Avian use of early-successional boreal forests in the postbreeding period
Bibliographic record
Abstract
The postbreeding period is critical for many forest birds and especially for juveniles, which must learn to forage on their own before the fall migration. During this period, many birds of the boreal forest are found in early-successional stands (ESS), where fruit abundance is typically high. Boreal forest birds may use ESS to exploit fruit or for reasons other than access to fruit, namely to forage along forest edges or simply to transit through clearcuts between patches of mature forest. We tested whether frugivory, edge use, and transit through small (<65 ha) clearcuts between mature-forest patches accounted for bird abundance in ESS in a boreal forest of Quebec during the summers 2007 and 2008. Fifteen of the 33 species captured in ESS were postbreeding frugivores. Removal of all fruits from Sambucus racemosa, a dominant fruiting plant, within 10 m of mist-netting sites reduced the number of frugivores captured by 45% but did not affect nonfrugivores. Numbers of birds captured were independent of distance from mature-forest edges, thus refuting the edge hypothesis, at least in a range of 20–60 m. Mist nets placed parallel to mature-forest edges intercepted more mature-forest birds than mist nets placed perpendicular to edges, as would be expected if mature-forest birds traveled straight through ESS. We conclude that frugivory and transit, but not edge proximity, contribute to the postbreeding abundance of mature-forest birds in boreal early-successional stands.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".