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Record W2015361234 · doi:10.1139/f09-060

Ontogenetic niche shifts and resource partitioning of lake trout morphotypes

2009· article· en· W2015361234 on OpenAlexvenueaboutno aff
Mara S. Zimmerman, Stephanie N. Schmidt, Charles C. Krueger, M. Jake Vander Zanden, Randy L. Eshenroder

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTroutSalvelinusOntogenyPelagic zoneBiologyBenthic zoneEcologyNicheEcological nicheHabitatZoologyFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Resource polymorphisms are widely observed in fishes; however, ontogenetic contributions to morphological and ecological differences are poorly understood. This study examined whether ontogenetic changes in niche partitioning could explain morphological and buoyancy differences between lake trout ( Salvelinus namaycush ) morphotypes in Great Slave Lake (Northwest Territories, Canada). Morphometric analysis, buoyancy, capture depth, diet, and stable isotope data were used in concert to determine whether (i) differences occur in small, as well as large, lake trout, (ii) ontogenetic changes in morphology and buoyancy correlate with shifts in depth or diet, and (iii) a subset of small trout, putatively identified as “humpers”, are distinct from other morphotypes. Ontogenetic changes in lake trout morphology were associated with an ecological shift between benthic and pelagic feeding. Resource partitioning between lean and siscowet-like trout occurred within benthic (small trout) and pelagic (large trout) habitats. The humper subset did not differ from small siscowet-like trout. By combining multiple methods and an ontogenetic perspective, our study provides novel perspectives on resource polymorphisms in large, deep lakes and on existing interpretations of stable isotope data from large lakes in general.

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 score0.122
Threshold uncertainty score0.892

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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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

Citations92
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicIsotope Analysis in EcologyFrench-language works237,207