544 Vizintin et al.vp Acta Bot. Croat. 71 (2), 299–310, 2012 CODEN: ABCRA 25 ISSN 0365-0588 eISSN 1847-8476 Influence of environmental and spatial factors on the distribution of surface sediment diatoms in Chaohu Lake, southeast China XU CHEN1, 2, XIANGDONG YANG2*, XUHUI DONG2, ENFENG LIU2 1 Department of Geography, Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, People’s Republic of China 2 State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, People’s Republic of China Abstract – The spatial distribution of surface sediment diatoms in Chaohu Lake (south- east China), and their relationships with environmental and spatial variables were ana- lyzed in this study. The diatom assemblages were dominated by planktonic species. Three dominant species Cyclostephanos dubius, Aulacoseira granulata and Aulacoseira alpi- gena are unevenly distributed across the lake. The distribution of surface sediment dia- toms must be subject to trophic status, hydrodynamics and other spatial variables in the lake. Keywords: Aulacoseira, Cyclostephanos, diatom, sediment, distribution, Chaohu Lake Abbreviations: c fd – the percentage frequency dependent susceptibility, LOI – loss on igni- tion, FPF – fine particle fraction, MD – median grain size, PCNM – principal coordinates of neighbour matrices Introduction The spatial structure of biological populations and communities plays a central role in many ecological theories, such as the theories of succession, adaptation, competition and so on (LEGENDRE and FORTIN 1989). In order to untangle the spatial patterns and include space as an explicit variable in ecological modeling, many methods have been proposed during the recent years, such as the geographic coordinate method, trend-surface analysis and principal coordinates of neighbour matrices (PCNM) analysis (cf. BORCARD et al. 2004). Among these methods, PCNM analysis can achieve a spectral decomposition of the spatial relationships among the sampling sites (BORCARD and LEGENDRE 2002). And ACTA BOT. CROAT. 71 (2), 2012 299 * Corresponding author, e-mail: xdyang@niglas.ac.cn Copyright® 2012 by Acta Botanica Croatica, the Faculty of Science, University of Zagreb. All rights reserved. PCNM analysis can be easily incorporated into canonical analysis models, providing a use- ful tool to assess the influence of spatial structure (DRAY et al. 2006). For instance, recent studies argued that the spatial factor had an important effect on the distribution of fish in lakes based on PCNM analysis (BRIND’AMOUR et al. 2005, SHARMA et al. 2011). The spatial patterns of not only large organisms but also of various microorganisms (e.g., phyto- plankton, cladoceran and chironomid) have attracted much attention recently (BEISNER et al. 2006, SWEETMAN et al. 2010, CAO et al. 2012). Diatoms are unicellular and eukaryotic organisms, occurring throughout the world, growing in almost all aquatic environments (BATTARBEE et al. 2001). The relationships be- tween diatoms and environmental variables act a vital role in limnological and paleo- limnological researches (SMOL and CUMMING 2000). Most studies ignore the effect of spa- tial variables on diatom distribution. However, YANG et al. (2009) indicated that spatial variables were a further influence on diatom distribution in the Round Loch of Glenhead, a small lake in south-west Scotland. Unlike small lakes, large lakes always exhibit complex spatial gradients (NÖGES et al. 2008, CÓZAR et al. 2012). It is assumed that spatial factors should impose an important role in diatom distribution in large lakes. In this study, we consider the importance of environmental and spatial factors on dia- tom distribution in Chaohu Lake, a large (770 km2) shallow lake in southeast China. Dia- toms and sedimentary proxies from surface sediments were analyzed in the laboratory. Meanwhile, spatial factors were calculated using PCNM analysis. Thereafter, we estimated the effects of spatial and environmental variables on surface sediment diatoms quantita- tively, using the canonical analysis model. Materials and methods Study site Chaohu Lake (N 31°25’–31°43’, E 117°17’–117°51’) is the fifth largest freshwater lake located in the Yangtze floodplain of southeast China (Fig. 1). The Chaohu watershed is lo- cated in a subtropical monsoon climate zone with an annual average temperature of ca. 16.1 °C, an annual average rainfall of ca. 996 mm and an annual mean wind speed of ca. 4.1 m s–1 (WANG and DOU 1998). The lake has an open water area of 770 km2 with a mean water depth of 3.0 m. Between 1984 and 2006, the average concentrations of total phosphorus (TP), total nitrogen (TN) and chlorophyll a (Chl a) were 256 mg L–1, 2850 mg L–1 and 20–40 mg L–1 respectively (XIE 2009). The main sources of nutrients come from the inputs of the rivers located in the western region, including Nanfei River and Paihe River (Fig.1) (SHANG and SHANG 2005). Due to the increasing nitrogen and phosphorus inputs from in- dustrial wastewater, domestic sewage and agricultural fertilizers since the late 1970s, the lake has suffered serious eutrophication in the past several decades. Sampling and laboratory analyses Thirty nine surface sediment samples were collected in June 2009 using a Kajak gravity corer. The top 1 cm sediment was removed for surface sediment samples. All samples were transported back to the laboratory and stored at 4 °C. Sedimentary proxies were analyzed in the State Key Laboratory of Lake Science and Environment, including particle size, mag- netic susceptibility, loss on ignition (LOI) and diatoms. Particle size spectra of the samples 300 ACTA BOT. CROAT. 71 (2), 2012 CHEN X., YANG X., DONG X., LIU E. were determined using a Malvern automated laseroptical particle-size analyzer (Master- sixer-2000). Magnetic susceptibility was measured using a Bartington Instruments MS2 sensor. LOI of samples was measured after 4 h of exposure at 550 °C (HEIRI et al. 2001). Di- atom samples were treated using hydrogen peroxide and hydrochloric acid in order to re- move all organic and carbonate components (BATTARBEE et al. 2001). Microspheres were added to calculate the diatom concentration (BATTARBEE and KNEEN 1982). All samples were mounted on microscope slides using the high refraction mountant Naphrax®. Diatoms were identified and counted using an Olympus BX51 microscope with an oil immersion objective at a magnification of 10×100. Diatom taxonomy mainly followed KRAMMER and LANGE-BERTALOT (1986; 1988; 1991a, b). A minimum of 500 valves were identified and counted for each sample. Spatial data GPS coordinates of 39 sampling sites were recorded in the field. The original coordi- nate values were z-score transformed and these standard coordinates were used to create the dataset of spatial variables derived from PCNM using the program Spacemaker2 (BORCARD and LEGENDRE 2004). A matrix of Euclidean distances between samples was computed and subsequently truncated based on truncation distance, which was equal to or larger than the largest distance between neighbours. And then, a principal coordinate analysis (PCoA) was performed on the truncated distance matrix. Thereafter, eigenvectors associ- ated with positive eigenvalues were kept and used in the subsequent ordination analysis. ACTA BOT. CROAT. 71 (2), 2012 301 DISTRIBUTION OF SURFACE SEDIMENT DIATOMS Fig.1. Location and map of Chaohu Lake (southeast China) and the sampling sites. Data analysis All statistical analyses of diatom assemblages were based on percent abundances and included diatom taxa with ³ 1% abundance in at least one sample. Diatom distribution data were represented in contour plots, performed using interpolation by a kriging approach with Surfer software (Golden software Inc.). And the percentage data of diatom were square root transformed prior to ordination analyses. Detrended correspondence analysis (DCA) was applied to the diatom percentage data to explore the patterns of species changes and biological species turnover (the gradient length) (JONGMAN et al. 1995). Redundancy analysis or canonical correspondence analysis was selected based on gradient length to ex- plore the relationships between diatom assemblages and explanatory variables (JONGMAN et al. 1995). To reduce the possible effects of the difference in the number of variables in- cluded in each set of explanatory variables, we used only those variables that were signifi- cant based on a forward selection (p<0.05; n=499 unrestricted permutations). The significant variables were then classified into spatial category [S] and environmen- tal category [E]. Three steps were required to partition variance in diatoms between spatial and environmental categories. First, an RDA with no covariables was used to measure the total amount of variance (as sum of canonical eigenvalues) in the diatom assemblages at- tributable to all explanatory variables [S+E] and the total unexplained variance (100– [S+E]). Second, a partial RDA was used to calculate variance explained by the unique ef- fects of each category ([S] or [E]). In this step, ordination of one explanatory category was run with the other category as covariable. Third, the interaction term [SE] was calculated by subtracting appropriate terms generated during steps 1 and 2 (i.e., [SE]=[S+E]–[S]– [E]). Variance partitioning analysis is based on standard ordination methods (BORCARD et al. 1992). The ordination was performed using the program CANOCO version 4.5 (TER BRAAK and [MILAUER 2002). Results Distribution of surface sediment diatoms A total of 24 genera and 68 species were identified in the 39 surface sediment samples. Diatom assemblages were characterized by planktonic diatoms such as Cyclostephanos dubius (Hustedt) Round, Aulacoseira granulata (Ehrenberg) Simonsen and Aulacoseira alpigena (Grunow) Krammer with few epiphytic or benthic species. And smaller abun- dances of Cyclotella meneghiniana Kützing, Nitzschia palea (Kützing) W. Smith, Tryblio- nella levidensis W.Smith, Gyrosigma acuminatum (Kützing) Rabenhorst and Stephano- discus sp. were observed in the samples. The total percentage of all three dominant species together (i.e., C. dubius, A. granulata and A. alpigena) was above 81% in each sample. The western part of the lake was dominated by C. dubius (Fig. 2). In the central region of the lake, A. granulata was more abundant than the other species. A. alpigena, N. palea and Stephanodiscus sp. were mainly found in the western and eastern basins. G. acumi- natum was concentrated in the eastern region. T. levidensis and C. meneghiniana were widely distributed in the lake but with small higher abundance patches in different regions. Environmental and spatial factors The environmental and spatial characteristics in the sampling sites are presented in fig- ure 3. The percentage frequency dependent susceptibility (cfd%) showed minor differences 302 ACTA BOT. CROAT. 71 (2), 2012 CHEN X., YANG X., DONG X., LIU E. across the lake. Unlike cfd, both LOI and particle size showed visible spatial variations. The values of LOI in the western basin were much higher than those in other basins. Sediments in the central basin were characterized by coarse particles. Spatial variables PCNM1 and PCNM2 were calculated and both of them were included in the later analysis. PCNM1 de- creased from the eastern basin to the western basin. High values of PCNM2 occurred in the central basin. ACTA BOT. CROAT. 71 (2), 2012 303 DISTRIBUTION OF SURFACE SEDIMENT DIATOMS Fig. 2. Spatial distribution of surface sediment diatoms concentrations (104 valves dry weight g–1) in Chaohu Lake. Ordination analyses The results showed that the gradient length of DCA axis 1 was 1.04 standard deviations, indicating that redundancy analysis (RDA) was suitable for exploring the relationships be- tween diatom assemblages and spatial and environmental factors (JONGMAN et al. 1995). The environmental variables included cfd, LOI, fine particle fraction (FPF) and median grain size (MD). Forward selection identified LOI, FPF and spatial variables (i.e. PCNM1 and PCNM2) as significant environmental variables for the later ordination analysis (Fig. 4). RDA captured the variance in the species-environment and space relationship quite well; 77.2% by the first two axes (Tab.1). The first ordination axis was positively related to LOI (r=0.703), while it was negatively related to PCNM1 (r=–0.766) and PCNM2 (r= –0.197). The second ordination axis was negatively related to FPF (r=–0.581) and PCNM1 (r=–0.260) (Tab.1). In the RDA biplot (Fig. 4), most of the samples in the eastern and cen- tral region were present in the ordination space characterized by high values of PCNM1 and PCNM2. Samples in the western region of the lake were present in the space character- ized by high values of LOI. Some of the samples in the central part of the lake were present in the space characterized by low values of FPF. Variation partitioning Spatial and environmental variables together were able to account for 30% of the vari- ance in diatom data. Pure environmental variance [E] (non-spatially structured environ- mental factors) accounted for 12.4%, pure spatial variance [S] for 11.2%, and 6.4% was atributed to the spatially structured components of the environmental variables [SE] in- cluded in the analysis. Seventy per cent of the variance in diatom data was unexplained by either the spatial or environmental variables considered in the analysis. 304 ACTA BOT. CROAT. 71 (2), 2012 CHEN X., YANG X., DONG X., LIU E. Fig. 3. Variations of environmental and spatial variables in the sampling sites. cfd – the percentage frequency dependent susceptibility, LOI – loss on ignition, FPF – fine particle fraction, MD – median grain size, PCNM – principal coordinates of neighbour matrices. Discussion Environmental factors and diatom distribution The diatom assemblages were dominated by three planktonic species (i.e., C. dubius, A. granulata and A. alpigena) in Chaohu Lake. The dominant species varied according to dif- ferent parts in the lake. For example, the percentage of C. dubius was steadily above 60% in the samples of the western region. However, the diatom assemblages in the central region ACTA BOT. CROAT. 71 (2), 2012 305 DISTRIBUTION OF SURFACE SEDIMENT DIATOMS Tab.1. Summary statistics for the redundancy analysis of diatom-environment and space. Only en- vironmental variables selected in the forward selection procedure are presented in the ordi- nation. Axes 1 2 3 4 Eigenvalue 0.137 0.095 0.049 0.019 Species-environment and space correlations 0.866 0.757 0.653 0.647 Cumulative percentage variance of species data 13.7 23.1 28.1 30.0 of species-environment and space relation 45.6 77.2 93.6 100.0 Correlation with species axes LOI 0.703 –0.051 –0.211 –0.313 FPF 0.071 –0.581 –0.374 –0.180 PCNM1 –0.766 –0.260 –0.163 –0.128 PCNM2 –0.197 –0.077 0.632 –0.019 Fig. 4. Redundancy analysis ordination plot (axes 1 and 2). The arrows represent the significant ex- planatory variables explaining variation in the diatom assemblage. were characterized by Aulacoseira granulata whose abundance was above 50% in each sample. Compared with the two dominant species, A. alpigena was less abundant in each sample. And A. alpigena was mainly found in the western and eastern basins. The total of percentages of other species was less than 20% in each sample. LOI has been widely used as a method to estimate the amount of organic matter in sedi- ments, and it can be used as a proxy for paleoproductivity (SANTISTEBAN et al. 2004). And in the Yangtze floodplain lakes, LOI can be indirectly indicative of lake trophic level (YAO and XUE 2009). RDA indicated that there was a significantly positive correlation between LOI and RDA axis 1 (r=0.703), suggesting the influence of trophic level on the diatom as- semblages. Cyclostephanos dubius is a typical eutrophic diatom in many shallow nutri- ent-rich lakes all over the world (BRADSHAW and ANDERSON 2003). Higher nutrient concen- trations (e.g, total nitrogen and total phosphorus) were observed in the western part of the lake due to the great amount of industrial and domestic sewage from Hefei City (XU et al. 2003, XIE 2009). Therefore, the aggregation of C. dubius in the western region was in re- sponse to the high nutrient level in this area. In addition, the western region was also one aggregated area of other species (e.g., A. alpigena, T. levidensis, N. palea and Cyclotella meneghiniana). These species were adapted to the mesotrophic or eutrophic status in the Yangtze floodplain lakes (YANG et al. 2008). Fine particle fraction was another significant factor influencing diatom distribution. Particle size fraction can provide information on hydrodynamic intensity in the Yangtze floodplain lakes (CHEN et al. 2011, LIU et al. 2012). Coarser grains may indicate strong cur- rents that are able to transport fine particles away; in contrast, finer grains may be indica- tive of weak hydrodynamic intensity. FPF strongly correlated to axis 2 (r=–0.581), suggest- ing the effect of hydrodynamic intensity on diatom distribution. For instance, the growth of A. granulata was sensitive to flow condition due to its fast sinking rate (HOTZEL and CROOME 1996). The strong hydrodynamic condition in the central basin, suggested by the coarser grains, would favor the development of A. granulata. The influence of spatial variables Spatial structures observed in ecological communities can arise from two independent processes: (1) Species’ response to environmental factors, which are usually spatially structured; and (2) spatial autocorrelation can also be created directly as a result of conta- gious biotic processes such as growth, seed dispersal or competition dynamics (FORTIN and DALE 2005, DRAY et al. 2006). In most situations, the spatial structure of communities is due to the synchronous effect of these two processes. Variance partitioning analysis (BORCARD et al. 1992) can be used to distinguish the effect of these two sources of spatial structure. In this study, we use partial ordination analysis to untangle the effects of »pure« spatial variance and spatially structured components of the environmental variables. The 11.2% of total variance in diatom data explained by the sole effect of spatial variables sug- gests that spatial factors must have a significant effect on diatom distribution in Chaohu Lake. Generally, the first PCNM variables represent coarse patterns, and the last ones rep- resent finer-scale patterns (BORCARD et al. 2004). PCNM1 showed the large-scale spatial gradient from the eastern to the western basin, while PCNM2 exhibited the finer-scale pat- tern. In addition, some environmental factors showed a visible spatial gradient, such as LOI declining from the western to the eastern basin. It is assumed that the combined effect of 306 ACTA BOT. CROAT. 71 (2), 2012 CHEN X., YANG X., DONG X., LIU E. spatial and environmental variables must exert an influence on diatom distribution. The re- sult indicated that 36.4% (=6.4/(6.4+11.2)) of the variance in spatial sub-models was due to the interactive effect of spatial and environmental factors. Further research In this study, the combined contribution of environmental and spatial variables to the variance in diatom data was only 30%. The high unexplained contribution may result from inadequate measurement of limnological variables. In shallow lakes, wind-driven resus- pension may be responsible for the spatial distribution of diatoms (YANG et al. 2009). In ad- dition, other chemical variables (e.g. TN, TP) of sediment can provide more information on microecological conditions influencing diatom distribution. Therefore, more data about the hydrodynamic condition and chemical factors would improve the conclusions about the spatial distribution of diatoms in Chaohu Lake. Conclusions Surface sediment diatom assemblages were dominated by three planktonic species (i.e. Cyclostephanos dubius, Aulacoseira granulata and A. alpigena) in Chaohu Lake. These three dominant species were unevenly distributed in the lake. The results of ordination analyses indicated that loss on ignition, fine particle fraction, PCNM1 and PCNM2 were four significant factors influencing diatom distribution. It is assumed that the distribution of surface sediment diatoms must be subject to trophic status, hydrodynamic intensity and other spatial variables in the lake. The unique effect of spatial variables (i.e., PCNM1 and PCNM2) captured 11.2% of the variance in diatom data. Although this value is not very high, the results recommend that spatial variables should be considered in the studies of di- atom distribution in large shallow lakes. Acknowledgements We would like to thank Zhang Enlou, Pan Hongxi, Yao Min, Meng Xianghua and Du Chenchang from Nanjing Institute of Geography and Limnology, Chinese Academy of Sci- ences for their help in the field. This study was supported by the National Natural Science Fund of China (40972217) and by National Basic Research Program of China (2012 CB956100). References BATTARBEE, R. 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