Quantitative Metabarcoding for Invertebrate Pest Monitoring and Management

Lachlan J. Gretgrix , Jack L. Scanlan, Francesco Martoni, Mark J. Blacket, Brendan C. Rodoni, Alexander M. Piper

Invertebrate pests are one of the most significant threats to global agriculture. To monitor these pests, invertebrate trapping methods are commonly used to collect a representation of the diversity of pest species present in the ecosystem. Assessing and monitoring such diversity is key to inform pest management strategies, but due to the complexity of bulk trap samples from non-selective trapping methods (hundreds or even thousands of individuals per sample), it can be difficult to obtain this information in a timely manner. Metabarcoding is a promising tool for shortening the time required to determine what agriculturally significant pests are present in trap samples, while providing more precise identifications (i.e. to species level). However, this method currently only provides semi-quantitative relative abundance data, instead of the accurate absolute abundance information required to effectively monitor and manage pest populations. There is a need to increase the capacity for quantitative metabarcoding, which is a rapidly growing avenue of research in similar fields of study, such as medical research and environmental DNA research. This review summarises existing advances in quantitative metabarcoding, highlighting key sources of bias, emerging correction methods, and studies that have driven progress toward truly quantitative applications in bulk invertebrate samples from agricultural systems. Through consolidating insights from across a diverse range of ecological applications of metabarcoding, we present a practical roadmap for improving the quantitative outputs and interpretation of metabarcoding results and integrating these novel methods into agricultural pest monitoring and management.