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Record W4401515059 · doi:10.1038/s42003-024-06503-z

Origin of the Laurentian Great Lakes fish fauna through upward adaptive radiation cascade prior to the Last Glacial Maximum

2024· article· en· W4401515059 on OpenAlexaff
Nathan J. C. Backenstose, Daniel J. MacGuigan, Christopher Osborne, Moisés A. Bernal, Elizabeth K. Thomas, Éric Normandeau, Daniel L. Yule, Wendylee Stott, Amanda S. Ackiss, Victor A. Albert, Louis Bernatchez, Trevor J. Krabbenhoft

Bibliographic record

VenueCommunications Biology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaUniversité Laval
FundersU.S. Geological SurveyUniversity at BuffaloGreat Lakes Fishery Commission
KeywordsFish <Actinopterygii>FaunaGlacial periodCascadeAdaptive radiationEcologyGeographyFisheryGeologyBiologyPaleontologyEngineering

Abstract

fetched live from OpenAlex

The evolutionary histories of adaptive radiations can be marked by dramatic demographic fluctuations. However, the demographic histories of ecologically-linked co-diversifying lineages remain understudied. The Laurentian Great Lakes provide a unique system of two such radiations that are dispersed across depth gradients with a predator-prey relationship. We show that the North American Coregonus species complex ("ciscoes") radiated rapidly prior to the Last Glacial Maximum (80-90 kya), a globally warm period, followed by rapid expansion in population size. Similar patterns of demographic expansion were observed in the predator species, Lake Charr (Salvelinus namaycush), following a brief time lag, which we hypothesize was driven by predator-prey dynamics. Diversification of prey into deep water created ecological opportunities for the predators, facilitating their demographic expansion, which is consistent with an upward adaptive radiation cascade. This study provides a new timeline and environmental context for the origin of the Laurentian Great Lakes fish fauna, and firmly establishes this system as drivers of ecological diversification and rapid speciation through cyclical glaciation.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.977

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.283
Teacher spread0.254 · 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 designNot applicable
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

Citations10
Published2024
Admission routes1
Has abstractyes

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