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Record W3113416836 · doi:10.1111/eva.13188

Temporal instability of lake charr phenotypes: Synchronicity of growth rates and morphology linked to environmental variables?

2020· article· en· W3113416836 on OpenAlexaff
Louise Chavarie, Steven L. Voelker, Michael J. Hansen, Charles R. Bronte, Andrew M. Muir, Mara S. Zimmerman, Charles C. Krueger

Bibliographic record

VenueEvolutionary Applications · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British Columbia
FundersGreat Lakes Fishery Commission
KeywordsEcotypeBiologySalvelinusEcologyIntraspecific competitionPhenotypePhenotypic plasticitySympatric speciationPhenotypic traitEvolutionary biologyTroutGenetics

Abstract

fetched live from OpenAlex

Abstract Pathways through which phenotypic variation among individuals arise can be complex. One assumption often made in relation to intraspecific diversity is that the stability or predictability of the environment will interact with expression of the underlying phenotypic variation. To address biological complexity below the species level, we investigated variability across years in morphology and annual growth increments between and within two sympatric lake charr Salvelinus namaycush ecotypes in Rush Lake, USA. A rapid phenotypic shift in body and head shape was found within a decade. The magnitude and direction of the observed phenotypic change were consistent in both ecotypes, which suggests similar pathways caused the variation over time. Over the same time period, annual growth increments declined for both lake charr ecotypes and corresponded with a consistent phenotypic shift of each ecotype. Despite ecotype‐specific annual growth changes in response to winter conditions, the observed annual growth shift for both ecotypes was linked, to some degree, with variation in the environment. Particularly, a declining trend in regional cloud cover was associated with an increase of early‐stage (ages 1–3) annual growth for lake charr of Rush Lake. Underlying mechanisms causing changes in growth rates and constrained morphological modulation are not fully understood. An improved knowledge of the biology hidden within the expression of phenotypic variation promises to clarify our understanding of temporal morphological diversity and instability.

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 score0.020
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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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