Area‐sensitivity by forest songbirds: theoretical and practical implications of scale‐dependency
Bibliographic record
Abstract
Songbird presence is often associated with the area of suitable habitat in the surrounding landscape. However, the size of landscape for which this association is maximized is generally unknown, likely to vary among species, and may affect our ability to incorporate songbirds in landscape management. We measured the occurrence and the persistence of forest songbirds in relation to the amount of habitat measured at several scales: local (100, 200 m radius), neighborhood (400, 800 m), landscape (1.6, 3.2, 6.4 km) and regional (12–24 km), based on data from Ontario's Forest Bird Monitoring Program (1987–2005). Songbird occurrence was obtained from point count sites distributed across southern Ontario and each revisited in multiple years (mean=5.8 yr). Presence of each species at a site was associated with forest habitat area measures that account for differences in preferred forest cover types among species. Area of coniferous, deciduous and mixed forest was derived from Landsat TM imagery. Thirty‐two of the 35 species studied were area‐sensitive, and area‐sensitivity was apparent for 13–25 species at each spatial scale. For 24 species, the strength of area‐sensitivity varied with scale, suggesting the importance of local, neighborhood, landscape and regional habitat for 3, 5, 5, and 11 species respectively. As a result, the list of the five most area‐sensitive species varied depending on the scale at which habitat was described. We conclude that area‐sensitivity can occur at a broader set of scales than generally assumed, and is most pronounced at the regional scale. We suggest that a broad set of scales should be examined before taking conservation decisions based on avian area‐sensitivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".