Seasonal increases in fish trophic niche plasticity within a flood‐pulse river ecosystem (Tonle Sap Lake, Cambodia)
Bibliographic record
Abstract
Abstract Species’ responses to seasonal environmental variation can influence trophic interactions and food web structure within an ecosystem. However, our ability to predict how species’ interactions will vary spatially and temporally in response to seasonal variation unfortunately remains inadequate within most ecosystems. Fish assemblages in the Tonle Sap Lake (TSL) of Cambodia—a dynamic flood‐pulse ecosystem—were studied for five years (2010–2014) using stable isotope and Bayesian statistical approaches to explore both within‐ and among‐species isotopic niche variation associated with seasonal flooding. Roughly 600 individual fish specimens were collected during 19 sampling events within the lake. We found that fishes within the same species tended to have a broader isotopic niche during the wet season, likely reflecting assimilation of resources from either a wider range of isotopically distinct prey items or a variety of habitats, or both. Furthermore, among‐species isotopic niches tended to overlap and range more broadly during the wet season, suggesting that floodplain inundation promotes exploitation of more diverse and similar resources by different species in the fish community. Our study highlights that the flood‐pulse dynamic that is typical of tropical aquatic ecosystems may be an essential element supporting freshwater fish community structure and the fish diversity that underpins the TSL food web. This flow regime is currently threatened by regional dam development, which may in turn impact the natural function and structure of the fishery food web.
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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 | 0.000 |
| 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".