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Data from: Partitioning drivers of spatial genetic variation for a continuously-distributed population of boreal caribou: implications for management unit delineation
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Priadka, Pauline; Manseau, Micheline; Trottier, Tim; Hervieux, Dave; Galpern, Paul; McLoughlin, Philip D.; Wilson, Paul J. — 2018-12-18 Isolation-by-distance (IBD) is a natural pattern not readily incorporated into theoretical models nor traditional metrics for differentiating populations, although clinal genetic differentiation can be characteristic of many wildlife species. Landscape features can also drive population structure additive to baseline IBD resulting in differentiation through isolation-by-resistance (IBR). We assessed the population genetic structure of boreal caribou across western Canada using non-spatial (STRUCTURE) and spatial (MEMGENE) clustering methods and investigated the relative contribution of IBD and IBR on genetic variation of 1221 boreal caribou multilocus genotypes across western Canada. We further introduced a novel approach to compare the partitioning of individuals into management units (MU) and assessed levels of genetic connectivity under different MU scenarios. STRUCTURE delineated five genetic clusters while MEMGENE identified finer-scale differentiation across the study area. IBD was significant and did not differ for males and females both across and among detected genetic clusters. MEMGENE landscape analysis further quantified the proportion of genetic variation contributed by IBD and IBR patterns, allowing for the relative importance of spatial drivers, including roads, water bodies and wildfires, to be assessed and incorporated into the characterization of population structure for the delineation of MUs. Local population units, as currently delineated in the boreal caribou recovery strategy, do not capture the genetic variation and connectivity of the ecotype across the study area. Here, we provide the tools to assess fine-scale spatial patterns of genetic variation, partition drivers of genetic variation and evaluate the best management options for maintaining genetic connectivity. Our approach is highly relevant to vagile wildlife species that are of management and conservation concern and demonstrate varying degrees of IBD and IBR with clinal spatial genetic structure that challenges the delineation of discrete population boundaries.
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Data from: When can model-based estimates replace surveys of wildlife populations that span many discrete management units?
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Priadka, Pauline; Brown, Glen S.; Fedy, Bradley C.; Mallory, Frank F. — 2022-05-23 <p>Monitoring widely distributed species on a budget presents challenges for the spatio-temporal allocation of survey effort. When there are multiple discrete units to monitor, survey alternatives such as model-based estimates can be useful to fill information-gaps but may not reliably reflect biological complexity and change. The spatio-temporal allocation of survey effort that minimizes uncertainty for the greatest number of units within a budget can help to ensure monitoring efforts are optimized.</p> <p>We used aerial survey-based population estimates of moose (Alces alces) across 30 Wildlife Management Units (WMUs) in Ontario, Canada to parameterize simulated populations and test the performance of different monitoring scenarios in capturing WMU-specific annual variation and trends. Firstly, we tested scenarios that prioritized conducting a survey for a unit based on one of three management criteria: population state, population uncertainty, or number of years between surveys. Also incorporated in the decision framework were WMU-specific costs and annual budget constraints. Secondly, we tested how using model-based estimates to fill information-gaps improved population and trend estimates. Lastly, we assessed how the utility (based on minimizing population uncertainty) of using a model-based estimate rather than conducting a survey was impacted by population density, severity of environmental stressors, and years since the last survey.</p> <p>Interval-based monitoring that minimized the number of years between surveys captured accurate trends for the highest number of WMUs, but annual variation was poorly captured regardless of management criteria prioritized. Using model-based estimates to fill information gaps improved trend estimation. Further, the utility of conducting a survey increased with time since the last survey and was greater for populations with low densities when the severity of environmental stressors was high, while being greater for populations with high densities when environmental severity was low.</p> <p>Overall, the utility of aerial survey monitoring was strongly associated with WMU-specific monitoring precision and the predictive power of model-based estimates. If long-term trends are evident then there is greater value in using alternatives such as model-based predictions to replace surveys, but model-based estimates may be a poor substitute when there is strong annual variation and when using a simple model.</p>

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(Author: Priadka, Pauline)

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