Ringed seal demography in a changing climate

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  • Climate change is affecting species’ distributions and abundances worldwide.
    Baseline population estimates, against which future observations may be compared, are necessary
    if we are to detect ecological change. Arctic sea ice ecosystems are changing rapidly and
    we lack baseline population estimates for many ice-associated species. Provided we can detect
    them, changes in Arctic marine ecosystems may be signaled by changes in indicator species
    such as ringed seals (Pusa hispida). Ringed seal monitoring has provided estimates of survival
    and fertility rates, but these have not been used for population-level inference. Using matrix
    population models, we synthesized existing demographic parameters to obtain estimates of historical
    ringed seal population growth and structure in Amundsen Gulf and Prince Albert
    Sound, Canada. We then formalized existing hypotheses about the effects of emerging environmental
    stressors (i.e., earlier spring ice breakup and reduced snow depth) on ringed seal pup
    survival. Coupling the demographic model to ice and snow forecasts available from the Coupled
    Model Intercomparison Project resulted in projections of ringed seal population size and
    structure up to the year 2100. These projections showed median declines in population size
    ranging from 50% to 99%. Corresponding to these projected declines were substantial changes
    in population structure, with increasing proportions of ringed seal pups and adults and declining
    proportions of juveniles. We explored if currently collected, harvest-based data could be
    used to detect the projected changes in population stage structure. Our model suggests that at
    a present sample size of 100 seals per year, the projected changes in stage structure would only
    be reliably detected by mid-century, even for the most extreme climate models. This modeling
    process revealed inconsistencies in existing estimates of ringed seal demographic rates. Mathematical
    population models such as these can contribute both to understanding past population
    trends as well as predicting future ones, both of which are necessary if we are to detect and
    interpret future observations.

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    Article (Published)
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    Attribution-NonCommercial 4.0 International