Behavioural mechanisms underlying functional response of sea stars Asterias vulgaris preying on juvenile sea scallops Placopecten magellanicus
Bibliographic record
Abstract
The functional response characterises the relationship between prey density and the consumption rate of individual predators. Typically, it is studied by fitting a model to observations of predation rate at different prey densities. The behavioural mechanisms underlying a functional response are not well understood, and estimates of model parameters seldom conform to observations of behaviour. We have developed a mechanistic approach that directly incorporates behavioural observations into characterisation of the functional response. Laboratory experiments were used to record predation rates and observe foraging behaviour of sea stars Asterias vulgaris preying on juvenile sea scallops Placopecten magellanicus at different densities. Experiments were conducted in tanks with no sediment and tanks with sediment. Behavioural data from the experiments were used to calculate parameters of functional response models Th (handling time per prey) and a (rate of successful search). On both substrates, Th remained constant across prey density, while a was density-dependent. An inverse quadratic was used to describe a and was incorporated into a functional response model. Estimates of a were also obtained by fitting the functional response model to the predation rate data using regression analysis. These estimates of a were highly consistent with the estimates calculated from behavioural data. On both substrates, sea stars preying on scallops had a Type III sigmoid-shaped functional response; on sediment, predation rate decreased at high prey densities. Sea star ability to capture attacked prey was probably the mechanism underlying the observed responses. In general, behavioural information can lead to better understanding of observed functional responses. © Inter-Research 2006.
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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 | 0.001 |
| 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".