On biotic and abiotic drivers of the microphytobenthos seasonal cycle in a temperate intertidal mudflat: a modelling study
Bibliographic record
Abstract
Abstract. Microphytobenthos (MPB) from intertidal mudflats are key primary producers at the land-ocean interface. MPB can be more productive than phytoplankton and sustain both benthic and pelagic higher trophic levels. The objective of this study is to assess the contribution of light, mud temperature, and gastropod Peringia ulvae grazing pressure in shaping the seasonal MPB dynamics on the Brouage mudflat (NW France). We use a physical-biological coupled model applied to the sediment first centimeter for the year 2008. The simulated data compare to observations including time-coincident remotely sensed and in situ data. The model suggests a MPB annual cycle characterized by a main spring bloom, a biomass depression in summer, and a moderate fall bloom. In early spring, high simulated photosynthetic rates due to mud surface temperature (MST) values close to the MPB temperature optimum for photosynthesis and to increasing solar irradiance trigger the onset of the MPB spring bloom. After the bloom, high MST values lead to synoptic events when MPB thermo-inhibition (39.5 % of summer) and limitation by P. ulvae grazing (8.7 % of summer) superimpose. During these synoptic events of thermo-inhibition and grazing combination, 14 % of the simulated annual MPB primary production is channeled towards the P. ulvae secondary production through ingestion. The model suggests that such a combined effect is highly linked to the MPB biomass depression in summer. The model ability to infer on biotic and abiotic mechanisms driving the seasonal MPB dynamics could open the door to a new assessment of the export flux of biogenic matter at the land-ocean interface and, more generally, of the contribution of productive intertidal biofilms to the coastal carbon cycle.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".