Autochthonous production in shallow littoral zones of five floodplain rivers: effects of flow, turbidity and nutrients
Bibliographic record
Abstract
Summary Comparative research and generalisations in lotic ecology are challenged by the dynamic hydrology of fluvial systems. The aim of this study was to understand more fully how factors such as light, nutrients and flow can predict variation in autochthonous production and algal biomass. We measured seasonal changes in percent bankfull discharge, inorganic nutrient concentrations, turbidity, instream primary production, respiration and algal biomass in the littoral zone of five floodplain rivers in one temperate and two tropical regions of the Western Hemisphere. The Brazos, Guadalupe and Neches rivers are in Texas, while the Tambopata River is in Peru and the Cinaruco River in Venezuela. Our study rivers represented a range of hydrological regimes, turbidity levels and nutrient concentrations. Flooding patterns were more seasonal in the tropical rivers than in the (temperate) Texas rivers. Inorganic nutrient concentrations were higher in the temperate rivers, probably due to anthropogenic nutrient loading. Turbidity was higher following periods of high flow in the Brazos, Tambopata and Guadalupe rivers than in the Neches and Cinaruco rivers. Littoral zones in the sediment‐laden Brazos and Tambopata rivers became heterotrophic during periods of high discharge, while littoral zones in the Guadalupe, Neches and Cinaruco rivers were consistently autotrophic. Regression tree analysis suggested that algal production and biomass in the water column responded more strongly to seasonal changes in nutrients and temperature than to turbidity, while benthic algae responded more strongly to turbidity. Our findings suggest that during periods of high flow and turbidity in rivers containing fine sediments, autochthonous production is limited and terrestrial‐based organic matter may assume greater importance in the aquatic food web.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".