Effects of selection harvesting on bark invertebrates and nest provisioning rate in an old forest specialist, the brown creeper (<i>Certhia americana</i>)
Bibliographic record
Abstract
The brown creeper (Certhia americana) is one of the forest bird species most sensitive to partial harvesting in North America. We examined the detailed response of this species and its food (bark-dwelling invertebrates) during the 3rd and 4th year after experimental selection harvesting (30–40% basal area removal) in northern hardwood forest. Relative to control plots, nest densities in treated plots were ca 50% lower. Because the density of nesting substrates was not significantly lower in treated plots than in controls, we investigated whether foraging substrates could be the limiting factor. Specifically, we tested for a treatment effect on 1) the abundance and species composition of bark invertebrate assemblages; 2) the biomass of bark invertebrates per unit area; and 3) the frequency of food provisioning. As predicted, treatment had a significant negative effect on food provisioning rate, though not on invertebrate biomass, when accounting for year effects. There was also no evidence for a treatment effect on the structure of bark invertebrate assemblages, which was mainly influenced by cumulative degree days. Selection harvesting thus appeared to reduce the amount of food delivered to brown creeper nestlings, unless greater amounts of food were delivered per feeding trip in treated plots. The lower density of foraging substrates in treated plots (77 versus 112 stems·ha−1 in controls) may require that adults perform longer foraging trips. Future studies should determine whether this extra effort has short- or long-term consequences for adults and nestlings.
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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".