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Record W2912532866 · doi:10.1111/ele.13225

Ionome and elemental transport kinetics shaped by parallel evolution in threespine stickleback

2019· letter· en· W2912532866 on OpenAlexafffund
Seth M. Rudman, Jared M. Goos, Joseph B. Burant, Kevin V. Brix, Taylor C. Gibbons, Colin J. Brauner, Punidan D. Jeyasingh

Bibliographic record

VenueEcology Letters · 2019
Typeletter
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Guelph
FundersDivision of Integrative Organismal SystemsUniversity of British ColumbiaCollege of Arts and Sciences, Oklahoma State UniversityOklahoma State UniversityNational Science Foundation
KeywordsSticklebackAdaptation (eye)EcologyBiologyEvolutionary ecologyParallel evolutionEcological nicheEcological speciationEvolutionary biologyPhylogeneticsGenetic variationFish <Actinopterygii>HabitatGeneGene flow

Abstract

fetched live from OpenAlex

Evidence that organisms evolve rapidly enough to alter ecological dynamics necessitates investigation of the reciprocal links between ecology and evolution. Data that link genotype to phenotype to ecology are needed to understand both the process and ecological consequences of rapid evolution. Here, we quantified the suite of elements in individuals (i.e., ionome) and differences in the fluxes of key nutrients across populations of threespine stickleback. We find that allelic variation associated with freshwater adaptation that controls bony plating is associated with changes in the ionome and nutrient recycling. More broadly, we find that adaptation of marine stickleback to freshwater conditions shifts the ionomes of natural populations and populations raised in common gardens. In both cases ionomic divergence between populations was primarily driven by differences in trace elements rather than elements typically associated with bone. These findings demonstrate the utility of ecological stoichiometry and the importance of ionome-wide data in understanding eco-evolutionary dynamics.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.421
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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
GenreCommentary

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

Citations23
Published2019
Admission routes2
Has abstractyes

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