Warming, stochastic diel thermal fluctuations affect physiological performance and gill plasticity in an amphibious mangrove fish
Bibliographic record
Abstract
Natural temperature variation in many marine ecosystems is stochastic and unpredictable, and climate change models indicate that this thermal irregularity is likely to increase. Temperature acclimation may be more challenging when conditions are highly variable and stochastic, and there is a need for empirical physiological data in these thermal environments. Using the hermaphroditic, amphibious mangrove rivulus (Kryptolebias marmoratus), we hypothesized that compared with regular, warming diel thermal fluctuations, stochastic warm fluctuations would negatively affect physiological performance. To test this, we acclimated fish to: (1) non-stochastic and (2) stochastic thermal fluctuations with a similar thermal load (27-35°C), and (3) a stable/consistent control temperature at the low end of the cycle (27°C). We determined that fecundity was reduced in both cycles, with reproduction ceasing in stochastic thermal environments. Fish acclimated to non-stochastic thermal cycles had growth rates lower than those of control fish. Exposure to warm, fluctuating cycles did not affect emersion temperature, and only regular diel cycles modestly increased critical thermal tolerance. We predicted that warm diel cycling temperatures would increase gill surface area. Notably, fish acclimated to either thermal cycle had a reduced gill surface area and increased intralamellar cell mass when compared with control fish. This decreased gill surface area with warming contrasts with what is observed for exclusively aquatic fish and suggests a preparatory gill response for emersion in these amphibious fish. Collectively, our data reveal the importance of considering stochastic thermal variability when studying the effects of temperature on fishes.
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 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 | 0.000 | 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) | 0.001 | 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".