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Disentangling taste and toxicity in
aposematic prey
Øistein Haugsten Holen
rspb.royalsocietypublishing.org
Research
Cite this article: Holen ØH. 2013 Disentangling taste and toxicity in aposematic prey.
Proc R Soc B 280: 20122588.
http://dx.doi.org/10.1098/rspb.2012.2588
Received: 1 November 2012
Accepted: 29 November 2012
Subject Areas:
ecology, evolution, behaviour
Keywords:
predation, taste-rejection, toxin, aposematism,
Batesian mimicry, automimicry
Author for correspondence:
Øistein Haugsten Holen
e-mail: o.h.holen@bio.uio.no
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rspb.2012.2588 or
via http://rspb.royalsocietypublishing.org.
Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biology, University of Oslo,
PO Box 1066 Blindern, 0316 Oslo, Norway
Many predators quickly learn to avoid attacking aposematic prey. If the prey
vary in toxicity, the predators may alternatively learn to capture and tastesample prey carefully before ingesting or rejecting them (go-slow behaviour).
An increase in prey toxicity is generally thought to decrease predation on
prey populations. However, while prey with a higher toxin load are more harmful to ingest, they may also be easier to recognize and reject owing to greater
distastefulness, which can facilitate a taste-sampling foraging strategy. Here,
the classic diet model is used to study the separate effects of taste and toxicity
on predator preferences. The taste-sampling process is modelled using signal
detection theory. The model is applicable to automimicry and Batesian mimicry.
It shows that when the defensive toxin is sufficiently distasteful, a mimicry complex may be less profitable to the predator and better protected against
predation if the models are moderately toxic than if they are highly toxic. Moreover, taste mimicry can reduce the profitability of the mimicry complex and
increase protection against predation. The results are discussed in relation to
the selection pressures acting on prey defences and the evolution of mimicry.
1. Introduction
Predators face the challenge of securing a high intake of nutrients while at the
same time limiting their intake of toxins. Many chemically defended prey use
bright, aposematic signals to warn that they are toxic, and predators can quickly
learn to avoid attacking aposematic prey [1]. However, if prey that share an
aposematic signal vary substantially in toxicity, the predators may instead
‘go slow’ and capture and taste each prey carefully before deciding whether
or not to reject it [2]; such taste-sampling allows the predators to preferentially
consume the least toxic ones [2–6]. Variation in toxicity is found within populations of visually identical prey (i.e. automimicry, [1,3,7– 12]), and between
different prey species that share the same warning signal (i.e. Batesian and
Müllerian mimicry, [1,13–15]). Since many prey are able to survive tasterejection [16,17], selection should favour prey defences that increase the
chance of being taste-rejected upon capture. Such defences include toxins
[16], but also compounds that exploit innate taste aversions in predators or
mimic the taste of toxins [18,19].
Aversions to the taste of harmful compounds can be learned or innate [3,19].
If a predator lacks innate aversion to the taste of a specific toxin, avoidance of
prey items containing it may be acquired through social learning or postingestive feedback [3,5,19,20]. Post-ingestive feedback allows a predator to
associate any ill effects that follow consumption of a novel food with a wide
range of food cues, including gustatory, olfactory and visual ones [21–23]. In
rats, such aversion learning can occur after a single trial, even if the delay
between ingestion and the onset of ill effects is several hours [21].
Although protective mimicry and aposematism have been studied for more
than a century [13,14,24], the effects of prey taste and prey toxicity on predator
preferences have rarely been separated (but see [5]). In fact, terms referring to distastefulness are often used as synonyms for toxicity in the literature [18]. This is
surprising given their different effects: increased model toxicity is likely to
increase the cost of ingesting model prey, while increased model distastefulness
& 2012 The Author(s) Published by the Royal Society. All rights reserved.
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Consider a predator that encounters three kinds of prey while
foraging. Prey type 1 (the mimic) is non-toxic and visually
identical to prey type 2 (the model), which contains toxins.
Prey type 3 (the alternative prey) is visually distinct from
prey types 1 and 2. According to the classic diet model, the
predator’s rate of energy intake is [26,27]:
c1 N1 E1 a1 þ c2 N2 E2 a2 þ c3 N3 E3 a3
;
1 þ c1 N1 h1 a1 þ c2 N2 h2 a2 þ c3 N3 h3 a3
ð2:1Þ
where Ei denotes the expected energy intake and hi the
expected handling time when attacking one prey item of
type i [ {1; 2; 3}. The product ciNi is the number of prey of
type i encountered by the predator in one unit of search
time, with Ni denoting prey density and ci search efficiency,
while ai is the probability that the predator will attack the
prey item. The energetic expenditure of the predator is
assumed to be the same over all parts of the foraging process
[26]; thus the long-term rate of energy gain is maximized by
maximizing expression (2.1). For mathematical tractability, I
will simply assume that the cost of ingesting a toxic prey
item can be measured in terms of the time needed for recovery before the predator can resume search (cf. [31]). The cost
thus enters into the handling time of the model prey.
The attack sequence is as follows: a predator that visually
detects a mimic or model will either attack it or ignore it.
Since the mimics and models are perfectly mimetic (i.e. visually
identical), they must be equally prone to being attacked. Let a12
denote the probability of attack for an item in the mimicry complex (i.e. a12 ¼ a1 ¼ a2). If the predator attacks, some time will be
spent on pursuit. If the prey is captured, the predator will taste
the prey and decide whether to ingest it or reject it. If the predator decides to ingest the prey, some time will be spent from the
start of ingestion until the predator is ready to resume search.
I adopt the standard assumption that the time spent by the
predator recognizing and deciding whether to attack a prey is
negligible [26,27]. Analogously, I assume that the time spent
by the predator to taste and decide whether to consume or
reject a prey is negligible.
Cognitive constraints and perceptual noise cause the distributions of perceived taste to overlap for the mimics and
models, making perfect taste discrimination impossible.
R(x)
0.5
0
0.5
x
1.0
Figure 1. The receiver operating characteristic (ROC) curve, denoted R(x). It gives
the probability of ingesting a mimetic prey after tasting as a function of the
probability of ingesting a model prey after tasting. Three different normal–normal
equal–variance ROC curves are shown, each with a different index of
discriminability, d0 . Dashed line, d0 ¼ 3; solid line, d0 ¼ 1.2; dotted line, d0 ¼ 0.
According to signal detection theory, the different compromises
that the predator can make when discriminating can be summarized in a ‘receiver operating characteristic’ (ROC) curve
[25]. Let x denote the probability of ingesting models upon tasting, and R(x) the probability of ingesting mimics upon tasting.
The function R(x) is the ROC curve (figure 1). A ROC curve
starts at the point (0,0) and ends at the point (1,1), and is
non-decreasing [25]. The predators gain information from the
taste stimuli, and if they use this information in an optimal
way—a routine assumption in foraging theory [27]—the ROC
curve is also necessarily concave ([25], p. 35). I return to the
shape of the ROC curve in a section below.
The taste-sampling process is incorporated into the
optimal diet model by expanding the terms describing the
expected energy gain and handling time from attacking
mimic and model prey items. Formally, let E1 ¼ q1 R(x) B1
and E2 ¼ q2 x B2, where Bi is the expected energy from
ingesting a prey item of type i and qi is the probability
of capture given attack. Let h1 ¼ h1p þ q1 R(x) h1c and
h2 ¼ h2p þ q2 x h2c, where hip is the expected time spent pursuing prey type i and hic is the expected time spent from the start
of ingestion until the predator is ready to resume search. The
cost of ingesting a toxic model is incorporated via h2c.
The rate of energy intake can now be stated as:
Fða12 ; a3 ; xÞ
¼
c1 N1 B1 q1 RðxÞa12 þc2 N2 B2 q2 xa12 þc3 N3 E3 a3
1þc1 N1 ðh1p þq1 RðxÞh1c Þa12 þc2 N2 ðh2p þq2 xh2c Þa12 þc3 N3 h3 a3 :
ð2:2Þ
The probabilities a12 and a3 comprise the predator’s attack
strategy, while the probability x comprises the predator’s tasterejection strategy. The probabilities a12, a3 and x can take any
value on the unit interval [0,1], while all other parameters in
equation (2.2) are strictly positive. The optimal foraging
strategy ð^a12 ; ^a3 ; ^xÞ is found by maximizing F(a12,a3,x). According to the zero–one rule [27], the optimal forager will neither
exhibit partial preferences for attacking the alternative prey nor
for attacking the mimicry complex (i.e. ^a12 and ^a3 will not take
values other than 0 or 1).
The model analysis is restricted to parameter values that
are biologically interesting and relevant to the subject.
Proc R Soc B 280: 20122588
2. The model
2
1.0
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may facilitate taste recognition and thus enable the predator to
reduce its consumption of model prey, all else being equal.
Here, I present an optimality model of foraging that separates the effects of taste and toxicity on predator preferences. A
signal detection theory model [25] of taste discrimination is
incorporated into the optimal diet model [26,27]. Signal detection theory has been much used in experimental studies of
stimulus discrimination [28], and when applied to the tastesampling process, it allows the probability of taste-rejection to
depend on the distribution of toxic and non-toxic prey in the
population at large, as was found in a recent study [29]. The optimal diet model predicts which prey types should be included in
a predator’s diet, and is well suited to account for the opportunity costs of attack and effects of alternative prey. This is
important, since the presence of alternative prey influences a predator’s willingness to forage on mimicry complexes [1,30]. The
model predicts whether the predator should avoid the mimicry
complex completely or rely on a taste-sampling strategy.
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ð2:3bÞ
is assumed to hold.
In the numerical explorations, mimic and model prey items
are assumed equal in all respects except toxin content.
Formally, let h2c ¼ h1c þ htoxin, where the non-negative parameter htoxin reflects the time needed to recover from the
effects of toxins before search can be resumed (the ‘toxin recovery time’). Moreover, let B1 ¼ B2 and h1p ¼ h2p. The probability
that prey type i survives taste-rejection is denoted si.
The perceived distastefulness of mimics and models is
assumed to be normally distributed with equal variance, as is
consistent with studies of taste discrimination [32]. An optimal
taste-discriminating predator will then use a simple acceptance
threshold and consume all prey perceived as being less distasteful than the threshold and taste-reject all prey perceived as more
distasteful. Each possible threshold will correspond to an interior
point on the ROC curve [25]. This resulting ROC curve is called a
normal–normal equal-variance ROC, and the distance between
the means of the two distributions of perceived distastefulness
measured in units of the standard deviation is termed the
index of discriminability, conventionally denoted d0 [25]. The
normal–normal equal–variance ROC for a given taste discriminability d0 (figure 1) is the parametric curve (with parameter
t) obtained when plotting Z(t) against Z(t 2 d0 ), where Z is the
cumulative standard normal distribution (figure 1).
Methods for calculating the optimal foraging strategy are
given in the electronic supplementary material, appendix S1,
including a graphical method applicable to any ROC and a
numerical procedure that can be used for the normal –
normal equal– variance ROC.
3. Results
(a) The optimal diet
Illustrative examples of the optimal diet of a predator are
given in figure 2, for a range of values of taste discriminability d0 and recovery time htoxin. The predator should typically
include the mimicry complex in the diet when mimics and
models are discriminable on the basis of taste (i.e. d0 is
high) and when the toxin recovery time is low (i.e. htoxin is
low). As expected, the parameter range for which the mimicry complex is included in the diet decreases when the
alternative prey is more profitable (i.e. h3 is low) and when
it is more abundant (i.e. N3 is high). The higher value of h3
chosen in figure 2a,b may be interpreted as more time being
needed to pursue, subdue or consume the alternative prey.
It is possible to get some analytical insight into how
the optimal diet changes with different parameters (see the
taste
discriminability (d¢ )
3
3
2
1
0
(c)
(d )
4
3
h3 = 1
B1
c3 N3 E3
.
h1c 1 þ c1 N1 h1p þ c2 N2 h2p þ c3 N3 h3
N3 = 3.0
2
1
0
10 20 30 40
recovery time (htoxin)
0
10 20 30 40
recovery time (htoxin)
Figure 2. The optimal diet comprises the mimicry complex only (dark grey
region), the alternative prey and the mimicry complex (white region), or the
alternative prey only (light grey region). The mimicry complex tends to be
included in the diet when it is easy to discriminate between mimics and
models on the basis of taste (i.e. d0 is high) and when the expected recovery
time after ingesting a model is short (i.e. htoxin is low). The mimicry complex
is excluded for a greater range of parameters when the alternative prey is
more profitable (i.e. h3 is low) and more abundant (i.e. N3 is high).
Parameter choices: N1 ¼ N2 ¼ 2, B1 ¼ B2 ¼ 1, c1 ¼ c2 ¼ c3 ¼ 1/4,
h1p ¼ h2p ¼ 9/10, h1c ¼ 1/10, h2c ¼ h1c þ htoxin, E3 ¼ 1, q1 ¼ q2 ¼ 1.
electronic supplementary material, appendix S1). The profitability of the mimicry complex decreases relative to the
profitability of the alternative prey if any of the parameters
h1c, h2c, h1p, h2p or E3 is increased, or any of the parameters
q1, B1, B2 or h3 is decreased (see the electronic supplementary
material, appendix S1). Such a change will widen the range
of (other) parameters for which the mimicry complex is
excluded from the diet, and reduce the range for which the
alternative prey is excluded from the diet. In figure 2, the
dark grey region will contract or disappear entirely while the
light grey region will expand. When either N3 or c3 increases,
foraging on the alternative prey will become a more profitable
option, causing the mimicry complex to be excluded from the
diet for a wider range of parameters. The effect of changing c1,
N1, c2, N2 or q2 depends on other parameters in a more
complex way (see the electronic supplementary material,
appendix S1). In cases where a predator that forages only on
the mimicry complex benefits from taste-rejecting models,
the profitability of the mimicry complex decreases with c2,
N2 and q2 and increases with c1 and N1. If a predator that
forages only on the mimicry complex benefits from consuming
models, then further investigation will be necessary to determine the effect of changes in the parameters c1, N1, c2, N2
and q2. This latter situation may arise if mimics and models
have very low abundance and/or search efficiency is low.
(b) The functional relationship between toxicity
and taste
A reasonable assumption is that prey containing a higher
amount of a distasteful toxin will appear more distasteful
to taste-sampling predators than prey containing a lower
Proc R Soc B 280: 20122588
In addition, the mimics are assumed sufficiently profitable
that ingestion is always beneficial (given capture). In other
words, a predator that attacks all prey types but only ingests
alternative prey would increase its energy intake rate if it
consumed a mimic. Formally, the inequality
taste
discriminability (d¢ )
ð2:3aÞ
(b)
4
h3 = 5
B1
B2
.
:
h1c h2c
N3 = 0.5
(a)
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By definition, it is more profitable for the predator to ingest a
mimic than a model. Throughout the paper it is therefore
assumed that the energy gain per unit time is higher when
ingesting mimics than models, or
probability of ingestion
1.0
probability of ingestion
1.0
energy intake rate
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1
0.8
0.6
0.4
0.2
0
0
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1.0
0.8
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0
prey killed per unit time
1.0
4
(iv)
0.25
prey killed per unit time
(iii)
0.25
prey killed per unit time
(ii)
0.25
4
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(a) (i)
taste discriminability (d¢ )
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0.20
0.15
0.10
0.05
0
energy intake rate
3
2
1
0.8
0.6
0.4
0.2
0
0
4
1.0
0.8
0.6
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0.2
0
Proc R Soc B 280: 20122588
taste discriminability (d¢ )
(b)
0.20
0.15
0.10
0.05
0
3
2
1
0
5
10
15
20
1.0
0.8
0.6
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5
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20
probability of ingestion
energy intake rate
taste discriminability (d¢ )
(c)
0.8
0.6
0.4
0.2
0
5
10
15
20
0.20
0.15
0.10
0.05
0
5
10
15
20
toxin recovery time (htoxin)
Figure 3. The optimal diet consists of the mimicry complex and the alternative prey (white regions) or only the alternative prey (grey regions). (i) The solid lines
show the hypothetical functional relationships between taste discriminability (d0 ) and toxin recovery time (htoxin) that are used in columns (ii– iv). (ii) The energy
intake rate of a predator that attacks only alternative prey (solid line), only the mimicry complex (dotted line) and both alternative prey and the mimicry complex
(dashed line). (iii) The probabilities by which a taste-sampling predator that attacks all prey will ingest mimics (dashed line) and models (solid line) upon capture.
(iv) The number of mimics and models that a taste-sampling predator that attacks all prey kills per unit time (dashed lines, mimics; solid lines, models; thick lines,
s1 ¼ s2 ¼ 1/4; thin lines, s1 ¼ s2 ¼ 3/4). Other parameters: N1 ¼ N2 ¼ N3 ¼ 2, B1 ¼ B2 ¼ 1, c1 ¼ c2 ¼ c3 ¼ 1/4, h1p ¼ h2p ¼ 9/10, h1c ¼ 1/10, h2c ¼
h1c þ htoxin, h3 ¼ 1, E3 ¼ 1, q1 ¼ q2 ¼ 1. Functional relationships: d0 ¼ m(1 2 exp(2htoxin/20)), (a) m ¼ 10, (b) m ¼ 5, (c) m ¼ 2.
amount of the same toxin, everything else being equal. An
evolutionary change in toxin level will therefore tend to
change model distastefulness (and thus taste discriminability,
d0 ) and harmfulness/recovery time (i.e. htoxin) in the same
direction. It may be useful to think of (monomorphic) populations of model prey that differ only in toxin load as points
lying on a monotonically increasing curve in the (htoxin, d0 )plane. The effect of a population increase in toxin load on
predator preferences (and ultimately on prey survival) will
depend on the exact shape of this curve, which in turn
depends on numerous factors, including the predator’s ability to detect the toxin at different concentrations and the
prey tissue(s) in which the toxin is stored.
Figure 3 provides some illustrative examples. When the
curve is steep, a small increase in toxin recovery time will
be accompanied by a great increase in toxin detectability. In
figure 3a, the predator keeps the mimicry complex in the
diet at all toxin levels, since taste-rejection suffices to keep
toxin consumption below acceptable levels. The mimics are
least likely to be ingested upon capture when the toxin
level in the model population is intermediate. By contrast,
the models are least likely to be ingested upon capture
when the toxin level is high. Because of this, the predators
spend less time recovering from toxic effects and more time
searching for prey when model toxicity is high, and thus
taste-sample more prey per unit time. Mortality is calculated
as kill rate (kills per predator per unit time, see the electronic
supplementary material, appendix S1). Mimic mortality is
lowest when model toxicity is intermediate. If the probability
of surviving taste-sampling and rejection is low (i.e. s1 ¼ s2 ¼
1/4), intermediate model toxicity gives the lowest kill rate
also for models (figure 3a). If the probability of surviving
taste-sampling and rejection is high (i.e. s1 ¼ s2 ¼ 3/4), the
models (but not the mimics) are best protected by high
model toxicity (figure 3a).
A qualitatively different situation arises in figure 3b,
where the curve relating taste discriminability d0 to recovery
time htoxin is moderately steep. Here, intermediate model toxicity lends the strongest protection to prey because it forces
the predator to exclude the mimicry complex from the diet
(the region of exclusion is indicated by the grey area, figure
3b). The mimicry complex is profitable when the models are
weakly toxic and thus not too harmful (i.e. recovery time is
short), and when the models are highly toxic and thus easy
to reject by taste. When the models are moderately toxic,
however, they will neither be sufficiently safe to consume
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(b) 0.25
2
1
0
0.20
0.15
0.10
0.05
0
1
2
3
taste discriminability (d´)
4
5
0.20
0.15
0.10
0.05
0
1
2
3
taste discriminability (d´)
4
Figure 4. (a) The optimal diet can include the mimicry complex only (dark grey region), the alternative prey and the mimicry complex (white region), or the alternative
prey only (light grey region). The dashed line shows the toxin recovery time and the range of taste discriminability explored in b and c. (b) The energy intake rate of a
predator that only attacks alternative prey (solid line), only attacks the mimicry complex (dotted line), and attacks all prey (dashed line). (c) The number of mimics and
models killed per unit time by a predator (dashed lines, mimics; solid lines, models; thick lines, s1 ¼ s2 ¼ 1/2; thin lines, s1 ¼ s2 ¼ 95/100). The predator attacks only
alternative prey (c, left line sections) attacks both alternative prey and the mimicry complex (c, middle line sections) and attacks only the mimicry complex (c, right line
sections). Other parameters: as in figure 3, except N3 ¼ 3, h3 ¼ 5, and in (b,c) htoxin ¼ 20.
nor sufficiently easy to recognize for the predator to benefit
from taste-sampling the mimicry complex. The predator
should therefore cease to attack the mimicry complex and
focus on alternative prey.
When the slope of the curve is shallow, great increases in
model harmfulness (i.e. recovery time) will be accompanied
by small increases in toxin detectability (figure 3c). Tastesampling may then provide too little information about
toxicity to be useful, and the mimicry complex should be
excluded from the diet whenever the model population is
sufficiently harmful (the region of exclusion is indicated by
the grey area, figure 3c). The same will happen if the curve
has zero slope (i.e. when taste discrimination is impossible
at any level of toxicity).
the predator is unable to use the energy in the model prey,
i.e. when B2 ¼ 0. Qualitatively similar results to those in
figure 2 were obtained (not shown). Second, I explored an
alternative cost of toxin consumption: I assumed that the
metabolism and excretion of toxins were energetically
costly to the predators (i.e. model consumption could have
a negative effect on energy reserves, B2 , 0), but that the
predators did not spend any time on recovery. Again,
qualitatively similar results were obtained (see the electronic supplementary material, figure S2 and appendix S1).
These results show that a protective advantage of intermediate
toxicity can arise also for metabolic costs (see the electronic
supplementary material, appendix S1).
(c) Taste mimicry
4. Discussion
In figure 4, toxin recovery time is kept constant, while the
taste discriminability d0 is allowed to vary. In this example,
the optimal diet includes alternative prey only (when d0 is
low), both alternative prey and the mimicry complex (when
d0 is intermediate), and the mimicry complex only (when d0
is high; figure 4a,b). The shifts in diet are caused by
the mimicry complex becoming more profitable as
taste-discriminability increases and are characterized by
discontinuous jumps in prey mortality (figure 4c). Both
mimics and models obtain best protection against predation
when taste discriminability is low and the mimicry complex
is excluded from the diet. When the mimicry complex is
included in the diet, the mimic mortality increases with
taste discriminability. As before, a higher taste discriminability can reduce model ingestion and thus the time spent
recovering from toxins, freeing the predator to attack more
prey per unit time. For a given predator diet, model mortality
could therefore both increase or decrease with taste
discriminability, depending on the probability of surviving
taste-sampling and rejection (figure 4c).
(d) Alternative assumptions
The optimal diet of the predator (shown in figure 2) was
briefly explored under two alternative assumptions. First, I
relaxed the assumption that B1 ¼ B2 to see what happens if
The model presented here explores optimal strategies for predators that forage on deceptive mimicry complexes (i.e.
Batesian mimicry and automimicry). The predators can
either avoid the mimicry complex entirely, or ‘go slow’
(sensu [2]) and capture and taste-sample each prey before
deciding whether or not to reject it. As expected, the model
predicts that avoidance of the mimicry complex is the best
strategy if the mimicry complex is unprofitable and the
alternative prey highly profitable and/or common. These
are classic predictions of the diet model that are in accordance
with available empirical data [1,27,30,33].
A more surprising result is that the mimicry complex may
be more unprofitable to the predator and thus better protected against predation if the models have an intermediate
level of a distasteful toxin than if they have a high level.
This contrasts with the long-standing consensus perspective
that better-defended model populations should provide
better protection from predation, everything else being
equal [1,7,15,34 –40]. This novel result has an intuitive explanation. Distasteful toxins affect the profitability of a mimicry
complex in two different ways: first, they increase the harm
associated with model consumption, which reduces profitability; second, they facilitate taste recognition. This allows
the predator to reduce its probability of consuming models
for a given probability of consuming mimics, and thus
Proc R Soc B 280: 20122588
10
20
30
40
toxin recovery time (htoxin)
no. prey killed per unit time
energy intake rate
3
(c) 0.25
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taste discriminability (d´)
(a) 4
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6
Proc R Soc B 280: 20122588
models will therefore enjoy a protective advantage of
intermediate model toxicity.
In the third example, the taste discriminability of the mimicry
complex increases slowly with model harmfulness (figure 3c).
When taste discriminability increases sufficiently slowly with
model harmfulness (or not at all), a protective advantage of
intermediate toxicity is not expected. Taste-sampling will be ineffective and the mimicry complex should be excluded from the
diet whenever the toxicity of the model population exceeds a
certain level. Innate aversions or learned aversions based on
post-ingestive feedback are then expected to form.
A key assumption in the model is that the consumption of
toxic prey items incurs a cost in terms of handling time (i.e.
recovery time). In the first example (figure 3a), the protective
advantage of intermediate toxicity arises through changes in
the taste-sampling strategies of the predators. For the model
prey, a protective advantage of intermediate model toxicity
is possible only because the time costs of model consumption
leads to a reduction in predator attack rates. Other types of
costs associated with toxin consumption (e.g. metabolic
costs) will not have the same effect. In the second example
(figure 3b), intermediate toxicity provides a protective advantage to mimics and models because it causes the predator to
cease attacking the mimicry complex. This mechanism works
equally well with metabolic costs of toxin consumption (see
the electronic supplementary material, appendix S1).
Toxic defences that are distasteful may yield benefits to the
individual carriers, since they facilitate taste rejection. Toxic
defences also yield benefits to the group via education of predators. Individual prey obtain these group-level benefits
regardless of their own defence level, and the benefits therefore
comprise a common good [10]. The model identifies the level of
toxicity that gives the strongest protection to the prey when
both individual benefits and group-level benefits are taken
into account. In general, this level of toxicity will not be evolutionarily stable [47]: if toxins are costly [48], mutant model prey
may invade that ‘cheat’ and reap some of the group-level
advantages of defence (e.g. being avoided by predators) without investing as heavily in defences as the others [7,49]. By
contrast, if defences are cheap, mutant model prey with toxin
loads (and levels of distastefulness) above the level that is optimal for the population may invade. Such mutants are also
‘cheaters’, since their own increase in the probability of being
taste-rejected comes at a cost to the population level of
protection (by increasing the taste-discriminability and thus
profitability of the mimicry complex). An investigation of evolutionarily stable levels of defence is outside the scope of this
paper; it would require a different model framework in
which costs of defence production were taken into account.
Natural selection acting on toxic prey may favour various
traits that increase the probability of taste recognition, such as
secretion of toxins upon attack [9,41,42], storage of the toxins
in outer tissues (e.g. wings, [43,44]) and production of nontoxic compounds with distinct taste. Undefended prey may
be under selection to mimic the taste of model prey to
reduce their chances of being recognized. The compounds
responsible for the resemblance in taste need not be toxic
themselves: in fact, it may be cheaper to store distasteful
non-toxins within the body than distasteful toxins, since the
latter may require costly alterations in prey physiology in
order to avoid autotoxicity [48]. Mimics that contain
compounds with a distinct taste that are not shared by the
models may be under selection to suppress the taste, either
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increases profitability. When the latter effect outweighs the
former, the profitability of the mimicry complex increases
with model toxicity.
The model shows that the shape of the function relating the
taste discriminability of the mimicry complex to model harmfulness plays a key role in determining whether a protective
advantage of intermediate toxicity might exist. The shape
depends on many factors, including the predator’s ability to
detect the toxin at different concentrations and its susceptibility
to the toxin; in addition, it will depend on the tissue(s) in
which the prey store the toxin and on whether the toxin is
excreted in response to predators [9,41–45]. Since little is currently known about the shape of this function, some
hypothetical examples have been explored (figure 3). The
first two examples illustrate two different ways in which a
protective advantage of intermediate toxicity may arise.
In the first example, taste discriminability increases
quickly with model harmfulness (figure 3a). Here, the mimicry complex remains sufficiently profitable to be kept in the
diet at all toxin levels. The predator commits fewer recognition errors when model toxicity (and thus distastefulness)
is high than when it is intermediate. This leads to a higher
probability of consuming mimics upon capture at high than
at intermediate model toxicity, but a lower probability of
consuming models. As a result of the reduced model
consumption, the predator also spends less time recovering
from toxic effects and more time searching for prey at high
than at intermediate model toxin levels, which ultimately
causes it to attack and taste-sample more prey per unit
time. Mimics are better protected by intermediate model toxicity than by high model toxicity. The models are attacked
more often but also taste-rejected more often when the
model population is highly toxic. The models are therefore
better protected by intermediate model toxicity when the
chance of surviving taste-rejection is low (25%) and by high
model toxicity when the chance of surviving taste-rejection
is high (75%) (figure 3a). Empirical studies have shown a
high probability of survival: one experimental study reported
100 per cent survival for aposematic bugs that were seized
and dropped (n ¼ 14) and 52 per cent for an equally
defended cryptic form (n ¼ 27) [17]; another study reported
an average survival of 84 per cent for five different aposematic insect species seized and held in the beak by naive
individuals of four different bird species [16].
In the second example, the protective advantage of intermediate toxicity is more dramatic (figure 3b). Here, the taste
discriminability increases moderately fast with model harmfulness, and the predators benefit from avoiding mimicry
complexes with intermediate model toxicity and attacking
mimicry complexes with high model toxicity (figure 3b).
Model populations that are intermediately toxic can be sufficiently difficult to recognize on the basis of taste and at the
same time sufficiently harmful to make avoidance the best
option. Although highly toxic models will be even more
harmful to ingest for predators, they might also be much
easier to recognize by taste, thus favouring a taste-sampling
strategy in the predator. It seems likely that taste-sampling
will always be of potential harm to a prey, even when the
prey is ultimately rejected. Soft-bodied prey with internal
toxin storage are particularly likely to be killed or seriously
damaged. Even tough prey that tolerate handling well are
likely to suffer smaller costs, such as minor injuries, toxin
depletion or opportunity costs [46]. Both mimics and
Downloaded from http://rspb.royalsocietypublishing.org/ on May 25, 2016
I thank Ø. Langangen, J. Mappes, C. Rowe, G. D. Ruxton and
T. O. Svennungsen for discussion and comments on the manuscript.
I also thank the two reviewers for their helpful comments. Funding
was provided by CEES (Centre for Ecological and Evolutionary
Synthesis), University of Oslo.
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The diet model identifies the foraging strategy that maximizes the long-term average energy intake of the predator
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post-ingestive feedback has shaped aversions), predators may
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models of the taste-sampling process. The model presented
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In conclusion, I have shown that an increase in toxicity in
a model population may increase mortality for both mimics
and models. Taste mimicry may reduce prey mortality,
since it makes taste-sampling difficult. The taste-sampling
process clearly deserves more attention in the study of
protective coloration.
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by ceasing production of the compound or by storing it
internally. The result is a coevolutionary race in taste characteristics that in many ways is analogous to a (visual) mimicry
chase. Although taste mimicry will tend to make the mimicry
complex less profitable to a predator and may lend protection
to both mimics and models (figure 4), it will typically be
favoured only in mimics.
The taste-sampling process might ultimately affect the evolution of warning signals. It is commonly held that the better
defended the model prey are, the less visually accurate the Batesian mimics need to be to obtain significant protection [15,36,38].
Likewise, better defended models are thought to be able to protect a higher number of Batesian mimics [38,39,50]. This
presupposes, however, that the best defended models create
the greatest aversions in the predators. If we take ‘best defended’
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intermediately toxic models may potentially lend more protection and therefore support a higher number of Batesian mimics.
The model also has ramifications for Müllerian mimicry.
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toxic co-mimic may render taste-sampling risky and unprofitable, and force the predators to exclude the mimicry complex
from their diet. The less defended co-mimic may thus contribute most of the protection enjoyed by a Müllerian pair.
Perhaps this could partly explain observed cases of Müllerian
mimicry where a less toxic species has acted as the model and
a more toxic species has adverged towards it [37].
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