Evaluation of functional trait diversity for marine zooplankton communities in the Northeast subarctic Pacific Ocean
Bibliographic record
Abstract
Functional trait diversity relates variation in the structure of biological communities to function and ecosystem processes. Zooplankton occupy a central position in marine food webs, modulating energy availability to higher trophic levels, while themselves affected by environmental variation. The use of traits with functional diversity potentially enables a more mechanistic understanding of variation and regulation of zooplankton communities than is possible with taxonomic diversity alone. Traits for 42 zooplankton species from the northeast subarctic Pacific were assembled from the literature and applied to a 16-year time series from the oceanographic Line P. We assembled traits on body size, ontogeny, habitat and feeding behaviours. Six major functional groupings were identified via a trait-based cluster dendrogram. Several functional diversity indices were also calculated, and compared with analogous taxonomic diversity indices. Analogue diversity indices were significantly correlated. Both types of diversity indices revealed some year-specific “anomalies” which were associated with broad-scale oceanographic and climatic regime shifts. We propose that the functional diversity approach may represent an additional ecological tool with which we can gain further understanding of zooplankton function and trophic linkages in a changing ocean, in part because functional traits are often based on easily measured morphological characters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".