Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Evolutionary Biology

Open Access

Are sexually selected traits affected by a poor environment early in life?

BMC Evolutionary BiologyBMC series – open, inclusive and trusted201616:263

https://doi.org/10.1186/s12862-016-0838-2

Received: 1 October 2016

Accepted: 25 November 2016

Published: 1 December 2016

Abstract

Background

Challenging conditions experienced early in life, such as a restricted diet, can detrimentally affect key life-history traits. Individuals can reduce these costs by delaying their sexual maturation, albeit at the price of the later onset of breeding, to eventually reach the same adult size as individuals that grow up in a benevolent environment. Delayed maturation can, however, still lead to other detrimental morphological and physiological changes that become apparent later in adulthood (e.g. shorter lifespan, faster senescence). In general, research focuses on the naturally selected costs of a poor early diet. In mosquitofish (Gambusia holbrooki), males with limited food intake early in life delay maturation to reach a similar adult body size to their well-fed counterparts (‘catch-up growth’). Here we tested whether a poor early diet is costly due to the reduced expression of sexually selected male characters, namely genital size and ejaculate traits.

Results

We found that a male’s diet early in life significantly influenced his sperm reserves and sperm replenishment rate. Shortly after maturation males with a restricted early diet had significantly lower sperm reserves and slower replenishment rates than control diet males, but this dietary difference was no longer detectable in older males.

Conclusions

Although delaying maturation to reach the same body size as well fed juveniles can ameliorate some costs of a poor start in life, our findings suggest that costs might still arise because of sexual selection against these males. It should be noted, however, that the observed effects are modest (Hedges’ g = 0.20–0.36), and the assumption that lower sperm production translates into a decline in fitness under sperm competition remains unconfirmed.

Keywords

AgeCatch-up growthDietMosquitofishSperm

Background

Conditions experienced early in life affect life-history trajectories [1, 2]. In particular, lower growth rates due to limited food availability during development tend to reduce adult body size [3]. In some species, however, individuals reduce the potential fitness costs of smaller adult body size. For example, if food again becomes available, they compensate by accelerating their growth (compensatory growth). Alternatively, they delay maturity to attain the same adult size as well-fed individuals (catch-up growth; reviews: [4, 5]). There are usually clear benefits to large adult size, such as increased survival and higher reproductive success (e.g. [6]), but reaching the same size as better fed individuals might generate other costs (e.g. [7]). An obvious cost of catch-up growth is a longer generation time and, if there is seasonal breeding, a shorter reproductive lifespan [8, 9]. More subtle costs arise when elevated or extended growth produces developmental abnormalities that can, for example, increase the risk of predation, decrease immune function, and lower resistance to stressors (e.g. [4, 1012]). Poor nutrition early in life has been shown to adversely affect adult behaviour [11, 13], locomotor performance [14], functional morphology [15, 16], and key adult life-history traits [1720]. But even if there are no obvious effects, a poor start in life can still decrease fitness. For example, individuals reared on a restricted diet might be morphologically indistinguishable from those reared on a standard diet, but have shorter telomeres or lower plasma antioxidant levels [2123], which should reduce their adult lifespan (but see [2]).

A major life history allocation decision is how to invest in naturally and sexually selected traits. In general, however, we know little about how conditions early in life affect allocation of resources to adult life history traits ([24], but see: [25]). In particular, far more studies have investigated the effects of early diet on naturally rather than sexually selected traits, (but see: [2629]). This is surprising because variation in male lifetime reproductive success is often predominantly attributed to differences in mating success (i.e. sexual selection; [30]). Most sexually selected traits are under strong directional selection, and food availability is often a major determinant of their condition-dependent expression. Pre-copulatory sexually selected traits (i.e. those that determine mating success) can be detrimentally affected by poor early nutrition. For example, male eye-span in stalked-eyed flies and song repertoire size in great reed warblers, which are both traits that affect female mate choice, are negatively affected by nutritional stress during development [31, 32]. This reduced investment could reflect a life history trade-off between sexually and naturally selected traits. For example, when early nutrition is poor, males sometimes reduce investment in sexual ornaments to maintain their oxidant defence systems [21, 29]. Similar trade-offs could also affect investment into different sexually selected traits [33, 34]. Male reproductive success usually depends on both mating success and sperm competitiveness (e.g. [3537]). Given lower resource availability, males might invest differently in traits under pre-copulatory and post-copulatory sexual selection (e.g. [38, 39]). This could shift the relative allocation of resources to sperm competitiveness versus attractive ornaments [40]. For example, greater investment in larger body size or weaponry can result in smaller testes and ejaculates [41, 42].

The condition-dependence of sperm traits has been examined in several species, but this is usually due to short-term effects of manipulating the adult diet (e.g. [4345]). Fewer studies, especially of vertebrates, have tested how a restricted juvenile diet affects sperm traits (but see: [35, 40, 46]). Male fertilization success is highly dependent on resource allocation to traits that are under post-copulatory sexual selection, especially when sperm competition is intense [47]. In such species, males tend to have relatively larger testes that produce more sperm [48, 49]. Of course, sperm production is not the sole predictor of sperm competitiveness. It can also depend on sperm viability, swimming speed, and even sperm length [50, 51]. Since ejaculates are costly to produce [44, 52], it follows that a poor juvenile diet could negatively affect the number, quality, and rate of sperm production (e.g. [53, 54]).

Here we test whether the juvenile diet of male mosquitofish (Gambusia holbrooki), despite having no effect on adult body size, affects two sexually selected adult traits: ejaculate production and genital size. In two earlier studies we showed that males with limited food intake as juveniles reach a similar size to males on a normal diet due to delayed maturation [55, 56]. Additionally, we showed that males with a poor start in life are less attractive to females than those reared on a regular diet [57]. Mosquitofish are poecillid fishes characterised by frequent, coercive mating attempts and intense sexual selection, including sperm competition [54, 58, 59]. Males internally inseminate females using a modified anal fin as an intromittent organ, the ‘gonopodium’ [60]. Several recent studies have linked greater gonopodium length to increased male reproductive success in G. holbrooki ([61, 62] but see [63]). We ask whether males initially raised on a restricted diet incur sexually selected costs, despite catch up growth, due to the production of lower quality ejaculates, or development of a shorter gonopodium.

Methods

Fish were bred as part of a larger study to test how inbreeding and food restriction affect compensatory growth [55]. We found no effects of inbreeding on any of the measured life history variables (growth trajectories and adult size). Here, we are specifically interested in whether early diet restriction influences sexually selected traits so, for clarity, we analyse the data excluding inbreeding from our models. Including inbreeding does not qualitatively alter our results because it had very small, non-significant effects. These are discussed elsewhere (J. Marsh, R. Vega-Trejo, M.L. Head, and M.D. Jennions ‘in preparation’).

We used mosquitofish descended from females captured in ponds in Canberra, Australia (35°14’27”S, 149°5’27”E and 35°14’13”S, 149° 5’55”E) in February-March 2013. Full methods are described in Vega-Trejo et al. [64]. In brief, in each experimental block we mated individuals from two families (e.g. A and B in block 1, C and D in block 2 and so on). Brothers and sisters from full sibling families were paired to create inbred (AA, BB) and outbred offspring with reciprocal male–female crosses (AB, BA; i.e. four cross-types). We set up 29 blocks and with one male and one to four full sisters per cross type. The resultant offspring were reared individually in 1 L tanks until maturity (N = 453 males) under a 14:10 h photoperiod at 28 °C. Males underwent a diet manipulation for 21 days between days 7 and 28 post-birth. Fish on the control diet were fed ad libitum with Artemia nauplii twice daily (i.e. standard laboratory feeding regime) whereas fish on the restricted diet were fed 3 mg of wet mass Artemia nauplii once every other day. We have previously shown that this restricted diet leads to minimal growth without elevating juvenile mortality [56]. Broods were split evenly between the control and restricted diet treatments. Males were considered mature when their gonopodium was translucent with a spine visible at the tip [65, 66]. These changes are associated with spermatogenesis in the testes [60, 66]. We have previously shown that inbreeding (i.e. mating with full-sibs) reduces the number of offspring at birth, but with no detectable effect on their likelihood of breeding, gestation time, offspring size at birth or growth rate [64]. In addition, there is no difference in juvenile survival between males on the control and the restricted diet (i.e. GLMM ran for food treatment: P = 0.952; [55]). We collected body size and sperm data from mature males (range: 2–18 weeks post-maturity). We define ‘developmental time’ as the number of days that males took to reach maturity, and ‘adult age’ as the post-maturation age at which sperm was extracted (i.e. total age – developmental time). Developmental time was 78.6 ± 34.3 days for males in the control treatment and 99.4 ± 37.5 for males in the restricted diet treatment. Adult age was 81.0 ± 17.1 days for males on the control diet, and 70.4 ± 21.4 days for males on the restricted diet (mean ± SD).

Sperm traits

We tested 453 males from 192 broods. Sperm was collected on three occasions: on day 0 we stripped virgin males of sperm (see below) to measure their maximum sperm reserves; one day later we stripped males to measure their sperm replenishment rate (i.e. sperm production over 24 h); on day 3 we stripped males to measure sperm velocity. We also calculated the proportion of sperm replenished (= number of sperm at day 1/number of sperm at day 0), which we arcsine-transformed to normalize the error distribution.

Sperm collection

To strip ejaculates, males were anaesthetized in ice-cold water. The male was then placed on a glass slide (coated with 1% polyvinyl alcohol solution (PVA) to prevent sperm bundles sticking to the slide) under a dissecting microscope. His gonopodium was swung forward and we applied gentle pressure to the abdomen to eject all the available sperm. We transferred the ejaculate to an Eppendorf tube with 100–900 μL of extender medium (207 mM NaCl, 5.4 mM KCl, 1.3 mM CaCl2, 0.49 mM MgCl2, 0.41 mM MgSO4, 10 mM Tris, pH 7.5). The amount of medium varied depending on the amount of ejaculate stripped to obtain accurate sperm counts, which require an intermediate sperm concentration. Sperm remain quiescent in this solution until activated [67]. Sperm counts and velocity measures were taken within 30 min of sperm collection (see [51] for further details). After the procedure each male was returned to his individual tank. Sperm collection was done blind to diet treatment by RVT.

Sperm number

To estimate the number of sperm we vortexed the sperm solution for 1 min and then mixed it repeatedly with a pipette (20–30 times) to break up sperm bundles and distribute the sperm evenly throughout the sample. We placed 3 μl of solution on a 20 micron capillary slide (Leja) and counted the sperm using a CEROS Sperm Tracker (Hamilton Thorne Research, Beverly, MA, USA) under 100× magnification. We counted five subsamples per sample. We estimated repeatability following Nakagawa and Schielzeth [68] using the rptR package in R 3.0.2 [69]. Repeatability was very high for sperm number (sperm at day 0: r = 0.85 ± 0.01 SE; sperm at day 1: r = 0.91 ± 0.006 SE). The mean of the five subsamples was used for further analyses. The threshold values defining cell detection were predetermined as elongation percentage 15–65, head size 5–15 μm, and the static tail filter was set off. Sperm were counted blind to male treatment.

Sperm velocity

For each ejaculate we analysed three samples. For each sample we collected 3 μL of the diluted sperm (above) and placed this in the centre of a cell of a 12-cell multitest slide (MP Biomedicals, Aurora, OH, USA) previously coated with 1% PVA. The sample was then activated with a 3 μL solution of 150 mM KCl and 2 mg ml−1 bovine serum albumin [70] and covered with a cover slip. We analysed sperm velocity within 30 s of activation for three subsamples to increase the number of velocity measures. We used an average of 109.3 ± 3.5 SE sperm tracks per ejaculate (minimum 10 sperm tracks/male). We excluded six of 399 available males from the velocity analysis because they had fewer than 10 sperm tracks. We recorded two standard measures of sperm velocity: (1) average path velocity (VAP): the average velocity over a smoothed cell path and (2) curvilinear velocity (VCL): the actual velocity along the trajectory using a CEROS Sperm Tracker. The threshold values defining static cells were predetermined at 20 μm/s for VAP and 15 μm/s for VCL. Repeatability was high for both parameters (VAP: r = 0.65 ± 0.02 SE; VCL: r = 0.58 ± 0.03) and we used the mean of the three subsamples in our analyses. Due to the near perfect correlation between VAP and VCL (r = 0.961, P < 0.001), as found in most comparable studies (e.g. [7173]), we only use VAP in our analyses.

Male morphology

All males were measured a week after sperm extraction. Males were anaesthetized by submersion in ice-cold water for a few seconds to reduce movement and then placed on polystyrene with a microscopic ruler (0.1 mm gradation) and photographed. We measured male standard length (SL = snout tip to base of caudal fin) and gonopodium length using Image J [74].

Statistical analysis

We removed one of 453 males from the analysis because he had a higher number of sperm on day 1 than day 0 indicating that not all sperm were collected during the first extraction. To analyse the effect of diet treatment on male sexual traits we used generalized linear mixed models (GLMM). We constructed separate models for each of our five response variables: gonopodium length, number of sperm at day 0, number of sperm at day 1 (i.e. replenishment rate), proportion of sperm replenished (arc-sine transformed), and VAP. In each model we included diet as a fixed factor, and male standard length, development time, and adult age as fixed covariates, as well as all two-way interactions with diet. Gonopodium length and body size were log-transformed. Adult age was not included in the model for gonopodium length as there is little post-maturity growth in G. holbrooki.

There were significant bivariate correlations between development time and body size (r = 0.62, P < 0.001) as well as development time and adult age (r = −0.77, P < 0.001). The former reflects a biological relationship and the latter is due to a logistic constraint (i.e. having to terminate the experiment). Even so, these correlations were not so large as to preclude including all three terms as covariates in a GLMM due to colinearity problems: running each model with one covariate at a time produced comparable effect sizes for focal terms.

More importantly, we needed to take into account that the mean development time differed significantly between the diets because of catch up growth by males on the restricted diet (GLMM with diet as the single fixed factor, and random factors as below: P <0.001). Including development time as a raw covariate could obscure a main effect of diet (i.e. it is a covariate measured post-treatment sensu A Gelman and J Hill [75], p.188 that might causally mediate any diet effect because it varies due to the diet itself). We therefore standardised developmental time within each diet treatment (both treatments: mean = 0, S.D =1). We also standardised male standard length within each diet for ease of interpretation of the results. However, male size did not differ between the diet treatments (P = 0.451; see Results) so the use of unstandardised male size yields almost identical results. In contrast, adult age was not standardised within diet treatments because it varied depending on when we were able to test males. Instead we standardised adult age across the study to aid in interpretation (i.e. the intercept is the value for an average aged male; [76]). Although mean adult age at testing differed significantly between the two diets, there was a large overlap in values (Additional file 1).

Centring the covariates within each diet affects their interpretation. The effect of development time (or its interaction with diet) should be interpreted relative to that of other males on the same diet. The main effects of diet are interpretable as those for a male of average size and development time for its treatment type, but of average age for males across the entire study (see [76]).

In all the GLMMs we specified a Gaussian error distribution and checked the distribution of model residuals to ensure this was appropriate. The use of Poisson error (for count data) and binomial error (for proportions) provided a worse fit to the data than the use of Gaussian error on raw or transformed dependent variables. Each model was fitted using the lme4 package in R 3.0.2 software with block, maternal identity, and sire identity as random factors (see [64]). All model terms were tested for significance using the Anova function in the car package specifying Type III Wald chi-square tests. Model simplification (i.e. removing non-significant interactions and main terms) did not change our results. Marginal R2 refers to the variance explained by fixed factors in a model, estimated on the link scale [77]. We present the marginal R2 (ΔR2) to show the decline when each fixed effect was dropped from the full model.

We also calculated the effect size (Hedges’ g) as the standardized difference between males on the control and restricted diets for the measured traits following Rosenberg et al. [78].

Figures are presented using raw data but with model estimates for regression lines, unless otherwise stated. Summary statistics are presented as mean ± SE.

Results

The correlations between the four ejaculate traits are provided in Table 1. Diet treatment means for the five male traits and effect sizes for diet are provided in Table 2. Parameter estimates from the GLMM models are provided in Table 3.
Table 1

Correlations between sperm traits measured

 

Number of sperm at day 1

Proportion of sperm replenished

Sperm velocity (VAP, μm/s)

Number of sperm at day 0

0.468 (<0.001)

−0.117 (0.015)

0.055 (0.271)

Number of sperm at day 1

 

0.664 (<0.001)

0.032 (0.526)

Proportion of sperm replenished

  

0.059 (0.246)

Estimates are followed by p-values in brackets. N = 452 males or 393 males (for sperm velocity)

Table 2

Treatment means ± SE (N) for the five traits measured

 

Control diet

Restricted diet

Hedges’ g

Gonopodium length (mm)

6.94 ± 0.05 (223)

7.03 ± 0.04 (226)

0.137

Number of sperm at day 0 (×105)

194.58 ± 6.37 (225)

176.20 ± 6.14 (227)

0.196

Number of sperm at day 1 (×105)

62.36 ± 2.72 (225)

47.92 ± 2.67 (227)

0.356

Proportion of sperm replenished

0.35 ± 0.02 (225)

0.29 ± 0.01 (227)

0.268

Sperm velocity (VAP, μm/s)

83.10 ± 1.11 (207)

81.88 ± 1.12 (186)

0.073

Table 3

Results from mixed models with parameter estimates and chi square (χ 2) tests for food treatment, size, developmental time, and adult age

 

Predictor

Estimate

SE

χ 2

P

∆R2

Gonopodium length [ln (mm)]

 Control diet (N = 223)

Intercept

0.838

0.002

152650

<0.001

 

 Restricted diet (N = 226)

Diet (restricted)

0.008

0.002

12.670

<0.001

0.047

Size

0.041

0.002

410.140

<0.001

0.332

Developmental time

0.003

0.002

2.553

0.110

0.032

Diet × Size

−0.020

0.003

43.952

<0.001

0.032

Diet × Developmental time

0.005

0.003

3.193

0.074

0.002

Number of sperm at day 0 (total count ×105)

 

Intercept

197.389

8.502

538.991

<0.001

 

 Control diet (N = 225)

Diet (restricted)

−10.103

8.904

1.2875

0.257

0.036

 Restricted diet (N = 227)

Size

25.761

6.978

13.627

<0.001

0.040

Adult age

−19.425

10.606

3.3546

0.067

0.033

Developmental time

−26.608

10.768

6.1057

0.013

0.009

Diet × Size

0.517

11.465

0.002

0.964

<0.001

Diet × Adult age

53.202

13.371

15.831

<0.001

0.028

Diet × Developmental time

22.339

14.83

2.269

0.132

0.004

Number of sperm at day 1 (total count ×105)

 

Intercept

62.4092

3.1771

385.8565

<0.001

 

 Control diet (N = 225)

Diet (restricted)

−10.9804

3.8738

8.0347

0.005

0.023

 Restricted diet (N = 227)

Size

12.9494

3.0323

18.237

<0.001

0.057

Adult age

−5.3597

4.568

1.3767

0.241

0.012

Developmental time

−21.5316

4.8045

20.0842

<0.001

0.045

Diet × Size

0.8841

5.3062

0.0278

0.868

<0.001

Diet × Adult age

14.6051

5.8465

6.2404

0.012

0.01

Diet × Developmental time

9.4495

6.6831

1.9992

0.157

0.003

Proportion of sperm replenished

 Control diet (N = 225)

Intercept

219.386

9.188

570.1931

<0.001

 

 Restricted diet (N = 227)

Diet (restricted)

−30.524

12.415

6.0454

0.014

0.013

Size

22.047

9.611

5.2624

0.022

0.02

Adult age

−4.278

14.272

0.0898

0.764

<0.001

Developmental time

−55.801

15.094

13.6668

<0.001

0.047

Diet × Size

6.203

16.712

0.1378

0.711

<0.001

Diet × Adult age

8.97

18.707

0.23

0.632

<0.001

Diet × Developmental time

9.292

21.251

0.1912

0.662

<0.001

Sperm velocity (VAP, μm/s)

 Control diet (N = 207)

Intercept

85.578

1.261

4603.423

<0.001

 

 Restricted diet (N = 186)

Diet (restricted)

−4.569

1.652

7.650

0.006

0.034

Size

−0.308

1.312

0.055

0.814

<0.001

Adult age

−8.066

1.932

17.426

<0.001

0.062

Developmental time

−0.293

1.999

0.022

0.883

0.007

Diet × Size

1.552

2.334

0.442

0.506

<0.001

Diet × Adult age

2.552

2.529

1.018

0.313

0.003

Diet × Developmental time

−3.568

2.909

1.505

0.220

0.003

P-values in bold indicate significant values. Covariates were standardised within food treatment. The sample sizes for control and restricted diets are given for each response variable. ∆R2 shows the change in marginal R2 when each fixed effect is dropped from the model

Effect of treatment on male morphology

There was no significant difference between the diet treatments in male body size at maturity (control: 23.52 ± 0.14 mm; restricted diet: 23.35 ± 0.11 mm; t = 0.92, P = 0.36). Against expectations (see [56]), males on the restricted diet had a significantly longer gonopodium than those on the control diet if they were of average or smaller body size, but a shorter gonopodium if they were of above average size (diet × size: P < 0.001; Table 3; Fig. 1). Correcting for body size, males that took relatively longer to mature on a given diet did not have a significantly longer gonopodium (P = 0.110), irrespective of the diet type (diet × development time: P = 0.074).
Fig. 1

Effect of diet treatment on the relationship between male body size and gonopodium length. Standard length is standardised within each diet. Black symbols and lines represent the control diet, grey symbols and lines represent the restricted diet. Lines represent simple model predictions. Grey shading represents 95% confidence intervals

What influences sperm traits?

Initial sperm reserves (Day 0)

As expected, larger males had significantly greater initial sperm reserves (P < 0.001), irrespective of their diet (diet × size interaction: P = 0.964). However, diet significantly influenced how sperm reserves changed with age (diet x adult age: P < 0.001). Initial sperm reserves of males on the control diet tended to decrease with age (P = 0.067), while there was a significant increase with age for males on the restricted diet (Estimate ± SE for males on the restricted diet: 33.777 ± 9.306, P <0.001; Fig. 2).
Fig. 2

Effect of diet treatment on the relationship between number of sperm at day 0 and adult age. Number of sperm represent total counts, adult age is standardised across diet. Black symbols and lines represent the control diet, grey symbols and lines represent the restricted diet. Lines represent simple model predictions. Grey shading represents 95% confidence intervals

Although the two slopes intersect, interpretation of the age-dependent change in sperm reserves is best confined to stating that, when younger, males on the restricted diet have lower sperm reserves than those on the control diet. This is because there are relatively few data points for males on the restricted diet when standardised male age exceeds 1 (see Fig. 2). There was also a significant effect of development time on initial sperm reserves (P = 0.013), irrespective of diet (diet × development time: P = 0.132).

Sperm replenishment rate (Day 1)

Larger males had significantly higher sperm replenishment rates than smaller males (P < 0.001), irrespective of their diet (diet × size: P = 0.868); but, controlling for body size, males that took longer to develop had a significantly lower sperm replenishment rate (P < 0.001) irrespective of their diet (diet × development time: P = 0.157). A male of average size and development time for its diet, and average age for the study, that was reared on the control rather than the restricted diet had a significantly higher replenishment rate (P = 0.005). There was, however, also a significant difference between the diets in how age related to replenishment rate (diet x adult age: P = 0.012): males on the control diet had no significant change in replenishment rate with age (P = 0.241; Table 3), while replenishment rate increased significantly with age for males on the restricted diet (Estimate ± SE for males on the restricted diet: 9.245 ± 3.977, P = 0.020).

Overall, males on the control diet replenished a significantly greater proportion of their initial sperm reserves within 24 h than those on the restricted diet (P = 0.014). The proportion replenished was significantly greater for larger males (P = 0.022), and for males with a shorter development time for their diet type (P < 0.001), but there was no significant effect of male age (P = 0.764). All these relationships held irrespective of diet (interactions with diet: all P > 0.632).

Finally, males on the control diet had significantly faster swimming sperm than did males on the restricted diet (P = 0.006). Sperm velocity also decreased significantly with adult age (P < 0.001; Fig. 3), but was unrelated to development time or body size. All these relationships held irrespective of diet (interactions with diet: all P > 0.220).
Fig. 3

The relationship between VAP (average path velocity) and adult age. VAP is given in μm/s, adult age is standardised across diet. The line represents model predictions. Grey shading represents 95% confidence intervals

Discussion

Nutritional constraints early in life can lower an individual’s fitness due to changes in their adult performance. Like many species [5, 24], juvenile mosquitofish that experience food restrictions early in life extend their development time to attain a similar body size to individuals on a regular diet (see [55, 56]). But do additional costs arise despite this equivalence in adult body size? Here we tested whether a poor juvenile diet has sexually selected costs for male Gambusia holbrooki due to a decline in ejaculate quality and the development of shorter genitalia. We found that early diet had a significant influence on initial sperm reserves, sperm replenishment rate, the proportion of sperm replenished, and on sperm velocity. Shortly after maturation males that had a restricted diet during development had smaller sperm reserves and lower sperm replenishment rates early in adulthood than males that did not have a restricted diet. However, these effects were not detectable for older males (see Fig. 2). In contrast, and unexpectedly, males on the control diet had relatively shorter gonopodia than those on the restricted diet, when they were of small or average body size. Our results, combined with those from our previous studies [56, 57], suggest that a poor diet early in life not only has the immediate cost of delayed maturation, but might impose additional costs if lower sperm production, slower swimming sperm, and deviations from the normal gonopodium-body size allometry reduce male fertilisation success under sperm competition.

Sperm production is condition dependent in a variety of species. When the diet of adult males is restricted they tend to have smaller sperm reserves (e.g. [44, 73, 7981]) and lower sperm replenishment rates (e.g. [54]). There are, however, far fewer studies that explore the effects of a poor juvenile diet on sperm reserves and sperm replenishment rates (but see: [35, 40]). We found that both sperm reserves and replenishment rates were affected by a male’s early diet in an age-dependent manner. Thus, in addition to the immediate condition-dependence of sperm production reported in previous studies, we have shown that early diet restriction can have much longer-term effects on sperm production. Whether the relatively small (albeit significant) effects that diet has on sperm production translate into differences in reproductive success remains to be tested. The effect size for the direct effect of diet on sperm production and reserves ranges from g = 0.20 to 0.36. To put this in context, by convention, effects of a 0.1 and 0.3 standard deviation change in means are usually described as ‘small’ and’medium’ respectively [82].

Although many factors affect ejaculate competitiveness under sperm competition, sperm number still tends to predict variation in male fertilization success under sperm competition [8385]. For example, it is a good predictor of fertilization success in another poeciliid fish, the guppy [51]. More generally, a consistent pattern in comparative analyses of diverse taxa is that species with more intense sperm competition have larger testes, and produce more sperm [8688]. This strongly suggests that sperm production is sexually selected under sperm competition. Our results imply that males that experience a restricted juvenile diet, even if they mate as often as males that had a regular diet, will have lower lifetime reproductive success due to reduced sperm competitiveness. Although sperm reserves increase with time since sexual maturity (here and [89]), males experiencing restricted diets during development may still be disadvantaged because they take longer to reach full sperm production. Additionally, it is worth noting that at our study site adult mosquitofish males do not overwinter [90]. The limited time available to breed (November-March) should favour males that reach full sperm reserves at maturation. However, although we believe that these arguments are compelling, to confirm that the lower sperm production we have reported affects male fitness we still need direct tests of fertilization success whereby males reared on a restricted and normal juvenile diets compete. It would also be interesting to look at how early diet influences the composition of seminal fluids, as this may influence sperm competiveness, for instance by altering sperm longevity ([91], but see [92]) and thus the potential for sperm to be stored over winter.

Our findings for juvenile dietary effects on ejaculatory traits are analogous to those in studies showing that variation in early nutrition due to parental care affects other adult sexual traits that determine male reproductive success [9395]. For example, in a dung beetle, developing larvae depend on nutrients provided by their parents which affects male body and horn size and thereby their mating success [93]. In some cases the diet or conditions that parents’ experience is transmitted to their own offspring (i.e., transgenerational effects: [1, 96, 97]), and can thereby affect their mating success. For example, in birds the amount of carotenoids available to mothers influences what they can deposit into egg yolks, which can then affect their sons’ adult ornamental coloration [98]. Our results highlight that, regardless of whether variation in the early nutritional environment is determined by parental care, an individual’s resource acquisition ability, or the habitat into which it is born, the effects on offspring fitness are potentially far reaching, and could extend into adulthood. More specifically, we suggest that it could be worthwhile to test for parental effects on sperm production.

Intriguingly, sperm velocity decreases with adult age in G. holbrooki. Sperm quality is expected to decline with age due to lower fertilising efficiency and/or the genetic quality of sperm produced by ageing males [99]. This expectation is supported by studies showing that sperm velocity deteriorates with male age (e.g. [100102]). The lower sperm quality of older males has been shown to reduce fertilization success under sperm competition in some cases (i.e. when competing with sperm from younger males; e.g. [103]) but not others [92, 104]. There are two possible reasons why older males might have lower quality sperm. One is that old males produce lower quality sperm (an effect of male age per se; e.g. [100, 105]). The other is that the sperm of older males deteriorates because it has spent more time in storage (an effect of sperm age; e.g. [106, 107]). In our study all sperm velocity measures were obtained from sperm that were at most three days old, so the observed lower sperm velocity is most likely due to an effect of male rather than sperm age. It is intriguing that sperm numbers increased with age (at least for males on the restricted diet), while sperm velocity declined with age for all males. This suggests that sperm number might be a more important determinant of fertilization success than sperm velocity, and is therefore more likely to be maintained given limited resources. This is supported by data from other poeciliids showing that sperm number is more important than sperm velocity under post-copulatory sexual selection (an effect of sperm age; e.g. [106, 107]). Additionally, unlike studies in other poecillids (see [43]), sperm velocity was significantly negatively affected by a restricted diet, albeit that the effect size was small (Hedges’ g = 0.07). This finding is nonetheless interesting given that we only manipulated diet early in life. Again, however, it remains to be directly shown that the observed dietary difference in sperm velocity affects male fertilization success under sperm competition.

Male fitness depends on the ability to acquire mates and gain paternity when females mate multiply (see [39, 43]). Although the quantity and the quality of sperm tends to strongly influence male fertilization success in most taxa, other traits can be important (e.g. genital morphology in dung beetles; [108]). We measured gonopodium length, which affects female mate choice in some poeciliids and has been implicated as a potentially important trait affecting sperm transfer [109112]. Unexpectedly, we found that small and medium-sized males on a restricted diet early in life had a longer gonopodium, corrected for body size, than those on a regular diet [60, 113]. The fitness consequences of this change in allometry are unclear. A female preference for males with a relatively longer gonopodium has been shown in G. holbrooki, but only for large bodied males (see [56] for a different finding). In addition, [109] failed to detect a female preference when using lines of males artificially selected for a relatively larger or shorter gonopodium. Insemination success seems to depend on both male body size and gonopodium length. Males with a relatively longer gonopodium are likely to be more successful, but only when they are large bodied [114]. Paternity studies of males free to compete for females have, however, produced contradictory results. Two studies [61, 62] found that males with a relatively longer gonopodium gained a greater share of paternity, while another study [63] found no difference in the reproductive success of males from lines selected for a relatively longer or shorter gonopodium. Consequently, the effects on male reproductive success of the observed diet-dependent change in relative gonopodium length remain unclear.

Conclusions

In sum, some ejaculate traits in G. holbrooki depend on an interaction between a male’s juvenile diet and his adult age. In a previous study we also showed that early life diet influences male attractiveness in G. holbrooki [57]. Together these studies suggest that early diet could have fitness consequences that only become apparent in adulthood. Our findings are similar to those in other species where males on different diets superficially look the same, but differ in social dominance [57], telomere length or plasma antioxidant levels (e.g. [13]). As with these studies it is assumed that the traits affected by diet influence male fitness. However, the actual effects of a poor early diet on adult male reproductive performance remain to be directly tested. Ideally, future studies should directly measure the relative reproductive success of males that undergo a poor start in life in a competitive mating context (but see [62]).

Declarations

Acknowledgments

We thank the ANU Animal Services team for fish maintenance. We thank Loeske E. B. Kruuk and Liam D. Bailey for statistical advice.

Funding

Our work was supported by the Australian Research Council (DP160100285). R.V.-T. is supported by fellowships from Consejo Nacional de Ciencia y Tecnología-México and the Research School of Biology.

Availability of data and material

Data available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.k86m5.

Authors’ contributions

RV-T participated in the design of the study, performed the sperm section of the laboratory work, performed the statistical analysis, and drafted the manuscript. MDJ participated in the design of the study, assisted in statistical analysis, and helped to draft the manuscript. MLH participated in the design of the study, assisted in statistical analysis, and helped to draft the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval

This work was conducted under the ANU animal ethics protocol, granted by animal use permit: ANU AEEC animal ethics protocol A2011/64. Collection permits were not required for this study as G. holbrooki are a pest species in Australia.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Division of Ecology and Evolution, Research School of Biology, The Australian National University
(2)
Wissenschaftskolleg zu Berlin

References

  1. Monaghan P. Early growth conditions, phenotypic development and environmental change. Philos Trans R Soc Lond B Biol Sci. 2008;363(1497):1635–45.View ArticlePubMedGoogle Scholar
  2. English S, Uller T. Does early-life diet affect longevity? A meta-analysis across experimental studies. Biol Lett. 2016;12(9):20160291.Google Scholar
  3. Dmitriew CM. The evolution of growth trajectories: what limits growth rate? Biol Rev. 2011;86(1):97–116.View ArticlePubMedGoogle Scholar
  4. Hector KL, Nakagawa S. Quantitative analysis of compensatory and catch-up growth in diverse taxa. J Anim Ecol. 2012;81(3):583–93.View ArticlePubMedGoogle Scholar
  5. Metcalfe NB, Monaghan P. Compensation for a bad start: grow now, pay later? Trends Ecol Evol. 2001;16(5):254–60.View ArticlePubMedGoogle Scholar
  6. Lee W-S, Monaghan P, Metcalfe NB. Experimental demonstration of the growth rate - lifespan trade-off. P Roy Soc B-Biol Sci. 2013;280(1752):20122370.Google Scholar
  7. Yearsley JM, Kyriazakis I, Gordon IJ. Delayed costs of growth and compensatory growth rates. Funct Ecol. 2004;18(4):563–70.View ArticleGoogle Scholar
  8. Marcil-Ferland D, Festa-Bianchet M, Martin AM, Pelletier F. Despite catch-up, prolonged growth has detrimental fitness consequences in a long-lived vertebrate. Am Nat. 2013;182(6):775–85.View ArticlePubMedGoogle Scholar
  9. Roff DA. The evolution of life histories. New York: Chapman and Hall; 1992.Google Scholar
  10. Oli MK, Hepp GR, Kennamer RA. Fitness consequences of delayed maturity in female wood ducks. Evol Ecol Res. 2002;4(4):563–76.Google Scholar
  11. Ali M, Nicieza A, Wootton RJ. Compensatory growth in fishes: a response to growth depression. Fish Fish. 2003;4(2):147–90.View ArticleGoogle Scholar
  12. Teder T, Vellau H, Tammaru T. Age and size at maturity: a quantitative review of diet-induced reaction norms in insects. Evolution. 2014;68(11):3217–28.View ArticlePubMedGoogle Scholar
  13. Royle NJ, Lindstrom J, Metcalfe NB. A poor start in life negatively affects dominance status in adulthood independent of body size in green swordtails Xiphophorus helleri. P Roy Soc B-Biol Sci. 2005;272(1575):1917–22.View ArticleGoogle Scholar
  14. Hector KL, Bishop PJ, Nakagawa S. Consequences of compensatory growth in an amphibian. J Zool. 2012;286(2):93–101.View ArticleGoogle Scholar
  15. Chin EH, Storm-Suke AL, Kelly RJ, Burness G. Catch-up growth in Japanese quail (Coturnix japonica): relationships with food intake, metabolic rate and sex. J Comp Physiol B. 2013;183(6):821–31.View ArticlePubMedGoogle Scholar
  16. Ohlsson T, Smith HG. Early nutrition causes persistent effects on pheasant morphology. Physiol Biochem Zool. 2001;74(2):212–8.View ArticlePubMedGoogle Scholar
  17. Taborsky B. The influence of juvenile and adult environments on life-history trajectories. P Roy Soc B-Biol Sci. 2006;273(1587):741–50.View ArticleGoogle Scholar
  18. Mugabo M, Marquis O, Perret S, Le Galliard JF. Immediate and delayed life history effects caused by food deprivation early in life in a short-lived lizard. J Evol Biol. 2010;23(9):1886–98.View ArticlePubMedGoogle Scholar
  19. Lindström J, Metcalfe NB, Royle NJ. How are animals with ornaments predicted to compensate for a bad start in life? A dynamic optimization model approach. Funct Ecol. 2005;19(3):421–8.View ArticleGoogle Scholar
  20. Orizaola G, Dahl E, Laurila A. Compensatory growth strategies are affected by the strength of environmental time constraints in anuran larvae. Oecologia. 2014;174(1):131–7.View ArticlePubMedGoogle Scholar
  21. Blount JD, Metcalfe NB, Arnold KE, Surai PF, Devevey GL, Monaghan P. Neonatal nutrition, adult antioxidant defences and sexual attractiveness in the zebra finch. P Roy Soc B-Biol Sci. 2003;270(1525):1691–6.View ArticleGoogle Scholar
  22. Birkhead TR, Fletcher F, Pellatt EJ. Nestling diet, secondary sexual traits and fitness in the zebra finch. P Roy Soc B-Biol Sci. 1999;266(1417):385–90.View ArticleGoogle Scholar
  23. Reichert S, Criscuolo F, Zahn S, Arrive M, Bize P, Massemin S. Immediate and delayed effects of growth conditions on ageing parameters in nestling zebra finches. J Exp Biol. 2015;218(3):491–9.View ArticlePubMedGoogle Scholar
  24. Barry KL. You are what you eat: food limitation affects reproductive fitness in a sexually cannibalistic praying mantid. Plos One. 2013;8(10):e78164.Google Scholar
  25. Runagall-McNaull A, Bonduriansky R, Crean AJ. Dietary protein and lifespan across the metamorphic boundary: protein-restricted larvae develop into short-lived adults. Scientific Reports. 2015;5:11783.Google Scholar
  26. Sentinella AT, Crean AJ, Bonduriansky R. Dietary protein mediates a trade-off between larval survival and the development of male secondary sexual traits. Funct Ecol. 2013;27(5):1134–44.View ArticleGoogle Scholar
  27. Fricke C, Adler MI, Brooks RC, Bonduriansky R. The complexity of male reproductive success: effects of nutrition, morphology, and experience. Behav Ecol. 2015;26(2):617–24.View ArticleGoogle Scholar
  28. Zajitschek F, Hunt J, Jennions MD, Hall MD, Brooks RC. Effects of juvenile and adult diet on ageing and reproductive effort of male and female black field crickets, Teleogryllus commodus. Funct Ecol. 2009;23(3):602–11.View ArticleGoogle Scholar
  29. Ohlsson T, Smith HG, Raberg L, Hasselquist D. Pheasant sexual ornaments reflect nutritional conditions during early growth. P Roy Soc B-Biol Sci. 2002;269(1486):21–7.View ArticleGoogle Scholar
  30. Shuster SM, Wade MJ. Mating systems and strategies. USA: Princeton University Press; 2003.Google Scholar
  31. David P, Bjorksten T, Fowler K, Pomiankowski A. Condition-dependent signalling of genetic variation in stalk-eyes flies. Nature. 2000;406(6792):186–8.View ArticlePubMedGoogle Scholar
  32. Nowicki S, Hasselquist D, Bensch S, Peters S. Nestling growth and song repertoire sire in great reed warblers: evidence for song learning as an indicator mechanism in mate choice. P Roy Soc B-Biol Sci. 2000;267(1460):2419–24.View ArticleGoogle Scholar
  33. Lewis Z, Sasaki H, Miyatake T. Sex starved: do resource-limited males ensure fertilization success at the expense of precopulatory mating success? Anim Behav. 2011;81(3):579–83.View ArticleGoogle Scholar
  34. Mehlis M, Rick IP, Bakker TCM. Dynamic resource allocation between pre- and postcopulatory episodes of sexual selection determines competitive fertilization success. P Roy Soc B-Biol Sci. 2015;282(1817):20151279.Google Scholar
  35. Tigreros N. Linking nutrition and sexual selection across life stages in a model butterfly system. Funct Ecol. 2013;27(1):145–54.View ArticleGoogle Scholar
  36. Fedina TY, Lewis SM. An integrative view of sexual selection in Tribolium flour beetles. Biol Rev. 2008;83(2):151–71.View ArticlePubMedGoogle Scholar
  37. Andersson M. Sexual selection. Princeton, NJ: Princeton University Press; 1994.Google Scholar
  38. Rahman MM, Kelley JL, Evans JP. Condition-dependent expression of pre- and postcopulatory sexual traits in guppies. Ecol Evol. 2013;3(7):2197–213.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Devigili A, Kelley JL, Pilastro A, Evans JP. Expression of pre- and postcopulatory traits under different dietary conditions in guppies. Behav Ecol. 2013;24(3):740–9.View ArticleGoogle Scholar
  40. Cordes N, Albrecht F, Engqvist L, Schmoll T, Baier M, Müller C, Reinhold K. Larval food composition affects courtship song and sperm expenditure in a lekking moth. Ecol Entomol. 2015;40(1):34–41.View ArticleGoogle Scholar
  41. Somjee U, Allen PE, Miller CW. Different environments lead to a reversal in the expression of weapons and testes in the heliconia bug, Leptoscelis tricolor (Hemiptera: Coreidae). Biol J Linn Soc. 2015;115(4):802–9.View ArticleGoogle Scholar
  42. Droney DC. The influence of the nutritional content of the adult male diet on testis mass, body condition and courtship vigour in a Hawaiian Drosophila. Funct Ecol. 1998;12(6):920–8.View ArticleGoogle Scholar
  43. Gasparini C, Devigili A, Dosselli R, Pilastro A. Pattern of inbreeding depression, condition dependence, and additive genetic variance in Trinidadian guppy ejaculate traits. Ecol Evol. 2013;3(15):4940–53.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Kahrl AF, Cox RM. Diet affects ejaculate traits in a lizard with condition-dependent fertilization success. Behav Ecol. 2015;26(6):1502–11.Google Scholar
  45. Lewis Z, Wedell N. Effect of adult feeding on male mating behaviour in the butterfly, Bicyclus anynana (Lepidoptera : Nymphalidae). J Insect Behav. 2007;20(2):201–13.View ArticleGoogle Scholar
  46. Rosenthal MF, Hebets EA. Temporal patterns of nutrition dependence in secondary sexual traits and their varying impacts on male mating success. Anim Behav. 2015;103:75–82.View ArticleGoogle Scholar
  47. Parker GA, Pizzari T. Sperm competition and ejaculate economics. Biol Rev. 2010;85(4):897–934.View ArticlePubMedGoogle Scholar
  48. Wedell N, Gage MJG, Parker GA. Sperm competition, male prudence and sperm-limited females. Trends Ecol Evol. 2002;17(7):313–20.View ArticleGoogle Scholar
  49. Kelly CD, Jennions MD. Sexual selection and sperm quantity: meta-analyses of strategic ejaculation. Biol Rev. 2011;86(4):863–84.View ArticlePubMedGoogle Scholar
  50. Mautz BS, Moller AP, Jennions MD. Do male secondary sexual characters signal ejaculate quality? A meta-analysis. Biol Rev. 2013;88(3):669–82.View ArticlePubMedGoogle Scholar
  51. Boschetto C, Gasparini C, Pilastro A. Sperm number and velocity affect sperm competition success in the guppy (Poecilia reticulata). Behav Ecol Sociobiol. 2011;65(4):813–21.View ArticleGoogle Scholar
  52. Olsson M, Madsen T, Shine R. Is sperm really so cheap? Costs of reproduction in male adders, Vipera berus. P Roy Soc B-Biol Sci. 1997;264(1380):455–9.View ArticleGoogle Scholar
  53. Bunning H, Rapkin J, Belcher L, Archer CR, Jensen K, Hunt J. Protein and carbohydrate intake influence sperm number and fertility in male cockroaches, but not sperm viability. P Roy Soc B-Biol Sci. 2015;282(1802):20142144.View ArticleGoogle Scholar
  54. O’Dea RE, Jennions MD, Head ML. Male body size and condition affects sperm number and production rates in mosquitofish, Gambusia holbrooki. J Evol Biol. 2014;27(12):2739–44.View ArticlePubMedGoogle Scholar
  55. Vega-Trejo R, Head ML, Jennions MD. Inbreeding depression does not increase after exposure to a stressful environment: a test using compensatory growth. BMC Evol Biol. 2016;16(1):68.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Livingston JD, Kahn AT, Jennions MD. Sex differences in compensatory and catch-up growth in the mosquitofish Gambusia holbrooki. Evol Ecol. 2014;28(4):687–706.View ArticleGoogle Scholar
  57. Kahn AT, Livingston JD, Jennions MD. Do females preferentially associate with males given a better start in life? Biol Lett. 2012;8(3):362–4.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Bisazza A, Marin G. Male size and female mate choice in the eastern mosquitofish. Copeia. 1991;1991:728–33.View ArticleGoogle Scholar
  59. Bisazza A, Marin G. Sexual selection and sexual size dimorphism in the eastern mosquitofish Gambusia holbrooki (Pisces Poeciliidae). Ethol Ecol Evol. 1995;7(2):169–83.View ArticleGoogle Scholar
  60. Pyke GH. A Review of the biology of Gambusia affinis and G.holbrooki. Rev Fish Biol Fish. 2005;15(4):339–65.View ArticleGoogle Scholar
  61. Head ML, Kahn A, Keogh JS, Jennions MD. Sexual selection on body size, genitals and heterozygosity: effects of demography and habitat complexity. bioRxiv. doi:10.1101/045724.
  62. Vega-Trejo R, Head ML, Keogh JS, Jennions MD. Experimental evidence for sexual selection against inbred males. J Anim Ecol. 2016. In Press.Google Scholar
  63. Booksmythe I, Head ML, Keogh JS, Jennions MD. Fitness consequences of artificial selection on relative male genital size. Nat Commun. 2016;7:11597.View ArticlePubMedPubMed CentralGoogle Scholar
  64. Vega-Trejo R, Head ML, Jennions MD. Evidence for inbreeding depression in a species with limited opportunity for maternal effects. Ecol Evol. 2015;5(7):1398–404.View ArticlePubMedPubMed CentralGoogle Scholar
  65. Stearns SC. The evolution of life-history traits in mosquitofish since their introduction to Hawaii in 1905 - rates of evolution, heritabilities, and developmental plasticity. Am Zool. 1983;23(1):65–75.View ArticleGoogle Scholar
  66. Zulian E, Bisazza A, Marin G. Determinants of size in male eastern mosquitofish (Gambusia holbrooki) - inheritance and plasticity of a sexual selected character. Boll Zool. 1993;60(3):317–22.View ArticleGoogle Scholar
  67. Gardiner DM. Utilization of extracellular glucose by spermatozoa of two viviparous fishes. Comp Biochem Physiol A. 1978;59A:165–8.View ArticleGoogle Scholar
  68. Nakagawa S, Schielzeth H. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biol Rev. 2010;85(4):935–56.PubMedGoogle Scholar
  69. R Development Core Team: R. A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2012.Google Scholar
  70. Billard R, Cosson MP. Some problems related to the assessment of sperm motility in freshwater fish. J Exp Zool. 1992;261:122–31.View ArticleGoogle Scholar
  71. Fitzpatrick LJ, Gasparini C, Fitzpatrick JL, Evans JP. Male–female relatedness and patterns of male reproductive investment in guppies. Biol Lett. 2014;10(5):20140166.Google Scholar
  72. Devigili A, Doldan-Martelli V, Pilastro A. Exploring simultaneous allocation to mating effort, sperm production, and body growth in male guppies. Behav Ecol. 2015;26(4):1203–11.View ArticleGoogle Scholar
  73. Rahman MM, Turchini GM, Gasparini C, Norambuena F, Evans JP. The Expression of Pre- and Postcopulatory Sexually Selected Traits Reflects Levels of Dietary Stress in Guppies. Plos One. 2014;9(8):e105856.Google Scholar
  74. Abramoff MD, Magelhaes PJ, Ram SJ. Image processing with ImageJ. Biophotonics Int. 2004;11:36–42.Google Scholar
  75. Gelman A, Hill J. Data analysis using regression and hierarchical/multilevel models. New York: Cambridge University Press; 2007.Google Scholar
  76. Schielzeth H. Simple means to improve the interpretability of regression coefficients. Methods Ecol Evol. 2010;1(2):103–13.View ArticleGoogle Scholar
  77. Nakagawa S, Schielzeth H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol. 2013;4(2):133–42.View ArticleGoogle Scholar
  78. Rosenberg MS, Rothstein HR, Gurevitch J. Effect sizes: Conventional Choices and Calculations. In: Koricheva J, Gurevitch J, Mengersen K, editors. Handbook of Meta-analysis in Ecology and Evolution. Princeton and Oxford: Princeton University Press; 2013. p. 61–71.Google Scholar
  79. Perry JC, Rowe L. Condition-dependent ejaculate size and composition in a ladybird beetle. P Roy Soc B-Biol Sci. 2010;277(1700):3639–47.View ArticleGoogle Scholar
  80. Alavi SMH, Pšenička M, Policar T, Rodina M, Hamáčková J, Kozák P, Linhart O. Sperm quality in male Barbus barbus L. fed different diets during the spawning season. Fish Physiol Biochem. 2009;35(4):683–93.View ArticlePubMedGoogle Scholar
  81. Donelson JM, Munday PL, McCormick MI, Pankhurst NW, Pankhurst PM. Effects of elevated water temperature and food availability on the reproductive performance of a coral reef fish. Mar Ecol Prog Ser. 2010;401:233–43.View ArticleGoogle Scholar
  82. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Secondth ed. Hillsdale: Lawrence Erlbaum; 1988.Google Scholar
  83. Parker GA. Sperm competition games - raffles and roles. P Roy Soc B-Biol Sci. 1990;242(1304):120–6.View ArticleGoogle Scholar
  84. Snook RR. Sperm in competition: not playing by the numbers. Trends Ecol Evol. 2005;20(1):46–53.View ArticlePubMedGoogle Scholar
  85. Gage MJG, Morrow EH. Experimental evidence for the evolution of numerous, tiny sperm via sperm competition. Curr Biol. 2003;13(9):754–7.View ArticlePubMedGoogle Scholar
  86. Simmons LW, Tomkins JL, Hunt J. Sperm competition games played by dimorphic male beetles. Proc Biol Sci. 1999;266(1415):145–50.View ArticlePubMed CentralGoogle Scholar
  87. Hosken DJ, Ward PI. Experimental evidence for testis size evolution via sperm competition. Ecol Lett. 2001;4(1):10–3.View ArticleGoogle Scholar
  88. Parker GA, Ball MA. Sperm competition, mating rate and the evolution of testis and ejaculate sizes: a population model. Biol Lett. 2005;1(2):235.View ArticlePubMedPubMed CentralGoogle Scholar
  89. Evans JP, Pitcher TE, Magurran AE. The ontogeny of courtship, colour and sperm production in male guppies. J Fish Biol. 2002;60(2):495–8.View ArticleGoogle Scholar
  90. Kahn AT, Kokko H, Jennions MD. Adaptive sex allocation in anticipation of changes in offspring mating opportunities. Nature Communications. 2013;4:1603.Google Scholar
  91. Gasparini C, Kelley JL, Evans JP. Male sperm storage compromises sperm motility in guppies. Biol Lett. 2014;10(11):20140681.Google Scholar
  92. Gasparini C, Marino IAM, Boschetto C, Pilastro A. Effect of male age on sperm traits and sperm competition success in the guppy (Poecilia reticulata). J Evol Biol. 2010;23(1):124–35.View ArticlePubMedGoogle Scholar
  93. Hunt J, Simmons LW. Maternal and paternal effects on offspring phenotype in the dung beetle Onthophagus taurus. Evolution. 2000;54(3):936–41.View ArticlePubMedGoogle Scholar
  94. Reinhold K. Maternal effects and the evolution of behavioral and morphological characters: a literature review indicates the importance of extended maternal care. J Hered. 2002;93(6):400–5.View ArticlePubMedGoogle Scholar
  95. Klug H, Alonzo SH, Bonsall MB. Theoretical foundations of parental care. In: Royle NJ, Smiseth PT, Kölliker M, editors. The Evolution of parental care. Oxford: Oxford University Press; 2012. p. 21–39.Google Scholar
  96. Klug H, Bonsall MB. What are the benefits of parental care? The importance of parental effects on developmental rate. Ecol Evol. 2014;4(12):2330–51.View ArticlePubMedPubMed CentralGoogle Scholar
  97. Alonso-Alvarez C, Velando A. Benefits and costs of parental care. In: Royle NJ, Smiseth PT, editors. The Evolution of parental care. Oxford: Oxford University Press; 2012. p. 40–61.View ArticleGoogle Scholar
  98. McGraw KJ, Adkins-Regan E, Parker RS. Maternally derived carotenoid pigments affect offspring survival, sex ratio, and sexual attractiveness in a colorful songbird. Naturwissenschaften. 2005;92(8):375–80.View ArticlePubMedGoogle Scholar
  99. Pizzari T, Dean R, Pacey A, Moore H, Bonsall MB. The evolutionary ecology of pre- and post-meiotic sperm senescence. Trends Ecol Evol. 2008;23(3):131–40.View ArticlePubMedGoogle Scholar
  100. Moller AP, Mousseau TA, Rudolfsen G, Balbontin J, Marzal A, Hermosell I, De Lope F. Senescent sperm performance in old male birds. J Evol Biol. 2009;22(2):334–44.View ArticlePubMedGoogle Scholar
  101. Sloter E, Schmid TE, Marchetti F, Eskenazi B, Nath J, Wyrobek AJ. Quantitative effects of male age on sperm motion. Hum Reprod. 2006;21(11):2868–75.View ArticlePubMedGoogle Scholar
  102. Cornwallis CK, Dean R, Pizzari T. Sex-specific patterns of aging in sexual ornaments and gametes. Am Nat. 2014;184(3):E66–78.View ArticlePubMedGoogle Scholar
  103. Radwan J, Michalczyk L, Prokop Z. Age dependence of male mating ability and sperm competition success in the bulb mite. Anim Behav. 2005;69:1101–5.View ArticleGoogle Scholar
  104. Hoysak DJ, Liley NR, Taylor EB. Raffles, roles, and the outcome of sperm competition in sockeye salmon. Can J Zool. 2004;82(7):1017–26.View ArticleGoogle Scholar
  105. Jones TM, Featherston R, Paris DBBP, Elgar MA. Age-related sperm transfer and sperm competitive ability in the male hide beetle. Behav Ecol. 2007;18(1):251–8.View ArticleGoogle Scholar
  106. Reinhardt K, Siva-Jothy MT. An advantage for young sperm in the house cricket Acheta domesticus. Am Nat. 2005;165(6):718–23.View ArticlePubMedGoogle Scholar
  107. Siva-Jothy MT. The young sperm gambit. Ecol Lett. 2000;3(3):172–4.View ArticleGoogle Scholar
  108. House CM, Simmons LW. Genital morphology and fertilization success in the dung beetle Onthophagus taurus: an example of sexually selected male genitalia. P Roy Soc B-Biol Sci. 2003;270(1514):447–55.View ArticleGoogle Scholar
  109. Kahn AT, Mautz B, Jennions MD. Females prefer to associate with males with longer intromittent organs in mosquitofish. Biol Lett. 2009;6(1):55–8.View ArticlePubMedPubMed CentralGoogle Scholar
  110. Langerhans RB, Layman CA, DeWitt TJ. Male genital size reflects a tradeoff between attracting mates and avoiding predators in two live-bearing fish species. Proc Natl Acad Sci U S A. 2005;102(21):7618–23.View ArticlePubMedPubMed CentralGoogle Scholar
  111. Brooks R, Caithness N. Female choice in a feral guppy population - are there multiple cues. Anim Behav. 1995;50:301–7.View ArticleGoogle Scholar
  112. Devigili A, Evans JP, Di Nisio A, Pilastro A. Multivariate selection drives concordant patterns of pre- and postcopulatory sexual selection in a livebearing fish. Nature Communications. 2015;6:8291.Google Scholar
  113. Head ML, Vega-Trejo R, Jacomb F, Jennions MD. Predictors of male insemination success in the mosquitofish (Gambusia holbrooki). Ecol Evol. 2015;5(21):4999–5006.View ArticlePubMedPubMed CentralGoogle Scholar
  114. Head ML, Jacomb F, Vega-Trejo R, Jennions MD. Male mate choice and insemination success under simultaneous versus sequential choice conditions. Anim Behav. 2015;103:99–105.View ArticleGoogle Scholar

Copyright

© The Author(s). 2016

Advertisement