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Record W7056756347

Evolutionary history of Early-Middle and Late Pleistocene equids, revealed by analysis of their paleogenomes

2020· other· en· W7056756347 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2020
Typeother
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsBeringiaAncient DNAPleistocenePopulationMitochondrial DNABiological dispersalMost recent common ancestorDemographic history
DOInot available

Abstract

fetched live from OpenAlex

DNA from archaeological, paleontological, and museum samples (ancient, or aDNA) provides a unique opportunity to trace eco-evolutionary history of populations affected by environmental shifts on geological time scale. Yet it is still unclear how climate-driven environmental change and biogeographical barriers affect diversification, population size and population structure of large-bodied herbivores inhabiting northern regions of the Northern Hemisphere. The goal of this dissertation is to fill in this gap by utilizing ancient DNA techniques and population genetic analysis to reveal the demographic and population history of extinct and present-day equids, genus Equus, focusing on their key ancient dispersal corridor - the Bering Land Bridge. In the following chapters, I explore the links between paleoenvironments and population history of various equid groups using high coverage paleogenomes recovered from fossil horse specimens sampled across Beringia. In my first chapter, I use in-solution DNA capture enrichment and mitochondrial genome assembly to reconstruct a whole mitochondrial genome of a specimen found in Western Beringia and initially identified as E. hemionus, or an Asiatic wild ass. With molecular phylogenetic analysis I demonstrate that the specimen belongs to a group of caballoid horses, E. ferus, rather than stenonid wild asses. The results obtained in Chapter 1 highlight the utility of ancient DNA studies in identification of incomplete, juvenile, or otherwise problematic museum specimens. In my second chapter I discover that Beringia was a key contact zone for populations of Late Pleistocene caballoid horses, E. ferus. I use new high coverage nuclear and mitochondrial paleogenomes, isolated from fossils of caballoid horses sampled across the Northern Hemisphere to infer that North American and Eurasian caballoid horse populations diverged around 0.8-1 million years ago. With coalescent simulations and genome-wide adamixtude inference I show that evolution of caballoid horses after this divergence continued in the presence of cross-continental gene flow. My demographic inference suggests that disappearance of the Bering Land Bridge likely exacerbated an already ongoing extinction of Beringian caballoid horse populations. In the third chapter, I recover the ~700,000 year old paleogenome of a previously unknown stenonid horse species inhabiting Klondike, Canada’s Yukon Territory - the oldest non-caballoid equid genome known to date. Using genotype likelihood approach on a dataset of present-day and ancient equid nuclear genomes, I show that the population of the newly discovered stenonid equid species was evolutionary close to the present-day zebras and Asiatic wild asses. I suggest that the new to genetics species likely represents an extinct branch of archaic stenonid ungulates that coexisted with “true”, or caballoid equids in the Early-Middle Pleistocene Yukon. In the fourth chapter I expand my study system to another iconic Bringian megafauna species - steppe bison, Bison priscus. Using molecular phylogeny reconstructed from new high coverage mitochondrial genomes, I explore the phylogenetic diversity of steppe bison in Western Beringia. I confirm the existence of the deeply divergent steppe bison clade and shed new light on the evolutionary history of bison during the Pleistocene to Holocene transition in ancient Siberia.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.018
GPT teacher head0.183
Teacher spread0.165 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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