Chemical ecology of predator–prey interactions in aquatic ecosystems: a review and prospectusThe present review is one in the special series of reviews on animal–plant interactions.
Bibliographic record
Abstract
The interaction between predator and prey is an evolutionary arms race, for which early detection by either party is often the key to success. In aquatic ecosystems, olfaction is an essential source of information for many prey and predators and a number of cues have been shown to play a key role in trait-mediated indirect interactions in aquatic communities. Here, we review the nature and role of predator kairomones, chemical alarm cues, disturbance cues, and diet cues on the behaviour, morphology, life history, and survival of aquatic prey, focusing primarily on the discoveries from the last decade. Many advances in the field have been accomplished: testing the survival value of those chemicals, providing field validation of laboratory results, understanding the extent to which chemically mediated learning may benefit the prey, understanding the role of these chemicals in mediating morphological and life-history adaptations, and most importantly, the selection pressures leading to the evolution of chemical alarm cues. Although considerable advances have been made, several key questions remain, the most urgent of which is to understand the chemistry behind these interactions.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".