Effects of alternative harvesting methods on boreal forest spider assemblages
Bibliographic record
Abstract
Forestry is among the most important disturbance forces in the boreal region, having caused drastic changes to the biota. Forest industries have recently introduced alternative logging techniques to better maintain forest diversity, but little is known on how these function. We studied the short-term effects of various logging methods on ground-dwelling spiders in Finland, using pitfall traps 1 year before and 2.5 years after logging. The compared logging regimes were (i) clear-cutting, (ii) retention felling, (iii) gap felling, (iv) thinning, and (v) control. We found that (1) clear-cutting and retention felling drastically changed the spider fauna. The abundance of forest species decreased with these methods, whereas most open-habitat species showed the opposite response, being caught only after logging. Moreover, multivariate analyses (nonmetric multidimensional scaling, multivariate regression trees) indicated that clear-cutting and retention felling produced similar spider faunas. (2) Increasing retention of trees caused less abrupt changes in spider fauna. Gap felling produced variable and intermediate responses, whereas thinning closely resembled control. We conclude that retention patches of a few tens of trees within clearcuts may not function as “lifeboats” for forest spiders; gap felling preserves some forest species but also supports the colonization of open-habitat species; and the studied thinning treatment well preserves the forest-floor spider assemblage.
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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 | 0.001 |
| 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".