Functional characteristics of zooplankton bioregions along the cross-shelf gradient
Bibliographic record
Abstract
• 10 functional groups were identified based on mesozooplankton traits. • Functional group contributions to the community traits varied between bioregions. • Bioregions differed in community weighted mean traits and functional diversity. • Assemblages with similar functional characteristics were found across bioregions. • Functional diversity and ecosystem functions showed weak and nonlinear relationships. The use of trait-based approaches complements taxonomic community analysis by linking species distributions with organismal traits. For marine zooplankton, traits are used to identify functional similarities between species and to quantify the roles of zooplankton in the food web and biogeochemical cycles. Efforts in understanding the functional biogeography of zooplankton have generally focused on copepods and the latitudinal gradient, while investigations on the wider zooplankton community and the cross-shelf gradient are limited. The objective of this study was to test whether taxonomically distinct zooplankton communities along the cross-shelf gradient are functionally distinct based on multiple functional characteristics. Two decades of zooplankton monitoring data from the Northeast subarctic Pacific Ocean were synthesized with a zooplankton trait database that provides a more extensive set of traits compared to previous functional biogeography studies. The 163 species of crustacean and soft-bodied zooplankton were first categorized into ten functional groups. The Offshore, Deep Shelf, Nearshore, and Deep Fjord bioregions were found to significantly differ in the relative composition of functional groups, community total trait values, community weighted means of traits, and functional diversity metrics. This study additionally explored assemblages with similar functional characteristics that are found in multiple bioregions and described the regional differences in the relationship between functional diversity and ecosystem functioning for zooplankton. The functional characterization of the bioregions provides a foundation for explaining how oceanographic drivers influence the functional characteristics of zooplankton communities and for improving predictions on how environmental changes would influence the distribution of traits and community-level functioning.
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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 | 0.000 |
| 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 | 0.001 |
| 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".