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Functional composition of tall-statured trees underpins aboveground biomass in tropical forests

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摘要 The influences of trait diversity(i.e.,the niche complementarity effect)and functional composition(i.e.,the mass ratio effect)on aboveground biomass(AGB)is a highly debated topic in forest ecology.Therefore,further studies are needed to explore these mechanisms in unstudied forest ecosystems to enhance our understanding,and to provide guidelines for specific forest management.Here,we hypothesized that functional composition would drive AGB better than trait diversity and stem size inequality in the(sub-)tropical forests of Nepal.Using data from 101 forest plots,we tested 25 structural equation models(SEMs)to link elevation,stem DBH inequality,trait diversity(i.e.,trait richness,evenness,dispersion and divergence),functional composition[i.e.,community-weighted of maximum height mean(CWM of Hmax),specific leaf area(CWM of SLA),leaf dry matter content(CWM of LDMC),and wood density(CWM of WD)]and AGB.The best-fitted SEMs indicated that CWM of Hmax promoted AGB while overruling the impacts of trait diversity indices on AGB.However,low trait diversity indices were linked with higher AGB while overruling the effects of CWM of SLA,LDMC and WD on AGB.In addition,AGB decreased with increasing elevation,whereas stem size inequality did not influence AGB.Our results suggest that divergent species’functional strategies could shape AGB along an altitudinal gradient in tropical forests.We argue that forest management practices should include plant functional traits in the management plan for the co-benefits of biodiversity conservation and carbon sequestration that underpins human wellbeing.
出处 《Journal of Forestry Research》 SCIE CAS CSCD 2023年第2期333-344,共12页 林业研究(英文版)
基金 supported by the Jiangsu Science and Technology Special Project(Grant No.BX2019084) Research Startup Funding at Nanjing Forestry University(Grant No.163010230) the Faculty Startup Funding(to Arshad Ali)for establishing Forest Ecology Research Group at Hebei University(Special Project No.521100221033)。
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