The total biomass of a stand is an indicator of stand productivity and is closely related to the density of plants. According to the self-thinning law, mean individual biomass follows a negative power law with plant d...The total biomass of a stand is an indicator of stand productivity and is closely related to the density of plants. According to the self-thinning law, mean individual biomass follows a negative power law with plant density. If the variance of individual biomass is constant, we can expect increased stand productivity with increasing plant density. However, Taylor's power law(TPL) that relates the variance and the mean of many biological measures(e.g. bilateral areal differences of a leaf, plant biomass atdifferent times, developmental rates at different temperatures, population densities on different spatial or temporal scales), affects the estimate of stand productivity when it is defined as the total biomass of large plants in a stand.Because the variance of individual biomass decreases faster than mean individual biomass, differences in individual biomass decline with increasing density, leading to more homogeneous timbers of greater economic value. We tested whether TPL in plant biomass holds for different species and whether the variance of individual biomass changes faster than the mean with increasing stand density.The height, ground diameter and fresh weight of 50 bamboo species were measured in 50 stands ranging from 1 m by 1 m to 30 m by 30 m to ensure more than 150 bamboos in every stand. We separately examined TPL in height,ground diameter, and weight, and found that TPL holds for all three biological measures, with the relationship strongest for weight. Using analysis of covariance to compare the regression slopes of logarithmic mean and variance against the logarithm of density, we found that the variance in individual biomass declined faster than the mean with increasing density. This suggests that dense planting reduced mean individual biomass but homogenized individual biomass. Thus, there exists a trade-off between effective stand productivity and stand density for optimal forest management. Sparse planting leads to large variation in individual biomass, whereas dense planting reduces mean individual biomass. Consequently, stand density for a plantation should be set based on this trade-off with reference to market demands.展开更多
Recent advances in spectral sensing techniques and machine learning(ML)methods have enabled the estimation of plant physiochemical traits.Nitrogen(N)is a primary limiting factor for terrestrial forest growth,but tradi...Recent advances in spectral sensing techniques and machine learning(ML)methods have enabled the estimation of plant physiochemical traits.Nitrogen(N)is a primary limiting factor for terrestrial forest growth,but traditional methods for N determination are labor-intensive,time-consuming,and destructive.In this study,we present a rapid,non-destructive method to predict leaf N concentration(LNC)in Metasequoia glyptostroboides plantations under N and phosphorus(P)fertilization using ML techniques and unmanned aerial vehicle(UAV)-based RGB(red,green,blue)images.Nine spectral vegetation indices(VIs)were extracted from the RGB images.The spectral reflectance and VIs were used as input features to construct models for estimating LNC based on support vector machine,ran-dom forest(RF),and multiple linear regression,gradient boosting regression and classification and regression trees(CART).The results show that RF is the best fitting model for estimating LNC with a coefficient of determination(R2)of 0.73.Using this model,we evaluated the effects of N and P treatments on LNC and found a significant increase with N and a decrease with P.Height,diameter at breast height(DBH),and crown width of all M.glyptostroboides were analyzed by Pearson correlation with the predicted LNC.DBH was significantly correlated with LNC under N treat-ment.Our results highlight the potential of combining UAV RGB images with an ML algorithm as an efficient,scalable,and cost-effective method for LNC quantification.Future research can extend this approach to different tree species and different plant traits,paving the way for large-scale,time-efficient plant growth monitoring.展开更多
Soil and climatic conditions are known to have close associations with plant morphological and stoichiometric traits at a regional scale along latitudinal gradients;however,how latitude drives biotic and abiotic facto...Soil and climatic conditions are known to have close associations with plant morphological and stoichiometric traits at a regional scale along latitudinal gradients;however,how latitude drives biotic and abiotic factors affecting plant nutrient acquisition to accommodate environmental nutrient deficiency remains unclear.We quantified soil,root,leaf,and leaf litter nitrogen(N)and phosphorus(P)concentrations to determine the potentially limiting nutrient and the simultaneous responses of root capture and leaf resorption to nutrient deficiency in seven Quercus acutissima forests across the North–South Transect of Eastern China.The results showed that the mean leaf and root N:P ratios in Q.acutissima were 21.58 and 20.23,respectively,which markedly exceeded the P limitation threshold of 16 for terrestrial plants.The mean leaf litter N and P were 10.63 mg/g and 0.51 mg/g,respectively,indicating that P resorption proficiency was relatively higher than N resorption proficiency.N displayed higher stoichiometric homeostasis than P in the leaf.The leaf and root N:P ratios showed a quadratic variation that first decreased and then increased as latitude increased,whereas the phosphorus resorption efficiency and root-soil accumulation factor of P displayed the opposite trend.Partial least square path modeling(PLS-PM)analysis demonstrated that root nutrient capture and leaf nutrient resorption were regulated by different influential factors.Overall,these findings provide new insights into plant strategies to adapt to environmental nutrient deficiency,as well as the scientific basis for predicting the spatial and temporal patterns of nutrient acquisition in the context of climate change.展开更多
基金supported by the National Natural Science Foundation of China(31870575)the Key Project of National Science&Technology Ministry(No.2015BAD04B02)the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘The total biomass of a stand is an indicator of stand productivity and is closely related to the density of plants. According to the self-thinning law, mean individual biomass follows a negative power law with plant density. If the variance of individual biomass is constant, we can expect increased stand productivity with increasing plant density. However, Taylor's power law(TPL) that relates the variance and the mean of many biological measures(e.g. bilateral areal differences of a leaf, plant biomass atdifferent times, developmental rates at different temperatures, population densities on different spatial or temporal scales), affects the estimate of stand productivity when it is defined as the total biomass of large plants in a stand.Because the variance of individual biomass decreases faster than mean individual biomass, differences in individual biomass decline with increasing density, leading to more homogeneous timbers of greater economic value. We tested whether TPL in plant biomass holds for different species and whether the variance of individual biomass changes faster than the mean with increasing stand density.The height, ground diameter and fresh weight of 50 bamboo species were measured in 50 stands ranging from 1 m by 1 m to 30 m by 30 m to ensure more than 150 bamboos in every stand. We separately examined TPL in height,ground diameter, and weight, and found that TPL holds for all three biological measures, with the relationship strongest for weight. Using analysis of covariance to compare the regression slopes of logarithmic mean and variance against the logarithm of density, we found that the variance in individual biomass declined faster than the mean with increasing density. This suggests that dense planting reduced mean individual biomass but homogenized individual biomass. Thus, there exists a trade-off between effective stand productivity and stand density for optimal forest management. Sparse planting leads to large variation in individual biomass, whereas dense planting reduces mean individual biomass. Consequently, stand density for a plantation should be set based on this trade-off with reference to market demands.
基金supported by the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(2022C02053)National Natural Science Foundation of China(NSFC)(32201632).
文摘Recent advances in spectral sensing techniques and machine learning(ML)methods have enabled the estimation of plant physiochemical traits.Nitrogen(N)is a primary limiting factor for terrestrial forest growth,but traditional methods for N determination are labor-intensive,time-consuming,and destructive.In this study,we present a rapid,non-destructive method to predict leaf N concentration(LNC)in Metasequoia glyptostroboides plantations under N and phosphorus(P)fertilization using ML techniques and unmanned aerial vehicle(UAV)-based RGB(red,green,blue)images.Nine spectral vegetation indices(VIs)were extracted from the RGB images.The spectral reflectance and VIs were used as input features to construct models for estimating LNC based on support vector machine,ran-dom forest(RF),and multiple linear regression,gradient boosting regression and classification and regression trees(CART).The results show that RF is the best fitting model for estimating LNC with a coefficient of determination(R2)of 0.73.Using this model,we evaluated the effects of N and P treatments on LNC and found a significant increase with N and a decrease with P.Height,diameter at breast height(DBH),and crown width of all M.glyptostroboides were analyzed by Pearson correlation with the predicted LNC.DBH was significantly correlated with LNC under N treat-ment.Our results highlight the potential of combining UAV RGB images with an ML algorithm as an efficient,scalable,and cost-effective method for LNC quantification.Future research can extend this approach to different tree species and different plant traits,paving the way for large-scale,time-efficient plant growth monitoring.
文摘Soil and climatic conditions are known to have close associations with plant morphological and stoichiometric traits at a regional scale along latitudinal gradients;however,how latitude drives biotic and abiotic factors affecting plant nutrient acquisition to accommodate environmental nutrient deficiency remains unclear.We quantified soil,root,leaf,and leaf litter nitrogen(N)and phosphorus(P)concentrations to determine the potentially limiting nutrient and the simultaneous responses of root capture and leaf resorption to nutrient deficiency in seven Quercus acutissima forests across the North–South Transect of Eastern China.The results showed that the mean leaf and root N:P ratios in Q.acutissima were 21.58 and 20.23,respectively,which markedly exceeded the P limitation threshold of 16 for terrestrial plants.The mean leaf litter N and P were 10.63 mg/g and 0.51 mg/g,respectively,indicating that P resorption proficiency was relatively higher than N resorption proficiency.N displayed higher stoichiometric homeostasis than P in the leaf.The leaf and root N:P ratios showed a quadratic variation that first decreased and then increased as latitude increased,whereas the phosphorus resorption efficiency and root-soil accumulation factor of P displayed the opposite trend.Partial least square path modeling(PLS-PM)analysis demonstrated that root nutrient capture and leaf nutrient resorption were regulated by different influential factors.Overall,these findings provide new insights into plant strategies to adapt to environmental nutrient deficiency,as well as the scientific basis for predicting the spatial and temporal patterns of nutrient acquisition in the context of climate change.
