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.
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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 | 0.000 | 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".