ORE Open Research Exeter

TITLE Macronutrient balance mediates the growth of sexually selected weapons but not genitalia in male broad horned

AUTHORS House, Clarissa M; Jensen, K; Rapkin, J; et al.

JOURNAL Functional Ecology

DEPOSITED IN ORE 15 January 2016

This version available at

http://hdl.handle.net/10871/25793

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A NOTE ON VERSIONS

The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication 1 Macronutrient balance mediates the growth of sexually selected weapons

2 but not genitalia in male broad horned beetles

3 Clarissa M. House1*, Kim Jensen1,2, James Rapkin1, Sarah Lane1, Kensuke Okada3, David J. 4 Hosken1 and John Hunt1

5 1. Centre for Ecology and Conservation, College of Life & Environmental Sciences, 6 University of Exeter, Penryn Campus, Cornwall, TR10 9EZ, UK. 7 2. Department of Entomology, North Carolina State University, Gardner Hall, Raleigh, 8 NC 27695-7613, USA. 9 3. Laboratory of Evolutionary Ecology, Graduate School of Environmental Science, 10 Okayama University, Tsushima-naka 1-1-1, Okayama, Japan.

11 *correspondence: E-mail: [email protected]

12

13 14 Running title: Macronutrients, weapons and genital traits

15 16 17 Summary

18 19 1. Condition is defined as the pool of resources available to an individual and can be 20 allocated to fitness-enhancing traits. Consequently, condition could influence 21 developmental trade-offs if any occur. Although many studies have manipulated diet to 22 demonstrate condition-dependent trait expression, few studies have determined the 23 contribution of specific nutrients that determine condition and mediate trade-offs. This can 24 occur if life history and morphological traits have different resource demands during 25 juvenile development. 26 27 2. We used nutritional geometry to quantify the effects of dietary protein and carbohydrate 28 content on larval performance and the development of adult morphology including body 29 size as well as primary and secondary sexually selected traits in male broad horned beetles, 30 Gnatocerus cornutus. 31 32 3. We found that development rate and morphological traits were highly affected by dietary 33 carbohydrate content and to a lesser extent by protein content, and that all traits were 34 maximized at a protein to carbohydrate ratio around 1:2. After controlling for body size, the 35 size of the mandibles responded to the increased availability and ratio of both 36 macronutrients with a proportionally greater allocation of resources to the mandibles. Male 37 genitalia, in contrast, were relatively insensitive to the increased availability of 38 macronutrients and varied proportionally with body size. 39 40 4. Overall, while nutrition influenced trait expression, the nutritional requirements of 41 development rate and morphological traits were largely complementary and resource 42 acquisition seems unlikely to implement trade-offs in this . 43 44 5. This finding contrasts with some resource constraint predictions, as beetles are able to

45 simultaneously meet the nutritional requirements of most traits. 46 47 48 Key-words: Condition-dependence, genitalia, larval diet, nutritional geometry, sexual 49 selection and weapons. 50

51 Introduction

52 Condition can be defined as the pool of resources that an organism has available to 53 allocate to life history and reproductive traits (Rowe & Houle 1996). As such, resource 54 acquisition and assimilation are important determinants of condition (Tomkins et al. 2004) 55 and all organisms face allocation trade-offs as they balance their limited resources between 56 competing demands. Although condition is notoriously difficult to measure (Blanckenhorn & 57 Hosken 2003; Cotton, Fowler, & Pomiankowski 2004; Hunt et al. 2004; Tomkins et al. 2004), 58 many traits are nonetheless condition dependent and sexually selected characters are 59 expected to be especially sensitive to variation in condition (Cotton et al. 2004; Tomkins et al. 60 2004; Rowe & Houle 1996).

61 Sexually selected traits are likely to be especially sensitive to allocation trade-offs as 62 these characters are costly to produce, play no role in resource acquisition, and reduce the 63 resources that can be allocated to other fitness-enhancing traits (Rowe & Houle 1996; 64 Tomkins et al. 2004; Wagner Jr et al. 2012). However, not all sexually selected traits are 65 sensitive to condition, with genitals being the prime example. Male genitalia are primarily 66 subjected to sexual selection (Eberhard 1985; Hosken & Stockley 2004; House et al. 2013), 67 but are frequently less sensitive to variation in condition than other sexually selected 68 characters (House & Simmons 2007). This seems to be a general pattern in invertebrates 69 where the plasticity in genital morphology in response to condition is typically limited 70 (Simmons 2013). Some evidence suggests that this may arise due to strong stabilizing sexual 71 selection that is imposed by female mate choice for reproductive isolation at one extreme 72 of the continuum or cryptic female at the other end (Eberhard 1985; McPeek et al. 2008; 73 Simmons 2013).

74 To a large extent, traditional tests of condition-dependent trait expression have 75 assumed that maximize the quantity or caloric content of food which is then 76 available to allocate between competing fitness components (quantitative resource 77 constraints) (Cotter, Simpson & Raubenheimer 2011; Cotton et al. 2003). As a result, 78 manipulation of condition usually involves the calorific content or quantity of food. An 79 alternative possibility is that the nutritional composition of available foods is of paramount 80 importance in building phenotypes (qualitative resource constraints) (Cotter et al. 2011; 81 Morehouse et al. 2010) and indeed, this has been found to be important in a number of 82 invertebrate models. For example, the expression of sexually selected traits in the field 83 cricket, Teleogryllus commodus (Maklakov et al. 2008), the cockroach, Nauphoeta cinerea 84 (South et al. 2011) and the neriid fly, Telostylinus angusticollis (Sentinella, Crean & 85 Bonduriansky 2013), are all affected by the specific balance of nutrients that males ingest. A 86 secondary consequence of the importance of the nutritional composition of food, is that 87 usually trade-offs will be fixed at the point of ingestion rather than later in the resource 88 gain-use chain (Cotter et al. 2011). This occurs when the nutritional requirements of 89 different traits vary, so that the optimal diet for one set of traits is not the same as that 90 optimising the expression of another (Cotter et al. 2011). Thus, a more complete 91 understanding of the consequences of dietary intake, and therefore condition, requires 92 estimation of not just the caloric content of food but also the combined effect of specific 93 nutrients for trait expression (Morehouse et al. 2010).

94 Nutritional Geometry (NG) provides a powerful approach to alter condition by 95 manipulating the levels of two vital macronutrients in the diet (South et al. 2011). Using this 96 approach, the ratio of macronutrients is fixed along a series of ‘nutritional rails’ but the 97 concentration of macronutrients vary along each rail (i.e. the total calorie content varies 98 from low to high). This allows the effects of total nutrition and the individual effects of 99 specific macronutrients to be partitioned using response surface methodologies and the 100 effects to be visualized as nutritional landscapes (Lee et al. 2008; Maklakov et al. 2008; 101 Simpson & Raubenheimer 2009; South et al. 2011). Research using the NG approach has 102 primarily focussed on investigations to understand the relationship between nutrition, 103 lifespan and reproduction (Archer et al. 2009; Simpson & Raubenheimer 2009). Fewer 104 studies have used a NG approach to investigate the link between condition and trait 105 elaboration. However, those studies that have taken this approach show that specific 106 nutrient combinations influence condition-dependent trait expression and fitness (Maklakov 107 et al. 2008; South et al. 2011; Sentinella et al. 2013). Thus, NG is a valuable tool to explore 108 condition dependent trait expression, especially for characters like secondary sexual traits 109 that are expected to demonstrate heightened condition-dependence. 110 The horned flour , Gnatocerus cornutus, feeds on a variety of grains, flours, 111 yeast and dry products and is known to respond plastically to changes in the larval 112 diet (Katsuki, Okada, & Okada 2012). On poor-quality larval diets, development is delayed 113 and the size of a secondary sexual trait, the mandible, is reduced (Katsuki et al. 2012). Male 114 mandibles are sexually selected (Okada, Miyanoshita & Miyatake 2006; Harano et al. 2010), 115 with large mandibles conferring an advantage in male-male competition and access to 116 mates (Harano et al. 2010; Okada et al. 2014). There is also evidence that the expression of 117 the mandibles trades off with other cephalic characters, such as the head horns, antenna 118 and eyes (Okada & Miyatake 2009). The negative genetic correlations between the 119 mandibles and these traits following artificial selection for increased mandible length may 120 have arisen due to resource competition (Okada & Miyatake 2009), but it is unknown if or 121 how genetic trade-offs between traits are influenced by nutrition. Sexual selection also acts 122 on aedeagus morphology in G. cornutus through a male’s ability to engage the female in 123 copula and inseminate and fertilize a female’s ovum following a non-competitive mating 124 (CMH unpublished data). It is currently unknown whether the trade-offs identified between 125 other characters extend to aedeagus morphology in this species.

126 A standard experimental approach to assess the condition-dependence of a sexual 127 trait is to compare the relative condition-dependence of a sexual trait(s) with one or more 128 nonsexual traits (Cotton et al. 2003). Here we used NG to determine the effects of protein 129 (P) and carbohydrate (C) as well as the effects of total nutritional content on the survival, 130 development time, body size and the expression of male primary (the aedeagus) and 131 secondary (the mandibles) sexual traits, as well as a non-sexual trait (the eye) in G. cornutus. 132 Although male mandibles are condition-dependent it is not clear if this relates to specific 133 nutrients or to total calories (Katsuki et al. 2012). It is also unknown whether the genitalia of 134 G. cornutus are impacted by nutrient consumption. First, we test whether the availability of 135 P and C during juvenile development influences survival, development time and body size, 136 as expected if condition is influenced by nutrition. Next, we test whether the expression of 137 condition-dependent mandibles have heightened sensitivity to nutrition whereas the 138 genitalia and a non-sexual trait are relatively insensitive to nutrition. Finally, we test 139 whether trade-offs among life history traits and/or primary and secondary sexual traits are 140 mediated by nutrition as these different traits are likely to have different optimal nutritional 141 requirements. 142

143

144 Materials and methods

145 STOCK POPULATIONS

146 Stock populations of G. cornutus were derived from the Japanese National Food Research 147 Institute, which has maintained cultures of G. cornutus for more than 50 years. In our 148 laboratory, mixed sex populations (n = 50 individuals per population) have been maintained 149 in pots (Thermoscientific Nalgene 500mL, 120mm OD) and reared on whole meal flour that 150 is enriched with 5% yeast and maintained at 27oC and 60% relative humidity under a 14:10 h 151 light:dark cycle for 2 years. At each generation, final instar larvae are randomly removed 152 from each stock population (n = 18) and mixed at random with larvae from all other 153 populations to maintain gene flow between the populations. These final instar larvae are 154 then placed in 24-well plates as pupation is inhibited at high larval density (Tsuda & Yoshida 155 1985). At eclosion, 25 male and 25 female adults (per pot) are randomly selected to form 156 the parents for the next generation.

157 EXPERIMENTAL DIETS

158 24 dry, artificial diets that varied in protein and carbohydrate content were produced 159 following the protocol established in Simpson and Abisgold (1985), and represent the same 160 diets used in South et al. (2011) and Bunning et al. (2015). The exact composition of each 161 diet is shown in Table S1 and the placement of the diets in nutritional space is shown in 162 Figure S1.

163 EXPERIMENTAL ANIMALS AND DESIGN 164 To obtain adults for the present experiment, final instar larvae were randomly collected 165 from 18 stock population pots and individually placed in a single cell (1.5cm x 1cm) of a 24 166 well plate (n = 42, 24-well plates). Pupae were checked daily for eclosion and eclosed adults 167 were separated into single sex, 24 well plates and provided with approximately 125 mg of 168 whole meal flour (Doves Farms Foods Ltd) that was enriched with 5% brewers yeast (ACROS 169 organics). Between 7 to 15 days post-eclosion, virgin males and females were collected and 170 randomly allocated to one of four mixed sex pots (Thermoscientific Nalgene 500mL, 120mm 171 OD; n = 125 females; n = 125 males) containing wholemeal flour (250 g) that was enriched 172 with 5% brewers yeast. After 7 days, mated females were transferred to one of four new 173 pots (Thermoscientific Nalgene 500mL, 120mm OD; n = 125 females) containing 250 g of 174 plain flour (Doves Farms Foods Ltd) for 24 hrs to oviposit. The duration of oviposition was 175 restricted to a single day to ensure that all resultant offspring were of the same age and 176 plain flour was used as 2nd instar larvae (that are approximately 1mm in length) are easier to 177 see against a white background. On the 14th day, 2nd instar larvae were separated from the 178 flour using a sieve. A fine paint brush was used to transfer the larvae from the sieve to 179 individual cells of a 24 well plate, each containing 125mg of one of the 24 artificial diets. All 180 cells of a given 24 well plate contained a single diet and a total of 96 replicate individuals 181 were allocated at random to one of the 24 diets (i.e. 4 x 24 well plates/per diet). 182 Larvae were checked daily for evidence of activity (i.e. burrows in the diet) and fed 183 an additional 125 mg of diet every second day of the experiment. If there was no activity 184 detected or when most of the pupae in a given 24 well plate had eclosed, the diet was 185 removed and sieved to locate any living larvae. Survivors were returned to a clean 24 well 186 plate with the appropriate diet until they either died or eclosed, which we scored as 0 or 1, 187 respectively. The dates of all eclosions were recorded and development time was calculated 188 as the time between larval being established on experimental diets (14 days of age) and 189 eclosion. 190 191 MEASURING MORPHOLOGICAL TRAITS 192 To measure adult morphology, beetles were placed on a slide on the stage of a dissecting 193 microscope (Leica M125) that was consistently oriented. A circular coverslip was gently 194 placed on top of the beetle to flatten the body so that the left and right side were 195 symmetrical along the midline of the body. Digital images of the head and thorax of each 196 beetle were captured using a mounted digital camera (Leica DFC 295) that was connected to 197 a PC. Using ImageJ (version 1.48), we measured the linear width of the pronotum as a 198 measure of general body size (Figure S2). We also used the “oval tool” in ImageJ to measure 199 the area of both eyes and the average area was used in subsequent analyses (Figure S2). 200 Due to their more complex structure, we used geometric morphometric analysis to 201 measure the size of the male mandibles and genitalia. Since the mandibles and genitalia 202 have few landmarks that are biologically homologous between specimens, we used a 203 protocol based on a mixture of landmarks (type-two landmarks) and semi-landmarks. For 204 the left side of the mandible, three homologous landmarks were placed along the outline of 205 the mandible and another 18 semi-landmarks were placed equidistance along the outer and 206 inner curve of the mandible using the TPSUTIL (version 1.46) and TPSDig (version 2.14) 207 programs (Rohlf 2009) (Figure S2). The Cartesian coordinates of these landmarks were 208 extracted using the tpsRELW (version 1.46) program (Rohlf 2008), and we estimated the 209 overall size of the mandible from the centroid size calculated as the square root of the sum 210 of squared distances between landmarks and their centroid (Cardini 2012). To measure the 211 size of the male genitalia, we separated the elytra and wings and removed the aedeagus 212 from the abdominal cavity using fine forceps. The aedeagus was then orientated in a 213 consistent position on a glass slide, mounted in a droplet of Hoyer’s solution, and an image 214 was taken. Using the same procedure and software as for mandible size, three homologous 215 landmarks were placed at the tip and base of the aedeagus, and another 26 semi-landmarks 216 were placed equidistance along its outline to estimate centroid size (Figure S3).

217 For a subset of 25 experimental males sampled at random across diets, we measured 218 each of our morphological measures twice to assess their repeatability using the R code 219 provided in Wolak et al. (2012). Each of our measurements of adult morphology were highly 220 repeatable (Pronotum width = 0.989, 95% CIs: 0.985, 0.991; Mean eye area = 0.943, 95% 221 CIs: 0.910, 0.988; Mandible size = 0.998, CIs: 0.998, 0.999; Genital size = 0.992, CIs: 0.989, 222 0.997).

223

224 SAMPLE SIZES

225 Based on our experimental design (i.e. 96 larvae per diet), a total of 2304 beetle larvae were 226 established on our experimental diets. For our analysis of larval survival to eclosion, 227 however, 76 beetles were not included because plate wells were empty but dead larva or 228 adults were not found meaning that the fate of the individual was uncertain. Thus, we had a 229 total sample size of n = 2227 for our survival analysis. It is important to note that our 230 analysis of larval survival contains both males and females as we were unable to determine 231 the sex of any larvae that died during development. Our analysis of development time and 232 adult morphology were based on 20 randomly selected male beetles per diet for 18 out of 233 the 24 artificial diets. For six of these diets (diets 1, 5, 9, 13, 17 and 21; Table S1, Fig. S1), too 234 few males survived to adulthood. We therefore had a sample size of n = 360 available for 235 the analysis of these traits.

236 237 STATISTICAL ANALYSIS

238 As our response variables (survival, development time and adult morphology) were 239 measured in different units, we standardized each response variable and the P and C 240 content of the diet to a mean of zero and standard deviation of one using a Z- 241 transformation prior to analysis. developing on diets of higher nutritional content 242 have a greater probability of survival to eclosion, have a reduced development time and 243 typically eclose at a larger body size (e.g. Hunt et al. 2004). The consumption of P and C in 244 our experiment is therefore expected to have contrasting effects on these response 245 variables (i.e. cause higher survival and larger body size but shorter development time). 246 Thus, even though faster development with a high intake of nutrients reflects more efficient 247 growth and development (i.e. increased performance), any statistical comparison of the 248 effects of nutrition on development time, body size and survival will necessarily show 249 significant differences due to the gradients having different signs, even if the absolute 250 magnitude of these effects in similar. We therefore transformed our standardized measures 251 of development time by multiplying the z scores by -1. After this transformation, beetle 252 larvae that developed slowly had negative z-scores whereas those developing faster had 253 positive z-scores.

254 As beetle larvae developing on a given diet were distributed across four separate 24- 255 well plates, there is the potential for variation across plates to influence our response 256 variables if each beetle in our experiment is treated as independent. We therefore 257 examined the linear and nonlinear effects of P and C consumption on our response variables 258 using Generalized Linear Mixed Models (GLMMs) that included ‘plate’ as a random effect. 259 Following conventional multivariate response surface methodologies (Lande & Arnold 260 1983), we first ran a GLMM that contained ‘plate’ as a random effect and the P and C 261 content of the diet as fixed effects. The parameter estimates from this model were used to 262 estimate the linear effects of these nutrients on our response variables. Next, we ran a 263 second GLMM that contained the same terms but also included the quadratic (P x P and C x 264 C) and cross-product (P x C) terms for these nutrients as fixed effects. The parameter 265 estimates from this model were used to estimate the nonlinear effects of these nutrients on 266 our response variables. While the parameter estimates (or nutritional gradients) from these 267 models are not biased when the response variable has a binary distribution, this has been 268 shown to bias significance testing of these parameters (Lande & Arnold 1983; Mitchell-Olds 269 & Shaw 1987; Janzen & Stern 1998). Consequently, as survival to eclosion was measured as 270 a binary response (i.e. 0 or 1) we ran the above GLMMs to estimate the linear and nonlinear 271 gradients for P and C but used a binary GLMM implemented in the ‘MCMCglmm’ package of 272 R (version 2.13.0; Hadfield 2010) to test the statistical significance of these gradients. Full 273 details of this analysis and accompanying R code are provided in Text S1.

274 When examining the effects of P and C on adult morphology (eye size, mandible size 275 and adeagus size), we compared two sets of models. First, we used the above GLMMs to 276 quantify the linear and nonlinear effects of P and C on absolute trait size. Second, we also 277 ran a GLMM for each trait that included body size as a fixed effect to examine the linear and 278 nonlinear effects of P and C on relative trait size. Including body size as a fixed effect 279 enables us to partition the independent responses of our morphological traits to nutrients 280 from those that are due to changes in body size across diets (Sentinella et al. 2013). With 281 the exception of the binary GLMM, all GLMMs were implemented in JMP®(version 8.0.2) 282 using Restricted Maximum Likelihood (REML) approximation.

283 Nonparametric thin-plate splines were used to visualize the nutritional landscapes 284 for each of our response variables and were constructed using the Tps function in the 285 ‘FIELDS’ package of R. In all cases, we used the smoothing parameter (λ) that minimized the 286 generalized cross-validation (GCV) score when visualizing the nutritional landscapes (Green 287 & Silverman 1994). Even though all analyses were conducted on standardized data, and in 288 the case of development time on transformed data, we present our nutritional landscapes 289 on the raw data to aid interpretation. 290 We used a sequential model building approach to test whether the linear and 291 nonlinear effects of P and C consumption differed across our response variables 292 (Chenoweth & Blows 2005; Draper & John 1988; South et al. 2011; Bunning et al. 2015). In 293 short, this sequential approach tests the difference in the sign and magnitude of the linear, 294 quadratic and correlational nutritional gradients across the different response variables by 295 comparing the change in variance explained by a GLMM that fits a single relationship 296 through the two response variables being compared (model 1) to a GLMM that fits a 297 separate relationship for each response variable (model 2). If model 2 explains significantly 298 more variance than model 1, as determined by a partial F test, this demonstrates that the 299 nutritional gradients differ across response variables. Full details of this analysis, including 300 linear equations, are included in Text S2. A potential limitation of this approach is that the 301 optimal expression of these traits may reside in a similar location in nutrient space even 302 though the magnitudes of these gradients differ. Therefore, in addition to using a sequential 303 model approach we also calculated the angle (θ) between the linear vectors for pairs of 304 response variables being compared as (Equation1) where a is the

305 linear effects of P and C consumption on the first response variable being compared, b is the 306 linear effects of these nutrients for the second response variable, and

307 . When the angle between the vectors is small (i.e. θ = 0°) the optima for the

308 two response variables reside in the same location in nutrient space, whereas when the 309 angle between the vectors is large (i.e. θ = 180°), the optima for the two response variables 310 are maximally divergent. To estimate and determine the statistical significance of θ, we 311 used a Bayesian approach implemented in the ‘MCMCglmm’ package of R. Full details of this 312 analysis and accompanying R code are provided in Text S3. 313 314

315 Results

316 SURVIVAL, DEVELOPMENT TIME and BODY SIZE 317 Survival, development rate and body size all increased linearly with the content of P and C in 318 the diet (Table 1). However, development rate and body size were over twice as responsive 319 (i.e. steeper gradient) to the consumption of C than P, whereas the consumption of P and C 320 had roughly equal effects on survival (Table 1). The significant negative quadratic terms 321 indicate a peak for all three traits with the consumption of C and a peak for survival (but not 322 development time or body size) with the consumption of P (Table 1). For each trait, this 323 peak was centred around a P:C ratio of approximately 1:2 on the nutritional landscape (Fig. 324 1). For survival, the significant negative correlational gradient suggests that longevity is 325 highest at a high C and low P consumption (Table 1, Fig. 1A). 326 A sequential model building approach showed significant differences in the linear, 327 quadratic and correlational effects of P and C consumption on survival and development 328 time (Table S2). The difference in the linear gradients for survival and development time 329 was due to the fact that survival was more responsive to P consumption than development 330 time, whereas the difference in quadratic gradients occurred because curvature of the peak 331 for P consumption was significant for survival and not for development time (Table S2). The 332 difference in the correlational gradient for survival and development time was due to the 333 fact that the negative gradient was significant for survival whereas it was not for 334 development time (Table S2). There were significant differences in the linear and quadratic 335 effects of P and C consumption on survival and body size but not in the correlational effect 336 of these nutrients (Table S2). The difference in linear gradients was due to the fact that body 337 size was more responsive to C consumption than survival, whereas the difference in 338 quadratic gradients was due to the curvature of the peak for P consumption being stronger 339 for survival than body size (Table S2). Finally, there were significant differences in the linear 340 effects of P and C consumption on development time and body size due to the fact that 341 body size was more responsive to C consumption than development time (Table S2). The 342 quadratic and correlational effects for these two traits did not differ significantly (Table S2). 343 Despite the differential effects of P and C consumption on survival, development 344 time and body size, the peaks for these traits occupy the same region on the nutritional 345 landscape (Fig. 1). This is evidenced by the small angle between the linear vectors for 346 survival and development time (θ = 8.83°, 95% CIs: 1.96°, 14.80°), between survival and 347 body size (θ = 2.85°, 95% CIs: 1.72°, 3.99°) and between development time and body size (θ 348 = 11.74°, 95% CIs: 2.97°, 18.99°). 349 350 THE ABSOLUTE SIZE OF MORPHOLOGICAL TRAITS 351 Mandible size and genital size both increased linearly with the consumption of P and C and 352 were over twice as responsive to the consumption of C than P (Table 2). In contrast, mean 353 eye area only increased linearly with the consumption of C (Table 2). There were significant 354 negative quadratic terms for the consumption of C for all three traits but not for the 355 consumption of P (Table 2). Inspection of the nutritional landscapes shows that these peaks 356 occur at a P:C ratio of approximately 1:2 for each trait (Fig.2). None of the correlational 357 gradients were statistically significant (Table 2). 358 Formal statistical comparison showed that the linear effects of P and C consumption 359 on mandible size differed significantly from the effects on mean eye area and genital size 360 (Table S3). In both cases, this was because mandible size was more responsive to the 361 consumption of P and C (Table S3). There was no difference in the linear effects of P and C 362 on mean eye area and genital size and none of the traits differed in the quadratic or 363 correlational effects of these nutrients (Table S3). Not surprisingly, the maximum size of 364 these three morphological traits occurred in the same region on the nutritional landscape 365 (Fig. 2), as evidenced by the small angle between the linear vectors for mandible and genital 366 size (θ = 9.74°, 95% CIs: 2.41°, 18.25°), mandible and eye size (θ = 3.64°, 95% CIs: 0.00°, 367 11.22°) and genital and eye size (θ = 13.32°, 95% CIs: 5.51°, 22.89°). 368 369 THE RELATIVE SIZE OF MORPHOLOGICAL TRAITS 370 Mean eye area, mandible size and genital size all scaled positively and significantly with 371 body size, although the linear gradient for mandible size was steeper than for mean eye 372 area and genital size (Table 3). After controlling for the effects of body size on these traits 373 (Sentinella et al. 2013), the consumption of P had a linear effect on mean eye area with this 374 trait decreasing in relative size as the consumption of P increased (Table 3). There were also 375 significant linear and quadratic effects of C consumption on relative mandible size (Table 3). 376 Relative mandible size increased with the consumption of C and there was also a negative 377 quadratic gradient indicative of a peak in relative mandible size with C consumption (Table 378 3). In contrast, the relative size of the genitalia was relatively insensitive to the consumption 379 of P and C (Table 3). 380 381

382 Discussion 383 One important source of variation in condition are environmental factors that influence 384 both the abundance of resources and the ease in which an individual may acquire these 385 resources from their environment (e.g. Hunt et al. 2004). Here we show that individuals that 386 consumed higher calorie diets were more likely to survive, developed faster and eclosed at a 387 larger body size. Thus, as predicted by theory (Hunt et al. 2004; Tomkins et al. 2004; Rowe & 388 Houle 1996), beetles that acquired more resources were in better ‘condition’ and able to 389 allocate more resources to their naturally and sexually selected traits. However, our work 390 shows that it is not simply the total intake of calories that is important for maximal trait 391 expression as the highest trait values did not occur universally on the most concentrated 392 diets. Rather, beyond a minimal caloric concentration, the ratio of ingested nutrients was 393 critical for the expression of life history traits, primary and secondary sexual traits, as well as 394 non-sexual traits, with linear increases in trait expression with the consumption of both 395 nutrients; the only exception being the effect of P consumption on mean eye area that was 396 not significant. Furthermore, despite variation in the responsiveness of survival, 397 development time and adult morphology to the consumption of P and C, the P:C ratio that 398 maximized the expression of these traits was the same (i.e. P:C = 1:2), resulting in nutritional 399 landscapes that were closely aligned across our traits. We therefore demonstrate that the 400 predicted trade-offs between life history and primary and secondary sexual traits is not 401 determined by the consumption of at least two important macronutrients (P and C).

402 Secondary sexual traits typically show a high degree of condition-dependent 403 expression as shown in a diversity of animal taxa (Cotton et al. 2003). In some instances, 404 secondary sexual trait exaggeration may be an extension of individuals’ body size (Cotton et 405 al. 2003), in which case changes in the secondary sexual trait occurs because of changes in 406 other components of the phenotype – for example as body size increases other traits may 407 also increase in size (i.e. indirect response). Alternatively, secondary sexual trait 408 exaggeration may reflect a wider response to condition, in which case changes in the trait 409 occur independently of changes in body size (i.e. direct response) (Cotton et al. 2003). We 410 demonstrate that the secondary sexual trait, the male mandibles, responded indirectly and 411 directly to variation in nutrition after controlling for body size although the indirect 412 response was much larger, suggesting that the mandibles are an exaggerated 413 representation of body size. We also show that the mandibles are significantly more 414 responsive to the intake of P and especially C in the larval diet of G. cornutus compared to a 415 primary sexual trait and a non-sexual trait, as predicted for a condition-dependent 416 exaggerated trait. This heightened sensitivity of the mandibles to nutrition is consistent with 417 previous work (Katsuki et al. 2012) and shows that a C-rich larval diet is likely to enhance 418 male fitness. In a contrasting invertebrate system, abundant P in the larval diet of 419 Telostylinus angusticollis is essential for the expression of characters that appear to be 420 strongly targeted by sexual selection. However there, C concentrations in the larval diet had 421 little effect, leading the authors to suggest that a P-rich larval diet may be a general 422 requirement of sexually-selected condition-dependent traits (Sentinella et al. 2013). Similar 423 findings were also found in the lesser wax moth, Achroia grisella, where the expression of 424 pre- and post-copulatory traits appeared to be increased in individuals that were reared on 425 high P larval diets (Cordes et al. 2015). The fact that we found contrasting effects in G. 426 cornutus is likely to reflect the evolutionary history of the species which feeds exclusively on 427 plant derived products (such as flour) that typically contain limited amounts of P (for 428 example, flour contains approximately 13% P and between 60 - 85 % C).

429 In contrast to the mandibles, the eyes were less responsive to the intake of dietary P. 430 This reflects the slower growth of the eye relative to the rest of the body with increasing 431 intake of P. Previously, it has been found that artificial selection for mandible length was 432 negatively, genetically correlated with the head horn, antenna and eye area which was 433 suggested to have arisen due to resource allocation trade-offs (Okada & Miyatake 2009). 434 Resource allocation trade-offs are predicted to occur when the precursors of the adult body 435 structures, (i.e. the imaginal discs) undergo a period of explosive growth after larval feeding 436 has stopped and therefore compete for limited resources (Nijhout & Emlen 1996). Evidence 437 suggestive of resource allocation trade-offs between a secondary sexual and a non-sexual 438 trait has been found in Onthophagine dung beetles; between horn length and eye size (O. 439 acuminatus and O. taurus; Nijhout & Emlen 1998) and relative horn length and wing length 440 (O. taurus; Tomkins et al. 2005). Similarly, in the butterfly, Precis coenia, the removal of the 441 hind wing imaginal discs from larvae gave rise to enlarged forewings, thorax and forelegs, 442 presumably because the manipulation increased the amount of resources that were 443 available for a smaller number of structures (Nijhout & Emlen 1998). In this study, the effect 444 of dietary protein may reinforce underlying resource allocation trade-offs between 445 neighbouring traits as dietary protein that is required to promote the growth of the body 446 and mandibles actually slows the growth of at least one neighbouring nonsexual trait – the 447 eye. Thus, in some instances, structures may compete for resources but in addition, the 448 macronutrients that they acquire may have negative effects for the growth of neighbouring 449 structures.

450 Unlike all other morphological traits, the growth of the genitalia only responded 451 indirectly and relatively weakly to variation in the consumption of P and C. Thus, although 452 the genitalia of G. cornutus are subject to sexual selection, it is clear that the size of the 453 aedeagus is not particularly sensitive to nutrition and is an unlikely signal of a male’s 454 underlying genetic quality. Similar patterns of environmentally canalized growth of genital 455 traits have been found in the hemipteran treehopper, Enchenopa binotata (Rodriguez & Al- 456 Wathiqui 2011, 2012), fly, Drosophila melanogaster (Shingleton et al. 2009, Dryer & 457 Shingleton 2011) and the dung beetle, O. taurus (House & Simmons 2007). However, this is 458 not always the case for genitalia, with evidence of environmentally induced plasticity in the 459 expression of the genitalia in the water strider, Gerris incognitos, albeit less than non-genital 460 traits (Arnqvist & Thornhill 1998), and fly D. mediopunctata (Andrade et al. 2005).

461 Canalization of the genitalia should reduce trait size variation and contribute to the 462 observed negative allometry of the male genitalia that is observed in many 463 species (Eberhard et al. 1998; Hosken et al. 2005; Dryer & Shingleton 2011; Simmons, 2013). 464 The adaptive significance of this pattern has been the subject of considerable discussion 465 (Bertin & Fairbairn 2007; Dryer & Shingleton 2011; Eberhard et al. 1998; Eberhard, 466 Rodriguez, & Polihronakis 2009; Simmons 2013), but a general explanation is that selection 467 favours intermediate male genital size (stabilising selection), which selects for canalized 468 growth (Eberhard et al. 2009; Dryer & Shingleton 2011; Simmons 2013). In G. cornutus, we 469 have found significant directional selection on aedeagus size such that males with a small 470 aedeagus are more effective in inseminating a female and consequently produce more 471 offspring following a non-competitive mating – although this pattern of selection may alter 472 when males compete to fertilize a females ovum (CMH unpulished data). This pattern of 473 apparent directional selection and the insensitivity to nutrition appear to be incompatible 474 unless directional selection for smaller genital size is stronger and more negative in large 475 males than it is in small males (Dryer & Shingleton 2011). In this context, environmental 476 canalization may protect the morphology of the aedeagus from environmental conditions 477 that increase body size and other traits via an indirect response to body size changes that 478 would otherwise lead to aedeagus size (i.e. large size) that confers lower fitness.

479 The mechanisms that generate nutritional reaction norms for body size and trait size, 480 such as we document here, have not yet been elucidated in G. cornutus. Past studies show 481 that the signalling pathways that regulate cell growth and proliferation are responsive to the 482 nutritional status of most animals (Mirth & Riddiford 2007; Shingleton et al. 2007, 2008). In 483 particular, the highly conserved insulin receptor-signalling pathway (IIS) and insulin-like 484 peptides (ILPs) that are responsive to nutrition have been implicated in the regulation of the 485 growth rate of extreme animal structures (Emlen et al. 2012). Although, the imaginal discs 486 themselves have been found to vary in their sensitivity to insulin-like peptides (ILPs) which 487 can lead to varied nutritional reaction norms (Shingleton et al. 2007; Warren et al. 2013). 488 For example, in the rhinoceros beetle Trypoxylus dichotomus, interference of the 489 transcription of the insulin receptor (lnR) gene in developing larvae significantly reduced the 490 size of nutrition dependent male horns while wing size was relatively insensitive and 491 genitalia size was not affected at all (Emlen et. al. 2012). Furthermore, it has been shown 492 that the insulin receptor-signalling pathway (IIS) is primarily regulated by dietary P in 493 Drosophila (Britton & Edgar 1998; Britton et al. 2002), A. grisella and T. angusticollis 494 although the precise signalling pathways are yet to be confirmed in A. grisella and T. 495 angusticollis (Cordes et al. 2015; Sentinella et al. 2013). In contrast, we find that the 496 secondary sexual traits of G. cornutus, responded directly to dietary C. This may arise if the 497 levels of circulating carbohydrates such as glucose have a role in the regulation of the insulin 498 receptor-signalling pathway (IIS) as has been shown in the silkworm Bombyx mori and 499 insects more broadly (Masumura et al. 2000). Our work illustrates the utility of NG to begin 500 to pin point the dominant macronutrients that stimulate growth and give rise to the 501 nutrient reaction norms for different traits. Further study is required to confirm the 502 involvement of specific pathways that mediate metabolic changes and differences in cell 503 growth and proliferation in different tissues in G. cornutus.

504 Interestingly, we found no evidence that the optimal nutritional requirements of one 505 trait constrain the dietary optimum of other traits. We show that life history traits (survival, 506 development time and body size), primary and secondary sexual traits, and a non-sexual 507 trait share a common requirement for P and C (despite their varied sensitivity) and optimize 508 their trait expression at a P:C ratio of 1:2. These findings contrast those of male T. 509 angusticollis flies and A. grisella wax moths that show that the positive effects of P for 510 secondary sexual trait expression is countered by the severe negative effects of P during the 511 developmental stages and therefore dietary P is likely to mediate trade-offs (Cordes et al. 512 2015; Sentinella et al. 2013). Similarly, in male N. cinerea varied requirements for dietary C 513 across different episodes of male reproductive effort is likely to mediate trade-offs. During 514 pre-copulatory sexual selection, male-male competition and female choice is mediated by 515 three pheromones, the expression of which is optimized at a P:C ratio of 1:8 (South et al. 516 2011), whereas sperm production is optimized at a P:C ratio of 1:2 (Bunning et al. 2015). 517 Consequently, the dietary optimum that enhances pre-copulatory performance is likely to 518 trade-off with the dietary optimum that enhances post-copulatory performance (Bunning et 519 al. 2015). Whether nutrition influences a putative trade-off between male morphology and 520 ejaculate traits in G. cornutus remains to be investigated.

521 Although nutrition seems unlikely to mediate trade-offs in G. cornutus, our results 522 show that nutrition is likely to have long lasting effects on male non-sexual and sexual 523 fitness. Extreme nutrient restriction during development was fatal at the lowest 524 concentrations of protein and carbohydrate, presumably because larvae were unable to 525 consume enough calories from these diets. Larvae that survived and developed on 526 suboptimal or unbalanced diets did so, but at a cost to their nonsexual fitness as they 527 developed more slowly. Prolonged development time is a plastic response which allows the 528 animal to achieve its required caloric and nutritional consumption in poor nutrient 529 environments (Chown & Nicolson 2004). For example, larvae of the African armyworm 530 Spodoptera exempta that were reared on poor quality grasses and larvae of the seed beetle, 531 limbatus that competed with siblings for resources were both able to attain the same 532 final size as those reared on good quality diets by prolonging development time (Chown & 533 Nicolson 2004). Here, larvae that developed more slowly were unable to compensate for 534 their poor larval diet and also were smaller adults. The fitness cost of delayed development, 535 small body size and reduced secondary sexual characters for males is likely to be 536 considerable in holometabolous species such as G. cornutus, as they are unable to 537 overcome these deficits in any adult environment (Adler et al. 2013; Boggs & Freeman 538 2005;). Given the probable fitness consequences of a poor quality larval environment it 539 would be of interest to determine whether G. cornutus can feed selectively to reach an 540 intake target that maximises the expression of life history traits and male secondary sexual 541 traits as has been reported for some other insects, e.g. Spodoptera littoralis (Simpson et al. 542 2004), Chortoicetes terminifera (Simpson et al. 2015) and N. cinerea (South et al. 2011).

543 In conclusion, we show that specific nutrients are important for the expression of a 544 condition dependent, secondary sexual trait but less so for a primary sexual trait. 545 Furthermore, among the life-history and morphological traits that we measured we find 546 that they have complementary nutritional requirements and therefore trade-offs are 547 unlikely to occur at the point of ingestion. Coupled with this, our estimates of sexual 548 selection on male primary (CMH unpublished data) and secondary sexual traits (Okada et al. 549 2006; Harano et al. 2010) suggest that development in an enriched C and weakly 550 supplemented P larval environment would confer high fitness as males can then invest in 551 large body size and condition-dependent weapons but also maintain a small aedeagus.

552

553 Acknowledgments

554 CMH was funded by a Leverhulme Trust fellowship and JH by a Royal Society University 555 Fellowship and the BBSRC. We thank Alastair Wilson for statistical advice on GLMMs.

556 Data accessibility

557 References

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727

728 Supporting Information

729 Additional Supporting Information may be found in the online version of this article. 730 Fig. S1. The distribution of artificial, holidic diets in nutrient space.

731 Table S1. The protein (P) and carbohydrate (C) content of artificial, holidic diets.

732 Table S2. Comparison of the effects of P and C on survival, development time and body size.

733 Table S3. Comparison of the effects of P and C on eye area, mandible size and genital size.

734 Text S1. Estimating the effects of P and C on larval survival.

735 Text S2. Sequential model building approach for comparing nutritional landscapes.

736 Text S3. Calculating the angle () and 95% CIs between linear nutritional gradients.

737 738 739 Table 1. Linear and nonlinear effects of the protein (P) and carbohydrate (C) content of 740 the diet on survival of beetle larvae (both sexes), as well as the development time and 741 adult body size of male G. cornutus. We used GLMMs, including plate as a random effect 742 and the linear and nonlinear effects of nutrients, to determine the gradients and used a 743 binary GLMM to test the significance of these gradients for survival (pMCMC).

Linear effects Nonlinear effects Response variable P C P x P C x C P x C Survival Gradient ± SE 0.49 ± 0.06 0.58 ± 0.06 -0.26 ± 0.04 -0.45 ± 0.04 -0.63 ± 0.07 t 7.98 9.49 7.25 11.79 9.00 df 93.08 93.14 86.94 87.08 90.31 pMCMC 0.0001 0.0001 0.0001 0.0001 0.0001 Development time Gradient ± SE 0.29 ± 0.10 0.65 ± 0.10 0.00 ± 0.11 -0.31 ± 0.14 0.19 ± 0.26 t 3.07 6.37 0.02 2.18 0.72 df 65.26 66.11 61.56 62.23 61.21 P 0.003 0.0001 0.99 0.033 0.47 Body size Gradient ± SE 0.38 ± 0.07 0.95 ± 0.07 -0.09 ± 0.06 -0.52 ± 0.07 -0.17 ± 0.13 t 5.44 13.08 1.41 7.15 1.29 df 67.44 58.32 64.94 65.35 63.56 P 0.0001 0.0001 0.16 0.0001 0.20 744 Response variables were Z-transformed prior to analysis so that the response of different traits to nutrition 745 was standardized and therefore comparable. The linear gradients describe the sign and magnitude of the 746 relationship between nutrients and the response variable. The quadratic gradients (P x P and C x C) describe 747 the curvature of the nutritional landscape with peaks indicated with a negative slope and a troughs by a 748 positive slope. The correlational gradient (P x C) describes how the covariance between P and C influences 749 the response variable, with a positive gradient indicating that the response variable is greater when both 750 the P and C content of the diet is high, whereas a negative gradient indicates that the response variable is 751 greatest when one nutrient is high and the other low.

752

753

754

755

756

757

758

759 760 Table 2. Linear and nonlinear effects of the protein (P) and carbohydrate (C) content of 761 the diet on the absolute size of a non-sexual trait (eye area), primary sexual trait 762 (mandibles), and secondary sexual trait (genitalia) in male G. cornutus.

Linear effects Nonlinear effects Response variable P C P x P C x C P x C Mean Eye Area Gradient ± SE 0.12 ± 0.07 0.75 ± 0.06 0.02 ± 0.07 -0.37 ± 0.08 -0.02 ± 0.15 t 1.66 10.54 0.25 4.68 0.14 df 65.62 66.34 62.60 62.11 59.65 P 0.10 0.0001 0.80 0.0001 0.89 Mandible Size Gradient ± SE 0.35 ± 0.07 0.89 ± 0.07 -0.02 ± 0.06 -0.52 ± 0.07 -0.17 ± 0.13 t 4.83 12.09 0.32 7.78 1.34 df 67.61 68.45 65.82 66.11 64.44 P 0.0001 0.0001 0.75 0.0001 0.19 Genital Size Gradient ± SE 0.22 ± 0.07 0.65 ± 0.07 -0.02 ± 0.08 -0.35 ± 0.08 0.00 ± 0.16 t 3.07 9.01 0.22 4.17 0.01 df 63.36 63.57 63.03 61.32 57.68 P 0.003 0.0001 0.83 0.0001 0.99 763 764

765 Table 3. The linear and nonlinear effects of the protein (P) and carbohydrate (C) content 766 of the diet on mean eye size, mandible size and genital size in male G. cornutus when 767 controlling for the effects of body size on these traits.

Linear effects Nonlinear effects Response variable P C Body size P x P C x C P x C Mean eye area Gradient ± SE -0.15 ± 0.05 0.12 ± 0.07 0.68 ± 0.06 0.07 ± 0.06 -0.07 ± 0.07 0.08 ± 0.12

t 3.04 1.78 12.28 1.20 1.03 0.68 df 71.34 108.20 183.10 61.94 80.66 56.85 P 0.003 0.08 0.0001 0.23 0.31 0.50 Mandible size Gradient ± SE 0.02 ± 0.03 0.08 ± 0.04 0.87 ± 0.03 0.05 ± 0.03 -0.12 ± 0.03 -0.03 ± 0.06

t 0.81 2.31 29.81 1.62 3.60 0.49 df 77.72 121.40 223.60 70.37 90.41 63.38 P 0.42 0.02 0.0001 0.11 0.0005 0.63 Genital size Gradient ± SE -0.05 ± 0.05 -0.01 ± 0.07 0.70 ± 0.06 0.05 ± 0.06 -0.01 ± 0.07 0.14 ± 0.12

t 1.06 0.10 11.82 0.80 0.19 1.24 df 77.93 106.7 130.70 65.70 78.09 50.48 P 0.29 0.92 0.0001 0.43 0.85 0.22 768 Body size was included in the model to partition out the independent responses of traits to nutrients from 769 those that were due to changes in body size across diets.

770

771 772 773 Fig. 1. Nutritional landscapes illustrating the effects of the protein and carbohydrate content 774 of the diet on (A) larval survival to eclosion, as well as the (B) developmental rate and (C) 775 body size of male G. cornutus. Hotter colours (red) represent areas of increased survival, a 776 slower development time and larger body size and colder colours (blue) represent areas of 777 reduced survival, faster development time and smaller body size. The circles show the 778 nutritional compositions of our artificial diets.

779 Fig. 2. Nutritional landscapes illustrating the effects of the protein and carbohydrate content 780 of the diet on (A) mean eye area, (B) mandible size and (C) genital size of male G. cornutus. 781 Hotter colours represent areas of larger trait size and colder colours represent areas of 782 smaller trait size. Note that no beetles survived at extremely nutrient poor diets (near the 783 origin) and hence adult morphology could not be measured for these animals. The circles 784 show the nutritional compositions of our artificial diets. 785

Figure 1 Carbohydrate content (%) Protein content (%) Development Survival Body Size time A B C Proportion Days mm

786

Figure

Carbohydrate content (%) 2 Protein Mandible Size Mean Eye Area Genital Size content (%) A B C Centroid size Centroid size mm 2