营养循环
自行车
温带气候
温带雨林
营养物
土壤水分
温带森林
生态学
环境科学
生物
农学
地理
生态系统
林业
作者
Felix Seidel,Carles Castaño,Josu G. Alday,Maximo Larry Lopez Caceres,José Antonio Bonet
标识
DOI:10.1016/j.apsoil.2024.105360
摘要
Understanding soil dynamics and nutrient cycling is crucial for the sustainable management of Japanese forests covering 70 % of the national land area. These forests are dominated by tree species with contrasting traits, influencing soil dynamics differently. We investigated how changes in soil characteristics across different forest stands shift in composition and functioning of fungal communities. Four different forest stands dominated by two different mycorrhizal types were selected: Fagus crenata and Larix kaempferi, representing ectomycorrhizal (ECM) types, and Cryptomeria japonica and Robinia pseudoacacia, representing arbuscular mycorrhizal (AM) types. In total, 62 composite topsoil samples from two depths were analyzed for their physicochemical properties and fungal communities were profiled by DNA sequencing. Ectomycorrhizal fungi dominated soils of Fagus crenata and Larix kaempferi forests, while fungal saprotrophs were more abundant in Cryptomeria japonica and Robinia pseudoacacia forests. Forest stand type rather than soil depth determined the composition and structure of soil fungal communities. Soil pH was positively correlated with abundances of saprotrophic fungi (P < 0.05) and negatively with ECM fungi. Soil C:N ratio was positively correlated, and nitrate was negatively correlated with relative abundances of root-associated fungi, primarily ECM fungi. No links between C nor N stocks with fungal guilds were found across the dataset. Observed links between soil C:N ratio and relative abundances of root-associated fungi and saprotrophs stress the importance of these guilds for influencing nutrient cycling economy across contrasting forest types. The lack of correlation between fungal communities and soil C and N stocks suggests distinct mechanisms driving stocks in these soils.
科研通智能强力驱动
Strongly Powered by AbleSci AI