Introduction
A defining feature of the angiosperms is their remarkable floral
diversity. (Armbruster 2014; Hernández-Hernández & Wiens 2020). Darwin
first recognized that the diversity of floral shape, size, colour and
scent could be attributable to selection by pollinators (Darwin 1877).
This realization has resulted in a large modern research program,
spawning ecological, evolutionary and genetic studies, investigating how
interactions between plants and pollinators drive floral evolution
(Harder & Johnson 2009; Van der Niet & Johnson 2012). While we are now
heading toward a strong mechanistic understanding of how flowers evolve
(Moyroud & Glover 2017; Shan et al. 2019; Fattorini & Glover
2020), the ecological processes involved in flower diversification
remain poorly understood (Kay & Sargent 2009; Johnson 2010; Van der
Niet & Johnson 2012; Armbruster 2017).
Most of our understanding of how flowers diversify derives from two
principles, which were combined into the Grant-Stebbins model (Johnson
2006, 2010). First, flowering plants should adapt to the most effective
pollinator in a given environment (Stebbins 1970); that is, the
pollinator that visits most frequently (number of visits) and
efficiently (per-visit pollen transport efficiency) (Armbruster 2014).
Second, since pollinator assemblages are geographically variable, plants
should be under divergent selection in different environments, resulting
in adaptation to different pollinators (Grant & Grant 1965).
However, the Grant-Stebbins model does not answer a fundamental question
in floral evolution: why is there so much floral diversity within
communities? In other words, why do the various species constituting
plant communities rarely converge on a single most effective pollinator?
Moreover, within-community diversity in degrees of flower
specialization, spanning from specialization on a single species of
pollinator to generalization on multiple unrelated pollinator species,
is equally challenging to reconcile with the perception that plants
should specialize on the most effective pollinator (Sargent & Otto
2006; Gómez et al. 2007).
In the last few decades, we have gained important insights into the
processes governing pollination success, allowing a better understanding
of the selective pressures acting on floral evolution (Mitchell et
al. 2009b). Several studies emphasize the importance of community
context in understanding the ecology and evolution of plant pollination
(Caruso 2000; Sargent & Ackerly 2008; Mitchell et al. 2009a;
Muchhala et al. 2010). Competition and facilitation among plant
species for access to pollinator visitation, and interspecific pollen
transfer play important roles in determining the outcome of pollination
(Geber & Moeller 2006; Morales & Traveset 2008; Sargent & Ackerly
2008; Mitchell et al. 2009a; Pauw 2013). Furthermore, competition
via interspecific pollen transfer offers a potential mechanism promoting
divergence in pollinator use by favoring reduced pollinator sharing
(Muchhala et al. 2010; Moreira-Hernández & Muchhala 2019).
However, despite a more comprehensive understanding of pollination
ecology, and several hypotheses having been proposed to explain either
variability in pollinator use or degree of specialization (Waseret al. 1996; Johnson & Steiner 2000; Aigner 2001; Gómez &
Regino 2006; Sargent & Otto 2006; Muchhala et al. 2010;
Moreira-Hernández & Muchhala 2019), we still lack a theory explaining
the broad patterns of floral diversity within and among communities.
A general mechanism promoting floral diversity both in pollinator use
and degree of specialization might derive from the consideration that
several processes governing pollination success—intraspecific
competition for access to pollinator visitation, interspecific pollen
transfer and pollen carryover—are modulated by floral abundance, which
intrinsically varies among species within communities. It is therefore
possible that different plant species of varying floral abundances face
divergent selective pressures from a same pollinator assemblage. For
example, interspecific pollen transfer is expected to have a stronger
impact on populations of low abundance, as the proportion of
interspecific pollinator visits increases with the proportion of
heterospecific individuals in plant communities (Rathcke 1983;
Feinsinger et al. 1991; Caruso 2002; Palmer et al. 2003;
Sargent & Otto 2006; Mitchell et al. 2009a; Runquist & Stanton
2013). Likewise, opportunities for pollen loss, whether passively or due
to pollinator grooming, should be greater for rare plant species because
pollinators visiting rare plants spend more time between conspecific
visits (Minnaar et al. 2019). Therefore, pollen carryover— the
proportion of the removed pollen carried to the next conspecific
flower—is expected to increase with floral abundance. conversely,
intraspecific competition for pollinator visitation should be stronger
at high floral abundance, as more flowers compete for visitation by the
same pollinator community (Rathcke 1983; Geber & Moeller 2006; Duffy &
Stout 2008; Pauw 2013; Benadi & Pauw 2018). While studies carried out
at limited spatial scales often find increased per flower pollinator
visitation with increasing floral density (due to greater attractiveness
of larger flowering patches) (see Ghazoul 2005), at the landscape
scale—the scale that ultimately matters for floral evolution—the
opposite trend is observed: the number of visits received per flower
decreases with a species abundance (see Pauw 2013 and references
therein; Hegland 2014; Benadi & Pauw 2018; Bergamo et al. 2020).
The reason for such scale dependency is simple: large floral patches are
more attractive to pollinators, but a population composed of multiple
large floral patches will saturate the pollinators available, leading to
stronger intraspecific competition.
In this article, I propose that the pollination system offering the
optimal evolutionary solution for a plant species is a function of the
plant’s relative abundance in a community. In this view, different
pollinators and degrees of specialization are favoured at different
floral abundances. Floral diversification can result from shifts in
relative floral abundance associated with the colonization of new
habitats or geographical ranges, creating new conditions under which
floral diversification can occur. Abundance has been previously
identified as a potential driver of floral specialization (Feinsinger
1983; Sargent & Otto 2006). However, the potential for variability in
floral abundance to drive adaptation to different pollinators has never
been considered before. To demonstrate the potential of floral abundance
to foster floral diversity, I develop a simple mathematical model of
pollen transfer considering the interaction of several pollination
processes—pollen carryover, pollen removal, intra- and interspecific
competition for pollinator visits, and interspecific pollen transfer. I
use the model to assemble plant-pollinator networks from simulated plant
and pollinator communities. In a community context, the interactions
between plants and their pollinators are generally investigated in terms
of networks of interactions. Using this approach, I assess if and how
interspecific variability in floral abundance generates diversity in
pollinator use and degree of specialization. In addition to supporting
the conceptual model, the mathematical model is consistent with, and
suggests explanations for, several patterns governing the evolution,
diversification and community assembly of flowers.
How floral abundance spurs flower diversification: a
conceptual
model
Since plants produce a finite number of gametes, optimizing reproductive
success requires maximizing the number of ovules fertilized by a finite
amount of pollen. When limited by pollinator quantity, plants should
benefit from being less restrictive in their flower accessibility. Any
visitor, regardless of its quality (pollen carryover efficiency and
specialization), is likely to increase the number of pollen grains
deposited on conspecific stigmas (Thomson et al. 2000; Thomson
2003; Muchhala et al. 2010). However, pollen grains have a higher
probability of reaching conspecific stigmas when carried by more
efficient pollinators. When enough pollinators are available to remove
most pollen grains, pollinator quality becomes more limiting to plant
reproductive success than pollinator quantity, and plants should
specialize on the most efficient pollinator (Thomson et al. 2000;
Thomson 2003).
Here I propose that the selective importance of pollinator quantity and
quality is modulated by floral abundance. At high floral abundance, as
more pollinators are required for sufficient pollination, plant
reproduction is more strongly limited by pollinator quantity.
Conversely, pollen loss to inefficient carryover and interspecific
visits is reduced as floral density increases. Therefore, increased
floral abundance should increase the selective importance of pollinator
quantity while reducing the selective importance of pollinator quality.
Under these conditions, plants should benefit from generalized
pollination where more pollinators, but perhaps more wasteful ones, have
access to flowers. At low abundance, the dynamic is reversed and plants
should benefit from specializing on efficient carriers of their pollen.
While I have so far considered a dichotomy between low and high
abundance, plants can exist in any state from extremely rare to
ubiquitous. Likewise, pollinators inherently vary in their abundance
(quantity component) and efficiency (quality component). As each
pollinator offers a unique combination of the quantity and quality
components of pollination, changes in a plant species’ relative
abundance in a community will shift the identity of the pollinator
representing the most effective pollinator, and therefore, the
pollination system resulting in a fitness optimum. In this view, floral
abundance shapes the plant selective landscape. Variations in plant
abundance move the fitness peak of the selective landscape toward
different pollination systems, fostering floral diversification.
Model of pollen transfer
Here I develop a mathematical model determining how the optimal
pollinator or set of pollinators for a plant population changes as a
function of floral abundance. The model measures the proportion of
pollen grains produced by a single flower of the focal species
(hereafter focal flower) that reaches conspecific stigmas. In the model,
this value is influenced by the interaction of several pollination
processes—pollen removal and carryover, intra- and interspecific
competition for pollinator visitation, and interspecific pollen
transfer—that are linked to floral abundance, as described above. In
the mathematical model, I treat pollinators as functional groups of
pollinator species with similar attributes (morphology, behaviour) that
produce similar selection on flowers (e.g. different species of
hummingbirds, large-bodied bees) (Fenster et al. 2004). The model
assumes that flowers are distributed homogeneously in space (i.e., that
flowers of the same are not more likely to be near one another). The
model therefore does not consider the potential for facilitation among
species sharing the same pollinators, although facilitation is expected
to beneficiate rarer species (Rathcke 1983; Steven et al. 2003;
Essenberg 2012), contributing to the predicted increase in quantity
limitation with abundance.
For a plant species a , the number of pollen grains produced per
flower is represented by Pt . The proportion of
grains removed with each pollinator visit to a focal flower is
represented by Ri , and depends on both the
attributes of the pollinator i and of the focal plant species.
While adaptation toward a pollination system could, theoretically,
increase R , I am more interested in the causes of shifts between
pollination systems rather the mechanisms leading to a subsequent better
fit to the system, so the model does not consider evolution of R .
With each new visit to the focal flower, the amount of pollen remaining
in the anthers diminishes, and the amount picked up with each new
pollinator visit diminishes proportionally to the number of visits
received, resulting in an exponential decline of pollen removed with
each new visit (Young & Stanton 1990; Robertson & Lloyd 1993).
Considering that pollinator i is the only visitor (floral
generalization is considered in equation 5), the total amount of pollen
removed, Pr , from the focal
flower by a given pollinator i is therefore
\({P_{r}=P}_{t}\left[1-\left(1-R_{i}\right)^{V_{\text{ij}}}\right]\)(1)
where Vij represents the number of visits by
pollinator i to flower j , which is a subset ofVi , the total number of visits made by the
pollinator in the plant community. The number of visits to the focal
flower depends on the abundance of the focal speciesAa and its proportional floral abundance in the
community, Aa / (Aa +
ΣA i) where ΣA i represent
the total abundance of flowers for each plant species visited by the
pollinator i excluding the focal species.
ΣA i is related to pollinator generalization. The
number of visits to the focal flower by pollinator i is therefore
\begin{equation}
V_{\text{ij}}=\frac{V_{i}(\frac{A_{a}}{A_{a}+\sum A_{i}})}{A_{a}}\nonumber \\
\end{equation}Which can be simplified as
\(V_{\text{ij}}=\frac{V_{i}}{A_{a}+\sum A_{i}}\) (2)
More simply, this second equation distributes the visits made by the
pollinator i equally among all flowers visited by the pollinator,
and therefore reflects competition for access to pollination by a
specific pollinator i . In this model I treat all plant species as
being equally attractive to each pollinator (variable attractiveness
could be considered by weighting Aa by the
relative attractiveness of the focal species). Considering the role of
floral abundance and competition for a limited amount of pollinator
visits, the number of pollen grains removed from the focal flower by a
given pollinator can be expressed as
\(P_{r}=P_{t}\left[1-\left(1-R_{i}\right)^{\frac{V_{i}}{A_{a}+\sum A_{i}}}\right]\)(3)
The proportion of those grains removed by the pollinator i that
reach a conspecific stigma depends on the pollen carryover capacity of
the pollinator, Ci —the proportion of pollen
carried over to each subsequent visit. For a given individual of the
pollinator i , as with each new visit the amount of pollen
remaining on the pollinator body declines at a rateCi , the proportion of grains remaining on the
individual pollinator follows an exponential decay (Lertzman & Gass
1983; Campbell 1985; Robertson 1992) (although longer or shorter than
exponential tails have been observed; Morris et al. , 1994;
Holmquist et al. , 2012). From the pollen grains deposited on the
individual pollinator body, as pollen is lost with each visit, the
amount that will reach a conspecific stigma is a function of the number
of interspecific visits made by the pollinator before reaching a
conspecific flower, which is a function of the reciprocal of the
proportional floral abundance of the focal species in the community of
flowers visited by pollinator i : (Aa +
ΣAi ) / Aa . Therefore,
assuming that the pollinator does not exhibit floral constancy (floral
constancy could be considered by weighting ΣAi by
the reciprocal of the degree of floral constancy exhibited by the
pollinator), the proportion of the removed pollen grains by individuals
of pollinator “\(i\)” that reach conspecific stigmas is equivalent to
\begin{equation}
{C_{i}}^{\frac{A_{a}+\sum A_{i}}{A_{a}}}\nonumber \\
\end{equation}Floral constancy can be considered as temporal specialization (Nickolas
M. Waser 1986; Amaya-Márque 2009), and therefore has a similar impact on
pollen transport as fixed specialization (not behaviourally flexible),
which is represented in the model by Aa +
ΣAi . Considering both the amount of pollen
removed by the pollinator i and the proportion of this pollen
that is deposited on conspecific stigmas, the total number of pollen
grains deposited on conspecific stigmas, Pd , is
expressed as
\(P_{d}=P_{t}\left[1-\left(1-R_{i}\right)^{\frac{V_{i}}{A_{a}+\sum A_{i}}}\right]\bullet{C_{i}}^{\frac{A_{a}+\sum A_{i}}{A_{a}}}\)(4)
Equation (4) determines the effect of specialization on a given
pollinator on the pollination success of the focal plant species. The
effect of specialization on different pollinators can be evaluated by
comparing the value of Pd for exclusive
pollination by different pollinators.
To determine the effect of pollination by different combination of
pollinators (generalization on different subsets of the available
pollinators), assuming random visitation order between individuals of
the different pollinators, the total amount of pollen produced,Pt is distributed between the different
pollinators visiting the focal species relative to 1) each pollinator’s
number of visits to the focal species and 2) their pollen removal rate.
The individual contribution of each pollinator toPr is summed and the total amount of pollen
deposited on conspecific stigmas is therefore
\(P_{d}=\sum_{i=1}^{n}{P_{t}\left\{\frac{{1-\left(1-\frac{\sum_{i=1}^{n}R_{i}}{n}\right)}^{\sum_{i=1}^{n}\frac{V_{\text{ij}}}{A_{a}+\sum A}}}{\left[\frac{\left(\sum_{i=1}^{n}\frac{V_{\text{ij}}}{A_{a}+\sum A}\right)}{\left(\frac{V_{i}}{A_{a}+\sum A_{i}}\right)}\bullet\frac{\left(\frac{\sum_{i=1}^{n}R_{i}}{n}\right)}{R_{i}}\right]}\right\}\bullet{C_{i}}^{\frac{A_{a}+\sum A_{i}}{A_{a}}}}\)(5)
By tracking pollen fate, the mathematical model explicitly measures the
pollination system maximizing male reproductive success. However,
selection through either male and female function is expected to reach
the same solution in terms of optimal pollination system as long as
pollen receipt and export are limiting and are governed by the same
variables for each sex (i.e. pollinator identity and abundance).
Methods
Importance of pollinator quality and
quantity
To determine the effect of floral abundance on the relative importance
of the quantity and quality components of pollination for pollination
success, using equation (4), I compared the impact of variations in
those components on conspecific pollen receipt at different floral
abundances. Equation (4) offers an explicit definition of which
parameters constitute the quantity and quality components of
pollination. Factors affecting pollen removal (the left part of the
equation)—pollinator abundance and pollen removal rate—are defined
as the quantity component. Factors affecting pollen deposition (the
right part of the equation)—pollinator carryover capacity and
specialization—are defined as the quality component. As a proxy for
pollinator specialization, I used ΣAi , the total
floral abundance of all the plant species visited by the pollinator (see
equation 4). The number of pollen grains produced by the focal flower
deposited on a conspecific stigma (equation 4) was compared at low and
high values of the parameters (Table 1) while maintaining the other
parameters constant. The proportional change (high – low )
/ high produced an estimate of the importance of variation of
those parameters on pollination success. The importance of the different
parameters for pollination success was compared for a range of floral
abundances from 2 to 1500 (at least two flowers are required for
cross-pollination).
Low, medium and high values of pollen carryover and pollen removal were
parameterized based on data from the literature (Table 1). Robertson
(1992) reported from a literature survey a range in pollen carryover
from 50.2% to 94.7%. I used values of 0.55, 0.73 and 0.9 as low,
medium and high values of pollen carryover in the model respectively.
The values of pollen removal were selected following Thomson (2003) who
modeled pollen delivery as a function of low and high values of pollen
removal of 0.3 and 0.7 respectively. In the model, I used values of 0.3,
0.5 and 0.7 as low, medium and high values of pollen removal
respectively. Those values encompass the pollen removal values measured
in various systems (e.g. Wolfe and Barrett 1989, Young and Stanton 1990,
Harder 1990, Thostesen and Olesen 1996). Medium values of total number
of visits by the pollinator in the community and abundance of the other
flower species were selected to produce intermediate limitation by
pollinator visits at intermediate floral abundance. This allowed
variable degrees of limitation by pollinator visits within the range of
floral abundance considered. The high and low values of total number of
visits by the pollinator in the community and abundance of the other
flower species represent a two-fold increase and decrease from the
medium values respectively.
Because the number of pollen grains deposited on a conspecific stigma
might be sensitive to the choice of values of the parameters used for
the mathematical model, I compared the impact of variations in pollen
carryover, pollen removal, pollinator visitation and specialization on
conspecific pollen receipt at each possible set of values of the other
parameters (low, medium, and high). I used those alternative values of
the different parameters to set upper and lower values of the estimated
importance of the quantity and quality components of pollination.
Intermediate values of the importance of a parameter on pollination
success correspond to the values obtained while all other parameters
were set to medium values while upper and lower values correspond to the
maximal and minimal values obtained among all the alternative values of
the other parameters respectively. Essentially, the upper and lower
values of the estimated importance of the quality and quantity
components of pollination indicate the degree to which the estimate
varies as a function of variation in the different parameters of the
mathematical model and serve as a confidence interval.
Plant-pollinator network
simulations
Using equation (5), I verified how variation in floral abundance affects
the structure of plant-pollinator networks. Each simulated network was
composed of a community of 10 pollinators and 12 plant species. The
pollinator communities were assembled by randomly sampling for each
pollinator values of carryover and removal from uniform distributions
using the runif function in R (R core team, 2020) with maximal and
minimal values of 0.9 and 0.55, and 0.7 and 0.3 for carryover and
removal respectively, except for the simulations testing niche
partitioning (see next paragraph and Table 2). The number of visits made
by the different pollinators (relative to their abundance) was sampled
from a Poisson log-normal distribution using the rpoilog function in the
R package sads (Prado et al. 2018). Poisson log-normal
distributions are often used to characterize community species-abundance
distributions (Baldridge et al. 2016). After randomizing the
plant species order, each plant species colonized the pollinator
community successively until all species had colonized the community.
For each colonization event, the plant species could evolve to be
pollinated by any possible combination of pollinators in the community,
and the combination resulting in the highest pollination success was
selected as the evolutionary outcome for the plant species (assuming no
restriction on the evolution of different pollination systems). Plant
pollination success associated with the evolution of pollination by the
different possible combinations of pollinators was calculated and
compared by inputting the simulated parameters (see Table 2) in equation
(5). Based on equation (5), the pollinators visited the plant species
that evolved pollination by those pollinators relative to each plant
species’ floral abundance. For a given plant species the number of
visits received by a pollinator was measured asVi / (Aa +
ΣAi ) were Vi is the total
number of visits made by the pollinator in the plant community,Aa is the floral abundance of the focal plant
species and ΣAi is the total floral abundance of
the other plant species visited by the pollinator (see equation 2).
Competition for visits by the different pollinators was dynamically
updated with each new colonization event. After all species colonized
the community, the number of visits by each pollinator to each plant
species was determined and used to build the plant-pollinator networks.
Sets of 100 simulations were run for plant communities of 1) variable
floral abundance 2) all low-abundance species and 3) all high-abundance
species. For the simulations with variable floral abundance, plant
communities were assembled by randomly sampling each plant abundance
from a Poisson log-normal
distribution. For the simulations
with plant species of low abundance, all plant species had the same
abundance and their abundance was low enough that pollinator quantity
was not limiting (see Table 2). For the simulations with plant species
of high abundance, the plant species abundance was set so that
pollination was quantitatively limited.
A final set of simulations was performed with all plant species of low
abundance in which each pollinator had similar values of pollen removal,
carryover and number of visits. I used this last set of simulations to
verify if the model would produce niche partitioning between plant
species as a result of selection to limit interspecific pollen transfer
when floral abundance is low, and thus pollinator efficiency is more
limiting than pollinator availability. Specifically, each pollinator’s
values of carryover and removal were determined by randomly sampling
from uniform distributions with minimum and maximum values of 0.72 and
0.74, and 0.49 and 0.51 for carryover and removal respectively. Each
pollinator’s number of visits made in the community (relative to its
abundance) was randomly sampled from a uniform distribution with minimum
and maximum values of 1490 and 1510 respectively.
Pollination system evolution in a new
community
Using the simulated plant-pollinator networks of the variable floral
abundance plant communities, I tested how floral abundance affects the
degree of floral specialization and if variation in floral abundance
leads to adaptation to different pollinators. For each simulated
plant-pollinator network, after all plant species had colonized the
community, a new plant colonist invaded the community. I varied the new
colonist’s abundance and recorded the subset of pollinators on which the
plant evolved at each abundance value.
Results
The impact pollinator abundance and pollen removal rate on plant
pollination success increased with floral abundance while the impact of
pollen carryover and pollinator specialization decreased with floral
abundance (fig. 1A-D). Overall, the quality component of pollination was
more important for plant pollination success than the quantity component
at low floral abundance, while the quantity component was more important
for pollination success at high abundance (fig. 1E).
For simulated plant communities in which plant species varied in floral
abundance, the resultant plant-pollinator networks exhibited variable
degrees of specialization among plant species (average number of links ±
standard deviation = 3.24 ± 2.00) (fig 2A, B). By contrast, in simulated
plant communities where all plant species were of low abundance, most
plant species specialized on a limited subset of the available
pollinators (average number of links ± standard deviation = 2.24 ± 1.06)
(Fig 2C, D). Moreover, in low-abundance plant communities,
specialization occurred even when all pollinators had very similar
abundance, carryover and removal. In this case, pollinator visits were
partitioned homogeneously among plant species, demonstrating the
presence of niche partitioning in the plant community (Fig 2G, H).
Conversely, in plant communities composed of abundant plant species,
most species generalized on most of the pollinators available (average
number of links ± standard deviation = 7.79 ± 2.43) (Fig 2E, F).
The subset of available pollinators on which a new plant colonist
evolved was a function of its floral abundance. At very low floral
abundance, the colonist specialized on pollinators weakly exploited by
the other plant species, thereby reducing competition via interspecific
pollen transfer (Fig 3). Those less-preferred pollinators were often
relatively rare and had a low carryover capacity, as pollinators with
high abundance and carryover were generally exploited by several plant
species. From very low to relatively low abundance, there was a tendency
for the new colonist to shift to specialization on a subset of
pollinators with high carryover rather than low competition. From
intermediate to high abundance, the new colonist favoured more abundant
pollinators. Generalization increased with abundance, but only at very
high abundance were the majority of the available pollinators exploited.
Discussion
Many plant lineages and communities are characterized by high floral
diversity (Van der Niet & Johnson 2012). However, the causes of floral
diversification and specialization remain elusive (Kay & Sargent 2009;
Johnson 2010; Van der Niet & Johnson 2012; Armbruster 2017). Here I
propose that a species’ relative abundance in a community determines the
pollination system offering the optimal evolutionary solution (Fig 1,
3). Given that abundance is evolutionarily and ecologically labile
(Ricklefs 2010; Loza et al. 2017), shifts in abundance associated
with the colonization of new habitats or geographic ranges could promote
floral diversification. This conceptual model complements the
Grant-Stebbins model in which flower diversification results from
geographical variation in pollinator assemblages (Grant & Grant 1965;
Stebbins 1970). In this more holistic view, floral diversification is
the result of variability in pollinator assemblages, floral abundance,
and plant community composition. This perception considerably relaxes
the conditions under which floral diversification occurs and offers an
explanation for the variability in pollinator use and degree of
specialization within and among communities.
In the simulated plant communities, flower specialization was observed
at low abundance while generalization was favoured at higher abundance
(Fig 2A, B, Fig 3), a pattern consistent with the frequently observed
link between abundance and degree of generalization in plant-pollinator
networks (Jordano et al. 2002; Bascompte et al. 2003;
Vázquez & Aizen 2003). While the cause of this pattern is debated
(Dorado et al. 2011; Fort et al. 2016), the model
presented here suggests that the link between abundance and
generalization can originate from a selective advantage of
generalization at high abundance. Furthermore, simulated plant
communities composed of plant species of low abundance resulted in
widespread specialization while communities of high abundance species
were associated with generalized pollination (Fig. 2). This observation
is consistent with the widespread floral specialization characterizing
highly diverse plant communities composed mostly of rare species, such
as in Mediterranean and tropical climates (Johnson & Steiner 2000;
Vamosi et al. 2006). In such communities, plants should be under
stronger selective pressure for specialization in order to avoid pollen
loss from inefficient carryover and interspecific pollen transfer
(Feinsinger 1983, Johnson and Steiner 2000). In the model, the
importance of reducing competition via interspecific pollen transfer was
further supported by the evolution of niche partitioning in communities
of rare plant species even when the different pollinators had very
similar attributes (Fig. 2G, H). Interestingly, while at moderately low
abundance plants specialized on a subset of pollinators with high
carryover capacity, at very low abundance the species frequently
specialized on pollinators of low abundance and carryover (Fig. 3).
Those pollinators were less exploited by more abundant plant species,
which instead evolved pollination by abundant and efficient carriers of
their pollen, offering a competition-free space for very rare species.
This pattern of plant community assembly can be explained by the
increasing probability of interspecific visits with increasing plant
rarity, exacerbating the importance of interspecific pollen loss at very
low abundance. The propensity for rare plant species to fill up
unexploited pollination niches has the potential to give rise to the
evolution of unique pollination systems, potentially contributing to the
impressive diversity in modes of pollination characterizing tropical and
Mediterranean communities.
The mathematical model and the simulated plant-pollinator networks
demonstrate that, from low to intermediate abundance, plants should
specialize on a subset of pollinators offering the optimal combination
of pollination quantity and quality components (Fig. 1, 3). However, as
plant abundance increases and most pollinators are not sufficiently
abundant to remove the majority of pollen grains, highly generalized
pollination should be favoured. But what happens at the extreme end of
the plant abundance spectrum, when the entire pollinator community
cannot provide enough visits to prevent pollen limitation? In those
conditions, perhaps the best strategy is for plants to relax their
dependence on biotic pollen vectors. While the evolution of wind
pollination from animal pollination has mostly been attributed to
reduced reliability of animal pollinators, most wind-pollinated plants
are characterized by large population size and high density (Culleyet al. 2002; Friedman & Barrett 2009). Indeed, it seems doubtful
that any combination of pollinators could adequately pollinate the
thousands of flowers per square metre characterizing the bloom of
temperate deciduous trees or Poaceae grasslands. Moreover, despite wind
representing a relatively inefficient system of pollen transport, the
high abundance characterizing most wind-pollinated plants reduces the
importance of pollen vector efficiency.
In the model, in order to demonstrate the role of floral abundance in
determining the optimal pollination system, plants could evolve on any
combination of pollinators without evolutionary restrictions. Plants,
however, have constrained abilities to track the most effective
pollination system (Fenster et al. 2004). Evolution occurs along
the lines of least resistance contingent on the genetic material
available, making some shifts in pollination systems more likely to
occur in certain plant lineages (Stebbins 1970; Van der Niet & Johnson
2012). Those evolutionary limitations represent an important nuance in
flower diversification. Plants should adapt to the most effective
pollination strategy that represent a line of least resistance.
Therefore, many plants are expected to not be optimally adapted to their
pollination environment. For instance, bilateral symmetry is considered
to facilitate floral specialization (Sargent 2004) and radially
symmetrical flowers may have limited ability to evolve floral
specialization.
Even in the absence of evolutionary constraints, because there is a net
flux of genes from large to small populations, the smallest, and often
peripheral, populations of a given plant species are expected to match
the local selective pressures relatively poorly (García-Ramos &
Kirkpatrick 1997; Kirkpatrick & Barton 1997; Kay & Sargent 2009).
Considering the importance of floral abundance in determining the
optimal pollination strategy, small peripheral populations should rarely
match such optimal conditions. Furthermore, large fluctuations in
population abundance might prevent adaptation to the most effective
pollinator when such fluctuations happen fast enough that adaptive
tracking is not possible.
Several theoretical models emphasize the importance of fitness
trade-offs in the evolution of flower specialization (Aigner 2001;
Sargent & Otto 2006; Muchhala 2007). Such trade-offs are expected to
occur when adaptation to a pollinator decreases the effectiveness of
pollination by other pollinators. However, despite having been detected
in some studies, fitness trade-offs in the effective use of different
pollinators are often weak or absent (see Armbruster 2014, 2017). Hence,
it seems unlikely that floral specialization is the sole result of
trade-offs. Here, similar to Muchhala et al. (2010), who investigated
the role of interspecific pollen transfer in flower specialization, I
demonstrate that specialization can evolve without trade-offs. Rather,
specialization should be advantageous when pollinator quantity is less
limiting than pollinator quality.
When present, fitness trade-offs in the effective use of different
pollinators should increase floral specialization. However, the model
presented here is consistent with the perception that floral
specialization might be governed not only by adaptation to increase
pollen removal and deposition by the most effective pollinator, but also
by the exclusion of less efficient ones (Thomson 2003; Castellanoset al. 2004; Muchhala et al. 2010; Armbruster 2017).
Paralleling evolution toward the most effective pollinator, exclusion of
less efficient pollinators through the evolution of pollinator filters
could produce trade-offs if it also excludes efficient but infrequent
pollinators. In other words, pollinator filters might rarely allow to
single out unwanted pollinators. For instance, the evolution of long
nectar spurs prevents access to pollinators with short mouthparts, even
if some of those pollinators might act as occasional but efficient
visitors. Plants might therefore often evolve a high degree of
evolutionary specialization despite several visitors acting as effective
pollinators due to the limited capacity to maintain pollination by a
subset of effective pollinators while precisely excluding the
ineffective ones.