Functional community structure modulates zooplankton production rates across boreal lakes
Bibliographic record
Abstract
Abstract Although there is a general consensus in ecology that more diverse communities tend to be more productive, it is still unclear whether this holds for aquatic ecosystems, especially with indicators of productivity rates and not just standing biomass proxies. Furthermore, most evidence is based on the results of experimental studies in which biodiversity was assessed through taxonomic richness, although functional indicators could be more relevant. Consequently, we still have a poor understanding of how real community performance is linked to the various features of community structure in natural aquatic ecosystems. Here we explicitly assessed the effect of functional traits and diversity on crustacean zooplankton productivity rates measured across 84 boreal Canadian lakes spanning wide limnological gradients. Productivity was assessed using the chitobiase enzyme method. Redundancy analyses and structured equation modelling were used to assess the relationship between zooplankton production rates, community structure (diversity and functional composition), while accounting for standing biomass and environmental factors After controlling for the effect of environmental factors, zooplankton production rates were positively linked to dominance (lower functional evenness) by certain feeding traits (Daphnia filtration and Chydorus filtration), demonstrating an identity (selection) effect. Overall, the magnitude of the functional compositional effect on production was comparable to the aggregate effect of the environmental variables. Our study demonstrates that zooplankton functional community structure plays an important role in the regulation of a pivotal lake ecosystem function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".