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McFarlane, Samantha; Manseau, Micheline; Steenweg, Robin; Hervieux, Dave; Hegel, Troy; Slater, Simon; Wilson, Paul 2021-08-16 <p class="List1" style="text-indent:0cm;margin-top:2px;margin-bottom:2px;"><span>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 an 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 (<i>Rangifer tarandus caribou</i>) 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 genotyping success rate of our empirical dataset (n = 7,210 samples) was 95.1%, and 1,755 unique individuals were identified. Analysis of the empirical data 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 absolute 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.</span></p> https://creativecommons.org/publicdomain/zero/1.0/
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McFarlane, Samantha; Manseau, Micheline; Wilson, Paul J. 2022-01-30 <p class="List1" style="margin-bottom:13px;">In social species, reproductive success and rates of dispersal vary among individuals resulting in spatially structured populations. Network analyses of familial relationships may provide insights on how these parameters influence population-level demographic patterns. These methods have however rarely been applied to genetically-derived pedigree data from wild populations.</p> <p class="List1" style="margin-bottom:13px;">Here we use parent-offspring relationships to construct familial networks from polygamous boreal woodland caribou (<i>Rangifer tarandus caribou</i>) 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 network measures, we assess the contribution of individual caribou to the population with several centrality measures and then determine which measures are best suited to inform on the population demographic structure. We investigate the centrality of individuals from eighteen different local areas, along with the entire population.</p> <p class="List1" style="margin-bottom:13px;">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 networks at different scales. The full network revealed that boreal caribou in Saskatchewan form a complex, interconnected familial network, as the removal of edges with high betweenness did not result in distinct subgroups. Alpha, betweenness, and eccentricity centrality were the most informative measures to characterize the population demographic structure and for spatially identifying areas of highest fitness levels and family cohesion across the range. We found varied levels of dispersal, fitness and cohesion in family groups.</p> <p class="List1" style="margin-bottom:13px;"><i>Synthesis and applications</i>: Our results demonstrate the value of different network measures in assessing genetically-derived familial networks. The spatial application of the familial networks identified individuals presenting different fitness levels, short and long-distance dispersing ability across the range in support of population monitoring and recovery efforts.</p>

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