Plasticity of Performance Curves in Ectotherms: Individual Variation Modulates Population Responses to Environmental Change
Bibliographic record
Abstract
Many ectothermic animals can respond to changes in their environment by altering the sensitivities of physiological rates, given sufficient time to do so. In other words, thermal acclimation and developmental plasticity can shift thermal performance curves so that performance may be completely or partially buffered against the effects of environmental temperature changes. Plastic responses can thereby increase the resilience to temperature change. However, there may be pronounced differences between individuals in their capacity for plasticity, and these differences are not necessarily reflected in population means. In a bet-hedging strategy, only a subsection of the population may persist under environmental conditions that favour either plasticity or fixed phenotypes. Thus, experimental approaches that measure means across individuals can not necessarily predict population responses to temperature change. Here, we collated published data of 608 mosquitofish ( Gambusia holbrooki ) each acclimated twice, to a cool and a warm temperature in random order, to model how diversity in individual capacity for plasticity can affect populations under different temperature regimes. The persistence of both plastic and fixed phenotypes indicates that on average, neither phenotype is selectively more advantageous. Fish with low acclimation capacity had greater maximal swimming performance in warm conditions, but their performance decreased to a greater extent with decreasing temperature in variable environments. In contrast, the performance of fish with high acclimation capacity decreased to a lesser extent with a decrease in temperature. Hence, even though fish with low acclimation capacity had greater maximal performance, high acclimation capacity may be advantageous when ecologically relevant behaviour requires submaximal locomotor performance. Trade-offs, developmental effects and the advantages of plastic phenotypes together are likely to explain the observed population variation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".