The implications of using multiple resources for consumer density dependence
Bibliographic record
Abstract
Questions: What is the relationship between the population size and per capita growth for a consumer species experiencing resource limitation? How is this relationship influenced by the presence of multiple resources that differ in their vulnerability to the consumer? How well do traditional models of density dependence represent these relationships? Methods: Simple differential equation models of one-consumer/multiple-resource systems are used to determine the relationship between the per capita mortality of the consumer and the equilibrium (or average) consumer density, as well as the associated relationship between per capita growth and population size. Most models have either two or very many resources. Key assumptions: Resources are nutritionally substitutable and different resources do not interact with each other. Predictions: If total mortality is low, use of multiple resources is likely to produce a decelerat-ing decrease in consumer population size with an increase in imposed mortality. Deceleration is caused by relaxed apparent competition between resources as consumer mortality increases. This represents a relaxation of the conditions for persistence in a game played between the strategies represented by different prey species. Because most functional responses saturate at high resource densities, the density–mortality relationship becomes accelerating at high mortal-ities. As a consequence, generalist consumers often have different curvature of density depend-ence at high and low population sizes (unlike the widely used ‘theta-logistic ’ model of density dependence).
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".