Seasonal variation in prey abundance influences habitat use by greater horseshoe bats (Rhinolophus ferrumequinum) in a temperate deciduous forest
Bibliographic record
Abstract
The hypothesis that patterns of habitat selection of greater horseshoe bats ( Rhinolophus ferrumequinum (Schreber, 1774)) vary across seasons in a temperate deciduous forest was investigated. Variables associated with potentially important ecological factors for greater horseshoe bats (physical structure of shrub stratum, crown canopy, insect availability, lunar phase, and weather) were collected for different seasons, and 75 sampling sites were established in the Luotong Mountain Nature Reserve in northeast China. Insect abundance was highest in late summer and lowest in late autumn. Poisson generalized linear models showed that the activity of greater horseshoe bats was positively related to the height and density of shrub stratum in late summer, whereas the activity of greater horseshoe bats was associated with insect abundance in early and late autumn. During periods of intermediate prey abundance (early summer), the height and density of shrub stratum, as well as insect abundance, influenced the activity of greater horseshoe bats. Shrub stratum may provide shelter against predation for foraging greater horseshoe bats. These results support our prediction that there was a trade-off between importance of food and cover among seasons for foraging bats. These findings are useful for the conservation and management of greater horseshoe bats.
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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 | 0.001 | 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".