Boreal forest landbirds in relation to forest composition, structure, and landscape: implications for forest management
Bibliographic record
Abstract
We examined a landbird community and its relationship to environmental variables within the boreal forest in north–central Ontario to evaluate its potential usefulness as an indicator of sustainable forest management. Our study had two components. First, we compared bird assemblages in mature forest stands inside Pukaskwa National Park (n = 17) with similar forested stands in a logged landscape (n = 18) over 3 years. We found significant separation of sites in the two treatments based on an ANOSIM (analogue of the standard univariate one-way ANOVA test) of the bird communities (R = 0.238, p < 0.001). We identified four significant indicators of the park landscape (bay-breasted warbler ( Dendroica castanea (Wilson, 1810)), black-throated green warbler ( Dendroica virens (J.F. Gmelin, 1789)), ovenbird ( Seiurus aurocapillus (L., 1766)), and red-eyed vireo ( Vireo olivaceus (L., 1766))) and five indicators of the logged landscape (black-backed woodpecker ( Picoides arcticus (Swainson, 1832)), brown creeper ( Certhia americana Bonaparte, 1838), winter wren ( Troglodytes troglodytes (L., 1758)), white-throated sparrow ( Zonotrichia albicollis (J.F. Gmelin, 1789)), and yellow-bellied sapsucker ( Sphyrapicus varius (L., 1776))). Some relationships were attributable to differences in vegetation, whereas other differences were attributable to the landscape context. Second, we used generalized additive models to examine the relationship of individual species with four sets of environmental data (understorey floristics, forest structure, overstorey composition, and landscape context) using the 35 sites noted above and 18 additional mature forest sites in the logged landscape (n = 53). We found that all four types of variables were frequently included in the best model based on Akaike's information criterion (AIC) (structure in 23 models, landscape in 20 models, overstorey in 19 models, and understorey in 15 models). We discuss our results in terms of their implications to forest management and note that our ability to map habitat for forest birds is substantially compromised by the lack of good spatial estimates of environmental variables that describe bird habitat.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".