A research team led by Prof. HOU Xingliang from the South China Botanical Garden of the Chinese Academy of Sciences has employed Genome-wide Association Studies (GWAS) and quantitative trait loci (QTL) mapping to pinpoint a Nuclear Factor-YA (NF-YA) gene located on chromosome 14, which they named Seed Weight 14 (SW14).
Researchers from Xishuangbanna Tropical Botanical Garden of the Chinese Academy of Sciences and the collaborators developed a new framework for transboundary conservation in the biodiversity-rich Gaoligong Mountains. This framework emphasizes integrating biodiversity hotspots, ecological gradients, and ecosystem services to create an effective conservation plan that transcends national boundaries.
A team led by Dr. SHEN Guozhen from the Institute of Botany, together with domestic and international collaborators, has revealed a "hidden extinction crisis" in China's flora over the past four decades. The study integrates satellite-derived land-cover data (1980-2018) with species-composition models to quantify, for the first time at a national scale, how habitat loss reshapes extinction risk for entire plant communities.
Researchers from Xishuangbanna Tropical Botanical Garden of the Chinese Academy of Sciences and their collaborators demonstrated that the original calibration for the widely adopted thermal-dissipation method often significantly underestimates water flow, particularly in fast-transporting species like lianas and ring-porous trees, leading to inaccurate environmental data.
An international research team has documented evidence of early barley harvesting and consumption in a pre-pottery Neolithic culture in Central Asia, a finding that challenges long-held narratives about the origins of agricultural practices beyond West Asia's Fertile Crescent and fills a critical gap in understanding global agricultural dispersal.
A research team led by Prof. JIANG Ni from the Institute of Genetics and Developmental Biology proposed a cost-effective method for in-field acquisition of flag leaf angle images and developed a lightweight deep learning model for accurate flag leaf angle estimation.
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