Many different factors,such as species traits,socio-economic factors,geographical and environmental factors,can lead to specimen collection preference.This study aims to determine whether grassland specimen collection...Many different factors,such as species traits,socio-economic factors,geographical and environmental factors,can lead to specimen collection preference.This study aims to determine whether grassland specimen collection in China is preferred by species traits(i.e.,plant height,flowering and fruiting period),environmental range(i.e.,the temperature and precipitation range)and geographical range(i.e.,distribution range and altitudinal range).Ordinary least squares models and phylogenetic generalized linear mixed models were used to analyze the relationships between specimen number and the explanatory variables.Random Forest models were then used to find the most parsimonious multivariate model.The results showed that interannual variation in specimen number between 1900 and 2020 was considerable.Specimen number of these species in southeast China was notably lower than that in northwest China.Environmental range and geographical range of species had significant positive correlations with specimen number.In addition,there were relatively weak but significant associations between specimen number and species trait(i.e.,plant height and flowering and fruiting period).Random Forest models indicated that distribution range was the most important variable,followed by flowering and fruiting period,and altitudinal range.These findings suggest that future floristic surveys should pay more attention to species with small geographical range,narrow environmental range,short plant height,and short flowering and fruiting period.The correction of specimen collection preference will also make the results of species distribution model,species evolution and other works based on specimen data more accurate.展开更多
Element cycling in the dominant plant communities including Rh. aureum, Rh. redowskianum and Vaccinium uliginosum in the Alpine tundra zone of Changbai Mountains in northeast China was studied. The results indicate th...Element cycling in the dominant plant communities including Rh. aureum, Rh. redowskianum and Vaccinium uliginosum in the Alpine tundra zone of Changbai Mountains in northeast China was studied. The results indicate that the amount of elements from litter decomposition was less than that of the plant uptake from soil, but that from plant uptake was higher than that in soil with mineralization process released. On the other hand, in the open system including precipitation input and soil leaching output, because of great number of elements from precipitation into the open system, the element cycling(except N, P) in the Alpine tundra ecosystem was in a dynamic balance. In this study, it was also found that different organ of plants had significant difference in accumulating elements. Ca, Mg, P and N were accumulated more obviously in leaves, while Fe was in roots. The degree of concentration of elements in different tissues of the same organ of the plants also was different, a higher concentration of Ca, Mg, P and N in mesophyll than in nerve but Fe was in a reversed order. The phenomenon indicates (1) a variety of biochemical functions of different elements, (2) the elements in mesophyll were with a shorter turnover period than those in nerve or fibre, but higher utilization rate for plant. Therefore, this study implies the significance of keeping element dynamic balance in the alpine tundra ecosystem of Changbai Mountains.展开更多
Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully...Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.展开更多
This study is aimed to investigate and analyze the vegetation landscape around Rivers of Hou-lung, Fa-tz, Da-li, Ching-shuei and Gau- ping, and to select the suitable plant species that could be applied for the area o...This study is aimed to investigate and analyze the vegetation landscape around Rivers of Hou-lung, Fa-tz, Da-li, Ching-shuei and Gau- ping, and to select the suitable plant species that could be applied for the area of riverbank ecological engineering in Taiwan. Studying the vegetation established the key point and procedure of ecological engineering in the riverside and revetment, to compile and edit the dominant plants' types, life form, propagating method, root systems' characteristics and functions for soil conservation. This research choses three dominant plants for roots strength test. The fitting models of plants pulling resistance(Rp, kg) between plant height (H, cm), diameter near ground (Dn, mm), diameter above ground 100 mm (Da, mm), The research finished the relative abundant, types and cluster analysis of riverbank dominant plants that generalize vegetative distribution and ecological restoration for different river types to apply and manage in Taiwan.展开更多
基金the Natural Science Foundation of Inner Mongolia,China(2023JQ01)the National Key R&D Program of China(2019YFA0607103)+2 种基金the Central Government Guides Local Science and Technology Development Fund Projects(2022ZY0224)the Open Project Program of Ministry of Education Key Laboratory of Ecology and Resources Use of the Mongolian Plateau,Hohhot,Inner Mongolia,China(KF2023003)Major Science and Technology Project of Inner Mongolia Autonomous Region:Monitoring,Assessment and Early Warning Technology Research of Biodiversity in Inner Mongolia(2021ZD0011)for financial support.
文摘Many different factors,such as species traits,socio-economic factors,geographical and environmental factors,can lead to specimen collection preference.This study aims to determine whether grassland specimen collection in China is preferred by species traits(i.e.,plant height,flowering and fruiting period),environmental range(i.e.,the temperature and precipitation range)and geographical range(i.e.,distribution range and altitudinal range).Ordinary least squares models and phylogenetic generalized linear mixed models were used to analyze the relationships between specimen number and the explanatory variables.Random Forest models were then used to find the most parsimonious multivariate model.The results showed that interannual variation in specimen number between 1900 and 2020 was considerable.Specimen number of these species in southeast China was notably lower than that in northwest China.Environmental range and geographical range of species had significant positive correlations with specimen number.In addition,there were relatively weak but significant associations between specimen number and species trait(i.e.,plant height and flowering and fruiting period).Random Forest models indicated that distribution range was the most important variable,followed by flowering and fruiting period,and altitudinal range.These findings suggest that future floristic surveys should pay more attention to species with small geographical range,narrow environmental range,short plant height,and short flowering and fruiting period.The correction of specimen collection preference will also make the results of species distribution model,species evolution and other works based on specimen data more accurate.
基金The National Natural Science Foundation of China(No. 90211003) and the Innovation Program of the Chinese Acdemy of Sciences(No. KZCX3 SW 332)
文摘Element cycling in the dominant plant communities including Rh. aureum, Rh. redowskianum and Vaccinium uliginosum in the Alpine tundra zone of Changbai Mountains in northeast China was studied. The results indicate that the amount of elements from litter decomposition was less than that of the plant uptake from soil, but that from plant uptake was higher than that in soil with mineralization process released. On the other hand, in the open system including precipitation input and soil leaching output, because of great number of elements from precipitation into the open system, the element cycling(except N, P) in the Alpine tundra ecosystem was in a dynamic balance. In this study, it was also found that different organ of plants had significant difference in accumulating elements. Ca, Mg, P and N were accumulated more obviously in leaves, while Fe was in roots. The degree of concentration of elements in different tissues of the same organ of the plants also was different, a higher concentration of Ca, Mg, P and N in mesophyll than in nerve but Fe was in a reversed order. The phenomenon indicates (1) a variety of biochemical functions of different elements, (2) the elements in mesophyll were with a shorter turnover period than those in nerve or fibre, but higher utilization rate for plant. Therefore, this study implies the significance of keeping element dynamic balance in the alpine tundra ecosystem of Changbai Mountains.
基金supported by the National Technology Extension Fund of Forestry,Forest Vegetation Carbon Storage Monitoring Technology Based on Watershed Algorithm ([2019]06)Fundamental Research Funds for the Central Universities (No.PTYX202107).
文摘Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.
基金Taiwan Science Council Research Project (94- 2313-B-235-001)
文摘This study is aimed to investigate and analyze the vegetation landscape around Rivers of Hou-lung, Fa-tz, Da-li, Ching-shuei and Gau- ping, and to select the suitable plant species that could be applied for the area of riverbank ecological engineering in Taiwan. Studying the vegetation established the key point and procedure of ecological engineering in the riverside and revetment, to compile and edit the dominant plants' types, life form, propagating method, root systems' characteristics and functions for soil conservation. This research choses three dominant plants for roots strength test. The fitting models of plants pulling resistance(Rp, kg) between plant height (H, cm), diameter near ground (Dn, mm), diameter above ground 100 mm (Da, mm), The research finished the relative abundant, types and cluster analysis of riverbank dominant plants that generalize vegetative distribution and ecological restoration for different river types to apply and manage in Taiwan.