Molecular Ecology Resources最新文献

Sequencing via target capture has been used to great effect in phylogenetic studies of organisms such as insects, arachnids and vertebrates. However, other taxa have received limited genomic attention despite their diversity and the intensity of research on such groups. Here, we describe generalised probe sets targeting ultraconserved elements (UCEs) for members of the crustacean orders Amphipoda and Isopoda in the superorder Peracarida. These sets employ ~10,000–100,000 probes targeting up to 10,000 loci. In silico analyses of these probe sets recovered an average of 5087 loci, while an average of 4633 was retained post-filtering. Phylogenetic analysis of these datasets resulted in well-supported trees that align with previously reconstructed relationships among the taxa selected while also providing resolution of previously uncertain nodes. Following the in silico analysis, an in vitro analysis targeting several amphipod and isopod families was conducted. This analysis extracted up to 4864 unique loci from the taxa sequenced, with an average of 1897 loci among all taxa. This represents an order-of-magnitude increase versus previously published sets, which were only able to recover < 250 UCEs among peracarid taxa. Phylogenetic analyses of the data generated in vitro resulted in well-supported trees that were resolved at both shallow and deep taxonomic levels. Both analyses demonstrate the utility of these probe sets for phylogenomic research within the Peracarida. Additional attention to members of the superorder using target enrichment will doubtlessly assist in resolving poorly understood aspects of their evolutionary history and expand current knowledge of this group.

Climate change and invasive species are leading drivers of biodiversity loss, with island ecosystems being especially vulnerable. South Georgia, a remote sub-Antarctic island, is 170 km long with approximately 30,000 ha of vegetated coastal areas, as snow and ice dominate the inland regions. Human activities on the island have historically introduced non-native species, resulting in 41 introduced vascular plant species compared with only 24 native ones. To address this imbalance, the South Georgia Non-Native Plant Management Strategy was implemented (2016–2020) to control non-native plant populations. We assessed emergent seedlings from South Georgia soil samples and wind-dispersed seeds to determine which species persist in the soil seed bank and contribute to dispersal. Using a molecular barcoding approach, we evaluated traditional markers (rbcL and matK) and optimized a high-throughput Angiosperms353 sequencing pipeline for accurate seedling identification. We generated a reference library covering all native and non-native species and applied this to 1,498 emergent seedlings and 737 trapped seeds. Molecular barcoding identified 21 species, including 10 non-natives and 11 natives. Strikingly, 84% of emergent seedlings were non-native, with Class III invasive species (Cerastium fontanum, Poa annua, Taraxacum officinale) dominating across most sites and in all wind traps. By contrast, Class I and II species occurred rarely and only at a few sites, indicating that management efforts have substantially reduced their spread, though viable seeds persist in the soil. These findings highlight both the continued threat from persistent seed banks of dominant invaders and the value of molecular barcoding for long-term monitoring. Our approach provides a framework for biosecurity and restoration management in South Georgia and other vulnerable ecosystems under climate change pressures.

Studying the taxonomic composition of phytoplankton has been revolutionised by the emergence of metabarcoding approaches which theoretically provides access to all phytoplankton diversity. However, metabarcoding has its limitations, including biases related to primers efficiency in covering all phytoplanktonic taxonomic groups, biases introduced during amplification and due to databases completion. To assess the importance of these biases, we compared the compositions of phytoplankton in two large peri-alpine lakes, using metabarcoding with primers targeting hypervariable regions of chloroplastic 16S and 23S rRNA, designed to cover the full taxonomic diversity of phytoplankton, and shotgun sequencing. To be able to directly compare the two methods, we extracted reads coming from the full sequences of these rRNA genes in shotgun sequencing data and used the same reference database for taxonomic assignation. The results show that the relative abundances of dominant groups of phytoplankton, including Cyanobacteria, Cryptophyta, Haptophyta, and Bacillariophyta, are consistent between the two approaches, validating the primers used for metabarcoding analysis. However, two phyla, Chlorophyta and non-diatom Ochrophyta showed greater divergence in their relative abundance, due to under-amplification or lack of amplification of certain taxonomic groups in metabarcoding. This is likely due to the high diversity of these groups, not covered yet by the reference databases, as well as a possible presence of introns in their choloroplastic ribosomal genes. These limitations are expected to be overcome with increasing reference database completion and the use of long-read metabarcoding. Overall, our study confirms the relevance of using chloroplastic primers for assessing the phytoplankton composition of lakes.

Structural variation is increasingly recognised as a pivotal contributor to genomic diversity in marine invertebrates, yet its extent and evolutionary significance remain poorly characterised in many species. Haplotype-phased genome assembly is an excellent resource for studying such variations by comparing homologous chromosomes. Here, we focus on abalones (genus Haliotis) that are iconic marine invertebrates originated in the Cretaceous period. They have long drawn attention for their ecological roles, distinctive morphology and cultural and economic value. In this study, we constructed a haplotype-phased genome assembly for the western Pacific abalone, Haliotis gigantea, using high-fidelity (HiFi) long-read sequencing and high-resolution chromosome conformation capture (Hi-C) data. The primary and alternative assemblies each comprised 18 long scaffolds (> 50 Mb), consistent with the species' diploid chromosome number (2n = 36), and contained 96.5% and 96.2% complete single-copy Metazoa Benchmarking Universal Single-Copy Orthologs genes, respectively, indicating high assembly quality. Comparative analysis of the two haplotypes revealed three homologous chromosomes with large-scale non-syntenic regions caused by extensive segmental duplications, with each enriched in distinct gene domains that may be related to adaptive evolution. These non-syntenic chromosomes likely originated in abalone evolution, as they were conserved across both closely and distantly related species, and led to the accumulation of duplicated genes in abalones. Our genome assembly highlights the evolutionary importance of non-syntenic structural variation in shaping genome architecture and suggests that such variation may play a broader role in functional diversification, adaptation and consequent prosperity across abalones.

Estimating the size of wild populations is a critical priority for ecologists and conservation biologists, but tools to do so are often labour intensive and expensive. A promising set of newer approaches are based on genetic data, which can be cheaper to obtain and less invasive than information from more direct observation. One of these approaches is close-kin mark-recapture (CKMR), a type of method that uses genetic data to identify kin pairs and estimates population size from these pairs. Although CKMR methods are promising, one limitation to using them more broadly is a lack of CKMR models that can deal with spatially structured populations and spatial heterogeneity in sampling. In this paper, we introduce a spatially explicit approach to CKMR that uses individual-based simulation in concert with a deep convolutional neural network to estimate population sizes. Using simulations, we show that our method, CKMRnn, is highly accurate, even in the face of spatial heterogeneity in sampling and spatial population structure, and demonstrate that it can account for potential confounders such as unknown population histories. Finally, to demonstrate the accuracy of our method in an empirical system, we apply CKMRnn to data from a Ugandan elephant population, and show that point estimates from our method recapitulate those from traditional estimators but that the confidence interval on our estimator is approximately 30% narrower.

Freshwater ecosystems are under increasing pressure from pollution, habitat degradation and climate change, highlighting the need for reliable biomonitoring approaches to assess ecosystem health and identify the causes of biodiversity and ecosystem service loss. Characterisation of freshwater microbiomes has the potential to be an important tool for understanding freshwater ecology, ecosystem health and ecosystem function. High-throughput sequencing technologies, such as Illumina short-read and Pacific Biosciences long-read sequencing, are widely used for microbial community analysis. However, the relative performance of these approaches for monitoring freshwater microbiomes has not been well explored. In this study, we compared the performance of long- and short-read sequencing approaches to assess archaeal and bacterial diversity in 42 river biofilm samples across seven distinct river sites in England by targeting the 16S ribosomal RNA gene. Our findings demonstrated that longer reads generated by PacBio sequencing provide a higher taxonomic resolution, enabling the classification of taxa that remained unassigned in the short-read Illumina datasets. This enhanced resolution is particularly beneficial for biodiversity assessments because it improves species-level identification, which is crucial for ecological monitoring. Despite this, both sequencing methods produced comparable bacterial community structures regarding taxon relative abundance, suggesting that the sequencing approach does not profoundly affect the comparative assessment of community composition. However, while Illumina offers higher throughput and cost efficiency, PacBio's ability to resolve complex microbial communities highlights its potential for studies requiring precise taxonomic identification.

Marine environmental DNA (eDNA) metabarcoding data are beginning to accumulate, even for remote and poorly studied areas, such as marine environments. These data enable us to identify distributions of target organisms and then to compare biological compositions between different marine areas. However, there is no platform to effectively utilise and accumulate these data. In this study, we developed eDNAmap, a web-based platform designed to analyse and store marine eDNA metabarcoding data. By uploading species or sequence composition data with location information, eDNAmap users can automatically (1) plot sampling locations on a map, (2) generate a heatmap to evaluate potential batch effects arising from methodological differences and (3) perform nonmetric multidimensional scaling and cluster analyses using similarity indices. Furthermore, users can specify scientific names to display species distributions and upload species lists to assess species compositions of the target sea area. As an example, fish sequence composition data obtained from 55 stations around the Watase line—believed to exist along a geographic canyon known as the Tokara Gap—were used to verify its existence using eDNAmap. The platform includes a database primarily consisting of teleost fish data from the Northwestern Pacific, which users can analyse similarly to their own uploads. Although originally designed for fish, eDNAmap is flexible enough to handle data from other marine organisms. Analysing multiple taxa enables the detection of concordant biogeographic patterns across different groups, which can strengthen ecological interpretations and lay the groundwork for identifying environmental drivers shaping community structures. eDNAmap is available at https://github.com/jun-inoue/eDNAmap.

Long terminal repeat retrotransposons (LTR-RTs) are recognised as a significant evolutionary force capable of shaping the structure and function of the genomes in eukaryotes, including animals, plants, and fungi. However, much remains largely unknown about how LTR-RTs influence the evolution of fungi at the chromosomal level. Here, we assembled the genome of an important plant pathogenic fungus, Diplocarpon coronariae (strain XN1), at the chromosomal level and obtained high-precision, full-length transcriptome annotations through transcriptome evidence and manual curation. Using high-quality genomes and gene annotations, we identified the two-speed genome and constructed a pan-genome graph of D. coronariae. Through comparative genomics, we discovered that LTR-RTs contributed to sequence and structural evolution among different strains of D. coronariae. Based on gene families constructed from the genomes of multiple species within Leotiomycetes, LTR-RTs were found to be involved in the formation of species-specific gene families as well as the expansion of gene families. Furthermore, through interspecies comparative genomics analysis, we identified a young chromosome, Chr15, specifically present in D. coronariae XN1. Chr15 likely originated from conserved topologically associating domains (TADs) and gradually expanded with the burst insertion of LTR-RTs, forming a completely new chromosome. This study provides new insights into the complexity and formation mechanisms of LTR retrotransposon-driven chromosomal and genomic structural evolution in fungi.

Even though environmental DNA metabarcoding is revolutionizing biomonitoring, many critical steps remain unstandardized, leading to arbitrary choices, particularly regarding the selection of metabarcode, clustering method and similarity threshold, among others. Additionally, these studies were hindered by biases resulting from the presence of mislabeled sequences in international databases such as GenBank and the lack of explicit definitions for taxonomic resolution. To address these issues, we developed a robust framework to compare the performance of 22 metabarcodes derived from the same mitogenomes (all available for Actinopterygians in NCBI) against a standardized taxonomic baseline based on COI Barcode Index Numbers (BINs). This framework allows for the separate quantification of over-splitting (splitting the same taxon/BIN) and over-merging (merging different taxon/BIN). Comparison of OTUs obtained with multiple de novo clustering methods to BINs confirmed the metabarcode ranking based on error sums. Although each metabarcode exhibited varying sensitivities to over-merging or over-splitting errors, the clustering threshold emerged as the most important factor influencing biodiversity estimates whatever the clustering method. This led us to propose optimal thresholds for each metabarcode to delineate taxonomic levels (metabarcode gaps). Additionally, we found that taxonomic resolution varied significantly among genes, orders and community diversity, but independently of metabarcode length. Overall, the choice of metabarcode and clustering threshold should aim to minimize over-merging or over-splitting while ensuring accurate lower taxonomic delineations. A set of documented R functions makes this evaluation of taxonomic resolution easily applicable to any other taxonomic group for which a representative set of full genes or mitogenomes is available.

The inclusion of functional traits of protists in environmental sequencing surveys, in addition to the traditional taxonomic framework, is essential for a better understanding of their roles and impacts on ecosystem processes. We provide a database of functional traits for a widespread and important clade of protists—the Amoebozoa—based on extensive literature research in eight trait categories: Habitat, locomotion, nutrition, morphology, morphotype, size, spore formation, and disease-relatedness. The comparison of community traits of the Amoebozoa with sympatric but highly divergent Cercozoa (Rhizaria) revealed both convergent evolution of morphology or locomotion and distinct differences in habitat preference and feeding selectivity. Amoebozoa seem to be rather unselective in their prey choice compared to Cercozoa. Indeed, the feeding preferences of Amoebozoa appeared to be related to cell size, whereas Cercozoa selectively feed on prey. Applications to metatranscriptomic data from soil, litter, and bark surfaces revealed differences in the average community trait compositions and ecosystem functioning, such as an increased proportion of disease-related Amoebozoa in soil or different proportions of nutrition types of Amoebozoa and Cercozoa on bark. This database will facilitate ecological analyses of sequencing data and improve our understanding of the diversity of adaptations of Amoebozoa to the environment and their functional roles in ecosystems.