Succession of protistan functional traits is influenced by bloom timing
Bibliographic record
Abstract
Surface ocean eukaryotic phytoplankton biogeography can be determined as chlorophyll-a using remote sensing techniques yet evaluating its community composition remains limited. Given our ability to track site-specific chlorophyll-a concentration, we tested which factors influenced protistan functional trait distribution, and whether the distributions can be inferred from bloom succession. Here we surveyed the Labrador Sea during spring over three consecutive years, sequenced 18S data over 15 stations and collected satellite-derived chlorophyll-a concentration from March to July for each year. We evaluated changes in distribution of taxonomic composition as well as the functional traits of protistan size, trophic strategy (defined as phototrophy, phagotrophy, and mixotrophy as capable of both), motility and dimethylsulfoxide or dimethylsulfoniopropionate production by building a functional trait database after an extensive literature review. More variability in the biogeography of protistan functional traits was explained across water masses, and among years than taxonomic composition and patterns in trait variability were more apparent when site-specific timing of peak chlorophyll-a was considered. We found that reconstructing bloom phenology using days before peak (DBP) chlorophyll explained a significant amount of variability in functional trait community structure that was previously attributed to water masses or years, suggesting that spatial and interannual variations can be explained by the sampling moment during succession. Approximately 30 days prior to peak, mixotrophy as a trophic strategy was replaced by phototrophic protists of typically larger size classes. Our work suggests DBP influences protistan community trait succession that could inform biogeochemical models, and likely acts a proxy for the onset of stratification.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".