Responses of Forest Net Primary Productivity to Climatic Factors in China during 1982-2015

Climate change and Land Use/Land Cover Change (LUCC) leading to spatial shifts in net primary productivity in Anhui Province, China

As an important part of terrestrial carbon cycle research, net primary productivity is an important parameter to evaluate the quality of terrestrial ecosystem and plays an important role in the analysis of global climate change and carbon balance. Anhui Province is in the Yangtze River Delta region in eastern China. Based on the theoretical basis of CASA model, this paper uses MODIS NDVI, vegetation type data, meteorological data, and LUCC to estimate the NPP of Anhui Province during 2001-2020 and analyzes its spatial-temporal pattern. The results showed that the average NPP in Anhui province was 508.95 gC· (m2 ·a) -1, and the spatial heterogeneity of NPP was strong, and the high value areas were mainly distributed in the Jiangnan Mountains and Dabie Mountains. NPP increased in most areas of Anhui Province, but decreased significantly in 17.60% of the area, mainly in the central area affected by urban and rural expansion and the transformation of the Yangtze River. The dynamic change of NPP in Anhui province is the result of climate change and land use change. Meteorological data are positively correlated with NPP. Among them, the correlation between temperature and solar radiation is higher, and the correlation between NPP and precipitation is the lowest among the three. The NPP of all land cover types was more affected by temperature than precipitation, especially forest land and grassland. The decrease of cultivated land and the increase of Artificial Surfaces (AS) may have contributed to the decrease of NPP in Anhui Province. Human activities have weakened the increase in NPP caused by climate change. In conclusion, this study refined the drivers of spatial heterogeneity of NPP changes in Anhui province, which is conducive to rational planning of terrestrial ecosystems and carbon balance measures.

Climate Factors Influence Above- and Belowground Biomass Allocations in Alpine Meadows and Desert Steppes through Alterations in Soil Nutrient Availability

Biomass is a direct reflection of community productivity, and the allocation of aboveground and belowground biomass is a survival strategy formed by the long-term adaptation of plants to environmental changes. However, under global changes, the patterns of aboveground-belowground biomass allocations and their controlling factors in different types of grasslands are still unclear. Based on the biomass data of 182 grasslands, including 17 alpine meadows (AMs) and 21 desert steppes (DSs), this study investigates the spatial distribution of the belowground biomass allocation proportion (BGBP) in different types of grasslands and their main controlling factors. The research results show that the BGBP of AMs is significantly higher than that of DSs (p < 0.05). The BGBP of AMs significantly decreases with increasing mean annual temperature (MAT) and mean annual precipitation (MAP) (p < 0.05), while it significantly increases with increasing soil nitrogen content (N), soil phosphorus content (P), and soil pH (p < 0.05). The BGBP of DSs significantly decreases with increasing MAP (p < 0.05), while it significantly increases with increasing soil phosphorus content (P) and soil pH (p < 0.05). The random forest model indicates that soil pH is the most important factor affecting the BGBP of both AMs and DSs. Climate-related factors were identified as key drivers shaping the spatial distribution patterns of BGBP by exerting an influence on soil nutrient availability. Climate and soil factors exert influences not only on grassland biomass allocation directly, but also indirectly by impacting the availability of soil nutrients.

Interannual Variations in Terrestrial Net Ecosystem Productivity and Climate Attribution in the Southern Hilly Region of China

The vegetation ecosystem in the southern hilly region of China (SHRC) plays a crucial role in the country's carbon reservoir. Clarifying the dynamics of net primary productivity (NPP) in this area and its response to climate factors in the context of climate change is important for national forest ecology, management, and carbon neutrality efforts. This study, based on remote sensing and meteorological data spanning the period 2001 to 2021, aims to unveil the spatiotemporal patterns of vegetation productivity and climate factors in the southern hilly region, explore interannual variation characteristics of vegetation productivity with altitude, and investigate the response characteristics of NPP to various climate factors. The results indicate that from 2001 to 2021, the annual average NPP in the southern hilly region had a significant increasing trend of 2.13 ± 0.78 g m-2 a-1. The trend of NPP varies significantly with altitude. Despite a general substantial upward trend in vegetation NPP, regions at lower elevations exhibit a faster rate of increase, suggesting a diminishing difference in the NPP of different elevation ranges. The overall rise in average temperature has positive implications for the southern hilly region, while the impact of precipitation on vegetation NPP demonstrates noticeable spatial heterogeneity. Regions in which vegetation NPP is significantly negatively correlated with precipitation are mainly concentrated in the southern areas of Guangdong, Fujian, and Jiangxi provinces. In contrast, other regions further away from the southeastern coast tend to exhibit a positive correlation. Over the past two decades, there has been an asymmetry in the diurnal temperature variation in the SHRC, with the nighttime warming rate being 1.8 times that of the daytime warming rate. The positive impact of daytime warming on NPP of vegetation is more pronounced than the impact of nighttime temperature changes. Understanding the spatiotemporal patterns of NPP in the SHRC and the characteristics of its response to climate factors contributes to enhancing our ability to protect and manage vegetation resources amidst the challenges of global climate change.

The impact of climate change and human activities on the change in the net primary productivity of vegetation-taking Sichuan Province as an example

Vegetation is an essential component of terrestrial ecosystems, influenced by climate change and human activities. Quantifying the relative contributions of climate change and human activities to vegetation dynamics is crucial for addressing global climate change. Sichuan Province is one of the essential ecological functional areas in the upper reaches of the Yangtze River, and its vegetation change is of great significance to the environmental function and ecological security of the Yangtze River Basin and southwest China. In this paper, the modified Carnegie-Ames-Stanford Approach(CASA) model was used to estimate the monthly NPP (Net Primary Productivity) of vegetation in Sichuan Province from 2000 to 2018, and the univariate linear regression analysis was used to analyze the temporal and spatial variation of vegetation NPP in Sichuan Province from 2000 to 2018. In addition, taking vegetation NPP as an index, Pearson correlation analysis, partial correlation analysis, and second-order partial correlation analysis were carried out to quantitatively analyze the contribution of climate change and human activities to vegetation NPP. Finally, the Hurst index and nonparametric Man-Kendall significance test were used to predict the future change trend of vegetation NPP in Sichuan Province. The results show that (1) from 2000 to 2018, the NPP of vegetation in Sichuan Province has a significant increasing trend (Slope = 6.09gC·m-2·a-1), with a multi-year average of 438.72 gC·m-2·a-1, showing a trend of low in the east and high in the middle. The response of vegetation NPP to altitude is different at different elevations; (2) the contribution rates of climate change and human activities to vegetation NPP change are 4.12gC·m-2·a-1 and 1.97gC·m-2·a-1, respectively. In contrast, the impact of human activities on NPP is more significant than climate change. Human activities are the main factors affecting vegetation restoration and degradation in Sichuan Province. However, the positive contribution to NPP change is less than climate change; (3) the future vegetation NPP change trend in Sichuan Province is mainly rising, and the same direction change trend is much larger than the reverse change trend. The areas with an increasing trend in the future account for 89.187% of the total area. This research helps understand the impact of climate change and human activities on vegetation change in Sichuan Province. It offers scientific bases for vegetation restoration and ecosystem management in Sichuan and the surrounding areas.

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

Functional plant traits include a plant's phenotypic morphology, nutrient element characteristics, and physiological and biochemical features, reflecting the survival strategies of plants in response to environmental changes [...].

The Seasonal Divergence in the Weakening Relationship between Interannual Temperature Changes and Northern Boreal Vegetation Activity

The response of boreal vegetation to global warming has shown a weakening trend over the last three decades. However, in previous studies, models of vegetation activity responses to temperature change have often only considered changes in the mean daily temperature (T_mean), with the diurnal temperature range (DTR) being neglected. The goal of this study was to evaluate the temporal trends of the relationships between two temperature factors (T_mean and DTR) and the vegetation activity across the boreal regions on both annual and seasonal timescales, by simultaneously employing satellite and climate datasets. We found that the interannual partial correlation between the growing season (GS) NDVI and T_mean (R_NDVI-Tmean) has shown a significant decreasing trend over the last 34 years. At the seasonal scale, the R_NDVI-Tmean showed a significant upward trend in the spring, while in the summer and autumn, the R_NDVI-Tmean exhibited a significant downward trend. The temporal trend characteristics of the partial correlation between the NDVI and DTR (R_NDVI-DTR), at both the GS and seasonal scales, were fully consistent with the R_NDVI-Tmean. The area with a significant decrease in the GS R_NDVI-Tmean and R_NDVI-DTR accounted for approximately 44.4% and 41.2% of the boreal region with the 17-year moving window, respectively. In stark contrast, the area exhibiting a significant increasing trend in the GS R_NDVI-Tmean and R_NDVI-DTR accounted for only approximately 22.3% and 25.8% of the boreal region with the 17-year moving window, respectively. With respect to the seasonal patterns of the R_NDVI-Tmean and R_NDVI-DTR, the area with a significant upward trend in the spring was greater than that with a significant downward trend. Nevertheless, more areas had a significant downward trend in the R_NDVI-Tmean and R_NDVI-DTR in summer and autumn than a significant upward trend. Overall, our research reveals a weakening trend in the impact of temperature on the vegetation activity in the boreal regions and contributes to a deeper understanding of the vegetation response to global warming.

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

With global warming, the concentrations of fine particulate matter (PM_2.5) 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 PM_2.5 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 PM_2.5 concentration, CO₂ emissions and NPP gradually strengthened over time. (3) High levels of PM_2.5 concentration and CO₂ emissions had negative effects on NPP, while high levels of MAT and MAP had positive effects on NPP.

Spatiotemporal dynamics of net primary productivity and its influencing factors in the middle reaches of the Yellow River from 2000 to 2020

Introduction

Net primary productivity (NPP) is an important indicator used to characterize the productivity of terrestrial ecosystems. The spatial distribution and dynamic change in NPP are closely related to regional climate, vegetation growth and human activities. Studying the spatiotemporal dynamics of NPP and its influencing factors plays a vital role in understanding ecosystem carbon sink capacity.

Methods

Based on MODIS-NPP data, meteorological data, and land use data from 2000 to 2020, we analyzed the spatiotemporal variation characteristics and influencing factors of NPP in the middle reaches of the Yellow River (MRYR) by using unary linear regression analysis, third-order partial correlation analysis, and Sen+Mann-Kendall trend analysis.

Results

The results showed that the annual average NPP of the MRYR was 319.24 gCm-2a-1 with a spatially decreasing trend from the southern part to the northern part. From 2000 to 2020, the annual average NPP experienced a fluctuating upward trend at a rate of 2.83 gCm-2a-1, and the area with a significant upward trend accounted for 87.68%. The NPP of different land use types differed greatly, in which forest had the greatest increase in NPP. Temperature had a negative correlation with NPP in most parts of the MRYR. Water vapor pressure promoted the accumulation of NPP in the northwestern MRYR. The areas with a positive correlation between NPP and water vapor pressure accounted for 87.6%, and 20.43% of the MRYR area passed the significance test of P< 0.05.

Conclusion

The results of the study highlight the impact of climate factors and land-use changes on NPP and provide theoretical guidance for high-quality sustainable development in the MRYR.