Use of native woodlands and traditional olive groves by foraging bats on a Mediterranean island: consequences for conservation
Bibliographic record
Abstract
Abstract We recorded bat activity on Zakynthos island (Greece) to test the hypotheses that (1) olive (Olea europea) groves and native woodlands provide comparable foraging habitat for insectivorous bats, (2) lower foraging activity occurs in olive groves treated with insecticide chemicals. We acoustically sampled bat activity (passes per minute) in four wooded habitats (organic and non‐organic olive groves, oak woodland (Quercus ilex and Quercus coccifera) and pine (Pinus halepensis) woodland from June to August 2005. Habitat type did not affect overall bat activity. A single application of insecticide chemicals annually did not affect activity over traditional olive groves. Habitat use on the island differed in several ways from that reported in studies at mainland sites. Most strikingly, pine woodland supported higher bat activity than expected relative to other habitat types, and we recorded unexpectedly high levels of M. capaccinii activity in woodland habitats. We suggest that traditional olive groves buffer some bat species from the effects of deforestation. Conservation plans for Mediterranean bats should consider the biodiversity value of these groves along with the need to conserve small woodland patches. Finally, understanding island‐specific patterns of habitat use is essential to bat conservation on small off‐shore islands.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".