Functional responses and ecosystem dynamics: how clearance rates explain the influence of satiation, food-limitation and acclimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| 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) | 0.000 | 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 it