Eva J P Lievens
email: eva.j.lievens@gmail.com

Nicolas O Rode

Julie Landes

Adeline Segard

Roula Jabbour-Zahab

Yannis Michalakis

Thomas Lenormand

Long-term prevalence data reveals spillover dynamics in a multi

Keywords: Artemia, host specificity, microsporidians, multi-host, reservoir host, seasonality, spillovers

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INTRODUCTION

Although many parasites infect multiple host species within their community [START_REF] Cleaveland | Diseases of humans and their domestic mammals: pathogen characteristics, host range and the risk of emergence[END_REF], Taylor et al. 2001), not all hosts are created equal. Host species vary widely in their degree of exposure to the parasite (e.g. [START_REF] Kilpatrick | Host heterogeneity dominates West Nile virus transmission[END_REF]), competence (e.g. LoGiudice et al. 2003[START_REF] Auld | Parasite transmission in a natural multihostmultiparasite community[END_REF], and population density (e.g. [START_REF] Dobson | The Ecology and Epidemiology of Rinderpest Virus in Serengeti and Ngorongoro Conservation Area[END_REF][START_REF] Rhodes | Rabies in Zimbabwe: reservoir dogs and the implications for disease control[END_REF][START_REF] Searle | Population density, not host competence, drives patterns of disease in an invaded community[END_REF]. These factors affect their contribution to the parasite's overall transmission, and thus to the maintenance of a persistent parasite population [START_REF] Dobson | Population dynamics of pathogens with multiple host species[END_REF][START_REF] Streicker | Differential sources of host species heterogeneity influence the transmission and control of multihost parasites[END_REF].

Quantifying the relative contribution of each host species to the persistence of a multi-host parasite is important for several reasons. Host species that contribute substantially to the parasite's maintenance have a strong influence on its epidemiology [START_REF] Viana | Assembling evidence for identifying reservoirs of infection[END_REF]) and evolutionary trajectory [START_REF] Holt | Virulence on the edge: a source-sink perspective[END_REF][START_REF] Benmayor | Host Mixing and Disease Emergence[END_REF][START_REF] Ching | Migration enhances adaptation in bacteriophage populations evolving in ecological sinks[END_REF], and can be pinpointed in the development of disease control strategies (Fenton andPedersen 2005, Streicker et al. 2013). Furthermore, asymmetrical transmission of parasites between host species can feed back into community structure [START_REF] Hatcher | How parasites affect interactions between competitors and predators[END_REF].

Unfortunately, identifying which host species contribute to the persistence of a parasite population is notoriously difficult [START_REF] Viana | Assembling evidence for identifying reservoirs of infection[END_REF]. Pathogens can persist in a host population if their basic reproduction number R0, which represents the number of secondary cases caused by a single infected host, is greater than one [START_REF] Dobson | Population dynamics of pathogens with multiple host species[END_REF][START_REF] Streicker | Differential sources of host species heterogeneity influence the transmission and control of multihost parasites[END_REF]). The host is then said to maintain the parasite population. In multi-host communities, each host species contributes to the parasite's overall R0, and an R0 greater than one can mask a lot of variation in those contributions. For example, [START_REF] Fenton | Are All Hosts Created Equal? Partitioning Host Species Contributions to Parasite Persistence in Multihost Communities[END_REF] considered a parasite infecting a simple two-host community, finding that it may fall into three underlying categories: i) a facultative multi-host parasite, which can be maintained by either host in the absence of the other (R0 would still be 4 greater than one if either host was removed from the community); ii) an obligate multi-host parasite, which can only persist when both hosts are present (R0 would be lower than one if either host was removed); iii) a spillover parasite, which can be maintained indefinitely by one of the host species, but not by the other (R0 in isolation would be respectively greater and lower than one). In the final case, the parasite's presence in the second, 'spillover' host is dependent on regular reintroductions from the 'reservoir' or 'maintenance' host [START_REF] Ashford | What it takes to be a reservoir host[END_REF][START_REF] Haydon | Identifying reservoirs of infection: a conceptual and practical challenge[END_REF]. Distinguishing between these fundamentally distinct, but superficially similar categories is challenging. Lab-based tests of host competence are insufficient, as the epidemiology of the system is key [START_REF] Searle | Population density, not host competence, drives patterns of disease in an invaded community[END_REF]; solutions therefore include the establishment of epidemiological, statistical, or genetic models, which may be labor-intensive and sensitive to assumptions [START_REF] Viana | Assembling evidence for identifying reservoirs of infection[END_REF].

Another way to identify into which category a multi-host parasite falls is to exploit variation in the composition of natural host communities (Fig. 1). For example, a parasite which can be found in communities containing both hosts A and B, but also in isolated populations of host A or host B, is clearly a facultative multi-host parasite. In contrast, a parasite that spills over from host A to host B can be identified by its presence in communities containing host A or hosts A and B, but repeated absence in communities containing only host B. This type of observation explicitly links the presence or absence of hosts to the presence or absence of disease, and can therefore lead to direct conclusions. Previously, such conclusions have mostly been drawn from human interventions, such as the vaccination or cull of a suspected reservoir (e.g. [START_REF] Dobson | The Ecology and Epidemiology of Rinderpest Virus in Serengeti and Ngorongoro Conservation Area[END_REF][START_REF] Caley | Effects of sustained control of brushtail possums on levels of Mycobacterium bovis infection in cattle and brushtail possum populations from Hohotaka, New Zealand[END_REF][START_REF] Macinnes | Elimination of rabies from red foxes in eastern Ontario[END_REF][START_REF] Nugent | The Role of Wild Deer in the Epidemiology and Management of Bovine Tuberculosis in New Zealand[END_REF][START_REF] Serrano | Decreasing prevalence of brucellosis in red deer through efforts to control disease in livestock[END_REF], or through the experimental construction of host communities (e.g. [START_REF] Power | Pathogen spillover in disease epidemics[END_REF]Mitchell 2004, Searle et al. 2016). However, the approach need not be limited to created variation: we can also look for 'natural experiments', host communities whose composition varies in the field. Of course, as natural experiments are not planned, due caution must be taken with regards to potential confounding factors. For instance, the epidemiology of a focal parasite may be shaped by an environmental variable (e.g. temperature, [START_REF] Altizer | Seasonality and the dynamics of infectious diseases[END_REF], Dunn et al. 2006), which happens to covary with the presence of a certain host. To ensure that the conclusions are robust to any such effects, the observations must be repeated across a range of relevant field conditions (e.g. different temperatures).

We illustrate the natural experiment approach using a two-host, two-parasite system: the brine shrimp Artemia franciscana and Artemia parthenogenetica, and their microsporidian parasites Anostracospora rigaudi and Enterocytospora artemiae. These species occur in sympatry in the French saltern of Aigues-Mortes (Rode et al. 2013a), whose interconnected basins form a metacommunity with patchy species composition. Taking advantage of this variation, we use long-term prevalence data to place the two parasites into the epidemiological categories described above (spillover, facultative multi-host, or obligate multi-host parasites), adding experimental tests of infectivity to investigate some of our conclusions in more depth. We find that the first microsporidian is a spillover parasite, whose spillover dynamics shape its seasonal prevalence. In contrast, we are unable to identify conclusively whether the second microsporidian is a spillover or a facultative multi-host parasite, and we discuss the relative merits of our approach compared to projections based on epidemiological models.

METHODS

Host-parasite system

Artemia (Branchiopoda: Anostraca), also called brine shrimp, is a genus of small crustaceans whose members populate salt lakes and salterns around the world. In Southern France, two 6 Artemia species coexist: A. parthenogenetica and A. franciscana. A. parthenogenetica is a parthenogenetic clade native to the area, while A. franciscana is a bisexual species native to the New World [START_REF] Thiéry | Bisexual populations of the brine shrimp Artemia in Sète-Villeroy and Villeneuve Saltworks (Languedoc, France)[END_REF]Robert 1992, Amat et al. 2005). A. franciscana was first introduced to this region in the 1970's (Rode et al. 2013c).

A. rigaudi and E. artemiae are microsporidian parasites of Artemia (Rode et al. 2013a). Although highly prevalent in Southern France (Rode et al. 2013c), they have only recently been described and little is known about their ecology. Both species parasitize the gut epithelium and are continuously transmitted to new hosts via free-living spores (Rode et al. 2013b(Rode et al. , 2013a)).

Previously, in a 'snapshot' sampling effort of the Mediterranean coast, A. rigaudi was found to be more prevalent in A. parthenogenetica, while E. artemiae was more prevalent in A. franciscana (Rode et al. 2013c).

We studied A. rigaudi and E. artemiae infecting A. franciscana and A. parthenogenetica in the saltern of Aigues-Mortes, in Southern France. This is a seasonal system, where both temperature (Fig. 2A) and salinity (Fig. 2B) vary throughout the year. Artemia hosts are present year-round in large quantities, but their average density varies by more than an order of magnitude between late winter and early summer (estimated at respectively ≤ 1 and 10-15 individuals/L; J. P.

Rullmann & P. Grillas, personal communication). The species composition of the Artemia community also varies seasonally: A. parthenogenetica are entirely absent in winter, but form the majority of the population in summer (Fig. 2C).

The Aigues-Mortes saltern forms a metacommunity of Artemia hosts with patchy species composition. The saltern is made up of a network of large interconnected basins, between which water is allowed to flow or not as a function of the salt production process. This causes environmental factors such as salinity and food quality to vary, leading to variation in the outcomes of inter-host competition: A. franciscana or A. parthenogenetica can outcompete one another or coexist [START_REF] Browne | Competition experiments between parthenogenetic and sexual strains of the brine shrimp, Artemia salina[END_REF][START_REF] Browne | Competition between sexual and parthenogenetic Artemia: a re-evaluation (Branchiopoda, Anostraca)[END_REF], Barata et al. 1996b). At any given time, therefore, adjoining basins can contain different host communities, though gene flow between the basins is regular enough that there is no genetic spatial structure in the Artemia populations [START_REF] Nougué | Automixis in Artemia: solving a century-old controversy[END_REF]. Within basins, Artemia populations are well-mixed [START_REF] Lenz | Ecology of Artemia[END_REF], so that we can assume that the microsporidians' spore pools are shared among the host species (cf. [START_REF] Fels | Transmission of the microsporidian Glugoides intestinalis in relation to spatial structure of the host Daphnia magna[END_REF].

Long-term field data

Data collection

We obtained prevalence data for A. rigaudi and E. artemiae from 94 samples of Artemia spp., collected at 14 different sites in the Aigues-Mortes saltern between 2008 and 2015 (Table 1).

There was no visible swarming behavior at any of the sampled locations at the time of collection (swarming skews microsporidian prevalence, Rode et al. 2013b). Samples were either processed immediately after collection, or stored in 96% ethanol and processed later; this prevented any infection-specific mortality from skewing the results. We tested a random subset of adult A. parthenogenetica and/or A. franciscana from each sample for the presence of A. rigaudi and E. artemiae (mean = 26.8 Artemia individuals/sample, sd = 24.3). In samples which contained both A. parthenogenetica and A. franciscana, we usually tested for infection in both host species.

Testing was done by PCR using species-specific microsporidian primers, following Rode et al. (2013a).

In addition to prevalence data, we had environmental and demographic data for most of the samples; we call these variables "sample-specific variables". First, we knew whether each sample came from a low-, middle-or high-salinity site. The absolute salinity at any given site can change dramatically from one day to the next if the water flow in the saltern is redirected.

However, the structure of the saltern means that the salinity at some sites is always lower, higher, or roughly equal to the average salinity of the saltern at that time. We classified these as low-, high-, and middle-salinity sites, respectively (n = 7, 3, and 3 sites; Table 1). This relative classification acts as a residual of salinity after seasonal effects are taken into account, and is not sensitive to the large variability of the absolute salinity measures. We were unable to assign a classification to Site 5, for which we lacked salinity information, or to the 8 samples with an unknown sampling site. Second, we had information on the species composition of each sample: whether it contained both A. franciscana and A. parthenogenetica (n = 65 samples), only A. franciscana (n = 25 samples), or only A. parthenogenetica (n = 3 samples). One sample had an unknown species composition.

Statistical analyses

The goal of our statistical analyses was to identify whether host community composition affected the prevalence of A. rigaudi and E. artemiae, while controlling for the two major environmental factors in the saltern (temperature and salinity). To do this, we analyzed the prevalence data of each microsporidian species in two steps: first, a general model of the prevalence over time; second, a more complex model that included sample-specific variables. All analyses were performed using generalized additive mixed models (in R version 3.1.3, R Core Team 2015, package "gamm4" Wood and Scheipl 2017), with the number of infected vs. non-infected hosts as the response variable (binomial response with logit link). Model comparison was done using the corrected AIC [START_REF] Hurvich | Regression and Time-Series Model Selection in Small Samples[END_REF]. 9 First, we constructed models to describe each microsporidian's yearly prevalence curve, using all of the collected data (n = 138 observations, of which 72 in A. franciscana and 66 in A. parthenogenetica). These models simply describe the seasonal dynamics of each parasite, i.e. they control for seasonal variation in temperature. We modelled Prevalenceij = Host speciesi x s(Monthj) + Samplej, where Prevalenceij represents the proportion of infected host species i in sample j, s(Monthj) represents a smoothing function of the continuous Month variable (the degree of smoothness is adjusted automatically, Wood and Scheipl 2017), and Samplej is a random effect controlling for the non-independence of prevalences in A. franciscana and A. parthenogenetica from the same sample. (We did not include the sampling site in our analyses because working salterns regularly re-distribute the water between basins, so Artemia sampled from the same site may or may not have the same genetic background.) The random effect Samplej was retained in all of the compared models. The resulting optimal models are hereafter referred to as the 'general models'.

Second, we investigated whether prevalence was affected by host species composition (our variable of interest) and the sample-specific salinity (which we wish to control for), independently of the variation already explained by our general models. We restricted our dataset to observations of A. franciscana, because we had no power to test the effect of species composition on prevalence in A. parthenogenetica (the latter was only present by itself in three samples). We further restricted our dataset to sites where the salinity classification and the species composition were known (n = 62 observations in total). For each microsporidian species, we used the general model from the previous section as a null model, and compared it to alternative models with added sample-specific terms. We modelled Prevalenceij = (fixed terms of the general model) x (Presence of A. parthenogeneticaj + Relative salinityj) + Samplej, where 10 Presence of A. parthenogeneticaj and Relative salinityj are the sample-specific variables of sample j, and Samplej is an observation-level random effect accounting for heterogeneity across samples (overdispersion, [START_REF] Harrison | A comparison of observation-level random effect and Beta-Binomial models for modelling overdispersion in Binomial data in ecology & evolution[END_REF]. Because Host speciesi was always A. franciscana, it was no longer necessary to include this fixed effect, as we did in the general models. The other fixed terms of the general model and the random effect Samplej were retained in all of the compared models.

In addition, we tested whether coinfection rates (with both A. rigaudi and E. artemiae) were significantly higher or lower than expected, as may be the case when parasites inhibit or facilitate each other, or when certain classes of hosts are more or less vulnerable. We used Cochran-Mantel-Haenszel tests (package "stats" in R version 3.1.3, R Core Team 2015) to test the independence of A. rigaudi and E. artemiae prevalence across samples; these tests were executed separately for A. franciscana and A. parthenogenetica.

Experimental tests of microsporidian infectivity

The results of our long-term field data revealed variation in prevalence among host species and on a seasonal basis (see Results). Since field patterns may not reflect functional relationships, we performed two experiments testing the infectivity of A. rigaudi and E. artemiae in each host and at two temperatures. In both cases, we detected infection using the PCR protocol described by Rode et al. (2013a), and we relied on their finding that an infection is detectable 5-6 days after the host has been exposed to parasite spores.

Experiment 1: Effect of temperature and host species/genotype on transmission

We tested the effects of temperature and recipient host species on parasite transmission by exposing non-infected A. franciscana or A. parthenogenetica to A. rigaudi and E. artemiae at 15 or 25°C. The purpose of this experiment was firstly to examine the effects of temperature on transmission, and secondarily to provide a rough idea of variation in susceptibility across host species. We did not aim to investigate asymmetric transmission (so we did not add a crossed 'donor host' factor), nor to thoroughly document variation in susceptibility across host genotypes (so we used a logistically feasible subset).

The non-infected, 'recipient' hosts were adult Artemia spp. pulled from laboratory stock collections of Aigues-Mortes lineages. For A. parthenogenetica, prior evidence suggests that microsporidian infectivity depends on host genotype (Rode et al. 2013b), so the recipient hosts were collected from four clones, P6 to P9 (P6 and P7 correspond to PAM6 and PAM7 in [START_REF] Nougué | Automixis in Artemia: solving a century-old controversy[END_REF]. For each clone, we used females from two lines, which had been maintained separately for several generations to standardize maternal effects. We named these recipient groups P6.1, P6.2, P7.1, P7.2, P8.1, P8.2, P9.1, and P9.2. For A. franciscana, we formed four replicate recipient groups, F1 to F4.

From each recipient group, 10 individuals were exposed to infection at 15°C, 10 individuals were exposed to infection at 25°C, 5 individuals served as negative controls at 15°C, and 5 individuals served as negative controls at 25°C. The recipient individuals were infected via exposure to infected 'donor' hosts. Donor hosts were a mixed group of A. parthenogenetica and A. franciscana, collected from sites in the Aigues-Mortes saltern with high prevalences of A.

rigaudi and E. artemiae (as ascertained by preliminary PCRs). Using a field-sampled group of mixed donors mimicked natural transmission conditions. Groups of 15 donors were placed in strainers above the jars of 10 recipient hosts. This allowed spores to pass through, but kept donors and recipients from mixing (Rode et al. 2013b). The strainers were rotated every 45 minutes to ensure randomized exposure. Exposure lasted for 9 hours, followed by a 6-day 12 incubation period. On day 6 of the incubation period, the surviving individuals were sacrificed and tested for the presence of A. rigaudi and E. artemiae as described previously.

Experiment 2: Effect of temperature and incubation time on A. rigaudi detection

In experiment 1, both microsporidians had low transmission success at 15°C (see Results).

However, the factors underlying this temperature effect were unclear: the reduced transmission could have been caused by a direct effect of temperature on the microsporidians (e.g. on spore germination, [START_REF] Undeen | The Effects of Temperature on the Survival of Edhazardia aedis (Microspora: Amblyosporidae), a Pathogen of Aedes aegypti[END_REF], or by indirect effects of temperature on the ectothermic hosts.

Cool temperatures lower the metabolic rate of Artemia [START_REF] Engel | The influence of salinity and temperature upon the respiration of brine shrimp nauplii[END_REF], thereby also lowering their defecation and ingestion rates (which would have reduced the effective inoculum size, [START_REF] Burns | Relation between filtering rate, temperature, and body size in four species of Daphnia[END_REF][START_REF] Larsen | Effect of temperature and viscosity on swimming velocity of the copepod Acartia tonsa, brine shrimp Artemia salina and rotifer Brachionus plicatilis[END_REF]) and dampening their cellular metabolism (which could have slowed the accumulation of microsporidian DNA in the host, [START_REF] Dunn | Transmission and burden and the impact of temperature on two species of vertically transmitted microsporidia[END_REF]. Since E. artemiae was present in the field in winter (see Results), we could infer that its low transmission success at 15°C must be due, at least in part, to indirect effects on the hosts. However, for A. rigaudi, which was not found in the field in winter (see Results), it was important to disentangle these confounding effects. To do this, we designed an experiment to compare the effects of temperature and incubation time on (detected) infectivity, while maintaining a standardized spore dose. We limited this experiment to testing A. rigaudi infecting A. franciscana.

In this experiment, we allowed A. rigaudi infections to incubate at 15°C or 25°C for different lengths of time (6 days vs. 12 days). We exposed adult A. franciscana from an uninfected laboratory stock population to feces containing A. rigaudi spores. The feces were collected from a laboratory stock of Artemia spp. infected with A. rigaudi; the spore concentration was unknown. Exposure occurred in groups: six groups of 20 hosts were placed in 50 mL autoclaved brine and 2.8 mL fecal solution was added. Four groups were exposed at 15°C, while two groups 13 were exposed at 25°C. Exposure lasted two days, during which time all spores could be ingested [START_REF] Reeve | The filter-feeding of Artemia I. In pure cultures of plant cells[END_REF]. After two days, hosts were separated and each individual was placed in a hemolymph tube containing 2.5 mL brine; this prevented between-recipient infections later in the experiment. The infection was allowed to incubate at the exposure temperature for four days, after which half of the surviving hosts from each group were sacrificed, and two of the groups exposed at 15°C were moved to 25°C. After a further six days of incubation, all remaining hosts were sacrificed and tested for the presence of A. rigaudi.

Statistical analyses

Statistical analyses of the experiments were performed using generalized linear mixed models (package "lme4", [START_REF] Bates | lme4: Linear mixed-effects models using Eigen and S4[END_REF], in R version 3.1.3, R Core Team 2015), with the number of infected vs. non-infected hosts as the response variable (binomial response with logit link). The significance of the predictors was tested using likelihood ratio tests.

For Experiment 1, we analyzed the probability of detecting an infection separately for A. rigaudi and E. artemiae. Fixed effects included Temperature, a Species/Genotype factor, and their interaction. As we expected to find differences among the A. parthenogenetica clones (Rode et al. 2013b), but could not distinguish between the mixed-together A. franciscana families, the Species/Genotype factor comprised 5 levels: A. franciscana, A. parthenogenetica P6, A. parthenogenetica P7, A. parthenogenetica P8, and A. parthenogenetica P9. We included Recipient group as a random effect to control for shared genetic and environmental effects.

For experiment 2, our statistical analyses used Exposure temperature, Periods incubating at 15°C, and Periods incubating at 25°C as fixed effects (with one period equal to six days), with Host group as a random variable controlling for pseudo-replication. Since the sensitivity of our 14 PCR was fixed, an increase in detectability over time reflects an increase in the quantity of parasite DNA present in the host (i.e. intra-host parasite reproduction).

RESULTS

Long-term field data

First, we described the prevalence dynamics for A. rigaudi and E. artemiae throughout the year (Fig. 3, Supp. Table 1). Anostracospora rigaudi was strongly seasonal: it was highly prevalent from August to October, but absent in winter (Month effect, ΔAICc ≥ 52.7; Fig. 3A &B). These seasonal dynamics were not different in the two hosts, but its prevalence was higher in A. parthenogenetica (effect of Host species, ΔAICc ≥ 156.2; Fig. 3A vs. B). The prevalence of E. artemiae was highly variable and this microsporidian was not strongly seasonal; nevertheless there was statistical support for temporal effects. The precise dynamics depended on the host species: E. artemiae's prevalence increased more steeply towards the end of the year in A. parthenogenetica than in A. franciscana (interaction between Host species and Month effect, ΔAICc ≥ 4.8; Fig. 3C &D). Overall, E. artemiae was more prevalent in A. franciscana (Fig. 3D vs. C).

Next, we introduced sample-specific effects to the general models, investigating the effects of salinity and the presence of A. parthenogenetica on the prevalence of the microsporidians in A. franciscana (Fig. 4, Supp. Table 2). For A. rigaudi, the species composition had a strong effect (ΔAICc ≥ 5.0): in the absence of A. parthenogenetica, the prevalence of A. rigaudi was almost always 0%, and never higher than 10% (Fig. 4B). In contrast, in the presence of A. parthenogenetica the prevalence could be very high, and the seasonal dynamics found in the general model reappeared (Fig. 4A). There was good support for an additional effect of salinity classification (ΔAICc = 2.1), with the high-salinity sites typically having lower prevalences. For E. artemiae, the salinity classification had no effect, and there was little support for an effect of species composition (ΔAICc ≤ -2.1). Finally, coinfection rates varied from 0% to 83% in our samples. Infection with A. rigaudi and E. artemiae was independent for A. parthenogenetica (Mantel-Haenszel χ 2 (1) < 0.1, p = 0.83, common odds ratio = 0.8), but was positively associated for A. franciscana (slightly more coinfection observed than expected; Mantel-Haenszel χ 2 (1) = 5.1, p = 0.02, common odds ratio = 1.9).

Experimental tests of microsporidian infectivity

Experiment 1: Effect of temperature and host/genotype on transmission

The P8 genotype of A. parthenogenetica and the F4 replicate of A. franciscana were previously infected (all controls tested positive for A. rigaudi and E. artemiae, respectively), so these recipient groups were removed from the analysis. Surprisingly, the previous infections appeared to have an inhibitory effect: none of the exposed P8s were infected with E. artemiae at the end of the experiment (n = 38), and none of the exposed F4s were infected with A. rigaudi (n = 21).

Temperature had a clear effect on the probability of infection of both A. rigaudi and E. artemiae; neither infected well at low temperatures (χ 2 (1) = 136.3 and 46.5, p < 0.001 in both cases; Fig. 5). Anostracospora rigaudi infected all three genotypes of A. parthenogenetica and A. franciscana equally well (χ 2 (3) = 0.8, p = 0.85), but infectivity was dependent on Species/Genotype in E. artemiae (χ 2 (3) = 18.1, p < 0.001; Fig. 5B). Post-hoc Tukey tests indicated that A. franciscana and P9 were equally susceptible to E. artemiae, while P6 and P7 were much less susceptible (z ≤ -2.9, p < 0.01). There were no significant interaction effects between temperature and Species/Genotype. Experiment 2: Effect of temperature and incubation time on A. rigaudi detection The probability of detecting A. rigaudi in A. franciscanai.e. the within-host accumulation of parasite DNAincreased significantly with incubation at 25°C, but not at 15°C (χ 2 (1) = 31.9 and 1.9, p < 0.001 and p = 0.18, respectively; Fig. 6). There was no significant effect of the exposure temperature on infectivity (χ 2 (1) = 0.0, p = 0.91). Therefore, the apparent reduction in infectivity of A. rigaudi at 15°C during experiment 1 was at least partly caused by slower parasite reproduction inside the hosts, delaying its detectability. The lower rates of detection after 6 days of incubation at 25°C in this experiment compared to experiment 1 could be explained by a lower initial spore dose.

DISCUSSION

When a parasite infects multiple host species, we cannot gain an adequate understanding of its epidemiology and evolution without knowing which of the host species actually contributes to its transmission and maintenance. In this study, we exploit natural variation in the composition of Artemia host communities to identify their microsporidian parasites as either spillover, facultative multi-host, or obligate multi-host parasites [START_REF] Fenton | Are All Hosts Created Equal? Partitioning Host Species Contributions to Parasite Persistence in Multihost Communities[END_REF]. Our primary result is that A. rigaudi is a spillover parasite, whose high infectivity in both hosts causes pseudo-endemic dynamics. Secondarily, we show that E. artemiae may be a spillover or a facultative multi-host parasite, and speculate that it is the first. Finally, we reflect on the link between the microsporidians' host specificity and seasonal dynamics, and discuss the merits of our method.

A. rigaudi is (secretly) a spillover parasite

Our long-term prevalence data revealed that A. rigaudi is a spillover parasite: it cannot persist on A. franciscana in the absence of A. parthenogenetica (Fig. 4). The presence of the parasite in A. franciscana must therefore be dependent on regular re-introductions from A. parthenogenetica, its maintenance host. This result is robust to variation in temperature (the effect can be found in every season, Fig. 3) and salinity (the effect is found across all salinity categories, results not shown), which are the main environmental factors that affect this system. Under the relevant natural conditions, populations of A. franciscana are unable to maintain A. rigaudi.

Parasites are unable to persist in a host species if that host provides little or no transmission, which may occur if its susceptibility, abundance, and/or lifetime spore production are low [START_REF] Dobson | Population dynamics of pathogens with multiple host species[END_REF][START_REF] Streicker | Differential sources of host species heterogeneity influence the transmission and control of multihost parasites[END_REF]. Our experiments ruled out the first possibility for A. franciscana and A. rigaudi, as both hosts were equally susceptible to the microsporidian (Fig. 5A). The second possibility, that sites containing only A. franciscana have consistently lower host densities, we also consider to be unlikely. Although the dataset used here does not contain demographic information, both observation and available evidence show that A. franciscanaonly sites can reach similar densities as sites containing both hosts (J. P. Rullmann & P. Grillas, unpublished data). Instead, Artemia biomass is mainly constrained by food availability [START_REF] Browne | Competition experiments between parthenogenetic and sexual strains of the brine shrimp, Artemia salina[END_REF], temperature (Barata et al. 1996a), and salinity (Wear and Haslett 1986), which are seasonal environmental factors (see Fig. 1). We therefore predict that the third possibility is most probable, namely that A. franciscana is a poor host for A. rigaudi because infected hosts produce few spores. Further experimental studies will be needed to confirm this hypothesis.

The contrast between A. rigaudi's generalist infectivity and status as a spillover parasite is particularly interesting. At first glance, its high infectivity in the two hosts, as tested in the lab and reflected in the field prevalences (Figs. 4A, 2A & B), may tempt us to conclude that A. rigaudi is a generalist parasite. Only a more detailed analysis of the field data belies this generalist infectivity, and reveals A. rigaudi's 'secret' host specificity. This result is a good demonstration of the dangers of using only infectivity as an indicator of parasite specialization and long-term success [START_REF] Agosta | How specialists can be generalists: resolving the "parasite paradox" and implications for emerging infectious disease[END_REF][START_REF] Lange | Genetic, ecological and geographic covariables explaining host range and specificity of a microsporidian parasite[END_REF]. In addition, the high infectivity of A. rigaudi in A. franciscana means that spillovers from A. parthenogenetica to A. franciscana occur frequently enough that the parasite appears to be independently present in both hosts (Fig. 2A &B). Some authors have termed such frequent spillovers 'apparent multi-host' or 'pseudoendemic' dynamics [START_REF] Fenton | Community epidemiology framework for classifying disease threats[END_REF][START_REF] Viana | Assembling evidence for identifying reservoirs of infection[END_REF])(cf. Dobson 1995[START_REF] Rhodes | Rabies in Zimbabwe: reservoir dogs and the implications for disease control[END_REF], terms which explicitly indicate the difficulty of distinguishing such dynamics from true endemism. This makes the danger of misinterpreting the epidemiology and evolution of pseudo-endemic parasites very high, if their community context is not investigated.

Anostracospora rigaudi itself is a good example of this problem. Based on previously available information, an earlier paper proposed that A. rigaudi could overwinter by infecting the invasive A. franciscana, thereby increasing its negative impact on the population of native A. parthenogenetica (a process known as "spillback", Rode et al. 2013c). In contrast, our current conclusion suggests that A. franciscana individuals act as inhibitory hosts, absorbing more A. rigaudi spores than they produce [START_REF] Holt | Parasite establishment in host communities[END_REF], and thereby diluting the effect of A. rigaudi on A. parthenogenetica (Ostfeld andKeesing 2000, Hall et al. 2009).

E. artemiae may be a spillover or a facultative multi-host parasite

Our prevalence data identified E. artemiae as either a spillover or a facultative multi-host parasite, but was insufficiently powerful to distinguish between the two possibilities. We were unable to analyze the ability of E. artemiae to persist on A. parthenogenetica because only three of our samples did not contain A. franciscana. Although these three samples were E. artemiaefree, this may have been due to chance. Based on our experimental test of infectivity, however, we suspect that E. artemiae is also a spillover parasite, in this case from A. franciscana to A. parthenogenetica. At high temperatures, E. artemiae is very infective in A. franciscana and the A. parthenogenetica genotype P9, but not at all in the genotypes P6 and P7 (Fig. 5B). These findings are consistent with the genotype-dependent prevalence data collected earlier by Rode et al. (2013b) in natural populations. Since A. parthenogenetica populations are a mix of different genotypes [START_REF] Nougué | Automixis in Artemia: solving a century-old controversy[END_REF], we can expect E. artemiae to infect A. parthenogenetica less well on average. (This interpretation is consistent with our field observations, but some caution is still merited. Since we only tested a small number of genotypes, it is possible that our results would change given a more thorough evaluation of the within-host genetic variation for resistance (cf. [START_REF] Luijckx | Cross-species infection trials reveal cryptic parasite varieties and a putative polymorphism shared among host species[END_REF].) The spillover effect of E. artemiae might therefore be even stronger than that of A. rigaudi, as the former appears to be more specifically infective than the latter. In the absence of conclusive evidence, however, this microsporidian serves as an excellent example of the difficulty of using observational data to identifying host contributions to parasite success (see discussion below).

Presence of maintenance hosts shapes parasite seasonality

When comparing the seasonal prevalence patterns of A. rigaudi and E. artemiae (Fig. 2) with the seasonal changes in host community composition (Fig. 1C), it quickly becomes clear that the two are linked. Anostracospora rigaudi's maintenance host, A. parthenogenetica, is entirely seasonal, being completely absent in winter and highly prevalent in late summer and autumn; logically, therefore, A. rigaudi is also strongly seasonal. In contrast, E. artemiae is able to persist throughout the year, with no overarching seasonal prevalence pattern. By testing infectivity at warm and cool temperature, and comparing the two parasites, we can gain further insight into A. rigaudi's absence in winter. Although low temperatures did slow parasite reproduction within the host when tested in A. rigaudi (Fig. 5), they did not make infection impossible for either parasite (A. rigaudi: filled triangle in Fig. 5;E. artemiae: open circles in Fig. 4B), and clearly do not preclude persistence of E. artemiae (Fig. 2D). Therefore, it is possible that A. rigaudi could persist in winter, if given the opportunity to do so by the presence of A. parthenogenetica. We therefore conclude that the general prevalence patterns of A. rigaudi and E. artemiae are predominantly shaped by host specialization, and not by environmental factors.

The seasonal cycles of A. rigaudi in its spillover host A. franciscana are of particular interest (Fig. 2B). These are clearly caused by the seasonal presence of the maintenance host A. parthenogenetica, which enforces seasonality on A. rigaudi and therefore on the frequency of spillovers. Seasonal cycles in prevalence occur in many host-parasite systems, and are often explained by factors such as climate, host behavior, and host immunity [START_REF] Hosseini | Seasonality and wildlife disease: how seasonal birth, aggregation and variation in immunity affect the dynamics of Mycoplasma gallisepticum in house finches[END_REF][START_REF] Duffy | Selective predators and their parasitized prey: Are epidemics in zooplankton under top-down control?[END_REF], 2009[START_REF] Grassly | Host immunity and synchronized epidemics of syphilis across the United States[END_REF][START_REF] Altizer | Seasonality and the dynamics of infectious diseases[END_REF][START_REF] Lass | Apparent seasonality of parasite dynamics: analysis of cyclic prevalence patterns[END_REF]. In contrast, our study presents a rare example of seasonal infections within a focal host (A. rigaudi in A. franciscana) being caused by the seasonality of an entirely different host (A. parthenogenetica). We know of one other description of such spillover-driven seasonality. [START_REF] Amman | Seasonal pulses of Marburg virus circulation in juvenile Rousettus aegyptiacus bats coincide with periods of increased risk of human infection[END_REF] investigated the prevalence of Marburg virus in bats, which causes severe hemorrhagic disease when it spills over to humans. They found that viral infections in bats peaked during birthing seasons, and that 83% of spillovers to humans occurred during these peaks. Such cases highlight the interconnected nature of communities hosting multi-host parasites, and are obviously of particular interest for the establishment of control strategies.

Extrapolating host contributions from observational data 21

In this study, we used variation in the composition of natural host communities to investigate how each host contributes to the maintenance of a shared parasite. The strength of this approach is illustrated by the results for A. rigaudi: we obtained direct evidence that under natural conditions, it is a spillover parasite dependent on A. parthenogenetica and unable to persist in A. franciscana. This conclusion, which we could not have drawn from tests of infectivity alone, has crucial consequences for our interpretation of the dynamics and evolution of both parasite and host. On the other hand, the results for E. artemiae highlight an important weakness of the method, namely that all possible combinations of communities must be sampled in order to obtain conclusive evidence. If one combination does not occur in the field, this constraint is inescapable. In this case, methods based on the construction of full epidemiological models, such as those described by [START_REF] Fenton | Are All Hosts Created Equal? Partitioning Host Species Contributions to Parasite Persistence in Multihost Communities[END_REF], may be more useful. These methods first quantify each host's contribution to parasite transmission using observed data on host abundance, parasite prevalence, and parasite shedding; they then infer the consequences for persistence from these results. While these techniques require the accurate measurement of all relevant parameters and an adequate mathematical description of the epidemiology, they are not dependent on wide-scale sampling across communities. Such methods are therefore useful where large-scale sampling is difficult or community composition is invariable, while the natural experiment approach could be particularly suited to highly variable and regularly sampled multi-host systems (e.g. Daphnia, [START_REF] Ebert | Host-parasite coevolution: Insights from the Daphnia-parasite model system[END_REF]). Independently of the method used, these (and our) studies consistently demonstrate the importance of studying a parasite within its entire host community before making inferences about its host specificity, epidemiological drivers, and selection environment.

Figure 1 .

 1 Figure 1. Using variation in the composition of host communities to categorize multi-host

Figure 2 .

 2 Figure 2. Seasonality in the Aigues-Mortes saltern. Each point represents one data point;

Figure 3 .

 3 Figure 3. General prevalence patterns of A. rigaudi (A & B) and E. artemiae (C & D) parasites

Figure 4 .

 4 Figure 4. Decomposition of Fig. 2B: prevalence of A. rigaudi in A. franciscana when A.

Figure 5 .

 5 Figure 5. Infectivity of A. rigaudi (A) and E. artemiae (B) as a function of temperature and host

Figure 6 .

 6 Figure 6. Detection of A. rigaudi infections in A. franciscana as a function of temperature

FIGURESFigure 1 .

 1 FIGURES

TABLES 641 Table 1 .

 6411 Sampling sites in the Aigues-Mortes saltern. 642

	Site	GPS coordinates	Salinity classification n(samples)
	Caitive Nord	43° 31' 10.84" N, 4° 14' 26.13" E	middle
	Caitive Sud	43° 31' 10.47" N, 4° 14' 26.12" E	low
	Fangouse	43° 30' 16.04" N, 4° 13' 28.75" E	high
	Pont de Gazette	43° 31' 04.63" N, 4° 10' 48.56" E	low	15
	Puit Romain	43° 30' 17.83" N, 4° 13' 27.16" E	middle
	Site 1	43° 29' 53.03" N, 4° 14' 23.06" E	low
	Site 3	43° 31' 02.65" N, 4° 14' 29.53" E	low
	Site 4	43° 32' 24.55" N, 4° 13' 25.90" E	middle	10
	Site 5	43° 32' 14.96" N, 4° 12' 41.52" E	(unknown)
	Site 8	43° 31' 37.17" N, 4° 10' 37.77" E	low
	Site 9	43° 32' 40.31" N, 4° 09' 16.59" E	high	20
	Site 10	43° 32' 40.10" N, 4° 09' 17.16" E	high
	Site 12	43° 31' 55.66" N, 4° 10' 23.92" E	low
	St. Louis	43° 32' 56.78" N, 4° 10' 07.95" E	low
	(unknown)	(unknown)	(unknown)

ACKNOWLEDGEMENTS

The authors thank F. Gout, O. Nougué, E. Flaven, and L.-M. Chevin for help with sampling, E.

Flaven for help with testing of sampled Artemia, and E. Decaestecker, M. A. Duffy and C. L.

Murall for their constructive comments on a previous version of the manuscript. We also thank the Salins d'Aigues-Mortes for access to the saltern. We acknowledge support from the QuantEvol (206734) grant from the European Research Council.

APPENDICES

Supplementary Table 1. Results of the overall models for A. rigaudi and E. artemiae infecting both Artemia hosts, before the addition of sample-specific variables. The full models included a smoothing function of Month, Host species, and their interaction. All models also included Sample as an individual-level random effect, to control for pseudoreplication. Models were compared using the difference in corrected AIC (ΔAICc); also provided are the degrees of freedom used (df) and the Akaike weights (w).

Model