Effects of Thinning and Herbicide Treatments on Nest-Site Selection by Songbirds in Young Managed Forests
Bibliographic record
Abstract
Abstract Routine silvicultural practices continue to alter the structure and composition of forests after logging by removing deciduous vegetation from regenerating coniferous forests. We identified nest trees and surveyed vegetation in a 5 m radius surrounding songbird nests (nest patch) and compared the nest patches to available habitat in nine 11–22 year old conifer plantations (22–47 ha) where 90–96% of deciduous stems were removed by two treatments: manual thinning, and manual thinning plus application of glyphosate (herbicide). The control and two treatments were replicated three times. We characterized the nest patches of five species: Warbling Vireo (Vireo gilvus), Dusky Flycatcher (Empidonax oberholseri), Swainson's Thrush (Catharus ustulatus), American Robin (Turdus migratorius), and Chipping Sparrow (Spizella passerina). During three post-treatment years, areas treated with thinning plus herbicide remained depauperate of deciduous vegetation whereas thinned sites experienced deciduous regrowth. Despite variation in the density of deciduous trees and in the type of tree used for nesting in the control and treatments, nest patches were positively correlated with the amount of remaining deciduous vegetation, representing habitat that either escaped or recovered from silvicultural treatments. That relationship was stronger in areas with the fewest deciduous trees. Nests were more likely to be successful in areas with more willow. Within a mosaic of managed forest stands, birds appear to use the same proximate habitat cues for selecting a nest patch despite varying fitness consequences across different silviculture regimes. Although birds appeared to compensate for changes in stand habitat by finding patches of untreated vegetation or altering the type of tree they nested in, there was a reproductive cost for some species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".