Molecular Ecology Resources最新文献

Admixture, the interbreeding of individuals from differentiated source populations, is now known to be a widespread phenomenon. Genomic studies of natural hybridisation can help to answer many questions on the impacts of admixture on adaptive evolution, reproductive isolation, and speciation. When a large variety of admixture proportions between two source populations exist, both geographic and genomic cline analysis are suitable methods for inferring biased, restricted or excessive gene flow at individual loci into the foreign genomic background, providing evidence for reproductive isolation, selection across an environmental transition, balancing selection, and adaptive introgression. Genomic cline analysis replaces geographic location with genome-wide hybrid index and is therefore useable in circumstances that violate geographic cline assumptions. Here, I introduce gghybrid, an R package for simple and flexible Bayesian estimation of Buerkle's hybrid index and Fitzpatrick's logit-logistic genomic clines using bi-allelic data, suitable for both small and large datasets. gghybrid allows any ploidy and uses Structure input file format. It has separate functions for hybrid index and cline estimation, treating each individual and locus respectively as an independent analysis, making it highly parallelisable. Admixture proportions from other software can alternatively be used in cline analysis, alongside parental allele frequencies. Parameters can be fixed and samples pooled for statistical model comparison with AIC or waic. Here, I describe the functions, pipeline, and statistical properties of gghybrid. Simulations reveal that model comparison with waic is preferred, and use of Bayesian posterior distributions and p values to select candidate non-null loci is problematic and should be avoided.

The accessibility to CRISPR/Cas (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein) genetic tools has given rise to applications beyond site-directed genome editing for the detection of DNA and RNA. These tools include precise diagnostic detection of human disease pathogens, such as SARS-CoV-2 and Zika virus. Despite the technology being rapid and cost-effective, the use of CRISPR/Cas tools in the surveillance of the causative agents of wildlife diseases has not been prominent. This study presents the development of a minimally invasive, field-applicable and user-friendly CRISPR/Cas-based biosensor for the detection of Pseudogymnoascus destructans (Pd), the causative fungal agent of white-nose syndrome (WNS), an infectious disease that has killed more than five million bats in North America since its discovery in 2006. The biosensor assay combines a recombinase polymerase amplification (RPA) step followed by CRISPR/Cas12a nuclease cleavage to detect Pd DNA from bat dermal swab and guano samples. The biosensor had similar detection results when compared to quantitative PCR in distinguishing Pd-positive versus negative field samples. Although bat dermal swabs could be analysed with the biosensor without nucleic acid extraction, DNA extraction was needed when screening guano samples to overcome inhibitors. This assay can be applied to help with more rapid delineation of Pd-positive sites in the field to inform management decisions. With further optimization, this technology has broad translation potential to wildlife disease-associated pathogen detection and monitoring applications.

Beetles, despite their remarkable biodiversity and a long history of research, remain lacking in reference genomes annotated with structural variations in loci of adaptive significance. We sequenced and assembled high-quality chromosome-level genomes of four Hercules beetles which exhibit divergence in male horn size and shape and body colouration. The four Hercules beetle genomes were assembled to 11 pseudo-chromosomes, where the three genomes assembled using Nanopore data (Dynastes grantii, D. hyllus and D. tityus) were mapped to the genome assembled using PacBio + Hi-C data (D. maya). We demonstrated a striking similarity in genome structure among the four species. This conservative genome structure may be attributed to our use of the D. maya assembly as the reference; however, it is worth noting that such a conservative genome structure is a recurring phenomenon among scarab beetles. We further identified homologues of nine and three candidate-gene families that may be associated with the evolution of horn structure and body colouration respectively. Structural variations in Scr and Ebony2 were detected and discussed for their putative impacts on generating morphological diversity in beetles. We also reconstructed the demographic histories of the four Hercules beetles using heterozygosity information from the diploid genomes. We found that the demographic histories of the beetles closely recapitulated historical changes in suitable forest habitats driven by climate shifts.

We present here a complete system for metagenomic analysis that allows performing the sequencing and analysis of a medium-size metagenome in less than one day. This unprecedented development was possible due to the conjunction of state-of-the-art experimental and computational advances: a portable laboratory suitable for DNA extraction and sequencing with nanopore technology; the powerful metagenomic analysis pipeline SqueezeMeta, capable to provide a complete analysis in a few hours and using scarce computational resources; and tools for the automatic inspection of the results via a graphical user interface, that can be coupled to a web server to allow remote visualization of data (SQMtools and SQMxplore). We have tested the feasibility of our approach in the sequencing of the microbiota associated to volcanic rocks in La Palma, Canary Islands. Also, we did a two-day sampling campaign of marine waters in which the results obtained on the first day guided the experimental design of the second day. We demonstrate that it is possible to generate metagenomic information in less than one day, making it feasible to obtain taxonomic and functional profiles fast and efficiently, even in field conditions. This capacity can be used in the further to perform real-time functional and taxonomic monitoring of microbial communities in remote areas.

PCR-based high-throughput sequencing has permitted comprehensive resolution analyses of zooplankton diversity dynamics. However, significant methodological issues still surround analyses of complex bulk community samples, not least as in prevailing PCR-based approaches. Marine drifting animals—zooplankton—play essential ecological roles in the pelagic ecosystem, transferring energy and elements to higher trophic levels, such as fishes, cetaceans and others. In the present study, we collected 48 size-fractionated zooplankton samples in the vicinity of a coral reef island with environmental gradients. To investigate the spatiotemporal dynamics of zooplankton diversity patterns and the effect of PCR amplification biases across these complex communities, we first took metatranscriptomics approach. Comprehensive computational analyses revealed a clear pattern of higher/lower homogeneity in smaller/larger zooplankton compositions across samples respectively. Our study thus suggests changes in the role of dispersal across the sizes. Next, we applied in silico PCR to the metatranscriptomics datasets, in order to estimate the extent of PCR amplification bias. Irrespective of stringency criteria, we observed clear separations of size fraction sample clusters in both metatranscriptomics and in silico datasets. In contrast, the pattern—smaller-fractioned communities had higher compositional homogeneity than larger ones—was observed in the metatranscriptomics data but not in the in silico datasets. To investigate this discrepancy further, we analysed the mismatches of widely used mitochondrial CO1 primers and identified priming site mismatches likely driving PCR-based biases. Our results suggest the use of metatranscriptomics or, although less ideal, redesigning the CO1 primers is necessary to circumvent these issues.

Age is necessary information for the study of life history of wild animals. A general method to estimate the age of odontocetes is counting dental growth layer groups (GLGs). However, this method is highly invasive as it requires the capture and handling of individuals to collect their teeth. Recently, the development of DNA-based age estimation methods has been actively studied as an alternative to such invasive methods, of which many have relied on used biopsy samples. However, if DNA-based age estimation can be developed from faecal samples, age estimation can be performed entirely non-invasively. We developed an age estimation model using the methylation rate of two gene regions, GRIA2 and CDKN2A, measured through methylation-sensitive high-resolution melting (MS-HRM) from faecal samples of wild Indo-Pacific bottlenose dolphins (Tursiops aduncus). The age of individuals was known through conducting longitudinal individual identification surveys underwater. Methylation rates were quantified from 36 samples collected from 30 individuals. Both gene regions showed a significant correlation between age and methylation rate. The age estimation model was constructed based on the methylation rates of both genes which achieved sufficient accuracy (after LOOCV: MAE = 5.08, R² = 0.33) for the ecological studies of the Indo-Pacific bottlenose dolphins, with a lifespan of 40–50 years. This is the first study to report the use of non-invasive faecal samples to estimate the age of marine mammals.

Testing the association between objects is central in ecology, evolution, and quantitative sciences in general. Two types of variables can describe the relationships between objects: point variables (measured on individual objects), and distance variables (measured between pairs of objects). The Mantel test and derived methods have been extensively used for distance variables. Yet, these methods have been criticized due to low statistical power and inflated type I error when spatial autocorrelation is present. Here, we assessed the statistical power between different types of tested variables and the type I error rate over a wider range of autocorrelation intensities than previously assessed, both on univariate and multivariate data. We also illustrated the performance of distance matrix statistics through computational simulations of genetic diversity. We show that the Mantel test and derived methods are not affected by inflated type I error when spatial autocorrelation affects only one variable when investigating correlations, or when either the response or the explanatory variable(s) is affected by spatial autocorrelation while investigating causal relationships. As previously noted, with autocorrelation affecting more variables, inflated type I error could be reduced by modifying the significance threshold. Additionally, the Mantel test has no problem of statistical power when the hypothesis is formulated in terms of distance variables. We highlight that transformation of variable types should be avoided because of the potential information loss and modification of the tested hypothesis. We propose a set of guidelines to help choose the appropriate method according to the type of variables and defined hypothesis.

Mesozooplankton is a very diverse group of small animals ranging in size from 0.2 to 20 mm not able to swim against ocean currents. It is a key component of pelagic ecosystems through its roles in the trophic networks and the biological carbon pump. Traditionally studied through microscopes, recent methods have been however developed to rapidly acquire large amounts of data (morphological, molecular) at the individual scale, making it possible to study mesozooplankton using a trait-based approach. Here, combining quantitative imaging with metabarcoding time-series data obtained in the Sargasso Sea at the Bermuda Atlantic Time-series Study (BATS) site, we showed that organisms' transparency might be an important trait to also consider regarding mesozooplankton impact on carbon export, contrary to the common assumption that just size is the master trait directing most mesozooplankton-linked processes. Three distinct communities were defined based on taxonomic composition, and succeeded one another throughout the study period, with changing levels of transparency among the community. A co-occurrences' network was built from metabarcoding data revealing six groups of taxa. These were related to changes in the functioning of the ecosystem and/or in the community's morphology. The importance of Diel Vertical Migration at BATS was confirmed by the existence of a group made of taxa known to be strong migrators. Finally, we assessed if metabarcoding can provide a quantitative approach to biomass and/or abundance of certain taxa. Knowing more about mesozooplankton diversity and its impact on ecosystem functioning would allow to better represent them in biogeochemical models.

Our limited knowledge about the ecological drivers of global arthropod decline highlights the urgent need for more effective biodiversity monitoring approaches. Monitoring of arthropods is commonly performed using passive trapping devices, which reliably recover diverse communities, but provide little ecological information on the sampled taxa. Especially the manifold interactions of arthropods with plants are barely understood. A promising strategy to overcome this shortfall is environmental DNA (eDNA) metabarcoding from plant material on which arthropods leave DNA traces through direct or indirect interactions. However, the accuracy of this approach has not been sufficiently tested. In four experiments, we exhaustively test the comparative performance of plant-derived eDNA from surface washes of plants and homogenized plant material against traditional monitoring approaches. We show that the recovered communities of plant-derived eDNA and traditional approaches only partly overlap, with eDNA recovering various additional taxa. This suggests eDNA as a useful complementary tool to traditional monitoring. Despite the differences in recovered taxa, estimates of community α- and β-diversity between both approaches are well correlated, highlighting the utility of eDNA as a broad scale tool for community monitoring. Last, eDNA outperforms traditional approaches in the recovery of plant-specific arthropod communities. Unlike traditional monitoring, eDNA revealed fine-scale community differentiation between individual plants and even within plant compartments. Especially specialized herbivores are better recovered with eDNA. Our results highlight the value of plant-derived eDNA analysis for large-scale biodiversity assessments that include information about community-level interactions.