Generalization of learned predator recognition in coral reef ecosystems: how cautious are damselfish?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Learned predator recognition provides animals with an adaptive mechanism to rapidly adapt to current levels of predation risk. Prey may be able to reduce the cost associated with learning if they can use information learned about known predators to respond to cues from closely related predators with which they are unfamiliar. The capacity of prey to generalize recognition and distinguish between novel predators and non‐predators is poorly understood, particularly in species‐diverse communities with many closely related predators and non‐predators. Lemon damselfish, Pomacentrus moluccensis , conditioned to recognize the odour of a predatory moon wrasse, Thalassoma lunare , as a risky stimulus, were subsequently tested for their response to T. lunare and a range of closely related predators and non‐predators from within the Labridae family, a distantly related non‐predator and a saltwater control. Pomacentrus moluccensis displayed antipredator responses not only to T. lunare odour, but also generalized their recognition to congeneric T. amblycephalum and T. hardwicke odours. Recognition was not extended to other species within (Labridae; Coris batuensis and Halichoeres melanurus ) or beyond (Pseudochromidae; Pseudochromis fuscus) the family. Individuals could not distinguish between the predator T. hardwicke and non‐predator T. amblycephalum when generalizing their recognition to congeneric species based on chemosensory assessment alone. Our results demonstrate that reef fishes may limit their generalization to congeneric species only, and may be unable to distinguish between predators and non‐predators using chemosensory cues. Recognition patterns may result from uncertainties in predicting the identities of predators in species‐diverse communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 it