Tracking the path of predator recognition in a predator-naive population of the pond snail
Bibliographic record
Abstract
Abstract Organisms evolve adaptive strategies to adjust to rapidly changing environmental stressors. Predation pressure is one of the strongest selective forces and organisms respond to predatory threats via innate and learned responses. We utilized a natural, experimental set-up, where two lakes Stoney and Margo in Canada containing natural populations of the prey Lymnaea stagnalis differed in the presence and absence of an invasive, predatory Northern crayfish, Faxonius virilis. We exploited the contrast in the predation backgrounds of the snail populations from the two lakes to test, 1) predator recognition in predator-experienced snails is innate, (2) predator-naive snails learn to detect a novel invasive predator, and 3) learning about a novel predator gets transmitted to the successive generations. We quantified predator fear memory formation using a higher-order learning paradigm called configural learning. We found that 1) predator recognition in predator-experienced snails is innate, 2) predator-naive snails learned to recognize the novel predator even after a brief exposure to predator cues highlighting the role of learning in combating invasive predators and the critical time-window during development that accounts for predator recognition, and 3) the learning and predator detection mechanism in predator-naive snails are not transmitted to successive generations. The population variation observed in the predator-detection mechanism may be due to the past and current experience of predators in one population over the other. We find an interesting study system to address how fear learning occurs and prospective future directions to understand the mechanism of innate fear recognition from a learned fear recognition.
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.
How this classification was reachedexpand
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) | 0.003 | 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 teacher head, 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".