Drivers of carabid functional diversity: abiotic environment, plant functional traits, or plant functional diversity?
Bibliographic record
Abstract
Understanding how community assembly is controlled by the balance of abiotic drivers (environment or management) and biotic drivers (community composition of other groups) is important in predicting the response of ecosystems to environmental change. If there are strong links between plant assemblage structure and carabid beetle functional traits and functional diversity, then it is possible to predict the impact of environmental change propagating through different functional and trophic groups. Vegetation and pitfall trap beetle surveys were carried out across twenty four sites contrasting in land use, and hence productivity and disturbance regime. Plant functional traits were very successful at explaining the distribution of carabid functional traits across the habitats studied. Key carabid response traits appeared to be body length and wing type. Carabid functional richness was significantly smaller than expected, indicating strong environmental filtering, modulated by management, soil characteristics, and by plant response traits. Carabid functional divergence was negatively related to plant functional evenness, while carabid functional evenness was positively correlated to plant functional evenness and richness. The study shows that there are clear trait linkages between the plant and the carabid assemblage that act not only through the mean traits displayed, but also via their distribution in trait space; powerful evidence that both the mean and variance of traits in one trophic group structure the assemblage of another.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".