Integrated framework for dynamic conservation of bamboo forest in giant panda habitat under climate change.

ABSTRACT

Climate change presents formidable challenges to forest biodiversity and carbon storage. Bamboo forests will be affected particularly in Southwest China's mountainous regions. Bamboo serves as not only a key food resource and habitat for giant panda Ailuropoda melanoleuca but also a potential carbon sink due to its rapid energy-to-matter conversion capability. We employ the MaxEnt model to project the distribution shifts of 20 giant panda foraged bamboo species in Sichuan Province under future climate scenarios, utilizing climate data of 30m resolution. Based on the changes in the diversity and distribution area of bamboo communities caused by climate change, the changing of giant pandas' food resources and the carbon storage of bamboo forests were calculated. The results indicated that the area of bamboo communities is projected to expand by 17.94%-60.88% more than now by the end of the 21st century. We analyzed the energy balance between the dietary needs of giant pandas and the energy provided by bamboo. We predicted that bamboo communities from 2000 to 2150 could support the continuous growth of the giant panda population (6533 wild individuals by 2140-2150 in an ideal state in Sichuan province). However, the species diversity and carbon storage of bamboo forests face out-of-sync fluctuations, both temporally and spatially. This is a critical issue for subalpine forest ecosystem management under climate change. Therefore, we propose a dynamic conservation management framework for giant panda habitats across spatial and temporal scales. This framework aims to facilitate the adaptation of subalpine forest ecosystems to climate change. This innovative approach, which integrates climate change into the conservation strategy for endangered species, contributes a conservation perspective to global climate action, highlighting the interconnectedness of biodiversity preservation and climate mitigation.