Elevated atmospheric CO 2 stimulates soil fungal diversity through increased fine root production in a semiarid shrubland ecosystem

Acc

epte

d A

rtic

le

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/gcb.12609 This article is protected by copyright. All rights reserved.

Received Date : 12-Nov-2013

Revised Date : 22-Feb-2014

Accepted Date : 24-Mar-2014

Article type : Primary Research Articles

Elevated atmospheric CO2 stimulates soil fungal diversity through increased fine root

production in a semiarid shrubland ecosystem

Running head: Elevated CO2 stimulates fungal diversity

David A. Lipson1, Cheryl R. Kuske2, La Verne Gallegos-Graves2, Walter C. Oechel1

1- San Diego State University, San Diego, CA 92182

2- Los Alamos National Laboratory, Los Alamos, NM 87545

Corresponding author:

David Lipson, [email protected], phone: (619) 594-4460, fax: (619) 594-5676

Acc

epte

d A

rtic

le

This article is protected by copyright. All rights reserved.

Key Words: Adenostoma fasciculatum, chaparral, free air CO2 enrichment (FACE), large subunit

rRNA (28S rRNA or LSU rRNA), 18S rRNA qPCR, Mediterranean-type ecosystem, microbial

community

Paper type: Primary Research Article

Abstract

Soil fungal communities are likely to be central in mediating microbial feedbacks to climate

change through their effects on soil carbon (C) storage, nutrient cycling and plant health. Plants

often produce increased fine root biomass in response to elevated atmospheric carbon dioxide

(CO2), but the responses of soil microbial communities are variable and uncertain, particularly in

terms of species diversity. In this study we describe the responses of the soil fungal community

to free air CO2 enrichment (FACE) in a semiarid chaparral shrubland in southern California

(dominated by Adenomstoma fasciculatum) using large subunit rRNA gene sequencing.

Community composition varied greatly over the landscape and responses to FACE were subtle,

involving a few specific groups. Increased frequency of Sordariomycetes and Leotiomycetes, the

latter including the Helotiales, a group that includes many dark septate endophytes known to

associate positively with roots, was observed in the FACE plots. Fungal diversity, both in terms

of richness and evenness, increased consistently in the FACE treatment, and was relatively high

compared to other studies that used similar methods. Increases in diversity were observed across

multiple phylogenetic levels, from genus to class, and were distributed broadly across fungal

lineages. Diversity was also higher in samples collected close to (5 cm) plants compared to

Acc

epte

d A

rtic

le


samples in canopy gaps (30 cm away from plants). Fungal biomass correlated well with soil

organic matter (SOM) content, but patterns of diversity were correlated with fine root production

rather than SOM. We conclude that the fungal community in this ecosystem is tightly linked to

plant fine root production, and that future changes in the fungal community in response to

elevated CO2 and other climatic changes will be primarily driven by changes in plant

belowground allocation. Potential feedbacks mediated by soil fungi, such as soil C sequestration,

nutrient cycling and pathogenesis, are discussed.

Introduction

Biological feedbacks to climate change represent major uncertainties in current models. For

example, the responses of soil microbial communities to elevated atmospheric carbon dioxide

(CO2) could strongly influence ecosystem functioning through effects on carbon (C)

sequestration, nutrient cycling and altered positive or negative relationships with plants (Bardgett

et al., 2008, Chapin et al., 2009). Concentrations of CO2 in soils are orders of magnitude higher

than atmospheric levels, and so impacts of elevated atmospheric CO2 on soil microbes are

generally mediated by plant responses. Plants from natural ecosystems commonly respond to

higher CO2 with increased allocation to roots (Dieleman et al., 2012). However, the responses of

soil microbial communities to such experiments have been highly variable (Drigo et al., 2008).

The aspect of microbial communities that is perhaps the subtlest and most challenging to

measure is diversity. Soil microbial communities comprise thousands of species of bacteria and

fungi and estimates of the phylogenetic richness of these communities can be limited by

Acc

epte

d A

rtic

le


inadequate sampling (Hughes et al., 2001). Improvements in sequencing technology are allowing

for more robust estimates of microbial diversity in natural environments, and these approaches

are being used increasingly to describe soil fungal communities (O’Brien et al., 2005). The

responses of soil fungal diversity to experimental manipulations of atmospheric CO2 vary

dramatically by ecosystem and no clear unifying factor has been found to explain these

variations (Weber et al., 2011).

In a previous study of the effects of free air CO2 enrichment (FACE) on a semiarid shrubland

ecosystem in southern California, the FACE treatment produced increased root biomass,

increased fungal biomass, and altered soil respiratory metabolism and extracellular enzymes

(Lipson et al., 2005). However, in this study there were no detectable changes in the bacterial

community composition in response to FACE. The altered microbial functioning and increased

fungal biomass without a concurrent shift in bacterial community structure suggested a change in

fungal community composition, but no data on this were available. In the present study we

analyze the soil fungal community in archived soils from this experiment using large subunit

ribosomal RNA (LSU rRNA) gene sequencing.

Materials and Methods

Site Description and Experimental Design

This study was conducted in a chaparral ecosystem at the Sky Oaks Field Station, operated by

San Diego State University, located in northeast San Diego County, California, U.S.A. (33°23′

Acc

epte

d A

rtic

le


N, 116°37′ W; 1,420 m above sea level). This is a semi-arid shrubland with a Mediterranean-

type climate, with hot dry summers and cool moist winters (349 mm mean annual precipitation).

The plant community at the FACE site was dominated by Adenostoma fasciculatum H. & A., an

evergreen shrub that regenerates after fire by re-sprouting from a lignotuber (Canadell & Zedler,

1995). The soil in the study area is a loamy sand, Ultic Haploxeroll, with a bulk density of 1.04g

cm-3, containing 32% rocks, and belonging to the Sheephead series (Bowman, 1973). The entire

area used in this study was burned in July 1992 prior to the establishment of the FACE treatment.

The purpose of the burning treatment was to minimize historical differences in vegetation and

soil properties that might have existed across the landscape. The FACE ring was 16 m in

diameter. The CO2 concentration was maintained near 550 ppm during the daylight hours. The

FACE treatment operated from January 1995 to May 2003. The surrounding landscape (>10 m

beyond the ring) served as a control with ambient levels of CO2 (around 360 ppm during the time

of the treatment). More details of the site and FACE treatment are available elsewhere (Lipson et

al., 2005, Luo, 2007, Roberts et al., 1998).

Soil samples used in this study were collected in March 2003 using a 10 cm diameter metal

coring device to a depth of 12-15 cm. Samples were collected at a distance of either 5 cm

(“plant”) or 30 cm (“gaps”) from the basal stem of A. fasculatum plants within the FACE ring or

from >10 m outside the ring (“control”). Samples were processed as described previously

(Lipson et al., 2005) and archived at -80°C until use in the present study. Microbial biomass and

activity in this ecosystem was correlated with soil organic matter (SOM), which was highly

variable across the landscape, and so Lipson (2005) controlled for differences in SOM between

FACE and control plots statistically using analysis of covariance. In the present study we

Acc

epte

d A

rtic

le


circumvented this issue by selecting a subset of soil samples with nearly identical SOM contents

for the FACE and control plots (six replicates each, including three plant and three gap samples

for both treatments) for construction of fungal LSU rRNA libraries (Table S1). These samples

covered a range of SOM from 1.8% to 4.5%, representing 88% of the total samples collected in

our previous study of this site. A larger set of soil samples (20 replicates) was used for the

analysis of landscape variation of total soil DNA and small subunit (SSU or 18S) rRNA genes.

DNA extraction, quantitative PCR and LSU rRNA sequencing

Soil DNA was extracted from duplicate aliquots of each soil sample, using the FastDNA SPIN

Kit (MP Biomedicals, LLC; Solon, OH, USA and quantified using the Quantit Pico Green

dsDNA Assay kit (Invitrogen Life Technologies, Grand Island, NY ). Fungal small subunit

(SSU) rRNA genes were quantified using quantitative PCR and SYBR Green detection (iQ

SYBR Green Supermix, BioRad Laboratories, Hercules CA). Quantitative PCR amplification

was performed using primers nu-SSU-1196F and nu-SSU-1536R (Borneman, 2000). The

quantitative standard for the fungal 18S rRNA qPCR was a single-copy fungal 18S rRNA gene

clone, related to Cortinarius violaceus, generated with primers SR8R and SR6

(www.biology.duke.edu/fungi/mycolab/primers). This plasmid DNA was linearized and DNA

standard curves of known concentration were generated for determination of gene copy numbers

in the soil samples. Samples were amplified on the MyiQ Single Color Real-Time PCR

Detection System (BioRad Laboratories, Hercules, CA).

Acc

epte

d A

rtic

le


Fungal clone/sequence libraries were generated from LSU rRNA genes PCR amplified from

each soil sample. Fragments of LSU rRNA genes (about 650 bp in length) were PCR amplified

using primers LR3 (5′-CCGTGTTTCAAGACGGG) and LR0R (5′-

ACCCGCTGAACTTAAGC) (http://biology.duke.edu/fungi/mycolab/primers.htm). Triplicate

50 μl PCR reaction consisted of 1x PCR Buffer (with 1.5 mM MgCl2), 0.8 mM dNTP mix (0.2

mM of each dNTP) (Applied Biosystems, Foster City, CA), 0.03U of Taq LD DNA Polymerase

(Applied Biosystems, Foster City, CA), 1 μM of each primer, and 2 μl of DNA Template

(following a ten- or hundred-fold dilution). Thermal cycling consisted of the following

conditions: (1) 950C for 2 minutes; (2) 940C for 1 minute, 550 for 1 minute, and 720C for 1

minute, (repeated 25x); (3) 720C for 7 minutes. The PCR products were electrophoresed through

a 1.2% agarose gel in 0.5x tris-borate-EDTA (TBE) and visualized with ethidium bromide. The

triplicate PCR products for each soil sample were pooled, purified using the Qiagen QIAquick

PCR Purification Kit (Valencia, CA), and cloned using Invitrogen TOPO TA Cloning Kit for

Sequencing (Invitrogen Carlsbad, CA) according to manufactures recommendations except that

the salt solution was omitted from the ligation reaction. For each soil sample, 384 cloned PCR

products were bi-directionally sequenced using Sanger technology.

Sequence analysis

Forward and reverse reads sequences were assembled using Fincon (unpublished software,

courtesy of Cliff Han, Los Alamos National Laboratory, NM) and assembled sequences were

oriented in the BioEdit program (Hall, 1999). Aligned sequences were imported into ARB where

alignments were manually edited and poor quality sequences were removed.

Acc

epte

d A

rtic

le


After preliminary quality analysis, 4107 sequences in total remained for the 12 soil libraries

(about 350 sequences/library). These sequences were analyzed in two different ways: (1)

sequences were classified using the fungal LSU classifier (Liu et al., 2012) available at the

ribosomal database project web site (http://rdp.cme.msu.edu/classifier/); (2) sequences were

aligned using the SINA alignment tool (Pruesse et al., 2012) (http://www.arb-silva.de/aligner/).

In analyses using the fungal LSU classifier, all sequences with less than 100% confidence scores

for fungi were removed, and for the analyses based on the SINA alignment, sequences with

identity score not greater than 60%, as well as several that proved to be Chlorophyta and

Alveolata (see below), were removed, leaving the numbers of sequences shown in Table S2. A

small number of Oomycetes were retained in this analysis, given their close association and

similar ecological roles to fungi (Beakes et al., 2012). The sequences were checked for chimeras

with USEARCH 6.0 (http://fungene.cme.msu.edu/FunGenePipeline/chimera_check/), using the

UCHIME algorithm in de novo mode, in which higher abundance sequences within the dataset

serve as reference parent sequences for candidate chimeric sequences with lower abundance

(Edgar et al., 2011). The default minimum score (minh) of 0.28 was used, and the minimum

divergence between the potential chimera and the nearest parent sequence was set at 2%, as this

would eliminate all experimentally relevant chimeras given the 98% clustering used in this study.

Sequences from the SINA alignment were clustered into operational taxonomic units (OTUs)

defined at 98% similarity using BLASTclust (http://toolkit.tuebingen.mpg.de/blastclust)

(Altschul et al., 1997). One sequence was chosen at random to represent each group (operational

taxonomic unit, OTU), and these sequences were used to create a maximum likelihood

phylogenetic tree (using FastDNAml in BioEdit, an optimized version of DNAML from the

Acc

epte

d A

rtic

le


Phylip package (Felsenstein & Churchill, 1996)). The ML model used empirical base

frequencies and a transition/transversion ratio of 2. Each major cluster in the tree was identified

using BLAST searches with a subset of sequences from each clade. This process identified

sequences belonging to Chlorophyta and Alveolata, which were then excluded from further

analysis. In cases where BLAST searches were ambiguous (uncultured groups), sequences were

aligned with guide sequences from GenBank and new trees were generated to verify the

placement of these clades. The number of occurrences of each OTU was used to generate

rarefaction curves and Chao1 estimates of OTU richness using EstimateS (Colwell et al., 2012).

The Shannon index was calculated for these OTUs and for each taxonomic level using the fungal

LSU classifier. The dereplicated maximum likelihood tree and the number of occurrences for

each OTU in each sample was used to compare community composition among samples with

UniFrac (Lozupone et al., 2006) (http://bmf.colorado.edu/unifrac/). The dereplicated sequences

were deposited in GenBank (accession numbers KF750183 - KF750567).

Statistical Analysis

Relationships among variables (such as SOM, DNA, SSU rRNA genes, diversity measures and

root biomass) were analyzed using regression analysis. The treatment effects (FACE and

proximity to plant) on measures of diversity were tested using two-way analyses of variance

(ANOVA). Classifications with confidence scores less than 80% proved to be unreliable based

on individual BLAST searches, and so these were generally excluded from analyses. This

resulted in a variable number of sequences when diversity was analyzed at different taxonomic

levels, and so to ensure that this did not bias results, ANOVA was run on both the entire data set

Acc

epte

d A

rtic

le


(Table 1, “all”) and on just those sequences classified at 80% confidence or better for that

taxonomic level (Table 1, “80%”). Correlation analysis was used to explore the relationships

among diversity measures and other variables. To test whether the effects of FACE were

significant over and above the effects of fine root biomass, we used an analysis of covariance

(ANCOVA).

To compare the relative proportion of fungal taxa by treatment, we used multivariate ANOVA

(MANOVA). This was done to limit the probability of a type I error given the large number of

potential tests and the likelihood of covariance among the major taxa. For example, there are 17

classes represented in the sequence data, and only 12 data points. We therefore included only the

most abundant classes in the MANOVA. When the overall treatment effect was found to be

significant, we then investigated the significant classes in more detail, carrying out MANOVA

on the orders within these classes. In the case of genera, the overall MANOVA was not

significant, but a post hoc test for a single genus was significant based on the Bonferroni

correction for multiple tests. In this case it was also necessary to rank transform the abundance

data to fit the model assumptions.

Results

As expected based on previous research at this experimental site (Lipson et al., 2005), indices of

total microbial biomass (soil DNA) and fungal biomass (18S rRNA copy number) were

correlated with soil organic matter (Figure 1). These indices were not significantly related to

plant root biomass (data not shown).

Acc

epte

d A

rtic

le


The fungal community was dominated by Ascomycota (Figure 2), which represented 66% of all

fungal sequences. The community included a diverse set of other fungal lineages (Figure 3),

including Basidiomycota, Chytridiomycota, Oomycota, the early diverging lineages

Mucoromycotina and Mortierellomycotina, and an uncultured clade of soil fungi. Based on

Figures 2 and 3 there is little obvious clustering of sequences from the FACE and control

treatments, though the FACE sequences are more dispersed throughout the trees (more black

than white symbols in Figures 2 and 3). As presented quantitatively below, these patterns

represent the subtle changes to overall community structure and the general increase in diversity

associated with the FACE treatment.

The SINA alignment of 3127 sequences produced 312 unique OTUs at 98% similarity (Figure

4a), with an overall Chao1 richness estimate of 478 (95% confidence interval: 416, 576) for the

entire site. The rarefaction analysis estimated a significantly higher OTU richness in the FACE

plots compared to control plots (Figure 4b). The Chao1 estimates of phylotype richness were

higher in FACE than in control plots, and within control plots Chao1 was higher under plant

canopies than in gaps (Figure 5). Beta diversity was high in this site, as can be seen by the fact

that the pooled Chao1 estimates (Figure 5a) are much higher than the means for the three

individual spatial replicates for the four treatment categories (Figure 5b). The ANOVA for

Chao1 was significant for both the effects of FACE and proximity to plant (Table 1).

These effects on diversity, particularly those of the FACE treatment, were observed at multiple

taxonomic levels. The Shannon index (which combines richness and evenness) showed the same

Acc

epte

d A

rtic

le


trends from the 98% similarity and genus level up to the class level (Figure 6). The effect of

FACE was significant at all levels and the effect of proximity to plant was significant at all levels

except for class (Table 1). The results were the same whether all classified sequences were

included or just those classifications with 80% or better confidence. In the latter analysis of

Shannon index scores, the R2 for the ANOVA peaked at the order level. We therefore also

investigated whether the trends in diversity at this level were distributed throughout the fungal

tree or whether they were restricted to a certain set of lineages. The Shannon index at the order

level was significantly higher in FACE plots both among Ascomycota (P=0.015) and non-

Ascomycota (P=0.032), though the canopy vs. gap comparison was only significant within the

non-Ascomycota (P=0.033). Therefore it appears that the stimulation of fungal diversity by

FACE occurred broadly across the major fungal lineages.

These treatment effects on fungal diversity can largely be explained by fine root biomass in the

plots: fine root biomass was significantly correlated with several measures of diversity (Table 2).

The strongest correlation occurred at the order level (Table 2, Figure 7). When controlling for

fine root biomass in an ANCOVA, the effects of FACE treatment and the FACE x fine root

interactions on H’ (order) were not significant. Unlike total microbial and fungal biomass,

diversity was not correlated with SOM (data not shown).

Treatment effects on fungal community composition were more variable and subtle than the

effects on diversity. The first two principle components of a PCA accounted for 66% of variation

in weighted UniFrac scores. The samples did not cluster obviously by treatment, though the

Acc

epte

d A

rtic

le


control gap samples were clearly quite distinct from the others in terms of relative abundance of

fungal taxa (Figure S1). Nevertheless, there was some indication that FACE plots may have

subtly altered fungal community structure. In the MANOVA that included only the six most

abundant classes, FACE and control treatments were significantly different (F=10.492, P=0.04),

while plant and gap samples were not (Table 3). The differences were due to higher abundances

in FACE plots of Sordariomycetes and Leotiomycetes. Because these two classes were affected

by the FACE treatment, we investigated the orders within each using MANOVA. The seven

orders within the Sordariomycetes were not significantly affected by FACE or plant, but the

three orders of the Leotiomycetes were significantly affected by FACE (F=14.909, P=0.003) and

plant (F=11.303, P=0.007) (interaction not significant). Helotiales (P=0.034) and Leotiomycetes

incertae sedis (P=0.007) were more abundant in the FACE treatment.

The FACE and control treatments were similar in terms of the most abundant genera: the

MANOVA for the eight most consistently abundant genera (Table 4) showed no overall

significant effect of FACE or proximity to plant. In a post-hoc test, the Hysteropatella genus was

more frequent in FACE plots (0.7±0.2%) vs. control plots (0.2±0.1%) at P=0.003 (on rank-

transformed data), significant given the Bonferroni adjustment (critical P=0.00625). There was

much spatial variability at the genus level: several genera were highly abundant in a single

sample only (Table 4).

Acc

epte

d A

rtic

le


Discussion

The soil fungal community in the chaparral ecosystem at Sky Oaks Field Station appears to be

relatively diverse, especially after eight years under the FACE treatment. The numbers of

observed and estimated fungal OTUs were higher than most other studies with similar methods

(though direct comparisons are difficult given the wide variety of OTU definitions, marker

genes, diversity measures and sequencing depth found among these studies). For example,

Chao1 estimates of fungal diversity in soils using similar molecular techniques include 121 in a

Colorado alpine soil (Schadt et al., 2003), 149 in a semiarid grassland in New Mexico (Porras-

Alfaro et al., 2011), 187 in an aspen stand in Wisconsin (Lesaulnier et al., 2008), 196-369 for

coastal sage scrub and grassland sites in southern California (Karst et al., 2013), and 361 in a

boreal spruce forest in Alaska (Allison et al., 2007), all lower than the value of 478 for the

present study. Waldrop (2006) reported ACE (abundance coverage-based estimator, similar to

Chao1) for fungal diversity (defined at 99% similarity for ITS sequences) around 90 or less for

experimental plots at the Cedar Creek LTER. The observed number of OTUs (defined at 98% for

ITS) at French alpine sites ranged from 142-432 in a study (Lentendu et al., 2011) with roughly

twice the sequencing effort per site than the present study, which is extrapolated to observe about

400 OTUs (98%, LSU rRNA) given a comparable sequencing effort (using the extrapolation

function of EstimateS, data not shown). However, the three fungal libraries (desert, prairie and

rainforest) presented by Fierer et al. (2007) appear to be more diverse than those in this study

based on the 207-235 unique OTUs (97%, 18S rRNA) observed among only 304-310 sequences

(only about 100 would be expected in the present study with a comparable sequencing effort

based on the rarefaction curve). Likewise, the pine plantation soil at Duke Forest appears to

harbor higher diversity than Sky Oaks: around 900-1200 OTUs (97%, LSU rRNA) were

Acc

epte

d A

rtic

le


observed in libraries of about 5800-8400 sequences each (Weber et al., 2013), a higher range

than the extrapolated value for the present study.

In addition to being exceptionally diverse, the soil fungal community at Sky Oaks harbored

previously undescribed, uncultured taxa. For example, the clade of uncultured fungi shown in

Figure 3 was related to LSU rRNA sequences found only in other culture-independent studies of

soil fungal diversity, including such diverse habitats as alpine environments in Colorado

(Nemergut et al., 2008) and Austria (Oberkofler, unpublished data), a piñon woodland in New

Mexico and a pine forest in North Carolina (Eichorst & Kuske, 2012), a Canadian white spruce

stand (Lamarche et al., 2011), and a southern California grassland (Karst et al., 2013). This clade

appears to represent either a novel class within the Basidiomycota or a separate phylum. The

uncultured clade in Figure 2 clustered most closely with the Ostropales (data not shown), and

may represent a novel family or order within the Leotiomyceta group.

Fungal community composition varied greatly across the landscape (Figure S1, Table 4). Against

this backdrop of variability, treatment effects of FACE and proximity to plants on community

structure were generally difficult to discern with the sample size of this study, though several

significant effects were observed. Some fungal taxa clearly have a patchy distribution in this

landscape. The patchy distribution of the genera listed in Table 4 that were highly abundant in

only single replicates might be explained by their ecological strategies. Metarhizium includes

pathogens of insects, and Cyllamyces are found in the guts of mammals, and so one dead insect

Acc

epte

d A

rtic

le


or animal dropping within the sample could have produced these patterns. Similarly puffball

mushrooms in Calvatia produce copious spores, potentially explaining hotspots in the landscape.

More impressive than the minor treatment effects on relative community composition were the

consistent changes in diversity at multiple levels and across major lineages of the fungal tree.

While elevated CO2 has been shown to alter fungal communities in a number of studies, reports

of positive responses of fungal diversity are rare. Of the seven sample types from five

ecosystems studied by Weber (2011), only two (aspen plantation and desert creosote bush)

showed increased diversity of celluloytic fungi in response to elevated CO2. Other studies found

no detectable change in fungal diversity in response to elevated CO2 (Antoninka et al., 2011, He

et al., 2010, Weber et al., 2013), and at least one found a trend towards decreased fungal

diversity (Lesaulnier et al., 2008). As noted previously, soil fungal responses to elevated CO2 are

clearly complex and ecosystem dependent (Weber et al., 2011). A possible factor for some of

these negative results is the high diversity of soil fungal communities and the costs associated

with describing them. Advances in sequencing technology are improving this situation; in the

present study we obtained an average of about 260 useable fungal sequences for each of 12

spatial replicates, allowing for more statistical power than some previous studies with lower

sequencing effort and cases in which replicate samples were pooled to reduce costs.

Both the landscape patterns and effects of elevated CO2 appear to be driven by plant fine roots.

Previous work in this ecosystem revealed increased root growth and respiration under elevated

CO2, and that the CO2-driven changes in fungal biomass and microbial community function

Acc

epte

d A

rtic

le


(metabolic quotient and enzyme activities) were likely related to this stimulation in belowground

allocation (Lipson et al., 2005). Similarly, elevated CO2 led to increased allocation by A.

fasciculatum to mycorrhizae, producing an altered AM community composition (Treseder et al.,

2003). (No Glomeromycota sequences were recovered in the present study, but the majority of

AM biomass is probably associated directly with roots rather than the soil). Increased production

of roots and root exudates could stimulate fungal diversity by a number of mechanisms. Fungi

that parasitize or decompose roots and exudates should become more productive under these

conditions, and the linkage between diversity and productivity is well established (Evans et al.,

2005). Root-associated endophytic fungi should enjoy a more expansive habitat, possibly

opening up new niches. And diversity can beget more diversity: the most abundant fungal genus

found in this study (Christiansenia) includes species that parasitize other fungi. A more diverse

fungal community provides a broader diversity of hosts for such parasites, and parasites in turn

can increase diversity and complexity of food webs (Dunne et al., 2013), and may be indicators

of a stable, resilient and productive ecosystem (Hudson et al., 2006). The fungi found in these

soils likely include general saprotrophs as well as mutualists and parasites of plants, animals and

other fungi. Despite the variety of ecological strategies, plants appear to be the driving force in

structuring the fungal communities, both in terms of generating the soil organic matter that

determines the amount of fungal biomass, and in terms of root biomass that strongly influences

the diversity of these communities.

Elevated CO2 could potentially influence microbial communities in other ways than through

changes in plant production and allocation. Lowered stomatal conductivity and increased water

use efficiency could lead to moister soil conditions. However in this ecosystem, FACE soils were

Acc

epte

d A

rtic

le


not wetter than control soils, possibly because increased leaf area compensated for more efficient

water use (Cheng, 2003). CO2-induced changes in plant chemistry could alter fungal

communities, however no effects of CO2 concentration were detected on emissions of

monoterpenes in this ecosystem (Baraldi et al., 2004).

As the increases in fungal diversity in response to elevated CO2 were very generalized, rather

than stimulating only a specific taxa or functional group, it is hard to speculate on the

implications for plant and ecosystem function. FACE soils were more abundant in Helotiales, an

order that includes dark septate root endophytes that often have beneficial effects on plant

growth (Newsham, 2011), and which increased in another elevated CO2 study, leading to

increased plant growth and N use efficiency (Alberton et al., 2010). Increased allocation to root

mutualists is a commonly observed effect of elevated atmospheric CO2 (Antoninka et al., 2011,

Drake et al., 2011, Drigo et al., 2010, Olsrud et al., 2010, Treseder et al., 2003). A more diverse

saprotrophic community could lead to more rapid decomposition of soil organic matter (Carney

et al., 2007), but given the high level of preexisting fungal diversity in this ecosystem, such

effects would likely be subtle (Setälä & McLean, 2004). It is notable that bacteria responded so

little to elevated CO2 in this ecosystem (Lipson et al., 2005), while fungi responded dramatically

(in terms of biomass and diversity). These results contrast those found in an agricultural

ecosystem (Anderson et al., 2011) and in a grassland (He et al., 2010), in which bacteria

responded more quickly to the FACE treatment. This change in microbial community structure,

combined with increased inputs of complex polymers from root biomass, could improve the

efficiency of C sequestration in these soils (Fontaine et al., 2011, Six et al., 2006, Treseder et al.,

2003).

Acc

epte

d A

rtic

le


Our study only addresses one dimension of global change, while clearly changes in temperature,

precipitation regime and other anthropogenic impacts will play major roles in shaping the fungal

communities of this ecosystem. For example, N fertilization is reported to have both significant

negative (Allison et al., 2007) and positive (Weber et al., 2013) impacts on fungal diversity.

Given the importance of roots in structuring fungal communities in this ecosystem, the

multifactorial effects of climate change on fungi will likely be determined by the responses of

plant belowground allocation to these multiple factors.

Acknowledgements

This study was supported in part by a U.S. Department of Energy Science Focus Area grant

(LANL2009F260) from the Biological and Environmental Research Division to CRK. Sanger

sequencing was conducted by the Los Alamos National Laboratory.

References

Alberton O, Kuyper TW, Summerbell RC (2010) Dark septate root endophytic fungi increase growth of Scots pine seedlings under elevated CO2 through enhanced nitrogen use efficiency. Plant Soil, 328, 459–470.

Allison SD, Hanson CA, Treseder KK (2007) Nitrogen fertilization reduces diversity and alters community structure of active fungi in boreal ecosystems. Soil Biology & Biochemistry, 39, 1878–1887.

Altschul S, Madden T, Schaffer A, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res, 258, 3389–3402.

Anderson T-H, Heinemeyer O, Weigel H-J (2011) Changes in the fungal-to-bacterial respiratory ratio and microbial biomass in agriculturally managed soils under free-air CO2 enrichment (FACE) - A six-year survey of a field study. Soil Biology & Biochemistry, 43, 895-904.

Acc

epte

d A

rtic

le


Antoninka A, Reich PB, Johnson NC (2011) Seven years of carbon dioxide enrichment, nitrogen fertilization and plant diversity influence arbuscular mycorrhizal fungi in a grassland ecosystem. New Phytologist, 192, 200–214.

Baraldi R, Rapparini F, Oechel WC, Hastings SJ, Bryant P, Cheng Y, Miglietta F (2004) Monoterpene emission responses to elevated CO2 in a Mediterranean-type ecosystem. New Phytologist, 161, 17-21.

Bardgett RD, Freeman C, Ostle NJ (2008) Microbial contributions to climate change through carbon cycle feedbacks. The ISME Journal, 2, 805–814.

Beakes GW, Glockling SL, Sekimoto S (2012) The evolutionary phylogeny of the oomycete “fungi”. Protoplasma, 249, 3–19.

Bergbauer M, Newell SY (1992) Contribution to lignocellulose degradation and DOC formation from a salt marsh macrophyte by the ascomycete Phaeosphaeria spartinicola. FEMS Microbiology Letters, 86, 341-347.

Bowman RH (1973) Soil survey of the San Diego area, California, part I., Washington, D.C., USDA Soil Conservation Service and Forest Service.

Canadell J, Zedler PH (1995) Underground structures of woody plants in mediterranean regions of California, Chile, and Australia. In: Ecology and Biogeography of Mediterranean Ecosystems in Chile, California, and Austrailia. (eds Kalin-Arroyo MT, Zedler PH, Fox MD) pp 177-210. New York, NY, Springer-Verlag.

Carney KM, Hungate BA, Drake BG, Megonigal JP (2007) Altered soil microbial community at elevated CO2 leads to loss of soil carbon. PNAS, 104, 4990–4995.

Chapin FS, Mcfarland J, Mcguire AD, Euskirchen ES, Ruess RW, Kielland K (2009) The changing global carbon cycle: linking plant–soil carbon dynamics to global consequences. Journal of Ecology, 97, 840–850.

Cheng Y (2003) Effects of manipulated atmospheric carbon dioxide concentrations on carbon dioxide and water vapor fluxes in Southern California chaparral. Unpublished Ph.D. San Diego State University, San Diego, 112 pp.

Colwell RK, Chao A, Gotelli NJ, Lin S-Y, Mao CX, Chazdon RL, Longino JT (2012) Models and estimators linking individual-based and sample-based rarefaction, extrapolation, and comparison of assemblages. Journal of Plant Ecology, 5, 3-21.

Dieleman WIJ, Vicca S, Dijkstra FA et al. (2012) Simple additive effects are rare: a quantitative review of plant biomass and soil process responses to combined manipulations of CO2 and temperature. Global Change Biology, 18, 2681–2693.

Drake JE, Gallet-Budynek A, Hofmockel KS et al. (2011) Increases in the flux of carbon belowground stimulate nitrogen uptake and sustain the long-term enhancement of forest productivity under elevated CO2. Ecology Letters, 14, 349–357.

Drigo B, Kowalchuk GA, Van Veen JA (2008) Climate change goes underground: effects of elevated atmospheric CO2 on microbial community structure and activities in the rhizosphere. Biol Fertil Soils, 44, 667–679.

Drigo B, Pijla AS, Duyts H et al. (2010) Shifting carbon flow from roots into associated microbial communities in response to elevated atmospheric CO2. PNAS, 107, 10938–10942.

Dunne JA, Lafferty KD, Dobson AP et al. (2013) Parasites Affect Food Web Structure Primarily through Increased Diversity and Complexity. PLoS Biol, 11, e1001579.

Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 27, 2194–2200.

Acc

epte

d A

rtic

le


Eichorst SA, Kuske CR (2012) Identification of cellulose-responsive bacterial and fungal communities in geographically and edaphically different soils by using stable isotope probing. Applied and Environmental Microbiology, 78, 2316-2327.

Evans KL, Warren PH, Gaston KJ (2005) Species–energy relationships at the macroecological scale: a review of the mechanisms. Biological Reviews, 80, 1 - 25.

Felsenstein J, Churchill GA (1996) A Hidden Markov Model approach to variation among sites in rate of evolution. Mol Biol Evol, 13, 93-104

Fierer N, Breitbart M, Nulton J et al. (2007) Metagenomic and small-subunit rRNA analyses reveal the genetic diversity of bacteria, archaea, fungi, and viruses in soil. Applied and Environmental Microbiology, 73, 7059-7066.

Fontaine S, Henault C, Aamor A et al. (2011) Fungi mediate long term sequestration of carbon and nitrogen in soil through their priming effect. Soil Biology & Biochemistry, 43, 86-96.

Hall TA (1999) BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT Nucl. Acids. Symp. Ser., 41, 95-98.

Hanson AM (1944) Three New Saprophytic Chytrids. Torreya, 44, 30-33. He Z, Xu M, Deng Y et al. (2010) Metagenomic analysis reveals a marked divergence in the

structure of belowground microbial communities at elevated CO2. Ecology Letters, 13, 564–575.

Hudson PJ, Dobson AP, Lafferty KD (2006) Is a healthy ecosystem one that is rich in parasites? TRENDS in Ecology and Evolution, 21, 381-385.

Hughes JB, Hellmann JJ, Ricketts TH, Bohannan BJM (2001) Counting the uncountable: statistical approaches to estimating microbial diversity. Applied and Environmental Microbiology, 67, 4399–4406.

Iannotta N, Noce ME, Ripa V, Scalercio S, Vizzarri V (2007) Assessment of susceptibility of olive cultivars to the Bactrocera oleae (Gmelin, 1790) and Camarosporium dalmaticum (Thüm.) Zachos & Tzav.-Klon. attacks in Calabria (Southern Italy). Journal of Environmental Science and Health, 42, 789-793.

Karst J, Piculell B, Brigham C, Booth M, Hoeksema JD (2013) Fungal communities in soils along a vegetative ecotone. Mycologia, 105, 61–70.

Kohzu A, Yohioka T, Ando T, Takahashi M, Koba K, Wada E (1999) Natural 13C and 15N abundance of field-collected fungi and their ecological implications. New Phytologist, 144, 323-330.

Lamarche J, Stefani FOP, Seguin A, Hamelin RC (2011) Impact of endochitinase-transformed white spruce on soil fungal communities undergreenhouse conditions. FEMS Microbiol Ecol, 76, 199–208.

Lentendu G, Zinger L, Manel S, Coissac E, Choler P, Geremia RA, Melodelima C (2011) Assessment of soil fungal diversity in different alpine tundra habitats by means of pyrosequencing. Fungal Diversity, DOI 10.1007/s13225-011-0101-5.

Lesaulnier C, Papamichail D, Mccorkle S et al. (2008) Elevated atmospheric CO2 affects soil microbial diversity associated with trembling aspen. Environmental Microbiology, 10, 926-941.

Lipson DA, Wilson RF, Oechel WC (2005) Effects of elevated atmospheric CO2 on soil microbial biomass, activity and diversity in a chaparral ecosystem. Appl Environ Microbiol, 71, 8573-8580.

Acc

epte

d A

rtic

le


Liu K-L, Porras-Alfaro A, Kuske CR, Eichorst SA, Xie G (2012) Accurate, rapid taxonomic classification of fungal large subunit rRNA genes. Applied and Environmental Microbiology, 78, 1523-1533.

Lozupone C, Hamady M, Knight R (2006) UniFrac - An online tool for comparing microbial community diversity in a phylogenetic context BMC Bioinformatics, 7, 371.

Luo H (2007) The importance of a Mediterranean type ecosystem in trace gas fluxes from the chaparral of Southern California. Unpublished Ph.D. Doctoral, SDSU, San Diego State University.

Lynch SC, Zambino PJ, Scott TA, Eskalen A (2013) Occurrence, incidence and associations among fungal pathogens and Agrilus auroguttatus, and their roles in Quercus agrifolia decline in California. Forest Pathology, doi: 10.1111/efp.12070.

Meiners J, Winkelmann T (2011) Morphological and genetic analyses of hellebore leaf spot disease isolates from different geographic origins show low variability and reveal molecular evidence for reclassification into Didymellaceae. J Phytopathol, 159, 665–675.

Nemergut DR, Townsend AR, Sattin SR et al. (2008) The effects of chronic nitrogen fertilization on alpine tundra soil microbial communities: implications for carbon and nitrogen cycling. Environmental Microbiology, 10, 3093–3105.

Newsham KK (2011) A meta-analysis of plant responses to dark septate root endophytes. New Phytologist, 190, 783–793.

O’brien HE, Parrent JL, Jackson JA, Moncalvo J-M, Vilgalys R (2005) Fungal Community Analysis by Large-Scale Sequencing of Environmental Samples. Applied and Environmental Microbiology, 71, 5544-5550.

Oberwinkler F, Bandoni RJ, Bauer R, Deml G, Kisimova-Horovitz L (1984) The Life-History of Christiansenia pallida, a Dimorphic, Mycoparasitic Heterobasidiomycete. Mycologia, 76, 9-22.

Olsrud M, Carlsson BA, Svensson BM, Michelsen A, Melillo JM (2010) Responses of fungal root colonization, plant cover and leaf nutrients to long-term exposure to elevated atmospheric CO2 and warming in a subarctic birch forest understory. Global Change Biology, 16, 1820–1829.

Ozkose E, Thomas BJ, Davies DR, Griffith GW, Theodorou MK (2001) Cyllamyces aberensis gen.nov. sp.nov., a new anaerobic gut fungus with branched sporangiophores isolated from cattle. Can. J. Bot., 79, 666–673.

Porras-Alfaro A, Herrera J, Natvig DO, Lipinski K, Sinsabaugh RL (2011) Diversity and distribution of soil fungal communities in a semiarid grassland. Mycologia, 103, 10-21.

Pruesse E, Peplies J, Glöckner FO (2012) SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics, 28, 1823-1829.

Rambelli A, Ciccarone C, Tempesta S, Raimondo FM (2011) Dematiaceous Hyphomycetes from Quercus suber litter. Flora Mediterranea, 21, 325-344.

Roberts SW, Oechel WC, Bryant PJ, Hastings SJ, Major J, Nosov V (1998) A field fumigation system for elevated carbon dioxide exposure in chaparral shrubs. Func. Ecol., 12, 708–719.

Schadt CW, Martin AP, Lipson DA, Schmidt SK (2003) Seasonal dynamics of previously unknown fungal lineages in tundra soils. Science, 301, 1359-1361.

Schneider M, Grünig CR, Holdenrieder O, Sieber TN (2009) Cryptic speciation and community structure of Herpotrichia juniperi, the causal agent of brown felt blight of conifers. Mycological Research, 113, 887–896.

Acc

epte

d A

rtic

le


Schoch CL, Shoemaker RA, Seifert KA, Hambleton S, Spatafora JW, Crous PW (2006) A multigene phylogeny of the Dothideomycetes using four nuclear loci. Mycologia, 98, 1041-1052.

Setälä H, Mclean MA (2004) Decomposition rate of organic substrates in relation to the species diversity of soil saprophytic fungi. Oecologia, 139, 98–107.

Shoemaker RA, Babcock CE (1989) Phaeosphaeria. Canadian Journal of Botany, 67, 1500-1599.

Six J, Frey SD, Thiet RK, Batten KM (2006) Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci. Soc. Am. J., 70, 555–569.

Sun Y, Wang Q, Lu XD, Okane I, Kakishima M (2011) Endophytic fungi associated with two Suaeda species growing in alkaline soil in China. Mycosphere, 2, 239–248.

Treseder KK, Egerton-Warburton LM, Allen MF, Cheng Y, Oechel WC (2003) Alteration of soil carbon pools and communities of mycorrhizal fungi in chaparral exposed to elevated carbon dioxide. Ecosystems, 6, 786–796.

Waldrop MP, Zak DR, Blackwood CB, Curtis CD, Tilman D (2006) Resource availability controls fungal diversity across a plant diversity gradient. Ecology Letters, 9, 1127–1135.

Weber CF, Vilgalys R, Kuske CR (2013) Changes in fungal community composition in response to elevated atmospheric CO2 and nitrogen fertilization varies with soil horizon. Front. Microbiol., doi: 10.3389/fmicb.2013.00078.

Weber CF, Zak DR, Hungate BA et al. (2011) Responses of soil cellulolytic fungal communities to elevated atmospheric CO2 are complex and variable across five ecosystems. Environmental Microbiology, 13, 2778–2793.

Zhang DW, Zhao MM, Chen J, Li C, Guo SX (2013) Isolation, idetification and anti-HIV-1 integrase activity of culturable endophytic fungi from Tibetan medicinal plant Phlomis younghusbandii Mukerjee. Acta Pharmaceutica Sinica, 48, 780-789.

Zimmermann G (2006) The entomopathogenic fungus Metarhizium anisopliae and its potential as a biocontrol agent. Pesticide Science, 37, 375–379.

Supporting Information Legends

Table S1. Properties of the soil samples used to generate fungal LSU rRNA sequences analyzed

in this study. SOM = soil organic matter, GWC = gravimetric water content, SIR = substrate-

induced respiration, Resp = soil respiration. Values are means and standard errors of three

replicates per treatment, 12 samples total.

Table S2. Number of sequences retained in each category for the two different analyses.

Acc

epte

d A

rtic

le


Figure S1. Principal component analysis of weighted UniFrac metric for the 12 samples used in

this study, showing the first two principle components.

Table 1. Summary of two-way ANOVAs for diversity measures. H’ is the Shannon index

calculated at various taxonomic levels; “all” indicates that all sequences with a classification

were included; “80%” indicates that only sequences classified with a score of 80% or better were

included. “Int.” is the FACE x plant interaction. The total number of sequences used in each

analysis is given by n. The number of replicates used in the ANOVA is 12 for all tests.

P-values

Variable

FACE vs.

control

plant vs.

gap Int. R2 n

H’, class (all) 0.047 0.245 0.413 0.495 3007

H’, order (all) 0.010 0.047 0.098 0.716 3007

H’, family (all) 0.014 0.015 0.041 0.759 3007

H’, genus (all) 0.006 0.014 0.020 0.802 3007

H’, class (80%) 0.015 0.246 0.643 0.588 2538

H’, order (80%) 0.001 0.013 0.069 0.831 2172

H’, family (80%) 0.040 0.022 0.122 0.680 1589

H’, genus (80%) 0.003 0.036 0.047 0.791 1114

H’, 98% similarity 0.008 0.019 0.028 0.780 3127

Chao1 0.022 0.033 0.118 0.689 3127

Acc

epte

d A

rtic

le


Table 2. Correlation coefficients (upper diagonal) and significance (P-values, lower diagonal) for

correlations among diversity measures and fine root biomass. H’ indicates the Shannon diversity

index for the taxonomic level in parentheses. N=12 for all tests.

H’(class) H’(order) H’(family) H’(genus) H’(98%) Chao1 FineRoots

H’(class) 0.916 0.829 0.808 0.775 0.704 0.666

H’(order) <0.001 0.896 0.918 0.939 0.817 0.687

H’(family) 0.001 <0.001 0.925 0.831 0.658 0.467

H’(genus) 0.001 <0.001 <0.001 0.831 0.658 0.581

H’(98%) 0.003 <0.001 0.001 0.001 0.874 0.530

Chao1 0.011 0.001 0.020 0.020 <0.001 0.545

FineRoots 0.018 0.014 0.126 0.048 0.076 0.067

Table 3. Representation (means and standard errors) of the six most abundant fungal classes

within LSU rRNA libraries from FACE and soil samples. The difference between FACE and

control treatments in the overall multivariate ANOVA including only the top six classes was

significant (P=0.04), while plant and gap samples were not (P=0.145).

Class Control

(%)

FACE

(%)

P

Dothideomycetes 47.7±7.8 38.5±3.5 0.346

Agaricomycetes 17.8±10.6 6.5±2.1 0.348

Sordariomycetes 5.9±1.1 14.3±4.1 0.050

Acc

epte

d A

rtic

le


Eurotiomycetes 7.6±2.3 11.7±1.8 0.186

Tremellomycetes 9.4±2.1 8.8±2.6 0.877

Leotiomycetes 1.4±0.5 7.7±2.7 0.040

Table 4. Relative abundance (and standard errors of 12 replicates) of the eight most abundant genera found consistently among the replicates, and four genera that were highly abundant in a single sample only. Typical ecological roles for each genus are listed based on cultured relatives (with supporting references).

Genus Rel. abundance

(%)

typical role reference

Christiansenia 1.2 (0.2) pathogen of fungi (Oberwinkler et al., 1984)

Chaetosphaeronema 1.0 (0.2) endophyte (Zhang et al., 2013)

Phaeosphaeria 0.9 (0.2) plant parasite,

saprotroph

(Shoemaker & Babcock,

1989), (Bergbauer &

Newell, 1992)

Herpotrichia 0.8 (0.4) plant pathogen (Schneider et al., 2009)

Camarosporium 0.6 (0.2) plant pathogen,

endophyte

(Iannotta et al., 2007, Sun et

al., 2011)

Hysteropatella 0.5 (0.1) saprotroph, “weak

parasite”

(Schoch et al., 2006)

Repetophragma 0.5 (0.1) saprotroph (Rambelli et al., 2011)

Coniozyma 0.5 (0.2) pathogen (Lynch et al., 2013, Meiners

& Winkelmann, 2011)

Abundant in one sample only

Acc

epte

d A

rtic

le


Catenomyces 1.0 (0.8) saprotroph (chytrid) (Hanson, 1944)

Calvatia 0.9 (0.9) saprotroph (puffball) (Kohzu et al., 1999)

Metarhizium 0.5 (0.4) pathogen of insects (Zimmermann, 2006)

Cyllamyces 0.4 (0.3) anaerobic gut fungus (Ozkose et al., 2001)

Figure 1. Regressions of total soil DNA and 18S rRNA genes on soil organic matter.

Figure 2. Maximum likelihood phylogenetic tree of fungal LSU rRNA sequences within the

Ascomycota phylum found in soils from FACE and control plots.

Figure 3. Maximum likelihood phylogenetic tree of fungal LSU rRNA sequences within all non-

Ascomycota phyla found in soils from FACE and control plots.

Figure 4. Rarefaction curves for OTUs defined at 98% similarity: (a) both treatments combined,

(b) FACE and control treatments shown separately. Error bars represent 95% confidence limits.

Figure 5. Chao1 estimates of fungal phylotype richness based on 98% similarity level: (a) Chao1

estimate and 95% confidence intervals for each category [FACE (F) or control (C) plots,

collected from under the plant canopy (p) or from gaps (g)], with all spatial replicates pooled

Acc

epte

d A

rtic

le


together; (b) means and standard errors of Chao1 for the three individual replicate soil samples

from each category.

Figure 6. Shannon index for fungal communities in soils from FACE (F) or control (C) plots,

collected from under the plant canopy (p) or from gaps (g). The index was calculated from the

relative abundance of sequences at each taxonomic level indicated. Each value is the mean and

standard error of three replicates (see Table 1 for statistical analysis).

Figure 7. Regression of Shannon index (H’) of fungal diversity at the order level on fine root

biomass, including samples from FACE and control plots.

Acc

epte

d A

rtic

le


Acc

epte

d A

rtic

le


Acc

epte

d A

rtic

le


Documents

Elevated atmospheric CO 2 stimulates soil fungal diversity through increased fine root production in a semiarid shrubland ecosystem