Trophic mismatch and its effects on the growth of young in an Arctic herbivore
Bibliographic record
Abstract
In highly seasonal environments, timing of breeding of organisms is typically set to coincide with the period of highest resource availability. However, breeding phenology may not change at a rate sufficient to keep up with rapid changes in the environment in the wake of climate change. The lack of synchrony between the phenology of consumers and that of their resources can lead to a phenomenon called trophic mismatch, which may have important consequences on the reproductive success of herbivores. We analyzed long-term data (1991-2010) on climate, plant phenology and the reproduction of a long-distance Arctic migrant, the greater snow goose (Chen caerulescens atlantica), in order to examine the effects of mismatched reproduction on the growth of young. We found that geese are only partially able to adjust their breeding phenology to compensate for annual changes in the timing of high-quality food plants, leading to mismatches of up to 20 days between the two. The peak of nitrogen concentration in plants, an index of their nutritive quality for goslings, occurred earlier in warm springs with an early snow melt. Likewise, mismatch between hatch dates of young and date of peak nitrogen was more important in years with early snow melt. Gosling body mass and structural size at fledging was reduced when trophic mismatch was high, particularly when the difference between date of peak nitrogen concentration and hatching was >9 days. Our results support the hypothesis that trophic mismatch can negatively affect the fitness of Arctic herbivores and that this is likely to be exacerbated by rising global temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".