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Record W2428738317 · doi:10.1139/cjfas-2015-0530

Sediment addition reduces the importance of predation on ecosystem functions in experimental stream channels

2016· article· en· W2428738317 on OpenAlexafffundvenue
Pauliina Louhi, John S. Richardson, Timo Muotka

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTrophic cascadePredationInvertebrateTrophic levelBenthic zoneRiparian zoneEcologyBiomass (ecology)EcosystemFood webAbiotic componentBiologyDetritivoreEnvironmental scienceHabitat

Abstract

fetched live from OpenAlex

Sedimentation is a pervasive cause of biological impairment in streams, and predation exerts strong control over lower trophic levels. However, studies combining these two factors are lacking. In a factorial experiment in flow-through channels, addition of sand (<0.5 mm) and predatory stoneflies (Perlidae) caused independent effects on benthic invertebrates, algal biomass, and leaf breakdown. Sand reduced invertebrate density by 55% and also reduced leaf breakdown and algal biomass. Predators reduced invertebrate densities by 40%, with the strongest impact on algal-feeding invertebrates. Predators also decreased densities of leaf-shredding invertebrates and reduced leaf breakdown, thereby inducing a trophic cascade via detritus-based food web. The two treatments exhibited an antagonistic interaction whereby sand obscured any effect of predators on algae, indicating that an abiotic stress may modify a trophic cascade. By contrast, we found no support for synergistic interactions between sand and predation. The strong effects of sedimentation on key ecosystem processes illustrate that stream management needs to exploit riparian and in-stream measures to reduce sediment inputs to headwater streams.

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 score0.358
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.212
Teacher spread0.197 · 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

Citations20
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicHydrology and Sediment Transport ProcessesFrench-language works237,207