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Allometric scaling laws critically examine structure-function relationships. In estimating the forest biomass carbon and its response under climate change, the issue of scaling has resulted in difficulties when modelling the biomass for different-sized trees, especially large ones, and has not yet been solved in either theory or practice. Here, we propose the concept of a dynamic allometric scaling relationship between stem biomass and above-ground biomass The allometric curve approaches an asymptote with an increase in tree size. An asymptotic allometric equation is presented that has a better fit to the data than the simple power-law allometric equation. The non-constant exponent is determined by the change in the biomass ratio for different organs and is governed by the dynamic allometric coefficient. This study presents a methodological framework to theoretically characterize allometric relationships and provides new insights in understanding the general scaling pattern and carbon sequestration capacity of large trees across global forests.
Xiaolu Zhou; Mingxia Yang; Zelin Liu; Peng Li; Binggeng Xie; Changhui Peng. Dynamic allometric scaling of tree biomass and size. Nature Plants 2021, 7, 42 -49.
AMA StyleXiaolu Zhou, Mingxia Yang, Zelin Liu, Peng Li, Binggeng Xie, Changhui Peng. Dynamic allometric scaling of tree biomass and size. Nature Plants. 2021; 7 (1):42-49.
Chicago/Turabian StyleXiaolu Zhou; Mingxia Yang; Zelin Liu; Peng Li; Binggeng Xie; Changhui Peng. 2021. "Dynamic allometric scaling of tree biomass and size." Nature Plants 7, no. 1: 42-49.
Rapid urbanization has led to the continuous deterioration of the surrounding natural ecosystem. It is important to identify the key urbanization factors that affect ecosystem services and analyze the potential effects of these factors on the ecosystem. We selected the Beijing, Tianjin, and Hebei (BTH) urban agglomeration to investigate these effects, and designed three indicators to map the urbanization level: Population density, gross domestic product (GDP) density, and the construction land proportion. Four indicators were chosen to quantify ecosystem services: Food production, carbon sequestration and oxygen production, water conservation, and soil conservation. To handle the nonlinear interactions, we used a random forest (RF) method to assess the effect of urbanization on ecosystem services in the BTH area from 2000 to 2014. Our study demonstrated that population density and economic growth were the internal driving forces affecting ecosystem services. We observed changing trends in the effect of urbanization: The effect of population density on ecosystem services increased, the effect of the proportion of construction land was consistent with population density, and the effect of GDP density on ecosystem services decreased. Our results suggest that controlling the population and GDP would significantly influence the sustainable development in large urban areas.
Shan Liu; Mingxia Yang; Yuling Mou; Yanrong Meng; Xiaolu Zhou; Changhui Peng. Effect of Urbanization on Ecosystem Service Values in the Beijing-Tianjin-Hebei Urban Agglomeration of China from 2000 to 2014. Sustainability 2020, 12, 10233 .
AMA StyleShan Liu, Mingxia Yang, Yuling Mou, Yanrong Meng, Xiaolu Zhou, Changhui Peng. Effect of Urbanization on Ecosystem Service Values in the Beijing-Tianjin-Hebei Urban Agglomeration of China from 2000 to 2014. Sustainability. 2020; 12 (24):10233.
Chicago/Turabian StyleShan Liu; Mingxia Yang; Yuling Mou; Yanrong Meng; Xiaolu Zhou; Changhui Peng. 2020. "Effect of Urbanization on Ecosystem Service Values in the Beijing-Tianjin-Hebei Urban Agglomeration of China from 2000 to 2014." Sustainability 12, no. 24: 10233.
Competition is an essential driving factor that influences forest community sustainability, yet measuring it poses several challenges. To date, the Competition Index (CI) has generally been the tool of choice for quantifying actual competition. In this study, we proposed using the Total Overlap Index (TOI), a CI in which the Area Overlap (AO) index has been adapted and modified to consider the “shading” and “crowding” effects in the vertical dimension. Next, based on six mixed forest plots in Xiaolong Mountain, Gansu, China, we assessed the results to determine the TOI’s evaluation capability. Individual-tree simulation results showed that compared to the modified Area Overlap index (AOM), the TOI has superior quantification capability in the vertical direction. The results of the basal area increment (BAI) model showed that the TOI offers the best evaluation capability among the four considered CIs in mixed forest (with Akaike Information Criterion (AIC) of 1041.60 and log-likelihood (LL) of −511.80 in the model fitting test, mean relative error of −28.67%, mean absolute percent error of 117.11%, and root mean square error of 0.7993 in cross-validation). Finally, the TOI was applied in the Kaplan–Meier survival analysis and Cox proportional-hazards analysis. The Kaplan–Meier survival analysis showed a significant difference between the low- (consisting of trees with the TOI lower than 1) and high-competition (consisting of trees with the TOI higher than 1) groups’ survival and hazard curves. Moreover, the results of the Cox proportional-hazards analysis exhibited that the trees in the low-competition group only suffered 34.29% of the hazard risk that trees in the high-competition group suffered. Overall, the TOI expresses more dimensional information than other CIs and appears to be an effective competition index for evaluating individual tree competition. Thus, the competition status quantified using this method may provide new information to guide policy- and decision-makers in sustainable forest management planning projects.
Boheng Wang; Yuankun Bu; Guanhu Tao; Chenran Yan; Xiaolu Zhou; Weizhong Li; Pengxiang Zhao; Yanzheng Yang; Ruikun Gou. Quantifying the Effect of Crown Vertical Position on Individual Tree Competition: Total Overlap Index and Its Application in Sustainable Forest Management. Sustainability 2020, 12, 7498 .
AMA StyleBoheng Wang, Yuankun Bu, Guanhu Tao, Chenran Yan, Xiaolu Zhou, Weizhong Li, Pengxiang Zhao, Yanzheng Yang, Ruikun Gou. Quantifying the Effect of Crown Vertical Position on Individual Tree Competition: Total Overlap Index and Its Application in Sustainable Forest Management. Sustainability. 2020; 12 (18):7498.
Chicago/Turabian StyleBoheng Wang; Yuankun Bu; Guanhu Tao; Chenran Yan; Xiaolu Zhou; Weizhong Li; Pengxiang Zhao; Yanzheng Yang; Ruikun Gou. 2020. "Quantifying the Effect of Crown Vertical Position on Individual Tree Competition: Total Overlap Index and Its Application in Sustainable Forest Management." Sustainability 12, no. 18: 7498.
Background In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future predictions of these responses, the current forest biomass carbon storage (FCS) should first be clarified as much as possible, especially at national scales. However, few studies have introduced how to verify an FCS estimate by delimiting the reasonable ranges. This paper addresses an estimation of national FCS and its verification using two-step process to narrow the uncertainty. Our study focuses on a methodology for reducing the uncertainty resulted by converting from growing stock volume to above- and below-ground biomass (AB biomass), so as to eliminate the significant bias in national scale estimations. Methods We recommend splitting the estimation into two parts, one part for stem and the other part for AB biomass to preclude possible significant bias. Our method estimates the stem biomass from volume and wood density (WD), and converts the AB biomass from stem biomass by using allometric relationships. Results Based on the presented two-step process, the estimation of China’s FCS is performed as an example to explicate how to infer the ranges of national FCS. The experimental results demonstrate a national FCS estimation within the reasonable ranges (relative errors: + 4.46% and − 4.44%), e.g., 5.6–6.1 PgC for China’s forest ecosystem at the beginning of the 2010s. These ranges are less than 0.52 PgC for confirming each FCS estimate of different periods during the last 40 years. In addition, our results suggest the upper-limits by specifying a highly impractical value of WD (0.7 t∙m− 3) on the national scale. As a control reference, this value decides what estimate is impossible to achieve for the FCS estimates. Conclusions Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-step process will help to verify national FCS and also to reduce uncertainty in related studies. While the true value of national FCS is immeasurable, our work should motivate future studies that explore new estimations to approach the true value by narrowing the uncertainty in FCS estimations on national and global scales.
Xiaolu Zhou; Xiangdong Lei; Caixia Liu; Huabing Huang; Carl Zhou; Changhui Peng. Re-estimating the changes and ranges of forest biomass carbon in China during the past 40 years. Forest Ecosystems 2019, 6, 1 -18.
AMA StyleXiaolu Zhou, Xiangdong Lei, Caixia Liu, Huabing Huang, Carl Zhou, Changhui Peng. Re-estimating the changes and ranges of forest biomass carbon in China during the past 40 years. Forest Ecosystems. 2019; 6 (1):1-18.
Chicago/Turabian StyleXiaolu Zhou; Xiangdong Lei; Caixia Liu; Huabing Huang; Carl Zhou; Changhui Peng. 2019. "Re-estimating the changes and ranges of forest biomass carbon in China during the past 40 years." Forest Ecosystems 6, no. 1: 1-18.
The method of forest biomass estimation based on a relationship between the volume and biomass has been applied conventionally for estimating stand above- and below-ground biomass (SABB, t ha−1) from mean growing stock volume (m3 ha−1). However, few studies have reported on the diagnosis of the volume-SABB equations fitted using field data. This paper addresses how to (i) check parameters of the volume-SABB equations, and (ii) reduce the bias while building these equations. In our analysis, all equations were applied based on the measurements of plots (biomass or volume per hectare) rather than individual trees. The volume-SABB equation is re-expressed by two Parametric Equations (PEs) for separating regressions. Stem biomass is an intermediate variable (parametric variable) in the PEs, of which one is established by regressing the relationship between stem biomass and volume, and the other is created by regressing the allometric relationship of stem biomass and SABB. A graphical analysis of the PEs proposes a concept of “restricted zone,” which helps to diagnose parameters of the volume-SABB equations in regression analyses of field data. The sampling simulations were performed using pseudo data (artificially generated in order to test a model) for the model test. Both analyses of the regression and simulation demonstrate that the wood density impacts the parameters more than the allometric relationship does. This paper presents an applicable method for testing the field data using reasonable wood densities, restricting the error in field data processing based on limited field plots, and achieving a better understanding of the uncertainty in building those equations.
Caixia Liu; Xiaolu Zhou; Xiangdong Lei; Huabing Huang; Carl Zhou; Changhui Peng; Xiaoyi Wang. Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship. Forests 2019, 10, 658 .
AMA StyleCaixia Liu, Xiaolu Zhou, Xiangdong Lei, Huabing Huang, Carl Zhou, Changhui Peng, Xiaoyi Wang. Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship. Forests. 2019; 10 (8):658.
Chicago/Turabian StyleCaixia Liu; Xiaolu Zhou; Xiangdong Lei; Huabing Huang; Carl Zhou; Changhui Peng; Xiaoyi Wang. 2019. "Separating Regressions for Model Fitting to Reduce the Uncertainty in Forest Volume-Biomass Relationship." Forests 10, no. 8: 658.
The spruce budworm (SBW) defoliates and kills conifer trees, consequently affecting carbon (C) exchanges between the land and atmosphere. Here, we developed a new TRIPLEX-Insect sub-model to quantify the impacts of insect outbreaks on forest C fluxes. We modeled annual defoliation (AD), cumulative defoliation (CD), and tree mortality. The model was validated against observed and published data at the stand level in the North Shore region of Québec and Cape Breton Island in Nova Scotia, Canada. The results suggest that TRIPLEX-Insect performs very well in capturing tree mortality following SBW outbreaks and slightly underestimates current annual volume increment (CAI). In both mature and immature forests, the simulation model suggests a larger reduction in gross primary productivity (GPP) than in autotrophic respiration (Ra) at the same defoliation level when tree mortality was low. After an SBW outbreak, the growth release of surviving trees contributes to the recovery of annual net ecosystem productivity (NEP) based on forest age if mortality is not excessive. Overall, the TRIPLEX-Insect model is capable of simulating C dynamics of balsam fir following SBW disturbances and can be used as an efficient tool in forest insect management.
Zelin Liu; Changhui Peng; Louis De Grandpré; Jean-Noël Candau; Xiaolu Zhou; Daniel Kneeshaw. Development of a New TRIPLEX-Insect Model for Simulating the Effect of Spruce Budworm on Forest Carbon Dynamics. Forests 2018, 9, 513 .
AMA StyleZelin Liu, Changhui Peng, Louis De Grandpré, Jean-Noël Candau, Xiaolu Zhou, Daniel Kneeshaw. Development of a New TRIPLEX-Insect Model for Simulating the Effect of Spruce Budworm on Forest Carbon Dynamics. Forests. 2018; 9 (9):513.
Chicago/Turabian StyleZelin Liu; Changhui Peng; Louis De Grandpré; Jean-Noël Candau; Xiaolu Zhou; Daniel Kneeshaw. 2018. "Development of a New TRIPLEX-Insect Model for Simulating the Effect of Spruce Budworm on Forest Carbon Dynamics." Forests 9, no. 9: 513.
Shulin Chen; Hong Jiang; Zhijian Cai; Xiaolu Zhou; Changhui Peng. The response of the net primary production of Moso bamboo forest to the On and Off-year management: A case study in Anji County, Zhejiang, China. Forest Ecology and Management 2018, 409, 1 -7.
AMA StyleShulin Chen, Hong Jiang, Zhijian Cai, Xiaolu Zhou, Changhui Peng. The response of the net primary production of Moso bamboo forest to the On and Off-year management: A case study in Anji County, Zhejiang, China. Forest Ecology and Management. 2018; 409 ():1-7.
Chicago/Turabian StyleShulin Chen; Hong Jiang; Zhijian Cai; Xiaolu Zhou; Changhui Peng. 2018. "The response of the net primary production of Moso bamboo forest to the On and Off-year management: A case study in Anji County, Zhejiang, China." Forest Ecology and Management 409, no. : 1-7.
For decades, researchers have thought it was difficult to remove the uncertainty from the estimates of forest carbon storage and its changes on national sales. This is not only because of stochasticity in the data but also the bias to overcome in the computations. Most studies of the estimation, however, ignore quantitative analyses for the latter uncertainty. This bias primarily results from the widely used volume‐biomass method via scaling up forest biomass from limited sample plots to large areas. This paper addresses (i) the mechanism of scaling‐up error occurrence, and (ii) the quantitative effects of the statistical factors on the error. The error compensators were derived, and expressed by ternary functions with three variables: expectation, variance and the power in the volume‐biomass equation. This is based on analysing the effect of power‐law function convexity on scaling‐up error by solving the difference of both sides of the weighted Jensen inequality. The simulated data and the national forest inventory of China were used for algorithm testing and application, respectively. Scaling‐up error occurrence stems primarily from an effect of the distribution heterogeneity of volume density on the total biomass amount, and secondarily from the extent of function nonlinearities. In our experiments, on average 94·2% of scaling‐up error can be reduced for the statistical populations of forest stands in a region. China's forest biomass carbon was estimated as approximately 6·0 PgC or less at the beginning of the 2010s after on average 1·1% error compensation. The results of both the simulated data experiment and national‐scale estimation suggest that the biomass is overestimated for young forests more than others. It implies a necessity to compensate scaling‐up error, especially for the areas going through extensive afforestation and reforestation in past decades. This study highlights the importance of understanding how both the function nonlinearity and the statistics of the variables quantitatively affect the scaling‐up error. Generally, the presented methods will help to translate fine‐scale ecological relationships to estimate coarser scale ecosystem properties by correcting aggregation errors.
Xiaolu Zhou; Xiangdong Lei; Changhui Peng; Weifeng Wang; Carl Zhou; Caixia Liu; Zhenggang Liu. Correcting the overestimate of forest biomass carbon on the national scale. Methods in Ecology and Evolution 2015, 7, 447 -455.
AMA StyleXiaolu Zhou, Xiangdong Lei, Changhui Peng, Weifeng Wang, Carl Zhou, Caixia Liu, Zhenggang Liu. Correcting the overestimate of forest biomass carbon on the national scale. Methods in Ecology and Evolution. 2015; 7 (4):447-455.
Chicago/Turabian StyleXiaolu Zhou; Xiangdong Lei; Changhui Peng; Weifeng Wang; Carl Zhou; Caixia Liu; Zhenggang Liu. 2015. "Correcting the overestimate of forest biomass carbon on the national scale." Methods in Ecology and Evolution 7, no. 4: 447-455.