Constraints on benthic algal response to nutrient addition in oligotrophic mountain rivers
Bibliographic record
Abstract
Abstract Nutrient availability has long been considered one of the most important factors regulating production of benthic algae in oligotrophic rivers; yet, empirical relationships do not have as wide an application as similar models derived for lentic systems. The aim of this research was to derive empirical relationships between nutrient concentrations and benthic algal abundance and to identify commonalities with other studies to improve our understanding of constraints on algae in oligotrophic rivers. Surveys of physical, chemical and biological attributes of oligotrophic mountain rivers in spring, summer and autumn for 2 years confirmed that small amounts of anthropogenic phosphorus (0.1–5.6 µg/L total phosphorus (TP)) resulted in 4‐ to 30‐fold increases in abundance of benthic algae and benthic macroinvertebrates (BMIs). Algal accrual along a gradient in nutrient availability was not masked by grazing pressure but was positively correlated with abundance of scrapers. Epilithic abundance was highest downstream of anthropogenic nutrient sources in autumn. We concluded that benthic algal abundance in these mountain rivers was weakly correlated with phosphorus availability if light was not limiting but ultimately controlled by temperature and river discharge. Therefore, we recommend more direct measures of nutrient limitation be used to predict changes in ecological integrity at the lower end of the resource gradient. Copyright © 2007 John Wiley & Sons, Ltd.
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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".