Chemical niches and ionoregulatory traits: applying ionoregulatory physiology to the conservation management of freshwater fishes
Bibliographic record
Abstract
Alterations in water chemistry can challenge resident fish species. More specifically, chemical changes that disrupt ion balance will negatively affect fish health and impact physiological and ecological performance. However, our understanding of which species and populations are at risk from ionoregulatory disturbances in response to changing freshwater environments is currently unclear. Therefore, we propose a novel framework for incorporating ionoregulatory physiology into conservation management of inland fishes. This framework introduces the concepts of fundamental chemical niche, which is the tolerable range of chemical conditions for a given species based on laboratory experiments, and realized chemical niche, which is the range of chemical conditions in which a species resides based on distribution surveys. By comparing these two niches, populations that may be at risk from ionoregulatory disturbances and thus require additional conservation considerations can be identified. We highlight the potential for commonly measured ionoregulatory traits to predict fundamental and realized chemical niches but caution that some traits may not serve as accurate predictors despite being important for understanding ionoregulatory mechanisms. As a sample application of our framework, the minimum pH distribution (realized niche) and survival limit pH (fundamental niche) of several North American fishes were determined by systematic review and were compared. We demonstrate that ionoregulatory capacity is significantly correlated with a realized niche for many species, highlighting the influence of ionoregulatory physiology on fish distribution patterns along chemical gradients. Our aim is that this framework will stimulate further research in this field and result in a broader integration of physiological data into conservation management decisions for inland waters.
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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 | 0.001 |
| 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".