Interspecific effects of forest fragmentation on bats
Bibliographic record
Abstract
Wind-farm development may be an important contributor to forest fragmentation, but how such developments impact bats is poorly understood. We hypothesized that bat activity at a wind farm would be explained, at least in part, by attraction and avoidance behaviours caused by deforestation. We tested predictions of this hypothesis via a landscape-level acoustic, capture, and radiotelemetry survey of little brown bats (Myotis lucifugus (Le Conte, 1831)) and northern long-eared myotis (Myotis septentrionalis (Trouessart, 1897)). Acoustic and capture data indicated no significant difference in magnitude of activity between the fragmented wind farm and the less-fragmented surrounding areas. However, only 2 of 19 radio-tracked bats were ever located inside the wind farm despite being captured adjacent to it. Bat locations were compared against randomly generated locations within the same area in a logistic regression framework to rank landscape variables in order of association with bats. A multicriteria evaluation of forest metrics showed that, over a 3-year period, there was an increase of suitable habitat inside the wind farm for M. lucifugus and a decrease for M. septentrionalis. These results support the contention that, at this level of disturbance, M. lucifugus may use the cleared areas, while M. septentrionalis is negatively impacted by increased deforestation caused by wind-farm development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".