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Ecology of Ectomycorrhizal-Basidiomycete Communities on a Local Vegetation Gradient
Author(s): Patrick Natel and Peter Neumann
Source:
Ecology,
Vol. 73, No. 1 (Feb., 1992), pp. 99-117
Published by: Wiley on behalf of the Ecological Society of America
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Ecology, 73(1), 1992, pp. 99-117
? 1992 by the Ecological Society of America
ECOLOGY OF ECTOMYCORRHIZAL-BASIDIOMYCETE COMMUNITIES ON
A LOCAL VEGETATION GRADIENT'
PATRICK NANTEL2 AND PETER NEUMANN
Departement de sciences biologiques, Universite de Montreal, C. P. 6128, succursale A,
Montreal, Quebec, Canada H3C 3J7
Abstract. To understand the factors that structure ectomycorrhizal-basidiomycete
communities at a local scale, we measured the strength of the relations among the fungal
communities, the tree communities, and the environment of a series of forest ecosystems
in southern Quebec. We collected fruit bodies belonging to ectomycorrhizal-basidiomycete
families and genera, sampled the woody vegetation, and described soils and landforms at
11 permanent sampling stations. We first calculated similarity matrices among stations,
one for each descriptor (fungal species abundance, woody species abundance, and abiotic
variables). We then explored the dependence among these matrices using Mantel and partial
Mantel tests, path analysis, and comparisons of ordinations and classifications. Similarity
among ectomycorrhizal fungus communities was strongly and significantly correlated with
tree community similarity, even when controlling for the effect of environmental similarity.
When the tests were made with a similarity matrix based on those tree species that are
known to be hosts of ectomycorrhizal fungi, abiotic similarity explained a significant portion
of the residual variation in the similarity among fungus communities. To explore this
complex relationship further, we analyzed species associations. The preference of fungus
associations for different sets of abiotic conditions showed that some factors affecting fungal
species distribution were different from those affecting the distribution of their tree hosts.
Direct and indirect gradient analyses showed that humus characteristics seemed to be
important niche dimensions of ectomycorrhizal fungi. The continuum concept was useful
to interpret the complex relations among symbiotic species. Trees were the main component
of the realized niche of ectomycorrhizal Basidiomycetes, but the fungal symbionts of a
particular tree species followed that tree species for only a part of the abiotic gradients
over which the host tree was found. This type of distribution predicts that beta diversity
of fungi would be generally higher than beta diversity of ectomycorrhizae-forming trees.
It also means that the ratio of fungus species richness to woody species richness would be
high for most community gradients. Our results and those of previous mycosynecological
studies agree with these predictions. The results have implications for the conservation of
biodiversity: site selection for conservation based on vegetation classification or mapping,
or on distribution of tree species, may miss important fungal species.
Key words: Basidiomycetes; biological associations; canonical correspondence analysis; diversity
of ectomycorrhizal fungi; ecological classification; ectomycorrhizae; gradient analysis; macrofungal
synecology; Mantel tests; path analysis; tree communities; trees-fungi-environment relationships.
INTRODUCTION
Synecology of macrofungi has been investigated
mostly by European mycologists (Cooke 1948, 1953,
1979, Hueck 1953, Apinis 1972, Darimont 1973). Many
of these studies were inspired by the "floristic-socio-
logical approach" that had been developed with vas-
cular plants. Results show generally a high fidelity of
many fungus species for particular plant associations
(e.g., Lisiewska 1974). However, the different eco-
physiological groups of fungi (saprotrophes, mycor-
rhizae, parasites) play quite different roles in an eco-
system and are linked in different ways to the host plant
' Manuscript received 5 March 1990; revised 28 March
1991; accepted 28 April 1991.
2 Present address: Departement de sciences biologiques,
University du Quebec a Montreal, C. P. 8888, succursale A,
Montreal, Quebec, Canada H3C 3P8.
species. Consequently, studies of trees-fungi-environ-
ment relationships must aim at one of these groups at
a time, as in the work of Bills et al. (1986) on ecto-
mycorrhizal-basidiomycete communities (EBC). Re-
cently, the work of Villeneuve et al. (1989) shows that
the diversity of ectomycorrhizal taxa is more clearly
related to percentage cover of ectomycorrhizal hosts
than to the diversity of vascular plants.
An association between two taxocenes, like EBC and
plant communities, poses an obvious problem of in-
terpretation. Indeed, it can imply: (1) that some en-
vironmental factors controlling the distribution of
symbiont (plant) species are the same as those struc-
turing the distribution of fungal species, (2) that the
composition of a plant community affects directly or
indirectly the composition of the fungal community
that shares the same habitat, or (3) both.
The purpose of this study was to identify the factors
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100 PATRICK NANTEL AND PETER NEUMANN Ecology, Vol. 73, No. 1
that affect the composition and structure of EBC at a
small ecological scale. Trees-fungi-environment rela-
tionships were treated as a problem of multiple cor-
respondence between three matrices (descriptors by
sampling station) where the descriptors were: (1) woody
species abundance (including the different growth stages
of the trees); (2) ectomycorrhizal-basidiomycete spe-
cies abundance, evaluated using the frequency and bio-
mass of basidiomata (sporophores of Basidiomycetes;
"fruit bodies"); and (3) the environmental variables.
We measured the relationship between such matrices
using a two-step procedure: (1) computation of simi-
larity matrices among stations, one for each type of
data, and (2) computation of a series of: (i) correlations
between all combinations of two of these similarity
matrices, and (ii) partial correlations between the same
pairs of matrices, removing the effect of the third one.
This last step involved Mantel tests (Mantel 1967) as
well as partial Mantel tests as described in Smouse et
al. (1986). The reasoning behind this approach is the
following: if the composition of EBC depends upon the
composition of the tree community, then when a pair
of tree communities are similar, the EBC of the same
two sample stations should also be similar to the same
extent. This approach was needed to provide an ob-
jective measure of the strength of the association be-
tween taxocenes and an unequivocal way to interpret
it. To our knowledge, our work represents the first
direct and quantitative approach to this problem.
The results are discussed within the framework of
the continuum concept (Whittaker 1967); ecological
relations between symbionts are viewed as relations
between distribution curves of the associated species
of the two taxocenes along an environmental gradient.
Most fungal species may follow their tree symbionts
for only a part of the abiotic gradients over which the
host tree was found or along all of these gradients; this
will have different consequences on the beta diversity
and on the ratio of fungus species richness to woody
species richness in the corresponding community gra-
dient (coenocline). This, in turn, has important impli-
cations for the conservation of biodiversity.
METHODS
Geographic and biogeographic aspects of
the sampling site
The sampling site was located at the "station de
biologic de l'Universite de Montr6al," which occupies
a portion of the lower Laurentides, 80 km north of
Montreal (740 W; 460 N; altitude: 400 m). Thle climate
is continental-temperate: the mean annual temperature
is 2.5%C (maximum: 100C, minimum: -2.5%C), total
annual precipitation is 1000 mm, thermic amplitude
is 32%C with a mean number of 110 d without frost
(Wilson 1971, Houde 1978). The site, with an undu-
lating and mountainous relief, lies on the southern flank
of the Precambrian Shield. Soil parent material is mostly
glacial till, but organic soils are common. The domi-
nant soils are ferro-humic podzols (Commission cana-
dienne de pedologie 1978) with a sandy-loam texture.
In Grandtner's classification of Quebec forests (1966)
the climax vegetation of the site is yellow birch-maple.
Today, it is dominated by white birch (Betula papyrife-
ra) stands mixed with sugar maple (Acer saccharum)
that occupy mesic sites disturbed in the past by cutting
and fire (Gagnon 1975). Pure sugar maple stands mixed
with yellow birch (Betula alleghaniensis) and beech
(Fagus grandifolia) are mostly found in mesic areas
where the till is thick and where the disturbances have
been minor. White pine (Pinus strobus) stands and bal-
sam fir (Abies balsamea) stands occupy rapidly drained
sites where soil is thin. Organic soils are colonized by
boggy vegetation in most places, but Thuja occidentalis
stands have developed on organic soils localized at the
bases of seepage slopes.
Vegetation sampling and description of
the stations
With aerial photographs and field experience we se-
lected 11 sampling plots of 20 x 20 m that represented
the six different major vegetation types that we per-
ceived. In each plot we recorded the woody vegetation
using the methods described in Gagnon and Bouchard
(1981), except that we sampled neither the herbaceous
nor the bryophyte strata. This method includes esti-
mates of abundance for all tree species in three different
diameter classes (seedlings, saplings, and trees) and of
all shrub species. For plot descriptions we recorded the
following geomorphological characteristics using the
guidelines and terminology of Day and McMenamin
(1983): altitude, aspect, slope (percentage, form), po-
sition of the station on the slope, superficial deposit
type and relative thickness, micro-relief, stoniness,
rockiness, and flooding. We also noted signs of past
disturbances.
Soil analyses
All soil samples were taken within 2 d in the 1st wk
of June to reduce variability imposed by the weather.
In the field we measured the horizon thickness, in-
cluding the humus layers (with the definitions found
in Day and McMenamin [1983]). The definition of min-
eral horizons was based on that of the Commission
canadienne de pedologie (1978): Ae = pale horizon
where leaching occurs (eluvial horizon); B1 = dark
horizon where accumulation of organic matter and iron
oxides occurs; B2 = lower horizon (BC) with a yellower
hue and a weaker color value. To record the color of
mineral horizons, we took fresh samples and used the
Munsell code of colors (Munsell Color Company 1969).
We measured pH with fresh soils, <24 h after the
sampling, using a Metrohm Herisau pH meter and a
0.01 mol/L CaCl2 solution. Soil samples were then
dried, allowing us to evaluate their water content-
here defined as the mass of water expressed as a per-
centage of the total mass of the undried sample (percent
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February 1992 ECTOMYCORRHIZAL-BASIDIOMYCETE COMMUNITIES 101
water). Dry samples were used for the remaining anal-
yses: (1) granulometric analysis (for particles >4 mm
in diameter, using the sieving technique; for particles
<2 mm, using the hydrometer method [Bouyoucos
1962] with the pre-treatment found in McKeague [1978:
16-17]); (2) determination of the organic matter con-
tent, which corresponds to the percentage of dry sample
mass that was lost on ignition (percent organic matter);
and (3) measure of major elements, including: (a) ex-
changeable hydrogen and total exchangeable bases, us-
ing the methods of Brown (1943); (b) concentration of
individual exchangeable cations Ca+2, Mg+2, and K+,
using a Perkin-Elmer 2380 atomic absorption spectro-
photometer, following the extraction procedures and
calculation found in McKeague (1978: 84-87); (c)
phosphorus soluble in water (as an availability index),
using the molybdenum blue technique (Olsen and Dean
1965); (d) the total nitrogen content, using the Kjeldahl
method.
Sampling of the ectomycorrhizal-basidiomycete
communities (EBC)
For sampling of the ectomycorrhizal Basidiomy-
cetes, we divided each station (plot) in 100 2 x 2 m
sub-quadrats to evaluate the spatial frequency of each
species according to the principles discussed in Bills et
al. (1986). During the summer of 1986, we regularly
visited six stations and collected all the basidiomata
of ectomycorrhizal species that could be found. Each
station was sampled at least eight times between 15
June and 30 August. In 1987 we regularly visited 11
stations and each was sampled 9 or 10 times between
28 May and 8 September. At each visit we recorded
the position in the grid of all basidiomata and identified
specimens in the laboratory. After dry-mass determi-
nation, all specimens were kept dried in a herbarium.
We collected all the Basidiomycetes taxa considered
as ectomycorrhizal by Trappe (1962) and Miller (1983).
We were also guided in our choice by Godbout and
Fortin (1985) and several taxonomic monographs
(Hesler and Smith 1963, Largent 1977, Pomerleau 1980,
1984, Singer 1986). For practical reasons we excluded
all the "Gasteromycetes" taxa which were, in any case,
rarely found in our sampling stations. On the other
hand, we included taxa for which the mycorrhizal sta-
tus had not been proven yet: some Hygrophorus and
Entolomaceae species in particular.
Data analysis
Before analysis we made the following manipula-
tions and transformations of the raw data. (1) Within
each station we calculated the relative dry mass for
each species of ectomycorrhizal Basidiomycetes by di-
viding its dry mass by the total dry mass of all the
species of the same plot and multiplying the result by
100. This was done for the biomass of one or two
seasons, according to the length of the record for each
plot. We also counted the number of sub-quadrats in
which each species was found (spatial frequency), for
one or two seasons as above, and computed a value of
relative spatial frequency by dividing its frequency by
the total frequency of all the species of the same plot.
(2) We calculated a relative "importance" value for
each species at each plot. For the ectomycorrhizal Ba-
sidiomycetes this was done by adding relative dry mass
and relative spatial frequency. For members of the
canopy layer (mature trees) we added relative domi-
nance (percentage of the total basal area) and relative
density. For the shrubs we combined the relative spa-
tial frequency with the relative dominance based on
total shrub cover. For the seedlings we added relative
frequency, relative cover, and relative density. For the
sapling stratum the importance value was simply the
relative density. By keeping canopy trees, saplings, and
seedlings separate we were able to search for relations
between fungal species and life stages of the tree species.
Indeed, successions of ectomycorrhizal species during
the developmental stages of different tree species are
documented by Marks and Foster (1967), Mason et al.
(1982), Dighton et al. (1986) and Garbaye et al. (1986).
Finally, (3) we removed from the final matrices: (a)
the fungi not completely identified (identification to
the genus only) and (b) the fungus and tree species that
occurred at one sampling station only. The abundance
values of unidentified and rare species contributed to
the percentage values of the other species in the same
plot but were not involved in the other steps of the
analysis.
Some soil descriptors were combined to reduce the
number of abiotic variables and to ease interpretation.
The concentration of all nutrients (Ca+2, Mg+2, K+,
available P, and total N) determined in the organic
horizon and in three mineral horizons (Ae, B1, B2)
were combined by horizon after standardization to a
mean of 0 and a variance of 1. These combinations
gave, respectively, an index of richness in mineral nu-
trients of the organic horizon (humus) and an index of
the richness in mineral nutrients of the mineral soil.
The total exchangeable H+ was combined with the total
exchangeable bases to give an index of the total ex-
change capacity of the different horizons. The total
exchange capacities of the three mineral horizons were
summed to give an index of exchange capacity of the
mineral soil. Finally, for mineral soil the pH, percent-
age of clay, silt, and sand, and the percentage of water
and organic matter were expressed as the mean of the
values for the three mineral horizons.
Comparison of data matrices. -The relationships
among the vegetation, EBC, and the environment can
be expressed as the relations among three matrices. We
began by computing three similarity matrices among
all pairs of plots, one for each type of data. For the
similarity among community sample, we chose the
Steinhaus coefficient for two reasons: (1) being an
asymmetrical coefficient, it does not consider double
zeros as an indication of resemblance; (2) Gower and
Legendre (1986) showed its reliability for measuring
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102 PATRICK NANTEL AND PETER NEUMANN Ecology, Vol. 73, No. 1
high and low similarities with the same fidelity. For
the abiotic similarity matrix, we used the Estabrook
and Rogers coefficient (Estabrook and Rogers 1966)
because it can handle data with different levels of pre-
cision. The equations and rationale for these two co-
efficients can be found in Legendre and Legendre (1983).
Mantel tests among similarity matrices. -To mea-
sure the relations among the three similarity matrices,
we computed a series of three Mantel tests (Mantel
1967) and three partial Mantel tests (Smouse et al.
1986). The Mantel test is a correlation test adapted to
similarity or distance matrices (Legendre and Fortin
1989). Using the standardized Mantel statistic as the
input, we then performed a path analysis (Sokal and
Rohlf 1981). This way, the analysis is not standard
because Mantel statistics are used instead of Pearson
correlation coefficients. Therefore, significance tests of
path coefficients cannot be used for interpretation. In
a second series of tests, we took a similarity matrix
among tree communities computed with only the tree
species that are known to be hosts of ectomycorrhizal
fungi, according to Trappe (1962), Malloch and Mal-
loch (1981, 1982), Brundrett and Kendrick (1988), and
Berliner and Torrey (1989).
Ordinations and classifications of community sam-
ples. -The main purpose of this step of the analysis
was to allow a finer resolution of the relationships re-
vealed by the Mantel tests. All three similarity matrices
were used to compute classifications and ordinations.
The latter were done through principal coordinate anal-
yses (PCOA). We chose this technique to project, on
an orthogonal system of axes, the similarities among
samples that have been described above. For the cluster
analyses of community samples, we used hierarchical,
agglomerative, proportional-link linkage clustering,
with connectedness between 0.50 and 0.60. These val-
ues of connectedness were chosen to overcome the
chaining phenomenon (Legendre and Legendre 1983).
Results of the classifications were superimposed on the
first two axes of the PCOA. We also placed the sam-
pling stations on the minimum spanning tree (MST)
of the clusters. MST is defined (in a single linkage
clustering) as the chain formed by the first similarity
link that put an object in a group or that allows two
groups to merge (Gower and Ross 1969, Legendre and
Legendre 1983). In his computer program for cluster
analysis, Legendre (1985) generalizes this concept to
the other agglomerative clustering methods. This su-
perimposition was useful to visually compare the struc-
ture of the two related taxocenes. To produce abiotic
groups of sampling stations, we used a K-means al-
gorithm (MacQueen 1967) with the three first axes of
PCOA extracted from the abiotic similarity matrix as
the input variables. To associate each station group
with the set of abiotic characteristics that caused it, we
computed Kendall's nonparametric correlations be-
tween the two first axes of the ordination and the en-
vironmental variables. We also computed the average
of each abiotic variable within each group.
Direct gradient analyses. -To analyze in more detail
the effect of soil variables on the structure of the tree
communities and on the structure of EBC, we com-
puted two canonical correspondence analyses (CCA)
(ter Braak 1986, 1987), one for each species data set,
but using the same soil variables. CCA is a technique
that selects the linear combination of environmental
variables that maximizes the dispersion of species
scores. We used it because, unlike PCOA, it is able to
detect unimodal relationships between species and ex-
ternal variables (ter Braak 1988). We took as the ex-
ternal variables the ones that describe the mineral ho-
rizons of the soil, in order to see the different responses
of the two taxocenes to the same gradient. The rest of
the abiotic variables were correlated with this edaphic
gradient after the extraction of the axes. We also com-
puted partial correlations between abiotic variables and
the two first axes of the CCA of fungal communities,
removing the effect of the corresponding axes of the
tree communities. This allowed us to identify the abi-
otic variables that might influence the distribution of
Basidiomycetes independently from the effect of the
composition of their associated tree community.
Relations between trees, Basidiomycetes, and the en-
vironment. -The objective of this series of analyses
was to bring out relationships at the species level rather
than at the community level. Grouping of species was
used mainly to reduce variability because the number
of samples is low compared to the high number of
species and abiotic variables. Also, we wanted to an-
alyze the effect of environmental variables when these
are considered collectively. Therefore, the analysis of
the distribution of clusters of species was made using
sets of abiotic variables to characterize their habitat.
Cluster analyses of species. -To study the relations
between each species of ectomycorrhizal Basidiomy-
cetes and individual tree species, we merged in a single
table the importance values of fungi and trees for each
sampling station. Following the method used by Ber-
geron and Bouchard (1983) we then computed an as-
sociation matrix among species using the chi-square
similarity coefficient (Roux and Reyssac 1975). With
this matrix, we computed a cluster analysis using the
agglomerative, complete-linkage method. Species as-
sociations were then defined by clusters established at
an arbitrarily fixed similarity value.
Biological associations and habitat preferences. -To
study the interrelations between sets of abiotic con-
ditions and the abundance of the species that belonged
to each biological association, we grouped in a two-
way contingency table (1) the stations of the same abi-
otic group and (2) the species of the same clusters of
the complete linkage. In this table each combination
of a station group with a species cluster forms a cell.
In each cell we computed the relative constancy (Rq)
of the species cluster in the abiotic group of stations.
This value (Rq) is a measure of the relative weighted
average ubiquity of a cluster of species within the series
of groups of stations representing different sets of abi-
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February 1992 ECTOMYCORRHIZAL-BASIDIOMYCETE COMMUNITIES 103
otic conditions. The weighted average ubiquity (U) of
a cluster of species (q) is given by the following formula:
m n /
Uq = 2 2 aiiln/ m,
j=l i=l
where aij = relative abundance of the ith species of
cluster q in the jth station of abiotic group k, n =
number of species in the cluster q, and m = number
of stations in the cluster k.
The relative weighted average ubiquity of a group
of species "q" is given by the following formula:
Rq =1 00 (Uq 2;( Uq)k)
( /k= I )
where p = number of abiotic groups of stations.
We repeated the computation of Rq using the total
relative abundance of each corresponding tree species
cluster in each station group. For this, we kept only
those tree species that are known to be ectomycor-
rhized (see references given above in Mantel tests among
similarity matrices). Then we computed a correspon-
dence statistic (Neu et al. 1974) with the relative con-
stancy of tree groups as the expected value of the con-
stancy of basidiomycete clusters (observed value). For
each comparison, the null hypothesis is that there is
no difference between the expected and the observed
value; this would indicate that the "habitat preference"
of a cluster of fungi is determined by the habitat pref-
erence of its associated cluster of ectomycorrhized trees.
The alternative hypothesis would indicate an avoid-
ance or a preference (depending on the sign of the
difference) of the fungal species clusters over different
sets of abiotic conditions, controlling for the effect of
ectomycorrhized trees.
Similarity matrices, principal coordinate analysis,
cluster analysis, Kendall's tau, and Mantel and partial
Mantel tests, were all computed with the "R" package
of Legendre and Vaudor (Legendre 1985). Canonical
correspondence analyses were computed with CANO-
CO (ter Braak 1988). Correspondence statistics were
computed using our own program.
RESULTS
During the two seasons of sampling (summer of 1986
and of 1987) we collected 240 species of ectomy-
corrhizal Basidiomycetes at the 11 sampling stations.
We were able to identify half of these to species (120);
59 of the species were found in more than 1 plot (Table
1). Unidentified species belong mostly to genera Cor-
tinarius and Russula and to the Entolomaceae family.
These groups are poorly known in eastern North Amer-
ica and especially in Quebec. At the same 11 sampling
stations 33 woody species were sampled: 15 trees and
18 shrubs. Of these, 13 species of trees, present in one
growth stage or another (Table 2), and 14 shrub species,
were identified in > 1 station. The similarity matrix
among plant communities that was used in the first
series of Mantel tests was computed with data of Table
2 only.
For each sampling station, we measured or observed
70 environmental variables: 28 and 15 of them, re-
spectively, describe the chemical and physical prop-
erties of the mineral horizons of the soil, 14 measure
the chemical and physical properties of the organic
horizon (humus), and 13 result from geomorphological
observations. After the combinations mentioned above,
37 variables remained (Table 3). They were used in
the computation of the abiotic similarity matrix among
stations.
Results of Mantel and partial Mantel tests show that
the structures of the tree community and the ecto-
mycorrhizal-basidiomycete community (EBC) are
strongly correlated, whether the similarity matrix used
was based on all tree species (Table 4) or ectomycor-
rhized tree species only (Table 5). Moreover, the partial
relation between composition of tree communities and
composition of EBC, removing the effect of abiotic
variables, is strong in both comparisons.
For the comparison based on all tree species, the
abiotic similarity matrix (SimABIO) is not significantly
correlated with any other similarity matrix (Table 4).
For the comparison based on ectomycorrhized tree
species, correlation and partial correlation between
abiotic similarity and EBC similarity, removing the
effect of tree communities, are weak but statistically
significant (Table 5). The path diagram in Fig. 1 further
summarizes these results. The path coefficients pre-
sented could not be tested in the usual manner for
statistical significance, because similarity matrices are
always non-independent variables; the partial Mantel
tests in Table 4 can be used as tests of significance of
these path coefficients, however. Fig. 1 shows the linear
relationships between similarity matrices, and we can
see that, in this context, most of the variance of the
two biotic similarity matrices remains unexplained by
the abiotic data used in this study. The main point,
however, is the strong causal relation in Fig. 1 between
SimEBC and SimTREES: it represents a significant
causal link, because it corresponds to a significant par-
tial Mantel statistic in Table 4, controlling for the effect
of the abiotic data.
The structures of the three data matrices are rather
similar (Figs. 2-4). Indeed, many pairs of sampling
stations are positioned near each other (e.g., 1 and 3,
10 and 7, 11 and 9) on the three ordination diagrams
and cluster in the same way. Ordination of either tree
communities or EBC separates clearly those commu-
nities dominated by deciduous tree species from those
dominated by coniferous trees.
The ordination and classification of stations based
on abiotic variables separates three groups of stations
(plots), each identified by a distinct symbol (Fig. 4).
First, there is a clear distinction between the "group"
with a wet organic soil (station 5) and the two groups
with mineral soils (the rest of the plots). Thus, the first
principal coordinate analysis (PCOA) axis is highly
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104 PATRICK NANTEL AND PETER NEUMANN Ecology, Vol. 73, No. 1
TABLE 1. Relative importance values* of ectomycorrhizal-basidiomycete species identified in more than one sampling station.
Stations were 20 x 20 m plots; here they are sorted according to the similarity of their tree community.
Sampling stations
Basidiomycete species
1 3 2 10 8 7
Hygrophorus ceraceus (Fr.) Fr. 39.17 64.26 0 0 0 0
Nolanea lutea Pk.t 10.66 0 17.6 0 0 0
Nolanea quadrate B. & C. 7.227 0.859 15.74 0 0 0
Nolanea strictior (Pk.) Pomerieau 5.715 3.637 5.334 3.78 0.706 7.492
Hygrophorus parvulus Pk. 3.402 3.271 0 0 0 0
Hygrophorus cantharellus (Schw.) Fr. 2.715 1.695 6.023 0 0 0
Hygrophorus marginatus Pk. 2.091 45.55 3.984 1.193 0 0
Laccaria laccata (Fr.) B. & Br. 1.143 0 1.269 3.779 7.272 0
Amanita muscaria (Fr.) Hooker var.
formosa (Fr.) Bertillon 0 26.8 0 18.1 3.256 0
Clavulinopsisfusiformis (Fr.) Cor. 0 7.674 29.04 0 6.06 0
Hygrophorus laetus (Fr.) Fr. 0 6.223 0 2.254 0.016 6.518
Amanita brunnescens Atk. 0 5.131 0 1.609 9.898 93.99
Ramariopsis kunzei (Fr.) Donk 0 5.037 6.805 0 0 0
Hygrophorus pallidus Pk. 0 4.586 0 0 0 0
Russulafragilis (Fr.) Fr. 0 3.132 2.163 9.851 12.66 9.967
Amanita citrina Schaeff. ex S.F.G. 0 2.645 0 13 0.758 0
Amanita porphyria (Fr.) Secr. 0 2.52 0 0 4.61 0
Hygrophorus pratensis (Fr.) Fr. 0 1.692 0 0 0 0
Lactarius thejogalus (Fr.) Fr. 0 1.509 29.33 0 0.719 3.004
Paxillus involutus (Fr.) Fr. 0 1.503 0 0 0 0
Hygrophorus unguinosus (Fr.) Fr. 0 1.021 0 0 0 2.185
Russula cyanoxantha (Schaeff.) Fr. 0 0 12.96 0 0.839 0
Amanitaflavoconia Atk. 0 0 12.51 0 0 0
Rozites caperata (Fr.) Karst. 0 0 8.106 0 6.228 10.03
Amanita vaginata (Fr.) Vitt. var. fulva Gill. 0 0 5.991 3.84 1.924 0
Hygrophorus nitidus Berk. & Curt. 0 0 5.898 0 0.622 0
Russula silvicola Shaffer 0 0 0 47.89 9.714 7.141
Boletus piperatus Fr. 0 0 0 11.91 0 0
Russula raoultii Quel. 0 0 0 3.293 0 0
Cortinarius armillatus (Fr.) Fr. 0 0 0 1.997 0 0
Cantharellus cibarius Fr. 0 0 0 1.509 3.986 0
Hebeloma mesophaeum (Pers.) Quel. 0 0 0 0 24.16 0
Russula claroflava Grove 0 0 0 0 6.827 0
Russula paludosa Britz. 0 0 0 0 4.395 0
Cortinariusflexipes (Fr.) Fr. 0 0 0 0 4.308 0
Inocybeumbrina Bres. 0 0 0 0 3.769 0
Suillus granulatus (Fr.) Kunt. 0 0 0 0 3.754 11.49
Lactarius glyciosmus (Fr.) Fr. 0 0 0 0 3.549 0
Amanita virosa Secr. 0 0 0 0 1.933 0
Russula puellaris Fr. (?) 0 0 0 0 1.422 0
Lactarius lignyotus (Fr.) Fr. 0 0 0 0 1.016 0
Lactarius thyinos Smith 0 0 0 0 0.948 0
Boletinus pictus (Pk.) Pk. 0 0 0 0 0 9.848
Leccinum scabrum (Fr.) S. F. Gray 0 0 0 0 0 3.32
Leccinum insigne Smith, Thiers & Watling 0 0 0 0 0 0
Russula roseipes (Secr.) Bres. 0 0 0 0 0 0
Cortinarius bolaris (Fr.) Fr. 0 0 0 0 0 0
Cortinarius evernius (Fr.) Fr. 0 0 0 0 0 0
Lactarius sordidus Pk. 0 0 0 0 0 0
Hebeloma testaceum Fr. (?) 0 0 0 0 0 0
Russula peckii Sing. 0 0 0 0 0 0
Clavulina cristata (Fr.) Schroet. 0 0 0 0 0 0
Leccinum holopus (Rostk.) Watling 0 0 0 0 0 0
Xerocomus subtomentosus (Fr.) Quel. 0 0 0 0 0 0
Cortinarius vibratilis (Fr.) Fr. 0 0 0 0 0 0
Tylopilusfelleus (Fr.) Karst. 0 0 0 0 0 0
Hygrophorus miniatus var. miniatus (Fr.) F 0 0 0 0 0 0
Leptoniaformosa (Fr.) Gill. 0 0 0 0 0 0
Abiotic group? 1 1 2 1 2 2
* Relative importance value = [relative dry mass (%) + relative spatial frequency (%)].
t Nomenclature follows that of Pomerleau (1980, 1984). Species are sorted according to their relative importance value in
all sampling plots.
: New species for the Quebec province.
? Based on total abiotic similarity (see Fig. 4).
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February 1992 ECTOMYCORRHIZAL-BASIDIOMYCETE COMMUNITIES 105
TABLE 1. Continued.
Sampling stations
6 11 4 9 5
o 0 0 0 0
0 0 0.542 1.437 0.965
0 0 4.514 0 5.067
1.893 1.022 2.241 2.982 0
0 0 0 0 0
0 0 0 1.185 7.788
0 0 1.678 1.175 0
2.426 2.815 0 1.609 21.74
0 0 0 0 0
0 0 0 0 0
6.24 0 1.59 0 5.503
29.18 0 0 0 0
0 0 4.404 0 0
0 0 0 0 1.014
2.972 5.039 0.567 21.83 4.192
0 0 0 0 0
4.381 0 3.722 0 0
0 0 0 0 0.481
2.027 8.144 17.23 7.947 6.231
0 0 0 0 1.069
0 0 2.519 1.456 0
0 0 0 0 0
0.346 17.77 0 30.41 0
0 0 5.004 0 1.873
0 8.647 0 3.203 1.64
0 0 12.67 0 2.072
25.85 9.519 1.592 38.17 2.558
0 1.462 0 0 0
2.479 0 0 0 0
0 0 2.786 8.141 0.535
0 0 0 0 0
0 0 0 0 0
0 0 0 2.186 0.493
13.32 3.219 1.235 3.033 1.823
0 4.446 0 0 22.69
0 2.194 0 0 0.631
0 0 0 0 0
0 0 0 0 0
0 12.03 0 0 0
2.435 6.63 0.565 0 1.011
0 1.947 0 16.63 0.55
0 0 0 0 1.861
7.837 0 75.94 8.748 0
0 7.166 0 4.553 3.641
27.52 0 0 1.149 0
8.592 3.549 0 0 0
2.189 0 5.249 0 0
0 17.58 0 0 1.095
0 13.26 9.102 0 0
0 9.395 1.556 0 3.957
0 7.316 0.692 0 0
0 5.912 0 0 1.431
0 1.273 0 7.372 0
0 1.247 2.48 0 0
0 0.934 0 0 2.022
0 0 1.862 8.049 0
0 0 1.671 0 8.275
0 0 0.533 0 1.626
2 2 1 2 3
correlated with moisture, which was mainly deter-
mined by the nature and the thickness of surface de-
posits. From right to left on Fig. 4 the soils are also
less acidic and richer in mineral nutrients. In the two
groups with a mineral soil, we can distinguish stations
1, 4, and 3 at the left bottom of the plan from the rest
of the stations at the upper left. Those three plots were
dominated by shade-tolerant trees (Table 2), and no
burned or cut stumps were observed in them. In the
other group of stations (2, 5, 6, 7, 8, 9, 10, and 11),
shade-intolerant trees such as Betula papyrifera and
Populus grandidentata, and burned or cut stumps were
more abundant. This suggests a disturbance gradient.
When examining the means of different variables for
each group of plots defined abiotically (Table 3), the
mineral horizons of group 1 (stations 1, 3, 4, and 10)
were rather rich in mineral nutrients, had a low per-
centage of water (weak capacity of water retention),
and a low percentage of organic matter (loss on igni-
tion). The humus was thick but had a low percentage
of organic matter and of water, and was poor in mineral
nutrients. Compared to group 1, group 2 (stations 2,
6, 7, 8, 9, 1 1) had mineral horizons that were poorer
in mineral nutrients, but had a greater exchange ca-
pacity and a higher percentage of water and of organic
matter. The thin humus had a higher percentage of
organic matter and of water, and was richer in mineral
nutrients.
Canonical correspondence analysis (CCA) dispersed
the community samples on environmental axes, i.e.,
on axes that were linear combinations of edaphic vari-
ables. The contribution of each variable involved in
the analysis is shown in the table of canonical coeffi-
cients (Table 6). The variables that combine a high
canonical coefficient with a high associated t value are
those that may have had the most effect on community
structure. These values have an exploratory use only,
since we cannot test their statistical significance (ter
Braak 1988).
For tree communities, the first two axes of CCA
explain a greater fraction of the variability (67.2%) than
for EBC (42.9%). Also, there are more variables with
high canonical coefficients for the first axis of tree com-
munities than for the same axis of EBC. All variables
have low coefficients on the first axis of EBC, but some
contribute more to the second axis (like the total ex-
change capacity and the mean richness). These results
suggest that the- edaphic gradient had more effect on
the distribution of tree species than on the distribution
of Basidiomycetes. Many external abiotic variables are
linked to the edaphic gradient, as shown by their cor-
relations with environmental axes (Table 7). For the
tree communities, these variables are the slope and
humus characteristics (percentage of water, percentage
of organic matter and richness in mineral nutrients).
For EBC, these variables are the thickness of the litter,
percentage of water in humus, slope, thickness and
nature of the surface deposit, and aspect.
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106 PATRICK NANTEL AND PETER NEUMANN Ecology, Vol. 73, No. 1
TABLE 2. Relative importance values* of tree species occurring as mature trees or saplings in more than one sampling station.
Stations were 20 x 20 m plots; here they are sorted according to the similarity of their tree community.
Sampling stations
Tree speciest 1 3 2 10 8 7
Acer saccharum (tree)t 200.00 161.75 0.00 0.00 0.00 0.00
Acer saccharum (sapling)t 60.00 100.00 65.00 4.84 64.90 0.00
Acer rubrum (tree)t 0.00 30.79 72.99 0.00 4.53 12.70
Populus grandidentata (tree) 0.00 7.46 14.69 111.80 38.20 93.50
Betula papyrifera (tree) 0.00 0.00 96.17 64.50 91.00 19.30
Abies balsamea (sapling) 0.00 0.00 7.50 9.68 18.90 15.80
Betula alleghaniensis (sapling) 0.00 0.00 5.00 0.00 0.00 0.00
Acer rubrum (sapling): 0.00 0.00 2.50 75.80 16.20 0.00
Populus tremuloides (tree) 0.00 0.00 0.00 19.15 0.00 0.00
Betula papyrifera (sapling) 0.00 0.00 0.00 9.68 0.00 57.90
Abies balsamea (tree) 0.00 0.00 0.00 4.56 49.10 0.00
Picea glauca (tree) 0.00 0.00 0.00 0.00 6.19 0.00
Picea mariana (tree) 0.00 0.00 0.00 0.00 5.81 0.00
Pinus strobus (tree) 0.00 0.00 0.00 0.00 5.15 74.70
Pinus strobus (sapling) 0.00 0.00 0.00 0.00 0.00 15.80
Populus grandidentata (sapling) 0.00 0.00 0.00 0.00 0.00 10.50
Thuja occidentalis (tree)t 0.00 0.00 0.00 0.00 0.00 0.00
Thuja occidentalis (sapling)# 0.00 0.00 0.00 0.00 0.00 0.00
Abiotic group? 1 1 2 1 2 2
* Relative importance value for trees = [relative dominance (%) + relative density (%)]; relative importance value for
saplings = [relative density (%)].
t Nomenclature follows that of Marie-Victorin (1964). Species are sorted according to their relative importance value in
all sampling stations.
t Endomycorrhized species.
? Based on total abiotic similarity (see Fig. 4).
Many variables show relatively strong partial cor-
relations with the axes of EBC, taking into account the
relation between axes of tree communities and axes of
EBC. These partial correlations may be interpreted as
a measure of the direct effect of variables with the
structure of EBC, removing the indirect effect of tree
communities. The variables with rather high partial
correlation are mostly humus descriptors (thickness of
the litter, percentage of organic matter, exchange ca-
pacity), mineral soil richness, and variables associated
with drainage (percentage of water in mineral soil, per-
centage of organic matter, slope).
Species associations (Table 8) may have resulted
partly from symbiotic relations with trees and partly
from simple co-occurrence due to similar habitat re-
quirements. Some tree-fungus associations revealed by
this analysis had been identified as symbiotic by pre-
vious investigations (Pinus strobus-Suillus granulatus;
Picea spp-Russula paludosa). On the other hand, the
clustering failed to bring out some well-known sym-
biotic relationships (e.g., Pinus strobus-Boletinus pic-
tus). Most of the symbiotic associations suggested by
Table 8 have not been documented yet, but many must
now be suspected as symbiotic.
For clusters of fungi that have been associated with
trees that are known to be endomycorrhized (like spe-
cies of the genus Acer, for example), we included in
Table 8 the ectomycorrhized trees that were ecologi-
cally associated with these trees. These associations
were identified by an independent cluster analysis of
tree species only. This caused some tree hosts to appear
in more than one set: therefore, tree-fungus sets can
be considered as "fuzzy sets."
According to the correspondence statistic (Neu et al.
1974), there are significant departures between relative
constancy of basidiomycete clusters (observed value)
and constancy of associated tree clusters (expected val-
ue) (Table 9). These departures are informative about
particular habitat preferences for Basidiomycetes.
Species of cluster 2 (Table 8) avoided hydric humi-
sol, the condition prevailing in the station of group 3
(Fig. 4), and preferred the two other groups of stations.
Therefore, they were associated with black spruce (Pic-
ea mariana) but mostly on dryer locations. Species of
cluster 3 preferred stations of group 1 and avoided
those of group 2. They were associated with Populus
grandidentata and P. tremuloides on soils that were
richer in mineral nutrients, with thick humus that were
poor in mineral nutrients. Species of clusters 4, 7, 9,
and 13 all preferred humid organic soils. Clusters 4
and 9 also avoided stations of group 1. Species of clus-
ter 10 avoided stations of group 2 and were found
almost exclusively on rich mesic stations dominated
by sugar maple (Acer saccharum). They were most
probably associated with beech (Fagus grandifolia).
Species of cluster 11 avoided rich mesic stations of
group 1 and preferred conditions prevailing in stations
of group 2. Finally, cluster 12 avoided rich mesic soils
and preferred: (1) soils that were poor in mineral nu-
trients but where the humus had a higher percentage
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February 1992 ECTOMYCORRHIZAL-BASIDIOMYCETE COMMUNITIES 107
TABLE 2. Continued.
Sampling stations
6 11 4 9 5
0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00
13.20 0.00 40.14 0.00 9.66
30.10 0.00 0.00 0.00 0.00
6.82 41.80 23.90 19.15 7.10
69.50 80.60 17.07 18.60 46.20
0.00 0.00 2.44 0.00 0.00
2.44 0.00 0.00 0.00 7.69
2.65 7.38 0.00 19.72 0.00
8.54 8.33 0.00 11.60 0.00
113.40 137.90 26.24 50.80 7.12
0.00 0.00 20.58 0.00 0.00
21.00 0.00 0.00 0.00 13.53
4.67 0.00 9.40 0.00 0.00
1.22 0.00 0.00 0.00 0.00
2.44 0.00 0.00 0.00 0.00
8.25 12.85 71.84 91.30 157.90
7.32 11.10 80.49 62.80 46.20
2 2 1 2 3
of organic matter, and (2) wet organic soils. Every other
cluster of Basidiomycetes seems to have followed their
tree symbionts wherever they grew.
DISCUSSION
Similarity matrices and ordinations
The partial correlation between composition of tree
communities and composition of the ectomycorrhizal-
basidiomycete communities (EBC), removing the ef-
fect of abiotic variables, is as strong as the simple cor-
relation between the two taxocenes (Table 4). This may
be partly caused by the absence of significant correla-
tion between the abiotic similarity and the similarity
among tree communities. Tree species showing a bi-
modal type of distribution along abiotic gradients cre-
ate conditions where station pairs may share many
species without sharing important abiotic conditions.
Abundant species in our samples, such as Thuja oc-
cidentalis, Abies balsamea, Picea mariana, and Acer
rubrum, are trees that show this pattern, for example
in their response to soil drainage.
The correlation and partial correlation between abi-
otic similarity and EBC similarity, removing the effect
of tree communities, are statistically significant when
we used only ectomycorrhized tree species in the anal-
ysis. This suggests that abiotic gradients have a more
linear effect on EBC structure than on structure of the
ectomycorrhized tree community.
We interpret the differentiation of tree communities
composition and structure as the effect of a complex
gradient of soil thickness (drainage) and richness and
of disturbance. For example, stations 1 and 3, at the
right of the principal coordinate analysis (PCOA) (Fig.
3), were dominated by sugar maple (Acer saccharum)
and were characterized by low disturbance and thick
mineral soils that were well drained. Stations 7 and 10,
at the bottom of the ordination, had in common an
abundance of poplar (Populus grandidentata) and white
birch (Betula papyrifera), two shade-intolerant species,
and were characterized by well-drained soils.
The relative positions of pairs of stations in the three
multidimensional spaces pertain to eight different types.
If A is the space defined by basidiomycete data, B the
one defined by tree data, and C the one defined by
abiotic variables, then pairs can be:
1) close in all three spaces;
2) distant in all three spaces;
3) distant in spaces A and B, close in space C;
4) distant in spaces A and C, close in space B;
5) distant in spaces B and C, close in space A;
6) close in spaces A and B, distant in space C;
7) close in spaces A and C, distant in space B;
8) close in spaces B and C, distant in space A.
Each of these combinations reflects a particular eco-
logical situation. If abiotic conditions strongly influ-
enced both tree community composition and EBC
composition (directly or via their effect on tree com-
munities), then the most common situations should
have been the first two. With those remarks in mind,
the position of some station pairs and some individual
stations is worth noticing. Station 2, for example, shared
few abiotic conditions and tree species with stations 4
and 5, but the EBC of the three had many species in
common. White birch (Betula papyrifera) was the main
tree species growing at the three stations, and it is pos-
sible that the EBC was mostly affected by the presence
of this species despite the widely different physical
properties of their substrate. This would suggest that
many ectomycorrhizal Basidiomycetes associated with
white birch were indifferent to some soil properties like
texture and water content. It can also mean that in the
past (before disturbances) the tree community of sta-
tion 2 had shared more species with stations 4 and 5
than it did when we sampled it. The ectomycorrhizal
Basidiomycetes may then have persisted in the station
while their associated trees had disappeared. Indeed,
we found that burned stumps belonged to coniferous
species in station 2 while there were but a few weak
saplings of balsam fir (Abies balsamea) growing at the
station or around it when we sampled. In his field
observations of Salix repens communities in the Brit-
ish Isles, Watling (1981) concluded that the species
composition of an EBC is influenced not only by the
present composition of the tree community but also
by its past composition.
In Fig. 4, station 5 is positioned far from all the
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108 PATRICK NANTEL AND PETER NEUMANN Ecology, Vol. 73, No. 1
TABLE 3. Abiotic variables used in the computation of the abiotic similarity among stations. Stations are sorted according
to the similarity of their physical environment.
Sampling stations
Variables 1 3 4 10 2 6 7
Abiotic groups 1 1 1 1 2 2 2
Humus descriptors
Thickness of L horizon (cm)* 1.5 0.6 2 0.5 0.5 0.5 2
Thickness of F horizon (cm)* 4 1.1 4.5 1.5 2 1.5 2
Thickness of H horizon (cm)* 4 5.6 25 2.25 2 5.5 1.5
Total thickness 9.5 8.3 21.5 4.25 4.5 7.5 5.5
% water 58.5 55.5 75 58 73 64.5 64
% organic matter 48.27 47.79 95.92 72.42 90.32 89.59 84.22
pH 3.5 3.1 2.6 3.9 2.9 3 3.1
Exchange capacity* 37.5 31 34 28.8 37.5 37.3 36.2
Richness* -4.045 0.378 -2.589 4.572 2.237 1.058 -1.909
Mineral soil descriptors
Hue of Blt 2 4 4 3 5 3 2
Hue of B2t 1 2 2 2 2 2 3
Chroma of B 1 4 6 6 10 4 8 8
Chroma of B2 4 6 4 10 6 10 8
Value of B1 3 4 3 4 2.5 4 4
Value of B2 4 4 5 5 5 4 4
Exchange capacity* 40.9 44.2 42.1 42.4 55.4 44.2 40.7
Average pH* 4 3.867 3.633 4.033 3.867 3.967 3.833
Richness* 3.098 4.766 -7.456 0.452 -0.713 -8.599 -2.823
% clay 5.25 5 6.675 3.375 5 4.125 4.625
% silt 21 22.5 20.515 16 20.625 16.5 22.5
% sand 73.75 72.5 72.81 80.625 74.375 79.375 72.875
% water* 27 28.5 25.667 15.5 37.333 28.833 18.667
% organic matter* 6.892 10.351 10.321 9.381 20.607 12.005 6.501
% of particules >4 mm 28.7 11.5 22.5 43.8 10.6 28.8 29.8
General descriptors
Altitude (m) 371 393 348 363 379 379 386
Aspect 2 3 1 4 8 8 7
Slope (?) 13 13 20 35 7.5 27 21
Microrelief 3 3 4 4 3 4 4
Stoniness* 2 3 1 4 3 4 5
Rockiness* 1 1 1 1 1 1 2
Disturbance* 2 2.5 1 2.5 3 3 3
Superficial deposit? 3 3 2 2 3 2 1
Position on slope* 3 3 2 3 1 2 3
Slope shape* 2 1 2 1 1 1 1
Floodingll 1 1 1 1 1 1 1
* See Methods: Vegetation sampling. . . and Methods: Soil analyses for details and references on these variables.
t Values are classes of the intensity of the red hue based on Munsell code: 1 = 2.5 Y; 2 = 10 YR; 3 = 7.5 YR; 4 = 5 YR;
5 = 2.5 YR.
: Values are classes of angular distances from the south of the aspect of a station.
? Values are qualitative descriptors: 1 = very thin till; 2 = thin till; 3 = thick till; 4 = organic deposit.
11 Presence (2) or absence (1) of the feature on the sampling station.
TABLE 4. Results of the first series of Mantel tests (above diagonal) and partial Mantel tests (below diagonal) between pairs
of similarity matrices. All tree species (Table 2) were used in the computation of community similarity (simTREES).t
simEBCt simTREES? simAB1Ojj
simEBCt - r = 0.48600 r = 0.26299
P = 0.004* P = 0.049
simTREES? r = 0.46431 r = 0.16917
P =0.005 P= 0.123
simABIOlI r= 0.20987 r= 0.04905 --
P= 0.049 P= 0.375 --
* Significant at the Bonferroni-corrected probability level of (.05/3 = .0167) for an overall significance level of .05 (Miller
1966).
t P = probability of H0 (= no relationship) after 1000 permutations; r = standardized Mantel statistic.
:: simEBC = similarity matrix of ectomycorrhizal-basidiomycete communities based on data of Table 1.
? simTREES = similarity matrix of tree community samples based on data of Table 2.
II simABIO = abiotic similarity matrix among stations based on variables of Table 3.
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February 1992 ECTOMYCORRHIZAL-BASIDIOMYCETE COMMUNITIES 109
TABLE 3. Continued.
Sampling stations
8 9 11 5
2 2 2 3
0.9 0.6 1 0
1.6 1.7 1.5 0
2 7.5 5.7 0
4.5 9.8 8.2 100
63 66.5 59 89
48.22 93.58 66.98 93.21
3.4 2.9 2.7 4.2
37.5 36.2 36.2 28.15
-1.504 0.95 -1.242 2.094
4 4 4 2
2 3 2 2
8 6 8 1
8 6 6 1
3 3 4 2.5
5 4 4 2.5
46.75 55.4 60.5 28.15
4 3.567 3.767 4.2
-1.084 -2.342 13.08 2.094
4.25 5.375 6.875 0
33.875 17.125 13.75 0
61.875 77.5 79.375 0
41 29.333 38.333 89
10.938 14.119 17.716 93.21
2.6 62.4 17.9 0
341 341 341 348
5 8 7 0
18 13 5 0
5 4 5 1
5 5 5 1
1 1 1 1
3 3 3 3
2 2 2 4
4 4 5 6
1 1 1 0
1 1 1 2
0.97138 simABIO
0.16917
simTREES
0.1861
0.45452
simEBC ) 0.73016
FIG. 1. Path diagram of the relationships among similarity
matrices. Numbers are "path" coefficients, computed from
the Mantel statistics in Table 4.
others, indicating that it was abiotically very different.
This station had two important uncommon character-
istics: it was wet-the water table remaining constantly
high during the summer-and it had a hydric humisol
(Commission canadienne de pedologie 1978). How-
ever, the tree community and EBC that it supported
makes it more similar to other dryer stations (4 and
9). Stations 4 and 5 shared many tree species (Thuja
occidentalis, Abies balsamea, Betula papyrifera) and
ectomycorrhizal species (Lactarius thejogalus, Hygro-
phorus nitidus, Rozites caperata, Russulapaludosa, etc.),
although the abiotic conditions seemed quite different.
However,the organic layer of soil was thick on both,
and this characteristic may be an important dimension
of the ectomycorrhizal species niche. Moreover, while
the dominant species of these two communities, Thuja
occidentalis, is not known as an ectomycorrhizae-form-
ing species, its abundance may have affected indirectly
the composition of EBC, by influencing, for example,
the chemical and microflora composition of the soil,
which affects the ectomycorrhizae formation (Slankis
1974).
Some pairs, like stations 1 and 3, shared abiotic
conditions, tree community composition, and EBC
TABLE 5. Results of the second series Mantel tests (above diagonal) and partial Mantel tests (below diagonal) between different
pairs of similarity matrices. Ectomycorrhizae-forming tree species (Table 3) were used in the computation of community
similarity (simEctoTREES).t
simEBCt simEctoTREES? simABIOlI
simEBCt - r = 0.47592 r = 0.26299
P= 0.004* P= 0.049
simEctoTREES? r = 0.48278 r = 0.03994
P= 0.004* P= 0.389
simABIOl r = 0.27763 r = -0.10044
P= 0.009* P= 0.370
* Significant at the Bonferroni-corrected probability level of (.05/3 = .0167) for an overall significance level of .05 (Miller
1966).
t P = probability of Ho (no relationship) after 1000 permutations; r = standardized Mantel statistic.
t simEBC = similarity matrix of ectomycorrhizal-basidiomycete communities based on data of Table 1.
? simEctoTREES = similarity matrix of tree community samples based on abundance data of ectomycorrhizae-forming
trees (see Table 2).
11 simABIO = abiotic similarity matrix among stations based on variables of Table 3.
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110 PATRICK NANTEL AND PETER NEUMANN Ecology, Vol. 73, No. 1
10 3
6
7
00 81
coniferous forests deciduous forests
9
C\J
C,)
X~~~~~~~~~~
11 42
AXIS 1 (25 %)
FIG. 2. Principal coordinates analysis (PCOA) and minimum spanning tree of ectomycorrhizal-basidiomycete community
(EBC) samples using the Steinhaus similarity matrix for stations 1 to 1 1. Stations belonging to the same group formed by
proportional-link linkage agglomeration are identified by the same symbol. Lines joining sampling stations represent the
minimum spanning tree (MST).
composition. There, the dominant tree (Acer saccha-
rum) is generally endomycorrhized (Brundrett and
Kendrick 1988, Berliner and Torrey 1989) and many
Basidiomycetes gathered in those stations were only
hypothetically ectomycorrhizal: Hygrophorus cera-
ceus, H. marginatus, Nolanea spp, Leptonia spp, etc.
However, the stipes of those species were always deeply
buried in the soil and their mycelia were diffuse and
rarely visible. This habit is not typical of most litter
saprotrophes. Therefore, it is possible that a tree spe-
cies that forms ectomycorrhizae, present as saplings or
seedlings (like Fagus grandifolia and Betula alleghani-
ensis) was associated with those Basidiomycetes.
Other pairs of sampling stations supported different
tree communities but their EBC were more similar.
For example, stations 6 and 7 shared important abiotic
characteristics, and two important ectomycorrhizae-
forming trees (Pinus strobus and Populus grandiden-
tata) were present in both, but not in the same pro-
portions. The similarity of EBC was due, here, to the
abundance of Amanita brunnescens and Boletinus pic-
tus, two species possibly associated with Pinus strobus
(Table 8; Trappe 1962). As in station 2, this suggests
that the presence of a particular tree species in a station
may have had a stronger influence on EBC composition
and structure than others.
Abiotic gradients
In Figs. 2 and 3 the configuration of the minimum
spanning tree (MST) superimposed onto the plan of
principal coordinates suggests the presence of the
"horseshoe" effect (Legendre and Legendre 1983). It
was then legitimate to rely on another technique of
5
4
93
matrix forstations1t1, cconiferous forests deciduous forests
C\J
C\J
C/) 6
7
AXIS 1 (36 %)
FIG. 3. Principal coordinates analysis and minimum spanning tree of plant communities using the Steinhaus similarity
matrix for stations 1 to 1 1, computed with relative importance values of trees and saplings (Table 2). Stations belonging to
the same group formed by proportional-link linkage agglomeration are identified by the same symbol. Lines joining sampling
stations represent the minimum spanning tree.
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February 1992 ECTOMYCORRHIZAL-BASIDIOMYCETE COMMUNITIES 111
"? 9
8 .> *10 5
7* * 2
0)
04~~0
CMj
X ) * group
< * group 2
*1 * group
AXIS 1 (27.2 %)
FIG. 4. Principal coordinates analysis and K-means cluster of stations 1 to 11, computed with abiotic similarity matrix,
using the Estabrook and Rogers coefficient.
axes extraction, such as canonical correspondence
analysis (CCA), for detection of abiotic gradients.
The direct gradient analysis pointed out the effect of
drainage quality (or moisture) of a station on plant
community composition and structure. This effect is
well documented (see, for example, Whittaker 1956,
1967, Bouchard and Maycock 1978, Gagnon and Bou-
chard 1981, Bergeron and Bouchard 1983, Gauvin and
Bouchard 1983). However, the EBC composition could
have also been affected by drainage since water avail-
ability is identified as a limiting factor of ectomycor-
rhizal development (references in Slankis 1974 and
Bowen 1972). Moreover, trees that grow on soils where
the mineral soil does not retain water well may depend
more on ectomycorrhizae for their water supply (Slan-
kis 1974). In our sample, more rapidly drained stations
were also richer in ectomycorrhizae-forming trees. Also,
as an indirect effect of plant community composition
or not, the humus of those stations had a higher per-
centage of organic matter (loss on ignition), a charac-
teristic identified as favorable for ectomycorrhizal for-
mation (Slankis 1974). On the other hand, mesic
stations were usually dominated by Acer saccharum, a
non-ectomycorrhized species, and the humus it created
had a lower percentage of organic matter.
Since most ectomycorrhizae are found in the humus
(see references in Slankis 1974), some variables asso-
ciated with this soil layer may directly affect the EBC
composition. Many properties of this layer may be
determined by plant community composition, which
is responsible for chemical composition and the phys-
ical structure of the litter. Correlations between humus
descriptors and edaphic gradients that structure tree
communities support this hypothesis. The interpreta-
tion of interrelations between abiotic and biotic gra-
dients is summarized in Fig. 5.
These results are similar to those of Favre (1948)
and Hering (1966) who compared the macrofungal spe-
cies composition of forests having similar tree com-
position but established on different soils. Favre (1948)
showed that chemical characteristics of the soils, most-
ly pH, affect the species composition of mycofloras.
Hering (1966) suggested that the nature of the soil par-
ent material is responsible for differences in macro-
fungal community composition. Also, Garbaye et al.
(1986), working on the distribution of ectomycorrhizae
types (which may or may not represent different spe-
cies) on young oaks, showed that soil texture, pH, and
organic-matter content are the main factors affecting
their distribution. The results of Bills et al. (1986) pre-
viously showed how plant communities at two extreme
poles of a complex environmental gradient support
different ectomycorrhizal communities. However,
knowledge of the composition of intermediate com-
munities was needed to bring out a deeper understand-
ing of factors affecting the composition of EBC.
TABLE 6. Canonical coefficients and associated t values for internal variables of canonical correspondence analysis (CCA)
of tree and basidiomycete communities.
Ectomycorrhizal-basiodio-
Tree communities mycete communities
Canonical coef. t values Canonical coef. t values
Edaphic variables Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2
Total exchange capacity -0.12 2.50 -0.15 2.74 0.69 -1.67 1.80 -2.79
Mean pH -0.88 0.74 -2.49 1.77 -0.02 -0.19 -0.11 -0.68
Mean richness -0.48 -0.64 -1.75 -1.95 0.18 1.04 1.28 4.79
Mean % of sand 1.21 -4.83 0.72 -2.42 0.21 1.99 0.27 1.65
Mean % of water 1.10 -5.21 0.79 -3.16 0.34 1.74 0.52 1.72
Mean % of organic matter 1.30 1.37 1.50 1.34 0.77 -0.56 1.82 -0.86
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112 PATRICK NANTEL AND PETER NEUMANN Ecology, Vol. 73, No. 1
TABLE 7. Correlations between abiotic variables and axes of canonical correspondence analysis (CCA) for tree communities
and ectomycorrhizal-basiodiomycete communities (EBC). Axes are linear combinations of internal variables (canonical
axes).
Weighted correlations Partial correlations
Ectomycorrhizal Ectomycorrhizal
Tree communities communities communities
Abiotic variables Axis 1 Axis 2 Axis 1 Axis 2 Axis 1* Axis 2t
External variables (not used in CCA)
Humus descriptors
Thickness of L horizon -0.2339 -0.1014 -0.5561 0.0281 -0.537 0.024
Thickness of F horizon -0.1409 -0.2188 -0.4763 -0.1468 -0.537 -0.174
Thickness of H horizon 0.3337 -0.2836 -0.2714 -0.1546 -0.581 -0.468
Total thickness 0.6006 -0.0618 0.3954 0.0948 0.199 0.062
% water 0.8111 0.1527 0.3329 -0.4502 -0.075 -0.395
% organic matter 0.7633 0.434 0.1175 -0.7406 -0.47 -0.635
pH -0.1743 0.2759 0.0071 0.2556 0.176 0.601
Exchange capacity -0.0815 -0.1965 0.0081 -0.3986 -0.052 -0.516
Richness 0.1321 0.5666 0.2641 0.2447 0.23 0.063
General descriptors
Aspect -0.05 0.2893 0.1737 -0.4784 0.155 -0.412
Slope -0.5218 0.414 -0.8136 -0.1553 -0.718 0.108
Microrelief -0.3666 -0.0043 -0.2014 -0.1099 0.067 -0.192
Stoniness -0.3467 0.2941 0.0425 -0.1511 0.231 -0.027
Disturbance 0.0195 0.2804 0.4633 -0.1228 0.56 0.0015
Superficial deposit 0.3017 -0.3204 0.4903 0.2689 0.413 0.139
Position on slope 0.2347 -0.1651 0.5145 0.3626 0.537 0.308
Internal variables (used in CCA)
Mineral soil descriptors
Exchange capacity -0.0226 -0.0827 0.4343 -0.2275 0.396 -0.355
Average pH -0.2747 0.0101 0.0338 0.3852 0.281 0.498
Richness -0.2235 -0.3837 0.5978 0.6927 0.875 0.619
% sand -0.4853 0.0176 -0.3764 -0.1093 -0.251 0.103
% water 0.5857 -0.1817 0.6834 0.0787 0.564 -0.064
% organic matter 0.5997 -0.0055 0.5855 0.0481 0.436 0.042
* Partial correlation between the first axis of EBC and variables, removing the effect of the first axis of tree communities.
t Partial correlation between the second axis of EBC and variables, removing the effect of the second axis of tree communities.
Interpretations of species associations
Since symbiotic partners were not identified with
certainty, we do not maintain that the habitat prefer-
ences described in the Results section are independent
of the distribution of actual (verified) symbiotic part-
ners.
As was the case in the results of CCA, the preference
patterns shown in Table 9 help to identify the abiotic
factors affecting fungus species distribution that were
different from those affecting the distribution of trees
(see Fig. 5). Also, while some abiotic conditions influ-
encing tree community structure were the same as those
affecting fungal communities, the responses of fungi
seem to have differed from those of their tree symbi-
onts. The effect of drainage is a good example of this.
The same phenomenon involving the soil acidity was
observed by Kotlaba (1953), who studied macromy-
cetes communities of peat bogs in Czechoslovakia. He
stated that some ectomycorrhizal species avoid peat
bogs although their tree hosts are present. Others, which
he called "acidophilic" species, follow their tree sym-
bionts wherever they grow. Moreover, it is known
(Slankis 1974) that ectomycorrhizae formation occurs
mainly when nutritional conditions for the tree asso-
ciate are suboptimal. This may partly explain why many
ectomycorrhizal Basidiomycetes seem to have avoided
rich mesic stations, a phenomenon observed by other
researchers (Ubrizsy 1972).
It must be said finally that the limited size of our
sample imposes limits on generalizations, mostly be-
cause of lack of replication in some of the abiotic con-
ditions. Concerning the gradient analyses, we cannot
tell to what extent lack of pattern is due to limited data.
Ideally, future work should include more plots that
would be sampled for more than two years. This would
mean a lot of basidiomata to identify, a difficult task
in the absence of taxonomic monographs on complex
genera such as Cortinarius and Russula. However, an
ecological investigation can be coupled with taxonomic
work if restricted to one family or a few genera. In this
perspective, collecting in permanent plots may provide
good material for an understanding of morphological
variations within species. On the other hand, the eco-
logical investigation requires that all material be iden-
tified, including damaged basidiomata. This is neces-
sary because the abundance of an ectomycorrhizal-
basidiomycete species is usually evaluated using the
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TABLE 8. Species associations defined statistically by complete linkage clustering using the chi-squared similarity coefficient
and importance values of species. To define these associations, the minimum similarity value for clusters was arbitrarily
fixed at 0.9700. The following associations are not necessarily symbiotic.
Cluster Basidiomycete species Tree species*
1 Amanita brunnescens Pinus strobus (tree)
Suillus granulatus Pinus strobus (sapling)
Populus grandidentata (sapling)
2 Amanita porphyria Picea mariana (tree)
Russula paludosa
Leccinum insigne
Russula roseipes
3 Amanita citrina Acer rubrum (sapling)t
Boletus piperatus Populus grandidentata (tree)
Russula raoultii Populus tremuloides (tree)
4 Amanita virosa Betula papyrifera (sapling)
Cortinarius evernius Betula papyrifera (seedling)
Russula peckii
Clavulina cristata
Hebeloma testaceum
5 Amanita flavoconia Abies balsamea (seedling)
Leccinum scabrum Abies balsamea (sapling)
Russulafragilis Abies balsamea (tree)
Russula puellaris
6 Amanitafulva Betula papyrifera (tree)
Hebeloma sp. Populus tremuloides (tree)
Russula silvicola
7 Hygrophorus laetus Acer rubrum (seedling)t
Hygrophorus unguinosus Populus grandidentata (tree)
Nolanea strictior
Rozites caperata
8 Boletinus pictus Picea glauca (tree)
Cortinarius bolaris Betula alleghaniensis (seedling)
Hygrophorus nitidus
Xerocomus subtomentosus
Lactarius sordidus
9 Cortinarius flexipes Picea glauca (seedling)
Cortinarius vibratilis Thuja occidentalis (seedling)t
Hygrophorus miniatus Thuja occidentalis (sapling)t
Leptonia formosa Thuja occidentalis (tree)t
Laccaria laccata Picea glauca (tree)
Lactarius thyinos Betula alleghaniensis (seedling)
10 Amanita muscaria Acer saccharum (seedling)t
Hygrophorus marginatus Acer saccharum (sapling)t
Hygrophorus pallidus Acer saccharum (tree)t
Hygrophorus ceraceus Fagus grandifolia (seedling)
Hygrophorus parvulus
Hygrophorus pratensis
Paxillus involutus
1 1 Lactarius lignyotus Pinus strobus (seedling)
Leccinum holopus Picea glauca (tree)
Tylopilus felleus Betula alleghaniensis (seedling)
Betula alleghaniensis (sapling)
Betula payrifera (tree)
12 Cantharellus cibarius Populus grandidentata (seedling)
Hebeloma mesophaeum Fagus grandifolia (seedling)
Lactarius glyciosmus
Russula claroflava
Inocybe umbrina
Cortinarius armillatus
13 Clavulinopsis fusiformis Betula alleghaniensis (sapling)
Hygrophorus cantharellus Acer pensylvanicum (seedling)t
Nolanea lutea Acer rubrum (tree)t
Nolanea quadrate Betula papyrifera (tree)
Lactarius thejogalus
Ramariopsis kunzei
Russula cyanoxantha
* Tree species names set in roman type are ectomycorrhizal species that are associated with the non-ectomycorrhized trees
according to a second cluster analysis computed with only the importance values of tree species.
t Endomycorrhized tree species.
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114 PATRICK NANTEL AND PETER NEUMANN Ecology, Vol. 73, No. 1
TABLE 9. Relative constancy* of species clusters in station groups for ectomycorrhizae fungal and tree species. Numbers in
boldface type indicate observed constancies that are significantly (P < .05) different from their expected one according to
the correspondence statistic (Neu et al. 1974), Bonferroni-corrected. Observed constancy (upper row for each station group)
is the constancy of fungal species; expected constancy (lower row) is the constancy of the associated tree species (ectomy-
corrhized only).
Station Species clusterst
group 1 2 3 4 5 6 7 8 9 10 11 12 13
Relative constancies
1 6.38 11.69 90.78 3.50 9.83 37.13 32.79 71.81 2.77 93.08 6.59 12.72 26.31
10.97 0.00 50.15 11.24 11.96 31.76 50.34 86.94 40.57 81.16 35.14 70.69 29.75
2 93.62 76.90 9.22 56.17 65.54 51.49 38.74 22.54 6.74 2.45 85.62 73.87 40.75
89.03 24.83 49.85 88.76 50.83 59.85 49.66 13.07 4.55 18.84 56.57 29.31 60.96
3 0.00 11.40 0.00 40.34 24.63 11.39 28.47 5.65 90.49 4.48 7.79 13.42 32.94
0.00 75.17 0.00 0.00 37.21 8.39 0.00 0.00 54.88 0.00 8.29 0.00 9.30
* Formula is explained in detail in Methods: Data analysis: Biological associations and habitat preferences.
t Based on complete linkage clustering using chi-square similarity coefficient (Table 8).
t Based on a cluster that minimizes variance of the first three principal coordinates extracted from the abiotic similarity
matrix (Fig. 4).
frequency or biomass of all its basidiomata. An alter-
native approach would consist of the identification of
the two symbionts (fungus and tree) directly for ecto-
mycorrhizae that would be sampled. Such an approach
would need a systematic or random excavation of roots
and thus be extremely time consuming; further, it will
become feasible only when more ectomycorrhizae de-
scriptions like those in Godbout and Fortin (1985),
Randall and Grand (1986), Agerer (1988), and Samson
and Fortin (1988), are available for associations of
North American tree and fungus species.
CONCLUSIONS
The distribution of ectomycorrhizal Basidiomycetes
resulted mainly from their biotic interactions with tree
communities. Partial Mantel tests appeared to be quite
powerful in bringing out dependence patterns between
linked biological communities (or taxocenes) and their
environment. Their results offered a much clearer pic-
ture of complex interactions among plant communi-
ties, environment, and ectomycorrhizal-basidiomycete
communities (EBC) than previous mycosociological
studies. We suggest their use in all studies involving
such material (like parasite-hyperparasite-environ-
ment relationships, etc.). Because the computation of
this statistic can handle geographical and chronological
distance matrices, it may also be useful where spatial
or temporal patterns of associated taxocenes are to be
revealed, as in the work of Taylor et al. (1984).
Some results of the analyses also suggested that the
composition and structure of EBC were not so depen-
dent on the precise composition and structure of the
tree community of the same habitat:
1) The partial Mantel test between EBC similarity
and abiotic similarity, removing the effect of the sim-
ilarity among ectomycorrhized tree communities,
showed an independent relationship between EBC and
the abiotic features of their habitat.
Exchange capacity of the mineral soil
drainage quality
mineral soil acidity mineral soil richness
structureG o --- structure o B
X ~~~~humus humidity
FIG. 5. Summary of the relationships between abiotic and biotic gradients, revealed by Mantel tests and canonical
correspondence analyses.
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February 1992 ECTOMYCORRHIZAL-BASIDIOMYCETE COMMUNITIES 115
2) The ecological distribution of basidiomycete spe-
cies associations showed preference/avoidance pat-
terns regarding environmental conditions that some-
times appeared independent of the ecological
distribution of hypothetical tree symbionts.
Environmental variables that seem to have affected
more directly the EBC structure were descriptors of the
physical and chemical properties of the humus, such
as litter thickness and percentage of organic matter.
Sometimes the composition and structure of EBC may
have been determined mainly by the presence of one
or two tree species (as with Pinus strobus and Betula
papyrifera) that were strongly associated with a few
highly productive Basidiomycetes.
In the framework of the continuum concept (Whit-
taker 1967), our results can be usefully interpreted and
visualized as relations between distribution curves of
associated species of two taxocenes. For the fungal
partner of ectomycorrhizae, there were three possible
types of relations between distribution curves:
1) Its curve followed that of its tree symbionts with
high fidelity independent of the environment;
2) It followed that of its tree symbionts only on a
part of the gradient;
3) It did not follow any tree distribution curve but
had its own independent one and formed ectomycor-
rhizae with whatever suitable hosts were available in
its habitat.
The second type best explains the partial correlation
between environmental gradient and EBC composition
gradient. This type of distribution implies that beta-
diversity of fungi is generally higher than beta-diversity
of ectomycorrhizae-forming trees, a fact suggested by
the results of previous mycosynecological studies
(Cooke 1948, 1953, 1979, Favre 1948, Kotlaba 1953,
Ubrizsy 1972, Apinis 1973, Darimont 1973, Lisiewska
1974, Villeneuve et al. 1989). Moreover, in our study
we had about 240 species of fungi to 27 species of
woody plants. From the perspective of species conser-
vation, these facts may both have consequences in the
choice of sites for the establishment of ecological pre-
serves: site selection based on vegetation classification
or mapping, or on distribution of tree species, may
miss several fungal species. Therefore, the general ob-
jective of conservation of biodiversity would be better
achieved by the conservation of many replicates of the
same forest types.
ACKNOWLEDGMENTS
This paper represents a portion of a thesis submitted to the
University de Montr6al in partial fulfillment of a master's
degree. We thank Mrs. Nathalie Marois for her assistance
during the sampling, description, and identification of fungi.
Dr. Pierre Legendre provided valuable comments on the use
of numerical methods and interpretation of results. We are
grateful to M. Stuart Hay who improved the English. We also
thank M. Robert Beausejour, Dr. Peter P. Harper and Dr.
Jean-Guy Pilon who have allowed us to use equipment of the
"station de biologie de l'Universit6 de Montreal" at Saint-
Hippolyte; M. Alain Vaudor for a copy of his program "Piste"
with which the path analysis was computed; Dr. John A.
Downing who allowed us to use some pieces of equipment of
his laboratory; MM. Jacques Dumont and Martin C6t6 (d&
partement de chimie, Universit6 de Montreal) who provided
technical assistance for atomic absorption spectrophotome-
try; Technitrol Canada Ltd. laboratories for nitrogen deter-
minations; and Dr. Yolande Dalp6, of the Biosystematic In-
stitut at Ottawa, for her aid in identification of some fungal
specimens. The comments of Dr. Peter S. White and two
anonymous reviewers greatly improved the manuscript. We
thank them very much. This research was supported by a
scholarship of the Natural Sciences and Engineering Research
Council of Canada and grants from the "Defi Canada" pro-
gram.
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