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This study analyzed seasonal physicochemical and phytoplankton data collected at 12 marine monitoring stations in Daya Bay from 1999 to 2002. Cluster analysis based on water quality and phytoplankton parameters measured at the 12 stations could be grouped into three clusters: cluster I – stations S1,S2 , S7 and S11 in the southern part and the north-eastern part of Daya Bay; cluster II – stations S5, S6,S9 ,S1 0 and S12 in the central and north-eastern parts of Daya Bay; cluster III – stations S3,S 4 and S8 in the cage culture areas in the south-western part of Daya Bay and in the north-western part of the Bay near Aotou harbor. Bivariate correlations between phytoplankton density and the major physical and nutrient factors were calculated for all stations. Factor analysis shows that there were high positive loadings of pH,T IN and the ratio of TIN to PO4-P in the three clusters, which indicates that all the stations in the three clusters were primarily grouped according to their respective nutrient conditions.
In order to demonstrate that silicate (SiO3-Si) can be used as an indicator to study upwelling in the northern South China Sea, hierarchical cluster analysis (CA) and principle component analysis (PCA) were applied to analyse the metrics of the data consisting of 14 physical-chemical-biological parameters at 32 stations. CA categorized the 32 stations into two groups (low and high nutrient groups). PCA was applied to identify five Principal Components (PCs) explaining 78.65% of the total variance of the original data. PCA found important factors that can describe nutrient sources in estuarine, upwelling, and non-upwelling areas. PC4, representing the upwelling source, is strongly correlated to SiO3-Si. The spatial distribution of silicate from the surface to 200 m depth clearly showed the upwelling regions, which is also supported by satellite observations of sea surface temperature.
One-year-old needles from 20 silver fir trees in the Tisovik Reserve (Belarus) were characterized in respect to 12 morphological and anatomical traits, and the data were analyzed statistically to determine variability between trees. Individuals within the population generally were homogenous for those traits. Needle length was the most variable trait within the studied population. The width/height ratio of the hypodermic cell exerted the weakest differentiating effect between trees. The results indicated that the Tisovik Reserve population is weakly differentiated.
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