Localized environmental heterogeneity drives the population differentiation of two endangered and endemic Opisthopappus Shih species
Bibliographic record
Abstract
BACKGROUND: Climate heterogeneity not only indirectly shapes the genetic structures of plant populations, but also drives adaptive divergence by impacting demographic dynamics. The variable localized climates and topographic complexity of the Taihang Mountains make them a major natural boundary in Northern China that influences the divergence of organisms distributed across this region. Opisthopappus is an endemic genus of the Taihang Mountains that includes only two spatially partitioned species Opisthopappus longilobus and Opisthopappus taihangensis. For this study, the mechanisms behind the genetic variations in Opisthopappus populations were investigated. RESULTS: Using SNP and InDel data coupled with geographic and climatic information, significant genetic differentiation was found to exist either between Opisthopappus populations or two species. All studied populations were divided into two genetic groups with the differentiation of haplotypes between the groups. At approximately 17.44 Ma of the early Miocene, O. taihangensis differentiated from O. longilobus under differing precipitation regimes due to the intensification of the Asian monsoon. Subsequently, intraspecific divergence might be induced by the dramatic climatic transformation from the mid- to late Miocene. During the Pleistocene period, the rapid uplift of the Taihang Mountains coupled with violent climatic oscillations would further promote the diversity of the two species. Following the development of the Taihang Mountains, its complex topography created geographical and ecological heterogeneity, which could lead to spatiotemporal isolation between the Opisthopappus populations. Thus the adaptive divergence might occur within these intraspecific populations in the localized heterogeneous environment of the Taihang Mountains. CONCLUSIONS: The localized environmental events through the integration of small-scale spatial effects impacted the demographic history and differentiation mechanism of Opisthopappus species in the Taihang Mountains. The results provide useful information for us to understand the ecology and evolution of organisms in the mountainous environment from population and species perspective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".