Discussion

Studies have applied social network analysis to quantify and explore the social structure of populations across numerous taxa (Brent, 2015; Brent, Lehmann, & Ramos-Fernández, 2011; Sueur et al., 2011), but to our knowledge this is the first to combine genetically-derived pedigree data with social network analysis to infer social structure of wild populations. Social network analyses are powerful and flexible methods for investigating the complex networks of interconnections between individuals within and between populations, providing numerous measures of individual sociality (Brent, 2015; Wasserman & Faust, 1994). With a large interconnected network of 1,562 nodes (individuals) and 1,866 edges (parent-offspring relationships) between individuals, it can be difficult to identify significant differences within the network. By bringing the familial network into a spatial framework and incorporating aspatial node-based centrality metrics, we were able to identify different levels of sociality within the network, with some local areas composed of stable family groups and others that are less sedentary. Comparing local area networks of management interest allowed us to identify areas of higher and lower fitness and connectivity in the overall boreal caribou familial network.
There are numerous network centrality metrics available, and many are often correlated (Brent, 2015; Freeman, 1977; Newman, 2003; Sueur et al., 2011; Wasserman & Faust, 1994; Wey & Blumstein, 2012). Indirect measures of centrality are commonly used in social network analysis to identify central individuals within a network (Brent, 2015; Wey, Blumstein, Shen, & Jordán, 2008), but there has been little research done to determine which of these metrics are suitable for measuring centrality in pedigree-based familial networks. As familial networks are not built on associations between individuals or constructed through direct observation, measures of network centrality apply differently, and not all metrics may be suitable for familial-based networks. By identifying local areas within the network, we were able to gain a better understanding of which areas contributed most to the familial network. We found significant differences in centrality measures between local areas in the full familial network, and these variations in individual centrality would have remained hidden if only the full familial network was examined. We used five centrality metrics in our social network analysis of familial networks (Figure 2), and found that alpha, betweenness, and eccentricity centrality were the most informative measures of individual centrality (Figure S1.1). Degree centrality in familial networks represents the parents of an individual (in-degree) and the offspring of an individual (out-degree), giving a direct measure of an individual’s reproductive output and fitness levels. It is important to note, however, that inferred individuals in the pedigree will always have an in-degree of 0, as it is not possible to infer the parents of inferred individuals. Alpha centrality is an important metric for familial networks, as it indicates those individuals who are connected to individuals who themselves are highly connected, giving an indication of individual status, even if that individual does not have a lot of direct connections (offspring). Reproductive output can be highly asymmetrical, with the number of offspring varying between individuals (McFarlane et al., 2018), and alpha centrality can indicate if that individual is part of a high-fitness family if they are connected to highly connected individuals. We found that local areas with high edge-to-node ratios had a wider distribution of alpha and degree centrality, indicating that more higher fitness individuals are found in these local areas than in low edge-to-node local areas (Figure 2C), and are better connected to other well-connected individuals. Three of the four high edge-to-node ratio local areas we identified are located in the western part of Saskatchewan’s Boreal Plains, which has the highest levels of both anthropogenic and fire disturbance in the Boreal Plains (Figure S3.2), and the tight family groups we observed in these areas may be a result of decreased dispersal propensity due to high levels of fragmentation between local areas.
Betweenness centrality is another important metric for network analysis, as it captures the interconnectedness of subgroups; individuals with high betweenness interact with individuals who do not interact with one another, therefore making betweenness important for maintaining group cohesion, and connecting disparate parts of the network (Brent, 2015). Our familial network was not comprised of subgroups, as most individuals (94.2%) had a betweenness centrality of 0, and 95.2% of all sampled individuals formed one large familial network. Even after the removal of edges with the highest edge betweenness, the overall network structure did not change, with most individuals still connected in one main network, with no clear subgroups (Figure S2.10). Our study species displays a polygamous mating system, with individuals potentially having multiple partners, producing a complex network of parent-offspring relationships and full- and half-siblings, with high interconnectedness among individuals across the network (Figure S2.1). Our highly interconnected network with no evidence of subgroups and low average betweenness centrality is the result of the polygamous mating system and high dispersal ability.
The high eccentricity centrality and low closeness centrality informs on the social structure and the presence of animals dispersing longer distances in the Boreal Shield. The Boreal Shield is less fragmented than the Boreal Plains, with significantly less anthropogenic disturbance (Figure S3.2; Table S3.1). Very few parent-offspring relationships occurred within or between the northern Boreal Shield local areas (Figure 1). This suggests that individuals in the Boreal Shield are not central to the familial network and have lower individual fitness, not reproducing many offspring that survive until fall (low degree centrality). Individuals in low edge-to-node local areas are not from the same familial lines and are not highly related to any other individuals in the network. The removal of high betweenness edges led to some individuals becoming disconnected from the full network, but these disconnected individuals were not from one local area, instead located throughout both ecozones, again highlighting the interconnectedness of the familial network.
In most animal network studies, nodes represent observed individuals, with relationships between pairs of individuals (dyads) defined by an association index (the time the pair of individuals spent together), with edges representing observed relationships, forming an interaction network (Morrison, 2016; Whitehead & Dufault, 1999). For many species, it is not possible or feasible to directly observe rare and elusive species, and therefore association information cannot be obtained. Pedigree reconstruction can give direct information about dyads between closely related individuals (parent-offspring and full-siblings), with these relationships forming the basis of the familial network. In comparison to association networks, in familial networks, only the sampled individuals are known or observed, and the edges between individuals and the unsampled individuals (parents) are inferred by the data analysis (Morrison, 2016). Reconstructing a familial network from genetically-derived pedigree data gives valuable information about the number of mating partners, the number of offspring, and the structure of the reproductive network of a population (McFarlane et al., 2018; Pemberton, 2008). Pedigrees represent historical and evolutionary connections between generations; these relationships have long been recognized as reticulating but are instead commonly presented as simplified trees instead of networks, where reticulations caused by inbreeding are absent (Morrison, 2016). Pedigrees represent a network of relationships, and therefore reconstructed pedigrees inherently contain information that can be used to construct a network. With a wide spectrum of mating systems present in species (Clutton-Brock, 1989), almost all species have pedigree networks, with multiple partners and/or offspring attributed to each individual, therefore creating a complex network of familial relationships (Morrison, 2016). Although boreal caribou have a skewed reproductive rate, with varying levels of individual fitness (McFarlane et al., 2018), our network does not appear to be vulnerable to sudden population crashes resulting from changes in social structure or social isolation and inbreeding. Due to the polygamous mating system and long-range dispersal ability, the boreal caribou network is highly interconnected, and removal of edges with high betweenness did not change the overall network structure or lead to distinct clusters or social groups, although family groups can be identified within the network, with varied levels of dispersal and fitness levels among family groups.
Social networks are powerful methods to assist in wildlife conservation (Snijders et al., 2017), but most wild populations cannot be directly observed, and social association networks cannot be constructed. By constructing a familial network based on genetically-derived parent-offspring relationships, we calculated informative metrics to draw a much finer picture of their individual fitness levels, pattern of demographic structure, and relative contribution of local areas to the larger population. The spatial application of the familial network allowed us to identify areas of higher fitness levels and social cohesion across the range in support of population monitoring and recovery efforts.