On Some Genetic Consequences of Social Structure, Mating Systems, Dispersal, and Sampling

On some genetic consequences of social structure, mating systems, dispersal, and sampling

Bárbara R. Parreiraa,b,1 and Lounès Chikhia,c

aInstituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal; bDepartamento de Biologia Vegetal, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal; and cCNRS, Université Paul Sabatier, Ecole Nationale de Formation Agronomique, UMR5174 EDB (Laboratoire Évolution & Diversité Biologique), F-31062 Toulouse, France

Edited by Mark G. Thomas, University College London, London, United Kingdom, and accepted by the Editorial Board May 13, 2015 (received for review August 7, 2014) Many species are spatially and socially organized, with complex 26). A mathematical theory was first formalized by Chesser (23, social organizations and dispersal patterns that are increasingly 24), who showed that the accumulation of genetic variation documented. Social species typically consist of small age-struc- within and among breeding groups can be predicted from mating tured units, where a limited number of individuals monopolize and dispersal strategies. This work demonstrated that breeding reproduction and exhibit complex mating strategies. Here, we groups may appear outbred despite high coancestry among the model social groups as age-structured units and investigate the group members. However, this and other seminal studies (22, genetic consequences of social structure under distinct mating 25–27) have been neglected and sometimes misinterpreted by strategies commonly found in mammals. Our results show that population geneticists and ecologists alike. For example, empirical sociality maximizes genotypic diversity, which contradicts the be- studies documenting outbreeding within social groups (SGs) have lief that social groups are necessarily subject to strong genetic drift interpreted it as a consequence of inbreeding avoidance or as and at high risk of inbreeding depression. Social structure gener- evidence for sex-biased migration. Although this is in agreement ates an excess of genotypic diversity. This is commonly observed in with Chesser’s study on female philopatry (23), it overlooked ecological studies but rarely reported in population genetic studies Chesser’s later results (24) showing that social structure can lead that ignore social structure. This heterozygosity excess, when to outbreeding even without sex-biased migration. detected, is often interpreted as a consequence of inbreeding Ecological data on relatedness and dispersal show that social avoidance mechanisms, but we show that it can occur even in species exhibit kin recognition and sex-biased dispersal. For ex- the absence of such mechanisms. Many seemly contradictory re- ample, active kin recognition has been described in many co- sults from ecology and population genetics can be reconciled by operatively breeding passerine species (28), and in mammalian genetic models that include the complexities of social species. We species, where SGs are generally composed of related females, find that such discrepancies can be explained by the intrinsic prop- males are usually the dispersing sex (1, 29). The amount of ge- erties of social groups and by the sampling strategies of real pop- netic data gathered from social species has also increased sig- ulations. In particular, the number of social groups and the nature nificantly over the last decades. Most genetic studies within of the individuals that compose samples (e.g., nonreproductive social units report a higher proportion of heterozygotes than and reproductive individuals) are key factors in generating out- expected under random mating [i.e., evidence for heterozygote breeding signatures. Sociality is an important component of pop- excess (outbreeding)]. This finding was described, for example, ulation structure that needs to be revisited by ecologists and in yellow-bellied and alpine marmots (30, 31), white-tailed deers population geneticists alike. (32), and in black-tailed prairie dogs (33). In comparison with the behavioral ecology studies that rec- sociality | social structure | mating system | genotypic diversity | ognize social units as the basic study unit, most population ge- inbreeding avoidance netic studies are carried out at the “population” level, where a “population” is frequently a conglomeration of SGs. The latter ociality is one of the most striking features in the Animal SKingdom. A large number of animal species, including hu- Significance mans, are social. Social systems have evolved in several distinct taxa, such as insects, birds, and mammals (1–4). Whereas some Many species live in socially structured populations, forming animals are highly social and live in groups for their entire life, cohesive units with kin structure. Yet, sociality has been others form groups only for a short period. The diversity of social neglected by population geneticists under the assumption that organizations ranges from eusociality in insects or communal social groups can be seen as small demes subjected to signifi- breeding in vertebrates to solitary life in some mammalian spe- cant genetic drift. Such demes are usually considered to be cies (5–9). Even among closely related species, this diversity can susceptible to inbreeding, with inbreeding avoidance becom- be wide (2, 10). For example, among the great apes, gorillas live ing a major force explaining dispersal strategies. We find that in single-male harem systems, whereas chimpanzees live in large social structure is highly effective in maintaining high geno- multimale/multifemale aggregations forming hierarchical com- typic and genetic diversity levels, without invoking sex-biased munities of related males with coalition strategies (11, 12), and dispersal or inbreeding avoidance mechanisms. These findings orangutans exhibit a semisolitary lifestyle where the territory of should change the way we perceive social groups. one male can include the territory of several related females (13). For decades, much of the theoretical work on sociality has fo- – Author contributions: B.R.P. and L.C. designed research; B.R.P. performed research; L.C. cused on its origins (14 18). It is generally thought that group-living contributed new reagents/analytic tools; B.R.P. and L.C. analyzed data; and B.R.P. and L.C. confers fitness benefits to individuals (19–21). This view has had a wrote the paper. profound impact on the interpretation of social behavior. Indeed, it The authors declare no conflict of interest. is still hotly debated whether these benefits are at the origin of This article is a PNAS Direct Submission. M.G.T. is a guest editor invited by the Editorial sociality, or if they are a side-effect of the existence of kin groups Board. with stable bonds that appeared for other reasons (6). 1To whom correspondence should be addressed. Email: [email protected]. Sociality has been discussed by population geneticists for This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. several decades, but mostly from a theoretical point of view (22– 1073/pnas.1414463112/-/DCSupplemental.

E3318–E3326 | PNAS | Published online June 16, 2015 www.pnas.org/cgi/doi/10.1073/pnas.1414463112 Downloaded by guest on October 2, 2021 studies, including those on highly social species, often find no prising 50 SGs until equilibrium, and measured genetic diversity PNAS PLUS signature of deviation from random-mating, [i.e., the proportion and inbreeding using traditional population genetics statistics. of heterozygotes meets Hardy–Weinberg (HW) proportions, in- dicating neither outbreeding nor inbreeding (25)]; this is, for Population Structure, Social Structure, and the Partitioning of Genetic example, the case of orangutans (34) and sifakas (35), among Diversity. We found that the subdivision of populations into SGs others. As a consequence, population geneticists have tended to lead to a higher (11–63%) genetic diversity than predicted under ignore sociality as an important source of genetic structure or the WF framework (Fig. 2, Fig. S1, and Table S1). This finding have considered SGs simply as small random-mating units (36, suggests that classic population genetics models may in many 37). This approach corresponds to the classic Wright–Fisher cases underestimate the genetic diversity found in real species model (WF) framework in which SGs are isolated demes with and hence lead to biased estimates (e.g., lower effective sizes or small effective sizes. This view has wide-ranging consequences. mutation rates) when applied to social species. Moreover, indi- First, because of the small effective sizes it is expected that SGs vidual heterozygosity within SGs (observed heterozygosity) was should be at high risk of inbreeding depression (36, 38). Second, equal to the expected heterozygosity at the population level based on the fact that small demes are subject to high genetic (HT), suggesting that the social structure is maintaining indivi- drift, the Bush–Wilson theory (39, 40) postulates that the fixation dual diversity at the maximum possible level rather than reducing of chromosomal rearrangements should be frequent and pro- it (Fig. S2). Therefore, social structure appears to be highly ef- mote an acceleration of the evolution rate in social species. This ficient in maintaining diversity within individuals and within SGs. theory has been criticized by behavioral ecologists who observed Interestingly, we found that genetic diversity was always high, higher, rather than lower, levels of heterozygosity in SGs (30). whichever sampling strategy we used, whereas genetic differen- Researchers working on sociality are thus faced with the con- tiation was influenced by the sampling scheme (see below). The flicting views of population geneticists, who treat SGs as demes, influence of sampling on genetic differentiation is in agreement and behavioral ecologists, who emphasize the complexity and with empirical studies reporting lower differentiation among details of social systems and have found empirical evidence that adult males or females in comparison with offspring (45–47) SGs are not simple WF units (30, 41–43). This has also led to the (Fig. S1 and Table S1). High diversity however seems to be an formation of a theoretical and communication gap between the intrinsic property of social structure. two fields, with behavioral ecologists considering themselves more in tune with the complexities of biological realism than Quantifying Inbreeding and Outbreeding in Social Systems. We found that the genotype proportions deviated from HW expec- population geneticists, who tend to reduce populations to simpler ANTHROPOLOGY units to make them more amenable to modeling. tations (i.e., random mating) toward an excess of heterozygotes F < Because there are so many social systems and so many pecu- (i.e., apparent outbreeding rather than inbreeding, IS 0, where F liarities in wild species, there would probably be no way to find a IS is a measure of departure from HW) (Fig. 3). This pattern was one-size-fits-all model that would satisfy field biologists and theoreticians alike. Here, we present a simple but general framework to investigate in detail the effects of sex-biased dispersal, philopatry, and breeding tactics on the distribution of AB genotypes and genetic diversity patterns within and among SGs in a population. By considering key life history parameters, we intend the framework presented here to be general enough to allow the interpretation of empirical observations from a wide array of social species. We studied different social organizations typically found in a vast range of living species. We simulated socially structured populations under different dispersal patterns and under a variety of mating systems (monogamy, polygynandry, and polygyny), ex- cluding any active mechanism of inbreeding avoidance. Our framework therefore provides a null-model against which theories of inbreeding avoidance, tolerance, or preference can be tested (44). We found that SGs allow species to maximize individual heterozygosity, hence minimizing inbreeding. Our results show that patterns of genetic diversity within and among SGs deviate from expectations under the WF framework. We find that the sampling design (e.g., how many groups and how many re- Fig. 1. Dispersal network and life cycle. A fully connected network of 10 productive individuals are sampled) is a key and highly under- SGs (A), in which each SG is a small unit where individuals undergo a life estimated issue that needs to be explicitly accounted for to cycle (B). (A) Nodes represent SGs and edges represent dispersal pathways. The network represents a typical 10-island model according to which in- understand signatures of outbreeding. Altogether, we show that it dividuals can migrate from and to any SG in the population. Note that the is possible to reconcile apparently contradictory results by pop- figure represents 10 SGs for simplicity, but in all our simulations we con- ulation geneticists and behavioral ecologists and bridge the gap sidered 50 SGs instead. (B) Within a SG individuals are assumed to go between these two communities. through three stages: young (between birth and weaning), juveniles (between weaning and reproductive age), and adults (above reproductive age). Results Adults can have a nonreproductive status (non-RS) or a reproductive status We modeled populations of social animals as a fully-connected (RS), which correspond to individuals that are above reproductive age but do network of SGs (Fig. 1A) among which individuals can disperse. not take part in reproduction events and individuals that actually mate at a We explicitly considered SGs as age-structured (Fig. 1B) units breeding season, respectively (SI Materials and Methods). Each box represents an age-class or a RS class. Transitions along age-classes are “natural” with overlapping generations and different mating and dispersal transitions, that is, they depend only on the age of individuals (black arrows), strategies without any explicit behavior to minimize inbreeding. whereas movement among reproductive-classes depend on the acquisition/loss This approach is different from demes in classic population genetics of a RS (gray arrows). Note that the transitions from non-RS to RS class (or in models where demes are modeled as WF units. For all of the results the opposite direction) occur following death events and correspond to mi- presented here we simulated the evolution of populations com- gration events in the case of the nonphilopatric sex (SI Materials and Methods).

Parreira and Chikhi PNAS | Published online June 16, 2015 | E3319 Downloaded by guest on October 2, 2021 Our framework also allows us to select which individuals are sampled and, thus, to quantify the effect of different sampling schemes on genetic diversity and differentiation patterns. For example, it allowed us to analyze samples composed of reproductive status (RS) individuals only. Because RS individuals are the only type of individuals in classic deme-based models, we also examined the effects of such sampling scheme, even though such biased sampling is rarely—if ever—done in field studies. Our results suggest that the way SGs are sampled affects the expected proportion of heterozygotes (expected heterozygosity, HES)as much, if not more, than differences in mating strategies and dispersal (Figs. 4 and 5). As described in detail below, the issue of sampling design is a neglected but important one that can explain contradictory results reported by behavioral ecologists and population geneticists. In most population genetic studies the SG is rarely the unit analyzed; instead, sampled individuals are usually combined using geographical criteria, and thus, samples include individuals from several SGs. We found that FIS values consistently increased when samples from different SGs were pooled and analyzed as if they corresponded to a single unit. The greatest increment was caused by combining samples from two SGs, with diminishing effects of additional SGs. When many SGs were pooled no major deviations from random-mating were detected (Discussion, Fig. 4, Fig. S4,andTable S2). Population genetics theory predicts a deficit in heterozygotes Fig. 2. Diversity in socially structured populations. Diversity (overall het- (i.e., a positive FIS) when samples from different random-mating erozygosity, HT) in the SG model in comparison with diversity in deme-based models for similar levels of genetic differentiation. The figure shows the results for all mating systems simulated under the non–sex-biased dispersal scenario (but the same qualitative effect was obtained under female philopatry) (Fig. S1 and Table S1). Dot sizes correspond to counts. Diversity values were estimated from a random sample (among all age classes and reproductive status), but similar levels were obtained when only RS individuals were sampled. A population subdivided into SGs accumulates more genetic diversity than a comparable traditionally structured population into demes. This is evidenced by the fact that most points are located above the dashed line.

consistent across all mating systems analyzed, with or without sex- biased dispersal (Fig. 3 and Fig. S3), suggesting that social structure minimizes inbreeding without the need of any inbreeding avoidance mechanism. Inbreeding avoidance mechanisms have been shown to exist and may contribute to increase even more heterozygosity levels. Although there were quantitative differences among the distinct mating systems examined, some rather different systems produced similar ranges of FIS values (e.g., polygynandry and extreme polygyny in Fig. 3: red and blue curves, respectively), whereas other apparently similar social systems would generate noticeably different (although overlapping) ranges of values. Overall, the consistent excess of heterozygotes within SGs was in agreement with the range of values observed in many natural species (Fig. 3, Upper) and suggests that social structure is unlikely to lead to inbreeding even in the absence of any active Fig. 3. Quantifying inbreeding in socially structured populations. In- inbreeding avoidance mechanism. breeding coefficient (FIS) under the four different types of social structure simulated. (Upper) Real data extracted from the literature on yellow-bellied Sampling Design Can Explain Contradictory Observations. The re- marmot (30), white-toothed shrew (36), soay-sheep (98), red-howler monkey sults above were obtained by sampling randomly within SGs (i.e., (45), fuit bat (46), and white sifaka (47) (see Table S3 for details). (Lower)The combining individuals from different ages and reproductive simulated datasets under four types of mating system. Black, red, blue, and green correspond to monogamy, polygynandry, extreme polygyny, and classes). This is the typical sampling design of empirical studies moderate polygyny, respectively. The solid and dashed lines represent fe- and is therefore the most appropriate to assess and confront with male philopatry and non–sex-biased dispersal, respectively. In real species real data. Most empirical studies are based on noncontrolled SGs typically show a deviation from HW equilibrium expectations toward an < sampling schemes. The results obtained from this type of sam- excess of heterozygotes (FIS 0). Such excess is found in several mammalian pling are thus what any modeling should explain. The fact that species that form groups ranging from small pair-bonded units to relatively F large aggregations. Negative FIS values are expected when populations are the IS values we found in our simulations overlap with those subdivided into SGs, whatever mating schemes, and dispersal strategies. The

reported for many social species shows that our model is realistic FIS values obtained in the simulation study were within the range of values enough to reproduce the results of some field studies (Fig. 3). measured for many real species.

E3320 | www.pnas.org/cgi/doi/10.1073/pnas.1414463112 Parreira and Chikhi Downloaded by guest on October 2, 2021 demes are combined, which is referred to as the Wahlund effect PNAS PLUS (48, 49). The expected positive FIS values are based on the implicit assumption that demes are at HW (FIS = 0). Our results showed that even though single SGs are characterized by an excess of heterozygotes (FIS < 0), this departure from random mating is more difficult to detect when samples from two or more SGs are pooled together (Fig. 4). This finding is consistent with a Wahlund effect where the pooled units are not at HW equilibrium but rather exhibit an excess of heterozygotes. Thus, FIS values reported in population genetic studies are often over- estimates that misrepresent the departures from random mating caused by social structure. However, these results also show that population genetics theory, in this case the Wahlund effect, still applies even when the unit of study is more complex than a simple WF deme. Our results also suggest that in addition to the effect of combining samples from different SGs, knowing the social status of sampled individuals is more important than population geneticists would typically recognize (but see refs. 22, 50, and 51 for important but undercited exceptions). When samples comprised only RS individuals, we did not find major deviations from random-mating, neither within SGs nor when SGs were pooled (Fig. 5, Fig. S4, and Table S2).

Fig. 5. Sampling design and inbreeding signals. Inbreeding coefficient (FIS) estimated from different sampling schemes within SGs obtained under the extreme polygyny non–sex-biased dispersal scenario. The dashed line rep- ANTHROPOLOGY resents FIS estimated when using the typical sampling design in ecological studies (random sample within SGs), whereas the black solid line represents

FIS values estimated when considering samples composed of reproductive individuals. The gray line represents the FIS distribution obtained from equivalent population genetics classic models assuming deme sizes and sex ratios as in the SGs’ simulations (see SI Materials and Methods for details). The figure shows that datasets similar to those obtained by field biologists and conservation geneticists produce signatures of outbreeding (excess of

heterozygotes; FIS < 0), whereas a sampling design based on the general assumptions of classic population genetics’ models produces a spurious sig-

nal of random mating (neither excess of heterozygotes, nor a deficit; FIS = 0) (see also Fig. S5 and Table S2).

Taken together, our results suggest that signatures of outbreeding reflect an organizational level below that of the population/ deme that should be recognized and reconciles the two apparent contradictory observations: (i) no departures from random-mating even in highly social-species (described in population genetic studies) (Fig. 5 and Fig. S5); and (ii) genetic evidence for outbreeding (described in ecological studies) (Fig. 3, Fig. S3, and Table S2). Discussion Species are spatially subdivided at several hierarchical levels. Fig. 4. The effect of pooling SGs on deviations from random-mating. In- How these subunits should be represented or modeled is likely to breeding coefficient (FIS) distributions obtained from data simulated under the extreme polygyny non–sex-biased dispersal scenario. The dashed line depend on the questions asked. For several decades population

is the FIS distribution obtained within SGs (same as represented in blue in Fig. geneticists have used simple but very powerful and useful hier- 3), whereas solid lines are FIS values obtained when samples from different archical representations to describe the overall relationships SGs are pooled and analyzed as one single unit. From left to right, blue lines between such subunits and to document the patterns of genetic correspond to grouping of 2, 4, and 10 SGs. This figure shows that the variation. Population genetics models have played a major role heterozygote excess signal (FIS < 0) is gradually lost when samples from more than one SG are pooled (solid lines). Indeed, the pooling of four SGs pro- in clarifying the role of space in maintaining genetic variation vided results that do not differ significantly from theoretical expectations and in generating patterns that could not be easily reproduced under an ideal population with random-mating. This also shows that the without structure (52, 53). However, in most studies this classic sampling strategy can lead to spurious signals of deficit in heterozygotes approach to population structure has tended to ignore the com- > (FIS 0, solid lines), which is an important result as in genetic studies of social plex relationships that can exist among individuals living within species, samples from more than one SG are usually analyzed together. This SGs and under the various mating systems that social species is misleading because pooling individuals from different SGs can lead to exhibit. This deme-based viewpoint has had a long-standing impact the conclusion that populations are structured into random-mating units when they actually have a different mating dynamics. Note that all of the on the interpretation of genetic data and has even produced an other mating systems analyzed showed the same pattern (see Fig. S4 and influential theory of molecular evolution. Here, we showed that: Table S2). (i) it is possible to build a simple and flexible framework with an

Parreira and Chikhi PNAS | Published online June 16, 2015 | E3321 Downloaded by guest on October 2, 2021 underlying model explicitly incorporating social structure and mechanisms (44, 67–69). There is indeed significant experimental mating systems; (ii) this model predicts patterns of genotypic and evidence of inbreeding avoidance and kin recognition (70, 71). genetic diversity that are different from those predicted by deme- We show that in a network of SGs where individuals are based models; and (iii) the sampling strategies are an important replaced at random among all of the potential reproductive in- issue that can explain real data and the lack of convergence be- dividuals available in the network, an excess of heterozygotes is tween what ecologist report and what populations genetics’ studies observed. Therefore, there is no need to invoke inbreeding find. The parameters used in our model are also more easily avoidance mechanisms to explain the outbreeding signature translated in real-world data than population genetics parameters observed in so many natural systems (see also refs. 23 and 24). (such as effective size or migration rates). Furthermore, there may also be no need to invoke inbreeding avoidance as a necessary cause for the evolution of sex-biased The Evolution of Social Behavior. Sociality has traditionally been dispersal. In fact, an increasing number of studies (reviewed in explained using ecological arguments over time scales beyond ref. 44) suggest that inbreeding avoidance may not be as wide- those of interest to most population geneticists. With few ex- spread as it was previously believed. In most species individuals ceptions, population genetics theory has rarely been invoked to with a high inbreeding coefficient are likely to be rare and, within explain sociality or the evolution of social species [but see the a population, inbreeding may vary widely among individuals. In Bush–Wilson hypothesis below (39, 40)]. Our results show that addition, the costs of outbreeding may have been neglected and social structure has a fundamental impact on the genetics of many species may be either tolerant to inbreeding or may even populations. In comparison with expectations from deme-based prefer to mate with relatives [“inbreeding preference” (44)]. models, socially structured populations exhibit an increase in Szulkin et al. (44) also discuss the difficulty in identifying inbreeding genetic and genotypic diversity, the latter being often associated avoidance strategies in animals, especially because there are no with fitness traits (54, 55). Several studies have found that higher clearly identifiable null-models to which real populations can be individual heterozygosity is associated with fitness in an impor- compared. As these authors rightly note, a precise measure of tant range of traits. For example, heterozygosity was found to individual inbreeding requires access to the exact pedigree or an increase neonatal survival and attractiveness in harbor seals (56, exhaustive sampling of the population under study. This is rarely 57) and territory size in a group-living bird (58). In addition, possible and may therefore lead to biases in the estimates of in- studies on the MHC show that heterozygosity increases longevity breeding obtained in most samples. Our results confirm this point in baboons (59) and the immune response in HIV-infected and our framework thus provides a null-model against which real Mauritian macaques (60). Sociality is usually explained by the case studies can be properly compared. fitness advantages it supposedly provides (21, 61), as for exam- Although we do claim that our framework provides new re- ple, increased mating opportunities and offspring survival (62– sults beyond those of classic population genetics models, we also 65). Because it also incurs costs, such as higher risks of disease wish to stress that deviations from HW expectations have already infection or parasite transmission, and inbreeding in small groups, been anticipated by theoretical deme-based models. Such models the maximization of observed heterozygosity would therefore ap- accounted for the effects of variance in reproductive success, pear to act against some of the main costs typically associated with unbalanced sex-ratios, and sex-biased dispersal (22, 72–75). In group living. Although costs and benefits of group living have been small populations the absence of selfing in dioecious organisms discussed for more than three decades (14, 21), interpretations of [i.e., separate sexes (51)], differences in allelic frequencies be- social behavior and explanations for sociality are still controversial tween males and females (73, 74), and sex-biased dispersal (22) today (6, 20). One possible reason for some of these debates is the can result in an excess of heterozygotes. Unfortunately, these difficulty in quantifying the relative importance of different evo- results have been either ignored or partly misinterpreted. For lutionary forces. For example, our results suggest that inbreeding example, behavioral ecologists have interpreted Chesser’s theo- avoidance might be easier to achieve through selection simply retical work as if it suggested that outbreeding was a consequence because social structure naturally generates outbreeding signa- of sex-biased dispersal. This interpretation may come from the tures. Our results therefore add a new dimension to this debate, fact that Chesser (23) did indeed use a model with female suggesting that maintaining the social structure is in itself an philopatry. However, in another study Chesser (24) actually advantage. showed that sex-biased dispersal was not necessary to generate outbreeding within SGs. Our study confirms that sex-biased dis- Inbreeding in Socially Structured Populations. Inbreeding depres- persal is not required to explain the observed excess of hetero- sion occurs in many natural populations. Because inbred indivi- zygotes but it also shows that modeling SGs as small WF units duals typically have decreased survival and fertility (66), inbreeding is not sufficient. Moreover, we identified nonreproductive in- depression is expected to be a powerful selective force favoring any dividuals as central in generating signals in real datasets beyond behavior that increases individual heterozygosity. However, this is those predicted by classical models. still a hotly debated issue and it is not clear how heterozygosity contributes to fitness increase. Balloux et al. (55) have suggested The Effect of the Sampling Scheme. Sampling is increasingly rec- that inbreeding is not necessarily correlated with individual het- ognized as an important issue in population genetics inference erozygosity. These authors noted that apparent heterosis could (51, 52, 76–78). Beyond the obvious effect of the sample size, result from linkage between markers experiencing balancing se- several recent studies (77, 78) have shown that the sampling lection and other genomic markers. Under such a scenario regions schemes typically used by population geneticists can be highly under balancing selection would drive the positive correlation misleading in population genetics inference. between heterozygosity and fitness in other genomic regions. Our Basset et al. (50), and Prout (22) before them, discussed the results show that the situation may be even simpler because in- importance of the timing of sampling (in relation to dispersal dividuals are represented in our model by independent markers events) in generating departures from HW proportions, but without any linkage with other loci and we do not assume any these results have been generally neglected even though there type of selection. Thus, the excess of heterozygotes detected in are real-case studies where such effects were observed (33, 46, our data cannot be an outcome of selective mechanisms. In 47). Although none of these studies are directly comparable to addition to heterosis, another common explanation is that the ours, they do point out that the interpretation of genetic data is outbreeding signatures found in many species are a consequence strongly affected by the composition of samples. We found that of sex-biased dispersal strategies. In particular, this heterozy- different samples’ composition (in terms of the status of in- gosity excess is seen as a consequence of inbreeding avoidance dividuals) can lead to different perceptions of the distribution of

E3322 | www.pnas.org/cgi/doi/10.1073/pnas.1414463112 Parreira and Chikhi Downloaded by guest on October 2, 2021 genotypic diversity, ranging from signals of outbreeding to no generations. Because this is expected to reduce the effective size, PNAS PLUS deviations from random mating within SGs. Basset et al. (50) it is unlikely to explain the differences found here. The second proposed that reproductive events drive a heterozygote excess difference is that our model incorporates individuals that do not that is counteracted by dispersal, and hence FIS values estimated reproduce but may be recruited when RS individuals die. These from juveniles are lower (more outbred) than when estimated individuals can be seen as “reservoirs” of genetic diversity, and from adults. Similarly, we found that when samples are only may be one reason for the increase in diversity found here. composed by RS individuals, the expected heterozygosity within Taken together, these two factors have opposite effects, and we SGs is larger than when individuals from any age and reproductive should therefore be cautious in extending our conclusions to all class are included. social species and environments. The crucial role played by the Unfortunately, in the wild it may be difficult to determine the non-RS individuals stresses again one of the main points of our reproductive status of individuals. This is a crucial point. Because study, namely the role of sampling. Non-RS individuals can it may not always be possible to identify RS individuals, it is represent a large proportion of the individuals that make up real important to be aware that the composition of samples has a populations, and are actually responsible for the genetic signa- great influence on genetic measures when drawing conclusions tures found in empirical studies. Non-RS individuals are not about social species. Samples often represent a mixture of indi- explicitly represented in WF models but can store genotypes or viduals from different SGs and social status. This finding is similar alleles that were lost among the RS individuals. to the mixing of demes, which is known to generate a Wahlund- Altogether, we stress that by forcing real data into deme-based effect (48, 49). The distinction is that the SGs are not at HW models, some signals, such as outbreeding described in many social equilibrium but rather exhibit negative FIS values and the Wahlund species, are ignored or seen as noise when these signals actually are effect brings them closer to, rather than further from, HW what we need to interpret and explain. We need to identify models proportions. that can incorporate the complexities of real populations, such as Our results show that apparent random-mating populations social structure and mating systems (84, 85), as argued by several found in traditional population genetic studies are difficult to ecologists in a recent critical review on ecological models (86). interpret. On one hand, deviations from random-mating that are usually attributed to ecological processes (inbreeding avoidance) Social Structuring, Karyotypic Evolution, and Speciation Rates. Rates may be a simple outcome of social structure. On the other hand, of speciation and chromosomal evolution have been described a real nonrandom mating population may exhibit HW pro- to occur faster in genera where social organization is typically

portions as a result of noncontrolled sampling strategies. As far found. This observation led Bush et al. (40) and Wilson et al. ANTHROPOLOGY as population genetic studies are concerned, it may in many cases (39) to hypothesize that the social structure is a key factor in be appropriate to consider a socially structured population as a promoting conditions that accelerate the rates of evolution (Bush– WF population (in the sense that it has the expected HW pro- Wilson hypothesis). This hypothesis had its roots in the population portions of genotypic diversity at the population level), but it is genetics theory for subdivided populations, and postulates that important to recognize that important deviations are ongoing at stronger genetic drift and inbreeding within demes are conditions the lower genotypic level. We provide a framework that recon- under which new gene arrangements have a better chance of ciles the results obtained by classic models and the results obtained becoming fixed and spread. The fixation of new mutations in in empirical genetic and ecological studies, whether they ignore small isolated demes might ultimately boost postmating isolation social structure or explicitly account for it. Some theoretical studies mechanisms and result in reproductive isolation (39). have produced important results that are similar to ours (50). However, this apparently reasonable and attractive hypothesis However, these results are typically ignored, perhaps because of was criticized by field ecologists who found no evidence for in- the fact that they are not reproducible by most existing simula- breeding. Indeed, social structure appears to minimize the loss tion software. In addition, the last 20 y have led to a major de- of genetic diversity in marmots, prairie dogs, sifakas, and deer, velopment of the coalescent theory, which makes assumptions among others (26, 30–32, 35, 45). This finding is in agreement that are incompatible with SG modeling. For example, multiple with what we find in our model. coalescent events are usually ignored (but see ref. 79 for generation by generation coalescent algorithms, or refs. 80 and 81). Concluding Remarks and Perspectives Similarly, the sample size is assumed to be negligible compared Social structure is ubiquitous among mammals and birds. The with the effective size (Ne) of the deme. fact that these two groups exhibit sex-biased dispersal patterns Moreover, the comparison between models is not always as that are widely conserved (male-biased in mammals and female- straightforward as it may seem. Even if one focuses only on biased in birds) (1) is striking and calls for an evolutionary deme-based models, if one compares n-island models and non- explanation that could be related to social structure, kin recog- structured WF models, Ne expressions are only valid under some nition, and inbreeding patterns. Inbreeding avoidance may be a specific assumptions. As discussed by Sjödin et al. (82) and by factor driving sex-biased dispersal evolution, and there is evi- Wakeley and Sargsyan (83), for some models of structure the dence for it, but its quantification requires proper testing with concept of effective size is not necessarily meaningful. Similarly, the identification of appropriate null-models and data. Our re- Chikhi et al. (77) have shown with others (52, 76, 78) that sults suggest that outbreeding rather than inbreeding is an emer- structured populations exhibit signals of population decrease gent property of social structure. The fact that inbreeding even though the population size remained constant. That is, avoidance has been questioned by numerous field studies sug- patterns of genetic diversity in an n-island model require a gests that our framework can provide the null-models required “dynamic Ne”, simply because no constant Ne could explain the to test hypotheses regarding the origin, maintenance, and con- data. One could find an Ne that would explain the HT, but not sequences of social structure. other statistics, such as Tajima’s D. The results presented here go A number of ecological and conservation studies have argued further and suggest that in social species, the concept of Ne may that changes in habitat composition affect group size, mate avail- thus not always be the most appropriate concept to understand ability, and mating behavior (9, 87, 88). Moreover, the increasingly the gene dynamics. reduced and fragmented landscapes (89) may limit dispersal, increase We believe that there are several differences between our isolation, and promote high relatedness within groups. Predicting SG model and deme-based models that could contribute to ex- the consequences of such changes with appropriate models, such as plaining why we found a higher level of genetic diversity (Fig. 2). our framework provides, may thus be critical for conservation The first difference is that our model allows for overlapping strategies.

Parreira and Chikhi PNAS | Published online June 16, 2015 | E3323 Downloaded by guest on October 2, 2021 Historically, SGs have been modeled as small demes (39, 40, chosen because it is most common among mammals (1). Altogether, eight (4 × 2) 75) and this has introduced a gap between behavioral ecology different combinations of mating system and dispersal were simulated. We observations and population genetic theory. Our work shows that aimed to represent some of the most diverse and representative systems social structure leads to genetic properties that cannot be mod- known in mammals (2). eled by considering a population as a network of small demes. In Genetic data on social species were taken from the literature. Datasets were distinguished according to the level at which analyses were performed particular, we identified non-RS individuals as crucial in generating (Table S3). Quantitative results from the SG simulations were compared with signals in real datasets that could not be reproduced by deme-based the ones obtained in ecological and population genetic studies. See Table S4 models, and as potential reservoirs of genetic diversity. In a few for parameter values of the SGs. words, we found differences in terms of (i) genotypic diversity, Because of the computational costs of our simulations and the required (ii) genetic diversity, and (iii) sampling effect (role of RS and postprocessing analyses, most simulations performed in this study assumed a non-RS individuals, grouping of SGs into arbitrary random g of 50. For example, in primates this would correspond to several hundred mating populations, and so forth). Accounting for fine structure individuals and habitats of several square kilometers for many species. should allow better understanding and quantifying the rela- However, validation tests and comparisons were also performed with larger = tive importance of factors, such as selection and sex-biased and smaller numbers of SGs (g 20, 100, 200). We were interested in both equilibrium and nonequilibrium conditions but, because very little work has dispersal. been done on the properties of SGs from a population genetic perspective However, more work is still needed and it is important to [but see the works of Prout (22) and Chesser (23, 24)], the results presented recognize that the island model considered here ignores spatial here focus mainly on equilibrium conditions. SGs were initialized with RS structure. Classic population genetics models have shown that individuals whose genotypes were obtained by sampling from a WF pop- spatial structure leads to important genetic consequences, such ulation (i.e., of stationary size). Our simulations were then run for 6,000 as isolation by distance (90). Because most species are spatially generations, even though population genetics theory predicted that equi- structured and dispersal is space-dependent, real populations librium would be reached in much fewer generations. Allelic frequencies of θ = may be more easily recognized in a classic stepping-stone model the founding populations were simulated with scaled mutation rate 20, where each deme is only connected to its neighbors (91). We using a user-friendly software (92) that uses the program ms (93) as an en- gine to simulate genetic data. carried out additional simulations of SGs and WF demes under a stepping-stone type of structure. The results we obtained confirm Genetic and Genotypic Diversity in Socially Structured Populations. To quantify that SGs exhibit genetic patterns that are significantly different departures from HW proportions (excess or deficit in heterozygotes), we

from those predicted by deme-based stepping-stone models (Fig. calculated Wright’s FIS statistic, using Weir and Cockerham’s estimator (94). S6). Finally, a combination of ecological data and modeling is This was done by using several sampling strategies either at the SG level (as is needed to increase our understanding of the genetic properties done in behavioral ecology studies) or by sequentially pooling samples from 2, 4, of social species. Our framework provides a bridge between and 10 SGs (as is usually done in population genetics studies that typically ignore population geneticists and behavioral ecologists and allows ex- SGs). For each mating strategy, we sampled four individuals from 20 SGs (in a plicit realistic modeling of the evolution of SGs. total of 80 samples). Within each SG we randomly sampled the four individuals (i) among all age classes or (ii) among the RS individuals, when possible (except Materials and Methods for monogamy, where only the two RS individuals were analyzed). To compare the effect of social structure with the effect of population General Framework. We consider a population of dioecious individuals con- structure we compared the diversity obtained (under models of social or- sisting of SGs between which individuals can move according to different ganization) with the diversity expected from typical population genetics (i.e., prespecified connectivity topologies (Fig. 1A) with or without sex-biased deme-based) models of structured populations, while maintaining the same migration. Individuals, males or females, go through a simplified life cycle levels of differentiation. We simulated a 50-island model with same number that involves several stages or age classes (Fig. 1B). As a consequence, an SG RS individuals per deme as the SG model. The same sex ratios were used for consists of a small age-structured unit, and the model can thus be seen as a both the deme-based and SG simulations. Because of this, the variance in model with overlapping generations. Newly born individuals go through the reproductive success should be similar or the same in both models. Simula- following age classes: young (between birth and weaning), juveniles (be- tions were also performed with two migration schemes: female philopatry tween weaning and reproductive age), and finally adults (i.e., above re- (sex-biased migration) and non–sex-biased migration. The deme-based productive age). We assume that not all adult individuals are effectively simulated data sets were obtained with the EASYPOP program (95), which reproductive, that is, we consider that only a limited and fixed (i.e., species- allows for the simulation of structured populations with variable sex ratios. specific) number of adults have the possibility to reproduce within an SG at a Diversity was compared using the overall unbiased heterozygosity according given reproductive event. These individuals are the effectively reproductive to Nei (96) and the within SG expected and observed heterozygosity “ ” individuals and are identified as having reproductive status (RS). At each according to Nei and Chesser (97). reproductive event, RS males are sampled with replacement and females without replacement, which allows for several offspring to be sired to a different mother while sharing the same father. For each independent locus, Table 1. The different scenarios of social structure considered in the genotypes of the offspring are sampled from the parents who contrib- the simulations ute with one allele each. We only considered microsatellite markers and we assumed that mutations occur according to a stepwise mutation model at an Mating system No. males No. females Structure Dispersing sex equal rate across all loci. We model different mating systems by allowing a variable number of male Monogamy 1 1 Island Male and female RS individuals per SG. This approach allowed us to simulate Male/Female different mating systems, such as monogamy and polygyny (Table 1). Other Polygynandry 2 2 Island Male more specific details of the model and implementation can be found in SI Male/Female Materials and Methods. Extreme polygyny 1 10 Island Male Male/Female Simulating Mating Systems. Four different mating systems (monogamy, Moderate polygyny 4 10 Island Male polygynandry, extreme polygyny, and moderate polygyny) (Table 1 and SI Male/Female Materials and Methods) were simulated. We assumed that the maximum number of SGs was fixed to g and that SGs could become extinct (see below The four types of mating system analyzed differ in the size and sex-ratios for details). In all simulations we assumed that dispersal events were taking of SGs. These are: monogamy, 1 breeding (RS) pair per SG; polygynandry, 2 place symmetrically among all SGs, that is, individuals could disperse to any breeding females per SG that each mate with 1 of 2 males at each breeding other group in the population. In other words, we assumed an implicit “island season; extreme polygyny, 1 male and 10 females per SG, which intends to model” network for dispersal and colonization events (Fig. 1A). Under each represent harem-polygyny; and moderate polygyny, 4 male and 10 females mating system we simulated dispersal events assuming either a sex-biased per SG, which intends to represent a dominance-polygyny mating system (female philopatry) or non–sex-biased dispersal regime. Female philopatry was (see SI Materials and Methods for details).

E3324 | www.pnas.org/cgi/doi/10.1073/pnas.1414463112 Parreira and Chikhi Downloaded by guest on October 2, 2021 Genetic differentiation between SGs/demes was measured by the Weir and particularly regarding the notion of effective size in structured populations; PNAS PLUS

Cockerham (94) FST. To have the same amount of genetic differentiation R. Heller and C. Chaouiya for reading carefully and discussing in detail drafts between demes as observed under the SG scenarios, we simulated datasets of this manuscript; M. Gomes, I. Gordo, J. Alves, I. Pais, R. Sharma, I. Alves, varying the migration rate from 0.3 up to 1 in EASYPOP (95). For comparison and T. Minhós for their helpful comments that improved the clarity of the purposes we used EASYPOP datasets that generated F values within the text; R. Chesser for discussions on the model; and E. Danchin, J. Howard, and the ST Fundação Calouste Gulbenkian for their continuous support and for additional range observed in the SG simulations (SI Materials and Methods). The financial assistance (to B.R.P.). This work was performed using HPC (High Per- EASYPOP values used for the comparison with SGs were sampled randomly formance Computing) resources from CALMIP (Calcul en Midi-Pyrénées Méso- among all of the values obtained within this range. Centre de Calcul) (Grant 2012, Projects 43 and 44) from Toulouse, France, and the Haystack from Instituto Gulbenkian de Ciência. This study was funded ACKNOWLEDGMENTS. We thank V. Sousa for numerous and fruitful by the Fundação para a Ciência e Tecnologia ref. SFRH /BD/41346/2007 (to discussions throughout the development of this work; C. van Schaik for B.R.P.) and PTDC/BIA- BIC/4476/2012 and PTDC/BIA-BEC/100176/2008 (to L.C.); discussion and valuable comments on the revised version of the manuscript; and LABEX (Laboratoire d’Excellence) entitled TULIP (vers une Théorie Uni- M. Beaumont for early discussions on our modeling framework and on the fiée des Interactions biotiques: rôle des Perturbations environnementales) scaling of time across models; the editor and three anonymous reviewers for (ANR-10-LABX-41) and the LIA BEEG-B (Laboratoire International Associé - Bio- useful comments that helped us to improve and clarify our manuscript, informatics, Ecology, Evolution, Genomics and Behaviour) (CNRS).

1. Greenwood PJ (1980) Mating systems, philopatry and dispersal in birds and mammals. 32. Mathews NE, Porter WF (1993) Effect of social structure on genetic structure of free- Anim Behav 28(4):1140–1162. ranging white-tailed deer in the Adirondack mountains. J Mammal 74(1):33–43. 2. Clutton-Brock TH (1989) Mammalian mating systems. Proc R Soc Lond B Biol Sci 33. Winterrowd MF, Dobson FS, Hoogland JL, Foltz DW (2009) Social subdivision in- 236(1285):339–372. fluences effective population size in the colonial-breeding black-tailed prairie dog. 3. Janson CH (2000) Primate socio-ecology: The end of a golden age. Evol Anthropol J Mammal 90(2):380–387. 9(2):73–86. 34. Goossens B, et al. (2005) Patterns of genetic diversity and migration in increasingly 4. Clutton-Brock T, Janson C (2012) Primate socioecology at the crossroads: past, present, fragmented and declining orang-utan (Pongo pygmaeus) populations from Sabah, and future. Evol Anthropol 21(4):136–150. Malaysia. Mol Ecol 14(2):441–456. 5. Clutton-Brock TH (2006) Cooperative breeding in mammals. Cooperation in Primates 35. Quéméré E, Crouau-Roy B, Rabarivola C, Louis EE, Jr, Chikhi L (2010) Landscape ge- and Humans, eds Kappeler PM, van Schaik CP (Springer, Berlin, Heidelberg), pp 173–190. netics of an endangered lemur (Propithecus tattersalli) within its entire fragmented 6. Clutton-Brock T (2009) Structure and function in mammalian societies. Philos Trans R range. Mol Ecol 19(8):1606–1621. Soc B Biol Sci 364(1533):3229–3242. 36. Balloux F, Goudet J, Perrin N (1998) Breeding system and genetic variance in the 7. Plowes N (2010) An introduction to eusociality. Nature Education Knowledge 3(10):7. monogamous, semi-social shrew, Crocidura russula. Evolution 52(4):1230–1235. Available at www.nature.com/scitable/knowledge/library/an-introduction-to-eusociality- 37. Lawson Handley LJ, Perrin N (2007) Advances in our understanding of mammalian – 15788128. Accessed February 20, 2014. sex-biased dispersal. Mol Ecol 16(8):1559 1578. 8. Swedell L (2012) Primate sociality and social systems. Nature Education Knowledge 3 38. Szulkin M, Garant D, McCleery RH, Sheldon BC (2007) Inbreeding depression along a – ANTHROPOLOGY (10):84. Available at www.nature.com/scitable/knowledge/library/primate-sociality- life-history continuum in the great tit. J Evol Biol 20(4):1531 1543. and-social-systems-58068905. Accessed February 20, 2014. 39. Wilson AC, Bush GL, Case SM, King MC (1975) Social structuring of mammalian pop- – 9. Schradin C, et al. (2012) Social flexibility and social evolution in mammals: A case study ulations and rate of chromosomal evolution. Proc Natl Acad Sci USA 72(12):5061 5065. of the African striped mouse (Rhabdomys pumilio). Mol Ecol 21(3):541–553. 40. Bush GL, Case SM, Wilson AC, Patton JL (1977) Rapid speciation and chromosomal – 10. Kappeler PM, Barrett L, Blumstein DT, Clutton-Brock TH (2012) Constraints and flex- evolution in mammals. Proc Natl Acad Sci USA 74(9):3942 3946. ibility in mammalian social behaviour: Introduction and synthesis. Philos Trans R Soc B 41. Frankham R (1995) Effective population size/adult population size ratios in wildlife: A review. Genet Res 66(2):95–107. Biol Sci 368(1618):20120337. 42. Nunney L (1995) Measuring the ratio of effective population size to adult members 11. Rowe N (1996) The Pictorial Guide to the Living Primates (Pogonias Press, East using genetic and ecological data. Evolution 49(2):389–392. Hampton, NY), 3rd Ed. 43. Palstra FP, Fraser DJ (2012) Effective/census population size ratio estimation: A com- 12. Inoue E, et al. (2013) Male genetic structure and paternity in western lowland gorillas pendium and appraisal. Ecol Evol 2(9):2357–2365. (Gorilla gorilla gorilla). Am J Phys Anthropol 151(4):583–588. 44. Szulkin M, Stopher KV, Pemberton JM, Reid JM (2013) Inbreeding avoidance, toler- 13. Atmoko SSU, et al. (2009) Orangutan mating behavior and strategies. Orangutans: ance, or preference in animals? Trends Ecol Evol 28(4):205–211. Geographic Variation in Behavioral Ecology and Conservation, eds Wich SA, Atmoko 45. Pope TR (1992) The influence of dispersal patterns and mating systems on genetic SSU, Setia TM, van Schaik CP (Oxford Univ Press, New York). differentiation within and between populations of the red howler monkey (Alouatta 14. Alexander RD (1974) The evolution of social behavior. Annu Rev Ecol Syst 5:325–383. seniculus). Evolution 46(4):1112–1128. 15. Emlen ST, Oring LW (1977) Ecology, sexual selection, and the evolution of mating 46. Storz JF, Bhat HR, Kunz TH (2001) Genetic consequences of polygyny and social systems. Science 197(4300):215–223. structure in an Indian fruit bat, Cynopterus sphinx. I. Inbreeding, outbreeding, and 16. Kappeler P, Van Schaik C (2002) Evolution of primate social systems. Int J Primatol population subdivision. Evolution 55(6):1215–1223. 23(4):707–740. 47. Lawler RR, Richard AF, Riley MA (2003) Genetic population structure of the white 17. Nowak MA, Tarnita CE, Wilson EO (2010) The evolution of eusociality. Nature sifaka (Propithecus verreauxi verreauxi) at Beza Mahafaly Special Reserve, southwest 466(7310):1057–1062. Madagascar (1992–2001). Mol Ecol 12(9):2307–2317. 18. Shultz S, Opie C, Atkinson QD (2011) Stepwise evolution of stable sociality in pri- 48. Hartl DL, Clark AG (2007) Principles of Population Genetics (Sinauer Associates, Sun- mates. Nature 479(7372):219–222. derland, MA), 4th Ed. 19. Clutton-Brock T (2002) Breeding together: Kin selection and mutualism in cooperative 49. Garnier-Géré P, Chikhi L (2013) Population subdivision, Hardy–Weinberg equilibrium – vertebrates. Science 296(5565):69 72. and the Wahlund effect. eLS (John Wiley & Sons, Chichester, UK). Available at www. 20. Silk JB (2007) The adaptive value of sociality in mammalian groups. Philos Trans R Soc els.net/WileyCDA/ElsArticle/refId-a0005446.html. Accessed June 4, 2015. – B Biol Sci 362(1480):539 559. 50. Basset P, Balloux F, Perrin N (2001) Testing demographic models of effective pop- 21. Silk JB (2007) Social components of fitness in primate groups. Science 317(5843): ulation size. Proc Biol Sci 268(1464):311–317. – 1347 1351. 51. Balloux F (2004) Heterozygote excess in small populations and the heterozygote- 22. Prout T (1981) A note on the island model with sex dependent migration. Theor Appl excess effective population size. Evolution 58(9):1891–1900. – Genet 59(5):327 332. 52. Wakeley J (1999) Nonequilibrium migration in human history. Genetics 153(4):1863–1871. – 23. Chesser RK (1991) Gene diversity and female philopatry. Genetics 127(2):437 447. 53. Rousset F (2004) Genetic Structure and Selection in Subdivided Populations (Princeton 24. Chesser RK (1991) Influence of gene flow and breeding tactics on gene diversity Univ Press, Princeton, NJ). – within populations. Genetics 129(2):573 583. 54. Hoffman JI, et al. (2014) High-throughput sequencing reveals inbreeding depression 25. Sugg DW, Chesser RK, Stephen Dobson F, Hoogland JL (1996) Population genetics in a natural population. Proc Natl Acad Sci USA 111(10):3775–3780. meets behavioral ecology. Trends Ecol Evol 11(8):338–342. 55. Balloux F, Amos W, Coulson T (2004) Does heterozygosity estimate inbreeding in real 26. Storz JF (1999) Genetic consequences of mammalian social structure. J Mammal 80(2): populations? Mol Ecol 13(10):3021–3031. 553–569. 56. Coltman DW, Bowen WD, Wright JM (1998) Birth weight and neonatal survival of 27. Sugg DW, Chesser RK (1994) Effective population sizes with multiple paternity. Ge- harbour seal pups are positively correlated with genetic variation measured by mi- netics 137(4):1147–1155. crosatellites. Proc Biol Sci 265(1398):803–809. 28. Szulkin M, Zelazowski P, Nicholson G, Sheldon BC (2009) Inbreeding avoidance under 57. Hoffman JI, Forcada J, Trathan PN, Amos W (2007) Female fur seals show active choice different null models of random mating in the great tit. J Anim Ecol 78(4):778–788. for males that are heterozygous and unrelated. Nature 445(7130):912–914. 29. Dobson FS (2013) The enduring question of sex-biased dispersal: Paul J. Greenwood’s 58. Seddon N, Amos W, Mulder RA, Tobias JA (2004) Male heterozygosity predicts ter- (1980) seminal contribution. Anim Behav 85(2):299–304. ritory size, song structure and reproductive success in a cooperatively breeding bird. 30. Schwartz OA, Armitage KB (1980) Genetic variation in social mammals: The marmot Proc Biol Sci 271(1550):1823–1829. model. Science 207(4431):665–667. 59. Huchard E, Knapp LA, Wang J, Raymond M, Cowlishaw G (2010) MHC, mate choice 31. Goossens B, Chikhi L, Taberlet P, Waits LP, Allainé D (2001) Microsatellite analysis of and heterozygote advantage in a wild social primate. Mol Ecol 19(12):2545–2561. genetic variation among and within Alpine marmot populations in the French Alps. 60. O’Connor SL, et al. (2010) MHC heterozygote advantage in simian immunodeficiency Mol Ecol 10(1):41–52. virus-infected Mauritian cynomolgus macaques. Sci Transl Med 2(22):22ra18.

Parreira and Chikhi PNAS | Published online June 16, 2015 | E3325 Downloaded by guest on October 2, 2021 61. Silk JB, et al. (2010) Strong and consistent social bonds enhance the longevity of fe- 80. Eldon B, Wakeley J (2006) Coalescent processes when the distribution of offspring male baboons. Curr Biol 20(15):1359–1361. number among individuals is highly skewed. Genetics 172(4):2621–2633. 62. Armitage KB, Schwartz OA (2000) Social enhancement of fitness in yellow-bellied 81. Nordborg M (2007) Coalescent theory. Handbook of Statistical Genetics, eds Balding DJ, marmots. Proc Natl Acad Sci USA 97(22):12149–12152. Bishop M, Cannings C (Wiley & Sons, Chichester, UK), 3rd Ed, Vol 2. 63. Silk JB, Alberts SC, Altmann J (2003) Social bonds of female baboons enhance infant 82. Sjödin P, Kaj I, Krone S, Lascoux M, Nordborg M (2005) On the meaning and existence survival. Science 302(5648):1231–1234. of an effective population size. Genetics 169(2):1061–1070. 64. Silk JB, et al. (2009) The benefits of social capital: Close social bonds among female 83. Wakeley J, Sargsyan O (2009) Extensions of the coalescent effective population size. baboons enhance offspring survival. Proc Biol Sci 276(1670):3099–3104. Genetics 181(1):341–345. 65. Wei TW, Blumstein DT (2012) Social attributes and associated performance measures 84. Guillot EG, Cox MP (2014) SMARTPOP: Inferring the impact of social dynamics on in marmots: Bigger male bullies and weakly affiliating females have higher annual genetic diversity through high speed simulations. BMC Bioinformatics 15:175. reproductive success. Behav Ecol Sociobiol 66(7):1075–1085. 85. Di Fiore A, Valencia LM (2014) The interplay of landscape features and social system 66. Charlesworth D, Willis JH (2009) The genetics of inbreeding depression. Nat Rev on the genetic structure of a primate population: An agent-based simulation study “ ” – Genet 10(11):783–796. using Tamarins . Int J Primatol 35(1):226 257. 67. Pusey A, Wolf M (1996) Inbreeding avoidance in animals. Trends Ecol Evol 11(5):201–206. 86. Evans MR, et al. (2013) Do simple models lead to generality in ecology? Trends Ecol – 68. Lukas D, Clutton-Brock TH (2011) Group structure, kinship, inbreeding risk and ha- Evol 28(10):578 583. 87. Cale PG (2003) The influence of social behaviour, dispersal and landscape fragmen- bitual female dispersal in plural-breeding mammals. J Evol Biol 24(12):2624–2630. tation on population structure in a sedentary bird. Biol Conserv 109(2):237–248. 69. Szulkin M, Sheldon BC (2008) Dispersal as a means of inbreeding avoidance in a wild 88. Banks SC, et al. (2005) The effects of habitat fragmentation on the social kin structure bird population. Proc Biol Sci 275(1635):703–711. and mating system of the agile antechinus, Antechinus agilis. Mol Ecol 14(6):1789–1801. 70. Mateo JM (2003) Kin recognition in ground squirrels and other rodents. J Mammal 89. Quéméré E, Amelot X, Pierson J, Crouau-Roy B, Chikhi L (2012) Genetic data suggest a 84(4):1163–1181. natural prehuman origin of open habitats in northern Madagascar and question the 71. Sharp SP, McGowan A, Wood MJ, Hatchwell BJ (2005) Learned kin recognition cues in deforestation narrative in this region. Proc Natl Acad Sci USA 109(32):13028–13033. a social bird. Nature 434(7037):1127–1130. 90. Kimura M, Weiss GH (1964) The stepping stone model of population structure and the 72. Chesser RK, Rhodes OEJ, Jr, Sugg DW, Schnabel A (1993) Effective sizes for subdivided decrease of genetic correlation with distance. Genetics 49(4):561–576. populations. Genetics 135(4):1221–1232. 91. Kimura M (1953) ‘Stepping stone’ model of population. Ann Rep Nat Inst Genetics 73. Pudovkin AI, Zaykin DV, Hedgecock D (1996) On the potential for estimating the effective Japan 3:62–63. number of breeders from heterozygote-excess in progeny. Genetics 144(1):383–387. 92. Parreira B, Trussart M, Sousa V, Hudson R, Chikhi L (2009) SPAms: A user-friendly 74. Luikart G, Cornuet J-M (1999) Estimating the effective number of breeders from software to simulate population genetics data under complex demographic models. – heterozygote excess in progeny. Genetics 151(3):1211 1216. Mol Ecol Resour 9(3):749–753. 75. Nunney L (1999) The effective size of a hierarchically structured populations. Evolu- 93. Hudson RR (2002) Generating samples under a Wright–Fisher neutral model of ge- – tion 53(1):1 10. netic variation. Bioinformatics 18(2):337–338. 76. Nielsen R, Beaumont MA (2009) Statistical inferences in phylogeography. Mol Ecol 94. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population – 18(6):1034 1047. structure. Evolution 38(6):1358–1370. 77. Chikhi L, Sousa VC, Luisi P, Goossens B, Beaumont MA (2010) The confounding effects 95. Balloux F (2001) EASYPOP (version 1.7): A computer program for population genetics of population structure, genetic diversity and the sampling scheme on the detection simulations. J Hered 92(3):301–302. and quantification of population size changes. Genetics 186(3):983–995. 96. Nei M (1978) Estimation of average heterozygosity and genetic distance from a small 78. Heller R, Chikhi L, Siegismund HR (2013) The confounding effect of population structure number of individuals. Genetics 89(3):583–590. on Bayesian skyline plot inferences of demographic history. PLoS ONE 8(5):e62992. 97. Nei M, Chesser RK (1983) Estimation of fixation indices and gene diversities. Ann Hum 79. Pope LC, Estoup A, Moritz C (2000) Phylogeography and population structure of an Genet 47(3):253–259. ecotonal marsupial, Bettongia tropica, determined using mtDNA and microsatellites. 98. Coltman DW, Pilkington JG, Pemberton JM (2003) Fine-scale genetic structure in a Mol Ecol 9(12):2041–2053. free-living ungulate population. Mol Ecol 12(3):733–742.

E3326 | www.pnas.org/cgi/doi/10.1073/pnas.1414463112 Parreira and Chikhi Downloaded by guest on October 2, 2021