Both environmental DNA (eDNA) and environmental RNA (eRNA) have been widely adopted for biodiversity assessment. While eDNA often persists longer in environments, eRNA offers a more current view of biological activities. In eRNA metabarcoding, extracted eRNA is reverse transcribed into complementary DNA (cDNA) for metabarcoding. However, the efficacy of various reverse transcription strategies has not been evaluated. Here we compared the biodiversity recovery efficiency of three strategies: random priming with hexamers, oligo(dT) priming, and taxa-specific priming using Mifish-U for fish in both high- and low-biodiversity regions. Our results demonstrate that reverse transcription strategies significantly impact biodiversity recovery. Random priming consistently detected the highest number of taxa in both low- and high-biodiversity regions. In low-biodiversity areas, oligo(dT) performed comparably to random hexamers; however, in high-biodiversity regions, random hexamers outperformed oligo(dT), particularly in recovering rare taxa. While taxa-specific priming was comparative to the other strategies for high-abundance taxa, it was less effective for rare taxa, thus limiting its utility for comprehensive biodiversity assessment. These differences are largely due to the multiple binding sites for random hexamers compared to the fewer or absent sites with oligo(dT) and taxa-specific primers under high eRNA degradation. Combining random hexamers and oligo(dT) significantly improved taxa recovery, especially for low-abundance species, supporting its best practice in eukaryotes. For prokaryotes or genes lacking polyadenylation, random priming is favored over taxa- or gene-specific priming. Collectively, these findings underscore the critical importance of selecting appropriate reverse transcription strategies in eRNA metabarcoding, with significant implications for effective biodiversity monitoring and conservation efforts.

Species-specific genomic information has the potential to transform modern conservation management strategies through improved genomic impact assessment and management outcomes. Gaining genomic insights into genetic diversity, adaptability, and potential resilience against infectious diseases is essential to enhance conservation efforts for threatened species. Here, we describe the development of a custom 50K SNP array for Pseudophryne corroboree, a critically endangered amphibian threatened by the amphibian chytrid fungus (Batrachochytrium dendrobatidis, Bd) and illustrate its application in characterising the species’ genomic architecture. The array comprises 48,386 SNPs, with an average density of 5.45 SNPs per Mb and was effective in genotyping multiple tissue types, including non-lethal buccal swabs. Of the SNPs, 82.1% were polymorphic across 910 captive-bred P. corroboree individuals derived from 54 families. Our analysis of this population revealed evidence of ancestral inbreeding, the presence of two historic bottlenecks occurring approximately 100 and 10 generations ago, with the latter coinciding with the arrival of Bd to Australia. We also demonstrate the array’s cross-species amplification potential, successfully converting 21,077 (43.6%) polymorphic loci across three closely related anurans. This array is a valuable resource for future investigations aimed at understanding the genetic basis of disease resistance and developing management strategies for improving reintroduction outcomes in P. corroboree.

not-yet-known not-yet-known not-yet-known unknown Global loss of biodiversity prioritizes the need for comprehensive and effective biomonitoring methods. Airborne eDNA has shown promise for monitoring terrestrial vertebrates but has not yet been rigorously compared to established biomonitoring methods. This study aims to compare species detection from airborne eDNA (eDNA) with observer-based monitoring (OBM), camera trapping (CT) and passive acoustic monitoring (PAM), focusing on birds and mammals. Monitoring was performed over the course of four weeks within an agroforestry ecosystem in the Netherlands. Birds were monitored using eDNA, OBM and PAM, while eDNA and CT were used for mammals. Four mammal species were detected by both CT and eDNA, while eDNA identified an additional 17 species, primarily small-bodied, including two invasive species. A total of 78 bird species was detected, with 28 identified by all three methods. In terms of species uniquely identified per method, eDNA detected the most (19 species), followed by PAM (10, but including 4 misidentifications) and OBM (2). All but three bird and four mammal species detected were consistent with known occurrences near the study site . Rarefaction shows that eDNA has the highest potential species diversity, but OBM is most efficient when limited time is available. Unique species can largely be explained by method characteristics or limitations; PAM detections are limited to species that make sound, while eDNA requires further research on detection range and sensitivity. This is the first study to compare airborne eDNA with OBM and acoustic data, further confirming the high potential of airborne eDNA for biodiversity monitoring.

As a collection of all the genetic variants in the gene pool, the pangenome is a concept that will become fundamental to conservation genomic studies. Unfortunately, most pangenomic approaches developed for humans and model organisms are financially impractical for conservation genomic studies of threatened or endangered species due to the high costs associated with deep sequencing multiple individuals using long read platforms. Here, by integrating metagenomic and iterative map-then-assemble approaches, we (1) propose novel workflows to construct graph pangenomes from multiple low-coverage short-read datasets; (2) benchmark these short-read pangenomes (both linear and graph) against a previously published long-read graph pangenome of the barn swallow; and (3) evaluate the utility of our workflows in population and conservation genomics. Our results indicate that economical short-read graph pangenomes can recover the vast majority of the variants identified through expensive long-read graph approaches, and that these variants accurately detect important biological signals (e.g., spatial structure and independent taxonomic delineations). These results mean that researchers can utilize their limited, conservation-oriented funding to more fully characterize all the variants in a particular gene pool for population-level analyses.

Population genetic clustering methods are widely used to detect hybridization events between closely related populations within species, as well as between deeply diverged lineages across phylogenetic time-scales. Their strengths and limitations in the latter cases, however, remain poorly explored. This study presents a systematic evaluation of the performance of the most popular population clustering method, STRUCTURE, under a variety of cross-species hybridization scenarios, including hybrid speciation, as well as introgression involving ghost (i.e., extinct or unsampled) lineages or otherwise. Our simulations demonstrate that STRUCTURE performs well in identifying hybrids and their parental donors when admixture happens very recently between sampled extant lineages, but generally fails to detect signals of admixture when hybridization occurs in deep time or when gene flow stems from ghost lineages. We find that symmetrical parental contribution in cases of hybrid speciation will often be revealed as extremely asymmetrical in STRUCTURE, especially when the admixture event occurred a long time ago. Our results suggest that population-genetic clustering methods may be inefficient for detecting ancient or ghost admixtures, which may partly explain why ghost introgression has escaped the attention of evolutionary biologists until recently.

The ability to assign biological samples to source populations based on genetic variation with high accuracy and precision is important for numerous applications from ecological studies through wildlife conservation to epidemiology. However, population assignment when genetic differentiation is low is challenging, and methods to address this problem are lacking. The application of artificial neural networks to population assignment using genomic data is highly promising. Here we present popfinder: a new, easy to use Python-based artificial neural network pipeline for genetic population assignment. We tested popfinder both with simulated genetic data from populations connected by varying levels of gene flow, and with reduced-representation sequence data for three species of seabirds with weak to no population genetic structure. Popfinder was able to assign individuals to their source populations with high accuracy, precision and recall in most cases, including both simulated and empirical datasets, except in the weakest empirical population structure dataset, where the comparator programs also performed poorly. Compared to other available software, popfinder was slower on the simulated data sets due to hyperparameter tuning and the fact that it does not reduce the dimensionality of the data set; however, all programs ran in seconds on empirical data sets. Additionally, popfinder provides a perturbation ranking method to help develop optimized SNP panels for genetic population assignment, and is designed to be user-friendly. Finally, we caution users of all assignment programs to watch both for leakage of data during model training, and for unequal detection probabilities.

Molluscs, characterised by various shapes and sizes, represent the most diverse group of animals after arthropods. This morphological disparity has historically complicated the establishment of phylogenetic relationships among the different classes of molluscs and subclasses of gastropods and their positions within the lophotrochozoans. In recent decades, ultraconserved elements (UCEs) have emerged as an effective technique for capturing large numbers of genes from preserved museum specimens across various animal groups. This study evaluates the efficiency of UCE capture across all classes of molluscs using a probe set designed for heterobranch gastropods. We analysed the effects of missing data using three levels of the GBLOCKS masker, ZORRO, and by generating various occupancy matrices. Our analyses successfully captured 2,119 out of 2,259 possible UCEs, recovering many genes from all molluscan classes and gastropod subclasses (144–1,604). As expected, efficiency decreases in groups more distantly related to gastropods, particularly heterobranchs. The more stringent maskers recovered shorter sequences with less missing data, yet fewer informative sites. Thus, our primary analyses focused on the use of more relaxed masking settings and the 50% occupancy matrix. These analyses resolved the well-established Conchifera and Aculifera hypothesis and further recovered a close relationship between Bivalvia and Scaphopoda (Diasoma). Moreover, with a wider taxon sampling, the interrelationships within, e.g., Gastropoda and Solenogastres are well resolved, highlighting the true universality of the probe set and encouraging its use for future research on any mollusc class.

Genetic data, including environmental DNA (eDNA), are regularly used to monitor escalating biodiversity concerns globally. In Aotearoa New Zealand, biodiversity is unique and cherished – many species are taonga (treasured) and cared for by kaitiaki (guardians with customary responsibilities), specifically mana whenua with custodial rights (Māori; the Indigenous people of New Zealand). Discussions are currently underway regarding the development of a reference DNA barcode database for biodiversity in Aotearoa New Zealand to improve outcomes for biosecurity surveillance and taking stock of biodiversity. A priority of these discussions is that the database development and eventual implementation accords with Te Tiriti o Waitangi (The Treaty of Waitangi). Here, we evaluate current practices for storing genetic data from samples collected in Aotearoa New Zealand by examining two major public data repositories – the National Centre for Biotechnology Information (NCBI) GenBank and the Barcode of Life Data System (BOLD). We find that current database practices limit opportunities for Māori sovereignty, with DNA (itself considered taonga) from many taonga species uploaded to public repositories with no associated restrictions or guidelines over use. This is an important finding that will help shape the development of a future reference DNA database for Aotearoa New Zealand that prioritises the rights and interests of Indigenous communities.

Next-generation sequencing (NGS) has the potential to transform mosquito-borne disease surveillance but remains under-utilized. This study introduces a comprehensive multi-loci metabarcoding-based MX (molecular xenomonitoring) approach to mosquito and arbovirus surveillance, enabling parallel identification of mosquito vectors, circulating arboviruses, and vertebrate hosts from bulk mosquito collections. The feasibility of this approach was demonstrated through its application to a large set (n=134) of bulk field collections. This set was complemented by a number (n=28) of single-species mosquito pools that had previously been screened for viruses using quantitative reverse transcription PCR (RT-qPCR) and metatranscriptomics. Universal alphavirus and flavivirus primer sets were used to screen for arboviruses in the resulting metabarcoding library. Viral amplicons were then indexed and combined with mosquito-specific (ITS2), universal invertebrate (COI), and vertebrate (Cyt b) barcode amplicons prior to sequencing. This approach confirmed the presence of all previously identified mosquito species, as well as those commonly misidentified morphologically, and enabled a degree of quantification regarding their relative physical abundance in each collection. Additionally, the developed approach identified a diverse vertebrate host community (18 species), demonstrating its potential for defining host preferences and, in tandem with the viral screens and associated vector data, understanding disease transmission pathways. Importantly, metabarcoding detected a diversity of regionally prevalent arboviruses and insect-specific viruses, with all three viral diagnostics demonstrating a similar sensitivity and specificity in detecting Ross River virus and Barmah Forest virus, Australia’s commonest arboviruses. In summary, multi-loci metabarcoding is an affordable and efficient MX tool that enables complete mosquito-borne disease surveillance.

Insect decline necessitates the development and use of standardized protocols for regular monitoring. These methods have to be rapid, efficient, and cost-effective to allow for large-scale implementation. Many insect sampling and molecular methods have been developed. This includes Malaise trapping, high-throughput DNA barcoding (“megabarcoding”), and metabarcoding. The latter allows for assessing the species diversity in whole samples using few steps, but sample heterogeneity in terms of body size remains a challenge since large insects contribute disproportionately more mtDNA than small ones, potentially overwhelming the template DNA from small species that then go undetected. Size-sorting can mitigate this problem, but no satisfying automated, rapid and non-destructive solutions are available. We introduce the EntoSieve, a low-cost and DIY motorized instrument that disentangles and sorts abundant insect bulk samples into several body-size fractions while minimizing damage to specimens thus reducing the risk of DNA contamination across size fractions (e.g., legs of large specimens in small body-size fraction). EntoSieve utilizes readily available components, 3D-printed parts and customizable meshes, thus enabling parallelization at low cost. We here show the efficiency of the EntoSieve for three samples with more than 10,000 specimens using three sieving protocols and assess the impact on specimen integrity. Efficiency ranged from 92-99% and achieved within 18-60 minutes and specimen damage was not significant for subsamples. By facilitating rapid pre-processing, the device contributes to producing morphologically valuable vouchers for megabarcoding and is likely to improve compositional diversity accuracy across size classes when using metabarcoding.

Escalating concern regarding the impacts of reduced genetic diversity on the conservation of endangered species has spurred efforts to obtain chromosome-level genomes through consortia such as the Vertebrate Genomes Project. However, assembling reference genomes for many threatened species remains challenging due to difficulties obtaining optimal input samples (e.g., fresh tissue, cell lines) that can characterize long-term conservation collections. Here, we present a pipeline that leverages genome synteny to construct high-quality genomes for species of conservation concern despite less-than-optimal samples and/or sequencing data, demonstrating its use on Hector’s and Māui dolphins. These endemic New Zealand dolphins are threatened by human activities due to their coastal habitat and small population sizes. Hector’s dolphins are classified as endangered by the IUCN, while the Māui dolphin is among the most critically endangered marine mammals. To assemble reference genomes for these dolphins, we created a pipeline combining de novo assembly tools with reference-guided techniques, utilizing chromosome-level genomes of closely related species. The pipeline assembled highly contiguous chromosome-level genomes (scaffold N50: 110 MB, scaffold L50: 9, miniBUSCO completeness scores >96.35%), despite non-optimal input tissue samples. We demonstrate that these genomes can provide insights relevant for conservation, including historical demography revealing long-term small population sizes, with subspecies divergence occurring ~20 kya, potentially linked to the Last Glacial Maximum. Māui dolphin heterozygosity was 40% lower than Hector’s and comparable to other cetacean species noted for reduced genetic diversity. Through these exemplar genomes, we demonstrate that our pipeline can provide high-quality genomic resources to facilitate ongoing conservation genomics research.

Short-read metabarcoding analysis is the gold standard to access to partial 16S and ITS genes with high read quality. With the advent of long-read sequencing, the amplification of full-length target genes is possible but with low read accuracy. Moreover, the amplification of 16S rDNA genes in seaweed or plant samples results in a large proportion of plastid reads, which are directly or indirectly derived from cyanobacteria. Primers designed not to amplify plastid sequences are available for short-read sequencing, while Oxford Nanopore Technology offers adaptive sampling, a unique way to remove reads in real-time. In this study, we compare three options to address the plastid read issue: deleting plastid reads with adaptative sampling, using optimized primers with Illumina MiSeq technology, and sequencing large numbers of reads with Illumina NovaSeq technology with universal primers. We showed that adaptive sampling using default settings of the MinKNOW software was ineffective for plastid depletion. We also demonstrated with a mock community that the SAMBA workflow provided the most accurate taxonomic assignment at the bacterial genus level compared to the IDTAXA and KRAKEN2 pipelines, but many false positives were generated at species level. Although NovaSeq sequencing with universal primer stood out for studying the algal bacterial community due to its deep coverage, the inclusion of eukaryotes and bacteria in the same sequencing run, and the low error rate. The combination of Illumina and ONT sequencing helped us explore the fungal diversity and allowed for the retrieval taxonomic information for genera poorly represented in the sequence databases.

Flower-derived eDNA holds great promise as a rapid and non-invasive tool for monitoring pollinators and their plant-associations. However, pollinators often only briefly interact with a plant and leave little eDNA, making them particularly challenging to detect. In addition, taxonomic biases in eDNA deposition and PCR amplification prevent quantitative analysis of pollinator diversity. These limitations have so far precluded the widespread use of eDNA in pollinator monitoring. Comparing flower-derived eDNA with conventional monitoring in flower strips, we here explore the utility of eDNA to detect community diversity, species abundances and ecological specificity of plant-associated arthropods. We show that read abundances are a bad predictor of true abundances at the community level. Instead, the occupancy of individual species in replicated flower eDNA samples provides reliable quantitative estimates of pollinator biodiversity and detects their ecological specificity very well. Also, we find that pollinator eDNA can be collected non-invasively, by washing off from flowers in the field. Our work highlights eDNA analysis as a powerful tool for the rapid future monitoring of plant-arthropod interactions and plant-pollinator networks.

Transcriptomics studies are more likely to achieve predictive results when they rely on tissue- and cell-specific transcriptional data. Identification of cell types in novel model organisms by their transcriptional profiles is difficult without data on transcriptional differences among major tissues and anatomical features. Here we report the first dataset on tissue- and organ-specific transcriptomics in freshwater plankton crustacean Daphnia magna, reporting markers of embryos, hemocytes, gut, carapace, antennae-2 and head, as well as the remaining carcass. Embryos are the most transcriptionally different from adults' features and antennae and the carapace are the most differentiated among them. We demonstrate that transcriptional markers of embryos vs. adults and of various adult anatomy features can be used to provide validation and functional explanation to published differential expression in response to environmental factors like infection, hypoxia, toxicants, or kairomones, to annotate Daphnia single cell data, and to ask questions about transcriptional diversification within extended gene families.

Inferring community composition from shotgun sequencing of environmental DNA is highly dependent on the completeness of reference databases used to assign taxonomic information as well as the pipeline used. While the number of complete, fully assembled reference genomes is increasing rapidly, their taxonomic coverage is generally too sparse to use them to build complete reference databases that span all or most of the target taxa. Low-coverage, whole genome sequencing, or skimming, provides a cost-effective and scalable alternative source of genome-wide information in the interim. Without enough coverage to assemble large contigs of nuclear DNA, much of the utility of a genome skim in the context of taxonomic annotation is found in its short read form. However, previous methods have not been able to fully leverage the data in this format. We demonstrate the utility of wholeskim, a pipeline for the indexing of k-mers present in genome skims and subsequent querying of these indices with short DNA reads. Wholeskim expands on the functionality of kmindex, a software which utilizes Bloom filters to efficiently index and query billions of k-mers. Using a collection of thousands of plant genome skims, wholeskim is the only software that is able to index and query the skims in their unassembled form. We also explore the effects of taxonomic and genomic completeness of the reference database on the accuracy and sensitivity of read assignment.

Biodiversity monitoring based on DNA metabarcoding depends on primer performance. Here, we develop a new metabarcoding primer pair that targets a ~ 318 bp fragment of the 28S rRNA gene. We validate the primer pair in assessing sponges, a notoriously challenging group for coral reef metabarcoding studies, by using mock and natural complex reef communities to examine its performance in species detection, amplification efficiency, and quantitative potential. Mock community experiments revealed a high number of sponge species (n = 94) spanning a broad taxonomic scope (15 orders), limited taxon-specific primer biases (only a single species exceeded a two-fold deviation from the expected number of reads), and its suitability for quantitative metabarcoding – there was a significant relationship between read abundance and visual percent coverage of sponge taxa (R = 0.76). In the natural complex coral reef community experiments, commonly used COI metabarcoding primers detected only 30.9% of sponge species, while the new 28S primer increased detection to 79.4%. These new 28S primers detect a broader taxonomic array of species across phyla and classes within the complex cryptobiome of coral reef communities than the Leray-Geller COI primers. As biodiversity assessments using metabarcoding tools are increasingly being leveraged for environmental monitoring and guide policymaking, these new 28S rRNA primers can improve biodiversity assessments for complex ecological coral reef communities.

Numerous specimens stored in natural history collections have been involuntarily preserved together with their associated microbiomes. We propose exploiting centuries-old soils occasionally found on the roots of herbarium plants to assess the diversity of ancient soil microbial communities originally associated with these plants. We validated this approach extracting and sequencing DNA from rhizospheric soils and roots of four plant species preserved in herbaria for more than 120 years. Extracted DNA displayed typical features of ancient DNA, with cytosine deamination at the ends of fragments predominantly shorter than 50 bp. When compared to extant microbiomes, herbarium microbial communities clustered with soil communities and were distinct from communities from other environments. Herbarium communities also displayed biodiversity features and assembly rules typical of soil and plant-associated ones. Soil communities were richer than root-associated ones with which they shared most taxa. Regarding community turnover, we detected collection site, soil versus root, and also plant species effects. Eukaryotic taxa that displayed a higher abundance in roots were mostly plant pathogens or obligate symbionts that were not identified among soil-enriched ones. Conservation of these biodiversity features and assembly rules in herbarium-associated microbial communities indicates that herbarium-extracted DNA could reflect the composition of the original plant-associated microbial communities and that preservation in herbaria seemingly did not alter these characteristics. Through the use of this approach, it should be possible to investigate historical soils and herbarium plant roots to explore the diversity and temporal dynamics of soil microbial communities.

Missing genotypes in DNA sequence data are an issue in many evolutionary genomic studies, especially of non-model organisms. It can be addressed using genotype imputation. However, algorithms that do not require additional genotype data as reference for imputation, which is often not available for non-model taxa, and are able to work with large whole-genome data sets are scarce. Therefore, we developed a new algorithm called GenoPop-Impute, which imputes the whole genome in separate batches and employs a random forest algorithm for imputation of correlated data sets. The batch-wise approach utilizes linkage disequilibrium to increase imputation accuracy and allows computational parallelization and thus efficiency. Tests on simulated data demonstrate that linkage disequilibrium between SNPs has a positive effect on imputation accuracy, due to correlation that originated in a shared evolutionary history. In comparison to two alternative algorithms, GenoPop-Impute is more accurate and is the only one computationally applicable to data sets of whole genomes. In addition, we found that GenoPop-Impute also increases the accuracy of commonly estimated population genomic metrics and mitigates biases due to missing data in demographic modeling experiments. We conclude that genotype imputation can be a valuable tool for evolutionary genomic studies of non-model taxa and that GenoPop-Impute is a highly suitable algorithm for this.