Declining adult body size and contracted life history in an unexploited boreal lake trout ( <i>Salvelinus namaycush</i> ) population during a period of warming
Bibliographic record
Abstract
Evidence of declines in fish body size with warming in the absence of exploitation is lacking. During a period of regional climate warming (1986–2016), we examined trends in growth, size-structure, and age-structure of an unexploited population of lake trout ( Salvelinus namaycush) from a small Canadian Shield Lake. These data were evaluated against long-term annual air and water temperature data, ice-cover data, and prey fish densities for the same period. Over time, the size of young immature lake trout did not change, while mature fish became smaller. The age-at-maturity of the population decreased by 4 years (from 9 to 5 years) and the body condition of immature and mature fish also declined. Though the adult lake trout population doubled over time (125–280 individuals), and the production (P), biomass (B), and biomass turnover (P/B) was constant, the population shifted to smaller, skinnier, younger individuals. By contrast, the relative abundance, biomass, and mean size of the prey fish community remained unchanged. Together this suggests that warming correlates with the contraction of the population’s life history.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".