Little brown bat activity patterns and conservation implications in agricultural landscapes in boreal Yukon, Canada
Bibliographic record
Abstract
Abstract Agriculture can threaten the persistence of bat populations by removing forests and wetlands and by intensifying production. Both processes are underway in expanding agricultural landscapes of boreal North America. To inform land planning and agricultural practices aimed at maintaining a viable population of the little brown bat (Myotis lucifugus), we assessed the use by bats of human‐modified (open fields, forest‐field edges, and cleared edges of ponds) and unmodified (forest ponds and forest interior) habitat features in agricultural landscapes in southern Yukon, Canada (60° N–61° N), using acoustic recordings. We summarized bat activity (number of 3‐s acoustic files with ≥1 pass/night) and bat feeding (files with >1 feeding buzz/night) at grouped sets of habitat features (sites) and used generalized linear mixed models to test predictions about relative use of habitats. The active season for bats was late April to early October. Little brown bat feeding was strongly correlated with general activity, but feeding comprised a significantly higher proportion of all activity at forest ponds and forest interiors compared to field edges, open fields, and ponds in fields. Total bat activity was highest at forest ponds, followed by field edges, and substantially less in forest interiors and open fields. Forest ponds were used more than the edges of nearby ponds with some riparian clearing for fields. Bats increased use of forest interiors and decreased use of fields as duration of darkness decreased close to summer solstice. We recommend exclusion of ponds, lakes, and other wetlands from future agricultural land disposition, and retention of a riparian forested buffer of ≥40 m around current water bodies on farms. We also recommend retention of strips or patches of forest bordering fields and connected to riparian areas and to more extensive forests on public lands. A relatively young agricultural landscape can avoid some of the risks of intensive agriculture with proactive planning and stewardship.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".