Evaluation of alternative prey-, predator-, and ratio-dependent functional response models in a zooplankton microcosm
Bibliographic record
Abstract
There is strenuous debate among ecologists regarding the inclusion of predator density into the originally prey-dependent functional response. We provided comprehensive empirical comparisons of alternative functional response models for the predatory ostracod Heterocypris incongruens (Ramdohr, 1808) and the rotifer Brachionus calyciflorus (Pallas, 1766) as its prey in small freshwater microcosms. Prey killed was measured at factorial combinations of four predator densities and five prey densities, and was recorded at 3 min intervals over 60 min experiments. To support the potential effect of predator interference on per capita kill rate, we recorded ostracod activity and aggression. Kill rate increased following a saturating function with increasing prey density and decreased with increasing predator density. Model evaluation using an information–theoretic approach indicated that the Arditi–Ginzburg type II ratio-dependent model performed best, followed by the Arditi–Akcakaya and Beddington–DeAngelis type II predator-dependent models, suggesting that predator interference was important in predicting kill rates. Interference among predators increased and their activity decreased with increasing predator density, providing confirmation that interference was responsible for the predator-dependent effect. By combining a microcosm experiment and behavioral observations, our results suggest that predator interference at realistic population densities influences ostracod kill rates and this form of interference was best accommodated by predator-dependent models.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".