Community structure of photosynthetic picoeukaryotes differs in lakes with different trophic statuses along the middle-lower reaches of the Yangtze River

Community structure of photosynthetic picoeukaryotes differs in lakes with different trophic... Abstract Photosynthetic picoeukaryotes (PPEs) play an important role in aquatic ecosystem functioning. There is still a relative lack of information on freshwater PPEs, especially in eutrophic lakes. We used a combination of flow cytometric sorting and pyrosequencing to investigate the PPEs community structure in more than 20 mesotrophic and eutrophic lakes along the middle-lower reaches of the Yangtze River in China. The abundance of PPEs ranged between 2.04 × 103 and 5.92 × 103 cells mL−1. The contribution of PPEs to total picophytoplankton abundance was generally higher in eutrophic lakes than in mesotrophic lakes. The sequencing results indicated that the Shannon diversity of PPEs was significantly higher in mesotrophic lakes than in eutrophic lakes. At the class level, PPEs were mainly dominated by three taxonomic groups, including Cryptophyceae, Coscinodiscophyceae and Chlorophyceae, and 15 additional known phytoplankton classes, including Synurophyceae, Dinophyceae, Chrysophyceae, Trebouxiophyceae and Prymnesiophyceae, were identified. Coscinodiscophyceae dominated in the most eutrophic lakes, while Chrysophyceae, Dinophyceae and other classes of PPEs were more abundant in the mesotrophic lakes. We also observed several PPEs operational taxonomic units, and those affiliated with Cyclotella atomus, Chlamydomonas sp. and Poterioochromonas malhamensis tended to be more prevalent in the eutrophic lakes. The canonical correspondence analysis and Mantel analysis highlighted the importance of environmental parameters as key drivers of PPEs community composition. photosynthetic picoeukaryotes, freshwater lake, eutrophic status, community structure, pyrosequencing INTRODUCTION Photosynthetic picoeukaryotes (PPEs), composed of cells less than 3 μm in size, are gaining recognition as not only key CO2 fixers (Bell and Kalff 2001; Jardillier et al.2010; Grob et al.2011) but also as organisms that control bacterioplankton abundance and simultaneously act as producers of organic matter and as predators (Hartmann et al.2012, 2013; Unrein et al.2014). Major advances in marine PPEs diversity, taxonomy and distribution patterns have been made in recent decades as a result of the introduction of new techniques, such as flow cytometry (Marie et al.2010; Shi et al.2011; Balzano et al.2012), and molecular approaches, such as amplification, cloning/sequencing (Fuller et al.2006; Lepère, Vaulot and Scanlan 2009; Kirkham et al.2013) and, most importantly, high-throughput sequencing (Cheung et al.2010; Yu et al.2015; Choi et al.2016). These studies highlighted the phylogenetic diversity of PPEs and shed light on the biogeographical distributions of PPEs at higher taxonomic levels. The PPEs community encompasses great diversity and comprises members of virtually every algal class (Vaulot et al.2008), of which Prymnesiophyceae, Chrysophyceae, Cryptophyceae, Bolidophyceae and Prasinophyceae are the most encountered classes of PPEs dominating the oceans (Not et al.2005; Balzano et al.2012; Kirkham et al.2013). However, the freshwater PPEs are still rather neglected, and only a few molecular explorations of picoeukaryotes or protists have been conducted in inland freshwater systems; however, these studies have revealed some of the diversity of freshwater PPEs, where sequences from heterotrophic organisms usually dominate, and the diversity of photosynthetic organisms is largely biased (Richards et al.2005; Lepere et al.2006; Mangot et al.2009; Charvet, Vincent and Lovejoy 2012; Simon et al.2015). These studies have indicated that Chlorophyte, Chrysophyceae, Dinophyceae, Cryptophyceae and Haptophyta are important in lakes and differ considerably from the taxa of PPEs that dominate marine systems. Nevertheless, most of the studies were performed in oligotrophic lakes, and the diversity, distribution and natural abundance of PPEs in different freshwater systems, such as eutrophic lakes, remain largely understudied; additionally, massive high-throughput sequencing techniques have been applied in very few of the studies (Li et al.2017). Thus, to explore the PPEs diversity and biogeography among inland lakes with higher trophic levels, we applied a combination of flow cytometry sorting and high-throughput pyrosequencing to provide an in-depth analysis of PPEs diversity and community structure in more than 20 mesotrophic and eutrophic lakes along the middle-lower reaches of the Yangtze River in China, which covers a long-distance scale of over 600 km. In addition, the use of flow cytometry to sort out PPEs cells as a prior can largely diminish the effects of heterotrophic organisms; thus, flow cytometry in combination with high-throughput pyrosequencing can better assess the diversity of PPEs (Marie et al.2010). Hence, the data obtained here can provide molecular insights into the diversity of PPEs in mesotrophic and eutrophic freshwater lakes and provide an important comparison with recent surveys of marine and oligotrophic planktonic systems. MATERIALS AND METHODS Sample collection and physicochemical analysis The middle-lower reaches of the Yangtze River are located in the east of China, which is the most urbanized and developed region of China. This area is also the most widespread area of freshwater lake distribution in China, and many lakes in this area suffer from severe eutrophication. Samples were collected in 30 lakes in the middle-lower reaches of the Yangtze River in April 2012 (Fig. 1). Samples used for flow cytometric sorting were fixed with paraformaldehyde at a final concentration of 1% and quick-frozen with liquid nitrogen. They were transported on ice to the laboratory and kept at –70°C prior to analysis. Water temperature, pH, conductivity (COND), nephelometric turbidity units (NTU) and dissolved oxygen (DO) were measured in situ using a multiparameter water quality probe (YSI 6600, Yellow Spring Instruments, Yellow Springs, OH, USA). Water transparency was measured using a Secchi disk (SD). Total suspended solids (TSS) were determined by measuring the weight of dry solid material remaining after vacuum filtration of a known sample volume (50–100 mL) through a GF/F filter (0.7 μm, Whatman, Maidstone, England, UK). Concentrations of nitrogen and phosphorous nutrients, dissolved organic carbon (DOC), chemical oxygen demand (COD), DOC and chlorophyll a (Chl-a) were analyzed as described previously (Li et al.2016). The trophic status of each lake was estimated from values of Chl-a, total nitrogen (TN), total phosphorous (TP), SD and COD using the revised trophic state index (Carlson 1977) for Chinese lakes (Wang 2012). The trophic level index (TLI) was calculated as $$TLI\ = \mathop \sum \nolimits_{j = 1}^m {W_j} \times TLI( j )\ $ $, where TLI(j) and Wj corresponded respectively to the trophic level of variable j (Chl-a, TN, TP, SD and COD) and its weight. The estimation of Wj and TLI(j) has been described in detail in Wang (2012). The lakes were then numbered according to the TLI values (Table 1). Figure 1. View largeDownload slide Locations of the investigated lakes along the middle-lower reaches of the Yangtze River. Figure 1. View largeDownload slide Locations of the investigated lakes along the middle-lower reaches of the Yangtze River. Table 1. The main environmental parameters and trophic states of the investigated lakes. TLI represents trophic level index estimated from values of Chlorophyll a (Chl-a), total nitrogen (TN), total phosphorous (TP), Secchi disk (SD) and chemical oxygen demand (COD). No.  Lake names  PPEs (cell mL−1)  PPEs (%)  SD (m)  TN (mg L−1)  TP (mg L−1)  Chl-a (μg L−1)  COD (mg L−1)  DOC (mg L−1)  Cond  TLI  Level  1  Lake Longwo  4086  67  4.98  1.06  0.017  2.99  2.64  3.91  0.54  34  Mesotrophic  2  Lake Chi  5189  55  0.75  1.54  0.011  7.50  2.44  10.09  0.31  42  Mesotrophic  3  Lake Sanshan  2043  32  0.92  0.53  0.017  12.57  3.27  4.22  0.41  43  Mesotrophic  4  Lake Lu  3081  25  0.97  0.65  0.021  9.99  3.37  3.95  0.26  43  Mesotrophic  5  Lake Zhusi  4313  22  0.43  1.29  0.027  3.99  2.20  3.43  0.16  44  Mesotrophic  6  Lake Wangtian  2367  31  1.30  0.83  0.036  15.71  4.41  5.70  0.21  47  Mesotrophic  7  Lake Yezhu  8140  67  0.80  0.65  0.043  19.04  3.49  4.84  0.47  48  Mesotrophic  8  Lake Xiaoshe  6583  36  0.60  1.09  0.022  20.96  3.63  4.41  0.31  49  Mesotrophic  9  Lake Gucheng  59 215  78  0.60  1.30  0.041  9.68  4.13  5.72  0.33  50  Eutrophic  10  Lake Ce  3178  60  1.08  1.44  0.052  22.16  4.82  5.71  0.25  52  Eutrophic  11  Lake Chenyao  18 452  33  0.38  1.48  0.038  14.90  4.74  7.36  0.28  54  Eutrophic  12  Lake Xinmiao  32 169  31  0.34  1.19  0.052  23.20  3.81  15.35  0.14  55  Eutrophic  13  Lake Zhupo  3859  49  0.60  1.41  0.075  47.18  3.29  2.45  0.29  56  Eutrophic  14  Lake Fengsha  5124  53  0.20  2.56  0.064  7.88  3.43  12.11  0.21  56  Eutrophic  15  Lake Shengjin  34 569  25  0.20  0.84  0.065  35.31  3.45  13.48  0.23  57  Eutrophic  16  Lake Huangni  9761  22  0.58  3.31  0.072  28.83  3.49  12.00  0.21  57  Eutrophic  17  Lake Chidong  15 566  48  0.73  1.32  0.060  71.19  4.92  5.35  0.20  57  Eutrophic  18  Lake Shaobo  7037  41  0.25  1.00  0.081  24.11  4.49  5.52  0.47  58  Eutrophic  19  Lake Dong  12 971  63  0.63  1.31  0.090  57.48  4.84  4.12  0.34  58  Eutrophic  20  Lake Qilibali  19 230  68  0.56  2.56  0.081  57.87  3.42  5.39  0.21  59  Eutrophic  21  Lake Taibo  11 480  26  0.28  3.41  0.069  27.57  3.21  4.71  0.13  59  Eutrophic  22  Lake Cheng  12 842  51  0.42  3.75  0.083  37.81  4.11  4.79  0.73  61  Eutrophic  23  Lake Qingling  40 665  59  0.55  1.53  0.138  57.54  6.19  5.74  0.51  62  Eutrophic  24  Lake Huangjia  37 812  53  0.50  1.85  0.098  96.36  6.57  5.63  0.37  63  Eutrophic  25  Lake Jiuluo  5513  70  0.56  3.70  0.138  68.89  5.10  5.28  0.72  64  Eutrophic  26  Lake Changbaidang  6129  58  0.43  3.54  0.158  57.67  5.03  5.40  0.72  65  Eutrophic  27  Lake Dianshan  9275  64  0.38  3.79  0.147  62.74  5.22  5.46  0.71  65  Eutrophic  28  Lake Zhulintang  23 446  54  0.50  2.23  0.151  154.75  6.46  3.91  0.36  67  Eutrophic  29  Lake Huanggangdong  24 743  63  0.52  1.72  0.175  153.83  7.39  6.44  0.44  67  Eutrophic  30  Lake Nanxing  13 912  69  0.40  3.76  0.215  111.34  6.65  5.85  0.72  69  Eutrophic  No.  Lake names  PPEs (cell mL−1)  PPEs (%)  SD (m)  TN (mg L−1)  TP (mg L−1)  Chl-a (μg L−1)  COD (mg L−1)  DOC (mg L−1)  Cond  TLI  Level  1  Lake Longwo  4086  67  4.98  1.06  0.017  2.99  2.64  3.91  0.54  34  Mesotrophic  2  Lake Chi  5189  55  0.75  1.54  0.011  7.50  2.44  10.09  0.31  42  Mesotrophic  3  Lake Sanshan  2043  32  0.92  0.53  0.017  12.57  3.27  4.22  0.41  43  Mesotrophic  4  Lake Lu  3081  25  0.97  0.65  0.021  9.99  3.37  3.95  0.26  43  Mesotrophic  5  Lake Zhusi  4313  22  0.43  1.29  0.027  3.99  2.20  3.43  0.16  44  Mesotrophic  6  Lake Wangtian  2367  31  1.30  0.83  0.036  15.71  4.41  5.70  0.21  47  Mesotrophic  7  Lake Yezhu  8140  67  0.80  0.65  0.043  19.04  3.49  4.84  0.47  48  Mesotrophic  8  Lake Xiaoshe  6583  36  0.60  1.09  0.022  20.96  3.63  4.41  0.31  49  Mesotrophic  9  Lake Gucheng  59 215  78  0.60  1.30  0.041  9.68  4.13  5.72  0.33  50  Eutrophic  10  Lake Ce  3178  60  1.08  1.44  0.052  22.16  4.82  5.71  0.25  52  Eutrophic  11  Lake Chenyao  18 452  33  0.38  1.48  0.038  14.90  4.74  7.36  0.28  54  Eutrophic  12  Lake Xinmiao  32 169  31  0.34  1.19  0.052  23.20  3.81  15.35  0.14  55  Eutrophic  13  Lake Zhupo  3859  49  0.60  1.41  0.075  47.18  3.29  2.45  0.29  56  Eutrophic  14  Lake Fengsha  5124  53  0.20  2.56  0.064  7.88  3.43  12.11  0.21  56  Eutrophic  15  Lake Shengjin  34 569  25  0.20  0.84  0.065  35.31  3.45  13.48  0.23  57  Eutrophic  16  Lake Huangni  9761  22  0.58  3.31  0.072  28.83  3.49  12.00  0.21  57  Eutrophic  17  Lake Chidong  15 566  48  0.73  1.32  0.060  71.19  4.92  5.35  0.20  57  Eutrophic  18  Lake Shaobo  7037  41  0.25  1.00  0.081  24.11  4.49  5.52  0.47  58  Eutrophic  19  Lake Dong  12 971  63  0.63  1.31  0.090  57.48  4.84  4.12  0.34  58  Eutrophic  20  Lake Qilibali  19 230  68  0.56  2.56  0.081  57.87  3.42  5.39  0.21  59  Eutrophic  21  Lake Taibo  11 480  26  0.28  3.41  0.069  27.57  3.21  4.71  0.13  59  Eutrophic  22  Lake Cheng  12 842  51  0.42  3.75  0.083  37.81  4.11  4.79  0.73  61  Eutrophic  23  Lake Qingling  40 665  59  0.55  1.53  0.138  57.54  6.19  5.74  0.51  62  Eutrophic  24  Lake Huangjia  37 812  53  0.50  1.85  0.098  96.36  6.57  5.63  0.37  63  Eutrophic  25  Lake Jiuluo  5513  70  0.56  3.70  0.138  68.89  5.10  5.28  0.72  64  Eutrophic  26  Lake Changbaidang  6129  58  0.43  3.54  0.158  57.67  5.03  5.40  0.72  65  Eutrophic  27  Lake Dianshan  9275  64  0.38  3.79  0.147  62.74  5.22  5.46  0.71  65  Eutrophic  28  Lake Zhulintang  23 446  54  0.50  2.23  0.151  154.75  6.46  3.91  0.36  67  Eutrophic  29  Lake Huanggangdong  24 743  63  0.52  1.72  0.175  153.83  7.39  6.44  0.44  67  Eutrophic  30  Lake Nanxing  13 912  69  0.40  3.76  0.215  111.34  6.65  5.85  0.72  69  Eutrophic  View Large Flow cytometric analysis, sorting and DNA extraction Samples were thawed on ice and filtered through a 300-mesh sieve before flow cytometric sorting. Analyses were performed using a FACSJazzSE flow cytometer (Becton Dickinson) equipped with two lasers emitting at 488 and 640 nm, as described previously. Full details of the discrimination and counting of different picophytoplankton groups are described in a previous study (Li et al.2015). PPEs were characterized by higher forward scattering (FSC) and rich far-red fluorescence from Chl-a, and phycocyanin-rich picocyanobacteria (PC-cells) were characterized by lower FSC and rich phycocyanin fluorescence. PPEs were sorted according to the protocol described by Li et al. (2017). The DNA of the sorted cells was extracted using the DNeasy Blood and Tissue Extraction Kit (Qiagen) modified by Marie et al. (2010). PCR and pyrosequencing PCR was performed using 454 pyrosequencing adaptor-linked universal primers: TAReuk454FWD1 (5’-CCAGCASCYGCGGTAATTCC-3’) and TAReukREV3 (5’-ACTTTCGTTCTTGATYRA-3’), which can amplify the hypervariable V4 region (∼380 bp) of the eukaryotic 18S rDNA/rRNA, and were generally suitable for all eukaryotes with some exceptions in the excavates and the microsporidia (Stoeck et al.2010). PCR mixtures (20 μL) were prepared in triplicate, and each contained 4 μL of 10× PCR buffer (50 mM KCl, 10 mM Tris-HCl and 1.5 mM MgCl2), 2.4 μL of dNTP (2.5 mM), 0.25 μL of each primer (10 μM), 0.5 μL of Herculase II fusion DNA polymerase (Stratagene, Agilent Technologies), 6 μL of DNA template and 6.6 μL of ddH2O. The PCR thermal regime consisted of an initial denaturation of 2 min at 95°C, followed by 30 cycles of 20 s at 95°C, 20 s at 55°C, 30 s at 72°C and a final cycle of 10 min at 72°C. Amplicons were checked in 1.5% agarose gels for successful amplification within the expected length. Triplicate amplicon reactions were pooled and purified using the PCR purification kit (Agencourt AMPure XP, Beckman) according to the manufacturer's instructions. DNA concentration and quality was determined with a NanoDrop 1000 spectrophotometer (Wilmington, DE, USA). Pyrosequencing of PCR products was performed on a Roche FLX 454 system (Roche Diagnostics Corporation, Branford, CT, USA) installed at the Center for Genetic & Genomic Analysis (Gene sky 385 Biotechnologies Inc., Shanghai, China). Sequence analysis and taxonomic affiliation All 454 raw reads were cleaned and filtered against the following quality criteria: (i) maximum mismatches of two in the forward primers, (ii) elimination of terminal bases with low-quality scores or truncation of the sequences at the reverse primer if one was detected and (iii) removal of sequences below 250 bp or those that contained one or more ambiguous base(s) or had an average quality score below 25. Subsequently, reads were dereplicated and clustered into operational taxonomic units (OTUs) at a 98% similarity threshold using UPARSE (Edgar 2010). Chimeras were detected and removed using UCHIME (Edgar et al.2011). A representative sequence of each OTU was aligned against the database SILVA 128 (BLAST threshold e-value = e−6) for taxonomy annotation. Non-target reads belonging to Streptophyta or Metazoa were removed from further analyses. Statistical analysis All statistical analyses and visualizations were carried out in the R environment (version 3.2.1, http://www.r-project.org). To avoid the effects of different sampling depths, each sequence library was randomly resampled to the minimum sample size before further analysis. The quality of the sampling effort was assessed through the calculation of rarefaction curves (Hughes et al.2001). The α-diversity indices were also estimated and included OTU richness, Shannon diversity and Simpson diversity. Exact permutation tests were used to identify the significance of differences between mesotrophic and eutrophic lakes. The sequence data were Hellinger-transformed before further multiple statistical analyses to decrease the effect of rare species (Legendre and Gallagher 2001). The pairwise Bray–Curtis distance matrices of picoeukaryote communities were then computed between all samples. Analysis of similarities (ANOSIM) was used to test the significance of the community composition differences between lakes with different trophic levels. Canonical correspondence analysis (CCA) was used to test the relationship between environmental parameters and picoeukaryote community variability. Concurrently, the Mantel test was employed to test the effects of geographic distances on picoeukaryote community composition. Geographic distances were estimated based on coordinates (http://biodiversityinformatics.amnh.org/open_source/gdmg/), using the mean Earth radius. RESULTS Environmental characterization of the investigated lakes All the lakes investigated were typical shallow lakes with an average depth of 1.8 m (0.7–6 m). The average temperature of the lakes was 17.5°C, with small variations between 16°C and 20°C during the sampling period. Temperature was the exception; the other parameters exhibited a wide range of variation among lakes. For instance, DOC ranged from 2.45 to 15.35 mg L−1, and DO ranged from 6.71 to 17.14 mg L−1. The main parameters that affected the trophic status of the lakes are summarized in Table 1. Most of the lakes were suffering from considerable nutrient pollution, with TN and TP concentrations ranging between 0.53–6.36 and 0.011–0.31 mg L−1, respectively. According to the TLI values, only 8 of the 30 lakes were classified as mesotrophic lakes, and the others were all classified as eutrophic lakes (Table 1). Spatial distribution of PPEs The abundance of PPEs ranged between 2.04 × 103 and 5.92 × 104 cells mL−1. The highest average abundance of PPEs was recorded in the eutrophic Lake Gucheng, while the lowest abundance of PPEs was observed in the mesotrophic Lake Wangtian. The contribution of PPEs to the total picoplankton abundance ranged between 22% and 78%. The correlation analysis between the cell abundance of PPEs and environmental factors revealed that the abundance of PPEs was strongly negatively correlated with SD and positively correlated with Chl-a and DOC concentration. The contribution of PPEs to the total picophytoplankton abundance was strongly positively correlated with nutrient concentrations, including TN, DTN, NH4-N, NO3-N, TP, DTP and PO4-P, and significantly positively correlated with COND, Chl-a and COD. Thus, PPEs tended to be more abundant than picocyanobacteria in eutrophic lakes. We conducted stepwise multiple regressions to highlight the significant factors that explained most of the variation in the abundances of PPEs as well as its contribution to total picophytoplankton abundance. The results of the multiple linear regression models are shown in Table 2. The selected variables explained 76.3% and 67.8% of the total variation of the PPEs abundance and PPEs percentage of total picophytoplankton, respectively. Temperature was the best explanatory factor for PPEs abundance and its percentage of total picophytoplankton. PPEs were more correlated with SD, TP, Chl-a and DOC, while the percentage of PPEs was related to COND, DO, TN, NH4-N, TP and TSS. This also implied that trophic status is the key factor for determining the PPEs abundance. Table 2. Multiple linear regression results for predicting the abundances of PPEs and its contribution to total picophytoplankton abundance.           Model    Variables  Standardized Coefficients  t  P  Ajusted R2  P    Constant    0  1        T  0.381  3.085  0.006        SD  −0.617  −4.351  0.000      PPEs  TP  −0.693  −3.143  0.006  0.763  0.000    Chla  0.847  3.996  0.001        DOC  0.496  4.403  0.000        Constant    0  1        T  −0.462  −2.654  0.017        COND  0.299  1.483  0.157        DO  0.420  2.835  0.012      %PPEs  TN  −0.660  −2.627  0.018  0.678  0.000    NH4-N  0.542  2.342  0.032        TP  0.695  2.875  0.011        TSS  −0.379  −2.246  0.039                Model    Variables  Standardized Coefficients  t  P  Ajusted R2  P    Constant    0  1        T  0.381  3.085  0.006        SD  −0.617  −4.351  0.000      PPEs  TP  −0.693  −3.143  0.006  0.763  0.000    Chla  0.847  3.996  0.001        DOC  0.496  4.403  0.000        Constant    0  1        T  −0.462  −2.654  0.017        COND  0.299  1.483  0.157        DO  0.420  2.835  0.012      %PPEs  TN  −0.660  −2.627  0.018  0.678  0.000    NH4-N  0.542  2.342  0.032        TP  0.695  2.875  0.011        TSS  −0.379  −2.246  0.039      View Large Diversity patterns of the PPEs assemblages Samples from a total of 30 lakes were sequenced, with an average of 4653 (946–8741) reads obtained from each sample. Before resampling and subsequent analyses, we eliminated 6 samples (S01, S09, S20, S23, S24 and S28) with read numbers below 3000 to avoid the effects of low sampling depth. Thus, the PPEs diversity was described from a total of 24 samples that were resampled to a minimum sequence number of 3527, which grouped into 572 OTUs. All rarefaction curves approached asymptote both before and after resampling (Fig. S1, Supporting Information), suggesting a further increase in sampling effort would not reveal more microbial diversity. The observed number of OTUs ranged between 53 and 167 in the lakes. The Shannon and Simpson diversity indices ranged between 1.07–3.96 and 0.31–0.96, respectively. The Shannon diversity indices in mesotrophic lakes were significantly higher than in eutrophic lakes (P = 0.028). Although not statistically significant (P > 0.05), the OTU numbers and the Simpson diversities also showed similar patterns in mesotrophic and eutrophic lakes (Fig. 2). In addition, pairwise correlation analyses indicated that both Shannon and Simpson diversity indices were strongly negatively correlated with TN in the investigated lakes (Fig. S2, Supporting Information). Figure 2. View largeDownload slide OTU numbers and diversity indexes in mesotrophic and eutrophic lakes after re-sampling. Figure 2. View largeDownload slide OTU numbers and diversity indexes in mesotrophic and eutrophic lakes after re-sampling. Composition and distribution of the PPEs assemblages The taxonomic affiliations of the OTUs are shown in Table S1, Supporting Information. Over 70% of the retrieved sequences from the flow cytometry sorted samples belonged to photosynthetic cells, which were grouped into at least 18 known classes of PPEs (Table S1, Supporting Information). In addition to the sequences belonging to PPEs, 6.68% (5735) of the sequences were classified as uncultured eukaryotes and were composed of environmental sequences without known close relatives in databases; thus, it was difficult or impossible to more precisely categorize the sequences. The composition of the PPEs assemblages among samples are shown in Fig. 3, where all the PPEs classes with mean sequence proportions below 1% (Table S1, Supporting Information) were classified as others. The relative abundances of taxonomic groups varied among lakes, even though OTUs affiliated with Cryptophyceae (35 OTUs), Coscinodiscophyceae (26 OTUs) and Chlorophyceae (69 OTUs) accounted for the majority of sequences in almost all libraries, which retrieved more than 50% of the total reads (Fig. 3). In addition, 22.78% (19 280) of the sequences were affiliated with non-pigmented picoeukaryotes, especially with Alveolata and fungi (Table S1, Supporting Information). In regard to OTUs belonging to non-pigmented eukaryotes, Chytridiomycota were the most encountered OTU within fungi. Alveolata was represented mainly by Ciliophora. Finally, 59 OTUs belonged to heterotrophic stramenopiles, Radiolaria, etc. Figure 3. View largeDownload slide The main class composition of PPEs in lakes along the middle-lower reaches of the Yangtze River. Others represent rare classes including Dictyochophyceae, Xanthophyceae, Eustigmatophyceae, Prymnesiophyceae and Raphidophyceae. Figure 3. View largeDownload slide The main class composition of PPEs in lakes along the middle-lower reaches of the Yangtze River. Others represent rare classes including Dictyochophyceae, Xanthophyceae, Eustigmatophyceae, Prymnesiophyceae and Raphidophyceae. At the class level, Coscinodiscophyceae sequences were found more often in eutrophic lakes than in mesotrophic lakes (Fig. 4), with a remarkable abundance in the most eutrophic lakes with the highest TLI values (Fig. 3). In contrast, sequences belonging to Chrysophyceae, Dinophyceae and other minor classes of PPEs, including Dictyochophyceae, Eustigmatophyceae, Prymnesiophyceae, Xanthophyceae and Raphidophyceae, were more abundant in mesotrophic lakes than in eutrophic lakes (P < 0.05, Fig. 4). Apart from these differences, no significant differences in the other groups of PPEs were found between lakes with different trophic levels (P > 0.1). Figure 4. View largeDownload slide Box plots of Coscinodiscophyceae, Chrysophyceae, Dinophyceae and Other rare PPEs class in mesotrophic and eutrophic lakes. Figure 4. View largeDownload slide Box plots of Coscinodiscophyceae, Chrysophyceae, Dinophyceae and Other rare PPEs class in mesotrophic and eutrophic lakes. The composition of OTUs within each primary high-rank taxa of PPEs from all samples are also illustrated (Fig. 5), where only the most abundant OTUs in each taxa are presented with colors. At the level of OTUs, Bacillariophyceae were mainly dominated by one OTU that was affiliated with Cyclotella atomus, which was highly abundant in the highly eutrophic water bodies. For Chlorophyta, one OTU affiliated with Chlamydomonas sp. tended to be more prevalent in the eutrophic lakes. For Cryptophyta, the major OTUs, OTU22 and OUT7, were widely distributed in both mesotrophic and eutrophic lakes. Of the other minor classes, OTU18, which belonged to Poterioochromonas malhamensis, was more frequently observed in eutrophic lakes than in mesotrophic lakes. Figure 5. View largeDownload slide The composition of OTUs within each primary high-rank taxa of PPEs. Figure 5. View largeDownload slide The composition of OTUs within each primary high-rank taxa of PPEs. Correlations between PPEs assemblages and environmental parameters and geographic distances According to the results of detrended correspondence analysis, the gradient length of the first axis was 6.7, which implied a large heterogeneity in the community composition of PPEs. Thus, CCA was used to analyze the correlation between PPEs assemblages and various environmental parameters (Fig. 6). The results indicated a significant correlation between the community of PPEs and the selected environmental parameters (P < 0.003). The first two axes explained 15.9% and 13.3% of the community variances, respectively. CCA showed that the lakes with similar trophic statuses tended to cluster together and presented more similar community compositions, although the ANOSIM results indicated insignificant differences between the communities of PPEs in the mesotrophic and eutrophic lakes (P = 0.208). The CCA results also revealed that the OTUs classified within different high-level taxonomic groups (i.e. class level) were linked to different environmental parameters. For example, most of the OTUs belonging to Chrysophyceae, Dinophyceae and the other classes of PPEs (i.e. ‘Others’ in Fig. 6) showed a strong preference for mesotrophic lakes with higher SD and TN/TP ratio; in contrast, the Chlorophyta and Bacillariophyta OTUs appeared to have a broader spectrum of environmental adaptations. In addition, the unclassified and non-pigmented OTUs seemed more prevalent in lakes with higher DO concentrations. Figure 6. View largeDownload slide CCA triplots of samples, environmental parameters and species of picoeukaryotes in lakes along the middle-lower reaches of the Yangtze River. Figure 6. View largeDownload slide CCA triplots of samples, environmental parameters and species of picoeukaryotes in lakes along the middle-lower reaches of the Yangtze River. To test whether the differences in the community compositions of PPEs were also affected by geographical distances between sites, we calculated Bray–Curtis distances based on the PPEs OTUs frequencies between sites of each trophic status. In addition, we performed Mantel and partial Mantel analysis to test the correlation between the Bray–Curtis and the geographical distance matrices. The results suggested that the PPE community was also significantly correlated with spatial distance (r = 0.17; P = 0.03). However, this significance disappeared in the partial Mantel test when environmental factors were considered as covariates (r = 0.077, P = 0.21). DISCUSSION PPEs were dominant in the eutrophic lakes The relative proportion of picophytoplankton to total phytoplankton decreased with increasing trophic status of aquatic systems (Callieri 2007). The contribution of PPEs to total picophytoplankton abundance differed according to environmental features. PPEs were prevalent typically in winter and spring, while PC-cells were prevalent in summer and autumn (Li et al.2016). PC-rich picocyanobacteria prevailed over phycoerythrin-rich cells in shallow, turbid and eutrophic lake areas (Vörös et al.1998). Previous studies stated that PPEs were not the major picophytoplankton components and that, in lakes, picocyanobacteria were generally an order of magnitude higher in abundance than their eukaryotic counterparts in both freshwater (Malinsky-Rushansky, Berman and Dubinsky 1995; Crosbie, Pöckl and Weisse 2003; Mózes, Présing and Vörös 2006; Callieri 2007; Winder 2009; Contant and Pick 2013) and marine systems (Worden, Nolan and Palenik 2004; Jiao et al.2005; Bouman et al.2011; Zhao et al.2011). Recently, it was reported that PPEs were the clearly dominant groups of picophytoplankton in two large eutrophic lakes, Lake Chaohu and Lake Taihu (Li et al.2016). Weisse has noted that small and shallow lakes should be treated separately from large lakes in terms of picophytoplankton abundance and their relationship to the trophic status of lakes (Weisse 1993). According to Weisse, in small and shallow lakes, factors unrelated to macronutrients have a stronger impact on APP abundance than in larger lakes (Weisse 1993). Our results, which were based on the survey of 30 small meso-eutrophic lakes, showed that the contribution of PPEs to total picophytoplankton averaged 49.1% and showed a strong positive relationship with nutrient concentrations and Chl-a. This was consistent with previous results based on field investigations and experiments that stated PPEs had a competitive advantage over picocyanobacteria in habitats with increasing nutrient availability. High diversity of PPEs dominated by Bacillariophyta, Chlorophyta and Cryptophyta in abundance The understanding of the diversity and community structure of PPEs is continually improved as a result of the increase in investigations in marine and some oligotrophic lakes in recent decades (Not et al.2005; Richards et al.2005; Kirkham et al.2013; Choi et al.2016). However, little is known about the structure of the PPE community over larger spatial scales or environmental gradients in inland lakes. Here, we have presented a large molecular dataset that begins to elucidate the community structure and distribution of PPEs in 24 mesotrophic-to-eutrophic lakes along the middle-lower reaches of the Yangtze River in East China (Fig. 1), which complements our previous research in the large eutrophic lakes, Lake Taihu and Lake Chaohu (Li et al.2017). The current dataset adds further weight to support the suggestion that the distributional pattern of PPEs differs at the class level based on the varying trophic levels of aquatic systems. Our results indicated that Cryptophyceae, Coscinodiscophyceae and Chlorophyceae were extremely abundant in the mesotrophic and eutrophic lakes that were investigated in this study (Figs 3–5). This is consistent with our previous observation in the eutrophic Lakes Taihu and Chaohu, which were largely dominated by sequences from Chlorophyta and Bacillariophyta (Li et al.2017). Furthermore, Chlorophyta have been reported as the major autotrophic organisms in lakes characterized by nutrient-rich systems (Lepère, Domaizon and Debroas 2007). Cryptophyceae were also associated with higher nutrient concentrations, even in the ocean (Kirkham et al.2011). The dominant taxonomic PPEs found in the freshwater systems described here also differ a lot with those in oceans, where Prymnesiophyceae, Chrysophyceae, and to a lesser extent, Prasinophyceae (here Mamiellophyceae) and Bolidophyceae have been reported to be the most common dominant groups of PPEs (Lepère, Vaulot and Scanlan 2009; Kirkham et al.2011, 2013; Choi et al.2016). Compared with the dominant freshwater algae, it seemed more difficult for the common marine lineages to conquer the diverse freshwater ecosystems (Hepperle and Schlegel 2002). In contrast, ecophysiological adaptations have likely led to the success of Chlorophyceae and Trebouxiophyceae in freshwater and terrestrial environments (Leliaert et al.2012). On the other hand, the recurrent discovery of ‘typically marine’ lineages in freshwater systems highlights the possibility of marine-freshwater barriers being transgressed (Massana et al.2014; Simon et al.2014; Triadó-Margarit and Casamayor 2015; Li et al.2017). However, these lineages were usually sparsely distributed in the corresponding freshwater systems, and there is still a long way to go before they become dominant or abundant. The difference in PPE community structure between mesotrophic and eutrophic lakes The investigated lakes encompassed a wide range of physical and chemical conditions, ranging from mesotrophic to eutrophic lakes with extremely high nutrients and Chl-a values (Table 1). PPEs richness and diversity appeared to be higher in the mesotrophic lakes than in the eutrophic lakes. This is, to some extent, consistent with previous observations on picoeukaryotes, where the oligo-mesotrophic systems possessed the most diversified library (Lefranc et al.2005; Cheung et al.2010). However, it would be premature to draw any conclusions on the classic unimodal diversity–productivity relationship unless further inclusion of more oligotrophic lakes occurred. Specifically, significant negative correlations were also observed between the diversity indices and TN concentrations in the investigated lakes along the middle-lower reaches of the Yangtze River. Despite the statistically insignificant differences of the PPE community structure between the mesotrophic and eutrophic lakes, Chrysophyceae, Dinophyceae and other minor classes of PPEs, including Dictyochophyceae, Eustigmatophyceae, Prymnesiophyceae, Xanthophyceae and Raphidophyceae, were significantly abundant in lakes with lower trophic levels (Fig. 4). The phylogenetic analysis also revealed that the dominant OTUs specific to mesotrophic lakes were mostly affiliated with Chrysophyceae and Dinophyceae. CCA analysis showed that the OTUs grouped within Chrysophyceae, Dinophyceae and other classes of PPEs were closely linked to relatively high SD and TN/TP ratios in the mesotrophic lakes (Fig. 6). None of the previous investigations had described the dominance of Chlorophyta or Bacillariophyta in the oligotrophic systems (marine or freshwater); rather, Chrysophyceae, Dinophyceae and Haptophyceae were usually more abundant (Lepere et al.2006; Shi et al.2011; Charvet et al.2012; Kirkham et al.2013; Lepère et al.2016). We also observed several PPEs OTUs affiliated with C.atomus, Chlamydomonas sp. and P.malhamensis that tended to be more prevalent in the eutrophic lakes. Cyclotella atomus was a nutrient-tolerant diatom taxa and has been found frequently in lakes with high trophic levels (Yang et al.2005, 2010). However, both Chlamydomonas sp. and P.malhamensis have been proved to be mixotrophic with high heterotrophic capability, which can be beneficial in nutrient-deficient conditions (Poerschmann, Spijkerman and Langer 2004; Holen 2010). The prevalence of these mixotrophic lineages in eutrophic lakes in the present study implied a complicated mechanism involved in their choices for nutritional pathways. Furthermore, the CCA and Mantel analysis performed here indicated that the PPE community structure variances were essentially explained by their local environmental parameters instead of geographic distances. This is in good agreement with the view that ‘everything is everywhere, but the environment selects’ (Baas-Becking 1934; Finlay 2002). Nevertheless, ecologists never reach consensus with regard to microbial geography, and opposite conclusions were drawn when considering different distance scales. Lepère et al. (2013) suggested that the beta diversity of picoeukaryotes was linked to the geographic distance of lakes, which were on average separated from each other by 133 km (Lepère et al.2013). In contrast, a recent study clearly rejected geographic distance as a driver of community composition when involving lake distances at the 10-km scale (Simon et al.2014, 2015), which is in accordance with our results, although our sampling sites were at a much larger distance scale (5–666 km, mean: 284 km), similar to Lepère et al. (2013). SUPPLEMENTARY DATA Supplementary data are available at FEMSEC online. Acknowledgements We are thankful to Feizhou Chen for their field sampling. FUNDING This research was supported by the National Natural Science Foundation of China (31670462 and 41431176); and the Sino-French International Collaborative Research Project (41661134036) and Investigation of basic science and technology resources (2017FY100300) financially sponsored this work. Conflict of interest. None declared. REFERENCES Baas-Becking LGM. Geobiologie of Inleiding tot de Milieukunde . The Hague, the Netherlands (in Dutch): WP Van Stockum & Zoon NV, 1934. 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Community structure of photosynthetic picoeukaryotes differs in lakes with different trophic statuses along the middle-lower reaches of the Yangtze River

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Abstract

Abstract Photosynthetic picoeukaryotes (PPEs) play an important role in aquatic ecosystem functioning. There is still a relative lack of information on freshwater PPEs, especially in eutrophic lakes. We used a combination of flow cytometric sorting and pyrosequencing to investigate the PPEs community structure in more than 20 mesotrophic and eutrophic lakes along the middle-lower reaches of the Yangtze River in China. The abundance of PPEs ranged between 2.04 × 103 and 5.92 × 103 cells mL−1. The contribution of PPEs to total picophytoplankton abundance was generally higher in eutrophic lakes than in mesotrophic lakes. The sequencing results indicated that the Shannon diversity of PPEs was significantly higher in mesotrophic lakes than in eutrophic lakes. At the class level, PPEs were mainly dominated by three taxonomic groups, including Cryptophyceae, Coscinodiscophyceae and Chlorophyceae, and 15 additional known phytoplankton classes, including Synurophyceae, Dinophyceae, Chrysophyceae, Trebouxiophyceae and Prymnesiophyceae, were identified. Coscinodiscophyceae dominated in the most eutrophic lakes, while Chrysophyceae, Dinophyceae and other classes of PPEs were more abundant in the mesotrophic lakes. We also observed several PPEs operational taxonomic units, and those affiliated with Cyclotella atomus, Chlamydomonas sp. and Poterioochromonas malhamensis tended to be more prevalent in the eutrophic lakes. The canonical correspondence analysis and Mantel analysis highlighted the importance of environmental parameters as key drivers of PPEs community composition. photosynthetic picoeukaryotes, freshwater lake, eutrophic status, community structure, pyrosequencing INTRODUCTION Photosynthetic picoeukaryotes (PPEs), composed of cells less than 3 μm in size, are gaining recognition as not only key CO2 fixers (Bell and Kalff 2001; Jardillier et al.2010; Grob et al.2011) but also as organisms that control bacterioplankton abundance and simultaneously act as producers of organic matter and as predators (Hartmann et al.2012, 2013; Unrein et al.2014). Major advances in marine PPEs diversity, taxonomy and distribution patterns have been made in recent decades as a result of the introduction of new techniques, such as flow cytometry (Marie et al.2010; Shi et al.2011; Balzano et al.2012), and molecular approaches, such as amplification, cloning/sequencing (Fuller et al.2006; Lepère, Vaulot and Scanlan 2009; Kirkham et al.2013) and, most importantly, high-throughput sequencing (Cheung et al.2010; Yu et al.2015; Choi et al.2016). These studies highlighted the phylogenetic diversity of PPEs and shed light on the biogeographical distributions of PPEs at higher taxonomic levels. The PPEs community encompasses great diversity and comprises members of virtually every algal class (Vaulot et al.2008), of which Prymnesiophyceae, Chrysophyceae, Cryptophyceae, Bolidophyceae and Prasinophyceae are the most encountered classes of PPEs dominating the oceans (Not et al.2005; Balzano et al.2012; Kirkham et al.2013). However, the freshwater PPEs are still rather neglected, and only a few molecular explorations of picoeukaryotes or protists have been conducted in inland freshwater systems; however, these studies have revealed some of the diversity of freshwater PPEs, where sequences from heterotrophic organisms usually dominate, and the diversity of photosynthetic organisms is largely biased (Richards et al.2005; Lepere et al.2006; Mangot et al.2009; Charvet, Vincent and Lovejoy 2012; Simon et al.2015). These studies have indicated that Chlorophyte, Chrysophyceae, Dinophyceae, Cryptophyceae and Haptophyta are important in lakes and differ considerably from the taxa of PPEs that dominate marine systems. Nevertheless, most of the studies were performed in oligotrophic lakes, and the diversity, distribution and natural abundance of PPEs in different freshwater systems, such as eutrophic lakes, remain largely understudied; additionally, massive high-throughput sequencing techniques have been applied in very few of the studies (Li et al.2017). Thus, to explore the PPEs diversity and biogeography among inland lakes with higher trophic levels, we applied a combination of flow cytometry sorting and high-throughput pyrosequencing to provide an in-depth analysis of PPEs diversity and community structure in more than 20 mesotrophic and eutrophic lakes along the middle-lower reaches of the Yangtze River in China, which covers a long-distance scale of over 600 km. In addition, the use of flow cytometry to sort out PPEs cells as a prior can largely diminish the effects of heterotrophic organisms; thus, flow cytometry in combination with high-throughput pyrosequencing can better assess the diversity of PPEs (Marie et al.2010). Hence, the data obtained here can provide molecular insights into the diversity of PPEs in mesotrophic and eutrophic freshwater lakes and provide an important comparison with recent surveys of marine and oligotrophic planktonic systems. MATERIALS AND METHODS Sample collection and physicochemical analysis The middle-lower reaches of the Yangtze River are located in the east of China, which is the most urbanized and developed region of China. This area is also the most widespread area of freshwater lake distribution in China, and many lakes in this area suffer from severe eutrophication. Samples were collected in 30 lakes in the middle-lower reaches of the Yangtze River in April 2012 (Fig. 1). Samples used for flow cytometric sorting were fixed with paraformaldehyde at a final concentration of 1% and quick-frozen with liquid nitrogen. They were transported on ice to the laboratory and kept at –70°C prior to analysis. Water temperature, pH, conductivity (COND), nephelometric turbidity units (NTU) and dissolved oxygen (DO) were measured in situ using a multiparameter water quality probe (YSI 6600, Yellow Spring Instruments, Yellow Springs, OH, USA). Water transparency was measured using a Secchi disk (SD). Total suspended solids (TSS) were determined by measuring the weight of dry solid material remaining after vacuum filtration of a known sample volume (50–100 mL) through a GF/F filter (0.7 μm, Whatman, Maidstone, England, UK). Concentrations of nitrogen and phosphorous nutrients, dissolved organic carbon (DOC), chemical oxygen demand (COD), DOC and chlorophyll a (Chl-a) were analyzed as described previously (Li et al.2016). The trophic status of each lake was estimated from values of Chl-a, total nitrogen (TN), total phosphorous (TP), SD and COD using the revised trophic state index (Carlson 1977) for Chinese lakes (Wang 2012). The trophic level index (TLI) was calculated as $$TLI\ = \mathop \sum \nolimits_{j = 1}^m {W_j} \times TLI( j )\ $ $, where TLI(j) and Wj corresponded respectively to the trophic level of variable j (Chl-a, TN, TP, SD and COD) and its weight. The estimation of Wj and TLI(j) has been described in detail in Wang (2012). The lakes were then numbered according to the TLI values (Table 1). Figure 1. View largeDownload slide Locations of the investigated lakes along the middle-lower reaches of the Yangtze River. Figure 1. View largeDownload slide Locations of the investigated lakes along the middle-lower reaches of the Yangtze River. Table 1. The main environmental parameters and trophic states of the investigated lakes. TLI represents trophic level index estimated from values of Chlorophyll a (Chl-a), total nitrogen (TN), total phosphorous (TP), Secchi disk (SD) and chemical oxygen demand (COD). No.  Lake names  PPEs (cell mL−1)  PPEs (%)  SD (m)  TN (mg L−1)  TP (mg L−1)  Chl-a (μg L−1)  COD (mg L−1)  DOC (mg L−1)  Cond  TLI  Level  1  Lake Longwo  4086  67  4.98  1.06  0.017  2.99  2.64  3.91  0.54  34  Mesotrophic  2  Lake Chi  5189  55  0.75  1.54  0.011  7.50  2.44  10.09  0.31  42  Mesotrophic  3  Lake Sanshan  2043  32  0.92  0.53  0.017  12.57  3.27  4.22  0.41  43  Mesotrophic  4  Lake Lu  3081  25  0.97  0.65  0.021  9.99  3.37  3.95  0.26  43  Mesotrophic  5  Lake Zhusi  4313  22  0.43  1.29  0.027  3.99  2.20  3.43  0.16  44  Mesotrophic  6  Lake Wangtian  2367  31  1.30  0.83  0.036  15.71  4.41  5.70  0.21  47  Mesotrophic  7  Lake Yezhu  8140  67  0.80  0.65  0.043  19.04  3.49  4.84  0.47  48  Mesotrophic  8  Lake Xiaoshe  6583  36  0.60  1.09  0.022  20.96  3.63  4.41  0.31  49  Mesotrophic  9  Lake Gucheng  59 215  78  0.60  1.30  0.041  9.68  4.13  5.72  0.33  50  Eutrophic  10  Lake Ce  3178  60  1.08  1.44  0.052  22.16  4.82  5.71  0.25  52  Eutrophic  11  Lake Chenyao  18 452  33  0.38  1.48  0.038  14.90  4.74  7.36  0.28  54  Eutrophic  12  Lake Xinmiao  32 169  31  0.34  1.19  0.052  23.20  3.81  15.35  0.14  55  Eutrophic  13  Lake Zhupo  3859  49  0.60  1.41  0.075  47.18  3.29  2.45  0.29  56  Eutrophic  14  Lake Fengsha  5124  53  0.20  2.56  0.064  7.88  3.43  12.11  0.21  56  Eutrophic  15  Lake Shengjin  34 569  25  0.20  0.84  0.065  35.31  3.45  13.48  0.23  57  Eutrophic  16  Lake Huangni  9761  22  0.58  3.31  0.072  28.83  3.49  12.00  0.21  57  Eutrophic  17  Lake Chidong  15 566  48  0.73  1.32  0.060  71.19  4.92  5.35  0.20  57  Eutrophic  18  Lake Shaobo  7037  41  0.25  1.00  0.081  24.11  4.49  5.52  0.47  58  Eutrophic  19  Lake Dong  12 971  63  0.63  1.31  0.090  57.48  4.84  4.12  0.34  58  Eutrophic  20  Lake Qilibali  19 230  68  0.56  2.56  0.081  57.87  3.42  5.39  0.21  59  Eutrophic  21  Lake Taibo  11 480  26  0.28  3.41  0.069  27.57  3.21  4.71  0.13  59  Eutrophic  22  Lake Cheng  12 842  51  0.42  3.75  0.083  37.81  4.11  4.79  0.73  61  Eutrophic  23  Lake Qingling  40 665  59  0.55  1.53  0.138  57.54  6.19  5.74  0.51  62  Eutrophic  24  Lake Huangjia  37 812  53  0.50  1.85  0.098  96.36  6.57  5.63  0.37  63  Eutrophic  25  Lake Jiuluo  5513  70  0.56  3.70  0.138  68.89  5.10  5.28  0.72  64  Eutrophic  26  Lake Changbaidang  6129  58  0.43  3.54  0.158  57.67  5.03  5.40  0.72  65  Eutrophic  27  Lake Dianshan  9275  64  0.38  3.79  0.147  62.74  5.22  5.46  0.71  65  Eutrophic  28  Lake Zhulintang  23 446  54  0.50  2.23  0.151  154.75  6.46  3.91  0.36  67  Eutrophic  29  Lake Huanggangdong  24 743  63  0.52  1.72  0.175  153.83  7.39  6.44  0.44  67  Eutrophic  30  Lake Nanxing  13 912  69  0.40  3.76  0.215  111.34  6.65  5.85  0.72  69  Eutrophic  No.  Lake names  PPEs (cell mL−1)  PPEs (%)  SD (m)  TN (mg L−1)  TP (mg L−1)  Chl-a (μg L−1)  COD (mg L−1)  DOC (mg L−1)  Cond  TLI  Level  1  Lake Longwo  4086  67  4.98  1.06  0.017  2.99  2.64  3.91  0.54  34  Mesotrophic  2  Lake Chi  5189  55  0.75  1.54  0.011  7.50  2.44  10.09  0.31  42  Mesotrophic  3  Lake Sanshan  2043  32  0.92  0.53  0.017  12.57  3.27  4.22  0.41  43  Mesotrophic  4  Lake Lu  3081  25  0.97  0.65  0.021  9.99  3.37  3.95  0.26  43  Mesotrophic  5  Lake Zhusi  4313  22  0.43  1.29  0.027  3.99  2.20  3.43  0.16  44  Mesotrophic  6  Lake Wangtian  2367  31  1.30  0.83  0.036  15.71  4.41  5.70  0.21  47  Mesotrophic  7  Lake Yezhu  8140  67  0.80  0.65  0.043  19.04  3.49  4.84  0.47  48  Mesotrophic  8  Lake Xiaoshe  6583  36  0.60  1.09  0.022  20.96  3.63  4.41  0.31  49  Mesotrophic  9  Lake Gucheng  59 215  78  0.60  1.30  0.041  9.68  4.13  5.72  0.33  50  Eutrophic  10  Lake Ce  3178  60  1.08  1.44  0.052  22.16  4.82  5.71  0.25  52  Eutrophic  11  Lake Chenyao  18 452  33  0.38  1.48  0.038  14.90  4.74  7.36  0.28  54  Eutrophic  12  Lake Xinmiao  32 169  31  0.34  1.19  0.052  23.20  3.81  15.35  0.14  55  Eutrophic  13  Lake Zhupo  3859  49  0.60  1.41  0.075  47.18  3.29  2.45  0.29  56  Eutrophic  14  Lake Fengsha  5124  53  0.20  2.56  0.064  7.88  3.43  12.11  0.21  56  Eutrophic  15  Lake Shengjin  34 569  25  0.20  0.84  0.065  35.31  3.45  13.48  0.23  57  Eutrophic  16  Lake Huangni  9761  22  0.58  3.31  0.072  28.83  3.49  12.00  0.21  57  Eutrophic  17  Lake Chidong  15 566  48  0.73  1.32  0.060  71.19  4.92  5.35  0.20  57  Eutrophic  18  Lake Shaobo  7037  41  0.25  1.00  0.081  24.11  4.49  5.52  0.47  58  Eutrophic  19  Lake Dong  12 971  63  0.63  1.31  0.090  57.48  4.84  4.12  0.34  58  Eutrophic  20  Lake Qilibali  19 230  68  0.56  2.56  0.081  57.87  3.42  5.39  0.21  59  Eutrophic  21  Lake Taibo  11 480  26  0.28  3.41  0.069  27.57  3.21  4.71  0.13  59  Eutrophic  22  Lake Cheng  12 842  51  0.42  3.75  0.083  37.81  4.11  4.79  0.73  61  Eutrophic  23  Lake Qingling  40 665  59  0.55  1.53  0.138  57.54  6.19  5.74  0.51  62  Eutrophic  24  Lake Huangjia  37 812  53  0.50  1.85  0.098  96.36  6.57  5.63  0.37  63  Eutrophic  25  Lake Jiuluo  5513  70  0.56  3.70  0.138  68.89  5.10  5.28  0.72  64  Eutrophic  26  Lake Changbaidang  6129  58  0.43  3.54  0.158  57.67  5.03  5.40  0.72  65  Eutrophic  27  Lake Dianshan  9275  64  0.38  3.79  0.147  62.74  5.22  5.46  0.71  65  Eutrophic  28  Lake Zhulintang  23 446  54  0.50  2.23  0.151  154.75  6.46  3.91  0.36  67  Eutrophic  29  Lake Huanggangdong  24 743  63  0.52  1.72  0.175  153.83  7.39  6.44  0.44  67  Eutrophic  30  Lake Nanxing  13 912  69  0.40  3.76  0.215  111.34  6.65  5.85  0.72  69  Eutrophic  View Large Flow cytometric analysis, sorting and DNA extraction Samples were thawed on ice and filtered through a 300-mesh sieve before flow cytometric sorting. Analyses were performed using a FACSJazzSE flow cytometer (Becton Dickinson) equipped with two lasers emitting at 488 and 640 nm, as described previously. Full details of the discrimination and counting of different picophytoplankton groups are described in a previous study (Li et al.2015). PPEs were characterized by higher forward scattering (FSC) and rich far-red fluorescence from Chl-a, and phycocyanin-rich picocyanobacteria (PC-cells) were characterized by lower FSC and rich phycocyanin fluorescence. PPEs were sorted according to the protocol described by Li et al. (2017). The DNA of the sorted cells was extracted using the DNeasy Blood and Tissue Extraction Kit (Qiagen) modified by Marie et al. (2010). PCR and pyrosequencing PCR was performed using 454 pyrosequencing adaptor-linked universal primers: TAReuk454FWD1 (5’-CCAGCASCYGCGGTAATTCC-3’) and TAReukREV3 (5’-ACTTTCGTTCTTGATYRA-3’), which can amplify the hypervariable V4 region (∼380 bp) of the eukaryotic 18S rDNA/rRNA, and were generally suitable for all eukaryotes with some exceptions in the excavates and the microsporidia (Stoeck et al.2010). PCR mixtures (20 μL) were prepared in triplicate, and each contained 4 μL of 10× PCR buffer (50 mM KCl, 10 mM Tris-HCl and 1.5 mM MgCl2), 2.4 μL of dNTP (2.5 mM), 0.25 μL of each primer (10 μM), 0.5 μL of Herculase II fusion DNA polymerase (Stratagene, Agilent Technologies), 6 μL of DNA template and 6.6 μL of ddH2O. The PCR thermal regime consisted of an initial denaturation of 2 min at 95°C, followed by 30 cycles of 20 s at 95°C, 20 s at 55°C, 30 s at 72°C and a final cycle of 10 min at 72°C. Amplicons were checked in 1.5% agarose gels for successful amplification within the expected length. Triplicate amplicon reactions were pooled and purified using the PCR purification kit (Agencourt AMPure XP, Beckman) according to the manufacturer's instructions. DNA concentration and quality was determined with a NanoDrop 1000 spectrophotometer (Wilmington, DE, USA). Pyrosequencing of PCR products was performed on a Roche FLX 454 system (Roche Diagnostics Corporation, Branford, CT, USA) installed at the Center for Genetic & Genomic Analysis (Gene sky 385 Biotechnologies Inc., Shanghai, China). Sequence analysis and taxonomic affiliation All 454 raw reads were cleaned and filtered against the following quality criteria: (i) maximum mismatches of two in the forward primers, (ii) elimination of terminal bases with low-quality scores or truncation of the sequences at the reverse primer if one was detected and (iii) removal of sequences below 250 bp or those that contained one or more ambiguous base(s) or had an average quality score below 25. Subsequently, reads were dereplicated and clustered into operational taxonomic units (OTUs) at a 98% similarity threshold using UPARSE (Edgar 2010). Chimeras were detected and removed using UCHIME (Edgar et al.2011). A representative sequence of each OTU was aligned against the database SILVA 128 (BLAST threshold e-value = e−6) for taxonomy annotation. Non-target reads belonging to Streptophyta or Metazoa were removed from further analyses. Statistical analysis All statistical analyses and visualizations were carried out in the R environment (version 3.2.1, http://www.r-project.org). To avoid the effects of different sampling depths, each sequence library was randomly resampled to the minimum sample size before further analysis. The quality of the sampling effort was assessed through the calculation of rarefaction curves (Hughes et al.2001). The α-diversity indices were also estimated and included OTU richness, Shannon diversity and Simpson diversity. Exact permutation tests were used to identify the significance of differences between mesotrophic and eutrophic lakes. The sequence data were Hellinger-transformed before further multiple statistical analyses to decrease the effect of rare species (Legendre and Gallagher 2001). The pairwise Bray–Curtis distance matrices of picoeukaryote communities were then computed between all samples. Analysis of similarities (ANOSIM) was used to test the significance of the community composition differences between lakes with different trophic levels. Canonical correspondence analysis (CCA) was used to test the relationship between environmental parameters and picoeukaryote community variability. Concurrently, the Mantel test was employed to test the effects of geographic distances on picoeukaryote community composition. Geographic distances were estimated based on coordinates (http://biodiversityinformatics.amnh.org/open_source/gdmg/), using the mean Earth radius. RESULTS Environmental characterization of the investigated lakes All the lakes investigated were typical shallow lakes with an average depth of 1.8 m (0.7–6 m). The average temperature of the lakes was 17.5°C, with small variations between 16°C and 20°C during the sampling period. Temperature was the exception; the other parameters exhibited a wide range of variation among lakes. For instance, DOC ranged from 2.45 to 15.35 mg L−1, and DO ranged from 6.71 to 17.14 mg L−1. The main parameters that affected the trophic status of the lakes are summarized in Table 1. Most of the lakes were suffering from considerable nutrient pollution, with TN and TP concentrations ranging between 0.53–6.36 and 0.011–0.31 mg L−1, respectively. According to the TLI values, only 8 of the 30 lakes were classified as mesotrophic lakes, and the others were all classified as eutrophic lakes (Table 1). Spatial distribution of PPEs The abundance of PPEs ranged between 2.04 × 103 and 5.92 × 104 cells mL−1. The highest average abundance of PPEs was recorded in the eutrophic Lake Gucheng, while the lowest abundance of PPEs was observed in the mesotrophic Lake Wangtian. The contribution of PPEs to the total picoplankton abundance ranged between 22% and 78%. The correlation analysis between the cell abundance of PPEs and environmental factors revealed that the abundance of PPEs was strongly negatively correlated with SD and positively correlated with Chl-a and DOC concentration. The contribution of PPEs to the total picophytoplankton abundance was strongly positively correlated with nutrient concentrations, including TN, DTN, NH4-N, NO3-N, TP, DTP and PO4-P, and significantly positively correlated with COND, Chl-a and COD. Thus, PPEs tended to be more abundant than picocyanobacteria in eutrophic lakes. We conducted stepwise multiple regressions to highlight the significant factors that explained most of the variation in the abundances of PPEs as well as its contribution to total picophytoplankton abundance. The results of the multiple linear regression models are shown in Table 2. The selected variables explained 76.3% and 67.8% of the total variation of the PPEs abundance and PPEs percentage of total picophytoplankton, respectively. Temperature was the best explanatory factor for PPEs abundance and its percentage of total picophytoplankton. PPEs were more correlated with SD, TP, Chl-a and DOC, while the percentage of PPEs was related to COND, DO, TN, NH4-N, TP and TSS. This also implied that trophic status is the key factor for determining the PPEs abundance. Table 2. Multiple linear regression results for predicting the abundances of PPEs and its contribution to total picophytoplankton abundance.           Model    Variables  Standardized Coefficients  t  P  Ajusted R2  P    Constant    0  1        T  0.381  3.085  0.006        SD  −0.617  −4.351  0.000      PPEs  TP  −0.693  −3.143  0.006  0.763  0.000    Chla  0.847  3.996  0.001        DOC  0.496  4.403  0.000        Constant    0  1        T  −0.462  −2.654  0.017        COND  0.299  1.483  0.157        DO  0.420  2.835  0.012      %PPEs  TN  −0.660  −2.627  0.018  0.678  0.000    NH4-N  0.542  2.342  0.032        TP  0.695  2.875  0.011        TSS  −0.379  −2.246  0.039                Model    Variables  Standardized Coefficients  t  P  Ajusted R2  P    Constant    0  1        T  0.381  3.085  0.006        SD  −0.617  −4.351  0.000      PPEs  TP  −0.693  −3.143  0.006  0.763  0.000    Chla  0.847  3.996  0.001        DOC  0.496  4.403  0.000        Constant    0  1        T  −0.462  −2.654  0.017        COND  0.299  1.483  0.157        DO  0.420  2.835  0.012      %PPEs  TN  −0.660  −2.627  0.018  0.678  0.000    NH4-N  0.542  2.342  0.032        TP  0.695  2.875  0.011        TSS  −0.379  −2.246  0.039      View Large Diversity patterns of the PPEs assemblages Samples from a total of 30 lakes were sequenced, with an average of 4653 (946–8741) reads obtained from each sample. Before resampling and subsequent analyses, we eliminated 6 samples (S01, S09, S20, S23, S24 and S28) with read numbers below 3000 to avoid the effects of low sampling depth. Thus, the PPEs diversity was described from a total of 24 samples that were resampled to a minimum sequence number of 3527, which grouped into 572 OTUs. All rarefaction curves approached asymptote both before and after resampling (Fig. S1, Supporting Information), suggesting a further increase in sampling effort would not reveal more microbial diversity. The observed number of OTUs ranged between 53 and 167 in the lakes. The Shannon and Simpson diversity indices ranged between 1.07–3.96 and 0.31–0.96, respectively. The Shannon diversity indices in mesotrophic lakes were significantly higher than in eutrophic lakes (P = 0.028). Although not statistically significant (P > 0.05), the OTU numbers and the Simpson diversities also showed similar patterns in mesotrophic and eutrophic lakes (Fig. 2). In addition, pairwise correlation analyses indicated that both Shannon and Simpson diversity indices were strongly negatively correlated with TN in the investigated lakes (Fig. S2, Supporting Information). Figure 2. View largeDownload slide OTU numbers and diversity indexes in mesotrophic and eutrophic lakes after re-sampling. Figure 2. View largeDownload slide OTU numbers and diversity indexes in mesotrophic and eutrophic lakes after re-sampling. Composition and distribution of the PPEs assemblages The taxonomic affiliations of the OTUs are shown in Table S1, Supporting Information. Over 70% of the retrieved sequences from the flow cytometry sorted samples belonged to photosynthetic cells, which were grouped into at least 18 known classes of PPEs (Table S1, Supporting Information). In addition to the sequences belonging to PPEs, 6.68% (5735) of the sequences were classified as uncultured eukaryotes and were composed of environmental sequences without known close relatives in databases; thus, it was difficult or impossible to more precisely categorize the sequences. The composition of the PPEs assemblages among samples are shown in Fig. 3, where all the PPEs classes with mean sequence proportions below 1% (Table S1, Supporting Information) were classified as others. The relative abundances of taxonomic groups varied among lakes, even though OTUs affiliated with Cryptophyceae (35 OTUs), Coscinodiscophyceae (26 OTUs) and Chlorophyceae (69 OTUs) accounted for the majority of sequences in almost all libraries, which retrieved more than 50% of the total reads (Fig. 3). In addition, 22.78% (19 280) of the sequences were affiliated with non-pigmented picoeukaryotes, especially with Alveolata and fungi (Table S1, Supporting Information). In regard to OTUs belonging to non-pigmented eukaryotes, Chytridiomycota were the most encountered OTU within fungi. Alveolata was represented mainly by Ciliophora. Finally, 59 OTUs belonged to heterotrophic stramenopiles, Radiolaria, etc. Figure 3. View largeDownload slide The main class composition of PPEs in lakes along the middle-lower reaches of the Yangtze River. Others represent rare classes including Dictyochophyceae, Xanthophyceae, Eustigmatophyceae, Prymnesiophyceae and Raphidophyceae. Figure 3. View largeDownload slide The main class composition of PPEs in lakes along the middle-lower reaches of the Yangtze River. Others represent rare classes including Dictyochophyceae, Xanthophyceae, Eustigmatophyceae, Prymnesiophyceae and Raphidophyceae. At the class level, Coscinodiscophyceae sequences were found more often in eutrophic lakes than in mesotrophic lakes (Fig. 4), with a remarkable abundance in the most eutrophic lakes with the highest TLI values (Fig. 3). In contrast, sequences belonging to Chrysophyceae, Dinophyceae and other minor classes of PPEs, including Dictyochophyceae, Eustigmatophyceae, Prymnesiophyceae, Xanthophyceae and Raphidophyceae, were more abundant in mesotrophic lakes than in eutrophic lakes (P < 0.05, Fig. 4). Apart from these differences, no significant differences in the other groups of PPEs were found between lakes with different trophic levels (P > 0.1). Figure 4. View largeDownload slide Box plots of Coscinodiscophyceae, Chrysophyceae, Dinophyceae and Other rare PPEs class in mesotrophic and eutrophic lakes. Figure 4. View largeDownload slide Box plots of Coscinodiscophyceae, Chrysophyceae, Dinophyceae and Other rare PPEs class in mesotrophic and eutrophic lakes. The composition of OTUs within each primary high-rank taxa of PPEs from all samples are also illustrated (Fig. 5), where only the most abundant OTUs in each taxa are presented with colors. At the level of OTUs, Bacillariophyceae were mainly dominated by one OTU that was affiliated with Cyclotella atomus, which was highly abundant in the highly eutrophic water bodies. For Chlorophyta, one OTU affiliated with Chlamydomonas sp. tended to be more prevalent in the eutrophic lakes. For Cryptophyta, the major OTUs, OTU22 and OUT7, were widely distributed in both mesotrophic and eutrophic lakes. Of the other minor classes, OTU18, which belonged to Poterioochromonas malhamensis, was more frequently observed in eutrophic lakes than in mesotrophic lakes. Figure 5. View largeDownload slide The composition of OTUs within each primary high-rank taxa of PPEs. Figure 5. View largeDownload slide The composition of OTUs within each primary high-rank taxa of PPEs. Correlations between PPEs assemblages and environmental parameters and geographic distances According to the results of detrended correspondence analysis, the gradient length of the first axis was 6.7, which implied a large heterogeneity in the community composition of PPEs. Thus, CCA was used to analyze the correlation between PPEs assemblages and various environmental parameters (Fig. 6). The results indicated a significant correlation between the community of PPEs and the selected environmental parameters (P < 0.003). The first two axes explained 15.9% and 13.3% of the community variances, respectively. CCA showed that the lakes with similar trophic statuses tended to cluster together and presented more similar community compositions, although the ANOSIM results indicated insignificant differences between the communities of PPEs in the mesotrophic and eutrophic lakes (P = 0.208). The CCA results also revealed that the OTUs classified within different high-level taxonomic groups (i.e. class level) were linked to different environmental parameters. For example, most of the OTUs belonging to Chrysophyceae, Dinophyceae and the other classes of PPEs (i.e. ‘Others’ in Fig. 6) showed a strong preference for mesotrophic lakes with higher SD and TN/TP ratio; in contrast, the Chlorophyta and Bacillariophyta OTUs appeared to have a broader spectrum of environmental adaptations. In addition, the unclassified and non-pigmented OTUs seemed more prevalent in lakes with higher DO concentrations. Figure 6. View largeDownload slide CCA triplots of samples, environmental parameters and species of picoeukaryotes in lakes along the middle-lower reaches of the Yangtze River. Figure 6. View largeDownload slide CCA triplots of samples, environmental parameters and species of picoeukaryotes in lakes along the middle-lower reaches of the Yangtze River. To test whether the differences in the community compositions of PPEs were also affected by geographical distances between sites, we calculated Bray–Curtis distances based on the PPEs OTUs frequencies between sites of each trophic status. In addition, we performed Mantel and partial Mantel analysis to test the correlation between the Bray–Curtis and the geographical distance matrices. The results suggested that the PPE community was also significantly correlated with spatial distance (r = 0.17; P = 0.03). However, this significance disappeared in the partial Mantel test when environmental factors were considered as covariates (r = 0.077, P = 0.21). DISCUSSION PPEs were dominant in the eutrophic lakes The relative proportion of picophytoplankton to total phytoplankton decreased with increasing trophic status of aquatic systems (Callieri 2007). The contribution of PPEs to total picophytoplankton abundance differed according to environmental features. PPEs were prevalent typically in winter and spring, while PC-cells were prevalent in summer and autumn (Li et al.2016). PC-rich picocyanobacteria prevailed over phycoerythrin-rich cells in shallow, turbid and eutrophic lake areas (Vörös et al.1998). Previous studies stated that PPEs were not the major picophytoplankton components and that, in lakes, picocyanobacteria were generally an order of magnitude higher in abundance than their eukaryotic counterparts in both freshwater (Malinsky-Rushansky, Berman and Dubinsky 1995; Crosbie, Pöckl and Weisse 2003; Mózes, Présing and Vörös 2006; Callieri 2007; Winder 2009; Contant and Pick 2013) and marine systems (Worden, Nolan and Palenik 2004; Jiao et al.2005; Bouman et al.2011; Zhao et al.2011). Recently, it was reported that PPEs were the clearly dominant groups of picophytoplankton in two large eutrophic lakes, Lake Chaohu and Lake Taihu (Li et al.2016). Weisse has noted that small and shallow lakes should be treated separately from large lakes in terms of picophytoplankton abundance and their relationship to the trophic status of lakes (Weisse 1993). According to Weisse, in small and shallow lakes, factors unrelated to macronutrients have a stronger impact on APP abundance than in larger lakes (Weisse 1993). Our results, which were based on the survey of 30 small meso-eutrophic lakes, showed that the contribution of PPEs to total picophytoplankton averaged 49.1% and showed a strong positive relationship with nutrient concentrations and Chl-a. This was consistent with previous results based on field investigations and experiments that stated PPEs had a competitive advantage over picocyanobacteria in habitats with increasing nutrient availability. High diversity of PPEs dominated by Bacillariophyta, Chlorophyta and Cryptophyta in abundance The understanding of the diversity and community structure of PPEs is continually improved as a result of the increase in investigations in marine and some oligotrophic lakes in recent decades (Not et al.2005; Richards et al.2005; Kirkham et al.2013; Choi et al.2016). However, little is known about the structure of the PPE community over larger spatial scales or environmental gradients in inland lakes. Here, we have presented a large molecular dataset that begins to elucidate the community structure and distribution of PPEs in 24 mesotrophic-to-eutrophic lakes along the middle-lower reaches of the Yangtze River in East China (Fig. 1), which complements our previous research in the large eutrophic lakes, Lake Taihu and Lake Chaohu (Li et al.2017). The current dataset adds further weight to support the suggestion that the distributional pattern of PPEs differs at the class level based on the varying trophic levels of aquatic systems. Our results indicated that Cryptophyceae, Coscinodiscophyceae and Chlorophyceae were extremely abundant in the mesotrophic and eutrophic lakes that were investigated in this study (Figs 3–5). This is consistent with our previous observation in the eutrophic Lakes Taihu and Chaohu, which were largely dominated by sequences from Chlorophyta and Bacillariophyta (Li et al.2017). Furthermore, Chlorophyta have been reported as the major autotrophic organisms in lakes characterized by nutrient-rich systems (Lepère, Domaizon and Debroas 2007). Cryptophyceae were also associated with higher nutrient concentrations, even in the ocean (Kirkham et al.2011). The dominant taxonomic PPEs found in the freshwater systems described here also differ a lot with those in oceans, where Prymnesiophyceae, Chrysophyceae, and to a lesser extent, Prasinophyceae (here Mamiellophyceae) and Bolidophyceae have been reported to be the most common dominant groups of PPEs (Lepère, Vaulot and Scanlan 2009; Kirkham et al.2011, 2013; Choi et al.2016). Compared with the dominant freshwater algae, it seemed more difficult for the common marine lineages to conquer the diverse freshwater ecosystems (Hepperle and Schlegel 2002). In contrast, ecophysiological adaptations have likely led to the success of Chlorophyceae and Trebouxiophyceae in freshwater and terrestrial environments (Leliaert et al.2012). On the other hand, the recurrent discovery of ‘typically marine’ lineages in freshwater systems highlights the possibility of marine-freshwater barriers being transgressed (Massana et al.2014; Simon et al.2014; Triadó-Margarit and Casamayor 2015; Li et al.2017). However, these lineages were usually sparsely distributed in the corresponding freshwater systems, and there is still a long way to go before they become dominant or abundant. The difference in PPE community structure between mesotrophic and eutrophic lakes The investigated lakes encompassed a wide range of physical and chemical conditions, ranging from mesotrophic to eutrophic lakes with extremely high nutrients and Chl-a values (Table 1). PPEs richness and diversity appeared to be higher in the mesotrophic lakes than in the eutrophic lakes. This is, to some extent, consistent with previous observations on picoeukaryotes, where the oligo-mesotrophic systems possessed the most diversified library (Lefranc et al.2005; Cheung et al.2010). However, it would be premature to draw any conclusions on the classic unimodal diversity–productivity relationship unless further inclusion of more oligotrophic lakes occurred. Specifically, significant negative correlations were also observed between the diversity indices and TN concentrations in the investigated lakes along the middle-lower reaches of the Yangtze River. Despite the statistically insignificant differences of the PPE community structure between the mesotrophic and eutrophic lakes, Chrysophyceae, Dinophyceae and other minor classes of PPEs, including Dictyochophyceae, Eustigmatophyceae, Prymnesiophyceae, Xanthophyceae and Raphidophyceae, were significantly abundant in lakes with lower trophic levels (Fig. 4). The phylogenetic analysis also revealed that the dominant OTUs specific to mesotrophic lakes were mostly affiliated with Chrysophyceae and Dinophyceae. CCA analysis showed that the OTUs grouped within Chrysophyceae, Dinophyceae and other classes of PPEs were closely linked to relatively high SD and TN/TP ratios in the mesotrophic lakes (Fig. 6). None of the previous investigations had described the dominance of Chlorophyta or Bacillariophyta in the oligotrophic systems (marine or freshwater); rather, Chrysophyceae, Dinophyceae and Haptophyceae were usually more abundant (Lepere et al.2006; Shi et al.2011; Charvet et al.2012; Kirkham et al.2013; Lepère et al.2016). We also observed several PPEs OTUs affiliated with C.atomus, Chlamydomonas sp. and P.malhamensis that tended to be more prevalent in the eutrophic lakes. Cyclotella atomus was a nutrient-tolerant diatom taxa and has been found frequently in lakes with high trophic levels (Yang et al.2005, 2010). However, both Chlamydomonas sp. and P.malhamensis have been proved to be mixotrophic with high heterotrophic capability, which can be beneficial in nutrient-deficient conditions (Poerschmann, Spijkerman and Langer 2004; Holen 2010). The prevalence of these mixotrophic lineages in eutrophic lakes in the present study implied a complicated mechanism involved in their choices for nutritional pathways. Furthermore, the CCA and Mantel analysis performed here indicated that the PPE community structure variances were essentially explained by their local environmental parameters instead of geographic distances. This is in good agreement with the view that ‘everything is everywhere, but the environment selects’ (Baas-Becking 1934; Finlay 2002). Nevertheless, ecologists never reach consensus with regard to microbial geography, and opposite conclusions were drawn when considering different distance scales. Lepère et al. (2013) suggested that the beta diversity of picoeukaryotes was linked to the geographic distance of lakes, which were on average separated from each other by 133 km (Lepère et al.2013). In contrast, a recent study clearly rejected geographic distance as a driver of community composition when involving lake distances at the 10-km scale (Simon et al.2014, 2015), which is in accordance with our results, although our sampling sites were at a much larger distance scale (5–666 km, mean: 284 km), similar to Lepère et al. (2013). SUPPLEMENTARY DATA Supplementary data are available at FEMSEC online. Acknowledgements We are thankful to Feizhou Chen for their field sampling. FUNDING This research was supported by the National Natural Science Foundation of China (31670462 and 41431176); and the Sino-French International Collaborative Research Project (41661134036) and Investigation of basic science and technology resources (2017FY100300) financially sponsored this work. Conflict of interest. None declared. REFERENCES Baas-Becking LGM. Geobiologie of Inleiding tot de Milieukunde . The Hague, the Netherlands (in Dutch): WP Van Stockum & Zoon NV, 1934. 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