A biomass equation dataset for common shrub species in China

Shrub biomass equations provide an accurate, efficient and convenient method in estimating biomass of shrubland ecosystems and biomass of the shrub layer in forest ecosystems at various spatial and temporal scales. In recent decades, many shrub biomass equations have been reported mainly in journals, books and postgraduate's dissertations. However, these biomass equations are applicable for limited shrub species with respect to a large number of shrub species widely distributed in China, which severely restricted the study of terrestrial ecosystem structure and function, such as biomass, production, and carbon budge. Therefore, we firstly carried out a critical review of published literature (from 1982 to 2019) on shrub biomass equations in China, and then developed biomass equations for the dominant shrub species using a unified method based on field measurements of 738 sites in shrubland ecosystems across China. Finally, we constructed the first comprehensive biomass equation dataset for China’s common shrub species. This dataset consists of 822 biomass equations specific to 167 shrub species and has significant representativeness to the geographical, climatic and shrubland vegetation features across China. The dataset is freely available at https://doi.org/10.11922/sciencedb.00641 for noncommercial scientific applications, and this dataset fills a significant gap in woody biomass equations and provides key parameters for biomass estimation in studies on terrestrial ecosystem structure and function.

Stem Biomass Equation of Eucalyptus urophylla S.T. Blake

Eucalyptus urophylla is one of the typical plants of the Province of East Nusa Tenggara, Indonesia whose distribution includes the islands of Timor, Alor, Wetor, Flores, Adonara, Lomblen, and Pantar. The best land for the growth of E. urophylla is an area with rainfall above 1000 mm every year. E. urophylla dominate the island of Timor hence the potential to absorb carbon and store it in biomass as part of climate change mitigation. This study aims to determine the allometric equation model to predict the potential of E. urophylla stem biomass. Calculation of the amount of stem biomass based on allometric equations is an analytical method used in this study. The sample trees used in equation modeling is 100 trees as a result of the inventory. The equations that can be used to estimate the biomass potential of the stem of E. urophylla in Timor Island were ln = -2.12 + 2.472 ln ( ) and (R2= 0.98); ln = -3.617 + 1.046 ln and (R2= 0.99); and ln = -3.510 + 2.157 ln ( ) + 0.983 ln and (R2= 0.99). The stem biomass potential with the model I amounting to 276.877 tons ha-1, model II of 279.671 tons ha-1, and model III of 280.209 tons ha-1.

Comparison of Allometric Equation and Destructive Measurement of Carbon Storage of Naturally Regenerated Understory in a Pinus rigida Plantation in South Korea

The forest understory plays an important role in the carbon and nutrient cycling and forest stability, but cost-efficient quantification of its biomass remains challenging. Most of the existing biomass allometric equations have been developed and designed only for mature forest trees (i.e., Diameter at breast height (DBH) ≥ 10 cm), and those for trees with DBH less than 10 cm are not readily available. In this study, we compared the biomass by plant component (i.e., foliage, branch, and stem) measured by a destructive method with those estimated by the existing biomass allometric equations for understory trees with DBH less than 10 cm in a Pinus rigida plantation. We also developed an allometric biomass equation for the identified understory tree species, namely, Quercus variabilis, Quercus acutissima, Quercus mongolica, Quercus serrata, and Carpinus laxiflora. The estimated biomass using allometric equations for foliage, branch, and stem was lower than the values obtained using the destructive method by 64%, 41%, and 18%, respectively. The biomass allometric equations developed in this study showed high coefficients of determination (mean R2 = 0.970) but varied depending on species and tree part (range: 0.824–0.984 for foliage, 0.825–0.952 for branch, and 0.884–0.999 for the stem, respectively). The computed biomass of the understory vegetation was 22.9 Mg ha−1, representing 12.0% of the total biomass of the P. rigida plantation. The present study demonstrates that understory trees with DBH less than 10 cm account for a considerable portion of carbon stock in forest ecosystems, and therefore suggests that more biomass allometric equations should be optimized for small-DBH trees to improve forest carbon stock estimation.

A review of biomass equations for China's tree species

Tree biomass equations are the most commonly used method to estimate tree and forest biomasses at various spatial and temporal scales because of their high accuracy, efficiency and conciseness. For decades, many tree biomass equations have been reported in diverse types of literature (e.g., journals, books and reports). These scattered equations are being compiled, and tree biomass equation datasets are currently available for many geographical regions (e.g., Europe, North America and sub-Saharan Africa) and countries (e.g., Australia, Indonesia and Mexico). However, one important country stands out as an area where a large number of biomass equations have not yet been reviewed and inventoried extensively: China. Therefore, in this study, we carried out a broad survey and critical review of the literature (from 1978 to 2013) on biomass equations in China and compiled a normalized tree biomass equation dataset for China. This dataset consists of 5924 biomass equations for nearly 200 tree species and their associated background information (e.g., geographical location, climate and stand description), showing sound geographical, climatic and forest vegetation coverage across China. The dataset is freely available at https://doi.org/10.1594/PANGAEA.895244 (Luo et al., 2018) for noncommercial scientific applications, and this dataset fills an important regional gap in global biomass equations and provides key parameters for biomass estimation in forest inventory and carbon accounting studies in China.

Can aphids be controlled by fungus? A mathematical model

The control of insect pests in agriculture is essential for food security. Chemical controls typically damage the environment and harm beneficial insects such as pollinators, so it is advantageous to identify targetted biological controls. Since predators are often generalists, pathogens or parasitoids are more likely to serve the purpose. Here, we model a fungal pathogen of aphids as a potential means to control of these important pests in cereal crops. Typical plant herbivore pathogen models are set up on two trophic levels, with dynamic variables the plant biomass and the uninfected and infected herbivore populations. Our model is unusual in that (i) it has to be set up on three trophic levels to take account of fungal spores in the environment, but (ii) the aphid feeding mechanism leads to the plant biomass equation becoming uncoupled from the system. The dynamical variables are therefore the uninfected and infected aphid population and the environmental fungal concentration. We carry out an analysis of the dynamics of the system. Assuming that the aphid population can survive in the absence of disease, the fungus can only persist (and control is only possible) if (i) the host grows sufficiently strongly in the absence of infection, and (ii) the pathogen transmission parameters are sufficiently large. If it does persist the fungus does not drive the aphid population to extinction, but controls it below its disease-free steady state value, either at a new coexistence steady state or through oscillations. Whether this control is sufficient for agricultural purposes will depend on the detailed parameter values for the system.

On the impact of biomass composition in constraint-based flux analysis

ABSTRACTThe biomass equation is a critical component in genome-scale metabolic models (GEMs): It is one of most widely used objective functions within constraint-based flux analysis formulation, describing cellular driving force under the growth condition. The equation accounts for the quantities of all known biomass precursors that are required for cell growth. Most often than not, published GEMs have adopted relevant information from other species to derive the biomass equation when any of the macromolecular composition is unavailable. Thus, its validity is still questionable. Here, we investigated the qualitative and quantitative aspects of biomass equations from GEMs of eight different yeast species. Expectedly, most yeast GEMs borrowed macromolecular compositions from the model yeast, Saccharomyces cerevisiae. We further confirmed that the biomass compositions could be markedly different even between phylogenetically closer species and none of the high throughput omics data such as genome, transcriptome and proteome provided a good estimate of relative amino acid abundances. Upon varying the stoichiometric coefficients of biomass components, subsequent flux simulations demonstrated how predicted in silico growth rates change with the carbon substrates used. Furthermore, the internal fluxes through individual reactions are highly sensitive to all components in the biomass equation. Overall, the current analysis clearly highlight that biomass equation need to be carefully drafted from relevant experiments, and the in silico simulation results should be appropriately interpreted to avoid any inaccuracies.

Biomass estimation equations for mesquite trees in the Americas

Mesquite trees are the preferred dendroenergy sources in arid and semi-arid forests. In spite of their relative importance, regional aboveground biomass (AGB) equations for mesquite trees are scarce in the scientific literature. For that reason, the aims of this study were to: (a) harvest trees and develop regional biomass equations; (b) contrast measured data with equations developed previously; and (c) test the applicability of the fitted equation for mesquite trees in the arid and semi-arid forests of the Americas. We harvested 206 new mesquite trees from arid and semi-arid forests in northern Mexico (Coahuila, Nuevo Leon, and Tamaulipas) in addition to using two other previously compiled data sets from Mexico (N = 304) to develop a regional equation. To test the validity of this equation, for biomass equations reported for the rest of the country, as well as for North and South American mesquite trees, we contrasted AGB measurements with predictions of fitted equations. Statistical analysis revealed the need for a single, regional, semi-empirical equation as together the three data sets represented the variability of the aboveground biomass of mesquite trees across northern Mexico, as well as mesquite trees in America’s arid and semiarid regions. Due to the large quantity of mesquite trees harvested for sampling and their variability, the regional biomass equation developed encompasses all other North and South American equations, and is representative of mesquite trees throughout the arid and semi-arid forests of the Americas.

ChinAllomeTree 1.0: China's normalized tree biomass equation dataset

The tree biomass equation, which is also called the tree allometric equation, is the most commonly used method to estimate tree and forest biomass at various spatial-temporal scales because of its high accuracy, efficiency and conciseness. For decades, many tree biomass equations have been reported in diverse types of literature (e.g., journals, books and reports). These scattered equations are being compiled, and tree biomass equation datasets are currently available for many geographical regions (e.g., Europe, North America and Sub-Saharan Africa) and countries (e.g., Australia, Indonesia and Mexico) except for in an important region of the world, Eastern Asia, specifically China. Therefore, in this study, we carried out an extensive survey and critical review of the literature (from 1978–2013) on biomass equations conducted in China and developed China’s normalized tree biomass equation dataset (ChinAllomeTree version 1.0). This dataset consists of 5,924 biomass component equations for nearly 200 species and their associated background information (e.g., geographical location, climate and stand description), showing sound geographical, climatic and forest vegetation coverages across China. The dataset is freely available at https://doi.pangaea.de/10.1594/PANGAEA.895244 for noncommercial scientific applications, which fills an important regional gap in global biomass datasets and provides key parameters for biomass estimation in forest inventory and carbon accounting in China.