Artificial steps mitigate the effect of fine sediment on the survival of brown trout embryos in a heavily modified river
Bibliographic record
Abstract
Summary Understanding the factors that determine successful salmonid embryo incubation in the many structurally modified river systems of the Northern Hemisphere is crucial for maintaining healthy salmonid populations. In this context, the joint impact of fine sediment accumulation together with anthropogenic river modifications on salmonid embryo survival has been rarely investigated. We investigated brown trout embryo survival to hatch (STH) together with ten physicochemical, hydraulic and morphological parameters in artificial brown trout redds in a heavily modified stream (i.e. channelized, artificial log steps) in central Switzerland. We were interested to understand whether (i)STHis more sensitive to the timing and duration of low oxygen rather than a mean oxygen concentration, (ii)STHwas negatively affected by increased fine sediment deposition decreasing redd gravel permeability, (iii) higher water levels, causing fine sediment resuspension, benefitSTH, (iv)STHwas negatively affected by organic content in the redds and especially (v) hydraulic gradients related to redd scale bed‐form and/or the artificial step structure benefit embryoSTH, and hence could mitigate the negative impact of fine sediment and/or organic content. Up to 50%, brown trout embryos survived with interstitial oxygen exceeding 3 mg L−1. Embryos endured up to 6 days ≤ 1 mg L−1but were more sensitive to oxygen depletion close to hatch. Therefore, timing and duration of low oxygen were important for embryoSTH, and hence, oxygen dynamics need to be considered when assessing in redd conditions for salmonidSTH. Partial least squares regression identified the horizontal hydraulic gradient, Fredle index, distance to artificial log steps upstream and amount of accumulated fine sediment as influential predictors for embryoSTH. The water level above the redd and total organic carbon content in the redd were not influential. Among the identified influential predictors, 70.9% of the variation inSTHcould be explained by a logistic regression model containing redd distance to the next upstream step (26.4%,P = 0.004), Fredle index (27.2%,P = 0.003) and horizontal hydraulic gradient (10.1%,P = 0.04). In the logistic regression, the amount of accumulated fine sediment (P = 0.75), field seasons (P = 0.93) and field sites (P = 0.66) was non‐significant. In summary, brown troutSTHwas sensitive to redd gravel permeability, which was measured as Fredle index and affected by fine sediment deposition. At the same time, hydraulic gradients related to artificial log steps, which enhanced hyporheic exchange, benefited embryoSTH, and hence mitigated fine sediment impact. This result can be probably transferred to other surface water‐dominated river systems with good hyporheic water quality. To what extent it can be transferred to river systems with other hydraulic boundary conditions remains to be evaluated. Altogether, our results clearly indicate that the impact of fine sediment on salmonid incubation success needs to be understood in the hydrological and morphological context of the particular river system.
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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 | 0.000 | 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 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".