Impact of Horizontal Edge–Interior and Vertical Canopy–Understory Gradients on the Abundance and Diversity of Bark and Woodboring Beetles in Survey Traps
Bibliographic record
Abstract
Semiochemical-baited intercept traps are important tools used to collect information about the presence/absence and population dynamics of forest insects. The performance of these tools is influenced by trap location along both horizontal edge-interior and vertical understory-canopy gradients. Consequently, the development of survey and detection programs requires both the development of effective traps and semiochemical lures but also deployment protocols to guide their use. We used field trapping experiments to examine the impact of both horizontal edge-interior and vertical understory-canopy gradients and their interactions with the species richness and abundance of Buprestidae, Cerambycidae and Curculionidae. Both gradients had significant effects on the diversity and abundance of all three families collected in traps and the pattern of gradient effects differed between the two experiments. In the first experiment, traps were deployed along transects involving large (>100 m) forest gaps and in the second experiment traps transected small (ca. 15 m) forest gaps. These results were consistent with the idea that gradient effects on the abundance and diversity of these three families of forest Coleoptera are context dependent. The results of this study suggest that monitoring programs for bark and woodboring beetles should deploy traps at multiple locations along both vertical understory-canopy and horizontal edge-interior gradients.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".