Biogeochemical water type influences community composition, species richness, and biomass in megadiverse Amazonian fish assemblages
Bibliographic record
Abstract
Amazonian waters are classified into three biogeochemical categories by dissolved nutrient content, sediment type, transparency, and acidity-all important predictors of autochthonous and allochthonous primary production (PP): (1) nutrient-poor, low-sediment, high-transparency, humic-stained, acidic blackwaters; (2) nutrient-poor, low-sediment, high-transparency, neutral clearwaters; (3) nutrient-rich, low-transparency, alluvial sediment-laden, neutral whitewaters. The classification, first proposed by Alfred Russel Wallace in 1853, is well supported but its effects on fish are poorly understood. To investigate how Amazonian fish community composition and species richness are influenced by water type, we conducted quantitative year-round sampling of floodplain lake and river-margin habitats at a locality where all three water types co-occur. We sampled 22,398 fish from 310 species. Community composition was influenced more by water type than habitat. Whitewater communities were distinct from those of blackwaters and clearwaters, with community structure correlated strongly to conductivity and turbidity. Mean per-sampling event species richness and biomass were significantly higher in nutrient-rich whitewater floodplain lakes than in oligotrophic blackwater and clearwater river-floodplain systems and light-limited whitewater rivers. Our study provides novel insights into the influences of biogeochemical water type and ecosystem productivity on Earth's most diverse aquatic vertebrate fauna and highlights the importance of including multiple water types in conservation planning.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".