Agroecosystems shape population genetic structure of the greenhouse whitefly in Northern and Southern Europe

RESEARCH ARTICLE Open Access Agroecosystems shape population genetic structure of the greenhouse whitefly in Northern and Southern Europe Irina Ovčarenko 1,2* , Despoina Evripidis Kapantaidaki 3,4 , Leena Lindström 1 , Nathalie Gauthier 5 , Anastasia Tsagkarakou 3 , Karelyn Emily Knott 1 and Irene Vänninen 2 Abstract Background: To predict further invasions of pests it is important to understand what factors contribute to the genetic structure of their populations. Cosmopolitan pest species are ideal for studying how different agroecosystems affect population genetic structure within a species at different climatic extremes. We undertook the first population genetic study of the greenhouse whitefly (Trialeurodes vaporariorum), a cosmopolitan invasive herbivore, and examined the genetic structure of this species in Northern and Southern Europe. In Finland, cold temperatures limit whiteflies to greenhouses and prevent them from overwintering in nature, and in Greece, milder temperatures allow whiteflies to inhabit both fields and greenhouses year round, providing a greater potential for connectivity among populations. Using nine microsatellite markers, we genotyped 1274 T. vaporariorum females collected from 18 greenhouses in Finland and eight greenhouses as well as eight fields in Greece. Results: Populations from Finland were less diverse than those from Greece, suggesting that Greek populations are larger and subjected to fewer bottlenecks. Moreover, there was significant population genetic structure in both countries that was explained by different factors. Habitat (field vs. greenhouse) together with longitude explained genetic structure in Greece, whereas in Finland, genetic structure was explained by host plant species. Furthermore, there was no temporal genetic structure among populations in Finland, suggesting that year-round populations are able to persist in greenhouses. Conclusions: Taken together our results show that greenhouse agroecosystems can limit gene flow among populations in both climate zones. Fragmented populations in greenhouses could allow for efficient pest management. However, pest persistence in both climate zones, coupled with increasing opportunities for naturalization in temperate latitudes due to climate change, highlight challenges for the management of cosmopolitan pests in Northern and Southern Europe. Keywords: Trialeurodes vaporariorum, Pest management, Microsatellite markers, Climate zone, Host adaptation Background The dispersal of phytophagous insect pests can be enhanced by worldwide trade and human movement [1,2]. In addition, climate change facilitates movement of various taxa polewards [3,4]. Following introduction to new habitats, the establishment of insect pest populations can be favored by benign climates, as well as by monocultures in agroecosystems, i.e. agricultural fields and greenhouses [5-7]. Low genetic diversity of insect pest populations in newly occupied habitats suggests that even a single successful founder event is enough to establish populations [8,9]. However, further spread of introduced pests into natural ecosystems depends on the environment surrounding the initial introduction and on the origin of the introduced species [10,11]. For example, pests of tropical origin may be more likely to establish themselves in the Mediterranean than in the boreal climate zone [12,13]. At * Correspondence: [email protected] 1 Department of Biological and Environmental Science, University of Jyvaskyla, P.O. Box 35, FI-40014 Jyvaskyla, Finland 2 MTT Agrifood Research Finland, Plant Production Research, Tietotie, Animale building, FI-31600 Jokioinen, Finland Full list of author information is available at the end of the article © 2014 Ovcarenko et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Ovčarenko et al. BMC Evolutionary Biology 2014, 14:165 http://www.biomedcentral.com/1471-2148/14/165 southern latitudes there are suitable climatic conditions and year-round availability of host plants, in both natural ecosystems and densely aggregated agroecosystems. In contrast, at northern latitudes, natural habitats are only seasonally available and greenhouses are often sparsely distributed. Thus, the extent of establishment and spread of invasive pests in the North might be more dependent on the distribution of agroecosystems, particularly greenhouses, than it is in the South. Enclosed greenhouse environments are designed to reduce evaporation, pest entry [14] and loss of expensive biological pest control agents [15], to ensure efficient crop maintenance. Because greenhouses are relatively closed environments, pest populations in greenhouses might be generally more affected by insecticide applications and host plant changes than are pest populations in fields. These crop management practices can lead to reductions in population size and selection for resistant genotypes in the pests leading to increased homozygosity within and differentiation between pest populations [16]. Thus, populations of insects inhabiting greenhouses might show more genetic differentiation than those in fields. Indeed, populations of phytophagous pests inhabiting greenhouses often show population genetic structure, e.g. Tetranychus urticae Koch [17,18], although dispersal and gene flow can also be restricted among pest populations inhabiting fields, e.g. Leptinotarsa decemlineata Say [8]. The greenhouse whitefly (Trialeurodes vaporariorum Westwood) is an invasive pest which was brought to Europe (UK) on Orchidaceae from Mexico in 1856 [19]. Soon after introduction, it spread to the European continent, and in 1920 it was recorded in greenhouses in Finland [20]. In Finland, it spread through transportation on plant seedlings above the Arctic Circle to Rovaniemi, bringing considerable damage to tomato and cucumber crops, as well as to ornamental plants [21,22]. T. vaporariorum was reported from the Mediterranean region only later, in 1963 [19]. The species was noticed in Greece (Crete) only in 1978, when it began to cause pest management problems due to its resistance to insecticides [23,24]. T. vaporariorum currently has an almost cosmopolitan distribution [25,26]. Its success can be attributed to the worldwide distribution of greenhouse habitats, polyphagy [27], its tolerance of higher or lower temperatures than its biological control agents [17,26], and its haplodiploid mode of reproduction [28]. However, the absence of an overwintering resting stage [29] potentially limits its spread to natural ecosystems. Since the development of T. vaporariorum ceases at 8.3°C [30], year-round populations might persist at southern latitudes, where some host plants are available during winter, but are not likely to persist at northern latitudes, where crop cultivation in fields is seasonal and wild host plants decay during winter. To date, population genetic structure has been analyzed in only a few whitefly species, and little is known about population genetic structure in T. vaporariorum.Therelated Bemisia tabaci species complex, particularly Mediterranean B. tabaci (Med), is characterized by high genetic diversity and differentiation of populations, as indicated by both mitochondrial and microsatellite markers [31,32] (except in recently introduced populations in Taiwan and France [33,34]). Populations of B. tabaci (Med) in Greece separated by just a few kilometers show population genetic structure, possibly due to separate founder events or an older population history in this country [35]. Unlike the B. tabaci species complex, T. vaporariorum populations have low genetic diversity in mitochondrial genes [36,37]. Recent findings indicate that sequences of three mitochondrial genes and composition of endosymbiont communities from populations sampled from different continents show little variation (Kapantaidaki et al., unpubl.). Analysis of a few nuclear genes (allozymes) in T. vaporariorum populations from greenhouses in South Korea revealed their subdivision possibly due to restricted gene flow by natural geographic barriers [38]. However, studies of population genetic structure in T. vaporariorum with other, more polymorphic genetic markers, allowing description of more recent evolutionary processes, have not been performed until now. Recent findings of variation in phenotypic responses, particularly diverse responses to insecticide treatments among geographically close populations, suggest differentiation and low gene flow among invaded greenhouses [39]. To understand how insect pests respond to different environmental conditions, such as climate, habitat, and crop management practices, and the role of agroecosystems in shaping population genetic structure, comparative studies of population genetic diversity of pests in different climate zones are necessary. In this study we present the first extensive genetic data on population structure of the greenhouse whitefly. We compare the genetic structure of T. vaporariorum populations in Finland and in Greece, representing boreal and Mediterranean climate zones, and evaluate the influence of host plants and agricultural practices on the spatial and temporal population genetic structure of this invasive species. We hypothesize that T. vaporariorum populations in Northern Europe are more likely to be genetically differentiated than populations in Southern Europe, because this species is expected to be restricted to greenhouses in the North. Methods Sampling In Finland we sampled commercial greenhouses that operate year-round and produce primarily tomato and cucumber crops of various cultivars (Table 1). Samples were collected from greenhouses belonging to different Ovčarenko et al. BMC Evolutionary Biology 2014, 14:165 Page 2 of 17 http://www.biomedcentral.com/1471-2148/14/165 growers. In total 18 greenhouses were sampled in spring of 2010–2012. Ten of these were sampled twice: in 2010 and in 2011. Sampling was concentrated in Ostrobothnia (16 greenhouses) but also included two distant locations in other parts of the country (Figure 1-I). Ostrobothnia was the focus of our study because in this area we could find multiple greenhouses with different management practices in terms of host plant species, their cultivars (Table 1) and the origin of seedlings. Two to five greenhouses belonging to different growers were sampled within Närpes, Töjby and Pjelax villages. The minimum and maximum distances between these villages were 9 and 32 km, respectively, measured as straight line distance between coordinates. The distances between greenhouses within villages ranged from 1.1 to 3.7 km in Närpes, 0.4 km in Töjby and from 0.28 to 0.9 km in Pjelax. In Greece we sampled 16 agroecosystems in different seasons over several years: 2004–2011. These included eight greenhouses and eight fields growing various crop plants (Table 1) which were distributed throughout mainland Greece, the Peloponnese and the Island of Crete (Figure 1-II). Sampling was concentrated in the fields of West Peloponnese because open environments in this region cover the expected range (7–20 km) of the potential dispersal abilities of whiteflies (natural or by wind, as known from the B. tabaci species complex; [40,41]). In West Peloponnese, the minimum distance between samples ranged from 3.4 km (between WP3 and WP4) to 24.4 km (between WP3 and WP5), and the maximum distance between locations reached 100 km. During sampling, both genders of whiteflies were collected using a mouth aspirator. The whiteflies were preserved in 90% Ethanol and stored at 4°C until they were sexed and used for genotyping. Since T. vaporariorum is a haplodiploid species, only adult females were chosen for genotyping. DNA extraction and microsatellite genotyping Total genomic DNA was extracted from each individual female as described in Tsagkarakou et al. [42]. Nine microsatellite markers (Table 2) out of the 13 characterized in Molecular Ecology Resources Primer Development Consortium et al. [43] were used to genotype 1274 T. vaporariorum females, 800 from Finland and 474 from Greece (Table 3). Four of the microsatellite markers described previously did not amplify consistently, and thus, were excluded from this study. Three multiplex amplification reactions were performed as described in Molecular Ecology Resources Primer Development Consortium et al. [43] with slight modifications. Diluted amplification products were separated on an ABI 3130xl genetic analyzer (Applied Biosystems). Allele sizes were scored against GeneScan™500 LIZ standard using GeneMapper® v 4.0 software (both Applied Biosystems) and were confirmed manually. Data analysis To analyze genetic distance between samples, both between and within countries, pairwise estimates of F ST were calculated in Arlequin 3.11 [44]. The significance of the genetic distances at the 0.05 level was tested by permuting the individuals or genotypes between the samples 110 times and adjusting Pvalues with strict Bonferroni correction. Since all pairwise F ST between samples from Finland and Greece showed statistically significant differences, and due to the low probability of gene flow between the two distant countries, all further analyses were done for each country separately. The samples taken in two consecutive years from the same greenhouse in Finland showed no genetic differentiation, except for one sampling location (TJ-2 a and TJ2 b). Therefore, in most analyses we used a combined dataset, which pooled samples collected from the same greenhouse in 2010 and 2011, (except for TJ-2 a and TJ2 b, which were considered as separate samples). For other analyses (specified below) we used a separated dataset, in which each sampling effort in Finland was considered a separate sample. Each sampling effort in Greece was considered a separate sample in all analyses, because none of the locations were sampled in consecutive years. For each sample, mean observed (H O ) and expected heterozygosity (H E ), and mean number of alleles (N A ) per locus were calculated using GenAlEx v. 6.5 [45]. Observed and expected heterozygosities were also calculated for each locus over the total data from each country. Departure from Hardy-Weinberg expectations (HWE) was tested with 1000 permutations using a global test across loci or samples as implemented in GENEPOP v. 4.2 [46]. The test was performed using Fisher’s method, testing hypotheses of heterozygote deficiency and heterozygote excess [47], and producing global p value estimates for each sample over all loci and for each locus over samples from Finland and Greece. Genotypic linkage disequilibrium for each pair of loci in the samples was tested using the log likelihood ratio statistic (Gtest) as implemented in GENEPOP v. 4.2. For multiple tests, statistical significance was adjusted using strict Bonferroni corrections [48]. The samples were analyzed for potential scoring errors in all loci using MICROCHECKER v. 2.2.3 and the frequency of null alleles (f) was estimated [49]. To investigate the relationship between genetic and geographic distance, isolation by distance was analyzed in GenAlEx v. 6.5 [45]. Genetic distance was defined by pairwise linear F ST, (F ST /(1F ST )), and geographic distance was defined as pairwise distances generated from geographical coordinates expressed in decimal degrees. The correlation between the two data matrices was assessed using a Mantel test and its significance estimated by Pvalues, the Ovčarenko et al. BMC Evolutionary Biology 2014, 14:165 Page 3 of 17 http://www.biomedcentral.com/1471-2148/14/165 Table 1 Description of the samples collected in Finland and Greece Geographical information Host plant Collection Geographical coordinates: Date: Country/Region Locality Sample code Latitude Longitude Species Cultivar Family Habitat Month-year Finland Ostrobothnia Härkmeri HR a 62.165219 21.467372 Cucumber 1 Imea Cucurbitaceae G May-10 HR b Tomato 1 Espero Solanaceae G Apr-11 Korsnäs KR a 62.778983 21.204792 Cucumber Cadense R2 Cucurbitaceae G May-10 KR b Cucumber Cadense R2 Cucurbitaceae G Apr-11 Malax ML a 62.938797 21.526186 Cucumber 1 Diligare Cucurbitaceae G May-10 ML b Tomato 1 DRW Solanaceae G Apr-11 Närpes NR 1a 62.476119 21.416114 Tomato Encore Solanaceae G May-10 NR 1b Tomato Encore Solanaceae G Apr-11 NR 2 62.479328 21.395703 Cucumber Imea Cucurbitaceae G May-10 NR 3a 62.467842 21.346608 Cherry tomato Gonchita Solanaceae G May-10 NR 3b Tomato Gonchita Solanaceae G Apr-11 Pjelax PJ 1a 62.393006 21.382206 Tomato Encore Solanaceae G May-10 PJ 1b Tomato Encore Solanaceae G Apr-11 PJ 2 62.395511 21.381911 Tomato Encore Solanaceae G May-10 PJ 3a 62.396372 21.382139 Tomato Encore Solanaceae G May-10 PJ 3b Tomato Encore Solanaceae G Apr-11 PJ 4 62.397450 21.375103 Tomato Dometica Solanaceae G Apr-11 PJ 5 62.389081 21.371075 Tomato Dometica Solanaceae G Apr-11 Pörtom PR a 62.710939 21.623539 Tomato Encore Solanaceae G May-10 PR b Tomato Encore Solanaceae G Apr-11 Töjby TJ 1a 62.664411 21.221228 Cucumber Ventura Cucurbitaceae G May-10 TJ 1b Cucumber Logica Cucurbitaceae G Apr-11 TJ 2a 62.661847 21.226625 Cucumber Annica Cucurbitaceae G May-10 TJ 2b Cucumber Annica Cucurbitaceae G Apr-11 Övermark OV 62.611700 21.471772 Tomato Several cultivars 4 Solanaceae G Apr-11 Uusimaa Lohja LH 60.176453 23.981306 Cucumber Imea Cucurbitaceae G Apr-11 Northern Savonia Nilsiä NL 63.151436 27.987397 Cucumber 2 Imea Cucurbitaceae G Jul-12 Greece West Peloponnese Kourtessi WP 1 37.966667 21.330278 Cucumber - Cucurbitaceae F Jun-04 Filiatra WP 2 37.119983 21.584281 Zuccini - Cucurbitaceae F Jul-04 Elea WP 3 37.372628 21.688894 Eggplant - Solanaceae F Aug-11 Prasidaki WP 4 37.397167 21.711822 Bean - Fabaceae F Aug-11 Anemochori WP 5 37.588725 21.538794 Tomato - Solanaceae F Sep-11 Terpsithea WP 6 37.227417 21.628542 Bean - Fabaceae F Sep-11 Andravida WP 7 38.007222 21.395833 Marrow - Cucurbitaceae F Sep-11 North Peloponnese Aigio NP 38.216853 22.114178 Rose - Rosaceae G Aug-11 West Greece Agrinio WG 38.579722 21.418056 Tomato - Solanaceae G Jun-11 East Peloponnese Nafplion EP 37.745556 22.850278 Bean - Fabaceae F Oct-11 Attica Athens AT 37.983147 23.706583 Eggplant - Solanaceae G Apr-05 Ovčarenko et al. BMC Evolutionary Biology 2014, 14:165 Page 4 of 17 http://www.biomedcentral.com/1471-2148/14/165 regression coefficient (R 2 ), and the mean correlation coefficient (R XY ) over 999 random permutations of linear F ST values as implemented in GenAlex v.6.5. Isolation by distance was assessed with smaller subsets of the data as well as using the full datasets, to evaluate the influence of scale on the relationships. Analyses of molecular variance (AMOVA) were performed using Arlequin 3.11 [44] to estimate and compare the percentage of genetic variation explained by different hierarchical groups (i.e. individual, sample, group of samples). Four analyses were constructed to test the following groups: 1) country (Finland vs. Greece), 2) host plant species (cucumber vs. tomato) in samples from Finland only, 3) host plant botanical family (Cucurbitaceae vs. Solanaceae vs. Fabaceae vs. Rosaceae) in samples from Greece only, and 4) habitat (greenhouse vs. field) in samples from Greece only. For analysis 2, samples HR, ML and NL were excluded since the whiteflies in these greenhouses might have been exposed to both cucumber and tomato grown in the same compartment or greenhouse. However, in analysis 3, sample MA 2 was not excluded because several hosts grown in the same compartment belonged to the same family (Solanaceae) (see Table 1). To assess the level of genetic differentiation between groups defined above for AMOVA, we compared summary statistics calculated for the different groups: F IS (inbreeding coefficient measuring heterozygote deficit within populations), F ST (a measure of population structure and heterozygote deficit among populations), allelic richness (measure of the number of alleles independent Table 1 Description of the samples collected in Finland and Greece (Continued) Island of Crete Fodele CR 1 35.398228 24.963689 Rose - Rosaceae G Mar-10 Sissi CR 2 35.305961 25.535006 Rose - Rosaceae G Apr-11 Malades CR 3 35.268528 25.104956 Datura - Solanaceae G Apr-11 Macedonia Serres MA 1 41.225933 23.361469 Tomato - Solanaceae G May-11 Drama MA 2 41.124744 24.162803 Sweet pepper 3 - Solanaceae G May-11 Lower case letters adjacent to population codes indicate the same location sampled in 2010 and 2011. G indicates samples collected from greenhouses, F –from fields. 1 Cucumber and tomato were growing in the same greenhouse compartment. 2 Cucumber and tomato were growing in different greenhouse compartments. 3 Tomato and eggplant were growing in the same greenhouse compartment. 4 Encore, Careza, Dometica, Dirk and Axxion cultivars grown in the same greenhouse compartment. CR 2 II Greece MA 2 100 Km AT WG MA 1 CR 1 CR 3 20 Km KR a,b ML a,b HR a,b NR 1,2,3 PJ 1,2,3,4,5 TJ 1, 2 PR OV I Finland 100 Km NL LH I II EP 50 Km WP 1 WP 2 WP 3,4 WP 5 WP 6 WP 7 NP Figure 1 Maps of sampling locations. Sample codes are listed in Table 1. I - Finland, II - Greece. Ovčarenko et al. BMC Evolutionary Biology 2014, 14:165 Page 5 of 17 http://www.biomedcentral.com/1471-2148/14/165 of sample size), H E (unbiased expected heterozygosity) and H O (observed heterozygosity). FSTAT v. 2.9.3 [50] was used to calculate the average (over samples and loci; weighted by sample size) of the chosen statistics for each group and for their comparison. Statistical significance was assessed after 1000 permutations. As in the AMOVA groups, some samples were excluded because multiple hosts were grown in the same compartment or greenhouse (see above). The relationship between environmental variables and genetic structure of the studied populations was estimated using default settings of the software GESTE v. 2.0 [51]. The software gives the highest posterior probability (Pr) to the model explaining genetic structure the best, evaluating environmental variables separately and in combination through a generalized linear model. In this analysis, the separated data set for the samples from Finland was used. Latitude, host plant species, cultivar, crop source and year of sampling were evaluated as explanatory variables of population structure in Finland. Latitude, longitude, four host plant families and habitat (field or greenhouse) were evaluated as explanatory variables of population structure in Greece. Bayesian clustering analysis implemented in STRUCTURE v.2.3.4 [52] was used to infer the number of genetically distinct clusters (K) in each country using a model of no admixture, correlated allele frequencies and including the sampling location as a prior [53]. Initial analyses were performed both with admixture and no admixture models, but the later was selected since visualization of the results was more straightforward and no differences in the most likely number of clusters were observed for the two models. Analysis parameters included a burn-in period of 250,000 followed by 500,000 MCMC iterations. For each dataset, Finland and Greece, we tested Kfrom 2 to 10, with ten replicate analyses per value of K. Subsets of each dataset were analyzed with the same settings. The most likely number of clusters in our samples was determined using the ΔKapproach [54] as implemented in Structure Harvester v. 0.56.3 [55]. Results were visualized as bar plots by finding the optimal alignment of the ten replicate analyses of the “best”Kin CLUMPP v. 1.1.2 [56] using the Greedy algorithm and 1000 random input orders, and then by creating graphics in Distruct v. 1.1 [57]. For Finland, the combined dataset was used first, and then two subsets of the data were created. In these subsets, the Table 2 Characteristics of the nine polymorphic microsatellite loci analysed in T. vaporariorum Locus (Genbank Accession no.) Primer sequence (5′-3′) (F: [dye]-forward; R: reverse) Repeat motif (cloned allele) Size range (bp) No. of alleles H 0 /H E Finland H 0 /H E Greece Tvap-1-1C F: [6-FAM]- GAGACTCCACGATGTCTGTC (GT) 6 GG(GT) 9 195-215 3 0.469/0.499* 0.455/0.461 (GF112015) R: TTCCCCTATCGTATGTTCAC Tvap-1-2 F: [VIC]- CTGTGAATCCCTCAGAAATC (GT) 6 233-236 2 0.094/0.108 0.299/0.308 (GF112025) R: TGACCTCTCTCAGGCTTTTA Tvap-3-1 F: [PET]- GAGATGGACAAACTACAACG (AC) 15 228-230 2 0.246/0.437*0.268/0.355* (GF112016) R: GATTGGATGTCGTGGTTG Tvap-3-2 F: [6-FAM]- GGAGGTCATTACTCATTTCG (AC) 6 170-182 4 0.401/0.405 0.522/0.581 (GF112017) R: CATAAATTTTCGGCTCACTC Tvap-3-3 F: [VIC]- CGCAAATCATACTTCCTTTC (CA) 5 235-237 2 0.417/0.412 0.496/0.459 (GF112019) R: AAATACAGGCGACTCATGTC Tvap-4-2 F: [NED]- GGTGGTATTGTGGCGTC (GA) 29 298-314 7 0.446/0.468 0.585/0.667 (GF112027) R: CTGCCTCTTATGACTCTTCC Tvap-1-4 F: [PET]- GATTTAGCCCAGTTCATTTG (TG) 5 265-267 2 0.091/0.097 0.137/0.179* (GF112020) R: CTTCAGTTGAGCTGCTGATG Tvap-1-5 F: [6-FAM]- CAGTTGTGGTAGTGTGGTG (TG) 12 124-146 10 0.416/0.411 0.703/0.757 (GF112028) R: CTCATCGGCTCATACATTC Tvap-2-2C F: [VIC]- CTGAAAGTCTTATTAGAGCC (TC) 8 GC (TC) 10 210-220 6 0.568/0.55 0.588/0.608 (GF112021) R: CTAACTGATTCCATAGTCG No. of alleles indicates the maximum number of alleles found in this study. H O , observed heterozygosity; H E , unbiased expected heterozygosity. *indicate H O /H E values with potential presence of null alleles with frequency > 0.2. H O /H E in bold indicate loci with significant deviations from Hardy–Weinberg equilibrium in terms of heterozygote deficiency after Bonferroni correction (no significant heterozygote excess was detected). Heterozygosities, deviations from HWE and null allele frequencies were estimated over 800 females from 18 samples in Finland and 474 females from 16 samples in Greece. Ovčarenko et al. BMC Evolutionary Biology 2014, 14:165 Page 6 of 17 http://www.biomedcentral.com/1471-2148/14/165 samples were grouped by host plant species and samples HR a, b and ML a, b were separated since they had been collected from different hosts (see Table 1). Samples HR, ML and NL were included in both data subsets since these samples might have been exposed to several hosts grown in the same compartment or greenhouse. For Greece, all samples were first analyzed together, then data subsets were created grouping samples by habitat (field or greenhouse). The definition of the data subsets (by host plant species or habitat) was chosen after considering the results of initial analyses with the full data sets and our analysis with GESTE v. 2.0 [51]. Table 3 Genetic diversity estimated over the nine microsatellite loci for samples of T. vaporariorum Country Region Locality Sample code NN A (±SE) H O /H E Finland Ostrobothnia Härkmeri HR a,b 30 + 30 2.556(±0.294) 0.375/0.435* Korsnäs KR a,b 29 + 30 2.333(±0.373) 0.222/0.254* Malax ML a,b 30 + 30 2.667(±0.236) 0.375/0.404 Närpes NR 1a,b 30 + 30 3.111(±0.423) 0.369/0.406 NR 2 30 2.333(±0.167) 0.256/0.341* NR 3a,b 30 + 30 2.889(±0.351) 0.308/0.375* Pjelax PJ 1a,b 30 + 30 2.889(±0.389) 0.375/0.429* PJ 2 30 2.667(±0.236) 0.422/0.422 PJ 3a,b 30 + 30 3.111(±0.455) 0.396/0.429* PJ 4 30 2.667(±0.289) 0.359/0.417* PJ 5 30 2.667(±0.333) 0.407/0.406 Pörtom PR a,b 30 + 30 3.444 (±0.669) 0.434/0.484 Töjby TJ 1a,b 30 + 30 3.222 (±0.494) 0.570/0.556* TJ 2a 30 3.333 (±0.577) 0.465/0.517 TJ 2b 30 3.667 (±0.707) 0.554/0.531* Övermark OV 30 3.556 (±0.648) 0.511/0.543* Uusimaa Lohja LH 21 3.667 (±0.799) 0.541/0.529 Northern Savonia Nilsiä NL 30 3.333 (±0.645) 0.448/0.512 Greece West Peloponnese Kourtessi WP 1 30 3.222(±0.494) 0.422/0.450 Filiatra WP 2 29 3.333 (±0.553) 0.415/0.524 Elea WP 3 30 3.444 (±0.626) 0.459/0.540 Prasidaki WP 4 30 2.667 (±0.236) 0.409/0.457 Anemochori WP 5 30 3.444 (±0.603) 0.437/0.394 Terpsithea WP 6 30 3.222 (±0.494) 0.428/0.469* Andravida WP 7 30 3.111 (±0.484) 0.441/0.419 North Peloponnese Aigio NP 30 3.222 (±0.494) 0.277/0.396 West Greece Agrinio WG 30 3.444 (±0.689) 0.395/0.455 East Peloponnese Navplion EP 30 3.222(±0.494) 0.422/0.450 Attica Athens AT 28 3.333 (±0.553) 0.415/0.524* Island of Crete Fodele CR 1 30 3.444 (±0.626) 0.459/0.540 Sissi CR 2 30 2.667 (±0.236) 0.409/0.457 Malades CR 3 30 3.444 (±0.603) 0.437/0.394* Macedonia Serres MA 1 27 3.222 (±0.494) 0.428/0.469* Drama MA 2 30 3.111 (±0.484) 0.441/0.419 For Finland the combined dataset, which pooled samples from consecutive years at the same location (except TJ 2) is described, since it was used in the majority of analyses. Lower case letters adjacent to population codes indicate the same location sampled in 2010 and 2011. N number of analyzed females, H O observed and H E expected heterozygosity and N A mean number of alleles per population averaged over 9 loci. * indicate H O /H E values in samples with null allele frequency > 0.2. H O /H E in bold indicate loci with significant deviations from Hardy–Weinberg equilibrium in terms of heterozygote deficiency after Bonferroni correction (no significant heterozygote excess was detected). Ovčarenko et al. BMC Evolutionary Biology 2014, 14:165 Page 7 of 17 http://www.biomedcentral.com/1471-2148/14/165 Results Genetic diversity of microsatellite loci and samples Significant deviations from HWE through heterozygote deficiencies were detected at one locus (Tvap 3–1) and 4 loci (Tvap-4-2, Tvap-1-4, Tvap-1-5 and Tvap-3-1) in the Finnish and Greek samples, respectively (Table 2). At the sample level, a test of HWE across the nine microsatellite loci indicated significant heterozygote deficiency in two Finnish and three Greek samples (Table 3). There were no cases of significant heterozygote excess. Three loci showed a null allele frequency > 0.2: Tvap-11C (2 samples), Tvap-3-1 (12 samples) and Tvap-1-4 (1 sample). For each of these loci, the frequency of null alleles within a sample varied: f= 0.110-0.258 (Tvap-1-1C), f= 0.164-0.401 (Tvap-3-1), and f= 0.166-0.238 (Tvap-1-4), and the average frequency of null alleles over the samples ranged from 0.119 to 0.250. No cases of large allele drop out were found. Even though null alleles are present, deviations from HWE could be also due to significant homozygosity in populations inhabiting the human-mediated environment (i.e. due to population bottlenecks and inbreeding), rather than due to significant genotyping errors. Genotypic linkage disequilibrium tested for each pair of loci for each sample revealed a potential association between loci Tvap-1-1 and Tvap 3–1 in sample PJ 4. Since locus Tvap-3-1 was characterized by homozygote excess and had a high frequency of null alleles only in sample PJ 4 (f= 0.401), we suspect that the linkage disequilibrium indicated for this sample does not reflect a true association between the loci. Therefore, data from all nine loci were used in the analyses. Differences between Finland and Greece AMOVA indicated significant genetic structure between the two geographic areas (Table 4). The percentage of variation explained by country of origin, Finland vs. Greece, was higher than that among the samples within each country (9.90% and 6.87%, respectively; Table 4), indicating that overall genetic variation might be explained by these groups. T. vaporariorum from the two countries also differed significantly in their global observed and expected heterozygosities (Finland: H O /H E = 0.350/0.385 vs. Greece: H O /H E = 0.451/0.496), and in allelic richness (2.498 vs. 3.234 for Finland vs. Greece, respectively) (all P= 0.001). However, Fstatistics calculated for each country did not differ statistically (Finland/Greece; F IS : 0.091/0.090, P= 0.157; F ST : 0.093/0.055, P= 0.976). Nevertheless, the range of pairwise F ST values between samples within countries was broader for Finland (−0.006 < F ST <0.533)than it was for Greece (−0.007 < F ST < 0.164) (Tables 5A and B). Population structure in Finland Seventy nine percent of pairwise F ST comparisons (121 of 154) between samples from Finland showed significant population differentiation (Table 5A). Some populations (HR, KR, PR, TJ 2b and LH) were differentiated from all other samples (Table 5A). However, one of the samples most distant from the Ostrobothnia region (NL) was not significantly different in pairwise F ST from one of the Ostrobothnian samples (OV). For samples collected from different greenhouses at the same location (NR, PJ and TJ), there was no significant genetic structure, except for TJ: TJ1 was not differentiated from TJ 2a, but both of Table 4 Distribution of the molecular variance between and within four groups of samples of T. vaporariorum Source of variation d.f. Sum of squares Variance components Percentage of variation Fixation indices Pvalues Between countries 1 275.947 0.221 9.900 F CT : 0.099 0 ± 0 Among samples within countries 32 425.048 0.153 6.870 F SC : 0.076 0 ± 0 Within samples 2514 4665.034 1.856 83.240 F ST :0.168 0 ± 0 Between host plant groups in Finland 1 1 34.818 0.031 1.690 F CT : 0.017 0.036 ± 0.006 Among samples within groups 13 195.663 0.157 8.590 F SC : 0.087 0 ± 0 Within samples 1285 2111.236 1.643 89.720 F ST : 0.103 0 ± 0 Among host plant groups in Greece 2 3 32.540 0.006 0.250 F CT : 0.002 0.266 ± 0.013 Among samples within groups 12 114.290 0.124 5.330 F SC : 0.053 0 ± 0 Within samples 932 2045.532 2.195 94.420 F ST : 0.056 0 ± 0 Between habitats in Greece 1 32.665 0.052 2.200 F CT : 0.022 0.001 ± 0.001 Among samples within groups 14 114.165 0.010 4.290 F SC : 0.044 0 ± 0 Within samples 932 2045.532 2.195 93.510 F ST : 0.065 0 ± 0 1 Between groups of samples collected from Cucurbitaceae, Solanaceae host plant families in Finland (samples HR, ML and NL are not included, see Methods for details). 2 Among groups of samples collected from Cucurbitaceae, Solanaceae, Fabaceae and Rosaceae host plant families in Greece. Ovčarenko et al. BMC Evolutionary Biology 2014, 14:165 Page 8 of 17 http://www.biomedcentral.com/1471-2148/14/165 Table 5 Pairwise estimates of F ST between samples in Finland (A) and Greece (B) over the nine microsatellite loci A Finland HR KR ML PJ 1 PJ 2 PJ 3 PJ 4 PJ 5 NR 1 NR 2 NR 3 PR OV TJ 1 TJ 2a TJ 2b LH NL HR 0.000 KR 0.207 0.000 ML 0.025 0.181 0.000 PJ 1 0.055 0.173 0.024 0.000 PJ 2 0.052 0.222 0.027 0.009 0.000 PJ 3 0.058 0.201 0.022 0.015 0.003 0.000 PJ 4 0.064 0.216 0.027 −0.004 −0.006 0.002 0.000 PJ 5 0.073 0.222 0.044 0.004 0.001 0.018 −0.005 0.000 NR 1 0.050 0.204 0.010 −0.004 0.017 0.012 0.006 0.013 0.000 NR 2 0.095 0.240 0.044 0.036 0.067 0.065 0.052 0.053 0.023 0.000 NR 3 0.079 0.207 0.024 0.029 0.059 0.051 0.044 0.043 0.010 0.001 0.000 PR 0.082 0.212 0.053 0.080 0.109 0.010 0.096 0.116 0.063 0.050 0.045 0.000 OV 0.048 0.217 0.015 0.024 0.026 0.028 0.023 0.033 0.024 0.039 0.027 0.034 0.000 TJ 1 0.113 0.311 0.064 0.077 0.104 0.094 0.088 0.087 0.050 0.011 0.021 0.056 0.044 0.000 TJ 2a 0.085 0.263 0.035 0.051 0.070 0.053 0.058 0.061 0.025 0.003 0.001 0.033 0.030 0.002 0.000 TJ 2b 0.248 0.533 0.236 0.197 0.225 0.215 0.212 0.206 0.193 0.183 0.211 0.232 0.198 0.129 0.185 0.000 LH 0.131 0.293 0.115 0.063 0.073 0.102 0.057 0.055 0.090 0.083 0.104 0.156 0.078 0.119 0.123 0.196 0.000 NL 0.060 0.327 0.031 0.066 0.089 0.045 0.086 0.111 0.034 0.109 0.077 0.050 0.035 0.096 0.061 0.294 0.227 0.000 Ovčarenko et al. 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