Genetic Structure and Evolutionary History of the tiger shrimp Penaeus monodon Across Its Wild and Introduced Ranges

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Sabatino, Armando Semo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7993803/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Mar, 2026 Read the published version in Biological Invasions → Version 1 posted 5 You are reading this latest preprint version Abstract Understanding the impacts of domestication and aquaculture on native populations is a key issue in conservation and evolutionary biology, particularly in the context of marine invasions. While aquaculture has greatly expanded the global distribution of species, its influence on genetic diversity and population structure remains poorly understood. This study examines the tiger shrimp Penaeus monodon , native to the Indo-Pacific and introduced across the Atlantic for aquaculture since the 1970s. However, the evolutionary consequences of secondary contact between native and introduced populations remain poorly resolved. Using mitochondrial DNA and microsatellite markers, we reconstructed the invasion process and explored patterns of genetic diversity, phylogeographic relationships, and demographic changes across native, invasive, and aquaculture populations. Samples were collected from Indo-Pacific populations, six Atlantic countries (four western and two eastern), and aquaculture facilities to assess the role of human-mediated dispersal in shaping genetic connectivity. Results revealed high genetic variability across all sites and three distinct genetic clusters, indicating complex historical and recent dispersal events. Significant intraspecific divergence between P. monodon and P. semisulcatus was detected, alongside demographic expansion in aquaculture populations. Conversely, both native and invasive populations exhibited signals of genetic bottlenecks. These findings suggest that aquaculture systems may mitigate founder effects and act as reservoirs of genetic diversity, enhancing invasion potential. Our results underscore the dual role of aquaculture as both a driver of biological invasions and a modifier of evolutionary trajectories, highlighting the need for greater understanding of domestication processes in shaping the genetic landscape of marine species. Biological invasions Atlantic Ocean domestic systems genetic variability demographic changes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Biological invasions are increasingly recognized as a significant threat to marine biodiversity worldwide (Lallias et al., 2015 ). Invasions occur when species colonize areas beyond their natural range due to human behavior. The origin of this phenomenon dates back to interoceanic travel during colonization, when people began traveling great distances by boat for various reasons, including to move to new areas, trade, and make war. Over the past few centuries, population growth and globalization has increased travel and trade exponentially, resulting in greater maritime transport to meet rising global demands. However, despite this threat, understanding the ecological and evolutionary circumstances and implications of successful invasions is not well established. One of the most important questions in invasion biology is why some species are successful at invasions and others are not (Schulz et al., 2019). Species typically remain restricted to their natural habitats due to their limited capacity for long-distance dispersal -often a consequence of short larval phases and/or a sessile or sedentary adult stage, which tends to restrict them to their native regions. Paradoxically, many documented cases of successful marine invasions involve species with limited dispersal (e.g., mollusks, crustaceans, ctenophores, cnidarians, tunicates). One explanation of this is that those with great dispersal capacities have dispersed as far as they can based on their fundamental niche. And, those invasions we do see are forced by the maritime activities over the past centuries in altering the distribution of taxa across large geographical scales (Goulletquer et al., 2002 ; Ruiz et al., 2000 ). Transport of marine organisms occurs mainly through the attachment or embedding of organisms in the hulls of ships (biofilm), ballast water, or introduction for cultivation/aquarium purposes. While attachment events are centuries old, introducing the ballast water system at the end of the 19th century to improve the stability and buoyancy of ships is more recent and created an unprecedented problem related to biological invasions (Molnar et al. 2008 ). Although ballast water is a very efficient means for the long-distance transport of many organisms, surviving the conditions of these systems during long voyages can indicate a certain adaptive capacity, contributing to the success of introduction and subsequent establishment. Therefore, diapause eggs in many marine species, especially crustaceans, can be favorable (Ricciardi, 2011 ). In contrast to the previously discussed vectors, potential invasions stemming from aquaculture introductions have received comparatively less attention. This is often attributed to the perception that aquaculture introductions result in lower propagule pressure or necessitate multiple introductions for successful establishment (Facon et al., 2003 ). However, it is important to note that a substantial proportion of invasive species originate, either directly or indirectly, from aquaculture (e.g., species from the genera Perna , Ostrea , Mytilus , Ruditapes, Crassostrea , Penaeus ). Despite this, the literature addressing aquaculture-mediated invasions remains relatively limited (Aguirre-Pabón et al., 2015; Lallias et al., 2015 ; Voisin et al., 2005 ), as research has primarily focused on broader topics, including biology, ecology, and evolution (Roman and Darling, 2007 ; Lejeusne et al., 2014 ), transport mechanisms and geographic pathways (Hulme, 2009 ; Wilson et al., 2009 ), factors influencing invasion success (Williamson, 2006; Blackburn et al., 2015), spatial distribution prediction (Muirhead and MacIsaac, 2005 ; Floerl et al., 2009 ; Larson et al., 2014 ), and local ecological impacts (Dick et al., 2013 ; Alexander et al., 2014 ; Jeschke et al., 2014 ). Regardless of the specific invasion scenario, a reduction in genetic diversity is generally anticipated due to the bottleneck effect, where only a limited subset of the source population's genetic variation is introduced (Dlugosch and Parker, 2008 ; Prentis et al., 2008). While some studies have shown that invasive species can establish and proliferate despite this reduced diversity (Genetic Paradox of Invasions; Frankham, 2004 ; Pérez et al., 2006 ; Roman and Darling, 2007 ; Hufbauer, 2008 ; Chandler et al., 2008 ), a substantial body of research highlights the significant role of propagule pressure and genetic mixing in driving invasion success (Oliveira et al., 2017 ; Darling et al., 2012 ; Ghabooli et al., 2011 ; Gillis et al., 2009 ; Dlugosch and Parker, 2008 ; Roman and Darling, 2007 ; Kolbe et al., 2004 ; Facon et al., 2003 ). These factors can effectively counteract the bottleneck effect, as evidenced by numerous studies that report comparable levels of genetic diversity between wild and introduced populations (Kelly et al., 2006 ; Rius et al., 2012 , 2015b), even in controlled experiments with varying propagule pressure (Clark and Johnston, 2009 ; Hedge et al., 2012 ; Arnott, 2016 ). Here we explore the genetics of the biological invasion of one of the most important commercial and aquaculture species worldwide, the tiger shrimp ( Penaeus monodon ). Penaeid shrimp are a group of decapods of significant commercial importance, distributed across tropical and subtropical regions worldwide, with the highest diversity found in the Indo-Pacific Ocean (Chan et al., 2008 ; Rajacumaran et al., 2014). This group includes the majority of shrimp species with high economic value, accounting for more than one-third of the annual wild crustacean catch. Additionally, over 20 species are critical to aquaculture. Despite their economic relevance, there is no consensus on the phylogeny of the Penaeidae family, and the taxonomic identification of some species remains uncertain (Samadi et al., 2016 ; Tavares and Gusmão, 2016; Ma et al., 2011 ). At least 10 species within this family have been reported as invasive or exotic (Aguirre-Pabón et al., 2015; Wakida-Kusunoki et al., 2011 ; Quigley et al., 2013 ; Özcan et al., 2006 ). Some of these species, such as Penaeus monodon , P. japonicus , P. aztecus , P. semisulcatus , P. pulchricaudatus , Litopenaeus vannamei , Fenneropenaeus merguiensis , and F. indicus , were introduced for aquaculture purposes in various countries outside their wild ranges. They have since been reported in natural environments, likely due to accidental escapes from aquaculture facilities or dispersion from adjacent areas (Kampouris et al., 2018 ; Scannella et al., 2017 ; Aguirre-Pabón et al., 2015; Wakida-Kusunoki et al., 2011 ; Quigley et al., 2013 ; Özcan et al., 2006 ). The tiger shrimp, P. monodon , is a wild species of the Indo-Pacific Ocean, distributed from Japan to Mozambique, including the islands of Australia, Indonesia, the Philippines, Malaysia, Sri Lanka, and Madagascar. This species was highly successful in aquaculture production during the 1970s and 1980s, leading to its introduction into many countries outside its wild range. In the Atlantic Ocean, it was introduced to the United States from Hawaii, with Hawaii receiving stocks from the Philippines, Tahiti, and Taiwan (DIAS, 2018). Similarly, P. monodon was introduced into Mexico (from Taiwan; DIAS, 2018), and Central and South America, including Panama, Colombia, Venezuela, and Brazil, from Taiwan and the Philippines (Ferreira et al., 2009 ; Álvarez-León and Gutiérrez-Bonilla, 2007). Additionally, Cuba received stocks from Ecuador (FAO, 2005 ). The first report of P. monodon outside its wild range was made in 1987 in Tutoia, Brazil (Fausto-Filho, 1987 ), followed by a second report in 1988 in South Carolina, Georgia, and Northeast Florida. In this area, approximately 300 individuals recovered following an accidental escape from aquaculture in South Carolina months earlier (Fuller et al., 2014 ). The species reappeared in 2000, and between that year and 2002, it was reported again in Tutoia (Brazil; Santos and Coelho, 2002 ), as well as in Alagoas and Pernambuco (Brazil; Coelho et al., 2001 ), Amapá (Silva et al., 2002 ), and Puerto Rico (Ramos, 2002). In subsequent years (2004–2007), P. monodon was reported in Venezuela, Colombia, Cuba, and the United States. By 2012, reports began to emerge from Central America, including Mexico, Guatemala, and Costa Rica. As of 2018, sightings and capture frequencies have been increasing throughout the Caribbean (Fig. 1 ). The species is now considered established in most areas of the Caribbean due to the rising catch rates and the presence of both mature and juvenile individuals, indicating successful completion of its reproductive cycle (Aguirre-Pabón et al., 2015; Fuller et al., 2014 ). Most studies on the invasion process of P. monodon in the Atlantic Ocean consist of reports (see above), descriptions of its spatiotemporal distribution (Fuller et al., 2014 ; Giménez et al., 2014 ; Sandoval et al., 2014 ), and analyses of size frequency (Fuller et al., 2014 ). The only study to date using molecular markers was conducted by Aguirre-Pabón et al. (2015, 2023), which reported low genetic diversity based on mitochondrial DNA but moderate diversity using microsatellites. These findings suggest multiple introductions and diverse origins of the invasive population within its wild range in the Pacific Ocean, highlighting the role of aquaculture-related translocations in shaping the genetic structure of these populations. Since reconstructing and understanding invasion mechanisms are crucial for developing strategies to mitigate future impacts (Pyšek and Richardson, 2010 ), this study employed mitochondrial DNA (COI) and microsatellites to reconstruct the invasion scenario of P. monodon in the Atlantic Ocean. The specific objectives were: (i) to infer the phylogenetic relationships of selected species within the Penaeidae family, with a primary focus on the divergence of P. monodon ; (ii) to determine the genetic diversity and structure of the invasive population and its relationship to wild and domestic populations; and (iii) to decipher the role of aquaculture in the invasion process of P. monodon in the Atlantic Ocean. Material and methods Sampling and laboratory protocols Muscle samples of tissue were collected in 15 localities of nine countries: 11 localities were distributed in six countries in the invasion area on the Atlantic Ocean (US, Colombia, Venezuela, Brazil, Senegal and GuineaBissau (AfricaW)), four localities of the natural environment in its wild area on the Indo-Pacific Ocean (Mozambique, India, Indonesia) and samples from the three farming systems were obtained of the wild area (Madagascar, India y Bangladesh) (Fig. 3 , Tables 1 ). All samples were preserved in absolute ethanol, and genomic DNA was isolated using a commercial DNA extraction kit (DNAeasy spin kit). To complement this matrix data, in the case of mtDNA-COI, sequences were obtained from GenBank from Brazil (21; PQ433754-PQ433770), Egypt (14; PP230876-PP2308889), Mozambique (5; KP297911-KP297913, KM508844- KM508845), India (65), Thailand (111; EF646151-EF646261), Indonesia (13; MT449912-MT449924), Philippines (139; KX459136-KX459272) and China (11; KP976327- KP976337). Mitochondrial DNA (COI) Mitochondrial DNA (COI) Polymerase Chain Reaction (PCR) was used to amplify the mitochondrial cytochrome c oxidase subunit I (COI-mtDNA) gene using the primers LCO1490 (TITCIACIAAYCAYAARGAYATTGG) and HCO2198 (TAIACYTCIGGRTGICCRAARAAYCA) as proposed by Geller et al. ( 2013 ). The reaction was conducted in a 25 µL volume under the following conditions: 3 µL of DNA template, 3 mM of MgCl₂, 0.3 mM of dNTPs, 0.4 µM of primers, and 2 U of Taq polymerase (Platinum™ Taq DNA Polymerase, Invitrogen, Paisley, UK). The thermal cycling profile was as follows: 95ºC for 5 minutes, followed by 35 cycles of 95ºC for 45 seconds, 48ºC for 45 seconds, and 72ºC for 1 minute, with a final extension at 72ºC for 10 minutes (BioRad T100™ Thermal Cycler). Amplification products were sent to Beckman Coulter Genomics (UK) for purification and Sanger sequencing in both directions (forward and reverse). Phylogeny and Divergence. To perform all analyses, the nucleotide substitution model (TN + G; Gamma = 0.81), which best fits the evolution of the sequences, was estimated using MrModelTest v2 (Nylander, 2004 ) and the Akaike Information Criterion (AIC). To assess the divergence within and between P. monodon and other penaeid species, a divergence matrix (MEGA v. 7; Kumar et al., 2007) and maximum likelihood estimates were calculated, using Euphausia longirostris (accession AF177189) as the outgroup. Sequences of P. monodon and P. semisulcatus were grouped based on areas where they are presumed to exhibit variations (e.g., Pacific Ocean, Indian Ocean, Arabian Peninsula, invasive areas, or domestic), which can help in understanding the degree of divergence compared to other species, with the outgroup serving as a reference. Genetic Diversity and Population Structure in the Invasive P. monodon. Genetic diversity levels were assessed in wild, invasive, and domestic populations using haplotype diversity (DH; Nei, 1987), share haplotypes (SH), private haplotypes (PH), and nucleotide diversity (π, the average number of nucleotide differences per site between two sequences; Nei, 1987). A haplotype network (median-joining; Bandelt et al., 1999 ) was constructed using PopART software (Leigh and Bryant, 2015 ), as well as the spatial distribution of the shared haplotypes was manually constructed using the shared haplotypes matrix obtained in Arlequin 3.0 (Excoffier et al., 2005 ); also, the pairwise F ST statistic was used to explore the spatial distribution of haplotypes to collection sites and the potential source of the invasive populations. Additionally, an Analysis of Molecular Variance (AMOVA; Excoffier et al., 1992 ) was performed using the previously obtained data (haplotype network, F ST , structure, and DAPC) to establish the different hierarchical groupings shown in Table 3 . Finally, tests to validate the neutrality model (Tajima’s D and Fu’s Fs) and assess changes in population size (mismatch distribution) were applied to understand the evolutionary processes in samples from invasive, wild, and domestic areas. All analyses were conducted in Arlequin 3.0 (Excoffier et al., 2005 ) using the Tamura and Nei model (TN + G; Gamma = 0) estimated in JModelTest (Posada, 2008 ) with 10,000 bootstrap permutations (significance level α = 0.05). Microsatellites Eight microsatellite loci (PmMS4CA, PmMS8, PmMS6, PmMS8A2, PmMS9, PmMS9GG, PmMS11AH, PmMS16) proposed by Li et al. ( 2007 ) were used to amplify DNA from P. monodon in a 10 µL multiplex PCR reaction, following the protocol outlined by the authors. PCR products were loaded onto an ABI 3130xl Genetic Analyzer (Applied Biosystems) using GeneScan 500 ROX size standard. Fragment lengths were determined using GeneMapper v.4.0 (Applied Biosystems). Genetic diversity was assessed by estimating allele frequencies, private alleles, allelic richness, observed heterozygosity (Ho), expected heterozygosity (He), and the inbreeding index (Fis). To understand the independence of genotypes between loci and avoid redundancy in the information obtained, genotypic linkage disequilibrium was tested using Markov chain exact probabilities (GENEPOP v.3.4, Raymond and Rousset, 1995 ). Additionally, the presence of null alleles in each locus was evaluated using Micro-Checker (Van Oosterhout et al., 2004 ) to avoid underestimation of genetic diversity parameters. Hardy-Weinberg equilibrium (HW-E) was tested for each population, and a global deficit of heterozygotes across loci and populations was assessed. All of these analyses, except for the null allele test, were performed using Arlequin 3.0 (Excoffier et al., 2005 ). The population differentiation level between sampling sites was tested using R ST and AMOVA (following the same groupings as above). These analyses were complemented with Discriminant Analysis of Principal Components (DAPC; ADEGENET; Jombart et al., 2010 ) and Bayesian structure analysis (STRUCTURE 2.3.3, Hubisz et al., 2009 ). The DAPC was used to explore the distribution of genotypes between different sites. In contrast, STRUCTURE was used to assign individuals to one or more groups based on their probability of belonging to those groups in a mixed population. This analysis was performed with a burn-in of 100,000 steps to minimize the effect of initial configuration, followed by 1,000,000 simulation steps (MCMC), assuming an admixture model appropriate for populations with gene flow, and the correlated allele model (Allele Frequencies Correlated), which assumes that populations diverged from a common ancestor and that allele frequency differences result from drift since divergence (Falush et al. 2003 ; Pritchard et al. 2000 ). The number of populations (K) was estimated using the method proposed by Evanno et al. ( 2005 ), based on the rate of change in Ln(K) between successive K values, using STRUCTURE SELECTOR (Li and Liu 2018 ). This analysis has the advantage of identifying the presence of discrete populations, hybrid zones, and migrant or admixed individuals. Results Mitochondrial DNA: Cytochrome Oxidase Subunit I (COI) Genetic diversity A total of 69 haplotypes were identified from 533 mtDNA-COI sequences of the P. monodon , with sequences spanning 497 bp (471 bp for Penaeidae in the phylogenetic analysis). Some invasive populations (USA, Colombia, Venezuela) in the western Atlantic Ocean exhibited the lowest genetic diversity across all parameters, particularly when compared to populations from the wild area and domestic systems. Brazil showed significant differences in genetic diversity parameters compared to the other locations; meanwhile, Bangladesh, West Africa, and Egypt demonstrated high haplotype diversity (HD) and nucleotide diversity (π) values, comparable to those of wild populations. This suggests that these locations behaved more like wild populations than invasive ones due to their similarity in genetic diversity to the wild area (Table 1 ). Private haplotypes were uncommon and found only in Colombia, Brazil, West Africa. Table 1. Genetic diversity indexes using mtDNA-COI in the native, culture and invasive populations of tiger shrimp P. monodon . N: number of samples, H: haplotypes, PH: private haplotypes, HD: haplotypes diversity, µ: nucleotide diversity, k: average number of paired differences, Tajima’s D and Fs’s Fu indexes. * p < 0.01, ** p < 0.01, *** p < 0.001. Population structure The Intraspecific divergence observed in some species (e.g., P. monodon and P. semisulcatus ) was notably high compared to others (e.g., P. murrayi vs P. investigatoris, P. longirostris vs P. americanus, F. merguiensis vs F. penicillatus, F. subtilis vs F. isabelae, L. setiferus vs L. schmitti ). The intraspecific variation in P. semisulcatus (Pacific Ocean vs Indian Ocean = 5.0%, Pacific Ocean vs Arabic Peninsula = 21.6%, Indian Ocean vs Arabic Peninsula = 18.6%) and P. monodon (Pacific Ocean vs Indian Ocean = 6.0%, Pacific Ocean vs Invasive area = 5.9%, Pacific Ocean vs Domestic = 6.3%, Indian Ocean vs Invasive area = 6.6%, Indian Ocean vs Domestic = 4.7%, Invasive area vs Domestic = 8.7%; or Pacific Ocean vs Indian Ocean = 8.3%) was similar to the interspecific variation observed between the species above (e.g., 6.1–15.3%; Fig. 2 A, Supplement 1). The haplotype network confirmed the significant intraspecific divergence in P. semisulcatus and P. monodon . In the case of P. monodon , the divergence was represented by four haplogroups: Haplogroup I (composed mainly of H1), separated by 12 mutations, and primarily consisting of invasive populations, along with some countries from the wild area such as Indonesia, China, India and Philippines; Haplogroup II (composed mainly of H2 and H5), separated by 10 mutations, and comprising populations from the Indo-Pacific Ocean and domestic systems, including some invasive countries to a lesser extent (primarily West Africa, Egypt and Brazil); Haplogroup III and IV (composed mainly of H52, H50 and H39), separated by 199 mutations, and consisting of Thailand and Philippines populations (Fig. 2 B). The divergence of these haplogroups was similar in P. semisulcatus , demonstrating how the haplotypes appeared mixed in two codistributed species, which suggests a possible effect of the domestic systems (Fig. 2 C). Additionally, a fourth haplogroup with a large divergence (58 mutations) was present, corresponding to the Arabian Peninsula (Fig. 2 C) F ST showed that the invasive populations from the USA, Colombia, Venezuela and Brazil were related to the wild populations of the Pacific Ocean (China, and Indonesia; F ST = -0.009 - 0.441), while West Africa and Egypt were more closely related to the Indian Ocean countries and domestic (F ST = -0.002 - 0.168; Table 2). Table 2. A) Genetic distance (Fst index) using mtDNA-COI in the native (black color), culture (blue color) and invasive (red color) population of the tiger shrimp P. monodon . B) Representation of the genetic distance using Neighbor-Joining tree. Text color in the table represent: red: invasive populations, blue: domestic populations and black: wild populations. AMOVA results for the hierarchical groupings proposed in Table 3 were significant for group IV (grouping IV: F ST = 0.901, p = 0.001, variance = 90.1%; Table 3). This assumes the relationship proposed by the genetic distance F ST , where the USA, Colombia, Venezuela, and Brazil are related to Indonesia and China. At the same time, West Africa appears to be related to Mozambique and India, and Egypt is related to domestic shrimp. Table 3. Analysis of Molecular Variance (AMOVA) results for different hierarchical grouping proposals in the tiger shrimp Penaeus monodon , based on mtDNA-COI and microsatellite markers. Text color in the table represent: red: invasive populations, blue: domestic populations and black: wild populations. The spatial distribution of the 69 haplotypes found in P. monodon showed a large proportion of private haplotypes (59 = 85.5%), of which only 10 were shared among almost all locations. Five share haplotypes were registered in the invasive area, in which four haplotypes were found in the Western Atlantic Ocean (H1, H2, H4 and H5) and four in the Eastern Atlantic Ocean (H1, H2, H5 and H12). H1 and H2 were the most frequently distributed haplotypes everywhere, except in Mozambique, Madagascar, Thailand, and the Philippines; H3 was found in the invasive area in West Africa, Egypt, and Brazil, and in the Indo-Pacific Ocean, except Thailand and the Philippines. H4 was shared by Brazil and Mozambique (Fig. 3 ; Table 4 ; Supplement 2). Table 4. Summary of the distribution of 69 haplotypes identified in 533 sequences of tiger shrimp. Colored cells indicate the ten haplotypes shared across different locations, while white cells represent private haplotypes. For private alleles, the numbers in brackets correspond to specific haplotypes. Text color in the table represent: red: invasive populations, blue: domestic populations and black: wild populations. The neutrality tests using Tajima's D and Fu's Fs were significantly negative for USA and Colombia, but only negative values were obtained to domestic India and Bangladesh; while the rest of the localities showed positive values (Table 1 ). Mismatch distribution analyses were conducted to test for signals of sudden population expansion, grouping localities in invasive, wild, and domestic, using the Ramos-Onsín and Rozas (R2) statistics and the raggedness index (r) to validate the congruence of the model. The results were significant for domestic, showing evidence of a population expansion event, with the best fit to a unimodal curve (Fig. 4 ). However, the trend of the curve in the other locations provided conflicting results. Microsatelites Genetic diversity Genetic diversity values were high and similar for all localities (Ho > 0.711, He > 0.744, and Fis < 1.75). The number of private alleles was slightly higher in the wild localities compared to the other ones, although South Carolina, Colombia (Guajira), the West African Coast, and Bangladesh registered private alleles (Table 5 ). There was no deviation from Hardy-Weinberg equilibrium (HW-E) and no linkage disequilibrium for all microsatellite loci used in this study. According to the bottleneck analysis using the TPM model and Wilcoxon statistical test, all wild and invasive localities were in mutation-drift disequilibrium, suggesting signs of a recent bottleneck process in these populations. Only the samples from domestic systems (India, Bangladesh, and Madagascar) did not show evidence of a recent bottleneck process (Fig. 5 ). Table 5. Genetic diversity indexes using microsatellites loci in the native, culture and invasive populations of tiger shrimp P. monodon. N: samples collected, PA: private alleles, Ho: heterozygosity observed, He: heterozygosity expected Fis: Index inbreeding, HW-E: Hardy-Weinberg Equilibrium. Text color in the table represent: red: invasive populations, blue: domestic populations and black: wild populations. Population structure Population differentiation in the invasive area ranged from low differentiation (R ST = -0.021 to 0.02) in the western Atlantic Ocean (USA, Colombia, and Venezuela) to moderate differentiation (R ST = 0.027 to 0.146) in the eastern Atlantic Ocean (West Africa). The lower values observed in these localities, when compared to the wild populations, were mainly associated with Indonesia (Rst = -0.023 to 0.053), India (R ST = -0.025 to 0.061), and domesticated from Bangladesh. Venezuela was the most similar (R ST = -0.025–0.034), while domesticated from Madagascar and Mozambique were the most distinct localities throughout the study area (R ST >0.232; Table 6 ). The AMOVA analysis, using the same hierarchical grouping as proposed with mtDNA-COI sequences, showed similar results, with the highest probability supported when assuming the relationships found with genetic distance R ST (grouping IV: F ST = 0.123, p = 0.007, variance = 12.37%; Table 3 ). This indicates that invasive populations in the USA, Colombia, and Venezuela are genetically related to wild populations from Indonesia, whereas those in West Africa are associated with domesticated from India and Bangladesh, as well as wild populations from India. Table 6. A) Genetic distance (Rst index) using microsatellites in the native (black color), culture (blue color) and invasive (red color) population of the tiger shrimp P. monodon . B) Representation of the genetic distance using Neighbor-Joining tree. Text color in the table represent: red: invasive populations, blue: domestic populations and black: wild populations. The results obtained from STRUCTURE revealed three most likely clusters (K = 3). One of these clusters predominated in the invasive area (red), while the other ones (blue and yellow) were associated with the wild and domestic areas. A third cluster was more restricted to Mozambique, Madagascar, and West Africa (Fig. 6 ). The genotypic mixture pattern remained similar across all K values used in this analysis (K = 2–10), except for the third cluster (yellow), and another that appeared in K = 4 (domestic India, fuchsia), which seemed not to mix with other populations (Fig. 6 ). Discriminant Analysis of Principal Components (DAPC) confirmed the results obtained from STRUCTURE, providing a more detailed view of the distribution within the three clusters (Fig. 7 ). The invasive area was most closely related to the Indonesia, India, and domesticated from India, and Bangladesh populations, which formed the second cluster. Meanwhile, West Africa, Mozambique, and domesticated from Madagascar were the most distinct group, with some West Africa points linking to other clusters (Fig. 7 ). Discussion Genetic diversity MtDNA-COI analysis revealed high genetic diversity in the tiger sgrimp P. monodon within its invasive range (Brazil, West Africa, Egypt) and in domestic systems (Bangladesh), comparable to that of native populations. These findings are consistent with previous studies in the Colombian Caribbean using mtDNA-CR and microsatellites (Aguirre-Pabón et al., 2015, 2023), which detected three haplotypes, two shared with four different Indo-pacific sources but at lower diversity levels. In the present study, three haplotypes were also identified in Colombia and five across the invasive range, with genetic affinities to populations from Asia and Africa. Notably, some haplotypes originated from regions with high divergence, such as the Philippines, India, and Mozambique (Fig. 3 , Table 4 , Supplement 2). The accumulation of nucleotide variation determines the rate of new haplotype emergence, and our results indicate low nucleotide diversity in domestic populations, likely due to the strong selective pressures imposed in hatchery systems. Conversely, invasive populations exhibited higher variability, which may reflect adaptive responses to colonization or the result of introductions from multiple sources (Oliveira et al., 2017 ; Darling et al., 2012 ; Ghabooli et al., 2011 ; Gillis et al., 2009 ; Dlugosch and Parker, 2008 ; Roman and Darling, 2007 ). This aligns with invasion scenarios in which human-mediated dispersal repeatedly bridges distant biogeographic regions, producing genetic mosaics that differ from natural distributions. Chronological records of the introduction process in the western Atlantic, with focal points in Brazil (1987) and the United States (1988; Fig. 1 ), further illustrate how aquaculture can establish parallel invasion fronts that subsequently expand and converge. Microsatellite analyses reinforce this pattern, showing consistently high diversity across sites, signs of demographic stability or expansion in aquaculture populations, and bottleneck signatures in both invasive and wild populations. Together, these outcomes exemplify a general pattern observed in many successful marine invaders: instead of suffering lasting genetic erosion, introduced populations maintain or even increase their variability (Estoup et al., 2016 ; Lawson et al., 2011). This so-called “ genetic paradox of invasions ” suggests that the invasion success of P. monodon is not an isolated case, but part of a broader dynamic where human-mediated dispersal compensates for founder effects. A central insight from this study is the role of aquaculture as a mechanism sustaining genetic diversity in invasive lineages. Unlike natural dispersal, aquaculture-mediated movements are intentional, recurrent, and geographically extensive, involving broodstock replacement, crossbreeding among lineages, and the transfer of gravid females across regions (Briggs et al., 2004; Benzie, 2000 ). These practices act as a counterweight to genetic drift, buffering bottlenecks that would otherwise constrain genetic variability (Gjedrem, 2005 ; Luvesuto et al., 2007 ; Araki and Schmid, 2010 ). Similar processes have been described in other aquaculture-related invasions, where domestication inadvertently enhances the evolutionary potential of non-native species (Sekar et al., 2014 ; Goyard et al., 2003 ). For instance, translocation of broodstock in Asia has generated artificial contact zones in which haplotypes from distant regions coexist (You et al., 2008 ; Ensing et al., 2011 ; Waqairatu et al., 2012 ; Vu et al., 2021 ). These artificial genetic assemblages, now detected in the Atlantic, underscore how aquaculture alters the tempo and mode of evolutionary processes in marine invasions. The rapid expansion of Asian aquaculture has further facilitated introductions of P. monodon into nearly all Caribbean and West African coasts (Aguirre-Pabón et al., 2015, 2023; Aguirre-Guzmán et al., 2020), usually outside of formal regulatory frameworks and without clear information on stock sizes or origins. Thus, the invasion patterns observed here reflect not only the biology of a single species, but also the broader reality that aquaculture operates as one of the most powerful anthropogenic forces shaping the global redistribution and genetic architecture of marine biodiversity. Phylogeny and structure population The phylogenetic analysis of penaeid species, with emphasis on P. monodon , revealed substantial intraspecific divergences (e.g., P. monodon : 4.7–8.3%; P. semisulcatus : 5–21.6%) that in some cases exceeded interspecific values (e.g., P. murrayi–P. investigatoris : 6.1%, P. longirostris–P. americanus : 7.3%, F. merguiensis–F. penicillatus : 6.5%, F. subtilis–F. isabelae : 6.1%, L. setiferus–L. schmitti : 6.5%; Fig. 2 A, Supplement 1). These results reinforce the need for refined phylogenetic work in penaeids, but in the broader context of invasions, they also illustrate a recurrent challenge: high within-species genetic differentiation provides reservoirs of variation that can be reshuffled through aquaculture and translocation, increasing invasion potential. This is particularly relevant given that many penaeids are intentionally moved across regions for aquaculture (e.g., P. monodon, L. vannamei, M. japonicus ; FAO DIAS database), or unintentionally via ballast water and canals. Several documented invasions—including P. monodon and P. aztec us (Kampouris et al., 2018 ; Scannella et al., 2017 ), M. japonicus (Rodriguez and Suarez, 2001), P. semisulcatus (Ragonese and Giusto, 2013 ), and F. merguiensis (Özcan et al., 2006 )—demonstrate how aquaculture-linked dispersal contributes to the redistribution of genetically structured species. Thus, phylogenetic divergence within penaeids is not just a taxonomic issue, but a key element influencing the evolutionary trajectories of invasive lineages. In P. monodon , COI-mtDNA and microsatellite markers yielded congruent signals of structure. The haplotype network, STRUCTURE analysis, and DAPC identified three groups, with the invasive population forming a distinct cluster (haplogroup I; red cluster in STRUCTURE). Such patterns align with scenarios where artificially assembled populations, derived from multiple native sources, fuel invasion fronts. Comparable findings in wild and domestic populations (Vu et al., 2021 ) highlight how mixing during domestication reshapes genetic baselines: high diversity is retained, divergence is generated, and invasive populations acquire an evolutionary toolkit to thrive in novel habitats. Evidence of this is the invasive populations in Brazil and West Africa, which displayed the highest genetic diversity and shared haplotypes while also retaining private ones, epitomize this process. For instance, H1—a haplotype traced to the Pacific (China and Indonesia, also present in India, the Philippines, and domesticated stocks from Bangladesh)—dominates the invasive range, while H2 and others (H4, H5) show Indo-Pacific and southeastern African origins (Fig. 3 , Table 4 , Supplement 2). This mosaic indicates that invasion pathways integrate multiple domestication sources, creating genetic combinations that natural dispersal alone might not produce. On the other hand, disrupted gene flow among Indo-Pacific localities (e.g., Thailand vs. Philippines) is consistent with previous reports of strong structure in P. monodon (Benzie et al., 2002; You et al., 2008 ; Waqairatu et al., 2012 ; Vu et al., 2021 ). Such natural discontinuities—often associated with historical vicariance, oceanographic barriers, or Pleistocene climate oscillations (Benzie et al., 2002)—contrast with the anthropogenic removal of barriers: aquaculture and translocations effectively reassemble lineages across oceans. This represents a fundamental shift for invasion biology: domestication transforms natural patterns of isolation-by-distance into artificially homogenized but demographically robust populations. Two explanatory frameworks—the Vagrant Member and Metapopulation models—have been proposed to understand penaeid historical biogeography (Hanski, 1998 ; Sinclair, 1988; Garant et al., 2000 ; Ensing et al., 2011 ). While these models emphasize restricted gene flow or extinction–recolonization dynamics, current data (Vu et al., 2021 ; You et al., 2008 ) suggest that neither alone captures the modern scenario. Instead, anthropogenic drivers dominate: domestication and aquaculture now dictate the geographic and genetic boundaries of populations, with implications for invasion spread and resilience. This anthropogenic influence is further reflected in the overlap between haplotypes of P. monodon and P. semisulcatus , separated by 12 mutational steps (Fig. 2 C). The co-occurrence of divergent haplogroups in aquaculture and invasive contexts raises broader evolutionary questions: to what extent does artificial hybridization or haplotype mixing across related species expand the adaptive capacity of invaders? Such processes highlight aquaculture not only as a vector but as a generator of novel genetic assemblages that may accelerate invasion success. Although our sampling lacked full coverage of wild and domestic stocks in the eastern Pacific, the general congruence across markers (mtDNA-COI and microsatellites) supports the conclusion that invasive Atlantic populations of P. monodon arise from multisource, human-mediated introductions with high levels of admixture. This contrasts with P. semisulcatus , whose domestication history also involves translocations but on a different scale (You et al., 2008 ; Ensing et al., 2011 ; Waqairatu et al., 2012 ; Vu et al., 2021 ). More broadly, these findings emphasize that phylogenetic diversity, when reshaped by aquaculture, provides a powerful substrate for successful invasions in marine ecosystems. Demographic history Signals of population expansion were strongest in the domestic population, whereas the wild and invasive populations exhibited negative or ambiguous patterns, as indicated by Tajima’s D and Fu’s Fs tests (Fig. 4 ). Mismatch distribution analyses yielded inconclusive results for the Ramos-Onsin and Rozas ( R2 ) statistic and the raggedness index ( r ). However, the unimodal trend observed in the domestic population (Fig. 4 ) suggests a signal of sudden demographic expansion (Slatkin and Hudson, 1991 ; Rogers and Harpending, 1992 ). Bottleneck analyses with microsatellites further supported this view: the Wilcoxon test revealed a significant heterozygote excess in both wild and invasive populations, consistent with recent bottlenecks, while the domestic population remained closer to mutation–drift equilibrium (Fig. 5 ). Taken together, these results point to two contrasting demographic scenarios: expansion in domestic systems and bottlenecks in wild and invasive populations. From an invasion biology perspective, these contrasting dynamics underscore how domestication and aquaculture reshape demographic trajectories in ways that directly influence invasion potential. In domestic systems, expansion is likely a consequence of stock management practices—such as frequent renewal of broodstock and mixing of individuals from multiple sources—which mitigate inbreeding, counteract disease-related losses, and maintain genetic variability. This artificial demographic buffering creates large, genetically diverse reservoirs that can serve as invasion sources, thereby altering the scale and speed at which non-native populations establish in the wild. By contrast, bottlenecks in invasive populations illustrate the classical expectation of founder effects, where successive reductions occur first during the transition from wild to domestic populations, and later during the establishment of invasive populations from these already reduced groups. Yet, the paradox revealed here is that high genetic diversity persists in invasive populations despite bottlenecks, a pattern increasingly observed in marine invaders. This paradox may be explained by the combination of two processes: (i) adaptation to local environments that favors rapid allele sorting, and (ii) repeated introductions or admixture from multiple wild sources, which reintroduce allelic diversity and buffer against genetic erosion. Such processes are not unique to P. monodon , but represent a broader mechanism by which aquaculture and human-mediated dispersal can counteract the stochastic losses expected under invasion scenarios. In this sense, the demographic signals observed here highlight a key question for invasion biology: to what extent does aquaculture transform demographic bottlenecks from liabilities into opportunities for invasion success? While bottlenecks are expected to constrain establishment, aquaculture-mediated introductions may invert this expectation by replenishing diversity and promoting demographic stability, ultimately shaping the long-term evolutionary dynamics of marine invaders. What is the role of the farming systems in global marine invasions? According to the FAO ( 2018 ), global aquaculture production has grown dramatically, increasing from 20 million tons in the 1950s to 186.7 million tons in 2015. This includes 54.1 million tons of fish, 17.1 million tons of mollusks, 7.9 million tons of crustaceans, and 938,500 tons of other aquatic animals, already surpassing wild capture production, which accounted for 92.7 million tons in 2015. This rapid growth, both in developed and developing countries, has largely depended on the deliberate or accidental introduction of non-native species (e.g., Shelton and Rothbard, 2006 ; De Silva et al., 2009 ). For instance, non-native species constitute 60% of aquaculture production in the Philippines, 50% in Brazil, more than 25% in China, and nearly 100% in Israel (Shelton and Rothbard, 2006 ). The scale of species introductions has increased in parallel with aquaculture expansion. Welcomme ( 1988 ) reported 1,354 introductions involving 237 species across 140 countries, of which 321 (24%) established wild populations and 89 (7%) generated significant ecological problems. Today, the FAO Database on Introductions of Aquatic Species (DIAS; http://www.fao.org/fishery/dias/en ) lists more than 5,500 introductions worldwide. China, the largest aquaculture producer, has introduced 179 species since 1920, more than 150 of them in the last 40 years, with production surpassing 400 million tons annually by 2000, despite official records listing only 56 species (Lin et al., 2015 ). These figures highlight not only the magnitude of aquaculture-driven introductions but also the gap in monitoring and regulation, particularly given that 76% of introduced species are classified as “unknown” in DIAS (Ling et al., 2015). Our findings add nuance to this global scenario by showing that farming systems are not only drivers of introductions but also key modulators of genetic outcomes in marine invasions. In the case analyzed here, invasive populations displayed high genetic diversity and evidence of multiple introductions from different origins (Fig. 2 B, Fig. 3 , Table 4 , Supplement 2). This aligns with broader invasion biology theory, where repeated introductions and admixture reduce founder effects and increase adaptive potential (Kolbe et al., 2004 ). Such processes may generate novel genotypes capable of coping with ecological pressures in novel environments, ultimately accelerating invasion success. Importantly, aquaculture facilities themselves may act as reservoirs of genetic diversity. Practices such as periodic renewal of broodstock or the exchange of individuals among hatcheries and regions can enhance gene flow and facilitate the persistence of diverse lineages (You et al., 2008 ; Ensing et al., 2011 ; Waqairatu et al., 2012 ; Vu et al., 2021 ). While this diversity may benefit aquaculture production by maintaining resilience against disease and environmental variability, it also inadvertently equips invasive populations with traits such as broad environmental tolerance, rapid growth, and high fecundity—characteristics shared by many globally invasive species. The broader implication is that aquaculture acts as a dual-force system: it sustains food production and economic development, but simultaneously shapes the evolutionary trajectory of non-native populations, often favoring traits that make them successful invaders. Finally, it is worth reflecting on the paradox posed by Lin et al. ( 2015 ): "Some species significantly contribute to the rapid expansion of aquaculture, while the negative effects associated with the unregulated introduction and irresponsible use of non-native species are increasing in both number and affected areas. However, lessons from these reported disasters are slowly being learned, yet the risks continue to be largely overlooked". Conclusions This study highlights the dual role of aquaculture in the evolutionary and biogeographic dynamics of Penaeus monodon . The maintenance of high genetic diversity across invasive populations, despite demographic bottlenecks, demonstrates that aquaculture-mediated translocations mitigate founder effects and promote genetic admixture among distant lineages. Aquaculture emerges not only as a dispersal vector but as an evolutionary catalyst, generating artificial admixture zones that enhance adaptive potential and promote invasion success. Domestic stocks, through broodstock exchange and recurrent introductions, act as reservoirs of genetic diversity that sustain invasion processes. Overall, aquaculture operates as a dual-force system: it supports global food production while simultaneously facilitating the spread and evolutionary diversification of non-native species. Recognizing this dual role is essential for future research, integrating genomic, ecological, and regulatory perspectives to evaluate how domestication-driven gene flow continues influences the success and resilience of marine invasions. Declarations Acknowledgments We thank Dr. Waiho Khor for him help in providing with the samples of the P. semisulcatus species from Sabah (Malaysia). Author contributions Juan Carlos Aguirre Pabon: designed the study, collected samples, laboratory processes, analysis data, writing original draft, reviewed and edited manuscript. Stephen Sabatino: support of laboratory processes and analysis data, conceptualization, writing, review and editing manuscript; Armando Semo: collected samples; review and editing manuscript. James Morris: collected samples; review and editing manuscript. Antonio Murias: financial support and project management; support of laboratory processes, conceptualization, investigation, review and finalized and approved the manuscript for submission. All authors read and approved of the final manuscript. Funding The authors have no relevant financial or non-financial interests to disclose. Data availability The datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. Conflict of interest The authors declare that they have no conflict of interest. References Aguirre-Guzmán G, López-Acevedo EA. (2020). 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1","display":"","copyAsset":false,"role":"figure","size":150631,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual catch reports of tiger shrimp \u003cem\u003eP. monodon\u003c/em\u003e in the western Atlantic Ocean. Red dots were provided by the Nonindigenous Aquatic Species Database of U.S. Geological Survey (USGS) and blue dots were obtained from bibliographics reviews. The number of the squares corresponds to the reports of a specific year.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7993803/v1/2a2a222966e9a8e1d1c13629.png"},{"id":96083237,"identity":"20328184-d4bc-49c1-b60d-820dda69f9fe","added_by":"auto","created_at":"2025-11-17 11:55:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eMaximum likelihood using sequences of mtDNA COI gen in the Penaeidae family. \u003cstrong\u003eB)\u003c/strong\u003eHaplotype network using TCS algorithm in samples of tiger shrimp \u003cem\u003eP. monodon \u003c/em\u003e(pink color)\u003cem\u003e.\u003c/em\u003e and C) green tiger prawn \u003cem\u003eP. semisulcatus\u003c/em\u003e (blue color).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7993803/v1/edeb6d5c7bec79b2a893b92f.png"},{"id":96246729,"identity":"6907657f-89c5-48b7-83b2-f1f7e1bfe097","added_by":"auto","created_at":"2025-11-19 07:26:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100581,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the 69 haplotypes identified in the tiger shrimp \u003cem\u003ePenaeus monodon\u003c/em\u003e. Sequences were obtained from invasive populations in the Atlantic Ocean, domestic systems in India, Bangladesh, and Madagascar, and wild populations from the Indo-Pacific region. Colors represent haplotypes shared between localities, while white indicates private haplotypes unique to specific locations.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7993803/v1/d69ef2a772658fc2d2400ba8.png"},{"id":96248370,"identity":"1ced7b26-a2fa-44fa-817f-fbb3cf217d97","added_by":"auto","created_at":"2025-11-19 07:28:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96636,"visible":true,"origin":"","legend":"\u003cp\u003eMismatch distribution analysis of mtDNA-COI was conducted to test signals of population expansion in \u003cem\u003eP. monodon\u003c/em\u003e, grouping localities into Invasive, Domesticand Wild categories, as well as considering the entire dataset as a single population* p \u0026lt; 0.01, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7993803/v1/1c37b85320b372bad877ba4d.png"},{"id":96083244,"identity":"a697d381-38c6-40c8-99ea-d901398baf8f","added_by":"auto","created_at":"2025-11-17 11:55:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55000,"visible":true,"origin":"","legend":"\u003cp\u003eBottleneck analysis results based on eight microsatellites, testing for significant population reductions (* p-value \u0026lt; 0.05) in the tiger shrimp \u003cem\u003eP. monodon\u003c/em\u003eacross different localities. The top images represent invasive populations, the middle images correspond to domestic populations, and the lower images depict wild populations.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7993803/v1/f1f9a46fafc23032e775b160.png"},{"id":96249658,"identity":"da332334-5acb-4f53-b954-e7dadd07608e","added_by":"auto","created_at":"2025-11-19 07:35:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":106450,"visible":true,"origin":"","legend":"\u003cp\u003eBayesian STRUCTURE results in the invasive, domestic and wild populations of tiger shrimp \u003cem\u003eP. monodon\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7993803/v1/07ea78a2fbb7531c03940e16.png"},{"id":96083246,"identity":"6d1974ed-15c6-4736-98b7-bddad9219122","added_by":"auto","created_at":"2025-11-17 11:55:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":64793,"visible":true,"origin":"","legend":"\u003cp\u003eDiscriminant Analyses of Principal Components (DAPC) in invasive, domestic and wild populations of tiger shrimp \u003cem\u003eP. monodon\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7993803/v1/dee5eca4d1c729c18687b7ac.png"},{"id":105755896,"identity":"f6cea162-8812-4b46-8ff8-7c6f211aa4ff","added_by":"auto","created_at":"2026-03-30 16:32:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1419720,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7993803/v1/a6fc823e-0903-4628-8a04-016dd94dad8b.pdf"},{"id":96083251,"identity":"6d015493-d267-43bb-b636-d462eaf83ec4","added_by":"auto","created_at":"2025-11-17 11:55:54","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":124043,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7993803/v1/b57f27f64e2fdaa537101861.docx"},{"id":96083248,"identity":"52cdc844-5607-44e6-b1d2-c03e48b01304","added_by":"auto","created_at":"2025-11-17 11:55:54","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":34440,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7993803/v1/d847761d734c077ccd249b42.docx"}],"financialInterests":"","formattedTitle":"Genetic Structure and Evolutionary History of the tiger shrimp Penaeus monodon Across Its Wild and Introduced Ranges","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBiological invasions are increasingly recognized as a significant threat to marine biodiversity worldwide (Lallias et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Invasions occur when species colonize areas beyond their natural range due to human behavior. The origin of this phenomenon dates back to interoceanic travel during colonization, when people began traveling great distances by boat for various reasons, including to move to new areas, trade, and make war. Over the past few centuries, population growth and globalization has increased travel and trade exponentially, resulting in greater maritime transport to meet rising global demands. However, despite this threat, understanding the ecological and evolutionary circumstances and implications of successful invasions is not well established.\u003c/p\u003e\u003cp\u003eOne of the most important questions in invasion biology is why some species are successful at invasions and others are not (Schulz et al., 2019). Species typically remain restricted to their natural habitats due to their limited capacity for long-distance dispersal -often a consequence of short larval phases and/or a sessile or sedentary adult stage, which tends to restrict them to their native regions. Paradoxically, many documented cases of successful marine invasions involve species with limited dispersal (e.g., mollusks, crustaceans, ctenophores, cnidarians, tunicates). One explanation of this is that those with great dispersal capacities have dispersed as far as they can based on their fundamental niche. And, those invasions we do see are forced by the maritime activities over the past centuries in altering the distribution of taxa across large geographical scales (Goulletquer et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ruiz et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTransport of marine organisms occurs mainly through the attachment or embedding of organisms in the hulls of ships (biofilm), ballast water, or introduction for cultivation/aquarium purposes. While attachment events are centuries old, introducing the ballast water system at the end of the 19th century to improve the stability and buoyancy of ships is more recent and created an unprecedented problem related to biological invasions (Molnar et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Although ballast water is a very efficient means for the long-distance transport of many organisms, surviving the conditions of these systems during long voyages can indicate a certain adaptive capacity, contributing to the success of introduction and subsequent establishment. Therefore, diapause eggs in many marine species, especially crustaceans, can be favorable (Ricciardi, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast to the previously discussed vectors, potential invasions stemming from aquaculture introductions have received comparatively less attention. This is often attributed to the perception that aquaculture introductions result in lower propagule pressure or necessitate multiple introductions for successful establishment (Facon et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, it is important to note that a substantial proportion of invasive species originate, either directly or indirectly, from aquaculture (e.g., species from the genera \u003cem\u003ePerna\u003c/em\u003e, \u003cem\u003eOstrea\u003c/em\u003e, \u003cem\u003eMytilus\u003c/em\u003e, \u003cem\u003eRuditapes, Crassostrea\u003c/em\u003e, \u003cem\u003ePenaeus\u003c/em\u003e). Despite this, the literature addressing aquaculture-mediated invasions remains relatively limited (Aguirre-Pab\u0026oacute;n et al., 2015; Lallias et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Voisin et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), as research has primarily focused on broader topics, including biology, ecology, and evolution (Roman and Darling, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lejeusne et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), transport mechanisms and geographic pathways (Hulme, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wilson et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), factors influencing invasion success (Williamson, 2006; Blackburn et al., 2015), spatial distribution prediction (Muirhead and MacIsaac, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Floerl et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Larson et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and local ecological impacts (Dick et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Alexander et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jeschke et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegardless of the specific invasion scenario, a reduction in genetic diversity is generally anticipated due to the bottleneck effect, where only a limited subset of the source population's genetic variation is introduced (Dlugosch and Parker, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Prentis et al., 2008). While some studies have shown that invasive species can establish and proliferate despite this reduced diversity (Genetic Paradox of Invasions; Frankham, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; P\u0026eacute;rez et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Roman and Darling, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hufbauer, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Chandler et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), a substantial body of research highlights the significant role of propagule pressure and genetic mixing in driving invasion success (Oliveira et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Darling et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ghabooli et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gillis et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dlugosch and Parker, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Roman and Darling, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kolbe et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Facon et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). These factors can effectively counteract the bottleneck effect, as evidenced by numerous studies that report comparable levels of genetic diversity between wild and introduced populations (Kelly et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rius et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, 2015b), even in controlled experiments with varying propagule pressure (Clark and Johnston, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hedge et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Arnott, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHere we explore the genetics of the biological invasion of one of the most important commercial and aquaculture species worldwide, the tiger shrimp (\u003cem\u003ePenaeus monodon\u003c/em\u003e). Penaeid shrimp are a group of decapods of significant commercial importance, distributed across tropical and subtropical regions worldwide, with the highest diversity found in the Indo-Pacific Ocean (Chan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rajacumaran et al., 2014). This group includes the majority of shrimp species with high economic value, accounting for more than one-third of the annual wild crustacean catch. Additionally, over 20 species are critical to aquaculture. Despite their economic relevance, there is no consensus on the phylogeny of the Penaeidae family, and the taxonomic identification of some species remains uncertain (Samadi et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tavares and Gusm\u0026atilde;o, 2016; Ma et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt least 10 species within this family have been reported as invasive or exotic (Aguirre-Pab\u0026oacute;n et al., 2015; Wakida-Kusunoki et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Quigley et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u0026Ouml;zcan et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Some of these species, such as \u003cem\u003ePenaeus monodon\u003c/em\u003e, \u003cem\u003eP. japonicus\u003c/em\u003e, \u003cem\u003eP. aztecus\u003c/em\u003e, \u003cem\u003eP. semisulcatus\u003c/em\u003e, \u003cem\u003eP. pulchricaudatus\u003c/em\u003e, \u003cem\u003eLitopenaeus vannamei\u003c/em\u003e, \u003cem\u003eFenneropenaeus merguiensis\u003c/em\u003e, and \u003cem\u003eF. indicus\u003c/em\u003e, were introduced for aquaculture purposes in various countries outside their wild ranges. They have since been reported in natural environments, likely due to accidental escapes from aquaculture facilities or dispersion from adjacent areas (Kampouris et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Scannella et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Aguirre-Pab\u0026oacute;n et al., 2015; Wakida-Kusunoki et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Quigley et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; \u0026Ouml;zcan et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe tiger shrimp, \u003cem\u003eP. monodon\u003c/em\u003e, is a wild species of the Indo-Pacific Ocean, distributed from Japan to Mozambique, including the islands of Australia, Indonesia, the Philippines, Malaysia, Sri Lanka, and Madagascar. This species was highly successful in aquaculture production during the 1970s and 1980s, leading to its introduction into many countries outside its wild range. In the Atlantic Ocean, it was introduced to the United States from Hawaii, with Hawaii receiving stocks from the Philippines, Tahiti, and Taiwan (DIAS, 2018). Similarly, \u003cem\u003eP. monodon\u003c/em\u003e was introduced into Mexico (from Taiwan; DIAS, 2018), and Central and South America, including Panama, Colombia, Venezuela, and Brazil, from Taiwan and the Philippines (Ferreira et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; \u0026Aacute;lvarez-Le\u0026oacute;n and Guti\u0026eacute;rrez-Bonilla, 2007). Additionally, Cuba received stocks from Ecuador (FAO, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe first report of \u003cem\u003eP. monodon\u003c/em\u003e outside its wild range was made in 1987 in Tutoia, Brazil (Fausto-Filho, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), followed by a second report in 1988 in South Carolina, Georgia, and Northeast Florida. In this area, approximately 300 individuals recovered following an accidental escape from aquaculture in South Carolina months earlier (Fuller et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The species reappeared in 2000, and between that year and 2002, it was reported again in Tutoia (Brazil; Santos and Coelho, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), as well as in Alagoas and Pernambuco (Brazil; Coelho et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), Amap\u0026aacute; (Silva et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and Puerto Rico (Ramos, 2002).\u003c/p\u003e\u003cp\u003eIn subsequent years (2004\u0026ndash;2007), \u003cem\u003eP. monodon\u003c/em\u003e was reported in Venezuela, Colombia, Cuba, and the United States. By 2012, reports began to emerge from Central America, including Mexico, Guatemala, and Costa Rica. As of 2018, sightings and capture frequencies have been increasing throughout the Caribbean (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The species is now considered established in most areas of the Caribbean due to the rising catch rates and the presence of both mature and juvenile individuals, indicating successful completion of its reproductive cycle (Aguirre-Pab\u0026oacute;n et al., 2015; Fuller et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMost studies on the invasion process of \u003cem\u003eP. monodon\u003c/em\u003e in the Atlantic Ocean consist of reports (see above), descriptions of its spatiotemporal distribution (Fuller et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gim\u0026eacute;nez et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sandoval et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and analyses of size frequency (Fuller et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The only study to date using molecular markers was conducted by Aguirre-Pab\u0026oacute;n et al. (2015, 2023), which reported low genetic diversity based on mitochondrial DNA but moderate diversity using microsatellites. These findings suggest multiple introductions and diverse origins of the invasive population within its wild range in the Pacific Ocean, highlighting the role of aquaculture-related translocations in shaping the genetic structure of these populations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSince reconstructing and understanding invasion mechanisms are crucial for developing strategies to mitigate future impacts (Pyšek and Richardson, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), this study employed mitochondrial DNA (COI) and microsatellites to reconstruct the invasion scenario of \u003cem\u003eP. monodon\u003c/em\u003e in the Atlantic Ocean. The specific objectives were: (i) to infer the phylogenetic relationships of selected species within the Penaeidae family, with a primary focus on the divergence of \u003cem\u003eP. monodon\u003c/em\u003e; (ii) to determine the genetic diversity and structure of the invasive population and its relationship to wild and domestic populations; and (iii) to decipher the role of aquaculture in the invasion process of \u003cem\u003eP. monodon\u003c/em\u003e in the Atlantic Ocean.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSampling and laboratory protocols\u003c/h2\u003e\u003cp\u003eMuscle samples of tissue were collected in 15 localities of nine countries: 11 localities were distributed in six countries in the invasion area on the Atlantic Ocean (US, Colombia, Venezuela, Brazil, Senegal and GuineaBissau (AfricaW)), four localities of the natural environment in its wild area on the Indo-Pacific Ocean (Mozambique, India, Indonesia) and samples from the three farming systems were obtained of the wild area (Madagascar, India y Bangladesh) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All samples were preserved in absolute ethanol, and genomic DNA was isolated using a commercial DNA extraction kit (DNAeasy spin kit).\u003c/p\u003e\u003cp\u003eTo complement this matrix data, in the case of mtDNA-COI, sequences were obtained from GenBank from Brazil (21; PQ433754-PQ433770), Egypt (14; PP230876-PP2308889), Mozambique (5; KP297911-KP297913, KM508844- KM508845), India (65), Thailand (111; EF646151-EF646261), Indonesia (13; MT449912-MT449924), Philippines (139; KX459136-KX459272) and China (11; KP976327- KP976337).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMitochondrial DNA (COI)\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eMitochondrial DNA (COI)\u003c/div\u003e\u003cp\u003ePolymerase Chain Reaction (PCR) was used to amplify the mitochondrial cytochrome c oxidase subunit I (COI-mtDNA) gene using the primers LCO1490 (TITCIACIAAYCAYAARGAYATTGG) and HCO2198 (TAIACYTCIGGRTGICCRAARAAYCA) as proposed by Geller et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The reaction was conducted in a 25 \u0026micro;L volume under the following conditions: 3 \u0026micro;L of DNA template, 3 mM of MgCl₂, 0.3 mM of dNTPs, 0.4 \u0026micro;M of primers, and 2 U of Taq polymerase (Platinum\u0026trade; Taq DNA Polymerase, Invitrogen, Paisley, UK). The thermal cycling profile was as follows: 95\u0026ordm;C for 5 minutes, followed by 35 cycles of 95\u0026ordm;C for 45 seconds, 48\u0026ordm;C for 45 seconds, and 72\u0026ordm;C for 1 minute, with a final extension at 72\u0026ordm;C for 10 minutes (BioRad T100\u0026trade; Thermal Cycler). Amplification products were sent to Beckman Coulter Genomics (UK) for purification and Sanger sequencing in both directions (forward and reverse).\u003c/p\u003e\u003cp\u003e\u003cem\u003ePhylogeny and Divergence.\u003c/em\u003e To perform all analyses, the nucleotide substitution model (TN\u0026thinsp;+\u0026thinsp;G; Gamma\u0026thinsp;=\u0026thinsp;0.81), which best fits the evolution of the sequences, was estimated using MrModelTest v2 (Nylander, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and the Akaike Information Criterion (AIC). To assess the divergence within and between \u003cem\u003eP. monodon\u003c/em\u003e and other penaeid species, a divergence matrix (MEGA v. 7; Kumar et al., 2007) and maximum likelihood estimates were calculated, using \u003cem\u003eEuphausia longirostris\u003c/em\u003e (accession AF177189) as the outgroup. Sequences of \u003cem\u003eP. monodon\u003c/em\u003e and \u003cem\u003eP. semisulcatus\u003c/em\u003e were grouped based on areas where they are presumed to exhibit variations (e.g., Pacific Ocean, Indian Ocean, Arabian Peninsula, invasive areas, or domestic), which can help in understanding the degree of divergence compared to other species, with the outgroup serving as a reference.\u003c/p\u003e\u003cp\u003e\u003cem\u003eGenetic Diversity and Population Structure in the Invasive P. monodon.\u003c/em\u003e Genetic diversity levels were assessed in wild, invasive, and domestic populations using haplotype diversity (DH; Nei, 1987), share haplotypes (SH), private haplotypes (PH), and nucleotide diversity (π, the average number of nucleotide differences per site between two sequences; Nei, 1987).\u003c/p\u003e\u003cp\u003eA haplotype network (median-joining; Bandelt et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) was constructed using PopART software (Leigh and Bryant, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), as well as the spatial distribution of the shared haplotypes was manually constructed using the shared haplotypes matrix obtained in Arlequin 3.0 (Excoffier et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); also, the pairwise F\u003csub\u003eST\u003c/sub\u003e statistic was used to explore the spatial distribution of haplotypes to collection sites and the potential source of the invasive populations. Additionally, an Analysis of Molecular Variance (AMOVA; Excoffier et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) was performed using the previously obtained data (haplotype network, F\u003csub\u003eST\u003c/sub\u003e, structure, and DAPC) to establish the different hierarchical groupings shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Finally, tests to validate the neutrality model (Tajima\u0026rsquo;s D and Fu\u0026rsquo;s Fs) and assess changes in population size (mismatch distribution) were applied to understand the evolutionary processes in samples from invasive, wild, and domestic areas. All analyses were conducted in Arlequin 3.0 (Excoffier et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) using the Tamura and Nei model (TN\u0026thinsp;+\u0026thinsp;G; Gamma\u0026thinsp;=\u0026thinsp;0) estimated in JModelTest (Posada, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) with 10,000 bootstrap permutations (significance level α\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e\n\u003ch3\u003eMicrosatellites\u003c/h3\u003e\n\u003cp\u003eEight microsatellite loci (PmMS4CA, PmMS8, PmMS6, PmMS8A2, PmMS9, PmMS9GG, PmMS11AH, PmMS16) proposed by Li et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) were used to amplify DNA from \u003cem\u003eP. monodon\u003c/em\u003e in a 10 \u0026micro;L multiplex PCR reaction, following the protocol outlined by the authors. PCR products were loaded onto an ABI 3130xl Genetic Analyzer (Applied Biosystems) using GeneScan 500 ROX size standard. Fragment lengths were determined using GeneMapper v.4.0 (Applied Biosystems).\u003c/p\u003e\u003cp\u003eGenetic diversity was assessed by estimating allele frequencies, private alleles, allelic richness, observed heterozygosity (Ho), expected heterozygosity (He), and the inbreeding index (Fis). To understand the independence of genotypes between loci and avoid redundancy in the information obtained, genotypic linkage disequilibrium was tested using Markov chain exact probabilities (GENEPOP v.3.4, Raymond and Rousset, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Additionally, the presence of null alleles in each locus was evaluated using Micro-Checker (Van Oosterhout et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) to avoid underestimation of genetic diversity parameters. Hardy-Weinberg equilibrium (HW-E) was tested for each population, and a global deficit of heterozygotes across loci and populations was assessed. All of these analyses, except for the null allele test, were performed using Arlequin 3.0 (Excoffier et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe population differentiation level between sampling sites was tested using R\u003csub\u003eST\u003c/sub\u003e and AMOVA (following the same groupings as above). These analyses were complemented with Discriminant Analysis of Principal Components (DAPC; ADEGENET; Jombart et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Bayesian structure analysis (STRUCTURE 2.3.3, Hubisz et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The DAPC was used to explore the distribution of genotypes between different sites. In contrast, STRUCTURE was used to assign individuals to one or more groups based on their probability of belonging to those groups in a mixed population. This analysis was performed with a burn-in of 100,000 steps to minimize the effect of initial configuration, followed by 1,000,000 simulation steps (MCMC), assuming an admixture model appropriate for populations with gene flow, and the correlated allele model (Allele Frequencies Correlated), which assumes that populations diverged from a common ancestor and that allele frequency differences result from drift since divergence (Falush et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Pritchard et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The number of populations (K) was estimated using the method proposed by Evanno et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), based on the rate of change in Ln(K) between successive K values, using STRUCTURE SELECTOR (Li and Liu \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This analysis has the advantage of identifying the presence of discrete populations, hybrid zones, and migrant or admixed individuals.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eMitochondrial DNA: Cytochrome Oxidase Subunit I (COI)\u003c/h2\u003e\n \u003cp\u003eGenetic diversity\u003c/p\u003e\n \u003cp\u003eA total of 69 haplotypes were identified from 533 mtDNA-COI sequences of the \u003cem\u003eP. monodon\u003c/em\u003e, with sequences spanning 497 bp (471 bp for Penaeidae in the phylogenetic analysis). Some invasive populations (USA, Colombia, Venezuela) in the western Atlantic Ocean exhibited the lowest genetic diversity across all parameters, particularly when compared to populations from the wild area and domestic systems. Brazil showed significant differences in genetic diversity parameters compared to the other locations; meanwhile, Bangladesh, West Africa, and Egypt demonstrated high haplotype diversity (HD) and nucleotide diversity (\u0026pi;) values, comparable to those of wild populations. This suggests that these locations behaved more like wild populations than invasive ones due to their similarity in genetic diversity to the wild area (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Private haplotypes were uncommon and found only in Colombia, Brazil, West Africa.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Genetic diversity indexes using mtDNA-COI in the native, culture and invasive populations of tiger shrimp \u003cem\u003eP. monodon\u003c/em\u003e. N: number of samples, H: haplotypes, PH: private haplotypes, HD: haplotypes diversity, \u0026micro;: nucleotide diversity, k: average number of paired differences, Tajima\u0026rsquo;s D and Fs\u0026rsquo;s Fu indexes. \u0026nbsp;* p \u0026lt; 0.01, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003ePopulation structure\u003c/p\u003e\n \u003cp\u003eThe Intraspecific divergence observed in some species (e.g., \u003cem\u003eP. monodon\u003c/em\u003e and \u003cem\u003eP. semisulcatus\u003c/em\u003e) was notably high compared to others (e.g., \u003cem\u003eP. murrayi\u003c/em\u003e vs \u003cem\u003eP. investigatoris, P. longirostris\u003c/em\u003e vs \u003cem\u003eP. americanus, F. merguiensis\u003c/em\u003e vs \u003cem\u003eF. penicillatus, F. subtilis\u003c/em\u003e vs \u003cem\u003eF. isabelae, L. setiferus\u003c/em\u003e vs \u003cem\u003eL. schmitti\u003c/em\u003e). The intraspecific variation in \u003cem\u003eP. semisulcatus\u003c/em\u003e (Pacific Ocean vs Indian Ocean\u0026thinsp;=\u0026thinsp;5.0%, Pacific Ocean vs Arabic Peninsula\u0026thinsp;=\u0026thinsp;21.6%, Indian Ocean vs Arabic Peninsula\u0026thinsp;=\u0026thinsp;18.6%) and \u003cem\u003eP. monodon\u003c/em\u003e (Pacific Ocean vs Indian Ocean\u0026thinsp;=\u0026thinsp;6.0%, Pacific Ocean vs Invasive area\u0026thinsp;=\u0026thinsp;5.9%, Pacific Ocean vs Domestic\u0026thinsp;=\u0026thinsp;6.3%, Indian Ocean vs Invasive area\u0026thinsp;=\u0026thinsp;6.6%, Indian Ocean vs Domestic\u0026thinsp;=\u0026thinsp;4.7%, Invasive area vs Domestic\u0026thinsp;=\u0026thinsp;8.7%; or Pacific Ocean vs Indian Ocean\u0026thinsp;=\u0026thinsp;8.3%) was similar to the interspecific variation observed between the species above (e.g., 6.1\u0026ndash;15.3%; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, Supplement 1).\u003c/p\u003e\n \u003cp\u003eThe haplotype network confirmed the significant intraspecific divergence in \u003cem\u003eP. semisulcatus\u003c/em\u003e and \u003cem\u003eP. monodon\u003c/em\u003e. In the case of \u003cem\u003eP. monodon\u003c/em\u003e, the divergence was represented by four haplogroups: Haplogroup I (composed mainly of H1), separated by 12 mutations, and primarily consisting of invasive populations, along with some countries from the wild area such as Indonesia, China, India and Philippines; Haplogroup II (composed mainly of H2 and H5), separated by 10 mutations, and comprising populations from the Indo-Pacific Ocean and domestic systems, including some invasive countries to a lesser extent (primarily West Africa, Egypt and Brazil); Haplogroup III and IV (composed mainly of H52, H50 and H39), separated by 199 mutations, and consisting of Thailand and Philippines populations (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). The divergence of these haplogroups was similar in \u003cem\u003eP. semisulcatus\u003c/em\u003e, demonstrating how the haplotypes appeared mixed in two codistributed species, which suggests a possible effect of the domestic systems (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Additionally, a fourth haplogroup with a large divergence (58 mutations) was present, corresponding to the Arabian Peninsula (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC)\u003c/p\u003e\n \u003cp\u003eF\u003csub\u003eST\u003c/sub\u003e showed that the invasive populations from the USA, Colombia, Venezuela and Brazil were related to the wild populations of the Pacific Ocean (China, and Indonesia; F\u003csub\u003eST\u003c/sub\u003e = -0.009 - 0.441), while West Africa and Egypt were more closely related to the Indian Ocean countries and domestic (F\u003csub\u003eST\u003c/sub\u003e = -0.002 - 0.168; Table 2).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 2. A)\u003c/strong\u003e Genetic distance (Fst index) using mtDNA-COI in the native (black color), culture (blue color) and invasive (red color) population of the tiger shrimp \u003cem\u003eP. monodon\u003c/em\u003e. \u003cstrong\u003eB)\u003c/strong\u003e Representation of the genetic distance using Neighbor-Joining tree. Text color in the table represent: red: invasive populations, blue: domestic populations and black: wild populations.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAMOVA results for the hierarchical groupings proposed in Table 3 were significant for group IV (grouping IV: F\u003csub\u003eST\u003c/sub\u003e= 0.901, p = 0.001, variance = 90.1%; Table 3). This assumes the relationship proposed by the genetic distance F\u003csub\u003eST\u003c/sub\u003e, where the USA, Colombia, Venezuela, and Brazil are related to Indonesia and China. At the same time, West Africa appears to be related to Mozambique and India, and Egypt is related to domestic shrimp.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Analysis of Molecular Variance (AMOVA) results for different hierarchical grouping proposals in the tiger shrimp \u003cem\u003ePenaeus monodon\u003c/em\u003e, based on mtDNA-COI and microsatellite markers. Text color in the table represent: red: invasive populations, blue: domestic populations and black: wild populations.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003cimg 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\"\u003e\n \u003cp\u003eThe spatial distribution of the 69 haplotypes found in \u003cem\u003eP. monodon\u003c/em\u003e showed a large proportion of private haplotypes (59\u0026thinsp;=\u0026thinsp;85.5%), of which only 10 were shared among almost all locations. Five share haplotypes were registered in the invasive area, in which four haplotypes were found in the Western Atlantic Ocean (H1, H2, H4 and H5) and four in the Eastern Atlantic Ocean (H1, H2, H5 and H12). H1 and H2 were the most frequently distributed haplotypes everywhere, except in Mozambique, Madagascar, Thailand, and the Philippines; H3 was found in the invasive area in West Africa, Egypt, and Brazil, and in the Indo-Pacific Ocean, except Thailand and the Philippines. H4 was shared by Brazil and Mozambique (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; Supplement 2).\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Summary of the distribution of 69 haplotypes identified in 533 sequences of tiger shrimp. Colored cells indicate the ten haplotypes shared across different locations, while white cells represent private haplotypes. For private alleles, the numbers in brackets correspond to specific haplotypes. Text color in the table represent: red: invasive populations, blue: domestic populations and black: wild populations.\u003c/div\u003e\n \u003cp\u003e\u003cimg 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hBBCeEaSTyGEEEII4ZnRJ5+aVnxEzXu2AlBVVYzv2GHGEolizN4ejqZBR4caFQFRKBTQNI1cLlcSj0QiaJrmPOJx+87q3oif557tOeu2134scmPf59139n3fFz4Imb1qqeeaN5n3cI+sVEv8V6CAZv2nE/C7lRVcJ3eQd7VQKH7+B/wOcCcGj7AmMtW593uQFY4X0BZqzr3fg+yj0x/QcXCVc+/3iUrXdec6aN8DXpQaffJpGHDHHfDQQ+BORgYG4P77zfLly81k8tgxc9swRl7HsaMDKith+3a1RARAoVAgHA6rYXK5HJlMBsMwMAyDvr4+amtr1WpjJg6k1aClGagG8j6sWnjkzGYOf/EzNeyPex6BnT+HfU9B504zGfVJqtv813gVNqw2E9EgCRMmTx4DgzbaKBDgC0jYdXK3WcloEIXDkM+bn/9tbWYyGlDr75zH+lcOsbn/M156Yj0nBo+oVQIjfE+Y/M48xj6Dts42CseDe1yffethHrjpMdbesoVd7+3go9MfqFXGvVwuR1tbG4ZhkM/nqaurU6uIsiSfF+vYMZg1q7g90tr2r78OmzbBa6/BgQNqqfBZKBQa9i4JFRUVhEIhZ3vXrl2e3l60B2hSg1ZDUMa6Lhf3zjs3XLWG71z9lBr2XmYvLP42zJxhbt9XDc9n1FqeefolWPld83miFrp2QmFIreUPHZ0YMULWGdNIIxvZqFYLBh2IuU7uRgjkruo6xGJgf0Y0NsLGIO4o7H95G/Oqv8eMyhsAiCZ+xO+eCdi3I4ue0YktjhGaaZ2r9zWy8flgHtfDJ/cw+5qbuXbyNwC4efpS9v/x12q1cW/r1q00NjaCdb2MRCLoAW/p94N3yefixfDCC2Z3vE39hRw4YCaoy5ebrZ/ZbGm5CCx34gnQ29t7TswPbUCdq1eydKDAFeTA28XEE+D6r/vW8lkYgvwQVEwvxsIVMPShu5Z/smSdxBOgiqrgtnxmlW9VVQFt+cxmi4kn1rCsgLZ89u/NMaPKTDwBplVUcmIgmC2f2QNZJ/EEqLq+KrAtn8c/Ocy1U8zEE+Caq6fz0amJ1/KZyWSocuU5oVCIgYGBkjrCy+Rz+XLYvx8GB88dH2rLZmGlNQBsxQrpeh+nCoWCp13uIylYvZE1Vstnu9X9fsVyZ3sBEKpQI8ExhzlqKLjGy67OGS87CtNnzlZDgTWnYvwc169Odn0BnsBmuXt5xbDKk3xe7IGeP9/sbt++3exWd7eCghlfsMBMTh9/XLrex6nt27ezZMkSNey5IWs4nD2trdX6N5jtAkIIIcSVoTzJ58VoaSnOel++3ExCBweLyeWBA1BfX5yQZBhm1/vWrSVvI4Lv6NGjRIdr2fZYhdXyKYD5N8Lew8Xt9/4Ei+a6a3gmVGF2s+feLMbyQxC92V3LPzXU0Euvsz3AALX435I/rBpw7SoMQCB3taYGel07OjAAAegdGU5kUZTDu191tk8ODTJ38dKSOkFRM7+G3r2uc/W9AWoXBfO4zvzKXAY/PuRsf/z5h1ReM6+kzkRQV1fHrl27nO1CoRCIxpigKU/yefvtZkulu5VyuKWS1q0rPrcT0fnzzX9TKVi7tliO1fW+LdjLXIhSQVpWImTNxUhZ2+rcjCtK3SLY/YfiOM8nfwV3LVBreabx+7D1t+bzVDc03aPW8E+CBGnSzjjPJEnq8W7y3CVJWMs82H92SQjkriYSkE4Xx3kmk2ZjQwAtuHsFh/p+48xwfzH1CLcsu0+tFgiJugTp3WlnnGfyyST1dwXzuM6ddhvvfPymM8M9N7SNG7+2SK027q1cuZKnn34arJnv/f39gWiMCZryJJ9r15pLLdld5ppmdqGrE4o2bSqW19ebY0CxBp+rk5EOHDATWnuMqJ2sCt9p1jJZ1dXV56z1uX37dlba43Y91Ax0WQ/3Wp891vVYsyYf9bjKvPDu2Rf5188f5FPjqP9rfT7zsLnc0sIH4dHVpROQPNbaYE480paaM987Xd9Lg6CPPsKE0dDoprtkAlLg9FnjSzSgO8Dfrvr6zOWWNA26u0snIAXMOr2X9XfOY01kKg90POfMfA+ivmf6CN8TRluo0f1od8kEpKBZceNPefath+k4uIpls5ucme8TSTQapbGxEU3TqK6uJpPxb1WRINOM4dbNEcJvI60D64NlU95XQ77pee1FNeQb4y8PqiF/LR0fH2WaEZxz+8LGxzEF2Jw/pYYC68d/nqKGAmvd6l+oocDa9PsfqiERUOVp+RRCCCGEEOIiSPIphBBCCCE8I8mnEEIIIYTwjCSfQgghhBDCM5J8CiGEEEIIz0jyKYQQQgghPCPJpxBCCCGE8Iwkn0IIIYQQwjOSfAohhBBCCM9I8imEEEIIITxTvuRzx47ifds1zbw3eyKh1jrXgQPlv5ViNAodHWrU1NJycfslhqXrOpqmoWka8Xi8pCyXyzllkUikpGysaa5HQS0Ecq5yrx05s9n/+7oDHD9h3td94YOQ2auWeq55k3lv98hKtcR/BQpo1n86ulocLAXXyR3kXS0UitcHPcg7CicGj7AmMpU1kansf3mbWhwoheMFtIUa2kINPRPs4/rR6Q/oOLiKjoOrOHxyj1o8ocTjcZqbm9WwsJQn+Wxpgfp62L8fDMN83Hsv7Nql1jzXggVqZHQSCXjtNTVatHFj4D/4gqpQKJDNZjEMA8MwSKfTpFIpp7y6upq+vj4MwyASiZSUjaW4dQdqA2gC6pTyZqAayPtwp+ojZzZz+IufqWF/3PMI7Pw57HsKOneayahPUt3mv8arsGG1mYgGSZgwefIYGLTRRmHYrzQBEXad3G0jfPsKgnAY8nnz+tDWZiajAbX+znmsf+UQm/s/46Un1nNi8IhaJTDC94TJ78xj7DNo62yjcDy4x/XZtx7mgZseY+0tW9j13g4+Ov2BWmVCiMfjpNNpNSxcypN8Pv44bNoE8+cXYwMDUFXlrjW8/fvVyOjoOtxxhxoVZRAKhejs7HS229vbOXr0aEmdiooKsOp6pcf1vMW6Dtt0IGNdl73bo6IbrlrDd65+Sg17L7MXFn8bZs4wt++rhuczai3PPP0SrPyu+TxRC107oTCk1vKHjk6MGCHrjGmkkY1sVKsFgw7EXCd3IwRyV3UdYjGwPxcaG82GgADa//I25lV/jxmVNwAQTfyI3z0TsG9HFj2jE1scIzTTOlfva2Tj88E8rodP7mH2NTdz7eRvAHDz9KXs/+Ov1WoTQk9PD01NTWpYuIw++bS7t9euVUsglys+b2kpdrlEo+5apexueHf3vTvuLj9wwHwvTTPfX2XXc5clEsXtkX6WuGhz5sxxnjc1NTndDJlMhtbWVldN74Rdz9usllC7V9J1Rl5ZDrxdTDwBrv+6by2fhSHID0HF9GIsXAFDH7pr+SdL1kk8AaqoCm7LZ1b5VlUV0JbPbLaYeILZMBHQls/+vTlmVJmJJ8C0ikpODASz5TN7IOskngBV11cFtuXz+CeHuXaKmXgCXHP1dD46NTFbPsWFjT75PHZMjZxrxw6znt0lj5UEDufee4v1Nm0qdsvb/9rld9xhxtatM+ttU8blrFtn1tu+3WyZPXDATDpfeKFYJ5WChx4y691/P2zd6n4HcQG9vb0lCabdKqppGv39/a6a3nkD2GA9L1itoDVWy2e71f1+xXJnewEQMhvJA2kOxS9VgTdedtX1RTXops+crYYCa07F+DmuX53s+gIsrmijTz5nzVIj5/rlL+H224vbTz45/HjQHTtKu+rXrjWTzB07it3zAwPF8oceguXLobKyGLNtsrpJli833yObNbt57r+/WEfXzVhVVWlSKi5I13Xa2tqGjbW3t6NpGgUfWjaygP21ZshqBbW37TTZ+70SQgghhG30yWdNjfnvcLPLE4liV/bFtJCiJJflNFyCag8FePHF0qRUnFculyObzRJ1DZ8oFAo0NDQQjUZpbW2lvb3d85l+EaA4IhUqlPGfV7T5N8Lew8Xt9/4Ei+a6a3gmVGF2s+feLMbyQxC92V3LPzXU0Euvsz3AALXUltQJjBpw7SoMQCB3taYGel07OjAAtUHcUYgsinJ496vO9smhQeYuXlpSJyhq5tfQu9d1rr43QO2iYB7XmV+Zy+DHh5ztjz//kMpr5pXUEVeO0Sef8+ebLZDr1pktlLaWFrNVdP58s9Xz8ceLZdksrFhR3LYtXw6Dg6WJ7MCAGb9cO3aM/B7btpnd8u6JUuK8CoUCGzZscLrYU6lUSQtnzjXO18tJR3FrYhFWy2bKGgoXs54zzNyMK0rdItj9h+I4zyd/BXeVeaWJS9D4fdj6W/N5qhua7lFr+CdBgjRpZ5xnkiT11KvVgiEBpF3N+UkI5K4mEpBOF8d5JpPmCikBtODuFRzq+40zw/3F1CPcsuw+tVogJOoSpHennXGeySeT1N8VzOM6d9ptvPPxm84M99zQNm782iK1mrhCaIZhD8IcpY4OMwG1PfRQ6WzGlpZiAnr//cXljjRr5cXKSjNJPHCgdPkle/fc9ZYsKXaT339/8fkddxQnOdn1cb2Hex82bTJbY+3tykoz8d20afjJUwJd12loaCiJxWIxenrM+ebu8nA4PLpxn+7f3wUMVzPvSjLt8jBwOXu0bMr7auiivXv2Rf718wcB+LI2h5rJo1vbrue1F9XQxTvYD6sfM58/utpMSEfB+Iv5/3W54g9Deq/ZCtpfjuHWS8vzUQaQI0e1NUK4m24SzuCN0dOM4c7YUci5BjN3u8aZlEX5jim5HFRbO9rdPfK4/8u0OX9KDV22/L5dbEqYLYgPdDzHgruHaSwZhR//eYoaumy5gzmqV1vn6qPdJOrKe1zXrf6FGrpsQ5/8G9vefhSAZbObmDutvOsfb/r9D9WQL5qbm+nq6gJrIq57lRhhKl/yKUQ5XULyOdZGk3yW26iSzzIbbfJZdmVMPsdS2ZPPMTU+jillTj7HWjmTz7FWzuRzrAUl+RQXNvpudyGEEEIIIS6SJJ9CiPHvr8dPC50xjloThRBiLEjyKYQY//5l/HRla8OOUhZCiCuHJJ9CCCGEEMIzknwKIYQQQgjPSPIphBBCCCE8I8mnEEIIIYTwjCSfQgghhBDCM5J8CiGEEEIIz4w++ayqMu9Go2kQjRbjHR3FuPue70GjaeYtPcVlKRQKaJrmPNz3efdTxLqtpmbd090vnxrv0HPqOnpOXce7Z32+O9HxE7DwQfOR2auWeq55E2hLIbJSLfFfgQKa9Z/u6xl0EQoBOdkvpFAoXhPs2ysH1InBI6yJTGVNZCr7X96mFgdK4XgBbaGGtlBDzwT7uH50+gM6Dq6i4+AqDp8c3a2Gg0zX9cBdE4Nm9MnnwIB5f3X3fdXBvD/6Qw/B9u2wfLn7FcFRVaVGxCXavn07hmE4j1DIvqO6f3QgY90Y0Cj3ra4vUfb0rdRMfoP4lPd5+4t/4FPjHbWKd+55BHb+HPY9BZ07zWTUJ6lu81/jVdiw2kxEgyRMmDx5DAzaaKNAgC8gYSBvnextVjIaROEw5PNgGNDWZiajAbX+znmsf+UQm/s/46Un1nNi8IhaJTDC94TJ78xj7DNo62yjcDy4x/XZtx7mgZseY+0tW9j13g4+Ov2BWmXcy+VytLW1YRgG+Xyeuro6tYooS/IJ0NoKr72mRmHWrOAmnliJs7hsuVyOZDKJpmnk3F88fNZgXY9TaoHH3j37IjMm3cmXtdkAVF71A46e6VSreSOzFxZ/G2bOMLfvq4bnM2otzzz9Eqz8rvk8UQtdO6EwpNbyh45OjBghzC9SjTSykY1qtWDQgRhYuwqNEMhd1XWIxcD+ctrYCBuDuKOw/+VtzKv+HjMqbwAgmvgRv3smYN+OLHpGJ7Y4Rmimda7e18jG54N5XA+f3MPsa27m2snfAODm6UvZ/8dfq9XGva1bt9LY2AhAKBQiEomgB7yl3w/lST7nzzdbPltairHhurKj0WK3i13e0gKJhPmv2nV/4MC5XfrumLvM7v4/cKD4XglXm1ciceFhAO6hAtIqekEVFRUYhkFfXx/V1dWB+AMruFo8k0CzWsFDJ8/udhJPgKnaLP9aPg+8XUw8Aa7/um8tn4UhyA9BxfRiLFwBQx+6a/knS9ZJPAGqqApuy2fWlXgCVAW05TObLSaeWL1OAW357N+bY0aVmXgCTKuo5MRAMFs+sweyTuIJUHV9VWBbPo9/cphrp5iJJ8A1V0/no1MTr+Uzk8lQ5cofQqEQA9LQdY7yJJ8A9fWwzTU2ZutWs+vdlkjA7bebXS6bNsG995rJ3uOPwwsvmHX27y9tQV2wwIwZ1r2Q7eR206bi+4DZ3e/+5W7caA4FsNmvMwxzGEB9fbHM7YknzHL7542UpAqw/qgAotEofX19tLW1qVU8574OG0CXz9fiv9Iq1ZB/3NleAIQq1EhwzGGOGgqu8bKrc8bLjsL0mcUvjUE3p2L8HNevTnZ9AZ7AZs2apYaEonzJ59q1MDhYTNiOHSst37XLTDQ1DdatM+va40Lvv7+0C+bAgeL7LFhgvua118z3nD/ffN2OHeb77N9ffN1IXn/dTHA1rZh4DtcyOzAAN9xg1hscVEvFeUSjUSKRiBr2XTsQkN5cIYQQQpQ1+QQzkdy0yUwMb79dLS22KtqPC6msLK3v7tatrzd/1vz57leMzG4ttR/DvS4aNVtkDcP82eKSBGGy0XD8amCbNmkxH54tjoX9zDjG9EmuYSVemn8j7D1c3H7vT7BorruGZ0IVZjd77s1iLD8E0ZvdtfxTQw299DrbAwxQS21JncCoAdeuwgAEcldraqDXtaMDA1AbxB2FyKIoh3e/6myfHBpk7uKlJXWComZ+Db17XefqewPULgrmcZ35lbkMfnzI2f748w+pvGZeSZ2JoK6ujl27djnbhUKBJUuWlNQR5U4+V640Wyg3bSrtcgdYssRsqbS5x2MOZ/lys/XRPY7Ufk1VlZno2j/DXeeINTZn1y6ztbOjw0yE3T+7peXcls8DB8x9l7EZl0XXdeYErFutYF2X/UqJvzXpXk6cfcUZ53n4i59x3aTvq9W8UbcIdv+hOM7zyV/BXQvUWp5p/D5s/a35PNUNTfeoNfyTIEGatDPOM0mSekYYquO3BJB2jS1JQiB3NZGAdLo4zjOZHHn4k88W3L2CQ32/cWa4v5h6hFuW3adWC4REXYL07rQzzjP5ZJL6u4J5XOdOu413Pn7TmeGeG9rGjV9bpFYb91auXMnTTz8N1qTc/v5+ou65LALKnnzaE4+GG++g66Vrgs6aVTrms6XF7GLH6mrHGgNqd9VrmjmrPpEwk1J33O7iv/9+8wPNnjB0//1mgmqPAXXXnz+/OKlowYLivtt1sFpX1SRVOJqbm521zLLZLK2trWoVz+muJQ/rgB61gsduu/qfyZ6+lZ5T1/Gdq58qmYDkuWceNpdbWvggPLq6dAKSx1obzIlH2lJz5nun67thEPTRR5gwGhrddJdMQAqcPmt5Bw3o9vHb1oX09ZnLLWkadHeXTkAKmHV6L+vvnMeayFQe6HjOmfkeRH3P9BG+J4y2UKP70e6SCUhBs+LGn/LsWw/TcXAVy2Y3OTPfJ5JoNEpjYyOaplFdXU0m49+qIkGmGcbF9H8L4TH7C0AALJvyvhryTc9rPi9U72L85UE15K+l4+OjTDOCc25f2Pg4pgCb86fUUGD9+M9T1FBgrVv9CzUUWJt+/0M1JAKqvC2fQgghhBBCnIckn0IIIYQQwjOSfAohhBBCCM9I8imEEEIIITwjyacQQgghhPCMJJ9CCCGEEMIzknwKIYQQQgjPSPIphBBCCCE8I8mnEEIIIYTwzOiTT/ctM92Pjg615uh0dID7/qjRaPl/hrggXdedW2rG4/ERy3RdLynzQhxoVmIF1+021TIvvXv2Rd76IqmGvfdcGu5tU6O+0ZaC3qtG/VeggGb9p+P9uXxJ3Cd5kHe1UCheH3z4fLgUJwaPsCYylTWRqex/eZtaHCiF4wW0hRraQg09E+zj+tHpD+g4uIqOg6s4fHKPWjzhNDc3+3ItHA9Gn3wODJj3TX/oITAM87F9O6xbBzt2qLUv39q1kMuZzxMJeO01tYYYY4VCgWw2i2EYGIZBOp0mlUqdU9bX10dDQ4P68jEVB9Jq0Eo4DevRBVhnkOf+9fMA3IryuTQ8+Ss16httqRoJjjBh8uQxMGijjQIFtUpwhIG8dZK3WcloEIXDkM+b14i2NjMZDaj1d85j/SuH2Nz/GS89sZ4Tg0fUKoERvidMfmceY59BW2cbhePBPa7PvvUwD9z0GGtv2cKu93bw0ekP1CoTRi6Xo6urSw0Ly+iTz+EsXw533AG7d6sl5aHr5vsLT4VCITo7O53t9vZ2jh496mzbZdFolFgs5sS90AM0qUGguLcQA1xt555564skc7/092rYe38bg0dXq1HfGK9CuEKN+k9HJ0aMECEAGmlkIxvVasGgWye2uavQCIHcVV2HWAxC1o42NsLGIO4o7H95G/Oqv8eMyhsAiCZ+xO+e2aRWCwQ9oxNbHCM00zpX72tk4/PBPK6HT+5h9jU3c+3kbwBw8/Sl7P/jr9VqE8bWrVs9vw6OJ2OTfO7YYbZMLl5sbh84YHa1JBLmvzt2mF3mdhdMVVXxtWr3vV3W0mK+XgTKnDlzwEpMbc3NzfT09Lhq+cfeK91qFPLaybN7mDbJ+jsQ40KWrJN4AlRRFdyWz6zrJAeoCmjLZzZbTDyxhmsFtOWzf2+OGVVm4gkwraKSEwPBbPnMHsg6iSdA1fVVgW35PP7JYa6dYiaeANdcPZ2PTk3Mls9UKkVLS4saFi7lSz4ff7yYMNbXw/79ZgsowIIF5r+33252uSxfDk88YXbPG4ZZZnfR293327eb2y++aCaqjz9ubovA6O3tpbW1tSSmaRpdXV1EIpGSuJ/iQANQ7cN1+d2zv+Jbk+5VwyLg5mB+qRoXxsuuWl9Ux4PpM2erocCaUzF+jutXJ89QQxNOwfpS5W6QEecqX/KpjvlcsMBsrQQzEcUat2kbGIAbbjCT1cHBYnzjRrOltL7efJ/5883XPfRQsY7wna7rtLWd25Zoj/nM5/OBGWjdYw2Hw+MeySNnNjPnKj+nOQkhhPDSxo0bz2mUEecqX/Lptny5OQlp23lmCUajcO+9ZrJaWVladu+9ZrJpt5yKQMnlcmSzWaLu1QdcotEoTU3DjcD0V7caGGODZ54ne/pWek5dx+EvfsbAmS1kT9+mVhMBU0MNvRSn4A8wQC21JXUCowZcuwoDEMhdramBXteODgxAbRB3FCKLohze/aqzfXJokLmLgzk7rmZ+Db17XefqewPULgrmcZ35lbkMfnzI2f748w+pvGZeSZ3xrlAo0NXV5az6kk6naWhooLlZGiFUY5N8HjgAL7wAS5aoJaYDB8wxoQMDaomZlC5ZUhyMLuMmAqVQKLBhwwZnclEqlXK6Gdy6urpIBGyM7hZgpRocQzWT9xCf8j7xKe8z90t/T9VVq6iZPPGXFxnvEiRIk3bGeSZJUk+9Wi0YEtYyD/afYBICuauJBKTTxXGeyaTZuxVAC+5ewaG+3zgz3F9MPcIty+5TqwVCoi5BenfaGeeZfDJJ/V3BPK5zp93GOx+/6cxwzw1t48avLVKrjWuhUMhZDcYwDGKxGN3d3SUTdYVp9MlnVZWZaLrHfC5YYM5Gt7td7TGf9uSh+fPNcrs+mB9EP/iBmZS+8EKx7PXXi2M+X3ihOPHotdfKv5yTOC9d1wmHw6TTaeebXW9vL6FQiFQq5cQ0TcOwx/J6pNlaSqnLtZ5nzrX8oWZNOBq+rfYKkdkLP30Gjn0QiLU+IyshPwQN/yN4a3320UeYMBoa3XSXTEAKnD5ruSXNat4P6q729ZnLLWkadHeXTkAKmHV6L+vvnMeayFQe6HjOmfkeRH3P9BG+J4y2UKP70e6SCUhBs+LGn/LsWw/TcXAVy2Y3OTPfxZVHM7zOEoS4GPaXkgBYNuV9NeSbntdeVEO+Mf4SgLVL3ZaOj48yzQjOuX1h4+OYAmzOn1JDgfXjP09RQ4G1bvUv1FBgbfr9D9WQCKjRt3wKIYQQQghxkST5FEIIIYQQnpHkUwghhBBCeEaSTyGEEEII4RlJPoUQQgghhGck+RRCCCGEEJ6R5FMIIYQQQnhGkk8hhBBCCOEZST6FEEIIIYRnJPkUQgghhBCeKX/yqWnmvdhHUlVVek93labBgQNq9NLs2FH8GYmEuT/2tn1/eVz7Er2i7/h9SXRdd+7fHo/H1WKnLJVKqUWe0133effbu2df5K0vkmrYW8dPwMIHzUdmr1rquchK0JaajyBKkSJCRA0HT8G6r7tmnfRBVSgUP4f1IO8onBg8wprIVNZEprL/5W1qcaAUjhfQFmpoCzX0TLCP60enP6Dj4Co6Dq7i8Mk9avGEYV8HNU2jUCioxaLsyWdHB1RWwvbtaokpkYAVK8AwzHrDJamGAfPnq9FLs3y5uQ933GF+yK1dC5s2mT9zYKBYb2AA7r8fcjn3q8UICoUC2WwWwzAwDIN0Ol2SZEYiEfr6+jAMg97eXnI+H9cGNeCjf/08APdBv+cR2Plz2PcUdO40k1GfxB+GDavBeBXaHzQT0SBJkSKJz18WLlYYyFu3YW+zktEgCochnzc/49vazGQ0oNbfOY/1rxxic/9nvPTEek4MHlGrBEb4njD5nXmMfQZtnW0Ujgf3uD771sM8cNNjrL1lC7ve28FHpz9Qq4x78XjcuUY2NTVRV1enVhFlTz5ff91M8l57bfjWy2PHYNYs8/nAgJkUjpXly0sTTftn7dhRjO3YAbffXtwW5xUKhejs7HS229vbOXr0KICTaEatVuRVq1axYcMGp67XmoF2NeiTt75IMvdLf6+GvZXZC4u/DTNnmNv3VcPzGbWWZ9J7IVFrPm9tgPwQ5N5Ua/mnlVa66VbDwaMDMSBkbTcCG5U6QaDrEItByNrRxkbYGMQdhf0vb2Ne9feYUXkDANHEj/jdM5vUaoGgZ3Rii2OEZprHtfG+RjY+H8zjevjkHmZfczPXTv4GADdPX8r+P/5arTbu9fT0OM9bWlrI5/Ml5cJUvuTzwAEzsVy+3GxhzGZLy6uqzKR03bpil3tVldkaqmnQ0mK+h7s73t5Wu8vt19jd6iNZsaK0dbWqCnbvLm7v3j22CfAVYM6cOQDs2rWLSKTYRTlr1iz6+/tdNb2TA2rUoE9Ont3DtEmL1bD3DrxdTDwBrv+6by2fhaHSfwHCFcXn4hJkXYknQFVAWz6z2WLiifVZHNCWz/69OWZUmYknwLSKSk4MBLPlM3sg6ySeAFXXVwW25fP4J4e5doqZeAJcc/V0Pjo18Vo+VeFwWA2Jsiaf2SystPrOVqw4t+t9YMDsBt+0yex2iUZhcNAsMwzzW/CCBaWvWbAA9u83y7G69Q8cgBdeMGP795vPR7J4sdkai5XIAmxzjd85dqz4XFyy3t5eWltbne2Q++Lio63Aeb6SeOrds7/iW5PuVcP+qJiuRnwRqjCTze3/opaIy2J+/ws+64vqeDB95mw1FFhzKsbPcf3qZNcX4CvAG2+84WsPYJCVL/ncvt1MFjUNHn985K53Wy5ntpD+zd8UY/v3F5/v2GEmq/b4T7ubfv58M/Hs6Dg3WVUtX15MTrNZePJJ8/mOHea+SZf7ZdN1nba2NjXsuxTQogZ9cuTMZuZcFZQpT8Gy5f+A5FPFCUf5IYjerNYSQojxK5vNkjhf7+wVrDzJ54EDUF9vJoX2o7IStm5Va14a95hNN00zWzTdyepI7r/fTDZff91MXJcsMbvbt26FmqB0zo4vuVyObDbrjO8EWLJkCZlMcQzhsWPHfBlo/bQ1/0IDkkAX+DZfefDM82RP30rPqes4/MXPGDizhezp29Rq3ph/I+w9XNx+70+waK67hqeiN5uTjYxXoekec9KRuAw1QK9rewCwxtIGSk0N9Lp2dGAAaoO4oxBZFOXw7led7ZNDg8xdHMwlGWrm19C7t3hcB94boHZRMI/rzK/MZfDjQ872x59/SOU180rqTCSRSKRkjoQoVZ7kM5U6d+zkihWlXdyXavlys1vePWazpcXctmexX4zbb4df/rI40elv/qa4X6OdVX8FKhQKbNiwwfmjSqVSFAoFJxG1Jx61tbWx0h6G4aF+a9KvYU04arJifqiZvIf4lPeJT3mfuV/6e6quWkXNZJ+WF6lbBLv/UBzn+eSv4K4L9Bx4IP6w+W9rkJYmGE8SQNo1zjMJ1Ct1giCRgHS6OM4zmTQbLAJowd0rONT3G2eG+4upR7hl2X1qtUBI1CVI70474zyTTyapvyuYx3XutNt45+M3nRnuuaFt3Pi1RWq1CSEejzuNMYVCIRBLDwbN6JPPqiqza9s9IejAAbPrfXDQbKXcsaN0wlE0WhzzWV9f7J63u9Ht99q+vThBSdPMMaVr15rvo2lwrzWWzv2zVTU15v4ttiZ9LF9u/msno+Ki6bpOOBwmnU47a5j19vY6Yz0zmQzV1dVomkZjY2NJy6gIgGceNpdbWvggPLq6dAKSx+zu9lXLoHOdWuo/HZ0GGsiTD/5an32u5v5uZQJSkPT1mcstaRp0d5dOQAqYdXov6++cx5rIVB7oeM6Z+R5Efc/0Eb4njLZQo/vR7pIJSEGz4saf8uxbD9NxcBXLZjc5M98nEk3TSKfThMNhNE0jHA5TH9AvWn7SDMOezSNEgLhXPfDZsinvqyHf9Lz2ohryjfGXgPWVLx0fH2WaEZxz+8LGxzEF2Jw/pYYC68d/nqKGAmvd6l+oocDa9PsfqiERUKNv+RRCCCGEEOIiSfIphBj//nr8tNAZ46g1UQghxoIkn0KI8e9fxk9Xtsb42VchhBgLknwKIYQQQgjPSPIphBBCCCE8I8mnEEIIIYTwjCSfQgghhBDCM5J8CiGEEEIIz0jyKYQQQgghPDP65LOqqnj7S/fjfLe8FONaoVBA0zTnPu6qXC5HPB5Xw55JWXca1IDh93Ds7fs8wVtfJNUw+z5P0HPqOvZ9nlCLxt7xE+atNRc+CJm9aqkvtKWg96pR/xUooFn/6ehqcbAUXCd8kHe1UCheH/Qg7yicGDzCmshU1kSmsv/lbWpxoBSOF9AWamgLNfRMsI/rR6c/oOPgKjoOruLwyT1q8YSh67pzC+pCoaAWi7IknwMDcP/98NBDYBjFx5Ilas3y6egw7xcvPFcoFAiHw2q4xKpVq9SQZ+xk07Buc+3Hnuz7PMGJs6+oYd76Isn0SVHiU95n+qQoR85sVquMrXsegZ0/h31PQedOMxn1kbZUjQRHmDB58hgYtNFGgQBfQMJA3jrp26xkNIjCYcjnzetDW5uZjAbU+jvnsf6VQ2zu/4yXnljPicEjapXACN8TJr8zj7HPoK2zjcLx4B7XZ996mAdueoy1t2xh13s7+Oj0B2qVcS+Xy9HW1oZhGOTzeerq6tQqoizJ50jG8pvtE0+oEeGRUCiEYYx8h5ZUKkVjY6Ma9kwF0Go9vxXw489+4dU6VVedm/YOnNnCdZO+D8B1k77P4S9+plYZO5m9sPjbMHOGuX1fNTyfUWt5yngVwhVq1H86OjFihAgB0EgjG9moVgsGHYiBtavQCIHcVV2HWAxC1o42NsLGIO4o7H95G/Oqv8eMyhsAiCZ+xO+e2aRWCwQ9oxNbHCM00zpX72tk4/PBPK6HT+5h9jU3c+3kbwBw8/Sl7P/jr9Vq497WrVuda2AoFCISiaCPZT40To1N8plQuhQ7Os7tln/44eLzjg6znt2FD9DSYr5PNGrGotFincFBqK8/9+cIX9ndC1U+Drmwr8FY1+BO17afTp41u5i+rM0u+fdT452SemPmwNvFxBPg+q/73vIZVFmyTuIJUEVVcFs+s8pJXxXQls9stph4Yn2OB7Tls39vjhlVZuIJMK2ikhMDwWz5zB7IOoknQNX1VYFt+Tz+yWGunWImngDXXD2dj05NvJbPTCZTcg0MhUIMDAyU1BHlTD4ff7yYTO7aVVq2bh3s3292t9xxB2zaBI89ZsYqK2HtWrPeihVmnY4O8/1eeMF8rWHAa6+Z8YEB8zXbt49t66q4ZBs3bqS11W539I89BK4L8G/k6bm+rM1RQ96qmK5GxAjm4PPv6lKMl12dM152FKbPNL8cjgdzKsbPcf3qZNcX4Als1qxZakgoypd8usd8quM9KyvhiOub47Fj5r/z55t17ZZP29q15vvdfz8sX27GHnoIXn+9tJ4IDF3XWblypRr2Rcg15jPt46QjIYQQQpyrfMmnm9oiuWmT2U1ud6m7x/r8zd+YrZg7dsDixcW4GFe2bNlCdXU1mqbR0NBAOp1Gs3/fPklYw+GCYNqk2/jUOOp0s39qvMOXtTlO9/uYm38j7D1c3H7vT7BorruGsNRQQy/FKfgDDFBLbUmdwKgB167CAARyV2tqoNe1owMDUBvEHYXIoiiHd7/qbJ8cGmTu4mDOjquZX0PvXte5+t4AtYuCeVxnfmUugx8fcrY//vxDKq+ZV1JnIqirq2OXq/e3UCiwRG2QE2OUfGKN82xpMZ9v2lRsFVWX57FbNtetKz63ubvvt20zE1URSD09PRiGgWEYdHd3E4vFzjsxyQsFoB+wRgv7ruqqVbx/9iUAjp7ppPKqH6hVxk7dItj9h+I4zyd/BXctUGsJIEGCNGlnnGeSJPXUq9WCIWE179vD/JIQyF1NJCCdLo7zTCbNBokAWnD3Cg71/caZ4f5i6hFuWXafWi0QEnUJ0rvTzjjP5JNJ6u8K5nGdO+023vn4TWeGe25oGzd+bZFabdxbuXIlTz/9NFgz3/v7+4nac1aEY/TJZ1WVOTbTPeZT08xk0m7hHBg4d8KRW339uV31KGuIrlhRTE6rqszXyC/UF3aLZnV19Yhrffql2bXkYZ2VfHrtrS+SDJzZwsCZLSVrfd70pXYGzzxPz6nr+NR4hxuuWlPyujH3zMPmcksLH4RHV5dOQPJBZCXkh6DhfwRvrc8++ggTRkOjm+6SCUiB02ctt6RZY02Cuqt9feZyS5oG3d2lE5ACZp3ey/o757EmMpUHOp5zZr4HUd8zfYTvCaMt1Oh+tLtkAlLQrLjxpzz71sN0HFzFstlNzsz3iSQajdLY2IimaVRXV5PJ+LuqSFBphhfNUy0tpV3t9hqddjLZ0WGOC3W3fLa0mGND1S58cWVQv6D4aNmU99WQb3pee1EN+cb4y4NqyF9Lx/6jrBw0Izjn9oWNj2MKsDl/Sg0F1o//PEUNBda61b9QQ4G16fc/VEMioEbf8nkhdhLp9stfliaar79+bpe7EEIIIYSYcMY++dy40Ry76e5yv/12s8zeVsdyupdasseNCiGEEEKIcW/sk0+sMZ/uW2/a63ra22qr59q1xbKA3gVDCCGEEEJcOm+STyGEEEIIIST5FEIIIYQQXpLkUwghhBBCeEaSTyGEEEII4RlJPoUQQgghhGck+RRCCCGEEJ4ZdfIZiUTQNI1IJKIWAVAoFNA0DU3TSKVSarEncrncBfdPXDz7mA13a814PO7777vgusWm3/fH+tR4h55T19Fz6jrePRuAuxM9l4Z729SoZ/Re0JaqUfNWm9pS85F7Uy31XoECmvWf7vtZdAFBOuHPp1Aoru0c8DvXnRg8wprIVNZEprL/5W1qcaAUjhfQFmpoCzX0TLCP60enP6Dj4Co6Dq7i8Mk9avGEYV8DNU2jUCioxaIcyWd/fz9NTU3k8/lhk5GN1jqdfX19tLa2qsVjrlAoUF1drYYdoVAIL+4wOlEUCgXC4bAaBivJb2trwzAMDMPw5feNdZvrvHVjwDbr2uyX7OlbqZn8BvEp7/P2F//Ap8Y7ahXvPJeGJ3+lRj2j95r3cVc1b4LMRjBehfYHofrv1BreCxMmTx4DgzbaKPh6Fl1AkE748wmHIZ83129uazOT0YBaf+c81r9yiM39n/HSE+s5MXhErRIY4XvC5HfmMfYZtHW2UTge3OP67FsP88BNj7H2li3sem8HH53+QK0y7sXjceca2NTURF1dnVpFlCP5tDU1NbFhwwY17LtQKERfX58aFpfpfMn6qlWrqK6uprm5WS3yjA7EgJC13Qj4dZuCd8++yIxJd/JlbTYAlVf9gKNnOtVq3vnbGDy6Wo16JlELff+oRqFzHYQqzOetDea/haGSKp7S0YkRI2SdRY00stG3s+gCgnTCn4+uQywGIWtHGxsDewOR/S9vY17195hReQMA0cSP+N0zm9RqgaBndGKLY4RmWufqfY1sfD6Yx/XwyT3MvuZmrp38DQBunr6U/X/8tVpt3Ovp6XGet7S0kM/nS8qFqWzJZ0tLC+l0uqT1U9d1Vq5cWVIPIJVKOU3S8Xjcibu7bN1N1rlcriRma25uJpVK0dzcjKZpNDc3l9RVuX+uTe2Sdw8T0HWd5uZmJ2bXs/dTd3Ud2a8ZqXv/SpDJZDAMg0wmU/J79VLWdR0GqPKxIejk2d1O4gkwVZvlb8vnOGIno37IknUST4AqqoLb8hmkE/58stli4glQVRXYls/+vTlmVJmJJ8C0ikpODASz5TN7IOskngBV11cFtuXz+CeHuXaKmXgCXHP1dD46NfFaPlUj9RRe6cqWfIZCIZqamti6dasTy2azRKPRknq5XI6jR486zdJYSSRAbW1tSby7u5tQKMSGDRvo7u7GMAxisRi6rpNKpejq6iKZTFJTU0NfXx9dXV1s2LABwzAIh8MlyaH97cN+DzupVLvkw+Ew+XwewzDIZrMUCoVzWk97enpKTqhIJEJfXx+GYVBXV+dry5+fQtbFpb+/n/7+/mGHYXhhjhrw0V9plWpInEfuTbPr3W9zAnUWXcB42dU542VHYfrM4pfGoJtTMX6O61cnz1BDE9obb7wRyB7hIChb8gmwcuVKurq6nNbKmpoatQpbt24tiXd2dpLJZACcMYKRSIT29nYSiQRYyV4ikUDTNNLptFO3qamppJ5d134Pt3A47Lx/W1sbmUzmnKRS13VisZiTRM25iA/LQqFAPp+nuroaTdOc//8r3YYNGzh27JgaFuK8tv622PUuhBDjWTabLclPRFFZk89oNEosFmPjxo3s2rVrxIM+MDCghhzNzc1EIpGSySp2t7rdalkOanI6WnaLrWEYJWM+rmSzZs1SQ2OuBuh1bQ8Ata5tL02btJgPzxZbfz8zjjF9UmlPgCiKPwwt96tR79VQQ6/rLBpggFrfzqILCNIJfz41NdDr2tGBAagN4o5CZFGUw7tfdbZPDg0yd/EwSzQEQM38Gnr3us7V9waoXRTM4zrzK3MZ/PiQs/3x5x9Sec28kjoTSSQSobPTxzH+AVfW5BOrVbGrq0sNO2pqakgmk872G2+8QWNjI1hjMjOZjJO82Uv1dHV1lXXQ7oYNG1i1apUaZtasWSXjVquqqkrK7X2wWzsbGswmmnA4XNLVfqV2u7u1tbWdM+TCCwkg7Rr2lgTqlTpe+dakezlx9hVnnOfhL37GdZO+r1YT1oz3tr8tjvVs9nF+R4IEadLOOM8kSep9O4suIEgn/PkkEpBOF8d5JpNQH8QdhQV3r+BQ32+cGe4vph7hlmX3qdUCIVGXIL077YzzTD6ZpP6uYB7XudNu452P33RmuOeGtnHj1xap1SaEeDzu9OgWCgXflh0MslEnn5FIhK6uLjRr3cdoNEo4HKa+vt6Z/ANQXV1NKpUikUjQ3t7uTNDJZrO0traSy+VIJpPk83mnzE5Sm5qaCIfDaJpGf38/DQ0NNDc3O2M+U6mUM3YzEokQj8dJp9M0NDQ4+9TY2Oi8b21tLYlEwhnzmc/nicfjRKNR2tvbnS50O7nEatW196Guro5wOOyMSe3v73eOgaZpw06ymkjcv1M7UVcnhfX39yuv8k6ftfqMBnQr8zG8dtvV/0z29K30nLqO71z9VMkEJM9l9sJPn4FjH/iy1mfuzeIySu61PiMroWunWWav9bnyu8VyP/TRR5gwGhrddJdMQAqcIJ3w59PXZy63pGnQ3V06ASlg1um9rL9zHmsiU3mg4zln5nsQ9T3TR/ieMNpCje5Hu0smIAXNiht/yrNvPUzHwVUsm93kzHyfSDRreKCdL9j5kCilGSOtmyNIpVL09vZKN7ofhlmtwC/LpryvhnzT81oAFqq3GH8JwMwgt6Xj46NMM4Jzbl/Y+DimAJvzp9RQYP34z1PUUGCtW/0LNRRYm37/QzUkAmrULZ9CCCGEEEJcLEk+R6DrOslkknQ6LWM4hRBCCCHKRJLPESQSCWf2usxYE0IIIYQoD0k+hRBCCCGEZyT5FEIIIYQQnpHkUwghhBBCeEaSTyGEEEII4RlJPoUQQgghhGck+RRCCCGEEJ6R5FMIIYQQQnhm1MlnPB4vuaf3pUilUsTjcWc7Ho+j63pJHRFM7nu5Dxfzc2F+3brNtQak1EIPvfVFknfPFm+H+e7ZF+k5dR09p65j3+eJkrqeOH4CFj5oPjJ71VJP5d4s3sc9slIt9V+BApr1n07AP5MKrhM+yLtaKJi3zdU0CPjn/InBI6yJTGVNZCr7X96mFgdK4XgBbaGGtlBDzwT7uH50+gM6Dq6i4+AqDp/coxaPa4VCAU3TyOVyahEAzc3Nvl8bg2TUyWdPTw/t7e3EYjEu5TbxuVyOZDJZEuvp6SGR8OGiLC5Jc3Mz1dXV5PN553deKBSorq52FubPZDKkUt6nfjmgzbojtQEkfboenzy7h4EzW5ztT413OHl2N/Ep7xOf8j4nzr7CkTObS14z5u55BHb+HPY9BZ07zWTUJxueA+NV8wHQvEmt4a8wYfLkMTBoo40CBbVKcISBvHXCt1nJaBCFw5DPg2FAW5uZjAbU+jvnsf6VQ2zu/4yXnljPicEjapXACN8TJr8zj7HPoK2zjcLx4B7XZ996mAdueoy1t2xh13s7+Oj0B2qVcalQKBAOh9WwQ9M0CoWC3LTGZdTJ5+WKRqO0t7erYRFwuq6TyWQwDINQKOTE33jjDZqampztDRs28PTTTzvbXtkFNLq224FiCuidd8/+ihmT7nS2v6zN5qYvFc/3uV/6e/7dGHS2x1xmLyz+NsycYW7fVw3PZ9Ranul5rPh8w2ooDLlL/aWjEyNGCPP8bqSRjWxUqwWDDsTA2lXz5A/iruo6xGJgf2Y0NsLGIO4o7H95G/Oqv8eMyhsAiCZ+xO+eCdi3I4ue0YktjhGaaZ2r9zWy8flgHtfDJ/cw+5qbuXbyNwC4efpS9v/x12q1cSkUCo3Y+BaPx2lvb6enp0ctuqKNSfKZSqVobm4mlUqhaVpJ17rdNK1pGkePHi15XSQScZqs3V24l9qdL8ZOW1sbdXV1zu/F/n0NDAxQcLVkzJo1y/Uq7xy1HrYq13OvHDmzmTlXXbhr5a+0SjU0dg68XUw8Aa7/uq8tn6pQhRrxT5ask3gCVFEV3JbPrCvxxDrhg7ir2Wwx8QSoqgpsy2f/3hwzqszEE2BaRSUnBoLZ8pk9kHUST4Cq66sC2/J5/JPDXDvFTDwBrrl6Oh+dmhgtnyPJ5XKk02mOHj2KpmlEIhG1yhWr7Mmnruskk0m6uroAyOfzpNNppzwcDjvdtZlMseUlHo+Tz+ed7Q0bNtDd3Y1hGMRiMRkLGgCFQoF8Pk9NTQ2GYdDe3k51dTUAS5YsIZ1OlySgfqgBzDPPH58a74DV0nk+H57NccNVa9Tw2KqYrkYCIfuv0HK/GvXXHOaooeAaL7s6Z7zsKEyfef6/3yCZUzF+jutXJ7u+AF8Bdu3aRVNTk3PNxBq2JsYg+UwkErS3t9PU1ERra6sTLxQK6LpOLBZzumsbG4sdpD09PSVjJuzxn5qmlSSvwj9DQ0OEw2FnXK79+y0UCkSjUZqamgiHw2iaRnV1NXV1dco7jL2E1Qtpz79oAGrVSmPo6JnOCyaV7559kfBV69TwFSn3JtR8J1gtn0IIUQ5Hjx5lzpw5zjVzw4YNvjfQBEXZk89ysWeG2S2fwn8VFRUlrdOqzs5OZ8IRQEtLi1rFEz3W3At7T4tfgcbWp8Y7DJzZ4sxoP3H2Ff718wd564vixLqTZ/dw8uxupk26reS1Y27+jbD3cHH7vT/BornuGp4rDJkTjxJefju4CDXU0Euvsz3AALWefoW5BDXg2lUY8Pjb1sWqqYFe144ODEBtEHcUIouiHN5tzYQDTg4NMnfx0pI6QVEzv4beva5z9b0BahcF87jO/MpcBj8+5Gx//PmHVF4zr6TORDNnzpxzhhcKk6fJ56xZs0in0844wd7eXtLp9LCzoru6us6b6AjvhUIhYrGY8/tSW7Jxjent6+sriXst55oE7JUva7Od2ezxKe8zY9KdfOfqp5yJRp8a75A/s8nZPnJms9NNP+bqFsHuPxTHeT75K7hrgVrLU3UtxYlHeq/ZChoECRKkSTvjPJMkqaderRYMCSDtGueZhEDuaiIB6XRxnGcyCfVB3FFYcPcKDvX9xpnh/mLqEW5Zdp9aLRASdQnSu9POOM/kk0nq7wrmcZ077Tbe+fhNZ4Z7bmgbN35tkVptQqmvr6erq8tp7dyyZQurVq1Sq12RNGOkKVoXKR6Pl3SLd3d309DQAEB7e3vJckqGYZBKpZyY3aLZ09NT8j75fJ6NGzc640btcaJ9fX1Eo1Hn/YQ/7Alg4XCY/v5+sBJR+/c+ylPKdJmTzFLW9TcMmHs2esumvK+GLsq+zxPMvCrBtybdy7tnX+RfP3+wpHzGpDtZePWljWXuea24buglO9gPq61s79HVZkI6CsZfSv9/LlbuTaj+u9JYuAL6t5bGLtnSMpx3lhw5qjHHM3fTTYLyLQGnGZd3bo8oB9auQreVkJZN+Y4puRxYY8Tp7jYT0jLanD+lhi5bft8uNllN8g90PMeCu1eoVUblx3+eooYuW+5gjurV1rn6aDeJuvIe13Wrf6GGLtvQJ//GtrcfBWDZ7CbmTitvD9Cm3/9QDXnGPTHanau4r43t7e0lwxGvZKNOPoUYE5eZfI6Fy00+x8Koks8yu9zkc8yUMfkcS2VPPsfU+DimlDn5HGvlTD7HWjmTz7HmZ/IpLo2n3e5CCCGEEOLKJsmnEGL8++vx00JnjKPWRCGEGAuSfAohxr9/GT9d2RrjZ1+FEGIsSPIphBBCCCE8I8mnEEIIIYTwjCSfQgghhBDCM5J8CiGEEEIIz0jyKYQQQgghPCPJpxBCCCGE8Myok89UKoWmacM+7HuARyIR537uFysej6Pr5q0Hc7kckUhErXLZIpGI897i4ui6XnL7MLd4PE5zc7Madu7zfqm/+9EqAJr18Pu3/KnxDj2nrqPn1HW8e9bnuxMdPwELHzQfmb1qqeeaN4G2FCIr1RL/FSigWf/pvp9FFxCkE/58dN28c5mmgXVtCJL/9cD3WROZyprI1JL4C//9J058uHK/xP8uTvP/LP3c1TM62kINbaFG/O/iJWV+6Ti4ynnY93UHOHxyjxP/p/zjJa/xWyQSQdO0YfOOXC6HpmnO/drPJx6Pl9Qb6Vppc19jh/vZE8mok8+jR4+Sz+cxDIOmpiba29sxDIO+vj6wDnY+n1dfdkE9PT0kEgkKhQLV9v2Ay6C5ufmy9udK5r43rSoej5NOp9UwhUKBcDishj0RBvLWjQHbrGuzX7Knb6Vm8hvEp7zP21/8A58a76hVvHPPI7Dz57DvKejcaSajPkl1m/8ar8KG1WYiGiRhwuTJY2DQRhsFX8+iCwjSCT+SXA7a2sAwzEcyaSajAfHbpx8n/uP/g839n1H9Xxp54b//xCmLLIqyuf8z5zGv+nslr/VD/O/ipHeXfu4WjhfIHshi7DMw9hmkd6dJPedvkv9P+cdZe8sW1t6yhf84o5Zf5R8D4KPTH3D8k8NO2Tsfv8m+P/5v9eW+aG5uJpPJYBgGkUjEaUTDSjwvNh+JRCKsWrWKUCgE57lW2tSkNJPJXHSSOx6NOvns7Ox0Dq5bNBqltbWVnp6eUSUhoVDISWTLobOzc1T7cyVKJBIj/g56enpoampSw4RCIQzD+zu56EAMsM/IRmCjUscr7559kRmT7uTL2mwAKq/6AUfPdKrVvJHZC4u/DTNnmNv3VcPzGbWWZ55+CVZ+13yeqIWunVAYUmv5Q0cnRoyQdRY10shG386iCwjSCX8+u3ZBY2Nxu70dtmxx1/DVLcvuI7xwCVjJZmRRFIATg0dYcPcKp15+3y5uX/4DZ9svPf/YQ1N96eduaGaIzv9W/Hxp/0k7R4eOltTx2v8n/JDzfME3l/HRqT8CcO3kb1A762+dsmjFCv5/p/37MuzW0tLi5DS1tbUsWWKeF1h5zcU0XqVSKRobG0kkEk5spGslVlI7Z86ckpid+6hJ6UQx6uTzUtjd8e4ub3c3vbt79nxd9c3Nzc5r3L8Y+1vCcO9nDw+Ix4PRFSHGRtZ1HQao8rEh6OTZ3U7iCTBVm+Vfy+eBt4uJJ8D1X/et5bMwBPkhqJhejIUrYOhDdy3/ZMk6iSdAFVXBbfkM0gl/PkePmg9bVZW71HczKm9wnvfvzTkJpzsOsO8lndnfWVQSC7I5FaUJjd+unfJNNeT46mTX55OP7MTTbnGMRs0vIpcimUxSX1+vhke0devWYevbP3ukXGg88yz5rK6uxjAM2tvbyWazYCWETU1NTnzr1q1wga76QqFAV1cXhmGQz+fp6uoC1/iIcDjsDAHYtWsXWN3Gvb29GIZBW1vbiO8tJoYgfdz+lVaphvzjzvYCIFShRoJjTqDOogsYD7taUwPWZ3VQ7X95G2siU+n7v5/mt0+PPAZRTUiDqndvL61/26qGffPeJwWWXL9cDQMw+PEhFn7zP6lh36RSKcLhMMlk8pLnh+RyOcLh8LA9wsNJpVK0tLSoYUdtba2Ty0wkniWf7m5b+xtFa2srnZ2dxONxksmkU36+rnq7O1fX9ZI6/f39ACWJ5VHrm/aWLVtYtWoVWN8kRnpvIYQQYyCRgFisOOGooQFqa9Vavlpw9wpnzOeLqUfUYvL7djF9ZrEnI8j0jE7bj9rUsK+Of3KYudNuU8McPrmH267/vhr2VWtrK4ZhEIvFaGu7tON47Nixi54sZOdCF0pU7VxmIvEs+RyOPYO6ra2N9vZ2tXhEmqaRzWalBVOcowbodW0PAH5d4qZNWsyHZ4vdJZ8Zx5g+6dK7cMpi/o2w93Bx+70/waK57hqeCVWY3ey5N4ux/BBEb3bX8k8NNfS6zqIBBqj17Sy6gCCd8BfS02NONrI/t1uD0yrndv//9aQaAuDIgd3csuw+NRw4uYM5sgeyRG/x6bNmGM8eai0Z42kb+uTfOP7JYSq+8h/UokDo7BzbMfrbt28nmUyiaZrTKKYOGZyofE0+t2zZQnd39yWNqdB1nVgsdkknRSgUcr69FAoF8vk8DQ0NE3YW2ZUsAaRdw96SwLkjabzxrUn3cuLsK844z8Nf/IzrJvn0Db9uEez+Q3Gc55O/grsWqLU80/h92Ppb83mqG5ruUWv4J0GCNGlnnGeSJPW+nUUXEKQT/mLkchAOFxPQANr/8jaq/4trcpTl8O5XA9/lXjheYMP/d4Mz8Sj1XIrCcX+vc/+Uf5z7wg+DNcvdntX+0ekP2PPeS05Suu+P/7tkKaYg2L59O43uiXIXYdasWU5P7IXYLaz2MEIAwzDOyYnUyUgTgWaUaUpyJBJxDl4sFqOnpweU5QXa29ud7vWmpiZqamqcJXzC4TD5fJ729nZ6e3ud17z66qssXbq05H01ay0s+zX2Nwb757t/Tnt7O62trc7+2XU3bNhQMhNNjExdXsJ9yjQ3Nzvjbpuamkq+FNi/J6xhF+of1HmNsKboxcgB9t52W9fn0Vg25X01dNFOnt3Dns//MwDfufopvjXpXrXKJel5bRRrhR7sh9XmUic8utpMSEfB+MuDauiSxB+G9F6zFbTfHO49OkvL8lEGQI4c1dZZ1E03iVGfRUWacfnn9rDKfcKXKNMxTaXM5ZXCYbjIC/Ol2pw/pYYu2vramzgxYCZp1f+l8ZzWz/y+XRw5sJvvNhZnb4/Gj/88RQ1dkub/2UzXdutzt76Jzv/WiZ7Rafhp6ZJ4scUxev7RvBZfrnWrf6GGLlrHQXO4m9sDNz3Ge58U+PU7pWOAZ19zc8ns+Mux6fc/VEOXzH3dsvMHm7qEYD6fH7bLPBKJkMlkSsrOd63E9d5qShaPx2lra7u06+c4ULbkU4iyGkXyWW6jST7LbVTJZ5mNNvksuzImn2Op7MnnmBofx5RRJp9eG23y6aXRJJ9eK0fyWQ722qDuxPVyFAoFmpubnca8icTXbnchhBBCiImktbWV3t7eS54prwqHw+e0kE4UknwKIYQQQpRRT08PW7Zsuey5JZFIBMMwhu3Wnwgk+RRCCCGEKLOenp7LTh4vdtLSeCXJpxBCCCGE8Iwkn0IIIYQQwjOSfAohhBBCCM9I8imEEEIIITwjyacQQgghhPCMJJ9CCCGEEMIzo04+4/E4mqY5j0uRSqWIx+NqeMzkcjkikYgaFpdJ/X1HIpGSc8HL363meoy0qtqlnZ3l8e7ZF+k5dR09p67jyJnNarG3jp+AhQ+aj8xetdRzkZWgLTUfuTfVUn8VKKBZ/+mMbqHoMVdwnfxB3tVCwbxzmabBKBffHmsnBo+wJjKVNZGp7H95m1ocKIXjBbSFGtpCDT0T7OP60ekP6Di4io6Dqzh8co9aPOHY18TRLjY/EY06+ezp6aG9vZ1YLHbOPUnPJ5fLOfdf90o0Gp3wa2d5pbm5uWQ7l8uRyWQwDAPDMOjr66O2trakzliJWzcBNIAmoE6tAJTurTdOnt3D21/8A/Ep7xOf8j6Hv/gZ75718faY9zwCO38O+56Czp1mMuqT5k3mreWNV6H7/4RV/6DW8FeYMHnyGBi00UZhxK80ARAG8tYfQNt5vn35LRyGfB4MA9razGQ0oNbfOY/1rxxic/9nvPTEek4MHlGrBEb4njD5nXmMfQZtnW0Ujgf3uD771sM8cNNjrL1lC7ve28FHpz9Qq0wIuVwOTdNobGzEMAwSiYRa5Yo36uTzckWjUdrb29WwGAdyuRxz5swpiVVUVJQsprtr1y7q6+tL6owV911vW6zrsFsOKN1bb/zZ2EvlVT9wtud+6e85fsanb8CZvbD42zBzhrl9XzU8n1FreWrO9ea/s76hlvhLRydGjBDm+dxIIxvZqFYLBh2IgbWr0AiB3FVdh1gM7M+IxkbYGMQdhf0vb2Ne9feYUXkDANHEj/jdM5vUaoGgZ3Rii2OEZlrn6n2NbHw+mMf18Mk9zL7mZq6dbP7B3zx9Kfv/+Gu12oRQXV1NX1/fqO/tPpGNSfKZSqVobm4mlUqd0/1aKBScbtmjR4+WvK65udkpc7esne/97G8Y6msikQiRSARd18nlcgDoul7yWnc3sTSLX7ytW7eek1iqd3Ho7e09J+aVsLK9FfAmDS7178Yg/24MOttTtVkl5Z468HYx8QS4/uu+tnyu/C4kn4LCEGz9LWz5P9Qa/smSdRJPgCqqgtvymXUlngBVAW35zGaLiSdAVVVgWz779+aYUWUmngDTKio5MRDMls/sgayTeAJUXV8V2JbP458c5topxW+a11w9nY9OTbyWz1QqRSwWY9WqVefkJaKo7Mmnruskk0m6uroAyOfzpNNppzwcDpPP5zEMg0ym2PKi6zpz5sxxum0zmQypVOq871coFKiuri55ja7r6LrOhg0byGQyNDQ0gJWk2s/tn2ffO7W7u5stW7Y4ZWJkqVSKlpYWNVyiUCh41uWuegPY4NpOWa2hfpg2aTEDZwJ0XlVMVyO+id5sdreHV0LNd8ztIJnjS1v5ZRovu6r0lgTZ9Jmz1VBgzakYP8f1q5NdX4AnKLvhJZPJkM/n6erqksatYZQ9+UwkErS3t9PU1FTS5FwoFNB1nVgs5rSINTY2OuVbtmxhyZIlJdu9vb3nfb833ngDrIkvmqaRz+cZGBjg1ltvpaGhgY0bN2IYBtFolGg0Snd3t/P6RCJBT08Pzc3NJUmpGFnBaqm4UIvm9u3bS36XXsoC9uga+/v/+fd27Hxr0r3MmHSnM+HoXz9/kOmTomq1K1b2XyG/FRr+B6SKf5pCCDFu9ff3s3LlSkKhEKFQiKamJgYGBtRqV7yyJ5+jcezYMTV0QfZEJ/vR2tpKKBTCMAxqampG7FK3u+trampKklIxsu3bt5NMJtE0jXDY7NzWNM0Z1mA7evQo0aj3SVYE6HRtbweS1iRguytes8aAemXh1TrxKe9TM9n8onTDVWvUKt6YfyPsPVzcfu9PsGiuu4an4g+bLZ6hCjMBtbvgg6CGGnrpdbYHGKAWf1ryL6gGXLsKAxDIXa2pgV7Xjg4MgE+9IxcSWRTl8O5Xne2TQ4PMXby0pE5Q1MyvoXev61x9b4DaRcE8rjO/MpfBjw852x9//iGV18wrqTMRRCKRy8plrjSeJp+zZs0inU47yUpvby/pdJpUKkVtbS1tbW1O3V27drFq1SrXq8916623kk6nneSyUCg4XfWpVIpEIkF3dzfZbFZ9KVu3bqW9vV1moV2C1tZWJ8nP581pPXbLss1uHfVaHLAHcRSs7vZW1yx4exKSAXidFp88u4fs6VudBNQXdYtg9x+K4zyf/BXctUCt5ansv5ZuhypKt/2SIEGatDPOM0mSel9GDV+EBJB2NfMnfRrgfCGJBKTTxXGeySR4NCHxUi24ewWH+n7jzHB/MfUItyy7T60WCIm6BOndaWecZ/LJJPV3BfO4zp12G+98/KYzwz03tI0bv7ZIrTburVq1qiSX6erqOmeOhADNMC5hfaRhxOPxkjGd3d3dTjd2e3t7yXJKhmGQSqWcWCwWA2u5JqwJR/bYzvb2dlpbW9F1/bzvl8vlqK6uLonpus6WLVuc/crn8wwNDTn1mpqaWLlypbNtj0Ntamqis9PddiZGUigUCIfDqKdPKpViyZIlo2/5vIQ1Y4ermVe62wtW6+flnOzLpryvhi7KkTObOfzFz/iyNoeayeVZ067ntVEs1XSwH1Y/Zj5/dLWZkI6C8ZcH1dAl0VyNSX3/WIZxn0sv57c7vBw5qjE/H7rpJuEM5hg9zRjujB2FHFi7Ct2ucSdlUb5jSi4H9md1d7eZkJbR5vwpNXTZ8vt2sSlhtiA+0PEcC+5eoVYZlR//eYoaumy5gzmqV1vn6qPdJOrKe1zXrf6FGrpsQ5/8G9vefhSAZbObmDvtNrXKqGz6/Q/VkC/cuUxfX9/or4cT0KiTTyHGxCUkn2PtcpPPsTCq5LPMRpt8ll0Zk8+xVPbkc0yNj2NKmZPPsVbO5HOslTP5HGtBST7FhXna7S6EEEIIIa5sknwKIYQQQgjPSPIphBBCCCE8I8mnEEIIIYTwjCSfQgghhBDCM5J8CiGEEEIIz0jyKYQQQgghPCPJpxBCCCGE8Iwkn0IIIYQQwjOSfAohhBBCCM+MOvmMRCJomlbyiMfjTnlzczOpVKrkNTZd10vqRiIRcrmcs61pGoVCwdm+HOV4D2H+rrRhbnkZj8ed37vKjkciEbVozKUA73/quT413qHn1HX0nLqOd8/6fGvM4ydg4YPmI7NXLfVc8ybz/u6RlWqJ/woU0Kz/dHS1OFgKgGY9Ar6rpFLgw+fBpVoTmVryeOG//0St4qv438XRFmpoC4f53LXi2kKNwnF/r31Dn/wbHQdXlTwOn9zjlNuxZw+1lrwuCCKRyDm5Sy6Xc65ratlw4vF4Sf4Rj8dpbm4uqWPH1dzJj+uml0adfPb399PU1ER7ezuGYZDP50mn007S2dXVpb7EkUgk6OnpAevg5/P5knLDMAiFQiWxS1WO97jS6bpOQ0ODGiaVStHW1oZhGDQ1NZX8UUUiEfr6+jAMg7q6upI/qrGWApJq0CfZ07dSM/kN4lPe5+0v/oFPjXfUKt655xHY+XPY9xR07jSTUZ+kus1/jVdhw2ozEQ2SMGHy5DEwaKONAv5exM8rDOSt27C3WcloEKVSkAzKX+bI9r+8jc39nzmPBzqeI7IoqlbzTeq5FG0/asPYZ9BU30Tz/yx+7sb/Lk77T9ox9hm0/6S9pMwPH58+ydpbtjiP/zijluu/Yl6Pnz3Uyoobf8raW7Yw+5r/F/+Uf1x9uW8ikcg5+QjAhg0bnDwnmUyet2ErEomwatUqJ/+Ix+Ok02m1Gs3NzdTW1mIYBrW1tU5Sm8lkJnTj2aiTT1UoFKKpqYlMJkNraytNTU1qlWH19PQQDofVsAiARCJBX1+fGqa+vp5o1PxQrqmpoaamBsD5Y7HLWlpaSKfTnv0RtQJWbuOrd8++yIxJd/JlbTYAlVf9gKNnOtVq3sjshcXfhpkzzO37quH5jFrLM0+/BCu/az5P1ELXTigMqbX8oaMTI0YI86LRSCMb2ahWCwYdiIG1q9AIQd1VWluhOwh/mec3+zuLSrb79+ZYcPeKkpif6u+qJ3qL9bk7v4aa+ebnrq3q+qqSf/1kJ5pu107+Bh+d/gCAiq/8BwAWfHMZ73z8phP3W39/P7FYrCRWKBScxrJQKEQsFhuxYSuVStHY2EgikXBiPT09w+ZDXV1d1NfXg3VNTVpf0EKhEH19fcO2lE4EZU8+sQ5mXV1dScxuVnYfyFQqNeKBLRQKJV258Xjc6fp1N3nb9dxldpIz0nvY++JuNk+lUs7rdb3Yd2U3hadSKc+Sp/HC/YeXzWadP7ShoaGSb40j/YFOdCfP7nYST4Cp2iz/Wj4PvF1MPAGu/7pvLZ+FIcgPQcX0YixcAUMfumv5J0vWSTwBqqgKbstn1pV4AlQFuOVznJhReUPJ9omBIyXbfgvNdH3uHsiSqCsmOKu+v4qGn5q9VFte2kLnf/Ppy67l2snfcJ4PffJvfHWy+Rn0yecn+ejUH4etF1Tu61gqlaKzc+Rjm0wmnYTyfOxhhvZ72/+qDTju4YgTRdmSz2Qy6SRv4XC45BeTTCZpa2ujr6+PTMZsbdF13cnwh+NuBW1ubiadTtPQ0IBhGPT19TlN3na9bDZ7TvfvSO/R1tZGd3c3vb29YO1Lb2+v05ze0NBAoVAglUrR19dHW1vbeff1SmYn/V1dXU4yb//BuJP4K9VfaZVqyD/ubC8AQhVqJDjmMEcNBdc42tXxJr9vF7cv/4Ea9p2e0dEWanRt7yL1XLERJVGXoP0n7WgLNdp+1FaSqPpt6JN+bvya2apst3i6x3+OF5FIhGQyOWJPbS6XIxwOX3Sjy0jvY6utrWXXrl1qeNwrW/Jpj/k0DIP+/v5zyuyExG4RSyQStLe3l9Rzc7ecdXZ2EovF6La6bKLRKLFYjDfeeMOpZye7dhfv+d7D3hd7P7PZLOl02kmcsVrvlixZQnV1Nbt27ZKxoyNIJBJO0u9O0Lu7u2loaCj5QiLHTwgxnux7ST+nGz4IEnUJZ8xn8sni5649wajvmT6qV1ejZ4LTADD48aGSFs5ls5v49TtdzqSja6d8c1y0gPb39zu5xXCTjo4dO1b2yUJHjx5VQ+Ne2ZLP8c6dPBuGQTQaJRqNYhgGWN3vE7Hpu1zULgg7KTUMg1gsxoYNG0rKrwTTJi3mw7PFc+Yz4xjTJ/k0cWH+jbD3cHH7vT/BornuGp4JVZjd7Lk3i7H8EERvdtfyTw019GL2igAMMEAttSV1AqMGXLsKAxDUXR2v1G74IFG71et+XMeS/7iE6C1R+p7pc7rg/fbR6Q+ovGZeSWzutNuciUizr7mZJdcvLykPslAodN7Gs4sVjUbJ5/MlQwWvlIaacZV8ZrNZsH5B6XS6ZDCvnRhu37592EG951NTU1Myc03XdXK5HKlUCl3XaW1tpb29fUI2fZeLruvDHvdIJEJtbW3J7+pK8a1J93Li7CvOOM/DX/yM6yZ9X63mjbpFsPsPxXGeT/4K7lqg1vJM4/dh62/N56luaLpHreGfBAnSpJ1xnkmS1HPh8Vu+SABp1zjPJAR1V8eb/L5dTJ9ZHLMdRHpGp6m+9HN31/9TvE6FZ52/S9crb/95LxVfGb418NlDrVReM4+5025TiwLt6aefHnZc56xZs87p/T2fpqYmtm/fDsDGjRtpbGxUqzBnzsQbWzPq5DMSidDV1UUymTxnOR17qaVkMkkqlaK6utp5jT3m0x4raC+1VF1dXTKW0918bU8gCofD58y+3rBhA5qm8fTTTzutcO73cI/5TKVSNDQ0kM/nicfjzhCAcDiMpmls2bLF6ZrfsmULmqaRTCZpbQ3eWmReyOVyzu/OPYHLvcZrNpt1jrv9e7KPpdfHTQcarNVnhv+4885tV/8z2dO30nPqOr5z9VMlE5A898zD5nJLCx+ER1eXTkDyWGuDOfFIW2rOfO9cp9bwVx99hAmjodFNd8kEpMDps5Zb0qxlHoK6q7oODQ2Qz4+LtT73vaRzy7L71LDvIvdGnHU8sweyJa2fmf+VIflkEm2hRvXqajL/y78VLdwGPz7kjPPEagm1u9tjVatZ+M3/VFLfb/aySHbugmt+g/3IZDLDtlDauYM6Qbm5uZmuri66urpKJlp3dnby9NNPo1mTpdXrZW9vL0uWLCmJTQSaYfcrB1w8HmfVqlXntKDZieo4+d8QF2uYRev9smzK+2rINz2v+bxQvYvxlwfVkL+Wjo/PAM0Izrl9YePjmAJszp9SQ4H14z9PUUOBtW71L9RQYG36/Q/VkC/shFVNJC9VoVCgubnZWeJpIhl1y6cQQgghhDC1trbS29s76hVf1JWDJpJxkXy6u8zVST/Ddc8LIa4wfz1+WuiMcdSaKIS4PD09PWzZsuWc7veLFYlEJvQqO+Mi+ezs7CyZhe420vJOQogryL+Mn65sjfGzr0KIy9fT03PZyeNEz2nGRfIphBBCCCEmBkk+hRBCCCGEZyT5FEIIIYQQnpHkUwghhBBCeEaSTyGEEEII4RlJPoUQQgghhGfKlnzmcrmSW0/ZK/Pjut3iSOz6Ynywf5/qmqu4bkEWBM3WHQeLNzLz3pEzm+k5dR09p67j5Nk9arG3jp8wb6258EHI7FVLfaEtBb1XjfqvQAHN+k9ndAtFj7mCdaJr1r1lg6pQMO9cpmnmrTYD7MTgEdZEprImMpX9L29TiwOlcLzg3G5TzwT7uLpvq3n4pM+fh2MsHo+X3EZTlCpL8mnftz2fzzvrbtbV1ZHJmPeVtReCH8lEXkh1orFvZzocXddpaGhQw77QrGuyAfh1fwg72YxPeZ/vXP0Ub37xE7WKt+55BHb+HPY9BZ07zWTUR9pSNRIcYcLkyWNg0EYbBQL85TgM5K2Tvc068YMoHDbv624Y0NZmJqMBtf7Oeax/5RCb+z/jpSfWc2LwiFolMML3hMnvzGPsM2jrbKNwPLjH9dm3HuaBmx5j7S1b2PXeDj46/YFaZUKw7w0vRlaW5DOZTNLd3V2SQPb39zt3Hcrn867aYjwLhUIYxvB3aEkkEvT19alhz8WBdsDvu+FO0a7nhqvWAHCt9v9mxiQfs63MXlj8bZg5w9y+rxqeN78c+sV4FcIVatR/OjoxYoQwP88aaWQjG9VqwaADMbB2FRohkLuq6xCLgX2NaGyEjUHcUdj/8jbmVX+PGZU3ABBN/IjfPbNJrRYIekYntjhGaKZ1rt7XyMbng3lcD5/cw+xrbubayd8A4ObpS9n/x1+r1SaEnp4empqa1LBwGXXyad+7NJFIqEX09JRe/t1d83Y3u9olH4/H0XWdeDyOpmmkUimnLBKJOK8f7T1TxcSUA9LAUav108+brn5Zm+08P3qmk5u+1F5S7qkDbxcTT4Drv+57y2dQZck6iSdAFVXBbfnMuhJPgKqAtnxms8XEE6CqKrAtn/17c8yoMhNPgGkVlZwYCGbLZ/ZA1kk8Aaqurwpsy+fxTw5z7RQz8QS45urpfHRqYrZ8igsbdfI5MDCghka0YcMGDMMgFovxxhtvgNIl776He1tbG93d3fT2mgPCdF137nXa3d3Nli1bnNcJYdsFNAE1Vi8kPo/5/NR4h55T1zFwZgv7Pj/3C5qnKqarETGCOcxRQ8E1XnZ1znjZUZg+s/jFMejmVIyf4/rVya4vwOKKNurks6qqSg2NyN0Saiet7i75zs5OYrEY3d3dzj3c7fubJhIJenp6aG5uDsy4QhE8R61rsZ3mbfC5IejL2mxnzOeJs6/4P+lICCGE8Nmok89bb70VXN3vbs3NzWWbxW532dfU1NDd3a0WCwFW4nlUDQbAtybdy4xJd6ph78y/EfYeLm6/9ydYNNddQ1hqqKGX4hT8AQaopbakTmDUgGtXYQACuas1NWD1YgEwMAC1QdxRiCyKcnj3q872yaFB5i72cbz2edTMr6F3r+tcfW+A2kXBPK4zvzKXwY8POdsff/4hldfMK6kjrhyjTj5DoRDt7e00NDSULL2TSqWYM2dO2Waxb926lfb29mHHlgphqwe6XK2dW4BVSh0/fGq8w6fGEaZNuk0t8kbdItj9h+I4zyd/BXctUGsJIEGCNGlnnGeSJPXUq9WCIWENcrZP+KT1RxA0iQSk08Vxnskk1AdxR2HB3Ss41PcbZ4b7i6lHuGXZfWq1QEjUJUjvTjvjPJNPJqm/K5jHde6023jn4zedGe65oW3c+LVFajVxhRh18gnQ2tpKd3c31dXVzoQgO45rXGckEnHGdSaTSXRdH7asoaGBVCpFQ0MD+XyeeDzOypUrSSaTaJpGW1sb6XRa1tDyif37ra6uLvnCkcvlqK6uLqnjtRDQba0+o1mNQH59XXnri6Szxue+z1dQM9nnLvdnHjaXW1r4IDy6unQCkg8iKyE/BA3/I3hrffbRR5gwGhrddJdMQAqcPtcJ361MQAqSvj5zuSVNg+7u0glIAbNO72X9nfNYE5nKAx3POTPfg6jvmT7C94TRFmp0P9pdMgEpaFbc+FOefethOg6uYtnsJmfm+0TT3NxMV1cXXV1dkqeMQDNGWjdHCD/5lLwOZ9mU99WQb3pee1EN+cb4y4NqyF9Lx8dHmWYE59y+sPFxTAE250+pocD68Z+nqKHAWrf6F2oosDb9/odqSARUWVo+hRBCCCGEuBiSfAohhBBCCM9I8imEEEIIITwjyacQQgghhPCMJJ9CCCGEEMIzknwKIYQQQgjPSPIphBBCCCE8I8mnEEIIIYTwjCSfQgghhBDCM6NOPqNR82Y09uNStLSYr7dFo9DR4a4hgqhQKKBpWsmtNW26rvt2a01bwbrToAboaqHH3j37onOLzSNnNqvF3jp+wry15sIHIbNXLfVc8ybQlpq32QyaAgU06z/d97PoAuIBOuHPp1AoXij0IO8onBg8wprIVNZEprL/5W1qcaAUjhfQFmpoCzX0TLCP60enP6Dj4Co6Dq7i8Emfbzc8huzroKZpFAoFtViUI/nM5eChh+COO+BSbtS5Ywc8/nhpLJeDtWtLYyJYCoUC4XBYDYP1B9fQ0KCGPRcG8taNAdusZNQPJ8/u4e0v/oH4lPeJT3mfw1/8jHfP+nh7zHsegZ0/h31PQedOMxn1Sarb/Nd4FTasNhPRIAkTJk8eA4M22ij4dhZdQMo6yQ3rvu4NPp7wFxIOQz5vXija2sxkNKDW3zmP9a8cYnP/Z7z0xHpODB5RqwRG+J4w+Z15jH0GbZ1tFI4H97g++9bDPHDTY6y9ZQu73tvBR6c/UKuMe7lcjra2NgzDIJ/PU1dXp1YR5Ug+L9fy5WbSKsaXUCiEMcK3jEQiQV9fnxr2lA7EgJC13QhsVOp45c/GXiqv+oGzPfdLf8/xMz61TGT2wuJvw8wZ5vZ91fB8Rq3lmadfgpXfNZ8naqFrJxSG1Fr+0NGJESNknUWNNLLRt7PoAloBu/coYX3zCshxLKHrEItByPrLbGyEjcE8pvtf3sa86u8xo/IGAKKJH/G7ZwL27ciiZ3Rii2OEZlrn6n2NbHw+mMf18Mk9zL7mZq6d/A0Abp6+lP1//LVabdzbunUrjY2NYF0vI5EIesBb+v0wJslnSwskEua/mlbatX7gQLHn5dgx96ugqspsEcVqGb3c7nxx5cq6Ek+AKh8bgv7dGOTfjUFne6o2q6TcUwfeLiaeANd/3beWz8IQ5IegYnoxFq6AoQ/dtfyTJeskngBVVAW35XM4FWogALLZYuKJ9WEf0JbP/r05ZlSZiSfAtIpKTgwEs+UzeyDrJJ4AVddXBbbl8/gnh7l2ipl4Alxz9XQ+OjXxWj4zmQxVVVXOdigUYmBgoKSOGIPks6PD7E5/4QVze/9+eO21YvmCBWbMMGDXrmI8GoXB4nWaTZvMh2GYXfoyFlRcrDlqwCfTJi1m4MwWNewfd7YXAKEgJkmWOYE5iy5BAahTvn0FyZzxc0ynz5ythgJrTsX4Oa5fnez6AjyBzZrlY0PDOFH25HPtWrM7/f77S3tVDhwwE8g77oD5883YihXF8lwOKitLt9euNVs93cmrEOPFtybdy4xJdzoTjv718weZPsnVDSBEOW0EOtWgEEIET9mTz3JJJMzE0275FOJi1AC9ru0BoNa17bWFV+vEp7xPzeQ3ALjhqjVqFW/MvxH2Hi5uv/cnWDTXXcMzoQqzmz33ZjGWH4Loze5a/qmhhl7XWTTAALW+nkUXoRkI4KoBjpoa6HX9ZQ4MQG0wj2lkUZTDu191tk8ODTJ38dKSOkFRM7+G3r2uc/W9AWoXBfO4zvzKXAY/PuRsf/z5h1ReM6+kzkRQV1fHLle3bqFQYMmSJSV1hMfJZ2Wl2Yppj+t8/XVzu6VFrWl22+/fr0aFOL8EkHaN80wC9Uodr508u4fs6VudBNQXdYtg9x+K4zyf/BXctUCt5ZnG78PW35rPU93QdI9awz8JEqRJO+M8kySp9/0sOo+U9a3LblRvVsqDIJGAdLo4zjOZhPpgHtMFd6/gUN9vnBnuL6Ye4ZZl96nVAiFRlyC9O+2M80w+maT+rmAe17nTbuOdj990ZrjnhrZx49cWqdXGvZUrV/L000+DNfO9v7+fqHvii4ByJJ/RqDnG87XXzJZK95jPlhZzjCfWWE97hnt9fXES0R13mN3z9pjP+nqzi/7++83XaJr5JXndumLSKvxlr+NZXV1dstZnLpejurq6pI4f+qxJv5q1+oxfQ+COnNlMz6nrePOLnxCf8j5f1nweR/bMw+ZySwsfhEdXl05A8lhrgznxSFtqznzvXKfW8FcffYQJo6HRTXfJBKRAiVvfsBpca33WqJUCoq/PXG5J06C7u3QCUsCs03tZf+c81kSm8kDHc87M9yDqe6aP8D1htIUa3Y92l0xACpoVN/6UZ996mI6Dq1g2u8mZ+T6RRKNRGhsb0TSN6upqMhn/VhUJMs0Yad0cIfzkY/KqWjblfTXkm57XfFwnVGH85UE15K+l4+OjTDOCc25f2Pg4pgCb86fUUGD9+M9T1FBgrVv9CzUUWJt+/0M1JAJq1C2fQgghhBBCXCxJPoUQQgghhGck+RRCCCGEEJ6R5FMIIYQQQnhGkk8hhBBCCOEZST6FEEIIIYRnJPkUQgghhBCekeRTCCGEEEJ4RpJPIYQQQgjhGUk+hRBCCCGEZ0adfFZVmXdCVB8dHWrN8mlpgURCjZ5fVdXl7dPl/KyJrlAooGlayX3dbZqmoWkaqVRKLfJUCoioQR+9e/ZF3voiqYa9dfyEeV/3hQ9CZq9a6gttKei9ajQYUqSIBOosOo+gnfDDKRSKFwhdV0sD5cTgEdZEprImMpX9L29TiwOlcLyAtlBDW6ihZ4J9XD86/QEdB1fRcXAVh0/uUYsnDPs6qGkahUJBLRblSD4HBuD+++Ghh8AwzMf27bBuHezYodYevY4OePxxNXp+iQQMDqrRC7ucnzXRFQoFwuGwGgYgEonQ19eHYRj09vYOm5x6IQX4nOad418/D8B90O95BHb+HPY9BZ07zWTUR9pSNRIcKVIkA3cWjSCIJ/xwwmHI582LRFubmYwG1Po757H+lUNs7v+Ml55Yz4nBI2qVwAjfEya/M4+xz6Cts43C8eAe12ffepgHbnqMtbdsYdd7O/jo9AdqlXEvHo9jGAaGYdDU1ERdXZ1aRZQj+RzO8uVwxx2we7daMnpr15qJ7qXQdaisVKMXdjk/a6ILhUIYhqGGnUQzGo0CsGrVKjZs2KDU8kYr0K0GffTWF0nmfunv1bC3Mnth8bdh5gxz+75qeD6j1vKU8SqEK9RoMLTSSnegzqLzCNoJPxxdh1gMQiFzu7ERNm5UawXC/pe3Ma/6e8yovAGAaOJH/O6ZTWq1QNAzOrHFMUIzzePaeF8jG58P5nE9fHIPs6+5mWsnfwOAm6cvZf8ff61WG/d6enqc5y0tLeTz+ZJyYRqT5HPHDnjtNVi8uBhzd8/b3d87dpjxHTuKZW5qV/5I3d/DvTdWl7mmgZUPlbDrV1UVY3b9HTvO/VmJxMjvJWDXrl1EIsV+v1mzZtHf319S50p08uwepk1y/SH45cDbxcQT4Pqv+97yKa4g2Wwx8cT60A5oy2f/3hwzqszEE2BaRSUnBoLZ8pk9kHUST4Cq66sC2/J5/JPDXDvFTDwBrrl6Oh+dmngtn6qRegqvdGVLPh9/vJjQ1dfD/v1mCyhW93VVldnbsmmT2S1/4IBZb3DQjBmG2Tppd9UnEsWu/E2bzJbU4YYJDffedvz11834unWl3e5VVWY9w4AlS4qJpl1/k/Il94UX4Pbbzf+n114rLRNFIffFRQDw7tlf8a1J96phf1RMVyNCeGfOHDUSWNNnzlZDgTWnYvwc169Odn0BvgK88cYbvvUABl3Zkk91zOeCBWZLIlb3dS5nJnnr1pmx+fPNepWVZpnNThJnzYJjx4rxgYHic7fh3hvM966vN58vX17sdj9wwPwZ9fVmovzCC6U/R9PM93Mnuvffb/4c24EDxedCjOTImc3MuapZDQshhLgCZLNZEmo3qoByJp9uy5ebCds2a5Kg3a1+++3ntiqOZONG2LXLfN26dfDii2oN0+W8NxQTZcMoJr+5nLkt3euXbsmSJWQyxTGEx44du+IHWg+eeZ7s6VvpOXUdh7/4GQNntpA9fZtazRvzb4S9h4vb7/0JFs111xBi7NTUQK9rWYOBAaitddcIjMiiKId3v+psnxwaZO7iYM6Oq5lfQ+/e4nEdeG+A2kXBPK4zvzKXwY8POdsff/4hldfMK6kzkUQiETo7O9WwsIxJ8nnggNmiuGSJuf3LX5oto+7WwwtpaSl2xxuG2VI6nJHee9YseOIJ87nd2mm3jFZWlo7ptJ/b4z8NQ7rXL5U90cieeNTW1sbKlSuVWleWmsl7iE95n/iU95n7pb+n6qpV1Ez2aXmRukWw+w/FcZ5P/gruWqDWEmJsJBKQThfHeSaTxa6pgFlw9woO9f3GmeH+YuoRbll2n1otEBJ1CdK70844z+STServCuZxnTvtNt75+E1nhntuaBs3fm2RWm1CiMfjTmNMoVDwfenBINKM4aYuX4KqquGXMbrjjmKL4o4dxc+Zykqzfl0d2A1ld9xh/msnfNu3mzPl1WWO7JZNO4l86CFzUpP63vffb3ab2/tmd7n/1/9aTFI11+Sm7dvN1tpotLgPDz1kJrDun+Xen9EdtfFNcx28vr4+J/F0L8PU3t5Oa2urU++SuX9Bl0gHGqznYWC0056WTXlfDV2yI2c28+/GIDd9qV0tuiQ9r43QBXAxDvbD6sfM54+uNhPSUTD+MrrloyIrIT9kPu/+PyEx2gabpeX7o9TRabDOojBh+kd9FhVpxuWf28Mq9wlfonzHlFwOqqvN593d587qHKXN+VNq6LLl9+1ik3VCPtDxHAvuXqFWGZUf/3mKGrpsuYM5qlebx7X70W4SdeU9rutW/0INXbahT/6NbW8/CsCy2U3MnVbenqBNv/+hGvKc+/poy+fzMidCMerkc6y0tJy7EsdwMTFBDfMH7JdyJJ/lMqrks8xGm3yWXRmTz7FU9uRzTI2PY0qZk8+xVs7kc6yVM/kca0FIPsXFGZNu99E6cODcVs8dO8yWSCGEEEIIMX4FMvmcP9/s5raXbtI0s8tdHdcphBAA/PX4aaEzxlFrohBCjIVAJp9Ys92Hm5EuhBDn+Jfx05WtMX72VQghxkJgk08hhBBCCDHxSPIphBBCCCE8I8mnEEIIIYTwjCSfQgghhBDCM5J8CiGEEEIIz0jyKYQQQgghPDPq5LOqylyH074vuurAgeJanS0taunIqqqgo0ONiiAoFApomubcx92m6zqapqFpGrqul5R5qQBo1sO/vTB9arxDz6nr6Dl1He+e9fnuRMdPwMIHzUdmr1rqOb0XtKXmI7JSLfVXgQKa9Z/u+1l0AUE64c+nUCheDHz8fLgYJwaPsCYylTWRqex/eZtaHCiF4wW0hRraQg09E+zj+tHpD+g4uIqOg6s4fHKPWjxhNDc3O9dCua/78EadfA4MmPdSHxw070Kkso/79u0Xf2vMRGL4+8UL/7nv367Gs9kshmHQ19dHQ4N9s2nvhYG8dWPANuva7Jfs6VupmfwG8Snv8/YX/8CnxjtqFe/c8wjs/Dnsewo6d5rJqI8G3gfjVfPRv1Ut9VeYMHnyGBi00UbB17PoAoJ0wp9POAz5vLlwc1ubmYwG1Po757H+lUNs7v+Ml55Yz4nBI2qVwAjfEya/M4+xz6Cts43C8eAe12ffepgHbnqMtbdsYdd7O/jo9AdqlXFP13UymQyGYZDP50kmkxQCfK77ZdTJp+3++827EJWDrkNlpRoVQRAKhTCM4e/Q0tnZCUA0GiUWi6nFntCBGBCythuBi/zOU3bvnn2RGZPu5MvabAAqr/oBR8+Yx8hzmb2w+Nswc4a5fV81PJ9Ra3km1Q3Jp8xWz6DR0YkRI2SdRY00stG3s+gCgnTCn4+uQywGIWtHGxsvvjXCY/tf3sa86u8xo/IGAKKJH/G7Z8p0cSszPaMTWxwjNNM6V+9rZOPzwTyuh0/uYfY1N3Pt5G8AcPP0pez/46/VahNCJBIB63ophle25LO1FV57rbT1s6MD/uZv3LVMdle9ppV2rbe0mLFo1F3bfE/3rTZt7i59tWs/Gj03hvWz7S59e1/d7+/+2SO9h6aZrbPq/l/J3H9kzc3N9PT0lJR7Jeu6DgNU+dgQdPLsbifxBJiqzfKv5fPA28XEE+D6r/va8ln/12aLZ/uDZgJaGFJr+CdL1kk8AaqoCm7LZ5BO+PPJZouJJ9YHcUBbg/r35phRZSaeANMqKjkxEMyWz+yBrJN4AlRdXxXYls/jnxzm2ilm4glwzdXT+ejUxGv5TCQSpNNpdF1H13Xa29slCR1G2ZLP+fPN1s9f/rIYe/11WL7cXctM1qqqzJ6XTZvM7ng7/vrrZnzdutJu902bzIdhwB13FBO+n/ykGH/oIfPnb9xoJpMDA2Z8+3bYZg3Z6eiA//pf4cUXzZ+BlcDW1xdv42kn0CO9hz22ddYss0zuN19K0zS6urqcb35+mKMGfPRXWoCa8CumqxHfhCrMf1sbzAR04wtqDX/NCdRZdAHjZVfnjJcdhekzi18ag25Oxfg5rl+d7PoCPIHl83kaGhrIZrO0traqxaKcySeYrZwvvGAmdDt2wO23qzXMZC2XM1sO7QQQa0xofb35fPny0m73XM58naaZyaFt1iw4dqy4bT9fvtxMHFtaiu8JUFNj/sxUykwcly83v5Dff3+xjh0f6T0GBsx/VwZsgkRQ2GM+8/m8r5OOxPjR2hCslk8hhBiNjRs3YhgGmUyGeDyuFotyJ5/Ll5stk6kU7N49fKug3cV9++0XP0bU7uK2Wz5tug6PP26WPf64maTi6o7HSmpt8+eb73H77aVd5u4E1jbSe4gLi0ajNDU1qWFP1AC9ru0BoNa17aVpkxbz4dniigCfGceYPkkZU+KV+TfC3sPF7ff+BIvmumv4ym4JDYIaauh1nUUDDFDr21l0AUE64c+npgZ6XTs6MAC1QdxRiCyKcnj3q872yaFB5i4O4OBkoGZ+Db17XefqewPULgrmcZ35lbkMfnzI2f748w+pvGZeSZ2JwD27vb+/n/7+fmmIGUZZk08wWxZfOE8X2i9/aXaRq4nprFnwxBPm8wMHzG73devM5y+8APv3l9bHSkr37y92mdu2bi12wbt1dJgtmWvXmonv66+bn4nusaoHDpj1RnoPcXG6urpIJBJqeMwlgLRr2FsScDVce+pbk+7lxNlXnHGeh7/4GddN+r5azRt1i2D3H4rjPJ/8Fdy1QK3li+ZNsPK7atQ/CRKkSTvjPJMkqfftLLqAIJ3w55NIQDpdHOeZTJZ2KQXIgrtXcKjvN84M9xdTj3DLsvvUaoGQqEuQ3p12xnkmn0xSf1cwj+vcabfxzsdvOjPcc0PbuPFri9RqE0ImUzqZc9asWSXbogzJZ1WVmRxqmpnA2V3mK1cWWznB/JxpaTG75u3WyieeMBO/RKK47Jumwb33mu+xaVNxLOmCBWbZwICZlNrJoh23Hzt2mD/b3id7/Kg9kej114vx1lbz/TdtMvdP08z3W7t25Pewx3wuCMZ12xea9Uutrq521vpMpVLOumaapo04I94LfdbqMxrQrczH8NptV/8z2dO30nPqOr5z9VMlE5A898zD5nJLCx+ER1eXTkDyWGRlcY3Pmu9A9Ga1hr/66CNMGA2NbrpLJiAFTpBO+PPp6zOXW9I06O4unYAUMOv0XtbfOY81kak80PGcM/M9iPqe6SN8TxhtoUb3o90lE5CCZsWNP+XZtx6m4+Aqls1ucma+TyStra1EIhHnWtjY2EhUnUUt0Aw/s4RRamk5t2VyuJgYh+xvLQGwbMr7asg3Pa/5vFC9i/GXB9WQv5aOj48yzQjOuX1h4+OYAmzOn1JDgfXjP09RQ4G1bvUv1FBgbfr9D9WQCKhRt3z6ZceO4gx0W0cHLF5cGhNCCCGEEMExbpPP5cthyZLSLvfhlnYSQgghhBDBcVHd7vYYPyGEEEIIMf5cRLrnmYtKPoUQQgghhCiHcdvtLoQQQgghxh9JPoUQQgghhGck+RRCCCGEEJ6R5FMIIYQQQnhGkk8hhBBCCOEZST6FEEIIIYRnJPkUQgghhBCekeRTCCGEEEJ4RpJPIYQQQgjhGUk+hRBCCCGEZyT5FEIIIYQQnpHkUwghhBBCeEaSTyGEEEII4RlJPoUQQgghhGck+RRCCCGEEJ6R5FMIIYQQQnhGkk8hhBBCCOEZST6FEEIIIYRnJPkUQgghhBCekeRTCCGEEEJ4RpJPIYQQQgjhmf8/ue6Q/rE5BMwAAAAASUVORK5CYII=\"\u003e\u003c/p\u003e\n \u003cp\u003eThe neutrality tests using Tajima\u0026apos;s D and Fu\u0026apos;s Fs were significantly negative for USA and Colombia, but only negative values were obtained to domestic India and Bangladesh; while the rest of the localities showed positive values (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Mismatch distribution analyses were conducted to test for signals of sudden population expansion, grouping localities in invasive, wild, and domestic, using the Ramos-Ons\u0026iacute;n and Rozas (R2) statistics and the raggedness index (r) to validate the congruence of the model. The results were significant for domestic, showing evidence of a population expansion event, with the best fit to a unimodal curve (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). However, the trend of the curve in the other locations provided conflicting results.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eMicrosatelites\u003c/h2\u003e\n \u003cp\u003eGenetic diversity\u003c/p\u003e\n \u003cp\u003eGenetic diversity values were high and similar for all localities (Ho\u0026thinsp;\u0026gt;\u0026thinsp;0.711, He\u0026thinsp;\u0026gt;\u0026thinsp;0.744, and Fis\u0026thinsp;\u0026lt;\u0026thinsp;1.75). The number of private alleles was slightly higher in the wild localities compared to the other ones, although South Carolina, Colombia (Guajira), the West African Coast, and Bangladesh registered private alleles (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). There was no deviation from Hardy-Weinberg equilibrium (HW-E) and no linkage disequilibrium for all microsatellite loci used in this study. According to the bottleneck analysis using the TPM model and Wilcoxon statistical test, all wild and invasive localities were in mutation-drift disequilibrium, suggesting signs of a recent bottleneck process in these populations. Only the samples from domestic systems (India, Bangladesh, and Madagascar) did not show evidence of a recent bottleneck process (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Genetic diversity indexes using microsatellites \u003cem\u003eloci\u003c/em\u003e in the native, culture and invasive populations of tiger shrimp \u003cem\u003eP. monodon.\u003c/em\u003e N: samples collected, PA: private alleles, Ho: heterozygosity observed, He: heterozygosity expected Fis: Index inbreeding, HW-E: Hardy-Weinberg Equilibrium. Text color in the table represent: red: invasive populations, blue: domestic populations and black: wild populations.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003ePopulation structure\u003c/p\u003e\n \u003cp\u003ePopulation differentiation in the invasive area ranged from low differentiation (R\u003csub\u003eST\u003c/sub\u003e = -0.021 to 0.02) in the western Atlantic Ocean (USA, Colombia, and Venezuela) to moderate differentiation (R\u003csub\u003eST\u003c/sub\u003e = 0.027 to 0.146) in the eastern Atlantic Ocean (West Africa). The lower values observed in these localities, when compared to the wild populations, were mainly associated with Indonesia (Rst = -0.023 to 0.053), India (R\u003csub\u003eST\u003c/sub\u003e = -0.025 to 0.061), and domesticated from Bangladesh. Venezuela was the most similar (R\u003csub\u003eST\u003c/sub\u003e = -0.025\u0026ndash;0.034), while domesticated from Madagascar and Mozambique were the most distinct localities throughout the study area (R\u003csub\u003eST\u003c/sub\u003e \u0026gt;0.232; Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The AMOVA analysis, using the same hierarchical grouping as proposed with mtDNA-COI sequences, showed similar results, with the highest probability supported when assuming the relationships found with genetic distance R\u003csub\u003eST\u003c/sub\u003e (grouping IV: F\u003csub\u003eST\u003c/sub\u003e = 0.123, p\u0026thinsp;=\u0026thinsp;0.007, variance\u0026thinsp;=\u0026thinsp;12.37%; Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This indicates that invasive populations in the USA, Colombia, and Venezuela are genetically related to wild populations from Indonesia, whereas those in West Africa are associated with domesticated from India and Bangladesh, as well as wild populations from India.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003cstrong\u003eTable 6. A)\u003c/strong\u003e Genetic distance (Rst index) using microsatellites in the native (black color), culture (blue color) and invasive (red color) population of the tiger shrimp \u003cem\u003eP. monodon\u003c/em\u003e. \u003cstrong\u003eB)\u003c/strong\u003eRepresentation of the genetic distance using Neighbor-Joining tree. Text color in the table represent: red: invasive populations, blue: domestic populations and black: wild populations.\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eThe results obtained from STRUCTURE revealed three most likely clusters (K\u0026thinsp;=\u0026thinsp;3). One of these clusters predominated in the invasive area (red), while the other ones (blue and yellow) were associated with the wild and domestic areas. A third cluster was more restricted to Mozambique, Madagascar, and West Africa (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe genotypic mixture pattern remained similar across all K values used in this analysis (K\u0026thinsp;=\u0026thinsp;2\u0026ndash;10), except for the third cluster (yellow), and another that appeared in K\u0026thinsp;=\u0026thinsp;4 (domestic India, fuchsia), which seemed not to mix with other populations (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Discriminant Analysis of Principal Components (DAPC) confirmed the results obtained from STRUCTURE, providing a more detailed view of the distribution within the three clusters (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The invasive area was most closely related to the Indonesia, India, and domesticated from India, and Bangladesh populations, which formed the second cluster. Meanwhile, West Africa, Mozambique, and domesticated from Madagascar were the most distinct group, with some West Africa points linking to other clusters (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eGenetic diversity\u003c/h2\u003e\u003cp\u003eMtDNA-COI analysis revealed high genetic diversity in the tiger sgrimp \u003cem\u003eP. monodon\u003c/em\u003e within its invasive range (Brazil, West Africa, Egypt) and in domestic systems (Bangladesh), comparable to that of native populations. These findings are consistent with previous studies in the Colombian Caribbean using mtDNA-CR and microsatellites (Aguirre-Pab\u0026oacute;n et al., 2015, 2023), which detected three haplotypes, two shared with four different Indo-pacific sources but at lower diversity levels. In the present study, three haplotypes were also identified in Colombia and five across the invasive range, with genetic affinities to populations from Asia and Africa. Notably, some haplotypes originated from regions with high divergence, such as the Philippines, India, and Mozambique (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplement 2).\u003c/p\u003e\u003cp\u003eThe accumulation of nucleotide variation determines the rate of new haplotype emergence, and our results indicate low nucleotide diversity in domestic populations, likely due to the strong selective pressures imposed in hatchery systems. Conversely, invasive populations exhibited higher variability, which may reflect adaptive responses to colonization or the result of introductions from multiple sources (Oliveira et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Darling et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ghabooli et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gillis et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dlugosch and Parker, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Roman and Darling, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This aligns with invasion scenarios in which human-mediated dispersal repeatedly bridges distant biogeographic regions, producing genetic mosaics that differ from natural distributions. Chronological records of the introduction process in the western Atlantic, with focal points in Brazil (1987) and the United States (1988; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), further illustrate how aquaculture can establish parallel invasion fronts that subsequently expand and converge.\u003c/p\u003e\u003cp\u003eMicrosatellite analyses reinforce this pattern, showing consistently high diversity across sites, signs of demographic stability or expansion in aquaculture populations, and bottleneck signatures in both invasive and wild populations. Together, these outcomes exemplify a general pattern observed in many successful marine invaders: instead of suffering lasting genetic erosion, introduced populations maintain or even increase their variability (Estoup et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lawson et al., 2011). This so-called \u0026ldquo;\u003cb\u003egenetic paradox of invasions\u003c/b\u003e\u0026rdquo; suggests that the invasion success of \u003cem\u003eP. monodon\u003c/em\u003e is not an isolated case, but part of a broader dynamic where human-mediated dispersal compensates for founder effects.\u003c/p\u003e\u003cp\u003eA central insight from this study is the role of aquaculture as a mechanism sustaining genetic diversity in invasive lineages. Unlike natural dispersal, aquaculture-mediated movements are intentional, recurrent, and geographically extensive, involving broodstock replacement, crossbreeding among lineages, and the transfer of gravid females across regions (Briggs et al., 2004; Benzie, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). These practices act as a counterweight to genetic drift, buffering bottlenecks that would otherwise constrain genetic variability (Gjedrem, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Luvesuto et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Araki and Schmid, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Similar processes have been described in other aquaculture-related invasions, where domestication inadvertently enhances the evolutionary potential of non-native species (Sekar et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Goyard et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor instance, translocation of broodstock in Asia has generated artificial contact zones in which haplotypes from distant regions coexist (You et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ensing et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Waqairatu et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Vu et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These artificial genetic assemblages, now detected in the Atlantic, underscore how aquaculture alters the tempo and mode of evolutionary processes in marine invasions. The rapid expansion of Asian aquaculture has further facilitated introductions of \u003cem\u003eP. monodon\u003c/em\u003e into nearly all Caribbean and West African coasts (Aguirre-Pab\u0026oacute;n et al., 2015, 2023; Aguirre-Guzm\u0026aacute;n et al., 2020), usually outside of formal regulatory frameworks and without clear information on stock sizes or origins. Thus, the invasion patterns observed here reflect not only the biology of a single species, but also the broader reality that aquaculture operates as one of the most powerful anthropogenic forces shaping the global redistribution and genetic architecture of marine biodiversity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePhylogeny and structure population\u003c/h2\u003e\u003cp\u003eThe phylogenetic analysis of penaeid species, with emphasis on \u003cem\u003eP. monodon\u003c/em\u003e, revealed substantial intraspecific divergences (e.g., \u003cem\u003eP. monodon\u003c/em\u003e: 4.7\u0026ndash;8.3%; \u003cem\u003eP. semisulcatus\u003c/em\u003e: 5\u0026ndash;21.6%) that in some cases exceeded interspecific values (e.g., \u003cem\u003eP. murrayi\u0026ndash;P. investigatoris\u003c/em\u003e: 6.1%, \u003cem\u003eP. longirostris\u0026ndash;P. americanus\u003c/em\u003e: 7.3%, \u003cem\u003eF. merguiensis\u0026ndash;F. penicillatus\u003c/em\u003e: 6.5%, \u003cem\u003eF. subtilis\u0026ndash;F. isabelae\u003c/em\u003e: 6.1%, \u003cem\u003eL. setiferus\u0026ndash;L. schmitti\u003c/em\u003e: 6.5%; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Supplement 1). These results reinforce the need for refined phylogenetic work in penaeids, but in the broader context of invasions, they also illustrate a recurrent challenge: high within-species genetic differentiation provides reservoirs of variation that can be reshuffled through aquaculture and translocation, increasing invasion potential.\u003c/p\u003e\u003cp\u003eThis is particularly relevant given that many penaeids are intentionally moved across regions for aquaculture (e.g., \u003cem\u003eP. monodon, L. vannamei, M. japonicus\u003c/em\u003e; FAO DIAS database), or unintentionally via ballast water and canals. Several documented invasions\u0026mdash;including \u003cem\u003eP. monodon\u003c/em\u003e and \u003cem\u003eP. aztec\u003c/em\u003eus (Kampouris et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Scannella et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), M. \u003cem\u003ejaponicus\u003c/em\u003e (Rodriguez and Suarez, 2001), \u003cem\u003eP. semisulcatus\u003c/em\u003e (Ragonese and Giusto, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and \u003cem\u003eF. merguiensis\u003c/em\u003e (\u0026Ouml;zcan et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u0026mdash;demonstrate how aquaculture-linked dispersal contributes to the redistribution of genetically structured species. Thus, phylogenetic divergence within penaeids is not just a taxonomic issue, but a key element influencing the evolutionary trajectories of invasive lineages.\u003c/p\u003e\u003cp\u003eIn \u003cem\u003eP. monodon\u003c/em\u003e, COI-mtDNA and microsatellite markers yielded congruent signals of structure. The haplotype network, STRUCTURE analysis, and DAPC identified three groups, with the invasive population forming a distinct cluster (haplogroup I; red cluster in STRUCTURE). Such patterns align with scenarios where artificially assembled populations, derived from multiple native sources, fuel invasion fronts. Comparable findings in wild and domestic populations (Vu et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) highlight how mixing during domestication reshapes genetic baselines: high diversity is retained, divergence is generated, and invasive populations acquire an evolutionary toolkit to thrive in novel habitats. Evidence of this is the invasive populations in Brazil and West Africa, which displayed the highest genetic diversity and shared haplotypes while also retaining private ones, epitomize this process. For instance, H1\u0026mdash;a haplotype traced to the Pacific (China and Indonesia, also present in India, the Philippines, and domesticated stocks from Bangladesh)\u0026mdash;dominates the invasive range, while H2 and others (H4, H5) show Indo-Pacific and southeastern African origins (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplement 2). This mosaic indicates that invasion pathways integrate multiple domestication sources, creating genetic combinations that natural dispersal alone might not produce.\u003c/p\u003e\u003cp\u003eOn the other hand, disrupted gene flow among Indo-Pacific localities (e.g., Thailand vs. Philippines) is consistent with previous reports of strong structure in \u003cem\u003eP. monodon\u003c/em\u003e (Benzie et al., 2002; You et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Waqairatu et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Vu et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such natural discontinuities\u0026mdash;often associated with historical vicariance, oceanographic barriers, or Pleistocene climate oscillations (Benzie et al., 2002)\u0026mdash;contrast with the anthropogenic removal of barriers: aquaculture and translocations effectively reassemble lineages across oceans. This represents a fundamental shift for invasion biology: domestication transforms natural patterns of isolation-by-distance into artificially homogenized but demographically robust populations.\u003c/p\u003e\u003cp\u003eTwo explanatory frameworks\u0026mdash;the Vagrant Member and Metapopulation models\u0026mdash;have been proposed to understand penaeid historical biogeography (Hanski, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Sinclair, 1988; Garant et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ensing et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). While these models emphasize restricted gene flow or extinction\u0026ndash;recolonization dynamics, current data (Vu et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; You et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) suggest that neither alone captures the modern scenario. Instead, anthropogenic drivers dominate: domestication and aquaculture now dictate the geographic and genetic boundaries of populations, with implications for invasion spread and resilience. This anthropogenic influence is further reflected in the overlap between haplotypes of \u003cem\u003eP. monodon\u003c/em\u003e and \u003cem\u003eP. semisulcatus\u003c/em\u003e, separated by 12 mutational steps (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The co-occurrence of divergent haplogroups in aquaculture and invasive contexts raises broader evolutionary questions: to what extent does artificial hybridization or haplotype mixing across related species expand the adaptive capacity of invaders? Such processes highlight aquaculture not only as a vector but as a generator of novel genetic assemblages that may accelerate invasion success.\u003c/p\u003e\u003cp\u003eAlthough our sampling lacked full coverage of wild and domestic stocks in the eastern Pacific, the general congruence across markers (mtDNA-COI and microsatellites) supports the conclusion that invasive Atlantic populations of \u003cem\u003eP. monodon\u003c/em\u003e arise from multisource, human-mediated introductions with high levels of admixture. This contrasts with \u003cem\u003eP. semisulcatus\u003c/em\u003e, whose domestication history also involves translocations but on a different scale (You et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ensing et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Waqairatu et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Vu et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More broadly, these findings emphasize that phylogenetic diversity, when reshaped by aquaculture, provides a powerful substrate for successful invasions in marine ecosystems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDemographic history\u003c/h2\u003e\u003cp\u003eSignals of population expansion were strongest in the domestic population, whereas the wild and invasive populations exhibited negative or ambiguous patterns, as indicated by Tajima\u0026rsquo;s \u003cem\u003eD\u003c/em\u003e and Fu\u0026rsquo;s \u003cem\u003eFs\u003c/em\u003e tests (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Mismatch distribution analyses yielded inconclusive results for the Ramos-Onsin and Rozas (\u003cem\u003eR2\u003c/em\u003e) statistic and the raggedness index (\u003cem\u003er\u003c/em\u003e). However, the unimodal trend observed in the domestic population (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) suggests a signal of sudden demographic expansion (Slatkin and Hudson, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Rogers and Harpending, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Bottleneck analyses with microsatellites further supported this view: the Wilcoxon test revealed a significant heterozygote excess in both wild and invasive populations, consistent with recent bottlenecks, while the domestic population remained closer to mutation\u0026ndash;drift equilibrium (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Taken together, these results point to two contrasting demographic scenarios: expansion in domestic systems and bottlenecks in wild and invasive populations.\u003c/p\u003e\u003cp\u003eFrom an invasion biology perspective, these contrasting dynamics underscore how domestication and aquaculture reshape demographic trajectories in ways that directly influence invasion potential. In domestic systems, expansion is likely a consequence of stock management practices\u0026mdash;such as frequent renewal of broodstock and mixing of individuals from multiple sources\u0026mdash;which mitigate inbreeding, counteract disease-related losses, and maintain genetic variability. This artificial demographic buffering creates large, genetically diverse reservoirs that can serve as invasion sources, thereby altering the scale and speed at which non-native populations establish in the wild.\u003c/p\u003e\u003cp\u003eBy contrast, bottlenecks in invasive populations illustrate the classical expectation of founder effects, where successive reductions occur first during the transition from wild to domestic populations, and later during the establishment of invasive populations from these already reduced groups. Yet, the paradox revealed here is that high genetic diversity persists in invasive populations despite bottlenecks, a pattern increasingly observed in marine invaders. This paradox may be explained by the combination of two processes: (i) adaptation to local environments that favors rapid allele sorting, and (ii) repeated introductions or admixture from multiple wild sources, which reintroduce allelic diversity and buffer against genetic erosion. Such processes are not unique to \u003cem\u003eP. monodon\u003c/em\u003e, but represent a broader mechanism by which aquaculture and human-mediated dispersal can counteract the stochastic losses expected under invasion scenarios.\u003c/p\u003e\u003cp\u003eIn this sense, the demographic signals observed here highlight a key question for invasion biology: to what extent does aquaculture transform demographic bottlenecks from liabilities into opportunities for invasion success? While bottlenecks are expected to constrain establishment, aquaculture-mediated introductions may invert this expectation by replenishing diversity and promoting demographic stability, ultimately shaping the long-term evolutionary dynamics of marine invaders.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eWhat is the role of the farming systems in global marine invasions?\u003c/h2\u003e\u003cp\u003eAccording to the FAO (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), global aquaculture production has grown dramatically, increasing from 20\u0026nbsp;million tons in the 1950s to 186.7\u0026nbsp;million tons in 2015. This includes 54.1\u0026nbsp;million tons of fish, 17.1\u0026nbsp;million tons of mollusks, 7.9\u0026nbsp;million tons of crustaceans, and 938,500 tons of other aquatic animals, already surpassing wild capture production, which accounted for 92.7\u0026nbsp;million tons in 2015. This rapid growth, both in developed and developing countries, has largely depended on the deliberate or accidental introduction of non-native species (e.g., Shelton and Rothbard, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; De Silva et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For instance, non-native species constitute 60% of aquaculture production in the Philippines, 50% in Brazil, more than 25% in China, and nearly 100% in Israel (Shelton and Rothbard, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe scale of species introductions has increased in parallel with aquaculture expansion. Welcomme (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) reported 1,354 introductions involving 237 species across 140 countries, of which 321 (24%) established wild populations and 89 (7%) generated significant ecological problems. Today, the FAO Database on Introductions of Aquatic Species (DIAS; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/fishery/dias/en\u003c/span\u003e\u003cspan address=\"http://www.fao.org/fishery/dias/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) lists more than 5,500 introductions worldwide. China, the largest aquaculture producer, has introduced 179 species since 1920, more than 150 of them in the last 40 years, with production surpassing 400\u0026nbsp;million tons annually by 2000, despite official records listing only 56 species (Lin et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These figures highlight not only the magnitude of aquaculture-driven introductions but also the gap in monitoring and regulation, particularly given that 76% of introduced species are classified as \u0026ldquo;unknown\u0026rdquo; in DIAS (Ling et al., 2015).\u003c/p\u003e\u003cp\u003eOur findings add nuance to this global scenario by showing that farming systems are not only drivers of introductions but also key modulators of genetic outcomes in marine invasions. In the case analyzed here, invasive populations displayed high genetic diversity and evidence of multiple introductions from different origins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplement 2). This aligns with broader invasion biology theory, where repeated introductions and admixture reduce founder effects and increase adaptive potential (Kolbe et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Such processes may generate novel genotypes capable of coping with ecological pressures in novel environments, ultimately accelerating invasion success.\u003c/p\u003e\u003cp\u003eImportantly, aquaculture facilities themselves may act as reservoirs of genetic diversity. Practices such as periodic renewal of broodstock or the exchange of individuals among hatcheries and regions can enhance gene flow and facilitate the persistence of diverse lineages (You et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ensing et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Waqairatu et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Vu et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While this diversity may benefit aquaculture production by maintaining resilience against disease and environmental variability, it also inadvertently equips invasive populations with traits such as broad environmental tolerance, rapid growth, and high fecundity\u0026mdash;characteristics shared by many globally invasive species.\u003c/p\u003e\u003cp\u003eThe broader implication is that aquaculture acts as a dual-force system: it sustains food production and economic development, but simultaneously shapes the evolutionary trajectory of non-native populations, often favoring traits that make them successful invaders. Finally, it is worth reflecting on the paradox posed by Lin et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e): \"Some species significantly contribute to the rapid expansion of aquaculture, while the negative effects associated with the unregulated introduction and irresponsible use of non-native species are increasing in both number and affected areas. However, lessons from these reported disasters are slowly being learned, yet the risks continue to be largely overlooked\".\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights the dual role of aquaculture in the evolutionary and biogeographic dynamics of \u003cem\u003ePenaeus monodon\u003c/em\u003e. The maintenance of high genetic diversity across invasive populations, despite demographic bottlenecks, demonstrates that aquaculture-mediated translocations mitigate founder effects and promote genetic admixture among distant lineages.\u003c/p\u003e\u003cp\u003eAquaculture emerges not only as a dispersal vector but as an evolutionary catalyst, generating artificial admixture zones that enhance adaptive potential and promote invasion success. Domestic stocks, through broodstock exchange and recurrent introductions, act as reservoirs of genetic diversity that sustain invasion processes.\u003c/p\u003e\u003cp\u003eOverall, aquaculture operates as a dual-force system: it supports global food production while simultaneously facilitating the spread and evolutionary diversification of non-native species. Recognizing this dual role is essential for future research, integrating genomic, ecological, and regulatory perspectives to evaluate how domestication-driven gene flow continues influences the success and resilience of marine invasions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Waiho Khor for him help in providing with the samples of the \u003cem\u003eP. semisulcatus\u003c/em\u003e species from Sabah (Malaysia). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eJuan Carlos Aguirre Pabon: designed the study, collected samples, laboratory processes, analysis data, writing original draft, reviewed and edited manuscript.\u0026nbsp;Stephen Sabatino: support of laboratory processes and analysis data,\u0026nbsp;conceptualization, writing, review and editing manuscript; Armando Semo: collected samples; review and editing manuscript. James Morris: collected samples; review and editing manuscript.\u0026nbsp;Antonio Murias:\u0026nbsp;financial support and project management;\u0026nbsp;support of laboratory processes,\u0026nbsp;conceptualization, investigation, review and\u0026nbsp;finalized and approved the manuscript for submission.\u0026nbsp;All authors read and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguirre-Guzm\u0026aacute;n G, L\u0026oacute;pez-Acevedo EA. 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(2008) Microsatellite and mitochondrial haplotype diversity reveals population differentiation in the tiger shrimp (\u003cem\u003ePenaeus monodon\u003c/em\u003e) in the Indo-Pacific region. Anim Genet. 39(3):267-77\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biological-invasions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binv","sideBox":"Learn more about [Biological Invasions](https://www.springer.com/journal/10530)","snPcode":"10530","submissionUrl":"https://submission.nature.com/new-submission/10530/3","title":"Biological Invasions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biological invasions, Atlantic Ocean, domestic systems, genetic variability, demographic changes","lastPublishedDoi":"10.21203/rs.3.rs-7993803/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7993803/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the impacts of domestication and aquaculture on native populations is a key issue in conservation and evolutionary biology, particularly in the context of marine invasions. While aquaculture has greatly expanded the global distribution of species, its influence on genetic diversity and population structure remains poorly understood. This study examines the tiger shrimp \u003cem\u003ePenaeus monodon\u003c/em\u003e, native to the Indo-Pacific and introduced across the Atlantic for aquaculture since the 1970s. However, the evolutionary consequences of secondary contact between native and introduced populations remain poorly resolved. Using mitochondrial DNA and microsatellite markers, we reconstructed the invasion process and explored patterns of genetic diversity, phylogeographic relationships, and demographic changes across native, invasive, and aquaculture populations. Samples were collected from Indo-Pacific populations, six Atlantic countries (four western and two eastern), and aquaculture facilities to assess the role of human-mediated dispersal in shaping genetic connectivity. Results revealed high genetic variability across all sites and three distinct genetic clusters, indicating complex historical and recent dispersal events. Significant intraspecific divergence between \u003cem\u003eP. monodon\u003c/em\u003e and \u003cem\u003eP. semisulcatus\u003c/em\u003e was detected, alongside demographic expansion in aquaculture populations. Conversely, both native and invasive populations exhibited signals of genetic bottlenecks. These findings suggest that aquaculture systems may mitigate founder effects and act as reservoirs of genetic diversity, enhancing invasion potential. Our results underscore the dual role of aquaculture as both a driver of biological invasions and a modifier of evolutionary trajectories, highlighting the need for greater understanding of domestication processes in shaping the genetic landscape of marine species.\u003c/p\u003e","manuscriptTitle":"Genetic Structure and Evolutionary History of the tiger shrimp Penaeus monodon Across Its Wild and Introduced Ranges","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 11:55:49","doi":"10.21203/rs.3.rs-7993803/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-11-06T10:46:42+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T01:39:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Biological Invasions","date":"2025-11-05T17:59:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-31T05:54:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biological Invasions","date":"2025-10-30T23:02:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biological-invasions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binv","sideBox":"Learn more about [Biological Invasions](https://www.springer.com/journal/10530)","snPcode":"10530","submissionUrl":"https://submission.nature.com/new-submission/10530/3","title":"Biological Invasions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4f7bb64f-6ecb-4622-afe2-9f794a16d813","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:28:55+00:00","versionOfRecord":{"articleIdentity":"rs-7993803","link":"https://doi.org/10.1007/s10530-026-03800-y","journal":{"identity":"biological-invasions","isVorOnly":false,"title":"Biological Invasions"},"publishedOn":"2026-03-24 16:09:17","publishedOnDateReadable":"March 24th, 2026"},"versionCreatedAt":"2025-11-17 11:55:49","video":"","vorDoi":"10.1007/s10530-026-03800-y","vorDoiUrl":"https://doi.org/10.1007/s10530-026-03800-y","workflowStages":[]},"version":"v1","identity":"rs-7993803","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7993803","identity":"rs-7993803","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}