The authors investigated the genetic diversity of 29 natural populations representing Pinus yunnanensis Franch. and its two close relatives, P. densata Mast. and P. kesiya Royle ex Gordn. var. langbianensis (A Chey.) ...The authors investigated the genetic diversity of 29 natural populations representing Pinus yunnanensis Franch. and its two close relatives, P. densata Mast. and P. kesiya Royle ex Gordn. var. langbianensis (A Chey.) Gaussen. Horizontal starch gel electrophoresis was performed for macrogametophytes collected from populations in Yunnan, Sichuan and Guangxi. Allozyme data for 33 loci of 14 enzymes demonstrated high levels of genetic variation at both population and species levels in comparison with other conifers, with the mean values for populations being P=0.694, A =2.0 and He =0.145 for P. yunnanensis; P=0.714, A=2.0 and He =0.174 for P. densata ; and P=0.758, A=2.1 and He =0.184 for P. kesiya var. langbianensis. Based on Wright’s F _statistics, the fixation index of P. yunnanensis, P. densata and P. kesiya var. langbianensis were 0.101, 0.054 and 0.143, respectively, indicating that the populations were largely under random mating. Based on Nei’s genetic distance, the genetic differentiation was not obvious among the three species (i.e. the genetic distance was less than 0.075). Because of the wider distribution of P. yunnanensis with greater variety of habitats, it was shown that the genetic differentiation among the P. yunnanensis populations was larger than that of the populations of the other two species. According to morphological, geographic and allozymic evidences, the authors suggested that the three species be better treated as varieties under a single species. In addition, the extensive gene flow among the three pine species resulted in great genetic diversity and evolutionary potential. Also, high level of genetic variation of P. yunnanensis provides important basis for its genetic improvement and breeding in future.展开更多
Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cyc...Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass 展开更多
文摘The authors investigated the genetic diversity of 29 natural populations representing Pinus yunnanensis Franch. and its two close relatives, P. densata Mast. and P. kesiya Royle ex Gordn. var. langbianensis (A Chey.) Gaussen. Horizontal starch gel electrophoresis was performed for macrogametophytes collected from populations in Yunnan, Sichuan and Guangxi. Allozyme data for 33 loci of 14 enzymes demonstrated high levels of genetic variation at both population and species levels in comparison with other conifers, with the mean values for populations being P=0.694, A =2.0 and He =0.145 for P. yunnanensis; P=0.714, A=2.0 and He =0.174 for P. densata ; and P=0.758, A=2.1 and He =0.184 for P. kesiya var. langbianensis. Based on Wright’s F _statistics, the fixation index of P. yunnanensis, P. densata and P. kesiya var. langbianensis were 0.101, 0.054 and 0.143, respectively, indicating that the populations were largely under random mating. Based on Nei’s genetic distance, the genetic differentiation was not obvious among the three species (i.e. the genetic distance was less than 0.075). Because of the wider distribution of P. yunnanensis with greater variety of habitats, it was shown that the genetic differentiation among the P. yunnanensis populations was larger than that of the populations of the other two species. According to morphological, geographic and allozymic evidences, the authors suggested that the three species be better treated as varieties under a single species. In addition, the extensive gene flow among the three pine species resulted in great genetic diversity and evolutionary potential. Also, high level of genetic variation of P. yunnanensis provides important basis for its genetic improvement and breeding in future.
基金supported by the State Forestry Administration of China under the national forestry commonwealth project grant#201404309the Expert Workstation of Academician Tang Shouzheng of Yunnan Province,the Yunnan provincial key project of Forestrythe Research Center of Kunming Forestry Information Engineering Technology
文摘Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass
