Voegtlin‐style suction traps measure insect diversity and community heterogeneity
Bibliographic record
Abstract
Abstract Comparing ecologically relevant communities of insects in heterogeneous environments requires methods capable of sampling a sufficient number of individuals and diversity of species to measure β diversity. A battery‐operated computer fan powers a 1.5 m high Voegtlin suction trap. These traps are efficient at capturing small, weakly flying insects, and can be used to sample the α and β diversity of Microhymenoptera in discrete habitats within a temperate forest ecosystem. During a preliminary study comparing Voegtlin‐style suction and Townes‐style Malaise traps, we found that the suction traps caught a greater number and a greater diversity of Hymenoptera than the Malaise traps, especially of those OTUs smaller than 1.5 mm. Placed along a transect at 50 m intervals, the suction traps also yielded more heterogeneous samples than the Malaise traps, suggesting they may be particularly useful for quantifying β diversity at small spatial scales. The same analyses with brachyceran Diptera were more nuanced. Malaise traps outperformed suction traps in terms of measuring α diversity, but suction traps resolved a higher degree of brachyceran community heterogeneity using β diversity. Insofar as Hymenoptera are amongst the most diverse of insect orders and the vast majority of species are specialist parasitoids of other insects, suction trapped Hymenoptera diversity may be a useful proxy for measuring α and β insect diversity in general.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".