Pritam Banerjee1, 2, Kathryn A. Stewart3, Caterina M. Antognazza4, Ingrid V. Bunholi5, Kristy Deiner6, Matthew A. Barnes7, Santanu Saha8, Héloïse Verdier9, Hideyuki Doi10, Jyoti Prakash Maity2, Michael W.Y. Chan1, Chien Yen Chen2*1Department of Biomedical Sciences, Graduate Institute of Molecular Biology, National Chung Cheng University, 168 University Road, Ming-Shung, Chiayi County 62102, Taiwan2Department of Earth and Environmental Sciences, National Chung Cheng University, 168 University Road, Ming-Shung, Chiayi County 62102, Taiwan.3 Institute of Environmental Science, Leiden University, 2333 CC Leiden, The Netherlands4 Department of Theoretical and Applied Science, University of Insubria, Via J.H. Dunant, 3, 21100, Varese, Italy5 Department of Biology, Indiana State University, Terre Haute, IN 47809, USA6 Department of Environmental Systems Science, ETH Zurich, Universitätstrasse 16, CH-8092 Zurich, Switzerland7 Department of Natural Resources Management, Texas Tech University, Lubbock, TX USA8Post Graduate Department of Botany, Bidhannagar College, Salt Lake City, Kolkata 700064, India9Univ Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA, Villeurbanne, France10Graduate School of Information Science, University of Hyogo, 7-1-28 Minatojima-minamimachi, Chuo-ku, Kobe, 650-0047, JapanAbstract Plant-animal interactions (PAI) represent major channels of energy transfer through ecosystems, where both positive and antagonistic interactions simultaneously contribute to ecosystem functioning. Monitoring PAI therefore increases understanding of environmental health, integrity and functioning, and studying complex interactions through accurate, cost-effective sampling can aid in the management of detrimental anthropogenic impacts. Environmental DNA (eDNA)-based monitoring represents an increasingly common, non-destructive approach for biomonitoring, which could help to elucidate PAI. Here, we focused our foundation to discuss the potential of eDNA in studying PAI on the literature existing from 2009 to 2021 using a freely accessible web search tool. The search was conducted by using key words involving eDNA and PAI, including both species-specific and metabarcoding approaches, recovering 43 studies. We summarise advantages and current limitations of such approaches, and we offer research priorities that will potentially improve future eDNA-based methods for PAI analysis. Our review has demonstrated that numerous studies exist using eDNA to identify PAI (e.g., pollination, herbivory, mutualistic, parasitic relationships), and although eDNA-based PAI studies remain in their infancy, to date they have identified higher taxonomic diversity in several direct comparisons to DNA-based gut/bulk sampling and conventional survey methods. Research into the influencing factors of eDNA detection involved in PAI (e.g., origin and types, methodological standardization, database limitations, validation with conventional surveys, and existing ecological models) will benefit the growth of this application. Thus, implementation of eDNA methods to study PAI can particularly benefit environmental biomonitoring surveys that are imperative for biodiversity health assessments.

In a recent paper, “Environmental DNA: What’s behind the term? Clarifying the terminology and recommendations for its future use in biomonitoring”, Pawlowski et al. argue that the term eDNA should be used to refer to the pool of DNA isolated from environmental samples, as opposed to only extra-organismal DNA from macro-organisms. We agree with this view. However, we are concerned that their proposed two-level terminology specifying sampling environment and targeted taxa is overly simplistic and might hinder rather than improve clear communication about environmental DNA and its use in biomonitoring. Not only is this terminology based on categories that are often difficult to assign and uninformative, but it ignores what is in our opinion the most important distinction within eDNA: the type of DNA (organismal or extra-organismal) from which ecological interpretations are derived.