Quantitative fatty acid signature analysis: a new tool to determine tropic interactions in soil food webs

定量脂肪酸特征分析：确定土壤食物网热带相互作用的新工具

• 批准号: 508823269
• 负责人: Professorin Dr. Liliane Rueß
• 金额: --
• 依托单位: Arbeitsgruppe Ökologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/508823269?language=en
• 关键词: Quantitative; fatty; acid; signature; analysis

Soil food webs are important drivers for ecological key functions in soil systems such as carbon and nutrient cycling. However, food web models generelly lack quantitative data from empirical studies to describe these energy flows. This is mainly due to the shortage in high-throughput methods to assess the dietary composition of the soil fauna. The aim of this project is to implement Quantitative Fatty Acid Signature Analysis (QFASA) to study belowground feeding relationships. In a precursor project, the classical QFASA model, designed for marine ecosystems and vertebrate predators, was applied to Collembola as important soil decomposers. This work resulted in essential model prerequisites, such as a comprehensive lipid library for basal resources in the green and brown food chain with 229 entries. Moreover, first sets of Calibration Coefficients (CCs) for common Collembola consumers were gained and were tested on a variety of single and mixed diets comprising basal food web resources (i.e. bacteria, fungi, algae, plants). However, model application revealed difficulties not encountered in marine ecosystems, such as highly variable CCs in soil invertebrates and smaller footprints of fatty acid (FA) signatures in the consumers at the food web base. In a first model adaptation a better QFASA performance was achieved by considering the fat content of the diet and adjusting the CCs via linear or exponential equations.The goal of the continuation application is a substantial model development, both empirically (CCs and FA subsets) and mathematically (iterative analyses). This includes experiments with formulated diets using FAs with highly variable CCs and offered over a broad range of fat contents. Thereby, the metabolic control, i.e. the variation of CCs by consumers, is better integrated into the model. The mathematical model application will be systematically elaborated with the focus on: i) CC robustness and distance measurements, ii) best-fit FA and library subsets, and iii) constrains by compositional data. This will result in CCs with significantly improved functionality. Additionally, a model extension from low trophic levels (Collembola) to top predators (mites) is performed. Finally, the best-fit model will be applied to field populations of Collembola and mites to assign their diets in situ. In sum, the proposed work will enable “shifting systems”, i.e. translating the application of QFASA from marine to soil food webs.

土壤食物网是土壤系统中碳和养分循环等生态关键功能的重要驱动力。然而，食物网模型普遍缺乏定量的数据，从实证研究来描述这些能量流。这主要是由于缺乏高通量的方法来评估土壤动物的饮食组成。本研究的目的是应用定量脂肪酸特征分析（QFASA）来研究地下摄食关系。在一个前期项目中，经典的QFASA模型，设计用于海洋生态系统和脊椎动物捕食者，被应用于弹尾目作为重要的土壤分解者。这项工作产生了基本的模型先决条件，如一个全面的脂质库的基础资源在绿色和棕色食物链与229个条目。此外，获得了普通弹尾目消费者的第一组校准系数（CC），并在包括基础食物网资源（即细菌、真菌、藻类、植物）的各种单一和混合饮食上进行了测试。然而，模型应用揭示了海洋生态系统中没有遇到的困难，例如土壤无脊椎动物中的CC高度可变，以及食物网基础消费者中脂肪酸（FA）特征的足迹较小。在第一个模型适应更好的QFASA性能，通过考虑饮食的脂肪含量，并通过线性或指数方程调整CCs.Continuation应用的目标是一个实质性的模型开发，经验（CC和FA子集）和数学（迭代分析）。这包括使用具有高度可变的CC的FA和在广泛的脂肪含量范围内提供的配制饮食的实验。因此，代谢控制，即消费者的CC变化，被更好地集成到模型中。数学模型的应用将被系统地阐述，重点是：i）CC鲁棒性和距离测量，ii）最佳拟合FA和库子集，以及iii）成分数据的约束。这将导致CC具有显著改进的功能。此外，从低营养级（弹尾目）的顶级捕食者（螨）的模型扩展。最后，最适合的模型将被应用到现场人口的弹尾虫和螨分配他们的饮食在原地。总之，拟议的工作将实现"转移系统"，即将QFASA的应用从海洋食物网转移到土壤食物网。