The biogeography of marine plankton traits
Bibliographic record
Abstract
Changes in marine plankton communities driven by environmental variability impact the marine food web and global biogeochemical cycles of carbon and other elements. To predict and assess these community shifts and their consequences, ecologists are increasingly investigating how the functional traits of plankton determine their relative fitness along environmental and biological gradients. Laboratory, field and modelling studies are adopting this trait-based approach to map the biogeography of plankton traits that underlies variations in plankton communities. Here, we review progress towards understanding the regulatory roles of several key plankton functional traits, including cell size, N2 -fixation and mixotrophy among phytoplankton, and body size, ontogeny and feeding behaviour for zooplankton. The trait biogeographical approach sheds light on what structures plankton communities in the current ocean, as well as under climate change scenarios, and also allows for finer resolution of community function because community trait composition determines the rates of significant processes, including carbon export. Although understanding of trait biogeography is growing, uncertainties remain that stem, in part, from the paucity of observations describing plankton functional traits. Thus, in addition to recommending widespread adoption of the trait-based approach, we advocate for enhanced collection, standardisation and dissemination of plankton functional trait data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".