Some gorgonians in the families, Primnoidae and Isididae within the suborder Calcaxonia were collected from subtidal zones between depths of 10 and 45 m in the coastal regions of King Sejong Station (62??13′S, 058?? 47′W), Korea Polar Research Institute of Korea Ocean Research and Development Institute (KORDI) by SCUBA diving from 2009 to 2011. Three species in the Primnoidae, Arntzia gracilis (Molander, 1929), Thouarella (Thouarella) antarctica (Valenciennes, 1846) and Onogorgia nodosa (Molander, 1929), and also one species in the family Isididae, Tenuisis microspiculata (Molander, 1929) are newly recorded to octocorallian fauna in Marian Cove and Potter Cove of King George Island. These four species have been described in detail

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Machine learning models are now capable of delivering coveted digital soil mapping (DSM) benefits (e.g., field capacity (FC) prediction); therefore, determining the optimal sample sites and sample size is essential to maximize the training efficacy. We solve this with a novel optimal sampling algorithm that allows the authentic augmentation of insufficient soil features using machine learning predictive uncertainty. Nine hundred and fifty-three forest soil samples and geographically referenced forest information were used to develop predictive models, and FCs in South Korea were estimated with six predictor set hierarchies. Random forest and gradient boosting models were used for estimation since tree-based models had better predictive performance than other machine learning algorithms. There was a significant relationship between model predictive uncertainties and training data distribution, where higher uncertainties were distributed in the data scarcity area. Further, we confirmed that the predictive uncertainties decreased when additional sample sites were added to the training data. Environmental covariate information of each grid cell in South Korea was then used to select the sampling sites. Optimal sites were coordinated at the cell having the highest predictive uncertainty, and the sample size was determined using the predictable rate. This intuitive method can be generalized to improve global DSM.

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