Molecular Ecology Resources最新文献

Strain-level identification of each microbe is crucial for understanding its role in the host. Most of the existing tools have primarily been evaluated on human metagenomic datasets, whereas the plant microbiome exhibits greater diversity and complexity and thus poses a challenge in the strain-level resolution of individual microbes. In this study, we conducted a comprehensive benchmarking of available reference-based tools for strain-level resolution of the plant microbiome. We evaluated seven tools on various performance parameters, like computational requirements, F1-score and relative abundances using synthetic datasets comprising microbes known to have strong associations with plants as well as real plant microbiome datasets. Our results demonstrated a better performance of StrainScan on the synthetic data, achieving higher F1-score and more accurate relative abundance estimates as compared to other tools, but its performance declined gradually with increasing strain diversity. However, StrainGE and StrainScan exhibited competitive performance on real plant metagenome data. Overall, though StrainGE exhibited better performance, it was more computationally expensive. However, StrainScan performed better in detecting low-abundance strains. Our findings suggest the comparative suitability of the available tools for the strain-level analysis of plant metagenome data and highlight the need for the development of more efficient and accurate taxonomic classifiers capable of handling the complex plant metagenome data while maintaining computational efficiency.

Genotyping by amplicon sequencing (GBAS) is a relatively low-cost approach for generating genotypic data compared with established genomic methods, making it highly scalable and particularly suitable for large-scale genetic monitoring projects. However, most existing analytical pipelines are either marker-specific, insufficiently scalable, or lacking efficient data management systems for the long-term integration of genotypic information, limiting the full potential of GBAS. Here, we address this gap by introducing GBAS-GUI (https://github.com/sonnenbe-dot/GBAS-GUI), a pipeline capable of generating GBAS-based genotypic data for a wide variety of loci at scale. GBAS-GUI integrates a graphical user interface with multiple checkpoints to improve accessibility and robustness. It implements multiprocessing architecture and a relational database that links genotypic data with associated sample metadata to enhance scalability and data management. The pipeline further enables marker screening through automated calculation of polymorphism information content (PIC) and implements a strategy to recover homologous genotypic information from paralogous loci with non-overlapping amplicon length ranges. Using multiple empirical datasets, we demonstrate substantial improvements in processing speed, database management and handling artefacts related to co-amplification of unspecific regions and duplicates of the same genomic region. We further show that incorporating the full sequence information captured by an amplicon increases marker information content beyond what is achievable with length-based genotyping alone and expands the analytical versatility of GBAS. Overall, GBAS-GUI provides a robust, scalable and versatile framework that unlocks the potential of GBAS for large-scale population genetic and phylogeographic studies.

Replication is central to most experimental and sampling designs, increasing inferential power and capturing fine-scale data heterogeneity. However, its importance remains poorly evaluated in some ecological and evolutionary settings. This is the case of metabarcoding studies using DNA recovered from sedimentary archives, in which biological signals integrate ecological information through depositional and burial processes, yet are commonly inferred from a single sediment core per site. Here, we evaluated the effect of different types of replication using sedimentary DNA metabarcoding data from two genetic markers (mitochondrial COI and nuclear 18S) using a nested sampling design. The design included three intertidal sites, three spatially separated sediment cores per site (biological replicates), two sediment horizons per core, and eight PCR (technical) replicates per sediment sample. Variance partitioning showed that site identity and sediment age group together explained > 70% of the variation in beta diversity, indicating that among-site spatial and stratigraphic differences were the dominant drivers of community composition. PERMANOVA likewise identified non-significant effects of biological replication. Among PCR replicates from the same sediment sample, richness varied substantially, whereas Shannon diversity was more consistent. Despite this variability, differences in community composition among technical replicates remained smaller than those associated with biological replication or site identity, indicating a limited influence on broader ecological patterns. Community composition was highly similar among replicate cores within sites, consistent with stratigraphic coherence. These results indicate limited within-site heterogeneity and suggest that, under stratigraphically coherent conditions, increasing biological replication may provide little additional information, whereas enhancing technical replication and stratigraphic resolution can improve ecological inference from sedimentary DNA metabarcoding datasets.

Target capture is widely used to enrich endogenous DNA from calcium phosphate skeletal material in vertebrates, but its performance on calcium carbonate hard parts widely produced by invertebrates remains poorly understood. Here, we compared DNA recovery from four fresh and 12 ancient (eight radiocarbon-dated to 1671–1135 years old before present) deep-sea vesicomyid clam shells, including species Archivesica marissinica, A. nanshaensis and A. okutanii, using whole-genome sequencing (WGS) or target capture of ultraconserved elements (UCEs). WGS achieved 16.65% on-target read recovery of UCEs from fresh soft tissue, but < 1% from shell specimens. By contrast, UCE capture in the same specimen increased on-target reads by up to 155-fold, reaching 29.84% in fresh shells and up to 72-fold, reaching 19.89% in ancient shells. Target capture of UCEs recovered 142–1001 loci per sample compared to 0–230 with WGS alone. Ancient shells of A. marissinica and A. okutanii, based on reads mapped with bwa-mem2 and bbmap, exhibited characteristic post-mortem DNA damage signals, with average 5′-end C-to-T misincorporation rates of 3.46% and 15.97%, respectively, exceeding the levels observed in fresh A. marissinica shells (maximum 1.24%). UCE-based phylogenetic reconstructions incorporating shell ancient DNA recovered two major clades within Pliocardiinae, consistent with published phylogenomic trees. Together, these findings demonstrate that target-capture enrichment enables effective recovery of highly degraded DNA from ancient mollusc shells and supports robust phylogenetic inference at the intrageneric scale, expanding the utility of shells—one of the most abundant invertebrate remains—for evolutionary, biogeographic and conservation studies.

Hybridization is ubiquitous throughout the tree of life. Its presence in genetic datasets often violates model assumptions or leads to erroneous inferences. Nonetheless, few parentage assignment methods have been developed that account for hybridization or admixture, particularly in non-model systems. Here, we describe a novel method of parentage analysis, MixedUpParents, that accounts for hybridization by using species-diagnostic single nucleotide polymorphisms while accounting for mixed ancestry within discrete genomic segments. We test MixedUpParents alongside other parentage assignment programs through simulations to assess assignment accuracy in scenarios with and without hybridization. Our simulations included a range of parameter values, including missing genotypes, migration (hybridization) rates and proportions of un-sampled individuals. Simulation results showed that in all scenarios with admixture, MixedUpParents outperformed the other parentage methods tested and performed well for parent–offspring assignment even when only one parent is sampled. In the best cases, MixedUpParents achieved $��\ge �$ 95% true-positive and $��\sim �$5% false-positive rates (i.e., assigning the wrong parent). Accuracy of all parentage methods increased when a higher proportion of the population was sampled, but decreased with higher levels of missing marker data. The most challenging cases for all methods were those with intermediate migration rates, and when full-siblings were included in the parent sample (representing age-agnostic field sampling). Overall, we show that ‘traditional’ methods can yield high false-positive rates when hybridization is present, emphasizing the importance of using appropriate methods in admixed systems. Our results and freely available software should advance population management and research on measures of reproductive success (fitness) in wild and captive populations, and parentage and pedigree reconstruction broadly.

Tropical dry forests host highly diverse insect communities and complex trophic interactions, yet these networks remain difficult to resolve using conventional molecular or rearing-based approaches. Here, we integrate long-read DNA barcoding, non-target sequence co-amplification, and host plant taxonomy to reconstruct tri-trophic interactions among leaf-mining insects, their host plants, and associated parasitoids in a Mexican tropical dry forest. Using Single Molecule, Real-Time (SMRT) sequencing on the PacBio Sequel IIe platform, we generated high-fidelity barcodes from individual leaf-miner larvae and from parasitoid larvae and pupae recovered from leaf mines. From 253 specimens, we obtained 214 sequences, and identified 83 operational taxonomic units including leaf-mining Lepidoptera, Diptera, Coleoptera, and their hymenopteran parasitoids, plus 31 non-target sequences. Of the 214 sequences, 69 were parasitoid wasps (32.2%). Of these, 40 were obtained from parasitoids dissected directly from leaf mines, while 29 were co-amplified from leaf-miner samples, demonstrating that SMRT-based co-amplification allows detection of parasitism without rearing. Our analysis revealed 156 trophic interaction types and 247 interaction events. Network analyses showed a highly modular tripartite network, with module structure primarily driven by host plant identity and a reduced set of influential leaf-miner and parasitoid taxa. Bipartite networks supported the hypothesis that interactions are more strongly compartmentalized between leafminers and host plants than at higher trophic levels. These results demonstrate that long-read DNA barcoding with co-amplification detection provides a scalable framework for reconstructing multi-trophic interactions from individual specimens, overcoming key limitations of rearing-based methods and enabling robust biodiversity and ecological network assessments in species-rich ecosystems.

The RZooRoH R package was implemented to characterize individual inbreeding levels. It identifies DNA segments inherited twice from a common ancestor through different paths, which are known as homozygous-by-descent (HBD) segments. The package accepts different data formats and provides multiple outputs: HBD segments, inbreeding rates and genome-wide and locus-specific HBD probabilities. In addition, it partitions HBD levels into multiple HBD classes. The length distribution varies between these classes, which therefore correspond to distinct groups of ancestors that can be traced back to different generations in the past. This provides information about mating structure and recent demographic history. The computational performance of the package has been substantially improved, enabling, for example, computing times to be reduced when working with whole-genome sequence data and more HBD classes to be fitted. It is now possible to fit one class per past generation, which facilitates interpretation of the results. Since we have previously demonstrated that the ZooRoH model can be used to characterize identity-by-descent (IBD) between haploid individuals or phased haplotypes, this option has been included in the new package version. Estimating kinship by characterizing IBD levels between the four possible pairs of haplotypes from two individuals is another feature we added to the package. Finally, new options allow models to be refined, for instance by defining HBD classes as intervals or constant inbreeding rates for neighbouring classes. Overall, the new version of the package offers improved computational efficiency and interpretability when characterizing inbreeding, IBD and relatedness levels.

Pont, D., P. Meulenbroek, V. Bammer, et al. 2023. “Quantitative Monitoring of Diverse Fish Communities on a Large Scale Combining eDNA Metabarcoding and qPCR.” Molecular Ecology Resources 23: 396–409. https://doi.org/10.1111/1755-0998.13715.

Due to an error in the calculation of teleo-eDNA concentrations (real time qPCR), all figures expressing the number of teleo-eDNA copies per litre or per sample must be divided by 100.

Figures 4, 5 and 7 are corrected (modifications of the axis numerical values and axis labels when necessary). Figures 1, 2, 3 and 6 are unchanged.

We apologize for this error.

The sustainability of Southeast Asian fisheries hinges on high-throughput tools for monitoring early life-stage fish biodiversity. However, applying DNA metabarcoding to hyper-diverse tropical ichthyoplankton requires rigorous calibration to ensure quantitative reliability. We systematically evaluated the metabarcoding workflow using controlled mock communities, revealing that taxonomic recovery is governed by a stochastic limit of detection at a normalised proxy biomass threshold of ≤ 0.05. Quantitative analysis confirmed a significant linear relationship between specimen size and read abundance (R² up to 0.817), demonstrating that biomass-driven template competition induces frequent false negatives for low-biomass taxa, a phenomenon exacerbated by increasing community complexity (ANOVA: p < 0.001). To mitigate these systemic biases, we applied a size-stratified specimen-balancing strategy intended to increase the representation of small-bodied components in natural bulk samples. Applying this optimised workflow to field samples from Khanh Hoa, Vietnam, we identified 139 species and unmasked a North–South biogeographic dichotomy (PERMANOVA: R² = 53%, p = 0.001) driven by transect-scale environmental gradients and local hydrography. Notably, we identified diversity hotspots requiring > 200,000 reads for saturation, suggesting these sites act as critical larval retention zones. The contrast between functional management zones was highly significant (p = 0.002), with the conservation area (Zone B) exhibiting higher alpha richness and a nine-fold increase in unique indicator species compared to high-activity areas (18 vs. 2). Our work demonstrates that comprehensive validation is vital for accurate metabarcoding, offering a robust framework to understand how ecological gradients and localised human pressures shape Vietnam's critical marine spawning sites and nursery grounds.

The proliferation of high-dimensional data in ecology and evolutionary biology raises the promise of statistical and machine learning models that are highly predictive and interpretable. However, high-dimensional data are commonly burdened with an inherent trade-off: in-sample prediction of outcomes will improve as additional variables are included in the model, but this may come at the cost of poor predictive accuracy and limited generalizability for future or unsampled observations (out-of-sample prediction). To confront this problem of overfitting, sparse models can focus on key variables by correctly placing low weight on unimportant variables. We compared nine methods to quantify their performance in variable selection and prediction using simulated data with different sample sizes, numbers of variables, and strengths of effects. Overfitting was typical for many methods and simulation scenarios. Despite this, in-sample and out-of-sample prediction converged on the true predictive target for simulations with more observations, larger causal effects, and fewer variables. Accurate variable selection to support process-based understanding will be unattainable for many realistic sampling schemes in ecology and evolution. We use our analyses to characterize data attributes for which statistical learning is possible, and illustrate how some sparse methods can achieve predictive accuracy while mitigating and learning the extent of overfitting.