Microbial composition alters the response of litter decomposition to environmental change

8/9/2019 Microbial composition alters the response of litter decomposition to environmental change

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Running head: Composition alters the response of litter decomposition

Microbial composition alters the response of litter decomposition to environmental change

Kristin L. Matulich and Jennifer B.H. Martiny5

Department of Ecology and Evolutionary Biology, University of California-Irvine, Irvine, CA

92697

Correspondence:10

Kristin L. Matulich

321 Steinhaus, Irvine, CA 92697


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Abstract

Recent studies demonstrate that microorganisms are sensitive to environmental change

and that their community composition influences ecosystem functioning. However, it is

unknown whether microbial composition interacts with the environment to affect the response of20

ecosystem processes to changing abiotic conditions. To investigate the potential for such

interactive effects on leaf litter decomposition, we manipulated microbial composition and

manipulated three environmental factors predicted to change in the future (moisture, nitrogen

availability, and temperature). We isolated fungal and bacterial taxa from leaf litter and used

them to construct unique communities. Communities were inoculated into microcosms25

containing sterile leaf litter and exposed to four environmental treatments (ambient conditions,

increased temperature, decreased moisture, and increased nitrogen availability). Respiration was

tracked over 60 days, and communities were pyrosequenced to assess compositional changes. As


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Introduction40

Biodiversity (both the richness and composition of taxa) has a direct influence on the rate

of ecosystem processes (reviewed in Cardinale et al. 2011). Yet until recently, it was often

assumed that microbial communities are so diverse and physiologically flexible that changes in

their diversity would not affect ecosystem processes (Schimel 2001). However, recent research

demonstrates that microbial diversity directly influences a variety of ecosystem processes such as45

litter decomposition, CO2 flux, and nitrogen cycling (Mille-Lindblom and Tranvik 2003, Bell et

al. 2005, Tiunov and Scheu 2005, Allison et al. 2009, Strickland et al. 2009, Reed and Martiny

2013).

In addition to these direct effects on process rates, biodiversity can affect the response of

ecosystem processes to changing abiotic conditions. Such interactive effects of biodiversity and50

abiotic change have been documented in plant communities. A recent meta-analysis showed that


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2005, Allison et al. 2009) isolates found that decomposition rate, as measured by CO2

production, varies with richness and composition. Similarly, microcosms including both fungal

and bacterial taxa had a higher decomposition rate than microbial communities containing just65

fungal or bacterial taxa alone (Mille-Lindblom and Tranvik 2003). Further, natural variation

found in complex microbial communities affected the rate of decomposition when inoculated

onto a common leaf litter (Strickland et al. 2009).

To test whether microbial diversity (specifically composition) alters an ecosystem’s

functional response to environmental change, we constructed mixed bacterial and fungal70

microcosms using isolates from natural leaf litter communities in a Mediterranean-type

ecosystem located in southern California. The use of cultured isolates allowed us to hold richness

constant while manipulating the identity of the taxa. We then measured microbial respiration (a

proxy for decomposition) and changes in composition over the course of the experiment. Each


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enzymatic activity generally increase under warmer temperatures (Lloyd and Taylor 1994). We

also expected that microbial composition would directly influence decomposition because

different taxa (and therefore communities) likely vary in their functional traits, including their

ability to decompose leaf litter. In addition to these direct effects, we further hypothesized that

the community and environment would interact to affect decomposition. Specifically, taxa would90

vary in the their responses to the environmental changes, resulting in differential shifts in

composition. Such variability in response traits, combined with that in functional traits, would

lead to differential functional responses among communities (Allison and Martiny 2008).

Finally, we predicted that the type of environmental change would affect the importance

of microbial composition for a microcosm’s functional response (i.e., the strength of the95

community-by-environment interaction). In particular, we hypothesized that the effect of the

temperature and moisture treatments would depend less on community composition than that of


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We isolated 32 bacterial taxa and 24 fungal taxa from leaf litter collected from the Loma

Ridge research site located 5 km north of Irvine, California, USA (33 44’N, 117 42’W, 365 m110

elevation). Briefly, litter was collected in January, April, July, and September of 2011. During

this period, the site was dominated by the annual grass genera Avena, Bromus, and Lolium the

annual forb genera Erodium and Lupinus and the native perennial grass Nassella pulchra. Once

collected, litter was ground and then washed with sterilized deionized (DI) water. Litter

fragments (106-212 μm) were suspended in sterile DI water and the resulting solution was115

passed through a 100 μm filter (Millipore).

For fungal isolation, the 100 μm filter containing the washed litter was collected and

placed into a Falcon tube containing 30 mL of 0.6% carboxymethyl cellulose (CMC) solution.

The resulting slurry was then added to 96-tube microplates containing malt extract agar, tap

water agar, minimal nutrient medium, or cornmeal agar media (modified from Rossman et al.120


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16S rRNA gene for bacteria (~1500 bp) or the 18S rRNA and internal transcribed spacer (ITS)

region (~700 bp) of the rRNA gene for fungi. For the fungal isolates, approximately 2.5 ng of

DNA extract was added to a PCR cocktail containing 1.2 units of HotMasterTaq polymerase

(5PRIME) 1x PCR buffer supplied by the manufacturer, 200 μM of each DNTP, 0.5 μM of each135

primer (ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and TW13 (5'-

GGTCCGTGTTTCAAGACG-3'); White et al. 1990, Gardes and Bruns 1993), and H2O to a

final volume of 25 μL. Following an initial denaturation step at 94°C for 1 min, PCR was cycled

34 times at 94°C for 1 min, 51°C for 1 min, 72°C for 1 min, and a final extension at 72°C for 8

min. All PCRs here and below were performed on a PTC-100 thermocycler (Bio-Rad). DNA140

extract from each bacterial isolate was added to a PCR cocktail containing 1.5 units of

HotMasterTaq polymerase (5PRIME) 1x PreMixF (FailSafe), 0.3 μM of each primer (pA (5'-

AGAGTTTGATCCTGGCTCAG-3') and pH (5'-AAGGAGGTGATCCAGCCGCA-3'); Edwards


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were amplified from residual DNA or viable organisms that escaped irradiation. Glass vials (40155

ml) with a sampling septum were filled with 4 g of sterile sand and autoclaved. Sterilized litter

(50 mg) was then added to each microcosm.

To create the communities, each of the 56 cultured taxa was randomly assigned to two

communities; this produced eight unique communities that each contained 8 bacterial and 6

fungal isolates (Appendix B: Table B1). Cultures of each isolate were first separated from the160

solid growth medium by mixing three agar plugs (7x2 mm) with 1.5 ml of 0.9% NaCl solution

and ~30 glass beads (2 mm diameter) in an eppendorf tube and shaken in a FastPrep-24

Instrument (MP Biomedicals, Solon, OH, USA) for 15 sec at 4.0 ms−1

. This procedure was used

to try to standardize the isolates in terms of growth stage rather than by abundance. For each

community, equal volumes of the 14 assigned isolates were combined, and the community165

inoculum was dispersed among replicate microcosms. The inoculated microcosms were then


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expected in California, including at least a 3°C increase in annual temperature, a decrease of 10-

25% in annual precipitation, and increasing rates of nitrogen deposition over the next 50 years

(IPCC 2013). 180

Respiration measurements

CO2 concentrations in the microcosm headspace were measured every 5-9 days to

estimate cumulative CO2 production over a 60-day incubation period. For each measurement, an

8 ml subsample of headspace gas was withdrawn by syringe and injected into an infrared gas

analyzer (PP-Systems EGM-4). To keep the microcosms at atmospheric CO2 concentrations, the185

vial caps were kept loose except for 24 hours before each sampling. At the end of the

experiment, cumulative CO2 production was divided by initial litter mass (50 mg) and total

duration of the experiment (60 days) to determine average daily CO2 production (measured as

mg C glitter-1

day-1

), our metric for litter decomposition.


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approximately 2 μl of a 1:10 dilution of each DNA extract was added to a PCR cocktail

containing 1.2 units of HotStarTaq polymerase (Qiagen), 1x PCR buffer supplied by the

manufacturer, 200 μM of each DNTP, 0.5 μM of each primer, and H2O to a final concentration

of 25 μL. 16S amplification used concatemers containing the universal primer 907R (Lane

1991), an 8-bp multiplex tag (Table A1), and the 454 ‘B’ adaptor205

CCTATCCCCTGTGTGCCTTGGCAGTCTCAG (in the reverse direction) and the

complimentary primer 515F (Turner et al. 1999) and the 454 ‘A’ adaptor

CCATCTCATCCCTGCGTGTCTCCGACTCAG (in the forward direction). Following an initial

denaturation step at 95°C for 5 min, PCR was cycled 34 times at 95°C for 30 sec, 62°C for 45

sec, 72°C for 1 min, and a final extension at 72°C for 10 min.210

For the fungi, the same PCR cocktail was used as above. 28S amplification used

concatemers containing the fungal-specific LROR (Tedersoo et al. 2008), an 8-bp multiplex tag


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experiment were detected by sequencing (42 out of 56); however, 3 fungi and 11 bacteria taxa

were not detected even in the inoculum samples (Table B1), suggesting they were present but not225

amplified during PCR. While the method detected clear differences in community composition

(see Results), this bias may have masked some correlations between functional (respiration rate)

and taxonomic responses.

Analyses

We analyzed average daily respiration rate and final community richness using a factorial230

mixed-model ANOVA. The model included environmental treatment as a fixed effect and

community composition as a random effect, and their interaction. To compare the initial and

final community richness in the control treatment (hereafter, “inoculum ANOVA”), we used a

factorial mixed-model ANOVA with time as a fixed effect and community composition as a

random effect. Significant pairwise differences were determined post-hoc using Tukey’s honest235


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To test the effect of environmental treatment on overall microbial composition, similarity

matrices were generated on rarified abundance data using the Bray–Curtis method in QIIME

(Bray and Curtis 1957, Caporaso et al. 2010). The resulting resemblance matrix served as a basis

for non-metric multidimensional scaling (NMDS), as well as a permutational multivariate

analysis of variance (PERMANOVA; Anderson et al. 2008) to test the effects of experimental250

factors on the final composition. PERMANOVA analyses were conducted using partial sums of

squares, on 999 permutations of residuals under a reduced model. Multivariate analysis was

conducted using PRIMER6 and PERMANOVA+ (Primer-E Ltd, Plymouth, UK). Statistical

routines are described in Clarke and Warwick (2001) and Anderson et al. (2008).

255

Results

Community responses


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All three environmental treatments – moisture, nitrogen, and temperature – altered both

microbial richness and composition in the microcosms. Richness was significantly lower in the270

three environmental perturbations than under ambient conditions (Table 1; Figure B2). This

reduction was most pronounced in the elevated nitrogen treatment, where on average 1.25 fewer

taxa were observed by the end of the experiment. Further, the response of richness to nitrogen

addition varied by community (χ 2

=6.0, p=0.01); the other environmental treatments did not show

an interactive effect (Table 1).275

Like richness, bacterial and fungal composition (as measured by relative abundance of

the taxa) was also influenced by environmental change. However, for all three perturbations,

bacterial and fungal composition was not directly affected by the environmental treatment alone.

Instead, the response to each environmental treatment varied by community; that is, the

community and environment interacted to affect composition (Table 1). This interaction was280


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ANOVA: χ 2=6.5, p=0.01). Environmental treatment also had a significant effect on respiration

(χ 2

=38.4, p


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(R 2=0.16, p=0.05; Figure 4). For example, none of the taxa in Community 7 differed in relative315

abundance when exposed to elevated temperature, and the average respiration rate of this

community did not significantly vary from the control conditions (Figure 4). In contrast, 40% of

taxa in Community 4 shifted in their relative abundances when exposed to nitrogen addition, and

the average respiration rate of this community was significantly higher than the control

treatment. This correlation was largely driven by the nitrogen treatment (R 2

=0.22, p=0.24; Figure320

4), suggesting that the compositional shifts resulting from increased nitrogen availability were

especially important for changes in overall respiration. In contrast, changes in physiology or total

microbial abundance may be more important in explaining the functional responses to moisture

and temperature shifts.

325

Discussion


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response traits vary would one observe an interactive effect of the community and the

environment.

Artificially assembled communities in microcosms allow researchers to test questions340

about microbial diversity that would be infeasible in natural communities; however, their

applicability to the ‘real-world’ is a relevant concern (Carpenter 1996). Our experimental

communities were highly reduced in diversity compared to natural litter-decomposing

communities (Voriskova and Baldrian 2013, Kim et al. 2014). Indeed, in a PCR survey of litter

from the same ecosystem (Matulich et al., in prep), we observed well over 2000 bacterial and345

800 fungal taxa (again defined by 97% sequence similarity of 16S rDNA). Notably, almost all of

the bacterial taxa used in the microcosms were detected in the natural community, where their

relative abundance ranged from 0.002% to 18.6% of the samples (Appendix: Table B1). Further,

fungal families containing our cultures represented almost 50% of the natural community,


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(increased abundance), or a combination (Ratkowsky et al. 1982, Lloyd and Taylor 1994, Sheik

et al. 2011). Likewise, nitrogen addition also increased decomposition, and elevated nitrogen

availability often stimulates decomposition rates by lowering the C:N ratio of the organic

material (Taylor et al. 1989, Allison et al. 2009, Bragazza et al. 2012). Contrary to our

predictions, however, microcosms subjected to reduced moisture showed significantly higher365

respiration rates than those in ambient conditions (Figure 2). Arid environments often have slow

rates of decomposition because water limits the physiological performance of microbes and the

diffusion of nutrients in the soil pore space (Harris 1981). We suspect that the higher respiration

rates in the drier microcosms may be caused by increased fungal activity, as many fungi grow

well in drier, Mediterranean conditions (Yuste et al. 2011). Indeed, both the bacteria and fungi370

used in this study were isolated from a Mediterranean-like ecosystem where high temperatures

and prolonged droughts are common, thus they may not only tolerate, but thrive in such


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and functional responses, such that changes in respiration were generally correlated with large

changes in the relative abundance of taxa. This result was most evident in the nitrogen addition385

treatment, where 11-50% of the taxa in all communities had significantly different relative

abundances than in the control treatment (Figure 4). However, these functional responses could

not be correlated to the presence or absence of particular taxa (i.e., sampling effects), although

this couldn’t be tested directly because each taxon was only present in two of the communities.

Moreover, environmental treatment, which had a main effect on respiration rates, did not directly390

alter microbial composition over the course of the experiment (Table 1). Together, these results

suggest that some of the changes in respiration rate may be due not only to changes in

composition, but also to changes in microbial activity or total abundance, which were not

measured here.

To our knowledge, this work is the first to directly demonstrate that microbial395


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Acknowledgements

We would like to thank S. Allison and D. Campbell for advice on the design and analysis

of the experiment, A. Amend for isolation of the fungal taxa, C. Weihe for assistance sequencing410

the communities, S. Nguyen for collecting measurements of CO2 production, and two

anonomous reviewers for comments that improved the clarity of the manuscript. Funding for this

project provided by the Office of Science (BER) US Department of Energy (program in

Microbial Communities and Carbon Cycling); the NSF Major Research Instrumentation program

(1126749); and the US Department of Education.415

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Table 2. The variance in mean difference of average respiration rate between control

microcosms and microcosms undergoing each of the three environmental treatments, as well as

Bartlett's K-squared test statistic for homogeneity of variances. A smaller K-squared value

corresponds to more homogenous variances between groups.

565

Treatment

Mean Difference

Variance

Bartlett's

K-squared Probability

Reduced Moisture 1.38 0.01 0.926

Elevated Nitrogen 6.65 5.99 0.014

Elevated Temperature 1.49 2.92 0.087


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Figure Legends570

Figure 1. Non-metric multidimensional scaling (NMDS) ordination based on Bray Curtis

similarities depicting fungal community composition. The samples inoculated with the same

initial microbial community are displayed in the same symbol color (8 communities total). The

shape of the symbols represent the environmental treatment, either control conditions (filled

squares), reduced moisture (open squares), elevated nitrogen (diamonds), and increased575

temperature (triangles). The initial microbial inoculum (stars) is also plotted but excluded from

analyses. N = 3 per community and treatment combination.

Figure 2. Daily microbial respiration rates for all microbial communities averaged over 60 days

for each environmental treatment. Blue boxes represent microcosms in control conditions,580

yellow in reduced moisture microcosms, red in elevated temperature microcosms, and green in


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Figure 4. Correlation of all community and functional responses to each of the three

environmental treatments. Each point represents the percent of taxa in a given community that

had significantly different relative abundances when exposed to a perturbation treatment and the

corresponding percent change in average respiration rate compared to the control treatment. The595

percent of significant taxa responses to the environmental treatments were moderately correlated

with the corresponding change in average community respiration ( R

2

=0.16, p=0.05), but this was

largely driven by the nitrogen addition treatment ( R2=0.22, p=0.24). Each point is labeled by

community and represents the average of a given community-environment combination.

600


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Microbial composition alters the response of litter decomposition to environmental change