Time Effects of Global Change on Forest Productivity in China from 2001 to 2017

Abstract

With global warming, the concentrations of fine particulate matter (PM2.5) and greenhouse gases, such as CO2, are increasing. However, it is still unknown whether these increases will affect vegetation productivity. Exploring the impacts of global warming on net primary productivity (NPP) will help us understand how ecosystem function responds to climate change in China. Using the Carnegie-Ames-Stanford Approach (CASA) ecosystem model based on remote sensing, we investigated the spatiotemporal changes in NPP across 1137 sites in China from 2001 to 2017. Our results revealed that: (1) Mean Annual Temperature (MAT) and Mean Annual Precipitation (MAP) were significantly positively correlated with NPP (p < 0.01), while PM2.5 concentration and CO2 emissions were significantly negatively correlated with NPP (p < 0.01). (2) The positive correlation between temperature, rainfall and NPP gradually weakened over time, while the negative correlation between PM2.5 concentration, CO2 emissions and NPP gradually strengthened over time. (3) High levels of PM2.5 concentration and CO2 emissions had negative effects on NPP, while high levels of MAT and MAP had positive effects on NPP.

Elevation Influences Belowground Biomass Proportion in Forests by Affecting Climatic Factors, Soil Nutrients and Key Leaf Traits

“…To examine the contribution of both abiotic and biotic factors to the spatial variation in BGBP and to account for the impact of random effects on biomass allocation, we utilized a linear mixed-effects model. The goodness of fit for these models was evaluated using the marginal R 2 value [ 44 ]. Climatic factors included the Mean Annual Temperature (MAT), Mean Annual Precipitation (MAP), Mean Hottest Monthly Temperature (MAHT), Mean Coldest Monthly Temperature (MCMT), and Annual Sunshine Duration (ASD).…”

The impact of vegetation phenology changes on the relationship between climate and net primary productivity in Yunnan, China, under global warming

“…For example, higher temperatures can stimulate plant metabolism and increase photosynthetic rates and NPP ( Piao et al., 2019 ; He et al., 2022 ). Wang et al. (2023) found that NPP in Chinese forests showed a significant positive correlation with both mean annual temperature and mean annual precipitation, but this positive correlation gradually weakened over time.…”

“…For example, higher temperatures can stimulate plant metabolism and increase photosynthetic rates and NPP (Piao et al, 2019;He et al, 2022). Wang et al (2023) found that NPP in Chinese forests showed a significant positive correlation with both mean annual temperature and mean annual precipitation, but this positive correlation gradually weakened over time. In addition, all plant phenological changes are almost highly correlated with temperature changes, especially in the months before seasonal life cycle events (Peñuelas and Filella, 2001).…”

Plant Morphology and Function, Geometric Morphometrics, and Modelling: Decoding the Mathematical Secrets of Plants

“…Functional plant traits play an essential role in exploring vegetation productivity and mitigating air pollution. Urban green spaces composed of various functional trait plants help to reduce air pollution levels [14] and serve as an important factor affecting urban ecosystem productivity [15,16].…”