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Pelagic food web interactions among benthic invertebrates and trout in mountain lakes

2011· article· en· W1577304018 on OpenAlexafffundabout
R. Paul Weidman, David W. Schindler, Rolf D. Vinebrooke

Bibliographic record

VenueFreshwater Biology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of AlbertaParks Canada
KeywordsPelagic zoneFood webBenthic zoneBiologyPredationTroutTrophic levelEcologyGammarusInvertebrateChironomidaeTrophic cascadeFisheryAmphipodaLarvaCrustaceanFish <Actinopterygii>

Abstract

fetched live from OpenAlex

1. Benthic chironomid larvae and the amphipod Gammarus lacustris have been observed in the pelagic habitats of many mountain lakes. The main goal of this study was to determine if chironomid larvae and gammarids potentially affect predator–prey and nutrient dynamics in pelagic food webs of mountain lakes. 2. Eighty-six mountain lakes were surveyed in Alberta and eastern British Columbia during the years 1965–1984, 1991–2004 and 2005–2007. Pelagic chironomid larvae were found in 86% of these lakes, and pelagic gammarids were found in 29% of lakes. Densities of pelagic chironomid larvae were 92% lower in lakes with pelagic gammarids and 76% lower in lakes with trout (P < 0.05). Intraguild predation of trout on gammarids appeared to reduce predation pressure on chironomid larvae. Gammarids consumed in vitro about 1 chironomid per gammarid per day or about 20% of their body mass in chironomid biomass per day. 3. Concentrations of total dissolved P and N, particulate C, and chlorophyll-a increased with increasing densities of pelagic gammarids and chironomid larvae in situ (R2 = 0.14 ± 0.19 SD, P < 0.1) and in vitro (P < 0.001). 4. Our findings suggest that gammarids and chironomid larvae are linked as predators and prey in pelagic food webs, possibly stimulating phytoplankton abundance via nutrient release.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.001

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 head0.024
GPT teacher head0.227
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

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

Citations15
Published2011
Admission routes3
Has abstractyes

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