Early avian functional assemblages after fire, clearcutting, and post-fire salvage logging in North American forests
Bibliographic record
Abstract
Increased demand for timber, the reduction in the available timber resources, and more frequent and severe forest fires under a changing climate have increased the use of salvage logging in North American forests despite concerns regarding impacts on biodiversity and long-term forest productivity. We aimed to complement previous approaches that used bird species richness or individual abundance in salvage-logged habitats to assess the sustainability of this practice. We looked for commonalities in the taxonomic, functional, and phylogenetic components of bird assemblages among these three post-disturbance habitats across a broad geographic range. We compiled six North American datasets selected from primary and grey literature that documented species composition of avian assemblages in habitats after recent fire, post-fire salvage logging, and traditional logging. Our results revealed contrasting patterns of bird trait assemblage among burned, post-fire salvage, and traditionally logged habitats. In salvage-logged habitats, taxonomic diversity, functional diversity, and functional and phylogenetic redundancy were significantly lower than in both burned and traditionally logged habitats. The frequency of insectivores was significantly lower after salvage logging than after both fire and traditional logging. These findings suggest that cumulative disturbances have a negative effect on early assembly of bird communities. The outcomes of this study encourage further assessments, at landscape level, of salvage logging intensity, burn size, and fire severity on bird functional structure to better plan for their conservation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".