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Digging deep: lessons learned from meiofaunal responses to a disturbance experiment in the Clarion-Clipperton Zone
Lefaible, N.; Macheriotou, L.; Purkiani, K.; Haeckel, M.; Zeppilli, D.; Pape, E.; Vanreusel, A. (2023). Digging deep: lessons learned from meiofaunal responses to a disturbance experiment in the Clarion-Clipperton Zone. Mar. Biodiv. 53(4): 48. https://dx.doi.org/10.1007/s12526-023-01353-0
In: Marine Biodiversity. Springer: Heidelberg; Berlin. ISSN 1867-1616; e-ISSN 1867-1624, more
Peer reviewed article  
Available in  Authors 
Keyword
    Marine/Coastal
Authors  Top 
  • Lefaible, N., more
  • Macheriotou, L., more
  • Purkiani, K.
  • Haeckel, M.
  • Zeppilli, D.
  • Pape, E., more
  • Vanreusel, A., more
Abstract
    The deep-sea mining industry is currently at a point where large-sale, commercial polymetallic nodule exploitation is becoming a more realistic scenario. At the same time, certain aspects such as the spatiotemporal scale of impacts, sediment plume dispersion and the disturbance-related biological responses remain highly uncertain. In this paper, findings from a small-scale seabed disturbance experiment in the German contract area (Clarion-Clipperton Zone, CCZ) are described, with a focus on the soft-sediment ecosystem component. Despite the limited spatial scale of the induced disturbance on the seafloor, this experiment allowed us to evaluate how short-term (< 1 month) soft-sediment changes can be assessed based on sediment characteristics (grain size, nutrients and pigments) and metazoan meiofaunal communities (morphological and metabarcoding analyses). Furthermore, we show how benthic measurements can be combined with numerical modelling of sediment transport to enhance our understanding of meiofaunal responses to increased sedimentation levels. The lessons learned within this study highlight the major issues of current deep-sea mining-related ecological research such as deficient baseline knowledge, unrepresentative impact intensity of mining simulations and challenges associated with sampling trade-offs (e.g., replication).
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