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Record W2051703014 · doi:10.1186/1471-2148-10-67

Surviving historical Patagonian landscapes and climate: molecular insights from Galaxias maculatus

2010· article· en· W2051703014 on OpenAlexafffund
Tyler Zemlak, Evelyn Habit, Sandra J. Walde, Cecilia Carrea, Daniel E. Ruzzante

Bibliographic record

VenueBMC Evolutionary Biology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsDalhousie University
FundersOffice of International Science and EngineeringNatural Sciences and Engineering Research Council of CanadaUniversidad Austral de ChileUniversidad de ConcepciónKillam TrustsConsejo Nacional de Investigaciones Científicas y TécnicasDalhousie UniversityBrigham Young UniversityNational Geographic SocietyNational Science Foundation
KeywordsBiologyAnalysis of molecular varianceEcologyPopulationPhylogeographymtDNA control regionLast Glacial MaximumBiological dispersalGenetic diversityMonophylyRefugium (fishkeeping)Mantel testDemographic historyGenetic structureGlacial periodPhylogeneticsPaleontologyHaplotypeCladeAlleleHabitatDemography

Abstract

fetched live from OpenAlex

BACKGROUND: The dynamic geological and climatic histories of temperate South America have played important roles in shaping the contemporary distributions and genetic diversity of endemic freshwater species. We use mitochondria and nuclear sequence variation to investigate the consequences of mountain barriers and Quaternary glacial cycles for patterns of genetic diversity in the diadromous fish Galaxias maculatus in Patagonia (approximately 300 individuals from 36 locations). RESULTS: Contemporary populations of G. maculatus, east and west of the Andes in Patagonia, represent a single monophyletic lineage comprising several well supported groups. Mantel tests using control region data revealed a strong positive relationship when geographic distance was modeled according to a scenario of marine dispersal. (r = 0.69, P = 0.055). By contrast, direct distance between regions was poorly correlated with genetic distance (r = -0.05, P = 0.463). Hierarchical AMOVAs using mtDNA revealed that pooling samples according to historical (pre-LGM) oceanic drainage (Pacific vs. Atlantic) explained approximately four times more variance than pooling them into present-day drainage (15.6% vs. 3.7%). Further post-hoc AMOVA tests revealed additional genetic structure between populations east and west of the Chilean Coastal Cordillera (coastal vs. interior). Overall female effective population size appears to have remained relatively constant until roughly 0.5 Ma when population size rapidly increased several orders of magnitude [100x (60x-190x)] to reach contemporary levels. Maximum likelihood analysis of nuclear alleles revealed a poorly supported gene tree which was paraphyletic with respect to mitochondrial-defined haplogroups. CONCLUSIONS: First diversifying in the central/north-west region of Patagonia, G. maculatus extended its range into Argentina via the southern coastal regions that join the Pacific and Atlantic oceans. More recent gene flow between northern populations involved the most ancient and most derived lineages, and was likely facilitated by drainage reversal(s) during one or more cooling events of the late Pleistocene. Overall female effective population size represents the end result of a widespread and several hundred-fold increase over approximately 0.5 Ma, spanning several climatic fluctuations of the Pleistocene. The minor influence of glacial cycles on the genetic structure and diversity of G. maculatus likely reflects the access to marine refugia during repeated bouts of global cooling. Evidence of genetic structure that was detected on a finer scale between lakes/rivers is most likely the result of both biological attributes (i.e., resident non-migratory behavior and/or landlocking and natal homing in diadromous populations), and the Coastal Cordillera as a dispersal barrier.

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 score0.418
Threshold uncertainty score0.595

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)0.0000.000

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 head0.007
GPT teacher head0.215
Teacher spread0.207 · 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

Citations80
Published2010
Admission routes2
Has abstractyes

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