A review of the empirical evidence for costs of plasticity in ectothermic animals
Bibliographic record
Abstract
Phenotypic plasticity can represent a vital adaptive response to environmental stressors, including those associated with climate change. Despite its evolutionary advantages, the expression of plasticity varies significantly within and among species, and is likely to be influenced by local environmental conditions. This variability in plasticity has important implications for evolutionary biology and conservation physiology. Theoretical models suggest that plasticity might incur intrinsic fitness costs, although the empirical evidence is inconsistent and there is ambiguity in the term 'cost of plasticity'. Here, we systematically review the literature to investigate the prevalence of costs associated with phenotypic plasticity in ectothermic animals. We categorized studies into those assessing 'costs of phenotype' (trade-offs between different plastic trait values) and 'costs of plasticity' (intrinsic costs of the capacity for plasticity). Importantly, the experimental designs required to detect costs of plasticity are inherently more complex and onerous than those required to detect costs of phenotype. Accordingly, our findings reveal a significant focus on costs of phenotype over costs of plasticity, with the former more frequently detecting costs. Contrary to theoretical expectations, our analysis suggests that costs of plasticity are neither universal nor widespread. This raises questions about the evolutionary dynamics of plasticity, particularly in stable environments. Our analysis underscores the need for precise terminology and methodology in researching costs of plasticity, to avoid conflating costs associated with plastic traits with costs more intrinsic to plasticity. Understanding these nuances is crucial for predicting how species might adapt to rapidly changing environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".