Linking Landscape-Scale Disturbances to Stress and Condition of Fish: Implications for Restoration and Conservation
Bibliographic record
Abstract
Humans have dramatically altered landscapes as a result of urban and agricultural development, which has led to decreases in the quality and quantity of habitats for animals. This is particularly the case for freshwater fish that reside in fluvial systems, given that changes to adjacent lands have direct impacts on the structure and function of watersheds. Because choices of habitat have physiological consequences for organisms, animals that occupy sub-optimal habitats may experience increased expenditure of energy or homeostatic overload that can cause negative outcomes for individuals and populations. With the imperiled and threatened status of many freshwater fish, there is a critical need to define relationships between land use, quality of the habitat, and physiological performance for resident fish as an aid to restoration and management. Here, we synthesize existing literature to relate variation in land use at the scale of watersheds to the physiological status of resident fish. This examination revealed that landscape-level disturbances can influence a host of physiological properties of resident fishes, ranging from cellular and genomic levels to the hormonal and whole-animal levels. More importantly, these physiological responses have been integrated into traditional field-based monitoring protocols to provide a mechanistic understanding of how organisms interact with their environment, and to enhance restoration. We also generated a conceptual model that provides a basis for relating landscape-level changes to physiological responses in fish. We conclude that physiological sampling of resident fish has the potential to assess the effects of landscape-scale disturbances on freshwater fish and to enhance restoration and conservation.
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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) | 0.001 | 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) | 0.000 | 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".