Ecology, 93(12), 2012, pp. 2674–2682
Ó 2012 by the Ecological Society of America
Predicting invertebrate herbivory from plant traits: evidence from
51 grassland species in experimental monocultures
JESSY LORANGER,1,2,4 SEBASTIAN T. MEYER,1,5 BILL SHIPLEY,2 JENS KATTGE,3 HANNAH LORANGER,1,6
CHRISTIANE ROSCHER,3,7 AND WOLFGANG W. WEISSER1,5
1
2
Institute of Ecology, Friedrich Schiller University of Jena, Dornburger Straße 159, 07749 Jena, Germany
De´partement de Biologie and Centre des Études de la Forêt, Universite´ de Sherbrooke, 2500, Boulevard de l’Universite´, Sherbrooke,
Quebec J1K 2R1 Canada
3
Max Planck Institute for Biogeochemistry, Hans-Knöll Straße 10, 07745 Jena, Germany
Abstract. Invertebrate herbivores can impact plant performance and plant communities.
Conversely, plants can affect the ability of herbivores to find, choose, and consume them
through their functional traits. While single plant traits have been related to rates of herbivory,
most often involving single herbivore–plant pairs, much less is known about which suite of
plant traits is important for determining herbivory for a pool of plant species interacting with
a natural herbivore community. In this study we measured aboveground herbivore damage on
51 herbaceous species growing in monocultures of a grassland biodiversity experiment and
collected 42 different plant traits representing four trait groups: physiological, morphological,
phenological, and herbivore related. Using the method of random forests and multiple
regression, we identified seven traits that are important predictors of herbivore damage (leaf
nitrogen and lignin concentration, number of coleopteran and hemipteran herbivores
potentially feeding on the plants, leaf life span, stem growth form, and root architecture);
leaf nitrogen and lignin concentration were the two most important predictors. The final
model accounted for 63% of the variation in herbivore damage. Traits from all four trait
groups were selected, showing that a variety of plant characteristics can be statistically
important when assessing folivory, including root traits. Our results emphasize that it is
necessary to use a multivariate approach for identifying traits affecting complex ecological
processes such as herbivory.
Key words: herbivore-related traits; invertebrate herbivory; Jena Experiment, Thuringia, Germany;
modeling; monoculture; morphology; phenology; physiology; plant trait; prediction.
INTRODUCTION
Herbivory is a major selective pressure affecting plant
physiology and plant fitness (Karban and Strauss 1993,
Hulme 1996a, Bigger and Marvier 1998), plant community composition and succession (Brown 1985, Gibson et
al. 1987, Brown and Gange 1992, Hulme 1996b, del-Val
and Crawley 2004), and plant evolution (Holeski et al.
2010 and references therein). Since the capacity of plants
to resist and/or tolerate herbivory is mediated by their
Manuscript received 25 February 2012; revised 18 June 2012;
accepted 19 June 2012. Corresponding Editor: B. D. Inouye.
4 Present address: Département de Biologie and Centre des
Études de la Forêt, Université de Sherbrooke, 2500,
Boulevard de l’Université, Sherbrooke, Quebec J1K 2R1
Canada. E-mail: [email protected]
5 Present address: Department of Ecology and Ecosystemmanagement, Technische Universität München, Hans-Carlvon-Carlowitz-Platz 2, 85350 Freising-Weihenstephan, Germany.
6 Present address: Institute of Biology and Environmental
Sciences, University of Oldenburg, Functional Ecology of
Plants, 26111 Oldenburg, Germany.
7 Present address: UFZ, Helmholtz Centre for Environmental Research, Department of Community Ecology,
Theodor-Lieser-Straße 4, 06120 Halle, Germany.
functional characteristics (i.e., traits sensu Violle et al.
2007), plant species differing in their traits can show
large differences in rates of herbivory.
Plants may alter herbivory through traits that affect
an herbivore’s ability to find, choose, or consume a
plant. The traits determining the susceptibility of a
particular plant to a particular herbivore, and hence to
rates of herbivory, are likely to differ among different
plant–herbivore interactions (Tanentzap et al. 2011). On
the other hand, there is a long history of research aimed
at identifying plant traits that generally structure
patterns of insect herbivory observed in the field
(Pérez-Harguindeguy et al. 2003 and references therein).
However, most of this research has focused on only one
or few plant traits or, when several traits were included
in the analysis, the focus was on pairwise correlations
between herbivory and single traits. This is potentially
problematic because plant traits often display complicated patterns of multivariate correlation that potentially mask the functional links underlying observed
correlations. Studies using a multivariate approach to
quantitatively analyze variation in herbivory rates have
identified three different groups of trait affecting levels
of herbivory: physiological (Coley 1983, Johnson et al.
2674
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PREDICTING HERBIVORY FROM PLANT TRAITS
2009, Kurokawa et al. 2010), morphological (Coley
1983, Kurokawa et al. 2010), and phenological (Johnson
et al. 2009). For example, physiological traits, such as
concentrations of nitrogen and secondary compounds,
impact the nutritional quality of plants. Morphological
traits such as height and growth form influence the
accessibility of plants and the ease with which herbivores
can locate them. Phenological traits, such as the growth
period, determine the availability of plants in a seasonal
context. In addition to these classical traits, other traits
can be defined. For example, the number of species from
a particular herbivore group that can potentially feed on
a plant species, which is an indirect measure of herbivore
pressure on the plant, may include information not
contained in the three other groups of traits. Several
studies have identified single traits such as leaf
toughness, foliar C:N ratios, or nitrogen concentration
that correlate with levels of herbivory (Pérez-Harguindeguy et al. 2003, Boege 2005, Peeters et al. 2007, Karley
et al. 2008, Kurokawa et al. 2010). Multivariate studies
linking herbivory to plant traits have so far only used
subsets of the groups of traits in their analysis, but it is
likely that they all act simultaneously to affect herbivory
in a plant community (Johnson et al. 2009, de Bello et al.
2010). Studies that combine several groups of plant
traits and assess their predictive power of explaining
herbivory for a large number of species are missing.
Thus, it remains unclear which combination of plant
traits is of importance in modulating plant susceptibility
to herbivory, especially when comparing several plant
species (Agrawal 2011).
The goals of the current study were (1) to determine
the correlation structure between a large number of
traits for a large pool of co-occurring herbaceous species
and (2) to identify the suite of traits that best predicts
invertebrate herbivory across monocultures of different
grassland plant species. Working with monocultures of a
range of species excludes the effects of complex
interactions that can arise when several plant species
co-occur (the subject of another study), but keeps the
generality of a multispecies approach by identifying how
variation in trait values across plant species affects
susceptibility to herbivores.
METHODS
Study site
This study was conducted as part of the Jena
Experiment, a grassland biodiversity experiment
(Roscher et al. 2004). The field site has a Eutric Fluvisol
(FAO/UNESCO 1997) and is located on the floodplain
of the Saale River (50855 0 N, 11835 0 E, altitude 130 m) at
the northern edge of Jena (Thuringia, Germany).
Established in 2002, the experiment has a 60-species
pool (Appendix A) consisting of herbaceous plants
commonly occurring in seminatural, mesophilic grasslands of the region (Molinio-Arrhenatheretea meadows,
Arrhenatherion community; Ellenberg 1996). In addition
to 80 main plots along a gradient in plant species
2675
richness, there is one monoculture plot of 1 m2 for each
plant species; only the monoculture plots were used in
this study. All plots are mown twice a year and weeded
regularly (twice or thrice a year), keeping only the target
species of each plot.
Herbivory and biomass measurements
In May and August 2010, plant material was sampled
from the species’ monocultures for herbivory measurements. Within the 1-m2 area of each monoculture plot, a
single whole individual, or all the rosette-/stolon-born
leaves on one spot, was cut 3 cm aboveground every 10
cm along a side-to-side transect. Additional transects
were added if fewer than 30 leaves were sampled in the
first transect. The plant material was stored in a cooler
in plastic bags with humid tissue and directly brought to
the laboratory after sampling, where 30 leaves per
sample were randomly selected (except for Ranunculus
repens in May, when 22 leaves were sampled). Invertebrate standing leaf herbivore damage was then measured
on each selected leaf as the proportion of consumed leaf
area (damaged leaf area/original undamaged leaf area).
Damage area was estimated by visual comparison to a
template card with a range of shapes of known area.
Four types of herbivory (i.e., chewing, rasping, sucking,
and mining) were separately estimated for each leaf. The
leaf was then measured using a LI-3000C area meter
(LI-COR Biosciences, Lincoln, Nebraska, USA). To
calculate the original undamaged leaf areas, the
estimated chewed area was added to the measured
remaining area of each leaf.
Due to lack of plant material, herbivory measurements were not possible for eight species. Also,
herbivore damage of Bromus hordeaceus was exceptionally high in August 2010 (at least 10 times higher than in
May 2010 and during a repeated measurement in May
and August 2011). As we could not explain this
extremely high value, this species was excluded from
the analyses. Thus, the final data set includes 51 of the
complete 60 plant species pool of the Jena Experiment
(see Appendix A for species names and inclusion status).
The four separately estimated herbivore damage types
were summed for each leaf and averaged for each species
over the two seasons to give 51 values of species-specific
standing herbivore damage.
Prior to sampling for herbivory measurements, the
biomass inside a 20 3 50 cm frame was cut 3 cm
aboveground in each monoculture, in both seasons.
Weeds were removed and the target species biomass was
oven dried (708C for 48 h) and weighted. This estimate
of biomass was used as a covariate in the analyses.
Plant traits
Trait data extracted from published literature and
databases.—Information on 84 traits was extracted from
the literature (a total of 148 sources; see Appendix B),
including published (Roscher et al. 2004, 2011a, b,
Gubsch et al. 2011) and unpublished trait data collected
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JESSY LORANGER ET AL.
in the Jena Experiment and international databases on
plant functional traits, i.e., TRY (Kattge et al. 2011),
LEDA (Kleyer et al. 2008), and Biolflor (Klotz et al.
2002). The data extracted from the TRY database
include several different sources listed in Appendix B
with their associated traits.
Trait measurements in the Jena Experiment.—Data on
specific leaf area (SLA), carbon and nitrogen tissue
concentration, and plant height were measured in the
monocultures of the Jena Experiment as follows: bulk
samples of fully expanded sun leaves (5–20 leaves
dependent on leaf size and number) were collected at
estimated peak biomass shortly before mowing in late
May and August. Samples from two replicate monoculture plots per species were taken from 2003 to 2005, and
one monoculture plot per species was sampled in May
2007. After measuring leaf area (including rachis and
petioles of compound leaves) with a leaf area meter (LI3100 area meter; LI-COR, Lincoln, USA) samples were
dried to a constant mass at 708C (48 h). SLA was
calculated as the ratio of total projected leaf area divided
by total leaf dry mass per sample (mm2 of leaf/mg of
leaf ). Dry leaf material was ground to a fine powder
with a ball mill and ;10–20 mg were analyzed for leaf
carbon and nitrogen concentration with an elemental
analyzer (Vario EL Element Analyzer, Hanau, Germany). Data of 13 grass species and 12 legume species
were collected in 2006 according to the procedures
described in Gubsch et al. (2011) and Roscher et al.
(2011a). The same sampling protocol was used for
measurements on 28 non-legume herb species in 2006
and the remaining species in 2008 or 2009. In spring and
summer 2003 and 2004, mean vegetative and total
(flowering) plant height were also measured in monocultures. Leaf lignin, cellulose, hemicellulose, and watersoluble matter concentration were measured following a
sequential extraction analysis (Vansoest et al. 1991) of
neutral detergent fiber (NDF), acid detergent fiber
(ADF), and acid detergent lignin (ADL). Leaf samples
were collected between May and October 2010. As only
1 g of dry material was necessary, the number of leaves
per species varied (at least 10 leaves per species in total,
from at least five different individuals; for most species
many more leaves). Subsamples were mixed and ground
to 1 mm particle size to obtain an average value of fiber
concentration per species over the growing season. For
the NDF and ADF analyses, an ANKOM 200 fiber
analyzer (ANKOM200, 65 rpm agitation; ANKOM
Technology, Macedon, New York, USA) was used,
and the ADL analysis was done in beakers with 72%
sulfuric acid. The samples were dried and weighed
between each analysis to calculate the different fiber
fractions. A complete list of method procedures is given
by ANKOM technology (ANKOM Technology
2011a, b, c).
The final data set, combining the information from
the literature and measurements, contained 105 traits
representing four main trait groups: physiological,
Ecology, Vol. 93, No. 12
morphological, phenological, and herbivore related.
See Appendix B for a detailed description of all traits
and associated references.
Statistical analyses
Data preparation.—In spite of best efforts to obtain
trait values for each plant species, there were between one
and 15 missing values for six of the traits (Appendix C).
Missing values of the traits stem growth form, seed
shedding height, beginning of seed shedding, and period
of seed shedding were imputed from reference traits or
reference species. Missing values of the traits leaf
phosphorus concentration and relative growth rate were
imputed via a multiple imputations method using the
function and package ‘‘mi’’ in R (Su et al. [2011]; this and
all subsequent statistical analyses were done using the R
statistical software version 2.10.0 [R Development Core
Team 2009]). Appendix C gives a detailed explanation of
the traits or species used as reference for every imputed
value. Finally, several traits and the standing herbivore
damage were loge-transformed to better approximate
normality (indicated in Appendix B).
When a given trait was available from different
sources or for different years, the values were averaged
in order to be as independent of individual measurements as possible. Highly correlated traits (r . 0.75)
were either averaged (when having the same units, e.g.,
different measures of plant height) to give a more
general new trait, or one (or more) of the correlated
traits were excluded (see Appendix B and the supplement for more details). The number of aphid herbivores
was separated from the total number of hemipteran
herbivores, because aphid damage is less likely to be
detected than that of larger hemipterans. Some traits
were combined even without being correlated, e.g., the
numbers of different secondary metabolites in several
similar groups were summed to form larger groups (see
Appendix B for details). Monophagous, oligophagous,
and polyphagous herbivore classes, even when not
correlated, were also combined to give the total number
of potential herbivores for each group of herbivores.
This is because (1) information on the specificity of
herbivores was often unavailable and (2) there was not
enough information when all classes were analyzed
separately. The resulting final data set for analysis
included 42 plant traits (Appendix B).
Data analyses.—The 42 traits were checked for
phylogenetic signals using the K statistic of Blomberg
et al. (2003) calculated by the ‘‘phylosignal’’ function of
the ‘‘picante’’ package in R (Kembel et al. 2010) and
based on a phylogeny of the 60 species pool of the Jena
Experiment (T. Jenkins, personal communication). As
none of the traits were clustered, a correction of trait
values for phylogenetic signals was not considered to be
necessary (Carvalho et al. 2006).
The random forest method was used employing the
‘‘RF’’ function of the ‘‘randomForest’’ package in R
(Liaw and Wiener 2002) to determine traits with which
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PREDICTING HERBIVORY FROM PLANT TRAITS
the 51 loge-transformed values of herbivore damage in
monocultures were correlated. This method determines
the most important factors that predict a response
variable among a large number of different factors by
calculating importance scores for each factor (Breiman
2001, Prasad et al. 2006). The method will work even for
highly correlated factors, assigning similar importance
scores to each. The identified traits were then used as
predictor variables in a multiple regression on herbivore
damage with plant biomass as a covariable. The
resulting model was simplified by a backward stepwise
procedure until all remaining traits in the regression
were significant (P , 0.05), resulting in an equation of
linear combinations of traits predicting degrees of
herbivore damage:
Predicted damage ¼ b0 þ b1 t1 þ b2 t2 þ . . . þ bi ti
ð1Þ
where b0 is the intercept of the model, and bi and ti are
the partial slope and the species-specific trait values of
the predictive trait ith, respectively. As trait values were
not standardized, the relative importance of the partial
slopes, or magnitude of effect, was calculated by
multiplying each partial slope by the range (maximum–minimum) of its associated trait. The predictive
potential of the resulting equation was cross-validated as
follows: a thousand subsets of 38 species (75% of the
total) were randomly chosen to calculate the intercept
and the partial slopes of the final multiple regression.
Using those parameters in Eq. 1, the predicted values of
herbivory were calculated for the remaining 13 species
(25%) of each subset. The observed damage values for
those 13 species were then regressed against their
respective predicted values obtained from the crossvalidation. The mean value of the residual standard
errors of those 1000 regressions (RSE*) was compared
to the residual standard error of the regression using the
full data set (RSE):
Errorð%Þ ¼
ðRSE RSEÞ
3 100:
RSE
ð2Þ
This equation calculates the percentage increase in the
error of prediction (Error) between the full model error
and the mean error from the 1000 cross-validations
(Efron and Tibshirani 1993).
RESULTS
Correlation and exclusion of traits
The original data set of 105 traits was reduced to 42
traits. Grazing tolerance was highly correlated with
trampling tolerance (r ¼ 0.95) and moderately correlated
with mowing tolerance (r ¼ 0.61). As grazing tolerance is
likely to be the most relevant variable predicting herbivore
damage, the two others were excluded. Leaf carbon and
nitrogen concentration were correlated with shoot carbon
and nitrogen concentration, respectively (r¼0.75, r¼0.84).
As the leaf herbivore damage was the variable of interest in
this study, shoot carbon and nitrogen concentrations were
2677
discarded. Leaf hemicellulose and cellulose concentrations
were also correlated (r¼0.79), so they were summed to give
a new variable: leaf primary fiber concentration. This
variable was, in turn, highly correlated (r¼0.88) with leaf
water-soluble matter concentration, which was excluded as
it can include both deterrent and attractive compounds for
herbivores (see Appendix B). The 11 different measures of
plant height were combined to give three plant height
traits: height spring, height summer, and height (Appendix
B). All original plant height measures were correlated (r .
0.75) to at least one of these three variables. Height spring
(summer) is the average of the different heights measured
in spring (summer) in the Jena Experiment (see Appendix
B). Height represents a general averaged value of several
different height measurements from the TRY database and
was highly correlated (r . 0.75) to all other excluded height
traits (see Appendix B). Interestingly, leaf lignin concentration was moderately positively and negatively correlated to leaf nitrogen and primary fiber concentrations,
respectively (r ¼ 0.59, r ¼ 0.62). It was also moderately
positively correlated to the number of coleopteran
herbivores potentially feeding on the plants (r ¼ 0.66)
and negatively correlated to stem growth form (r ¼0.59).
Predicting herbivore damage
The random forest method selected 13 traits as being
important to predict loge-transformed values of herbivore damage (Table 1), whereas the decrease in
importance score between each of the other ‘‘less’’
important traits was very small (see Appendix D for
details). In a multiple regression, these 13 traits were
further reduced to seven traits significantly predicting
herbivore damage using a backward stepwise selection.
The resulting highly significant model explained 63% of
the variation in loge-transformed damage values (Table
1). Fig. 1 shows the correlation between observed and
predicted values of damage from the multiple regression.
Herbivore damage increased with increasing leaf
nitrogen concentration, leaf life span (1, deciduous; 2,
partly deciduous; 3, evergreen), the number of coleopteran herbivores potentially feeding on the plants, and
with root architecture score (1, long-living primary root
system; 2, secondary fibrous roots in addition to the
primary root system; 3, short-living primary root
system, extensive secondary root system). Root architecture is regarded here as an ordered variable that
increases with increasing importance and size of the
secondary root system and with decreasing life span and
size of the primary root system. Herbivore damage
decreased with increasing values of stem growth form
( percentage of erection of the stem), leaf lignin
concentration, and the number of hemipteran herbivores
potentially feeding on the plants. Based on their
magnitude scores (Table 1), the physiological traits (leaf
nitrogen and lignin concentrations) were the most
important predictors for herbivore damage, followed
by the traits related to the herbivore community
(coleopteran and hemipteran herbivores), the life-history
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Ecology, Vol. 93, No. 12
TABLE 1. Plant functional traits selected by a random forest (RF) approach to be of importance in predicting leaf standing
herbivore damage in 51 different species monocultures at the field site of the Jena Experiment (Thuringia, Germany).
Order
Variable
Importance
Removal
RF value
P
Intercept
Biomass Leaf nitrogen concentration
Coleopteran herbivores
Leaf lignin concentration
Stem growth form
Root architecture
Orthopteran herbivores
Leaf life span
Leaf distribution
Aphid herbivores
Leaf phosphorus concentration
N-containing compounds
Leaf dry matter content
Hemipteran herbivores
1
2
3
4
5
6
7
8
9
10
11
12
13
e
g
c
d
b
f
a
110.6
50.9
48.2
43.6
34.9
25.1
19.5
16.6
12.6
11.7
11.6
11.6
9.4
,0.001
0.246
,0.001
,0.001
0.003
0.039
0.032
0.134
,0.001
0.765
0.393
0.819
0.196
0.865
0.003
Regression coefficient
10.649 6
1.749 6
0.547 6
0.073 6
0.007 6
0.268 6
0.370 6
0.406 6
1.520
0.392
0.118
0.023
0.003
0.121
0.092
0.127
Magnitude
1.75
1.48
1.56
0.70
0.54
0.74
0.79
Notes: Order of importance (1–13) is the ordered importance of traits as given by the RF. Order of removal (shown with letters
a–g) is the order in which the traits were removed in the backward stepwise selection. Ellipses in this column show traits that
remained in the final model after the stepwise backward selection. RF value is the importance score given by the RF for each trait.
P values are for the intercept and the partial slopes of the traits in the multiple regression of loge-transformed specific herbivore
damage against each trait at the time it was removed by backward stepwise selection (P . 0.05) or in the final model (P , 0.05).
Regression coefficient shows the intercept and partial slopes of the remaining traits in the final model, with standard error (in the
final model, R 2 ¼ 0.631 and 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. Ellipses indicate that data were not applicable in those columns.
The biomass measured in the monocultures was used as a covariable in the model. It was never significant together with other
variables and was consequently removed from the final model.
trait (leaf life span), and finally, the morphological traits
(stem growth form and root architecture).
The cross-validation results showed that the model is
robust and consistent (RSE ¼ 0.498, RSE* 6 SD ¼ 0.515
6 0.112). The mean residual standard errors (RSE*,
over 1000 regressions) were only 3.43% higher when
fitting was restricted to 25% of the species not used to
calculate the partial slopes, compared to the residual
standard errors when fitting all species in the original
regression (RSE). Moreover, the mean correlation
coefficient between observed and predicted damage
values for those 1000 regressions was only 10% lower
than the correlation coefficient for the whole model.
Thus, the presence of particular species has little impact
on the goodness of fit of the model.
selected traits are consistently good predictors of herbivore damage among different subsets of the 51 species.
Our results are similar to those from two other studies
that quantitatively linked invertebrate herbivory to
plant traits in a large interspecific context using multiple
DISCUSSION
Standing invertebrate herbivore leaf damage in monocultures of 51 herbaceous species was successfully
predicted from plant traits, explaining 63% of the
variation in herbivore damage. The seven traits selected
as predictors come from all four trait groups included in
the study: physiological, morphological, phenological,
and herbivore related. To our knowledge, this study is the
first where several trait groups have been combined in a
predictive model for general invertebrate herbivory on
herbaceous species. The resulting model accounted for a
surprisingly high proportion of the total variance in
herbivore damage and was strongly supported by crossvalidation results that showed that the influence of any
single plant species on the model was low and that the
FIG. 1. Correlation (r ¼ 0.83, P , 0.001) between leaf
herbivore damage in monocultures (HD) measured as the
percentage of the damaged leaf area at the field site of the Jena
Experiment (Thuringia, Germany) and the damage predicted
by a model based on seven traits. The axes are log-scaled. For
the model see Table 1.
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PREDICTING HERBIVORY FROM PLANT TRAITS
regression despite the fact that both Coley (1983) and
Kurokawa et al. (2010) addressed tropical tree species,
did not use monocultures, and focused on only two trait
groups (physiological and morphological). Although we
used more plant and herbivore species, our model
explained more variation in herbivore damage than
Kurokawa et al.’s (2010), which was based on four traits
(specific leaf area, leaf C:P, N:P, and total phenolics)
and explained 48% of the variation in leaf consumption
by one herbivore on 41 woody species. The model of
Coley (Coley 1983) explained 70% of the variation in
natural invertebrate herbivory on mature leaves of 46
tree species. Note, however, that 16 traits were included
in Coley’s multiple regression, even though most of them
were not significant, which yields a higher R 2 value than
if only significant traits had been kept in the model.
Thus, with less than a half of the traits used in Coley’s
study, we could account for a similar fraction of the
variation in herbivore damage. Moreover, while their
final model included many more traits, we both
identified leaf nitrogen and lignin concentrations as
being the two most important predictors of herbivore
damage, although leaf lignin concentration was an
important and significant predictor only in a multivariate context including at least three other variables.
Physiological traits
As hypothesized by Pérez-Harguindeguy et al. (2003),
and in line with Coley (1983)’s findings, leaf nitrogen and
lignin concentrations had the largest effect on the
herbivore damage since leaf consumption should be
directly linked to leaf nutritional quality. Invertebrate
herbivores have been shown to feed preferentially on
plant tissue with higher nitrogen concentrations since
they are nitrogen limited because of the much lower C:N
ratio in animal compared to plant tissue (Elser et al.
2000). Correlations between herbivory and leaf nitrogen
concentration have also been found in many other
invertebrate herbivory-related studies (Karley et al.
2008, Cronin et al. 2010, Kurokawa et al. 2010). While
the herbivore damage increased with higher leaf nitrogen
concentration, it decreased with leaf lignin concentration,
which is known to be a compound decreasing herbivore
performance (Wainhouse et al. 1990). While correlations
between herbivory and lignin concentration have been
documented before (Coley 1983, Wainhouse et al. 1990,
Poorter et al. 2004), results have not been consistent
(Coley 1983, Kurokawa and Nakashizuka 2008, Kurokawa et al. 2010). Coley (1983) suggested that leaf
toughness, which is related to leaf fiber and lignin
concentration, could be a better predictor of herbivory,
which is in line with findings of Pérez-Harguindeguy et al.
(2003). While precise information on leaf toughness was
unavailable for the species in this study, leaf lignin
concentration had a consistent impact on the observed
damage. This is of interest as most previous studies
documenting the importance of lignin have worked with
woody plant species from tropical rainforests (Coley
2679
1983, Poorter et al. 2004, Kurokawa and Nakashizuka
2008, Kurokawa et al. 2010). We found that leaf lignin
concentration may have an underestimated role for the
interactions between invertebrate herbivores and herbaceous vegetation in temperate grasslands.
Morphological traits
The observed degree of herbivore damage was
negatively correlated with values of stem growth form
(percentage of erection of the stem) suggesting that more
prostrate species are more fed upon. Plant height could
represent a physical barrier to some invertebrate herbivores (Pérez-Harguindeguy et al. 2003). Because only
invertebrate herbivore damage was measured, including
damage from mollusks and other flightless herbivores,
prostrate or rosette-shaped plant species are likely to be
generally more easily grazed on than erect species.
We know much less about plant–herbivore interactions below ground than above ground, and even less
about feedbacks between the two compartments. While
the current study demonstrates a significant positive
correlation between observed aboveground herbivore
damage and the plant’s root architecture, the underlying
link is somewhat difficult to interpret. On the one hand,
different root systems have different resource acquisition
potentials (Fitter et al. 1991, Doussan et al. 2003), which
impacts aboveground leaf quality with obvious implications for herbivores. On the other hand, belowground
herbivore preferences might depend on root architecture, and their feeding can induce physiological effects in
the plants which can enhance (Newingham et al. 2007)
or reduce (Kaplan et al. 2011 and references therein)
aboveground herbivory. In addition, variation in root
architecture could correlate with some other aboveground trait that is important for invertebrate herbivores, but that was not included in this study.
Regardless of the mechanisms, the fact that root
architecture was selected in our model highlights the
importance of considering characteristics of the whole
plant even when assessing only folivory.
Phenological traits
In the model, the degree of herbivore damage was
positively related to leaf life span (1, deciduous, 2, semideciduous, 3, evergreen). The positive partial regression
coefficient associated with this variable reflects the effect
of these different types of leaf life spans after controlling
for the other variables in the model, including leaf
nitrogen and lignin concentrations. Thus, for leaves with
the same concentrations of nitrogen and lignin, those
whose leaves typically persist longer on the plant were
more susceptible to herbivore damage. This is opposite
to published results from trees (Coley et al. 1985, Coley
1988). However, in contrast to trees in these studies, for
the herbaceous vegetation in the present study, leaf life
span is not a measure of the actual life spans for
individual leaves; rather it is a measure of potential leaf
availability for herbivores. Since the field site is mown
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JESSY LORANGER ET AL.
twice a year and all leaves had been produced in the
periods between harvests, the effect of leaf life span does
not simply reflect an accumulation of herbivory over
different periods. Rather, herbivores (especially specialists) might prefer host plants that, when controlling for
other important variables such as nitrogen and lignin
concentrations, provide food (leaves) for the longest
period by being available earlier in spring or providing
opportunities to overwinter.
Herbivore-related traits
The number of coleopteran herbivores known to feed
on a given plant species was selected as an important
factor positively correlated to values of herbivore
damage. In line with that importance, coleopterans were
the most numerous herbivore group found on the field
site in 2010, followed by hemipterans (A. Ebeling,
personal communication). This agrees with herbivorous
coleopterans playing a major role in grasslands as
important invertebrate herbivores.
A surprising result, at first glance, is the negative
correlation between herbivore damage and the species
richness of potential hemipteran herbivores. Generally,
since damage by sap-sucking insects is difficult to
quantify on the basis of damaged or removed leaf area
(as was done in this study), this causes an underestimate
of their direct impacts. If there are indirect effects
between sap-sucking herbivores and herbivores that
cause more visible damage (e.g., by competition), then
these interactions could explain the lower damage with
higher potential (and likely realized) hemipteran infestation. Such competitive effects have been shown for
two sap-sucking herbivore species (Inbar et al. 1995),
and similar competitive effects might extend to other
herbivore guilds as Kaplan and Denno (2007) found in
their review that the strength of competition between
invertebrate herbivores does not change significantly
between intra- and inter-guild interactions. We found
that plant species with high levels of sap-sucking damage
had low levels of other types of damage, which might
indicate competitive interactions, although contrasting
correlations with other plant traits cannot be excluded.
Secondary metabolites
Surprisingly, only leaf lignin concentration was
selected in the model as a chemical herbivore deterrent
of importance, and there is no correlation with the
secondary metabolites we investigated. Many previous
studies have emphasized the importance of at least one
group of secondary metabolites in modulating herbivory
(Carroll and Hoffman 1980, Bernays and Chapman
1994, Schoonhoven et al. 2005, Johnson et al. 2009). In
contrast to the current study, most of these reports have
focused on specific interactions between single plant and
herbivore species or specific compounds, whereas
interactions involving many herbivore and plant species
have rarely been addressed. The minor importance of
secondary compounds in our model is confirmed by a
Ecology, Vol. 93, No. 12
recent meta-analysis aiming to determine what traits
generally influence plant susceptibility to herbivores.
Carmona et al. (2011) and Schuldt et al. (2012) found no
secondary metabolites that were correlated to susceptibility; instead, life-history, morphological, and geographical traits were important. A possible reason is
that particular compounds within a group of secondary
compounds (e.g., phenolics, terpenoids) can have a
range of functions other than anti-herbivory defenses
(Bernays and Chapman 1994). Secondary compounds
might even have at the same time both stimulating and
inhibiting effects depending on the herbivore species
(Carroll and Hoffman 1980). Consequently, the effect of
a particular secondary compound is often specific to a
particular herbivore species or group of species, making
such metabolites poor predictors of herbivory by the
entire community. Also, because of the ‘‘specific’’
function of several secondary metabolites, using concentrations of particular compounds as predictors rather
than the number of different compounds might increase
their predictive power for herbivory patterns.
Patterns of correlation
It is interesting to note the positive correlations
among leaf lignin concentration, leaf nitrogen concentration, and coleopteran herbivores, the three most
important predictors of our final model. The negative
effect of leaf lignin concentration on herbivore damage
disappears when both other variables are excluded, and
is not significant when only one of them is included in
the model. Thus, leaf lignin concentration seems to
negatively affect herbivores for leaves with the same
nitrogen concentration, and it seems to have a particularly important effect on coleopteran herbivores (which
are mostly chewers), but a more detailed analysis of this
interaction is needed. Consequently, pre-selecting a few
traits believed to be important runs the risk of drawing
false conclusions due to unknown interactive effects
between traits, and working with as many traits as
possible increases the reliability of the identified
relations between plant traits and herbivore damage.
CONCLUSION
Beginning with 105 traits, a final set of seven traits,
belonging to all trait groups addressed in this study,
predicted leaf standing herbivore damage in monocultures. This confirmed our expectations that (1) herbivore
damage can be predicted from a relatively small number
of plant traits and (2) one trait group is not sufficient to
capture the major sources of variation in degree of
herbivore damage. Multiple aspects of plants are
important in controlling herbivory. It is important to
remember that our results are based on monocultures. In
natural plant communities, in which different plant
species grow together in close proximity, the presence of
species with less desirable traits might either reduce or
enhance herbivory on species possessing more desirable
traits, beyond the levels expected in plant monocultures.
December 2012
PREDICTING HERBIVORY FROM PLANT TRAITS
If interactions in multispecies communities are not
important, then it would be possible to extrapolate our
results to multispecies communities simply by weighting
the expected herbivore damage of each species by its
relative abundance in the community. Testing whether
the relations expressed in monocultures in this study
remain consistent in more complex plant communities is
an important next step toward understanding how
plants and invertebrate herbivores impact each other.
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). The study was also
supported by the TRY initiative on plant traits, and we thank
all the contributors who have provided trait data via the TRY
database. TRY is supported by DIVERSITAS, IGBP, the
Global Land Project, the UK 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. We thank Enrica de Luca for the
biomass data, Annett Lipowsky and Marlén Gubsch for some
of the plant trait data, and Tania Jenkins for the phylogeny.
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, Germany.
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SUPPLEMENTAL MATERIAL
Appendix A
Species pool of the Jena Experiment (Thuringia, Germany) with inclusion status in the analyses (Ecological Archives E093-248-A1).
Appendix B
Detailed list of all traits considered for this manuscript with detailed references (Ecological Archives E093-248-A2).
Appendix C
Details on the imputation analyses used in this manuscript (Ecological Archives E093-248-A3).
Appendix D
Graph of the results from the random forest (RF) analysis, explaining how the selection of traits was done (Ecological Archives
E093-248-A4).
Supplement
Pairwise correlation between each pair of plant traits (Ecological Archives E093-248-S1).