Acidification can directly affect olfaction in marine organisms
Bibliographic record
Abstract
In the past decade, many studies have investigated the effects of low pH/high CO2 as a proxy for ocean acidification on olfactory-mediated behaviours of marine organisms. The effects of ocean acidification on the behaviour of fish vary from very large to none at all, and most of the maladaptive behaviours observed have been attributed to changes in acid-base regulation, leading to changes in ion distribution over neural membranes, and consequently affecting the functioning of gamma-aminobutyric acid-mediated (GABAergic) neurotransmission. Here, we highlight a possible additional mechanism by which ocean acidification might directly affect olfaction in marine fish and invertebrates. We propose that a decrease in pH can directly affect the protonation, and thereby, 3D conformation and charge distribution of odorants and/or their receptors in the olfactory organs of aquatic animals. This can sometimes enhance signalling, but most of the time the affinity of odorants for their receptors is reduced in high CO2/low pH; therefore, the activity of olfactory receptor neurons decreases as measured using electrophysiology. The reduced signal reception would translate into reduced activation of the olfactory bulb neurons, which are responsible for processing olfactory information in the brain. Over longer exposures of days to weeks, changes in gene expression in the olfactory receptors and olfactory bulb neurons cause these neurons to become less active, exacerbating the problem. A change in olfactory system functioning leads to inappropriate behavioural responses to odorants. We discuss gaps in the literature and suggest some changes to experimental design in order to improve our understanding of the underlying mechanisms and their effects on the associated behaviours to resolve some current controversy in the field regarding the extent of the effects of ocean acidification on marine fish.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".