Root functional traits are important predictors for plant resource acquisition strategies in subtropical forests

生物 植物生态学 营养物 比叶面积 亚热带 下层林 桉树 草本植物 植物 农学 生态学 光合作用 天蓬 医学 传统医学 草药
作者
Guangcan Yu,Yufang Wang,Andi Li,Senhao Wang,Jing Chen,Jiangming Mo,Mianhai Zheng
出处
期刊:Ecological Applications [Wiley]
卷期号:35 (1): e3082-e3082 被引量:6
标识
DOI:10.1002/eap.3082
摘要

Intercorrelated aboveground traits associated with costs and plant growth have been widely used to predict vegetation in response to environmental changes. However, whether underground traits exhibit consistent responses remains unclear, particularly in N-rich subtropical forests. Responses of foliar and root morphological and physiological traits of tree and herb species after 8-year N, P, and combined N and P treatments (50 kg N, P, N and P ha-1 year-1) were examined in leguminous Acacia auriculiformis (AA) and nonleguminous Eucalyptus urophylla (EU) forests in southern China. N addition did not significantly impact all leaf and root traits except root N concentration per root length. Root traits responded to P addition more than leaf traits in trees; however, both traits responded similarly to P addition in herbs. Tree species deviated from the expected leaf economics spectrum; however, all species aligned with the root economics spectrum. The P and combined N and P treatments significantly altered the position of principal components analysis of root functional traits for herb species compared to the control. However, these changes did not reflect a classic shift in nutrient acquisition strategy within the root economics spectrum. As leguminous species experienced greater P limitation, AA responded more to P addition than EU; their understories indicated no significant differences. This study reveals how plant aboveground and underground traits adapt to nutrient-rich environments. These findings highlight the importance of incorporating plant underground traits, which show significant and specific responses to nutrient additions, into Earth system models for accurately predicting plant responses to global change.
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