Meiofauna in a Potential Deep-Sea Mining Area—Influence of Temporal and Spatial Variability on Small-Scale Abundance Models

• Published in: Diversity (Basel) Vol. 13; no. 1; p. 3
• Main Authors: Uhlenkott, Katja, Vink, Annemiek, Kuhn, Thomas, Gillard, Benjamin, Martínez Arbizu, Pedro
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2021
• Subjects: Abundance; Backscattering; Bathymeters; Bathymetry; Benthic communities; Biodiversity; Clarion Clipperton Fracture Zone; Computation; Continuity (mathematics); Correlation coefficient; Correlation coefficients; Deep sea mining; distribution modelling; Grain size; Investigations; license area; Meiofauna; meiofauna abundance; Metals; Nematodes; Nodules; polymetallic nodules; random forest; Sea nodules; Sediments; Shear strength; Taxonomy; Topography; Variability
• ISSN: 1424-2818; 1424-2818
• DOI: 10.3390/d13010003

In large areas of the Clarion Clipperton Fracture Zone (northeast Pacific), exploration of deep-sea polymetallic nodules as a potential source of high-technology metals is ongoing. Deep-sea mining may have a severe impact on the benthic communities. Here, we investigated meiofauna communities in the abyss at the scale of a prospective mining operation area. Random forest regressions were computed to spatially predict continuous layers of environmental variables as well as the distribution of meiofauna abundance across the area. Significant models could be computed for 26 sediment and polymetallic nodule parameters. Meiofauna abundance, taxon richness and diversity were also modelled, as well as abundance of the taxon Nematoda. Spatial correlation is high if the predictions of meiofauna are either based on bathymetry and backscatter or include sediment and nodule variables; Pearson’s correlation coefficient varies between 0.42 and 0.91. Comparison of differences in meiofauna abundance between different years shows that spatial patterns do change, with an elevated abundance of meiofauna in the eastern part of the study area in 2013. On the spatial scale of a potential mining operation, distribution models prove to be a useful tool to gain insight into both temporal variability and the influence of potential environmental drivers on meiofauna distribution.