Acarological Risk of Borrelia burgdorferi Sensu Lato Infections Across Space and Time in The Netherlands

Abstract

A longitudinal investigation on tick populations and their Borrelia infections in the Netherlands was undertaken between 2006 and 2011 with the aim to assess spatial and temporal patterns of the acarological risk in forested sites across the country and to assess variations in Borrelia genospecies diversity. Ticks were collected monthly in 11 sites and nymphs were examined for Borrelia infections. Tick populations expressed strong seasonal variations, with consistent and significant differences in mean tick densities between sites. Borrelia infections were present in all study sites, with a site-specific mean prevalence per month ranging from 7% to 26%. Prevalence was location-dependent and was not associated with tick densities. Mean Borrelia prevalence was lowest in January (4%), gradually increasing to reach a maximum (24%) in August. Borrelia afzelii represented 70% of all infections, with Borrelia burgdorferi sensu stricto, Borrelia garinii, and Borrelia valaisiana represented with 4%, 8%, and 10%, respectively. The density of infected nymphs and the proportional distribution of the four Borrelia genospecies, were significantly different between sites. The results show a consistent and significant spatial and temporal difference in acarological risk across the Netherlands.

Introduction

In recent decades, Lyme borreliosis has become an increasing public health concern throughout the northern hemisphere. This infectious disease, caused by spirochetes of the Borrelia burgdorferi sensu lato (s.l.) group, is vectored by ticks of the genus Ixodes. In western Europe, Ixodes ricinus is virtually the only vector transmitting this pathogen to humans. I. ricinus is found in a wide variety of habitats, characterized by shade-providing woodland, a thick and preferably moist litter layer and with an abundant wildlife population to serve as blood hosts.

Larvae predominantly feed on small rodents and birds, whereas nymphs and adult ticks feed on larger animals, including wild boar, deer, and other grazers (Gray 1998, Mannelli et al. 2012, Pacilly et al. 2014, Hofmeester et al. 2016). Presently in Europe, at least eight species of the B. burgdorferi complex have been reported, with Borrelia afzelii, Borrelia garinii, B. burgdorferi sensu stricto (s.s.), and Borrelia valaisiana the most common genospecies (Margos et al. 2011). B. afzelii and B. burgdorferi s.s. are associated with small rodents and B. garinii, B. valaisiana, and Borrelia turdi with birds (Kurtenbach et al. 2001, Mannelli et al. 2005, Heylen et al. 2013, Norte et al. 2013).

In the Netherlands, Lyme borreliosis has been reported since 1984 (Houwerzyl et al. 1984). Frequent national surveys of the incidence of Lyme borreliosis have been undertaken since 1994, and from the last published survey it became clear that there has been a steep increase in the incidence of erythema migrans, with more than 22,000 cases (134/100,000) in 2009 (Hofhuis et al. 2015). The reasons for this increase have been ascribed to successful restoration of nature reserves, loss of farmland in favor of forests, and an increase in the wildlife population, notably roe deer (Sprong et al. 2012).

The increase in Lyme borreliosis has been reported from many areas of the country, but these areas could not be used for risk assessment for lack of countrywide data on spatial and temporal distributions of I. ricinus populations and their associated Borrelia infections. Previously, we reported on tick population dynamics and associated Borrelia infections in 24 sites across the Netherlands during an 18-month period (Gassner et al. 2011). The current study is a continuation of that study, but designed to study spatial and temporal variations in tick population dynamics over 5 successive years. The distribution and intensity of B. burgdorferi s.l. infections in tick vectors in relation to habitat and season was studied with emphasis on acarological risk for Borrelia infection, as a proxy for Lyme disease risk (Mannelli et al. 2003, Eisen et al. 2010).

Materials and Methods

Study sites

Eleven sites, distributed across the Netherlands, were selected for tick collections (Fig. 1). All sites were classified as nature areas, with forests and/or forest/shrubland as the dominant habitat description. Criteria for selection were (1) likelihood of presence of I. ricinus ticks and (2) cooperation of land owners and/or foresters and (3) the availability of a team of volunteers to make monthly collections (Gassner et al. 2011). Detailed description of the sites, including soil type and vegetation have been provided by Gassner et al. (2011). With exception of site no. 8 (Kwade Hoek), which was dominated by coastal shrub, all sites were forested with a mixture of deciduous and nondeciduous trees, and a rich ground cover of grass, in some sites interspaced with Vaccinium spp. Within each location, two marked transects of 100 m² were monitored consistently. Geographic coordinates of each transect were established with Global Positioning System (GPS), and a description of the habitat was made and classified according to Braun-Blanquet (1964) and Weeda et al. (2005).

FIG. 1.

Map of the Netherlands and the location of the study sites. 1, Schiermonnikoog; 2, Gieten; 3, Montferland; 4, Ede; 5, Hoog Baarlo; 6, Bilthoven; 7, Twiske; 8, Kwade Hoek; 9, Wassenaar; 10, Veldhoven; 11, Eijsden.

Tick sampling

Ticks were collected monthly from July 2006 to June 2011, each time within the first 7 days of the month. Teams of volunteers, recruited through the Association for Environmental Education (IVN), were given a training session by the researchers. At each of the 11 sites (Fig. 1), tick collections were made by dragging a white cotton cloth (1 m²) over the two marked transects (Daniels et al. 2000). The cloth was inspected for the presence of ticks at intervals of 25 m (Tälleklint and Jaenson 1996). Larvae, nymphs, and adult ticks were collected using forceps, placed in 70% ethanol and dispatched by mail to the research team in Wageningen, where all ticks were recounted and identified to species and life stage.

Borrelia identifications

Nymphal ticks were examined for the presence of Borrelia infections. If on any sampling day and site fewer than 30 nymphs were collected, all ticks were examined; if more than 30 nymphs were found, a random sample of 30 specimens collected at that site was selected for Borrelia analysis. For all samples, DNA extraction was performed as described by Schouls et al. (1999) and 60 μL of each extract was stored at −80°C until further analyses. Until January 2009, identification of Borrelia species was done using PCR followed by the reverse line blot (RLB) procedure modified after Schouls et al. (1999). From January 2009 until June 2010, Borrelia infections were established using quantitative PCR (Q-PCR) followed by RLB of only those ticks that were found positive for B. burgdorferi s.l. with Q-PCR (van Overbeek et al. 2008). As of June 2010, Borrelia identifications in nymphal ticks were done with another Q-PCR (Heylen et al. 2013). From all Q-PCR-positive tick lysates, B. burgdorferi sensu lato genospecies was determined by PCR amplification and sequencing of the variable 5S–23S (rrfA–rrlB) intergenic spacer region (Heylen et al. 2013). The different PCR methods were compared. Negligible differences in the infection rate between the methods were found (not shown).

Statistical analysis

A generalized linear model (GLM) assuming a negative binomial distribution with log link function and dispersion estimated was used to investigate the effect of location and month on the number of nymphs collected. In another GLM, assuming a binomial distribution with logit link function and dispersion estimated, the effect of location, month, and number of nymphs collected on the percentage of Borrelia-infected nymphs was investigated. The percentage Borrelia infection was expressed as the number of Borrelia-infected nymphs per month per location divided by the total number of nymphs analyzed from the same location and in the same month (Jouda et al. 2004, Ferdin et al. 2010). A third GLM, assuming a gamma distribution with log link function and dispersion estimated was used to investigate the effect of location and month on the acarological risk, expressed by the density of infected nymphs +0.1 (DIN).

The effects of location, month, and number of nymphs collected and their interaction were fitted in the models and the nonsignificants were dropped. Year was not included in the model because in 2006 and 2011 not all months were monitored. Models were compared by the corrected Akaike's information criterion (AICC). Differences between locations were tested by pairwise comparisons with Bonferroni correction. Effects were considered significant at p < 0.05. All analyses were performed using IBM SPSS statistical software, version 22.

Results

Population dynamics of I. ricinus

I. ricinus was found in all study sites (Fig. 1), with a strong seasonal dynamics (Fig. 2). The peak of (active) larval abundance was in July–August, and nymphs and adults reached maximum population densities in May–June (Table 1). With the exception of the winter of 2006/2007, when a low tick activity was recorded, in all other years not one or only few ticks were found between December and March (Supplementary Table S1; Supplementary Data are available online at www.liebertpub.com/vbz). Mean annual tick numbers collected per site, estimated from July to June, ranged from 0.4 to 1573 (larvae), 57–397 (nymphs), and 12–119 (adults). Between study sites, large variations in tick abundance were found (Supplementary Table S1), but differences in temporal variation were less apparent. The lowest abundance of ticks was observed in De Kwade Hoek (site no. 8; mean annual density of larvae/nymphs/adults [l/n/a] 0.4/66/31), while Gieten (site no. 2) had the highest abundance (mean annual density of l/n/a 1577/276/25). In De Kwade Hoek, larvae were found on only one occasion (from 59 sampling dates).

FIG. 2.

Mean larval, nymph, and adult collections (log) per month per site from July 2006 to July 2011. Error bars indicate standard errors of the mean (±1 SE).

Table 1.

Summary of Data Collection (56 Months) per Location

	Mean density				Borrelia genospecies distribution (100%)					Acarological risk
Location	Larvae	Nymphs	Adults	Prevalence nymphs (%)	afzelii	sensu stricto	garinii	valaisiana	Other	Months	DIN ± SEM
Bilthoven	2.5	4.8	1.8	14	54.2	4.2	8.3	20.8	12.5	33	1.36 ± 0.28^b
Ede	132.0	35.3	4.4	14	74.2	2.3	8.3	9.1	6.1	38	7.56 ± 1.30^c
Eijsden	0.2	23.6	0.9	11	69.7	6.1	6.1	15.2	3	37	3.82 ± 0.68^c
Gieten	140.4	24.6	2.2	12	44	9.3	21.3	8	17.3	37	4.81 ± 0.88^c
Hoog Baarlo	106.2	20.0	1.1	7	45	7.5	15	22.5	10	38	2.11 ± 0.50^b
Kwade Hoek	0.0	5.9	2.8	26	82.1	1.8	1.8	12.5	1.8	25	3.83 ± 2.04^b
Montferland	32.4	24.8	1.6	14	66.3	4.5	10.1	4.5	14.6	37	5.48 ± 0.99^c
Schiermonnikoog	18.1	4.3	2.0	8	50	16.7	16.7	16.7	0	29	0.60 ± 0.19^a
Twiske	35.9	23.8	1.4	15	80.3	6.6	1.6	4.9	6.6	34	5.32 ± 0.87^c
Veldhoven	21.9	31.8	11.3	14	81.2	2.9	5.8	5.8	4.3	36	7.49 ± 2.01^c
Wassenaar	8.0	18.5	1.4	16	84.6	0	3.1	6.2	6.2	39	6.29 ± 1.23^c

Mean density of questing ticks collected per month (tick stage per 200 m²). Borrelia burgdorferi s.l. prevalence in nymphs (per site%). Distribution of Borrelia genospecies. “Other” includes minor B. burgdorferi s.l. genospecies, such as Borrelia bavariensis, and untypable genospecies. The acarological risk was expressed as the DIN ± SEM per 200 m². Different superscript letters indicate significant differences between locations (p < 0.05, GLM, Acarological risk = x ₁ + location × x ₂ + month × x ₃ + (location:month) × x ₄). “Month” is the number of monthly collections included for the calculation of DIN. Detailed data on tick collection, prevalences, and B. burgdorferi s.l. genospecies distribution are found in Supplementary Tables S1–S3.

DIN, density of infected nymphs; GLM, generalized linear model; SEM, standard error of the mean.

Both location and collection month had a significant effect on the number of nymphs collected (GLM, p < 0.001, no. of nymphs = x ₁ + location × x ₂ + month × x ₃). Pairwise comparisons between locations showed that the monthly mean number of nymphs collected on Schiermonnikoog (site no. 1), Bilthoven (site no. 6), and De Kwade Hoek (range 4–6 nymphs/200 m²) was significantly lower than at all other locations (range 19–33 nymphs/200 m²). Ede (site no. 4) and Veldhoven (site no. 10) had the highest collections of nymphs, which were significantly higher than the locations Schiermonnikoog, Bilthoven, De Kwade hoek, and Hoog Baarlo (Fig. 3, p < 0.05). All mobile life stages of I. ricinus were found throughout the year, but with very low densities in December, January, and February, when mean tick densities ranged from 0 to 5.09 per 200 m² (Supplementary Table S1).

FIG. 3.

Mean number of nymphs collected at each location. Error bars represent standard errors of the mean (±1 SEM). Different letters indicate significant differences between locations (p < 0.05, GLM). GLM, generalized linear model.

Borrelia prevalence

Of the 12,005 nymphal ticks collected, 6987 were analyzed for Borrelia infections. The mean Borrelia prevalence of nymphs per month over all sites during the 5-year study period was 13.7% (Supplementary Tables S2 and S3), with little variation between the years. The percentage of infected nymphs, however, differed significantly between months (GLM, p < 0.001, %bor = x ₁ + location × x ₂ + month × x ₃). The mean percentage of infected nymphs was 4.2% in January and gradually increased to reach a value of 24.4% in August (GLM, estimated mean, Fig. 4). No correlation was found between the number of nymphs collected and the percentage of nymphs infected (GLM, p = 0.082, Supplementary Fig. S2).

FIG. 4.

Estimated mean (GLM) Borrelia prevalence of nymphs collected each month (GLM, %bor = x ₁ + location × x ₂ + month × x ₃). Error bars represent standard errors of the mean.

Borrelia-infected ticks were present at all study sites, but with significant differences in estimated monthly mean prevalence between sites, varying from 7% ± 2.8% in Hoog Baarlo to 26% ± 4.3% in De Kwade Hoek (GLM, estimated mean p < 0.001, Fig. 5, Supplementary Tables S2 and S3). The percentage of Borrelia infection in Hoog Baarlo, Schiermonnikoog, Eijsden (site no. 11), and Gieten (site no. 2) was significantly lower than in De Kwade Hoek (GLM, p < 0.05, Fig. 5). In Hoog Baarlo (site no. 5), with a mean Borrelia prevalence of 7% and mean (active) nymphal abundance of 224 per 200 m² per year, no Borrelia infections were found between December 2006 and June 2008 (Supplementary Table S2). In contrast, in De Kwade Hoek the mean Borrelia prevalence was 26% with a mean (active) nymphal abundance of only 66.6 per 200 m² per year.

FIG. 5.

Estimated mean (GLM) Borrelia prevalence of nymphs collected per location. Error bars indicate standard errors of the mean. Different letters indicate significant differences between locations (p < 0.05, GLM, %bor = x ₁ + location × x ₂ + month × x ₃).

Of the 54 sampling months over which ticks were examined for Borrelia infections (Supplementary Table S2), only four moments occurred in which all study sites contained Borrelia-positive ticks.

Borrelia species composition

Genotype identification showed that 69.7% of the infected nymphs were infected with B. afzelii, 9.5% with B. valaisiana, 8.3% B. garinii, 8.1% Borrelia s.l., and 4.4% with B. burgdorferi s.s. (Fig. 6 and Supplementary Table S3).

FIG. 6.

Borrelia genospecies distribution in infected nymphs (n = 683). Borrelia burgdorferi stands for B. burgdorferi sensu stricto, and Borrelia s.l. for other, minor and untypable genospecies of B. burgdorferi sensu lato. Distribution per location in Supplementary Tables S1 and S2.

Geographic variation in Borrelia genospecies prevalence

Four Borrelia species were identified, but with distinctly variable species compositions between sites. In all sites in all months B. afzelii was the dominant species, with infections varying from 44.0% to 84.6%. Those of B. burgdorferi s.s. varied from 0% to 16.7%, of B. garinii from 1.6% to 21.3%, and of B. valaisiana from 4.5% to 22.5%. Four sites had >80% B. afzelii and two sites <50% of this Borrelia species. B. afzelii was not found in Wassenaar. One site (Schiermonnikoog) had relatively high proportions of B. burgdorferi s.s. (16.7%), B. garinii (16.7%), and B. valaisiana (16.7%). Because of the many sampling events with only few ticks, we could not run a statistical analysis of Borrelia diversity between sites. The variation in Borrelia infection prevalence between the five study years was negligible (Supplementary Fig. S1).

Acarological risk

Significant differences in acarological risk, expressed as the density of infected nymphs (DIN), were found between sites (GLM, p < 0.001, Table 1) and time of year (months). The DIN was significantly lower at Schiermonnikoog (0.60) than at all other sites and more than 12 times lower than Ede, which had the highest risk value (7.56, GLM, p < 0.05, Table 1). The DIN at Bilthoven, Hoog Baarlo and De Kwade Hoek was significantly higher than at Schiermonnikoog and significantly lower than at all other sites (GLM, p < 0.05, Supplementary Table S1). Month also had a significant effect on the acarological risk and was highest in the summer months (GLM, p < 0.001, Fig. 7).

FIG. 7.

Mean acarological risk values ± SEM between March and November of the 5-year study period. Acarological risk is expressed as Borrelia prevalence × density of infected nymphs.

Discussion

I. ricinus was present in all sites studied, and B. burgdorferi infections were continuously found in all sites. This finding implies that the common hosts of I. ricinus larvae in the Netherlands are relevant reservoir hosts of B. burgdorferi s.l. B. afzelii was the dominant Borrelia species, with B. garinii, B. burgdorferi s.s., and B. valaisiana widely distributed but in much lower percentages. Strong site-specific differences in Borrelia prevalence of nymphal populations were found, which remained stable over the 5-year study period. Borrelia infections in I. ricinus were lower in the spring than in the summer and fall. Estimates of acarological risks for Borrelia infection across the Netherlands suggests large, location- and time-dependent, differences.

I. ricinus populations exhibit a well-known seasonal variation (Gray 1991, Tälleklint and Jaenson 1996, Mannelli et al. 2012, Pangracova et al. 2013, Schulz et al. 2014), which is driven by temperature, and occasionally, saturation deficit. Our study shows that, with the exception of the months of December to February, when low temperatures precluded their activity, adult ticks were abundant in all seasons, but with a clear peak in late May. This peak is assumedly the result of newly emerging adults being added to the population at that time. Peak populations of nymphs coincided with those of adult ticks, suggesting a strong cyclic rhythm per developmental stage that is temperature driven (Randolph et al. 2002, Hancock et al. 2011).

The peak activity of larvae, by contrast, was more widespread and occurred later in the season, and was followed by a sharp decline in the fall. Larvae of I. ricinus are strongly affected by saturation deficit, succumbing to drought when they do not get an opportunity to rehydrate in the litter layer (Randolph et al. 2002). In the Netherlands, abiotic conditions that are harmful to ticks occur frequently in late July/August, when short periods of drought and high temperatures lead to high saturation deficits. It appears therefore that larvae emerging earlier in the summer have a higher chance of survival than those appearing later in the year. This may explain the steep seasonal decline in larval populations observed in this study.

The seasonal fluctuation in population densities of larvae, nymphs, and adults of I .ricinus as observed in the Netherlands is not much different from that observed in other western European countries, although region-specific differences may occur, for example, in areas where winters are more severe than in the maritime climate of coastal Europe such as the Netherlands. The relatively high activity of nymphs and adults in mild winters, as seen in 2006/2007 (Table 1), can be explained by the fact that I. ricinus expresses questing activity at T > 5°C, which occurred regularly that winter (Daniel et al. 2015). Such effects would occur less often in continental Europe including Germany, Poland, and the Czech Republic, for example, which are not exposed to the influence of the Atlantic Ocean.

The differences in tick abundance between sites we observed appeared significant and constant over time. Schiermonnikoog, Bilthoven, and De Kwade Hoek had a similar, but low, tick density compared to the other areas. In the first two locations this can be due to unfavorable microclimate, leading to high saturation deficits in the summer. This is unlikely to be the case, however, in Kwade Hoek where annual flooding with sea water of parts of the site during winter storms may affect tick survival. As will be discussed below, the overall differences in tick population density between sites was not associated with Borrelia prevalence and agrees with our previous study over many more sites than the current study (Gassner et al. 2011).

The mean Borrelia prevalence of nymphs over the 5 study years demonstrates a relatively high level of Borrelia infections in natural populations throughout the country. This is in agreement with our previous findings in which 24 sites were investigated during 18 months (Gassner et al. 2011). Unlike the previous study, however, which lasted only 1.5 years and was too short to observe longitudinal trends, in the present study we find large and temporal consistent differences in prevalence of I. ricinus between sites: Schiermonnikoog (7.8%) and Hoog Baarlo (7.9%) are sites with low prevalence, and De Kwade Hoek (32.3%) represents a site with high Borrelia prevalence. These variations cannot be ascribed to differences in climate: Schiermonnikoog and De Kwade Hoek both are coastal habitats directly exposed to typical marine environmental conditions from the North Sea. While Schiermonnikoog has a forest vegetation consisting of pine and birch trees with a grassy undercover, De Kwade Hoek is characterized by coastal shrub (mainly Hippophae rhamnoides) interspaced with grassy glades, and these differences in vegetation structure may perhaps explain some, but not all, of the differences found.

The high Borrelia prevalence in Kwade Hoek might be explained by a near-total dependence on rodents as blood source for larvae. In Schiermonnikoog there is higher diversity of (small) wildlife, possibly causing the much lower prevalence. Hoog Baarlo, however, is situated far inland and not exposed to maritime influences. The site is dominated by Quercus petraea and Pinus sylvestris, with an understorey of Vaccinium myrtillus, a habitat described as optimal for I. ricinus (Wielinga et al. 2006, Tack et al. 2012). This raises the question of the differences in Borrelia prevalence could be explained by the composition of blood host species? On Schiermonnikoog, deer are absent and most blood meals must therefore be provided by rabbits, hares, and rodents, and possibly sheep (S. van Wieren, pers. comm.). Jaenson et al. (2009) describe a site in Sweden where, in the absence of large grazers, I. ricinus is mainly dependent on hares. A similar limited host range may explain the presence of I. ricinus on Schiermonnikoog, where roe deer are absent but lagomorphs are abundant. In De Kwade Hoek, there is a large population of roe deer, and small rodents, but not much other fauna (Gassner et al. 2013). A high Borrelia prevalence as found in De Kwade Hoek requires a combination of tick reproductive hosts (roe deer) and an abundant rodent population to feed the juvenile ticks. Roe deer are incompetent hosts for B. burgdorferi, but rodents are highly suitable hosts and responsible for most of the B. burgdorferi populations (Hofmeester et al. 2016). When these conditions are met, Borrelia prevalence can be high, as was apparently the case in De Kwade Hoek.

In Hoog Baarlo rodents are abundant, as well as numerous other fauna including Borrelia-incompetent red deer and wild boar (Pacilly et al. 2014); the relatively low Borrelia prevalence in this site may possibly be explained by the large number of ticks feeding on a more diverse fauna leading to a dilution effect (Schmidt and Ostfeld 2001). Although forest birds are not very abundant inland, large congregations of migratory birds are found in the coastal zones, possibly providing food for resident ticks. The latter hypothesis, however, is not very likely as B. afzelii was the most dominant Borrelia genospecies in all areas, representing 82.5% of all Borrelia infections found in De Kwade Hoek. B. afzelii is associated with small rodents and not with birds (Kurtenbach et al. 1995, Hanincova et al. 2003). We suggest that the consistent differences in Borrelia infection prevalences between the sites are most likely caused by the composition of the resident blood hosts of I. ricinus, with unknown factors leading to low, or high, Borrelia prevalence.

Apart from the role of small rodents like Apodemus and Myodes species. and Erinaceus europaeus, which are reservoir hosts of various B. burgdorferi s.l. genospecies, and that of roe deer and red deer, which are poor hosts of B. burgdorferi s.l., the role of other mammal species in transmission of B. burgdorferi s.l. in the Netherlands is poorly studied, and no more can currently be said that might explain the differences in infection prevalence of ticks between the sites. As comparative studies of the Borrelia prevalence of ticks between sites are rare, and therefore our hypothesis is untested, a field of research lies waiting to be initiated.

The Borrelia diversity among sites was variable, and somewhat surprising. The three coastal sites, Schiermonnikoog, Wassenaar, and De Kwade Hoek, are characterized by relatively large numbers of migratory birds. Given the strong association of B. garinii and B. valaisiana with birds (Kurtenbach et al. 2002, Heylen et al. 2014), these Borrelia species would have been expected to be dominant in these coastal sites. As migratory birds, however, are only present there in early spring and in the autumn, when larval ticks are not yet abundant, the presence of migratory birds is out of tune with that of the peak larval season, July–August, which may explain why the bird-associated Borrelia genospecies were not dominant in the coastal areas. B. garinii and B. valaisiana were, however, more often found in inland sites: Gieten, Hoog Baarlo, and Bilthoven. In the closed-canopy forests that are typical for the latter three locations, birds are not as abundant as small rodents and other hosts associated with B. afzelii, and these results are therefore unexpected. The Borrelia genospecies identified as B. burgdorferi s.l. may have consisted of Borrelia bavariensis, for which we did not have a good diagnostic tool at the time of study. B. bavariensis is, however, present in the Netherlands (Coipan et al. 2013a, Tijsse-Klasen et al. 2013) and is likely to have been present among the B. burgdorferi samples collected in this study.

Unlike most locations, where Borrelia-infected ticks were found throughout the study period, the study site in Hoog Baarlo did not provide any Borrelia-infected tick from December 2006 until June 2008, although before and after this period infected ticks were found regularly (Supplementary Fig. S1 and Supplementary Table S2). This is the only site where red deer were present, and given that this deer species is an incompetent host for B. burgdorferi s.l. (Ragagli et al. 2011, Mannelli et al. 2012) while it is an excellent host for I. ricinus (Pacilly et al. 2014), it appears that during this period of our study few Borrelia parasites were circulating in the area, leading to a temporary extinction of Borrelia infections from the rodent population, which serves as the main host of larval I. ricinus in the Netherlands (Wielinga et al. 2006, Gassner et al. 2013). Because we did not observe a temporary decline in I. ricinus populations in Hoog Baarlo compared to the other study years (Supplementary Fig. S1), such temporary clearances of a parasite population are of great interest when considering Lyme borreliosis preventive measures.

The longitudinal nature of our study, with monthly collections conducted during 5 years, allows for a rare comparison of the periodicity of Borrelia infections in the questing tick population in multiple sites. Not any or few questing ticks were found during the winter months, and it is assumed that larvae feeding on Borrelia-infectious hosts in any of the study years may develop into infected nymphs in the spring of the following year. In the present study a mean prevalence of 12% of nymphs collected in the spring (March–May) was found, indicating that a considerable part of the larvae in the previous year had ingested an infectious blood meal. Yet, Borrelia prevalence values in summer and fall were significantly higher than those of the spring population.

As the intra-stadial development period of I. ricinus in western Europe is about 12 months (Milne 1945, Cerny et al. 1974, Randolph et al. 2002), we speculate that the spring population had acquired its Borrelia infections in the spring of the previous year, when small rodent populations are still low (Wilson et al. 1993). The summer/autumn populations of ticks that we assayed for Borrelia infections may have derived from larvae that emerged and blood fed in the summer/autumn of the previous year, having acquired Borrelia infections from infected hosts that were more abundant at that time compared to the spring. This may explain the variation in prevalence between spring and summer/autumn.

In this study, and our previous study (Gassner et al. 2011), there was no relationship between the apparent density of questing ticks and their corresponding Borrelia prevalence. This result suggests a density-independent feeding behavior of larvae, where each larva has the same chance of becoming infected while feeding. This finding was also reported by Jaenson et al. (2009), from a longitudinal study across 13 different sites in Sweden. Unlike in our study, Jaenson et al. reported the mammalian fauna composition, which highly varied with small rodents, lagomorphs, and (roe)deer. Given the high abundance of rodents, and the close association between larvae and rodents, the proportion of larvae feeding on roe deer, even should these be present in higher numbers, is almost negligible compared to the proportion feeding on rodents (Humair et al. 1999, Hanincova et al. 2003, Sinski et al. 2006, Gassner et al. 2008, 2013). As the larvae acquire their Borrelia infections (mostly B. afzelii and B. burgdorferi sensu stricto) from rodents, and hence the nymphs derived from these larvae, (Hofmeester et al. submitted), we conclude that Borrelia transmission is dependent mostly on the prevalence of Borrelia parasites in the rodent ear(s), that is, the proportion of infected rodents, and not on the abundance of feeding larvae (Duijvendijk et al. 2015). In other words, the prevalence in nymphs should be a function of infected mouse abundance and larva abundance on mice.

Although observed previously, one striking result is the relatively low variation in Borrelia prevalences, but higher variations in DIN over the years at each site (Coipan et al. 2013b). Apparently, the resident reservoir populations for B. burgdorferi s.l. (e.g., rodents) are relatively stable over time, in spite of the occasional mast years, which favors small rodents and should, in theory, also favor larval feeding success. The latter might increase the DIN, but not necessarily the infection rate of ticks. As the roe deer population is the driving factor for tick abundance in the Netherlands, and this was not likely to vary between the years, the tick population could therefore remain fairly stable, which is also shown from our population density data.

The results from this study imply that forested sites in the Netherlands constitute a spatially and temporally highly varied acarological risk to visitors, which may explain the high prevalence of Lyme disease recorded (Hofhuis et al. 2015). These findings are in agreement with recent work from other European countries (Halos et al. 2010, Estrada-Pena et al. 2011, Perez et al. 2012, Schwarz et al. 2012, Medlock et al. 2013, Richter et al. 2013, Tveten 2013, Mysterud et al. 2016), which show that I. ricinus populations and associated B. burgdorferi populations, the two main drivers of the DIN, are common and widely distributed. The site-specific acarological risk as found in this study is an interesting observation, and can be exploited when considering preventive measures. Adequate knowledge on the ecology of Borrelia transmission in woodland areas, therefore, will assist in the development of such preventive tools and therefore, disease prevention.

Footnotes

Acknowledgments

The authors are highly appreciative of the monthly tick collections done by the group of volunteers. We thank the managers of several nature reserves for permission to collect ticks in often closed areas, which has greatly facilitated the work presented. Manoj Fonville and Christa Drenth are thanked for excellent technical and analytical assistance with (Q)PCR, reverse line blotting, and DNA sequencing. We thank Tobias Homan for his assistance in the production of .

Author Disclosure Statement

No competing financial interests exist.

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