hazard [58,65] and others finding no relationship at all [29,60,67]. Thus underlining the importance of directly measuring host species diversity when testing the dilution effect hypothesis [59,62]. Studies that did directly measure host species diversity found limited evidence for dilution effects in the Lyme disease system. For example, Logiudice et al. [29] found no relationship between Shannon diversity and NIP, and a significant but weak negative relationship between species richness and NIP that disappeared when sites with small sample sizes (< 30 ticks) were removed from the analyses [29]. States et al. [61] compared the NIP and DIN of B. burgdorferi -infected I. scapularis ticks between species-poor islands and species-rich mainland communities and found no difference in NIP and higher DIN on islands, contrary to what would be expected based on the dilution effect hypothesis. Werden et al. [68] found that the role of host species diversity was context-dependent, as the interaction between rodent abundance and species richness resulted in either dilution or amplification. In Europe, Ruyts et al. [40] did not find any relationship of host species diversity (as measured by species richness and exponentiated Shannon diversity) with either NIP or DIN. While Gandy et al [41], found that deer density (non-competent hosts) had no effect on Lyme disease hazard, as their role in amplifying tick densities negated their effect of reducing pathogen prevalence. Importantly, Ruyts et al. [40] already highlighted how the ecology of B. burgdorferi s.l. is much more complex in Europe than in the US, with important implications for the role of host species diversity. In Europe, different host species are associated with different Borrelia genospecies, so that more diverse wildlife communities support more diverse Borrelia communities [40]. As these different Borrelia genospecies vary in severity of Lyme disease manifestations [69], adding additional host species could potentially increase Lyme disease hazard by providing larval ticks with bloodmeals from host species that carry more virulent genospecies [40]. In our study, correlations between different measures of host diversity and the NIP and DIN for Borrelia garinii (a genospecies that can cause neuroborreliosis) were statistically non-significant, but the direction of these correlations were all positive. Given that current forest management strategies in the Netherlands are targeted at further increasing biodiversity within forests, future studies should re-evaluate this relationship. Although our results suggest that increasing host species diversity is unlikely to reduce tick-borne disease hazard at the local, within-forest scale, the potential effect of other “biodiversity conservation” strategies in other habitats and at other spatial scales warrants further investigation. For example, the Dutch government committed to increase nature areas by 80,000 ha by 2027. This not only includes different types of forests, but also natural grasslands, heathlands, wetlands, and open dune habitats. In some cases, forest patches within Natura 2000 areas are cleared to make way for other habitat types [70]. Such habitats are characterized by different abiotic conditions and host communities than forests, and hence have different tick densities and infection prevalence [71,72]. Whether and how increasing biodiversity at the landscape-scale (e.g. by intermingling forests with other types of habitat) can reduce tick-borne disease hazard, is an open question that warrants further investigation. Declarations Acknowledgements Not applicable Authors ’ contributions Conceptualization: HS, HE, KT, MH; Data curation: KT,HS; Formal Analysis: KT,HE; Funding acquisition: HS; Investigation: HS, HE,MH; Methodology: KT, HE; Project administration: HS; Resources: HS; Software: KT, HE; Supervision: HS; Visualization: KT; Writing – original draft KT, HS, HE; Writing – review & editing, KT, HS, HE, MH Funding This research was financially supported by the Dutch Ministry of Health, Welfare and Sport (VWS) and a grant from the European Interreg North Sea Region program, as part of the NorthTick project. Availability of data and material The datasets used and/or analysed during the current study are in the supplementary datafile. More information is available from the corresponding author on reasonable request Ethics approval and consent to participate Animal experimental handling procedures were approved by the Animal Experiments Committee of Wageningen University (WUR-2013055 and WUR-2014019) and by the Dutch ministry of Economic affairs (FF/75A/2013/003). Consent for publication Not applicable Competing interests The authors declare that they have no competing interests References Bogovic P, Strle F. Tick-borne encephalitis: A review of epidemiology, clinical characteristics, and management. World J Clin Cases WJCC. Baishideng Publishing Group Inc; 2015;3:430. Rizzoli A, Hauffe HC, Carpi G, Vourc’h G, Neteler M, Rosa R. Lyme borreliosis in Europe. Eurosurveillance. European Centre for Disease Prevention and Control; 2011;16:19906. Heyman P, Cochez C, Hofhuis A, Van Der Giessen J, Sprong H, Porter SR, et al. A clear and present danger: tick-borne diseases in Europe. Expert Rev Anti Infect Ther. 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Proc R Soc B Biol Sci. 2017;284:20170453. Hofmeester TR, Sprong H, Jansen PA, Prins HHT, van Wieren SE. Deer presence rather than abundance determines the population density of the sheep tick, Ixodes ricinus, in Dutch forests. Parasit Vectors. 2017;10:433. Otis DL, Burnham KP, White GC, Anderson DR. Statistical inference from capture data on closed animal populations. Wildl Monogr. JSTOR; 1978;3–135. Hofmeester TR, Rowcliffe JM, Jansen PA. A simple method for estimating the effective detection distance of camera traps. Remote Sens Ecol Conserv. Wiley Online Library; 2017;3:81–9. Arthur DR. British ticks. Br Ticks. London: Butterworths; 1963; Hillyard PD. Ticks of north-west Europe. Field Studies Council; 1996. Hofmeester TR, Rowcliffe JM, Jansen PA. Quantifying the availability of vertebrate hosts to ticks: a camera-trapping approach. Front Vet Sci. 2017;4:115. Hofmeester TR, Krawczyk AI, Van Leeuwen AD, Fonville M, Montizaan MG, Van Den Berge K, et al. Role of mustelids in the life-cycle of ixodid ticks and transmission cycles of four tick-borne pathogens. Parasit Vectors. Springer; 2018;11:1–13. Fabri ND, Sprong H, Heesterbeek H, Ecke F, Cromsigt JPGM, Hofmeester TR. The circulation of Anaplasma phagocytophilum ecotypes is associated with community composition of vertebrate hosts. Ecosphere. Wiley Online Library; 2022;13:e4243. R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2008. Available from: http://www.R-project.org/ RStudio Team. RStudio: Integrated Development Environment for R. [Internet]. Boston: RStudio, PBC; 2022. Available from: http://www.rstudio.com Vavrek MJ. fossil: palaeoecological and palaeogeographical analysis tools. Palaeontol Electron. 2011;14:1T. Navarro D. Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.6) [Internet]. Sydney, Australia: University of New South Wales; 2015. Available from: https://learningstatisticswithr.com Allan BF, Keesing F, Ostfeld RS. Effect of forest fragmentation on Lyme disease risk. Conserv Biol. 2003;17:267–72. Linske MA, Williams SC, Stafford III KC, Ortega IM. Ixodes scapularis (Acari: Ixodidae) reservoir host diversity and abundance impacts on dilution of Borrelia burgdorferi (Spirochaetales: Spirochaetaceae) in residential and woodland habitats in Connecticut, United States. J Med Entomol. Oxford University Press US; 2018;55:681–90. Zolnik CP, Falco RC, Kolokotronis S-O, Daniels TJ. No observed effect of landscape fragmentation on pathogen infection prevalence in blacklegged ticks (Ixodes scapularis) in the Northeastern United States. PLoS One. Public Library of Science San Francisco, CA USA; 2015;10:e0139473. States S, Brinkerhoff R, Carpi G, Steeves T, Folsom-O’Keefe C, DeVeaux M, et al. Lyme disease risk not amplified in a species-poor vertebrate community: similar Borrelia burgdorferi tick infection prevalence and OspC genotype frequencies. Infect Genet Evol. Elsevier; 2014;27:566–75. Diuk-Wasser MA, VanAcker MC, Fernandez MP. Impact of land use changes and habitat fragmentation on the eco-epidemiology of tick-borne diseases. J Med Entomol. Oxford University Press US; 2021;58:1546–64. Turney S, Gonzalez A, Millien V. The negative relationship between mammal host diversity and Lyme disease incidence strengthens through time. Ecology. Wiley Online Library; 2014;95:3244–50. Wang YX, Matson KD, Prins HH, Xu Y, Huang ZY, de Boer WF. Risk factors for Lyme disease: a scale-dependent effect of host species diversity and a consistent negative effect of host phylogenetic diversity. Ticks Tick-Borne Dis. Elsevier; 2022;102073. Brownstein J, Skelly D, Holford T, Fish D. Forest fragmentation predicts local scale heterogeneity of Lyme disease risk. Oecologia. 2005;146:469–75. Fahrig L. Effects of habitat fragmentation on biodiversity. Annu Rev Ecol Evol Syst. 2003;34:487–515. Diuk-Wasser MA, Hoen AG, Cislo P, Brinkerhoff R, Hamer SA, Rowland M, et al. Human risk of infection with Borrelia burgdorferi, the Lyme disease agent, in eastern United States. Am J Trop Med Hyg. The American Society of Tropical Medicine and Hygiene; 2012;86:320. Werden L, Barker IK, Bowman J, Gonzales EK, Leighton PA, Lindsay LR, et al. Geography, deer, and host biodiversity shape the pattern of Lyme disease emergence in the Thousand Islands Archipelago of Ontario, Canada. PLoS One. Public Library of Science San Francisco, USA; 2014;9:e85640. Jahfari S, Krawczyk A, Coipan EC, Fonville M, Hovius JW, Sprong H, et al. Enzootic origins for clinical manifestations of Lyme borreliosis. Infect Genet Evol. Elsevier; 2017;49:48–54. Bos voor de toekomst [Internet]. Den Haag, the Netherlands: ministerie van Landbouw, Natuur en Voedselkwaliteit en Interprovinciaal Overleg; 2020 Nov p. 60. Report No.: 1120–001. Available from: https://open.overheid.nl/repository/ronl-d6ac7db2-0d36-45b0-9507-f76638a48c0d/1/pdf/Bos%20voor%20de%20toekomst_Uitwerking%20ambities%20en%20doelen%20landelijke%20Bossenstrategie%20en%20beleidsagenda%202030.pdf Wielinga PR, Gaasenbeek C, Fonville M, de Boer A, de Vries A, Dimmers W, et al. Longitudinal analysis of tick densities and Borrelia, Anaplasma, and Ehrlichia infections of Ixodes ricinus ticks in different habitat areas in The Netherlands. Appl Environ Microbiol. Am Soc Microbiol; 2006;72:7594–601. Hartemink N, van Vliet A, Sprong H, Jacobs F, Garcia-Martí I, Zurita-Milla R, et al. Temporal-spatial variation in questing tick activity in the Netherlands: The effect of climatic and habitat factors. Vector-Borne Zoonotic Dis. Mary Ann Liebert, Inc., publishers; 2019;19:494–505. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2368686","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":159446666,"identity":"4439a95a-5018-4e9e-a2b3-1fa02f6b9808","order_by":0,"name":"Helen Joan Esser","email":"","orcid":"","institution":"Wageningen University \u0026 Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Helen","middleName":"Joan","lastName":"Esser","suffix":""},{"id":159446667,"identity":"a2089397-c63a-4d87-81d2-8fb804f9bbf8","order_by":1,"name":"Katsuhisa Takumi","email":"","orcid":"","institution":"National Institute for Public Health and the Environment","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Katsuhisa","middleName":"","lastName":"Takumi","suffix":""},{"id":159446668,"identity":"099bbb92-67e1-44ae-9a69-8f654efd0400","order_by":2,"name":"Maya Louise Holding","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maya","middleName":"Louise","lastName":"Holding","suffix":""},{"id":159446669,"identity":"e3ed4604-18d8-4d4c-8608-b7922eca6ced","order_by":3,"name":"Hein Sprong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACAwbGBhib8QGQCwTMDbhUY2hhNoBoQYjg0IIAbBJQ2/BrMWdvbnvA8OdOPr90+7XqioLD0QzSjfi1WPYcbDdgbHtmOXPOmbKbZwwO5zbIHCTgsBuJbRKMDYcNDG7kpN1sMEjLbZBIJKDl/sM2CYY/EC2FxGm5wQjUwgbSkn6MscHAhggtZ4AOS2x7ZiA5I4dZEqSljaCW48efSXz4c8eAXyL94ceGPxK5/RLJB/BqAYMEBpAiHkgcsRFWDwYgLewPiFQ8CkbBKBgFIw0AAAycSv9ej747AAAAAElFTkSuQmCC","orcid":"","institution":"National Institute for Public Health and the Environment","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hein","middleName":"","lastName":"Sprong","suffix":""}],"badges":[],"createdAt":"2022-12-12 06:59:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2368686/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2368686/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":30342198,"identity":"c463e91a-6d42-4524-9a0f-bc03c774a74b","added_by":"auto","created_at":"2022-12-14 22:39:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":212327,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix of NIP and biodiversity indices for ten different tick-borne pathogens and symbionts in \u003cem\u003eIxodes ricinus\u003c/em\u003e. Diversity indices are based on the proportional abundance of host species. The size of a circle is proportional to Kendall’s rank correlation coefficient. A cross indicates an insignificant correlation coefficient.\u003c/p\u003e","description":"","filename":"Fig1encounterratesNIP.png","url":"https://assets-eu.researchsquare.com/files/rs-2368686/v1/12326da76de3adac8103eff1.png"},{"id":30342200,"identity":"07f92b6a-5b17-44a7-ba05-f6a475f4939a","added_by":"auto","created_at":"2022-12-14 22:39:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209638,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix for DIN and biodiversity indices for ten different tick-borne pathogens and symbionts in \u003cem\u003eIxodes ricinus\u003c/em\u003e. Diversity indices are based on the proportional abundance of host species. The size of a circle is proportional to Kendall’s rank correlation coefficient. A cross indicates an insignificant correlation coefficient.\u003c/p\u003e","description":"","filename":"Fig2encounterratesDIN.png","url":"https://assets-eu.researchsquare.com/files/rs-2368686/v1/23599420fc13436aec0c4c4e.png"},{"id":30342745,"identity":"8987c4a3-b542-4b70-afdf-4800203902a7","added_by":"auto","created_at":"2022-12-14 22:47:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":226810,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix for DIN and biodiversity indices for ten different tick-borne pathogens and symbionts in \u003cem\u003eIxodes ricinus\u003c/em\u003e. Diversity indices are based on the proportion of tick meals provided by each host species. The size of a circle is proportional to Kendall’s rank correlation coefficient. A cross indicates an insignificant correlation coefficient.\u003c/p\u003e","description":"","filename":"Fig3larvaefedNIP.png","url":"https://assets-eu.researchsquare.com/files/rs-2368686/v1/ad2755c92fe91e8d6729a552.png"},{"id":30342202,"identity":"3552b62b-efda-4fbd-8b80-252096ed8f65","added_by":"auto","created_at":"2022-12-14 22:39:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":245862,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix for DIN and biodiversity indices for ten different tick-borne pathogens and symbionts in \u003cem\u003eIxodes ricinus\u003c/em\u003e. Diversity indices are based on the proportion of tick meals provided by each host species. The size of a circle is proportional to Kendall’s rank correlation coefficient. A cross indicates an insignificant correlation coefficient.\u003c/p\u003e","description":"","filename":"Fig4larvaefedDIN.png","url":"https://assets-eu.researchsquare.com/files/rs-2368686/v1/1d066cd6bbeb97373118ea83.png"},{"id":33210102,"identity":"0be57c07-d51a-42c2-88e2-dc83c80928e6","added_by":"auto","created_at":"2023-02-21 05:14:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1204607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2368686/v1/f8d2834a-c285-4130-b86e-9cd5224a3a64.pdf"},{"id":30342746,"identity":"13d005b9-3238-4021-9298-99a72debfcae","added_by":"auto","created_at":"2022-12-14 22:47:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":518710,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-2368686/v1/98023aa36a215878f2f8311e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"No evidence for a dilution effect of high vertebrate diversity on tick-borne disease hazard in Dutch forests","fulltext":[{"header":"Background","content":"\u003cp\u003eIn several European countries, long-lasting increases in the incidences and geographic expansion of Lyme borreliosis and tick-borne encephalitis have been observed [1,2]. Furthermore, infections and diseases involving other tick-borne pathogens, such as \u003cem\u003eAnaplasma phagocytophilum\u003c/em\u003e, \u003cem\u003eBorrelia miyamotoi\u003c/em\u003e, \u003cem\u003eNeoehrlichia mikurensis\u003c/em\u003e, \u003cem\u003eRickettsia helvetica\u003c/em\u003e, and \u003cem\u003eSpiroplasma ixodetis\u003c/em\u003e are emerging, as evidenced by the accumulating cases involving these agents [3\u0026ndash;6]. The transmission cycles of these pathogens are driven by poorly understood ecological processes, involving intricate interactions between wildlife, ticks, and environmental and climatic factors [7]. The challenge is to disentangle these interactions and to identify potential management strategies that could interfere with pathogen transmission cycles and reduce the hazard of tick-borne diseases.\u003c/p\u003e \u003cp\u003eAlthough direct evidence is lacking, the increase in tick-borne diseases may be caused in part by the increased population sizes and spread of \u003cem\u003eIxodes ricinus\u003c/em\u003e ticks [8,9]. Tick-suitable areas in Europe have been expanding, largely due to land-use changes such as agricultural abandonment, habitat fragmentation, and reforestation, as well as climatic changes [8\u0026ndash;10]. These environmental changes have also impacted the distribution and population sizes of wildlife, including species that are important hosts to ticks [11]. Wild ungulates in particular, such as roe deer (\u003cem\u003eCapreolus capreolus\u003c/em\u003e), red deer (\u003cem\u003eCervus elaphus\u003c/em\u003e) and fallow deer (\u003cem\u003eDama dama\u003c/em\u003e), have increased in densities and expanded their ranges in recent decades [12]. Positive relationships between ungulate density and tick abundance is well established: These species act as feeding hosts for all stages of \u003cem\u003eI. ricinus\u003c/em\u003e, and principal propagation hosts for adult \u003cem\u003eI. ricinus\u003c/em\u003e ticks [13\u0026ndash;16]. Fencing out or culling of ungulates has therefore been implemented to reduce tick-borne disease hazard [17]. However, such strategies are costly and more effective on islands, as near-total removal of ungulates is required for sufficient reductions in tick densities to reduce tick-borne disease risk [17\u0026ndash;19]. Besides practical and financial limitations, fencing out or culling of ungulates can also cause issues related to public opinion, ethics, land management objectives, economic revenue, and ecological cascades (e.g. impacts on local predator populations and vegetation structure), and may therefore be undesirable [14].\u003c/p\u003e \u003cp\u003eAnother potential strategy that is increasingly promoted as an effective measure to control tick-borne diseases, is the conservation of endemic vertebrate diversity. This is often termed as biodiversity conservation in the literature [20\u0026ndash;24]. A proposed mechanism is that habitats with high vertebrate species diversity, will have a larger proportion of incompetent reservoir species. This diverts tick bites away from more competent reservoir species, thereby reducing pathogen transmission [25\u0026ndash;27]. This \u0026ldquo;dilution effect\u0026rdquo; hypothesis was promoted by studies of the Lyme borreliosis system in the northeastern United States [20,23,28\u0026ndash;30]. These studies fueled lively debates on the applicability and generality of the dilution effect hypothesis for other geographic regions where Lyme disease is endemic, as well as other zoonotic disease systems [31\u0026ndash;38]. Currently, empirical evidence for dilution effects on Lyme disease hazard in Europe is limited at best, while it remains unclear whether dilution effects operate for other tick-borne diseases [39\u0026ndash;41]. Yet, understanding whether high host species diversity is indeed associated with reduced tick-borne disease hazard is important, because of its potential societal and management implications.\u003c/p\u003e \u003cp\u003eHere, we evaluate whether promoting high vertebrate diversity in forested areas can be an effective strategy to reduce tick-borne disease hazard in the Netherlands, by testing how frequently the dilution effect occurs for a wide range of tick-borne micro-organisms. We considered both human pathogens, including three \u003cem\u003eBorrelia burgdorferi\u003c/em\u003e sensu lato genospecies (causing Lyme borreliosis), \u003cem\u003eAnaplasma phagocytophilum\u003c/em\u003e (causing human granulocytic anaplasmosis), \u003cem\u003eNeoehrlichia mikurensis\u003c/em\u003e (causing neoehrlichiosis), and \u003cem\u003eBorrelia miyamotoi\u003c/em\u003e (causing tick-borne relapsing fever) as well as typical \u003cem\u003eI. ricinus\u003c/em\u003e symbionts, such as \u003cem\u003eRickettsiella spp.\u003c/em\u003e [42] and \u003cem\u003eMidichloria midichondrii\u003c/em\u003e [43]. Although the transmission of symbionts preliminary relies on vertical transmission, most, if not all, are also transmitted horizontally, and thus may be affected by vertebrate community composition [44]. In a previous study, we monitored wildlife communities from 19 forest sites by camera trapping and live-trapping [13,45,46]. Here, we quantified host species diversity for each site, using six different indices as well as total host availability. We then tested whether these indices were significantly correlated with nymphal infection prevalence (NIP) and density of infected nymphs (DIN) for each of the tick-borne micro-organisms. We considered negative correlations of host species diversity with NIP to be consistent with a dilution effect (i.e. reduced transmission) and negative correlations between host species diversity and DIN to reflect reduced disease hazard. Hence, potential outcomes of our study are:\u003c/p\u003e \u003cp\u003e1) dilution of tick-borne pathogens (NIP) and reduction of disease hazard (DIN),\u003c/p\u003e \u003cp\u003e2) dilution of tick-borne pathogens but no reduction of hazard,\u003c/p\u003e \u003cp\u003e3) no or limited dilution of tick-borne pathogens and no reduction of hazard, or\u003c/p\u003e \u003cp\u003e4) no or limited dilution of tick-borne pathogens but a reduction of hazard.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy sites and data collection\u003c/h2\u003e \u003cp\u003eData on wildlife hosts, tick density and tick-borne pathogens (TBPs) were collected in 19 forest sites of 1 ha each across the Netherlands (Supplementary Figure S1). Details on study locations and data collection were previously described [13,45,46]. In short, ten sites were sampled in 2013, and nine in 2014. Five sites were dominated by Scots pine (\u003cem\u003ePinus sylvestris\u003c/em\u003e), five by European oak (\u003cem\u003eQuercus robur\u003c/em\u003e), and nine by a mixture of these. All sites had a distance of at least 5 km between them (Supplementary Table S1).\u003c/p\u003e \u003cp\u003eCommunities of wildlife hosts were sampled by live trapping of small mammals and camera trapping of medium- to large-sized mammals and birds. Live trapping was conducted once per site, either in July or August, by placing 64 longworth live traps (Heslinga traps, Groningen, the Netherlands) in a 8x8 grid with 12m interspacing. Live traps were pre-baited for 3 days with a mixture of grains, mealworms, and carrot, and contained hay as insulation material. After the pre-baiting period, traps were checked during six consecutive trapping sessions at 12h intervals. Captured small mammals were identified to species and their population densities estimated using capture-mark-recapture models for closed populations [47]. Camera trapping followed the protocol of Hofmeester et al. [48]. Briefly, two camera traps (HC500, Reconyx Inc, Holmen, WI) were mounted on trees 40 cm above ground-level without bait or lure at randomly generated locations within each site. Cameras traps were at least 30 m apart and were deployed for 28 days, after which they were rotated to another location within the site for 9 consecutive rounds. Thus, each site had 18 camera trap locations, totaling 504 camera trapping days.\u003c/p\u003e \u003cp\u003eQuesting nymphal ticks were collected by drag sampling. Each site was sampled six times; once every four weeks from April up to September, the period in which \u003cem\u003eI. ricinus\u003c/em\u003e is most active in the Netherlands. Each time, twenty transects of 10m were sampled using a 1m\u003csup\u003e2\u003c/sup\u003e cotton cloth, totaling 1200 m\u003csup\u003e2\u003c/sup\u003e per site. Dragging was only performed when vegetation was dry and temperatures were above 10\u0026deg;C. Collected ticks were stored in Eppendorf tubes and identified to species by an experienced technician using morphological keys as described in [49] and [50].\u003c/p\u003e \u003cp\u003ePathogens and symbionts were detected using standard qPCR techniques as described previously [43]. In brief, all ticks were hydrolyzed with ammonium hydroxide and analyzed individually for the presence of tick-borne pathogens with different (multiplex) real-time quantitative PCR (qPCR) protocols, based on various target genes. Samples positive for \u003cem\u003eB. burgdorferi\u003c/em\u003e s.l. were subjected to conventional PCR and Sanger sequencing of the intergenic spacer region for genospecies identification. All qPCRs were carried out on a LightCycler 480 (Roche Diagnostics Nederland B.V., Almere, the Netherlands) in a final volume of 20 \u0026micro;l with iQ Multiplex Powermix, 3 \u0026micro;l of sample, and 0.2 \u0026micro;M for all primers and different concentrations for probes [43]. Positive controls, highly diluted synthetic plasmids with unique sequence, and negative water controls were used on every plate tested. To minimize contamination and false-positive samples, DNA extraction, PCR mix preparation, sample addition, and qPCR analyses were performed in separate air-locked dedicated labs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of host species diversity\u003c/h2\u003e \u003cp\u003eWe calculated six different biodiversity indices based on camera trapping and live trapping data to characterize the host community of each forest site: 1) species richness (\u003cem\u003eS\u003c/em\u003e), calculated as the total number of recorded species, 2) Shannon\u0026rsquo;s effective number of species, which is the exponentiated form of the Shannon diversity index (\u003cem\u003eeH\u003c/em\u003e), 3) Shannon\u0026rsquo;s evenness (\u003cem\u003eJ\u003c/em\u003e), also called Pielou\u0026rsquo;s evenness, 4) Simpson\u0026rsquo;s effective number of species, which is the reciprocal of the Simpson diversity index (\u003cem\u003e1/D\u003c/em\u003e), 5) Simpson\u0026rsquo;s evenness (\u003cem\u003eE\u003c/em\u003e), and 6) Berger-Parker dominance (\u003cem\u003eBP\u003c/em\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Apart from species richness, all of these indices require estimations of each host species\u0026rsquo; relative abundance. We quantified this relative abundance by estimating the average passage rate (d\u003csup\u003e-1\u003c/sup\u003e) of 32 vertebrate host species captured by camera trapping and live trapping. These passage rates are directly proportional to encounter rates between hosts and ticks (see [51] for details).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiversity indices and their formulas used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies Richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u0026rsquo;s diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}^{{\\prime }}=-\\sum _{i=1}^{S}{p}_{i}*{ln}{p}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u0026rsquo;s effective number of species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({e}^{{H}^{{\\prime }}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u0026rsquo;s evenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(J=\\frac{H{\\prime }}{{ln}S}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimpson\u0026rsquo;s diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(D=\\sum _{i=1}^{S}{{p}_{i}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimpson\u0026rsquo;s effective number of species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{D}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimpson\u0026rsquo;s evenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(E=\\frac{1}{DS}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBerger-Parker dominance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(BP=\\frac{{n}_{max}}{N}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u0026rsquo;s diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}^{{\\prime }}=-\\sum _{i=1}^{S}{p}_{i}*{ln}{p}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u0026rsquo;s effective number of species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({e}^{{H}^{{\\prime }}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u0026rsquo;s evenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(J=\\frac{H{\\prime }}{{ln}S}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimpson\u0026rsquo;s diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(D=\\sum _{i=1}^{S}{{p}_{i}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimpson\u0026rsquo;s effective number of species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{D}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimpson\u0026rsquo;s evenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(E=\\frac{1}{DS}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBerger-Parker dominance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(BP=\\frac{{n}_{max}}{N}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBecause different host species do not contribute equally to feeding larval ticks, we also calculated these six biodiversity indices based on the expected number of larvae fed by each host species per plot per day. Following Schmidt and Ostfeld [28], we multiplied the average passage rate of each host species by the average larval burden of that host species in Europe, as obtained from Hofmeester et al. [52] and Fabri et al. [53]. Hence, our first set of diversity indices were based on the proportional abundance of each host species in the community, while the second set was based on the proportion of tick meals provided by each host species, thereby accounting for the differential contribution of hosts to feeding larval ticks. One additional index was calculated for this study: the sum of the passage rates of all 32 vertebrate host species, as a measure of total host availability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAll statistical analyses were carried out using R version 4.1.2 [54] on the RStudio platform [55]. Biodiversity indices were calculated using the \u0026lsquo;fossil\u0026rsquo; package [56] and correlation plots were constructed using the \u0026lsquo;lsr\u0026rsquo; package [57]. We checked assumptions for normality based on visual inspection of QQ-plots, histograms, and the Shapiro-Wilk test. These assumptions were not all met so we proceeded with Kendall rank correlation tests to evaluate the relationship of the biodiversity indices with two measures of tick-borne disease hazard, namely NIP and DIN. The density of questing, infected nymphs (DIN) was calculated by multiplying the nymphal infection prevalence (NIP) by the density of questing nymphal \u003cem\u003eI. ricinus\u003c/em\u003e ticks (DON) for each plot [58]. P-values were adjusted using the Holm method.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHost species diversity across sites\u003c/h2\u003e \u003cp\u003eA total of 32 vertebrate host species were identified based on camera trapping and live trapping across the 19 forest sites (Supplementary Table S2). Species diversity and availability differed widely among sites. For diversity indices based on the proportional abundance of host species the following ranges were observed: species richness from 6 to 16, Shannon\u0026rsquo;s effective number of species from 1.20 to 9.63, Shannon\u0026rsquo;s evenness from 0.07 to 0.84, Simpson\u0026rsquo;s effective number of species from 1.05 to 7.57, Simpson\u0026rsquo;s evenness from 0.09 to 0.52, Berger-Parker dominance from 0.22 to 0.97, and total host availability from 0.05 to 2.72 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For diversity indices based on the proportion of tick meals provided by each host species, species richness ranged from 6 to 14, Shannon\u0026rsquo;s effective number of species ranged from 1.13 to 6.39, Shannon\u0026rsquo;s evenness ranged from 0.07 to 0.80, Simpson\u0026rsquo;s effective number of species ranged from 1.04 to 5.37, Simpson\u0026rsquo;s evenness ranged from 0.09 to 0.64, Berger-Parker dominance ranged from 0.27 to 0.98, and the total host availability ranged from 0.05 to 2.72 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although each species diversity index had approximately the same range of values when either the proportional abundance of each host species or the proportion of tick meals from each host species was used, they were not correlated with each other.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiodiversity indices for 19 forest sites, based on camera and live trapping data More information on the forest sites can be found in Tables S2 to S4 and Figure S1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies richness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShannon diversity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShannon evenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimpson diversity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSimpson evenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBerger-Parker dominance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAvailability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNIP and DIN across sites\u003c/h2\u003e \u003cp\u003eA total of 16,568 questing nymphs were collected across all forest sites, of which 16,555 were successfully tested for the presence of pathogens and symbionts. We detected ten different pathogens and symbionts, which varied widely in nymphal infection prevalence (NIP) between sites. These were \u003cem\u003eAnaplasma phagocytophilum\u003c/em\u003e (0%-15%), \u003cem\u003eBorrelia afzelii\u003c/em\u003e (0.3%-12%), \u003cem\u003eBorrelia garinii\u003c/em\u003e (0%-4%), \u003cem\u003eBorrelia\u003c/em\u003e valaisiana (0%-2%), \u003cem\u003eBorrelia miyamotoi\u003c/em\u003e (0%-5%), \u003cem\u003eMidichloria mitochondrii\u003c/em\u003e (47%-77%), \u003cem\u003eNeoehrlichia mikurensis\u003c/em\u003e (0%-13%), \u003cem\u003eRickettsia helvetica\u003c/em\u003e (1%-64%), \u003cem\u003eRicketsiella\u003c/em\u003e spp. (20%-96%), and \u003cem\u003eSpiroplasma ixodetis\u003c/em\u003e (5%-44%) (Supplementary Table S3).\u003c/p\u003e \u003cp\u003eThe density of nymphs (DON) among forest sites ranged from 22 to over 2,200 ticks per 1200 m\u003csup\u003e2\u003c/sup\u003e (Supplementary Table S4). Multiplying the DON and NIP resulted in large variations in the density of infected nymphs (DIN) between sites: \u003cem\u003eAnaplasma phagocytophilum\u003c/em\u003e (0-113), \u003cem\u003eBorrelia afzelii\u003c/em\u003e (1-106), \u003cem\u003eBorrelia garinii\u003c/em\u003e (0\u0026ndash;25), \u003cem\u003eBorrelia\u003c/em\u003e valaisiana (0\u0026ndash;15), \u003cem\u003eBorrelia miyamotoi\u003c/em\u003e (0\u0026ndash;70), \u003cem\u003eMidichloria mitochondrii\u003c/em\u003e (17-1436), \u003cem\u003eNeoehrlichia mikurensis\u003c/em\u003e (0-294), \u003cem\u003eRickettsia helvetica\u003c/em\u003e (4-522), \u003cem\u003eRicketsiella\u003c/em\u003e spp. (16\u0026ndash;839), and \u003cem\u003eSpiroplasma ixodetis\u003c/em\u003e (6-591) (Supplementary Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between biodiversity and tick-borne disease hazard\u003c/h2\u003e \u003cp\u003eKendall rank correlation tests indicated that neither the NIP nor the DIN of any of the ten tick-borne micro-organisms was significantly correlated with any of the six indices of host species diversity or with total host availability. This was regardless of whether the proportional abundance of host species or the proportion of tick meals provided by each host species was used to calculate these indices (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe evaluated whether maintaining high host species diversity could be a potential strategy to reduce tick-borne disease hazard in forested areas in the Netherlands. To this end, we collected questing ticks from 19 forest fragments with distinct wildlife assemblages and tested these for the presence of tick-borne micro-organisms. Using six different indices of host species diversity as well as total host availability, we found no significant correlations with the NIP or DIN for any of the ten tick-borne symbionts that we detected. These results were consistent regardless of whether we used the relative abundance of host species, or the proportion of larvae fed by each host species to calculate the diversity indices. Thus, our results do not support evidence for a dilution effect in Dutch forests, suggesting that maintaining high species diversity of native wildlife is unlikely to reduce tick-borne disease hazard at the scale of local forest patches.\u003c/p\u003e \u003cp\u003eOur results are in line with a number of empirical studies that challenge the concept that high host species diversity buffers against tick-borne disease hazard [39,40,59\u0026ndash;61]. Initial studies that advocated the dilution effect hypothesis focused on the Lyme disease system and relied heavily on computer simulation models [23,28], with limited empirical data from a single research site [20,29,30]. Moreover, the latter studies focused on NIP rather than DIN, and hence did not address the actual acarological risk [31,33,62]. Other studies have focused on human Lyme disease incidence, reporting either negative or positive correlations with host species richness, depending on the spatial scale [26,63,64]. Besides several concerns regarding the use of human incidence data, these studies were conducted across larger spatial scales and their results are therefore difficult to interpret or apply at the local, within-forest scale.\u003c/p\u003e \u003cp\u003eAnother issue is that past studies often relied on proxies for host species diversity, such as forest fragment size [58,60,65]. The underlying assumption is that smaller forest fragments have lower host species diversity and higher relative abundances of competent reservoir hosts [58]. This however, is not necessarily true. Fragmentation can sometimes increase overall habitat diversity, edge effects, and landscape complementation, which in turn can increase host species diversity [66]. Moreover, many wildlife species have adapted to anthropogenic environments, including incompetent reservoir hosts such as deer in the case of \u003cem\u003eB. burgdorferi\u003c/em\u003e s.l., which may reach high abundances in suburban environments [59,62]. For example, Linske et al. [59] reported significantly higher host species richness and encounter abundance in small, fragmentated residential woodlands than in large, unfragmented woodlands of Connecticut. Although residential habitats had reduced \u003cem\u003eB. burgdorferi\u003c/em\u003e infection prevalence in rodents, this finding was driven by host encounter abundance, not host diversity [59]. The complex and variable effects of fragmentation on wildlife communities may explain the discrepancies between studies, with some reporting positive relationships with Lyme disease hazard [58,65] and others finding no relationship at all [29,60,67]. Thus underlining the importance of directly measuring host species diversity when testing the dilution effect hypothesis [59,62].\u003c/p\u003e \u003cp\u003eStudies that did directly measure host species diversity found limited evidence for dilution effects in the Lyme disease system. For example, Logiudice et al. [29] found no relationship between Shannon diversity and NIP, and a significant but weak negative relationship between species richness and NIP that disappeared when sites with small sample sizes (\u0026lt;\u0026thinsp;30 ticks) were removed from the analyses [29]. States et al. [61] compared the NIP and DIN of \u003cem\u003eB. burgdorferi\u003c/em\u003e-infected \u003cem\u003eI. scapularis\u003c/em\u003e ticks between species-poor islands and species-rich mainland communities and found no difference in NIP and higher DIN on islands, contrary to what would be expected based on the dilution effect hypothesis. Werden et al. [68] found that the role of host species diversity was context-dependent, as the interaction between rodent abundance and species richness resulted in either dilution or amplification. In Europe, Ruyts et al. [40] did not find any relationship of host species diversity (as measured by species richness and exponentiated Shannon diversity) with either NIP or DIN. While Gandy et al [41], found that deer density (non-competent hosts) had no effect on Lyme disease hazard, as their role in amplifying tick densities negated their effect of reducing pathogen prevalence.\u003c/p\u003e \u003cp\u003eImportantly, Ruyts et al. [40] already highlighted how the ecology of \u003cem\u003eB. burgdorferi\u003c/em\u003e s.l. is much more complex in Europe than in the US, with important implications for the role of host species diversity. In Europe, different host species are associated with different \u003cem\u003eBorrelia\u003c/em\u003e genospecies, so that more diverse wildlife communities support more diverse \u003cem\u003eBorrelia\u003c/em\u003e communities [40]. As these different \u003cem\u003eBorrelia\u003c/em\u003e genospecies vary in severity of Lyme disease manifestations [69], adding additional host species could potentially increase Lyme disease hazard by providing larval ticks with bloodmeals from host species that carry more virulent genospecies [40]. In our study, correlations between different measures of host diversity and the NIP and DIN for \u003cem\u003eBorrelia garinii\u003c/em\u003e (a genospecies that can cause neuroborreliosis) were statistically non-significant, but the direction of these correlations were all positive. Given that current forest management strategies in the Netherlands are targeted at further increasing biodiversity within forests, future studies should re-evaluate this relationship.\u003c/p\u003e \u003cp\u003eAlthough our results suggest that increasing host species diversity is unlikely to reduce tick-borne disease hazard at the local, within-forest scale, the potential effect of other \u0026ldquo;biodiversity conservation\u0026rdquo; strategies in other habitats and at other spatial scales warrants further investigation. For example, the Dutch government committed to increase nature areas by 80,000 ha by 2027. This not only includes different types of forests, but also natural grasslands, heathlands, wetlands, and open dune habitats. In some cases, forest patches within Natura 2000 areas are cleared to make way for other habitat types [70]. Such habitats are characterized by different abiotic conditions and host communities than forests, and hence have different tick densities and infection prevalence [71,72]. Whether and how increasing biodiversity at the landscape-scale (e.g. by intermingling forests with other types of habitat) can reduce tick-borne disease hazard, is an open question that warrants further investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAuthors\u003cspan dir=\"RTL\"\u003e\u0026rsquo;\u0026nbsp;\u003c/span\u003econtributions\u003c/h2\u003e\n\u003cp\u003eConceptualization: HS, HE, KT, MH;\u0026nbsp;Data curation: KT,HS;\u0026nbsp;Formal Analysis: KT,HE; Funding acquisition: HS; Investigation: HS, HE,MH; Methodology: KT, HE; Project administration: HS; Resources: HS; Software: KT, HE; Supervision: HS; Visualization: KT; Writing \u0026ndash; original draft KT, HS, HE; Writing \u0026ndash; review \u0026amp; editing, KT, HS, HE, MH\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was financially supported by the Dutch Ministry of Health, Welfare and Sport (VWS) and a grant from the European Interreg North Sea Region program, as part of the NorthTick project.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are in the supplementary datafile. More information is available from the corresponding author on reasonable request\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eAnimal experimental handling procedures were approved by the Animal Experiments Committee of Wageningen University (WUR-2013055 and WUR-2014019) and by the Dutch ministry of Economic affairs (FF/75A/2013/003).\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBogovic P, Strle F. Tick-borne encephalitis: A review of epidemiology, clinical characteristics, and management. World J Clin Cases WJCC. Baishideng Publishing Group Inc; 2015;3:430.\u003c/li\u003e\n\u003cli\u003eRizzoli A, Hauffe HC, Carpi G, Vourc\u0026rsquo;h G, Neteler M, Rosa R. 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Mary Ann Liebert, Inc., publishers; 2019;19:494\u0026ndash;505.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ixodes ricinus, nature restoration, biodiversity, Lyme borreliosis, tick-borne disease","lastPublishedDoi":"10.21203/rs.3.rs-2368686/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2368686/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMaintaining high vertebrate diversity is promoted as a potential strategy to control Lyme disease hazard via a so-called dilution effect, which occurs when increasing diversity of an ecological community reduces the transmission of a pathogen. However, empirical evidence from Europe is limited at best, while it remains unclear whether dilution effects operate for other tick-borne diseases. Here, we evaluated how often the dilution effect occurs for a wide range of tick-borne pathogens and symbionts in forest areas in the Netherlands.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData on wildlife, tick densities, and tick-borne microorganisms were collected in 19 forest sites. We calculated six different biodiversity indices based on camera trapping and live trapping data to characterize the vertebrate community of each forest site. These indices were correlated with the nymphal infection prevalence (NIP) and density of infected nymphs (DIN) of three \u003cem\u003eBorrelia burgdorferi\u003c/em\u003e sensu lato genospecies as well as seven other tick-borne pathogens and symbionts.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eVertebrate host species diversity, tick densities and infection prevalence varied widely among sites. However, neither the NIP nor the DIN of any of the ten tick-borne pathogens or symbionts was significantly correlated with any of the six indices of vertebrate species diversity or with total host availability. These results were consistent regardless of whether we used the relative abundance of vertebrate species or the proportion of larvae fed by each host species to calculate the diversity indices.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur results do not support evidence for a dilution effect in Dutch forests, suggesting that facilitating high species diversity of native wildlife is unlikely to reduce tick-borne disease hazard at the scale of local forest patches. Whether (other) nature conservation strategies in other types of habitats and at other spatial scales can reduce tick-borne disease hazard warrants further investigation.\u003c/p\u003e","manuscriptTitle":"No evidence for a dilution effect of high vertebrate diversity on tick-borne disease hazard in Dutch forests","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-14 22:39:10","doi":"10.21203/rs.3.rs-2368686/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"083e9658-e678-4656-ba39-3079fd8d04b6","owner":[],"postedDate":"December 14th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-02-21T05:14:10+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-14 22:39:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2368686","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2368686","identity":"rs-2368686","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}