Integrated landscape genomics reveals biogeography and climate-driven local adaptation in high-altitudes
Wang, N.; Ghimire, P.; Chhetri, P.; Ghimire, N.; Yi, C.; Zhang, T.; Zhuoga, S.; Jiangyong, Z.; Lamichhaney, S.
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High-elevation environments harbor unique species adapted to altitudinal and environmental extremes. However, due to logistical challenges of sampling such species throughout their distribution across steep and varied terrains, studies often lack comprehensive evidence to understand patterns of population divergence, local adaptation, and how current adaptations may influence future vulnerability. In this study, we utilized Tibetan Partridge (Perdix hodgsoniae), a high-altitude endemic bird found between 2800-5000 meters across the arid western and humid northeastern regions of the Sino-Himalayan landscape. This regions complex topography-characterized by tall mountains, deep valleys, and contrasting climatic conditions provided opportunities for investigating population divergence, local adaptation, and climate-related vulnerability. We integrated population-scale whole-genome sequencing with ecological, climatic, landscape, and morphological data to examine current patterns of local adaptation and forecast future risks. Our findings show that both biogeographic barriers and climatic variation drive rapid population divergence in P. hodgsoniae, reflected in distinct morphological traits and population genetic structure. Western populations, inhabiting dry and fragmented landscapes, exhibit adaptations to temperature extremes with low genetic diversity, reduced habitat suitability, limited gene flow, and weak connectivity--factors that increase their vulnerability to future environmental changes. In contrast, northeastern populations, living in more humid regions, show genetic adaptations linked to precipitation, maintain high genetic diversity and habitat connectivity, and may serve as evolutionary refugia under future climate scenarios. This study underscores the value of integrating genomic, ecological, and landscape data to reveal mechanisms of divergence and adaptation, and to develop robust predictions for conservation planning under rapidly changing environmental conditions.
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