Description: There is a strong economic interest in commercial deep‐sea mining of polymetallic nodules and therefore a need to define suitable preservation zones in the abyssal plain of the Clarion Clipperton Fracture Zone (CCZ). However, besides ship‐based multibeam data, only sparse continuous environmental information is available over large geographic scales. We test the potential of modelling meiofauna abundance and diversity on high taxonomic level on large geographic scale using a random forest approach. Ship‐based multibeam bathymetry and backscatter signal are the only sources for 11 predictor variables, as well as the modelled abundance of polymetallic nodules on the seafloor. Continuous meiofauna predictions have been combined with all available environmental variables and classified into classes representing abyssal habitats using k‐means clustering. Results show that ship‐based, multibeam‐derived predictors can be used to calculate predictive models for meiofauna distribution on a large geographic scale. Predicted distribution varies between the different meiofauna response variables. To evaluate predictions, random forest regressions were additionally computed with 1,000 replicates, integrating varying numbers of sampling positions and parallel samples per site. Higher numbers of parallel samples are especially useful to smoothen the influence of the remarkable variability of meiofauna distribution on a small scale. However, a high number of sampling positions is even more important, integrating a greater amount of natural variability of environmental conditions into the model. Synthesis and applications. Polymetallic nodule exploration contractors are required to define potential mining and preservation zones within their licence area. The biodiversity and the environment of preservation zones should be representative of the sites that will be impacted by mining. Our predicted distributions of meiofauna and the derived habitat maps are an essential first step to enable the identification of areas with similar ecological conditions. In this way, it is possible to define preservation zones not only based on expert opinion and environmental proxies but also integrating evidence from the distribution of benthic communities.

Description: 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.

Description: Accurate and reliable biodiversity estimates of marine zooplankton are a prerequisite to understand how changes in diversity can affect whole ecosystems. Species identification in the deep sea is significantly impeded by high numbers of new species and decreasing numbers of taxonomic experts, hampering any assessment of biodiversity. We used in parallel morphological, genetic, and proteomic characteristics of specimens of calanoid copepods from the abyssal South Atlantic to test if proteomic fingerprinting can accelerate estimating biodiversity. We cross-validated the respective molecular discrimination methods with morphological identifications to establish COI and proteomic reference libraries, as they are a pre-requisite to assign taxonomic information to the identified molecular species clusters. Due to the high number of new species only 37% of the individuals could be assigned to species or genus level morphologically. COI sequencing was successful for 70% of the specimens analysed, while proteomic fingerprinting was successful for all specimens examined. Predicted species richness based on morphological and molecular methods was 42 morphospecies, 56 molecular operational taxonomic units (MOTUs) and 79 proteomic operational taxonomic units (POTUs), respectively. Species diversity was predicted based on proteomic profiles using hierarchical cluster analysis followed by application of the variance ratio criterion for identification of species clusters. It was comparable to species diversity calculated based on COI sequence distances. Less than 7% of specimens were misidentified by proteomic profiles when compared with COI derived MOTUs, indicating that unsupervised machine learning using solely proteomic data could be used for quickly assessing species diversity.