Are we heading towards a global decrease in coregonine catches?
Bibliographic record
Abstract
Coregonines have specific ecological needs, making them potentially very vulnerable to changes in lake conditions. A contemporary concern is that many lakes worldwide are experiencing environmental changes due to anthropogenic pressure and climate warming. Here, we compiled long-term data of coregonine catches from 27 lakes from three continents in the northern hemisphere. Declines in catch were observed in 67% of the lakes during the first two decades of the 21st century, with a significant trend (p-value<0.05) in 44% of the cases. An analysis to determine whether trends are globally linked to environmental conditions and specific lake attributes was carried out on 26 lakes for the period 2000–2019. Several local declines in catches had already been documented in the literature and are likely to be due to local forcing such as nutrients, species invasions and changes in fishing practices. Nevertheless, on a global scale, our results indicate that lakes, which exhibited a significant decrease in catch were larger and more nutrient-poor than other lakes. The rate of change in catches appeared to be related to the trophic state of the lake. The specific effect of warming is difficult to determine during the studied period. When warming occurred outside the period of egg incubation, decreasing trends were more frequently observed in nutrient-poor than in mesotrophic lakes. In conclusion, our findings suggest that achieving oligo- or ultraoligotrophic conditions, as required in developed countries to control phytoplankton blooms and enhance water quality, could pose significant challenges for future management of coregonine fisheries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".