Environment and physiology shape Arctic ungulate population dynamics
Bibliographic record
Abstract
Species conservation in a rapidly changing world requires an improved understanding of how individuals and populations respond to changes in their environment across temporal scales. Increased warming in the Arctic puts this region at particular risk for rapid environmental change, with potentially devastating impacts on resident populations. Here, we make use of a parameterized full life cycle, individual-based energy budget model for wild muskoxen, coupling year-round environmental data with detailed ontogenic metabolic physiology. We show how winter food accessibility, summer food availability, and density dependence drive seasonal dynamics of energy storage and thus life history and population dynamics. Winter forage accessibility defined by snow depth, more than summer forage availability, was the primary determinant of muskox population dynamics through impacts on calf recruitment and longer term carryover effects of maternal investment. Simulations of various seasonal snow depth and plant biomass and quality profiles revealed that timing of and improved/limited winter forage accessibility had marked influence on calf recruitment (±10-80%). Impacts on recruitment were the cumulative result of condition-driven reproductive performance at multiple time points across the reproductive period (ovulation to calf weaning) as a trade-off between survival and reproduction. Seasonal and generational condition effects of snow-rich winters interacted with age structure and density to cause pronounced long-term consequences on population growth and structure, with predicted population recovery times from even moderate disturbances of 10 years or more. Our results show how alteration in winter forage accessibility, mediated by snow depth, impacts the dynamics of northern herbivore populations. Further, we present here a mechanistic and state-based model framework to assess future scenarios of environmental change, such as increased or decreased snowfall or plant biomass and quality to impact winter and summer forage availability across the Arctic.
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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 | 0.000 | 0.001 |
| 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".