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Demography, dynamics and disease transmission in a population of Dianthus pavonius, an alpine
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carnation, heavily diseased by anther smut, Microbotryum sp.
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Emily Bruns1*, Michael Hood2, and Janis Antonovics1
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University of Virginia, Dept. Biology, Charlottesville, VA, 2Amherst College, Dept. Biology, Amherst
MA, * corresponding author. elb5m@virginia.edu
Key words: Aster-models, Endemic, Epidemiology Density-dependence, Fitness, Frequency-dependent
transmission, Juvenile-infection, Pollinator
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Running head: Disease dynamics within an endemic plant population
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ABSTRACT
1. To date most demographic studies of disease in natural plant populations have been carried out in
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systems with meta-population dynamics and high extinction colonization rates. In contrast, we
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know very little about disease dynamics in stable, endemic species.
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2. Anther-smut disease (Microbotryum spp) causes sterilizing symptoms on a wide variety of
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species in the Caryophyllaceae that differ in life history and ecology. Previous studies have
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focused on anther-smut diseases infecting geographically widespread plant species with meta-
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population dynamics. We investigated the dynamics of anther-smut disease on Dianthus
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pavonius, an endemic alpine carnation, with a continuous distribution and high disease
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prevalence.
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3. Marked plants were followed for six years in a heavily diseased (>40%) population of Dianthus
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pavonius. Demographic estimates and long-term census data were used to parameterize and
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validate a population dynamic model, and to determine the population and disease trajectories.
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4. Even though theory suggests that sterilizing diseases with frequency-dependent transmission
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could drive host populations to extinction, our model predicted long-term, stable host-pathogen
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coexistence even at high disease prevalence. Transmission to non-flowering juvenile plants was
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found to be essential for disease persistence.
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5. Aster models used to analyze lifetime fitness showed that this pollinator transmitted disease
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causes high sterility with little recovery, has no effect on host longevity. These fitness effects are
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significantly greater than those observed on the model anther-smut host, Silene latifolia.
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6. Synthesis: The stable dynamics of anther-smut disease on D. pavonius differ substantially from
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the extinction-colonization dynamics observed for other anther-smut systems, and, indeed for
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many other natural plant–pathogen systems. The effect of disease on plant populations depends
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on transmission dynamics and life history of the host. Thus similarities in disease natural history
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and pathology is insufficient for predicting population dynamics, and these results underscore the
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necessity of long-term demographic studies.
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INTRODUCTION
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Disease can affect host population growth in myriad ways, ranging from very minor effects (Alexander &
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Mihail 2000; Prendeville, Tenhumberg & Pilson 2014) to strong population regulation (Brunhamt &
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Anderson 1991; Antonovics 2004) or pathogen-induced extinction (Skerratt et al. 2007; McCallum et al.
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2009). Transmission mode and virulence play critical roles in determining these outcomes (Anderson &
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May 1991; De Castro & Bolker 2004; Antonovics 2009; Best et al. 2011). However, tracking disease
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transmission and evaluating fitness effects in natural populations can be challenging, particularly in
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systems where hosts and pathogens are long-lived, and where fitness effects can be cumulative and/or
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vary from year to year. Here we use demographic approaches to understand transmission and disease
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dynamics of a sterilizing anther-smut disease of the long-lived alpine carnation Dianthus pavonius.
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Anther-smut, caused by fungi in the genus Microbotryum, is a vector-borne, sterilizing disease of
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plants in the Caryophyllaceae that has become a model system for disease ecology (Bernasconi et al.
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2009). The disease has a fascinating natural history that greatly impacts our current understanding of its
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transmission and its fitness effect on the host. The fungus alters host flowering, causing the plant’s
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anthers to produce spores in place of pollen. Insect pollinators visiting diseased plants can disperse the
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spores to new hosts (Alexander & Maltby 1990; Roche, Alexander & Maltby 1994; Altizer, Thrall &
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Antonovics 1998). While infection with anther-smut has little effect on host mortality (Antonovics &
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Alexander 1989; Carlsson, Elmqvist & Url 1992), host fitness is significantly impacted because infected
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flowers are sterilized. Moreover, the disease most often appears systemic throughout the flowering stems,
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resulting in complete sterilization.
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This charismatic form of pollinator-born spore dispersal indicates transmission of anther-smut
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disease should share many features with vector and sexually transmitted diseases (Antonovics 2005).
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Vector-born and sexually transmitted diseases typically exhibit frequency-dependent transmission rather
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than mass-action (density-dependent) transmission because the number of contacts per individual
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generally does not increase with host density. Thus the probability that a given contact involves an
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infected individual is a function of the frequency of disease in the population (Anderson 1981,
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Antonovics 1989, Thrall 1993). Theory shows that the combination of frequency-dependent transmission
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and virulence in the form of sterility can be a potent recipe for disease-driven host extinction (Getz &
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Pickering 1983; Best et al. 2011): frequency-dependent diseases can persist even at low susceptible host
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densities (Getz & Pickering 1983; Antonovics 2009), and infected, sterilized individuals persist and
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continue to transmit disease resulting in high prevalence even through the loss of all host individuals
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(Anderson 1981; Thrall, Antonovics & Hall 1993; O’Keefe & Antonovics 2002). Observations from
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long-term census of anther-smut disease on a meta-population of Silene latifolia shows that infected
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populations tend to be smaller and have higher extinction rates than healthy populations (Antonovics
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2004). However, S. latifolia populations experience high background rates of extinction and colonization
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even in the absence of disease with both host and pathogen only persisting at the meta-population level
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(Antonovics et al. 1994). Indeed, to date most of our information on the disease dynamics in natural plant
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populations also come from species with meta-population-like dynamics (Thrall & Burdon 2003;
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Antonovics 2004; Laine 2007; Carlsson-Granér, Giles & Thrall 2014). We have relatively little
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information on how disease affects the abundance and persistence of continuous populations of plants.
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Here we report the results of a six-year demographic study of a heavily diseased population of the
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long-lived perennial Dianthus pavonius (the alpine carnation). Dianthus pavonius is endemic to the
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Maritime Alps region of Italy and France, and is found in high abundance and in continuous populations
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in meadow habitats above 1600m. We frequently observe extraordinarily high levels of disease incidence
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and prevalence (30-60%) in populations of D. pavonius (Antonovics, Hood, unpublished). This striking
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level of disease raises the obvious question of whether and how D. pavonius host populations are
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maintained in the face of such strong fitness impacts.
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Our first goal was to quantify fitness components and lifetime fitness for both the hosts and
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pathogens. To this end we used aster-models of life-history (Geyer, Wagenius, & Shaw 2007) to estimate
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expected lifetime fitness of healthy and diseased plants. Aster models provide a powerful new statistical
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approach for predicting lifetime fitness in perennial organisms, and evaluating the contribution of fitness
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components (Shaw et al. 2008). Our second goal was to evaluate the disease dynamics and determine
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whether the host and pathogen populations are likely to persist or die out as a result of the interactions.
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We therefore constructed a predictive model of disease dynamics using field estimates of mortality,
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flowering, and disease transmission.
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Our results show that anther-smut disease can persist stably, and at high prevalence within
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populations of D. pavonius despite strong negative effects on host fitness. Moreover we find that disease
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transmission to non-flowering plants plays a key role in maintaining the pathogen, demonstrating that
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transmission modes beyond those inferred from natural history observations are critically important to
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understanding the dynamics of this charismatic disease.
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METHODS
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Study site and species
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Alpine carnation, Dianthus pavonius (= D. neglectus) is a perennial herbaceous plant endemic to
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the Maritime Alps in France and western Italy, typically found in meadow habitats between 1600m and
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2300m in elevation. Flowering occurs for a 2-3 week period in mid summer, but individual plants do not
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necessarily flower each year. Infected plants produce the typical spore-bearing anthers that are seen in
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other anther-smut systems and the flowers are sterilized by the disease as the ovary also fails to mature
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properly. The Microbotryum species infecting D. pavonius is genetically distinct from those infecting
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Silene and other genera in the Caryophyllaceae (le Gac et al. 2007; Kemler et al. 2012). Three putative
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lineages of Microbtoryum have been found on D. pavonius plants in the Maritime Alps (Hood et al.
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unpublished) but only one of these lineages has ever been observed in the population studied here.
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We studied a population of D. pavonius at ca. 2000m near Rifugio Garelli, in the Parco Naturale
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del Marguareis (formerly Parco Naturale Alta Valle Pesio) in North-Western Italy. Formal census
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surveys and natural history observations at the Rifugio Garelli field site and across the park have found
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that D. pavonius is widespread from above 1600m (tree-line) to ca. 2300m. The population appears to be
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nearly continuous across the region, often reaching high densities of plants, and disease prevalence is
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extremely high (30-60%). In 2005, a 50 x 5m transect, which we called “Middle plot”, was established
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near the Rifugio Garelli field site. All flowering plants within this plot were counted and scored for
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disease status. The plot was re-censused in 2007 and 2014. In 2007 a “Lower plot” (30 x 10m) was
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established directly downslope of the Middle plot. Flowering plants in these plots were counted in 2007
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and 2014.
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Demography
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To understand the dynamics of disease spread, we set up a demographic study of marked plants
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within the 100m transect. Individuals were only included if they were flowering, so that disease status
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could be determined, and if they were distinct from other individuals. A maximum of 2 plants per 0.5 x
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0.5m quadrat were marked to avoid undue disturbance. Plants were marked using both green plastic
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coated wire as well as a 10 cent US coin (dimes) placed in the ground ca. 2cm downhill from the plant
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which could then be located using a metal detector. The first ‘cohort’ of 112 plants were marked in 2008,
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(90 healthy, 22 diseased) in the “Lower” transect plot. The term ‘cohort’ is used only to distinguish the
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year that plants were marked; the age of individual plants within each cohort was not known. Two
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additional cohorts were marked in 2009 (188 healthy, 76 diseased) and in 2012 (72 healthy, 42 diseased)
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throughout all sections of the transect.
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Survival, flowering or vegetative status, disease status, and the number of inflorescences were
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recorded for all marked plants in all years except 2012. In 2012 the majority of individuals flowered
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several weeks before the census period due to low snow cover and therefore disease status was estimated
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based on the presence of teliospores in old flowers, or the presence of healthy, developing fruits. If fruits
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were sterile but no spores were visible, the status was recorded as unknown since not only anther-smut
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disease but also seed predators such as hadenid moths can prevent seed production. Out of the 503 total
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marked plants, only 53 (11%) were lost or the scoring was ambiguous.
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Host fitness
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We used the ‘aster’ models of Geyer et al. (2007) to evaluate the effect of disease on host lifetime
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inflorescence production. These models provide a statistically rigorous method of estimating total
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lifetime fitness from multiple fitness components by explicitly modelling the dependence of later life
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history stages on the expression of earlier life history stages while taking into account differing sampling
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distributions (Shaw et al. 2008). Our model had three distinct life history stages each conditioned upon
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the previous: (a) survival to the next year, (b) flowering (i.e. whether the plant flowered or remained
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vegetative), and (c) the number of inflorescences produced in a year (Fig. S1). We used Bernoulli
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distributions to model survival and flowering, and a zero-truncated Poisson distribution to model
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inflorescence number. Distance along the transect was included as a covariate in all models. All analyses
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were carried out in R v2.12.0 (The R Foundation for Statistical Computing, 2010) using the ‘aster’
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package (Geyer et al. 2007).
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To evaluate the fitness cost of infection to the host, we categorized plants that were diseased at
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any point during the five-year period as ‘diseased’ and then used nested, unconditional aster models and
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likelihood ratio tests to evaluate the significance of disease on lifetime production of healthy
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inflorescences. For this analysis we used only plants marked in the 2008 and 2009 cohorts (N=376). We
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estimated means and 95% confidence intervals using the ‘predict.aster’ function.
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Pathogen manipulation of host traits
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To determine if the pathogen manipulated the expression of host life history traits we used aster
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models to compare survival and expected lifetime inflorescence production of healthy and diseased
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plants. We used only the subset of plants that did not change disease status: Since only 6% of plants were
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observed to change status this did not represent a significant reduction in sample size. We used likelihood
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ratio tests to compare aster models that did and did not include disease status as a factor.
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Rates of state transitions
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We used all marked plant cohorts to calculate conditional transition rates (equation 1-4) for three
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classes of plants: flowering-healthy (Nfh), flowering-diseased (Nfd), and vegetative (Nv). We calculated a
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single mortality and flowering rate for all vegetative plants rather than separating into healthy (Nvh) and
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diseased (Nvd) classes since we could not be sure of the disease status for vegetative plants that never
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flowered again.
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Probability of dying =
𝑁𝑖(𝑡+1)
𝑁𝑖(𝑡 )
= 𝜇𝑖
(1)
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Probability of flowering given survival =
𝑁𝑓ℎ𝑖(𝑡+1) +𝑁𝑓𝑑𝑖(𝑡+1)
𝑁𝑖(𝑡+1)
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= 𝜙𝑖 ′
(2)
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Probability of infection given survival and flowering =
𝑁𝑓𝑑(𝑡+1)
𝑁𝑓ℎ𝑡
= 𝑃𝑖′
(3)
= 𝛾𝑖′
(4)
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Probability of recovery given survival and flowering =
𝑁𝑓ℎ(𝑡+1)
𝑁𝑓𝑑𝑡
We then calculated the unconditional parameters by dividing the conditional probability by the
probability of detection. For example: 𝜙𝑖 = 𝜙𝑖′ /(1 − 𝜇𝑖 )
We assumed that all infections occurred during flowering (Fig S2). To calculate transmission
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rates, we first calculated the force of infection, P, which is the probability that an individual will become
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infected within a year, and does not depend on an assumption of frequency or density-dependent
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transmission mode. To estimate the transmission coefficient, 𝛽 we initially assumed frequency-dependent
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transmission 𝛽𝑖 = 𝑃𝑖 (𝑁𝑓𝑑/(𝑁𝑓𝑑 + 𝑁𝑓ℎ)). Since we did not have census data for each year of the study,
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we used the average prevalence observed in 2007 in the middle plot, (0.41) to calculate 𝛽. Census data in
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2014 show little change in prevalence (0.39) suggesting that disease remained fairly constant (Antonovics
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et al. in prep).
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Population model
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We used the estimated mortality, flowering, recovery, and transmission rates to parameterize a
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difference equation model of D. pavonius population growth and infection (Equations 5-10). We assumed
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that new individuals were recruited from the flowering healthy class into the vegetative healthy class, at a
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rate of b, limited by host population density, such that b’, the rate of establishment was
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𝑏 ′ = 𝑏⁄(1 + 𝑘𝑁)
(5)
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where k is a constant that describes the strength of density-dependence and N is the total
population size.
We initially assumed that the force of infection, 𝑃 was frequency-dependent such that 𝑃 =
𝑁𝑓𝑑
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𝛽 (𝑁𝑓ℎ+𝑁𝑓𝑑). We also assumed that only flowering plants could become infected since pollinators are
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unlikely to visit non-flowering plants. The dynamics of the model are described by the equations (6-9).
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For simplicity, the subscript t has been left out of the right hand side of the equation.
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𝑁𝑓ℎ(𝑡+1) = 𝑁𝑓ℎ(1 − 𝜇𝑓ℎ )𝜙𝑓ℎ (1 − 𝑃) + 𝑁𝑓𝑑(1 − 𝜇𝑓𝑑 )𝜙𝑓𝑑 𝛾 + 𝑁𝑣ℎ (1 − 𝜇𝑣 )𝜙𝑣 +
𝑁𝑣𝑑 (1 − 𝜇𝑣 )𝜙𝑣 𝛾
(6)
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𝑁𝑓𝑑(𝑡+1) = 𝑁𝑓𝑑(1 − 𝜇𝑓𝑑 )𝜙𝑓𝑑 (1 − 𝛾) + 𝑁𝑓ℎ(1 − 𝜇𝑓ℎ )𝜙𝑓ℎ 𝑃 + 𝑁𝑣𝑑(1 − 𝜇𝑣 )𝜙𝑣 (1 − 𝛾)
(7)
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𝑁𝑣ℎ(𝑡+1) = 𝑁𝑓ℎ ∗ 𝑏 ′ + 𝑁𝑣ℎ(1 − 𝜇𝑣 )(1 − 𝜙𝑣 ) + 𝑁𝑓ℎ(1 − 𝜇𝑓ℎ )(1 − 𝜙𝑓ℎ )(1 − 𝑃) +
𝑁𝑓𝑑(1 − 𝜇𝑓𝑑 )(1 − 𝜙𝑓𝑑 )𝛾 + 𝑁𝑣𝑑(1 − 𝜇𝑣 )(1 − 𝜙𝑣 )𝛾
(8)
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𝑁𝑣𝑑(𝑡+1) = 𝑁𝑣𝑑(1 − 𝜇𝑣 )(1 − 𝜙𝑣 )(1 − 𝛾) + 𝑁𝑓ℎ(1 − 𝜇𝑓ℎ )(1 − 𝜙𝑓ℎ )𝑃 +
𝑁𝑓𝑑(1 − 𝜇𝑓𝑑 )(1 − 𝜙𝑓𝑑 )(1 − 𝛾)
(9)
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We used the parameterized model to predict the attrition rate of marked plants assuming that
establishment was not possible (b=0), and that disease transmission was frequency dependent. We used
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the number of marked flowering healthy and diseased plants in 2009 as our starting conditions, and
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compared the predicted results to the observed change in the number and disease prevalence of marked
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plants.
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Next, we predicted changes in overall population size and disease prevalence, assuming
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establishment was possible. A full census of all flowering plants in 2005, 2007, and 2014 was available
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for the ‘middle’ section of the transect (5 x 50 m). In 2007 and 2014 a census was also carried out in
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lower section of the transect, a 10 x 30m section down-slope from the middle section (Antonovics et al. in
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prep).
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To estimate the birth rate we used the number of healthy and diseased plants in the middle
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transect plot in 2005 as the starting conditions, and then ran 10-year simulations over a range of birth
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rates to determine which values best predicted the observed population size and prevalence in 2014. We
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repeated the simulations using the data from the 2007 lower transect plot census as the starting conditions.
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RESULTS
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Host fitness
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The results from our aster analysis showed that the disease had a very strong negative impact on
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expected lifetime fitness of D. pavonius (Df=1, Dev.=15.025, p<.0001). Predicted fertile inflorescence
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production for healthy plants over 6 years was 7.73 ± 0.38 (95% CI), but was just 4.74 ± .67 (95% CI)
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for plants that were diseased at some point. There was no evidence of increased mortality in infected hosts
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(Table S1). Mortality rates tended to be higher for diseased plants in 2012 (Fig.1A) when overall
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mortality was higher but the difference was not statistically significant (Dev=4.055, p=0.1317). Partial
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infection was rare; only 7% of infected plants were ever observed to simultaneously produce both healthy
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and diseased flowers. Position along the transect also had a significant effect on expected lifetime
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inflorescence production (Df=1, Dev=44.39, p<0.0001), with healthy plants at the upper end of the
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transect producing fewer inflorescences. Since spatial variation is not the focus of this paper, we do not
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pursue this result further, but we left the transect position in the model.
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Pathogen manipulation of host traits
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Aster analysis found that diseased plants were more likely to flower (Dev= 4.3424, p =0.0372)
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and to produce more inflorescences than healthy plants (Dev =15.025, p= 0.0001). Expected inflorescence
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production over 6 years was 8.06 ± 0.53 (95% CI) for healthy plants and 8.89± 0.43 (95% CI) for
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infected plants. Although the difference in fitness components any given year was not statistically
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significant (Fig. 1) these life history differences added up to a small, but statistically significant greater
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lifetime inflorescence production as the result of infection.
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Transmission and recovery rates
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Transmission and recovery events were rare: only 27 plants (6%) were observed to change
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disease status over the 6-year period. Of these 19 were unambiguous transitions: 15 infections and 4
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recoveries. The other 8 plants were observed to change status multiple times and were excluded from
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further analysis, as they were likely diseased plants that experienced temporary recovery or partially
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diseased plants, or clumped individuals of several intertwined stems. The overall low rates of infection
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and permanent recovery make it extremely unlikely that the same plant would go through more than one
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transition in a six-year period. True recoveries appeared quite rare (Table 1), and we calculated Υ =
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0.029.
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The force of infection varied over the six years of the study with the highest rate occurring in
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2012 (Fig. 2). The weighted average force of infection over all years was 0.07, resulting in a frequency-
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dependent transmission coefficient of 𝛽𝑓 = 0.171 (Table S2).
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Attrition model
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We tested the parameterized population dynamic model (Equations 5-10) by inputting the
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numbers of healthy and infected marked plants in 2009 and comparing the attrition rate and change in
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disease prevalence predicted by the model with the observed data. We found the model provided
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reasonable predictions of plant attrition and disease prevalence over time with the exception of 2012,
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where the observed disease prevalence was much lower than expected (Fig. 3). There was a summer
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drought in 2012 that lead to high mortality rates, especially among the diseased plants (Fig. 1A) and
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lower flowering rates.
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Dynamic model
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Next we tested the ability of the model to predict changes in the census population size and
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disease prevalence observed in the ‘middle’ and ‘lower’ census plot. We ran simulations with the number
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of flowering healthy and diseased plants in the first census year as the starting conditions and allowed
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birth rates to range from b= 0 to 20. We assumed that the population sizes at the initial census (815 for
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the middle plot, 1936 for the lower plot) were close to carrying capacity as most of the open space in both
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plots contained D. pavonius plants (Antonovics et al. in prep), and we therefore set the carrying capacity
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to K=1000, and 2000, respectively, by setting the density dependent parameter to k=0.001 and 0.0005.
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For the middle plot, we found that a birth rate of b=1.8 predicted a reasonable match to the observed
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flowering population size in 2014 (Fig. 4A) but drastically underestimated the disease prevalence in 2014
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(Fig. 4B). Indeed no combination of b or k could predict the disease prevalence in 2014. Results for the
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lower plot were similar: b=1.8 provided the best prediction of population size (Fig. 4C), but resulted in an
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under-prediction of disease prevalence (Fig. 4D).
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Transmission to juveniles
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The consistent under-prediction of disease in all transmission models strongly indicates that an
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important transmission parameter in our model is either missing or severely underestimated (see also
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Discussion). One hypothesis is that the missing transmission is occurring among vegetative plants: we
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attempted to quantify separate transmission rates to flowering and vegetative plants (𝛽𝑓 , 𝛽𝑣 ) from the data
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by assuming that transmission occurred during the last calendar year rather than the last flowering year
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(Fig. S2). Using this method we found that transmission to vegetative plants appeared significantly higher
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than transmission to flowering plants (𝛽𝑓 = 0.142, 𝛽𝑣 = 0.581), however the sample sizes for detecting
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vegetative transmission were extremely low (Table S2), and could be upwardly biased if vegetative
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diseased plants are more likely to flower. Thus, we have little confidence in this estimate. More
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importantly, we can think of no biological reason why non-flowering adult plants should have higher rates
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of disease exposure or be more susceptible to infection than their flowering counterparts. A second
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hypothesis is that the missing transmission is occurring among pre-flowering juvenile plants.
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To test this latter hypothesis, we constructed a model (equations 6-11) that distinguishes between
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vegetative, pre-flowering juvenile and vegetative, adult plants, and has separate disease transmission
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functions for each (𝛽𝑗 , 𝛽𝑣 ). We defined juveniles as pre-flowering plants. New individuals are born into
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the healthy juvenile class (Njh) at rate of b’ and die at a rate of 𝜇𝑗 . We allowed juveniles to transition into
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a diseased class (Njd) at a rate of Pj. We initially assumed a frequency-dependent transmission function of
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𝑃𝑗 = 𝛽𝑗 (𝑁𝑓ℎ+𝑁𝑓𝑑). Both healthy and diseased juveniles transition into an adult flowering class at a rate of
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𝜙𝑗 . Since it seems unlikely that vegetative adults would experience zero transmission while vegetative
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juveniles were able to become infected, we allowed vegetative adult plants to become infected at the same
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rate as flowering plants (Pf =Pv). Equations 10-15 describe the model.
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𝑁𝑗ℎ(𝑡+1) = 𝑁𝑓ℎ ∗ 𝑏 ′ + 𝑁𝐽ℎ(1 − 𝜇𝑗 )(1 − 𝜙𝑗 )(1 − 𝑃𝑗 )
(10)
𝑁𝑗𝑑(𝑡+1) = 𝑁𝑗𝑑(1 − 𝜇𝑗 )(1 − 𝜙𝑗 ) + 𝑁𝐽ℎ(1 − 𝜇𝑗 )(1 − 𝜙𝑗 )𝑃𝑗
(11)
𝑁𝑓𝑑
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𝑁𝑓ℎ(𝑡+1) = 𝑁𝑗ℎ(1 − 𝜇𝑗 )𝜙𝑗 (1 − 𝑃𝑗 ) + 𝑁𝑓ℎ(1 − 𝜇𝑓ℎ )𝜙𝑓ℎ (1 − 𝑃𝑓 ) + 𝑁𝑓𝑑(1 −
𝜇𝑓𝑑 )𝜙𝑓𝑑 𝛾 + 𝑁𝑣ℎ(1 − 𝜇𝑣ℎ )𝜙𝑣ℎ (1 − 𝑃𝑣 ) + 𝑁𝑣𝑑 (1 − 𝜇𝑣𝑑 )𝜙𝑣𝑑 𝛾
(12)
312
313
𝑁𝑓𝑑(𝑡+1) = 𝑁𝑗ℎ(1 − 𝜇𝑗 )𝜙𝑗 𝑃𝑗 + 𝑁𝑗𝑑(1 − 𝜇𝑗 )𝜙𝑗 + 𝑁𝑓𝑑(1 − 𝜇𝑓𝑑 )𝜙𝑓𝑑 (1 − 𝛾) +
314
𝑁𝑓ℎ(1 − 𝜇𝑓ℎ )𝜙𝑓ℎ 𝑃𝑓 + 𝑁𝑣ℎ(1 − 𝜇𝑣ℎ )𝜙𝑣ℎ 𝑃𝑣 + 𝑁𝑣𝑑(1 − 𝜇𝑣𝑑 )𝜙𝑣𝑑 (1 −
315
𝛾)
(13)
316
317
318
𝑁𝑣ℎ(𝑡+1) = 𝑁𝑓ℎ ∗ 𝑏 ′ + 𝑁𝑣ℎ(1 − 𝜇𝑣ℎ )(1 − 𝜙𝑣ℎ )(1 − 𝑃𝑣 ) + 𝑁𝑣ℎ(1 − 𝜇𝑓ℎ )(1 −
𝜙𝑓ℎ )(1 − 𝑃𝑓 ) + 𝐹𝑑(1 − 𝜇𝑓𝑑 )(1 − 𝜙𝑓𝑑 )𝛾 + 𝑉𝑑(1 − 𝜇𝑣𝑑 )(1 − 𝜙𝑣𝑑 )𝛾
(14)
319
320
321
𝑁𝑣𝑑(𝑡+1) = 𝑁𝑣𝑑(1 − 𝜇𝑣𝑑 )(1 − 𝜙𝑣𝑑 )(1 − 𝛾) + 𝑁𝑓ℎ(1 − 𝜇𝑓ℎ )(1 − 𝜙𝑓ℎ )𝑃𝑓 +
𝑁𝑓𝑑(1 − 𝜇𝑓𝑑 )(1 − 𝜙𝑓𝑑 )(1 − 𝛾) + 𝑁𝑣ℎ(1 − 𝜇𝑣ℎ )(1 − 𝜙𝑣ℎ )𝑃𝑣
(15)
322
323
To determine the juvenile transmission rate that best explains the observed population dynamics
324
we ran simulations starting with the 2005 census data from the middle transect plot and varied both 𝛽𝑗
325
and b. We used data from an on-going implant experiment to estimate juvenile mortality and flowering
326
rates. In the experiment 1200 first-year D. pavonius plants were transplanted into the field near the current
327
demography study and were tracked for survival (𝜇=0.043) and flowering (𝜙𝑗 =0.17). We used chi-
328
squared tests to compare predicted number of flowering healthy and diseased individuals to the observed
329
numbers in 2014 under each parameter combination.
15
330
For the middle plot we found that a juvenile transmission rate between 𝛽𝑗 = 0.23 to 0.3 and a
331
birth rate of b=2 provided the best fit to the data (Fig. S3). In the lower plot, we could find no values of
332
𝛽𝑗 or b that could accurately predict the change in population size and disease frequency when k=0.0005.
333
However, if k=0.001, then a juvenile transmission rate between 𝛽𝑗 = 0.21 to 0.3 and a birth rate of b=4
334
provided the best fit to the data (Fig. S3). If we assumed that all non-flowering plants (juvenile and adult)
335
were infected at the same rate (𝛽𝑗 = 𝛽𝑣 ) we still found that transmission to juveniles was higher (0.2 to
336
0.26 for both plots) than that observed for flowering plants. Taken together, these results demonstrate that
337
juvenile infection rates must be as high as adult infection rates for disease to be maintained at its observed
338
frequency
339
If transmission to non-flowering plants occurs through passive wind, or splash dispersal of spores
340
from nearby diseased plants, rather than pollinator transfer, the transmission function is likely to be
341
density-dependent rather than frequency-dependent. To model density dependence, we changed the force
342
of infection for juvenile and vegetative adult plants in equations 6-9 to 𝑃𝑖 = 𝛽𝑖 𝑁𝑓𝑑. In the middle plot,
343
with k=0.001, we found that 𝛽𝑗 = 𝛽𝑣 0.0006 and b=1.8 provided the best fit to the observed 2014 census
344
data. In the lower plot we found the model that best fit the observed data was one where 𝛽𝑗 = 𝛽𝑣 =0.0002
345
with b=6 and k=0.001 or b=12 and k=0.0005.
346
Long term predictions
347
Long term predictions for host and pathogen persistence over the next 50 years depended on the
348
rate of juvenile infection. Models that included transmission to juveniles predicted long-term coexistence
349
of the host and pathogen for both frequency and density-dependent transmission modes (Fig. 5), while
350
models that did not include juvenile infection predicted local extinction of the pathogen (Fig. S4).
351
Interestingly, the long-term predictions for population size and disease did not depend strongly on the
352
transmission mode to non-flowering plants (Fig. 5).
353
16
354
DISCUSSION
355
Natural history observations have always been an important tool for understanding disease
356
transmission biology. Sir Ronald Ross’ remarkable discovery that mosquitos were responsible for malaria
357
transmission was aided by natural history observations of the malaria disease co-occurrence with
358
mosquito-laden swamps (Cox 2010). Likewise, compiled observations of elevated rat mortality paved the
359
way for Paul-Louis Simon’s discovery that rat fleas, Xenopsylla cheopis, were responsible for
360
transmission of bubonic plague (Gross 1995). Anther-smut has arguably one of the most fascinating
361
natural histories of all plant pathogens: the co-option of the anthers immediately suggests pollinator-borne
362
transmission, with disease spreading between flowering, adult plants. Indeed, it is frequently regarded as
363
a model plant-system for understanding sexually transmitted disease (Antonovics 2005; Bernasconi et al.
364
2009). However, the results of our demographic study reveal that modes of transmission beyond those
365
suggested by the natural history must play a critical role in the dynamics of the disease on Dianthus
366
pavonius. We find that transmission rates to adult flowering plants are far too low to explain the high-
367
sustained level of disease, indicating that a significant component of pathogen fitness must come from
368
transmission to non-flowering plants.
369
Our simulation results show that disease can only be maintained if transmission rates to pre-
370
flowering, juvenile plants are equal to or higher than transmission rates to adult plants. Transmission rates
371
are a joint measure of two factors: the average exposure to disease, (i.e. number pollinator visits per
372
plant) and the physiological susceptibility (i.e. the probability that a plant will become infected given that
373
spores are deposited on it). It seems unlikely that juveniles would have a higher disease exposure rate
374
than flowering plants since pollinators are less likely to visit them, but age-dependent physiological
375
susceptibility could compensate for low levels of disease exposure. Differences in the level of disease
376
resistance between juveniles and adults have been observed in many other plants (van der Plank 1968;
377
Burdon et al. 2014) and animals (Ahmed, Oldstone & Palese 2007). In crop plants, adult and seedling
378
resistance can have different mechanisms: so-called ‘seedling resistance’ is typically qualitative,
17
379
controlled by major genes that confer complete resistance against specific pathogen strains (Parker &
380
Ellis 2010; Thrall, Bever & Burdon 2010), while ‘adult-resistance’ develops later and tends to be
381
quantitative, and often with the effect of reducing pathogen fitness (Poland et al. 2009; Lannou 2012).
382
While the molecular mechanisms underlying anther-smut resistance are currently unknown, seedling
383
inoculation experiments with D. pavonius yield infection rates ranging from (50-80% - Antonovics et al.
384
unpublished). Moreover, the low observed floral infection rate cannot simply be explained by low
385
pathogen encounter rates: we found that 87% out of a sample of 107 healthy flowering plants in the study
386
had Microbotryum spores deposited on their flowers (Bruns et al unpublished).
387
Alternatively, the higher than expected disease frequency could be the result of temporal variation
388
in transmission and flowering. If a high transmission rate year was followed by a year with low flowering
389
rates, this could result in a large amount of disease being hidden in a ‘vegetative bank’, and would result
390
in an underestimation of the true prevalence. We did find moderate year-to-year variation in mortality,
391
flowering, and infection rates. Indeed, the prevalence observed in the 2014 may be an over-estimate of
392
the true prevalence since more diseased plants flowered in 2014 than healthy plants. However, the
393
magnitude of the difference between the observed prevalence in 2014 and the prevalence predicted under
394
the adult-only transmission model is large enough that it seems unlikely that temporal variation in
395
flowering alone could account for it. Thus is likely that juvenile infection plays an important role in the
396
maintenance of anther-smut disease in D. pavonius.
397
Vegetative infection of pre-flowering juveniles with anther-smut disease has been detected in
398
demographic studies of other host species (Alexander & Antonovics 1988; Carlsson-Granér 2006).
399
Alexander and Antonovics (1988) found that juvenile infection rates were similar to floral infection rates
400
in S. latifolia. Carlsson-Granér (2006) also found that rates of juvenile infection in Lychinis alpina and
401
Silene rupestris were similar to rates of flowering adult infection in a four-year year demographic study.
402
Carlsson-Granér (2006) then tested the role of juvenile infection for disease persistence by constructing a
403
population dynamic model similar to the one used here, and predicting the results when disease
18
404
transmission was restricted to adults. She found disease could not persist in populations of S. rupestris in
405
the absence of juvenile infection, similar to our results for D. pavonius.
406
The large contribution of juvenile transmission to overall disease dynamics could strongly alter
407
our understanding of the spatial-dynamics of disease. Transmission of anther-smut spores to flowering
408
plants through insect pollinators suggests frequency-dependent dynamics, similar to those observed in
409
vector-borne or sexually transmitted diseases (Lockhart, Thrall & Antonovics 1996; Antonovics 2005),
410
because pollinators are likely to visit a relatively constant number of plants. In the model plant Silene
411
latifolia, frequency-dependent transmission has been implied by spore deposition experiments
412
(Antonovics & Alexander 1992; Roche et al. 1994) and also has been shown to provide a better prediction
413
of disease spread in the field than the assumption of density-dependent transmission (Biere & Honders
414
1998; Antonovics 2004). However, the transmission mode for vegetative plants, including juveniles, is
415
more likely to be density-dependent (Roche et al. 1994), with spore deposition occurring through passive
416
wind or splash dispersal transmission from nearby infected plants. Antonovics and Alexander (1992)
417
found that seedlings planted within 5cm of diseased S. latifolia plants were infected at a high rate. An
418
important question for future investigation is whether healthy plants with high adult resistance also play
419
an important role in transmission to nearby seedlings by attracting spore-bearing pollinators. Since D.
420
pavonius drops its seeds near the parent plant, this scenario could generate a Janzen-Connell type
421
dynamic (Janzen 1970; Connell 1971) where seedlings closest to healthy parents have a higher disease
422
risk than those further away.
423
Mixed frequency and density-dependent transmission modes could also affect patterns of host and
424
pathogen persistence at broader spatial scales. Theoretical models show that frequency-dependent
425
diseases with some amount of density-dependent transmission can increase the likelihood of maintaining
426
disease but host populations are still at higher risk of disease driven extinction than largely density-
427
dependent diseases (Ryder et al. 2007). However, results of our best-fit models for D. pavonius predict
428
long-term coexistence of host and pathogen, rather than extinction, under either frequency or density-
19
429
dependent models of juvenile transmission. It is worth noting that the population of D. pavonius we
430
studied is part of a much larger population (Antonovics et al., in prep), which may reduce the probability
431
of local extinction. While the distribution of D. pavonius is relatively continuous within alpine meadow
432
habitats, we do observe spatial heterogeneity in host density. In particular, populations near the lower
433
elevational range limit tend to be significantly smaller (Antonovics et al, in prep). Juvenile transmission
434
mode could have a critical effect on host and pathogen persistence in these marginal populations. If
435
juvenile infection rates decline with density, then disease may not be able to persist in low-density
436
populations near the range margin. However, if juvenile transmission rates remain high then the
437
combination of frequency and density-dependent transmission dynamics could greatly increase the risk of
438
pathogen driven extinction (Ryder et al. 2007). .
439
Fitness effects of disease
440
We found that infection with anther-smut effectively halved the expected lifetime fitness of D.
441
pavonius, a moderately long-lived endemic species. Interestingly, the magnitude of this fitness cost
442
appears to be much greater than that suffered by the shorter lived, weedy species, Silene latifolia (Biere &
443
Antonovics 1996; Rausher 1996). While we found that complete sterilization was the norm in D.
444
pavonius (93% of all infections), complete host sterilization is relatively rare in S. latifolia (0-60% of all
445
infections; Buono et al. 2014). In addition, over-winter recovery rates appear negligible in D. pavonius (0-
446
6%), but are quite high in S. latifolia (64%: Biere and Antonovics 1996; Buono et al. 2014). Thus, disease
447
severity appears to be much higher in D. pavonius than S. latifolia despite the biological similarity of the
448
pathogens.
449
Fitness components of the Microbotryum pathogen species on D. pavonius and S. latifolia also
450
differ, and they do so in ways that are consistent with life history theory. Studies have consistently shown
451
that anther-smut pathogens are able to manipulate the flowering frequency and phenology of their hosts
452
(Alexander & Maltby 1990; Carlsson et al. 1992; Biere & Antonovics 1996; Jennersten 1998; Shykoff &
20
453
Kaltz 1998). However, since the fungus also requires a living host for overwinter survival, the magnitude
454
of floral manipulation is likely constrained the same allocation trade-offs faced by its host; an over-
455
investment in reproduction could lead to decreased survival. Life history theory predicts that longer-lived
456
hosts will invest more resources in longevity than reproduction. Consistent with the prediction, we find
457
that longevity is a more important component of fitness in the anther-smut pathogen infecting D.
458
pavonius than it is for the pathogen infecting the short-lived S. latifolia. We found that infection with
459
anther-smut results in only a modest 9.5% increase in lifetime inflorescence production of D. pavonius,
460
and we found no evidence that pathogen reduces host survival. It may be that the resources for the extra
461
inflorescence production would have been used for seed production rather than survival. In contrast,
462
increases in annual flower production of up to 50% have been reported for infected S. latifolia plants
463
(Alexander & Maltby 1990; Shykoff & Kaltz 1997), and studies have found that infected S. latifolia
464
plants experience higher levels of over-winter mortality than healthy plants when conditions are poor
465
(Thrall et al. 1994; Alexander and Antonovics 1995; Hood 2003 but see Buono et al. 2014).
466
In conclusion, this study provides an in-depth look at the dynamics of a sterilizing disease within
467
an endemic perennial species. While the remarkably high disease prevalence (30-40%), severe fitness
468
impacts on fecundity, and possible frequency-dependent transmission immediately suggest a high
469
extinction risk, the dynamics in this system appear to be close to a stable equilibrium: long-term
470
projections predict little change in population size or disease frequency. These dynamics differ
471
substantially from the extinction-colonization dynamics of anther-smut disease documented in the model
472
species, S. latifolia (Antonovics et al. 1994; Alexander et al. 1996; Antonovics 2004) and S. dioica
473
(Carlsson-Granér 2006; Carlsson-Granér et al. 2014). Our model does not factor in genetic variation in
474
host resistance, which can play an important role in disease persistence (Antonovics, Thrall, & Jarosz
475
1997, Carlsson-Granér & Thrall 2002). The high prevalence and cost of infection indicate that selection
476
pressure for resistance in D. pavonius must be high, and indeed, the low floral infection rate may be a
477
result of resistance evolution. Overall the differences in transmission and fitness effects between anther-
21
478
smut disease on D. pavonius and anther-smut on S. latifolia demonstrate that biological similarly of
479
disease life-history is insufficient for predicting dynamics, and underscore the necessity of long-term
480
demographic studies.
481
482
ACKNOWLEDGEMENTS
483
We sincerely thank the staff of the Parco Naturale del Marguareis especially Valentina Carasso, Bruno
484
Gallino, and Ivan Pace for their help and collaboration, and Adrianna and Guido Colombo for their
485
hospitality at Rifugio Garelli. The data was gathered with the help of a travel grant from the University of
486
Sheffield to Mike Boots and Alex Best. Additional field assistance was provided by Jessie Abbate, Ben
487
Adams, Colin Antonovics, Amy Blair, Lidia Castagnoli, Dylan Childs, Ruth Hamilton, Amy Johnson, Ed
488
Jones, Ian Miller, Anthony Ortiz, Tim Park, Robbie Richards, Ian Sorrell, Molly Scott, Casey Silver,
489
Adrianna Turner, Monroe Wolfe, and Sarah Yee. We also thank the following high school students from
490
Liceo Scientifico Tecnologico I.I.S. "G. Cigna" High School in Mondovì for their hard work in the field:
491
Arianna Bottero, Maddalena Graci, Eleonora Ornati, and Vincent Venezia. We gratefully acknowledge
492
grant support from the National Science Foundation, DEB-1115899 to JA and DEB- 1115765 to MEH.
493
The authors have no conflicts of interest to declare.
494
495
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TABLES AND FIGURES
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Table 1. Estimated parameters used in the attrition and population dynamic models. Values shown are the
624
weighted average parameter estimate over five years and 95% confidence intervals.
State
Parameter
Estimate
Lower 95% CI
625
Upper 95%
CI
Flowering, healthy
mortality (𝜇𝑓ℎ )
0.106
0.082
0.129
Flowering, diseased
mortality (𝜇𝑓𝑑 )
0.132
0.093
0.172
Vegetative
mortality (𝜇𝑣 )
0.215
0.184
0.246
Flowering, healthy
flowering (𝜙𝑓ℎ )
0.549
0.485
0.613
Flowering, diseased
flowering (𝜙𝑓𝑑 )
0.602
0.500
0.703
Vegetative
flowering (𝜙𝑣 )
0.241
0.177
0.305
Flowering, diseased
Recovery (𝛾)
0.026
0.000
0.097
Flowering, healthy
transmission (𝛽)
0.171
0.065
0.277
626
627
FIGURE LEGENDS
628
629
Figure 1. Mean annual rates of mortality, flowering, and inflorescence production for healthy (grey lines)
630
and diseased (black lines) plants.
631
Figure 2. Estimated force of infection P, the probability of healthy plant becoming infected, on marked
632
plants in the demography study.
29
633
Figure 3. Attrition model: predicted fate of marked plants in the demography study when no recruitment
634
was permitted (b=0). Solid lines show the observed data, dashed lines show the model predictions. A)
635
change in number of flowering individuals (B) change in disease prevalence.
636
Figure 4. Predicted change in the flowering population size (A,C) and disease prevalence (B,D) for
637
the middle and lower transect plots. Dark circles show the observed values from the census data.
638
Dashed lines show the model predictions, +/- 95% CI around the transmission estimated transmission
639
parameter, 𝛽 (Table 1). Parameters: b=1.8, and k=0.001 (A,B) and k=0.0005 (C,D), all other parameters
640
as in Table 1. Initial values of vegetative plants at time t0 (not counted in the census surveys) were
641
assumed to be equal to the proportion of flowering plants at t0 multiplied by the probability of not
642
flowering: 𝑁𝑣ℎ 𝑡0 = 𝑁𝑓ℎ(1 − 𝜙𝑓ℎ ) and 𝑁𝑣𝑑 𝑡0 = 𝑁𝑓𝑑(1 − 𝜙𝑓𝑑 ).
643
Figure 5. Predicted long-term change in number of flowering plants (A) and prevalence (B) under two
644
best-fit models of disease dynamics (see text). Circles indicate observed census counts for the middle
645
transect plot. Solid lines- All disease transmission is frequency dependent: 𝛽𝑓 = 𝛽𝑣 = 0.171 𝛽𝑗 = 0.26.
646
Dashed lines= Transmission to vegetative adults and juveniles is density–dependent. 𝛽𝑓 = 0.171, 𝛽𝑣 =
647
𝛽𝑗 = 0.0006. Other parameters b=2, k=0.001.
648
649
650
30
651
652
Figure 1.
653
31
Force of infection
0.3
0.25
0.2
0.15
0.1
0.05
0
2009
2011
2012
year
654
655
2010
Figure 2.
656
32
2013
657
658
659
Figure 3.
33
660
661
Figure 4.
662
663
664
34
665
666
Figure 5.
667
668
669
670
671
35
672
36