生态系统
生物群落
陆地生态系统
植被(病理学)
环境科学
生态学
生产力
大气科学
生物
地质学
医学
宏观经济学
病理
经济
作者
Mirco Migliavacca,Talie Musavi,Miguel D. Mahecha,Jacob A. Nelson,Jürgen Knauer,Dennis Baldocchi,Óscar Pérez‐Priego,Rune Christiansen,Jonas Peters,Karen Anderson,Michael Bahn,T. Andrew Black,Peter D. Blanken,Damien Bonal,Nina Buchmann,Silvia Caldararu,Arnaud Carrara,Nuno Carvalhais,Alessandro Cescatti,Jiquan Chen
出处
期刊:Nature
[Nature Portfolio]
日期:2021-09-22
卷期号:598 (7881): 468-472
被引量:208
标识
DOI:10.1038/s41586-021-03939-9
摘要
Abstract The leaf economics spectrum 1,2 and the global spectrum of plant forms and functions 3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species 2 . Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities 4 . However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability 4,5 . Here we derive a set of ecosystem functions 6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems 7,8 .
科研通智能强力驱动
Strongly Powered by AbleSci AI