# On the function of flower number: disentangling fertility from pollinatormediated selection

Estelle Barbot, Mathilde Dufaÿ, Jeanne Tonnabel, Cécile Godé, Isabelle de Cauwer

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Estelle Barbot, Mathilde Dufaÿ, Jeanne Tonnabel, Cécile Godé, Isabelle de Cauwer. On the function of flower number: disentangling fertility from pollinatormediated selection. 2023. hal-04083938

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1 On the function of flower number: disentangling fertility from pollinator-mediated selection

3 Estelle Barbot¹,², Mathilde Dufaÿ³, Jeanne Tonnabel²,³, Cécile Godé¹ and Isabelle De Cauwer¹

5 ¹Univ. Lille, CNRS, UMR 8198 - Evo-Eco-Paleo, F-59000 Lille, France

6 ²ISEM, Univ Montpellier, CNRS, IRD, Montpellier, France

7 ³CEFE, Univ Montpellier, CNRS, Univ Paul Valéry Montpellier 3, EPHE, IRD, Montpellier, France
9 Abstract

10 In animal-pollinated angiosperms, the "male-function" hypothesis claims that male
11 reproductive success should benefit from large floral displays, through pollinator
12 attraction, while female reproductive success is expected to be mainly limited by
13 resource availability. As appealing as this theory might be, studies comparing
14 selection strength on flower number in both sexes rarely document the expected
15 asymmetry. This discrepancy could arise because flower number impacts both
16 pollinator attraction and overall gamete number. In this study, we artificially
17 manipulate floral displays to disentangle the fertility versus pollinator attraction
18 components of selection, both in terms of mating and reproductive success. In
19 females, flower number was under strong fertility selection, as predicted in the
20 absence of pollen limitation. In contrast, in males, flower number was mainly under
21 sexual selection, which in turn increased male reproductive success. However, these
22 selection patterns were not different in males with artificially increased floral
23 displays. This suggests that sexual selection acting on flower number in males do not
24 occur because flower number increases pollinator attraction, but rather because more
25 pollen is available to disperse on more mates. Our study illustrates the power of
26 disentangling various components of selection with potentially sex-specific effects for
27 understanding the evolution of sexual dimorphism.

28 **Keywords:** sexual selection – pollinator attraction – gametes – sexual dimorphism –
29 "male-function" hypothesis – phenotypic manipulation
30 **Introduction**

31 Darwin himself introduced the notion of sexual selection, advocating that certain
32 traits are selected because they enhance individual fitness through mating success,
33 i.e. the number of sexual partners [1]. According to theory, anisogamy should result
34 in male reproductive success being more limited by gamete acquisition (e.g. through
35 mate acquisition) than female reproductive success [2–5]. Across the animal
36 kingdom, this statement is widely supported by two lines of evidence. First,
37 empirical studies measuring sex-specific Bateman's gradients, the slope of the
38 regression of reproductive success on mating success, generally demonstrate this
39 relation to be steeper in males than in females [6]. Second, male biased sexual
40 dimorphism for armament and ornament is pervasive in animal taxa, particularly in
41 reptiles, birds and mammals, suggesting stronger sexual selection on male versus
42 female phenotypes [7,8].

43 The scope for such sex differences in the dependence of reproductive success on
44 access to sexual partners has long been suggested to include all sexually reproducing
45 anisogamous organisms, thus including plants [2,8]. In animals, during the pre-
46 copulation phase of reproduction, sexual selection can involve (i) competition for the
47 access to sexual partners (usually among males) and/or (ii) choice of sexual partners
48 (usually by females [4]). In plants, during the pre-pollination phase of reproduction
49 when pollen is dispersed from anthers to stigmas, sexual selection can operate
50 through male intrasexual competition to access receptive stigmas [9]. Sexual selection
can also take the form of female intrasexual competition to receive viable pollen in the common case of pollen limitation [10,11]. One might thus expect between-sex differences in the intensity of sexual selection to decrease with increasing pollen limitation, as observed in broadcast spawning animals when male gametes become limiting [12].

Yet, the pre-pollination phase of plants strongly differs from sexual selection in animals given that it involves a third party – the pollinators – in a vast majority of angiosperm species [13]. Sexual selection mediated by pollinators may thus drive the evolution of phenotypic traits linked with pollinator attraction, with obvious parallels with mate attraction in animals [9,14]. In this respect, a wide range of floral traits has been demonstrated to be involved in pollinator attraction, such as nectar quantity [15], flower size [16,17] or flower number (reviewed in [18]). In dioecious plants, i.e. species with separate sexes, sexual dimorphism in floral traits is generally thought to have evolved through differential sexual selection between sexes [9,19].

In dioecious species, males generally carry more flowers than females, at least in temperate regions, suggesting stronger directional selection on this trait in males than in females [20–22]. Similarly, in hermaphroditic species, flower number has been historically considered as a typical ‘male’ trait, as earlier work in plants argued that flower number, beyond a certain threshold, served the purpose of the male function only [23,24]. Indeed, hermaphroditic plants often display fruit-sets lower than one (i.e. produce many flowers that do not mature into fruits), even in optimal
pollination conditions [25,26]. The rationale for this "male-function" hypothesis is based on the common observation that flower number affects pollen donation more than seed production [23,24,27,28]. One process behind this involves pollinator attraction: large number of flowers often increase the number of pollinators visits [17,18,29,30]. Thus, if pollen donation depends more on the number of pollinators visits compared to ovule fertilization, large floral displays would mainly benefit male fitness after a certain threshold [23]. Yet, empirical support for the male-function hypothesis remains ambiguous [31]. Indeed, several studies quantifying selection on flower number through both sexual functions in hermaphroditic species found either (i) weaker selection on flower number through male function compared to female function [32,33], (ii) no selection through the male function [34] or even (iii) negative selection through the male function [35,36]. Quantifying the relative effect of flower number through different components of selection in each sex may help understand the current discrepancy between verbal expectations and empirical data.

Selection on flower number may arise through two distinct but simultaneous selective processes. First, flower number can be under sexual selection if individuals with larger display benefit from increased reproductive success through enhanced pollinator attraction (i.e. pollinator-mediated sexual selection), ultimately acquiring a higher number of sexual partners. Second, flower number can be under fertility selection if individuals with larger displays benefit from higher gamete production at the plant level. Comparing the strength of selection between arrays of hand-
pollinated and open-pollinated plants has allowed to partition the effect of
pollinator-mediated and fertility selection on flower number in a number of study
systems [37–40]. One important caveat of this approach, however, is that it focuses on
female fitness exclusively, precluding any explicit test of the male-function
hypothesis [31]. A powerful method to alleviate this issue is the use of phenotypic
manipulation [41]. In animals, experiments manipulating traits thought to be
involved in mate acquisition has proven to be an effective method in sexual selection
studies [42–45]. While phenotypic manipulation has allowed to explain the evolution
of various floral traits through pollinator-mediated selection [46–48], this approach
has not yet been applied to explicitly test the male-function hypothesis.

In this study, we performed phenotypic manipulation in order to test whether flower
number benefitted more the male rather than the female reproductive success,
through its impact on pollinator attraction. By adding dummy flowers on half of the
individuals in an experimental population of the dioecious species *Silene dioica*, at
the population flowering peak, we amplified the visual signal displayed by some
plants without changing their gamete production. Such approach allows
disentangling pollinator-mediated from fertility components of selection on flower
number with a common methodology for both sexes. Within each sex, two
components of individual fitness will be considered: (i) the number of offspring
produced, referred to as reproductive success hereafter, which corresponds to the
number of seeds for females, and to an estimate of the number of sired offspring for
males, obtained through paternity analysis and (ii) the number of acquired mates, referred to as mating success hereafter, which corresponds to the number of genetic mates for both sexes. Because female reproduction is supposed to be limited mainly by resource availability for ovule production and maturation, at least when pollinators are not a limiting factor, we predict that neither female reproductive success, nor its relationship with the number of real flowers, should be impacted by our treatment (control vs. enhanced visual signal). We however hypothesize that female mating success can be affected by our treatment if the addition of dummy flowers enhances pollinator attraction and mate acquisition. In males, we predict that enhancing the visual signal to pollinators should increase both mating and reproductive success, and possibly strengthen the relationship between the number of real flowers and mating/reproductive success assuming that the benefit of displaying a showier signal increases with the number of gametes produced at the plant level.

## 128 Material and methods

### 129 *Study system*

*Silene dioica* (L.) Clairv. is a short-lived perennial herb of the Caryophyllaceae family. This dioecious plant is widespread throughout northern and central Europe [49,50]. Sex determination is genetic, with males being the heterogametic sex [51,52]. The species is sexually dimorphic for flower number and size, with males being more
conspicuous than females [22,53]. *Silene dioica* is insect-pollinated and displays a generalist pollination system with bees and hoverflies as main pollinators [49,54].

## Plant material and experimental design

Plant material was generated using seeds collected in three forests from the same region where the experiment was conducted (Lille University in France, 50°36'27.9''N 3°08'36.3''E), in order to reach a sufficient sample size while avoiding the inclusion of half or full-sibs. All individuals were sown in a greenhouse and placed in separate 0.7-L pots filled with a standard soil mixture.

An array of 172 plants with 1:1 sex-ratio was kept in an insect-proof greenhouse until the beginning of the experiment. The surface area of the experimental plot was of 50 m² and local density was 3.3 individuals.m⁻², mirroring typical densities observed in nature. No wild populations of *Silene dioica* were growing in the vicinity. At the flowering peak (31th May 2019), plants were randomly assigned to our two treatments and placed in the experimental garden for ten days with individual spatial positions arranged to follow a grid pattern where sexes and treatments were alternated.

In the control group (C), flower number was left unmanipulated. It included 43 females (FC) and 43 males (MC). In the treatment group (T), which also included 43 females (FT) and 43 males (MT), the visual signal was amplified by adding enough dummy flowers to double the flower number of each individual (i.e. different
numbers of dummy flowers were thus used depending on individual flower
production). Dummy flowers were created using crepe paper which was selected
based on its color spectra to approach average S. dioica petals' reflectance, and circles
of 3.5cm diameter were cut in the paper with the closest spectra and rolled to roughly
mimic S. dioica's flower shape and size (Figure S1). These dummy flowers were then
tied on plants using an invisible nylon thread.

In addition to these open-pollinated plants, a group of 20 females (FHP) were placed
in the same experimental garden over the same period and artificially hand-
pollinated to quantify pollen limitation of female reproductive success. All open
flowers were supplemented every other day with pollen from a pool of males from
the same initial collection, isolated in a greenhouse. Each FHP female received pollen
from an average of 20 different males over the course of the experiment, with two
males being used for each pollination session for each FHP female. After ten days, all
females (FT, FC and FHP) were placed in an insect-proof greenhouse until fruits
reached maturity and could be collected, typically two to three weeks after flower
opening.

## Pollinator observations

In order to verify our expectation that dummy flowers increased pollinator
attraction, pollinator visitation patterns were reported and compared between
treatments. We conducted observations on groups of ten neighbouring plants
through three observational sessions of 20 minutes spread over the course of the experiment. Accordingly, each plant was observed for a total of sixty minutes. For each pollinator entering the group under scrutiny, the identity of the visited plants and the number of visits per plant were noted. A visit was defined as a clear contact between the insect and the reproductive structures of a flower. For each plant, three variables were derived from these observations (Figure 1): (i) whether a plant was visited or not over the course of the sixty minutes, defined hereafter as visitation, (ii) the number of visiting pollinators (i.e. the number of independent insects visiting a given plant), and (iii) the total number of visits (i.e. the total number of contacts between any pollinator and the reproductive structures of a given plant). Note that we did not observe any contact between pollinators and a fake flowers.

## 185 *Trait measurement*

**Floral traits** – A total of twelve floral traits were measured on all plants. Three traits were measured three times during the course of the experiment and averaged across dates: (1) the number of open flowers, (2) corolla width and (3) calyx height. The nine remaining traits were measured once, just before the beginning of the experiment: (4) nectar volume, (5) sugar concentration, (6) petal colour luminance, (7) petal colour saturation, (8) petal reflectance in UV wavelengths, (9) petal length, (10) petal largest width, (11) petal area and (12) petal cleft length. Details about floral trait measurements are available in appendix (Text S1; Figure S4).
194 **Gamete production** - The number of gametes per flower was assessed for both sexes.
195 For females, ovule number was assessed by imaging the contents of a random
196 sample of fruits. For males, pollen quantity was estimated on two anthers using a
197 particle counter. Details about estimation of gamete production in each sex are
198 available in appendix (Text S1).

199 *Female reproductive success*

200 Seed production was assessed on half of the mature fruits (one to six fruits per plant)
201 using image processing, as described in Text S1. Germination rates were estimated
202 on a sub-sample of 60 seeds per female that were sown in petri dishes filled with
203 40mL of 10g/L agar in sterile water (photoperiod 14:10 and temperature 21-15°C) and
204 surveyed during 15 days. For each female, reproductive success over the course of
205 the experiment was then obtained by multiplying three terms: total number of fruits,
206 mean seed number per fruit, and germination rate.

207 *Male reproductive success*

208 Male reproductive success was estimated by genotyping a subsample of offspring.
209 The number of seedlings genotyped for each female reflected its contribution to the
210 total seed pool (23.6 ± 11.4 seeds genotyped per female). Within each female,
211 seedlings were randomly sampled across fruits. Total genomic DNA from adults (86
212 females and 86 males) and seedlings (2030 individuals) was extracted, and PCR
213 assays were used to amplify five nuclear microsatellites (Table S1, Text S2). Using
214 genotypes and individual spatial data, we then used a spatially explicit mixed
215 mating model (MEMM model, [55]) to assess variation in male reproductive success
216 and in pollen dispersal kernels, as described in Text S3.

217 *Estimation of mating success*

218 We performed categorical paternity assignments using CERVUS 3.07, allowing for a
219 2% genotyping error and using an 80% confidence criterion [56,57]. To assess the
220 number of effective mating partners for each female and male (i.e. genetic mating
221 success), we used a bootstrapping procedure that sampled 8 seeds per mother 1000
222 times. Fixing the number of offspring per mother allows avoiding the establishment
223 of an artificially positive correlation between reproductive and mating success that
224 would solely reflect variation in seed production across females [58]. The mean
225 number of effective mating partners by individual across bootstraps was used in the
226 analysis. All data (pollinator visits, floral traits, male and female reproductive and
227 mating success, as well as pollen dispersal distances) is available on Dryad
228 (doi:10.5061/dryad.0rxwdbs4d).

229 *Statistical analysis*

230 **Phenotypic homogeneity between groups** – Prior to all analyses, we removed one
231 male from the treatment with manipulated flower number (MT) because it produced
232 over one standard-deviation more flowers than the second most-floriferous male
233 individual. One female was also removed from dataset because it did not produce
any seeds (FC). For each sex, a multivariate analysis of variance (MANOVA) was
then performed to ascertain that values for the thirteen measured traits (twelve floral
traits and gamete number, see above) were homogeneous within sex and among
treatments, using Wilks' Lambda statistic.

**Assessing pollen limitation in the experimental plot** – The occurrence of pollen limitation was tested by assessing whether the treatment (control FC, manipulated FT or hand-pollinated FHP) affected seed set. Seed set was calculated for each dissected fruit as seed number divided by ovule number. Comparisons were performed using a generalized linear model with a binomial distribution. A random individual effect was added because several fruits were sampled for each female (lme4 package, [59]), and a likelihood ratio test was used to test the effect of pollination treatment, by comparing models without vs. with pollination treatment as a fixed effect.

**Testing the effect of phenotypic manipulation on pollinator visits, pollen dispersal distances, reproductive success and mating success** – In the following models, we included as fixed effects (1) the first axis of a PCA on all morphological floral traits, (2) the number of real flowers, and (3) gamete number per flower. The latter two were explicitly included in models as fixed effects to account for fertility variation between plants while the former described the general floral phenotype of individuals. PCAs were performed on males and females separately. The first axis accounted for 27.6% of floral traits' variance in females and 29.8% in males. We only
255 retained the first axis of the PCA on basis of the broken-stick method ([60]; Figure 256 S2). In both sexes, this first axis mainly represents flower size variation (Figure S2).

257 **Pollinator visits** – Three groups of generalized linear models were built to explain 258 pollinator attraction, one for each estimate (visitation, number of pollinators visits, 259 total visits). For each model, likelihood ratio tests were used to test the significance of 260 the following fixed effects: (i) the number of real flowers, (ii) the first axis of PCA 261 defined above, (iii) treatment, (iv) sex and (v) the interaction effect between treatment 262 and sex, by comparing the likelihood of models including or not those effects. 263 Because pollen production can also play a role in pollinator attraction [61], additional 264 models testing for the effect of gamete production and its interaction with sex were 265 also built. Visitation was modelled using a GLM with a binomial distribution. For 266 models explaining either the number of pollinators or the total number of visits, 267 GLMs with negative binomial distributions were retained based on their greater AIC 268 values compared to hurdle or zero-inflated models (i.e. accounting for 269 overdispersion and/or excess of zeros).

270 **Pollen dispersal distance** – Variation in mean pollen dispersal distances among 271 males was assessed using multiple linear regressions including the following 272 explanatory variables: (i) the number of real flowers, (ii) the number of gametes per 273 flower and (iii) the first axis of PCA defined above. Separate analyses were first 274 performed on control males (MC) and on manipulated males (MT). Then, a second 275 analysis on both groups of males was performed to test differences between groups.
T-tests were conducted on estimates to assess their significance in separate models, and likelihood ratio tests were used to test the effect of interactions between treatment and each of the three explanatory variables in the complete model. Prior weights were set up to take into account uncertainty of a posteriori individual mean pollen dispersal distance estimated using the MEMM model (Text S3).

**Patterns of selection on flower number** – We used structural equation modelling (SEM, [62]) which allows to jointly assess the direct and indirect effects of floral traits (number of real flowers, gamete number and first PCA axis) on both mating and reproductive success. Specifically, we evaluated how floral traits affected mating success (Figure 2) while testing the relationship between mating success and reproductive success (i.e. indirect effect of floral traits on reproductive success through mating success; Figure 2). We also evaluated the direct relationship between floral traits and reproductive success, independently of an indirect effect through mating success (Figure 2). This was done on two separate datasets, one for each sex, using *piecewiseSEM* package in R [63]. We then performed a multigroup analysis using local estimation of the parameters for each sex, which is carried out in two steps. The first step provides statistics for each treatment × variable interactions using type III ANOVA to assess which selection patterns were similar between groups (i.e. treatment × variable interaction effects that are not significant). The second step provides correlation estimates, and significant treatment × variable interaction are
296 free to vary between each group. Estimates were then plotted on the network using
297 *semPlot* package in R [64].

298 Another aim of the study was to evaluate if enhanced pollinator attraction through
299 phenotypic manipulation resulted in higher mating success (MS) and/or
300 reproductive success (RS). To that aim and in addition to the SEM analyses, we
301 tested for a main effect of the treatment on MS and/or RS, which cannot be achieved
302 using SEMs. We thus built multiple linear models, predicting either reproductive or
303 mating success, again considering males and females separately. Apart from the
304 treatment variable, we tested for the same independent variables than those included
305 in the SEM analyses, i.e. number of real flowers, number of gametes per flower and
306 the first PCA axis. In models predicting reproductive success, mating success was
307 added as an additional explanatory variable. Significance of estimates were assessed
308 using T-tests for separate models, and likelihood ratio tests were used to test effects
309 of (i) treatment as main effect, (ii) treatment × trait interactions, as well as (iii)
310 treatment × mating success interaction in reproductive success models. Uncertainties
311 in the estimation of male reproductive success were again accounted for by
312 modelling prior weights using a posteriori distributions of the MEMM model. The
313 analyses were performed using as response variables either relative reproductive or
314 mating success (i.e. estimates were divided by the mean), and used standardized trait
315 values as explanatory variables (i.e. traits were centred and scaled), following
316 standard recommendations [65,66]. Relativization and standardization were
performed per sex and across treatments, since the methods used to estimate fitness
differed between sexes.

## 319 **Results**

320 *No evidence for pollen limitation*

We found no evidence of pollen limitation in our experimental garden. Hand-pollinated females (FHP) did not show higher seed sets than open-pollinated females
(FC: 0.62 ± SE 0.012; FT: 0.64 ± SE 0.011; FHP: 0.59 ± SE 0.018; $\chi^2_{2,556}=1.45, P=.48$).

324 *Phenotypic manipulation increased pollinator visitation*

A total of 280 pollinators were observed, including Syrphidae (60.7% of visitors),
*Bombus* species (28.6%), Halictidae (6.8%) and Lepidopterae (3.9%). Pollinators were
more attracted by males than females as revealed by a significant sex effect for all
three descriptors of pollinator visitation patterns: visitation ($\chi^2_{1,160}=14.68, P<.001$),
number of pollinators ($\chi^2_{1,160}=63.19, P<.001$) and total number of visits ($\chi^2_{1,160}=92.36, P<.001$; Figure 1). MANOVAs on the thirteen measured traits did not
indicate phenotypic differences between treatments (females: $F_{15,67}=1.34, P=.20$ ; males : $F_{15,62}=0.71, P=.77$ ), so any effect of treatment on visitation patterns should
be ascribable to the phenotypic manipulation. Results showed that dummy flowers
did not increase visitation ($\chi^2_{1,160}=1.82, P=.17$), but increased both the number of
visiting pollinators and the total number of visits ($\chi^2_{1,160}=6.04, P=.014$ and
$\chi^2_{1,160}=6.91, P<.01$ respectively; Figure 1). Although the effect of the treatment
337 appeared stronger in females compared to males, none of sex × treatment interactions
338 were significant (visitation: $\chi_{1,160}^2=1.98, P=.16$ ; pollinator number:
339 $\chi_{1,160}^2=2.43, P=.12$ ; total visits: $\chi_{1,160}^2=2.58, P=.11$ ). Pollinators were also
340 attracted by higher numbers of real flowers (pollinator number:
341 $\chi_{1,160}^2=25.37, P<.001$ ; total visits: $\chi_{1,160}^2=35.87, P<.001$ ), but this effect was
342 weaker for visitations ($\chi_{1,160}^2=3.25, P=.71$ ). Finally, larger values along PC1
343 (representing larger flower sizes) did not increase visitation ($\chi_{1,160}^2=0.0021, P=.96$ )
344 nor the number of pollinators ($\chi_{1,160}^2=2.088, P=.15$ ) but increased the total number
345 of visits ($\chi_{1,160}^2=6.44, P=.11$ ). When included in the models, gamete number had
346 no effect on pollinator attraction, and did not modify the results described above.

347 *Pollen dispersal distance was influenced by the number of real flowers*
348 Across treatments, average pollen dispersal distance was 1.50 meters (± 0.32 SD)
349 within our experimental garden. Mean pollen dispersal distance did not differ
350 between control (MC) and manipulated (MT) males ($\chi_{1,77}^2=0.44, P=.51$ ; Figure S3).
351 Mean pollen dispersal distance increased with the number of real flowers in the
352 manipulated group. This effect was weaker in the control group, although the effect
353 of the interaction between the number of real flowers and treatment was not
354 significant (Table 1). Neither the number of gametes per flower nor PC1
355 (representing variation in flower size) affected pollen dispersal distances in both
356 groups.
357 *Selection patterns differed between sexes*

358 Results from SEM analyses and linear models were globally congruent. Estimates 359 and statistics from linear models are thus reported in supplementary (Table S2). SEM 360 analyses showed the same selection patterns in control and manipulated females 361 (Table 2). In both groups, we detected a significantly positive direct effect of the 362 number of real flowers on both reproductive and, to a lesser degree, mating success 363 (Table 2, Figure 2A), as well as a significant positive effect of the number of gametes 364 per flower on reproductive success. In both groups of females, MS and RS were not 365 significantly correlated. In the linear model, treatment did not increase reproductive 366 success (i.e. treatment as main effect was not significant; $\chi^2_{1,74}=1.23, P=.27$ ), but 367 significantly increased mating success ($\chi^2_{1,76}=4.97, P=.26$ ).

368 In both control and manipulated males, the number of real flowers was significantly 369 positively correlated with plant mating success (Table 2; Figure 2B). Because the SEM 370 analyses also detected a strong correlation between MS and RS in males, this 371 suggests an indirect effect of real flower number on reproductive success. The only 372 difference detected between manipulated and control males concerned the 373 correlation between the number of gametes per flower and reproductive success: 374 whereas this correlation was marginally positive in manipulated plants, it was 375 negative in the control group. In the linear model, neither the average reproductive 376 nor mating success were affected by the treatment (respectively $\chi^2_{1,74}=0.87, P=.35$ 377 and $\chi^2_{1,76}=0.10, P=.75$ ).
378

**Discussion**

379

The current study aimed at exploring the male-function hypothesis [23,27], by measuring the relationship between flower number and fitness in both sexes, through its potential impact on pollinator attraction. Specifically, our aims were (i) to investigate the impact of flower production on reproductive and mating success in male and female plants of *S. dioica*, and (ii) to explore whether an experimental enhancement of the floral signal would mainly impact selection patterns in males, highlighting the role of flower number in pollinator attraction and pollen export.

380

381

382

383

384

385

386

Adding dummy flowers indeed increased pollinator attraction, enabling us to test our predictions. As expected, we found that neither female reproductive success, nor its relationship with flower number were impacted by this enhanced attraction. We also found that this manipulation increased female mating success (in terms of mate acquisition), but with no detected impact on seed production. In males, contrarily to our hypotheses, we found no effect of enhanced attraction to pollinators on either reproductive or mating success. Whereas large flower numbers seem to be associated with a higher mating success, we found no difference in this selection pattern between control and manipulated males. The reach of these results for the historical discussion on the male function hypothesis are discussed below.

387

388

389

390

391

392

393

394

395

396

*Pollination context*
397 Seed production was not statistically different between open- and hand-pollinated plants, suggesting that female reproductive success was not limited by pollen receipt in the experimental garden. In such conditions, according to sexual selection theory, one expects fitness of male plants to depend more on their ability to attract pollinators compared to that of females [9,10], provided that pollinator attraction is associated with the probability to reach sexual partners.

403 The strongest determinant of pollinator attraction was clearly sex (males received on average twelve-fold more visits than females), confirming previous observations in *S. dioica* [53,67]. It should be noted that this preference for male *S. dioica* plants is known to be at least partly driven by the high degree of sexual dimorphism for flower number [68]. This effect of flower number on pollinator attraction is confirmed in our study: adding dummy flowers resulted in an overall increase in the number of pollinators attracted, as well as in the total number of visits.

410 *Female reproductive success was mainly driven by gamete production*

411 In females, the number of real flowers was shown to positively affect reproductive success, and this effect was similar in both manipulated and control plants. This observation, along with the fact that no pollen limitation was detected, suggests that the benefit for the female function of carrying more flowers is only linked to a higher ovule production at the plant level, and not to any effect on pollinator attraction. In other words, our results support the sole effect of fertility selection on the number of
417 flowers, in alignment with general expectations from sexual selection theory. Our
418 analysis also revealed positive selection on the number of ovules per flower, but no
419 selection on flower size (a trait often associated with pollinator attraction [16,17]).

420 Taken together, these findings support that, as long as the pollination context is
421 favourable, traits that increase female reproductive success are associated with
422 fertility, as found in several other plant species [37,39,69].

423 In our experiment, female mating success increased both when plants received more
424 pollinator visits (treatment effect) and when they carried more real flowers. These
425 two effects are likely to emerge from mechanistic aspects of pollen dispersal. On the
426 one hand, an increase in the number of independent pollinator visits is expected to
427 increase the diversity of pollen arriving on the stigmas. On the other hand, because
428 producing more flowers implies that more ovules are available for fertilization, an
429 increase in flower number should result in a parallel increase in the number of mates.

430 However, because female mating and reproductive success were uncorrelated, a
431 result equivalent to non-significant Bateman gradients found in females of many
432 animal species ([6] but see exceptions within) and in another plant species [70], the
433 detected positive effects on mating success did not translate in an increase in
434 reproductive success. In other words, females did not benefit from an increased seed
435 number because they accessed more sexual partners, confirming that only fertility
436 selection occurred in females of our experiment.
437 *Males do not seem to benefit from enhanced pollinator attraction*

438 Contrary to our expectations, artificially enhanced floral display did no impact male
439 reproductive success or patterns of selection on floral traits. The only difference
440 between control and manipulated male plants concerned selection on the number of
441 pollen grains per flower, with control plants experiencing negative selection on this
442 trait, whereas manipulated plants seemed to experience positive selection. Although
443 we could not find any satisfactory explanation for this difference between control
444 and manipulated plants, this effect is unlikely to be driven by treatment differences
445 in pollinator attraction, since the quantity of gamete per flower had no significant
446 impact on pollinators visits in our analyses. While a negative trade-off between
447 pollen quantity and quality could contribute to the observed change in selective
448 patterns between control and manipulated males, our previous evaluation of pollen
449 phenotypes suggests that this correlation is rather positive [22].

450 In both categories of males, the number of real flowers positively impacted mating
451 success. In strong contrast with the patterns observed in females, however, mating
452 and reproductive success were strongly associated, reflecting the positive Bateman
453 gradient that is often observed in male animals [6] and in a couple of plant species
454 [70,71]. Thus, although flower number did not have any detectable direct effect on
455 male reproductive success, our results support an indirect effect through mating
456 success: male plants with more (real) flowers tended to have a better access to mates,
457 which in turn significantly increased the number of seeds they sired. This part of our
results thus seems consistent with the expectation of flower number being under
sexual selection in males. Whereas observational studies cannot assess whether such
selective pattern is mediated by an increased pollinator attraction or an increased
gamete production, our manipulation of floral display -that increased pollinator
attraction only- allowed us going further. Under the hypothesis of a male function
through pollinator attraction, we were expecting differences between manipulated
and control males. Our results did not fit our expectations. Manipulated males did
not benefit from increased mating or reproductive success, and they appeared to
experience the same selection patterns on real flower number compared to control
males. This suggests that the possible benefit of exhibiting a high number of flowers
in males is not mediated by pollinator attraction per se. One likely explanation for
our results is that producing many flowers results in larger pollen loads on
pollinators (e.g. through increased visit time, [72]) and, ultimately, in reaching a
larger number of females. The fact that the number of real flowers was positively
associated with longer dispersal distance in both control and manipulated males
indirectly supports this hypothesis. Pollen production at the plant level is a trait that
typically falls in the realm of what is sometimes referred to as "the grey zone" of
sexual selection [73], since it can both be argued to be mainly under fertility selection
or under sexual selection.

This study documented extremely restricted pollen dispersal distances (typical for
entomogamous species, [74-76]), which might weaken the opportunity for sexual
479 selection, since male-male competition would typically occur among directly
480 neighbouring individuals. Nevertheless, our result support that males producing
481 more flowers gain higher fitness at through enhanced access to females. It thus
482 seemed that sexual selection played a part in the evolution of flower number in
483 males of *Silene dioica*, whereas selection on the same trait in females was
484 undoubtedly based on fertility selection. Nonetheless, this difference between males
485 and females could not be attributable to a differential in the relationships between
486 male vs. female fitness and pollinator attraction. Taken together, our results are thus
487 not congruent with the male-function hypothesis, when interpreted as an advantage
488 of producing many flowers to the male function through an increased capacity of
489 attracting pollinators [22,26].

490 **Data accessibility**

491 Data are available on Dryad: https://dx.doi.org/10.5061/dryad.0rxwdbs4d [77].

492 **Funding**

493 This work is a contribution to the CPER research project CLIMIBIO. The authors
494 thank the French Ministère de l'Enseignement Supérieur et de la Recherche, the
495 Hauts de France Region and the European Funds for Regional Economical
496 Development for their financial support to this project. JT was funded by a Marie-
497 Sklodowska-Curie grant (#844321).
498 Acknowledgements

499 We would like to thank E. Klein for letting us access the MEMM model. We thank S.

500 P. De Lisle for precious advice about reproductive and mating success relativization

501 and trait standardization in selection gradient analysis. We also thank Céline

502 Devaux, Eric Imbert and François Rousset for their advice about the analyses. This

503 work has been performed using infrastructure and technical support of the

504 "Plateforme Serre, cultures et terrains expérimentaux - Université de Lille". We are

505 grateful to Eric Schmitt for the support in plant collection maintenance. For

506 laboratory assistance, we thank L. Debacker, S. Flourez and N. Le Roux.
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<table><caption>722 **Table 1:** Effect of floral traits on pollen dispersal distances and differences between
723 groups.</caption><thead><tr><th></th><th>Control males (MC)</th><th>P</th><th>Manipulated males (MT)</th><th>P</th><th>Treatment × trait</th><th>P</th></tr></thead><tbody><tr><td></td><td>R<sup>2</sup><sub>S</sub>=-0.014</td><td></td><td>R<sup>2</sup><sub>S</sub>=0.11</td><td></td><td>R<sup>2</sup><sub>S</sub>=-0.048</td><td></td></tr><tr><td>β Number of real flowers</td><td>0.0045 ± 0.0038</td><td>.24</td><td>0.012 ± 0.0045</td><td>.014</td><td>χ<sup>2</sup><sub>1,77</sub>=1.60</td><td>.21</td></tr><tr><td>β Gametes number per flower</td><td>8.4 × 10<sup>-7</sup> ± 1.7 × 10<sup>-6</sup></td><td>.62</td><td>4.4 × 10<sup>-7</sup> ± 1.8 × 10<sup>-6</sup></td><td>.80</td><td>χ<sup>2</sup><sub>1,77</sub>=0.029</td><td>.87</td></tr><tr><td>β PC1</td><td>0.016 ± 0.024</td><td>.51</td><td>0.031 ± 0.025</td><td>.22</td><td>χ<sup>2</sup><sub>1,77</sub>=0.65</td><td>.65</td></tr></tbody></table>

724 Note: Estimates (± SE) from linear models are reported for each effect in both groups (MC and MT). are reported from likelihood ratio tests to assess the interaction between treatment and each trait. *P*-value in bold are significant. The proportion of variance explained by each model is reported using adjusted R-squared ( R<sup>2</sup><sub>S</sub> ).
<table><caption>**Table 2:** F-stats (type III ANOVA) for multigroup SEM analyses</caption><thead><tr><th></th><th></th><th>Females</th><th>P</th><th>Males</th><th>P</th></tr></thead><tbody><tr><td rowspan="4">Reproductive success</td><td>Treatment × Mating success</td><td>F<sub>1,77</sub>=0.3</td><td>.25</td><td>F<sub>1,77</sub>=12.4</td><td>.44</td></tr><tr><td>Treatment × Number of real flowers</td><td>F<sub>1,77</sub>=0.3</td><td>.52</td><td>F<sub>1,77</sub>=12.4</td><td>.75</td></tr><tr><td>Treatment × Gametes number per flower</td><td>F<sub>1,77</sub>=0.3</td><td>.33</td><td>F<sub>1,77</sub>=12.4</td><td><strong>.030</strong></td></tr><tr><td>Treatment × PC1</td><td>F<sub>1,77</sub>=0.3</td><td>.29</td><td>F<sub>1,77</sub>=12.4</td><td>.50</td></tr><tr><td rowspan="3">Mating success</td><td>Treatment × Number of real flowers</td><td>F<sub>1,77</sub>=0.3</td><td>.079</td><td>F<sub>1,79</sub>=5.2</td><td>.50</td></tr><tr><td>Treatment × Gametes number per flower</td><td>F<sub>1,77</sub>=0.3</td><td>.86</td><td>F<sub>1,79</sub>=5.2</td><td>.51</td></tr><tr><td>Treatment × PC1</td><td>F<sub>1,77</sub>=0.7</td><td>.22</td><td>F<sub>1,79</sub>=5.2</td><td>.99</td></tr></tbody></table>
<figure><img src="image_2.png" /></figure>

Visitation 1

1

0

Number of visiting pollinators 2

1

0

Total number of visits 2

2

0

<figure><img src="image_3.png" /></figure>

*

<figure><img src="image_4.png" /></figure>

**
**Figure 1:** Illustration of the three proxies used to characterize pollinator visits (A): visitation, the number of visiting pollinators and the total number of visits. Boxplots representing the number of visiting pollinators (B) and the total number of visits (C), according to sex and treatment. P-values for the treatment effect are represented with \* < 0.05 and \*\* < 0.01. Males (MC: yellow, MT: orange) were more visited than females (FC: light green, FT: dark green), and both sexes were more visited with the addition of dummy flowers. Means are represented with white points.
A

♀

<figure><img src="image_5.png" /></figure>

B

<figure><img src="image_6.png" /></figure>

♂
**Figure 2:** Results from multigroup SEM analyses in females (A) and males (B). Each trait can have (I) a direct effect on MS and/or RS, as well as (ii) an indirect effect on RS, depending of the strength of the relationship between RS and MS (i.e. Bateman gradient), through a direct effect on MS. Estimates for each correlations are plotted with associated *P*-values in brackets, with green arrows for positive values and orange arrows for negative values. Covariances between traits are represented with double headed dotted arrows. In the SEM analyses, only one correlation differed between control (C) and manipulated (T) group, with estimates plotted in grey for the correlation between gamete number per flower and reproductive success in males.