The role of echolocation strategies for niche differentiation in bats
Bibliographic record
Abstract
Guilds subdivide bat assemblages into basic structural units of species with similar patterns of habitat use and foraging modes, but do not explain mechanisms of niche differentiation. Bats have evolved four different echolocation strategies allowing the access to four different trophic niche spaces differing in niche dimensions. Bats foraging in open and edge spaces use the “aerial hawking or trawling strategy” and detect and localize prey by evaluating pulse–echo trains in which the prey echo is unmasked. The pulse–echo pairs deliver mainly positional information on the prey and only little information on its nature. Signals are highly variable and are adapted for detection and localization in open space and (or) edge space. In narrow space, bats identify prey by solving a pattern recognition task. Bats using the “flutter detecting strategy” evaluate glint pattern in prey echoes; bats using the “active gleaning strategy” evaluate the spectral–temporal pattern of the prey–clutter echo complex; and bats using the “passive gleaning strategy” evaluate the pattern of prey-generated cues to find food and use echolocation only for spatial orientation. The less variable signals of narrow space bats are adapted for pattern recognition. The diverse and species-rich tropical bat assemblage at Barro Colorado Island, Panama, is here used as an exemplar for assigning bats to guilds, and we discuss the role of echolocation and other adaptations for niche differentiation within guilds.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".