Natural regeneration determines wintering bird presence in wind-damaged coniferous forest stands independent of postdisturbance logging
Bibliographic record
Abstract
Natural disturbances in European production forests are undesired from an economic perspective but are important drivers of biodiversity. The removal of damaged timber to restore economic value is accompanied by negative effects on various species groups, particularly breeding bird communities, and can hinder natural regeneration. However, little is known about the effect of postdisturbance logging on bird assemblages in winter when temperatures are low and food resources are unpredictable. We conducted fixed-radius point counts of wintering birds in forest stands logged or unlogged after windthrows and in mature forest stands to test our predictions that bird species densities and abundances (i) are lower in postdisturbance logged stands and (ii) depend on the amount of natural regeneration, (iii) which differs between logged and unlogged stands. Our generalized linear mixed model did not support the expected differences in bird abundance or species densities between postdisturbance logged and unlogged stands but indicated that mature forest stands harbor the highest bird abundances and species densities. The number of regenerated coniferous trees significantly positively affected wintering bird abundances in both postdisturbance logged and unlogged stands. Hence, the number of coniferous trees appears to be a main predictor of wintering bird presence.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".