Coexistence and limiting similarity of consumer species competing for a linear array of resources
Bibliographic record
Abstract
Consumer-resource systems with linear arrays of substitutable resources form the conceptual basis of much of present-day competition theory. However, most analyses of the limiting similarity of competitors have only employed consumer-resource models as a justification for using the Lotka-Volterra competition equations to represent the interaction. Unfortunately, Lotka-Volterra models cannot reflect resource exclusion via apparent competition and are poor approximations of systems with nonlogistic resource growth. We use consumer-resource models to examine the impact of exclusion of biotic resources or depletion of abiotic resources on the ability of three consumer species to coexist along a one-dimensional resource axis. For a wide range of consumer-resource models, coexistence conditions can become more restrictive with increasing niche separation of the two outer species. This occurs when the outer species are highly efficient; in this case they cause extinction or severe depletion of intermediate resources when their own niches have an intermediate level of separation. In many cases coexistence of an intermediate consumer species is prohibited when niche separation of the two outer species is moderately large, but not when it is small. Coexistence may be most likely when the intermediate species is closer to one of the two outer species, contrary to previous theory. These results suggest that competition may lead to uneven spacing of utilization curves. The implications and range of applicability of the models are discussed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".