Assessment of the variation and influencing factors of vegetation NPP and carbon sink capacity under different natural conditions

Wei, Xindong; Yang, Jie; Luo, Pingping; Lin, Liangguo; Lin, Kangli; Guan, Jiamin

doi:10.1016/j.ecolind.2022.108834

Cited by 133 publications

(48 citation statements)

References 37 publications

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“…In the current study, the square average annual temperature (MAT) and square annual precipitation (MAP) were the main factors affecting community productivity (Fig. S1, 2), and similar results were also obtained in previous studies (Ji et al 2020;Wei et al 2022). As the temperature increased, the productivity of the community first decreased and then increased, showing a binomial decreasing trend (Fig.…”

Section: Discussionsupporting

confidence: 88%

Forest productivity is determined by climate and stand properties rather than species diversity

Li¹,

Wang²,

Liu³

et al. 2022

Preprint

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A positive diversity–productivity relationship has been demonstrated in planned experiments, but the evidence from field studies of natural forests is contradictory. We studied aboveground forest productivity in 28 subtropical montane forests on Mt. Fanjingshan and assessed the relationship between productivity and tree diversity, as well as climatic, edaphic, topography, and stand structural factors likely to influence productivity. The main determinants of aboveground net primary productivity (NPP) were climate factors and stand properties. They explained 35% of the variance in NPP. The percentage of the variance in NPP explained by stand properties, climate, soil properties, topography, and species richness were 55%, 30%, 7%, 5%, and 3%, respectively. Topography had no direct effect on production, but they had a significant indirect effect. Elevation had an indirect effect on production via tree height, and slope steepness had a significant indirect effect through climate factors. However, tree diversity had nonsignificant direct and indirect effects on productivity. We concluded that the productivity of highly diverse montane forests is primarily controlled by climate factors and stand structural properties rather than species diversity.

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Section: Discussionsupporting

confidence: 88%

Forest productivity is determined by climate and stand properties rather than species diversity

Li¹,

Wang²,

Liu³

et al. 2022

Preprint

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“…The resolution was 1 km. The China Land Use Remote Sensing Monitoring Database (https://resdc.cn/data.aspx?DATAID=97) is currently the most accurate remote sensing monitoring data product of land use in China (Wei et al 2022), with an overall accuracy of more than 90%. It has played an important role in national land resource survey, hydrological and ecological research.…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

The change of ecosystem resilience and its response to economic factors in Yulin, China

Wang

et al. 2023

Environ. Res. Commun.

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Ecosystem resilience at a regional scale is an important indicator and component of ecological health assessment. Ecosystem resilience refers to the self-regulating and restoring capacity of an ecosystem to restore itself to its initial state after deviation from the equilibrium state due to disturbances. In this study, Fragstats and ArcGIS software were used to calculate various indicators of ecosystem resilience, then a model of ecosystem resilience was used to evaluate and analyze the characteristics of ecosystem resilience in 12 counties of Yulin Prefecture, China. The ecosystem resilience–economy responses were discussed using Pearson correlation coefficients and multiple regression analysis accordingly. The results suggested that ecosystem resilience in Yulin increased steadily during 1995–2015, with the self-regulating and self-restoring capacity strengthened over time. The study also found that the total output value of agriculture, forestry, animal husbandry, and fishery, fiscal expenditure and gross investment in fixed assets were significantly and positively correlated with ecosystem resilience, with correlation coefficients of 0.716 (p = 0.000 < 0.01), 0.589 (p = 0.002 < 0.01) and 0.411 (p = 0.046 < 0.05), respectively. The proportion of primary industry were negatively correlated with ecosystem resilience, with correlation coefficients of -0.445 (p = 0.029 < 0.05). The research revealed the development and changes of ecological resilience in Yulin and the response to the social economy. The findings provided support for ecological health diagnosis and decision-making concerning sustainable development in the study area and beyond.

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“…PM 2.5 changes the net photosynthetic rate by affecting plant photosynthesis, thereby affecting ecosystem NPP [ 15 , 16 , 17 , 18 ]. NPP is an essential variable to characterize plant activities and a crucial factor in determining ecosystem carbon sinks and regulating ecological processes [ 19 ]. Estimating vegetation NPP on a large spatial and temporal scale using multisource remote sensing data and comprehensive spatial pattern and dynamic analysis with GIS technology has become an important method to quantify NPP [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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

Gao

2023

Plants

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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.

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Assessment of the variation and influencing factors of vegetation NPP and carbon sink capacity under different natural conditions

Cited by 133 publications

References 37 publications

Forest productivity is determined by climate and stand properties rather than species diversity

Forest productivity is determined by climate and stand properties rather than species diversity

The change of ecosystem resilience and its response to economic factors in Yulin, China

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

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