https://orcid.org/0000-0002-7315-8680
Figures
Abstract
The maize weevil, Sitophilus zeamais, is a ubiquitous pest of maize and other cereal crops worldwide and remains a threat to food security in subsistence communities. Few population genetic studies have been conducted on the maize weevil, but those that exist have shown that there is very little genetic differentiation between geographically dispersed populations and that it is likely the species has experienced a recent range expansion within the last few hundred years. While the previous studies found little genetic structure, they relied primarily on mitochondrial and nuclear microsatellite markers for their analyses. It is possible that more fine-scaled population genetic structure exists due to local adaptation, the biological limits of natural species dispersal, and the isolated nature of subsistence farming communities. In contrast to previous studies, here, we utilized genome-wide single nucleotide polymorphism data to evaluate the genetic population structure of the maize weevil from the southern and coastal Mexican states of Oaxaca and Chiapas. We employed strict SNP filtering to manage large next generation sequencing lane effects and this study is the first to find fine-scale genetic population structure in the maize weevil. Here, we show that although there continues to be gene flow between populations of maize weevil, that fine-scale genetic structure exists. It is possible that this structure is shaped by local adaptation of the insects, the movement and trade of maize by humans in the region, geographic barriers to gene flow, or a combination of these factors.
Citation: Baltzegar J, Jones MS, Willcox M, Ramsey JM, Gould F (2023) Population genetic structure of the maize weevil, Sitophilus zeamais, in southern Mexico. PLoS ONE 18(4): e0264469. https://doi.org/10.1371/journal.pone.0264469
Editor: Aditya Pratap, ICAR-Indian Institute of Pulses Research, INDIA
Received: February 9, 2022; Accepted: February 22, 2023; Published: April 12, 2023
Copyright: © 2023 Baltzegar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data that support the findings of this study are openly available in Dryad (doi: 10.5061/dryad.zpc866tcr).
Funding: This work was supported by the National Science Foundation – IGERT 1068676 (https://www.nsf.gov/)(FG) and the Genetic Engineering and Society Center, North Carolina State University, Raleigh, NC. (https://research.ncsu.edu/ges) (JB & MJ) There was no additional external funding received for this study.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The maize weevil, Sitophilus zeamais, is a ubiquitous pest of maize and other cereal crops worldwide. Although there are effective control solutions, these are not always available to subsistence farmers. Therefore, maize weevils remain a threat to food security in these communities [1]. Recent research to control the maize weevil has focused on developing maize varieties resistant to the insect, improving storage conditions, and identifying chemical compounds toxic to the species [e.g., 2–4]. Maize varieties that are resistant to damage tend to have higher levels of phenolic compounds, which increases the hardness of grain and provides mechanical resistance to the maize from the maize weevil [5]. However, subsistence farmers, especially in Mexico, often prefer to grow their own landraces, which are maize varieties adapted for the specific climate, taste, and texture preferences in the region and may not have been specifically selected to protect against storage pests [6]. Hermetic storage solutions can be very effective, but they can also be prohibitively expensive for subsistence farmers. Recently, advances in more affordable hermetic storage bags have been made and programs exist to give access to silos and these improved storage solutions. However, silos and other hermetic storage containers are still not available to all who need them. Insecticides are used frequently, but insecticide resistance has been reported in this species. And resistance to any new chemical control agents would be expected to develop with sustained use [7]. Therefore, despite having a myriad of control methods, maize weevils continue to cause grain loss, especially in subsistence communities [1].
Few population genetic studies have been conducted on the maize weevil and its sister species, the rice weevil, S. oryzae [8–12]. Those studies have shown that there is very little genetic differentiation between geographically dispersed populations [9, 11]. For maize weevil, eight nuclear microsatellite markers were previously used to assess 15 populations from 9 countries over 4 continents [9]. The conclusion of the study was that very little genetic structure exists globally. A second study used mitochondrial markers to identify population structure among 9 countries in West and Central Africa [11]. Again, the conclusion was that very little phylogeographic genetic structure exists in this species. Together, these results suggest the recent worldwide expansion of the maize weevil. Similarly, studies in the rice weevil and other pest of stored grain have also shown evidence for low levels of population structure [12, 13].
While a recent range expansion of the maize weevil has likely occurred within the last few hundred years, we speculate that more fine-scaled population genetic structure exists due to local adaptation, the biological limits of natural species dispersal, or the isolated nature of subsistence farming communities. Maize weevils are not strong fliers. The typical flight range for the species was roughly estimated to be one quarter mile [14]. While human mediated movement of grain via trade is generally understood to be a primary source of movement for stored pests, subsistence farmers are likely participating in grain trade at a reduced rate compared to larger commercial farmers, particularly at the international scale. Thus, if genetic differences are to be found among maize weevil populations, they are likely to be observed between subsistence communities in socially isolated regions.
Mexico was chosen as the study location as it is near the center of origin for maize and the maize weevil is the most common pest present in stored maize in Oaxaca and Chiapas. The region continues to have strong subsistence farming communities and each community grows landraces of maize specific to their region and preferences. Human mediated transport via the grain trade is likely the most common way that maize weevils migrate in this region. The specific amount of trade among subsistence households in Mexico is not well characterized because farmers do not prioritize this type of record keeping [6]. However, population genetic studies of maize grain in Oaxaca and Chiapas have shown that there are high amounts of gene flow at the same time that local population structure exists and it appears to correlate well with the social origin of the farmer [15, 16]. Seed is primarily saved from one year to the next on the same farm and when needed, is first traded among farmers within the same community. Only occasionally will farmers acquire seed from regional markets [16]. These trade practices have been associated with the farmers’ perception of risk. The least risky trades are with local people who are well known and the riskiest trades are with strangers from other communities [16]. While information about the grain trade can inform our hypotheses about maize weevil population structure, it cannot act as a direct proxy as maize weevils can easily move from one variety of maize to another.
Natural migration via flight is also a possible method of movement for the maize weevil. Oaxaca and Chiapas are separated by the Isthmus of Tehuantepec. The isthmus is the shortest land distance between the Pacific Ocean and the Gulf of Mexico and is a great source for wind energy [17]. Given that maize weevils typically fly short distances (~0.25 miles) and are not particularly strong fliers, the isthmus may provide a barrier to gene flow [14]. In addition, both states exhibit a wide range of elevations and climates, providing further opportunity for local adaptation to occur and population structure to be identified.
It is imperative to better understand the basic population dynamics underlying this species so that traditional control methods can better be applied. Furthermore, the information in this study will be vital for informing the socio-ecological context in which maize weevil exists, allowing researchers who might develop emerging technologies for maize weevil control to identify relevant stakeholders from which to seek approval [18].
Mitochondrial and nuclear microsatellite markers are classic methods to assess the population genetic structure in species of interest [19]. They are especially good at identifying population structure that has existed for a significant period of time. However, the use of so few markers limits the statistical power that these studies have to identify recently developed genetic structure. In addition, mtDNA should not be used as the primary marker to study the recent evolutionary history in species that carry inherited symbionts because indirect selection on mtDNA can occur due to disequilibrium with other maternally inherited genes [20]. Maize weevils host two endosymbionts: Sodalis pierantonius and Wolbachia [9, 10, 21–23]. In these cases, the finding of no structure is not proof for the absence of structure, rather it is likely that these studies are underpowered or contain biases [19, 20]. It is prudent to utilize a more statistically powerful technique to identify possible genetic structure. Sets of single nucleotide polymorphisms (SNPs) provide genetic markers that have higher statistical power to answer these types of questions and are now used regularly to elucidate population structure. One individual bi-allelic SNP is less powerful than an individual microsatellite because of the fewer number of alleles sampled. But, when hundreds or thousands of SNPs are sampled at once, the combined statistical power is higher than the few dozen microsatellite loci that may be available for a species [24]. Maize weevil has only 8 polymorphic and amplifiable microsatellite loci identified [9].
Historically, it was challenging to generate a large number of informative SNPs for a non-model organism. However, reduced genome representation sequencing methods allow researchers to sample genome-wide SNPs without the need for a priori knowledge of the genome. Reduced representation methods have given researchers a greater ability to characterize the genetic population structure of important, but non-model organisms. Here we use double-digest restriction-site associated sequencing (ddRadSeq) to identify informative SNPs and assess the genetic population structure of the maize weevil collected from subsistence communities in southern Mexico.
Materials and methods
Field collections
From 2 August to 18 August 2016, maize weevils were collected from a total of 61 small to medium sized farms within rural communities in southern Mexico. Weevils collected from each community were treated as one population in subsequent analyses. The sample locations were chosen based on previously established relationships with community members. Members of each residence verbally consented to specimen collection, but the need for review was waived by the ethics committee. We sampled 7 communities in Oaxaca, Mexico and 5 communities in Chiapas, Mexico (Fig 1). In each community, we collected approximately 200 maize weevils from 3 to 7 individual residences. These samples were stored in 100% ethanol at ambient temperature during the duration of field collecting. Upon returning to the United States, samples were stored at -20˚C in the laboratory until processed. At each sampling location, we recorded latitude, longitude, and elevation using a Garmin GPS unit (Garmin International, Inc., Olathe, KS, USA) (Table 1). The sites in Oaxaca ranged in elevation from 23.5 m to 2,005 m and those in Chiapas ranged from 8.0 m to 1,142.5 m.
Communities marked with red dots were sequenced in Lane 1, samples marked with blue dots were sequenced in Lane 2. Full community names are listed in Table 1. A) A map of Mexico showing relative location of sampled communities located in the southern and coastal region of Mexico. B) A zoomed in view centered on Oaxaca and Chiapas and the location of the communities where maize weevils were collected.
Genomic DNA isolations
Genomic DNA was isolated from the head and thorax of individual weevils following the DNeasy Qiagen Kit (Qiagen Inc., Germantown, MD, USA) manufacturer’s suggestions in our laboratory at North Carolina State University in Raleigh, NC. DNA from 24 weevils from each of four communities (SRL, YAV, HUX, and NVL) were isolated for the proof-of-concept sequencing run. DNA from 12 weevils from each of the 8 remaining communities were isolated for the second sequencing run. Homogenized weevil tissue samples were incubated overnight in the lysis buffer solution at 55˚C and on day two of the isolation a RNaseA (4mg/mL) treatment was performed before the recommended wash steps. Finally, samples were eluted into 300 μl of 70˚C dH2O and stored at -20˚C for short term storage or -80˚C for long term storage.
ddRadSeq library preparation and sequencing
Genomic DNA isolated from individual weevils as described above was prepared for sequencing following the ddRadSeq protocol first described by Peterson et al. [25] and adapted to our laboratory as described by Fritz et al. [26]. Two separate libraries were prepared and sequenced on two different lanes of an Illumina HiSeq 2500 125 bp SE. The first library included 96 individual weevils, 24 each from the 4 most distant communities sampled: Santa Maria Yavesia, Santa Rosa de Lima, Huixtla, and Nuevo Leon. This sampling regime included the communities with the highest and lowest elevation from each state. This particular library was used to test the hypothesis that genetic differentiation could be found by genotyping SNPs. A second library, sequenced in the same manner as the first, was prepared after confirmation that genetic differentiation could be identified. This library included 12 individual weevils from each of the remaining 8 communities sampled, Santa Ana Zegache, Santos Reyes Nopala, San Isidro Campechero, Santiago Yaitepec, Pochutla, Golondrinas, Montecristo, and Villa Corzo. The communities in the second library represent intermediate distances and elevations as compared to the first library.
Bioinformatic and statistical analyses
Data produced by each lane was first checked with FastQC, then reads were trimmed with trimmomatic to remove adapter and Illumina indices [27, 28]. The trimming was verified by FastQC and a multiQC report was generated to compare results from the different indices [27–29]. Data from both lanes were analyzed together in Stacks v. 1.48 following the de novo procedure described by Rochette and Catchen [30, 31]. The procedure is outlined here to specify certain parameter choices. Samples were first cleaned, demultiplexed, and truncated to 115 bp using the program process_radtags. The per sample coverage was checked. Then, ustacks was run on all samples (-M = 5, -m = 3). The 4 highest coverage individuals from each of the 12 populations were chosen to create the catalog, with the exception of sample zag.c1, which had much higher coverage than other samples. Using the catalog popmap created in the previous step, cstacks was implemented (-n = 5). Then, sstacks was run on all samples. To improve calls, rxstacks was used with the flags—prune_haplo and—conf_lim = 0.10. Then, cstacks and sstacks were run again using the improved catalog. Finally, genotype information for each individual was exported from Stacks in genepop format from the populations program. Using the adegenet package in R, these data were stringently filtered [32, 33]. Individuals with greater than 80% missing genotype calls were removed. Then, loci with greater than 35% of missing data were removed. Principal components were then computed and plotted with the same program. F statistics were calculated in hierfstat package in R [34].
Results
ddRadSeq
The first sequencing run resulted in 111,204,487 reads after trimming and filtering with process_radtags, with an average 1,158,380 reads per individual and a range of 288,190–3,079,538. The second sequencing library resulted in 186,219,498 trimmed and processed reads, with a range of 15,068–4,730,073 and an average 1,939,786 reads per individual. Although the initial quality check appeared similar in each sequencing run, there were clear sequencing lane differences upon inspection of the PCA plot produced after running populations with both lanes together in Stacks (Fig 2) therefore, stringent filtering criteria were applied. There were four populations, Santos Reyes Nopala (NOP), Golondrinas (GOL), Villa Corzo (VLC), and Nuevo Leon (NVL), where every individual had greater than 80% missing genotypes in the populations output, these were removed from further analysis. The populations with missing data were from both sequencing lanes. The mean number of reads for these populations following the process_radtags procedure was 1,680,432, while the mean number of reads for all populations was 1,549,083. Therefore, the reason for the high number of missing genotypes is likely due to specific parameter selection downstream of the process_radtags program. Following individual removal, a missing data threshold for individual SNPs was set at 35%, this resulted in the removal of 21,365 SNPs from the analysis. The final number of individuals included in the analysis was 132. The final number of SNPs included was 17,966.
Each population is color coded and labeled. Each effect group is circled and labeled. The four populations missing genotype calls were missing at least 80% of the genotypes output by populations in Stacks.
Statistical analyses
Plotting principal components 1 and 2 for the filtered SNPs shows that the first principal component explains the difference between the two sequencing lanes (Fig 3). Plotting principal components 2 and 3 removes the lane effect and shows that weevils from each community group tightly together. There is a group of four communities in Oaxaca that share gene flow and cluster together, while the remaining communities are distinct from one another (Fig 4). In addition, principal component 3 can be explained by which state the community is located. The two communities on the bottom of the graph, Huixtla (HUX) and Montecristo (mtc), are in Chiapas, while all other communities are located in Oaxaca (Fig 4). Nei’s pairwise FST was calculated using the hierfstat package in R (Table 2). All population pairs have negative FST values, which should be interpreted as no difference between populations.
Weevils from each community are color coded. Principal components 1 and 2 are plotted. Communities SRL, YAV, and HUX are from the first sequencing lane. The remaining populations are from the second sequencing lane. Principal component one is primarily explaining the difference between the two sequencing lanes.
Weevils from each community are color coded. Principal components 2 and 3 are plotted. Communities SRL, YAV, and HUX are from the first sequencing lane. The remaining populations are from the second sequencing lane.
Discussion
Many studies have shown that low genetic structure is a common occurrence in pests of stored grain [12, 13], including in maize weevil [9, 11]. However, few of these studies have used markers with the power to identify recent and ongoing local adaptation [20, 35]. In this study, we utilized genome-wide SNPs to evaluate the genetic population structure of maize weevil, S. zeamais, from the southern and coastal Mexican states of Oaxaca and Chiapas. While other studies have attempted to identify genetic structure of this species at a larger scale, our smaller geographic scale study is the first to find fine-scale genetic population structure in the maize weevil.
In our data, we observe both negative pairwise FST estimates and apparent population genetic structure through principal components analysis. Negative FST values can generally be interpreted as zero and indicate that there are similar allele frequencies between populations [36]. This can occur if populations are artificially selected along a continuous gradient or if selection is acting on only a subset of loci sampled. FST estimates take into account information from all loci equally. The effect is that these estimates are biased toward older evolutionary events and that recent selection events or selection events on specific loci are dampened. PCA reduces the dimensionality of the dataset while maximizing variance. The effect of this approach gives more weight to the recently diverged loci. This allows PCA to identify local adaptation even when little to no population structure can be identified through FST analysis [37].
While there remains a high amount of gene flow between the sampled communities, the principal components analysis was able to identify some genetic differentiation based on geography. Primarily, there was a clear distinction between communities in Oaxaca and Chiapas. Four of the communities in Oaxaca (SRL, POC, YAI, ZEG) clustered very tightly and share a high amount of genetic similarity, but maize weevils from San Isidro Campecheros (ISD) and Santa Maria Yavesia (YAV) in Oaxaca, each formed their own distinct clusters. This is an interesting result as ISD is geographically close to the four communities that clustered together and is actually closer to SRL and YAI than POC and ZEG are. The exact reasons underlying the genetic structure are unknown, but we observed that the roads between certain communities in Oaxaca were easier to traverse and the travel time between sites was subsequently reduced. ISD was harder to reach via car and was more socially isolated than its neighboring communities. Likewise, YAV, the other genetically distinct community in Oaxaca was the most socially and geographically isolated community sampled in that state. The communities sampled in Chiapas were also much more isolated than most of those sampled in Oaxaca. These patterns resemble those found in the genetic population structure of maize from this region. While there is a high amount of gene flow among populations, fine-scale structure exists and is correlated with the social structure of farmers [15, 16]. It appears that maize weevil population structure may follow similar patterns.
The fact that the communities are more connected in Oaxaca than in Chiapas may help to facilitate grain trade between communities. These social interactions may be leading to more gene flow between maize weevil populations of the more connected Oaxacan communities versus the more isolated communities in each state. An interesting future study could incorporate farmer trade patterns into the genetic analysis to further explore the potential effect of human interactions on the genetic structure of maize weevil. This could be especially informative if supply chain nodes and trade endpoints were included in the sampling regime. If the effect is found to be significant, trade practices could also inform pest management strategies.
An alternative hypothesis to explain the difference between the populations found in Oaxaca and Chiapas is the existence of the Isthmus of Tehuantepec, an extremely windy area of Mexico. This region may form a natural barrier to weevil dispersal if migration is dominated by weevil flight rather than human-mediated grain trade. One or a combination of these factors may be shaping the genetic population structure of maize weevil in southern Mexico. Additional studies should seek to elucidate the underlying mechanisms contributing to the genetic structure observed in this study.
An important result presented here is the control of sequencing lane effects caused by combining independent sequencing lanes. The effects were controlled by stringent filtering of both individuals and loci. Removing under representative SNPs from the dataset greatly decreased the obvious lane effects. At the same time, a sufficient number of informative SNPs were retained to allow for clear relationships between populations to be observed. While it is preferable to design a ddRadSeq experiment where samples from all populations are randomized across all sequencing lanes, it is reassuring that with proper filtering and conservative SNP selection, this method is robust to identify population structure when randomization cannot be achieved.
This study is the first to characterize fine-scale population structure in the maize weevil. Previous research showed that the maize weevil has experienced a worldwide range expansion over the last several hundred years [9]. Here, we show that although there continues to be gene flow between populations of maize weevil, that fine-scale genetic structure exists. It is possible that this structure is shaped by the movement and trade of maize by humans in the region, by geographic barriers to gene flow, or a combination of factors.
Acknowledgments
We would like to thank all of the farmers who gave their time, knowledge, and weevils to us. We would also like to thank our guides in Mexico who facilitated our interactions with the farmers. Without their assistance we would not have been able to conduct this study.
References
- 1. García García-Lara S, García-Jaimes E, Bergvinson DJ. Mapping of maize storage losses due to insect pests in central Mexico. J Stored Prod Res. 2019;84: 101529.
- 2. García-Lara S, Bergvinson DJ. Identification of maize landraces with high level of resistance to storage pests Sitophilus zeamais Motschulsky and Prostephanus truncatus horn in Latin America. Rev Fitotec Mex. 2013;36(Suppl. 3-A): 347–356.
- 3. Chigoverah AA, Mvumi BM. Efficacy of metal silos and hermetic bags against stored-maize insect pests under simulated smallholder farmer conditions. J Stored Prod Res. 2016;69: 179–189.
- 4. Oliveira AP, Santos AA, Santana AS, Lima APS, Melo CR, Santana EDR, et al. Essential oil of Lippia sidoides and its major compound thymol: toxicity and walking response of populations of Sitophilus zeamais (Coleoptera: Curculionidae). Crop Prot. 2018;112: 33–38.
- 5. Arnason JT, Baum B, Gale J, Lambert JDH, Bergvinson D, Philogene BJR, et al. Variation in resistance of Mexican landraces of maize to maize weevil Sitphilus zeamais, in relation to taxonomic and biochemical parameters. Euphytica. 1994;74: 227–236.
- 6. Hellin J, Bellon MR, Hearne SJ. Maize Landraces and Adaptation to Climate Change in Mexico. J Crop Improv. 2014;28(4): 484–501.
- 7. Koul O, Walia S, Dhaliwal GS. Essential oils as green pesticides: potential and constraints. Biopestic Int. 2008;4(1): 63–84.
- 8. Ojo JA, Valero MC, Sun W, Coates BS, Omoloye AA, and Pittendrigh BR. Comparison of full mitochondrial genomes for the rice weevil, Sitophilus oryzae and the maize weevil, Sitophilus zeamais (Coleoptera: Curculionidae). AGGENE. 2016;2: 1–33.
- 9. Corrêa AS, Vinson CC, Braga LS, Guedes, RNC, de Oliveira LO. Ancient origin and recent range expansion of the maize weevil Sitophilus zeamais, and its genealogical relationship to the rice weevil S. oryzae. Bull Entomol Res. 2017;107(1): 9–20.
- 10. Silva AA, Braga LS, Corrêa AS, Holmes VR, Johnston JS, Oppert B, et al. Comparative cytogenetics and derived phylogenic relationship among Sitophilus grain weevils (Coleoptera, Curculionidae, Dryophthorinae). Comparative Cytogenetics 2018; 12(2): 223–245. pmid:29997743
- 11. Ndiaye MR, Sembène M. Genetic structure and phylogeographic evolution of the West African populations of Sitophilus zeamais (Coleoptera, Curculionidae). J Stored Prod Res. 2018;77: 135–143.
- 12. Thangaraj SR, McCulloch GA, Subtharishi S, Chandel RK, Debnath S, Subramaniam C, et al. Genetic diversity and its geographic structure in Sitophilus oryzae (Coleoptera; Curculionidae) across India–implications for managing phosphine resistance. Journal of Stored Products Research, 2019;84.
- 13. McCulloch GA, Mohankumar S, Subramanian S, Sonjai Rajan T, Rahul C, Surendran R, et al. Contrasting patterns of phylogeographic structuring in two key beetle pests of stored grain in India and Australia. Journal of Pest Science, 2019;92: 1249–1259.
- 14. Giles PH. Observations in Kenya on flight activity of stored products insects particularly Sitophilus zeamais Motsch. J Stored Prod Res. 1969;4(4): 317–329.
- 15. Brush SB, and Perales HR. A maize landscape: Ethnicity and agro-biodiversity in Chiapas Mexico. Agriculture, Ecosystems and Environment, 2007;121(3), 211–221.
- 16. Badstue LB, Bellon MR, Berthaud J, Ramírez A, Flores D, & Juárez X. The Dynamics of Farmers’ Maize Seed Supply Practices in the Central Valleys of Oaxaca, Mexico. World Development, 2007;35(9), 1579–1593.
- 17. International Finance Corporation. Investments for a windy harvest: IFC support of the Mexican wind sector drives results. 2014;1: 1–4. Available from: https://www.ifc.org/wps/wcm/connect/a0f55458-988a-4756-8ebd-f456235bc644/IFC_CTF_Mexico.pdf?MOD=AJPERES&CVID=kCCelk9
- 18. Baltzegar J, Barnes JC, Elsensohn JE, Gutzmann N, Jones MS, King S, et al. Anticipating complexity in the deployment of gene drive insects in agriculture. J Responsible Innov. 2018;5(S1): S81–S97.
- 19. Morin PA, Martien KK, Taylor BL. Assessing statistical power of SNPs for population structure and conservation studies. Mol Ecol Resour. 2009;9(1): 66–73. pmid:21564568
- 20. Hurst GDD and Jiggins FM. Problems with mitochondrial DNA as a marker in population, phylogeographic and phylogenetic studies: the effects of inherited symbionts. Proc R Soc B. 2005;272: 1525–1534. pmid:16048766
- 21. Carvalho GA, Corrêa AS, de Oliveira LO, and Guedes RNC. Evidence of horizontal transmission of primary and secondary endosymbionts between maize and rice weevils (Sitophilus zeamais and Sitophilus oryzae) and the parasitoid Theocolax elegans. J of Stored Prod Res. 2014;59: 61–65.
- 22. Corrêa AS, de Oliveira LO, Braga LS, and Guedes RNC. Distribution of the related weevil species Sitophilus oryzae and S. zeamais in Brazil. Insect Sci. 2013;20(6), 763–770. pmid:23956213
- 23. Vieira JL, and Guedes RNC. Endosymbiont load, personality and reproductive output of maize weevils (Sitophilus zeamais). Journal of Pest Science, 2021;94(3), 691–701.
- 24. Liu N, Chen L, Wang S, Oh C, Zhao H. 2005. Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure. BMC Genet. 2009;6(Suppl I): S26–5.
- 25. Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE. Double Digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS One. 2012;7(5): e37135. pmid:22675423
- 26. Fritz ML, Paa S, Baltzegar J, Gould F. Application of a dense genetic map for assessment of genomic responses to selection and inbreeding in Heliothis virescens. Insect Mol Biol. 2016;25(4): 385–400.
- 27. Andrews S, Krueger F, Segonds-Pichon A, Biggins L, Krueger C, Wingett S, et al. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics. Babraham Institute. 2010–2020. Available from: http://www.bioinformatics.babraham.ac.uk/projects/fastqc.
- 28. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15): 2114–2120. pmid:24695404
- 29. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19): 3047–3048. pmid:27312411
- 30. Rochette NC, Catchen JM. Deriving genotypes from RAD-seq short-read data using Stacks. Nat Protoc. 2017;12: 2640–2659. pmid:29189774
- 31. Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA. Stacks: an analysis tool set for population genomics. Mol Ecol. 2013;22(11): 3124–3140. pmid:23701397
- 32. Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008; [accessed 2020 Mar 24];24(11): 1403–1405. Available at: https://academic.oup.com/bioinformatics/article/24/11/1403/191127. pmid:18397895
- 33.
R Project for Statistical Computing. © The R Foundation. 2020. R Foundation for Statistical Computing, Vienna, Austria. Available from: https://www.R-project.org/.
- 34. Goudet JT. Hierfstat: Estimation and Tests of Hierarchical F-Statistics. 2018. Available from: http://www.r-project.org, http://github.com/jgx65/hierfstat.
- 35. Cordeiro EMG, Campbell JF, Phillips T, and Akhunov E. Isolation by Distance, Source-Sink Population Dynamics and Dispersal Facilitation by Trade Routes: Impact on Population Genetic Structure of a Stored Grain Pest. G3, 2019;9(5): 1457–1468. pmid:30808690
- 36. Holsinger KE, and Weir BS. Genetics in geographically structured populations: Defining, estimating and interpreting FST. Nature Reviews Genetics 2009;10(9): 639–650. pmid:19687804
- 37. Duforet-Frebourg N, Luu K, Laval G, Bazin E, and Blum MGB. Detecting genomic signatures of natural selection with principal component analysis: Application to the 1000 genomes data. Mol Biol Evol. 2016;33(4), 1082–1093. pmid:26715629