Benjamin Dechant , Jens Kattge, Ryan Pavlick, Fabian Schneider, Francesco Sabatini, Alvaro Moreno-Martinez, Ethan Butler, Peter van Bodegom, Helena Vallicrosa, Teja Kattenborn, Coline Boonman, Nima Madani, Ian Wright, Ning Dong, Hannes Feilhauer, Josep Penuelas, Jordi Sardans, Jesus Aguirre-Gutierrez, Peter Reich, Pedro Leitao, Jeannine Cavender-Bares, Isla H. Myers-Smith , Sandra Duran, Holly Croft, Ian Colin Prentice, Andreas Huth, Karin Rebel, Sönke Zaehle, Irena Simova, Sandra Diaz, Markus Reichstein, Christopher Schiller, Helge Bruelheide, Miguel Mahecha, Christian Wirth, Yadvinder Malhi, Philip Townsend

Foliar traits such as specific leaf area (SLA), leaf nitrogen (N) and phosphorus (P) concentrations play an important role in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations. Here, we intercompare such global upscaled foliar trait maps at 0.5° spatial resolution (six maps for SLA, five for N, three for P), categorize the upscaling approaches used to generate them, and evaluate the maps with trait estimates from a global database of vegetation plots (sPlotOpen). We disentangled the contributions from different plant functional types (PFTs) to the upscaled maps and characterized the differences between two trait metrics: community weighted mean (CWM) and top-of-canopy weighted mean (TWM). We found that the global foliar trait maps of SLA and N differ drastically and fall into two groups that are almost uncorrelated (for P only maps from one group were available). The primary factor explaining the differences between these groups is the exclusive use of PFT information combined with remote sensing-derived land cover products in one group while the other group mostly relied on environmental predictors. The impact of using TWM or CWM on spatial patterns was considerably smaller than that of including PFT and land cover information. The maps that used PFT and land cover information exhibit considerable similarities in spatial patterns that are strongly driven by land cover. The maps not using PFTs show a lower level of similarity and tend to be strongly driven by individual environmental variables. Overall, the maps using PFT and land cover information better reproduce the between-PFT trait differences and trait distributions of the plot-level sPlotOpen data, while the two groups performed similarly in capturing within-PFT trait variation. Upscaled maps of both groups were moderately correlated to grid-cell-level sPlotOpen data (R = 0.2-0.6), with considerable differences between upscaling approaches and overall higher correlations for SLA and N. Our findings highlight the importance of explicitly accounting for within-grid-cell trait variation, which has important implications for applications using existing maps and future upscaling efforts.