The relations between ‘standard’ fluvial habitat variables and turbulent flow at multiple scales in morphological units of a gravel‐bed river
Bibliographic record
Abstract
Abstract Fluvial fish habitat is often characterized by highly turbulent flow conditions. Several laboratory experiments suggest that unpredictable turbulent fluctuations can increase the swimming energy costs of fish. At the scale of fish habitat models, it can be hypothesized that turbulence can be captured by the combined effects of the standard habitat variables: depth, velocity and substrate. However, recent studies conducted at the reach scale suggest that turbulent properties are more controlled by the large‐scale bed morphology than by individual roughness elements. In this study, we investigate the spatial structure of turbulent flow and the potential relationships between ‘standard’ habitat variables and turbulent flow properties in pools and riffles of a shallow gravel‐bed river. The study explores these relations at multiple spatial scales. Mean turbulent properties and turbulent flow structures statistics were computed from 1932 near bed velocity time series sampled with acoustic Doppler velocimeters on a regular grid in four morphological units (two pools and two riffles) presenting a gradient of complexity. We used a novel multivariate variation partitioning analysis involving principal coordinates of neighbour matrices (PCNM) to partition turbulent flow properties into six significant spatial scales (VF: 0.35, F: 0.75, M: 1.25, L: 2, XL: 2.5 and XXL: 3 m). Between 45 and 70% of the variance of the turbulent flow properties were explained by the spatial PCNM. In the four units, turbulent properties exhibited a spatial dependence across the entire range of scales. However, the proportion of variation explained by the larger‐scaled PCNMs was higher in the most homogeneous units. In general, the spatial dependence of turbulent flow was lower in the riffles than in the pools, where the mean flow velocity was slower. The capacity of ‘standard’ fish habitat variables to explain turbulent properties was relatively low, especially in the smaller scales, but varied greatly between the units. From a practical point of view, this level of complexity suggests that turbulence should be considered as a ‘distinct’ ecological variable within the range of spatial scales included in this study. Further research should attempt to link the spatial scales of turbulent flow variability to benthic organism patchiness and fish habitat use. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".