Bibliographic record
Abstract
The history of the idea that predation rates are functions of the ratio of prey density to predator density, known as ratio dependence, is reviewed and updated. When the term was introduced in 1989, it was already known that higher predator abundance often reduced an individual predator's average intake rate of prey. However, the idea that this effect was a universally applicable inverse proportionality was new. That idea was widely criticized in many articles in the early 1990s, and many of these criticisms have never been addressed. Nevertheless, ratio dependence seems to be gaining in popularity and is the subject of a recent monograph by the originators. This article revisits the most important objections to this theory, and assesses to what extent they have been answered by the theory's proponents. In this process, several new objections are raised. The counterarguments begin with the lack of a plausible, generally applicable mechanism that could produce ratio dependence. They include the fact that ratio dependence is a special case of predator-density effects, which, in turn, are only one of many non-prey species effects that influence the consumption rate of a particular prey. The proclaimed simplicity advantage of ratio dependence is at best small and is outweighed by its disadvantages; it predicts biologically implausible phenomena, and cannot easily be extended to describe multi-species systems, trait-mediated interactions, coevolution, and a number of other important ecological phenomena. Any potential small simplicity advantage disappears with corrections to remove unrealistic low-density dynamics caused by ratio dependence. The frequent occurrence of strong predator dependence does not make ratio dependence a better 'default' model of predation than prey dependence, and empirical studies of the full range of non-prey species effects on the consumption rates of predators are needed.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.011 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".