Integrating Soil Biodiversity into Ecosystem Services

University of Catania

Established in 1434, the University of Catania (UNICT) is a very large (uniRank enrollment range: 40,000-44,999 students) public higher education institution. This almost 600-year-old Italian institution is the oldest university in Sicily, and 17 independent departments run the educational system. International applicants are eligible to apply for enrollment and several academic and non-academic facilities, museums and services to students and staff are provided. A special unit is the Scuola Superiore di Catania, a higher education centre based on excellence founded in 1998 for the selection and recognition of the brightest young minds, offering a variety of studies, including analysis, research and experimentation.

Role within SOB4ES

The institution will determine the relationships between SOB data from WP2 and existing soil functional indicators supporting ES. We will first collate georeferenced data produced in T2.1 as proxies for ES, including plant biomass, food supply and net CO2 uptake (calculated from Sentinel-2 data at high spatial resolution), soil erosion, water retention and the intensity of cropping, and changes in above-ground forest biomass (Sentinel-1 C-band radar and ALOS-2 (L-band radar)). Models for these proxies will incorporate effects of land history, site and landscape heterogeneity and be linked to land cover (CORINE) and climate data. A parallel mechanistic modelling approach, using earth observation (EO) data, will parameterise spatially-explicit process models of ES supply (e.g., satellite-derived leaf area index (LAI) in the Soil and Water Assessment Tool (SWAT) model). These ES proxies will then be linked to network metrics. The SOB count data from T2.1 (project-collected) and existing data from databases, such as PREDICTS and EUdaphobase, will be converted to abundance change metrics of count up, down and no-change between samples using χ2 tests. This circumvents many problems of compositional bias and puts all taxa on a common metric scale. The food webs will be reconstructed using logic-based Machine Learning in the pythonPython package PyGol98. This reconstruction is expected to yield more complete networks than created to date, to be validated using literature information and expert knowledge. Standardised network metrics of connectance, complexity, compartmentalisation, and motifs for the reconstructed food webs will be analysed for relationships to the predicted ES proxies.

Main contacts

Photo of Prof. Christian Mulder
Prof. Christian Mulder

Professor in Ecology and Climate Emergency

Photo of Dr. Erminia Conti
Dr. Erminia Conti

Senior Researcher in Ecology and Ecotoxicology