Boreal songbirds and variable retention management: a 15-year perspective on avian conservation and forestry
Bibliographic record
Abstract
Partial retention harvest (PRH) has received attention as an alternative to clear-cutting, yet most studies of its effects on boreal songbirds have been conducted shortly after harvest. We assessed responses of songbird assemblages to PRH over a 15-year post-harvest period at the EMEND experiment in the mixedwood forest of Alberta, Canada. Four partial retention levels (10%, 20%, 50%, and 75% of stems) were applied in a series of 10 ha “compartments” during winter 1998–1999 with matching clearcuts and unharvested control compartments in each of three replicates in four common mixedwood cover-types. Songbirds were surveyed using the point count method in 1998 (pre-harvest) and after harvest in 1999, 2000, 2005, 2006, 2012, and 2013. Partial retention harvests that left ≥20% of merchantable stems mitigated changes to songbird assemblages away from, and accelerated recovery toward, the unharvested benchmark. However, assemblages of 14- to 15-year-old controls differed from those observed prior to harvest, notably in composition of old-forest associated species, suggesting effects of experiment-scale processes and (or) regional trends not attributable to forestry activities. Although retention levels ≥20% appear to better conserve old-forest birds than clear-cutting in the short term, long-term trade-offs with increasing harvest footprint to compensate for unharvested merchantable volume should be investigated.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".