insects
Article
Distribution and Molecular Diversity of Whitefly Species
Colonizing Cassava in Kenya
Florence M. Munguti 1,2,* , Dora C. Kilalo 1 , Evans N. Nyaboga 3, Everlyne N. Wosula 4 , Isaac Macharia 2
and Agnes W. Mwango’mbe 1
1 Department of Plant Science and Crop Protection, University Nairobi, Kangemi,
Nairobi P.O. Box 29053-00625, Kenya; dchao@uonbi.ac.ke (D.C.K.); wakesho123@gmail.com (A.W.M.)
2 Kenya Plant Health Inspectorate Service, GPO, Nairobi P.O. Box 49592-00100, Kenya;
macharia.isaac@kephis.org
3 Department of Biochemistry, University Nairobi, GPO, Nairobi P.O. Box 30197-00100, Kenya;
nyaboga@uonbi.ac.ke
4 International Institute of Tropical Agriculture, Dar es Salaam P.O. Box 34441, Tanzania; E.Wosula@cgiar.org
* Correspondence: fmunguti@kephis.org
Simple Summary: The whitefly, Bemisia tabaci (Gennadium, Hemiptera) is a crop pest and plant-
virus vector known to transmit more than 300 plant viruses. Among other plant viruses, the vector
transmits viruses that cause the two major devastating viral diseases of cassava in sub-Saharan
Africa namely cassava mosaic disease (CMD) and cassava brown streak disease (CBSD). In order
to achieve effective implementation of whitefly management programs including prevention of
spread of the species, accurate species identification is vital. Morphological identification approaches



 toward whitefly species limits the capacity to accurately identify new species, especially the presence


of cryptic species such as the numerous B. tabaci genetic groups. Using the mitochondrial DNA
Citation: Munguti, F.M.; Kilalo, D.C.; cytochrome oxidase 1 (mtCO1) sequences, four distinct whitefly species namely Bemisia tabaci,
Nyaboga, E.N.; Wosula, E.N.; Bemisia afer, Aleurodicus dispersus and Paraleyrodes bondari were identified in samples collected from
Macharia, I.; Mwango’mbe, A.W.
major cassava growing regions in Kenya. The study presents the first report of P. bondari (Bondar’s
Distribution and Molecular Diversity
nesting whitefly) on cassava in Kenya. We found three B. tabaci genetic groups of SSA1, SSA2 and
of Whitefly Species Colonizing
Cassava in Kenya. Insects 2021, 12, Indian Ocean (IO) putative species colonizing cassava in Kenya. The information is useful to inform
875. https://doi.org/10.3390/ better management strategies of the whitefly vectors to reduce the impact of cassava viral diseases,
insects12100875 which continue to be a threat to food security in major cassava growing regions.
Academic Editor: Dorith Rotenberg Abstract: The whitefly, Bemisia tabaci (Gennadium, Hemiptera) has been reported to transmit viruses
that cause cassava mosaic disease (CMD) and cassava brown streak disease (CBSD) in many parts
Received: 30 July 2021 of sub-Saharan Africa (SSA). Currently, there is limited information on the distribution, species
Accepted: 4 September 2021 and haplotype composition of the whitefly populations colonizing cassava in Kenya. A study
Published: 27 September 2021 was conducted in the major cassava growing regions of Kenya to address this gap. Analyses of
mitochondrial DNA cytochrome oxidase 1 (mtCO1) sequences revealed the presence of four distinct
Publisher’s Note: MDPI stays neutral whitefly species: Bemisia tabaci, Bemisia afer, Aleurodicus dispersus and Paraleyrodes bondari in Kenya.
with regard to jurisdictional claims in The B. tabaci haplotypes were further resolved into SSA1, SSA2 and Indian Ocean (IO) putative species.
published maps and institutional affil-
The SSA1 population had three haplogroups of SSA1-SG1, SSA-SG2 and SSA1-SG3. Application
iations.
of KASP genotyping grouped the Bemisia tabaci into two haplogroups namely sub-Saharan Africa
East and Southern Africa (SSA-ESA) and sub-Saharan Africa East and Central Africa (SSA-ECA).
The study presents the first report of P. bondari (Bondar’s nesting whitefly) on cassava in Kenya.
Bemisia tabaci was widely distributed in all the major cassava growing regions in Kenya. The increased
Copyright: © 2021 by the authors.
detection of different whitefly species on cassava and genetically diverse B. tabaci mitotypes indicates
Licensee MDPI, Basel, Switzerland.
a significant influence on the dynamics of cassava virus epidemics in the field. The study highlights
This article is an open access article
distributed under the terms and the need for continuous monitoring of invasive whitefly species population on cassava for timely
conditions of the Creative Commons application of management practices to reduce the impact of cassava viral diseases and prevent
Attribution (CC BY) license (https:// potential yield losses.
creativecommons.org/licenses/by/
4.0/). Keywords: Bemisia tabaci; mtCOI gene; P. bondari; KASP genotyping; haplotype
Insects 2021, 12, 875. https://doi.org/10.3390/insects12100875 https://www.mdpi.com/journal/insects
Insects 2021, 12, 875 2 of 16
1. Introduction
Whiteflies (Hemiptera: Aleyrodidae) are mainly tropical insects but are also found
in all warmer parts of the world, as well as in greenhouses in temperate regions [1,2].
There are more than 1500 whitefly species, of which only a few have been reported to
transmit plant viruses [3–5]. The whitefly, Bemisia tabaci (Genaddius) is the most important
in terms of virus transmission and has been reported to correspond to a complex of more
than 40 morphologically indistinguishable species that attack several host crops [6–12].
This poses a challenge in developing sustainable effective control approaches since the
species differ in terms of their susceptibility to natural enemies, insect resistance, fecundity
and vector competence [13]. The Bemisia tabaci is regarded as a super vector due to the
global spread of the viruses they transmit [14,15]. It is known to have over 1000 host-plant
species including cassava, sweet potato, tomato, cucurbits, cotton as well as various fruits
and ornamental plants [15,16]. Bemisia tabaci is known to transmit over 300 economically
important plant viruses including begomoviruses, criniviruses, carlaviruses, torradoviruses and
ipomoviruses [4,17–20].
The two major devastating viral diseases of cassava in sub-Saharan Africa, namely cassava
mosaic disease (CMD) and cassava brown streak disease (CBSD), are caused by begomoviruses and
ipomoviruses, respectively, and are transmitted by the whitefly, Bemisia tabaci [11,21]. Combined
losses from the two diseases have been estimated to a value of USD 1 billion per year [22,23],
threatening the livelihoods of more than one billion smallholder households that depend on
cassava as a staple food crop in sub-Saharan Africa. Bemisia tabaci transmits cassava mosaic
begomoviruses (CMBs) in a persistent manner and cassava brown streak ipomoviruses
(CBSIs) in a semi-persistent manner [4,23–27]. The rapid spread of CMD and CBSD is
associated with the super abundance of B. tabaci [11,28]. Other than B. tabaci, reports in
Kenya indicate transmission of CBSI’s by other whitefly species namely spiraling whitefly
(Aleurodicus dispersus) [26,29] and Trialeuroides vaporariorum [29]. The first occurrence of
Aleurodicus dispersus in Kenya was in 2009 and was confirmed to be involved in transmission
of CBSIs [26,30]. Khamisi et al. [31] identified B. afer and A. dispersus on cassava in Kenya,
although their role in transmission of viruses infecting the crop has not been elucidated. In
addition, a recent report of P. bondari on cassava in Uganda by Omongo et al. [32] indicates
the need to understand its potential role in transmission of cassava viruses.
Most whitefly species studies in Africa have been mainly focused on B. tabaci and in
sub-Saharan Africa. Two major groups of B. tabaci have been characterized as the “cassava
type” that is unique to cassava as a host and the “non-cassava type” that colonizes other
host plants such as tomato and sweet potato but does not colonize cassava [11,33,34]. The
non-cassava types of B. tabaci that do not establish on cassava belong to groups designated
as Indian Ocean (IO), Mediterranean (MED), Middle East Asia minor 1 (MEAM 1) and
Uganda [11,33,35]. Although the Bemisia tabaci MEAM 1 and MED formerly termed biotype
B and biotype Q, respectively, [17] have not been reported on cassava, they are the most
widely disseminated and impactful species globally.
Five genetically distinct groups of B. tabaci putative species have been reported for
cassava in Africa, namely sub-Saharan Africa 1 (SSA-1 to 5), SSA-2, SSA-3 SSA-4 and
SSA-5) [11,12,36]. Sub-Saharan Africa 1 (SSA-1) is the most prevalent species and has been
reported to occur throughout sub-Saharan Africa and has further been divided into five
subgroups (SG) namely SSA1-SG1, SSA1-SG2, SSA1-SG3, SSA1-SG4 and SSA-SG5 [11,19].
Sub-Saharan Africa 2 (SSA-2) has been reported to occur in East and West Africa, SSA-3
and SSA-4 in Central and West Africa and SSA5 in South Africa [11,37,38]. In East and
Central Africa, the most common cassava B. tabaci species have been identified to belong to
the three sub-Saharan Africa taxa (SSA1, SSA2 and SSA3) [11,12,36,38] with SSA1 as the
most prevalent species in the region. Despite extensive studies done on B. tabaci putative
species in many cassava-growing countries in Eastern and Central Africa, there are limited
Insects 2021, 12, 875 3 of 16
studies on diversity of whitefly species and the circulating B. tabaci species haplotypes on
cassava in Kenya.
To achieve effective implementation of whitefly management programs including
prevention of spread of the species, accurate species identification is vital [39]. This is due
to the wide variation in the species host preference, insecticide resistance profiles, pathogen
vectoring range and increased fecundity of the different biotypes [13,40]. Morphological
identification approaches of whitefly species are challenging and limits the capacity to accu-
rately identify new species especially the presence of cryptic species such as the numerous
B. tabaci mitotypes [41]. The use of mitochondrial DNA cytochrome oxidase I gene (mtCO1),
first reported by Frohlich et al. [42] has been the most widely used molecular approach to
distinguish different whitefly species that can be associated with specific biotypes. The
region provides the highest genetic variability among the whitefly species and has been
widely used in identification of the B. tabaci complex groups [8,11,12,32,33,41,43,44]. Use
of mtCO1 gene allows B. tabaci sequence data to be applied to more global phylogenetic
questions with a large number of mtCO1 Bemisia species sequences currently available
in the Genbank [44]. However, mtCO1 has the limitation that it is a single locus that is
maternally inherited; therefore, it is likely to yield inadequate genetic resolution to distin-
guish populations, and it does not provide a full delineation of phylogenetic history [45].
Therefore, there is need to complement mtCO1 markers with low cost and informative
single nucleotide polymorphism (SNP)-based markers such as Kompetitive-Allele-Specific
PCR (KASP) for identification and genetic diversity studies of whitefly species.
A rapid and high throughput genotyping tool based on Kompetitive-Allele-Specific
PCR (KASP) approach has been reported to characterize and categorize cassava-colonizing
The results were over 99% consistent with the results obtained from NextEra restriction-site
associated DNA sequencing (NextRAD) [34], proving KASP genotyping assay as a reliable
SNP-based assay for identification of B. tabaci whiteflies on cassava.
There is inadequate information on the genetic variability and geographical distribu-
tion of whitefly species colonizing cassava in different cassava growing agro-ecological
zones Kenya. Data are urgently required regarding the genetic groups, haplotype diversity,
geographical distribution, and the phylogenetic relationships of whitefly species in Kenya.
Therefore, the aims of the study were to (i) determine the identity and distribution of the
whitefly species, and (ii) the genetic diversity of B. tabaci complex colonizing cassava in
different cassava growing regions in Kenya. This information facilitates the design of future
studies that aim toward prevention and management of this economically significant pest
as well as development of control strategies for the viral diseases they transmit and spread.
2. Materials and Methods
2.1. Sampling Sites, Sample Collection and Preservation
A survey was carried out between July 2018 and July 2020 to determine the whitefly
species associated with cassava in different cassava growing regions in coastal (Kilifi, Kwale
and Taita-Taveta counties), Eastern (Kitui, Makueni, Machakos and Embu counties), central
(Murang’a county), Nyanza (Migori, Kisumu and Homa Bay counties) and Western (Busia,
Bungoma and Kakamega county) parts of Kenya (Figure 1 and Supplementary Table S1).
The regions sampled represented different agro-ecological zones with different rainfall
and temperature regimes. Western and Nyanza region counties receive bimodal rainfall
ranging from 950 to 1500 mm annually, temperatures ranging from 14.4 to 25.4 ◦C and
altitude ranges from 100 to 1800 m above sea level. In Eastern region, altitude ranges from
100 to 1800 m with rainfall potential of 500–760 mm. The coastal region has rainfall ranging
from 500 to 1000 mm annually, and a temperature range between 22.4 to 30.3 ◦C.
Insects 2021, 12, x FOR PEER REVIEW 4 of 18 
 
titude ranges from 100 to 1800 m with rainfall potential of 500–760 mm. The coastal re-
Insects 2021, 12, 875 gion has rainfall ranging from 500 to 1000 mm annually, and a temperature range4 boef -16
tween 22.4 to 30.3 °C. 
 
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2.2. Genomic DNA Extraction and Polymerase Chain Reaction Amplification
2.2. Genomic DNA Extraction and Polymerase Chain Reaction Amplification 
Genomic DNA was extracted from individual adult whiteflies using the Chelex
metGheondo[m46ic] wDiNthAs lwigahst emxotrdaicfitecdat ifornosm.  Einadchiviindduiavl idaduualltw whhitietflefyliepsr euvsiionugs ltyhep rCesheerlveexd 
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Grant SUB Aqua 18 machine (Grant Instruments, Cambridge, England). The lysis mixture
was centrifuged in a microcentrifuge (Sigma 1-14 model 10014; Sigma Laborzentrifugen
 GmbH, Osterode am Harz, Germany) at 13,000 rpm for 10 min and the lysate collected and
Insects 2021, 12, 875 5 of 16
stored at −20 ◦C awaiting downstream processes. The DNA quantity and quality were
checked using NanodropONE (Thermo Scientific, Wilmington, DE, USA).
The PCR amplification reactions were performed using two sets of primers; the uni-
versal DNA primer pair (LCO1490: 5′-GGTCAACAAATCATAAAGATATTGG-3′ and
HCO2198: 5′-TAAACTTCAGGGTGACCAAAAAATCA-3′) targeting all species [47] and
the standard mtCOI primer pair (2195Bt: 5′ TGRTTTTTTGGTCATCCRGAAGT3′ and
C012/Btsh2 5′-TTTACTGCACTTTCTGCC-3′) targeting mainly Bemisia species as de-
scribed by Mugerwa et al. [12] amplifying ~867bp region. The PCR reaction was carried in
a total of 25 µL reaction volume containing 12.5 µL OneTaq 2×MM with standard buffer
(New England biolabs), 0.5 µL each of 10 µM for both forward and reverse primer and
2 µL of DNA template. This was carried out in Eppendorf master cycler machine pro 6321
(Eppendorf, Taunton, MA, USA) with thermal cycling conditions as follows: 94 ◦C for
5 min, 37 cycles of 94 ◦C for 60 sec, 53 ◦C for 60 sec and 72 ◦C for 60 sec, and final extension
at 72 ◦C for 10 min.
The PCR amplicons were resolved on 1.5% agarose gel in 1× TBE buffer stained with
0.01% Gel Red (Biotium, Fremont, CA, USA) at 100 volts for 30 min and the amplified prod-
uct gel bands visualized in a C280 gel documentation system (Azure Biosytems, Dublin, CA,
USA). Amplicons of expected band sizes were estimated by comparison to a direct Load
PCR 100-bp low ladder (Sigma D3687). Purification of the amplified PCR products was
carried out using GeneJet Purification kit (Thermo Scientific cat K0702; Thermoscientific,
Vilnius, Lithuania) according to manufacturer’s instructions. Quantification of the purified
PCR product was performed using Nano drop One (Thermo Scientific, Wilmington, DE,
USA) and the purified PCR products submitted for bi-directional Sanger sequencing at
Macrogen (Macrogen Europe, B.V, Amsterdam, The Netherlands). All the 76 whitefly
samples were successfully sequenced and used for the downstream analysis.
2.3. KASP Genotyping of Whitefly Samples
Thirty-eight samples representing the samples that had amplified for the differ-
ent B. tabaci mitotypes with CO1 primers were selected and used for KASP genotyping
(Supplementary Table S2). A set of six primers (BTS99-319, BTS22-762, BTS141, BTS55-473,
BTS613 and BTS46203) as described by Wosula et al. [48] were used. Conventional primers
were used to generate PCR products of genome portions containing target SNPs and the
PCR products were then used as DNA template in KASP genotyping [48]. The KASP
reaction mixture (total volume of 10 µL) contained 5 µL of 2× KASP master mix, 0.14 µL of
KASP primer assay mix and 5 µL DNA template (1 µL of PCR product/DNA extract + 4 µL
of sterile water).
KASP genotyping was performed in a Stratagene MX 3000P (Agilent Technologies,
Santa Clara, CA, USA). The following cycling conditions were used: Stage1: 30 ◦C 60 sec
(pre-read); Stage 2: 94 ◦C for 15 min hot-start Taq activation (1 cycle); Stage3: 94 ◦C for
20 sec, 61 ◦C (61 ◦C decreasing 0.6 ◦C per cycle to achieve a final annealing/extension
temperature of 55 ◦C) for 60 sec (10 cycles); Stage 4: 94 ◦C for 20 sec, 55 ◦C for 60 sec
(29 cycles); Stage 5: 94 ◦C for 20 sec, 57 ◦C for 60 sec (3 cycles); Stage 6: 37 ◦C for 60 sec
(1 cycle, cooling) followed by an end-point fluorescent read. These conditions were used
for four primers (BTS99-319, BTS22-762, BTS55-473, BTS141), while stage 3: 94 ◦C for 20 sec,
68 ◦C (68 ◦C decreasing 0.6 ◦C per cycle to achieve a final annealing/extension temperature
of 62 ◦C) was used for two primers BTS613 and BTS46-203. The quality of genotyping
cluster plots was visually assessed and only samples in distinct clusters with respective
positive controls were considered for manual SNP calling using the MxPro 5 software
incorporated into the Stratagene MX 3000P unit.
2.4. Sequence Quality Control, BLAST Search and Phylogenetic Analysis
Quality control of the resulting sequences including trimming was performed using
CLC Main workbench software version 8.1.3 (QIAGEN, Aarhus, Denmark). Sequences
that were not of good quality were discarded. The trimming was performed to 609 bp. The
Insects 2021, 12, 875 6 of 16
sequences were assembled and the consensus sequences resulting from the overlapping
portions of high quality reads from each of the forward and reverse sequences saved as fasta
format. Similarity searches were performed by querying the consensus sequences via Basic
Local Alignment Search Tool (BLAST) at GenBank database hosted by the National Centre
of Biotechnology Information (NCBI), Bethesda, MD, USA and on the Barcode of Life
Database (BOLD) (https://www.boldsystems.org/ accessed on 20 July 2021). The BLASTn
algorithm was used to identify regions of similarity between the consensus sequences from
the study and the sequences available in the GenBank.
The resulting BLASTn search results were tabled with the percentage identity, E-value,
percentage coverage as a well as the accession number per sample. Multiple sequence
alignment was performed using Clustal W in MEGA (version 6.06) [49] for phylogenetic and
molecular evolutionary analysis. Reference sequences were retrieved from the GenBank
and included in the analysis [50]. The maximum likelihood algorithm based on Kimura 2-
parameter model [51] was used to infer the evolutionary relationship between the various
samples and phylogenetic tree constructed using 1000 bootstrap replicates in MEGA
(version 6.06). The representative resulting sequences were submitted to GenBank through
Bankit in the online submission portal [52]. The extent of nucleotide sequence variation
within the identified B. tabaci (groups with at least 5 sequences) and B. afer was examined.
Estimates were obtained for the number of haplotypes, polymorphic sites (S), average
number of nucleotide differences (k), nucleotide diversity (Pi), haplotype diversity (Hd),
Theta per sequence and Theta per site. Tajima’s D and Fu’s Fs were obtained using the
mismatch distribution procedure of Dna-SP 6.12.03 [53].
3. Results
3.1. Presense of Whiteflies in Inspected Cassava Fields
Samples of adult whiteflies were collected in all the inspected cassava growing regions
in Kenya (Figure 1 and Supplementary Table S1). Adult whiteflies were predominantly
observed on the underside of the young upper cassava leaves (Figure 2A,B). For the
spiraling whiteflies, characteristic spiraling pattern of the eggs spirals was evident on the
Insects 2021, 12, x FOR PEER REVIEWu nderside of the leaves (Figure 2C,D). Characteristic wilting and sooty mo7l dof 1c8a used by
 
heavy whitefly infestation was observed on cassava leaves and stems (Figure 2E,F).
 
FFiigguurree 22. .AAdduult lwt whihteiftleieflsi eosn othnet huneduenrsdiedres oidf ecaosfsacvaas slaeavvaelse (aAv,eBs).( SAp,iBra).liSnpg imraalrinksg lmefta brkys spleifrtalb-y spiraling
winhg iwtehflityefslyp espcieecsieos nont htheeu unnddeerrssiiddee oof fcacsassasvaav laealveeasv (C,D). Characteristic wilting and sooty mold fungus caused by heavy whitefly infestation on caesssa(vCa ,pDla).nCts h(Ea,rFa)c. teristic wilting and sooty mold
fungus caused by heavy whitefly infestation on cassava plants (E,F).
3.2. Determination of Whitefly species and Phylogenetic Analysis 
Amplification of partial mtCO1 gene for each of the adult whitefly using primer pair 
LCO1490/HCO2198 [48,54] gave the expected size of the amplicons of 710 bp. A total of 
75 mtCO1 high-quality sequences from individual whiteflies were used in the subse-
quent analysis. Similarity search of the 75 new mtCO1 generated in the current study, 
through BLASTn in NCBI and in the BOLD systems database revealed four different 
whitefly species (B. tabaci, B. afer, P. bondari and A. dispersus) with percentage similarities 
between 99–100%. Two of the whitefly species (A. dispersus and P. bondari) identified 
were non-Bemisia. Phylogenetic analysis conducted on mtCOI sequences of the 
non-Bemisia whitefly species with already defined sequences from the GenBank, grouped 
our non-Bemisia whitefly species into two clusters based on the topology of the phylo-
genetic tree (Figure 3). Representative sequences for A. dispersus and P. bondari were de-
posited in the Genbank with accessions MZ331821-MZ331823 and MZ 329 
998-MZ330000, respectively. 
 
Insects 2021, 12, 875 7 of 16
3.2. Determination of Whitefly Species and Phylogenetic Analysis
Amplification of partial mtCO1 gene for each of the adult whitefly using primer
pair LCO1490/HCO2198 [48,54] gave the expected size of the amplicons of 710 bp. A
total of 75 mtCO1 high-quality sequences from individual whiteflies were used in the
subsequent analysis. Similarity search of the 75 new mtCO1 generated in the current
study, through BLASTn in NCBI and in the BOLD systems database revealed four different
whitefly species (B. tabaci, B. afer, P. bondari and A. dispersus) with percentage similarities
between 99–100%. Two of the whitefly species (A. dispersus and P. bondari) identified were
non-Bemisia. Phylogenetic analysis conducted on mtCOI sequences of the non-Bemisia
Iwnsehcitst e2fl02y1, s1p2,e xc iFeOsRw PEitEhR aRlErVeIaEdWy defined sequences from the GenBank, grouped our non- 8 of 18 
 Bemisia whitefly species into two clusters based on the topology of the phylogenetic tree
(Figure 3). Representative sequences for A. dispersus and P. bondari were deposited in the
Genbank with accessions MZ331821-MZ331823 and MZ 329 998-MZ330000, respectively.
 T04 
 E7 
 KY574540-Aleurodicus dispersus 
100
 E38-C 
 E3B-C 
100 
 T03 
 T24 
 MK159738-Aleurodicus rugioperculatus
 LC491421-Paraleyrodes minei 
 MK333262-Paraleyrodes bondari
 T13
100
 T02
74 
 T46
0.05 
 
Figure 3. Maximum likelihood phylogenetic tree constructed using mitochondrial cytochrome
oxidaFsiegu1rsee q3u. eMncaexsimobutmain leikdeflrihomoodno pnh-Byelomgiseinaewtihc ittreefley csopnesctireuscctoeldle uctseidngo nmciatosscahvoanpdlraianlt sciyntoKcehnroyma e oxidase 1 sequences ob-
betwteaeinneJudl yfro20m1 8noannd-BJeumlyis2ia0 w20h. iRteeffleyr esnpceeciseesq cuoelnleccetsed(  o)nf rcoamssaNvCaB pIlawnetrse inin Kcleundyead bfeotrwcoeemnp Jaurliyso 2n0.18 and July 2020. Reference u
sequences () from NCBI were included for comparison. 
Alignment of sequences generated using primers specific for Bemisia followed by
phylogenetic analysis revealed thrAeeliggnenmeetinct gorfo usepqsuoefnBc.etsa bgaecni ecroamtepdl euxs, iwngh ipchrimineclrus dsepde:cific for Bemisia followed by 
sub-Saharan Africa 1 (SSA1), spuhby-SloagheanraenticA afnriaclay2si(sS rSeAv2e)alaendd tIhnrdeiea gneOnceetiacn g(rIoOu)pms iotof tBy.p teasbaci complex, which included: 
(Figure 4). The members of SSsAub1-BSa. htaabraacni  gArofruipcae d1 i(nStSoAth1)r,e esucblu-Sstaehrasr/asnu bAgfrroicuap s2 o(fSSSASA2)1 -and Indian Ocean (IO) mito-
SG1, SSA1-SG2 and SSA1-SG3ty(pFeigs u(Freig4u).reA 4n)o. tThheer cmaessmabvearws ohfi tSeSflAy 1s pBe.c tiaebsaicdi egnrtoiufipededw iansto three clusters/subgroups of 
Bemisia afer, which clustered dSiSsAta1n-tSlGy1fr, oSmSAth1-eSSGS2A a1n,dS SSASA2 a1n-SdGI3n d(FiaignuOrec e4a).n Asnuobtghreoru cpasssava whitefly species identi-
(Figure 4). fied was Bemisia afer, which clustered distantly from the SSA1, SSA2 and Indian Ocean 
Using KASP genotyping,stuhbegtreostuepdsw (Fhiigteuflrye s4a).m ples grouped into two clusters namely
SSA-ESA and SSA-ECA whereby SSA-ECA composed of the subgroups SSA1-SG1 and
SSA1-SG2 whereas SSA-ESA comprised of SSA1-SG3 (Supplementary S2). The mitotype
SSA2 was not tested using KASP as samples failed to amplify due to degradation of DNA
during transportation for KASP genotyping.
 
Insects 2021, I1n2s,e8ct7s5 2021, 12, x FOR PEER REVIEW 8 of 16 9 of 18 
 
 
Figure 4. MFaixgiumreu m4. lMikealxihimooudmp hliykleoligheonoedti cpthreyelocgoennsetrtuicc tterdeeu csoinngstmruitcotechdo unsdirniga lmcyitochroonmderioaxl icdyatsoech1rsoemquee onxceidsaosbet a1i nsedquences ob-
from Bemisitaaitnabeadc if(rcoamss aBveamtiysipae tsa)bcaocil l(eccatesdsaivnat htyispsetsu) dcyo.llReecfteerde nince thseisq usteundcyes. R(uef)efrreonmceN sCeBquI wenecreesi n(clu) dfreodmfo Nr cCoBmI pwaerirseo inn.cluded for 
Only selectceodmsepqauriesnocne.s Oanrelys hseolwecnteidn stheqeupehnycleosg aernee stihcotwrene .in the phylogenetic tree. 
3.3. DistributionUosfinWg hKiteAflSyPS pgeecnieostaynpdinBge,m tihsiea ttaebsatecidH waphloitteyfplyes samples grouped into two clusters 
Of thneafmouerlyw hSiSteAfl-yESsApe caiensdi dSenSAtifi-EeCdAba swedheornebmyt CSOS1As-EeqCuAe nceosm, Bp.otsaebda ciotfh etmheo sstubgroups 
abundantS, SfoAu1n-dSGin1 7a0n%d SoSfAth1e-SsGeq2u wehneceredasa SmSApl-eEsSfAo lclowmepdribseydB o. fa SfeSrA( 13-S%G),3A (S. udipspelresmusentary S2). 
(8%) and TP.hbeo mndiatority(5p%e )S.SBAe2m wisiaas tnaboatc tiewstaesd wusidinegly KdAisStPri baus tseadmipnleasll ftahileecdo tuon atimespolifyt hdeue to deg-
surveyedrmadaajotironca osfs aDvNaAg rdouwring trreagnisopnosrtinatiKonen fyoar K(FAigSuPr gee5nAot)y. pBienmgi.s ia afer was iden-
tified in Nyanza region (Migori and Homabay counties) and in Eastern (Makueni and
Machakos3c.3o.u Dntiisetrsi)bauntidonC oefn Wtrahlit(eMflyu rSapnegc’iaesc aonudn Btye)m. Tishiae ttawbaocin Hona-pBloetmyipseias type of whiteflies
(A. dispersus anOdf Pt.hbeo fnoduarr iw) ihdietenftliyfi sepdeicnietsh iedsetnutdifyiewd ebraeseodn loynf omutnCdOi1n sseaqmuepnlecsesc,o Bll.e tcatbeadci the most 
from the caobausntadlarnetg, ifoonunodf K ine n7y0a%.  Tohf ethsep sireaqluinegncwedh isteaflmyp(lAes. dfoislploewrseuds) bwya Bs.t ahfeerm (1o3s%t p),r Ae-. dispersus 
dominant(8n%on) -aBnedm Pis. ibaowndhairtie fl(5y%s)p. eBceimesisaian dtabwacidi ewlyasd wistirdiebluyt eddisitnribaulltethde inth arelle tchoeu cnotuiensties of the 
(Kwale, KsiluifirvaenydedTa mitaa-joTra vceatsas)aovfa cgoraoswtailnKge rneygaio(nFsig iunr Ke e5nAy)a.  T(FhiegPu.rbeo 5nAda)r. iBwemasisdiae taefecrt ewdaisn identified 
only threeins amNpylaenszfar omregTiaointa (TMavigetoaria nadndK ilHifiocmouabnatiye s.counties) and in Eastern (Makueni and 
Machakos counties) and Central (Murang’a county). The two non-Bemisia type of white-
flies (A. dispersus and P. bondari) identified in the study were only found in samples col-
 
Insects 2021, 12, x FOR PEER REVIEW 10 of 18 
 
lected from the coastal region of Kenya. The spiraling whitefly (A. dispersus) was the most 
predominant non-Bemisia whitefly species and widely distributed in all the three coun-
ties (Kwale, Kilifi and Taita-Taveta) of coastal Kenya (Figure 5A). The P. bondari was de-
tected in only three samples from Taita Taveta and Kilifi counties. 
The B. tabaci haplotype distribution per region is shown in Figure 5B. The major B. 
tabaci mitotype cluster was SSA1-SG3, which was predominant in coastal and eastern 
regions of Kenya with 90% and 50% samples, respectively, clustering under this sub-
group. The subgroup SSA1-SG3 was not identified from any whitefly samples collected 
in western and Nyanza region of Kenya. SSA1-SG2 was identified on whitefly samples 
from one county each in western (Busia county) and Nyanza (Migori county). SSA1-SG1 
was predominant in Western and Nyanza region of Kenya especially in the counties of 
Busia, Kakamega, Migori, Kisumu and Homabay. Only two whitefly samples were 
identified as SSA-SG1 in Coastal region of Kenya (one from Taita-Taveta and the other 
from Kwale County). The subgroup SSA1-SG1 was not identified from any tested white-
fly samples from Eastern and Central region of Kenya (Figure 5B). The sub-Saharan Af-
rica subgroup 2 (SSA2) was identified in six whitefly samples from Western and Nyanza 
region of Kenya (Busia, Bungoma, Homabay and Kakamega counties), and in one sample 
collected in the coastal region (Taita-Taveta county) of Kenya. SSA2 was not detected in 
any of the samples from Eastern and Central region of Kenya. The Indian Ocean (IO) 
Insects 2021, 12, 875 9 of 16
putative species was detected only in two whitefly samples from Eastern region of Ken-
ya. 
Insects 2021, 12, x FOR PEER REVIEW 11 of 18 
 
 
 
 
Figure 5.F Aig muraep5 o. fA Kmenaypao afnKde cnuyta-oauntd mcaupt-so ouft cmasaspasvao fgcraoswsainvga cgoruowntiinesg icno Kunentiyesa isnhKoweninyga dshiso-wing distri-
tributionb ouf ttihoen foofutrh we hfoituerflwy hspiteecflieyss (pAe)c aiensd( AB.) taanbadciB h.atapbloactyi pheasp l(oBt)y opfe csa(sBsa)voaf-ccaoslosanvizai-ncgo lwonhiizteinflgy whitefly
identifiedid fernotmifi tehde fsraomptlhees scaomllepclteesdc dolulericntegd thdeu srtinugdyth. e study.
3.4. PopulatioTn hGeenBe.titca bAancialhyasips lotype distribution per region is shown in Figure 5B. The major
B. tabaci mitotype cluster was SSA1-SG3, which was predominant in coastal and eastern
Popreuglaiotinosno gfeKneentiycas wanitahly9s0i%s oafn wdh5i0t%eflsya msppelceise,sr eBs.p teacbtaicvie alyn,dc lBu.s taeferrin rgevuenadleerd tshiixs sBu. bgroup.
tabaci mtCO1 haplotypes, although only SSA1-SG1 and SSA1-SG3 haplogroups had more 
than four sequences. SSA1-SG1 had only one haplotype with 17 samples, while SSA1-SG3 
had two haplotypes with 25 and 6 samples, respectively. The Tajima’ D p-value was sig-
nificant for all B. tabaci indicating no deviation from the expectation of neutral selection, 
but values were non-significant for SSA1-SG3 indicating no deviation from the expecta-
tion of neutral selection. Bemisia afer had only two haplotypes each with 8 and 2 samples, 
respectively and the Tajima’ D p-value was non-significant indicating no deviation from 
the expectation of neutral selection (Table 1). 
Table 1. Population genetic analysis of whitefly species Bemisia tabaci and B. afer found colonizing 
cassava in Kenya. 
Parameter All B. tabaci SSA1-SG1 SSA1-SG3 B. afer 
Sample size 56 17 31 10 
Number of 
6 1 2 2 
haplotypes 
Polymorphic 
162 0 1 5 
sites (S) 
Average no of 
nucleotide 19.24091 0.0000 0.3226 1.77778 
differences (k) 
Nucleotide 
0.0245 0.0000 0.0004 0.0023 
diversity (Pi) 
 
Insects 2021, 12, 875 10 of 16
The subgroup SSA1-SG3 was not identified from any whitefly samples collected in western
and Nyanza region of Kenya. SSA1-SG2 was identified on whitefly samples from one
county each in western (Busia county) and Nyanza (Migori county). SSA1-SG1 was
predominant in Western and Nyanza region of Kenya especially in the counties of Busia,
Kakamega, Migori, Kisumu and Homabay. Only two whitefly samples were identified
as SSA-SG1 in Coastal region of Kenya (one from Taita-Taveta and the other from Kwale
County). The subgroup SSA1-SG1 was not identified from any tested whitefly samples
from Eastern and Central region of Kenya (Figure 5B). The sub-Saharan Africa subgroup 2
(SSA2) was identified in six whitefly samples from Western and Nyanza region of Kenya
(Busia, Bungoma, Homabay and Kakamega counties), and in one sample collected in the
coastal region (Taita-Taveta county) of Kenya. SSA2 was not detected in any of the samples
from Eastern and Central region of Kenya. The Indian Ocean (IO) putative species was
detected only in two whitefly samples from Eastern region of Kenya.
3.4. Population Genetic Analysis
Population genetics analysis of whitefly species B. tabaci and B. afer revealed six
B. tabaci mtCO1 haplotypes, although only SSA1-SG1 and SSA1-SG3 haplogroups had
more than four sequences. SSA1-SG1 had only one haplotype with 17 samples, while SSA1-
SG3 had two haplotypes with 25 and 6 samples, respectively. The Tajima’ D p-value was
significant for all B. tabaci indicating no deviation from the expectation of neutral selection,
but values were non-significant for SSA1-SG3 indicating no deviation from the expectation
of neutral selection. Bemisia afer had only two haplotypes each with 8 and 2 samples,
respectively and the Tajima’ D p-value was non-significant indicating no deviation from
the expectation of neutral selection (Table 1).
Table 1. Population genetic analysis of whitefly species Bemisia tabaci and B. afer found colonizing
cassava in Kenya.
Parameter All B. tabaci SSA1-SG1 SSA1-SG3 B. afer
Sample size 56 17 31 10
Number of haplotypes 6 1 2 2
Polymorphic sites (S) 162 0 1 5
Average no of nucleotide
differences (k) 19.24091 0.0000 0.3226 1.77778
Nucleotide diversity (Pi) 0.0245 0.0000 0.0004 0.0023
Haplotype diversity (Hd) 0.702 0 0.323 0.356
Variance of Hd 0.0017 0.000 0.0078 0.0253
Standard deviation of Hd 0.0079 0.000 0.088 0.159
Theta per sequence 39.1849 - 0.2503 1.7674
Theta per site 0.0499 - 0.0003 0.0023
Fu’s Fs statistic 25.868 - 0.864 3.636
Tajima’s D −1.80960 - 0.44525 0.02348
p p < 0.05 - p > 0.10 p > 0.10
4. Discussion
This study is the first extensive sampling and molecular identification of whitefly
species colonizing cassava along a geographical transect from coastal Kenya through east-
ern, central, Nyanza to western part of Kenya. Using partial mtCO1 gene sequences [48],
our study revealed the occurrence of four different species of whitefly (Bemisia tabaci,
Aleurodicus dispersus, Bemisia afer and Paraleyrodes bondari) colonizing cassava in Kenya.
The whitefly species, B. tabaci was the most abundant whitefly species colonizing cassava,
similar to previous reports by Mware et al. [26] and Njoroge et al. [27]. The study by
Njoroge et al. [27], identified B. tabaci and Trialeurodicus vaporarium in whitefly samples
collected from different agro-ecological zones in cassava growing regions of Kenya. How-
ever, the whitefly species Trialeurodicus vaporarium was not detected in any of the whitefly
samples collected in our study. Recent findings by Khamis et al. [31] reported A. dispersus
Insects 2021, 12, 875 11 of 16
and B. afer on whitefly samples collected on cassava host in Kenya, and did not detect any
B. tabaci, probably due to the limited number of whitefly samples collected on cassava host.
The genetic diversity and geographical distribution of B. tabaci are of great interest because
of its importance as insect vector for cassava brown streak ipomoviruses and cassava
mosaic begomoviruses [11,23]. The use of mtCO1 primers specific to Bemisia tabaci [12] in
this study, revealed three genetic groups of B. tabaci haplotypes (SSA1, SSA2 and IO). The
species SSA1 was the most abundant B. tabaci haplotype with three subgroups (SSA1-SG1,
SSA-SG2 and SSA1-SG3) identified from the whitefly samples. Previous studies in Kenya
by Manani et al. [55], confirmed Bemisia tabaci species belonging to five distinct clades
within the sub-Saharan Africa (SS1 and SS2) genetic groups, but could not link the different
clades to the specific SSA1 haplotypes. SSA1 and SSA2 Bemisia tabaci haplotypes have
been reported to colonize cassava and are vectors of viruses associated with CMD and
CBSD [24].
Among the SSA-1 haplotypes of B. tabaci reported in this study, SSA1-SG3 sub-group
was the predominant and was found in samples collected from Coastal and Eastern regions
of Kenya. SSA1-SG3 has been reported to be the indigenous B. tabaci species in coastal
regions of the East Africa [21,56]. SSA1-SG1 sub-group was the second most frequent SSA1
B. tabaci haplotype and was found mostly in samples from Western and Nyanza region.
The subgroup SSA1-SG2 was identified only in three samples from Western region with
none identified in Eastern, central and coastal region of Kenya.
The sub-Saharan Africa 2 (SSA2) genetic group was found in whitefly samples col-
lected from Western region of Kenya and only one sample collected from Coastal region of
Kenya. This is contrary to previous studies that reported SSA2 as the most diverse haplo-
type in geographical coverage in Eastern and Central Africa regions [55,57]. The presence
of SSA2 haplotype in coastal region of Kenya for the first time could be associated with the
possibility of inadvertent movement whitefly nymphs or eggs on leaf material transported
with cassava stems during the exchange of cassava planting material by farmers. Previous
reports in Uganda have associated SSA2 genetic group with regions having high incidences
and severity of cassava mosaic disease [58]. Future studies need to be conducted to explore
the role SSA2 genetic group in single and/or dual transmission of viruses causing CMD
and CBSD, in order to inform management strategies for the diseases they spread.
Various non-cassava B. tabaci haplotypes have been reported on cassava and other
hosts including Mediterranean (MED) formerly biotype Q, Middle-East Asia Minor 1
(MEAM-1), Indian Ocean (IO) and Uganda [33,35]. Our study identified the Indian Ocean
(IO) putative species from only two samples collected in Eastern and Central region of
Kenya. Homology search on our sequences BLASTn search and BOLD systems shared
similarity percentages of 100%, with B. tabaci Indian Ocean on tomato (Accession Number
AJ550171.1). The Indian Ocean whitefly species has been reported as a non-cassava colo-
nizer and is mostly found on sweet potato and other vegetables [59]. The Indian Ocean
(IO) species are unable to reproduce on and colonize or utilize cassava and therefore their
presence on cassava in this study could be due to the fact that they are transient individuals
moving through cassava in search of the preferred hosts such as tomato and sweet potato.
Using KASP genotyping, the representative samples of B. tabaci haplotypes grouped
into two clusters; sub-Saharan Africa, East and central Africa (SSA-ECA) and sub-Saharan
Africa, East and Southern Africa (SSA-ECA) whereby SSA-ECA composed of the subgroups
SSA1-SG1 and SSA1-SG2 whereas SSA-ESA comprised of only SSA1-SG3. The results were
consistence with previous studies [46,57]. SSA-ECA comprising of SSA1-SG1 and SSA1-
SG2 have been reported in areas with severe CMD and CBSD [46]. SSA1 –SGA and
SSA1-SG2 (SSA-ECA) in western region of Kenya indicates a possible correlation with the
high incidence and severity of cassava viral diseases in the region [60,61].
The wide distribution of B. tabaci in all major cassava growing regions of Kenya,
suggest the ability of this species to adapt to many different agro-ecological zones, contrary
to other species detected in this study (B. afer, Aleurodicus disperses and Paraleyrodes bondari).
From this study, Bemisia afer was reported as the second most abundant whitefly species
Insects 2021, 12, 875 12 of 16
and was more frequent on samples collected from Eastern and Western region with none
identified from samples collected in the coastal region of Kenya. Previous studies have
associated B. afer with cassava as a host [27,31,62] as well as insect vector in transmission of
Sweet Potato Chlorotic Stunt Virus (SPCSV) [63]. There is therefore a need for transmission
studies to explain the possible role of B. afer whitefly species in transmission of viruses as-
sociated with CMD and CBSD. Spiraling whitefly Aleurodicus dispersus Russel (Homoptera,
Aleyrodidae) identified in this study was predominantly from samples collected in the
coastal region of Kenya. Aleurodicus dispersus has been associated with the resurgence of
cassava brown streak disease and its role as a vector for the cassava brown streak viruses
in the coastal region of Kenya was confirmed by Mware et al. [26].
This study identified Paraleyrodes bondari (Bondar’s nesting whitefly) in four whitefly
samples collected from the coastal regions. Although we did not analyze nymph samples,
the failure to detect this species in other regions suggests a restricted occurrence. Homology
search on our sequences BLASTn search and BOLD systems shared similarity percentages
of 99.84% and 100%, respectively, with P. bondari isolates from India on guava (Accession
Number MW488201.1) and coconut (Accession number MW488198). Based on phylogenetic
analysis, the identified P. bondari isolate was closely related to P. bondari and P. minei
isolates but distantly related to A. dispersus whitefly species based on the clustering on
the phylogeny tree. This is in agreement with previous reports, confirming the identity
of the isolates detected in this study [32,64,65]. This is the first report of Bondar’s nesting
whitefly species, Paraleyrodes bondari (Hemiptera Aleyrodidae) colonizing cassava in Kenya,
specifically in the coastal region of Kenya.
Bondar’s nesting whitefly, Paraleyrodes bondari has in previous studies been reported
on coconut and guava hosts, respectively [64,66]. These plant species (coconut and guava)
are important crops commonly grown by majority of farmers in the coastal region of
Kenya where P. bondari whitefly species was identified and could have been the possible
main host for the whitefly species. Host adaptation may be a more important component
affecting the low predominance of P. bondari in cassava. In coastal Kenya, cassava is
predominantly grown as a subsistence crop, usually side by side with coconut, guava and
other vegetable crops. The role of other host plants was a factor that we did not investigate—
P. bondari adults may have moved from sources other than the identified source (cassava).
For example, coconut and guava hosts may sustain reproduction and development of P.
bondari whitefly species, with adults migrating/dispersing to cassava and therefore the P.
bondari whitefly occurring on cassava were present as visitors and were not colonizing the
crop. However, recent reports on occurrence and distribution of P. bondari on cassava in
Uganda by Omongo et al. [32] and Isabirye [67] indicates a possibility of adaptability of
the whitefly species on cassava crop. Being an invasive whitefly species, further extensive
surveillance to map the species distribution and possibly elucidate the possible origin, host
range and the possible role in transmission of cassava viruses or viruses infecting other
crops in Kenya.
Even though we detected Paraleyrodes bondari associated to cassava, we cannot infer its
potential to effectively colonize this host since only adults were collected. In addition, the
very low frequency with which it was detected, indicate that Paraleyrodes bondari is poorly
adapted to cassava. It will be important to monitor Paraleyrodes bondari populations in
cassava over the next years, to assess its possible adaptation to this host. Further sampling
in those sites or free-choice experiments is necessary to confirm the potential of Paraleyrodes
bondari to colonize and utilize cassava.
Previous studies have reported occurrence of different whitefly species on cassava
crop [26,28,31,32,68–70]. Transmission of cassava viruses by some of the whitefly species
has also been confirmed [24,26]. There is therefore need for studies to infer the possible
role of the reported whitefly species in transmission of cassava viruses and especially
the cassava brown streak ipomoviruses causing cassava brown streak disease, which has
continued to be a threat to many cassava-growing regions. Based on the results from
this study, there is therefore need for further surveys to identify the whitefly species on
Insects 2021, 12, 875 13 of 16
cultivated and non-cultivated hosts, as this could have implications on the dynamics of
the emergence, re-emergence and spread of cassava viruses—especially the cassava brown
streak ipomoviruses causing brown streak disease in cassava.
5. Conclusions
In this study, four different whitefly species B. tabaci, B. afer, Aleurodicus dispersus
and P. bondari were identified on the whitefly samples collected from cassava in Kenya.
This is the first report of P. bondari (Bondar’s nesting whitefly) on cassava in Kenya. Even
though we detected Paraleyrodes bondari associated to cassava, we cannot infer its potential
to effectively colonize this host since only adults were collected. Bemisia tabaci species
complex was the most prevalent and widely distributed in all the regions (Coastal, Western,
Nyanza, Eastern and Central). Three genetic groups of B. tabaci: SSA1 (SSA1-SG1, SSA1-
SG2 and SSA1-SG3), SSA2 and Indian Ocean putative species were identified. The major B.
tabaci haplotype was SSA1-SG3, which was predominant in coastal and Eastern regions.
Our study also demonstrated application of KASP genotyping as a rapid tool for analysis
of cassava B. tabaci in laboratories with limited access to sequencing technologies. The
information will be useful to inform better management strategies of the vectors to reduce
the impact of the viral diseases, which continue to be a threat to food security in major
cassava growing regions.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/
10.3390/insects12100875/s1, Table S1: Whitefly samples, sampled sites and whitefly species de-
tected in cassava in Kenya based on MtCO1 sequences. Supplementary Table S2: Identification
of Bemisia tabaci haplotypes in selected whitefly samples based on mtCO1 gene sequencing and
KASP genotyping.
Author Contributions: Conceptualization, F.M.M., D.C.K., I.M., E.N.N. and A.W.M.; Methodology,
F.M.M., E.N.W., D.C.K. and E.N.N.; Data Analysis, F.M.M., E.N.W. and E.N.N.; Funding Acquisition,
A.W.M.; Writing—Initial Draft, F.M.M.; Writing—Review and Editing, D.C.K., E.N.W., I.M., E.N.N.
and A.W.M. All authors have read and agreed to the published version of the manuscript.
Funding: This work was financed by the Regional Universities Forum for Capacity Building in
Agriculture (RUFORUM) through the Transforming African Agricultural Universities to meaningfully
contribute to Africa’s growth and development (TAGDEv) through the MasterCard Foundation.
Institutional Review Board Statement: This study does not contain any studies with human parti-
icipants or vertebrate performed by any of the authors. The whitefly samples were collected from
farmers fields upon obtaining their permission.
Informed Consent Statement: Cassava farmers who agreed to participate in the study signed an
informed consent letter before each whitefly sampling.
Data Availability Statement: Partial MtCOI Sequences of the whitefly species including the B. tabaci
haplotypes obtained in this study were deposited in Genebank through online submission portal
under accession numbers (MZ33092-096, MZ331821-823, MZ331 385-396, and MZ329998-3000).
Acknowledgments: The authors acknowledge the support of Kenya Plant Health Inspectorate
Service and the University of Nairobi for their support. We thank all the farmers where the sampling
was conducted.
Conflicts of Interest: Authors declare that no competing interests on this publication.
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