Spatially explicit population genetic models have long been developed, yet have rarely been used to test hypotheses about the spatial distribution of genetic diversity or the expected neutral levels of genetic divergence between populations. Here, we use spatially explicit coalescence simulations to explore the properties of the island model and the two-dimensional stepping stone model under a wide range of scenarios with spatio-temporal variation in deme size. We avoid the simulation of genetic data, using the fact that under the studied models, summary statistics of genetic diversity and divergence between demes can be approximated from coalescence times. We perform the simulations using gridCoal, a flexible spatial wrapper for the software msprime developed herein. In gridCoal, deme sizes can change arbitrarily across space and time, and migration rates between individual demes can be specified. We identify the different factors that can cause a deviation from the theoretical expectations, such as the simulation time in comparison to the effective deme size and the spatio-temporal autocorrelation across the grid. Our results highlight that Fst, a measure of the strength of population structure, principally depends on recent demography, which makes it robust to temporal variation in deme size. We also warn that predicting genetic diversity from coalescence times requires a much longer run time than needed for the estimation of Fst. Finally, we illustrate the use of gridCoal on a real-world example, the range expansion of silver fir (Abies alba Mill.) since the Last Glacial Maximum, using different degrees of spatio-temporal variation in deme size.

Conducting large-scale phylogeographic studies to understand processes affecting population structure and genetic diversity across multiple species is difficult because the key genetic (NCBI) and spatial (GBIF) repositories are disconnected. In this issue of Molecular Ecology Resources, Pelletier et al. (2022) demonstrate the power of connecting these in the program phylogatR. This program assembled 87,852 species and 102,268 sequence alignments in a taxonomic hierarchy, yielding multiple sequence alignments per species, mainly for animals (88%), composed mostly of mtDNA data. The authors discuss several caveats with these alignments and provide flags identifying particular problems associating locality and genetic data with certain taxa (e.g., multiple localities per individuals). They provide a test that nucleotide diversity should increase with area, but find a significant relationship in only 32% of taxa with no clear taxonomic or ecological factors accounting for this. To examine the potential of this program, I tested the idea that the degree of population expansion should increase with latitude given potential environmental stability in the tropics and instability in temperate regions. In under two hours, I downloaded all squamates (lizards and snakes) and regressed Tajima’s D on latitude and found a weak but significant negative relationship, indicating a potential association between latitude and population expansion. The phylogatR database is a powerful resource for researchers wanting to test the relationship between genetic diversity and some aspect of space or environment.

Schistosomiasis is a neglected tropical disease of humans caused by blood flukes of the genus Schistosoma – the only dioecious parasitic flatworms. Although aspects of sex determination, differentiation and reproduction have been studied in some Schistosoma species, almost nothing is understood for Schistosoma japonicum - the causative agent of schistosomiasis japonica. This relates mainly to a lack of high-quality genomic and transcriptomic resources for this species. As current draft genomes for S. japonicum are highly fragmented, we assembled here a chromosome-level reference genome (seven autosomes, the Z-chromosome and partial W-chromosome), achieving a substantially enhanced gene annotation. Utilising this genome, we discovered that the sex chromosomes of S. japonicum and its congener S. mansoni independently suppressed recombination during evolution, forming four and two ‘strata’, respectively. By exploring the W-chromosome and sex-specific transcriptomes, we identified 35 W-linked genes and 257 female-preferentially transcribed genes (FTGs) and identified a signature for sex determination and differentiation in S. japonicum. These FTGs cluster within autosomes or the Z-chromosome and exhibit a highly dynamic transcription profile during the pairing of female and male schistosomules (advanced juveniles), representing a critical phase for the maturation of the female worms, suggesting distinct layers of regulatory control of gene transcription at this stage of development. Collectively, these data provide a valuable resource for further functional genomic characterisation of S. japonicum, shed light on the evolution of sex chromosomes in this highly virulent human blood fluke and provide a pathway to identify novel targets for development of intervention tools against schistosomiasis.

Prevailing 16S rRNA gene-amplicon methods for characterizing the bacterial microbiome are economical, but result in coarse taxonomic classifications, are subject to primer and 16S copy number biases, and do not allow for direct estimation of microbiome functional potential. While deep shotgun metagenomic sequencing can overcome many of these limitations, it is prohibitively expensive for large sample sets. We evaluated the ability of shallow shotgun metagenomic sequencing to characterize taxonomic and functional patterns in the fecal microbiome of a model population of feral horses (Sable Island, Canada). Since 2007, this unmanaged population has been the subject of an individual-based, long-term ecological study. Using deep shotgun metagenomic sequencing, we determined the sequencing depth required to accurately characterize the horse microbiome. In comparing conventional versus high-throughput shotgun metagenomic library preparation techniques, we validate the use of more cost-effective lab methods. Finally, we characterize similarities between 16S amplicon and shallow shotgun characterization of the microbiome, and demonstrate that the latter recapitulates biological patterns first described in a published amplicon dataset. Unlike amplicon data, we demonstrate how shallow shotgun metagenomic data also provided useful insights about microbiome functional potential which support previously hypothesized diet effects in this study system.

Over the last two decades, there has been a huge increase in our understanding of microbial diversity, structure and composition enabled by high throughput sequencing (HTS) technologies. Yet, it is unclear how the number of sequences translates to the number of cells or species within the community. Additional observational data may be required to ensure relative abundance patterns from sequence reads are biologically meaningful or presence absence data may be used instead of abundance. The goal is to obtain robust community abundance data, simultaneously, from environmental samples. In this issue of Molecular Ecology Resources, Karlusich et al., (2022) describe a new method for quantifying phytoplankton cell abundance. Using Tara Oceans datasets, the authors propose the photosynthetic gene psbO for reporting accurate relative abundance of the entire phytoplankton community from metagenomic data. The authors demonstrate improved correlations with traditional optical methods including microscopy and flow cytometry, improving upon current molecular identification typically using rRNA markers genes. Furthermore, to facilitate application of their approach, the authors curated a psbO gene database for accessible taxonomic queries. This is an important step towards improving species abundance estimates from molecular data and eventually reporting of absolute species abundance, enhancing our understanding of community dynamics.

Environmental DNA (eDNA) analyses are powerful for describing marine biodiversity but must be optimized for their effective use in routine monitoring. To maximize eDNA detection probabilities of sparsely distributed populations, water samples are usually concentrated from larger volumes and filtered using fine-pore membranes, often a significant cost-time bottleneck in the workflow. This study aimed to streamline eDNA sampling by investigating plankton net versus bucket sampling, direct versus sequential filtration including self-preserving filters. Biodiversity was assessed using metabarcoding of the small ribosomal subunit (18S rRNA) and mitochondrial cytochrome c oxidase I (COI) genes. Multi-species detection probabilities were estimated for each workflow using a probabilistic occupancy modelling approach. Significant workflow-related differences in biodiversity metrics were reported. Highest amplicon sequence variant (ASV) richness was attained by the bucket sampling combined with self-preserving filters, comprising a large portion of micro-plankton. Less diversity but more metazoan taxa were captured in the net samples combined with 5 µm pore size filters. Pre-filtered 1.2 µm samples yielded few or no unique ASVs. The highest average (~32%) metazoan detection probabilities in the 5 µm pore size net samples confirmed the effectiveness of pre-concentrating plankton for biodiversity screening. These results contribute to streamlining eDNA sampling protocols for uptake and implementation in marine biodiversity research and surveillance.

Despite the increasing accessibility of high-throughput sequencing, obtaining high-quality genomic data on non-model organisms without proximate well-assembled and annotated genomes remains challenging. Here we describe a workflow that takes advantage of distant genomic resources and ingroup transcriptomes to select and jointly enrich long open reading frames (ORFs) and ultraconserved elements (UCEs) from genomic samples for integrative studies of microevolutionary and macroevolutionary dynamics. This workflow is applied to samples of the African unionid bivalve tribe Coelaturini (Parreysiinae) at basin and continent-wide scales. Our results indicate that ORFs are efficiently captured without prior identification of intron-exon boundaries. The enrichment of UCEs was less successful, but nevertheless produced substantial datasets. Exploratory continent-wide phylogenetic analyses with ORF supercontigs (>515,000 parsimony informative sites) resulted in a fully resolved phylogeny, the backbone of which was also retrieved with UCEs (>11,000 informative sites). Variant calling on ORFs and UCEs of Coelaturini from the Malawi Basin produced ~2,000 SNPs per population pair. Estimates of nucleotide diversity and population differentiation were similar for ORFs and UCEs. They were low compared to previous estimates in mollusks, but comparable to those in recently diversifying Malawi cichlids and other taxa at an early stage of speciation. Skimming off-target sequence data from the same enriched libraries of Coelaturini from the Malawi Basin, we reconstructed the maternally-inherited mitogenome, which displays the gene order inferred for the most recent common ancestor of Unionidae. Overall, our workflow and results provide exciting perspectives for integrative genomic studies of microevolutionary and macroevolutionary dynamics in non-model organisms.

Despite recent advances in high-throughput DNA sequencing technologies, a lack of locally relevant DNA reference databases may limit the potential for DNA-based monitoring of biodiversity for conservation and biosecurity applications. Museums and national collections represent a compelling source of authoritatively identified genetic material for DNA database development yet obtaining DNA barcodes from long-stored specimens may be difficult due to sample degradation. We demonstrate a sensitive and efficient laboratory and bioinformatic process for generating DNA barcodes from hundreds of invertebrate specimens simultaneously via the Illumina MiSeq system. Using this process, we recovered full-length (334) or partial (105) COI barcodes from 439 of 450 (98 %) national collection-held invertebrate specimens. This included full-length barcodes from 146 specimens which produced low-yield DNA and no visible PCR bands, and which produced as little as a single sequence per specimen, demonstrating high sensitivity of the process. In many cases, the identity of the most abundant sequences per specimen were not the correct barcodes, necessitating the development of a taxonomy-informed process for identifying correct sequences among the sequencing output. The recovery of only partial barcodes for some taxa indicates a need to refine certain PCR primers. Nonetheless, our approach represents a highly sensitive, accurate, and efficient method for targeted reference database generation, providing a foundation for DNA-based assessments and monitoring of biodiversity.

Revegetation projects face the major challenge of sourcing the optimal plant material. This is often done with limited information about plant performance and increasingly requires to factor resilience to climate change. Functional traits can be used as quantitative indices of plant performance and guide provenancing, but trait values expected under novel conditions are often unkown. To support climate-resilient provenancing efforts, we develop a trait prediction model that integrates the effect of genetic variation with fine-scale temperature variation. We train our model on multiple field plantings of Arabidopsis thaliana and predict two relevant fitness traits -- days-to-bolting and fecundity -- across the species' European range. Prediction accuracies were high for days-to-bolting and moderate for fecundity, with the majority of trait variation explained by temperature differences between plantings. Projection under future climate predicted a decline in fecundity, although this response was heterogeneous across the range. In response, we identified novel genotypes that could be introduced to genetically offset the fitness decay. Our study highlights the value of predictive models to aid seed provenancing and improve the success of revegetation projects.

Polyploids are cells or organisms with a genome consisting of more than two sets of homologous chromosomes. Polyploid plants have important traits that facilitate speciation and are thus often model systems for evolutionary, molecular ecology and agricultural studies. However, due to their unusual mode of inheritance and dou-ble-reduction, diploid models of population genetic analysis cannot properly be ap-plied to polyploids. To overcome this problem, we developed a software package en-titled VCFPOP to perform a variety of population genetic analyses for autopolyploids, such as parentage analysis, analysis of molecular variance, principal coordinates analysis, hierarchical clustering analysis and Bayesian clustering. We make this soft-ware freely available, downloadable from http://github.com/huangkang1987/vcfpop.

The ithomiine butterflies (Nymphalidae: Danainae) represent the largest known radiation of Mullerian mimetic butterflies. They dominate by number the mimetic butterfly communities, which include species such as the iconic neotropical Heliconius genus. Despite recent studies carried out on ithomiine ecology and genetic structure, no reference genome was available for the tribe. Here, we generated high-quality, chromosome-scale genome assemblies of two Melinaea species, Melinaea marsaeus and Melinaea menophilus, and a draft genome of Ithomia salapia. We obtained genomes with a size ranging from 396 Mb to 503 Mb across the three species and scaffold N50 of 40.5 Mb and 23.2 Mb for the two chromosome-scale assemblies. Using collinearity analyses we identified massive rearrangements between the two closely related Melinaea species. A detailed annotation of transposable elements and genes was performed, resulting in the identification of 24,341, 31,081 and 31,976 genes in I. salapia, M. marsaeus and M. menophilus, respectively. We used a specialist annotation to target chemosensory genes, which is crucial for host plant detection and mate recognition in mimetic species. A comparative genomic approach revealed independent gene expansions in ithomiines and particularly in gustatory receptor genes. These first three genomes of ithomiine mimetic butterflies constitute a valuable addition and a welcome comparison to existing biological models of mimicry, such as Heliconius, and will enable further understanding of the mechanisms of adaptation and the genetic bases underpinning mimicry.

Inserts of DNA from extranuclear sources, such as organelles and microbes, are common in eukaryote nuclear genomes. However, sequence similarity between the nuclear and extranuclear DNA, and a history of multiple insertions, make the assembly of these regions challenging. Consequently, the number, sequence, and location of these vagrant DNAs cannot be reliably inferred from the genome assemblies of most organisms. We introduce two statistical methods to estimate the abundance of nuclear inserts even in the absence of a nuclear genome assembly. The first (intercept method) only requires low-coverage (<1x) sequencing data, as commonly generated for population studies of organellar and ribosomal DNAs. The second method additionally requires that a subset of the individuals carry extra-nuclear DNA with diverged genotypes. We validated our intercept method using simulations and by re-estimating the frequency of human NUMTs (nuclear mitochondrial inserts). We then applied it to the grasshopper Podisma pedestris, exceptional for both its large genome size and reports of numerous NUMT inserts, estimating that NUMTs make up 0.056% of the nuclear genome, equivalent to >500 times the mitochondrial genome size. We also re-analysed a museomics dataset of the parrot Psephotellus varius, obtaining an estimate of only 0.0043%, in line with reports from other species of bird. Our study demonstrates the utility of low-coverage high-throughput sequencing data for the quantification of nuclear vagrant DNAs. Beyond quantifying organellar inserts, these methods could also be used on endosymbiont-derived sequences. We provide an R implementation of our methods called “vagrantDNA” and code to simulate test datasets.

We analyzed robustness of species identification based on proteomic composition to data processing and intraspecific variability, specificity and sensitivity of species-markers as well as discriminatory power of proteomic fingerprinting and its sensitivity to phylogenetic distance. Our analysis is based on MALDI-TOF MS data from 32 marine copepod species coming from 13 regions (North and Central Atlantic and adjacent seas). A random forest (RF) model correctly classified all specimens to species level with only small sensitivity to data processing, demonstrating the strong robustness of the method. Compounds with high specificity showed low sensitivity i.e., identification was rather based on complex pattern-differences than on presence of single markers. Proteomic distance was not consistently related to phylogenetic distance. A species-gap in proteome composition appeared at 0.8 Euclidean distance when using only specimens from the same sample. When other regions or seasons were included, intra-specific variability increased, resulting in overlaps of intra- and inter-specific distance. Highest intra-specific distances (> 0.8) were observed between specimens from brackish and marine habitats i.e., salinity likely affects proteomic patterns. When testing library sensitivity of the RF model to regionality, strong misidentification was only detected between two congener pairs. Still, choice of reference library may have an impact on identification of closely related species and should be tested before routine application. We envision high relevance of this time- and cost-efficient method for future zooplankton monitoring as it provides not only in-depth taxonomic resolution for counted specimens but also add-on information e.g., on developmental stage or environmental conditions.

Lifespan is a key attribute of a species’ life cycle and varies extensively among major lineages of animals. In fish, lifespan varies by several orders of magnitude, with reported values ranging from less than one year to approximately 400 years. Lifespan information is particularly useful for species management, as it can be used to estimate invasion potential, extinction risk and sustainable harvest rates. Despite its utility, lifespan is unknown for most fish species. This is due to the difficulties associated with accurately identifying the oldest individual(s) of a given species, and/or deriving lifespan estimates that are representative for an entire species. Recently it has been shown that CpG density in gene promoter regions can be used to predict lifespan in mammals and other vertebrates, with variable accuracy across taxa. To improve accuracy of lifespan prediction in a non-mammalian vertebrate, here we develop a fish-specific genomic lifespan predictor. Addressing previous issues of low sample size and sequence dissimilarity, we incorporate more than eight times the number of fish species used previously (n = 442) and use fish-specific gene promoters as reference sequences. Our model predicts fish lifespan from genomic CpG density alone (measured as CpG observed/expected ratio), explaining 64 % of the variance between known and predicted lifespans. The results demonstrate the value of promoter CpG density as a universal predictor of fish lifespan that can applied where empirical data are unavailable, or impracticable to obtain.

Understanding landscape connectivity has become a global priority for mitigating the impact of landscape fragmentation on biodiversity. Link-based methods traditionally rely on relating pairwise genetic distance between individuals or demes to their landscape distance (e.g., geographic distance, cost distance). In this study, we present an alternative to conventional statistical approaches to refine cost surfaces by adapting the Gradient Forest (GF) approach to produce a resistance surface. Used in community ecology, GF is an extension of random forest (RF), and has been implemented in genomic studies to model species genetic offset under future climatic scenarios. By design, this adapted method, resGF, has the ability to handle multiple environmental predicators and is not subjected to traditional assumptions of linear models such as independence, normality and linearity. Using genetic simulations, resGF performance was compared to other published methods. In univariate scenarios, resGF was able to distinguish the true surface contributing to genetic diversity among competing surfaces better than the compared methods. In multivariate scenarios, the GF approach performed similarly to the other RF-based approach using least-cost transect analysis (LCTA). Additionally, two worked examples are provided using two previously published datasets. This machine learning algorithm has the potential to improve our understanding of landscape connectivity and can inform long-term biodiversity conservation strategies.

Genomics can play important roles in biodiversity conservation, especially for Extinct-in-the-Wild species where genetic factors can influence total extinction risk 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 contiguous reference genomes for both species, including the XY chromosome pair in the skink. We then analyze patterns of genetic diversity to infer ancient demography and more recent histories of inbreeding. We observe high genome-wide heterozygosity in the blue-tailed skink (0.007) and Lister’s gecko (0.005), consistent with large historical population sizes. However, nearly 10% of the skink reference genome falls within long runs of homozygosity (ROH), resulting in homozygosity at all major histocompatibility complex (MHC) loci, whereas we detect only a single ROH in the 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 species’ histories and implications for management. We show how reference genomes can provide evolutionary and conservation insights in the absence of resequencing data, and we provide a resource for future population-level and comparative genomic studies in reptiles.

Innovations in ancient DNA (aDNA) preparation and sequencing technologies have exponentially increased the quality and quantity of aDNA data extracted from ancient biological materials. The additional temporal component from the incoming aDNA data can provide improved power to address fundamental evolutionary questions like characterising selection processes that shape the phenotypes and genotypes of contemporary populations or species. However, utilising aDNA to study past selection processes still involves considerable hurdles such as how to eliminate the confounding effect of genetic interactions in the inference of selection. To circumvent this challenge, in this work we extend the method introduced by He et al. (2022) to infer temporally variable selection from the data on aDNA sequences with the flexibility of modelling linkage and epistasis. Our posterior computation is carried out through a robust adaptive version of the particle marginal Metropolis-Hastings algorithm with a coerced acceptance rate. Moreover, our extension inherits their desirable features like modelling sample uncertainties resulting from the damage and fragmentation of aDNA molecules and reconstructing underlying gamete frequency trajectories of the population. We assess the performance and show the utility of our procedure with an application to ancient horse samples genotyped at the loci encoding base coat colours and pinto coat patterns.

Dispersal is a crucial mechanism to living beings, allowing them to reach new resources such that populations and species can explore new environments. However, directly observing the dispersal mechanisms of widespread species can be costly or even impracticable, which is the case for mangrove trees. The influence of ocean currents on the mangroves’ propagules’ movement has been 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 dispersal and connectivity of Rhizophora mangle along the Southwest Atlantic. We inferred population genetic structure and migration rates based on single nucleotide polymorphisms, simulated the displacement of propagules along the region and tested our hypotheses with Mantel tests and redundancy analysis. We observed a two populations structure, north and south, which is corroborated by other studies with Rhizophora and other coastal plants. The inferred recent migration rates do not indicate gene flow between the sampled 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 suggests 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 power of the dispersal process, which has ecological and evolutionary implications.