Ecological restoration of mangrove ecosystems that became susceptible to recent habitat perturbations is crucial for tropical coast conservation. The white mangrove Laguncularia racemosa, a pioneer species inhabiting intertidal environments of the Atlantic East Pacific (AEP) region, has been used for reforestation in China for decades. However, the molecular mechanisms underlying its fast growth and high adaptive potential remain unknown. Using PacBio single-molecule real-time sequencing, we completed a high-quality L. racemosa genome assembly covering 1105 Mb with scaffold N50 of 3.46 Mb. Genomic phylogeny shows that L. racemosa invaded intertidal zones during a period of global warming. Multi-level genomic convergence analyses between L. racemosa and three native dominant mangrove clades show that they experienced convergent changes in genes involved in nutrient absorption and high salinity tolerance. This may explain successful L. racemosa adaptation to stressful intertidal environments after introduction. Without recent whole-genome duplications or activated transposable elements, L. racemosa has retained many tandem gene duplications. Some of them are involved in auxin biosynthesis, intense light stress and cold stress response pathways, associated with L. racemosa's ability to grow fast under high light or cold conditions when used for reforestation. In summary, our study identifies shared mechanisms of intertidal environmental adaptation and unique genetic changes underlying fast growth in mangrove-unfavourable conditions and sheds light on the molecular mechanisms of the white mangrove utility in ecological restoration.

The Harbour porpoise (Phocoena phocoena) is a highly mobile cetacean species primarily occurring in coastal and shelf waters across the Northern hemisphere. It inhabits heterogeneous seascapes broadly varying in salinity and temperature. Here, we produced 74 whole genomes at intermediate coverage to study Harbour porpoise's evolutionary history and investigate the role of local adaptation in the diversification into subspecies and populations. We identified ~6 million high quality SNPs sampled at eight localities across the North Atlantic and adjacent waters, which we used for population structure, demographic and genotype-environment association analyses. Our results suggest a genetic differentiation between three subspecies (P.p. relicta, P.p. phocoena and P.p. meridionalis), and three distinct populations within P.p. phocoena: Atlantic, Belt Sea and Proper Baltic Sea. Effective population size and Tajima's D suggest population contraction in Black Sea and Iberian porpoises, but expansion in the P.p. phocoena populations. Phylogenetic trees indicate post-glacial colonization from a southern refugium. Genotype-environment association analysis identified salinity as major driver in genomic variation and we identified candidate genes putatively underlying adaptation to different salinity. Our study highlights the value of whole genome resequencing to unravel subtle population structure in highly mobile species, shows how strong environmental gradients and local adaptation may lead to population differentiation, and how neutral and adaptive markers can give different perspectives on population subdivision. The results have great conservation implications as we found inbreeding and low genetic diversity in the endangered Black Sea subspecies and identified the critically endangered Proper Baltic Sea porpoises as a separate population.

Single-nucleotide polymorphism (SNP) analysis is a powerful tool for population genetics, pedigree reconstruction and phenotypic trait mapping. However, the untapped potential of SNP markers to discriminate the sex of individuals in species with reduced sexual dimorphism or of individuals during immature stages remains a largely unexplored avenue. Here, we developed a novel protocol for molecular sexing of birds based on the detection of unique Z- and W-linked SNP markers. Our method is based on the identification of two unique loci, one in each sexual chromosome. Individuals are considered males when they show no calls for the W-linked SNP and are heterozygous or homozygous for the Z-linked SNP, while females exhibit both Z- and W-linked SNP calls. We validated the method in the Jackdaw (Corvus monedula). The reduced sexual dimorphism in this species makes it difficult to identify the sex of individuals in the wild. We assessed the reliability of the method using 36 individuals of known sex and found that their sex was correctly assigned in 100% of cases. The sex-linked markers also proved to be widely applicable for discriminating males and females from a sample of 927 genotyped individuals at different maturity stages, with an accuracy of 99.5%. Since SNP markers are increasingly used in quantitative genetic analyses of wild populations, the approach we propose has great potential to be integrated into broader genetic research programmes without the need for additional sexing techniques.

Infectious diseases impact numerous organisms. Knowledge of host-pathogen interactions and host responses to infection is crucial for conservation and management. Obtaining this knowledge quickly is made increasingly possible by a variety of genomic approaches, yet, for many species the bottleneck to understanding this, remains access to appropriate samples and data. Lack of sample availability has also limited our understanding of how pathogens and the immune responses of hosts change over time. Archival materials may provide a way to explore pathogen emergence and host responses over multiple-possibly hundreds-of years. Here, we tested whether formalin-fixed paraffin-embedded (FFPE) tissue samples could be used to understand an unknown pathology, lamprey reddening syndrome (LRS), affecting pouched lampreys (Geotria australis). Our differential expression analyses of dermal tissues from four unaffected lampreys and eight affected lampreys collected in 2012 alluded to several potential agents associated with LRS. Interestingly, the pathways associated with viral infections were overrepresented in affected versus unaffected lamprey. Gene ontology analyses of the affected and non-affected lampreys also provided new insights into the largely understudied immune responses of pouched lampreys. Our work confirms that FFPE samples can be used to infer information about the transcriptional responses of a wildlife species affected by unknown historical pathologies/syndromes. In addition, the use of FFPE samples for transcriptomics offers many opportunities to investigate the genomic responses of a species to a variety of environmental changes. We conclude with a discussion about how to best sample and utilize these unique archival resources for future wildlife transcriptomic studies.

Molecular methods including metabarcoding and quantitative polymerase chain reaction have shown promise for estimating species abundance by quantifying the concentration of genetic material in field samples. However, the relationship between specimen abundance and detectable concentrations of genetic material is often variable in practice. DNA mixture analysis represents an alternative approach to quantify specimen abundance based on the presence of unique alleles in a sample. The DNA mixture approach provides novel opportunities to inform ecology and conservation by estimating the absolute abundance of target taxa through molecular methods; yet, the challenges associated with genotyping many highly variable markers in mixed-DNA samples have prevented its widespread use. To advance molecular approaches for abundance estimation, we explored the utility of microhaplotypes for DNA mixture analysis by applying a 125-marker panel to 1179 Chinook salmon (Oncorhynchus tshawytscha) smolts from the Sacramento-San Joaquin Delta, California, USA. We assessed the accuracy of DNA mixture analysis through a combination of mock mixtures containing DNA from up to 20 smolts and a trophic ecological application enumerating smolts in predator diets. Mock DNA mixtures of up to 10 smolts could reliably be resolved using microhaplotypes, and increasing the panel size would likely facilitate the identification of more individuals. However, while analysis of predator gastrointestinal tract contents indicated DNA mixture analysis could discern the presence of multiple prey items, poor and variable DNA quality prevented accurate genotyping and abundance estimation. Our results indicate that DNA mixture analysis can perform well with high-quality DNA, but methodological improvements in genotyping degraded DNA are necessary before this approach can be used on marginal-quality samples.

The negative genetic impacts of gene flow from domestic to wild populations can be dependent on the degree of domestication and exacerbated by the magnitude of pre-existing genetic differences between wild populations and the domestication source. Recent evidence of European ancestry within North American aquaculture Atlantic salmon (Salmo salar) has elevated the potential impact of escaped farmed salmon on often at-risk wild North American salmon populations. Here, we compare the ability of single nucleotide polymorphism (SNP) and microsatellite (SSR) marker panels of different sizes (7-SSR, 100-SSR and 220K-SNP) to detect introgression of European genetic information into North American wild and aquaculture populations. Linear regression comparing admixture predictions for a set of individuals common to the three datasets showed that the 100-SSR panel and 7-SSR panels replicated the full 220K-SNP-based admixture estimates with low accuracy (r2 of .64 and .49, respectively). Additional tests explored the effects of individual sample size and marker number, which revealed that ~300 randomly selected SNPs could replicate the 220K-SNP admixture predictions with greater than 95% fidelity. We designed a custom SNP panel (301-SNP) for European admixture detection in future monitoring work and then developed and tested a python package, salmoneuadmix (https://github.com/CNuge/SalmonEuAdmix), which uses a deep neural network to make de novo estimates of individuals' European admixture proportion without the need to conduct complete admixture analysis utilizing baseline samples. The results demonstrate the mobilization of targeted SNP panels and machine learning in support of at-risk species conservation and management.

Phytophthora agathidicida is a virulent soil pathogen of Aotearoa New Zealand's iconic kauri tree species (Agathis australis (D. Don) Lindl.) and the primary causal agent of kauri dieback disease. To date, only a few control options are available to treat infected kauri that are expressing symptoms of dieback disease. Previous research has identified strains of Penicillium and Burkholderia that inhibited the mycelial growth of P. agathidicida in vitro. However, the mechanisms of inhibition remain unknown. By performing whole genome sequencing, we screened the genomes of four Penicillium and five Burkholderia strains to identify secondary metabolite encoding biosynthetic gene clusters (SM-BGCs) that may be implicated in the production of antimicrobial compounds. We identified various types of SM-BGCs in the genome of each strain, including polyketide synthases (PKSs), non-ribosomal peptide synthetases (NRPSs), and terpenes. Across all four of the Penicillium strains, five SM-BGCs were detected that encoded the biosynthesis of napthopyrone, clavaric acid, pyranonigrin E, dimethyl coprogen and asperlactone. Across all five of the Burkholderia strains, three SM-BGCs were detected that encoded the biosynthesis of ornibactin, pyochelin and pyrrolnitin. Our analysis detected numerous SM-BGCs which could not be characterised. Further efforts should be made to identify the compounds encoded by these SM-BGCs so that we can explore their antimicrobial potential. The potential inhibitory effects of the compounds encoded by the SM-BGCs identified in this study may be worthy of further investigation for their effect on the growth and virulence of P. agathidicida.

Dispersal is a crucial mechanism to living beings, allowing them to reach new resources such that populations and species can occupy new environments. However, directly observing the dispersal mechanisms of widespread species can be costly or even impractical, which is the case for mangrove trees. The influence of ocean currents on mangrove dispersal is increasingly evident; however, few studies mechanistically relate the patterns of population distribution with the dispersal by oceanic currents under an integrated framework. Here, we evaluate the role of oceanic currents on connectivity of Rhizophora mangle along the Southwest Atlantic. We inferred population genetic structure and migration rates, simulated the displacement of propagules and tested our hypotheses with Mantel tests and redundancy analysis. We observed populations structured in two major groups, north and south, which is corroborated by other studies with Rhizophora and other coastal plants. Inferred recent migration rates do not indicate ongoing gene flow between sites. Conversely, long-term migration rates were low across groups and contrasting dispersal patterns within each one, which is consistent with long-distance dispersal events. Our hypothesis tests suggest that both isolation by distance and isolation by oceanography (derived from the oceanic currents) can explain the neutral genetic variation of R. mangle in the region. Our findings expand current knowledge of mangrove connectivity and highlight how the association of molecular methods with oceanographic simulations improve the interpretation of the dispersal process. This integrative approach is a cost- and time-efficient strategy to include dispersal and connectivity data into marine protected areas planning and management.

Genomics can play important roles in biodiversity conservation, especially for Extinct-in-the-Wild species where genetic factors greatly influence risk of total extinction and probability of successful reintroductions. The Christmas Island blue-tailed skink (Cryptoblepharus egeriae) and Lister's gecko (Lepidodactylus listeri) are two endemic reptile species that went extinct in the wild shortly after the introduction of a predatory snake. After a decade of management, captive populations have expanded from 66 skinks and 43 geckos to several thousand individuals; however, little is known about patterns of genetic variation in these species. Here, we use PacBio HiFi long-read and Hi-C sequencing to generate highly contiguous reference genomes for both reptiles, including the XY chromosome pair in the skink. We then analyse patterns of genetic diversity to infer ancient demography and more recent histories of inbreeding. We observe high genome-wide heterozygosity in the skink (0.007 heterozygous sites per base-pair) and gecko (0.005), consistent with large historical population sizes. However, nearly 10% of the blue-tailed skink reference genome falls within long (>1 Mb) runs of homozygosity (ROH), resulting in homozygosity at all major histocompatibility complex (MHC) loci. In contrast, we detect a single ROH in Lister's gecko. We infer from the ROH lengths that related skinks may have established the captive populations. Despite a shared recent extinction in the wild, our results suggest important differences in these species' histories and implications for management. We show how reference genomes can contribute evolutionary and conservation insights, and we provide resources for future population-level and comparative genomic studies in reptiles.

Understanding the evolutionary consequences of anthropogenic change is imperative for estimating long-term species resilience. While contemporary genomic data can provide us with important insights into recent demographic histories, investigating past change using present genomic data alone has limitations. In comparison, temporal genomics studies, defined herein as those that incorporate time series genomic data, utilize museum collections and repeated field sampling to directly examine evolutionary change. As temporal genomics is applied to more systems, species and questions, best practices can be helpful guides to make the most efficient use of limited resources. Here, we conduct a systematic literature review to synthesize the effects of temporal genomics methodology on our ability to detect evolutionary changes. We focus on studies investigating recent change within the past 200 years, highlighting evolutionary processes that have occurred during the past two centuries of accelerated anthropogenic pressure. We first identify the most frequently studied taxa, systems, questions and drivers, before highlighting overlooked areas where further temporal genomic studies may be particularly enlightening. Then, we provide guidelines for future study and sample designs while identifying key considerations that may influence statistical and analytical power. Our aim is to provide recommendations to a broad array of researchers interested in using temporal genomics in their work.

There is growing interest in the role of structural variants (SVs) as drivers of local adaptation and speciation. From a biodiversity genomics perspective, the characterization of genome-wide SVs provides an exciting opportunity to complement single nucleotide polymorphisms (SNPs). However, little is known about the impacts of SV discovery and genotyping strategies on the characterization of genome-wide SV diversity within and among populations. Here, we explore a near whole-species resequence data set, and long-read sequence data for a subset of highly represented individuals in the critically endangered kākāpō (Strigops habroptilus). We demonstrate that even when using a highly contiguous reference genome, different discovery and genotyping strategies can significantly impact the type, size and location of SVs characterized genome-wide. Further, we found that the mean number of SVs in each of two kākāpō lineages differed both within and across generations. These combined results suggest that genome-wide characterization of SVs remains challenging at the population-scale. We are optimistic that increased accessibility to long-read sequencing and advancements in bioinformatic approaches including multireference approaches like genome graphs will alleviate at least some of the challenges associated with resolving SV characteristics below the species level. In the meantime, we address caveats, highlight considerations, and provide recommendations for the characterization of genome-wide SVs in biodiversity genomic research.

For both undivided and subdivided populations, the consensus method to maintain genetic diversity is the Optimal Contribution (OC) method. For subdivided populations, this method determines the optimal contribution of each candidate to each subpopulation to maximize global genetic diversity (which implicitly optimizes migration between subpopulations) while balancing the relative levels of coancestry between and within subpopulations. Inbreeding can be controlled by increasing the weight given to within-subpopulation coancestry (λ). Here we extend the original OC method for subdivided populations that used pedigree-based coancestry matrices, to the use of more accurate genomic matrices. Global levels of genetic diversity, measured as expected heterozygosity and allelic diversity, their distributions within and between subpopulations, and the migration pattern between subpopulations, were evaluated via stochastic simulations. The temporal trajectory of allele frequencies was also investigated. The genomic matrices investigated were (i) the matrix based on deviations of the observed number of alleles shared by two individuals from the expected number under Hardy-Weinberg equilibrium; and (ii) a matrix based on a genomic relationship matrix. The matrix based on deviations led to higher global and within-subpopulation expected heterozygosities, lower inbreeding and similar allelic diversity than the second genomic and pedigree-based matrices when a relatively high weight was given to the within-subpopulation coancestries (λ ≥ 5). Under this scenario, allele frequencies moved only slightly away from the initial frequencies. Therefore, the recommended strategy is to use the former matrix in the OC methodology giving a high weight to the within-subpopulation coancestry.

Genomic information can aid in the establishment of sustainable management plans for commercially exploited marine fishes, aiding in the long-term conservation of these resources. The southern African hakes (Merluccius capensis and M. paradoxus) are commercially valuable demersal fishes with similar distribution ranges but exhibiting contrasting life histories. Using a comparative framework based on Pool-Seq genome-wide SNP data, we investigated whether the evolutionary processes that shaped extant patterns of diversity and divergence are shared among these two congeneric fishes, or unique to each one. Our findings revealed that M. capensis and M. paradoxus show similar levels of genome-wide diversity, despite different census sizes and life-history features. In addition, M. capensis shows three highly structured geographic populations across the Benguela Current region (one in the northern Benguela and two in the southern Benguela), with no consistent genome-environment associations detected. In contrast, although population structure and outlier analyses suggested panmixia for M. paradoxus, reconstruction of its demographic history suggested the presence of an Atlantic-Indian Ocean subtle substructuring pattern. Therefore, it appears that M. paradoxus might be composed by two highly connected populations, one in the Atlantic and one in the southwest Indian Ocean. The reported similar low levels of genomic diversity, as well as newly discovered genetically distinct populations in both hake species can thus assist in informing and improving conservation and management plans for the commercially important southern African Merluccius.

Genomic data and machine learning approaches have gained interest due to their potential to identify adaptive genetic variation across populations and to assess species vulnerability to climate change. By identifying gene-environment associations for putatively adaptive loci, these approaches project changes to adaptive genetic composition as a function of future climate change (genetic offsets), which are interpreted as measuring the future maladaptation of populations due to climate change. In principle, higher genetic offsets relate to increased population vulnerability and therefore can be used to set priorities for conservation and management. However, it is not clear how sensitive these metrics are to the intensity of population and individual sampling. Here, we use five genomic datasets with varying numbers of SNPs (NSNPs  = 7006-1,398,773), sampled populations (Npop  = 23-47) and individuals (Nind  = 185-595) to evaluate the estimation sensitivity of genetic offsets to varying degrees of sampling intensity. We found that genetic offsets are sensitive to the number of populations being sampled, especially with less than 10 populations and when genetic structure is high. We also found that the number of individuals sampled per population had small effects on the estimation of genetic offsets, with more robust results when five or more individuals are sampled. Finally, uncertainty associated with the use of different future climate scenarios slightly increased estimation uncertainty in the genetic offsets. Our results suggest that sampling efforts should focus on increasing the number of populations, rather than the number of individuals per populations, and that multiple future climate scenarios should be evaluated to ascertain estimation sensitivity.

Haplotype networks can be used to demonstrate the genealogical relationships of DNA sequences within species, and thus are widely applied in population genetics, molecular ecology, epidemiology and evolutionary studies. However, existing programs become computationally infeasible as the sample size increases. Here, we present fastHaN, an efficient and scalable program suitable for constructing haplotype networks for large samples. On a data set with the haplotype length of 30 kb, the Median Joining Network (MJN) algorithm implemented by fastHaN is 3000 times faster than PopART and 70 times faster than NETWORK in single-threaded mode. The implementation of the Templeton-Crandall-Sing (TCS) algorithm is 100 times faster than PopART and 5800 times faster than the TCS software. Moreover, fastHaN also enables multi-threaded mode with scalability. The source code is freely available on https://github.com/ChenHuaLab/fastHaN/. A web-based version is also available on https://ngdc.cncb.ac.cn/haplotype/.

A high-quality reference genome can be a valuable resource for threatened species by providing a foundation to assess their evolutionary potential to adapt to future pressures such as environmental change. We assembled the genome of a female hihi (Notiomysits cincta), a threatened passerine bird endemic to Aotearoa New Zealand. The assembled genome is 1.06 Gb, and is of high quality and highly contiguous, with a contig N50 of 7.0 Mb, estimated QV of 44 and a BUSCO completeness of 96.8%. A male assembly of comparable quality was generated in parallel. A population linkage map was used to scaffold the autosomal contigs into chromosomes. Female and male sequence coverage and comparative genomics analyses were used to identify Z-, and W-linked contigs. In total, 94.6% of the assembly length was assigned to putative nuclear chromosome scaffolds. Native DNA methylation was highly correlated between sexes, with the W chromosome contigs more highly methylated than autosomal chromosomes and Z contigs. 43 differentially methylated regions were identified, and these may represent interesting candidates for the establishment or maintenance of sex differences. By generating a high-quality reference assembly of the heterogametic sex, we have created a resource that enables characterization of genome-wide diversity and facilitates the investigation of female-specific evolutionary processes. The reference genomes will form the basis for fine-scale assessment of the impacts of low genetic diversity and inbreeding on the adaptive potential of the species and will therefore enable tailored and informed conservation management of this threatened taonga (treasured) species.

The global sulphur cycle has implications for human health, climate change, biogeochemistry and bioremediation. The organosulphur compounds that participate in this cycle not only represent a vast reservoir of sulphur but are also used by prokaryotes as sources of energy and/or carbon. Closely linked to the inorganic sulphur cycle, it involves the interaction of prokaryotes, eukaryotes and chemical processes. However, ecological and evolutionary studies of the conversion of organic sulphur compounds are hampered by the poor conservation of the relevant pathways and their variation even within strains of the same species. In addition, several proteins involved in the conversion of sulphonated compounds are related to proteins involved in sulphur dissimilation or turnover of other compounds. Therefore, the enzymes involved in the metabolism of organic sulphur compounds are usually not correctly annotated in public databases. To address this challenge, we have developed HMSS2, a profiled Hidden Markov Model-based tool for rapid annotation and synteny analysis of organic and inorganic sulphur cycle proteins in prokaryotic genomes. Compared to its previous version (HMS-S-S), HMSS2 includes several new features. HMM-based annotation is now supported by nonhomology criteria and covers the metabolic pathways of important organosulphur compounds, including dimethylsulphoniopropionate, taurine, isethionate, and sulphoquinovose. In addition, the calculation speed has been increased by a factor of four and the available output formats have been extended to include iTol compatible data sets, and customized sequence FASTA files.

Recent studies have highlighted the significant role of copy number variants (CNVs) in phenotypic diversity, environmental adaptation and species divergence across eukaryotes. The presence of CNVs also has the potential to introduce genotyping biases, which can pose challenges to accurate population and quantitative genetic analyses. However, detecting CNVs in genomes, particularly in non-model organisms, presents a formidable challenge. To address this issue, we have developed a statistical framework and an accompanying r software package that leverage allelic-read depth from single nucleotide polymorphism (SNP) data for accurate CNV detection. Our framework capitalises on two key principles. First, it exploits the distribution of allelic-read depth ratios in heterozygotes for individual SNPs by comparing it against an expected distribution based on binomial sampling. Second, it identifies SNPs exhibiting an apparent excess of heterozygotes under Hardy-Weinberg equilibrium. By employing multiple statistical tests, our method not only enhances sensitivity to sampling effects but also effectively addresses reference biases, resulting in optimised SNP classification. Our framework is compatible with various NGS technologies (e.g. RADseq, Exome-capture). This versatility enables CNV calling from genomes of diverse complexities. To streamline the analysis process, we have implemented our framework in the user-friendly r package 'rCNV', which automates the entire workflow seamlessly. We trained our models using simulated data and validated their performance on four datasets derived from different sequencing technologies, including RADseq (Chinook salmon-Oncorhynchus tshawytscha), Rapture (American lobster-Homarus americanus), Exome-capture (Norway spruce-Picea abies) and WGS (Malaria mosquito-Anopheles gambiae).

The current biodiversity and climate crises highlight the need for efficient tools to monitor terrestrial ecosystems. Here, we provide evidence for the use of airborne eDNA analyses as a novel method for detecting terrestrial vertebrate communities in nature. Metabarcoding of 143 airborne eDNA samples collected during 3 days in a mixed forest in Denmark yielded 64 bird, mammal, fish and amphibian taxa, of which the detected 57 'wild' taxa represent over a quarter of the around 210 terrestrial vertebrates that occur in the overall area. We provide evidence for the spatial movement and temporal patterns of airborne eDNA and for the influence of weather conditions on vertebrate detections. This study demonstrates airborne eDNA for high-resolution biomonitoring of vertebrates in terrestrial systems and elucidates its potential to guide global nature management and conservation efforts in the ongoing biodiversity crisis.

Clonal propagation and extensive dispersal of seeds and asexual propagules are two important features of aquatic plants that help them adapt to aquatic environments. Accurate measurements of clonality and effective clonal dispersal are essential for understanding the evolution of aquatic plants. Here, we first assembled a high-quality chromosome-level genome of a widespread emergent aquatic plant Sparganium stoloniferum to provide a reference for its population genomic study. We then performed high-depth resequencing of 173 individuals from 20 populations covering different basins across its range in China. Population genomic analyses revealed three genetic lineages reflecting the northeast (NE), southwest (SW) and northwest (NW) of its geographical distribution. The NE lineage diverged in the middle Pleistocene while the SW and NW lineages diverged until about 2400 years ago. Clonal relationship analyses identified nine populations as monoclonal population. Dispersal of vegetative propagules was identified between five populations covering three basins in the NE lineage, and dispersal distance was up to 1041 km, indicating high dispersibility in emergent aquatic plant species. We also identified lineage-specific positively selected genes that are likely to be involved in adaptations to saline wetlands and high-altitude environments. Our findings accurately measure the clonality, determine the dispersal range and frequency of vegetative propagules, and detect genetic signatures of local adaptation in a widespread emergent aquatic plant species, providing new perspectives on the evolution of aquatic plants.