The complexity of data collection – the ALFAwetlands approach

ALFAwetlands is a complex, multi-annual EU Horizon project with project partners and their respective Living Labs from across Europe. Further, there are nine different wetland types from restored peat extraction areas (Fig. 1) over wetland forests to coastal wetlands included in our project. Consequently, any kind of data collection should be synchronized, hereby following the principle of “the lowest common denominator” on the one hand but maximum possible data set on the other, and, of course, also having in mind different levels of sampling experience within the specific fields at each and every partner. Thus, the ALFAwetlands Work Package 3 (WP 3) aims to strengthen the existing knowledge and gather new experimental data amongst partners and their respective sites, to enhance our mutual understanding of how ecosystems respond to different wetland management and restoration practices.

Figure 1: Restored peat extraction field, Laiuse/Living Lab Estonia. Image by T. Schindler, University of Tartu

In brief, the WP3 team develops common method standards as core element (step 1) to collate existing data and collect new data (step 2) to estimate greenhouse gas fluxes, the carbon stock, biodiversity responses and quantify the role of microbial and fungal communities from different management and restoration practices, and integrates this as step 3 into a complex joint database (Fig. 2). 

Figure 2: Simplified WP3 scheme with main tasks.

The challenge, to be understood positively, is to cross-link existing (literature, unpublished partner data) and new (collected) data, vertically from biomolecular level (such as RNA, DNA) to remote sensed models, and horizontally across the different Living labs and wetland types across Europe to finally quantify GHG fluxes and C stock changes over time (fourth dimension) and evaluate the biodiversity and ecosystem service responses in wetlands under different land use, restoration management regimes.

What to expect from all those efforts?  

Spatially detailed GHG flux measurement data and environmental variable-determined models provide an excellent opportunity to build machine learning models to predict GHG fluxes and C stock changes in wetlands with higher precision. Additional, newly applied analytical methods (Fourier transform infrared spectroscopy (FTIR), remote sensing) will ease not only the characterization and comparisons of wetlands at a larger scale but provide scalable tools to monitor certain responses to restoration actions. Analyzing the local microbial community characteristics is predicted to raise our understanding of cause-effect of differing greenhouse gas emissions, between different types and different geographical locations. Last but not least, the collated data can be used to upscale results and impacts even on national and EU levels by modelling the climate change mitigation and identify feasible climate change mitigation solutions.  


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