Ecology, 94(7), 2013, pp. 1499–1509
Ó 2013 by the Ecological Society of America
Predicting invertebrate herbivory from plant traits:
Polycultures show strong nonadditive effects
JESSY LORANGER,1,2,6 SEBASTIAN T. MEYER,1,7 BILL SHIPLEY,2 JENS KATTGE,3 HANNAH LORANGER,1,8
CHRISTIANE ROSCHER,4 CHRISTIAN WIRTH,5 AND WOLFGANG W. WEISSER1,7
1
Institute of Ecology, Friedrich Schiller University of Jena, Dornburger Str. 159, 07749 Jena, Germany
De´partement de biologie and Centre des e´tudes de la forêt, Université de Sherbrooke, Sherbrooke, QC J1K 2R9 Canada
3
Max Planck Institute for Biogeochemistry, Hans-Knöll Str10, 07745 Jena, Germany
4
UFZ, Helmholtz Centre for Environmental Research, Department of Community Ecology, Theodor-Lieser-Str. 4,
06120 Halle, Germany
5
Department of Special Botany and Functional Biodiversity, Universität Leipzig, Ritterstr. 26, 04109 Leipzig, Germany
2
Abstract. Plant functional traits affect the capacity of herbivores to find, choose, and
consume plants. However, in a community composed of different plant species, it is unclear
what proportion of herbivory on a focal plant is explained by its own traits and which is
explained by the characteristics of the surrounding vegetation (i.e., nonadditive effects).
Moreover, nonadditive effects could be positive or negative, and it is not known if they are
related to community properties such as diversity. To quantify nonadditive effects, we
developed four different additive models based on monoculture herbivory rates or plant traits
and combined them with measurements of standing invertebrate herbivore damage along an
experimental plant diversity gradient ranging from monocultures to 60-species mixtures.
In all four models, positive nonadditive effects were detected, i.e., herbivory levels were
higher in polycultures than what was expected from monoculture data, and these effects
contributed up to 25% of the observed variance in herbivory. Importantly, the nonadditive
effects, which were defined as the deviance of the models’ predictions from the observed
herbivory, were positively correlated with the communities’ plant species richness.
Consequently, interspecific interactions appear to have an important impact on the levels of
herbivory of a community. Identifying those community properties that capture the effects of
these interactions is a next important challenge for our understanding of how the environment
interacts with plant traits to drive levels of herbivory.
Key words: community-weighted traits; consumers; diversity; grassland; insects; interactions;
invertebrate herbivory; Jena Experiment; monocultures; polycultures; Saale River floodplain, Thuringia,
Germany.
INTRODUCTION
Herbivory is a major selective pressure affecting plant
communities (Allan and Crawley 2011) and the capacity
of plants to avoid, resist, or tolerate herbivory is
mediated by their functional traits. Consequently, plant
species differing in such traits can drastically differ in
rates of herbivory (Coley and Barone 1996, Rasmann
and Agrawal 2009). Moreover, the rate of herbivory on
a focal plant in a multispecies community depends on its
own traits, but could also depend on the characteristics
Manuscript received 27 November 2012; revised 7 February
2013; accepted 13 February 2013. Corresponding Editor: N. J.
Sanders.
6 E-mail: [email protected]
7 Present address: Technische Universität München, Terrestrial Ecology Group, Department of Ecology and Ecosystem Management, Center for Food and Life Sciences
Weihenstephan, Hans-Carl-von-Carlowitz-Platz 2, 85350
Freising, Germany.
8 Present address: Institute of Biology and Environmental
Sciences, University of Oldenburg, Functional Ecology of
Plants, 26111 Oldenburg, Germany.
of the surrounding vegetation, such as its taxonomic and
functional composition.
Consider first the null hypothesis H0, which assumes
that the decision of the herbivore to consume tissue of a
given plant depends only on the functional traits of the
focal species, irrespective of the surrounding vegetation.
If this is true, then the herbivory experienced by a
species is the same in multispecies vegetation as in a
monoculture, assuming that the trait values of the
species do not change in the two types of vegetation.
Consequently, the total rate of herbivory of the entire
vegetation is the sum of the monoculture rates of
herbivory (measured or predicted from traits) of each
species in the vegetation weighted by its relative
abundance. This is called ‘‘additive scaling’’ of herbivory
(H0) because (1) the total rate is an additive function of
the monoculture rates and (2) these monoculture rates
are scaled up to multispecies mixtures.
Under the alternative hypothesis H1 (‘‘nonadditive
scaling’’), the probability that a herbivore will consume
tissue of a given plant depends on properties of the
surrounding vegetation in addition to the functional
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JESSY LORANGER ET AL.
traits of the focal species. The scaling from monocultures to mixtures is ‘‘nonadditive’’ because one must
include some factor beyond an additive function of the
monoculture rates. For instance, herbivory on the focal
plant could increase relative to monoculture if herbivores are attracted to it by the surrounding vegetation;
the inverse mechanism would result in decreased
herbivory. Because some studies (Huntly 1991, Hambäck et al. 2000, Finch and Collier 2012 ) have
demonstrated a decreased herbivory that is mediated
by the presence of another neighboring species, it is
likely that nonadditive effects do occur in mixed
communities. However, we do not know their strength
relative to the additive scenario nor whether such
nonadditive effects are consistently positive or negative.
Although statistically additive models based on community-weighted traits, but not scaling up from monoculture herbivory, do not test the additive scaling
hypothesis, they can still be used to quantify the size
and direction of nonadditive effects by calculating the
deviation between community herbivory predicted from
the models and herbivory measured in mixed-species
communities. In addition, comparing communities that
differ in the magnitude of this deviation can give insights
into which community properties determine the strength
of nonadditive effects.
In this study, we developed four models (M1 to M4) to
predict herbivory measured in experimental herbaceous
grassland communities, ranging from monocultures to
60-species mixes (Jena Experiment; Roscher et al. 2004)
that are exposed to a natural community of invertebrate
herbivores. M1 and M2 test the additive scaling
hypothesis (H0) because they were directly based on
monoculture herbivory data. M3 and M4 were based on
community-weighted traits and therefore do not scale up
from the levels of herbivory measured in monocultures.
As a result, these models cannot test the additive (or
nonadditive) scaling hypothesis, but are rather designed
to investigate how the nonadditive effects of multispecies
communities on herbivory are mediated by plant traits.
The specific goals of this study were (1) to test the
models’ ability to predict herbivory in polycultures
correctly (all models) and to see whether results from
monocultures can be scaled up to polycultures (M1 and
M2), (2) to quantify if there are nonadditive effects and
determine their magnitude and direction (all models),
and (3) to analyze if any nonadditive effects are related to
community properties of the vegetation (all models).
METHODS
Study site
The field site was located on the floodplain of the
Saale River at the northern edge of Jena (50855 0 N,
11835 0 E; altitude 130 m a.s.l.), Thuringia, Germany, on
a Eutric Fluvisol (1997 update of FAO 1988). The Jena
Experiment, established in 2002, is a biodiversity
experiment consisting of 80 large plots (originally 20 3
20 m, now 5 3 7 m) in a randomized-block design,
Ecology, Vol. 94, No. 7
containing 1, 2, 4, 8, 16, or 60 plant species with 14, 16,
16, 16, 14, and 4 replicates of these levels of species
richness, respectively. The pool of 60 species (Appendix
A) consists of herbaceous plant species commonly
occurring in seminatural, mesophilic grasslands in
Central Europe: Molinio–Arrhenatheretea meadows,
Arrhenatherion community (Ellenberg 1996). Furthermore, there is one small monoculture plot (1 3 1 m) of
each of the 60 species on the experimental site. All plots
are mown twice a year and weeded 2–3 times annually,
keeping only the target species in each plot. A detailed
description of the setup of the experiment is given in
Roscher et al. (2004).
Biomass and herbivory measurements
In May and August 2010, biomass inside a 20 3 50 cm
frame was cut 3 cm above the ground from a
randomized position in each large plot. Unsown species
were separated and the remaining biomass was sorted to
species, oven-dried (at 788C for 48 h), and weighed.
Before drying, the same samples were used to measure
invertebrate leaf standing herbivore damage (hereafter
‘‘herbivory’’) as follows. For each species per plot, 30
leaves were chosen randomly. In some large plots, less
than 30 leaves were available for rare species (minimum
¼ 1 leaf per species; mean ¼ 21 leaves per species; on
average, 65% of the initially sown species per plot
occurred in the samples). The damaged surface area of
all leaves (in square millimeters) was estimated visually
by comparing the damaged leaf area to a series of
circular and square templates ranging in size from 1
mm2 to 500 mm2. The total damaged area per leaf
included four types of herbivory: chewing, rasping,
sucking, and mining. The remaining surface area of each
leaf was measured using a LI-3000C Area Meter (LICOR, Lincoln, Nebraska, USA). The potential undamaged leaf area before herbivory was estimated by adding
the proportion of the area lost to chewing damage (a
factor previously estimated for all species) to the
measured area. Herbivory was calculated as the total
damaged area divided by the potential undamaged leaf
area. Following the same protocol, herbivory was
estimated for leaf samples from the small-area monocultures (1 plot per species, 30 leaves per plot), hereafter
simply referred to as monocultures; see Loranger et al.
(2012) for details on sampling in monocultures.
From the sampled large plots, a total of 10 were
excluded because of missing herbivory measurements
(six plots) or because they contained more than 15%
biomass of one or more of the nine species for which
reliable herbivory measurements in monoculture were
not available (four plots; see Appendix A). When these
species accounted for ,15% of the biomass in a plot,
they were excluded (also for the calculation of community-weighted traits as we will detail), but the plot was
kept in the analyses with the remaining species. For each
large plot (hereafter referred to as ‘‘communities,’’
including 12 large-area monoculture plots), a commu-
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COMMUNITY PROPERTIES AFFECT HERBIVORY
1501
nity-level estimate of herbivory was calculated by
summing up herbivory per species in that plot multiplied
by the respective relative biomass of each species. Values
for the two harvests (May and August) were averaged,
giving 70 values of measured community herbivory.
is the abundance-weighted mean of the species-specific
monoculture herbivory, as follows:
Predicting herbivory in monocultures
where hi is the herbivory experienced by an average
individual of plant species i when growing in a
monoculture, and raij is the relative abundance of
species i in a community j composed of S species. The
nonadditive scaling hypothesis (H1) assumes that the
herbivory suffered by species i in monoculture changes
by an amount di when growing in mixture. This implies
that the predicted total herbivory experienced by the
entire plant community j deviates from the sum of the
predicted herbivory experienced by each species in
monoculture by an amount Dj :
As an initial step to predict herbivory in monocultures
(Loranger et al. 2012), we assembled a data set of 42
plant traits, including physiological, morphological,
phenological, and herbivore-related traits, from data
collected in the Jena Experiment (Roscher et al. 2004,
2011a, b, Gubsch et al. 2011; M. Gubsch, A. Lipowsky,
and C. Roscher, unpublished data), and from international plant trait databases: TRY (Kattge et al. 2011,
including the following main references: Kuhn et al.
2004, Garnier et al. 2007, Pakeman et al. 2008, 2009,
Fortunel et al. 2009), LEDA (Kleyer et al. 2008), and
Biolflor (Klotz et al. 2002). Appendix B, Table B1 gives
a detailed description of the traits and a full list of
references. In the next step, the method of random
forests (RF; Breiman 2001) was applied, which uses a
series of regression trees to derive importance scores that
indicate the most important traits for predicting
herbivory among a large number of traits (see Appendix
C for more details on the methodology of the random
forests selection technique). Following the random
forests, a multiple regression with model simplification
via stepwise selection was done and seven of the 42
initial plant traits were identified as being significant
predictors of herbivory: leaf nitrogen concentration
(loge-transformed), leaf lignin concentration, number
of coleopteran and hemipteran (excluding aphids)
herbivores potentially feeding on the plants (logetransformed), leaf life span, stem growth form (percentage erection of the stem), and root architecture. The
final model from Loranger et al. (2012), which explained
63% of the variation in herbivory measured in monocultures (Appendix B: Fig. B1) and was supported by
cross-validation tests, is:
lnðmeasured herbivoryÞ
’ 10:65 þ 1:75 3 lnðleaf½nitrogenÞ
0:07 3 leaf½lignin
þ 0:55 3 lnðcoleopteran herbivoresÞ
0:41 3 lnðhemipteran herbivoresÞ
þ 0:37 3 leaf life span
0:007 3 stem growth form
þ 0:27 3 root architecture:
ĥj ðH0 Þ ¼
S
X
ð2Þ
raij hi
i¼1
hj ðH1 Þ ¼
S
X
raij ðhi þ di Þ ¼
i¼1
S
X
i¼1
raij hi þ
S
X
raij di
i¼1
hj ðH1 Þ ¼ hj ðH0 Þ þ Dj :
ð3Þ
Note that the deviation term di does not represent a
purely statistical error term (i.e., a random value from a
distribution having a zero mean), but rather the
deviation from the additive scaling hypothesis between
measured and predicted herbivory (nonadditive effects).
However, because hi contains sampling variation and
measurement error, di also contains these errors. If the
deviations from additive scaling (d i ) vary randomly and
independently for each species in the plant community,
and if they are equally likely to be positive or negative,
then they will tend to cancel each other (D j ; 0) and
additive scaling would be a reasonable approximation;
otherwise nonadditive scaling occurs and Dj is an
approximation of the strength of nonadditive effects in
determining herbivory in plant communities.
Models predicting herbivory in polycultures that are
not based on monoculture data and thus do not scale up
from monoculture, cannot be tests of the additive (or
nonadditive) scaling hypothesis. Given this, theory and
terms related to Eq. 3 cannot be applied to these models.
However, if these models are statistically additive, i.e.,
they are a linear combination of predicting factors, an
equivalent term for Dj can be calculated to separate
additive from nonadditive effects. This term is also
calculated in the same way as the deviation between
measured and predicted herbivory and it will also be an
approximation of the strength of nonadditive effects in
the community.
Testing the additive scaling hypothesis
ð1Þ
Additive vs. nonadditive scaling
Given the additive scaling hypothesis (H0), the
predicted herbivory of the plant community j (ĥ j(H0))
The additive scaling hypothesis was tested using two
different models. First (M1), community herbivory was
predicted (ĥ ) from community-weighted species-specific
herbivory measured in monocultures (Eq. 2). Second
(M2), predictions were based on plant functional traits
and previous relationships between traits and herbivory
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JESSY LORANGER ET AL.
(Loranger et al. 2012) obtained in monocultures. To do
so, the species-specific trait values in the monoculture
model (Eq. 1) were replaced by the associated community-weighted trait values, while keeping the intercept
(b0) and partial slopes (bk) at the values estimated in the
monoculture model:
ĥj ðH0 Þ ¼
S
X
raij hi ¼
i¼1
S
X
rai ðb0 þ
i¼1
T
X
bk tik Þ
k¼1
¼ b0 þ b1 t1 þ . . . þ bk tk :
ð4Þ
Predicted herbivory from M1 and M2 was regressed
against measured herbivory for all 70 plant communities
included in the study. If the additive scaling hypothesis
is correct, the regression slopes will not be significantly
different from unity. A paired t test was additionally
used to verify if, on average, predicted values of
community herbivory from these two models were
significantly lower or higher than measured values.
Testing the relative importance of plant traits in
monocultures vs. polycultures
Eq. 5 represents a model that assumes that the best
traits predicting herbivory in monocultures and in
polycultures are the same, but that the values of the
parameters (b) can change by an unknown amount d.
Thus (M3), the measured values of community herbivory were freely regressed against the communityweighted version of the same seven traits selected in
the monoculture model, giving new partial slopes (bk0 ):
ĥj ðH0 Þ ¼
S
X
raij hi0
i¼1
¼ ðb0 þ d00 Þ þ ðb1 þ d10 Þt1 þ . . . þ ðbK þ dK0 Þtk
ĥj ðH0 Þ ¼ b00 þ b10 t1 þ . . . þ bK0 tk :
ð5Þ
For this model, if the only difference between monocultures and multispecies communities is that the relative
importance (i.e., regression slopes) of the different traits
changes between monocultures and mixtures, an equivalent percentage of explained variation relative to the
result in monocultures (i.e., ;63% variance explained) is
to be expected. Because these regression slopes are now
freely estimated from the multispecies communities,
rather than being fixed by the monoculture values, M3 is
not a true test of the additive scaling hypothesis, but is
still an additive model that does retain the assumption
that the same traits are important in determining
herbivory levels in monocultures and mixtures.
Finally, a new trait-based model (M4) was created
using community-weighted values of all 42 traits by
selecting traits of potential importance in a random
forests analysis (see Appendix C) followed by a
backward stepwise selection in a multiple regression
until the partial slope of all traits in the model were
significantly different from 0. M4 is not a test of the
additive scaling hypothesis, but allows us to investigate
Ecology, Vol. 94, No. 7
whether different traits are governing community
herbivory compared to monoculture herbivory, while
still maintaining the assumptions of statistical additive
effects.
In addition, we related the deviations (difference
between measured and predicted herbivory) of models
M1–M4 to a series of community properties. We initially
calculated seven properties: sown species richness,
community biomass, realized species richness, Shannon
diversity, evenness (diversity divided by species richness), functional diversity via Rao’s quadratic entropy,
and functional dispersion (Laliberté and Shipley 2011).
However, sown species richness was selected as a
surrogate for other community properties because (1)
there were highly significant correlations between all of
these community characteristics except for community
biomass and evenness (see Appendix D), (2) sown
species richness was the actual community property that
was experimentally manipulated in the experiment, and
(3) qualitative results were the same for all community
properties.
Besides simple sampling variation and ‘‘missing’’
variables causing variation in herbivory levels, deviations between the measured and predicted values in our
four models can be caused by nonadditive effects. We
will treat these deviations as an approximation of
nonadditive effects and will call them so. In doing so
we are assuming that measurement error and sampling
variation are negligible, relative to the range of variation
in these values. To estimate the relative importance of
additive and nonadditive effects in determining community herbivory in all models, the log ratio of their
absolute contributions was calculated. Because our four
models are statistically additive, the additive contribution is the value predicted by each model and the
nonadditive contribution is the difference between
measured and predicted herbivory. This difference can
be positive or negative, and the log ratio of the absolute
values quantifies their relative importance (Appendix E).
All statistical analyses were done in R version 2.10.0
(R Development Core Team 2009). The values of
herbivory, deviation of predicted herbivory from measured herbivory, sown species richness, and of several
plant traits (see Appendix B) were loge-transformed. We
present R 2 values adjusted for bias due to differing
numbers of predictor variables, as provided by the ‘‘lm’’
function in R. This measures the proportion of the
biologically relevant variation that is explained by the
model and is defined as R 2(N 1)/(N k 1), where N
is the number of observations and k is the number of
predictors of each regression. The relative importance of
the partial slopes, or magnitude of effect, was calculated
by multiplying the absolute value of each partial slope
by the range (maximum minus minimum) of its
associated community-weighted trait. In addition, the
goodness of fit of the models (M1–M4) was compared
using the corrected version of the Akaike information
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COMMUNITY PROPERTIES AFFECT HERBIVORY
criterion for small ‘‘N’’ size (AICc), as defined by
Burnham and Anderson (2010).
RESULTS
Testing the hypothesis of additive scaling
The additive scaling hypothesis (H0) asserts that the
predicted herbivory of multispecies vegetation is the
abundance-weighted average of the species-specific
monoculture herbivory values. Predicted herbivory
based on this relation (M1) underestimated measured
herbivory (slope 0.43 6 0.09) and explained only 23%
(AICc ¼ 84.84) of the variation in measured community herbivory (Fig. 1A, Table 1). The actual level of
herbivory in the communities, on average, was greater
than expected from monoculture (t test: t ¼ 2.39; df ¼ 69,
P ¼ 0.020) and this positive bias increased significantly
with sown species richness of the communities (Fig. 1B,
Table 1). Thus, we rejected the additive scaling
hypothesis.
The community-weighted version (M2) of a previously published model linking herbivory in monoculture to
species-specific traits (Eq. 4) explained only 6% (Table
1) of the variation in measured community herbivory
and the fit was worse than for M1 (AICc ¼ 70.70).
Again, the model underestimated actual levels of
herbivory (Fig. 1C, Table 1). The deviation between
measured and predicted values, on average, was higher
and more significantly differed from zero (t ¼ 3.31, df ¼
69, P ¼ 0.001) than with predictions based on measured
monoculture herbivory. Deviations increased with increasing sown species richness of the plant communities
(Fig. 1D, Table 1). Again, the additive scaling hypothesis was rejected.
Testing the relative importance of plant traits
in monocultures vs. in polycultures
For our third model (M3), we regressed the measured
levels of community herbivory against the same seven
traits as in the monoculture model, but estimated new
regression slopes rather than fixing them to the values of
the initial model. In this multiple regression, the seven
traits explained 25% (Table 2) of the variation in
community herbivory. Thus, the predictive power was
weaker than for the model in monocultures (R 2 ¼ 0.63;
Appendix B: Fig. B1), but was better than with M1 and
M2 (Fig. 1E, Table 1). In contrast, the goodness of fit of
the model was in between those of the first two models
(AICc ¼ 72.37), because the increase in explained
variation was counterbalanced by many more parameters that M3 had to estimate to obtain a higher R 2.
Although this model was highly significant, only the
slopes associated with coleopteran herbivores and leaf
life span were (marginally) significant and the relative
importance of the traits changed considerably in
comparison to the model for monocultures (Table 2).
Again, the deviation between measured and predicted
herbivory correlated significantly and positively with
sown species richness (Fig. 1F, Table 1).
1503
Given that the results of M3 indicated a change from
monocultures to multispecies communities in the relative
importance of plant traits in predicting herbivory, we
used a new random forests analysis on the 42 initial
traits to select a new set of traits that best predicted
community herbivory. In total, 20 traits were selected
(Table 3; Appendix C). From those 20 traits, a
backward stepwise selection in a multiple regression
identified five traits to significantly predict herbivory in
communities (M4; Table 3). These five traits explained
55% of the variation in measured community herbivory,
which was still less than that explained by the
monoculture model. Only the trait ‘‘coleopteran herbivores’’ was selected in both the community and
monoculture models. However, ‘‘height summer’’ (selected in the communities) is naturally related to, and
correlated with (r ¼ 0.43, P , 0.001) stem growth form
(chosen in the original monoculture model). Similarly,
leaf primary fiber concentration (selected in communities) is correlated (r ¼ 0.72, P , 0.001) with leaf lignin
concentration (selected in monocultures). Predicted
herbivory in M4 correlated more closely with measured
herbivory than did predictions from the first three
models (Fig. 1G, Table 1; AICc ¼ 113.39), yet the
deviation between measured and predicted herbivory
correlated even more significantly and more positively
with sown species richness (Fig. 1H, Table 1).
Quantifying the strength and direction
of nonadditive effects
The deviance of all four models increased with
diversity, ranging from an average deviance close to
zero with some strongly negative or positive values at
low levels of species richness, to a clearly positive
deviance at highest diversity; i.e., all models increasingly
underestimated levels of herbivory with increasing
diversity (Fig. 1). In general, measured herbivory was
higher than expected from the four different models
(Fig. 1, Appendix E: Fig. E1; solid circles). To quantify
how much additive, compared to nonadditive, effects
contributed to the observed levels of herbivory, the
measured herbivory needs to be partitioned into these
two components. Therefore, we calculated the log ratio
of the predicted herbivory (additive effect) and the
absolute value of the difference between measured and
predicted herbivory, i.e., the absolute deviance (nonadditive effect).
In contrast to the deviance, the log ratio of
nonadditive to additive effects did not change significantly with species richness (Appendix E: Table E1),
even if levels for the highest diversity level were much
higher for all models. On average, the additive effects
estimated by the models M1–M3 exceeded 2.7 times the
nonadditive effects. In other words, more than onequarter of the measured herbivory was determined by
nonadditive effects (assuming that measurement error
was small and there were no missing variables). For M4,
which was designed to explain as much variation as
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JESSY LORANGER ET AL.
Ecology, Vol. 94, No. 7
FIG. 1. Herbivory (percentage leaf standing herbivore damage for whole plant communities) as predicted from different models
regressed against measured herbivory at the field site of the Jena Experiment, Thuringia, Germany, and the relationship between
deviance of the models (difference between measured and predicted herbivory) and species richness (number of plant species sown)
of the plant communities. Note the logarithmic axis for herbivory and species richness. Dashed lines give expected relationships (a
slope of 1 between predicted and measured herbivory in the left-hand panels and a constant average deviance of 0 in the right-hand
panels). Solid lines are best-fit lines of significant additive models, given in detail in Table 1. (A, B) In the upper row of panels,
predictions are based on herbivory levels of the species in monocultures (M1, Eq. 2). (C, D) In the second row, predictions are from
a model, based on seven plant functional traits, that was developed to predict species-specific herbivory in plant monocultures and
that was used with community-weighted traits for the different plant communities (M2, Eqs. 1 and 4). (E, F) In the third row,
predictions are based on a trait model in which new partial slopes were estimated for the same seven community-weighted traits
directly for the herbivory measured in communities (M3; the resulting model is given in Table 2). (G, H) In the bottom row,
predictions are based on a trait-based model for which a new trait selection identified five community-weighted traits out of a set of
42 plant traits to be the best predictors of herbivory measured in communities (M4; the resulting model is given in Table 3).
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COMMUNITY PROPERTIES AFFECT HERBIVORY
1505
TABLE 1. Statistics for models presented in Fig. 1 on the relationship between (1) herbivory predicted from four different models
and herbivory measured in plant communities of differing diversity and (2) the deviance of these models and diversity of the
plant communities.
Intercept 6 SE
Slope 6 SE
r2
F
P
M1, predicted from monocultures
log(HpM1) ; log(Hm)
log(DevM1 þ 15) ; log(sowndiv)
0.15 6 0.07
2.70 6 0.02
0.43 6 0.09
0.02 6 0.01
0.23
0.07
21.7
6.06
,0.001
0.016
M2, predicted from monoculture trait-based model
log(HpM2) ; log(Hm)
log(DevM2 þ 10) ; log(sowndiv)
0.16 6 0.08
2.29 6 0.03
0.22 6 0.10
0.04 6 0.01
0.06
0.08
5.28
7.10
0.025
0.010
M3, predicted from relaxed trait-based model
log(HpM3) ; log(Hm)
log(DevM3 þ 3) ; log(sowndiv)
0.37 6 0.05
0.98 6 0.07
0.33 6 0.06
0.08 6 0.03
0.32
0.07
33.5
6.33
,0.001
0.014
M4, predicted from new trait-based model
log(HpM4 ) ; log(Hm)
log(DevM4 þ 3) ; log(sowndiv)
0.23 6 0.05
0.95 6 0.06
0.59 6 0.06
0.09 6 0.03
0.58
0.12
96.0
10.7
,0.001
0.002
Model for predicting herbivory
Notes: Variable definitions: HpM1 is community herbivory predicted from monoculture herbivory; Hm is measured community
herbivory; DevM1 ¼ Hm HpM1; sowndiv ¼ sown species richness; HpM2 is community herbivory predicted from a trait-based
monoculture model; DevM2 ¼ Hm HpM2; HpM3 is community herbivory predicted from a trait-based model; DevM3 ¼ Hm HpM3;
HpM4 is community herbivory predicted from a new trait-based model; DevM4 ¼ Hm HpM4. For all F statistics, df ¼ 1, 68.
possible with community-weighted traits, i.e., to minimize the ratio of nonadditive to additive effects,
nonadditive effects still accounted for ;10% of the
observed herbivory in polycultures (Appendix E: Fig.
E1, Table E1). For all four models, the percentage of
observed herbivory attributable to nonadditive effects
did not change with diversity, but this contribution
became increasingly positive as diversity in the communities increased.
DISCUSSION
Additive scaling assumes that the amount of herbivory experienced by an average individual of a given
plant species will be the same, irrespective of what other
species occur with it in the community. If so, then the
total herbivory of the plant community is an abundanceweighted sum of the monoculture levels. Our results
contradict this hypothesis, with nonadditive effects
accounting for up to 25% of the measured herbivory
in communities in the tests of additive scaling (i.e., M1
and M2). Although the percentage of additive effects
was always larger than that of nonadditive effects, the
percentage of measured herbivory accounted for by
nonadditive effects was surprisingly high. Even for M4,
designed to explain as much variation as possible by
additive effects of plant traits, nonadditive effects still
accounted for 10% of the variation in herbivory, and the
deviance from the additive models increased with species
richness, indicating an unexplained increase in observed
herbivory in more diverse plant communities. Furthermore, the results of models M3 and M4 suggest that the
relative importance of plant traits changes depending on
which combinations of species occur in a community.
Therefore, all four models confirm the occurrence of
important nonadditive effects, which is in line with other
studies in experimental and seminatural grassland
systems showing that community properties such as
diversity, evenness, or species richness were correlated
with levels of invertebrate herbivory (Scherber et al.
2006a, b, 2010b, Unsicker et al. 2006, Stein et al. 2010,
Allan and Crawley 2011). It seems that the choice to feed
TABLE 2. Multiple regression model of the loge-transformed leaf standing herbivore damage
measured in 70 plant communities at the field site of the Jena Experiment, Germany.
Trait
Intercept
Leaf nitrogen concentration
Leaf lignin concentration
Coleopteran herbivores
Hemipteran herbivores
Leaf lifespan
Stem growth form
Root architecture
Regression coefficient 6 SE
4.202
0.381
0.042
0.356
0.037
0.222
0.005
0.227
6
6
6
6
6
6
6
6
1.212
0.327
0.034
0.185
0.130
0.124
0.004
0.153
P
Magnitude
,0.001
0.248
0.216
0.059
0.776
0.078
0.194
0.143
0.35
0.75
0.85
0.07
0.44
0.47
0.45
Notes: Damage was determined by seven community-weighted traits that are important to predict
herbivory in monocultures (M3). The regression parameters were freely estimated. The regression
coefficient shows the intercept or partial slope (for the complete model, R 2 ¼ 0.254, P , 0.001).
‘‘Magnitude’’ is the magnitude of the maximal effect of a trait on herbivory: the absolute value of the
partial slope multiplied by the range (maximum–minimum) of the trait. Traits in the table are in
decreasing order of magnitude score from the monoculture results (Loranger et al. 2012).
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JESSY LORANGER ET AL.
Ecology, Vol. 94, No. 7
TABLE 3. Plant functional traits selected by a random forests (RF) approach to be important to
predict loge-transformed leaf standing herbivore damage of 70 different communities at the field
site of the Jena Experiment, Germany.
Trait
RF value
P
Intercept
Leaf lignin concentrationa
Leaf carbon concentration
Coleopteran herbivores
Stem growth forml
Phosphorus leaf concentrationd
Silica
Ruderalc
Aromatic compoundsj
Aphid herbivores8
Period of seed sheddingg
Orthopteran herbivorese
Relative growth ratek
Heighth
Nitrogen leaf concentrationb
Mollusc herbivoresi
Lepidopteran herbivoresm
Leaf primary fiber concentration
SLAf
Height summer
Flowering phasen
71.8
62.0
52.2
41.8
40.4
38.8
36.4
35.9
31.2
28.9
28.8
28.5
26.7
26.5
26.3
25.5
24.4
24.1
23.4
21.0
,0.001
0.984
,0.001
,0.001
0.357
0.854
,0.001
0.779
0.325
0.080
0.414
0.797
0.434
0.555
0.837
0.342
0.367
,0.001
0.784
,0.001
0.075
Regression coefficient 6 SE
16.918 6
0.018 6
0.570 6
1.00 6
1.466 6
0.022 6
1.824
0.003
0.129
0.240
0.416
0.005
Magnitude
1.58
1.37
1.00
1.41
1.14
Notes: Traits that remained in the model after a stepwise backward selection (M4) are in
boldface. RF value is the importance scores given by the RF for each trait. Superscript letters (a–o)
indicate the order in which the traits have been removed in the backward stepwise selection. P
values are for the intercept and partial slopes of the traits in the multiple regression of logetransformed herbivore damage in communities against each trait at the time it was removed by
backward stepwise selection (P . 0.05) or in the final resulting model (P , 0.05). The regression
coefficient shows intercept and partial slopes of the remaining traits, with standard error (in the
final model, R 2 ¼ 0.553, P , 0.001). Magnitude is the magnitude of the maximal effect of an
explanatory variable on the response variable calculated as the absolute value of the partial slope
multiplied by the range (maximum – minimum) of the explanatory variable. Where cells are blank,
data were not applicable.
on a particular species depends both on traits of that
species and on traits of surrounding species.
Why would trait–herbivore relationships that were
strong and robust predictors when herbivores were
confronted with monocultures not apply as well when
herbivores were confronted with multispecies vegetation? Why would predictions from additive models
systematically underestimate actual levels of herbivory,
and even more so as plant diversity increases? There are
at least five possible explanations, the first three being
related more directly to the herbivores’ reaction to
increasing biodiversity and the last two being more
related to effects of plant–plant interactions.
First, it is likely that insect herbivores do not choose
the vegetation to feed on at the plot scale (5 3 7 m2), but
at a much finer scale, choosing individual plants from a
heterogeneous mixture of food sources differing in
quality. Consequently, the insect would not perceive a
mixture of a high- and low-quality species in a plot as a
‘‘vegetation’’ of intermediate quality, as predicted by
additive scaling hypothesis. In a monoculture of
intermediate quality, levels of herbivory would also
be intermediate. However, in the mixture of
low- and high-quality species, herbivores could concentrate feeding on the high-quality species because of
feeding preferences. Imagine that in monocultures
containing 10 g of leaves, the herbivore eats 1 of 10 g
of species A (the more palatable species) and 0.5 g of 10
g of species B. The predicted herbivory in a 1:1 mixture
of the two species would be 0.5 3 10% þ 0.5 3 5% ¼
7.5%. In the actual mixture, the herbivore concentrates
its feeding entirely on the preferred species A, eating 1 g
of the 5 g while ignoring the 5 g of leaves of species B.
The observed herbivory would be 1 g of the total 10 g:
thus 10%. If so, then levels of community herbivory
would be close to the levels of the monocultures of the
most attractive species. As a consequence, additive
scaling models would underestimate herbivory in mixed
communities, as we observed.
Second, generalist herbivores can change their preference for food plants based on the composition of
available plant species. For example, diverse communities also provide a diverse nutritional regime, thus
potentially diluting deterrent chemicals or improving the
nutrient balance of generalists (Bernays and Bright
1993). These effects can contribute to the decrease in
importance of nutritional characteristics (leaf nitrogen
and lignin concentration) in predicting herbivory in
communities compared to herbivory in monocultures.
Third, the community of generalist and specialist
herbivores can change with plant composition, thus
changing herbivore loads and levels of herbivory and
their relations to plant traits. In fact, there is a positive
correlation between higher diversity and abundance of
July 2013
COMMUNITY PROPERTIES AFFECT HERBIVORY
herbivores and plant diversity in the Jena Experiment
(Scherber et al. 2010a). A likely mechanism is that
increasing plant diversity leads via increased plant
architectural complexity to more protection for more
types of herbivores by providing more hiding and resting
places (Southwood et al. 1979, Lawton 1983). The
higher importance of architectural than nutritionalquality traits in M3 is consistent with this mechanism.
In addition, higher plant diversity increased plant
productivity in the Jena Experiment (Marquard et al.
2009) and elsewhere (Naeem et al. 1994, Tilman et al.
1996). Such a higher primary productivity has been
shown to correlate with higher herbivore pressure
(Haddad et al. 2001).
Fourth, the effect of trait values of a given plant
species can be modulated by traits of other surrounding
plants. For example, the level of herbivory in a
community could be partly driven by magnet species
(or their absence) that initially attract herbivores in a
similar way as documented for plant–pollinator interactions (Johnson et al. 2003). For instance, highly
nutritious species could attract herbivores to the site.
Once the preferred plant species has been consumed, it
might be advantageous for the herbivore to feed on
surrounding less nutritious species rather than leaving
the site and using time to search for a new site or
accepting risks associated with moving to a new site. This
spillover effect (White and Whitham 2000) has been
found in the Jena Experiment, where the presence of
legumes increased the rates of invertebrate herbivory on
the other species (Scherber et al. 2006b). An associational
resistance between plant species can also occur, leading
to a decrease of herbivore damage on attractive plant
species due to interference in detection by associated
non-host plant species (Tahvanainen and Root 1972,
Hambäck et al. 2000, Finch and Collier 2012).
Fifth, the trait values expressed by a species can
change depending on the other species that are growing
in close proximity. For example, where legume species
are present in the Jena Experiment, leaf nitrogen
concentration in grasses may increase (Gubsch et al.
2011). It has also been shown in a case study with
Plantago lanceolata in the Jena Experiment that the
allocation to chemical defense compounds may change
with increasing diversity (Mraja et al. 2011). Although
trait variation certainly is an important mechanism
causing the observed nonadditive effects, the values of
several categorical traits taken from the literature that
were important in monocultures do not change across
communities (e.g., root architecture or leaf life span).
Consequently, trait variation cannot be the only
mechanisms causing nonadditive effects.
All five of these mechanisms can also contribute to
the observed differences in the choice of traits that were
selected in the models. The most striking difference is
that leaf nitrogen concentration, which was the most
important predictor in monocultures, was not selected
in multispecies communities. This could be caused by
1507
nutritional quality being generally less important in
mixed vegetation compared to a single plant species,
due to selection of attractive plants and mixing of
different resources as previously explained. A similar
argument can explain why leaf life span was selected in
the monoculture model (where it quantifies the
seasonal availability of foliage) but was not selected
in mixtures where temporal complementarity between
species means that there is always foliage available. As
for root architecture, it had a positive effect on
herbivory in monoculture. However, its communityweighted value in mixtures was related to the relative
abundance of grasses, which can decrease herbivory
(H. Loranger et al., unpublished manuscript). These
contrary effects may have canceled each other in
communities. The effect of grasses on herbivory in
mixtures might also account for silica being selected as
a significant trait, as Poaceae is the main plant family
defended by silica.
CONCLUSION
The hypothesis of additive scaling of herbivory was
rejected and nonadditive effects were detected in each
model. This indicates that complex plant–insect interactions are of importance in determining the levels of
herbivory in multispecies communities. Which of the
proposed potential mechanisms causes predictions of
community herbivory to deviate from our models
remains to be investigated, concentrating on (1) the
variation of relevant traits, (2) changes in the herbivore
community along the diversity gradient, and (3) direct
investigations on feeding preferences and behavior of
important herbivore groups. To do this, some of the
unmeasured potential causes described here (or traits
associated with them) should be included in future
models: vegetation properties such as canopy height,
biomass, diversity, and/or the distinctiveness (uniqueness) of the target species. Such an approach can also
help one to understand how environmental conditions
(abiotic and biotic) interact with the traits governing the
level of herbivory experienced by a plant.
ACKNOWLEDGMENTS
We thank Anne Ebeling, the gardeners, and technical staff
who have worked on the Jena Experiment for maintaining the
site. The Jena Experiment was funded by the Deutsche
Forschungsgemeinschaft (FOR 1451). We thank Enrica de
Luca, who provided biomass data, and Annett Lipowsky and
Marlén Gubsch, who provided some plant trait data. This study
was funded by the Natural Sciences and Engineering Research
Council of Canada (NSERC), the Fonds Québécois de
Recherche sur la Nature et les Technologies (FQRNT), and
the AquaDiva@Jena project financed by the state of Thuringia.
The study has also been supported by the TRY initiative on
plant traits, and we thank all the contributors who have
provided trait data via the TRY database (www.trydb.org).
TRY is/has been supported by DIVERSITAS, IGBP, the
Global Land Project, the U.K. Natural Environment Research
Council (NERC) through its program QUEST (Quantifying
and Understanding the Earth System), the French Foundation
for Biodiversity Research (FRB), and GIS ‘‘Climat, Environnement et Société’’ France.
1508
JESSY LORANGER ET AL.
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SUPPLEMENTAL MATERIAL
Appendix A
Species pool of the Jena Experiment with inclusion status in the analyses (Ecological Archives E094-136-A1).
Appendix B
Detailed list of the 42 traits initially considered to predict leaf standing herbivore damage and graph of predicted against
measured values of herbivory in monocultures (Ecological Archives E094-136-A2).
Appendix C
Description of the Random Forests (RF) analysis and graph of the results, explaining how traits were selected (Ecological
Archives E094-136-A3).
Appendix D
Correlation matrix of the different community properties of the communities in the Jena Experiment (Ecological Archives
E094-136-A4).
Appendix E
Log-response ratios for nonadditive and additive effects based on the different models (Ecological Archives E094-136-S1).