Evolution of Drosophila Buzzatii Wings: Modular Genetic Organization

bioRxiv preprint doi: https://doi.org/10.1101/2021.06.16.448721; this version posted June 17, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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-ND 4.0 International license.

1 Evolution of Drosophila buzzatii wings: Modular genetic organization,

2 sex-biased integrative selection and intralocus sexual conflict

3 short running title: Evolution of Drosophila buzzatii wings

4 Iglesias PP1†*, Machado FA2†*, Llanes S3, Hasson E3, Soto EM3

5 1 Laboratorio de Genética Evolutiva, Universidad Nacional de Misiones – CONICET, Félix de

6 Azara 1552, N3300LQH, Misiones, Argentina.

7 2 Department of Biological Sciences, Virginia Polytechnic Institute and State University,

8 Blacksburg, United States.

9 3 Instituto de Ecología, Genética y Evolución de Buenos Aires (IEGEBA – CONICET), DEGE,

10 Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires,

11 Argentina.

12 †These authors contributed equally to this work.

13 *Corresponding authors; e-mail: [email protected] , [email protected]

15 Abstract

16 The Drosophila wing is a structure shared by males and females with the

17 main function of flight. However, in males, wings are also used to produce songs, or

18 visual displays during courtship. Thus, observed changes in wing phenotype depend

19 on the interaction between sex-specific selective pressures and the genetic and

20 ontogenetic restrictions imposed by a common genetic architecture. Here, we

21 investigate these issues by studying how the wing has evolved in twelve populations

22 of Drosophila buzzatii raised in common-garden conditions and using an isofemale

1 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.16.448721; this version posted June 17, 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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-ND 4.0 International license.

23 line design. The between-population divergence shows that sexual dimorphism is

24 greater when sex evolves in different directions. Multivariate Qst-Fst analyses

25 confirm that male wing shape is the target for multiple selective pressures, leading

26 males’ wings to diverge more than females’ wings. While the wing blade and the

27 wing base appear to be valid modules at the genetic (G matrix) and among-

28 population (D matrix) levels, the reconstruction of between-population adaptive

29 landscapes (Ω matrix) shows selection as an integrative force. Also, cross-sex

30 covariances reduced the predicted response to selection in the direction of the

31 extant sexual dimorphism, suggesting that selection had to be intensified in order to

32 circumvent the limitations imposed by G. However, such intensity of selection was

33 not able to break the modularity pattern of the wing. The results obtained here show

34 that the evolution of D. buzzatii wing shape is the product of a complex interplay

35 between ontogenetic constraints and conflicting sexual and natural selections.

37 Keywords

38 Adaptive landscapes, Genetic architecture, Intralocus sexual conflict, Morphological

39 integration/modularity, Multivariate selection.

41 Introduction

42 Understanding changes in morphological structures requires an integrative

43 approach that also considers constraints upon change. How is morphology

44 produced during development in the first place? Is selection in line with these

45 developmental rules? Does selection differ between the sexes? Morphological

46 integration, selection, and between-sex pleiotropy are key factors generating

47 association among traits at larger scales. An integrated developmental pattern or

2 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.16.448721; this version posted June 17, 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-ND 4.0 International license.

48 the occurrence of sex-specific selection on homologous structures controlled by

49 common genetic machinery (i.e. intralocus sexual conflict) may constrain the

50 evolutionary trajectories of such structures. It is only after evaluating the relative

51 effect of these factors that morphological changes can be understood in the light of

52 the interaction between constraints and their potential for functional adaptation.

53 The wing of Drosophila is a complex structure involved in different functions

54 such as flight and acoustic or visual communication (Wooton, 1992). For a long time,

55 it has been considered as a developmentally integrated structure that constrains

56 adaptive evolution (Houle, Bolstad, Van der Linde, & Hansen, 2017; Klingenberg,

57 2009; Klingenberg & Zaklan, 2000). While these conclusions are mostly based on

58 the hypothesis that the wing is divided into anterior and posterior (AP)

59 compartments (Klingenberg & Zaklan, 2000), recently Muñoz-Muñoz et al. (2016)

60 showed evidence supporting the compartmentalization of this structure along the

61 proximo-distal (PD) axis, forming two different modules, the wing base and the wing

62 blade. These modules were recognized not only on the phenotypic level but also at

63 the genetic and environmental levels, suggesting that it is possibly a consequence of

64 a modular developmental program.

65 From the wing primary task perspective (i.e. flight), the developmental

66 modules seem to match functional modules: the wing base transmits the forces

67 generated by the flight muscles and the wing blade generates the aerodynamic

68 forces necessary to lift the body (Dudley 2002). Although fly's ability is common to

69 both sexes, sex-biased dispersal has been documented in Drosophila (Begon, 1976;

70 Powell, Dobzhansky, Hook, & Wistrand, 1976; Fontdevila & Carson, 1978; Markow

71 & Castrezana, 2000; Mishra, Tung, Shree Sruti, Srivathsa, & Dey, 2020). An

3 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.16.448721; this version posted June 17, 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-ND 4.0 International license.

72 asymmetric dispersal of the sexes can exert different selective pressures on wing

73 morphology in each sex, leading to a sex-biased evolution of the modules.

74 Drosophila’s wings are also involved in premating behaviors that markedly

75 differentiate the wing’s function between sexes (Ewing, 1983; Dickson, 2008).

76 Except for a few species (within the Drosophila virilis species group; Satokangas,

77 Liimatainen, & Hoikkala, 1994), only males use wings for acoustic or visual

78 communication. Therefore, if wing morphology influences sound production or

79 visual display, only male wings will be subject to selection. This selection on males

80 can cause the displacement of females from their phenotypic optimum, reducing

81 their fitness. It is well documented that the direction and intensity of selection on

82 courtship song have been found to differ among populations and species according

83 to female preferences (Iglesias & Hasson, 2017; Iglesias et al., 2018a; Klappert,

84 Mazzi, Hoikkala, & Ritchie, 2007).

85 Here, we address these issues by investigating how the wing has evolved in

86 twelve populations of the cactophilic species Drosophila buzzatii raised in common-

87 garden conditions and using an isofemale line design. Only the males of the D.

88 buzzatii species use wings to produce a courtship song (Iglesias & Hasson, 2017;

89 Iglesias et al., 2018a; Iglesias, Soto, Soto, Colines, & Hasson, 2018b), and a previous

90 study has shown a rapid divergence of courtship song parameters among these

91 populations (Iglesias et al., 2018a). Thus, we first investigated whether the wings of

92 males and females are evolving differently among the sampled populations. At

93 present, wing modularity at the genetic level has only been tested in the model

94 species D. melanogaster (but see Soto, Carreira, Soto, & Hasson, 2008); however, D.

95 buzzatii is a cactophilic species distantly related to it. The effect of selection and drift

96 accumulate through time in each evolving lineage and can even change the modular

97 pattern in an evolutionary context (Martín-Serra, Figueirido, & Palmqvist, 2020;

98 Melo & Marroig, 2015). Thus, we then tested whether the wing in each sex is

99 organized in two modules along the PD (proximo-distal) or the AP (antero-

100 posterior) axis in D. buzzatii. We test these hypotheses by estimating the posterior

101 distribution of the Among-Population (D) and Additive Genetic (G) within and

102 between sexes covariances from a landmark-based analysis. We also estimated the

103 covariance pattern among peaks on the realized adaptive landscape for these

104 populations (Ω) to evaluate the potential effect of selection in defining between-

105 populations patterns of phenotypic integration and modularity. We also applied a

106 multivariate FSTq –FST approach to determine the extent to which selection and

107 drift contributed to the evolution of the wing. Finally, we investigated how cross-sex

108 covariances constrain or facilitate the predicted responses to multivariate selection.

109 To do that, we combined the multivariate breeder’s equation with random and

110 empirical selection gradients and compared the predicted response to selection

111 when using a G with and without between sex covariances. If intralocus sexual

112 conflict is not fully resolved, we expect high and positive intersexual genetic

113 correlations resulting in an augmented response when cross-sex covariances are

114 setting to zeroes. Otherwise, if the genetic architecture of wings evolves to allow

115 independent adaptation in each sex, thus resolving intralocus conflict, we expect the

116 response to selection to be the same. However, if cross-sex covariances facilitate the

117 response to selection, we expect the magnitude of the response to be higher when

118 including them.

119

120 Materials and Methods

121

122 Data collection and measurements

123 For this study, we analyzed 12 populations of D. buzzatii flies that have been

124 previously used to study variation in male courtship song (Iglesias et al., 2018a).

125 Each population was characterized by eight to 15 isofemale lines founded with wild-

126 collected gravid females. Flies were raised under common-garden and controlled-

127 density conditions (40 first-instar larvae per vial), and a photoperiod regimen of 12-

128 h light: 12-h dark cycle. First, they were raised on standard Drosophila medium for

129 four generations and then were moved one more generation to a ‘semi-natural’

130 medium prepared with fresh cladodes of the cactus Opuntia ficus indica (see Iglesias

131 et al., 2018a for more details). This cactus species represents the more widespread

132 host used by D. buzzatii in the study area.

133 We removed the right-wing of between three to five adult emerged flies per

134 sex and line, and mounted them on glass microscope slides for image acquisition.

135 Following Muñoz-Muñoz et al. (2016), a set of 15 landmarks was digitized in each

136 wing using the TPSdig software (Rohlf, 2001). Shape information was obtained from

137 the configurations of landmarks using standard geometric morphometrics methods

138 as implemented in the Geomorph package v3.0.7 (Adams, Collyer, Kaliontzopoulou,

139 & Sherratt, 2018). To examine shape variation, we performed a principal component

140 (PC) analysis based on the covariance matrix of Procrustes residuals. To evaluate

141 how many PCs should we retain as wing shape variables in subsequent analyses we

142 employed the broken-stick model criterion implemented in the package vegan

143 (Oksanen et al., 2019), which indicated that only the first 5 axes should be kept

144 (68.28% of total variation).

145

146 Genetic covariation, population divergence and selection

147 We estimated the posterior distribution of the Among-Population (D) and

148 additive genetic (G) covariance matrices by using a Bayesian multivariate mixed

149 model implemented in the R package MCMCglmm (Hadfield, 2019). Population and

150 Line (nested within Population) were included as fixed and random factors,

151 respectively. The analysis was run for 105 generations with 50% burn-in, after

152 which we extracted 500 samples. Convergence was verified using trace plots for all

153 parameters.

154 The covariance matrices for the fixed effect Population and random effect

155 Line were considered as the Among-Population (D) and the genetic (G) covariance

156 matrices, respectively. Each sex-trait combination was treated as a different trait

157 (Sztepanacz & Houle, 2019) resulting in 10 traits for each level of comparison. Thus,

158 G and D are composed of four submatrices representing both patterns of integration

159 and divergence between and within sexes as follows

� � 160 � = � �

161 where � is the full covariance matrix, �� and � are the male and female specific

162 covariance submatrices, and � and � are the between-sex covariance

163 submatrices, with t denoting a transpose. For G, the � submatrix is also called B,

164 and encodes the pleiotropic trait association and constraint between sexes.

165 To evaluate the potential effect of selection in defining between-populations

166 patterns of divergence, we calculated the Ω matrix, which expresses the covariance

167 pattern among peaks on the realized adaptive landscape for these populations. Ω is

168 calculated as Ω = ��(Felsenstein, 1988; Marroig, & Cheverud, 2010), which

169 assumes that all populations share a single and unique common ancestral

170 population. Ω was calculated for each sample of the posterior distribution,

171 producing a total of 500 matrices.

172 Because all matrices were calculated on a reduced morphospace comprised

173 of only five PCs, for each sample of the posterior we back-projected all matrices on

174 the original image space to obtain the landmark variation associate with G, D and Ω.

175 The covariance matrix Z on the original space can be obtained as follows � =

176 �� where � is either G, D or Ω calculated on the first five PCs (as above), V is a

177 matrix of the leading five eigenvectors. These back-projected matrices were then

178 used on our modularity analysis.

179

180 Sexual dimorphism in the sampled populations

181 To determine whether wing’s shape changes are sex-specific, we calculated

182 the magnitude (Procrustes distance) and alignment (vector correlation) of between-

183 sex shape differences for each sample of the posterior distribution of the fixed effect

184 Population. For each population, the magnitude of the average differentiation

185 between males and females in Procrustes distance was plotted against the Fisher z

186 transformed vector correlation between males and females. A positive correlation

187 between these variables is indicative that between sexes differences are

188 accumulating along the same lines of divergence. A negative correlation would

189 indicate that sexes tend to diverge in opposite directions, independently from each

190 other.

191

192 Modularity analyses

193 Muñoz-Muñoz et al. (2016) found evidence supporting the hypothesis of

194 wing modularity along the PD axis in D. melanogaster. However, locating the

195 boundaries between the modules is hard given that development is principally

196 characterized by variations in terms of gradients (Matamoro-Vidal, Salazar-Ciudad,

197 & Houle, 2015). Muñoz-Muñoz et al. (2016) also discuss the role of the wing hinge

198 contraction during late pupal development as an important process potentially

199 impacting the covariance structure and thereby on the PD modularity. However,

200 given that hinge contraction occurs while cells at the margin of the blade are

201 attached to the surrounding pupal cuticle (see Figure 5 in Matamoro-Vidal et al.,

202 2015), it is possible that the proximal portion of the blade covariate more with the

203 hinge than with the distal portion of the blade. Thus, whether to include landmarks

204 8 and 9 (following Muñoz-Muñoz et al., 2016) in one module or another is somewhat

205 arbitrary. Therefore, in this study, we tested two hypotheses for the PD axis that

206 only vary in assigning those landmarks to either the blade or the base. Hypothesis A

207 of PD modularity includes landmarks 8 and 9 in the wing base module (Figure 1A)

208 while hypothesis B includes those landmarks in the wing blade module as Muñoz-

209 Muñoz et al. (2016; Figure 1B). For comparative purposes, we also tested the

210 classical hypothesis in which the wing is divided into an anterior and a posterior

211 compartment following Klingenberg (2009; also see Klingenberg & Zaklan, 2000;

212 Figure 1C).

213 The hypotheses of modular organization of the wing were evaluated using

214 the covariance ratio effect sizes (ZCR) statistic (Adams & Collyer, 2019), which was

215 shown to be robust even for small sample sizes. ZCR is a function of the covariance

216 ratio statistic (CR) which measures the intensity of association between modules in

217 relation to the intensity of trait association within a module as measured by the

218 covariance among those traits (Adams, 2016). ZCR is then calculated as a

219 transformation of the observed CR value (CRobs) as follows

�� − �̂ 220 � = �

221 were � and � are, respectively, the average and standard deviation of CR under the

222 null distribution of no modularity, and are calculated by shuffling matrices rows and

223 columns 1000 times. For each matrix type, ZCR was calculated for each sample of the

224 posterior, producing 1000 values for each matrix. More negative values of ZCR

225 indicates a more modular structure, and positive values indicate less modular

226 structures. Because ZCR was proposed to compare the modularity signal among

227 different datasets, here we use this statistic to both select the best modularity

228 hypothesis for G (Figure 1), and to compare modularity signal among G, D and Ω.

229 For G, the best hypothesis was chosen as the one with the lower ZCR. The best genetic

230 hypothesis was then used to calculate modularity signals on the other levels of

231 analysis. For all matrices, ZCR was calculated for each MCMC sample, resulting in a

232 posterior distribution of modularity signal values.

233

234 Figure 1. Digitized landmarks showing the three hypotheses tested for wing 235 modularity. Landmarks of the same color belong to the same module. 236

237 Multivariate FSTq –FST comparisons

238 To evaluate if phenotypes were evolving due to selection or drift we

239 employed Chenoweth & Blows (2008) multivariate generalization of the QST-FST

240 test called FSTq–FST. FSTq is a matricial transformation analogous to the calculation

241 of the univariate Qst and can be obtained as follows:

242 �� = [� + 2�]/ � [� + 2�]/ (1)

243 Under neutrality, the ith eigenvalue of FSTq (λi) is expected to be equal to FST

244 estimates. Eigenvalues that are larger than FST are thought to be generated by

245 directional selection, and values that are lower are thought to be generated by

246 stabilizing selection (Chenoweth & Blows, 2008). In this way, we can evaluate how

247 directions of divergence were differentially affected by selection.

248 One common issue in multivariate systems is the overestimation of the

249 leading eigenvalues due to sampling error (Marroig et al., 2012). Thus, using FST as

250 the sole parameter as the null-expectation for λi can be considered unrealistic. Here

251 we circumvent this issue by producing a distribution of expected eigenvalues using

252 simulations, and confronting them against the observed ones. This was done by

253 obtaining the expected patterns of between-population divergence E(D) as follows

254 �(�) = � (2)

255 (Martin, Chapuis, & Goudet, 2008) using the Gs from the posterior sample as a

256 starting point. To simulate population divergence and to account for sampling error,

257 we generated 12 observations for each E(D) and calculated the covariance among

258 those values. This new covariance matrix of divergence was then used in equation

259 (1) to calculate an empirical distribution of FSTq under drift. This was performed

260 10000 times by randomly drawing Gs from the posterior. Each simulated FSTq was

261 subjected to an eigendecomposition and their eigenvalues were used as a null

262 expectation for each dimension of the empirical FSTq. The null distribution was then

263 confronted against the observed values, both for individual axes (λi) as well as for

264 the average eigenvalue (�̅) as a global test of the adaptive hypothesis. This analysis

265 was performed for the full G and for Gf and Gm separately. Additionally, we

266 computed the amount of between population divergence for each sample of the

267 posterior distribution of FSTq as the sum of the variances for males and females

268 specific traits individually. This produces a measure of how much each sex diverged

269 from the ancestral population. We also conducted QST–FST comparisons using the R

270 package driftsel (Karhunen, Merilä, Leinonen, Cano, & Ovaskainen, 2013).

271 The coancestry coefficient (FST) for each pair of populations were estimated

272 by using data from eight microsatellite markers previously genotyped (Iglesias et

273 al., 2018a) and the bayesian R package RAFM (Karhunen, 2012). Analyses were run

274 for 106 generations with a 50% burn-in, resulting in 500 posterior distribution

275 samples.

276

277 Cross-sex (co)variances and the response to selection

278 Finally, we investigated how cross-sex genetic covariances may constrain or

279 facilitate the response to selection combining the multivariate breeder’s equation,

280 the random skewers method and the R metric (Sztepanacz & Houle, 2019). The

281 multivariate breeder’s equation (Lande, 1979) can be written to includes

282 differences in selection and inheritance in the two sexes as follows:

∆�̅ � � � 283 = (3) ∆�̅ � � �

284 where ∆� and � are the selection responses and selection gradients, respectively, in

285 males (m) and females (f). By generating a large number of random selection

286 gradients and applying to our G estimates we can evaluate how those populations

287 will evolve under natural selection (Cheverud & Marroig, 2007). To investigate the

288 constraining effect of between-sexes covariances (B), one can confront the

289 magnitude of evolution (the norm of ∆�) between a G with and without between sex

290 covariances (B is set to a matrix of zeroes; Sztepanacz & Houle, 2019). If the ratio

291 between both magnitudes (the R metric) is less than one, then the cross-sex genetic

292 covariances can constraining the response to selection, whereas R>1 suggests they

293 are facilitating the response to selection. We calculated R by drawing a vector from

294 a spherical multivariate normal distribution and applying it to a randomly drawn G

295 matrix from our posterior distribution. This was performed 10000 times, producing

296 a distribution of Rs. Complementarily, we also calculated the R statistic for the

297 empirical divergence between populations. We did that by rearranging equation 3

298 to estimate �s from ∆� drawn from our posterior distribution of fixed effects. We

299 then used these empirically derived �s to calculate a distribution of empirical R

300 values as above.

301

302 Results

303 Sex-specific wing shape in the sampled populations

304 The inspection of the relation between magnitude (Procrustes distance) and

305 alignment (z-transformed vector correlation) of between sex divergence for each of

306 the 12 populations of D. buzzatii show a negative association (Figure 2A). The

307 distribution of correlation among alignment and magnitude showed a 95% highest

308 density interval that was entirely negative, with a median correlation of -0.629

309 (Figure 2B). This result suggests that higher sexual dimorphism was achieved when

310 males and females evolved in different directions instead of in the same direction.

A. B. 1.0 0.99

0.95 0.5

0.7 0.0 cor 0

−0.5 Vector correlation Vector −0.7

−0.95 −1.0 0.000 0.005 0.010 0.015 0.751.001.25 Procrustes distance x 311

312 Figure 2. Magnitude (Procrustes distance) and alignment (vector correlation) of 313 between-sexes shape differentiation among the 12 populations of D. buzzatii. A. 314 Representation of between-sex divergence and alignment obtained from the 315 posterior distribution of fixed effects. The Y axis is shown in a fisher-z transformed 316 scale. B. Distribution of Pearson-correlation values between Procrustes distance 317 and fisher-z transformed vector correlation for all samples of the posterior 318 distribution. Horizontal solid lines inside the distribution highlights the 95% higher 319 density intervals. 320

321 Patterns of phenotypic integration and modularity

322 The analysis of the modularity signal through ZCR on G suggests that our PD

323 hypothesis A (HA) is the best one for both males and females (Figure 3A). For HA, all

324 ZCR from the posterior distribution were significantly different from 0 for both sexes

325 (p<0.001), with values being generally lower than -2. While the hypothesis B (HB)

326 also showed values that were generally significant, the intensity of modularity signal

327 was weaker, with values closer to zero (no modularity). Lastly, the classical antero-

328 posterior hypothesis C (HC) showed values that were consistently non-significant

329 (p>0.10) and were larger than zero.

330 The comparison of the modularity signal for HA on D and Ω showed different

331 intensities of between-module integration as measured by ZCR (Figure 3B). While D

332 appears to behave in a similar way to G, with the whole distribution of ZCR falling

333 below the zero line for both sexes, Ω shows weaker modularity signal, with

334 distributions that overlaps zero. This suggests that, while population divergence

335 followed the genetic modularity pattern, the adaptive landscape was not aligned

336 with those same patterns. The investigation of others modularity hypothesis (HB and

337 HC) for D and Ω showed distributions that overlap or were superior to zero,

338 respectively, suggesting that evolution and selection did not fit those hypotheses

339 either.

A. Female Male 2

0 R C Z −2

HA HB HC HA HB HC

B. D Ω

0 −1 R C

Z −2 −3 −4 Female Male Female Male

340

341 Figure 3. A. The posterior distributions of the ZCR statistic for modular hypothesis 342 comparisons. A. Comparison among the three different modularity hypotheses (see 343 Figure 1) on the G matrix for each sex separately. B. Posterior distribution of the 344 modularity signal for D and Ω discriminated by sex. 345

346 Sex-biased selection of wing morphology

347 For the multivariate FSTq–FST analysis of both sexes combined, the

348 posterior distribution of the average eigenvalues of FSTq (�) was higher than the

349 simulated ones under drift, suggesting a pervasive action of directional selection in

350 the among-population differentiation (Figure 4A). This was also true for individual

351 axes of variation, especially the leading six axes (λ1-6), which showed no overlap

352 between the 95% highest density intervals and the null-expectation. Inspection of

353 the between-population divergence shows that males have diverged proportionally

354 more than females (Figure 4B). On the analysis of the females and males separately,

355 both sexes rejected the null-hypothesis of drift on the global test (�̅), but only the

356 leading axis of females had eigenvalues superior to the expected under drift (λ1),

357 while males had at least three axes that could be reliably assigned to directional

358 selection (λ1-3; Figure 5).

359 An inspection of the shape changes associated with each axis of directional

360 selection on the combined FSTq analysis reveal that males and females are evolving

361 in different directions, a fact that is particularly true for the three leading

362 eigenvectors, in which the correlation between the male and female-specific shape

363 change are inferior to 0.58 (Figure 4C). This suggests that the leading axes of

364 population differentiation represents a misalignment between phenotypic changes

365 of males and females.

366 The driftsel analysis showed that, irrespective of the dataset (sexes combined

367 or males and females separately), the resulting statistics S was always 1, reinforcing

368 the pervasive role of directional selection shaping the wing-shape divergence.

369

370

371 372 Figure 4. Tests of adaptive evolution using the eigenvalue decomposition of 373 the FSTq matrix combining D. buzzatii male and female wing traits. A. Posterior 374 distribution of eigenvalues of the FSTq matrix. Bars indicate posterior median and 375 95% highest density intervals for multivariate (λ) and univariate (λi) genetic 376 differentiation among populations. Dashed lines indicate upper and lower 95% 377 highest density limits for the estimated FSTq under neutrality. B. Posterior 378 distribution for the between-population amount of divergence. C. Representation of 379 the leading six eigenvectors of the median FSTq matrix as shape deformations from 380 the consensus configurations. Shapes are depicted as positive deviations from the 381 consensus, and were amplified 20 times for visualization. Colors represent the 382 amount of shape difference interpolated through a thin-plate spline. Lines represent 383 the wing venation pattern. Correlation values are the vector correlation between 384 female and male vectors. 385

386

females males

0.3

0.2 FSTq

0.1

0.0

λ λ1 λ2 λ3 λ4 λ5 λ λ1 λ2 λ3 λ4 λ5 387

388 Figure 5. Tests of adaptive evolution for D. buzzatii wing traits using the 389 eigenvalue decomposition of the FSTq matrix analyzing sexes separately. Bars 390 indicate posterior median and 95% highest density intervals for multivariate (λ) 391 and univariate (λi) genetic differentiation among populations. Dashed lines indicate 392 upper and lower 95% highest density limits for the estimated FSTq under neutrality. 393

394 Effect of cross-sex (co)variances on the response to selection

395 The posterior distributions of G show that the intersexual correlations for

396 the five PCs (rmf) were moderate to high. The 95% highest density intervals for rmf

397 values ranged from 0.502 to 0.763, suggesting a moderate to a high degree of

398 between-sex pleiotropy on wing traits.

399 The R statistics based on the simulated random selection gradients shows a

400 distribution that is centered on 1, and spreads evenly towards high and low values

401 (Figure 6), suggesting that cross-sex covariances can either constrain or facilitate

402 the evolution of D. buzzattii wing. On the other hand, the R values calculated based

403 on empirically informed �s shows a distribution that is highly skewed to lower

404 values (Figure 6), indicating that the observed populational differences occurred

405 along directions that were in fact constrained.

406

density 1

0 0.5 1.0 1.5 R 407 408 Figure 6. The R metric of random (gray distribution) and empirically informed 409 (purple distribution) selection gradients. Dashed line indicates R=1, in which there 410 is no constraining effect of between-sexes correlation. 411

412 Discussion

413 We examined wing morphological changes in D. buzzatii in terms of

414 constraints and evolutionary flexibility. We found that the wing blade and the wing

415 base appear to be valid modules at the genetic level, providing developmental

416 flexibility and phenotypic variation. However, reconstruction of between-

417 population adaptive landscapes shows selection as an integrative force.

418 Investigation of population's morphological divergence suggests that each sex is

419 diverging independently from each other, with males diverging more than females.

420 As expected when sex-specific selection acts on a common genetic machinery

421 (Bonduriansky & Chenoweth, 2009; Cox & Calsbeek, 2009), we found that

422 populations are evolving in directions that are constrained by G, highlighting the

423 restrictive role of between-sex pleiotropy in the evolution of sexually dimorphic

424 traits.

425 Our multivariate QST analysis (FSTq) showed a clear signal of directional

426 selection on wing shape, and that divergence was mostly concentrated on males

427 rather than females. The leading eigenvectors of FSTq, which are the axes of

428 morphological variation most affected by directional selection, show moderate

429 correlations between the directions of divergence between sexes, suggesting that

430 males and females are evolving somewhat independently. Evaluations of between-

431 population divergence show that greater divergence between sexes is usual. The

432 FSTq analyses of each sex separately revealed three morphological axes under

433 directional selection in males, but only one axis in females. This asymmetry suggests

434 a wider array of functional demand on male wings than on female's, which agrees

435 with the double function of wings in males, like flight and courtship song production.

436 Iglesias et al. (2018a) showed that males of these same sampled populations have

437 divergent courtship songs, which they produce with their wings. These authors

438 found evidence consistent with the role of directional selection in the divergence of

439 courtship song traits, which could be associated with wing shape changes shown

440 here. This male-specific selection on the wing for song production could also explain

441 the asymmetry found in the number of axes under selection between male and

442 female D. buzzatii. In summary, our results suggest that male wing shape is probably

443 a target for multiple selective pressures, which lead to this sex diverging more than

444 females in their phenotype.

445 The results of the evolutionary simulations showed that cross-sex

446 covariances reduced the predicted response to selection in the direction of the

447 extant sexual dimorphism, in line with previous results in D. melanogaster's wing

448 (Sztepanacz & Houle, 2019). However, while in D. melanogaster the predicted

449 response to selection in random directions is also reduced, in D. buzzatii responses

450 can be either reduced or augmented. This discrepancy may reflect the lower and

451 more variable intersexual correlations (rmf) found in the wing of D. buzzatii (rmf =

452 0.502-0.763), relative to values observed in D. melanogaster's wing (rmf = 0.907-

453 0.940; Sztepanacz & Houle, 2019). Although lower than D. melanogaster, rmf values

454 found here were higher than, for instance, rmf values found in the brightly colored

455 dewlap of Anolis lizards (rmf = 0.39–0.41; Cox, Costello, Camber, & McGlothlin,

456 2017). In Anolis lizards, cross-sex covariances (B) had little effect on the predicted

457 response to selection either in random directions and in the direction of sexual

458 dimorphism (Cox et al., 2017), reinforcing the idea that the intensity of correlations

459 determines if populations will be constrained by B.

460 However, evaluation of between-population divergence of D. buzzatii shows

461 that sexual dimorphism is greater when sex evolve in different directions,

462 suggesting that selection along constrained directions had to be intensified in order

463 to circumvent the limitations imposed by G (Machado, 2020), a fact that could have

464 an indirect effect on the structure of G itself (Melo & Marroig, 2015). Theory shows

465 that sexually antagonistic selection will favor a reduction in cross-sex genetic

466 covariance when the strength of selection is highly asymmetric between the sexes

467 (McGlodhlin et al., 2019). Otherwise, sexually antagonistic selection will tend to

468 maintain strong cross-sex genetic covariance when the strength of selection is

469 similar in each sex (McGlodhlin et al., 2019). If that holds for the populations

470 investigated here, that means that the differences in rmf values observed between D.

471 buzzatii and D. melanogaster can be a consequence of different intensities and

472 directions of sexual selection, with D. buzzatii being subjected to stronger sexual

473 selection than D. melanogaster. Although both species rely on acoustic signals for

474 mating success, the use of courtship song's playbacks with wingless males does not

475 reach the mating success of winged males in D. melanogaster (Rybak, Sureau, &

476 Aubin, 2002), while they do in D. buzzatii (Iglesias & Hasson, 2017). This suggests

477 that song preference is more relevant for the reproductive success of D. buzzatii

478 males, and that the strength of selection related to female preference for courtship

479 songs is stronger in this species than in D. melanogaster. Taken together, these

480 results suggest that D. buzzatii is under stronger sexual antagonistic selection than

481 D. melanogaster, leading not only to differences in patterns of divergence, but also

482 having an impact on the genetic architecture of wing traits.

483 One possible genetic mechanism behind the decoupling of phenotypes

484 among sexes could be related to the frequency of inversions in D. buzzati. A previous

485 study in this species showed that inversion arrangements and the intensity of

486 selection for adult viability on body size (a trait correlated with wing length and

487 width; Norry, Vilarde, Fernandez Iriarte, & Hasson, 1997) are sex-specific

488 (Rodriguez, Fanara, & Hasson, 1999). Changes in inversion frequencies after

489 selection for adult viability (longevity) are concordant with expectations based on

490 the average effect of inversions on body size and the intensity of selection on body

491 size (Rodriguez et al. 1999). Moreover, changes in inversion frequencies are known

492 to be under intense selection in D. buzzatti on a macrogeografic scale (Hasson,

493 Rodriguez, Fanara, Reig, & Fontdevila, 1995; Rodriguez, Piccinali, Levi, & Hasson,

494 2000; Soto et al 2010). Given the differential frequency between the sexes, its

495 relevance in trait determination, and the available evidence of natural selection, the

496 patterns of variation in the inversion polymorphism may provide the genetic basis

497 of sex-specific selection on morphological traits.

498 However, if selection was strong enough to overcome pleiotropic effects

499 between sexes, why was it unable to change the modularity pattern of the wing into

500 an integrated one, as expressed in the adaptive landscape? Studies of the association

501 among traits in D. melanogaster wings suggests that, while it is possible to change

502 trait integration through selection, these changes are not permanent and quickly

503 revert to their ancestral patterns once selection is suspended (Bolstad et al., 2015).

504 This suggests that internal stabilizing selection produced by the relation between

505 ontogenetic pathways is an important component in maintaining integration

506 patterns in complex traits, as it weeds away sub-optimal trait combinations due to

507 maladaptive pleiotropic effects (Cheverud, 1988). However, changing between-sex

508 integration does not necessarily result in (maladaptive) changes in the ontogenetic

509 program, as phenotypes are still produced following the same general ontogenetic

510 rules. If this argument holds, then it might explain why sexual dimorphism is more

511 frequent than restructuration of integration patterns in complex phenotypes (Porto

512 et al., 2009), even though both could be seen as instances of selection changing

513 pleiotropic effects (Melo & Marroig, 2015).

514 While here we focused on the possible action of sexual selection in explaining

515 between-sex divergence, it is possible that natural selection on wing shape,

516 specifically through its relation to flight, could produce the observed differences as

517 well. Wing shape is known to affect flight performance in Drosophila (Chin &

518 Lentink, 2016; Ray, Nakata, Henningsson, & Bomphrey, 2016), and males are known

519 to be the more dispersive sex in various species (Begon, 1976; Powell et al., 1976;

520 Fontdevila & Carson, 1978; Markow & Castrezana, 2000; Mishra et al., 2020). Sex

521 bias in dispersal is affected by many factors and interactions such as predispersal

522 context, mate shortage, and availability of resources, which might vary among

523 populations and exert different selective pressures (Mishra et al., 2018; Tung et al.,

524 2018). Therefore, it is possible that natural selection for aerodynamics could

525 produce between-sex coordinated and uncoordinated evolution. This is consistent

526 with our FSTq analysis, which showed a mixture of aligned and non-aligned changes

527 between sexes. Despite this, previous investigations into the evolution of sexually

528 dimorphic traits in Drosophila have suggested that sexual selection has a stronger

529 capacity to produce between-sex divergence (Chenoweth et al., 2008), suggesting

530 that, even if aerodynamics affect the evolution of sexual dimorphism, it is more likely

531 obfuscated by the role of sexual selection.

532 In short, the evolution of D. buzzatii wing shape seems to be the product of a

533 complex interplay between ontogenetic constraints, conflicting sexual and natural

534 selections, and presents a natural experiment for the evolution of sexual

535 dimorphism on complex morphologies. Future studies on the causal links between

536 wing morphology, song production and aerodynamics of D. buzzatii are needed to

537 provide us with a better picture of how wings and cross-sex covariances can

538 indirectly evolve as a by-product of selection on mating success and biomechanical

539 performance.

540

541 Acknowledgements

542 We sincerely thank Gladys Hermida for allowed access to its laboratory facilities for

543 the production of the photographs used in this study. We also thank Consejo

544 Nacional de Investigaciones Científicas y Técnicas (CONICET), ANPCyT (PICT 2013-

545 1121 and PICT-2019-2554) and NSF (DEB 1350474 and DEB 1942717 to L. Revell)

546 for financial support.

547

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703 Data Archiving Statement

704 The data that support the findings of this study will be openly available upon

705 acceptance on Dryad (http://datadryad.org/) or Github (https://github.com/).

706 707 Author Contributions 708 P.P.I., F.A.M. and E.M.S. conceived the project; E.H. supervised the global project in

709 which the present work is embedded and contributed materials and reagents; E.M.S.

710 performed wing dissections; S.L. took the photographs; P.P.I. and F.A.M. conducted

711 analyses; P.P.I. and F.A.M. wrote the original draft, which was discussed, edited and

712 revised by all authors.