Navigational behaviour of <i>Lymnaea stagnalis</i> in response to chemical and flow cues
Bibliographic record
Abstract
Many aquatic animals use chemical cues for navigation relative to prey and predators. Navigation strategies such as chemotaxis and chemical-gated rheotaxis vary depending on the flow conditions. Moreover, sources of attractive versus aversive chemical cues are distinct goals which may use different strategies. Navigation by the great pond snail, Lymnaea stagnalis, presents an interesting case in that they can experience a range of flow environments in nature, including no flow, laminar flow and turbulent flow. In a series of behavioural experiments, we documented movement patterns relative to both sources of attractive and aversive chemical cues in all three flow conditions. The results indicate that L. stagnalis can use chemotaxis in the absence of flow, either chemotaxis or chemical-gated rheotaxis in laminar flow and probably chemical-gated rheotaxis in turbulent flow. Since navigation behaviour also differed between light and dark conditions, visual cues are also likely to be used in parallel with chemical cues to guide navigation. Responses to aversive sources of chemical cues were categorically distinct, with no evidence of crawling away from the source. Instead, we found an increased frequency of detachment from the substrate, leading the animals to float up to the water surface. Overall, our findings provide the first evidence of an animal switching between the distinct navigation strategies required in different flow environments and set the stage for more detailed analyses of the proximate mechanisms that produce the navigation behaviours in L. stagnalis.
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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 | 0.000 |
| 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) | 0.001 | 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".