MacArthur’s consumer-resource model: a Rosetta Stone for competitive interactions
Bibliographic record
Abstract
Recent developments in competition theory, namely, Modern Coexistence Theory (MCT), have aided empiricists in formulating tests of species persistence, coexistence, and evolution from simple to complex community settings. However, the parameters used to predict competitive outcomes, such as interaction coefficients, invasion growth rates, or stabilizing differences, remain biologically opaque, making findings difficult to generalize across ecological settings. Here, our article is structured around five goals, towards clarifying MCT by first making a case for the modern-day utility of MacArthur’s consumer-resource model, a model with surprising complexity and depth: (i) to describe the model in uniquely accessible language, deciphering the mathematics towards cultivating deeper biological intuition about competition’s innerworkings regardless of what empirical toolkit one uses, (ii) to provide translation between biological mechanisms from MacArthur’s model and parameters used to predict coexistence in MCT, (iii) to make explicit important but understated assumptions of MacArthur’s model in plain terms, (iv) provide empirical recommendations, and (v) to examine how key ecological concepts (e.g., r/K selection) can be understood with renewed clarity through MacArthur’s lens. We end by highlighting opportunities to explore mechanisms in tandem with MCT and to compare and translate results across ecological currencies towards a more unified ecological science.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".