Functional responses and ecosystem dynamics: how clearance rates explain the influence of satiation, food-limitation and acclimation
Bibliographic record
Abstract
Modellers have long been aware that the mathematical form of zooplankton mortality, or closure, significantly affects the dynamics of planktonic ecosystem models. Another important formulation is the functional response, i.e. how ingestion rates change with prey density. Here we explain why different grazing responses can have profoundly differing influences on modelled dynamics, and how common practices may limit models due to misguided characterization of feeding behaviours. Use of different ingestion functions in a Nutrient–Phytoplankton–Zooplankton (NPZ) model results in oscillating versus constant densities. Contrary to the conclusions of previous studies, it is shown that these results are not due to zooplankton satiation versus non-satiation. Analysis of a predator-prey model is used to derive the necessary condition for ecological stability, which is related to food-limited clearance rates. Sensitivity studies demonstrate that zooplankton clearance rates have a strong influence on the dynamics of more complex models. Moreover, it is shown that acclimation time lags can dramatically alter results from those where zooplankton instantly adapt to changing prey densities due to the corollary effect on clearance rates. These results are discussed in terms of practical advice to modellers who face uncertainty in choosing expressions for the functional response.
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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 | 0.003 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".