Primary attraction and random landing in host‐selection by wood‐feeding insects: a matter of scale?
Bibliographic record
Abstract
Abstract 1 Most plant‐feeding insects show some degree of specialization and use a variety of cues to locate their host. Two main mechanisms of host location, primary attraction and random landing, have been investigated for such insects. 2 Research has led to contradictory conclusions about those hypotheses, especially for wood‐feeding insects; however, recent studies suggest that both mechanisms may take place in a single taxon but at different scales. 3 We developed a field experiment to test the hypothesis that primary attraction occurs at larger scale and random landing at finer scale in wood‐feeding insects. Landing rates, measured using sticky traps, were compared first between patches and then between individual trees according to their distance to a baited central tree. 4 Polynomial functions describing landing rate to distance relationships were compared with a function produced by a null model describing what should occur under the random landing hypothesis. Scolytidae and Cerambycidae (Coleoptera) responded to volatiles at the patch scale, supporting the primary attraction hypothesis, but the landing patterns of some groups at finer scale matched closely the predictions of our null model, giving support to the random landing hypothesis. 5 Our results show that the primary attraction and random landing hypotheses are not mutually exclusive and that prelanding use of host‐produced volatile is scale‐dependant. Scale considerations should thus be included in the study of prelanding host‐selection of wood‐feeding insects.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".