Abstract

Deep-water seamount ecosystems are sensitive to human activities and have a slow recovery rate after being disturbed. Bottom trawling and potential deep-sea mineral extraction could severely damage benthic communities on seamounts and seriously impact deep-sea ecosystems. Inadequate knowledge of the distribution of megabenthos on seamounts or their community structure hinders deep-sea conservation and management. In this study, based on a multidisciplinary dataset generated from recordings taken by human-occupied vehicle (HOV) and remotely operated vehicles (ROVs) along transects and environmental variables, a range of megabenthic morphotaxa were observed on two adjacent deep-water seamounts and predicted using species distribution models (SDMs). Accordingly, based on the predicted distribution of each morphotaxon, five distinct communities were identified through cluster analysis. The results of SDMs showed that environmental variables varyingly impacted the distribution of different morphotaxa, among which the average velocity and eastness direction of near-bottom currents, bathymetric position index (BPI), and backscatter intensity exerted the most significant influence on megabenthic distribution patterns. The distribution of five distinct communities showed a similarity of community composition on the two deep-water seamounts, suggesting a potential connectivity between the two seamounts. The distribution of communities revealed the spatial characteristics of vulnerability of deep-water seamounts at the community level, which could provide a direct basis for marine spatial planning of deep-sea ecosystems.

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