Forum
Editorial
Blackwell Publishing Ltd
New Phytologist – an
evolving host for mycorrhizal
research
‘Sam Gamgee planted saplings in all the places where
specially beautiful or beloved trees had been destroyed,
and he put a grain of the precious dust from Galadriel
in the soil at the root of each. The little silver nut he
planted in the Party Field where the tree had once
been; and he wondered what would come of it. All through
the winter he remained as patient as he could, and
tried to restrain himself from going round constantly
to see if anything was happening. Spring surpassed his
wildest hopes. His trees began to sprout and grow, as
if time was in a hurry and wished to make one year
do for twenty. In the Party Field a beautiful young
sapling leaped up: it had silver bark and long leaves
and burst into golden flowers in April. It was indeed
a mallorn, and it was the wonder of the neighbourhood.
In after years, as it grew in grace and beauty, it was known
far and wide and people would come long journeys to see
it: the only mallorn west of the Mountains and east
of the Sea, and one of the finest in the world.’
( J. R. R. Tolkien, The Return of the King, Book Six,
Chapter IX, The Grey Havens)
Next time you walk in the misty woods captivated by the
sheer beauty and majesty of trees, thank the cast of a million
www.newphytologist.org
species of soil organisms living in the endless foam of tiny
niches of weathered rock, mineral particles and decomposing
soil organic matter. As a tree forms, it interacts with guilds
of beneficial microorganisms promoting its growth and
development. The box of dust given by the Elven queen of
Lothlorien, Galadriel, contained the needed mycorrhizal
inoculum for promoting the growth of mallorn trees,
but infortunately this ancestral knowledge was lost for
millenia.
It is now very strange to realize that before Professor Jack
Harley began his research on the mycorrhiza of beech (Fagus
sylvatica) in the middle of last century, botanists and foresters
regarded mycorrhizas as being obscure and of little importance.
Harley’s series of outstanding, now classical, experiments on
ectomycorrhizas elucidated the mechanisms by which tree
mycorrhizas take up essential nutrients such as phosphate
from the soil (Harley, 1953). He clarified the nature of what
is undoubtedly the commonest and most important symbiosis
in the world. Simply stated, nearly all families of plants form
root symbiotic organs, termed mycorrhizas, with soil fungi
belonging to all the main phyla; namely Glomeromycota,
Ascomycotina and Basidiomycotina. Within days of their
emergence in the upper soil profiles, up to 95% of short
roots of trees are colonized by mycorrhizal fungi. The
importance of this symbiosis in controlling plant nutrient
status and growth is now well established (Read & PerezMoreno, 2003). New Phytologist hosted Harley’s seminal
papers on ectomycorrhizal physiology, and from this a
strong association between the journal and the mycorrhizal
community has developed. Indeed, from ISI citation analysis,
it is clear that mycorrhizal research still contributes greatly to
the success of the journal; the most widely cited and influential
article in recent years being the Tansley review by Read &
Perez-Moreno (2003) discussing the key ecological role of
the different types of mycorrhizal symbioses in plant nutrition.
Today, with the advent of new tools and techniques, the
possibility of integration across a wide range of disciplines
from genomics to molecular ecology and field ecology is
becoming a reality that is much encouraged by New Phytologist.
In this Editorial we will highlight some of the recent innovative
mycorrhizal research published in the journal and look to
future challenges that lie ahead. This theme is continued
throughout the Forum of this issue, including Commentaries
on selected papers and a series of Letters stimulated by discussions and the ideas exchanged at the last International
Conference on Mycorrhiza (ICOM5: July 2006, Granada,
Spain).
Primary research papers in the last few years have broken
the ground for new lines of research from regulation of gene
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226 Forum
Editorial
expression to the ecological relevance of mycorrhizal symbioses.
To cite a few, these studies have provided a new perspective
on how the mycorrhizal symbionts play a critical role in
biogeochemical cycles. The main stumbling block has been
that a large proportion of mycorrhizal fungi do not produce
conspicuous fruit bodies or cannot be grown in laboratory
cultures, but most importantly there were no techniques
available to assess the extensive and highly active webs of
extraradical hyphae permeating the soil. The techniques and
approaches of above-ground ecology do not translate well
to the soil environment. However, during the past decade,
PCR-based molecular methods and DNA sequencing have
been routinely used to identify mycorrhizal fungi, and the
application of these molecular methods has provided detailed
insights into the complexity of mycorrhizal fungal communities and populations, and offers exciting prospects for
elucidation of the processes that structure ectomycorrhizal
fungal communities (Horton & Bruns, 2001). These tools
have managed to reveal the tremendous diversity of mycorrhizal
fungi interacting with their host in space (Genney et al.,
2006) and time (Koide et al., in press), but also how different environmental factors and forest land usage could alter
the composition of these soil fungal communities (Richard
et al., 2005; Toljander et al., 2006). These molecular ecology studies will spur work on dynamics and functions of
mycorrhizal communities and populations, but also generate
hypotheses about their role in the changing forest ecosystems.
For example, it appears that the formidable webs of extramatrical
hyphae of mycorrhizal fungi not only permeate the mineral
soil horizons, but are also very abundant in litter and decaying
wood debris (Rosling et al., 2003; Tedersoo et al., 2003).
With improvements in molecular techniques and appropriate
DNA databases (Kõljalg et al., 2005), identification of taxa
in fungal ecology has expanded from fruit bodies to mycorrhizal
roots to extraradical hyphae (Anderson & Cairney, 2004).
Combined with isotopic tools, these techniques provide
novel insights into soil fungal ecology. In an elegant study,
Lindahl et al. (2007) reported on the spatial patterns of
ectomycorrhizal and saprotrophic fungi from soil profiles in
a Pinus sylvestris forest in Sweden, and compared those patterns with profiles of bulk carbon:nitrogen (C:N) ratios, and
15N and 14C contents (as a proxy for age). Saprotrophic
fungi were found to primarily colonize relatively recently
shed litter components on the surface of the forest floor,
where organic C was mineralized while N was retained.
Mycorrhizal fungi were prominent in the underlying, more
decayed litter and humus, where they apparently mobilized
N and made it available to their host plants. Mycorrhizas
not only shape the plant communities, they also affect the
functional diversity of rhizospheric bacteria (Frey-Klett
et al., 2005). In their seminal paper, Schrey et al. (2005)
have shown that a molecular cross-talk is taking place
between the members of these multitrophic associations. But
beyond a gross understanding of their demography, the
New Phytologist (2007) 174: 225–228
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specific spatiotemporal dynamics of mycorrhizal species and
communities in the underground remain elusive. The physical,
chemical and biological complexity of the soil makes this
kind of investigation a daunting prospect. The current situation
could be eased by the development of high-throughput
molecular diagnostic tools, such as DNA oligoarrays, for
cataloging soil microbes on the larger scale imposed by field
studies of a very heterogeneous subterranean world.
The use of molecular approaches to inform the ecology
and evolution of mycorrhizal symbioses has been a hallmark
of Marc-André Selosse’s research programs, and we are
pleased to announce his appointment to the Editorial Board.
His group at the University of Montpellier (France) has
contributed much to the understanding of the ecology and
evolution of mycorrhizal symbioses (Richard et al., 2005;
Selosse et al., 2006). In a fascinating example of how molecular
tools have provided new cues to understand plant ecology,
he showed that the endomycorrhizal symbionts of forest
achlorophyllous orchids, such as Neottia nidus-avis, belong
to the genus Sebacina, a common ectomycorrhizal taxon
associated with temperate trees (Selosse et al., 2002). This
study of myco-heterotrophic plants has profoundly modified
our view of the specificity of mycorrhizal fungi toward their
host plants and the carbon fluxes between the different
inhabitants of forest soils (Bidartondo, 2004). Marc-André’s
interests and expertise in the ecology and evolution of
symbioses mesh well with the mycorrhizal expertise of the
journal board which includes Iver Jakobsen, Alastair Fitter,
Francis Martin, and Ian Alexander, whose perspectives range
from genomics to field ecology.
The next challenge on the agenda is to identify the
functions played by the assemblages of mycorrhizal fungi in
situ (Read & Perez-Moreno, 2003). As a prerequisite of such
large-scale functional ecology studies, we now need to discover
genes controlling the functioning of the mycorrhizal symbioses.
Critical in this endeavor will be the use of genomic information
on the recently sequenced Populus trichocarpa (Tuskan et al.,
2006) and its mycorrhizal mutualists. The completion or
impending completion of the genome sequences of the ectomycorrhizal Laccaria bicolor and endomycorrhizal Glomus
intraradices (Martin et al., 2004; http://genome.jgi-psf.org/
Lacbi1/Lacbi1.home.html) provides an unprecedented
opportunity to identify the key components of interspecific
and organism–environment interactions (Whitham et al.,
2006). By examining, modeling and manipulating patterns
of gene expression, we can identify the genetic control
points regulating the mycorrhizal response to changing host
physiology, and better understand how these interactions
control ecosystem function.
Complex biological systems such as symbiosis are thought to
be caused by the interaction of many genes and the environment,
and the genetic components can be determined by association
with genetic variation. Association mapping and ecotilling
(Gilchrist et al., 2006) compare genomes in wide-ranging
www.newphytologist.org © The Authors (2007). Journal compilation © New Phytologist (2007)
Editorial
natural populations of individuals with different phenotypes to
allow ‘associations’ between genetic markers and phenotypic
traits, such as nutrient acquisition or symbiosis efficiency.
This approach is sparking the development of higher density
genotyping arrays with greater power to detect common
genetic variations, such as single nucleotide polymorphisms
(SNPs) and copy number variants (CNVs); the latter being
likely involved in ectomycorrhiza development (Le Quéré
et al., 2006). Mycorrhiza-regulated genes involved in N and
phosphate absorption and organic matter decay have now
been identified (Tuskan et al., 2006; Couturier et al., in
press). Analysis of their sequence polymorphisms in wild
populations will set the stage for understanding the adaptation
of the subsurface symbiotic duet to changes in the environment.
In addition, novel DNA sequencers based on massively
parallel sequencing of millions of fragments will provide
a cost-effective, efficient tool for conducting these candidategene based association genetic studies on a large scale in situ.
The development of highly parallel genomic assays is still a
relatively young field and has not yet been applied to soil
microbial ecology. Sequencing of PCR-amplified ribosomal
DNA will be substituted by genome sequencing of
hundreds of environmental mycorrhizal samples and selected
soil metagenomes in the near future. There is no doubt that
massive sequencing of soil entities will be fertile ground for
novel hypotheses about how mycorrhizal symbioses drive
ecosystems. Future efforts in this area will advance our
general perspective on mycorrhizal ecology and evolution and
elucidate the biological dynamics that mediate the flux of
matter and energy in terrestrial ecosystems. New Phytologist is pleased to continue to host and to support these
innovative studies.
Acknowledgements
FM would like to thank David and Nicolas Martin for
sharing their in-depth expertise on Middle-Earth. Research
conducted in Martin’s laboratory on the molecular ecology
and genomics of mycorrhizal symbioses is funded by INRA,
the Région Lorraine and the European Network of Excellence
EVOLTREE.
Francis Martin
Interaction Section Editor
Holly Slater
Managing Editor
References
Anderson IC, Cairney JWG. 2004. Diversity and ecology of soil fungal
communities: increased understanding through the application of
molecular techniques. Environmental Microbiology 6: 769–779.
Bidartondo MI. 2004. The evolutionary ecology of myco-heterotrophy.
New Phytologist 167: 335–352.
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Couturier C, Montanini B, Martin F, Brun A, Blaudez D, Chalot M.
2007. The expanded family of ammonium transporters in the perennial
poplar plant. New Phytologist 174: 137–150.
Frey-Klett P, Chavatte M, Clausse ML, Courrier S, Le Roux C,
Raaijmakers J, Martinotti MG, Pierrat JC, Garbaye J. 2005.
Ectomycorrhizal symbiosis affects functional diversity of rhizosphere
fluorescent pseudomonads. New Phytologist 165: 317–328.
Genney DR, Anderson IC, Alexander IJ. 2006. Fine-scale distribution of
pine ectomycorrhizas and their extramatrical mycelium. New Phytologist
170: 381–390.
Gilchrist EJ, Haughn GW, Ying CC, Otto SP, Zhuang J, Cheung D,
Hamberger B, Aboutorabi F, Kalynyak T, Johnson L, Bohlmann J,
Ellis BE, Douglas CJ, Cronk QCB. 2006. Use of Ecotilling as an
efficient SNP discovery tool to survey genetic variation in wild
populations of Populus trichocarpa. Molecular Ecology 15:
1367–1378.
Harley JL. 1953. A note on the effect of sodium azide upon the
respiration of beech mycorrhizas. New Phytologist 52: 83–85.
Horton TR, Bruns TD. 2001. The molecular revolution in
ectomycorrhizal ecology: peeking into the black-box. Molecular
Ecology 10: 1855–1871.
Koide RT, Shumway DL, Bing X, Sharda JN. 2007. On temporal
partitioning of a community of ectomycorrhizal fungi. New Phytologist
174: 420–429.
Kõljalg U, Larsson KH, Abarenkov K, Nilsson RH, Alexander IJ,
Eberhardt U, Erland S, Høiland K, Kjøller R, Larsson E, Pennanen T,
Sen R, Taylor AFS, Tedersoo L, Vrålstad T, Ursing BM. 2005.
UNITE: a database providing web-based methods for the molecular
identification of ectomycorrhizal fungi. New Phytologist 166: 1063–
1068.
Le Quéré A, Eriksen KA, Rajashekar B, Schützendübel A, Canbäck B,
Johansson T, Tunlid A. 2006. Screening for rapidly evolving genes in
the ectomycorrhizal fungus Paxillus involutus using cDNA microarrays.
Molecular Ecology 15: 535–550.
Lindahl B, Ihrmark K, Boberg J, Trumbore SE, Högberg P, Stenlid J,
Finlay RD. 2007. Spatial separation of litter decomposition and
mycorrhizal nitrogen uptake in a boreal forest. New Phytologist 173:
611–620.
Martin F, Tuskan GA, Difazio SP, Lammers P, Newcombe G, Podila
GK. 2004. Symbiotic sequencing for the Populus mesocosm: DOE
tackles the genomes of endomycorrhizal Glomus intraradices and
ectomycorrhizal Laccaria bicolor. New Phytologist 161: 330–335.
Read DJ, Perez-Moreno J. 2003. Mycorrhizas and nutrient cycling in
ecosystems – a journey towards relevance? New Phytologist 157: 475–
492.
Richard F, Millot S, Gardes M, Selosse MA. 2005. Diversity and
specificity of ectomycorrhizal fungi retrieved from an old-growth
Mediterranean forest dominated by Quercus ilex. New Phytologist 166:
1011–1023.
Rosling A, Landeweert R, Lindahl BD, Larsson KH, Kuyper TW, Taylor
AFS, Finlay RD. 2003. Vertical distribution of ectomycorrhizal fungal
taxa in a podzol soil profile. New Phytologist 159: 775–783.
Schrey SD, Schellhammer M, Ecke M, Hampp R, Tarkka MT. 2005.
Mycorrhiza helper bacterium Streptomyces AcH 505 induces differential
gene expression in the ectomycorrhizal fungus Amanita muscaria. New
Phytologist 168: 205–216.
Selosse MA, Richard F, He X, Simard SW. 2006. Mycorrhizal
networks: des liaisons dangereuses? Trends in Ecology and Evolution 21:
621–628.
Selosse MA, Weiß M, Jany JL, Tillier A. 2002. Communities and
populations of sebacinoid basidiomycetes associated with the
achlorophyllous orchid Neottia nidus-avis (L.) L.C.M. Rich. and
neighbouring tree ectomycorrhizae. Molecular Ecology 11:
1831–1844.
Tedersoo L, Kõljalg U, Hallenberg N, Larsson KH. 2003. Fine scale
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Commentary
distribution of ectomycorrhizal fungi and roots across substrate layers
including coarse woody debris in a mixed forest. New Phytologist 159:
153–165.
Toljander JF, Eberhardt U, Toljander YK, Paul LR, Taylor AFS. 2006.
Species composition of an ectomycorrhizal fungal community along a
local nutrient gradient in a boreal forest. New Phytologist 170: 873–884.
Tuskan GA, DiFazio S, Jansson S et al. 2006. The genome of black
cottonwood, Populus trichocarpa. Science 313: 1596–1604.
Whitham TG, Bailey JK, Schweitzer JA, Shuster SM, Bangert RK, LeRoy
CJ, Lonsdorf EV, Allan GJ, DiFazio FP, Potts BM, Fischer DG,
Gehring CA, Lindroth RL, Marks JC, Hart SC, Wimp GM, Wooley
SC. 2006. A framework for community and ecosystem genetics: from
genes to ecosystems. Nature Reviews Genetics 7: 510–523.
Key words: evolution, fungal genomics, Glomus intraradices, Laccaria
bicolor, molecular ecology, mycorrhiza, poplar, symbiosis.
Commentary
Trehalose synthesis in
ectomycorrhizas –
a driving force of carbon
gain for fungi?
Carbohydrates are synthesized by photosynthesis in plants
and are partitioned in the form of sucrose, via the phloem,
to organs and tissues which have a demand for carbon and
form a ‘sink’. Ectomycorrhizal fungi, which live in intimate
symbiosis with trees, receive up to 30% of the total carbon
fixed by the plant host (Finlay & Söderström, 1992) and
thus function as an important sink. In exchange, the tree
receives mineral nutrients from the fungus. Understanding
how the fungus can increase its ‘sink strength’, and hence
demand carbon from the host, is clearly important. In this
issue of New Phytologist (pp. 389–398), Lopez et al. have
worked towards this by investigating carbon partitioning in
the poplar (Populus tremula × tremuloides)–Amanita muscaria
ectomycorrhizal symbiosis.
New
©
The
Phytologist
Authors (2007).
(2007) doi:
Journal
10.1111/j.1469-8137.2007.00@@@.x
compilation © New Phytologist (2007)
‘Thus, while trehalose is not the only fungal carbon
sink synthesized upon feeding glucose, it would seem
to be particularly important in the Hartig net.’
A. muscaria, Lopez et al. hypothesized that trehalose may act
as an important carbon sink and thus set about determining
the compartmentalization of trehalose biosynthesis between
the hyphae of the fungal sheath and the Hartig net. Through the
use of advanced techniques and much skill, the authors
succeeded in physically separating the fungal sheath from
the Hartig net. Transcript levels of the genes encoding key
enzymes of fungal trehalose biosynthesis were found to be
higher in the Hartig net compared to the other tissues; in
particular, trehalose phosphate synthase (TPS), trehalose
phosphate phosphatase (TPP) and trehalose phosphorylase
(TP) were increased. The TPS and TPP enzymes form the
classic pathway for trehalose synthesis, whereas TP is thought
to function as trehalose synthase when glucose is abundant.
Further expression analysis has shown that the Amanita
genes (AmTPS, AmTPP and AmTP) are largely unaffected by
sugar and nitrogen supply. This indicates that their increased
trehalose gene expression observed in the Hartig net is under
developmental control. It is of note that global gene expression
studies addressing ectomycorrhizal development have not
discovered an up-regulation of genes encoding enzymes of
trehalose biosynthesis (Duplessis et al., 2005; Wright et al.,
2005). Lopez et al. go on to show that both TPS activity
and trehalose concentrations are considerably higher in the
Hartig net than in the fungal sheath, correlating directly
with the transcript abundance and enzyme activity data. The
authors propose that in this fungal tissue, both pathways of
trehalose synthesis, TPS/TPP and TP, are operating, and that
the transformation of two glucose molecules into trehalose is
important in maintaining the sink for glucose.
What is known about trehalose and
ectomycorrhizal fungi?
Knowing that trehalose is used as an intermediate storage
pool for carbohydrates and is present in large quantities in
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174: 228–230
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In previous work, Martin et al. (1998) showed that the
glucose accumulating in Eucalyptus globulus roots was
utilized by the ectomycorrhizal fungus Pisolithus tinctorius,
www.newphytologist.org © The Authors (2007). Journal compilation © New Phytologist (2007)
Commentary
and that it was converted to short chain polyols (namely,
arabitol and erythritol) and trehalose. At that time it was not
known whether the accumulation of these soluble carbohydrates was located in the fungal sheath or in the Hartig net.
A number of studies have suggested that trehalose fulfils
multiple functions in ectomycorrhiza. Trehalose (and
mannitol) concentrations have been found to relate to fungal
vitality (Niederer et al., 1989), and, when exposed to desiccation by frost the concentration of trehalose in excised
mycorrhizal roots was shown to double (Niederer et al., 1992).
Several Hebeloma strains were able to survive to −10 °C and
accumulated arabitol, mannitol and trehalose, apparently for
cryoprotection (Tibbett et al., 2002). In a global change
study under an atmosphere of elevated CO2, an increase in
the uptake of glucose and synthesis of trehalose was found
in nutrient-rich but not in nutrient-poor soils; increased
trehalose synthesis was also found to correlate with an increase
in fungal biomass (Wiemken et al., 2001). Pisolithus tinctorius
has been reported to accumulate large amounts of trehalose
during growth on media containing glucose. A shift to a
carbon-free medium resulted in the consumption of this
large trehalose pool, while the arabitol pool decreased by only
approx. 50%. During the formation of the ectomycorrhizal
symbiosis following contact between axenically grown
Pisolithus and pine seedlings, the fungal trehalose pool was
consumed in the first 10 days but then refilled after the
establishment of symbiosis, especially in the extraradical
mycelium (Ineichen & Wiemken, 1992). This demonstrates
an important function of trehalose as an easily available
source of glucose for energy and carbon.
Is trehalose synthesis necessary for the gain of
carbon by the fungus?
Lopez et al. investigated trehalose biosynthesis at the
plant–fungus interface and considered trehalose as a relevant
carbohydrate sink in symbiosis. However, when glucose is fed
to ectomycorrhizal fungi (Cenococcum graniforme, Hebeloma
crustuliniforme), it can be transformed into various carbohydrates as well as lipids (Martin et al., 1984a,b; Laczko
et al., 2004). Martin et al. (1998), experimenting with
Eucalyptus–Pisolithus ectomycorrhizas, detected trehalose,
mannitol, arabitol and erythritol in similar amounts after
feeding with labelled glucose. Thus, while trehalose is not
the only fungal carbon sink synthesized following feeding
with glucose, it would seem to be particularly important in
the Hartig net, as was revealed by Lopez and colleagues. We
are left curious regarding what the reasons for this might be.
What could be the advantages of forming
trehalose for the fungus?
1 A certain advantage comes from the fact that trehalose is
not accumulated by plants (Eastmond & Graham, 2003).
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2 Trehalose is considered as a transport sugar in ectomycorrhizal fungi, in analogy to sucrose in plants (Söderström
et al., 1988).
3 The storage and transport of carbon in the form of
trehalose and the later gain of two molecules of glucose by
only one step for degradation is an energetically favourable
process at the site of consumption, compared to the conversion of polyols to glucose.
4 It was demonstrated that trehalose protects proteins and
membranes from heat and cold stress (see, e.g. Crowe, 2007).
A large group of ectomycorrhizal fungi form hydrophobic
surfaces which allow growth in dry areas, such as litter
layers that might be exposed to daily desiccation. In these
instances trehalose could act as a protectant for proteins and
membranes.
5 The fungus might use trehalose to ‘manipulate’ the plant
in order to increase by some means the sink for sucrose in the
roots. A recent study has shown that trehalose-6-phosphate
is implicated in sugar signalling in Arabidopsis (Lunn et al.,
2006). Furthermore, Nicotiana tabacum transformed with
E. coli trehalose biosynthetic genes had an enhanced photosynthetic capacity, pointing to a role for trehalose (or trehalose
phosphate) as a signal in carbon allocation (Pellny et al., 2004).
Similarly, photosynthesis is enhanced in mycorrhizal compared
to non-mycorrhizal trees (Durall et al., 1994). Therefore, an
interaction between the trehalose metabolism of fungal origin
with plant signalling processes has to be borne in mind.
One aspect that might be considered in future work is the
possibility that trehalose synthesis could occur by several
additional pathways which have not yet been investigated
in Basidiomycetes (Fig. 1). For example, in Mycobacteria
trehalose is synthesized from two molecules of glucose cleaved
from the glycogen polymer by a single enzyme (DeSmet
et al., 2000) and thus, upon demand, the carbohydrate
reserve in the form of glycogen can easily be converted into
trehalose.
In conclusion, the work of Lopez et al. highlights the
importance of trehalose and trehalose metabolism in ectomycorrhizal symbiosis. With their painstaking work, they
have clearly shown that trehalose accumulates strongly in
the Hartig net, most probably because of the combined
Fig. 1 Pathways for trehalose synthesis. TPS, trehalose phosphate
synthase; TPP, trehalosephosphate phosphorylase; TS, trehalose
synthase; TreY, maltooligosyl trehalosesynthase; TreZ,
maltooligosyltrehalose trehalosehydrolase; TP, trehalose phosphorylase.
© The Authors (2007). Journal compilation © New Phytologist (2007) www.newphytologist.org
New Phytologist (2007) 174: 228–230
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Commentary
actions of TPS and TPP, and possibly that of TP. It will be a
future challenge to define the biological role of trehalose
accumulation in the Hartig net.
Verena Wiemken
Zürich-Basel Plant Science Center,
Botanical Institute, University of Basel,
Hebelstr. 1, CH-4056 Basel, Switzerland
(tel +41 61 267 23 28; fax +41 61 267 23 30;
email [email protected])
References
Crowe JH. 2007. Trehalose as a ‘chemical chaperon’: fact and fantasy.
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DeSmet KAL, Weston A, Brown IN, Young DB, Robertson BD. 2000.
Three pathways for trehalose biosynthesis in mycobacteria. Microbiology
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Duplessis S, Courty PE, Tagu D, Martin F. 2005. Transcript patterns
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Lopez MF, Manner P, Willmann A, Hampp R, Nehls U. 2007.
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Lunn E, Feil R, Hendriks JH, Gibon Y, Mocuende R, Scheible WR,
Osuna D, Carillo P, Hajirezaei MR, Stitt M. 2006. Sugar-induced
Host preference, niches and
fungal diversity
Commentary
Ectomycorrhizal fungi occur in remarkably species-rich
assemblages. One of the prevailing hypotheses to explain this
diversity is niche differentiation; by occupying distinct ecological
niches within a site, multiple fungal species are able to co-occur
(Bruns, 1995). In this issue of New Phytologist (pp. 430– 440),
Ishida and colleagues make a significant contribution to our
understanding of niche differentiation by showing that co-
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increases in trehalose 6-phosphate are correlated with redox activation of
ADPglucose pyrophosphorylase and higher rates of starch synthesis in
Arabidopsis thaliana. Biochemical Journal 397: 139–148.
Martin F, Boiffin V, Pfeffer PE. 1998. Carbohydrate and amino
acid metabolism in the Eucalyptus globules–Pisolithus tinctorius
ectomycorrhiza during glucose utilization. Plant Physiology 118:
627–635.
Martin F, Canet D, Marchal JP. 1984a. In vivo natural abundance 13C
NMR studies of the carbohydrate storage in ectomycorrhizal fungi.
Physiologie Végétale 22: 733–743.
Martin F, Canet D, Marchal JP, Brondeau J. 1984b. In vivo
natural-abundance 13C nuclear magnetic resonance studies of living
ectomycorrhizal fungi. Plant Physiology 75: 151–153.
Niederer M, Pankow W, Wiemken A. 1989. Trehalose synthesis in
mycorrhiza of Norway spruce – an indicator of vitality.
European Journal of Forest Pathology 19: 14–20.
Niederer M, Pankow W, Wiemken A. 1992. Seasonal changes of soluble
carbohydrates in mycorrhizas of Norway spruce and changes induced by
exposure to frost and desiccation. European Journal of Forest Pathology
22: 291–299.
Pellny TK, Ghannoum O, Conroy JP, Schluepmann H, Smeekens S,
Andralojc J, Krause KP, Goddijn O, Paul JM. 2004. Genetic
modification of photosynthesis with E. coli genes for trehalose synthesis.
Plant Biotechnology Journal 2: 71–82.
Söderström B, Finlay RD, Read DJ. 1988. The structure and function
of the vegetative mycelium of ectomycorrhizal plants 4. Qualitative
analysis of carbohydrate contents of mycelium interconnecting host
plants. New Phytologist 109: 163–166.
Tibbett M, Sanders FE, Cairney JWG. 2002. Low-temperature-induced
changes in trehalose, mannitol and arabitol associated with enhanced
tolerance to freezing in ectomycorrhizal basidiomycetes (Hebeloma spp.).
Mycorrhiza 12: 249–255.
Wiemken V, Ineichen K, Boller T. 2001. Development of
ectomycorrhizas in model beech-spruce ecosystems on siliceous and
calcareous soil: a 4-year experiment with atmospheric CO2 enrichment
and nitrogen fertilization. Plant Soil 234: 99–108.
Wright DP, Johannson T, LeQuéré A, Söderström B, Tunlid A. 2005.
Spatial pattern of gene expression in the extramatrical mycelium and
mycorrhizal root tips formed by the ectomycorrhizal association with
birch (Betula pendula) Seedlings in Soil Microcosms. New Phytologist
167: 579–596.
Key words: ectomycorrhiza, gene expression, trehalose,
trehalose-6-phosphate, trehalose-6-phosphate phosphatase, TPP,
trehalose-6-phosphate synthase, TPS, Trehalose posphorylase, TP.
occurring host species have distinct mycorrhizal communities,
reflecting both host taxonomy and, arguably, successional status.
Although host specificity is a well-known phenomenon
(Molina & Trappe, 1982), it has not previously been clear to
what extent co-occurring species of plants support different
species of ectomycorrhizal fungi. Using individual root
collections from co-occurring plants, Ishida and colleagues
have effectively demonstrated that host specificity (or, more
accurately, host preference) is an important factor in local
diversity. Regrettably, statistical power issues prevent a robust
determination of whether host preference is more common
at the family than at the genus level. Nonetheless, there are
www.newphytologist.org © The Authors (2007). Journal compilation © New Phytologist (2007)
Commentary
strong indications that both host family and successional
status are important in determining plant–fungal associations.
‘… they show unequivocal evidence that host preference is an important component of the correlation of
ectomycorrhizal fungal diversity with plant diversity’
The estimate of over 300 fungal species in Ishida and colleagues’
study represents the highest ectomycorrhizal fungal species
richness yet described. For comparison, I used data from other
recent papers where species richness has been calculated using
the same estimator of total species richness and my own
unpublished data. Although based on a small data set, a
remarkably clear pattern emerges: estimated fungal richness
is a linear function of the number of ectomycorrhizal host
species (n = 11, P < 0.001, r2 = 0.95; Fig. 1, Table 1). Thus, while
the extremely high diversity found by Ishida and colleagues
is indeed remarkable, it falls exactly in line with previous data
Fig. 1 Estimated total ectomycorrhizal fungal species richness as a
function of the number of ectomycorrhizal plant species; data from
published reports of below-ground fungal diversity where total
richness has been estimated (fungal richness = 2.4 + 49.1 × plant
richness; P < 0.001; adjusted r2 = 0.95). Circles and the regression
line are based on second-order jackknife estimates of species richness.
Data from Ishida et al. (2007) are indicated by a closed circle and
included in the regression. For comparison, additional points have
been added from reports using other richness estimators (first-order
jackknife (crosses) or Chao2 (triangle)), but are not included in the
analysis. The outlier Chao2 estimate with three plant species of only
37 fungal species is from an early successional community on Mt
Fuji (Nara, 2006). See Table 1 for data.
Forum
from systems with fewer ectomycorrhizal plant species. As
the rapid development of molecular tools permits ever larger
and more comprehensive surveys of fungal communities, it
will be interesting to see if, and at what level, the increase in
fungal diversity reaches an asymptote.
Host preference is only one explanation of increased fungal
diversity with increasing number of plant species. Increased
plant diversity is also likely to create more heterogeneous
litter inputs, which may create opportunities for niche
differentiation by ectomycorrhizal hyphae (Conn & Dighton,
2000; Wardle, 2006). Alternatively, species richness of
ectomycorrhizal plants may be correlated with site conditions
that independently favor high species richness of ectomycorrhizal fungi. This is where the detailed work of Ishida and
colleagues is invaluable; by independently sampling roots of
eight plant species they provide unequivocal evidence that
host preference is an important component of the correlation
of ectomycorrhizal fungal diversity with plant diversity.
Causality and directionality remain, of course, unproven.
Mechanisms of host preference
While there are genetic and physiological barriers to certain
plant–fungus associations (Molina & Trappe, 1982), host
specificity of ectomycorrhizal fungi does not appear to be
absolute. It has been noted that plant–fungus associations
that form under laboratory conditions are not always indicative
of host specificity under natural conditions, a phenomenon
sometimes termed ‘ecological specificity’. The observation of
ecological specificity implies that environmental factors have
a direct role in determining host specificity. Thus, host
preference of mycorrhizal fungi reflects a realized, rather
than fundamental, niche.
Restricted realized niches generally result from competition.
Ectomycorrhizal fungi compete for roots (Wu et al., 1999),
and we know that small differences in the rate of initial
stages of mycelial growth onto roots can have longer term
impacts on competitive outcomes, through priority effects
(Kennedy et al., 2006). Nonetheless, while competitive
interactions are generally important in soil fungal communities,
our understanding of ectomycorrhizal competition and the
influences that plant hosts may have on this competition
remains limited (Wardle, 2006).
An alternative hypothesis to strict competition would be
direct plant selection of one fungal associate over another. It
may be that plants, in the presence of multiple potential
symbiotic partners, are able to selectively allocate resources
to ‘preferred’ mycorrhizal associates. If this occurs, a
hypothetical species ‘A’ might be able to form mycorrhiza
with a plant host under laboratory conditions, but be excluded
in the presence of a hypothetical species ‘B’ under field
conditions. Nonetheless, evidence for preferential plant
allocation of resources to one fungal partner over another is
limited. It is also interesting that in arbuscular mycorrhiza,
© The Authors (2007). Journal compilation © New Phytologist (2007) www.newphytologist.org
New Phytologist (2007) 174: 230–233
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231
232 Forum
Commentary
Table 1 Literature values for estimated richness as a function of number of host speciesa
Citation
Host
species
Observed
richness
Bootstrap
Chao1
Chao2
Jackknife 1
Jackknife 2
Ishida et al. (2007)
Tedersoo et al. (2006)
Izzo et al. (2005)b
Dickie et al. (unpublished data)c
Luoma et al. (2006)
Toljander et al. (2006)
Nara (2006)
Walker et al. (2005)
Kjøller (2006)
Cline et al. (2005)d
Cline et al. (2005)e
Koide et al. (2005)
Korkama et al. (2006)
Saari et al. (2005)
8
6
4
4
4
3
3
2
1
1
1
1
1
1
205
172
101
125
101
66
36
75
31
43
20
27
34
16
–
–
–
148
–
–
39.9
–
–
–
–
30.8
–
–
–
–
–
167
–
–
–
–
–
53.3
27.1
–
–
–
362
322
230
–
–
–
37.3
–
–
57.1
62.7
–
–
–
315
–
163
175
136
112
–
116
43.3
56.8
30.7
36.0
46.5
19
387
329
207
194
145
149
–
143
48.1
63.2
35.5
42.0
–
–
a
Values in bold represent the data points shown in Fig. 1. Data were obtained by searching Google Scholar using the terms ‘ectomycorrhiza
and diversity and (Chao1 OR Chao2 OR Jackknife OR Bootstrap)’ with all papers including a jackknife estimate of diversity based on
molecular identification included. Host species was the total number of hosts present for studies using soil cores, or the number of species
sampled for studies using bioassay seedlings or direct root identification. Where more than one estimate was provided (e.g. for different
treatments) the highest estimate was used.
b
Richness estimates; personal communication from A. Izzo, based on data in Izzo et al. (2005).
c
Data from Cedar Creek Long-term ecological research (LTER) site, MN, USA. Host species are Quercus ellipsoidalis, Quercus macrocarpa,
Corylus americana, and Helianthemum bicknellii. Data were collected by Dickie, Avis, Dentiger, McLaughlin et al.
d
Mature trees.
e
Seedlings near mature trees.
at least, an opposite pattern has emerged: the mycorrhizal
community developing under particular plant species can be
inferior in terms of increasing plant growth (Bever, 2002).
This may suggest that plant selection for ‘preferred’ symbionts
is either nonexistent or ineffective at optimizing fungal
community composition.
The n-dimensional hypervolume of mycorrhizal
niche space
Hutchinson (1957) defined a niche as ‘an n-dimensional
hypervolume … every point in which corresponds to a state
of the environment which would permit the species to exist
indefinitely’. The work by Ishida et al. confirms the importance
of host preference as one environmental dimension (or
niche axis) upon which fungal niche differentiation can
occur. Other known ectomycorrhizal niche axes include soil
depth (Dickie et al., 2002; Genney et al., 2006), seasonality
(Koide et al., 2007), and distance from trees (Dickie &
Reich, 2005). Factors such as stand age (Gebhardt et al., in
press) or soil type (Lekberg et al., 2007) are also important
in structuring mycorrhizal communities; however, these
larger scale factors would generally increase between-site (or
β) diversity, rather than within-site (or α) diversity.
Both at the plant interface of the ectomycorrhizal root-tip
and in the soil as hyphae, ectomycorrhizal fungi encounter a
highly variable environment with myriad possible niche
New Phytologist (2007) 174: 230–233
000–000
dimensions. Many of these niche dimensions are relatively
narrow in breadth. Nonetheless, dimension breadth is
relatively unimportant compared with dimension numbers
(n), as available niche space in a community, i.e. the ‘ndimensional hypervolume’, increases multiplicatively with
niche breadth but exponentially with increasing dimension
numbers. Given this, it is perhaps not surprising to find that
ectomycorrhizal fungi occur in such species-rich communities.
Other factors, such as dispersal limitation (Lekberg et al.,
2007), trophic interactions (Wardle, 2006) and soil disturbance, are likely to further contribute to this fungal diversity.
Coda: the jack-of-all-trades
There is at least one notable exception to the rule of niche
differentiation: the ectomycorrhizal fungus Cenococcum geophilum.
It comes as no surprise that C. geophilum was found on
every host tree species studied by Ishida and colleagues. The
same species has been found across soil profiles (Dickie
et al., 2002), at all stages of stand development (Gebhardt
et al., in press), at every distance from forest edges (Dickie &
Reich, 2005), and at every season of the year (Koide et al.,
2007). Even accepting that C. geophilum may be a closely
related species complex, such a wide distribution of a genus
is still remarkable, particularly given that C. geophilum has no
known long-distance dispersal mechanism. The invocation of
niche differentiation as an explanation for fungal diversity has
www.newphytologist.org © The Authors (2007). Journal compilation © New Phytologist (2007)
Commentary
to be tempered by the recognition that some fungi, such as C.
geophilum, have yet to show any real evidence of niche restriction.
Acknowledgements
R. T. Koide, R. G. FitzJohn, P. G. Kennedy and P. G. Avis
provided helpful comments and discussion. The author is
supported by research funds from the Foundation for
Research, Science and Technology of New Zealand.
Ian A. Dickie
Landcare Research, PO Box 40, Lincoln 7640, New Zealand
(tel +64 3 321 9646; fax +64 3321 9998;
email [email protected])
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Cline ET, Ammirati JF, Edmonds RL. 2005. Does proximity to mature
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Conn C, Dighton J. 2000. Litter quality influences on decomposition,
ectomycorrhizal community structure and mycorrhizal root surface acid
phosphatase activity. Soil Biology and Biochemistry 32: 489– 496.
Dickie IA, Reich PB. 2005. Ectomycorrhizal fungal communities at forest
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Dickie IA, Xu W, Koide RT. 2002. Vertical niche differentiation of
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Phytologist 156: 527–535.
Gebhardt S, Neubert K, Wöllecke J, Münzenberger B, Hüttl RF. (in
press). Ectomycorrhiza communities of red oak (Quercus rubra L.) of
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Mycorrhiza.
Genney DR, Anderson IC, Alexander IJ. 2006. Fine-scale distribution of
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170: 381–390.
Hutchinson GE. 1957. Concluding remarks. Cold Spring Harbour
Symposium on Quantitative Biology 22: 415– 427.
Ishida TA, Nara K, Hogetsu T. 2007. Host effects on ectomycorrhizal
fungal communities: insight from eight host species in mixed
conifer-broadleaf forests. New Phytologist 174: 430– 440.
Letters from ICOM – digging
deeper into mycorrhizal
research
Commentary
In this issue of New Phytologist, the Forum section is devoted
to mycorrhizal research. In his Editorial (pp. 225–228),
Forum
Izzo A, Agbowo J, Bruns TD. 2005. Detection of plot-level changes in
ectomycorrhizal communities across years in an old-growth mixed
conifer forest. New Phytologist 166: 619–630.
Kennedy PG, Bergemann SE, Hortal S, Bruns TD. 2006. Determining
the outcome of field-based competition between two Rhizopogon
species using real-time PCR. Molecular Ecology doi:10.1111/j.1365294X.2006.03191.x.
Kjøller R. 2006. Disproportionate abundance between ectomycorrhizal
root tips and their associated mycelia. FEMS Microbiology Ecology 58:
214–224.
Koide RT, Shumway DS, Xu B, Sharda JN. 2007. On temporal
partitioning of a community of ectomycorrhizal fungi. New Phytologist
174: 420–429.
Koide RT, Xu B, Sharda J. 2005. Contrasting below-ground views of an
ectomycorrhizal fungal community. New Phytologist 166: 251–262.
Korkama T, Pakkanen A, Pennanen T. 2006. Ectomycorrhizal
community structure varies among Norway spruce (Picea abies) clones.
New Phytologist 171: 815–824.
Lekberg Y, Koide RT, Rohr JR, Aldrich-Wolfe L, Morton JB. 2007. Role
of niche restrictions and dispersal in the composition of arbuscular
mycorrhizal fungal communities. Journal of Ecology 95: 95–105.
Luoma DL, Stockdale CA, Molina R, Eberhart JL. 2006. The spatial
influence of Pseudotsuga menziesii retention trees on ectomycorrhizal
diversity. Canadian Journal of Forest Research 36: 2561–2573.
Molina R, Trappe J. 1982. Patterns of ectomycorrhizal host specificity and
potential among Pacific Northwest conifers and fungi. Forest Science 28:
423–458.
Nara K. 2006. Pioneer dwarf willow may facilitate tree succession by
providing late colonizers with compatible ectomycorrhizal fungi in a
primary successional volcanic desert. New Phytologist 138: 619–627.
Saari SK, Campbell CD, Russell J, Alexander IJ, Anderson IC. 2005. Pine
microsatellite markers allow roots and ectomycorrhizas to be linked to
individual trees. New Phytologist 165: 295–304.
Tedersoo L, Suvi T, Larsson E, Kõljalg U. 2006. Diversity and
community structure of ectomycorrhizal fungi in a wooded meadow.
Mycological Research 110: 734–748.
Toljander JF, Eberhardt U, Toljander YK, Paul LR, Taylor AFS.
2006. Species composition of an ectomycorrhizal fungal community
along a local nutrient gradient in a boreal forest. New Phytologist 170:
873–884.
Walker JF, Miller OKJ, Horton JL. 2005. Hyperdiversity of ectomycorrhizal
fungus assemblages on oak seedlings in mixed forests in the southern
Appalachian mountains. Molecular Ecology 14: 829–838.
Wardle DA. 2006. The influence of biotic interactions on soil biodiversity.
Ecology Letters 9: 870–886.
Wu B, Nara K, Hogetsu T. 1999. Competition between ectomycorrhizal
fungi colonizing Pinus densiflora. Mycorrhiza 9: 151–159.
Key words: Cenococcum geophilum, ecological specificity, realized niche,
species richness, symbiosis.
Francis Martin (Interaction Section Editor) opens the
discussion with a brief ‘look back’ at the history of
mycorrhizal research published in New Phytologist and
brings us up to the present day with a focus on the impacts
of genomics and modern molecular tools on the ecology and
evolution of mycorrhizas. Commentary authors Verena
Wiemken and Ian Dickie take a closer look at individual
research papers published in this issue, dealing with trehalose
biosynthesis (pp. 228–230) and niche differentiation (pp. 230–
© The Authors (2007). Journal compilation © New Phytologist (2007) www.newphytologist.org
New Phytologist (2007) 174: 233–235
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233
234 Forum
Commentary
233), respectively. In this final section of the Forum, we
further encourage the exchange of ideas and open debate
by featuring a series of Letters inspired by the Fifth
International Conference on Mycorrhiza ( July 2006,
Granada, Spain; Selosse & Duplessis, 2006). These Letters
focus on some of the important and intriguing issues
currently facing mycorrhizal research, provide discussion
in a wider context, and suggest future directions and
perspectives.
Flow rates and pathways involved in the exchange of matter
between sources and sinks are core issues for understanding
mycorrhiza function in quantitative terms. Examples are the
early data for phosphate flux in arbuscular mycorrhiza fungi
provided by Sanders & Tinker (1971) and the recent model
for nitrogen (N) uptake (Govindarajulu et al., 2005). Tools
are now emerging for quantifying all metabolic fluxes in
cells or organisms, and the prospects for such fluxomics
studies in mycorrhizas are discussed by Yair Shachar-Hill
(pp. 235–240). Two powerful approaches are described:
(1) dynamic analysis of time-course data for the distribution of isotopic label, and (2) steady-state analysis of
metabolic labeling patterns under conditions of isotopic
steady state. Examples are given of what fluxomics
could teach us about the exchange of carbon for mineral
nutrients, the importance of which was emphasized in a
recent Letter (Fitter, 2006). Fluxomics studies may
eventually provide the information required to understand
the background of the observed functional diversity in
mycorrhizas.
Suggestions for future work to elucidate the variation in
functional aspects of mycorrhizas are provided by Koide
et al. (pp. 240–243) for ectomycorrhiza fungi and by van
der Heijden & Scheublin (pp. 244–250) for arbuscular
mycorrhizal fungi. The prediction of impacts of a given
community of mycorrhizal fungi on nutrient cycling and
productivity of ecosystems will require that we know which
functional traits are variable across species and isolates and
which are more robust. Functional grouping of fungi would
be helpful and may well become possible; hence phosphorus
(P) transport on a length-specific basis was robust at the
intraspecific level of mycorrhizal fungi, but differed between
species (Munkvold et al., 2004). Methods required to identify
the crucial traits must be carefully chosen, as exemplified by
the work of Smith et al. (2004), who measured a major
contribution of arbuscular mycorrhizal fungi to plant P uptake
by means of radiotracer isotopes, even when no difference
could be detected in total plant P content. Both Koide et al.
and van der Heijden & Scheublin emphasize that studies of
function and diversity of mycorrhizas will also need to measure
how perturbations in the environment (soil and host plant)
influence the function of individual fungi. Having identified the crucial functional traits of mycorrhizal fungi and the
variation in these traits across species and isolates, the next
step would be to use this information to predict the
New Phytologist (2007) 174: 233–235
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impact of a given community of mycorrhizal fungi at the
ecosystem level.
Molecular tools have provided many new insights into the
composition and structure of communities of mycorrhizal
fungi. Recent examples of this are studies on effects of
environmental factors on communities of ectomycorrhizal
fungi (Parrent et al., 2006) and the contribution of temporal
and spatial variation to the structure of an arbuscular
mycorrhizal fungal community in undisturbed vegetation
(Rosendahl & Stukenbrock, 2004). The need for more work
in this area is highlighted by Lilleskov & Parrent (pp. 250–
256) with the aim of generating models suitable for predicting how communities of mycorrhizal fungi are affected by
the environment. Their ambition originates in the presumed
impact of human-accelerated environmental change on
communities of mycorrhizal fungi on a global scale and
they provide a solid framework for sampling strategies and
experimental designs, methods for identification of the
fungi and selection of the most appropriate environmental data.
The authors appreciate the complex nature of the required
data collection and modeling efforts and emphasize the need
for increased collaboration and resource allocation. Achieving
this goal is important in the context of climate change, and
the incorporation of the functional traits of the fungi will
further allow prediction of the role of mycorrhizas in the
flow of matter in ecosystems.
The final contribution by Rubini et al. (pp. 256–259)
provides exciting insights into the life cycle of truffles and
discusses possible implications for management of these
precious ascocarps. Molecular tools revealed that the haploid
phase prevails in the truffle life cycle, with the dikaryotic
phase being confined to the initial stages of ascocarp development. It appears that outcrossing may be more common
in truffles than previously assumed and fruiting of these
fungi may accordingly turn out to depend on the presence
of strains that are genetically distinct or of opposite sexuality.
The potential impact of this on choice of procedures for
truffle cultivation is highlighted.
Although this forum special does not intend to cover all
aspects of mycorrhiza research, it will hopefully be a source
of inspiration for digging deeper into the biology and function
of these symbioses in their shaping of ecosystems. We do
need a continued research effort to determine the role of
mycorrhizas in nutrient exchanges between above- and
below-ground compartments, which appear to have high
potential impacts on the productivity of ecosystem components and on the sequestration of atmospheric carbon.
Iver Jakobsen
Biosystems Department, Risø National Laboratory,
Technical University of Denmark, PO Box 49, DK-4000,
Roskilde, Denmark (tel +45 46 77 41 54;
fax +45 46 77 41 09; email [email protected])
www.newphytologist.org © The Authors (2007). Journal compilation © New Phytologist (2007)
Letters
References
Dickie IA. 2007. Host preference, niches and fungal diversity. New
Phytologist 174: 230–233.
Fitter AH. 2006. What is the link between carbon and phosphorus fluxes
in arbuscular mycorrhizas? A null hypothesis for symbiotic function.
New Phytologist 172: 3 – 6.
Govindarajulu M, Pfeffer PE, Jin H, Abubaker J, Douds DD, Allen JW,
Bücking H, Lammers PJ, Shachar-Hill Y. 2005. Nitrogen transfer in
the arbuscular mycorrhizal symbiosis. Nature 435: 819 –823.
van der Heijden MGA, Scheublin TR. 2007. Functional traits in
mycorrhizal ecology: their use for predicting the impact of arbuscular
mycorrhizal fungal communities on plant growth and ecosystem
functioning. New Phytologist 174: 244 –250.
Koide RT, Courty P-E, Garbaye J. 2007. Research perspectives on
functional diversity in ectomycorrhizal fungi. New Phytologist 174: 243–246.
Lilleskov EA, Parrent JL. 2007. Can we develop general predictive models
of mycorrhizal fungal community–environment relationships? New
Phytologist 174: 250–256.
Martin F, Slater H. 2007. New Phytologist – an evolving host for
mycorrhizal research. New Phytologist 174: 225–228.
Munkvold L, Kjøller R, Vestberg M, Rosendahl S, Jakobsen I. 2004.
High functional diversity within species of arbuscular mycorrhizal fungi.
New Phytologist 164: 357–364.
Forum
Parrent JL, Morris WF, Vilgalys R. 2006. CO2-enrichment and nutrient
availability alter ectomycorrhizal fungal communities. Ecology 87: 2278 –
2287.
Rosendahl S, Stukenbrock EH. 2004. Community structure of arbuscular
mycorrhizal fungi in undisturbed vegetation revealed by analyses of LSU
rDNA sequences. Molecular Ecology 13: 3179–3186.
Rubini A, Riccioni C, Arcioni S, Paolocci F. 2007. Troubles with truffles:
unveiling more of their biology. New Phytologist 174: 256–259.
Sanders FE, Tinker PB. 1971. Mechanism of absorption of phosphate
from soil by endogone mycorrhizas. Nature 233: 278–279.
Selosse M-A, Duplessis S. 2006. More complexity in the mycorrhizal
world. New Phytologist 172: 600–604.
Shachar-Hill Y. 2007. Quantifying flows through metabolic networks and
the prospects for fluxomic studies of mycorrhizas. New Phytologist 174:
238–242.
Smith SE, Smith FA, Jakobsen I. 2004. Functional diversity in arbuscular
mycorrhizal (AM) symbioses: the contribution of the mycorrhizal P
uptake pathway is not correlated with mycorrhizal responses in growth
or total P uptake. New Phytologist 162: 511–524.
Wiemken V. 2007. Trehalose synthesis in ectomycorrhizas – a driving force
of carbon gain for fungi. New Phytologist 174: 228–230.
Key words: fluxomics, functional traits, fungal communities, fungal
diversity, mycorrhiza, predictive models, symbiosis, truffles.
Letters
Quantifying flows through
metabolic networks and
the prospects for fluxomic
studies of mycorrhizas
What is fluxomics?
The goal of fluxomics is to quantify all the metabolic fluxes
in a cell, tissue or organism (Sauer et al., 1999; Sauer, 2004).
Investigation of the flow of matter through biochemical
systems has always been central to the study of metabolism,
and analysis of the rates of metabolic transformations – the
study of enzyme kinetics – is likewise a long-established
aspect of understanding any biological system in detail.
However, the conceptual, experimental and computational
tools for quantifying the integrated functioning of metabolic
networks began to become available only in recent decades,
and are still very much under development. Like many
other omic approaches, fluxomics has yet to attain the goal
of generating comprehensive system-wide data sets. However,
progress in the last 10 yr has been rapid and fluxomics has
grown beyond its origins in bacterial systems and has begun
to make significant contributions to the study of plant systems
(Kruger et al., 2003; Schwender et al., 2004a; Ratcliffe &
Shachar-Hill, 2006).
How is network flux analysis performed?
The analysis of multiple flows through a network involves
both direct and indirect determination of metabolic and
transport fluxes. Direct determination of fluxes involves
individually measuring the rates of substrate uptake, product
secretion, and the accumulation of storage or structural
compounds (lipids, carbohydrates and proteins) of known
composition. Indirect determination of fluxes is performed
in two ways. In the first method, fluxes measured directly
are used to deduce other net fluxes; this is done by balancing
the influxes and effluxes from individual metabolite pools
using the known stoichiometries of biochemical reactions
(flux balancing). The second method is based on interpreting
the results of labeling experiments. Labeling measurements
using radioactive isotopes are made by fractionation or
chromatographic separation methods followed by scintillation
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Fig. 1 A model of nitrogen flow in the arbuscular mycorrhizal symbiosis, suitable for initiating a fluxomic analysis. The movements of carbon
and phosphorus with which nitrogen (N) fluxes are associated are also outlined. Inorganic N is taken up by the fungal extraradical mycelium
and assimilated via nitrate reductase (for nitrate) and the glutamine synthetase/glutamate synthase cycle. It is then incorporated into arginine
(Arg), which is translocated along the coenocytic fungal hyphae from the extraradical mycelium (ERM) into the intraradical mycelium (IRM).
Arg is then broken down in the IRM, releasing urea and ornithine, which are further broken down by the actions of urease and ornithine
aminotransferase. NH4+ released from Arg breakdown passes to the host via ammonium transporters or perhaps other mechanisms Chalot
et al. (2006). Pi, orthophosphate; PolyP, polyphosphate. Reproduced from Jin et al. (2005) with the permission of New Phytologist©.
counting of different intermediate and product metabolites.
Measurements of stable isotopic labeling usually involve 13C
(or less commonly 15N or 2H) and are made by nuclear
magnetic resonance (NMR) spectroscopy and mass spectrometry.
The interpretation of labeling and flux measurement
data in terms of multiple fluxes is nontrivial and almost
always involves computer-aided modeling. Models are used
to estimate fluxes by finding the values that result in a best
fit of computed (simulated) to experimental results.
This reliance on fitting to a metabolic model means that a
fluxomic investigation requires some prior knowledge of and
assumptions about the metabolic architecture of the system.
This knowledge is certainly much less complete in mycorrhizas
than in many bacteria or model fungi and plants. However,
progress in delineating metabolic and transport networks in
mycorrhizal systems has been steady in recent years and has
reached a point where models can be constructed for the
quantitative interpretation of labeling data in the best-studied
cases (Chalot & Brun, 1998; Bago et al., 2000; Bucking &
Shachar-Hill, 2005; Govindarajulu et al., 2005). The outline
of a working model of central metabolism and transport in
the arbuscular mycorrhizal symbiosis that could serve as a
starting point for flux analysis is shown in Fig. 1. The advent
of mycorrhizal plant and fungal genome sequences will be of
enormous help in building and in filling in the molecular
mechanisms of such models. When transcript and proteomic
data sets for mycorrhizas become much more complete than
they are at present – which can be expected to happen sooner
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rather than later – they too will be important in defining
and validating model networks for use in flux analysis.
There are two general approaches to the analysis of metabolic
fluxes through a network; they differ in the conditions and
the systems to which they are suited as well as in the measurements required and the information they yield. The first
approach, dynamic or kinetic analysis, yields metabolic
fluxes from the analysis of time-course data on the distribution of isotopic label through the network (Morgan &
Rhodes, 2002). The parameters involved in analyzing
dynamic labeling experiments include kinetic rate constants
(either Km and Vmax values or pseudo first-order rate constants)
and the concentrations of products and precursors for the
enzymatic reactions and transport processes being studied.
Some of the pool sizes are typically measured as part of the
investigation, and enzyme activities and kinetic properties
may be measured or estimated from knowledge about other
systems. Dynamic labeling analysis yields estimates of the
unknown or uncertain parameters (forward and reverse
fluxes, pools sizes, and rate constants) by fitting a kinetic
metabolic model to the experimental data. Kinetic models
consist of a set of rate equations that describe the fluxes
through each metabolic and transport step in the network of
interest in terms of the relevant pool sizes and kinetic
parameters of enzymes and transporters. The values of these
parameters are iteratively adjusted until the model produces
simulated time-courses of labeling and pool sizes that best
match the experimental data.
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Fig. 2 (a) A model used in a dynamic labeling analysis of the kinetics of glycine betaine synthesis in transgenic tobacco (Nicotiana tabacum).
Leaf discs were incubated with [14C]choline, and pool sizes as well as labeling time-courses were measured for total choline (Cho; external,
Choex; in the cytosol, Chocyt; in the chloroplast, Chochl; in the vacuole, Chovac), phosphocholine (P-Cho), phosphatidylcholine (Ptd-Cho), and
glycine betaine (GlyBet). Vmax and Km values for fluxes B, C, D, E, G and H, and the first-order rate constants for fluxes A, F and I, were
obtained by optimizing the fit between the simulated kinetics and the labeling time-courses. BetAld, betaine aldehyde; P-Bases,
phosphobases; Ptd-Bases, phosphatidylbases. The figure is adapted from McNeil et al. (2000) with the permission of the American Society of
Plant Biologists. (b) A steady-state model used for the fluxomic analysis of the network of central metabolism in maize (Zea mays) root tips.
Measurements included accumulation rates of starch cell wall and sucrose, and nuclear magnetic resonance (NMR) analysis of the 13C
labeling in different atomic positions of metabolic products after labeling to steady state with 13C-glucose. Modeling of these data yielded
estimates of the following flux values. Vg, rate of glucose uptake; Vppp3, fluxes catalyzed by transaldolase; Vhk, flux through hexokinase;
Vppp4, fluxes catalyzed by transketolase; Vgpase, flux from glucose-6-phosphate (G6P) to glucose; Valdp, flux catalyzed by plastidic
aldolase; Vwall, rate of wall biosynthesis; Vpk, oxidative flux through pyruvate kinase (PK); Vhcp, exchange of cytosolic and plastidic
hexose-P; Vpepc, anaplerotic flux through phosphoenolpyruvate carboxylase; Vgf, fluxes catalyzed by G6P isomerase; Vpdh, flux catalyzed
by pyruvate dehydrogenase; Vsuc, rate of sucrose synthesis; Vcs, flux through citrate synthase; Vald, fluxes catalyzed by aldolase; Vca, flux
catalyzed by aconitase; Vgly, glycolytic flux; Vglu, rate of glutamate synthesis; Vsts, fluxes of starch synthesis and degradation; Vsfa, flux
through 2-oxoglutarate DH; Vsta, rate of starch accumulation; Vfum, flux catalyzed by fumarase; Vppp1, flux of the oxidative part of the
pentose-P pathway; Vasp, rate of aspartate production; Vppp2, fluxes catalyzed by transketolase; Vme, flux through malic enzyme.
Abbreviations: GLC, glucose; GLCext, external glucose; SUC, sucrose; G6P, glucose-6-phosphate; F6P, fructose-6-phospate; H6P, hexose6-phospate; STA, starch; TP, triose-phosphate; P5PP, plastidic pentose-5-phosphate; S7PP, plastidic sedoheptulose-7-phosphate; E4PP,
plastidic erythrose-4-phosphate; PEP, phosphoenolpyruvate; PYR, pyruvate; AcCoA, acetyl coenzyme A; CIT, citrate; OAA, oxaloacetate;
FUM, fumarate; AKG, alphaketoglutarate; Glu, glutamate; Ala, alanine. From unpublished work by Ana Alonso.
The second approach, steady-state analysis, is also known
as metabolic flux analysis (MFA). Here the values of
metabolic and transport fluxes are derived from measurements
of metabolite labeling patterns when the latter have reached
stable levels (referred to as isotopic steady state). In steadystate analyses, measurements of isotopic enrichments at different atomic positions of metabolites are used to deduce
flux values. No measurements of metabolite levels or estimates
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of rate constants are required for steady-state analyses, nor
are values for these parameters obtained. The models used in
this type of analysis are based on rate equations describing
metabolic transformations, but they differ from dynamic
models in two important ways. First, steady-state models
treat the fluxes themselves rather than the underlying metabolite
pools and rate constants as the variables (parameters) to be
used in the fitting process. Secondly, these models focus on
metabolic branch points, with any series of reaction steps
between two branch points being treated as a single entity.
Accordingly, steady-state analyses can only be made of systems
that are in metabolic steady state long enough to reach isotopic
steady state. The flux maps obtained in these studies are less
detailed than those from kinetic analyses, but the smaller
number of parameters being considered makes it easier to
obtain sufficient data to compute them robustly (experimental
over-determination).
Steady-state fluxomic studies yield a quantitative description
of the flows through a metabolic network, whereas the dynamic
models yielded by kinetic studies are mechanistic. Thus,
only kinetic analyses can be predictive of flux patterns under
conditions other than those of the experiments used in the
analysis. Another advantage of kinetic analyses is that they
can be carried out when the fluxes are changing during an
experiment (such as when a bolus of substrate is supplied),
whereas steady-state analyses require the fluxes to remain
fixed during the labeling period. Furthermore, the isotopic steady
state required for steady-state analyses can take many hours
or even days to be reached. These differences have thus far
restricted the use of steady-state analyses of plant systems to
cell cultures, isolated root tissues, and seeds developing in
culture. However, steady-state studies can cover larger parts
of the metabolic network and are also much better suited than
dynamic studies to the analysis of central metabolism with its
complex patterns of reversible, cyclic, multicompartmented fluxes.
What has fluxomics taught us about plant
systems?
Figure 2 illustrates kinetic and steady-state models used in
studies of plant metabolism. The flux values obtained for
metabolic networks such as these constitute a flux map, and
such maps have been derived by fluxomic studies for various
plant systems. These range from cyanobacteria, through
higher plant cell cultures, to root, leaf, flower, and seed tissues
and have been reviewed in greater detail elsewhere (Kruger
et al., 2003; Ratcliffe & Shachar-Hill, 2006). Here, several
examples of the findings made in plant fluxomic studies are
described. These have been chosen for their relevance to
possible future mycorrhizal investigations, the potential for
which is discussed further in the following section.
Figure 2(a) describes the metabolic network involved in
the synthesis of glycine betaine (an osmoprotectant; McNeil
et al., 2000). This model was used to pursue the rational
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metabolic engineering of this compound into plants that
normally lack the ability to protect themselves from drought
and salt stress in this manner. Using 33P and 14C radiolabeling experiments and kinetic modeling, McNeil et al. (2000)
were able to identify the constraints that had hitherto
limited the successful metabolic engineering of glycine
betaine production into betaine nonproducing species. This
research, together with a larger set of bacterial fluxomic
studies, illustrates how metabolic network flux analysis can
make key contributions to metabolic engineering. In particular, the ability to map fluxes of phosphorus (P) and carbon
(C) units through different parts of metabolism is of direct
interest to mycorrhizal research. The rational engineering of
these types of metabolic and transport processes is a longterm goal of understanding mycorrhizas, and the development
of predictive, mechanistic models such as that illustrated
in Fig. 2(a) (McNeil et al., 2000) would be a powerful aid
to achieving this. Another illustrative kinetic study concerns
the synthesis and emission of plant signaling compounds. In
a study by Boatright et al. (2004), stable isotopic labeling
and gas chromatography–mass spectrometry (GC-MS)
measurement were used to map the pathways and dynamics
by which flowers make and release volatile scent compounds. Recent progress towards discovering the plant signaling compounds involved in establishing the arbuscular
mycorrhizal symbiosis (Akiyama et al., 2005) suggests the
possibility of using flux analysis in understanding the synthesis of such compounds.
The steady-state model shown in Fig. 2(b) covers most
of central metabolism in growing root tips (Ana Alonso,
unpublished work). Steady-state flux investigations of root
metabolism have uncovered a high degree of metabolic inefficiency in the form of futile cycling that dissipates much of
the ATP produced by respiration in this heterotrophic tissue (Dieuaide-Noubhani et al., 1995; Alonso et al., 2005).
Cycling of carbohydrates from hexose to mannitol and back
in ectomycorrhizal fungi may constitute just such a futile
cycle (Martin et al., 1988). The conversion of hexose to lipid
and back by arbuscular mycorrhizal fungi in the symbiotic state
(Bago et al., 2000) results in the loss of over half the carbon
involved and may in this sense be deemed a futile cycle,
although it may serve an important functional role in carbon
transport through the arbuscular mycorrhizal fungal mycelium.
Figure 2(b) shows the kind of flux model used in such
studies to map carbon fluxes through central metabolism.
The ability to map the fluxes of carbon metabolism, especially
carbohydrate handling, in roots is a necessary precursor to a
full understanding of carbon exchange at the plant–fungus
interface of mycorrhizas. The implication of fluxomic studies
on roots and other heterotrophic cells is that roots may not
be carbon limited, which might be taken as support for the
theory of ‘luxury resource exchange’ (in which plants trade
surplus carbon for fungal nutrients) that was recently proposed
by Kiers & Van der Heijden (2006) to form a key part of
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the evolutionary basis for mutualism in the arbuscular
mycorrhizal symbiosis.
In another example of what steady-state analyses of plant
metabolic fluxes can reveal, Schwender et al. (2004b) discovered
the operation of a novel metabolic route through primary
metabolism in developing seeds of Brassica napus (canola or
oilseed rape). This metabolic route involves known enzymatic reactions operating to substantially increase the efficiency with which carbon supplied by the maternal plant is
used by the embryo. This example is relevant to potential
investigations of mycorrhizal fungi as these have already shown
the potential for just this sort of novel combination of enzymatic
reactions (Bago et al., 2000; Govindarajulu et al., 2005).
What might network flux analysis tell us about
the functioning of mycorrhizas?
The description of metabolic and transport flows through a
whole network or a functional subnetwork allows one to
address questions that cannot be answered at the single
enzyme or even pathway level. Such questions include:
‘What are the dominant routes of carbon and nitrogen flow
from sources to sinks and from substrates to secondary
metabolic products? What are the relative sizes of flux
through alternative metabolic and transport routes? How are
reductant and ATP produced and consumed during growth
and development? What determines the overall efficiency
of carbon utilization?’ In my opinion, addressing questions of
this type is central to understanding the functioning of
mycorrhizal systems. Thus, a quantitative analysis of fluxes
will be needed if we are to discover the contributions of
different routes of P (polyphosphate vs other forms of
P; fungal vs direct P uptake), nitrogen (N) (organic vs
inorganic), and C (lipid vs carbohydrate) flows between and
within plant roots and mycorrhizal fungi. Flux analysis is
also required to address questions about mycorrhizal
functioning and efficiency such as the exchange rate of C for
P. Progress in such areas is necessary for understanding the
ecology and evolution of mycorrhizal function (Van der
Heijden & Sanders, 2002).
Experience in the last decade shows that the analysis of
metabolic flux is extremely valuable in the successful modification of microbial functioning for industrial purposes
(Petersen et al., 2000; Dauner et al., 2001). As illustrated by
the studies of plant metabolism discussed above, fluxomics
can contribute both to the rational design of genetic alterations and to the analysis of transgenic strains. If and when
we are to successfully genetically alter the metabolic and
transport characteristics of mycorrhizal fungi and their hosts
to improve mycorrhizal performance in different settings,
fluxomics will probably have an important part to play.
Another area in which metabolic flux analysis has the
potential to contribute to mycorrhizal research in the future
is that of secondary metabolism, an area that we are only
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beginning to explore. Experience with plant systems shows
that delineation of the pathways by which metabolites
involved in signaling and defense are made can be greatly
facilitated by applying flux analytical methods.
The existence of in vitro model arbuscular and ecto
mycorrhizas together with the establishment of stable isotopic
and radiolabeling methods for mycorrhizal research provide
the analytical tools needed for applying flux analysis
approaches developed in microbes and plants to mycorrhizal research. The development of model mycorrhizas that
retain the advantages of current in vitro model mycorrhizas
but are closer to natural situations will also be important if
mycorrhizal fluxomics is to be relevant to ecological and
agricultural settings.
Some questions in mycorrhizal research could best be
addressed using dynamic labeling approaches, where others
are likely to prove more amenable to steady-state analysis.
Thus studies on the routes, rates and regulation of P movement
through mycorrhizal symbioses are a promising area for
dynamic labeling analysis using radiolabeling and kinetic
models. By contrast, steady-state analysis based on 13C labeling
offers the potential to significantly advance our understanding of C handling by mycorrhizal fungi and mycorrhizas, for
those cases where metabolic and isotopic steady states are
achievable under physiologically meaningful conditions.
Another area in which dynamic labeling analysis is likely to
provide valuable insights is that of N metabolism and transport. Recent results of short-term labeling with 15N and mass
spectrometric analysis in the arbuscular mycorrhizal symbiosis
(Cruz et al., 2006) show great promise for such studies.
Yair Shachar-Hill
Department of Plant Biology, Michigan State University,
Wilson Drive, East Lansing, MI 48824, USA
(tel +1517 432 0719; fax +1517 353 1926;
e-mail [email protected])
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Research perspectives on
functional diversity in
ectomycorrhizal fungi
Taxonomic and functional diversity in
ectomycorrhizal fungal communities
Communities of ectomycorrhizal fungi
We have recently witnessed an increasing number of studies
of ectomycorrhizal fungal communities. This interest, in part,
stems from the need to understand human impacts on the
functioning of natural ecosystems and it has been facilitated
by the advent of nucleic acid-based fungal detection methods.
We have learned that ectomycorrhizal fungal communities are
frequently species-rich, in some cases exceeding 100 taxa in
relatively small plots of land (Izzo et al., 2004). Most comprise
few, frequently occurring species and many more rare species
(Taylor, 2002; Buée et al., 2005; Koide et al., 2005a). Species
may spatially partition the forest floor (Dickie et al., 2002;
Genney et al., 2006) and interact with each other both
positively and negatively (Agerer et al., 2002; Koide et al.,
2005b). Moreover, the relationships between the frequency
of soil hyphae and the numbers of fruiting structures and
colonized roots differ markedly among species (Gardes &
Bruns, 1996; Gehring et al., 1998; Koide et al., 2005a).
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Key words: fluxomics, metabolic flux analysis, metabolic networks, mycorrhiza.
From the standpoint of ecosystem function, taxonomic
diversity is only relevant insofar as it is reflective of functional
diversity. For example, variation in the composition of the
ectomycorrhizal fungal community on individual plants
influences host growth ( Jonsson et al., 2001) probably because
the species vary in ability to transport nutrients to the host
or in their demand for carbon. This emphasis on function
was reflected in the session entitled ‘Functional diversity in
mycorrhiza’ at the last International Conference on Mycorrhiza
(Granada, July 2006). Because we feel, as did Bengtsson
(1998), that there is more utility in understanding the
relationship between ecosystem functions and species traits
than between ecosystem functions and taxonomic diversity
per se (about which there has been much debate), our purpose
here is to highlight some methods that can be used to document
functional variability among species of ectomycorrhizal
fungi, as well as to discuss briefly the utility in doing so. It
seems reasonable to concentrate on functions that
influence the success of both the fungi and their hosts.
Thus, functions relating to the acquisition of water, carbon
(C), phosphorus (P) and nitrogen (N), and the exchange of
resources between plants and fungi may be among the
most relevant.
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letters
Field-based methods for documenting variation
in function
While there is accumulating evidence that species of ectomycorrhizal fungi differ functionally, most of it results from
in vitro studies of a limited number of culturable fungi, or
from very simplified experimental systems with young seedlings
under controlled conditions. An extrapolation to the function
of the fungi in real ecosystems is therefore difficult at present,
but some approaches may be of particular help. We highlight
them here.
Courty et al. (2005) recently demonstrated a promising
field-based method to determine the activities of various
enzymes of ectomycorrhizal roots, including phosphatase,
glucosaminidase, peptidase, glucosidase, oxidase and others.
This approach allows one to characterize differences among
roots colonized by different fungal species in their potential
ability to access nutrients from various organic substrates.
While ectomycorrhizal roots consist of plant tissue, fungal
mycelium (ectomycorrhizal and possibly associated saprotrophs)
and adhering and encrusted bacteria (Garbaye, 1994), they
nevertheless remain ecologically relevant functional units
that are frequently enumerated by researchers according to
fungal species (morphotypes). Significant variation among
species in their enzyme activities (Buée et al., 2005; Courty
et al., 2005, 2006) may explain, in part, why species vary in
their capacity to absorb and transport N or P to their hosts,
or in their demand for host C. Such enzyme assays may be
especially relevant for species of ectomycorrhizal fungi that
possess the contact type of hyphal exploration strategy
(Agerer, 2001). For other species, the hyphae that grow into
the soil may be at least as important to nutrient capture as
colonized roots. Accounting for the enzyme activities of
ectomycorrhizal hyphae in the field is difficult, but one
approach is to transplant intact mycorrhizal microcosms from
laboratory to field (Nara, 2006). The difficulty in distinguishing between the activities of ectomycorrhizal and saprotrophic
fungi may be partly overcome by using sand-filled mesh bags,
which allow growth of ectomycorrhizal fungi to the partial
exclusion of decomposer fungi (Wallander et al., 2001).
Enzyme assays must be interpreted in light of the fact that
incubation conditions are generally chosen to insure rapid
catalysis and nonlimiting substrate availability. Obviously the
conditions in nature may be different. Nevertheless, significant
variation among species in potential enzyme activity assessed
under standard conditions may be ecologically informative
in much the same way that measurements of photosynthesis
under standardized conditions have proven to be with respect
to plant distributions in nature (Schulze et al., 2005).
Stable isotope probing is another powerful tool that could
elucidate variation in hyphal function among ectomycorrhizal
fungal species in field settings. In this method, 13C-labeled
(Radajewski et al., 2000) or 15N-labeled (Cadisch et al., 2005)
substrate is applied to the soil. If an organism has access to
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the substrate, the heavier isotope will be incorporated into
its DNA. Extraction of community DNA from the soil
is followed by separation of isotope-enriched DNA from
unenriched DNA on the basis of density. The organisms
with access to the labeled substrate are then identified by
amplification of the denser DNA by PCR and subsequent
analysis, such as by T-RFLP or DGGE. The challenges in
using this technique are related to sensitivity, as dilution of
the heavy isotopic signal by the more common, lighter
isotope can occur, and accuracy, as incorporation of the heavy
isotope by microbes without access to the labeled substrate
may occur following uptake of metabolic intermediates
released by the death of microbes that do have access
(Radajewski et al., 2000).
Significant functional variability occurs among species of
ectomycorrhizal fungi in their ability to utilize complex organic
sources of N, particularly protein (Abuzinadah & Read,
1986). The significance of this observation is highlighted by
the distributions of species along N availability gradients and
changes in communities in response to nutrient additions.
For example, the ability to use protein in culture by the
various species of ectomycorrhizal fungi found along a N
concentration gradient was inversely correlated with the
availability of inorganic N at the sites they occupied (Lilleskov
et al., 2002). Moreover, sporocarp δ15N was correlated with
the ability to use protein (Lilleskov et al., 2002).
The quantity of naturally occurring stable isotopes may
also prove to be useful in other contexts. For example, a
significant source of functional variability occurs among
ectomycorrhizal fungal isolates in their propensity to transfer
N to their hosts (Abuzinadah & Read, 1989), and the amount
of this transfer may be indicated by natural abundances of
15N in fungal tissue (Hobbie et al., 2005). In general, however,
methods based on the quantification of naturally occurring
stable isotopes must be used with caution. For example, host
specificity may be reflected in the δ15N and δ13C of fungal
tissues (Högberg et al., 1999; Kohzu et al., 1999), but those
studies also indicate that considerable variation exists among
ectomycorrhizal fungal species of a given host in both δ15N
and δ13C, which may reflect their use of different sources of
C or N in the environment.
Although perhaps less exciting than DNA- or isotope-based
approaches, good old-fashioned observation of morphological
and anatomical properties among species of ectomycorrhizal
fungi is also very important insofar as they influence resource
acquisition and transport (exploration types; Agerer, 2001).
Villarreal-Ruiz et al. (2006), for example, showed in a Scots
pine chronosequence that there was a marked shift from
ectomycorrhizal fungal communities with long distance to
fringe exploration types as stands aged. Variation among species
in the production of large rhizomorphs capable of extracting
water from the soil may also determine whether or not ectomycorrhizal fungi influence other microorganisms, such as
those responsible for decomposition (Koide & Wu, 2003).
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Future research
The ability to document functional variability among ectomycorrhizal fungi allows us to address a number of exciting
ecological questions, which we discuss here. If we first assume
that dispersal is not the primary limitation to the distributions
of ectomycorrhizal fungi, then a most intriguing question
concerns the relative importance of host plants and the physical
environment as determinants of ectomycorrhizal fungal functional diversity. Many studies have shown that ectomycorrhizal
fungal communities of particular hosts change with an assortment of environmental variables. For example, the ratio of
Basidiomycete to Ascomycete ectomycorrhizal colonization
may decrease with drought (Gehring et al., 1998; Swaty et al.,
2004). Are such shifts simply caused by individualistic responses
of the fungal species to the environment, or can the host
additionally select for fungal species based on functions that are
most beneficial to it when conditions change, perhaps by
disproportionately allocating carbon to the favored species?
Selection of function by host plants may be important in
another context. There is limited evidence that host-specific
fungal species are more effective in transporting nutrients to
their hosts than generalist fungi (Hobbie et al., 2005). Is this
sort of functional superiority a prerequisite for the evolution of host specificity? Does functional superiority lead to
disproportionate allocation of carbon from host to superior
fungi, in turn leading to the evolution of specificity? Do all
host-specific fungal species possess particular functions that
make them more valuable to the host than those possessed
by generalist species?
Whether host plants can select for particular functions as
opposed to particular species can be addressed in systems in
which individual host plants support fungal communities that
are distinct from those on nearby, conspecific hosts (Gehring
et al., 1998). In such situations there may be little selection
for species of ectomycorrhizal fungi, but is there selection
for function? Is the distribution of fungi on hosts random,
or is there selection by hosts that results in certain functions
being represented on each host irrespective of fungal species?
Does this reflect a low degree of functional redundancy
among the fungal species on single trees, but a high degree
of functional redundancy among trees?
We actually know little about the degree to which the
physical environment selects for particular functions of
ectomycorrhizal fungi, but this question is easily approached
in several existing systems. For example, when disturbances
such as clear-cutting or wildfire occur, host plants may be
removed wholesale from the ecosystem, and this could place
new selection pressures on the mycorrhizal fungi. Will this
result in a community of fungi with greater saprotrophic
capacity? Many experiments have been conducted to examine
the effects of climate change on vegetation. Researchers have
also capitalized on natural experiments of climate variation
(Swaty et al., 2004). These could provide valuable opportunities
New Phytologist (2007) 174: 240–243
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to determine the functional responses of ectomycorrhizal
fungal communities to environmental change. For example,
long-term warming or cooling trends may increase or decrease
rates of mineralization. Will this select for fungi with differences
in ability to acquire N and P from organic compounds? Will
long-term drying or wetting trends lead to selection for or
against species that produce water-transporting rhizomorphs?
Another fascinating question concerns the contribution
of ectomycorrhizal fungi to overall ecosystem functional
diversity. It is clearly possible for communities of ectomycorrhizal fungi and host plants to influence the composition of
the other. How much of ecosystem functional diversity as related
to nutrient cycling or carbon sequestration, for example,
that is currently ascribed to plant diversity is actually the
result of ectomycorrhizal fungal diversity?
Within ectomycorrhizal fungal communities, species may
interact in both negative and positive ways. On small spatial
scales, greater than expected co-occurrence of pairs of ectomycorrhizal fungi may occur (Agerer et al., 2002; Koide
et al., 2005b). Does this reflect complementarity of function
by the species that allows each of them to posses a higher fitness
when growing together than when growing separately? Do
negative interactions (less than expected co-occurrence: Agerer
et al., 2002; Koide et al., 2005b) occur primarily among
functionally similar species?
In addition to the substantial interspecific functional variability, significant within-species functional variability exists in
ectomycorrhizal fungi (Cairney, 1999). If a variety of functions
are necessary in every ecosystem or on every host, can intraspecific functional variability substitute for interspecific variability? Do we find that communities of low species diversity
have higher degrees of intraspecific functional diversity?
Finally, studies of the functions of rare vs frequent and/or
abundant species may also prove to be valuable. Do rare species
duplicate the functions of frequent species, and will rare species
assume the functions of frequent species in the community if
for some reason the frequent species becomes locally extinct,
thus preserving that function in the community despite
community shifts? Or do rare species (individually or collectively) perform functions not performed by frequent species?
Conclusions
Many tools are now available for the study of the functions
of ectomycorrhizal fungi, which will allow us to address the
mechanistic bases for many fascinating phenomena reported
in the past. We hope to witness a growing interest in the
functional diversity of this ecologically important guild of
fungi.
Acknowledgements
We thank Håkan Wallander and Iver Jakobsen for organizing
the ICOM session entitled ‘Functional diversity in mycorrhiza’,
www.newphytologist.org © The Authors (2007). Journal compilation © New Phytologist (2007)
Letters
the New Phytologist for inviting us to write this article, and
the anonymous reviewers for their stimulating ideas that
resulted in considerable improvement of this manuscript.
Roger T. Koide1*, Pierre-Emmanuel Courty2
and Jean Garbaye2
1Department of Horticulture, The Pennsylvania
State University, University Park, PA 16802 USA;
2INRA Nancy, UMR 1136 INRA/UHP Interactions
Arbres-Microorganismes, 54280 Champenoux, France
(*Author for correspondence:
tel +1 814 863 0710; fax +1 814 863 6139;
email [email protected])
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Functional traits in
mycorrhizal ecology: their
use for predicting the impact
of arbuscular mycorrhizal
fungal communities on plant
growth and ecosystem
functioning
The majority of land plants form symbiotic associations with
communities of arbuscular mycorrhizal fungi (AMF). These
mutualistic soil fungi usually promote plant growth (Smith
& Read, 1997; Klironomos, 2003) and AMF communities
influence a number of important ecosystem processes, including
plant productivity, plant diversity and soil structure (Grime
et al., 1987; van der Heijden et al., 1998, 2006; Vogelsang
et al., 2006).
Recent studies have provided some fascinating insights
into the structure and diversity of AMF communities in the
field. Differences in AMF communities have been found
between plant species, ecosystems, locations and seasons
(Bever et al., 2001; Husband et al., 2002; Öpik et al., 2006),
but also between different parts of the same root system,
such as the roots and root nodules of legumes (Scheublin
et al., 2004). However, most studies remain at the level of
observation, and it is difficult to link the identification of
AMF communities in the field with the functional significance
of these AMF communities.
Here we evaluate the problems that can be encountered
in linking identification and functional significance of AMF
and discuss possible approaches to deal with these problems.
We present a list of 13 different functional traits that could
be used to determine the existing functional diversity present
within AMF communities. This mycorrhizal functional trait
diversity could subsequently be used to predict the impact of
specific AMF communities on plant growth and ecosystem
functioning.
Problems in linking AMF community
composition and functional traits of AMF
Although knowledge of the composition of AMF communities
in plant roots in the field is expanding, unfortunately this
knowledge is rarely connected to studies on the function
of the AMF symbiosis (Helgason et al. 2002; Read, 2002).
It appears difficult to make this link between AMF
community composition and function for several reasons:
1. The species concept of AMF is still poorly developed and
it has not been possible to show that particular AMF taxa
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have specific functions, as with animal and plant species,
where a species is a genetically, morphologically and functionally
distinct entity. Several recent studies have shown that AMF
species or taxa, identified either by spore morphology or by
ribosomal DNA, are highly variable in several functional
traits (Hart & Reader, 2002a; Munkvold et al., 2004; Koch
et al., 2006; T. R. Scheublin et al. unpublished). For example,
Munkvold et al. (2004) compared the effects of 24 AMF
isolates on plant growth and phosphorus (P) nutrition. They
observed that growth and P uptake varied as much between
isolates of the same AMF species as between different AMF
species. Hence, these results indicate that the effects of AMF
communities on plant growth cannot be predicted based on
the species composition of AMF communities.
2. Several molecular studies have shown that unknown and
uncultured AMF types are abundant in the field, especially
in undisturbed ecosystems (Fitter, 2005; Stukenbrock &
Rosendahl, 2005). For example, Helgason et al. (2002)
reported that sequences of c. 60% of AMF types detected in
the roots of woodland plants had no cultured representative. The functional significance of these uncultured AMF
types is unknown and it is not possible to determine their
impact on plant growth or ecosystem functioning. Hence,
further studies should attempt to cultivate these uncultured
AMF in order to identify their ecological function.
Apart from these problems in linking AMF community
composition with functional significance, there are also
some difficulties in the determination of AMF communities
itself, which could disturb the subsequent link with
function:
3. Some of the AMF types that are identified in the plant
roots are never found as spores, and some of the spore types
are never found in roots (Clapp et al., 2002). Hence a
characterization of AMF communities in the field based on
either spore communities or molecular profiling of AMF
communities in plant roots alone is insufficient to cover the
whole spectrum of AMF present within a community. Molecular
methods are, in our view, more suitable to characterize AMF
communities because these methods can target active AMF
communities that are present within plant roots. However,
molecular characterisation of AMF communities in speciesrich natural communities can be time consuming because
roots of many plant species need to be analysed to cover
potential host specific AMF types (Bever et al., 2001) and
because AMF types might be present in specific soil horizons
(Oehl et al., 2005) or during specific times in the year
(Merryweather & Fitter, 1998). Hence, in order to assess the
total AMF community present at a specific site, it will be
useful to use both methods because they can complement
each other. In addition to this, the abundance of the mycelial networks of the different AMF types is generally not
measured, and is likely to be very important for AMF traits
such as nutrient uptake, and therefore for the functioning of
the AMF symbiosis.
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Letters
4. Most studies have used qualitative approaches to investigate
AMF communities and only a few have actually attempted
to determine the relative abundance of various AMF types
using quantitative molecular techniques (Alkan et al.,
2006). It is necessary to know which AMF types are rare
or abundant because ecological theory predicts that
dominant species often have the largest impact on ecosystem
functioning.
5. Most studies have assessed the composition of AMF
communities at one time-point and at one place; few have
searched for repeated patterns measuring throughout seasons,
at several locations and sampling enough plant individuals
to determine natural existing variation. It will be difficult to
link AMF community composition to a particular ecological
function if AMF communities in plant roots are highly
dynamic and rapidly changing. Hence, for a better understanding of AMF function in nature it is essential to search
for repeated patterns. The use of DNA microarrays for the
rapid and simultaneous detection of thousands of genes in
environmental samples such as plant roots (van Straalen &
Roelofs, 2006) provides a powerful technology to search for
such repeated patterns. Similarly, it is important to know
whether AMF isolates have the same impact on plant
growth when experiments are repeated. Surprisingly, few
ecological experiments with specified AMF isolates have
been performed to test for such repeated patterns and this is
something that needs attention. Thus, the search for repeated
patterns in mycorrhizal ecology is necessary for finding
general rules and patterns.
Potential approaches for linking AMF
community composition and functional
significance
It appears difficult to link observations on the composition
of AMF communities with the functional significance of
AMF because AMF isolates of the same species can be
functionally highly diverse (see above). Hence, in our
opinion, it will be necessary to develop a method to use
functional traits and functional groupings of AMF to
characterize AMF communities. Experiments with plants
and soil organisms have already shown that ecosystem properties
can be successfully explained using functional traits and
functional diversity (Diaz & Cabido, 2001; Heemsbergen
et al., 2004). For example, Tilman et al. (1997) observed
that functional composition and functional diversity rather
than species diversity were the principal factors explaining
effects of increased plant species diversity on plant productivity
and nutrient acquisition. Functional diversity is defined here
as ‘the value and range of those species and organismal traits
that influence ecosystem functioning’. For further information
regarding trait selection and measures of functional diversity
in ecology, we refer to Lavorel & Garnier (2002), Cornelissen
et al. (2003) and Petchey & Gaston (2006).
Forum
Functional traits and functional diversity
A number of traits have been used to distinguish AMF types
from each other. Some of these traits are useful for taxonomic
purposes (spore morphology, ribosomal gene sequences),
while other traits tell something about the functional significance
of a particular AMF type for plant performance or for ecosystem
functioning. We have identified 13 different functional traits
of AMF that could be used to determine the functional
diversity present within a specific AMF community (Table 1).
Several of these functional traits could be used to predict the
effect of AMF communities on plant performance and on
specific ecosystem processes. This list is not all-embracing;
other traits could also be added.
Some of the functional traits listed in Table 1 are known
to vary between different AMF and are of functional importance for plant growth and ecosystem functioning. For
example, several studies have shown that P uptake by plants
is related to the amount of external hyphae produced by a
specific fungus. Plants colonized by AMF types that produce large amounts of external mycelium usually acquire
more P from AMF compared with plants that are colonized
by AMF that form low amounts of hyphae ( Jakobsen et al.,
1992). Similarly, plant communities grown with AMF types
that produce an extensive mycorrhizal mycelium acquire
more P compared with plant communities that are colonized
by AMF producing few hyphae (van der Heijden et al.,
1998, 2006). Hence, the hyphal length of AMF present in
the soil could be used to make predictions about the mycorrhizal
contribution to plant P acquisition and plant productivity
(in case P is limiting plant growth). Arbuscular mycorrhizal
fungi can also be responsible for a high proportion of total P
uptake when there is no AMF effect on plant growth (Smith
et al., 2004; van der Heijden et al., 2006). Hyphal length
(together with AMF colonization) could, under these circumstances, be used to estimate the mycorrhizal contribution
to P acquisition independent from plant productivity.
Moreover, the formation and stability of soil aggregates is
also related to hyphal length (Miller & Jastrow, 2000) pointing
to the importance of this trait for ecosystem functioning.
Other fungal traits listed in Table 1 are promising in that
they could be used to characterize additional functional
diversity present within AMF communities; for example, it
has been shown that there is temporal and spatial variation in
root and soil colonization by different AMF (Merryweather
& Fitter, 1998; Smith et al., 2000). Some AMF appeared to
be active in summer while other fungi were more abundant
in autumn (Merryweather & Fitter, 1998). Plant productivity
could be higher in communities where both fungal types are
present, assuming that both fungal types are beneficial and
supply nutrients to the plant. Hence, this trait could be
important for sustaining plant productivity throughout the
growing season. Moreover, some AMF acquire nutrients
near the roots while other AMF forage further away and
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Table 1 Functional traits of arbuscular mycorrhizal fungi (AMF) and their potential influence on plant performance and ecosystem processes
Functional trait
Morphological traits
1. Hyphal length1,2,3
2. Mycelium structure5
3. Stability of hyphal networks
(e.g. occurrence of hyphal fusions6)
4. Hyphal life span7
5. Speed of root colonization3
6. Degree of root colonization8
7. Spore production9
8. Formation of auxiliary cells
9. Formation of vesicles
Physiological traits
10. Physiological diversity and
efficiency of nutrient uptake10
phosphorus uptake
nitrogen uptake
copper uptake
iron uptake
11. Temporal11 and spatial12 variation in
fungal activity
12. Fungal carbon acquisition
13. Host preference13/functional compatibility14
Potential effects on plant performance and ecosystem processes
Nutrient acquisition1,2; plant productivity1,2; soil aggregation and stability4
Nutrient acquisition; plant productivity
Resistance against disturbance; soil stability; nutrient acquisition
Carbon storage; nutrient acquisition
Seedling establishment; plant productivity
Protection against fungal pathogens8; nutrient acquisition; plant productivity
Seedling establishment after disturbance or extreme events9
?
?
Complementary resource use
Nutrient acquisition; plant productivity
Complementary resource use; nutrient acquisition; plant productivity
Plant productivity; carbon storage
Plant community structure2; plant diversity2; plant productivity2
1
Jakobsen et al. (1992); 2van der Heijden et al. (1998, 2006); Vogelsang et al. (2006); 3Hart & Reader (2002b); 4Miller & Jastrov (2000);
Rillig & Mummey (2006); 5Friese & Allen (1991); 6Giovannetti et al. (1999); de la Providencia et al. (2005); 7Staddon et al. (2003);
8
Newsham et al. (1995); 9Hart et al. (2001); 10Benedetto et al. (2005); Harrison (2005); Burleigh et al. (2002); 11Merryweather & Fitter
(1998); Husband et al. (2002); 12Smith et al. (2000); 13Helgason et al. (2002); Scheublin et al. (2004); Öpik et al. (2006); 14Ravnskov and
Jakobsen (1995).
explore a different soil volume (Smith et al., 2000). This can
lead to increased productivity because there could be
complementary resource use. A similar phenomenon has
been observed in plants with different rooting depth. Host
preference and host range are two functional traits that are,
potentially, very important. Plant diversity and plant
productivity could be very dependent on fungal diversity in
case AMF have a restricted host range or strong host preference,
and when different fungi promote growth of different plant
species (van der Heijden et al., 1998). It is, however, still
unclear if there are many (uncultured) AMF with restricted
host range or strong host preference and whether they are
abundant. A recent suggestion that AMF identity rather
than AMF diversity is more important in explaining effects
of fungal diversity on plant productivity (Vogelsang et al.,
2006) does not point to the importance of host preference.
The functional significance of other traits is still unclear.
Different AMF vary in a wide range of characters, including
speed of root colonization (Hart & Reader, 2002b), amount
of root colonization (Smith & Read, 1997), spore production (Bever et al., 2001), the frequency of hyphal fusions
and the integrity of hyphal networks (Giovannetti et al.,
1999; de la Providencia et al., 2005), formation of vesicles
and auxiliary cells (Morton & Benny, 1990) and the physio-
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logical activities of nutrient uptake and transport pathways
(Boddington & Dodd, 1999; Burleigh et al., 2002). The
integrity and stability of hyphal networks might for example
be important for disturbed or dry environments where AMF
types with stable networks provide a better linkage with the
soil and protect the vegetation against drought or disturbance.
Moreover, it has been shown that some AMF types can
acquire nitrogen (N) and transfer it to the plant (Johansen
et al., 1992; Frey & Schüepp, 1993; Hodge et al., 2001). It
is unclear whether all AMF are able to do this. Fungal N
acquisition would be an important functional trait if there is
variation among different AMF types and if AMF transfer
significant amounts of N to the plant. Functional diversity
of AMF is especially important if different AMF have different
functions (e.g. one fungus provides drought resistance and
another fungus is responsible for nutrient acquisition).
Physiological studies that show that different fungi provide
different services are extremely scarce, despite observations
that fungal diversity increases plant productivity (van der
Heijden et al., 1998; Gustafson & Casper, 2006; Lekberg
et al., 2007) and plant diversity (van der Heijden et al., 1998;
Vogelsang et al., 2006).
Early work has shown that plants can be classified into
different functional groups according to their ability to tolerate
www.newphytologist.org © The Authors (2007). Journal compilation © New Phytologist (2007)
Letters
stress and disturbance (Grime, 1979). Perhaps such functional
groupings are also useful to distinguish different fungal
strategies: For example, some AMF types (e.g. Glomus
intraradices and Glomus mosseae) appear to have a more
ruderal lifestyle as they produce large amounts of spores and
are also found in disturbed sites ( Jansa et al., 2003; Öpik
et al., 2006). Such AMF types could be classified as being
disturbance tolerant. Other AMF types form mycelial
networks in soil, produce few (or no) spores and are
characteristic for undisturbed infertile ecosystems (Stukenbrock & Rosendahl, 2005; Öpik et al., 2006). Such AMF types
might be adapted to nutrient-poor conditions and could perhaps
be classified as stress tolerant. It is likely that most cultured
AMF belong to the first group, pointing to the need to isolate
more AMF types from undisturbed natural ecosystems.
Several traits appear to have a taxonomic basis, especially
at higher taxonomic resolution. Hyphal networks produced
by members of the two major AMF suborders, the Glomeraceae
and Gigasporacea vary. Hyphal networks of the Glomeraceae
are thought to be better integrated compared with those of
the Gigasporacea because there are many more hyphal
fusions (de la Providencia et al., 2005; Voets et al., 2006).
Moreover, members of the Glomeraceae are usually fast root
colonizers, allocate a larger fraction of fungal biomass within
the root (Hart & Reader, 2002b) and they form vesicles for
storage of lipids (Morton & Benny, 1990). By contrast, members
of the Gigasporacea lack vesicles (Morton & Benny, 1990)
and they are thought to produce relatively more external
mycelium (Hart & Reader, 2002b). These different fungal
strategies suggest that AMF communities with a higher
diversity of AMF genera or families contain larger functional
diversity. Moreover, one fungal trait, the P uptake per unit
hyphal length appeared to be AMF species specific (Munkvold
et al., 2004) and this trait could perhaps be used to discriminate
different AMF species.
When using the functional traits discussed above it is also
very important to consider that plants vary in their response
to AMF and that some plant species are much more
sensitive to changes in AMF community composition (van
der Heijden et al., 1998). Hence, the response of plant
communities to changes in AMF communities also depends
on the identity of the plants present in a specific plant community.
Functional genes
In order to identify functional groups of AMF it is necessary
to develop a method to rapidly characterize functional AMF
traits, for example by using functional genes present within
AMF communities. However, currently, AMF genetics and
the relation between AMF genes, their expression levels, and
functional traits such as hyphal length and nutrient
acquisition are not yet fully understood. For example,
different AMF taxa contain different P-transporter genes
(Benedetto et al., 2005; Harrison, 2005). Genetic variations
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in P-acquisition pathways (hyphal uptake, hyphal storage as
polyphosphate, hyphal transport, and supply to the plant)
could potentially explain why different AMF supply
different amounts of P to plants (Boddington & Dodd,
1999; Harrison, 2005). It is, however, still unclear whether
such functional genes (and their activities) can be used to
explain the effects of AMF communities on plant nutrition
and plant growth. This is an area that is still largely
unexplored and where much progress can be made.
Hopefully, the sequencing of Glomus intraradices, which is
currently being performed, will increase our understanding
of AMF genetics and direct us to suitable functional
genes as biomarkers for AMF functional diversity. Moreover,
enzyme tests have been successfully used to characterize
functional diversity of ectomycorrhizal fungal communities
present on root tips (Courty et al., 2005). Such approaches
could be useful for AMF, if there is a way to apply this
method in situ.
Manipulation of entire AMF communities
At present, AMF identified in the field cannot be linked to
their ecological function and their impact on plant growth.
Therefore, the only way to link AMF community composition
with functional traits of AMF is to make use of artificial
ecosystems. In artificial ecosystems, the AMF community
composition can be controlled, and AMF isolates can be
selected that can be distinguished by molecular identification
methods. The diversity of AMF communities can be
manipulated, and the performance of the AMF community
as a whole can be compared with the performance of
each of the individual AMF isolates that compose the
community. The functional diversity of the AMF
communities can be calculated using the functional attribute
diversity of the AMF types composing the communities
(Petchey & Gaston, 2006) and using a selection of traits listed
in Table 1. Subsequently, it can be tested whether specific
ecosystem functions (e.g. productivity, nutrient acquisition
and soil structure) are influenced by AMF functional
diversity.
The controlled environment of artificial ecosystems also
requires that many important factors of the natural situation,
such as interactions with other organisms are eliminated
(Read, 2002). Another approach to connect AMF community
composition and function is to manipulate entire AMF
communities and compare changes in community composition
and functional properties of the entire community. Examples
could be treatments such as fertilization or use of the fungicide
benomyl, but also more recent technologies such as the use
of hyphal compartments (Schweiger & Jakobsen, 1999) and
bioassay plants (Johnson et al., 2005). Although the natural
situation is reflected as best as possible with these methods,
they are indirect, and therefore sometimes less conclusive
because other factors are simultaneously affected.
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To date, experiments investigating the effects of AMF
diversity on plant growth or ecosystem functioning have
been performed in one or two growing seasons, and usually
include widespread, easily culturable ‘ruderal type’ fungi.
Future experiments need to be performed for a longer, ecologically realistic, growth period and with fungi that are
abundant in the ecosystem studied (and not necessarily easy
to culture). Observations that adult plants and seedlings are
colonized by different AMF communities (Husband et al.,
2002), and that juveniles and adults sometimes benefit from
different AMF (van der Heijden et al., 2006), suggest that
AMF types may vary in ways that we still do not completely
understand. In this respect it is also important to use stable
isotope probing of DNA (Friedrich, 2006) or fatty acids
(Olsson & Johnson, 2005) to analyse which fungi are
physiological active, where they are active and in which type
of ecosystems.
Conclusions
Several experiments have now shown that AMF isolates are
functionally diverse and that the composition of AMF
communities has a large impact on plant performance, plant
community structure and ecosystem functioning. At the
same time an increasing number of studies have investigated
AMF communities in plant roots in the field for a large
number of plants and in a wide range of ecosystems.
However, few studies have made a direct link between the
composition of AMF communities in the field and the
functional significance of such AMF communities for plant
growth and ecosystem functioning. This is an area that
needs much more attention, not only to better understand
the ecology of plants and plant communities but also to
monitor whether introductions of AMF into the field
have been successful (e.g. to enhance agricultural production or increase the effectiveness of nature restoration
projects). We have suggested a number of approaches
that are helpful in this respect. In our opinion, defining
functionally distinct AMF groups is essential if we want
to understand fully the interactions between plant and
AMF communities in agricultural and natural ecosystems.
We have therefore identified a number of functional
traits (see Table 1) that could be used to predict the
effects of AMF communities on plant growth and
ecosystem functioning. This, together with microcosm
studies and novel technologies will help us to better
understand the functional significance of AMF for plants
and ecosystems.
Acknowledgements
This research has been supported by a grant from the
Netherlands Organisation for Scientific Research (NWO
Vernieuwingsimpuls grant 016.001.023 awarded to MvdH).
New Phytologist (2007) 174: 244
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–250
We thank the editor and the reviewers for constructive and
helpful comments.
Marcel G. A. van der Heijden1 and Tanja R. Scheublin1,2
1Institute
of Ecological Science, Vrije Universiteit, de Boelelaan
1085, NL−1081 HV Amsterdam, the Netherlands;
2(Present address) Department of Ecology and Evolution,
University of Lausanne, CH−1015 Lausanne,
Switzerland
(Correspondence: Marcel G. A. van der Heijden:
email: [email protected]; Tanja R.
Scheublin: email: [email protected])
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Can we develop general
predictive models of
mycorrhizal fungal
community–environment
relationships?
A major motivation for this effort is the high rate of
human-accelerated environmental change, including elevated
atmospheric ozone (O3), CO2, nitrogen (N) deposition,
climate change and land use/land cover change (Vitousek
et al., 1997; Cubasch et al., 2001; Tilman & Lehman,
2001). It is clear that these changes can affect mycorrhizal
fungal species, but it is also clear that we do not yet have data
sets sufficiently saturated, or models sufficiently powerful, to
determine the exact nature, timing and spatial pattern of
fungal community responses. Given that mycorrhizal fungi
are phylogenetically and functionally diverse, consume a
significant portion of global terrestrial production, play a
critical role in nutrient cycling and food webs, and exhibit
high sensitivity to environmental change, the ability to predict
such community responses is critical for conserving fungal
diversity and maintaining ecosystem processes.
Given these concerns, an efficient way to focus our efforts
in obtaining community information would be to optimize
sampling and experimental designs to address questions on
the effects of human-accelerated environmental change on
mycorrhizal fungal communities at a global scale. This would
achieve several related objectives. First, it would allow us to
develop saturated databases of fungal community composition, structure and spatio-temporal dynamics in relation
to variable resources and conditions. Second, it would provide a baseline against which to measure the effects of future
environmental change. Third, it would permit us to determine
where and how fungal communities are presently responding
to environmental change. Last, it would identify sites with large
components of unidentified fungi that could be foci for muchneeded investigation by fungal taxonomists (Korf, 2005).
In order to accomplish this, we must understand how
communities of fungi change in response to all the key
anthropogenic and natural environmental drivers. This
requires the development of quantitative models of species–
environment relationships built on several key elements:
appropriate study designs, community data, environmental
data, and models. In the following sections we describe some
initial considerations in bringing these elements together.
Letters
Our understanding of the controls on mycorrhizal fungal
species distribution and community organization is in its
early childhood – especially when compared with that of the
more mature fields of plant and animal community ecology
and biogeography – largely because of the historical difficulty
of gathering species distribution information. This challenge,
arising from the paucity of mycorrhizal morphological characteristics, is magnified because of high diversity, particularly in
ectomycorrhizal fungal communities. Although some regional
models of ectomycorrhizal sporocarp–environment relationships
have been developed (e.g. Tyler, 1985; Hansen, 1988, 1989;
Rydin et al., 1997), sporocarps represent a biased subsample
of the below-ground community (Gardes & Bruns, 1996).
The advent of molecular tools has allowed us to move forward
with many detailed below-ground mycorrhizal community
analyses (Horton & Bruns, 2001). Many of these analyses have
been linked with experiments, gradients and chronosequences,
leading to an increased understanding of environmental
controls on species distribution and abundance. Although
an essential first step, these studies are mostly carried out
at a local scale, leading to a highly fragmented picture of
species distribution and relationship with the environment
that cannot be extrapolated to other sites.
We believe that, in addition to the aforementioned local
approach, there is much to be gained by wedding regional- to
continental-scale mycorrhizal fungal community characterizations, environmental measurements and the best new
modelling approaches to develop a more general understanding of the relationship between mycorrhizal fungal communities
and their environment. This approach would allow us to begin
to develop species– or community–environment predictive
models sufficiently accurate that for any site we could predict
the potential pool of dominant fungal taxa (recognizing that
stochastic processes will probably determine the actual pool).
While ambitious, this approach is essential in order to predict
species–environment relationships beyond a narrow set of sites.
New Phytologist (2007) 174: 250–256
000–000
Appropriate study designs
Experiments vs gradients
Species–environment response functions cannot be derived
from experimental studies involving only two levels of a
perturbation, unless those functions are known a priori to
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be linear. Multilevel experiments or gradient studies are necessary
for determining the shape of a response curve. However,
once we move beyond the local scale, multilevel experiments
become difficult to fund and manage, making sampling of
replicate gradients or related stratified sampling techniques
the only viable alternative for generating large-scale species–
environment relationships. Combining multilevel experiments
at a strategic subset of sites with large-scale gradientbased sampling could provide the greatest information and
insights.
Using gradients to tie data collection to environmental
change
Most environmental changes are spatially variable (e.g. Galloway
& Cowling, 2002; Chandra et al., 2003), and multiple
change agents can be correlated. By identifying where
gradients of environmental change are steepest we can define
areas of greatest interest for investigation. To maximize our
potential to determine the community response to diverse
environmental changes, we should sample across multiple
types of gradients (climate, pollution, land use, disturbance,
etc.), and incorporate sites that break down correlations between
multiple change agents (e.g. between O3 and N deposition).
Some environmental changes – notably, elevated CO2 – are
less amenable to gradient analysis, because they are relatively
uniform at the global scale. Although there is some possibility
of using localized natural or anthropogenic gradients of
CO2 (e.g. Rillig et al., 2000), experimental approaches will
probably play a larger role in developing response functions
to CO2.
Appropriate species distribution data
The development of DNA-based molecular tools has led
to an explosion of investigations into mycorrhizal fungal
community ecology (Horton & Bruns, 2001). This
development holds great promise, but in order to maximize
our ability to use these data to build general predictive models,
several requirements must be met.
Sequence-based identification
Species distribution/abundance data across sites must be
comparable. This requires that mycorrhizal fungal identity
be established using a common metric, with the most useful
being internal transcribed spacer (ITS) ribosomal DNA
sequences (Horton & Bruns, 2001; Kõljalg et al., 2005).
Sequence data are preferable over other approaches because
they reduce the ambiguity of species identifications, allowing
for comparison among sites and studies. The ITS provides
sufficient variation to discriminate at approximately the species
level and is readily amplified from small amounts of material
using primers of varying specificity. Other ribosomal DNA
Forum
regions, such as portions of the large subunit (LSU) and
small subunit (SSU), are useful in the phylogenetic placement
of unknowns when ITS sequences are not informative
because of insufficiently saturated databases (Horton & Bruns,
2001), but these regions lack the taxonomic resolution needed
for species-level modelling.
Processing of samples from a large-scale sampling program
would require high-throughput approaches to sequencing,
such as those used by the Fungal Metagenomics Project
(Senkowsky, 2006). The rice genome required over 7 million
sequences (Goff et al., 2002) and the human genome required
over 27 million sequences (Venter et al., 2001), and costs of
sequencing continue to decline, so generating several million
sequences to characterize the global diversity, distribution and
response to environmental change by one of the most important
classes of mutualists seems both achievable and reasonable.
Consistent high-throughput methods must be used
Consistent sampling methods would improve the quality of
a global data set. Method choice will depend on whether
the study focuses on ectomycorrhizal fungi alone or on all
mycorrhizal fungi. Unlike other mycorrhizal fungi, ectomycorrhizal fungi are typically monodominant on root tips,
permitting sorting of tips into morphotypes followed by
DNA analysis. This permits the characterization of frequency,
biomass and number of root tips of different taxa (Horton
& Bruns, 2001). Caution must be used in interpretation of
these data, because each root tip does not represent a separate
individual (Taylor, 2002). In addition, this approach is
susceptible to lumping species of similar morphologies
during the sorting process, and can be labor intensive.
By contrast, all mycorrhizal fungi can be sampled via random sampling of individual mycorrhizal root tips followed
by polymerase chain reaction (PCR)-based identification
(e.g. Peter et al., 2001; Parrent et al., 2006), although a cloning step is required for most nonectomycorrhizal types. This
approach is compatible with presence–absence or frequencybased metrics of abundance.
Similarly, bulk DNA extraction of pooled mycorrhizal
root tips is viable for all classes of mycorrhizae. Although
soil or hyphae can also be extracted, these will have a higher
proportion of nonmycorrhizal fungi than roots, so are more
appropriate for total soil fungal community analysis. These
mixtures can then be subjected to PCR, separated and
sequenced. PCR is a very powerful approach, but results
for mixtures are subject to bias, sensitivity limitations for
amplifying rare or divergent sequences, and the potential for
chimera formation, which need to be taken into account
during sampling and analysis.
Current PCR-based approaches used for analyzing DNA
mixtures are semiquantitative. A common approach is the
cloning and sequencing of PCR products. Methods for highthroughput cloning and sequencing are rapidly evolving (e.g.
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Hutchison et al., 2005; Metzker, 2005) and could be adapted
for large-scale community analysis. The major drawback of
this approach is the redundant sequencing of dominant taxa
required to obtain sequences of rarer taxa, but costs of sequencing are dropping quickly enough that this is less of an issue.
Another commonly used approach is slab gel electrophoresisbased separation approaches, such as temperature or denaturation gradient gel electrophoresis (TGGE and DGGE,
respectively) followed by sequencing of unique fragments
(Anderson & Cairney, 2004). These approaches are generally
labor intensive and therefore are relatively low throughput.
Potential, but as-yet untapped, high-throughput analogs of
the above methods are carried out by either capillary electrophoresis (CE) or denaturing high pressure liquid chromatography (DHPLC) combined with automatic fraction collectors
(e.g. Berka et al., 2003; Domann et al., 2003). DHPLC is
presently commercially available (e.g. Domann et al., 2003),
but it is not widely used. Although CE has the potential for
parallel processing via capillary arrays, which could greatly
accelerate throughput (Berka et al., 2003), it is not yet
commercially available.
As an alternative to sequencing approaches, community
microarrays are under development (Anderson & Cairney,
2004; DeSantis et al., 2005; Sessitsch et al., 2006) that hybridize target DNA with a high density array of thousands of
probes, providing a rapid evaluation of whole-community
composition and semiquantitative abundance determined
from hybridization intensity. If technical challenges are overcome and the cost per microarray chip becomes reasonable,
this would permit very rapid characterization of high numbers
of samples, providing the possibility of more replicate samples
per site and a resultant high sampling density that would
improve modelling efforts. The main disadvantage is that
species not included in the array will be missed in the analysis,
making it less valuable in systems where many community
members are unknown. Thus, the microarray approach would
be most useful after intensive high-throughput sequencing-
based approaches have generated sufficiently saturated sequence
databases.
Appropriate environmental data
Scale affects choice of predictor variables
The scale of investigation will affect the environmental variable
selection. In local models, variables such as disturbance or land
use history, host community, soil pH and nutrients, host
nutrition, parent material, slope and aspect are likely to be
important. As the scale of investigation expands, additional
variables, such as temperature, precipitation and biogeographic
constraints (e.g. endemism), will probably emerge as significant
variables. Some of these data will be readily available in
geographic information system (GIS)-based data sets, but
other data must be collected on site.
Distal vs proximal variables
Variable choice affects both model quality and data collection
costs. An important choice to make is between distal and
proximal variables. Distal variables are farther removed
from, and hence do not act directly on, the dependent
variable. By contrast, proximal variables are closer to, and
hence may directly act on, the dependent variable. In Fig. 1
we present an example of selected distal and proximal variables
that could be used in characterizing the community response
to components of changing atmospheric chemistry.
There may be advantages and disadvantages of using
distal vs proximal variables in modelling species distribution
and abundance. The main advantage of distal variables is that
they are usually easier to measure or estimate, and are often
available as GIS layers. For example, latitude and longitude,
topography, geology, climate, N deposition, atmospheric O3,
and foliar N might be much easier to measure or model (e.g.
Smith et al., 2002) than soil moisture, soil N, soil texture, or
Fig. 1 A simplified conceptual diagram of
the interactions of mycorrhizal fungal
communities with selected aspects of
changing atmospheric chemistry. Distal
predictor variables are shown as ovals.
Proximal predictor variables are shown as
rectangles.
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below-ground carbon (C) allocation by the host tree. When
distal variables are easier to measure and highly correlated
with proximal variables it will be advantageous to use the
distal variable. However, in some cases distal variables will be
poorly correlated with the proximal variable. Two examples
illustrate the complexities involved in variable choice: belowground C allocation; and soil N.
Most models of plant C allocation suggest that belowground C allocation is a function of plant C gain and nutrient
status (Le Roux et al., 2001). The response of the fungal
community to environmental changes, such as N deposition,
CO2 and O3, could depend very much on complex interactions among host nutrition, C gain and below-ground C
allocation, although the exact nature of these interactions
and their effect on mycorrhizal fungal communities is at present
poorly understood. Although it would be ideal to measure
below-ground C allocation directly, these measurements are
notoriously difficult to make (Giardina et al., 2005), so it
would have to be either ignored or modelled using easier to
measure, but more distal, variables, such as foliar nutrients,
tree growth and atmospheric chemistry. In this case, it becomes
important to incorporate C allocation models that capture
below-ground allocation dynamics and can be appropriately
parameterized across a broad range of species.
In contrast, the proximal variable soil N (e.g. extractable
mineral pools, organic horizon C : N) is relatively easy to
measure and appears to be a good predictor of ectomycorrhizal species or genus abundance (Lilleskov et al., 2001, 2002).
Given that soil N is a complex product of multiple distal
variables (e.g. N deposition, site history, soil type, biota,
climate), during model parameterization it would be preferable
to measure soil N directly, rather than attempting to model
it using distal variables. However, efforts to extend these predictions beyond sampled sites would still require input from
biogeochemical models that use distal predictors to estimate
soil N at unsampled locations (e.g. Rowe et al., 2005).
An additional problem with certain distal/indirect variables (e.g. elevation) is that as the scale of studies expands,
predictions using these variables become worse (Guisan &
Zimmerman, 2000), limiting their utilities in more general,
large-scale models.
Class and continuous variables
Once we have determined the most relevant predictors for
characterizing species–environment relationships, we need
to determine the most appropriate way to measure them.
Some predictors, such as host identity, are clearly class variables.
Others, such as soil pH or N, are clearly continuous
variables. However, many factors can be conceptualized as
either class or continuous variables (e.g. disturbance, host
community or substrate). When possible, it is more useful
for defining response functions to conceptualize and measure
variables in a continuous manner. For example, rather than
Forum
specifying stands as disturbed or undisturbed, more useful
metrics would be related continuous variables such as time
since disturbance, forest floor biomass or host species
biomass. Similarly, host biomass data are preferred to host
presence/absence data. If necessary, continuous data can
always be converted to class data, but not vice versa.
Appropriate models
An in-depth discussion of models is beyond the scope of this
article, and many issues are discussed elsewhere (e.g. Guisan
& Zimmermann, 2000; Austin, 2002; Guisan & Thuiller,
2005; Ferrier & Guisan, 2006). We focus here on two key
issues: whether to model distributions of species or
communities; and whether to use static or dynamic models.
Modelling species vs communities
Most current models focus on predicting individual species
distributions rather than whole communities (Guisan &
Zimmermann, 2000; Austin, 2002). This derives not only
from the relative simplicity of modelling individual species,
but also from our understanding, derived largely from plant
ecology, of the individualistic nature of species assemblages
(Gleason, 1926), as evidenced by their independent assortment
along environmental gradients (Whittaker, 1967) and lability
of community composition over time and space (Davis, 1981).
Community models are either in conflict with this theoretical formulation or an extension of it, depending on the
approach used. During community modelling, (1) whole
communities can be characterized then modelled as a function of the environmental variables, (2) multiple species can
be simultaneously modelled as a function of environmental
variables, or (3) the net result of multiple individual species–
environment models can be assembled into a community
prediction (Ferrier & Guisan, 2006). The first (and least
‘Gleasonian’) class does not allow individualistic species
responses, provide individual species maps, or extrapolate
beyond known communities. The third class should do best
at modelling individualistic species responses and defining
individual species distributions. The first and second classes
have a variety of strengths, for example they can rapidly analyze
large numbers of species and perform well when species are
encountered infrequently (Ferrier & Guisan, 2006), as in
ectomycorrhizal fungal community sampling. The most
appropriate methods for modelling mycorrhizal fungal
communities will have to be determined by comparative
analysis of different approaches, but will probably derive
from the second or third class of models.
Static vs dynamic models
Most models used to predict distribution and abundance of
species are static (Guisan & Zimmermann, 2000). Static
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models predict current distribution in relation to environmental
variables, assuming equilibrium conditions. The major
advantages of static models are that they are relatively easy to
build, parameterize and test, and are therefore favored for largescale species distribution modelling efforts. One of the simplest
classes of static models is regression. Other static models
allow more flexibility in modelling species–environment
relationships (e.g. generalized linear models, generalized
additive models, ordination methods, regression and
classification tree analysis) (Guisan & Zimmermann, 2000).
However, the equilibrium assumption may not be valid when
modelling fungal communities in a changing environment.
Nonequilibrium conditions arise in response to naturally
dynamic conditions (e.g. disturbance, climate change), but
human-accelerated environmental changes may increase
disequilibria. For example, it appears that there may be significant lags in the ectomycorrhizal fungal below-ground
community response to elevated N deposition (Lilleskov,
2005). Lags of this sort could lead to poor static model
parameterization.
By contrast, dynamic models can address nonequilibrium processes, such as succession, changing soil chemistry,
changing below-ground C allocation and climate change.
Forest ecologists have long used dynamic models to predict
spatio–temporal dynamics in species distribution and community structure and composition (e.g. Urban et al., 1991;
Carey, 1996; Gao et al., 1996; He et al., 2002; Gratzer et al.,
2004), and simple dynamic models have been explored for
fungal communities (e.g. Halley et al., 1994). However, to
structure and parameterize these models correctly requires
much more information than for static models, and so they
are rarely parameterized for species distribution modelling at
large scales. Characterizing the spatio–temporal dynamics of
mycorrhizal fungal species assemblages in relation to multiple
variables across a broad range of environments would be
extremely challenging, requiring data that are not easily
obtainable.
To deal with these difficulties, a viable two-pronged
approach would be initially to build and test static species
distribution and abundance models. If serious deficiencies
are apparent that are probably the result of disequilibria,
then we can work towards the parameterization of dynamic
models, based on the results of experimental, gradient,
chronosequence and longitudinal studies.
Conclusion
Although it is tempting to throw up our hands given the complexity of this challenge, we believe that the attempt should be
made to begin to build global data sets and predictive species/
community models, recognizing that this will be an iterative
process, involving continual improvement of tools, data and
models (Fig. 2). An efficient approach to providing high crosscomparability of both species and environmental data would
New Phytologist (2007) 174: 250–256
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Fig. 2 A flow diagram describing the outline of an approach to
developing general predictive models of mycorrhizal fungal
community distribution. Solid lines represent the primary
approach based on currently available and appropriate methods:
gradient-based site selection; polymerase chain reaction (PCR),
cloning and sequencing of community DNA; combination of
sampled, pre-existing and modelled environmental data; static
modelling of both individual and multiple species relationship with
environment; evaluation of predictions; and iterative improvements
in data and models. Dotted lines represent alternative approaches
that are under development (community microarrays) or that can
be tried if primary approach results indicate that they are required
(dynamic modelling of communities based on experiments and
other data sources). GIS, geographic information system; ITS,
internal transcribed spacer.
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be to develop a research consortium that uses a mutually agreed
upon sampling scheme to achieve maximum coverage for
minimum effort, similar to the community effort that supported
the Deep Hypha project (http://ocid.nacse.org/research/
deephyphae/projects.php). The price of not acting now will
be a lost opportunity to define baseline species distribution
data in the face of rapid global change. We have touched on
a few issues. The key next steps are rallying a diverse group
of researchers to collaborate in this process, and finding the
resources to support large-scale data collection and modelling
efforts. The time to take these steps has come.
Acknowledgements
We thank Dr Andy Taylor for the invitation to give the talk
at the 5th International Conference on Mycorrhiza, 23–27
July 2006, Granada, Spain, on which this paper was based.
Erik A. Lilleskov1* and Jeri L. Parrent2
1USDA Forest Service, Northern Research Station,
RWU4159, 410 MacInnes Dr., Houghton, MI, 49901,
USA; 2Department of Forest Mycology and Pathology,
Swedish University of Agricultural Sciences, SE 750 07
Uppsala, Sweden
(*Author for correspondence:
tel +906 482 6303, ext. 18;
fax +906 482 6355;
email [email protected])
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Troubles with truffles:
unveiling more of their
biology
the assessment of intraspecific genetic variability, particularly
in species of economic interest such as Tuber magnatum Pico
and Tuber melanosporum Vittad., which produce the most
appreciated white and black truffles, respectively.
Bertault et al. (1998, 2001) were the first to claim that
T. melanosporum experienced a strong genetic bottleneck
during the last glaciation, so explaining the apparent absence
of phylogeographic signals in T. melanosporum populations.
A major implication of this is that the environmental
conditions dictate the qualitative differences among
truffles of different geographic origin. In addition,
embracing the hypothesis that ascocarps result from the
fusion of two haploid mycelia, and are therefore to be
considered diploid structures, these authors also proposed a
selfing reproductive system in Tuber spp. to interpret the
absence of heterozygotes found when truffle ascocarps were
screened with codominant markers. Bertault et al.’s
conclusions and propositions have influenced all the
subsequent studies on genetic structure within both
T. melanosporum and T. magnatum, with a major impact on
Letter
Truffles are the hypogeous fruit bodies of the ascomycetes
Tuber spp. They are highly praised and priced gourmet food,
and their aroma and taste are known world-wide. But actually,
with the exception of ‘specialists’, very few people are aware
that the reproductive system of Tuber spp. is a real riddle.
This is largely a consequence of the difficulties of growing
and the impossibility of mating these symbiotic fungal species
under controlled conditions. Molecular markers are now
allowing us to look more closely into truffle population genetics
and, in turn, into their life cycle and reproduction. Molecular
investigations started more than a decade ago, originating
from the primary need to reliably type morphologically similar
truffle species (Henrion et al., 1994). The focus then shifted to
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Key words: community microarrays, community modelling,
human-accelerated environmental change, internal transcribed spacer
(ITS), mycorrhizal fungi, sequencing, species distribution modelling,
static modelling.
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Letter
sampling strategies and mating models (Frizzi et al., 2001;
Murat et al., 2004; Rubini et al., 2004; Mello et al., 2005).
Yet, through enlarging the sampling areas and adopting
increasingly informative markers, it has emerged that these
two species are not so genetically depauperated as was
previously thought. Indeed, it is possible to differentiate their
populations genetically and track for each species a
postglacial expansion pattern that fits nicely with that of
most plant species with which these fungi have to establish
mutualistic symbiosis (Murat et al., 2004; Rubini et al.,
2005).
Even more intriguingly, simple sequence repeat (SSR)
studies in T. magnatum have shown the occurrence of an
extensive genetic exchange among geographically closed
populations, as per two-locus and multilocus linkage
disequilibrium analyses (Rubini et al., 2005). This extensive
gene flow is hard to reconcile with the absence of any
heterozygotic individuals. To resolve this conundrum, we
hypothesized that T. magnatum outcrosses and, as most of
the ascomycetes, has a prevalently haploid life cycle, with
a ‘cryptic’ dikaryotic phase in the ascocarps (Rubini et al.,
2005). In this scenario, the absence of heterozygotes results
from a sampling bias as the DNA recoverable from ascocarps is contributed largely from the haploid, maternal
Forum
tissue of the gleba. Conversely, the paternal DNA is not easy
to recover because it is present only in the ascospores, and
these structures are not usually broken during the DNA
extraction process. Direct support for this idea comes from a
new strategy allowing the differential recovery and analysis
of DNA of pools of ascospores from the DNA of the
surrounding gleba within single ascocarps. As expected, in
most of the truffles analysed, the SSR patterns of the spores
displayed alleles, of clear-cut paternal origin, in addition to
those present in the gleba (Paolocci et al., 2006). Furthermore,
in the same study we showed that primary mycelia, generated
from germinating spores, are very likely homokaryotic as the
mycorrhizas resulting from the inoculation of host trees
with SSR-genotyped pools of spores were individually haploid.
Thus, taken together these data argue against the thesis that
truffle mycorrhizas are formed only after heterokaryotic
mycelia are established (Lanfranco et al., 1995).
All in all, the SSR-assisted studies of T. magnatum ascocarps
and mycorrhizas suggest the prevalence of the haploid phase
in the truffle life cycle, a situation that typifies most ascomycetes, and substantiate the view that the fertilization
process, and the resulting dikaryotic phase in Tuber spp.,
are spatially and/or temporally confined in the first stages
of ascocarp development (Fig. 1).
Fig. 1 Simple sequence repeat (SSR) patterns and schematic representation of the Tuber magnatum life cycle. (a) Allelic configurations at two
SSR loci displayed by the gleba and pools of ascospores from a single T. magnatum ascocarp and by mycorrhizal tips resulting from hostplant inoculation with the same truffle. (b) The T. magnatum life cycle as inferred from SSR studies. (1) Early stages of ascocarp development:
the ascocarp primordia probably contain a dikaryotic mycelium (shown as white hyphae) resulting from fertilization, embedded in a network
of haploid (homokaryotic) maternal hyphae (shown as grey hyphae). The dikaryotic mycelia develop with the formation of crozier and ascus
mother cells (1a) where karyogamy takes place (1b). Karyogamy is shortly followed by meiosis, resulting in the formation of asci containing
a variable number of ascospores (1c). (2) Mature ascocarp with asci and spores surrounded by hyphae of maternal origin. At this stage the
paternal alleles can only be detected within spores. (3) Ascospores producing homokaryotic primary mycelia. (4) Homokaryotic mycorrhizas
resulting from root colonization by primary mycelia. (5) The fertilization process.
© The Authors (2007). Journal compilation © New Phytologist (2007) www.newphytologist.org
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258 Forum
Letter
Perspectives
These findings in T. magnatum raise some new, enticing
questions.
1 Can all Tuber spp. outcross?
2 Are truffles prevalently outcrossing or heterothallic species?
3 What is the morphology of the mating structures in these
fungi?
Whilst it is most likely that some other Tuber species,
such as Tuber aestivum Vittad. syn. Tuber uncinatum Chatin
(Wedén et al., 2004; Wedén, 2004), also outcross, and
studies following the approach adopted for T. magnatum
would help to assess this, the answer to the second question
is more complex. Indeed, despite several attempts, the genes
of the mating type have not yet been isolated in these fungi.
Once again, population genetics studies, although indirectly,
might offer a further opportunity to gain insights into
the truffle life cycle. As a matter of fact, it should be
possible to estimate the outcrossing rate in these fungi
by comparing the expected and observed rates of heterozygosity within populations. Notably, the sampling should
concern both the gleba and the spores in each ascocarp.
Because each truffle ascocarp results from an independent
mating event, that truffles with identical genetic profiles
in their gleba represent clones of the same individual
(Bertault et al., 2001; Murat et al., 2004) is no longer to be
taken for granted.
Despite all these advances in our understanding of the
truffle reproductive cycle, one black hole still remains. This
is the morphology of the fertilization process. However, the
apparent absence of male hyphae in the gleba and the fact
that specialized male structures (antheridia) have never been
described in these fungi let us argue that the male gamete
function may be fulfilled by any detached cells, such as
ascospores, hyphal fragments or even mitotic spores (spermatia). Although further studies are needed to shed light on
this key point of truffle biology, further support for this
hypothesis is provided by the recent and fascinating finding
that Tuber spp. produce mitospores (Urban et al., 2004).
Practical implications
Certainly, the discovery of a genetic and phylogeographic
structure in T. magnatum and T. melanosporum is likely to
have a major impact on attempts to elucidate whether, in
addition to environmental conditions, genetic determinants
shape the morphology of, and dictate the organoleptic
differences within, any given truffle species over its
geographical range. However, how this discovery will affect
the development of strategies for the cultivation and
marketing of these fungi is equally relevant. Our prediction
is that the availability of more molecular markers will
increasingly make it possible to trace natural truffle populations
according to their geographic origin. Far from being a
New Phytologist (2007) 174: 256–259
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secondary issue, this is of great practical importance for the
associations of truffle harvesters and local governments who
are actively promoting the economic and social development
of rural and marginal areas. At the same time, these results
pose intriguing new questions about the potential problems
linked to microbial competition and loss of fungal biodiversity.
Not least, this is also a relevant ecological problem. Artificial
truffle plantations are often established in naturally productive
areas to counterbalance the sharp decline in wild truffle
harvests (Hall et al., 2003). However, the possible consequences
of the deliberate introduction in naturally truffle-producing
areas of host trees that have been nursery-inoculated with
nonindigenous fungal strains have largely been unexplored.
The notion that T. magnatum, at least, is not an exclusively
selfing species is important for successful truffle cultivation.
If truffles are the product of a preferentially outcrossing or
heterothallic species, the presence of genetically distinct
strains or of strains with opposite sexuality at the cultivation site would be the major requirement to allow these
fungi to fruit. Thus, we believe that a careful re-evaluation
of the procedures for host-plant inoculation is opportune.
Such a re-evaluation should promote the presence of as
much genetic variability within an artificial truffle plantation
as possible. The routine in nursery practice is to inoculate host
plants with ascocarps. However, it would be extremely
interesting to investigate whether mycorrhizas result from the
germination of many or only a few spores or, alternatively,
result prevalently from the maternal hyphae of the gleba.
Following the same reasoning, the envisaged procedure of
large-scale host-plant inoculation using in vitro cultivable
individual mycelial strains is not likely to be an advisable
practice.
Last but not least, there remains a very attractive hypothesis
that still needs to be tested. This is whether, aside from the
impact of environmental factors, the underrepresentation of
local truffle biodiversity is one of the underlying reasons for
unsuccessful production in some artificial truffle plantations.
Andrea Rubini, Claudia Riccioni, Sergio Arcioni
and Francesco Paolocci*
Consiglio Nazionale delle Ricerche (CNR), Istituto di
Genetica Vegetale – Perugia, Via Madonna Alta n.
130, 06128 Perugia, Italy
(*Author for correspondence:
tel +39 075501 4861; fax +39 075501 4869;
email [email protected])
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