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Subtropical forest productivity is significantly affected by both natural disturbances (local and regional climate changes) and anthropogenic activities (harvesting and planting). Monthly measures of forest aboveground productivity from natural forests (primary and secondary forests) and plantations (mixed and single-species forests) were developed to explore the sensitivity of subtropical mountain productivity to the fluctuating characteristics of climate change in South China, spanning the 35-year period from 1981 to 2015. Statistical analysis showed that climate regulation differed across different forest types. The monthly average maximum temperature, precipitation, and streamflow were positively correlated with primary and mixed-forest aboveground net primary productivity (ANPP) and its components: Wood productivity (WP) and canopy productivity (CP). However, the monthly average maximum temperature, precipitation, and streamflow were negatively correlated with secondary and single-species forest ANPP and its components. The number of dry days and minimum temperature were positively associated with secondary and single-species forest productivity, but inversely associated with primary and mixed forest productivity. The multivariate ENSO (EI Niño-Southern Oscillation) index (MEI), computed based on sea level pressure, surface temperature, surface air temperature, and cloudiness over the tropical Pacific Ocean, was significantly correlated with local monthly maximum and minimum temperatures (Tmax and Tmin), precipitation (PRE), streamflow (FLO), and the number of dry days (DD), as well as the monthly means of primary and mixed forest aboveground productivity. In particular, the mean maximum temperature increased by 2.5, 0.9, 6.5, and 0.9 °C, and the total forest aboveground productivity decreased by an average of 5.7%, 3.0%, 2.4%, and 7.8% in response to the increased extreme high temperatures and drought events during the 1986/1988, 1997/1998, 2006/2007, and 2009/2010 EI Niño periods, respectively. Subsequently, the total aboveground productivity values increased by an average of 1.1%, 3.0%, 0.3%, and 8.6% because of lagged effects after the wet La Niña periods. The main conclusions of this study demonstrated that the influence of local and regional climatic fluctuations on subtropical forest productivity significantly differed across different forests, and community position and plant diversity differences among different forest types may prevent the uniform response of subtropical mountain aboveground productivity to regional climate anomalies. Therefore, these findings may be useful for forecasting climate-induced variation in forest aboveground productivity as well as for selecting tree species for planting in reforestation practices.
Subtropical forest productivity is significantly affected by both natural disturbances (local and regional climate changes) and anthropogenic activities (harvesting and planting). Monthly measures of forest aboveground productivity from natural forests (primary and secondary forests) and plantations (mixed and single-species forests) were developed to explore the sensitivity of subtropical mountain productivity to the fluctuating characteristics of climate change in South China, spanning the 35-year period from 1981 to 2015. Statistical analysis showed that climate regulation differed across different forest types. The monthly average maximum temperature, precipitation, and streamflow were positively correlated with primary and mixed-forest aboveground net primary productivity (ANPP) and its components: Wood productivity (WP) and canopy productivity (CP). However, the monthly average maximum temperature, precipitation, and streamflow were negatively correlated with secondary and single-species forest ANPP and its components. The number of dry days and minimum temperature were positively associated with secondary and single-species forest productivity, but inversely associated with primary and mixed forest productivity. The multivariate ENSO (EI Niño-Southern Oscillation) index (MEI), computed based on sea level pressure, surface temperature, surface air temperature, and cloudiness over the tropical Pacific Ocean, was significantly correlated with local monthly maximum and minimum temperatures (Tmax and Tmin), precipitation (PRE), streamflow (FLO), and the number of dry days (DD), as well as the monthly means of primary and mixed forest aboveground productivity. In particular, the mean maximum temperature increased by 2.5, 0.9, 6.5, and 0.9 °C, and the total forest aboveground productivity decreased by an average of 5.7%, 3.0%, 2.4%, and 7.8% in response to the increased extreme high temperatures and drought events during the 1986/1988, 1997/1998, 2006/2007, and 2009/2010 EI Niño periods, respectively. Subsequently, the total aboveground productivity values increased by an average of 1.1%, 3.0%, 0.3%, and 8.6% because of lagged effects after the wet La Niña periods. The main conclusions of this study demonstrated that the influence of local and regional climatic fluctuations on subtropical forest productivity significantly differed across different forests, and community position and plant diversity differences among different forest types may prevent the uniform response of subtropical mountain aboveground productivity to regional climate anomalies. Therefore, these findings may be useful for forecasting climate-induced variation in forest aboveground productivity as well as for selecting tree species for planting in reforestation practices.
Forest restoration is being scaled-up globally to deliver critical ecosystem services and biodiversity benefits, yet we lack rigorous comparison of co-benefit delivery across different restoration approaches. In a global synthesis, we use 25,950 matched data pairs from 264 studies in 53 countries to assess how delivery of climate, soil, water, and wood production services as well as biodiversity compares across a range of tree plantations and native forests. Carbon storage, water provisioning, and especially soil erosion control and biodiversity benefits are all delivered better by native forests, with compositionally simpler, younger plantations in drier regions performing particularly poorly. However, plantations exhibit an advantage in wood production. These results underscore important trade-offs among environmental and production goals that policymakers must navigate in meeting forest restoration commitments.
The e-commerce industry has brought convenience, fundamentally realizing a mode of life in which purchases can be made without leaving home. Civil and commercial law is an effective guarantee to ensure that the rights and interests of buyers and sellers are not infringed so that the whole transaction process of buying and selling has clear rights and responsibilities and improves the e-commerce market. The article uses a discrete regression model to innovate the development path of e-commerce and commercial and civil law through four stages: analysis of influencing factors of e-commerce development under civil, and commercial law, hierarchical clustering analysis of e-commerce data, prediction of rights and interests of buyers and sellers in the e-commerce industry, and generation of development path integrated into civil, commercial law. An apparel department store enterprise is used as the research object to test the practical effect of the innovative development path. The adjusted goodness-of-fit coefficient of the civil and commercial law factors is 0.932, and the Pearson correlation coefficient is 0.954, which has the greatest influence on this e-commerce enterprise. The cumulative turnover in the recent year was as high as $11210142, and the realized revenue was $2045277. The turnover growth rate in the second quarter reached 251 percentage points. This is sufficient to show that the innovative research on the development path can innovate e-commerce by integrating civil and commercial law and make the e-commerce industry develop in a better direction.
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