Quantitative metabarcoding for invertebrate pest monitoring and management

Invertebrate pests pose one of the most significant threats to global agriculture. Trap-based surveillance is widely used to monitor the presence and abundance of pests, beneficial taxa and broader agroecosystem communities; however nonselective traps often collect hundreds or even thousands of individuals per sample, making conventional sorting and morphological identification labor-intensive and delaying the delivery of actionable information. Metabarcoding offers a scalable alternative for rapidly identifying agriculturally significant taxa in mixed trap samples, while providing more precise identifications (i.e. to species level). However, metabarcoding currently only provides semiquantitative estimates of relative abundance, rather than the accurate absolute abundance information required for many pest-monitoring and management decisions. Improving the quantitative capacity of metabarcoding is therefore an important and rapidly developing area of research across ecological, medical, microbiome, and environmental DNA research. This review summarizes progress toward quantitative metabarcoding of bulk invertebrate samples, highlighting key sources of bias, emerging correction methods, and the opportunities and challenges associated with their translation into agricultural monitoring systems. By consolidating insights from diverse ecological applications, we present a practical roadmap for improving the quantitative outputs and interpretation of metabarcoding data and integrating these novel approaches into agricultural pest monitoring and management.