Samia ricini, a gigantic saturniid moth, has the potential to be a novel lepidopteran model species. Since S. ricini is much more tough and resistant to diseases than the current model species Bombyx mori, the former can be easily reared compared to the latter. In addition, genetic resources available for S. ricini rival or even exceed those for B. mori: at least 26 eco-races of S. ricini are reported and S. ricini can hybridise with wild Samia species, which are distributed throughout Asian countries, and produce fertile progenies. Physiological traits such as food preference, integument colour, larval spot pattern, etc. are different between S. ricini and wild Samia species so that those traits can be the target for forward genetic analysis. In order to facilitate genetic research in S. ricini, we determined the whole genome sequence of S. ricini. The assembled genome of S. ricini was 458 Mb with 155 scaffolds, and the N50 length of the assembly was approximately 21 Mb. 16,702 protein coding genes were predicted in the assembly. Although the gene repertoire of S. ricini was not so different from that of B. mori, some genes, such as chorion genes and fibroin genes, seemed to have specifically evolved in S. ricini.

The Ocean Barcode Atlas (OBA) is a user friendly web service designed for biologists who wish to explore the biodiversity and biogeography of marine organisms locked in otherwise difficult to mine planetary scale DNA metabarcode datasets. Using just a web browser, a comprehensive picture of the diversity of a taxon or a barcode sequence is visualized graphically on world maps and interactive charts. Interactive results panels allow dynamic threshold adjustments and the display of diversity results in their environmental context measured at the time of sampling (temperature, oxygen, latitude, etc.). Ecological analyses such as alpha and beta-diversity plots are produced via publication quality vector graphics representations. Currently, the Ocean Barcode Altas is deployed online with the i) Tara Oceans eukaryotic 18S-V9 rDNA metabarcodes, ii) Tara Oceans 16S/18S rRNA miTags, and iii) 16S-V4V5 metabarcodes collected during the Malaspina-2010 expedition. Additional prokaryotic or eukaryotic plankton barcode datasets will be added upon availability, given they provide the required complement of barcodes (including raw reads to compute barcode abundance) associated with their contextual environmental variables. Ocean Barcode Atlas is a freely-available web service at: http://oba.mio.osupytheas.fr/ocean-atlas/.

Advances in sequencing technologies and declining costs are increasing the accessibility of large-scale biodiversity genomic datasets. To maximise the impact of these data, a careful, considered approach to data management is essential. However, challenges associated with the management of such datasets remain, exacerbated by uncertainty among the research community as to what constitutes best practices. As an interdisciplinary team with diverse data management experience, we recognise the growing need for guidance on comprehensive data management practices that minimise the risks of data loss, maximise efficiency for stand-alone projects, enhance opportunities for data reuse, facilitate Indigenous data sovereignty and uphold the FAIR and CARE Guiding Principles. Here, we describe four fictional personas reflecting user experiences with data management to identify data management challenges across the biodiversity genomics research ecosystem. We then use these personas to demonstrate realistic considerations, compromises, and actions for biodiversity genomic data management. We also launch the Biodiversity Genomics Data Management Hub (https://genomicsaotearoa.github.io/data-management-resources/), containing tips, tricks and resources to support biodiversity genomics researchers, especially those new to data management, in their journey towards best practice. The Hub also provides an opportunity for those biodiversity researchers whose expertise lies beyond genomics and are keen to advance their data management journey. We aim to support the biodiversity genomics community in embedding data management throughout the research lifecycle to maximise research impact and outcomes.

Scale insects are hemimetabolous, showing “incomplete” metamorphosis and no true pupal stage. Ericerus pela, commonly known as the white wax scale insect (hereafter, WWS), is a wax-producing insect found in Asia and Europe. WWS displays dramatic sexual dimorphism, with notably different metamorphic fates in males and females. Males develop into winged adults, while females are neotenic and maintain a nymph-like appearance, which are flightless and remain stationary. Here we report the de novo assembly of the WWS genome with its size of 638.30 Mb (69.68Mb for scaffold N50) by PacBio sequencing and Hi-C. From these data, we constructed a robust phylogenetic analysis of 24,923 gene families from 16 representative insect genomes, which indicates that holometabola evolved from incomplete metamorphosis insects in the Late Carboniferous, about 50 million years earlier than previously thought. To study the distinct development of males and females, we analyzed the methylome landscape in either sex. Surprisingly, WWS displayed high levels of methylation (4.42% for males) when compared to other insects. We observed differential methylation patterns for genes involved in steroid and sesquiterpenoids production as well as related fatty acid metabolism pathways. We show here that both males and females exhibit distinct titer profiles for ecdysone, the principal insect steroid hormone, and juvenile hormone (a sesquiterpenoid), suggesting that these hormones are the primary drivers of sexually dimorphic features. Our results provide a comprehensive genomic and epigenomic resource of scale insects that provide new insights into the evolution of metamorphosis and sexual dimorphism in insects.

Gene annotation is a critical bottleneck in genomic research, especially for the comprehensive study of very large gene families in the genomes of non-model organisms. Despite the recent progress in automatic methods, state-of-the-art tools used for this task often produce inaccurate annotations, such as fused, chimeric, partial or even completely absent gene models for many family copies, errors that require considerable extra efforts to be corrected. Here we present BITACORA, a bioinformatics solution that integrates popular sequence similarity-based search tools and Perl scripts to facilitate both the curation of these inaccurate annotations and the identification of previously undetected gene family copies directly in genomic DNA sequences. We tested the performance of BITACORA in annotating the members of two chemosensory gene families with different repertoire size in seven available genome sequences, and compared its performance with that of Augustus-PPX, a tool also designed to improve automatic annotations using a sequence similarity-based approach. Despite the relatively high fragmentation of some of these drafts, BITACORA was able to improve the annotation of many members of these families and detected thousands of new chemoreceptors encoded in genome sequences. The program creates general feature format (GFF) files, with both curated and newly identified gene models, and FASTA files with the predicted proteins. These outputs can be easily integrated in genomic annotation editors, greatly facilitating subsequent manual annotation and downstream evolutionary analyses.

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

Genotyping novel complex multigene families is particularly challenging in non-model organisms. Target primers frequently amplify simultaneously multiple loci leading to high PCR and sequencing artefacts such as chimeras and allele amplification bias. Most genotyping pipelines have been validated in non-model systems whereby the real genotype is unknown and the generation of artefacts may be highly repeatable. Further hindering accurate genotyping, the relationship between artefacts and genotype complexity (i.e. number of alleles per genotype) within a PCR remains poorly described. Here we investigated the latter by experimentally combining multiple known major histocompatibility complex (MHC) haplotypes of a model organism (chicken, \textit{Gallus gallus}, 43 artificial genotypes with 2-13 alleles per amplicon). In addition to well defined “optimal” primers, we simulated a non-model species situation by designing “cross-species” primers, with sequence data from closely related Galliforme species. We applied a novel open-source genotyping pipeline (ACACIA; \url{https://gitlab.com/psc_santos/ACACIA}), and compared its performance with another, previously published pipeline (AmpliSAS). Allele calling accuracy was higher when using ACACIA (98.5\% vs 97\% and 77.8\% vs 75.2\% for the “optimal” and “cross-species” datasets respectively). Systematic allele dropout of three alleles owing to primer mismatch in the “cross-species” dataset explained high allele calling repeatability (100\% when using ACACIA) despite low accuracy, demonstrating that repeatability can be misleading when evaluating genotyping workflows. Genotype complexity was positively associated with non-chimeric artefacts, chimeric artefacts (nonlinearly by leveling when amplifying more than 4-6 alleles) and allele amplification bias. Our study exemplifies and demonstrates pitfalls researchers should avoid to reliably genotype complex multigene families.

Identifying local adaptation in bottlenecked species is essential for conservation management. Selection detection methods have an important role in species management plans, assessments of adaptive capacity, and looking for responses to climate change. Yet, the allele frequency changes exploited in selection detection methods are similar to those caused by the strong neutral genetic drift expected during a bottleneck. Consequently, it is often unclear what accuracy selection detection methods have across bottlenecked populations. In this study, simulations were used to explore if signals of selection could be confidently distinguished from genetic drift across 23 bottlenecked and reintroduced populations of Alpine ibex (Capra ibex). The meticulously recorded demographic history of the Alpine ibex was used to generate comprehensive simulated SNP data. The simulated SNPs were then used to benchmark the confidence we could place in outliers identified in empirical Alpine ibex SNP data. Within the simulated dataset, the false positive rates were high for all selection detection methods but fell substantially when two or more methods were combined. True positive rates were consistently low and became negligible with increased stringency. Despite finding many outlier loci in the empirical Alpine ibex SNPs, none could be distinguished from genetic drift-driven false positives. Unfortunately, the low true positive rate also prevents the exclusion of recent local adaptation within the Alpine ibex. The baselines and stringent approach outlined here should be applied to other bottlenecked species to ensure the risk of false positive, or negative, signals of selection are accounted for in conservation management plans.

Biodiversity inventory remains limited in marine systems due to unbalanced access to the three ocean dimensions. The use of environmental DNA (eDNA) for metabarcoding allows fast and effective biodiversity inventory and is forecast as a future biodiversity research and biomonitoring tool. However, in poorly understood ecosystems, eDNA results remain difficult to interpret due to large gaps in reference databases and PCR bias limiting the detection of some major phyla. Here, we aimed to circumvent these limitations by avoiding PCR and recollecting larger DNA fragments to improve assignment of detected taxa through phylogenetic reconstruction. We applied capture by hybridization (CBH) to enrich DNA from deep-sea sediment samples and compared the results with those obtained through an up-to-date metabarcoding PCR-based approach (MTB). Originally developed for bacterial communities by targeting 16S rDNA, the CBH approach was applied to 18S rDNA to improve the detection of species forming benthic communities of eukaryotes, with particular focus on metazoans. The results confirmed the possibility of extending CBH to metazoans with two major advantages: i) CBH revealed a broader spectrum of prokaryotic, eukaryotic, and particularly metazoan diversity, and ii) CBH allowed much more robust phylogenetic reconstructions of full-length barcodes with up to 1900 base pairs. This is particularly important for taxa whose assignment is hampered by gaps in reference databases. This study provides a database and probes to apply 18S CBH to diverse marine systems, confirming this promising new tool to improve biodiversity assessments in data-poor ecosystems like those in the deep sea.

Published literature suggests that indigenous cultural practices, specifically traditional medicine, are commonplace among urban communities contrary to the general conception that such practices are associated to rural societies. We reviewed literature for records of herptiles sold by traditional health practitioners in urban South Africa, then used visual confirmation surveys, DNA barcoding, and folk taxonomy to identify the herptile species that were on sale. Additionally, interviews with 11 SePedi and IsiZulu speaking traditional health practitioners were used to document details of the collection and pricing of herptile specimens along with the practitioners’ views of current conservation measures aimed at traditional medicine markets. The herptile specimens sold by traditional health practitioners included endangered and non-native species. The absorbance ratios of DNA extracted from the tissue of herptiles used in traditional medicine were found to be unreliable predictors of whether those extractions would be suitable for downstream applications. From an initial set of 111 tissue samples, 81 sequencing reactions were successful and 55 of the obtained sequences had species level matches to COI reference sequences on the NCBI GenBank and/or BOLD databases. Molecular identification revealed that traditional health practitioners sometimes mislabel the species they use. The mixed methodology employed here is useful for conservation planning as it updates knowledge of animal use in indigenous remedies and can accurately identify species of high conservation priority. Furthermore, the study highlights the possibility of collaborative conservation planning with traditional health practitioners.

A large part of the soil protist diversity is missed in metabarcoding studies based on 0.25 g of soil environmental DNA (eDNA) and universal primers due to ca. 80 % co-amplification of non-target plants, animals and fungi. To overcome this problem, enrichment of the substrate used for eDNA extraction is an easyly implemented option but its effect has not yet been tested. In this study, we evaluated the effect of a 150 µm mesh size filtration and sedimentation method to improve the recovery of protist eDNA, while reducing the co-extraction of plant, animal and fungal eDNA, using a set of contrasted forest and alpine soils from La Réunion, Japan, Spain and Switzerland. Biodiversity of the whole eukaryotic community was estimated with V4 18S rRNA metabarcoding and classical amplicon sequence variant calling. A 2-3-fold enrichment in shelled protists (Euglyphida, Arcellinida and Chrysophyceae) was observed at the sample level with the proposed method, with, at the same time, a 2-fold depletion of Fungi and a 3-fold depletion of Embryophyceae. Protist alpha diversity was slightly lower in filtered samples due to reduced coverage in Variosea and Sarcomonadea, but significant differences were observed in only one region. Beta diversity was mostly impacted by region and habitat, and explained the same variance in bulk soil and filtered samples. The increase resolution in the soil protist diversity provided by the filtration-sedimentation method is a strong argument to include it in the standard preparation of any future soil for protist eDNA metabarcoding studies.

Environmental DNA is an effective tool for describing fish biodiversity in lotic environments, but the downstream transport of eDNA released by organisms makes it difficult to interpret species detection at the local scale. In addition to biophysical degradation and exchanges at the water-sediment interface, hydrological conditions control the transport distance. We have developed an eDNA transport model that considers downstream retention and degradation processes in combination with hydraulic conditions and assumes that the sedimentation rate of very fine particles is a correct estimate of the eDNA deposition rate. Based on meta-analyses of available studies, we successively modelled the particle size distribution of fish eDNA (PSD), the relationship between the sedimentation rate and the size of very fine particles in suspension, and the influence of temperature on the degradation rate of fish eDNA. After combining the results in a mechanistic-based model, we correctly simulated the eDNA uptake distances observed in a compilation of previous experimental studies. eDNA degradation is negligible at low flow and temperature but has a comparable influence to background transfer when hydraulic conditions allow a long uptake distance. The wide prediction intervals associated with the simulations reflect the complexity of the processes acting on eDNA after shedding. This model can be useful for estimating eDNA detection distance downstream from a source point and discussing the possibility of false positive detection in eDNA samples, as shown in an example.

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.

High-throughput sequencing for analysis of environmental microbial diversity has evolved vastly over the last decade. Currently the go-to method for microbial eukaryotes is short-read metabarcoding of variable regions of the 18S rRNA gene with <500 bp amplicons. However, there is a growing interest in long-read sequencing of amplicons covering the rRNA operon for improving taxonomic resolution. For both methods, the choice of primers is crucial. It determines if community members are covered, if they can be identified at a satisfactory taxonomic level, and if the obtained community profile is representative. Here, we designed new primers targeting 18S and 28S rRNA based on 177,934 and 21,072 database sequences, respectively. The primers were evaluated in silico along with published primers on reference sequence databases and marine metagenomics datasets. We further evaluated a subset of the primers for short- and long-read sequencing on environmental samples in vitro and compared the obtained community profile with primer-unbiased metagenomic sequencing. Of the short-read pairs, a new V6-V8 pair and the V4_Balzano pair used with a simplified PCR protocol provided good results in silico and in vitro. Fewer differences were observed between the long-read primer pairs. The long-read amplicons and ITS1 alone provided higher taxonomic resolution than V4. Together, our results represent a reference and guide for selection of robust primers for research on and environmental monitoring of microbial eukaryotes.

Understanding the evolutionary consequences of anthropogenic change is imperative for estimating long-term species resilience. While contemporary genomic data can provide us with important insights into recent demographic histories, investigating past change using present genomic data alone has limitations. In comparison, temporal genomics studies, defined herein as those that incorporate time series genomic data, leverage museum collections and repeated field sampling to directly examine evolutionary change. As temporal genomics is applied to more systems, species, and questions, best practices can be helpful guides to make the most efficient use of limited resources. Here, we conduct a systematic literature review to synthesize the effects of temporal genomics methodology on our ability to detect evolutionary changes. We focus on studies investigating recent change within the past 200 years, highlighting evolutionary processes that have occurred during the past two centuries of accelerated anthropogenic pressure. We first identify the most frequently studied taxa, systems, questions, and drivers, before highlighting overlooked areas where further temporal genomic studies may be particularly enlightening. Then, we provide guidelines for future study and sample designs while identifying key considerations that may influence statistical and analytical power. Our aim is to provide recommendations to a broad array of researchers interested in using temporal genomics in their work.

Environmental (e)DNA methods have enabled rapid, sensitive, and specific inferences of taxa presence throughout diverse fields of ecological study. However, use of eDNA results for decision-making has been impeded by uncertainties associated with false positive tests putatively caused by contamination. Sporadic contamination is a process that is inconsistent across samples and systemic contamination occurs consistently over a group of samples. Here, we used empirical data and lab experiments to (1) estimate the sporadic contamination rate for each stage of a common, targeted eDNA workflow employing best practice quality control measures under simulated conditions of rare and common target DNA presence, (2) determine the rate at which negative controls (i.e., “blanks”) detect varying concentrations of systemic contamination, (3) estimate the effort that would be required to consistently detect sporadic and systemic contamination. Sporadic contamination rates were very low across all eDNA workflow steps, and, therefore, an intractably high number of negative controls (>100) would be required to determine occurrence of sporadic contamination with any certainty. Contrarily, detection of intentionally introduced systemic contamination was more consistent; therefore, very few negative controls (<5) would be needed to consistently alert to systemic contamination. These results have considerable implications to eDNA study design when resources for sample analyses are constrained.

Dietary metabarcoding has vastly improved our ability to analyse the diets of animals, but it is hampered by a plethora of technical limitations including potentially reduced data output due to the disproportionate amplification of the DNA of the focal predator, here termed ‘the predator problem’. We review the various methods commonly used to overcome this problem, from deeper sequencing to exclusion of predator DNA during PCR, and how they may interfere with increasingly common multi-predator-taxon studies. We suggest that multi-primer approaches with an emphasis on achieving both depth and breadth of prey detections may overcome the issue to some extent, although multi-taxon studies require further consideration, as highlighted by an empirical example. We also review several alternative methods for reducing the prevalence of predator DNA that are conceptually promising but require additional empirical examination. The predator problem is a key constraint on molecular dietary analyses but, through this synthesis, we hope to guide researchers in overcoming this in an effective and pragmatic way.

The molecular characterisation of complex behaviours is a challenging task as a range of different factors are often involved to produce the observed phenotype. An established approach is to look at the overall levels of expression of brain genes – known as ‘neurogenomics’ – to select the best candidates that associate with patterns of interest. This approach has relied so far on a set of powerful statistical tools capable to provide a snapshot of the expression of many thousands of genes that are present in an organism’s genome. However, traditional neurogenomic analyses have some well-known limitations; above all, the limited number of biological replicates compared to the number of genes tested – often referred to as “curse of dimensionality”. Here we implemented a new Machine Learning (ML) approach that can be used as a complement to established methods of transcriptomic analyses. We tested three types of ML models for their performance in the identification of genes associated with honeybee waggle dance. We then intersected the results of these analyses with traditional outputs of differential gene expression analyses and identified two promising candidates for the neural regulation of the waggle dance: the G-protein coupled receptor boss and hnRNP A1, a gene involved in alternative splicing. Overall, our study demonstrates the application of Machine Learning to analyse transcriptomics data and identify genes underlying social behaviour. This approach has great potential for application to a wide range of different scenarios in evolutionary ecology, when investigating the genomic basis for complex phenotypic traits.