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