Linking demography and host dispersal to Trichuris arvicolae distribution in a cyclic vole species

International Journal for Parasitology 37 (2007) 813–824 www.elsevier.com/locate/ijpara

Linking demography and host dispersal to Trichuris arvicolae distribution in a cyclic vole species Julie Deter a,*, Yannick Chaval a, Maxime Galan a, Karine Berthier a, Alexis Ribas Salvador b, Juan Carlos Casanova Garcia b, Serge Morand a, Jean-Franc¸ois Cosson a, Nathalie Charbonnel a a

Centre de Biologie et de Gestion des Populations (CBGP), Campus International de Baillarguet, CS 30016, 34988 Montferrier sur Lez, France b Laboratori de Parasitologia, Departament de Microbiologia i Parasitologia Sanita`ries, Facultat de Farma`cia, Universitat de Barcelona, Av. Joan XXIII s/n, E-08028 Barcelona, Spain Received 12 October 2006; received in revised form 21 December 2006; accepted 2 January 2007

Abstract Spatial structure in the distribution of pathogen infection can influence both epidemiology and host–parasite coevolutionary processes. It may result from the spatial heterogeneity of intrinsic and extrinsic factors, or from the local population dynamics of hosts and parasites. In this study, we investigated the effects of landscape, host dispersal and demography (population abundance and phase of the fluctuation) on the distribution of a gastro-intestinal nematode Trichuris arvicolae in the fossorial water vole Arvicola terrestris sherman. This rodent exhibits outbreaks occurring regularly in Franche-Comte´ (France). Thirteen out-of-phase populations were studied in autumn 2003. They exhibited highly different T. arvicolae prevalences. The heterogeneity in prevalences was not explained by population structure, landscape or vole abundance, but by the phase of the vole population fluctuations. Populations at the end of the high density phase showed null prevalence whereas populations in increase or outbreak phases exhibited higher prevalences. Population genetic analyses based on microsatellites revealed significant differentiation between vole populations, and higher dispersal rates of young voles compared with old ones. These younger individuals were also infected more frequently than older voles. This suggested a role of host dispersal in the distribution of T. arvicolae. However, there was a strong discrepancy between the spatial patterns of prevalence and of host genetics or demographic phase. Genetic differentiation and differences in demographic phase exhibited significant spatial autocorrelations whereas prevalence did not. We concluded that the distribution of T. arvicolae is influenced by vole dispersal, although this effect might be overwhelmed by local adaptation processes or environmental conditions.  2007 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved. Keywords: Arvicola terrestris; Trichuris arvicolae; Syphacia nigeriana; Genetic; Local adaptation; Nematode; Parasite; Spatial autocorrelation

1. Introduction Many wildlife hosts live in spatially variable, often fragmented, habitats. Spatial heterogeneity in abiotic and biotic factors, or metapopulation structure, is now recognised to influence ecological and evolutionary dynamics of host– parasite interactions (Thompson, 1997; Wilson et al., 2002). *

Corresponding author. Tel.: +33 (0) 4 99 62 33 46; fax: +33 (0) 4 99 62 33 45. E-mail address: [email protected] (J. Deter).

It may determine important demographic characteristics of host populations such as their survival or reproduction and consequently have epidemiological consequences. Alternatively, the potential for parasites to disperse is strongly linked to the dispersal ability of their hosts, especially in monoxenous parasites (Criscione et al., 2005). The landscape structure may thus be a key determinant of the spread of parasites. The effect of habitat heterogeneity and fragmentation on parasite distribution has already been described for intestinal helminths of mountain hares (Hulbert and Boag, 2001), the Lyme disease pathogen

0020-7519/$30.00  2007 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijpara.2007.01.012

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Borrelia burgdorferi (Van Buskirk and Ostfeld, 1998) and gregarine parasites of damselflies (Taylor and Merriam, 1996). Variations in infection patterns have mostly been considered at the local scale of isolated populations for coevolutionary studies. Taking into account broader geographic scales and groups of connected populations has revealed the strong role of spatial structuring in the evolutionary potential of hosts and parasites (Thompson, 1994; Lively, 1999), and in the patterns of local adaptation (Gandon et al., 1996). In turn, the resulting spatial distribution of parasites within and among host populations might lead to the regulation or destabilisation of host population dynamics, depending on the level of parasite aggregation (Bolker and Grenfell, 1995; Holt and Boulinier, 2005). The study reported here examines the spatial heterogeneities of infection with the obligate parasitic nematode, Trichuris arvicolae, within and among populations of the fossorial water vole Arvicola terrestris sherman. In various regions of France and Switzerland, populations of the fossorial water vole fluctuate widely (Saucy, F. 1988. Dynamique de population, dispersion et organisation sociale de la forme fouisseuse du campagnol terrestre, (Arvicola terrestris scherman (Shaw)). University of Neuchaˆtel, Neuchaˆtel; Giraudoux et al., 1997) and were recognised to be cyclic (Saucy, 1994; Turchin and Hanski, 2001). Outbreaks occur every 5–8 years and cause severe damage to grasslands (Delattre et al., 1998). These outbreaks spread spatially as a ‘travelling wave’ from epicentres to over 2,500 km2 (Giraudoux et al., 1997; Berthier et al., 2005). Recently, Berthier et al., have suggested the importance of effective dispersal for the travelling wave spreading (Berthier (2005); Berthier et al., 2005). Population genetic analyses have revealed high dispersal rates in A. terrestris (Berthier et al., 2006) with distances up to a few kilometres (for the subaquatic form see Telfer et al., 2003). The potential role of parasitic helminths in these vole demographic fluctuations and/or in the travelling wave has rarely been examined for water voles. The only study concerned the larval platyhelminth Taenia taeniaeformis and revealed that this cestode was not involved in the demographic cycle of the water vole populations (Deter et al., 2006). Among the parasites infecting water voles, the nematode T. arvicolae could affect vole population abundance. Heavy burdens of this monoxenous parasite may be responsible for disease (Koski and Scott, 2001; Hayes et al., 2004) and even reduce individual growth on a low protein diet (Michael and Bundy, 1992). A clear negative impact on host fitness has been demonstrated recently: laboratory mice, Mus musculus, infected with the related parasite, Trichuris muris, produced a reduced number of litters, fewer pups per litter, and had young with lower weights at weaning when compared with controls (Porcherie, 2005. Susceptibilite´ aux parasites des hybrides entre Mus musculus musculus et Mus musculus domesticus: origine du phe´nome`ne et roˆle dans la contrese´lection des hybrides. University of Montpellier II, Montpellier). This impact on reproduction is particularly

interesting because theoretical studies have shown that pathogen impact on host fecundity instead of on survival may be more important for inducing host cycles (May and Anderson, 1978) as observed in fossorial water vole populations. The A. terrestris–T. arvicolae system is thus a relevant model to investigate the role of this helminth species in the demographic cycle of its host populations. An important prerequisite is to study the parasite distribution and investigate whether it is determined by heterogeneities in the host population or in extrinsic factors, or whether it is generated by host dispersal. In this paper, we assessed the distribution of T. arvicolae within and among 13 populations of A. terrestris. Samples were collected in autumn 2003 as higher prevalences are often observed at the end of the summer for parasitic helminths of mammals (Haukisalmi et al., 1988; Umur and Yukari, 2005). The environmental parameters, including landscape and host demographic attributes of these populations, were characterised. Individual vole hosts were characterised according to age, sex, body condition and infection with the nematode Syphacia nigeriana. We examined whether these factors influenced the probability of infection with T. arvicolae and we compared the spatial structure of these factors with the spatial distribution of the infection. Positive spatial autocorrelation in infection at short distance classes can be observed if the environmental factors that drive diversity are also spatially autocorrelated, or if individual host movements are important at this geographical scale. Such methods have previously been used to investigate the spatial distribution of diseases (Real and McElhany, 1996), tick infection (McCoy et al., 1999) and spatial genetic structure (Epperson and Li, 1996). To disentangle these effects, we also investigated the spatial structure of A. terrestris dispersal. The comparison with the spatial distribution of infection provided evidence of the potential importance of host dispersal for the outcome of T. arvicolae infection. 2. Materials and methods 2.1. The host–parasite system: A. terrestris and T. arvicolae The fossorial water vole (A. terrestris scherman Shaw) is an arvicoline rodent, which typically inhabits grasslands. It is distributed from Central Europe to Central Romania and in the mountains of North Spain, United Kingdom and Fennoscandia to Russia (Wilson and Reeder, 2005). The fossorial water vole’s breeding season usually ranges from early spring (April to March) to early autumn (September to October). Reproduction may be observed during the winter period in demographically increasing populations (Saucy, F. 1988. Dynamique de population, dispersion et organisation sociale de la forme fouisseuse du campagnol terrestre, (Arvicola terrestris scherman (Shaw)). University of Neuchaˆtel, Neuchaˆtel; Le Louarn, H.and Que´re´, J.-P. 2003. Les rongeurs de France, faunistique et biologie. Paris).

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Trichuris arvicolae has long been confounded with T. muris, a cosmopolitan gastro-intestinal worm parasitizing various species of rodents. Recently, Feliu et al. (2000), using molecular markers, distinguished these two species. They showed that T. arvicolae is specific to Arvicolinae and T. muris to Murinae. Several arvicoline rodents (e.g. the common vole, Microtus arvalis and the bank vole, Clethrionomys glareolus) can potentially be infected with T. arvicolae (Feliu et al., 2000). This worm is a monoxenous parasite of caeca (Feliu et al., 2000). Unembryonated eggs are passed with the faeces of infected rodents and become infective in the soil. After ingestion by soil-contaminated food or grooming, eggs hatch in the small intestine and release larvae that mature and establish themselves as adults in the caecum. Heavy infection can induce an immune-mediated worm expulsion (dependent on a Th2 type response) followed by a temporary protective immunity (Hayes et al., 2004; Helmby and Grencis, 2004; Artis, 2006). The prevalence and abundance of helminths may be influenced by synergistic or antagonistic interactions with other parasite species (see for a review Behnke et al., 2001). Such interactions have been clearly demonstrated with intestinal helminths in wild rabbits (Lello et al., 2004) and mice (Behnke et al., 2005). Recent laboratorybased experiments revealed a negative influence due to the presence of the monoxenous nematode S. nigeriana (relatively non pathogenic) on the success of infection by T. muris (Porcherie, 2005. Susceptibilite´ aux parasites des hybrides entre Mus musculus musculus et Mus musculus domesticus: origine du phe´nome`ne et roˆle dans la contrese´lection des hybrides. University of Montpellier II, Montpellier). To date, no data confirming the existence of such competitive interactions have been described in natura for these parasite species. 2.2. Study area, landscape, abundance and demographic phase at sampling localities The study area was the canton of Nozeroy. It covers about 250 km2 in the mid-east of France (Franche-Comte´, Jura, Nozeroy: 4647 0 N, 603 0 E). This is an area of low lying mountains ranging in altitude from 370 to 970 m. The landscape is essentially constituted of large areas of open grasslands interrupted with forests and mixed habitat (i.e. countryside with many hedges, trees and small fields). Thirteen sites corresponding to 1–2 ha grasslands, separated by 2–15 km, were sampled (Fig. 1). As the fossorial water vole lives exclusively in grassland and meadows (Le Louarn, H.and Que´re´, J.-P. 2003. Les rongeurs de France, faunistique et biologie. Paris), landscape may influence individual movements and thus gene flow. Indeed, recent population genetic studies conducted in the same area revealed that effective dispersal was restricted in space and influenced by sharp relief (Berthier et al., 2005, 2006). In order to characterise the variability of the landscape surrounding the

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Fig. 1. Sites of sampling for genetic and parasitological analyses in the canton of Nozeroy (Franche-Comte´, France). The map is a binary image representing suitable habitat for vole (grassland) in white and all other kinds of habitat in grey (i.e. forest, town, etc.). Numbers represent the site codes, and the arrow the direction of the ‘travelling wave’.

sampling sites, we used Indian Remotely Sensed Data: a panchromatic image, at a spatial resolution of 7 m. From this image, we produced a binary description of the landscape as a patchwork of suitable habitat (grasslands) within a hostile matrix (all other kinds of habitats). We estimated the percentage of non-grassland area within a radius of 500 m (L500) around the site sampled. Larger radii were not considered as they revealed less variance (1,000 m) or the same percentage of non-grassland area (>1,000 m) whatever the site. Vole abundance was determined within each site using the surface index method developed by Giraudoux et al. (1995). Two perpendicular transects of 250 m were analysed by considering 10-m intervals for the presence of water vole surface activities. The index of abundance corresponds to the percentage of intervals containing water vole tumuli (mounds produced due to the vole fossorial activity) within the site. A temporal survey of this index has been conducted since 2002 in the 13 sites studied, as well as over the entire canton at 79 other sites. The demographic phase, i.e. low density, increase, outbreak (less than 1-year-old), persisting outbreak (longer than 1-year-old) and decline, was determined using variation in abundance through time (results not shown here), according to the definition provided by Krebs and Myers (1974).

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2.3. Sampling and parasite identification

2.4. Host microsatellite genotyping

The survey was carried out in autumn 2003 between the 20th of September and the 30th of October. We used 80 traps per site, with only one live-trap per colony (i.e. nest) to avoid the sampling of related individuals. This protocol leads to the capture of approximately 30 animals per site after a single day. To minimise the effects of trapping on vole abundance, we released 10 individuals after collection of DNA samples from the tail. About 20 rodents were kept per site, brought to the laboratory and housed individually for 2 weeks in standardised conditions for physiological experiments (unpublished data). As the parasite life expectancy and prepatent period usually exceed 2 weeks (Morand, 1996), it is unlikely that vole infection status changed during this period in relation to both T. arvicolae and S. nigeriana infection. Voles were euthanised by cervical dislocation, weighed and dissected. Digital tissue was kept in 95 alcohol for DNA analysis. Eye lenses were removed and weighted to estimate the age of rodents following Martinet (1966) using the formula established by Boujard (Boujard, T. 1982. Contribution a` l’e´tude de la structure et de la cine´tique d’une population de la forme fouisseuse du campagnol terrestre (Arvicola terrestris scherman (Shaw)) dans le Doubs (France) pendant la phase pullulante du cycle. DEA Thesis, University Pierre et Marie Curie – Paris VI, Paris) for water voles sampled near the study area. The residuals of body mass on age provided information on the body condition (Saucy, F. 1988. Dynamique de population, dispersion et organisation sociale de la forme fouisseuse du campagnol terrestre, (Arvicola terrestris scherman (Shaw)). University of Neuchaˆtel, Neuchaˆtel; Schulte-Hostedde et al., 2001). Sexual maturity was recorded for females according to uterus size (mature if >1 mm) and the reproductive status (lactation, pregnancy), and for males by considering the testis size (mature if >6 mm; Saucy, F. 1988. Dynamique de population, dispersion et organisation sociale de la forme fouisseuse du campagnol terrestre, (Arvicola terrestris scherman (Shaw)). University of Neuchaˆtel, Neuchaˆtel) Only sexually mature individuals were kept for the study, and pregnant females or females that gave birth in the laboratory were removed from the analyses as they were too few to include this effect in statistical analyses. Consequently, the role of juveniles in the dispersal and dynamics of T. arvicolae cannot be evaluated although it probably represents the main part of the population in autumn. Finally, rodent caeca were dissected and T. arvicolae and S. nigeriana specimens were identified and counted using dissecting microscopes. All animals used in these experiments were housed and maintained in accordance with the INRA animal care guideline (Veissier, 1999) and all procedures were approved by Departmental Veterinary Service (B34-169-1), an institution accredited by the National Ministry of Agriculture and Fisheries.

Host genetic variation was assessed using eight microsatellite loci developed for the fossorial water vole: AT03, AT09, AT13, AT19, AT22, AT23, AT24 and AT25 (Berthier et al., 2004). DNA was extracted from A. terrestris tissue samples using DNAeasy Tissue Kit Qiagen following the manufacturer’s instructions. Primer sequences are given in Berthier et al. (2004) and multiplexed amplification conditions have been modified to some extent. For each locus and individual, we used a total reaction volume of 10 lL, containing about 30 ng of DNA (1 lL of one toe extraction), 5 lL of Qiagen Multiplex PCR Master Mix, 0.05 lL of each primer at 10 lM except for AT23, 0.15 lL and ultragrade water. PCR was performed on a Mastercycler epgradient S, Eppendorf. It started with 15 min of initial denaturation at 95 C, followed by 37 cycles with: 30 s of denaturation at 94 C, 90 s of annealing at 57 C and 60 s of extension at 72 C (30 min for the last one cycle at 60 C). PCR products (0.5 lL) were mixed with 18.5 lL ultragrade formamide and 1 lL Genescan-400Rox size standard (Applied Biosystems). Loci were detected using fluorescent PCR and a monocapillary ABI PRISM 310 DNA automated sequencer (Applied Biosystems). Migration was performed at 15 kV, 11 mW and 12 lA at 60 C for 24 min. Note that genetic analyses have not been conducted on T. arvicolae samples as no molecular marker has yet revealed polymorphism at the intraspecific level (results not shown). 2.5. Statistical analyses 2.5.1. Trichuris arvicolae distribution within A. terrestris populations Trichuris arvicolae distribution was described in terms of prevalence and intensity. The prevalence is defined as the percentage of hosts infected in a sampling site. Mean and median intensity, respectively, correspond to the mean and the median number of T. arvicolae worms per infected individual in a sampling site. Parasite aggregation was estimated using the parameter k when the observed parasite distribution did not depart from the negative binomial (Poulin, 1998). Otherwise, we used the ratio of the variance to the mean number of worms per host (Hudson et al., 2002). Results and confidence intervals were obtained using the software Quantitative Parasitology 3.0 (Reiczigel, J.and Ro´zsa, L. 2001, posting date. Quantitative Parasitology 3.0). A v2 test was conducted to test the influence of sampling sites on the prevalence of T. arvicolae infection. 2.5.2. Trichuris arvicolae distribution among A. terrestris populations We investigated the spatial pattern in the parasitological data—i.e. whether highly infected vole populations were aggregated in space—by analyzing the spatial autocorrelation of prevalence (Sokhal and Oden, 1978).

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Moran’s I indices, which correspond to the correlation of the studied variable among the different sampling sites and vary between 1 and +1, were calculated for vole samples (with infection status) situated at different distances. Six distance classes were chosen to maximise the similarity of the number of pairs of samples for each Moran’s I index. Upper limits were 3.5, 5.5, 7.5, 9.5, 10.5 and 16 km. The significance of each Moran’s I statistics was calculated using a randomization procedure (10,000 permutations). The spatial correlogram as a whole can be considered significant at a given significance level a if at least one of its coefficients is significant at a/k, where k is the number of distance classes used (Bonferroni criterion – (Oden, 1984)). Three basic correlogram profiles are usually found in ecological data (Diniz-Filho et al., 2003). The first is obtained when there is positive autocorrelation in short distance classes, coupled with negative spatial autocorrelation at large distance classes. In this case, the correlogram profile can be interpreted as a linear gradient at a large scale. A second common type occurs when only small distance autocorrelation is found, indicating that spatial variation is structured in patches. In this case, the distance up to which spatial autocorrelation is observed can be interpreted as the average patch size in the variable. Third, if no Moran’s I coefficient is significant; there is no spatial pattern in the data. Coordinates of sampling sites were used to compute distance matrices and spatial autocorrelation statistics using the software AutocorQ 2.0 (Hardy and Vekemans, 2002). 2.5.3. Probability of infection with T. arvicolae We conducted a logistic regression with a binomial error distribution and a logit function to analyse the influence of individual and site factors on the probability of infection with T. arvicolae. The dependant binary variable was the infection status for T. arvicolae. The individual parameters included were the age (continuous variable), body condition, sex (two levels: female and male) and the presence of S. nigeriana (two levels: 0 and 1). The parameters related to the sampling sites were vole abundance (continuous variable), demographic phase (three levels only observed among the five possible: increase, outbreak and persisting outbreak) and the landscape (continuous variable called L500). The modelling started with all two-way interactions. We used the Akaike Information Criterion to select the best model considering fit and complexity (Johnson and Omland, 2004). The significance level of all statistical tests was set at 0.05. Statistical analyses were carried out using GenStat 6.2 (Payne et al., 2003) with reference to Legendre and Legendre (1998). Factors found to have a significant effect on the probability of T. arvicolae infection were then analysed for the influence of sampling sites, because the spatial pattern of T. arvicolae infection could be caused by changes in population structure. Similarly, we investigated the role of environmental factors selected in the model on the spatial pattern of T. arvicolae infection by

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testing their spatial autocorrelation (see the procedure above). 2.5.4. Genetic differentiation among A. terrestris populations Genetic variation was estimated over all loci within each population using the observed (HO) and expected (HE) heterozygosities (unbiased estimates, Nei 1978) using the program Genetix 4.05 (Belkhir, K. 2001, posting date. GENETIX, program in WindowsTM for the genetics of populations. Laboratoire Ge´nome, Populations, Interactions CNRS UMR 5000, Universite´ de Montpellier II). The observed number of alleles in a sample is strongly dependent on sample size. To compare the allelic richness in the different sampling sites, we used the rarefaction procedure implemented in Fstat 2.9.3.2 (Goudet, J. 2001. FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3)) and estimated the expected number of alleles in subsamples of 27 individuals, i.e. the smallest number of individuals typed for a locus in a sample (Goudet, 1995). Genotypic linkage disequilibria between all pairs of loci, and conformation to the Hardy–Weinberg equilibrium for each locus separately and over all loci, were tested within each population using Markov chain methods with the sofware Genepop 3.4 (Raymond and Rousset, 1995). Deviations from the Hardy–Weinberg proportions were quantified by the unbiased Wright inbreeding coefficient FIS, estimated according to Weir and Cockerham (1984). We analysed the spatial genetic differentiation among A. terrestris populations to assess the potential effects of host dispersal on the spatial pattern of T. arvicolae infection. Tests for genotypic differentiation over all loci and for all pairs of sampling sites were performed using Markov chain methods in Genepop 3.4. A sequential Bonferroni correction was applied (Rice, 1989). The estimator h of FST (Weir and Cockerman, 1984) was calculated over all loci, both over all sites and for all pairs of sites. We expected gene flow, and thus effective dispersal, to be strongly influenced by distance (Berthier et al., 2005, 2006). The null hypothesis of independence between geographic and genetic distances was thus tested against the hypothesis of a positive correlation (isolation by distance), using the method developed by Rousset (1997). Under isolation by distance, a correlation is expected between the logarithm of the geographic distance and FST/(1  FST). This correlation was tested using a Mantel test and a rank correlation coefficient (Rousset, 1997). These analyses were conducting using Genepop 3.4. The spatial pattern of genetic variation was also investigated using spatial autocorrelation analyses (Hardy and Vekemans, 1999), which unlike Mantel testing, provide results on the shape of the relationship (Stow et al., 2001; Brouat et al., 2003). Moran’s I statistics for multilocus genotypes were computed with the program Spagedi 1.2 (Hardy and Vekemans, 2002) for the six distance classes previously described. Significant isolation by distance or spatial genetic autocorrelation would reveal the influence of geography on

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A. terrestris genetic differentiation and thus on its effective dispersal. To separate the effects of dispersal and distance on T. arvicolae spatial distribution, we analysed the correlation between pairwise differences in prevalence levels and pairwise genetic differentiation corrected for distance i.e. the residuals of the linear regression of FST/(1  FST) over the logarithm of the geographical distance. This was tested using a Mantel test (10,000 permutations) in Genetix 4.05. A significant correlation would reveal a strong influence of vole gene flow on T. arvicolae spatial distribution, independent of the distance between sites. Alternatively, an absence of correlation would reveal that the geographical distance, including both site similarity and vole dispersal, and/or host dispersal without any gene flow, may influence T. arvicolae distribution. Finally, individual factors found to influence T. arvicolae infection were tested for a biased effect on host effective dispersal using Fstat 2.9.3.2. Following Goudet et al. (2002), an optimal detection is assumed using one-tailed tests for Assignment Index (AI) (Favre et al., 1997) especially the mean of AIc (mAIc). For each individual in a given locality, we calculate the probability that its genotype at a given locus should appear in a sample (as the squared frequency of the allele if the individual is homozygous or twice the product of the frequencies for a heterozygous individual). We obtained a value indicating if a genotype is more likely than average to occur in its sample (probably a resident individual), or less likely than average (potentially a disperser) (Goudet et al., 2002). We expected the ‘disperser group’ to be less genetically structured and then to have a lower mAIc on average than the ‘philopatric group’. Differences in mean of AIc were tested by permutation of individuals in groups. Following the same method, a biased effect of infection on host effective dispersal was investigated.

Table 1 Landscape and demographic characteristics of 13 sites sampled for infection of Trichuris arvicolae in Arvicola terristis Site code

Number of voles trapped

% of grasslanda

Abundance 2002

2003

2004

03 04 05 07 10 12 13 14 15 16 17 43 87

30 30 27 27 29 29 37 29 30 29 29 30 27

7.28 50.11 43.54 13.25 37.28 35.69 14.60 15.51 29.51 11.24 21.84 21.85 31.43

98 77 48 70 50 4 44 0 12 0 4 5 0

99 100 95 99 76 95 98 89 39 76 71 74 12

72 89 99 80 74 85 95 96 67 92 90 80 91

Demographic stateb P P O O O O O I I I I I I

Nc is the number of voles trapped per site for genetic purposes. Abundances are given for Autumn 2002, 2003 and 2004. a Corresponds to the percentage of non-grassland area within a radius of 500 m around the centre of the site. b P, persisting outbreaks; O, recent outbreaks; and I, demographic increases.

influenced by the sampling site (v20:05;12 ¼ 33:42, P < 0.001). Globally, T. arvicolae was weakly aggregated within a site: the estimates of k were, respectively, 0.71, 0.42 and 0.76 for sites 7, 10 and 13. Aggregation could not be estimated at the other sites. The variance/mean ratio generally reached 1.95, although it varied between 0.75 and 2.48. The prevalence of S. nigeriana was also variable among sites from 9% to 55%. The mean intensity ranged between 1 and 37 worms and the median between 1 and 14.5 worms per infected host. These data are summarised in Table 2.

3. Results For the current study, 383 animals were trapped and 235 were kept for parasitological screening. None of the females captured were pregnant or in lactation. The composition of the landscape surrounding the sampling sites ranged between 7.28% and 50.11% of non-grassland area (Table 1). The vole abundance indices varied between 11.87 and 100, and the temporal survey of these indices revealed that in 2003, vole populations experienced a persisting outbreak in two sites, an outbreak in five sites and a demographic increase in six sites (Table 1). 3.1. Trichuris arvicolae distribution within A. terrestris populations The prevalence of T. arvicolae varied from 0% to 68.42% among sampling sites, with mean intensity ranging between 1 and 2.67 and median intensity between 1 and 3 worms per host individual depending on sampling sites. The prevalence with T. arvicolae was significantly

Table 2 Prevalence (in %) and 95% confidence interval (CI) for prevalence (indicated in brackets) of Trichuris arvicolae and Syphacia nigeriana for each sampling site Site code

03 04 05 07 10 12 13 14 15 16 17 43 87

No. of voles

20 18 19 16 15 20 22 18 18 18 17 17 17

Prevalence of Trichuris arvicolae [CI]

Syphacia nigeriana [CI]

0.00 0.00 68.42 37.50 33.30 15.00 27.30 22.20 16.70 27.80 29.40 17.60 35.30

10.00 38.90 42.11 50.00 60.00 21.05 9.09 33.33 22.22 55.56 47.06 35.29 17.65

[43.44–87.43] [15.19–64.57] [11.82–61.62] [3.38–39.58] [10.72–50.23] [6.4–47.64] [3.57–41.42] [9.69–53.49] [10.31–55.96] [3.79–43.44] [14.2–61.68]

[1.23–31.70] [17.29–64.26] [20.25–66.51] [24.65–75.35] [32.28–83.67] [5.73–43.67] [1.12–29.17] [13.34–59.01] [6.40–47.64] [30.75–78.47] [22.98–72.19] [14.20–61.68] [3.79–43.44]

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3.2. Trichuris arvicolae distribution among A. terrestris populations The autocorrelation analysis of infection with T. arvicolae showed a significantly positive intraclass correlation coefficient (t = 0.1080, P < 104, not shown in Fig. 2). The correlogram showed an absence of spatial structure of T. arvicolae prevalence (Fig. 2a). None of the Moran’I coefficients were statistically significant.

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Table 3 Logistic regression of Trichuris arvicolae infection in Arvicola terrestris Variables

df

Deviance

Deviance ratio

P-value

Age Demographic phase

1 2

16.4 25.1

16.41 12.53

<0.001 <0.001

Only significant terms are shown. P-values were estimated by comparison with reduced models not containing the term in question. This model is highly significant (P < 0.001) with a deviance ratio of 12.84, deviance 21.73, R2 = 0.15 and 3 df N = 235 animals.

3.3. Probability of infection with T. arvicolae The single best model selected to explain the variation in T. arvicolae infection was highly significant (Total deviance = 38.5, n = 235, P < 0.001). This model included the age (P < 0.001) and the demographic phase (P < 0.001) (Table 3). These parameters accounted for 15% of the total deviance in T. arvicolae infection. In contrast, sex, body condition and S. nigeriana infection were not statistically significant (respectively P = 0.228, P = 0.103, P = 0.492). Furthermore, landscape and vole abundance did not explain significant variation in the T. arvicolae infection model (respectively, P = 0.410, P = 0.388). Voles infected with T. arvicolae were significantly younger than uninfected ones. This was particularly significant when considering voles younger than 7.6 months (Fig. 3).

Fig. 3. Effect of host age (in months) on Trichuris arvicolae prevalence estimated over all sites sampled. N is the number of animals sampled.

Prevalences in older age classes were lower (0–17%) than in younger age classes (30–40%). No T. arvicolae were found in animals older than 8 months. The same patterns

Fig. 2. Spatial correlogram of water vole (a) infection by Trichuris arvicolae, (b) demographic phase (92 sites in plane line and 13 sites with parasitological data in dotted line) and (c) genetic differentiation (FST/(1  FST)) in populations situated at different distances within the canton of Nozeroy. Dark squares are significant Moran’s I showing a positive or negative correlation of prevalence for the distance considered. Distance classes correspond to the maximal distance in km defining the class.

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J. Deter et al. / International Journal for Parasitology 37 (2007) 813–824

Fig. 4. Effect of Arvicola terrestris demographic phase (I, increase; O, recent outbreak and P, persisting outbreak) on Trichuris arvicolae prevalence estimated over all the sites sampled. The vertical bars indicate 95% confidence interval. N is the number of animals sampled. *** Indicates a highly significant difference between pairs. NS indicates a non-significant difference.

were observed when considering intensities (results not shown). Voles trapped in populations experiencing a persisting outbreak exhibited lower prevalences compared with those trapped in the two other demographic phases (Fig. 4). Posthoc tests (contingency table) revealed that these differences were significant when comparing persisting outbreak versus recent outbreak or persisting outbreak versus demographic increase. However, no statistical difference was detected between prevalences observed in populations exhibiting a demographic increase or recent outbreak. Note that the demographic phase did not significantly influence the age structure (ANOVA, F2,231 = 1.15, P = 0.317). We also tested whether there was a link between the day of trapping and the age of the animals, supposing that older individuals might be sampled at the end of trapping, and younger ones at the beginning of trapping. The effect of the date of trapping on the age of the voles trapped was not significant (ANOVA F12,231 = 1.171, P = 0.3119). 3.4. Demographic phase of A. terrestris populations At the scale of the canton, vole abundance characterised at the 92 sites surveyed exhibited a significant spatial autocorrelation. The degree of similarity in vole demographic phase decreased with geographical distance following a linear gradient (Fig. 2b), with a domain of similarity of vole dynamics being similar to genetics: 5.5 km. Including the 13 sites for which parasitological data were available revealed the same pattern of spatial autocorrelation, although none of the values was significant. 3.5. Genetic differentiation among A. terrestris populations All loci were highly variable, the number of alleles per locus ranging between five and 14. Allelic richness averaged

over loci ranged between 6.25 and 7.75. Mean expected heterozygosis varied from 0.66 to 0.75 (Table 4). After Bonferroni correction, none of the sampling sites showed a significant genotypic disequilibrium, likely, FIS values were low for all sites (Table 4). We concluded that theses alleles were effectively independent. The Fisher exact test for genotypic differentiation conducted over all sites was highly significant (P < 0.0001). The estimate of FST over all sites and loci was 0.021. The pairwise tests of differentiation were significant for 12 over 78 pairs of sampling sites after Bonferroni correction (Table 5). Estimates of FST ranged between 0.005 and 0.040. A highly significant isolation by distance was observed throughout the study area (mantel test, P = 6 · 104, r = 6 · 103, slope = 6 · 103). The spatial autocorrelation analysis showed a significant decreasing pattern consistent with isolation by distance (Fig. 2c). The size of genetically similar patches, estimated by the intercept with the x-axis on the autocorrelogram, ranged between 5.5 and 10.5 km. Both patterns of isolation by distance and spatial autocorrelation revealed the strong correlation between genetic differentiation – and thus effective dispersal – and geographic distance. The Mantel test revealed a non-significant association between pairwise differences in prevalence and genetic differentiation (Z = 304.694, r = 0.124, P = 0.266). As age significantly influenced infection status, we tested if effective dispersal depended on age using assignment index. We found no biased effect of infection with T. arvicolae on A. terrestris dispersal rates (mAIc of infected group = 0.363 and non-infected = 0.148, P = 0.899), i.e. no differential dispersal depending on the infection status. However, a significantly biased effective dispersal rate was found in favour of animals younger than 7.6 months compared with older ones (mAIc of older group = 0.461 and younger group = 0.154, P = 0.040). Younger animals were the principal dispersers.

Table 4 Characteristics of the populations surveyed (coded by sites) including sample size, allelic richness, observed heterozygosity (HO), estimates of expected heterozygosity (HE), intrapopulation fixation indices (FIS), and probabilities associated to the rejection of the Hardy–Weinberg equilibrium (HW) Site code

Sample size

Allelic richness

HO

HE

FIS

HW equilibrium (probability)

03 04 05 07 10 12 13 14 15 16 17 43 87

30 30 27 27 29 29 37 29 30 29 29 30 27

7.00 6.50 7.00 6.25 7.13 7.13 6.50 7.00 6.87 6.87 7.13 7.75 7.13

0.68 0.70 0.70 0.70 0.74 0.65 0.70 0.67 0.66 0.62 0.75 0.72 0.68

0.73 0.70 0.70 0.68 0.75 0.68 0.68 0.68 0.68 0.66 0.71 0.71 0.69

0.083 0.020 0.016 0.009 0.026 0.065 0.018 0.023 0.042 0.075 0.037 0.002 0.033

0.220 0.248 0.519 0.346 0.047 0.137 0.587 0.111 0.319 0.258 0.149 0.993 0.050

J. Deter et al. / International Journal for Parasitology 37 (2007) 813–824

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Table 5 Genetic differentiation between sampling sites of Arvicola terrestris at eight microsatellite loci Site code 03 04 05 07 10 12 13 14 15 16 17 43 87

03 0.007 0.026 0.024 0.012 0.029 0.021 0.016 0.016 0.040 0.017 0.025 0.031

04

05

07

10

12

13

14

15

16

17

43

87

a

a

a

a

a

a

a

a

a

a

a

a

a

b

a

a

a

a

a

a

a

a

a

a

b

a

b

NS

a

NS

b

a

a

a

a

a

a

a

a

a

a

a

a

b

a

a

a

a

a

a

a

b

a

a

a

a

b

a

a

a

a

b

b

b

a

a

a

b

b

a

a

a

a

b

a

a

0.034 0.012 0.012 0.017 0.028 0.009 0.017 0.029 0.016 0.029 0.027

0.021 0.018 0.015 0.013 0.017 0.011 0.019 0.0006 0.0068 0.0307

0.015 0.017 0.019 0.021 0.026 0.034 0.019 0.019 0.037

0.021 0.025 0.012 0.016 0.026 0.011 0.013 0.021

a

0.017 0.013 0.016 0.011 0.010 0.012 0.014

0.022 0.013 0.023 0.014 0.013 0.025

0.005 0.011 0.008 0.021 0.014

0.011 0.009 0.014 0.021

0.014 0.024 0.014

0.010 0.021

a

0.027

FST estimates are presented below the diagonal and the level of significance above the diagonal. NS, non-significant probability. a P values significant at P < 0.05. b P values significant after Bonferroni correction (P < 6 · 104).

4. Discussion Our study reports strong heterogeneities in T. arvicolae prevalence among the water vole populations sampled. The positive intraclass correlation coefficient observed for autocorrelation analyses of infection with T. arvicolae revealed the importance of local factors, of a genetic basis of resistance/susceptibility or of maternal effects, which have already been suspected (Blackwell and Els, 2002; Kristan, 2002; Behnke et al., 2006). However we did not find any significant spatial pattern at the scale of the Nozeroy canton. Contrary to our expectations, closest sites did not present similar prevalences, and distant sites did not exhibit significantly different prevalences. Several authors have demonstrated that host movement capacity can determine parasite dispersal (Nadler et al., 1990; Blouin et al., 1995; McCoy et al., 2005). The spatial patterns of genetic differentiation and differences in prevalence among vole populations showed important discrepancies. Different reasons might explain why we did not detect any correlation between host effective dispersal pattern and T. arvicolae distribution. First, vole dispersal might not affect T. arvicolae distribution. This is to be expected if voles that disperse are weakly infected or if infected voles suffer from lower survival than non-infected ones during and after migration. These possibilities seem unlikely, because our study revealed that vole dispersal is mainly achieved by young voles (see also Saucy, F. 1988. Dynamique de population, dispersion et organisation sociale de la forme fouisseuse du campagnol terrestre (Arvicola terrestris scherman (Shaw)). University of Neuchaˆtel, Neuchaˆtel.; Saucy and Schneiter, 1998), which correspond to the most heavily infected part of the population. Furthermore, this nematode does not seem to have a negative effect on vole survival (Porcherie, 2005. Susceptibilite´ aux parasites des hybrides entre Musculus musculus musculus et Mus musculus domesticus: origine du phe´nome`ne et roˆle

dans la contre-se´lection des hybrides. University of Montpellier II, Montpellier). Second, vole dispersal might affect the distribution of T. arvicolae but this influence is overwhelmed by other factors. Three hypotheses can be proposed: (i) local adaptation by T. arvicolae to its host might affect the transmission success of the nematode from one population to a genetically different one. Local adaptation of parasites to their hosts (i.e. higher fitness in local hosts compared with foreign hosts) is commonly observed in host–parasite interactions (see a recent review in Kawecki and Ebert, 2004). Theoretical models predict that it occurs when parasite dispersal rates are higher than those of hosts (Gandon et al., 1996). Population genetic studies of T. arvicolae are now required to test for the existence of local adaptation in this system. (ii) adaptationby T. arvicolae to its local environment might lead to low transmission rates of this nematode after migration into a new site. The viability of helminth eggs is known to strongly depend on soil conditions (Pietrock and Marcogliese, 2003; Bungiro and Cappello, 2004; Jenkins et al., 2006). Spatial fluctuations of these environmental conditions at the scale of the canton could result in low survival rates of helminth eggs and thus could overwhelm the effects of parasite dispersal on its distribution. (iii) the distribution of T. arvicolae may not be determined solely by dispersal through A. terrestris. Other arvicoline rodents such as the common vole, M. arvalis, and the bank vole, C. glareolus, can potentially be infected with T. arvicolae (Feliu et al., 2000) and thus disperse this nematode. However C. glareolus lives preferentially in forest and hedge habitats, and is rarely infected with this nematode (between 0% and 6.66%, unpublished data). M. arvalis lives in sympatry with A. terrestris (Le Louarn, H. and Que´re´, J.-P. 2003. Les rongeurs de France, faunistique et biologie. Paris), exhibits similar prevalences (between 17% and 52%, Cerqueira, D. 2001. Implication des communaute´s parasitaires dans les cine´tiques des populations de rongeurs: application aux

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J. Deter et al. / International Journal for Parasitology 37 (2007) 813–824

populations sympatriques de deux espe`ces de campagnols, Arvicola terrestris sherman (Shaw, 1801) et M. arvalis (Pallas, 1779). University of Montpellier II, Montpellier) but disperses at smaller distances (Smith and Batzli, 2006; Wijnhoven et al., 2006). It is thus unlikely that the effects of M. arvalis dispersal on the distribution of T. arvicolae overwhelm those linked to dispersal of A. terrestris. This study reports a significant effect of the demographic phase on T. arvicolae prevalence. Vole populations which experienced persisting outbreaks were characterised by a total absence of T. arvicolae. This result must, however, be interpreted cautiously. Indeed only two of the populations surveyed were classified in this demographic phase in 2003. This result could reflect a population effect only and not the influence of the demographic phase. Analysing the prevalence of T. arvicolae in other populations experiencing persisting outbreaks is required to confirm the influence of the demographic phase on this observation. Alternatively, our trapping procedure excluded the capture of juveniles. Finding no parasitised voles in populations undergoing persisting outbreaks does not necessarily imply that no parasites were present in the population. If the infection pressure was strong locally, voles may have been infected earlier in life and then have developed a protective immunity (Koski and Scott, 2001; Hayes et al., 2004). Persisting outbreak populations could present very high risks of infection that would not be obvious in our data set. This scenario could be verified by trapping juveniles or by testing voles serologically for T. arvicolae antibodies. Some tests based on T. muris/ mice protocols have been performed (Jan Bradley, personal communication) but were not decisive, probably due to differences in anti-T. muris and anti-T. arvicolae antibodies. If the absence of T. arvicolae in persisting outbreak populations is reliable, the senescence hypothesis (Boonstra, 1994) offers a theoretical context to explain this pattern. It proposes that the social inhibition of maturation during peak densities causes a shift in age structure towards older animals, leading to senescence and population decline. This phenomenon appears after several years of high densities. We showed in this study that older individuals were less likely to be infected than younger ones, perhaps because of the development of a protective immunity. Changes in age structure towards older individuals in declining vole populations have already been reported (Tkadlec and Zejda, 1998; Cerqueira, 2001. Implication des communaute´s parasitaires dans les cine´tiques des populations de rongeurs: application aux populations sympatriques de deux espe`ces de campagnols, Arvicola terrestris sherman (Shaw, 1801) et M. arvalis (Pallas, 1779). University of Montpellier II, Montpellier; Ja´nova´ et al., 2003). Physiological differences could also exist between individuals sampled in populations experiencing recent or persisting outbreaks. Indeed, a consequence of long-term peak densities is chronic stress,

which results in accelerated senescence and impairment of immune function (Bradley et al., 1980; Dhabhar and McEwen, 1997; Boonstra, 2005). The detrimental effect on immunity could benefit opportunist parasites or pathogens which could compete with T. arvicolae. We did not find any evidence of antagonistic relationships between S. nigeriana and T. arvicolae, but interactions with other macro- and microparasites should be investigated. Finally, the absence of a detectable relationship between prevalence and host abundance was surprising. Such a relationship was expected according to mathematical models (May and Anderson, 1978) and based on previous results involving the prevalence of another helminth, T. taeniaeformis (Deter et al., 2006). This result suggests that higher levels of host abundance may not increase the transmission rates of T. arvicolae. This may occur if the water vole territories do not overlap, even when abundance increases. We found that demography and gene flow presented the same spatial patterns with geographical distance, whereas T. arvicolae did not. These results showed that although the changes in demography and vole dispersal are likely to be associated with one another (Berthier, 2005. Roˆle de la dispersion dans le fonctionnement et le maintien a` long terme des populations cycliques de rongeurs. University of Franche-Comte´, Besanc¸on; Berthier et al., 2005; Berthier et al., 2006), they are unlikely to induce or result from a homogenisation of T. arvicolae prevalences at this spatial scale. The disappearance of this nematode in vole populations, if confirmed, could be the indicator of a forthcoming decline, and investigating the reasons of this disappearance could help us determine factor(s) which are responsible for vole cycles. Acknowledgements We thank Josef Bryja, Matthieu Faure, Esteve Martinez Garcia, Herve´ Le´pissa and Sylvain Piry for assistance with the field work, Jan Bradley for serological analyses, and Olivier Hardy and Karen McCoy for their relevant comments on this manuscript. This work was financed by a grant from the Ministry of Research and supported financially by the Institut National de la Recherche Agronomique, the region Franche-Comte´, the DIREN Franche-Comte´ and the Ministry of Environment. References Artis, D., 2006. New weapons in the war on worms: identification of putative mechanisms of immune-mediated expulsion of gastrointestinal nematodes. Int. J. Parasitol. 36, 723–733. Behnke, J.M., Bajer, A., Sinski, E., Wakelin, D., 2001. Interactions involving intestinal nematodes of rodents: experimental and field studies. Parasitology 122, S39–S49. Behnke, J.M., Gilbert, F.S., Abu-Madi, M.A., Lewis, J.W., 2005. Do the helminth parasites of wood mice interact?. J. Anim. Ecol. 74 982–993.

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