Our current understanding of ecological and evolutionary processes underlying island biodiversity is heavily shaped by empirical data from plants and birds, although arthropods comprise the overwhelming majority of known animal species. This is due to inherent problems with obtaining high-quality arthropod data. Novel high throughput sequencing approaches are now emerging as powerful tools to overcome such limitations, and thus comprehensively address existing shortfalls in arthropod biodiversity data. Here we explore how, as a community, we might most effectively exploit these tools for comprehensive and comparable inventory and monitoring of insular arthropod biodiversity. We first review the strengths, limitations and potential synergies among existing approaches of high throughput barcode sequencing. We consider how this can be complemented with deep learning approaches applied to image analysis to study arthropod biodiversity. We then explore how these approaches can be implemented within the framework of an island Genomic Observatories Network (iGON) for the advancement of fundamental and applied understanding of island biodiversity. To this end, we identify seven island biology themes at the interface of ecology, evolution and conservation biology, within which collective and harmonised efforts in HTS arthropod inventory could yield significant advances in island biodiversity research.

Metabarcoding of DNA extracted from community samples of whole organisms (whole organism community DNA, wocDNA) is increasingly being applied to terrestrial, marine and freshwater metazoan communities to provide rapid, accurate and high resolution data for novel molecular ecology research. The growth of this field has been accompanied by considerable development that builds on microbial metabarcoding methods to develop appropriate and efficient sampling and laboratory protocols for whole organism metazoan communities. However, considerably less attention has focused on ensuring bioinformatic methods are adapted and applied comprehensively in wocDNA metabarcoding. In this study we examined over 600 papers and identified 111 studies that performed COI metabarcoding of wocDNA. We then systematically reviewed the bioinformatic methods employed by these papers to identify the state-of-the-art. Our results show that the increasing use of wocDNA COI metabarcoding for metazoan diversity is characterised by a clear absence of bioinformatic harmonisation, and the temporal trends show little change in this situation. The reviewed literature showed (i) high heterogeneity across pipelines, tasks and tools used, (ii) limited or no adaptation of bioinformatic procedures to the nature of the COI fragment, and (iii) a worrying underreporting of tasks, software and parameters. Based upon these findings we propose a set of recommendations that we think the wocDNA metabarcoding community should consider to ensure that bioinformatic methods are appropriate, comprehensive and comparable. We believe that adhering to these recommendations will improve the long-term integrative potential of wocDNA COI metabarcoding for biodiversity science.