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1 Centre for Bioengineering and Biotechnology, China University of Petroleum (East China), Qingdao 266555, China
2 Key Laboratory of Marine Geology and Environment, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
3 Life Sciences and Technology College, Ocean University of China, Qingdao 266003, China
Correspondence
Hongyue Dang
DangHY20042000{at}yahoo.com.cn
Tiegang Li
tgli{at}ms.qdio.ac.cn
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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The GenBank/EMBL/DDBJ accession numbers for the archaeal amoA gene sequences determined in this study are EU025140 to EU025186.
Four supplementary figures are available with the online version of this paper.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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), is a key component of the global nitrogen (N) biogeochemical cycle. Over geological timescales, the N cycle is thought to have affected the global biogeochemical cycle of carbon and hence the content of atmospheric CO2 (Falkowski, 1997
). On a local scale, microbial nitrification also contributes to the bioremediation of anthropogenic N eutrophication in estuarine and coastal environments (Francis et al., 2005; Caffrey et al., 2007), via coupled nitrification–denitrification or nitrification–anammox (anaerobic oxidation of ammonium) processes (Seitzinger, 1988; Coolen et al., 2007; Lam et al., 2007).
The recent discovery that some mesophilic archaea in the kingdom Crenarchaeota possess the potential for chemoautotrophic ammonia oxidation, the first and rate-limiting step in microbial nitrification, suggests an important role of archaea in the N cycle (Venter et al., 2004; Konneke et al., 2005; Treusch et al., 2005). Crenarchaeota are ubiquitous and abundant in marine waters and sediments (DeLong, 1992; Vetriani et al., 1999). Genomic and environmental microbiological studies indicated that the crenarchaeota microbiota could be chemoautotrophic (Wuchter et al., 2003; Herndl et al., 2005; Hallam et al., 2006a, b; Ingalls et al., 2006), heterotrophic (Ouverney & Fuhrman, 2000; Teira et al., 2006) or mixotrophic. The crenarchaeotal ammonia mono-oxygenase gene (amoA) was found to be pervasive in the ocean (Francis et al., 2005; Beman & Francis, 2006; Beman et al., 2007; Lam et al., 2007; Nakagawa et al., 2007). Quantitative studies also indicated that ammonia-oxidizing archaea (AOA) appear to be more abundant than ammonia-oxidizing bacteria (Wuchter et al., 2006; Mincer et al., 2007; Nakagawa et al., 2007), and the potential estuarine nitrification rates increased as abundance of AOA amoA increased (Caffrey et al., 2007), further suggesting the ecological importance of the AOA.
Although nitrification is of particular importance in estuarine and coastal sediments, and the ability to oxidize ammonia may be broadly distributed in the crenarchaeota (Nicol & Schleper, 2006; Caffrey et al., 2007; Cavicchioli et al., 2007; Francis et al., 2007), only a few studies have actually examined the diversity and spatial distribution of the sedimentary AOA communities (Francis et al., 2005; Beman & Francis, 2006). These studies have shown that diverse AOA phylotypes and distinct AOA communities exist in different marine environments both on a large geographical scale and in local estuarine gradients, demonstrating that their spatial distribution may be associated with environmental conditions (Francis et al., 2005; Beman & Francis, 2006). However, these studies were limited to the temperate and subtropical coast of the East Pacific Ocean. The sedimentary diversity and spatial distribution of AOA are largely unknown in other coastal areas of the world oceans, including most large river estuaries.
The Changjiang River (historically called the Yangtze River) is the third-largest river in the world, with a huge water discharge of 9.24x1011 m3 per year, equivalent to 1/50 of the water volume of the adjacent East China Sea (Yanagi, 1994), the largest continental marginal sea in the western Pacific. This river also delivers more than 7.5x1010 moles per year of N nutrients to the East China Sea (Zhu et al., 2005). Thus, the estuary and shelf region has been the research focus of a series of international programmes, such as JGOFS (Joint Global Ocean Flux Study), GLOBEC (Global Ocean Ecosystems Dynamics), LOICZ (Land–Ocean Interactions in the Coastal Zone) and IMBER (Integrated Marine Biogeochemistry and Ecosystem Research), due to its importance in fishery, climate change and environmental issues, such as pollution, eutrophication, red tides and hypoxia (Li et al., 2002; Li & Daler, 2004; Chai et al., 2006). Being the interface of land, freshwater and marine environments, this region is extremely complicated and dynamic, due to the variability of freshwater input, currents and anthropogenic inputs, as well as the construction and operation of the Three Gorges Dam in the middle reaches of the Changjiang River (Zhang et al., 1999; Jiao et al., 2007). Micro-organisms may play important roles in this unique large river estuarine ecosystem, particularly in biogeochemical cycles and food webs. However, studies of microbial ecology started here only recently (Sekiguchi et al. 2002; Zhang & Jiao, 2007). It has been shown that marine crenarchaeota dominate the pelagic archaeal community, and 16S rRNA gene sequences related to autotrophic ammonia-oxidizing ‘Candidatus Nitrosopumilus maritimus’ were detected in the estuarine area (Zeng et al., 2007). Due to environmental heterogeneity and intense riverine input of anthropogenic N, we hypothesized that the Changjiang Estuary and the adjacent East China Sea could harbour diverse sedimentary AOA, with distinct community structures and diversity in response to specific estuarine, geochemical and eutrophication gradients. To test this hypothesis, a molecular study based on the archaeal amoA functional marker gene was conducted.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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). Sediments were collected with a stainless steel 0.1 m2 Gray O'Hara box corer and only undisturbed core samples with clear overlying water were used (Jonasson & Olausson, 1966). Replicate surface sediment subcore samples down to 5 cm depth for microbiological and environmental analyses were taken with sterile 60 ml syringes (luer end removed), homogenized and stored in airtight sterile plastic bags at –20 °C during the cruise and –80 °C after returning to the laboratory.
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). Other parameters were measured in the laboratory. A nutrient AutoAnalyser (QUAATRO; Bran+Luebbe) was used to measure pore-water dissolved N and P concentrations: nitrate (NO3-N), nitrite (NO2-N), ammonium (NH4-N) and phosphate (PO4-P). Sediment total organic carbon (OrgC) and nitrogen (OrgN) contents were measured with a PE2400 series II CHNS/O elemental analyser (Perkin Elmer). A Cilas 940L laser granulometer (Company Industrielle des Lasers) was used for sediment grain size analysis and median grain size, sorting coefficient, skewness and kurtosis were calculated (Table 1).
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; Beman & Francis, 2006). Replicate DNA extractions from three subcore samples were pooled for each sampling station. Archaeal amoA fragments (
635 bp) were amplified in a PTC-200 thermal cycler (Bio-Rad) with published primers Arch-amoAF and Arch-amoAR and the corresponding PCR protocol (Francis et al., 2005; Beman & Francis, 2006). PCR products from five reactions were pooled to minimize PCR bias, gel-purified, and ligated into pMD19-T simple vectors (Takara) according to the manufacturer's instructions. The hybrid vectors were used to transform Escherichia coli TOP10 competent cells prepared by using the calcium chloride protocol (Sambrook & Russell, 2001). Recombinants were selected by using X-Gal-IPTG Luria–Bertani (LB) indicator plates supplemented with 100 µg ampicillin ml–1. Each clone library was constructed by random selection of approximately 100 white colonies from a single plate. A plasmid miniprep method was used for recombinant plasmid preparations (Dang & Lovell, 2000). Cloned amoA fragments were reamplified using primers M13-D (5'-AGGGTTTTCCCAGTCACGACG-3') and RV-M (5'-GAGCGGATAACAATTTCACACAGG-3'), flanking the insertion site of the vector. PCR products were screened for correct size and purity by 1 % agarose gel electrophoresis. Amplimers with the correct size were digested using MspI and HhaI (Fermentas). Restriction fragments were resolved by electrophoresis on 4 % agarose gels in 0.5x TBE, and digitally photographed with an ImageMaster VDS imaging system (Pharmacia Biotech). The band patterns of the RFLP analysis were compared in order to identify redundant clones.
Clone vector primer RV-M was used for sequencing with an ABI 3770 automatic sequencer (Applied BioSystems). Several random clones from the same RFLP patterns resulted in identical amoA sequences; the genetic variation in each of the RFLP patterns was probably quite low. The resultant unique sequences were grouped into operational taxonomic units (OTUs) based on 95 % DNA sequence similarity calculated with the DOTUR program (Schloss & Handelsman, 2005), to facilitate comparison with other studies (Francis et al., 2005; Beman & Francis, 2006; Park et al., 2006; Beman et al., 2007). The sequences of the putative archaeal amoA genes were translated into conceptual amino acid sequences using the BioEdit program (Hall, 1999), and the online BLAST program (Altschul et al., 1997) from the GenBank database was used for retrieval of the closest matched sequences. AmoA sequences were aligned using the CLUSTAL_X program (Thompson et al., 1994) and phylogenetic trees constructed using the PROTDIST and NEIGHBOR programs of the PHYLIP package (version 3.66) (Felsenstein, 1989).
Statistical analyses.
The estimated coverage of the constructed archaeal amoA gene libraries was calculated as C=[1 – (n1/N)]x100, where n1 is the number of unique (frequency=1) amoA RFLP genotypes or OTUs detected in a library and N is the total number of clones in the same library (Mullins et al., 1995). This value approximated the probability that all the unique sequences present in a given sample were represented at least once in the library.
Indices of the amoA genotype diversity (Shannon–Wiener H and Simpson D) and evenness (J) were calculated with amoA RFLP genotypes or OTUs of the clone libraries (Brown & Bowman, 2001). Rarefaction analysis and two nonparametric richness estimators, the abundance-based coverage estimator (SACE) and the bias-corrected Chao1 (SChao1), were calculated using the DOTUR program (Schloss & Handelsman, 2005; Beman & Francis, 2006). These diversity indices and richness estimators are useful statistical tools to compare the relative complexity of communities and to estimate the completeness of sampling.
The AOA community classification was determined with weighted UniFrac environmental clustering and principal coordinates analyses (PCoA) (Lozupone & Knight, 2005; Lozupone et al., 2007). The online UniFrac program (http://bmf.colorado.edu/unifrac/index.psp) takes molecular evolutionary distances of the sequences and their environmental occurrences for microbial community similarity analyses, particularly suitable for sequence data. Correlations between AOA communities and environmental factors were analysed by canonical correspondence analysis (CCA) using the software Canoco (version 4.5, Microcomputer Power) (ter Braak & 
milauer, 2002). High occurrence of zero entries (64 %) in the species table (i.e. the OTU table) indicated that the unimodal CCA analysis was more suitable than the linear-model-based redundancy analysis (RDA) for our data (Lep
& milauer, 2003). The percentage frequency data of the amoA OTUs were used as the species input, and the environmental variables entered into the CCA were normalized (i.e. adjusted for a mean of 0 and SD of 1 via Z transformation) (Magalhâes et al., 2007). Manual deselection of collinear environmental variables and forward selection with significance tests of Monte Carlo permutations were used to build the optimal models of the microbe–environment relationship (Lep & milauer, 2003). These multivariate statistical methods cope with major issues in microbial ecology, such as the distribution and relatedness of diversity and community structure with environmental variables or along an environmental gradient (Lozupone et al., 2007; Magalhâes et al., 2007).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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7.5x1010 moles nitrate per year) (Zhu et al., 2005). Gradients of surface-water salinity, density and conductivity along the two sampling transects indicated that strong freshwater runoff from the Changjiang River formed the characteristic Changjiang Diluted Water (CDW) in the East China Sea (Table 1
). The northward intrusion of the Taiwan Warm Current (TWC) from the south along the 50 m isobath is another permanent feature of the East China Sea circulation in summer (Ichikawa & Beardsley, 2002; Chen et al., 2003). The TWC saline water met with the CDW freshwater just off the Changjiang River mouth, forming salinity fronts around 122. ° E. Most of the fine particles carried by the CDW were deposited in this estuarine mixing zone, and were responsible for the creation of the estuarine maximum turbidity zone (Table 1). Surface-water salinity and turbidity gradients showed that part of the CDW flowed in the south-eastward branch along the coastline during our sampling time, in good accordance with previous findings in summer seasons (Chen et al., 2003; Zhu et al., 2005).
Due to the intrusion of the TWC, bottom water at the sampling stations usually had higher salinity than surface water (Table 1). The most significant gradients among the various bottom-water physico-chemical parameters were dissolved oxygen and turbidity. The S7 station was usually located inside the Changjiang Estuary seasonal oxygen minimum zone (Li et al., 2002). Although the bottom water did not reach anoxic conditions during our sampling period, the lowest dissolved oxygen value did occur at station S7 and obvious gradients existed along both the sampling transects as expected (Table 1). The bottom-water turbidity maxima at stations S18 and S7 indicated that, besides surface-water particulate matter sedimentation, seafloor surface sediment resuspension occurs intensely in this area. This was consistent with the fact that seasonal upwelling occurred in the area in summer (Zhu, 2003; Zhu et al., 2005), which might have a strong influence on the bottom-water physical and geochemical characteristics.
The sediment and pore-water geochemical parameters, especially NH4-N (ranging from 153.90 µM to 1808.07 µM) and N/P (from 48.44 to 359.91) (Table 1), indicated significant eutrophication of most of our sampling stations. The highest values occurred at station S29 and the lowest at station S10.
Diversity of the amoA libraries
From the seven archaeal amoA libraries constructed, 637 clones were screened, and 47 RFLP genotypes and 29 OTUs were identified. Sampling station S7 had the highest diversity of the archaeal amoA genotypes, and stations S9 and S29 had the lowest diversity, based on the values of the Shannon–Wiener, reciprocal of Simpson and evenness indices calculated with both the RFLP genotypes and the OTUs data, except for station S9's evenness index calculated with the OTUs data (Table 2). The estimated coverages of the clone libraries were quite high (Table 2), together with the rarefaction analyses (Supplementary Fig. S1, available with the online version of this paper), indicating that these libraries might have captured the majority of the archaeal amoA sequence types in the studied environments with the primers used. However, analyses of the richness estimators SACE and SChao1 indicated that most of our sampling stations might have higher predicted archaeal amoA genotype diversity than recovered in the current study (Table 2).
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The constructed phylogenetic tree showed that two AmoA sequence clusters could be identified based on 20 % sequence distance cutoff determined via the DOTUR program (Fig. 2). Sequences associated with coastal sediment (San Francisco Bay, Gulf of California and Huntington Beach) or marine water (Black Sea) environments composed cluster I (Francis et al., 2005; Lam et al., 2007), and sequences having their closest matches mainly from terrestrial environments (Chinese, American, Australian and German soils) composed cluster II (Treusch et al., 2005; Leininger et al., 2006; He et al., 2007), although some estuarine sediment sequences were also affiliated within this cluster (Francis et al., 2005; Beman & Francis, 2006; Park et al., 2006). The putative soil-related sequences of cluster II in our clone libraries mainly occurred at stations S7, S8, S18, S29, and occasionally at station S10. Their relative abundance accounted for 33.3 %, 16.7 %, 37.5 %, 33.3 % and 7.1 % of the RFLP genotypes and 17.2 %, 9.8 %, 13.3 %, 4.3 % and 1.1 % of the clones in each of the corresponding libraries. The change of their relative abundances in the various libraries indicated that the AOA of these sequences might mainly occur in the area close to the Changjiang River mouth.
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). These sequences mainly occurred in sampling stations S7, S8, S9, S10 and S33. Five sequences (109 clones) were closely (94.3–96.2 % identity) related to the AmoA sequence of ‘Candidatus Nitrosopumilus maritimus’ SCM1 (Konneke et al., 2005), and they had wide distribution in the Changjiang Estuary and the adjacent East China Sea (Fig. 2).
Nearly 60 % of the AmoA sequences occurred in only one of the sampling stations. Four sequences, S7-A-01, S7-A-02, S7-A-03 and S7-A-04, which were related to Gulf of California and San Francisco Bay sediment and Black Sea water AmoA sequences, occurred in all the stations (Fig. 2). Some other sequences, such as S7-A-05, S33-A-06, S7-A-06 and S7-A-07, which were related to Cenarchaeum symbiosum or sedimentary archaeal AmoA sequences from San Francisco Bay and Huntington Beach, occurred in five or four of the sampling stations. These common sequences were also the most abundant ones (80.8 % of the 637 clones screened) in the libraries.
AOA community distribution in response to estuarine gradients
UniFrac environmental clustering of the archaeal amoA clone libraries showed clear grouping of the AOA communities in the Changjiang Estuary and the adjacent East China Sea (data not shown). The amoA genotype assemblages of stations S8, S9 and S10 on the transect perpendicular to the East China Sea coastline (the ‘perpendicular transect’ hereafter) were grouped together, those of stations S18, S29 and S33 on the transect aslant to the coastline (the ‘slant transect’ hereafter) were grouped together, and the amoA genotype assemblage of station S7, which was at the intersection of the two transects, formed the third group. PCoA showed similar clustering of the archaeal amoA genotype assemblages, and the first two principal coordinates (P1 and P2) explained 78.29 % of the total community variability (Fig. 3). Environmental clustering (data not shown) and PCoA (Supplementary Fig. S2) based on the archaeal AmoA protein sequences showed similar clustering of the sampling stations.
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). The first two CCA axes explained 65.2 % of the total variance in the amoA genotypes composition and 71.6 % of the cumulative variance of the genotype–environment relationship. However, only surface-water salinity contributed significantly (P=0.015, F=1.92, 1000 Monte Carlo permutations) to the amoA genotype–environment relationship, and this factor alone provided 27.8 % of the total CCA explanatory power. The putative soil-related archaeal amoA OTUs had very restricted distribution in the CCA graph, being mainly located on the negative half of the surface-water salinity vector (Fig. 4a). The distribution of these OTUs in response to low surface-water salinity indicated that the potential source of these putative soil-related AOA might be freshwater input from the Changjiang River. Although none of the other factors made a statistically significant contribution to the amoA genotype–environment relationship (P>0.200, 1000 Monte Carlo permutations), the combination of these environmental factors provided additionally 62.3 % of the total CCA explanatory power. Moreover, the bottom-water turbidity seemed to have a positive correlation with the distribution of the putative soil-related amoA OTUs.
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). However, only the sediment sorting coefficient, a measure of the distribution or variability of particle sizes in the sediment, contributed significantly (P=0.045, F=2.49, 1000 Monte Carlo permutations) to the amoA genotype–environment relationship. This sedimentological factor alone provided 45.6 % of the total CCA explanatory power. Neither NH4-N nor OrgN made a statistically significant contribution to the microbe–environment relationship (P>0.25, 1000 Monte Carlo permutations), although distinct spatial distribution of the AOA assemblages along their gradients could be identified (Fig. 4b). Moreover, the geochemical variable OrgN seemed to have a positive correlation with the distribution of the putative soil-related amoA OTUs (Fig. 4b). |
DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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) (Francis et al., 2005; Treusch et al., 2005; Beman & Francis, 2006; Hallam et al., 2006a; Leininger et al., 2006; Park et al., 2006; He et al., 2007; Lam et al., 2007). Comparison with the sediment archaeal amoA communities of the hypernutrified subtropical Bahía del Tóbari Estuary (Beman & Francis, 2006) indicated that the Changjiang Estuary and the adjacent East China Sea harboured similar AOA diversity. The high sequence similarity between the two estuaries indicates that similar archaeal AOA communities might exist in similar estuarine environments across the Pacific Ocean, despite the great geographical distance.
The Changjiang Estuary is an important interface of the terrestrial and marine environments; it is also highly complex and dynamic. The CDW has been found to split into two branches upon entering the East China Sea near the Changjiang River mouth in summer. One flowed south-eastwards, and the other northeast-northwards (Chen et al., 2003; Zhu et al., 2005). The slow change of surface-water salinity along the slant transect indicated that part of the CDW flowed south-eastwards along the coastline during our sampling period. The sharp increase of surface-water salinity along the perpendicular transect indicated that the Changjiang freshwater runoff in the eastward direction was blocked by the offshore marine water or the northward intrusion of the TWC. Due to these differences, there were differences in the water physico-chemical properties between the two sampling transects. Surface water on the slant transect was more turbid than that on the perpendicular transect (not including station S7). This distinction could also be found in the difference of the sedimentary AOA assemblages (Figs 3 and 4; Supplementary Fig. S2).
The archaeal diversity in estuaries might be greater than that in the adjacent open oceans due to allochthonous terrestrial inputs, of which river water runoff might form the major source of particle-attached archaea in estuarine waters (Crump & Baross, 2000; Wells & Deming, 2003; Wells et al., 2006). Some studies have indicated that the AOA communities in terrestrial environments are distinct from those in marine environments (Leininger et al., 2006), while other studies have indicated that estuaries might harbour mixed populations of both soil and sediment AOA (Francis et al., 2005; Beman & Francis, 2006). The deposition of microbes from the water column with freshwater input could potentially explain the existence of the putative soil-related archaeal AmoA sequences in the estuarine sedimentary environments. In the Changjiang estuarine area, besides freshwater, nutrients, organic matter and suspended particles, the CDW might also contribute to the transport of terrestrial micro-organisms into the seawater and sediments along its flow path. The distribution of terrestrial AOA in estuarine environments might be a common phenomenon based on AmoA phylogenetic analyses (Fig. 2) (Francis et al., 2005; Beman & Francis, 2006), illustrating the potentially important effect of river freshwater on the coastal sedimentary AOA composition. Our results also indicate that the putative soil-related AOA in estuarine sedimentary environments might serve as a bioindicator or biotracer of riverine impact on the coastal benthic microbial ecosystem.
Station S7 had the highest diversity of archaeal amoA genotypes among all the sampling stations. This station received the strongest impact of freshwater discharge from the Changjiang River. It was also located in the estuarine mixing zone, and the consequential maximum-turbidity zone of the East China Sea. Particles could be a source of riverine archaea in estuaries (Crump & Baross, 2000; Wells & Deming, 2003; Wells et al., 2006). Particles might also be a source of small organic compounds and potentially serve as a nutrient source for marine archaea, as studies have indicated that some marine crenarchaeota could utilize amino acids heterotrophically (Ouverney & Fuhrman, 2000; Teira et al., 2006). The positive correlation of the S7 station amoA genotype assemblage with the sediment OrgN (Fig. 4b), although not statistically significant (P>0.25, 1000 Monte Carlo permutations), provides certain clues to the above reasoning. Furthermore, the distribution of the sedimentary archaeal amoA genotype assemblages also strongly correlated with surface-water turbidity (Supplementary Fig. S3). However, because of the collinearity between surface-water salinity and turbidity (r=–0.814), the exact contributions of these environmental factors could hardly be distinguished from each other. The edge effect of strong riverine–marine water interactions at the sharp estuarine salinity front around the S7 sampling station might provide a unique ecotone for the development and maintenance of a species-rich AOA assemblage (Chen et al., 2003; Ries et al., 2004; Zhu et al., 2005).
Some 30.0 % of our sedimentary AmoA sequences had their closest match to sequences originally retrieved from marine water environments (Lam et al., 2007). This indicates that sediment and seawater might share some common or similar AOA micro-organisms (Beman & Francis, 2006), or that exchange of AOA between sediment and seawater environments might occur in the Changjiang Estuary and East China Sea, via particle deposition, sediment resuspension or other mechanisms. A recent study indicated that the deep-sea sediments might harbour AOA communities distinct from those of the water columns of the deep oceans (Nakagawa et al., 2007). Significant differences might exist between coastal and deep-sea environments in the effect of the water–sediment interactions on the sedimentary AOA communities. High rates of particle deposition and sediment resuspension in coastal waters, especially near large river estuaries, might provide stronger exchange between the water and sediment AOA communities.
Multivariate statistical analyses indicated that the spatial distribution of the sedimentary amoA genotype assemblages correlated significantly with the surface-water salinity and sediment sorting coefficient (Fig. 4a, b), indicating potential influences of Changjiang River freshwater runoff and sedimentological condition on the sedimentary AOA community. The influence of the sedimentological condition on the sedimentary AOA community could be complicated. Sedimentological conditions were mainly related to the in situ hydrological regime, such as currents, tides, waves, upwelling, lateral transport, water mixing and exchange, and the intensity and dynamics of these activities. The correlation of the sediment sorting coefficient with archaeal amoA genotype assemblages could be related directly or indirectly to hydrological activities, via their impact on the sediment source, composition, organic matter content, pore-water redox, nutrient composition and concentration, and other physico-chemical, sedimentological or geochemical factors. Although we do not know the exact mechanism at present, our work is probably the first to show a direct correlation of a sedimentological factor with the distribution and structure of the sedimentary AOA community in an estuarine and continental shelf environment. However, because of the covariability of some of the environmental factors (Supplementary Figs S3 and S4), the exact influence and contribution of each environmental factor cannot be determined with certainty at present.
In summary, the diversity and spatial distribution of the sedimentary AOA have been studied in the Changjiang Estuary and adjacent East China Sea for the first time via analyses of the functional marker gene amoA. Our work indicated that this estuarine area might harbour similar sediment AOA communities to those of the East Pacific Ocean, and the transport of terrestrial AOA via river freshwater runoff might contribute to the composition and diversity of the estuarine sedimentary AOA communities.
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ACKNOWLEDGEMENTS |
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Edited by: W. Liesack
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Received 24 September 2007;
revised 8 February 2008;
accepted 12 February 2008.
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