Sediment distributions in deep sea influence the benthic community structure and thus play an important role in shaping the marine ecosystem. Several studies on sediment characteristics had been conducted in South China Sea (SCS), but only limited to coastal areas of regions within SCS territories. Therefore, this study was carried out to analyze the benthic sediment profile in an area beyond 12 nautical miles off the coast of Sarawak, southern SCS. Sediment samples were collected from 31 stations, comprising three depth ranges: (I) 20-50 m, (II) 50-100 m, and (III) 100-200 m. The total organic matter (TOM) contents were determined and subjected to dry and wet sieving methods for particle size analysis. TOM contents in the deep area (>50 m) were significantly higher (p = 0.05) and positively correlated (r = 0.73) with silt-clay fraction. About 55% and 82% of stations in strata II and III, respectively, were dominated by silt-clay fractions (<63 μm mean diameter), coherent with TOM data. In addition, sediments in the deep area (>50 m) tend to be poorly sorted, very fine skewed, and platykurtic. Unlike data obtained 20 years ago which reported high content of silt-clay (58%), this study recorded a lower content (35%); therefore, changes in sediment load had been observed in southern SCS.
Plasmodium knowlesi infections in Malaysia are a new threat to public health and to the national efforts on malaria elimination. In the Kapit division of Sarawak, Malaysian Borneo, two divergent P. knowlesi subpopulations (termed Cluster 1 and Cluster 2) infect humans and are associated with long-tailed macaque and pig-tailed macaque hosts, respectively. It has been suggested that forest-associated activities and environmental modifications trigger the increasing number of knowlesi malaria cases. Since there is a steady increase of P. knowlesi infections over the past decades in Sarawak, particularly in the Kapit division, we aimed to identify hotspots of knowlesi malaria cases and their association with forest activities at a geographical scale using the Geographic Information System (GIS) tool. A total of 1064 P. knowlesi infections from 2014 to 2019 in the Kapit and Song districts of the Kapit division were studied. Overall demographic data showed that males and those aged between 18 and 64 years old were the most frequently infected (64%), and 35% of infections involved farming activities. Thirty-nine percent of Cluster 1 infections were mainly related to farming surrounding residential areas while 40% of Cluster 2 infections were associated with activities in the deep forest. Average Nearest Neighbour (ANN) analysis showed that humans infected with both P. knowlesi subpopulations exhibited a clustering distribution pattern of infection. The Kernel Density Analysis (KDA) indicated that the hotspot of infections surrounding Kapit and Song towns were classified as high-risk areas for zoonotic malaria transmission. This study provides useful information for staff of the Sarawak State Vector-Borne Disease Control Programme in their efforts to control and prevent zoonotic malaria.