Monitoring biodiversity is essential to protect, preserve and restore ecosystems, particularly in the context of current challenges such as climate change, habitat destruction and globalization (Baird & Hajibabaei, 2012). Biomonitoring is needed for developing biotic indices for assessing ecological status, measuring impacts of anthropogenic activities in natural ecosystems, evaluating biodiversity loss, surveying nonindigenous species, conservation, and identifying cryptic species (Balvanera et al., 2006;Fišer et al., 2018). Thus, spatially and temporally structured biomonitoring activities provide a powerful tool for the implementation of regional, national and international regulations, directives and policies for nature conservation.However substantial impediments exist including access to remote locations, limited specialist taxonomic knowledge, cost, slow pace of human-driven data analyses, and typically low sensitivity for detection of rare and elusive species (Zinger et al., 2020). These drawbacks are often translated into expensive monitoring activities with limited spatial, temporal and taxonomic coverage. In this context, new approaches for biomonitoring are being explored, among which advanced DNA-based technologies are emerging (Kissling et al., 2018). The field of biodiversity monitoring through the analysis of the pool of DNA isolated from environmental samples, referred to as environmental DNA or eDNA (Pawlowski et al., 2020;Taberlet et al., 2012), is rapidly growing. This growth is being driven through improved approaches for sampling, data generation and analyses, and with recent advances on how eDNA should be interpreted for biodiversity assessments (Bohmann et al., 2014). The success of eDNA-based biomonitoring is reflected in exponential growth of publications within this area and increasing submissions to Molecular Ecology Resources in particular (Figure 1). Molecular Ecology Resources aims to publish high quality eDNA studies that serve as broad resources, including innovative methodologies for DNA sampling, enhanced laboratory protocols for data generation, or new computer programs and statistical advances for data analyses. Thus, the aim of this editorial is to contribute to producing good quality DNA dataderived essential biodiversity variables (EBVs) (Kissling et al., 2018) by providing guidance to the community submitting articles on the subject. For that purpose, we have summarized best practices established in published literature related to the different phases involved in the process, namely sampling, laboratory work, bioinformatic analyses and data interpretation (Figure 2).