Plant-enzyme interactions along gradients of resource stoichiometry (PEGasuS)

识别多种宏观和微观养分的化学计量生态位越来越被认为是了解和预测不同环境条件下植物表现的一种有前途的工具。植物、土壤、土壤微生物和生态酶在不同元素循环中的化学计量学是相互耦合的，这是一个强有力的基础。根际是这种相互作用的热点。将化学计量学的概念与现实世界草原(地上和地下)的形态特征联系起来仍处于初级阶段。Pegasus的目标是解开13种草地植物的这些相互作用，这些植物沿着土地利用强度(LUI)的梯度具有不同的生态位最佳，以及不同的地上和地下功能性状，以及它们的根际生态酶动力学，以评估这些相互作用对植物与根际微生物营养(C：N：P：S和其他生物元素：镁、钙、钾、铜、锰、锌、铁)的影响。混合模型和信息论方法相结合的方法将确定驱动五个化学计量生态位体积(叶、根、根际土壤溶液、微生物生物量和生态酶动力学)的因素。尽管它们很敏感，但酶底物亲和力或催化效率还没有被用来解开植物-土壤相互作用中的化学计量关系。我们将使用非线性混合效应模型通过扩展的Michaelis-Menten方法来检测酶反应。与过去10年建立的低LUI和/或低LUI的参考地相比，这一结果将提高人们对化学计量动态平衡程度如何限制物种在不同LUI的真实草原中的分布及其竞争能力，以及在LUI减少后植物酶化学计量反馈如何恢复的机械性理解。在60个草原EPs(45个EPs的联合多点试验REX加上15个低Lui EPs)和45个简化的Lui样地(RP)上，我们将确定物种特定的化学计量调控策略(所有三个探索)。描述“植物群落环境”的生物多样性探索数据和描述“目标植物物种环境”的项目数据一起，能够识别驱动植物酶反馈的机制，以及它们对三年来LUI减少的响应。通过这种方式，我们将在多元素化学计量学框架下，在小区和个体水平上解开植物分类和功能多样性以及LUI诱导的资源梯度对植物-酶相互作用的影响。这些知识预计将为生物多样性探索的框架带来可观的附加值，并将有助于从机械上更好地了解Lui和Lui减少对生态系统功能(碳和养分周转)以及相关生产服务(饲料质量和数量)的影响。

Identifying stoichiometric niches for multiple macro and micro nutrients is increasingly considered as a promising tool to understand and predict plant performance under different environmental conditions. There is a strong basis that the stoichiometries of plants, soils, soil microbes and ecoenzymes active in different element cycles are coupled. The rhizosphere is a hotspot of such interactions. Connecting concepts of stoichiometry with morphological traits in real-world grasslands (above- and belowground) is still in its infancy. PEGasuS targets at disentangling these interactions for 13 grassland plant species, characterized by different niche optima along the gradient of land-use intensity (LUI) as well as by different above- and belowground functional traits, and their rhizosphere ecoenzyme kinetics to assess the consequences of these interactions for plant versus rhizosphere microbial nutrition (C:N:P:S and further bioelements: Mg, Ca, K, Cu, Mn, Zn, Fe). The factors driving five coupled stoichiometric niche volumes (leaves, roots, rhizosphere soil solution, microbial biomass and ecoenzyme kinetics) will be identified by combining mixed model and information theoretic approaches. Despite their sensitivity, enzymatic substrate affinities or catalytic efficiencies are not yet used to unravel stoichiometric relationships in plant-soil interactions. We will detect enzyme reactions by an extended Michaelis-Menten approach using non-linear mixed effect models. The outcome will improve the mechanistic understanding on how the degree of stoichiometric homeostasis may constrain the species’ distribution and their competitive abilities in real-world grasslands with different LUI and how plant-enzyme stoichiometric feedbacks recover after LUI reduction compared to established reference plots with low LUI and/or LUI components over the last >10 years. At 60 grassland EPs (45 EPs of the joint multi-site experiment REX plus 15 low LUI EPs) and 45 reduced LUI plots (RPs), we will identify species-specific stoichiometric regulation strategies (all three exploratories). Biodiversity Exploratories data characterizing the “plant community environment” together with our project data characterizing the “target plant species environment” enable the identification of mechanisms driving plant-enzyme feedbacks along gradients of LUI and their response to three years of LUI reduction. In this way, we will disentangle the effects of plant taxonomic and functional diversity at plot and individuum level as well as of LUI-induced resource gradients on the plant-enzyme interaction in a multielement stoichiometric framework. This knowledge is expected to generate substantial added value to the framework of the Biodiversity Exploratories and will contribute to a better mechanistic understanding of the effects of LUI and LUI reduction on ecosystem functioning (carbon and nutrient turnover) as well as related production services (fodder quality and quantity).