FEMS Microbiology Ecology 27 (1998) 21^32
Interspeci¢c fungal interactions in spatially heterogeneous systems
Nia A. White a; *, Craig Sturrock a , Karl Ritz b , William B. Samson c ,
James Bown c , Harry J. Staines c , John W. Palfreyman a , John Crawford
b
b
a
School of Molecular and Life Sciences, University of Abertay Dundee, Bell Street, Dundee DD1 1HG, UK
Unit of Integrative Bioscience, Cellular and Environmental Physiology Department, Scottish Crop Research Institute, Invergowrie,
Dundee DD2 5DA, UK
c
School of Computing, University of Abertay Dundee, Bell Street, Dundee DD1 1HG, UK
Received 21 January 1998; revised 8 June 1998; accepted 8 June 1998
Abstract
The community dynamics of two- and three-fungal species interactions derived for a tessellated agar model system are
described. The microcosm allows for the varied prescription of: (1) the number of fungal species interacting; (2) the spatial
configuration (patchiness) of the distribution of individuals; (3) the magnitude of scale of spatial occupation by different fungal
individuals ; and (4) the operation of antagonistic mechanisms based on contact or longer range diffusible components.
Stepwise logistic regressions for two-species interactions are used to inform the design of the multi-species interaction
tessellations. The model prescribes and investigates complex parameters, such as spatiotemporal heterogeneity and microcosm
scale (e.g. population patchiness and crossing times). Data are quantified as proportion, interface class and state transition
class of viable fungal species. Spatiotemporal heterogeneity is represented using a novel application of principal component
analysis which shows good intuitive agreement with visual assessment of the interaction outcome patterns, and allows effective
comparison of the data as a whole. The model demonstrates the influence of the complex and coordinated behaviour of fungal
mycelia on community development: interaction outcome of three-species interactions cannot be directly extrapolated from the
relevant binary component interactions ; interaction outcomes of the multi-species tessellations appears to be neither random
nor fully deterministic ; a degree of stochasticity is apparent in all tessellation arrangements; the smaller scale tessellations
produce more consistent interaction outcome results, probably because experimental scale affects the duration of transient
behaviour; and different initial spatial configurations of inoculum (irrespective of inoculum quantity or proportion) influence
community development and reproducibility. z 1998 Published by Elsevier Science B.V. All rights reserved.
Keywords : Fungal interaction; Spatial heterogeneity; Biological model
1. Introduction
Individual fungal mycelia are indeterminate structures displaying considerable developmental plastic* Corresponding author. Tel.: +44 (1382) 308674;
Fax: +44 (1382) 308663; E-mail: [email protected]
ity, thereby conferring a versatility for colonizing
and transcending spatially and temporally heterogeneous terrestrial environments. Mycelial organisation
is directed by interactions between the genotype of a
particular organism, and the prevailing external
abiotic (e.g. microclimate) and biotic (other organism) environments, mediated at the boundaries both
0168-6496 / 98 / $19.00 ß 1998 Published by Elsevier Science B.V. All rights reserved.
PII: S 0 1 6 8 - 6 4 9 6 ( 9 8 ) 0 0 0 5 2 - X
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N.A. White et al. / FEMS Microbiology Ecology 27 (1998) 21^32
of colonies as a whole and of individual hyphal
tubes. Considerable evidence con¢rms that heterogeneous microclimate and resource quality have a substantial in£uence on mycelial growth and even combative ability e.g. [1^7], though the e¡ect of the
biotic environment on fungal morphogenesis is less
clear [8^10]. Studies of fungal growth in mixed cultures, i.e. within a heterogeneous biotic environment,
have largely centred around in situ natural ecology.
Such studies have produced a wealth of empirical
and qualitative data on community dynamics which
indicate the complexity of such systems (for examples see [1]). Theory arising from such research is,
however, often di¤cult to test in an experimentally
rigorous fashion because of a paucity of suitable experimental models. So whilst quanti¢cation of community dynamics in vitro would permit a more precise manipulative control of experimental systems,
this is likely to be at the expense of accommodating
complex parameters, such as spatiotemporal heterogeneity and the e¡ects of scale. There is, however, a
growing awareness that the spatiotemporal complexity of ecosystems, whilst currently impeding reliable
quantitative understanding of natural ecosystems, is
a key to promoting the persistence of diversity in
these systems, e.g. [11,12].
Most current studies on community dynamics focus on the interaction between a limited number of
individuals at small spatial scales ^ often confrontations between two organisms on a common nutrient
source. However, at larger scales, it seems probable
that contrasting emergent behaviour may arise because individuals in a group or patch behave di¡erently to individuals which are isolated, by processes,
such as modi¢cation of the environment, resource
translocation and hyphal networking or anastomoses, e.g. [5,9,12]. Moreover, most mycologists recognise, and a diversity of publications cite, that
cultures of a fungal individual can often exhibit a
variety of responses when subjected to ostensibly
the same set of conditions (e.g. [5], and Gwyn S.
Gri¤th, personal communication). Such phenomena
are quantitatively most noticeable during assessment
of interaction outcomes in traditional agar confrontation studies, even within a few studied replicates.
This paper describes the application of a simple
experimental system (microcosm) which permits the
controlled study of the spatiotemporal dynamics of
fungal communities. We describe the application of
the system to investigation of the interspeci¢c behaviour of three di¡erent fungal species, which show a
variety of growth patterns (i.e. cord formation, sporulation), which were isolated from a speci¢c timber
within the historic ship, the Frigate Unicorn [13].
Interaction outcome data are derived for spatially
heterogeneous (with respect to patchiness and scale)
distributions of isolates within a nutritionally homogeneous system. We speci¢cally study whether heterogeneous results displayed during small-scale experiments are ampli¢ed or bu¡ered as the spatial
scale of the systems is increased, and discuss the
application of such data to mathematical modelling
approaches.
2. Materials and methods
2.1. Microcosm and species confrontation design
The microcosm used in all experiments was based
on that described by Ritz [4]. Square tiles
(10U10U3 mm) of 2% (w/v) malt extract agar
(MEA; 20 g oxoid malt extract, 15 g oxoid technical
agar in 1 l distilled water, approximate pH 5.6) were
arranged in either 2U1, 3U3 or 6U6 arrays in Petri
dishes. Tiles were separated by 2 mm air gaps unless
stated otherwise (restricting di¡usion of metabolites,
but allowing di¡usion of volatiles and formation of
bridging hyphae between tiles). Tiles were tessellated
with respect to the fungal species provisionally identi¢ed as Poria placenta, Coniophora marmorata and
Paecilomyces variotii hereafter referred to as species
Pp, Cm, and Pv (viz. species A, D and H), respectively [13].
Confrontations were established as either precolonised or simultaneously inoculated tiles. Precolonised tiles were cut from the actively growing
margins of 2% MEA cultures and confronted in a
radial direction with respect to mycelial growth. Simultaneously inoculated tiles were prepared by
placing 4 mm diameter cores obtained from the actively growing margins of 2% MEA cultures, at the
centre of individual tiles. Aseptic technique was
maintained throughout. Petri dishes were sealed
with strips of Para¢lm, placed in loosely sealed
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23
Fig. 1. Example maps showing the spatial distribution of fungal species in the 3U3 tessellated agar tile arrays at the onset of the experiments. Bold lines denote air-gaps between individual tiles, ¢ne lines denote quadrants which were sampled and species occupancy determined (see text). (a) Tessellation M; (b) tessellation N; (c) tessellation O; (d) tessellation P; (e) tessellation Q; (f) tessellation R; (g) tessellation S; (h) tessellation T; (i) tessellation U; (j) tessellation X. Dimension of each tile is 1 cm2 .
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plastic bags together with a Petri dish containing
water-saturated cotton wool (to maintain humidity),
and incubated at ambient temperature (20^25³C
range), in the dark.
2.2. Arrangement of 2U1 tessellated confrontations
For the 2U1 arrays, confrontations in all permutations were established between fungal species Pp,
Cm and Pv, as both precolonised tiles and simultaneously inoculated tiles (6 replicates of each). Interaction outcomes were assessed after 2, 4 and 6
weeks.
2.3. Arrangement of 3U3 tessellated confrontations
For the 3U3 arrays, simultaneously inoculated
tiles of Pp, Cm and Pv were confronted in 10 randomly generated starting con¢gurations (labelled M,
N, O, P, Q, S, T, U, W, X; ¢ve replicates each) all
in the ratio 1:4:4, respectively (see Fig. 1 for the
starting con¢gurations of all 3U3 arrays). The
1:4:4 species ratio was selected because initial experiments indicated that A was the most aggressive
species, and maintaining its initial presence to a minimum would be most likely to provide dynamic interactions over experimentally reasonable time
scales. Interaction outcomes were assessed after
7 weeks.
2.4. Arrangement of 6U6 tessellated confrontations
For the 6U6 arrays, simultaneously inoculated
tiles of Pp, Cm and Pv were confronted in four
randomly generated starting con¢gurations (labelled
G, J, A, K; ¢ve replicates each) in the ratio 1:4:4,
respectively (see Fig. 2 for the starting con¢gurations
of all 6U6 arrays). Interaction outcome was assessed
at 7 weeks (for arrangements G and J) and at 28
weeks (for arrangements A and K).
2.5. Assessment of interaction outcomes and data
analysis
Replicates were harvested at various time intervals
according to the experiment. The spatial con¢guration of the distribution of species, hereafter referred
to as the interaction outcome, was determined by
dividing each tile into quarters, placing each quarter
onto fresh 2% MEA plates and identifying the
emerging mycelium within 5 days. For the 2U1 tessellations, the presence of individual fungal species
in each quarter was expressed as the proportion of
each species existing within each quarter tile. Stepwise logistic regression was used to determine if the
presence of a fungus was related to pre- or simultaneous inoculation, sampling period, species pairing,
or the identity of the subject tile [15].
Parameters were derived which represent key spatial and temporal descriptors of the tessellation, respectively, viz.
(1) `Interface classes', i.e. the number of interfaces
between adjacent tiles of the combinations Pp:Pp,
Cm:Cm, Pv:Pv, Pp:Cm, Pp:Pv, Cm:Pv. Where
there was multiple-occupancy of tiles, the numbers
were expressed as a proportion of the whole tile, i.e.
Fig. 2. Example maps showing the spatial distribution of fungal species in the 6U6 tessellated agar tile arrays at the onset of the experiments. Bold lines denote air-gaps between individual tiles, ¢ne lines denote quadrants which were sampled and species occupancy determined (see text). (a) Tessellation A; (b) tessellation G; (c) tessellation J; (d) tessellation K. Dimension of each tile is 1 cm2 .
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N.A. White et al. / FEMS Microbiology Ecology 27 (1998) 21^32
the total number of interfaces scored was constant at
12 for 3U3 and 60 for 6U6 tessellations.
(2) `State transition classes', i.e. the number of
tiles which showed a particular transition from one
state of occupancy at a particular time point, to
another at the time of sampling (e.g. Pp s Cm,
PpCm s PpCmPv, etc.). There were 21 such classes.
25
Thus for each tessellation, a multinomial data set
of 27 values (i.e. 6 interface and 21 state transition)
was fed into a principal component analysis (PCA)
using the sums of squares and products (SSP) method within GENSTAT [16], in order to test for the
degree of similarity between the various states of the
tessellations.
Table 1
Proportion of fungal species Pp, Cm and Pv during 2U1 confronting tessellations
Experiment and sampling period (weeks)
Simultaneously inoculated tiles
0
2
4
6
0
2
4
6
0
2
4
6
Precolonised tiles
0
2
4
6
0
2
4
6
0
2
4
6
Proportion of fungal species in confronted inoculated tiles
Tile 1
Tile 2
Species
Species
Pp
Cm
Pp and Cm
Pp
Cm
Pp and Cm
24
24
24
22
0
0
0
1
0
0
0
1
0
2
7
17
24
18
15
3
0
4
2
4
Pp
Pv
Pp and Pv
Pp
Pv
Pp and Pv
24
16
21
17
0
0
0
1
0
8
3
6
0
0
0
0
24
21
24
23
0
3
0
1
Pv
Cm
Pv and Cm
Pv
Cm
Pv and Cm
24
0
0
2
0
2
5
19
0
22
19
3
0
0
0
0
24
23
24
20
0
1
0
4
Pp
Cm
Pp and Cm
Pp
Cm
Pp and Cm
24
24
24
24
0
0
0
0
0
0
0
0
0
1
9
3
24
18
13
16
0
5
2
5
Pp
Pv
Pp and Pv
Pp
Pv
Pp and Pv
24
17
23
21
0
0
0
0
0
7
1
3
0
0
0
0
24
24
20
24
0
0
4
0
Pv
Cm
Pv and Cm
Pv
Cm
Pv and Cm
24
0
0
0
0
13
16
20
0
11
8
4
0
0
0
0
24
22
24
24
0
2
0
0
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3. Results
tessellations; Pp usually defended its territory
against Cm, whilst invading Cm's domains; Pp usually defended its territory against Pv and vice versa;
and Pv was usually invaded by Cm, whilst Cm defended its domains against Pv. Similar outcomes
were obtained during precolonised interactions;
however, Pp always defended its territory against
Cm, whilst invading Cm's domain.
The presence of a species appeared dependent on
four factors: its presence within the subject tile, the
pairing combination, the longest sampling period
and whether the inoculations were simultaneous.
This conclusion was tested using stepwise logistic
regression. Separate stepwise logistic regression
models were ¢tted for each species and pairs of species, with factors entering the model in their order of
importance, but only if the deviance was signi¢cantly (P 6 0.05) reduced. Table 2 shows the order
that each factor entered the model and the goodness-of-¢t for the overall model. Thus, the presence
of Pp depended most importantly on its presence in
the subject tile; of secondary importance was the
species with which it was paired, and thirdly, the
longest sampling period. Adding the simultaneous
inoculation term did not signi¢cantly improve the
model. The models all had highly signi¢cant goodness-of-¢t statistics (PI0.01 in all instances).
3.1. Qualitative macromorphological description of
Pp, Cm and Pv interactions for simultaneously
inoculated tiles
In general, during the interactions between Pp and
Cm, hyphae initially met on tiles initially occupied
by Pp after approximately 5^6 days, and formed a
yellow interaction line after approximately. The extension rate of species Cm was reduced as it bridged
the gap between the tiles (data not shown). The
interaction line subsequently became dark brown
by 2^3 weeks, during which time, Pp formed a mycelial £ush that advanced through the interaction
front to invade the Cm colonies. A resulting brown
`reverse' [14] colouration was formed beneath both
species' tiles, and a yellowing of Cm mycelium
formed within 4^5 weeks. During interactions between species Pp and Pv, both species grew and
colonised their respective tiles with subsequent deadlock. Occasionally, Pv colonies were rapidly formed
on uncolonised regions of Pp tiles, probably due to
spread of spores, with subsequent deadlock. During
Cm and Pv interactions, Cm grew and produced a
dense mycelial £ush over the colonised Pv tile, completely engul¢ng the Pv tile within 2 weeks. However, evidence of sporulation by Pv remained visible
within the Cm mycelium for some time. Negligible
reduction of Cm colony extension rate was observed
during the crossing of the air gap between tiles.
3.3. Interaction outcomes during 3U3 and 6U6
tessellation studies
Replicates of both the 3U3 and 6U6 tessellations
produced a variety of spatial patterns in outcome
(Figs. 3^5). Clustering and scattering of 1st and
2nd principal components was intuitively concordant
with the visual interaction outcome patterns (compare with Fig. 5). The initial conformations for both
the 3U3 and 6U6 tessellations clustered strongly in
3.2. The proportion of interaction outcomes for 2U1
tessellations
The proportion of fungal species extant within
tessellations over time are given in Table 1. These
data indicate that during simultaneously inoculated
Table 2
Factors that in£uenced the presence of a species in a tile, as determined by stepwise regression
Factor
Presence within subject tile
Pairing combination
Longest sampling period
Inoculation mode
Species
Pp
Cm
Pv
Pp and Cm
Pp and Pv
Cm and Pv
1
2
3
^
1
2
^
3
1
2
4
3
2
1
3
^
2
1
3
^
2
1
3
^
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27
Fig. 3. Example maps showing the spatial distribution of fungal species in the 3U3 tessellated agar tile arrays at the onset of the experiments (START) and after 7 weeks incubation. Bold lines denote air-gaps between individual tiles, ¢ne lines denote quadrants which were
sampled and species occupancy determined (see text). (a) Tessellation X; (b) tessellation O. Dimension of each tile is 1 cm2 . Symbols indicate the species isolated from each quadrant.
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N.A. White et al. / FEMS Microbiology Ecology 27 (1998) 21^32
Fig. 4. Example maps showing the spatial distribution of fungal species in the 6x6 tessellated agar tile arrays at the onset of the experiments (START) and after 7 weeks incubation. Bold lines denote air-gaps between individual tiles, ¢ne lines denote quadrants which were
sampled and species occupancy determined (see text). (a) Tessellation J; (b) Tessellation G. Dimension of each tile is 1 cm2 . Symbols as
for Fig. 1.
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29
Fig. 5. Example images of the 6U6 tessellated agar tile arrays after 7 weeks incubation, prior to assessment of interaction outcome (results given in Fig. 4). Tessellation J: (a) Rep 1; (b) Rep 3; and (c) Rep 5. Tessellation G: (d) Rep 1; (e) Rep 2; and (f) Rep 4.
the PCA due to the prescription of the initial 1:4:4
arrangement of the species. There were generally two
classes of outcome, characterised by a high degree of
similarity between replicates (e.g. arrangements X
and J; Figs. 3a and 4a) or a wide divergence between replicates (e.g. arrangements O and G; Figs.
3b and 4b). Overall, the 6U6 tessellations showed
little consistency of outcome despite the clustered
starting con¢gurations. Replicates for the 3U3 tessellations produced more consistent interaction outcomes, with 20 out of 50 coincident data points, and
C
Fig. 6. Plot of ¢rst and second principal components from analysis of interface classes and state transition classes of tessellated
agar tile arrays (see text for further description). Open symbols
denote 3U3 arrays, closed symbols denote 6U6 arrays. Key refers to the tessellation codes used in the text. Squared cluster denotes start con¢guration for 3U3 arrays; circled cluster denotes
start con¢guration for 6U6 arrays. Symbols adjacent to J1, J3,
J5, G1, G2 and G4 indicate the PCA positions of the tessellations presented in Fig. 5. Asterisk indicates the clustering of 20
coincident data points for the 3U3 arrays (see text).
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N.A. White et al. / FEMS Microbiology Ecology 27 (1998) 21^32
a further 18 proximal to these points (Fig. 6). Arrangements Q, O and N showed particularly variable
outcomes between replicates.
4. Discussion
A linear hierarchy of `combatibility' (i.e. invasiveness and resistance to invasion) was not displayed by
the three species studied; Pp s Cm, Cm s Pv, but Pp
showed deadlock with Pv. The binary (2U1 tessellations) experimental data, if extrapolated to a three
species interaction situation, might instinctively suggest that Pv would be replaced by Cm, which would
then be replaced entirely by Pp. Such inferences are
common in microbial community dynamics studies
(see [1] for examples). However, the three species
tessellations show that such extrapolations should
be made with caution, and that variable interaction
outcomes are often achieved as would be expected in
vivo communities.
Of course, the complex and coordinated behaviour
of the fungal mycelium may have profound e¡ects
on species combatibility and hence community development. For example, the fusion (anastomoses) between hyphae and, therefore, continuity of protoplasm, of the same or similar species of ascomycete
or basidiomycete fungi may improve combatibility of
species within ecosystems by improving the survival
of the populations of genomic organelles, nuclei and
mitochondria [12,17]. Furthermore, changes in mycelial organisation (e.g. cords, fans, sheets etc.) particularly amongst the basidiomycetes are fundamental to interaction outcome [9,12]. Whilst other
authors have attempted to model fungal community
development, even the most advanced model [5]
ignores the correlated or co-operative behaviour of
the mycelium, dispersal by spores and coexistence of
species at the same location. The current paper demonstrates that the initial spatial con¢guration of inoculum does in£uence community development (and
reproducibility), regardless of inoculum quantity or
proportion. The system described therefore can re£ect the complex and cooperative behaviour of the
fungal mycelium and indicates that consideration of
spatial heterogeneity should therefore be incorporated into any successful theoretical model.
The stepwise logistic regression model for the bi-
nary tile interactions indicated the relative importance of experimental parameters which in£uenced
the eventual outcome of the interactions. This information was used to de¢ne the experimental design in
the larger tessellation. Thus simultaneous inoculation
of tiles during experiments involving Cm and/or Pv
was important in determining their occupancy on
outcome. Neither simultaneous nor preinoculation
was important in determining the existence of Pp
in a tile. Simultaneous inoculation of the larger tessellations was therefore adopted to avoid rapid extinction of Cm and Pv in the designs.
Mapping complex spatial and temporal data for
analysis has often proved problematic in studies involving heterogeneity as expressing and quantifying
this heterogeneity is di¤cult. The use of interface
classes to produce a guide to the spatial organisation
of the community allows information on both the
area occupied by individuals and the perimeter size
of their spatial domains to be utilised. Whereas the
use of state transition classes provides temporal information about the system. PCA was used as means
of statistically describing key spatial and temporal
aspects of the state of the tessellations with respect
to species occupancy and distribution. Although this
is a non-conventional application of PCA, it proved
to be an e¡ective way of summarizing and visualizing the otherwise complex maps produced, in such a
way that comparisons were more rigorous than by
subjective visual analysis. However, the PCA representation of the data did show good intuitive agreement with a visual assessment of the interaction outcome patterns, and allowed e¡ective comparison of
the data as a whole. The interaction outcomes of
replicates for the 3U3 and 6U6 arrangements appeared to be neither random nor fully deterministic,
and as might be expected with such multi-component
systems, a degree of stochasticity was apparent in the
outcomes. Theory suggests that microcosm scale
would e¡ect the in£uence of stochasticity on the outcome of interactions, with larger systems averagingout stochasticity and behaving more consistently. It
would also be expected intuitively that the e¡ects of
stochasticity on community dynamics would be less
in spatially larger systems compared with smaller.
This is con¢rmed by a general result [18] applicable
to any system subject to internal (microscopic) £uctuations, viz. that the magnitude of the £uctuations
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in macroscopic quantities, such as the relative proportion of species, is inversely proportional to the
volume of the system. Therefore, we would expect
the outcomes from exact replicate tessellations to
be more consistent in the larger grid sizes. By the
same argument, we would also expect outcomes in
large tessellations to be less sensitive to initial conditions compared with small. However, this behaviour was not observed here; following a 7 week incubation period, the 3U3 tessellations gave slightly
more consistent results than the 6U6 arrangements,
with 20 coincident outcomes observed during the
smaller scale experiments. The more likely explanation for our observations is that scale a¡ects the
duration of transient behaviour and that smaller
scale experiments achieve a more stable outcome
faster relative to larger scale experiments. This is
simply a consequence of a characteristic time of the
dynamics being determined by the crossing time (i.e.
the time taken for an individual to move stepwise
from one side of the tile system to the other). Crossing times are greater in larger systems, and therefore
the dynamical time scales are longer.
The current study has concentrated on spatial aspects of community development in a relatively uniform environment. However, the system used also
o¡ers the potential to introduce a range of aspects
of natural complexity, for example: (1) variation of
the nutritional status of the supporting media for
individuals; (2) varying the inoculum potential of
individual confronters; (3) investigating the potential
ecological strategies of individuals during confrontations and any e¡ects of mycelial organisation; and
(4) study of the e¡ects of continuous or discrete environmental stress, metabolite production and biomass distribution on the range of potential outcomes.
The probability data produced in the binary-tile
experiments, are currently being used as the basis
for a set of rules for a cellular automaton computer
modelling programme, designed to predict the behaviour of the larger scale, spatially heterogeneous
experiments (Bown et al. (in preparation)). The experimental system described in this paper also allows
the e¡ective study of the complex spatial dynamics
of biological community dynamics in a manner
which is highly conducive to mathematical description, analysis and development. Furthermore, the
31
theoretical issues raised in this study will be relevant
to any situation where emergent behaviour depends
upon the interaction of parallel events, e.g. spatial
epidemiology, economic modelling, behavioural
modelling. The particular signi¢cance of the system
described in this paper is that it allows biological and
theoretical developments to be linked in a fully
integrated manner so that, for example, the results of the theoretical study can be used to inform
design of more appropriate biological experiments.
Analysis of other types of emergent behaviour can
rarely be undertaken in such an integrated, two-way
process.
Acknowledgments
We would like to acknowledge the ¢nancial support of the Leverhulme Trust and the Scottish O¤ce
Agriculture, Environment and Fisheries Department
in this work.
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