Biological Control 84 (2015) 1–10
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Biological Control
journal homepage: www.elsevier.com/locate/ybcon
Population genetics of the predatory lady beetle Hippodamia convergens
Arun Sethuraman a,b,⇑, Fredric J. Janzen b, John Obrycki c
a
Center for Computational Genetics and Genomics, Department of Biology, Temple University, Philadelphia, PA 19102, United States
Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50010, United States
c
Department of Entomology, University of Kentucky, Lexington, KY 40546, United States
b
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
Variation at 7 microsatellite loci was
determined in 117 adults from 11
populations of Hippodamia
convergens.
Detected the presence of genetic
population structure (at least K = 2
subpopulations).
Population demography was
explained using a source-sink model.
Determined a steep and recent
population size decline in Eastern
populations.
a r t i c l e
i n f o
Article history:
Received 5 September 2014
Accepted 17 January 2015
Available online 29 January 2015
Keywords:
Augmentative release
Population structure
Source-sink model
Bayesian MCMC
Microsatellite
Demography
a b s t r a c t
Quantifying non-target effects of augmentative releases on populations of conspecifics is key to understanding the long-term impacts of augmentation biological control. Potential deleterious (and advantageous) allelic variation carried over to augmented populations from ‘source’ populations could shape
adaptive evolutionary trajectories. Variation at seven microsatellite loci was determined in 117 adults
from 11 populations (2 populations from California, 1 from Arizona, 1 from South America, and 7 from
regions east of the Rocky Mountains in the United States [hereafter, Eastern]) of the widely distributed
predatory lady beetle, Hippodamia convergens Guerin (Coleoptera: Coccinellidae). Our study was designed
to examine possible introgression of genes from adult H. convergens that are mass-collected in California
annually and released in eastern North America for augmentative biological control. The average
observed heterozygosity was 0.44 and all loci were polymorphic (mean = 20.57 alleles/locus). The number of genetically distinct subpopulations of H. convergens was estimated to be at least two. Our analyses
indicate that Californian multilocus genotypes are admixed within Eastern populations of H. convergens.
We also determined the sizes of California populations to be larger than all sampled Eastern populations,
suggesting recent declines in the latter. Additional study of the population demography of H. convergens
and its local ecological adaptations is required to determine if these augmentative releases are causing
large-scale non-target effects.
Ó 2015 Elsevier Inc. All rights reserved.
1. Introduction
⇑ Corresponding author at: Center for Computational Genetics and Genomics,
Department of Biology, Temple University, Philadelphia, PA 19102, United States.
E-mail addresses: arun@temple.edu (A. Sethuraman), fjanzen@iastate.edu (F.J.
Janzen), john.obrycki@uky.edu (J. Obrycki).
http://dx.doi.org/10.1016/j.biocontrol.2015.01.002
1049-9644/Ó 2015 Elsevier Inc. All rights reserved.
An understanding of population-level differences in widely-distributed species transported or manipulated by humans may be
critical to our understanding of the consequences of these activities. Repeated releases of organisms in a new environment may
result in population mixing and hybridization, e.g., the commercial
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A. Sethuraman et al. / Biological Control 84 (2015) 1–10
use of bumblebees for pollination (Kraus et al., 2011), frogs for
medicinal purposes (Zhang et al., 2013), and releases of non-native
frogs in Europe (Holsbeek et al., 2009). Similarly, human-assisted
movement and release of insect parasitoids and predators for suppression of insect pests, which represents one of the major practices of biological control (O’Neil and Obrycki, 2009), has
associated risks that may affect non-target organisms.
Potential non-target effects of importation biological control,
which attempts to permanently establish exotic species to reduce
populations of introduced pests, have received considerable attention (e.g., Howarth, 1991; Follett et al., 2000; Louda et al., 2003).
For example, the effects of two introduced species of predatory Coccinellidae, Coccinella septempunctata and Harmonia axyridis, on
native North American species have been the focus of numerous
studies during the past four decades (e.g., review by Obrycki et al.,
2000; Brown, 2003; Harmon et al., 2007; Moser and Obrycki,
2009; Kajita et al., 2012). In contrast, relatively few studies have
focused on the potential non-target effects of augmentative releases
(review by Van Lenteren et al., 2003; Bjornson, 2008; Michaud et al.,
2012), in which repeated releases of a natural enemy are made without the expectation of permanent establishment in the environment. The genetic consequences of augmentative biological
control are seldom studied, owing to unpredictability in population-level dynamics with other species (interspecific competition)
and genetic incompatibilities between populations of the same species (intraspecific interactions). Study of genetic population structure and population demography allows us to make predictions
about (a) compatibility, (b) survival of the species, and (c) invasivity
(or the propensity of an invasive species to adapt to a new
environment).
Predatory coccinellids are some of the most abundant species in
agroecosystems (Honek et al., 2012), yet the basic population
genetics of many of these beneficial species is poorly known
(Sloggett et al., 2012). During the first half of the 20th century,
genetic studies of variation in elytral patterns of coccinellids were
conducted (e.g., Dobzhansky, 1933; Komai, 1956).
More recently, a series of studies evaluated variation in allozymes in selected species of North American lady beetles and compared levels of genetic variation with introduced species (Krafsur
et al., 2005). In Hippodamia convergens, heterozygosity averaged
over 27 loci in adults from Iowa was 21%, a level similar to several
other North American species (Krafsur et al., 2005). Based on variation in 18 microsatellite markers, Lombaert et al. (2010) proposed a series of geographical movements to explain the recent
rapid increase in the distribution of H. axyridis. Using 37 polymorphic microsatellite markers to genotype offspring, Haddrill et al.
(2008) documented high rates of multiple mating by female Adalia
bipunctata collected at two field sites. In a recent study, based on
genetic variation in mitochondrial DNA (cytochrome oxidase I),
Kajita et al. (2012) concluded that the current North American distribution of C. septempunctata is a result of multiple human
releases of this species and local expansion from release sites.
Augmentative releases of the predatory lady beetle, H. convergens (Coleoptera: Coccinellidae), represent a unique example of
augmentative biological control that allows examination of several
potential non-target effects of these releases (Michaud et al., 2012).
Adult H. convergens are collected from overwintering sites in the
western USA, stored at low temperatures, and sold for release
throughout the USA (commercial sources listed in White and
Johnson (2010). These releases may be appropriate for pest suppression (particularly aphids and whiteflies) in the western USA
(e.g., Dreistadt and Flint, 1996; Flint and Dreistadt, 2005; Hagler
and Naranjo, 2004; Hagler, 2009), but releases may create several
non-target issues for Eastern populations of H. convergens. For
example, previous studies have documented the presence of
pathogens and parasitoids in field-collected adult H. convergens
in the western USA that are then released in the eastern USA
(e.g., Lipa and Steinhaus, 1959; O’Neil et al., 1998; Bjornson,
2008). H. convergens is widely distributed in North America and
various aspects of seasonal biology, ecology and predator–prey
interactions of selected geographic populations have been studied
(e.g., Hagen, 1962; Michaud and Qureshi, 2006; Phoofolo et al.,
2008; Hagler, 2009). In this study, we examined the population
structure of H. convergens using microsatellite markers. Based on
our findings, we address questions about the potential effects of
augmentative releases of H. convergens from the western USA on
the genetics of Eastern (east of the Rocky Mountains) populations
of this species. Of particular interest in this context are three
important questions: (1) What is the population structure of H.
convergens across the Americas, and is there a signal of admixture
from the California populations that have been released annually
into Eastern populations? (2) What is the ancient population
demography of sampled beetle populations – do they fit a model
of panmixia (which would be expected in the absence of population structure) or more of a source-sink model (which would be
expected in the presence of structure with signs of admixture)?
(3) Are there genetic signatures of recent population size decline
in any sampled populations of H. convergens?
2. Materials and methods
2.1. Field work – H. convergens populations
H. convergens were obtained with various methods. Adults were
purchased from suppliers in California and Arizona (Table 1). While
exact sampling information was not available for these purchased
samples, we assume that they were sampled from the same geographical location for all further analyses. Adults from Arkansas
were collected from roadside vegetation in 1994 and stored in
alcohol at 20 °C. Arkansas, Iowa, Kansas, Kentucky, and Oklahoma
collections were made using sweep nets in alfalfa fields and roadside vegetation. The California-A population was from an overwintering site in the Angeles National Forest. Adults from Georgia were
collected from two cotton fields in different counties. The beetles
from Chile in South America were collected from agricultural fields
near Santiago.
2.2. Genotyping
A total of 117 individuals from 11 sampling locales were genotyped at 7 microsatellite loci (Hcv7, Hcv17, Hcv15, HcvT19, Hcv4,
Hcv13, Hcv30 - see Table 2) (A’Hara et al., 2012) on an Applied Biosystems 3730 DNA Analyzer at Iowa State University using the ROX
(FAM/HEX dye sets. Prior to genotyping, genomic DNA was extracted
from each beetle with Qiagen DNeasy Blood and Tissue Kits, following the extraction protocol for insect tissues. PCR amplification of
microsatellite loci was performed in 12.5 ll reactions, comprising
7.65 ll of dH2O, 1.25 ll of 10x PCR buffer (containing 25 lM of
MgCl2), 0.12 ll of dNTPs, 0.2 ll of forward primer (labeled with
FAM/HEX), 0.2 ll of reverse primer, 0.08 ll of Taq DNA polymerase,
0.5 ll of Q-solution, and 2.5 ll of extracted DNA. Both negative and
positive controls were performed in each set of PCRs, and genotyped.
1.5 ll of PCR product was used in each genotyping reaction. Genotypes were then analyzed using GeneMapper v.1.0 (Applied Biosystems). Indeterminate allele sizes or failed PCRs and genotyping
reactions were recorded as missing alleles – 46 out of 819 genotypes
were missing (6%). All individuals used in this study contained
information for at least 3 out of 7 genotyped loci.
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A. Sethuraman et al. / Biological Control 84 (2015) 1–10
Table 1
Sampling locales, GPS coordinates, collection information for H. convergens individuals.
Population
Location
Longitude-Latitude
Collector(s)
Collection
Dates
Number of
Individuals
Arkansas
Arizona
California
CaliforniaA
Georgia
Roadside vegetation
Purchased Arbico
Purchased Rincon-Vitova
Mt Baldy Village, CA
Coffee Cty and Tift Cty cotton fields
33.72°N, 94.40°W
Tim Kring
Chris Wheeler
John Ruberson
12
16
18
9
18
Iowa
Kansas-Lawrence
Kansas-Manhattan
Alfalfa fields in central Iowa
Roadside Vegetation
Kansas State University alfalfa
fields
University of Kentucky North Farm
Norman-Oklahoma
34.24°N 117.66°W
31.24°N 83.00°W 31. 30°N
83.33°W
41.73°N 93.60°W
38.97°N 95.24°W
39.19°N 96.59°W
1993-June
2011-May
2011-May
2011-Oct
2011-Aug
Laura Jesse & JJO
JJO
Jim Nechols & JJO
2011-May
2011-May
2011-May
11
9
8
Jake Hillard
Yukie Kajita & Eric
O’Neal
Audrey Grez
2011-May
2011-Jun
5
8
2011-Nov
3
Kentucky
Oklahoma
South AmericaChile
Santiago-Chile
38.03°N 84.49°W
35.22°N 97.44°W
-33.45°N 70.67°S
Table 2
Primer sequences, names, modifications and size ranges as estimated from both our study, and that of A’Hara et al. (2012).
Sequence
Primer
Repeat
50 Mod
Allele size
ACCACTTATGTCTTGCAAACCC
AGTAGGTATTGGGGCACCTG
AGTTAGAAAAGAAAGACCTTTTGCC
ATGGGTGAGGTTCCTCGTG
AGGAGATGTCAAAAGGATAAATTGG
CCAAATGTTTGATAGGATTTCTTCG
CACTGATAAGCCAATAACTAAACTTGA
TTCCTGGTGTCGTAATCGTG
AATAGGTCCAGTTCGCCAGA
CAGCCTGTGCTACCTCTCC
TCTTTCTTGTTAGCTCTTCTTCGG
TGTTTATTCTGCTGTTGTGTCTG
GACGATTGTGCGAGCCAG
TGGAGTTGAAATAGATGTATGAAAAT
Hcv4F
Hcv7F
Hcv13F
Hcv15F
Hcv17F
HcvT19F
Hcv30F
Hcv4R
Hcv7R
Hcv13R
Hcv15R
Hcv17R
HcvT19R
Hcv30R
Dinucleotide
Dinucleotide
Dinucleotide
Dinucleotide
Dinucleotide
Trinucleotide
Dinucleotide
Dinucleotide
Dinucleotide
Dinucleotide
Dinucleotide
Dinucleotide
Trinucleotide
Dinucleotide
FAM
FAM
FAM
FAM
FAM
FAM
FAM
134–161
170–221
154–178
148–227
123–136
210–232
152–227
Table 3
P values from X2 tests of Hardy–Weinberg Equilibrium across all loci and populations.
P values correspond to the probability that an observed chi-squared statistic is as high
as or higher than one observed under the null hypothesis of H0 of HWE. Values shown
in red indicate violation of HWE.
Arkansas
Arizona
California
CaliforniaA
Georgia
Iowa
KansasL
KansasM
Kentucky
Oklahoma
SouthAmerica
Hcv7
Hcv17
Hcv15
HcvT19
Hcv4
Hcv13
Hcv30
0.72
0.00
0.00
0.00
0.00
0.00
0.03
0.00
0.00
0.00
1.00
0.02
0.36
0.09
0.08
0.00
0.01
0.00
0.21
0.11
0.08
NA
0.00
0.00
0.02
0.07
0.00
0.01
0.00
0.00
0.00
0.00
0.53
0.00
0.00
0.00
1.00
0.00
0.00
0.00
0.00
0.00
0.18
0.00
0.01
0.15
0.10
1.00
0.01
1.00
0.00
0.51
0.19
NA
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.20
0.35
0.01
0.00
0.00
0.00
0.00
0.98
0.00
0.00
0.00
0.00
0.00
0.19
0.00
2.3. Genetic diversity analysis
All populations and loci were tested for deviations from Hardy–
Weinberg Equilibrium (HWE) prior to further analyses, using X2
tests with the ADEGENET R package (Jombart and Ahmed, 2011).
Analyses of polymorphism levels, and expected and observed heterozygosities, were performed in GDA v.1.1 (Weir et al., 1996;
Lewis et al., 2001). X2 tests of genotypic disequilibrium were
employed to test for independence of loci using Genepop v.4.3.2
(Raymond and Rousset, 1995; Rousset, 2008). 1000 bootstrap replicates were used to construct 95% confidence intervals of F statistics (f or FIS or genetic differentiation among individuals within
populations, F = FIT or genetic differentiation in individuals in the
total population, and h = FST or genetic differentiation within populations, relative to the total population), using GDA v.1.1 (Weir
et al., 1996; Lewis et al., 2001).
2.4. Detection of population structure
To detect population structure and the presence of introgression between the California populations (hereafter denoted as
Western populations) and the Eastern (all other populations except
California) populations, we conducted two sets of analyses. The
first approach was to use methods to infer population structure
under both the mixture and the admixture models (Pritchard
et al., 2000). The mixture model assumes that each individual’s
multilocus genotype is derived entirely from one ancestral subpopulation, while the admixture model assumes that each allele at a
locus can be derived from a different ancestral subpopulation.
We assumed a subpopulation structure comprising K = 1 to 12
ancestral subpopulations and ran 100 different initializations (replicates) of MULTICLUST v.1.0 (Sethuraman et al., 2013) under both
the mixture and admixture models. MULTICLUST implements an
Expectation–Maximization algorithm for maximum likelihoodbased estimation of subpopulation allele frequencies and mixture/admixture proportions (under a model with specified number
of ancestral subpopulations, here denoted by K). We used information criteria (AIC) from MULTICLUST to infer the ‘true’ number of
ancestral subpopulations.
We also performed the same analyses using STRUCTURE v.2.3.4
(Pritchard et al., 2000; Falush et al., 2007), with 100,000 iterations
of burn-in for the MCMC, and 100,000 iterations of MCMC after
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A. Sethuraman et al. / Biological Control 84 (2015) 1–10
burn-in. Five replicate runs of STRUCTURE were performed by
varying the number of subpopulations (K) between 1 and 11. The
‘true’ number of ancestral subpopulations was then inferred using
the method of Evanno et al. (2005) with STRUCTURE HARVESTER
(Earl and et al., 2012).
2.5. Estimation of migration
The second approach was to infer migration rates (M) and effective population sizes (Ne) under an island model using MIGRATE
v.3.6.4 (Beerli and Felsenstein, 2001; Beerli, 2008). Under this
model, sampled populations of a constant Ne continue to exchange
genes at a constant migration rate (M) individuals per generation,
backwards in time, until all genes have coalesced to a common
ancestor. The aim of this analysis was to infer migration (if any)
between sampled populations of H. convergens, and determine Ne
at each sampled locale. Our goal for the MIGRATE analyses was
twofold: (a) to determine rates of augmentation as a measure of
migration out of the ‘‘source’’ (here, California populations), and
(b) to determine the relative distribution of ancestral population
sizes, and detect discrepancies between the Western and Eastern
populations (if any). To do this, we ran 5 replicates of MIGRATE,
reflecting a source-sink model of population demography, assuming that there is no migration between all the sampled populations,
except for augmentation (emigration) out of the California source
populations (California and California-A). We only assumed bidirectional migration between the two source populations in the
model. MIGRATE is a Bayesian Markov-Chain Monte Carlo (MCMC)
based method that estimates the posterior density distribution of
demographic parameters (here, Ne and M) by sampling genealogies. We assumed a Brownian model of microsatellite evolution
considering the high levels of polymorphism deduced (see Table 4).
To ensure good mixing of the MCMC, we utilized 1 106 burn-in
iterations, followed by 5 105 iterations of MCMC. 4 chains were
heated to improve mixing of the MCMC (Metropolis Coupling),
with temperatures of chains ranging from 1 to 100,000 as recommended by Beerli (2008). Runs of MIGRATE were distributed
among 8 CPUs (1 CPU monitors the progress of the run, 7 CPUs,
one for each locus). We monitored acceptance rates of genealogies
(and other parameter updates), effective sample sizes (ESS), and
autocorrelations between parameters throughout the run. We performed 5 replicate runs in order to ensure parameter estimates
were unchanging across runs. All computational analyses were
performed on the Temple University HPC (Owlsnest).
We also ran 2 replicates of the full model (island model, with
bidirectional migration between all sampled populations) for comparison with the source-sink model. All other computational
parameters were held the same across replicate runs. Priors on
migration rates and population sizes were adjusted to be uniform
between 0 and 2000 for migration, and 0 and 100 for population
Table 4
Total allelic information (i.e. number of individuals with complete diploid genotypes
at a locus) (n), polymorphism per locus (P), average number of alleles per locus (A)
and polymorphic locus (Ap), expected proportion of heterozygotes (He), observed
proportion of heterozygotes (Ho), and inbreeding coefficient (f). All values are
reported across 11 tested populations.
Locus
n
P
A
Ap
He
Ho
f
Hcv7
Hcv17
Hcv15
HcvT19
Hcv4
Hcv13
Hcv30
All
111
105
103
96
105
111
111
106
1
1
1
1
1
1
1
1
30
11
25
20
15
16
27
20.57
30
11
25
20
15
16
27
20.57
0.926
0.760
0.924
0.802
0.520
0.874
0.872
0.811
0.550
0.514
0.398
0.417
0.305
0.324
0.532
0.434
0.408
0.325
0.570
0.482
0.415
0.629
0.392
0.466
size after multiple runs. Metropolis Coupling was invoked in the
MCMC runs by using a total of 100 short chains and 20 long chains
to explore the posterior density surface more efficiently. Since the
source-sink model is nested within the full model, we determined
the better model by comparing the marginal posterior density of
parameters given the data (proportional to the likelihood) from
both models.
MIGRATE estimates Ne and M under the island model, assuming
that M is constant through evolutionary time. To test for the presence or absence of recent migration (only migration rates estimated until the first coalescent event backwards in time)
between sampled populations (particularly in the Eastern locales),
we used the program BayesAss v.1.3 (Wilson and Rannala, 2003) to
perform MCMC-based inference of recent migration. BayesAss was
run using a full model as above, with 3,000,000 iterations of MCMC
with 999,999 iterations discarded as burn-in. We performed three
separate runs of BayesAss with the same parameters to ensure
unchanging posterior densities and parameter estimates across
runs. Convergence of MCMC in both MIGRATE and BayesAss runs
was assessed by using the program Tracer (Rambaut et al., 2003),
which plots the log-probability of data given parameters across
an MCMC run.
2.6. Population size change
To test for signatures of population size change (increase or
decline) in sampled populations, we used the Bayesian MCMC program MSVAR v.1.3 (Beaumont, 1999; Girod et al., 2011). MSVAR
uses standard assumptions of the coalescent to estimate the posterior density distribution of Ne backwards in coalescent time
under a model of panmixia (assuming that all chromosomes, here
loci were sampled from the same panmictic population, as our full
model above). Using an exponential growth or decline model for
population size, we ran 2 108 iterations of MCMC to ensure convergence before inference. We used a conservative estimate of generation time of 1 year per generation (Hagen, 1962), standard
microsatellite mutation rate of 5 104 mutations per generation
(as used by Lombaert et al. (2011) in another coccinellid, H. axyridis), 50 years since the start of a population decline or expansion
(used the tinv prior used by Lombaert et al. (2011), to coincide with
the time of invasion of H. axyridis), and prior ancestral and current
population sizes of 10,000. Convergence of MCMC was adjudged as
above by plotting the log likelihood across the run, and ensuring no
discernible pattern throughout the run. Three separate runs of
MSVAR were performed, using the (1) Western populations, (2)
Eastern populations, and (3) all populations.
3. Results
3.1. Genetic diversity
An average of 10 individuals were tested per population (ranging from 3–16; Table 5). All populations contained polymorphic
loci, with an average observed heterozygosity of 0.44 (0.308 in
Arkansas – 0.599 in CaliforniaA). All loci were polymorphic, with
an average of 20.57 alleles per locus. The average observed heterozygosity across all loci was 0.434 (0.305 – 0.550; Table 4).
Most loci were at HWE in all populations at a significance level
of p = 0.05. The Hcv17 locus showed the most deviation from HWE
in several populations (Arkansas, Arizona, California, CaliforniaA,
KansasM, Kentucky, and Oklahoma). Similarly, Hcv4 showed considerable deviation from HWE in the Arizona, California, CaliforniaA, KansasM, Kentucky, Georgia, and South American
populations. CaliforniaA population seemed to be deviating from
HWE at 4 out of 7 loci tested (see Table 3). Tests showed that none
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A. Sethuraman et al. / Biological Control 84 (2015) 1–10
Table 5
Average number of sampled individuals per population (n), polymorphism per locus (P), average number of alleles per locus (A) and polymorphic locus (Ap), expected proportion
of heterozygotes (He), observed proportion of heterozygotes (Ho), and inbreeding coefficient (f). All values are reported across 7 tested loci.
ID
Population
n
P
A
Ap
He
Ho
f
1
2
3
4
5
6
7
8
9
10
11
Arkansas
Arizona
California
CaliforniaA
Georgia
Iowa
KansasL
KansasM
Kentucky
Oklahoma
SouthAmerica
Mean
11.143
13.857
16.000
7.429
16.286
10.429
8.429
7.714
4.429
7.714
2.571
9.636
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.857
1.000
0.987
6.000
8.286
9.143
5.286
10.000
6.857
6.714
6.571
4.286
4.714
3.000
6.442
6.000
8.286
9.143
5.286
10.000
6.857
6.714
6.571
4.286
5.333
3.000
6.498
0.727
0.793
0.798
0.721
0.802
0.720
0.807
0.815
0.755
0.656
0.786
0.762
0.308
0.509
0.536
0.600
0.449
0.332
0.349
0.367
0.317
0.390
0.690
0.441
0.587
0.367
0.336
0.178
0.448
0.552
0.585
0.567
0.623
0.422
0.176
0.450
of the loci were linked (see Table 7), and that indeed the loci used
in the following analyses were independent.
When compared across all 7 tested loci by population, the genetic
differentiation in individuals within populations (FIS) was estimated
to be 0.45 (95% c.i. of 0.366–0.530), which indicates high levels of
within-population genetic diversity. Bootstrap confidence intervals
(95%) constructed around estimates of population differentiation
(using a priori population structure as defined by the locations of
sampling) show high levels of population differentiation within
individuals relative to the total population (FIT = 0.468140; 95% c.i.
of 0.394–0.545), and low levels of between-population differentiation (FST = 0.039108; 95% c.i. of 0.029–0.046) – see Table 6.
3.2. Population structure
MULTICLUST determined the ‘true’ number of subpopulations
in the data under both the admixture and mixture models to be
K = 2 (Fig. 1). Analysis of admixture and mixture plots indicates
several interesting patterns. Largely, the sampled localities of
Arkansas, Arizona, California, KansasM, Kentucky, Georgia, and
Oklahoma seem to cluster together as one distinct subpopulation.
The Iowa, CaliforniaA, KansasL and South America (Chile) localities
formed a separate cluster, but also indicated admixture with the
first group (see Fig. 2).
Replicate runs of STRUCTURE, and determining the most likely
number of subpopulations using the method of Evanno et al.
(2005), were inconclusive under the admixture model owing to
low standard deviations in likelihood between replicate runs. But
the mixture model suggested the ‘true’ number of subpopulations
to be K = 3 (Fig. 1). This addition of a subpopulation (compared to
K = 2 by MULTICLUST) provides further distinction in patterns of
mixed ancestry between sampled populations (see Fig. 2). Particularly, individuals in Iowa, Georgia, KansasL, and Kentucky show
signs of mixed ancestry with individuals in Arkansas, Arizona,
and California. The South American population also seems to be
admixed with the California populations.
3.3. Estimation of migration
Comparison of the marginal posterior densities (proportional to
the data likelihood) under the full (panmixia) and the source-sink
Table 6
F statistics, and bootstrap confidence intervals constructed using 1000 replicates
using GDA. For estimation of FST, we assumed that there were a total of 11
subpopulations, a priori assumed to be the locations of sampling.
Value/bound
FIS
FIT
FST
Mean
Upper
Lower
0.446
0.530
0.367
0.468
0.546
0.395
0.039
0.047
0.029
Table 7
X2 tests of genotypic linkage disequilibrium across all pairs of loci, under the null
hypothesis H0 that genotypes at one locus are independent of genotypes at another
locus.
Locus 1
Locus 2
X2
df
P-Value
Hcv7
Hcv7
Hcv17
Hcv7
Hcv17
Hcv15
Hcv7
Hcv17
Hcv15
HcvT19
Hcv7
Hcv17
Hcv15
HcvT19
Hcv4
Hcv7
Hcv17
Hcv15
HcvT19
Hcv4
Hcv13
Hcv17
Hcv15
Hcv15
HcvT19
HcvT19
HcvT19
Hcv4
Hcv4
Hcv4
Hcv4
Hcv13
Hcv13
Hcv13
Hcv13
Hcv13
Hcv30
Hcv30
Hcv30
Hcv30
Hcv30
Hcv30
10.96
0
12.96
9.27
8.24
6.00
2.76
8.93
12.69
3.99
18.45
14.01
8.84
6.41
14.23
14.53
1.038
9.48
8.04
12.20
3.12
12
8
14
10
16
14
14
18
16
16
14
20
16
18
18
10
16
12
14
10
14
0.53
1
0.53
0.51
0.94
0.97
1
0.96
0.70
1
0.19
0.83
0.92
0.99
0.71
0.15
1
0.66
0.89
0.27
1
models (see Methods) showed that the latter had a greater likelihood (Bezier Ln Probability = 152752.12 under source-sink versus
Bezier Ln Probability = 289624.59 under the full model). Using
the source-sink model of population demography, MIGRATE
revealed considerable differences in Ne between the ‘‘source’’, or
the Californian populations, versus the ‘‘sink’’, or all other populations (see Table 9). Population size estimates at the California and
CaliforniaA populations were several fold larger than the other
populations (Ne > 8860 individuals). All other populations of H.
convergens in the eastern USA had smaller Ne (60–3400 individuals). The Oklahoma population was the smallest, with an Ne of 60
individuals (0–6260; 95% c.i.). M (migration rate) estimates under
the source-sink model revealed interesting patterns of ancient
and contemporary gene flow between sampled populations. Of
note are the large emigration rates from the California localities
into the Chile, KansasL, Kentucky, and Arkansas localities. All other
M estimates were comparatively lower (especially in comparison
with the other California population), indicating that the CaliforniaA locality is a central source for all other sampled sites. Bidirectional migration was negligible between the two California
localities (see Table 8).
Analysis of contemporary M using BayesAss, on the other hand
revealed no substantial immigration or emigration between any of
the sampled populations (see Table 10). Mean M estimates did
however show a relatively higher immigration from all Eastern
6
A. Sethuraman et al. / Biological Control 84 (2015) 1–10
Fig. 1. (L) AIC estimates from runs of MULTICLUST from K = 1–10. Least AIC determines the ‘true’ subpopulation structure under both admixture and mixture models. (R) DK
estimates using the method of Evanno et al. (2005) to pick the most likely number of subpopulations in 5 replicate runs of STRUCTURE v.2.3.4 under the mixture model.
Admixture model runs were not amenable to be used under the same method owing to low standard deviations between replicate runs.
Fig. 2. Admixture/mixture proportion plots from all populations. Each distinct subpopulation is represented by a color. Under the mixture model, each individual’s multilocus
genotype is entirely derived from a subpopulation. Under the admixture model, an allele can be derived from one of K subpopulations. K = 2 was determined by replicate runs
of MULTICLUST v.1.0 under both the mixture (Left) and admixture (Center) models, while replicate runs of STRUCTURE v.2.3.4 estimated K = 3 under the mixture model
(Right).
populations (except KansasM) into the Georgia population, but the
95% confidence interval still included a net M of 0.0, indicative of
no substantial migration. Mean estimates of parameters (and confidence intervals) were consistent through the three replicate runs.
MCMC trace plots of the logarithmic probability of the data given
estimated parameters also showed oscillation around a mean
value, with no definitive pattern, indicative of convergence of
MCMC.
3.4. Population size change
MSVAR analyses using the Western and Eastern populations
detected Ne decline in both groups of populations (29-fold in
Western, mean Current Ne = 48966.61 (variance = 1.50), mean
Ancestral Ne = 1402813.705 (variance = 1.31), 12-fold in Eastern,
mean Current Ne = 13901.77 (variance = 1.59), mean Ancestral
Ne = 173087.62 (variance = 1.30)).
Across all populations, MSVAR determined that the current
overall population size under the panmictic model has declined
considerably (16-fold, mean Current Ne = 19054.61 (variance = 1.29), mean Ancestral Ne = 316227.8 (variance = 1.27)) over
approximately the last 4.41 (variance = 0.157) years, across all loci
(Tables 11 and 12). Mutation rate across all loci was determined to
be 3 104 sites per generation, on similar scales as the assumed
microsatellite mutation rate per generation, across loci.
4. Discussion
Augmentative release of biological control organisms (often
from one, or more source populations) presents a unique opportu-
7
South America
Oklahoma
Kentucky
KansasM
KansasL
Iowa
Georgia
CaliforniaA
California
SouthAmerica
0.013(0.0000–
0.0383)
0.011(0.0000–
0.0325)
0.0118(0.0000–
0.0346)
0.015(0.0000–
0.0443)
0.0126(0.0000–
0.0363)
0.0144(0.0000–
0.0415)
0.016(0.0000–
0.0455)
0.0163(0.0000–
0.0477)
0.1087(0.0000–
0.0555)
0.0151(0.0000–
0.0437)
0.705(0.6667–
0.7682)
0.0128(0.0000–
0.0365)
0.0111(0.0000–
0.0333)
0.0114(0.0000–
0.0339)
0.015(0.0000–
0.0437)
0.0124(0.0000–
0.0366)
0.0142(0.0000–
0.0409)
0.016(0.0000–
0.0455)
0.0159(0.0000–
0.0449)
0.0187(0.0000–
0.0544)
0.6861(0.6667–
0.7224)
0.0242(0.0000–
0.0725)
Oklahoma
Kentucky
0.0128(0.0000–
0.0369)
0.011(0.0000–
0.0324)
0.0117(0.0000–
0.0347)
0.0151(0.0000–
0.044)
0.0123(0.0000–
0.0362)
0.0141(0.0000–
0.0414)
0.0163(0.0000–
0.0471)
0.0163(0.0000–
0.0459)
0.6942(0.6667–
0.7435)
0.0146(0.0000–
0.0424)
0.0233(0.0000–
0.0706)
0.0139(0.0000–
0.0405)
0.0108(0.0000–
0.0315)
0.0117(0.0000–
0.035)
0.0149(0.0000–
0.044)
0.0127(0.0000–
0.0371)
0.0142(0.0000–
0.0417)
0.0166(0.0000–
0.0389)
0.6919(0.6667–
0.7381)
0.0197(0.0000–
0.0581)
0.0151(0.0000–
0.0452)
0.0234(0.0000–
0.0706)
KansasM
KansasL
Iowa
0.013(0.0000–
0.038)
0.011(0.0000–
0.0319)
0.0116(0.0000–
0.0327)
0.0152(0.0000–
0.0447)
0.0121(0.0000–
0.0352)
0.0142(0.0000–
0.0414)
0.693(0.6667–
0.7367)
0.0162(0.0000–
0.0471)
0.0189(0.0000–
0.0542)
0.0146(0.0000–
0.0437)
0.024(0.0000–
0.0713)
Arkansas
nity for studying the dynamics of genetic admixture. H. convergens,
has been used widely in augmentation biological control across the
Americas, particularly as predators of aphids and whiteflies. However, the population genetic effects of augmentative releases of H.
convergens from source populations in California on other native
populations of the beetle have yet to be characterized. Our analysis
of structure among sampled locations strongly indicates the presence of ancestral admixture between at least 2 genetically divergent subpopulations. Consistent with that conclusion of ancestral
admixture, no recent migration is discernible between sampled
populations, as indicated by the BayesAss analyses. Estimation of
ancestral migration rates between sampled populations using
MIGRATE indicates greater support (Bezier ln probability of 10
scales larger) for a source-sink model of population demography,
at least historically, rather than panmixia. Correspondingly, our
analyses show that California multilocus genotypes are indeed
admixed into Eastern populations, supporting the hypothesis of
non-target effects of augmentation programs that utilize commercially distributed H. convergens from California.
Estimated population sizes were considerably larger in the California (source) H. convergens populations, compared to all Eastern
0.0284(0.0000–
0.0898)
0.0471(0.0000–
0.1552)
0.017(0.0000–
0.0522)
0.0149(0.0000–
0.0446)
0.0232(0.0000–
0.0709)
0.7326(0.6667–
0.8591)
0.0572(0.0000–
0.1804)
0.0469(0.0000–
0.157)
0.0593(0.0000–
0.1843)
0.0704(0.0000–
0.2201)
0.0275(0.0000–
0.0822)
2200(0–5460)
3260(0–6540)
10740(7060–14260)
8860(5200–12540)
3000(0–6140)
740(0–4660)
2200(0–5740)
2740(0–6000)
2860(0–6260)
60(0–6260)
3400(0–6660)
Georgia
Ne (h/4u), 95% c.i.
1.10(0.00–2.73)
1.63(0.00–3.27)
5.37(3.53–7.13)
4.43(2.60–6.27)
1.50(0.00–3.07)
0.37(0.00–2.33)
1.10(0.00–2.87)
1.37(0.00–3.00)
1.43(0.00–3.13)
0.03(0.00–3.13)
1.70(0.00–3.33)
0.13(0.0000–
0.2407)
0.1155(0.0000–
0.2477)
0.09(0.0000–
0.2502)
0.0739(0.0000–
0.2263)
0.7957(0.6667–
0.9021)
0.1295(0.0000–
0.2399)
0.1087(0.0000–
0.2115)
0.1008(0.0000–
0.2134)
0.0899(0.0000–
0.1952)
0.1233(0.0000–
0.232)
0.0588(0.0000–
0.1581)
h (mode, 95% c.i.)
Arizona
Arkansas
California
CaliforniaA
Georgia
Iowa
Kansas-L
Kansas-M
Kentucky
Oklahoma
South America
CaliforniaA
Population
0.0618(0.0000–
0.1977)
0.0459(0.0000–
0.154)
0.075(0.0000–
0.2363)
0.7339(0.6667–
0.869)
0.0695(0.0000–
0.2194)
0.0221(0.0000–
0.0668)
0.026(0.0000–
0.0794)
0.036(0.0000–
0.1244)
0.0208(0.0000–
0.0607)
0.0153(0.0000–
0.044)
0.0384(0.0000–
0.1239)
Table 9
Population size estimates from fitting a source-sink model to the data using MIGRATE
3.6.4. Parameters estimated are mutation scaled population sizes h = 4Neu. Also
shown are 95% confidence intervals around the mode (maximum likelihood in the
posterior density distribution of estimated parameter given the data). Mean Ne
estimate is across 5 replicate runs of MIGRATE.
California
40.337
800.8679
3211.1
75.18
73.8481
1252.995
159
93.4879
205.4721
613.8
1426.337
167.14
537.9579
559.6019
2813.282
1.1799
27.6201
692.461
3172.2
0.0167(0.0000–
0.0483)
0.044(0.0000–
0.1539)
0.736(0.6667–
0.8767)
0.0725(0.0000–
0.2243)
0.0242(0.0000–
0.0758)
0.0155(0.0000–
0.0455)
0.0171(0.0000–
0.0515)
0.0263(0.0000–
0.0903)
0.02(0.0000–
0.0569)
0.016(0.0000–
0.0462)
0.0247(0.0000–
0.0745)
Number
1.1
1.63
1.63
5.37
4.43
1.5
1.5
0.37
0.37
1.1
1.1
1.37
1.37
1.43
1.43
0.03
0.03
1.7
1.7
Arkansas
h
(0.00–70.67)
(112.00–838.67)
(1272.00–2000.00)
(0.00–46.67)
(0.00–56.00)
(586.67–1052.00)
(32.00–234.67)
(78.67–358.67)
(425.33–873.33)
(286.67–1264.00)
(689.33–1794.67)
(0.00–325.33)
(124.00–1132.00)
(212.00–1712.00)
(1012.00–2000.00)
(0.00–110.67)
(697.33–1945.33)
(118.67–770.66)
(625.33–2000.00)
0.014(0.0000–
0.0413)
0.6821(0.6667–
0.7129)
0.0122(0.0000–
0.0367)
0.015(0.0000–
0.0443)
0.0129(0.0000–
0.0371)
0.0153(0.0000–
0.0445)
0.0174(0.0000–
0.05)
0.0177(0.0000–
0.0529)
0.0203(0.0000–
0.059)
0.0147(0.0000–
0.0427)
0.0248(0.0000–
0.0759)
95% c.i.
36.67
491.33
1970.00
14.00
16.67
835.33
106.00
252.67
555.33
558.00
1296.67
122.00
392.67
391.33
1967.33
39.33
920.67
407.33
1866.00
0.6837(0.6667–
0.7149)
0.0104(0.0000–
0.0302)
0.0116(0.0000–
0.0337)
0.0146(0.0000–
0.0427)
0.0124(0.0000–
0.0363)
0.014(0.0000–
0.0405)
0.0157(0.0000–
0.0478)
0.0156(0.0000–
0.0445)
0.0185(0.0000–
0.055)
0.0149(0.0000–
0.0427)
0.0259(0.0000–
0.0765)
Mode
M4->1
M3->2
M4->2
M4->3
M3->4
M3->5
M4->5
M3->6
M4->6
M3->7
M4->7
M3->8
M4->8
M3->9
M4->9
M3->10
M4->10
M3->11
M4->11
Arizona
Parameter
Arizona
Table 8
Migration rate estimates from fitting a source-sink model to the data using MIGRATE
3.6.4. Parameters estimated are bidirectional migration rates (scaled by a standard
microsatellite mutation rate of 5 104 mutations per site per generation). Number
of immigrants is calculated as hM, where h are the mutation scaled population sizes.
Also shown are 95% confidence intervals. The first column indicates which migration
parameter as Mfrom->to. Population ID’s are the same as in Table 5. The second column
indicates mode of the posterior density distribution of migration rates M.
Table 10
Mean pairwise contemporary migration rate (in fraction of population that are immigrants/emigrants) as estimated by BayesAss v.3.0.3. 95% confidence intervals are shown in parentheses. Means and confidence intervals were
computed over three replicate runs of BayesAss with different random number seeds.
A. Sethuraman et al. / Biological Control 84 (2015) 1–10
8
A. Sethuraman et al. / Biological Control 84 (2015) 1–10
Table 11
Estimates of population sizes (ancestral and current) under a panmictic model across 7 polymorphic microsatellite loci using MSVAR v.1.3.
Locus
Mutations
TMRCA
log10 (N0)
log10 (N1)
log10 (l)
log likelihood
1
2
3
4
5
6
7
1504
1443
1517
1509
1431
1345
1525
3.05E+01
6.74E+01
2.92E+01
6.66E+01
6.53E+01
3.22E+01
4.73E+01
4.40
4.17
4.43
4.18
4.20
4.39
4.24
5.54
5.42
5.37
5.52
5.53
5.48
5.51
3.50
3.46
3.47
3.54
3.57
3.52
3.44
1346.27
651.50
1536.17
451.31
324.93
1029.43
995.29
Table 12
Estimates of population sizes, and time since population decline across 7 polymorphic microsatellite loci using MSVAR v.1.3. Also shown are variances across loci in parentheses.
The full model (analysis using all populations) is shown by the row labeled ‘‘All’’.
Population
log10 Ncurrent
log10 Nancestral
log10 T
log likelihood
Time (years)
Eastern
Western
All
4.14(0.20)
4.69(0.18)
4.28(0.11)
5.24(0.11)
6.15(0.12)
5.50(0.11)
3.42(0.21)
3.989(0.27)
3.51(0.04)
3464.64
7391.32
6334.91
3.91(0.22)
5.45(0.64)
4.41(0.16)
populations. The South American beetles (as well as some of the
larger Eastern populations) seem to be most closely related to
the CaliforniaA population, and have a size comparable to all the
other sampled populations. Also, if the entire sampled population
were considered to be panmictic, a very recent and drastic population decline has occurred in all sampled populations (across loci).
While the MSVAR analyses strongly indicate a population decline
(Ncurrent/Nancestral = 0.06), the method has lower precision in estimating demographic parameters under very recent population size
changes (Girod et al., 2011). MSVAR also assumes that alleles are
sampled from a panmictic population (K = 1), which is not entirely
true in our sampled locations, as indicated by the MULTICLUST and
STRUCTURE analyses (which show the presence of at least K = 2
ancestral subpopulations). We also utilized a generation time of
1 generation per year (based on the univoltine source populations
from California – Hagen (1962) in estimating population size
decline. We acknowledge that this estimate is conservative. Several
native populations, particularly in our Eastern sampling locales are
multivoltine (Hagen, 1962). Correspondingly, our estimate of time
since population size decline may be biased. We hence treat the
detection of population decline with MSVAR as circumstantial,
and any inferences from it only augmenting what has already been
observed in ecological studies of some eastern H. convergens populations (Smith and Gardiner, 2013).
We acknowledge that the sample sizes in this study are relatively small, compared to the expected total population size, but
several points support their validity in our study. First, levels of
polymorphism were high – all loci were polymorphic (P = 1, across
all sampled individuals), with an average of >20 alleles/locus. For
such highly polymorphic loci, lower sample sizes should not affect
estimates of heterozygosity, allele frequencies, and subsequent
statistics (Hale et al., 2012). Moreover, estimated levels of inbreeding (FIS = 0.446) and population structure (K P 2 subpopulations)
are high. Thus, each sampled location should have lower heterozygosity on average if a Wahlund Effect holds true (sense Table 3,
despite HWE). As long as polymorphic loci are homozygous, the
sample should be representative of a population regardless of the
number of sampled individuals. Furthermore, our analyses of
structure, polymorphism, heterozygosity, and disequlibrium
assume no populations labels, being computed across individuals
in the total sample, and thus should be unaffected by local sample
sizes. Finally, the coalescent analyses used (e.g., MSVAR) do not
require many individuals to permit demographic inferences
(Pluzhnikov and Donnelly, 1996).
Pervasive inbreeding was implicated by microsatellite analysis,
indicating that most genetic variation is explained by variation
among individuals within sampled locations. Also, the low heterozygosity in any individual relative to the total population (either as
a result of non-random mating, inbreeding, or drift), and relatively
lower levels of differentiation combined with declining population
sizes (16-fold), indicates large effects of genetic drift, perhaps due
to founder effects. This outcome is of particular concern for the
long-term evolutionary adaptability of the species, considering
resource competition and intra-guild predation by other invasive
predatory Coccinellidae across North America. Reduced populations of H. convergens in selected regions of eastern North America
correlate with increased densities of introduced Eurasian species,
specifically C. septempunctata and H. axyridis (Smith and
Gardiner, 2013). Our analyses provide evidence that Eastern populations of H. convergens have been affected by augmentative
released from the west, although no detectible migration is ongoing between the sampled populations. In light of no apparent
reproductive barriers between populations from Iowa and California (Gomez et al., 1998; Obrycki et al., 2001), we hypothesize that
continual admixture has not affected the rate of evolution of reproductive barriers. However, questions remain as to the rates at
which beetles from California mate with local beetles, whether
eastern and western populations express genetic differences
related to local environmental adaptations, and whether the
crosses have deleterious consequences for Eastern populations.
Reduced population sizes and inbreeding could lead to parallel
effects in bacterial symbionts and pathogens, with further potential consequences for this species. Many species of lady beetles
contain maternally-inherited bacterial symbionts that alter sex
ratios by killing developing male eggs (Majerus, 2006). The presence of these bacteria can compromise the use of mitochondrial
DNA for population genetic studies (Hurst and Jiggins, 2005). However, no male-killing bacteria have been reported from H. convergens (Majerus, 2006; Weeks et al., 2003). Still, (Majerus and
Majerus, 2012) predicted their occurrence or invasion based on
H. convergens’ oviposition behavior of laying eggs in clusters, the
risk of first instar starvation, and sibling egg cannibalism by neonates. Augmentative releases of overwintering adult H. convergens
collected from the Sierra Nevada Mountains in California have
been conducted for over a century (see description of practices in
Hagen (1962)). Although many releases initially targeted pest species in California and Arizona agricultural systems (Hagen, 1962;
Dreistadt and Flint, 1996; Flint and Dreistadt, 2005; Hagler and
A. Sethuraman et al. / Biological Control 84 (2015) 1–10
Naranjo, 2004; Hagler, 2009), over the past 50 years, H. convergens
from California have been widely released east of the Rocky Mountains. These beetles can be infected with pathogenic misrosporidia
and parasitized by the braconid wasp Dinocampus coccinellae,
which may parasitize other species of Coccinellidae (Lipa and
Steinhaus, 1959; O’Neil et al., 1998; Saito and Bjornson, 2006;
Bjornson, 2008; Ceryngier et al., 2012). Nonetheless, evidence is
lacking that quantifies any effects of these potential mortality factors on Eastern populations of H. convergens.
Further characterization of intra-specific variation in selected
traits of H. convergens is needed to assess the potential effects of
releases of California beetles on local H. convergens populations.
For example, studies of geographic variation in H. convergens populations from Arizona, Cuzco (Peru), New York, and Oregon examined the thermal requirements for development (Butler and
Dickerson, 1972; Escalante, 1972; Obrycki and Tauber, 1982;
Miller, 1992). Consistency in developmental thresholds across geographically separated populations in North America has been
reported by Miller (1992). However, earlier studies noted differences in thermal responses between populations from Arizona
and New York (Butler and Dickerson, 1972; Obrycki and Tauber,
1982). Analysis of the genetics of thermal regulation in tandem
with the population structure and admixture of this species are
thus warranted to understand the genetic consequences of localized adaptation in these predatory beetles.
Several larger practical and conceptual questions are raised by
this study. How does human-mediated movement of organisms
(and subsequent genetic admixture into native conspecifics, as
indicated by this study) for commercial purposes affect the longterm adaptability of species to local environments? What are the
benefits and non-target genetic effects of augmented releases of
H. convergens on local populations of cohabiting species? Particularly, can predator–prey demographics between competing species
explain large scale population size declines as detected by this
study? What other factors (genetic and ecological) affect the
demographics of augmented species, having determined pervasive
inbreeding and population size decline? Addressing these and
other questions will require a closer look at species occupying similar habitats in which H. convergens are released. A combined analysis of population demography (using genetic information) and
ecological information (including habitat and prey use, and parasitoids, records of augmentation) could shed more light on the
large-scale impacts of augmentative biological control programs
that involve geographic displacement of individuals.
Author contributions
JJO and FJ designed the study. Genotyping was performed by AS
and JJO. AS analyzed the data, and AS, FJ, and JJO wrote the
manuscript.
Acknowledgments
This research was supported by a USDA-NIFA sabbatical grant
(2011-67014-30194) to JJO. We thank Andy Michel and Mary
Gardiner, Department of Entomology, Ohio Agricultural Research
and Development Center, Wooster, Ohio for sharing their microsatellite markers.We thank Morgan Becker, Audra Loy, Chelsea Sawyers, Leighfonda Allen, Daniela Flores, other members of the
Janzen Lab at Iowa State University, and Lalitha Sethuraman for
assistance with genotyping. We are very grateful to all the collectors of H. convergens listed in Table 1. We thank Dr. Ric Bessin,
Department of Entomology, University of Kentucky, for the image
of H. convergens. All analyses were performed on the Temple HPC
9
(Owlsnest) which was supported in part by the NSF major research
instrumentation grant number CNS-09-58854.
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