Resource availability affects temporal variation of phytoplankton size structure in the Kuroshio east of Taiwan
Bibliographic record
Abstract
Abstract The on‐going discussion concerning how environmental factors determine phytoplankton size structure has centered around two hypotheses: (H1) The resource‐size relationship predicts that normalized biovolume size spectrum (NBSS) slopes for phytoplankton are progressively shallower with increasing resource availability and (H2) The temperature‐size relationship predicts that phytoplankton NBSS slopes steepen with increasing water temperature. To test these hypotheses, we examined 72 phytoplankton assemblage collections in the Kuroshio east of Taiwan. Total phytoplankton biomass was used as a proxy for resource availability instead of nutrients because nutrients are depleted and do not represent resource availability for oligotrophic seas. We found no significant relationship between NBSS slopes with temperature, providing little support for the temperature‐size rule. In contrast, a positive relationship between NBSS slopes and total biomass for most of the year lends general support to the resource‐size relationship, except during the winter and early spring. To explain this exception, we hypothesize that resource pulses occurring during the cold seasons are used more efficiently by small cells and promote faster growth of small relative to large phytoplankton because these pulses take place after a long period of resource depletion in oligotrophic seas; thus, the NBSS slopes become much steeper than would be expected from a positive resource‐size relationship. This deviation can be considered as nonsteady state in terms of phytoplankton size structure relative to resources. Nevertheless, we cannot rule out the possibility that grazing effects also play an important role in controlling phytoplankton size structure.
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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 | 0.001 | 0.001 |
| 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".