Effects of Nutrients, Temperature and Their Interactions on Spring Phytoplankton Community Succession in Lake Taihu, China

<div><p>We examined the potential effects of environmental variables, and their interaction, on phytoplankton community succession in spring using long-term data from 1992 to 2012 in Lake Taihu, China. Laboratory experiments were additionally performed to test the sensitivity of the phytoplankton community to nutrient concentrations and temperature. A phytoplankton community structure analysis from 1992 to 2012 showed that <i>Cryptomonas</i> (Cryptophyta) was the dominant genus in spring during the early 1990s. Dominance then shifted to <i>Ulothrix</i> (Chlorophyta) in 1996 and 1997. However, <i>Cryptomonas</i> again dominated in 1999, 2000, and 2002, with <i>Ulothrix</i> regaining dominance from 2003 to 2006. The bloom-forming cyanobacterial genus <i>Microcystis</i> dominated in 1995, 2001 and 2007–2012. The results of ordinations indicated that the nutrient concentration (as indicated by the trophic state index) was the most important factor affecting phytoplankton community succession during the past two decades. In the laboratory experiments, shifts in dominance among phytoplankton taxa occurred in all nutrient addition treatments. Results of both long term monitoring and experiment indicated that nutrients exert a stronger control than water temperature on phytoplankton communities during spring. Interactive effect of nutrients and water temperature was the next principal factor. Overall, phytoplankton community composition was mediated by nutrients concentrations, but this effect was strongly enhanced by elevated water temperatures.</p></div

Phytoplankton community succession during the experiment.

<p>Phytoplankton community succession during the experiment.</p

Trends in the total concentration of chlorophyll-<i>a</i> in the different water temperature treatments.

<p>L, M and H represent the low nutrient concentration treatment (TN∼2 mg·L⁻¹, TP∼0.06 mg·L⁻¹), medium nutrient concentration treatment (TN∼7 mg·L⁻¹, TP∼0.3 mg·L⁻¹) and high nutrient concentration treatment (TN∼10 mg·L⁻¹, TP∼1 mg·L⁻¹), respectively.</p

Results of linear regression models.

<p>Models with and without interaction were both fitted to phytoplankton community (Ratio). The way calculating ratio please refer to methods. TN×WT means the interaction between TN and water temperature.</p><p>** <i>p</i><0.01.</p><p>-Not included in the model.</p><p>Results of linear regression models.</p

Pearson correlations between conductivity and main ions.

<p>** Correlation is significant at the 0.01 level (2-tailed).</p><p>Pearson correlations between conductivity and main ions.</p

Trends of the physical and chemical variables.

<p>All data are shown as the mean ± SD.</p

Ordination biplot.

<p>(a) Environment variables and dominated species against redundancy analysis axes 1 and 2. (b) Variance partitioning of phytoplankton community, explained by trophic state and climatic variables. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113960#s2" target="_blank">methods</a> for the abbreviations of environmental variables.</p

Location of Lake Taihu in China and the sampling sites.

<p>Map was redrawn from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113960#pone.0113960-Xu1" target="_blank">[26]</a>. Phytoplankton biovolume together with water quality were monitored monthly at THL1#, THL3#, THL4# and THL5#, generally in the middle of each month.</p

Nitrogen (N) and phosphorus (P) resuspension rate at each site during the observation period.

<p>Figures of sites 1, 2, 3 and 6 with gray shading have the same value for y-axis, and figures of sites 4 and 5 have smaller value for y-axis.</p

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<p>Lake Taihu is a large shallow eutrophic lake with frequent recurrence of cyanobacterial bloom which has high variable distribution in space and time. Based on the field observations and remote sensing monitoring of cyanobacterial bloom occurrence, in conjunction with laboratory controlled experiments of mixing effects on large colony formation and colonies upward moving velocity measurements, it is found that the small or moderate wind-induced disturbance would increase the colonies size and enable it more easily to overcome the mixing and float to water surface rapidly during post-disturbance. The proposed mechanism of wind induced mixing on cyanobacterial colony enlargement is associated with the presence of the extracellular polysaccharide (EPS) which increased the size and buoyancy of cyanobacteria colonies and promote the colonies aggregate at the water surface to form bloom. Both the vertical movement and horizontal migration of cyanobacterial colonies were controlled by the wind induced hydrodynamics. Because of the high variation of wind and current coupling with the large cyanobacterial colony formation make the bloom occurrence as highly mutable in space and time. This physical factor determining cyanobacterial bloom formation in the large shallow lake differ from the previously documented light-mediated bloom formation dynamics.</p