Effects of Habitat Structure and Lid Transparency on Pitfall Catches
Bibliographic record
Abstract
We present a methodological study that aims to help placate some of the criticism surrounding the use of pitfall trapping for carabid beetles in ecological studies. Because pitfall trap catches are dependent on the activity of carabids and not solely on density, characteristics of the trap construction may influence the success of the trap in different habitat types. Specifically, traditional opaque wooden lids may change the temperature and sun exposure of a trap relative to its surroundings. These abiotic factors may vary with the vegetation structure present around the trap. Thus, traditional opaque lids may offer a shade refuge in low vegetation habitats. We hypothesized that a change in microclimate associated with lid transparency would alter the behavior of ground beetles and thereby lead to a bias in trap catch results. To test this hypothesis, we performed a replicated, two-factor experiment manipulating lid transparency (opaque, partially transparent, and completely transparent) and vegetation height (>2, 1, <0.5 m) around 27 pitfall traps. Soil temperatures beneath each lid varied significantly with lid transparency and vegetation height. There was no effect of either treatment on carabid species richness, whereas species assemblages varied significantly with respect to vegetation height but not lid transparency. However, total carabid catch rates and overall carabid species composition varied significantly with vegetation height but not lid transparency. Therefore, our results show that lid transparency does not bias carabid beetle catch and lend support to the use of pitfall trapping to assess the effects of habitat change on epigaeic communities.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".