1. food and drink
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Ecological Modelling 192 (2006) 618–626
Why fruits rot, seeds mold and meat spoils: A reappraisal
Thomas N. Sherratt a,∗ , David M. Wilkinson b , Roderick S. Bain a
a
b
Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, Ont., Canada K1S 5B6
Biology and Earth Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
Received 3 December 2004; received in revised form 2 June 2005; accepted 18 July 2005
Available online 28 September 2005
Abstract
It has been argued that micro-organisms may gain a selective advantage by rendering fruit, seeds and meat as objectionable to
larger animals as possible, thereby increasing the likelihood that the micro-organisms retain the resource. Here, we demonstrate
that if spoiling carries a cost then not even group selection can enable a spoiling strategy to persist. In the absence of such a cost,
then spoilers will be able to persist even without the actions of a larger animal, yet spread from rarity only under a limited set of
conditions. We therefore question whether this verbally attractive theory is tenable, and offer alternative explanations for why
rotting fruit, seeds and meat tend to be repellent to larger animals.
© 2005 Elsevier B.V. All rights reserved.
Keywords: Microbial competition; Spoiling; Group selection; Free riders
1. Introduction
In 1977, Daniel Janzen proposed a characteristically imaginative and unorthodox theory. He argued
that the consumption of a microbe and its resources
by an animal would be deleterious to most microbes,
hence microbes would be (p. 691) “under strong selection pressure to render seeds, fresh fruit, or carcasses
as objectionable or unusable to larger organisms as is
possible in the shortest period of time”. To dramatise
the largely overlooked possibility of microbe–macrobe
competition, Janzen (1979) described a scenario of a
∗ Corresponding author. Tel.: +1 613 520 2600x1748;
fax: +1 613 520 3539.
E-mail address: sherratt@ccs.carleton.ca (T.N. Sherratt).
0304-3800/$ – see front matter © 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.ecolmodel.2005.07.030
youngster left alone for a short time in the kitchen
with two strawberries, one fresh and one moldy. If
this youngster pops the fresh one in its mouth, then
“the microbe has won”! Like the allegorical youngster, a range of bird species do indeed exhibit preferences for consuming fresh fruits over putrefying
fruits (e.g. Borowicz, 1988; Buchholz and Levey,
1990; Cipollini and Stiles, 1993). While Janzen (1977,
1979) stressed that he did not believe that all repellent
chemicals released by microbes were solely selected
to deter larger animals, he did propose that animals
may have “played a large and virtually unrecognized
role in evolution of their production”. Thus, fruits
rot, seeds mold and meat spoils in large measure
because that is the way microbes compete with bigger
organisms.
T.N. Sherratt et al. / Ecological Modelling 192 (2006) 618–626
Since its publication, Janzen’s paper has been
widely cited in the primary literature (a Web of
Science® search on May 27, 2005 indicated 150 citations) as well as in more general texts (Cockburn, 1991,
p. 333; Stiling, 2002, p. 140). The vast majority of these
citations have viewed the idea favourably, indeed one of
us has previously described this idea as “a fine example
of the importance of thinking about microbial ecology”
(Wilkinson, 1998). However, somewhat surprisingly,
the conditions (if any) under which Janzen’s proposal
might work have never been formally identified. Here,
we show using a numerical simulation model that if
the generation of spoiling chemicals carries an individual cost to the “spoiler”, then larger animals cannot
by themselves facilitate selection for spoiling. In this
case, spoiling forms will be rapidly undermined by
“free-riding” (Axelrod and Dion, 1988) non-spoilers
that enjoy the same benefit as spoilers (namely, not
being eaten) but do not pay the cost. Many microbes
are predominantly clonal (Maynard Smith et al., 1993),
and while it was proposed that “a kind of group selection” (Janzen, 1977) at the level of the patch might
allow spoiling to persist, here we argue that the rate of
movement of microbes among fruit, seeds, and cadavers is likely to be far too high for this form of selection
to operate. If spoiling does not carry an individual cost
then clearly we do not need to invoke preferential foraging behaviour of animals to explain the persistence of
this strategy. Yet even here, spoiling microbes will only
spread from rarity under a restricted set of conditions.
2. The model
In the following model, we consider an environment containing a fixed number of available microbial
resource patches (n), which for simplicity we call fruit,
but they might equally be thought of as seeds or cadavers. We have wind-fallen fruits in mind, or at least a
resource that is potentially available for consumption
by a vertebrate. These fallen fruits can be colonised
by microbes that render the patch less attractive to
large animals (spoilers), and/or by microbes that do
not render the patch less attractive (non-spoilers). By
definition, individual fruits that support a high amount
of spoiling microbes are less likely to be eaten by a
frugivore than similar fruits with fewer spoilers. The
model combines continuous-time processes (growth
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of microbial populations and concomitant depletion
of resources) with discrete-time processes (consumption of fruit and transmission of microbes between
fruit, followed by fruit replacement) which occur at
the end of every unit time step. Our simulation was
implemented in Microsoft® Visual Basic 6, and utilized a BASIC Numerical Analysis Library (BNALib)
for the numerical solution of differential equations via a
fourth-order Runge-Kutta method. The continuous and
discrete events were combined by first allowing fruit
consumption and transmission of microbes between
fruit (in random order), then numerically integrating
our differential equations over a unit time period (see
below) and finally replacing decayed fruit, iterating the
whole process over many time steps.
2.1. Growth of microbes and depletion of
resources
The continuous-time equations for microbial growth
on each fruit i were of the form:
dRi
= −kN Ni Ri − kS Si Ri
dt
(1.1)
dNi
= fN Ri Ni − bNi
dt
(1.2)
dSi
= fS Ri Si − bSi
dt
(1.3)
where Ri is the amount of resource (comprising everything in the fruit available for microbes), Ni and Si the
amount of non-spoilers and spoilers on fruit i, respectively, b the microbe mortality coefficient, kN and kS
and fN and fS are the consumption and reproduction
coefficients for non-spoilers and spoilers, respectively
(see Table 1). In this way, a single fruit starting with
a resource of R = 25 and initial amount of microbes of
N = 5 (with no spoiling forms present) at t = 0 would be
depleted by microbial action to one tenth of its resource
value in approximately 25 time units when kN = 0.01,
fN = 0.02, b = 0.2 (Fig. 1). As fruit may typically decay
in a matter of a few weeks, one can conveniently consider a unit time in many of our simulations as a single
day. However, we stress that the qualitative conclusions
we draw from our model are only dependent on the relative sizes of key parameters such as fN and fS , and they
are in no way dependent on the exact sizes of parameters chosen.
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Table 1
Glossary of terms used in the model
Parameter or rule
Interpretation
n
Ri
Ni , S i
kN , k S
fN , fS
c
b
x
R*
maxreplace1, maxreplace2
q
maxdonatep
maxN
maxS
SD
CR1-3
RR1-2
SC1-2
Number of fruits available (assumed constant)
Amount of resources on fruit i
Amount of non-spoilers and spoilers on fruit i
Consumption coefficients for non-spoilers and spoilers
Reproduction coefficients for non-spoilers and spoilers
Weighting coefficient used in consumption rule 2
Microbe mortality coefficient
Number of fruit consumed each discrete-time step
Critical fruit resource level, below which replacement occurs
Maximum amount of microbes on any fruit that is replaced under microbial replacement rules 1 and 2
Probability that a given fruit donates microbes to another per time step
Maximum proportion of microbes donated in a transfer event
Initial maximum amount of non-spoilers in each fruit
Initial maximum amount of spoilers in each fruit (starting condition 1 only)
Starting amount of spoilers in a single fruit (starting condition 2 only)
x Fruit selected at: 1 = random; 2 = highest Ri − c Si ; 3 = highest (Ri /Si )
Inoculum in replaced fruit from: 1 = all other fruit; 2 = single random fruit
Spoilers initially in: 1 = all fruit; 2 = single random fruit
2.2. Fruit consumption
At each discrete unit time step, a fixed number x
of the available n fruit (x < n) were consumed by frugivores. Three different fruit consumption rules (CR)
were considered. Under CR1 (a control condition),
the x fruit were selected at random from the available
fruit, independent of their resource value or microbial
community. Under CR2, the x fruit with the highest
Ri − cSi were selected for consumption, where c is a
weighting coefficient (the higher c, the more important it is for foragers to avoid spoiling microbes than
to obtain high value resources). Under CR3, the x
fruit with the highest Ri /Si were chosen. In all cases
of tied preference for the most desirable fruit, the
fruit with the highest R within this equally preferred
subset were taken. Clearly, CR2 and CR3 represent
extreme cases of selection against non-spoilers given
complete information—if such behaviour cannot generate selection for spoilers, then it is unlikely that
spoilers could evolve through another form of frugivore
behaviour.
2.3. Fruit replacement
Fig. 1. Standard graph for the depletion of a single fruit resource
(continuous line) by microbes, and the subsequent decline of
microbes (dotted line). R(0) = 25, kN = 0.01, fN = 0.02, b = 0.2. The
fruit starts with a microbial amount of 5 units. A unit time is considered a day.
When fruit were eaten, or depleted by microbial
activity to a value R* , then they were replaced with
“fresh” fruit with initial resource R(0). These fresh
fruit were seeded with an initial inoculum of microbes,
whose exact composition was dependent on the specific replacement rule (RR) invoked. Under RR1, any
new fruit was given an inoculum drawn from a discrete uniform distribution ranging from 1 to maxreplace1 while the proportion of spoilers in this inoculum
was equal to the weighted proportion of spoilers in
the rest of the global microbial community (excluding
T.N. Sherratt et al. / Ecological Modelling 192 (2006) 618–626
those microbes on fruit selected for consumption in
the current time step). Under RR2, the size of the
inoculum was simply maxreplace2 and the proportion
of spoilers in the inoculum was equal to the proportion of spoilers in a single randomly chosen surviving
fruit. These two rules clearly lie at opposite extreme
ends of a continuum. Of course, our assumption of
maintaining a fixed number of available fruit is somewhat arbitrary, but preferable to any other set of rules
given that the net number of fruit in natural systems will vary in a complex way in both space and
time.
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3. Results
In the following sections, we have independently
varied several key parameters to elucidate their effect.
However, we have confirmed that our qualitative conclusions are the same for a range of other parameter
combinations. We have also reached an identical set of
conclusions using discrete-time models with different
sets of assumptions (not shown), and draw on a simple
phenomenological model (Maynard Smith, 1998, see
discussion) to interpret our findings.
3.1. Oscillatory solutions for resources
2.4. Microbial dispersal
To represent chance dispersal events, a small amount
of microbes on any given fruit i were passed to any
given fruit j (i = j) with probability q per pair per
unit time step. All possible pairs of fruit in the system were considered for dispersal events, both as
potential donors of microbes and as potential recipients. If dispersal occurred, then the proportion of
a donor’s microbes transferred was randomly drawn
from a uniform distribution between 0 and maxdonatep and comprised the same mixture of spoilers and
non-spoilers present in the donor. Microbial dispersal
could occur either before or after the fruit consumption
(and concomitant replacement) at each discrete-time
step. To avoid any priority effects (e.g. see Ruxton and
Saravia, 1998), we let the probability that dispersal
occured before consumption be 0.5.
The mean amount of resource per fruit and the number of microbes per fruit frequently exhibited oscillatory dynamics (Fig. 2). This form of periodic dynamics
was evident even without consumption of fruit, and
included both damped oscillations and more permanent cycles (see Fig. 2). The oscillations arise simply
as a consequence of “pulses” in resource availability as
decayed fruit are replaced with fresh fruit. Thus, under
most foraging rules frugivores tend to concentrate on
2.5. Starting conditions
Our starting conditions (SC) were of two different forms, mirroring our fruit replacement rules. In
both cases, all n fruit started with R(0) resources, and
each fruit was given a quantity of non-spoilers drawn
from a discrete uniform distribution between 1 and
maxN. Thus, the starting densities of microbes on each
fruit were not identical, although the initial amount of
resource that each fruit contained was. In SC1, each
fruit also received an integer amount of spoilers drawn
from a uniform distribution 1 to maxS. However, to
analyse whether a rare mutant spoiling form might
spread from rarity, in SC2 we allowed only a single
randomly chosen fruit to have spoilers, and the amount
of these spoilers was given by SD.
Fig. 2. How the mean amount of resources per fruit (continuous
line) and the mean amount of spoiling and non-spoiling microbes
per fruit (dotted lines) fluctuate over time in a single replicate simulation. Parameter values: n = 100 fruit, kN = 0.01, kS = 0.01, b = 0.2,
fN = 0.03, fS = 0.03, R(0) = 25, R* = 2.5, q = 0.1, maxreplace2 = 0.1,
maxreplacep = 0.001, maxN = 5, maxS = 5, x = 20 fruit consumed,
consumption rule 3 (highest difference), starting rule 1 (spoilers initially on all fruit) and replacement rule 2 (inoculum from a single
fruit). Note that the oscillations do not represent fluctuations in any
individual fruit (Ri always declines), but reflect changes in the total
amount of resources in the system as a consequence of decayed and
consumed fruit being replaced with fresh fruit.
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Fig. 3. Change in the proportion of spoilers in the population over
time under the parameter combinations listed in Table 2. Error bars
represent ±1 standard deviation of the mean proportion of spoilers
per fruit per unit time, calculated over 50 replicate simulations. Starting condition 1 (spoilers initially on all fruit) and replacement rule 2
(inoculum from a single fruit) assumed.
the freshly available fruit—when a cohort of decayed
and unattractive fruit is eventually replaced, it generates a temporary increase in fruit availability. One way
to see this is through varying the resource threshold R*
beyond which a fruit is replaced. When we reduce the
threshold R* from 2.5 to 0.5 and keep all other variables
the same as Fig. 2, then the resource dynamics moves to
permanent cycles (not shown) and the period increases
from 16 to 52 units (as estimated using autocorrelation
analysis). Clearly by lowering R* , fruit stays around for
longer before it is completely decayed and replaced,
and this alteration consequently lengthens the period
of any cycle that is generated. It is important to note
however that in all of our simulations, the proportion
of microbes that were spoilers did not oscillate and
always reached an equilibrium, generally within 500
time steps (see Fig. 3, for example).
3.2. Scenario A: spoiling is cost free to the spoiler
Here, we ask whether a spoiling form would spread
in a microbial population if it could spoil fruit at no
cost to its reproductive output (fN = fS ). Such a condition would arise, for instance, if the repellent chemicals
were a by-product of microbial metabolism (alcohols
are a possible example). If this condition holds, we find
that spoiling microbes can persist, and indeed (depending on starting conditions), they may come to represent
a reasonably high proportion of the population. Here,
the proportion of spoilers increases in the overall microbial population directly as a result of the selective action
of frugivores. For example, we note that the mean proportion of spoilers in the microbial population was
significantly higher in all selective predation simulations compared to no predation and random predation
simulations (Table 2). As such, our analysis supports
Janzen’s argument that selective predation may help
promote the evolution of spoiling microbes when spoiling carries no reproductive cost.
However, there is an important caveat. Note that the
starting conditions have a significant influence on the
equilibrium mean proportion of spoilers in the population, and that spoilers never evolve to fixation. The
central reason for these observations is that the nonspoilers in any fruit gain just as much as spoilers. Thus,
as more and more exchange between spoiling and nonspoiling microbes occurs among the fruit (and fruit
are replaced, in some cases with a representative general inoculum), then fruit begin to support increasingly
similar proportions of spoilers and non-spoilers, and
selection can no longer act. This phenomenon can be
seen in several ways. For example, in a single run of the
model under conditions in Fig. 3 the mean proportion
of spoiling microbes per fruit rose from 0.500 to a stable 0.673 in 500 time steps but the standard deviation
of the proportion of spoiling microbes among individual fruit within the system fell from 0.120 to 0.001.
Secondly, when the microbial dispersal parameter was
reduced from 0.1 to 0.001 then a higher proportion of
spoiling microbes could be reached before fruit became
homogeneous in content, and further selection was prevented (cf Table 2 versus Table 3). To take an extreme,
when dispersal was removed altogether (q = 0, all other
conditions as for Table 3) then under starting rule 1
and replacement rule 2 the proportion of microbes that
were of the spoiling form rose from an overall mean
of 0.524 (S.D. 0.011 over replicates) in generation 1 to
stable mean proportion of 0.892 (S.D. 0.021) by generation 1000, based on 50 replicate simulations. Clearly,
there is no simple dispersal threshold at which spoilers will flourish. However, lower dispersal rates simply
extend the period of time before all fruit become similar
in their mix of spoilers and non-spoilers. When there
is little or no variability among fruit, frugivores cannot exercise their preference for non-spoiled fruit, and
selection can no longer act on this basis.
T.N. Sherratt et al. / Ecological Modelling 192 (2006) 618–626
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Table 2
The mean proportion of microbes that were spoilers after time step 1 (first line, with standard deviation in brackets) and after time step 1000
(second line in cell, with standard deviation) under different predation rules, fruit replacement rules and starting conditions
Predation
Highest Ri − cSi (CR2)
Highest Ri /Si (CR3)
Spoilers initially in all fruit (SC1)
Replacement inoculum from all other fruit (RR1)
0.49817 (0.00876)
0.49983 (0.01136)
0.49628 (0.01239)
0.49697 (0.01967)
0.52256 (0.01108)
0.61507 (0.02144)
0.52248 (0.00986)
0.59103 (0.01550)
Replacement inoculum from single fruit (RR2)
0.50048 (0.01028)
0.50034 (0.01230)
0.00000a (0.00000)
0.49677 (0.03549)
0.52163 (0.01277)
0.68940 (0.03141)
0.52081 (0.01286)
0.68638 (0.02938)
Spoilers initially in single fruit (SC2)
Replacement inoculum from all other fruit (RR1)
0.00018 (0.00001)
0.00018 (0.00006)
0.00056 (0.00052)
0.00026 (0.00046)
0.00020 (0.00001)
0.00070 (0.00064)
0.00020 (0.00001)
0.00059 (0.00031)
Replacement inoculum from single fruit (RR2)
0.00018 (0.00001)
0.00018 (0.00006)
0.00000a (0.00000)
0.00016 (0.00028)
0.00021 (0.00001)
0.00498 (0.00644)
0.00020 (0.00001)
0.00860 (0.00825)
No predation
Random predation (CR1)
Mean and standard deviations are all based on 50 separate simulations, each for 1000 time units (effectively 1000 days based on natural decay
rates). Here, we assume no reproductive costs to spoiling (fN = fS ). Parameter values: n = 100, kN = 0.005, kS = 0.005, b = 0.2, fN = 0.02, fS = 0.02,
R(0) = 25, R* = 2.5, c = 1000, x = 10, q = 0.1, maxreplace1 = 20, maxreplace2 = 0.1, maxdonatep = 0.001, maxN = 10, maxS = 10, SD = 0.1.
a All microbes eventually go extinct due to continual depletion of resources.
Finally, we note that changing the actual number of
fruit consumed per unit time step (x) had little influence
on our general conclusions. For instance, increasing the
number of fruit consumed from 10 to 30 but keeping
all other conditions the same as in Table 3 tended to
increase the overall mean proportion of spoilers in the
microbial population slightly, but the same general patterns were apparent.
3.3. Scenario B: spoiling incurs a small
reproductive cost
In this instance, we assume that there is a metabolic
cost to the production of spoiling chemicals, such that
a non-spoiling colonist would eventually multiply and
out-compete any spoilers within the same fruit (assuming the fruit lasts that long). If these spoiling chemicals
were produced at least in part to deter large animals
from consuming the resource, then we feel that such a
cost would be likely. For instance, the antibiotic tetracycline requires over 70 separate enzymatic steps in its
synthesis (Madigan et al., 2000) and it is known that
in soils, populations of antibiotic-producing bacteria
grow more slowly than many non-antibiotic producers
(Wiener, 2000).
When fN > fS then spoiling microbes invariably go
globally extinct. For instance, in replicate simulations
identical to that depicted in Table 2 but with fS = 0.019
then all spoiling microbes went extinct by t = 1000 in
all simulations. Even if dispersal of microbes between
fruit was reduced to very low levels, then spoiling
microbes were either extinct by t = 1000 or at least
declining towards extinction (Table 4). Since nonspoilers will eventually be selected over spoilers within
any given fruit, then the only way spoilers can persist is if they have a chance of occupying a fruit alone
for much of the fruit’s existence. With even low levels of dispersal then this group-beneficial trait is outcompeted by non-spoilers in the same fruit that share
the benefit, but pay none of the costs.
4. Discussion
We have argued that for Janzen’s provocative spoiling theory to work, then there should be no benefits from “free-riding”, otherwise microbes would be
selected to take the benefits of not being eaten by a
larger animal without paying the cost. Such instability is widely reported in n-player cooperative games
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Table 3
The mean proportion of microbes that were spoilers after time step 1 (first line, with standard deviation in brackets) and after time step 1000
(second line in cell, with standard deviation) under different predation rules, fruit replacement rules and starting conditions
Predation
Highest Ri − cSi (CR2)
Highest Ri /Si (CR3)
Spoilers initially in all fruit (SC1)
Replacement inoculum from all other fruit (RR1)
0.50062 (0.00850)
0.49879 (0.01135)
0.50023 (0.01103)
0.50125 (0.02094)
0.52168 (0.01244)
0.61932 (0.01949)
0.52020 (0.01103)
0.58706 (0.02003)
Replacement inoculum from single fruit (RR2)
0.49924 (0.00942)
0.50045 (0.01200)
0.00000a (0.00000)
0.49467 (0.16296)
0.52251 (0.01199)
0.88747 (0.02752)
0.51909 (0.01131)
0.88930 (0.02654)
Spoilers initially in single fruit (SC2)
Replacement inoculum from all other fruit (RR1)
0.00018 (0.00001)
0.00017 (0.00007)
0.00047 (0.00062)
0.00026 (0.00038)
0.00020 (0.00001)
0.00062 (0.00068)
0.00020 (0.00001)
0.00079 (0.00055)
Replacement inoculum from single fruit (RR2)
0.00018 (0.00001)
0.00019 (0.00006)
0.00000a (0.00000)
0.00003 (0.00016)
0.00020 (0.00001)
0.02209 (0.02721)
0.00020 (0.00001)
0.02490 (0.02196)
No predation
Random predation (CR1)
Mean and standard deviations are all based on 50 separate simulations, each for 1000 time units. Here, we assume no reproductive costs to
spoiling (fN = fS ). Parameter values as for Table 2 except dispersal parameter q = 0.001.
a All microbes eventually go extinct due to continual depletion of resources.
(e.g. Dugatkin, 1997). The presence of putrefying
chemicals in rotting food may well dissuade potential
consumers from eating it, but this in itself does not
provide sufficient grounds to argue that the repellent
chemicals have arisen, or are maintained, for this reason. As we have shown, even group-level selection is
unlikely to facilitate the persistence of spoilers because
the dispersal rates of microbes are almost certainly
Table 4
The mean proportion of microbes that were spoilers after time step 1 (first line, with standard deviation in brackets) and after time step 1000
(second line in cell, with standard deviation) under different predation rules, fruit replacement rules and starting conditions
Predation
Highest Ri − cSi (CR2)
Highest Ri /Si (CR3)
Spoilers initially in all fruit (SC1)
Replacement inoculum from all other fruit (RR1)
0.49516 (0.01059)
0.49532 (0.01110)
0.00000 (0.00001)
0.00000 (0.00000)
0.51636 (0.01558)
0.00000 (0.00000)
0.51674 (0.01391)
0.00000 (0.00000)
Replacement inoculum from single fruit (RR2)
0.49202 (0.00918)
0.49131 (0.01219)
0.00000 (0.00000)
0.00000 (0.00000)
0.51698 (0.01111)
0.02162 (0.00280)
0.51836 (0.01201)
0.00059 (0.00012)
Spoilers initially in single fruit (SC2)
Replacement inoculum from all other fruit (RR1)
0.00018 (0.00001)
0.00018 (0.00005)
0.00000 (0.00000)
0.00000 (0.00000)
0.00020 (0.00001)
0.00000 (0.00000)
0.00020 (0.00001)
0.00000 (0.00000)
Replacement inoculum from single fruit (RR2)
0.00018 (0.00001)
0.00019 (0.00005)
0.00000 (0.00000)
0.00000 (0.00000)
0.00020 (0.00001)
0.00017 (0.00031)
0.00020 (0.00001)
0.00000 (0.00000)
No predation
Random predation (CR1)
Mean and standard deviations are all based on 50 separate simulations, each for 1000 time units. Here, we assume a small reproductive costs to
spoiling (fN > fS ). Parameter values as for Table 2 except q = 0.001 (as Table 3) and fS = 0.019.
T.N. Sherratt et al. / Ecological Modelling 192 (2006) 618–626
too high to allow patches containing only spoilers to
arise.
Our consideration of the dynamics of spoilers and
non-spoilers reflects more general questions in evolutionary biology; indeed in some ways our simulations
can be seen as unnecessarily detailed. For example,
an illuminating model for the spread of an altruistic
trait that promotes the persistence of individuals within
patches has already been presented by Maynard Smith
(1998). As with our study, Maynard Smith argued
that the dispersal rates of more selfish forms between
groups would have to be exceptionally low to promote the spread of altruistic traits. Edible fruit typically decay or are eaten in a matter of days or weeks.
Microbes may be transferred between fallen fruit in a
variety of ways ranging from wind-assisted movement,
to transfer by fruit feeding organisms such as insects
(e.g. see Ehlers and Olesen, 1997). Many microbes
are also likely to be present even before the fruit falls.
Despite the ephemeral nature of the resource, and considerable uncertainty over the way microbes disperse,
we feel that a dispersal rate as low as that required
to allow spoiling microbes to spread significantly
from rarity via group selection, would be extremely
unlikely.
Some plausible alternative explanations for why
fruits rot, seeds mold and meat spoils, are simply that (i)
it has arisen as a result of chemically mediated competition between microbes and/or that (ii) repellency
has arisen primarily as a by-product of other metabolic
activities, such as extra-cellular digestion. In effect,
explanation (i) moves the assumed competitor from
vertebrate back to microbe, but the nature of the competitive interactions can be qualitatively different. For
example, in relation to explanation (i), it is possible that
the repellent compounds are made without cost by the
spoilers, but that they also inhibit the growth of the nonspoilers. This might happen for instance, if spoilers are
immune to their own repellent by-products. In parallel
work (not shown) we have found that it is the nonspoilers invariably go extinct under these conditions.
Non-spoilers in these circumstances effectively face
the “double whammy” by being poorer competitors
and eliciting higher extinction rates through frugivory.
However, it should be noted that non-spoilers go extinct
even if frugivores are non-selective, suggesting that this
type of spoiling behaviour does not owe its success to
frugivore behaviour.
625
In relation to explanation (ii) above, one might
wonder why it does not pay microbes to free-ride on
the extra-cellular digestion products of others, in the
same way non-spoilers free-ride on spoilers. An analogous example is of the production of the precursors
of dimethyl sulfide by marine plankton, which some
workers have argued may benefit the population as
a whole (see review by Simo, 2001). Here, however,
there is perhaps more potential for asymmetries in that
the chemicals are likely to benefit the individual that
produced it and its relatives more than its unrelated
conspecifics or heterospecifics (see Brown, 1999 for
a game-theoretical solution to this type of problem).
Indeed many bacteria appear to have adaptations that
keep the enzymes used in extracellular digestion in very
close proximity to the cell membranes (Fenchel et al.,
1998). Costly traits that benefit the individual and the
group may spread when populations exhibit high viscosity. However, in the case of rotting fruit, it is likely
that dispersal can reduce the between-group variability sufficiently to undermine the selective advantage of
any group-beneficial trait that appears in the system.
Overall, our analyses indicate that spoilers can coexist with non-spoilers if creating repellent chemicals
carries no higher costs to the spoilers within the same
resource as non-spoilers (scenario A, fN = fS ). Yet even
under these circumstances, it is important to note that
spoilers can only spread from rarity under a restricted
set of conditions. Of course, as we have seen, given this
selective neutrality of spoilers and non-spoilers within
a fruit, then spoilers could in theory persist in any system where they were present, even in the absence of
selective behaviour of larger animals.
By quantifying and formalizing Janzen’s imaginative idea, we have argued that the spoiling of fruit,
seeds and meat is unlikely to have arisen primarily
as a mechanism to deter larger animals from consuming them. The theory simply will not work when,
as might be expected, there is an individual cost to
spoiling and microbes readily disperse. In the absence
of such a cost, then spoilers will be able to persist even without the actions of a larger animal, and
spread from rarity only under a limited set of conditions. Of course, one could argue that our model
is abstract and simplistic, for instance, in considering only dichotomous strategies of “spoil” or “do not
spoil”, and that more realistic sets of assumptions with
continuous strategies would generate stronger support
626
T.N. Sherratt et al. / Ecological Modelling 192 (2006) 618–626
for Janzen’s idea. At very least we hope this work
will stimulate those who advocate Janzen’s fascinating
theory, to show just how such a hypothesis could be
justified.
Acknowledgement
We thank NSERC (TNS) and the UK Royal Society
(DMW) for funding and Rees Kassen and our anonymous referees for helpful comments.
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