Functional diversity of crustacean zooplankton communities: towards a trait‐based classification
Bibliographic record
Abstract
Summary 1. While studies of phytoplankton and terrestrial plant communities have increasingly emphasised the use of functional traits in ecological research, few have yet to apply this approach to zooplankton communities. 2. This study reviews laboratory and observational studies on zooplankton feeding and life history and provides a series of functional trait tables for the North American freshwater zooplankton. Qualitative and quantitative trait tables highlight areas where data were more scarce and point to which types of studies could fill in gaps in our knowledge of zooplankton niches. 3. Data were most complete for the Cladocera across most traits, while feeding information for cyclopoids was most sparse. Qualitative data that distinguished congeneric species were lacking for most groups. 4. A regional community dendrogram for common north‐eastern North American zooplankton species was generated and shows that taxonomic differences between species do not capture fully functional differences based on the traits of body length, habitat, trophic group and feeding type. 5. The data collected here, combined with readily measurable species attributes, can be used to generate a multivariate measure of the functional niche of each species found in a community. Armed with this information, functional relationships that are useful for ecological studies of lake ecosystems can be more easily conducted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".