- 1University of Valencia, Image Processing Laboratory (IPL), Image Signal Processing Group, Paterna, Spain (alvaro.moreno@uv.es)
- 2Department of Natural Resources and the Environment, University of Connecticut
- 3University of Edinburgh School of Geosciences, United Kingdom
- 4Department of Ecology, Evolution & Marine Biology, University of California, Santa Barbara
- 5German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- 6Department of Biology, Section for Ecoinformatics and Biodiversity, Aarhus University, Denmark
- 7Max Planck Institute for Biogeochemistry, Jena, Germany
- 8Chair of Sensor-based Geoinformatics (geosense), Faculty of Environment and Natural Resources, University of Freiburg, Germany
- 9Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium
- 10Laboratory of Catchment Hydrology and Geomorphology, School of Architecture, Civil and Environmental Engineering, EPFL Valais Wallis, Sion, Switzerlan
- 11CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, Spain
Plant hydraulic traits are critical in regulating plant–water interactions and essential for understanding vegetation responses to environmental stress. Building on our earlier methodology for mapping global plant functional traits, we now incorporate a newly-available dataset of hydraulic traits for 55,779 tree species. This integrated framework leverages remotely sensed imagery, crowdsourced biodiversity data, and trait databases to estimate and map key hydraulic parameters, including maximum stomatal conductance (gsMAX), xylem pressure at 50% and 88% conductance loss (P50, P88), and photosynthetic water use efficiency (WUE).
The tree trait data underlying our study accounts for the large phylogenetic signals inherent in these hydraulic traits by leveraging phylogenetically-informed machine learning models and novel trait imputation methods. These enhanced predictions of hydraulic traits are subsequently integrated into our trait-mapping workflow, which has previously demonstrated high accuracy (r > 0.5; rME < 6%; rRMSE < 11%) for leaf-level traits at a 1 km spatial resolution. While the hydraulic trait maps have not yet been validated due to a lack of independent validation data, the observed patterns are consistent with a meta-analysis based on recent literature.
We also capture the full distribution of hydraulic traits (standard deviation, skewness, and kurtosis) at the grid-cell level to reflect the non-Gaussian variability of community-level traits. This added detail helps elucidate the ecological strategies of species assemblages and refines our understanding of ecosystem vulnerability to climate extremes. Overall, this work offers a new avenue for improving global ecosystem models and Earth system simulations by providing spatially explicit community-level hydraulic trait estimates at large scales. Our results highlight the importance of merging global remote sensing data with state-of-the-art trait imputation and phylogenetic information to advance research on plant functioning and ecosystem dynamics.
How to cite: Moreno-Martínez, Á., Muñoz-Marí, J., Adsuara, J., Knighton, J., Sanchez-Martinez, P., Anderegg, L., Dechant, B., Schneider, F. D., Kattge, J., Katternborn, T., Lusk, D., Koppa, A., Miralles, D., Piles, M., Mencuccini, M., Chaparro, D., and Camps-Valls, G.: Mapping Tree Hydraulics and Assemblages at Continental Scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17820, https://doi.org/10.5194/egusphere-egu25-17820, 2025.
