Trophic Ecology of<i>Arapaima</i>sp. in a ria lake—river–floodplain transition zone of the Amazon
Bibliographic record
Abstract
Abstract Neotropical floodplains are usually productive systems that are maintained by the nutrient, detritus and sediment inputs provided by the main river channel flood pulse. Ria lakes represent a special feature and habitat in the Amazonian floodplains, being characterised by a dendritic morphology and dependence on terrestrial inputs provided by an intricate stream network. Our objective was to evaluate the trophic ecology of the arapaima ( Arapaima sp.). We combined stomach content analysis with measurements of carbon and nitrogen stable isotope values from dorsal muscle to infer the ontogenetic changes in trophic level and isotopic niche width in floodplain and ria lakes. Arapaima diet was dominated by fish from low trophic positions. While most of the stomachs sampled in the study ria lake were full, empty stomachs predominated in samples taken in the floodplain lakes. These differences indicate that ria lakes may provide better feeding grounds for arapaima during the dry season, presumably because ria lakes are interconnected with a large stream network and the main river channel year round. Nitrogen stable isotope results further indicated an ontogenetic dietary shift in arapaima, with piscivory increasing as a function of length in both environments. Carbon stable isotope analysis indicated that energy sources used by arapaima varied by environment, with arapaima using a greater diversity of food sources in ria lakes than in floodplain lakes. Information about the main carbon sources is useful for fishery management because stakeholders may choose to conserve key vegetal groups to ensure the productivity and diversity of aquatic ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".