Abstract ID: 3.9423 | Accepted as Talk | Talk/Oral | TBA | TBA

Woodlands in the Qinghai-Xizang Plateau contain China’s largest intact forest ecosystems and, as other high-elevation systems in the region, are threatened by climate change and human disturbance. Despite their importance for ecological conservation and sustainable management, few studies have explored the long-term trajectories of these Plateau forests, their main drivers and threats. To address this gap, we analyzed forest distribution area and age-group structure dynamics of three typical functional forest types (broadleaved, pine, and mixed spruce-fir forests) using 1973–2018 forest inventory data from over 80,000 ground-based sample plots and multi-temporal remote sensing (NDVI) data. We then evaluated the relative effects of climate change and human influence (mostly afforestation programs) on forest dynamics. Based on field surveys, an optimized Maximum Entropy model predicted the habitats suitable for the different forest groups under current and future climate scenarios. Our results showed a dual increase in both, forest area and plant biomass, over the past five decades, dominated by mature forest stands, while the proliferation in young and middle-aged stands improved age-group structural balance. After 1998, the spruce-fir forest area and biomass temporarily declined and broad-leaved and pine forests expanded. Remote sensing data analysis confirmed an overall increase in forest NDVI, but still 47.39 % of forest pixels showed browning trends. Climate change increasingly influenced forest growth, with the minimum temperature of the coldest month iden-tified as a key constraint on tree distribution. Climate projections showed suitable habitat expansion for all forest types, relative to the present, with broadleaved forests showing greater resistance to warming. However, by the late 21st century, suitable habitat areas decreased under both low and high-emission scenarios. Our data evidence the response of forests to combined impacts of climate change and distrubances, and emphasize the need to assist sustainable management programs of subalpine forests with solid scientific data under global climate change.