Polar Bear (<i>Ursus maritimus</i>) Life History and Population Dynamics in a Changing Climate
Bibliographic record
Abstract
There is now an increasing body of scientific evidence that rapid changes in the earth’s climate over the last half-century are influencing the physiology, phenology, distribution, and abundance of species (Hughes, 2000; McCarty, 2001; Stenseth et al., 2002; Root, 2003). As a result, understanding how climate change will affect the distribution and abundance of species has become a major concern in ecology. Among long-lived vertebrates, environmental variation is known to influence growth (Post et al., 1997), survival (Gaillard et al., 1997), reproductive success (Albon et al., 1983), and consequently the demography of populations. Although large-scale patterns of climatic variability such as the El Nino Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) are known to influence the life history and population dynamics of both marine and terrestrial species (Stenseth et al., 2002), relatively little information exists on how rapidly occurring but persistent change in the earth’s climate (i.e., global warming) will affect species life-history traits and population dynamics. The research I am conducting for my PhD will examine how environmental variation influences the life-history traits and population dynamics of a large carnivore species, the polar bear (Ursus maritimus), through climate-mediated shifts in the availability of essential prey resources. Polar bear life history is intimately linked to the sea ice environment, with sea ice providing the platform from which bears hunt, travel, mate, and in some areas, den (Amstrup, 2003). Over the last 20 years, in association with climate warming, there have been significant declines in both the temporal and spatial extent of sea ice cover in the Arctic (Parkinson and Cavalieri, 1989; Parkinson et al., 1999; Comiso, 2002; Comiso and Parkinson, 2004; Stroeve et al., 2007). It has been suggested that spatial and temporal changes in the sea ice environment will result in reduced availability and abundance of the polar bears’ primary prey, seals (Derocher et al., 2004). In turn, reduced prey availability has the potential to influence the life history of individuals (growth, reproduction, and survival) and thereby the population dynamics of polar bears. The effects of reduced prey availability are already evident in western Hudson Bay, where polar bears are forced ashore during an extensive icefree period that can last for up to four months each summer. Higher air temperatures and earlier sea ice breakup in spring have extended this period and resulted in significant declines in the body mass of adult female polar bears (Stirling et al., 1999; Stirling and Parkinson, 2006). Sea ice– mediated changes in individual phenotypic quality have the potential to influence a number of individual life history traits (e.g., age at first reproduction, litter size, and longevity), all of which can influence the demography of polar bear populations. The purpose of my PhD research is to determine to what extent changes in the Arctic sea ice environment are influencing the growth, reproduction, and survival of polar bears and how changes in key life history traits and vital rates are influencing polar bear population dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".