Bibliographic record
Abstract
Abstract This book explores how mathematical models can illuminate the interaction known as interspecific competition. Competition occurs whenever two or more species share at least some of the same limiting resources. It is likely to affect all species, as well as many higher-level aspects of community and ecosystem dynamics. Interspecific competition shares many of the same features as density dependence (intraspecific competition) and evolution (competition between genotypes). In spite of this, a robust theoretical framework for understanding its outcomes and many potential effects on ecological communities is lacking. Despite its prominence in the ecological literature, the theory seems to have lost direction in recent decades, with many synthetic papers promoting outdated ideas, failing to use resource-based models, and having little utility in applied fields such as conservation and environmental management. The book examines how theory that began to be developed half a century ago can be extended to illuminate the effects of environmental change on the abundances of competing species. Current competition theory needs to incorporate findings regarding consumer–resource interactions in the context of larger food webs containing behaviourally or evolutionarily adapting components. Overly simple models and methods of analysis have led to past theory contributing less than it should have to practical applications. The book also discusses the related interactions of intraspecific competition and apparent competition, and examines the evolutionary as well as the ecological effects of this important process.
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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 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".