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Record W4410020614 · doi:10.1093/nsr/nwaf157

Contrasting evolutionary trajectories of terrestrial vertebrates in the Hengduan Mountains hotspot

2025· article· en· W4410020614 on OpenAlexaff
Chen-Qi Lu, Wenna Ding, Wei Xu, Quan Li, Shui-Wang He, Fei Wu, Wenjie Dong, Jie-Qiong Jin, Dong Feng, Xuelong Jiang, Kai Wang, Peng Guo, Robert W. Murphy, Ya‐Ping Zhang, Jing Che

Bibliographic record

VenueNational Science Review · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsRoyal Ontario Museum
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaChinese Academy of SciencesSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsHotspot (geology)Evolutionary biologyBiologyGeologyGeophysics

Abstract

fetched live from OpenAlex

ABSTRACT The Hengduan Mountains (HDM) harbor the richest temperate diversity in the Northern Hemisphere, yet our understanding of how this exceptionally diverse biota evolved remains obscure. Large-scale historical biogeographic analyses of 851 terrestrial vertebrate species and their relatives (totaling 4862 species) reveal that multiple evolutionary pathways formed this biodiversity hotspot. Whereas in situ speciation dominates in amphibians and non-avian reptiles, near-equal in situ speciation and colonization occurs in mammals, and colonization happens primarily in birds. HDM are a ‘cradle’ for neo-endemics and a ‘sink’ receiving surrounding biotas, mostly (>30%) coming from the Indo-Malay region. Orogenesis and monsoon intensification triggered in situ speciation initiated in the early Oligocene and peaking around 7–8 Ma. Analyses of different taxonomic groups reveal contrasting evolutionary processes and how major geo-climatic events override taxon-specific attributes. Results highlight the need to incorporate taxon-specific traits into future conservation planning to effectively address the unique needs and challenges of different groups.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.326
Teacher spread0.291 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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