Spatial and temporal distribution of nitrite-dependent anaerobic methane-oxidizing bacteria in an intertidal zone of the East China Sea

2017 ◽  
Vol 101 (21) ◽  
pp. 8007-8014 ◽  
Author(s):  
Jiaqi Wang ◽  
Lidong Shen ◽  
Zhanfei He ◽  
Jiajie Hu ◽  
Zhaoyang Cai ◽  
...  
Keyword(s):  
East China Sea ◽  
Intertidal Zone ◽  
Temporal Distribution ◽  
East China ◽  
Spatial And Temporal Distribution ◽  
The East China Sea ◽  
China Sea ◽  
Methane Oxidizing Bacteria

Spatio-temporal distribution and environmental risk of arsenic in sediments of the East China Sea

2013 ◽  
Vol 340 ◽  
pp. 21-31 ◽  
Author(s):  
Liqin Duan ◽  
Jinming Song ◽  
Huamao Yuan ◽  
Xuegang Li ◽  
Ning Li
Keyword(s):  
East China Sea ◽  
Environmental Risk ◽  
Temporal Distribution ◽  
East China ◽  
The East China Sea ◽  
China Sea ◽  
Spatio Temporal

scholarly journals ​Spatial and Temporal Distribution of Portunus trituberculatus in the Northern East China Sea based on Different Modelling Approaches

2021 ◽  
Author(s):  
Xiaodong Li ◽  
Jing Wang ◽  
Ya Liu ◽  
Yingbin Wang
Keyword(s):  
East China Sea ◽  
Temporal Distribution ◽  
Environmental Data ◽  
East China ◽  
Portunus Trituberculatus ◽  
Trawl Survey ◽  
Spatial And Temporal Distribution ◽  
Generalised Additive Model ◽  
China Sea ◽  
Fishing Vessels

Background: Portunus trituberculatus is an important economic crab in the East China Sea. With the increase of tonnage and power of offshore fishing vessels, fishing intensity also increases, which has caused great pressure on P. trituberculatus resources. Protecting P. trituberculatus and achieving sustainable utilisation of resources are urgent problems that need to be solved. Therefore, protection and rational development of P. trituberculatus resources are important to accurately understand its spatial and temporal distribution. Methods: In this study, the temporal and spatial distribution predictive models of P. trituberculatus in the northern East China Sea were built on the basis of three analysis methods (generalised additive model [GAM], random forest [RF] and artificial neural network [ANN]) using bottom trawl survey data and environmental data from 2006 to 2007. The fitting and prediction performances of these three models were compared. Result: Season and sea bottom temperature were the most important factors on the distribution of P. trituberculatus. The fitting performance of ANNs was better than those of GAMs and RFs, but its predictive performance was worse than those of GAMs and RFs. Therefore, RFs was the appropriate model in predicting the distribution of P. trituberculatus in the northern East China Sea. The abundance of P. trituberculatus was significantly higher in summer than in other seasons (P less than 0.01) and generally higher in the northern part of the study area than in the southern part in all seasons.

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