Wild soybean (Glycine soja Sieb.et Zucc; WS) has been used as a traditional food in China for many years and contains significantly higher levels of isoflavones than cultivated soybean (Glycine max; CS), but the secondary metabolites, including flavonoids and the phenolic composition differences between them, remain unclear. The results showed that WS possessed significantly higher total phenolic and flavonoid content and exhibited better antioxidant and α-glucosidase inhibition activities as well as excellent protective effects against H₂O₂-induced oxidative injury in a human endothelial cell line. Through metabolomic analysis, 642 metabolites were identified, and 238 showed differential expression, with 151 upregulated and 87 downregulated. A total of 79 flavonoid compounds were identified, 42 of which were upregulated in WS. 2′-Hydroxygenistein, garbanzol, protocatechuic aldehyde, ligustilide, and resveratrol were the most discriminated compounds in WS. The metabolic pathway analysis of differential metabolites related to the biosynthesis of flavonoids and phenolic acids were the biosynthesis of phenylpropanoids, flavonoids, isoflavonoids, flavones, and flavonols. This study substantially elucidated differences in the content of flavonoids and biological activities between WS and CS, which is useful information for the effective utilization of these two black soybean species in food processing.

Loss of biodiversity due to the insufficient conservation of resources caused by land use is a serious problem humankind is facing. It is important to determine the priorities of biodiversity conservation. Based on the construction of an identification framework for a priority biodiversity conservation area, Xingguo County, a typical county administrative unit in the southern forested area of China, is used as an example. First, this paper constructs the priority identification framework for regional biodiversity conservation based on MCDA methods. Second, it adopts the InVEST-habitat quality model to evaluate the habitat quality in 2015. Third, factors such as elevation, slope, distance to nearest road and revenue loss for the forestry industry are selected to reflect the transportation cost, infrastructure cost, and opportunity cost of biodiversity conservation in the study area. By spatializing these factors and assigning weights to them, a spatial pattern of the biodiversity conservation costs in the study area is constructed. Last, a priority biodiversity conservation area is determined by an overlaid habitat quality map and the spatial pattern of conservation costs. The first priority area covers 620.61 km2, accounting for 19.31% of the total area of Xingguo County. Through well-financed biodiversity measures, the second and third priority areas can be selected using a stepwise approach. Our study may provide ideas and technical methods to help underdeveloped areas to achieve effective protection of biodiversity.