Physiological and behavioural strategies of aquatic animals living in fluctuating environments
Bibliographic record
Abstract
Shallow or near-shore environments, such as ponds, estuaries and intertidal zones, are among the most physiologically challenging of all aquatic settings. Animals inhabiting these environments experience conditions that fluctuate markedly over relatively short temporal and spatial scales. Living in these habitats requires the ability to tolerate the physiological disturbances incurred by these environmental fluctuations. This tolerance is achieved through a suite of physiological and behavioural responses that allow animals to maintain homeostasis, including the ability to dynamically modulate their physiology through reversible phenotypic plasticity. However, maintaining the plasticity to adjust to some stresses in a dynamic environment may trade off with the capacity to deal with other stressors. This paper will explore studies on select fishes and invertebrates exposed to fluctuations in dissolved oxygen, salinity and pH. We assess the physiological mechanisms these species employ to achieve homeostasis, with a focus on the plasticity of their responses, and consider the resulting physiological trade-offs in function. Finally, we discuss additional factors that may influence organismal responses to fluctuating environments, such as the presence of multiple stressors, including parasites. We echo recent calls from experimental biologists to consider physiological responses to life in naturally fluctuating environments, not only because they are interesting in their own right but also because they can reveal mechanisms that may be crucial for living with increasing environmental instability as a consequence of climate change.
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) | 0.001 | 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 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".