Trioza erytreae is one of the major threats to citrus industry as the vector of the incurable disease known as huanglongbing (HLB) or citrus greening. The psyllid invaded the northwest of the Iberian Peninsula 6 years ago. The invasion alarmed citrus growers in the Mediterranean basin, the largest citrus producing area in Europe, which is still free of HLB. Before our study, no research had been carried out on the genetic diversity of T. erytreae populations that have invaded the Iberian Peninsula and the archipelagos of the Macaronesia (Madeira and the Canary Islands). In this study, combining microsatellites markers and mtDNA barcoding analysis, we characterize the genetic diversity, structure and maternal relationship of these new invasive populations of T. erytreae and those from Africa. Our results suggest that the outbreaks of T. erytreae in the Iberian Peninsula may have derived from the Canary Islands. The populations of T. erytreae that invaded Macaronesia and the Iberian Peninsula are likely to have originated from southern Africa. We anticipate our results to be a starting point for tracking the spread of this invasive pest outside of Africa and to be important for optimizing contingency and eradication plans in newly invaded and free areas.The African citrus psyllid  Trioza erytreae (Del Guercio) (Hemiptera: Triozidae) is an invasive pest that has become one of most severe threats to the Mediterranean citrus industry3. Along with the Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Psyllidae), T. erytreae is a vector of the phloem-restricted Gram-negative bacteria Candidatus Liberibacter species that occur in citrus. These bacteria are the causative agents of citrus greening disease, an incurable and most devastating disease that affect citrus, also known as huanglongbing (yellow dragon disease) or simply by its acronym HLB5. Spread of HLB by T. erytreae had a devastating impact on citrus production in the cooler highland regions of Kenya and Tanzania, where it caused losses of 25–100%6. In Florida, the largest orange growing area in the United States of America, the spread of HLB by D. citri caused citrus production decline by 74%, resulting in losses of about USD 4554 million8. Besides their role as HLB vectors, the nymphs of both psyllid species excrete large amount of honeydew that facilitates the growth of sooty moulds on infested trees9.The African citrus psyllid Trioza erytreae was first reported in 1897 in the South African regions of the Eastern Cape and Stellenbosch10, but it was not until 1918 that Del Guercio first described the African citrus psyllid from samples collected on lemon trees in Eritrea (Ethiopia)11. Trioza erytreae has been reported in Sub-Saharan Africa, e.g. Kenya (Eastern Africa), South Africa (Southern Africa), the South Atlantic island of Saint Helena, in the Indian Ocean islands of Madagascar, in addition to Sudan (in north-eastern Africa)4. Trioza erytreae has also been found in the Arabian Peninsula, where it was likely introduced into Yemen from Ethiopia12. In 1994, T. erytreae was found in the archipelagos of the Macaronesia, in the North Atlantic Ocean. It was first reported in Madeira13 and 8 years later in the Canary Islands14. Trioza erytreae has become adventive in a wide range of different geographic locations, altitudinal tiers (from sea level to 1300 m above sea level), different environmental conditions 15, and to a wide range of host species mainly within Rutaceae16.T. erytreae was recorded for first time in the summer of 2014, specifically in the northwest of the Iberian Peninsula, in Pontevedra (Spain), and Porto 17. Since then, its distributional range in the Iberian Peninsula has expanded rapidly to the point of becoming a serious threat to the entire citrus industry based in the Mediterranean basin2. Between 2017 and 2018, the psyllid moved more than 200 km southwards along the west coast of Portugal, from Figueira da Foz (Coimbra) in the north to Pontes  in the south. Currently, the distribution of T. erytreae in the west coast of the Iberian Peninsula covers a straight uninterrupted line of 600 km from Cedeira  to Setúbal 18. At the present, no further southerly movement of the psyllid has been detected towards the closet major European citrus growing areas . In Spain, the distribution of T. erytreae has expanded in a north-eastern direction throughout to the Cantabrian coast, and specific outbreaks have been reported recently in Asturias, Cantabria and the Basque Country19. So far it is still unknown how T. erytreae reached the Iberian Peninsula.In mainland Europe, T. erytreae. The wide distribution of T. erytreae makes any contingency measure a challenging effort20. It has been demonstrated that the parasitoid wasp Tamarixia dryi (Waterston) (Hymenoptera: Eulophidae) is a highly specific parasitoid of T. erytreae22, and its use in classical biological control programme has decreased the African citrus psyllid populations in the Canary Islands drastically until they have almost totally disappeared23. A T. dryi-assisted biological control program against T. erytreae was also carried out successfully in the Indian Ocean island of Réunion 24 and Mauritius25.Although the HLB has not been detected in the mainland of Spain and Portugal, the alarm of citrus growers in the Mediterranean basin, particularly in Spain—the largest citrus fruit producer in Europe—is increasing due to the rapid spread of 26. The first report on the intraspecific genetic diversity of the African citrus psyllid, suggested that T. erytreae recorded in Europe most likely originated from South Africa, although the possibility of a Kenyan origin could not be ruled out based solely on the use of the COI mitochondrial genetic marker (mtDNA barcoding)27.Knowledge of the geographical origin of invasive insects is essential for developing effective contingency measures against these threatening pests. In this regard, it has been demonstrated that information on genetic variation among populations can be used to assess this questionSimple Sequence Repeats) have been used widely, because of their extensive genome distribution and high level of polymorphism, to study the genetic diversity and phylogeography of several crop pest insects such the tomato leaf miner Tuta absoluta (Meyrick)28 (Lepidoptera: Gelechiidae); the beet armyworm Spodoptera exigua (Hübner)29 (Lepidoptera: Noctuidae); the sugar cane aphid Melanaphis sacchari (Zehntner)30 (Hemiptera: Aphididae); the cotton mealybug Phenacoccus solenopsis (Tinsley)31 (Hemiptera: Pseudococcidae); the whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae); as well as the Asian citrus psyllid D. citri34. The main objective of the present work is to gain insights in the genetic structure of the T. erytreae populations, as very little is known about their genetic traits. The inference of the origin and colonisation routes of the T. erytreae invasive populations in Europe is critical for the design and implementation of accurate management strategies against this devastating pest. We used nuclear SSR markers and mtDNA barcoding to analyse the genetic diversity and phylogeny of T. erytreae populations collected from different locations in Galicia and Canary Islands, Madeira, Portugal mainland, and Africa (Kenya and South Africa).Apart from mtDNA barcoding, nuclear markers such as the microsatellites . Analysis of SSR distribution revealed that the di-nucleotide motifs  were the most abundant SSRs, with a frequency of 79.4%. Both tetra-  and tri-  nucleotide repeats comprised about 5–15%  and CT/AG (31.9%) are the most frequent motif pairs, with a total frequency of 68.5% , Tery6_s02825  and Tery6_s07841 (26739 bp) were randomly selected based on their sequence length . SSRs were selected on the base of their type of repeat motif , nucleotide composition and length (number of in tandem repeated motifs) . The scaffolds Tery6_s00034, Tery06, − 07, − 13 and − 14 were found in inter-genic regions, while Tery08, − 12 and − 15 were mapped into introns. For Tery05, − 9, − 10 and − 11 was not possible to establish whether they targeted coding or non-coding regions. SSR loci Tery01, − 02 and − 03 were found in intron regions in the scaffold Tery6_s07841, and SSR locus Tery04 in an inter-genic region in the sequence corresponding to the scaffold Tery6_s02825. For amplification of SSR loci, specific PCR primers were designed on the sequence flanking the in tandem repeated motifs. Blast of the different amplicons against the T. erytreae draft genome sequence showed that PCR primers would result in the specific amplification of their specific SSR locus. Experimental validation of PCR primers was carried out on a testing panel of individuals collected in different locations in the Canary Islands and South Africa. Primers pairs for SSR loci Tery04, − 05, − 06, − 08, − 09, − 10, − 11, − 12, − 13 and − 15 yielded DNA fragments of the expected size and were chosen for carry on further population genetic analysis. These loci contain eight di-nucleotides , one tri-nucleotide (TGA), and three tetra-nucleotides , which arranged in microsatellites of different length (from 5 to 30 in tandem repeated motifs)  was seven. SSR markers Tery08 and Tery11 had the highest number of alleles , whereas Tery13 had the lowest . The expected (He) and observed (Ho) heterozygosity per locus in the entire population ranged from 0.20 to 0.77 and from 0.03 to 0.84, respectively. SSRs Tery11 and Tery08 displayed the highest diversity , and Tery09 and Tery13  were the least informative markers. Most of the SSR markers used in this work showed He values higher than 0.5, apart from Tery05, − 09 and − 13 . With the only exception of Tery04 and Tery15, for most of the analysed SSRs He was higher than Ho. It can be also observed that the whole population displayed a deficit of average Ho (0.31) compared with the He value (0.51) under Hardy–Weinberg equilibrium. This observation agrees with the positive value of the Wright’s fixation index (Fw) estimated for all analysed SSR markers over the whole population (Fw = 0.41). The SSR markers Tery12 and Tery13 showed Fw values close to 1.0 , suggesting that their alleles were considerably fixed in the population.The individuals of ya Table , were anT. erytreae sampled in newly invaded areas from Spain and Portugal, including Madeira and the Canary Islands, and those from the previous invaded areas in Africa (South Africa and Kenya), we used a Bayesian clustering method to analyse the SSR multi-locus genotyping data. The STRUCTURE analysis according to the method of ΔK36 showed that the overall genetic profile of all the individuals sampled could be described with two or three different hypothetically original populations corresponding to the highest ΔK values  are 2 or 3. Nevertheless, Pritchard’s method37 showed a posterior probability of data at K = 7  as was suggested by the method of ΔK36 whereby most of the analysed individuals were classified with high probability (Q > 0.90) in two clusters . Almost of all individuals from Moreno, Poiso, and Farrobo  were entirely reassigned to Cluster 3 along with several individuals from the Canary Islands and Galicia (Spain). In addition, individuals from Vairão (Porto) and São Vicente de Pereira Jusã (Aveiro)  were also assigned to Cluster 3, while those individuals sampled from southern locations up to Sobreda  were assigned to Cluster 1. The exceptions to this pattern were the individuals from Ribamar (Ericeira), which were assigned to Cluster 3. Most notably, samples from Kenya were genetically different from those of South Africa and grouped in Cluster 1. At K = 7 the population structure scenario was more hierarchical, but 73% of all individuals (108 out from 147) could be assigned to one of the seven clusters with more than 90% probability (Q > 0.9). The assignment of half of the remaining individuals (21 out of 39) could be done with more than 70% probability (Q > 0.7). Among the different groups, Cluster 1 (in green) and 2 (in beige) are restricted to the populations of South Africa and Kenya, respectively, with almost no presence of individuals from any of the newly invaded areas. Clusters 3 (in purple) and 4 (in pink) are mostly exclusive to the individuals from Madeira and Portugal mainland, although with some membership in the Canary Islands and Galicia. Cluster 5 (in light blue) and Cluster 6 (in orange) are represented by individuals from Madeira, the Canary Islands and Galicia, while the individuals from Camacha (Madeira) –the only ones that were collected from Casimiroa edulis La Llave & Lex. —form exclusively Cluster 7 (in dark blue). Remarkably, Q fractions corresponding to Cluster 7 are present in the individuals from Nelspruit, Tzaneen, and some in Pretoria.To assess the differentiation and genetic diversity among the local populations of ues Fig. . It meanT. erytreae populations was also assessed by means of a distance-based clustering method. The scored SSR allelic data obtained from the ten SSR loci developed in this study were used to calculate a genetic dissimilarity matrix and to compute a Neighbor Joining (NJ) tree. A preliminary dendogram constructed using only the African populations of T. erytreae showed that the individuals from South Africa grouped together into a single cluster clearly separated from the Kenyan population. The robustness of the tree clustering was supported by the high bootstrap values obtained for nearly all branches  on that previous topology. Inspection of the constrained tree topology revealed seven clusters that were in congruence with the structural population at K = 7 suggested by the STRUCTURE analysis  performed well in our distance-based clustering analysis.The genetic diversity of hes Fig. . To confsis Fig. . It is nT. erytreae individuals collected in this study was assessed using mitochondrial DNA (mtDNA) barcoding, the sequence analysis of nucleotide variations in the 5’ region of the Cytochrome C Oxidase I gene (COI)38. Comparison of the nucleotide sequence of COI barcode fragments from this study with other T. erytreae GenBank accessions demonstrated that all sequences are highly conserved (Supplementary Data 3). With the only exception of the fragments amplified from Kenyan individuals, all remaining sequences showed absolute identity (100%) to the GenBank accession numbers that we previously deposited in the GenBank39, corresponding to COI barcode sequences from T. erytreae individuals collected in the Canary Islands (MK285551-MK285553), Galicia (MK285548-MK285550), Madeira (MK285558) and South Africa  (Supplementary Data 4). Alignment of sequences amplified in this study from individuals collected in Homa Bay (Kenya) shared absolute identity with those extracted from the entire mitochondrion genome sequences from Eritrea and Uganda27, and 97–98% identity with those COI barcode fragment sequences from individuals from other locations in Kenya39.The maternal phylogenetic relationship among the T. erytreae populations that have invaded Spain and Portugal, with those from South Africa and Kenya 39. The few exceptions to this observation were three South African individuals, one from Pretoria (Pretoria-100), and two from West Acres (TeSA1 and TeSA7), which may correspond to migrants from Nelspruit or Tzaneen, and Pretoria, respectively. In a sister clade position to the South African clade, the GenBank accessions of Kenyan and Tanzania COI sequences39 included in this phylogenetic analysis formed a monophyletic group. The COI barcode sequences from Homa Bay (Kenya) clustered separately as an outgroup in a different clade with the corresponding fragment extracted from the mitochondrion genome sequences from Ethiopia and Uganda 27, and Cameroon (MG989238)40 present in GenBank.The nucleotide sequences of the COI barcode fragments generated in this study (n = 39) and some previously deposited in the GenBank (n = 37) were used to analyse the maternal phylogenetic relationship of nya Fig. . From alria Fig. , as the 44. However, although the contribution of SSRs in studies of population diversity and structure is undeniable, their comprehensive characterisation and development as molecular markers are only possible after genome sequences are available. In Hemiptera, microsatellites have been characterized for several invasive and devastating pests, including the Asian citrus psyllid D. citri45. The recent sequencing of T. erytreae genome has made this first report of a comprehensive identification and distribution analysis of SSRs for this invasive and devastating citrus pest possible. We found that the total number and frequency of T. erytreae SSR markers  are about 3.6 and 3.8 times lower, respectively, compared to D. citri 38. The estimated frequency in other Hemiptera range from 0.16 for a vector of Chagas disease Rhodnius prolixus Stål (Hemiptera: Reduviidae), to 33 SSRs/Kb for the rice brown plant hopper Nilaparvata lugens Stål (Hemiptera: Delphacidae)45. Since the genome of T. erytreae is not yet fully annotated, we could not estimate the total number of SSRs in exons, introns or intergenic regions, but it was possible to get information for most of the SSR loci selected for further analysis. When comparing the number of various classes of SSRs in T. erytreae, we found that our data were in accordance with the averages estimated for other insect genomes38. The average of di- and trinucleotide SSRs is significantly higher than those observed for longer repeat types. Among the grouped motif pairs in T. erytreae, we found that CT/AG and GA/TC were predominant, while GC/GC and CG/CG were the least frequent di-nucleotide motifs. These data agree with published results in other insect species, including members of the order Hemiptera45.It is well known that SSR markers are useful to study population genetics and phylogeography in insect species and have been proven to be an efficient molecular tool to assist in the optimisation of integrated pest management programsT. erytreae populations that have invaded Spain and Portugal. The Wright’s fixation index (Fw) for all loci was higher than zero (0.41) considering the whole individuals sampled, indicating an overall heterozygote deficiency. This also suggested a slight degree of inbreeding among individuals within local populations. Our results show that the T. erytreae individuals analysed in this study are structured in seven genetic clusters (K = 7).In our study we developed ten informative microsatellites markers to determine the genetic variability and to estimate the gene flow and the structure of T. erytreae was recorded in the Iberian Peninsula in 2014, just 7 years ago17, our results suggest that genetic structure was already present among the newly invaded areas in the west coast of the Iberian Peninsula, from Galicia (Spain) to Setúbal , between 2015 and 2019. Since T. erytreae was spotted in Galicia 12 years after of being reported in the Canary Islands, and that individuals from both locations share a very similar genetic structure, the Canary Islands emerge as the most likely source of the T. erytreae that invaded the north-western coast of the Spainish mainland. The little genetic homogeneity found in local populations suggests the presence of different colonizing lineages. In this regard, there is strong and growing evidence suggesting that multiple introductions, complex global movement, and population admixture contribute to increase the genetic diversity in invasive insect species, and that mixing of different lineages may contribute to rapid evolution and to invasiveness46.Although T. erytreae was first reported in Madeira in 199413 and 8 years later (2002) in the Canary Islands14, it is possible that colonization of the latter archipelago has derived, at least in part, from Madeira. According to the genetic clusters identified in both archipelagos, the colonisation of the Canary Islands could have originated mainly from Quebradas and Farrobo , and in a minor extension from Moreno and/or Poiso (both in Madeira). Our study shows that T. erytreae individuals from Madeira, the Canary Islands and Galicia are genetically more diverse compared to those from the sample locations in the coast of Portugal, South Africa and Kenya. Conversely to the high genetic diversity observed in Galicia, the local populations of T. erytreae sampled along the coast of Portugal seem relatively homogeneous with only two main genetic clusters. It is probable that the expansion of T. erytreae observed in recent years along the coast of Portugal18 has had its origin in the invaded locations of Madeira, but we cannot rule out that it may have also been derived from Galicia. Remarkably, our distance-based clustering analysis of SSR allelic data suggest that the Camacha linage may have been derived from South Africa, somewhere in or around the triangle made up for the localities of Pretoria, Nelspruit, or Tzaneen, as the emerging position of the Cluster 7 in the base of the Cluster 2 supports this hypothesis.Since the outbreak of T. erytreae populations from South Africa and Kenya seems different from each other. Moreover, their genetic diversity is also different from those populations of T. erytreae sampled in Spain and Portugal. Therefore, we cannot conclude from our study that the T. erytreae populations that invaded the Macaronesia and the Iberian Peninsula derived directly from one of these two African countries. Recently, a study on the intraspecific diversity of T. erytreae assessed by COI barcoding suggested that the populations of this species that have been found in Europe is most likely originated from South Africa27. The study that was strongly biased towards COI barcode accessions from Kenya with 74.2% of all sequences, compared to 14.6% from South Africa and only 7.9% from Spain and Portugal, and could not exclud the possibility of a Kenyan origin. However, according to our STRUCTURE analysis, the presence in Pretoria (South Africa) of discrete membership fractions of the genetic clusters detected in the new invaded areas in Spain and Portugal suggests that the T. erytreae individuals that have invaded Europe may have their origin in South Africa. Our phylogenetic analysis using mtDNA barcoding also supports this hypothesis, as the individuals from Spain and Portugal (including those from the Canary Islands and Madeira) formed a monophyletic group with those individuals from Pretoria, Tzaneen and Nelspruit, suggesting that the T. erytreae individuals that invaded the Iberian Peninsula have their maternal origin somewhere in the northeast of South Africa. Furthermore, the fact that T. erytreae populations in the Canary Islands have been drastically reduced by its natural enemy Tamarixia dryi (Del Guercio) (Hymenoptera: Eulophidae), a highly specific parasitoid of T. erytreae imported from Pretoria22 and released in the archipelago in 201823, also supports the idea that the T. erytreae individuals that invaded the Macaronesia and the Iberian Peninsula have derived from the northeast of South Africa near Pretoria. Nevertheless, considering that Pretoria (Gauteng) is not a citrus growing area, it could be possible that the individuals of T. erytreae sampled in Pretoria could have derived from the neighbouring province of Limpopo, the largest citrus producing area in South Africa47.The genetic diversity of Aphis citricidus  (Hemiptera: Aphididae), the main vector of citrus tristeza virus (CTV). This aphid species first invaded Madeira in 199448, and 8 years later the north-western region of the Iberian Peninsula49. This similar invasive behaviour suggests a common pathway for both citrus pests. T. erytreae and A. citricidus are oligophagous species, with Citrus species as principal host plants but also feeding on other Rutaceae, and that the importation of citrus plants from non-European countries is forbidden in the EU. Taking all of this into consideration, we hypothesise that the introduction pathway of T. erytreae in the Macaronesia and Iberian Peninsula could be related to the commercial trade or transportation by travellers of ornamental Rutaceae. In the case of T. erytreae, another possibility was recently suggested which showed that the adults of the psyllid can survive several days on citrus fruits in low temperatures20.A similar spatial pattern of invasion was observed in the case of the black citrus aphid . erytreae individuals from 23 locations across Spain, Portugal, Kenya, and South Africa were collected between 2017 and 2019  was isolated from individual insects following a modified Salting Out method50.A total of 147 TT. erytreae individuals was used as template for PCR amplification of a 714 bp fragment of the 5’-coding region of the mitochondrial Cytochrome C Oxidase I gene , from positions + 6 to + 719 with respect to the start codon. The COI barcode fragment was amplified using the specific primers Te-6U30 and Te-720L26, previously published by us22. PCR fragments were bi-directional sequenced using the same both primers. COI barcode fragments were sequenced by capillary electrophoresis using a 3130XL Genetic Analyzer , at the DNA Sequencing Unit of the IBMCP . Sequences were analysed and trimmed to remove primer sequences, using the Sequencer DNA Sequence Analysis Software v4.9 . Forward and reverse high-quality reads obtained for each T. erytreae individual were assembled into consensus sequences and submitted to the GenBank public sequence repository. Multiple alignment of nucleotide sequences of COI barcode fragments was performed in MEGAX42 using the built-in ClustalW alignment interface. For each COI barcode sequence, a fragment of 657 bp (from positions + 36 to + 692 with respect to the ATG) was used for multiple sequence alignment. The phylogenetic analysis was also carried out on MEGA X using the Maximum Likelihood Method based on the Tamura-Nei model41. The reliability of the constructed tree was evaluated using a bootstrap test of 10,000 replicates. Initial tree for the heuristic search were obtained automatically by applying the Maximum Parsimony method. A discrete Gamma distribution was used to model evolutionary rate differences among sites .Total DNA (10 ng) of selected T. erytreae, obtained in collaboration with the Genome Assembly and Annotation Team , with an estimated size of 763 Mb , was mined for SSRs by means of the GMATA35 software . The parameters of motif unit length were set to a minimum of two (excluding mononucleotide motifs) and maximum of ten nucleotides repeats, while the minimum of in tandem repeat copies of detected motifs (from di- to octa-nucleotides) was set to three.A genome sequence draft of 4.4 PCR amplification and Genotyping of SSR loci. Excluding mononucleotides motives, SSRs of three or more in tandem repeated motifs were targeted to design primers for PCR amplification. Primer pairs with sufficient flaking sequence to amplify genomic SSR loci between 250 and 600 bp were designed in Primer Analysis Software Oligo 7.6 , using very high constrain parameters with Tm ranged between 58 and 60 °C. Amplicons sequence were blasted via BlastN against the T. erytreae draft genome sequence using the CLC Genomics Workbench v.9.5.3 , to determine whether PCR primers would result in the amplification of single DNA fragments for each SSR loci. Finally, primer pairs were tested for amplification in PCR reactions using the Phire Hot Start II DNA Polymerase . PCR reactions were carried out in a total reaction volume of 20 µl containing 1X Phire Reaction Buffer (provided with 1.5 mM MgCl2), 200 μM of all four dNTPs, 500 μM of forward and reverse primers, 0.4U of DNA Polymerase, and 20 ng of total DNA. Cycled conditions were set as: initial denaturation at 95 °C for 30 s, followed by 35 cycles of denaturation at 94 °C for 5 s, annealing at 60 °C for 5 s and extension at 72 °C for 10 s; and 10 min of extension at 72 °C. The PCR products were stained with gelRed and then resolved through a 2% agarose gel and visualised under UV light. Genotyping of selected SSR loci was performed on a capillary genetic fragment analyser. PCR reactions were set up as mentioned above with the only exception that forward primers labelled with wellRED fluorescent dye were used instead of unlabelled oligonucleotides. Denaturation and capillary electrophoresis were carried out on a CEQ™ 8000 Genetic Analysis System , using linear polyacrylamide gel according to the manufacturer’s instructions. The PCR amplified fragments were sized based on 400 or 600 bp DNA size standards . Genetic analysis system software GenomeLab™ GeXP v10.0 was used for data collection and analysis, and subsequently to estimate the genetic diversity.51 . For each SSR locus, amplicons were scored as allelic data to calculate the genetic dissimilarity matrix using the simple matching dissimilarity index between pairs of accessions (units). Neighbour-Joining (NJ) tree was computed using the obtained dissimilarity matrix. The robustness of the consensus tree branches was tested using 10,000 bootstrap iterations.Population diversity organization was analysed with the bioinformatic software DARwin v6T. erytreae that invaded the Macaronesia (Madeira and Canary Islands) and the Iberian Peninsula , a multivariate analysis approach that uses no priori genetic assumptions for relationships between allelic differences and genetic distances. By means of GENETIX, descriptive statistics of SSR allelic data, including the mean number of alleles per locus (Na), the expected (He) and observed heterozygosity (Ho), Wright's fixation index over the whole population (Fw) and the inbreeding coefficient (Fis) were estimated (Table 37 (http://cbsuapps.tc.cornell.edu/structure) was used to infer the genetic structure of the T. erytreae local populations. The software assumes a model in which there are K unknown populations, each of which is characterized by a set of allele frequencies at each analysed locus. The STRUCTURE analysis was done using Admixture model but allowing allele frequencies to vary independently across populations. The additional parameters used were different values of FST for different subpopulations, prior FST mean 0.01, standard deviation 0.05, and constant lambda valued was set to 1. The length of the initial burn-in period was set to 500,000 iterations of burning followed by 1,000,000 Monte Carlo Markov Chain (MCMC) repetitions in the data collection phase, with ten independent runs for each value of K, and the number of inferred clusters varying from one to ten. Individuals in the populations were probabilistically assigned to hypothetical original populations, or jointly to two or more populations if their genotypes indicate that they are admixed. The most likely number of K was jointly determined by the method of ΔK developed by Evanno et al36 and the Pritchard’s method37 based on the estimation of the K value at which the likelihood distribution began to decrease. The run at which K value yield the highest likelihood of the data given the parameters values was used for plotting the distribution of individuals membership coefficients (Q). Individuals analysed in this study were assigned with high probability (Q membership coefficient > 0.90), while the assignment of admixed individuals to the most likely population was set at Q > 0.70.Two analyses were conducted to examine the populations of la Table . Polymored Table . A BayesSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.


Actinidia eriantha, which occurs across the eastern moist evergreen broad-leaved forests of subtropical China, as a case study to test hypotheses of refugia along the oceanic–continental gradient and ‘oceanic’ adaptation.Refugia is considered to be critical for maintaining biodiversity; while discerning the type and pattern of refugia is pivotal for our understanding of evolutionary processes in the context of conservation. Interglacial and glacial refugia have been studied throughout subtropical China. However, studies on refugia along the oceanic–continental gradient have largely been ignored. We used a liana A. eriantha was explored using a combination of three cpDNA markers and 38 nuclear microsatellite loci, Species distribution modelling and dispersal corridors analysis. Our data showed intermediate levels of genetic diversity  both at the species and population level. Microsatellite loci revealed five clusters largely corresponding to geographic regions. Coalescent time of cpDNA lineages was dated to the middle Pliocene (ca. 4.03 Ma). Both geographic distance and climate difference have important roles for intraspecific divergence of the species. The Zhejiang-Fujian Hilly Region was demonstrated to be a refugium along the oceanic–continental gradient of the species and fit the ‘refugia in refugia’ pattern. Species distribution modelling analysis indicated that Precipitation of Coldest Quarter (importance of 44%), Temperature Seasonality (29%) and Mean Temperature of Wettest Quarter (25%) contributed the most to model development. By checking the isolines in the three climate layers, we found that A. eriantha prefer higher precipitation during the coldest quarter, lower seasonal temperature difference and lower mean temperature during the wettest quarter, which correspond to ‘oceanic’ adaptation. Actinidia eriantha expanded to its western distribution range along the dispersal corridor repeatedly during the glacial periods.The phylogeographic pattern of Actinidia eriantha in subtropical China and that speciation is attributed to ‘oceanic’ adaptation. This study gives a deeper understanding of the refugia in subtropical China and will contribute to the conservation and utilization of kiwifruit wild resources in the context of climate change.Overall, our results provide integrated evidence demonstrating that the Zhejiang-Fujian Hilly Region is a refugium along the oceanic–continental gradient of The online version contains supplementary material available at 10.1186/s12870-022-03464-5. Distinguishing the type of refugia is crucial for our understanding of evolutionary processes such as adaptation and speciation, while also forecasting how current climate change may affect the species in the context of conservation , 2. RefuLindera aggregata (Sims) Kosterm [Castonopsis eyrei (Champ.) Tutch [Machilus thunbergii Sieb. et Zucc [Loropetalum chinensis (R. Br.) Oliver [Sargentodoxa cuneata (Oliv.) Rehd. et Wils [Emmenopterys henry Oliv [Rosa sericea Lindl. complex [Lindera aggregata [Extensive literature has recently emerged on the phylogeographic patterns of plant species in subtropical China e.g. –10). Sub. Sub10]) Kosterm , Castono.) Tutch ., Machil et Zucc ., Lorope) Oliver  and Sarg et Wils  while innry Oliv . and Ros complex . Large mggregata . The varggregata . Despiteggregata , refugiaActinidia eriantha Benth.  is a suitable model for testing hypotheses of refugia along the oceanic–continental gradient and ‘oceanic’ adaptation, on account of its distribution and moist habitat. The species is an important component of liana species in eastern moist evergreen broad-leaved forests (EBLF) of subtropical China at an altitude of 200 to 1500 m [Actinidia eriantha is a functionally dioecious, perennial liana species characterized by young branchlets, petioles, inflorescences, sepals and fruits densely tomentose with milky-white to dirty yellow hairs or appressed tomenta [A. eriantha with limited sampling from narrow geographical areas have been conducted [o 1500 m . The mor tomenta . The spe tomenta . To assionducted –31. The A. eriantha comprehensively. We asked: i) What are the patterns of genetic diversity and population divergence of A. eriantha? ii) Which factors have an effect on the genetic structure of the species? iii) What is the location and type of the refugia of the species? iv) What is the direction of dispersal of the species? Our goals are hence to test the hypotheses of refugia along the oceanic–continental gradient and ‘oceanic’ adaptation, revealing how the species responded to past climate changes and provide information for collecting kiwifruit germplasm resources and conserve the species.Here, we used extensive sampling covering the whole distribution range of the species combined with nuclear SSR and cpDNA evidence, Species Distribution Models (SDM) and dispersal corridors analyses to explore the patterns of historical demography of A. eriantha and two outgroup species i.e. A. fulvicoma Hance and A. chinensis Planch., with a complete length of 1605 bp. Twenty-three cpDNA haplotypes were identified within A. eriantha, with eight being singletons  and nucleotide diversity (πT = 9.1 × 10− 4) within the species. Significant phylogeographic structure  was detected. In the SAMOVA analyses, K = 3 when FCT values reaches the maximum (0.821), which means that three regional groups of populations were identified. These three groups are denoted as “Southeast edge”, “Southwest edge” and “Main part” according to their relative positions in the distribution area of the species. The “Southeast edge” and “Southwest edge” groups only contain a single population, i.e. HA in “Southeast edge” and LP in “Southwest edge”. Hierarchical AMOVA showed a great amount of variation (82.1%) occurred among the three regional population groups and only 6.6% presented differences among populations within groups, and 11.3% of the variation within populations   and Fu’s FS  also supported that the “Main part” had experienced regional expansions in the past. Since similar results were estimated for all populations of the species, we did not show them here. The small size and low number of haplotypes of the other regional groups “Southeast edge” and “Southwest edge” are unsuitable for estimating possible population spatial expansion, therefore no MDA, BSP and neutrality tests were conducted for them.The regional population group “Main part”, which contains all haplotypes in Lineage 2, was used for mismatch distribution analysis (MDA) and bayesian skyline plots (BSP) to estimate the possible population spatial expansion indicated by the clear star-like phylogeny of haplotypes in Lineage 2. The BSP showed a slight population expansion occurring between c. 3.5 and 1.5 Ma  analysis. The ‘east area’ was identified as the most likely ancestral area of the Among the 38 loci, two were detected as outliers by Arlequin (AET22 and AET167) and six by SamBada . Of these, locus AET167 was detected as an outlier by both tests and considered a locus under selection. The locus is significantly correlated with four bioclimatic variables: Temperature Seasonality (BIO4), Annual Precipitation (BIO12), Precipitation of Driest Month (BIO14) and Precipitation of Coldest Quarter (BIO19). No genetic function was annotated for the sequence containing AET167 by GO analyses. In total, 31 loci (including 26 EST-SSRs and 5 genomic SSRs) displayed no evidence of selection in either outlier tests were therefore considered neutral loci.HE ranged from 0.293 to 0.938. Over all populations, 22 of the 38 loci displayed a significant heterozygosity deficit . For all 38 loci, Ae , RS , Ho  and UHE  indicate moderate diversity within populations. Significant heterozygosity deficit was displayed in 21 of the 28 populations. Higher diversity was identified in eastern A. eriantha populations   occurred in all populations except YM and DK.For each population across the 31 neutral loci, the genetic diversity parameters are listed in detail  possessed lower values of genetic divergence than western populations  range from 0.001 (0-0.006) to 0.169 (0.155-0.173)  to 0.047 (0.012-0.085)  which corresponds to c. 3.22 Ma (95% CI: 0.74 - 18.55). Then, Cluster I and Cluster IV diverged from Cluster V at approximately 294,000  and 321,000  generations ago, or c. 2.06 Ma (95% CI: 0.62 - 9.03) and 2.25 Ma (95% CI: 0.75 - 5.50), respectively. The split between Cluster II and Cluster IV was estimated to approximately 143,000 generations ago  or c. 1.00 Ma (95% CI: 0.27 - 2.52). No bottleneck was found by comparing the population sizes of Ni and these of NDBi . The population size of the species expended along with divergence from 522,000 (N3) to 3,145,000 (N1 + N2 + N3 + N4 + N5).Scenarios of historic processes were evaluated in a three-step analysis  simulation based on the SSR neutral data was moderate , suggesting a significant fit to this data, albeit not for cpDNA . The MMRR results  indicated that geographic distance had a significant effect on neutral SSR divergence, whereas the effects of environment factors were not significant . Our analysis indicated that Precipitation of Coldest Quarter , Temperature Seasonality  and Mean Temperature of Wettest Quarter  contributed the most to model development. The ensemble model for each period is illustrated in Fig. A. eriantha generally matches observed distributions, except for predicted but unsupported areas north of the Yangtze River. During the LIG, A. eriantha experienced a drastic contraction compared to its current range with scattered areas in the Zhejiang-Fujian Hilly Region near the East China Sea and the eastern part of Taiwan province. Under LGM conditions, the estimated climatic suitability for A. eriantha underwent a radical range expansion with continuous suitable habitat from the Yungui Plateau to Taiwan. The future potential areas are contracted, fragmenting into several small suitable habitats scattered in the current distribution range of the species. The Zhejiang-Fujian Hilly Region (the current distribution area of Cluster V and Cluster III) near the East China Sea was always included in the predicted potential distribution areas of the species during the four glacial/interglacial periods  and WD  were both significantly lower (P < 0.01) than expected from a random distribution  and the eastern distribution (ED) range of the species, 158 occurrence points of u et al.   and haplotype diversity (hT = 0.498) across the 28 populations. The lower values of Ho than that of UHE in most populations possibly resulted from inbreeding, which was confirmed by the significant heterozygosity deficit displayed in 21 of the 28 populations  was previously observed in six populations located at the border between Hunan and Guangxi Province (nine genomic microsatellite loci) [A. eriantha.Patterns of genetic diversity of rategies . Howevermic SSRs . In facty hT = 0.98 acrosste loci) . The higFST = 0.177) are almost identical to the mean values of genetic differentiation among populations of the late-successional (FST = 0.17) or long-lived (FST = 0.19) plants [FST = 0.116) [A. eriantha is a relatively favorite desirable food source for frugivory animals. Also, seeds of A. eriantha can germinate readily upon maturity and are potentially capable of establishing new populations.The genetic differentiation based on SSR markers  plants  and cons= 0.116) . The lowNST and GST. Although three regional groups were identified by SAMOVA  F. K. Mey [Betula genus [The significant phylogeographic structure was supported by chloroplast data according to SAMOVA Fig. , HierarcOVA Fig.  based onOVA Fig. . Five clOVA Fig. b, demons. K. Mey  and Betula genus .A. eriantha. The Zhejiang-Fujian Hilly Region is always included in the predicted potential distribution areas of the species during the four glacial/interglacial periods in SDM , Temperature Seasonality  and Mean Temperature of Wettest Quarter  contributed the most to model development. By checking the isolines in the three climate layers during the four glacial/interglacial periods, we found that A. eriantha prefers higher precipitation during the coldest quarter, lower seasonal temperature difference and lower mean temperature during the wettest quarter  based on SDM. Accumulating evidence suggests glacial expansion was not a rare event in subtropical China ..ActinidiThe more highly fragmented distribution area of the species is evident from the LGM to the future based on SDM Fig. . This isA. eriantha. Environment effects can contribute to patterns of genetic structure of species either genome-wide or only in particular genomic regions [A. eriantha since the result of the identity test supports the existence of niche differentiation between the two groups    Fig. b, which ci Table , suggestActinidia eriantha along the oceanic–continental gradient and ‘oceanic’ adaptation of the species in the subtropical China. Molecular phylogeography and species distribution modelling revealed west-east geographically distinct divergence of Actinidia eriantha. Both geographic distance and climate difference all have important roles for intraspecific divergence of the species. The Zhejiang-Fujian Hilly Region was demonstrated to be a refugium along the oceanic–continental gradient and fit the ‘refugia in refugia’ pattern. After originating from the Zhejiang-Fujian Hilly Region with ‘oceanic’ adaptation, A. eriantha expanded to its western distribution range along the dispersal corridor repeatedly during the glacial periods. This study gives a deeper understanding of the refugia in subtropical China and will contribute to the conservation and utilization of kiwifruit wild resources since the identification of the refugium and the genetic diversity it harbour enables the refugium to be targeted for protection and collection in the context of climate change.In summary, this is the first integrated evidence of a refugium of A. eriantha , rps16-trnQ , and trnE-trnT . The cpDNA of A. eriantha is inherited paternally [A. eriantha, three individuals of A. fulvicoma and one of A. chinensis were sequenced. Polymerase Chain Reactions were conducted in 20 μl reactions containing 10 μl 2× Taq PCR MasterMix (Bioteke), 0.5 μl each primer (0.2 μM), 1 μl template DNA (ca. 50-100 ng) and 8 μl ddH2O. Thermo-cycling conditions were as follows: 94 °C for 4 min; 35 cycles of 94 °C for 30 s, 58 °C for 60 s, and 72 °C for 60 s; a final 10 min extension at 72 °C. Sequences were generated with an ABI 377XL DNA sequencer (Applied Biosystems).For the phylogeographic cpDNA analysis, we sequenced three non-coding intergenic spacer (IGS) regions: ternally . In totaA. chinensis . Amplif751, 761 . Fluores751, 761 .https://digitalworldbiology.com/FinchTV). Three cpDNA regions were aligned and trimmed separately in MEGA v. 6 [https://users-birc.au.dk/~palle/php/fabox/index.php). Haplotype (h) and nucleotide (π) diversity were calculated for each population and the overall species using DnaSP v. 5 [GST, NST) were analyzed using the program PermutCpSSR v. 1.2 (http://www.mybiosoftware.com/tag/permutcpssr).All sequences were checked using Finch TV v. 1.4 (EGA v. 6  and combaSP v. 5 . Two parK) was determined by repeatedly running the program SAMOVA with 2–28 groups and choosing those partitions with a maximum value of differences among groups of populations (FCT). Hierarchical analysis of molecular variance (AMOVA) [To infer possible regional groups of populations, spatial analysis of molecular variance (SAMOVA) of cpDNA was implemented in SAMOVA v. 1.0 . The mos (AMOVA)  was also (AMOVA)  to quantActinidia has been previously elucidated [A. chinensis and the two other species of 11 Myr (95%CI: 4.7-17.3 Myr) and the split between A. eriantha and A. fulvicoma of 5.5 Myr (95%CI: 4.2-6.8 Myr) [A. eriantha and its closest relative A. latifolia since A. latifolia shows up in the ingroup of A. eriantha in the tree constructed with the three cpDNA markers. The length of the Markov Chain Monte Carlo (MCMC) algorithm was set to 1 billion generations, sampling every 100,000 generations with the first 20% discarded as burn-in. We checked the convergence of parameters using Tracer v. 1.6 [http://tree.bio.ed.ac.uk/software/figtree/).Genealogical relationships of identified haplotypes were inferred from a 95% statistical parsimony network constructed in TCS v.1.2 . To idenucidated . Therefo6.8 Myr) . We did r v. 1.6 . The conμ [μ is the neutral mutation rate of the entire cpDNA sequence per generation. The value for μ was calculated as μ = uk, where u is the substitution rate  [A. eriantha under natural conditions [D [FS [We used bayesian skyline plots (BSP) in BEAST2 v. 2.4 and the mismatch distribution analysis (MDA) in Arlequin v. 3.5 to detect population spatial expansion events. BSP was conducted with the same settings as in previous analyses except that the priors setting was changed to Coalescent Bayesian Skyline. BSP has the advantage of not assuming any demographic scenario a priori . The gooμ , where μn(s/s/g) , and k rnditions . We alsotions [D  and Fu’ss [D [FS  to assesThe ancestral areas were reconstructed using the BBM (Bayesian Binary MCMC) implemented in RASP v. 3.0 . In totahttp://www.bioclim.org) served as environmental variables. Two sets of loci were obtained: loci under selection and neutral loci. A locus was considered to be under selection if both outlier tests showed significance. The correlation between the outlier loci and environment factors was checked using SamBada. Gene Ontology (GO) annotations [To identify putative locally adaptive loci that may affect analysis of neutral population structure and demographic history, we used two methods: genetic-environment association analysis in SamBada v. 0.5.3  and diffotations  of the sNA), the observed (HO) and the excepted heterozygosity (HE), and polymorphism information content (PIC) was calculated using CERVUS v. 3.0 [FIS was calculated using FSTAT v. 2.9 [http://genepop.curtin.edu.au/). Significance was corrected for multiple testing using the Bonferroni method. To account for any departure from HWE due to the presence of null alleles in each locus, we estimated their frequency within each population in FreeNA with a cutoff value of 0.2 [Polymorphism of each microsatellite locus, i.e. the number of observed alleles (S v. 3.0 . The meaT v. 2.9 . Signifie of 0.2 . LinkageAT), the number of private alleles (Ap), the effective number of alleles (Ae), the observed (HO) and the excepted heterozygosity (HE), unbiased expected heterozygosity (UHE) [FIS across 31 neutral loci using GeneAlex v. 6.5 [RS, standardized for four individuals using rarefaction) was calculated using FSTAT. The two genetic parameters UHE and RS were used for removing the bias caused by the strong variation in the sample size. Genetic diversity based on standardized Ae and UHE were visualized as genetic landscapes in ArcGIS v. 10.2 , using the Genetic Landscapes Toolbox [FST was estimated using GeneAlex. Genetic distance based on standardized FST was visualized as divergence landscape.Intrapopulation genetic variation was estimated by the total number of alleles (ty (UHE) , and thex v. 6.5 . The all Toolbox . SignifiA. eriantha. A principal coordinate analysis (PCoA) of populations was performed in GenAlex and the first two PC axes (PC1 and PC2) were used to explore the genetic relationships among populations. The genetic relationships were also evaluated by generating a neighbor-joining (NJ) network based on DA distances [K = 1 to K = 28 clusters and 10 runs per K value. The most probable number of clusters was determined using the ΔK approach [Several methods were used to investigate the genetic structure of istances  using POistances . The mosistances . Assuminapproach  in STRUCapproach .M) with the Brownian motion approximation. Three independent analyses were run, recording every 100 steps with 500,000 genealogies and a 10,000 genealogy burn-in. Immigration rate (m) was calculated as m = Mμ with μ being the mutation rate . BayesA× 10− 4,  was usedWe used ABC simulations in DIYABC v2.0  to deterR function ‘MMRR’ and partial Mantel test in the package vegan v. 2.4 [FST/(1-FST) [www.worldclim.org). To reduce bioclimatic covariance, principal component analysis (PCA) was conducted using the R package vegan. The first three PC axes  explaining > 89% of the total variation were used to compute Climatic (Euclidian) distance.To evaluate the effect of geographic and environmental conditions on genetic divergence, we tested for isolation by distance (IBD) and isolation by environment (IBE) based on neutral SSR loci and cpDNA dataset. IBE based on potentially adaptive loci was not tested since only one outlier locus was obtained. Two methods were used: multiple matrix regression with randomization (MMRR)  using thn v. 2.4 . We used/(1-FST)  to repreA. eriantha across a complete glacial-interglacial cycle, 168 occurrence points of A. eriantha including 28 sampled locations, six location studied by Liu et al. [http://www.cvh.ac.cn/) were used to compute species distribution models (SDM) in Maxent v. 3.2 [http://www.worldclim.org). Model performance was evaluated using the area under the Receiver Operating Characteristic Curve (AUC). To avoid over-fitting, we selected five bioclimatic variables (|r| > 0.8): BIO2 , BIO4 , BIO8 (Mean Temperature of Wettest Quarter), BIO16 (Precipitation of Wettest Quarter) and BIO19 (Precipitation of Coldest Quarter), based on correlation analysis using ENMTools v. 1.3 [http://www.diva-gis.org/gdata). To determine whether the WD and ED part of the species occupy identical climatic environments (‘niches’), we performed SDM in Maxent and niche identity tests in ENMTools. A one-tailed t-test was used to estimate the significant difference between observed values of Warren et al.’s I [D [D and I generated by randomly assigning samples to either part.To infer the potential distribution areas of u et al.  and 134 t v. 3.2  based ons v. 1.3  and thei al.’s I  and Scho.’s I [D  and 100 A. eriantha were established using SDM toolbox.Dispersal corridors at the current time, LGM, and LIG were mapped by applying the CLCP method and SDM toolbox  in ArcGIAdditional file 1. Genetic characteristics of cpDNA in 28 Actinidia eriantha populations.Additional file 2. Analysis of molecular variance (AMOVA) for 28 sampled populations of Actinidia eriantha based on cpDNA.Additional file 3. The ancestral areas were reconstructed using the BBM (Bayesian Binary MCMC) method. (a) The ancestral areas were reconstructed using the BBM implemented in RASP v. 3.0. Three regions  were defined according to the precision of consensus tree and the distribution range of the species. (b) Geographical locations of the three regions and the most likely dispersal direction.Additional file 4. Characteristics of 38 nuclear microsatellite loci.Additional file 5. Genetic diversity of each population based on 31 neutral nuclear microsatellite loci.Additional file 6. The most probable number of clusters determined using the Delta K approach. (a) When K = 2 for all individuals, Delta K has the highest value. (b) When K = 4 for western subset (plus HA and NJ) of individuals, Delta K has the highest value.Additional file 7. Migration rates (m) across the five clusters of Actinidia eriantha based on 31 neutral nuclear SSRs.Additional file 8. Prior distributions of the parameters used in DIYABC.Additional file 9. Set A, B, C and D of scenarios in DIYABC of Actinidia eriantha. Posterior probability of each scenario obtained by logistic regression of 1% of the closest simulated datasets is shown on the top of the scenario. Scenario outlined in red is the best option.Additional file 10. Estimations of posterior distributions of parameters revealed by DIY-ABC for the best scenarios of set A, B, C, D and E, respectively.Additional file 11. The isolines in the three climate layers Bio 4, Bio 8 and Bio 19.Additional file 12. Information of 134 herbaria specimens of Actinidia eriantha.Additional file 13. The results of niche identity test between West group and East group in subtropical China. The histogram indicate the randomized distributions of Warren et al.’s I and Schoener’s D and the arrow indicates the observed values of I and D. The x-axis indicates values of I and D, and the y-axis indicates the number of randomizations.Additional file 14. cpDNA haplotype with its Gene Bank accession number.


Apis mellifera). Methods: We collected honey bee workers that belonged to 14 populations, including 13 subspecies, totaling 1347 colonies, who were individually  and pool-sequenced (14 pools). We compared allele frequencies, genetic diversity estimates, and population structure as inferred by the two approaches. Results: Pool-seq and ind-seq revealed near identical population structure and genetic diversities, albeit at different costs. While pool-seq provides genome-wide polymorphism data at considerably lower costs, ind-seq can provide additional information, including the identification of population substructures, hybridization, or individual outliers. Conclusions: If costs are not the limiting factor, we recommend using ind-seq, as population genetic structure can be inferred similarly well, with the advantage gained from individual genetic information. Not least, it also significantly reduces the effort required for the collection of numerous samples and their further processing in the laboratory.Background: Whole-genome sequencing has become routine for population genetic studies. Sequencing of individuals provides maximal data but is rather expensive and fewer samples can be studied. In contrast, sequencing a pool of samples (pool-seq) can provide sufficient data, while presenting less of an economic challenge. Few studies have compared the two approaches to infer population genetic structure and diversity in real datasets. Here, we apply individual sequencing (ind-seq) and pool-seq to the study of Western honey bees ( Studying population genetic structure and diversity is the basis of our understanding of biodiversity and the conservation of species . Due to Allele frequencies are one of the key parameters to study population genetic structure and to estimate genetic distances between populations , and theThe cost-effectiveness of pool-seq becomes obvious when considering the cost of individual handling and library preparation for sequencing: as one pool represents a single sample, only one library needs to be prepared ,10. As sPool-seq is especially suitable for applications that require large sample sizes and the analysis of multiple samples/populations; however, for an optimal design of pools, previous knowledge of population structure is beneficial or even necessary. On the other hand, the disadvantage of pool-sequencing is the loss of genetic information at the individual level. Therefore, this technique may not be suitable for certain applications ,11, for Most of the advantages and disadvantages of the pool and individual sequencing approaches have been discussed theoretically or by using simulation studies ,10,11,15A. mellifera L.). Both approaches, whole-genome ind-seq and pool-seq, have been previously applied in this species, albeit not with a comparative purpose . Eur. EurA. me , as well as ten individuals per population (ind-seq). Data obtained by either pool-seq and ind-seq were then compared to their ability to detect the population structure and diversity of European honey bees. Finally, we discuss the advantages and disadvantages of both approaches in practice and recommend future studies.In this study, we empirically assess the consistency of two sequencing approaches, pool-seq and ind-seq, in estimating allele frequency, genetic diversity and documenting population structure in European A. mellifera in Europe , the saal. 1997 ; Lineageoti 1965 , and A. rov 1991 . Based o lineage . In thisFor this study, we pooled about 90 individuals per population following Schlötterer et al. , who recA. m. adami with only nine workers available, from each of the 14 pools, were extracted individually using the CTAB method [® Ultra DNA Library Prep Kit for Illumina®  following the manufacturer’s recommendations. Libraries were sequenced on the Illumina X Ten platform . The very same samples sequenced individually have also been included in the respective pools.DNA from the thorax of ten workers (each one from a different colony), except for B method . SequencFor the two approaches, two different types of DNA extractions were used from different parts of the bodies , followed by different library preparation protocols, and finally, two different sequencing platforms were used. This strategy was chosen in order to compare the two methods in very realistic settings, that is, we used the same initial populations, then applied the two approaches independently, including the sequencing at different facilities, and bioinformatics analyses by different researchers, such that finding good agreement between the two approaches would indicate robustness to the method used.http://broadinstitute.github.io/fastqc, accessed on 3 November 2019). High-quality sequences were mapped against the honey bee reference genome Amel4.5 [http://broadinstitute.github.io/picard/, accessed on 3 November 2019) were used to convert between SAM and BAM formats, remove duplicate reads, sort the BAM files, remove reads with low-quality mapping (MAPQ < 20), and keep only properly mapped pairs. Subsequently, the data were processed following the steps of the PoPoolation package [https://github.com/jlanga/smsk_popoolation (accessed on 3 November 2019).Bioinformatics processing of the generated pool sequence data was performed using best practices following Schlötterer et al. . Illumin Amel4.5  using bw Amel4.5 . SAMtool Amel4.5  and Pica package . The map package ,52, and  package ,54 at htFor individual sequencing data, reads were filtered by fastp  to excluTo compare the genetic variability identified based on the two different sequencing approaches, the allele frequencies of the commonly called SNPs in each population were calculated using PLINK1.9  for ind-ST distances [ST in overlapping window sizes of 20 kb and 10 kb step-size. For ind-seq data, we first used ANGSD [ST between pairs of populations, using a sliding windows approach .Genetic diversity between populations was inferred using Fistances . For pooistances  to calcued ANGSD  to estimExpected heterozygosity (He), as a measure of genetic diversity within populations, was calculated for each population with a custom R script using the above-estimated allele frequencies by pool-seq and ind-seq and the standard formula (2pq) .ST results were plotted in R [ST estimates as calculated by the pool-seq and ind-seq approaches, respectively, using the ggpubr package in R [Genetic diversity and Fted in R  using thted in R . Pearsonted in R  were calage in R .To infer population structure with the pool-seq data, a principal component analysis was performed using the allele frequencies for each population. For ind-seq data, we first used ANGSD  to estimFor ind-seq data, population structure was further investigated using a model-based approach. NGSadmix  was usedPrincipal components (PCs) and individual model-based ancestries were plotted in R  with theFor the pool sequencing approach, we obtained 4,372,477,988 raw reads in total, resulting in an overall genome depth of coverage >1800×. Mean coverage for each pool ranged from 70.3× (cec_grc) to 264.5× (rod_bgr) . For thep > 0.001).A total of 1.6 M and 3.7 M SNPs were called for pool- and ind-seq, respectively. Variants called by both approaches were extracted, leaving 607 K SNPs to calculate the correlations between the allele frequencies generated with either method in each population. The allele frequencies generated with the two methods were very highly correlated with each other, but high variation was observed for single variants  , and its visualization as a distance heatmap revealed near identical results  . While F results B.p = 0.037), and revealed nearly identical results of diversity within populations as estimated by both approaches  than the mean C lineage  and mean M lineage  diversity. The lowest diversity overall, as estimated by pool-seq and ind-seq, was found in the car_aut_hun pool from Austria and Hungary belonging to the C-lineage.Similarly to the allele frequencies and genetic distances, expected heterozygosities were very highly correlated between pool- and ind-seq  based on data generated with both sequencing approaches was nearly identical, showing a clear separation of the populations into the four main lineages : The firModel-based ancestry was further investigated in individual samples. The optimal number of clusters as inferred by Evanno’s DeltaK was K = 3  that sepA. mellifera populations. Comparing the two different sequencing approaches (pool-seq and ind-seq), we found that both revealed a population genetic structure and genetic diversities of European honey bees that were nearly identical. Moreover, the results are in good concordance with previous findings  was higher than the one for pools . In addition, by subsampling pool sequence data to uniform coverage and requiring a strict filter of minimum read count 3 to call a variant, we could only detect SNPs with a minor allele frequency of >0.06. This was already pointed out by Cutler and Jensen , who shoThe total sample size of individuals that are pooled is a crucial parameter that influences the accuracy of the allele frequency estimations ,76. By pST values  were identified between pool-seq and ind-seq. Thus, although only few studies empirically evaluated the accuracy of allele frequency estimates derived from pool-seq and ind-seq in natural populations, all arrive at very high correlations and demonstrate that either approach applies to population genomics studies.A remarkable finding in our study is that, despite the huge difference in sampling coverage between pool-seq and ind-seq, allele frequencies obtained with the two methods correlate extremely well with each other  or isolated (mel_irl) populations, which may thus potentially have lost diversity through genetic drift during recolonization. In particular, in the case of the A. m. mellifera island population of Ireland, where limited human inference through importation, is suspected [The highest genetic diversity by far was identified in the rut_mlt population as a representative of the African evolutionary lineage A, which iversity ,38. A pobreeding . Among tbreeding ,88. Morebreeding ,90, whiluspected ,91.Since similar results are obtained with both sequencing approaches and results are concordant with published literature, the issue of costs involved with either approach gains additional weight. In general, the cost of pool-seq is considered lower ,12,13. TOn the other hand, besides the cost of sequencing itself, we need to consider the actual cost of collecting the samples required for either approach. In our case, the sampling of ~90 individuals per population, each one originating from a different colony and apiary and a total of 16 different countries, constituted a huge effort and generated extra costs. In comparison, the sampling of only ten individuals per population is less complex and labor-intensive.A. m. remipes and A. m. caucasia on the borders between Turkey and Armenia, and between lineages as has been reported previously [A. m. ruttneri on the island of Malta [While pool-seq and ind-seq reveal similar overall results on population structure and genetic diversity, albeit, at different costs, ind-seq can provide additional information at the individual level. In this way, we could identify individuals with a mixed genetic background that is indicative of the existence of hybridization between subspecies and that would give rise to a greater resemblance between populations when analyzed by pooling. For instance, by natural hybridizations in the contact areas of two subspecies, as we find between eviously ,94,95,96of Malta .An additional advantage of individual whole-genome sequence data is that further analyses can be performed, such as the inference of evolutionary and demographic histories ,99,100. A. mellifera is a good example to evaluate two methods. The species is well studied, and we could compare our results with previous studies that used different tools and populations. While overall results of both approaches were very similar, our final verdict for the empirical comparison between pool-seq and ind-seq to investigate population structure in the honey bee is that ind-seq, albeit more expensive, could give us similar or even equal information, while additionally providing insights into individual-based admixture. Based on our experience, we would thus not necessarily recommend to only applying the pool-seq approach in similar studies. Nevertheless, pool-seq can be a useful and cost-efficient option, for instance for variant discovery [Our case study on iscovery ,102, andBoth approaches, pool-seq and ind-seq, enabled us to get a global vision of European honey bee diversity. The population structure of some of the studied populations was genetically examined for the first time. It will be interesting to analyze them more in-depth to shed light on complex patterns of diversity. For instance, within the C lineage, where multiple highly interrelated subspecies exist in close geographical proximity, we found some level of mixed genetic background in several populations. The comprehensive genomic dataset generated by this study will therefore be the basis for future studies to explore further the genetic variation within and among subspecies and to identify signatures of local adaptations.


Xerocrassa montserratensis, an endangered land snail endemic to Catalonia (northeastern Iberian Peninsula), is an excellent model to study the processes affecting the phylogeography of specialized species of conservation concern. This species is restricted to xerophilous stony slopes and occurs within a small and fragmented area of ca. 500 km2. We sequenced the COI barcode region of 152 individuals from eight sites covering the entire range of the species. We found four genetic groups mostly coincident with their geographic distribution: a central ancestral group containing shared haplotypes among five localities and three groups restricted to a single locality each. Two of these derived groups were geographically and genetically isolated, while the third and most differentiated group was not geographically isolated. Geomorphologic and paleoclimatic processes during the Pleistocene can explain the divergence found between populations of this low dispersal species with historical fragmentation and secondary contacts. Nonetheless, recent passive large dispersal through streams was also detected in the central group. Overall, our study uncovered four evolutionary units, partially matching morphologically described subspecies, which should be considered in future conservation actions.Species with small geographic ranges do not tend to have a high genetic structure, but some land snail species seem to be an exception.  Land snails are highly diverse in the number of species although frequently unknown because of cryptic morphological speciation2. Projections since the 1980s estimate that 7% of land snails have been probably lost in front of the suggested 0.04% considering all taxa3. Although few genetic studies focused on European Mediterranean land snails, this group is composed by ca. 2700 species with a high rate of endemism and evolutionary diversification4. Land snails are organisms with very low dispersal abilities5. Some snail species have large distribution ranges, often associated to generalist ecological requirements8. However, many species have small distribution ranges and a considerable ecological specialization10. Life-history traits such as low mobility and ecological specialization make land snails good candidates to exhibit a high genetic structure11. Phylogeographic studies can provide information on genetic diversity and historical demographic processes such as isolation, gene flow and range expansion/contraction13. This approximation can help delineating conservation measures of threatened endemic species, such as some land snails14, and thus in setting species recovery priorities15.Invertebrate species represent the majority of multicellular organisms but are often neglected from conservation policies mostly due to lack of knowledgeXerocrassa are distributed across the Mediterranean basin16, and include ca. 50 described species in the European side of the basin plus many subspecies17. Some species are morphologically cryptic and only molecular studies may uncover specific delimitations14. Many species are concentrated in Greece and in the Balearic Islands, and most of them are endemics , indicating the potential for genetic differentiation due to ecological specialization and low dispersal ability18. High endemism is also observed in the mainland, as exemplified by Xerocrassa montserratensis, a species restricted to the north-eastern part of the Iberian Peninsula with a small geographic distribution (< 3600 km2) and an area of occupancy of 448 km219, fragmented in several isolated patches20. This snail shows a high habitat specialization, living almost exclusively in mountain xerophilous bare stony slopes of conglomerate lithology with narrow soil and little shrub and grass vegetation21. Currently, bare stony slopes are patchily distributed in isolated points surrounded by dense unsuitable forest and scrubland22.Land snails of the genus X. m. montserrantesis23, X. m. betulonensis24 and X. m. delicatula25. The distribution of these subspecies was limited to a few localities25. However, there is some controversy on their taxonomic status in the literature. One morphological study suggested that the subspecies X. m. betulonensis was a different species26. Nonetheless, a recent study that analysed characters of the shell and anatomical traits of the reproductive organs in individuals from the different areas failed to identify morphological differences in the traits historically used to discriminate among the three subspecies20.At the end of the XIXth century three subspecies were described based on morphological traits of the shell: Xerocrassa montserratensis across its whole distribution range. Specifically, we (1) evaluate the genetic diversity of the species; (2) analyse the differentiation among populations; (3) test if the population genetic structure is explained by expansion, isolation by distance, or barriers to gene flow, and (4) investigate the validity of its subspecies. We hypothesize that the species will present high inter-population genetic differentiation caused by its suitable habitat fragmentation and low dispersal ability.Our study aims at understanding the processes structuring the populations of ecological specialist species with reduced distribution ranges by analysing the genetic structure of Xerocrassa montserratensis individuals from eight localities across the whole distribution range of the species  barcode fragment were aligned for 152 ies Fig. . Overallies Fig. , of whicies Fig. . Howeveries Fig.  with Mon23. The Montcau group contained 7 haplotypes all from that locality, from where the subspecies X. m. delicatula was initially described 25. In the case of Montcau and Sant Jeroni, it is interesting to emphasize that we also found haplotypes clustering in the central group  were significant after FDR correction except for the comparison between Sentmenat and Gallifa  of the global variance, as revealed by an AMOVA without any a priori grouping. The rest of the total variation 22.5%) was explained by differences within populations. In both cases, the differences were significant (P < 0.001). All pairwise populations’ comparisons  and Bayesian inference (BI), using X. chiae as outgroup, were concordant and grouped all haplotypes with high support values , within the range of the rate calculated for other land snails27. All X. montserratensis haplotypes grouped with high support values . We detected significant genetic differentiation among localities as expected for a species with a low mobility and a high habitat specialization. The population differentiation followed an isolation by distance model with some exceptions. On one hand, the two closest neighbouring localities showed a high differentiation. On the other hand, one of the most geographically distant localities showed a low differentiation. Finally, three of the main mitochondrial genetic groups encountered matched the distribution area of subspecies morphologically described in the XIXth century but the taxonomy of which had recently been discussed controversially.In this work, we assessed the population structure of the xerophilous land snail Xerocrassa montserratensis populations, as found in other terrestrial-snails28. This high genetic diversity could be explained by cryptic initial speciation processes, fragmentation of the suitable habitat, secondary contacts, local adaptation and ultimately reduced gene flow29. We found differences in genetic diversity among localities that could be related to their extent of suitable habitats, as observed in other species where a positive correlation between allelic richness and habitat patch size had been reported30. This pattern seems to be concordant in Sant Llorenç del Munt i l’Obac Natural Park  are known to shape drainage rearrangements40. In the study area, homoclinal shifting occurred thanks to the rifting along transversal basement faults , could also be considered a different subspecies with slightly morphological differences since individuals in this locality have more rounded shell and a less marked carinate shell border20. Despite morphological variation in the shell shape and size among different X. montserratensis populations, Martinez-Ortí & Bros20 recently stated that morpho–anatomical characters from both the shell and reproductive system cannot be used to discriminate the described taxa. Morpho-static evolution has been described in some snail species arising from molecular but non-ecological differentiation43. Moreover, discrepancies between nuclear, mitochondrial and morphological data can result from initial stages of the speciation processes with ongoing gene flow45. Thus, the subspecies status in X. montserratensis should be considered preliminary since only mitochondrial data has been used. Additional studies using genome-wide markers should be undertaken to evaluate whether parapatric X. montserratensis populations have current gene flow, further providing the potential to uncover adaptation processes47.The subspecies described more than 100 years ago are concordant with our observed mitochondrial analyses. Thus, the nominal form X. montserratensis populations are located. The genetically differentiated groups found in this species could be considered different evolutionary significant units, matching the initially described morphological subspecies, with the identification of a potential new subspecies. According to IUCN, X. montserratensis is listed as endangered because its reduced distribution range and habitat specialization. Moreover, its populations are decreasing since a low number of specimens have been found in some populations (https://www.iucnredlist.org/species/22254/9368348 accessed December 2020)19. The fact that we have found high genetic structuring implies that there is not only the need of species protection but also to protect each genetic group in a coordinated manner since the risk of extinction is higher. Although the species inhabits protected Natural Parks, conservation plans are mostly based on larger-sized fauna and flora. Our study helps to put more emphasis in this kind of fauna (no arthropod invertebrates), which normally are not considered flag species and deserve less interest from conservation institutions, and highlights the role of genetic studies in setting species priorities in conservation management plans.Our study provides new information for conservation management actions to be considered by policymakers and stakeholders at the Natural Parks, where most of Xerocrassa montserratensis is a land snail species of less than 1.5 cm of shell diameter20 inhabiting bare stony slopes of conglomerate lithology in the northeastern Iberian Peninsula  and Marina . Finally, X. m. montserrantesis was described from individuals collected in the Montserrat Mountain, where the species was initially described23. In a recent morphologic study, a more globose shell shape and less carinated shell periphery was described in individuals from Els Munts20.At the end of the XIXth century three subspecies were described based on morphological traits of the shell Fig. : X. m. mSnails were collected from eight locations, most of them in Natural Park reserves, covering the known range of the species including the localities where the different subspecies were described and with previous morphologic analyses Fig. . All theX. ripacurcica from Congost de Montrebei (Lleida) were collected and sequenced in the present work.A total of 152 snail individuals were collected between autumn 2013 and autumn 2014, except for La Mola which was sampled in winter 2015. For conservation management of this endangered species, juveniles were prioritized over adults for collection. Juveniles are more abundant in the localities and have lower survival capacity during the summer drought experienced in Mediterranean environments. The permit to collect the specimens was granted by the corresponding authorities of the natural parks managed by Diputació de Barcelona, Patronat de la Muntanya de Montserrat, and Servei de Fauna i Flora of the Generalitat de Catalunya. Approximately 20 samples were taken from each locality and preserved in absolute ethanol for further genetic analyses (Table 48 were used to amplify the barcode region of the Cytochrome Oxidase I (COI) gene. PCR amplifications were carried out in a total volume of 20 µl including: 2 µl of 5 × Buffer , 1 µl of 25 nmol MgCl2, 0.5 µl of dNTP (1 mM), 0.4 µl of each primer (10 µM), 0.2 µl of Taq polymerase corresponding to 1 unit  and 1 µl of DNA. The PCR started with an initial denaturation at 94 °C for 5 min, followed by 35 cycles of a denaturation step at 94 °C for 1 min, an annealing step at 50 °C for 1 min and an elongation step at 72 °C for 1 min 30 s, and a final elongation step at 72 °C for 7 min. The amplified DNA was purified with Exo-SAP (0.2 U/µl Exonuclease and 0.2 U/µl Shrimp Phosphatase) at a proportion of 1:2 (ExoSap:PCR product) and the forward strand was sequenced by Macrogen or Scientific and Technologic Services at the Universitat de Barcelona. Sequence chromatograms were visually checked, aligned and cut to the same length with MEGA X49. We only used the forward primer because the obtained sequences had clear nucleotide peaks. Doubtful sequences were repeated for amplification and sequenced from both primers for reliability.For each specimen, the shell was broken and a small fragment of foot tissue was cut and dried to remove ethanol. Total genomic DNA was extracted using QIAamp DNA Mini Kit (QIAGEN), according to the manufacturer’s protocol, and resuspended in 50 µl AE buffer. The universal primers LCO1490/HCO219850. To evaluate differences in diversity between localities we carried out a permutation test with 10,000 replicates using genetic_diversity_diffs v1.0.6 (https://github.com/laninsky/genetic_diversity_diffs) 51. To compare the number of haplotypes among localities with different number of analysed individuals we calculated allelic richness with Contrib52. To identify signs of population demographic events deviating from neutrality we computed Tajima’s D neutrality test and R2 test of demographic expansion using DnaSP 6 for each locality separately and the whole area combined. Haplotype frequencies per locality, pairwise genetic distances (FST) between localities and its significance, and the amount of variation found within and among localities (AMOVA) were calculated using Arlequin ver. 3.5.253. The Benjamini-Yekutieli False Discovery Rate (FDR) correction54 was applied to account for multiple comparisons. The genetic relations among populations were visualized in a bi-dimensional plot by a principal coordinates analysis (PCoA) with GenAlEx55 using pairwise FST values. Correlations between pairwise population genetic (FST) and geographic distance matrices were evaluated by Mantel tests and its significance assessed with 999 permutations using GenAlEx. Pairwise geographic distances were computed as the Euclidean distance in km between each two collecting sites. Finally, barriers to gene flow among localities were calculated with Alleles In Space56 using Monmonier Maximum Difference Algorithm and Interpolate Genetic Landscape Shape. Raw distances and the option “pseudoslope”, which corrects the genetic distances with the geographic ones, were also used to identify the barriers.Haplotype diversity, nucleotide diversity and their standard deviations were calculated for each population using DnaSP 6https://fluxus-engineering.com/).To show the number of nucleotide changes among haplotypes and their relationships we built a haplotype network using the Median Joining Network Algorithm with the software Network 10  and Bayesian Inference (BI) methods. X. montserratensis was estimated using a Bayesian approach implemented in BEAST 1.1059. The analysis was based on the geographic calibration followed by Chueca et al.18 considering that the three main clades of Xerocrassa species within the Balearic Island diverged during the Messinian Salinity Crisis, 5.3 ± 0.3 Mya. Overall, we used 11 additional species .The time of divergence between groups of es Table , six froSupplementary Information.