1. In many social species, reproductive success varies between individuals within a population, resulting in socially structured populations. Social network analyses of familial relationships may provide insights on how fitness influences population-level demographic patterns. These methods have however rarely been applied to genetically-derived pedigree data from wild populations. 2. Here we use social networks to reconstruct parent-offspring relationships and create a familial network from polygamous boreal woodland caribou (Rangifer tarandus caribou) in Saskatchewan, Canada, to inform recovery efforts. We collected samples from 933 individuals at 15 variable microsatellite loci along with caribou-specific primers for sex identification. Using social network metrics, we assess the contribution of individual caribou to the population with several centrality metrics and then determine which metrics are best suited to inform on the population demographic structure. We look at the centrality of individuals from eighteen different local areas, along with the entire population. 3. We found substantial differences in centrality of individuals in different local areas, that in turn contributed differently to the full network, highlighting the importance of analyzing social networks at different scales. The full network revealed that boreal caribou in Saskatchewan form a complex, interconnected social network with strong familial ties, as the removal of edges with high betweenness did not result in distinct subgroups. Alpha, betweenness, and eccentricity centrality were the most informative metrics to characterize the population demographic structure and for spatially identifying areas of highest fitness levels and social cohesion across the range. 4. Synthesis and applications: Our results demonstrate the value of different network metrics in assessing genetically-derived familial networks. The spatial application of the familial networks identified areas of higher fitness levels and social cohesion across the range in support of population monitoring and recovery efforts.

Accurately estimating abundance is a critical component of monitoring and recovery of rare and elusive species. Spatial capture-recapture (SCR) models are an increasingly popular method for robust estimation of ecological parameters. We provide a maximum likelihood analytical framework to assess results from empirical studies to inform SCR sampling design, using both simulated and empirical data from non-invasive genetic sampling of seven boreal caribou populations (Rangifer tarandus caribou) which varied in range size and estimated population density. We use simulated population data with varying levels of clustered distributions to quantify the impact of non-independence of detections on density estimates, and empirical datasets to explore the influence of varied sampling intensity on the relative bias and precision of density estimates. Simulations revealed that clustered distributions of detections did not significantly impact relative bias or precision of density estimates. The empirical genotyping success rate was 95.1%. Empirical results indicated that reduced sampling intensity had a greater impact on density estimates in smaller ranges. The number of captures and spatial recaptures were strongly correlated with precision, but not relative bias. The best sampling designs did not differ with estimated population density but differed between large and small ranges. We provide an efficient framework implemented in R to estimate the detection parameters required when designing SCR studies. The framework can be used when designing a monitoring program to minimize effort and cost while maximizing effectiveness, which is critical for informing wildlife management and conservation.

Accurately estimating abundance is a critical component of monitoring and recovery of rare and elusive species. Spatial capture-recapture (SCR) models are an increasingly popular method for robust estimation of ecological parameters. We provide a maximum likelihood analytical framework to assess results from empirical studies to inform SCR sampling design, using both simulated and empirical data from non-invasive genetic sampling of seven boreal caribou populations (Rangifer tarandus caribou) which varied in range size and estimated population density. We use simulated population data with varying levels of clustered distributions to quantify the impact of non-independence of detections on density estimates, and empirical datasets to explore the influence of varied sampling intensity on the relative bias and precision of density estimates. Simulations revealed that clustered distributions of detections did not significantly impact relative bias or precision of density estimates. The empirical genotyping success rate was 95.1%. Empirical results indicated that reduced sampling intensity had a greater impact on density estimates in smaller ranges. The number of captures and spatial recaptures were strongly correlated with precision, but not relative bias. The best sampling designs did not differ with estimated population density but differed between large and small ranges. We provide an efficient framework implemented in R to estimate the detection parameters required when designing SCR studies. The framework can be used when designing a monitoring program to minimize effort and cost while maximizing effectiveness, which is critical for informing wildlife management and conservation.