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To investigate the relationship between the alkenone unsaturation index (U37K′) and sea surface temperature (SST) in coastal and continental shelf waters, 58 surface sediment samples were collected from the South China Sea (SCS), Taiwan Strait, and East China Sea (ECS). We combined the new results with the previously published 71 data points from the SCS, the shallow water areas of the Yellow Sea (YS) and northern ECS, to form a dataset with sample sites spanning across 6°N and 37°N (including annual SST calibration between 14.3°C and 28.6°C). With this dataset, we examined the U37K′-SST relationship based on 129 samples from the Western North Pacific (WNP) margin as well as using 85 samples from specific WNP shallow water. The U37K′ index from the low-mid latitudinal WNP margin demonstrated a good correlation with the surface annual mean SST (0–50 m water depth; R2=0.89). The slope of linear regression (U37K′-SST) based on the coastal-continental shelf samples with water depth less than 200 m is similar to that of the published global open ocean regression equation. These results confirm that U37K′ can be used as a shallow sea water SST proxy in mid-low latitudes of the WNP marginal seas. In addition, our reintegrated U37K′-SST results based on 172 global shallow water samples are similar (similar slopes and intercepts) to the shallow ocean results in the WNP marginal seas. However, the similarity of the regression formula to the open ocean does not imply that the formula is applicable. For example, of the 85 data in the shallow waters from the marginal sea in this study, the majority of data points lie above the regression line, showing positive residuals for U37K′. This regression bias appears to be caused by specific marine environments, such as warm ocean currents and/or high nutrient conditions that result in positive U37K′ residuals. Taken together, considering the specific temperature and environmental factors in the shallow waters of the low-mid-latitude WNP margin, we propose a nonlinear U37K′-SST regression formula: U37K′ = −1.2488+0.1740×SST−0.0035×(SST)2, R2=0.93, N=85, specifically for the environments with SST below 24°C. [ABSTRACT FROM AUTHOR]

A total of 126 traditional villages in Guangdong were selected for research to analyze their spatial distribution pattern, spatial autocorrelation, and Han subgroup characteristics through the spatial analysis with GIS. The results showed that the traditional villages in Guangdong were distributed compactly, which was similar to that in other provinces; on the prefecture-level city scale, they were mainly distributed in Meizhou, Qingyuan, Zhanjiang, and Zhaoqing, showing a central distribution; on the regional scale, they were mainly distributed in northern Guangdong and the Pearl River Delta, indicating an unbalanced distribution; their density of spatial distribution showed a significant difference with a provincial distribution density of 0.59 per thousand km2 and the highest distribution density of 2.14 per thousand km2; there were four gathering areas accommodating many traditional villages, namely Meizhou-Chaoshan area, Guangfo-Dongguan area, Leizhou in Zhanjiang, and Iianzhou in Qingyuan; the spatial distribution of traditional villages, which presented a pattern of hot spot--sub-hot spot-- sub-cold spot--cold spot, had a significant global spatial autocorrelation, and areas in similar sizes were tending towards gathering; traditional villages in Guangdong belonged to the Cantonese-speaking group and Hakka-speaking group with a density of 0.59 per thousand km² and 0.48 per thousand km2 respectively, and Han subgroup with the highest distribution density was the minority areas of 1.31 per thousand km², followed by Shaozhou dialect area with a density of 1.00 per thousand km². [ABSTRACT FROM AUTHOR]