Fragments are not islands: patch vs landscape perspectives on songbird presence and abundance in a harvested boreal forest
Bibliographic record
Abstract
The boreal mixed‐wood forest of northern Alberta. Canada is characterized by a mosaic of deciduous and coniferous forest patches. Recently, the deciduous portion of the forest was allocated for industrial logging. Widespread habitat loss and fragmentation may negatively affect birds and other wildlife. Most research on the effects of habitat loss on bird abundance has focussed on the forest as a patch or island in a matrix of non‐habitat, but some species of songbird may use both the forest patch and the matrix. We hypothesized that some species of songbird might be able to compensate for a loss of deciduous forest by moving into other habitat types (termed “habitat compensation”). We report on a replicated field investigation in which we assessed the response of songbirds to commercial timber harvest by first examining their abundance within deciduous forest only, and then adding the clearcuts and coniferous forest in the surrounding areas to the analysis for a broader, landscape view of the system. Bird communities in deciduous and coniferous habitats had significant overlap in species composition: there was less overlap between forest and clearcuts. The shift from patch‐centred to landscape sampling altered our interpretation of over half of the most common species' responses to logging in at least one year, suggesting that habitat compensation may have been occurring. However, significant variation in responses of species was observed between the two study areas. Our past reliance on island biogeographic and other single habitat approaches may be inappropriate for this system, and we stress that a broad, landscape view is required to properly assess and interpret species' responses to habitat loss and fragmentation.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".