Contrasting effects of rising temperatures on trophic interactions in marine ecosystems
Bibliographic record
Abstract
In high-latitude marine environments, primary producers and their consumers show seasonal peaks of abundance in response to annual light cycle, water column stability and nutrient availability. Predatory species have adapted to this pattern by synchronising life-history events such as reproduction with prey availability. However, changing temperatures may pose unprecedented challenges by decoupling the predator-prey interactions. Here we build a predator-prey model accounting for the full life-cycle of fish and zooplankton including their phenology. The model assumes that fish production is bottom-up controlled by zooplankton prey abundance and match or mismatch between predator and prey phenology, and is parameterised based on empirical findings of how climate influences phenology and prey abundance. With this model, we project possible climate-warming effects on match-mismatch dynamics in Arcto-boreal and temperate biomes. We find a strong dependence on synchrony with zooplankton prey in the Arcto-boreal fish population, pointing towards a possible pronounced population decline with warming because of frequent desynchronization with its zooplankton prey. In contrast, the temperate fish population appears better able to track changes in prey timing and hence avoid strong population decline. These results underline that climate change may enhance the risks of predator-prey seasonal asynchrony and fish population declines at higher latitudes.
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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 | 0.001 |
| 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".