Spider Assemblages in the Overstory, Understory, and Ground Layers of Managed Stands in the Western Boreal Mixedwood Forest of Canada
Bibliographic record
Abstract
Logging is the main human disturbance in the boreal forest; thus, understanding the effects of harvesting practices on biodiversity is essential for a more sustainable forestry. To assess changes in spider composition because of harvesting, samples were collected from three forest layers (overstory, understory, and ground) of deciduous and conifer dominated stands in the northwestern Canadian boreal mixedwood forest. Spider assemblages and feeding guild composition were compared between uncut controls and stands harvested to 20% retention. In total, 143 spider species were collected, 74 from the ground, 60 from the understory, and 71 from the overstory, and species composition of these three pools differed considerably among layers. Distinctive spider assemblages were collected from the canopy of each forest cover type but these were only slightly affected by harvesting. However, logging had a greater impact on the species composition in the understory and ground layers when compared with unharvested controls. Guild structure differed among layers, with wandering and sheet-weaving spiders dominant on the ground while orb-weaving and ambush spiders were better represented in the understory and overstory, respectively. Given the ecological importance of spiders and the expectation of faunal changes with increased harvesting, further efforts toward the understanding of species composition in higher strata of the boreal forest are needed.
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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 | 0.000 |
| 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 | 0.000 |
| 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".