Models that predict a forest stand’s evolution are essential for developing plans for sustainable management.A simple mathematical framework was developed that con-siders the individual tree and stand basal area unde...Models that predict a forest stand’s evolution are essential for developing plans for sustainable management.A simple mathematical framework was developed that con-siders the individual tree and stand basal area under random resource competition and is based on two assumptions:(1)a sigmoid-type stochastic process governs tree and stand basal area dynamics of living and dying trees,and(2)the total area that a tree may potentially occupy determines the number of trees per hectare.The most effective method to satisfy these requirements is formalizing each tree diameter and potentially occupied area using Gompertz-type stochastic differential equations governed by fixed and mixed-effect parameters.Data from permanent experimental plots from long-term Lithuania experiments were used to construct the tree and stand basal area models.The new models were relatively unbiased for live trees of all species,including silver birch(Betula pen-dula Roth)and downy birch(Betula pubescens Ehrh.),[spruce(Picea abies),and pine(Pinus sylvestris)].Less reliable predic-tions were made for the basal area of dying trees.Pines gave the highest accuracy prediction of mean basal area among all live trees.The mean basal area prediction for all dying trees was lower than that for live trees.Among all species,pine also had the best average basal area prediction accuracy for live trees.Newly developed basal area growth and yield models can be recommended despite their complex formulation and implementation challenges,particularly in situations when data is scarce.This is because the newly observed plot provides sufficient information to calibrate random effects.展开更多
Growth and yield modeling has a long history in forestry. The methods of measuring the growth of stand basal area have evolved from those developed in the U.S.A. and Germany during the last century. Stand basal area m...Growth and yield modeling has a long history in forestry. The methods of measuring the growth of stand basal area have evolved from those developed in the U.S.A. and Germany during the last century. Stand basal area modeling has progressed rapidly since the first widely used model was published by the U.S. Forest Service. Over the years, a variety of models have been developed for predicting the growth and yield of uneven/even-aged stands using stand-level approaches. The modeling methodology has not only moved from an empirical approach to a more ecological process-based approach but also accommodated a variety of techniques such as： 1） simultaneous equation methods, 2） difference models, 3） artificial neural network techniques, 4） linear/nonlinear regression models, and 5） matrix models. Empirical models using statistical methods were developed to reproduce accurately and precisely field observations. In contrast, process models have a shorter history, developed originally as research and education tools with the aim of increasing the understanding of cause and effect relationships. Empirical and process models can be married into hybrid models in which the shortcomings of both component approaches can, to some extent, be overcome. Algebraic difference forms of stand basal area models which consist of stand age, stand density and site quality can fully describe stand growth dynamics. This paper reviews the current literature regarding stand basal area models, discusses the basic types of models and their merits and outlines recent progress in modeling growth and dynamics of stand basal area. Future trends involving algebraic difference forms, good fitting variables and model types into stand basal area modeling strategies are discussed.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
Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management...Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.展开更多
Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function,non-Gaussian distributions,multicollinearit...Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function,non-Gaussian distributions,multicollinearity,outliers and noise in the data.The problems of backpropagation models using artificial neural networks include determination of the structure of the network and overlearning courses.According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province,a back-propagation artificial neural network model(BPANN)and a support vector machine model(SVM)for basal area of Chinese fir(Cunninghamia lanceolata)plantations were constructed using four kinds of prediction factors,including stand age,site index,surviving stem numbers and quadratic mean diameters.Artificial intelligence methods,especially SVM,could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models.SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.展开更多
The objective of this study is to evaluate the capability of satellite imagery for the estimation of basal area in Northern Zagros Forests. The data of the high resolution geometric (HRG) sensor of SPOT-5 satellite da...The objective of this study is to evaluate the capability of satellite imagery for the estimation of basal area in Northern Zagros Forests. The data of the high resolution geometric (HRG) sensor of SPOT-5 satellite dated in July 2005 were used. Investigation of the quality of Satellite images shows that these images have no radiometric distortion. Overlaying of geocoded images with the digital topographic maps indicated that the images have high geometric precision. A number of 319 circular plots (0.1 ha) were established using systematic random method in the study area. All trees having diameter at breast height (DBH) (i.e. 1.3 m above ground) greater than 5 cm were callipered in each plot. Basal area in each plot was determined using field data. Main bands, artificial bands such as vegetation indices and principle component analysis (PCA) were studied. Digital numbers related to each plot were extracted from original and artificial bands. All plots were ordinated by major geographic aspects and the best fitted regression models were determined for both the study area without consideration of aspects and with consideration of major geographic aspects by multiple regression analysis (step wise regression). The results from regression analysis indicated that the square root of basal area without consideration of aspects has a high correlation with band B1 (r = –0.60). The consideration of aspects resulted in correlation of different indices with square root of basal area such that in northern forests, band B1 had higher correlation coefficient(r = –0.67) among other indices. In Eastern forests, the same band showed correlation of basal area with different correlation coefficient (r = –0.65). In southern and western forests, the square root of basal area had higher correlation (r = –0.68) with RVI. The use of the square root of basal area as a dependent variable in multivariate linear regression improved the results. The assessment of model validity indicated that the proposed models are properly valid.展开更多
Bitterlich sampling is an extensively used technique in worldwide forest inventories. Although it has been proved that estimates of basal area from Bitterlich sampling are mathematically unbiased, its precision for in...Bitterlich sampling is an extensively used technique in worldwide forest inventories. Although it has been proved that estimates of basal area from Bitterlich sampling are mathematically unbiased, its precision for individual forest stands may be fairly poor. An extension of validation efforts to different forest biomes could therefore provide more comprehensive assessment and understanding of the Bitterlich sampling technique. In this study, this technique was quantitatively evaluated by using simulated sparse boreal forests and dense tropical forests from an empirical forest structure model (EFSM). Theoretical estimation of basal areas and practical estimation influenced by the hidden-tree effect were both compared with true basal areas of the simulated forests. The evaluation results indicated that: 1) Bitterlich sampling can yield acceptable accuracy and precision when the count number (CN) of trees was set to 10 for the studied boreal and tropical forests with distinct characteristics, 2) the theoretical estimation of basal area can be improved by increasing the CN values for both forests, and 3) when the hidden-tree effect is encountered, the accuracy for tropical forests will be decreased by increasing the CN values, whereas the accuracy for boreal forests can still be improved. Accordingly, a relatively high CN, at a reasonable cost, is recommended for sparse boreal forests to improve the accuracy of basal area estimation. In contrast, for dense tropical forests, a CN of ten is appropriate to mitigate the hidden-tree effect.展开更多
Nonlinear mixed effects model（NLMEM） is based on the relationship between the fixed and random effects in the regression function.The NLMEM has a competitive advantage in analyzing repeated measures data,the longitu...Nonlinear mixed effects model（NLMEM） is based on the relationship between the fixed and random effects in the regression function.The NLMEM has a competitive advantage in analyzing repeated measures data,the longitudinal data and multilevel data.This paper chose two kinds of two-level nonlinear mixed model to analyze basal area growth for Chinese Fir（Cunninghamia lanceolata）. Model 1 is a general two-level NLMEM and Model 2 is based on Model 1 to further consider the fixed effects parameters changes with a specific factor. Firstly,through the analysis of these two models, this paper defined the basic model to build the two-level NLMEM.Secondly,665 kinds of models derived from Model 1 and 2 703 kinds of models derived from Model 2 were calculated and compared. The results showed that:for Model 1,there were 57 kinds of models converging,and when the formal parameter b<sub>0</sub> considered the block effects and plot effects,b<sub>1</sub> and b<sub>4</sub> only considered the block effects, the model fitted the best;and for Model 2,there were 24 kinds of model converging,and when the formal parameter bs considered the block effects and plot effects,b<sub>1</sub> only considered block effects and the fixed effects b<sub>0</sub> changed with any level of block level, Model 2 fitted the best.Finally,by comparing the traditional nonlinear regression model,Model 1 and Model 2,the results showed that Model 1 and Model 2 fitted better than the traditional nonlinear regression, and Model 2 was best fitting model.展开更多
The amygdaloid complex receives information from all sensory systems, especially from vision. Some researches (Amaral DG, 2003) show that in the primate brain, the amygdala
Microbiological and physical\|chemical characteristics of subtropical forest, grassland and cropfield soils from the karst areas of Southwest China were investigated. The study revealed that the conversion of natural ...Microbiological and physical\|chemical characteristics of subtropical forest, grassland and cropfield soils from the karst areas of Southwest China were investigated. The study revealed that the conversion of natural forest to other forms of land would lead to a reduction in soil organic C(26.2%-35.3%), total N(37.2%-55.8%), total P(32.9%-43.6%), microbial biomass C(35.4%-49.1%), N(37.2%-55.8%), and P(25.8%-41.9%). Comparative analysis of microbial activity in terms of basal soil respiration showed maximum activity in forest soil and minimum in cropfield soil. Analysis of microbial metabolic respiratory activity indicated a relatively greater respiratory loss of CO\-2—C per unit microbial biomass in cropfield and grassland than in forest soil. Considering the importance of microbial components in soil, it is concluded that land use in different ways will lead to the reduction of biological stability of soil.展开更多
A tree's basal area(BA)and wood volume scale exponentially with tree diameter in species-specifc patterns.Recent observed increases in tree growth suggest these allometric relationships are shifting in response to...A tree's basal area(BA)and wood volume scale exponentially with tree diameter in species-specifc patterns.Recent observed increases in tree growth suggest these allometric relationships are shifting in response to climate change,rising CO_(2) levels,and/or changes in forest management.We analyzed 9,214 cores from nine conifer and 11 broadleaf species grown in managed mixed-species stands in the upper Midwest to quantify how well diameter(diameter at breast height(DBH))serves to predict BA growth and above-ground wood and carbon(C).These samples include many large trees.We ft mixed models to predict BA growth and above-ground biomass/C from diameter,tree height,and the BA of nearby trees while controlling for site effects.Models account for 55%–83%of the variance in log(recent growth),improving predictions over earlier models.Growth-diameter scaling exponents covary with certain leaf and stem(but not wood)functional traits,reflecting growth strategies.LogBA increment scales linearly with log(diameter)as trees grow bigger in 16/20 species and growth actually accelerates in Quercus rubra L.Three other species plateau in growth.Growth only decelerates in red pine,Pinus resinosa Ait.Growth in whole-tree,above-ground biomass,and C accelerate even more strongly with diameter(mean exponent:2.08 vs.1.30 for BA growth).Sustained BA growth and accelerating wood/C growth contradict the common assumption that tree growth declines in bigger trees.Yield tables and silvicultural guidelines should be updated to reflect these current relationships.Such revisions will favor delaying harvests in many managed stands to increase wood production and enhance ecosystem values including C fxation and storage.Further research may resolve the relative roles of thinning,climatic conditions,nitrogen inputs,and rising CO2 levels on changing patterns of tree growth.展开更多
基金supported by the Horizon Europe Framework Programme(HORIZON),call Teaming for Excellence(HORIZONWIDERA-2022-ACCESS-01-two-stage)-Creation of the Centre of Excellence in Smart Forestry“Forest 4.0”No.101059985″This research was cofunded by FOREST 4.0-“Ekscelencijos centras tvariai miško bioekonomikai vystyti”(Nr.10-042-P-0002).
文摘Models that predict a forest stand’s evolution are essential for developing plans for sustainable management.A simple mathematical framework was developed that con-siders the individual tree and stand basal area under random resource competition and is based on two assumptions:(1)a sigmoid-type stochastic process governs tree and stand basal area dynamics of living and dying trees,and(2)the total area that a tree may potentially occupy determines the number of trees per hectare.The most effective method to satisfy these requirements is formalizing each tree diameter and potentially occupied area using Gompertz-type stochastic differential equations governed by fixed and mixed-effect parameters.Data from permanent experimental plots from long-term Lithuania experiments were used to construct the tree and stand basal area models.The new models were relatively unbiased for live trees of all species,including silver birch(Betula pen-dula Roth)and downy birch(Betula pubescens Ehrh.),[spruce(Picea abies),and pine(Pinus sylvestris)].Less reliable predic-tions were made for the basal area of dying trees.Pines gave the highest accuracy prediction of mean basal area among all live trees.The mean basal area prediction for all dying trees was lower than that for live trees.Among all species,pine also had the best average basal area prediction accuracy for live trees.Newly developed basal area growth and yield models can be recommended despite their complex formulation and implementation challenges,particularly in situations when data is scarce.This is because the newly observed plot provides sufficient information to calibrate random effects.
基金This study was supported by the National Natural Science Foundation of China （Grant No. 30471389）
文摘Growth and yield modeling has a long history in forestry. The methods of measuring the growth of stand basal area have evolved from those developed in the U.S.A. and Germany during the last century. Stand basal area modeling has progressed rapidly since the first widely used model was published by the U.S. Forest Service. Over the years, a variety of models have been developed for predicting the growth and yield of uneven/even-aged stands using stand-level approaches. The modeling methodology has not only moved from an empirical approach to a more ecological process-based approach but also accommodated a variety of techniques such as： 1） simultaneous equation methods, 2） difference models, 3） artificial neural network techniques, 4） linear/nonlinear regression models, and 5） matrix models. Empirical models using statistical methods were developed to reproduce accurately and precisely field observations. In contrast, process models have a shorter history, developed originally as research and education tools with the aim of increasing the understanding of cause and effect relationships. Empirical and process models can be married into hybrid models in which the shortcomings of both component approaches can, to some extent, be overcome. Algebraic difference forms of stand basal area models which consist of stand age, stand density and site quality can fully describe stand growth dynamics. This paper reviews the current literature regarding stand basal area models, discusses the basic types of models and their merits and outlines recent progress in modeling growth and dynamics of stand basal area. Future trends involving algebraic difference forms, good fitting variables and model types into stand basal area modeling strategies are discussed.
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.
基金The National Forestry Commission of Mexico and The Mexican National Council for Science and Technology(CONAFOR-CONACYT-115900)。
文摘Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.
基金supported by the National Scientific and Technological Task in China(Nos.2015BAD09B0101,2016YFD0600302)National Natural Science Foundation of China(No.31570619)the Special Science and Technology Innovation in Jiangxi Province(No.201702)
文摘Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function,non-Gaussian distributions,multicollinearity,outliers and noise in the data.The problems of backpropagation models using artificial neural networks include determination of the structure of the network and overlearning courses.According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province,a back-propagation artificial neural network model(BPANN)and a support vector machine model(SVM)for basal area of Chinese fir(Cunninghamia lanceolata)plantations were constructed using four kinds of prediction factors,including stand age,site index,surviving stem numbers and quadratic mean diameters.Artificial intelligence methods,especially SVM,could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models.SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.
文摘The objective of this study is to evaluate the capability of satellite imagery for the estimation of basal area in Northern Zagros Forests. The data of the high resolution geometric (HRG) sensor of SPOT-5 satellite dated in July 2005 were used. Investigation of the quality of Satellite images shows that these images have no radiometric distortion. Overlaying of geocoded images with the digital topographic maps indicated that the images have high geometric precision. A number of 319 circular plots (0.1 ha) were established using systematic random method in the study area. All trees having diameter at breast height (DBH) (i.e. 1.3 m above ground) greater than 5 cm were callipered in each plot. Basal area in each plot was determined using field data. Main bands, artificial bands such as vegetation indices and principle component analysis (PCA) were studied. Digital numbers related to each plot were extracted from original and artificial bands. All plots were ordinated by major geographic aspects and the best fitted regression models were determined for both the study area without consideration of aspects and with consideration of major geographic aspects by multiple regression analysis (step wise regression). The results from regression analysis indicated that the square root of basal area without consideration of aspects has a high correlation with band B1 (r = –0.60). The consideration of aspects resulted in correlation of different indices with square root of basal area such that in northern forests, band B1 had higher correlation coefficient(r = –0.67) among other indices. In Eastern forests, the same band showed correlation of basal area with different correlation coefficient (r = –0.65). In southern and western forests, the square root of basal area had higher correlation (r = –0.68) with RVI. The use of the square root of basal area as a dependent variable in multivariate linear regression improved the results. The assessment of model validity indicated that the proposed models are properly valid.
文摘Bitterlich sampling is an extensively used technique in worldwide forest inventories. Although it has been proved that estimates of basal area from Bitterlich sampling are mathematically unbiased, its precision for individual forest stands may be fairly poor. An extension of validation efforts to different forest biomes could therefore provide more comprehensive assessment and understanding of the Bitterlich sampling technique. In this study, this technique was quantitatively evaluated by using simulated sparse boreal forests and dense tropical forests from an empirical forest structure model (EFSM). Theoretical estimation of basal areas and practical estimation influenced by the hidden-tree effect were both compared with true basal areas of the simulated forests. The evaluation results indicated that: 1) Bitterlich sampling can yield acceptable accuracy and precision when the count number (CN) of trees was set to 10 for the studied boreal and tropical forests with distinct characteristics, 2) the theoretical estimation of basal area can be improved by increasing the CN values for both forests, and 3) when the hidden-tree effect is encountered, the accuracy for tropical forests will be decreased by increasing the CN values, whereas the accuracy for boreal forests can still be improved. Accordingly, a relatively high CN, at a reasonable cost, is recommended for sparse boreal forests to improve the accuracy of basal area estimation. In contrast, for dense tropical forests, a CN of ten is appropriate to mitigate the hidden-tree effect.
文摘Nonlinear mixed effects model（NLMEM） is based on the relationship between the fixed and random effects in the regression function.The NLMEM has a competitive advantage in analyzing repeated measures data,the longitudinal data and multilevel data.This paper chose two kinds of two-level nonlinear mixed model to analyze basal area growth for Chinese Fir（Cunninghamia lanceolata）. Model 1 is a general two-level NLMEM and Model 2 is based on Model 1 to further consider the fixed effects parameters changes with a specific factor. Firstly,through the analysis of these two models, this paper defined the basic model to build the two-level NLMEM.Secondly,665 kinds of models derived from Model 1 and 2 703 kinds of models derived from Model 2 were calculated and compared. The results showed that:for Model 1,there were 57 kinds of models converging,and when the formal parameter b<sub>0</sub> considered the block effects and plot effects,b<sub>1</sub> and b<sub>4</sub> only considered the block effects, the model fitted the best;and for Model 2,there were 24 kinds of model converging,and when the formal parameter bs considered the block effects and plot effects,b<sub>1</sub> only considered block effects and the fixed effects b<sub>0</sub> changed with any level of block level, Model 2 fitted the best.Finally,by comparing the traditional nonlinear regression model,Model 1 and Model 2,the results showed that Model 1 and Model 2 fitted better than the traditional nonlinear regression, and Model 2 was best fitting model.
基金supported by grants of the National Natural Science Foundation of China (No. 90208013)Shanghai Leading Academic Discipline Project (#B111)
文摘The amygdaloid complex receives information from all sensory systems, especially from vision. Some researches (Amaral DG, 2003) show that in the primate brain, the amygdala
文摘Microbiological and physical\|chemical characteristics of subtropical forest, grassland and cropfield soils from the karst areas of Southwest China were investigated. The study revealed that the conversion of natural forest to other forms of land would lead to a reduction in soil organic C(26.2%-35.3%), total N(37.2%-55.8%), total P(32.9%-43.6%), microbial biomass C(35.4%-49.1%), N(37.2%-55.8%), and P(25.8%-41.9%). Comparative analysis of microbial activity in terms of basal soil respiration showed maximum activity in forest soil and minimum in cropfield soil. Analysis of microbial metabolic respiratory activity indicated a relatively greater respiratory loss of CO\-2—C per unit microbial biomass in cropfield and grassland than in forest soil. Considering the importance of microbial components in soil, it is concluded that land use in different ways will lead to the reduction of biological stability of soil.
文摘A tree's basal area(BA)and wood volume scale exponentially with tree diameter in species-specifc patterns.Recent observed increases in tree growth suggest these allometric relationships are shifting in response to climate change,rising CO_(2) levels,and/or changes in forest management.We analyzed 9,214 cores from nine conifer and 11 broadleaf species grown in managed mixed-species stands in the upper Midwest to quantify how well diameter(diameter at breast height(DBH))serves to predict BA growth and above-ground wood and carbon(C).These samples include many large trees.We ft mixed models to predict BA growth and above-ground biomass/C from diameter,tree height,and the BA of nearby trees while controlling for site effects.Models account for 55%–83%of the variance in log(recent growth),improving predictions over earlier models.Growth-diameter scaling exponents covary with certain leaf and stem(but not wood)functional traits,reflecting growth strategies.LogBA increment scales linearly with log(diameter)as trees grow bigger in 16/20 species and growth actually accelerates in Quercus rubra L.Three other species plateau in growth.Growth only decelerates in red pine,Pinus resinosa Ait.Growth in whole-tree,above-ground biomass,and C accelerate even more strongly with diameter(mean exponent:2.08 vs.1.30 for BA growth).Sustained BA growth and accelerating wood/C growth contradict the common assumption that tree growth declines in bigger trees.Yield tables and silvicultural guidelines should be updated to reflect these current relationships.Such revisions will favor delaying harvests in many managed stands to increase wood production and enhance ecosystem values including C fxation and storage.Further research may resolve the relative roles of thinning,climatic conditions,nitrogen inputs,and rising CO2 levels on changing patterns of tree growth.
