Accurate understanding of global photosynthetic capacity(i.e.maximum RuBisCO carboxylation rate,Vc,max)variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cy...Accurate understanding of global photosynthetic capacity(i.e.maximum RuBisCO carboxylation rate,Vc,max)variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cycles with climate change,but a holistic understanding and assessment remains lacking.Here we hypothesized that V_(c,max)was dictated by both factors of temperature-associated enzyme kinetics(capturing instantaneous ecophysiological responses)and the amount of activated RuBisCO(indexed by V_(c,max)standardized at 25℃,V_(c,max25)),and compiled a comprehensive global dataset(n=7339 observations from 428 sites)for hypothesis testing.The photosynthesis data were derived from leaf gas exchange measurements using portable gas exchange systems.We found that a semi-empirical statistical model considering both factors explained 78%of global V_(c,max)variability,followed by 55%explained by enzyme kinetics alone.This statistical model outperformed the current theoretical optimality model for predicting global V_(c,max)variability(67%),primarily due to its poor characterization on global V_(c,max25)variability(3%).Further,we demonstrated that,in addition to climatic variables,belowground resource constraint on photosynthetic machinery built-up that directly structures the biogeography of V_(c,max25)was a key missing mechanism for improving the theoretical modelling of global V_(c,max)variability.These findings improve the mechanistic understanding of global V_(c,max)variability and provide an important basis to benchmark process-based models of terrestrial photosynthesis and carbon cycling under climate change.展开更多
Soil thickness determines the soil productivity in the black soil region of northeast China,which is important for national food security.Existing information on the spatial variation of black soil thickness is inadeq...Soil thickness determines the soil productivity in the black soil region of northeast China,which is important for national food security.Existing information on the spatial variation of black soil thickness is inadequate.In this paper,we propose a model framework for spatial estimation of the black soil thickness at the watershed scale by integrating field observations,unmanned aerial vehicle variations of topography,and satellite variations of vegetation with the aid of random forest.We sampled 141 sample profiles over a watershed and identified the black soil thickness based on indices of the mollic epipedon.Topographic variables were derived from a digital elevation model and vegetation variables were derived from Landsat 8 imagery.Random forest was used to determine the relationship between black soil thickness and environmental variables.The resulting model explained 61%of the black soil thickness spatial variation,which was more than twice that of traditional interpolation methods(ordinary kriging,universal kriging and inverse distance weighting).Topographic variables contributed the most toward explaining the thickness,followed by vegetation indices.The black soil thickness over the watershed had a clear catenary soil pattern,with thickest black soil in the low depositional areas and thinnest at the higher elevations that drain into the low areas.The proposed model framework will improve estimates of soil thickness in the region of our study.展开更多
基金supported by National Natural Science Foundation of China(31922090 and 31901086)Hong Kong Research Grant Council Early Career Scheme(27306020)+4 种基金HKU Seed Fund for Basic Research(201905159005 and 202011159154)supported by the Innovation and Technology Fund(funding support to State Key Laboratories in Hong Kong of Agorobiotechnology)of the HKSAR,Chinasupported by the Carbon Mitigation Initiative of the Princeton Universitysupport from the National Science Foundation(DEB-2045968)Texas Tech University.
文摘Accurate understanding of global photosynthetic capacity(i.e.maximum RuBisCO carboxylation rate,Vc,max)variability is critical for improved simulations of terrestrial ecosystem photosynthesis metabolisms and carbon cycles with climate change,but a holistic understanding and assessment remains lacking.Here we hypothesized that V_(c,max)was dictated by both factors of temperature-associated enzyme kinetics(capturing instantaneous ecophysiological responses)and the amount of activated RuBisCO(indexed by V_(c,max)standardized at 25℃,V_(c,max25)),and compiled a comprehensive global dataset(n=7339 observations from 428 sites)for hypothesis testing.The photosynthesis data were derived from leaf gas exchange measurements using portable gas exchange systems.We found that a semi-empirical statistical model considering both factors explained 78%of global V_(c,max)variability,followed by 55%explained by enzyme kinetics alone.This statistical model outperformed the current theoretical optimality model for predicting global V_(c,max)variability(67%),primarily due to its poor characterization on global V_(c,max25)variability(3%).Further,we demonstrated that,in addition to climatic variables,belowground resource constraint on photosynthetic machinery built-up that directly structures the biogeography of V_(c,max25)was a key missing mechanism for improving the theoretical modelling of global V_(c,max)variability.These findings improve the mechanistic understanding of global V_(c,max)variability and provide an important basis to benchmark process-based models of terrestrial photosynthesis and carbon cycling under climate change.
基金supported by the National Key R&D Program of China(Grant Nos.2018YFC0507006).
文摘Soil thickness determines the soil productivity in the black soil region of northeast China,which is important for national food security.Existing information on the spatial variation of black soil thickness is inadequate.In this paper,we propose a model framework for spatial estimation of the black soil thickness at the watershed scale by integrating field observations,unmanned aerial vehicle variations of topography,and satellite variations of vegetation with the aid of random forest.We sampled 141 sample profiles over a watershed and identified the black soil thickness based on indices of the mollic epipedon.Topographic variables were derived from a digital elevation model and vegetation variables were derived from Landsat 8 imagery.Random forest was used to determine the relationship between black soil thickness and environmental variables.The resulting model explained 61%of the black soil thickness spatial variation,which was more than twice that of traditional interpolation methods(ordinary kriging,universal kriging and inverse distance weighting).Topographic variables contributed the most toward explaining the thickness,followed by vegetation indices.The black soil thickness over the watershed had a clear catenary soil pattern,with thickest black soil in the low depositional areas and thinnest at the higher elevations that drain into the low areas.The proposed model framework will improve estimates of soil thickness in the region of our study.
