Permeability of three boreal forest landscape types to bird movements as determined from experimental translocations
Bibliographic record
Abstract
Efficient dispersal is critical to metapopulation persistence in fragmented landscapes. Yet, this phenomenon is poorly understood because it is difficult to study. We used an indirect method, experimental translocation, to investigate the permeability of three landscape types of the boreal mixedwood forest region of Canada to movements of a forest specialist, the ovenbird ( Seiurus aurocapillus ), and a habitat generalist, the white‐throated sparrow ( Zonotrichia albicollis ). We captured a total of 148 males (84 ovenbirds; 64 sparrows), which were then colour banded and displaced ca. 2 km away from their territories in landscapes fragmented either by agriculture, timber harvesting, or natural disturbances. We measured the probability and time of return of individuals to their territories during the 48 h following their translocation. We examined the relative influence of landscape type, territory quality, and age, physical characteristics, and pairing status of individuals on their probability or time of return. For both species, landscape type was the only significant predictor of the probability and time of return of individuals. For the ovenbird, the agricultural landscape was least permeable, followed by the harvested and naturally patchy landscapes. The agricultural and harvested landscapes were equally permeable to white‐throated sparrow movements, and the naturally patchy landscape was the least permeable. Permeability to ovenbird movements increased with the proportion of forest in the landscape. Because matrix type and the proportion and configuration of forest differed significantly among the three landscape types, we could not determine their relative influence on landscape permeability to bird movements. However, our results do indicate that even a long‐distance migrant such as the ovenbird can move more rapidly and efficiently across the landscape as the proportion of suitable (or permeable) habitat increases.
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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 | 0.001 |
| 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".