Consumer trophic positions respond variably to seasonally fluctuating environments
Bibliographic record
Abstract
Abstract The effects of environmental seasonality on food web structure have been notoriously understudied in empirical ecology. Here, we focus on seasonal changes in one key attribute of a food web, consumer trophic position. We ask whether fishes inhabiting tropical river–floodplain ecosystems behave as seasonal omnivores, by shifting their trophic positions in relation to the annual flood pulse, or whether they feed at the same trophic position all year, as much empirical work implicitly assumes. Using dietary data from the Tonle Sap Lake, Cambodia, and a literature review, we find evidence that some fishes, especially small piscivores, increased consumption of invertebrates and/or plant material during the wet season, as predicted. However, nitrogen stable isotope (δ 15 N) data for 26 Tonle Sap fishes, spanning a broader range of functional groups, uncovered high variation in seasonal trophic position responses among species (0 to ±0.52 trophic positions). Based on these findings, species respond to the flood pulse differently. Diverse behavioral responses to seasonality, underpinned by spatiotemporal variation at multiple scales, could be central for rerouting matter and energy flow in these dynamic ecosystems. Seasonally flexible foraging behaviors warrant further study given their potential influence on food web dynamics in a range of fluctuating environments.
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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 | 0.000 | 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".