The stress of Arctic warming on polar bears

Arctic ecosystems are changing rapidly in response to climate warming. While Arctic mammals are highly evolved to these extreme environments, particularly with respect to their stress axis, some species may have limited capacity to adapt to this change. We examined changes in key components of the stress axis (cortisol and its carrier protein-corticosteroid binding globulin [CBG]) in polar bears (Ursus maritimus) from western Hudson Bay (N = 300) over a 33 year period (1983-2015) during which time the ice-free period was increasing. Changing sea ice phenology limits spring hunting opportunities and extends the period of onshore fasting. We assessed the response of polar bears to a standardized stressor (helicopter pursuit, darting, and immobilization) during their onshore fasting period (late summer-autumn) and quantified the serum levels of the maximum corticosteroid binding capacity (MCBC) of CBG, the serum protein that binds cortisol strongly, and free cortisol (FC). We quantified bear condition (age, sex, female with cubs or not, fat condition), sea ice (breakup in spring-summer, 1 year lagged freeze-up in autumn), and duration of fasting until sample collection as well as cumulative impacts of the latter environmental traits from the previous year. Data were separated into "good" years (1983-1990) when conditions were thought to be optimal and "poor" years (1991-2015) when sea ice conditions deteriorated and fasting on land was extended. MCBC explained 39.4% of the variation in the good years, but only 28.1% in the poor ones, using both biological and environmental variables. MCBC levels decreased with age. Changes in FC were complex, but more poorly explained. Counterintuitively, MCBC levels increased with increased time onshore, 1 year lag effects, and in poor ice years. We conclude that MCBC is a biomarker of stress in polar bears and that the changes we document are a consequence of climate warming.

Ringed seal demography in a changing climate

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.