Spatial pattern and environmental drivers of Acid Phosphatase activity in Europe
Sun, Yan (Centre National de la Recherche Scientifique. Laboratoire des Sciences du Climat et de l'Environnement (France))
Goll, Daniel (University of Augsburg. Institute of Geography (Germany))
Ciais, Philippe (Centre National de la Recherche Scientifique. Laboratoire des Sciences du Climat et de l'Environnement (France))
Peng, Shushi (Peking University. College of Urban and Environmental Sciences. Sino-French Institute for Earth System Science (China))
Margalef, Olga (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Asensio, Dolores (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Sardans i Galobart, Jordi (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals)

Data:	2020
Resum:	Acid phosphatase produced by plants and microbes plays a fundamental role in the recycling of soil phosphorus (P). A quantification of the spatial variation in potential acid phosphatase activity (AP) on large spatial scales and its drivers can help to reduce the uncertainty in our understanding of bio-availability of soil P. We applied two machine-learning methods (Random forests and back-propagation artificial networks) to simulate the spatial patterns of AP across Europe by scaling up 126 site observations of potential AP activity from field samples measured in the laboratory, using 12 environmental drivers as predictors. The back-propagation artificial network (BPN) method explained 58% of AP variability, more than the regression tree model (49%). In addition, BPN was able to identify the gradients in AP along three transects in Europe. Partial correlation analysis revealed that soil nutrients (total nitrogen, total P, and labile organic P) and climatic controls (annual precipitation, mean annual temperature, and temperature amplitude) were the dominant factors influencing AP variations in space. Higher AP occurred in regions with higher mean annual temperature, precipitation and higher soil total nitrogen. Soil TP and Po were non-monotonically correlated with modeled AP for Europe, indicating diffident strategies of P utilization by biomes in arid and humid area. This study helps to separate the influences of each factor on AP production and to reduce the uncertainty in estimating soil P availability. The BPN model trained with European data, however, could not produce a robust global map of AP due to the lack of representative measurements of AP for tropical regions. Filling this data gap will help us to understand the physiological basis of P-use strategies in natural soils.
Ajuts:	European Commission 610028
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Back-propagation artificial network ; Europe ; Phosphorus cycling ; Partial correlation analysis ; Regression tree ; Soil acid phosphatase
Publicat a:	Frontiers in Big Data, Vol. 2 (January 2020) , art. 51, ISSN 2624-909X

DOI: 10.3389/fdata.2019.00051
PMID: 33693374

13 p, 1.5 MB

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