Phenotypic Plasticity, Costs of Phenotypes, and Costs of Plasticity
Bibliographic record
Abstract
Why are some traits constitutive and others inducible? The term costs often appears in work addressing this issue but may be ambiguously defined. This review distinguishes two conceptually distinct types of costs: phenotypic costs and plasticity costs. Phenotypic costs are assessed from patterns of covariation, typically between a focal trait and a separate trait relevant to fitness. Plasticity costs, separable from phenotypic costs, are gauged by comparing the fitness of genotypes with equivalent phenotypes within two environments but differing in plasticity and fitness. Subtleties associated with both types of costs are illustrated by a body of work addressing predator-induced plasticity. Such subtleties, and potential interplay between the two types of costs, have also been addressed, often in studies involving genetic model organisms. In some instances, investigators have pinpointed the mechanistic basis of plasticity. In this vein, microbial work is especially illuminating and has three additional strengths. First, information about the machinery underlying plasticity--such as structural and regulatory genes, sensory proteins, and biochemical pathways--helps link population-level studies with underlying physiological and genetic mechanisms. Second, microbial studies involve many generations, large populations, and replication. Finally, empirical estimation of key parameters (e.g., mutation rates) is tractable. Together, these allow for rigorous investigation of gene interactions, drift, mutation, and selection--all potential factors influencing the maintenance or loss of inducible traits along with phenotypic and plasticity costs. Messages emerging from microbial work can guide future efforts to understand the evolution of plastic traits in diverse organisms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".