Trophic level modulates carabid beetle responses to habitat and landscape structure: a pan‐European study
Bibliographic record
Abstract
1. Anthropogenic pressures have produced heterogeneous landscapes expected to influence diversity differently across trophic levels and spatial scales. 2. We tested how activity density and species richness of carabid trophic groups responded to local habitat and landscape structure (forest percentage cover and habitat richness) in 48 landscape parcels (1 km 2 ) across eight European countries. 3. Local habitat affected activity density, but not species richness, of both trophic groups. Activity densities were greater in rotational cropping compared with other habitats; phytophage densities were also greater in grassland than forest habitats. 4. Controlling for country and habitat effects, we found general trophic group responses to landscape structure. Activity densities of phytophages were positively correlated, and zoophages uncorrelated, with increasing habitat richness. This differential functional group response to landscape structure was consistent across Europe, indicated by a lack of a country × habitat richness interaction. Species richness was unaffected by landscape structure. 5. Phytophage sensitivity to landscape structure may arise from relative dependency on seed from ruderal plants. This trophic adaptation, rare in Carabidae, leads to lower phytophage numbers, increasing vulnerability to demographic and stochastic processes that the greater abundance, species richness, and broader diet of the zoophage group may insure against.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".