Songbird communities in a pyrogenic habitat mosaic
Bibliographic record
Abstract
Wildfires play a key role in shaping the boreal forest landscape, yet the response of wildlife to the patchy mosaics they create is poorly understood. We studied songbirds 5–6 years post-fire in a large burn (9600 ha) in the boreal mixed wood forest of north-eastern Alberta. In the spring of 1995 and 1996 we estimated abundance of songbirds in four areas, each with four replicate sites: unburned patches within the fire ( Isolates ); burned patches ( Burns ); patches that had been clear-cut prior to burning by wildfire ( Cut-Burns ); and the unburned, continuous forest adjacent to the burn ( Peripherals ). We also sampled shrub-based arthropods with sweep-nets at each site. To investigate the role of Isolates , we compared them to Peripherals and to Burns . We compared Cut-Burns to Burns to examine the effect of logging prior to burning. In general, Isolates supported higher numbers of species and individuals than Peripherals , mainly due to higher numbers of aerial-foraging birds. Isolates and Burns had similar species richness and abundance, but Burns supported more aerial foragers while Isolates had more foliage gleaners. Cut-Burns generally supported fewer individuals than Burns , in particular fewer shrub-nesters, aerial foragers, and neotropical migrants. More bird species reached their highest densities in Burns than in any other area. Songbird species richness and abundance were positively related to dry arthropod biomass, with Burns and Isolates having the highest arthropod biomass. These patterns indicated that, 5–6 years post-burn, the patchy mosaic created by this wildfire supported more species than the mature forest surrounding the fire. Clear-cut logging prior to burning resulted in a diminished songbird community compared to that found in burned stands.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".