Drivers of population dynamics and juvenile mortality in northwest Atlantic harp seals
Bibliographic record
Abstract
Human-induced threats to terrestrial and marine wildlife are on the rise, and while some species may face a single major threat that is easily identifiable, others face multiple concurrent threats. Harp seals, an abundant pinniped in the north Atlantic that was historically depleted by human harvest, are one such species: while commercial and subsistence harvests remain a significant source of mortality, in recent decades their environment has undergone significant changes which could also impact population dynamics. Inferring the relative importance of various threats as drivers of population dynamics can be challenging, particularly for marine species where monitoring abundance is difficult: the use of integrated population models (IPM), which leverage multiple data sources to parameterize process-based models of population dynamics, provide one solution to this challenge. We developed a hierarchical Bayesian IPM with which to explore the shifting roles of anthropogenic and environmental factors in driving population trends. We used a competing hazards formulation for survival, enabling the partitioning of mortality into multiple discreet causes (hazards), and allowing us to assess how these different hazards varied over 7 decades (1952 – 2019). We fitted the model to available data on pup production, fecundity, age structure, human removals and environmental conditions, and used the fitted model to produce annual estimates of pup production and total abundance. We conducted a Bayesian life stage simulation analysis (LSA) to compare the relative contributions of various hazards to variation in population growth. We found that harvests of young of the year and adults were the primary contributors to variation in population trends from 1951-1982; however, after 1983 the relative importance of harvest mortality decreased while the impacts of natural mortality increased, especially for young of year (YOY), and since 2000 the impacts of YOY mortality from ice cover anomalies have become one of the strongest drivers of trends. Based on current climate models, which project warmer water and decreasing ice cover, we expect continued high levels of YOY mortality from environmental factors such as deteriorating ice conditions. These climate-related hazards are likely to become the dominant drivers of population dynamics in coming decades, which will in turn affect sustainable harvest levels for both Canada and Greenland. Our model will provide a useful tool for exploring future scenarios of climate impacts and management strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".