Assessing differences in the response of forest aboveground biomass and composition under climate change in subtropical forest transition zone

Sci Total Environ. 2020 Mar 1:706:135746. doi: 10.1016/j.scitotenv.2019.135746. Epub 2019 Nov 25.

Abstract

The subtropical forest transition zone in southern China is a typical transition zone with high coverage and diverse vegetation. Projected climate change will affect physiological processes of trees, which would consequently alter the forest aboveground biomass (AGB) and composition at broad spatial scales. However, spatially heterogeneous responses may also be shaped by climate change, succession, and harvesting in different forest habitats. The objectives of this study were to assess the changes in subtropical forest AGB and composition in response to climate change, while comparing the responses of two similar forest landscapes: Taihe County (TH) and Longnan County (LN). We used a loose-coupling of PnET-II with LANDIS-II to simulate changes in forest AGB and composition under climate change scenarios (Current climate, RCP2.6, RCP4.5, RCP6.0, and RCP8.5) with harvest disturbances. Our simulation results demonstrated that forest AGB and composition were significantly affected by climate change in both landscapes. Changes in forest AGB was mostly driven by succession and harvest, but climate change also greatly contribute to the variation in AGB of deciduous broad-leaved forests (DBF), and coniferous forests (CF). Moreover, a larger area of LN experienced biomass reduction compared to TH, specifically under the RCP8.5 scenario. Given our estimates of the response in forest AGB and composition under climate change scenarios across different periods, we recommend that the regional forest management should be localized and should consider the effects of climate change through time in their planning schemes.

Keywords: China; Forest succession; LANDIS-II; Landscape modeling; Subtropical transition zone.

MeSH terms

  • Biomass
  • China
  • Climate Change*
  • Forests*
  • Trees