Urbanization and eco-environment coupling is a research hotspot.Dynamic simulation of urbanization and eco-environment coupling needs to be improved because the processes of coupling are complex and statistical methods are limited.Systems science and cross-scale coupling allow us to define the coupled urbanization and eco-environment system as an open complex giant system with multiple feedback loops.We review the current state of dynamic simulation of urbanization and eco-environment coupling and find that:(1)The use of dynamic simulation is an increasing trend,the relevant theory is being developed,and modeling processes are being improved;(2)Dynamic simulation technology has become diversified,refined,intelligent and integrated;(3)Simulation is mainly performed for three aspects of the coupling,multiple regions and multiple elements,local coupling and telecoupling,and regional synergy.However,we also found some shortcomings:(1)Basic theories are inadequately developed and insufficiently integrated;(2)The methods of unifying systems and sharing data are behind the times;(3)Coupling relations and the dynamic characteristics of the main driving elements are not fully understood or completely identified.Additionally,simulation of telecoupling does not quantify parameters and is not systemically unified,and therefore cannot be used to represent spatial synergy.In the future,we must promote communication between research networks,technology integration and data sharing to identify the processes governing change in coupled relations and in the main driving elements in urban agglomerations.Finally,we must build decision support systems to plan and ensure regional sustainable urbanization. 相似文献
Shell-boring species Polydora brevipalpa Zachs, 1933 is redescribed based on morphological observations and molecular approach for future unambiguous identification. Genetic distance analyses showed that the interspecific polydorid variation(16.7%–25.6%) was at least 15 times higher than the intraspecific one(0.2%–0.9%) based on the cytochrome c oxidase subunit I(CO1) gene sequences of polydorids. However, 18 S rDNA variation pattern demonstrated a rather narrow barcoding gap, with the interspecific polydorid variation(0.5%–5.6%) being very close to the intraspecific one(0.0%–0.4%). As such, the CO1 gene exhibited better DNA barcode for identification of polydorids than the 18 S rDNA gene because of the su ciently large barcoding gaps. Analysis of molecular variance results based on CO1 gene sequences showed that most variations in sequences(97.79%) lay within groups of adult worms and egg capsules rather than between them. This indicated that egg capsules from Crassostrea gigas(Thunberg,1793) in Ningbo and Nantong were related to the adult worms from Patinopecten yessoensis(Jay, 1857) in Dalian, and both of them belonged to P. brevipalpa. This result was further supported by parsimony network analysis, which showed that egg capsules collected from dif ferent localities and adult worms shared a single haplotype. This study was the first to report both P. brevipalpa infestation on C. gigas and to utilise the known CO1 sequences of the adult polydorids to validate morphologically unidentified egg capsules or early larvae. P. brevipalpa was most possibly brought to Chinese waters through transportation of Pa. yessoensis brood stock from Japan. 相似文献
A model integrating geo-information and self-organizing map (SOM) for exploring the database of soil environmental surveys was established. The dataset of 5 heavy metals (As, Cd, Cr, Hg, and Pb) was built by the regular grid sampling in Hechi, Guangxi Zhuang Autonomous Region in southern China. Auxiliary datasets were collected throughout the study area to help interpret the potential causes of pollution. The main findings are as follows: (1) Soil samples of 5 elements exhibited strong variation and high skewness. High pollution risk existed in the case study area, especially Hg and Cd. (2) As and Pb had a similar topo-logical distribution pattern, meaning they behaved similarly in the soil environment. Cr had behaviours in soil different from those of the other 4 elements. (3) From the U-matrix of SOM networks, 3 levels of SEQ were identified, and 11 high risk areas of soil heavy metal-contaminated were found throughout the study area, which were basically near rivers, factories, and ore zones. (4) The variations of contamination index (CI) followed the trend of construction land (1.353) > forestland (1.267) > cropland (1.175) > grassland (1.056), which suggest that decision makers should focus more on the problem of soil pollution surrounding industrial and mining enterprises and farmland.