Asynchrony of spring phytoplankton response to temperature driver within a spatial heterogeneity bay of Three-Gorges Reservoir, China

Abstract

a b s t r a c tThe increased air temperature is expected to have important driver on spring phytoplankton dynamics. To test whether spatial heterogeneity modifies the synchronous responses of phytoplankton to regional temperature driver, we evaluate temporal coherences for physical factors (temperature, water stability and non-algal light extinction), nutrients (nitrogen, phosphorus and silicon), and biomass and density of phytoplankton by Pearson correlation analysis and synchrony for phytoplankton community dynamics by Mantel test and nonmetric multi-dimensional scaling (NMS), during spring bloom (February 23-April 28, 2005) within Xiangxi Bay, a high spatial gradient bay of Three-Gorges Reservoir (China). The high level of temporal coherences for surface water temperature (r = 0.946, p < 0.01) and relative water column stability (r = 0.750, p < 0.01) were found between pair sites (A and B), in which the increase trends occurred with increase in regional air temperature during the study period. However, the low synchrony for phytoplankton dynamics were indeed observed between Site A and B, especially for the density of common dominant taxa (Cyclotella spp.: r = 0.155, p = 0.388) and community structure (Mantel test: r = 0.351). Moreover, the local habitat characteristics such as nutrient (nitrogen and phosphorus) and non-algal light extinction showed low levels of temporal coherence. It indicated that local community of phytoplankton varies rather independently within the single lentic bay with high spatial heterogeneity and that dispersal of algal organisms among locations cannot overwhelm out these local dynamics. Contrary to many studies, the present results argued that, in a small geographic area (i.e., a single reservoir bay of approximately 24 km length), spatial gradients also may influence spring phytoplankton response to regional temperature driver.

Seasonal variability of water quality and metazooplankton community structure in Xiaowan Reservoir of the upper Mekong River

“…In aquatic systems, the higher flows appear to dilute dissolved material, as reflected by lower conductivity, and this finding is consistent with the results of lower Mekong River (Irvine et al, 2011;Prathumratana et al, 2008). In the Xiaowan Reservoir, the runoff generation process is supposed to create the suspended sediments since the effect of the relatively low Chl-a content on turbidity (algae turbidity) was minimal (Xu et al, 2011). This speculation is supported by the variation in the DO concentration, although this concentration is not only affected by the turbulence which caused by high flow.…”

Patterns of asynchrony for phytoplankton fluctuations from reservoir mainstream to a tributary bay in a giant dendritic reservoir (Three Gorges Reservoir, China)

“…Using Mantel's test, the correlation between the taxon-date matrices of the two sampling sites was calculated as a measure of coherence (r) for community composition (Xu et al 2011a). The Squared Euclidean coefficient was used as a distance measure in the Mantel test setup (McCune and Mefford 1999).…”

“…Nevertheless, synchrony of the succession of community composition has been rarely detected during the long term (Kent et al 2007;Rusak et al 2009). Further, detection of phytoplankton synchrony accounting for community composition is necessary to determine whether local-scale regulators or regional drivers play a key role on phytoplankton dynamics (Xu et al 2011a) because the variability of all natural communities may be measured by two distinct but related quantities: aggregate variability (total abundance) and compositional variability (community composition) (Brown and Lawson 2010).…”

Vertical and seasonal changes of water quality in Keban dam reservoir

“…Water density (D) was calculated as a function of the water temperature (T) using the Krambeck equation (Xu et al, 2011): Normality of data was checked by Shapiro-Wilk's test. The differences of variables with normal distribution among the sampling depths were tested using analysis of variance (ANOVA), whereas in case of non-normality, the signed rank test (Wilcoxon) followed by Student's t-test was used.…”