Ecological Entomology (2013), 38, 290–302
DOI: 10.1111/een.12017
The effects of drought and herbivory on plant–herbivore
interactions across 16 soybean genotypes in a field
experiment
R O S E G R I N N A N,1 T H O M A S E . C A R T E R Jr.2 and M A R C T . J .
J O H N S O N 3 1 Department of Plant Biology, North Carolina State University, Raleigh, North Carolina, U.S.A.,
2
USDA-ARS, Raleigh, North Carolina, U.S.A. and 3 Department of Biology, University of Toronto at Mississauga, Toronto,
Canada
Abstract. 1. As the Earth’s climate continues to change, drought and insect
population outbreaks are predicted to increase in many parts of the world. It is therefore
important to understand how changes in such abiotic and biotic stressors might impact
agroecosystems.
2. The plant stress hypothesis predicts that, owing to physiological and biochemical
changes, plants experiencing drought will be more susceptible to insect herbivory,
which could have synergistic negative effects on plant performance. By contrast, the
plant vigor hypothesis predicts that insects will preferentially feed on fast-growing
vigorous plants.
3. These hypotheses were tested in a field experiment using 16 soybean (Glycine
max (L.) Merr.) genotypes to determine: (i) the combined effects of drought and
herbivory on plant performance; (ii) the impact of drought on soybean resistance to
herbivores; and (iii) how genetically variable phenotypic traits in soybean correlate
with these responses.
4. It was found that drought had a greater effect on soybean performance than
herbivory, and drought and herbivory did not interact to impact on any measure of plant
performance. Drought caused decreased insect herbivory on average, suggesting that
the plant vigor hypothesis is consistent with the effects of drought stress on soybean
resistance to leaf-chewing insect herbivores. This conclusion is further supported by
genotypic correlations which show that plant growth rate is positively correlated with
the amount of herbivory plants received.
5. These results suggest that, although the effects of climate-associated changes in
drought and herbivory will have negative effects on soybean, these potential effects are
quantifiable with simple experiments and can be mitigated through continued breeding
of varieties that are tolerant and resistant to these abiotic and biotic stressors.
Key words. Climate change, insect outbreak, plant defences, plant–insect interac-
tions, plant stress, insect resistance, tolerance.
Introduction
Most climate change models predict increased variability in
precipitation around the globe (Easterling et al ., 2000; Portmann et al ., 2009; Seager et al ., 2009). While it is still unclear
Correspondence: Marc Johnson, Department of Biology, University
of Toronto-Mississauga, Mississauga, ON, L5L 1C6, Canada.
E-mail: [email protected]
290
which specific regions will experience changes in precipitation,
it is generally accepted that drought will become more common in many areas (IPCC, 2007). Predictions regarding the
impacts of drought on communities and ecosystem processes
are more complex, and therefore less resolved (Parmesan,
2006; Williams & Jackson, 2007; Tylianakis et al ., 2008). This
uncertainty is a reflection of the fact that the various species
in ecosystems are likely to respond differently to changes
in their environment (Andrewartha & Birch, 1954; Parmesan
Published 2013. This article is a U.S. Government work and is in the public domain in the USA.
Drought and plant–herbivore interactions
& Yohe, 2003; Willis et al ., 2008; Wang et al ., 2012). It is
therefore imperative to build a solid understanding of the ecological responses to predicted climate change.
Plant–insect interactions are among the dominant species
interactions on Earth and accordingly they have large effects
on the ecology and functioning of natural and managed ecosystems (Strong et al ., 1984; Agrawal, 2011; Johnson, 2011).
Thus, understanding how plants and insects will jointly respond
to climate change is important to basic and applied problems
in biology (Painter, 1951; Walters, 2011). It has been hypothesized that increases in drought associated with climate change
could increase the frequency and severity of insect population
outbreaks (White, 1984; Koricheva et al ., 1998; Huberty &
Denno, 2004; Rouault et al ., 2006; Easterling et al ., 2007; Paritsis & Veblen, 2011). If true, the negative effects of drought
on plants could be exacerbated by increased insect damage
(Hawkes & Sullivan, 2001; Hale et al ., 2005). It is therefore
imperative to understand how the combined effects of drought
and damage by herbivorous insects (i.e. herbivory) might affect
plant performance. For instance, drought and herbivory may
have independent (additive) effects on plants (Hawkes & Sullivan, 2001). Alternatively, drought and herbivory could interact
to cause synergistic negative effects on plants, resulting in a
larger decrease in performance than predicted by their additive
effects. A third possible outcome is that drought and herbivory could have antagonistic effects on plants, resulting in a
smaller decrease in performance than predicted by their additive effects. The exact nature of the combined effects of abiotic
and biotic stressors will be particularly important to cropping systems, where strategies are needed to mitigate harmful
effects of multiple environmental stresses that act simultaneously (Long & Ort, 2010; Sinclair, 2011).
Two conflicting hypotheses offer general predictions about
how drought can affect a plant’s susceptibility to insect
herbivores – the ‘plant stress hypothesis’ (PSH, White, 1984;
Huberty & Denno, 2004) and the ‘plant vigor hypothesis’
(PVH, Price, 1991). The PSH posits that environmental stress
increases a plant’s susceptibility to at least some types of
insect herbivory by altering leaf chemistry and whole-plant
physiology (Koricheva et al ., 1998; White et al ., 2011). Under
this hypothesis, drought can cause a reduction in plant chemical defences and an increase in available nitrogen relative to
carbon, causing plants to be more susceptible to insect attack
(White, 1984; Huberty & Denno, 2004; Gutbrodt et al ., 2011).
A corollary of this hypothesis is that plants subject to drought
are likely to experience both direct negative effects of drought
on plant performance and indirect negative effects of drought
mediated by increased herbivory. The combination of these
stressors can potentially cause a synergistic decrease in plant
performance. By contrast, the PVH posits that insects preferentially feed and perform best on fast-growing plants and least
on stressed plants (Price, 1991). Herbivory is higher under this
scenario because there would be smaller investment in plant
defences by fast-growing plants (Coley et al ., 1985; Endara
& Coley, 2011), or because slow-growing stressed plants
would exhibit reduced carbohydrate synthesis and reduced
water uptake, resulting in lower nutritional content (Herms &
Mattson, 1992; Daane & Williams, 2003; Huberty & Denno,
291
2004; Hale et al ., 2005; Gutbrodt et al ., 2011). If PVH is
true then the negative effects of drought on plant performance
are unlikely to be synergistic. These conflicting hypotheses
regarding the effects of drought on herbivory perpetuate the
difficulty in predicting the effects of climate change on species
interactions (Koricheva et al ., 1998; Huberty & Denno, 2004).
It is therefore important to understand the conditions under
which these hypotheses make accurate predictions.
Identifying phenotypic traits that mediate a plant’s response
to abiotic and biotic stresses is critical to building a predictive
framework that models plant and insect responses to climate
change (Manavalan et al ., 2009; Skirycz & Inze, 2010;
Sinclair, 2011). Ecological genetics studies provide a powerful
approach to identifying genetically controlled phenotypic traits
that affect drought tolerance and insect resistance (Glynn et al .,
2004b; Leimu & Koricheva, 2006; James et al ., 2008; Charlson
et al ., 2009; Maherali et al ., 2010; Carmona et al ., 2011;
Franks, 2011). Crop species are especially useful in this regard,
because most crops have been bred for many decades, are well
studied, and contain many varieties or landrace accessions with
known phenotypes and pedigrees. Moreover, identifying traits
involved in mitigating the effects of drought and insects within
crop species can assist breeders in identifying germplasm for
the breeding of new crop varieties that will be better suited to
future climates (Manavalan et al ., 2009).
Here we seek to investigate how drought and an experimentally created insect herbivore population outbreak affect
plant–herbivore interactions, and whether or not any genetically variable plant traits could predict a plant’s response
to both drought and herbivory in a model crop system, soybean. To address these goals, we conducted a field experiment
in which we asked the following questions: (i) how do the
combined effects of drought and herbivory affect soybean performance; (ii) how does drought impact soybean resistance to
insect herbivores; and (iii) can we identify genetically variable
plant traits correlated with plant performance and susceptibility to herbivores under different environmental conditions? We
discuss the implications of our results in the context of the
potential effects of climate change on agroecosystems.
Materials and methods
Study system
Soybean is a leguminous herbaceous crop of global
importance as a food source and it provides an economically
important model for testing the effects of abiotic and biotic
stresses on plants and plant–insect interactions (Carter et al .,
2004; Manavalan et al ., 2009). Soybean breeding has produced
a wide array of phenotypic traits across thousands of varieties
or accessions (Carter et al ., 2004). For example, soybean
accessions exhibit substantial variation in avoidance, resistance
and tolerance to drought (Carter et al ., 1999; Carter et al .,
2003; Narvel et al ., 2004; Manavalan et al ., 2009), herbivores
(Kraemer et al ., 1988; Zhu et al ., 2006; Zhu et al ., 2007), and
other stressors (Carter et al ., 2004; Lam et al ., 2010). We initially quantified phenotypic variation among 51 genotypes in
a greenhouse experiment (Grinnan et al ., 2012) and chose 16
Published 2013. This article is a U.S. Government work and is in the public domain in the USA., Ecological Entomology, 38, 290–302
292
Rose Grinnan, Thomas E. Carter, Jr. and Marc T.J. Johnson
phenotypically diverse genotypes (Table S1) from this earlier
work to answer the research questions posed in the current
field study. These genotypes represent zones of adaptation that
correspond to varying latitudes, from Ohio to Georgia, U.S.A.
(Table S1). This variation in zone of adaptation is largely due
to differences in photoperiod (day length) response and quantified agronomically according to ‘maturity group’. We used four
major maturity groups (IV–VII) commonly grown in North
America, where each increasing maturity group number corresponds to an approximately 1-week delay in harvest maturity
under natural day length (Fehr & Caviness, 1977, Table S1).
Field experiment
We timed our planting and harvest to be consistent with
conventional farming practices in the PIEDMONT of North
Carolina, where we conducted our work. On 8 June 2010, we
planted seed of 16 soybean genotypes at the Lake Wheeler
Field Laboratory of North Carolina State University. The
site was dominated by an Appling fine sandy loam soil.
We prepared the field by first killing weeds with Round Up
herbicide followed by tilling 2 weeks and again 1 week prior
to planting. After soybeans emerged, weeds were removed by
a combination of ploughing and hand-weeding. Plough shoes
were kept at least 24 cm from plants to minimize the damage
to roots. The field was arranged into 16 spatial blocks that
were laid out in a 4 × 4 rectangular grid pattern with 3–4 m
spacing between blocks (Figure S1). Within each block, we
planted the 16 genotypes into individual 1.83 m rows, with
1 m spacing between rows. Rows were arranged in a 4 × 4
grid within a block (Figure S1); individual rows were the unit
of replication for the study. Each maturity group (IV–VII)
was represented by four soybean genotypes, and all genotypes
within a maturity group were planted in adjacent rows within
a block to minimize asymmetric competitive effects that could
arise from phenological differences in development, for traits
such as plant height and leaf area. The spatial position of
maturity groups within a block, and the position of genotypes
within a maturity group were fully randomized. The 16 blocks
were employed in a Latin square design, in which drought and
herbivory treatments were assigned in a factorial fashion as
follows: (i) drought and herbivory (i.e. naturally occurring herbivores plus insect herbivores added to plants); (ii) drought and
suppressed herbivory; (iii) irrigation and herbivory; and (iv)
irrigation and suppressed herbivory. We reduced edge effects
by planting border rows of soybean around each block, such
that the maturity groups of border plants matched the maturity
group of the adjacent experimental plants. We thinned plants to
eight to nine plants per 0.3 m (48–54 plants per row) at 18 days
after planting.
The water treatment (irrigation or drought) was applied to
entire blocks using irrigation and plastic covering (Figure S1).
Irrigation was delivered to individual blocks through PVC
piping from a water tank using a water pump. We controlled
the delivery of water to each block with independent valves
and pressure gauges that allowed precise control of water
volume (Figure S1). Water was delivered from the PVC
headers to individual plant rows using Netafim™ dripperline
(Techline emitter spacing was 30.5 cm, which dispenses water
at a flow rate of 3.4 litres h –1 ) (Netafim, Fresno, California).
As the dripperline emitters were standardized, we were able to
ensure that watered blocks received the same relative amount
of water by timing the length of watering events. The dripperline was run in long U-formation around each row, including
border beans, such that six dripperline ‘U’ formations were
used to water each block. The emitters were staggered such
that rows received water approximately every 15 cm. Over the
irrigation we laid 0.15-mm-thick polyethylene white plastic
sheeting, cut into 11 m × 1 m strips. Strips were laid alongside
each row such that seven strips were used to cover each block
completely (Figure S1), including a 1-m border of plastic
extending past the edges of each treatment block. Although
some precipitation inevitably reached the soil, the plastic
barrier effectively diverted most natural precipitation away
from blocks so that we had a high level of control over the
amount of water that reached plants. As the field was on a
slight slope, we dug trenches (25 cm wide, 13 cm deep) along
the upslope side of each block that received drought to divert
water runoff.
We initiated drought by ceasing to water plants in mid-July
(37 days after planting) and we laid plastic 9 days later before
any rain. We ended the drought in early October, 11 weeks
later (122 days after planting) by removing the plastic. This
timing corresponded with a very heavy rain and when many
plants had approached reproductive maturity. Our drought
treatment was facilitated by the fact that our experiment was
conducted during the second hottest summer on record in
Wake County, NC. The mean daytime highs in July, August
and September were 34.1 ◦ C, 33.0 ◦ C, and 31.8 ◦ C, respectively, and ranged from 1.8 ◦ C (July) to 3.0 ◦ C (September)
above average. This hot weather increased plant demand for
water and accentuated the effects of drought in June, July and
nearly all of September (Table S2). In general, the effects of
drought were mild initially and then increased through time,
being most severe in August and September (Table 1). We
confirmed that our drought treatment successfully reduced soil
moisture by measuring soil water content in mid-August using
four replicate soil cores from each block. At this time we
observed a 35% reduction in the gravimetric water content of
the soil (8.9 ± 0.3% water in irrigated soil versus 5.8 ± 0.3%
in water-stressed soil; F 1,14 = 29.65, P < 0.001). Visible
signs of wilting were also observed and our physiological
measurements of stomatal conductance showed that plants
transpired less in response to drought.
We manipulated insect damage by suppressing herbivory
in half of the blocks and simulating a herbivore outbreak
in the other half. We reduced natural herbivory by spraying
®
control blocks (suppressed herbivory) with Baythroid XL
(Bayer CropScience, Research Triangle Park, North Carolina)
at the manufacturer’s recommended rate (118 ml acre –1 ) every
second week starting in late June. We increased the spray
frequency to every week from mid-July to mid-August because
of intense herbivory during this time, and we added an
extra spray in mid-September, again to address a high
level of herbivory. We simulated an herbivore outbreak
in mid-July (44 days after planting) by introducing ˜300
Published 2013. This article is a U.S. Government work and is in the public domain in the USA., Ecological Entomology, 38, 290–302
0.76
1.79
0.21
0.8
0.7
0.63
0.78
0.99
0.0604
0.03
1,12
0.1
0.07
0.16
0.24
0.08
0
4.29
6.19
1,12
0.95
0.07
0.18
0.78
P
1,15
1,12
1,12
1,12
1,14
1,12
1,12
0
3.89
2.06
0.08
F
1,12 3.35 0.09
1,12 30.88 < 0.001
1,12 4.41 0.06
1,12 4.97 0.046
1,14 0.08 0.79
1,13 0.51 0.49
1,13 0.84 0.38
1,12
1,15
1,13
1,12
d.f.
1,12 2.32 0.15
1,11 16.81 0.002
–
–
< 0.001
1,12 11.36 0.006
0.001
0.71
0.15
0.06
P
Herbivory
1,12 0.68 0.43
1,12 11.2
0.006
1,16 1,12 24.05
1,13 5.7
0.03
1,14 17.98
1,12 0.14
1,12 2.36
1,14 4.2
F
Drought
1,12
1,12
1,12
1,12
1,12
1,14
1,12
1,12
1,12
1,12
1,12
1,12
1,12
1,12
1,12
1,12
d.f.
1.58
2.1
0.07
0.15
0.05
0.5
1.35
0.49
0.49
0.27
0.89
0.76
0.01
0.01
0.13
0.71
F
F
P
15,15
12,214
12,222
12,216
15,225
15,15
15,15
12,222
12,15
12,15
12,15
0.66
2.24
1.49
1.83
2.58
0.92
0.34
52.5
31.42
48.2
22.8
0.78
0.01
0.13
0.046
0.001
0.56
0.34
< 0.001
< 0.001
< 0.001
< 0.001
12†,15 4.01 0.007
15†,15 10.71 < 0.001
12,12 27.55 < 0.0001
15,15 24.59 < 0.001
d.f.
Genotype
F
P
d.f.
F
P
–
3,12
3,12
3,12
–
–
–
–
0.15
0.5
0.03
0.11
0.15
0.15
0.15
> 0.15
– >
0.83
4.05
2.46
– >
– >
– >
3,12 6.63 0.007
3,12 9.16 0.002
3,12 16.38 < 0.001
3,12 11.98 0.001
–
–
–
–
–
–
15,210
15,208
–
> 0.15
– > 0.15
– > 0.15
– > 0.15
– > 0.15
– > 0.15
1.4
0.15
1.47 0.12
–
15,210
–
–
–
–
–
–
–
2.2
–
–
–
–
–
–
> 0.15
0.008
> 0.15
> 0.15
> 0.15
> 0.15
> 0.15
> 0.15
–
– > 0.15
15,209 1.97 0.02
–
– > 0.15
–
– > 0.15
d.f.
Herbivory ×
genotype
–
– > 0.15
–
– > 0.15
15,207 1.98 0.018
15,209 1.5
0.11
P
Drought ×
genotype
–
– > 0.15
15,209 2.24 0.006
–
– > 0.15
–
– > 0.15
F
Maturity
group
3,12 2.76 0.088 15,206 1.61 0.073
–
– > 0.15‡ –
– > 0.15
3,12 3.83 0.04 –
– > 0.15
–
– > 0.15 15,209 1.77 0.04
d.f.
0.23 15,224 33.49 < 0.001 –
0.17
0.8
0.7
0.83
0.49
0.27
0.5
0.5
0.61
0.36
0.4
0.92
0.94
0.72
0.41
P
Drought ×
herbivory
∗Block (drought × herbivory) P < 0.05.
†The numerator d.f. of plant genotype was 12 when the reduced model contained genotype nested within maturity group. The effect of maturity group was removed from the model when this
effect and interactions containing maturity group all had P > 0.15
‡No d.f. and F -value are provided because effect had P > 0.15 and was removed from the final model during stepwise backward selection procedure (see Methods)
§Drought × maturity group, P < 0.05.
¶Herbivory × maturity group, P < 0.05
We show the summary of mixed-model anova results for the factors that were of primary interest in this study. Given the large number of factors studied, we used a stepwise backward selection
procedure to find the best-fitting model with reduced complexity and greater statistical power. Thus, different traits often had different final models, which accounts for the variability in numerator
and denominator degrees of freedom associated with individual tests. The denominator degrees of freedom were typically synthetic fractional composites comprising multiple factors, and we
rounded all d.f. values to the nearest whole number. The full anova tables are available upon request. P -values in bold show effects that are statistical significant at P < 0.05.
Performance
Seed yield∗
–
Growth rate (early)∗
0
Growth rate (late)∗§¶
14
Height∗
–
Phenology
First flower∗
–
First fruits§
–
–
First mature fruits∗
Maturation rate∗
–
Physiology
Conductance (late July)∗ 16
Conductance (mid Aug)∗ 30
Conductance (late Aug)∗ 44
Conductance (early Sept)∗ 51
Water potential∗
56
% leaf water content∗
21
Specific leaf area∗
21
Other
Trichomes∗
0
Trait
Days
drought d.f.
Table 1. Anova results of the effects of drought, herbivory and soybean genotype on plant traits associated with performance, phenology, physiology and trichomes.
Drought and plant–herbivore interactions
Published 2013. This article is a U.S. Government work and is in the public domain in the USA., Ecological Entomology, 38, 290–302
293
294
Rose Grinnan, Thomas E. Carter, Jr. and Marc T.J. Johnson
Helicoverpa zea Boddie (Noctuidae) first-instar larvae to
each 1.8-m experimental row of herbivory-treated blocks.
This corresponded to around five additional caterpillars per
plant, which is approximately five to 10 times the average
density of first-instar H. zea larvae observed in other crops
(Sansone & Smith, 2001). Thus, these densities accurately
simulate an outbreak. H. zea is a common generalist herbivore
of soybean in the southeastern U.S., where this study was
conducted (Turnipseed & Kogan, 1976; Kogan & Turnipseed,
1987). The manipulation of increased insect population size
was timed to correspond with 1 week following the initiation
of drought, as might be expected to occur for a natural droughtinduced herbivore outbreak (White, 1984).
In late September, we began harvesting rows as they matured
to quantify seed yield. At harvest, we cut the centre 1.2 m of
the row close to the soil surface, allowed the plants to dry
at ambient temperatures in a greenhouse for a minimum of
2 weeks, and threshed them to harvest seed. We dried the seed
in paper bags at ambient room temperature in a laboratory for
at least 2 weeks and then weighed total seed mass per row.
Harvest was completed by 30 October 2010 (145 days after
planting). Average moisture in the seed was approximately 9%.
Measures of resistance against herbivores
We assessed the effects of drought and plant genotype on
insect leaf herbivory in the field at 1, 4 and 9 weeks following
the initiation of drought. Herbivory was quantified by visually
estimating percentage leaf area consumed (i.e. herbivory) by
leaf-chewing insects on five evenly distributed trifoliate leaves
from each of three plants per row (i.e. 15 leaves total per
row). These values were then averaged into a single herbivory
estimate for each row. The timing of these surveys allowed us
to assess the impact of increasing drought stress through time
on susceptibility to herbivores across diverse plant genotypes.
We also assessed the effect of drought and plant genotype
on the performance of two generalist leaf-chewing herbivores
in a no-choice detached leaf bioassay. Six weeks after the
drought began, we punched two 3.8-cm-diameter round leaf
disks from haphazardly selected leaves at the top of the
canopy of each row using a Marvy Uchida LVEJCP hole
punch (Uchida of America, Torrance, California). We placed
these leaf disks individually into 60 × 15 mm polystyrene Petri
dishes lined with moistened filter paper. We then placed
newly hatched caterpillars of H. zea and Spodoptera exigua
Hübner (Noctuidae) (Benzon Research, Carlisle, Pennsylvania)
individually on leaves, such that we assayed resistance of each
replicate row to both insect species. The insects were allowed
to feed on a leaf punch in the Petri dish for 1 week, at which
point we removed all insects and placed them in a 1.5-ml
microcentrifuge tube. They were allowed to void their gut
contents for 24 h and were then frozen before measuring their
fresh weight on a Mettler AT20 FACT microbalance (MettlerToledo, Inc., Columbus, Ohio).
Herbivory estimates on field plants showed that the insecticide treatment was effective at reducing herbivory. Mean
cumulative tissue loss to leaf-chewing insect herbivores was
2.8% (SE = 0.1%) in late July, 3.8% (SE = 0.1) in mid-August,
and 9.6% (SE = 0.4%) in mid-September. Insecticide consistently reduced herbivory (effect of insecticide, P < 0.05 for
all dates) by 27–63%, depending on the date of measurement. Moreover, the damage observed in our herbivory-treated
blocks fell within the natural range of herbivory observed in
soybean planted by farmers in the surrounding area. This was
confirmed by measuring herbivory on 10 plants from each
of 10 soybean fields in late August in Wake and Johnston
Co. Herbivory in these fields ranged from 2.9% to 15.6%
(mean = 7.1%, SE = 4.0).
Plant trait measurements
An important objective of our research was to understand
how variation in plant traits might mediate a plant’s response
to drought and herbivory. To this end, we focused on traits
associated with plant physiology, leaf morphology, plant
growth, flowering phenology, and seed yield. We measured
stomatal conductance four times throughout the drought period,
ranging from 2 to 8 weeks after the start of the drought
treatment. Stomatal conductance was measured from a single
leaf in each row using a Delta-T AP4 Porometer (Delta-T
Devices, Burwell, Cambridge, UK). Leaves were selected from
the top of the canopy, provided they were fully expanded and
facing the sun. Eight weeks following the initiation of drought,
we measured water potential with a Scholander Pressure
Chamber (PMS Instrument Company, Albany, Oregon) on
a terminal leaflet also used for stomatal conductance. Three
weeks into the drought treatment, we collected a 3.8-cmdiameter round hole punch from a leaf at the top of the canopy
of each experimental row, then dried the leaves at 40 ◦ C for
24 h and recorded the mass of leaf punches. With these data
we calculated specific leaf area as leaf surface area/dry weight.
Leaf trichome density was measured several days prior to the
beginning of drought from a single fully expanded trifoliate
leaf near the apical meristem by punching a 7-mm-diameter
hole between major veins near the base of the terminal leaflet,
and counting the number of trichomes on one-half of the circle
from both the upper and lower surfaces.
Plant growth rate was calculated as a change in plant height
per day. We measured the height of two plants per experimental
row three times prior to flowering, from the soil surface to the
primary shoot apical meristem. The two plants were tagged
during the first measurement so that we measured the same
plant each time. From these data we calculated growth rate in
plant height in early (day 14–29, June) and mid (day 30–50,
July) growing season.
We quantified reproductive phenology for each row as
time to flowering and fruit pod set. We recorded flowering
phenology as days until the first open flower. Pod initiation was
defined as the first day when 2–3 mm of a pod emerged from
the senescing flower. Pod maturity date was estimated as the
first day when 95% of pods in the row were brown. For a small
number of rows, it was necessary to harvest them before 95%
of the pods were brown, because of their potential to dehisce
rapidly in the field. We calculated the pod-filling period as the
length of time between pod emergence and maturation.
Published 2013. This article is a U.S. Government work and is in the public domain in the USA., Ecological Entomology, 38, 290–302
Drought and plant–herbivore interactions
Statistical analysis
We examined the effects of drought, herbivory, plant
genotype and maturity group using general linear mixed
models in proc glm of SAS (SAS Institute, Cary, NC, USA).
The statistical models employed reflected the blocking and
nesting structure of the experimental design. When analysing
plant traits as the response variable (e.g. seed yield, physiological and phenological traits), we started with the statistical
model: plant trait = meanoverall + block (drought × herbivory) + water + herbivory + drought × herbivory + maturity
group + genotype(maturity group) + herbivory × maturity
group + drought × maturity group + drought × genotype
(maturity group) + herbivory × genotype(maturity group)
+ drought × herbivory × genotype(maturity group) + drought
× herbivory × maturity group + error. All effects in italics
were treated as random and the correct denominator expression and degrees of freedom were determined using the ‘test’
statement in proc glm. Parentheses indicate nested terms,
where the term outside of the parentheses is nested within the
term shown inside of the parentheses. As noted earlier, we
selected plant genotypes that exhibited phenotypic variation
within and between maturity groups, and therefore our
selection of genotypes was not truly random. We nevertheless
treated genotypes as a random effect in statistical models,
because we view them as representing a sample of possible
genotypes within soybean. To increase statistical power of
our tests, we decreased model parameterization by removing
non-significant interactions (cutoff of P > 0.15), and the effect
of block and maturity group using a sequential stepwise
backwards selection procedure (Myers, 1990). We always
retained the drought × herbivory interaction term in the
reduced model because this project was in part motivated by
testing whether or not this interaction existed. We examined
the residuals of the untransformed data for each variable
separately to determine whether assumptions of homogeneity
of variance or normality were violated. In such cases, we
examined whether log or square-root transformations of the
data improved the residuals.
We used a similar approach to analyse effects of
plant genotype and experimental treatments on herbivore susceptibility, but we used a simpler model because
these models only included blocks that herbivores were
allowed to naturally colonize (i.e. unsprayed plots). In the
analysis of herbivory, we used the full model: herbivory
= meanoverall + block (drought) + drought + maturity group +
genotype(maturity group) + drought × maturity group +
drought × genotype(maturity group) + error.
We also analysed herbivore susceptibility according to
the weight gain and survival of caterpillars in a no-choice
Petri dish bioassay. When analysing weight gain we started
with the same full model as described earlier. For caterpillar
survival we used generalized linear mixed effect models with
a binomial error distribution and logit-link function, where
genotype was designated as a random effect. In analyses
involving H. zea, the likelihood statistics would not converge,
so we employed proc glimmix to investigate the effects of
drought and maturity group in one analysis (Model 1), and
295
proc genmod to examine the effects of drought and genotype
in a second analysis (Model 2).
We plotted pairwise correlations in jmp 8.0 (SAS) based on
genotypic least-squares means for phenotypic traits to determine how traits predicted plant performance and resistance to
herbivory.
Results
Plant performance
Our first research question sought to determine how drought
and herbivory affect soybean performance and, in particular,
whether they interact to affect plants. Overall, drought had
the largest effect on performance, causing a 37.5% reduction
in seed yield (Fig. 1, Table 1). Plant genotypes showed
weakly significant variation in tolerance to drought (Fig. 1,
Table 1), with most genotypes experiencing varying degrees of
reduced yield (Fig. 1). Drought had little effect on mid-season
(mid-July) growth rate (Table 1). However, drought caused a
7.4% reduction in final height and this effect varied among
genotypes, as indicated by the significant genotype × drought
interaction (Table 1). Drought slightly accelerated flowering
and pod maturity date (P < 0.05) but never by more than 2
days (Table 1).
Negative effects of drought on plant performance have
been causally linked to multiple phenotypic traits in soybean,
especially physiological traits associated with leaf water loss
(Manavalan et al ., 2009). Consistent with this, plants subject
to drought consistently exhibited 35–58% lower stomatal
conductance of water (Table 1, Table S1), and this reduction
was strongest in late September at the height of the drought.
Drought did not influence water potential or specific leaf
area, although the latter was measured when the effects of
drought were just beginning to be realized according to our
stomatal conductance results (Table 1). Most other traits
varied significantly among plant genotypes (Table 1), but
drought × genotype interactions were rare. Maturity group had
only small effects on overall seed yield or physiological traits
(Tables 1 and S1).
Herbivory had a weaker effect than drought on plant
performance. Herbivory was associated with reductions in
early (June) and mid-season (July) growth rates (i.e. rate
of change in plant height) of 9.4% (P = 0.07) and 10.2%
(P = 0.18), respectively (Fig. 1, Table 1). These weak effects on
growth rate were due to large genetic variation in response to
herbivory (herbivory × genotype, P = 0.006), especially early
in the season (Fig. 1, Table 1). For example, in June our
herbivory treatment had no effect on growth rate for some
genotypes, whereas others experienced a 25% reduction in
growth rate (Fig. 1b). Despite this effect on early season
growth, we found no effect of suppressed herbivory on
seed yield or final height (Table 1). Although we saw no
treatment effect from herbivory, genotypic correlations suggest
that insect damage may have, in fact, caused substantial
reductions in seed yield that were undetected due to insufficient
statistical power. Seed yield was negatively correlated with
percent herbivory in irrigated and drought treatments (Fig. 2),
Published 2013. This article is a U.S. Government work and is in the public domain in the USA., Ecological Entomology, 38, 290–302
296
Rose Grinnan, Thomas E. Carter, Jr. and Marc T.J. Johnson
(a) 400
410
Ambient (r = –0.66, P = 0.005)
Drought (r = –0.37, P = 0.15)
320
330
Seed Yield (g)
Seed yield (g)
Drought: P = 0.001
Drought x genotype: P = 0.073
240
160
Ambient H20
Early growth rate (cm/day)
250
170
80
(b)
Ambient
Drought
Drought
Herbivory: P = 0.07
Herbivory x genotype: P = 0.006
1.3
90
0
10
20
30
%Herbivory
1.1
Fig. 2. Genotypic correlations between seed yield and percentage
herbivory. Each point represents the least squares mean for a soybean
genotype in either irrigated (ambient) or drought water conditions
with linear trend lines added. The herbivory rates shown were taken
from mid-September when the largest difference was observed between
irrigated and drought conditions.
0.9
0.7
0.5
Herbivory
Fig. 1. Reaction norm plots showing the effects of drought and
herbivory on components of plant performance across 16 soybean
genotypes. (a) Effects of irrigated (‘ambient H2 0’) and drought
conditions on seed yield. Each line depicts the reaction norm for
a single plant genotype, where the ends of each line are the leastsquares mean (LSM) of the untransformed data. The dashed line
shows a genotype (PI 471931) that exhibited a severe decline in
yield in response to drought and the dotted line shows the single
genotype (Stressland) that exhibited increased yield. (b) Response of
early growth rate across 16 genotypes to suppressed herbivory versus
plots with ambient insect damage. The large black dots outside the
genotype lines represent the overall means across genotypes within
each treatment.
but the correlation was significant only in the irrigated
treatment. This result suggests that increased herbivory does
indeed cause decreased plant performance. Consistent with
this interpretation, plants treated with suppressed herbivory
exhibited as much as a 3-day delay in fruit pod emergence and
maturation (Table 1). One possible reason for the discrepancy
between our herbivory treatment effects and our genotypic
correlations is that, even though spraying with insecticides
quantitatively suppressed insect damage, it did not completely
eliminate herbivory (see ‘Methods’).
A major objective of this project was to assess whether
drought and herbivory interact to affect plant performance.
Our results clearly show that drought and herbivory did not
interact to affect any measure of plant performance (Table 1, all
P > 0.40), and therefore the combined effects of these stresses
on soybean performance were additive.
Resistance to insect herbivores
Our second research question asked whether drought or
plant genotype affected the susceptibility of soybean to insect
30
Drought: P = 0.056
Drought x genotype: P < 0.0001
25
%Herbivory
Insects suppressed
20
15
10
5
0
Ambient
Drought
Fig. 3. Reaction norm plot showing the effect of drought on
percentage tissue consumed by insects (herbivory) across 16 soybean
genotypes. Each line depicts the reaction norm for a single plant
genotype, where the ends of each line are the least-squares mean
(LSM) of the untransformed data in the treatment environment. The
large black dots outside the genotype lines represent overall means
within each treatment. The data depicted were taken in mid-September.
herbivores. Drought had little effect on the amount of herbivory
incurred by plants 1 week (mid-July) to 4 weeks (mid-August)
after the initiation of drought, but it caused a 28% reduction in
herbivory after 9 weeks of drought, when the effects of water
stress were most severe (Fig. 3, Table 2). A highly significant
drought × genotype interaction was detected for herbivory in
the unsprayed blocks towards the end of the drought, indicating
that drought may have modified the susceptibility of some
genotypes. For example, PI 567352B (Table S1) exhibited 58%
lower damage in drought conditions, whereas other genotypes
such as a Benning variety recombinant inbred line (RIL) (G04Ben229IR-MGH) experienced a 12% increase in herbivory
with drought (Fig. 3, Tables 2 and S1).
Our no-choice bioassays showed no clear effect of drought
on the performance or survival of either H. zea or S.
Published 2013. This article is a U.S. Government work and is in the public domain in the USA., Ecological Entomology, 38, 290–302
Drought and plant–herbivore interactions
Table 2. Anova results of the effects of experimental treatments and
soybean genotype on insect herbivory.
Herbivory mid-July
Block (drought)∗
Drought†
Genotype∗
Herbivory mid-August
Block (drought)∗
Drought†
Genotype∗
Herbivory mid-September
Block (drought)∗
Drought‡
Maturity group§
Genotype (maturity group)¶
Drought × genotype (maturity group)∗
d.f. SS
F
6
1
15
11.17
0.01
53.65
2.51 0.03
0.01 0.94
4.82 <0.001
6
1
15
41.71
2.07
45.62
4.47 0.0005
0.3 0.61
1.96 0.03
6
1
3
12
15
86.28
174.69
727.14
676.54
489.01
2.42
4.26
4.3
1.73
5.5
297
Table 3. (a) Pairwise genotypic correlations between seed yield
and phenological, physiological, morphological traits plus percentage
herbivory; and (b) genotype correlations between percentage herbivory
and these same traits.
P
0.03
0.0558
0.0281
0.1572
<0.0001
∗Denominator for testing these effects was Mean Square (MS) error.
†Denominator was MS block (drought).
‡Denominator was MS block (drought) + MS drought × genotype −
MS error.
§Denominator was MS genotype (maturity group).
¶Denominator was MS drought × genotype (maturity group).
We include results from anova for three measures of percent insect
damage to each soybean row (mid-July, mid-August, and midSeptember).
exigua (Table S3), while plant genotypes varied in their
resistance to H. zea but not S. exigua (Table S3). Although
drought × genotype interactions were not significant, two slowwilting types identified in previous research (N93-110-6
derived from slow wilting PI 416937 and N94-7784 from
Egypt) exhibited a trend to be more insect-resistant after
being exposed to extended drought. N74-7784 was also
the most resistant genotype based on herbivory ratings in
the field. Despite these trends, the lack of any detectable
effect of drought on the performance of bioassay caterpillars
suggests that the effects of drought and drought × genotype
interactions on susceptibility to herbivores were largely driven
by differences in herbivore preference rather than herbivore
performance.
Traits that predict plant performance and resistance to
herbivory
Only two traits were significantly genetically correlated with
seed yield. As described earlier, the strongest predictor of yield
was percentage herbivory (Fig. 2), but this correlation was only
evident in irrigated water treatments (Table 3). Water potential
was also significantly negatively correlated with yield, but
again only in the irrigated treatment. Surprisingly, none of the
traits measured provided significant predictors of seed yield in
drought conditions (Table 3).
Growth rate was the strongest predictor of the amount of herbivory (Table 3, Fig. 4), which was positively genetically correlated with herbivory in both irrigated (r = 0.71, P = 0.002)
Irrigated
Predictor
(a) Seed yield
Growth rate (mid)
Date first flower open
Date first pod emerged
Date 95% pods mature
Pod maturation rate
% herbivory
Trichome density
Specific leaf area
% leaf water
Stomatal conductance
Water potential
(b) Percentage herbivory
Growth rate (mid)
Date first flower open
Date first pod emerged
Date 95% pods mature
Pod maturation rate
Trichome #
Specific leaf area
% leaf water
Stomatal conductance
Water potential
Drought
r
P
r
P
−0.48
+ 0.39
+ 0.40
+ 0.49
+ 0.48
−0.66
−0.04
+ 0.10
−0.22
−0.41
−0.62
0.060
0.134
0.119
0.056
0.057
0.005
0.881
0.714
0.403
0.112
0.012
−0.30
+ 0.03
−0.00
+ 0.10
+ 0.17
−0.37
−0.05
+ 0.26
+ 0.09
+ 0.14
−0.24
0.249
0.915
0.977
0.688
0.532
0.146
0.828
0.323
0.727
0.585
0.370
+ 0.71
−0.46
−0.48
−0.40
−0.30
+ 0.20
−0.05
+ 0.22
+ 0.46
+ 0.47
0.002
0.075
0.059
0.130
0.265
0.467
0.830
0.426
0.078
0.067
+ 0.59
−0.50
−0.57
−0.37
−0.22
+ 0.10
−0.26
+ 0.14
−0.10
+ 0.14
0.015
0.050
0.021
0.150
0.387
0.780
0.327
0.633
0.663
0.571
Correlations with P < 0.05 are shown in bold.
and drought conditions (r = 0.59, P = 0.015) (Table 3, Fig. 4).
Early flowering and fruiting phenology were both negatively
genetically correlated with herbivory (Table 3), suggesting that
plants that reproduce sooner had the least damage. It is important to note that these genotypic correlations of growth rate and
phenology are not a simple effect of maturity group variation
on these responses, as the genotypic means were calculated
while simultaneously partitioning the variation in maturity
group using partial-sums-of-squares. No other traits showed
a clear relationship with herbivory.
Although we performed multiple independent pairwise
correlations with herbivory, the probability of detecting the
four observed significant correlations at the P = 0.05 level by
chance was very low (binomial expansion test, P = 0.01). If
we use the Bonferonni correction method (critical P = 0.0025)
then only growth rate in the irrigated water treatment is a
significant predictor of herbivory.
Discussion
Four results from our experiment are most important to understanding the consequences of climate change for plant–insect
interactions in soybean. First, experimentally induced drought
had larger effects than herbivory on plant performance.
Drought affected most measures of plant performance,
Published 2013. This article is a U.S. Government work and is in the public domain in the USA., Ecological Entomology, 38, 290–302
298
Rose Grinnan, Thomas E. Carter, Jr. and Marc T.J. Johnson
30
Ambient
Drought
%Herbivory
25
20
15
10
5
Ambient (r = +0.71, P = 0.002)
Drought (r = +0.59, P = 0.015)
0
1
2
3
4
Growth Rate (cm/day)
Fig. 4. Genotypic correlation between percentage herbivory and the
rate of increase in plant height (i.e. growth rate) during July. Each
point represents the least-squares mean for each soybean genotype for
either irrigated (ambient) or drought treatments.
including seed yield, final height and phenology (Table 1). By
contrast, our insect herbivory treatment only affected early vegetative growth rate and phenology (Table 1, Fig. 1), although
genotypic correlations suggest that herbivory may also have
reduced seed yield. Secondly, drought caused plants to be less
susceptible to insect herbivores on average (Fig. 3), which
supports the PVH and contradicts the PSH (White, 1984;
Price, 1991). Further support for this interpretation stems from
genotypic correlations which show that fast-growing plant
genotypes typically suffered the most herbivory (Price, 1991).
Thirdly, drought and herbivory never interacted to affect plant
performance, and therefore the effects of these stresses were
independent. Fourthly, genetic variation in specific physiological and growth traits was genotypically correlated with seed
yield and resistance to herbivores (Table 3). Together these
results have important implications for understanding how to
mitigate the effects of climate change on plant–insect interactions in agroecosystems.
Effects of abiotic and biotic stresses on plant performance
A primary objective of our study was to understand whether
abiotic and biotic stresses have additive or non-additive effects
on plant performance. In our study, drought and herbivory
impacted plant performance independently of one another,
indicating that the effects of these factors were strictly
additive. These results are similar to our previous experiments
using soybean in controlled environments (Grinnan et al .,
2012), where there was also a lack of strong interactions
between drought and herbivory across two experiments. The
consistency in these results across experiments and multiple
genotypes implies that the effects of these stressors on soybean
can be studied independently. Whether these conclusions
extend to other crop systems is unknown and will require
independent study in each system, but previous reviews from
non-crop plants suggest that additive effects of environmental
factors may be common (Hawkes & Sullivan, 2001).
Numerous studies support our general finding that drought
decreases soybean performance (Hoogenboom et al ., 1987;
Ray & Sinclair, 1998; Liu et al ., 2003; James et al .,
2008; Sinclair, 2011). Similarly, many studies report negative
impacts of herbivory on soybean performance (Turnipseed &
Kogan, 1976; Smelser & Pedigo, 1992; Terry et al ., 1999;
Rypstra & Marshall, 2005; Costamagna et al ., 2007). However,
few studies have investigated the combined effects of abiotic
and biotic stresses on soybean or other crops (Hawkes &
Sullivan, 2001). For example, Smelser and Pedigo (1992)
found that drought and herbivory from bean leaf beetles
(pod feeders) did not result in increased yield loss, but
instead, drought-stressed plants experienced less herbivory
than plants that received water. By contrast, drought led to
increased susceptibility to herbivores in rice (Boling et al .
2004), and together drought and greater insect damage caused
synergistic losses in yield (Litsinger et al ., 2011). Hawkes and
Sullivan (2001) provided the most comprehensive review of
the combined effects of herbivory and the abiotic environment
(nutrients water and light) on plant performance. They found
that herbivory and environmental factors (including drought)
rarely interact, but instead typically have additive effects on
plant performance. Their analysis included mostly non-crop
plants, and thus our results suggest that their conclusions may
frequently extend to crops such as soybean.
Effects of drought on susceptibility to herbivores
Abiotic stresses have long been predicted to affect the
susceptibility of plants to herbivores (White, 1984; Price,
1991). Meta-analyses show complex effects of drought on
herbivory, which depend on plant attributes and herbivore
feeding guild (Koricheva et al ., 1998; Huberty & Denno,
2004). For example, drought tends to have positive effects on
the survival and density of stem-boring insects, and negative
effects on the survival and density of sap-feeding insects.
Other groups of insects, such as leaf-chewing herbivores,
show no consistent response to drought (Huberty & Denno,
2004). Our finding of lower herbivory on plants subjected to
drought (Fig. 3), as well as positive genotypic correlations
between plant growth rate and herbivory (Fig. 4), support
the prediction that, on average, drought makes plants less
susceptible to chewing herbivores via effects on plant vigour
(Price, 1991). Moreover, our results show that drought can
have strong negative effects on leaf-chewing insects, contrary
to the general conclusion offered by Huberty and Denno
(2004). However, our results also indicate that the effect of
drought on susceptibility to herbivores depends on soybean
genotype. Many genotypes exhibited substantial decreases in
herbivory in response to drought, but several genotypes showed
no response or slight increases in herbivory. In other words,
support for the PVH may depend on which plant genotype
is studied. We found a similar interaction in a greenhouse
experiment that used a wider diversity of soybean genotypes
(Grinnan et al ., 2012). Thus, there is substantial genetic
variation in soybean for how drought modifies resistance to
leaf-chewing herbivores, which could facilitate breeders in
Published 2013. This article is a U.S. Government work and is in the public domain in the USA., Ecological Entomology, 38, 290–302
Drought and plant–herbivore interactions
creating new crops that are resistant to insects under varying
conditions of water stress.
The effects of drought on plant resistance to herbivores
in our field experiment were opposite to our results from a
previous greenhouse experiment in the same system, which
found higher average herbivore performance following drought
(Grinnan et al ., 2012). Two important differences between
these studies probably explain the discrepancy. First, our
field experiment simulated a long continuous drought, whereas
our greenhouse experiment simulated a single severe ‘pulsed’
drought, followed by a recovery period. Huberty and Denno
(2004) proposed that continuous drought is expected to
cause decreased insect performance while pulsed stresses
are most likely to have positive effects on herbivores.
Their logic follows from differences in how chronic versus
pulsed droughts affect available nitrogen and water content
in plants. Drought typically causes increased leaf nitrogen
in non-leguminous crops (Daane & Williams, 2003; Glynn
et al ., 2004a; Mody et al ., 2009), which can have positive
effects on herbivore performance (Mattson, 1980; Kursar
& Coley, 2003; Hale et al ., 2005). Increased leaf water
content can also have positive effects on herbivores (Scriber
& Feeny, 1979; Agrawal, 2004; Johnson, 2008), and while
plants recovering from a pulsed stress maintain high water
content, plants experiencing chronic stress typically have
low leaf water content. Therefore the benefits of increased
nitrogen are negated by the effects of low water content
under continuous water stress (Huberty & Denno, 2004;
Mody et al ., 2009).
The second likely explanation for the differences between
the field and greenhouse relates to the details of the assays
we employed for herbivore performance. In the greenhouse,
we performed detached leaf no-choice Petri dish bioassays. In
the field we conducted similar bioassays, but we focused on
percentage herbivory in the field because it incorporates the
natural performance and preference of insects for plants. The
discrepancy between the field and greenhouse suggests that
herbivore preference was largely responsible for the patterns
observed in the field.
Traits that predict plant performance and resistance to
herbivory
One way that experiments can provide a predictive framework to understand how climate change will influence plants is
to identify plant traits that mediate interactions with the environment (McGill et al ., 2006). Our results show that genotypic
variation in water potential and susceptibility to herbivores are
individually the best predictors of soybean yield under irrigated
conditions. It is important to exercise caution in interpreting
these results because these correlations do not implicate causal
relationships. For example, growth rate is a product of complex
biochemical, physiological and development processes under
polygenic control. The genotypic correlations could therefore
stem from pleiotropic effects of genes affecting other traits, or
variation in genes that are in linkage disequilibrium with the
loci controlling the traits identified. Nevertheless, these results
299
require further study to assess their potential as targets for
breeding of crops with improved yield. Unfortunately, none of
the measured traits predicted yield under drought, indicating
that a greater diversity of traits (e.g. photosynthetic rate, rooting depth, etc.) are needed to provide an accurate prediction
of soybean under drought stress.
Variation in resistance to herbivores is well known from
soybean (Turnipseed & Kogan, 1976; Terry et al ., 1999;
Underwood & Rausher, 2000; Carter et al ., 2004) and other
plant systems (Fritz & Simms, 1992; Anderson & MitchellOlds, 2011; Walters, 2011). We found that a combination of
traits related to soybean growth and reproductive phenology
predicted susceptibility to herbivores. In both irrigated and
drought conditions, the fastest-growing genotypes incurred the
greatest leaf herbivory (Table 3, Fig. 4), and consequently fastgrowing genotypes also tended to exhibit the lowest yield under
herbivory. Plant genotypes that flowered and fruited later also
experienced the least damage, and this effect was strongest
under drought conditions, suggesting that late flowering under
continuous drought can provide some resistance to herbivores
(but see McPherson et al ., 2001). This result can be explained
if the most damaging insects emerge early in the season
(Agrawal et al ., 2012), and if these insects are negatively
affected by drought (Huberty & Denno, 2004). Physiological
traits and leaf pubescence showed no clear correlations with
herbivory. These results are consistent with recent findings that
show life-history traits (e.g. growth rate, biomass allocation)
and phenological traits often provide the strongest predictors of
resistance against herbivores (Kursar & Coley, 2003; Carmona
et al ., 2011; Turley et al ., 2012) and might be important targets
in future crop development of resistant varieties.
Implications for climate change and agriculture
Our results have important implications for mitigating the
effects of climate change in agriculture. First, drought and
herbivory can reduce plant performance and crop yield. If the
severity of these stressors increases as predicted through time
(IPCC, 2007), antagonistic or synergistic interactions between
multiple stressors could lead to unpredictable effects on crop
yield which would jeopardize the security and sustainability
of agricultural production. Our observation that drought and
herbivory have additive effects on plant performance and the
expression of many plant traits implies that it is possible
to predict the consequences of these factors on soybean
by studying them independently in field experiments. We
recognize that this conclusion requires further experimental
support. However, if stressors typically have additive effects
on soybean performance, then understanding the effects of
drought and pest outbreaks on soybean could be relatively
straightforward, studied with simple single factor experiments
involving many candidate plant genotypes (Carter et al .,
1999; Manavalan et al ., 2009). Secondly, our observation
of genetic variation in resistance to drought and insects
(Carter et al ., 2004) further supports the notion that existing
germplasm in soybean contains a wealth of genetic resources
for breeding the next generation of crops that will be adapted
Published 2013. This article is a U.S. Government work and is in the public domain in the USA., Ecological Entomology, 38, 290–302
300
Rose Grinnan, Thomas E. Carter, Jr. and Marc T.J. Johnson
to future climates (Manavalan et al ., 2009; Long & Ort, 2010;
Sinclair, 2011).
Acknowledgements
We thank the staff at the NCSU Lake Wheeler Road Field
Laboratory and especially Chad Carter. This work was made
possible through laboratory and field assistance from J. Bailey,
W. Bolin, B. Geisler, K. Geisler, M. Gibbons, D. Gonzales, M.
Grinnan, A. Hayden, E. Hersch-Green, E. Huie, H. Huie, S.
Jaconis, J. Konowski, S. Kreilcamp, R. Marchin, R. Masson,
C. Myburg, A. Noble, M. Novitzky, C. O’Dell, A. Parmar,
J. Rabon, N. Turley and A. Wines. J. Bachelor, E. Fiscus,
S. Griggs and B. Hoffman provided insight, equipment and
technical assistance. B. Hoffman also provided assistance in
interpreting physiological data. This work was facilitated by
the USDA-ARS Soybean and Nitrogen Fixation Research Unit
in Raleigh NC. We also thank NC State University (RG,
MTJJ), USDA-ARS (TEC) and NSERC Canada for funding.
Supporting Information
Additional Supporting Information may be found in the online
version of this article under the DOI reference:
10.1111/een.12017
Fig. S1. Figures illustrating experimental setup of field
experiment. (A) A block set up with four rows down
representing four maturity groups, and six rows across
with the central four rows representing different genotypes
within a maturity group; the two outermost rows represent a
border genotype of the same maturity group. Entire blocks
experienced either suppressed insects or ambient plus added
insect herbivory, crossed with suppressed water (drought)
or water irrigation to mimic typical ambient conditions. (B)
Dripperline attached to PVC at the top of a block to deliver
water to irrigated blocks. (C) Dripperline in U-formation at the
bottom of blocks allowed delivery of water to all plants. (D)
Plastic sheeting over a block to control rainfall; the field was
sloped slightly downwards in the direction of the photograph,
such that water quickly ran off the plastic to the end of the
block. Water above the block was diverted with a trench that
is partially visible towards the bottom of the photograph.
Table. S1. Genotype information including genotype means
of phenotypic traits and herbivory in each treatment.
Table. S2. Monthly precipitation (cm) measured at the
Lake Wheeler Field Laboratory weather station during the
experiment in 2010 (June–November). Data presented include
monthly total, deviation from the monthly mean, greatest
amount of precipitation received in a 24-h period and the
day of the month on which that occurred, plus the number
of days during each month that 0.25, 1.25, and 2.5 cm or
more precipitation fell in 24 h. Note that, although September
experienced much higher than average precipitation, most of
this occurred at the end of the month.
Table. S3. Effects of drought and soybean genotype on
herbivore performance in a detached leaf no-choice bioassay.
We used two generalist feeding caterpillars, Helicoverpa zea
and Spodoptera exigua and recorded their biomass gain after
7 days of growth starting from a freshly hatched first-instar
neonate caterpillar. Caterpillar mass was analysed using the
same model as described for percentage herbivory, while we
used generalized linear models with a binomial distribution
and a logit link function when analysing survival. Because the
maximum likelihood model only converged when drought and
maturity groups were included in the model (Model 1), we
used generalized linear mixed models and pseudo-likelihood
statistics to assess the effects of genotype in relation to drought
(Model 2; see Methods) with significance tests calculated
according to log-likelihood ratio tests.
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