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

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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 assign the accurate dietary information in dominant food web members. The aim of the proposed project is to implement Quantitative Fatty Acid Signature Analysis (QFASA) as new tool to study cryptic belowground feeding relationships. This model approach, recently designed for marine ecosystems and vertebrate predators, will be adapted to soil ecosystems and invertebrate consumers. Over the last decade fatty acids have been established as biochemical marker molecules in soil food webs. Starting from this qualitative fatty acids profiling as prerequisite, the project will generate the major input factors for the QFASA model as a premise to its application in soil systems. Collembola as frequent soil decomposer are used as model organisms. Firstly, a lipid library for major resources is conducted and prey-on-prey simulations are run to reveal overlap in signatures of resource types. Secondly, calibration coefficients as weighting factors for specific fatty acids are developed to account for consumer metabolism using formulated diets in laboratory experiments. And thirdly, the QFASA model is validated in simulation studies with pseudoconsumers. The best-fit model finally is applied to field populations of Collembola to assign their diets at a deciduous forest, grassland, and arable site. In sum, the proposed project will found the base for the application of QFASA to a wide range of soil decomposers, for gathering quantitative data to describe food web interactions and energy flows under realistic conditions in the field.