MétaCan
Menu
Back to cohort
Record W4387259467 · doi:10.3390/d15101059

Genetic and Ecological Approaches to Introduced Populations of Pumpkinseed Sunfish (Lepomis gibbosus) in Southwestern Europe

2023· article· en· W4387259467 on OpenAlexaboutno aff
Ángela Lambea-Camblor, Felipe Morcillo, Jesús Muñoz, Anabel Perdices

Bibliographic record

VenueDiversity · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
Fundersnot available
KeywordsLepomisBiologyEcologyIntroduced speciesPopulationNicheInvasive speciesPredationDemography

Abstract

fetched live from OpenAlex

Freshwater systems are among the most affected by the introduction of exotic species. The pumpkinseed sunfish Lepomis gibbosus, a centrarchid native to eastern North America, is listed among the top ten introduced freshwater fishes with the greatest ecological impact globally. Despite this, genetic and evolutionary studies of the species are still scarce. Here, we analyzed the genetic variability of introduced populations of L. gibbosus using three mitochondrial genes (COI, d-loop, and ND1). In addition, we used species distribution modeling to compare the niche of introduced versus native populations to assess the present and potential future distribution of the species under different climate change scenarios. Compared with the native populations, introduced ones present a lower level of genetic variability, indicating these populations originated from a small number of individuals from the native (Atlantic) population in the USA and Canada. The low variability was likely driven by a founder effect and subsequent bottleneck, as often occurs in invasive species. Our modeling results suggest not only that L. gibbosus modified its niche during the invasion process in Europe but also the possible global expansion of the species under future climatic conditions, which could facilitate its establishment in almost all continents.

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 score0.006
Threshold uncertainty score0.373

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 science0.0000.001
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 head0.090
GPT teacher head0.238
Teacher spread0.148 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueDiversitySame topicGenetic diversity and population structureFrench-language works237,207