Download Below-ground ectomycorrhizal communities: the effect of small scale

Symbiosis (2012) 57:57–71
DOI 10.1007/s13199-012-0179-x
Below-ground ectomycorrhizal communities: the effect of small
scale spatial and short term temporal variation
Richard O’Hanlon
Received: 15 March 2012 / Accepted: 12 July 2012 / Published online: 1 August 2012
# Springer Science+Business Media B.V. 2012
Abstract Ectomycorrhizal (ECM) associations are an important form of symbiosis for the majority of tree species. In
forest ecosystems where nutrients are often limited, ECM
associations are vital for seedling establishment and tree
survival. ECM communities are often very species rich
and frequently follow the log normal distribution—with a
few species being abundant and many species being rare.
Much of the current knowledge of ECM communities has
been revealed through a combination of above-ground sporocarp and below-ground root tip analyses. However,
above-ground surveys of ECM communities reveal largely
different findings regarding ECM diversity and community
structure to below-ground surveys. Therefore below-ground
surveys are vital to our understanding of ECM biology,
ecology and community dynamics. In this article I review
the recent findings regarding how ECM communities vary
both spatially and temporally in forests. Spatial variation
occurs at the centimetre scale both horizontally and vertically in forests, and can be explained by the separation of
the ECM community into distinct niches. Temporal variation occurs over relatively short time scales, with ECM
communities showing large changes even on a monthly
basis. I then apply the niche concept to ECM fungi, and
review a recent theory, ECM functional morphology, and
examine how this may be used to explain a significant
amount of spatial and temporal variation in forest ECM
communities. The functional morphology theory is particularly useful in explaining patterns of ECM community variation across the distinct successional stages of the forest
cycle. However, the effect of other abiotic and biological
variables on ECM communities should not be ignored.
Finally, as ECM communities are non-randomly distributed
R. O’Hanlon (*)
World Forestry Centre,
4033 SW Canyon road,
Portland, OR 97221, USA
e-mail: [email protected]
and vary widely in species richness over time, I lay out a
sampling strategy to provide representative samples of the
actual ECM community in the study area. Using (i) an
extensive sampling methodology, (ii) separation of samples
at distances greater than levels of spatial autocorrelation,
and (iii) samples collected throughout the year, over a number of years, an ample picture of the ECM community in
temperate forests can be collected.
Keywords Ectomycorrhizas . Sporocarps . Functional
morphology . Spatiotemporal variation . Niche separation .
Successional patterns . Phylogenetically defined niche
1 Ectomycorrhizal communities: a diverse
and important component of forests
Mycorrhizal associations were key to the colonisation of
terrestrial habitats by plants (Brundrett 2002). Indeed up to
85 % of angiosperm and 90 % of gymnosperm species show
mycorrhizal associations (Wang and Qiu 2006). One group
of mycorrhizal associations, the ectomycorrhizas, are particularly important to their hosts in boreal and temperate forest
ecosystems where competition for available nutrients is very
high (Read and Perez-Moreno 2003; Smith and Read 2008).
Ectomycorrhizal (ECM) fungi form a sheathing structure
around the roots of plants, which also penetrates between
plant root cells, called a Hartig net. The presence of a Hartig
net distinguishes ECM fungi from saprotrophic fungi present on plant roots (Brundrett 2007). Here after, any reference
to below-ground ECM fungi refers to these sheathing structures, and not to below-ground fruiting hypogeous (truffle)
fungi. A large number of ECM fungi also produce aboveground structures, in the form of sporocarps to disseminate
their spores; hereafter referred to above-ground ECM fungi.
ECM fungi are known to be able to acquire nutrients for
their hosts from a wide range of sources including
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decomposing litter (Leake and Read 1997), pollen (PerezMoreno and Read 2001a), saprotrophic fungi (Lindahl et al.
1999), from soil fauna such as springtails (Klironomos and
Hart 2001) and nematodes (Perez-Moreno and Read 2001b),
and from stone particles in the soil (van Schöll et al. 2008).
In temperate and boreal forests, below-ground ECM fungi
colonize more than 90 % of the fine roots of trees (Teste et
al. 2009; Ma et al. 2010; Pickles et al. 2010; Leski and
Rudawska 2012). In some forests below-ground ECM species richness has been found to be very high (Table 1), often
numbering hundreds of species per ca. 5,000 m2 forested
area (Richard et al. 2005; Tedersoo et al. 2006) and even
hundreds per individual tree in certain old-growth forests
(Bahram et al. 2011). However, examples of below-ground
species poor ECM communities are known from certain
forest types, such as ancient bristlecone pine Pinus longaeva
Bailey forests in California (Bidartondo et al. 2001) and
some tropical forests (see Tedersoo and Nara 2010).
The abundance of individuals within these below-ground
communities typically follow the log-normal distribution
(Horton and Bruns 2001; Kranabetter 2004; Luoma et al.
2006; Cline et al. 2005; Courty et al. 2008; O’Hanlon and
Harrington 2012a) with a few taxa being dominant and the
majority of taxa being present at low frequencies. A number
of factors have been suggested to account for this large
species richness (see Kernaghan 2005), including host specificity (Ishida et al. 2007; Lang et al. 2011), forest age
(Twieg et al. 2007; Ma et al. 2010), soil edaphic factors
(Kranabetter et al. 2009), disturbance regimes (Dickie et al.
2009; Diedhiou et al. 2009) and climatic variables (O’Dell
et al. 1999; Querejeta et al. 2009). From a host centric viewpoint, the necessity of supporting such a diverse community
of ECM fungi is questionable, as each ECM fungus feeds
off the host’s photosynthate (up to 20 % net productivity;
Hobbie 2006). However, recent research by Corrêa et al.
(2012) has indicated that the photosynthate supplied by the
host to the ECM community may be in excess, or produced
directly in response to the need for increased root growth,
and thus at no net cost to the host growth. Others have
questioned if ECM symbiosis should always be classed as
a mutualistic association: as it is known that ECM species
can occupy different positions along the mutualismparasitism (Jones and Smith 2004) or biotrophysaprotrophy continuum (Koide et al. 2008). A recent and
ecologically sound hypothesis has come from girdling
experiments in Germany, and states that a high species
richness of ECM symbionts may provide insurance to the
host in case environmental conditions change and the most
dominant ECM fungus is no longer capable of supplying the
host with necessary nutrients (Druebert et al. 2009; Pena et
al. 2010). This insurance hypothesis is analogous to the
functional redundancy hypothesis often cited in biodiversity
and ecosystem functioning research (see Bolger 2001 for
R. O’Hanlon
review), where high biodiversity (of ECM fungi) allows for
healthy functioning in the face of environmental
perturbations.
2 Aims of this article
In this article I will focus on the causes of spatial and
temporal variation in below-ground ECM communities,
and also how this variation affects the realized ECM communities. The sampling methods applied to above-ground
(sporocarp) and below-ground (root tip) ECM communities
are also briefly reviewed. The majority of examples used
come from boreal and temperate forests, a reflection of the
fact that this has been the focus of most ECM research
(Dickie and Moyerson 2008; Tedersoo and Nara 2010). I
will apply the niche concept to ECM communities, through
a combination of two relatively new hypotheses to be applied to ECM fungi—the phylogenetically defined niche
and the functional morphology hypothesis. When dealing
with the spatial positioning of samples, I will explain the
main factors understood to cause the structuring of ECM
communities in terms of horizontal variation (distance from
tree base) and vertical variation (position in the soil profile).
In terms of temporal variation, I will focus only on the short
term (from months to 1–3 years) effect of time on ECM
communities. I will not deal with the effect of long term
variation in ECM communities, as this would introduce
many confounding factors (e.g. effect of succession and
climate) and is outside of the scope of this article. A final
purpose of this article is to provide some guidelines regarding when and where ECM communities should be sampled,
so that the best possible representative sample of the actual
ECM community can be collected. It will not deal with other
aspects of ECM investigation, which are aptly dealt with in
Brundrett (2009).
3 Sampling methods applied to ECM communities
We can define a community as comprising all organisms
that live together at the same place and same time. The
community concept can be further broken down to include
a particular subset of individuals, such as only the ECM
fungi. In order to assess the population structure of the
community we need a method of defining the abundances
of the individuals (measured at the interspecific or intraspecific level; see Douhan et al. 2011) within the community.
The application of the community concept to ECM fungi
has been reviewed recently (Koide et al. 2011) and will not
be dealt with here. Much of the current knowledge of ECM
community ecology to date has been gained by the use of
either above-ground or below-ground ECM fungi in forests.
Spatial and temporal variation in ectomycorrhizal communities
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Table 1 Selection of below-ground ectomycorrhizal studies in boreal and temperate forests along with details of the sampling intensity carried out
Country
Forest type
ECM richness
Number of samples (sample volume cm3)
Root tips counted
Reference
Canada
Canada
Canada
England
Estonia
Estonia
Germany
Iran
TC
TC
TC
TC
TD
TD
TC
TC
69
26
65
13
14
172
23
160
120 (294)
Whole trees (N/a)
Whole trees (N/a)
40 (191)
Not given (113)
90 (4500)
Whole roots (N/a)
7 (2250)
17500
35410
8858
118000
20401
27015
38219
863
Goodman and Trofymow 1998
Durall et al. 1999
Robertson et al. 2006
Palfner et al. 2005
Püttsepp et al. 2004
Tedersoo et al. 2006
kaldorf et al. 2004
Bahram et al. 2012
Italy
Japan
Lithuania
Lithuania
New Zealand
Poland
Scotland
Scotland
Sweden
Sweden
Sweden
USA
USA
USA
TC
TC
TC
TC
TC
TC
TC
TC
BC
BC
BC
TC
TB
TC
31
37
33
14
19
17
13
24
135
37
39
42
140
17
32 (15000)
Whole roots (N/a)
Whole trees (N/a)
60 (1571)
84 (785)
Whole trees (N/a)
30 (126)
217 (98)
120 (92)
30 (92)
Whole roots (N/a)
Whole roots (N/a)
64 (900)
17 (Variable)
3947
3493
14364
15000
31520
9600
19170
92000
10484
5371
8275
4500
6400
9994
Scattolin et al. 2008
Matsuda and Hijii 2004
Menkis et al. 2005
Aučina et al. 2011
Walbert et al. 2010
Rudawska et al. 2006
Flynn et al. 1998
Pickles et al. 2010
Jonsson et al. 1999
Taylor 2002
Rosling et al. 2003
Lilleskov et al. 2002
Morris et al. 2008
Wurzburger et al. 2004
Forest types: TC Temperate coniferous; TD Temperate deciduous; BC Boreal coniferous. N/a studies where sample volume was not measured
A number of studies have compared the two methods, with
the majority discovering that they reveal largely dissimilar
findings for species richness and community structure
(Gardes and Bruns 1996; Dahlberg et al. 1997; Peter et al.
2001; Horton and Bruns 2001; Richard et al. 2005; Porter et
al. 2008; Trocha et al. 2011; O’Hanlon and Harrington
2012a; van der Linde et al. 2012). The ecological significance of counts of above-ground sporocarps as a measure of
abundance is debatable, as sporocarps are highly ephemeral
(Richardson 1970) and exhibit unpredictable fruiting patterns (Straatsma et al. 2001; Harrington 2003; Martínez de
Aragón et al. 2007). This being said, Tóth and Barta (2010)
encourage the continuation of above-ground sporocarp sampling studies as they are suitable for identifying ECM species responses to environmental change over wide spatial
and temporal ranges. The question of how to assess belowground ECM species abundance is even more problematic,
as below-ground species are concealed, difficult to identify
morphologically, and their biomass is not as easily estimated
as with above-ground sporocarps. Below-ground ECM fungi also differ widely in their patch sizes (Genney et al. 2006;
Pickles et al. 2010), even between members of the same
genus (Kretzer et al. 2005), therefore sampling scale is of
vital importance if a true representative of the ECM community is to be attained from soil samples (Horton and
Bruns 2001; Taylor 2002).
In many previous studies, researchers have used counts
of below-ground ECM root tips as a measure of ECM
species abundance. Following the advice of Taylor (2002),
researchers have generally counted within the region of
several thousand tips (Table 1). However, others have questioned the use of root tips as an abundance measure (Dickie
et al. 2009)—because it is incorrect to treat each root tip as
an individual, and also because of difficulties in the counting of ECM species that form clumped or multi-branched
ECM tips (e.g. many Pinus sylvestris L. mycorrhizas;
O’Hanlon and Harrington 2012a; O’Hanlon unpublished
results). Thus a number of studies have used the frequency
of occurrence of below-ground ECM root across a number
of samples (soil cores; Koide et al. 2011) or within samples
using randomly selected soil core sections (Eberhart et al.
1996) as the method for defining species abundance in
below-ground ECM communities.
An important, yet oft unconsidered component of belowground ECM communities is the extramatrical mycelium
(EMM) (Read 1992; Leake et al. 2004; Cairney 2005;
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Wallander 2006; Anderson and Cairney 2007; Finlay 2008).
A recent review by Cairney (2012) investigated the role of
EMM in the carbon cycle of soils, and highlighted that
EMM is a vast store of carbon in forest soils, a store which
will likely be strongly affected by climate change, thus
identifying EMM responses to climate change as a key
future research area. The EMM in forest soil can be measured directly from soil samples (Koide et al. 2005; Genney
et al. 2006), or it can be baited using substrate filled root
restricting mesh bags buried in the soil (Kjøller 2006;
Wallander et al. 2010; Kjøller et al. 2012; Walker et al.
2012). The use of molecular methods then allows for the
taxonomic identification of the EMM, which is unidentifiable to species level using morphological methods alone
(Agerer 2006). Results from studies comparing belowground ECM roots and the EMM have found that these
two methods provide contrasting views of the mycorrhizal
communities, thus examination of both the ECM community and its EMM is recommended (Koide et al. 2005; Genney
et al. 2006; Kjøller 2006).
Within below-ground ECM examinations it is common
that studies use a combination of morphological and molecular identification methods for taxa identification (Rosling
et al. 2003; Kaldorf et al. 2004; Menkis et al. 2005; Parádi
and Baar 2006; Buée et al. 2007; Gebhardt et al. 2007; Ma et
al. 2010; Walbert et al. 2010; Aučina et al. 2011; O’Hanlon
and Harrington 2012a). Sakakibara et al. (2002) found that
preliminary sorting of ectomycorrhizas by morphotyping
followed by molecular identification allowed for the effective use of research time and funds. The techniques and
important characteristics for morphotyping ECM roots are
now well established (Agerer 1987–2002; 2006; Ingleby et
al. 1990; Goodman et al. 1997), however, morphotyping
alone has been shown to fail at distinguishing between
certain morphologically similar ECM species (see
Sakakibara et al. 2002 and references therein). The use and
suitability of molecular biology identification methods for
ECM fungi has increased in recent years (see Douhan et al.
2011 and references therein). However, before the application of molecular methods to ECM studies, many ECM
types could not be matched to morphological descriptions
of ECM taxa and so were often lumped into tenuous groupings which revealed little about ECM ecology, leading to
investigators terming these unidentified ECM types the
“black-box” (Allen 1991). Modern studies often have identification rates of >90 % of taxa to species level (e.g. Courty
et al. 2008; Tedersoo et al. 2008) using morphological and
molecular identification methods. The most popular molecular identification methods applied to ECM communities
have been reviewed relatively recently (Bidartondo and
Gardes 2005; Anderson and Cairney 2004; Martin 2007)
and so will not be addressed in this article. A recent advance
in molecular biology has been the application of next
R. O’Hanlon
generation sequencing technologies such as pyrosequencing
(“sequencing by synthesis”) to ECM communities. Recent
studies to use pyrosequencing on fungal communities are
reviewed in Hibbett et al. (2011), and studies often find
much greater species richness of OTUs (Operational
Taxonomic Units) than expected; such as the ECM dominated 470 OTU found by Blaalid et al. (2012) looking at soil
fungi found along a successional gradient near a retreating
glacier, or the unexpectedly high number (89 OTU) of ECM
fungi in a highly disturbed forest in British Columbia
(Walker et al. 2012). The pyrosequencing method and analysis of its results, however, have some intrinsic faults. These
including biases related to the treatment of rare OTUs, the
use of read abundance as a measure of species abundance,
and the relatively short read sequences attained causing
concern among some ECM community researchers
(Amend et al. 2010; Dickie 2010; Tedersoo et al. 2010a).
However, all of the advances in morphological and molecular methods to identify ECM fungi count for little if the
roots examined do not provide a good representative sample
of the total ECM community. Sampling schemes lacking
sufficient replication do not take into account the environmental heterogeneity evident in the rhizosphere, thus biased
samples gives a biased description of the community, even
when using the most advanced identification methods. To
paraphrase Prosser (2010), “even the most powerful techniques become powerless if used in a poorly designed experiment”. ECM communities have been shown to be nonrandomly distributed (Bruns 1995; Tedersoo et al. 2003;
2008), most likely related to distinct niche preferences within the ECM community (Horton and Bruns 2001; Dickie et al.
2002; Taylor 2002; Buée et al. 2007; Tedersoo et al. 2008).
Thus, in such a heterogeneous environment, the realised
ECM community is strongly dependent on the sampling
scheme utilized (Taylor 2002; Lilleskov et al. 2004). It is
this aspect—the positioning of samples in space and time
which I will examine further.
4 Niche preferences in ECM communities
Before going into details about the exact variation found in
the below-ground ECM communities across space and time,
it may be enlightening to briefly investigate the niche effect
and its consequences for the ECM community. In ecology, a
niche is generally defined as set of biotic and abiotic conditions under which a species can persist (also known as the
Hutchinsonian niche; see Holt 2009). In ECM communities,
the biotic conditions can include root presence, species
competition, species reproductive patterns; while the abiotic
conditions include, for example, soil edaphic factors, climate and land use history. Spatial autocorrelation patterns
have been found along horizontal and vertical transects in
Spatial and temporal variation in ectomycorrhizal communities
forest soil (Lilleskov et al. 2004; Izzo et al. 2005; Genney et
al. 2006)—with ECM communities of temperate forests
often displaying high autocorrelation in the region of 1 to
4 meters (Peter et al. 2001; Lilleskov et al. 2004; Genney et
al. 2006; Pickles et al. 2010; Bahram et al. 2011). These
values are much lower than those found for ECM communities in Tropical forests (>8 meters; Tedersoo et al. 2010b),
which the authors hypothesize was due to the much lower
ECM species richness per sample in these tropical versus
temperate forests. Methods for testing for spatial autocorrelation and designing sampling methods to reduce its effect
are dealt with later in the review.
As early as Darwin, it was noticed that species with
shared ancestry had high phenotypic similarity (CavenderBares et al. 2009). This later became an area of intense study
in evolutionary ecology, and is now referred to as the phylogenetically defined niche concept. The phlyogenetically
defined niche concept was developed using macroorganisms as the model (Cavender-Bares et al. 2009;
Vamosi et al. 2009) and has been highlighted as a useful
hypothesis in explaining significant amounts of the variation
in species distributions (Wiens et al. 2010), including in
ECM communities (Parrent et al. 2010; Jumpponen et al.
2012). It states that closely related taxa (e.g. within the same
genus) often occupy similar niches, possibly due to a number of factors—such as similar dispersal habits, sharing
preferences for specific nutrient sources, and/or susceptibility to the same consumers, pathogens or parasites. The
phylogenetically defined niche hypothesis has recently been
tested with fungi, specifically arbuscular mycorrhizal fungi
(Maherali and Klironomos 2007, 2012; Powell et al. 2009),
and it has been found that phylogenetically closely related
species often compete for similar niches due to variation in
colonization strategies. There are numerous examples of
ECM taxa which display a distinct niche preference—such
as the order Thelephorales with coarse woody debris
(Tedersoo et al. 2003), members of the genus Tomentella
for the A1 soil horizon (Visser 1995; Courty et al. 2008),
Russula spp. dominance in fertile clay soils (Peay et al.
2010a) and the preference of some Lactarius species for
either the organic or mineral soil horizons (Geml et al.
2009), to name just a few.
As ECM fungi are symbiotic organisms, it can be stated
that the presence of host roots is the main factor delineating
ECM distributions. Indeed, as roots are the habitat for all
ECM fungi, root density may explain many of the community and successional patterns in ECM assemblages
(Newton 1992; Peay et al. 2011). As ECM fungi are obligate
symbionts, they cannot complete their life cycle under natural conditions in the absence of carbohydrates supplied by
a plant host (Högberg et al. 2001; Taylor and Alexander
2005). Therefore ECM communities are confined to areas
with host roots available, but roots can often reach many
61
meters from individual trees (Saari et al. 2005) so care must
be taken in densely stocked forests. The presence of some
ECM species many meters from possible host trees (Taylor
and Alexander 2005) and the proven ability in-vitro of
several ECM species to acquire carbon directly from organic
matter when tree host carbon supply is limited (Buée et al.
2005; Courty et al. 2007; Cullings et al. 2008; Vaario et al.
2012), has led to the hypothesis that not all ECM species are
obligate symbionts and some exist as facultative saprotrophs
(Talbot et al. 2008). However, Baldrian (2009) refutes this
claim, providing another hypothesis to account for the increased enzymatic activities of ECM fungi under host carbon limited conditions. He states that the increased activity
could be part of an autolytic process to allow for the fungus
to escape from the dying root and find a new host. In a reply
to this, Cullings and Courty (2009) highlight that ECM
fungi respond to litter addition by increasing EMM growth
into the litter, which the authors believe was used as a
nutrient source by the ECM fungi. They also point out that
many ECM species express the genes and produce the
enzymes necessary to utilize litter as a nutrient source;
which coupled with the fact that there are numerous examples of ECM taxa reverting back to the saprotrophic lifestyle
(Hibbett et al. 2000) indicates that ECM fungi may exist
somewhere along the biotrophy-saprotrophy continuum
(Koide et al. 2008).
The continued host supply of carbohydrates to the fungus
is very important in the further spreading and colonization
of new roots by ECM fungi (Högberg et al. 2001).
Uncolonized sapling baiting experiments and post-harvest
root assessments in numerous forest types have highlighted
that areas in close proximity to existing ECM tree roots have
higher ECM infection rates and more diverse ECM communities (Kranabetter and Wylie 1998; Hagerman et al. 1999;
Kranabetter and Friesen 2002; Outerbridge and Trofymow
2004; Cline et al. 2005; Luoma et al. 2006; Jones et al.
2008; Teste et al. 2009). Luoma et al. (2006) examined the
spatial influence of retained mature trees in Oregonian
Douglas fir forests. They found that ECM richness exhibited
a 50 % decline when they took their sample at distances of
greater than 8 meters from the tree. This decline of ECM
species richness as they sampled further away from the tree
was found to be related to the decline in root density at
increasing distances. Although fungal spores are mass produced (105 spores per m−2; Dahlberg and Stenlid 1994),
very small and well suited to long distance dispersal; conventional wisdom says that most of the spores fall close to
their source of origin. Li (2005) found that few spores
(<5 %) of basidiomycete Amanita muscaria Lam. escaped
more than 5 meters from their sporocarp origin, while
Galante et al. (2011) used a field based and statistical modelling approach to predict that only 5 % of basidiospores fall
further than 1 meter of the sporocarp origin. Moreover,
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ECM population genetics has found that the spores of many
ECM species experience dispersal limitations at landscape
and continental scales (Bergemann and Miller 2002; Kretzer
et al. 2004; Douhan and Rizzo 2005; Taylor et al. 2006;
Grubisha et al. 2007; Carriconde et al. 2008; Geml et al.
2008; Douhan et al. 2011). In a seminal ECM community
ecology paper by Peay et al. (2007) examining ECM communities on different sized “tree islands” in single tree
species even aged pine forests in California, ECM fungal
species richness decreased as distance from nearest inoculum source increased. Thus showing that for ECM fungi,
“everything is not everywhere” and ECM fungi, unlike
many other micro-organisms (Finlay 2002), are geographically restricted. Indeed, a follow up study in the same area
but on older trees showed that ECM species richness was
approximately 50 % lower at 1,000 m from the forest than at
the forest edge (Peay et al. 2010b).
5 Horizontal and vertical variation in ectomycorrhizal
communities
Along the horizontal plane, studies have shown that belowground ECM communities show large amounts of variation,
i.e. with communities differing along a transect from the
trunk of the tree (Dickie and Reich 2005; Dickie et al. 2005;
Gebhardt et al. 2007; Pickles et al. 2010; Bahram et al.
2011). The most recent of these studies was carried out in
old-growth mixed aspen forests in Estonia (Bahram et al.
2011) and found that ECM species richness showed a stochastic pattern, not directly related to distance from the tree
base. Community structure however, was found to be structured and non-stochastic, a finding which the authors believe is due to certain groups of species having
phylogenetically defined niches. Further support for this
explanation comes from the fact that levels of spatial autocorrelation were weaker when samples were analysed at the
species level, with strong spatial autocorrelation was present
at the lineage level. Greater spatial autocorrelation at the
ECM lineage level compared with species level suggests
that species within lineages have a clumped distribution due
to biotic and/or abiotic factors. Work by University of
Minnesota researchers in bur oak Quercus macrocarpa
Michx. forests investigated the effect of forest edges on
ECM communities (Dickie and Reich 2005; Dickie et al.
2005). Their work highlighted the fact that the ECM community of trees was most diverse in the soil close to the tree
where high root density allows for high ECM abundance.
ECM communities also show vertical variation in the soil
profile (Dickie et al. 2002; Landeweert et al. 2003; Rosling
et al. 2003; Genney et al. 2006; Baier et al. 2006; Lindahl et
al. 2007; Courty et al. 2008; Scattolin et al. 2008). Rosling
et al. (2003) used morphotyping and DNA sequencing to
R. O’Hanlon
examine the vertical distribution of ECM types in a podzol
soil from a spruce and pine forest in Sweden. They found
that more than half of the ECM taxa were restricted to the
mineral soil horizons, thus indicating the importance of
including the mineral soil layers in future sampling
schemes. Genney et al. (2006) used a novel method to
examine the fine scale distribution of ECM tips and ECM
mycelium in the soil profile of a Scot’s pine forest in
Scotland. They took slices of the soil profile and divided
this into small cubes of soil. They were then able to map (at
the centimetre scale) the distribution of the different ECM
species and identify preferences for some ECM species to
certain positions in the soil profile, indicating niche preferences in their ECM community. The effect of niche preferences and how they structure the ECM community have
been described using both (i) abiotic and (ii) biotic factors
(Jumpponen and Egerton-Warburton 2005). Under (i) abiotic factors, studies have found that certain ECM taxa have
distinct preferences for soil moisture content (Agerer 2006),
pH (Grebenc et al. 2009; Ishida et al. 2009), nitrogen
(Lilleskov et al. 2002; Kranabetter et al. 2009), phosphorus
(Harrington and Mitchell 2005a; Twieg et al. 2009; Cairney
2011), zinc (Colpaert et al. 2005) and others (Erland and
Taylor 2002). (ii) Biotic factors include tree species identity
(Ishida et al. 2007; Tedersoo et al. 2008; O’Hanlon and
Harrington 2012a), interspecific ECM competition
(Kennedy 2010), presence of helper bacteria (Tedersoo et
al. 2009) and arthropod grazing (Schneider et al. 2005).
6 Temporal variation in ectomycorrhizal communities
across short timescales
Temporal variation has been recognised in above-ground
ECM sporocarp studies. Many species have distinct fruiting
times, for example Hebeloma sinapizans (Paulet ex Fr.) Gill
is known to sporulate early in the season (Martínez de
Aragón et al. 2007) whilst Hygrophorus hypothejus Fries.
fruits very late in the foray season (Phillips 2006). Some
species fruit yearly Russula ochroleuca (Pers.) Fr.
(Straatsma et al. 2001) whilst some Cortinarius camphoratus Fr.; (Martínez de Aragón et al. 2007) fruit only sporadically. Parallel to this, it has been recognised that some ECM
species are easily cultured in-vitro from spores, whilst others
are extremely difficult to culture from spore based medium
(Trape 1977). Species such as Laccaria spp. (Gherbi et al.
1999) and Hebeloma spp. (Guidot et al. 2001) have high
spore germination rates and also produce many sporocarps.
Also, these species are frequently found in disturbed or early
successional habitats (Dighton et al. 1986; Jumpponen et al.
1999, 2012; Nara et al. 2003a, b). Incidentally, recent genetic work has shown that for some ECM genera, for example the genus Laccaria, no clear species delineation is
Spatial and temporal variation in ectomycorrhizal communities
currently available (Douhan et al. 2011). Also, the species
Laccaria amethystina Cooke contains many morphospecies
(species based on sporocarp morphology) across Europe
(Vincenot et al. 2011), and associates with many different
host trees (Roy et al. 2008); a factor which some believe
leads to higher intra-specific variation due to allochronous
speciation (Dickie et al. 2010). Thus a future focus of ECM
population genetics should be in clearing up issues regarding ECM species delineation levels, so that the importance
of intra-specific diversity in ECM communities can be further examined (Johnson et al. 2012).
In contrast to this, species that are difficult to culture from
spores and infrequently produce sporocarps e.g. Russula brevipes Peck (Bergemann and Miller 2002) and Tricholoma
matsutake Singer (Amend et al. 2009) are often more common
as ECM fungi in undisturbed habitats. This has led researchers
to postulate that ECM species often focus their resources into
either spore based or mycelia based methods of colonization
(Newton 1992; Ishida et al. 2008). Termed the “trade-off in
species’ life histories” mechanism, Kennedy (2010) reviews
this hypothesis and comes to the conclusion that whilst it may
explain a noteworthy amount about ECM distributions, other
factors listed by Bruns (1995), such as resource partitioning,
ECM fungus host specificity and abiotic preferences of some
ECM fungi are also important.
The well known ephemerality of fungal sporocarps
(Watling 1995; Straatsma et al. 2001) is to some extent
mirrored in the below-ground ECM communities. Studies
of below-ground ECM communities over multiple sampling
occasions have shown that the ECM communities are not in
a static state (Guidot et al. 2004; Harrington and Mitchell
2005b; Izzo et al. 2005; Koide et al. 2007; Courty et al.
2008; Pickles et al. 2010). Of the previously mentioned
temporal studies, the long term study (3 years) of Izzo et
al. (2005) examined the effect of spatiotemporal variation on
the below-ground ECM community of fir forests in
California. They found that at large spatial scales the dominant species occurred every year whereas at small spatial
scales dominant species were sometimes completely
replaced. Similarly, albeit using a more restrictive sampling
protocol, Guidot et al. (2004) found that their target belowground ECM fungus, Hebeloma cylindrosporum Romag.,
was frequently eliminated from, and reintroduced to the
ECM community over a three year study carried out in of
Pinus pinaster Aiton forests in France. Over a 2 year study
in Scottish Scots pine forests, Pickles et al. (2010) found that
some Cortinarius species (called Cortinarius complex)
reappeared in a similar position on both years, while
Tomentellopsis submollis (Svrcek) Hjortstam displayed the
opposite pattern appearing in a different location in each
year. However, for the most part the authors noted no
significant correlation (positive or negative) between the
positions of most ECM species over the study period.
63
Studies examining ECM community temporal variation
over shorter scales (ca. 1 year) cannot be said to fully
capture the seasonal variation of the ECM community.
While longer term studies (over several years) run the risk
of introducing confounding factors such as changes in host
age and also the chance of immigration of new ECM species
into the community. Tree host age has been identified as
having a strong influence on the ECM communities using
above- (Smith et al. 2002) and below-ground (Palfner et al.
2005; Twieg et al. 2007) ECM studies in the past. In terms
of surveying above-ground fungal communities, a general
time-frame of 3 years of surveying in normally carried out,
although one exceptionally long term study was still finding
new species after 21 years (Straatsma et al. 2001). For
below-ground communities there has not been a general
consensus reached as to the minimal time frame, although
studies surveying over 1 year are not common amongst the
literature (Tóth and Barta 2010). Ideally, a study sampling
the ECM community of a plot every month over 2/3 years
would address temporal variations between months while
also including sufficient replication of results across years.
However, a study of this magnitude is likely to be very time
and cost intensive. Of the short term studies to address
temporal patterns in ECM communities, the study of Koide
et al. (2007) in a plantation pine forest in Pennsylvania, USA
examined the ECM communities by sampling every two
months over a period of 13 months. They found that taxa
richness and community structure varied widely across
months, and suggested that meteorological variables and the
availability of uncolonized roots might have a strong influence on their findings. Harrington and Mitchell (2005b) also
identified meteorological variables as the most likely factor
affecting the fluctuations in abundances of many species in
the ECM communities of dryas heaths in Ireland over a
14 month sampling period. Similar to the conclusions of
Koide et al. (2007), Courty et al. (2008) identified meteorological variation and available root space as significant in
explaining ECM community variation in a 14 month study
in a French oak forest. The French study also identified a
community in which the most abundant species fluctuated
vastly between months, with September being the month with
the highest species richness. Another interesting finding of
this study was that temporal variation was especially evident
in the upper soil horizons, possibly due to changes in soil
moisture levels through the seasons.
7 Functional morphology: a theory to explain stand level
ECM community variations?
A recent paper published by researchers associated with the
Bruns lab in UC Berkeley, seeks to unite many of the ideas
discussed in this article that account for the spatial and
64
R. O’Hanlon
temporal variation in ECM communities. Peay et al. (2011)
tested and discussed their functional morphology hypothesis
with data from forests of Bishops pine Pinus muricata D.
Don in California. They described how three zones exist in
forest soils with regard to root densities and forest succession (Fig. 1). In the spore competitive phase, species that
spread primarily by spores are dominant on the roots, such
as in early successional forests or afforested landscapes. The
next stage, termed the early mycelia competitive phase, less
abundant less reactive spores establish at low frequency and
vegetative competition ensues. In the ultimate phase, the
zoned competitive phase, root density explains a large portion of the community structure, with high density root
zones dominated by short-range exploration morphotypes
and lower density root areas dominated by long-range exploration types. There are many examples of ECM species
distributions which corroborate with the functional morphology theory: such as (i) the abundance of Laccaria spp.
(Fig. 1a, b), Inocybe spp. (Fig. 1c, d) and Hebeloma spp. in
both above- (Deacon and Fleming 1992; Nara et al. 2003b;
Jumpponen et al. 2012) and below-ground (Nara et al.
2003a) ECM communities in recently established forests,
(ii) the abundance of sporocarps and medium range exploration types of Cortinarius spp. (Fig. 1e, f) in old growth
(sparsely stocked) forests (Peter et al. 2001; Smith et al.
2002) and (iii) the abundance of Russula spp. (Fig. 1g, h)
sporocarps and contact exploration type ectomycorrhizas in
mature forests with high root densities (Kranabetter et al.
2005; Twieg et al. 2007; O’Hanlon and Harrington 2012a, b).
Thus, the functional morphology hypothesis unites many
theories from previous ECM research—including effects of
forest successional stages on ECM communities, ECM exploration types and colonization-competition theories.
However, this theory is relatively new, and has had little
time to be tested using data from other forest habitats,
although some contemporary studies have acknowledged
its worth (Kennedy et al. 2011; Hobbie et al. 2012;
O’Hanlon and Harrington 2012a; Velmala et al. 2012).
Peay and colleagues propose that the functional morphology
hypothesis will be most useful for explaining the variation
in ECM communities across the early successional stages of
forests, as this is when the greatest changes in root density
occur. They also recognize that leaf litter quality, soil edaphic qualities and the host tree identity are also likely to be
important factors explaining variation in ECM communities.
Fig. 1 Some applicable ectomycorrhizal species (above-ground and
below-ground structures; vertical axis) and the phase (horizontal axis)
in the functional morphology hypothesis (Peay et al. 2011) which they
fit into. The species are (a) Laccaria amethystina sporocarps and (b)
ectomycorrhizas; (c) Inocybe spp. sporocarps and (d) ectomycorrhizas;
(e) Cortinarius spp. sporocarps and (f) ectomycorrhizas; (g) Russula
ochroleuca sporocarps and (h) ectomycorrhizas
8 Guidelines for sampling ECM communities
Spatial and temporal variation is widely recognised in ECM
communities. ECM communities are structured by numerous factors, including tree species present, edaphic factors,
substrate preferences, ecosystem disturbance, competition,
and root density. The combined effects of these factors
appear to cause the non-random distributions seen in many
species of ECM fungi. This article has focussed on what
previous research has revealed about small scale spatial and
temporal variation on below-ground ECM communities. I
Spatial and temporal variation in ectomycorrhizal communities
will now propose some sampling guidelines which if followed, will allow for the collection of root samples which
give the best representation of the actual below-ground
ECM communities in the forest. To the best of my knowledge, guidelines dealing with the below-ground sampling of
ECM communities in temperate forests have not been proposed, while best practices for sampling epigeous and hypogeous fungal communities have been described several
times (Orton 1987; Vogt et al. 1992; Watling 1995; Mueller
et al. 2004; Halme et al. 2012).
Firstly, extensive sampling (few roots from many samples) is better than intensive sampling (many roots from few
samples) for revealing ECM species richness and actual
community structure (Gehring et al. 1998; Horton and
Bruns 2001; Peter et al. 2001; Taylor 2002; Nara et al.
2003a; Tedersoo et al. 2003; Kaldorf et al. 2004; Menkis
et al. 2005; Martin 2007). It follows that a trade-off is
needed in sample size (i.e. core size), so that the sorting
and analysis of the below-ground morhotypes does not
become unmanageable. Thus sample sizes smaller than
100 cm3, as used in a number of studies (Taylor 2002;
Parádi and Baar 2006; Pickles et al. 2010), are more manageable than those of larger volume; especially in areas of
high root abundance, such as in densely stocked forests. If
large soil samples are used, then a random selection of roots
from within this sample (see Eberhart et al. 1996) can be
taken and analysed to provide a realistic picture of the
below-ground ECM community (Nara et al. 2003a;
Tedersoo et al. 2003). These samples (e.g. cores) should
then be randomly dispersed (incorporating sufficient separation between samples to remove effect of spatial autocorrelation; see below) throughout all plots, as this provides the
study with sufficient replication within and between plots.
For example, O’Hanlon and Harrington (2012a) examined
the ECM communities of three forest types (oak, Scots pine
and Sitka spruce) in Ireland. They sampled the ECM communities of four plots of each forest type, and collected five
samples from each plot. Thus, they had replication present
for between- and within-plot comparisons. The use of replication is vital to allow for the environmental heterogeneity
typically seem in natural ecosystems. However, it has recently
been pointed out that many studies using “cutting edge”
molecular technologies (i.e. pyrosequencing) for describing
microbial communities lack sufficient replication, and thus
have little “real world” scientific meaning (Prosser 2010).
Second, in an effort to minimize the effect of spatial
autocorrelation, and thus pseudo-replication, the samples
should be spaced further apart than the known levels of
spatial autocorrelation in the examined forest. If no data
exists for levels of spatial autocorrelation in the forest type
in question—then using the data from previous research into
the ECM communities of temperate forests as a guide,
spacing samples at least 4 m apart will reduce the effects
65
of spatial autocorrelation between samples. During the analysis of results, tests for spatial autocorrelation, using a
Mantel correlogram should be applied. In a Mantel correlogram, a Mantel correlation is computed between a multivariate distance matrix and a matrix of geographic separation
between the samples. The multivariate distance matrix can
use numerous distance measures (e.g. Jaccard, Sørensen)
while the matrix of physical distances can represent actual
physical distances between samples or the geographic coordinates of the samples (i.e. X and Y values). However,
previous research has shown that using the physical distances between samples is preferable to using XY coordinates,
as physical distances include variation such as surface slope,
while XY coordinates assume that the samples are on the
same plane in calculating distance between samples (Urban
et al. 2002). The Mantel statistic is evaluated through a
permutational Mantel test and progressive correction tests
(e.g. Bonferroni) to correct for multiple testing (Legendre
and Legendre 1998). Software for carrying out Mantel correlograms is freely available through the R (R Development
Core Team 2011) suite of programs, the packages Vegan
(Oksanen et al. 2012) and Ecodist (Goslee and Urban 2007)
being two of the most popular packages.
Thirdly, where possible, samples should be collected
throughout the year, with a particular focus of sampling in
autumn. Previous studies have found September to be the
most above-ground (Hering 1966) and below-ground
(Courty et al. 2008) fungal species rich month in European
temperate forests. A study spanning several years and measuring the below-ground ECM community on a regular
(monthly) basis would allow for better comparisons with
the above-ground ECM community, as differences in sampling periods may be one of the main reasons for disparities
between above- and below-ground ECM communities in
previous studies (Tóth and Barta 2010; O’Hanlon and
Harrington 2012a). Sampling throughout the year will also
give an indication of how the ECM community changes
over time, and how quickly this change occurs; a key area
for future research to address (Taylor 2002; Courty et al.
2010). There have been several studies that sampled multiple years, yet did not sample very frequently throughout the
year (Izzo et al. 2005; Pickles et al. 2010). There have also
been studies that sampled several times per year, yet did not
sample over multiple years (Harrington and Mitchell 2005b;
Koide et al. 2007; Courty et al. 2008). Such 2 year, regularly
(monthly) sampled studies have been carried out for bacterial communities (Meier et al. 2008), although shorter studies are also more common for soil bacterial communities
(Smit et al. 2001; Juottonen et al. 2008).
In closing, it is exciting to note the massive developments
in the techniques for describing ECM communities that
have been made in recent years (Martin 2007; Hibbett et
al. 2009; Douhan et al. 2011). These methodological
66
advances will help discover where the “undescribed fungi”
may be hidden (Hawksworth and Rossman 1997), and thus
give a clearer idea of exactly how high fungal biodiversity
really is (Blackwell 2011). However, in order for these
methodological advances to be useful, it is vital that samples
of the below-ground ECM community used in future studies
acknowledge and deal with the high spatial and temporal
variation in the heterogeneous environment that is forest
soil; to again quote Prosser (2010), we must “replicate or
lie”.
Acknowledgements Much of this work was prepared while the
author was an International fellow at the World Forest Institute and
funded by the Council for Forest Research and Development
(COFORD), and the Harry A. Merlo foundation. The insightful comments of two anonymous reviewers added to the quality of this review.
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