Introduction

The influence of genetic relatedness on social interactions of conspecifics has been an ongoing question in conservation of wild populations for a long time (Wilson, 1975). On the one hand, pedigree reconstructions have provided insights into mating patterns, individual fitness, and social and genetic structure of populations (Harcourt, Kingston, Cameron, Waas, & Hindell, 2006; Lucena-Perez et al., 2018; vonHoldt et al., 2008). On the other hand, analyses of social connectivity and stability of individuals have produced valuable information on the viability of wild populations, especially at current selective environments due to human-induced environmental changes (Snijders, Blumstein, Stanley, & Franks, 2017). Yet social network analyses and genetically-derived pedigree reconstruction have been used as two separate methodological frameworks to assist conservation of wild populations. The combination of these methods may highlight the interconnectedness between individuals, differences in reproductive success, and ultimately inform on the demographic structure of a population.
Reconstructing a reasonably complete and accurate familial network from pedigree data is especially relevant for endangered species, providing information on mating patterns and reproductive success (Lucena-Perez et al., 2018; Manlik et al., 2016). However, collecting reliable parentage information for cryptic and elusive species is difficult or directly unfeasible; pedigree information obtained through direct field observations are often limited to females, and may consistently overlook cryptic mating (Coltman et al., 1999; Gottelli, Wang, Bashir, & Durant, 2007). Molecular markers, such as microsatellites, have been used to infer parentage and familial relationships in wild populations (Pemberton, 2008) and assess individual heterogeneity in survival and reproduction (Bolnick et al., 2011; Hamel, Gaillard, Festa-Bianchet, & Côté, 2009; Kendall, Fox, Fujiwara, & Nogeire, 2011). Such heterogeneity can be the result of a number of common processes, such as persistent social rank (e.g. Holst et al., 2002; Stockley & Bro-Jørgensen, 2011), unequal allocation during parental care (e.g. Manser & Avey, 2000; Johnstone, 2004), fine-scale spatial habitat heterogeneity (Bollinger & Gavin, 2004; Franklin, Anderson, Gutiérrez, & Burnham, 2000; Manolis, Andersen, & Cuthbert, 2002), and genetics (Meyers & Bull, 2002; Nussey, 2005).
Social networks have been used to investigate complex webs of interconnections between individuals, providing an array of measurements of individual sociality and the extent to which an individual is connected to other individuals (Borgatti, Mehra, Brass, & Labianca, 2009; Wasserman & Faust, 1994). Several network-based measures are commonly used in social network analysis to quantify indirect connections between individuals in a network (Table 1). Although some network-based centrality measures may overlap, each measure captures a distinct aspect of the social network of a population; individuals with high scores for one measure may not necessarily have a high score in other measures (Brent, 2015; Sueur, Jacobs, Amblard, Petit, & King, 2011). Highly directly connected individuals may not necessarily be highly indirectly connected; individuals with the same degree (same number of social ties) may have different betweenness, depending if that individual’s partners are from the same subgroup (low betweenness) or from different subgroups (high betweenness). In sperm whale (Physeter macrocephalus ) association networks, centrality varied between and within individuals, with one sperm whale having the highest scores for strength and eigenvector centrality, but the lowest clustering coefficient (Lusseau, Whitehead, & Gero, 2008). In a captive chimpanzee (Pan troglodytes ) population, the individuals with the largest degree (number of grooming partners) were not the individuals with the greatest betweenness or clustering coefficient (Kanngiesser, Sueur, Riedl, Grossmann, & Call, 2011). The extent to which an individual is directly and indirectly connected to the network has considerable quantifiable differences. Strongly directly connected individuals with weak indirect connections may have great influence over their immediate social partners, but have minimal influence over the rest of the population, whereas individuals that are weakly directly connected but with strong indirect connections may be the single tie that links otherwise unconnected network sections together, exerting considerable influence over the population (Brent, 2015).
Here, we infer population demographic structure by assessing different node-based metrics of centrality obtained from a familial pedigree network. First, we use microsatellite data to identify parent-offspring relationships and construct a spatial familial network from all relationships (familial pedigree) of boreal caribou in Saskatchewan, Canada. Then we create a spatial familial network to identify local area networks with varying distributions of centrality metrics, determining whether high centrality metrics and edge-to-node ratios at the fine scale correspond to high centrality in the full network. We also assess the community structure and cohesiveness within the full network using edge removal to identify boundaries that run between subgroups, with a particular focus on parts of the range presenting different levels of anthropogenic disturbance. Our findings allow us to discuss how different metrics of network centrality can be used to spatially identify areas of highest fitness levels and social cohesion across the landscape in support of population monitoring and recovery efforts.