LANDSCAPE STRUCTURE AND SPATIAL SCALE AFFECT SPACE USE BY SONGBIRDS IN NATURALLY PATCHY AND HARVESTED BOREAL FORESTS
Bibliographic record
Abstract
Knowledge of how landscape structure influences patterns of animal space use is critical to developing sustainable landscape management practices. For breeding songbirds that defend a territory embedded within a larger home range, effects of structural change on movement may be manifested at multiple spatial scales. We used radio-telemetry to assess within-territory and home range space use as functions of the proportion of clearcut-harvested versus naturally unforested land for two species of Neotropical migrant songbirds. We tested whether these relationships varied with spatial scale by assessing landscape structure in both the local neighborhood (115 m radius around an individual's territory center) and across the landscape (1250 m radius). Territory size for riparian-associated male Northern Waterthrushes (Seiurus noveboracensis) was curvilinearly related to the proportion of harvested versus naturally unforested land and varied by greater than two orders of magnitude. Waterthrush territories were largest in the most heavily harvested landscapes. Home range space use by male Blackpoll Warblers (Dendroica striata), which are habitat generalists, was influenced by the ratio of clearcuts to natural gaps in both the neighborhood and landscape. Blackpolls may modify their behavior as a result of anthropogenic processes acting at both small and larger spatial scales, but we observed considerable interannual variability. Our results suggest that boreal forest–breeding passerines may be capable of modifying their space use behavior in response to moderate levels of structural change caused by forestry.
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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 | 0.000 |
| 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".