Wetlands DOI 10.1007/s13157-013-0506-x
ARTICLE
Aboveground Vegetation Influences Belowground Microeukaryotic Community in a Mangrove Nature Reserve Zheng Yu & Jun Yang & Xiaoqing Yu & Lemian Liu & Ye Tian
Received: 24 April 2013 / Accepted: 1 December 2013 # Society of Wetland Scientists 2013
Abstract Over the last decades, invasion by the exotic cordgrass (Spartina alterniflora) has become one of the most serious and challenging environmental issues threatening mangrove wetland ecosystems in China. The purpose of the study was to investigate the microeukaryotic diversity and community composition after S. alterniflora invasion in a mangrove nature reserve and to elucidate the factors that are driving succession or shifts in microeukaryotic communities. In this study, the spatiotemporal distributions of microeukaryotic communities in coastal wetland sediments of the Jiulong River Estuary, southeast China were investigated. The microeukaryotic communities from the wetland were distributed within six major groups (i.e. Alveolata, Stramenopiles, Rhizaria, Viridiplantae, Fungi and Metazoa). Our results indicated that vegetation changes were the primary factor driving the shift in microeukaryotic community composition, rather than seasonal separation. Total nitrogen (TN) and oxidized nitrogen (nitrite and nitrate) were significant environmental factors in explaining the largest portion of the variation in microeukaryotic composition. The exotic cordgrass invasions affected the microeukaryotic communities and played a key role in shaping community structure. It appears that microeukaryotic community could be used as a
Electronic supplementary material The online version of this article (doi:10.1007/s13157-013-0506-x) contains supplementary material, which is available to authorized users. Z. Yu : J. Yang (*) : X. Yu : L. Liu : Y. Tian Aquatic EcoHealth Group, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, People’s Republic of China e-mail:
[email protected] Z. Yu University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
bioindicator in assessing the recovery process of mangrove ecosystems. Keywords Microeukaryotic communities . Spatiotemporal distributions . Mangrove . DGGE . Nitrogen . Invasive cordgrass
Introduction Mangroves are salt-tolerant evergreen forests, characterized by high biomass and primary production, and found along coastlines and deltas in tropical and subtropical areas around the world (Barbier et al. 2008). Mangrove ecosystems have important direct and indirect ecological, economic and social values to humans: they protect coastal settlements against erosion, cyclones and wind (Dahdouh-Guebas et al. 2005; Das and Vincent 2009), serve as a source and sink of nutrients for other inshore marine habitats, including coral reefs and sea grass nurseries (Dorenbosch et al. 2004; Duke et al. 2007), and provide critical habitat for diverse species range from terrestrial to marine organisms, as well as a variety of prokaryotic and eukaryotic microbes (Primavera 1998; Kathiresan and Bingham 2001; Luther and Greenberg 2009). However, the world’s mangroves have been declining at an alarming rate due to unsustainable human activities and exotic plant invasion (Feist and Simenstad 2000). Over the last decades, Spartina alterniflora (smooth cordgrass) invasion had seriously threatened the mangrove ecosystems in China (Zhang et al. 2011). S. alterniflora, a native of the Atlantic and Gulf coasts of North America, is a perennial deciduous and deep-rooted salt marsh grass, which plays an important ecological role in its native ecosystems (Feist and Simenstad 2000). With its great capacity for reducing tidal wave energy, mitigating erosion and trapping sediments, this plant has been widely introduced to many coastal and
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estuarine regions of the world as a species for ecological engineering (Ahn and Mitsch 2002; Teal and Weishar 2005). S. alterniflora has rapidly spread in eastern coastal areas of China due to its large seed production and fast germination rate. Recently, increasing evidence suggest that S. alterniflora may out-compete native plants, impact the native ecosystems and coastal aquaculture, and cause declines in native species richness (Huang and Zhang 2007). This exotic plant first intentionally introduced to the coastal wetland of Jiulong River, Fujian Province of China was in 1979 and has replaced mangroves in the Jiulong River Estuary at an alarming speed over the past years (Liao et al. 2007). Eukaryotic microorganisms are of vital importance to biogeochemical processes in coastal ecosystems (e.g. mangroves and salt marshes). They represent the base of the food web and changes in their composition and structure can lead to profound changes at all trophic levels (Laurin et al. 2008; Harding et al. 2011). In the past decades, the use of molecular techniques in microeukaryotic ecology has increased our ability to analyze the relationship between microeukaryotes and plants (Jousset et al. 2010). Garbeva et al. (2004) found that microbial communities and plants are closely related to each other, as plants are the main providers of specific carbon and energy sources for microbial communities and microorganisms can promote plant growth via chemical signals. Understanding of microbial community characteristics and potential factors that drive its variation is essential in developing effective approaches for mangrove ecosystem management. However, little is known about the genetic diversity of microeukaryotes in the sediments of coastal mangrove wetland and how this is Fig. 1 Map of Jiulong River estuary showing the sampling locations and their vegetation types
affected by S. alterniflora invasion and seasonal variation in different habitats (Murase et al. 2012). The aims of this study were i) to characterize the belowground microeukaryotic diversity and community composition over four seasons and four different habitats in a mangrove nature reserve, and ii) to determine the extent to which the environmental conditions, seasonality and S. alterniflora invasion affect the microeukaryotic community structure.
Materials and Methods Study Site and Sample Collection Sampling sites were located in the mangrove nature reserve of the Jiulong River Estuary (117°53′–117°55′ E, 24°25′–24°29′ N) in Fujian province, southeast China. Over the last decades, S. alterniflora has invaded the native mangrove habitat and flourished in the tidal wetland in Jiulong River Estuary (Zhang et al. 2011). The sampling sites were located in the middle tidal wetland where sediments or soils are not always covered with water. Mangrove and S. alterniflora are the overwhelmingly dominant plants in these areas throughout the year. Four different habitats were selected as sample sites: unvegetated bare mudflat (Mu), S. alterniflora-invaded zone (Co), ecotone area with S. alterniflora and mangrove growing mixed together in the same area (Ec) and native mangrove zone (Ma) (Fig. 1). Sample cores were collected in four seasons, April (spring), July (summer), October (autumn) 2010 and January (winter) 2011. In each zone, triplicate samples were collected
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from the top 0–5 cm layer of bulk soil using a polyvinyl chloride (PVC) pipe (7 cm in diameter), thereby creating 48 sediment samples in total. The sediment cores were frozen immediately after collection using dry ice and transported to the laboratory. Each core was lyophilized at −55 °C (Freeze Dry System 7960030, Labconco, USA), then homogenized and divided into two portions. One portion was used to measure the physical and chemical data; the other was filtered through a 150 μm mesh to remove the large plant debris and metazoan, and finally stored at −80 °C until DNA extraction. Environmental Data Ten environmental variables were measured in this study. Porewaters were sampled at the top 0–5 cm sediment using porewater diffusion equilibrators with three replicates in each zone (Hesslein 1976). Salinity of sediment porewater was measured with an ATAGO salt-meter. Porewater pH was measured with a Starter 2C pH meter. The volume median diameter D [0.5], which is the diameter where 50 % of the distribution is above and 50 % is below, was analyzed by a laser granulometer (Malvern Mastersizer 2000, Malvern Instruments, Malvern, UK). The total carbon (TC), total nitrogen (TN) and total sulfur (TS) contents (as % dry weight of the sediment) were determined using an elemental analyzer (Vario MAX CNS, Elementar, Hanau, Germany), while total phosphorus (TP), ammonium nitrogen (NH4-N) and nitrite and nitrate nitrogen (NOx-N) concentrations were measured by a flow injection analyzer (QC8500, Lachat Instruments, Milwaukee, USA) following standard methods. DNA Extraction, PCR Amplification and DGGE The total DNA was obtained from 0.5 g dry sediment using an E.Z.N.A Soil DNA Kit (Omega Bio-Tek, USA) following the manufacturer’s instructions. The extracted DNA was dissolved in 50 μl TE buffer, quantified by a spectrophotometer and stored at −20 °C until further use. To determine the microeukaryotic composition, the amplification of specific fragments of the gene encoding the 18S ribosome subunit of the microeukaryotes was performed using the primers Euk1A (5′-CTG GTT GAT CCT GCC AG-3′) and Euk516r-GC (5′ACC AGA CTT GCC CTC CCG CCC GGG GCG CGC CCC GGG CGG GGC GGG GGC ACG GGG GG-3′) (Diez et al. 2001). The 50-μl PCR mixture contained 1 μl of the primer set (25 pmol each), 0.5 μl (1.25 U) of Ex Taq DNA polymerase (Takara Bio, Otsu, Japan), 5 μl of Ex Taq buffer (20 mM MgCl2), 4 μl of deoxyribonucleotide triphosphate mixture (2.5 mM each, Takara Bio, Otsu, Japan) and 100 ng of DNA template. Touchdown PCR was performed for microeukaryotic primers included an initial denaturation at 94 °C for 5 min and 10 touchdown cycles of denaturation at
94 °C for 1 min, annealing at 65 °C (with the temperature decreasing 1 °C each cycle) for 1 min, and extension at 72 °C for 1 min, followed by 20 cycles of 94 °C for 1 min, 55 °C for 1 min, and 72 °C for 1 min. Finally, a primer extension at 72 °C for 10 min was performed. PCR reactions were performed with a Mastercycler 5333 (Eppendorf AG, Hamburg, Germany). Denaturing gradient gel electrophoresis (DGGE) was performed with a DCode mutation detection system (Bio-Rad, Laboratories, Hercules, USA). Approximately 2.4 μg of PCR product for each sample was mixed with the loading dye (5:1) and loaded on the gel. The DGGE gels (25 to 45 % of urea and formamide) were prepared with a solution of polyacrylamide (6 %, wt vol−1) in 1× Tris-acetate-EDTA (TAE) buffer. The electrophoresis was performed at 60 °C with a constant voltage of 100 V for 16 h. The DGGE gels were stained with SYBR Green I nucleic acid stain for 30 min in 1× TAE buffer, rinsed in distilled water, and then visualized with UV radiation by using Gel Doc EQ imager (Bio-Rad Laboratories, Hercules, USA). The above steps, including the PCR and DGGE, were replicated three times independently to verify repeatability of the results. Sequencing and Phylogenetic Analysis Dominant DGGE bands were excised from the DGGE gels and eluted overnight in sterilized distilled water at 4 °C. The eluted DNA was used as template for re-amplification with the original primer set without the GC clamp. PCR products were purified with the TaKaRa Agarose Gel DNA Purification Kit (Takara, Otsu, Japan), then cloned into a pMD18-vector (Takara, Otsu, Japan) and transformed into Escherichia coli DH5α competent cells (Takara, Otsu, Japan) following the procedures of Liu et al. (2011). Finally, successfully inserted plasmids were sequenced unidirectionally using an automated sequencer (ABI3730, Applied Biosystems, USA). Each sequence was compared with sequences available in GenBank databases using BLAST, and the closest relatives were identified for phylogenetic analysis. The sequences were realigned and manually edited with the ClustalX aligner and the phylogenetic analyses were performed with the MEGA5 software (Tamura, et al. 2011) package using neighbor-joining methods (Saitou and Nei 1987). The sequences with similarities greater than 97 % were regarded as one operational taxonomic unit (OTU). Data Analysis DGGE patterns were scored with the aid of Quantity One fragment analysis software (Bio-Rad Laboratories, Hercules, USA), as described previously (Schauer et al. 2000). The bands occupying the same position in different lanes of the gel were identified. Each band was carefully checked and
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manually corrected. All bands detected by the software were above 0.2 % relative intensity in a lane, which was the threshold applied. This band intensity was then expressed as a proportion of the total intensity of all bands comprising a particular community profile. Binary matrices were constructed for all lanes, taking into account the relative abundance matrix. The Shannon-Wiener index (H’) and Pielou’s Evenness index (J’) were calculated for the microeukaryotic diversity of DGGE profiles. The H’ was determined with the following equation: H’=-ΣPilnPi. The term Pi was calculated as follow: Pi=ni/N, where ni is the band intensity for individual bands and N is the sum of the intensities of bands in a lane (Eichner et al. 1999). The J’ was determined with the following equation: J’=H’/lnS. The S is the number of ribotypes (Pielou 1966). Bray-Curtis similarity (presence/absence-based) matrices were constructed with the DGGE profiles generated from each site and non-metric multidimensional scaling (MDS) ordination was used to investigate differences in microeukaryotic communities. Significant differences (P< 0.01) between groups were evaluated using the randomization/permutation procedure ANOSIM (analysis of similarities). The global R statistic ranges from 0 to 1 and indicates the overall degree of separation between groups of sites, and no separation is indicated by R=0, whereas R=1 suggests complete separation. Preliminary detrended correspondence analysis (DCA) on the microeukaryotic data revealed that the longest gradient length was shorter than 3.0, indicating that the majority of species exhibited linear responses to the environmental variation. In order to investigate which environmental variables best explained the variability in DGGE profiles, a redundancy analysis (RDA) was applied. RDA analysis examines variations in the community structure by constraining ordination axes to linear combinations of environmental variables. A forward manual selection RDA and Monte Carlo permutation tests identified a minimal subset of environmental variables that explained significant proportions (P<0.05) of the variations in the community data. The environmental variables are represented by arrows pointing in the direction of maximum change, and the arrow length is indicative of the importance of each environmental variable. S. alterniflora and mangrove are considered to be a set of potential explanatory variable associated with vegetation and 0/1 matrix corresponding absence/ presence of each plant. All data analyses were performed with the STATISTICA 6.0, the PRIMER 5.0 and the CANOCO 4.5 software packages.
Nucleotide Sequence Accession Numbers Sequences generated in this study were registered in GenBank under the accession numbers JN846836 to JN846882.
Results Genetic Diversity of Microeukaryotic Communities DGGE profiles of microeukaryotic community showed high variation in band’s position and intensity among the four seasons and different vegetation types. A total of 95 different bands in the different lanes of the gel were identified, with an average of 34 bands (range from 17 to 45) per sample (Fig. 2). The Shannon-Wiener index ranged from 2.03 to 3.45 (mean 3.02), the evenness index varied between 0.72 and 0.93. ANOVA indicated that only the interaction of season and vegetation type showed significant differences in the number of DGGE band diversity (P=0.008). There were no significant differences in the Shannon-Wiener and the Pielou’s evenness index among different seasons and vegetation types as determined using a two-way ANOVA (Table 1). However, non-metric multidimensional scaling (MDS) analysis suggested that the 48 sample sites were divided into four groups corresponding to the four habitats, indicating vegetation types had the most important influence on the microeukaryotic community (Fig. 3). Analysis of similarity (ANOSIM) based on pairwise tests also revealed that microeukaryotic communities could be distinguished from each other by the four habitats (Global R=0.685, P=0.001). The greatest difference in microeukaryotic community composition was observed between mangrove and ecotone sites (R=0.869, P=0.001) (Table 2). In contrast, microeukaryotic communities among four seasons could not be distinguished (Global R=0.029, P=0.800). Microeukaryotic Community Composition The microeukaryotic communities from four habitats were composed of highly diverse organisms and widely distributed within six major groups (i.e., Alveolata, Stramenopiles, Rhizaria, Viridiplantae, Fungi and Metazoa) (Fig. 4). Fiftysix percent of all sequences were classified in the metazoan group, followed by Alveolata (20 %), Rhizaria (11 %), Fungi (7 %), Viridiplantae (4 %), and Stramenopiles (1 %). The dominant phyla were Nematoda (24 %) and Annelida (19 %) in the metazoan group. Some clone sequences were regarded as unclassified microeukaryotes due to the fact that they had no clear close relatives among published sequences in GenBank databases (<90 % similarity), and thus their phylogenetic positions were unclear. Compared to the three other habitat sites, the Metazoa group showed the highest relative abundance in the mudflat samples (63 %) and the Alveolata was the second most abundant group (19 %) (Fig. 5). There was a decrease in relative abundance of Metazoa from 63 % in non-vegetation (mudflat) sediment to 52 %, 54 % and 42 % in the cordgrass, ecotone and mangrove sites, respectively. The dominant
Wetlands Fig. 2 Number of DGGE bands (a) and Shannon-Wiener index (b) of the microeukaryotic community among four seasons and four vegetation types. Data are means of three independent samples±SE. Significant differences (P<0.05) between sites are indicated by different letters
phylum Nematoda, remained relatively stable in mudflat, cordgrass and ecotone sites, but changed rapidly in mangrove sites, and their relative values were 39 %, 41 %, 39 % and 30 %, respectively. Instead, the phylum Annelida remained relatively stable in cordgrass (34 %), ecotone (39 %) and mangrove (37 %) sites, but decreased in mudflat sites (27 %). Relationship Between Environmental Factors and Microeukaryotic Communities Our data indicated cordgrass and ecotone stations exhibited similar chemical characterization. The total sulfur (TS), pH and D [0.5] were somewhat higher at mudflat stations. Salinity was lower at the mangrove station, relative to other stations.
The biotic and abiotic factors in mudflat stations were very different in comparison to the other three stations. RDA ordination revealed that the DGGE fingerprints were related to the environmental variables (TN and NOx-N), and the axes 1 and 2 explained 29.1 % and 17.3 % of the data variability, respectively (Fig. 6). Nitrogen (TN, NOx-N) together with vegetation type were found to be statistically significantly related to the composition of the microeukaryotic communities. RDA ordination of the species in relation to those variables showed that the 48 samples were separated into four groups: the mangrove vegetation sites, the cordgrass sites, the
Table 1 Factorial designs derived from season and vegetation to genetic diversity of microeukaryotic communities in coastal wetland surface sediments of the Jiulong River estuary, by ANOVA Factor
Number of bands
df F
P
ShannonWiener index df F
P
Pielou’s evenness df F
P
Season 3 0.431 0.732 3 0.423 0.738 3 0.513 0.676 Vegetation 3 1.419 0.255 3 1.091 0.368 3 1.527 0.228 Season× 9 3.119 0.008** 9 2.040 0.069 9 0.978 0.477 Vegetation ** P<0.01
Fig. 3 Non-metric multidimensional scaling (MDS) ordination of the microeukaryotic community fingerprints in the costal wetland sediments of the Jiulong River estuary
Wetlands Table 2 Global ANOSIM statistics for tests involving vegetation at four sampling stations Groups
Sample statistic R
No. of Monte Carlo permutations with scores ≥ R
P value
Mudflat vs Cordgrass Mudflat vs Ecotone Mudflat vs Mangrove Cordgrass vs Ecotone Cordgrass vs Mangrove Ecotone vs Mangrove
0.576 0.487 0.823 0.589 0.741 0.869
999 999 999 999 999 999
0.001 0.001 0.001 0.001 0.001 0.001
ecotone sites, and the mudflat sites. The same four-group patterns were confirmed by the MDS result.
Discussion Spatial-Temporal Patterns of Microeukaryotic Communities Microorganisms are an important component of wetland ecosystems and play a pivotal role in biogeochemical cycles (DeLong et al. 2006; Ushio et al. 2008). Microorganisms are characterized by a small size, short generation time, and a high sensitivity to environmental changes, so they are ideal subjects for monitoring environmental conditions (Santos et al. 2010; Yang et al. 2012). Previous studies indicated the species composition and spatial-temporal dynamics of soil microbial communities are associated with local habitat characteristics, plant types and human activities (Bossio et al. 1998; Berg and Smalla 2009; Eddie et al. 2010). Our results indicated that microeukaryotic diversity was high and relatively stable in sediments of the Jiulong River estuarine wetland, and both MDS and RDA ordinations revealed that the microeukaryotic community structure significantly differed according to soil properties and vegetation types. It was a good evidence to prove that soil and plant types are the major determinant of the structure of microeukaryotic community (Lawley et al. 2004; Gutknecht et al. 2006). Seasonal changes had more of an effect in aquatic environments than in soil and this pattern is supported by previous studies on microbial communities (Bastida et al. 2009; Peralta et al. 2010). The reason maybe due to soil environment is more stable than the aquatic environment (Balser and Firestone 2005; Ikenaga et al. 2009). DGGE is useful for investigating differences in microbial community structure but is of limited use for assessing diversity (Santos et al. 2010). We confirmed that the microeukaryotic communities were different among different kinds of habitats and relatively stable over four different seasons. However for the diversity assessment, more molecular techniques should be used together to improve the
Fig. 4 Phylogenetic tree of the microeukaryotic 18S rRNA gene phylotypes from the Jiulong River estuarine sediments. The tree was rooted using the neighbour-joining method. The Jukes-Cantor model numbers at the branches show the bootstrap percentages (above 50 % only) after 1000 replications of bootstrap sampling
credibility of the results (Murase et al. 2012). The algal composition of our sediment samples was low, and only one DGGE band was identified as an alga. It’s possible that most
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Influence of Cordgrass Invasion on Microeukaryotic Community
Fig. 5 Relative abundance of 18S rRNA gene fragments for major microeukaryotic groups in coastal wetland sediments of the Jiulong River estuary
algae live in the water column and few species are found in the sediment. Another possibility is that the isolation of nucleic acids from algae may not have been as efficient as from other microeukaryotes, or that our primers were less likely to amplify algal DNA (Kaiser et al. 2001; Berney et al. 2004).
The microeukaryotic communities in the unvegetated bare mudflat were differed from the vegetated stations. For example, small size Metazoans accounted for 63 % of sequences obtained from the mudflat zone, but only 42 % of sequences from the mangrove zone. Previous studies have confirmed that plants create unique environments for the microorganisms living in the rhizosphere, and the removal of specific plant groups can change the microeukaryote community structure (Grayston et al. 1998; Wardle et al. 1999). Plants can also change the physicochemical conditions of soil under the canopy (Ehrenfeld et al. 2001; Menon et al. 2013): fallen leaves can enhance soil fertility, and plant roots can release a variety of compounds into the surrounding soil (Garbeva et al. 2004). Mudflats appear to undergo cycles of erosion and deposition, and it is generally believed that mudflats undergoing erosion have a concave upwards profile, and those experiencing deposition have variability with season (Dyer et al. 2000). More importantly, our data from the ecotone zone provided the evidence of the importance of cordgrass invasion on microeukaryotic community structure. In this study, the ecotone zone was a transition area between cordgrass and mangrove. Such edge effects can cause changes in population or community structure that occur at the boundary of two habitats (Yates et al. 2004; Laurance et al. 2007). MDS indicated that the microeukaryotic community in the ecotone zone was more similar to that in the cordgrass than that associated with mangrove. Thus, it appeared that the alien plant invasions affected the microeukaryotic communities and played a key role in shaping the community composition. Sequencing and phylogenetic analysis also revealed that species composition changed due to the presence of cordgrass, with an increase in the proportion of Nematoda and Annelida in cordgrass stations. Nematodes have been used in biomonitoring studies and are suitable indicators of the impacts of pollution on marine ecosystems (Moreno et al. 2009; Santos et al. 2010). Chen et al. (2007) demonstrated that S. alterniflora invasions had generally lower nematode diversity, compared with the two other native Scirpus mariqueter and Phragmites australis dominant cordgrass sites. Implications for Mangrove Ecosystem Management
Fig. 6 RDA ordination showing the effect of vegetation and soil properties on the microeukaryotic community. Only statistically significant environmental variables are marked with an asterisk (*) according to Monte Carlo permutation test (P<0.05)
Soil microeukaryotic communities, vegetation and nitrogen were closely related to each other (Ushio et al. 2008; Wang et al. 2010). Smooth cordgrass invasion had caused large losses of mangrove ecosystems, and restoring the desired ecosystem may require drastic and expensive
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intervention (An et al. 2007). Soil microeukaryotic communities are intimately connected to plant community dynamics and diversity, and neglecting the belowground community may come with a heavy cost to society (Chen et al. 2012). Our results showed that both TN and NOx-N were significant environmental factors affecting the distribution of microeukaryotic communities. In fact, nitrogen is the major limiting nutrient in most terrestrial ecosystems (Muruganandam et al. 2010), and human activity has profoundly altered the global biogeochemical cycle of nitrogen (Galloway 1995; Vitousek et al. 1997). In recent years, agricultural activities in the upper Jiulong River watershed were the major sources of nitrogen because of both heavy chemical fertilizer application and intensive livestock production (Tian et al. 2008). Nitrogen was one of the main factors influencing microeukaryotic community composition in the Jiulong River estuarine wetland. Further, the NH4-N and NOx-N were more correlated with microeukaryotic communities in the mangrove-cordgrass ecotone and the mangrove, respectively. In this study, TS, pH and D [0.5] were highly correlated with each other, but its possibility that all three environment variables weren’t actually influencing microeukaryotic community structure. Previous studies proved that the anthropogenic N addition increases primary productivity, also in some cases, facilitating exotic plants invasion (Liao et al. 2008), thereby the invasive cordgrass plays a fundamental role in shaping the soil microeukaryotic communities. The mutual dependence between plants and microorganisms is a crucial biological interaction that has been largely ignored in mangrove ecology and virtually all mangrove reforestation projects (Gomes et al. 2010). Over the last few decades, the exotic cordgrass (S. alterniflora) invasion had become one of the most challenging environmental issues in part because of threats to mangrove wetland ecosystems (Daehler and Strong 1996). Better management of mangrove wetland is required to control the continuing expansion of S. alterniflora. At present, there is ongoing restoration of the mangrove wetland in the Jiulong River Estuary. Not only will this project deal with the mangrove restoration but will also consider the belowground environment. Microeukaryotic community structure could be used as a bioindicator during the recovery process of this mangrove ecosystem.
Acknowledgments This research was supported by the National Basic Research Program of China (2012CB956103), the Knowledge Innovation Program of the Chinese Academy of Sciences (KZCX2-YW-QN401 and KZCX2-YW-Q02-04), the National Natural Science Foundation of China (31370471 and 41276133), and the International Science and Technology Cooperation Program of China (2011DFB91710). We thank Edward Mitchell, David Wilkinson, Xueling Li, Stefano Amalfitano and Yaniv Douieb for improving the English of the manuscript.
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Author Contributions Conceived and designed the experiments: JY. Performed the experiments: ZY XQY. Analyzed the data: ZY JY XQY LML YT. Contributed reagents/materials/analysis tools: ZY JY XQY LML. Wrote the paper: ZY JY.