Global Ecosystem Functional Types

Monitoring ecosystem functioning at the global scale requires consistent methodologies capable of capturing spatial and temporal variability across diverse environments. Traditional ecosystem classifications have largely relied on structural attributes such as vegetation physiognomy, which only partially reflect ecological complexity. Functional approaches, focused on ecosystem processes and fluxes of matter and energy, provide a complementary perspective that can improve global ecosystem monitoring. In this study, we identify key ecosystem functional attributes (EFAs) that represent the spatial and temporal variability of terrestrial biomes worldwide using satellite-derived data. Specifically, we evaluate which EFAs best capture ecosystem functional dynamics and assess their complementarity to delineate biome-like entities termed Global Ecosystem Functional Types (GEFTs). We analysed seasonal dynamics of four remotely sensed variables linked to major ecosystem processes: carbon uptake (Enhanced Vegetation Index, EVI), surface radiation balance (albedo), sensible heat fluxes (land surface temperature, LST), and water dynamics (evapotranspiration, ET). Using MODIS data (2001–2015), we derived a set of EFAs describing key functional properties of ecosystems. Principal component analyses revealed that most global variability in ecosystem functioning can be summarized by three EFAs representing magnitude (mean), seasonality (standard deviation), and phenological timing (date of maximum). Across biomes, ecosystem differences were primarily associated with variations in mean functional levels, while seasonal dynamics and timing contributed additional but complementary information. Correlation analyses showed weak coupling among functional attributes, indicating that different ecosystem processes provide largely non-redundant information. Using these attributes, we identified 35 multifunctional Ecosystem Functional Types, forming coherent spatial patterns across the globe. Our results demonstrate that a small set of EFAs derived from remote sensing can capture the main dimensions of ecosystem functioning at the planetary scale. This framework provides a consistent basis for identifying functional biogeographical units and offers a scalable tool for monitoring global ecosystem dynamics under environmental change.