Using functional traits to investigate the determinants of crustacean zooplankton community structure
Bibliographic record
Abstract
Understanding the various processes contributing to community assembly is among the central aims of ecology. As a means of exploring this topic we quantified the relative influences of habitat filtering and competition in establishing patterns of community functional trait diversity across a landscape of lakes. Habitat filtering has been invoked in shaping community structure when co‐occurring taxa are more similar in their traits than expected by chance (under‐dispersion), and competition has been inferred as a structuring agent when co‐occurring taxa are less similar (over‐dispersion). We tested these hypotheses in crustacean zooplankton communities using a functional trait‐based approach based on five traits defining zooplankton feeding and habitat preferences across 51 lakes spanning several large limnological gradients. In general, zooplankton communities were functionally less diverse than random assemblages created from the same regional species pool. Furthermore, functional diversity was strongly correlated with variables related to lake productivity, suggesting that access to resources was the chief habitat filtering process constraining zooplankton functional diversity. This pattern was driven by the predominantly herbivorous cladocerans as opposed to the more commonly omnivorous, and sometimes carnivorous, copepods.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".