Pitfall Trap Size and Capture of Three Taxa of Litter-Dwelling Arthropods: Implications for Biodiversity Studies
Bibliographic record
Abstract
Cost-effective and ecologically sensitive monitoring techniques are required to assess effects of anthropogenic disturbances on biodiversity. Pitfall trapping is widely used in biodiversity monitoring programs to measure the diversity of organisms active within leaf-litter. We compared catch rates and species richness of ground beetles (Coleoptera: Carabidae), rove beetles (Coleoptera: Staphylinidae), and spiders (Araneae) across five different diameters of pitfall traps (4.5, 6.5, 11, 15, and 20 cm) and three sizes of rain covers (64, 79.2, and 225 cm2) to determine optimal trap size for studying litter-dwelling arthropod biodiversity. In general, larger pitfall traps collected more individuals, and more species, of all three taxa. Further tests on data standardized to trap circumference showed that catch rates are not directly proportional to trap size, and even the smallest traps collected a disproportionately high number of certain taxa. When catch rate data were standardized by trap circumference smaller traps collected more small-bodied carabid and staphylinid species and large traps collected more wolf spiders (Lycosidae) than smaller traps. Roof size had no effect on species richness or catch rate of beetles or spiders. For the purposes of ecological monitoring, using more small pitfall traps would be the most efficient sampling technique to characterize the dominant epigaeic arthropod fauna; small traps collect few nontarget vertebrates, and sorting the samples involves generally less processing time. From a conservation perspective, however, including several large pitfall traps in the sampling regime would help detect rare species.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".