Inducible competitors and adaptive diversification
Bibliographic record
Abstract
Identifying the causes of diversification is central to evolutionary biology. The ecological theory of adaptive diversification holds that the evolution of phenotypic differences between populations and species––and the formation of new species––stems from divergent natural selection, often arising from competitive interactions. Although increasing evidence suggests that phenotypic plasticity can facilitate this process, it is not generally appreciated that competitively mediated selection often also provides ideal conditions for phenotypic plasticity to evolve in the first place. Here, we discuss how competition plays at least two key roles in adaptive diversification depending on its pattern. First, heterogenous competition initially generates heterogeneity in resource use that favors adaptive plasticity in the form of “inducible competitors”. Second, once such competitively induced plasticity evolves, its capacity to rapidly generate phenotypic variation and expose phenotypes to alternate selective regimes allows populations to respond readily to selection favoring diversification, as may occur when competition generates steady diversifying selection that permanently drives the evolutionary divergence of populations that use different resources. Thus, competition plays two important roles in adaptive diversification––one well-known and the other only now emerging––mediated through its effect on the evolution of phenotypic plasticity [Current Zoology 59 (4): 537–552, 2013].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".