Molecular Ecology Resources最新文献

The major histocompatibility complex (MHC) is a highly polymorphic gene family that is crucial in immunity, and its diversity can be effectively used as a fitness marker for populations. Despite this, MHC remains poorly characterised in non-model species (e.g., cetaceans: whales, dolphins and porpoises) as high gene copy number variation, especially in the fast-evolving class I region, makes analyses of genomic sequences difficult. To date, only small sections of class I and IIa genes have been used to assess functional diversity in cetacean populations. Here, we undertook a systematic characterisation of the MHC class I and IIa regions in available cetacean genomes. We extracted full-length gene sequences to design pan-cetacean primers that amplified the complete exon 2 from MHC class I and IIa genes in one combined sequencing panel. We validated this panel in 19 cetacean species and described 354 alleles for both classes. Furthermore, we identified likely assembly artefacts for many MHC class I assemblies based on the presence of class I genes in the amplicon data compared to missing genes from genomes. Finally, we investigated MHC diversity using the panel in 25 humpback and 30 southern right whales, including four paternity trios for humpback whales. This revealed copy-number variable class I haplotypes in humpback whales, which is likely a common phenomenon across cetaceans. These MHC alleles will form the basis for a cetacean branch of the Immuno-Polymorphism Database (IPD-MHC), a curated resource intended to aid in the systematic compilation of MHC alleles across several species, to support conservation initiatives.

Mayflies (Ephemeroptera) are among the crucial water and habitat quality bioindicators. However, despite their intensive long-term use in various studies, more reliable mayfly DNA barcode data have been produced in a negligible number of countries, and only ~40% of European species had been barcoded with less than 50% of families covered. Despite being carried out in a small area, our study presents the second-most species-rich DNA reference library of mayflies from Europe and the first comprehensive view from an important biodiversity hotspot such as the Western Carpathians. Within 1153 sequences, 76 morphologically determined species were recorded and added to the Barcode of Life Data System (BOLD) database. All obtained sequences were assigned to 97 BINs, 11 of which were unique and three represented species never barcoded before. Sequences of 16 species with high intraspecific variability were divided into 40 BINs, confirming the presence of cryptic lineages. Due to the low interspecific divergence and the non-existing barcoding gap, sequences of six species were assigned to three shared BINs. Delimitation analyses resulted in 79 and 107 putative species respectively. Bayesian and maximum-likelihood phylogenies confirmed the monophyly of almost all species and complexes of cryptic taxa and proved that DNA barcoding distinguishes almost all studied mayfly species. We have shown that it is still sufficient to thoroughly investigate the fauna of a small but geographically important area to enrich global databases greatly. In particular, the insights gained here transcend the local context and may have broader implications for advancing barcoding efforts.

Diapause, a form of dormancy to delay or halt the reproductive development during unfavourable seasons, has evolved in many insect species. One example is aestivation, an adult-stage diapause enhancing malaria vectors' survival during the dry season (DS) and their re-establishment in the next rainy season (RS). This work develops a novel genetic approach to estimate the number or proportion of individuals undergoing diapause, as well as the breeding sizes of the two seasons, using signals from temporal allele frequency dynamics. Our modelling shows the magnitude of drift is dampened at early RS when previously aestivating individuals reappear. Aestivation severely biases the temporal effective population size ($��N_e�$), leading to overestimation of the DS breeding size by $��1�/�{\left(�1�-�\alpha �\right)}^2�$ across 1 year, where $��\alpha �$ is the aestivating proportion. We find sampling breeding individuals in three consecutive seasons starting from an RS is sufficient for parameter estimation, and perform extensive simulations to verify our derivations. This method does not require sampling individuals in the dormant state, the biggest challenge in most studies. We illustrate the method by applying it to a published data set for Anopheles coluzzii mosquitoes from Thierola, Mali. Our method and the expected evolutionary implications are applicable to any species in which a fraction of the population diapauses for more than one generation, and are difficult or impossible to sample during that stage.

The analyses of environmental DNA (eDNA) and environmental RNA (eRNA) released by organisms into their surrounding environment (water, soil and air) have emerged as powerful tools for monitoring biodiversity. While eDNA has been widely adopted for the non-invasive detection of species and characterization of community composition, the utilization of eRNA is still in its infancy. Due to its functional nature, eRNA holds intriguing potential for biodiversity monitoring offering new avenues of research beyond species detection. For example, conspecifics that are almost genetically identical can exhibit distinct transcriptomic differences depending on their life stage. In this issue of Molecular Ecology Resources, Parsley and Goldberg (2024) demonstrate, through a lab-validated field study, that eRNA can be used to detect distinct life stages of amphibians. This study elegantly demonstrates that eRNA can be used not only to detect invasive or endangered species but also to reveal population demographic information important for guiding effective conservation strategies.

The ability to sex individuals is an important component of many behavioural and ecological investigations and provides information for demographic models used in conservation and species management. However, many birds are difficult to sex using morphological characters or traditional molecular sexing methods. In this study, we developed probabilistic models for sexing birds using quantitative PCR (qPCR) data. First, we quantified distributions of gene copy numbers at a set of six sex-linked genes, including the sex-determining gene DMRT1, for individuals across 17 species and seven orders of birds (n = 150). Using these data, we built predictive logistic models for sex identification and tested their performance with independent samples from 51 species and 13 orders (n = 209). Models using the two loci most highly correlated with sex had greater accuracy than models using the full set of sex-linked loci, across all taxonomic levels of analysis. Sex identification was highly accurate when individuals to be assigned were of species used in model building. Our analytical approach was widely applicable across diverse neognath bird lineages spanning millions of years of evolutionary divergence. Unlike previous methods, our probabilistic framework incorporates uncertainty around qPCR measurements as well as biological variation within species into decision-making rules. We anticipate that this method will be useful for sexing birds, including those of high conservation concern and/or subsistence value, that have proven difficult to sex using traditional approaches. Additionally, the general analytical framework presented in this paper may also be applicable to other organisms with sex chromosomes.

Genetic diversity is frequently described using heterozygosity, particularly in a conservation context. Often, it is estimated using single nucleotide polymorphisms (SNPs); however, it has been shown that heterozygosity values calculated from SNPs can be biased by both study design and filtering parameters. Though solutions have been proposed to address these issues, our own work has found them to be inadequate in some circumstances. Here, we aimed to improve the reliability and comparability of heterozygosity estimates, specifically by investigating how sample size and missing data thresholds influenced the calculation of autosomal heterozygosity (heterozygosity calculated from across the genome, i.e. fixed and variable sites). We also explored how the standard practice of tri- and tetra-allelic site exclusion could bias heterozygosity estimates and influence eventual conclusions relating to genetic diversity. Across three distinct taxa (a frog, Litoria rubella; a tree, Eucalyptus microcarpa; and a grasshopper, Keyacris scurra), we found heterozygosity estimates to be meaningfully affected by sample size and missing data thresholds, partly due to the exclusion of tri- and tetra-allelic sites. These biases were inconsistent both between species and populations, with more diverse populations tending to have their estimates more severely affected, thus having potential to dramatically alter interpretations of genetic diversity. We propose a modified framework for calculating heterozygosity that reduces bias and improves the utility of heterozygosity as a measure of genetic diversity, whilst also highlighting the need for existing population genetic pipelines to be adjusted such that tri- and tetra-allelic sites be included in calculations.

Biomonitoring of marine life has been enhanced in recent years by the integration of innovative DNA-based approaches, which offer advantages over more laborious techniques (e.g. microscopy). However, trade-offs between throughput, sensitivity and quantitative measurements must be made when choosing between the prevailing molecular methodologies (i.e. metabarcoding or qPCR/dPCR). Thus, the aim of the present study was to demonstrate the utility of a microfluidic-enabled high-throughput quantitative PCR platform (HTqPCR) for the rapid and cost-effective development and validation of a DNA-based multi-species biomonitoring toolkit, using larvae of 23 commercially targeted bivalve and crustacean species as a case study. The workflow was divided into three main phases: definition of (off-) target taxa and establishment of reference databases (PHASE 1); selection/development and assessment of molecular assays (PHASE 2); and protocol optimization and field validation (PHASE 3). 42 assays were eventually chosen and validated. Genetic signal not only showed good correlation with direct visual counts by microscopy but also showed the ability to provide quantitative data at the highest taxonomic resolution (species level) in a time- and cost-effective fashion. This study developed a biomonitoring toolkit, demonstrating the considerable advantages of this state-of-the-art technology in boosting the developmental testing and application of panels of molecular assays for the monitoring and management of natural resources. Once developed, this approach provides a cost and time-effective alternative compared to other multi-species approaches (e.g. metabarcoding). In addition, it is transferable to a wide range of species and will aid future monitoring programmes.

Characterizing the processes underlying reproductive isolation between diverging lineages is central to understanding speciation. Here, we present RIDGE—Reproductive Isolation Detection using Genomic polymorphisms—a tool tailored for quantifying gene flow barrier proportion and identifying the relevant genomic regions. RIDGE relies on an Approximate Bayesian Computation with a model-averaging approach to accommodate diverse scenarios of lineage divergence. It captures heterogeneity in effective migration rate along the genome while accounting for variation in linked selection and recombination. The barrier detection test relies on numerous summary statistics to compute a Bayes factor, offering a robust statistical framework that facilitates cross-species comparisons. Simulations revealed RIDGE's efficiency in capturing signals of ongoing migration. Model averaging proved particularly valuable in scenarios of high model uncertainty where no migration or migration homogeneity can be wrongly assumed, typically for recent divergence times <0.1 2N_e generations. Applying RIDGE to four published crow data sets, we first validated our tool by identifying a well-known large genomic region associated with mate choice patterns. Second, while we identified a significant overlap of outlier loci using RIDGE and traditional genomic scans, our results suggest that a substantial portion of previously identified outliers are likely false positives. Outlier detection relies on allele differentiation, relative measures of divergence and the count of shared polymorphisms and fixed differences. Our analyses also highlight the value of incorporating multiple summary statistics including our newly developed outlier ones that can be useful in challenging detection conditions.

The approach of combining cost-effective nanopore sequencing and emerging environmental DNA (eDNA) metabarcoding could prove to be a promising tool for biodiversity documentation, especially in Malaysia. Given the substantial funding constraints in recent years, especially in relation to the country's biodiversity, many researchers have been limited to conduct restricted research without extended monitoring periods, potentially hindering comprehensive surveys and could compromise the conservation efforts. Therefore, the present study aimed to evaluate the application of eDNA metabarcoding on freshwater fish using short reads generated through nanopore sequencing. This assessment focused on species detection in three selected rivers within the Endau Rompin Landscape in Malaysia. Additionally, the study compared levels of species detection between eDNA metabarcoding and conventional sampling methods, examined the effectiveness of primer choice, and applied both metabarcoding and shotgun sequencing to the eDNA approach. We successfully identified a total of 22 and 71 species with an identification threshold of >97% and >90%, respectively, through the MinION platform. The eDNA metabarcoding approach detected over 13% more freshwater fish species than when the conventional method was used. Notably, the distinction in freshwater fish detection between eDNA primers for 12S rRNA and cytochrome oxidase I was insignificant. The cost for eDNA metabarcoding proved to be more effective compared to conventional sampling with cost reduction at 33.4%. With favourable cost-effectiveness and increased species detection, eDNA metabarcoding could complement existing methods, enhance holistic diversity documentation for targeted habitats and facilitate effective conservation planning.

Species delimitation is a contentious topic. The genomics revolution initially brought hope that identifying and classifying species would be easier through better methods and more data, but genomics has also brought complexity and controversy to delimitation. One solution can be to collect a larger sample of individuals at a finer geographic scale. But what if taxa are rare and collecting more samples is difficult or detrimental to the organisms at hand? In this issue of Molecular Ecology Resources, Opatova et al. (2023) tackle the ambiguity of species delimitation in rare and endangered trapdoor spiders (genus Cyclocosmia). The authors propose a framework for delimiting species when samples are hard to come by, such as in these rare and cryptic spiders. The authors combine extensive genomic sampling with statistical approaches that consider both the genetic distinctiveness of each population of spiders and how much gene flow occurs between these populations. Their proposed taxonomy balances two opposing signals, structure and gene flow, to count eight lineages of Cyclocosmia, and to point the way for future taxonomic studies of the rare or difficult to obtain.