Developing and validating the efficacy of DNA metabarcoding for nationwide insect biomonitoring schemes

Abstract

Next-generation sequencing (NGS) approaches are revolutionizing the study of biodiversity and ecosystem functioning by providing scalable, economic and efficient methods, with a range of immediate applications for national insect biomonitoring schemes. However, such approaches are still in their infancy and are yet to be applied to established schemes in the UK. With insect biodiversity being under threat and the recent evidence on global insect declines, there is a clear need for reliable and fast methods for describing insect communities that overcome the pitfalls of traditional monitoring. This thesis tests and validates the application of DNA metabarcoding to identify insect communities in bulk samples of insects collected as part of two major biomonitoring schemes in the UK. First, I give an overview of insect monitoring, and the molecular approaches used for insect monitoring particularly in an agricultural context, highlighting limitations and potential future directions. Second, I assess the efficacy of DNA-metabarcoding to identify aphid archival samples of a 16-year time series from the Rothamsted Insect Survey (RIS). I show how DNA-metabarcoding can reliably identify samples that are as old as 18 years with high congruence (~80%), but without destroying their morphological integrity. I apply these methods to the corresponding bycatch (i.e non-target taxa captured) samples from the same trap and reveal over 800 insect taxa caught over the same period, many of which can be regarded as ecosystem service providers, pests and/or newly discovered species in the UK. I highlight the potential of bycatch samples to construct time-series for hundreds of insect taxa, enabling previously intractable questions to be addressed. I then apply these methods to analyse bycatch samples from the Fera’s Yellow Water Pan Trap Network and show the utility of DNA metabarcoding for describing and comparing biodiversity without relying on conventional taxonomy. Finally, I show how and why combining molecular approaches and long-term schemes can help to understand and mitigate insect declines. I discuss the potential biases and limitations of these methods, whilst identifying direct applications for existing and future insect biomonitoring schemes