Does water velocity influence optimal escape behaviors in stream insects?
Bibliographic record
Abstract
Optimal escape theory can successfully explain variation in the distance to an approaching predator at which prey initiate flight (the flight initiation distance, FID).However, for animals without access to refuges, optimal escape theory may also explain variation in the distance that prey flee (the retreat distance, RD).In benthic stream habitats, both the risk of predation and the costs of escape may be mediated by water velocity; optimal escape theory then predicts that FID and RD of slow-current insects should vary little with increasing current velocity, whereas the FID and RD of fast-current insects should decrease.To test this prediction, a simulated predator (SP) was used to initiate escape responses in 3 mayflies found in different habitats-Ameletus (slow pools), Baetis (fast riffles), and Epeorus (very fast cascades)-across a range of water velocities.Unexpectedly, the FID of all 3 prey did not vary with water velocity.In contrast, the RD of Epeorus decreased with velocity (RD at the lowest velocity about 4.53 greater than the highest velocity), whereas the RD of Ameletus did not vary significantly with velocity.Escape behaviors of Baetis did not vary strongly with velocity.Variation in the proportion of larvae that escaped by drifting or swimming rather than crawling (Ameletus .Baetis, Epeorus) suggests that for fast-current prey, the costs associated with leaving the streambed exceed the risks of benthic predation.Water velocity may thus influence ecological processes such as predator-prey interactions and emigration from patches in substantial, but previously unexplored, ways.
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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 | 0.002 |
| 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 | 0.001 | 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".