MétaCan
Menu
Back to cohort
Record W4404168164 · doi:10.2305/iucn.uk.2016-2

On the demographic history of chimpanzees and some consequences of integrating population structure in chimpanzees and other great apes

2024· preprint· en· W4404168164 on OpenAlexaff
Camille Steux, Clément Couloigner, Armando Arredondo, Willy Rodríguez, Olivier Mazet, Rémi Tournebize, Lounès Chikhi, Sanaga River

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsUniversité Laval
FundersMinistère de l'Enseignement Supérieur et de la RechercheRégion Occitanie Pyrénées-MéditerranéeAgence Nationale de la RechercheIndian National Science Academy
KeywordsPopulationPopulation structureGeographyEvolutionary biologyPsychologyBiologyDemographySociology

Abstract

fetched live from OpenAlex

Reconstructing the evolutionary history of great apes is of particular importance for our understanding of the demographic history of humans. The reason for this is that modern humans and their hominin ancestors evolved in Africa and thus shared the continent with the ancestors of chimpanzees and gorillas. Common chimpanzees (Pan troglodytes) are our closest relatives with bonobos (Pan paniscus) and most of what we know about their evolutionary history comes from genetic and genomic studies. Most evolutionary studies of common chimpanzees have assumed that the four currently recognised subspecies can be modelled using simple tree models where each subspecies is panmictic and represented by one branch of the evolutionary tree. However, several studies have identified the existence of significant population structure, both within and between subspecies, with evidence of isolation-by-distance (IBD) patterns. This suggests that demographic models integrating population structure may be necessary to improve 1 our understanding of their evolutionary history. Here we propose to use n-island models within each subspecies to infer a demographic history integrating population structure and changes in connectivity (i.e. gene flow). For each subspecies, we use SNIF (structured non-stationary inference framework), a method developed to infer a piecewise stationary n-island model using PSMC (pairwise sequentially Markovian coalescent) curves as summary statistics. We then propose a general model integrating the four subspecies metapopulations within a phylogenetic tree. We find that this model correctly predicts estimates of within subspecies genetic diversity and differentiation, but overestimates genetic differentiation between subspecies as a consequence of the tree structure. We argue that spatial models integrating gene flow between subspecies should improve the prediction of between subspecies differentiation and IBD patterns. We also use a simple spatially structured model for bonobos and chimpanzees (without admixture) and find that it explains signals of admixture between the two species that have been reported and could thus be spurious. This may have implications for our understanding of the evolutionary history of the Homo genus.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
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)0.0010.000

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 head0.034
GPT teacher head0.307
Teacher spread0.273 · 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
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

Citations18
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicPrimate Behavior and EcologyFrench-language works237,207