土壤学
物种丰富度
生态学
生态系统
横断面
土壤pH值
α多样性
生物
生物地球化学
非生物成分
β多样性
生物多样性
土壤有机质
土壤水分
作者
Andrew T. Nottingham,Noah Fierer,Benjamin L. Turner,Jeanette Whitaker,Nick Ostle,Niall P. McNamara,Richard D. Bardgett,Jonathan W. Leff,Norma Salinas,Miles R. Silman,Loeske E. B. Kruuk,Patrick Meir
摘要
Summary More than 200 years ago, von Humboldt reported decreases in tropical plant species richness with increasing elevation and decreasing temperature. Surprisingly, co-ordinated patterns in plant, bacterial and fungal diversity on tropical mountains are yet to be observed, despite the central role of soil microorganisms in terrestrial biogeochemistry. We studied an Andean transect traversing 3.5 km in elevation to test whether the species diversity and composition of tropical forest plants, soil bacteria and fungi can follow similar biogeographical patterns with shared environmental drivers. We found co-ordinated changes with elevation in all three groups: species richness declined as elevation increased, and the compositional-dissimilarity of communities increased with increased separation in elevation, although changes in plant diversity were larger than in bacteria and fungi. Temperature was the dominant driver of these diversity gradients, with weak influences of edaphic properties, including soil pH. The gradients in microbial diversity were strongly correlated with the activities of enzymes involved in organic matter cycling, and were accompanied by a transition in microbial traits towards slower-growing, oligotrophic taxa at higher elevations. We provide the first evidence of co-ordinated temperature-driven patterns in the diversity and distribution of three major biotic groups in tropical ecosystems: soil bacteria, fungi and plants. These findings suggest that, across landscape scales of relatively constant soil pH, inter-related patterns of plant and microbial communities with shared environmental drivers can occur, with large implications for tropical forest communities under future climate change.
科研通智能强力驱动
Strongly Powered by AbleSci AI