Brown Creeper (<i>Certhia americana</i>) demographic response to hardwood forests managed under the selection system
Bibliographic record
Abstract
The Brown Creeper (Certhia americana Bonaparte, 1838) has been identified as one of the most sensitive passerines to partial forest harvest in North America. The effect of selection logging on Brown Creeper density, nest timing, nest survival, and nest and foraging site selection was examined in five silviculture treatments (intensive group selection, typical group selection, old single-tree selection, recent single-tree selection, and control forests) of Algonquin Provincial Park, Canada. As Brown Creeper nests under the bark of large, decaying trees, we hypothesized that Brown Creeper density, timing of breeding, nest survival, and nest and foraging site selection would be negatively affected by silviculture through the removal of large, decaying trees as part of providing safe conditions for loggers. We monitored 101 nests of Brown Creeper during the 2010 and 2011 breeding seasons, mapped territories to estimate density, and conducted foraging surveys. Brown Creeper density was reduced by about 42% in logged stands compared with control stands. Despite that, silviculture did not significantly alter timing of breeding or nest survival. However, the loss of large trees through partial harvesting meant that Brown Creeper nested closer to adjacent, small forested wetlands and often in balsam fir (Abies balsamea (L.) Mill.) in treated stands. In control stands, Brown Creeper nested further from forested wetlands, disproportionately in greater numbers in upland hardwoods, and preferentially in the bark of snags of yellow birch (Betula alleghaniensis Britton). The change in the species of tree used for nesting and the general forest type as a result of logging also resulted in consequences for the selection of foraging substrates. To maintain higher densities of Brown Creeper in logged stands in Algonquin Park, we recommend retaining larger diameter yellow birch, both snags and live trees, preferably within strategically located uncut reserves based on habitat supply planning, that maintains patches roughly the size of Brown Creeper territories (10 ha).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".