Evolutionary drivers of invasion: hybridization and local adaptation in the Amazon sailfin catfish

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The armored catfish (Pterygoplichthys sp.) is an invasive species that has negative ecological impacts. Morphological and genetic data, based on mitochondrial DNA, suggested that armored catfish from the Grijalva-Usumacinta basins, Mexico, are hybrids. We used a double digest restriction-site associated DNA (ddRAD-seq) approach to gain understanding about the identity of the armored catfish of the Grijalva-Usumacinta River Basins, and to identify candidate loci that could be facilitating adaptation to environmental conditions in invaded areas. We sampled 103 armored catfish (Pterygoplichthys sp). We assembled a transcriptome to be used as reference for SNP calling. We performed de novo and transcriptome reference-based SNP calling (22,595 SNPs and 3,083 SNPs, respectively). High levels of genetic diversity (HE = 0.378 de novo, HE = 0.265 reference), heterozygotes excess (FIS = -0.0129 de novo, FIS = -0.429 reference) and low levels of genetic differentiation (FST = 0.0083 de novo and FST = 0.0086 reference), support the hypothesis of rapid dissemination and population expansion from a population of hybrid origin. We identified 54 candidate loci; of these, one relates to stress tolerance, and another to osmotic stress. Hybridization, high genetic variation and selection may play a relevant role in the invasion success of armored catfish. To control the populations of this invasive species, it is advisable to minimize habitat degradation and implement habitat restoration. Hybridization in aquariums and other facilities for its reproduction and commercialization should be prevented. Biological sciences/Evolution/Population genetics Biological sciences/Ecology/Invasive species Biological sciences/Genetics/Evolutionary biology ddRADseq genotype-environment association invasion genomics Mexico Pterygoplichthys reference transcriptome selection Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The introduction of invasive species is one of the main threats to biodiversity [1]. With the constant increase in connectivity and anthropogenic global changes, the rates of species introductions and their impacts are accelerating [2,3]. These generate direct and indirect effects on native species, communities and ecosystems, becoming a significant component of ecosystem change [4]. In addition, biological invasions have been reported at increasing rates. Invasive species adapt rapidly to novel environments as many invasive species maintain high levels of genetic diversity [5-7]. Thus, high levels of genetic variation may be correlated with invasion success [8,9]. To reduce the negative impacts of invasive species, it is necessary to better-understand functional genetic variation, and to identify the environmental variables that are involved in geographic or temporal expansion for invasive species. Uncovering the mechanisms underlying rapid genetic adaptation can provide insights into adaptive evolution to plan on invasive species control, and natural resource management [10,11]. In the context of genotype-environment association (GEA) studies, the availability of a reference genome allows annotating candidate loci that may be involved in adaptive processes [12-16], which otherwise would remain anonymous [17]. For many non-model species, the availability of a reference genome limits the possibility of conducting GEA analyses [18]. A cost-effective alternative is using a transcriptome to annotate candidate loci [17]. The transcriptome contains information from the coding regions of an organism's genome [19]; thus, SNP mapping with a reference transcriptome guarantee that SNPs are located within the coding regions of the genome and will be more likely to have a direct functional impact [17]. This approach would contribute to better-understand the mechanisms that regulate key biological processes and functions [17]. Also, the cost to obtain a transcriptome is lower than that of a reference genome, and more data may be available in repositories [17]. Several species of the Amazon sailfin catfish ( Pterygoplichthys spp), also known as armored catfish, are native to tropical areas in Central and South America, and they are considered as invasive in parts of North America, Central America, Asia and Europe, where hybridization is common [20-23]. These species have important negative ecological impacts, and socioeconomic and health implications for human populations [23-30]. Climate change is expected to facilitate range expansion for invasive species, such as the armored catfish [31]. Furthermore, armored catfish inhabit freshwater, but it has been reported in coastal areas, where it tolerates high salinity [32-34]. Therefore, identifying genomic characteristics that may confer adaptation and favor its establishment in new areas is of vital importance to determine genome-based management strategies. Previous genetic studies based on mitochondrial DNA suggested that hybridization may be playing an important role in invasion success [35-38]; but formal analyses on hybridization based on nuclear molecular markers are lacking. Such is the case in Mexico, where the armored catfish distributed in the Grijalva-Usumacinta region show morphological variation in ventral spot patterns, and a single mitochondrial haplotype corresponding to P. pardalis , suggesting a possible hybrid origin for armored catfish in this region followed by rapid dissemination [38]. In the present study we implemented a double digest restriction-site associated DNA (ddRAD-seq) method to obtain single nucleotide polymorphisms (SNPs). For SNP calling, we followed two approaches: a de novo approach and a reference approach using a transcriptome to annotate SNPs. We aimed to solve the taxonomic identity of the armored catfish of the Grijalva-Usumacinta River Basins, to estimate its levels of genetic variation and genetic differentiation, and to identify candidate loci that could be facilitating its adaptation to novel environmental conditions in invaded areas. As we suspect this population to be from hybrid origin, we expected to find low correspondence between genetic differentiation and taxon identity based on ventral spot patterns, together with high levels of genetic variation. We expect to find low genetic differentiation between sampled areas corresponding to rapid dissemination after introduction. Finally, we expect to find candidate loci associated with environmental conditions, such as salinity, that may be allowing armored catfish to colonize new areas. Materials and Methods (a) Sample collection, DNA extraction and genotyping-by-sequencing The armored catfish distributed in the Grijalva-Usumacinta region are P. pardalis , or hybrids, P. pardalis x P. disjunctivus , based on mtDNA [38]; therefore, from hereon we will refer to these specimens as Pterygoplichthys sp. A total of 110 individuals were collected along canals and main rivers of the Grijalva-Usumacinta region (Lacantún n=9, Tenosique n= 14, Catazajá n= 46, Tres Brazos n= 31 and, Amacohite n= 10) (Fig. 1). The same sampling effort was implemented at each site; therefore, we can assume that sampling sizes are in accordance with armored catfish abundance [38, 39]. Each individual was identified, in accordance to ventral spot patterns, as P. pardalis (dots, n=27 organisms), P. disjunctivus (vermiculations, n= 34 organisms) or intermediate (mixture of spots and vermiculations, n= 49 organisms) [21, 22, 36, 38, 40-43]. We took a muscle tissue sample (0.5 x 0.5 mm) from the caudal peduncle of each fish. Tissue was placed in 96% ethanol and maintained at -20 °C until DNA extraction. We performed a phenol-chloroform-isoamyl alcohol protocol for DNA extraction [44]. DNA was sent to the University of Minnesota Genomics Center (genomics.umn.edu), where a ddRAD-seq [45] protocol, with restriction enzymes Pstl and Mspl, and 300 bps fragments size selection, was conducted. Next generation sequencing (NGS) was performed in an Illumina NovaSeq 2x100 SP platform. First, we conducted a pilot project with eight samples to determine the number of reads per sample needed to obtain robust data (https://genomics.umn.edu/service/gbs-pilot-projects); accordingly, 2 million reads per sample provide enough coverage to obtain robust results. We obtained an average of ≈ 2.8 million reads per sample (1,882,474 - 6,673,571 raw reads per sample) with average quality values of ≥Q30 across all libraries. Raw reads are deposited in NCBI SRA database (Bioproject PRJNA1144408). Sequence quality was assessed using FastQC v 0.12 [46]. Adapters were removed from each read using the gbstrim.pl script provided by the University of Minnesota (https://bitbucket.org/jgarbe/gbstrim/src/master/; parameters: --enzime1 PstI --enzime2 MspI --fastqfile --read R1 or R2 --outputfile R1.trim.fastq or R2.trim.fastq --verbose --threads 2 --minlength 50). Because overall R2 reads showed lower quality scores, we decided to use only the R1 files for SNP calling. Trimmomatic was used to cut the reads (R1) to the same length (parameters: CROP:90 and MINLEN:90) to implement SNP calling in Stacks [47, 48]. We retained 1,486,698 to 5,507,094 reads per sample after quality filtering. We conducted de novo SNP calling with Stacks vers. 2.62 [48, 49]. First, we followed the optimization protocol proposed by [50], testing M and n from 2 to 6. Accordingly, optimized values were M = 5 and n = 5. We implemented two approaches: one approach considers individuals assigned to the previously mentioned morphotypes ( P. pardalis , P. disjunctivus and mixed or intermediate); the other approach considers the geographical region from which the samples were obtained, regardless of the morphotype, because armored catfish in the Grijalva-Usumacinta region showed a single mitochondrial haplotype [38]. We tested values 0.6, 0.7 and 0.8 for the minimum percentage of individuals in a population required to process a locus for that population (-r); the final run was performed with a -r = 0.8. To minimize linkage disequilibrium, we restricted data to one random SNP per locus (--write-random-snp). We used VCFtools 0.1.17 [51] to filter SNPs for minimum allele frequency (maf = 0.01), Hardy-Weinberg equilibrium (hwe = 0.00001), number of alleles per site (min-alleles = 2) and maximum proportion of missing data (max-missing = 0.8). Of the total number of individuals (110), seven were eliminated because they had more than 10 % missing data. All analyses were performed on 103 individuals with a final de novo database of 22,595 SNPs. For the reference-based SNP calling, we downloaded available transcriptome data for P. pardalis (SRR5997830) to assemble a reference transcriptome. Low quality reads as well as adapters were removed with AdapterRemoval v2 [52], retaining those with a minimum quality value of Q28. The resulting reads were used to perform de novo reconstruction using the Trinity assembler with default parameters [53]. The subsequent contigs were cleaned with the SeqClean program (https://github.com/gpertea/gsrc/blob/master/scripts/seqclean). AlignWise [54] was used to identify coding regions (CDS). For this purpose, we used as reference fish genomes from different taxa of the class Siluriformes: Bagris yarrelli , Danio rerio , Hemibagrus wyckioides , Ictalurus punctatus , Oncorhynchus gorbuscha , Pangasianodon gigas , Tachysurus fuvidraco and Takifugu rbripes . The assembled contigs were filtered with the BlastClust to obtain non-redundant unigenes, and proteins <40 amino acids were removed. For the reference-based SNP calling, filtered raw reads were mapped to the reference transcriptome with BWA-mem. Files were transformed to bam with Samtools [55], and SNP calling was performed with Stacks vers. 2.62 [48, 49]. Finally, the populations module was run considering sampling locations. VCFtools [51] was used to perform SNP filtering with the previously mentioned parameters. We obtained a database consisting of 103 individuals and 3,059 mapped SNPs. (b) Diversity and genetic structure We used the de novo database that considered morphotypes to conduct an exploratory analysis and confirm the validity of the morphotypes. We conducted principal component analysis (PCA) and discriminant analysis of principal components (DACP) from adegenet [56, 57] in R version 4.3.3 [58] to observe if individuals with the same morphotype form clusters in accordance with ventral patterns. For DACP, we retained three PCs and two discriminant functions [56]. Since we did not find signs of genetic differentiation according to morphotype, for subsequent analyses, data were grouped according to sampling sites (Fig. 1). We performed PCA and DACP for the de novo and mapped datasets that consider sampling site to observe if individuals cluster in accordance with geography. For both databases, de novo and mapped SNPs, we used the vcfR [59], adegenet [57] and hierfstat [60] libraries for R [58] to estimate summary statistics (allele frequencies, uH E , H O , inbreeding coefficient ( F IS ) and genetic differentiation ( F ST )). A Wilcoxon rank test was applied to test for statistically significant differences between databases for H O and uH E . We performed an Admixture analysis [61] to confirm genetic structure by testing values from K = 0 to K = 10, and selected the best K from cross-validation (CV) error. (c) Environmental data and analysis Environmental parameters were measured in situ in triplicate at all sampling sites using a Hanna HI9829 multiparameter probe: temperature (°C), hydrogen potential (pH), dissolved oxygen (mg/L-1), conductivity (mS/cm), total dissolved solids (mg/L-1), salinity, depth (m), transparency (m) [62]. Also, the ecological quality of the riverbanks was evaluated at each site using the riparian ecological quality index (RQI) protocol, where seven attributes were evaluated [63, 64] (see detailed methods in supplementary material). The RQI was determined from the sum of the seven attributes assessed with a scoring system from 0 to 150, as indicated in the literature, in the following categories: very good (150-130), good (129-100), moderate (99-70), poor (69-40), bad (39-10) and very bad (<10) [63, 64]. To estimate the correlation between physicochemical water variables, we performed an analysis of covariance using Pearson's correlation coefficient. We retained in the dataset only those variables with a correlation value ≤ 0.7. Although the temperature and RQI parameters showed a correlation coefficient above the threshold (-0.74), we retained these variables because temperature is an important parameter for fish development [65], while RQI is a composite measure of habitat quality which may have an impact on fish population parameters [66]. The variables included in subsequent analyses were temperature (°C), dissolved oxygen (mg/L-1), salinity, depth (m), transparency (m) and RQI. A principal components analysis (PCA) was performed for the selected variables using R [58]. An analysis of variance (ANOVA) was performed in R [58] to detect differences among sampling sites with respect to environmental parameters, using the average values of the three measurements taken at each site. When statistical differences between sites were found, we performed a TukeyHSD setting a significance level of 0.05 (α=0.05). (d) Tests for candidate loci We used the mapped SNP database to identify genes that could be involved in rapid adaptation to new environmental conditions in this invasive species. It has been proposed that tests for candidate loci are susceptible to false positives, so more than one test should be used. Sites with selection signals that are recovered with more than one method will be less likely to be false positives [67]. Therefore, we implemented three tests. First, we used pcadapt [68] implemented in R, where candidate SNPs are identified as those that correlate significantly with the set of PCs that maximizes variance under a specific false positive rate (FDR). For this, we evaluated the optimal value of K (the optimal number of gene clusters), from 1 to 10, using a plot of the proportion of variance explained by each PC. Accordingly, we retained K = 2, and we estimated the FDR of the p-values associated with the Bonferroni correction by using the qvalue function of the R package “ qvalue ” [69]. Finally, we obtained the list of candidate SNPs with FDR α= 0.05, i.e. 5 % of candidate SNPs are expected to be false positives [68]. The second method implemented was a latent factor mixed model (LFMM) from the LEA package [70] in R software. These linear mixed models test correlations between allele counts and an environmental variable. Here, we used two environmental variables: salinity and RQI to test the neutral structure through latent factors [71]. We focused on these variables because the armored catfish is a freshwater fish found to inhabit high salinity environments in the Grijalva-Usumacinta River Basins and RQI has been previously associated with fish body condition in the same area [66]. We ran LFMM with several iterations of 50,000, 5,000 sweeps and we set k = 2 for the number of latent factors for the analysis. We performed ten replicates of the analysis [70]. We took the median score of the 10 replicates, and we estimated the genomic inflation factor. Subsequently, we obtained the adjusted p-values using the genomic inflation factor. These p-values were used to predict the significance of the association between the SNP and the environmental variable. The list of candidate SNPs was obtained using the Benjamin-Holchberg procedure with a false discovery rate of 0.05 for the two environmental variables included in the analysis. Finally, we used BayeScan [72] to identify candidate loci. This program uses the Bayesian method to directly estimate the posterior probability for each locus based on population-specific F ST coefficients [72]. This method accommodates differences in demographic history and the extent of genetic drift between populations. It is based on a logistic regression model that decomposes genetic variation into locus population-specific effects [73]. We used the default string parameters to conduct the runs (n= 5000, thin= 10, nbp= 20, pilot= 5000, burn= 50000), and, for the model parameters, we used the default value -pr_odds [76]. The candidate SNPs identified by any of the above methods were annotated by homology and gene ontology analysis. For this purpose, we constructed a database with the proteome of the Siluriformes species previously mentioned, and the proteome obtained from P. pardalis. The Blastp [74] algorithm was used to perform the search for homologous sequences with an expectancy value (e-value) < 1x10 -5 . Subsequently, we used the InterproScan program [75] to assign gene ontology terms (GOterms) [76]. Finally, we performed a functional enrichment analysis with g:Profiler [77]. Danio rerio was selected as the reference organism to perform this analysis with g:GOST values < 0.05. Results (a) Genetic Diversity and Structure The two data sets yielded comparable results regarding genetic structure. In the exploratory analysis, which considered morphotypes, all samples overlapped, suggesting that there is no genetic differentiation between morphotypes in the armored catfish collected in the Grijalva-Usumacinta region (Fig. S1). When examining genetic structure according to sampling location, using de novo and mapped datasets, population structure in the armored catfish of the Grijalva-Usumacinta River basins is similar. In the PCA analyses, most samples overlap, and there is no clear clustering (Fig. 2). The DAPC analysis shows that the Lacantún region exhibits some genetic differentiation in comparison to the other regions analyzed (Fig. 3b and 3d). The results of the Admixture analysis are consistent with those obtained in the DAPC, with K = 2 (cv = 0.60) showing admixture in all populations except the Lacantún site, where individuals have high assignation probability to one of the clusters (Fig. S2). It is worth mentioning that levels of overall genetic differentiation are low ( F ST = 0.008 for both datasets; Table 1). The patterns of genetic diversity observed in both datasets are similar (Table 1). We find statistically significant differences for heterozygosity parameters ( H E and H O ) between datasets (Wilcoxon-rank-test; p = 0.007 and r = 0.82, in each case). We obtained high levels of overall genetic diversity and similar values between sites, and F IS values close to zero. (b) Environmental analyses Principal component analysis (PCA) of the environmental variables indicates that the first two axes explain 73.6 % of the variation, the first one explaining (PC1) 46.2 % and the second one (PC2) 27.4 % (Fig. 3). The analysis of variance indicates that temperature, dissolved oxygen, salinity and transparency exhibit statistical differences between sampling sites (p > 0.05). PCA results suggest that environmental conditions in Amacohite and Ostitán, both located in the Grijalva River, are similar. Also, the environmental conditions in Chilapa and Tres Brazos, located in the lower part of the basins, are similar. In contrast, Lacantún and Jonuta, both located on the Usumacinta River, show differences in their environmental conditions. Based on TukeyHSD post hoc analysis, temperature is homogeneous in the Grijalva river and heterogeneous in the Usumacinta river (Fig. 4a). For dissolved oxygen, differences occur at the Lacantún site with the highest values (Fig. 4b). While in salinity, differences occur at the Tres Brazos site with the highest salinity value compared to the rest of the sampling sites (Fig. 4c). Transparency shows differences between the Grijalva and Usumacinta rivers (Fig. 4d). (c) Tests for candidate loci The pcadapt analysis identified two SNPs as significant outliers (0.06 % of 3,059 SNPs), while LFMM identified 24 outlier SNPs associated with salinity (0.78 % of 3,059 SNPs) and 28 outlier SNPs associated with RQI (0.91 % of 3,059 SNPs). BayeScan analysis identified a total of 1,960 SNPs at α = 0.05 (64 % of 3,059 SNPs). The strongest candidates for selection were 54 SNPs, which were retrieved in at least two of the three methods. Two candidate SNPs are shared between pcadapt and BayeScan. Similarly, all the candidate loci identified with LFMM for salinity and RQI are shared with BayeScan (Fig. S3). In the GOTerms enrichment analysis for SNP annotation with unigenes (Table S1), we identified some interesting categories such as: nine isoforms of the unigene "TRINITY_DN1064", which could be involved with the synthesis of teneurin; two unigenes TRINITY_DN385_c0_g1_i4 and TRINITY_DN542_c0_g1_i10 which are identified with homologous function to genes of the mitogen-activated protein kinase (MAPK) family; and the unigene TRINITY_DN1538_c0_g1_i2, homologous to interferon induced protein-35 (IFP35), which is associated with multiple functions of the immune system. Discussion In the present study, the SNP annotation approach utilizing the reference transcriptome proved to be valuable for identifying potential candidate sites, as it ensured that the candidate sites under selection are indeed coding loci. This approach facilitated a more comprehensive understanding of underlying factors associated with the invasion success of the armored catfish ( Pterygoplichthys sp.) in the Grijalva-Usumacinta River basins. (a) Genetic diversity and genetic structure We observed some differences in genetic diversity but similar patterns of genetic structure with the de novo and mapped SNPs datasets. The estimated overall and per locality levels of genetic diversity from the mapped dataset were lower than those estimated with the de novo approach. This is to be expected, as the transcriptome represents only the coding regions of the genome, which tend to be highly conserved and accumulate less genetic variation than non-coding regions [19, 78]. The results of this study support that the armored catfish ( Pterygoplichthys sp.) from the Grijalva and Usumacinta rivers belong to a single genetic group. These results are consistent with those showing that specimens distributed in the Grijalva-Usumacinta region have the same mitochondrial haplotype and correspond to a single species or are hybrids [38]. It has been suggested that P. pardalis and P. disjunctivus could be morphological varieties of a single species or that these fish can hybridize in invaded areas [21-23, 35-38]. Also, our results support the hypothesis that released individuals may be hybrids because of intentional human intervention in aquariums [35-38, 79]. Hybridization may play a key role in the ability of the armored catfish to adapt to new environments and to its invasion success. This phenomenon has been observed in other invasive species, such as lionfish, in which hybridization events in the native distribution area could have driven rapid adaptation in invaded areas ( Pterois ) [80, 81]. However, genomic data from the native distribution of P. pardalis and P. disjunctivus are needed to conduct formal analyses of hybridization and adaptive introgression. Obtaining demographic information and carrying out experimental studies is also needed to test the hypothesis that heterosis is related to invasive potential in the armored catfish [82-85]. High genetic diversity ( H O and H E ) was observed in the armored catfish ( Pterygoplichthys sp.) with both de novo ( H O = 0.3824 and H E = 0.378) and mapped datasets ( H O = 0.2667 and H E = 0.2653). Lionfish ( Pterois volitans ), which is a marine invasive species, has lower genetic diversity both in invaded areas and in its native range ( H E = 0.079 – 0.141) [81]. The mosquitofish ( Gambusia holbrooki ), another invasive freshwater fish, also has lower levels of mean genetic diversity than the armored catfish ( H E = 0.149 ± 0.155 for invasive populations in America) [86]. Several evolutionary processes are related to successful invasions, such as propagule pressure or high genetic diversity, but the rapid adaptive response to new selective processes during propagation and colonization is usually more important [87]. It has been proposed that invasive species may loose genetic variation due to founder events, but this does not imply that the species necessarily losses their adaptive potential, because phenotypic plasticity may allow organisms to respond to environmental heterogeneity [88]. Even so, it is still unknown which traits help predict invasion success, or which traits help predict the differences in local adaptation between invaders [7]. The high genetic diversity observed in these fish is indicative of their high evolutionary potential, which is influenced by selection pressures on the founding population [89]. Furthermore, it has been reported that these fish can maintain their fitness in adverse environments due to the plasticity of their phenotypic traits, including a slower growth rate and smaller size at reproductive maturity [90]. The later favors their colonization capacity. The assessment of population structure using F ST , DACP and admixture revealed a regional structure in the armored catfish, with a single population group in the Grijalva-Usumacinta region. Low values of global genetic differentiation support the hypothesis that the armored catfish was introduced to the Grijalva-Usumacinta region on a single or a few occasions, subsequently expanding and rapidly disseminating throughout the region [38]. In the lionfish ( Pterois volitans ), low values of genetic differentiation are also observed in some of the invaded areas of the Gulf of Mexico and the Caribbean ( F ST = 0 – 0.006) [81]. In contrast, mosquitofish ( Gambusia holbrooki ) exhibit higher genetic differentiation among sites, however, differentiation between populations within the invaded range remains lower than that observed among populations from the native range. This pattern is consistent with rapid population expansion following establishment in invaded areas [86]. It is notable that the fish collected in the Lacantún locality exhibited some degree of genetic differences from the rest of the sampled locations. It is noteworthy that this locality exhibited high values of dissolved oxygen and RQI, which suggests that it is a better conserved area. We cannot rule out that the genetic differentiation of the Lacantún locality is related to lower densities due to better conserved environmental conditions. Accordingly, there is a lower biomass of armored catfish in conserved areas of the Usumacinta River, like in Lacantún, compared to the Grijalva River, which is impacted by diverse anthropogenic activities [39]. Furthermore, it has been observed that these fish prefer burrowing in uncovered areas of vegetation [91]. Therefore, it is hypothesized that conserved areas represent in some way a barrier for the establishment of armored catfish, since its reproductive activity, which is important for its establishment and invasion, is affected by this condition (high density of roots) [91]. However, to adequately test these hypotheses, experimental studies that include testing for adaptation to salinity and root density should be implemented. Disturbance is considered one of the factors that increases the susceptibility to invasion, as it creates new niches and increases resource availability [75], while simultaneously disrupting the balance of native communities [92]. It has been documented that armored catfish prefer areas impacted by land use changes and pollution [31]. Thus, the conservation and restoration of the diversity of competing species in invaded water bodies can reduce armored catfish growth and reproduction [90]. The establishment of buffer zones between wetland areas and intensive land use areas can mitigate the negative effects of human activity on aquatic ecosystems, while also providing benefits to local communities [93]. (b) Tests for candidate loci A total of 54 candidate loci were identified with two of the three methods used for SNP detection. It has been suggested that demographic processes such as population expansion and hybridization can result in false positives in F ST outlier and genotype-environmental association analyses [14]. Therefore, we used three complementary methods to identify candidate loci; nonetheless, these results should be taken with caution, and future studies should implement an experimental design to validate these loci. Here we highlight some candidate SNPs that are related to salinity and RQI. The candidate loci correlated with salinity are unigenes related to catalytic and regulatory activity of transcription, translation of DNA- and RNA-associated signals. Regarding the candidate loci that showed correlation with RQI; these unigenes are associated with protein binding activity, DNA and RNA binding transport and cellular communication (Fig. S4). In the functional characterization of these candidate loci, tenurins and teneurin C-terminal associated peptide (TCAP) are transmembrane proteins that are essential for nervous system development in vertebrates; these unigenes are involved with stress response in Danio rerio and other animal species [94-97]. Teneurin-3 could be a factor in local adaptation of armored catfish in the Grijalva-Usumacinta Rivers basins. The armored catfish is a freshwater species; nevertheless, these organisms have already been documented in various brackish areas [33,34]. It is possible that candidate loci identified in the present study are a factor in the invasion process, because, for example, the family of protein kinase (MAPK) is involved in various processes such as growth, development and response to various types of stress (osmotic, heat shock, ultraviolet radiation, oxidative stress and environmental stress) [98]. In channel catfish ( Ictalurus punctatus ), this gene family plays an important role in the response to high salinity stress [98], and in the armored catfish this gene family may be playing a role in the colonization of brackish areas such as those observed in Tres Brazos located in the Pantanos de Centla Natural Reserve [32-34]. It has been described that teneurins are involved in cell metabolism, morphology and migration in teleost fish, such as rainbow trout ( Oncorhynchus mykiss ). These transmembrane proteins stimulate cell proliferation and stimulate levels of cAMP in rainbow trout’s neuronal cells [94]. Furthermore, teneurins and C-terminal teneurin-associated peptide (TCAP) are molecular components used to mitigate the response to stress [97], and they increase metabolic performance in zebrafish ( Danio rerio ) [96]. Finally, the interferon protein (IPF35) is involved in the innate antiviral response of the mandarin fish ( Siniperca chuatsi ) [99,100]. This protein may be implicated in the development and the environmental tolerance of the armored catfish. Experimental analyses should be conducted to validate the role of these candidate genes, and to determine whether expression regulation is related to the invasive success in the armored catfish. If this is verified, it will be important to assess their frequency in natural populations. This will provide further insight into the invasive potential of the armored catfish into new areas. Conclusions Using a reference transcriptome for SNP calling represents a viable alternative for the analysis of non-model species and species with large and/or complex genomes, with the objective of identifying candidate loci under selection. Our results support that the armored catfish in the Grijalva and Usumacinta rivers are of hybrid origin and underwent processes of population expansion and rapid dissemination after their introduction. In addition, high genetic variability may be allowing them to adapt to novel environments. To control the population of armored catfish, it is advisable to reduce the negative impact of anthropogenic activities on these rivers and to minimize habitat degradation. Where feasible, riverbank restoration activities should be conducted, and the densities of this fish should be monitored to assess the efficacy of these measures. Hybridization may be playing an important role in invasion success of the armored catfish via heterosis. Therefore, hybridization in aquariums and other facilities for its reproduction and commercialization should be monitored and prevented. In addition, genomic data from the native distribution areas of all armored catfish species that have been recorded as invasive species are needed to gain a better understanding about the role of hybridization in adaptation to new environments and invasion success. Our analyses suggest that the genes TCAP, MAPK and IPF35 may be involved in the colonization of brackish areas, but experimental validation is required. Finally, obtaining high quality, well annotated, reference genomes will forward our understanding about the role of additional genes, gene families, as well as structural variants, in the invasion success of the armored catfish. Declarations Acknowledgments This research was funded by Fondo de Investigación Científica y Desarrollo Tecnológico de El Colegio de la Frontera Sur (FID-784 project 30013 to GC-M). United Nations Development Programme (PPD-FMAM F06-México-2019 to GC-M and EB. World Wildlife Fund (WWF-ECOSUR 2017-2018 to EB). Operative funds (project 3103711920 to GC-M). Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCyT; scholarship No. 627848 granted for doctoral studies to JFM-V). The authors would like to express gratitude to the Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCyT) for the scholarship (No. 627848) granted for doctoral studies to JFM-V. We also thank Conservación de la Biodiversidad del Usumacinta A.C. (COBIUS A.C.) for the resources provided for this project. To the Universidad Juárez Autónoma de Tabasco (UJAT) for the personnel and equipment provided for sample collection. To all the personnel and volunteers that participated in sampling trips. To institutional laboratories for the instruments, equipment and materials for DNA extraction. Also, we thank the Instituto de Ecología, A.C. (INECOL) for granting access to the high-performance computing system (HUTZILIN) used to carry on the transcriptome assembly and annotation; specially to Dr. Enrique Ibarra-Laclette (P.I.) and M.Sc. Emmanuel Villafan for his technical support. Thank you to Dr. Erika Aguirre-Planter for her constructive comments on the manuscript. Authors' contributions Conceptualization: GC-M, JFM-V. Original drafting: JFM-V, GC-M. Data curation: MG-B, YTG-G, AC-T, JFM-V. Formal analysis: AC-T, JFM-V, YTG-G, GC-M. Acquisition of funds: GC-M, EB. Methodology: GC-M, JFM-V. Resources: GC-M. Software: GC-M, AC-T. 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Estimated levels of genetic diversity for the armored catfish population ( Pterygoplichthys sp.) from the Grijalva-Usumacinta region. The data was derived from 22,595 single nucleotide polymorphisms (SNPs) from de novo dataset and 3,059 SNPs from the mapped dataset. The following metrics were calculated: N: number of individuals; H O : observed heterozygosity; uH E : unbiased expected heterozygosity; π: nucleotide diversity; F ST : genetic differentiation; and F IS : inbreeding coefficient. Region de novo dataset mapped dataset N H O uH E π F ST F IS H O uH E π F ST F IS Lacantún 9 0.3859 0.3795 0.3796 -0.0123 0.2852 0.2772 0.2772 -0.0228 Tenosique 14 0.3787 0.3806 0.3806 0.0043 0.2877 0.2667 0.2767 -0.0276 Catazajá 44 0.3780 0.3800 0.3800 0.0045 0.2735 0.2673 0.2673 -0.0105 Tres Brazos 27 0.3886 0.3779 0.3779 -0.0253 0.2845 0.2717 0.2717 -0.0271 Amacohite 9 0.3838 0.3716 0.3717 -0.0293 0.2870 0.2693 0.2693 -0.0514 Mean 103 0.3830 0.3779 0.3779 0.0083 -0.0152 0.2836 0.2725 0.2724 0.0086 -0.0279 Additional Declarations There is no duality of interest Supplementary Files SupplementaryMaterials061025.docx Supplementary Materials Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Villahermosa","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"","lastName":"Miranda-Vidal","suffix":""},{"id":557054022,"identity":"14c75164-1fd5-4246-9aab-e0dd237c0673","order_by":2,"name":"Yocelyn Gutiérrez-Guerrero","email":"","orcid":"","institution":"University of California, Berkeley","correspondingAuthor":false,"prefix":"","firstName":"Yocelyn","middleName":"","lastName":"Gutiérrez-Guerrero","suffix":""},{"id":557054023,"identity":"f25634c7-19c2-48a7-916e-659afa76705a","order_by":3,"name":"Anahí Canedo-Téxon","email":"","orcid":"","institution":"El Colegio de la Frontera Sur (ECOSUR), Unidad Villahermosa","correspondingAuthor":false,"prefix":"","firstName":"Anahí","middleName":"","lastName":"Canedo-Téxon","suffix":""},{"id":557054024,"identity":"f844678c-27d2-401f-ac68-6c2fbab880ef","order_by":4,"name":"Maricela García-Bautista","email":"","orcid":"","institution":"El Colegio de la Frontera Sur (ECOSUR), Unidad SCLC","correspondingAuthor":false,"prefix":"","firstName":"Maricela","middleName":"","lastName":"García-Bautista","suffix":""},{"id":557054025,"identity":"7e66faf7-1ccb-4af5-b097-6a385c4de8b1","order_by":5,"name":"Everardo Barba","email":"","orcid":"","institution":"El Colegio de la Frontera Sur (ECOSUR), Unidad Villahermosa","correspondingAuthor":false,"prefix":"","firstName":"Everardo","middleName":"","lastName":"Barba","suffix":""},{"id":557054026,"identity":"84917938-1717-44f8-812f-b5dde2fa40a4","order_by":6,"name":"Gabriela Castellanos-Morales","email":"","orcid":"","institution":"El Colegio de la Frontera Sur (ECOSUR), Unidad Villahermosa","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"","lastName":"Castellanos-Morales","suffix":""}],"badges":[],"createdAt":"2025-12-08 14:32:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8308737/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8308737/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100106449,"identity":"a2e71654-578a-434b-b795-7ffbb3e3c474","added_by":"auto","created_at":"2026-01-13 05:13:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34103125,"visible":true,"origin":"","legend":"\u003cp\u003eInvasive armored catfish (\u003cem\u003ePterygoplichthys\u003c/em\u003esp.) sampling sites in the Grijalva-Usumacinta River basin, Mexico, SSUR: Sampling site Usumacinta River, SSGR: Sampling site Grijalva River, PNA: Protected Natural Area.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8308737/v1/c9b803ac4b4263838f581f87.png"},{"id":100106445,"identity":"9e63a361-a595-445a-9097-ffd60abfd792","added_by":"auto","created_at":"2026-01-13 05:13:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":244287,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of genetic variation by sampling location, as a function of principal component analysis (PCA, explains 4.43 % of variance) and discriminant analysis of principal components (DAPC, explains 18.14 % of variance), was determined based on 22,595 single-nucleotide polymorphisms (SNPs) of the armored catfish (\u003cem\u003ePterygoplichthys\u003c/em\u003e sp.) from the Grijalva–Usumacinta River basins, resulting from \u003cem\u003ede novo\u003c/em\u003e dataset (a and b, respectively). The distribution of genetic variance by region as a function of PCA (explains 3.98 % of variance) and DAPC (explains 16.2 % of variance) was also determined for 3,059 SNPs with mapped dataset (c and d, respectively). Catazajá (CAT), Grijalva (GRIJ), Lacantún (LAC), Tres Brazos (TB) and Tenosique (TEN).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8308737/v1/0b6c4bf09cf54c9aa4ddf7da.png"},{"id":100106446,"identity":"6d6e7ef5-3b02-4308-88db-507e89749da5","added_by":"auto","created_at":"2026-01-13 05:13:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75452,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) for the physicochemical variables of the sites analyzed in the Grijalva (depicted in red) and the Usumacinta (depicted in bold) River basins. Tr: transparency, T: temperature, RQI: riparian quality index, DO: dissolved oxygen, Sal: salinity, D: depth.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8308737/v1/b1a51abe549c2d704332f8ab.png"},{"id":100106447,"identity":"b37123f1-68cf-4085-9264-d351133fb69e","added_by":"auto","created_at":"2026-01-13 05:13:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122520,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of variance of a) temperature, b) dissolved oxygen, c) salinity and d) transparency recorded at the sites sampled in the Grijalva river (in red; A: Amacohite, O: Ostitán, C: Chilapa) and Usumacinta (in gray; L: Lacantún, J: Jonuta, TB: Tres Brazos).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8308737/v1/4d1885c552cc66523725dc78.png"},{"id":106093002,"identity":"e7efe1d0-07cd-40e3-bbc0-e66c4e77419f","added_by":"auto","created_at":"2026-04-03 11:32:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":50657334,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8308737/v1/acddb2e0-2b80-4c37-9e88-a8cbcd73543d.pdf"},{"id":100106448,"identity":"f0381488-17e2-4cf6-8fac-285f28b52a63","added_by":"auto","created_at":"2026-01-13 05:13:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":930488,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Materials\u003c/p\u003e","description":"","filename":"SupplementaryMaterials061025.docx","url":"https://assets-eu.researchsquare.com/files/rs-8308737/v1/77dbf47d53a31fb6d9a7feb5.docx"}],"financialInterests":"There is no duality of interest","formattedTitle":"Evolutionary drivers of invasion: hybridization and local adaptation in the Amazon sailfin catfish","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe introduction of invasive species is one of the main threats to biodiversity [1]. With the constant increase in connectivity and anthropogenic global changes, the rates of species introductions and their impacts are accelerating [2,3]. These generate direct and indirect effects on native species, communities and ecosystems, becoming a significant component of ecosystem change [4]. In addition, biological invasions have been reported at increasing rates. Invasive species adapt rapidly to novel environments as many invasive species maintain high levels of genetic diversity [5-7]. Thus, high levels of genetic variation may be correlated with invasion success [8,9]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo reduce the negative impacts of invasive species, it is necessary to better-understand functional genetic variation, and to identify the environmental variables that are involved in geographic or temporal expansion for invasive species. Uncovering the mechanisms underlying rapid genetic adaptation can provide insights into adaptive evolution to plan on invasive species control, and natural resource management [10,11].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the context of genotype-environment association (GEA) studies, the availability of a reference genome allows annotating candidate loci that may be involved in adaptive processes [12-16], which otherwise would remain anonymous [17]. For many non-model species, the availability of a reference genome limits the possibility of conducting GEA analyses [18]. A cost-effective alternative is using a transcriptome to annotate candidate loci [17]. The transcriptome contains information from the coding regions of an organism\u0026apos;s genome [19]; thus, SNP mapping with a reference transcriptome guarantee that SNPs are located within the coding regions of the genome and will be more likely to have a direct functional impact [17]. This approach would contribute to better-understand the mechanisms that regulate key biological processes and functions [17]. Also, the cost to obtain a transcriptome is lower than that of a reference genome, and more data may be available in repositories [17].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral species of the Amazon sailfin catfish (\u003cem\u003ePterygoplichthys\u003c/em\u003e spp), also known as armored catfish, are native to tropical areas in Central and South America, and they are considered as invasive in parts of North America, Central America, Asia and Europe, where hybridization is common [20-23]. These species have important negative ecological impacts, and socioeconomic and health implications for human populations\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[23-30]. Climate change is expected to facilitate range expansion for invasive species, such as the armored catfish [31]. Furthermore, armored catfish inhabit freshwater, but it has been reported in coastal areas, where it tolerates high salinity [32-34]. Therefore, identifying genomic characteristics that may confer adaptation and favor its establishment in new areas is of vital importance to determine genome-based management strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious genetic studies based on mitochondrial DNA suggested that hybridization may be playing an important role in invasion success [35-38]; but formal analyses on hybridization based on nuclear molecular markers are lacking. Such is the case in Mexico, where the armored catfish distributed in the Grijalva-Usumacinta region show morphological variation in ventral spot patterns, and a single mitochondrial haplotype corresponding to \u003cem\u003eP. pardalis\u003c/em\u003e, suggesting a possible hybrid origin for armored catfish in this region followed by rapid dissemination [38]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the present study we implemented a double digest restriction-site associated DNA (ddRAD-seq) method to obtain single nucleotide polymorphisms (SNPs). For SNP calling, we followed two approaches: a \u003cem\u003ede novo\u003c/em\u003e approach and a reference approach using a transcriptome to annotate SNPs. We aimed to solve the taxonomic identity of the armored catfish of the Grijalva-Usumacinta River Basins, to estimate its levels of genetic variation and genetic differentiation, and to identify candidate loci that could be facilitating its adaptation to novel environmental conditions in invaded areas. As we suspect this population to be from hybrid origin, we expected to find low correspondence between genetic differentiation and taxon identity based on ventral spot patterns, together with high levels of genetic variation. We expect to find low genetic differentiation between sampled areas corresponding to rapid dissemination after introduction. Finally, we expect to find candidate loci associated with environmental conditions, such as salinity, that may be allowing armored catfish to colonize new areas.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e(a) Sample collection, DNA extraction and genotyping-by-sequencing\u003c/p\u003e\n\u003cp\u003eThe armored catfish distributed in the Grijalva-Usumacinta region are \u003cem\u003eP. pardalis\u003c/em\u003e, or hybrids, \u003cem\u003eP. pardalis\u0026nbsp;\u003c/em\u003ex \u003cem\u003eP. disjunctivus\u003c/em\u003e, based on mtDNA [38]; therefore, from hereon we will refer to these specimens as \u003cem\u003ePterygoplichthys\u0026nbsp;\u003c/em\u003esp. A total of 110 individuals were collected along canals and main rivers of the Grijalva-Usumacinta region (Lacant\u0026uacute;n n=9, Tenosique n= 14, Catazaj\u0026aacute; n= 46, Tres Brazos n= 31 and, Amacohite n= 10) (Fig. 1). The same sampling effort was implemented at each site; therefore, we can assume that sampling sizes are in accordance with armored catfish abundance [38, 39]. Each individual was identified, in accordance to ventral spot patterns, as \u003cem\u003eP. pardalis\u0026nbsp;\u003c/em\u003e(dots, n=27 organisms), \u003cem\u003eP. disjunctivus\u003c/em\u003e (vermiculations, n= 34 organisms) or intermediate (mixture of spots and vermiculations, n= 49 organisms) [21, 22, 36, 38, 40-43]. We took a muscle tissue sample (0.5 x 0.5 mm) from the caudal peduncle of each fish. Tissue was placed in 96% ethanol and maintained at -20 \u0026deg;C until DNA extraction.\u003c/p\u003e\n\u003cp\u003eWe performed a phenol-chloroform-isoamyl alcohol protocol for DNA extraction [44]. DNA was sent to the University of Minnesota Genomics Center (genomics.umn.edu), where a ddRAD-seq [45] protocol, with restriction enzymes Pstl and Mspl, and 300 bps fragments size selection, was conducted. Next generation sequencing (NGS) was performed in an Illumina NovaSeq 2x100 SP platform. First, we conducted a pilot project with eight samples to determine the number of reads per sample needed to obtain robust data (https://genomics.umn.edu/service/gbs-pilot-projects); accordingly, 2 million reads per sample provide enough coverage to obtain robust results. We obtained an average of \u0026asymp; 2.8 million reads per sample (1,882,474 - 6,673,571 raw reads per sample) with average quality values of \u0026ge;Q30 across all libraries. Raw reads are deposited in NCBI SRA database (Bioproject PRJNA1144408).\u003c/p\u003e\n\u003cp\u003eSequence quality was assessed using FastQC v 0.12 [46]. Adapters were removed from each read using the gbstrim.pl script provided by the University of Minnesota (https://bitbucket.org/jgarbe/gbstrim/src/master/; parameters: --enzime1 PstI --enzime2 MspI --fastqfile --read R1 or R2 --outputfile R1.trim.fastq or R2.trim.fastq --verbose --threads 2 --minlength 50). Because overall R2 reads showed lower quality scores, we decided to use only the R1 files for SNP calling. Trimmomatic was used to cut the reads (R1) to the same length (parameters: CROP:90 and MINLEN:90) to implement SNP calling in Stacks [47, 48]. We retained 1,486,698 to 5,507,094 reads per sample after quality filtering.\u003c/p\u003e\n\u003cp\u003eWe conducted \u003cem\u003ede novo\u003c/em\u003e SNP calling with Stacks vers. 2.62 [48, 49]. First, we followed the optimization protocol proposed by [50], testing M and n from 2 to 6. Accordingly, optimized values were M = 5 and n = 5. We implemented two approaches: one approach considers individuals assigned to the previously mentioned morphotypes (\u003cem\u003eP. pardalis\u003c/em\u003e, \u003cem\u003eP. disjunctivus\u003c/em\u003e and mixed or intermediate); the other approach considers the geographical region from which the samples were obtained, regardless of the morphotype, because armored catfish in the Grijalva-Usumacinta region showed a single mitochondrial haplotype [38]. We tested values 0.6, 0.7 and 0.8 for the minimum percentage of individuals in a population required to process a locus for that population (-r); the final run was performed with a -r = 0.8. To minimize linkage disequilibrium, we restricted data to one random SNP per locus (--write-random-snp).\u003c/p\u003e\n\u003cp\u003eWe used VCFtools 0.1.17 [51] to filter SNPs for minimum allele frequency (maf = 0.01), Hardy-Weinberg equilibrium (hwe = 0.00001), number of alleles per site (min-alleles = 2) and maximum proportion of missing data (max-missing = 0.8). Of the total number of individuals (110), seven were eliminated because they had more than 10 % missing data. All analyses were performed on 103 individuals with a final \u003cem\u003ede novo\u0026nbsp;\u003c/em\u003edatabase of 22,595 SNPs.\u003c/p\u003e\n\u003cp\u003eFor the reference-based SNP calling, we downloaded available transcriptome data for \u003cem\u003eP. pardalis\u003c/em\u003e (SRR5997830) to assemble a reference transcriptome. Low quality reads as well as adapters were removed with AdapterRemoval v2 [52], retaining those with a minimum quality value of Q28. The resulting reads were used to perform \u003cem\u003ede novo\u003c/em\u003e reconstruction using the Trinity assembler with default parameters [53]. The subsequent contigs were cleaned with the SeqClean program (https://github.com/gpertea/gsrc/blob/master/scripts/seqclean). AlignWise [54] was used to identify coding regions (CDS). For this purpose, we used as reference fish genomes from different taxa of the class Siluriformes: \u003cem\u003eBagris yarrelli\u003c/em\u003e, \u003cem\u003eDanio rerio\u003c/em\u003e, \u003cem\u003eHemibagrus wyckioides\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Ictalurus punctatus\u003c/em\u003e, \u003cem\u003eOncorhynchus gorbuscha\u003c/em\u003e, \u003cem\u003ePangasianodon gigas\u003c/em\u003e, \u003cem\u003eTachysurus fuvidraco\u003c/em\u003e and \u003cem\u003eTakifugu rbripes\u003c/em\u003e. The assembled contigs were filtered with the BlastClust to obtain non-redundant unigenes, and proteins \u0026lt;40 amino acids were removed.\u003c/p\u003e\n\u003cp\u003eFor the reference-based SNP calling, filtered raw reads were mapped to the reference transcriptome with BWA-mem. Files were transformed to bam with Samtools [55], and SNP calling was performed with Stacks vers. 2.62 [48, 49]. Finally, the populations module was run considering sampling locations. VCFtools [51] was used to perform SNP filtering with the previously mentioned parameters. We obtained a database consisting of 103 individuals and 3,059 mapped SNPs.\u003c/p\u003e\n\u003cp\u003e(b) Diversity and genetic structure\u003c/p\u003e\n\u003cp\u003eWe used the \u003cem\u003ede novo\u003c/em\u003e database that considered morphotypes to conduct an exploratory analysis and confirm the validity of the morphotypes. We conducted principal component analysis (PCA) and discriminant analysis of principal components (DACP) from \u003cem\u003eadegenet\u0026nbsp;\u003c/em\u003e[56, 57] in R version 4.3.3 [58] to observe if individuals with the same morphotype form clusters in accordance with ventral patterns. For DACP, we retained three PCs and two discriminant functions [56]. Since we did not find signs of genetic differentiation according to morphotype, for subsequent analyses, data were grouped according to sampling sites (Fig. 1). We performed PCA and DACP for the \u003cem\u003ede novo\u0026nbsp;\u003c/em\u003eand mapped datasets that consider sampling site to observe if individuals cluster in accordance with geography.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor both databases, \u003cem\u003ede novo\u003c/em\u003e and mapped SNPs, we used the \u003cem\u003evcfR\u003c/em\u003e [59], \u003cem\u003eadegenet\u0026nbsp;\u003c/em\u003e[57]\u003cem\u003e\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;hierfstat\u0026nbsp;\u003c/em\u003e[60] libraries for R [58] to estimate summary statistics (allele frequencies, \u003cem\u003euH\u003csub\u003eE\u003c/sub\u003e\u003c/em\u003e, \u003cem\u003eH\u003csub\u003eO\u003c/sub\u003e\u003c/em\u003e, inbreeding coefficient (\u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e) and genetic differentiation (\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e)). A Wilcoxon rank test was applied to test for statistically significant differences between databases for \u003cem\u003eH\u003csub\u003eO\u003c/sub\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003euH\u003csub\u003eE\u003c/sub\u003e\u003c/em\u003e. We performed an Admixture analysis [61] to confirm genetic structure by testing values from \u003cem\u003eK\u0026nbsp;\u003c/em\u003e= 0 to \u003cem\u003eK\u0026nbsp;\u003c/em\u003e= 10, and selected the best \u003cem\u003eK\u0026nbsp;\u003c/em\u003efrom cross-validation (CV) error.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(c) Environmental data and analysis\u003c/p\u003e\n\u003cp\u003eEnvironmental parameters were measured \u003cem\u003ein situ\u003c/em\u003e in triplicate at all sampling sites using a Hanna HI9829 multiparameter probe: temperature (\u0026deg;C), hydrogen potential (pH), dissolved oxygen (mg/L-1), conductivity (mS/cm), total dissolved solids (mg/L-1), salinity, depth (m), transparency (m) [62]. Also, the ecological quality of the riverbanks was evaluated at each site using the riparian ecological quality index (RQI) protocol, where seven attributes were evaluated [63, 64] (see detailed methods in supplementary material). The RQI was determined from the sum of the seven attributes assessed with a scoring system from 0 to 150, as indicated in the literature, in the following categories: very good (150-130), good (129-100), moderate (99-70), poor (69-40), bad (39-10) and very bad (\u0026lt;10) [63, 64].\u003c/p\u003e\n\u003cp\u003eTo estimate the correlation between physicochemical water variables, we performed an analysis of covariance using Pearson\u0026apos;s correlation coefficient. We retained in the dataset only those variables with a correlation value \u0026le; 0.7. Although the temperature and RQI parameters showed a correlation coefficient above the threshold (-0.74), we retained these variables because temperature is an important parameter for fish development [65], while RQI is a composite measure of habitat quality which may have an impact on fish population parameters [66]. The variables included in subsequent analyses were temperature (\u0026deg;C), dissolved oxygen (mg/L-1), salinity, depth (m), transparency (m) and RQI.\u003c/p\u003e\n\u003cp\u003eA principal components analysis (PCA) was performed for the selected variables using R [58]. An analysis of variance (ANOVA) was performed in R [58] to detect differences among sampling sites with respect to environmental parameters, using the average values of the three measurements taken at each site. When statistical differences between sites were found, we performed a TukeyHSD setting a significance level of 0.05 (\u0026alpha;=0.05).\u003c/p\u003e\n\u003cp\u003e(d) Tests for candidate loci\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We used the mapped SNP database to identify genes that could be involved in rapid adaptation to new environmental conditions in this invasive species. It has been proposed that tests for candidate loci are susceptible to false positives, so more than one test should be used. Sites with selection signals that are recovered with more than one method will be less likely to be false positives [67]. Therefore, we implemented three tests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, we used pcadapt [68] implemented in R, where candidate SNPs are identified as those that correlate significantly with the set of PCs that maximizes variance under a specific false positive rate (FDR). For this, we evaluated the optimal value of K (the optimal number of gene clusters), from 1 to 10, using a plot of the proportion of variance explained by each PC. Accordingly, we retained K = 2, and we estimated the FDR of the p-values associated with the Bonferroni correction by using the qvalue function of the R package \u0026ldquo;\u003cem\u003eqvalue\u003c/em\u003e\u0026rdquo; [69]. Finally, we obtained the list of candidate SNPs with FDR \u0026alpha;= 0.05, i.e. 5 % of candidate SNPs are expected to be false positives\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[68].\u003c/p\u003e\n\u003cp\u003eThe second method implemented was a latent factor mixed model (LFMM) from the LEA package [70] in R software. These linear mixed models test correlations between allele counts and an environmental variable. Here, we used two environmental variables: salinity and RQI to test the neutral structure through latent factors [71]. We focused on these variables because the armored catfish is a freshwater fish found to inhabit high salinity environments in the Grijalva-Usumacinta River Basins and RQI has been previously associated with fish body condition in the same area [66]. We ran LFMM with several iterations of 50,000, 5,000 sweeps and we set k = 2 for the number of latent factors for the analysis. We performed ten replicates of the analysis [70]. We took the median score of the 10 replicates, and we estimated the genomic inflation factor. Subsequently, we obtained the adjusted p-values using the genomic inflation factor. These p-values were used to predict the significance of the association between the SNP and the environmental variable. The list of candidate SNPs was obtained using the Benjamin-Holchberg procedure with a false discovery rate of 0.05 for the two environmental variables included in the analysis.\u003c/p\u003e\n\u003cp\u003eFinally, we used BayeScan [72] to identify candidate loci. This program uses the Bayesian method to directly estimate the posterior probability for each locus based on population-specific \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e coefficients [72]. This method accommodates differences in demographic history and the extent of genetic drift between populations. It is based on a logistic regression model that decomposes genetic variation into locus population-specific effects [73]. We used the default string parameters to conduct the runs (n= 5000, thin= 10, nbp= 20, pilot= 5000, burn= 50000), and, for the model parameters, we used the default value -pr_odds [76].\u003c/p\u003e\n\u003cp\u003eThe candidate SNPs identified by any of the above methods were annotated by homology and gene ontology analysis. For this purpose, we constructed a database with the proteome of the \u003cem\u003eSiluriformes\u003c/em\u003e species previously mentioned, and the proteome obtained from \u003cem\u003eP. pardalis.\u003c/em\u003e The Blastp [74] algorithm was used to perform the search for homologous sequences with an expectancy value (e-value) \u0026lt; 1x10\u003csup\u003e-5\u003c/sup\u003e. Subsequently, we used the InterproScan program [75] to assign gene ontology terms (GOterms) [76]. Finally, we performed a functional enrichment analysis with g:Profiler [77]. \u003cem\u003eDanio rerio\u003c/em\u003e was selected as the reference organism to perform this analysis with g:GOST values \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e(a) Genetic Diversity and Structure\u003c/p\u003e\n\u003cp\u003eThe two data sets yielded comparable results regarding genetic structure. In the exploratory analysis, which considered morphotypes, all samples overlapped, suggesting that there is no genetic differentiation between morphotypes in the armored catfish collected in the Grijalva-Usumacinta region (Fig. S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen examining genetic structure according to sampling location, using \u003cem\u003ede novo\u003c/em\u003e and mapped datasets, population structure in the armored catfish of the Grijalva-Usumacinta River basins is similar. In the PCA analyses, most samples overlap, and there is no clear clustering (Fig. 2). The DAPC analysis shows that the Lacant\u0026uacute;n region exhibits some genetic differentiation in comparison to the other regions analyzed (Fig. 3b and 3d). The results of the Admixture analysis are consistent with those obtained in the DAPC, with K = 2 (cv = 0.60) showing admixture in all populations except the Lacant\u0026uacute;n site, where individuals have high assignation probability to one of the clusters (Fig. S2). It is worth mentioning that levels of overall genetic differentiation are low (\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e = 0.008 for both datasets; Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe patterns of genetic diversity observed in both datasets are similar (Table 1). We find statistically significant differences for heterozygosity parameters (\u003cem\u003eH\u003csub\u003eE\u003c/sub\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eH\u003csub\u003eO\u003c/sub\u003e\u003c/em\u003e) between datasets (Wilcoxon-rank-test; p = 0.007 and r = 0.82, in each case). We obtained high levels of overall genetic diversity and similar values between sites, and \u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u0026nbsp;\u003c/em\u003evalues close to zero.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(b) Environmental analyses\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA) of the environmental variables indicates that the first two axes explain 73.6 % of the variation, the first one explaining (PC1) 46.2 % and the second one (PC2) 27.4 % (Fig. 3). The analysis of variance indicates that temperature, dissolved oxygen, salinity and transparency exhibit statistical differences between sampling sites (p \u0026gt; 0.05). PCA results suggest that environmental conditions in Amacohite and Ostit\u0026aacute;n, both located in the Grijalva River, are similar. Also, the environmental conditions in Chilapa and Tres Brazos, located in the lower part of the basins, are similar. In contrast, Lacant\u0026uacute;n and Jonuta, both located on the Usumacinta River, show differences in their environmental conditions.\u003c/p\u003e\n\u003cp\u003eBased on TukeyHSD post hoc analysis, temperature is homogeneous in the Grijalva river and heterogeneous in the Usumacinta river (Fig. 4a). For dissolved oxygen, differences occur at the Lacant\u0026uacute;n site with the highest values (Fig. 4b). While in salinity, differences occur at the Tres Brazos site with the highest salinity value compared to the rest of the sampling sites (Fig. 4c). Transparency shows differences between the Grijalva and Usumacinta rivers (Fig. 4d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(c) Tests for candidate loci\u003c/p\u003e\n\u003cp\u003eThe pcadapt analysis identified two SNPs as significant outliers (0.06 % of 3,059 SNPs), while LFMM identified 24 outlier SNPs associated with salinity (0.78 % of 3,059 SNPs) and 28 outlier SNPs associated with RQI (0.91 % of 3,059 SNPs). BayeScan analysis identified a total of 1,960 SNPs at \u0026alpha; = 0.05 (64 % of 3,059 SNPs). The strongest candidates for selection were 54 SNPs, which were retrieved in at least two of the three methods. Two candidate SNPs are shared between pcadapt and BayeScan. Similarly, all the candidate loci identified with LFMM for salinity and RQI are shared with BayeScan (Fig. S3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the GOTerms enrichment analysis for SNP annotation with unigenes (Table S1), we identified some interesting categories such as: nine isoforms of the unigene \u0026quot;TRINITY_DN1064\u0026quot;, which could be involved with the synthesis of teneurin; two unigenes TRINITY_DN385_c0_g1_i4 and TRINITY_DN542_c0_g1_i10 which are identified with homologous function to genes of the mitogen-activated protein kinase (MAPK) family; and the unigene TRINITY_DN1538_c0_g1_i2, homologous to interferon induced protein-35 (IFP35), which is associated with multiple functions of the immune system.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, the SNP annotation approach utilizing the reference transcriptome proved to be valuable for identifying potential candidate sites, as it ensured that the candidate sites under selection are indeed coding loci. This approach facilitated a more comprehensive understanding of underlying factors associated with the invasion success of the armored catfish (\u003cem\u003ePterygoplichthys\u003c/em\u003e sp.) in the Grijalva-Usumacinta River basins.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(a) Genetic diversity and genetic structure\u003c/p\u003e\n\u003cp\u003eWe observed some differences in genetic diversity but similar patterns of genetic structure with the \u003cem\u003ede novo\u003c/em\u003e and mapped SNPs datasets. The estimated overall and per locality levels of genetic diversity from the mapped dataset were lower than those estimated with the \u003cem\u003ede novo\u003c/em\u003e approach. This is to be expected, as the transcriptome represents only the coding regions of the genome, which tend to be highly conserved and accumulate less genetic variation than non-coding regions [19, 78].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of this study support that the armored catfish (\u003cem\u003ePterygoplichthys\u003c/em\u003e sp.) from the Grijalva and Usumacinta rivers belong to a single genetic group. These results are consistent with those showing that specimens distributed in the Grijalva-Usumacinta region have the same mitochondrial haplotype and correspond to a single species or are hybrids [38]. It has been suggested that \u003cem\u003eP. pardalis\u0026nbsp;\u003c/em\u003eand \u003cem\u003eP. disjunctivus\u0026nbsp;\u003c/em\u003ecould be morphological varieties of a single species or that these fish can hybridize in invaded areas [21-23, 35-38]. Also, our results support the hypothesis that released individuals may be hybrids because of intentional human intervention in aquariums [35-38, 79].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHybridization may play a key role in the ability of the armored catfish to adapt to new environments and to its invasion success. This phenomenon has been observed in other invasive species, such as lionfish, in which hybridization events in the native distribution area could have driven rapid adaptation in invaded areas (\u003cem\u003ePterois\u003c/em\u003e) [80, 81]. However, genomic data from the native distribution of \u003cem\u003eP. pardalis\u003c/em\u003e and \u003cem\u003eP. disjunctivus\u003c/em\u003e are needed to conduct formal analyses of hybridization and adaptive introgression. Obtaining demographic information and carrying out experimental studies is also needed to test the hypothesis that heterosis is related to invasive potential in the armored catfish [82-85]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHigh genetic diversity (\u003cem\u003eH\u003csub\u003eO\u003c/sub\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eH\u003csub\u003eE\u003c/sub\u003e\u003c/em\u003e) was observed in the armored catfish (\u003cem\u003ePterygoplichthys\u0026nbsp;\u003c/em\u003esp.) with both \u003cem\u003ede novo\u003c/em\u003e (\u003cem\u003eH\u003csub\u003eO\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= 0.3824 and \u003cem\u003eH\u003csub\u003eE\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= 0.378) and mapped datasets (\u003cem\u003eH\u003csub\u003eO\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= 0.2667 and \u003cem\u003eH\u003csub\u003eE\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= 0.2653). Lionfish (\u003cem\u003ePterois volitans\u003c/em\u003e), which is a marine invasive species, has lower genetic diversity both in invaded areas and in its native range (\u003cem\u003eH\u003csub\u003eE\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= 0.079 \u0026ndash; 0.141) [81]. The mosquitofish (\u003cem\u003eGambusia holbrooki\u003c/em\u003e), another invasive freshwater fish, also has lower levels of mean genetic diversity than the armored catfish (\u003cem\u003eH\u003csub\u003eE\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.149 \u0026plusmn; 0.155 for invasive populations in America) [86]. Several evolutionary processes are related to successful invasions, such as propagule pressure or high genetic diversity, but the rapid adaptive response to new selective processes during propagation and colonization is usually more important [87]. It has been proposed that invasive species may loose genetic variation due to founder events, but this does not imply that the species necessarily losses their adaptive potential, because phenotypic plasticity may allow organisms to respond to environmental heterogeneity [88]. Even so, it is still unknown which traits help predict invasion success, or which traits help predict the differences in local adaptation between invaders [7]. The high genetic diversity observed in these fish is indicative of their high evolutionary potential, which is influenced by selection pressures on the founding population [89]. Furthermore, it has been reported that these fish can maintain their fitness in adverse environments due to the plasticity of their phenotypic traits, including a slower growth rate and smaller size at reproductive maturity [90]. The later favors their colonization capacity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe assessment of population structure using \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e,\u003c/em\u003e DACP and admixture revealed a regional structure in the armored catfish, with a single population group in the Grijalva-Usumacinta region. Low values of global genetic differentiation support the hypothesis that the armored catfish was introduced to the Grijalva-Usumacinta region on a single or a few occasions, subsequently expanding and rapidly disseminating throughout the region [38]. In the lionfish (\u003cem\u003ePterois volitans\u003c/em\u003e), low values of genetic differentiation are also observed in some of the invaded areas of the Gulf of Mexico and the Caribbean (\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= 0 \u0026ndash; 0.006) [81]. In contrast, mosquitofish (\u003cem\u003eGambusia holbrooki\u003c/em\u003e) exhibit higher genetic differentiation among sites, however, differentiation between populations within the invaded range remains lower than that observed among populations from the native range. This pattern is consistent with rapid population expansion following establishment in invaded areas [86].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is notable that the fish collected in the Lacant\u0026uacute;n locality exhibited some degree of genetic differences from the rest of the sampled locations. It is noteworthy that this locality exhibited high values of dissolved oxygen and RQI, which suggests that it is a better conserved area. We cannot rule out that the genetic differentiation of the Lacant\u0026uacute;n locality is related to lower densities due to better conserved environmental conditions. Accordingly, there is a lower biomass of armored catfish in conserved areas of the Usumacinta River, like in Lacant\u0026uacute;n, compared to the Grijalva River, which is impacted by diverse anthropogenic activities [39]. Furthermore, it has been observed that these fish prefer burrowing in uncovered areas of vegetation [91]. Therefore, it is hypothesized that conserved areas represent in some way a barrier for the establishment of armored catfish, since its reproductive activity, which is important for its establishment and invasion, is affected by this condition (high density of roots) [91]. However, to adequately test these hypotheses, experimental studies that include testing for adaptation to salinity and root density should be implemented.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDisturbance is considered one of the factors that increases the susceptibility to invasion, as it creates new niches and increases resource availability [75], while simultaneously disrupting the balance of native communities [92]. It has been documented that armored catfish prefer areas impacted by land use changes and pollution [31]. Thus, the conservation and restoration of the diversity of competing species in invaded water bodies can reduce armored catfish growth and reproduction [90]. The establishment of buffer zones between wetland areas and intensive land use areas can mitigate the negative effects of human activity on aquatic ecosystems, while also providing benefits to local communities [93]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(b) Tests for candidate loci\u003c/p\u003e\n\u003cp\u003eA total of 54 candidate loci were identified with two of the three methods used for SNP detection. It has been suggested that demographic processes such as population expansion and hybridization can result in false positives in \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e outlier and genotype-environmental association analyses [14]. Therefore, we used three complementary methods to identify candidate loci; nonetheless, these results should be taken with caution, and future studies should implement an experimental design to validate these loci. Here we highlight some candidate SNPs that are related to salinity and RQI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe candidate loci correlated with salinity are unigenes related to catalytic and regulatory activity of transcription, translation of DNA- and RNA-associated signals. Regarding the candidate loci that showed correlation with RQI; these unigenes are associated with protein binding activity, DNA and RNA binding transport and cellular communication (Fig. S4). In the functional characterization of these candidate loci, tenurins and teneurin C-terminal associated peptide (TCAP) are transmembrane proteins that are essential for nervous system development in vertebrates; these unigenes are involved with stress response in \u003cem\u003eDanio rerio\u0026nbsp;\u003c/em\u003eand other animal species [94-97]. Teneurin-3 could be a factor in local adaptation of armored catfish in the Grijalva-Usumacinta Rivers basins.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe armored catfish is a freshwater species; nevertheless, these organisms have already been documented in various brackish areas [33,34]. It is possible that candidate loci identified in the present study are a factor in the invasion process, because, for example, the family of protein kinase (MAPK) is involved in various processes such as growth, development and response to various types of stress (osmotic, heat shock, ultraviolet radiation, oxidative stress and environmental stress) [98]. In channel catfish (\u003cem\u003eIctalurus punctatus\u003c/em\u003e), this gene family plays an important role in the response to high salinity stress [98], and in the armored catfish this gene family may be playing a role in the colonization of brackish areas such as those observed in Tres Brazos located in the Pantanos de Centla Natural Reserve [32-34]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt has been described that teneurins are involved in cell metabolism, morphology and migration in teleost fish, such as rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e). These transmembrane proteins stimulate cell proliferation and stimulate levels of cAMP in rainbow trout\u0026rsquo;s neuronal cells [94]. \u0026nbsp;Furthermore, teneurins and C-terminal teneurin-associated peptide (TCAP) are molecular components used to mitigate the response to stress [97], and they increase metabolic performance in zebrafish (\u003cem\u003eDanio rerio\u003c/em\u003e) [96]. Finally, the interferon protein (IPF35) is involved in the innate antiviral response of the mandarin fish (\u003cem\u003eSiniperca chuatsi\u003c/em\u003e) [99,100]. This protein may be implicated in the development and the environmental tolerance of the armored catfish.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExperimental analyses should be conducted to validate the role of these candidate genes, and to determine whether expression regulation is related to the invasive success in the armored catfish. If this is verified, it will be important to assess their frequency in natural populations. This will provide further insight into the invasive potential of the armored catfish into new areas.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eUsing a reference transcriptome for SNP calling represents a viable alternative for the analysis of non-model species and species with large and/or complex genomes, with the objective of identifying candidate loci under selection. Our results support that the armored catfish in the Grijalva and Usumacinta rivers are of hybrid origin and underwent processes of population expansion and rapid dissemination after their introduction. In addition, high genetic variability may be allowing them to adapt to novel environments. To control the population of armored catfish, it is advisable to reduce the negative impact of anthropogenic activities on these rivers and to minimize habitat degradation. Where feasible, riverbank restoration activities should be conducted, and the densities of this fish should be monitored to assess the efficacy of these measures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHybridization may be playing an important role in invasion success of the armored catfish via heterosis. Therefore, hybridization in aquariums and other facilities for its reproduction and commercialization should be monitored and prevented. In addition, genomic data from the native distribution areas of all armored catfish species that have been recorded as invasive species are needed to gain a better understanding about the role of hybridization in adaptation to new environments and invasion success. Our analyses suggest that the genes TCAP, MAPK and IPF35 may be involved in the colonization of brackish areas, but experimental validation is required. Finally, obtaining high quality, well annotated, reference genomes will forward our understanding about the role of additional genes, gene families, as well as structural variants, in the invasion success of the armored catfish.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Fondo de Investigaci\u0026oacute;n Cient\u0026iacute;fica y Desarrollo Tecnol\u0026oacute;gico de El Colegio de la Frontera Sur (FID-784 project 30013 to GC-M). United Nations Development Programme (PPD-FMAM F06-M\u0026eacute;xico-2019 to GC-M and EB. World Wildlife Fund (WWF-ECOSUR 2017-2018 to EB). Operative funds (project 3103711920 to GC-M). Consejo Nacional de Humanidades, Ciencias y Tecnolog\u0026iacute;as (CONAHCyT; scholarship No. 627848 granted for doctoral studies to JFM-V). The authors would like to express gratitude to the Consejo Nacional de Humanidades, Ciencias y Tecnolog\u0026iacute;as (CONAHCyT) for the scholarship (No. 627848) granted for doctoral studies to JFM-V. We also thank Conservaci\u0026oacute;n de la Biodiversidad del Usumacinta A.C. (COBIUS A.C.) for the resources provided for this project. To the Universidad Ju\u0026aacute;rez Aut\u0026oacute;noma de Tabasco (UJAT) for the personnel and equipment provided for sample collection. To all the personnel and volunteers that participated in sampling trips. To institutional laboratories for the instruments, equipment and materials for DNA extraction. Also, we thank the Instituto de Ecolog\u0026iacute;a, A.C. (INECOL) for granting access to the high-performance computing system (HUTZILIN) used to carry on the transcriptome assembly and annotation; specially to Dr. Enrique Ibarra-Laclette (P.I.) and M.Sc. Emmanuel Villafan for his technical support. Thank you to Dr. Erika Aguirre-Planter for her constructive comments on the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: GC-M, JFM-V. Original drafting: JFM-V, GC-M. Data curation: MG-B, YTG-G, AC-T, JFM-V. Formal analysis: AC-T, JFM-V, YTG-G, GC-M. Acquisition of funds: GC-M, EB. Methodology: GC-M, JFM-V. Resources: GC-M. Software: GC-M, AC-T. Supervision: GC-M. Drafting - revising and editing: JFM-V, YTG-G, AC-T, MG-B, EB, GC-M.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest/Competing Interests:\u0026nbsp;\u003c/strong\u003eThe authors have no conflict of interest/competing interests to declare that are relevant to the content of this article. The authors declare they have no financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data are deposited at NCBI SRA (Bioproject PRJNA1144408). \u0026nbsp;Scripts used for data analyses can be found in https://github.com/GenomicConservationLab-ECOSUR/Pterygoplichthys_JFMV\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch ethics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study complies with animal handling ethics approved by institutional ethics for research committee.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDue\u0026ntilde;as, M. A. et al. The role played by invasive species in interactions with endangered and threatened species in the United States: a systematic review. \u003cem\u003eBiodivers. 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Resour\u003c/em\u003e. \u003cstrong\u003e17\u003c/strong\u003e, 67\u0026ndash;77 (2017).\u003c/li\u003e\n \u003cli\u003eStorey, J.D. \u0026amp; Bass, A.J. Bioconductor\u0026rsquo;s qvalue package. Version 2.12.0: 1\u0026ndash;13 (2018).\u003c/li\u003e\n \u003cli\u003eFrichot, E. \u0026amp; Fran\u0026ccedil;ois, O. LEA: An R package for landscape and ecological association studies. \u003cem\u003eMethods Ecol. Evol\u003c/em\u003e. \u003cstrong\u003e6\u003c/strong\u003e, 925\u0026ndash;929 (2015).\u003c/li\u003e\n \u003cli\u003eFrichot, E., Schoville, S. D., Bouchard, G. \u0026amp; Fran\u0026ccedil;ois, O. Testing for associations between loci and environmental gradients using latent factor mixed models. \u003cem\u003eMol. Biol. Evol\u003c/em\u003e. \u003cstrong\u003e30\u003c/strong\u003e, 1687\u0026ndash;1699 (2013).\u003c/li\u003e\n \u003cli\u003eFoll, M. \u0026amp; Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. \u003cem\u003eGenetics\u003c/em\u003e \u003cstrong\u003e180\u003c/strong\u003e, 977\u0026ndash;993 (2008).\u003c/li\u003e\n \u003cli\u003eBeaumont, M. A. \u0026amp; Balding, D. J. Identifying adaptive genetic divergence among populations from genome scans. \u003cem\u003eMol. Ecol\u003c/em\u003e. \u003cstrong\u003e13\u003c/strong\u003e, 969\u0026ndash;980 (2004).\u003c/li\u003e\n \u003cli\u003eAltschul, S. F., Gish, W., Miller, W., Myers, E. W. \u0026amp; Lipman, D. J. Basic local alignment search tool. \u003cem\u003eJ. Mol. Biol\u003c/em\u003e. \u003cstrong\u003e215\u003c/strong\u003e, 403\u0026ndash;410 (1990).\u003c/li\u003e\n \u003cli\u003eZdobnov, E. M. \u0026amp; Apweiler, R. InterProScan - An integration platform for the signature-recognition methods in InterPro. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 847\u0026ndash;848 (2001).\u003c/li\u003e\n \u003cli\u003eAshburner, M. et al. Gene Ontology: tool for the unification of biology. \u003cem\u003eNat. Genet\u003c/em\u003e. \u003cstrong\u003e25\u003c/strong\u003e, 25\u0026ndash;29 (2000).\u003c/li\u003e\n \u003cli\u003eKolberg, L. et al. G:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update). \u003cem\u003eNucleic Acids Res\u003c/em\u003e. \u003cstrong\u003e51\u003c/strong\u003e, W207\u0026ndash;W212 (2023).\u003c/li\u003e\n \u003cli\u003eSiepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. \u003cem\u003eGenome Res\u003c/em\u003e. \u003cstrong\u003e15\u003c/strong\u003e, 1034\u0026ndash;1050 (2005).\u003c/li\u003e\n \u003cli\u003eSahoo, L., Swain, P. P., Hussan, A., Mohapatra, A. \u0026amp; Sundaray, J. K. K. DNA barcoding delineates the identity of invasive South American sucker mouth armoured catfishes of genus \u003cem\u003ePterygoplichthys\u003c/em\u003e of East Kolkata Wetland, West Bengal, India as single species. \u003cem\u003eIndian J. Fish\u003c/em\u003e. \u003cstrong\u003e69\u003c/strong\u003e, (2022).\u003c/li\u003e\n \u003cli\u003eWilcox, C. L., Motomura, H., Matsunuma, M. \u0026amp; Bowen, B. W. Phylogeography of lionfishes (\u003cem\u003ePterois\u003c/em\u003e) indicate taxonomic over splitting and hybrid origin of the invasive \u003cem\u003ePterois volitans\u003c/em\u003e. \u003cem\u003eJ. Hered\u003c/em\u003e. \u003cstrong\u003e109\u003c/strong\u003e, 162\u0026ndash;175 (2018).\u003c/li\u003e\n \u003cli\u003eBurford Reiskind, M. O. et al. The genomics of invasion: characterization of red lionfish (\u003cem\u003ePterois volitans\u003c/em\u003e) populations from the native and introduced ranges. \u003cem\u003eBiol. Invasions\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 2471\u0026ndash;2483 (2019).\u003c/li\u003e\n \u003cli\u003eVidal, O., Garc\u0026iacute;a-Berthou, E., Tedesco, P. A. \u0026amp; Garc\u0026iacute;a-Mar\u0026iacute;n, J. L. Origin and genetic diversity of mosquitofish (\u003cem\u003eGambusia holbrooki\u003c/em\u003e) introduced to Europe. \u003cem\u003eBiol. Invasions\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 841\u0026ndash;851 (2010).\u003c/li\u003e\n \u003cli\u003eMwanja, W. W., Kaufman, L. \u0026amp; Fuerst, P. A. Comparison of the genetic and ecological diversity of the native to the introduced tilapiines (Pisces: Cichlidae), and their population structures in the Lake Victoria region, East Africa\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003cem\u003eAquat. Ecosyst. Health Manag\u003c/em\u003e. \u003cstrong\u003e13\u003c/strong\u003e, 442\u0026ndash;450 (2010).\u003c/li\u003e\n \u003cli\u003eSuarez-Salgado, D., Herrera-Camacho, J., Lara-Rivera, A. L. \u0026amp; Parra-Bracamonte, G. M. Diversidad y origen gen\u0026eacute;tico de poblaciones introducidas de bagre de canal (\u003cem\u003eIctalurus punctatus\u003c/em\u003e Rafinesque, 1818), en el centro occidental de M\u0026eacute;xico.\u0026nbsp;\u003cem\u003eLat. Am. J. Aquat. Res\u003c/em\u003e. \u003cstrong\u003e44\u003c/strong\u003e, 525\u0026ndash;534 (2016).\u003c/li\u003e\n \u003cli\u003eLu, G. et al. Evolution and genetics of bighead and silver carps: Native population conservation versus invasive species control. \u003cem\u003eEvol. Appl\u003c/em\u003e. \u003cstrong\u003e13\u003c/strong\u003e, 1351\u0026ndash;1362 (2020).\u003c/li\u003e\n \u003cli\u003eVera, M., D\u0026iacute;ez‐del‐Molino, D. \u0026amp; Garc\u0026iacute;a‐Mar\u0026iacute;n, J. Genomic survey provides insights into the evolutionary changes that occurred during European expansion of the invasive mosquitofish (\u003cem\u003eGambusia holbrooki\u003c/em\u003e ). \u003cem\u003eMol. Ecol\u003c/em\u003e. \u003cstrong\u003e25\u003c/strong\u003e, 1089\u0026ndash;1105 (2016).\u003c/li\u003e\n \u003cli\u003eBarrett, S. C. H. Foundations of invasion genetics: The Baker and Stebbins legacy. \u003cem\u003eMol. Ecol\u003c/em\u003e. \u003cstrong\u003e24\u003c/strong\u003e, 1927\u0026ndash;1941 (2015).\u003c/li\u003e\n \u003cli\u003eDlugosch, K. M. \u0026amp; Parker, I. M. Founding events in species invasions: Genetic variation, adaptive evolution, and the role of multiple introductions. \u003cem\u003eMol. Ecol\u003c/em\u003e. \u003cstrong\u003e17\u003c/strong\u003e, 431\u0026ndash;449 (2008).\u003c/li\u003e\n \u003cli\u003eAğdamar, S. \u0026amp; Tarkan, A. S. High genetic diversity in an invasive freshwater fish species, \u003cem\u003eCarassius gibelio\u003c/em\u003e, suggests establishment success at the frontier between native and invasive ranges. \u003cem\u003eZool. Anz\u003c/em\u003e. \u003cstrong\u003e283\u003c/strong\u003e, 192\u0026ndash;200 (2019).\u003c/li\u003e\n \u003cli\u003eWei, H. et al. Environmental related variation in growth and life-history traits of non-native sailfin catfishes (\u003cem\u003ePterygoplichthys\u003c/em\u003e spp.) across river basins of South China. \u003cem\u003eAquat. Invasions\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 92\u0026ndash;109 (2022).\u003c/li\u003e\n \u003cli\u003eLienart, G. D. H., Rodiles-Hern\u0026aacute;ndez, R. \u0026amp; Capps, K. A. Nesting burrows and behavior of nonnative catfishes (Siluriformes: Loricariidae) in the Usumacinta-Grijalva Watershed, Mexico. \u003cem\u003eSouthwest. Nat\u003c/em\u003e. \u003cstrong\u003e58\u003c/strong\u003e, 238\u0026ndash;243 (2013).\u003c/li\u003e\n \u003cli\u003eJauni, M., Gripenberg, S. \u0026amp; Ramula, S. Non-native plant species benefit from disturbance: A meta-analysis. \u003cem\u003eOikos\u003c/em\u003e \u003cstrong\u003e124\u003c/strong\u003e, 122\u0026ndash;129 (2014).\u003c/li\u003e\n \u003cli\u003eCamacho-Valdez, V., Rodiles-Hern\u0026aacute;ndez, R., Navarrete-Guti\u0026eacute;rrez, D. A. \u0026amp; Valencia-Barrera, E. Tropical wetlands and land use changes: The case of oil palm in neotropical riverine floodplains. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 1\u0026ndash;23 (2022).\u003c/li\u003e\n \u003cli\u003eReid, R. M., Freij, K. W., Maples, J. C. \u0026amp; Biga, P. R. Teneurins and Teneurin C-Terminal Associated Peptide (TCAP) in Metabolism: What\u0026rsquo;s Known in Fish? \u003cem\u003eFront. Neurosci\u003c/em\u003e. \u003cstrong\u003e13\u003c/strong\u003e, 1\u0026ndash;7 (2019).\u003c/li\u003e\n \u003cli\u003eLee Reid, R. M. Investigating growth-related hormones and their roles in metabolism in fish skeletal muscle. vol. 5 (University of Alabama Birmingham, 2020).\u003c/li\u003e\n \u003cli\u003eReid, R. M., Reid, A. L., Lovejoy, D. A. \u0026amp; Biga, P. R. Teneurin C-Terminal Associated Peptide (TCAP)-3 Increases Metabolic Activity in Zebrafish. \u003cem\u003eFront. Mar. Sci\u003c/em\u003e. \u003cstrong\u003e7\u003c/strong\u003e, 1\u0026ndash;16 (2021).\u003c/li\u003e\n \u003cli\u003eAbramov, T. et al. A novel role for Teneurin C-terminal Associated Peptide (TCAP) in the regulation of cardiac activity in the Sydney rock oyster, Saccostrea glomerata. \u003cem\u003eFront. Endocrinol. (Lausanne).\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1\u0026ndash;14 (2023).\u003c/li\u003e\n \u003cli\u003eDuan, Y. et al. Genome‐wide identification and expression analysis of mitogen‐activated protein kinase (MAPK) genes in response to salinity stress in channel catfish (\u003cem\u003eIctalurus punctatus\u003c/em\u003e). \u003cem\u003eJ. Fish Biol\u003c/em\u003e. \u003cstrong\u003e101\u003c/strong\u003e, 972\u0026ndash;984 (2022).\u003c/li\u003e\n \u003cli\u003eLi, L., Chen, S. N., Li, N. \u0026amp; Nie, P. Transcriptional and subcellular characterization of interferon induced protein-35 (IFP35) in mandarin fish, \u003cem\u003eSiniperca chuatsi.\u003c/em\u003e \u003cem\u003eDev. Comp. Immunol.\u003c/em\u003e \u003cstrong\u003e115\u003c/strong\u003e, 103877 (2021).\u003c/li\u003e\n \u003cli\u003eChen, J. et al. Interferon-inducible Myc/STAT-interacting protein Nmi associates with IFP 35 into a high molecular mass complex and inhibits proteasome-mediated degradation of IFP 35. \u003cem\u003eJ. Biol. Chem\u003c/em\u003e. \u003cstrong\u003e275\u003c/strong\u003e, 36278\u0026ndash;36284 (2000).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1. Estimated levels of genetic diversity for the armored catfish population (\u003cem\u003ePterygoplichthys\u003c/em\u003e sp.) from the Grijalva-Usumacinta region. The data was derived from 22,595 single nucleotide polymorphisms (SNPs) from \u003cem\u003ede novo\u003c/em\u003e dataset and 3,059 SNPs from the mapped dataset. The following metrics were calculated: N: number of individuals; \u003cem\u003eH\u003csub\u003eO\u003c/sub\u003e\u003c/em\u003e: observed heterozygosity; \u003cem\u003euH\u003csub\u003eE\u003c/sub\u003e\u003c/em\u003e: unbiased expected heterozygosity; \u0026pi;: nucleotide diversity; \u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e: genetic differentiation; and \u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e: inbreeding coefficient.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"823\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 98px;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 382px;\"\u003e\n \u003cp\u003e\u003cem\u003ede novo\u0026nbsp;\u003c/em\u003edataset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 344px;\"\u003e\n \u003cp\u003emapped dataset\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003csub\u003eO\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003euH\u003csub\u003eE\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026pi;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003csub\u003eO\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003euH\u003csub\u003eE\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026pi;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eLacant\u0026uacute;n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTenosique\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eCatazaj\u0026aacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTres Brazos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eAmacohite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.3779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.2724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.0279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ddRADseq, genotype-environment association, invasion genomics, Mexico, Pterygoplichthys, reference transcriptome, selection","lastPublishedDoi":"10.21203/rs.3.rs-8308737/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8308737/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Uncovering the mechanisms underlying rapid genetic adaptation can provide insights into adaptive evolution of invasive species. The armored catfish (Pterygoplichthys sp.) is an invasive species that has negative ecological impacts. Morphological and genetic data, based on mitochondrial DNA, suggested that armored catfish from the Grijalva-Usumacinta basins, Mexico, are hybrids. We used a double digest restriction-site associated DNA (ddRAD-seq) approach to gain understanding about the identity of the armored catfish of the Grijalva-Usumacinta River Basins, and to identify candidate loci that could be facilitating adaptation to environmental conditions in invaded areas. We sampled 103 armored catfish (Pterygoplichthys sp). We assembled a transcriptome to be used as reference for SNP calling. We performed de novo and transcriptome reference-based SNP calling (22,595 SNPs and 3,083 SNPs, respectively). High levels of genetic diversity (HE = 0.378 de novo, HE = 0.265 reference), heterozygotes excess (FIS = -0.0129 de novo, FIS = -0.429 reference) and low levels of genetic differentiation (FST = 0.0083 de novo and FST = 0.0086 reference), support the hypothesis of rapid dissemination and population expansion from a population of hybrid origin. We identified 54 candidate loci; of these, one relates to stress tolerance, and another to osmotic stress. Hybridization, high genetic variation and selection may play a relevant role in the invasion success of armored catfish. To control the populations of this invasive species, it is advisable to minimize habitat degradation and implement habitat restoration. Hybridization in aquariums and other facilities for its reproduction and commercialization should be prevented.","manuscriptTitle":"Evolutionary drivers of invasion: hybridization and local adaptation in the Amazon sailfin catfish","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 05:13:24","doi":"10.21203/rs.3.rs-8308737/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49c6e3b0-933f-4dfc-97e2-3cc8b429c343","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59286025,"name":"Biological sciences/Evolution/Population genetics"},{"id":59286026,"name":"Biological sciences/Ecology/Invasive species"},{"id":59286027,"name":"Biological sciences/Genetics/Evolutionary biology"}],"tags":[],"updatedAt":"2026-03-27T09:42:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 05:13:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8308737","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8308737","identity":"rs-8308737","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}