Spatiotemporal variation in activity of bat species differing in hunting tactics: effects of weather, moonlight, food abundance, and structural clutter
Bibliographic record
Abstract
Different foraging tactics in related animal taxa may be expected to cause species-specific differences in sensitivity to temporal and spatial variations of resources. To test this, we studied spatiotemporal dynamics of flight and foraging activity in seven insectivorous bat species in northern Poland using broadband ultrasound detection, recording of weather conditions, insect abundance, moon phase, and cover of floating vegetation. The seven species studied comprised six (genera Eptesicus Rafinesque, 1820, Pipistrellus Kaup, 1829, and Nyctalus Bowdich, 1825) that were classified as aerial hawkers and one ( Myotis daubentonii (Kuhl, 1817)) that was classified as a water-surface forager. Stepwise forward multiple regression models indicated that the prominent limiting factors for aerial hawkers were biomass of potential prey and air temperature. Analysis of the activity of the water-surface forager revealed no effect of food abundance or air temperature, but activity was negatively affected by floating vegetation (which masks echoes of prey items), fog (which absorbs echolocation calls), and moonlight (possible increased predation risk). Hence, trophic resources appear to have no significance as a limiting factor for species using microhabitats with unusually high prey abundance (e.g., water surface). Activities of such species, however, may be more affected by temporally changing detectibility of food items and vulnerability to predation pressure.
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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 | 0.001 |
| 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".