Temporal dynamics of Puumala hantavirus infection in cyclic populations of bank voles

1 Scientific RepoRts | 6:21323 | DOI: 10.1038/srep21323 www.nature.com/scientificreports Temporal dynamics of Puumala hantavirus infection in cyclic populations of bank voles Liina Voutilainen1,2, Eva R. Kallio3, Jukka Niemimaa1, Olli Vapalahti2,4 & Heikki Henttonen1 Understanding the dynamics of zoonotic pathogens in their reservoir host populations is a prerequisite for predicting and preventing human disease epidemics. The human infection risk of Puumala hantavirus (PUUV) is highest in northern Europe, where populations of the rodent host (bank vole, Myodes glareolus) undergo cyclic fluctuations. We conducted a 7-year capture-mark-recapture study to monitor seasonal and multiannual patterns of the PUUV infection rate in bank vole populations exhibiting a 3-year density cycle. Infected bank voles were most abundant in mid-winter months during years of increasing or peak host density. Prevalence of PUUV infection in bank voles exhibited a regular, seasonal pattern reflecting the annual population turnover and accumulation of infections within each year cohort. In autumn, the PUUV transmission rate tracked increasing host abundance, suggesting a density-dependent transmission. However, prevalence of PUUV infection was similar during the increase and peak years of the density cycle despite a twofold difference in host density. This may result from the high proportion of individuals carrying maternal antibodies constraining transmission during the cycle peak years. Our exceptionally intensive and long-term dataset provides a solid basis on which to develop models to predict the dynamic public health threat posed by PUUV in northern Europe. Understanding the dynamics of zoonotic pathogens in their reservoir host populations is an important step towards predicting the risk of zoonotic diseases to humans, such as infections caused by hantaviruses that account for ca. 50 000 cases worldwide each year1,2. Hantaviruses (family Bunyaviridae) are carried by rodents, soricomorphs, and bats3. Some of them cause haemorrhagic fever with renal syndrome (HFRS; viruses harboured by Old World rats and mice, as well as arvicolines) or hantavirus cardio pulmonary syndrome (HCPS; viruses harboured by New World rats and mice) in humans. In terms of human health, the most important European avirus is Puumala hantavirus (PUUV) which causes nephropathia epidemica (NE), a mild form of HFRS4,5. The principal host of PUUV is the bank vole (Myodes glareolus), which is distributed over most of Europe6 and is the dominant rodent species in boreal forests. In general, the incidence of human hantavirus infections tracks the abundance of the local rodent host population7–12. In temperate Europe, bank vole populations show seasonal fluctuations and occasional, irregular eruptions caused by masting, i.e., a heavy seed crop of oak and beech12–14. Masting has been linked to human NE epidemics, likely brought about by growth of the local vole population12,15,16. In boreal forests of northern Europe, deciduous trees do not undergo masting events and vole population abundance cycles of 3 to 5 years are thought to be driven by specialist predators17–19. Across Europe, the incidence of NE is highest in Finland, northern Sweden and northern Russia5,20. Although bank vole population dynamics have proven useful for predicting NE epidemics in both temperate and boreal biomes9–12,21, detailed analyses of long-term PUUV transmission in reservoir populations are lacking. Furthermore, the relationship between rodent abundance and human infection rate is somewhat obscure; for example, a higher incidence of human NE was observed during increasing rather than peak phases in two vole cycles in central Finland where PUUV is endemic9. Our objective was to fully describe the pattern of PUUV transmission in cyclic populations of boreal bank voles. More specifically, we aimed to characterize the seasonal and multiannual fluctuations in 1) the abundance of PUUV-infected bank voles, which is presumably the main driver of NE epidemics in humans, 2) the prevalence of PUUV infection, and 3) the rate of PUUV transmission (as indicated by the acquisition rate of PUUV-specific 1Natural Resources Institute Finland, Vantaa, Finland. 2University of Helsinki, Department of Virology, Finland. 3University of Jyvaskyla, Department of Biological and Environmental Science, Finland. 4Helsinki University Hospital, Department of Virology, Finland. Correspondence and requests for materials should be addressed to L.V. (email: liina. [email protected]) Received: 25 June 2015 accepted: 15 January 2016 Published: 18 February 2016 OPEN www.nature.com/scientificreports/ 2 Scientific RepoRts | 6:21323 | DOI: 10.1038/srep21323 antibodies), which we expected to shed more light on within-host transmission of PUUV. In addition, we studied whether the accumulation or rate of PUUV infections differed between annual cohorts born in different cycle phases. For these purposes, we monitored PUUV infections in bank voles through nearly three vole cycles using a capture-mark-recapture method. For the first time, PUUV infection dynamics were also studied in detail during the winter when the incidence of human NE is highest in the boreal zone. Methods Ethical statement. All handling procedures of wild bank voles followed the Finnish Act on the Use of Animals for Experimental Purposes (62/2006) and took place with permission from the Finnish Animal Experiment Board (license numbers HY 45-02, HY 122-03, and HY 54-05). All efforts were made to minimize animal suffering. The species studied is not protected in Finland or included in the Red List of Finnish Species. The animal trapping took place with permission from the landowners. Rodent trapping and sampling. The study took place at Konnevesi in central Finland (62°34′ N, 26°24′ E), where PUUV is highly endemic in bank voles9,22–24. In this region, the ground is covered by snow for an average of 160 days from late November to late April25. PUUV transmission dynamics were studied in detail using a capture-mark-recapture (CMR) method on a large “core grid”. Additionally, trappings were performed on 14 smaller “satellite grids” to extend the geographic scale of the dataset. The core grid included a young stand of birch and willow on drained soil surrounded by mature spruce-dominated coniferous forest with the understorey vegetation dominated by mosses and dwarf shrubs (e.g., Vaccinium). Satellite grids were situated in pine, spruce, and mixed forests of different ages within 5 km of the core grid and at least 750 m from it and each other. Trappings took place from April 2002 to May 2009. The core grid consisted of 246 trapping stations at 15 m intervals, covering a total area of 5.8 ha. One Ugglan Special live-trap (Grahnab, Sweden) was placed at each trapping station under a sheet metal chimney that provided shelter for captured animals but enabled access to traps during the snowy period. In all, 60 trapping sessions were conducted on the core grid. During the snow-free period, the trapping interval was approximately one month and longer during winters, so that the intervals lasted from 17 to 166 days (median 37, interquartile range 30–43 days). Traps were baited with oat seeds and potato and set in the evening. After setting, traps were checked ten times at ca. 8-hour intervals. During low temperatures (< – 5 °C) in winter, traps were checked 3 times per day at 4-hour intervals and left open (non-trapping) overnight. Newly-trapped bank voles were subcutaneously tagged with a transponder (ID-100A Microtransponder, Trovan Ltd, U.K.). On each trapping session, voles were bled through the retro-orbital sinus and their body mass, age (based on pelage26), sex, and sexual maturity (perforate and/or lactating females and males with scrotal testes regarded as mature) were recorded. Animals recaptured during a single trapping session were immediately released. A total of 21 trapping sessions on the 14 satellite grids were conducted in the beginning (May), middle (July), and after the end (October) of the bank vole breeding season. Nine Ugglan Special live traps baited with oat seeds and potato were set in a grid of 3*3 traps with 15 m intervals for 3 nights and checked once per day. Occupied traps were replaced with freshly-baited ones and animals were brought into the laboratory, bled through retro-orbital sinus, and sacrificed. In October 2004 and July and October 2006, live traps were replaced with standard snap traps baited with rye bread. The sex and breeding status of all animals snap-trapped in the satellite grids were determined before the heart was dissected and placed into 200 μ L phosphate-buffered saline and stored at –20 °C prior to the analysis of PUUV antibodies. Determination of PUUV infection status for each animal in each trapping session (t). All blood (diluted 1:10 in phosphate-buffered saline) and heart samples were tested for the presence of PUUV-specific antibodies using an immunofluorescent antibody test (IFAT) described elsewhere27. Based on IFAT results, an infection status was determined for each animal for each trapping session (t). The classifications of infection status are shown in Table1 (hereafter, uppercase letters indicate infection status deduced from the animal’s serological history, and lowercase letters indicate that the status was assigned according to other criteria). As PUUV causes a chronic infection in the bank vole28, a PUUV-seropositive (P) result was interpreted as an infected (I) animal. However, young animals captured between May and October may be seropositive as a result of maternal antibodies (MatAb) received from an infected female which provide temporary immunity for up to 80 days from birth29. Therefore, initially seropositive young animals that were later captured and determined to be seronegative were considered as being MatAb + (M) when first captured. Determining the infection status of young animals that were caught in more than one trapping session and which always tested seropositive was more complex. For those seropositive animals captured in the core and satellite grids, the number genuinely infected (i) was estimated for each trapping session by summing the assigned individual probabilities (estimate based on body mass) of being genuinely infected. These probabilities were calculated from a statistical model (i.e., MatAb model, Supplementary Fig. S1; see below) based on 588 captures with a known serological history in the core area; i.e., 1) animals that had acquired or/and lost antibodies between the two trapping sessions, and 2) seropositive animals that were old enough (> 8 months) to exclude the possibility of a positive result due to MatAb. In the MatAb model, infection status was examined in relation to body mass using generalized additive models (GAM) with a binomial distribution (0 = MatAb +, 1 = genuinely infected) and logit link function (gam function of gamm4 library30 in the R software package31). Because the growth rate of young animals varies over the breeding season depending on if they mature immediately or delay reproduction, separate models were run for each month of capture (Supplementary Fig. S1). If an animal was assigned a probability >0.9 of being genuinely infected, it was considered as genuinely infected (probability of being infected = 1) in later trapping sessions. For example, if a summer-born bank vole was first captured as seropositive in August weighing 19.4 grams, seropositive in September weighing 18.4 grams, and seropositive in October weighing 16.7 grams, it was allocated respective www.nature.com/scientificreports/ 3 Scientific RepoRts | 6:21323 | DOI: 10.1038/srep21323 probabilities 0.924, 0.908, and 0.963 of being genuinely infected by the GAM models. However, as the initial probability (0.924) exceeded 0.9, the animal was assigned a probability of 1 of being infected, and infection status II (infected in t – 1 and t), for September and October. Seronegative animals were considered as non-infected and therefore susceptible (S). In some rare cases, an adult individual was seronegative despite being previously considered genuinely infected. These antibody results were interpreted as false negatives. When an animal was not captured during a particular trapping session (t), but captured with an unchanged PUUV antibody status between the preceding (t – 1) and the following (t + 1) trapping sessions, that same antibody status was also determined for trapping session t. Determination of PUUV infection status in the previous trapping session (t–1) for each animal captured in trapping session t. To calculate the seroconversion rate (the number of individuals that acquired PUUV antibodies between trapping sessions t – 1 and t out of those susceptible at t – 1) in the core grid, the infection status in the previous trapping session (t – 1) for each animal captured in trapping session t needed to be determined. The possible preceding (t – 1) status were: I (Infected), S (Susceptible), M (MatAb+ ), and 0 (suckling pups and thus not a part of the population). For animals that were captured and tested for antibodies at both t – 1 and t, determination was straightforward. However, for individuals that were first time captured at t, probabilities of belonging to the susceptible group at the previous trapping session were assigned (see details in Table1). Firstly, young animals likely to have been suckling pups and therefore not part of the susceptible population at t–1 were excluded from the analysis. The exclusion criterion was a body mass at t that was lower than the body mass of the lightest animal captured for the second time in the same month of any year. Secondly, young individuals that were seronegative (?S) at their first capture (t) were divided into seronegative and therefore susceptible (sS) at t – 1, and seropositive due to maternal antibodies and therefore non-susceptible (mS) at t – 1: their assigned numbers were calculated using the proportion susceptible at t–1 (SS) among the seronegative individuals at t (SS + MS), so that (sS) = (?S)*[SS/(MS + SS)], and (mS) = (?S) – (sS). In nine early summer trapping sessions, none of the young summer-born animals (N = 38) had a known serological history and therefore this calculation method could not be applied. In these cases, the proportion of individuals carrying MatAb at t – 1 [mS/(sS + mS)] was determined to be the same as the infection prevalence among over-wintered PUUV antibody/infection status Number Seronegative; susceptible at t1871 SS: negative, captured as negative at t–11027 MS: negative, captured as positive at t–178 0S: assigned by weight as not in population at t–197 ?S: negative, not captured but assigned by weight as in population at t–1669 sS: assigned as susceptible at t–1555.0 mS: assigned as immune due to maternal antibodies at t–1114.0 Seropositive (P): infected (I/i) or protected by maternal antibodies (M/m) at t1415 II: positive, captured as positive at t–1 828 SI: positive, captured as negative at t–1 223 ?P: positive, not captured at t–1364 0M: positive year-born, later captured as negative; assigned not in population at t–147 mM: positive year-born, later captured as negative; assigned as immune at t–1 31 0m: positive year-born, not re-captured as negative; assigned not in population at t–152 mm: positive year-born, not re-captured as negative; assigned as immune by weight 49.6 ?i: positive adult, or a year-born assigned as infected by weight 184.4 ii: assigned as infected and seroconverted before t–1118.2 si: assigned as infected and seroconverted between t–1 and t59.1 xi: no data to assign PUUV status for t–1 7 Total PUUV infection status Susceptible (SS + MS + 0 S + ?S) 1871.0 Infected (II + SI + ?i) 1235.4 Maternal antibody positive (0M + mM + 0m + mm) 179.6 Total N of captures 3286 Table 1. The known (uppercase) and assigned (lowercase) PUUV status of all bank vole captures on the core grid. P = s eropositive, S/s = susceptible, M/m = maternal antibody positive, I/i = infected, 0 = not in population, ? = unknown, x = undetermined. Capital letters indicate status deduced from serological history, small letters the assigned status. In PUUV infection/antibody status, the first character indicates PUUV status at t−1 (previous trapping session), and second character the PUUV status at t (current trapping session). Seroconversion rates for total population and yearly cohorts per trapping session were calculated from (SI + si)/ (SS + SI + ss + si), marked in bold. www.nature.com/scientificreports/ 4 Scientific RepoRts | 6:21323 | DOI: 10.1038/srep21323 females in the preceding trapping session (t – 1); according to earlier results, PUUV infection has no effect on breeding success in early summer among over-wintered individuals29,32. Thirdly, for individuals that were first captured as seropositive, and from which the MatAb and 0 individuals at t – 1 had been extracted, i.e., (?i) = (?P– 0M– mM– 0m– mm), the number that seroconverted between t – 1 and t (si) was estimated using the proportion that seroconverted between t – 1 and t (SI) among the known positive individuals (SI + II) so that (si) = (?i)*[SI/(SI + II)]. Especially during the breeding season, animals born in different years (i.e., old over-wintered and young summer-born voles) coexisted on the study grid. Given that different-age animals likely fall into the four infection classes (0, M, S, I) on divergent proportions, all the above-mentioned assignments were made per year cohort, i.e., animals born in the same summer. Statistical analyses. Data sets. The population-level dynamics of (a) the abundance of infected animals, (b) the prevalence of infection, and (c) the per capita seroconversion rate were examined using two population-level datasets (datasets “Prevalence of PUUV per trapping session” and “Seroconversion rate per trapping session”, available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.g8140 33). In addition to population-level dynamics, we examined the effects of age and cycle phase on the accumulation of infections and the seroconversion rate in yearly cohorts of bank voles using two cohort-level datasets (datasets “PUUV prevalence per year cohort” and “Seroconversion rate per year cohort”, available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.g8140 33). The seasonal and multiannual variation in the abundance of infected individuals was studied using a dataset (“Prevalence of PUUV per trapping session”33) of trapping indices (individuals/100 trap nights) of PUUV-infected bank voles from all trapping sessions conducted on the core grid (60 trapping sessions, 246 traps) and the 14 satellite grids (21 trapping sessions, 126 traps). The same dataset was used to study the temporal variation in PUUV infection prevalence. For the satellite grids, infection prevalence was calculated as the number of individuals known to be (or assigned as) infected per the total number tested, and for the core grid as the minimum number of infected per the minimum population size (i.e., including animals that were not captured at t but at least once before and once after t). The dataset used to study temporal variation in seroconversion rate (“Seroconversion rate per trapping session”33) consisted of the per capita seroconversion rate calculated for each trapping session on the core grid by dividing the total number of seroconverted by the total days of exposure, i.e., (SI + si)/ [(SS + SI + ss + si)* interval], where (SI + si) was the number of animals known to be or scored as seroconverted between t – 1 and t, (SS + SI + ss + si) was the number of animals known to be or scored as susceptible on t – 1, and “interval” was the number of days between t – 1 and t. Individuals with MatAb were excluded from the susceptible population. According to population abundances observed here and in an earlier study9, the trapping sessions were divided into two categorical variables: three phases of the density cycle (variable “cycle phase”: “increase”, “peak”, and “low” phases; onset of phase on 1st June) and three vole density cycles (variable “cycle ID”: 2001− 2003, 2004− 2006, and 2007− 2009; onset of cycle on June 1st of the increase phase). June was selected as the onset of a biological year, as the first summer-born individuals usually entered the population in that month. Also a dichotomous variable “breeding season” was included in the data set, indicating whether the time interval between t – 1 and t predominantly belonged to the yearly breeding season or not. In addition to studying infection prevalence and seroconversion rate in the whole population, we examined the effects of age and cycle phase on the accumulation of infections and the seroconversion rate in yearly cohorts of bank voles. Thus, infection prevalences (“PUUV prevalence per year cohort”33) and seroconversion rates (“Seroconversion rate per year cohort”33) were calculated for each birth year cohort and for each trapping session they were present on the core grid. Age was determined as the number of months after 1st May of the year of birth, i.e., the earliest month when individuals of the year-born cohort could be expected to enter the population (although they were not observed in our trapping data). Birth years were divided into “increase”, “peak”, and “low” phases as above. Candidate models. The population-level dynamics of (a) the abundance of infected animals, (b) the prevalence of infection, and (c) the per capita seroconversion rate (datasets “Prevalence of PUUV per trapping session” and “Seroconversion rate per trapping session”33) were examined within two time-frames: a biological year and a three-year population density cycle. In yearly cohorts of bank voles (datasets “PUUV prevalence per year cohort” and “Seroconversion rate per year cohort”33), the accumulation of PUUV infections and the temporal variation in seroconversion rate were studied in a time-frame of 16 months, the assumed maximum life span of most bank voles34. Generalized additive models (GAM, gamm4 package30 for R software31) were applied in all analyses that included continuous temporal variables (i.e. “cycle month”, “month”, and “cohort age”). GAMs are nonparametric regression models often used for characterizing associations where the shape of the relationship between the predictors and the response is not known a priori – for instance, in temporal variation35. Generalized additive mixed models (GAMMs, function “gamm4”), i.e. GAMs including random effects, were used for population-level analyses on the abundance of infected individuals and infection prevalence (dataset “Prevalence of PUUV per trapping session”33), since the dataset included observations from several sites (core and 14 satellites). Models without random effects (function “gam”) were used for other analyses. The abundance of infected individuals was analysed using a log link function with Poisson error distributions. The trapping effort (number of trap nights) was used as an offset variable. The prevalence of infection and seroconversion rate (proportional outcomes) were analysed using a logit link function with binomial error distributions, weighted by the total number of individuals and exposure time (N susceptible individuals*interval between t – 1 and t), respectively. Overdispersion was accounted for by including an observation-level random effect in mixed models, and a dispersion parameter in models without random effects. www.nature.com/scientificreports/ 5 Scientific RepoRts | 6:21323 | DOI: 10.1038/srep21323 For the population-level dynamics (i.e., abundance of infected animals, infection prevalence and the per capita seroconversion rate in the total population), a set of candidate models was examined for each time-frame (see Table2). The “within-cycle” candidate model set included a full model consisting of factorial variable “cycle ID” and separate smooth terms of months elapsed (i.e. “cycle month”) from the cycle onset (1st of June) for each “cycle ID”. The full model of the “within-year” model set included the factorial variable “cycle phase” and separate smooth terms of time elapsed from 1st June (“month”) for each “cycle phase” level (Table2). All submodels nested within the full models were included in the candidate model sets. Seasonal variation in seroconversion rate was further studied using the dichotomous variable “breeding season” in dataset “Seroconversion rate per trapping session”33 as a temporal predictor instead of a continuous time variable. The full model of the candidate model set (“Variation in relation to breeding season”, Table2) was a generalized linear model with binomial error distributions and a logit link function, where the interaction between cycle phase and breeding season was set as an explanatory variable. In yearly cohorts of bank voles, the accumulation of PUUV infections and the temporal variation in seroconversion rate were studied with a candidate model set that included a full model consisting of the factorial variable “Birth year cycle phase” and separate smooth terms of cohort age (from 1st May) for each cycle phase, and all submodels nested within the full model (model set “Variation in relation to cohort age and birth year cycle phase”, Table2). All candidate model sets were ranked according to Akaike information criteria adjusted for sample size (AICc36, in MuMIn package37 for R software31). In model sets where dispersion parameters were used to account for overdispersion, quasi-AICc (QAICc) was used for model ranking38. In each candidate model set, the most parsimonious model within 2 AICc/QAICc units from the lowest AICc/QAICc score was considered the best supported by the data and subsequently used for statistical inference. For mixed model sets, an optimal random effects structure was chosen for each full model in a similar fashion prior to the selection of fixed effects, so that models with random variance attributable to a) trapping site b) trapping site for each factor level (“cycle ID” or “cycle phase”), and c) observation (to account for overdispersion), and combinations a + b and a + c were ranked according to AICc scores. In all analyses, P values below 0.05 were considered statistically significant. Results During the 7-year study, bank vole abundance exhibited a distinct three-year cycle in which the population increased, peaked and then crashed (Fig.1a). During the increase and peak phases, the highest abundances were Candidate models Dependent variable Abundance of infected Infection prevalence Seroconversion rate Within-cycle dynamics cycle ID + s(cycle month by cycle ID) 626.4 543.3 169.1 cycle ID + s(c ycle month) 646.9 533.2 186.0 s(cycle month) 762.9 530.1 184.3 cycle ID 643.9 953.7 193.9 intercept only 761.0 951.2 196.5 Within-year dynamics cycle phase + s(month by cycle phase) 578.1 507.4 134.6 cycle phase + s(month) 600.1 490.5 134.1 s(month) 610.3 497.2 136.5 cycle phase 1374.8 549.0 132.1 intercept only 1381.2 550.9 135.5 Variation in relation to breeding season (dichotomous) cycle phase * breeding season 130.4 cycle phase + breeding season 127.9 breeding season 130.4 cycle phase 130.8 intercept only 134.1 Variation in relation to cohort age and birth year cycle phase cycle phase + s(cohort age by cycle phase) 183.3 156.1 cycle phase + s(cohort age) 182.2 158.3 s(cohort age) 205.3 160.9 cycle phase 519.4 158.7 intercept only 642.2 161.9 Table 2. AICc (Akaike information criterion adjusted for sample size) scores of all candidate generalized additive models (GAMs). s(…) denote GAM smooth terms. The AICc scores of the best-supported models are written in bold. www.nature.com/scientificreports/ 6 Scientific RepoRts | 6:21323 | DOI: 10.1038/srep21323 reached at the end of the breeding season (August-October) while abundance remained low during the same period in low phases. A total of 1 108 bank voles were captured on the core grid, of which 615 (56%) were captured in at least two trapping sessions. At first capture, 337 individuals were PUUV seropositive, 766 were seronegative, and the antibody status could not be determined for five animals. The median number of captures per animal during separate trapping sessions was 2 (interquartile range 1− 4), and the maximum number of trapping sessions the same individual was captured was 14. In total, 3 286 captures (i.e., individuals known to be alive at a given trapping session t but for which an infection status could not be determined) were obtained: 1 415 (43%) testing PUUV seropositive and 1 871 (57%) seronegative (Table1). A total of 1 152 bank voles were caught on the satellite grids, of which 324 (28%) were PUUV seropositive, 790 (69%) seronegative and 38 (3%) could not be tested. Of the 766 initially-seronegative voles caught on the core grid, 499 were recaptured at least once during following trapping sessions, yielding a total of 1 240 recaptures where an animal’s infection status was SS or SI (Table1). Among these recaptures of susceptible animals, 223 seroconversions (18%) were recorded, of which 35% took place between the first and second capture, 31% between the second and third capture, and the remaining 34% between the third and fourteenth capture. Of the 337 initially-seropositive animals, 236 were first captured in their birth year between May and October and potentially, due to their young age, carried MatAb. Of these 236, 126 were recaptured at least once and 78 (62%) became seronegative and were thus considered to have been carrying MatAb. For the 200 captures of the remaining 158 initially-seropositive first-year animals, the assigned probabilities (calculated for each capture from the MatAb model based on body mass, see Supplementary Fig. S1) of being infected summed to 98.4, meaning that a total of 200 − 98.4 = 101.6 captures (51%, 0m + mm in Table1) were considered MatAb positive. MatAb+ animals were more common in peak phase cohorts than those born in other cycle phases and accounted for approximately 25–35% of seropositive animals in July of peak phases (Fig.1b). The number of susceptible, infected, and MatAb+ animals on the core grid were similar to pooled numbers of animals caught on the satellite grids (Fig.1b). Temporal patterns in PUUV infection. Abundance of infected voles. On the timescale of a three-year density cycle, the dynamics in the abundance of PUUV-infected bank voles on the core and satellite grids were best explained by a GAMM that included the factorial variable “cycle ID” and separate smooth terms of time elapsed from the cycle onset for each cycle (“cycle month”, Tables2 and 3). According to the model, infection patterns during density cycles 2004–2006 and 2007–2009 were similar, when the abundance of infected voles reached high peaks during both increaseand peak-phase winters (Fig.2a, Table3). On the core grid during increase phases, the peak abundance of infected individuals was 8.3 (95% confidence interval [CI]: 6.1–10.6) and 9.2 (95% CI: 5.0– 13.5) infected individuals/100 trap nights during the 2004–2006 and 2007–2009 cycles, respectively, and occurred in February. In peak phases, 7.8 (95% CI: 6.2–9.5) and 10.0 (95% CI: 5.5–14.5) infected individuals/100 trap nights were attained in late October and early December in 2004–2006 and 2007–2009 cycles, respectively. During the increase phases of these cycles, the peak abundance of infected voles followed the peak in total abundance with a lag of approximately 3.5 to 4.5 months, whereas during peak phases, the lag was only 2 months Figure 1. The number of bank voles on the core (lines) and satellite grids (crosses), (a) in total and (b) according to PUUV infection status. Values for the 14 satellite grids are pooled. Shaded areas indicate phases (Jun 1st to May 31st) of increasing (light grey), peak (dark grey) and low (white) vole density. In a, vertical lines indicate trapping sessions on the core grid. In b, colours indicate infection status: susceptible for PUUV infection (blue), infected with PUUV (red), and MatAb+ (green). www.nature.com/scientificreports/ 7 Scientific RepoRts | 6:21323 | DOI: 10.1038/srep21323 (Figs1a and 2a). The 2001–2003 cycle (data from April 2002 to June 2003) differed from the other two cycles in that the abundance of infected voles remained much lower and peaked during the summer of the peak phase with 3.1 (95% CI: 1.6–4.7) individuals/100 trap nights, then declined towards winter (Fig.2a). Within a time frame of one year, the abundance of infected animals was best explained by a model that included the factorial variable “cycle phase” and separate smooth terms for temporal variation (“month”) in each cycle phase (Tables2 and 3). During increase phases, a seasonal pattern could be seen with the abundance of infected animals increasing from 0.3 (95% CI: 0.1–0.8) in June to 4.3 (95% CI: 2.1–6.5) in early February, and thereafter declining to 3.1 (95% CI: 1.7–4.5) individuals/100 trap nights by the end of May (Fig.2b). During peak phases, the model suggested a rather stable level of infection from June to late November (4.6–5.0 infected individuals/100 trap nights, 95% CI: 2.1–7.2) thereafter declining to 1.1 individuals (95% CI: 0.4–1.7) by the end of Dependent variable/source of variation Within-cycle dynamics Within-year dynamics Abundance of Infected Parametric coefficients Estimate (SE) zP value Estimate (SE) zP value intercept − 4.15 (0.24) − 17.1 < 0.001 − 3.85 (0.15) − 24.9 < 0.001 cycle ID 2004–2006 − 0.02 (0.25) − 0.1 0.95 2007–2009 − 0.06 (0.71) − 0.1 0.938 cycle phase peak 0.40 (0.17) 2.4 0.016 low − 2.67 (0.36) − 7.5 < 0.001 Smooth terms Edf (rdf) χ2P value Edf (rdf) χ2P value cycle month by cycle ID 2001–2003 1 (1) 55 < 0.001 2004–2006 7.08 (7.08) 190 < 0.001 2007–2009 5.70 (5.70) 124 < 0.001 month by cycle phase increase 2.08 (2.08) 13.6 0.001 peak 2.08 (2.08) 22.7 < 0.001 low 1.95 (1.95) 4.4 0.105 Random effects σ2sd σ2sd site within cycle 2001–2003 0.64 0.80 within cycle 2004–2006 0.31 0.56 within cycle 2007–2009 1.34 1.16 observation 0.70 0.84 site 0.09 0.31 Infection prevalence Parametric coefficients Estimate (SE) zP value Estimate (SE) zP value intercept − 1.11 (0.15) − 7.3 < 0.001 − 0.85 (0.22) − 3.8 < 0.001 cycle phase peak 0.19 (0.30) 0.6 0.517 low − 0.77 (0.36) − 2.1 0.032 Smooth terms Edf (rdf) χ2P value cycle month/month 6.82 (6.82) 41.7 < 0.001 3.07 (3.07) 31.1 < 0.001 Random effects σ2sd σ2sd site within cycle 2001–2003 0.75 0.87 within cycle 2004–2006 0.98 0.99 within cycle 2007–2009 1.42 1.19 observation 0.43 0.65 Seroconversion rate Parametric coefficients Estimate (SE) zP value Estimate (SE) zP value intercept − 6.06 (1.28) − 4.7 < 0.001 − 5.31 (0.14) − 37.4 < 0.001 cycle ID 2004–2006 0.20 (1.30) 0.2 0.88 2007–2009 1.27 (1.85) 0.7 0.497 cycle phase peak − 0.27 (0.19) − 1.4 0.161 low − 2.19 (1.41) − 1.5 0.128 Smooth terms Edf (rdf) χ2P value cycle month by cycle ID 2001–2003 1.43 (1.76) 3.8 0.036 2004–2006 2.12 (2.71) 2 0.129 2007–2009 4.85 (5.53) 3.5 0.007 Table 3. Parameter estimates of the best-supported models analysing the within-cycle and within-year dynamics in the abundance of PUUV infected bank voles, infection prevalence and seroconversion rate. Edf = Estimated degrees of freedom; rdf = residual degrees of freedom; σ2 = the variance attributable to random effect. sd = standard deviation of σ2. www.nature.com/scientificreports/ 8 Scientific RepoRts | 6:21323 | DOI: 10.1038/srep21323 May. During low phases, the predicted abundance of infected animals was nearly constant and remained below one individual/100 trap nights (Fig.2b). Infection prevalence. The prevalence of PUUV infection on the core and satellite grids showed a clear seasonal fluctuation both within cycles (Fig.2c) and within years (Fig.2d). Models with distinct temporal patterns for separate cycles or separate cycle phases were not supported (Table2). The best supported within-cycle model only included the temporal component, i.e., a smooth term for the time elapsed from cycle onset, and the best within-year model included the seasonal component, i.e., a smooth term of time elapsed since the onset of the biological year and separate intercepts for cycle phases (Table3). According to the within-cycle model, the PUUV prevalence peaked in mid-March of the increase phase (prevalence 69.3%, 95% CI: 49.9–88.7%), and a second, but lower peak followed in mid-March of the peak phase (prevalence 52.7%, 95% CI: 30.7–74.7%). Within a cycle, the lowest infection prevalences occurred in early September during both increasing and peak phases (PUUV Figure 2. Variation in the (a,b) abundance of PUUV-infected bank voles/100 trap nights, (c,d) PUUV infection prevalence, and (e,f) seroconversion rate within the time frame of (a,c,e) a vole density cycle and (b,d,f) a year. Lines represent predicted values for the core grid from the best-supported models ((a) cycle ID + s[c ycle month by cycle ID]; (b) cycle phase + s[month by cycle phase]; (c) s[cycle month]; (d) cycle phase + s[month]; (e) cycle ID + s[cycle month by cycle ID]; (f) cycle phase; s[…] denote GAM smooth terms). Shaded areas indicate the 95% confidence intervals of parameter estimates. “C” and “S” in a− e denote observed data in core and pooled satellite grids, respectively. Numbers 1− 8 in f denote biological years 2001− 2008. The character sizes indicate the number of animals (c,d) and total days of exposure (e,f). Green, red, and blue colours indicate different cycles in (a,c,e) and different cycle phases in (b,d,f). www.nature.com/scientificreports/ 9 Scientific RepoRts | 6:21323 | DOI: 10.1038/srep21323 prevalences 12.4% [95% CI: 4.1–20.7%] and 17.8% [95% CI: 9.1–26.5%], respectively), and in early December during low phase (prevalence 4.2%, 95% CI: 0–10.2%; Fig.2c). The within-year model predicted a yearly prevalence peak in mid-March, when 59% (95% CI: 39–83%), 64% (95% CI: 44–86%), and 40% (95% CI: 20–65%) of bank voles were infected during increase, peak and low phases of the cycle, respectively (Fig.2d). The yearly bottom prevalence was predicted to occur in mid-September, when 15% (95% CI: 6–23%), 17% (95% CI: 8–26%), and 7% (95% CI: 2–13%) of animals were infected during increase, peak and low phases, respectively. In the within-year model, there was no significant difference between increase and peak phase prevalences, but during low phases the prevalence was significantly lower than during increase phases (Table3). Per capita seroconversion rate. From the within-cycle model set examining the per capita seroconversion rate, the best-supported model included separate smooth terms of time for different cycles (Tables2 and 3), and the patterns of seroconversion rate showed no similarity between cycles (Fig.2e). According to model predictions for cycle 2007− 2009, the seroconversion rate increased from July to January in increase phase 2007 and from August to October in peak phase 2008. In the within-year model set, the best-supported model included separate intercepts for cycle phases and no within-year temporal variation, although there was no significant difference between cycle phases (Tables2 and 3, Fig.2f). Although within-year models with temporal components were not well supported (Table2), an increasing trend in seroconversion rate was observed from August to October in increase phases 2004 and 2007, and peak phases 2005 and 2008 (Fig.2f). This increase was captured in within-cycle model predictions for years 2007 and 2008 (cycle 2007− 2009 in Fig.2e). In the model set where the continuous time variables were replaced with the dichotomous variable “breeding season”, the model “breeding season + cycle phase” was best supported (Table2), so that the seroconversion rate was significantly higher outside than during the breeding season (coefficient for non-breeding season = 0.39, SE = 0.18, z = 2.1, P = 0.036). PUUV infection prevalence and seroconversion rate in relation to cohort age and cycle phase at birth. Of the model set examining infection prevalence in relation to age and cycle phase of the birth year, the best-supported model included a common smooth term of cohort age but separate intercepts for each birth year cycle phase (Tables2 and 4). The gradual increase of infection prevalence with age was similar in increaseand peak-phase cohorts (Fig.3a): in both cohorts, nearly all surviving individuals were PUUV infected by the end of their second summer (increase phases: 91.4%, 95% CI: 83.4–99.4%; peak phases: 89.1%, 95% CI: 78.8–99.4%). In cohorts born during low phases, infection prevalence was significantly lower (Table4), reaching only 27.4% (95% CI: 0.0–65.5%) by the end of their second summer. Overall, the infection prevalence increased steadily with cohort age, although a period of steeper increase was observed between September and January. The total number of animals reached its peak between August and October in every year cohort (Fig.3a, dashed lines). However, the peak abundances of increase-phase cohorts (2004 and 2007: Fig.3a dashed red lines) were 40–50% lower than those of peak-phase cohorts (2005 and 2008: Fig.3a dashed blue lines). Seroconversion rate between two trapping sessions in a year cohort was best explained by a model that included separate smooth terms of age for cohorts born in different cycle phases (Tables2 and 4). The temporal patterns of predicted seroconversion rates were dissimilar (Fig.3b): seroconversion rate showed a steady increase from September to December in increase-phase cohorts, resulting in a steep increase in infection prevalence (Fig.3a). From December to March, the rate declined and increased thereafter coinciding with the onset of the breeding season and continuing into summer, although with wide confidence intervals. In peak-phase cohorts, no temporal trend was seen in seroconversion rate except for a slight, steady decline (Fig.3b). However, Dependent variable/source of variation Parameter estimates Infection prevalence Parametric coefficients Estimate (SE) zP value intercept − 0.16 (0.13) − 1.2 0.217 birth year cycle phase peak − 0.27 (0.15) − 1.8 0.08 low − 3.41 (0.93) − 3.7 < 0.001 Smooth terms Edf (rdf) χ2P value cohort age 3.89 (4.83) 48.5 < 0.001 Seroconversion rate Parametric coefficients Estimate (SE) zP value intercept − 5.53 (0.25) − 22.2 < 0.001 birth year cycle phase peak − 0.11 (0.30) − 0.4 0.716 low − 2.12 (1.70) − 1.2 0.219 Smooth terms Edf (rdf) χ2P value cohort age by birth year cycle phase increase 3.62 (4.49) 1.8 0.125 peak 1.00 (1.00) 0.1 0.714 low 1.00 (1.00) 0.9 0.358 Table 4. Parameter estimates of the best-supported models analysing the PUUV infection prevalence and seroconversion rate during the lifespan of a yearly bank vole cohort. Edf = Estimated degrees of freedom; rdf = Residual degrees of freedom.