Kalahari skinks eavesdrop on sociable weavers to manage predation by pygmy falcons and expand their realized niche
Bibliographic record
Abstract
Abstract Eavesdropping on community members has immediate and clear benefits. However, little is known regarding its importance for the organization of cross-taxa community structure. Furthermore, the possibility that eavesdropping could allow species to coexist with a predator and access risky foraging habitat, thereby expanding their realized niche, has been little considered. Kalahari tree skinks (Trachylepis spilogaster) associate with sociable weaver (Philetairus socius) colonies as do African pygmy falcons (Polihierax semitorquatus), a predator of skinks and weavers. We undertook observational and experimental tests to determine if skinks eavesdrop on sociable weavers to mitigate any increase in predation threat that associating with weaver colonies may bring. Observations reveal that skinks use information from weavers to determine when predators are nearby; skinks were more active, more likely to forage in riskier habitats, and initiated flight from predators earlier in the presence of weavers compared with when weavers were absent. Playback of weaver alarm calls caused skinks to increase vigilance and flee, confirming that skinks eavesdrop on weavers. Furthermore, skinks at sociable weaver colonies were more likely to flee than skinks at noncolony trees, suggesting that learning is mechanistically important for eavesdropping behavior. Overall, it appears that eavesdropping allows skinks at colony trees to gain an early warning signal of potential predators, expand their realized niche, and join communities, whose predators may otherwise exclude them.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".