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Published by Oxford University Press on behalf of Entomological Society of America 2018.
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Plant–Insect Interactions
Landscape Effects on Reproduction of Euschistus servus
(Hemiptera: Pentatomidae), a Mobile, Polyphagous,
Multivoltine Arthropod Herbivore
Dawn M.Olson,1,8 Kristina R.Prescott,2 Adam R.Zeilinger,3 SuqinHou,4
Alisa W.Coffin,5 Coby M.Smith,5 John R.Ruberson,6 and David A.Andow7
1Crop Protection, Research, and Management Unit, USDA-ARS, Tifton, GA 31794, 2Department of Ecology, Evolution and Behavior,
University of Minnesota, St. Paul, MN 55108, 3Department of Environmental Science, Policy, and Management, University of
California Berkeley, Berkeley, CA 94720, 4Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115,
5Southeast Watershed Research Laboratory, USDA-ARS, Tifton, GA 31794, 6Department of Entomology, Kansas State University,
Manhattan, KS 66506, 7Department of Entomology and Center for Community Genetics, University of Minnesota, St. Paul, MN 55108,
and 8Corresponding author, e-mail: dawn.olson@ars.usda.gov
Subject Editor: ChristopherRanger
Received 30 January 2018; Editorial decision 15 March 2018
Abstract
Landscape factors can significantly influence arthropod populations. The economically important brown stink
bug, Euschistus servus (Say) (Hemiptera: Pentatomidae), is a native mobile, polyphagous and multivoltine pest
of many crops in southeastern United States and understanding the relative influence of local and landscape
factors on their reproduction may facilitate population management. Finite rate of population increase (λ) was
estimated in four major crop hosts—maize, peanut, cotton, and soybean—over 3 yr in 16 landscapes of southern
Georgia. Ageographic information system (GIS) was used to characterize the surrounding landscape structure.
LASSO regression was used to identify the subset of local and landscape characteristics and predator densities that
account for variation in λ. The percentage area of maize, peanut and woodland and pasture in the landscape and
the connectivity of cropland had no influence on E.servus λ. The best model for explaining variation in λ included
only four predictor variables: whether or not the sampled field was a soybean field, mean natural enemy density in
the field, percentage area of cotton in the landscape and the percentage area of soybean in the landscape. Soybean
was the single most important variable for determining E.servus λ, with much greater reproduction in soybean
fields than in other crop species. Penalized regression and post-selection inference provide conservative estimates
of the landscape-scale determinants of E.servus reproduction and indicate that a relatively simple set of in-field and
landscape variables influences reproduction in this species.
Key words: brown stink bug, least angle regression
There is increasing evidence that community structure, species abun-
dance, and biotic interactions of invertebrate species in farmlands
are inuenced by larger-scale processes occurring at the landscape
level (habitat size, spatial arrangement, connectivity and quality, and
landscape matrix: Andow 1983, Marino and Landis 1996, Colunga-
Garcia etal. 1997, Thies et al. 2003, Tscharntke and Brandl 2004,
Schweiger etal. 2005, Bianchi etal. 2006, Tscharntke etal. 2007,
Gardiner et al. 2009, Yasuda et al. 2011). Arable landscapes are
often intensely managed and frequent application of agrochem-
icals can be a signicant cause of biodiversity loss (e.g., Matson
etal. 1997, Wilson etal. 1999). Further, structurally more complex
landscapes—i.e., those with higher amounts of non-crop area such
as woodland, hedgerows, grassland, fallows, and pastures—may
compensate for locally reduced diversity inside intensively managed
crop elds mainly through rapid re-colonization of resource-rich crop
elds by highly dispersive organisms (Rusch etal. 2016). These non-
crop areas can also provide insects with overwintering sites, sum-
mer aestivation sites, resting sites, mating sites, and sites that have
spatially separated resources that are required to meet their needs
(Holland and Fahrig 2000, Tscharntke etal. 2012). Additionally,
spillover across habitats often increases with increasing edge density,
or perimeter-area ratios, which can enhance or inhibit functional
connectivity among habitats (Olson and Andow 2008, Tscharntke
etal. 2012). Further, Sivakoff etal. (2013) and Meisner etal. (2017)
found that crop composition immediately adjacent to a cotton eld
was associated with substantial differences in cotton yield, the pest
Environmental Entomology, XX(X), 2018, 1–9
doi: 10.1093/ee/nvy045
Research
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species Lygus hesperus Knight (Hemiptera: Miridae) density and
pesticide use, suggesting that spillover effects of arthropod species
among crops of different quality may also occur in landscapes.
The ability of a population to persist and increase after colonizing
habitats is reected in its nite rate of population increase (λ) in that
habitat. Factors that inuence λ include micro- and macro-climates,
resource availability, competition, predation, and dispersal (Norton
etal. 2005). Knowledge of the local and landscape variables inu-
encing λ in different landscapes may lead to a better understanding
of the factor(s) contributing to population buildup in specicareas.
The southern green stink bug Nezara viridula (L.) (Hemiptera:
Pentatomidae), the brown stink bug Euschistus servus (Say)
(Hemiptera: Pentatomidae), and the green stink bug Chinavia hilaris
(Say) (Hemiptera: Pentatomidae)are important agricultural pests in
southeastern United States, that became more prominent in cotton
after widespread adoption of Bt cotton and eradication of the cotton
boll weevil (Turnipseed etal. 1995, Greene etal. 2006, Zeilinger
etal. 2011). The major row crops economically damaged by stink
bugs are eld and sweet corn, soybean and cotton (McPherson and
McPherson 2000; Koch and Pahs 2014, 2015; Soria etal. 2017).
Although peanut—another major crop in the Southeast—is also a
host of stink bugs (Tillman etal. 2009), they have not been reported
to cause economic damage to this crop. In this study we concen-
trate on E.servus because during the last several years it has become
the dominant stink bug species in southern Georgia (Olson et al.
2012). E. servus is a native species occurring from the southeast-
ern United States west through Louisiana, Texas, New Mexico, and
Arizona into California (McPherson 1982). It is bivoltine through-
out its range, and overwinters in the adult stage under crop residue,
leaves, pieces of bark, and in bunches of grass, preferring open elds
(McPherson and McPherson 2000). This species is highly mobile and
polyphagous and prefers feeding on the seeds/fruit of host plants
(McPherson and McPherson 2000); thus, they move among host
plants in response to changing phenology of the hosts (McPherson
and McPherson 2000, Blinka 2008). Maize is an early planted host
with some overlap in occurrence with later-planted peanut, cotton
and soybean. All of these crops are reproductive hosts for E.servus
(Herbert and Toews 2011, Koch and Pahs 2014), colonization pref-
erence for soybean is much higher than for peanut and cotton (Olson
etal. 2011) and peanut is a poorer quality host in terms of adult
longevity than are cotton and soybean (Olson etal. 2016). In add-
ition, numerous non-crop E.servus hosts can exist in non-woodland
and woodland eld borders surrounding the crops (McPherson and
McPherson 2000), and are suspected to be sources of stink bugs to
the adjacent crops in the spring (Reay-Jones 2010, Olson etal. 2012,
Tillman etal. 2014).
Evidence for the importance of landscape-level determinants of
natural enemy populations and their role in natural pest control is
increasing (Werling etal. 2011, Avelino 2012, Fabian etal. 2013,
Rusch etal. 2016). The role of natural enemies in stink bug popula-
tion dynamics remains poorly understood, at either the eld or land-
scape level. Olson and Ruberson (2012) indicated the importance
of re ants (Solenopsis invicta Buren, Hymenoptera: Formicidae) as
predators of stink bug eggs in unsprayed cotton and peanut, whereas
long-horned grasshoppers (Orthoptera:Tettigoniidae) were domin-
ant egg predators in unsprayed soybean. Geocoris spp. (Hemiptera:
Geocoridae) also feed on stink bug eggs (Olson and Ruberson 2012)
and are abundant predators in cotton and soybean (Naranjo and
Simac 1985, Pfannenstiel and Yeargan 1998). There is little known
of the ecology of these predators in agroecosystems with respect to
growth of E.servus populations. Given the variation among crop
species for E.servus host quality and predation rates, a landscape
approach to the study of E.servus populations encompassing major
crop and non-crop hosts may reveal population patterns that can
account for the present-day outbreaks in cotton and othercrops.
The cross-habitat spillover hypothesis put forth by Tshcarntke
etal. (2012) suggests that more mobile species and species that need
multiple cover types may spillover and ourish in landscapes with
high functional connectivity. Therefore, we tested the hypotheses
1a) that as the percentage area of woodland and pasture, maize,
peanut, cotton and/or soybean increases in the landscape, the net
reproduction of E. servus increases in the landscape, 1b) as the
number of maize, peanut, cotton and soybean elds closest to the
focal elds increases, the net reproduction of E.servus increases in
the landscape, and 2)higher densities of predators—specically, re
ants, longhorned grasshoppers and Geocoris spp. in sampled elds
reduces the net reproduction of E.servus in thateld.
Materials and Methods
SamplingPlan
We identied two areas (Southwest and East-Central) of the Coastal
Plain of Georgia (Fig.1) which to sample stink bugs in crop land-
scapes during the years 2009–2011. The areas were separated by
approximately 150 km. Within each area, we randomly identied
two or three 4.8 ×4.8 km (2,330 ha) landscapes, with two land-
scapes in 2011 in the Southwest area being 5.3×8.3 km (4,399 ha)
to encompass enough of the required elds. The Southwest area had
two landscape samples during 2009 (designated Shirah and Baggs),
and three landscape samples during 2010 and 2011 (Vinwell, Wright
and Moultrie). The East-Central area had two landscapes sampled
during 2009 (North and South), and three landscape samples dur-
ing 2010 (Davis, Henderson and Rufus) and 2011 (Henderson,
Irwin and Rufus). Landscape samples differed in location each year
because of shifting crop patterns. Working with local landowners in
each landscape, we identied three elds of maize, peanut, soybean,
and cotton. Each crop eld was sampled weekly, from early June
to August in maize, and from mid-July to late September for the
other crops. The elds were commercial elds that were managed in
accordance with the growers’ practices. We removed 43 elds where
E.servus λ could not be estimated because of low numbers and out-
lier soybean elds that had a high frequency of insecticide applica-
tions by two growers in eight soybean elds (4–6 applications) or
crop failure (plants less than 30cm in height) in one grower’s three
small elds (≤0.40 ha). This resulted in a total of 138 elds used in
the analyses over the 3-yr period of thisstudy.
Each eld was sampled using two permanent parallel transects
(spaced 30.5 m apart) running perpendicular to the edge of the
eld. All sampled eld edges were adjacent to woodland. Atotal of
20 sampling points in 2009 and 15 sampling points in 2010 and
2011 were established along each transect. The rst sample point
was placed 1 m from the crop edge for all years and in 2009, the
next 19 samples were spaced at 5 m intervals, whereas in 2010 and
2011, samples 2 through 10 were spaced at 5 m intervals and the
last ve samples were spaced at 10 m intervals (=101 m from crop
edge for all elds). Samples alternated weekly from crop rows 1–5
to the left and the right sides of transects to reduce repetitive plant
and population disturbance. Sampling of maize was done using a
two-person, whole-plant visual count with the samplers on opposite
sides of the maize row for a total sampling distance of 1.5 m (ca 8
plants per sample). Peanut was sampled using a Vortis suction sam-
pler (15cm diameter inlet, Burkard Manufacturing Company, Ltd.,
Hertfordshire, UK) with 12×3-s suctions at 7–8cm from the soil
at each sampling point (2,121cm2 sampled per sampling station).
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Cotton and soybean were sampled using a 1.5 m drop cloth with
10 shakes per sample. In all crops, about 1.5 linear row-meters of
plants were sampled at each point. All stink bugs were identied and
assigned to a developmental stage, but only E.servus was common
enough over the 3 yr to analyze in detail.
Landscape Characteristics
For each landscape, we used a geographic information system (GIS)
to determine the percentage of area in the landscape that contained
E.servus host crops (intensively produced maize, peanut, cotton and
soybean); semi-natural habitat consisting of woodland and pasture
or ‘green-veining’ (GV), and the number of maize, peanut, cotton and
soybean elds within 100, 500, and 1,000 m radii from the focal
sampling eld as a measure of cropland connectivity. Assuming no
physical barriers to E.servus movement, we calculated straight-line
connections from the edges of sampled crop elds to the closest edges
of nearby crops. Females of an ecologically similar species, N.virid-
ula, have been observed dispersing up to 1,000 m per day by ight
in search of feeding or oviposition sites (Kiritani and Sasaba 1969).
Natural Enemies
We included S. invicta, adults and nymphs of longhorned grass-
hoppers, and adults and nymphs of Geocoris spp. in our weekly
samples. In addition to generalist predator sampling, we estimated
parasitism rates by collecting and incubating a subsample of stink
bug adults and nymphs. Numbers of natural enemies and parasitism
rates were averaged over eld samples to coincide with our estimates
of λ per eld. However, because parasitism of adults (1.4%) and
nymphs (0%) was very low, overall parasitism was excluded from
subsequent analysis. Longhorned grasshopper density was very low
so they were also excluded from subsequent analysis.
Relative Finite Rate of Population Increase
Relative nite rate of population increase (λ generation−1) was esti-
mated for each sampled eld where adults were observed in the eld.
The date that adults were rst observed was considered to be the date
of rst colonization and the dates that peaks in adult density occurred
were noted. If late-instar nymphs were observed before adults were
observed, the date of rst adult colonization was assumed to have
occurred 20 d before the date of rst sighting of nymphs, based on
E.servus developmental time (Munyaneza & McPherson 1994). On
average over all crops, the number of days from the rst adult peak
to the second adult peak was 39.7± 2.5 d, so during the 40 d fol-
lowing the date of rst adult colonization, any adults observed were
considered to be colonists. After this period, adults were considered
to be progeny of the colonists. Eggs were too infrequently sampled to
be used to estimate an oviposition period or an initial density of the
offspring generation, and nymphs were found less frequently than
adults. Thus, we used colonizing and progeny adults to estimate λ.
Using Southwood’s method (Southwood 1978), we calculated the
area under the colonist-incidence curve (Ac) and the progeny-inci-
dence curve (Ap), and estimated relative net population growth as the
ratio of these two (Ap/Ac). An example is shown in Fig.2.
Statistical Analyses
First, we investigated cropland and non-cropland heterogeneity
over years. We analyzed the effects of year on arcsine-square root
transformed percentage area of cropland hosts, GV (woodland and
Fig.1. A 2009 landscape within the Coastal Plain of Georgia illustrating crop types and sampled sites.
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pasture), as well as the perimeter-to-area ratio of the sampled elds,
and the number of cropland elds within 100, 500, and 1,000 m
from the sampled eld with ANOVA and used Tukey’s HSD to sep-
arate themeans.
Second, we investigated which factors explained E. servus pop-
ulation increase, λ. We included three factors in this analysis: 3 yr,
four or six landscapes (landscapes varied over years), and three
maize, peanut, cotton and soybean elds in each landscape. During
2009, four landscapes were sampled and during 2010 and 2011
six landscapes were sampled. The design was a nested ANOVA
with years as whole plot factors and whole plot error dened by
landscapes within years (error 1). The subplots were the crops (and
crop interactions with year), and groups were the unit of replica-
tion (error 2)after removing all landscape interactions with crops
(and landscape by crop interactions with year). The natural loga-
rithm of λ (lnλ) was analyzed as the untransformed λ was highly
skewed and heteroscedastic. The standardized residuals for error 2
were normal (Supplementary Fig.S1a and b), and there was no cor-
relation between the standardized residuals and the predicted val-
ues (Supplementary Fig. S2) thereby, satisfying assumptions of an
ANOVA. However, lnλ error was still slightly heteroscedastic, so we
also conducted a randomization test, using the same ANOVA model.
Values of lnλ were randomized among crops and groups within land-
scapes, and landscapes were randomized among years, making sure
that the unbalanced structure was preserved. These randomizations
independently permuted the units of replication with respect to year
at the whole plot level and with respect to crop (and its interactions)
at the subplot level. The estimated F-value from each randomization
for each term of interest was used to construct null distributions, and
the observed F was used to calculate the P-value. The simulations
were repeated 2.5 million times so that the estimated P-value was
accurate to ve signicant digits.
Third, we determined the set of landscape and local scale vari-
ables that best explained variation in E.servus reproduction using
the LASSO method of variable selection (Hastie, Tibshirani, and
Friedman 2009). Stepwise regression and model averaging are the
other two ways to select a subset of variables. Stepwise regression
generally overestimates parameter values, and underestimates stand-
ard errors. Model averaging requires a priori specication of a set
of models. The LASSO procedure uses a regularization penalty to
‘shrink’ coefcient estimates of a linear combination of covariates,
using a pre-determined estimate of the regulator or penalty param-
eter, γ1 (note, while other authors have represented the LASSO reg-
ulator as λ, we adopt the notation of Hooten and Hobbs (2015) to
differentiate it from our estimates of stink bug reproduction, λ). The
LASSO procedure is a special case of the elastic net method (Hastie
et al. 2009); we conducted K-fold cross-validation of the elastic
net tuning parameter, α (results not shown) the minimum mean-
squared error (MSE) indicated that α ≈ 0, indicating that the LASSO
method was sufcient (Hastie etal. 2009, Hooten and Hobbs 2015).
Broadly speaking, all of these regularization methods for variable
selection—such as LASSO and elastic net—are particularly useful
in cases of strong multicollinearity among covariates (Hooten and
Hobbs 2015), which are common among landscape ecology studies
and which we suspected a priori.
To efciently solve the LASSO procedure and estimate model
coefcients, we used the least angle regression (LAR) algorithm
(Efron et al. 2004). The best value of the regulator parameter, γ1,
was chosen using a modied AIC selection procedure: the best value
is based on the number of steps through the LASSO path at which
AIC increases after two consecutive steps (Tibshirani etal. 2017).
The best model (i.e., set of non-zero coefcients) was based on the
selected value of γ1. From this, P-values and 95% condence inter-
vals were calculated using the truncated Gaussian test described by
Tibshirani etal. (2016).
To conduct the LASSO, we included the following local scale
covariates: in-eld mean densities of Geocoris spp., re ants, and
total of the two natural enemies. We also included the factors: crop
sampled for stink bugs (maize, peanut, cotton, soybean) and the
year. Factors with more than two levels were split into multiple
binary dummy variables. For example, we created a new binary
variable for each crop species sampled; the ‘soybean’ variable was
coded as a ‘1’ for each observation from a soybean eld and ‘0’ for
observations from the other three crop species. This was repeated
for the other three crop species. The landscape covariates included
the percentage area of specic crop hosts in the landscapes (maize,
peanut, cotton, soybean) and total crop hosts, percentage area of
GV, and the number of maize, peanut, cotton and soybean elds
within 100, 500, and 1,000 m radii from the sampled eld. This
analysis included 32 total covariates: three natural enemy density
variables, four crop species dummy variables, 3-yr dummy varia-
bles, ve percentage area variables, PA ratio, percentage of GV, and
15 variables on the number of elds (of specic crops and of all
crops) at different distances.
Estimated densities of natural enemies were natural log trans-
formed, and all covariates were transformed to standardized normal
variables, by centering each covariate around its mean and scaling
its standard deviation. For comparison, we also include the ordinary
least-squares coefcient estimates, which are easily derived from
LAR (Tibshirani etal. 2016). The signicance level for all hypothesis
tests was set at α=0.05.
All geospatial manipulations and analyses were conducted using
ArcGIS for Desktop (Version 10.3, Advanced; ArcGIS for Desktop
2014). ANOVA tests were conducted in SAS (SAS Institute Inc.
1998). Randomization tests and LASSO were conducted in R 3.3.2
(R Core Team 2016). Elastic net cross-validation was conducted
using the glmnet and glmnetUtils packages (Friedman etal. 2010,
Ooi and Microsoft 2017); the LAR and truncated Gaussian test were
conducted using the selective Inference package (Tibshirani et al.
2017). R code for running LASSO analysis can be found at https://
github.com/arzeilinger/stink_bug_reproduction_lasso.
Fig. 2. An example of incidence data: the average number of brown stink
bug adults, nymphs, and eggs per sample versus day of year in maize in
the North Coffee landscape of the East-Central region. Using Southwood’s
method (Southwood 1978), we calculated the area under the colonist-
incidence curve (Ac) and the progeny-incidence curve (Ap), and estimated
relative net population growth as the ratio of these two (Ap/Ac).
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Results
Landscape Characteristics
The percentage area of all crops combined varied over the years
being higher in 2009 and 2010 than in 2011 (Supplementary
TableS1, Table1). The percentage area of maize, soybean and pas-
ture in the landscapes were higher in 2009 than in 2010 and 2011
(Supplementary TableS1, Table1), and the percentage area of cot-
ton in the landscapes was lower in 2009 than in 2010 and 2011
(Supplementary Table S1, Table 1). The perimeter-to-area ratios
of sampled elds, percentage area of peanut and GV in the land-
scape and the number of crop elds within 100 and 500 m radii
from the sampled eld were not signicantly different over years
(Supplementary TableS1, Table1). The number of crop elds within
a 1,000 m radius from the sampled eld was signicantly higher in
2010 than 2011 (Supplementary TableS1, Table1). The percentage
area of cotton in the landscapes (mean ± SD: 11.56 ± 5.68) was
highest overall, intermediate in maize (7.16 ± 5.00) and peanut
(8.03±3.14) and lowest in soybean (2.00±1.11).
Relative Finite Rate of Population Increase
Finite rates of population increase (λ generation−1) of E.servus var-
ied signicantly with crop (Table 2). Overall, λ was signicantly
higher in soybean than in maize, cotton and peanut (Table3). There
was high variance in λ in all of the crops, indicating that all crops
were periodically low in reproduction, but sometimes they were
highly productive habitats (Supplementary TableS2).
For the LASSO analysis of E.servus λ, modied AIC selection
indicated that γ1=9.97 produced the best model. LASSO is a method
of variable selection; as such, all covariates with non-zero coefcient
estimates are considered to be included in the ‘best model’. Of the
32 variables included in the analysis, only four were selected in the
best model: 1)whether or not the sampled eld was a soybean eld,
2) mean natural enemy in-eld density, 3)percentage area of cot-
ton in the landscape, and 4)percentage area of soybean in the land-
scape (Table4). All other variables were dropped from the LASSO
model. Soybean eld identity was the only variable with a signicant
truncated Gaussian test (Table4). In contrast to the LASSO results,
the least squares regression model included 29 variables, some with
extremely large coefcient estimates (Table4).
Discussion
We found that the soybean crop was the single most important
predictor of increases in the relative net reproductive rate (λ gen-
eration−1) of E.servus populations, which is inconsistent with our
rst hypothesis. Our results also suggest that the total area of cot-
ton and soybean in the landscape and natural enemy density in
focal elds are important in affecting λ, but only in combination
with each other and the soybean crop. Given that the condence
intervals for the non-signicant variables overlap zero, little can
be said about how they are affecting λ. Overall, we found some
support that natural enemy density and cotton and soybean in the
landscape have respective impacts on stink bug λ, but we were
unable to isolate clear impacts of these processes on their own. In
contrast to the LASSO results, the model based on least-squares
regression includes 29 variables with coefcient estimates as large
as ± 1014. Collinearity among covariates—which is common in
spatial and landscape ecology studies—can inate coefcient esti-
mates in least-squares regression, as seen in our analysis (Taylor
and Tibshirani 2015). LASSO and related procedures are more
appropriate for such problems; the inclusion of a regulator or pen-
alty parameter produces more conservative coefcient estimates,
with more estimates equal to zero (i.e., dropped from the model)
(Hooten and Hobbs 2015).
The Southwood (1978) method of estimating relative net pop-
ulation growth rate has several potential sources of error and bias.
Stink bug densities were generally low, so sampling error for a
sample date was generally large. The use of areas under the inci-
dence curve rather than incidence itself reduces bias and improves
precision (Manly 1977) because the daily errors are averaged by
the calculation of the area under the curve. Another source of
potential bias is adult mortality and emigration from the eld. If
the time a colonizing adult stays in a habitat is less (or greater)
than the time a progeny adult stays in the same habitat (leaving
either by dispersal or death), then λ per capita will be over- (or
under-) estimated. However, if such a bias in λ occurred, it would
likely be a consistent bias for any particular habitat, as the fac-
tors affecting differential mortality and dispersal in the colonizing
versus the progeny adults are likely to be determined by habitat
factors, such as systematic variation in microclimate, nutritional
quality, competition, predation, and parasitism. Thus, compar-
isons among landscapes within a crop habitat are likely to be
accurate. If potential variation in adult mortality and emigration
rates is carefully considered, differences in the estimated λ would
be due to differences in net reproduction, or immature mortality
as would be expected.
Table1. Percentage of land use types and cropland perimeter-to-area
ratio (m) for 16 landscapes within years (2009, 2010, and 2011)
Index Year Mean ± SE
% Cropland hosts 2009 30.65±0.020a
2010 29.66±0.007a
2011 25.57±0.008b
% Maize 2009 0.101±0.009a
2010 0.067±0.006b
2011 0.057±0.004b
% Peanut 2009 0.073±0.006
2010 0.077±0.004
2011 0.064±0.004
% Cotton 2009 0.082±0.003b
2010 0.132±0.006a
2011 0.124±0.009a
% Soybean 2009 0.052±0.004a
2010 0.022±0.001b
2011 0.010±0.001b
% Green-veining 2009 0.421±0.019
2010 0.390±0.013
2011 0.405±0.009
Perimeter-area-ratio 2009 136.72±13.72
2010 134.52±8.59
2011 119.09±7.71
Number of crop elds (100 m) 2009 4.00±0.30
2010 3.39±0.20
2011 3.83±0.20
Number of crop elds (500 m) 2009 9.83±0.64
2010 9.48±0.54
2011 8.00±0.49
Number of crop elds (1,000 m) 2009 18.74±1.10ab
2010 20.04±0.98a
2011 16.00±0.67b
Cropland hosts=maize, peanut, cotton and soybean. GV=pastures, wood-
land and non-crop hosts in the woodland. Different letters within indices are
signicantly different at P<0.05.
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Our results indicate that immature E.servus mortality from re
ant and Geocoris spp. predation may have reduced net reproduction
across the study area. In addition, mortality from insecticides likely
contributed to lower λ in landscapes with a higher proportion of cot-
ton, as insecticides were generally applied 2–3 times on this crop. As
it was not possible to obtain accurate and timely agrochemical appli-
cation information from growers, we based our estimated frequency
of applications on direct observations, conversations with growers
and the presence in some samples of only Hippodamia convergens
Guérin-Méneville (Coleoptera: Coccinellidae), a species resistant to
the pyrethroids and organophosphates typically applied in Georgia
(Barbosa etal. 2016). However, it was unlikely that the variance in
λ among elds of a given crop was due to differential insecticide
application, because cotton was similarly treated with insecticides
throughout the study area, and insecticides were seldom applied on
maize, peanut, and soybean. This is supported by the consistently
high diversity of insect herbivores, predators and parasitoids that
we observed in maize, peanut, and soybean elds (D.M.O. & J.R.R.,
personal observation).
It is unlikely that differential emigration rates by colonizing ver-
sus progeny adult stink bugs were the major cause of variation in
λ. This is because herbivorous stink bugs typically move frequently
to and from nearby habitats in search of food, mates and oviposi-
tion sites (Kiritani etal.1965, Todd 1989), but they tend to remain
closely associated with food plants, either foraging nearby or return-
ing to habitats with good food plants (Kiritani etal. 1965). It is only
when the food plant quality deteriorates, especially when the plant
matures or is harvested, that stink bugs leave the habitat (Todd and
Herzog 1980, Todd 1989, Reisig etal. 2013).
We found that the higher combined densities of the generalist
predators Geocoris spp. and S. invicta may have been associated
with lower E. servus λ. Such a relation may have been caused by
1)density-dependent regulation of E. servus populations by gener-
alist predators, 2)predator densities are determined by factors unre-
lated to E. servus population density and proportionally suppress
stink bug reproduction, or 3)predator densities and E.servus repro-
duction co-vary because they both are responding to some common
cause. While the third hypothesis cannot be ruled out, we suggest
that the rst hypothesis is unlikely. Given that both Geocoris spp.
and S.invicta are extreme generalists, it is unlikely that stink bug
densities would strongly inuence predator population dynamics
resulting in regulation of stink bug populations. Previous work pro-
vides some support for the second hypothesis; both predators prey
on stink bug eggs (Olson and Ruberson 2012), which would reduce
λ. Thus, our results are the rst to suggest that predation inuences
stink bug reproduction in the region.
A higher percentage area of either cotton or soybean in the land-
scape was associated with lower E.servus λ. Cotton was the dom-
inant crop in the landscapes while soybean was the least abundant
crop. Cotton may be an acceptable but not a very good reproductive
host for E.servus, especially when considering the higher insecticide
use on this crop. Therefore, the more cotton in the landscape, the
lower would be λ in the landscape. The reasons for the negative rela-
tionship between λ and the percentage area of soybean are less clear.
We found the highest E.servus λ in soybean compared to the other
crops, and there was a relatively high correlation between E.servus
λ and soybean suggesting that soybean can be a very good reproduc-
tive host for E.servus, as has been found for other stink bug species
(Panizzi and Slansky 1985). Subsequent analyses of Geocoris spp.
density indicated that soybean had strong and positive effects on
their density (D. M.Olson etal. unpublished data). Higher Geocoris
spp. densities and E.servus reproduction in soybean suggests that
high predation on immatures may have occurred in this crop,
accounting for the overall negative relationship between E.servus
reproduction and the percentage area of soybean in the landscape.
The stink bug N.viridula and presumably E.servus can move
over distances of 1,000 m per day in search of feeding and oviposition
sites (Kiritani and Sasaba 1969). However, E.servus may not need
to traverse such a distance in the landscapes in the Georgia coastal
plain region where crops are often closely spaced and preferred crop
phenologies for feeding are present throughout the season. This is
supported by the lack of any relationship between the distances of
crops from the sampled eld and stink bug reproductionrates.
The percentage area of GV in the landscape had no relationship
with λ in the sampled elds. This is contrary to the often positive rela-
tionship found for insect natural enemies, butteries and vertebrate
species (Tscharntke etal. 2012, Rusch etal. 2016), but is consistent
with the relatively few studies of pest insect species (Bianchi etal.
2006, Chaplin-Kramer 2011). Woodlands in the studied landscapes
were mainly comprised of natural and planted pine and oak species
which likely have few nutritional resources available for stink bug
species. These woodlands may have provided E.servus with summer
aestivation sites, resting sites or mating sites, or temporary refuge
Table 3. Distribution of the relative finite rate of population
increase (λ generation−1) of E.servus per crop
Level of crop nλ Generation−1
Mean Standard error
Maize 41 1.33b 0.15
Cotton 28 1.13b 0.14
Peanut 36 1.33b 0.29
Soybean 24 3.96a 0.63
Means with the same letter are not signicantly different at P<0.05.
Table2. ANOVA of the effects of year, crop, and their interactions on ln-tranformed relative finite rate of population increase (λ generation−1)
of E.servus. Degrees of freedom (df), Sum of Squares (SS), Mean Square (MS)
Source df Type III SS MS F-value P-value GLM P-value randomization
Level 1
Year 2 0.81078484 0.40539242 0.57 0.560 0.989
Error 1 5 6.64969660 2.2223104
Level 2
Crop 3 7.51726378 1.67410628 3.50 0.018 <0.001
Year × crop 6 6.36069670 1.06011612 1.48 0.193 0.408
Error 2 99 70.9751941 0.7169212
6 Environmental Entomology, 2018, Vol. XX, No. X
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from eld disturbance and adverse abiotic conditions, (Holland and
Fahrig 2000, Tscharntke et al. 2012), but these factors had mini-
mal inuence on E. servus net reproduction in the crops studied
here. In a previous study, we found that E.servus was not found
at the eld edges of peanut, cotton and soybean that were adjacent
to woodlands (Olson etal. 2012). We concluded in that study, that
these woodlands were not a major source of stink bug crop colonists
to peanut, cotton or soybean. This is in contrast to what Tillman
and Cottrell (2016) recently found where several stink bug species,
including E.servus, moved from elderberry in woodland to adjacent
crops. Elderberry was not found near the crops in our study areas
(Appendix A.1.1 and A.2.2 inOlson etal. 2012). The results from
this study also suggest that the woodlands of our study were not very
productive habitats for E.servus.
Understanding the response of arthropod herbivores to land-
scape ‘complexity’ has been a focus of two recent reviews (Bianchi
etal. 2006, Chaplin-Kramer etal. 2011). Bianchi etal. (2006) used
the proportion of non-crop habitat and perimeter-to-area ratios and
boundary density of elds as measures of complexity, and found that
in 45% of the cases complexity reduced pest pressure. However, 40%
of the cases showed no response to complexity. Chaplin-Kramer etal.
(2011) expanded the concept of landscape complexity, and consid-
ered ve measures: % natural habitat, % non-crop habitat, % crop
habitat, habitat diversity (Shannon and Simpson indexes), and other
measures (distance to natural habitat and length of woodland edges).
They found no effect of landscape complexity on pest abundance or
plant damage. Both reviews recognized that few landscape studies
have measured arthropod pest responses, and Chaplin-Kramer etal.
(2011) identied a need to standardize measurements of landscape
complexity. They also suggest that a strong context-specic response
may preclude simple standardizations. Arecent study found that ovi-
position by two mobile, polyphagous and multivoltine moth species
is dynamic and depends on the composition, arrangement, attract-
iveness, and preference for crops in the landscape (Parry etal. 2017).
Therefore, generalist herbivores may have specic responses to a
suite of factors that depend on the species and landscape context,
thereby precluding simple standardization.
In summary, our results showed that the landscape charac-
teristics of the percentage area of maize, peanut and GV in the
landscape and the number of crops at various distances from the
sampled elds had no inuence on E. servus reproduction in the
landscapes. Overall, soybean was the strongest single local scale
variable explaining E.servus λ. But, the combined local scale char-
acteristics of soybean and natural enemy density and the landscape
scale characteristics of the percentage area of cotton and soybean
better explained E.servus λ than did soybean by itself. These results
suggest that a relatively simple set of in-eld and landscape varia-
bles related to differences in habitat prevalence and relative host
quality inuences reproduction in this mobile, polyphagous and
multivoltine species.
SupplementaryData
Supplementary data are available at Environmental Entomology
online.
Acknowledgments
We thank Andy Hornbuckle, Melissa Thompson, and numerous student work-
ers for their help in the eld. We also thank two anonymous reviewers for
their comments which have greatly improved the manuscript. The project was
supported by the National Institute of Food and Agriculture (grant number
2008-35302-04709 to D.A.A., D.M.O., and J.R.R.).
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