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Landscape Effects on Reproduction of Euschistus servus (Hemiptera: Pentatomidae), a Mobile, Polyphagous, Multivoltine Arthropod Herbivore


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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. A geographic 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.
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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 SuqinHou,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:
Subject Editor: ChristopherRanger
Received 30 January 2018; Editorial decision 15 March 2018
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. Ageographic 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 inuenced 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 etal. 1997, Thies et al. 2003, Tscharntke and Brandl 2004,
Schweiger etal. 2005, Bianchi etal. 2006, Tscharntke etal. 2007,
Gardiner et al. 2009, Yasuda et al. 2011). Arable landscapes are
often intensely managed and frequent application of agrochem-
icals can be a signicant cause of biodiversity loss (e.g., Matson
etal. 1997, Wilson etal. 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 etal. 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 etal. 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
etal. 2012). Further, Sivakoff etal. (2013) and Meisner etal. (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
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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 reected in its nite rate of population increase (λ) in that
habitat. Factors that inuence λ include micro- and macro-climates,
resource availability, competition, predation, and dispersal (Norton
etal. 2005). Knowledge of the local and landscape variables inu-
encing λ in different landscapes may lead to a better understanding
of the factor(s) contributing to population buildup in specicareas.
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 etal. 1995, Greene etal. 2006, Zeilinger
etal. 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 etal. 2017).
Although peanut—another major crop in the Southeast—is also a
host of stink bugs (Tillman etal. 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
etal. 2011) and peanut is a poorer quality host in terms of adult
longevity than are cotton and soybean (Olson etal. 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 etal. 2012,
Tillman etal. 2014).
Evidence for the importance of landscape-level determinants of
natural enemy populations and their role in natural pest control is
increasing (Werling etal. 2011, Avelino 2012, Fabian etal. 2013,
Rusch etal. 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 othercrops.
The cross-habitat spillover hypothesis put forth by Tshcarntke
etal. (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—specically, re
ants, longhorned grasshoppers and Geocoris spp. in sampled elds
reduces the net reproduction of E.servus in thateld.
Materials and Methods
We identied 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 identied
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 identied 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 30cm 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 thisstudy.
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. Atotal 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 (15cm diameter inlet, Burkard Manufacturing Company, Ltd.,
Hertfordshire, UK) with 12×3-s suctions at 7–8cm from the soil
at each sampling point (2,121cm2 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 identied 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 themeans.
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 dened 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 signicant 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 specication of a set
of models. The LASSO procedure uses a regularization penalty to
‘shrink’ coefcient 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 sufcient (Hastie etal. 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 efciently solve the LASSO procedure and estimate model
coefcients, we used the least angle regression (LAR) algorithm
(Efron et al. 2004). The best value of the regulator parameter, γ1,
was chosen using a modied 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 etal. 2017).
The best model (i.e., set of non-zero coefcients) was based on the
selected value of γ1. From this, P-values and 95% condence inter-
vals were calculated using the truncated Gaussian test described by
Tibshirani etal. (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 specic 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 specic 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 coefcient estimates, which are easily derived from
LAR (Tibshirani etal. 2016). The signicance 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 etal. 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://
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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Landscape Characteristics
The percentage area of all crops combined varied over the years
being higher in 2009 and 2010 than in 2011 (Supplementary
TableS1, Table1). The percentage area of maize, soybean and pas-
ture in the landscapes were higher in 2009 than in 2010 and 2011
(Supplementary TableS1, Table1), 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 signicantly different over years
(Supplementary TableS1, Table1). The number of crop elds within
a 1,000 m radius from the sampled eld was signicantly higher in
2010 than 2011 (Supplementary TableS1, Table1). 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 signicantly with crop (Table 2). Overall, λ was signicantly
higher in soybean than in maize, cotton and peanut (Table3). 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 TableS2).
For the LASSO analysis of E.servus λ, modied 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 coefcient
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 (Table4). All other variables were dropped from the LASSO
model. Soybean eld identity was the only variable with a signicant
truncated Gaussian test (Table4). In contrast to the LASSO results,
the least squares regression model included 29 variables, some with
extremely large coefcient estimates (Table4).
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 condence
intervals for the non-signicant 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 coefcient estimates as large
as ± 1014. Collinearity among covariates—which is common in
spatial and landscape ecology studies—can inate coefcient 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 coefcient 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.
Table1. 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
signicantly 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 etal. 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 etal.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 etal. 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 etal. 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 inuence 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 inuences
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 etal. 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 reproductionrates.
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, butteries and vertebrate
species (Tscharntke etal. 2012, Rusch etal. 2016), but is consistent
with the relatively few studies of pest insect species (Bianchi etal.
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 signicantly different at P<0.05.
Table2. 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 inuence 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 etal. 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 inOlson etal. 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
etal. 2006, Chaplin-Kramer etal. 2011). Bianchi etal. (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 etal.
(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 etal.
(2011) identied a need to standardize measurements of landscape
complexity. They also suggest that a strong context-specic response
may preclude simple standardizations. Arecent 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 etal. 2017).
Therefore, generalist herbivores may have specic 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 inuence 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 inuences reproduction in this mobile, polyphagous and
multivoltine species.
Supplementary data are available at Environmental Entomology
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.).
Andow, D. A. 1983. The extent of monoculture and insect pest populations
with particular reference to wheat and cotton. Agr. Ecosyst. Environ. 9:
ArcGIS for Desktop. 2014. 10.3, Advanced. Environmental Systems Research
Institute, Inc., Redlands, CA.
Avelino, J., A. Romero-Gurdian, H. F.Vruz-Cueller, and F. A.J. Declerck. 2012.
Landscape context and scale differentially impact coffee leaf rust, coffee
berry borer, and coffee root-knot nematodes. Ecol. Appl. 22: 584–596.
Barbosa, P. R.R., J. P.Michaud, A. R.S.Rodrigues, and J. B. Torres. 2016.
Dual resistance to lambda-cyhalothrin and dicrotophos in Hippodamia
convergens (Coleoptera: Coccinellidae). Chemosphere. 159: 1–9.
Blinka, E. L. 2008. Biological and ecological studies on green stink bug,
Acrosternum hilare, and brown stink bug, Euschistus servus (Hemiptera:
Pentatomidae), in eastern North Carolina cropping systems, PhD disserta-
tion. North Carolina State University, Raleigh, NC.
Table 4. Results from LASSO analysis relating stink bug relative
finite rate of population increase (λ generation−1) to a set of in-field
and landscape covariates
Effect LASSO estimate
(± 95% CI)a
Soybean 0.338
(0.203, 1.13)
0.000746 0.661
Mean re ant and Geocoris
(−0.484, 4.88)
0.822 0.0366
Percentage of cotton −0.0477
(−1.62, 1.38)
0.417 −2.63E^14
Percentage of soybean −0.0183
(−1.95, 3.34)
0.603 −9.59E^13
GV 0 NA 0.498
PA 0 NA −0.252
Mean Geocoris spp. 0 NA −0.177
Mean ants 0 NA −0.172
Percentage of maize 0 NA −2.20E^14
Percentage of peanut 0 NA −1.77E^14
Percentage of all crops 0 NA 3.95E^14
Number of maize (100 m) 0 NA −1.07E^13
Number of cotton (100 m) 0 NA −1.47E^13
Number of peanut (100 m) 0 NA −1.27E^13
Number of soybean (100 m) 0 NA −1.16E^13
Number of all crops (100 m) 0 NA 1.99E^13
Number of maize (500 m) 0 NA 0.158
Number of cotton (500 m) 0 NA −0.324
Number of peanut (500 m) 0 NA 0.0509
Number of soybean (500 m) 0 NA 0.289
Number all crops (500 m) 0 NA 0
Number of maize (1,000 m) 0 NA −0.187
Number of cotton (1,000 m) 0 NA 0.233
Number of peanut (1,000 m) 0 NA 0.184
Number of soybean (1,000 m) 0 NA −0.0777
Number of all crops (1,000 m) 0 NA 0
Maize 0 NA 0.119
Cotton 0 NA 0
Peanut 0 NA 0.012
Year 2009 0 NA 1.85E^14
Year 2010 0 NA 2.12E^14
Year 2011 0 NA 2.05E^14
aCoefcient estimates from LASSO analysis. 95% condence interval (±
95% CI) and P-values were calculated from truncated Gaussian test.
bOrdinary least-squares coefcient estimates (LS Estimates) derived from
LAR output.
Environmental Entomology, 2018, Vol. XX, No. X 7
Downloaded from
by DigiTop USDA's Digital Desktop Library user
on 04 April 2018
Bianchi, F. J.J. A., C. J.H. Booij, and T. Tscharntke. 2006. Sustainable pest
regulation in Agricultural landscapes: a review on landscape composition:
biodiversity and natural pest control. Proc. Royal Soc. B. 273: 1715–1727.
Chaplin-Kramer, R., M. E.O’Rourke, E. J.Blitzer, and C. Kremen. 2011. A
meta-analysis of crop pest and natural enemy response to landscape com-
plexity. Ecol. Lett. 14: 922–932.
Colunga-Garcia, M., S. H. Gage, and D. A. Landis. 1997. Response of an
assemblage of Coccinellidae (Coleoptera) to a diverse agricultural land-
scape. Environ. Entomol. 26: 797–804.
Efron B, T. Hastie, I. Johnstone, and R. Tibshirani. 2004. Least angle regres-
sion. Ann. Stat. 32: 407–499.
Fabian, Y., N. Sandau, O. T. Bruggisser, A. Aebi, P. Kehrli, R. P. Rohr, R. E.
Naisbit, and L. F.Bersier. 2013. The importance of landscape and spa-
tial structure for hymenopteran-based food webs in an agro-ecosystem. J.
Anim. Ecol. 82: 1203–1214.
Friedman, J., T. Hastie, and R.Tibshirani. 2010. Regularization paths for gen-
eralized linear models via coordinate descent. J. Stat. Softw. 33: 1–22.
Gardiner, M. M., D. A. Landis, C.Gratton, C. D. DiFonzo, M.O’Neal, J.
M.Chacon, M. T.Wayo, N. P.Schmidt, E. E.Mueller, and G. E.Heimpel.
2009. Landscape diversity enhances biological control of an introduced
crop pest in the north-central USA. Ecol. Appl. 19: 143–154.
Greene, J. K., C. S. Bundy, P. M. Roberts, and B. R. Leonard. 2006.
Identication and management of common boll feeding bugs in cotton.
Clemson University, Blackville, SC.
Hastie, T., R. Tibshirani, and J. H. Friedman. 2009. The elements of statistical
learning: data mining, inference, and prediction. Springer, New York, NY.
Herbert, J. J., and M. D. Toews. 2011. Seasonal abundance and population struc-
ture of brown stink bug (Hemiptera: Pentatomidae) in farmscapes containing
corn, cotton, peanut, and soybean. Ann. Entomol. Soc. Am. 104: 99–918.
Holland, J., and L. Fahrig. 2000. Effect of woody borders on insect density and
diversity in crop elds: a landscape-scale analysis. Agr. Ecosyst. Environ.
78: 115–122.
Hooten, M. B., and T. Hobbs. 2015. A guide to Bayesian model selection for
ecologists. Ecol. Monogr. 85: 3–28.
Kiritani, K., and T. Sasaba. 1969. The difference in bio- and ecological char-
acteristics between neighbouring populations in the southern green stink
bug, Nezara viridula L.Jpn. J.Ecol. 19: 177–184.
Kiritani, K., N.Hokyo, K.Kimura, and F. Nakasuji. 1965. Imaginal dispersal
of the southern green stink bug Nezara viridula L., in relation to feeding
and oviposition. Jpn. J.Appl. Entomol. Z. 9: 291–297.
Koch, R. L., and T.Pahs. 2014. Species composition, abundance, and seasonal
dynamics of stink bugs (Hemiptera: Pentatomidae) in Minnesota soybean
elds. Environ. Entomol. 43: 883–888.
Koch, R. L., and T.Pahs. 2015. Species composition and abundance of stink
bugs (Hemiptera: Heteroptera: Pentatomidae) in Minnesota Field Corn.
Environ. Entomol. 44: 233–238.
Manly, B. F. J. 1977. A further note on Kiritani and Nakasuji’s model for
stage-frequency data including comments on the use of Tukey’s jackknife
technique for estimating variance. Res. Popul. Ecol. 18: 177–186.
Marino, P. C., and D. A. Landis. 1996. Effect of landscape structure on parasi-
toid diversity in agroecosystems. Ecol. Appl. 6: 276–284.
Matson, P. A., W.J. Parton, A. G. Power, and M. J.Swift. 1997. Agricultural
intensication and ecosystem properties. Science. 277: 504–509.
McPherson, J. E. 1982. The Pentatomoidea (Hemiptera) of northeastern
North America with emphasis on the fauna of Illinois. Southern Illinois
University Press, Carbondale and Edwardsville, IL, 240 p.
McPherson, J. E., and R. M. McPherson. 2000. Stink bugs of economic impor-
tance in America North of Mexico. CRC Press, Boca Raton, FL.
Meisner, M. H., T. Zaviezo, and J. A.Rosenheim. 2017. Landscape crop com-
position effects on cotton yield, Lygus hesperus densities and pesticide use.
Pest Manag. Sci. 73: 232–239.
Munyaneza, J., and J. E. McPherson. 1994. Comparative-study of life-histo-
ries, laboratory rearing, and immature stages of Euschistus servus and
Euschistus variolarius (Hemiptera, Pentatomidae). Great Lakes Entomol.
26: 263–274.
Naranjo, S.E., and J. L. Stimac. 1985. Development, survival, and reproduc-
tion of Geocoris punctipes (Hemiptera: Lygaeidae): effects of plant feeding
on soybean and associated weeds. Environ. Entomol. 14: 523–530.
Norton, L. R., L. G. Firbank, A. Scott, and A. R. Watkinson. 2005.
Characterising spatial and temporal variation in the nite rate of popu-
lation increase across the northern range boundary of the annual grass
Vulpia fasciculata. Oecologia. 144: 407–415.
Olson, D., and D. Andow. 2008. Patch edges and insect populations.
Oecologia. 155: 549–558.
Olson, D. M., and J. R. Ruberson. 2012. Crop-specic mortality of south-
ern green stink-bug eggs in Bt- and non-Bt cotton, soybean and peanut.
Biocontrol Sci. Techn. 22: 1417–1428.
Olson, D. M., J. R. Ruberson, A. R. Zeilinger, and D. A. Andow. 2011.
Colonization preference of Euschistus servus and Nezara viridula in trans-
genic cotton varieties, peanut and soybean. Entomol. Exp. Appl. 139:
Olson, D. M., J. R. Ruberson, and D. A. Andow. 2012. Effects on stink
bugs of field edges adjacent to woodland. Agr. Ecosyst. Environ. 156:
Olson, D. M., J. R. Ruberson, and D. A. Andow. 2016. Relative longevity
of adult Nezara viridula in eld cages of cotton, peanut, and soybean.
Entomol. Exp. Appl. 159: 30–36.
Ooi, H., and Microsoft. 2017. glmnetUtils: Utilities for “Glmnet.” R package
version 1.0.2. https://CRAN.
Panizzi, A. R., and F. Slansky. 1985. Review of phytophagous pentatomids
(Hemiptera: Pentatomidae) associated with soybean in the Americas. Fla.
Entomol. 68: 184–214.
Parry, H. R., C. A. Paull, M. P. Zalucki, A. R. Ives, A. Hulthen, and
N. A. Schellhorn. 2017. Estimating the landscape distribution of eggs by
Helicoverpa spp., with implications for Bt resistance management. Ecol.
Model. 365: 129–140.
Pfannenstiel, R. S., and K. V. Yeargan. 1998. Association of predaceous
Hemiptera with selected crops. Environ. Entomol. 27: 232–239.
R Core Team. 2016. R: Alanguage and environment for statistical computing.
R Foundation for Statistical Computing, Vienna, Austria.
Reay-Jones, F. P. 2010. Spatial and temporal patterns of stink bugs (Hemiptera:
Pentatomidae) in wheat. Environ. Entomol. 39: 944–955.
Reisig, D. D., M.Roe, and A.Dhammi. 2013. Dispersal pattern and dispersion
of adult and nymph stink bugs (Hemiptera: Pentatomidae) in wheat and
corn. Environ. Entomol. 42: 1184–1192.
Rusch, A., R. Chaplin-Kramer, M. M. Gardiner, V. Hawro, J. Holland,
D.Landis, C.Thies, T.Tscharntke, W. W.Weisser, C.Winqvist, etal. 2016.
Agriculture landscape simplication reduces natural pest control: a quan-
titative synthesis. Agr. Ecosyst. Environ. 21: 198–204.
SAS Institute Inc. 1998. SAS version 6. SAS/STAT User Guide Release, Cary,
Schweiger, O., J. P.Maelfait, W.van Wingerden, F.Hendrickx, R.Billeter, M.
I.Speelmans, I.Augenstein, B.Aukema, S.Aviron, D.Bailey, etal. 2005.
Quantifying the impact of environmental factors on arthropod commu-
nities in agricultural landscapes across organizational levels and spatial
scales. J. Appl. Ecol. 42: 1129–1139.
Sivakoff, F. S., J. A.Rosenheim, P.Dutilleul, and Y.Carrière. 2013. Inuence of
the surrounding landscape on crop colonization by a polyphagous insect
pest. Entomol. Exp. Appl. 49: 11–21.
Soria, M. F., P.E. Degrande, A. R. Panizzi, and M. D.Toews. 2017. Economic
injury level of the Neotropical brown stink bug euschistus heros (F.) on
cotton plants. Neotrop. Entomol. 46: 324–335.
Southwood, T. R. E. 1978. The construction, description, and analysis
of age-specic life-tables, pp. 356–374. In T. R.E. Southwood (ed.),
Ecological methods, with particular reference to the study of insect
populations, 2nd ed. Chapman and Hall, London, United Kingdom.
Taylor, J., and R. J.Tibshirani. 2015. Statistical learning and selective infer-
ence. Proc. Natl. Acad. Sci. U. S. A. 112: 7629–7634.
Thies, C., I. Steffan-Dewenter, and T. Tscharntke. 2003. Effects of landscape
context and parasitism at different spatial scales. Oikos. 101: 18–25.
Tibshirani, R. J., J. Taylor, R. Lockhart, and R. Tibshirani. 2016. Exact
post-selection inference for sequential regression procedures. J. Am. Stat.
Assoc. 111: 600–620.
Tibshirani, R. J., R.Tibshirani, J.Taylor, J.Loftus, and S.Reid. 2017. selec-
tiveInference: Tools for Post-Selection Inference. R package version 1.2.2.
8 Environmental Entomology, 2018, Vol. XX, No. X
Downloaded from
by DigiTop USDA's Digital Desktop Library user
on 04 April 2018
Tillman, P. G., and T. E.Cottrell. 2016. Density and egg parasitism of stink
bugs (Hemiptera: Pentatomidae) in elderberry and dispersal into crops. J.
Insect Sci. 16: 1–14.
Tillman, P. G., T. D. Northeld, R. F. Mizell, and T. C. Riddle. 2009.
Spatiotemporal patterns and dispersal of stink bugs (Heteroptera:
Pentatomidae) in peanut-cotton farmscapes. Environ. Entomol. 38:
Tillman, P. G., T. E.Cottrell, R. F.Mizell, and E.Kramer. 2014. Effect of eld
edges on dispersal and distribution of colonizing stink bugs across farms-
capes of the southeast USA. Bull. Entomol. Res. 104: 56–64.
Todd, J. W. 1989. Ecology and behavior of Nezara viridula. Annu. Rev.
Entomol. 34: 273–292.
Todd, J. W., and D. C. Herzog. 1980. Sampling phytophagous Pentatomidae
on soybean, pp. 438–478. In M.Kogan and D. C.Herzog (eds.), Sampling
methods in soybean entomology. Springer-Verlag, New York, NY.
Tscharntke, T., and R.Brandl. 2004. Plant-insect interactions in fragmented
landscapes. Annu. Rev. Entomol. 49: 405–430.
Tscharntke, T., R.Bommarco, Y.Clough, T. O.Crist, D.Kleijn, T. A.Rand,
J. M.Tylianakis, S.Nouhuys, and S.Vidal. 2007. Conservation biologi-
cal control and enemy diversity on a landscape scale. Biol. Control. 43:
Tscharntke, T., J.M. Tylianakis, T. A. Rand, R. K. Didham, L. Fahrig, P. Batáry,
J. Bengtsson, Y. Clough, T. O. Crist, C. F. Dormann, etal. 2012. Landscape
moderation of biodiversity patterns and processes - eight hypotheses. Biol.
Rev. Camb. Philos. Soc. 87: 661–685.
Turnipseed, S. G., M. J.Sullivan, J. E.Mann, and M. E. Roof. 1995. Secondary
pests in transgenic Bt cotton in South Carolina, pp 768–769. 1995
Proceedings Beltwide Cotton Conferences, San Antonio, TX, USA, January
4-7, Vol 2. Memphis: National Cotton Council.
Werling, B. P., T. D.Meehan, B. A.Robertson, C.Gratton, and D. A.Landis.
2011. Biocontrol potential varies with changes in biofuel-crop plant com-
munities and landscape perenniality. GCB Bioenergy. 3: 347–359.
Wilson, B. P., A. J.Morris, B. E.Arroyo, S. C.Clark, and R. B. Bradbury. 1999.
A review of the abundance and diversity of invertebrate and plant foods
of granivorous birds in northern Europe in relation to agricultural change.
Agr. Ecosyst. Environ. 65: 13–30.
Yasuda, M., T.Mitsunaga, A.Takeda, K.Tabuchi, K.Oku, T.Yasuda, and T.
Watanabe. 2011. Comparison of the effects of landscape composition on two
mirid species in Japanese rice paddies. Appl. Entomol. Zool. 46: 519–525.
Zeilinger, A. R., D. M. Olson, and D. A. Andow. 2011. Competition between
stink bug and heliothine caterpillar pests on cotton at within-plant spatial
scales. Entomol. Exp. Appl. 141: 59–70.
Environmental Entomology, 2018, Vol. XX, No. X 9
Downloaded from
by DigiTop USDA's Digital Desktop Library user
on 04 April 2018
... Our data also suggest that perennial crops might provide suitable shelters for overwintering (e.g., under tree bark) when seminatural habitats are not available (i.e., in highly simplified landscapes). Moreover, our analyses showed that stink bugs best responded to landscape processes consistently at 2 km scale, confirming the available information in the literature regarding their dispersal ability (Olson et al., 2018;Taki et al., 2014). Response to landscape composition was similar for the most abundant species except for A. germari, which showed no relationship with forest cover in the landscape. ...
Semi-natural habitats are considered fundamental for biodiversity conservation and the provision of biological control services in agroecosystems. However, crop pests that exploit different types of habitats during their life cycle might thrive in complex landscapes. Understanding how crop pests use a range of resources across the agroecosystem is fundamental to plan sustainable crop protection strategies. Here we explored the effects of local habitat type (i.e., annual crop, perennial crop, dry grassland and forest) and landscape composition (increasing cover of forest and dry grassland) on stink bug pests in Mediterranean agroecosystems. Stink bugs (Hemiptera: Pentatomoidea) are polyphagous and highly mobile organisms considered a serious threat for numerous crops worldwide. To better understand how stink bugs used different habitats, we sampled active adults and juveniles in spring and summer, and overwintering individuals in autumn and winter. Our results showed that semi-natural habitats supported more abundant stink bug populations, potentially providing alternative feeding, reproduction, and overwintering sites. Specifically, we found more active adults and juveniles in dry grasslands, while forests hosted greater numbers of overwintering individuals. Moreover, forest cover in the landscape was positively related to active stink bug abundance in all sampled habitats. Finally, we found complex landscapes rich in overall semi-natural habitats to support higher abundance of overwintering individuals in both forests and dry grasslands, while perennial crop might provide suitable overwintering sites in highly simplified landscape. These results have important implications for pest management as crop fields situated in complex landscapes might be more susceptible to pest infestation. Effective control strategies may require a landscape-based approach.
... If the crops near the focal fields constitute alternative hosts of the pests, population built-up in the focal fields can be favoured (Meisner et al., 2017;Olson et al., 2018a;Panizzi and Slansky, 1985). Furthermore, population built-up of generalists herbivores in a focal crop field can be significantly increased by the immigration of the pests triggered after harvest of the nearby crops (Mueller and Stern, 1974). ...
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In modern agricultural landscapes, the conservation of natural enemies constitutes an alternative method of regulating economically important pests. Landscape diversity around the fields and plant diversity at local scales can provide vital resources for beneficial arthropods. For this reason, the abundance and diversity of predators and parasitoids is higher in heterogeneous than in homogeneous landscapes. However, pest suppression is not necessarily positively linked with increased abundance and diversity of biological agents in heterogeneous landscapes. The aim of this review is to shed light on the reasons for this. My hypothesis is that the effects of within-field and habitat plant diversity on pest densities are contingent on the spatial scales examined and on the functional role of the cover types found in the landscape for sustaining natural enemy populations. The results obtained by analysing the associated literature confirm this hypothesis. Large-scale heterogeneity strongly influences the effect of within-plant diversity on pest suppression. The functional role of the different land cover types in the agricultural landscapes may be positive, negative or neutral for natural enemies and pests. This depends on the functional traits of the plant species found in the land cover types and their arrangement in space, thereby affecting accordingly natural pest control.
... The largest peaks in E. servus population density, as well as the likelihood of significant aggregations at trap sites, typically corresponded to periods when cotton fruit (i.e., bolls) and peanut plants were available in fields. A previous study showed that the composition of host crops, especially corn and peanut, within a landscape had a greater positive influence on E. servus reproduction than the proportion of non-crop habitat, including forest, pasture, and non-crop hosts [40]. Nonetheless, in this study, E. servus adults and nymphs aggregated at traps in field edges of each crop that were located near early-season fruiting black cherry and mid-season elderberry in forests. ...
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Stink bugs (Hemiptera: Pentatomidae) are polyphagous pests that cause significant economic losses to a variety of crops. Although many species have been documented to aggregate within agricultural fields, much less is known regarding the timing and distribution of adults and nymphs within and between surrounding non-crop habitat. Therefore, we explored the spatiotem-poral distribution of Euschistus servus (Say), Euschistus tristigmus (Say), and Chinavia hilaris (Say), three species of North American origin, and examined whether distribution patterns varied between species according to habitat. Stink bugs were monitored weekly for three years within an 18 km 2 grid of pheromone-baited traps. We tested whether habitat affected distribution patterns, used spatial analysis by distance indices (SADIE) to identify aggregations, and visualized distributions with interpolated maps. Overall, E. servus adults were captured in crops, whereas E. tristigmus adults and nymphs were mainly captured in forests. Accordingly, distribution patterns of E. tris-tigmus were relatively stable over time, whereas aggregations of adult E. servus varied over space, and the timing of aggregations reflected the phenology of major crops. Chinavia hilaris was most often captured in forest, followed by crop habitat. Pest management strategies for stink bugs may require an area-based management approach that accounts for movement in agricultural fields and surrounding habitat.
... During 2018 and 2019, fields that were planted to soybean the previous year, and particularly those managed using no-till practices where the corn crop was planted into the soybean residue appeared to have a greater incidence of stink bug damage. The relationship with no-till practices and soybean as the previous crop has been proposed before (Edwards et al. 1985, Olson et al. 2018, Babu et al. 2019). Brown stink bug infestations in early vegetative stage corn are sporadic from year to year, and from field to field within years. ...
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Brown stink bug, Euschistus servus (Say) (Hemiptera: Pentatomidae), is a common insect that can infest corn fields in the Mid-South and Southeastern U.S. Infestations and damage are sporadic, thus little research has been conducted on the impact of brown stink bug infesting corn seedlings. Two experiments were conducted in eleven commercial corn fields in the Mississippi Delta to evaluate the impact of damage from natural stink bug infestations during the seedling stage (<V4) on corn yield and growth during 2018 and 2019. Single plants and 3-meter sections of the row were marked at each location. Plant damage for the single plant experiment was rated on a 0–3 scale and every single plant was given a damage rating based on visible symptomology. As damage severity increased, plant height and yield decreased. Some plants with the most severe damage did not produce any grain. At each location, sections of row (plots) with 0%, 10%, 20%, 30%, or 40% damaged plants were identified. All levels of damaged plants resulted in lower yield compared to the nondamaged control. These results demonstrate the brown stink bug infestations during the seedling stage (<V4) can reduce corn yield. The magnitude of yield reductions can be dependent on several factors including the severity of damage to individual plants and the percentage of plants with damage within the field. Although detecting infestations with current scouting methods is difficult, fields should be scouted and infestations managed to minimize yield loss.
... It is unlikely that host quality or nutrition alone is responsible for the differences in flight distance observed among the plant hosts in our study, especially since a low flight potential was observed from the relatively nutritious soybean-fed population. Compared with many of the nonagronomic E. servus hosts and crops hosts, soybean is regarded as highly suitable food plant for E. servus adult survival and nymphal development (Panizzi 1997, Herbert and Toews 2011, Olson et al. 2018. Moreover, it is probable that most of the adult individuals collected from both the early season and late season weed hosts in this study utilized soybean as food while they were nymphs (Pilkay et al. 2015, Babu 2018. ...
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The brown stink bug, Euschistus servus (Say), is a damaging pest of multiple crops in the southeastern United States. In addition to crops, both the weedy field borders and wooded areas of a typical farmscape in this region harbor E. servus host plants, many of which are temporally and spatially limiting in availability or nutritional suitability. Therefore, local dispersal is required so that individuals efficiently track and utilize host resources. This research sought to establish the baseline flight capacity of adult E. servus across the season in relation to body weight, sex, and plant host use with a flight mill system. Across this 2-yr study, among the individuals with a flight response in the flight mill, 90.1% of individuals flew in a range of >0–1 km, with an individual maximum flight distance of 15.9 km. In 2017, mean total distance flown varied across the season. Except for the individuals collected from corn in 2019, during both 2017 and 2019, the highest numerical mean flight potential occurred soon after overwintering emergence and a relatively low flight potential occurred during the cropping season. Individuals collected from wheat, corn, and early season weeds lost a higher proportion of body weight after flight than did individuals from soybean and late season weeds. The baseline dispersal potential information generated from this study can be extrapolated to the farmscape level aiming to develop, plan, and implement E. servus management programs.
... We used lasso regression to evaluate covariate importance in the presence of collinearity (e.g., Olson et al., 2018). Lasso regression estimates a shrinkage parameter (λ) that is applied to each covariate, resulting in covariates being 'shrunk' toward zero such that the estimates of less influential covariates are zero (Hastie, Tibshirani & Friedman 2009). ...
Animal home ranges are influenced by diverse intrinsic and extrinsic factors. For example, habitat heterogeneity may affect the spatial distribution of resources leading to larger home ranges where resources are spatially dispersed or, conversely, smaller home ranges where resources are concentrated or abundant. Other landscape features may lead to smaller home ranges by constraining or restricting animal movements. Understanding the relative importance of these two processes is increasingly important given the prevalence of anthropogenic features across contemporary landscapes. We test the relative importance of habitat heterogeneity and movement restriction on the home range size of a wide-ranging, habitat and dietary generalist, the federally threatened eastern indigo snake (Drymarchon couperi). We used data from 83 radio-tracked individuals in a multi-scale analysis of home range size as a function of multiple landscape features representing land cover and habitat heterogeneity. We found that home range size was negatively correlated to habitat heterogeneity (i.e., the standard deviation of normalized difference vegetation index [NDVI]) and urban intensity. Smaller home ranges in areas with high habitat heterogeneity and low urban intensity likely reflected reduced resource dispersion through the concentration of diverse foraging habitats. Home ranges were smallest in urban landscapes which, combined with previously documented avoidance of urban habitats by eastern indigo snakes, suggests that urban land cover restricts home range size. Our results demonstrate the importance of considering both the influence of resource dispersion and movement barriers in understanding animal space use. Moreover, we highlight the need to consider the potential role of anthro-pogenically subsidized resources (e.g., prey, shelter sites) to understand variation in eastern indigo snake home range sizes within urban areas.
... As a polyphagous insect, N. viridula includes feeds more than 30 plant families. Soybean is one of the legumes that is suitable food for this stink bug (Olson et al., 2018). Stink bugs have stylets, piercing-sucking mouthparts. ...
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The research was conducted to study the agronomical characters as the resistance attributes of twenty soybean varieties to Nezara viridula. The results showed that the twenty soybean varieties had a different response to stink bug infestation. Gepak Kuning, Seulawah, and Sinabung were resistant to stink bug, while Argomulyo were highly susceptible, and Grobogan and Malabar were susceptible. Besides resistance to stink bug, Sinabung also had the highest seed yield (2.95 t/ha). The seed yield of Gepak Kuning and Seulawah were not high, i.e. 2.20 and 1.82 t/ha respectively. The three highly susceptible or susceptible varieties also showed the lowest seed yield, i.e. 0.68 t/ha (Argomulyo), 0.42 t/ha (Grobogan) and 0.99 t/ha (Malabar). The negative correlation was shown between resistance to pest with days to maturity, duration of the reproductive phase, the number of unfilled pods, and weight of 100 seeds. Seed yield also had a negative correlation with duration of reproductive phase and weight of 100 seeds. It indicated that varieties with short duration of reproductive phase and small seed size were preferred by Nezara viridula as food sources. Therefore, these two characters can be used to determine the soybean resistance to Nezara viridula.
• Crop production sequences influence arthropod populations in temporally unstable row crop systems. Winter wheat (Triticum aestivum L.) represents one of the earliest abundant crops in south‐eastern United States. This study aims to understand primary source habitats driving brown stink bug, Euschistus servus (Say), and tarnished plant bug, Lygus lineolaris (Palisot de Beauvois), population abundance in wheat. • To better understand these relationships, adult and nymphal densities were in wheat fields weekly from flowering through harvest in 2019 and 2020. Geospatial data were used to measure landscape composition surrounding sampled fields. We investigated the influence of landscape predictors on E. servus and L. lineolaris abundance using generalized linear mixed modelling. • Field size, proportion of agriculture, proportion of wheat area, and proportion of soybean Glycine max L.) area from the previous year in the surrounding landscape were associated with E. servus abundance in wheat. Similarly, L. lineolaris abundance was associated with proportion of wheat area and soybean area from the previous year. • These results reveal the influence of soybean area planted the previous year on insect pest densities the following spring in wheat. Further, results suggest agricultural landscapes dominated by wheat are associated with decreased pest abundance across the sampled region.
Understanding the relationship between integrated landscape patterns (coupled land use, soil properties, and topography) and stream water quality in different seasons promotes appropriate landscape planning. However, this relationship is unclear. Here, water quality nitrogen (N) and phosphorus (P) levels and the integrated landscape patterns were investigated in ten Chinese subtropical catchments during 2010–2017, using the least absolute shrinkage and selection operator (LASSO) regressions method and redundancy analysis (RDA). The results suggested that stream water N and P levels were significantly higher in the fallow season than in the rice-growing season (p < 0.05). The N and P levels in the rice-growing season were elevated with the increasing area proportions of tea fields in Ultisols on the medium slope (16.06–28.02°), and larger isolation, diversity, and geometric complexity of landscape patches, but decreased with the increasing area proportions of forests in Ultisols on the steep slope (28.02–80.30°) and interspersion of landscape patches. Stream water N and P levels were more likely influenced by landscape configuration in the rainy rice-growing season, mainly due to the rapid velocities and high quantities of surface flow strengthening landscape configuration effects on the N and P migration and exchange. In the fallow season, the N and P levels were heightened with the increasing area proportions of tea fields, residential areas, and paddy fields in Ultisols on the relative flat (0–16.06°) and medium slopes, and larger isolation of landscape, and could be greatly reduced if the area proportions of forests in Ultisols on the steep slope is increased. The N and P levels were more likely determined by landscape composition in the dry fallow season, associated with the slow and poor subsurface and underground hydrological flows. Therefore, the results promoted reasonable landscape management in different seasons and suitable soil and topography conditions for improving stream water quality.
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Brown stink bug, Euschistus servus (Say), is a damaging pest of corn, Zea mays L. (Cyperales: Poaceae), in the southeastern United States. In North Carolina, during the spring, winter-planted wheat, Triticum aestivum L. (Cyperales: Poaceae), serves as the earliest available crop host, and E. servus seems to prefer this crop over seedling corn. In the absence of wheat in the agroecosystem, weeds serve as a bridge host for a portion of overwintered E. servus populations until they move to corn and other subsequent crops. Our objective was to reduce densities of E. servus in corn by manipulating the weedy field borders with mowing and applications of dicamba herbicide. During the study, multiple species of stink bugs (n =16) were found associated with weed plots. However, E. servus was the predominant (>94%) stink bug species in the corn. In this farmscape, density of E. servus adults in the unmanaged weed plots began declining around the second week of May, followed by an increase in density in adjacent corn plots. This movement coincided with the seedling growth of corn. In 2016, applications of dicamba in the weedy field border resulted in a lower density of E. servus in herbicide-treated weed plots compared with untreated plots. Despite this difference, manipulations of weeds did not lead to any significant changes in density of E. servus adults in corn. Further evidence suggested that a prominent external source of E. servus, other than field-bordering weeds, in the farmscape was likely driving densities in corn.
Many scientists have reported an extensive amount of information on the biology, life history, and damage potential of stink bugs. However, this information is scattered among numerous journals, periodicals, and other publications. Stink Bugs of Economic Importance in America North of Mexico brings together the applied and nonapplied literature in one complete and concise format. The book gives you: • Section by section discussions of various economic stink bug species and damage to individual crops • Separate tables of host plants organized by common name, scientific name, and family name • General biology for each economic stink bug species Strategies for the control of destructive species • Keys for identification of stink bug species • Numerous unique line drawings • Over 700 references on stink bug publications Written by two top-notch researchers whose experience is complementary, the book examines these constant pests. The first comprehensive resource on this fascinating and destructive group of insects, Stink Bugs of Economic Importance in America North of Mexico provides you with a reference that you can use in the laboratory or in the field for easy identification of pentatomids.
Transgenic crops expressing insecticidal toxins of Bacillus thuringiensis (Bt) have been deployed in agricultural landscapes around the globe. While the key strategy to delay resistance is the mandatory planting of a non-Bt refuge crop that is preferred by the target pest, the efficacy of this resistance management strategy across different landscape contexts over time is rarely considered. Here, we develop an individual-based model to simulate the spatio-temporal distribution of a highly mobile, polyphagous, global pest, Helicoverpa spp, across agricultural landscapes dominated by transgenic cotton. The simulation model allows us to explore refuge ‘electivity’, the relative utilization of refuge habitat by female Helicoverpa, in relation to Bt cotton habitat. Refuge electivity is an emergent function of egg distributions resulting from individual moth behavior, within multiple landscapes during different seasons and crop phenology. The individual-based model is validated against independent data collected from the field. Our findings suggest that refuge electivity is sensitive to the spatial and temporal context of the attractiveness of host crops in the landscape and the preferences of the moths. The attractiveness of mandated refuges, such as pigeon pea relative to Bt cotton, influences how effective they are in the landscape. Dynamics between other host crops, such as sorghum, also play an important role that varies over time and space. We use the model to identify scenarios where refuge strategies are likely to be most effective in terms of boosting susceptible populations and increasing landscape movement (genetic mixing). This dynamic approach has potential to inform better refuge design for Bt resistance management across a wide range of landscape contexts. For example, these findings justify the removal of sorghum as an option for mandated refuge in the Risk Management Plan (RMP) for Bt cotton in Australia.
The construction of a number of life-tables is an important component in the understanding of the population dynamics of a species. Although some animal ecologists, such as Richards (1940), had expressed their results showing the successive reductions in the population of an insect throughout a single generation, Deevey (1947) was really the first to focus attention on the importance of this approach. Life-tables have long been used by actuaries for determining the expectation of life of an applicant for insurance and thus the column indicating the expectation of life at a given age (the e x column) is an essential feature of human life-tables. However, the fundamental interests of the ecologist and, even more so, of the economic entomologist are essentially different from those of the actuary and it is a mistake to believe that these approaches and parameters of primary interest in the study of human populations are also those of greatest significance to the animal ecologist. Because many insects have discrete generations and their populations are not stationary, the age-specific life-table is more widely applicable than the timespecific life-table.
In Brazil, the Neotropical brown stink bug, Euschistus heros (F.) (Hemiptera: Pentatomidae), commonly disperses from soybeans to cotton fields. The establishment of an economic treatment threshold for this pest on cotton crops is required. Infestation levels of adults of E. heros were evaluated on cotton plants at preflowering, early flowering, boll filling, and full maturity by assessing external and internal symptoms of injury on bolls, seed cotton/lint production, and fiber quality parameters. A completely randomized experiment was designed to infest cotton plants in a greenhouse with 0, 2, 4, 6, and 8 bugs/plant, except at the full-maturity stage in which only infestation with 8 bugs/plant and uninfested plants were evaluated. Results indicated that the preflowering, early-flowering, and full-maturity stages were not affected by E. heros. A linear regression model showed a significant increase in the number of internal punctures and warts in the boll-filling stage as the population of bugs increased. The average number of loci with mottled immature fibers was significantly higher at 4, 6, and 8 bugs compared with uninfested plants with data following a quadratic regression model. The seed and lint cotton was reduced by 18 and 25% at the maximum level of infestation (ca. 8 bugs/plant) in the boll-filling stage. The micronaire and yellowing indexes were, respectively, reduced and increased with the increase of the infestation levels. The economic injury level of E. heros on cotton plants at the boll-filling stage was determined as 0.5 adult/plant. Based on that, a treatment threshold of 0.1 adult/plant can be recommended to avoid economic losses.
Chinavia hilaris (Say), Euschistus servus (Say), Euschistus tristigmus (Say), and Thyanta custator custator (F.) (Hemiptera: Pentatomidae) are serious pests of crops in the southeastern United States but little is known concerning their dispersal from noncrop hosts in woodlands into crops. This 2-yr study was conducted to investigate whether elderberry [Sambucus nigra subsp. canadensis (L.) R. Bolli] in woodlands serves as a source of stink bugs dispersing into adjacent crops and to examine parasitism of C. hilaris and E. servus eggs on this plant. Elderberry was a reproductive host for each of the four stink bug species; females oviposited on plants with subsequent nymphs feeding on elderberry and developing into adults. Anastatus mirabilis (Walsh & Riley) (Hymenoptera: Eupelmidae), Anastatus reduvii (Howard), and Trissolcus edessae Fouts (Hymenoptera: Scelionidae) were prevalent egg parasitoids of C. hilaris but A. reduvii was the prevalent parasitoid of E. servus. Newly developed stink bug adults were first detected on elderberry around mid-July. Then in late July and early August, as elderberry fruit senesced and cotton bolls became available, stink bugs began dispersing from elderberry into cotton based on recapture of stink bugs on cotton that had previously been marked on elderberry. In addition, in 2015, density of C. hilaris, E. servus, and E. tristigmus was higher in cotton with elderberry than in cotton without it. Over the study, economic threshold was reached for four of seven cotton fields. Elimination of elderberry in woodlands adjacent to cotton may be a viable management tactic for control of stink bugs in cotton.
We propose new inference tools for forward stepwise regression, least angle regression, and the lasso. Assuming a Gaussian model for the observation vector y, we first describe a general scheme to perform valid inference after any selection event that can be characterized as y falling into a polyhedral set. This framework allows us to derive conditional (post-selection) hypothesis tests at any step of forward stepwise or least angle regression, or any step along the lasso regularization path, because, as it turns out, selection events for these procedures can be expressed as polyhedral constraints on y. The p-values associated with these tests are exactly uniform under the null distribution, in finite samples, yielding exact Type I error control. The tests can also be inverted to produce confidence intervals for appropriate underlying regression parameters. The R package selectiveInference, freely available on the CRAN repository, implements the new inference tools described in this article. Supplementary materials for this article are available online.
Background: Landscape crop composition surrounding agricultural fields is known to affect the density of crop pests, but quantifying these effects, as well as measuring how they translate to changes in yield, is difficult. Using a large dataset consisting of 1498 records of commercial cotton production in California between 1997 and 2008, we explored the relationship between landscape composition and cotton yield, the density of Lygus hesperus (a key cotton pest) at field-level and within-field spatial scales, and pesticide use. Results: We found that the crop composition immediately adjacent to a cotton field was associated with substantial differences in cotton yield, L. hesperus density, and pesticide use. Furthermore, crops that tended to be associated with increased L. hesperus density also tended to be associated with increased pesticide use and decreased cotton yield. Conclusion: Our results suggest a possible mechanism by which landscape composition can affect cotton yield: by increasing the density of pests that in turn damage cotton plants. Our quantification of how surrounding crops affect pest densities, and in turn yield, in cotton fields, has significant impacts for cotton farmers who can use this information to help optimize crop selection and ranch layout.