Drivers of diet patterns in a globally distributed freshwater fish species
Bibliographic record
Abstract
We analysed data of a globally distributed model organism (brown trout, Salmo trutta) in an attempt to understand relationships among biogeography, prey communities, and climate on diet composition at regional spatial scales (Scandinavia) and thereafter explored whether diet patterns remained the same at global scales. At regional scales, we uncovered comprehensive patterns in diet composition among neighbouring freshwater ecoregions, with site-specific prey communities as the best predictor of the observed prey utilization patterns. Thus, we posit that environmental gradients altering site-specific prey communities and consequently the trophic niche of the predator through bottom-up mechanisms are key in understanding spatial dietary patterns. Proximity was also important for the revealed biogeographic patterns at global scales. We suggest that geographic location (latitude and elevation) as a proxy of environmental heterogeneity is key at small spatial scales, and climate at global extents, to understand spatial dietary patterns. Our findings support the hypothesis that future shifts in prey communities due to climate change will strengthen biographical patterns in feeding of freshwater fishes, with consequences for invasiveness assessment and nature management and conservation.
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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 | 0.001 | 0.001 |
| 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".