Journal of Ecology 2010, 98, 985–992
doi: 10.1111/j.1365-2745.2010.01693.x
Can optimal defence theory be used to predict the
distribution of plant chemical defences?
Andrew C. McCall1* and James A. Fordyce2
1
Department of Biology, 150 Ridge Road, Denison University, Granville, OH 43023, USA; and 2Department of Ecology
and Evolutionary Biology, 569 Dabney Hall, University of Tennessee, Knoxville, TN 37996, USA
Summary
1. The optimal defence theory (ODT) of chemical defence provides a predictive framework for the
distribution of anti-herbivore defences in plants. One of its predictions is that chemical defences will
be allocated within a plant as a function of tissue value, where value is correlated with the cost of
having that tissue removed. While many studies have examined intra-plant variation in defence
chemistry, these results have rarely been compiled quantitatively to assess whether defence allocation is consistent with the prediction of ODT that more valuable tissues should be more defended
than less valuable tissues.
2. We performed a formal meta-analysis of published and unpublished studies to examine the
predictive utility of ODT. Specifically, we examined whether defence chemicals occur at higher
concentrations in flowers versus leaves and in younger leaves compared to older leaves, under the
assumption that younger leaves are more valuable than older leaves. We also examined whether the
expansion status of younger leaves, nodal position of the leaves, growing conditions and chemical
class of defence compounds affected the mean effect sizes.
3. We found that tissues with higher assumed value had significantly higher concentrations of
defence chemicals than tissues with lower value. In particular, we found that younger leaves had
higher concentrations of defence chemicals than older leaves, consistent with the predictions of
ODT. The magnitude of this difference was higher in the younger leaf⁄ older leaf comparison than
in the flower ⁄ leaf comparison, with no evidence that flowers were more defended than leaves. The
overall results were not affected by chemical class, young leaf expansion status, growing conditions
or leaf position on the plant.
4. Synthesis. We found general agreement between the predictions of ODT and the intraplant
distribution of chemical defence and conclude it is a useful model. The effect size varied depending
on the tissue compared. Explicit measures of tissue value, in particular as it relates to relative fitness,
are required to further validate the predictive utility and general applicability of ODT.
Key-words: chemical defence, florivory, herbivory, leaf age, leaf value, optimal defence theory,
optimality models, plant–animal interactions, plant–herbivore interactions
Introduction
Plants employ a wide variety of chemical defences against
herbivores. These chemical defence compounds are not evenly
distributed across plant tissues or organs. Optimal defence theory (ODT) was developed to explain the distribution of defensive chemicals within a plant (McKey 1974), and it has been
useful in predicting some aspects of intra-plant distributions of
secondary compounds (McKey 1974; Zangerl & Rutledge
1996). Optimality models, such as ODT, predict that traits are
selected to maximize an organism’s fitness. Such models can
*Correspondence author. E-mail: [email protected]
provide a useful framework for understanding the distribution
and variation of traits across taxa. For example, optimality
models have been used in predicting or modelling the foraging
behaviour (e.g. Charnov 1976; Iwasa, Higashi & Yamamura
1981) and the evolution of reproductive effort (Shertzer &
Ellner 2002) in animals. Recently, the marginal value theorem
has been used to predict root foraging in plants (McNickle &
Cahill 2009) and optimality models have been developed to
predict root density under various resource scenarios (Craine
2006).
The specific model we investigate in this paper, ODT, predicts that the allocation of defensive chemistry within a plant
should be a function of tissue or organ value in terms of fitness
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986 A. C. McCall & J. A. Fordyce
(McKey 1974). An important assumption of ODT is that
defensive compounds are costly to produce. Plants would presumably be selected to employ high concentrations of secondary compounds throughout all of their organs constitutively if
costs were negligible and herbivory significantly impacted fitness. These costs can be physiological or ecological (Strauss
et al. 2002). Physiological costs can include autotoxicity and
the allocation of resources for defence that could otherwise be
used for growth and reproduction (but see Herms & Mattson
1992). Ecological costs can include a change in strength or
direction of ecological interactions, such as the cost if allocation of defence makes a plant more susceptible to competition
from neighbouring plants or if the allocation of defence in
flowers deters pollinators (Strauss et al. 2002).
If defence is costly, ODT predicts that defence should be
allocated to different plant parts as a function of: (i) the rate at
which the tissue is attacked in absence of any defence, (ii) the
cost, ecological or physiological, of employing the defence in
that tissue (McKey 1974) and (iii) the value of a particular tissue to the plant or the cost of removing that tissue (McKey
1974). Workers have hypothesized that young leaves are more
valuable than older leaves according to photosynthetic capacity and other physiological features (McKey 1974; Harper
1989; Coleman & Leonard 1995), and ODT has been invoked
to explain why younger leaves are more defended than older
ones (van Dam et al. 1995a; Traw & Feeny 2008). Similarly,
researchers have suggested that flowers might be more valuable than leaves because they are more directly related to sexual reproduction (Strauss, Irwin & Lambrix 2004).
Over a decade ago, Zangerl & Bazzaz (1992) observed that
research on intraplant chemical defence was relatively rare.
Since then, more studies comparing defensive compounds in
different tissues have accumulated which permits tests of some
of the predictions of ODT. A recent review also suggested the
utility of systematic study of defence chemistry in both vegetative and reproductive tissues like flowers in order to understand the link between defence and pollination service (Kessler
& Halitschke 2009). This review reported that flowers had
higher levels of defensive compounds than leaves in 13 of 14
studies under consideration (Kessler & Halitschke 2009). A
possible shortcoming of this summary was that the authors
only reported what proportion of all studies showed a difference in the predicted direction, which does not take into
account sample sizes or the variation in concentrations of compounds, which could yield a very small overall effect size
between leaves and flowers (Gurevitch et al. 1992). The review
also did not report whether the differences in chemical defence
between flowers and leaves were significant, or whether the
defensive compound under scrutiny had any effects on herbivores for any particular study (Kessler & Halitschke 2009).
Because of the relative lack of quantitative information on
the effectiveness of ODT in predicting defensive chemical concentration in plant tissues of different age or of different organ
identity, we asked whether lower-value tissues were less
defended compared to higher-value tissues. It is important to
note that we are not testing the specific value of different tissues
in this paper, but are testing whether, given common assump-
tions about tissue value, defensive compounds are allocated in
proportion to the assumed values. In particular, we asked a
general question, (i) Are more valuable tissues more chemically
defended than less valuable leaves?, and two more specific
questions: (ii) Are younger leaves more defended than older
leaves?, and (iii) Are flowers more defended than leaves?
Materials and methods
META-ANALYSIS
We performed a formal meta-analysis to explore whether ODT is useful in predicting the concentration of putative defensive chemicals
among tissues or organs of different values. This method provides a
means to summarize and analyse the results of many studies that
might differ in their methodology or study organisms, and can statistically separate the effects of different subcategories in a large data set
(Rosenberg, Adams & Gurevich 2000). Meta-analyses compute an
effect size between two or more groups of subjects and then determine
if those effect sizes are significantly different from zero.
Our primary question was whether tissues assumed to be higher in
value are more defended than tissues of lesser value. We examined
whether young leaves are more defended compared to old leaves, and
whether this difference is larger than the difference between leaves
and flowers. If the magnitude of the difference is larger for leaves of
different ages, it would be consistent with the hypothesis that tissue
age is more important in determining value than tissue identity or
origin.
Additionally, we asked if other factors might affect the strength of
the hypothesized difference in chemical concentrations between tissues of different value. We tested whether differences in defence intensity were influenced by the type of chemical defence examined,
whether the experiment was performed on field-collected leaves or
artificially reared plants, whether younger leaves were expanded to
full size at the time of chemical quantification, and whether nitrogen
content was associated with tissue value.
COMPARISONS AND PREDICTIONS
Younger versus older leaves
Our first prediction was that younger leaves with greater potential to
contribute to future fitness would be more defended than older ones
(Harper 1989; Iwasa et al. 1996). We define leaf age as how long, in
absolute time, a leaf has been on a plant, with new leaves hypothesized to be more valuable than leaves that have been on a plant for a
longer time because photosynthetic rates and nitrogen concentrations
generally decrease in leaves over time (Mooney & Gulmon 1982).
Workers have suggested that younger leaves are more valuable than
older leaves (Harper 1989; Coleman & Leonard 1995) and thus
should be more defended according to ODT.
Another factor that could influence leaf value is the position of the
leaf on a stem or branch. Recent studies have shown that leaves at older
nodes, usually those at the base of an herbaceous plant, had higher
nitrogen concentrations than leaves found at younger nodes when leaf
age was held constant (Wiedemuth et al. 2005), or that leaves at older
nodes tended to be higher in value than leaves at younger nodes in
Brassica nigra, when the age of the leaf was held constant (Traw &
Feeny 2008). This implies that a leaf sprouting from an older node contributes relatively more to the fitness of a plant than a younger-node
leaf, all other factors being equal (Mooney & Gulmon 1982).
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Predicting the distribution of plant chemical defences 987
Many of the studies we included in this study sampled leaves of different ages during a single moment in a plant’s lifetime, so that leaves
of different ages were produced from nodes of different ages. This
design could decrease the difference in value and subsequent defence
input between old and young leaves if old leaves are produced at older
nodes and if leaves from older nodes contribute more to a plant’s fitness than leaves from younger nodes (Mooney & Gulmon 1982). To
investigate whether this factor impacted effect sizes, we ran our metaanalyses with and without studies in which young leaves were also
known to be sampled at younger nodes. In many cases we were
unable to determine the node of the sampled leaves, so these studies
were excluded from these analyses.
for terpenes versus other classes of chemicals. Although many individual chemicals are used throughout plants for defence, we used
three very broad categories for classification: phenolics, terpenes and
defences that contain nitrogen (e.g. alkaloids and cyanogenic compounds). There is a great deal of variation in the defensive efficacy
and mode of biological activity within each of these categories, but
they are dissimilar in structure and consistent with traditional classification schemes historically used in the literature. Furthermore, a narrower criterion for chemical classification would result in categories
with very few samples, reducing our power for statistical comparisons
among categories.
Artificial versus field conditions
Expanded versus unexpanded young leaves
Newly emerged leaves go through a period of expansion before
attaining full size. Because some of the studies examined young leaves
still in the expansion stage, we also asked whether the differences in
chemical defence between leaf ages depended on whether the young
leaves in a study were still expanding or had reached maximum size.
Lower concentrations of defensive compounds in older leaves might
be due to passive diffusion of chemicals as a leaf ages and expands
(McKey 1979; Read et al. 2003) or a more active breakdown ⁄ reallocation of defensive compounds away from older leaves and into
younger leaves (van Dam et al. 1995b). We tested whether the effect
size depended on whether the young leaves from a study were fully
expanded at the time of chemical sampling or were at a pre-expansion
stage.
Because artificial conditions in glasshouses, gardens or field plots supplemented with water or fertilizer could affect the difference in
defence between low and high-value tissues, we also tested whether
the effect size depended on growing conditions. Our prediction was
that plants grown under artificial conditions might be less resourcelimited compared to plants grown under natural field conditions,
thereby decreasing the overall costs of employing defence and leading
to smaller differences in defensive chemical concentrations between
young and old leaves. This prediction is consistent with Koricheva’s
(2002) finding that the costs associated with chemical defence were
significantly lower in controlled environments compared to field studies. We excluded those studies for which we were unable to determine
if the plants were grown under artificial conditions.
NITROGEN-BASED MEASURES OF VALUE:
Flowers versus leaves
META-ANALYSIS
Flowers are required for sexual reproduction in angiosperms, and
thus might be more valuable compared to leaves (Strauss, Irwin &
Lambrix 2004). Following the predictions of ODT, flowers might
have higher concentrations of defensive compounds than leaves, especially in plants that are not self-compatible (Smallegange et al. 2007;
Kessler & Halitschke 2009). Our prediction was that flowers would
have higher levels of defensive compounds than leaves produced concomitantly with flowering because the costs of deterring pollinators
might be offset by the benefit of protecting flowers against florivores.
Alternatively, if flowers tend to be more ephemeral, this property
might select for lower defences than in longer-lived leaves (Strauss,
Irwin & Lambrix 2004). It is important to note that we did not consider studies that looked at chemical defences in nectar only because
these compounds might have other functions besides deterring herbivores (Rhoades & Berghdahl 1981) and because most of our studies
on flowers (10 ⁄ 12) did not separate petal tissue from the rest of the
flower, so chemicals in the nectar could contribute to the overall
chemical concentrations in those tissues.
For our meta-analyses we used generally held hypotheses about the
value of different tissues, based on the assumption that removal of tissue is a good way to measure value. Another way to determine value
is to use the concentration of nitrogen in tissues, because nitrogen is
often a limiting nutrient in terrestrial ecosystems (Xia & Wan 2008).
Younger leaves might be valuable because they can contribute more
to the future fitness of a plant (Harper 1989). For example, Traw &
Feeny (2008) found that leaf value was significantly correlated with
nitrogen concentration in Brassica nigra and B. kaber. We performed
a meta-analysis on the subset of studies that included nitrogen concentration of plant tissues being compared in order to determine if
younger leaves had higher concentrations of nitrogen compared to
older leaves (nitrogen concentrations were not available for studies
examining flowers).
Chemical class
Optimal defence theory explicitly assumes that plants incur some cost
when employing defences; we were therefore interested in examining
intra-plant defence across various classes of chemical compounds.
Evidence for different costs for different chemicals has been found
previously: Koricheva (2002) showed that the relationship between
growth and chemical concentration was negative for phenolics and
alkaloids but was positive for terpenes, suggesting smaller costs in the
production of terpenes. Thus, we also investigated whether the effect
size between high and low-value tissues depended on chemical class,
with the prediction that the difference would be significantly lower
DATA SELECTION AND ANALYSIS
We found studies by searching the ISI Web of Science for combinations of keywords pertaining to defensive compounds and different
plant tissues or leaves of different ages. Examples of these search combinations were ‘optimal defence theory’, ‘chemical defence and plant
tissue’, and ‘chemical defence and flower ⁄ leaf’. In order to find the
greatest number of useful articles we did not restrict our search to any
particular journals. Although the search window was set from 1900
to 2009, the earliest study we were able to use was from 1980. We also
consulted relevant reviews for cited references [e.g. Zangerl & Bazzaz
(1992); Kessler & Halitschke (2009)].
Because meta-analyses depend on the quality of data that can be
found and screened, we employed a number of criteria to ensure the
quality and relevance of studies. We included those studies that
reported mean chemical concentrations, standard deviations (in
! 2010 The Authors. Journal compilation ! 2010 British Ecological Society, Journal of Ecology, 98, 985–992
988 A. C. McCall & J. A. Fordyce
numerical or graphical form), and sample size of leaves of different
ages on a single plant. When leaves were sampled at many different
ages or stages, we chose the most extreme ages for comparison. We
excluded studies on cultivated species because ODT was originally
developed as a framework to explain how natural selection might
shape defensive traits. By definition, cultivated species have been, and
continue to be, under artificial selection. Artificial selection has been
shown to affect the distribution and concentrations of defensive
compounds (Rosenthal & Dirzo 1997; Lindig-Cisneros, Dirzo &
Espinosa-Garcia 2002; Mondolot et al. 2008). Thus, ODT provides
no expectation for the distribution of defensive compounds in cultivated plants and including them in the present study would be problematic. We only used data if the chemical or members of the same
class of compounds were shown to be effective at reducing herbivory
in the plant species under study, or members of the same plant family,
either through deterrence or decreased herbivore performance. This
criterion excluded many studies with putative defensive chemicals
that have no demonstrated effect on herbivores. Since ODT is concerned with the evolution of defences in plant populations, we also
used only those studies that explicitly used individual plants as replicates, and thus where measures of variance were computed among
individuals. This criterion also excluded many studies where either tissues or leaves of a certain age were pooled, and in which replicates
consisted of repeated samplings of the same pooled quantity (e.g. Vetter 1995). Whenever needed, and if possible, the original authors of
studies were consulted to clarify details of the methods or the results.
To limit the number of comparisons within each species, and thus
avoiding pseudoreplication (Abrami et al. 1988), we used the following guidelines. (i) If there were multiple populations examined within
a single species, we chose a single population at random. (ii) For work
that examined a single species and analysed multiple chemicals of the
same class, we used the chemical found at the highest mean concentration, as long as it conformed to the other requirements of our analysis. (iii) If a study examined more than one chemical class per
species, we included each chemical class, with a maximum of three
chemical classes per species.
Our methods excluded many of the studies listed by Zangerl &
Bazzaz (1992) and Kessler & Halitschke (2009) because estimates of
variation or sample sizes were not provided in the original studies.
When only graphs were available in a study, we measured means and
standard deviations or standard errors directly from the figures in
ImageJ (U.S. National Institutes of Health) employing a modification
of a method used by Sistrom & Mergo (2000). If only standard errors
were available, we calculated the standard deviations using the
sample size reported within each group.
To calculate effect size we used Hedge’s d (Hedges, Gurevitch &
Curtis 1999; Rosenberg, Adams & Gurevich 2000), which uses the
difference in values between the experimental and control treatments
divided by the pooled standard deviation multiplied by a correction
term to take into account small sample sizes. We assigned the tissue
with the hypothesized higher value to the experimental group, so
positive values of d show that higher-value tissues have higher defensive chemical concentrations. We also ran the analyses using another
effect size estimate, the response ratio (Rosenberg, Adams &
Gurevich 2000), which yielded results qualitatively and quantitatively
similar to results from when Hedge’s d was used.
Data exploration was performed by visually inspecting the normal
quantile–quantile plot between standardized effect sizes and those
predicted under the normal distribution (Wang & Bushman 1998;
Rosenberg, Adams & Gurevich 2000). As the visual inspection
showed that the effect sizes were approximately normal, we used
parametric statistics throughout the analyses. When testing for
significant differences among subsets of the data, we used the Q statistic of heterogeneity with a mixed model to account for random effects
within studies. Accounting for within-study random effects is more
appropriate for ecological studies compared to a fixed-effects model
that assumes the error within each study is relatively constant across
studies (Rosenberg, Adams & Gurevich 2000)
We also calculated Rosenthal’s number, which is the number of
unpublished non-significant studies that would need to be added to
the analysis before the overall effect size becomes insignificant. This
phenomenon is sometimes called the ‘file-drawer effect’ because nonsignificant results often remain unpublished. In general, if this number is greater than 5n + 10, where n is the number of comparisons,
then the results are considered robust to the effects of publication bias
(Rosenthal 1979). All analyses were performed using MetaWin 2.0
(Rosenberg, Adams & Gurevich 2000).
Results
We found 140 appropriate comparisons from 33 published
studies and 3 unpublished studies that met our criteria for
inclusion in the meta-analysis (see Appendix S1 for a list of the
studies, Table S1 for the plant species examined in and explanatory categories assigned to each study and Table S2 for chemicals measured, effect sizes and variances for each study, all
available in Supporting Information). Consistent with the predictions of ODT, tissues with higher predicted value had significantly higher concentrations of defensive chemicals compared
to less valuable tissues (Fig. 1, Table 1). When studies confounding leaf age and nodal position were excluded, the effect
size was smaller, although still significant (Fig. 1, Table 1),
and whether studies looked at unexpanded vs. expanded
young leaves did not affect the effect size (Q = 0.03,
P = 0.88, Fig. 1, Table 1). The effect size of the leaves vs.
flowers comparison was significantly lower than the effect size
of younger vs. older leaves (Q = 5.59, P = 0.05, Fig. 1,
Table 1). Chemical class did not significantly influence effect
size (Q = 1.37, P = 0.64, Fig. 1, Table 1), and there was no
effect of artificial vs. field conditions on the effect size
(Q = 0.24, P = 0.69, Fig. 1, Table 1). For the meta-analysis
examining within-plant nitrogen concentration, we found 19
comparisons from six published studies showing that younger
leaves had significantly higher concentrations of nitrogen than
older leaves (Fig. 1, Table 1).
Discussion
Given the importance and prevalence of ODT in many studies
investigating chemical defence, it is important to determine if
the data are consistent with its predictions. Our analysis shows
that the distribution of defensive compounds in plants is consistent with the predictions of ODT. Specifically, concentrations of defensive chemicals were higher in tissues with higher
putative values than in tissues with lower values. The mean
effect size was significantly greater than zero, and robust to the
file-drawer effect, with 1899 studies with null results needed in
order to render the effect size non-significant at a = 0.05 – a
number of studies much larger than the critical value of 710
(5n + 10) studies.
! 2010 The Authors. Journal compilation ! 2010 British Ecological Society, Journal of Ecology, 98, 985–992
Predicting the distribution of plant chemical defences 989
Effect size (Hedge's d)
4
3
29
7
45
2
27
58
70
15
36
18
37
34
12
1
0
NPT
F L
W/o
confounded
All
Tissue
A F
EU
Nitrogen
Conditions
Expansion
Chemical
class
Fig. 1. Results of the meta-analysis showing mean effect sizes and
95% confidence intervals of defensive chemical concentrations in tissues of different value. Numbers above the bars are the number of
studies in each category. All effect sizes are based on chemical concentrations in higher-value tissues minus concentrations in lower-value
tissues, with positive effect sizes supporting the predictions of optimal
defence theory (ODT). ‘All’ refers to all of the studies examined. ‘Tissue’ refers to whether the difference between flowers and leaves (F) or
the difference between young leaves and old leaves (L) was examined.
‘W ⁄ o confounded’ are studies where leaf age (young or old) was not
confounded with position on the plant. ‘Chemical class’ refers to
whether the studies looked at nitrogen-containing compounds (N),
phenolics (P) or terpenes (T). ‘Conditions’ refers to the growing conditions of the plant, artificial (A) or field (F). ‘Expansion’ refers to
whether the young leaves were at full expansion (E) or unexpanded
(U) when leaves were sampled for defensive compounds. ‘Nitrogen’
is the mean effect size between nitrogen concentrations in morevaluable versus less-valuable tissues.
Table 1. Report of Q statistics showing whether there is a significant
effect of different levels of explanatory factors (e.g. chemical class) on
effect sizes. For rows without different levels, Rosenthal’s number
indicates how many studies of no effect are needed to render the effect
size insignificant
Category (no. of
comparisons)
All studies (70)
Leaves vs. flowers (12)
Younger leaves vs. older
leaves (58)
Without confounded
studies (45)
Nitrogen-containing (34)
Phenolics (7)
Terpenes (29)
Artificial conditions (27)
Field conditions (37)
Expanded young leaves (36)
Unexpanded young leaves (15)
Nitrogen and value (18)
Rosenthal’s
number
Q (d.f.)
P
N⁄A
5.59 (1,68)
N⁄A
0.05
1899
N⁄A
N⁄A
1081
1.37 (2,67)
0.64
0.24 (1,62)
0.69
0.03 (1,50)
0.88
N⁄A
N⁄A
106
Our results on leaf age and chemical defence are consistent
with the predictions of ODT, given that younger leaves are
more valuable than older leaves. Younger leaves usually are
more photosynthetically active than older leaves (Wiedemuth
et al. 2005) and thus are hypothesized to be more valuable than
older leaves, all other factors being equal. There was little effect
of whether young leaves were expanded or not, suggesting that
the differences among leaves of different ages are not simply
due to expansion of young leaf area as that leaf matures.
Excluding studies where nodal position was confounded with
leaf age had little impact on the overall effect sizes suggesting
that absolute leaf age might be more important than position
on the stem. We note, however, that many studies had no
information regarding nodal position of leaves.
It is important to note that while we performed our analysis
under the assumption that younger leaves are more valuable
than older leaves, this may not always be the case. A common
strategy among shade-tolerant tropical plants is to delay greening and employ very rapid expansion of young leaves (Kursar
& Coley 2004). In this case, plants should be selected to defend
older leaves more because they are the tissues with the highest
potential lifetime contribution to photosynthesis. Although
Kursar & Coley (2003) showed that plants with fast-expanding
young leaves were less defended than plants with slowly
expanding young leaves, the data in that work were not appropriate for our meta-analysis because defensive compound concentrations of unmanipulated plants were not reported.
Because tropical plants comprise a large proportion of the planet’s biodiversity and delayed greening and rapid expansion
are common strategies in the tropics, future work could use the
meta-analytic framework to test whether defensive compounds
in young leaves are found in lower concentrations in those species that employ the delayed greening tactic versus plants with
young leaves that expand more slowly.
We restricted our analysis to studies that address the original
intent of ODT, i.e. explaining the intraplant distribution of
defensive secondary compounds (McKey 1974). However, the
roles of inducible defence and resistance (Karban & Baldwin
1997), indirect defences such as mutualistic, biotic defences
(e.g. Heil & McKey 2003), mechanical defences such as trichomes and laticifer systems (e.g. Farrell et al. 1991), and the
interactions among them, are increasingly playing an important role in plant defence theory. For example, researchers
have found that extrafloral nectar production rates (Wäckers
& Bonifay 2004; Radhika et al. 2008; Holland, Chamberlain
& Horn 2009) and trichome densities (Traw & Feeny 2008) are
correlated with assumed tissue value. Volatile organic compounds (VOCs) are also emitted at higher rates from younger
leaves than from older leaves in Glycine max (Rostás & Eggert
2008). Similarly, the inducibility of chemical defences might be
correlated with tissue value (Zangerl & Rutledge 1996; Strauss,
Irwin & Lambrix 2004). As our understanding of the ecology
and evolution of these defensive strategies continues to mature,
it will be important to incorporate them into future optimality
models aimed at elucidating the selective factors responsible
for plant defence strategies.
There was a significant effect of whether comparisons were
made on flowers versus leaves or younger versus older leaves.
In particular, there was no evidence that flowers were more
defended than leaves, but younger leaves were more defended
! 2010 The Authors. Journal compilation ! 2010 British Ecological Society, Journal of Ecology, 98, 985–992
990 A. C. McCall & J. A. Fordyce
than older leaves. This suggests that tissue age might affect
defence allocation more than tissue identity alone. We note,
however, that the sample size was much smaller for the flowers
versus leaves comparison and that our results might be due to
a lack of power. Alternatively, our findings that flowers are not
defended more than leaves might indicate that the generally
held assumption that flowers are more valuable than leaves is
incorrect. For example, if a plant makes many flowers and a
small proportion of them actually set fruit, flowers might be
less valuable than a single leaf that could produce enough
resources to mature several fruits. We did not have data on the
number of flowers produced per plant in many of our studies
that looked at defences in flowers, but this would be important
data to provide in future experiments. Other factors besides
value might select for less intense defences in flowers. For
example, because flowers are generally more ephemeral than
leaves, reproductive tissues might have a lower probability of
attack than leaves (McCall & Irwin 2006). The composition of
defence chemicals in flowers might also be different from that
in leaves, and it could lead to erroneous conclusions if we
assumed the same defensive compounds are used throughout
the plant in different concentrations. This is a general drawback of the ODT, because it assumes that similar defences with
similar costs are used throughout a plant to defend different
tissues. Future studies could examine a wider range of potentially defensive compounds in flowers and leaves to ensure
multiple defences are considered. A final problem with our floral defence results is that defences in the nectar could mask the
true concentrations of chemicals in other floral tissues like the
corolla. It would thus be useful for future researchers to
remove nectar before processing the rest of the flower or to
consider the corolla alone in their studies.
Some of our categorizations failed to explain the variation in
the effect sizes. For example, we failed to detect a significant
effect of chemical class on effect size. This does not support the
suggestion by Koricheva (2002) that terpenes are produced at a
lower cost than phenolics or nitrogen-containing defences like
alkaloids. As a caveat, we note that there were relatively few
(seven) studies looking at phenolics in our analysis, perhaps limiting the power to discern larger differences in effect size among
the classes. Artificial conditions often yield different results than
field settings when examining chemical defence (Ormeno et al.
2008). However, we found no evidence that environment
affected the effect size among tissues of different value.
We also found that nitrogen concentration was significantly
greater in higher value tissues compared to lower value tissues,
suggesting that nitrogen concentration can serve as a useful
proxy to initially determine value if other methods are not
available. Some caution should be taken with these results,
however, since relatively few (106) studies of no effect were
needed to render the effect size not significant, which is only
slightly above the critical number of 105 (5n + 10) studies.
Optimality models such as ODT are useful because they usually apply to a broad range of organisms and are often very
good at predicting life-history traits or behaviours (Mäkelä
et al. 2002). For example, we showed that younger leaves,
being more valuable, are better defended than older leaves. In
another example, the optimal gas exchange model has been
successful at predicting the daily fluctuations in photosynthesis
and stomatal conductance (e.g. Hari et al. 1999). Despite their
general usefulness, optimality models can be improved when
applied to particular groups of organisms like plants. For
example, because leaves produced at old nodes may be more
valuable than leaves produced at younger nodes when age is
held constant (Wiedemuth et al. 2005; Traw & Feeny 2008),
we believe that it is important that workers report the absolute
positions of old versus young leaves when testing defences
between these tissues. Another suggestion for future work is
that the value of different tissues should be tested, rather than
assumed, when ODT is invoked to explain differences in defensive chemical concentrations. Removal of different tissues followed by fitness estimates would be the most direct method of
determining value (McKey 1979). This method has been used
in several studies (Nitao & Zangerl 1987; Barto & Cipollini
2005; Traw & Feeny 2008), but it should be used more often.
Researchers employing tissue removal to assess value should
consider what part of the tissue is removed and how equivalent
amounts of tissues are measured. This is especially important
when testing the value of flowers and leaves because certain
structures like the ovary may be more valuable than other
structures in the flower, like the petals. For example, if one
removes 1g of leaf tissue, does one simply remove an equivalent mass of entire flowers, or does one remove only the structures that are actually consumed by herbivores? We suspect
that removing those structures or areas that are actually consumed in nature is the most appropriate method, but this
requires appropriate natural history knowledge of the plant–
herbivore interaction under investigation.
More studies should also consider whether inducible
defences are being measured when they are not explicitly considered in the experimental design. Although most of the studies we included here tacitly considered only constitutive
measures, our results could have been affected if tissues had
already been induced. This could especially be true in field studies, where damage to other parts of the plants could induce
chemicals in undamaged tissues. Ignoring induced defences
could also be problematic if they are more effective than constitutive defences in some environmental contexts, such as areas
that have variable levels of herbivory over generational time.
We conclude that ODT is useful in predicting the allocation
of defensive compounds among tissues of differing absolute
age and there is little evidence that this effect is influenced by
leaf expansion, chemical class or growing conditions. As other
authors have made clear (Stamp 2003), general theories for the
distribution of chemical defences in plants are difficult to formulate and test explicitly, but as more data are published, we
hope that meta-analyses that consider several taxa and situations at once will help us think more clearly about this important question in plant ecology.
Acknowledgements
We would especially like to thank Jean Langenheim, M. M. Hay-Roe
and Rebecca Irwin for sharing unpublished data. We thank Joe Bailey, Judie
! 2010 The Authors. Journal compilation ! 2010 British Ecological Society, Journal of Ecology, 98, 985–992
Predicting the distribution of plant chemical defences 991
Bronstein, Richard Karban, Jennifer Lau, the Handling Editor and three
anonymous reviewers for very helpful comments on earlier drafts of this work.
This work was supported, in part, by a U.S. National Science Foundation grant
(DEB-0614223) to J.A.F.
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Table S1. List of studies and species grouped into categorical variables.
Received 4 November 2009; accepted 8 June 2010
Handling Editor: Martin Heil
Table S2. List of studies, species, chemicals measured, effect sizes and
the variance of the effect sizes.
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Appendix S1. List of references used for the meta-analyses.
! 2010 The Authors. Journal compilation ! 2010 British Ecological Society, Journal of Ecology, 98, 985–992