Relating extremes of flow and air temperature to stream fish communities
Bibliographic record
Abstract
ABSTRACT The disruptive potential of floods, drought, ice, and high water temperature on fishes in streams have been well documented. We examined the relationship between unexpected high flow events, low flow events coupled with low and high air temperature events, and a variety of ecological measures frequently used to quantify fish communities in streams, e.g. density. We developed a severity index to quantify concurrent extreme disturbances occurring over annual and summer periods. We anticipated that years or summers of high severity would result in changes in the fish community measures, e.g. low abundance. Despite the occurrence of severe events, there were relatively few instances of environmental severity resulting in consistent negative/positive changes in fish communities. Of the fish community measures, young‐of‐the‐year (YOY) growth was most responsive to extremes. Low flow in combination with high temperature events significantly lowered YOY growth; whereas, unexpected high flows increased growth. Unexpected high flow events were associated with a significant negative effect on fish abundance and positive effects on biomass, YOY growth, and diversity during the summer period. The predictive power of abiotic–biotic regressions from the summer time period was generally greater than that from the annual time period. We suggest that high amounts of groundwater flow into the streams may buffer the impact of extreme environmental conditions. Our methodology of measuring extremes in flow and air temperature could be implemented over much larger scales for use in long‐term monitoring impacts related to climate and land use change. Copyright © Her Majesty the Queen in Right of Canada 2012
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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 | 0.000 |
| 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 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".