Sampling Saproxylic Coleoptera: Scale Issues and the Importance of Behavior
Bibliographic record
Abstract
Some currently used tree-scale sampling techniques targeting saproxylic insects capture individuals that are attracted to or landing on specific potential hosts. The success of such techniques is entirely dependent on strong primary attraction in targeted insects. However, up to this point, field experiments testing the primary attraction hypothesis have produced contradictory results. To test the efficiency of such techniques, and consequently, the strength of primary attraction for saproxylic Coleoptera, we sampled insects landing on contrasting snag types including new and old snags of five different tree species using sticky traps in a single mixed 135-yr-old boreal stand in Western Quebec, Canada. Ordination analyses showed homogenous assemblages among the different snag types and stovepipe controls, when considering either all species captured or only targeted functional groups, and very few species showed strong affinities to specific snag types. Species composition of assemblages was in several cases correlated with the species and status of trees neighboring the sampling units, which suggest that prelanding host selection mechanisms do not allow insects to single out a potential host while in flight. Our results suggest that primary attraction may play a role at larger spatial scales and help insects identify potential habitat patches, while selection of a single host at the local scale is done by trial-and-error through random landing. In such a context, future studies aiming at describing precise host-use patterns of saproxylic insects should rely on methods targeting larvae or emerging adults such as wood dissection and rearing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".