The Sanjiang Plain, the largest inland freshwater marshland in China, was extensive reclaimed into agricultural land. To assess the effects of marshland reclamation on Collembola, we investigated collembolan communities in a chronosequence of soybean plantations (2, 15, and 25 years) in Sanjiang marshland, Northeastern China. We found that: 1) the densities and species richness of Collembola were promoted after short-term (2 years) cultivation of soybean, but significantly decreased after medium-term cultivation (15 years); 2) the densities of epi-edaphic Collembola increased while the densities of hemi-edaphic Collembola decreased as the elongation of soybean cultivation; 3) compared with S0, two species of Collembola appeared while five species disappeared in S25. The changes of plant communities and the soil traits were supposed to be the key factors affecting the composition of soil Collembola. We thus suggest that original marshland should be saved for preserving high diversity and densities of Collembola in the Sanjiang Plain.
Russian Journal of Ecology - The present study characterized the nutrients availability of three rare earth tailings deserted in different time stages in Southern Jiangxi of China, and revealed the... 相似文献
An improved energy demand forecasting model is built based on the autoregressive distributed lag (ARDL) bounds testing approach and an adaptive genetic algorithm (AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization, and technological progress as the input variables. On the basis of historical annual data from 1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively. 相似文献