- Research article
- Open Access
Mating behavior and reproductive morphology predict macroevolution of sex allocation in hermaphroditic flatworms
BMC Biology volume 20, Article number: 35 (2022)
Sex allocation is the distribution of resources to male or female reproduction. In hermaphrodites, this concerns an individual’s resource allocation to, for example, the production of male or female gametes. Macroevolutionary studies across hermaphroditic plants have revealed that the self-pollination rate and the pollination mode are strong predictors of sex allocation. Consequently, we expect similar factors such as the selfing rate and aspects of the reproductive biology, like the mating behaviour and the intensity of postcopulatory sexual selection, to predict sex allocation in hermaphroditic animals. However, comparative work on hermaphroditic animals is limited. Here, we study sex allocation in 120 species of the hermaphroditic free-living flatworm genus Macrostomum. We ask how hypodermic insemination, a convergently evolved mating behaviour where sperm are traumatically injected through the partner’s epidermis, affects the evolution of sex allocation. We also test the commonly-made assumption that investment into male and female reproduction should trade-off. Finally, we ask if morphological indicators of the intensity of postcopulatory sexual selection (female genital complexity, male copulatory organ length, and sperm length) can predict sex allocation.
We find that the repeated evolution of hypodermic insemination predicts a more female-biased sex allocation (i.e., a relative shift towards female allocation). Moreover, transcriptome-based estimates of heterozygosity reveal reduced heterozygosity in hypodermically mating species, indicating that this mating behavior is linked to increased selfing or biparental inbreeding. Therefore, hypodermic insemination could represent a selfing syndrome. Furthermore, across the genus, allocation to male and female gametes is negatively related, and larger species have a more female-biased sex allocation. Finally, increased female genital complexity, longer sperm, and a longer male copulatory organ predict a more male-biased sex allocation.
Selfing syndromes have repeatedly originated in plants. Remarkably, this macroevolutionary pattern is replicated in Macrostomum flatworms and linked to repeated shifts in reproductive behavior. We also find a trade-off between male and female reproduction, a fundamental assumption of most theories of sex allocation. Beyond that, no theory predicts a more female-biased allocation in larger species, suggesting avenues for future work. Finally, morphological indicators of more intense postcopulatory sexual selection appear to predict more intense sperm competition.
Sex allocation theory predicts how organisms allocate resources to reproduction via their male or female function [1, 2]. Sex allocation has been studied intensively in separate-sexed organisms—generally estimated as the offspring sex ratio [1, 2]—and found to be largely determined by the level of local mate competition (LMC) [3, 4]. LMC occurs in structured populations when only one or a few females lay eggs in a patch (e.g., fig wasps [5, 6]), leading to mate competition between a female’s sons. From the mother’s perspective, making many sons thus wastes reproductive resources that could be reallocated to the production of daughters. Consequently, such mothers produce a female-biased sex ratio to reduce competition between their sons and provide more potential mating partners for them [3, 7].
In contrast, sex allocation in hermaphrodites concerns the allocation of resources by a single individual into its own male or female sex function, either separated in time in sequential hermaphrodites or concurrently in simultaneous hermaphrodites (we focus on the latter, referring to them simply as hermaphrodites). Hermaphroditism is predicted to occur when at least one sex function yields diminishing returns on investment (e.g., the production of sperm or eggs), which then favours resource reallocation to the other sex function, resulting in biased sex allocation [8,9,10]. It is thought that the male function more often experiences diminishing returns . This results from competition between related sperm, sometimes called local sperm competition (LSC), in analogy to LMC . Specifically, high LSC is expected under monogamy or frequent selfing because both lead to competition between closely related sperm. Sex allocation is thus expected to be strongly female-biased in such organisms [1, 13]. Low population density can also lead to LSC since a reduced encounter probability may decrease the number of (sperm) donors contributing ejaculates to a given (sperm) recipient, and sex allocation is therefore expected to depend on the mating group size [9, 12, 14]. Furthermore, even when mating rates are high, postcopulatory sexual selection mechanisms, such as sperm removal, sperm displacement and/or cryptic female choice, can increase LSC [12, 15,16,17,18].
Many hermaphroditic animals and plants exhibit phenotypic plasticity of sex allocation, generally following theoretical predictions [12, 19]. In plants, a more male-biased allocation (i.e., a relative shift towards male allocation) is observed with higher population density, likely because this results in more linear male fitness returns on investment [19,20,21]. Size-dependent sex allocation is also common and may be linked to the pollination mode. Larger individuals are generally more female-biased in animal pollinated plants but more male-biased in wind pollinated plants . Finally, outcrossing frequency is a strong predictor of sex allocation, with more female-biased allocation under high selfing rates . Similar patterns are found in animals with generally a more male-biased allocation with higher density and/or mating group size (both of which likely correlate with sperm competition intensity) (e.g., [22,23,24,25]). Furthermore, there is some evidence of a more female-biased allocation with increasing body size (reviewed in ) and with increased selfing [26, 27].
The macroevolution of sex allocation has been studied across numerous taxa in plants (reviewed in [19, 28, 29]). These analyses show an association between the selfing rate and reduced investment into pollen, frequently resulting in convergent changes in flower morphology, which has been termed the selfing syndrome (e.g., [30,31,32], reviewed in ). Sex allocation in plants also depends on the pollination mode, generally showing more male-biased allocation in wind-pollinated plants, potentially because animal pollination limits pollen export . Furthermore, across angiosperm plants, species with larger flowers allocate a larger proportion of total flower biomass to male structures .
In contrast to the extensive research in plants, comparative work on sex allocation in hermaphroditic animals is much more limited. A study of six sea bass species found a positive relationship between male allocation and sperm competition intensity, and male allocation was potentially also influenced by spawning frequency and behaviour during gamete release . Moreover, sex allocation was also compared between five goby species. While species always contained some (simultaneous) hermaphrodites with varying allocation to the male function and pure sex females [35, 36], they differed in the presence and frequency of pure sex males, which occurred only in the species with high population density. This suggests that a pure male strategy (i.e., an extremely male-biased allocation) might only occur under intense sperm competition (and thus reduced LSC). Finally, sex allocation was recently investigated in seven Macrostomum flatworm species . The results tentatively suggested that the mating behavior and the ability to perform selfing might predict sex allocation (but see below for more detail).
Thus, similar factors predict sex allocation plasticity within species in animals and plants. However, due to the lack of studies, it is unclear if there are general predictors of sex allocation evolution in hermaphroditic animals. A priori, we expect that, like in plants, increased selfing should favor a more female-biased allocation [1, 2, 38]. We also expect–in analogy to the pollination mode–that the reproductive mode, including the mating behaviour, will predict sex allocation. For example, there should be clear differences between internal and external fertilization [12, 39, 40]. Moreover, in internally fertilizing species, we expect factors shaping sperm competition dynamics (e.g., the sperm receiving organ morphology) to predict sex allocation . Describing macroevolutionary predictors of sex allocation in hermaphroditic animals will reveal differences and commonalities between plants and animals and add a missing piece to this successful evolutionary theory [1, 2].
Here, we search for macroevolutionary predictors of sex allocation across the free-living flatworm genus Macrostomum. The worms in this species-rich group of hermaphrodites are microscopic and transparent, allowing in vivo measurements of reproductive trait morphology, and testis and ovary size. From the gonad sizes, we can then calculate gonadal sex allocation (GSA) as the ratio of testis size over total gonad size  (Fig. 1C). Moreover, we can assign most Macrostomum species to one of two contrasting mating syndromes [43, 44]. These represent convergent combinations of morphological and behavioral characters that indicate different mating behaviours, namely reciprocal copulation or hypodermic insemination (Fig. 1). Additionally, the species show extensive variation in several fascinating reproductive morphologies (Fig. 1D–F) that may serve as indicators of the intensity of postcopulatory sexual selection (“morphological indicators”), including the complexity of the (female) antrum (the female genitalia), the size and shape of the stylet (the male intromittent organ), and different aspects of the sperm design [43,44,45]. These morphological indicators likely also constitute non-gonadal components of male and female sex allocation (see the “Methods” section).
In species with the reciprocal mating syndrome [43, 44], the donors reciprocally insert their stylet into the recipient’s antrum, which often shows a thickened epithelium (Fig. 1A) and stores the received sperm  (Fig. 1F). Furthermore, in many reciprocally mating species, one can observe the so-called postcopulatory suck behavior, where recipients put their mouth on their own female genital opening, probably to remove received ejaculate [45, 47,48,49]. Such ejaculate removal could allow the recipient to exert cryptic female choice and/or represent a female resistance trait in sexual conflict over the fate of the ejaculate . The sexual conflict interpretation is supported by the presence of stiff lateral sperm bristles, a possible male persistence trait (Fig. 1A, E), which is hypothesized to anchor sperm inside the antrum during the suck behaviour and to thereby defend against the recipient’s sperm removal attempts . Moreover, several seminal fluid proteins identified in the main model species, Macrostomum lignano, reduce the recipient’s propensity to perform the suck behaviour , further supporting this sexual conflict view. In contrast, species with the hypodermic mating syndrome [43, 44] mate via hypodermic insemination (HI), during which donors traumatically inject sperm into the recipient’s tissues using a needle-like stylet (Fig. 1B). Comparative analyses indicate that HI has convergently evolved at least nine times in the genus [43, 44] and that its emergence coincides with convergent changes in sperm design and both female and male genital morphology. Specifically, species with HI have a simplified antrum, shorter and sharper stylets, and shorter and simpler sperm with either absent or drastically reduced bristles (Fig. 1B) [43, 44].
HI probably affects the intensity and mode of sperm competition and thus the optimal sex allocation, but the direction of the effect is currently unclear [44, 50]. On the one hand, HI could lead to increased male investment. In reciprocally mating species, donated sperm is stored inside the antrum, where it can be exposed to the recipient’s suck behavior and sperm displacement by competing donors, both of which likely increase the realized level of LSC [15, 17, 18]. In contrast, in species with HI, the donor circumvents the antrum, thus possibly reducing the recipient’s ability to control the fate of the received ejaculate. Furthermore, already stored sperm is likely inaccessible to competing donors, hindering sperm removal or displacement. Therefore, under HI, sperm competition could be more fair-raffle like , which would reduce LSC and thus favor an increased male allocation [44, 50]. The fact that species with HI have shorter sperm than reciprocally mating species supports this prediction . This is because sperm length and number likely trade-off [52,53,54,55], suggesting that under HI, selection favors more numerous but also shorter sperm. On the other hand, HI could lead to decreased male allocation because it facilitates selfing [26, 37, 56,57,58], leading to a drastic increase in LSC [1, 13]. The hypodermically mating species, Macrostomum hystrix, is thought to perform selfing by hypodermically inseminating into its own anterior body region . Several other hypodermically mating species also self , but how fertilization is finally achieved is currently unknown, and not all species have been examined. Furthermore, at least one reciprocally mating species, Macrostomum mirumnovem, can also self , so while HI may facilitate selfing [26, 57, 58], it is not required.
As briefly mentioned above, a previous study investigated GSA evolution in seven Macrostomum species . While not conclusive, the data suggested that the inferred mating syndrome and the ability to self might predict sex allocation. Two of the three hypodermically mating species and three of the four selfing species had the most ovary-biased sex allocation. However, the abovementioned hypodermically mating and facultatively selfing M. hystrix had the most testis-biased sex allocation . Therefore, the general effect of HI on the evolution of sex allocation remains unclear.
Here we use a recent and robust phylogenetic framework  to study the macroevolution of sex allocation across 120 Macrostomum species. First, we ask if the mating syndrome predicts GSA. Second, we estimate heterozygosity as a proxy for the degree of inbreeding/selfing to determine how it relates to the mating syndrome and if it predicts GSA. Third, we ask if changes in GSA capture reallocation between the sex functions. Finally, we elucidate if the abovementioned morphological indicators of the intensity of postcopulatory sexual selection can predict GSA.
Sex allocation evolution
We recently collated a morphological and phylogenetic dataset including 145 Macrostomum species from across the globe . For the present study, we estimated the gonadal sex allocation (GSA; Fig. 1C) for 120 of these species (1092 specimens, Additional file 1: Tables S1-S3). While for 26 species, we measured only a few specimens (N=1: 14 species, N=2: 12 species), we had larger sample sizes for the remaining species (N=3–4: 13 species, N=5–10: 37 species, N>10: 44 species). Furthermore, standard errors of GSA estimates were generally low (Fig. 2), suggesting single specimens represent useful estimates of species means. The arithmetic mean GSA across all species was 0.50 (median 0.52), but species varied considerably, ranging from strongly ovary-biased (0.08) to strongly testis-biased (0.91, Fig. 2). Note that theory predicts absolute allocation should not exceed 0.5. However, GSA measures represent relative rather than absolute allocation (see the “Methods and discussion”). Closely related species tended to have a similar GSA, resulting in non-zero estimates for the indicators of phylogenetic signal lambda (λ=0.64) and Bloomberg’s K (K=0.33), but they covaried less than expected under a Brownian motion model (where λ and K would equal 1). We found qualitatively similar patterns for residual testis size (λ=0.47, K=0.23), residual ovary size (λ=0.55, K=0.47), and body size (λ=0.80, K=0.39), with all reported values being significantly different from both 0 and 1 (all P ≤ 0.01).
Ancestral state reconstruction suggested that the large clade containing only species assigned to the hypodermic mating syndrome (clade I in Fig. 2) had ancestors with a strongly ovary-biased GSA. In clade I, only the single specimen measured for M. sp. 99 (Mac099) had a substantially testis-biased GSA (Fig. 2). In contrast, clade II—containing primarily species assigned to the reciprocal mating syndrome—generally had a more testis-biased GSA. Moreover, while two subclades within clade II showed a more ovary-biased GSA (clades III and IV in Fig. 2), also these contained some species with more testis-biased GSA, such as Macrostomum paradoxum (Macpar) in clade III, and Macrostomum gieysztori (Macgie) and relatives in clade IV.
Sex allocation is bimodally distributed
GSA was bimodally distributed (Fig. 3A, solid black line), with an ovary-biased peak at 0.21, a testis-biased peak at 0.70, and a local minimum at 0.53. Separating species by inferred mating syndrome shows that the ovary-biased and testis-biased peaks largely correspond to the hypodermic and reciprocal mating syndromes, respectively (Fig. 3A, yellow and green distributions). Almost all species assigned to the hypodermic syndrome had a GSA <0.5, while most species assigned to the reciprocal syndrome had values >0.5.
We explored if peaks in GSA density corresponded to distinct peaks in a macroevolutionary landscape by fitting three evolutionary models to the data using the “Bounded Brownian Motion with potential” approach [60, 61]. Specifically, we assessed whether our data best fit a landscape that is flat (i.e., Brownian motion, BM), has a single peak (i.e., Ornstein-Uhlenbeck, OU), or has two peaks (BBMV, ). We found that a two-peak landscape best fit our data (Table 1, Fig. 3C). The preferred model had an ovary-biased peak at 0.22 (Fig. 3C), closely corresponding to the first peak of the measured GSA distribution (Fig. 3A). The model had a second peak at 0.76, which was slightly more testis-biased than the measured peak. Besides determining the shape of the evolutionary landscape, BBMV also estimates how strong the attraction towards peaks is, with larger peaks representing stronger attraction (see the “Methods” and ). Interestingly, the ovary-biased peak was three times higher than the testis-biased peak (i.e., 3.34 vs. 1.11), indicating that species quickly evolve towards a low GSA while they do not increase their GSA as rapidly.
Hypodermic species have an ovary-biased sex allocation
We tested if the inferred mating syndrome predicted GSA using weighted phylogenetic generalized least squares (PGLS) regression. We found that the hypodermic mating syndrome was associated with a significant reduction in GSA compared to the reciprocal mating syndrome (Fig. 3B, Additional file 1: Table S4). Moreover, this association persisted when we excluded clade I (Additional file 2: Figure S1D, Additional file 1: Table S4), suggesting the result was not primarily driven by this rather uniform clade. Furthermore, we recovered qualitatively similar associations when using other predictors closely associated with the mating syndromes, namely received sperm location, sperm bristle state, and antrum state (Additional file 2: Figure S1A-C, Additional file 1: Table S4).
Observed heterozygosity predicts sex allocation
Selfing rates are expected to affect GSA, but they have not been estimated for any Macrostomum species and obtaining such estimates was outside of the scope of this study. However, regular selfing is expected to reduce heterozygosity, which can be estimated from transcriptome data, giving some indication of genetic diversity across species. Reduced heterozygosity could, however, also be caused by other factors leading to small effective populations size, such as founder effects and biparental inbreeding . We estimated per-site heterozygosity in all specimens with transcriptomes using a superTranscript approach  (Additional file 3: Figure S2, Additional file 1: Table S5).
We observed lower heterozygosity in species assigned to the hypodermic mating syndrome (Fig. 3D), consistent with increased selfing in these species leading to reduced effective population size. As expected from these results, we found a significant positive relationship between heterozygosity and GSA across all species (Fig. 3C, Additional file 1: Table S6). The logarithmic fit of our PGLS analysis further revealed that this relationship was non-linear, in that GSA initially increased sharply with heterozygosity but then decelerated. Interestingly, the relationship persisted within both the hypodermic and the reciprocal mating syndrome, with the effect size being smaller in the former (Fig. 3C), suggesting a general link between heterozygosity and GSA.
Changes in gonadal sex allocation represent reallocation
Because GSA is a ratio, it is affected by changes in testis size, ovary size, or both. Therefore, we performed PGLS regression of both testis and ovary size on body size to investigate whether GSA differences between the syndromes represent reallocation. We either excluded (allometry models) or included (covariate models) the inferred mating syndrome as a predictor. Testis size was positively related to body size, but larger species had proportionally smaller testes (negative allometry, Fig. 4A left, Additional file 1: Table S7A). When correcting for body size, the testis size was significantly larger in reciprocal species than in hypodermic species (Fig. 4A right, Additional file 1: Table S7B). Ovary size scaled isometrically with body size (Fig. 4B left, Additional file 1: Table S7A), and when correcting for body size, hypodermically mating species had larger ovaries (Fig. 4B right, Additional file 1: Table S7B). Therefore, GSA differences between the syndromes resulted from changes in both testis and ovary size. As expected from these results, GSA was negatively related to body size (Fig. 4C, Additional file 1: Table S6), but body size did not differ between the inferred mating syndromes (Fig. 4D, Additional file 1: Table S6). We then also tested for a trade-off between the male and the female gonads using PGLS regression of residual testis size on residual ovary size. Residual testis size was negatively related to residual ovary size (Fig. 4E, Additional file 1: Table S8). Across Macrostomum, an increase in ovarian tissue thus coincides with reduced testis tissue.
Morphological indicators predict sex allocation
Next, we explored the relationships between GSA and four sexual traits that are potential morphological indicators of the intensity of postcopulatory sexual selection [43,44,45], namely antrum complexity, stylet length, sperm length, and sperm bristle length. Across all species, univariate PGLS of sexual traits suggested that GSA was positively related to antrum complexity, stylet length, total sperm length, and bristle length (Fig. 5, Additional file 1: Table S6). A multivariate PGLS, including all sexual traits and body size, suggested a similar pattern, except that bristle length was not significant (Additional file 1: Table S9). Since sperm bristle length was positively related with both stylet length and sperm length (data not shown), its effect was likely masked in the multivariate analysis.
Across species classified as hypodermically mating, only the antrum complexity score was still significant in both the univariate and multivariate analyses (Fig. 5, Additional file 1: Table S6 and Table S9), indicating a negative relationship of GSA with antrum complexity. However, the R2pred of the univariate model was low (0.07) since only four species had a complexity score >0. In contrast, analysis of species assigned to the reciprocal syndrome broadly recapitulated the findings of the complete dataset. Again, GSA was positively related to antrum complexity and sperm length, in both the univariate and multivariate cases (Fig. 5, Additional file 1: Table S6 and Table S9). Stylet length was not significant in the univariate analysis, but was positively related again in the multivariate analysis, presumably because the multivariate model accounts for body size. To investigate this further, we repeated the stylet length PGLS with body size included as a covariate. Now, stylet length significantly predicted GSA (λ=0.6, β=0.48, SE=0.13, t value=3.8, p<0.001), indicating that relative, not absolute, stylet length predicts GSA.
Sex allocation and hypodermic insemination
Across the genus Macrostomum, gonadal sex allocation (GSA) is bimodally distributed. Since the intensity of local sperm competition (LSC) is likely the main driver of GSA evolution, this indicates that most species are exposed to either fairly low or high, but not to intermediate, intensities of LSC. By fitting evolutionary models, we showed that the two modes in the GSA distribution correspond to two adaptive optima on a macroevolutionary landscape, with rapid evolution towards the peak of ovary-biased GSA and slower evolution towards the testis-biased peak. Thus, across the genus GSA primarily becomes more ovary-biased and only rarely becomes more testis-biased. Furthermore, we found that the type of mating behaviour exhibited by the species explained the bimodal distribution of GSA well, with most species with the hypodermic mating syndrome having an ovary-biased GSA, while species with the reciprocal mating syndrome usually have a testis-biased GSA. Across the genus, hypodermic insemination (HI) has evolved multiple times but there is no clear evidence for reversions to reciprocal copulation, suggesting that HI canalizes morphology and could lock species in this mating behavior . The evolutionary model of GSA suggests that HI has a similarly canalizing effect on sex allocation.
Our findings suggest that HI is associated with processes that lead to reduced male allocation [1, 13]. Additionally, they suggest a more gradual change of sex allocation in reciprocally mating species, potentially in response to the observed male-female coevolution in these species [43, 50]. A reduced male allocation in species with HI contradicts the prediction that HI may lead to a more male-biased sex allocation by reducing the potential for sperm displacement and cryptic female choice and thus leading to more fair-raffle sperm competition [44, 50]. Instead, HI could be linked to the ability to perform selfing [37, 56,57,58], and to perhaps even prefer selfing, with some species showing no evidence of inbreeding depression or delayed selfing . Furthermore, we have established laboratory cultures of several additional species in the hypodermically mating clade (I in Fig. 2) from single individuals, suggesting that selfing could be pervasive in this clade (Brand and Schärer, pers. obs.). Finally, several reciprocally mating species in clade II (Fig. 2) appear to be unable to self (Singh and Schärer, pers. obs.). In agreement with this notion, we observed lower heterozygosity in species assigned to the hypodermic mating syndrome, further supporting the link between HI and selfing.
Naturally, reduced heterozygosity alone is not conclusive evidence of selfing. It simply indicates a smaller effective populations size, which could also result from biparental inbreeding or founder effects . Therefore, hypodermically mating species could also have an ovary-biased GSA because they mostly occur in small fragmented populations. Unfortunately, little is known about the ecology of Macrostomum, partially due to the extensive effort required in sample extraction. Our field collections indicate that population densities vary from one to hundreds of animals in 100 ml of sediment/substrate, and we have collected both hypodermic and reciprocally mating species across the density spectrum (Brand and Schärer, pers. obs.). Alternatively, HI could lead to LSC even in dense populations if injected sperm only rarely encounter rival sperm (e.g., if once injected, hypodermic sperm rapidly became non-functional). Unfortunately, no data on the rate of hypodermic sperm transfer or sperm longevity is currently available for any hypodermic species.
Does hypodermic insemination constitute a selfing syndrome?
The observed convergent reduction in sex allocation and heterozygosity, combined with replicated changes in sexual traits , is reminiscent of the selfing syndrome in plants. The selfing syndrome is defined as an association between the selfing rate and reduced investment into pollen, in combination with convergent changes in flower morphology (e.g., [30,31,32, 64,65,66], reviewed in ). In hermaphroditic Biomphalaria snails, a reduced waiting time (i.e., the time that an isolated individual waits before it resorts to selfing), combined with reduced inbreeding depression, has analogously been called the selfing syndrome [67,68,69], but few morphological traits have been assessed to evaluate correlated changes. In Caenorhabditis nematodes, hermaphroditic selfing lineages have originated a least three times  and these origins are associated with replicated behavioral, morphological, and genomic changes [71, 72].
In agreement with our inferred link between selfing and reduced male allocation, some hermaphroditic animals reduce their male allocation under selfing [26, 27], but others show no or inconsistent patterns [68, 73]. However, reduced male allocation could also result from other factors. For example, in the free-living flatworm Schmidtea polychroa, populations with sperm-dependent parthenogenesis show reduced male allocation compared to outcrossing populations, presumably due to limited outcrossing opportunities in the former [74, 75]. Assuming the observed reduction in heterozygosity results from selfing, our data suggest that HI constitutes a selfing syndrome. Selfing may thus be a wide-spread phenomenon across different groups of flatworms [76,77,78,79] and other hermaphroditic invertebrates [80,81,82] that have evolved HI. It would further make it likely that the selfing rate can predict sex allocation in hermaphroditic animals generally.
It has been suggested that transitions to selfing are irreversible, leading to lineages suffering an increased risk of extinction and thus representing an evolutionary “dead-end” [83, 84]. However, others have argued that the reproductive isolation and increased dispersion potential resulting from selfing can drive speciation . Our data supports both hypothesis to some extent. On the one hand, we observe several single species with HI at the tips of the phylogeny. On the other hand, we see multiple clades with HI that are old (but note that due to the lack of fossils in these small invertebrates no calibrated phylogeny is available). Particularly, in the hypodermic clade (Fig. 2, clade I), extensive (cryptic) speciation has occurred . This suggests that the hypodermic clade has purged inbreeding depression, which is supported by a loss of waiting time and no obvious inbreeding depression in one member of this clade (Macrostomum pusillum, ).
Broader implications on sex allocation theory
We find a negative relationship between residual testis and ovary size, supporting the trade-off assumption, a central tenet of sex allocation theory [1, 11]. Moreover, we show that our GSA estimates do not simply reflect changes in, for example, relative testis size, but that they instead indicate reallocation and thus changes in sex allocation. Since theory predicts that a male allocation >0.5 should destabilize hermaphroditism , our GSA estimates likely represent the relative rather than the absolute ratio of resource allocation. Mature eggs are large in Macrostomum (up to ~10% of body size) and yolk provisioning, which largely occurs once the oocytes have left the ovary, likely accounts for a substantial fraction of female allocation [85, 86]. Therefore, our GSA estimate likely omits a larger proportion of female compared to male allocation. We did not include developing or mature eggs in our sex allocation estimate because their presence and size can be temporally variable (e.g., in measures are made just before or after egg laying), while ovary size is relatively constant.
It should be noted that although we find a significant sex allocation trade-off, the variance explained by the model was relatively low (R2pred = 0.11). Therefore, investment into testes vs ovaries may, at least to some extent, be decoupled. Evidence for the trade-off assumption is equivocal in plants (reviewed in [19, 28]) and rather scant in animals (reviewed in ). This is unsurprising because a large between-individual variance in resource budget will often result in a positive covariance between traits, even if an underlying trade-off exists [87,88,89]. While comparative analyses using species means are potentially better suited to reveal such trade-offs, results can still be affected by variable acquisition ability between species , offering a possible explanation for the apparent decoupling between ovaries and testes. Furthermore, resources could also be reallocated to other non-gonadal sex allocation components (see the “Discussion” section below).
Interestingly, we show that testis size exhibits negative allometry, while ovary size scales isometrically. Consequently, we find that larger Macrostomum species tend to have a more ovary-biased GSA, revealing a form of size-dependent sex allocation (reviewed in ). This between-species effect recapitulates the intraspecific finding in Macrostomum lignano . Another form of size-dependent sex allocation is found in angiosperm plants, where species with larger flowers have an increased male allocation to flower biomass, most likely due to male-male competition for pollinators . Similarly, selfing plant species tend to have smaller, more female-biased flowers . However, a species’ mean size does not seem to predict sex allocation (e.g., ). Within populations, sex allocation has been predicted to be negatively related to body size [93, 94], but because these are intraspecific models, it is not clear what relationship is expected in between-species comparisons. One explanation for our findings is that antrum size and consequently sperm storage capacity show a negative allometry with body size. Given some simplifying assumptions (e.g., constant mating rate and isometric scaling of sperm size), this would limit how much sperm a worm can donate and lead to faster diminishing returns on investment into sperm production in larger species. To test this hypothesis, it would be necessary to quantify antrum size. However, such measurements are challenging because the antrum is highly transparent and flexible. Furthermore, theoretical work should parameterize our argument to generate general macroevolutionary predictions.
Sex allocation and morphological indicators
Across all species and restricted to species mating reciprocally, antrum complexity, stylet length, and sperm length were positively related to GSA. A positive relationship between sperm length and testis size is found in several species (e.g., [95,96,97], but see ) and does not necessarily imply that selection acts both on sperm size and number. Instead, selection for longer sperm could result in slower spermatogenesis, requiring a larger testis to produce an equal number of sperm [96, 99,100,101,102].
The morphological indicators may represent components of non-gonadal sex allocation. Integrating them into a more inclusive measure of sex allocation would mean that in species with high GSA, male allocation would be further increased due to the sperm and stylet traits, but at the same time, female allocation would be increased due to greater antrum complexity likely also representing higher allocation. Integrating non-gonadal components would be desirable and could potentially explain the observed decoupling between male and female allocation. However, as mentioned earlier, several other sex allocation components (e.g., yolk and seminal fluid production) would also have to be considered to capture a more complete measure of sex allocation.
All measured morphologies likely represent adaptations to postcopulatory processes, such as cryptic female choice (antrum complexity and sperm design), sperm removal (stylet morphology and sperm design), and control over the mating duration (antrum and stylet morphology) and thus should be good proxies for the intensity of postcopulatory sexual selection [43, 44, 91]. Furthermore, we recently showed covariation between male and female genital morphology in reciprocally copulating species, which points to ongoing coevolution. Because these traits also predict GSA, sperm competition likely increases with the intensity of postcopulatory sexual selection. Interestingly, stylet length, sperm length, and bristle length were not significantly related to GSA in hypodermic species and antrum complexity even showed a weak (and non-significant) negative relationship. Therefore, selection on components relevant to postcopulatory sexual selection may cease or even reverse under HI. Because under HI the stylet and sperm no longer interact with the antrum [45, 57, 58], this makes intuitive sense and further supports the view that male-female coevolution drives the evolution of the morphological indicators in reciprocally mating species.
However, while our data is consistent with this interpretation, selfing could also determine GSA in reciprocally mating species. We observed several reciprocally mating species with low levels of heterozygosity, as expected under selfing. While evidence for selfing in these species is scant, one reciprocally mating species, M. mirumnovem, can self under laboratory conditions (albeit with reduced fecundity of isolated animals compared to pairs)  and it has an ovary-biased GSA (0.29). However, M. mirumnovem has comparably high heterozygosity (19 sites per 10kb), which seems to contradict the selfing hypothesis. But this could be due to the unusual karyotype and an accompanying whole genome duplication of M. mirumnovem [103, 104], likely violating the assumptions of our heterozygosity analysis. In summary, we cannot exclude that selfing is also important for GSA evolution in reciprocally mating species, but it is unlikely to be the only predictor.
The bimodal distribution of sex allocation indicates that hypodermic insemination is associated with reduced GSA, likely because it coincides with increased selfing and/or biparental inbreeding. Therefore, the mating behavior predicts macroevolutionary patterns of sex allocation in Macrostomum flatworms. This association between inbreeding and reduced investment into the production of male gametes agrees with finding in plants and with theoretical predictions. We, therefore, provide clear comparative evidence that these predictions can be generalized to hermaphroditic animals. We also find a relative shift towards female allocation in large species, which is currently not predicted by theory, highlighting that more theoretical work is needed. Furthermore, we find that morphological indicators of the intensity of postcopulatory sexual selection are positively related to GSA, suggesting that they are associated with intense sperm competition.
Study species and morphological measurements
Most specimens were collected during multiple field sampling trips across the globe. We have previously given a detailed account of the collected specimens, including image and video documentation . Because these animals are transparent, it is possible to observe the testes, the ovaries, the seminal vesicle, and the male and female genitalia in vivo from animals lightly squeezed dorsoventrally between a coverslip and a microscope slide (Fig. 1C). We have previously collected measurements from these specimens , including body size, sperm length, sperm bristle length, and stylet length, by tracing the structures using ImageJ  (Fig. 1C–E). Images were calibrated using a stage micrometer. We further categorized the female genitalia on a per species basis to produce a compound complexity score, with increasing values representing more complex genitalia (Fig. 1F). Finally, we previously assigned species to an inferred mating syndrome according to their genital morphology and the location of received sperm . In total, 40 species were assigned to the hypodermic mating syndrome, 69 species to the reciprocal mating syndrome, two species were assigned to an intermediate mating syndrome, and nine species could not be assigned. As discussed in , the inferred mating syndrome differs slightly from the mating syndrome originally used in , in that the latter included information on behavioral data. Of the unassigned species, three (M. sp. 14, M. sp. 51 and M. sp. 89) had a morphology indicative of hypodermic insemination, but we observed received sperm in the antrum, suggesting they may also represent intermediate states between the syndromes. The other species were not assigned because of contradictory information (M. sp. 10) or because we lacked information on reproductive morphology (i.e., no observation of the sperm and/or the antrum). See the supporting information of  for a more detailed discussion.
Here we additionally estimated GSA as testis area/(testis area + ovary area) for 120 species (calculated on untransformed gonad area values), with most specimens being documented within a few days after field extraction. We estimated the gonad area by tracing their outline manually using ImageJ (Fig. 1C). Whenever possible, we traced both paired testes and ovaries and used their sums as the datum. When tracing one gonad was impossible due to the animal’s position, but we could confirm it was present, we doubled the value of the measured gonad. When it was unclear if the second gonad was present, we used only the estimate for one gonad as the datum (all described Macrostomum species have paired gonads, but individual specimens can sometimes lack some gonads). Gonads were generally traced at 400x magnification (1459 testes, 1366 ovaries), but some at 100x (17 testes, 20 ovaries) or 200x (378 testes, 387 ovaries) when very large, or at 1000x (74 testes, 71 ovaries) when very small. We usually measured gonads at the same magnification within an individual (1001 out of 1102 cases). We only estimated GSA for specimens with active gonads, which we judged by the testes having visible elongating spermatids towards the lumen, and ovaries having visible oocytes, yolk granules at the posterior end of the ovary, and in most cases developing eggs. We also required that specimens had a complete male reproductive system, consisting minimally of a seminal vesicle, a vesicula granulorum, and the stylet. Given the variable body sizes of the species and specimens, we could not standardize the thickness of the squeeze preparation (which is usually done when measuring GSA in studies on laboratory-reared Macrostomum [42, 58, 106]), potentially making direct comparisons of testis and ovary area between species problematic. In a small side study, we found that, given their relative nature, GSA estimates were unaffected by squeezing intensity in four species (Brand and Schärer, unpublished). We, therefore, expect GSA estimates to be robust. Note also that the cost of the testis and ovary per unit area may not be equivalent and that the testis area likely approximates allocation towards sperm production better than ovary area does allocation towards egg production, making GSA necessarily a relative measure (see also the “Discussion” section).
Several Macrostomum species show considerable phenotypic plasticity in GSA , which could be problematic if plasticity were of a similar magnitude to between-species differences. However, a comparative study of sex allocation plasticity in response to mate availability and sperm competition intensity in seven Macrostomum species found that interspecific variation in GSA was nearly three times larger than intraspecific variation . Therefore, GSA estimates from field-collected specimens should approximate species properties, and this is further supported by the generally low standard errors of GSA in our data (Fig. 2).
Before statistical analysis, we square root-transformed the body, testis, and ovary area measurements to convert them to a linear size estimate. We then log10-transformed these size estimates and all other linear measurements (stylet length, bristle length, sperm length). To compare testis and ovary size directly between species, we then also calculated the residuals of a linear regression of the testis and ovary size on the body size across all species (using geometric species means, testis: F1,118=36.99, β = 0.64, p < 0.001, adjusted R2 = 0.24; ovary: F1,118=179.8, β = 1.04, p < 0.001, adjusted R2 = 0.60). All statistical analyses were performed in R (version 3.6.0, ).
We used a recently generated ultrametric molecular phylogeny of the genus Macrostomum that is based on a combination of whole-body transcriptome RNA-Seq (98 species) and Sanger-sequenced 28S rRNA data (47 species, ). While the major phylogenetic groupings were consistent between different inference methods and sequence alignments used, the previous study found some discrepancies in the backbone of the phylogeny . We here present results from a phylogeny, called C-IQ-TREE, made from an alignment containing 385 genes (94,625 amino acids in the trimmed alignment) from the 98 species with a transcriptome, plus a 28S rRNA fragment including an additional 47 species and calculated with a maximum likelihood approach. To assess whether tree uncertainty influenced our conclusions, we also performed all analyses on two phylogenies that only include species with transcriptomic data available, calculated using a maximum likelihood (H-IQ-TREE) or Bayesian approach (H-ExaBayes). The analyses on these alternate topologies were quantitatively similar and qualitatively identical (this was also the case in the analyses in ), and we here only present results from the analysis performed on the more comprehensive C-IQ-TREE phylogeny.
Estimation of heterozygosity
Heterozygosity is a putative correlate of the selfing rate, because a high selfing rate would be expected to reduce the effective population size (Ne) and thus lead to a reduction in heterozygosity. In order to obtain estimates of per-site heterozygosity for different species, we generated superTranscripts  for each of the transcriptomes assembled for , excluding transcriptomes from species without GSA estimates or for which we pooled RNA samples from many specimens, leaving us with 130 transcriptomes across 90 species. We then called haplotypes using the GATK 3.8 RNA variant calling pipeline . Briefly, we mapped the trimmed reads to the superTranscripts using STAR in 2-pass mode, post-processed the alignment using Picard, translated mapping scores using GATK SplitNCigarReads, and finally called variants using the GATK HaplotypeCaller. We discarded SNP clusters (three or more SNPs within a 35-base sliding window) and low confidence SNPs (FS>30, QD<5), which represents more aggressive filtering than the standard pipeline . We calculated the per-site heterozygosity as the number of SNPs divided by the total number of bases across all superTranscripts. Most assemblies are derived from a single individual, and we thus simply mapped the reads used for the assembly. Eight of the transcriptomes were derived from sequencing data from multiple individuals per species (seven species with three samples and one species with two samples), collected from the same location. In these cases, we called variants for each individual separately by mapping the reads to the species reference transcriptome and averaged estimates for each species. We assessed the repeatability of the estimates by calculating the Intraclass Correlation Coefficient (ICC) for species with more than one sample using the R package ICC . Repeatability was quite low (N=22, k=2.78, ICC=0.32, 95%CI=0.04–0.6), but this was mainly due to variation between different sampling sites since the ICC was large when we compared specimens collected from the same site (N=13, k=2.53, ICC=0.87, 95%CI=0.69–0.96, Additional file 3: Figure S2).
Evolution of sex allocation
We calculated the empirical density of GSA using the density function in R (version 3.5.1) and determined the bandwidth using the method described in . Next, we estimated phylogenetic signal (both lambda, λ, as well as Bloomberg’s K) in the distribution of GSA using the phylosig function from the R package phytools (version 0.6, ) and conducted likelihood ratio tests to determine if the signal differed from zero (indicating no phylogenetic signal) or 1 (indicating the expected phylogenetic signal under Brownian motion). Next, we calculated ancestral states of GSA using the contMap function from phytools, which determines maximum-likelihood estimates for all internal nodes using the method of Felsenstein .
We used the R package BBMV (version 2.1, ) to model GSA evolution across the phylogeny based on the Fokker-Planck-Kolmogorov (FPK) diffusion equation. BBMV can fit Brownian motion (BM), Ornstein-Uhlenbeck (OU), and more complex evolutionary models. BM describes trait evolution as a random walk determined by a species mean trait value and the evolutionary rate parameter σ2. Under BM, trait values will drift randomly in proportion to the evolutionary time that separates them. The OU model additionally includes a long term mean trait value θ that species are attracted towards. The attraction is proportional to the α parameter, which is sometimes called the “rubber band” parameter (e.g., ) because it pulls drifting trait values back to θ. Using the FPK equation, BBMV extends OU models by allowing more than one long-term mean value and complex variation in the attractiveness of these mean values. BBMV models the probability density of a trait (x, GSA in our case) as a combination of an evolutionary rate σ2 and a constant deterministic force, proportional to the evolutionary potential function V(x). Species are attracted to minima of V(x) defined by e-V(x). To allow a more intuitive interpretation, e-V(x) is scaled using a normalization factor N, converting it into a macroevolutionary landscape Ne-V(x). In this landscape, peaks correspond to trait values species are attracted towards, and peak height indicates how fast species move toward the trait value (i.e., how attractive the peak is). The shape of V(x) can be adjusted to compare models specifying different types of evolutionary landscapes. We used the polynomial equation suggested by [60, 61], ax4 + bx2 + cx, for V(x), bounded GSA (0,1), and fit all models with 200 discretization windows (Npts = 200). We then compared the AIC weights of the full model with up to two peaks (estimating a, b, and c), the OU model with up to one peak (a = 0), and the BM model without peaks (a = 0, b = 0, c = 0). We incorporated uncertainty in trait estimation by providing all individual GSA estimates to the lnL_BBMV function and determined the 95% confidence interval of the polynomial terms using the “Uncertainty_FPK” function. Because the full model only has two peaks when b is negative, we tested if the 95% confidence interval of b remains below 0.
General phylogenetic regression approach
We tested a number of predictors of GSA evolution (see next section) using phylogenetic generalized least squares (PGLS) regression, using the gls function in the R package nlme (version 3.1). We incorporated phylogenetic signal in the residuals of the regression and simultaneously accounted for varying sample size, using the number of estimates for the dependent variable as weights (“~1/Nspecimens”). We fit evolutionary models (BM, λ, and OU) for the covariance in the residuals and selected the model with the lowest corrected AIC. Occasionally, both the λ and OU models had the same corrected AIC value, in which case we used the λ model. Due to this rule, the λ model was preferred in all analyses. Therefore, the PGLS covariances are best captured by a model that reduces the weight of the phylogeny compared to the expectation under BM. We checked the distribution of the phylogeny-corrected residuals for normality and profiled the likelihood of the parameter of the correlation structure (i.e., λ). In some smaller subsets of the data, we had issues with model convergence because the likelihood profile indicated λ<0. In these cases, we restricted λ to zero, making the model equivalent to a weighted ordinary least squares (OLS) regression. Since R2 values are problematic for OLS models , we calculated R2pred using the R package rr2  to show model fits. Specifically, we compared the model fit of an intercept-only model (Y ~ 1 + ε) with a model including the predictor(s) and the phylogeny (Y ~ Xi + Xj + ε | Ψ). R2pred thus represents the predictive power of both the data and the phylogeny.
Predictors of sex allocation
We performed univariate analysis, using four binary categorical predictors, namely the inferred mating syndrome (hypodermic vs. reciprocal, excluding the two species classified as intermediate), the received sperm location (at least some hypodermic sperm vs. sperm in antrum only), the sperm bristle state (bristles absent/reduced vs. bristles present), and the antrum state (thin vs. thickened), classified as in . Next, we performed univariate PGLS with the estimate of per-site heterozygosity of the species for which we have a transcriptome (see above). Specifically, we log10-transformed the heterozygosity estimate and tested whether hypodermic insemination is associated with a reduction in heterozygosity, using a PGLS with the inferred mating syndrome as the predictor. We also used a PGLS analysis to evaluate the relationship between heterozygosity and GSA, first across all species, as well as restricted to the species showing the hypodermic or reciprocal mating syndrome, respectively.
Since GSA could change due to changes in testis size, ovary size, or both, we performed univariate PGLS analyses with testis size and ovary size as the dependent variables. First, we fit a model only including body size as the predictor to investigate the evolutionary allometry of each gonad (allometry models), performing a t test against a null model of an isometric slope of one. Second, we fit models that additionally included the inferred mating syndrome as a predictor to explore what changes in gonad size underlie differences in GSA (covariate models). Furthermore, we performed univariate PGLS analyses assessing whether GSA was predicted by body size. Additionally, to test the fundamental assumption of a sex allocation trade-off, we also performed a univariate PGLS of residual testis size on residual ovary size to determine if GSA changes can be seen as reallocation between sex functions.
Next, we explored the relationships between GSA and four reproductive morphology traits that may serve as indicators of the intensity of postcopulatory sexual selection (“morphological indicators”). These morphological indicators may also represent non-gonadal components of sex allocation in Macrostomum, with one trait summarising investment into the female genitalia (antrum complexity) and three traits representing aspects of male allocation (stylet length, sperm length, sperm bristle length). Specifically, this was done by performing univariate PGLS analyses with GSA as the dependent variable. Finally, we explored complex effects of specific trait combinations using multivariate PGLS analysis for GSA, using these four morphological indicators as well as body size as predictors. We performed all analyses across the entire dataset, as well as restricted to species classified as belonging to the hypodermic or reciprocal mating syndrome. Even though hypodermic species have low variability in antrum complexity and bristle length, we still performed PGLS analyses for these traits for consistency between the analyses. We do not correct for the resulting multiple testing since the data used varies slightly between tests. But we note that the p values were usually low and would, in most cases, remain significant after adjustment.
Availability of data and materials
Bounded Brownian motion with evolutionary potential
Gonadal sex allocation
Intraclass correlation coefficient
Local sperm competition
Local mate competition
Ordinary least squares
Phylogenetic generalized least squares
Charnov EL. The theory of sex allocation. Monogr Popul Biol. 1982;18:1–355.
West SA. Sex allocation. Princeton: Princeton University Press; 2009.
Hamilton WD. Extraordinary sex ratios. Science. 1967;156:477–88.
West SA, Shuker DM, Sheldon BC. Sex-ratio adjustment when relatives interact: a test of constraints on adaptation. Evolution. 2005;59:1211–28. https://doi.org/10.1111/j.0014-3820.2005.tb01772.x.
Frank SA. Hierarchical selection theory and sex ratios. II. on applying the theory, and a test with fig wasps. Evolution. 1985;39:949–64. https://doi.org/10.1111/j.1558-5646.1985.tb00440.x.
West SA, Herre EA. Stabilizing selection and variance in fig wasp sex ratios. Evolution. 1998;52:475. https://doi.org/10.2307/2411083.
Taylor PD. Intra-sex and inter-sex sibling interactions as sex ratio determinants. Nature. 1981;291:64–6. https://doi.org/10.1038/291064a0.
Charnov EL, Bull JJ, Maynard SJ. Why be an hermaphrodite? Nature. 1976;263:125–6. https://doi.org/10.1038/263125a0.
Charnov EL. Sex allocation and local mate competition in Barnacles. Mar Biol Lett. 1980;1:269–72.
Fischer EA. Sexual allocation in a simultaneously hermaphroditic coral reef fish. Am Nat. 1981;117:64–82. https://doi.org/10.1086/283686.
Charnov EL. Simultaneous hermaphroditism and sexual selection. Proc Natl Acad Sci U S A. 1979;76:2480–4.
Schärer L. Tests of sex allocation theory in simultaneously hermaphroditic animals. Evolution. 2009;63:1377–405. https://doi.org/10.1111/j.1558-5646.2009.00669.x.
Charlesworth D, Charlesworth B. Allocation of resources to male and female functions in hermaphrodites. Biol J Linnean Soc. 1981;15:57–74. https://doi.org/10.1111/j.1095-8312.1981.tb00748.x.
Charnov EL, Los-den Hartogh RL, Jones WT, van den Assem J. Sex ratio evolution in a variable environment. Nature. 1981;289:27–33. https://doi.org/10.1038/289027a0.
Charnov EL. Sperm competition and sex allocation in simultaneous hermaphrodites. Evol Ecol. 1996;10:457–62. https://doi.org/10.1007/BF01237878.
Pen I, Weissing FJ. Sperm competition and sex allocation in simultaneous hermaphrodites: a new look at Charnov’s invariance principle. Evol Ecol Res. 1999;1:517–25.
van Velzen E, Schärer L, Pen I. The effect of cryptic female choice on sex allocation in simultaneous hermaphrodites. Proc R Soc B. 2009;276:3123–31. https://doi.org/10.1098/rspb.2009.0566.
Schärer L, Pen I. Sex allocation and investment into pre- and post-copulatory traits in simultaneous hermaphrodites: the role of polyandry and local sperm competition. Philos Transact R Soc B Biol Sci. 2013;368:20120052. https://doi.org/10.1098/rstb.2012.0052.
de Jong TJ, Klinkhamer PGL. Evolutionary ecology of plant reproductive strategies. Cambridge; New York: Cambridge University Press; 2005.
Goldman DA, Willson MF. Sex allocation in functionally hermaphroditic plants: a review and critique. Bot Rev. 1986;52:157–94.
Klinkhamer PGL, de Jong TJ, Metz H. Sex and size in cosexual plants. Trends Ecol Evol. 1997;12:260–5. https://doi.org/10.1016/S0169-5347(97)01078-1.
Baeza JA. Sex allocation in a simultaneously hermaphroditic marine shrimp. Evolution. 2007;61:2360–73. https://doi.org/10.1111/j.1558-5646.2007.00199.x.
Singh P, Vellnow N, Schärer L. Variation in sex allocation plasticity in three closely related flatworm species. Ecol Evol. 2019:ece3.5566. https://doi.org/10.1002/ece3.5566.
Hart MK, Svoboda A, Mancilla CD. Phenotypic plasticity in sex allocation for a simultaneously hermaphroditic coral reef fish. Coral Reefs. 2011;30:543–8. https://doi.org/10.1007/s00338-011-0737-3.
Tamechika MM, Matsuno K, Wada S, Yusa Y. Different effects of mating group size as male and as female on sex allocation in a simultaneous hermaphrodite. Ecol Evol. 2020;10:2492–8. https://doi.org/10.1002/ece3.6075.
Winkler L, Ramm SA. Experimental evidence for reduced male allocation under selfing in a simultaneously hermaphroditic animal. Biol Lett. 2018;14:20180570. https://doi.org/10.1098/rsbl.2018.0570.
Johnston MO, Das B, Hoeh WR. Negative correlation between male allocation and rate of self-fertilization in a hermaphroditic animal. Proc Natl Acad Sci U S A. 1998;95:617–20.
Campbell DR. Experimental tests of sex-allocation theory in plants. Trends Ecol Evol. 2000;15:227–32. https://doi.org/10.1016/S0169-5347(00)01872-3.
Sicard A, Lenhard M. The selfing syndrome: a model for studying the genetic and evolutionary basis of morphological adaptation in plants. Ann Bot. 2011;107:1433–43. https://doi.org/10.1093/aob/mcr023.
Plitmann U, Levin DA. Breeding systems in the Polemoniaceae. Pl Syst Evol. 1990;170:205–14. https://doi.org/10.1007/BF00937704.
Barrett SCH, Harder LD, Worley AC. The comparative biology of pollination and mating in flowering plants. Phil Trans R Soc Lond B. 1996;351:1271–80. https://doi.org/10.1098/rstb.1996.0110.
Jürgens A, Witt T, Gottsberger G. Pollen grain numbers, ovule numbers and pollen-ovule ratios in Caryophylloideae: correlation with breeding system, pollination, life form, style number, and sexual system. Sex Plant Reprod. 2002;14:279–89. https://doi.org/10.1007/s00497-001-0124-2.
Paterno GB, Silveira CL, Kollmann J, Westoby M, Fonseca CR. The maleness of larger angiosperm flowers. Proc Natl Acad Sci USA. 2020;117:10921–6. https://doi.org/10.1073/pnas.1910631117.
Petersen CW. Sex allocation in hermaphroditic sea basses. Am Nat. 1991;138:650–67. https://doi.org/10.1086/285240.
St. Mary CM. Sex allocation in Lythrypnus (Gobiidae): variations on a hermaphroditic theme. Environ Biol Fishes. 2000;58:321–33. https://doi.org/10.1023/A:1007644010331.
Maxfield JM, Van Tassell JL, St. Mary CM, Joyeux J-C, Crow KD. Extreme gender flexibility: using a phylogenetic framework to infer the evolution of variation in sex allocation, phylogeography, and speciation in a genus of bidirectional sex changing fishes(Lythrypnus, Gobiidae). Mol Phylogenet Evol. 2012;64:416–27. https://doi.org/10.1016/j.ympev.2012.04.016.
Singh P, Schärer L. Self-fertilization, but not mating strategy, predicts the evolution of sex allocation plasticity in a hermaphroditic flatworm genus. bioRxiv. 2020. https://doi.org/10.1101/2020.06.12.149351.
Brunet J. Sex allocation in hermaphroditic plants. Trends Ecol Evol. 1992;7:79–84. https://doi.org/10.1016/0169-5347(92)90245-7.
Henshaw JM, Marshall DJ, Jennions MD, Kokko H. Local gamete competition explains sex allocation and fertilization strategies in the sea. Am Nat. 2014;184:E32–49. https://doi.org/10.1086/676641.
Levitan DR, Petersen C. Sperm limitation in the sea. Trends Ecol Evol. 1995;10:228–31. https://doi.org/10.1016/S0169-5347(00)89071-0.
Parker GA, Immler S, Pitnick S, Birkhead TR. Sperm competition games: Sperm size (mass) and number under raffle and displacement, and the evolution of P2. J Theor Biol. 2010;264:1003–23. https://doi.org/10.1016/j.jtbi.2010.03.003.
Schärer L, Ladurner P. Phenotypically plastic adjustment of sex allocation in a simultaneous hermaphrodite. Proc R Soc B Biol Sci. 2003;270:935–41. https://doi.org/10.1098/rspb.2002.2323.
Brand JN, Harmon LJ, Schärer L. Frequent origins of traumatic insemination involve convergent shifts in sperm and genital morphology. Evol Lett. 2022. https://doi.org/10.1002/evl3.268.
Schärer L, Littlewood DTJ, Waeschenbach A, Yoshida W, Vizoso DB. Mating behavior and the evolution of sperm design. Proc Natl Acad Sci. 2011;108:1490–5. https://doi.org/10.1073/pnas.1013892108.
Vizoso DB, Rieger G, Schärer L. Goings-on inside a worm: functional hypotheses derived from sexual conflict thinking. Biol J Linnean Soc. 2010;99:370–83.
Luther A. Untersuchungen an rhabdocoelen Turbellarien VI. Macrostomiden aus Finnland. Fauna Fennica. 1947;49:1–38.
Schärer L, Joss G, Sandner P. Mating behaviour of the marine turbellarian Macrostomum sp.: these worms suck. Mar Biol. 2004;145:373–80. https://doi.org/10.1007/s00227-004-1314-x.
Patlar B, Weber M, Temizyürek T, Ramm SA. Seminal fluid-mediated manipulation of post-mating behavior in a simultaneous hermaphrodite. Curr Biol. 2020;30:143–9. https://doi.org/10.1016/j.cub.2019.11.018.
Weber M, Patlar B, Ramm SA. Effects of two seminal fluid transcripts on post-mating behaviour in the simultaneously hermaphroditic flatworm Macrostomum lignano. J Evol Biol. 2020:jeb.13606. https://doi.org/10.1111/jeb.13606.
Schärer L, Janicke T. Sex allocation and sexual conflict in simultaneously hermaphroditic animals. Biol Lett. 2009;5:705–8. https://doi.org/10.1098/rsbl.2009.0100.
Parker GA. Sperm competition games: raffles and roles. Proc R Soc London Ser B Biol Sci. 1990;242:120–6. https://doi.org/10.1098/rspb.1990.0114.
Parker GA. Selection on non-random fusion of gametes during the evolution of anisogamy. J Theor Biol. 1978;73:1–28. https://doi.org/10.1016/0022-5193(78)90177-7.
Parker GA. Why are there so many tiny sperm? Sperm competition and the maintenance of two sexes. J Theor Biol. 1982;281–94.
Parker GA. Sperm competition games: sperm size and sperm number under adult control. Proc R Soc London Ser B Biol Sci. 1993;253:245–54. https://doi.org/10.1098/rspb.1993.0110.
Immler S, Pitnick S, Parker GA, Durrant KL, Lüpold S, Calhim S, et al. Resolving variation in the reproductive tradeoff between sperm size and number. PNAS. 2011;108:5325–30. https://doi.org/10.1073/pnas.1009059108.
Ramm SA, Vizoso DB, Schärer L. Occurrence, costs and heritability of delayed selfing in a free-living flatworm. J Evol Biol. 2012;25:2559–68. https://doi.org/10.1111/jeb.12012.
Ramm SA, Schlatter A, Poirier M, Schärer L. Hypodermic self-insemination as a reproductive assurance strategy. Proc R Soc Biol Sci. 2015;282:20150660. https://doi.org/10.1098/rspb.2015.0660.
Giannakara A, Ramm SA. Self-fertilization, sex allocation and spermatogenesis kinetics in the hypodermically inseminating flatworm Macrostomum pusillum. J Exp Biol. 2017;220:1568–77. https://doi.org/10.1242/jeb.149682.
Brand JN, Viktorin G, Wiberg RAW, Beisel C, Schärer L. Large-scale phylogenomics of the genus Macrostomum (Platyhelminthes) reveals cryptic diversity and novel sexual traits. Mol Phylogenet Evol. 2022;166:107296. https://doi.org/10.1016/j.ympev.2021.107296.
Boucher FC. BBMV: an R package for the estimation of macroevolutionary landscapes. Ecography. 2019;42:558–64. https://doi.org/10.1111/ecog.04045.
Boucher FC, Démery V, Conti E, Harmon LJ, Uyeda J. A general model for estimating macroevolutionary landscapes. Syst Biol. 2018;67:304–19. https://doi.org/10.1093/sysbio/syx075.
Falconer DS, Mackay T. Introduction to quantitative genetics. 4. ed., [16. print.]. Harlow: Pearson, Prentice Hall; 2009.
Davidson NM, Hawkins ADK, Oshlack A. SuperTranscripts: a data driven reference for analysis and visualisation of transcriptomes. Genome Biol. 2017;18:148. https://doi.org/10.1186/s13059-017-1284-1.
Gallardo R, Dominguez E, Muñoz JM. Pollen-ovule ratio, pollen size, and breeding system in Astragalus (Fabaceae) subgenus Epiglottis: a pollen and seed allocation approach. Am J Bot. 1994;81:1611–9. https://doi.org/10.1002/j.1537-2197.1994.tb11473.x.
Damgaard C, Abbott RJ. Positive correlations between selfing rate and pollen-ovule ratio within plant populations. Evolution. 1995;49:214–7.
Galloni M, Podda L, Vivarelli D, Cristofolini G. Pollen presentation, pollen-ovule ratios, and other reproductive traits in Mediterranean Legumes (Fam. Fabaceae - Subfam. Faboideae). Plant Syst Evol. 2007;266:147–64. https://doi.org/10.1007/s00606-007-0526-1.
Escobar JS, Auld JR, Correa AC, Alonso JM, Bony YK, Coutellec M-A, et al. Patterns of mating-system evolution in hermaphroditic animals: correlations among selfing rate, inbreeding depression, and the timing of reproduction. Evolution. 2011;65:1233–53. https://doi.org/10.1111/j.1558-5646.2011.01218.x.
Noël E, Chemtob Y, Janicke T, Sarda V, Pélissié B, Jarne P, et al. Reduced mate availability leads to evolution of self-fertilization and purging of inbreeding depression in a hermaphrodite: selfing evolution in a hermaphroditic snail. Evolution. 2016;70:625–40. https://doi.org/10.1111/evo.12886.
Tian-Bi Y-NT, N’goran EK, N’guetta S-P, Matthys B, Sangare A, Jarne P. Prior selfing and the selfing syndrome in animals: an experimental approach in the freshwater snail Biomphalaria pfeifferi. Genet Res. 2008;90:61–72. https://doi.org/10.1017/S0016672307008919.
Kiontke KC, Félix M-A, Ailion M, Rockman MV, Braendle C, Pénigault J-B, et al. A phylogeny and molecular barcodes for Caenorhabditis, with numerous new species from rotting fruits. BMC Evol Biol. 2011;11:339. https://doi.org/10.1186/1471-2148-11-339.
Cutter AD. Reproductive transitions in plants and animals: selfing syndrome, sexual selection and speciation. New Phytol. 2019;224:1080–94. https://doi.org/10.1111/nph.16075.
Cutter AD. Caenorhabditis evolution in the wild. BioEssays. 2015;37:983–95. https://doi.org/10.1002/bies.201500053.
Hughes RN, Wright PJ, Carvalho GR, Hutchinson WF. Patterns of self compatibility, inbreeding depression, outcrossing, and sex allocation in a marine bryozoan suggest the predominating influence of sperm competition. Biol J Linnean Soc. 2009;98:519–31. https://doi.org/10.1111/j.1095-8312.2009.01312.x.
Weinzierl RP, Berthold K, Beukeboom LW, Michiels NK. Reduced male allocation in the parthenogenetic hermaphrodite Dugesia polychroa. Evolution. 1998;52:109–15. https://doi.org/10.1111/j.1558-5646.1998.tb05143.x.
Weinzierl RP, Schmidt P, Michiels NK. High fecundity and low fertility in parthenogenetic planarians. Invert Biol. 1999;118:87. https://doi.org/10.2307/3227051.
Janssen T, Vizoso DB, Schulte G, Littlewood DTJ, Waeschenbach A, Schärer L. The first multi-gene phylogeny of the Macrostomorpha sheds light on the evolution of sexual and asexual reproduction in basal Platyhelminthes. Mol Phylogenet Evol. 2015;92:82–107. https://doi.org/10.1016/j.ympev.2015.06.004.
Michiels NK, Newman LJ. Sex and violence in hermaphrodites. Nature. 1998;391:647.
Macdonald S, Caley J. Sexual reproduction in the monogenean <I>Diclidophora merlangi</i>: tissue penetration by sperms. Z Parasitenk. 1975;45:323–34. https://doi.org/10.1007/BF00329822.
Llewellyn J. Sperm transfer in the monogenean gill parasite Gastrocotyle trachuri. Proc R Soc London Ser B Biol Sci. 1983;219:439–46. https://doi.org/10.1098/rspb.1983.0083.
Lange R, Reinhardt K, Michiels NK, Anthes N. Functions, diversity, and evolution of traumatic mating: function and evolution of traumatic mating. Biol Rev. 2013;88:585–601. https://doi.org/10.1111/brv.12018.
Tatarnic NJ. Traumatic insemination and copulatory wounding. Reference Module in Life Sciences: Elsevier; 2018. https://doi.org/10.1016/B978-0-12-809633-8.20730-9.
Apelt G. Fortpflanzungsbiologie, Entwicklungszyklen und vergleichende Frühentwicklung acoeler Turbellarien. Mar Biol. 1969;4:59.
Stebbins GL. Self fertilization and population variability in the higher plants. Am Nat. 1957;91:337–54.
Glémin S, François CM, Galtier N. Genome evolution in outcrossing vs. selfing vs. asexual species. In: Anisimova M, editor. Evolutionary Genomics: Statistical and Computational Methods. New York: Springer; 2019. p. 331–69. https://doi.org/10.1007/978-1-4939-9074-0_11.
Gremigni V. Platyhelminthes-Turbellaria. Reproductive Biology of Invertebrates, Vol 1 Oogenesis, oviposition, and oosorption. New York: Wiley; 1983. p. 67–107.
Gremigni V, Falleni A, Lucchesi P. An ultrastructural study of oogenesis in the Turbellarian Macrostomum. Acta Embryol Morphol Exper. 1987;8:257–62.
Van Noordwijk AJ, de Jong G. Acquisition and allocation of resources: their influence on variation in life history tactics. Am Nat. 1986;128:137–42.
Reznick D, Nunney L, Tessier A. Big houses, big cars, superfleas and the costs of reproduction. Trends Ecol Evol. 2000;15:421–5. https://doi.org/10.1016/S0169-5347(00)01941-8.
Schärer L, Sandner P, Michiels NK. Trade-off between male and female allocation in the simultaneously hermaphroditic flatworm Macrostomum sp. J Evol Biol. 2005;18:396–404. https://doi.org/10.1111/j.1420-9101.2004.00827.x.
Simmons LW, Lüpold S, Fitzpatrick JL. Evolutionary trade-off between secondary sexual traits and ejaculates. Trends Ecol Evol. 2017;32:964–76. https://doi.org/10.1016/j.tree.2017.09.011.
Vizoso DB, Schärer L. Resource-dependent sex-allocation in a simultaneous hermaphrodite. J Evol Biol. 2007;20:1046–55. https://doi.org/10.1111/j.1420-9101.2007.01294.x.
Teixido AL, Valladares F. Heat and drought determine flower female allocation in a hermaphroditic Mediterranean plant family. Plant Biology. 2019;21:1024–30. https://doi.org/10.1111/plb.13031.
Angeloni L, Bradbury JW, Charnov EL. Body size and sex allocation in simultaneously hermaphroditic animals. Behav Ecol. 2002;13:419–26. https://doi.org/10.1093/beheco/13.3.419.
Angeloni L. Sexual selection in a simultaneous hermaphrodite with hypodermic insemination: body size, allocation to sexual roles and paternity. Anim Behav. 2003;66:417–26. https://doi.org/10.1006/anbe.2003.2255.
Gage MJ. Associations between body size, mating pattern, testis size and sperm lengths across butterflies. Proc R Soc Lond B. 1994;258:247–54. https://doi.org/10.1098/rspb.1994.0169.
Pitnick S. Investment in testes and the cost of making long sperm in Drosophila. Am Nat. 1996;148:57–80. https://doi.org/10.1086/285911.
Rowley A, Locatello L, Kahrl A, Rego M, Boussard A, Garza-Gisholt E, et al. Sexual selection and the evolution of sperm morphology in sharks. J Evol Biol. 2019;32:1027–35. https://doi.org/10.1111/jeb.13501.
Gage MJG, Freckleton RP. Relative testis size and sperm morphometry across mammals: no evidence for an association between sperm competition and sperm length. Proc R Soc London Ser B Biol Sci. 2003;270:625–32. https://doi.org/10.1098/rspb.2002.2258.
Pitnick S, Markow TA, Spicer GS. Delayed male maturity is a cost of producing large sperm in Drosophila. Proc Natl Acad Sci. 1995;92:10614–8. https://doi.org/10.1073/pnas.92.23.10614.
LaMunyon CW, Ward S. Evolution of sperm size in nematodes: sperm competition favours larger sperm. Proc R Soc B Biol Sci. 1999;266:263–7. https://doi.org/10.1098/rspb.1999.0631.
Lüpold S, Linz GM, Rivers JW, Westneat DF, Birkhead TR. Sperm competition selects beyond relative testes size in birds. Evolution. 2009;63:391–402. https://doi.org/10.1111/j.1558-5646.2008.00571.x.
Ramm SA, Stockley P. Sperm competition and sperm length influence the rate of mammalian spermatogenesis. Biol Lett. 2010;6:219–21. https://doi.org/10.1098/rsbl.2009.0635.
Schärer L, Brand JN, Singh P, Zadesenets KS, Stelzer C-P, Viktorin G. A phylogenetically informed search for an alternative Macrostomum model species, with notes on taxonomy, mating behavior, karyology, and genome size. J Zool Syst Evol Res. 2020;58:41–65. https://doi.org/10.1111/jzs.12344.
Zadesenets KS, Jetybayev IY, Schärer L, Rubtsov NB. Genome and karyotype reorganization after whole genome duplication in free-living flatworms of the genus Macrostomum. IJMS. 2020;21:680. https://doi.org/10.3390/ijms21020680.
Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671–5. https://doi.org/10.1038/nmeth.2089.
Janicke T, Marie-Orleach L, De Mulder K, Berezikov E, Ladurner P, Vizoso DB, et al. Sex allocation adjustment to mating group size in a simultaneous hermaphrodite. Evolution. 2013;67:3233–42. https://doi.org/10.1111/evo.12189.
R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2019.
Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics. 2013;11:11.10.1-11.10.33. https://doi.org/10.1002/0471250953.bi1110s43.
Wolak ME, Fairbairn DJ, Paulsen YR. Guidelines for estimating repeatability. Methods Ecol Evol. 2012;3:129–37. https://doi.org/10.1111/j.2041-210X.2011.00125.x.
Sheather SJ, Jones MC. A reliable data-based bandwidth selection method for kernel density estimation. J R Stat Soc Ser B (Methodological). 1991;53:683–90.
Revell LJ. phytools: an R package for phylogenetic comparative biology (and other things): phytools: R package. Methods Ecol Evol. 2012;3:217–23. https://doi.org/10.1111/j.2041-210X.2011.00169.x.
Felsenstein J. Phylogenies and the comparative method. Am Nat. 1985;125:1–15. https://doi.org/10.1086/284325.
Cooper N, Thomas GH, Venditti C, Meade A, Freckleton RP. A cautionary note on the use of Ornstein Uhlenbeck models in macroevolutionary studies. Biol J Linn Soc. 2016;118:64–77. https://doi.org/10.1111/bij.12701.
Ives AR. R2s for correlated data: phylogenetic models, lmms, and glmms. Syst Biol. 2019;68:234–51. https://doi.org/10.1093/sysbio/syy060.
Ives A, Li D. rr2: An R package to calculate R2s for regression models. JOSS. 2018;3:1028. https://doi.org/10.21105/joss.01028.
We thank R. Axel W. Wiberg and Pragya Singh for helpful discussions and John Pannell for comments on earlier versions of the manuscript.
This work was supported by the Swiss National Science Foundation (SNSF) research grants 31003A_162543 and 310030_184916 to LS. The funding bodies had no role in the study design, the data collection and analysis, the decision to publish, or the preparation of the manuscript.
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Details on all specimens included in this study. Table S2. Mean species values for all variables. Table S3. Sample size per species for all quantitative traits. Table S4. PGLS analysis of gonadal sex allocation and various indicators of the inferred mating syndrome. Table S5. Heterozygosity estimates. Table S6. Univariate PGLS with GSA as the dependent variable. Table S7. Results from PGLS regression analysis of (A) testis size and ovary size on body size and (B) a covariate model including body size and the inferred mating syndrome. Table S8. PGLS of the residual testis and residual ovary size. Table S9. Multivariate PGLS of gonadal sex allocation using four sexual traits and body size as predictors.
Gonadal sex allocation of species dependent on (A) received sperm location, (B) sperm bristle state, (C) antrum state, and (D) inferred mating syndrome. For the analysis in panel (D), the species belonging to the large clade only containing hypodermically mating species (clade I in Fig. 2) were removed to evaluate the robustness of the association shown in Fig. 3B. Boxes show the second and third quartile and the whiskers extend up to 1.5 times the interquartile range. Within each panel the main results of the PGLS analysis comparing the yellow and green data points (excluding the grey data) are given. Detailed results are in Additional file 1: Table S4.
. Per-site heterozygosity for all evaluated specimens. Specimens are grouped by species and ordered by mean heterozygosity. Colours indicate specimens collected from different sampling locations, likely corresponding to different populations. Heterozygosity estimates resulting from mapping to a common reference are indicated as squares. Variation within species is largely driven by differences between populations in species with intermediate values, while agreement between populations is high for low values.
About this article
Cite this article
Brand, J.N., Harmon, L.J. & Schärer, L. Mating behavior and reproductive morphology predict macroevolution of sex allocation in hermaphroditic flatworms. BMC Biol 20, 35 (2022). https://doi.org/10.1186/s12915-022-01234-1
- Convergent evolution
- Local sperm competition
- Sperm competition
- Sexual conflict
- Sexual selection
- Comparative morphology
- Selfing syndrome
- Traumatic insemination