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In the face of global biodiversity declines, surveys of beneficial and antagonistic arthropod diversity as well as the ecological services that they provide are increasingly important in both natural and agro-ecosystems. Conventional survey methods used to monitor these communities often require extensive taxonomic expertise and are time-intensive, potentially limiting their application in industries such as agriculture, where arthropods often play a critical role in productivity (e.g. pollinators, pests and predators). Environmental DNA (eDNA) metabarcoding of a novel substrate, crop flowers, may offer an accurate and high throughput alternative to aid in the detection of these managed and unmanaged taxa. Here, we compared the arthropod communities detected with eDNA metabarcoding of flowers, from an agricultural species (Persea americana—‘Hass’ avocado), with two conventional survey techniques: digital video recording (DVR) devices and pan traps. In total, 80 eDNA flower samples, 96 h of DVRs and 48 pan trap samples were collected. Across the three methods, 49 arthropod families were identified, of which 12 were unique to the eDNA dataset. Environmental DNA metabarcoding from flowers revealed potential arthropod pollinators, as well as plant pests and parasites. Alpha diversity levels did not differ across the three survey methods although taxonomic composition varied significantly, with only 12% of arthropod families found to be common across all three methods. eDNA metabarcoding of flowers has the potential to revolutionize the way arthropod communities are monitored in natural and agro-ecosystems, potentially detecting the response of pollinators and pests to climate change, diseases, habitat loss and other disturbances.

Mesorhizobia are soil bacteria that establish nitrogen-fixing symbioses with various legumes. Novel symbiotic mesorhizobia frequently evolve following horizontal transfer of symbiosis-gene-carrying integrative and conjugative elements (ICESyms) to indigenous mesorhizobia in soils. Evolved symbionts exhibit a wide range in symbiotic effectiveness, with some fixing nitrogen poorly or not at all. Little is known about the genetic diversity and symbiotic potential of indigenous soil mesorhizobia prior to ICESym acquisition. Here we sequenced genomes of 144 Mesorhizobium spp. strains cultured directly from cultivated and uncultivated Australian soils. Of these, 126 lacked symbiosis genes. The only isolated symbiotic strains were either exotic strains used previously as legume inoculants, or indigenous mesorhizobia that had acquired exotic ICESyms. No native symbiotic strains were identified. Indigenous nonsymbiotic strains formed 22 genospecies with phylogenomic diversity overlapping the diversity of internationally isolated symbiotic Mesorhizobium spp. The genomes of indigenous mesorhizobia exhibited no evidence of prior involvement in nitrogen-fixing symbiosis, yet their core genomes were similar to symbiotic strains and they generally lacked genes for synthesis of biotin, nicotinate and thiamine. Genomes of nonsymbiotic mesorhizobia harboured similar mobile elements to those of symbiotic mesorhizobia, including ICESym-like elements carrying aforementioned vitamin-synthesis genes but lacking symbiosis genes. Diverse indigenous isolates receiving ICESyms through horizontal gene transfer formed effective symbioses with Lotus and Biserrula legumes, indicating most nonsymbiotic mesorhizobia have an innate capacity for nitrogen-fixing symbiosis following ICESym acquisition. Non-fixing ICESym-harbouring strains were isolated spo-radically within species alongside effective symbionts, indicating chromosomal lineage does not predict symbiotic potential. Our observations sugges

Metabarcoding of environmental DNA (eDNA) when coupled with high throughput sequencing is revolutionising the way biodiversity can be monitored across a wide range of applications. However, the large number of tools deployed in downstream bioinformatic analyses often places a challenge in configuration and maintenance of a workflow, and consequently limits the research reproducibility. Furthermore, scalability needs to be considered to handle the growing amount of data due to increase in sequence output and the scale of project. Here, we describe eDNAFlow, a fully automated workflow that employs a number of state-of-the-art applications to process eDNA data from raw sequences (single-end or paired-end) to generation of curated and noncurated zero-radius operational taxonomic units (ZOTUs) and their abundance tables. This pipeline is based on Nextflow and Singularity which enable a scalable, portable and reproducible workflow using software containers on a local computer, clouds and high-performance computing (HPC) clusters. Finally, we present an in-house Python script to assign taxonomy to ZOTUs based on user specified thresholds for assigning lowest common ancestor (LCA). We demonstrate the utility and efficiency of the pipeline using an example of a published coral diversity biomonitoring study. Our results were congruent with the aforementioned study. The scalability of the pipeline is also demonstrated through analysis of a large data set containing 154 samples. To our knowledge, this is the first automated bioinformatic pipeline for eDNA analysis using two powerful tools: Nextflow and Singularity. This pipeline addresses two major challenges in the analysis of eDNA data; scalability and reproducibility.

Metabarcoding of environmental DNA (eDNA) when coupled with high throughput sequencing is revolutionising the way biodiversity can be monitored across a wide range of applications. However, the large number of tools deployed in downstream bioinformatic analyses often places a challenge in configuration and maintenance of a workflow, and consequently limits the research reproducibility. Furthermore, scalability needs to be considered to handle the growing amount of data due to increase in sequence output and the scale of project. Here, we describe eDNAFlow, a fully automated workflow that employs a number of state-of-the-art applications to process eDNA data from raw sequences (single-end or paired-end) to generation of curated and noncurated zero-radius operational taxonomic units (ZOTUs) and their abundance tables. This pipeline is based on Nextflow and Singularity which enable a scalable, portable and reproducible workflow using software containers on a local computer, clouds and high-performance computing (HPC) clusters. Finally, we present an in-house Python script to assign taxonomy to ZOTUs based on user specified thresholds for assigning lowest common ancestor (LCA). We demonstrate the utility and efficiency of the pipeline using an example of a published coral diversity biomonitoring study. Our results were congruent with the aforementioned study. The scalability of the pipeline is also demonstrated through analysis of a large data set containing 154 samples. To our knowledge, this is the first automated bioinformatic pipeline for eDNA analysis using two powerful tools: Nextflow and Singularity. This pipeline addresses two major challenges in the analysis of eDNA data; scalability and reproducibility.

Mousavi‐Derazmahalleh, M., Stott, A., Lines, R., Peverley, G., Nester, G., Simpson, T., ... Christophersen, C. T. (2021). eDNAFlow, an automated, reproducible and scalable workflow for analysis of environmental DNA sequences exploiting nextflow and singularity. Molecular Ecology Resources, 21(5), 1697-1704. https://doi.org/10.1111/1755-0998.13356