Algal priming mediates the effects of light and nutrients on organic matter processing: insights from artificial and natural streams
Bibliographic record
Abstract
Environmental factors such as light and nutrients may play important roles in determining detrital decomposition through their effects on primary production. Thus, human activities that alter the availabilities of light and nutrients may have direct consequences on organic matter (OM) processing and nutrient cycling in freshwater ecosystems. I compared heterotrophic function (OM processing and respiration) between two P levels (5 µg P L-1 and 51 µg P L-1) and two shade levels (0% and 80% shade) in natural streams, as well as 3 P levels (10 µg P L-1, 50 µg P L-1 and 100 µg P L-1) and 4 shade levels (0%, 50%, 80% and 100%) in artificial streams using the cotton-strip assay (CSA). Data from these experiments show a negative association between algal abundance (GPP and chlorophyll a) and recalcitrant OM (ROM) processing, implying a negative priming effect. Light was an important driver of negative priming which disproportionately affected ROM decomposition at lower P treatments over time. Overall, there were limited interactive effects between light and nutrient availability; rather, ROM processing was positively associated with P availability at all light levels while it was negatively associated with light availability at all P levels. There was no evidence to support positive priming (i.e., algal stimulation of ROM processing) in either experiment. Overall, results from this study illustrate the importance of considering light levels and nutrient availability when considering long term C and nutrient budgets in freshwater ecosystems.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".