Bats respond positively to local drainage ditch vegetation and forest amount in the broader landscape in a North American agroecosystem
Bibliographic record
Abstract
Abstract Bat populations are declining globally. Maintaining high‐quality habitat for bats can help mitigate extinction risk. Natural and semi‐natural linear vegetation features have been shown to provide shelter and foraging habitat for bats in temperate agroecosystems in Europe, yet their value for bats in North America has received little attention. Using automated ultrasonic recorders, we assessed bat species richness and activity across agricultural drainage ditches that varied in mean vegetation height, variability in vegetation height, and mean width in agroecosystems in eastern Ontario, Canada. Landscapes surrounding recording sites also varied in forest amount and mean field size, and recording sites were located at different distances from the nearest forest patch. We found that in general, bat activity at the community level and at the individual species level was positively associated with mean vegetation height and mean vegetation width; however, species appeared to vary in their response to variation in vegetation height. We also found a general positive relationship within and across species for bat activity with forest amount at the landscape scale. Overall, our results suggest maintaining or increasing vegetation height along drainage ditches and field margins as well as maintaining or increasing forest amount at the landscape scale will best support bats in temperate North American agroecosystems.
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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 | 0.000 |
| 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 | 0.000 | 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".