The model

We used a simple population genetic model to simulate and track the prevalence of a sexually antagonistic allele, x, through a diploid population over time. To allow for the allele to have a sex-specific effect and to be either recessive or dominant, we split the populations into males (m) and females (f) that are further divided according to the allelic composition of the genetic locus in question. That is, we account for individuals that are homozygous wrt x (m xx and f xx ), homozygous wrt y (m yy and f yy ) or heterozygous (m xy and f xy ). We assumed mating to be frequency-dependent and for simplicity assumed the population size remains constant over time. The population dynamics can then be described by the following set of differential equations:

$$\frac{{{\mathrm{d}}m_{ij}}}{{{\mathrm{d}}t}} = \mu \left( {1 - \rho } \right)G_{ij} - \mu _m\,m_{ij},i,j \in \left\{ {x,y} \right\}$$ (1)

$$\frac{{{\mathrm{d}}f_{ij}}}{{{\mathrm{d}}t}} = \mu \,\rho \,G_{ij} - \mu _f\,f_{ij}$$ (2)

with

$$\mu = \mathop {\sum }\limits_{i,j} \mu _mm_{ij} + \mu _ff_{ij}$$ (3)

$$G_{xx} = \pi _{xx}^f\left( {\pi _{xx}^m + 0.5\pi _{xy}^m} \right) + \pi _{xy}^f\left( {0.5\pi _{xx}^m + 0.25\pi _{xy}^m} \right)$$ (4)

$$G_{yy} = \pi _{yy}^f\left( {\pi _{yy}^m + 0.5\pi _{xy}^m} \right) + \pi _{xy}^f\left( {0.5\pi _{yy}^m + 0.25\pi _{xy}^m} \right)$$ (5)

$$G_{xy} = \pi _{xx}^f\left( {\pi _{yy}^m + 0.5\pi _{xy}^m} \right) + \pi _{yy}^f\left( {\pi _{xx}^m + 0.5\pi _{xy}^m} \right) + \pi _{xy}^f\left( {0.5\pi _{xx}^m + 0.5\pi _{xy}^m + 0.5\pi _{yy}^m} \right)$$ (6)

where

$$\pi _{ij}^s = \frac{{s_{ij}}}{{\mathop {\sum }

olimits_{i,j} s_{ij}}},s \in \{ m,f\}$$ (7)

is the proportion of males or females with locus (ij). ρ denotes the female sex bias (if considered). We further assumed that the cost or benefit associated with carrying allele x is solely manifested through a decrease or increase in lifetime reproductive success. We cannot model costs to females as reduced health per se; if reductions in health do not reduce female fitness then trivially, any male-benefit alleles will spread. We therefore assume that alleles that severely reduce female health, reduce female fitness by reducing how well women care for their offspring and grand-offspring. Under this assumption, a shortening or lengthening of an individual’s reproductive period is equivalent to a reduction or increase in reproductive fitness. In the model this can be incorporated by changing \(\pi _{ij}^s\) as follows

$$\pi _{ij}^m = \frac{{b_{ij}m_{ij}}}{{\mathop {\sum }

olimits_{i,j} b_{ij}m_{ij}}}$$ (8)

and

$$\pi _{ij}^f = \frac{{c_{ij}m_{ij}}}{{\mathop {\sum }

olimits_{i,j} c_{ij}f_{ij}}}$$ (9)

where b ij ≥ 1 denotes the male-benefit and c ij ≤ 1 denotes the cost to females. Considering x to take an effect only in homozygous form this results in

$$b_{xx}

e 0,b_{xy} = 0,b_{yy} = 0$$

$$c_{xx}

e 0,c_{xy} = 0,c_{yy} = 0$$

whereas the situation in which x is dominant can be described as

$$b_{xx}

e 0,b_{xy}

e 0,b_{yy} = 0$$

$$c_{xx}

e 0,c_{xy}

e 0,c_{yy} = 0$$

This formulation also allows us to consider dominant and recessive effects in females and males differentially. To obtain equilibrium population frequencies, we solved Eqs. (1) and (2) numerically using the odeint solver from the NumPy Python package. For each combination of male-fitness benefits (b ij ) and female fitness costs (c ij ), we simulated the model forward in time until an equilibrium point was reached, using the following initial conditions: m xx (0) = 0.05, m xy (0) = 0.3, m yy (0) = 0.1, f xx (0) = 0.1, f xy (0) = 0.35, f yy (0) = 0.1.

Selecting on late-life male fertility affects female lifespan

If intralocus sexual conflict is responsible for the health-survival paradox, then selecting for late-life male reproduction should result in the accumulation of late-acting male-benefit alleles that reduce female fitness. To broadly test this idea, we selected for late-life male fertility in replicate populations of Drosophila simulans but relaxed selection on females. If sexual conflict is responsible for the health-survival paradox, then in populations where males experienced the greatest increase in fertility late-in-life, females should experience the greatest reduction in lifespan (relative to controls).

Selecting on male fertility involved establishing five experimental populations and five female-supply populations (see below) using flies collected from our large outbreeding, free-mating lab-stock population (100 males and 100 females (all virgins) per experimental and female-supply population). For 28 days, fly food in the experimental populations was changed every five days to ensure that no eggs laid emerged as adults. On day 28, flies were anaesthetised with CO 2 , and removed from the cage. One hundred virgin 3- to 5-day-old females were taken from their paired-female-supply populations (to ensure experimental populations were independently evolving), and added to the appropriate experimental population. This should reduce selection for old age reproduction in females. Old (28 days) males from each experimental population and young females from paired-female supply were then left for three days to lay eggs. From these, 100 male and 100 female offspring were collected on emergence and these seeded the next generation in each experimental population. This procedure was repeated for 12 generations. Female-supply populations were also fed every five days, but here new food was added on day 15, removed on day 18 and offspring collected from eggs laid between days 15 and 18. This regime is shown in Supplementary Figure 1.

To test for male responses to our artificial selection, we collected 30 virgin males from each experimental population and from the lab-stock population. Each male was housed in a 40 mL vial, with 8 mL of medium for two days to mature. Then two virgin lab-stock females were added and these were replaced with young virgin females every week to ensure males had continuous access to young virgins (as is the case in selection cages). Any female that died was replaced with a like-age virgin. On day 28, male fertility was assayed. Each focal male was paired with a virgin stock female. Males were removed eight hours later. Females were allowed to lay eggs in three vials over seven days and all offspring that emerged from these vials were counted as our measure of male fertility (full details in Supplementary Figure 2). Males that did not appear to mate (i.e. counts of offspring = zero), were excluded from analyses.

To test for female impacts resulting from selecting on male fertility, 40 females from each experimental population and the lab-stock population were collected as virgins and housed individually in 40 mL vials contain 8 mL of Jazz mix medium. After four days, flies were moved into a new vial. The following morning two stock virgin males (three to five days old) were added and kept with the females for three hours before being removed. This regime was repeated across the entire lifespan of each female fly (full details in Supplementary Figure 3). Pairing females with two males for three hours every five days ensures that females reproduce as normal but lifespan is not reduced due to the direct costs of mating or harassment30. Females were checked daily for survival and adult lifespan was calculated.

To assess how late-life male fertility evolved in the experimental populations, mean male fertility in the stock population was subtracted from the mean value for each experimental population. High positive values meant that males from the experimental population performed much better than non-selected males, and a negative value meant that experimental males had worse late-life fertility. Female lifespan was treated in the same way (i.e. average population value—average stock-population value) and a Pearson’s correlation coefficient was calculated in R version 3.4.1.31 to analyse the correlation between male late-life fertility and female lifespan.

Association between late-life male fitness and female health

The above “selection then assay protocol” tests the general principle of the health-survival paradox, but does not directly test for female health declines as late-life male fitness increases. If intralocus sexual conflict is responsible for the health-survival paradox, then genotypes that produce high fitness males should produce females with poor health and vice versa. This was tested using Drosophila melanogaster Genetic Reference Panel (DGRP) lines and two biomarkers of female physical function to reveal underlying health.

Ten DGRP lines were used (ID = 28, 101, 136, 360, 382, 443, 595, 737, 783, 796). Flies were maintained under a 12:12 L:D cycle at 25 °C. Lines were excluded from analyses, if fewer than two animals survived to assay. The final sample sizes for each line and trait are given in Supplementary Table 1. Experimental flies were collected as virgins and aspirated into individual 100 mL vials on their day of hatching with 8 mL of medium. Dahomey tester flies were used as mating partners to assess reproductive performance. Ivanov et al.32 recorded lifespan in DGRP lines and median lifespan ranged between 21 and 79 days. We therefore conducted tests when adults were 35 days old, approximately the median lifespan for some of the shorter lived genotypes.

Negative geotaxis (vertical climbing in response to shock) was one measure of fly health. It is a measure of motor ability that shows an age-dependent decline in Drosophila33. To assay negative geotaxis, flies were aspirated into 15 mL vials attached to a drop mechanism, which was raised 10 cm and dropped. A camera recorded every trial, to record the distance that flies climbed in the two minutes after dropping. Flies were then given two minutes to recover before the process was repeated. All observations were made blind—flies were labelled with a random number and videos were analysed independently by two different observers, and any values that differed by >3 mm were observed by a third experimenter to reach consensus. Recovery time from anaesthesia was also used as a measure of female health, as this can indicate metabolic performance. Assay flies were transferred into a 15 mL vials where they were exposed to CO 2 (1 L/min) for thirty seconds. Flies were then put onto a piece of white paper and the time until flies stood upright was recorded and used as our measure of performance. All assays were recorded within two hours of lights going (i.e. 9–11 am) and flies assayed in a random order. Measures were made blind; flies were labelled with a random number by one lab member, before vials were passed to the observer.

Reproductive performance of the DGRP males was assessed after matings with virgin females from our wildtype stock animals. Each experimental male was housed with two virgin tester females, aged between 3 and 6 days old, that had been left overnight in 40 mL mating vials containing surplus food. Flies were then left for 48 h, after which, females were transferred to a vial for oviposition for a further 48 h, while males were removed and frozen. Females were then moved to one more vial for a further 48 h, such that their egg laying over 6 days was recorded. Oviposition vials were then incubated at the temperature from which their sire originated and offspring were counted 8 days after the first day of offspring eclosion.

Please note, differences in timings between the two male fertility assays represent species-specific lab protocols. Additionally, males were kept as virgins prior to being assayed in this experiment to allow comparison with females who were also maintained as virgins to ensure that direct physiological damage caused by male harassment did not reduce their physical performance.

To test for effects, we created a single average value, for each line and each trait for analyses (i.e. we conducted derived variable analyses). For male fertility, zero counts were once more excluded as we could not be sure that males had mated if no offspring were produced, but note this is conservative for our hypothesis. For female geotaxis distance, we included zero values but note that the results of analyses are the same irrespective of whether we include all data for both traits or exclude zeros. We then used a Pearson’s correlations, calculated in R, to analyse the associations between male late-life fertility, and female health measures.