Periphyton Phosphorus Uptake in Response to Dynamic Concentrations in Streams: Assimilation and Changes to Intracellular Speciation
Bibliographic record
Abstract
Effective modeling and management of phosphorus (P) losses from landscapes to receiving waterbodies requires an adequate understanding of P retention and remobilization along the terrestrial-aquatic continuum. Within aquatic ecosystems, the stream periphyton can transiently store bioavailable P through uptake and incorporation into biomass during subscouring and baseflow conditions. However, the capacity of stream periphyton to respond to dynamic P concentrations, which are ubiquitous in streams, is largely unknown. Our study used artificial streams to impose short periods (48 h) of high SRP concentration on stream periphyton acclimated to P scarcity. We examined periphyton P content and speciation through nuclear magnetic resonance spectroscopy to elucidate the intracellular storage and transformation of P taken up across a gradient of transiently elevated SRP availabilities. Our study demonstrates that the stream periphyton not only takes up significant quantities of P following a 48-h high P pulse but also sustains supplemental growth over extended periods of time (10 days), following the reestablishment of P scarcity by efficiently assimilating P stored as polyphosphates into functional biomass (i.e., phospho-monoesters and phospho-diesters). Although P uptake and intracellular storage approached an upper limit across the experimentally imposed SRP pulse gradient, our findings demonstrate the previously underappreciated extent to which the periphyton can modulate the timing and magnitude of P delivery from streams. Further elucidating these intricacies in the transient storage potential of periphyton highlights opportunities to enhance the predictive capacity of watershed nutrient models and potentially improve watershed P management.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".