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1 Ann Bot 2 3 Geographic consistency and variation in conflicting selection generated by pollinators 4 and seed predators 5 6 7 Shi-Guo Sun1, W. Scott Armbruster2, 3 and Shuang-Quan Huang1* 8 9 10 1School of Life Sciences, Central China Normal University, Wuhan 430079, China 11 2School of Biological Sciences, University of Portsmouth, Portsmouth PO1 2DY, UK 12 3Institute of Arctic Biology, University of Alaska, Fairbanks AK 99775-7000, USA 13 14 Running head: Conflicting selection on floral traits 15 16 The manuscript has four figures, one table and two Appendices (Table S1, and S2), and a 17 supplemental Excel file containing data on 12 phenotypic traits, stigmatic pollen loads in 18 14 populations and seed survival data in 12 populations of Pedicularis rex. 19 20 * Corresponding address: Tel: +86-27-67867221, Fax: +86-27-67861147; 21 E-mail: [email protected] 22

1 23 ABSTRACT 24  Backgrounds and Aims Floral traits that attract pollinators may also attract seed 25 predators, which, in turn, may generate conflicting natural selection on such traits. 26 Although such selection trade-offs are expected to vary geographically, few studies have 27 investigated selection mediated by pollinators and seed predators across a geographic 28 mosaic of environments and floral variation. 29  Methods Floral traits were investigated in 14 populations of the bumblebee-pollinated 30 herb, Pedicularis rex, in which tubular flowers are subtended by cupular bracts holding 31 rain water. To study potentially conflicting selection on floral traits generated by 32 pollinators and florivores, we measured stigmatic pollen loads, initial seed set, 33 pre-dispersal seed , and final viable-seed production in 12-14 populations in the 34 field. 35  Key Results GLM analyses indicated that the pollen load on stigmas was positively 36 related to the exsertion of the corolla beyond the cupular bracts and size of the lower 37 corolla lip, but so too was the rate of , creating conflicting selection on 38 both floral traits. A geographic mosaic of selection mediated by seed predators, but not 39 pollinators, was indicated by significant variation in levels of seed predation and the 40 inclusion of 2-, 3- and 4-way interaction terms between population and seed predation in 41 the best model (lowest AICc) explaining final seed production. 42  Conclusions These results indicate opposing selection in operation: pollinators 43 generated selection for greater floral exsertion beyond the bracts, but seed predators 44 generated selection for reduced exsertion above the protective pools of water, although 45 the strength of the latter varied across populations. 46 47 Key words: Corolla-tube length, cupular bract, geographic selection mosaic, Pedicularis rex, 48 pre-dispersal seed predation, phenotypic selection analysis, stigmatic pollen load, seed 49 survival. 50

2 51 INTRODUCTION 52 The evolution of floral traits has traditionally been thought to be moulded by the most 53 frequent and effective pollinators (Stebbins, 1970). Floral traits attractive to pollinators, 54 however, may also attract plant enemies such as that eat flower parts (florivores 55 and seed predators). Although most early studies of plant- interactions investigated 56 either attraction of pollinators or instead defence against herbivores, an increasing number of 57 papers now suggest that most floral traits experience selection generated by both mutualists 58 and antagonists (Strauss et al., 1996; Strauss and Armbruster, 1997; Galen, 1999; 59 Mothershead and Marquis, 2000; Steffan-Dewenter et al., 2001; Irwin et al., 2003, 2004; 60 McCall and Irwin, 2006; Rey et al., 2006; Strauss and Whittall, 2006; Parachnowitsch and 61 Caruso, 2008; Bartkowska and Johnston, 2012; Theis and Adler, 2012; Kessler et al., 2013; 62 Talluto and Benkman, 2014). For example, selection on floral traits generated by 63 pre-dispersal seed predators might usually be in the opposite direction as that generated by 64 pollinators, given that seed predators and pollinators may use the same floral traits (e.g. floral 65 shape, colour and scent) to find flowers for oviposition or mutualistically feeding on nectar, 66 respectively (e.g. Ehrlén et al., 2002; de Waal et al., 2012; de Jager and Ellis, 2013; 67 Pérez-Barrales et al., 2013). This effect might be especially strong if ovipositing seed 68 predators can use floral traits that influence pollination success as a way to predict later 69 host-substrate quality for their offspring (e.g. Pérez-Barrales et al., 2013). 70 It is also increasingly apparent that geographic variation in the pollinator fauna, 71 seed-predator fauna, and/or floral-trait values often creates geographic mosaics of phenotypic 72 selection (Thompson, 2005). Covariation between plant traits and pollinator and/or 73 traits is an expected outcome of this situation, and such covariation observed in nature may, 74 in turn, provide evidence for the existence of the selection mosaic (see Herrera et al., 2006). 75 For example, corolla-tube lengths in both Zaluzianskya microsiphon and Lapeirousia anceps 76 covary geographically with tongue length of long-proboscid fly pollinators in South Africa. 77 These were interpreted as a geographic selection mosaic because seed set and/or pollen 78 deposition depended on the length of flower tube relative to the length of the fly tongue 79 (Anderson and Johnson, 2008; Pauw et al., 2009). Pollinators with longer tongues relative to 80 floral-tube length could ingest more nectar, and plants with longer tubes relative to 81 pollinator-tongue length benefited with higher pollen deposition. However, long-tubed 82 flowers may experience selection in the opposite direction if seed predators (or nectar robbers) 83 differentially exploit larger flowers. For example, conflicting selection was generated by 84 pollinators and damage-inflicting ants, and was shown to influence the evolution of flower

3 85 shape in bumblebee-pollinated Polemonium viscosum. Plants bearing flowers with short, 86 flared corollas were more attractive to bumblebee pollinators but more vulnerable to ant 87 predation (Galen and Cuba, 2001). While considerable research now suggests that selection 88 mediated by seed predators (or nectar thieves) often runs counter to selection on the same 89 traits mediated by pollinators (Herrera, 2000; Gómez, 2003, 2008; Irwin et al., 2003, 2010; 90 Cariveau et al., 2004; Strauss and Whittall, 2006; Pérez-Barrales et al., 2013), geographic 91 variation in selection generated by both pollinators and herbivores remains largely 92 unexplored (but see Thompson and Pellmyr, 1992; Galen and Cuba, 2001; Siepielski and 93 Benkman, 2010; Ågren et al., 2013; and review in Thompson 2005, 2013). 94 Here we investigate variation in floral traits over a large geographical area in a 95 bumblebee-pollinated subalpine herb, Pedicularis rex. We also assess variation in 96 components of reproductive success as influenced by both pollinators and pre-dispersal seed 97 predators. This study permits us to ask whether: (1) floral traits vary geographically; (2) there 98 is conflicting selection generated by pollinators and seed predators within populations; (3) 99 there is variation among populations in these selective pressures. To examine the possible 100 effects of selection by pollinators and herbivores on floral traits, we measured stigmatic 101 pollen loads in flowers in 14 populations and seed production and seed predation in 12 of 102 these populations. If flowers with wider corolla lobes or longer corolla tubes attract more 103 pollinators and/or receive more pollen on stigmas, do they also attract more enemies, such as 104 seed predators? We thus not only investigate possible conflicting selection by pollinators and 105 florivores, we also attempt to compare patterns of selection on floral traits across multiple 106 populations. 107 108 MATERIALS AND METHODS 109 Study species and sites 110 Pedicularis rex Franch. (Orobanchaceae) is a self-compatible, perennial herb endemic to 111 southwest China (Yang et al., 1998; Tang et al., 2007). It flowers from late June to early 112 August. Flowering individuals can grow up to 1.5 m and produce numerous vertical 113 spike-like racemes with highly zygomorphic pink or yellow flowers. Flowers are arranged in 114 whorls on each raceme (usually 3-5 flowers per whorl) and open in sequence from bottom to 115 top. The pinnatisect to pinnatipartite leaves are borne in whorls, and the base of each whorl 116 forms a cupular “bract” (CB). Flowers are “approach” herkogamous (Lloyd and Webb, 1992), 117 with the receptive stigma exserted from the corolla and the anthers enclosed in the corolla. 118 Very little self-pollination occurs in the absence of pollinators (Huang and Sun, personal

4 119 observations). The corolla comprises a tube, a trilobate lower lip and a galeate upper lip 120 enclosing the four introrse anthers (Fig. 1). The plants occur in various habitats including dry 121 open slopes, forest edges or in shade, at elevations from 2500 m to 4300m across Sichuan and 122 Yunnan Provinces. Our field survey revealed remarkable variation in plant size, flower 123 number per whorl, and flower size across populations. 124 The genus Pedicularis is extremely diverse in Southwest China, with over 300 species, 125 most of which are pollinated almost entirely by bumblebees (Yang et al., 1998; Tang et al., 126 2007; Eaton et al., 2012; Huang and Shi, 2013; Armbruster et al., 2014; Liu et al., 2016). 127 Pedicularis rex is pollinated by several species of bumble bees, including Bombus frieseanus 128 and Bombus festivus. 129 The seeds of P. rex are fed upon by larvae of both fly (Diptera) and moth (Lepidoptera) 130 pre-dispersal seed predators (Tang, 2011). The seed predators lay eggs on the ovaries after 131 flowers are open but prior to the ovaries swelling, by piercing the sepals or corolla tubes from 132 the outside the flowers (cf. Thompson and Pellmyr, 1991). Because these larvae are not easily 133 reared, we have identifications only to order. 134 Cupular bracts in P. rex (and its closest relatives) are usually full of rain water in which 135 the capsules and the base of flowers are submerged (Fig. 1). We hypothesized, therefore, that 136 cupular bracts (containing water) might function to protect flowers from oviposition by 137 predispersal seed predators. 138 139 Traits measured

140 Twelve traits were measured on 16-36 (mean  SE = 21.38  1.04) individual plants in

141 each field population. We used digital calipers precise to 0.1 mm to measure six vegetative 142 traits (stem diameter, leaf length and width, width of top and bottom of cupular bracts (CB), 143 and CB height. We also randomly chose two flowers from different whorls on each plant and 144 used calipers to measure six floral traits [flower length, corolla-tube length (the distance 145 between corolla base and the point where the lower lip extends out), corolla-tube diameter, 146 lower lip length and width, and corolla opening (the distance between the galea tip and lower 147 lip; Fig. 1)]. The mean of the two floral measurements from each plant was used in all 148 subsequent analyses. We also calculated a composite trait to capture the proportion of the 149 flower exserted above the water-bearing cupular bracts: 150 151 Corolla exsertion = (flower length - bract height)/ flower length

5 152 153 Geographical variation in traits 154 To investigate geographical variation in traits, we measured phenotypic variation in 14 155 populations in Sichuan and Yunnan, southwest China. Detailed information on sampled 156 populations is provided in Table S1. 157 158 Measurement of fitness components 159 Lifetime reproductive fitness of individual plants was not easily assessed, nor could we 160 measure the male component of fitness (dispersed pollen producing seeds on other plants). 161 We instead measured three components that seemed likely to contribute to female fitness and 162 would also be sensitive to the activities of pollinators and seed predators. Because seed 163 production was almost certainly pollen limited (see Results), the number of pollen grains on 164 the stigma at the end of a flower's period of receptivity should be a good indicator of potential 165 seed production. Indeed, the relationship between initial seed set and number of pollen grains 166 on the stigma number was linear (results not shown). Proportion of ovules growing into seeds 167 and those escaping seed predation also seemed to be important components of female fitness. 168 Stigmatic pollen load was based on assessment of the same two flowers per plant measured 169 above, seed set and seed survival were based on a mean of 12.51 (SD = 5.60) capsules per 170 plant. The plant mean of the above variables was used to characterise fitness components 171 for each plant (see Cariveau et al., 2004). 172 173 Stigmatic pollen loads -- We measured pollen present on stigmas at the end of floral 174 receptivity as an index of pollination success (i.e. pollen arrival) and a possible indicator of 175 pollinator-mediated selection on floral traits (see Pauw et al., 2009). We counted the pollen 176 on the stigmas of the same flowers that we measured morphologically. Specifically, we 177 crushed onto slides the stigmas from flowers in late anthesis and counted under a microscope

178 (see Fang and Huang, 2013) the spheroidal pollen grains of P. rex (diameter ca. 20 m; Yu 179 and Wang, 2008). 180 181 Estimation of initial seed set, seed predation, and seed survival -- Seed set, seed 182 predation, and seed survival were estimated by counting viable seeds, damaged seeds, and 183 unfilled ovules in six capsules each from 20 different individuals in each population (= 120 184 fruits per population) three weeks after initial flowering. In seven populations (1, 3, 5, 8, 9,

6 185 10, 11; see table S1), these were the same plants as used for measurement of floral traits and 186 pollination success, although different flowers usually had to be selected. In five populations 187 (2, 4, 6, 7, 12; see table S2) labels were lost during the intervening period, and we were 188 unable to relate rates of seed production and predation to plant-mean floral morphology. The 189 data from these five populations were used only to assess variation in seed predation rates 190 across populations (Figure 4; Table S2). 191 Undamaged seeds, seeds damaged by larval seed predators, and unfertilized ovules were 192 identified in each capsule based on their morphological differences, and counted. Fully 193 developed, viable seeds were obviously enlarged and undamaged. Unfertilized ovules were 194 pale and much smaller. Seeds damaged by larvae were black and were usually missing part of 195 the seed coat. The estimated total number of ovules (and maximum potential seed production 196 per flower) is the sum of all three counts. We then calculated four parameters for each 197 measured flower, the first two of which are proportions of maximum possible seed 198 production for that flower (to correct for among-flower variation in ovule number). 1) 199 Initial seed set is the sum of undamaged and damaged seeds divided by the total number of 200 ovules. 2) Final seed production (intact seeds) is the number of undamaged seeds divided by 201 the total number of ovules. 3) Proportional seed survival is the number of undamaged seeds 202 divided by the sum of intact and damaged seeds. 4) Proportional seed predation is the number 203 of damaged seeds divided by the sum of undamaged and damaged seeds (= 1 - seed survival). 204 Sometimes in heavily attacked populations capsule contents were completely consumed by 205 larvae. In these cases (up to 5 fruits per population) seed number was impossible to ascertain, 206 and these fruits could not be used in the analyses. 207 208 Data analysis 209 All statistical analyses were conducted using IBM SPSS 22 (IBM Corporation, 2014). 210 211 Variation among populations -- MANOVAs (multivariate analyses of variance) were 212 conducted separately on 1) vegetative traits and 2) floral traits to assess whether there was 213 significant among-population variation in vegetative and floral traits. The difference between 214 the initial seed set and the final seed production (= seed predation) was also compared across 215 populations, and the variation was assessed using Chi-square analysis. 216 217 Relating fitness to phenotype (General Linear Models) --We conducted all analyses of 218 fitness using individual-plant means of traits and fitness components. In doing so, we

7 219 assumed that most of the important floral variation affecting pollination and seed predation 220 occurred at the among-plant rather than among-flower level. We examined the effects of 221 variation in the measured traits (flower length, corolla-tube length, corolla-tube diameter, 222 lower lip length and width, corolla opening, and corolla exsertion) on the four interrelated 223 components of reproductive fitness described in the previous section. All proportional traits 224 and proportional fitness components were arcsin-squareroot transformed in order to meet 225 better the normality assumptions for analyses that follow. Of the seven floral traits measured 226 or calculated, only corolla exsertion and lower corolla-lip width were included in the final 227 models, because only they explained enough additional variance to reduce the information 228 criterion examined (Burnham and Anderson, 2002; see below). 229 To examine and compare across multiple populations the floral trait-fitness relationships 230 at the individual plant level (within-population covariation), we used a general linear model 231 (GLM) approach (with normal error distribution) similar to those described by Heisler and 232 Damuth (1987), Scheiner et al. (2001), Okasha (2004), and Bolstad et al. (2010), these being 233 extensions of the Lande-Arnold model of natural selection on quantitative traits (Lande and 234 Arnold, 1983; see also Phillips and Arnold, 1989). By pooling population data and including 235 both population and population-interaction terms, we were able to estimate overall mean 236 population effects and test whether phenotypic selection on floral traits varied among 237 populations (i.e. whether selection operated in a mosaic fashion). 238 We first assessed the number of pollen grains arriving on stigmas (mean = 12.28, SD =

239 5.30) in relation to floral traits, source population, and trait  population interactions. We 240 used a second model to evaluate controls over seed predation rates (proportion of seeds in a 241 fruit damaged by seed predators; mean = 0.127, SD = 0.120) in relation to measured floral

242 traits, amount of pollen on the stigmas, initial seed set, source population, and the seed-set 

243 population interaction. We used a third model to examine initial seed set (proportion of the 244 ovules in a fruit developing into seeds; mean = 10.53, SD = 2.42) in relation to floral traits,

245 amount of pollen grains on the stigma, the source population, population  trait interactions,

246 and the population  pollen-number interaction. We used a fourth model to evaluate controls

247 over final production of viable seeds in relation to amount of pollen on stigmas, initial seed 248 set, seed-predation rate, population identity, and corresponding interactions. 249 The models were evaluated using model selection, where the lowest 250 finite-sample-corrected Akaike Information Criterion (AICc) was assumed to indicate the 251 best model, at least when the difference was substantial (e.g. delta AICc > ca. 5, or a relative

8 252 likelihood greater than 20:1; see next section and Burnham and Anderson, 2002). In other 253 words, the simpler model (fewer terms) was chosen over the more complex model when the 254 explanatory powers were not significantly different (cf. Neter et al., 1985). 255 256 Relating fitness to phenotype (Path analysis) -- We used path analysis to visualize in one 257 integrated causal model the relationships among components of reproductive success (see 258 Scheiner et al., 2000), as well as effects of phenotypic traits on these components (i.e. 259 operation of phenotypic selection), and the correlative relationships between the phenotypic 260 traits present in the four best GLM models described above. This allowed the causal effects 261 of variables and interrelated fitness parameters to be modelled using "biological common 262 sense" not necessarily captured in pure statistical models (Scheiner et al., 2000). 263 Because the path analysis that follows involves pooling the population data on the 264 assumption of no significant interactions, we tested the best interaction models against the 265 overall best model, following the procedure of Burnham and Anderson (2002), where the 266 probability that the more complex model including the interaction minimizes information loss 267 as well as the best model (which is also the relative likelihood) is calculated as:

268 exp((AICbest - AICcinteraction)/2) 269 270 The variables included in each portion of the path diagram were those shown by GLM 271 model selection to be important as well as ones we expected to have potential direct effects 272 on each component of reproductive success. (This approach does not create significant 273 relationships, it just precludes impossible ones; Scheiner et al., 2000.) Note that inferences 274 reported here include the caveat that true causality could be the result of effects of 275 unmeasured correlates of the measured variables; only manipulative experiments can resolve 276 this (see Discussion). To remove the significant population effects identified by the GLM 277 analyses above and to transform all fitness observations to within-population relative fitness 278 (Endler 1984), each observation was standardized to its populations mean by subtracting

279 from each observation, i, its respective (jth) population mean: (xij - x-barj). 280 Ordinary least-squares multiple regression was conducted on the 281 population-mean-corrected observations to yield standardized regression coefficients, which 282 are numerically identical to path coefficients calculated using simultaneous equations (Li, 283 1975; Shipley, 2000). The advantage of this approach is that the strength of standardized path 284 coefficients can be compared numerically (all path coefficients vary theoretically between -1 285 and +1; Li, 1975; Scheiner et al., 2000). P-values reported for path coefficients in Figure 2

9 286 come, however, from the more robust GLM analyses. The interactions were found to be 287 ordinal, i.e., the result of differences in slope steepness but not direction. This meant that the 288 main effects were biologically, as well as statistically, interpretable, despite several 289 significant interactions (Pedzahur, 1997). 290 291 RESULTS 292 Trait variation 293 The two MANOVA analyses indicated significant phenotypic variation among populations in

294 both vegetative traits (F5, 280 = 3.835, p < 0.001) and floral traits (F5, 280 = 29.485, p < 0.001), 295 with the effect size of the latter being much greater. Thus populations have differentiated 296 phenotypically, with this being especially dramatic for floral traits. 297 298 Pollination and seed predation 299 The mean number of pollen grains deposited per stigma was 12.53 (SD = 5.36) (N = 299 300 plants, 598 flowers) across the 14 populations. Because each flower had on average 25.96 301 (SD = 6.33) ovules (N = 120), this result indicates that probably all populations experienced 302 pollen limitation of seed production. Indeed, initial seed set in 12 populations varied from 303 31.10% to 48.53%, consistent with the inference of pollen limitation. Chi-square tests showed 304 that final seed production differed significantly (p < 0.05) from initial seed set in six of 12 305 populations (Table S2), indicating that these six populations probably experienced more seed 306 predation than the others. Levels of seed predation ranged from 0.8% and 1.36% in 307 Populations 11 and 3, respectively, to 18.5 and 27.42% in populations 12 and 5, respectively 308 (Table S2). 309 310 Multi-population patterns of phenotypic selection within populations 311 General Linearized Models -- GLM analysis (with model selection) of the data from all 312 14 populations pooled indicated that pollen arrival on stigmas (Model 1) was affected 313 positively by the exsertion of the corolla beyond the bracts and positively, but more weakly, 314 by the width of the lower corolla lobe (lower-lip width). There was also a strong population 315 effect; i.e. the mean amounts of pollen on stigmas varied significantly across populations 316 (Table 1). 317 GLM analysis of seed-predation rates from 7 populations pooled (Model 2) indicated that 318 the level of attack was affected positively by corolla exsertion, lip width, and the amount of 319 pollen on the stigmas. Attack rates also varied among populations (significant population

10 320 term), consistent with the Chi-square results above (Table 1). 321 Initial seed set (Model 3) was affected positively by the amount of pollen on the stigma 322 and by the population term (Table 1). 323 Final viable seed production (Model 4) was affected positively by the initial seed set and 324 negatively by the seed predation rate, as expected, although the steepness (but not direction) 325 of the seed-predation effects varied across populations (interactions were significant but 326 ordinal). The population means of final viable seed also varied (the population effect was 327 significant; Table 1). 328 329 Path analysis -- Testing the information content of the most biologically relevant

330 interaction models against the best models suggested that population  trait interactions were

331 statistically negligible in Models 1 and 3 and that pooling populations for the path-analytical 332 model was appropriate. We found that for Model 1 (amount of pollen on stigmas), the model

333 including the exsertion  population interaction was 0.0068 times as likely as the best model 334 (see Table 1) to minimize the information loss, indicating no significant interaction. For

335 Model 3 (initial seed set), the model including the pollen-load  population interaction was

336 0.0120 times as likely as the best model to minimize the information loss, also indicating no 337 significant interactions. For Model 2 (amount of seed predation), the likelihood analysis was

338 less clear. The model including the exsertion  population interaction was 0.3450 times as

339 likely as the best model (without interaction) to minimize the information loss. Despite weak 340 support for the no-interaction model, we included it in the path diagram without interactions. 341 In contrast, the best model for Model 4 (final seed production) included interaction terms (see 342 Table 1), so interactions were indicated in the path diagram and path coefficients were not 343 calculated. 344 The path diagram (Fig. 2) shows the postulated causal, independent effects (i.e. holding 345 other variable constant) of various floral characteristics on fitness components of Pedicularis 346 rex plants after population effects have been removed (and assuming no interactions affecting 347 pollen arrival, seed predation, and initial see set). The diagram also includes the detected 348 direct and indirect causal influences on total viable seed production (plant means), from 349 which interactions were not excluded. This integrated analysis indicates that flower exsertion 350 strongly affected pollinator attraction as reflected in pollen arrival. In turn, pollen arrival 351 strongly influenced initial seed set, which in turn had a strong effect on total number of 352 surviving seeds. Floral exsertion had an even stronger effect on rates of attack by seed

11 353 predators, which in turn had an effect on final seed production, mostly via 354 population-interactive effects (i.e. populations varied significantly in the effect of seed 355 predation on final seed production; Fig. 2, Table 1). In other words, there was a geographic 356 mosaic in selection mediated by seed predators. In contrast, there was no evidence that 357 selection mediated by pollinators varied among populations. Regardless, most population 358 experienced conflicting selection on flower exsertion beyond bracts, as mediated by 359 pollinators vs. seed predators (Figs. 2, 3). 360 361 Among-population covariation between traits and mean fitness 362 An examination of the population means of initial seed set, seed survival, and floral exsertion 363 reveals a similar relationship to that seen within populations. Increasing floral exsertion (as a 364 proportion of the corolla length) was associated with both increasing seed set and decreasing 365 proportion of seeds surviving seed predation (Fig. 4). 366 367 DISCUSSION 368 Population differences 369 We found that the study populations of Pedicularis rex had diverged significantly in floral 370 phenotype. Populations also differed significantly from one another in mean pollen loads on 371 the stigma, mean seed set, and mean rates of seed predation. 372 373 Pollination success and seed predation 374 We found that, as predicted, corollas that were more exserted from the bracts and those with 375 wider lower lips attracted both more pollinators and more seed predators, indicating direct 376 positive and negative selection, respectively, acted on these traits. The apparent direct 377 selection on floral exertion (e.g. from the animal perspective) is actually correlational 378 selection (from the plant perspective), because corolla exsertion is itself a relationship 379 between one floral trait (corolla-tube length) and one vegetative trait (bract height). 380 The apparent choice of flowers with broader lower corolla lips by both pollinators and 381 seed predators suggests that both are responding to the same advertisement signals (see 382 Pérez-Barrales et al., 2013). The same may be true of floral exsertion from the bracts (at least 383 for pollinators), although in the case of the seed predators, the oviposition success rates may 384 be the critical factor. Indeed, that less exserted flowers had lower rates of seed predation is 385 consistent with the hypothesis that submergence of the base of the flower in water provides 386 protection against seed predators. This interpretation has been confirmed experimentally in a

12 387 companion study assessing the effects of draining the water from the cupular bracts 388 (effectively increasing corolla exsertion from the water but not the bracts) on seed-predation 389 and pollination rates. The experimental study showed that flowers that were not submerged 390 (i.e. high floral exsertion beyond water) had higher rates of seed predation (Sun and Huang, 391 2015). Greater exsertion beyond the water did not, however, increase pollination rates 392 detectably (Sun and Huang, 2015), although the exsertion beyond the green bracts (more 393 likely to influence showiness) remains to be manipulated and assessed. This prior study 394 differs from the one reported here in that only six populations were studied and neither 395 multivariate phenotypic selection nor its variation among populations were addressed. 396 Similar experimental results have been obtained in a water-calyx plant, Chrysothemis 397 friedrichsthaliana (Gesneriaceae), in Costa Rica, where floral buds were frequently attacked 398 by ovipositing moths. Experimental manipulation of water levels in the cup-like calyces 399 showed that a liquid barrier over buds reduced per-flower egg deposition and subsequent 400 herbivory by 50%, suggesting that the water protected buds from florivores (Carlson and 401 Harms, 2007). 402 We found, unexpectedly, that there were higher rates of seed predation on flowers that 403 had more pollen on their stigmas (Table 1, Fig. 2). Although this makes intuitive sense, as 404 well pollinate flowers provide the more seed resources for the adult seed predators' larvae, the 405 mechanisms generating this relationship are unclear. Oviposition occurs in the flowering 406 stage rather than the fruiting stage, so direct assessment of seed resources by ovipositing 407 adults is impossible. Adult seed predators appear instead to be making oviposition choices 408 using floral cues (e.g. pollen odours or unmeasured floral traits influencing pollination) that 409 predict which flowers are likely later to produce fruits with more seeds. This relationship 410 seems unlikely to be simply the result of shared floral apparency to both pollinators and seed 411 predators (as for corolla lip), because most floral traits likely to involved in attraction were 412 measured and no direct effects on seed-predation rates could be found. If our interpretation is 413 correct, this would be only the second or perhaps third time, to our knowledge, that such 414 “predictive” behaviour by seed predators has been detected using phenotypic-selection 415 analysis (Pérez-Barrales et al., 2013; see also Carlson and Holsinger, 2013). We suspect such 416 predictive behaviour is actually common, however, given how adaptive it must be; it is just 417 hard to distinguish from apparency. Indeed, use of predictive seed set signals may be the 418 critical first step in the evolution of active pollination by seed predators (see Yoder et al., 419 2010). 420 Because the study presented here was based on standing variation without experimental

13 421 manipulation of phenotype, it is possible that environmental correlations or other unknown 422 correlates of the measured independent variables (e.g. microclimate, regional climate, soil 423 conditions, activity of unknown mutualists or antagonists, etc.) could have contributed to the 424 patterns we found. This could lead to misattributing causal significance to corolla exsertion 425 and lower-lip width, despite strong statistical support (see Rausher, 1992; Scheiner, 2002; 426 Stinchcombe et al., 2002). However, the results of the companion study described above (Sun 427 and Huang, 2015) confirm experimentally the operation of one mechanism invoked here, the 428 protection of flowers from seed predators by deeper, water-bearing cupular bracts. 429 430 Conflicting selection on floral traits generated within populations by insect mutualists and 431 antagonists 432 There is ample evidence of the influence of pollinator-mediated selection on floral evolution 433 (see review in Fenster et al., 2004), and a growing number of studies have more recently 434 shown the importance of florivore-mediated selection (e.g. Galen, 1999; Herrera, 2000; 435 Cariveau et al., 2004; Irwin et al., 2004; Strauss and Whittall 2006; Gómez, 2008; Carlson 436 and Holsinger, 2010). Indeed it is increasingly apparent that floral traits are very commonly 437 sculpted by conflicting selection generated by both mutualists and antagonists (Irwin et al., 438 2004; Theis and Adler, 2012; Kessler et al., 2013). For example, smaller (female) flowers 439 experienced less attack by florivores than larger (hermaphrodite) flowers in a gynodioecious 440 orchid (Huang et al., 2009), suggesting that an increase of flower size may increase 441 attractiveness to pollinators but involve a higher risk of floral herbivory. In a recent study of 442 Dalechampia scandens, blossoms with larger bracts received more pollen on their stigmas, 443 but seed predators laid more eggs on these blossoms, indicating that selection for larger bract 444 size exerted by pollinators was counteracted by the selection exerted by seed predators 445 (Pérez-Barrales et al., 2013). 446 Previously studies have emphasized that, in Pedicularis, most of the diversity of flower 447 morphology has been driven by selection generated by bumble bee pollinators (Macior, 1982; 448 Eaton et al., 2012). This emphasis is understandable, given the paucity of empirical studies of 449 florivores on Pedicularis. Interestingly, there is one early study that reports intense 450 pre-dispersal seed predation: lepidopteran larvae damaged 39% of mature capsules of P. 451 furbishiae in Northeastern America (Menges et al., 1986). To this observation we can add 452 data presented here suggesting that evolution of floral traits in Pedicularis rex is influenced 453 by antagonists as well as mutualists. Seed predators on P. rex generated selection conflicting 454 with that mediated by pollinators for at least two floral traits: corolla exsertion from the

14 455 cupular bracts and width of the lower lip. The conflicting selection on floral exsertion was 456 particularly dramatic (Fig. 3). 457 458 Additive population effects 459 All four GLM models indicated that there were additive population effects influencing rates 460 of pollination, seed predation, initial seed set, and final viable seed production. This means 461 that, for a given value of a phenotypic trait, e.g. floral exsertion, the average amount of pollen 462 on stigmas, average rates of seed predation, etc., varied significantly among populations. This 463 is consistent with the idea that some populations are pollination or seed predation "hot-spots" 464 and others relative "cold-spots" because of different abundances of pollinators and seed 465 predators. The lack of interactions for the first three models meant that populations could be 466 safely combined to understand general trends in selection. 467 468 Among-population covariation between traits and mean fitness 469 The phenotypic variation among populations, if it has a genetic basis, might reflect a 470 history of response to stronger selection by more abundant seed predators in some 471 populations, leading to inconspicuous, less exserted flowers and lower pollination. In other 472 populations, a history of less seed predation may have led to more conspicuous flowers and 473 hence overall greater attractiveness to pollinators, relative to other plant species that 474 generalist bumblebee pollinators might otherwise visit in the same community. This 475 interpretation is supported by analyses of population means of trait values and rates of 476 pollinations, seed set, and seed predation. 477 As was observed within populations, increasing mean floral exsertion was associated with 478 increasing mean seed set and decreasing mean proportion of seeds surviving seed predation 479 (Fig. 4). Thus, populations with large proportional corolla exsertion received more pollen on 480 average (and set more seeds), but suffered higher average rates seeds predation. Populations 481 with lower proportional corolla exsertion received less pollen on average, but experienced 482 lower average seed predation. Although consistent with the within-population relationships, 483 this result is somewhat surprising because natural selection is usually detected by comparing 484 individuals within populations. It is possible that pollinators and seed predators are finding 485 and choosing among nearby populations in the same fashion as they choose among flowers 486 and/or plants within populations, although this seems a little unlikely. Nevertheless, 487 populations can be spread out across the same governing adaptive surface as individuals 488 within populations (e.g. Armbruster, 1990). If this is the case here, it is puzzling that the

15 489 populations have not converged more closely onto a single adaptive peak. Perhaps the surface 490 actually comprises multiple adaptive peaks. Further complicating our interpretation is the fact 491 that populations may experience annual fluctuations in selection, so that the true selection 492 history for any given populations is not reflected in any contemporary patterns. 493 494 Mosaic selection generated by seed predators but not pollinators 495 No population interaction term was retained in the best GLM model of pollen arrival on 496 stigmas, which suggests that pollinator-mediated selection on floral traits was similar across 497 all populations. Indeed, the best interaction model (mosaic selection) was 0.0068 times as 498 likely as the non-interaction model to minimize the information loss. 499 In contrast, the interaction model explaining seed predation was about one-third as likely 500 as the non-interaction model to minimize the information loss; i.e. the two models were 501 almost equally good, except for inclusion of an extra term and loss of a degree of freedom. 502 This indicates that there may be geographic variation in selection mediated by seed predators, 503 despite the interaction term not having been retained in the best model (perhaps because of 504 relatively small sample sizes in each population). Even stronger evidence of a geographic 505 mosaic in selection mediated by seed predators is the retention of the population x 506 seed-predation interaction term in the final seed-production model. The retention of 3- and 507 4-way interactions involving seed predation also supports the geographic heterogeneity in 508 selection generated by seed predators. The direct evidence of significant variation in intensity 509 of seed predation across populations also supports the interpretation of a geographic mosaic 510 in selection generated by seed predators. Taken together, these results are consistent with the 511 expectation that selection commonly varies geographically, leading to the potential for 512 geographic mosaics of evolution or coevolution (Thompson 2005). 513 514 Concluding remarks 515 Populations of Pedicularis rex in southwestern China exhibited large amounts of variation in 516 both morphology, levels of pollination and, most dramatically, levels seed predation. Despite 517 this variation among populations, there was also evidence of consistent conflicting selection 518 within most populations on floral exsertion beyond the water-bearing bracts. Pollinators 519 selected for greater exsertion, perhaps because such flowers are easier to see. Seed predators, 520 in contrast, mediated selection against exsertion, as highly exserted flowers were less 521 protected by water. There was also evidence of a geographic mosaic in selection generated by 522 seed predators, but not in selection generated by pollinators. Many questions remain

16 523 unanswered, however, especially with regards among-population patterns of mean fitness in 524 relation to floral trait variation. Future studies of this kind should focus on larger sample 525 sizes (for each population) in order to increase parameter precision and improve the ability to 526 detect geographic variation in selection generated by mutualists and antagonists. 527 528 ACKNOWLEDGEMENTS 529 We thank Jin Ma, Xiao-Yue Wang, Min Yang, Jing-Ju Zhang for their help in the field study, 530 Sarah Corbet, Jeff Karron, Mark Rausher, Xiao-Xing Tang and three anonymous reviewers 531 for valuable comments on this work. This work was supported by the National Science 532 Foundation of China (NSFC no. 31030016, 31270281) to SQH, and a UK Royal Society 533 International Research grant to WSA. 534 535 LITERATURE CITED 536 Ågren J, Hellström F, Toräng P, Ehrlén J. 2013. Mutualists and antagonists drive 537 among-population variation in selection and evolution of floral display in a perennial herb. 538 Proceedings of the National Academy of Sciences, USA 110: 18202-18207. 539 Anderson B, Johnson SD. 2008. The geographical mosaic of coevolution in a plant-pollinator 540 mutualism. Evolution 62: 220-225. 541 Armbruster WS. 1990. Estimating and testing the shapes of adaptive surfaces: the 542 morphology and pollination of Dalechampia blossoms. American Naturalist 135: 14-31. 543 Armbruster WS, Shi X-Q, Huang S-Q. 2014. Do specialised flowers promote reproductive 544 isolation? Realised pollination accuracy of three sympatric Pedicularis species. Annals of 545 Botany 113: 331-340. 546 Bartkowska MP, Johnston MO. 2012. Pollinators cause stronger selection than herbivores on 547 floral traits in Lobelia cardinalis (Lobeliaceae). New Phytologist 193: 1039–1048. 548 Bolstad GH, Armbruster, WS, Pélabon C, Pérez-Barrales R, Hansen TF. 2010. Direct 549 selection at the blossom level on floral reward by pollinators in a natural population of 550 Dalechampia schottii: full-disclosure honesty? New Phytologist 188: 370–384. 551 Burnham KP, Anderson DR. 2002. Model selection and multimodel inference: a practical 552 information-theoretic approach. New York, UK: Springer-Verlag. 553 Carlson JE, Harms KE. 2007. The benefits of bathing buds: water calyces protect flowers 554 from a microlepidopteran herbivore. Biology Letters 3: 405-407. 555 Carlson JE, Holsinger KE. 2010. Natural selection on inflorescence color polymorphisms in 556 wild Protea populations: the role of pollinators, seed predators, and intertrait correlations.

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22 702 Supporting information 703 Additional supporting information includes two tables and one Excel file. 704 Table S1. Detailed information on location and altitude of 14 sampled populations of 705 Pedicularis rex. 706 Table S2. Initial seed set (%), final seed set (%), seed predation (%) and comparison of 707 between initial and final seed set (Chi-Square analyses) shown for 12 populations, with P 708 < 0.05 indicated in bold. 709 Appendix Excel file. Means and Standard errors for 12 phenotypic traits and pollination 710 success in 14 populations of Pedicularis rex. F-values are extracted from one-way 711 ANOVAs with population as a class variable and trait as a dependent variable. Means 712 followed by the same letter are not significantly different based on Bonferroni 713 multi-comparison test. * P < 0.001. CB: cup-like bracts.

23 Table 1. Generalized linear models of relationships between floral morphology, pollination, seed predation, and seed production (all populations analysed jointly), as supported by the AICc model-selection procedure (those with minimum AICc values were assumed to be best). All proportions were arcsin-squareroot transformed to meet model assumptions of normality. (These transformations resulted in slightly lower AICs compared to untransformed data, but did not substantially change the results.) Models 1 and 3 are based on data from populations 1-14, whereas models 2 and 4 are based on data from populations 1, 3, 5, and 8-11. Note that Model 4 has only two models listed because the interaction model had a lower AIC than the no-interaction model. Wald Chi Square p-values for model-term effects: *p < 0.05; **p < 0.01; ***p < 0.001; no asterisk: p > 0.05. 1. Determinants of pollination (number of pollen grains on stigmas) Pollen = constant + exsertion + lip width + population + AICc = a. Full model population×exsertion + population×lip width 1728.99 Best Pollen = constant + exsertion + lip width* + population + AICc = b. interaction population×exsertion 1714.83 model AICc = c. Best model Pollen = constant + exsertion** + lip width + population*** 1700.10

2. Determinants of seed predation Seed predation = constant + exsertion*** + lip width + AICc = a. Full model pollen** + population + population×pollen + -128.48 population×exsertion + population×pollen + population×lip Best Seed predation = constant + exsertion*** + lip width* + AICc = b. interaction pollen* + population + population×exsertion -151.03 model Seed predation = constant + exsertion*** + lip width* + AICc = c. Best model pollen* + population*** -156.11

24

3. Determinants of initial seed set Initial seed set = constant + exsertion* + lip + pollen*** + AICc = a. Full model population*** + population×exsertion + population×lip* + -452.54 population×pollen Best Initial seed set = constant+ pollen** + population** + AICc = b. interaction population×pollen -480.30 model AICc = c. Best model Initial seed set = constant + population*** + pollen*** -493.44

4. Determinants of total surviving seeds Total seeds = constant + pollen + initial seed set*** - seed predation + exsertion + lip + population*** + population×initial seed set*** + population×seed AICc = a. Full model predation*** + population×pollen + population×exsertion*** -971.091 + population×lip + population×initial-seed-set×predation*** + populaiton×initial-seed-set×predation×exsertion ***

Total seeds = constant + initial seed set*** - seed predation + population*** + population×initial seed set*** + AICc = b. Best model population×seed predation*** population×exsertion*** + -1000.03 population×initial-seed-set×predation*** + population×initial-seed-set×predation×exsertion ***

25 Figure legends Fig. 1. Measurements of six floral and six vegetative traits in Pedicularis rex. FL, flower length; TL, tube length; TD, tube diameter; LLL, lower lip length; LLW, lower lip width; CO, corolla opening; SD, stem diameter; LL, leaf length; LW, leaf width; WTC, width of top of cupular bract; WBC, Width of bottom of cupular bract; CBH, cupular bract height. Arrows show water in cupular bract.

Fig. 2. Path diagram, based in part on the four best GLM models obtained by AIC model selection of among-individual (within-population) relationships between floral morphology, pollination, initial seed set, seed predation, and final seed production (after standardizing each observation to its respective population mean). Numbers are standardized regression coefficients (= path coefficients), which vary between 1 and -1 (0 = no effect, 1 / -1 = complete determination). Coefficients of relationships affecting, or affected by, the interaction terms cannot be calculated in the same fashion as for the other paths and have not been included. (For details of the interactive effects on total seed set, see Table 1.) The main effects of seed predation and initial seed set on final seed production are interpretable despite interactions, because the interactions result largely from significant variation in slope steepness but not direction (i.e. interactions are ordinal), although the effect sizes (path coefficients) are not easily interpreted and have been omitted. Depicted significance levels come from the GLM models: * P < 0.05, ** P < 0.01, *** P < 0.001.

Fig. 3. Conflicting selection detected by comparing variation in two components of female fitness, pollination and proportion of seeds escaping predation, in relation to flower exsertion beyond the water-bearing bracts. All variables were transformed by subtracting the mean of the population to which each observation belonged. This captures the pooled within-population relationships by removing the significant population effect detected in the GLM. Pooling of populations after standardization was statistically indicated because there were no detectable interactions. Note that other explanatory variables have not been partialled out as in the statistical models, hence large scatter of observations around the central tendencies. A. Relationship between mean-corrected number of pollen grains on stigmas and the arcsin-squareroot-transformed proportional corolla exsertion (R2 =

26 0.024, P = 0.007). B. Relationship between mean-corrected proportion of seeds surviving seed predation (arcsin-squareroot-transformed ) and the arcsin-squareroot-transformed proportional corolla exsertion (R2 = 0.267, P < 0.001).

Fig. 4. Relationship between population-mean values of the number of pollen grains on stigmas, proportion of seeds surviving predation, and proportional exsertion of flower beyond water-bearing bracts.

27 Fig. 1

28 Fig. 2

29 Fig. 3

30 Figure 4

31