Comparative Physiology and Relative Swimming Performance of Three Redhorse (<i>Moxostoma</i>spp.) Species: Associations with Fishway Passage Success
Bibliographic record
Abstract
Our understanding of biological criteria to inform fish passage design is limited, partially due to the lack of understanding of biological motivators, cues, and constraints, as well as a lack of biological performance evaluations of structures once they are built. The Vianney-Legendre vertical slot fishway on the Richelieu River, Quebec, Canada, passes large numbers of migrating redhorse (Moxostoma spp.) upriver to spawning grounds each year. We evaluated the physiological capacity and relative swimming ability of three redhorse species (Moxostoma anisurum, Moxostoma carinatum, Moxostoma macrolepidotum; silver, river, and shorthead redhorse, respectively) to determine how these biotic factors relate to variation in fishway passage success and duration. Shorthead redhorse had higher maximum metabolic rates and were faster swimmers than silver and river redhorse at their species-specific peak migration temperatures. Blood lactate and glucose concentrations recovered more quickly for river redhorse than for silver and shorthead redhorse, and river redhorse placed second in terms of metabolic recovery and swim speed. Interestingly, fish sampled from the top of the fishway had nearly identical lactate, glucose, and pH values compared to control fish. Using passive integrated transponders in 2010 and 2012, we observed that passage success and duration were highly variable among redhorse species and were not consistent among years, suggesting that other factors such as water temperature and river flows may modulate passage success. Clearly, additional research is needed to understand how organismal performance, environmental conditions, and other factors (including abundance of conspecifics and other comigrants) interact with fishway features to dictate which fish will be successful and to inform research of future fishways. Our research suggests that there may be an opportunity for a rapid assessment approach where fish chased to exhaustion to determine maximal values of physiological disturbance are compared to fish sampled from the top of the fishway, which could reveal which species (or sizes of fish) are approaching or exceeding their physiological capacity during passage.
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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".