Species-resolved metagenomics reveal ecological effects on the microbiota in a global pest, the whitefly, using 2bRAD-M

Species-resolved metagenomics reveal ecological effects on the microbiota in a global pest, the whitefly, using 2bRAD-M | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Species-resolved metagenomics reveal ecological effects on the microbiota in a global pest, the whitefly, using 2bRAD-M Kun Yang, Yuxin Zhang, Yitong He, Hongran Li, Jincheng Zhou, Youjun Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4321283/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Microbial communities including symbionts play vital roles in insect hosts. Abiotic factors, especially ecological factors also have significant influence on the structure of the microbiome and the abundance of symbionts within hosts. However, the effects of the bacterial symbionts and ecological factors on the microbiota in host whitefly remains poorly understood. Results In this study, 49 Bemisia tabaci MED populations collected in 23 locations around the world were sequenced using 2bRAD-M, to explore the relationships among ecological factors, symbionts and microbial diversities in whiteflies. Results revealed that microbial community structures significantly differed in the different geographical B. tabaci MED populations, and the abundance of many symbionts including Portiera , Hamiltonella , Rickettsia , Cardinium , and Wolbachia , significantly influenced with one another. Also, the diversity of bacterial communities in whiteflies were significantly affected by the relative abundance of symbionts including Cardinium and Hamiltonella . Meanwhile, environmental factors including temperature, precipitation, longitude and latitude significantly influenced the abundance of many symbionts and the diversity of bacterial communities in B. tabaci MED. Conclusions Overall, our results revealed complex interactions among ecological factors, among ecological factors, microbiota diversity and symbionts in B. tabaci MED. This helps to comprehend the complex interactions among these factors in insect hosts. symbiont whitefly 2bRAD-M sequencing microbial diversity ecological factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is notorious for its destruction to host plants and extremely rapid invasive capacity which has enabled its colonization in almost every part of the world including temperate, sub-tropical and tropical locations [1,2]. It is a pecies complex, with at least 44 cryptic species identified up to date, which have a wide range of host plants [3,4,5]. More importantly, B. tabaci is the main insect vector of many begomoviruses which cause enormous damages to field crops [6,7]. B. tabaci MED is considered one of the most invasive species and widespread globally including in countries such as China, USA, and many Mediterranean countries [3,8,9]. Especially in China, the B. tabaci MED has replaced B. tabaci MEAM1 to be the dominant cryptic species in the past few years [10,11]. Bacterial communities have vital influence on the development, health, and biological processes of insect hosts. For example, many bacteria (especially endosymbionts) provide hosts with nutrients including vitamins and essential amino acids, as well as protect their hosts from adverse ecological factors including heat shock, predators and pesticides [9,12,13,14,15,16]. Biotic and abiotic factors such as diet, shape the structure of bacterial communities in insects [17,18,19,20,21,22,23,24]. Also, high temperatures significantly influence the diversity and abundance of symbionts in whiteflies [24] and host genotype and Wolbachia infection affect the densities of other Wolbachia strains [21]. Further, the temporal and spatial distributions of hosts influence the bacterial communities within them [25]. Endosymbionts are widespread in insects and always have vital influence on hosts. They are vertically heritable bacteria which mostly reside in the bacteriocytes of insect hosts, especially primary endosymbionts [26,27,28,29]. In B. tabaci , there is one primary endosymbionts Candidatus Portiera aleyrodidarum and 6 other secondary endosymbionts, which include Rickettsia , Cardinium , Fritschea , Wolbachia , Hamiltonella and Arsenophonus [4,9,30,31]. They are both important for host whiteflies. For instance, Portiera and Hamiltonella cooperate to provide host B. tabaci MED with essential nutrition including co-factors and vitamins [32]. Cardinium infection significantly affects the fitness, fecundity, thermotolerance and microRNA expression in host B. tabaci [9,33,34,35,36]. Rickettsia also enhances the thermotolerance [37] and provisions of host whitefly with nutrition and resistance [38], as well as alter the defense pattern of host plants [39]. However, the interactions among different symbionts in B. tabaci remain poorly explored. The presence of endosymbionts in insects can significantly change the structure of bacterial communities in insect hosts [40,41,42]. Also, the existence and abundance of symbionts in insects can be significantly influenced by abiotic factors [43,44,45]. Additonally, the influence of environment on symbionts may contribute to changes in the microbiota structure in host insects [46]. Further, more extreme high temperatures in the future [47,48], will have impacts on insects and their bacterial communities [49]. However, related research in whiteflies are limited, and the relationship among microbial communities and symbionts in whiteflies and ecological factors require clarification. 2bRAD-M is a novel metagenome sequencing method especially for low-biomass or degraded microbiomes, which accurately generate species level taxonomic profiles of samples [50], which has been widely applied in medical and agricultural research [51,52,53;54]. Herein, 2bRAD-M sequencing was applied to 49 B. tabaci MED populations collected from 23 locations spread globally, to reveal the influence of ecological factors as well as the symbionts on the microbiota in B. tabaci. The 3 main purposes were: 1) clarifying the structure and constients of different B. tabaci MED populations from various geographical areas; 2) exploring the relationships among different symbionts and the influence of disparate symbionts on microbiota diversities in whiteflies; 3) illustrating the effects of ecological factors on bacterial communities as well as symbiont abundance in B. tabaci MED. The results showed a significant influence of ecological factors including abiotic factors on microbial communities and strong effects of symbionts on the bacterial diversity in B. tabaci MED. Materials and methods Global collection of B. tabaci MED populations We collected 49 populations of B. tabaci MED on different host plants, from 23 locations in eight countries spanning Asia, Europe, and North America, from 2006 to 2018. The whitefly samples were placed in alcohol after collection and transported to the laboratory. Identification of B. tabaci and Cardinium in whiteflies were performed using conventional PCR following previously described methods [9]. Female adults were used in all experiments, with the detailed information provided in Table 1 and Table S1 . One replicate means one B. tabaci female adult. Table 1 Collection information of Bemisia tabaci MED populations used in this experiment Group Name Population Name Collected location Collected year Host plant Replicates Kr Kr1 Gyeonggi, Korea 2018 cucumber 5 Kr2 South Chungcheong Province, Korea 2018 cucumber 5 Kr3 Jeollabuk-do, Korea 2018 sweet pepper 5 Kr4 Jeollanam-do, Korea 2018 cucumber 5 Kr5 South Gyeongsang Province, Korea 2018 cucumber 5 Kr6 North Gyeongsang Province, Korea 2018 muskmelon 5 FJ FJ Fuzhou, Fujian Province, China 2018 eggplant 5 JX JX Nanchang, Jiangxi Province, China 2018 eggplant 5 AH AH Hefei, Anhui Province, China 2018 eggplant 5 JS JS1 Nanjing, Jiangsu Province, China 2012 eggplant 10 JS2 Nanjing, Jiangsu Province, China 2018 eggplant 5 ZJ ZJ Hangzhou, Zhejiang Province, China 2018 gynura bicolor 5 GD GD Guangzhou, Guangdong Province, China 2018 eggplant 5 JL JL Tonghua, Jilin Province, China 2018 cucumber 5 JN JN1 Jinan, Shandong Province, China 2008 eggplant 10 JN2 Jinan, Shandong Province, China 2013 eggplant 10 JN3 Jinan, Shandong Province, China 2015 eggplant 10 JN4 Jinan, Shandong Province, China 2017 eggplant 10 HN HN Lingshui, Hainan Province, China 2017 tomato 9 BJ BJ Beijing, China 2012 eggplant 10 XJ XJ Wulumuqi, Xinjiang Province, China 2012 eggplant 10 HuB HuB Wuhan, Hubei Province, China 2012 eggplant 10 USA USA1 Lowes, Tucson, Arizona, USA 2006 poinsettia 10 USA2 Kmart, Tucson, Arizona, USA 2008 poinsettia 10 USA3 Trader Joe’s, Tucson, Arizona 2008 poinsettia 10 Group Name Population Name Collected location Collected year Host plant Replicates USA USA4 Walmart#2, Tucson, Arizona 2008 poinsettia 10 ESP ESP1 Madrid, Spain 2006 tomato 10 ESP2 Madrid, Spain 2007 tomato 10 ESP3 Madrid, Spain 10 CR CR Croatia 2007 poinsettia 10 BH BH Bosnia and Herzegovina 2007 eggplant 10 CY CY Cyprus 2007 10 IL IL Israel 2017 10 DZ DZ1 Dezhou, Shandong Province, China 2008 10 DZ2 Dezhou, Shandong Province, China 2013 10 DZ3 Dezhou, Shandong Province, China 2015 10 DZ4 Dezhou, Shandong Province, China 2017 10 LC LC1 Liaocheng, Shandong Province, China 2008 10 LC2 Liaocheng, Shandong Province, China 20013 10 LC3 Liaocheng, Shandong Province, China 2015 10 LC4 Liaocheng, Shandong Province, China 2017 10 SG SG1 Shouguang, Shandong Province, China 2008 10 SG2 Shouguang, Shandong Province, China 2013 10 SG3 Shouguang, Shandong Province, China 2015 10 SG4 Shouguang, Shandong Province, China 2017 10 ZZ ZZ1 Zaozhuang, Shandong Province, China 2008 10 ZZ2 Zaozhuang, Shandong Province, China 2013 10 ZZ3 Zaozhuang, Shandong Province, China 2015 10 ZZ4 Zaozhuang, Shandong Province, China 2017 10 A replicate means one female adult and all replicates of one population were collected in the same place within 1 hm 2 . DNA extraction and 2bRAD-M sequencing 2bRAD-M sequencing methods were used to measure the bacterial communities in whiteflies. The 2bRAD-M sequencing and libraries constructions were performed by Qingdao OE Biotech Co., Ltd. (Qingdao, China). The library constructions followed previously published protocols [55] with minor modifications. Briefly, the whitefly DNA (100 ng) of each single sample digestion was performed with 4 U of the enzyme BcgI (NEB) under 37°C for 3 h. After that, the DNA fragments were all ligated with specific adaptors. Then, a mix of 5 µL digested DNA combined with 10 µL of ligation master mix containing 0.2 µM each of two adaptors and 800 U of T4 DNA ligase (NEB) was processed for ligation reaction. The next ligation reaction was performed under 4°C for 12 h. Subsequently, the ligation products were PCR amplified; then, 8% polyacrylamide gel was used for electrophoresis. Based on the electrophoresis results, an approximately 100-bp band of the gel was excised, and the DNA fragments from the gel were then reconstiuted with DEPC water under 4°C for 12 h. PCR test was conducted with platform-specific barcode-bearing primers to introduce sample-specific barcodes. Each PCR sample was 20 µL, with a 25-ng gel-extracted PCR product, 0.2 µM each of forward and reverse primers, 0.3 mM dNTP mix, 0.4 U Phusion high-fidelity DNA polymerase (NEB), and 1×Phusion HF buffer. After PCR amplification and electrophoresis, QIAquick PCR purification kit (50) (Qiagen) was used to purify the PCR products and the Illumina Nova PE150 platform was used for sequencing. Data analysis of 2bRAD-M sequencing results Information from the NCBI database for microbial genomes (including 173,165 species of fungi, bacteria, and archaea) was used for 2bRAD-M analysis. The restriction enzymes of 16 type 2B were used to restrict the sample to fragments with built-in Perl scripts. RefSeq (GCF) number was subsequently used to assign the set of 2bRAD-M tags from all microbial genome information. Moreover, the whole genome’s corresponding GCF taxonomic information was also assigned. After that, every GCF’s 2bRAD tags that only occurred once were selected for comparison with those of all the other 2bRAD-M tags. The 2bRAD-M tags that were unique to a species were considered a species-specific 2bRAD-M marker, and those markers were used to form a reference database. A detection threshold of 0.0001 (0.01%) relative abundance was used as a default [56]. To calculate the relative abundance of each bacterium, the microbial species within each sample were first identified. All sequenced 2bRAD tags after quality control were mapped (using a built-in Perl script) against the 2bRAD marker database which contains all 2bRAD tags theoretically unique to each of 26,163 microbial species in the database. To control the false-positive in the species identification, a G score was derived for each species identified within a sample as below, which is a harmonious mean of read coverage of 2bRAD markers belonging to a species and the number of all possible 2bRAD markers of this species. The threshold of the G score for a false positive discovery of microbial species was set to 5 [51]. $${\text{G} \text{s}\text{c}\text{o}\text{r}\text{e}}_{\text{s}\text{p}\text{e}\text{c}\text{i}\text{e}\text{s} \text{i}}=\sqrt{{S }_{i}\times {t}_{i}}$$ S the number of reads assigned to all 2bRAD markers belonging to species i within a sample. t number of all 2bRAD markers of species i that have been sequenced within a sample. Then, the average read coverage of all 2bRAD markers for each species was calculated, which represent the number of individuals belonging to a species present in a sample at a given sequencing depth. The relative abundance of a given species is then calculated as the ratio of the number of microbial individuals belonging to a species against the total number of individuals from known species that can be detected within a sample. $${Relative abundance}_{species i}=\frac{\raisebox{1ex}{${S}_{i}$}\!\left/ \!\raisebox{-1ex}{${T}_{i}$}\right.}{{\sum }_{\text{i}=1}^{\text{n}}\raisebox{1ex}{${S}_{i}$}\!\left/ \!\raisebox{-1ex}{${T}_{i}$}\right.}$$ S the number of reads assigned to all 2bRAD markers of species i within a sample. T the number of all theoretical 2bRAD markers of species i. The diversity and abundance indexes, and infection of symboints as influenced by climate or geographical factors were described by two structural equations models (SEM) with Satorra-Bentler correction, respectively. To reduce heteroscedasticity, the log value of “precipitation” was used in SEMs. The SEM models was accepted when the P value > 0.05 and CFI > 0.95 after excluding the redundant paths step by step based on a lower AIC value. To exclude the variance of each parameter, the coefficients of SEM were estimated by the standardized transformation. The relative abundances of Cardinium and Rickettsia (Fig. 1 ) and microbial diversities of samples (Fig. 3 ) were all first tested for normality (Kolmogorov-Smirnov test) and homogeneity of group variances (Levene’s test). When the data followed normal distribution, they were analysed with a one-way ANOVA with post-hoc Tukey HSD analysis. Data which did not follow a normal distribution, were analyzed by a Kruskal-Wallis test and Dunn’s test with Bonferroni correction for multiple comparisons. To analyze the relationship between symbionts and microbiota diversities, all data of symbiont relative abundance (s) were logit transformed as follows (Figs. 4 , 5 , 6 , 7 and 8 ): $$logit\left(s\right)=\text{l}\text{g}(\text{s}+1)$$ When analyzing the relationship between symbionts and microbiota diversities, the diversity indexes were recounted after removing the analyzed symbiont (Fig. 4 ). The vegan and picante packages in R were used to compute the α-diversity indexes of microbiota in B. tabaci . QIIME software was used to generate the PCoA plots of Bray-Curtis intersample distances and the classification probabilities [57]. Spearman analysis was used for analyzing the relationship symbionts and microbiota diversity indexes. Pearson analysis was used for the relationship between symbiont and ecological factors as well as the relationship between different symbionts. SPSS 21.0 was used to carry out all statistical tests. Both Arsenophonus and Wolbachia in B. tabaci had too many 0 values, therefore a categorical analysis was used to analyze the relationships between the 2 symbionts and other symbionts as well as ecological factors. All B. tabaci populations were categorized into 2 groups with the Arsenophonus or Wolbachia abundance: one group with Arsenophonus or Wolbachia being present (abundance > 0) and one group with Arsenophonus or Wolbachia being absent (abundance = 0). Mann-Whitney U -test was used to compare the symbiont abundance and diversity indexes between the 2 groups (Fig. S1 and S2). All figures were made by GraphPad Prism 9.0.0. Analysis with generalized linear models was carried out in SPSS 21.0. Results Cardinium and Rickettsia abundance in Bemisia tabaci MED collected globally The 2bRAD-M sequencing results showed that Cardinium infection rate varied significantly among B. tabaci MED collected from the different areas ( P < 0.001, Kruskal–Wallis test, Fig. 1 left panel). Whiteflies collected in Israel (IL population) had no Cardinium (no sample’s relative abundance of Cardinium exceeded 0.01%). Whiteflies collected from South Korea (Kr) had a relative higher Cardinium abundance (21.54 ± 3.24%, Mean ± SEM) compared to whiteflies collected in other areas (Fig. 1 , left panel). Rickettsia was abundant in B. tabaci MED populations with low Cardinium infection such as IL (68.07 ± 4.57%, Mean ± SEM), CY (69.84 ± 7.14%, Mean ± SEM), ZJ (48.21 ± 13.19%, Mean ± SEM) and FJ (43.76 ± 5.30%, Mean ± SEM) populations, but was lower in Cardinium -abundant populations, such as in Kr population (relative abundance of Rickettsia lower than 0.01%) (Fig. 1 , right panel). Another interesting finding was that the relative abundance of Cardinium was higher in high-latitude populations compared to those in relatively low-latitude populations. In East Asia, high-latitude populations such as JL, XJ and Kr populations (more than 7.38%) had a relatively higher Cardinium abundance than low-latitude populations including FJ, GD, HN populations (lower than 4.16%). In Europe and the Mediterranean region, the relatively higher latitude populations including such as CR population also had higher Cardinium abundance (above 14.16%) than BH, CY and IL populations (lower 1.00%) which were collected in relatively low-latitude areas (Fig. 1 , left panel). Microbial composition of different Bemisia tabaci MED populations The microbial communities in each B. tabaci MED population, was identified to the species level. One primary symbiont Portiera aleyrodidarum and five secondary symbionts ( Hamiltonella , Cardinium , Wolbachia , Rickettsia and Arsenophonus ) were discovered in all 49 B. tabaci MED populations. Portiera aleyrodidarum was the most abundant bacteria in almost all geographical populations except for IL and CY populations (in which the abundance of Rickettsia was higher than Portiera ), with a relative abundance being above 28.82% (lowest in CY population) in all whitefly populations. Rickettsia was abundant in the CY population (69.84% of all bacteria) and IL population (68.07%). However, no sequences of Rickettsia were identified in the AH, JX, HuB, XJ and all South Korea populations. The Rickettsia species identified in the samples included Rickettsia sp. Strain MEAM1 , Rickettsia hoogstraalii , Rickettsia felis and Rickettsia bellii. Of these, Rickettsia sp. Strain MEAM1 was the dominant species in all Rickettsia -infected samples. Rickettsia bellii showed a lower infection rate in ZZ, JN, HN and USA populations (relative abundance lower than 0.1‰). Likewise, Rickettsia hoogstraalii showed a lower infection rate in SG, HN, JL, CR, CY populations (relative abundance lower than 0.1‰). Rickettsia felis (relative abundance 1.14‰) was identified only in the BH population. Interestingly, in all populations which had a high Rickettsia abundance, showed lower Cardinium abundance. On the other hand, Cardinium -abundant populations (such as the Korea populations) showed no or less abundant Rickettsia , which imply a potential antagonistic interaction between Cardinium and Rickettsia in B. tabaci MED. Four Cardinium species were identified in this study ( Cardinium cBtQ, Cardinium cEper1, Cardinium endosymbiont of Culicoides punctatus and Cardinium cSfur ). Of these, Cardinium cBtQ was the dominant species in all Cardinium -infected samples. Cardinium cEper1 had a lower infection rate (relative abundance lower than 0.1‰), in the JS, Kr2, Kr5, ZZ, LC, AH and JL populations. Likewise, Cardinium cSfur had a lower infection rate in the GD, SG, SPA3 and LC populations, while Cardinium endosymbiont of Culicoides punctatus was only detected in the Kr1 population. The results showed that all whitefly populations were infected with Hamiltonella defensa , with a relative abundance ranging from 0.02% (lowest in the JX population) to 18.37% (highest in the HuB population). Seven Wolbachia species were identified from the whitefly samples ( Wolbachia endosymbiont of Bemisia tabaci, Wolbachia endosymbiont of Cylisticus convexus, Wolbachia endosymbiont of Diaphorina citri, Wolbachia endosymbiont of Folsomia candida, Wolbachia endosymbiont of Laodelphax striatellus, Wolbachia endosymbiont of Leptopilina clavipes, Wolbachia endosymbiont of Nilaparvata lugens, Wolbachia pipientis wAlbB ). Of these, Wolbachia endosymbiont of Bemisia tabaci was the dominant in all Wolbachia -infected samples. However, its abundance was relatively low (below 5.94%) in all samples, except for the JS2 population, which had a relative abundance of 21.12% of all bacterial communities. The relative abundance of the other Wolbachia species was much lower (relative abundance lower than 0.1‰) in all samples. In some East Asia populations including BJ, HN, Kr1, Kr2 and Kr3 populations, no Wolbachia was detected. Arsenophonus was detected in several populations (including AH, JX, BH, CR, CY and IL populations) with a low-abundance rate below 2.8% (Fig. 2 ). Four Arsenophonus species was identified from all samples in this study ( Arsenophonus endosymbiont of Bemisia tabaci Asia II3 , Arsenophonus endosymbiont of Aleurodicus floccissimus , Arsenophonus endosymbiont str Hangzhou of Nilaparvata lugens , Arsenophonus nasoniae ). Of these, Arsenophonus endosymbiont of Bemisia tabaci Asia II3 was the dominant in all Arsenophonus -infected samples. Bacterial diversity in geographically different Bemisia tabaci MED populations Three α-diversity indexes (including Chao1, Simpson and Shannon indexes) were applied to determine the diversities of the bacterial communities in each whitefly population. The diversity indexes were all analyzed with the Kruskal–Wallis test and Dunn’s test with Bonferroni correction for multiple comparisons (Fig. 3 ), as none followed a normal distribution. The Shannon and Simpson index analysis showed a similar result for the community diversity index for all whitefly populations. Results showed that the JL population had the highest Simpson index (0.55 ± 0.02, Mean ± SEM) and a relatively high Shannon index (0.97 ± 0.05, Mean ± SEM), while the CR population had the highest Shannon index (1.04 ± 0.12, Mean ± SEM) and a relatively high Simpson index (0.49 ± 0.06, Mean ± SEM). On the other hand, the JX population had the lowest Shannon index (0.21 ± 0.05, Mean ± SEM) and the lowest Simpson index (0.09 ± 0.03, Mean ± SEM) (Fig. 3 A and 3 B). Results of the Chao1 diversity index showed that the AH population had the highest bacterial community richness (60.40 ± 9.18, Mean ± SEM), while the BJ population had the lowest Chao1 index (17.00 ± 1.91, Mean ± SEM) (Fig. 3 C). Interestingly, the α-diversity indexes of Cardinium abundant populations (such as JL, CR, USA and Kr populations) were higher than diversity indexes of Cardinium limited populations (such as JX, BJ, IL and HuB populations) (Fig. 3 ). Relationship between symbionts and bacterial diversities in Bemisia tabaci MED The correlations between the relative abundance of symbionts and the α-diversity indexes (including Shannon, Chao1 and Simpson indexes) of microbial communities in whitefly, were analyzed using Spearman correlation. The microbiota diversity indexes were computed after removing the compared symbiont in this experiment. The results revealed that Cardinium abundance significantly influenced the B. tabaci microbiota Simpson diversity index negatively (r = -0.449, P < 0.01) (Fig. 4 B). Hamiltonella had a significant negative effect on both the Shannon diversity (r = -0.498, P < 0.01) and Simpson diversity (r = -0.498, P < 0.01) of host whitefly (Fig. 4 G and 4 H), while Portiera and Rickettsia did not significantly influence the diversity indexes of bacterial communities in B. tabaci (Fig. 4 ). Categorical analysis showed that both Wolbachia and Arsenophonus did not significantly affect the diversity of host whitefly (Fig. S1 and S2). Relationships among various symbionts in Bemisia tabaci MED Pearson correlation was applied to clarify the complex relationships among the different symbionts in B. tabaci MED. Results showed that the relative abundance of Hamiltonella significantly negatively correlated with the relative abundance of Rickettsia (r = -0.403, P < 0.01) and Wolbachia (r = -0.351, P < 0.05) (5ig. 4A and 5B). The relative abundance of Portiera had a significant positive correlation with the relative abundance of Hamiltonella (r = 0.296, P < 0.05), but a significant negative correlation with the relative abundance of Rickettsia (r = -0.886, P < 0.001) (Fig. 5 C and 5 D). Results from the generalized linear models revealed a significant correlation between Rickettsia and Hamiltonella (Table 2 ). Categorical analysis showed that Wolbachia and Arsenophonus significantly affected the abundance of each other positively (Fig. S1 E and S2E). Table 2 Generalized linear models of symbionts, bacterial diversities and environmental factors in Bemisia tabaci MED. Dependent Variable Covariates B Wald Chi-Square P-value Rickettsia Hamiltonella 0.156 14.496 0.003 Portiera X 0.039 14.913 < 0.001 Y -0.365 7.255 0.007 Bio1 -0.202 9.324 0.002 Bio12 0.003 10.085 0.001 X*bio1 0.001 5.536 0.019 X*bio5 -0.002 17.685 < 0.001 X*bio12 -0.000014 9.398716 0.002 Y*bio5 0.011 4.996 0.025 Rickettsia X -0.44 17.247 < 0.001 Y -0.365 7.255 < 0.001 Bio1 -0.202 9.324 0.002 Bio12 0.003 10.085 0.001 X*bio1 0.001 5.536 0.019 X*bio5 -0.002 17.685 < 0.001 X*bio12 -0.000014 9.398716 0.002 Y*bio5 0.011 4.996 0.025 Hamiltonella X 0.006 4.132 0.042 Shannon Y 0.312 4.396 0.036 X*bio1 -0.001 6.702 0.010 X*bio5 0.001 7.969 0.005 X*bio12 0.000012 5.911 0.015 Simpson X -0.016 4.982 0.026 Y 0.234 5.895 0.015 X*bio1 -0.001 6.007 0.014 X*bio5 0.001 8.947 0.003 X*bio12 0.000009 7.838 0.005 Y*bio5 -0.007 4.020 0.045 Chao1 Y 29.251 4.619 0.032 X*bio12 0.001 5.763 0.016 Y*bio1 0.361 4.817 0.028 Y*bio5 -1.081 4.627 0.031 Note: “X” means “longitude”, “Y” means “latitude”. Influence of ecological factors on the microbial communities in global Bemisia tabaci MED populations To understand whether and to what extent ecological factors shaped the microbial communities across the B. tabaci MED populations, Pearson analysis and structural equation model (SEM) were used to resolve the relationships between microbial community structure (including symbionts and α-diversity indexes) and 5 putative predictor variables including annual mean temperature, mean temperature of warmest quarter, annual precipitation, latitude, and longitude. The diversity and abundance indexes of symbionts as influenced by climate or geographical factors were described by a structural equations model (SEM). Results showed that the annual mean temperature significantly and positively influenced the relative abundance of Rickettsia (r = 0.326, P < 0.05) (Fig. 6 A), while annual precipitation significantly and positively influenced the Arsenophonus abundance (r = 0.325, P < 0.05) and the Chao1 index (r = 0.405, P < 0.01) (Fig. 6 B and 6 C) in global whitefly populations. Generalized linear models also revealed a significantly influence of ecological factors on the abundance of symbionts Portiera , Rickettsia and Hamiltonella (Table 2 ). The relationships between the microbial communities of B. tabaci MED and longitude were also explored. Results showed that longitude positively influenced the abundance of Portiera (r = 0.499, P < 0.001) but negatively influenced the abundance of Rickettsia (r = -0.411, P < 0.01) (Fig. 7 ). Also, longitude negatively influenced the Shannon (r = -0.402, P < 0.01) and Simpson indexes (r = -0.321, P < 0.05) (Fig. 8 ). The SEM analysis was used to clarify the complex interactions among symbionts, bacterial communities and ecological factors. The SEM models were simplified based on the lowest value of Akaike Information Criterion (AIC). The simplified models were not rejected based on the P value of chi-square test > 0.05, comparative Index (CFI) > 0.90, standardized root mean square residual (SRMR) < 0.05. For the relationships among different symbionts and ecological factors (Fig. 9 ). Results showed that longitude positively influenced Portiera abundance ( r = 0.40 ± 0.12, z = 2.04, P < 0.05), but negatively influenced Rickettsia abundance ( r = -0.42 ± 0.12, z = 3.40, P < 0.001). Further, longitude positively influenced Hamiltonella abundance but negatively influenced Wolbachia and Arsenophonus abundances. Latitude positively influenced both the abundance of Cardinium and Hamiltonella , but negatively influenced Rickettsia abundance. Annual mean temperature negatively affected Portiera abundance and positively affected Hamiltonella abundance, but mean temperature did not significantly influence Rickettsia abundance. The mean temperature of warmest quarter negatively influenced Hamiltonella abundance and Wolbachia abundance, but positively influenced Portiera abundance. Annual precipitation positively influenced Arsenophonus abundance and positively influenced Wolbachia abundance, but negatively affected Hamiltonella abundance. Also, SEM analysis indicated a negative correlation between Portiera and Rickettsia , as well as a negative correlation between Hamiltonella and Wolbachia . Additionally, SEM analysis also showed a negative correlation between Rickettsia and Cardinium abundance ( r = -0.27 ± 0.12, z = 2.36, P < 0.05) (Fig. 9 ). The relationships between the diversities of microbial communities and ecological factors were also explored by SEM (Fig. 10 ). Results showed that longitude negatively influenced the Shannon index, while latitude positively influenced both the Shannon and Chao1 indexes. Annual precipitation also positively influenced the Chao1 index, which result was similar to the Pearson analysis (Fig. 6 C), and also positively influenced the Shannon index. Annual temperature and mean temperature positively influenced both the Shannon and Simpson indexes, while the mean temperature of warmest quarter negatively influenced both the Shannon and Simpson indexes (Fig. 10 ). Discussion We collected 49 B. tabaci MED populations from 23 locations in Asia, Europe and USA, using techniques and experiments including 2bRAD-M sequencing, to explore the relationships among the microbial communities including symbionts in whiteflies and ecological factors. The results revealed that the structures of microbial communities in different B. tabaci MED populations were significantly different, and their diversities were significantly negatively affected by the relative abundance of Cardinium and Hamiltonella . The abundance of symbionts influenced one other, and the effect was always negative, except for Portiera and Hamiltonella . Many ecological factors including temperature, precipitation, longitude and latitude had effects on symbiont abundance and bacterial diversities in B. tabaci . Our results clarified a significant influence of symbiont infection on the microbial diversities in B. tabaci MED, as well as exploring the impacts of ecological factors on the bacterial communities including symbionts infection and microbial diversities. In this experiment, we identified six symbionts including one primary symbiont Portiera and five secondary symbionts from all B. tabaci MED samples. These results are similar to that from other studies which also revealed that whiteflies are co-infected with a variety of symbionts globally [58,59,60,61,62]. The results confirmed that the primary symbiont Portiera is the most abundant bacteria in almost all B. tabaci MED populations (Fig. 2 ), which play s many important roles in whiteflies such as provision of essential amino acids [63,64,65,66]. Also, the results showed that Hamiltonella infected all B. tabaci MED populations, although it showed lower abundance (lower than 18.37% in all populations). The co-infection of Hamiltonella and Portiera was prevalent in whiteflies and is vital for B. tabaci MED. A previous study revealed that Hamiltonella completes the missing steps in some of the pathways of Portiera to provide a full complement of nutrients to their whitefly hosts [32]. Arsenophonous also infected many populations such as the AH, BH, CY and IL populations, and provided host whiteflies with B vitamin, similar to Hamiltonella [67]. The existence and abundance of different symbionts in insects promote interactions among them, especially for secondary symbionts [41,68,69,70]. For example, Wolbachia infection in the fruit fly, Drosophila neotestacea , significantly promoted the infection of Spiroplasma [69]. Similar results reported from wild Laodelphax striatellus populations showed that Wolbachia infection enriched the abundance of other bacteria groups including Thermus , Spiroplasma and Ralstonia [41]. Our 2bRAD-M sequencing results showed that the presence of Hamiltonella significantly and negatively influenced the abundance of Rickettsia and Wolbachia , while the presence of Portiera positively influenced Hamiltonella , but negatively influenced Rickettsia abundance (Fig. 4 ). The positive correlation between Portiera and Hamiltonella confirms the cooperation to provide a full complement of nutrients to their whitefly hosts [32], and which helps to promote their spread in whitefly populations. On the other hand, antagonistic interactions among bacteria including symbionts in other insects have been reported. For example, the relative abundance of 154 bacteria genera in Laodelphax striatellus (SBPH) significantly decreased with Wolbachia infection [41]. In the D. melanogaster gut, the prevalence of various bacteria is generally negatively correlated [71] and many isolated bacteria inhibit the growth of the primary symbiont Paenibacillus larvae in the honeybee [68]. Another study reported of an antagonistic interaction between Hamiltonella and Cardinium in B. tabaci [72]. The co-sharing of the bacteriocytes in B. tabaci by symbionts may promote such antagonistic interactions, as a result of competition for limited space and resources in host whiteflies [31,73,74]. The infection and titer of symbionts in arthropods including in whiteflies are affected by many ecological factors including temperature, precipitation, longitude, latitude and so on [25,43,44,45,70]. For example, Cardinium infection in the spider mite, Tetranychus truncatus is positively influenced by latitude but decreases with increased annual precipitation [70]. We are the first to report that longitude and latitude significantly affect the abundance of many symbionts including Portiera , Rickettsia and Cardinium in whiteflies (Figs. 6 and 8 ). Our result showed a positive influence of latitude on Cardinium abundance, as well as a negative influence of precipitation on Hamiltonella. However, precipitation positively influenced the abundance of Wolbachia and Arsenophonus and temperature postively affected Rickettsia abundance (Fig. 4 A), which is congruent to a previous study which revealed the Rickettsia infection significantly increased the thermotolerance of host whiteflies [37]. The frequency and abundance of symbionts are significantly influenced by temperature, especially for Cardinium [43,44,45]. We previously reported that Cardinium infection increased the thermotolerance of host whiteflies [9]. However, in this study the correlation between the abundance of Cardinium and temperature was not significant. Therefore, the relationships between Cardinium and temperature in whiteflies should be further explored. Results showed that annul mean temperature had negative effects on Portiera abundance. A previous study revealed that Portiera titer was significantly reduced under high temperatures [75]. There is increasing awareness of the vital role of the microbiome on host insects such as the provisioning of essential nutrients, synthesis of specific toxins or modification of the insect immune system [13,76]. However, the structure and diversities of microbial communities in insects are influenced by many factors, including host sex, growth stages, the abundance of symbionts, antibiotics as well as many geographic and climatic factors [13,41,66,77,78]. In this experiment, we revealed that the diversity of whiteflies’ bacterial communities (Shannon, Simpson and Chao1 indexes) is significantly influenced by many ecological factors including temperature, precipitation, longitude and latitude (Fig. 4 C, 6 and 7 ). Similar results were also reported on the SBPH, whic showed that the microbial structure in SBPH was also significantly influenced by annual mean precipitation and longitude [41], while the microbial communities in stoneflies were significantly influenced by geographical distances [46]. How ecological factors influence the structure of microbial communities in B. tabaci MED should be explored further, although studies have shown that insects acquire microbiome from the environment [79,80,81]. Our results also showed that the infection and abundance of many symbionts in whiteflies were significantly influenced by ecological factors (Fig. 6 A, 6 B, 7 and 9 ), which results are similar to other related studies [43,70,82]. This indicate that the change in symbiont abundance caused by ecological factors may indirectly alter the diversity of bacterial community in B. tabaci MED. We discovered that symbiont abundance had significant effects on the diversities of microbial communities in whiteflies, which results are congruent with that in other related studies [40,41,42]. For example, Wolbachia infection significantly changed the structure of microbial communities in SBPH [41] and D. melanogaster [42]. The complex interactions among different symbionts and microbial communities in insect host should be explored further. In summary, 2bRAD-M sequencing analysis of 49 global B. tabaci populations provided a new and comprehensive perspective of the relationship among the structure and diversity of bacterial community, ecological factors as well as the symbionts in B. tabaci MED populations. The results showed that the symbionts within host whiteflies affected one another, and the abundance of symbionts and microbiota diversities were significantly influenced by many ecological factors. However, further studies are needed to clarify how ecological factors influence the diversity of bacterial communities in different B. tabaci MED populations under global ecological timescales. Declarations Author Contributions: Conceptualization, Chu D; sample preparation: Li HR; methodology and data analysis: Yang K, Zhang YX, Zhou JC and Zhang YJ; writing and editing: Yang K, Zhang YX and Chu D; funding acquisition: Chu D and Yang K. Acknowledgments: The authors wish to acknowledge Dr Ary Hoffman, for the help in analyzing the data of the results of this study. Funding This research was supported by the National Key Research and Development Program of China (2022YFD1401200), the Science and Technology Benefiting the People Demonstration Project of Qingdao (24-1-8-xdny-10-nsh), the Taishan Scholar Foundation of Shandong Province, and the Qingdao Agricultural University High-level Talent Fund (663-1121025). Availability of data and materials The raw data of the 2b-RAD sequencing were deposited in the Sequence Read Archive Database of the NCBI (BioProject number: SRR10541684). Ethics approval and consent to participate All authors consent to participate in this study. Consent for publication All authors give their consent for publication. Competing interests The authors declare no competing interests. References Bertin S, Luigi M, Parrella G, et al. Survey of the distribution of Bemisia tabaci (Hemiptera: Aleyrodidae) in Lazio region (Central Italy): a threat for the northward expansion of Tomato leaf curl New Delhi virus (Begomovirus: Geminiviridae) infection. Phytoparasitica. 2018;46:171-182. DOI: 10.1007/s12600-018-0675-1 De Barro PJ, Liu SS, Boykin LM, Dinsdale AB. Bemisia tabaci : A statement of species status. Annu Rev Entomol. 2011;56:1-19. DOI: 10.1146/annurev-ento-112408-085504 Boykin LM, De Barro PJ. A practical guide to identifying members of the Bemisia tabaci species complex: and other morphologically identical species. Front Ecol Evol. 2014;2:1-5. DOI: 10.3389/fevo.2014.00045 Kanakala S, Ghanim M. Global genetic diversity and geographical distribution of Bemisia tabaci and its bacterial endosymbionts. PLoS ONE. 2019;14(3):e0213946. DOI: 10.1371/journal.pone.0213946 Zhu DT, Xia WQ, Rao Q, Liu SS, Ghanim M, Wang XW. Sequencing and comparison of the Rickettsia genomes from the whitefly Bemisia tabaci Middle East Asia Minor I. Insect Sci. 2016;23:531-542. DOI: 10.1111/1744-7917.12347 Islam W, Akutse KS, Qasim M, Khan KA, Ghramh HA, Idrees A, Latif S. Bemisia tabaci-mediated facilitation in diversity of begomoviruses: Evidence from recent molecular studies. Microb Pathog. 2018;123:162-168. DOI: 10.1016/j.micpath.2018.07.029 Gilbertson RL, Batuman O, Webster CG, Adkins S. Role of the Insect Supervectors Bemisia tabaci and Frankliniella occidentalis in the emergence and global spread of plant viruses. Annu Rev Virol. 2015;2:67-93. DOI: 10.1146/annurev-virology-100114-055141 Liu Y, Yang K, Wang JC, Chu D. Cardinium infection alters cotton defense and detoxification metabolism of its whitefly host. Insect Sci. 2022. DOI: 10.1111/1744-7917.13086 Yang K, Yuan MY, Liu Y, Guo CL, Liu TX, Zhang YJ, Chu D. First evidence for thermal tolerance benefits of the bacterial symbiont Cardinium in an invasive whitefly, Bemisia tabaci. Pest Manag Sci. 2021;77:5021-5031. DOI: 10.1002/ps.6631 Chu D, Wan FH, Zhang YJ, Brown JK. Change in the biotype composition of Bemisia tabaci in Shandong Province of China from 2005 to 2008. Environ Entomol. 2010;39:1028-1036. DOI: 10.1603/en09192 Guo CL, Zhu YZ, Zhang YJ, Keller MA, Liu TX, Chu D. Invasion biology and management of sweetpotato whitefly (Hemiptera: Aleyrodidae) in China. J Integr Pest Manag. 2021;12:1-16. DOI: 10.1093/jipm/pmaa026 Ballinger MJ, Perlman SJ. Generality of toxins in defensive symbiosis: Ribosome-inactivating proteins and defense against parasitic wasps in Drosophila. PLoS Pathog. 2017;13(7):e1006431. DOI: 10.1371/journal.ppat.1006431 Douglas AE. Multiorganismal insects: diversity and function of resident microorganisms. Annu Rev Entomol. 2015;60(1):17-34. DOI: 10.1146/annurev-ento-010814-021120 Engel P, Martinson VG, Moran NA. Functional diversity within the simple gut microbiota of the honeybee. Proc Natl Acad Sci USA. 2012;109:11002-11007. DOI: 10.1073/pnas.1202970109 Engel P, Moran NA. The gut microbiota of insects - diversity in structure and function. FEMS Microbiol Rev. 2013;37(5):699-735. DOI: 10.1111/1574-6976.12025 Paniagua Voirol LR, Enric F, Martin K, Monika H, Fatouros NE. Bacterial symbionts in Lepidoptera: their diversity, transmission, and impact on the host. Front Microbiol. 2018;9. DOI: 10.3389/fmicb.2018.00556 Chung SH, Scully ED, Peiffer M, Geib SM, Rosa C, Hoover K, Felton GW. Host plant species determines symbiotic bacterial community mediating suppression of plant defenses. Sci Rep. 2017;7:39690. DOI: 10.1038/srep39690 Gallo-Franco JJ, Toro-Perea N. Variations in the bacterial communities in Anastrepha obliqua (Diptera: Tephritidae) according to the insect life stage and host plant. Curr Microbiol. 2020;77(7):1283-1291. DOI: 10.1007/s00284-020-02134-0 Hussain M, Akutse KS, Ravindran K, Lin Y, Bamisile BS, Qasim M, Dash CK, Wang L. Effects of different temperature regimes on survival of Diaphorina citri and its endosymbiotic bacterial communities. Environ Microbiol. 2017;19(9):3439-3449. DOI: 10.1111/1462-2920.13825 Jones AG, Mason CJ, Felton GW, Hoover K. Host plant and population source drive diversity of microbial gut communities in two polyphagous insects. Sci Rep. 2019;9(1):2792. DOI: 10.1038/s41598-019-39351-4 Kondo N, Shimada M, Fukatsu T. Infection density of Wolbachia endosymbiont affected by co-infection and host genotype. Biol Lett. 2005;1(4):488–491. DOI: 10.1098/rsbl.2005.0327 Priya NG, Ojha A, Kajla MK, Raj A, Rajagopal R. Host plant induced variation in gut bacteria of Helicoverpa armigera . PLoS One. 2012;7:e30768. DOI: 10.1371/journal.pone.0030768 Yang K, Chen H, Bing XL, Xia X, Zhu YX, Hong XY. Antibiotic-induced changes in Tetranychus truncatus bacterial community alter its fecundity, longevity and sex-ratio. Syst Appl Acarol. 2020;25(9):1668-1682. DOI: 10.11158/saa.25.9.7 Yang K, Qin PH, Yuan MY, Chen L, Zhang YJ, Chu D. Infection density pattern of Cardinium affects the responses of bacterial communities in an invasive whitefly under heat conditions. Insect Sci. 2022; DOI: 10.1111/1744-7917.13141 Martiny JB, Bohannan BJ, Brown JH, Colwell RK, Fuhrman JA, Green JL, et al. Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol. 2006;4:102-112. DOI: 10.1038/nrmicro1341 Feldhaar H, Gross R. Insects as hosts for mutualistic bacteria. Int J Med Microbiol. 2009;299(1):1-8. DOI: 10.1016/j.ijmm.2008.06.003 Luan JB. Insect bacteriocytes: adaptation, development, and evolution. Annu Rev Entomol. 2024;6981-6998. Mondo SJ, Lastovetsky OA, Gaspar ML, et al. Bacterial endosymbionts influence host sexuality and reveal reproductive genes of early divergent fungi. Nat Commun. 2017;8:1843. DOI: 10.1038/s41467-017-01752-w Werren JH, Baldo L, Clark ME. Wolbachia : master manipulators of invertebrate biology. Nat Rev Microbiol. 2008;6(10):741-751. DOI: 10.1038/nrmicro1969 Bing XL, Yang J, Zchori-Fein E, Wang XW, Liu SS. Characterization of a newly discovered symbiont of the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae). Appl Environ Microbiol. 2013;79(2):569-575. DOI: 10.1128/AEM.02154-12 Gottlieb Y, Ghanim M, Gueguen G, Kontsedalov S, Vavre F, Fleury F, Zchori-Fein E. Inherited intracellular ecosystem: symbiotic bacteria share bacteriocytes in whiteflies. FASEB J. 2008;22(7):2591-2599. DOI: 10.1096/fj.07-101121 Rao Q, Rollat-Farnier P, Zhu D, Santos-Garcia D, Silva FJ, Moya A, et al. Genome reduction and potential metabolic complementation of the dual endosymbionts in the whitefly Bemisia tabaci. BMC Genomics. 2015;16:226. DOI: 10.1186/s12864-015-1447-4 Fang YW, Liu LY, Zhang HL, Jiang DF, Chu D. Competitive ability and fitness differences between two introduced populations of the invasive whitefly Bemisia tabaci Q in China. PLoS One. 2014;9(6):e100423. DOI: 10.1371/journal.pone.0100423 Li HR, Harwood JD, Liu TX, Chu D. Novel proteome and acetylome of Bemisia tabaci Q in response to Cardinium infection. BMC Genomics. 2018;19:523. DOI: 10.1186/s12864-018-4881-5 Li HR, Wei XY, Ding TB, Chu D. Genomewide profiling of Cardinium-responsive microRNAs in the exotic whitefly, Bemisia tabaci (Gennadius) biotype Q. Front Physiol. 2018;9:1580. DOI: 10.3389/fphys.2018.01580 Lv N, Peng J, Chen XY, Guo CF, Sang W, Wang XM, et al. Antagonistic interaction between male-killing and cytoplasmic incompatibility induced by Cardinium and Wolbachia in the whitefly. Bemisia tabaci . Insect Sci. 2020;28:330-346. DOI: 10.1111/1744-7917.13086 Brumin M, Kontsedalov S, Ghanim M. Rickettsia influences thermotolerance in the whitefly Bemisia tabaci B biotype. Insect Sci. 2011;18:57-66. DOI: 10.1111/j.1744-7917.2010.01389.x Fan ZY, Liu Y, He ZQ, Wen Q, Chen XY, Khan MM, Osman MM, Mandour NS, Qiu BL. Rickettsia infection benefits its whitefly hosts by manipulating their nutrition and defense. Insects. 2022;13:1161. DOI: 10.3390/insects13091161 Shi PQ, Chen XY, Chen XS, Lv N, Liu Y, Qiu BL. Rickettsia increases its infection and spread in whitefly populations by manipulating the defense patterns of the host plant. FEMS Microbiol Ecol. 2021;97(4):fiab032. DOI: 10.1093/femsec/fiab032 Audsley MD, Seleznev A, Joubert DA, Woolfit M, O'Neill SL, McGraw EA. Wolbachia infection alters the relative abundance of resident bacteria in adult Aedes aegypti mosquitoes, but not larvae. Mol Ecol. 2018;27:297–309. DOI: 10.1111/mec.14482 Duan XZ, Sun JT, Wang LT, Shu XH, Guo Y, Keiichiro M, et al. Recent infection by Wolbachia alters microbial communities in wild Laodelphax striatellus populations. Microbiome. 2020;8:104. DOI: 10.1186/s40168-020-00867-0 Simhadri RK, Fast EM, Guo R, Schultz MJ, Vaisman N, Ortiz L, Bybee J, Slatko BE, Frydman HM. The gut commensal microbiome of Drosophila melanogaster is modified by the endosymbiont Wolbachia. mSphere. 2017;2:e00287-17. DOI: 10.1128/mSphere.00287-17 Charlesworth J, Weinert LA, Araujo EV Jr, Welch JJ. Wolbachia , Cardinium and climate: an analysis of global data. Biol Lett. 2019;15(8):20190273. DOI: 10.1098/rsbl.2019.0273 Corbin C, Heyworth ER, Ferrari J, Hurst GD. Heritable symbionts in a world of varying temperature. Heredity. 2016;118(1):10-20. DOI: 10.1038/hdy.2016.77 Morag N, Klement E, Saroya Y, Lensky I, Gottlieb Y. Prevalence of the symbiont Cardinium in Culicoides (Diptera: Ceratopogonidae) vector species is associated with land surface temperature. FASEB J. 2012;26(10):4025-4034. DOI: 10.1096/fj.12-208009 Zhu YX, Huo QB, Wen T, Wang XY, Zhao MY, Du YZ. Mechanisms of fungal community assembly in wild stoneflies moderated by host characteristics and local environment. npj Biofilms Microbiomes. 2022;8:31. DOI: 10.1038/s41522-022-00258-0 Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason BE, et al. Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res. 2006;111:D05109. DOI: 10.1029/2005JD006290 Meehl GA, Tebaldi C. More intense, more frequent, and longer-lasting heat waves in the 21st century. Science. 2004;305:994–997. DOI: 10.1126/science.1098704 Ma CS, Ma G, Pincebourde S. Survive a warming climate, insect responses to extreme high temperatures. Ann Rev Entomol. 2021;66:8.1–8.22. Sun Z, Huang S, Zhu P, Tzehau L, Zhao H, Lv J, Zhang RC, Zhou LS, Niu QY, Wang XP, Zhang M, Jing GC, Bao ZM, Liu JQ, Wang S, Xu J. Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M. Genome Biol. 2022;23:36. DOI: 10.1186/s13059-022-02600-7 Sun Z, Huang S, Zhu P, Tzehau L, Zhao H, Lv J, et al. Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M. Genome Biol. 2022;23(1):36. DOI: 10.1186/s13059-021-02576-9. Sun Z, Liu J, Zhang M, Wang T, Huang S, Weiss ST, Liu YY. Removal of false positives in metagenomics-based taxonomy profiling via targeting Type IIB restriction sites. Nat Commun. 2023;14(1):5321. DOI: 10.1038/s41467-023-41099-8. Song X, Huang Q, Yang Y, Ma L, Liu W, Ou C, et al. Efficient therapy of inflammatory bowel disease (IBD) with highly specific and durable targeted Ta 2 C modified with chondroitin sulfate (TACS). Adv Mater. 2023;35(36):e2301585. DOI: 10.1002/adma.202301585. Yang K, Zhang HY, Wang P, Jin GX, Chu D. Both symbionts and environmental factors contribute to shape the microbiota in a pest insect, Sogatella furcifera . Front Microbiol. 2024;14:1336345. DOI: 10.3389/fmicb.2023.1336345. eCollection 2023. Wang S, Meyer E, McKay J, Matz MV. 2b-RAD: a simple and flexible method for genome-wide genotyping. Nat. Methods. 2012;9:808–810. DOI: 10.1038/nmeth.2023 Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M, Weingart G, et al. Species-level function-al profiling of metagenomes and metatranscriptomes. Nat. Methods. 2018;15:962–968. DOI: 10.1038/s41592-018-0176-y Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335-336. DOI: 10.1038/nmeth.f.303 Ansari PG, Singh RK, Kaushik S, Krishna A, Wada T, Noda H. Detection of symbionts and virus in the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae), vector of the Mungbean yellow mosaic India virus in Central India. Appl Entomol Zool. 2017;52:567–579. DOI: 10.1007/s13355-017-0504-0 Hashmi TE, Devi SR, Meshram NM, Prasad R. Assessment of bacterial endosymbionts and the host, Bemisia tabaci (Hemiptera: Aleyrodidae), using rRNA and mitochondrial cytochrome oxidase I gene sequences. Commun Integr Biol. 2018;11(1):e1433442. DOI: 10.1080/19420889.2018.1433442 Kurata A, Fujiwara A, Haruyama N, et al. Multiplex PCR method for rapid identification of genetic group and symbiont infection status in Bemisia tabaci (Hemiptera: Aleyrodidae). Appl Entomol Zool. 2016;51:167-172. DOI: 10.1007/s13355-015-0402-8 Pan H, Li X, Ge D, Wang S, Wu Q, Xie W, Jiao X, Chu D, Liu B, Xu B, Zhang Y. Factors affecting population dynamics of maternally transmitted endosymbionts in Bemisia tabaci. PLoS ONE. 2012;7(2):e30760. DOI: 10.1371/journal.pone.0030760 Tang X, Cai L, Shen Y, Du Y. Diversity and evolution of the endosymbionts of Bemisia tabaci in China. PeerJ. 2018;6:e5516. DOI: 10.7717/peerj.5516 Bao XY, Yan JY, Yao YL, Wang YB, Visendi P, Seal SE, Luan JB. Lysine provisioning by horizontally acquired genes promotes mutual dependence between whitefly and two intracellular symbionts. PLoS Pathog. 2021;17(11):e1010120. DOI: 10.1371/journal.ppat.1010120 Jiang ZF, Xia F, Johnson KW, Brown CD, Bartom E, Tuteja JH, et al. Comparison of the genome sequences of " Candidatus Portiera aleyrodidarum" primary endosymbionts of the whitefly Bemisia tabaci B and Q biotypes. Appl Environ Microbiol. 2013;79(5):1757-1759. DOI: 10.1128/AEM.03537-12 Jiang ZF, Xia F, Johnson KW, Bartom E, Tuteja JH, Stevens R, et al. Genome sequences of the primary endosymbiont " Candidatus Portiera aleyrodidarum" in the whitefly Bemisia tabaci B and Q biotypes. J Bacteriol. 2012;194(23):6678-6679. DOI: 10.1128/JB.01708-12 Li NN, Jiang S, Lu KY, Hong JS, Wang YB, Yan JY, Luan JB. Bacteriocyte development is sexually differentiated in Bemesia tabaci. Cell Rep. 2022;38(9):110455. DOI: 10.1016/j.celrep.2022.110455 Wang YB, Ren FR, Yao YL, Sun X, Walling LL, Li NN, Bai B, Bao XY, Xu XR, Luan JB. Intracellular symbionts drive sex ratio in the whitefly by facilitating fertilization and provisioning of B vitamins. ISME J. 2020;14(12):2923-2935. doi: 10.1038/s41396-020-0717-0. Evans JD, Armstrong TN. Antagonistic interactions between honey bee bacterial symbionts and implications for disease. BMC Ecol. 2006;6(1):4. DOI: 10.1186/1472-6785-6-4 Fromont C, Adair KL, Douglas AE. Correlation and causation between the microbiome, Wolbachia and host functional traits in natural populations of drosophilid flies. Mol Ecol. 2019;28(8):1826-1841. DOI: 10.1111/mec.15067 Zhu YX, Song YL, Zhang YK, Hoffman AA, Zhou JC, Sun JT, Hong XY. Incidence of facultative bacterial endosymbionts in spider mites associated with local environments and host plants. Appl Environ Microbiol. 2018;84(6):AEM.02546-17. DOI: 10.1128/AEM.02546-17 Wong AC, Chaston JM, Douglas AE. The inconstant gut microbiota of Drosophila species revealed by 16S rRNA gene analysis. ISME J. 2013;7:1922-1932. DOI: 10.1038/ismej.2013.86 Zhao DX, Hoffmann AA, Zhang ZC, Niu HT, Guo HF. Interactions between facultative symbionts Hamiltonella and Cardinium in Bemisia tabaci (Hemiptera: Aleyrodoidea): Cooperation or Conflict? J Econ Entomol. 2018;111(6):2660-2666. DOI: 10.1093/jee/toy243 Shi PQ, Wang L, Liu Y, An X, Chen XS, Ahmed MZ, Qiu BL, Sang W. Infection dynamics of endosymbionts reveal three novel localization patterns of Rickettsia during the development of whitefly Bemisia tabaci. FEMS Microbiol Ecol. 2018;94(11):fiy165. DOI: 10.1093/femsec/fiy165 Skaljac M, Zanic K, Ban SG, Kontsedalov S, Ghanim M. Co-infection and localization of secondary symbionts in two whitefly species. BMC Microbiol. 2012;12:142. DOI: 10.1186/1471-2180-12-142 Shan HW, Lu YH, Bing XL, Liu SS, Liu YQ. Differential responses of the whitefly Bemisia tabaci symbionts to unfavorable low and high temperatures. Microb Ecol. 2014;68:472-482. DOI: 10.1111/1574-6941.12296 Prosdocimi EM, Mapelli F, Gonella E, Borin S, Crotti E. Microbial ecology-based methods to characterize the bacterial communities of non-model insects. J Microbiol Methods. 2015;119:110-125. DOI: 10.1016/j.mimet.2015.10.008 Martinson VG, Douglas AE, Jaenike J. Community structure of the gut microbiota in sympatric species of wild Drosophila. Ecol Lett. 2017;20:629-639. DOI: 10.1111/ele.12760 Näpflin K, Schmid-Hempel P. Host effects on microbiota community assembly. J Anim Ecol. 2018;87:331-340. DOI: 10.1111/1365-2656.12764 Adair KL, Douglas AE. Making a microbiome: the many determinants of host-associated microbial community composition. Curr Opin Microbiol. 2017;35:23–29. DOI: 10.1016/j.mib.2016.11.002 Chevrette MG, Carlson CM, Ortega HE, Thomas C, Ananiev GE, Barns KJ, et al. The antimicrobial potential of Streptomyces from insect microbiomes. Nat Commun. 2019;10:516. DOI: 10.1038/s41467-019-08438-0 Moro CV, Tran FH, Raharimalala FN, Ravelonandro P, Mavingui P. Diversity of culturable bacteria including Pantoea in wild mosquito Aedes albopictus. BMC Microbiol. 2013;13:70. DOI: 10.1186/1471-2180-13-70 Zhu YX, Song ZR, Zhang YY, Hoffmann AA, Hong XY. Spider mites singly infected with either Wolbachia or Spiroplasma have reduced thermal tolerance. Front Microbiol. 2021;12:706321. DOI: 10.3389/fmicb.2021.706321 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4321283","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":295688386,"identity":"9cfbef52-c059-43fe-a1af-4822bb61873a","order_by":0,"name":"Kun Yang","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Yang","suffix":""},{"id":295688387,"identity":"6f208cd9-97c3-43cb-82cb-81ffa95d97d2","order_by":1,"name":"Yuxin Zhang","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Zhang","suffix":""},{"id":295688388,"identity":"ee748800-3838-4772-8bd2-48f2359b1f26","order_by":2,"name":"Yitong He","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yitong","middleName":"","lastName":"He","suffix":""},{"id":295688389,"identity":"015ecb66-94a0-46bf-912b-efce23f006c3","order_by":3,"name":"Hongran Li","email":"","orcid":"","institution":"Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hongran","middleName":"","lastName":"Li","suffix":""},{"id":295688390,"identity":"909e6bc3-8965-48c4-bc3e-2137fdcdbaf3","order_by":4,"name":"Jincheng Zhou","email":"","orcid":"","institution":"Shenyang Agricultural Universit","correspondingAuthor":false,"prefix":"","firstName":"Jincheng","middleName":"","lastName":"Zhou","suffix":""},{"id":295688391,"identity":"f0d7ef47-e41c-4bfd-b9d8-a765f87bd17a","order_by":5,"name":"Youjun Zhang","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Youjun","middleName":"","lastName":"Zhang","suffix":""},{"id":295688392,"identity":"f4d79d6e-cea8-4951-a02f-824bfe657d88","order_by":6,"name":"Dong Chu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACCcYGIHkAjBg+QMQMiNfCOIM4LWASooWZhxgt/LOb2x78qLmT2He89/BrmzK7xAb25m0SDDV3cFty52C7Yc+xZ4kzz5xLs845l5zYwHOsTILh2DOcWgwkEtskeBsOJ264kWNmnNvGnNggkWMG9OBhvFok/8K0WLbVJzbIvyGsRRpqi/FjxrbDQFt48GuRuAHUInPssPHMM2fMGHvOHTdu40krtkg4hlsL/4z0Z5Jvag7L9h3vMf7wo6xatp/98MYbH2pwa0EGbBIMbAwgxMCQQJQGYEx+gKgfBaNgFIyCUYAKAPczXNuhXl5nAAAAAElFTkSuQmCC","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Dong","middleName":"","lastName":"Chu","suffix":""}],"badges":[],"createdAt":"2024-04-25 03:40:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4321283/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4321283/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55712436,"identity":"134f9e11-f9c0-459d-a35d-5e256fe2b47f","added_by":"auto","created_at":"2024-05-02 06:46:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151171,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of \u003cem\u003eCardinium\u003c/em\u003e(left panel) and \u003cem\u003eRickettsia\u003c/em\u003e(right panel) by 2bRAD-M sequencing and collection sites (middle panel) of \u003cem\u003eBemisia\u003c/em\u003e \u003cem\u003etabaci\u003c/em\u003e MED across 23 geographical locations. As the symbiont infection data didnot follow a normal distribution, the difference in \u003cem\u003eCardinium\u003c/em\u003eand \u003cem\u003eRickettsia\u003c/em\u003e abundance in various \u003cem\u003eB. tabaci\u003c/em\u003e MED populations was analyzed by Kruskal–Wallis test and Dunn’s test with Bonferroni correction for multiple comparisons. Different letters indicatesignificant difference.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4321283/v1/f02065e35d7e8633437468c7.png"},{"id":55711916,"identity":"7f5fb7ec-9e42-44d5-bb53-d63a9150ad36","added_by":"auto","created_at":"2024-05-02 06:38:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":166295,"visible":true,"origin":"","legend":"\u003cp\u003eThe abundance of microbial communities in 49 \u003cem\u003eBemisia\u003c/em\u003e \u003cem\u003etabaci\u003c/em\u003e MED populations collected in 23 locations, based on 2bRAD-M sequencing. The relative abundance of the top 6 abundant bacteria at the genus level is shown indifferent countries and continents. The relative abundance of bacteria in whiteflies from23 locations (A), in South Korea area (B), Jiangsu Province, China (C), Jinan, China (D), United States (E), Spain (F), Dezhou, China (G), Liaocheng, China (H), Shouguang, China (I) and Zaozhuang, China (J) are shown.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4321283/v1/09342263df9832aeab53f83a.png"},{"id":55711921,"identity":"378343b8-25af-470e-956b-43a7164cf3b0","added_by":"auto","created_at":"2024-05-02 06:38:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":419396,"visible":true,"origin":"","legend":"\u003cp\u003eα-diversities of all 24 \u003cem\u003eBemisia\u003c/em\u003e \u003cem\u003etabaci\u003c/em\u003e MED populations collected globally. The Chao1 index (\u003cstrong\u003eA\u003c/strong\u003e), Simpson index (\u003cstrong\u003eB\u003c/strong\u003e) and Shannon index (\u003cstrong\u003eC\u003c/strong\u003e) are presented based on 2bRAD-M sequencing results. As the diversity data did not follow a normal distribution, they were analyzed by Kruskal–Wallis test and Dunn’s test with Bonferroni correction for multiple comparisons.Different letters indicate significant difference.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4321283/v1/c099f8b6ab9e8dedc56f7512.png"},{"id":55711923,"identity":"0588b2f9-d27d-4a88-b940-c5ddbff9786d","added_by":"auto","created_at":"2024-05-02 06:38:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":226880,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between the \u003cem\u003eB. tabaci\u003c/em\u003e diversity indexes and symbiont logit transformed abundance. The relationship between \u003cem\u003eCardinium\u003c/em\u003e and Shannon index (\u003cstrong\u003eA\u003c/strong\u003e), Simpson index (\u003cstrong\u003eB\u003c/strong\u003e) and Chao1 index (\u003cstrong\u003eC\u003c/strong\u003e), the relationship between \u003cem\u003ePortiera\u003c/em\u003e and Shannon index (\u003cstrong\u003eD\u003c/strong\u003e), Simpson index (\u003cstrong\u003eE\u003c/strong\u003e) and Chao1 index (\u003cstrong\u003eF\u003c/strong\u003e), the relationship between \u003cem\u003eHamiltonella \u003c/em\u003eand Shannon index (\u003cstrong\u003eG\u003c/strong\u003e), Simpson index (\u003cstrong\u003eH\u003c/strong\u003e) and Chao1 index (\u003cstrong\u003eI\u003c/strong\u003e), the relationship between \u003cem\u003eRickettsia\u003c/em\u003eand Shannon index (\u003cstrong\u003eJ\u003c/strong\u003e), Simpson index (\u003cstrong\u003eK\u003c/strong\u003e) and Chao1 index (\u003cstrong\u003eL\u003c/strong\u003e), respectively. The data were from all 49 \u003cem\u003eBemisia\u003c/em\u003e \u003cem\u003etabaci\u003c/em\u003e MED populations collected from 23 geographical locations and analysed using Spearman correlation, based on the 2bRAD-M sequencing results. r values and \u003cem\u003eP\u003c/em\u003e values of each linear regression plots are provided.\u003c/p\u003e","description":"","filename":"FIgure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4321283/v1/c385a98f15e112bf08ecf42c.png"},{"id":55712438,"identity":"4649b0ad-d3fa-493c-90e9-0c3741c8d118","added_by":"auto","created_at":"2024-05-02 06:46:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":122722,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between the proportions of \u003cem\u003eHamiltonella\u003c/em\u003e and the proportions of \u003cem\u003eRickettsia\u003c/em\u003e (\u003cstrong\u003eA\u003c/strong\u003e) or the proportions of \u003cem\u003eWolbachia\u003c/em\u003e (\u003cstrong\u003eB\u003c/strong\u003e), and the relationship between the proportions of \u003cem\u003ePortiera\u003c/em\u003e and \u003cem\u003eHamiltonella\u003c/em\u003e (\u003cstrong\u003eC\u003c/strong\u003e) or \u003cem\u003eRickettsia\u003c/em\u003e (D) among all 49 \u003cem\u003eBemisia\u003c/em\u003e \u003cem\u003etabaci\u003c/em\u003e MED populations collected from 23 geographical locations and analysed using Pearson correlation, based on 2bRAD-M sequencing results. R\u003csup\u003e2\u003c/sup\u003e values and \u003cem\u003eP\u003c/em\u003e values of each linear regression plots are provided.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4321283/v1/0b7ecc7e3108d03f3261bb3e.png"},{"id":55712437,"identity":"cd5e0f41-9dcc-4e2c-8f54-0dd103815adb","added_by":"auto","created_at":"2024-05-02 06:46:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76465,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between the proportions of \u003cem\u003eRickettsia\u003c/em\u003e and the annual mean temperature (A), and the relationship between annual precipitation and the relative abundance of \u003cem\u003eArsenophonus\u003c/em\u003e (B) or Chao1 index (C) among all 49 \u003cem\u003eBemisia\u003c/em\u003e \u003cem\u003etabaci\u003c/em\u003e MED populations collected from 23 geographical locations. Data was analysed using Pearson correlation, based on 2bRAD-M sequencing results. R\u003csup\u003e2\u003c/sup\u003e values and \u003cem\u003eP\u003c/em\u003e values of each linear regression plots are provided.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4321283/v1/b7090995dff61a8ea5840522.png"},{"id":55711922,"identity":"1329e4f8-f8d8-486e-aecf-1dfde3a093fb","added_by":"auto","created_at":"2024-05-02 06:38:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":89534,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between the longitude and the relative abundance of \u003cem\u003ePortiera\u003c/em\u003e (A) and \u003cem\u003eRickettsia\u003c/em\u003e (B) among all 49 \u003cem\u003eBemisia\u003c/em\u003e \u003cem\u003etabaci\u003c/em\u003e MED populations collected from 23 geographical locations. Data was analysed using Pearson correlation, based on 2bRAD-M sequencing results. R\u003csup\u003e2\u003c/sup\u003e values and \u003cem\u003eP\u003c/em\u003e values of each linear regression plots are provided.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4321283/v1/fa6248905fc4a6a8ad2dd580.png"},{"id":55711920,"identity":"11c39b87-81d0-4099-bbc9-b908fee45dae","added_by":"auto","created_at":"2024-05-02 06:38:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":90977,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between the longitude and the Shannon diversity index (A) and Simpson index (B) among all 49 \u003cem\u003eBemisia\u003c/em\u003e \u003cem\u003etabaci\u003c/em\u003e MED populations collected from 23 geographical locations. Data was analysed using Pearson correlation, based on 2bRAD-M sequencing results. R\u003csup\u003e2\u003c/sup\u003e values and \u003cem\u003eP\u003c/em\u003e values of each linear regression plots are provided.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4321283/v1/258a1514d1f9bfe0c9a30a03.png"},{"id":55711926,"identity":"1dbdc846-f2bc-4989-bfdc-128857d5fe2a","added_by":"auto","created_at":"2024-05-02 06:38:52","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":341482,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram of the structural equation model (SEM) for environmental factors (including annual mean temperature, mean temperature of warmest quarter, annual precipitation, longitude and latitude) and symbionts’ abundance (including \u003cem\u003ePortiera\u003c/em\u003e, \u003cem\u003eRickettsia\u003c/em\u003e, \u003cem\u003eHamiltonella\u003c/em\u003e, \u003cem\u003eCardinium\u003c/em\u003e, \u003cem\u003eWolbachia\u003c/em\u003e and \u003cem\u003eArsenophonus\u003c/em\u003e). Statistically significant negative paths are indicated by blue arrows, while positive paths are indicated by red arrows. The R\u003csup\u003e2\u003c/sup\u003e values in each box indicate the amount of variation in that variable explained by the input arrows. Numbers next to arrows are unstandardized slopes.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4321283/v1/4036ea7c31b2ff5dc48d5418.png"},{"id":55711917,"identity":"f8ec68d8-810e-471e-8d7f-f6268accdfdc","added_by":"auto","created_at":"2024-05-02 06:38:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":242998,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram of the structural equation model (SEM) for environmental factors (including annual mean temperature, mean temperature of warmest quarter, annual precipitation, longitude and latitude) and diversity indexes (including Shannon index and Simpson index). Statistically significant negative paths are indicated by blue arrows, while positive paths are indicated by red arrows. The R\u003csup\u003e2\u003c/sup\u003e values in each box indicate the amount of variation in that variable explained by the input arrows. Numbers next to arrows are unstandardized slopes.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4321283/v1/8682f7a79dd1da8e032c9894.png"},{"id":57012505,"identity":"c6fddd7c-8bea-4200-909d-b4ab0dbd2448","added_by":"auto","created_at":"2024-05-23 11:48:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2594482,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4321283/v1/70ac8869-9aaa-4db4-b572-6afc32e1c9aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Species-resolved metagenomics reveal ecological effects on the microbiota in a global pest, the whitefly, using 2bRAD-M","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eBemisia tabaci\u003c/em\u003e (Gennadius) (Hemiptera: Aleyrodidae) is notorious for its destruction to host plants and extremely rapid invasive capacity which has enabled its colonization in almost every part of the world including temperate, sub-tropical and tropical locations [1,2]. It is a pecies complex, with at least 44 cryptic species identified up to date, which have a wide range of host plants [3,4,5]. More importantly, \u003cem\u003eB. tabaci\u003c/em\u003e is the main insect vector of many begomoviruses which cause enormous damages to field crops [6,7]. \u003cem\u003eB. tabaci\u003c/em\u003e MED is considered one of the most invasive species and widespread globally including in countries such as China, USA, and many Mediterranean countries [3,8,9]. Especially in China, the \u003cem\u003eB. tabaci\u003c/em\u003e MED has replaced \u003cem\u003eB. tabaci\u003c/em\u003e MEAM1 to be the dominant cryptic species in the past few years [10,11].\u003c/p\u003e \u003cp\u003eBacterial communities have vital influence on the development, health, and biological processes of insect hosts. For example, many bacteria (especially endosymbionts) provide hosts with nutrients including vitamins and essential amino acids, as well as protect their hosts from adverse ecological factors including heat shock, predators and pesticides [9,12,13,14,15,16].\u003c/p\u003e \u003cp\u003eBiotic and abiotic factors such as diet, shape the structure of bacterial communities in insects [17,18,19,20,21,22,23,24]. Also, high temperatures significantly influence the diversity and abundance of symbionts in whiteflies [24] and host genotype and \u003cem\u003eWolbachia\u003c/em\u003e infection affect the densities of other \u003cem\u003eWolbachia\u003c/em\u003e strains [21]. Further, the temporal and spatial distributions of hosts influence the bacterial communities within them [25].\u003c/p\u003e \u003cp\u003eEndosymbionts are widespread in insects and always have vital influence on hosts. They are vertically heritable bacteria which mostly reside in the bacteriocytes of insect hosts, especially primary endosymbionts [26,27,28,29]. In \u003cem\u003eB. tabaci\u003c/em\u003e, there is one primary endosymbionts \u003cem\u003eCandidatus\u003c/em\u003e Portiera aleyrodidarum and 6 other secondary endosymbionts, which include \u003cem\u003eRickettsia\u003c/em\u003e, \u003cem\u003eCardinium\u003c/em\u003e, \u003cem\u003eFritschea\u003c/em\u003e, \u003cem\u003eWolbachia\u003c/em\u003e, \u003cem\u003eHamiltonella\u003c/em\u003e and \u003cem\u003eArsenophonus\u003c/em\u003e [4,9,30,31]. They are both important for host whiteflies. For instance, \u003cem\u003ePortiera\u003c/em\u003e and \u003cem\u003eHamiltonella\u003c/em\u003e cooperate to provide host \u003cem\u003eB. tabaci\u003c/em\u003e MED with essential nutrition including co-factors and vitamins [32]. \u003cem\u003eCardinium\u003c/em\u003e infection significantly affects the fitness, fecundity, thermotolerance and microRNA expression in host \u003cem\u003eB. tabaci\u003c/em\u003e [9,33,34,35,36]. \u003cem\u003eRickettsia\u003c/em\u003e also enhances the thermotolerance [37] and provisions of host whitefly with nutrition and resistance [38], as well as alter the defense pattern of host plants [39]. However, the interactions among different symbionts in \u003cem\u003eB. tabaci\u003c/em\u003e remain poorly explored.\u003c/p\u003e \u003cp\u003eThe presence of endosymbionts in insects can significantly change the structure of bacterial communities in insect hosts [40,41,42]. Also, the existence and abundance of symbionts in insects can be significantly influenced by abiotic factors [43,44,45]. Additonally, the influence of environment on symbionts may contribute to changes in the microbiota structure in host insects [46]. Further, more extreme high temperatures in the future [47,48], will have impacts on insects and their bacterial communities [49]. However, related research in whiteflies are limited, and the relationship among microbial communities and symbionts in whiteflies and ecological factors require clarification.\u003c/p\u003e \u003cp\u003e2bRAD-M is a novel metagenome sequencing method especially for low-biomass or degraded microbiomes, which accurately generate species level taxonomic profiles of samples [50], which has been widely applied in medical and agricultural research [51,52,53;54]. Herein, 2bRAD-M sequencing was applied to 49 \u003cem\u003eB. tabaci\u003c/em\u003e MED populations collected from 23 locations spread globally, to reveal the influence of ecological factors as well as the symbionts on the microbiota in \u003cem\u003eB. tabaci.\u003c/em\u003e The 3 main purposes were: 1) clarifying the structure and constients of different \u003cem\u003eB. tabaci\u003c/em\u003e MED populations from various geographical areas; 2) exploring the relationships among different symbionts and the influence of disparate symbionts on microbiota diversities in whiteflies; 3) illustrating the effects of ecological factors on bacterial communities as well as symbiont abundance in \u003cem\u003eB. tabaci\u003c/em\u003e MED. The results showed a significant influence of ecological factors including abiotic factors on microbial communities and strong effects of symbionts on the bacterial diversity in \u003cem\u003eB. tabaci\u003c/em\u003e MED.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003eGlobal collection of\u003c/b\u003e \u003cb\u003eB. tabaci\u003c/b\u003e \u003cb\u003eMED populations\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe collected 49 populations of \u003cem\u003eB. tabaci\u003c/em\u003e MED on different host plants, from 23 locations in eight countries spanning Asia, Europe, and North America, from 2006 to 2018. The whitefly samples were placed in alcohol after collection and transported to the laboratory. Identification of \u003cem\u003eB. tabaci\u003c/em\u003e and \u003cem\u003eCardinium\u003c/em\u003e in whiteflies were performed using conventional PCR following previously described methods [9]. Female adults were used in all experiments, with the detailed information provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. One replicate means one \u003cem\u003eB. tabaci\u003c/em\u003e female adult.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCollection information of \u003cem\u003eBemisia tabaci\u003c/em\u003e MED populations used in this experiment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCollected location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCollected year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHost plant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReplicates\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eKr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGyeonggi, Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecucumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKr2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouth Chungcheong Province, Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecucumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJeollabuk-do, Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003esweet pepper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKr4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJeollanam-do, Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecucumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKr5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouth Gyeongsang Province, Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecucumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorth Gyeongsang Province, Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emuskmelon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuzhou, Fujian Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNanchang, Jiangxi Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHefei, Anhui Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eJS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNanjing, Jiangsu Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNanjing, Jiangsu Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHangzhou, Zhejiang Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003egynura\u0026nbsp;bicolor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGuangzhou, Guangdong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTonghua, Jilin Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecucumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eJN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJinan, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJinan, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJinan, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJinan, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLingshui, Hainan Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003etomato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeijing, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWulumuqi, Xinjiang Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWuhan, Hubei Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLowes, Tucson, Arizona, USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epoinsettia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKmart, Tucson, Arizona, USA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epoinsettia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrader Joe\u0026rsquo;s, Tucson, Arizona\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epoinsettia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003cp\u003eName\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003cp\u003eName\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCollected location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCollected year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHost plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReplicates\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWalmart#2, Tucson, Arizona\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epoinsettia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eESP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMadrid, Spain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003etomato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMadrid, Spain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003etomato\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMadrid, Spain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCroatia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epoinsettia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBosnia and Herzegovina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCyprus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsrael\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDZ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDezhou, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDZ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDezhou, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDZ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDezhou, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDZ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDezhou, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiaocheng, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiaocheng, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiaocheng, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiaocheng, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShouguang, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShouguang, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShouguang, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShouguang, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eZZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZZ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZaozhuang, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZZ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZaozhuang, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZZ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZaozhuang, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZZ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZaozhuang, Shandong Province, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eA replicate means one female adult and all replicates of one population were collected in the same place within 1 hm\u003csup\u003e2\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and 2bRAD-M sequencing\u003c/h2\u003e \u003cp\u003e2bRAD-M sequencing methods were used to measure the bacterial communities in whiteflies. The 2bRAD-M sequencing and libraries constructions were performed by Qingdao OE Biotech Co., Ltd. (Qingdao, China). The library constructions followed previously published protocols [55] with minor modifications. Briefly, the whitefly DNA (100 ng) of each single sample digestion was performed with 4 U of the enzyme BcgI (NEB) under 37\u0026deg;C for 3 h. After that, the DNA fragments were all ligated with specific adaptors. Then, a mix of 5 \u0026micro;L digested DNA combined with 10 \u0026micro;L of ligation master mix containing 0.2 \u0026micro;M each of two adaptors and 800 U of T4 DNA ligase (NEB) was processed for ligation reaction. The next ligation reaction was performed under 4\u0026deg;C for 12 h.\u003c/p\u003e \u003cp\u003eSubsequently, the ligation products were PCR amplified; then, 8% polyacrylamide gel was used for electrophoresis. Based on the electrophoresis results, an approximately 100-bp band of the gel was excised, and the DNA fragments from the gel were then reconstiuted with DEPC water under 4\u0026deg;C for 12 h. PCR test was conducted with platform-specific barcode-bearing primers to introduce sample-specific barcodes. Each PCR sample was 20 \u0026micro;L, with a 25-ng gel-extracted PCR product, 0.2 \u0026micro;M each of forward and reverse primers, 0.3 mM dNTP mix, 0.4 U Phusion high-fidelity DNA polymerase (NEB), and 1\u0026times;Phusion HF buffer. After PCR amplification and electrophoresis, QIAquick PCR purification kit (50) (Qiagen) was used to purify the PCR products and the Illumina Nova PE150 platform was used for sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData analysis of 2bRAD-M sequencing results\u003c/h2\u003e \u003cp\u003eInformation from the NCBI database for microbial genomes (including 173,165 species of fungi, bacteria, and archaea) was used for 2bRAD-M analysis. The restriction enzymes of 16 type 2B were used to restrict the sample to fragments with built-in Perl scripts. RefSeq (GCF) number was subsequently used to assign the set of 2bRAD-M tags from all microbial genome information. Moreover, the whole genome\u0026rsquo;s corresponding GCF taxonomic information was also assigned. After that, every GCF\u0026rsquo;s 2bRAD tags that only occurred once were selected for comparison with those of all the other 2bRAD-M tags. The 2bRAD-M tags that were unique to a species were considered a species-specific 2bRAD-M marker, and those markers were used to form a reference database. A detection threshold of 0.0001 (0.01%) relative abundance was used as a default [56].\u003c/p\u003e \u003cp\u003eTo calculate the relative abundance of each bacterium, the microbial species within each sample were first identified. All sequenced 2bRAD tags after quality control were mapped (using a built-in Perl script) against the 2bRAD marker database which contains all 2bRAD tags theoretically unique to each of 26,163 microbial species in the database. To control the false-positive in the species identification, a G score was derived for each species identified within a sample as below, which is a harmonious mean of read coverage of 2bRAD markers belonging to a species and the number of all possible 2bRAD markers of this species. The threshold of the G score for a false positive discovery of microbial species was set to 5 [51].\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${\\text{G} \\text{s}\\text{c}\\text{o}\\text{r}\\text{e}}_{\\text{s}\\text{p}\\text{e}\\text{c}\\text{i}\\text{e}\\text{s} \\text{i}}=\\sqrt{{S }_{i}\\times {t}_{i}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eS\u003c/strong\u003e \u003cp\u003ethe number of reads assigned to all 2bRAD markers belonging to species i within a sample.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003et\u003c/strong\u003e \u003cp\u003enumber of all 2bRAD markers of species i that have been sequenced within a sample.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThen, the average read coverage of all 2bRAD markers for each species was calculated, which represent the number of individuals belonging to a species present in a sample at a given sequencing depth. The relative abundance of a given species is then calculated as the ratio of the number of microbial individuals belonging to a species against the total number of individuals from known species that can be detected within a sample.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${Relative abundance}_{species i}=\\frac{\\raisebox{1ex}{${S}_{i}$}\\!\\left/ \\!\\raisebox{-1ex}{${T}_{i}$}\\right.}{{\\sum }_{\\text{i}=1}^{\\text{n}}\\raisebox{1ex}{${S}_{i}$}\\!\\left/ \\!\\raisebox{-1ex}{${T}_{i}$}\\right.}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eS\u003c/strong\u003e \u003cp\u003ethe number of reads assigned to all 2bRAD markers of species i within a sample.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eT\u003c/strong\u003e \u003cp\u003ethe number of all theoretical 2bRAD markers of species i.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe diversity and abundance indexes, and infection of symboints as influenced by climate or geographical factors were described by two structural equations models (SEM) with Satorra-Bentler correction, respectively. To reduce heteroscedasticity, the log value of \u0026ldquo;precipitation\u0026rdquo; was used in SEMs. The SEM models was accepted when the \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 and CFI\u0026thinsp;\u0026gt;\u0026thinsp;0.95 after excluding the redundant paths step by step based on a lower AIC value. To exclude the variance of each parameter, the coefficients of SEM were estimated by the standardized transformation.\u003c/p\u003e \u003cp\u003eThe relative abundances of \u003cem\u003eCardinium\u003c/em\u003e and \u003cem\u003eRickettsia\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and microbial diversities of samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were all first tested for normality (Kolmogorov-Smirnov test) and homogeneity of group variances (Levene\u0026rsquo;s test). When the data followed normal distribution, they were analysed with a one-way ANOVA with post-hoc Tukey HSD analysis. Data which did not follow a normal distribution, were analyzed by a Kruskal-Wallis test and Dunn\u0026rsquo;s test with Bonferroni correction for multiple comparisons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo analyze the relationship between symbionts and microbiota diversities, all data of symbiont relative abundance (s) were logit transformed as follows (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equc\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$logit\\left(s\\right)=\\text{l}\\text{g}(\\text{s}+1)$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen analyzing the relationship between symbionts and microbiota diversities, the diversity indexes were recounted after removing the analyzed symbiont (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The vegan and picante packages in R were used to compute the α-diversity indexes of microbiota in \u003cem\u003eB. tabaci\u003c/em\u003e. QIIME software was used to generate the PCoA plots of Bray-Curtis intersample distances and the classification probabilities [57]. Spearman analysis was used for analyzing the relationship symbionts and microbiota diversity indexes. Pearson analysis was used for the relationship between symbiont and ecological factors as well as the relationship between different symbionts. SPSS 21.0 was used to carry out all statistical tests.\u003c/p\u003e \u003cp\u003eBoth \u003cem\u003eArsenophonus\u003c/em\u003e and \u003cem\u003eWolbachia\u003c/em\u003e in \u003cem\u003eB. tabaci\u003c/em\u003e had too many 0 values, therefore a categorical analysis was used to analyze the relationships between the 2 symbionts and other symbionts as well as ecological factors. All \u003cem\u003eB. tabaci\u003c/em\u003e populations were categorized into 2 groups with the \u003cem\u003eArsenophonus\u003c/em\u003e or \u003cem\u003eWolbachia\u003c/em\u003e abundance: one group with \u003cem\u003eArsenophonus\u003c/em\u003e or \u003cem\u003eWolbachia\u003c/em\u003e being present (abundance\u0026thinsp;\u0026gt;\u0026thinsp;0) and one group with \u003cem\u003eArsenophonus\u003c/em\u003e or \u003cem\u003eWolbachia\u003c/em\u003e being absent (abundance\u0026thinsp;=\u0026thinsp;0). Mann-Whitney \u003cem\u003eU\u003c/em\u003e-test was used to compare the symbiont abundance and diversity indexes between the 2 groups (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). All figures were made by GraphPad Prism 9.0.0. Analysis with generalized linear models was carried out in SPSS 21.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eCardinium\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eRickettsia\u003c/b\u003e \u003cb\u003eabundance in\u003c/b\u003e \u003cb\u003eBemisia tabaci\u003c/b\u003e \u003cb\u003eMED collected globally\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe 2bRAD-M sequencing results showed that \u003cem\u003eCardinium\u003c/em\u003e infection rate varied significantly among \u003cem\u003eB. tabaci\u003c/em\u003e MED collected from the different areas (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Kruskal\u0026ndash;Wallis test, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e left panel). Whiteflies collected in Israel (IL population) had no \u003cem\u003eCardinium\u003c/em\u003e (no sample\u0026rsquo;s relative abundance of \u003cem\u003eCardinium\u003c/em\u003e exceeded 0.01%). Whiteflies collected from South Korea (Kr) had a relative higher \u003cem\u003eCardinium\u003c/em\u003e abundance (21.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24%, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) compared to whiteflies collected in other areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, left panel). \u003cem\u003eRickettsia\u003c/em\u003e was abundant in \u003cem\u003eB. tabaci\u003c/em\u003e MED populations with low \u003cem\u003eCardinium\u003c/em\u003e infection such as IL (68.07\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57%, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM), CY (69.84\u0026thinsp;\u0026plusmn;\u0026thinsp;7.14%, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM), ZJ (48.21\u0026thinsp;\u0026plusmn;\u0026thinsp;13.19%, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) and FJ (43.76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.30%, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) populations, but was lower in \u003cem\u003eCardinium\u003c/em\u003e-abundant populations, such as in Kr population (relative abundance of \u003cem\u003eRickettsia\u003c/em\u003e lower than 0.01%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, right panel).\u003c/p\u003e \u003cp\u003eAnother interesting finding was that the relative abundance of \u003cem\u003eCardinium\u003c/em\u003e was higher in high-latitude populations compared to those in relatively low-latitude populations. In East Asia, high-latitude populations such as JL, XJ and Kr populations (more than 7.38%) had a relatively higher \u003cem\u003eCardinium\u003c/em\u003e abundance than low-latitude populations including FJ, GD, HN populations (lower than 4.16%). In Europe and the Mediterranean region, the relatively higher latitude populations including such as CR population also had higher \u003cem\u003eCardinium\u003c/em\u003e abundance (above 14.16%) than BH, CY and IL populations (lower 1.00%) which were collected in relatively low-latitude areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, left panel).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicrobial composition of different\u003c/b\u003e \u003cb\u003eBemisia tabaci\u003c/b\u003e \u003cb\u003eMED populations\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe microbial communities in each \u003cem\u003eB. tabaci\u003c/em\u003e MED population, was identified to the species level. One primary symbiont \u003cem\u003ePortiera aleyrodidarum\u003c/em\u003e and five secondary symbionts (\u003cem\u003eHamiltonella\u003c/em\u003e, \u003cem\u003eCardinium\u003c/em\u003e, \u003cem\u003eWolbachia\u003c/em\u003e, \u003cem\u003eRickettsia\u003c/em\u003e and \u003cem\u003eArsenophonus\u003c/em\u003e) were discovered in all 49 \u003cem\u003eB. tabaci\u003c/em\u003e MED populations. \u003cem\u003ePortiera aleyrodidarum\u003c/em\u003e was the most abundant bacteria in almost all geographical populations except for IL and CY populations (in which the abundance of \u003cem\u003eRickettsia\u003c/em\u003e was higher than \u003cem\u003ePortiera\u003c/em\u003e), with a relative abundance being above 28.82% (lowest in CY population) in all whitefly populations.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRickettsia\u003c/em\u003e was abundant in the CY population (69.84% of all bacteria) and IL population (68.07%). However, no sequences of \u003cem\u003eRickettsia\u003c/em\u003e were identified in the AH, JX, HuB, XJ and all South Korea populations. The \u003cem\u003eRickettsia\u003c/em\u003e species identified in the samples included \u003cem\u003eRickettsia sp. Strain MEAM1\u003c/em\u003e, \u003cem\u003eRickettsia hoogstraalii\u003c/em\u003e, \u003cem\u003eRickettsia felis\u003c/em\u003e and \u003cem\u003eRickettsia bellii.\u003c/em\u003e Of these, \u003cem\u003eRickettsia sp. Strain MEAM1\u003c/em\u003e was the dominant species in all \u003cem\u003eRickettsia\u003c/em\u003e-infected samples. \u003cem\u003eRickettsia bellii\u003c/em\u003e showed a lower infection rate in ZZ, JN, HN and USA populations (relative abundance lower than 0.1\u0026permil;). Likewise, \u003cem\u003eRickettsia hoogstraalii\u003c/em\u003e showed a lower infection rate in SG, HN, JL, CR, CY populations (relative abundance lower than 0.1\u0026permil;). \u003cem\u003eRickettsia felis\u003c/em\u003e (relative abundance 1.14\u0026permil;) was identified only in the BH population.\u003c/p\u003e \u003cp\u003eInterestingly, in all populations which had a high \u003cem\u003eRickettsia\u003c/em\u003e abundance, showed lower \u003cem\u003eCardinium\u003c/em\u003e abundance. On the other hand, \u003cem\u003eCardinium\u003c/em\u003e-abundant populations (such as the Korea populations) showed no or less abundant \u003cem\u003eRickettsia\u003c/em\u003e, which imply a potential antagonistic interaction between \u003cem\u003eCardinium\u003c/em\u003e and \u003cem\u003eRickettsia\u003c/em\u003e in \u003cem\u003eB. tabaci\u003c/em\u003e MED.\u003c/p\u003e \u003cp\u003eFour \u003cem\u003eCardinium\u003c/em\u003e species were identified in this study (\u003cem\u003eCardinium cBtQ, Cardinium cEper1, Cardinium endosymbiont of Culicoides punctatus\u003c/em\u003e and \u003cem\u003eCardinium cSfur\u003c/em\u003e). Of these, \u003cem\u003eCardinium cBtQ\u003c/em\u003e was the dominant species in all \u003cem\u003eCardinium\u003c/em\u003e-infected samples. \u003cem\u003eCardinium cEper1\u003c/em\u003e had a lower infection rate (relative abundance lower than 0.1\u0026permil;), in the JS, Kr2, Kr5, ZZ, LC, AH and JL populations. Likewise, \u003cem\u003eCardinium cSfur\u003c/em\u003e had a lower infection rate in the GD, SG, SPA3 and LC populations, while \u003cem\u003eCardinium endosymbiont of Culicoides punctatus\u003c/em\u003e was only detected in the Kr1 population.\u003c/p\u003e \u003cp\u003eThe results showed that all whitefly populations were infected with \u003cem\u003eHamiltonella defensa\u003c/em\u003e, with a relative abundance ranging from 0.02% (lowest in the JX population) to 18.37% (highest in the HuB population).\u003c/p\u003e \u003cp\u003eSeven \u003cem\u003eWolbachia\u003c/em\u003e species were identified from the whitefly samples (\u003cem\u003eWolbachia endosymbiont of Bemisia tabaci, Wolbachia endosymbiont of Cylisticus convexus, Wolbachia endosymbiont of Diaphorina citri, Wolbachia endosymbiont of Folsomia candida, Wolbachia endosymbiont of Laodelphax striatellus, Wolbachia endosymbiont of Leptopilina clavipes, Wolbachia endosymbiont of Nilaparvata lugens, Wolbachia pipientis wAlbB\u003c/em\u003e). Of these, \u003cem\u003eWolbachia endosymbiont of Bemisia tabaci\u003c/em\u003e was the dominant in all \u003cem\u003eWolbachia\u003c/em\u003e-infected samples. However, its abundance was relatively low (below 5.94%) in all samples, except for the JS2 population, which had a relative abundance of 21.12% of all bacterial communities. The relative abundance of the other \u003cem\u003eWolbachia\u003c/em\u003e species was much lower (relative abundance lower than 0.1\u0026permil;) in all samples. In some East Asia populations including BJ, HN, Kr1, Kr2 and Kr3 populations, no \u003cem\u003eWolbachia\u003c/em\u003e was detected.\u003c/p\u003e \u003cp\u003e \u003cem\u003eArsenophonus\u003c/em\u003e was detected in several populations (including AH, JX, BH, CR, CY and IL populations) with a low-abundance rate below 2.8% (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Four \u003cem\u003eArsenophonus\u003c/em\u003e species was identified from all samples in this study (\u003cem\u003eArsenophonus endosymbiont of Bemisia tabaci Asia II3\u003c/em\u003e, \u003cem\u003eArsenophonus endosymbiont of Aleurodicus floccissimus\u003c/em\u003e, \u003cem\u003eArsenophonus endosymbiont str Hangzhou of Nilaparvata lugens\u003c/em\u003e, \u003cem\u003eArsenophonus nasoniae\u003c/em\u003e). Of these, \u003cem\u003eArsenophonus endosymbiont of Bemisia tabaci Asia II3\u003c/em\u003e was the dominant in all \u003cem\u003eArsenophonus\u003c/em\u003e-infected samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBacterial diversity in geographically different\u003c/b\u003e \u003cb\u003eBemisia tabaci\u003c/b\u003e \u003cb\u003eMED populations\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThree α-diversity indexes (including Chao1, Simpson and Shannon indexes) were applied to determine the diversities of the bacterial communities in each whitefly population. The diversity indexes were all analyzed with the Kruskal\u0026ndash;Wallis test and Dunn\u0026rsquo;s test with Bonferroni correction for multiple comparisons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), as none followed a normal distribution. The Shannon and Simpson index analysis showed a similar result for the community diversity index for all whitefly populations. Results showed that the JL population had the highest Simpson index (0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) and a relatively high Shannon index (0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM), while the CR population had the highest Shannon index (1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) and a relatively high Simpson index (0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM). On the other hand, the JX population had the lowest Shannon index (0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) and the lowest Simpson index (0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Results of the Chao1 diversity index showed that the AH population had the highest bacterial community richness (60.40\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM), while the BJ population had the lowest Chao1 index (17.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Interestingly, the α-diversity indexes of \u003cem\u003eCardinium\u003c/em\u003e abundant populations (such as JL, CR, USA and Kr populations) were higher than diversity indexes of \u003cem\u003eCardinium\u003c/em\u003e limited populations (such as JX, BJ, IL and HuB populations) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationship between symbionts and bacterial diversities in\u003c/b\u003e \u003cb\u003eBemisia tabaci\u003c/b\u003e \u003cb\u003eMED\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe correlations between the relative abundance of symbionts and the α-diversity indexes (including Shannon, Chao1 and Simpson indexes) of microbial communities in whitefly, were analyzed using Spearman correlation. The microbiota diversity indexes were computed after removing the compared symbiont in this experiment. The results revealed that \u003cem\u003eCardinium\u003c/em\u003e abundance significantly influenced the \u003cem\u003eB. tabaci\u003c/em\u003e microbiota Simpson diversity index negatively (r = -0.449, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). \u003cem\u003eHamiltonella\u003c/em\u003e had a significant negative effect on both the Shannon diversity (r = -0.498, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and Simpson diversity (r = -0.498, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) of host whitefly (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eG and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eH), while \u003cem\u003ePortiera\u003c/em\u003e and \u003cem\u003eRickettsia\u003c/em\u003e did not significantly influence the diversity indexes of bacterial communities in \u003cem\u003eB. tabaci\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Categorical analysis showed that both \u003cem\u003eWolbachia\u003c/em\u003e and \u003cem\u003eArsenophonus\u003c/em\u003e did not significantly affect the diversity of host whitefly (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationships among various symbionts in\u003c/b\u003e \u003cb\u003eBemisia tabaci\u003c/b\u003e \u003cb\u003eMED\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePearson correlation was applied to clarify the complex relationships among the different symbionts in \u003cem\u003eB. tabaci\u003c/em\u003e MED. Results showed that the relative abundance of \u003cem\u003eHamiltonella\u003c/em\u003e significantly negatively correlated with the relative abundance of \u003cem\u003eRickettsia\u003c/em\u003e (r = -0.403, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u003cem\u003eWolbachia\u003c/em\u003e (r = -0.351, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (5ig. 4A and 5B). The relative abundance of \u003cem\u003ePortiera\u003c/em\u003e had a significant positive correlation with the relative abundance of \u003cem\u003eHamiltonella\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.296, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but a significant negative correlation with the relative abundance of \u003cem\u003eRickettsia\u003c/em\u003e (r = -0.886, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Results from the generalized linear models revealed a significant correlation between \u003cem\u003eRickettsia\u003c/em\u003e and \u003cem\u003eHamiltonella\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Categorical analysis showed that \u003cem\u003eWolbachia\u003c/em\u003e and \u003cem\u003eArsenophonus\u003c/em\u003e significantly affected the abundance of each other positively (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE and S2E).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized linear models of symbionts, bacterial diversities and environmental factors in \u003cem\u003eBemisia tabaci\u003c/em\u003e MED.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald Chi-Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRickettsia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHamiltonella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cem\u003ePortiera\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.000014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.398716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY*bio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cem\u003eRickettsia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.000014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.398716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY*bio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHamiltonella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY*bio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX*bio12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY*bio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY*bio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: \u0026ldquo;X\u0026rdquo; means \u0026ldquo;longitude\u0026rdquo;, \u0026ldquo;Y\u0026rdquo; means \u0026ldquo;latitude\u0026rdquo;.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInfluence of ecological factors on the microbial communities in global\u003c/b\u003e \u003cb\u003eBemisia tabaci\u003c/b\u003e \u003cb\u003eMED populations\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo understand whether and to what extent ecological factors shaped the microbial communities across the \u003cem\u003eB. tabaci\u003c/em\u003e MED populations, Pearson analysis and structural equation model (SEM) were used to resolve the relationships between microbial community structure (including symbionts and α-diversity indexes) and 5 putative predictor variables including annual mean temperature, mean temperature of warmest quarter, annual precipitation, latitude, and longitude. The diversity and abundance indexes of symbionts as influenced by climate or geographical factors were described by a structural equations model (SEM). Results showed that the annual mean temperature significantly and positively influenced the relative abundance of \u003cem\u003eRickettsia\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.326, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), while annual precipitation significantly and positively influenced the \u003cem\u003eArsenophonus\u003c/em\u003e abundance (r\u0026thinsp;=\u0026thinsp;0.325, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the Chao1 index (r\u0026thinsp;=\u0026thinsp;0.405, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) in global whitefly populations. Generalized linear models also revealed a significantly influence of ecological factors on the abundance of symbionts \u003cem\u003ePortiera\u003c/em\u003e, \u003cem\u003eRickettsia\u003c/em\u003e and \u003cem\u003eHamiltonella\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relationships between the microbial communities of \u003cem\u003eB. tabaci\u003c/em\u003e MED and longitude were also explored. Results showed that longitude positively influenced the abundance of \u003cem\u003ePortiera\u003c/em\u003e (r\u0026thinsp;=\u0026thinsp;0.499, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but negatively influenced the abundance of \u003cem\u003eRickettsia\u003c/em\u003e (r = -0.411, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Also, longitude negatively influenced the Shannon (r = -0.402, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and Simpson indexes (r = -0.321, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe SEM analysis was used to clarify the complex interactions among symbionts, bacterial communities and ecological factors. The SEM models were simplified based on the lowest value of Akaike Information Criterion (AIC). The simplified models were not rejected based on the \u003cem\u003eP\u003c/em\u003e value of chi-square test\u0026thinsp;\u0026gt;\u0026thinsp;0.05, comparative Index (CFI)\u0026thinsp;\u0026gt;\u0026thinsp;0.90, standardized root mean square residual (SRMR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. For the relationships among different symbionts and ecological factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Results showed that longitude positively influenced \u003cem\u003ePortiera\u003c/em\u003e abundance (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, z\u0026thinsp;=\u0026thinsp;2.04, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but negatively influenced \u003cem\u003eRickettsia\u003c/em\u003e abundance (\u003cem\u003er\u003c/em\u003e = -0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, z\u0026thinsp;=\u0026thinsp;3.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Further, longitude positively influenced \u003cem\u003eHamiltonella\u003c/em\u003e abundance but negatively influenced \u003cem\u003eWolbachia\u003c/em\u003e and \u003cem\u003eArsenophonus\u003c/em\u003e abundances. Latitude positively influenced both the abundance of \u003cem\u003eCardinium\u003c/em\u003e and \u003cem\u003eHamiltonella\u003c/em\u003e, but negatively influenced \u003cem\u003eRickettsia\u003c/em\u003e abundance. Annual mean temperature negatively affected \u003cem\u003ePortiera\u003c/em\u003e abundance and positively affected \u003cem\u003eHamiltonella\u003c/em\u003e abundance, but mean temperature did not significantly influence \u003cem\u003eRickettsia\u003c/em\u003e abundance. The mean temperature of warmest quarter negatively influenced \u003cem\u003eHamiltonella\u003c/em\u003e abundance and \u003cem\u003eWolbachia\u003c/em\u003e abundance, but positively influenced \u003cem\u003ePortiera\u003c/em\u003e abundance. Annual precipitation positively influenced \u003cem\u003eArsenophonus\u003c/em\u003e abundance and positively influenced \u003cem\u003eWolbachia\u003c/em\u003e abundance, but negatively affected \u003cem\u003eHamiltonella\u003c/em\u003e abundance. Also, SEM analysis indicated a negative correlation between \u003cem\u003ePortiera\u003c/em\u003e and \u003cem\u003eRickettsia\u003c/em\u003e, as well as a negative correlation between \u003cem\u003eHamiltonella\u003c/em\u003e and \u003cem\u003eWolbachia\u003c/em\u003e. Additionally, SEM analysis also showed a negative correlation between \u003cem\u003eRickettsia\u003c/em\u003e and \u003cem\u003eCardinium\u003c/em\u003e abundance (\u003cem\u003er\u003c/em\u003e = -0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, z\u0026thinsp;=\u0026thinsp;2.36, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe relationships between the diversities of microbial communities and ecological factors were also explored by SEM (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Results showed that longitude negatively influenced the Shannon index, while latitude positively influenced both the Shannon and Chao1 indexes. Annual precipitation also positively influenced the Chao1 index, which result was similar to the Pearson analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), and also positively influenced the Shannon index. Annual temperature and mean temperature positively influenced both the Shannon and Simpson indexes, while the mean temperature of warmest quarter negatively influenced both the Shannon and Simpson indexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe collected 49 \u003cem\u003eB. tabaci\u003c/em\u003e MED populations from 23 locations in Asia, Europe and USA, using techniques and experiments including 2bRAD-M sequencing, to explore the relationships among the microbial communities including symbionts in whiteflies and ecological factors. The results revealed that the structures of microbial communities in different \u003cem\u003eB. tabaci\u003c/em\u003e MED populations were significantly different, and their diversities were significantly negatively affected by the relative abundance of \u003cem\u003eCardinium\u003c/em\u003e and \u003cem\u003eHamiltonella\u003c/em\u003e. The abundance of symbionts influenced one other, and the effect was always negative, except for \u003cem\u003ePortiera\u003c/em\u003e and \u003cem\u003eHamiltonella\u003c/em\u003e. Many ecological factors including temperature, precipitation, longitude and latitude had effects on symbiont abundance and bacterial diversities in \u003cem\u003eB. tabaci\u003c/em\u003e. Our results clarified a significant influence of symbiont infection on the microbial diversities in \u003cem\u003eB. tabaci\u003c/em\u003e MED, as well as exploring the impacts of ecological factors on the bacterial communities including symbionts infection and microbial diversities.\u003c/p\u003e \u003cp\u003eIn this experiment, we identified six symbionts including one primary symbiont \u003cem\u003ePortiera\u003c/em\u003e and five secondary symbionts from all \u003cem\u003eB. tabaci\u003c/em\u003e MED samples. These results are similar to that from other studies which also revealed that whiteflies are co-infected with a variety of symbionts globally [58,59,60,61,62]. The results confirmed that the primary symbiont \u003cem\u003ePortiera\u003c/em\u003e is the most abundant bacteria in almost all \u003cem\u003eB. tabaci\u003c/em\u003e MED populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which play\u003cem\u003es\u003c/em\u003e many important roles in whiteflies such as provision of essential amino acids [63,64,65,66]. Also, the results showed that \u003cem\u003eHamiltonella\u003c/em\u003e infected all \u003cem\u003eB. tabaci\u003c/em\u003e MED populations, although it showed lower abundance (lower than 18.37% in all populations). The co-infection of \u003cem\u003eHamiltonella\u003c/em\u003e and \u003cem\u003ePortiera\u003c/em\u003e was prevalent in whiteflies and is vital for \u003cem\u003eB. tabaci\u003c/em\u003e MED. A previous study revealed that \u003cem\u003eHamiltonella\u003c/em\u003e completes the missing steps in some of the pathways of \u003cem\u003ePortiera\u003c/em\u003e to provide a full complement of nutrients to their whitefly hosts [32]. \u003cem\u003eArsenophonous\u003c/em\u003e also infected many populations such as the AH, BH, CY and IL populations, and provided host whiteflies with B vitamin, similar to \u003cem\u003eHamiltonella\u003c/em\u003e [67].\u003c/p\u003e \u003cp\u003eThe existence and abundance of different symbionts in insects promote interactions among them, especially for secondary symbionts [41,68,69,70]. For example, \u003cem\u003eWolbachia\u003c/em\u003e infection in the fruit fly, \u003cem\u003eDrosophila neotestacea\u003c/em\u003e, significantly promoted the infection of \u003cem\u003eSpiroplasma\u003c/em\u003e [69]. Similar results reported from wild \u003cem\u003eLaodelphax striatellus\u003c/em\u003e populations showed that \u003cem\u003eWolbachia\u003c/em\u003e infection enriched the abundance of other bacteria groups including \u003cem\u003eThermus\u003c/em\u003e, \u003cem\u003eSpiroplasma\u003c/em\u003e and \u003cem\u003eRalstonia\u003c/em\u003e [41]. Our 2bRAD-M sequencing results showed that the presence of \u003cem\u003eHamiltonella\u003c/em\u003e significantly and negatively influenced the abundance of \u003cem\u003eRickettsia\u003c/em\u003e and \u003cem\u003eWolbachia\u003c/em\u003e, while the presence of \u003cem\u003ePortiera\u003c/em\u003e positively influenced \u003cem\u003eHamiltonella\u003c/em\u003e, but negatively influenced \u003cem\u003eRickettsia\u003c/em\u003e abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The positive correlation between \u003cem\u003ePortiera\u003c/em\u003e and \u003cem\u003eHamiltonella\u003c/em\u003e confirms the cooperation to provide a full complement of nutrients to their whitefly hosts [32], and which helps to promote their spread in whitefly populations. On the other hand, antagonistic interactions among bacteria including symbionts in other insects have been reported. For example, the relative abundance of 154 bacteria genera in \u003cem\u003eLaodelphax striatellus\u003c/em\u003e (SBPH) significantly decreased with \u003cem\u003eWolbachia\u003c/em\u003e infection [41]. In the \u003cem\u003eD. melanogaster\u003c/em\u003e gut, the prevalence of various bacteria is generally negatively correlated [71] and many isolated bacteria inhibit the growth of the primary symbiont \u003cem\u003ePaenibacillus larvae\u003c/em\u003e in the honeybee [68]. Another study reported of an antagonistic interaction between \u003cem\u003eHamiltonella\u003c/em\u003e and \u003cem\u003eCardinium\u003c/em\u003e in \u003cem\u003eB. tabaci\u003c/em\u003e [72]. The co-sharing of the bacteriocytes in \u003cem\u003eB. tabaci\u003c/em\u003e by symbionts may promote such antagonistic interactions, as a result of competition for limited space and resources in host whiteflies [31,73,74].\u003c/p\u003e \u003cp\u003eThe infection and titer of symbionts in arthropods including in whiteflies are affected by many ecological factors including temperature, precipitation, longitude, latitude and so on [25,43,44,45,70]. For example, \u003cem\u003eCardinium\u003c/em\u003e infection in the spider mite, \u003cem\u003eTetranychus truncatus\u003c/em\u003e is positively influenced by latitude but decreases with increased annual precipitation [70]. We are the first to report that longitude and latitude significantly affect the abundance of many symbionts including \u003cem\u003ePortiera\u003c/em\u003e, \u003cem\u003eRickettsia and Cardinium\u003c/em\u003e in whiteflies (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Our result showed a positive influence of latitude on \u003cem\u003eCardinium\u003c/em\u003e abundance, as well as a negative influence of precipitation on \u003cem\u003eHamiltonella.\u003c/em\u003e However, precipitation positively influenced the abundance of \u003cem\u003eWolbachia\u003c/em\u003e and \u003cem\u003eArsenophonus\u003c/em\u003e and temperature postively affected \u003cem\u003eRickettsia\u003c/em\u003e abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), which is congruent to a previous study which revealed the \u003cem\u003eRickettsia\u003c/em\u003e infection significantly increased the thermotolerance of host whiteflies [37]. The frequency and abundance of symbionts are significantly influenced by temperature, especially for \u003cem\u003eCardinium\u003c/em\u003e [43,44,45]. We previously reported that \u003cem\u003eCardinium\u003c/em\u003e infection increased the thermotolerance of host whiteflies [9]. However, in this study the correlation between the abundance of \u003cem\u003eCardinium\u003c/em\u003e and temperature was not significant. Therefore, the relationships between \u003cem\u003eCardinium\u003c/em\u003e and temperature in whiteflies should be further explored. Results showed that annul mean temperature had negative effects on \u003cem\u003ePortiera\u003c/em\u003e abundance. A previous study revealed that \u003cem\u003ePortiera\u003c/em\u003e titer was significantly reduced under high temperatures [75].\u003c/p\u003e \u003cp\u003eThere is increasing awareness of the vital role of the microbiome on host insects such as the provisioning of essential nutrients, synthesis of specific toxins or modification of the insect immune system [13,76]. However, the structure and diversities of microbial communities in insects are influenced by many factors, including host sex, growth stages, the abundance of symbionts, antibiotics as well as many geographic and climatic factors [13,41,66,77,78]. In this experiment, we revealed that the diversity of whiteflies\u0026rsquo; bacterial communities (Shannon, Simpson and Chao1 indexes) is significantly influenced by many ecological factors including temperature, precipitation, longitude and latitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Similar results were also reported on the SBPH, whic showed that the microbial structure in SBPH was also significantly influenced by annual mean precipitation and longitude [41], while the microbial communities in stoneflies were significantly influenced by geographical distances [46]. How ecological factors influence the structure of microbial communities in \u003cem\u003eB. tabaci\u003c/em\u003e MED should be explored further, although studies have shown that insects acquire microbiome from the environment [79,80,81].\u003c/p\u003e \u003cp\u003eOur results also showed that the infection and abundance of many symbionts in whiteflies were significantly influenced by ecological factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), which results are similar to other related studies [43,70,82]. This indicate that the change in symbiont abundance caused by ecological factors may indirectly alter the diversity of bacterial community in \u003cem\u003eB. tabaci\u003c/em\u003e MED. We discovered that symbiont abundance had significant effects on the diversities of microbial communities in whiteflies, which results are congruent with that in other related studies [40,41,42]. For example, \u003cem\u003eWolbachia\u003c/em\u003e infection significantly changed the structure of microbial communities in SBPH [41] and \u003cem\u003eD. melanogaster\u003c/em\u003e [42]. The complex interactions among different symbionts and microbial communities in insect host should be explored further.\u003c/p\u003e \u003cp\u003eIn summary, 2bRAD-M sequencing analysis of 49 global \u003cem\u003eB. tabaci\u003c/em\u003e populations provided a new and comprehensive perspective of the relationship among the structure and diversity of bacterial community, ecological factors as well as the symbionts in \u003cem\u003eB. tabaci\u003c/em\u003e MED populations. The results showed that the symbionts within host whiteflies affected one another, and the abundance of symbionts and microbiota diversities were significantly influenced by many ecological factors. However, further studies are needed to clarify how ecological factors influence the diversity of bacterial communities in different \u003cem\u003eB. tabaci\u003c/em\u003e MED populations under global ecological timescales.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contributions:\u0026nbsp;Conceptualization, Chu D; sample preparation: Li HR; methodology and data analysis: Yang K, Zhang YX, Zhou JC and Zhang YJ; writing and editing: Yang K, Zhang YX and Chu D; funding acquisition: Chu D and Yang K.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Acknowledgments: The authors wish to acknowledge Dr Ary Hoffman, for the help in analyzing the data of the results of this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Key Research and Development Program of China (2022YFD1401200), the Science and Technology Benefiting the People Demonstration Project of Qingdao (24-1-8-xdny-10-nsh), the Taishan Scholar Foundation of Shandong Province, and the Qingdao Agricultural University High-level Talent Fund (663-1121025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe raw data of the 2b-RAD sequencing were deposited in the Sequence Read Archive Database of the NCBI (BioProject number: SRR10541684).\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All authors consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Consent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors give their consent for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBertin S, Luigi M, Parrella G, et al. Survey of the distribution of \u003cem\u003eBemisia tabaci \u003c/em\u003e(Hemiptera: Aleyrodidae) in Lazio region (Central Italy): a threat for the northward expansion of Tomato leaf curl New Delhi virus (Begomovirus: Geminiviridae) infection. Phytoparasitica. 2018;46:171-182. DOI: 10.1007/s12600-018-0675-1\u003c/li\u003e\n\u003cli\u003eDe Barro PJ, Liu SS, Boykin LM, Dinsdale AB. \u003cem\u003eBemisia\u003c/em\u003e\u003cem\u003etabaci\u003c/em\u003e: A statement of species status. Annu Rev Entomol. 2011;56:1-19. DOI: 10.1146/annurev-ento-112408-085504\u003c/li\u003e\n\u003cli\u003eBoykin LM, De Barro PJ. A practical guide to identifying members of the Bemisia tabaci species complex: and other morphologically identical species. Front Ecol Evol. 2014;2:1-5. DOI: 10.3389/fevo.2014.00045\u003c/li\u003e\n\u003cli\u003eKanakala S, Ghanim M. Global genetic diversity and geographical distribution of \u003cem\u003eBemisia tabaci\u003c/em\u003e and its bacterial endosymbionts. PLoS ONE. 2019;14(3):e0213946. DOI: 10.1371/journal.pone.0213946\u003c/li\u003e\n\u003cli\u003eZhu DT, Xia WQ, Rao Q, Liu SS, Ghanim M, Wang XW. Sequencing and comparison of the Rickettsia genomes from the whitefly Bemisia tabaci Middle East Asia Minor I. Insect Sci. 2016;23:531-542. DOI: 10.1111/1744-7917.12347\u003c/li\u003e\n\u003cli\u003eIslam W, Akutse KS, Qasim M, Khan KA, Ghramh HA, Idrees A, Latif S. Bemisia tabaci-mediated facilitation in diversity of begomoviruses: Evidence from recent molecular studies. Microb Pathog. 2018;123:162-168. DOI: 10.1016/j.micpath.2018.07.029\u003c/li\u003e\n\u003cli\u003eGilbertson RL, Batuman O, Webster CG, Adkins S. Role of the Insect Supervectors Bemisia tabaci and Frankliniella occidentalis in the emergence and global spread of plant viruses. Annu Rev Virol. 2015;2:67-93. DOI: 10.1146/annurev-virology-100114-055141\u003c/li\u003e\n\u003cli\u003eLiu Y, Yang K, Wang JC, Chu D. \u003cem\u003eCardinium\u003c/em\u003e infection alters cotton defense and detoxification metabolism of its whitefly host. Insect Sci. 2022. DOI: 10.1111/1744-7917.13086\u003c/li\u003e\n\u003cli\u003eYang K, Yuan MY, Liu Y, Guo CL, Liu TX, Zhang YJ, Chu D. First evidence for thermal tolerance benefits of the bacterial symbiont Cardinium in an invasive whitefly, Bemisia tabaci. Pest Manag Sci. 2021;77:5021-5031. DOI: 10.1002/ps.6631\u003c/li\u003e\n\u003cli\u003eChu D, Wan FH, Zhang YJ, Brown JK. Change in the biotype composition of \u003cem\u003eBemisia tabaci\u003c/em\u003e in Shandong Province of China from 2005 to 2008. Environ Entomol. 2010;39:1028-1036. DOI: 10.1603/en09192\u003c/li\u003e\n\u003cli\u003eGuo CL, Zhu YZ, Zhang YJ, Keller MA, Liu TX, Chu D. Invasion biology and management of sweetpotato whitefly (Hemiptera: Aleyrodidae) in China. J Integr Pest Manag. 2021;12:1-16. DOI: 10.1093/jipm/pmaa026\u003c/li\u003e\n\u003cli\u003eBallinger MJ, Perlman SJ. Generality of toxins in defensive symbiosis: Ribosome-inactivating proteins and defense against parasitic wasps in Drosophila. PLoS Pathog. 2017;13(7):e1006431. DOI: 10.1371/journal.ppat.1006431\u003c/li\u003e\n\u003cli\u003eDouglas AE. Multiorganismal insects: diversity and function of resident microorganisms. Annu Rev Entomol. 2015;60(1):17-34. DOI: 10.1146/annurev-ento-010814-021120\u003c/li\u003e\n\u003cli\u003eEngel P, Martinson VG, Moran NA. Functional diversity within the simple gut microbiota of the honeybee. Proc Natl Acad Sci USA. 2012;109:11002-11007. DOI: 10.1073/pnas.1202970109\u003c/li\u003e\n\u003cli\u003eEngel P, Moran NA. The gut microbiota of insects - diversity in structure and function. FEMS Microbiol Rev. 2013;37(5):699-735. DOI: 10.1111/1574-6976.12025\u003c/li\u003e\n\u003cli\u003ePaniagua Voirol LR, Enric F, Martin K, Monika H, Fatouros NE. Bacterial symbionts in Lepidoptera: their diversity, transmission, and impact on the host. Front Microbiol. 2018;9. DOI: 10.3389/fmicb.2018.00556\u003c/li\u003e\n\u003cli\u003eChung SH, Scully ED, Peiffer M, Geib SM, Rosa C, Hoover K, Felton GW. Host plant species determines symbiotic bacterial community mediating suppression of plant defenses. Sci Rep. 2017;7:39690. DOI: 10.1038/srep39690\u003c/li\u003e\n\u003cli\u003eGallo-Franco JJ, Toro-Perea N. Variations in the bacterial communities in Anastrepha obliqua (Diptera: Tephritidae) according to the insect life stage and host plant. Curr Microbiol. 2020;77(7):1283-1291. DOI: 10.1007/s00284-020-02134-0\u003c/li\u003e\n\u003cli\u003eHussain M, Akutse KS, Ravindran K, Lin Y, Bamisile BS, Qasim M, Dash CK, Wang L. Effects of different temperature regimes on survival of Diaphorina citri and its endosymbiotic bacterial communities. Environ Microbiol. 2017;19(9):3439-3449. DOI: 10.1111/1462-2920.13825\u003c/li\u003e\n\u003cli\u003eJones AG, Mason CJ, Felton GW, Hoover K. Host plant and population source drive diversity of microbial gut communities in two polyphagous insects. Sci Rep. 2019;9(1):2792. DOI: 10.1038/s41598-019-39351-4\u003c/li\u003e\n\u003cli\u003eKondo N, Shimada M, Fukatsu T. Infection density of \u003cem\u003eWolbachia\u003c/em\u003e endosymbiont affected by co-infection and host genotype. Biol Lett. 2005;1(4):488\u0026ndash;491. DOI: 10.1098/rsbl.2005.0327\u003c/li\u003e\n\u003cli\u003ePriya NG, Ojha A, Kajla MK, Raj A, Rajagopal R. Host plant induced variation in gut bacteria of \u003cem\u003eHelicoverpa\u003c/em\u003e\u003cem\u003earmigera\u003c/em\u003e. PLoS One. 2012;7:e30768. DOI: 10.1371/journal.pone.0030768\u003c/li\u003e\n\u003cli\u003eYang K, Chen H, Bing XL, Xia X, Zhu YX, Hong XY. Antibiotic-induced changes in \u003cem\u003eTetranychus truncatus \u003c/em\u003ebacterial community alter its fecundity, longevity and sex-ratio. Syst Appl Acarol. 2020;25(9):1668-1682. DOI: 10.11158/saa.25.9.7\u003c/li\u003e\n\u003cli\u003eYang K, Qin PH, Yuan MY, Chen L, Zhang YJ, Chu D. Infection density pattern of \u003cem\u003eCardinium\u003c/em\u003e affects the responses of bacterial communities in an invasive whitefly under heat conditions. Insect Sci. 2022; DOI: 10.1111/1744-7917.13141\u003c/li\u003e\n\u003cli\u003eMartiny JB, Bohannan BJ, Brown JH, Colwell RK, Fuhrman JA, Green JL, et al. Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol. 2006;4:102-112. DOI: 10.1038/nrmicro1341\u003c/li\u003e\n\u003cli\u003eFeldhaar H, Gross R. Insects as hosts for mutualistic bacteria. Int J Med Microbiol. 2009;299(1):1-8. DOI: 10.1016/j.ijmm.2008.06.003\u003c/li\u003e\n\u003cli\u003eLuan JB. Insect bacteriocytes: adaptation, development, and evolution. Annu Rev Entomol. 2024;6981-6998.\u003c/li\u003e\n\u003cli\u003eMondo SJ, Lastovetsky OA, Gaspar ML, et al. Bacterial endosymbionts influence host sexuality and reveal reproductive genes of early divergent fungi. Nat Commun. 2017;8:1843. DOI: 10.1038/s41467-017-01752-w\u003c/li\u003e\n\u003cli\u003eWerren JH, Baldo L, Clark ME. \u003cem\u003eWolbachia\u003c/em\u003e: master manipulators of invertebrate biology. Nat Rev Microbiol. 2008;6(10):741-751. DOI: 10.1038/nrmicro1969\u003c/li\u003e\n\u003cli\u003eBing XL, Yang J, Zchori-Fein E, Wang XW, Liu SS. Characterization of a newly discovered symbiont of the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae). Appl Environ Microbiol. 2013;79(2):569-575. DOI: 10.1128/AEM.02154-12\u003c/li\u003e\n\u003cli\u003eGottlieb Y, Ghanim M, Gueguen G, Kontsedalov S, Vavre F, Fleury F, Zchori-Fein E. Inherited intracellular ecosystem: symbiotic bacteria share bacteriocytes in whiteflies. FASEB J. 2008;22(7):2591-2599. DOI: 10.1096/fj.07-101121\u003c/li\u003e\n\u003cli\u003eRao Q, Rollat-Farnier P, Zhu D, Santos-Garcia D, Silva FJ, Moya A, et al. Genome reduction and potential metabolic complementation of the dual endosymbionts in the whitefly Bemisia tabaci. BMC Genomics. 2015;16:226. DOI: 10.1186/s12864-015-1447-4\u003c/li\u003e\n\u003cli\u003eFang YW, Liu LY, Zhang HL, Jiang DF, Chu D. Competitive ability and fitness differences between two introduced populations of the invasive whitefly Bemisia tabaci Q in China. PLoS One. 2014;9(6):e100423. DOI: 10.1371/journal.pone.0100423\u003c/li\u003e\n\u003cli\u003eLi HR, Harwood JD, Liu TX, Chu D. Novel proteome and acetylome of Bemisia tabaci Q in response to Cardinium infection. BMC Genomics. 2018;19:523. DOI: 10.1186/s12864-018-4881-5\u003c/li\u003e\n\u003cli\u003eLi HR, Wei XY, Ding TB, Chu D. Genomewide profiling of Cardinium-responsive microRNAs in the exotic whitefly, Bemisia tabaci (Gennadius) biotype Q. Front Physiol. 2018;9:1580. DOI: 10.3389/fphys.2018.01580\u003c/li\u003e\n\u003cli\u003eLv N, Peng J, Chen XY, Guo CF, Sang W, Wang XM, et al. Antagonistic interaction between male-killing and cytoplasmic incompatibility induced by \u003cem\u003eCardinium \u003c/em\u003eand \u003cem\u003eWolbachia \u003c/em\u003ein the whitefly. \u003cem\u003eBemisia tabaci\u003c/em\u003e. Insect Sci. 2020;28:330-346. DOI: 10.1111/1744-7917.13086\u003c/li\u003e\n\u003cli\u003eBrumin M, Kontsedalov S, Ghanim M. Rickettsia influences thermotolerance in the whitefly Bemisia tabaci B biotype. Insect Sci. 2011;18:57-66. DOI: 10.1111/j.1744-7917.2010.01389.x\u003c/li\u003e\n\u003cli\u003eFan ZY, Liu Y, He ZQ, Wen Q, Chen XY, Khan MM, Osman MM, Mandour NS, Qiu BL. Rickettsia infection benefits its whitefly hosts by manipulating their nutrition and defense. Insects. 2022;13:1161. DOI: 10.3390/insects13091161\u003c/li\u003e\n\u003cli\u003eShi PQ, Chen XY, Chen XS, Lv N, Liu Y, Qiu BL. \u003cem\u003eRickettsia\u003c/em\u003e increases its infection and spread in whitefly populations by manipulating the defense patterns of the host plant. FEMS Microbiol Ecol. 2021;97(4):fiab032. DOI: 10.1093/femsec/fiab032\u003c/li\u003e\n\u003cli\u003eAudsley MD, Seleznev A, Joubert DA, Woolfit M, O\u0026apos;Neill SL, McGraw EA. \u003cem\u003eWolbachia\u003c/em\u003e infection alters the relative abundance of resident bacteria in adult \u003cem\u003eAedes aegypti\u003c/em\u003e mosquitoes, but not larvae. Mol Ecol. 2018;27:297\u0026ndash;309. DOI: 10.1111/mec.14482\u003c/li\u003e\n\u003cli\u003eDuan XZ, Sun JT, Wang LT, Shu XH, Guo Y, Keiichiro M, et al. Recent infection by \u003cem\u003eWolbachia\u003c/em\u003e alters microbial communities in wild \u003cem\u003eLaodelphax\u003c/em\u003e\u003cem\u003estriatellus\u003c/em\u003e populations. Microbiome. 2020;8:104. DOI: 10.1186/s40168-020-00867-0\u003c/li\u003e\n\u003cli\u003eSimhadri RK, Fast EM, Guo R, Schultz MJ, Vaisman N, Ortiz L, Bybee J, Slatko BE, Frydman HM. The gut commensal microbiome of Drosophila melanogaster is modified by the endosymbiont Wolbachia. mSphere. 2017;2:e00287-17. DOI: 10.1128/mSphere.00287-17\u003c/li\u003e\n\u003cli\u003eCharlesworth J, Weinert LA, Araujo EV Jr, Welch JJ. \u003cem\u003eWolbachia\u003c/em\u003e, \u003cem\u003eCardinium\u003c/em\u003e and climate: an analysis of global data. Biol Lett. 2019;15(8):20190273. DOI: 10.1098/rsbl.2019.0273\u003c/li\u003e\n\u003cli\u003eCorbin C, Heyworth ER, Ferrari J, Hurst GD. Heritable symbionts in a world of varying temperature. Heredity. 2016;118(1):10-20. DOI: 10.1038/hdy.2016.77\u003c/li\u003e\n\u003cli\u003eMorag N, Klement E, Saroya Y, Lensky I, Gottlieb Y. Prevalence of the symbiont Cardinium in Culicoides (Diptera: Ceratopogonidae) vector species is associated with land surface temperature. FASEB J. 2012;26(10):4025-4034. DOI: 10.1096/fj.12-208009\u003c/li\u003e\n\u003cli\u003eZhu YX, Huo QB, Wen T, Wang XY, Zhao MY, Du YZ. Mechanisms of fungal community assembly in wild stoneflies moderated by host characteristics and local environment. npj Biofilms Microbiomes. 2022;8:31. DOI: 10.1038/s41522-022-00258-0\u003c/li\u003e\n\u003cli\u003eAlexander LV, Zhang X, Peterson TC, Caesar J, Gleason BE, et al. Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res. 2006;111:D05109. DOI: 10.1029/2005JD006290\u003c/li\u003e\n\u003cli\u003eMeehl GA, Tebaldi C. More intense, more frequent, and longer-lasting heat waves in the 21st century. Science. 2004;305:994\u0026ndash;997. DOI: 10.1126/science.1098704\u003c/li\u003e\n\u003cli\u003eMa CS, Ma G, Pincebourde S. Survive a warming climate, insect responses to extreme high temperatures. Ann Rev Entomol. 2021;66:8.1\u0026ndash;8.22.\u003c/li\u003e\n\u003cli\u003eSun Z, Huang S, Zhu P, Tzehau L, Zhao H, Lv J, Zhang RC, Zhou LS, Niu QY, Wang XP, Zhang M, Jing GC, Bao ZM, Liu JQ, Wang S, Xu J. Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M. Genome Biol. 2022;23:36. DOI: 10.1186/s13059-022-02600-7\u003c/li\u003e\n\u003cli\u003eSun Z, Huang S, Zhu P, Tzehau L, Zhao H, Lv J, et al. Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M. Genome Biol. 2022;23(1):36. DOI: 10.1186/s13059-021-02576-9. \u003c/li\u003e\n\u003cli\u003eSun Z, Liu J, Zhang M, Wang T, Huang S, Weiss ST, Liu YY. Removal of false positives in metagenomics-based taxonomy profiling via targeting Type IIB restriction sites. Nat Commun. 2023;14(1):5321. DOI: 10.1038/s41467-023-41099-8. \u003c/li\u003e\n\u003cli\u003eSong X, Huang Q, Yang Y, Ma L, Liu W, Ou C, et al. Efficient therapy of inflammatory bowel disease (IBD) with highly specific and durable targeted Ta\u003csub\u003e2\u003c/sub\u003e C modified with chondroitin sulfate (TACS). Adv Mater. 2023;35(36):e2301585. DOI: 10.1002/adma.202301585.\u003c/li\u003e\n\u003cli\u003eYang K, Zhang HY, Wang P, Jin GX, Chu D. Both symbionts and environmental factors contribute to shape the microbiota in a pest insect, \u003cem\u003eSogatella furcifera\u003c/em\u003e. Front Microbiol. 2024;14:1336345. DOI: 10.3389/fmicb.2023.1336345. eCollection 2023.\u003c/li\u003e\n\u003cli\u003eWang S, Meyer E, McKay J, Matz MV. 2b-RAD: a simple and flexible method for genome-wide genotyping. Nat. Methods.\u003cem\u003e \u003c/em\u003e2012;9:808\u0026ndash;810. DOI: 10.1038/nmeth.2023\u003c/li\u003e\n\u003cli\u003eFranzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M, Weingart G, et al. Species-level function-al profiling of metagenomes and metatranscriptomes. Nat. Methods. 2018;15:962\u0026ndash;968. DOI: 10.1038/s41592-018-0176-y\u003c/li\u003e\n\u003cli\u003eCaporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335-336. DOI: 10.1038/nmeth.f.303\u003c/li\u003e\n\u003cli\u003eAnsari PG, Singh RK, Kaushik S, Krishna A, Wada T, Noda H. Detection of symbionts and virus in the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae), vector of the Mungbean yellow mosaic India virus in Central India. Appl Entomol Zool. 2017;52:567\u0026ndash;579. DOI: 10.1007/s13355-017-0504-0\u003c/li\u003e\n\u003cli\u003eHashmi TE, Devi SR, Meshram NM, Prasad R. Assessment of bacterial endosymbionts and the host, Bemisia tabaci (Hemiptera: Aleyrodidae), using rRNA and mitochondrial cytochrome oxidase I gene sequences. Commun Integr Biol. 2018;11(1):e1433442. DOI: 10.1080/19420889.2018.1433442\u003c/li\u003e\n\u003cli\u003eKurata A, Fujiwara A, Haruyama N, et al. Multiplex PCR method for rapid identification of genetic group and symbiont infection status in Bemisia tabaci (Hemiptera: Aleyrodidae). Appl Entomol Zool. 2016;51:167-172. DOI: 10.1007/s13355-015-0402-8\u003c/li\u003e\n\u003cli\u003ePan H, Li X, Ge D, Wang S, Wu Q, Xie W, Jiao X, Chu D, Liu B, Xu B, Zhang Y. Factors affecting population dynamics of maternally transmitted endosymbionts in Bemisia tabaci. PLoS ONE. 2012;7(2):e30760. DOI: 10.1371/journal.pone.0030760\u003c/li\u003e\n\u003cli\u003eTang X, Cai L, Shen Y, Du Y. Diversity and evolution of the endosymbionts of \u003cem\u003eBemisia tabaci \u003c/em\u003ein China. PeerJ. 2018;6:e5516. DOI: 10.7717/peerj.5516\u003c/li\u003e\n\u003cli\u003eBao XY, Yan JY, Yao YL, Wang YB, Visendi P, Seal SE, Luan JB. Lysine provisioning by horizontally acquired genes promotes mutual dependence between whitefly and two intracellular symbionts. PLoS Pathog. 2021;17(11):e1010120. DOI: 10.1371/journal.ppat.1010120\u003c/li\u003e\n\u003cli\u003eJiang ZF, Xia F, Johnson KW, Brown CD, Bartom E, Tuteja JH, et al. Comparison of the genome sequences of \u0026quot;\u003cem\u003eCandidatus\u003c/em\u003e Portiera aleyrodidarum\u0026quot; primary endosymbionts of the whitefly \u003cem\u003eBemisia tabaci \u003c/em\u003eB and Q biotypes. Appl Environ Microbiol. 2013;79(5):1757-1759. DOI: 10.1128/AEM.03537-12\u003c/li\u003e\n\u003cli\u003eJiang ZF, Xia F, Johnson KW, Bartom E, Tuteja JH, Stevens R, et al. Genome sequences of the primary endosymbiont \u0026quot;\u003cem\u003eCandidatus\u003c/em\u003e Portiera aleyrodidarum\u0026quot; in the whitefly \u003cem\u003eBemisia tabaci \u003c/em\u003eB and Q biotypes. J Bacteriol. 2012;194(23):6678-6679. DOI: 10.1128/JB.01708-12\u003c/li\u003e\n\u003cli\u003eLi NN, Jiang S, Lu KY, Hong JS, Wang YB, Yan JY, Luan JB. Bacteriocyte development is sexually differentiated in Bemesia tabaci. Cell Rep. 2022;38(9):110455. DOI: 10.1016/j.celrep.2022.110455\u003c/li\u003e\n\u003cli\u003eWang YB, Ren FR, Yao YL, Sun X, Walling LL, Li NN, Bai B, Bao XY, Xu XR, Luan JB. Intracellular symbionts drive sex ratio in the whitefly by facilitating fertilization and provisioning of B vitamins. ISME J. 2020;14(12):2923-2935. doi: 10.1038/s41396-020-0717-0. \u003c/li\u003e\n\u003cli\u003eEvans JD, Armstrong TN. Antagonistic interactions between honey bee bacterial symbionts and implications for disease. BMC Ecol. 2006;6(1):4. DOI: 10.1186/1472-6785-6-4\u003c/li\u003e\n\u003cli\u003eFromont C, Adair KL, Douglas AE. Correlation and causation between the microbiome, Wolbachia and host functional traits in natural populations of drosophilid flies. Mol Ecol. 2019;28(8):1826-1841. DOI: 10.1111/mec.15067\u003c/li\u003e\n\u003cli\u003eZhu YX, Song YL, Zhang YK, Hoffman AA, Zhou JC, Sun JT, Hong XY. Incidence of facultative bacterial endosymbionts in spider mites associated with local environments and host plants. Appl Environ Microbiol. 2018;84(6):AEM.02546-17. DOI: 10.1128/AEM.02546-17\u003c/li\u003e\n\u003cli\u003eWong AC, Chaston JM, Douglas AE. The inconstant gut microbiota of Drosophila species revealed by 16S rRNA gene analysis. ISME J. 2013;7:1922-1932. DOI: 10.1038/ismej.2013.86\u003c/li\u003e\n\u003cli\u003eZhao DX, Hoffmann AA, Zhang ZC, Niu HT, Guo HF. Interactions between facultative symbionts Hamiltonella and Cardinium in Bemisia tabaci (Hemiptera: Aleyrodoidea): Cooperation or Conflict? J Econ Entomol. 2018;111(6):2660-2666. DOI: 10.1093/jee/toy243\u003c/li\u003e\n\u003cli\u003eShi PQ, Wang L, Liu Y, An X, Chen XS, Ahmed MZ, Qiu BL, Sang W. Infection dynamics of endosymbionts reveal three novel localization patterns of Rickettsia during the development of whitefly Bemisia tabaci. FEMS Microbiol Ecol. 2018;94(11):fiy165. DOI: 10.1093/femsec/fiy165\u003c/li\u003e\n\u003cli\u003eSkaljac M, Zanic K, Ban SG, Kontsedalov S, Ghanim M. Co-infection and localization of secondary symbionts in two whitefly species. BMC Microbiol. 2012;12:142. DOI: 10.1186/1471-2180-12-142\u003c/li\u003e\n\u003cli\u003eShan HW, Lu YH, Bing XL, Liu SS, Liu YQ. Differential responses of the whitefly Bemisia tabaci symbionts to unfavorable low and high temperatures. Microb Ecol. 2014;68:472-482. DOI: 10.1111/1574-6941.12296\u003c/li\u003e\n\u003cli\u003eProsdocimi EM, Mapelli F, Gonella E, Borin S, Crotti E. Microbial ecology-based methods to characterize the bacterial communities of non-model insects. J Microbiol Methods. 2015;119:110-125. DOI: 10.1016/j.mimet.2015.10.008\u003c/li\u003e\n\u003cli\u003eMartinson VG, Douglas AE, Jaenike J. Community structure of the gut microbiota in sympatric species of wild Drosophila. Ecol Lett. 2017;20:629-639. DOI: 10.1111/ele.12760\u003c/li\u003e\n\u003cli\u003eN\u0026auml;pflin K, Schmid-Hempel P. Host effects on microbiota community assembly. J Anim Ecol. 2018;87:331-340. DOI: 10.1111/1365-2656.12764\u003c/li\u003e\n\u003cli\u003eAdair KL, Douglas AE. Making a microbiome: the many determinants of host-associated microbial community composition. Curr Opin Microbiol. 2017;35:23\u0026ndash;29. DOI: 10.1016/j.mib.2016.11.002\u003c/li\u003e\n\u003cli\u003eChevrette MG, Carlson CM, Ortega HE, Thomas C, Ananiev GE, Barns KJ, et al. The antimicrobial potential of \u003cem\u003eStreptomyces\u003c/em\u003e from insect microbiomes. Nat Commun. 2019;10:516. DOI: 10.1038/s41467-019-08438-0\u003c/li\u003e\n\u003cli\u003eMoro CV, Tran FH, Raharimalala FN, Ravelonandro P, Mavingui P. Diversity of culturable bacteria including \u003cem\u003ePantoea\u003c/em\u003e in wild mosquito Aedes albopictus. BMC Microbiol. 2013;13:70. DOI: 10.1186/1471-2180-13-70\u003c/li\u003e\n\u003cli\u003eZhu YX, Song ZR, Zhang YY, Hoffmann AA, Hong XY. Spider mites singly infected with either \u003cem\u003eWolbachia\u003c/em\u003e or \u003cem\u003eSpiroplasma\u003c/em\u003e have reduced thermal tolerance. Front Microbiol. 2021;12:706321. DOI: 10.3389/fmicb.2021.706321\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"symbiont, whitefly, 2bRAD-M sequencing, microbial diversity, ecological factors","lastPublishedDoi":"10.21203/rs.3.rs-4321283/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4321283/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMicrobial communities including symbionts play vital roles in insect hosts. Abiotic factors, especially ecological factors also have significant influence on the structure of the microbiome and the abundance of symbionts within hosts. However, the effects of the bacterial symbionts and ecological factors on the microbiota in host whitefly remains poorly understood.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study, 49 \u003cem\u003eBemisia tabaci\u003c/em\u003e MED populations collected in 23 locations around the world were sequenced using 2bRAD-M, to explore the relationships among ecological factors, symbionts and microbial diversities in whiteflies. Results revealed that microbial community structures significantly differed in the different geographical \u003cem\u003eB. tabaci\u003c/em\u003e MED populations, and the abundance of many symbionts including \u003cem\u003ePortiera\u003c/em\u003e, \u003cem\u003eHamiltonella\u003c/em\u003e, \u003cem\u003eRickettsia\u003c/em\u003e, \u003cem\u003eCardinium\u003c/em\u003e, and \u003cem\u003eWolbachia\u003c/em\u003e, significantly influenced with one another. Also, the diversity of bacterial communities in whiteflies were significantly affected by the relative abundance of symbionts including \u003cem\u003eCardinium\u003c/em\u003e and \u003cem\u003eHamiltonella\u003c/em\u003e. Meanwhile, environmental factors including temperature, precipitation, longitude and latitude significantly influenced the abundance of many symbionts and the diversity of bacterial communities in \u003cem\u003eB. tabaci\u003c/em\u003e MED.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOverall, our results revealed complex interactions among ecological factors, among ecological factors, microbiota diversity and symbionts in \u003cem\u003eB. tabaci\u003c/em\u003e MED. This helps to comprehend the complex interactions among these factors in insect hosts.\u003c/p\u003e","manuscriptTitle":"Species-resolved metagenomics reveal ecological effects on the microbiota in a global pest, the whitefly, using 2bRAD-M","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 06:38:46","doi":"10.21203/rs.3.rs-4321283/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28cc5e48-8ceb-4e99-bc5a-0febaadd0e64","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-23T11:40:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 06:38:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4321283","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4321283","identity":"rs-4321283","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}