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Record W2031295581 · doi:10.1139/f05-025

Flexible diet and trophic position of dreissenid mussels as inferred from stable isotopes of carbon and nitrogen

2005· article· en· W2031295581 on OpenAlexvenueno aff
David W. Garton, Christopher D. Payne, Joseph P. Montoya

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
FundersOhio State University
KeywordsDreissenaSestonTrophic levelZooplanktonMusselFood webStable isotope ratioDetritusEcologyBiologyIsotope analysisPhytoplanktonEnvironmental scienceFisheryBivalviaMolluscaNutrient

Abstract

fetched live from OpenAlex

In this study, the trophic position and food-web impacts of invading zebra mussels (Dreissena polymorpha) were investigated by sampling mussels, seston (= phytoplankton), macrophytes, zooplankton, and surficial sediment in two small inland lakes (1999–2002) and similarly in western Lake Erie (1999 only). Tissues from quagga mussels (Dreissena bugensis) from Lake Erie were also analyzed. Stable-isotope ratios ( 15 N/ 14 N and 13 C/ 12 C) were used to identify likely food sources and estimate relative trophic position. For Lake Erie, stable-isotope ratios indicated no diet differences between the two mussel species. For all lakes, zooplankton δ 13 C was indicative of phytoplanktivory. The 13 C stable isotope ratios indicated that seston comprised ~50% of food sources for mussels in Lake Erie, but 73%–97% and 52%–100% of the diet of mussel populations in Lake Wawasee and Clark Lake, respectively. Stable nitrogen isotope ratios placed zooplankton at trophic levels equal to or higher than those of mussels in seven of eight comparisons. Dreissena polymorpha and D. bugensis are able to exploit suspended detritus as a significant energy source, as well as compete directly with zooplankton for seston as a food source and with each other in areas of sympatry.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.992

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)0.0010.000

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 head0.013
GPT teacher head0.213
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations41
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicAquatic Invertebrate Ecology and BehaviorFrench-language works237,207