pH influences the importance of niche-related versus neutral processes in lacustrine ...

37 Pages·2015·1.6 MB·English

AEM Accepted Manuscript Posted Online 27 February 2015
Appl. Environ. Microbiol. doi:10.1128/AEM.04042-14
Copyright © 2015, American Society for Microbiology. All Rights Reserved.
1 pH influences the importance of niche-related versus neutral processes in lacustrine
2 bacterioplankton assembly
3
4 Lijuan Rena, Erik Jeppesenb, c, Dan Hea#, Jianjun Wanga, Lone Liboriussenb, Peng Xinga, and
5 Qinglong L. Wua, c*
D
o
6 w
n
lo
7 a
d
e
d
8 State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and
f
r
o
9 Limnology, Chinese Academy of Sciences, Nanjing, Chinaa; Department of Bioscience, Aarhus m

h
t
10 University, Silkeborg, Denmarkb; Sino-Danish Centre for Education and Research, Beijing, tp
:
/
/
a
11 Chinac. e
m
.
a
12 s
m
.
13 Running Head: Bacterial community assembly in freshwater lakes. o
r
g
/

14 o
n

J
15 *Address correspondence to Qinglong L. Wu, [email protected]. a
n
u
a
16 r
y

5
17 # Present address: Dan He, State Key Laboratory of Soil and Sustainable Agriculture, Institute of ,

2
0
1
18 Soil Science, Chinese Academy of Sciences, Nanjing, China.
9

b
y

g
u
e
s
t
1
19 ABSTRACT pH is an important factor that shapes the structure of bacterial communities.
20 However, we have very limited information about the patterns and processes by which overall
21 bacterioplankton communities assemble across wide pH gradients in natural freshwater lakes.
22 Here, we used pyrosequencing to analyze the bacterioplankton communities in 25 discrete
23 freshwater lakes in Denmark with pH levels ranging from 3.8 to 8.8. We found that pH was the D
o
w
24 key factor in impacting lacustrine bacterioplankton community assembly. More acidic lakes n
lo
a
25 imposed stronger environmental filtering, which decreased the richness and evenness of d
e
d
26 bacterioplankton operational taxonomic unit (OTU) and largely shifted community composition. f
r
o
m
27 Although environmental filtering was detected to be the most important determinant of
h
t
t
p
28 bacterioplankton community assembly, the importance of neutral assembly processes must also :
/
/
a
e
29 be considered notably in acidic lakes, where the species (OTU) diversity was low. We observed m
.
a
30 that the strong effect of environmental filtering in more acidic lakes was weakened by the s
m
.
o
31 enhanced relative importance of neutral community assembly, and bacterioplankton communities r
g
/

o
32 tended to be less phylogenetically clustered in more acidic lakes. In summary, we propose that n

J
a
33 pH was a major environmental determinant in freshwater lakes, regulating the relative n
u
a
34 importance and interplay between niche-related and neutral processes and shaping the patterns of r
y

5
,
35 freshwater lake bacterioplankton biodiversity. 2
0
1
36 9

b
y
37 Keywords: bacterioplankton community, phylogenetic assembly, freshwater lake, pH gradient g
u
e
s
t
2
38 INTRODUCTION
39 Bacterioplankton are a crucial component of aquatic ecosystems, and they play an important role
40 in the ecological processes of freshwater lakes (1, 2). Understanding the mechanisms governing
41 bacterioplankton community assembly (e.g., community composition, diversity, and
42 phylogenetic structure) in natural freshwater lakes is a long-standing challenge in microbial D
o
w
43 community ecology. There is increasing evidence that the community structure of freshwater n
lo
a
44 lake bacterioplankton is influenced by niche-related (deterministic) and neutral (stochastic) d
e
d
45 processes (e.g., 2-4). Niche-related processes (5) include selection imposed by the abiotic f
r
o
m
46 environment (environmental filtering) and species interactions (competition, facilitation,
h
t
t
p
47 mutualism, and predation), whereas neutral effects (6) include stochastic processes such as :
/
/
a
e
48 unpredictable disturbances, probabilistic dispersal, and random birth-death events (7, 8). Neutral m
.
a
49 processes likely result in random fluctuations in community composition along a given s
m
.
o
50 environmental gradient (9). The observation that both niche and neutral processes are important r
g
/

o
51 in shaping bacterioplankton community assembly raises the following question: what affect the n

J
a
52 relative contributions and interplay between stochastic and deterministic processes in structuring n
u
a
53 lacustrine bacterioplankton assembly? r
y

5
,
54 Multiple environmental factors regulate the distribution of bacterioplankton communities in 2
0
1
55 freshwater lakes (e.g., 10-16). pH is proposed to be an important environmental factor 9

b
y
56 influencing both overall bacterioplankton community composition (BCC) (2, 17) and the g
u
e
57 composition of abundant bacterioplankton groups including Polynucleobacter and s
t
58 Limnohabitans (e.g., 18, 19) in freshwater lakes. Newton et al. (20) performed clone-library
59 sequencing and observed that pH was also a key environmental determinant of the phylogenetic
60 relatedness of the freshwater lake Actinobacteria acI lineage; acidic lakes (pH from 5.1 to 6)
3
61 contained more closely related acI clades than expected at random. However, so far few papers
62 have systematically discussed the assembly of overall lacustrine bacterioplankton communities
63 across wide pH gradients with more detailed phylogenetic information, and therefore, we don’t
64 know much about the patterns of overall lacustrine bacterioplankton diversity and community
65 composition across wide pH gradients. Furthermore, how pH influences the relative importance D
o
w
66 and interplay between niche-related and neutral processes in structuring overall communities is n
lo
a
67 also largely unknown. d
e
d
68 One promising approach to understand the underlying niche-related versus neutral processes f
r
o
m
69 in shaping community assembly is the use of community phylogenetic structure test (e.g., 21-
h
t
t
p
70 23). When comparing community phylogenetic structure with randomization procedures, known :
/
/
a
e
71 as “null models”, and estimating the deviations from null model expectations, the changes in the m
.
a
72 relative influences of deterministic and stochastic processes across environmental gradients can s
m
.
o
73 be inferred (e.g., 21-23). The foundation of this phylogenetic framework is the assumption that r
g
/

o
74 closely related taxa are more likely to have similar niches, often termed phylogenetic niche n

J
a
75 conservatism (21, 24). If the phylogenetic niche conservatism is supported then a non-random n
u
a
76 community phylogenetic structure is expected to indicate the occurrence of niche-based r
y

5
,
77 processes such as environmental filtering and competitive interactions (24). Although the 2
0
1
78 phylogenetic framework has several notable advantages, it is rarely used to study bacterial 9

b
y
79 communities in freshwater lakes, especially when the communities span a broad range of g
u
e
80 environmental characteristics (25). However, freshwater ecosystem studies have observed that s
t
81 freshwater bacterial communities tend to be more clustered in more “stressful” habitats; for
82 example, there is increased clustering of Betaproteobacteria in less productive freshwater
83 mesocosms (25) and increased clustering of the Actinobacteria acI lineage in more acidic lakes
4
84 (20).
85 The intracellular pH of most microorganisms is usually within 1 pH unit of neutral pH (26).
86 Therefore, we hypothesized that the “stress” habitats such as acidic lakes in our study might
87 directly impose a greater environmental filtering on community assembly, thereby decreasing
88 overall bacterioplankton diversity and shifting the community composition to taxa that can better D
o
w
89 adapt to the environments. Additionally, the increased pH filtering effects in acidic lakes might n
lo
a
90 increase the phylogenetic clustering of the bacterioplankton communities. Although d
e
d
91 environmental filtering is an important determinant of community assembly (20, 25), other f
r
o
m
92 mechanisms, such as neutral processes, must also be considered. Recent researches have showed
h
t
t
p
93 that neutral processes have a strong impact on the accumulation of rare taxa in a community, and :
/
/
a
e
94 the relative influence of neutral processes may increase on community assembly when m
.
a
95 community populations are small (6, 7). Therefore, we also hypothesized that the enhanced s
m
.
o
96 neutral assembly processes in small bacterioplankton communities might balance or weaken the r
g
/

o
97 relative importance of environmental filtering on overall bacterioplankton communities and n

J
a
98 would also alter phylogenetic assembly patterns of overall communities across the pH gradient. n
u
a
99 To test these two hypotheses, we selected 25 discrete freshwater lakes in Central Jutland, r
y

5
,
100 Denmark. The pH of freshwater lakes, including bog humic lakes, is generally between 4 and 9, 2
0
1
101 but pH values can be lower than 4 in extreme volcanic or mining water (27). To sample a wider 9

b
y
102 pH range, we included two lakes with pH values ≤ 4 in our study; these lakes were formed from g
u
e
103 abandoned mining operations approximately 70 years ago. Using high-throughput s
t
104 pyrosequencing, we analyzed the composition and diversity of the bacterioplankton communities
105 in 25 discrete freshwater lakes with pH values ranging from 3.8 to 8.8. We also analyzed the
106 bacterioplankton phylogenetic community structure across the pH gradient and studied the
5
107 mechanisms shaping this structure.
108
109 MATERIALS AND METHODS
110 Sample collection and chemical analyses. During May and June 2012, we collected water
111 samples from 25 discrete freshwater lakes in Central Jutland, Denmark. These lakes are located D
o
w
112 in the same geographical area, and pairwise distances between the lakes are less than 40 km. n
lo
a
113 They have no surface water inputs and span a pH range of 3.8 to 8.8 (Table S1). Some acidic d
e
d
114 lakes, such as lake No name 1 and No name 2 (Table S1), were formed from mining activities f
r
o
m
115 conducted in the 1940s. Based on the lake shape and size (area < 0.76 km2, Table S1), we
h
t
t
p
116 selected six different sampling points in each lake. The sampling points were generally :
/
/
a
e
117 distributed uniformly. At each sampling point, bacterial samples (400 to 500 mL of water) were m
.
a
118 collected from surface waters (top 50 cm) and filtered through 0.2-µm-pore Isopore filters s
m
.
o
119 (Millipore, Billerica, Massachusetts, USA). The filters were stored at −20°C. Water pH and r
g
/

o
120 conductivity were measured in the field at each location. Untreated surface water samples n

J
a
121 (approximately 300 mL) were collected from each sampling point for chemical analysis. Using n
u
a
122 standard methods (28), the total organic carbon (TOC), total nitrogen (TN), total phosphorus r
y

5
123 (TP), total iron (TFe), and nitrate nitrogen (NOx-) content of the water were determined. , 2
0
1
124 Additionally, historical data were obtained for most of the examined lakes and are cited in our 9

b
y
125 study. The geographical, physical, and chemical characteristics of the examined lakes are shown g
u
e
126 in Table S1. Environmental variables, including conductivity, TOC, TN, TP, TFe, NO -, and lake s
x t
127 area, were transformed by log (x+1) in all subsequent analyses.
128
6
129 DNA extraction, amplification and 454 sequencing. Genomic DNA was extracted from the
130 biomass collected on the filters using a standard phenol-chloroform extraction method (11) and
131 was purified using the Wizard DNA clean-up kit (Promega, Madison, Wisconsin, USA). The
132 bacterial 16S rRNA genes were amplified with the forward primer 341F (5′ CCT ACG GGA
133 GGC AGC AG 3′) with the Roche 454 ‘A’ pyrosequencing adapter and a unique 7-bp barcode D
o
w
134 sequence and with the reverse primer 1073R (5′ACG AGC TGA CGA CAG CCA TG 3′) with n
lo
a
135 the Roche 454 ‘B’ sequencing adapter at the 5′-end. Three replicates of each sample were d
e
d
136 amplified in 20-µL reactions under the following conditions: 24 cycles of denaturation at 94°C f
r
o
m
137 for 30 s, annealing at 55°C for 30 s, and extension at 72°C for 30 s, with a final extension at
h
t
t
p
138 72°C for 5 min. The PCR products were visualized by electrophoresis, and the replicates were :
/
/
a
e
139 pooled. The purified PCR products were combined in equimolar ratios and then sequenced using m
.
a
140 a Roche 454 FLX+ pyrosequencing machine. s
m
.
o
141 r
g
/

o
142 Pyrosequencing data analysis. Raw reads of the 16S rRNA gene sequences were processed n

J
a
143 using the software package Mothur (V. 1.30.0, http://www.mothur.org) according to the standard n
u
a
144 454 operating procedure (SOP, 29). In brief, the raw reads were denoised, trimmed, quality- r
y

5
,
145 filtered, and aligned to the Silva database (30) using Mothur. Clean sequences of six replicates 2
0
1
146 for each lake were then aggregated into one group. To build the OTU matrix, we first generated a 9

b
y
147 distance matrix from the clean sequences using the dist.seqs command in Mothur. In the process, g
u
e
148 we excluded any pairwise distance larger than 0.15. Next, we clustered the clean sequences into s
t
149 OTUs based on the newly created distance matrix by using the cluster command and the furthest
150 neighbor algorithm method in Mothur. Finally, we generated an OTU-shared file at a cutoff
151 value of 0.03 (OTUs ) using the make.shared command in Mothur. To correct for sequencing
0.03
7
152 bias, we randomly sub-sampled the minimum number of sequences (i.e., 19 000 sequences) from
153 each lake, and OTUs with fewer than 3 sequences were excluded from the OTU table using the
154 Remove.rare command in Mothur. Representative sequences of OTUs were generated within
0.03
155 Mothur and then classified into taxonomic groups using the typical freshwater bacterial database
156 provided by Newton et al. (2) at the recommended bootstrap threshold of 80% (31). Unclassified D
o
w
157 representative sequences of OTUs were further identified using the Silva bacterial database n
0.03
lo
a
158 and by BLAST against GenBank entries (http://www.ncbi.nlm.nih.gov/blast/blast.cgi). A d
e
d
159 phylogenetic tree was constructed using the FastTree algorithm (v. 2.1.3, 32) (FastTree flagged f
r
o
m
160 conditions: -fastest, -gtr, -nt). By matching with the corrected OTU table, a new phylogenetic
h
t
t
p
161 tree was generated by using the match.phylo.comm command in the picante package in the R :
/
/
a
e
162 statistical language (R Development Core Team 2008). m
.
a
163 s
m
.
o
164 Diversity analyses. Taxonomic richness (i.e., the number of OTUs0.03, 33) and Pielou's evenness rg
/

o
165 (34) were calculated using the vegan package in R. Faith’s phylogenetic diversity (PD, 35) was n

J
a
166 measured using the phylogenetic tree and the OTU0.03 table with the pd command in the picante n
u
a
167 package in R. The BCC dissimilarities between lakes (beta diversity) were examined using the r
y

5
,
168 taxonomy-based Bray-Curtis distance and the phylogeny-based UniFrac distance, which were 2
0
1
169 calculated using the vegan package and the picante package, respectively, in R. 9

b
y
170 g
u
e
171 Community phylogenetic structure. To exclude the impact of OTU richness on PD variation, s
t
172 we compared the PD of a particular community to randomized null communities of the same size
173 and calculated the standardized effect size of PD (SES.PD) using the ses.pd command in the
174 picante package in R. The null model was performed by shuffling taxa labels 999 times across
8
175 the tips of the phylogenetic tree using a given tree topology and branch lengths to randomize
176 phylogenetic relationships among OTUs . SES.PD equals the differences in PD between the
0.03
177 observed communities and the mean value of the 999 null communities divided by the
178 standardized deviation of the PD in the 999 null communities (21).
179 The phylogenetic signal was examined by conducting Mantel correlograms with 999 D
o
w
180 randomizations between the pairwise matrix of OTU0.03 phylogenetic distances and OTU0.03 n
lo
a
181 ecological niche distances (i.e., the difference in habitat requirements for each OTU , 36, 37) d
0.03 e
d
182 as proposed by Wang et al. (37). The metrics measuring the mean phylogenetic distance to each f
r
o
m
183 taxon’s closest relative (the mean nearest taxon distance (MNTD) and the standardized effect
h
t
t
p
184 size of MNTD (SES.MNTD, 22) were also examined in our study. Using the ses.mntd command :
/
/
a
e
185 in the picante package of R, the abundance-unweighted MNTD and SES.MNTD were calculated. m
.
a
186 The null model used for SES.PD was also used for SES.MNTD. SES.MNTD equals the s
m
.
o
187 differences in MNTD between the observed communities and the mean value of the 999 null r
g
/

o
188 communities divided by the standardized deviation of the MNTD in the 999 null communities n

J
a
189 (21). Using a sample T-test, the mean SES of PD and MNTD across all lakes were compared n
u
a
190 with zero, which is the value expected when the average phylogenetic structure of local r
y

5
,
191 communities is the same as the null communities. SES values greater than zero indicate 2
0
1
192 phylogenetic over-dispersion (taxa more distantly related than expected at random), and SES 9

b
y
193 values lesser than zero suggest clustering (taxa more closely related than expected at random). g
u
e
194 s
t
195 Statistical analyses. A Heuristic method was used to estimate the relative weight of the
196 environmental variables to the relative abundances of the different phyla in multiple regression
197 (38). Both of the relative abundances and the number of OTUs of the main lineages or clades of
9
198 bacterioplankton communities along the pH gradient were depicted in a heat map using the
199 pheatmap command in the pheatmap (Pretty Heatmaps) package in R.
200 The taxonomy-based and phylogeny-based distances were visualized using non-metric
201 multidimensional scaling (NMDS) in the vegan package in R. By using the mantel.partial
202 command in the vegan package in R, the partial Mantel statistic with 9999 permutations (39) was D
o
w
203 performed to calculate the partial matrix correlation between three dissimilarity matrices: BCC n
lo
a
204 matrix, one of the examined environmental variables, and the conditioned matrix of the rest d
e
d
205 environmental variables. Analyses of similarity (ANOSIM) were also performed to test the f
r
o
m
206 differences in BCC across different pH categories (pH 3-4, 5-6, 6-7, and 7-9) using the anosim
h
t
t
p
207 command in the vegan package of R. Additionally, multiple regression on distance matrices :
/
/
a
e
208 (MRM) with 9999 permutations (40) was used to partition the variations in BCC into pure m
.
a
209 environment, pure space, mixed space/environment, and unexplained components by using the s
m
.
o
210 MRM command in the ecodist package of R. The best subset of environmental parameters r
g
/

o
211 applied in the MRM analysis was chosen by the BIOENV procedure using the vegan package in n

J
a
212 R (41). n
u
a
213 We also performed univariate models to predict bacterioplankton alpha diversity and the r
y

5
,
214 metrics of community phylogenetic structure with various environmental characteristics by using 2
0
1
215 the lm command in stats package of R. In each case, we fitted a linear and a quadratic model, and 9

b
y
216 results are shown for the model with the lowest Akaike information criteria (AIC) value. g
u
e
217 s
t
218 Sequence data submission. The sequence data were submitted to the National Center for
219 Biotechnology Information (NCBI) Sequence Read Archive (SRA;
220 http://www.ncbi.nlm.nih.gov/sra) under the study accession number SRP034651. The accession
10