RNA‐seq is a widely used approach to investigate gene expression and increasingly is used in time course studies to characterize transcriptomic changes over time. Two primary options are available as controls in time course experiments: samples collected at the first sampling time are used as controls (temporal control, TC) and samples collected in parallel at each individual sampling time are used as controls (biological control, BC). While both approaches are used in experimental studies, we know of no analyses performed to date that directly compare effects of control type choices on identifying differentially expressed gene (DEG) and subsequent functional analysis. In the current study, we compare experimental results using these different control types for time course RNA‐seq drought stress experiments in two wild grass species in the genus Paspalum. Our results showed BC assemblies obtained higher number of loci in both species. The number of DEGs increased with increasing stress and then decreased dramatically at the recovery timepoint using both control types. Expression levels of the same DEGs were highly correlated between control types in both species, ranging from r = 0.653 to r = 0.852. We also observed similar rank orders of shared enriched GO term lists using the two different control types. Collectively, our findings suggest similar results in differential gene expression and functional annotation between control types. The ultimate choice of control type will rely on the experimental length and organism type with labor time and sequencing costs as additional factors to be considered.

Sponge gourd (Luffa cylindrica (L.) Roem.) or luffa is a diploid herbaceous plant with 26 chromosomes (2n = 26) and belongs to the family Cucurbitaceae. To address the limited knowledge of the genome of Luffa species, the chromosome‐level genome of L. cylindrica was assembled and analysed using PacBio long reads and Hi‐C data. We combined Hi‐C data with a draft genome assembly to generate chromosome‐length scaffolds. Thirteen scaffolds corresponding to the 13 chromosomes were assembled from 1,156 contigs to a final size of 669 Mb with a contig N50 size of 5 Mb and a scaffold N50 size of 53 Mb. After removing redundant sequences, 416.31 Mb (62.18% of the genome) of repeat sequences was detected. Subsequently, 31,661 protein‐coding genes with an average of 5.69 exons per gene were identified in the L. cylindrica genome using de novo methods, transcriptome data and homologue‐based approaches. In addition, 27,552 protein‐coding genes (87.02%) were annotated in five databases. According to the phylogenetic analysis, L. cylindrica is closely related to Cucurbita and Cucumis species and diverged from their common ancestor ~28.6–67.1 million years ago. Genome collinearity analysis was performed in Cucurbita moschata, Cucumis sativus and L. cylindrica, and it demonstrated a high degree of conserved gene order in these three species. The completeness of the genome will provide high‐quality genomic knowledge on breeding and reveal genetic variation in L. cylindrica.

The ability to detect the identity of a sample obtained from its environment is a cornerstone of molecular ecological research. Thanks to the falling price of shotgun sequencing, genome skimming, the acquisition of short reads spread across the genome at low coverage, is emerging as an alternative to traditional barcoding. By obtaining far more data across the whole genome, skimming has the promise to increase the precision of sample identification beyond traditional barcoding while keeping the costs manageable. While methods for assembly‐free sample identification based on genome skims are now available, little is known about how these methods react to the presence of DNA from organisms other than the target species. In this paper, we show that the accuracy of distances computed between a pair of genome skims based on k‐mer similarity can degrade dramatically if the skims include contaminant reads; i.e., any reads originating from other organisms. We establish a theoretical model of the impact of contamination. We then suggest and evaluate a solution to the contamination problem: Query reads in a genome skim against an extensive database of possible contaminants (e.g., all microbial organisms) and filter out any read that matches. We evaluate the effectiveness of this strategy when implemented using Kraken‐II, in detailed analyses. Our results show substantial improvements in accuracy as a result of filtering but also point to limitations, including a need for relatively close matches in the contaminant database.

The genomic era has led to an unprecedented increase in the availability of genome‐wide data for a broad range of taxa. Wildlife management strives to make use of these vast resources to enable refined genetic assessments that enhance biodiversity conservation. However, as new genomic platforms emerge, problems remain in adapting the usually complex approaches for genotyping of non‐invasively collected wildlife samples. Here, we provide practical guidelines for the standardized development of reduced single nucleotide polymorphism (SNP) panels applicable for microfluidic genotyping of degraded DNA samples, such as faeces or hairs. We demonstrate how microfluidic SNP panels can be optimized to efficiently monitor European wildcat (Felis silvestris S.) populations. We show how panels can be set up in a modular fashion to accommodate informative markers for relevant population genetics questions, such as individual identification, hybridization assessment and the detection of population structure. We discuss various aspects regarding the implementation of reduced SNP panels and provide a framework that will allow both molecular ecologists and practitioners to help bridge the gap between genomics and applied wildlife conservation.

The house sparrow is an important model species for studying physiological, ecological and evolutionary processes in wild populations. Here, we present a medium density, genome wide linkage map for house sparrow (Passer domesticus) that has aided the assembly of the house sparrow reference genome, and that will provide an important resource for ongoing mapping of genes controlling important traits in the ecology and evolution of this species. Using a custom house sparrow 10K iSelect Illumina SNP chip we have assigned 6,498 SNPs to 29 autosomal linkage groups, based on a mean of 430 informative meioses per SNP. The map was constructed by combining the information from linkage with that of the physical position of SNPs within scaffold sequences in an iterative process. Averaged between the sexes; the linkage map had a total length of 2004 cM, with a longer map for females (2,240 cM) than males (1801 cM). Additionally, recombination rates also varied along the chromosomes. Comparison of the linkage map to the reference genomes of zebra finch, collared flycatcher and chicken, showed a chromosome fusion of the two avian chromosomes 8 and 4A in house sparrow. Lastly, information from the linkage map was utilized to conduct analysis of linkage disequilibrium (LD) in eight populations with different effective population sizes (Ne) in order to quantify the background level LD. Together, these results aid the design of future association studies, facilitate the development of new genomic tools and support the body of research that describes the evolution of the avian genome.

Yellow perch, Perca flavescens, is an ecologically and economically important species native to a large portion of the northern United States and southern Canada and is also a promising candidate species for aquaculture. No yellow perch reference genome, however, has been available to facilitate improvements in both fisheries and aquaculture management practices. By combining Oxford Nanopore Technologies long‐reads, 10X genomics Illumina short linked reads and a chromosome contact map produced with Hi‐C, we generated a high‐continuity chromosome scale yellow perch genome assembly of 877.4 Mb. It contains, in agreement with the known diploid chromosome yellow perch count, 24 chromosome‐size scaffolds covering 98.8% of the complete assembly (N50 = 37.4 Mb, L50 = 11). We also provide a first characterization of the yellow perch sex determination locus that contains a male‐specific duplicate of the anti‐Mullerian hormone type II receptor gene (amhr2by) inserted at the proximal end of the Y chromosome (chromosome 9). Using this sex‐specific information, we developed a simple PCR genotyping assay which accurately differentiates XY genetic males (amhr2by+) from XX genetic females (amhr2by‐). Our high‐quality genome assembly is an important genomic resource for future studies on yellow perch ecology, toxicology, fisheries, and aquaculture research. In addition, the characterization of the amhr2by gene as a candidate sex determining gene in yellow perch provides a new example of the recurrent implication of the transforming growth factor beta pathway in fish sex determination, and highlights gene duplication as an important genomic mechanism for the emergence of new master sex determination genes.

Microbial communities, which drive major ecosystem functions, consist of a wide range of interacting species. Understanding how microbial communities are structured and the processes underlying this is crucial to interpreting ecosystem responses to global change but is challenging as microbial interactions cannot usually be directly observed. Multiple efforts are currently focused to combine next‐generation sequencing (NGS) techniques with refined statistical analysis (e.g., network analysis, multivariate analysis) to characterize the structures of microbial communities. However, most of these approaches consider a single table of sequencing data measured for several samples. Technological advances now make it possible to collect NGS data on different taxonomic groups simultaneously for the same samples, allowing us to analyse a pair of tables. Here, an analytical framework based on co‐correspondence analysis (CoCA) is proposed to study the distributions, assemblages and interactions between two microbial communities. We show the ability of this approach to highlight the relationships between two microbial communities, using two data sets exhibiting various types of interactions. CoCA identified strong association patterns between autotrophic and heterotrophic microbial eukaryote assemblages, on the one hand, and between microalgae and viruses, on the other. We demonstrate also how CoCA can be used, complementary to network analysis, to reorder co‐occurrence networks and thus investigate the presence of patterns in ecological networks.

The superb fairy‐wren, Malurus cyaneus, is one of the most iconic Australian passerine species. This species belongs to an endemic Australasian clade, Meliphagides, which diversified early in the evolution of the oscine passerines. Today, the oscine passerines comprise almost half of all avian species diversity. Despite the rapid increase of available bird genome assemblies, this part of the avian tree has not yet been represented by a high‐quality reference. To rectify that, we present the first high‐quality genome assembly of a Meliphagides representative: the superb fairy‐wren. We combined Illumina shotgun and mate‐pair sequences, PacBio long‐reads, and a genetic linkage map from an intensively sampled pedigree of a wild population to generate this genome assembly. Of the final assembled 1.07‐Gb genome, 975 Mb (90.4%) was anchored onto 25 pseudochromosomes resulting in a final superscaffold N50 of 68.11 Mb. This high‐quality bird genome assembly is one of only a handful which is also accompanied by a genetic map and recombination landscape. In comparison to other pedigree‐based bird genetic maps, we find that the fairy‐wren genetic map more closely resembles those of Taeniopygia guttata and Parus major maps, unlike the Ficedula albicollis map which more closely resembles that of Gallus gallus. Lastly, we also provide a predictive gene and repeat annotation of the genome assembly. This new high‐quality, annotated genome assembly will be an invaluable resource not only regarding the superb fairy‐wren species and relatives but also broadly across the avian tree by providing a novel reference point for comparative genomic analyses.

EnTAP (Eukaryotic Non‐Model Transcriptome Annotation Pipeline) was designed to improve the accuracy, speed, and flexibility of functional gene annotation for de novo assembled transcriptomes in non‐model eukaryotes. This software package addresses the fragmentation and related assembly issues that result in inflated transcript estimates and poor annotation rates of protein‐coding transcripts. Following filters applied through assessment of true expression and frame selection, open‐source tools are leveraged to functionally annotate the reduced set of translated proteins. Downstream features include fast similarity search across five repositories, protein domain assignment, orthologous gene family assessment, and Gene Ontology (GO) term assignment. The final annotation integrates across multiple databases and selects an optimal assignment from a combination of weighted metrics describing similarity search score, taxonomic relationship, and informativeness. Researchers have the option to include additional filters to identify and remove contaminants, identify associated pathways, and prepare the transcripts for enrichment analysis. This fully featured pipeline is easy to install, configure, and runs significantly faster than comparable annotation packages. EnTAP is optimized to generate extensive functional information for the gene space of organisms with limited or poorly characterized genomic resources.

Although environmental DNA shed from an organism is now widely used for species detection in a wide variety of contexts, mobilizing environmental DNA for management requires estimation of population size and trends in addition to assessing presence or absence. However, the efficacy of environmental‐DNA‐based indices of abundance for long‐term population monitoring have not yet been assessed. Here we report on the relationship between six years of mark‐recapture population estimates for eulachon (Thaleichthys pacificus) and “eDNA rates” which are calculated from the product of stream flow and DNA concentration. Eulachon are a culturally and biologically important anadromous fish that have significantly declined in the southern part of their range but were historically rendered into oil and traded. Both the peak eDNA rate and the area under the curve of the daily eDNA rate were highly predictive of the mark‐recapture population estimate, explaining 84.96% and 92.53% of the deviance, respectively. Even in the absence of flow correction, the peak of the daily eDNA concentration explained an astonishing 89.53% while the area under the curve explained 90.74% of the deviance. These results support the use of eDNA to monitor eulachon population trends and represent a >80% cost savings over mark‐recapture, which could be further increased with automated water sampling, reduced replication, and focused temporal sampling. Due to its logistical ease and affordability, eDNA sampling can facilitate monitoring a larger number of rivers and in remote locations where mark‐recapture is infeasible.

The Tetraodontidae family are known to have relatively small and compact genomes compared to other vertebrates. The obscure puffer fish Takifugu obscurus is an anadromous species that migrates to freshwater from the sea for spawning. Thus the euryhaline characteristics of T. obscurus have been investigated to gain understanding of their survival ability, osmoregulation, and other homeostatic mechanisms in both freshwater and seawater. In this study, a high‐quality chromosome‐level reference genome for T. obscurus was constructed using long‐read Pacific Biosciences (PacBio) Sequel sequencing and a Hi‐C‐based chromatin contact map platform. The final genome assembly of T. obscurus is 381 Mb, with a contig N50 length of 3,296 kb and longest length of 10.7 Mb, from a total of 62 Gb of raw reads generated using single‐molecule real‐time sequencing technology from a PacBio Sequel platform. The PacBio data were further clustered into chromosome‐scale scaffolds using a Hi‐C approach, resulting in a 373 Mb genome assembly with a contig N50 length of 15.2 Mb and and longest length of 28 Mb. When we directly compared the 22 longest scaffolds of T. obscurus to the 22 chromosomes of the tiger puffer Takifugu rubripes, a clear one‐to‐one orthologous relationship was observed between the two species, supporting the chromosome‐level assembly of T. obscurus. This genome assembly can serve as a valuable genetic resource for exploring fugu‐specific compact genome characteristics, and will provide essential genomic information for understanding molecular adaptations to salinity fluctuations and the evolution of osmoregulatory mechanisms.

Determining the origin of individuals in mixed population samples is key in many ecological, conservation and management contexts. Genetic data can be analyzed using Genetic Stock Identification (GSI), where the origin of single individuals is determined using Individual Assignment (IA) and population proportions are estimated with Mixed Stock Analysis (MSA). In such analyses, allele frequencies in a reference baseline are required. Unknown individuals or mixture proportions are assigned to source populations based on the likelihood that their multilocus genotypes occur in a particular baseline sample. Representative sampling of populations included in a baseline is important when designing and performing GSI. Here we investigate the effects of family sampling on GSI, using both simulated and empirical genotypes for Atlantic salmon (Salmo salar). We show that non‐representative sampling leading to inclusion of close relatives in a reference baseline may introduce bias in estimated proportions of contributing populations in a mixed sample, and increases the amount of incorrectly assigned individual fish. Simulated data further show that the induced bias increases with increasing family structure, but that it can be partly mitigated by increased baseline population sample sizes. Results from standard accuracy tests of GSI (using only a reference baseline and/or self‐assignment) gave a false and elevated indication of the baseline power and accuracy to identify stock proportions and individuals. These findings suggest that family structure in baseline population samples should be quantified and its consequences evaluated, before carrying out GSI.

Molecular ecology regularly requires the analysis of count data that reflect the relative abundance of features of a composition (e.g., taxa in a community, gene transcripts in a tissue). The sampling process that generates these data can be modeled using the multinomial distribution. Replicate multinomial samples inform the relative abundances of features in an underlying Dirichlet distribution. These distributions together form a hierarchical model for relative abundances among replicates and sampling groups. This type of Dirichletmultinomial modelling (DMM) has been described previously, but its benefits and limitations are largely untested. With simulated data, we quantified the ability of DMM to detect differences in proportions between treatment and control groups, and compared the efficacy of three computational methods to implement DMM—Hamiltonian Monte Carlo (HMC), variational inference (VI), and Gibbs Markov chain Monte Carlo. We report that DMM was better able to detect shifts in relative abundances than analogous analytical tools, while identifying an acceptably low number of false positives. Among methods for implementing DMM, HMC provided the most accurate estimates of relative abundances, and VI was the most computationally efficient. The sensitivity of DMM was exemplified through analysis of previously published data describing lung microbiomes. We report that DMM identified several potentially pathogenic, bacterial taxa as more abundant in the lungs of children who aspirated foreign material during swallowing; these differences went undetected with different statistical approaches. Our results suggest that DMM has strong potential as a statistical method to guide inference in molecular ecology.

Museum collections are essential for reconstructing and understanding past biodiversity. Many museum specimens are, however, challenging to identify. Museum samples may be incomplete, have an unusual morphology, or represent juvenile individuals, all of which complicate accurate identification. In some cases, inaccurate identification can lead to false biogeographic reconstructions with cascading impacts on paleontological and paleoecological research. Here we analyze an unusual Equid mandible found in the Far North of the Taymyr peninsula that was identified morphologically as Equus hemionus, an ancestor of present‐day Asiatic wild asses. If correct, this identification represents the only finding of a putative Late Pleistocene hemione in the Arctic region, and is therefore critical to understanding wild ass evolution and paleoecology. To confirm the accuracy of this specimen's taxonomic assignment, we used ancient DNA and mitochondrial hybridization capture to identify and place this specimen in the larger equid phylogeny. We find that the specimen is actually a member of E. caballus, the ancestor of domestic horses. Our study demonstrates the utility of ancient DNA to validate morphological identification, in particular of incomplete, otherwise problematic, or taxonomically unusual museum specimens.

Genomic technologies continue to shed light on important ecological and evolutionary questions. Nonetheless, these new tools are applied disproportionately in a small fraction of global biodiversity, partly because of technical challenges to studying highly diverse taxa that occur in low abundances in an environment (e.g., marine and microbial communities). As a result, our understanding of ecological and evolutionary processes lags in many taxa. In a From the Cover manuscript in this issue of Molecular Ecology Resources, Galen, Borner, Williamson, Witt, and Perkins (2020) present a novel approach for characterizing diversity that combines metatranscriptomics with rigorous bioinformatic processing to dramatically improve detection and identification of diverse, low‐abundance avian blood parasites. Their approach is an exciting application of available tools that increases our potential for a deeper understanding of diversity in other communities of low‐abundance, highly diverse taxa.

We implemented multilocus selection in a spatially‐explicit, individual‐based framework that enables multivariate environmental gradients to drive selection in many loci as a new module for the landscape genetics programs, CDPOP and CDMetaPOP. Our module simulates multilocus selection using a linear additive model, providing a flexible platform to evaluate a wide range of genotype‐environment associations. Importantly, the module allows simulation of selection in any number of loci under the influence of any number of environmental variables. We validated the module with individual‐based selection simulations under Wright‐Fisher assumptions. We then evaluated results for simulations under a simple landscape selection model. Next, we simulated individual‐based multilocus selection across a complex selection landscape with three loci linked to three different environmental variables. Finally, we demonstrated how the program can be used to simulate multilocus selection under varying selection strengths across different levels of gene flow in a landscape genetics framework. This new module provides a valuable addition to the study of landscape genetics, allowing for explicit evaluation of the contributions and interactions between gene flow and selection‐driven processes across complex, multivariate environmental and landscape conditions.

Multiple male mating (MMM) causes sperm competition, which may play an important role in the evolution of reproductive traits. The frequency of multiple paternity (MP), where multiple males sire offspring within a single litter, has been used as an index of MMM frequency. However, MP frequency is necessarily lower than MMM frequency. The magnitude of the difference between MMM and MP frequency depends on litter size (LS) and fertilization probability skew (FPS), and this difference may be meaningfully large in animals with small LSs. In this study, we propose a method to estimate MMM frequency using an individual‐based model with three variables (MP frequency, LS and FPS). We incorporated observed paternity skew data to infer a possible range of FPS that cannot be measured in free‐living populations and tested the validity of our method using a data set from a grey‐sided vole (Myodes rufocanus) population and from hypothetical populations. MP was found in 50 out of 215 litters (23.3%) in the grey‐sided vole population, while MMM frequency was estimated in 67 of 215 litters (31.2%), with a certainty range of 59–88 (27.4%–40.9%). The point estimation of MMM frequency was realized, and the certainty range was limited within the practical range. The use of observed paternity skew was very effective at narrowing the certainty range of the estimate. Our method could contribute to a deeper understanding of the ecology of MMM in free‐living populations.

Management of biological invasions and conservation activity in the fight against habitat fragmentation both require information on how ongoing dispersal of organisms is affected by the environment. However, there are few landscape genetic programs that map resistance to dispersal at small spatiotemporal scales. To facilitate such analyses, we present an R package named ResDisMapper for the mapping of resistance to dispersal at small spatiotemporal scales, without the need for prior knowledge on environmental features or intensive computation. Based on the concept of isolation by distance (IBD), ResDisMapper calculates resistance using deviations of each pair of samples from the general IBD trend (IBD residuals). The IBD residuals are projected onto the studied area, which allows construction and visualization of a fine‐scale map of resistance based on spatial accumulation of positive or negative IBD residuals. In this study, we tested ResDisMapper with both simulated and empirical datasets and compared its performance with two other popular landscape genetic programs. Overall, we found that ResDisMapper can map resistance with relatively high accuracy. The latest version of the package and associated documentation are available on GitHub (https://github.com/takfung/ResDisMapper).

Metatranscriptomics is a powerful method for studying the composition and function of complex microbial communities. The application of metatranscriptomics to multispecies parasite infections is of particular interest, as research on parasite evolution and diversification has been hampered by technical challenges to genome‐scale DNA sequencing. In particular, blood parasites of vertebrates are abundant and diverse although they often occur at low infection intensities and exist as multispecies infections, rendering the isolation of genomic sequence data challenging. Here, we use birds and their diverse haemosporidian parasites to illustrate the potential for metatranscriptome sequencing to generate large quantities of genome‐wide sequence data from multiple blood parasite species simultaneously. We used RNA‐sequencing of 24 blood samples from songbirds in North America to show that metatranscriptomes can yield large proportions of haemosporidian protein‐coding gene repertoires even when infections are of low intensity (<0.1% red blood cells infected) and consist of multiple parasite taxa. By bioinformatically separating host and parasite transcripts and assigning them to the haemosporidian genus of origin, we found that transcriptomes detected ~23% more total parasite infections across all samples than were identified using microscopy and DNA barcoding. For single‐species infections, we obtained data for >1,300 loci from samples with as low as 0.03% parasitaemia, with the number of loci increasing with infection intensity. In total, we provide data for 1,502 single‐copy orthologous loci from a phylogenetically diverse set of 33 haemosporidian mitochondrial lineages. The metatranscriptomic approach described here has the potential to accelerate ecological and evolutionary research on haemosporidians and other diverse parasites.

Molecular Ecology Resources publishes broad resources to enhance studies in evolution, ecology, and conservation. The journal consistently ranks high among journals in Ecology and Evolutionary Biology, with an h5‐index of 64 and Impact Factor of 7.06 for the current year. The journal aims to publish high quality resources for broad use in the community including computer programs, statistical and methodological advances, and extensive molecular tools. Standards for manuscripts reflect advances across multiple fields and we encourage authors to review guidelines prior to submitting manuscripts to Molecular Ecology Resources since previous content may not reflect current standards.

Museum genomics has transformed the field of collections‐based research, opening up a range of new research directions for paleontological specimens as well as natural history specimens collected over the past few centuries. Recent work demonstrates that it is possible to characterize epigenetic markers such as DNA methylation in well preserved ancient tissues. This approach has not yet been tested in traditionally prepared natural history specimens such as dried bones and skins, the most common specimen types in vertebrate collections. In this study, we developed and tested methods to characterize cytosine methylation in dried skulls up to 76 years old. Using a combination of ddRAD and bisulphite treatment, we characterized patterns of cytosine methylation in two species of deer mouse (Peromyscus spp.) collected in the same region in Michigan in 1940, 2003, and 2013–2016. We successfully estimated methylation in specimens of all age groups, although older specimens yielded less data and showed greater interindividual variation in data yield than newer specimens. Global methylation estimates were reduced in the oldest specimens (76 years old) relative to the newest specimens (1–3 years old), which may reflect post‐mortem hydrolytic deamination. Methylation was reduced in promoter regions relative to gene bodies and showed greater bimodality in autosomes relative to female X chromosomes, consistent with expectations for methylation in mammalian somatic cells. Our work demonstrates the utility of historic specimens for methylation analyses, as with genomic analyses; however, studies will need to accommodate the large variance in the quantity of data produced by older specimens.

High‐throughput sequencing of amplicons from environmental DNA samples permits rapid, standardized and comprehensive biodiversity assessments. However, retrieving and interpreting the structure of such data sets requires efficient methods for dimensionality reduction. Latent Dirichlet Allocation (LDA) can be used to decompose environmental DNA samples into overlapping assemblages of co‐occurring taxa. It is a flexible model‐based method adapted to uneven sample sizes and to large and sparse data sets. Here, we compare LDA performance on abundance and occurrence data, and we quantify the robustness of the LDA decomposition by measuring its stability with respect to the algorithm's initialization. We then apply LDA to a survey of 1,131 soil DNA samples that were collected in a 12‐ha plot of primary tropical forest and amplified using standard primers for bacteria, protists, fungi and metazoans. The analysis reveals that bacteria, protists and fungi exhibit a strong spatial structure, which matches the topographical features of the plot, while metazoans do not, confirming that microbial diversity is primarily controlled by environmental variation at the studied scale. We conclude that LDA is a sensitive, robust and computationally efficient method to detect and interpret the structure of large DNA‐based biodiversity data sets. We finally discuss the possible future applications of this approach for the study of biodiversity.

The data used for profiling microbial communities is usually sparse with some microbes having high abundance in a few samples and being nearly absent in others. However, current bioinformatics tools able to deal with this sparsity are lacking. pime (Prevalence Interval for Microbiome Evaluation) was designed to remove those taxa that may be high in relative abundance in just a few samples but have a low prevalence overall. The reliability and robustness of pime were compared against existing methods and tested using 16S rRNA independent data sets. pime filters microbial taxa not shared in a per treatment prevalence interval started at 5% prevalence with increasing increments of 5% at each filtering step. For each prevalence interval, hundreds of decision trees were calculated to predict the likelihood of detecting differences in treatments. The best prevalence‐filtered data set was user‐selected by choosing the prevalence interval that kept a large portion of the 16S rRNA sequences in the data set while also showing the lowest error rate. To obtain the likelihood of introducing type I error while building prevalence‐filtered data sets, an error detection step based was also included. A pime reanalysis of published data sets uncovered other expected microbial associations than previously reported, which may be masked when only relative abundance was considered.

Plastic marine debris (PMD) affects spatial scales of life from microbes to whales. However, understanding interactions between plastic and microbes in the “Plastisphere”—the thin layer of life on the surface of PMD—has been technology‐limited. Research into microbe–microbe and microbe–substrate interactions requires knowledge of community phylogenetic composition but also tools to visualize spatial distributions of intact microbial biofilm communities. We developed a CLASI‐FISH (combinatorial labelling and spectral imaging – fluorescence in situ hybridization) method using confocal microscopy to study Plastisphere communities. We created a probe set consisting of three existing phylogenetic probes (targeting all Bacteria, Alpha‐, and Gammaproteobacteria) and four newly designed probes (targeting Bacteroidetes, Vibrionaceae, Rhodobacteraceae and Alteromonadaceae) labelled with a total of seven fluorophores and validated this probe set using pure cultures. Our nested probe set strategy increases confidence in taxonomic identification because targets are confirmed with two or more probes, reducing false positives. We simultaneously identified and visualized these taxa and their spatial distribution within the microbial biofilms on polyethylene samples in colonization time series experiments in coastal environments from three different biogeographical regions. Comparing the relative abundance of 16S rRNA gene amplicon sequencing data with cell‐count abundance data retrieved from the microscope images of the same samples showed a good agreement in bacterial composition. Microbial communities were heterogeneous, with direct spatial relationships between bacteria, cyanobacteria and eukaryotes such as diatoms but also micro‐metazoa. Our research provides a valuable resource to investigate biofilm development, succession and associations between specific microscopic taxa at micrometre scales.

Ribosomal RNA genes have long been a favoured locus in phylogenetic and metabarcoding studies. Within a genome, rRNA loci are organized as tandem repeated arrays and the copies are homogenized through the process of concerted evolution. However, some level of rRNA variation (intragenomic polymorphism) is known to persist and be maintained in the genomes of many species. In nematode worms, the extent of rRNA polymorphism (RP) across species and the evolutionary and life history factors that contribute to the maintenance of intragenomic RP is largely unknown. Here, we present an extensive analysis across 30 terrestrial nematode species representing a range of free‐living and parasitic taxa isolated worldwide. Our results indicate that RP is common and widespread, ribosome function appears to be maintained despite mutational changes, and intragenomic variants are stable in the genome and neutrally evolving. However, levels of variation were varied widely across rRNA locus and species, with some taxa observed to lack RP entirely. Higher levels of RP were significantly correlated with shorter generation time and high reproductive rates, and population‐level factors may play a role in the geographic and phylogenetic structuring of rRNA variants observed in genera such as Rotylenchulus and Pratylenchus. Although RP did not dramatically impact the clustering and recovery of taxa in mock metabarcoding analyses, the present study has significant implications for global biodiversity estimates of nematode species derived from environmental rRNA amplicon studies, as well as our understanding of the evolutionary and ecological factors shaping genetic diversity across the nematode Tree of Life.

High‐throughput DNA metabarcoding of amplicon sizes below 500 bp has revolutionized the analysis of environmental microbial diversity. However, these short regions contain limited phylogenetic signal, which makes it impractical to use environmental DNA in full phylogenetic inferences. This lesser phylogenetic resolution of short amplicons may be overcome by new long‐read sequencing technologies. To test this idea, we amplified soil DNA and used PacBio Circular Consensus Sequencing (CCS) to obtain an ~4500‐bp region spanning most of the eukaryotic small subunit (18S) and large subunit (28S) ribosomal DNA genes. We first treated the CCS reads with a novel curation workflow, generating 650 high‐quality operational taxonomic units (OTUs) containing the physically linked 18S and 28S regions. To assign taxonomy to these OTUs, we developed a phylogeny‐aware approach based on the 18S region that showed greater accuracy and sensitivity than similarity‐based methods. The taxonomically annotated OTUs were then combined with available 18S and 28S reference sequences to infer a well‐resolved phylogeny spanning all major groups of eukaryotes, allowing us to accurately derive the evolutionary origin of environmental diversity. A total of 1,019 sequences were included, of which a majority (58%) corresponded to the new long environmental OTUs. The long reads also allowed us to directly investigate the relationships among environmental sequences themselves, which represents a key advantage over the placement of short reads on a reference phylogeny. Together, our results show that long amplicons can be treated in a full phylogenetic framework to provide greater taxonomic resolution and a robust evolutionary perspective to environmental DNA.

Environmental DNA (eDNA) sampling, the detection of species‐specific genetic material in water samples, is an emerging tool for monitoring aquatic invasive species. Optimizing eDNA sampling protocols can be challenging because there is imperfect understanding of how each step of the protocol influences its sensitivity. This paper develops a probabilistic model that characterizes each step of an eDNA sampling protocol to evaluate the protocol's overall detection sensitivity for one sample. The model is then applied to analyse how changes over time made to the eDNA sampling protocol to detect bighead (BH) and silver carp (SC) eDNA have influenced its sensitivity, and hence interpretation of the results. The model shows that changes to the protocol have caused the sensitivity of the protocol to fluctuate. A more efficient extraction method in 2013, new species‐specific markers with a qPCR assay in 2014, and a more efficient capture method in 2015 have improved the sensitivity, while switching to a larger elution volume in 2013 and a smaller sample volume in 2015 have reduced the sensitivity. Overall, the sensitivity of the current protocol is higher for BH eDNA detection and SC eDNA detection compared to the original protocol used from 2009 to 2012. The paper shows how this model of eDNA sampling can be used to evaluate the effect of proposed changes in an eDNA sampling and analysis protocol on the sensitivity of that protocol to help researchers optimize their design.

Advances in DNA sequencing have made it feasible to gather genomic data for non‐model organisms and large sets of individuals, often using methods for sequencing subsets of the genome. Several of these methods sequence DNA associated with endonuclease restriction sites (various RAD and GBS methods). For use in taxa without a reference genome, these methods rely on de novo assembly of fragments in the sequencing library. Many of the software options available for this application were originally developed for other assembly types and we do not know their accuracy for reduced representation libraries. To address this important knowledge gap, we simulated data from the Arabidopsis thaliana and Homo sapiens genomes and compared de novo assemblies by six software programs that are commonly used or promising for this purpose (ABySS, CD‐HIT, Stacks, Stacks2, Velvet and VSEARCH). We simulated different mutation rates and types of mutations, and then applied the six assemblers to the simulated data sets, varying assembly parameters. We found substantial variation in software performance across simulations and parameter settings. ABySS failed to recover any true genome fragments, and Velvet and VSEARCH performed poorly for most simulations. Stacks and Stacks2 produced accurate assemblies of simulations containing SNPs, but the addition of insertion and deletion mutations decreased their performance. CD‐HIT was the only assembler that consistently recovered a high proportion of true genome fragments. Here, we demonstrate the substantial difference in the accuracy of assemblies from different software programs and the importance of comparing assemblies that result from different parameter settings.

We have compiled a database of functional traits for two widespread and ecologically important groups of protists, Cercozoa and Endomyxa (Rhizaria). The functional traits of microorganisms are crucially important for interpreting results from environmental sequencing surveys. Linking morphological and ecological traits to environmental factors is common practice in studies involving micro‐ and macroorganisms, but is rarely applied to protists. Our database provides functional and ecologically significant traits linked to morphology, nutrition, locomotion and habitats. We discuss how the use of functional traits may help to unveil underlying ecosystem processes. This database is intended as a common reference for the molecular ecology community and will boost the understanding of ecosystem functions, especially those driven by biological interactions.

Age‐related changes in DNA methylation do occur. Taking advantage of this, mammalian and avian epigenetic clocks have been constructed to predict age. In fish, studies on age‐related DNA methylation changes are scarce and no epigenetic clocks have been constructed. However, in fisheries and population dynamics studies there is a need for accurate estimation of age, something that is often impossible for some economically important species with the currently available methods. Here, we used the European sea bass, a marine fish the age of which can be determined with accuracy, to construct a piscine epigenetic clock, the first one in a cold‐blooded vertebrate. We used targeted bisulfite sequencing to amplify 48 CpGs from four genes in muscle samples and applied penalized regressions to predict age. We thus developed an age predictor in fish that is highly accurate (0.824) and precise (2.149 years). In juvenile fish, accelerated growth due to elevated temperatures had no effect on age prediction, indicating that the clock is able to predict the chronological age independently of environmentally‐driven perturbations. An epigenetic clock developed using muscle samples accurately predicted age in samples of testis but not ovaries, possibly reflecting the reproductive biology of fish. In conclusion, we report the development of the first piscine epigenetic clock, paving the way for similar studies in other species. Piscine epigenetic clocks should be of great utility for fisheries management and conservation purposes, where age determination is of crucial importance.

Comparability of findings from qPCR‐based telomere studies is hampered by such measurement results being assay‐specific, precluding a direct quantitative comparisons of observed differences and/or slopes of associations between studies. It is proposed that this can be partially alleviated by expressing qPCR‐based telomere data as Z‐scores.

Reptiles and other nonmammalian vertebrates have transcriptionally active nucleated red blood cells. If blood transcriptomes can provide quantitative data to address questions relevant to molecular ecology, this could circumvent the need to euthanize animals to assay tissues. This would allow longitudinal sampling of animals’ responses to treatments, as well as sampling of protected taxa. We developed and annotated blood transcriptomes from six reptile species and found on average 25,000 proteins are being transcribed in the blood, and there is a CORE group of 9,282 orthogroups that are found in at least four of six species. In comparison to liver transcriptomes from the same taxa, approximately two‐thirds of the orthogroups were found in both blood and liver; and a similar percentage of ecologically relevant gene groups (insulin and insulin‐like signalling, electron transport chain, oxidative stress, glucocorticoid receptors) were found transcribed in both blood and liver. As a resource, we provide a user‐friendly database of gene ids identified in each blood transcriptome. Although on average 37% of reads mapped to haemoglobin, importantly, the majority of nonhaemoglobin transcripts had sufficient depth (e.g., 97% at ≥10 reads) to be included in differential gene expression analysis. Thus, we demonstrate that RNAseq blood transcriptomes from a very small blood sample (<10 μl) is a minimally invasive option in nonmammalian vertebrates for quantifying expression of a large number of ecologically relevant genes that would allow longitudinal sampling and sampling of protected populations.

The high‐throughput capacities of the Illumina sequencing platforms and the possibility to label samples individually have encouraged wide use of sample multiplexing. However, this practice results in read misassignment (usually <1%) across samples sequenced on the same lane. Alarmingly high rates of read misassignment of up to 10% were reported for lllumina sequencing machines with exclusion amplification chemistry. This may make use of these platforms prohibitive, particularly in studies that rely on low‐quantity and low‐quality samples, such as historical and archaeological specimens. Here, we use barcodes, short sequences that are ligated to both ends of the DNA insert, to directly quantify the rate of index hopping in 100‐year old museum‐preserved gorilla (Gorilla beringei) samples. Correcting for multiple sources of noise, we identify on average 0.470% of reads containing a hopped index. We show that sample‐specific quantity of misassigned reads depends on the number of reads that any given sample contributes to the total sequencing pool, so that samples with few sequenced reads receive the greatest proportion of misassigned reads. This particularly affects ancient DNA samples, as these frequently differ in their DNA quantity and endogenous content. Through simulations we show that even low rates of index hopping, as reported here, can lead to biases in ancient DNA studies when multiplexing samples with vastly different quantities of endogenous material.

Freshwater fish biodiversity is quickly decreasing and requires effective monitoring and conservation. Environmental DNA (eDNA)‐based methods have been shown to be highly sensitive and cost‐efficient for aquatic biodiversity surveys, but few studies have systematically investigated how spatial sampling design affects eDNA‐detected fish communities across lentic systems of different sizes. We compared the spatial patterns of fish diversity determined using eDNA in three lakes of small (SL; 3 ha), medium (ML; 122 ha) and large (LL; 4,343 ha) size using a spatially explicit grid sampling method. A total of 100 water samples (including nine, 17 and 18 shoreline samples and six, 14 and 36 interior samples from SL, ML and LL, respectively) were collected, and fish communities were analysed using eDNA metabarcoding of the mitochondrial 12S region. Together, 30, 35 and 41 fish taxa were detected in samples from SL, ML, and LL, respectively. We observed that eDNA from shoreline samples effectively captured the majority of the fish diversity of entire waterbodies, and pooled samples recovered fewer species than individually processed samples. Significant spatial autocorrelations between fish communities within 250 m and 2 km of each other were detected in ML and LL, respectively. Additionally, the relative sequence abundances of many fish species exhibited spatial distribution patterns that correlated with their typical habitat occupation. Overall, our results support the validity of a shoreline sampling strategy for eDNA‐based fish community surveys in lentic systems but also suggest that a spatially comprehensive sampling design can reveal finer distribution patterns of individual species.

In the context of parentage assignment using genomic markers, key issues are genotyping errors and an absence of parent genotypes because of sampling, traceability or genotyping problems. Most likelihood‐based parentage assignment software programs require a priori estimates of genotyping errors and the proportion of missing parents to set up meaningful assignment decision rules. We present here the R package APIS, which can assign offspring to their parents without any prior information other than the offspring and parental genotypes, and a user‐defined, acceptable error rate among assigned offspring. Assignment decision rules use the distributions of average Mendelian transmission probabilities, which enable estimates of the proportion of offspring with missing parental genotypes. APIS has been compared to other software (CERVUS, VITASSIGN), on a real European seabass (Dicentrarchus labrax) single nucleotide polymorphism data set. The type I error rate (false positives) was lower with APIS than with other software, especially when parental genotypes were missing, but the true positive rate was also lower, except when the theoretical exclusion power reached 0.99999. In general, APIS provided assignments that satisfied the user‐set acceptable error rate of 1% or 5%, even when tested on simulated data with high genotyping error rates (1% or 3%) and up to 50% missing sires. Because it uses the observed distribution of Mendelian transmission probabilities, APIS is best suited to assigning parentage when numerous offspring (>200) are genotyped. We have demonstrated that APIS is an easy‐to‐use and reliable software for parentage assignment, even when up to 50% of sires are missing.

Boraginales (the forget‐me‐not order) is a core group within the lamiids clade. However, until now, no genome from Boraginales has been reported, and published transcriptomes are also rare. Here, we report the first Boraginales species de novo genome (i.e. Echium plantagineum genome) and seven other Boraginales species transcriptomes to probe three issues: (i) Boraginales' phylogenetic position within the lamiids clade; (ii) potential whole genome duplications (WGDs) in Boraginales; and (iii) candidate key enzyme genes in the alkannin/shikonin core pathway. The results showed that: (i) Boraginales was most probably closer to the Solanales/Gentianales clade than the Lamiales clade, at least based on the single‐copy orthologous genes from genome/transcriptome data; (ii) after the gamma (γ) event, Boraginaceae (classified into the Boraginales I clade) probably underwent at least two rounds of WGD, whereas Heliotropiaceae and Ehretiaceae (classified into the Boraginales II clade) probably underwent only one round of WGD; and (iii) several candidate key enzyme genes in the alkannin/shikonin core pathway were inferred, e.g. genes corresponding to geranyl cyclase, naphthol hydroxylase and O‐acyl transferase.

Environmental DNA (eDNA) is rapidly growing in popularity as a tool for community assessments and species detection. While eDNA approaches are now widely applied, there is not yet agreement on best practices for sample collection and processing. Investigators looking to integrate eDNA approaches into their research programme are required to examine a growing collection of disparate studies to make an often uncertain decision about which protocols best fit their needs. To promote the application of eDNA approaches and to encourage the generation of high‐quality data, here we review the most common techniques for the collection, preservation and extraction of metazoan eDNA from water samples. Specifically, we focus on experimental studies that compare various methods and outline the numerous challenges associated with eDNA. While the diverse applications of eDNA do not lend themselves to a one‐size‐fits‐all recommendation, in most cases, capture/concentration of eDNA on cellulose nitrate filters (with pore size determined by water turbidity), followed by storage of filters in Longmire's buffer and extraction with a DNeasy Blood & Tissue Kit (or similar) has been shown to provide sufficient, high‐quality DNA. However, we also emphasize the importance of testing and optimizing protocols for the system of interest.

Pyropia haitanensis (Bangiales, Rhodophyta), a major economically important marine crop, is also considered as an ideal research model of Rhodophyta to address several major biological questions such as sexual reproduction and adaptation to intertidal abiotic stresses. However, comparative genomic analysis to decipher the underlying molecular mechanisms is hindered by the lack of high‐quality genome information. Therefore, we integrated sequencing data from Illumina short‐read sequencing, PacBio single‐molecule sequencing and BioNano optical genome mapping. The assembled genome was approximately 53.3 Mb with an average GC% of 67.9%. The contig N50 and scaffold N50 were 510.3 kb and 5.8 Mb, respectively. Additionally, 10 superscaffolds representing 80.9% of the total assembly (42.7 Mb) were anchored and orientated to the 5 linkage groups based on markers and genetic distance; this outcome is consistent with the karyotype of five chromosomes (n = 5) based on cytological observation in P. haitanensis. Approximately 9.6% and 14.6% of the genomic region were interspersed repeat and tandem repeat elements, respectively. Based on full‐length transcriptome data generated by PacBio, 10,903 protein‐coding genes were identified. The construction of a genome‐wide phylogenetic tree demonstrated that the divergence time of P. haitanensis and Porphyra umbilicalis was ~204.4 Ma. Interspecies comparison revealed that 493 gene families were expanded and that 449 were contracted in the P. haitanensis genome compared with those in the Po. umbilicalis genome. The genome identified is of great value for further research on the genome evolution of red algae and genetic adaptation to intertidal stresses.

With the continual improvement in high‐throughput sequencing technology and constant updates to fungal reference databases, the use of amplicon‐based DNA markers as a tool to reveal fungal diversity and composition in various ecosystems has become feasible. However, both primer selection and the experimental procedure require meticulous verification. Here, we computationally and experimentally evaluated the accuracy and specificity of three widely used or newly designed internal transcribed spacer (ITS) primer sets (ITS1F/ITS2, gITS7/ITS4 and 5.8S‐Fun/ITS4‐Fun). In silico evaluation revealed that primer coverage varied at different taxonomic levels due to differences in degeneracy and the location of primer sets. Using even and staggered mock community standards, we identified different proportions of chimeric and mismatch reads generated by different primer sets, as well as great variation in species abundances, suggesting that primer selection would affect the results of amplicon‐based metabarcoding studies. Choosing proofreading and high‐fidelity polymerase (KAPA HiFi) could significantly reduce the percentage of chimeric and mismatch sequences, further reducing inflation of operational taxonomic units. Moreover, for two types of environmental fungal communities, plant endophytic and soil fungi, it was demonstrated that the three primer sets could not reach a consensus on fungal community composition or diversity, and that primer selection, not experimental treatment, determines observed soil fungal community diversity and composition. Future DNA marker surveys should pay greater attention to potential primer effects and improve the experimental scheme to increase credibility and accuracy.

Multigene and genomic data sets have become commonplace in the field of phylogenetics, but many existing tools are not designed for such data sets, which often makes the analysis time‐consuming and tedious. Here, we present PhyloSuite, a (cross‐platform, open‐source, stand‐alone Python graphical user interface) user‐friendly workflow desktop platform dedicated to streamlining molecular sequence data management and evolutionary phylogenetics studies. It uses a plugin‐based system that integrates several phylogenetic and bioinformatic tools, thereby streamlining the entire procedure, from data acquisition to phylogenetic tree annotation (in combination with iTOL). It has the following features: (a) point‐and‐click and drag‐and‐drop graphical user interface; (b) a workplace to manage and organize molecular sequence data and results of analyses; (c) GenBank entry extraction and comparative statistics; and (d) a phylogenetic workflow with batch processing capability, comprising sequence alignment (mafft and macse), alignment optimization (trimAl, HmmCleaner and Gblocks), data set concatenation, best partitioning scheme and best evolutionary model selection (PartitionFinder and modelfinder), and phylogenetic inference (MrBayes and iq‐tree). PhyloSuite is designed for both beginners and experienced researchers, allowing the former to quick‐start their way into phylogenetic analysis, and the latter to conduct, store and manage their work in a streamlined way, and spend more time investigating scientific questions instead of wasting it on transferring files from one software program to another.

The rice stem borer, Chilo suppressalis, is one of the most damaging insect pests to rice production worldwide. Although C. suppressalis has been the focus of numerous studies examining cold tolerance and diapause, plant–insect interactions, pesticide targets and resistance, and the development of RNAi‐mediated pest management, the absence of a high‐quality genome has limited deeper insights. To address this limitation, we generated a draft C. suppressalis genome constructed from both Illumina and PacBio sequences. The assembled genome size was 824.35 Mb with a contig N50 of 307 kb and a scaffold N50 of 1.75 Mb. Hi‐C scaffolding assigned 99.2% of the bases to one of 29 chromosomes. Based on universal single‐copy orthologues (BUSCO), the draft genome assembly was estimated to be 97% complete and is predicted to encompass 15,653 protein‐coding genes. Cold tolerance is an extreme survival strategy found in animals. However, little is known regarding the genetic basis of the winter ecology of C. suppressalis. Here, we focused our orthologous analysis on those gene families associated with animal cold tolerance. Our finding provided the first genomic evidence revealing specific cold‐tolerant strategies in C. suppressalis, including those involved in glucose‐originated glycerol biosynthesis, triacylglycerol‐originated glycerol biosynthesis, fatty acid synthesis and trehalose transport‐intermediate cold tolerance. The high‐quality C. suppressalis genome provides a valuable resource for research into a broad range of areas in molecular ecology, and subsequently benefits developing modern pest control strategies.

Translocations of threatened species can reduce the risk of extinction from a catastrophic event. For plants, translocation consists of moving individuals, seeds, or cuttings from a native (source) population to a new site. Ideally a translocation population would be genetically diverse and consist of fit founding individuals. In practice, there are challenges to designing such a population, including constraints on the availability of material, and tradeoffs between different goals. Here, we present an approach for designing a translocation population that identifies sets of founders that are optimized according to multiple criteria (e.g., genetic diversity), while also conforming to constraints on the representation of different founders (e.g., propagation success). It uses flexible inputs, including SNP genotypes, matrices of similarity between individuals, and vectors of phenotype data. We apply the approach to a critically endangered plant, Hibbertia puberula subsp. glabrescens (Dilleniaceae), which was genotyped at thousands of SNP loci. The goals of minimizing genetic similarity among the founding individuals and maximizing genetic diversity were largely complementary: populations optimized for one of these criteria were near‐optimal for the other. We also performed analyses in which we minimized genetic similarity among founding individuals while imposing selection (against hypothetical deleterious alleles, and against undesirable phenotypes, respectively), and here characterized sharp tradeoffs. This was useful in allowing the benefits of selection to be weighed against costs in terms of genetic similarity. In summary, we present an approach for designing a translocation population that allows flexible inputs, the imposition of realistic constraints, and examination of conflicting goals.

Despite their ubiquity, in most cases little is known about the impact of eukaryotic parasites on their mammalian hosts. Comparative approaches provide a powerful method to investigate the impact of parasites on host ecology and evolution, though two issues are critical for such efforts: controlling for variation in methods of identifying parasites and incorporating heterogeneity in sampling effort across host species. To address these issues, there is a need for standardized methods to catalogue eukaryotic parasite diversity across broad phylogenetic host ranges. We demonstrate the feasibility of a metabarcoding approach for describing parasite communities by analysing faecal samples from 11 nonhuman primate species representing divergent lineages of the primate phylogeny and the full range of sampling effort (i.e. from no parasites reported in the literature to the best‐studied primates). We detected a number of parasite families and regardless of prior sampling effort, metabarcoding of only ten faecal samples identified parasite families previously undescribed in each host (x̅ = 8.5 new families per species). We found more overlap between parasite families detected with metabarcoding and published literature when more research effort—measured as the number of publications—had been conducted on the host species' parasites. More closely related primates and those from the same continent had more similar parasite communities, highlighting the biological relevance of sampling even a small number of hosts. Collectively, results demonstrate that metabarcoding methods are sensitive and powerful enough to standardize studies of eukaryotic parasite communities across host species, providing essential new tools for macroecological studies of parasitism.

Data on hundreds or thousands of single nucleotide polymorphisms (SNPs) provide detailed information about the relationships between individuals, but currently few tools can turn this information into a multigenerational pedigree. I present the r package sequoia, which assigns parents, clusters half-siblings sharing an unsampled parent and assigns grandparents to half-sibships. Assignments are made after consideration of the likelihoods of all possible first-, second- and third-degree relationships between the focal individuals, as well as the traditional alternative of being unrelated. This careful exploration of the local likelihood surface is implemented in a fast, heuristic hill-climbing algorithm. Distinction between the various categories of second-degree relatives is possible when likelihoods are calculated conditional on at least one parent of each focal individual. Performance was tested on simulated data sets with realistic genotyping error rate and missingness, based on three different large pedigrees (N = 1000-2000). This included a complex pedigree with overlapping generations, occasional close inbreeding and some unknown birth years. Parentage assignment was highly accurate down to about 100 independent SNPs (error rate <0.1%) and fast (<1 min) as most pairs can be excluded from being parent-offspring based on opposite homozygosity. For full pedigree reconstruction, 40% of parents were assumed nongenotyped. Reconstruction resulted in low error rates (<0.3%), high assignment rates (>99%) in limited computation time (typically <1 h) when at least 200 independent SNPs were used. In three empirical data sets, relatedness estimated from the inferred pedigree was strongly correlated to genomic relatedness.

Plastid sequencing is an essential tool in the study of plant evolution. This high-copy organelle is one of the most technically accessible regions of the genome, and its sequence conservation makes it a valuable region for comparative genome evolution, phylogenetic analysis and population studies. Here, we discuss recent innovations and approaches for de novo plastid assembly that harness genomic tools. We focus on technical developments including low-cost sequence library preparation approaches for genome skimming, enrichment via hybrid baits and methylation-sensitive capture, sequence platforms with higher read outputs and longer read lengths, and automated tools for assembly. These developments allow for a much more streamlined assembly than via conventional short-range PCR. Although newer methods make complete plastid sequencing possible for any land plant or green alga, there are still challenges for producing finished plastomes particularly from herbarium material or from structurally divergent plastids such as those of parasitic plants.

Connectivity and movement patterns of populations are influenced by past and present environmental and biotic factors, which are reflected in genetic relatedness among populations. Methods that estimate the "commute time" between populations based on electrical resistance (i.e., isolation-by-resistance [IBR]) have been widely applied to either infer movement patterns directly from environmental factors or detect possible barriers to gene flow given empirical genetic relatedness. Yet, the commute time is only equivalent to the coalescence time between populations under symmetric migration with isotropic landscapes. Asymmetric gene flow is relatively common when populations are expanding, retreating, or with source-sink dynamics. In a From the Cover paper in this issue of Molecular Ecology Resources, Lundgren and Ralph (Molecular Ecology Resources, 19, 2019) describe a Bayesian method to infer bidirectional gene flow rates and population sizes without the assumption of symmetry. The method shows great accuracy in connectivity estimations under symmetric, as well as asymmetric gene flow scenarios where resistance methods fail. However, computational complexity limits the method to a few populations, preventing its application to finescale environmental maps. Also, as a discrete-deme static model, the inferred differences in gene flow rates are sensitive to population discretization and cannot be directly used to differentiate among processes (e.g., past expansion vs. current barrier). Here, we discuss scenarios where the new method can best be utilized and provide potential directions to identify the underlying processes causing deviations in gene flow patterns.

In spite of the usefulness of codominant markers in population genetics, the existence of null alleles raises challenging estimation issues in natural populations that are characterized by positive inbreeding coefficients (F > 0). Disregarding the possibility of F > 0 in a population will generally lead to overestimates of null allele frequencies. Conversely, estimates of inbreeding coefficients (F) may be strongly biased upwards (excess homozygotes), in the presence of nontrivial frequencies of null alleles. An algorithm has been presented for the estimation of null allele frequencies in inbred populations (van Oosterhout method), using external estimates of the F-statistics. The goal of this study is to introduce a modification of this method and to provide a formal comparison with an alternative likelihood-based method (Chybicki-Burczyk). Using simulated data, we illustrate the strengths and limitations of these competing methods. Under most circumstances, the likelihood method is preferable, but for highly inbred organisms, a modified van Oosterhout method offers some advantages.

GenBank is the database of record for public sequence data. Results reported in the scientific literature that are based on sequence data cannot be evaluated if the underlying data is not in the public record.

Increasingly, data on shape are analysed in combination with molecular genetic or ecological information, so that tools for geometric morphometric analysis are required. Morphometric studies most often use the arrangements of morphological landmarks as the data source and extract shape information from them by Procrustes superimposition. The MorphoJ software combines this approach with a wide range of methods for shape analysis in different biological contexts. The program offers an integrated and user-friendly environment for standard multivariate analyses such as principal components, discriminant analysis and multivariate regression as well as specialized applications including phylogenetics, quantitative genetics and analyses of modularity in shape data. MorphoJ is written in Java and versions for the Windows, Macintosh and Unix/Linux platforms are freely available from http://www.flywings.org.uk/MorphoJ_page.htm.

The accelerating pace of scientific discovery is being driven by the development of transformative technologies that are expanding traditional scientific boundaries. In ecology, the concept that ecosystem structure can be monitored through the stew of genetic material released into the environment by local organisms is now a highly active area of research. At the same time that the use of environmental DNA (eDNA) has gained popularity in ecology, CRISPR genome editing technology has been revolutionizing organismal biology and the biomedical sciences. In a From the Cover article in this issue of Molecular Ecology Resources, Williams et al. (2019) report about the fusion of CRISPR technology with molecular ecology to improve the in situ processing capabilities of eDNA applications. Piloting this new CRISPR technology on aquatic systems, the authors describe tools to accurately identify the presence of Atlantic salmon (Salmo salar) from eDNA, using conditions that are highly amenable to implementation in the field. This overcomes a major barrier restricting the use of eDNA, opening a door to expanded use of the technology in ecological research.

Environmental DNA (eDNA) promises to ease noninvasive quantification of fish biomass or abundance, but its integration within conservation and fisheries management is currently limited by a lack of understanding of the influence of eDNA collection method and environmental conditions on eDNA concentrations in water samples. Water temperature is known to influence the metabolism of fish and consequently could strongly affect eDNA release rate. As water temperature varies in temperate regions (both seasonally and geographically), the unknown effect of water temperature on eDNA concentrations poses practical limitations on quantifying fish populations using eDNA from water samples. This study aimed to clarify how water temperature and the eDNA capture method alter the relationships between eDNA concentration and fish abundance/biomass. Water samples (1 L) were collected from 30 aquaria including triplicate of 0, 5, 10, 15 and 20 Brook Charr specimens at two different temperatures (7 °C and 14 °C). Water samples were filtered with five different types of filters. The eDNA concentration obtained by quantitative PCR (qPCR) varied significantly with fish abundance and biomass and types of filters (mixed-design ANOVA, P < 0.001). Results also show that fish released more eDNA in warm water than in cold water and that eDNA concentration better reflects fish abundance/biomass at high temperature. From a technical standpoint, higher levels of eDNA were captured with glass fibre (GF) filters than with mixed cellulose ester (MCE) filters and support the importance of adequate filters to quantify fish abundance based on the eDNA method. This study supports the importance of including water temperature in fish abundance/biomass prediction models based on eDNA.

Inferring and quantifying recent barriers to connectivity is increasingly important for conservation and management in a world undergoing rapid environmental change. Traditional measures of genetic differentiation can take many generations to reflect a new barrier to connectivity. Although methods that use the linkage disequilibrium signal in mixed genetic samples are able to reflect recent levels of gene flow, they are not suitable for use in situations with low levels of genetic differentiation. Kinship-based methods, those that assess the spatio-temporal distribution of related individuals, have been used in this context, but a formal statistical framework for such approaches has been lacking. In this issue of Molecular Ecology Resources, Escoda, et al. adapt the assortativity coefficient, AC, to analyse the networks of kin relationships in the Pyrenean desman (Galemys pyrenaicus) across potential barriers to dispersal. Their modified AC quantifies the proportion of missing kin relationships across putative dispersal barriers with respect to the expected proportion if there was no barrier. This application highlights that AC can be used to test the null hypothesis that a putative barrier has no effect on gene flow, in which case AC is not significantly different from 0. The method represents a useful step forward in conservation genomics by developing and adapting tools to assess contemporary connectivity using genomic data.

It is the dream of all researchers working with ancient DNA to identify prior to DNA extraction from bone the specimens or specific zones within them that contain the highest proportion of endogenous DNA. As it impacts the sacrifice of precious ancient specimens and the financial support needed for the analyses, the question is of high importance to the scientific field of palaeogenomics. The "Holy Grail" of palaeogenomics was reached when Cristina Gamba et al. () discovered that it was in the petrosal part of the temporal bone, the densest part of the mammalian skeleton, where DNA is exceptionally well preserved. As a consequence, osteological collections experienced a rush from palaeogenomicists to "harvest" these precious bone parts. In this issue of Molecular Ecology Resources, Alberti et al. () describe the discovery of another promising source of relatively well-preserved endogenous DNA, that they had identified through computed tomography (CT scans), the outermost layer of cortical bone. These bones being larger and more abundant than petrous bones, this discovery increases markedly the source material for high-quality palaeogenomic studies and releases the pressure on osteological collections.

Molecular markers have been used to identify the sex of sampled individuals for several decades, but the time-consuming development phase prevented their application in many systems. Recently, a growing number of papers have applied reduced-representation sequencing (RRS) protocols to the identification of sex-specific markers without the use of test crosses or prior genomic information. While such an approach has great advantages in terms of versatility and ease of use, the "shotgun sequencing" nature of RRS data sets leads to a high amount of missing data, which results in statistical challenges to the confident assignment of sex to individuals. In this issue of Molecular Ecology Resources, Stovall et al. (Molecular Ecology Resources, 18, 2018) provide a statistical framework to answer two questions: (1) how many individuals of one sex only must possess a genotype for this locus to be considered significantly sex-specific? and (2) How many sex-specific loci must an individual of unknown sex possess (in a given data set) to be confidently assigned a sex? The statistical pipeline introduced, and applied to samples of New Zealand fur seal (Arctocephalus forsteri) to identify 90 sex-specific loci, should be broadly applicable to a large number of species and constitutes a nice addition to the molecular ecology toolkit in the genomics era.

Bigheaded carps are invasive fishes threatening to invade the Great Lakes basin and establish spawning populations, and have been monitored using environmental DNA (eDNA). Not only does eDNA hold potential for detecting the presence of species, but may also allow for quantitative comparisons like relative abundance of species across time or space. We examined the relationships among bigheaded carp movement, hydrography, spawning and eDNA on the Wabash River, IN, USA. We found positive relationships between eDNA and movement and eDNA and hydrography. We did not find a relationship between eDNA and spawning activity in the form of drifting eggs. Our first finding demonstrates how eDNA may be used to monitor species abundance, whereas our second finding illustrates the need for additional research into eDNA methodologies. Current applications of eDNA are widespread, but the relatively new technology requires further refinement.

Mollusc shells, beyond the treasure of information inherently conveyed through their morphology and chemical composition also have the capacity to preserve DNA sequences over the long term in their inner structure. This has been clearly demonstrated for the first time in the study published in this issue of Molecular Ecology Resources by Der Sarkissian et al. (). With a methodology specifically dedicated to ancient DNA and solid matrices, the authors were able to successfully extract and amplify DNA from marine shells spanning the last 7,000 years. Furthermore, using metagenomic analyses, they could identify important factors affecting DNA recovery. Using reference genomes and sequences in a targeted approach to assign high-throughput sequencing reads, the authors revealed both the presence of endogenous mollusc DNA and a potent pathogen of Manilla clam. Collectively, the results presented in this study open extremely promising research avenues, from palaeogenomics and evolutionary biology to ecological genomics at population and community levels, as well as the opportunity to fine-tune diagnostic tools for conservation and aquaculture purposes. Last but not least, this study also offers exciting perspectives in epigenomics and the evolution of regulatory processes in the context of adaptation to global change. It can be easily expected that the approach developed by Der Sarkissian et al. () will be pursued and extensively investigated in the near future by the scientific community interested in these issues.