Overcoming the pitfalls of merging dietary metabarcoding into ecological networks. Issue 3 (17th January 2022)

• Record Type: Journal Article
• Authors: Cuff, Jordan P.; Windsor, Fredric M.; Tercel, Maximillian P. T. G.; Kitson, James J. N.; Evans, Darren M.
• Is Part Of: Methods in ecology and evolution. Volume 13:Issue 3(2022)
• Page Start: 545
• Page End: 559
• Publication Date: 2022-01-17
• Subjects: DNA barcoding -- food webs -- metagenomics -- multilayer networks -- network ecology -- quantification -- trophic interactions; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13796
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: The construction of increasingly detailed species interaction networks is extending the potential applications of network ecology, providing an opportunity to understand complex eco‐evolutionary interactions, ecosystem service provision and the impacts of environmental change on ecosystem functioning. Dietary metabarcoding is a rapidly growing tool increasingly used to construct ecological networks of trophic interactions, enabling the determination of individual animal diets including difficult‐to‐distinguish prey taxa and even for species where traditional dietary analyses are unsuitable (e.g. fluid feeders and small invertebrates). Several challenges, however, surround the use of dietary metabarcoding, especially when metabarcoding‐based interactions are merged with observation‐based species interaction data. We describe the difficulties surrounding the quantification of species interactions, sampling perspective discrepancy (i.e. zoocentric vs. phytocentric sampling), experimental biases, reference database omissions and assumptions regarding direct and indirect consumption events. These problems are not, however, insurmountable. Effective experimental design and data curation with appropriate attention paid to these problems renders the incorporation of dietary metabarcoding into ecological network analysis a powerful tool for the construction of highly resolved networks. Throughout, we discuss how these problems should be addressed when merging data to construct ecological networks.