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Genetic diversity and population structure of two subspecies of western honey bees (Apis mellifera L.) in the Republic of South Africa as revealed by microsatellite genotyping

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Apis mellifera scutellata and A.m. capensis, two native subspecies of western honey bees in the Republic of South Africa (RSA), are important to beekeepers in their native region because beekeepers use these bees for honey production and pollination purposes. Additionally, both bees are important invasive pests outside of their native ranges. Recently, whole mitogenome sequencing and single nucleotide polymorphisms were used to study their genetic diversity. To add to our knowledge of the molecular ecology of both bees, we tested the ability of microsatellites to be used as a tool to discriminate between A.m. capensis and A.m. scutellata. We analyzed the genetic variability and overall population structure of both bee subspecies and hybrids of the two by genotyping individuals collected from RSA (N = 813 bees from 75 apiaries) at 19 microsatellite DNA loci. Overall, populations averaged between 9.2 and 11.3 alleles per locus, with unbiased heterozygosity values ranging from 0.81 to 0.86 per population. Bayesian clustering analyses revealed two distinct evolutionary units, though the results did not match those of earlier morphometric and molecular analyses. This suggests that the microsatellites we tested were not sufficient for subspecies identification purposes, especially for Cape and hybrid bees. Nevertheless, the microsatellite data highlight the considerable genetic diversity within both populations and a larger-than-expected hybridization zone between the natural distributions of A.m. capensis and A.m. scutellata.
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Genetic diversity and population structure
of two subspecies of western honey bees
(Apis mellifera L.) in the Republic of South
Africa as revealed by microsatellite
genotyping
Amin Eimanifar
1,2
, Johanna T. Pieplow
3
, Alireza Asem
4
and
James D. Ellis
1
1Entomology and Nematology Department, Honey Bee Research and Extension Laboratory,
Gainesville, FL, USA
2Independent Senior Scientist, Industrial District, Easton, MD, USA
3Molekulare Ökologie, Institut Für Biologie, Molekulare Ökologie, Martin-Luther-Universität
Halle-Wittenberg, Halle, Germany
4College of Fisheries and Life Science, Hainan Tropical Ocean University, Yucai Road, Sanya, China
ABSTRACT
Apis mellifera scutellata and Apis mellifera capensis, two native subspecies of western
honey bees in the Republic of South Africa (RSA), are important to beekeepers in
their native region because beekeepers use these bees for honey production and
pollination purposes. Additionally, both bees are important invasive pests outside of
their native ranges. Recently, whole mitogenome sequencing and single nucleotide
polymorphisms were used to study their genetic diversity. To add to our knowledge
of the molecular ecology of both bees, we tested the ability of microsatellites to
be used as a tool to discriminate between A.m. capensis and A.m. scutellata.
We analyzed the genetic variability and overall population structure of both bee
subspecies and hybrids of the two by genotyping individuals collected from RSA
(N= 813 bees from 75 apiaries) at 19 microsatellite DNA loci. Overall, populations
averaged between 9.2 and 11.3 alleles per locus, with unbiased heterozygosity values
ranging from 0.81 to 0.86 per population. Bayesian clustering analyses revealed
two distinct evolutionary units, though the results did not match those of earlier
morphometric and molecular analyses. This suggests that the microsatellites we
tested were not sufcient for subspecies identication purposes, especially for Cape
and hybrid bees. Nevertheless, the microsatellite data highlight the considerable
genetic diversity within both populations and a larger-than-expected hybridization
zone between the natural distributions of A.m. capensis and A.m. scutellata.
Subjects Biodiversity, Entomology, Genetics
Keywords Apis mellifera scutellate,Apis mellifera capensis, Microsatellite genotyping,
Population structure
INTRODUCTION
Apis mellifera L. (western honey bees) are eusocial bees native to Eurasia and Africa.
A. mellifera is one of nine honey bee (Apis spp.) species, with the other eight species being
How to cite this article Eimanifar A, Pieplow JT, Asem A, Ellis JD. 2020. Genetic diversity and population structure of two subspecies of
western honey bees (Apis mellifera L.) in the Republic of South Africa as revealed by microsatellite genotyping. PeerJ 8:e8280
DOI 10.7717/peerj.8280
Submitted 1 October 2019
Accepted 22 November 2019
Published 3 January 2020
Corresponding author
Amin Eimanifar,
amineimanifar1979@gmail.com
Academic editor
Ruslan Kalendar
Additional Information and
Declarations can be found on
page 14
DOI 10.7717/peerj.8280
Copyright
2020 Eimanifar et al.
Distributed under
Creative Commons CC-BY 4.0
native to and distributed in Asia (Radloff, Hepburn & Engel, 2011). A. mellifera has
diversied into approximately 2530 subspecies which are further classied into six
evolutionary groups, or lineages(A, M, C, O, Z and Y) (Ruttner, 1988;Garnery,
Cornuet & Solignac, 1992;Franck et al., 2000;Alburaki et al., 2013). At least 11 of the
A. mellifera subspecies are native to Africa. Two subspecies native to the Republic of
South Africa (RSA) are of particular interest to us. They are Apis mellifera scutellata
(the savannah or lowland honey bee) and Apis mellifera capensis (the Cape honey bee).
These two subspecies hybridize readily in the intermediate areas where their natural
distributions overlap (Crewe, Hepburn & Moritz, 1994;Moritz, Beye & Hepburn, 1998;
Eimanifar et al., 2018b;Bustamante, Baiser & Ellis, in press).
Both A.m. scutellata and A.m. capensis are important to beekeepers in the RSA but are
considered invasive pests outside of their native ranges (Eimanifar et al., 2018a,2018b).
A.m. scutellata is the bee introduced into Brazil in the mid 1950s, later becoming known in
the Americas as the killer bee(Caron, 2001) due to the heightened defensiveness it
exhibits. Cape bees can reproduce thelytokously (worker bees produce diploid, female
offspring), a trait that allows them to be social parasites (Aumer et al., 2019). This behavior
makes A.m. capensis a signicant problem in northern parts of the RSA where they are not
native. There, they socially parasitize A.m. scutellata colonies. Though A.m. capensis
workers can produce female offspring, they reproduce at much lower rates than do queen
honey bees, rendering them unable to support a robust colony population. Consequently,
A.m. scutellata colonies infested with A.m. capensis laying workers suffer dwindling
populations and ultimately die. Beekeepers in the RSA call this phenomenon the capensis
calamity(Allsopp, 1992).
A.m. capensis and A.m. scutellata show a distinct genetic structure (Jaffe et al., 2009;
Neumann et al., 2011;Eimanifar et al., 2018b). Some studies have been published on this
topic already, but the sample sizes used render them less informative at the level of
subspecies differentiation (Wallberg et al., 2014;Chapman et al., 2015;Harpur et al., 2015;
Nelson et al., 2017). Recently, the population structure and genetic diversity of both
subspecies was determined using Genotyping-by-Sequencing analyses for 474 individuals,
representative of 28 geographical regions in the RSA (Eimanifar et al., 2018b) and whole
mitogenome analyses of 25 individuals from across the RSA (Eimanifar et al., 2018a).
Eimanifar et al. (2018b) produced a list of 83 divergent SNP loci that can be used to
distinguish the two subspecies. In contrast, whole mitogenomes were not useful for
distinguishing between the subspecies, though the high genetic diversity of the bees
was highlighted (Eimanifar et al., 2018a). In recent years, there have been substantial
applications of microsatellite markers to assess the genetic variability, population structure
and conservation genetics of many organisms (Wang et al., 2017;Frias-Soler et al., 2018;
De Melo Moura et al., 2019;Yu et al., 2019). Thus, to add to our knowledge of the
molecular ecology of both bee populations in the RSA, we used 19 microsatellite loci to
discern the range-wide genetic variation and population genetic structure of 813 honey
bees from 75 different apiaries (29 geographical regions) across the beesnatural
distribution in the RSA. Our microsatellite data, when viewed in context with our earlier
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 2/18
work (Eimanifar et al., 2018a,2018b), highlight the high genetic diversity of the honey bee
populations inhabiting the RSA.
MATERIALS AND METHODS
Honey bee sampling and DNA extraction
In April/May 2013 and April 2014, a total of 60+ bees were collected from each of over
1,100 managed honey bee colonies located in 75 different apiaries grouped into 29
geographical regions (13 apiaries/region) across the A.m. scutellata and A.m. capensis
distribution in RSA (Mortensen et al., 2016). A region was dened as a cluster of 13
apiaries that were located close to one another (i.e., 550 km between apiaries) or was
otherwise isolated by itself in instances where a region was composed of only one apiary.
The regions were named for the closest city, town, village, or other geographically dened,
nearby area. Detailed information on the sampling regions and apiaries, the number of
bees analyzed per apiary and the geographical coordinates of the sampling regions are
shown in Table 1.
The bees were collected and stored by colony in a 50 ml tube containing 95%
ethanol. The samples were transported (per USDA-APHIS regulations) to the Honey
Bee Research and Extension Laboratory at the University of Florida where the identity
of each colony (A.m. capensis, A.m. scutellata or hybrid) was determined using classical
morphometrics conducted on at least ten bees from each colony (for procedure,
Bustamante, Baiser & Ellis, in press). The results of the morphometric classication are
shown in Table S1. Colony identity was cross-checked using a distribution map published
by Hepburn & Radloff (1998). For later analysis, the sample tubes of bees were kept
at 80 C.
For this study, 813 worker bees were sampled from 101 selected colonies across the
75 different apiaries, with 2233 bees used per region (510 bees per colony). The DNA
was extracted from the dissected thoraces of each individual bee using WizardÒGenomic
DNA Purication kit (Promega, Durham, NC, USA) according to the manufacturers
instructions. DNA quality was assessed using a 1% agarose gel and quantied using QubitÒ
3.0 Fluorometer based on manufacturers guidelines (Thermo Scientic Inc., Waltham,
MA, USA).
Microsatellite genotyping
Nineteen microsatellite loci were selected from previous studies and deemed appropriate
candidates for the genotyping of the target bee populations (Shaibi, Lattorff & Moritz,
2008;Chen et al., 2005;Alburaki et al., 2013;Rowe et al., 1997;Solignac et al., 2003;
Weinstock et al., 2006). The forward primers were labeled at the 5end with one of four
uorescent dyes (FAM, VIC, PET and NED; Applied Biosystems, Foster, CA, USA) and
distributed into six multiplex PCR reactions. A list of labeled primers and associated
information is included in Table S2. PCR reactions were performed in a thermal cycler
(Eppendorf, Hamburg, Germany) in 20 µl volumes containing 10 µl of Master Mix
Maxima Hot Start 2X Qiagen, 0.6 µl of each primer (10 pmol/µl) and one µl of DNA
(>10 ng/µl). The PCR program started with a denaturation phase at 95 C for 5 min,
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 3/18
Table 1 Summary information for honey bee samples collected in the Republic of South Africa.
Apiary No = 75 total apiaries sampled. Sampling regions/apiaries = city/town closest to all apiaries in
the region + apiary in/around that city (AC apiaries). Apiary identier = code generated from two-letter
city/town abbreviation and apiary letter. N= number of honey bees examined in a given region.
Geographical coordinates = GPS location of each apiary.
Apiary no. Sampling regions/apiaries Apiary identier NGeographical coordinates
1 Bloemfontein/A BL/A 14 29.24S26.95E
2 Bloemfontein/B BL/B 13 29.20S27.20E
3 Bloemfontein/C BL/C 5 29.24S26.94E
4 Kroonstad/A KR/A 10 27.58S27.30E
5 Kroonstad/B KR/B 10 27.33S27.50E
6 Kroonstad/C KR/C 8 27.27S27.50E
7 Pretoria/A PT/A 10 25.74S28.26E
8 Pretoria/B PT/B 17 25.70S28.10E
9 Springbok/A SP/A 10 29.65S17.83E
10 Springbok/B SP/B 10 29.71S17.78E
11 Springbok/C SP/C 10 29.67S17.81E
12 Upington/A UP/A 10 28.48S21.18E
13 Upington/B UP/B 10 28.72S20.98E
14 Upington/C UP/C 9 28.52S21.24E
15 Vryburg/A VR/A 30 26.96S24.76E
16 Bredasdorp/A BD/A 10 34.50S20.35E
17 Bredasdorp/B BD/B 9 34.57S20.26E
18 Bredasdorp/C BD/C 10 34.53S20.29E
19 Citrusdaal/A CD/A 9 32.86S19.21E
20 Citrusdaal/B CD/B 10 32.84S19.24E
21 Citrusdaal/C CD/C 10 32.67S19.06E
22 Cape Town/A CT/A 8 33.80S18.36E
23 Cape Town/B CT/B 12 33.97S18.51E
24 Cape Town/C CT/C 10 33.96S18.45E
25 George/A GE/A 8 33.90S22.33E
26 George/B GE/B 8 33.95S22.75E
27 George/C GE/C 8 33.98S22.47E
28 Grahamstown/A GT/A 15 33.31S26.49E
29 Grahamstown/B GT/B 14 33.37S26.42E
30 Knysna/A KN/A 15 34.05S22.99E
31 Knysna/B KN/B 15 34.02S22.97E
32 Langebaan/A LA/A 10 33.04S18.09E
33 Langebaan/B LA/B 10 33.00S18.31E
34 Langebaan/C LA/C 10 33.03S18.10E
35 Laingsburg/A LB/A 5 33.27S20.85E
36 Laingsburg/B LB/B 13 33.28S20.97E
37 Laingsburg/C LB/C 8 3337S2116E
38 Moorreesburg/A MB/A 10 33.10S18.74E
39 Moorreesburg/B MB/B 10 33.11S18.56E
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 4/18
followed by 35 cycles of 30 s at 95 C, 30 s at 55 C, 30 s at 72 C and a nal elongation
phase at 72 C for 30 min. The resulting PCR products were diluted 25100 fold before
genotyping and submitted to the University of Floridas Interdisciplinary Center for
Biotechnology Research for sequencing. A volume of 990 µl Hi-DiTM Formamide
Table 1 (continued).
Apiary no. Sampling regions/apiaries Apiary identier NGeographical coordinates
40 Moorreesburg/C MB/C 10 33.02S18.85E
41 Modderfontein/A MF/A 30 33.18S25.80E
42 Oudtshoorn/A OD/A 10 33.50S22.51E
43 Oudtshoorn/B OD/B 10 33.53S22.54E
44 Oudtshoorn/C OD/C 10 33.58S22.49E
45 Plettenburg Bay/A PB/A 22 34.05S23.36E
46 Plettenburg Bay/B PB/B 8 34.09S23.34E
47 Port Elizabeth/A PE/A 10 33.87S25.39E
48 Riversdale/A RD/A 10 34.31S21.50E
49 Riversdale/B RD/B 10 34.23S21.58E
50 Riversdale/C RD/C 10 34.10S21.20E
51 St. Francis/A SF/A 20 34.17S24.81E
52 Stellenbosch/A ST/A 10 33.89S18.89E
53 Stellenbosch/B ST/B 10 33.91S18.81E
54 Stellenbosch/C ST/C 10 33.85S18.82E
55 Swellendam/A SW/A 10 34.05S20.65E
56 Swellendam/B SW/B 10 34.40S20.60E
57 Swellendam/C SW/C 9 34.19S20.30E
58 Touwsrivier/A TR/A 8 33.15S20.47E
59 Touwsrivier/B TR/B 9 33.17S20.53E
60 Touwsrivier/C TR/C 10 33.17S20.26E
61 Worcester/A WD/A 10 33.59S19.45E
62 Worcester/B WD/B 9 33.52S19.49E
63 Worcester/C WD/C 9 33.62S19.69E
64 Beaufort West/A BW/A 10 32.34S22.64E
65 Beaufort West/B BW/B 10 32.34S22.64E
66 Beaufort West/C BW/C 10 32.34S22.62E
67 East London/A EL/A 10 33.04S27.86E
68 East London/B EL/B 8 32.94S27.97E
69 East London/C EL/C 9 32.97S27.90E
70 Graaff-Reinet/A GR/A 10 32.25S24.53E
71 Graaff-Reinet/B GR/B 10 32.17S24.56E
72 Graaff-Reinet/C GR/C 9 32.26S24.54E
73 Klawer/A KL/A 10 32.02S18.78E
74 Klawer/B KL/B 10 32.17S18.51E
75 Klawer/C KL/C 10 32.10S18.84E
Total number of bees 813
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 5/18
(Catalog # 4311320; Life Technologies, Grand Island, NY, USA) was mixed with 10 µl of
the GeneScanTM 600 LIZ size standard (Catalog # 1406056; Life Technologies, Grand
Island, NY, USA). A volume of 10 µl of the solution was mixed with one µl of PCR product
per sample and distributed in a 96 well plate. Fragment lengths were determined using a
37301 DNA Analyzer (Model 325-0020; Applied Biosystems, Foster City, CA, USA)
and alleles were scored using GeneMarker version 2.4.0 (Applied Biosystems, Foster City,
CA, USA) (Hulce et al., 2011). All electropherograms were checked manually and
conrmed for further statistical analyses.
Statistical analyses
Population genetic analyses
MICRO-CHECKER 2.2.3 was used to identify the likelihood of scoring errors due to
null alleles, stutter bands and large allele dropout (Van Oosterhout et al., 2004).
We genotyped 10% of the samples twice to evaluate the rate of genotyping errors.
Microsatellite diversity was evaluated by calculating the mean number of alleles per
locus (Na), the number of effective alleles (Ne), observed (H
obs
) and expected (H
exp
)
heterozygosities, unbiased expected heterozygosity (uHe), Shannon index (I) per
sampling region, and the number of private alleles per region (NP) using GenAlEx 6.5
(Peakall & Smouse, 2012). The inbreeding coefcient (F
IS
) was estimated based on Wrights
F-statistics using GenAlEx 6.5 (Peakall & Smouse, 2012). Allelic richness (Ar) per sampling
region was calculated with FSTAT 2.9.3 (Goudet, 2001) using the rarefaction method.
Bee samples from BW, CD, PE, and WD were not included in the population diversity
analyses because the microsatellites assigned them to bee groups that were different
from those to which they were assigned using single nucleotide polymorphisms (SNPs)
(Eimanifar et al., 2018b) and morphometrics (Bustamante, Baiser & Ellis, in press). Thus,
their identities were questionable, making their assignment to a bee group for population
analysis injudicious.
The deviation of genetic markers from HardyWeinberg equilibrium was examined
for each combination of locus and region based on the exact test following Markov
chain parameters including dememorization = 5,000, batches = 5,000, iterations per
batch = 1,000 (Guo & Thompson, 1992) as implemented in Genepop 4.2 (Raymond &
Rousset, 1995). Overall population differentiation indices were calculated among regions
(again, excluding bees from BW, CD, PE and WD) based on Wrights F-statistics index
(F
ST
) using GenAlEx 6.5 (Peakall & Smouse, 2012). We also calculated HedricksG
ST
and
Josts D for each subspecies as genetic differentiation indicated by F
ST
may remain
undetected for highly variable, multi-allelic markers such as microsatellites (Meirmans &
Hedrick, 2011). G
ST
is the original G
ST
as dened by Nei (1978) and standardized by the
maximum value it can obtain (G
ST (max)
)(Hedrick, 2005). Josts D is calculated based
on the effective number of alleles instead of heterozygosity, which is considered a more
intuitive diversity estimate (Jost, 2008). All three pairwise genetic differentiation metrics
were calculated and their signicance was inferred based on 9,999 permutations in
GenAlEx 6.5 (Peakall & Smouse, 2012). The overall genetic differentiation indices (F
ST
,
G
ST
and Josts D) were compared statistically between subspecies using a one-way
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 6/18
ANOVA test as implemented in SPSS Statistica (IBM Corp, 2013). A signicance value of
95% for condence intervals was applied to analyze the pairwise data set. The matrix of
genetic distance was calculated based on Nei index among regions using GenAlEx 6.5
(Peakall & Smouse, 2012).
Partitioning of genetic variation between and within regions was determined by a
hierarchical analysis of molecular variance in Arlequin v. 3.5 package (Excofer & Lischer,
2010). The signicance of these values was examined based on 9,999 permutations.
The structure of dening regions was adjusted based on clustering patterns of honey bees
distributions described by Hepburn & Radloff (1998).
Bayesian population structure analysis
Analyses of genetic clustering among regions were performed using STRUCTURE 2.3.4
(Pritchard, Stephens & Donnelly, 2000). An admixture model with correlated allele
frequencies were assumed. We applied a number of clusters (K), varying from 1 to 10, with
ve independent runs per each Kto estimate the most reliable number of genetic clusters
(K) using the posterior probability (LnP (N/K)) (Falush, Stephens & Pritchard, 2007)
and ad hoc quantity DK for each Kpartition. We conducted the analysis with no prior
information about population identity with the following parameters: 100,000 pre-burn
steps and 750,000 post-burn iterations of the MCMC algorithm for each run. Posterior
probability changes with respect to Kbetween different runs were assigned as a method for
determining the true Kvalue (Evanno, Regnaut & Goudet, 2005). The most likely value
for Kwas estimated applying EvannosΔKmethod (Evanno, Regnaut & Goudet, 2005)
using STRUCTURE HARVESTER (Earl & Von Holdt, 2012). We used individual Q
matrices to visualize structure bar plots using STRUCTURE PLOT (Ramasamy et al.,
2014).
RESULTS
The result from MICRO-CHECKER revealed no issues with scoring alleles due to
stutters or allelic dropout in any of the 19 loci. We detected the occurrence of homozygote
excess for three loci (UN351, HB-SEX-01 and HB-THE-03), likely indicating the
occurrence of null alleles. Because of this, all datasets were rerun excluding these loci.
In each case, the results were similar to those obtained using all 19 loci, so we included all
loci in the analyses. In order to estimate population genetic indices, we divided all regions
based on subspecies identity determined by Eimanifar et al. (2018b) and Bustamante,
Baiser & Ellis (in press). The results of the population genetics indices are depicted in
Table 2 for each region.
The mean number of alleles per locus was 10.01 ± 0.11 (mean ± s.e. here and hereafter)
and varied from 5.8 (A24) to 13.44 (A107). The mean number of alleles for A.m. scutellata
(six regions), A.m. capensis (11 regions) and hybrid populations (eight regions) was
10.23 ± 0.42, 9.94 ± 0.6 and 9.96 ± 0.8, respectively. The H
exp
value ranged from 0.72 at
locus A24 to 0.87 at locus A14, while the H
obs
value ranged from 0.35 at locus HB-SEX-01
to 0.88 at locus AC6. The H
exp
value across all loci was highest in the KL region, with
an average of 0.84. The lowest H
exp
values were found in the VR, CT, GE, LA and SF
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 7/18
regions, with an average of 0.8, respectively. Observed heterozygosity ranged from 0.7 in
the SW region to 0.8 in the RD region. Mean H
exp
and H
obs
values were 0.81 and 0.75,
respectively. The highest and lowest unbiased expected heterozygosities were found in KL
(0.86), and CT/GE (0.81), respectively. Multilocus values of F
IS
per region ranged from
Table 2 Population genetic characteristics, determined using 19 microsatellite loci of honey bees sampled from 25 geographical regions in the
Republic of South Africa (excluding bees from BW, CD, WD and PEsee text). Region abbreviations are explained in Table 1. Bee identity was
determined using Eimanifar et al. (2018a,2018b) and (Bustamante, Baiser & Ellis, in press). Abbreviations: N
a
, mean number of observed allele per
locus; N
e
, mean number of effective population size; Ar, mean allelic richness; H
obs
, observed heterozygosity; H
exp
, expected heterozygosity; uHe,
mean unbiased estimate of expected heterozygosity; I, Shannon index; F
IS
; Fixation index and N
P
(%), percentage of mean number of private alleles
per region. The standard deviation is indicated in parentheses. The data are the mean with s.e. below in parentheses (Mean (s.e.)).
Pop. Na Ne Ar H
obs
H
exp
uHe I F
IS
Np (%)
A.m. scutellata regions
BL 10.7 (0.8) 6.4 (0.5) 8.73 (2.33) 0.79 (0.04) 0.83 (0.01) 0.84 (0.01) 2 (0.08) 0.05 (0.04) 15.8 (8.6)
KR 10.4 (0.7) 6.5 (0.5) 8.83 (2.27) 0.75 (0.03) 0.83 (0.01) 0.85 (0.01) 2.01 (0.07) 0.10 (0.03) 0 (0)
PT 10.7 (0.7) 5.9 (0.4) 8.86 (2.18) 0.79 (0.03) 0.82 (0.01) 0.83 (0.01) 1.97 (0.07) 0.03 (0.03) 26.3 (10.4)
SP 9.9 (0.5) 5.7 (0.3) 8.14 (1.51) 0.77 (0.04) 0.81 (0.01) 0.83 (0.01) 1.92 (0.05) 0.06 (0.04) 10.5 (7.2)
UP 9.8 (0.6) 5.8 (0.4) 8.19 (1.92) 0.74 (0.03) 0.81 (0.01) 0.83 (0.01) 1.91 (0.07) 0.09 (0.03) 5.3 (5.3)
VR 9.9 (0.6) 5.4 (0.3) 8.00 (1.65) 0.74 (0.04) 0.80 (0.01) 0.82 (0.01) 1.88 (0.06) 0.08 (0.04) 10.5 (7.2)
A.m. scutellata
mean (SE)
10.23 (0.42) 5.95 (0.42) 8.46 (0.39) 0.76 (0.02) 0.82 (0.01) 0.83 (0.01) 1.95 (0.05) 0.07 (0.03) 11.4 (9.06)
A.m. capensis regions
BD 9.8 (0.6) 5.8 (0.4) 8.11 (1.81) 0.78 (0.04) 0.81 (0.02) 0.82 (0.02) 1.91 (0.07) 0.04 (0.04) 0 (0)
CT 9.3 (0.5) 5.3 (0.3) 7.84 (1.50) 0.73 (0.05) 0.80 (0.01) 0.81 (0.01) 1.86 (0.06) 0.09 (0.05) 0 (0)
GE 9.2 (0.5) 5.3 (0.3) 8.13 (1.65) 0.74 (0.03) 0.80 (0.01) 0.81 (0.01) 1.86 (0.06) 0.07 (0.04) 15.8 (8.6)
LB 10.3 (0.6) 6.4 (0.4) 8.91 (2.15) 0.75 (0.04) 0.83 (0.01) 0.85 (0.01) 2.01 (0.07) 0.10 (0.04) 0 (0)
LA 9.6 (0.5) 5.4 (0.4) 7.92 (1.53) 0.77 (0.03) 0.80 (0.01) 0.82 (0.01) 1.87 (0.06) 0.04 (0.04) 0 (0)
MB 10.2 (0.6) 6.1 (0.5) 8.55 (1.90) 0.77 (0.03) 0.81 (0.02) 0.83 (0.02) 1.96 (0.08) 0.06 (0.03) 10.5 (7.2)
OD 11.3 (0.6) 6.3 (0.4) 9.30 (1.91) 0.73 (0.04) 0.83 (0.01) 0.84 (0.01) 2.05 (0.06) 0.12 (0.05) 0 (0)
RD 10.5 (0.7) 6.1 (0.4) 8.58 (1.95) 0.80 (0.04) 0.82 (0.02) 0.84 (0.02) 1.98 (0.07) 0.03 (0.03) 10.5 (7.2)
ST 9.7 (0.5) 5.5 (0.3) 8.08 (1.49) 0.76 (0.04) 0.81 (0.01) 0.82 (0.01) 1.89 (0.05) 0.06 (0.04) 5.3 (5.3)
SW 9.6 (0.7) 5.9 (0.5) 8.21 (2.17) 0.70 (0.04) 0.81 (0.02) 0.83 (0.02) 1.92 (0.07) 0.14 (0.05) 5.3 (5.3)
TR 9.8 (0.6) 6.1 (0.4) 8.50 (2.10) 0.71 (0.05) 0.82 (0.01) 0.84 (0.01) 1.94 (0.07) 0.13 (0.05) 5.3 (5.3)
A.m. capensis
mean (SE)
9.94 (0.60) 5.84 (0.40) 8.38 (0.44) 0.75 (0.03) 0.81 (0.01) 0.83 (0.01) 1.93 (0.06) 0.08 (0.04) 4.79 (5.49)
Hybrid regions
EL 10.8 (0.5) 6.2 (0.4) 9.00 (1.67) 0.73 (0.03) 0.82 (0.01) 0.84 (0.02) 2 (0.06) 0.12 (0.04) 15.8 (8.6)
GR 9.5 (0.6) 5.7 (0.4) 7.92 (1.83) 0.77 (0.03) 0.81 (0.02) 0.82 (0.02) 1.88 (0.07) 0.05 (0.03) 0 (0)
GT 10.1 (0.5) 5.9 (0.3) 8.44 (1.50) 0.75 (0.04) 0.82 (0.01) 0.84 (0.01) 1.96 (0.05) 0.09 (0.05) 10.5 (7.2)
KL 10.8 (0.6) 6.9 (0.4) 9.10 (1.86) 0.75 (0.04) 0.84 (0.01) 0.86 (0.01) 2.07 (0.06) 0.11 (0.04) 15.8 (8.6)
KN 9.9 (0.5) 6.1 (0.4) 8.36 (1.63) 0.73 (0.03) 0.82 (0.01) 0.84 (0.01) 1.96 (0.06) 0.11 (0.04) 5.3 (5.3)
MF 10.0 (0.7) 5.9 (0.3) 8.35 (1.77) 0.77 (0.03) 0.82 (0.02) 0.83 (0.02) 1.94 (0.06) 0.06 (0.03) 21.1 (9.6)
PB 10.3 (0.5) 5.7 (0.3) 8.28 (1.60) 0.71 (0.03) 0.81 (0.01) 0.83 (0.01) 1.92 (0.06) 0.12 (0.04) 15.8 (8.6)
SF 8.3 (0.5) 5.3 (0.3) 7.59 (1.63) 0.75 (0.05) 0.80 (0.01) 0.82 (0.01) 1.80 (0.05) 0.06 (0.06) 5.3 (5.3)
Hybrid
mean (SE)
9.96 (0.80) 5.96 (0.47) 8.38 (0.50) 0.75 (0.02) 0.82 (0.01) 0.84 (0.01) 1.94 (0.08) 0.09 (0.03) 11.20 (7.14)
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 8/18
0.03 (PT and RD regions) to 0.14 (SW region), and the overall value was signicantly
positive 0.08 ± 0.008. Allelic richness among regions of A.m. scutellata and capensis varied
from 7.84 (CT region) to 9.3 (OD region). In hybrid regions, it varied from 7.59 (SF region)
to 9.1 (KL region) (Table 2;Fig. 1).
HardyWeinberg equilibrium tests were performed for each region and each locus.
No regions or loci deviated from HardyWeinberg equilibrium (P< 0.05). The percentage
of the mean number of private alleles varied from 0 (KR, BD, CT, LB, LA, OD, and
GR regions) to 26.3% (PT region). The percent mean number of private alleles in
A.m. scutellata,A.m. capensis, and hybrid bees was 11.4%, 4.79% and 11.2% respectively
(Table 2). The highest and the lowest value of genetic distance was observed between
UP-RD and MF-ST (0.68), and BD-RD (0.30), respectively (Table S3). A.m. capensis had
higher F
ST
,G
ST
and Josts D values than did A.m. scutellata and hybrid bees but there was
no signicant difference observed between them (Table 3).
The AMOVA results indicated that most of the genetic variation (78.97%) was
attributed within populations (i.e., between bees within a region) with only 5.05% of the
variation occurring between the 25 regional populations (Table 4). In the STRUCTURE
analysis, the most likely number of clusters across regions was estimated based on
likelihood and Delta Kscores across regions. Two genetically heterogeneous clusters were
detected by STRUCUTRE as the value of Delta Kwas greatest at K= 2. The average value
Figure 1 A heat map showing the value of allelic richness of honey bees from 29 regions in the
Republic of South Africa (generated using ArcGIS). The abbreviations of the regions are explained
in Table 1.Full-size
DOI: 10.7717/peerj.8280/g-1
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 9/18
of posterior probabilities and Delta Kfor each Kare shown in Table 5. The results suggest
that the sampled honey bees belong to two large genetic groups at the subspecies level,
with evidence of admixture patterns across the regions of the two dened populations
(Fig. 2). The regions were assigned with unequal probability to each genetic group
(Fig. 3). When assigning all genotypes at the subspecies level for morphometrically
Table 3 Estimation of pairwise mean genetic differentiation indices for A.m. scutellata,
A.m. capensis and hybrid honey bees collected from the Republic of South Africa. Columnar
means sharing the same letter are not signicantly different; ANOVA, Tukey test, P> 0.05. Fvalue refers
to the ratio of the between groups to within groups mean square. All tests are calculated based on 9,999
bootstraps. CI: 95% condence intervals.
F
ST
(mean ± SD)
[95% CI]
G
ST
(mean ± SD)
[95% CI]
Josts D (mean ± SD)
[95% CI]
A.m. scutellata 0.06 ± 0.01
a
[0.040.11] 0.04 ± 0.01
a
[0.020.08] 0.29 ± 0.09
a
[0.120.44]
A.m. capensis 0.07 ± 0.01
a
[0.050.12] 0.05 ± 0.01
a
[0.030.09] 0.31 ± 0.10
a
[0.180.53]
Hybrid 0.06 ± 0.01
a
[0.050.09] 0.04 ± 0.01
a
[0.030.07] 0.28 ± 0.08
a
[0.130.50]
F-value 2.41 2.28 0.498
Pvalue 0.099 0.111 0.610
Table 4 Analysis of molecular variance on genetic partitioning for honey bees. The results of an
Analysis of Molecular Variance on genetic partitioning for honey bees from the Republic of South Africa
using 19 microsatellite loci and recognizing three populations (A.m. scutellata,A.m. capensis and hybrid
bees).
Source of variation df Sum of
squares
Estimated
variance
Percentage of
variation (%)
Between regional
populations
24 812.89 0.42 5.05
Among individuals 691 6,491.30 1.35 15.98
Within populations (between
bees within a region)
716 4,788.50 6.68 78.97
Total 1,431 12,092.69 8.56 100
Table 5 Estimated posterior probabilities and Delta Kfor each K partition.
K Reps Mean LnP (K) Stdev LnP (K) Ln(K) |Ln(K)|Delta K
15 65,504.2 0.49295 –– –
25 64,668.3 2.65 835.90 248.80 93.85
35 64,081.2 18.37 587.10 205.68 11.20
45 63,699.8 308.14 381.42 262.48 0.85
55 63,055.9 19.78 643.90 179.82 9.09
65 62,591.8 43.82 464.08 26.76 0.61
75 62,154.5 97.66 437.32 48.46 0.50
85 61,765.7 190.04 388.86 62.70 0.33
95 61,314.1 52.00 451.56 244.74 4.71
10 5 61,107.3 279.83 206.82 ––
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 10/18
A.m. scutellata-like bees, 23% and 77% of the bees were assigned to the K1 and K2 genetic
groups, respectively. For morphometrically A.m. capensis-like bees, 63% and 37% of
the bees were assigned to the K1 and K2 genetic groups respectively. For morphometrically
hybrid bees, 45.5% and 54.5% were assigned to the K1 and K2 genetic groups respective
Figure 3 Geographic distribution of 75 sampling apiaries (black dots) of honey bees in the Republic
of South Africa. Adjacent apiaries around each town are clustered into a single region (29 total regions
grayed in the gure) abbreviated with the name of the closest town (the region abbreviations are dened
in Table 1). The pie charts represent the composition of the two genetic clusters for each geographical
region shown as blue (more A.m. scutellata) and orange (more A.m. capensis). The colors indicate the
different proportion of allele frequencies assigned to bees from each region.
Full-size
DOI: 10.7717/peerj.8280/g-3
Figure 2 Bayesian clustering assignment of 813 honey bees using STRUCTURE. Bayesian clustering
assignment of 813 honey bees of A.m. scutellata,A.m. capensis and hybrid colonies from 29 geographical
regions in the Republic of South Africa using STRUCTURE. The regions are dened in Table 1. Cluster
analysis of subspecies are shown at K= 2, without prior information of population identity. The different
regions are depicted on the X-axis and were dened using SNPs (Eimanifar et al., 2018b). Each honey bee
is represented by a bar that is, segmented into two colors based on the assignment into inferred clusters
given the assumption of Kpopulations. The length of the colored segment is the estimated proportion of
alleles of the individuals belonging to that cluster. The analysis was performed in ve replicates for each K
so the replicate with the highest likelihood is shown. Full-size
DOI: 10.7717/peerj.8280/g-2
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 11/18
(Fig. 3). The microsatellite genotypes generated for all honey bees analyzed are provided in
Table S4.
DISCUSSION
In the present study, we present information resulting from microsatellite genotyping
to evaluate the detailed genetic diversity and population structure of two subspecies
(A.m. scutellata and A.m. capensis) and their hybrids from 29 regions in the RSA.
Microsatellite loci have been widely used to characterize the structure of honey bee
populations across different geographical regions since they have a high degree of
sensitivity and provide a reasonable level of polymorphism (Franck et al., 2001;
Loucif-Ayad et al., 2015;Techer et al., 2015).
Genetic diversity
In an analysis of 19 microsatellite loci, we detected a high level of genetic variability
based on the mean number of alleles observed per microsatellite locus and heterozygosity
within and among examined honey bee regions. The high level of genetic diversity
observed for all honey bee groups was consistent with that shown in previous studies
(Estoup et al., 1995;Franck et al., 1998;Jaffe et al., 2010;Eimanifar et al., 2018a,2018b).
This may be true in RSA given the high densities of wild honey bee colonies in certain areas
of the country (Vaudo et al., 2012a,2012b).
The microsatellites we used tended to classify bees from certain regions as hybrids, more
so than did SNPs, which tended to separate the subspecies more predictably (Eimanifar
et al., 2018b). In fact, both the A.m. capensis and A.m. scutellata regions in RSA were
smaller when dened by microsatellites than they were when dened using SNPs or whole
mitogenomes (Eimanifar et al., 2018a,2018b). Consequently, we feel the microsatellites
possibly overestimated the size of the hybrid zone. In contrast, it is possible that the
microsatellites provided evidence of greater hybridization between the two subspecies than
rst thought.
As expected, honey bees in the hybrid region in RSA contain a gene pool derived
from hybridization of A.m. scutellata and A.m. capensis bees. The hybrid bees in our study
had many polymorphic alleles that likely originated from recombination of alleles of the
two subspecies (Alda & Doadrio, 2014). We suspect that the occurrence of new alleles
in the hybrid bees could be a result of hybridization of the two subspecies, as is known to
happen for populations in hybrid zones in general (Arnold et al., 1999).
The mean number of alleles observed across all loci (Na = 10.01) and the mean value
of observed heterozygosity (H
obs
= 0.75) were greater for bee populations in our study
(N= 716) than those reported in the literature for Algeria (N= 414, Na = 9.5, H
obs
= 0.69)
(Loucif-Ayad et al., 2015), Croatia (N= 225, Na = 9.25, H
obs
= 0.67) (Muñoz et al., 2009)
and on Rodrigues Island (N= 524, Na = 7.63, H
obs
= 0.64) (Techer et al., 2016). The
greater mean number of alleles/locus in our study than in others likely resulted from our
large sample size and generally large populations of the two subspecies we examined
(Loucif-Ayad et al., 2015). African honey bees exhibit several exceptional characteristics,
including pronounced migratory behavior (absconding and swarming), which possibly
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 12/18
could explain their high level of genetic diversity with low levels of differentiation
(Franck et al., 1998;Hepburn & Radloff, 1998).
Population genetic structure
The set of microsatellite markers we used yielded a distinctive population structure
within the examined groups, though the microsatellite results did not align completely
with those of the morphometric (Bustamante, Baiser & Ellis, in press) and other molecular
results (Eimanifar et al., 2018b). Nevertheless, our STRUCTURE results support the
presence of some genetic structure at the subspecies level, reinforcing other work showing
that there are two different gene pools existing in A.m. capensis and A.m. scutellata honey
bees from the RSA (Eimanifar et al., 2018a,2018b).
We found additional evidence of the allelic introgression of A.m. capensis from their
native range into that of A.m. scutellata regions (Neumann & Moritz, 2002;Moritz et al.,
2008). These results highlight the decades-old, continuing impact of the capensis
calamityon A.m. scutellata colonies in the RSA. Our STRUCUTRE results (Fig. 3) suggest
that the A.m. capensis/scutellata hybrid zone has moved north and east from that originally
delineated by Hepburn & Radloff (1998) over two decades ago.
In conclusion, honey bees in the RSA are composed of two genetically different
populations, both with signicant genetic structure. Given our large sample size and
analytical tools, we show two distinct evolutionary units, though the results did not
match those of earlier morphometric and molecular analyses (Eimanifar et al., 2018b;
Bustamante, Baiser & Ellis, in press), suggesting that the microsatellites we tested were not
sufcient for subspecies identication purposes, especially for Cape and hybrid bees.
Nevertheless, our data highlight the considerable genetic diversity within both populations,
a possibly larger-than-expected hybridization zone between the natural distributions of
A.m. capensis and A.m. scutellata, and evidence of the expansion of A.m. capensis into
A.m. scutellata regions. The genetic introgression of A.m. capensis into A.m. scutellata
territory continues from the days of the capensis calamityand highlights the invasive
potential of A.m. capensis. Our data suggest that the hybrid zone has expanded in the RSA
beyond that shown using morphological data (Hepburn & Radloff, 1998;Bustamante,
Baiser & Ellis, in press), possibly owing to migratory beekeeping activities in the RSA.
ACKNOWLEDGEMENTS
We thank current and former members of the University of Florida Honey Bee
Research and Extension Laboratory who collected honey bee samples across the Republic
of South Africa (RSA): Tomas Bustamante, Mark Dykes, Ashley Mortensen, and Daniel
Schmehl. We also thank Mathias Ellis for his assistance with sample collection.
We graciously acknowledge Mike Allsopp (ARC-Plant Protection Research Institute,
RSA), Christian Pirk (University of Pretoria, South Africa), and Garth Cambray for the
assistance they provided in coordinating eld sample collections and/or providing
samples. We also thank the RSA beekeepers who allowed us to sample their colonies.
We thank the genotyping facilities at the University of Florida, Interdisciplinary Center for
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 13/18
Biotechnology Research, for sequencing of 813 honey bees. We gratefully acknowledge
Robin Moritz who provided comments on multiple drafts of the manuscript.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
This project was supported through a cooperative agreement provided by the United States
Department of Agriculture, Animal and Plant Health Inspection Service (USDA-APHIS)
and by the Florida Department of Agriculture and Consumer Services through the
guidance of the Honey Bee Technical Council. The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
A cooperative agreement provided by the United States Department of Agriculture,
Animal and Plant Health Inspection Service (USDA-APHIS).
Florida Department of Agriculture and Consumer Services through the guidance of the
Honey Bee Technical Council.
Competing Interests
Amin Eimanifar is employed by the Eurons EAG Agroscience, Johanna T. Pieplow
is employed by the Martin-Luther-Universität Halle-Wittenberg, Alireza Asem
is employed by the Hainan Tropical Ocean University and James D. Ellis is
employed by the University of Florida. The authors declare that they have no competing
interests.
Author Contributions
Amin Eimanifar conceived and designed the experiments, performed the experiments,
analyzed the data, contributed reagents/materials/analysis tools, prepared gures and/or
tables, authored or reviewed drafts of the paper, approved the nal draft.
Johanna T. Pieplow analyzed the data, contributed reagents/materials/analysis tools,
authored or reviewed drafts of the paper, approved the nal draft.
Alireza Asem analyzed the data, prepared gures and/or tables, authored or reviewed
drafts of the paper, approved the nal draft.
James D. Ellis conceived and designed the experiments, analyzed the data, contributed
reagents/materials/analysis tools, prepared gures and/or tables, authored or reviewed
drafts of the paper, approved the nal draft.
Field Study Permissions
The following information was supplied relating to eld study approvals (i.e., approving
body and any reference numbers):
Field permits were not required. Samples were collected from beekeeper colonies, not
wild colonies, with the beekeeperspermission.
Eimanifar et al. (2020), PeerJ, DOI 10.7717/peerj.8280 14/18
Data Availability
The following information was supplied regarding data availability:
Raw data is available in Table S4.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.8280#supplemental-information.
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... There are also techniques for screening for unwanted species/subspecies of honey bees, though they vary in degree of accuracy. For example, the African honey bee, A. m. scutellata, and its hybrids can be identified using a reduced set of single nucleotide polymorphisms (SNPs), a real-time qPCR assay, or combinations of morphological features (Pinto et al., 2014;Harpur et al., 2015;Munoz et al., 2015;Eimanifar et al., 2018Eimanifar et al., , 2020Boardman et al., 2021;Momeni et al., 2021). Geo-morphometric analyses of honey bee wings coupled with SNP data (Calfee et al., 2020;Henriques et al., 2020), or geo-morphometrics alone (Nawrocka et al., 2018;Bustamante et al., 2020) have been used to identify A. m. scutellata populations as well. ...
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... It is noted that in areas that are natural contact zones of different A. mellifera subspecies, natural hybridization occurs [40,41] and the hybridization is inevitable in regions where human interference due to beekeeper preferences is high, which occurred in the C lineage native area of distribution [24][25][26]42,43]. Serbia, located in the center of the Balkan Peninsula, is geographically in the middle of the distribution range of the C lineage. ...
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