bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

1 Sex signal and stability covary with fitness 2 3 Thomas Blankers1*, Rik Lievers*1, Camila Plata1, Michiel van Wijk1, Dennis van 4 Veldhuizen1 & Astrid T. Groot1,2. 5 6 1 Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Science Park 7 904, Amsterdam, the Netherlands 8 2 Max Planck Institute for , Hans-Knöll-Strasse 8, Jena, Germany 9 * These authors contributed equally 10 11 Author for correspondence: Thomas Blankers, [email protected] 12 13 14 bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

15 ABSTRACT 16 If sexual signals are costly to produce or maintain, covariance between signal expression and 17 fitness is expected. This signal-fitness covariance is important evolutionarily, because it can 18 contribute to the maintenance of genetic variation in signal traits, despite selection from mate 19 preferences. Chemical signals, such as sex , have traditionally been assumed to 20 be stereotypical species-recognition signals, but their relationship with fitness is unclear. Here 21 we test the hypothesis that for chemical signals that are primarily used for conspecific mate 22 finding, there is covariation between signal properties and fitness in the noctuid moth Heliothis 23 subflexa. Additionally, as moth signals are synthesized de novo every night throughout the 24 's reproductive life, the maintenance of the signal can be costly. Therefore, we also 25 hypothesized that fitness covaries with signal stability (i.e. the lack of intra-individual variation 26 over time). We measured among- and within-individual variation in pheromone amount and 27 composition as well as fecundity, fertility, and fitness in two independent groups of that 28 differed in the time in between two consecutive pheromone samples. In both groups, we found 29 reproductive success and longevity to be correlated with pheromone amount, composition, and 30 stability, supporting both our hypotheses. This study is the first to report a correlation between 31 fitness and sex pheromone composition in , solidifying previous indications of condition- 32 dependent moth pheromones and highlighting how signal-fitness covariance may contribute to 33 heritable variation in chemical signals both among and within individuals.

34 Keywords: sexual communication, fitness, trade-offs, sex pheromone, Lepidoptera

35

36 INTRODUCTION 37 Many sexually reproducing organisms discriminate among potential mates. By selecting a mate, 38 choosing individuals may receive direct benefits, e.g. protection or nutrients, and indirect 39 benefits, e.g. by receiving 'good' genes which result in more viable or sexy offspring (1–5). Mate 40 choice commonly occurs through sexual signals, and variation in reproductive success across 41 individuals producing different sexual signals is the basis of (3,6,7).

42 In general, signals under sexual selection are subject to directional selection, as those individuals 43 are chosen that confer the highest direct or indirect benefits. Additionally, in many organisms, bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

44 sexual signals are used to localize potentially suitable, conspecific mates. These so-called species 45 recognition signals are often under stabilizing selection, because variation in these signals 46 renders them less reliable. Both directional and stabilizing selection are expected to erode genetic 47 variation (8,9). However, sexual signals are shown to have high levels of genetic variance 48 (10,11) and many observations indicate that sexual signals evolve rapidly, diverge early on 49 during speciation, and are important barriers to gene flow among closely related species (6,12– 50 15). To understand how sexual signals evolve, it is important to understand how (genetic) 51 variation in these signals is maintained.

52 If signals are costly to produce or maintain, their expression and their composition are expected 53 to be correlated with fitness (5,16). Negative correlations between signal and fitness indicate that 54 signal investment trades off with fitness (one-trait trade-offs sensu (17)). Positive correlations 55 between signal expression and fitness are expected when only high-quality senders are able to 56 bear the cost of the signal (two-trait trade-offs sensu (17) and indicate that the signal is 57 condition-dependent (18). Covariation between sexual signal variation and fitness can maintain 58 genetic variation in sexual signals, even in the face of selection (19–22). This is because fitness 59 is the result of the combined effect of many different traits and thus controlled by many different 60 loci (23).

61 Signal-fitness covariance has been studied mostly in species with acoustic and visual signals, 62 while chemical signals have received much less attention (24,25). This may be because chemical 63 signals, such a sex pheromones, are generally assumed to be biosynthetically cheap (24,26,27) 64 and have traditionally been assumed to be independent of signaler quality (24,28). Specifically, 65 moth sex pheromones have typically been considered as species-recognition signals (29,30) and 66 empirical evidence suggests moth pheromone signal composition is under stabilizing selection 67 (31–35). However, sexual signals probably do not function solely as species recognition or mate 68 choice signals, but rather range along a continuum (20,36,37). This idea is supported by 69 empirical studies that showed a relationship between nutrition and pheromone amount (38), body 70 size and sex pheromone amount (39), and body size and sex pheromone composition (40) in 71 moths, and between nutritional state, age, and parasite load and pheromone composition in 72 beetles (41). However, even though moth sex pheromones have been studied extensively in the 73 past forty to fifty years and the sex pheromone of > 2000 moth species has been identified, very bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

74 little is known about the relationship between signal variation and fitness or about variation 75 within individuals. Moreover, we lack empirical insight into the relationship between sexual 76 signal variation and fitness for chemical mate attraction in general.

77 Here, we tested the hypothesis that even for stereotypical species-recognition signals, there is 78 covariation between sexual signal composition and fitness. Since many sexual signals need to be 79 maintained throughout an individual’s reproductive lifetime, and maintenance likely requires 80 continuous investment, we also hypothesized that the ability of an individual to maintain its 81 signal covaries with fitness as well. We tested these hypotheses for the female sex pheromone in 82 the noctuid moth Heliothis subflexa. Specifically, we examined how sex pheromone signaling 83 activity (calling activity from hereon), and pheromone amount and composition changed over 84 time.

85 In moths, older (virgin) females tend to have reduced activity and reduced mating success 86 (42–48), and virgin female moths generally keep investing in signaling (49). Thus, prolonged 87 virginity is expected to trigger physiological responses that modulate resource allocation 88 between somatic maintenance and reproduction. We assessed these trade-offs by comparing a 89 biologically realistic delay to first mating (3 days) with an extreme case of prolonging female 90 virginity (8 days), here referred to as ‘early’ and ‘late’ maters, respectively. We then asked 91 whether a) the composition and amount of the pheromone signal in early and later maters 92 covaried with fitness, and b) the stability (within-individual variation) of the pheromone during 93 prolonged virginity was correlated with fitness. We addressed these questions separately in the 94 early and in the late maters to explore the robustness of our findings to a) variation in age and b) 95 the time span over which intra-individual variation was measured. We expected that high-fitness 96 females produced more pheromone compared to low-fitness females, and that maintaining high 97 pheromone amounts and stable pheromone composition trades off with fitness.

98

99 METHODS

100 Insects

101 The laboratory population of H. subflexa originated from North Carolina State University and 102 has been reared at the University of Amsterdam since 2011, with occasional exchange between bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

103 NCSU, Amsterdam, and the Max Planck Institute for Chemical Ecology in Jena to maintain 104 genetic diversity. The rearing was kept at 25°C and 60% relative humidity with 14h:10h light- 105 dark cycle. Larvae were reared on a wheat germ/soy flour-based diet (BioServ Inc., Newark, DE, 106 USA) in 37-mL individual cups. Pupae were separated by sex and checked for emergence every 107 60 minutes from 2 hours before until 7 hours after the onset of scotophase. Only females that 108 emerged within this timeframe were included in the experiments. After emergence, females were 109 transferred to clear 475-mL observation cups covered with fine mesh gauze. Males were kept in 110 the 37-mL individual cups. Adults were provided with regularly replaced cotton soaked in 10% 111 sugar water.

112 Phenotyping

113 The pheromone of each female was sampled at two time points. The first sample was taken in the 114 first night after emergence. The second sample was taken in the third or eight night after 115 emergence for the ‘early’ and ‘late’ treatment, respectively. Early maters were females that were 116 kept virgin for three days and mated on the fourth day, while late maters were females that were 117 kept virgin for eight days and mated on the ninth day. To avoid possible variation due to 118 differences between sampling time during the night (50–52), care was taken to take the first and 119 second pheromone sample at the same time during the night (no more than 10 minutes 120 difference) and the sex pheromone was always sampled within a narrow time interval during 121 peak calling times (3rd – 6th hour of scotophase (50).

122 Pheromone was collected from the gland surface from females using optical fibers coated with a 123 100-μm PDMS (Polymicro Technologies Inc., Phoenix, AZ, USA), as described in detail in (53), 124 where the authors showed strong correspondence between pheromone measurements following 125 the non-invasive method used here and following traditional (lethal) gland extractions. 126 Pheromone glands were extruded and fixed by gently squeezing the abdomen and gently rubbed 127 over a period of 2 minutes. Fibers were then submerged for 1-2 hours and rinsed in 50 µL hexane 128 with 200 ng pentadecane as internal standard and discarded. Pheromone extracts were analyzed 129 by injecting the concentrated samples into a splitless inlet of a 7890A GC (Agilent Technologies, 130 Santa Clara, CA, USA) and integrating the areas under the pheromone peaks, using Agilent 131 ChemStation (version B.04.03). bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

132 Throughout this study, we focused on the sex pheromone components that have been shown to 133 be important for male attraction (Z11-16:Ald, Z11-16:OAc, Z11-16:OH, and [Z7-16:Ald + Z9- 134 16:Ald],), as well as the total amount of pheromone. Z7-16:Ald and Z9-16:Ald were difficult to 135 separate by GC and were therefore integrated as one peak (referred to a Z7/Z9-16:Ald), as has 136 been done in previous studies (54). Absolute amounts (in ng) and percentages (of the total 137 amount) of each compound were calculated relative to a 200 ng pentadecane internal standard. 138 Samples containing < 10 ng could not be reliably integrated and were excluded from subsequent 139 analyses. We globally standardized the absolute amount of each of the four pheromone 140 components by subtracting the global average amount across all females and then dividing the 141 mean-centered value by the variance. We then performed principle component analysis on the 142 standardized pheromone measurements, retaining four principle components (PCs). Differences 143 in PC scores between the first and second pheromone samples were tested using a paired 144 student's t-test, using the statistical package R (55).

145 Relationship between fitness and female calling activity

146 To determine whether calling activity of virgin females increased or decreased over their lifetime 147 or whether it peaked at some point in their reproductive live, we randomly assigned 40 and 51 148 females to the ‘early mating’ and ‘late mating’ treatments, respectively, and observed the calling 149 activity of each female every night from eclosion until her death. For each female, we measured 150 pupal mass, calling behavior, fecundity, fertility, and lifespan. Pupae with a visible wing pattern 151 were weighed before the start of scotophase (the 10-hour dark phase during the day-night cycle). 152 Extremely small pupae (< 0.1 gram) were discarded. The time spent calling before and after 153 mating was recorded daily every 30 minutes between 1-8 hours after the onset of scotophase. To 154 measure fecundity and fertility, females were mated in 475-mL observation cups with virgin 2-3- 155 day-old males one day after the second pheromone sample was taken. For all mated females, we 156 scored the amount of eggs, the number of hatching larvae, and the life span. To stimulate 157 oviposition, freshly cut Physalis peruviana berries were supplied daily onto the gauze. Before 158 the onset of a scotophase, eggs laid in the previous scotophase were collected by transferring the 159 female to a new observation cup. Eggs were counted before emergence, and artificial diet was 160 provided for emerging larvae. Emerged larvae were counted daily. After female death, lifespan 161 was recorded, and mating success was confirmed by checking for the presence of a bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

162 spermatophore. The percentage of calling females, the per-individual onset of calling and the 163 per-individual duration of calling were visually examined before and after mating. We tested 164 whether early maters differed from late maters in fecundity (overall and per day), fertility 165 (overall and per day), and life span using two-sample t-tests.

166 Relationship between fitness and inter-individual variation

167 To test whether fecundity, fertility, and life span variation could be predicted by variation in the 168 pheromone, we fitted generalized linear models with quasi-Poisson error and used a stepwise 169 model selection approach to identify significant predictors and interactions. In the base model, 170 the response was either fecundity, fertility, or life span and the predictors were each of the four 171 PCs as well as the pupal mass of the female, the pupal mass of her mate, and the time spent 172 calling by the female before mating. Only the PCs from pheromone measurements taken on 173 timepoint 2 were used, as these were the samples taken immediately before the were set 174 up that were used to measure the individual’s fitness. Model selection was done by first adding 175 interactions among the independent variables and then purging independent variables that did not 176 significantly explain variation in the response. Adding and removing variables and interactions 177 was done using Analysis of Deviance, where significance of an increase/decrease in residual 178 Deviance after removing/adding a variable or interaction was assessed using a Chi-square test. 179 Improvement of model fit was considered significant for p < 0.05. For the final model, the 180 pseudo-R2 was calculated as the ratio between the null deviance and 1 minus the residual 181 deviance, similar to calculating the coefficient of determination in linear regression. The pseudo- 182 R2 gives the proportion of deviance explained, which is informative about model fit and about 183 the explanatory value of the independent variables (56).

184 185 Relationship between fitness and intra-individual variation

186 To test whether fecundity, fertility, and life span variation could be predicted by the degree to 187 which females kept their pheromone stable, we calculated intra-individual variation in 188 pheromone by taking the absolute difference between PC scores for pheromone measurements 189 taken after 24 hours and PC scores for pheromone measurements taken three or eight days post- 190 eclosion, for early and late maters respectively. We fitted the same generalized linear models 191 (with the same covariates) and employed the same model selection procedure as described bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

192 above, except that the predictor variables were now the absolute change in PC scores between 193 timepoint 1 and timepoint 2. 194 195 Heritability of stability 196 To determine the heritability of pheromone composition (PC scores) and stability (difference 197 between PC scores at timepoint one and two), we selected 20 females that covered the range of 198 intra-individual variation observed across all females. Subsequently, we sampled 5-20 daughters 199 per female (N = 240) at both timepoints, i.e. three or eight days post-eclosion for early and late 200 maters, respectively. We used Bayesian Monte Carlo Markov Chain models combined with 201 pedigree information of the two generations of females and their mates. We ran independent 202 models for each principle component (inter-individual variation) or for the change in PC scores 203 between timepoint 1 or 2 (intra-individual variation). The models were implemented with the R 204 package “MCMCglmm” (57) using inverse gamma priors. All MCMC models were run for 205 1,000,000 iterations, the initial 100,000 samples were discarded (burn-in period) and chains were 206 sampled every 100 iterations (thinning) to reduce autocorrelation. The narrow-sense heritability 207 (h2) was estimated as the ratio of additive genetic variance (‘animal’ effect) over the sum of all 208 variance compounds (‘animal’ plus ‘unit’ effects). 209 210 RESULTS 211 Relationship between fitness and female calling activity

212 When observing female calling activity, we found that calling effort of all females and duration 213 of individual calling activity initially increased and then decreased after the second or third night 214 (Fig 1A,B). Upon mating, calling activity was almost entirely suspended, after which it slowly 215 recovered. There was no apparent relationship between the time at which females started calling 216 and female age or mating status (Fig S1). bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

217 Fig 1. 218 Calling activity and fitness. For both groups, early (blue) and late (yellow) maters, the collective calling effort per 219 night and the distribution of calling duration across females is shown for each night. A) Percent of females calling 220 on any given night, B) Calling duration. Boxes and whiskers show the interquartile ranges and the median, and lines 221 connect mean values for each night. Individual dots show observations > 1.5 times the interquartile range. The 222 dashed, vertical grey and black lines show the days at which pheromone samples were collected and females were 223 mated, respectively. C) Fitness variation within and between early and late maters. **: P < 0.01.

224 Late maters invested in pheromone calling for an additional five days relative to early maters 225 (Fig 1). Both fecundity and fertility were significantly lower in late maters relative to early 226 maters (Fig 1C). Reduced fecundity was likely due to the ‘lost’ 5 days rather than due to 227 allocation of time and energy, because fecundity per day was not significantly different between 228 early and late maters (46.50 versus 40.96 eggs per day; t = 1.39; P = 0.1669). Fertility per day 229 was lower in late versus early maters (19.00 versus 32.84 larvae per day; t = 3.102; P = 0.0026), 230 indicating that females that mated later in life laid eggs with lower hatching success compared to bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

231 early maters. We further observed that late-mated females lived on average two days longer than 232 early-mated females (Fig 1C).

233 Variation in sex pheromone signal strength and composition

234 In quantifying the sex pheromone variation using principle component scores for the 235 standardized absolute amounts of four components that are important for male response in H. 236 subflexa, we found that the first PC accounted for 60.5% of the total variation among individuals. 237 Since this axis was almost perfectly correlated with the total amount of pheromone (Pearson’s r 238 = -0.99; P < 0.0001), PC1 thus described variation in the total amount of pheromone. The 239 remaining three PCs described different dimensions of pheromone composition space: higher 240 scores on PC2 (19.8% var. expl.) indicated decreasing amounts of Z11-16:OAc relative to all 241 other compounds, higher scores on PC3 (13.2% var. expl.) indicated increasing amounts of Z11- 242 16:OH relative to Z7/Z9-16:Ald, and higher scores on PC4 (6.54% var. expl.) indicated 243 increasing amounts of the major component, Z11-16:Ald, relative to all other compounds (Table 244 S1; Fig S2). Neither of these three ‘composition’ PCs were correlated with the total amount of 245 pheromone (Pearson’s r ranged from -0.05 to 0.08; P ranged from 0.3337 to 0.4987) and a 246 significant proportion of the variation in all PC scores was additive genetic variance, except PC1 247 in the late maters (posterior mode of h2 ranged from 0.17 to 0.58; for PC1 in late maters h2 < 248 0.01; Fig S3). There was thus no need to calculate log-contrasts for the components to break the 249 unit-sum constraints that trouble the use of relative amounts (58), thereby avoiding inadvertent 250 effects resulting from the choice of the divisor.

251 With the exception of PC4, all PCs were significantly different between timepoint 1 and 252 timepoint 2, both for early and late maters (Fig 2). This shows that there is intra-individual 253 variation in both amount and composition of the pheromone signal. Over time, pheromone 254 amount decreased, while relative amounts of Z11:16:OAc and Z11-16:OH increased (Fig 2). 255 However, there was substantial variation around the mean direction and magnitude of change, 256 with some individuals showing much more or much less change in the amount and composition 257 of the pheromone over time (Fig 2). This shows that there is variation in the magnitude of intra- 258 individual variation. Estimates of the narrow-sense heritability of intra-individual variation in the 259 PCs were between 0.1 and 0.2, indicating that a significant proportion of the variation is additive 260 genetic variation (Fig S3). bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

261

262 Fig 2. Pheromone variation within and between females. Box-and-whisker plots show distribution of principal 263 component (PC) scores at day one and at day three or eight for early (blue) and late (yellow) maters, respectively. 264 Dashed lines connect samples taken from the same individual. For each PC, the amount variance explained and the 265 main pheromone component that loads on the PC, as well as the sign of the loading (positive, +, or negative, - ), is 266 indicated above each panel.

267

268 Relationship between fitness and inter-individual pheromone variation

269 When we determined correlations between the female pheromone signal and her fecundity, 270 fertility and life span, we found that all fitness measurements depended on variation in the 271 pheromone signal. In all models, both in early and late maters, we found at least one PC 272 describing variation in the composition of the pheromone blend to explain variation in fitness 273 (Fig 3A). For fecundity (in both early and late maters) we also observed a correlation with PC1. 274 Partial pseudo-R2 values ranged from three to thirty percent for the pheromone components. 275 Time spent calling before mating and the pupal mass of the female also explained fitness bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

276 variation in most models and the combined effect from the PCs, covariates, and their interactions 277 explained between 15 – 59% of the variation in fitness measurements (Table S2).

278

279 Fig 3. Fitness variation explained by pheromone traits. Each panel depicts one model for either early (blue) or late 280 maters (yellow), for fecundity, fertility, or lifespan as a response variable, and for pheromone amount and 281 composition as a predictor. For each of the four PCs, columns depict the partial pseudo-R2 of the PC and all 282 interactions in which the PC is involved in the model.

283

284 Producing more pheromone (lower scores on PC1) or higher relative amounts of Z11-16:OAc 285 (lower scores on PC2), Z11-16:OH (higher scores on PC3), and Z11-16:Ald (higher scores on 286 PC4) was universally associated with higher fitness (higher fecundity and fertility, longer 287 lifespan) with one exception: higher scores for PC4 were associated with lower fecundity in the 288 early maters (Fig 4; Fig S4; Table S2). In the case of interactions between a pheromone PC and 289 female pupal mass, we examined the modulating effect of pupal mass on the correlation between 290 fitness and pheromone by drawing separate regression lines for the females with the lowest 33% 291 pupal mass, those with pupal mass in the middle tercile, and those in the top 33% of pupal mass, 292 using the r-package “interactions” (59). This showed that higher relative amount of Z11-16:OH 293 (higher PC3 score) was associated with higher fertility in the heaviest 33% females, but not in bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

294 the bottom 67% (Fig 4). Also, higher relative amount of Z11-16:OAc (lower PC2 score) was 295 associated with longer life span in heavy females, but with lower life span in lighter females (Fig 296 4) .

297

298

299 Fig 4. Relationship between pheromone variation and fitness, showing the correlation between PC scores and fitness 300 from both early maters (blue) and late maters (yellow). Points show individual coordinates, lines show regression 301 paths, i.e. the effect of the PC on the x-axis on the response on the y-axis, accounting for the effects from all other 302 predictors in the model. Higher scores on PC2 correspond to lower relative amounts of Z11-16:OAc, while higher 303 scores on PC3 correspond to higher relative amounts of Z11-16:OH. Interaction effects are shown as regression 304 paths split by terciles. For the PCs where we found interaction effects between pupal mass and fitness measures, 305 relationships were determined separately for pupal masses in the lower tercile (median of the lower tercile = 0.224 306 g), middle tercile (median = 0.238 g), and upper tercile (median = 0.275 g).

307 Relationship between fitness and intra-individual variation

308 To test the hypothesis that females with stable pheromone signals during their life time have 309 higher fitness, we calculated intra-individual variation as the absolute difference between PC 310 scores for pheromone measurements taken 24 hours post-eclosion and for pheromone 311 measurements taken three or eight days post-eclosion, for early and late maters respectively. We 312 found that between 6% and 45% of the variation in fitness measures was predicted by intra- bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

313 individual variation in either the total amount (PC1), the relative amount of Z11-16:OAc (PC2) 314 or both (Fig 5).

315

316 Fig 5. Fitness variation explained by intra-individual variation in pheromone amount and composition (here referred 317 to as stability). Each panel depicts one model for either early (blue) or late maters (yellow), for fecundity, fertility, or 318 lifespan as a response variable, and for intra-individual variation in pheromone PCs as a predictors. Column height 319 corresponds to the partial pseudo-R2 of intra-individual variation in the PC and all interactions in which the PC is 320 involved in the model. PCs absent from the model are shown as partial pseudo-R2 = 0.

321 In early maters, more stability in the total amount (PC1) was associated with higher fecundity, 322 fertility, and longevity, while in late maters stability in total amount was uncoupled from fitness 323 (Fig 6; Fig S5). For PC2, we similarly found a predominantly positive correlation between 324 stability and fitness (i.e. lower values on the plasticity axis are associated with higher values on 325 the fitness axis). We also observed examples of effects in the opposite direction for both fertility 326 and life span (Fig 6; Fig S5). bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

327

328 Fig 6. Relationship between change in pheromone and fitness, showing the magnitude and direction of the 329 correlation between change in PC scores and fitness in early maters (blue) and late maters (yellow). Change in PC 330 scores was calculated as the absolute difference between the PC score for the first sample and second sample of an 331 individual. Points show individual coordinates, lines show regression paths, i.e. the effect of the PC on the x-axis on 332 the response on the y-axis, accounting for the effects from all other predictors in the model. For pupal mass, the 333 lower tercile median = 0.224 g, the middle tercile median = 0.238 g, the upper tercile median = 0.275 g.

334

335 DISCUSSION

336 If sex pheromone signals are costly, calling activity, pheromone amount and/or pheromone 337 composition should covary with fitness. We showed that in H. subflexa an extreme delay in 338 mating of 8 days and a continued investment in signaling was associated with lower reproductive 339 output. We also found that while virgin females maintained high signaling activity, their signal 340 changed over time: the total amount decreased and the ratios of components changed. Signal 341 variation was proportional to the delay in mating, but varied considerably across females. Some 342 females showed high intra-individual variation, while others had stable signals over time. The 343 heritability estimates of the intra-individual variation in pheromone composition (roughly 344 between 0.1 and 0.2) indicated that this variation can evolve in response to selection. Lastly, we bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

345 found that longevity, fecundity, and fertility were correlated with pheromone amount, with 346 pheromone composition, and with the stability in both pheromone amount and composition 347 within females. We thus conclude that our results meet expectations for costly pheromone 348 signaling and that the amount, composition, and maintenance of the long-distance mate attraction 349 pheromone produced by H. subflexa females likely depend on the genetic quality of the female.

350 Calling activity and fitness

351 In a synthesis of empirical results on calling activity in female moths, the majority of moth 352 species were found to increase their calling effort over time (49). This finding is in line with 353 theoretical predictions for a costly signal, for which the production depends on the number of 354 male arrivals: a virgin female should not invest much in signaling if this draws in too many 355 males, but she should increase signaling efforts if no males visit at all (49). Our results show that 356 for H. subflexa, calling activity initially indeed increased, but then decreased (Fig 1). Females 357 that spent more nights calling had reduced reproductive success, but slightly longer life span 358 relative to females that called fewer days, which is in line with general patterns across moths and 359 suggest both time constraints (fewer days remaining to lay eggs in late mated females) and 360 energy constraints (e.g., resorption of eggs to support somatic maintenance) to sex pheromone 361 calling (60). Together, these findings support our hypothesis that there are costs associated with 362 sex pheromone calling in female moths.

363 Pheromone variation and fitness

364 We hypothesized that these costs would manifest in signal-fitness correlations in virgin moths. 365 We found evidence for covariation between fitness and sex pheromone amount, composition, 366 and stability, regardless of the fitness measure (reproductive output or life span). In addition, we 367 found similar patterns in two independent groups of females that differed in their age at the time 368 of sampling and in their remaining life span to lay eggs. Even though in nature females are 369 unlikely to stay virgin for 8 days (late maters), our finding that her signal still correlates with 370 fitness shows that the patterns that we found are independent of female age. Our findings thus 371 strongly support a scenario for costly sex pheromone signals in moths and provide context to 372 earlier findings that suggested condition-dependence of moth sex pheromone amount (38,39) and 373 composition (40), by revealing positive covariance between signal and fitness components. bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

374 On the one hand, the consistent positive correlations between fitness and pheromone amount as 375 well as between fitness and the relative abundance of pheromone components important to male 376 mate attraction are surprising from a physiological perspective. It is unlikely that pheromone 377 production presents significant metabolic costs to signaling females, because the nutrients used 378 for pheromone production are negligible compared to available resources (27). However, there 379 may be indirect costs, for example if enzymes that convert sex pheromone component precursors 380 into their final products (in the appropriate ratios of species-specific blends) are also used for 381 other critical physiological processes.

382 On the other hand, the fitness costs to sex pheromone composition inferred here can explain field 383 observations of sex pheromone variation among wild populations of H. subflexa. The acetate 384 ester Z11-16:OAc slightly improves the attractiveness of the H. subflexa blend, while strongly 385 deterring the closely-related tobacco budworm, H. virescens (61,62). In regions where H. 386 virescens co-occurs with H. subflexa, the relative amount of Z11-16:OAc of field-caught females 387 is significantly higher compared to regions where H. virescens is absent (63). Apparently, Z11- 388 16:OAc is only produced when communication interference is likely to happen. As we found that 389 the PC2 axis, associated with variation in Z11-16:OAc, explained 10-20% of the variation in 390 fecundity, fertility, and life span (Fig 3), such that females with higher fitness had higher relative 391 amounts of Z11-16:OAc, selection likely favors females that produce lower relative amounts of 392 acetate esters when there is not risk of heterospecific mate attraction.

393 There is limited data available for male response to intra-specific levels of variation in moth sex 394 pheromones and these data often come from different methods, including sticky traps, field traps 395 and wind tunnel assays. It is therefore difficult to judge the relevance of the levels of observed 396 variation to male response. The two axes that described most of the variation in pheromone 397 composition (and that correlated with fitness), PC2 and PC3, are driven by variation in Z11- 398 16:OAc, Z11-16:OH, and Z9-16:Ald, which all have been shown to be important for male 399 attraction (61,62). The relative amount of Z11-16:OAc ranged from 5% to 15%. Previous studies 400 have shown that a) as little as 1% is enough to completely deter the heterospecific H. virescens 401 (64), b) adding Z11-16:OAc to a synthetic blend increases the attractivity of H. subflexa males 402 (61,65), and c) increased levels of acetate esters (from roughly 1-3 % to 10-15%) also increased 403 male response (62). For Z11-16:OH, the lowest and highest scores on PC3 corresponded to a 2- bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

404 fold difference, i.e. from ~3% to ~6%. Previous studies have shown that increasing or reducing 405 the relative amount of Z11-16:OH by a factor 2 can reduce male attraction to sticky traps by 10% 406 in the field (66), while in wind tunnel experiments a change in male attraction was not found 407 (61). Finally, for Z9-16:Ald we found a range from ~15% to ~35%. Previous studies have found 408 a 38% reduction in male precopulatory behavior when relative amounts of Z9-16:Ald were 409 below 15% (67), while in wind tunnel assays Z9-16:Ald levels below 10% reduced male 410 attraction by 20% (61). Hence, the observed levels of variation in the pheromone summarized by 411 PC2 and PC3 fall within a range that is likely relevant for variation in male responses.

412 We also hypothesized that for costly signals, maintenance is costly too and we thus expected 413 stability of the signal to covary with fitness. We found that individuals with more stable sex 414 pheromone signals over time had higher fitness, although negative correlations were found as 415 well. The evidence thus supports our hypothesis for fitness costs to sex pheromone stability. 416 Since in moths older females are typically less attractive, as measured by mating success (42– 417 48), it is possible that a female benefits from maintaining a young-female-like blend. This would 418 be similar to cricket song and mouse urinary protein pheromones, for which signal composition 419 reliably reflects age and in both cases the scent of senescence is associated with reduced mate 420 attraction (68,69). Additionally, female moths produce more pheromone earlier in life. Possible 421 physiological explanations for decreasing pheromone amounts in aging females are that changes 422 in Juvenile Hormone may result in hormonal suppression of pheromone production later in life 423 (70,71) or that older females may have a reduced capability to synthesize pheromone 424 components (71). Lower pheromone amounts can reduce the attractiveness of the blend, however 425 there is mixed evidence for this hypothesis (49). Therefore, an evolutionary mechanism that 426 would explain a benefit to costly stability is that stability counters signal decay due to 427 senescence.

428 In summary, we find evidence for signal-fitness covariation, which thus indicate costs to sex 429 pheromone signaling. Our results are in line with earlier findings for condition-dependence of 430 pheromone amount in moths and beetles, but go beyond these findings by revealing (i) a 431 relationship between a moth sex pheromone signal and fitness and (ii) finding this relationship 432 not only for the amount of pheromone produced, but also for the composition and for the extent 433 to which females keep their signal amount and composition stable. These results add multiple bioRxiv preprint doi: https://doi.org/10.1101/2021.02.05.429875; this version posted February 6, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.

434 new dimensions in which genetic quality differences between female moths can contribute to 435 pheromone variation within species. As genetic variation underlying sexual signals ultimately 436 provides the raw material from which species barriers may originate, the relationship between 437 signal variation and fitness components presented here thus provides a mechanism for the 438 evolution of sex pheromone signals and the diversity of moth species. 439 440 ACKNOWLEDGEMENTS 441 The authors thank the Evolutionary and Population Biology group members, especially Isabel 442 Smallegange and Emily Burdfield-Steel for their helpful discussions and the editorial board 443 member and three referees for their constructive criticism. This project is funded by the 444 Netherlands Organization for Scientific Research (NWO-ALW, award no. 822.01.012 awarded 445 to ATG), the National Science Foundation (award no. IOS-1052238 and IOS-1456973 awarded 446 to MW), and by the Marie Skłodowska-Curie Individual fellowship (grant agreement No 794254 447 awarded to TB). 448 449

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