Behavioral Ecology
doi:10.1093/beheco/arp111
Advance Access publication 12 August 2009
Food quality affects search strategy in the
acellular slime mould, Physarum polycephalum
Tanya Latty and Madeleine Beekman
Behaviour and Genetics of Social Insects Laboratory, School of Biological Sciences A12, University of
Sydney, Sydney, NSW 2006, Australia
When searching for resources, organisms can increase the efficiency of search and exploitation behavior by using information
about the quality of a current resource patch in their decision making. The search strategy used by an organism can in turn affect
its performance in different landscapes. Here we examine the effect of resource quality on 2 foraging decisions: how much time
to allocate to explore the environment for new resources and what search strategy to use during exploration. We used the slime
mould Physarum polycephalum as our model system. Physarum polycephalum is an amoeboid organism that forages as a flowing mass
of pseudopods. We quantified the search pattern of plasmodia after engulfment of food of 6 different qualities. Food quality had
a significant, positive effect on how long plasmodia waited before resuming search behavior and on how long it took to abandon
food disks. Food quality had a positive effect on fractal dimension, indicating that the amount of localized search performed by
plasmodia increased with food quality. Our results suggest that increasing food quality results in a shift from extensive to intensive
search. Next, we examined foraging performance in landscapes with different patch structures. Plasmodia in correlated landscapes (half the patches contained only high-quality food, half contained only low-quality food) gained more weight than
plasmodia foraging in noncorrelated landscapes (patches contained both high- and low-quality food disks). Our results show
that food quality affects exploitation and search behavior and that both behaviors influence foraging performance in different
landscapes. Key words: amoeboid, area-restricted search, exploitation, exploration, foraging, myxomycophyta. [Behav Ecol
20:1160–1167 (2009)]
ndividual foragers searching for food may make use of information about food density, abundance, and quality to make
decisions on how to search for further resources. Once a resource has been located, foragers must decide how much time
to allocate toward exploiting that resource and how much to
spend sampling the environment for new, and possibly better,
food sources. Theory predicts that organisms foraging on depletable patches should spend a greater amount of time on
high-quality patches than on low-quality patches (McNair
1982). There is some empirical support for this prediction.
Chipmunks foraging on seed patches allocate progressively
more time to exploration as patch quality decreases (Kramer
and Weary 1991). Similarly, the amount of time fungal biomass remains on a food source before resuming mycelial
extension depends partly on the quality and size of the food
resource (reviewed in Boddy 1999).
In addition to influencing how long an individual spends
exploiting a known resource, contact with a food item can affect
the movement pattern used by organisms to search for new
resources. For example, a forager may increase its search effort
after detecting a food item, a behavior known as area-restricted or
area-concentrated search (Kareiva and Odell 1987; Benhamou
1992). Area-restricted search has been observed in a wide variety
of organisms including (but by no means limited to) mammals
(Benedix 1993), insects (Hassell and Southwood 1978; Bond
1980; Carter and Dixon 1982), and birds (Nolet and Mooij
2002; Weimerskirch et al. 2007). Further, the use of arearestricted search is not limited to animal foragers. Fungal mycelia have also been shown to alter their growth pattern after
contact with food sources in a manner consistent with the use
of a more intensive, area-restricted search strategy (Dowson et al.
I
Address correspondence to T. Latty. E-mail: [email protected].
Received 31 December 2008; revised 11 June 2009; accepted 7
July 2009.
The Author 2009. Published by Oxford University Press on behalf of
the International Society for Behavioral Ecology. All rights reserved.
For permissions, please e-mail: [email protected]
1988; Boddy 1999; Zakaria and Boddy 2002). After food depletion, foragers may adopt an ‘‘extensive’’ search strategy characterized by rapid movement and low path sinuosity (Benhamou
1992; Fortin 2002). This movement pattern may increase the
searcher’s probability of locating a new patch.
Although several studies have demonstrated that the use of
area-restricted search can be influenced by food density
(Benedix 1993) and hunger levels (Bond 1980; Carter and
Dixon 1982), the role of food quality in determining search
strategy has received much less attention (but see Donnelly
and Boddy 1997). Foragers would be expected to increase
their use of area-restricted search after contact with a highquality food item, whereas contact with a lower quality food
item could induce increased exploratory (extensive) search.
The type of search strategy employed by an organism may impact its foraging success depending on the distribution of forage
patches within a landscape and their relative quality. Modeling
suggests that the use of area-concentrated search after contact
with a profitable resource, and straighter, faster movements in
the absence of such a resource, is an efficient search strategy that
maximizes contacts with patchily distributed resources in continuous habitats (Benhamou 1992). The use of flexible, foodinduced search tactics may increase an organism’s probability
of encountering high-quality resource patches while ensuring
that it rapidly leaves poor-quality patches. This type of strategy
would be the most effective when the quality of any given food
item within the patch is correlated with the quality of other
items in that patch. However, it may be less efficient when
patches contain mixed food qualities, so that sampling a single
food item does not give an accurate representation of the
patches’ overall quality. Patch structure could therefore have
a significant effect on an organism’s foraging success.
Our goal in this paper is 2-fold. First, we examine the effect
of food quality on the exploitation and search tactics of the
acellular slime mould Physarum polycephalum (Myxomycophyta: Myxomycetae) by offering food of different quality.
Latty and Beekman • Search strategy in Physarum polycephalum
1161
from Southern Biological Supplies (Nunawading, Australia).
Laboratory cultures were fed 1–5 flakes of old-fashioned rolled
oats (Carmen‘s Organic, Cheltenham, Victoria, Australia) daily.
We subcultured plasmodia onto new agar plates weekly.
We made food disks by adding 1%, 3%, 5%, 7%, or 10%
weight/volume of oatmeal to 2% agar. Different oatmeal
concentrations were created by mixing finely ground oatmeal
(Soland health foods, Arana Hills, Australia) with 2% agar. We
also included a 0% oatmeal treatment that consisted of plain
2% agar. While still liquid, the oatmeal–agar mixture was
poured into 15 mm diameter by 2-mm high moulds and
allowed to set.
Plasmodia will engulf and grow on the range of oatmeal
concentrations we used in our study, and growth is positively
correlated with oatmeal content (Latty and Beekman 2009).
Our food disks therefore represent a range of acceptable food
qualities.
Experiment 1: exploitation and search behavior
Figure 1
A Physarum polycephalum plasmodium. The plasmodium has
commenced searching after engulfing a food disk (5% oatmeal agar
w/v). The tiny finger-like projections are pseudopods. A collection of
pseudopods make up a search front.
Second, we test P. polycephalum’s foraging success in 2 different
types of patchy landscape: one in which all food disks within
a patch were the same (correlated patch structure) and one
in which each patch contained food disks of mixed quality
(noncorrelated patch structure).
Physarum polycephalum is a single celled, multinucleate amoeboid organism that can cover areas exceeding 930 cm2 (Kessler
1982). During its vegetative stage, P. polycephalum is an active,
motile plasmodium capable of migrating at speeds of up to 5
cm/h (Kessler 1982). Migrating plasmodia are typically organized into an extending fan-like sheet at the front, followed by
a network of interconnected strands or veins at the rear (Kessler
1982). When an extended front comes into contact with a food
source, the plasmodium completely or partially engulfs it and
enters into a sedentary ‘‘exploitation’’ phase during which digestion of the food item occurs (Halvorsrud and Wagner 1998).
Some time after engulfment, the plasmodium will resume exploration by extending pseudopods into the surrounding environment while remaining in physical contact with the initial
food source via a network of veins (Halvorsrud and Wagner
1998; see Figure 1). After a variable period of exploitation (presumably ending with the depletion of the food), the plasmodium will move from the food source and reallocate the
biomass previously involved in digestion to searching. Physarum
polycephalum may therefore face a trade-off between allocating
time and biomass to exploratory structures, and allocating time
and biomass to exploiting a currently occupied food source.
Although qualitative descriptions suggest that hunger status
(starved or not starved) may affect plasmodial morphology
(e.g., Dove and Rusch 1980), no study to date has explicitly
examined the impact of food quality on subsequent search
and morphology of a P. polycephalum plasmodium.
MATERIALS AND METHODS
General procedures
We maintained P. polycephalum plasmodia on 2% agar plates,
incubated at 22 C in the dark. The original culture was obtained
Exploitation behavior
To start an experiment, we cut a plasmodial fragment from the
extending fronts of actively growing plasmodia. When severed
from the main cell, plasmodial fragments become fully functioning, separate individuals within minutes (Kobayashi
et al. 2006). In all, 15 plasmodia were randomly assigned to
each of the treatment groups. All plasmodia were weighed
prior to the start of the experiment. The mean weight of
plasmodial fragments was 0.037 6 0.002 g.
We placed each P. polycephalum plasmodium into the center
of a standard sized 2% agar plate (150 mm diameter) and
allowed it to acclimatize for 3 h. Once the acclimatization
period had elapsed, a 0%, 1%, 3%, 5%, 7%, or 10% oatmeal
(depending on treatment) food disk was placed in contact
with the edge of the largest expanding front. If more than
one equally sized expanding front was present, we randomly
selected one of them to place the food disk. At this stage, the
search front had usually migrated only 1–2 cm from the
inoculation point.
The plates with P. polycephalum were then returned to normal culture conditions (22 C in the dark) except when photographs were taken. To document the exploration and
exploitation pattern of the plasmodia, we checked plasmodia
every hour for the first 12 h and recorded whether or not the
plasmodia had engulfed the food source and if searching
fronts were visible. We also took digital photographs at
12 and 24 h using a high-definition Sony Handycam (HDRHC5E) positioned at a height of 50 cm. All images were taken
against a black background and we placed a ruler in all photographs for calibration.
We were interested in examining the effect of food quality on
the amount of time a plasmodium spent on the food source
before resuming search behavior. To quantify time before
search, we determined when the food source was completely
engulfed and when the plasmodium resumed search behavior.
We recorded a food disk as ‘‘fully engulfed’’ when the entire
food disk was covered by the plasmodium. This was easy to discern as P. polycephalum plasmodia are bright yellow, whereas
the food disks were beige. We scored the initiation of searching when an expanding front was observed on the agar. Our
pilot studies revealed that some plasmodia initiated search
behavior before they had completely engulfed the food disk.
We categorized these plasmodia as ‘‘early searchers.’’ We were
also interested in examining the total amount of time plasmodia remained on a food disk before abandoning it completely.
We checked plasmodia at 12, 24, 48, 72, and 96 h and
noted whether or not the food disk was still engulfed. Plasmodia in the 0% oatmeal group (which never engulfed the agar
1162
disks) were omitted from our analysis of exploitation
behavior.
Search behavior
We processed and analyzed digital images using Image J
(National Institutes of Health, Bethesda, MD). We digitally
altered the images to remove extraneous objects such as the
edge of the dish. We then subtracted the image background
using a rolling ball algorithm which smoothes out background inconsistencies and yields a solid black background.
We manually adjusted the image contrast to ensure that the
entire plasmodium was clearly differentiated from the background. We converted the image to binary (black and white)
using a thresholding technique that resulted in a black and
white image where the plasmodium was white and the background showed as black.
Most studies have used a measure of path sinuosity combined with average speed to characterize search strategies of
animals and to distinguish area-restricted search strategies
from exploratory strategies (e.g., Bond 1980; Carter and
Dixon 1982; Benedix 1993; Munyaneza and Obrycki 1998).
Such a procedure is logistically difficult for a P. polycephalum
plasmodium because it moves as a flowing mass of branching
and extending pseudopods. Thus, the morphology of the organism can be seen as the embodiment of its search strategy.
We therefore quantified P. polycephalum’s search behavior using 4 morphological metrics: total pseudopod length, the
mass fractal dimension (D ; see below) of the entire plasmodium, the mass fractal dimension of the plasmodium within
5 cm of the food source, and ‘‘local search area.’’ We calculated
the latter metric by dividing the area covered by a plasmodium
within a 5-cm radius of the food disk by the total area covered
by the plasmodium. This allowed us to asses the extent to
which plasmodia were focusing their search effort on the area
immediately surrounding the food disk. Area was calculated
by counting the number of white pixels in the processed image. We calculated total pseudopod length by measuring and
adding the length of all primary pseudopods starting at the
food source and ending at the outermost tip. In the 0% oatmeal treatment, plasmodia did not engulf the plain agar disk;
thus, we measured total distance traveled from the inoculation point. We defined primary pseudopods as those pseudopods that terminated in a search front (see Figure 1). Search
fronts are easily recognizable as flat, fan-shaped structures.
In some cases, plasmodia exhibited a symmetrical, compact
morphology that lacked pseudopodia. In these cases, we
calculated total length as the distance from the food source
or inoculation point to the outermost edge of the plasmodium.
Fractal dimension, D, is a quantitative measure of the spacefilling properties of an object (Liebovitch 1998). Higher
values of D indicate greater space filling. Fractal dimension
has been used to quantify growth patterns in many systems
including bacterial colonies (Fujikawa and Matsushita 1989,
1991), blood vessels in healthy and hypertensive lungs (Boxt
et al. 1994), mycelial growth patterns (Obert et al. 1990;
Boddy et al. 1999), and root architecture (Ozier-Lafontaine
et al. 1999). We calculated D in Image J, using the box-counting
method with box sizes of 12–64 pixels. The box-counting
method calculates D by covering the image with a grid of boxes
and counting the number of boxes that contain white pixels.
The measurement is then repeated using grids composed of
different sized boxes. A plot is generated with the log of box
size on the x axis and the log of the number of boxes containing white pixels on the y axis. The data are then fitted with
a straight line. The slope (S) of the line is the negative of the
fractal dimension (for more information, see Smith et al. 1996;
Liebovitch 1998).
Behavioral Ecology
Experiment 2: foraging performance in correlated and
noncorrelated landscapes
To test P. polycephalum’s foraging ability in landscapes with
correlated (patches containing food disks of equal quality)
and noncorrelated (patches containing food disks of mixed
quality) patch structures, we created patchy foraging arenas
consisting of 8 food patches. Each food patch contained
4 food disks, arranged in a square (Figure 2). Food disks
within a patch were 0.5 cm apart; patches were 2.5 cm apart.
We used 2 qualities of food disks: low (1% oatmeal) and high
(10% oatmeal). These food disks were made as described in
the general procedures. In the landscape with correlated
patch structure, all food disks within a patch were of the same
quality. In all the noncorrelated landscapes, each patch consisted of two 1% and two 10% food disks located diagonally
from one another (Figure 2b). Both landscapes had the same
number of high- and low-quality food disks, the only difference being their spatial arrangement.
The arenas were made by filling 145-mm diameter petri
dishes with plain 2% agar. Once the agar had set, we punched
0.5-mm holes into the agar and filled them with either our 1%
or 10% oatmeal–agar mixture. This procedure resulted in
relatively uniform, equally sized food disks.
To start an experiment, we cut a small plasmodial fragment
from the extending front of an actively growing plasmodium.
All plasmodia were weighed prior to placement. The mean initial weight of plasmodia was 0.027 6 0.002. We placed the plasmodial fragment in the centre of the arena and allowed it to
forage throughout the landscape for 4 days. We examined the
number of high- and low-quality food disks engulfed by each
Figure 2
Experimental setup for landscape foraging experiment. The ‘‘X’’
marks the inoculation point. The dark circles represent high-quality
(10% oatmeal) disks. The white circles are low-quality (1% oatmeal)
food disks. A patch consists of 4 food disks, 0.5 cm apart. Each patch
was 3 cm away from the inoculation point and 2.5 cm from other
adjacent patches. (a) Correlated landscape and (b) noncorrelated
landscape.
Latty and Beekman • Search strategy in Physarum polycephalum
1163
Table 1
Results of statistical analyses on the effect of food quality on exploitation and search behavior
Dependent variable
Independent variable
P
Coefficient estimate
Time to engulf (h)
Food quality
Weight (g)
Food quality
Weight (g)
Food quality
Weight (g)
Food quality
Weight (g)
Food quality
Weight (g)
Food quality
Weight
Food quality
Weight
Food quality
Weight
0.21
0.94
<0.0001
0.066
0.0003
0.52
0.004
0.40
0.34
0.15
<0.0001
0.88
<0.0001
0.2
0.002
0.06
0.01
0.09
0.30
237.18
20.37
17.16
2.38
271.38
20.207
48.85
0.008
20.05
0.025
20.87
0.02
22.2
Time before search (h)
Early search?
Abandonment time (h)
Total pseudopod length (mm, Box–Cox, k ¼ 0.02)
Fractal dimension (D, log)
Local fractal dimension (D, Box–Cox, k ¼ 2.5)
2
Local area search (cm , arcsine)
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
0.0009
1.3
0.06
19.9
0.11
26.8
0.64
85.8
0.2
34
0.002
0.33
0.004
0.67
0.007
1.1
Test statistic
N
F ¼ 1.5
F ¼ 0.0047
F ¼ 20.01
F ¼ 3.47
v2 ¼ 13.07
v2 ¼ 0.4
F ¼ 13.6
F ¼ 0.69
F ¼ 0.9
F ¼ 2.05
F ¼15.9
F ¼ 0.02
F ¼ 33.1
F ¼ 1.66
F ¼ 9.86
F ¼ 3.5
70
67
67
75
89
89
89
89
With the exception of early search, all data were analyzed using a multiple linear regression model. Early search was analyzed using a logistic
regression. Statistically significant results are in bold.
plasmodium every 12 h. We also counted the total number of
patches discovered. A patch was designated as ‘‘discovered’’
when at least one food disk in the patch was in direct contact
with a pseudopod. After 72 h, we removed the plasmodium
from the dish and reweighed it.
Statistical analyses
We examined the effect of food quality on search behavior using multiple linear regression models. Plasmodium weight was
included as an independent variable to control for the slight
differences in the starting weights of plasmodia. Variance inflation factors (VIFs) were checked to ensure that the assumption
of independence of variables was met. VIFs greater than
10 indicate strong colinearity (Quinn and Keough 2002).
Variables were therefore conservatively rejected from our
model if they had a VIF greater than 2. After initial model fitting, the distribution of residuals was tested using a Shapiro–
Wilk goodness-of-fit test. If residuals deviated from normality,
transformations were used as appropriate, and diagnostic procedures were repeated. We also visually examined residual
plots to ensure that the assumption of homogeneity of variance
was also met. As a result of this procedure, fractal dimension
was log transformed and local area search was arcsine transformed. Local fractal dimension and total pseudopod length
were transformed using the Box–Cox family of transformations
(k ¼ 2.5 and k¼ 0.02, respectively). Values of lamda were
selected using an iterative process that finds the best transformation (in terms of normality and homogeneity of variance)
(Sokal and Rohlf 1995). For our analysis of ‘‘early search,’’
we used a logistic regression model. We present the results
of our multiple regression analyses using leverage plots. Leverage plots account statistically for variance caused by all
other effects in the model.
We examined the effect of landscape structure on foraging
success using multiple linear regression models. Plasmodial
weight was included as a covariate. All analyses were done using
JMP 7 (SAS).
RESULTS
Experiment 1: exploitation and search behavior
Exploitation behavior
Overall, 5 plasmodia failed to completely engulf the food item
within 24 h (2 from the 1% treatment group and 3 from the 5%
treatment group). These were omitted from the analysis of
exploitation behavior. Plasmodia took a mean 5.4 6 0.19 h
to completely engulf the food disks. Neither treatment nor
weight had a significant effect on how long plasmodia took
to engulf food disks (Table 1).
Three plasmodia (1 in each of the 5%, 7%, and 10% treatment groups) did not initiate search behavior within the 12-h
observation period and so are excluded from the analysis of
time until search. After engulfing the food source, plasmodia
remained quiescent for a mean 0.39 6 0.24 h before initiating
search. Plasmodia on higher quality food disks took longer to
resume search than did those on low-quality food disks (Table 1
and Figure 3a). Plasmodium weight did not have a detectable
effect on how long plasmodia took to initiate search (Table 1).
The likelihood of initiating search before engulfment was
complete (early searching) increased with decreasing food
quality (Table 1).
Plasmodia remained on food disks for a mean 62 6 2 h
before abandoning food disks completely. Plasmodia that
had engulfed higher quality food disks remained on them
for longer than those that had engulfed lower quality food
disks (Table 1 and Figure 3b).
Search behavior
In general, plasmodia in the lower food quality groups (0%,
1%, and 3%) had a thinner, more elongate appearance
(Figure 4a–c). In contrast, plasmodia that had fed on higher
quality food sources (5%, 7%, and 10%) developed a feathery
morphology (Figure 4d–f).
One plasmodium in the 10% treatment group had not initiated search behavior within the 24-h observation period and
so is omitted from the analysis of search morphology. The
mean total length of pseudopods was 10.8 6 0.97 cm. Neither
food quality nor weight had a significant effect on total pseudopod length (Table 1). Because plasmodia in the 0% oatmeal
treatment did not engulf food sources, total pseudopod
length was measured from the inoculation point. Omitting
the 0% oatmeal treatment group from the analysis of total
pseudopod length did not change the results.
Plasmodia had a mean fractal dimension (D) of 1.376 0.02.
The fractal dimension of plasmodia after 24 h was affected
by food quality such that fractal dimension increased linearly
with increasing food quality (Table 1 and Figure 5a). Plasmodial weight did not have a significant effect on fractal
dimension.
Behavioral Ecology
1164
when plasmodia in the correlated landscapes encountered
low-quality patches, they tended to engulf 1 or 2 of the food
disks before leaving the patch entirely. In both landscapes,
plasmodia maintained networks of tubes attaching the active
searching fronts to previously engulfed food disks.
Plasmodia found a mean 6.2 6 0.38 patches during the 72-h
trial. Patch structure (correlated or noncorrelated) had no effect on the total number of patches discovered by plasmodia
(Table 2). Initial weight had a significant impact on the number of patches found such that plasmodia with higher initial
weights located more patches than lighter plasmodia. Patch
structure had a significant effect on the number of low-quality
food disks found by foraging plasmodia, such that plasmodia
in the correlated environment found a mean of 7.5 6 0.9 lowquality food disks, compared with 10.6 6 0.9 low-quality food
disks in the noncorrelated landscape (Table 2). Plasmodia
with heavier initial weights engulfed more low-quality patches
then did lighter plasmodia (Table 2).
Patch structure had no effect on the number of high-quality
food disks found, with plasmodia finding a mean 12 6
0.69 high-quality food disks over 72 h. Heavier plasmodia
engulfed more high-quality patches than lighter plasmodia
(Table 2).
Plasmodia in the correlated landscape gained more weight
than those in the noncorrelated environment (mean weight
0.05 6 0.006 g and 0.03 6 0.003 g, respectively). Plasmodia
with a higher initial weights gained more weight than those
with a lower initial weights (Table 2).
DISCUSSION
Figure 3
Leverage plots showing the effect of food quality on exploitation
behaviour. Dotted lines show 95% confidence intervals. (a) The
effect of food quality on how long it took for plasmodia to initiate
search behaviors. Note that negative numbers on the y axis are from
plasmodia that initiated search behaviors before they finished
engulfing the food disk. (b) The effect of food quality on
abandonment time. Note that values on the x axis have been shifted
slightly to prevent overlap of points.
The mean local fractal dimension (fractal dimension within
5 cm radius of food disk) of plasmodia was 1.3 6 0.02. Local
fractal dimension increased linearly with increased food quality (Table 1 and Figure 5b) but was not affected by plasmodial
weight.
The mean local search area covered by plasmodia was 0.4 6
0.09. Food quality had a significant impact on the amount of
localized search area such that plasmodia increasingly concentrated their search within 5 cm of the food source as food
quality increased (Table 1 and Figure 5c).
Experiment 2: foraging performance in correlated and
noncorrelated landscapes
Plasmodia initially grew by sending out pseudopods in multiple
directions. Once the first food disk had been contacted, plasmodia retracted the remaining pseudopods and concentrated
their biomass on the newly discovered food disk. In the noncorrelated landscapes, patches were typically encountered and
exploited sequentially, with plasmodia moving from one patch
to an adjacent patch only after all food disks (irrespective
of quality) within the patch had been engulfed. By contrast,
Food quality influenced several aspects of P. polycephalum’s
exploitation behavior. Plasmodia that had engulfed lower
quality food sources initiated search earlier and abandoned
food disks sooner than did plasmodia that had engulfed higher quality food disks. Plasmodia that had engulfed lower quality food disks were also more likely to initiate search behavior
before they had finished engulfing the food disk. Our results
are consistent with the marginal value theorem (McNair
1982) and with results from organisms as diverse as chipmunks (Kramer and Weary 1991) and fungi (Boddy 1999).
Interestingly, food quality had no influence on how long it
took for a plasmodium to engulf a food disk. The ability to
quickly engulf a potential food source, regardless of quality,
may help the plasmodium to rapidly monopolize resources,
thereby preventing competition from other decomposing
organisms such as bacteria and fungi.
In addition to affecting exploitation behavior, food quality
also had a strong influence on subsequent search behavior. Local area search increased with food quality, suggesting that plasmodia that had engulfed high-quality food items were focusing
their search within 5 cm of the food disk. This is consistent with
the use of an area-restricted search tactic. By contrast, plasmodia that had fed on lower quality food items were concentrated
away from the local area, using an extensive search tactic.
Further, the fractal dimension of plasmodia increased with
food quality, suggesting that plasmodia that had engulfed
high-quality food items grow in a more compact, space-filling
manner. The same trend held when we examined local fractal
dimension. This is again consistent with our suggestion that
plasmodia that have encountered high-quality resources subsequently engage in more intense, localized search, whereas
those that had fed on lower quality food sources engage in
a more extensive search, presumably aimed at maximizing
exploration.
Plasmodia in all treatment groups initiated searching while
still exploiting their current food disk. The amoeboid nature of
P. polycephalum allows it to simultaneously invest in both search
Latty and Beekman • Search strategy in Physarum polycephalum
1165
Figure 4
The effect of food quality on
plasmodial morphology after
24 h. (a–f) They show typical
morphology of plasmodia that
fed on 0%, 1%, 3%, 5%, 7%,
and 10% oatmeal food disks,
respectively. Note that the original images have been converted to binary images.
and exploitation behaviors. Physarum polycephalum’s resource
exploitation strategy, thus, appears to be as follows: the plasmodium crawls over the food source until it has been completely covered with a film of plasmodial biomass. Upon
contact with a nonnutritive surface (plain agar, in our experiment), the excess biomass immediately begins spreading
while maintaining a connection to the engulfed food item.
This behavior ensures that enough plasmodial biomass remains to digest the food source, while maximizing exploration by allocating any excess biomass to search behavior. This
search strategy has parallels in the leaf cutter ant Atta colombica where more foragers arrive at a patch than are necessary
to exploit it (Shepherd 1982). These ‘‘extra’’ foragers immediately begin searching the area around the food source
(Shepherd 1982). The allocation of extra resources to exploration tasks may optimize the trade-off between exploration
and exploitation.
Modeling studies have suggested that the use of areaconcentrated search after contact with a resource and
straighter, faster movements in the absence of resource cues
Figure 5
Leverage plots showing the effect of food quality on search behaviour. (a) Food quality versus
fractal dimension. (b) Food
quality versus local fractal dimension (fractal dimension
within 5 cm of the food disk).
Local fractal dimension has
been transformed using a Box–
Cox transformation, k ¼ 2.5. (c)
Food quality versus proportion
of local search (arcsine transformed). Note that values on
the x axis have been shifted
slightly to prevent overlap of
points.
Behavioral Ecology
1166
Table 2
Statistical details of multiple linear regression analysis on foraging
performance in correlated and noncorrelated landscapes
Dependent
variable
Independent
variable
P
No. of patches
engulfed
No. of low-quality
food disks engulfed
No. of high-quality
food disks engulfed
Weight gain (g)
Landscape
Weight (g)
Landscape
Weight (g)
Landscape
Weight (g)
Landscape
Weight (g)
Coefficient
estimate
F -test
statistic
0.169
20.39 6 0.27
2.05
0.0003 139.0 6 30.63 20.59
0.02
1.56 6 0.6
5.97
0.002 254.69 6 2.09 12.47
0.17
0.69 6 0.49
1.97
0.0006 253.33 6 55.8 86.95
0.04
0.009 6 0.004 4.58
0.01
1.43 6 0.51
7.98
N ¼ 20. Statistically significant results are in bold.
is an efficient search strategy that maximizes contacts
with patchily distributed resources in continuous habitats
(Benhamou 1992). Our results support this. When foraging
in different landscapes P. polycephalum achieved greater foraging success (measured as weight gain) when it foraged in
correlated landscapes compared with noncorrelated landscapes. The difference in foraging success seems to be due
to plasmodia in the correlated landscape consuming a smaller
proportion of low quality food disks than those in the noncorrelated landscape. On several occasions, plasmodia in the
correlated landscapes consumed only 1 or 2 items in a lowquality patch before leaving the patch entirely. This supports
our suggestion that P. polycephalum’s search strategy allows it
to rapidly leave low-quality patches. We suggest that the search
strategy of P. polycephalum is less efficient in the noncorrelated
landscape and that this results in an increased amount of
energy lost while searching.
In the wild, slime moulds feed on a variety of microorganisms such as bacterial colonies, yeasts, and fungi (Alexopoulos
1963). These vary in nutritional quality and are likely patchily
distributed. Our results suggest that P. polycephalum’s search
strategy is more efficient in landscapes with correlated patch
structure. However, it should be noted that plasmodia gained
weight in both landscapes, suggesting that their search strategy is generally effective in patchy environments.
The flexible search strategy of P. polycephalum is an interesting example of collective behavior. Plasmodial fragments as
small as 1 mm2 can become fully independent individuals
(Nakagaki and Guy 2008). The overall behavior of the organism appears to be the collective result of the behavior of these
small units of protoplasm (Nakagaki and Guy 2008). This
decentralized system of organization is similar to that of filamentous fungi, where a network of mycelial cords collectively
search for resources. Mycelia of the fungus Stropharia caerulea
slow the rate of hyphal extension after contact with a wood
bait (Donnelly and Boddy 1997). Contact with small wood
baits, an attractive food source, results in the greatest slowing
of extension rate (Donnelly and Boddy 1997). This is thought
to reflect resource-induced changes in the organism’s foraging strategy. Other studies have found that contact with food
sources of different size or type affect the postcontact ‘‘search
strategy’’ of fungal mycelia (Dowson et al. 1989; Zakaria and
Boddy 2002).
Social insects such as ants, bees, and termites are, perhaps,
the most well-studied examples of collective behavior. In these
systems, foraging trails are functionally analogous to the mycelial chords of fungi or the pseudopods of P. polycephalum.
Indeed, the colony-level foraging strategy of social insects
has been referred to as ‘‘amoeboid’’ (Seeley 1997). For species
using foraging trails or tunnels, the colony-level search strat-
egy is embodied in the network of trails and tunnels that link
the colony to its foraging and exploration sites. In the Formosan subterranean termite (Coptotermes formosanus), food
size has a significant effect on the construction of search tunnels by workers (Hedlund and Henderson 1999). When fed
a large food source, search tunnel volume and length
increased, whereas the number of tunnels decreased
(Hedlund and Henderson 1999). The resulting change in
tunnel network topology is thought to reflect a switch toward
increased resource exploitation and decreased exploration.
Despite its apparent simplicity, P. polycephalum is capable of
several complex behaviors such as finding the shortest Euclidian path between points (Nakagaki 2001; Nakagaki, Kobayashi,
et al. 2004; Nakagaki, Yamada, and Hara 2004) and anticipating
periodic events (Saigusa et al. 2008). Our study has shown that
P. polycephalum plasmodia alter their exploitation and search
behavior depending on the quality of recently engulfed food
sources. We then demonstrated how this behavior affects the
organisms foraging success on different landscapes. Our study,
thus, is an interesting example of behavioral flexibility in a
unicellular organism.
Funding
Human Frontier Science Program (RGP51/2007 to M.B.);
Australian Research Council (DP0878924 to M.B.); The University of Sydney (International Research Fellowship to M.B.).
We would like to thank Marcus McHale for help in the laboratory.
We would also like to thank Ben Oldroyd and other members of
the Behaviour and Genetics of Social Insects laboratoty for helpful
and insightful comments on early versions of the manuscript. Thanks
also to Toshiyuki Nakagaki for helpful tips on culture techniques. The
comments of our three referees greatly contributed to the manuscript.
REFERENCES
Alexopoulos C. 1963. The Myxomycetes II. Bot Rev. 29:1–78.
Benedix JH Jr. 1993. Area-restricted search by the plains pocket
gopher (Geomys bursarius) in tallgrass prairie habitat. Behav Ecol.
4:318–324.
Benhamou S. 1992. Efficiency of area-concentrated searching behaviour in a continuous patchy environment. J Theor Biol. 159:67–81.
Boddy L. 1999. Saprotrophic cord-forming fungi: meeting the challenge of heterogeneous environments. Mycologia. 91:13–32.
Boddy L, Wells JM, Culshaw C, Donnelly DP. 1999. Fractal analysis in
studies of mycelium in soil. Geoderma. 88:301–328.
Bond AB. 1980. Optimal foraging in a uniform habitat: the search
mechanism of the green lacewing. Anim Behav. 28:10–19.
Boxt LM, Katz J, Liebovitch LS, Jones R, Esser PD, Reid L. 1994.
Fractal analysis of pulmonary arteries: the fractal dimension is lower
in pulmonary hypertension. J Thorac Imaging. 9:8–13.
Carter MC, Dixon FG. 1982. Habitat quality and the foraging behaviour of coccinellid larvae. J Anim Ecol. 51:865–878.
Donnelly DP, Boddy L. 1997. Resource acquisition by the mycelialcord-former Stropharia caerulea: effect of resource quantity and
quality. FEMS Microbiol Ecol. 23:195–205.
Dove WF, Rusch HP, editors. 1980. Growth and differentiation in Physarum polycephalum. Princeton (NJ): Princeton University Press.
Dowson CG, Rayner ADM, Boddy L. 1988. Foraging patterns of Phallus impudicus, Phanerochaete laevis and Steccherinum fimbriatum
between discontinuous resource units in soil. FEMS Microbiol Lett.
53:291–298.
Dowson CG, Springham P, Rayner ADM, Boddy L. 1989. Resource
relationships of foraging mycelial systems of Phanerochaete velutina
and Hypholoma fasciculare in soil. New Phytol. 111:501–509.
Fortin D. 2002. Optimal searching behaviour: the value of sampling
information. Ecol Model. 153:279–290.
Fujikawa H, Matsushita M. 1989. Fractal growth of Bacillus subtilis on
agar plates. J Physical Soc Japan. 58:3875–3878.
Fujikawa H, Matsushita M. 1991. Bacterial fractal growth in the
concentration field of nutrient. J Physical Soc Japan. 60:88–94.
Latty and Beekman • Search strategy in Physarum polycephalum
Halvorsrud R, Wagner G. 1998. Growth patterns of the slime mold
Physarum on a nonuniform substrate. Phys Rev E. 57:941–948.
Hassell MP, Southwood TRE. 1978. Foraging strategies of insects.
Annu Rev Ecol Syst. 9:75–98.
Hedlund JC, Henderson G. 1999. Effect of available food size on
search tunnel formation by the Formosan subterranean termite
(Isoptera: rhinotermitidae). J Econ Entomol. 92:610–616.
Kareiva P, Odell G. 1987. Swarms of predators exhibit ‘‘preytaxis’’ if
individual predators use area-restricted search. Am Nat. 130:233–270.
Kessler D. 1982. Plasmodial structure and motility. Cell biology of
Physarum and Didymium. In: Aldrich HC, Daniel JW, editors.
Sydney (Australia): Academic Press.
Kobayashi R, Tero A, Nakagaki T. 2006. Mathematical model for rhythmic protoplasmic movement in the true slime mold. J Math Biol.
53:273–286.
Kramer DL, Weary DM. 1991. Exploration versus exploitation: a field
study of time allocation to environmental tracking by foraging chipmunks. Anim Behav. 41:443–449.
Latty TM, Beekman M. Forthcoming 2009. Going into the light: food
quality and the risk of light exposure affect patch choice decisions
in the acellular slime mould Physarum polycephalum. Ecology.
Liebovitch LS. 1998. Fractals and chaos: simplified for the life sciences. Oxford: Oxford University Press.
McNair JN. 1982. Optimal giving-up times and the marginal value
theorem. Am Nat. 119:511–529.
Munyaneza J, Obrycki JJ. 1998. Searching behavior of Coleomegilla
maculata larvae feeding on colorado potato beetle eggs. Biol Control. 13:85–90.
Nakagaki T. 2001. Smart behavior of true slime mold in a labyrinth.
Res Microbiol. 152:767–770.
Nakagaki T, Guy RD. 2008. Intelligent behaviors of amoeboid movement based on complex dynamics of soft matter. Soft Matter. 4:57–67.
1167
Nakagaki T, Kobayashi R, Nishiura Y. 2004. Obtaining multiple separate food sources: behavioural intelligence in the Physarum plasmodium. Proc R Soc Lond B Biol Sci. 271:2305–2310.
Nakagaki T, Yamada H, Hara M. 2004. Smart network solutions in an
amoeboid organism. Biophys Chem. 107:1–5.
Nolet BA, Mooij W. 2002. Search paths of swans foraging on spatially
autocorrelated tubers. J Anim Ecol. 71:451–462.
Obert M, Pfeifer P, Sernetz M. 1990. Microbial growth patterns
described by fractal geometry. J Bacteriol. 172:1180–1185.
Ozier-Lafontaine H, Lecompte F, Sillon J. 1999. Fractal analysis of the
root architecture of Gliricidia sepium for the spatial prediction of
root branching, size and mass: model development and evaluation
in agroforestry. Plant Soil. 209:167–179.
Quinn GP, Keough MJ. 2002. Experimental design and data analysis
for biologists. Cambridge: Cambridge University Press.
Saigusa T, Tero A, Nakagaki T, Kuramoto Y. 2008. Amoeba anticipate
periodic events. Phys Rev Lett. 100:018101.
Seeley T. 1997. Honey bee colonies are group level adaptive units. Am
Nat. 150:S22–S41.
Shepherd JD. 1982. Trunk trails and the searching strategy of a leafcutter ant, Atta colombica. Behav Ecol Sociobiol. 11:77–84.
Smith TG, Lange GD, Marks WB. 1996. Fractal methods and results
in cellular morphology—dimensions, lacunarity and multifractals.
J Neurosci Methods. 69:123–136.
Sokal RR, Rohlf FJ. 1995. Biometry: the principles and practices of
statistics in biological research. New York: W.H. Freeman and Co.
Weimerskirch H, Pinaud D, Pawlowski F, Bost CA. 2007. Does prey
capture induce area-restricted search? A fine-scale study using GPS
in a marine predator, the wandering albatross. Am Nat. 170:734–743.
Zakaria AJ, Boddy L. 2002. Mycelial foraging by Resinicium bicolor:
interactive effects of resource quantity, quality and soil composition.
FEMS Microbiol Ecol. 40:135–142.