Elevational Disease Distribution in a Natural Plant– Pathogen System: Insights from Changes Across Host Populations and Climate

Elevational disease distribution in a natural plant– pathogen system: insights from changes across host populations and climate

Jessica L. Abbate and Janis Antonovics

J. L. Abbate ([email protected]), Centre d ’ É cologie Fonctionelle et É volutive (CEFE), UMR 5175, CNRS, 1919 route de Mende, FR-34293, Montpellier, France. – J. Antonovics, Dept of Biology, Univ. of Virginia, Charlottesville, VA 22904, USA.

Understanding the factors determining the distribution of parasites and pathogens in natural systems is essential for making predictions about the spread of emerging infectious disease. Here, we report the distribution of the fungal anther-smut disease, caused by Microbotryum spp., on populations of the European wildfl ower Silene vulgaris over a range of elevations. A survey of several geographically distinct mountains in the southern French alps found that anther- smut disease was restricted to high elevations, rarely observed below 1300 m despite availability of hosts below this elevation. Anther smut causes host-sterility, and is recognized as a model system for natural host – pathogen interactions, sharing common features with vector-borne and sexually-transmitted disease in animals. In such systems, many biotic and abiotic factors likely to change over ecological gradients can infl uence disease epidemiology, including host spatial structure, pathogen infectivity, host resistance, and vector behavior. Here, we tested whether host population size, density, or connectivity also declined across elevation, and whether these epidemiologically relevant factors explained the observed disease distribution. We found that while none of these factor means changed across elevation, disease was signifi cantly more likely to occur at both higher elevations and in larger populations, the majority of which were found above 1300 m. Th e break in disease incidence was also associated with an apparent scarcity of these larger host populations between 1000 and 1300 m in elevation. Examining variation in climatic factors among host populations, we also showed that the probability of disease was higher in areas with historically colder, wetter, and more stable conditions. Th e restricted distribution of anther-smut disease in high-elevation S. vulgaris provides an opportunity for empirical study on range limits and disease distribution in natural alpine communities that are considered particularly sensitive to the eff ects of climate change.

Once marginalized in classical ecology textbooks, parasitic stability (Harvell et al. 2002, IPCC 2007, Laff erty 2009). organisms are now understood to play a major role in However, the determinants of disease distribution in natural structuring biological communities, population dynamics, systems have been relatively under-studied, limiting species evolution and global biodiversity (Anderson and our understanding of the full consequences of current and May 1981, Altizer et al. 2003, Guernier et al. 2004, future ecosystem perturbations. Hudson et al. 2006). Understanding how parasites and Anther smut is a naturally-occurring endemic disease pathogens are distributed in space and time is essential of fl owering plants in the pink family, Caryophyllaceae, for guiding vaccination programs (Real and Biek 2007), caused by host-specifi c lineages of the obligate basidiomy- setting quarantine guidelines, managing wildlife and cete, Microbotryum spp. Th e disease has large advantages nature reserves (Monz ó n et al. 2011), selecting agricultural for distributional studies because it is easily observable in cultivars (Wolfe 1985), and making predictions about dis- the fi eld without the need for special equipment, and it ster- ease emergence (Jones et al. 2008). Th e concurrent map- ilizes rather than kills its host, thereby facilitating assessment ping of species distributions and habitat features can off er of disease prevalence and occurrence not only in the powerful insight into ecological requirements and factors fi eld (Antonovics et al. 2002) but also using herbarium spec- potentially important for survival, reproduction, or disper- imens (Hood et al. 2010). As a pollinator-transmitted sal (Shea and Chesson 2002). For host – pathogen systems, sterilizing disease, anther smut shares characteristics these factors can aff ect the spread and persistence of disease with sexually-transmitted diseases in animals (Lockhart (Anderson and May 1981). With rapid climate change, this et al. 1996), and has been investigated with regard to knowledge is particularly important for anticipating the many broader ecological and evolutionary topics such as timing and intensity of endemic and epidemic diseases genome evolution, metapopulation dynamics, and speciation which may threaten human health, food supply and social (Bernasconi et al. 2009). Because its hosts are generally

1126 abundant and economically unimportant, it is a disease density and frequency of disease (prevalence) have complex whose natural distribution can be studied without the need impacts on sexual and vector-mediated transmission, and for quarantine or intervention, in contrast to many animal reduced transmission within populations can reduce or agricultural systems, whose study in unperturbed condi- spread and persistence in the metapopulation (Antonovics tions is diffi cult for logistic, economic or ethical reasons. et al. 1995, Biere and Honders 1998, Carlsson-Grané r and Anther-smut disease of the Caryophyllaceae is distributed Th rall 2002, Essenberg 2012). Additionally, delayed host world-wide, with disease generally occurring throughout phenology at higher elevations could further restrict the native range of each particular host (Hood et al. the availability of hosts if fl owering seasons do not overlap, 2010). However, in one particularly wide-spread host spe- and such temporal avoidance of disease within populations cies, Silene vulgaris, natural history observations suggested is associated with anther-smut resistance in S. latifolia that host-specifi c endemic disease on this species was (Biere and Antonovics 1996). restricted to high elevation populations above ca 2000 m Here, we conducted systematic surveys of Silene across much of its natural geographic range (Hood et al. vulgaris and its anther-smut disease to quantify disease dis- 2010, Antonovics and M. Hood unpubl.). Below this eleva- tribution across elevational transects within the host’ s tion, diseased S. vulgaris had been documented only in sym- native range in the French Alps. We tested whether concur- patry with diseased congener Silene latifolia, and molecular rent changes in host population size, density, or connectiv- markers showed that the disease on S. vulgaris at these low ity suffi ciently explained the observed pattern of disease elevations was caused by cross-species transmission from the incidence, and whether elevation was still important after S. latifolia -specifi c fungal lineage (Hood et al. 2003). Th ough these host spatial factors were considered. We then used common occurrence of anther-smut disease in high-elevation these survey localities and dates to explore the contribution S. vulgaris was noted as early as 1957 (Marsden-Jones of climate and host phenology, two ecological factors that and Turrill 1957), its incidence had never explicitly could aff ect disease spread in this system and are commonly been studied. Furthermore, despite the general extent of known to correlate with elevation. S. vulgaris distribution (Jalas and Suominen 1986), few details on the availability of host populations across the region were known. Methods High-elevation species distributions typically point to abiotic environmental explanations, particularly for plants Study species (K ö rner 2003). In disease systems, both temperature and rainfall can have varying eff ects on pathogen viability, Silene vulgaris host resistance and disease transmission (Harvell et al. Silene vulgaris (commonly known as ‘ bladder campion ’ ) is a 2002, Strange 2003, Kutz et al. 2009). A few studies on gynodioecious perennial member of the Caryophyllaceae, Microbotryum species infecting S. latifolia have noted that widely distributed across and native to Eurasia. Th e species is temperatures can alter fungal spore germination, mating, easily identifi ed by the infl ated hanging calyx with a and development of infectious hyphae (Hood and crown of fi ve deeply-notched petals, the dichasial infl ores- Antonovics 1998) as well as in planta infection success of cence, and opposing simple oblong-shaped leaves protrud- teliospore inocula (Schä fer et al. 2010). Whereas these two ing from each swollen node along the stem. Each plant can studies showed reduced activity under colder temperatures, have a large number of stems arising from a single taproot, Alexander and Maltby (1990) noted delayed disease allowing for counts of discrete individuals. S. vulgaris is expression after an incubator overheated to above 30° C for found from sea level to roughly 2400 m, largely in meadows, over 48 h in the weeks prior to fl owering. However, in this mown or grazed fi elds, and along steep embankments system, it is neither clear how climatic changes would or other land features prone to disturbance. aff ect epidemiological parameters, nor is it clear that climate Silene vulgaris is morphologically variable and several factors are the only important changes occurring across subspecies have been recognized (Marsden-Jones and Turrill elevation. 1957, Chater and Walters 1964, Aeschimann 1983), with Many spatially structured ecological changes can infl u- some having been distinguished as separate species in regional ence both host- and pathogen-mediated aspects of disease fl oras (e.g. Silene vulgaris ssp. maritima ϭ Silene unifl ora ). epidemiology (Antonovics et al. 1995, Ostfeld et al. Th ree subspecies have been noted as occurring in the study 2005), so it is important to distinguish changes in pathogen region (S. vulgaris ssp. vulgaris , S. v. spp. prostrata and S. v. distribution from changes in the distribution of its host spp. glareosa ). While S. vulgaris spp. vulgaris is the more (Laine and Hanski 2006). Th is is particularly important widely distributed and variable type, S. v. spp. prostrata and across environmental gradients, where the obvious changes S. v. spp. glareosa are described by smaller features and high- in abiotic factors may correlate with, but not be directly er-elevation rocky habitats. However, in crossing studies responsible for, changes in disease occurrence. In particular, these sub-species were found to be interfertile (Marsden- land use and habitat fragmentation, host life-history or Jones and Turrill 1957); there is no evidence that they have reproductive timing, vector community composition and separate pollinators or lack gene fl ow in the areas where they presence of biological competitors can all be aff ected by overlap, and individuals with intermediate morphology are changes in climate or geology. For example, declines in host found in the fi eld at the boundaries between their habitats population size and connectivity can reduce the attraction of (Marsden-Jones and Turrill 1957, Abbate and Antononvics vectors carrying infectious spores between stationary plant unpubl.). Furthermore, only a few populations at the highest populations (Essenberg 2012). Within-population host elevations of transects 3 and 4 were identifi ed as likely to be

1127 S. v. spp. prostrata , and removal of these populations from was carried out along six replicate elevational transects, the dataset did not qualitatively alter the results. Lacking spanning several separate peak and valley systems (Fig. 1). concrete species delimitations, in this study we therefore included all populations displaying characters within the Sampling protocol spectrum of S. vulgari s morphology and refer to them simply as Silene vulgaris . Each transect was traversed by car, initially stopping to mark and score all populations that were found, or where populations were continuously present stopping regularly Microbotryum spp. every 0.1 km. Th e upper portion of transect 1 (above 1700 m) Anther-smut disease is characterized by dark fungal was accessed only by foot. At upper elevations, plants some- spores produced in the place of pollen in the host’s anthers. times occurred continuously from fi eld to fi eld, and it was Disease is easily identifi able visually in the fi eld when the not possible to count all individuals. Instead, individual hosts are fl owering. Th e disease is caused by lineages of the populations were delimited by an area of ca 40 ϫ 40 m fungal species complex Microbotryum violaceum sensu lato, around the sampling point, which is similar to methods a basidiomycete in Microbotryales (Le Gac et al. 2007). used in previous meta-population studies of the congener All Microbotryum species produce similar disease symptoms S. latifolia (Antonovics et al. 1994). Th e majority of pollen in their hosts, with pollen being replaced by teliospores and dispersal in S. latifolia, primarily carried out by noctuid reduction of the female reproductive organs, leading to com- moths with relatively wide foraging ranges, occurs within plete sterility. After teliospores germinate, they undergo 40 m of a source (Richards et al. 1999), meaning the true meiosis and mate to produce a dikaryotic hyphal stage that inter-breeding group size in S. vulgaris could be even infects a new host, growing down into the plant where newly smaller if more short-range pollinators are involved. All developing infl orescences are infected. Th e pathogen over- populations were oriented by landmarks (such as fences, winters along with the host, and disease is expressed again groves of trees, roads, large boulders, and waterways) in during subsequent growing seasons. Permanent recovery of order to precisely re-locate if re-sampled. Th e location of diseased individuals as well as disease-induced mortality is each population was recorded (longitude, latitude) and thought to be rare based on observations of closely-related elevation (altitude above sea-level) was derived by spatial pathogen species on the related host S. latifolia (Antonovics query of coordinates to the United States Geological et al. 2002). Survey’ s Seamless Elevation data sets hosted at USGS/ Th ree distinct Microbotryum species specifi c to EROS (using an online interface ‘ E-Query ’ from Zonum Silene vulgaris have been identifi ed: M. silenes-infl atae , Solutions: Ͻ www.zonums.com/online/equery.phpϾ ). As M. lagerheimii and M. violaceo-irregularis (V á nky 1994, populations were located across habitats with a variety Le Gac et al. 2007, Lutz et al. 2008). Microbotryum of human disturbance and phenological timing, we periodi- species from closely-related S. latifolia and S. dioica cally re-traced transects 2 – 6 and added any missed popula- hosts have also been found to infect sympatric S. vulgaris tions to the database. We re-visited previously-marked sites (Antonovics et al. 2002, Hood et al. 2003). Species are when possible, but systematic re-sampling of all populations distinguished by their ribosomal internal transcribed was not attempted. spacer (ITS) sequence using basidiomycete-specifi c Population density of S. vulgaris was measured by count- forward and reverse primers (Hood et al. 2010), and ing the number of individuals within the population and M. violaceo-irregularis is also distinguished under a light dividing by the estimated area (no. mϪ 2). Density in larger microscope from the other species by the irregularly populations was measured within a 100-m 2 area deemed verrucose (i.e. with small projections on the surface rather representative of the density for the entire population. than a complete ‘ reticulated ’ network) morphology of its Total population size was determined by counting the total spore coat (V á nky 1994, Lutz et al. 2008). In this study, all number of individuals, or estimating this number for smut-infected S. vulgaris populations were included as populations with more than 200 individuals. Typically, ‘ diseased ’ , regardless of the fungal species responsible. most individuals in a given population fl owered synchro- Where possible (see results), teliospores from one fl ower nously, and only plants with open fl owers were included in bud per host plant were collected for Microbotryum species the counts. Where populations were surveyed multiple identifi cation. times, only counts from the sampling date on which the largest number of individuals were detected were used; Study area hence, we only used a single data point for each population in the analysis. Population connectivity was given as the Th e study was centered on the areas surrounding the Jardin inverse of distance to nearest neighbor. As populations Alpin du Lautaret, located in the confl uence of occurred over heterogeneous terrain, a Pythagorean measure several alpine mountain ranges east of Grenoble, France. of distance to nearest neighbor was calculated: the square Disease on S. vulgaris was fi rst noted here by Marsden- root of the sum of squared Euclidian distance and squared Jones and Turrill (1957). Over a period of fi ve years, from change in altitude. To meet assumptions of parametric August 2007 to October 2011 (Fig. 1; Supplementary statistical analyses, density was square-root-transformed, material Appendix 1 Table A1) 191 natural S. vulgaris and size and connectivity were each log-transformed. populations were identifi ed, in habitats ranging from Disease prevalence was calculated as the number of dis- open meadows and grazing fi elds, fallow agricultural and eased individuals (D) out of the total number of individuals garden land, rocky slopes, and along roadsides. Sampling scored in that population (N). To control for ascertainment

1128 Allevard Diseased Hosts 200m Elevation Healthy Hosts Transect Transect 1

St.-Michel-de- Maurienne

Transect 2

Transect 4 Transect 3

Transect 6 Transect 5

Figure 1. Map of presence (ﬁ lled circles) and absence (open circles) of anther-smut disease in Silene vulgaris populations detected from 2007– 2011 along multiple elevational transects (dashed lines). Lighter background colors denote higher elevations, and gray lines indicate 200 m clines in elevation.

bias introduced by non-randomly selecting populations regression, with population size, density, connectivity, with a minimum of one diseased individual (necessarily and elevation as fi xed factors, and disease status (diseased attributing higher prevalence with smaller populations), or healthy) as the binary response variable. All interaction prevalence was corrected by multiplying by (D – 1/N – 1) terms were tested, and those not signifi cant were removed (Antonovics et al. 1997). Disease incidence (defi ned as the in a step-wise fashion. Transect was also included as a fraction of populations with at least one diseased individual) fi xed factor to test whether the pattern was only specifi c was calculated as the proportion of diseased populations to particular mountains, and Moran’ s test on model out of the total number of populations present in a given residuals was used to check for spatial auto-correlation. area; incidence is given for each 100-m section of elevation Linear models were analyzed using R ver. 2.13.2, and surveyed, grouped over all transects. logistic regressions were performed with proc LOGISTIC in the SAS statistical package ver. 9.3. Data on prevalence, Analysis size and density were missing for just a few of the populations, and were thus excluded from the corresponding Th e relationship between disease incidence and elevation analyses. Due to the change in sampling methods along was tested using logistic regression (generalized linear transect 1 aff ecting detection of populations, connectivity model with a binomial logit link) of the raw proportion of data from this transect were removed from all analyses; no diseased populations weighted by the total number of other analyses were qualitatively aff ected by its inclusion. host populations present in each 100-m section. To test whether prevalence within diseased populations changed Elevational correlates across elevation, the arc-sin transformed ascertainment- corrected proportions, weighted by the total number of Elevational gradients are associated with a wide suite of sea- individuals per population, was regressed onto elevation. sonal and ecological changes over relatively short geographi- Th e within-population relationship between disease preva- cal distances. In this study, we did not directly measure any of lence (corrected and transformed) and population size and these correlated elevational factors, but climate and pheno- density was analyzed using multiple regression. logical information could respectively be extracted from the Assessing among-population processes, analysis of localities and dates at which population sampling occurred. covariance was used to test whether diseased populations Climate information for each population was tended to be larger, denser, or more connected than obtained from the WorldClim database of current average healthy ones along each transect; separate linear regres- monthly minimum, mean, and maximum temperature sions were used to test whether population size, density or and precipitation (years 1950– 2000) recorded from a net- connectivity also declined across elevation along each work of weather stations and interpolated over a 30-arc transect. Th e relationship between disease occurrence, second resolution grid (Ͻ www.worldclim.org/Ͼ ). Nineteen host distribution, and elevation was tested using logistic descriptive ‘ bioclimatic ’ variables derived from these data

1129 summarize the conditions experienced at a given location, 1.0 such as seasonal means and variability (Hijmans et al. 2005). Th e value of each variable was extracted from the grid for 0.8 each S. vulgaris population location using nearest-neighbor interpolation with the Spatial Analyst Tool in ArcMap 9.2 0.6 software (ESRI 2009). Principal components analysis was conducted on these 19 variables using the SAS procedure 0.4 PRINCOMP, as this procedure descriptively reduces a highly correlated dataset to a few orthogonal variables. Th e Disease Incidence 0.2 relationship between the major eigenvectors and elevation

was shown using linear regression, and logistic regression was (proportion of populations diseased) 0.0 performed as above to test whether disease was more likely to 500 1000 1500 2000 2500 be found under similar climatic conditions while correcting Elevation (meters) for transect and host population factors. To qualitatively determine whether the phenology of Figure 2. Anther-smut disease incidence in S. vulgaris across higher-elevation diseased plants was delayed relative to elevation, measured as the proportion of populations diseased lower-elevation healthy plants, possibly limiting contact for each 100 m in elevation surveyed. Circle size indicates the opportunity for transmission, the dates (month and number of populations surveyed in each 100-m section across all transects in the study area. Solid line (all populations, sig- day) of fl owering observed for both healthy and diseased nifi cant) and dashed line (populations above 1300 m, not sig- populations were compiled from all populations that were nifi cant) indicate the weighted regression of disease incidence visited throughout the growing season over the fi ve year across elevation. period of the study. Th e dates were then plotted against elevation to identify whether or not there were periods of temporal overlap in the phenology of healthy and diseased Fig. 3c), nor did means diff er above and below the 1300 m ϭ ϭ populations. limit to disease distribution (size: F1,166 1.41, p 0.24; ϭ ϭ ϭ density: F1,157 0.71, p 0.40; connectivity F1,168 1.28, p ϭ 0.26). Mean connectivity was signifi cantly diff erent Results ϭ ϭ between transects (F4,164 3.67, p 0.007), but inclusion of transect as a co-factor acted only to further weaken any Disease distribution ϭ evidence for a relationship with elevation (F1,164 0.46, ϭ ϭ p ϭ 0.5). Despite this lack of change across elevation, dis- Both diseased (n 51) and healthy (n 137) populations ϭ ϫ eased populations were signifi cantly larger (F1,156 20.28, were found across the 50 60 km extent of the survey Ͻ ϭ ϭ ϭ p 0.001), denser (F1,152 6.19, p 0.014), and closer area (Fig. 1). Disease occurred in ca. 36.8% (n 133) of ϭ ϭ all populations between 1300 m and 2600 m in elevation, to their neighbors (F1,160 5.05, p 0.026) than healthy above which hosts were scarce; diseased populations below populations in each transect. Signifi cance of these diff erences 1300 m in elevation were exceptionally rare (3.6%, n ϭ 55). were weakened only slightly by explicitly controlling for Correspondingly, disease incidence signifi cantly increased elevation. with elevation (Fig. 2; likelihood ratio χ 2 ϭ 21.2; p Ͻ 0.0001). When all measured factors were considered together However, considering only populations above 1300 m, in a single logistic regression model with disease occurrence the relationship between disease incidence and elevation as a binary response, probability of disease occurrence was lost (Fig. 1; likelihood ratio χ 2 ϭ 0.52; p ϭ 0.47). increased signifi cantly with rises in both host population size (likelihood ratio χ 2 ϭ 9.3, p ϭ 0.0024) and elevation DNA sequencing of the ITS region for fungal spores χ2 ϭ ϭ from 56 individuals including 34 diff erent populations (likelihood ratio 10.3, p 0.0013), and showed an showed that the predominant species present was upward trend with higher connectivity (likelihood ratio χ2 ϭ 2.2, p ϭ 0.13) (Table 1). Disease was more abundant M. silenes-infl atae (73.2% of all samples), and that it was χ2 ϭ Ͻ present in all transects and at all elevations (Supplementary in populations of 50 – 300 individuals ( 25.9, p 0.001), which were less numerous at lower elevations (χ 2 ϭ 21.2, material Appendix 1 Table A2). Microbotryum violaceo- Ͻ irregularis was only present in very high-elevation populations p 0.001; Fig. 3d); while population size means did not ( Ͼ 2000 m, except for one population at 1794 m) along diff er above and below the limit, there was a greater propor- transects 3 and 4, which straddle the highest peak surveyed tion of populations with 50– 300 individuals relative to those in other size classes above (56%) versus below (37%) (Col du Galibier, 3400 m). Just two populations contained χ2 ϭ ϭ M. lagerheimii, and both of these occurred at lower eleva- 1300m ( 4.7, p 0.03). Th ese larger populations tions on transect 2. were also noted as being abruptly absent from elevations between 1000 and 1300 m (Fig 3a). Moran ’ s I statistic indi- Host factors cated no confounding spatial-autocorrelation among the data in this model (p ϭ 0.61). When this analysis was Contrary to disease incidence, host population size, conducted on just populations above the 1300 m limit to density, and connectivity showed no signifi cant change disease, elevation was no longer signifi cant (p ϭ 0.42), ϭ ϭ 2 ϭ across elevation (size: F1,166 1.28, p 0.26, R 0.002, but disease probability was more strongly linked to ϭ ϭ 2 ϭ ϭ ϭ Fig. 3a; density: F1,157 1.31, p 0.25, R 0.002, population size (p 0.0012) and connectivity (p 0.08) ϭ ϭ 2 ϭ Fig. 3b; connectivity: F1,168 1.48, p 0.23, R 0.03, (Supplementary material Appendix 1 Table A3). Th ese

1130 (a) (c) 1.0 2.5

1.5 2.0

2.0 1.5

2.5 1.0

3.0 0.5 Host population connectivity (log 1/m) Host population

Host population size (log number plants) (log number size Host population 3.5 0.0

500 1000 1500 2000 2500 500 1000 1500 2000 2500 Elevation (m) Elevation (m) (b) (d)

Host population density Host population 2 (log number plants/sq. meter) (log number

500 1000 1500 2000 2500

Elevation (m)

Figure 3. Natural host population (a) size (log number of individuals), (b) density (log no. of individuals mϪ 2), and (c) connectivity (log distance (m) to nearest neighbor) across elevation for healthy (open circles) and diseased (closed circles) S. vulgaris populations. (d) Frequency of healthy (open bars) and diseased (fi lled bars) populations in each size class above and below 1300 m in elevation. results show that while host population size is important for positively with density (Fig. 4b; β ϭ 0.153, p ϭ 0.03). Host determining disease across the entire host distribution, the population size and density were highly positively correlated elevational contribution may only be important below some with each other when all populations were considered sort of threshold. (n ϭ 159, r ϭ 0.381, p Ͻ 0.001), and among just healthy populations (n ϭ 111, r ϭ 0.372, p Ͻ 0.001), but were Disease prevalence not signifi cantly correlated among diseased populations (n ϭ 47, r ϭ 0.228, p ϭ 0.12). Within populations where disease was present, prevalence showed no change across elevation (Supplementary material Elevational correlates Appendix 1 Fig. A1; for all diseased populations ϭ ϭ 2 ϭϪ (F1,47 0.009, p 0.92, adjusted R 0.021); for just Variation in climatic conditions across populations was ϭ ϭ Ͼ those above 1300 m (F1,45 0.259, p 0.61, adjusted suffi ciently described ( 95%) by the fi rst two composite R2 ϭ Ϫ0.016)). Prevalence did not relate to host popula- variables (Table 2). Th e fi rst climatic variable component, tion size (Fig. 4a; β ϭ Ϫ 0.02, p ϭ 0.74), but increased PC1, explained 89.7% of the variation among site values, and loaded equally with variables describing temperatures (positively), diurnal and seasonal temperature fl uctuations (positively), and precipitation (negatively) (Table 2). Th ere Table 1. Logistic regression model for probability of anther-smut was a strong negative linear relationship between PC1 and disease occurrence in S. vulgari s populations as a function of host ∗∗ population factors and elevation. Asterisks ( ) indicate signifi cant elevation, and this was slightly improved by a second- effects at p Ͻ 0.01. order polynomial fi t (Supplementary material Appendix 1 Fig. A2a; F ϭ 1416, p Ͻ 0.0001, R2 ϭ 0.98). Th e Host distribution and elevation Ϫ 2 Log L ϭ 144.28 7,178 AIC ϭ 162.28 second component, PC2, loaded strongly with precipita- Source DF Estimate Std. errorχ 2 p tion seasonality and less strongly with mean temperature of Transect 4 2.264 0.69 the driest season (both positively) (Table 2). Explaining ∗∗ Population sze 1 0.6765 0.2225 9.245 0.0024 a further 5.12% of the variation, PC2 had a signifi cant Host density 1 0.2058 0.7486 0.076 0.78 non-linear relationship with elevation (Supplementary Connectivity 1 0.2665 0.1782 2.237 0.13 ϭ Ͻ ∗∗ material Appendix 1, Fig. A2b, F8,177 21.3, p 0.0001, Elevation 1 0.0014 0.0004 10.283 0.0013 R 2 ϭ 0.46), such that the lowest values were on average

1131 (a) 0.30 found at intermediate elevations. Disease status of the population showed only a marginal trend for association with smaller PC2 values (Supplementary material Appendix 1 Fig. A2b; F ϭ 2.64, p ϭ 0.11). However, when PC1 0.20 1,177 and PC2 were together substituted for elevation in the logistic regression with transect, density, size and connectivity (Table 3), we found that the probability of disease 0.10 occurrence increased signiﬁ cantly with decreases in both PC1 (likelihood ratio χ2 ϭ 8.9, p ϭ 0.0028) and PC2 (likelihood ratio χ 2 ϭ 7.3, p ϭ 0.0068). According to the Disease prevalence (D-1/N-1) 0.00 signs and magnitudes of variables contributing to the 1.2 1.4 1.6 1.8 2.0 2.2 2.4 eigenvectors, disease was more likely to be found where

Host population size (log10 number total plants) overall temperatures were lower, fl uctuations in diurnal and seasonal temperatures were smaller, and where precipi- (b) 0.30 tation totals were greater and more stable. Inclusion of these climatic components improved the overall fi t of the model (Akaike information criterion (AIC) Score improved from χ2 ϭ 0.20 162.28 to 155.29, likelihood ratio test 8.99, p ϭ 0.0027), and Moran’ s I also showed these results were not aff ected by spatial autocorrelation (p ϭ 0.98). Again, when tested only on populations above 1300 m, population 0.10 size (p ϭ 0.0008) and connectivity (p ϭ 0.06) were the only factors related to disease occurrence.

Disease prevalence (D-1/N-1) Phenological overlap of both healthy and diseased 0.00 populations above and below the 1300m limit was detected –1.0 –0.5 0.0 (Supplementary material Appendix 1 Fig. A3a). While –2 Ͼ Host population density (log10 plants m ) host populations at very high elevations ( 2000 m) did not fl ower until later in the season (late July), those at Figure 4. Univariate plots of anther smut prevalence (proportion of mid-elevations regularly began fl owering much earlier (May), diseased hosts) within S. vulgaris populations as a function of and hosts at lower elevations continued to fl ower into the (a) size (total number of hosts), and (b) density (number of hosts per m2 ). Prevalence was corrected for ascertainment bias, calculated later part of the season. Diseased populations above 1300 m as specifi ed in the methods (D – 1/N – 1). Th e regression line indi- were found fl owering at the same time as the low-elevation cates signifi cant linear relationship (p ϭ 0.03). hosts throughout the season. Th e pattern did not change

Table 2. Results of principal components analysis on WorldClim climatic variables for S. vulgaris populations. The variables loading most heavily into each eigenvector are highlighted in bold print.

Component Eigenvalue Difference Proportion Cumulative PC1 17.0490755 16.0378351 0.8973 0.8973 PC2 1.0112404 0.3493519 0.0532 0.9505 PC3 0.6618885 0.5085376 0.0348 0.9854 Contribution of variables to components Variable PC1 PC2 PC3 Bio1 Annual mean temp0.241403 0.023772 Ϫ 0.043593 Bio2 Mean diurnal temp range0.23905 Ϫ 0.033132 0.015126 Bio3 Isothermality (Bio2/Bio7) 0.235732 Ϫ 0.061816 0.073075 Bio4 Temp seasonality 0.23813 Ϫ 0.056205 Ϫ 0.062867 Bio5 Max temp warmest month0.24155 Ϫ 0.00043 Ϫ 0.030042 Bio6 Min temp coldest month0.238784 0.076346 Ϫ 0.061887 Bio7 Temp annual range0.238715 Ϫ 0.061617 Ϫ 0.003823 Bio8 Mean temp wettest quarter0.234256 0.060593 Ϫ 0.151567 Bio9 Mean temp driest quarter 0.1346050.389909 0.900239 Bio10 Mean temp warmest quarter0.241401 0.015439 Ϫ 0.047765 Bio11 Mean temp coldest quarter0.241015 0.044999 Ϫ 0.05262 Bio12 Annual precipitation ؊ 0.240796 0.079511 0.00902 Bio13 Precip wettest month ؊ 0.235327 0.189334 Ϫ 0.053094 Bio14 Precip driest month ؊ 0.240949 0.021062 Ϫ 0.021522 Bio15 Precip seasonality 0.0909830.86766 ؊ 0.374392 Bio16 Precip wettest quarter ؊ 0.239179 0.131724 Ϫ 0.007691 Bio17 Precip driest quarter ؊ 0.241347 0.04266 0.024786 Bio18 Precip warmest quarter ؊ 0.241029 Ϫ 0.000054 0.016543 Bio19 Precip coldest quarter ؊ 0.239486 0.095652 0.029957

1132 Table 3. Logistic regression model for probability of anther-smut disease occurrence in S. vulgaris populations as a function of host ∗∗ population factors and climate. Asterisks ( ) indicate signiﬁ cant effects at p Ͻ 0.01.

Host distribution and climate Ϫ 2 Log L ϭ 135.29 AIC ϭ 155.29 Source DF Estimate Std. errorχ 2 p Transect 4 2.982 0.56 ∗∗ Population size 1 0.7611 0.2394 10.104 0.0015 Host density 1 0.5717 0.8181 0.488 0.48 Connectivity 1 0.3056 0.1841 2.754 0.097 ∗∗ climate PC1 ( ϩ Temp, ϩ Temp var, -precip) 1 Ϫ 0.2099 0.0703 8.919 0.0028 ∗∗ climate PC2 ( ϩ Precip var, ϩ Temp driest quarter) 1 Ϫ 0.8777 0.3243 7.326 0.0068 when each year and transect was considered independently artifactual, it could indicate that contrary to S. latifolia , vec- (Supplementary material Appendix 1 Fig. A3b). tor limitation is not important for the spread of anther smut in S. vulgaris . While host distribution appeared to be roughly similar Discussion among high and low elevations, there was a striking dearth of populations – particularly larger populations Th is study shows a clear elevational decline in the ( Ͼ 30 individuals) – detected between 1000 and 1300 m in incidence of anther-smut disease in Silene vulgaris in the elevation, where the incidence of disease also declined French Alps, and suggests that changes in both elevation sharply (Fig. 3a). Mid-elevation sections in the study area and host population size are involved in determining this were notably steep and rocky, leaving larger populations distribution. Common to one in three populations above either un-detected from the road or simply non-existent. 1300 m, the disease was exceptionally rare among Th is is important to note because small isolated host populations of the widespread wildfl ower below this populations – whether due to a change in land use, ecology elevation – a pattern repeated on several distinct mountains. or geology – could present a barrier to dispersal of the Th ese data confi rm and quantify earlier natural history pathogen (Antonovics et al. 1994, Carlsson-Gran é r and observations regarding the pattern of disease distribution in Th rall 2002). On the other hand, theoretical predictions S. vulgaris (Hood et al. 2010). We also documented the also argue that genetic susceptibility can be highest when occurrence of Microbotryum silenes-infl atae and M. lagerheimii host distribution is patchy, allowing the disease to persist in on S. vulgaris at low elevations, even though such metapopulations with fairly low levels of connectivity occurrences are rare, as well as the presence of M. violaceo- (Antonovics et al. 1994, 1997, Carlsson-Grané r and irregularis at the highest elevations. Th is suggests that the Th rall 2002). three Microbotryum species may have further distributional Climatic conditions that occur along elevational diff erences within that of diseased S. vulgaris (Supplemen- gradients were also strong predictors of disease occur- tary material Appendix 1 Table. A2). rence, and their inclusion alongside host distribution Diseased populations were larger, more dense, and more factors signifi cantly strengthened the model. It is there- connected than healthy populations, but no signifi cant fore likely that changes in temperature or precipitation declines in the means of these factors were detected at across elevation are involved in limiting disease to higher lower elevations. A more detailed analysis showed that dis- elevations. Among the locations surveyed in our study, ease was most often present in populations with between the lower temperatures and higher levels of precipitation 50 – 300 individuals, and that these populations were more characteristic of higher altitudes were also highly frequent at elevations above 1300 m (Fig. 3d). Th is is correlated with lower seasonal and diurnal temperature partially consistent with previous studies of the incidence of variation. Diurnal temperature fl uctuations in particular M. violaceum on the related host species S. latifolia which have been shown to be particularly infl uential for disease found that mid-sized populations were more likely to persistence in several systems (Lambrechts et al. 2006, be diseased than very small or very large populations Vale and Little 2009, Duncan et al. 2011). We also found (Antonovics et al. 1994, 1997). Such a non-linear pattern is that more stable precipitation conditions, particularly low potentially due to a balance between epidemiological pro- in the mid-elevation ranges, were also signifi cantly associ- cesses: smaller populations attract fewer long-distance pol- ated with increased probability of disease occurrence. In linators carrying spores from other populations (Essenberg other fungal pathogen systems, whose restive spore stages 2012) and are more prone to loss of disease due to stochastic are typically thought to protect against seasonal desicca- eff ects, while transmission in very large (often dense) tion, temperature and rainfall eff ects on epidemiological populations may decline due to low pollinator/host ratios parameters are well-known (Strange 2003). In contrast, (Antonovics et al. 1995). We did not fi nd a signifi cant what little confl icting information we have about decline in the incidence of disease among the largest size environmental eff ects on anther-smut infection has only class (Ͼ 300 individuals), though it is possible that our been conducted on a diff erent set of host and pathogen methods of population sampling in areas where there was species (S. latifolia and M. violaceum; Alexander and continuous host presence artifi cially minimized the sizes of Maltby 1990, Hood and Antonovics 1998, Schä fer et al. the largest populations. If this linear relationship is not 2010). In our study it is unclear what environmental

1133 mechanisms could lead to reduced incidence of disease the fl owering period during which disease transmission in low-elevation populations, but physiological impacts occurs. on pathogen infectivity or viability, host susceptibility, Compared to anther-smut disease in some of its conge- recovery, or longevity are all hypotheses that could be ners, we actually know very little about the agent and tested. Steady incidence and the loss of elevational eff ects mechanism of transmission in S. vulgaris . In this study, on disease distribution above 1300 m suggest that if there we determined that disease prevalence – occurrence within are physiological impacts of climate limiting disease to populations – did not decline along with disease incidence, high elevations, the mechanism is likely to involve a nor were there signifi cant changes in population density threshold. Th ermodynamic thresholds of enzymes dictate implicated in the decline. Biere and Honders (1998) many essential cellular processes, so this would be found that while infection rate of anther-smut disease in biologically reasonable to expect. natural S. latifolia populations was a function of the initial Climatic changes can also have indirect eff ects on frequency of infection and not density of hosts, they disease transmission, either through staggered phenology, found that host density did contribute to prevalence for community composition, or vector behavior. Overlapping transmission of disease in the pink-fl owered sister-species, phenology of hosts from diff erent elevations suggested S. dioica . Th ey posited that the short foraging range of there is at least some opportunity for contact between high bees pollinating S. dioica make contact more sensitive to elevation diseased and low elevation healthy populations. plant density than in S. latifolia populations, pollinated Moreover, in S. vulgaris , pollination and presumably mostly by noctuid moths that forage over larger distances disease transmission is accomplished by a suite of generalist (van Putten et al. 2007). We also found that disease pollinators spanning the orders of Coleoptera, Lepidoptera, prevalence was higher in more dense S. vulgaris populations Hymenoptera and Diptera (Marsden-Jones and Turrill (Fig. 4b) suggesting that short-range foragers are indeed 1957). Th is suggests that contact between host populations important vectors, at least at the high elevations where this does not change across elevation, even if there are changes was testable. in pollinator community composition. However, it is Th e presence of S. vulgaris -specifi c fungal lineages possible that changes in vector behavior, timing, and causing disease in at least two populations below 1300 m transmission effi cacy could result in changes in the disease suggests that there is no absolute barrier to transmission. If epidemiology and thus its distribution. there is some change in vector community or behavior In extending the distributional data to cover many across elevation (such as high-elevation distribution of a replicate valley systems, it was not possible to revisit every key moth species), it is possible that rare long-range disper- population, every year, nor multiple times per year. How- sal of disease to low elevations is accompanied by further ever, we noted qualitative presence or absence of disease in rarity of long-range dispersal from those newly infected 49 re-sampled populations (and quantitative prevalence in populations. If this is then coupled with some decrease in 25 of those). We found just two new disease colonizations, environmental suitability along with changes in the avail- two possible disease extinctions and one population in ability of large, attractive host populations, occasional epi- which the single diseased plant was detected over multiple demics at low elevations may not persist long enough to years in the exact same location within the population, but be sustained endemically in the metapopulation. We had not detected for one year in-between. All of the locations neither a sampling design nor suffi cient data to formally where there was disease colonization or extinction were test for persistence of disease in populations, and the two above 1300 m, so a higher ‘ turnover ’ of disease in popula- diseased sites below 1300 m were observed only in the tions appears unlikely to be a factor in aff ecting the second-to-last year of the study. diff erence between high and low elevation distributions. No Correlative studies such as these demonstrating the populations of hosts for which we attempted re-sampling relationship between disease distribution and various went extinct – even if they were not fl owering, vegetative environmental (biotic and abiotic) factors are useful for or disturbed remnants were always detected; no new host generating hypotheses (Rohr et al. 2011), but cannot claim populations were found in areas previously marked as to provide proof of causation. As an established model sys- unoccupied. Th e spatial patterns reported therefore appear tem for the study of natural host– pathogen dynamics to be relatively stable over years. As the observations of (Antonovics et al. 2002, Bernasconi et al. 2009, Alexander disease distribution in any given year were biased towards 2010), the characterization of a restricted disease distribu- under-reporting of previously observed populations, compli- tion provides an opportunity for empirical testing of cating their interpretation, we did not include this factor in hypotheses regarding the factors that determine disease dis- our analyses. Nevertheless, using logistic regression and tribution that may have direct analogies to disease systems repeated-measure mixed-model analyses, we determined that of economic and public health importance. Th ere have been any such natural or artifactual changes in both qualitative few studies on pathogen range limits in natural systems – disease distribution and quantitative prevalence, population and even fewer documented in systems where testing size and density across years in our dataset did not qualita- hypotheses about the cause of such range limits is tractable. tively alter the results or interpretation presented herein Exceptions include rust in California dwarf fl ax (Springer (Abbate and Antonovics unpubl.). In addition to such 2007), powdery mildew of Plantago (Laine and Hanski changes in natural factors, human land use diff erences 2006, Laine 2008), larval nematode infection of three- (e.g. mowing, grazing, farming) between high and low spined sticklebacks (Macnab and Barber 2012), and several elevation populations may play a role, potentially impacting arctic zoonotic diseases (Kutz et al. 2009). Th ese studies both the availability of suitable habitats, and the duration of reported mixed conclusions about the eff ect of environment

1134 on epidemiological parameters, with some systems more Alexander, H. M. 2010. Disease in natural plant populations, infl uenced by host resistance diversity (Springer 2007) communities and ecosystems: insights into ecological or local adaptation (Laine 2008), rather than direct envi- and evolutionary processes. – Plant Disease 94: 492 – 503. ronmental eff ects (Kutz et al. 2009, Macnab and Barber Alexander, H. M. and Maltby, A. 1990. Anther-smut infection of Silene alba caused by Ustilago violacea : factors determining 2012). Th ese varied results have led many to argue that fungal reproduction. – Oecologia 84: 249 – 253. climate impacts on disease distribution should be consid- Altizer, S. et al. 2003. Rapid evolutionary dynamics and disease ered on a case-by-case basis, but that very little data cur- threats to biodiversity. – Trends Ecol. Evol. 18: 589– 596. rently exist to support any broad generalizations (Laff erty Anderson, R. M. and May, R. M. 1981. Th e population 2009, Rohr et al. 2011). dynamics of microparasites and their invertebrate hosts. – Phil. In conclusion, this study shows that the rarity of Trans. R. Soc. B 291: 451 – 524. disease occurrence among host populations at low eleva- Antonovics, J. et al. 1994. Ecological genetics of metapopulations: tions may be due to changes in both climatic conditions the Silene – Ustilago plant – pathogen system. – In: Real, L. A. (ed.), Ecological genetics. Princeton Univ. Press, pp. 146 –170. and host distribution. Our results at this point cannot Antonovics, J. et al. 1995. A generalized model of parasitoid, exclude other hypotheses about what may be contributing venereal and vector-based transmission processes. – Am. Nat. to the limitation of disease distribution in this natural 145: 661 – 675. host – pathogen system, especially the role of host and Antonovics, J. et al. 1997. Genetics and the spatial ecology of pathogen genetic variation. Nor does our study point species interactions: the Silene –Ustilago system. – In: directly at a single mechanism whereby the observed envi- Tilman, D. and Kareiva, P. (eds), Spatial ecology: the role of ronmental gradient may result in reduced disease incidence. space in population dynamics and interspecifi c interactions. Such a connection between incidence of disease and Princeton Univ. Press, pp. 158 – 180. Antonovics, J. et al. 2002. Th e ecology and genetics of a host shift: climate has been well-established in agricultural systems. Microbotryum as a model system. – Am. Nat. 160: S40 – 53. Our study suggests that even in natural systems, where Bernasconi, G. et al. 2009. Silene as a model system in ecology there is the added complexity that individuals are geneti- and evolution. – Heredity 103: 5 – 14. cally variable, and occur at a range of densities in very Biere, A. and Antonovics, J. 1996. Sex-specifi c costs of resistance diff erent communities, there may still be a fairly direct to the fungal pathogen Ustilago violacea (Microbotryum connection between climatic factors and disease distribu- violaceum ) in Silene alba . – Evolution 50: 1098 – 1110. tion, something of great importance for predicting response Biere, A. and Honders, S. J. 1998. Anther smut transmission in of natural communities to climate change. Silene latifolia and Silene dioica : impact of host traits, disease frequency and host density. – Int. J. Plant Sci. 159: 228– 235. Carlsson-Gran é r, U. and Th rall, P. H. 2002. Th e spatial distribution Acknowledgements – We are enormously grateful to the following: of plant populations, disease dynamics and evolution of Christopher Winstead-Derlega, Kerri Coon, Andrea Berardi, resistance. – Oikos 97: 97 – 110. Peter Fields, Kim Gilbert, Colin Antonovics, Alia El-Kadi, Ahlia Chater, A. O. and Walters, S. M. 1964. Silene L. – In: Tutin, T. G. Sekkarie and Carolyn Farnsworth for their help and enthusiasm in et al. (eds), Flora Europea. Cambridge Univ. Press, the fi eld, lab, or both; Serge Aubert, Rolland Douzet, Karl Grigulis pp. 158 – 181. and the staff , researchers and volunteers at the Laboratoire Duncan, A. B. et al. 2011. Temporal variation in temperature d’ Ecologie Alpine and the Station Alpin du Lautaret for guidance, determines disease spread and maintenance in Paramecium facilities and endless hospitality; Doug Taylor, Laura Galloway, microcosm populations. – Proc. R. Soc. B 278: 3412 – 3420. David Carr, Butch Brodie, Keith Kozminski, Stephen Keller, ESRI (Environmental Systems Resource Institute) 2009. Michael Hood and Gael Kergoat for helpful discussions, ArcMap 9.2. ESRI, Redlands, California. and Sebastien Lion and Sylvain Gandon for post-doctoral support Essenberg, C. J. 2012. Explaining variation in the eff ect of fl oral of JLA. Th is work was funded in part by the National Science density on pollinator visitation. – Am. Nat. 180: 153 – 166. Foundation (DEB-0910212, DEB-0747222, and DEB-1115899 Guernier, V. et al. 2004. Ecology drives the worldwide distribution – part of the joint NSF-NIH Ecology of Infectious Disease of human diseases. – PLoS Biol. 2: e141. program), Th e Univ. of Virginia Dept of Biology, Sigma Xi Harvell, C. D. et al. 2002. Climate warming and disease risks Grants-In-Aide-of-Research, Univ. of Virginia Centers and Labs for terrestrial and marine biota. – Science 296: 2158 – 2162. Union Small Grants, Univ. of Virginia Small Research Grants, and Hijmans, R. J. et al. 2005. Very high resolution interpolated ANR grant 10-PDOC- 017-01 ‘ SPATEVOLEPID ’ . Sequencing climate surfaces for global land areas. – Int. J. Climatol. 25: was provided by Tatiana Giraud, Pierre Gladieux and Alodie Snirc, 1965 – 1978. and funded by the ‘ Consortium National de Recherche en Hood, M. 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Supplementary material (available as Appendix oik-01001 at Ͻ www.oikosjournal.org/readers/appendixϾ ). Appendix 1.

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