Global arthropod beta-diversity is spatially and temporally structured by latitude.

Global biodiversity gradients are generally expected to reflect greater species replacement closer to the equator. However, empirical validation of global biodiversity gradients largely relies on vertebrates, plants, and other less diverse taxa. Here we assess the temporal and spatial dynamics of global arthropod biodiversity dynamics using a beta-diversity framework. Sampling includes 129 sampling sites whereby malaise traps are deployed to monitor temporal changes in arthropod communities. Overall, we encountered more than 150,000 unique barcode index numbers (BINs) (i.e. species proxies). We assess between site differences in community diversity using beta-diversity and the partitioned components of species replacement and richness difference. Global total beta-diversity (dissimilarity) increases with decreasing latitude, greater spatial distance and greater temporal distance. Species replacement and richness difference patterns vary across biogeographic regions. Our findings support long-standing, general expectations of global biodiversity patterns. However, we also show that the underlying processes driving patterns may be regionally linked.

Sequence signatures within the genome of SARS-CoV-2 can be used to predict host source.

We conducted an in silico analysis to better understand the potential factors impacting host adaptation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in white-tailed deer, humans, and mink due to the strong evidence of sustained transmission within these hosts. Classification models trained on single nucleotide and amino acid differences between samples effectively identified white-tailed deer-, human-, and mink-derived SARS-CoV-2. For example, the balanced accuracy score of Extremely Randomized Trees classifiers was 0.984 ± 0.006. Eighty-eight commonly identified predictive mutations are found at sites under strong positive and negative selective pressure. A large fraction of sites under selection (86.9%) or identified by machine learning (87.1%) are found in genes other than the spike. Some locations encoded by these gene regions are predicted to be B- and T-cell epitopes or are implicated in modulating the immune response suggesting that host adaptation may involve the evasion of the host immune system, modulation of the class-I major-histocompatibility complex, and the diminished recognition of immune epitopes by CD8+ T cells. Our selection and machine learning analysis also identified that silent mutations, such as C7303T and C9430T, play an important role in discriminating deer-derived samples across multiple clades. Finally, our investigation into the origin of the B.1.641 lineage from white-tailed deer in Canada discovered an additional human sequence from Michigan related to the B.1.641 lineage sampled near the emergence of this lineage. These findings demonstrate that machine-learning approaches can be used in combination with evolutionary genomics to identify factors possibly involved in the cross-species transmission of viruses and the emergence of novel viral lineages.IMPORTANCESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible virus capable of infecting and establishing itself in human and wildlife populations, such as white-tailed deer. This fact highlights the importance of developing novel ways to identify genetic factors that contribute to its spread and adaptation to new host species. This is especially important since these populations can serve as reservoirs that potentially facilitate the re-introduction of new variants into human populations. In this study, we apply machine learning and phylogenetic methods to uncover biomarkers of SARS-CoV-2 adaptation in mink and white-tailed deer. We find evidence demonstrating that both non-synonymous and silent mutations can be used to differentiate animal-derived sequences from human-derived ones and each other. This evidence also suggests that host adaptation involves the evasion of the immune system and the suppression of antigen presentation. Finally, the methods developed here are general and can be used to investigate host adaptation in viruses other than SARS-CoV-2.

Looking for the sponge loop: analyses of detritus on a Caribbean forereef using stable isotope and eDNA metabarcoding techniques.

Coral reefs are biodiverse ecosystems that rely on trophodynamic transfers from primary producers to consumers through the detrital pathway. The sponge loop hypothesis proposes that sponges consume dissolved organic carbon (DOC) and produce large quantities of detritus on coral reefs, with this turn-over approaching the daily gross primary production of the reef ecosystem. In this study, we collected samples of detritus in the epilithic algal matrix (EAM) and samples from potential sources of detritus over two seasons from the forereef at Carrie Bow Cay, Belize. We chose this location to maximize the likelihood of finding support for the sponge loop hypothesis because Caribbean reefs have higher sponge abundances than other tropical reefs worldwide and the Mesoamerican barrier reef is an archetypal coral reef ecosystem. We used stable isotope analyses and eDNA metabarcoding to determine the composition of the detritus. We determined that the EAM detritus was derived from a variety of benthic and pelagic sources, with primary producers (micro- and macroalgae) as major contributors and metazoans (Arthropoda, Porifera, Cnidaria, Mollusca) as minor contributors. None of the sponge species that reportedly produce detritus were present in EAM detritus. The cnidarian signature in EAM detritus was dominated by octocorals, with a scarcity of hard corals. The composition of detritus also varied seasonally. The negligible contribution of sponges to reef detritus contrasts with the detrital pathway originally proposed in the sponge loop hypothesis. The findings indicate a mix of pelagic and benthic sources in the calmer summer and primarily benthic sources in the more turbulent spring.

A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses.

Environmental DNA (eDNA) metabarcoding has gained growing attention as a strategy for monitoring biodiversity in ecology. However, taxa identifications produced through metabarcoding require sophisticated processing of high-throughput sequencing data from taxonomically informative DNA barcodes. Various sets of universal and taxon-specific primers have been developed, extending the usability of metabarcoding across archaea, bacteria and eukaryotes. Accordingly, a multitude of metabarcoding data analysis tools and pipelines have also been developed. Often, several developed workflows are designed to process the same amplicon sequencing data, making it somewhat puzzling to choose one among the plethora of existing pipelines. However, each pipeline has its own specific philosophy, strengths and limitations, which should be considered depending on the aims of any specific study, as well as the bioinformatics expertise of the user. In this review, we outline the input data requirements, supported operating systems and particular attributes of thirty-two amplicon processing pipelines with the goal of helping users to select a pipeline for their metabarcoding projects.

Minimalist revision of Mesochorus Gravenhorst, 1829 (Hymenoptera: Ichneumonidae: Mesochorinae) from Área de Conservación Guanacaste, Costa Rica, with 158 new species and host records for 129 species / Revisión minimalista de Mesochorus Gravenhorst, 1829 (Hymenoptera: Ichneumonidae: Mesochorinae) del Área de Conservación Guanacaste, Costa Rica, con 158 especies nuevas y registros de hospedantes para 129 especies

Introduction: Species of Mesochorus are found worldwide and members of this genus are primarily hyperparasitoids of Ichneumonoidea and Tachinidae. Objectives: To describe species of Costa Rican Mesochorus reared from caterpillars and to a lesser extent Malaise-trapped. Methods: The species are diagnosed by COI mtDNA barcodes, morphological inspection, and host data. A suite of images and host data (plant, caterpillar, and primary parasitoid) are provided for each species. Results: A total of 158 new species of Mesochorus. Sharkey is the taxonomic authority for all. Conclusions: This demonstrates a practical application of DNA barcoding that can be applied to the masses of undescribed neotropical insect species in hyperdiverse groups.

Introducción: Las especies de Mesochorus se encuentran en todo el mundo y los miembros de este género son principalmente hiperparasitoides de las familias Ichneumonoidea y Tachinidae. Objetivos: Describir las especies de Mesochorus costarricenses obtenidas de orugas y en menor medida por trampas Malaise. Métodos: Las especies se diagnosticaron mediante el uso de código de barra molecular por COI del ADNmt, inspección morfológica y datos del huésped. Se proporciona un conjunto de imágenes y datos de los huéspedes (planta, oruga y parasitoide primario) para cada especie. Resultados: Se encontró un total de 158 nuevas especies de Mesochorus. Sharkey es la autoridad taxonómica para todas las especies. Conclusiones: Se demuestra una aplicación práctica del código de barras de ADN que se puede aplicar a grandes cantidades de especies de insectos neotropicales no descritas para grupos hiperdiversos.

Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon Sequencing Data.

Developing an understanding of how microbial communities vary across conditions is an important analytical step. We used 16S rRNA data isolated from human stool samples to investigate whether learned dissimilarities, such as those produced using unsupervised decision tree ensembles, can be used to improve the analysis of the composition of bacterial communities in patients suffering from Crohn's disease and adenomas/colorectal cancers. We also introduce a workflow capable of learning dissimilarities, projecting them into a lower dimensional space, and identifying features that impact the location of samples in the projections. For example, when used with the centered log ratio transformation, our new workflow (TreeOrdination) could identify differences in the microbial communities of Crohn's disease patients and healthy controls. Further investigation of our models elucidated the global impact amplicon sequence variants (ASVs) had on the locations of samples in the projected space and how each ASV impacted individual samples in this space. Furthermore, this approach can be used to integrate patient data easily into the model and results in models that generalize well to unseen data. Models employing multivariate splits can improve the analysis of complex high-throughput sequencing data sets because they are better able to learn about the underlying structure of the data set. IMPORTANCE There is an ever-increasing level of interest in accurately modeling and understanding the roles that commensal organisms play in human health and disease. We show that learned representations can be used to create informative ordinations. We also demonstrate that the application of modern model introspection algorithms can be used to investigate and quantify the impacts of taxa in these ordinations, and that the taxa identified by these approaches have been associated with immune-mediated inflammatory diseases and colorectal cancer.

Comparative analysis of fish environmental DNA reveals higher sensitivity achieved through targeted sequence-based metabarcoding.

Environmental DNA (eDNA)-based methods of species detection are enabling various applications in ecology and conservation including large-scale biomonitoring efforts. qPCR is widely used as the standard approach for species-specific detection, often targeting a fish species of interest from aquatic eDNA. However, DNA metabarcoding has the potential to displace qPCR in certain eDNA applications. In this study, we compare the sensitivity of the latest Illumina NovaSeq 6000 NGS platform to qPCR TaqMan assays by measuring limits of detection and by analysing eDNA from water samples collected from Churchill River and Lake Melville, NL, Canada. Species-specific, targeted next generation sequencing (NGS) assays had significantly higher sensitivity than qPCR, with limits of detection 14- to 29-fold lower. For example, when analysing eDNA, qPCR detected Gadus ogac (Greenland cod) in 21% of samples, but targeted NGS detected this species in 29% of samples. General NGS assays were as sensitive as qPCR, while simultaneously detecting 15 fish species from eDNA samples. With over 34,000 fish species on the planet, parallel and sensitive methods such as NGS will be required to support effective biomonitoring at both regional and global scales.

Influence of plant species, mycorrhizal inoculant, and soil phosphorus level on arbuscular mycorrhizal communities in onion and carrot roots.

Arbuscular mycorrhizal fungi (AMF) are ancient and ecologically important symbionts that colonize plant roots. These symbionts assist in the uptake of water and nutrients, particularly phosphorus, from the soil. This important role has led to the development of AMF inoculants for use as biofertilizers in agriculture. Commercial mycorrhizal inoculants are increasingly popular to produce onion and carrot, but their specific effects on native mycorrhizal communities under field conditions are not known. Furthermore, adequate availability of nutrients in soils, specifically phosphorus, can reduce the diversity and abundance of AMF communities in the roots. The type of crop grown can also influence the composition of AMF communities colonizing the plant roots. This study aimed to investigate how AMF inoculants, soil phosphorus levels, and plant species influence the diversity of AMF communities that colonize the roots of onion and carrot plants. Field trials were conducted on high organic matter (muck) soil in the Holland Marsh, Ontario, Canada. The treatments included AMF-coated seeds (three to five propagules of Rhizophagus irregularis per seed) and non-treated onion and carrot seeds grown in soil with low (~46 ppm) and high (~78 ppm) phosphorus levels. The mycorrhizal communities colonizing the onion and carrot roots were identified by Illumina sequencing. Five genera, Diversispora, Claroideoglomus, Funneliformis, Rhizophagus, and Glomus, were identified in roots of both plant species. AMF communities colonizing carrot roots were more diverse and richer than those colonizing onion roots. Diversispora and Funneliformis had a 1.3-fold and 2.9-fold greater abundance, respectively, in onion roots compared to carrots. Claroideoglomus was 1.4-fold more abundant in carrot roots than in onions. Inoculation with R. irregularis increased the abundance and richness of Rhizophagus in AMF communities of onion roots but not in carrot roots. The soil phosphorus level had no effect on the richness and diversity of AMF in the roots of either crop. In summary, AMF inoculant and soil phosphorus levels influenced the composition of AMF communities colonizing the roots of onion and carrot plants, but the effects varied between plant species.

MetaWorks: A flexible, scalable bioinformatic pipeline for high-throughput multi-marker biodiversity assessments.

Multi-marker metabarcoding is increasingly being used to generate biodiversity information across different domains of life from microbes to fungi to animals such as for molecular ecology and biomonitoring applications in different sectors from academic research to regulatory agencies and industry. Current popular bioinformatic pipelines support microbial and fungal marker analysis, while ad hoc methods are often used to process animal metabarcode markers from the same study. MetaWorks provides a harmonized processing environment, pipeline, and taxonomic assignment approach for demultiplexed Illumina reads for all biota using a wide range of metabarcoding markers such as 16S, ITS, and COI. A Conda environment is provided to quickly gather most of the programs and dependencies for the pipeline. Several workflows are provided such as: taxonomically assigning exact sequence variants, provides an option to generate operational taxonomic units, and facilitates single-read processing. Pipelines are automated using Snakemake to minimize user intervention and facilitate scalability. All pipelines use the RDP classifier to provide taxonomic assignments with confidence measures. We extend the functionality of the RDP classifier for taxonomically assigning 16S (bacteria), ITS (fungi), and 28S (fungi), to also support COI (eukaryotes), rbcL (eukaryotes, land plants, diatoms), 12S (fish, vertebrates), 18S (eukaryotes, diatoms) and ITS (fungi, plants). MetaWorks properly handles ITS by trimming flanking conserved rRNA gene regions as well as protein coding genes by providing two options for removing obvious pseudogenes. MetaWorks can be downloaded from https://github.com/terrimporter/MetaWorks and quickstart instructions, pipeline details, and a tutorial for new users can be found at https://terrimporter.github.io/MetaWorksSite.

Demystifying eDNA validation.

As environmental DNA (eDNA) approaches gain momentum for biodiversity analysis, validation becomes a key consideration. I focus on four facets of eDNA validation. Validation through technical processes, legal use, official statements, and 'good enough' scenarios can advance the field to aid societal issues such as climate emergency and biodiversity crisis.

Comparison of traditional and DNA metabarcoding samples for monitoring tropical soil arthropods (Formicidae, Collembola and Isoptera).

The soil fauna of the tropics remains one of the least known components of the biosphere. Long-term monitoring of this fauna is hampered by the lack of taxonomic expertise and funding. These obstacles may potentially be lifted with DNA metabarcoding. To validate this approach, we studied the ants, springtails and termites of 100 paired soil samples from Barro Colorado Island, Panama. The fauna was extracted with Berlese-Tullgren funnels and then either sorted with traditional taxonomy and known, individual DNA barcodes ("traditional samples") or processed with metabarcoding ("metabarcoding samples"). We detected 49 ant, 37 springtail and 34 termite species with 3.46 million reads of the COI gene, at a mean sequence length of 233 bp. Traditional identification yielded 80, 111 and 15 species of ants, springtails and termites, respectively; 98%, 37% and 100% of these species had a Barcode Index Number (BIN) allowing for direct comparison with metabarcoding. Ants were best surveyed through traditional methods, termites were better detected by metabarcoding, and springtails were equally well detected by both techniques. Species richness was underestimated, and faunal composition was different in metabarcoding samples, mostly because 37% of ant species were not detected. The prevalence of species in metabarcoding samples increased with their abundance in traditional samples, and seasonal shifts in species prevalence and faunal composition were similar between traditional and metabarcoding samples. Probable false positive and negative species records were reasonably low (13-18% of common species). We conclude that metabarcoding of samples extracted with Berlese-Tullgren funnels appear suitable for the long-term monitoring of termites and springtails in tropical rainforests. For ants, metabarcoding schemes should be complemented by additional samples of alates from Malaise or light traps.

Multi-marker DNA metabarcoding detects suites of environmental gradients from an urban harbour.

There is increasing need for biodiversity monitoring, especially in places where potential anthropogenic disturbance may significantly impact ecosystem health. We employed a combination of traditional morphological and bulk macroinvertebrate metabarcoding analyses to benthic samples collected from Toronto Harbour (Ontario, Canada) to compare taxonomic and functional diversity of macroinvertebrates and their responses to environmental gradients. At the species rank, sites assessed using COI metabarcoding showed more variation than sites assessed using morphological methods. Depending on the assessment method, we detected gradients in magnesium (morphological taxa), ammonia (morphological taxa, COI sequence variants), pH (18S sequence variants) as well as gradients in contaminants such as metals (COI & 18S sequence variants) and organochlorines (COI sequence variants). Observed responses to contaminants such as aromatic hydrocarbons and metals align with known patchy distributions in harbour sediments. We determined that the morphological approach may limit the detection of macroinvertebrate responses to lake environmental conditions due to the effort needed to obtain fine level taxonomic assignments necessary to investigate responses. DNA metabarcoding, however, need not be limited to macroinvertebrates, can be automated, and taxonomic assignments are associated with a certain level of accuracy from sequence variants to named taxonomic groups. The capacity to detect change using a scalable approach such as metabarcoding is critical for addressing challenges associated with biodiversity monitoring and ecological investigations.

LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data.

BACKGROUND: Identification of biomarkers, which are measurable characteristics of biological datasets, can be challenging. Although amplicon sequence variants (ASVs) can be considered potential biomarkers, identifying important ASVs in high-throughput sequencing datasets is challenging. Noise, algorithmic failures to account for specific distributional properties, and feature interactions can complicate the discovery of ASV biomarkers. In addition, these issues can impact the replicability of various models and elevate false-discovery rates. Contemporary machine learning approaches can be leveraged to address these issues. Ensembles of decision trees are particularly effective at classifying the types of data commonly generated in high-throughput sequencing (HTS) studies due to their robustness when the number of features in the training data is orders of magnitude larger than the number of samples. In addition, when combined with appropriate model introspection algorithms, machine learning algorithms can also be used to discover and select potential biomarkers. However, the construction of these models could introduce various biases which potentially obfuscate feature discovery. RESULTS: We developed a decision tree ensemble, LANDMark, which uses oblique and non-linear cuts at each node. In synthetic and toy tests LANDMark consistently ranked as the best classifier and often outperformed the Random Forest classifier. When trained on the full metabarcoding dataset obtained from Canada's Wood Buffalo National Park, LANDMark was able to create highly predictive models and achieved an overall balanced accuracy score of 0.96 ± 0.06. The use of recursive feature elimination did not impact LANDMark's generalization performance and, when trained on data from the BE amplicon, it was able to outperform the Linear Support Vector Machine, Logistic Regression models, and Stochastic Gradient Descent models (p ≤ 0.05). Finally, LANDMark distinguishes itself due to its ability to learn smoother non-linear decision boundaries. CONCLUSIONS: Our work introduces LANDMark, a meta-classifier which blends the characteristics of several machine learning models into a decision tree and ensemble learning framework. To our knowledge, this is the first study to apply this type of ensemble approach to amplicon sequencing data and we have shown that analyzing these datasets using LANDMark can produce highly predictive and consistent models.

Towards reproducible metabarcoding data: Lessons from an international cross-laboratory experiment.

Advances in high-throughput sequencing (HTS) are revolutionizing monitoring in marine environments by enabling rapid, accurate and holistic detection of species within complex biological samples. Research institutions worldwide increasingly employ HTS methods for biodiversity assessments. However, variance in laboratory procedures, analytical workflows and bioinformatic pipelines impede the transferability and comparability of results across research groups. An international experiment was conducted to assess the consistency of metabarcoding results derived from identical samples and primer sets using varying laboratory procedures. Homogenized biofouling samples collected from four coastal locations (Australia, Canada, New Zealand and the USA) were distributed to 12 independent laboratories. Participants were asked to follow one of two HTS library preparation workflows. While DNA extraction, primers and bioinformatic analyses were purposefully standardized to allow comparison, many other technical variables were allowed to vary among laboratories (amplification protocols, type of instrument used, etc.). Despite substantial variation observed in raw results, the primary signal in the data was consistent, with the samples grouping strongly by geographical origin for all data sets. Simple post hoc data clean-up by removing low-quality samples gave the best improvement in sample classification for nuclear 18S rRNA gene data, with an overall 92.81% correct group attribution. For mitochondrial COI gene data, the best classification result (95.58%) was achieved after correction for contamination errors. The identified critical methodological factors that introduced the greatest variability (preservation buffer, sample defrosting, template concentration, DNA polymerase, PCR enhancer) should be of great assistance in standardizing future biodiversity studies using metabarcoding.

Biotic signals associated with benthic impacts of salmon farms from eDNA metabarcoding of sediments.

Environmental DNA (eDNA) metabarcoding can rapidly characterize the composition and diversity of benthic communities, thus it has high potential utility for routine assessments of benthic impacts of marine finfish farming. In this study, 126 sediment grab samples from 42 stations were collected at six salmon farms in British Columbia, Canada. Benthic community changes were assessed by both eDNA metabarcoding of metazoans and macrofaunal polychaete surveys. The latter was done by analysing 11,466 individuals using a combination of morphology-based taxonomy and DNA barcoding. Study objectives were to: (i) compare biotic signals associated with benthic impacts of salmon farming in the two data sources, and (ii) identify potential eDNA indicators to facilitate monitoring in Canada. Alpha diversity parameters were consistently reduced near fish cage edge and negatively correlated with pore-water sulphide concentration, with coefficients ranging from -0.62 to -0.48. Although Polychaeta are a common indicator group, the negative correlation with pore-water sulphide concentration was much stronger for Nematoda OTU richness (correlation coefficient: -0.86) than for Polychaeta (correlation coefficient: -0.38). Presence/absence of Capitella generally agreed well between the two methods despite that they differed in the volume of sediments sampled and the molecular marker used. Multiple approaches were used to identify OTUs related to organic enrichment statuses. We demonstrate that eDNA metabarcoding generates biotic signals that could be leveraged for environmental assessment of benthic impacts of fish farms in multiple ways: both alpha diversity and Nematoda OTU richness could be used to assess the spatial extent of impact, and OTUs related to organic enrichment could be used to develop local biotic indices.

Freshwater diatom biomonitoring through benthic kick-net metabarcoding.

Biomonitoring is an essential tool for assessing ecological conditions and informing management strategies. The application of DNA metabarcoding and high throughput sequencing has improved data quantity and resolution for biomonitoring of taxa such as macroinvertebrates, yet, there remains the need to optimise these methods for other taxonomic groups. Diatoms have a longstanding history in freshwater biomonitoring as bioindicators of water quality status. However, multi-substrate periphyton collection, a common diatom sampling practice, is time-consuming and thus costly in terms of labour. This study examined whether the benthic kick-net technique used for macroinvertebrate biomonitoring could be applied to bulk-sample diatoms for metabarcoding. To test this approach, we collected samples using both conventional multi-substrate microhabitat periphyton collections and bulk-tissue kick-net methodologies in parallel from replicated sites with different habitat status (good/fair). We found there was no significant difference in community assemblages between conventional periphyton collection and kick-net methodologies or site status, but there was significant difference between diatom communities depending on site (P = 0.042). These results show the diatom taxonomic coverage achieved through DNA metabarcoding of kick-net is suitable for ecological biomonitoring applications. The shift to a more robust sampling approach and capturing diatoms as well as macroinvertebrates in a single sampling event has the potential to significantly improve efficiency of biomonitoring programmes that currently only use the kick-net technique to sample macroinvertebrates.

Harnessing the power of eDNA metabarcoding for the detection of deep-sea fishes.

The deep ocean is the largest biome on Earth and faces increasing anthropogenic pressures from climate change and commercial fisheries. Our ability to sustainably manage this expansive habitat is impeded by our poor understanding of its inhabitants and by the difficulties in surveying and monitoring these areas. Environmental DNA (eDNA) metabarcoding has great potential to improve our understanding of this region and to facilitate monitoring across a broad range of taxa. Here, we evaluate two eDNA sampling protocols and seven primer sets for elucidating fish diversity from deep sea water samples. We found that deep sea water samples (> 1400 m depth) had significantly lower DNA concentrations than surface or mid-depth samples necessitating a refined protocol with a larger sampling volume. We recovered significantly more DNA in large volume water samples (1.5 L) filtered at sea compared to small volume samples (250 mL) held for lab filtration. Furthermore, the number of unique sequences (exact sequence variants; ESVs) recovered per sample was higher in large volume samples. Since the number of ESVs recovered from large volume samples was less variable and consistently high, we recommend the larger volumes when sampling water from the deep ocean. We also identified three primer sets which detected the most fish taxa but recommend using multiple markers due the variability in detection probabilities and taxonomic resolution among fishes for each primer set. Overall, fish diversity results obtained from metabarcoding were comparable to conventional survey methods. While eDNA sampling and processing need be optimized for this unique environment, the results of this study demonstrate that eDNA metabarcoding can facilitate biodiversity surveys in the deep ocean, require less dedicated survey effort per unit identification, and are capable of simultaneously providing valuable information on other taxonomic groups.

Drivers of tropical soil invertebrate community composition and richness across tropical secondary forests using DNA metasystematics.

Tropical forests are fundamental ecosystems, essential for providing terrestrial primary productivity, global nutrient cycling, and biodiversity. Despite their importance, tropical forests are currently threatened by deforestation and associated activities. Moreover, tropical regions are now mostly represented by secondary forest regrowth, with half of the remaining tropical forests as secondary forest. Soil invertebrates are an important component to the functioning and biodiversity of these soil ecosystems. However, it remains unclear how these past land-use activities and subsequent secondary forest developments have altered the soil invertebrate communities and any potential ecological consequences associated with this. DNA metabarcoding offers an effective approach to rapidly monitor soil invertebrate communities under different land-use practices and within secondary forests. In this study, we used DNA metabarcoding to detect community-based patterns of soil invertebrate composition across a primary forest, a 23-year-old secondary forest, and a 33-year-old secondary forest and the associated soil environmental drivers of the soil invertebrate community structure in the Maquenque National Wildlife Refuge of Costa Rica (MNWR). We also used a species contribution analysis (SIMPER) to determine which soil invertebrate groups may be an indication of these soils reaching a pre-disturbed state such as a primary forest. We found that the soil invertebrate community composition at class, order, family, and ESV level were mostly significantly different across that habitats. We also found that the primary forest had a greater richness of soil invertebrates compared to the 23-year-old and 33-year-old secondary forest. Moreover, a redundancy analysis indicated that soil moisture influenced soil invertebrate community structure and explained up to 22% of the total variation observed in the community composition across the habitats; whereas soil invertebrate richness was structured by soil microbial biomass carbon (C) (Cmic) and explained up to 52% of the invertebrate richness across the primary and secondary forests. Lastly, the SIMPER analysis revealed that Naididae, Entomobryidae, and Elateridae could be important indicators of soil and forest recuperation in the MNWR. This study adds to the increasing evidence that soil invertebrates are intimately linked with the soil microbial biomass carbon (Cmic) and that even after 33 years of natural regrowth of a forest, these land use activities can still have persisting effects on the overall composition and richness of the soil invertebrate communities.

Increase in abundance and decrease in richness of soil microbes following Hurricane Otto in three primary forest types in the Northern Zone of Costa Rica.

Little is known of how hurricane-induced deposition of canopy material onto tropical forest floors influences the soil microbial communities involved in decomposition of these materials. In this study, to identify how soil bacterial and fungal communities might change after a hurricane, and their possible roles in the C and N cycles, soils were collected from five 2000 m2 permanent plots in Lowland, Upland and Riparian primary forests in Costa Rica 3 months before and 7 months after Hurricane Otto damaged the forests. The soil Water, inorganic N and Biomass C increased and total organic C decreased Post-Hurricane, all of which best predicted the changes in the Post-Hurricane soil microbial communities. Post-Hurricane soils from all forest types showed significant changes in community composition of total bacteria, total fungi, and five functional groups of microbes (i.e., degrading/lignin degrading, NH4+-producing, and ammonium oxidizing bacteria, and the complex C degrading/wood rot/lignin degrading and ectomycorrhizal fungi), along with a decrease in richness in genera of all groups. As well, the mean proportion of DNA sequences (MPS) of all five functional groups increased. There were also significant changes in the MPS values of 7 different fungal and 7 different bacterial genera that were part of these functional groups. This is the first evidence that hurricane-induced deposition of canopy material is stimulating changes in the soil microbial communities after the hurricane, involving changes in specific taxonomic and functional group genera, and reduction in the community richness while selecting for dominant genera possibly better suited to process the canopy material. These changes may represent examples of taxonomic switching of functionally redundant microbial genera in response to dramatic changes in resource input. It is possible that differences in these microbial communities and genera may serve as indicators of disturbed and recovering regional soil ecosystems, and should be evaluated in the future.

Using DNA-barcoded Malaise trap samples to measure impact of a geothermal energy project on the biodiversity of a Costa Rican old-growth rain forest.

We report one year (2013-2014) of biomonitoring an insect community in a tropical old-growth rain forest, during construction of an industrial-level geothermal electricity project. This is the first-year reaction by the species-rich insect biodiversity; six subsequent years are being analyzed now. The site is on the margin of a UNESCO Natural World Heritage Site, Área de Conservación Guanacaste (ACG), in northwestern Costa Rica. This biomonitoring is part of Costa Rica's ongoing efforts to sustainably retain its wild biodiversity through biodevelopmental integration with its societies. Essential tools are geothermal engineering needs, entomological knowledge, insect species-rich forest, government-NGO integration, common sense, DNA barcoding for species-level identification, and Malaise traps. This research is tailored for integration with its society at the product level. We combine an academic view with on-site engineering decisions. This biomonitoring requires alpha-level DNA barcoding combined with centuries of morphology-based entomological taxonomy and ecology. Not all desired insect community analyses are performed; they are for data from subsequent years combined with this year. We provide enough analysis to be used by both guilds now. This biomonitoring has shown, for the first year, that the geothermal project impacts only the biodiversity within a zone less than 50 m from the project margin.