Abstract
Climate warming is expected to influence forest growth, composition and distribution. However, accurately estimating and predicting forest biomass, potential productivity or forest growth is still a challenge for forest managers dealing with land-use at the stand to regional levels. In the present study, we predicted the potential productivity (PP) of forest under current and future climate scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) in Jilin province, northeastern China by using Paterson’s Climate Vegetation and Productivity (CVP) index model. The PP was validated by comparing it with the mean and maximum net primary production calculated from light energy utilization (GLM_PEM). Our results indicated that using the CVP index model is partially valid for predicting the potential forest productivity in northeastern China. PP exhibited obvious spatial heterogeneity varying from 4.6 to 8.9 m3 ha−1 year−1 with an increasing tendency from northwest to southeast driven by the precipitation across the region. The number of vegetation-active months, precipitation and insolation coefficient were identified as the primary factors affecting PP, but no significant relationship was found for warmest temperature or temperature fluctuation. Under future climate scenarios, PP across the Jilin Province is expected to increase from 1.38% (RCP2.6 in 2050) to 15.30% (RCP8.5 in 2070), especially in the eastern Songnen Plain (SE) for the RCP8.5 scenarios.
Highlights
Because forests are so important as carbon (C) sinks for mitigating climate change and provide other essential ecosystem services (Chapin et al 2008; Coomes et al 2014), climate warming is expected to directly influence forest structure and functions, such as productivity, by altering abiotic conditions (Morin et al 2018; Correia et al 2018; Wang et al 2019)
potential productivity (PP) increased with growing season length and precipitation, first increasing and decreasing with the insolation coefficient and warmest temperature, but no significant relationship was found for temperature fluctuation
Our result indicated that the PP was positively correlated with precipitation, which is consistent with the result of He et al (2015) who found that the net primary production (NPP) of a Larix olgensis forest in northeastern China was significantly correlated with annual precipitation
Summary
Because forests are so important as carbon (C) sinks for mitigating climate change and provide other essential ecosystem services (Chapin et al 2008; Coomes et al 2014), climate warming is expected to directly influence forest structure and functions, such as productivity, by altering abiotic conditions (e.g., temperature, precipitation and atmospheric CO2 concentration) (Morin et al 2018; Correia et al 2018; Wang et al 2019). The site index is linked to a specific species and forest structure (Monserud and Sterba 1996; Bravo and Montero 2003; Benavides et al 2009)
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