Biological and environmental drivers of trophic ecology in marine fishes - a global perspective
Bibliographic record
Abstract
Dietary niche width and trophic position are key functional traits describing a consumer's trophic ecology and the role it plays in a community. However, our understanding of the environmental and biological drivers of both traits is predominantly derived from theory or geographically restricted studies and lacks a broad empirical evaluation. We calculated the dietary niche width and trophic position of 2,938 marine fishes and examined the relationship of both traits with species' maximum length and geographic range, in addition to species richness, productivity, seasonality and water temperature within their geographic range. We used Generalized Additive Models to assess these relationships across seven distinct marine habitat types. Fishes in reef associated habitats typically had a smaller dietary niche width and foraged at a lower trophic position than those in pelagic or demersal regions. Species richness was negatively related to dietary niche width in each habitat. Species range and maximum length both displayed positive associations with dietary niche width. Trophic position was primarily related to species maximum length but also displayed a non-linear relationship with dietary niche width, whereby species of an intermediate trophic position (3-4) had a higher dietary niche width than obligate herbivores or piscivores. Our results indicate that trophic ecology of fishes is driven by several interlinked factors. Although size is a strong predictor of trophic position and the diversity of preys a species can consume, dietary niche width of fishes is also related to prey and competitor richness suggesting that, at a local level, consumer trophic ecology is determined by a trade-off between environmental drivers and biological traits.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".