Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches
Dechant, Benjamin (Leipzig University)
Kattge, Jens (Max Planck Institute for Biogeochemistry)
Pavlick, Ryan (California Institute of Technology)
Schneider, Fabian D. (California Institute of Technology)
Sabatini, Francesco M. (Alma Mater Studiorum University of Bologna)
Álvaro Moreno-Martínez, Álvaro (Universitat de Valéncia)
Butler, Ethan E. (University of Minnesota. Department of Forest Resources)
van Bodegom, Peter M. (Leiden University)
Vallicrosa Pou, Helena (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Kattenborn, Teja (University of Freiburg. Department for Sensor-based Geoinformatics)
Boonman, Coline C.F. (Aarhus University. Department of Biology)
Madani, Nima (California Institute of Technology)
Wright, Ian J. (Western Sydney University)
Dong, Ning (Macquarie University. Department of Biological Sciences)
Feilhauer, Hannes (Leipzig University)
Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Sardans i Galobart, Jordi (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Aguirre-Gutiérrez, Jesús (University of Oxford)
Reich, Peter B. (University of Minnesota. Department of Forest Resources)
J. Leitão, Pedro J. (German Centre for Integrative Biodiversity Research)
Townsend, Philip A. (University of Wisconsin)

Fecha:	2024
Resumen:	Foliar traits such as specific leaf area (SLA), leaf nitrogen (N), and phosphorus (P) concentrations play important roles 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 quantified the impacts of using different plot-level trait metrics on the evaluation with sPlotOpen: 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 use of PFT information combined with remote sensing-derived land cover products in one group while the other group mostly relied on environmental predictors alone. The maps that used PFT and corresponding 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. Upscaled maps of both groups were moderately correlated to sPlotOpen data aggregated to the grid-cell level (R = 0. 2-0. 6) when processing sPlotOpen in a way that is consistent with the respective trait upscaling approaches, including the plot-level trait metric (CWM or TWM) and the scaling to the grid cells with or without accounting for fractional land cover. The impact of using TWM or CWM was relevant, but considerably smaller than that of the PFT and land cover information. The maps using PFT and land cover information better reproduce the between-PFT trait differences of sPlotOpen data, while the two groups performed similarly in capturing within-PFT trait variation. 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. Remote sensing information has great potential to reduce uncertainties related to scaling from in-situ observations to grid cells and the regression-based mapping steps involved in the upscaling.
Ayudas:	European Commission 855187
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Foliar trait ; Specific leaf area ; Leaf nitrogen ; Leaf phosphorus ; Global mapUpscaling
Publicado en:	Remote Sensing of Environment, Vol. 311 (September 2024) , art. 114276

DOI: 10.1016/j.rse.2024.114276

19 p, 12.0 MB

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Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals)
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