Periodic Reporting for period 4 - FLUFLUX (Fluvial Meta-Ecosystem Functioning: Unravelling Regional Ecological Controls Behind Fluvial Carbon Fluxes)
Reporting period: 2021-06-01 to 2023-03-31
Understanding mechanisms behind spatial features of biodiversity, organic matter (or in general resources) and ecosystem functioning is vital for predicting the future role of rivers in the Earth´s carbon cycle. It is further important for maintaining biodiversity and functioning in river networks, which belong to the most biodiverse and most strongly human-affected ecosystems on Earth.
The overall objectives of FLUFLUX were (i) to first principally test whether the interaction of organic matter diversity with consumer biodiversity can help to explain organic matter metabolism to a reasonable degree, and (ii) to test whether gradients of topological variation and fragmentation can lead to sizeable variation of spatial patterns of organic matter metabolism. The project empirically investigated two organic matter-consumer pairs: dissolved organic matter consumed by microbes, and particulate organic matter consumed by invertebrates – different patterns are expected due to differences in transport traits of OM and dispersal traits of consumers. In addition, the project studied the analogue interactions between resources, environmental factors and biodiversity – again in the spatial domain of river networks – controlling the buildup of organic matter (and concomitant removal of CO2) by primary producers.
In parallel to this observational work, we developed a meta-ecosystem model for river networks. This model has meanwhile been made available as a R-package and is capable of modelling network-scale biodiversity, resource distribution and ecosystem functioning as the result of the interaction between biodiversity and available resources. The model is in fact blending a metacommunity model with a biogeochemical transport-reaction model. As such it allows simulation of river meta-ecosystem functioning based on mechanistic principles involving species and resources with particular traits. Besides this mechanistic model, we also advanced on the network-scale statistical analysis of empirical data. Mostly using a Bayesian framework we build models explaining the distribution of microbes, invertebrates and fish, and further develop those to analyze ecosystem functions. In addition to this modelling work, we developed a prototype for a laboratory meta-ecosystem. Work on this experimental approach has been slower than anticipated and could not be brought to the intended level that could support the planned series of hypothesis tests about meta-ecosystem functioning.