A meta‐analysis of zooplankton functional traits influencing ecosystem function
Bibliographic record
Abstract
The use of functional traits to characterize community composition has been proposed as a more effective way to link community structure to ecosystem functioning. Organismal morphology, body stoichiometry, and physiology can be readily linked to large-scale ecosystem processes through functional traits that inform on interspecific and species-environment interactions; yet such effect traits are still poorly included in trait-based approaches. Given their key trophic position in aquatic ecosystems, individual zooplankton affect energy fluxes and elemental processing. We compiled a large database of zooplankton traits contributing to carbon, nitrogen, and phosphorus cycling and examined the effect of classification and habitat (marine vs. freshwater) on trait relationships. Respiration and nutrient excretion rates followed mass-dependent scaling in both habitats, with exponents ranging from 0.70 to 0.90. Our analyses revealed surprising differences in allometry and respiration between habitats, with freshwater species having lower length-specific mass and three times higher mass-specific respiration rates. These differences in traits point to implications for ecological strategies as well as overall carbon storage and fluxes based on habitat type. Our synthesis quantifies multiple trait relationships and links organisms to ecosystem processes they influence, enabling a more complete integration of aquatic community ecology and biogeochemistry through the promising use of effect traits.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".