MetaBridge: Metabarcode data in marine environmental monitoring - bridging the gap between science and management

Which biological organisms that are found at a given location are dependent on environmental conditions, and can thus be used to assess environmental impact from human activities. While finding out which organisms are present is usually done through collecting and identifying organisms by hand, advances in molecular sequencing and computer technology now makes it possible to detect organisms directly based on identification of DNA sequences that are present in an environmental sample, so-called environmental DNA (eDNA). The MetaBridge project uses eDNA in marine sediments as a method for monitoring environmental impact on seafloor organisms from offshore oil and gas extraction. By detecting the species composition at sampling localities, presence or lack of indicator species can tell whether environmental contamination is present. Since eDNA is a new technology, methodological standardization and more data are needed to provide enough information to interpret results and develop ways in which eDNA data can be used in routine monitoring. MetaBridge will build a reference dataset of eDNA data from sampling stations on the Norwegian shelf and at international sites and develop methods to allow practical classification of the environmental status of seafloor organism communities from eDNA data. Environmental impact monitoring of the health of marine bottom communities is a regulatory requirement of marine industry operations. Developing eDNA as an alternative or complement to existing methods to examine seafloor sediment communities will increase the cost-effectiveness of marine environmental monitoring, and allow a more complete picture of ecosystems that also include tiny animals and microorganisms not part of current monitoring: Increasing the capabilities of marine monitoring represents an important aspect of ensuring good ocean stewardship in the face of increased ocean stress from human activities and climate change. The project started in July 2021 and a postdoc was recruited shortly thereafter. The postdoc was selected from a large number of extremely well qualified applicants and started the work in Bergen on September 1. For the project to get a head start, samples were collected by Equinor during their regular survey work in May 2021. Analyses of these samples are ongoing.

The Norwegian oil and gas sector spends considerable resources on environmental monitoring of offshore installations on the Norwegian Shelf. Environmental DNA (eDNA) technologies such as metabarcoding has the potential to increase cost-effectiveness and sensitivity to environmental impact, and can unlock targeted detection of specific indicator species using automated samplers or AUVs. Implementation of eDNA in marine monitoring is hampered by small-scale study design and lack of established methodology, making it difficult to move beyond a proof-of-concept stage. A more complete ecological characterization and validation of metabarcoding data, correlated to existing biological and chemical monitoring parameters, and development of validated biotic indices based on this data, are unavoidable steps in a realistic and practical roadmap towards eDNA environmental monitoring acceptance and implementation. The main objective of MetaBridge is to validate and consolidate the large scale use of metabarcoding for environmental monitoring of marine sediments in the context of oil and gas extraction activities. Through the ongoing MetaMon project, we have developed best-practice sampling and lab processing methodology for marine sediment and demonstrated that metabarcoding data from the North Sea can be used to detect environmental impact through correlation with existing environmental parameters. Designed to specifically address stated regulator and stakeholder needs, MetaBridge builds on this momentum to A) significantly expand the extent of metabarcoding data, including time series data and a range of habitats, to explore the range of and validate metabarcoding data results, B) use this data to develop and test biotic indices, based on machine learning and sensitivity indices/indicator species, that are necessary for routine monitoring.