How Does the Evolution of Universal Ecological Traits Affect Population Size? Lessons from Simple Models
Bibliographic record
Abstract
This article argues that adaptive evolutionary change in a consumer species should frequently decrease (and maladaptive change should increase) population size, producing adaptive decline. This conclusion is based on analysis of multiple consumer-resource models that examine evolutionary change in consumer traits affecting the universal ecological parameters of attack rate, conversion efficiency, and mortality. Two scenarios are investigated. In one, evolutionary equilibrium is initially maintained by opposing effects on the attack rate and other growth rate parameters; the environment or trait is perturbed, and the trait then evolves to a new (or back to a previous) equilibrium. Here evolution exhibits adaptive decline in up to one-half of all cases. The other scenario assumes a genetic perturbation having purely fitness-increasing effects. Here adaptive decline in the consumer requires that the resource be self-reproducing and overexploited and requires a sufficient increase in the attack rate. However, if the resource exhibits adaptive defense via behavior or evolution, adaptive decline may characterize consumer traits affecting all parameters. Favorable environmental change producing parameter shifts similar to those produced by adaptive evolution has similar counterintuitive effects on consumer population size. Many different food web models have already been shown to exhibit such counterintuitive changes in some species.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".