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Record W2129211542 · doi:10.1139/cjfas-57-4-725

Within- and among-population variation in the trophic position of a pelagic predator, lake trout (<i>Salvelinus namaycush</i>)

2000· article· en· W2129211542 on OpenAlexvenueaboutno aff
M. Jake Vander Zanden, Brian J. Shuter, Nigel P. Lester, Joseph B. Rasmussen

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTrophic levelSalvelinusTroutPelagic zoneForage fishPopulationBiologyEcologyPredationApex predatorIsotope analysisTrophic state indexTrophic cascadeFisheryFood webFish <Actinopterygii>PhytoplanktonNutrient

Abstract

fetched live from OpenAlex

Many aquatic consumers have flexible feeding habits, and the diet and trophic position of a species can be expected to vary both within and among populations. In this study, we quantify the importance of both within- and among-population trophic variation for lake trout (Salvelinus namaycush) using stable isotope designations of trophic position from 13 Ontario and Quebec lakes. Lake-to-lake differences explained 78% of the total variation in lake trout trophic position. Analysis using both stable isotopes and published dietary data demonstrated that the trophic position of lake trout failed to increase appreciably as a function of animal body size. This finding was attributed to weak pred - ator size - prey size relationships as well as to there being no relationship between prey fish trophic position and body size. The variance in trophic position of a population reflects the extent to which individuals forage as trophic special - ists; however, we did not identify any one factor that was correlated with within-population trophic variation. Our find - ing that much of the total variation in trophic position represents among-population differences indicates that

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)0.001
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 score0.302
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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

Citations137
Published2000
Admission routes2
Has abstractyes

Explore more

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