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沉水植物苦草根系功能性状对基质类型和种植密度的响应及其与个体表现关系
陈俊格1, 陈 矿1, 王和云1, 易春龙2, 高健1, 文冬华1, 张润龙1
1.:湖北工业大学土木建筑与环境学院;2.:中工武大设计集团有限公司
摘要:
根系功能性状体现了植物细根的生长状况及其对外部环境的适应性,然而根系功能性状响应环境变化的时间稳定性如何仍不清楚。苦草(Vallisneria natans)是水体沉水植被修复的先锋种类,细根在其种群重建初期起着重要作用。该研究设计了草垫、草垫+沙子、草垫+黄泥及草垫+底泥四种生长基质和100株?m-2以及200株?m-2两种种植密度并将其两两组合,以模拟自然生境不同的基质和密度情况,并在不同时期对苦草功能性状指标进行取样测定,通过重复测量二元方差分析研究基质和密度对根系功能指标的影响,并通过线性拟合模型探讨个体水平的生长表现(生物量分配、营养吸收、个体定植)与具体的根系功能性状之间的关系对环境变化的响应。结果表明:所有测量指标均受到基质条件的显著影响,部分根系功能性状指标如根冠比(RSR)、根干重、比根长(SRL)、根比表面积(SRA)、根组织密度(RTD)、根系锚定力和根体积受密度的影响显著。苦草生物量的分配主要受基质条件的影响,对营养的吸收、运输及根系固着能力受基质和密度的共同影响。线性拟合模型表明SRA、RTD、根直径、根表面积以及根体积在个体水平上能一定程度预测RSR对环境变化的响应,且RTD的预测效果最好;根冠比、SRA、生根数、总根长、根表面积以及根体积在个体水平上能一定程度预测SRL对环境变化的响应,其中生根数的预测效果最好;SRA、根直径、根表面积以及根体积在个体水平上能一定程度预测根系锚定力对环境变化的响应,并且SRA的预测效果最好。但是基质和密度在不同时期对苦草根系功能性状的影响方向会发生改变,当我们用根系功能性状预测植物个体表现时,需要考虑种群重建所处的时期。
关键词:  根系功能性状  基质  密度  苦草  种群构建  性状×环境交互作用  性状-表现关系
DOI:
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基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
Study on the responses of root functional traits of submerged plant Vallisneria natans to substrate type and planting density and its relationship with individual growth performance
Chen Junge1, Chen Kuang1, Wang Heyun1, Yi Chunlong2, Gao Jian1, Wen Donghua1, Zhang Runlong1
1.: Key Laboratory of Ecological Remediation of Lakes and Rivers and Algal Utilization of Hubei Province,School of Civil and Environment,Hubei University of Technology;2.: CEWUD GROUP Company,Limited
Abstract:
Root functional traits reflect the growth status of plant fine roots and their adaptability to external environment. However, the time stability of root functional traits in response to environmental changes is still unclear. Vallisneria natans is a pioneer species for submerged vegetation restoration in water bodies and fine roots play an important role in the early stage of population reconstruction. In this study, four growth substrates (straw pad, straw pad+sand, straw pad+yellow mud, and straw pad+sediment) and two planting densities (100 plants?m-2 and 200 plants?m-2) were designed to simulate different substrates and densities in natural habitats. The functional traits of V. natans were sampled and measured in different periods. Two-way ANOVA with repeated measures were used to analyze the effects of time, substrate, density and their interactions on functional trait. And linear fitting models (R 4.2.0) were used to study the relationship between root functional traits and growth performance (biomass allocation, nutrient uptake, individual colonization) in responses to varied substrates, densities and periods. The results revealed that all measurement indicators were significantly affected by substrate, and some root functional traits such as root shoot ratio ( RSR ), root dry weight, specific root length ( SRL ), root specific surface area ( SRA ), root tissue density ( RTD ), root anchoring force and root volume were significantly affected by density. The biomass allocation was mainly affected by substrate, and the absorption, transportation and root fixation of nutrients were affected both by substrate and density. The linear fitting model revealed that SRA, RTD, root diameter, root surface area, and root volume could predict the responses of RSR to environmental changes at the individual level,with RTD highest predication value. RSR, SRA, root number, total root length, root surface area, and root volume could partly predict the response of SRL to environmental changes at the individual level, with root number highest prediction value. SRA, root diameter, root surface area, and root volume could predict the response of root anchorage force to environmental changes at the individual level to a certain extent, and SRA had the highest prediction values. However, the influence direction of substrate and density on the functional traits of V. natans roots would change in different periods. When we use root functional traits to predict growth performance at the individual level, the period of population reconstruction needs to be considered.
Key words:  Root functional traits  substrate  density  Vallisneria natans  population construction  trait×environment interactions  trait–performance relationships
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