Phenotypic evolutionary response to temporally limited pollinator access in Brassica rapa

pollinator communities, experimental evolution, selection, phenotypic response, 18 generalist plant species, plant—pollinator interaction 19 20 Type of article: letter 21 22 Author Contributions: LF and SA acquired the University Research Priority Program 23 ‘Evolution in action” funding, conceptualized and designed the experiment, EA performed the 24 experiment with the contribution of LF, EA performed the analyses, EA wrote the original draft, 25 LF and SA reviewed and edited the manuscript. 26 27 Data Availability Statement: Data and code are available in Zenodo 28 (10.5281/zenodo.15425805). 29 30 Metrics: 131 words in the abstract, 4971 words in the main text, 84 references, 4 figures, and 31 1 table. 32 33 Correspondence: Dr. Léa Frachon, e -mail: [email protected], phone: +33681504491, 34 address: UMR1347 AGROECOLOGIE, 17 rue Sully, BP 86540, 21065 Dijon, Cedex, France 35 36 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 2

37 Plant–pollinator interactions are essential for plant reproductive success and pollinator 38 food supply. However, the ongoing pollinator decline threatens many wild and cultivated 39 flowering plants, urgently requiring studies on its impact on plant populations and their potential 40 evolutionary responses. We combined an experimental evolution with a resurrection approach 41 to test phenotypic evolutive changes in response to artificially limited access of plants to natural 42 pollinators in a common garden setting using Brassica rapa. After six generations, we detected 43 putative adaptive responses to the pollination treatments, including changes in phenology, 44 floral morphology and flower volatile organic compounds, associated with changes in overall 45 attractiveness to hoverflies. Although the generalist plant B. rapa shows ability to rapidly 46 respond to strong limitation s to its natural pollinator community, the observed decrease of 47 fitness could threaten the population. 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 3

65 About 88% of all species of flowering plants worldwide are pollinated by animal pollinators 66 (Ollerton et al. 2011) and depend on them for their reproduction. However, in the last few 67 decades, a worldwide decline of domesticated and wild insect pollinators has been observed 68 (Kluser & Peduzzi 2007; Potts et al. 2010). This loss of pollinators has also been observed to 69 negatively impact ecosystem services and food production (Aizen et al. 2009; Klein et al. 2007; 70 Singh & Adhikary 2021). For instance, local declines in bee and hoverfly species abundance 71 in Britain and the Netherlands over the past century (Biesmeijer et al. 2006; Powney et al. 72 2019) support growing concerns about the persistence of local pollinator communities. Indeed, 73 a decrease in pollinator diversity and abundance leads to pollen limitation and reduces plant 74 reproductive success (Bennett et al. 2020; Thomann et al. 2013). For instance, lower insect 75 pollinator diversity negatively affects fruit and seed set in Raphanus sativus (Albrecht et al. 76 2012). 77 The extent to which a decrease in pollinator diversity and abundance affects plants may 78 depend on the degree to which plants are specialised to their pollinators ( Ramos-Jiliberto et 79 al. 2020). Generalist -pollinated plant species, i.e., plant species interacting with a wide 80 diversity of pollinator species for their reproductive success (Ollerton et al. 2007), play a central 81 role in plant–pollinator networks due to their higher connectivity than other specialist-pollinated 82 plant species (Bascompte & Jordano 2007). Indeed, thanks to their broader interaction 83 networks and inherent resilience, generalist plants are less prone to extinction and potentially 84 better able to adapt to pollinator loss than specialist species. However, few studies have 85 examined the adaptive potential of these generalist -pollinated plant specie s in response to 86 pollinator declines or to the destabilization of their pollination networks. Understanding the 87 adaptive potential of generalist plants is particularly critical in crops, where pollinator declines 88 could directly threaten crop yields and food security (Klein et al. 2007, Aizen et al. 2009). 89 Moreover, the loss of generalist plant species can destabilize plant –pollinator interactions, 90 decrease pollinator abundance (Biella et al. 2019; Palacio et al. 2016), and lead to chain 91 extinctions of plant and pollinator species from the ecological network (Bascompte et al. 2019). 92 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 4 Understanding whether and how generalist-pollinated species respond to changes in access 93 to pollinators is crucial for ensuring ecosystem stability. However, only a few studies have 94 explored the adaptive response of generalist plant species to pollinator community 95 disturbance. For instance, rapid phenotypic response s to a single pollinator ha ve been 96 observed in generalists Mimulus guttatus (Bodbyl Roels & Kelly 2011) and Brassica rapa 97 (Gervasi & Schiestl 2017) in greenhouse conditions. Interestingly, Schiestl et al. (2018) found 98 that Brassica rapa exposed to a synthetic pollinator community with one bumblebee and one 99 hoverfly species under controlled conditions evolved a ‘generalised -pollination phenotype’ 100 overlapping with, but distinct from, the phenotypes seen under pollination by only one of the 101 two pollinator species. However, in natural ecosystems, generalist plants interact with complex 102 pollinator communities involving more than two pollinator species. To our knowledge, the 103 evolutionary response of generalist plant species to changes in the extent of access to their 104 natural pollinator communities under field conditions remains unknown. 105 In response to limited access to pollinators, flowering plant species can adopt two opposite 106 strategies: decreasing their dependency on pollinators or reinforcing their interaction with 107 pollinators (Thomann et al. 2013). Specifically, the first strategy for plants in response to limited 108 access to pollinators is to enhance selfing ability, i.e., reproducing using their own pollen, 109 ensuring reproductive success in the absence of pollinators (Lloyd 1992) and is referred to as 110 reproductive assurance. By default, selfing is suppressed in many plant species by physical 111 and molecular barriers (Rea & Nasrallah 2008; Wang & Filatov 2023). However, these barriers 112 can disappear within just a few generations in the absence of pollinators, as demonstrated by 113 increased selfing ability in Mimulus guttatus experimental evolution study (Bodbyl Roels & 114 Kelly 2011). Similarly, selfing ability increased in Brassica rapa in response to less efficient 115 pollinators for pollination, like hoverflies (Gervasi & Schiestl 2017). In these two examples, the 116 increase in selfing ability was associated with a decrease in herkogamy, a key physical barrier 117 defined as the distance between the stigma and anthers in individual flowers, which promotes 118 gamete contact within the same flower (Opedal 2018). However, the evolution of selfing is 119 complex and involves a set of distinct traits, collectively termed the ‘selfing syndrome’ such as 120 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 5 smaller flowers, decreased scent emission, etc. (Barrett 2003; Cutter 2019). However, this 121 increased selfing capacity also leads to population -level costs, such as reduced genetic 122 diversity. A second potential strategy in response to limited access to pollinators is to 123 strengthen interactions with pollinators (Thomann et al. 2013) by increasing plant 124 attractiveness (pollinator visitation rates) and pollen transfer efficiency. Flower traits that 125 determine flower attractiveness are associated with pollinator detection and access to flowers 126 (Bauer et al. 2017; Gervasi & Schiestl 2017) such as plant size, flower number, flower 127 morphology, flower colour, composition and amount of nectar, and floral scent produced 128 (Klinkhamer & De Jong 1993; Klumpers et al. 2019; Majetic et al. 2009; Wright & Schiestl 129 2009). However, it remains unclear whether plants preferentially adopt one strategy over the 130 other in response to limited pollinator access. 131 An efficient method to study plant responses to environmental changes is an experimental 132 evolution study (Kawecki et al. 2012). Experimental evolution offers the possibility of 133 manipulating a set of environmental conditions to exert selection pressures (Bennett & Lenski 134 1999). However, if the aim of an experimental evolution study is to quantify the evolutionary 135 response of an organism to a set of manipulated conditions under otherwise natural conditions, 136 it may be difficult to separate the effect of the focal conditions from those of the uncontrolled 137 natural conditions. Thus, such an in situ experimental evolution approach provides ecological 138 realism but also introduces the possibility of confounding environmental effects. For instance, 139 climatic conditions cannot be fully controlled in in situ experiments but may elicit responses 140 that enhance or attenuate the effects of the experimental manipulation. Distinguishing the 141 effects of the experimental manipulation from those of the climate therefore requires recording 142 the climatic conditions over the course of the experiment and including them as explanatory 143 covariates in the statistical analyses. Moreover, the expression of genetically determined focal 144 traits may be affected by uncontrolled environmental conditions and parental effects. These 145 factors may thus confound the comparison of traits measured before and after the 146 experimental treatment. In plants, it is possible to mitigate both of these factors by regenerating 147 from stored seeds the generations from before and after the evolution experiment under the 148 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 6 same controlled conditions in a so -called resurrection approach (Franks et al. 2008, 2018; 149 Weider et al. 2017). 150 The overarching aim of our study is to better understand how generalist -pollinated 151 flowering plant species respond to temporal limitation in access to their natural pollinator 152 community. To achieve this aim, we designed an experimental evolution study to assess i) 153 whether and how morphological traits and fitness components respond, and ii) how the 154 attractiveness of plants to pollinators evolves. 155 156

157 Study system and experimental evolution design 158 We used the fast-cycling standard variety of Brassica rapa from Wisconsin Fast Plants® 159 (Carolina Biological Supply, Burlington, USA) as our study system (Wendell & Pickard 2007). 160 We randomly divided 108 full-sib seed families (supplementary information) into three sets of 161 36, hereafter referred to as “replicates”. Each replicate underwent an independent six -162 generation evolutionary experiment with three pollination treatments ( “Pollination treatments” 163 subsection). Throughout the manuscript, we refer to a treatment × replicate combination as a 164 “replicate population”. We started the experiment with nine initial replicate populations (3 165 treatments × 3 replicates) and propagated these nine replicate populations individually over 166 the experimental generations for each of the three pollination treatments (Fig. 1 , 167 supplementary information). 168 We grew the 324 individual plants (9 replicate populations x 36 individual plants) in a 169 phytotron for two weeks, potted them in standardized soil, and moved them into nine outdoor 170 cages surrounded by an insect -proof net that were set up at the Botanical Garden of the 171 University of Zürich (Fig. S1). Inside each cage, we randomly positioned 36 individual plants 172 from a given replicate population (Fig. S1). 173 174 175 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 7 176 FIGURE 1 | Experimental design. a) 108 full sib -seed families of fast -cycling B. Rapa have been generated from 400 B. rapa seeds, and b) 177 propagated over six generations (G1 to G6) in three pollination treatments in common garden, each comprising three parallel replicate populations 178 of 36 individuals. c) To remove maternal and environmental effects, seeds from the full -sib families and from the sixth generations of the 179 experimental evolution were regrown in a resurrection experiment under similar greenhouse conditions for two generations (fir st and second 180 resurrections). Due to set up constraint, we regrow a second time the seeds of the first resurrection, leading to a third resurrection. d) Treatments 181 included one control treatment and two pollination treatments in which access of pollinators was temporally limited. e) To pr opagate plants from 182 one generation to the next, we estimated relative fitness from the product of the total number of seeds produced times the ge rmination rate and 183 corrected the number of seedlings in each pot by the estimated relative fitness of its maternal plant. 184 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 8 During the experimental evolution study, we measured 17 traits including seven 185 architectural and morphological traits , and 10 fitness-related traits at each generation (see 186 table S 1, supplementary information). Climate data was recorded (temperature and light 187 intensity, supplementary information) using a datalogger installed in the central outdoor cage, 188 and principal component analysis was performed to summarize the variation of the 10 climatic 189 variables (Table S2). 190 191 Pollination treatments 192 After moving plants into outdoor cages, we applied three pollination treatments by 193 artificially manipulating temporal pollinator access to simulate a decrease in pollinator richness 194 and abundance. 1) Control treatment: we hand -pollinated plants by transferring pollen from 195 flowers of a given plant to one to six flowers of the nearest plant. We kept the three cages 196 assigned to the three replicates in this treatment constantly closed to exclude insect pollinators. 197 2) Full Access treatment: we let the natural surrounding insect pollinator community pollinate 198 the plants. We opened each of the three cages once during the life cycle for five consecutive 199 hours during the highest pollinator activity (between 11 am and 4 pm). 3) Limited Access 200 treatment: we temporally reduced the access of the natural pollinator community to plants 201 compared to the Full-Access treatment. We opened each of the three cages assigned to the 202 three replicates in this treatment once during the life cycle for one hour during the highest 203 pollinator activity (between 12 pm and 1 pm). Our observations of pollinator visits during 204 treatment application (supplementary information) revealed significantly lower pollinator 205 abundance and diversity in the Limited Access treatment compared to the Full Access one 206 (Supplementary information, Table S3, Dataset 1). 207 208 Resurrection experiments 209 To reduce maternal and environmental effects that could confound generation effects, we 210 conducted two refresher generation resurrection experiments (Fig. 1c , supplementary 211 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 9 information). We sowed seeds from the first and sixth generations together to generate a total 212 of 12 replicate resurrection populations (three initial replicate populations plus three treatments 213 × three replicate populations from the sixth generation). We grew these replicate resurrection 214 populations in a phytotron for two weeks, and then transferred them into a greenhouse (Irchel 215 Campus, University of Zürich). Two weeks later, within each replicate resurrection population, 216 we cross-pollinated eight flowers per plant with pooled pollen from the same replicate 217 resurrection population. At fruit maturity, we generated a second resurrection generation to 218 decrease maternal effect by growing one offspring per maternal plant (n = 36) following the 219 same protocol and conditions as previously described. Finally, we repeated a third resurrection 220 experiment in order to estimate changes in mating system using seeds from the first 221 resurrection (Fig. 1c). In the manuscript, unless contraindicated, we use the term 'resurrection 222 experiment' without distinction, referring to either the second or third resurrection depending 223 on the context. 224 During the resurrection experiments, we measured 33 traits including: flowering time, eight 225 architectural and morphological traits, 13 flower volatile organic compounds (VOCs), and 11 226 fitness-related traits (Table S1, supplementary information). 227 228 Pollinator preference 229 To investigate the effect of pollination treatments on the overall flower attractiveness to 230 pollinators, we performed a four-choice test. In this test, an insect pollinator was given a choice 231 among four focal plant individuals from the four different resurrection populations (initial 232 generation, sixth generation of Control, Full Access, and Limited Access) . We used the 233 pollinator’s first choice as a preference proxy. During the four -choice test, we released one 234 bumblebee (Bombus terrestris) or one adult hoverfly (Episyrphus balteatus) inside a cage. In 235 total, we performed 106 four -choice tests for each pollinator species . On the test day, we 236 counted the number of open flowers for each of the four focal plants. 237 238 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 10 Statistical analyses 239 We tracked phenotypic changes over time during the experimental evolution. Based on the 240 mean values of traits across plant individuals within each replicate population times generation, 241 we tested separately (1) the overall effect of pollination treatment, and (2) the overall effect of 242 generation with Kruskal—Wallis test. 243 To quantify both the effect of climate and that of generations, we performed two 244 independent multi-way ANOVAs to explain trait changes by treatment, and either by generation 245 or by climate for the ten traits showing a normal distribution in R v4.4.1 according to the 246 following equations: 247 Trait ~ treatment x generation + (1 | replicate population) (anova 1) 248 Trait ~ treatment x climatePCs + (1 | replicate population) (anova 2) 249 where Trait represents the mean of 10 phenotypic and fitness-related traits, treatment the three 250 pollination treatment s, generation a categorial explanatory variable representing the six 251 generations, climatePCs the first two principal components derived from the climate PCA, and 252 replicate population as random factor. 253 To detect phenotypic and fitness -related trait evolutionary changes, we used arithmetic 254 mean for each trait from the resurrection experiment dataset. First, to understand changes in 255 trait relationships across treatments, we calculated Spearman’s pairwise rank correlation 256 coefficients (package Hmisc v5.0-1, Harrell Jr 2003) in R environment among traits within each 257 four resurrection populations. Additionally, to assess evolutionary changes occurred over six 258 generations, we performed Kruskal—Wallis tests on arithmetic mean of 33 traits within each 259 replicate resurrection population, with treatment as a categorical explanatory variable . For 260 traits with significant results (p -value < 0.05), we performed multiple pairwise Kruskal -Wallis 261 tests (R package pgirmess v 2.0.3, Giraudoux et al. 2024). 262 To quantify directional selection, we applied the Lande & Arnold framework (Lande & 263 Arnold 1983) to explain the effect of uncorrelated (significant rhoSpearman < 0.8) phenotypic traits 264 (fixed-effect explanatory variables) on relative fitness estimate. We performed three directional 265 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 11 selection analysis based (1) on five uncorrelated phenotypic traits that passed the Shapiro test, 266 (2) on seven uncorrelated phenotypic traits related to phenology and floral morphology traits, 267 and (3) on 11 uncorrelated traits associated with VOCs. The relative fitness estimate was the 268 relative number of seeds per plant within the respective replicate resurrection population. We 269 included replicate as a random slope, and the phenotypic traits were centred and scaled within 270 their replicate resurrection population for each generation. We interpreted the partial 271 regression coefficients pertaining to the explanatory variables as directional selection 272 gradients. 273 To test changes in overall plant attractiveness, we performed a four-choice experiment and 274 tested the pollinator preference for one of the four resurrection treatments. We performed 275 pairwise comparison of proportions (pairwise.prop.test function) of chosen plants between the 276 initial and the sixth generation for the three treatments. To see if the distribution of the 277 phenotypic traits differs between plants chosen and not chosen by pollinators, we performed 278 pairwise Wilcoxon tests in R for each phenological and phenotypic trait by using the arithmetic 279 means between plants chosen and not chosen by either bumblebees or hoverflies. 280 281

282 Tracking phenotypic changes over generations 283 We assessed the overall effects of treatment and generation on 17 traits using pairwise 284 Kruskal-Wallis tests. Additionally, to disentangle the potential confounding effects between 285 generation and climate, we performed two multi -way ANOVAs: included treatment and 286 generation as factors (anova 1) , or treatment and climate as factor s (anova 2) . First, we 287 observed a significant overall effect of treatment on seven out of ten fitness-related traits, but 288 no treatment effect on floral morphological traits (Table S4). Second, we observed a significant 289 overall effect of generation (considering all replicate population together) on 11 traits related 290 to morphological traits and fitness-related traits (Fig. 2, Table S4). We also detected significant 291 effects of both generation (anova 1) and climate (anova 2) for all tested traits (“within” replicate 292 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 12 populations in Dataset 2), as illustrated by mean flower diameter (Fig. 2a) . These results 293 revealed variations across generations , which is confounded by the distinct climate 294 experienced by each generation , emphasizing the importance of combining experimental 295 evolution in natural condition with resurrection approach. 296 297 298 299 300 301 302 303 FIGURE 2 | Tracking phenotypic trait evolution across six generations of the experimental 304 evolution study in common garden. Empirical distribution of (a) mean flower diameter and (b) 305 mean herkogamy per pollination treatment as measured directly in the experimental evolution 306 study. 307 308 Evolutionary changes in phenotypic trait correlations 309 To assess the variation in phenotypic correlations between generations and according to 310 treatments, pairwise Spearman correlations among 30 phenotypic traits and fitness-related 311 traits measured in resurrection experiment were performed. These correlations showed 312 different patterns according to the resurrection treatments (initial generation , three evolved 313 pollination treatments), and to the trait sets considered (Fig. S2). For instance, we observed a 314 decrease in the strength of positive correlations between the flowering time and flower 315 morphology traits from the initial generation to the last generation across all treatments ( Fig. 316 S2). Moreover, we observed a shift from negative correlation in initial generation to positive 317 correlation between some flower morphology traits and amounts of flower scent components, 318 especially in Full and Limited access treatments (Fig. S2) . In contrast, a slight change in 319 correlation from positive to negative appear ed between fitness -related traits and the floral 320 VOCs from initial to last generation for Control and Limited access treatments (Fig. S2). Finally, 321 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 13 for certain sets of traits, the correlations within them remain overall stable during the 322 evolutionary process like the floral VOCs, the floral morphological traits, and the fitness-related 323 traits. 324 325 Rapid phenotypic evolutionary response to pollination treatments 326 To unravel the phenotypic differentiation among the replicate resurrection populations, we 327 performed a linear discriminant analysis (LDA) on different categories of traits. We observed 328 an overall phenotypic differentiation among the initial generation and the replicate resurrection 329 populations of the last generation (Fig. S3). We found phenotypic differentiation among 330 treatment populations in LDA based on floral morphological traits and on fitness-related traits 331 (Fig. S3ac) highlighted by a non -overlapping of centroid. However, these phenotypic 332 differentiation within trait categories are slight, as indicated by the overlap of the ellipses in the 333 space described in LDA. In contrast, i n LDA based on floral VOCs, the centroid of replicate 334 populations from evolved pollination treatments clustered together but remained distinct from 335 the initial generation (Fig. S3e). These results indicate d some phenotypic changes of 336 combination of traits in response to different selection-mediated treatments. 337 To investigate the treatment effect (initial generation and the three evolved pollination 338 treatments) on phenotypic trait variations, we performed Kruskal —Wallis test. Overall, we 339 observed significant evolutionary changes across treatments for 19 out of 33 measured traits 340 related to morphological and fitness-related traits i.e. significant differences between the initial 341 generation and at least one of the evolved populations (Table 1). Interestingly, our findings 342 revealed significant differences between the initial and the last generations in the Full and/or 343 344 345 346 347 348 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 14 TABLE 1| Results of Kruskal—Wallis tests to explain the variation of 33 traits measured in the 349 resurrection experiments by treatment as a categorial explanatory variable. Two sets of tests 350 have been performed: (1) an overall Kruskal—Wallis tests and (2) multiple pairwise Kruskal—351 Wallis tests (between each per of treatments). For each trait, the arithmetic mean within each 352 treatment has been calculated and indicated on the right of the table. The four pollination 353 treatments are: Initial sib -seed families ("G0"), Control ("C"), Full access ("FA"), and Limited 354 access ("LA"). Significant p-values (<0.05) are indicated in bold. 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 Limited Access treatment, but not in the Control, across 13 traits associated with different trait 380 categories, highlighting evolution ary response to the pollination treatment, rather than 381 uncontrolled environmental effects that would also be present in the control treatment . For 382 instance, we detected a significant delay in flowering time for both Limited and Full access 383 treatments, but not in Control treatment (Fig. 3a, Table 1). Moreover, we observed significant 384 evolutionary changes for 11 out of 13 floral VOCs with, for instance, a decrease in benzyl nitrile 385 in Limited Access and Full Access compared to the initial generation (Fig. 3b, Table 1). Finally, 386 we detected significant evolutionary changes in outcrossing ability with a decrease in fruit 387 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 15 length and seed weight across generations in Limited and Full Access treatments (Fig. 3c , 388 Table 1). However, we did not observe significant evolutionary changes for autogamy ability 389 for none of the pollination treatments (Table 1). Finally, we observed evolutionary changes in 390 six traits where significant changes occurred in the Limited and/or Full Access treatments, but 391 also in the Control treatment (Table 1). This introduces uncertainty in attributing these 392 evolutionary changes to the pollination treatment (s) rather than uncontrolled environmental 393 effects selecting in our populations. 394 395 396 FIGURE 3 | Evolutionary changes observed in resurrection approach for the different 397 pollination treatments. Empirical distribution of a) the flowering time; b) the amount of benzyl 398 nitrile in floral VOCs; and (c) the number of seeds per fruit under outcrossing. For each plot, 399 the initial populations and the three evolved populations (Control, Full Access, and Limited 400 Access) are represented. Significant pairwise comparison between means of distributions 401 (pairwise Kruskal-Wallis test) are emphasized. 402 403 Changes in the strength of directional selection 404 Finally, to quantify changes in the strength and the direction of the selection for phenotypic 405 traits before and after potential selection within different pollination treatments, we investigated 406 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 16 directional selection analyses in resurrection experiment. These analyses have been 407 performed on two different set of traits: (1) with all plant architecture and flower morphology; 408 and (2) VOCs floral traits. In initial generation, two phenotypic traits were under positive 409 selection (height of the plant, number of inflorescences, Table S5a), and one under negative 410 selection (pistil length). The strength of directional selection increased in the control treatment 411 for these traits. However, we observed different patterns for both pollination treatments. In the 412 Full Access, we observed the emergence of negative selection for flowering time, and positive 413 selection for petal width (Table S5a). In the Limited Access, we observed a strengthening of 414 positive selection on plant height (Table S5a). Finally, we did not observe directional selection 415 in traits related to scent compounds, either in the initial generation or in evolved populations 416 (Table S5b). 417 418 Plant evolution driven by pollinator preferences 419 To determine whether the phenotypic responses observed in our experiment is associated 420 with evolutionary changes in overall plant attractiveness, we tested for potential shifts in 421 pollinator preference among resurrection treatments (initial generation, Control, Limited 422 Access, and Full Access). In four-choice test experiment, we assessed the preference of two 423 common pollinators (bumblebees and hoverflies) in our common garden that visited our plants 424 during experimental evolution (Dataset 1). We did not observe distinct pollinator preferences 425 between the initial generation and the evolved populations, except for a lower preference of 426 hoverflies for plants in Limited Access over the ones from initial generation (Fig. 4ad, Table 427 S6). 428 429 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 17 430 FIGURE 4 | Preference of Bombus terrestris and Episyrphus balteatus for treatment and 431 phenotypic traits. Preference of a) bumblebees (Bombus terrestris) and d) hoverflies 432 (Episyrphus balteatus) for resurrection pollination treatments. Distributions of trait values of 433 plants chosen vs. not chosen as the first preference by bumblebees (light bars) and hoverflies 434 (dark bars) for b) the number of flowers, c) herkogamy, and amounts of e) nonanal and f) 435 benzyl nitrile in floral VOCs. The ‘number of first choices’ refers to the number of times a plant 436 from a specific treatment was chosen as the first plant visited during the four-choice tests. For 437 a) and b), significant pairwise comparisons of proportions are emphasized (***: 0 ≤ p ≤ 0.001, 438 **: 0.001 < p ≤ 0.01, *: 0.01 < p ≤ 0.05, ▪: 0.05 < p < 0.1, ns: non-significant). For c), d), e), and 439 f) significant pairwise Wilcoxon tests are emphasized (***: 0 ≤ p ≤ 0.001, **: 0.001 < p ≤ 0.01, 440 *: 0.01 < p ≤ 0.05, ns: non-significant). 441 442 443 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 18 Moreover, we tested the overall preference of bumblebee and hoverfly for specific 444 phenotypic traits. For instance, we observed a preference for high number of flowers for both 445 species of pollinators (Fig. 4b, Table S6), as well as a preference of bumblebees for earlier 446 flowering time (Table S6). Additionally, we observed a significant preference of bumblebees 447 for smaller pistil length, negative herkogamy, and lower amount of methyl benzoate and higher 448 amount of phenylethyl alcohol in floral VOCs (Fig. 4, Table S6). Similarly, we found that 449 hoverflies preferred higher amount of limonene and methyl salicylate, and lower amount of 2-450 amino benzaldehyde (Table S6). Lastly, we observed a shared preference of bumblebees and 451 hoverflies for higher amount of nonanal and benzyl nitrile in floral VOCs (Fig. 4e and f, Table 452 S6). 453 454

455 In response to disturbances in natural pollinator communities, generalist flowering plants 456 may rapidly adapt by increasing their pollinator attractiveness or enhancing selfing ability. In a 457 six-generation common-garden evolutionary experiment with three varying temporal access to 458 natural pollinators, we observed evolutionary changes in morphological and fitness -related 459 traits. However, distinguishing these changes from the effects of distinct environmental 460 conditions across generations is challenging. To minimize environmental and maternal effects, 461 we conducted a resurrection experiment in a controlled greenhouse, growing together the 462 initial and the last generations. We found rapid phenotypic changes in response to variation in 463 pollinator access, including i) reduced outcrossing ability under Limited and Full Access and ii) 464 shifts in phenology (delay of flowering), floral morphology (decrease in petal size), and floral 465 scent under Limited Access . Finally, contrary to our assumptions, we detected reduced 466 attractiveness to hoverflies in Limited Access. 467 468 Our resurrection approach revealed that temporally limited access to natural pollinators 469 can drive rapid shifts in plant mating systems. Specifically, we observed a decrease in 470 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 19 outcrossing ability in both the Limited and Full Access treatments. This mating system shift 471 was associated with reduced fruit length under outcrossing , and fewer seeds per fruit under 472 outcrossing, with higher effect in the Limited Access. Such reduction in outcrossing ability was 473 also documented in a controlled greenhouse evolutionary experiment in response to hoverfly 474 pollination (Gervasi and Schiestl 2017, Kofler et al. 2024). These changes could result from a 475 decrease in pollinator visits, which may reduce pollen quantity — suggested but not measured 476 — potentially leading to pollen limitation (Burd 1994). However, while a decrease in 477 outcrossing ability is known as adaptive response to reduced pollinator availability, it is often 478 associated with an increase in selfing (Acoca-Pidolle et al. 2024; Cheptou et al. 2022; Bodbyl 479 Roels & Kelly 2011). In our study, we did not observe such significant changes; however, we 480 did observe a non -significant trend toward increased selfing ability. The lack of significant 481 changes in selfing ability may reflect known barrier s preventing selfing in Brassica (e.g., 482 genetic self-incompatibility, floral morphology, Brugière et al. 2000, Nasrallah 2017, Murase et 483 al. 2020), or a latent period before any evolutionary shift in selfing ability becomes detectable. 484 Our findings suggested that small populations facing pollinator community disturbance are at 485 high risk of decline. In fact, the reduction in outcrossing compromises genetic diversity, while 486 the absence of increased selfing, which could provide reproductive insurance, further reduces 487 reproductive success, creating a dual threat to population viability. 488 In addition, our study highlighted rapid evolution of phenology and floral morphology in 489 response to pollination treatments. Both Limited and Full Access treatments exhibited a delay 490 in flowering time, with a delay more pronounced in the Limited Access. In our study, this delay 491 in flowering time was associated with a reduced preference of both bumblebees and hoverflies. 492 Our finding is in line with previous studies in which pollinators drive changes in flowering time 493 (Elzinga et al. 2007, Xu 2023). Although flowering time is often linked to climate adaptation 494 (Fournier-Level et al. 2022; Geissler et al. 2023; Preston & Fjellheim 2022), we detected no 495 significant differences between the initial population and the Control treatment, confirming that 496 changes arose from pollination treatments rather than environmental factors in our study . 497 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 20 However, while we observed this delay in flowering time, we highlighted a significant negative 498 selection of this phenological trait in Full Access (delay flowering time correlated with lower 499 fitness). This finding could be explained by several hypotheses: (1) selection acting on a trait 500 negatively correlated with flowering time, (2) a recent shift in selection that has not yet 501 manifested in phenotypic changes, or (3) selection favouring synchrony with pollinator peak 502 activities rather than early flowering per se. Moreover, we observed shifts in floral morphology 503 with a decrease in petal size in the Limited Access treatment. This flower reduction pattern is 504 often observed as a response to loss of pollinators (Tusuubira and Kelly 2024, Acoca-Pidolle 505 et al. 2024). Indeed, an absence of pollinator can lead to a shift in mating -system, from 506 outcrossing animal-pollination to selfing pollination. This transition, called ‘selfing syndrome’, 507 is accompanied by phenotypic changes reducing the plant attractiveness to pollinators, 508 including decrease in flower size (Tsuchimatsu and Fujii, 2022 ). While we did not observe 509 significant increase in selfing ability , only a non -significant trend , this floral morphological 510 change may represent an early evolutionary shift toward a selfing syndrome, potentially 511 leading to increased selfing rates in subsequent generations. 512 Additionally, we observed evolutionary changes in many floral scent compositions in only 513 six generations. Pollinators often show strong preferences for a specific or a combination of 514 floral VOCs , and previous studies suggested that pollinator -mediated selection can drive 515 changes in floral scent composition in B. rapa (Dorey & Schiestl 2024; Gervasi & Schiestl 2017; 516 Ramos & Schiestl 2019). Among the observed changes in VOCs, most floral VOCs are known 517 to influence pollinator preferences . For instance, while methyl salicylate is associated with 518 hoverfly preference, limonene, p-anisaldehyde, 2-aminobenzaldehyde, benzaldehyde, methyl 519 salicylate, and nonanal are linked to preferences in Hymenoptera (Dötterl & Gershenzon 520 2023). For example, we confirmed that plants with lower nonanal levels are less frequently 521 chosen by both bumblebees and hoverflies. Additionally, we observed evolutionary reductions 522 in nonanal production across six generations in both Limited and Full Access treatments, 523 further demonstrating that our pollination treatments decrease plant attractiveness. Due to the 524 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 21 involvement of many VOCs and their shared biosynthetic pathways (Dötterl & Gershenzon 525 2023), the target of selection in our study is unlikely to be a specific VOC compound, as 526 confirmed by the lack of consistent directional selection for these traits. Instead, selection likely 527 acts on the floral scent bouquet as a whole. 528 Lastly, we detected an overall decrease in plant attractiveness to hoverflies in evolved 529 plants from the Limited Access treatment , for which we observed a significantly lower 530 abundance of hoverflies over the six generations of selection compared to the Full Access 531 treatment. The decrease in hoverfly attractiveness in Limited Access can be explained by 532 changes observed in a combination of phenotypic traits, including VOCs, in response to low 533 occurrence of these pollinators. For instance, we documented evolutionary reductions in VOCs 534 such as limonene, nonanal, and benzyl nitrile across both Limited and Full Access treatments. 535 Correspondingly, plants producing lower levels of these compounds were significantly less 536 attractive to hoverflies, while these compounds are known to be involved in plant attractiveness 537 to hoverflies (Dötterl & Gershenzon 2023). Additionally, we observed that the Limited Access 538 treatment reduced petal size, which aligns with the overall decrease in plant attractiveness . 539 Because producing floral scent and enhancing floral visibility to improve plant attractiveness 540 are costly (Spigler et al. 2020), one plausible hypothesis for this reduced hoverfly preference 541 for plants evolved in Limited Access is that these plants have adapted by reducing their overall 542 attractiveness in response to reduced pollinator community. In contrast, we observed no such 543 trend in bumblebee preferences, possibly because there was no substantial difference in 544 bumblebee abundance between the Limited and Full Access treatments. However, we tested 545 plant attractiveness using only two common pollinators under controlled conditions, which does 546 not reflect the ecological realism of the environments where these plants have evolved. It would 547 therefore be valuable to test the attractiveness of the evolved plants to a broader range of 548 pollinators, including honeybees or small bees – groups for which we observed abundance 549 differences between treatment s - as well as within the context of a complete pollinator 550 community. Moreover, six generations are a short time period for selective processes, and the 551 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 22 variation of our selective pressure (pollinator community composition) across generations, may 552 have buffered the speed of selection and likely imposed diffuse selection on traits. 553 554 Overall, our study showed that temporally limiting generalist plants’ access to their natural 555 pollinator community can drive short-term changes in mating system and overall plant 556 attractiveness. It therefore appeared that flowering plants have substantial adaptive capacity, 557 particularly in traits affecting pollinator attractiveness (floral morphology and scent). However, 558 the short time period of our experimental evolution provides only a partial view of how plants 559 evolve in response to a decline in pollinators, and the long-term consequences of pollinator 560 decline remain unclear. Reducing pressure on natural pollinator communities should therefore 561 remain a priority. 562 563

564 We thank Markus Meierhofer, Rayko Jonas, Daniel Schlagenhauf, Matthias Furler, Franz 565 Huber, Laura Dällenbach for their help during experiments. We thank Florian Schiestl, Tobias 566 Züst, Maxime Bonhomme and Carolin Kosiol for helpful discussions and inputs during the 567 study. This work was supported by the University of Zürich and the University Research Priority 568 Program ‘Evolution in action’. 569 570 571 572 573 574 575 576 577 578 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 23 Supplementary Materials 579 Dataset 1: raw data pollinator abundance and diversity 580 Dataset 2: result of multi-ways ANOVAs (models 1 and 2) – experimental evolution 581 Dataset 3: raw data individual traits – experimental evolution 582 Dataset 4: raw data flower morphology – experimental evolution 583 Dataset 5: raw data fruit length – experimental evolution 584 Dataset 6: temperature and light recording – experimental evolution 585 Dataset 7: raw data individual traits – second resurrection 586 Dataset 8: raw data flower morphology – second resurrection 587 Dataset 9: raw data fruit length – second resurrection 588 Dataset 10: raw data individual traits – third resurrection 589 Dataset 11: raw data fruit length – third resurrection 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 .CC-BY 4.0 International licenseperpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for thisthis version posted January 23, 2026. ; https://doi.org/10.64898/2026.01.23.701299doi: bioRxiv preprint 24

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