Conservation physiology of freshwater fishes: an illustration of pressing questions and implications for management
Bibliographic record
Abstract
Abstract Rivers, lakes, and wetlands are facing threats that continue to grow in intensity and frequency from climate change, habitat fragmentation, invasive species, changes in food availability, natural disasters, various forms of pollution (e.g., trace metals, light, noise), and emerging infectious diseases. These disruptions to freshwater environments are driving population declines in freshwater fishes as well as threatening migratory species that need freshwater habitats to complete their life cycle. To improve freshwater fish conservation efforts, it is essential to understand the magnitude and nature of the threats fish are currently facing. Here, we present a series of case studies that illustrate the utility of employing physiological methods to assess both the threats facing freshwater fishes, and the conservation efforts being used to help preserve freshwater biodiversity. We present an array of physiological tools that can be used across multiple levels of biological organization, from molecular to population-level, to address a variety of questions. Finally, we share what we view to be pressing questions in freshwater fish conservation physiology and highlight strategies to help bridge gaps across different user groups.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".