The effects of small-scale disturbance on forest birds: a meta-analysis
Bibliographic record
Abstract
Small-scale disturbance is a significant process in all major forest biomes. Some silvicultural practices, particularly group selection harvesting, intend to emulate natural small-scale disturbance by harvesting small clearcuts in the continuous forest. We conducted a meta-analysis on the effects of small-scale harvesting on North American breeding forest birds. We extracted species richness and relative abundance of several functional bird groups and guilds from published studies and compared them between gap-dominated and unlogged forest as a function of forest type and the size and age of the gap. The abundance of many bird groups was higher in the gap-dominated than in the continuous forest. Species preferring interior parts of the forest had the most negative association with the presence of gaps but this relationship was not statistically significant. Abundances of many bird groups increased with increasing gap size, while its effect on abundance of some bird groups disappeared quickly. Our review suggests that silvicultural practices that bring about small gaps do not negatively affect the abundances of most forest birds and often even enhance it. However, more studies are needed to examine optimal size and abundance of gaps in a forest and whether emulated small-scale disturbance effectively mimics natural processes.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".