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Aligning population models with data: Adaptive management for big game harvests

  • Author(s) / Creator(s)
  • Models of population dynamics are a central piece for harvest management, allowing
    managers to evaluate alternative strategies and to identify uncertainty. Here we present a
    density-dependent population dynamics model that can be used in conjunction with
    adaptive management to optimize big game management, designed to use data commonly
    collected by state and provincial wildlife agencies. We review a case study for white-tailed
    deer (Odocoileus virginianus) in North Dakota, USA, where we evaluate how harvest
    composition and monitoring frequency affect the maximum sustainable yield (MSY). Data
    were obtained from winter aerial surveys and hunter questionnaires over six years between 2009 and 2019. Harvest composition moderately skewed towards antlered individuals (37.5% antlerless deer and 62.5% antlered deer, i.e., antlerless:antlered harvest
    ratio ¼ 0.6) resulted in a harvest rate of 0.2, which translates to a long-term harvest that is
    more than double that obtained if the harvest composition matched the population
    composition. However, given environmental uncertainty, we recommend that managers
    adopt a harvest strategy that is at least 10e15% lower than the maximum sustainable yield
    to buffer against environmental variability. Despite the fact that contrasting monitoring
    schemes resulted in similar optimal harvest rates, we illustrated how adopting an adaptive
    harvest strategy (i.e., where harvests change with population size) affords lower risks of
    overexploitation than a static strategy in which populations are assessed only occasionally.
    An adaptive harvest strategy features resilience allowing harvested populations to return
    to equilibrium even after substantial perturbation events.

  • Date created
    2021-01-01
  • Subjects / Keywords
  • Type of Item
    Article (Published)
  • DOI
    https://doi.org/10.7939/r3-116t-cz94
  • License
    Attribution-NonCommercial 4.0 International