Landscape heterogeneity supports bat diversity: Importance of wetlands and intermediate forest cover in coastal British Columbia
Bibliographic record
Abstract
Despite widespread recognition of the critical ecosystem services provided by bats and the dire conservation status of many species, key knowledge gaps remain about the habitat and landscape features that promote bat diversity. These gaps hinder effective conservation planning, particularly as climate change and land-use intensification influence ecosystems. We investigated how bat species richness and activity vary with local habitat type, landscape forest cover, and protected area status on Vancouver Island, British Columbia. Using ultrasonic acoustic detectors, we surveyed 132 forest gaps between mid-May and mid-July across three years (2022–2024) in both protected and unprotected areas. Richness and activity were consistently higher at wetlands than at non-wetland meadows or anthropogenic openings, particularly for Myotis species. Richness and activity typically peaked in landscapes with intermediate forest cover, although the optimal amount and spatial scale varied among species groups defined by overlapping call characteristics. Protected areas supported higher activity of the Little Brown Myotis ( Myotis lucifugus )–Long-legged Myotis ( M. volans ) group, but protection status offered no consistent benefit for other species groups. Species richness and activity also increased with warmer nightly temperatures and peaked mid-season. Our results emphasize the ecological importance of wetlands and forest–wetland mosaics for supporting bat communities, including species of conservation concern such as Little Brown Myotis. More broadly, they highlight the need to integrate wetland protection and structural heterogeneity into conservation planning, ensuring that working landscapes contribute to biodiversity protection in regions facing intensifying land-use and climate pressures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".