Effect of landscape tree species composition on beetle (Coleoptera) communities in a temperate hardwood forest
Bibliographic record
Abstract
While studies show that beetles have extremely diverse niches and spatial requirements, the drivers of insect communities in forests are relatively understudied. Most studies are conducted at the stand level, with landscape level assessments limited to course level data inputs. Recent advances in satellite imagery and processing, however, have allowed the creation of relatively fine-scale (20 m) maps of tree abundances over large areas. This paper examines landscape level tree composition in the Great Lakes St. Lawrence Forest region to evaluate relationships between communities of forest beetles (Coleoptera) and tree species. This is a region which has experienced historical conifer loss due to extensive logging. The objectives were to better understand the ‘zone of influence’ around trees using high-resolution tree estimates. I predicted that: (1) certain tree species would be more influential to beetle communities than others, and (2) insects of conifers would be more influenced by landscape-scale tree communities than beetles of broadleaf trees. I used a data set which included beetle abundances from traps located in live broad leaf and conifer trees and on deadwood. The tree species composition within 100 m to 800 m radii of trap sites was used to evaluate tree influences on beetle composition. Results showed that beetle communities were typically most influenced by stand level tree composition, but communities of deadwood appeared to support high dispersing Staphylinids, which were most influenced by rare, deciduous tree species on the landscape. Conservation efforts should focus on maintaining large, diverse forested regions to support a variety of forest taxa and their spatial needs.
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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 | 0.001 | 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".