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Record W2080892890 · doi:10.1111/fwb.12284

Artificial steps mitigate the effect of fine sediment on the survival of brown trout embryos in a heavily modified river

2013· article· en· W2080892890 on OpenAlexaff
Christian Michel, Y. Schindler Wildhaber, Jannis Epting, Karen L. Thorpe, Peter Huggenberger, Christine Alewell, Patricia Burkhardt‐Holm

Bibliographic record

VenueFreshwater Biology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of Alberta
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsBrown troutTroutSedimentContext (archaeology)Environmental scienceEcologyEmbryoOxygenBiologyEnvironmental chemistryHydrology (agriculture)FisheryChemistryGeology

Abstract

fetched live from OpenAlex

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) STH is more sensitive to the timing and duration of low oxygen rather than a mean oxygen concentration, (ii) STH was negatively affected by increased fine sediment deposition decreasing redd gravel permeability, (iii) higher water levels, causing fine sediment resuspension, benefit STH , (iv) STH was 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 embryo STH , 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 −1 but were more sensitive to oxygen depletion close to hatch. Therefore, timing and duration of low oxygen were important for embryo STH , and hence, oxygen dynamics need to be considered when assessing in redd conditions for salmonid STH . 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 embryo STH . 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 in STH could 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 trout STH was 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 embryo STH , 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.013
GPT teacher head0.216
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2013
Admission routes1
Has abstractyes

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