Effects of altered resource consumption rates by one consumer species on a competitor
Bibliographic record
Abstract
Abstract Non‐mechanistic models of competition suggest that harming one of two competing species will increase the population density of the other. These models also suggest that any change in a fitness component of one competitor will make the densities of the two competitors change in opposite directions. However, models of competition that incorporate resource dynamics show that neither conclusion holds generally. Reducing the consumption abilities of one competitor may decrease the population size of the other by decreasing resource overexploitation by the first and thereby increasing its density. It is also possible for decreased consumption abilities of one species to increase the population densities of both species, when the increased density of the focal species is offset by its decreased ability to consume the main resources of its competitor. Finally, decreases in consumption may have the effects predicted by phenomenological models; a decrease in the focal species and an increase in its competitor. Unstable systems may exhibit more complicated patterns of changes in densities with changes in consumption rates. These counterintuitive effects depend on the presence of overexploitation of biotic resources, about which little is known. More generally, there have been few theoretical or empirical studies examining the indirect effects of changes in consumption rates of a focal species in a food web; these are termed ‘trait‐initiated indirect effects’. A better understanding of the potential consequences of altered consumption rates will be important for understanding biotic shifts in communities undergoing environmental change, and in using simple community modules to understand larger food webs.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".