Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests ...Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests remain highly uncertain. It is critically important to determine the relative importance of different biotic and abiotic factors between plants and soil, particularly with respect to their influence on plant regrowth. Consequently,it is necessary to quantitatively characterize the dynamicspatiotemporal distribution of forest carbon sinks at a regional scale. This study used a large, long-term dataset in a boosted regression tree（BRT） model to determine the major components that quantitatively control forest biomass increments in a mid-subtropical forested region（Wuyishan National Nature Reserve, China）. Long-term,stand-level data were used to derive the forest biomass increment, with the BRT model being applied to quantify the relative contributions of various biotic and abiotic variables to forest biomass increment. Our data show that total biomass（t） increased from 4.62 9 106 to 5.30 9 106 t between 1988 and 2010, and that the mean biomass increased from 80.19 ± 0.39 t ha-1（mean ± standard error） to 94.33 ± 0.41 t ha-1in the study region. The major factors that controlled biomass（in decreasing order of importance） were the stand, topography, and soil. Stand density was initially the most important stand factor, while elevation was the most important topographic factor. Soil factors were important for forest biomass increment but have a much weaker influence compared to the other two controlling factors. These results provide baseline information about the practical utility of spatial interpolationmethods for mapping forest biomass increments at regional scales.展开更多
Background: Over the last decades, many forest simulators have been developed for the forests of individual European countries. The underlying growth models are usually based on national datasets of varying size, obta...Background: Over the last decades, many forest simulators have been developed for the forests of individual European countries. The underlying growth models are usually based on national datasets of varying size, obtained from National Forest Inventories or from long-term research plots. Many of these models include country-and location-specific predictors, such as site quality indices that may aggregate climate, soil properties and topography effects. Consequently, it is not sensible to compare such models among countries, and it is often impossible to apply models outside the region or country they were developed for. However, there is a clear need for more generically applicable but still locally accurate and climate sensitive simulators at the European scale, which requires the development of models that are applicable across the European continent. The purpose of this study is to develop tree diameter increment models that are applicable at the European scale, but still locally accurate. We compiled and used a dataset of diameter increment observations of over 2.3 million trees from 10 National Forest Inventories in Europe and a set of 99 potential explanatory variables covering forest structure, weather, climate, soil and nutrient deposition.Results: Diameter increment models are presented for 20 species/species groups. Selection of explanatory variables was done using a combination of forward and backward selection methods. The explained variance ranged from10% to 53% depending on the species. Variables related to forest structure(basal area of the stand and relative size of the tree) contributed most to the explained variance, but environmental variables were important to account for spatial patterns. The type of environmental variables included differed greatly among species.Conclusions: The presented diameter increment models are the first of their kind that are applicable at the European scale. This is an important step towards the development of a new generation of forest development simulators that can be applied at the European scale, but that are sensitive to variations in growing conditions and applicable to a wider range of management systems than before. This allows European scale but detailed analyses concerning topics like CO2 sequestration, wood mobilisation, long term impact of management, etc.展开更多
The aim of this study was to estimate a basal area growth model for individual trees in uneven-aged Caspian forests.A survey was conducted in order to find a natural forest without any harvesting activities,a so call...The aim of this study was to estimate a basal area growth model for individual trees in uneven-aged Caspian forests.A survey was conducted in order to find a natural forest without any harvesting activities,a so called ＇untouched forest＇ and an area was selected from the Iranian Caspian forest.Three sample plots in the same aspect and of the same forest type were selected.In each plot,total tree height,diameter at breast height,distance of neighbor trees and azimuth were measured.Thirty trees were selected and drilled with increment borer to determine the increment model.Regression analysis was used to estimate the growth model.Results show that,for individual trees,there is a significant nonlinear relationship between the annual basal area increment,as the dependent variable,and the basal area.The results also show that the basal area of competing trees has a positive influence on growth.That the increment is higher with more competing neighboring trees is possibly because plots with higher volume per hectare and more competition,most likely also have higher site index or better soil or better site productivity than the plot with lower volume per hectare.展开更多
Based on the research of predictingβ-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predictβ-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 ...Based on the research of predictingβ-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predictβ-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and thefixed-length pattern of 12 amino acids are selected. When using the same characteristic parameters and the same test method, Random Forest algorithm is more effective than Support Vector Machine. In addition, because of Random Forest algorithm doesn’t produce overfitting phenomenon while the dimension of characteristic parameters is higher, we use Random Forest based on higher dimension characteristic parameters to predictβ-hairpin motifs. The better prediction results are obtained;the overall accuracy and Matthew’s correlation coefficient of 5-fold cross-validation achieve 83.3% and 0.59, respectively.展开更多
基金supported by National Forestry Public Welfare Foundation of China(201304205)National Science Foundation of China(31470578 and 31200363)+2 种基金Fujian Provincial Department of S&T Project(2016Y0083,2013YZ0001-1,2014J05044 and 2015Y0083)Xiamen Municipal Department of Science and Technology(3502Z20130037 and 3502Z20142016)Youth Innovation Promotion Association CAS
文摘Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests remain highly uncertain. It is critically important to determine the relative importance of different biotic and abiotic factors between plants and soil, particularly with respect to their influence on plant regrowth. Consequently,it is necessary to quantitatively characterize the dynamicspatiotemporal distribution of forest carbon sinks at a regional scale. This study used a large, long-term dataset in a boosted regression tree（BRT） model to determine the major components that quantitatively control forest biomass increments in a mid-subtropical forested region（Wuyishan National Nature Reserve, China）. Long-term,stand-level data were used to derive the forest biomass increment, with the BRT model being applied to quantify the relative contributions of various biotic and abiotic variables to forest biomass increment. Our data show that total biomass（t） increased from 4.62 9 106 to 5.30 9 106 t between 1988 and 2010, and that the mean biomass increased from 80.19 ± 0.39 t ha-1（mean ± standard error） to 94.33 ± 0.41 t ha-1in the study region. The major factors that controlled biomass（in decreasing order of importance） were the stand, topography, and soil. Stand density was initially the most important stand factor, while elevation was the most important topographic factor. Soil factors were important for forest biomass increment but have a much weaker influence compared to the other two controlling factors. These results provide baseline information about the practical utility of spatial interpolationmethods for mapping forest biomass increments at regional scales.
基金funded by the SIMWOOD project(Grant Agreement No.613762)of the EU H2020 Programmefacilitated by the Alter For project(Grant Agreement No.676754)+3 种基金the VERIFY project(Grant Agreement No.776810)Co-funding was received from the topsector Agri&Food under No.AF-EU-15002The Dutch National Forest Inventory is funded by the Ministry of Economic AffairsThe regional forest inventory in Piemonte was produced with the support of EU structural funds
文摘Background: Over the last decades, many forest simulators have been developed for the forests of individual European countries. The underlying growth models are usually based on national datasets of varying size, obtained from National Forest Inventories or from long-term research plots. Many of these models include country-and location-specific predictors, such as site quality indices that may aggregate climate, soil properties and topography effects. Consequently, it is not sensible to compare such models among countries, and it is often impossible to apply models outside the region or country they were developed for. However, there is a clear need for more generically applicable but still locally accurate and climate sensitive simulators at the European scale, which requires the development of models that are applicable across the European continent. The purpose of this study is to develop tree diameter increment models that are applicable at the European scale, but still locally accurate. We compiled and used a dataset of diameter increment observations of over 2.3 million trees from 10 National Forest Inventories in Europe and a set of 99 potential explanatory variables covering forest structure, weather, climate, soil and nutrient deposition.Results: Diameter increment models are presented for 20 species/species groups. Selection of explanatory variables was done using a combination of forward and backward selection methods. The explained variance ranged from10% to 53% depending on the species. Variables related to forest structure(basal area of the stand and relative size of the tree) contributed most to the explained variance, but environmental variables were important to account for spatial patterns. The type of environmental variables included differed greatly among species.Conclusions: The presented diameter increment models are the first of their kind that are applicable at the European scale. This is an important step towards the development of a new generation of forest development simulators that can be applied at the European scale, but that are sensitive to variations in growing conditions and applicable to a wider range of management systems than before. This allows European scale but detailed analyses concerning topics like CO2 sequestration, wood mobilisation, long term impact of management, etc.
文摘The aim of this study was to estimate a basal area growth model for individual trees in uneven-aged Caspian forests.A survey was conducted in order to find a natural forest without any harvesting activities,a so called ＇untouched forest＇ and an area was selected from the Iranian Caspian forest.Three sample plots in the same aspect and of the same forest type were selected.In each plot,total tree height,diameter at breast height,distance of neighbor trees and azimuth were measured.Thirty trees were selected and drilled with increment borer to determine the increment model.Regression analysis was used to estimate the growth model.Results show that,for individual trees,there is a significant nonlinear relationship between the annual basal area increment,as the dependent variable,and the basal area.The results also show that the basal area of competing trees has a positive influence on growth.That the increment is higher with more competing neighboring trees is possibly because plots with higher volume per hectare and more competition,most likely also have higher site index or better soil or better site productivity than the plot with lower volume per hectare.
文摘Based on the research of predictingβ-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predictβ-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and thefixed-length pattern of 12 amino acids are selected. When using the same characteristic parameters and the same test method, Random Forest algorithm is more effective than Support Vector Machine. In addition, because of Random Forest algorithm doesn’t produce overfitting phenomenon while the dimension of characteristic parameters is higher, we use Random Forest based on higher dimension characteristic parameters to predictβ-hairpin motifs. The better prediction results are obtained;the overall accuracy and Matthew’s correlation coefficient of 5-fold cross-validation achieve 83.3% and 0.59, respectively.
