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Protistology 9 (3/4), 133-142 (2015)
Protistology
Annual variation of protozoan communities and its relationship to environmental conditions in a sub-tropic urban wetland ecosystem, southern China
Xinlu Shi1, Xiangwei Meng1, Guijie Liu1, Yong Jiang2, Songlu Liu1, Cuicui Hou1, Qingjuan Meng1 and Henglong Xu2
1 Hangzhou Key Laboratory for Animal Adaptation and Evolution, Hangzhou Normal University, Hangzhou 310036, China
2 Laboratory of Microbial Ecology, Department of Marine Ecology, Ocean University of China, Qingdao 266003, China
Summary
With ease of collection, short life cycles, lack of complex developmental stages, and rapid response to environmental changes, protozoa attract the increasing attention as suitable indicators for bioassessment. In order to reveal the annual variation of protozoan communities and its relation to the environmental conditions in a sub-tropic urban wetland, the protozoan species composition, abundance, diversity, and their correlations with abiotic factors were studied in Xixi wetland, Hangzhou, China. A total of 89 protozoan species comprising 34 ciliates, 13 flagellates, and 42 rhizopods were recorded; 7 of those were the dominant species. The protozoan abundance ranged from 3*104 ind. l-1 to 19.65*104 ind. l-1; ciliates (69.3%) were the primary contributors in terms of relative abundance. The cluster analysis discriminated the protozoan communities into three annual stages: spring, summer-autumn and winter at a 30% similarity level with significant difference. Multivariate correlation analysis showed that temporal variation in protozoan communities was significantly related to the changes of environmental variables, especially water temperature, dissolved oxygen, chemical oxygen demand (COD) and nutrients. Three diversity indices (species richness, diversity and evenness) were significantly correlated with the COD and nutrients. The results demonstrated that the annual variation in protozoan abundance represented a clear seasonal shift in response to environmental changes and thus may be used as a potential indicator for assessing water quality in a sub-tropic urban wetland ecosystem.
Key words: annual variation, protozoan communities, urban wetland, water quality
© 2015 The Author(s)
Protistology © 2015 Protozoological Society Affiliated with RAS
Introduction
Protozoa are an important ecological group which plays an invaluable role in the functioning of microbial food webs by transferring energy to higher trophic levels in aquatic ecosystems (Cairns et al., 1972; Jiang et al., 2007, 2011; Xu et al., 2009). With ease of collection, short life cycles, lack of complex developmental stages and rapid response to environmental changes, protozoa have proved to be useful bioindicators of water quality in many aquatic ecosystems (Cereceda-Martin et al., 1996; Madoni, 1994; Lee et al., 2004; Jiang and Shen, 2005). Therefore, increasing attention is being focused on protozoa as suitable indicators for bioassessment (Panswad and Chavalparit, 1997; Kchaou et al., 2009; Xu et al., 2005, 2008, 2009).
In recent years, many investigations on the changes of protozoan communities have been carried out in various aquatic ecosystems. Xu et al. (2001) reported the relationship between protozoan community diversity and water quality in the Baiyangdian lake, Xie et al. (2005) demonstrated the relationship of zooplankton community structure and water pollution of the Jinjiang river valley, and Shi et al. (2012) revealed the spatial patterns of protozoan communities for assessing water quality in the Hangzhou section of Jing-Hang Grand Canal in China. However, those investigations were commonly focused on the lakes and rivers, but less — on the sub-tropic urban wetland.
Xixi wetland (30°3'35"N-30°21'28"N, 120°0'26"E -120°9'27"E) is located in the sub-tropic area near Hangzhou city, southern China. It is an urban, farming and cultural wetland in the national wetland park, which has been one of the national 5A-level scenic spots since 2012. The 70% of the area are formed by river, pond, lake overflow, and marshes. It has many environmental functions and plays an important role in controlling flood, regulating runoff, resisting drought, preventing pollution, regulating climate, controlling soil erosion and sedimentation, and beautifying the environment. Up to now, however, human activities have become increasingly frequent in the wetland with the development of economy and society, garbage accumulation, sewage from aquaculture, tourism etc., that cause the destruction of wetland' ecological environment, especially the water environment.
In order to reveal the annual variation of protozoan communities and its relationship to the environmental conditions in a sub-tropic urban wetland, a 1 -year research was performed in the protection engineering area of Xixi wetland from May 2012 to April 2013. The aims of this study
were: (1) to document the taxonomic composition of protozoan communities; (2) to reveal temporal dynamics in terms of species number, abundance and biodiversity; (3) to determine their relationships to environmental parameters and use as a potential indicator of water quality in sub-tropic urban wetland ecosystems.
Material and methods
Study areas and sampling stations
Xixi wetland is divided into three areas: the eastern ecological protection cultivation area, ecological tourism and leisure area in the central region, and the western ecological landscape closure area; only the central area is open to visitors. Eight sampling stations (Stations 1-8) were selected as the study zones in the central open areas (Fig. 1). A total of 12 sampling events were monthly carried out during the study period of May 2012 through April 2013.
Sampling, identification, and measurements
In order to get quantitative biological samples, a 1,000 ml mixed water sample from the upper-mid-bottom water layers was collected monthly at each station with depths 1—3 m. Samples for counting were preserved in situ using Lugol's iodine solution (1.5% final concentration, v/v), precipitated for 48 h, and the final volume was set to 50 ml of the concentrated sediments. A 0.1 ml subsample of this final concentrate was placed in a perspex counting chamber, observed and counted under a light microscope at a magnification of *400 (Shen et al., 1990). All counts were repeated five times, and three subsamples from each sample yielded a standard error of <8% of the mean values of counts (Xu et al., 2008, 2010).
Identification of protist species was performed according to Shen et al. (1990), Patterson and Hedley (1992) and Foissner et al. (1999). Examination of all live samples was usually completed as soon as possible after sampling (generally within 2-4 h) in order to prevent significant changes in species composition due to predation in the samples, etc. (Shen et al., 1990).
Analysis of environmental factors
Water temperature (T) and dissolved oxygen (DO) were measured using a water quality analyzer (model: HQ30d, HACH), while hydrogen ion
concentration (pH) was detected with a pH meter (model: ST20, OHAUS) in situ; the determination of chemical oxygen demand (COD), biological oxygen demand (BOD5), total nitrogen (TN), and total phosphorus (TP) followed the standard procedures of American Public Health Association (APHA, 1989).
Data analyses
Species diversity (Shannon-Wiener, H) evenness (Pielou, J') and richness (Margalef, d) indices are commonly employed in the community-level investigations and are suitable for simple statistical analyses (Ismael and Dorgham, 2003). The three indices were computed following the equations:
s
H = - ^ P(ln Pi)
i=1
J' = H' / lnS
d = (S - 1) / lnN
where H' = observed diversity index; Pi = proportion of the i-species in the total count; S = total number of species; and N = total number of individuals.
Multivariate analyses of temporal variations in protozoan community patterns were carried out using the PRIMER v6.0. Bray-Curtis similarity
matrices were constructed from log-transformed species abundance data. The clustering analyses of biological samples were conducted using the routine cluster and multidimensional scaling (MDS) ordination (Bray and Curtis, 1957; Anderson et al., 2008; Xu et al., 2011). Differences in samples among sampling stations were tested by the submodule ANOSIM (analysis of similarities) (Clarke and Gorley, 2006). The submodule biota-environment (BIOENV) was used to explore potential relationships between biotic parameters and the abiotic data. The significance of biota-environment correlations was tested using the routine RELATE (Clarke and Gorley, 2006).
Univariate correlation analyses were conducted using the statistical program SPSS v16.0. Data were standardized by logarithmic transformation before analyses (Tan et al., 2010).
Results
Environmental parameters
The monthly mean values of the 1-year long measurements of the environmental variables from eight sampling stations are shown in Table 1.
The change of water temperature (T) was comparatively regular and consistent with the change of season. The study area is located in the
Table 1. Mean values for physicochemical parameters of every month from the eight sampling stations in Xixi Wetland during the study period (25-May-12 to 24-Apr-13).
Date T (°C) pH DO (mg l-1) COD (mg l-1) bod5 (mg l-1) TN (mg l-1) TP (mg l-1)
25-May-12 24.76±0.82 6.44±0.14 5.99±0.89 16±7.01 6.34±1.42 1.21±0.37 0.067±0.05
26-Jun-12 25.01±0.62 6.33±0.12 4.03±1.33 17.25±10.12 3.58±0.99 1.015±0.52 0.045±0.02
26-Jul-12 31.98±0.93 6.32±0.14 3.24±0.74 19.88±5.54 4.49±1.44 0.824±0.21 0.04±0.02
28-Aug-12 29.83±1.06 7.37±0.23 4.81±1.55 19.38±2.45 7.1±2 0.703±0.16 0.038±0.03
27-Sep-12 25.23±0.64 7.3±0.12 4.16±1.05 16.75±5.31 6.78±1.47 0.966±0.33 0.063±0.03
24-Oct-12 20.95±0.46 7.5±0.28 4.97±1.19 19.88±5.84 5.24±1 0.86±0.31 0.12±0.06
24-Nov-12 13.66±0.67 7.59±0.05 5.61±0.9 13±2.56 3.34±0.64 1.177±0.44 0.066±0.02
26-Dec-12 7.08±1.13 7.66±0.18 7.68±1.37 16.38±7.95 5.9±1.37 1.415±0.34 0.068±0.03
23-Jan-13 7.11±0.77 7.61±0.07 8.2±1.47 12±5.53 5.19±2.3 1.466±0.28 0.047±0.03
27-Feb-13 11.28±0.55 7.06±0.35 8.5±0.54 16±15.19 4.08±1.27 1.493±0.12 0.042±0.01
29-Mar-13 12.85±0.34 6.41±0.18 6.8±1.18 9.13±3.56 4.26±0.73 0.973±0.37 0.034±0.02
24-Apr-13 18.66±0.71 6.36±0.15 6.4±0.6 21.25±14.32 3.71±0.82 0.985±0.25 0.065±0.05
Notes: T — water temperature, DO — dissolved oxygen, COD — chemical oxygen demand, BOD5 — biological oxygen demand, TN — total nitrogen, TP — total phosphorus.
subtropical climate zone; the temperature was relatively high, ranging from 7.08 to 31.98 °C (annual mean value 19.03 °C), and the wetland water was not frozen throughout the year.
The values of pH ranged from 6.32 to 7.66, averaging 7. Some numerical values showed partial acidulous which was due to silt at the bottom of wetland waters.
The dynamics of DO values and the change of T were basically inverse, DO ranging from 3.24 to 8.5 mg l-1 (mean 5.87 mg l-1).
Variation range of COD was from 9 to 21.25 mg l-1 (mean 15.18 mg l-1), and the BOD5 varied from 3.83 to 7.99 mg l-1 (mean 5.34 mg l-1).
The concentration of TN peaked two times during the year: the bigger peak from November to February and the minor peak from May to June, with little change during the other months. The concentration of TP was low almost all year round, and only in October TP was high.
Species composition and dominant species
The taxonomic composition of protozoan communities observed during the study period is summarized in Table 2. A total of 89 protozoan species comprising 34 ciliates, 13 flagellates, and 42 rhizopods were identified from 25-May-12 to 24-April-13 in the study area of Xixi wetland.
Dominant species were defined as species with an abundance that exceeded 10% of the total protozoan density in certain months during the study period. There were seven dominant species: Strobilidium gyrans, Cercomonas crassicauda,
Tintinnopsis wangi, Holophrya simplex, Strobilidium velox, Halteria grandinella and Difflugia urceolata. Only one of those species (Strobilidium gyrans) peaked during more than one season of the one-year cycle, whereas dominance of the other six species was confined to one season only (Fig. 2).
Annual variation in species number and abundance
The temporal variation in protist species number showed the highest peak in August with 32 species among which ciliates (53.13%) and rhizopods (40.63%) were primary contributors. The lowest values were registered in January and March, only 8 species each month, flagellates (62.5%) being the greatest contributors in January and ciliates (50%) - in March (Fig. 3a and b). Ciliates (42.79%) and rhizopods (40.93%) were primary contributors to the annual variation of species number.
The annual variation in abundance showed a clear seasonal shift (Fig. 3c). The highest abundance value (19.65 x104 ind. l-1) was recorded in summer (August), when ciliates (77%) were primary contributors. Then, abundance values reduced gradually since autumn (September), the lowest abundance value (3*104 ind. l-1) was registered in winter (February), when ciliates and flagellates (35% each) were primary contributors. Finally, abundance values started to rise gradually in spring, from April onward. in the total protozoan abundance, the input of ciliates was 69.28%, rhizopods — 18.89%, flagellates — 11.83%. Ciliates were the primary contributors to the annual variation of protozoan community structure in terms of relative abundance (Fig. 3d).
Table 2. List of protozoan species observed during the study period, their annual abundance, biomass range and occurrence.
Species Abund. Biom. Occurr. (%)
Heliophrya erharsi + + + 8
Halteria grandinella ++++ ++ 58
Glaucoma macrostoma + + + 17
Tintinnidium fluviatile +++ + 50
Plagiocampa mutabilis + + + 17
Enchelydium fusidens + + + 8
Holophrya sulcata + + + 8
Podophrya fixa + + + 8
Pleuronema cornatum + + + 8
Ophryoglena atra + + + 8
Urotricha agilis + + + 8
Holophrya simplex ++ + + 67
Podophrya maupasi + + + 17
Tetrahymena pyriformis ++ + + 25
Trachelius ovum + + + 8
Strombidium viride ++ + + 33
Colpoda patella + + + 8
Urotricha farcta + + + 25
Urotricha furcata + + + 17
Paramecium aurelia + + + 17
Pseudoglaucoma musorum + + + 8
Euplotus terricoda ++ + + 42
Askenasia volvox + + + 8
Strobilidium velox ++ + ++ 42
Sathrophilus ovatus + + + 8
Loxocephalus ellipticus + + + 8
Tintinnopsis wangi ++ + ++ 50
Frontonia atra ++ + 8
Urotricha armata ++ + + 17
Didinium balbianii nanum ++ + 25
Trochilia minuta ++ + 8
Strobilidium gyrans +++ + +++ 100
Aspidisca costata ++ + 8
Lagynophrya conifera ++ + 8
Phyllomitus undulans ++ + 8
Cercomonas bodo ++ + + 42
Cercomonas crassicauda ++ + + 67
Bodo edax ++ + 8
Cercomonas ovatus ++ + + 25
Mastigella vitrea ++ + 8
Bodo globosus ++ + + 25
Trepomonas steinii ++ + 8
Bodo minimus ++ + + 33
Cercomonas longicauda ++ + 8
Cercomonas radiatus +++ + 33
Cercomonas agilis ++ + 8
Monosiga ovata ++ + 8
Cyclopyxis arcelloides ++ + 25
Raphidiophrys pallida + + + 8
Difflugia gramen + + + 17
Phryganella nidulus + + + 8
Pelomyxa palustris + + + 8
Discamoeba guttula + + + 8
Acanthocystis brevicirrhis + + + 8
Actinophrys sol +++ + 25
Astrodisculus radians + + + 8
Pyxidicula operculata + + + 25
Diplophrys archer + + + 8
Pinaciophora fluviatilis + + + 8
Difflugia avellana ++ + + 58
Clathrulina elegans + + + 17
Difflugia acuminata ++ + + 33
Euglypha tuberculata + + + 8
Nebela collaris + + + 17
Raphidiophrys viridis + + + 17
Pseudodifflugia gracilis + + + 17
Difflugia urceolata ++ + ++ 58
Fig. 2. Annual variations in abundances (ind. l-1) of the seven dominant species in Xixi wetland during the study period of May 2012-April 2013.
Table 2. (Continuation).
Species Abund. Biom. Occurr. (%)
Vannella miroides ++ + 8
Lithocolla globosa ++ + 8
Difflugia globulosa ++ + + 42
Trichamoeba villosa ++ + 8
Hyalodiscus actinophorus ++ + + 42
Cyphoderia ampulla ++ + 8
Striamoeba striata ++ + 8
Cochliopodium bilimbosum ++ + 25
Hedriocystis reticulata ++ + 8
Sphenoderia lenta ++ + 25
Cochliopodium minutum ++ + 8
Hyalosphenia minuta ++ + 25
Tnnema enchelys ++ + + 33
Acanthamoeba astronyxis ++ + 8
Euglypha acanthophora ++ + 25
Cucurbitella mespiliformis ++ + 8
Difflugia oblonga ++ + 17
Saccamoeba limna ++ + 8
Lesquereusia epitomium ++ + 8
Euglypha laevis ++ + 8
Centropyxis aerophila ++ + 8
Arcella hemisphaerica ++ + 8
Notes: Abund., abundances (ind. l-1): + = 0-50, + + = 50-500, + + + = 500-5000, ++++ over 5000; Biom., biomass (|g l-1): + = 0-10, ++ = 10-100, +++ = 100-1000, ++++ over 1000.
Fig. 3. Species number (a), relative species number (b), abundance (c), and relative abundance (d) of protozoan communities in Xixi wetland during the study period of May 2012—April 2013.
Temporal patterns of protozoan community
structure
A dendrogram of the temporal distribution was plotted using the group-average clustering on Bray-Curtis similarities for the protozoan community species-abundance average value of eight sampling stations from every month. The cluster analysis resulted in protozoan communities falling into three groups (I-III) at a 30% similarity level: group i was composed of data from January and December, corresponding to the winter; group ii was composed of data from February, April and May, corresponding to the spring; group iii including June to November, corresponding to the summer and autumn. Data for March stayed separately. ANOSIM analysis showed a significant difference between the protozoan communities in various seasons (^=0.745, P=0.001). According to the results, the annual variation of protozoan communities in Xixi wetland had an obvious seasonal feature (Fig. 4.).
Annual variation in species biodiversity parameters
The annual variation in species richness and diversity showed an obvious seasonal pattern (Fig. 5); the values were relatively low during the one-year cycle, with maximum in August and May, and minimum in January. Evenness values ranged from 0.75 to 1, with little variation amplitude throughout the year; protozoan communities were evenly distributed.
Linkage between biotic and abiotic data
The results of Spearman correlation analysis showed that, with the exception of pH and Tp, biological factors and environmental parameters had a correlation relationship (Table 3). In particular, species numbers highly significantly positively correlated with T (P<0.01), and significantly negatively correlated with TN (P<0.05). Besides, species abundance significantly positively correlated
Fig. 4. Multidimensional scaling (MDS) ordination with clustering analysis for protozoan communities in Xixi wetland during the study period of May 2012-Apr 2013. 1=23-Jan-13, 2=27-Feb-13, 3=29-Mar-13, 4=24-Apr-13, 5=25-May-12, 6=26-Jun-12, 7=26-Jul-12, 8=28-Aug-12, 9=27-Sep-12, 10=24-0ct-12, 11=24-Nov-12, 12=26-Dec-12.
with BOD5, while species number significantly positively correlated with COD and significantly negatively correlated with DO.
In addition, species richness, diversity and evenness also demonstrated the correlation relationship with water environmental factors: species richness significantly positively correlated with T and COD (P<0.05); species diversity highly significantly positively correlated with T (P<0.01), significantly positively correlated with COD, significantly negatively correlated with D O and TN; evenness highly significantly negatively correlated with T, highly significantly positively correlated with D O, significantly positively correlated with TN.
Multivariate correlation analysis (BIOENV analysis) demonstrated that temporal variation in protozoan communities significantly correlated with the variation of environmental factors, especially
Fig. 5. Temporal variations in species richness (d), diversity (H) and evenness ( J) of protozoan communities in Xixi wetland during the study period of May 2012-April 2013.
T, pH, DO, COD, TN and TP, either alone or in combinations with one another (Table 4). The highest correlation was between the combination of T, pH, DO, COD and TP (R=0.539; P<0.01). These results confirm that protozoan communities are potential indicators of water quality in wetland systems.
Discussion
Until recently, there has been little understanding of the annual variation in protozoan community pattern in Xixi wetland although many investigations on temporal features of protozoan community patterns and their relationships to changes of environmental conditions were carried out in different aquatic ecosystems (Panswad and Chavalparit, 1997; Kchaou et al., 2009; Xu et al., 2005, 2008, 2009; Sun et al., 2013).
In our study, a total of 89 protozoan species were registered, comprising 34 ciliates, 13 flagellates, and 42 rhizopods; 7 of those were dominant species, of which ciliates (69.28%) were the primary contributors in terms of relative abundance. Our results were also similar to other previously published reports. Shi et al. (2012) reported 88 protozoan species from the Hangzhou section of Jing-Hang Grand Canal during a 1-year cycle from February 2008 to January 2009. Xu et al. (2000) identified 77 protozoan species in Baiyangdian Lake. Xie and Zheng (2000) reported 83 protozoan species
Table 3. Correlation analysis between the abundance (N), species number (S), species richness (d), species diversity (W) and species evenness (J') of protozoan communities and environmental factors.
N S d H' J'
T 0.731** 0.792** 0.689* 0.741** -0.766**
pH 0.019 0.016 0.023 -0.029 0.146
DO -0.550 -0.597* -0.495 -0.604* 0.877**
COD 0.383 0.619* 0.663* 0.596* -0.229
bod5 0.635* 0.572 0.542 0.542 -0.175
TN -0.604* -0.595* -0.524 -0.599* 0.673*
TP -0.009 0.216 0.409 0.428 -0.033
Notes: Abund., abundances (ind. l-1): + = 0-50, + + = 50-500, +++ = 500-5000, ++++ over 5000; Biom., biomass (ug l-1): + = 0-10, ++ = 10-100, +++ = 100-1000, ++++ over 1000.
in Poyang Lake, and Tan et al. (2010) identified 53 protozoan species during only half a year from May to September 2003 in Songhua River, northeast China.
In the Xixi wetland system, the protozoan abundance ranged from 3* 104 ind. l-1 to 19.65 * 104 ind. l-1, the annual average abundance was 80.875* 103 ind. l-1. According to relevant reports, the abundances of protozoa in other inland areas of China were, for example, as follows: Dianshan Lake, 3.4*103 ind. l-1 ~ 5.3* 103 ind. l-1 (Zheng et al., 2009), Yuexiu Lake, 54.9* 103 ind. l-1 ~ 69.1* 103 ind. l-1 (Xu and Zheng, 2000), Baiyangdian Lake, 34.5* 103 ind. l-1 ~ 37.5* 103 ind. l-1 (Xu et al., 2001). By comparing protozoan abundances in our study and in other fresh waters mentioned above, we can conclude that protozoan abundance was the highest in Xixi wetland.
A number of reports have demonstrated that ciliate assemblages were strongly related to eutrophi-cation status (Beaver and Crisman, 1982, 1989; Jiang et al., 2007). Of the total protozoan abundance in Xixi wetland, ciliates were the primary contributors in the annual variation of protozoan community structure in terms of relative species abundance. This suggests that ciliates played an important role in the eutrophication of the study area which is consistent with the previously reported facts.
Multivariate analyses is a sensitive tool for detecting changes in community structure, and extremely useful for revealing variations of communities on temporal scales (Jiang et al., 2007, 2011; Chen et al., 2008; Xu et al., 2008). In our study, both cluster analyses and MDS ordination demonstrated that temporal patterns of protozoan communities were obviously seasonal. Protozoan communities in Xixi wetland were different in winter, spring,
Table 4. Summary of results from biota-environment (BIOENV) analysis, with the correlations between temporal variation in protozoan communities and different environmental variables.
Rank Correlation coefficient Environmental factors
1 0.539 T, pH, DO, COD, TP
2 0.538 T, pH, DO, TP
3 0.537 T, pH, DO, TN, TP
4 0.537 T, DO, COD, TP
5 0.537 T, pH, DO, COD
6 0.536 T, DO, TP
7 0.535 T, DO, COD
8 0.533 T, pH, DO, COD, TN
9 0.532 T, pH, DO, TN
10 0.532 T, DO
summer and autumn. Moreover, BIOENV analysis and Spearman analysis demonstrated that temporal variation in protozoan communities was significantly related to different environmental variables, especially to T and the concentration of DO, COD, TN and TP. This suggests that the annual variation of protozoan communities is associated with the changes of environmental variables, and that the protists can be used as indicators of water quality in wetland systems (Cereceda-Martin et al., 1996; Madoni, 1994; Lee et al., 2004; Jiang and Shen, 2005).
Species diversity, evenness and richness indices are comprehensive indices for protozoan community survey, and are widely used as the important parameters for evaluating water quality (Xu et al., 2005, 2008; Chen et al., 2008; Kiss et al., 2009; Tan et al., 2010). In general, the higher these indices were, the better the water conditions represented (Ismael and Dorgham, 2003). In our study, the results showed that all three biodiversity parameters were significantly correlated with the chemical factors. Thus, we suggested that these three biodiversity parameters can be used as suitable indicators for assessing water quality in wetland systems (Ismael and Dorgham, 2003; Xu et al., 2005; Jiang et al., 2007).
In summary, the protozoan community structure showed an obvious seasonal feature, and successive annual dynamics of protozoan communities were significantly related to environmental factors. No other study about urban wetland ecosystem was reported yet; our results of survey provide first-hand data for urban wetland research. All three diversity indices (species richness, diversity and evenness) were significantly correlated with the COD and nutrients. The results demonstrated that the annual
variation in protozoan abundance represented a clear seasonal shift in response to environmental changes and thus may be used as a potential indicator for assessing water quality in a sub-tropic urban wetland ecosystem.
Acknowledgements
This work was supported by the Natural Science Foundation of China (project numbers: 31272262, 31071880 and 41076089), the Zhejiang Key Scientific & Technological Innovation Team Project (2010R50039-20), and the Hangzhou Key Laboratory for Animal Adaptation and Evolution (20100333T05). We wish to thank Yong Jiang, Tao Xiao, Liang Zhao, Li Wang, Qingjuan Meng for improving the manuscript and thank Songlu Liu, Cuicui Hou for help in sampling.
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Address for correspondence: Henglong Xu. Laboratory of Microbial Ecology, Department of Ocean University of China, Ocean University of China, Qingdao 266003, China; e-mail: [email protected]
