Early seral hardwood vegetation increases adult and fledgling bird abundance in Douglas-fir plantations of the Oregon Coast Range, USA
Bibliographic record
Abstract
Recent evidence suggests that population declines of bird species associated with early-successional forest conditions may be associated with reduced quality of breeding habitat. Increasing intensity of forest management on private lands and decreased harvest rates on federal lands in the Pacific Northwest, USA, have resulted in a loss of diverse young forest stands, typically called early seral forest. Previous studies suggest that the amount of early seral broadleaf cover within conifer forests is linked to the composition of foliage-gleaning bird communities. However, information regarding productivity and juvenile use of post-breeding habitat in highly modified plantation habitat is lacking. We examined the relationship between vegetation structure resulting from intensive forest management practices and the abundance of five species of leaf-gleaning, neotropical migrants: orange-crowned warbler ( Oreothlypis celata (Say, 1823)), Wilson’s warbler ( Wilsonia pusilla (A. Wilson, 1811)), MacGillivray’s warbler ( Oporornis tolmiei (J.K. Townsend, 1839)), Swainson’s thrush ( Catharus ustulatus (Nuttall, 1840)), and black-headed grosbeak ( Pheucticus melanocephalus (Swainson, 1827)). All species, except MacGillivray’s warbler, showed positive associations with the amount of early seral hardwood cover as fledglings, breeding adults, or both. However, the relative magnitude of these associations varied, suggesting that other factors may also have influenced avian responses. Abundances of breeding pairs and young are likely to show positive responses to management practices that increase early seral hardwood cover in regenerating stands. If adopted at landscape and regional scales, such practices may positively influence population trends of several declining bird species.
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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 | 0.000 | 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) | 0.001 | 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".