Temporal and spatial distribution of phytoplankton functional groups and role of environment factors in a deep subtropical reservoir

Phytoplankton and environment factors were investigated in 2015 and phytoplankton functional groups were used to understand their temporal and spatial distribution and their driving factors in Wanfeng Reservoir. Seventeen functional groups(B, D, E, F, G, J, Lo, MP, P, S1, T, W1, W2, X1, X2, Xph, Y) were identified based on 34 species. The dominant groups were: J/B/P/D in dry season, X1/J/Xph/G/T in normal season and J in flood season. Phytoplankton abundance ranged from 5.33×10~4 cells/L to 3.65×10~7 cells/L, with the highest value occurring in flood season and lowest in dry season. The vertical profi le of dominant groups showed little differentiation except for P, which dominated surface layers over 20 m as a result of mixing water masses and higher transparency during dry season. However, the surface waters presented higher values of phytoplankton abundance than other layers, possibly because of greater irradiance. The significant explaining variables and their ability to describe the spatial distribution of the phytoplankton community in RDA diff ered seasonally as follows: dry season, NH4-N, NO_3-N, NO_2-N, TN:TP ratio and transparency(SD); normal season, temperature(WT), water depth, TN, NH4-N and NO_3-N; flood season, WT, water depth, NO_3-N and NO_2-N. Furthermore, nitrogen, water temperature, SD and water depth were significant variables explaining the variance of phytoplankton communities when datasets included all samples. The results indicated that water physical conditions and hydrology were important in phytoplankton community dynamics, and nitrogen was more important than phosphorus in modifying phytoplankton communities. Seasonal differences in the relationship between the environment and phytoplankton community should be considered in water quality management.

Spatial Strategy for Quality Labor in Rural Development——A Case Study of Jiangsu Province, China

This study examines the issue of high-quality labor in rural enterprises of China. It develops a spatial strategy which consists of two dimensions: geographical space and administrative space. Different combinations of these two dimensions form a variety of approaches such as local internalization, local externalization, regional/national inter- nalization, and regional/national externalization. In the local internalization approach, rural enterprises hire such high-quality labor and ask them to work on site, while in the local externalization approach, rural enterprises seek help from employees working in other local enterprises. In the regional/national internalization approach, rural enterprises set up research and development centers in big cities to take advantages of the high-quality labor pool there. Finally in the regional/national externalization approach, rural enterprises hire people from big cities on temporary contracts. Three approaches, hiring retired technical workers, shuttling between the rural site and country seats, and setting up R&D centers in big cities are demonstrated through cases in Zhangjiagang, a leading county-level city in the southern Jiangsu Province. It is argued that rural enterprises need to broaden their perspectives of administrative space and geographical space and think creatively to deal with the shortage of quality labor in rural settings.

Spatial Localization for Nonlinear Dynamical Stochastic Models for Excitable Media

Nonlinear dynamical stochastic models are ubiquitous in different areas.Their statistical properties are often of great interest,but are also very challenging to compute.Many excitable media models belong to such types of complex systems with large state dimensions and the associated covariance matrices have localized structures.In this article,a mathematical framework to understand the spatial localization for a large class of stochastically coupled nonlinear systems in high dimensions is developed.Rigorous mathematical analysis shows that the local effect from the diffusion results in an exponential decay of the components in the covariance matrix as a function of the distance while the global effect due to the mean field interaction synchronizes different components and contributes to a global covariance.The analysis is based on a comparison with an appropriate linear surrogate model,of which the covariance propagation can be computed explicitly.Two important applications of these theoretical results are discussed.They are the spatial averaging strategy for efficiently sampling the covariance matrix and the localization technique in data assimilation.Test examples of a linear model and a stochastically coupled Fitz Hugh-Nagumo model for excitable media are adopted to validate the theoretical results.The latter is also used for a systematical study of the spatial averaging strategy in efficiently sampling the covariance matrix in different dynamical regimes.