ABSTRACT
The role of relatedness in animal societies has received important consideration as a process driving social groups. In the marine world, most studies which have investigated this question have focused on marine mammals such as whales and dolphins. For sharks, recent studies have demonstrated preferential associations among individuals from which social communities emerge. Assortment patterns have been found according to phenotypic or behavioural traits but the role of genetic relatedness and family structure in shaping the social structure of adult shark populations has never been investigated. Here, we used a social network analysis crossed with DNA microsatellite genotyping to investigate the role of the genetic relatedness in the social structure of a blacktip reef shark (Carcharhinus melanopterus) population. We found an established social network spatially organized but dynamic through time with different communities merging during the mating periods. Such recombination shows the males being more gregarious. Kinship was not a predictor of associations among sharks both at the dyad or community levels as individuals tended to associate independently and randomly regardless of the genetic relatedness between individuals. The lack of parental care in this species may contribute to the breakdown of family links in the population early in life, thereby preventing the formation of kin-based social networks.
INTRODUCTION
Socialising is an adaptive strategy which is widespread across the animal kingdom that can take various forms, from temporary unstable associations to long-term stable groups in complex societies (Krause and Ruxton 2002). Understanding the factors that influence the formation and evolution of social groups is important to understand the evolution of animal societies as well as to gain insight into population dynamics and conservation strategy (Snijders et al. 2017). Associations among individuals can provide benefits to improve individual fitness by reducing predation risk or improving foraging efficiency (Krause and Ruxton 2002). While individuals can benefit by simply associating with other conspecifics (Kerth et al. 2011), the benefit of grouping can be enhanced by associating with similar individuals, also called social assortativity. By associating with individuals of the same size or the same sex, individuals are more likely to avoid conflict or harassment (Dadda et al. 2005) and their risk of predation is reduced via the confusion effect (Landeau and Terborgh 1986). Further assorting with kin can also provide indirect fitness benefits (Hamilton 1964). Kin assortment has been shown to provide benefits in reducing aggression (Olsén and JäUrvi), increasing growth rate (Brown and Brown 1993) or allowing cooperative behaviour such as predator inspection (Milinski 1987).
Kin structuring has received extensive attention in cooperative breeding societies in which groups arise from the retention of offspring and delayed dispersal which facilitates the formation of interactions with relative and kin-based groups (Wolf and Trillmich 2008; Hatchwell Ben J. 2010; Wiszniewski et al. 2010). However, the role of relatedness in structuring animal societies that are characterised by a dynamic fission-fusion social system without parental care has not been demonstrated. While the link between social networks and kinship has been extensively studied in terrestrial animals (Holekamp et al. 2012; Carter et al. 2013; Arnberg et al. 2015), kinship structure in social networks of marine and freshwater organisms has been primarily limited to marine mammals (Wiszniewski et al. 2010; Mann et al. 2012; Reisinger et al. 2017). Several cetacean societies show strong kin-based social network structures. However, in fishes, kin structure is less clear. Work on shoaling fish, for example, did not find kin assortment, even in species that have the abilities for kin discrimination (Croft et al. 2012). While sharks have recently been shown to be able to develop preferred associations and organise into structured social networks (Mourier et al. 2018), kinship has only been explored in one case study that focused on juvenile sharks (Guttridge et al. 2011), highlighting a lack of information on the potential for kin-based associations to arise in shark populations.
Overall, most studies that have explored the relationship between genetic relatedness and social interactions have focused on highly social species and in particular, on species that exhibit parental care (Wolf and Trillmich 2008; Wiszniewski et al. 2010; Kerth et al. 2011). Studying less social vertebrates should significantly improve our understanding of how social and genetic structure interact to shape the evolution of sociality in the animal kingdom.
In this study, we investigate the interaction between socio-spatial patterns and genetic relatedness in a population of blacktip reef sharks (Carcharhinus melanopterus) monitored over a 3-year period on the North shore of Moorea island (French Polynesia). Sharks represent an interesting and unique model to explore the extent to which individuals interact with genetically related associates due to ecological traits that differ from most social vertebrates. First, sharks are now increasingly recognised as being capable of complex social interactions, developing preferred social affiliations (Guttridge et al. 2009; Jacoby et al. 2010; Mourier et al. 2012), showing surprising learning abilities (Guttridge et al. 2013; Mourier et al. 2017) and developing patterns of leadership and dominance hierarchy (Guttridge et al. 2011; Jacoby et al. 2016; Brena et al. 2018). Then, as opposed to many social organisms, reef sharks do not show parental care and many shark species drop their progeny in specific nurseries outside adult habitats (Mourier and Planes 2013). These discrete nurseries are chosen to potentially provide the neonates with a safe environment where they will spend their first months of life. When juvenile sharks reach a certain size or age, they leave their nursery to explore a wider home range (Chapman et al. 2009) and integrate within the adult population and interact with others. Therefore, these shark populations do not show social viscosity (i.e., philopatry and delayed dispersal) as in some kin-based societies (Wolf and Trillmich 2008). In such aggregations, interactions between kin are also diluted by the presence of numerous neighbours and average relatedness quickly drops with increasing group size. In some shark species, the likelihood of associating with a related peer is reduced due to small litter size and a high mortality rate at the juvenile stage, leading to a lack of first order relatives to reach adulthood. However, in a closed system, such as an isolated island, and in the case of blacktip reef sharks which spends its entire life cycle within Moorea (Mourier and Planes 2013), relatives have more chance encounter each other and interact in social groups.
To understand the assortative forces which underpin the structural properties of the system is challenging for elusive underwater animals. As the blacktip reef shark displays a high degree of site attachment (Papastamatiou et al. 2009) and shares some of its areas with a variety of neighbours (Mourier et al. 2012), exploring this network holds the potential to work out the relationship between spatial, social and genetic structure in a reef shark. As reef sharks do not show parental care but display ontogenetic habitat segregation with females leaving their young in discrete nurseries, we will test: (1) whether the social network changes between the non-mating and mating seasons and (2) whether genetic relatedness plays a role in structuring the network at both the individual and community levels. Here, for the first time, we explore the link between social network and relatedness in an adult shark population.
MATERIAL AND METHODS
Field observations and network construction
Between 2008 and 2010, observation surveys were conducted along approximately 10km of coastline of the Northern reef of Moorea Island (French Polynesia) (Figure 1). The surveys consisted of 40 min dives at 6 sites along a 10-km portion of reef in which individual blacktip reef sharks were identified using photo-identification based on the colour-shape of the dorsal fin. Previous surveys of the areas demonstrated that shark social network might be dynamic and change during the mating period (Mourier et al. 2012). Therefore, we split the dataset into non-mating and mating periods to analyse social networks of blacktip reef sharks separately for both periods. This segregation was made based on observed mating scars on the bodies of females as a sign of mating activity (Chin et al. 2015) and pregnancy monitoring from underwater surveys (Porcher 2005; Mourier and Planes 2013). Overall, the non-mating period extends between March and August and the mating period starts in September and lasts until February the following year. Using R package asnipe (Farine 2013), we calculated dyadic association strengths for each period (i.e. associations among pairs of individuals) from the spatio-temporal co-occurrences, and the proportion of time two individuals were observed together at the same site, using the half-weight index (HWI) (Cairns and Schwager 1987).
Network metrics
A randomization procedure was implemented to test if there is a relationship between the social network and factors such as the genetic relatedness, the spatial overlap and the sex category of each dyad. We therefore used a data stream permutation procedure and, based on the stack of individual by individual matrix for each sampling period, swaps individuals between associations within sampling periods, and then recalculated the weighted summary network after each swap. Each of the 1000 permutations implemented in this study created a new weighted network, resulting in a new stack of matrices (Farine 2013). This procedure performed permutations which controlled for the number of observations and group size.
We used an eigenvector-based modularity method to determine if the population was divided into different communities (clusters) (Newman 2006), and whether groups of close affiliates (i.e. individuals connected by edges with high HWI values) could be identified. We calculated modularity using the difference between the proportion of total co-occurrences within clusters and their expected proportion given the sum of co-occurrences of inter-cluster individuals.
We then compared the gregariousness of individual sharks between the non-mating and mating seasons. For this, we used two measures of gregariousness: node degree (or binary degree) which is the number of direct neighbours each individual is connected to in the network and node strength (or weighted degree) that is the sum of associations of an individual. We then used these network metrics in order to determine (1) whether individuals differed in their gregariousness between non-mating and mating seasons and (2) whether males and females differed in their gregariousness with respect to season. To test if there is a difference, we used data permutations described above and compared the t-statistic of the observed difference between the two seasonal networks (or between males and females for each network depending on the analysis) to the distribution of t-values from the randomised data (Farine and Whitehead 2015; Farine 2017). If the observed t-statistic sits outside the random distribution, then the difference is significant. The significance is therefore calculated by counting the number of randomised test statistics that had a greater value than the observed statistic if the statistic is positive (or a lower value than the observed statistic if the statistic is negative), and then dividing by the number of randomisations.
DNA sampling and laboratory procedures
Shark fishing sessions using rod and reel and barbless hooks were conducted to obtain tissue samples for genetic analysis. Once hooked, sharks were brought alongside the boat where they were inverted and placed in tonic immobility while biological data and tissue samples were collected. Each shark was identified by photo-identification of the dorsal fin, sexed and measured (Mourier et al. 2012; Mourier, Mills, et al. 2013). Fishing sessions were conducted directly after underwater surveys to avoid perturbations of the experimental setup (Mourier et al. 2017) and to increase the chance of getting DNA samples from sharks that were part of the social network. Fishing effort was maintained until sharks failed to respond to the bait (generally <30 min and after catching 2-3 individuals). A fin clip was collected from the second dorsal fin or anal fin and samples were individually preserved in 95% ethanol and returned to the laboratory for genotyping (Mourier and Planes 2013). DNA was extracted using the QIAGEN DX Universal Tissue Sample DNA Extraction protocol. PCR amplification and the microsatellite loci used are described in detail in previous studies (Mourier and Planes 2013; Vignaud et al. 2013; Vignaud et al. 2014). The software MICROCHECKER (Van Oosterhout Cock et al. 2004) was used to test for null alleles and other genotyping errors.
We compared the suitability of four pairwise relatedness estimators, Queller and Goodnight (QG) (Queller and Goodnight 1989), Lynch and Ritland (LR) (Lynch and Ritland 1999), Li et al. (LL) (Li et al. 1993) and Wang (W) (Wang 2002) in the R package related (Pew et al. 2015) and determined that the Wang estimator was best suited to our microsatellite panel (Fig. S1). This function generates simulated individuals of known relatedness based on the observed allele frequencies and calculates the genetic relatedness using four different estimators. The correlation between observed and expected genetic relatedness was obtained for each estimator, and the one with the highest correlation coefficient was selected for further analysis.
Spatial profiles
We describe spatial patterns for all individuals at each survey site and for each season using a residency index R that takes into account the unequal sampling effort between sites using the following equation: with Rij representing the Residency index of individual i at site j and Sij represents the number of sighting of individual i at site j. The left part of the equation is the siting rate of individual i at site j while the right part of the equation represents a weight, where the sampling bias is considered. We then generated a Bray-Curtis similarity matrix to construct a matrix of spatial overlap between individuals for each season.
Crossing network analysis with genetic relatedness
Several different approaches were used to investigate the role of genetic relatedness in driving social structure in this population. First, across both non-mating and mating social networks, we asked what most influenced social association strength among sharks: genetic relationships, sex or spatial relationships. We used the multiple regression quadratic assignment procedure (MRQAP) [49] modified by Farine (Farine 2013) that enables null models built from pre-network data permutations to be used in conjunction with a MRQAP regression. This approach was shown to be more accurate than classic MRQAP procedures (Farine 2017). The dependent matrix in this procedure contained the observed association strengths (HWI values) in the social network. The three predictor matrices came from the spatial overlap among dyads, the sex homophily and the genetic relatedness values among dyads, allowing us to explore how spatial preferences, sex and genetic relationships, respectively, influence network structure during both seasons. In the sex homophily matrices, similar dyads received a value of 1, while dissimilar dyads received a value of 0. We performed all the network analyses, including permutations and MRQAP tests, using the package asnipe (Farine 2013) in R v. 3.3.0 (R Core Team 2019).
Next, to determine whether individuals within communities were more or less closely related than expected, we compared the observed values for each community against a distribution of expected relatedness values generated by randomly shuffling individuals between community groups for 1000 permutations, where size was kept constant using the R package related (Pew et al. 2015). If the observed mean relatedness was greater than that of the permuted data (P > 0.95), then the null hypothesis which predicted that the mean within-community relatedness is random, was rejected. If only a few closely related individuals were present, then it is possible that their within-community overabundance compared to between social communities might not be detected using mean coefficient of relatedness (Buston et al. 2009).
Similarly, we verified whether the proportion of closely related pairs was higher within than between social communities using a chi-squared test following the same approach as the precedent analysis with mean relatedness. We compared the χ2 statistics of the observed difference in proportions of relatedness values above a certain threshold between within- and between-communities to that of expected relatedness values generated by randomly shuffled individuals between community groups for 1000 permutations and keeping size constant. We tested with the two threshold relatedness values 0.125 and 0.25 corresponding to the theoretical relatedness of cousins and half-sibs, respectively.
RESULTS
Data summary
The network was built with 105 individual sharks observed at least 3 times (range 3-20) during 136 sampling weeks. The threshold of three observations per shark was chosen to remove transient sharks from the network. The non-mating season network consisted of 103 individuals (67 males and 36 females) and the mating season network of 101 individuals (67 males and 34 females), with both networks having 99 individual sharks in common (65 males and 34 females). Both networks are presented in Figure 2. The non-mating season network was divided into five distinct communities while the mating season network was only divided into three large communities (Figure 2). Gregariousness was generally higher during the mating season, with a significantly higher degree during the mating season (t = −2.803, Prand = 0.001) and a higher strength, although the latter was not significant (t = −2.883, Prand = 0.052) (Figure 3). Males were generally more gregarious than females, as they interacted with more individuals (higher degree) and had stronger relationships (higher strength) for both seasons, although only degree was significantly higher for males during the non-mating period (Figure 3; Table 1).
A total of 225 adult sharks were genotyped from the studied area. About 80% of the individuals included in our social network analyses were genotyped: 79.6% for the non-mating period (i.e. 82/103 individuals) and 78.2% for the mating period (79/101 individuals). From the 17 microsatellite markers taken from our previous study (Mourier and Planes 2013), the presence of null alleles was detected at Cli12 which was then removed from our dataset for further genetic analyses. We conducted the genetic analyses with 16 loci (Table S1).
Crossing of genetic relatedness and association patterns
MRQAP regressions revealed that only spatial overlap was a significant predictor of associations among sharks for both mating and non-mating seasons (p < 0.05; Table 2; Table 3). Indeed, sex homophily was not a significant factor and this was also confirmed by the lack of significance of male and female homophily in explaining association patterns among sharks.
With a negative regression coefficient, genetic relatedness was not a significant predictor of association patterns in both seasons (β = −0.014 for the non-mating season and β = −0.002 for the mating season; Table 2 and Table 3).
Average pairwise relatedness among individuals was 0.0006 ± 3.98 10-6 (mean ± SE) ranging from −0.462 to 0.909. Within-community relatedness estimate was inferred for each community with five or more samples for both non-mating and mating seasons; therefore, during the non-mating period, the purple community (Figure 2) which had only three individuals, was excluded from this analysis. In general, relatedness within all communities was not higher than expected if communities were randomly organized (non-mating period: mean ± SE = 0.017 ± 0.005, p < 0.322; mating period: mean ± SE = 0.016 ± 0.005, p < 0.297) (Figure 4). Relatedness within each community was also not higher than expected: for communities green (mean ± SE = 0.058 ± 0.017, p < 0.069), blue (mean ± SE = 0.011 ± 0.007, p < 0.378), red (mean ± SE = −0.004 ± 0.008, p < 0.535) and yellow (mean ± SE = - 0.014 ± 0.041, p < 0.563) during the non-mating season, and green (mean ± SE = 0.038 ± 0.012, p < 0.245), blue (mean ± SE = 0.012 ± 0.006, p < 0.339) and red (mean ± SE = −0.001 ± 0.009, p < 0.534) during the mating season (Figure 4).
Among the 25 200 potential pairs, 5 614 (22.3%) had relatedness values higher than 0.125 and 1910 (7.5%) had values higher than 0.25. In addition, there was no difference in the proportion of close relatives within and between communities for either threshold relatedness value during the non-mating season (chi-squared test for threshold 0.125: d.f. = 1, χ2 = 0.543, p = 0.684; chi-squared test for threshold 0.25: d.f. = 1, χ2 = 1.539, p = 0.344) and during the mating season (chi-squared test for threshold 0.125: d.f. = 1, χ2 = 1.524, p = 0.491; chi-squared test for threshold 0.25: d.f. = 1, χ2 = 2.040, p = 0.314) (Table 4).
Together, these results suggest that no differences exist for within- and between-community membership with respect to the genetic relatedness of their members.
DISCUSSION
We found that the social network of blacktip reef sharks in Moorea was dynamic with five mixed-sex communities during the non-mating season merging into three larger communities during the mating season (Figure 2). Social proximity was not predicted by the genetic relatedness between sharks both at the association and community levels and was only explained by spatial overlap between individuals. A genetic relatedness analysis also revealed that individuals had low probabilities of interacting with a close-kin which could explain the lack of influence of kinship in structuring the social network in this population. These results therefore suggest that genetic relatedness does not drive the structure of the social network in this shark population.
Our previous work on this population suggested that association patterns between sharks could be dynamic and may be seasonally dependent (Mourier et al. 2012). By analysing the non-mating and mating seasons separately, we revealed that the overall population network changed between seasons. During the non-mating season, the network was composed of five communities in which members were more connected to each other that they were with members of other communities. During the mating season, the network communities merged to form three larger communities. This dynamic appears to be driven by the movements and increased activity of some individual sharks during the mating season, switching from one community to another (Figure 2). Males that move among communities during the mating season may gain a selective advantage in courting novel females as shown in other fishes (Kelley et al. 1999). Individuals tend to be more connected during the mating period and males are more gregarious than females during both seasons (Figure 3). Having strong and stable social affinities, especially in females, has been shown to increase resilience of associations to male harassment or novel intruders (Jacoby et al. 2010). This could partly suggest that, even if genetic relatedness does not influence the structure of the network, developing strong associations in shark populations can provide fitness benefits, at least within a reproductive context.
Among the adult sharks in our population, there was a generally low level of relatedness, and only a small number of dyads had close familial relatives. Interactions frequently occurred between distant kin and non-kin. This implies that the social structure among adult blacktip reef sharks was not based on associations between close kin as demonstrated by our analyses which compared association patterns with genetic relatedness among dyads at the pairwise or community levels. This is confirmed by the low number of close kin available for each shark in the population (<8 % pairs with R>0.25), thereby limiting the probability of an individual to encounter a family member and to develop strong associations with them. These results can be explained in part by the life history and life cycle of blacktip reef sharks. In fact, in contrast to most social animals that show some forms of family structure and parental care, female reef-associated sharks such as blacktip reef shark, leave their pups in their nursery after birth (Mourier and Planes 2013). Moreover, litter size in this species does not exceed five pups (Mourier, Mills, et al. 2013) while litter size in Moorea was limited to a maximum of two pups (Mourier and Planes 2013). In addition, blacktip reef sharks follow a yearly breeding cycle with females giving birth every year and potentially being fertilized by multiple males within or across years, which increases the probability of having maternal and paternal half-siblings. Our ongoing long-term nursery monitoring shows that capture probabilities rapidly decline after March (unpublished data), a couple of months after parturition, which suggests a dramatic mortality rate within the nursery areas during the first months of life (i.e. survival rate expected to be inferior to 50% during the first year of life). Together with a small litter size and absence of parental care, this high mortality rate, which is common in many shark species, is likely to limit the opportunity to find family members and develop strong affiliations with close relatives at adulthood. Even in nurseries, juvenile lemon sharks did not clearly assort by relatedness (Guttridge et al. 2011), even if the probability of finding a relative is higher for this species with a larger litter size. When juvenile sharks grow, they progressively explore their environment and increase their home range (Chin A et al. 2013), creating an opportunity to find related individuals such as parents or maternal half-siblings from previous reproductive seasons. At adulthood, our results confirm that preferred associations (Mourier et al. 2012) are not driven by genetic relatedness as sharks are associating with conspecifics of variable genetic distances. This suggests that sharks might not have the ability for kin recognition simply based on visual or olfactory cues and that kin-based preferred associations may only develop within nursery areas from increased familiarity with littermates.
The emerging literature suggests that genetic structure of animal social networks can vary dramatically, from highly cohesive kin-based groups like African elephants (Loxodonta africana) (Archie et al. 2006) or spotted hyenas (Crocuta crocuta) (Holekamp et al. 2012), to groups with moderate levels of genetic relatedness due to limited dispersal like the Galápagos sea lion (Zalophus wollebaeki) (Wolf and Trillmich 2008) or the eastern grey kangaroo (Macropus giganteus) (Best et al. 2014), or to groups with little to no genetic relatedness like guppies (Poecilia reticulata) (Croft et al. 2012), the common raccoon (Procyon lotor) (Hirsch et al. 2013) or migratory golden-crowned sparrows (Zonotrichia atricapilla) (Arnberg et al. 2015). These patterns of variation provide opportunities to explore how ecological factors interact with kinship to produce variations in the structures of animal societies. Kinship is expected to promote the evolution of cooperation and sociality in animals (Hamilton 1964). However, our understanding of the evolution of sociality results to a great extent from the study of closed societies, in which interactions mainly involve relatives and can hence be explained by kin selection (Hamilton 1964). However, the kin selection theory has recently been challenged by results from studies showing that fitness benefit can emerge in social groups composed mainly of non-relatives (e.g., Cameron et al. 2009; Riehl 2011; Wilkinson et al. 2016). In many natural populations, dispersal tends to be limited, favouring local competition between neighbours and the emergence of a social component, whether it be selfish, aggressive, cooperative or altruistic (Lehmann and Rousset 2010). But how social behaviours translate into fitness costs and benefits depends considerably on life-history features, as well as on local demographic and ecological conditions. The fission – fusion social dynamics lead to unstable group membership, and dispersal and occasional recruitment of unrelated individuals lead to low average relatedness in groups. Then under such conditions, selection is not expected to favour kin recognition mechanisms based on familiarity alone.
Therefore, contrary to the kin selection hypothesis which predicts stronger associations among kin, sharks tended to assort randomly according to relatedness. As kinship does not explain the strength of social affiliations in blacktip reef sharks, the question remains as to how and why sharks form preferred associations organised in social communities (Mourier et al. 2012). Although cooperation has been mainly explained in the context of kin selection, there might be potential benefits of non-kin sociality in blacktip reef sharks. While evidence of shark cooperation has not been confirmed, grouping can have several benefits in sharks (Jacoby et al. 2012), including increased foraging success by hunting in groups (Weideli et al. 2015; Mourier et al. 2016), protection from predators (Mourier, Planes, et al. 2013), or increased tolerance relationships and reduced aggression rate (Brena et al. 2018). These benefits do not necessarily imply kin selection and can simply build on the development of familiarity from repeated interactions. Social structure in reef sharks can arise from multiple simple ecological factors such as the distribution of resources in space and time leading to aggregations of individuals even in the absence of benefits of direct social affiliation (Ramos-Fernández et al. 2006) or mitigation of the cost of unnecessary aggression when competing for resources mediated by individual recognition (Brena et al. 2018). Regardless of the exact cause of social preferences in reef sharks, the absence of kinship as an important factor in structuring association patterns suggests that there are important benefits of sociality in sharks that we still need to uncover. With an increasing use of social network analyses applied to shark populations (Mourier et al. 2018), future work on social networks and genetic relatedness in different populations or species is necessary to confirm our results and to improve our understanding of population dynamics in sharks and the evolution of sociality.
SUPPLEMENTARY MATERIAL
Supplementary information including a table and a figure can be found online.
FUNDING
This study was supported by the Direction à l’Environnement (DIREN) of French Polynesia and the Coordination Unit of the Coral Reef Initiatives for the Pacific (CRISP).
Ethics statment
This work was approved by the Direction à l’Environnement (DIREN) of French Polynesia and Ministère de la promotion des langues, de la culture, de la communication et de l’environnement de Polynésie française under Arrêté 9324 of 30 October 2015.
Acknowledgements
We are grateful to the Centre de Recherche Insulaire et Observatoire de l’Environnement (CRIOBE) staff, volunteers and students who assisted in field data collection. We are also grateful to Jeanine Almany for English reviewing.