The complex interaction of ecology and hydrology in a small catchment: a salmon's perspective
Bibliographic record
Abstract
Abstract For the past 22 years, we have monitored hydro‐meteorological conditions and fish population dynamics in Catamaran Brook, a 52 km2 catchment in the Miramichi River system of New Brunswick, Canada. Given the long‐term nature of the multidisciplinary dataset, we are able to provide an overview of the complex interaction between streamflow and fish population dynamics drawing on previously published material as well as new data analyses. For autumn‐spawning fishes like the Atlantic salmon, access to headwater reaches was directly related to streamflow during late October (when they ascend spawning tributaries), as well as the propensity of beaver dams in the stream (also a function of streamflow). Winter streamflow was positively correlated with egg survival, except when rain‐on‐snow conditions induced severe ice break‐up events that likely caused the highest mortalities on record for salmon and other fishes. Juvenile recruitment was significantly influenced by density‐dependent processes of growth and competition but further mediated by density‐independent factors like winter flow. In spring, fry emergence was largely temperature‐driven, although peaks in fry drift were sometimes synchronized with secondary discharge peaks and temperature. Tributaries like Catamaran Brook provide thermal refugia for coolwater fishes like salmon and trout during the summer when wide, shallow main‐stem Atlantic rivers experience low discharge and high water temperatures that induce physiological and behavioural stress (i.e. > 23 °C). These phenomena are discussed in detail, especially in terms of how they may be compromised by future changes in hydrologic conditions resulting from predicted climate change scenarios. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".