Supporting information
Linking wetland sediment redox potential with functional genes subjected
to warming: implications for phosphorus mobilization
Zhijian Zhang1,2*, Hang Wang1, Jizhong Zhou3, Hongyi Li1, Joy D. Van Nostrand3,
Zhaode Wang4, Xinhua Xu1.
1
College of Environmental and Resource Science, Zhejiang University, Hangzhou 310058,
China;
2
China Academy of West Region Development, Zhejiang University, Hangzhou 310058,
China.
3Institute
for Environmental Genomics, Department of Microbiology and Plant Science,
University of Oklahoma, Norman, OK 73019, USA;
4
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography
and Limnology, Chinese Academy of Science, Nanjing 210008, China;
*
Corresponding Author:
College of Environmental and Resource Science, Zhejiang University, 886th Yuhangtang
Ave, Hangzhou 310058, China.
Email: zhangzhijian@zju.edu.cn, xuxinhua@zju.edu.cn
Phone: +86 571 8898 2057; Fax: +86 571 8898 1719
A. Materials and methods
A-1: Microcosm configuration
A microcosm setup (Fig. S1) for simulating climate warming at a minute-scale under both
daily and seasonal scenarios was developed for this study by using independently monitored
water-bath jackets. Microcosms consisted of four major components: a storage section, a
1
heating section, a water circulation section, and a real-time controlling section. The storage
section was composed of two stainless steel incubation boxes: one was for the present-day
ambient temperature treatment (control), and the other was for the +5oC-increased
temperature treatment (warmed). The real-time controlling section was composed of a
computer (HP a6315cn), a custom-built controller (TZ2008, Jiaxing China), digital
temperature probes (NB 407-25a, China), and lab-designed software (C++ language). The
temperature probes, heater, and pump were programmed through the custom-built controller
by the computer. With the help of the software, the temperatures in both incubation boxes
were continuously recorded and the differences in the box temperatures were compared by
digital probes at two-minute intervals. The temperature difference between the two
incubation boxes was set at 5oC±1oC. The custom-built controller simultaneously turned the
heater and the pump on or off when the instantaneous temperature difference was lower than
4oC or over 6oC, respectively. Except for the computer and the controller, the rest of the
microcosm components were set up outdoors in May 2008. This novel microcosm offers a
high resolution temperature comparison, repeatability, and the capability for simulating more
realistic warming conditions.
A-2: Study sites and sampling regime
The study sites were located in the southern region of the Taihu Lake Basin and the
NingShao plain within the delta of the Yangtze River, in China. The climate in this area is
subtropical monsoon with an annual average rainfall of 1350 mm and an annual average
temperature of 26 oC in summer and 4oC in winter. Three nutrient-enriched wetlands, i.e.,
2
YaTang riverine wetland (YT), XiaZhuhu aquaculture wetland (XZ) and the wetland in
XiXi National Wetland Park (XX) with a large spatial and temporal variability in
sediment-water nutrient exchanges, were chosen for wetland sediment sampling. The basic
physico-chemical parameters of the sediment samples in-situ were described in our
previous studies (6, 8) or could be found in Table S1. These nutrient-enriched wetlands
have shallow waterbodies approximately 0.8-1.5 m in depth. YT wetland is in an advanced
state of eutrophication and is classified as eutrophic with the highest sediment organic
matter (114 g kg-1), nutrient (total phosphorus: 2530 mg kg-1, total nitrogen: 6.81 g kg-1)
and water content (68.7%) of all the study sites. XZ and XX wetlands are in a
meso-eutrophic state, while the organic matter and phosphorus content in XZ sediment are
nearly twice that in XX sediment.
Transparent PVC wetland columns (45.0 cm in height and 10.0 cm in internal
diameter) were prefabricated before sampling. Sediment cores of 0-15 cm from the surface
were collected with a stainless steel column sampler. After most of the larger visible roots,
stones, and macrobenthos were removed from the surface, each sediment core (mixed with
its zoobenthos community, mainly including Olisochaeta, Crustacea and Mollusca) were
divided into a 0-5 cm top-sediment and 5-15 cm sub-sediment manually. Then the 5-15 cm
sub-sediment were first transferred into each PVC wetland column, whereafter the 0-5 cm
top-sediment were refilled to the remaining 0-5 cm column space of sediment layer
carefully. After 20-cm-depth sediment refilling, each column was filled with 20 cm of the
ambient overlying water. The field sampling was conducted in May 2008. All of the
3
wetland columns were shipped back to the laboratory within 3 h where 6 replicates of each
wetland sample were placed inside each of the two incubation boxes (Fig. S1). A sediment
solution sampler (0.5 μm porous polyacrylonitrile follow fiber, Chinese Academy of
Sciences, Nanjing) was horizontally imbedded and kept in the sediment pore in each
column at a fixed depth of 5 cm to allow pore-water sampling (5). Some of floating-leaved
(e.g., Lemna minor L., Trapa spp.) and submerged (e.g., Ceratphyllum demersum L.)
aquatic vegetation was found growing after two months incubation.
A-3: Laboratory incubation for sediment oxygen demand and reducing capability
Sediments collected from YT wetland columns in the summer (Jul-2010) were chosen for
laboratory assays of sediment oxygen demand (SOD) and reducing capability, considering
that the impact of experimental warming on YT wetland was the most significant among
three tested wetlands in term of sediment redox potential shifts, and the summer samples
have relatively high microbial activities, where the effects of elevated temperature and
associated low redox potential are more prominent than other sampling seasons.
The 0-5 cm top sediment cores were collected using thin-walled plastic core tubes. For
measurement of SOD, each core was transferred to 5-cm-id, 20 cm-long glass container
and filled with a 10-cm water column above the core. After flushing with N2, the glass
container was closed tightly using stoppers, and then incubated at a constant temperature of
25 oC in the dark. Dissolved oxygen (DO) concentration was measured using a DO meter
(HQ30d, HACH Corporation, American) at each sampling time point. The SOD, expressed
as DO uptake rate (mmol m-2d-1), was calculated from the DO concentration versus time
4
and the cross-sectional area of the core (4).
For measurement of the sediment reducing capability, a sediment suspension, prepared
by re-suspending 10 g of wet sediment from a homogenized 0-5 cm top sediment core in 50
mL distilled water, was incubated at a constant temperature of 25oC in the dark. Substrate
acetate (20 mmol L-1) was first added three days before the beginning of the assay as a
carbon source to stimulate the activity of microorganisms. After that, three electron
acceptors and substrate acetate were added separately in each core in order to measure
potential nitrate, sulfate and ferric iron reduction rates, respectively (3): (1) KNO3 (140 mg
N L-1) and acetate (20 mmol L-1), (2) K2SO4 (20 mg S L-1) and acetate (20 mmol L-1), (3)
FeC6H5O7·H2O (200 mg Fe L-1) and acetate (20 mmol L-1). For each sampling time point, 2
mL of homogeneous sample were taken, centrifuged at 500 x g for 10 min to remove
sediment particles and then diluted before analysis.
For measurement of NH4 and H2S in trace gas form, 10 g of wet sediment from the
sediment core without added water was immediately placed into a glass container with a
chamber containing 10 mL of 0.2 mol L-1 H2SO4 or 0.07 mol L-1 zinc acetate as NH4 or
H2S absorbents, respectively. Electron acceptors and substrate acetate were separately
added: (1) KNO3 (0.70 mg N g-1) and acetate (8.30 mg g-1), (2) K2SO4 (0.10 mg S g-1) and
acetate (8.30 mg g-1). The dissolved ammonium and sulfide in absorbents was analyzed at
each sampling time point.
Six replicates were conducted simultaneously and the corresponding chemical
analyses were performed according to standard methods (7): the phenanthroline
5
spectrophotometric method for Fe(II) and Fe(III), and the iodometric method for sulfite and
H2S. Nitrate and NH4 was measured using a continuous flow analyzer (Autoanalyzer III,
BRAN+LUEBBE, Germany). The rates are expressed in units of ug analyte per g dry
sediment per hour or day. The incubations were terminated when measured concentrations
in liquid or trace gas forms reached (near-) constant values.
A-4: Statistical Analysis
Two-factor analysis of variance (ANOVA) with repeated measurement was performed to
show the effects of experimental warming (treatment), and observation date (season) on
DO, the thickness of the oxidized layer and the redox potential, as well as phosphorus (P)
concentration variations of three tested wetland samples in-situ investigation, followed by
Post Hoc Multiple Comparisions by Duncan’s multiple range test. SOD and sediment
reducing capability in laboratory incubation testing were examined by the Student’s t-test
for each single sampling point. All statistical analyses disscussed above were performed
using SPSS (version 15.0) software.
For GeoChip 4.0 hybridization data, pre-processed data was used for anlysis.
Hierarchical clustering of detected genes involved in key biogeochemical categories
present in at least two out of six samples was performed with CLUSTER 3.0 using
Spearman rank correlation and the complete linkage method for both genes and samples,
and trees were visualized in TREEVIEW. Functional gene biodiversity indices were
calculated using Shannon-Weiner’s H’, Simpson’s 1/D, and Simpson’s 1/D EH.
Non-parametric multivariate analysis of variance (MANOVA) based on dissilimarities in
6
detected gene abundances among samples and their rank order was used to examine
whether warming has significant effects on specific microbial functional groups. Diversity
indices
and
non-parametric
analysis
were
performed
using
R
2.9.1
(http://www.r-project.org/). The two-tailed paired Student’s t-test was performed to
examine significant (p < 0.05) changes in relative abundance of each specific functional
gene between warmed samples and the control.
Detrended correspondence analysis (DCA) of key biogeochemical categories,
including carbon degradation and fixation, methanogenesis and aerobic methane oxidation,
ammonification,
nitrogen
fixation,
assimilatory/dissimilatory
nitrogen
reduction,
nitrification, denitrification, phosphorus utilization, iron reduction and metabolism as well
as sulfur reduction was carried out to show the overall shifts of detected functional genes in
response to experimental warming. Based on the length of the first DCA ordination axis,
redundancy analysis (RDA) was performed to link specific genes related to microbial
oxidation-reduction
metabolism
(including
genes
involved
in
carbon
fixation,
methanogensis, denitrification, assimilatory/dissimilatory nitrogen reduction, iron and
sulfur reduction) with the in-situ investigated environmental variables (including
temperature, DO, thickness of oxidized layer, redox potential, soil moisture, and pH).
Forward selections were performed to test which environmental variables have a significant
influence on these specific genes (2). In addition, partial RDA for co-variation analysis
(variation partitioning analysis, VPA) was conducted to examine the proportion of variation
solely explained by each single environmental variable (1, 2), in order to show the relative
7
importance of each environmental variable in shaping microbial functional gene diversity.
Monte Carlo permutation with 499 unrestricted permutations was used to test significance
at the p < 0.05 levels. DCA, RDA and VPA were all performed by Canoco (version 4.5,
Centre for Biometry, Wageningen, The Netherlands).
References
1.
Borcard, D., P. Legendre, and P. Drapeau. 1992. Partialling out the spatial component of
ecological variation. Ecology 73:1045-1055.
2.
Lepš, J., and P. Šmilauer. 2003. Multivariate analysis of ecological data using CANOCO.
Cambridge University Press, Cambridge, UK.
3.
Martins, G., A. Terada, D. C. Ribeiro, A. M. Corral, A. G. Brito, B. F. Smets, and R.
Nogueira. 2011. Structure and activity of lacustrine sediment bacteria involved in nutrient
and iron cycles. FEMS Microbiol. Ecol. 77:666-679.
4.
Shin, W. S., J. H. Pardue, and W. A. Jackson. 2000. Oxygen demand and sulfate reduction
in petroleum hydrocarbon contaminated salt marsh soils. Water Res 34:1345-1353.
5.
Song, J., Y. M. Luo, Q. G. Zhao, and P. Christie. 2003. Novel use of soil moisture samplers
for studies on anaerobic ammonium fluxes across lake sediment-water interfaces.
Chemosphere 50:711-715.
6.
Wang, H., Z. L. He, Z. M. Lu, J. Z. Zhou, J. D. Van Nostrand, X. H. Xu, and Z. J. Zhang. 2012.
Genetic Linkage of Soil Carbon Pools and Microbial Functions in Subtropical Freshwater
Wetlands in Response to Experimental Warming. Appl Environ Microb 78:7652-7661.
7.
Wei, F. 2002. Water and wastewater monitoring and analysis. Environmental Science Press,
Beijing, China.
8.
Zhang, Z. J., Z. D. Wang, J. Holden, X. H. Xu, H. Wang, J. H. Ruan, and X. Xu. 2012. The
release of phosphorus from sediment into water in subtropical wetlands: a warming
microcosm experiment. Hydrol Process 26:15-26.
8
B. Supporting tables
Table S1 Descriptions of the study sites and the basic physico-chemical sediment propertiesa.
Total
Latitude and
Main
Annual mean Annual mean flow
Organic matter Total nitrogen
Water contents
pH
phosphorus
Dominant macrophytes
longitude wetland use water depth (m) velocity, (m min-1)
(g kg-1)
(g kg-1)
(%)
(mg kg-1)
Phragmites communis,
YaTang
Trapa spp, Acorus
riverine 120°29'13"E,
Mixed use
0.80
1.02
7.4
114
6.81
2530
68.7
calamus, Sagittaria
wetland 30°43'15"N
sagittifolia, Miscanthus
(YT)
floridulus
Trapa bispinosa,
XiaZhuhu
Alternanthera
aquaculture 120°02'54"E, Aquaculture
1.50
0.12
7.3
64.7
4.32
906
64.5
philoxeroides, Trapa
wetland 30°31'28"N and tourism
spp, Arundo dona,
(XZ)
Arundo donax
Phragmites communis,
XiXi
Trapa spp, Acorus
National 120°03'59"E,
calamus, Sagittaria
Tourism
0.85
0.10
7.4
32.6
3.87
521
55.0
Wetland 30°16'23"N
sagittifolia, Phragmites
Park (XX)
communis, Miscanthus
floridulus
Wetland ID
a
The organic matter, total nitrogen, total phosphorus in wetland sediments were calculated based on dry sediments, while water contents were
calculated based on fresh sediments.
9
Table S2 The average values of biodiversity indices of functional genes involved in key biogeochemical categories (i.e., carbon, nitrogen,
phosphorus, iron, and sulfur cycling) detected in sediments collected from three wetlands, i.e., YaTang riverine wetland (YT), XiaZhuhu
aquaculture wetland (XZ) and the wetland in XiXi National Wetland Park (XX) under control (ambient temperature) and warmed (ambient
temperature + 5oC) treatments in the microcosm experiment. All data are represented as mean (SD). The p-values are from paired Student’s t-test
on the difference in mean values of indices from these biogeochemical categories between control and warmed treatments. Significant (< 0.05)
p-values are in bold.
Key gene categories
Carbon
Nitrogen
Phosphorus
Sulfur
Iron
p-value
Shannon-Weiner Index (H’)
control
6.79 (0.25)
6.14 (0.16)
4.32 (0.39)
5.17 (0.14)
5.21 (0.28)
warmed
7.02 (0.13)
6.38 (0.09)
4.48 (0.15)
5.33 (0.09)
5.66 (0.17)
< 0.001
Simpson’s
Diversity Index (1/D)
control
warmed
536 (34.9)
583 (23.9)
268 (11.4)
295 (17.5)
33.9 (7.78)
36.9 (2.85)
90.2 (1.36)
97.8 (4.62)
94.7 (22.1)
108 (12.5)
0.014
Evenness (EH)
control
warmed
0.380 (0.06)
0.326 (0.05)
0.403 (0.11)
0.336 (0.07)
0.192 (0.07)
0.186 (0.02)
0.298 (0.10)
0.244 (0.04)
0.284 (0.11)
0.277 (0.04)
0.043
10
Table S3 Phosphorus concentrations (mg L-1) in the pore-water and overlying water between treatments (control vs. warmed) and among
different sampling months in wetland columns incubated in the microsom experiment measured from Feb 2009 to Nov 2010. The incubated
sediment samples were collected from from three wetlands, i.e., YaTang riverine wetland (YT), XiaZhuhu aquaculture wetland (XZ) and the
wetland in XiXi National Wetland Park (XX). Differences between treatments were examined by two-way ANOVA. Post Hoc Multiple
Comparisions by Duncan’s multiple range test was futhur applied to show the statistical differences in phosphorus seasonal variations of 2009.
Sampling months
Experimental warming
Mean comparison
Wetlands
Control
Pore-water
YT
12.2
XX
1.22
XZ
1.60
Overlying water
YT
2.49
XX
0.156
XZ
0.211
Warmed
2009
Sig.
2010
Mean multiple comparison
Feb
Mar
May
Jul
Sep
Nov
Sig.
Mean comparison
Aug
Nov
Sig.
16.2
2.04
2.09
*
***
*
26.1a
1.39c
2.35b
17.3b
1.73bc
2.06b
29.2a
1.36c
2.38b
16.8b
2.02ab
2.04b
19.1b
2.27a
3.44a
11.4c
2.26a
2.16b
***
**
*
9.94
1.72
0.613
10.90
3.28
2.62
NS
*
**
3.01
0.225
0.239
NS
NS
NS
3.26c
0.163b
0.324b
1.03d
0.089b
0.085d
4.43c
0.218b
0.540a
9.03a
0.838a
0.650a
6.61b
0.275b
0.201c
0.247d
0.088b
0.059d
***
**
***
2.36
0.192
0.206
0.389
0.099
0.060
**
NS
**
Indices with the same superscript are not significantly different at the 0.05 levels.
ANOVA significance levels: * p < 0.05; ** p < 0.01;*** p < 0.001.
NS: not significant.
11
Table S4 The p-values showing significant levels of differences in phosphorus concentations between control and warmed treatments for each
sampling month (2009 to 2010 inclusive). The measured phosphorus was from sediment pore-water and overlying water in wetland columns
incubated under our field experimental warming system. The sediments in wetland columns were from three tested wetlands, i.e., YaTang
riverine wetland (YT), XiaZhuhu aquaculture wetland (XZ), the wetland in XiXi National Wetland Park (XX). The p-values less 0.05 are bold
significance using Student’s t-test.
2009
2010
Wetlands
Feb
Mar
May
Jul
Sep
Nov
Aug
Nov
Pore-water
YT
XZ
XX
0.043
0.017
0.007
0.023
0.182
0.043
0.023
0.058
0.129
0.007
0.063
0.003
0.002
0.021
0.169
0.182
0.008
0.001
0.005
0.007
0.023
0.195
0.023
0.649
Overlying water
YT
0.898
XZ
0.899
XX
0.262
0.304
0.081
0.003
0.041
0.423
0.014
0.025
0.034
0.382
0.058
0.445
0.475
0.776
0.645
0.828
0.749
0.012
0.408
0.154
0.057
0.188
12
C. Supporting figures
A
B
Fig. S1 (A) Schematic of the experimental wetland microcosm system developed
using independently monitored water-bath jackets under the current climate condition
(Left: ambient temperature, control) and the simulated climate warming condition
(Right: ambient temperature + 5oC, warmed). (B) Photographs of the microcosm
temperature-controlled system represented in this study. Six wetland columns as
replicates for each study site were put in each stainless steel box and operated
outdoors.
13
Fig. S2 Manipulated temperature variations in the overlying water of incubated
wetland columns and field precipitation obtained from Hangzhou meteorological
station records during a study year of 2010 as an example. Lines represent daily
temperature (black squares for control, red circles for warmed) and bars represent
daily precipitation patterns. The actual in-situ temperature in the overlying water
recorded by the data-logger is +5oC higher in warmed than the control. The
temperature data from Jan, 15 to Apr, 20 is missing.
14
Fig. S3 Detrended correspondence analysis (DCA) of GeoChip 4.0 data involved in
key biogeochemical categories, including carbon degradation and fixation,
methanogenesis
and
methane
oxidation,
ammonification,
nitrogen
fixation,
assimilatory/dissimilatory nitrogen reduction, nitrification, denitrification, phosphorus
utilization, iron reduction and metabolism as well as sulfur reduction. Experimental
warming altered the microbial functional community structure in the similar pattern
and direction as seen the arrow symbols for three tested wetlands, namely YaTang
riverine wetland (YT), XiaZhuhu aquaculture wetland (XZ) and the wetland in XiXi
National Wetland Park (XX). YT-c, XZ-c and XX-c represented control samples and
YT-w, XZ-w and XX-w represented warmed samples. The first two canonical axes of
the DCA plot explained 26.0% and 24.8% of gene variations, respectively. The
warmed samples were well separated from those in the control.
15
Fig. S4 Hierarchical cluster analysis of functional genes involved in carbon fixation categories
(i.e., aclB, CODH, ppc, and rubisco) detected in sediments from three tested wetlands, i.e.,
YaTang riverine wetland (YT), XiaZhuhu aquaculture wetland (XZ) and the wetland in XiXi
National Wetland Park (XX). Results were generated in CLUSTER 3.0 using Spearman rank
correlation and the complete linkage method and visualized using TREEVIEW. Red indicates
signal intensities above background while black indicates signal intensities below background.
Brighter red coloring indicates higher signal intensities. YT-c, XZ-c and XX-c represent
control samples and YT-w, XZ-w and XX-w represent warmed samples. Warmed samples
clustered together and were well separated from the control. The abundance of genes indicated
by mean values of normalized signal intensities detected in sediments from these wetlands
was plotted in three separate groups (Group 1, Group 2, and Group 3). Genes clustered in
Group 2 showed a significant (p < 0.05) difference in abundance of detected genes between
warmed and control samples. Error bars are ± standard deviation. Asterisks represent
significant paired Student’s t-test differences between control and warmed samples (* p < 0.05,
** p < 0.01).
16
Group 1
Group 2
Group 3
Group 1
Group 2
17
Group 3
Fig. S5 Hierarchical cluster analysis of cytochrome c gene (Group1, Group 2, and Group 3)
involved in metal reduction (including iron reduction) detected in sediments from three tested
wetlands, i.e., YaTang riverine wetland (YT), XiaZhuhu aquaculture wetland (XZ) and the
wetland in XiXi National Wetland Park (XX). Results were generated in CLUSTER 3.0 using
Spearman rank correlation and the complete linkage method and visualized using
TREEVIEW. Red indicates signal intensities above background while black indicates signal
intensities below background. Brighter red coloring indicates higher signal intensities. YT-c,
XZ-c and XX-c represented control samples and YT-w, XZ-w and XX-w represented warmed
samples. The warmed samples with relatively higher signal intensities clustered together and
were well separated from the control.
18
Fig. S6 Dissolved oxygen (DO) concentration dynamics for a typical sediment
oxygen demand (SOD) measurement in YaTang riverine wetland (YT) sediment
samples under control (ambient temperature) and warmed (ambient temperature + 5oC)
treatments. Error bars show ± SD. The differences between control and warmed
treatments were tested by Student’s t-test for each sampling point, indicated by * p <
0.05, ** p < 0.01.
19
Fig. S7 Nitrate (NO3-) and ammonia (NH4+) concentration dynamics measured in
9-day laboratory incubation for YaTang riverine wetland (YT) sediment samples
under control (ambient temperature) and warmed (ambient temperature + 5oC)
treatments. Error bars show ± SD. The differences between control and warmed
treatments were tested by Student’s t-test for each sampling point, indicated by * p <
0.05, ** p < 0.01.
20
Fig. S8 Sulfite (SO32-) and hydrogen sulfide (H2S) concentration dynamics measured
in 9-day laboratory incubation for YaTang riverine wetland (YT) sediment samples
under control (ambient temperature) and warmed (ambient temperature + 5oC)
treatments. Error bars show ± SD. The differences between control and warmed
treatments were tested by Student’s t-test for each sampling point, indicated by * p <
0.05, ** p < 0.01.
21
Fig. S9 Ferric iron (Fe3+) and ferrous iron (Fe2+) concentration dynamics measured in
13-day laboratory incubation for YaTang riverine wetland (YT) sediment samples
under control (ambient temperature) and warmed (ambient temperature + 5oC)
treatments. Error bars show ± SD. The differences between control and warmed
treatments were tested by Student’s t-test for each sampling point, indicated by * p <
0.05, ** p < 0.01.
22
Fig. S10 Seasonal variations (from May 2010 to Feb 2011) of dissolved oxygen
concentration at the bottom of water (A), thickness of top oxidized sediment layer (B),
and redox potential of uppermost sediment centimeter (C) observed in-situ in wetland
columns incubated under control (ambient temperature) and warmed (ambient
temperature + 5oC) treatments. YaTang riverine wetland (YT), XiaZhuhu aquaculture
wetland (XZ) and the wetland in XiXi National Wetland Park (XX) are three tested
wetlands in this study. Error bars are ± standard deviation.
23