Habitat relationships of boreal forest birds in managed mixedwood forests.
Bibliographic record
Abstract
Disturbance ecology suggests that if patterns created by boreal harvesting more closely resemble effects of natural disturbance, then boreal birds should more easily cope with habitat changes associated with harvesting. I tested this idea by documenting avian community responses to partial-cutting treatments applied in Fort Nelson, British Columbia. The purposes of this study were threefold: 1) to investigate changes in the bird community following partial-cutting in boreal forest stands; 2) to compare point-count and transect bird survey methods and determine the degree of correlation between the two datasets; and 3) to test the applicability of bird-habitat models developed in Alberta\u2019s boreal forest for predicting species abundance in a boreal forest environment in northern British Columbia. Two bird survey protocols, point-counts and fixed-width transects, were employed in 4 partial-cut and 2 uncut (control) stands. Each stand was surveyed 4 times per season over 2 breeding seasons. Detailed habitat information was collected in 212-0.04 ha plots. Similar numbers of species were observed in each year with 50 and 52 species observed in 1999 and 2000, respectively. More than half (51.7%) the total species observed were neotropical migrants. Differences in cumulative species per point count station between years were not explained by treatment effect. There were differences in species distribution across sites with mourning warbler and Connecticut warbler consistently detected only at the partial-cut sites in both years. None of the detected bird species occurred only at the control sites when data for both years was combined; however, in each year there were 3 different rare species detected only at the uncut sites. Species diversity differed between partial-cuts and controls and between years. Significant correlations between the two survey methods depended on bird species, habitat and timing of survey in the breeding season (i.e., early or late in the season). For the bird-habitat model comparison, models containing local and neighbourhood habitat variables were generated for 13 candidate boreal forest bird species. There was a lack of agreement between predicted species abundances and those observed.
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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".