Toward a reliable assessment of potential ecological impacts of deep‐sea polymetallic nodule mining on abyssal infauna. (26th July 2021) - Lins, Lidia; Zeppilli, Daniela; Menot, Lénaïck; Michel, Loïc N.; Bonifácio, Paulo; Brandt, Miriam; Pape, Ellen; Rossel, Sven; Uhlenkott, Katja; Macheriotou, Lara; Bezerra, Tania Nara; Sánchez, Nuria; et al.
Abstract: The increasing demand for metals is pushing forward the progress of deep‐sea mining industry. The abyss between the Clarion and Clipperton Fracture Zones (CCFZ), a region holding a higher concentration of minerals than land deposits, is the most targeted area for the exploration of polymetallic nodules worldwide, which may likely disturb the seafloor across large areas and over many years. Effects from nodule extraction cause acute biodiversity loss of organisms inhabiting sediments and polymetallic nodules. Attention to deep‐sea ecosystems and their services has to be considered before mining starts but the lack of basic scientific knowledge on the methodologies for the ecological surveys of fauna in the context of deep‐sea mining impacts is still scarce. We review the methodology to sample, process and investigate metazoan infauna both inhabiting sediments and nodules dwelling on these polymetallic‐nodule areas. We suggest effective procedures for sampling designs, devices and methods involving gear types, sediment processing, morphological and genetic identification including metabarcoding and proteomic fingerprinting, the assessment of biomass, functional traits, fatty acids, and stable isotope studies within the CCFZ based on both first‐hand experiences and literature. We recommend multi‐ and boxcorers for the quantitative assessments of meio‐ and macrofauna, respectively. The assessment of biodiversity at species level should be focused and/or the combination of morphological with metabarcoding or proteomic fingerprinting techniques. We highlight that biomass, functional traits, and trophic markers may provide critical insights for biodiversity assessments and how statistical modeling facilitates predicting patterns spatially across point‐source data and is essential for conservation management.