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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. [ABSTRACT FROM AUTHOR]

Coral reefs are biodiversity hotspots, places of high endemicity and provide essential services to billions of people globally. With increasing threats to these reefs worldwide, there is a need to implement faster, more efficient ways to monitor spatial and temporal patterns of biodiversity. Environmental DNA (eDNA) metabarcoding offers a promising tool to address this issue, as it has revolutionized our ability to monitor biodiversity from complex environmental samples such as seawater. However, the capacity for eDNA to resolve fine scale shifts in community composition across habitats in seascapes is yet to be fully explored. Here, we applied eDNA metabarcoding using the rRNA 18S Universal eukaryote assay to explore differences in community profiles between samples collected from the lagoon and reef slope habitats across more than 170 km of the Ningaloo Coast World Heritage Area in Western Australia. We recovered 2061 amplicon sequence variants that comprised of 401 taxa spanning 14 different metazoan phyla such as cnidarians, poriferans, molluscs, algae, worms, and echinoderms. Our results revealed strong clustering of samples by habitat type across the length of the reef. Community dissimilarity (beta diversity) between samples collected from the reef slope and lagoon habitats was high and was driven largely by a strong rate of spatial turnover, indicating a distinct set of taxa representing each reef zone community. We also detected a strong pattern of isolation by distance within our slope samples, suggesting that communities are spatially stratified across the length of the reef. Despite high connectivity due to regular flushing of the lagoon environment, our results demonstrate that metabarcoding of seawater eDNA from different habitats can resolve fine scale community structure. By generating multi-trophic biodiversity data, our study also provided baseline data for Ningaloo from which future changes can be assessed. [ABSTRACT FROM AUTHOR]

Accurate identification of the botanical components of honey can be used to establish its geographical provenance, while also providing insights into honeybee (Apis mellifera L.) diet and foraging preferences. DNA metabarcoding has been demonstrated as a robust method to identify plant species from pollen and pollen‐based products, including honey. We investigated the use of pollen metabarcoding to identify the floral sources and local foraging preferences of honeybees using 15 honey samples from six bioregions from eastern and western Australia. We used two plant metabarcoding markers, ITS2 and the trnL P6 loop. Both markers combined identified a total of 55 plant families, 67 genera, and 43 species. The trnL P6 loop marker provided significantly higher detection of taxa, detecting an average of 15.6 taxa per sample, compared to 4.6 with ITS2. Most honeys were dominated by Eucalyptus and other Myrtaceae species, with a few honeys dominated by Macadamia (Proteaceae) and Fabaceae. Metabarcoding detected the nominal primary source provided by beekeepers among the top five most abundant taxa for 85% of samples. We found that eastern and western honeys could be clearly differentiated by their floral composition, and clustered into bioregions with the trnL marker. Comparison with previous results obtained from melissopalynology shows that metabarcoding can detect similar numbers of plant families and genera, but provides significantly higher resolution at species level. Our results show that pollen DNA metabarcoding is a powerful and robust method for detecting honey provenance and examining the diet of honeybees. This is particularly relevant for hives foraging on the unique and diverse flora of the Australian continent, with the potential to be used as a novel monitoring tool for honeybee floral resources. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]