Periodic Reporting for period 2 - FLUFLUX (Fluvial Meta-Ecosystem Functioning: Unravelling Regional Ecological Controls Behind Fluvial Carbon Fluxes)
Reporting period: 2018-10-01 to 2019-11-30
Understanding mechanisms behind spatial features of biodiversity, organic matter and its metabolism 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 are (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 investigates 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. This observational work in real river networks will generate hitherto unexisting databases on the spatial distribution of organic matter, biodiversity and ecosystem functioning in river networks. Further, the project uses laboratory-based experimental setups, in which the topology of connected chemostats is manipulated, and modeling approaches studying river meta-ecosystem functioning in-silico.
In parallel to this observational work, we also started to test various designs of chemostats considered for building laboratory meta-ecosystems. For the sake of efficient field work, work on this experimental approach has been slower than anticipated and intensification is planned for the third project period.
A strong modeler entered the group in the second year of the project and is currently developing a meta-ecosystem model for river network metabolism using a Bayesian framework. Data from the Mara, Vjosa and Thur will be the first case studies delivering data to use for model testing and validation.