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Scale-dependent environmental control of mesozooplankton community structure in three aquaculture subtropical bays of China

Sun D., Liu Z., Wang C.

Oceanologia

2016

No. 58 (2)

124--134

Most subtropical bays of China have been under heavy pollution since the late 1990s, mainly because of the rapid development of aquaculture and discharge of industrial and agricultural wastewater. Some projects were conducted to investigate the zooplankton community in these bays, but those studies were less focused on the relationship between spatial structure of mesozooplankton community and environmental variables in/among bays. The mesozooplankton community structures in relation to physical, chemical and biological variables were studied in three subtropical bays of China with seasons and different spatial scales during 2000 and 2002–2003. Data were collected on temperature (T), salinity (S), concentration of chlorophyll a (Chl a), pH, dissolved oxygen (DO), soluble reactive phosphate (SRP), dissolved inorganic nitrogen (DIN), chemical oxygen demand (COD), suspended particle material (SPM) and mesozooplankton taxonomic abundances. Correlation analysis showed that the main environmental factors correlated to the total abundance of mesozooplankton in these subtropical bays were Chl a, temperature, COD and SRP. Multivariate analysis indicated that DO, Chl a and temperature were the principal factors in influencing spatial differentiation of zooplankton community structure in the inter-bay scale. At the within-bay scale, the influencing factors were different among bays; the main factors were physical variables for Xiangshan Bay and Sanmen Bay, while chemical variables for Yueqing Bay, respectively. The results revealed that the environmental variables that affected spatial structure of mesozooplankton community were different at inter-bay scale and within-bay scales, and zooplankton community was more influenced by chemical (e.g. nutrients/ammonia) variables when under serious eutrophication condition, while it would be more influenced by physical variables (temperature/salinity) when under less eutrophic conditions.

Optimization of tau indentification in ATLAS experiment using multivariate tools

Janyst Ł., Kaczmarska A., Szymocha T., Wolter M., Zemła A.

Computer Science

2008

Vol. 9

35-45

Elementary particle physics experiments, which search for very rare processes, require the efficient analysis and selection algorithms able to separate a signal from the overwhelming background. Four learning machine algorithms have been applied to identify τ leptons in the ATLAS experiment: projective likelihood estimator (LL), Probability Density Estimator with Range Searches (PDE-RS), Neural Network, and the Support Vector Machine (SVM). All four methods have similar performance, which is significantly better than the baseline cut analysis. This indicates that the achieved background rejection is close to the maximal achievable performance.

W eksperymentach fizyki wysokich energii, poszukujących bardzo rzadkich procesów, dużego znaczenia nabierają algorytmy umożliwiające separację sygnału od przeważającego tła. Cztery algorytmy uczące się na przykładach zostały zastosowane do identyfikacji leptonów tau w eksperymencie ATLAS: rzutowane rozkłady prawdopodobieństw (projective likelihood estimator - LL), PDE-RS (Probability Density Estimator with Range Searches), sieć neuronowa oraz maszyna wektorów wspierających (SVM). Algorytmy te mają zbliżone wydajności znacząco lepsze od standardowej analizy z użyciem cięć. Sugeruje to, że osiągnięte wydajności są bliskie maksymalnej osiągalnej granicy.