Seasonal variation of water quality and phytoplankton response patterns in Daya Bay, China

Abstract

Data collected from 12 stations in Daya Bay in different seasons in 2002 revealed the relation between water quality and phytoplankton response patterns. The results showed that Daya Bay could be divided into wet and dry seasons by multivariate statistical analysis. Principal component analysis indicated that temperature, chlorophyll a and nutrients were important components during the wet season (summer and autumn). The salinity and dissolved oxygen were the main environmental factors in the dry season (winter and spring). According to non-metric multidimensional scaling, there was a shift from the large diatoms in the dry season to the smaller line-chain taxa in the wet season with the condition of a high dissolved inorganic nitrogen and nitrogen to phosphorous concentration ratio. Nutrient changes can thus alter the phytoplankton community composition and biomass, especially near the aquaculture farm areas. There was no evidence of an effect of thermal water from the nearby nuclear power plants on the observed changes in phytoplankton community and biomass in 2002.

Keywords: Daya Bay (DYB); South China Sea; human activities; multivariate analysis; phytoplankton; water quality.

Figure 1

Sampling stations in Daya Bay (Adapted from [17,24]).

Figure 2

Changes of temperature, salinity and dissolved oxygen (DO) at the surface and bottom water (W: winter, SP: spring, S: summer, A: autumn).

Figure 2

Changes of temperature, salinity and dissolved oxygen (DO) at the surface and bottom water (W: winter, SP: spring, S: summer, A: autumn).

Figure 3

Changes of the nutrient concentration and Chl-a at surface and bottom (W: winter, SP: spring, S: summer, A: autumn).

Figure 3

Changes of the nutrient concentration and Chl-a at surface and bottom (W: winter, SP: spring, S: summer, A: autumn).

Figure 4

Relationship between Chl-a and salinity.

Figure 5

Principal component analysis (PCA) (Axis I and II) made on the loadings of environment variables (a) and the scores of the 12 stations in four seasons (b) in surface.

Figure 5

Principal component analysis (PCA) (Axis I and II) made on the loadings of environment variables (a) and the scores of the 12 stations in four seasons (b) in surface.

Figure 6

Principal component analysis (PCA) (Axis I and II) made on the loadings of environment variables (a) and the scores of the 12 station in four seasons (b) in bottom.

Figure 6

Principal component analysis (PCA) (Axis I and II) made on the loadings of environment variables (a) and the scores of the 12 station in four seasons (b) in bottom.

Figure 7

NMDS of phytoplankton community structure (numbers from 1 to 12 represent winter stations 1–12; numbers from 13 to 24 represents spring stations 1 to 12; numbers from 25 to 36 represent summer stations from 1 to 12; numbers from 37 to 48 represent autumn stations from 1 to 12).