Biomass Equations Research Articles

Allometric scaling models for tree saplings: Assessing potential fodder for ungulate herbivores

Knowledge of the quantity of twig dry mass (available fodder) of saplings is crucial for understanding tree recruitment under wild ungulate browsing pressure in forest ecosystems. Therefore, this study aimed to develop allometric equations for estimating twig dry mass using sapling structural data and to model the twig dry mass of saplings of various species in various size classes while quantitatively describing the variations among tree species. Sapling biomass samples were taken for six widely spread tree species in two national parks in Poland. After drying the biomass samples in the oven for 72 h at 65 °C, the data were analyzed, and models were generated using the ordinary least squares and generalized additive methods. The results confirmed our hypothesis that different tree species differ in their allometric properties. The twig dry masses of Acer pseudoplatanus and Carpinus betulus were best estimated by basal diameter, whereas those of Picea abies, Abies alba, and Fagus sylvatica were best estimated by sapling height. In contrast, the model quality for Fagus sylvatica and Sorbus aucuparia was enhanced by combining the sapling height with the mean largest crown length (MLCL). For all species, the twig dry mass distribution was linear along sapling height classes and exponentially increased with shoot diameter classes, with coniferous species exhibiting the highest correlation. On the other hand, the proportion of twig dry mass to total sapling dry mass followed the order S. aucuparia > A. alba > P. abies > F. sylvatica > C. betulus > A. pseudoplatanus. The developed allometric equations contribute significantly to the already available collection of published biomass equations. Moreover, they extremely useful in estimating the browsing potential of tree saplings falling within size ranges conducive to herbivore browsing.

Relationship between net primary productivity and stand age in typical plantation forests in China.

Plantation forests play an important role in carbon sink in terrestrial ecosystems. Based on tree rings of five main plantation tree species (Robinia pseudoacacia, Quercus variabilis, Cunninghamia lanceolata, Pinus sylvestris var. mongolica, and Pinus tabuliformis) at 25 sites in China, we calculated the average annual NPP of standard trees in each study area by the biomass equations and extended to the stand scale. The relationships between NPP and stand age were fitted by the InTEC and Law models. The results showed that NPP of R. pseu-doacacia, C. lanceolata, and P. tabuliformis plantations increased to a peak and then leveling off with stand age, while that of Q. variabilis and P. sylvestris var. mongolica plantations reached a peak and then showed a decreasing trend. The inflection points of NPP-stand age curve for different planatations was 11 years for P. sylvestris var. mongolica, 14 years for C. lanceolata, 16 years for P. tabuliformis, and 20 years for R. pseudoacacia. The NPP peak was 6.65, 7.58, 4.70 and 2.59 t·hm-2·a-1, respectively. Both the InTEC and Law NPP-stand age models had high fitting accuracy at a large scale, with the lowest R2 of 0.95 and RMSE of 0.55 t·hm-2·a-1 for the P. sylvestris var. mongolica InTEC model and the highest R2 of 0.99 and RMSE of 0.26 t·hm-2·a-1 for the C. lanceolata InTEC model. The construction of NPP-stand age relationship for major plantations in China provided mechanistic support for the estimation of carbon sinks in plantations at long time scales and provided a reference for the diversification of afforestation tree species selection.

The potential of bamboo forests as a carbon sink and allometric equations for estimating their aboveground biomass

Bamboo forests in Colombia and the Andean region of South America represent high-value ecosystems that provide ecological and economic benefits with local and global impacts. One of the ecosystem services provided by these forests is associated with their capacity to store carbon. In this study, data collected from monitoring plots were used to estimate the carbon content in different pools. Bamboo biomass (Bba), tree biomass (Btree), litter (Cli) and soil organic carbon (SOC) were assessed. The approximate total ecosystem carbon stock (TECaprox) ranged from 198.4 Mg C ha−1 to 330.9 Mg C ha−1 and bamboo carbon Cba represents approximately 50%. In addition, considering the relevance of developing tools to facilitate bamboo inventory and biomass estimates, allometric equations (AE) to estimate bamboo aboveground biomass (AGB) were fitted using the diameter of culms at breast height (dbh) and the total culm length (l) as predictor variables. The fitted AEs included the weighted linear, weighted log-transformed and weighted nonlinear fixed effect models. To compliance the additivity of biomass components a simultaneous systems of biomass equations (seemingly unrelated regressions) were also fitted. The precision and accuracy were assessed considering the residual diagnostic plots and statistics, such as the root-mean-square error (RMSE), RMSE percentage error (RMSEPE) and the Furnival’s index (Fln) for weighted log-transformed models and cross-validation. The performance of the models was similar with an RMSE of approximately 10 kg and 26% of RMSEPE, with slightly lower error for the weighted log-transformed model for the fitting and validation phases. A proper performance was also evidenced for the simultaneous approach for predicting AGB. Bamboo forests showed high relevance as carbon sinks and therefore might be considered strategic tropical ecosystems for climate change mitigation. On the other hand, the fitted AE exhibited proper performance and therefore provided reliable possibilities for estimating the AGB of bamboo during inventories. For practical reasons, the use of models with dbh as a predictor variable is recommended.

Effects of Climate on Stand-Level Biomass for Larch Plantations in Heilongjiang Province, Northeast China

Climate change affects forest resource availability, growing season length, and thus forest biomass accumulation. However, only a limited number of studies have been conducted on forest biomass management based on climate effects, particularly at the stand-level. Thus, an allometric biomass equation based on conventional and climate-based stand biomass models, was developed and compared for larch trees (Larix spp.). A total of 160 experimental plots of larch plantations have been collected in Heilongjiang Province, Northeast China. In this study, we developed four types of additive model systems for stand-level biomass: two types of the stand-level biomass basic models (M-1 and M-2) with stand variables (stand basal area (BA) and stand mean height (Hm)) as the predictors, and two types of the proposed stand-level biomass climate-based models (M-3 and M-4) with stand variables (BA and Hm) and climatic variables (mean annual temperature (MAT) and annual precipitation (AP)) as the predictors. Accordingly, this study evaluated the effects of climatic variables (MAT and AP) and stand variables (BA and Hm) on the model’s performance. Model fitting and validation results revealed that the climatic variables significantly improved the model performance of the fitted equation by increasing the coefficient of determination (R2) values and reducing the root mean square error (RMSE) values. A higher R2 and a lower RMSE were consistently generated by M-2 and M-4, whereas M-1 and M-3 consistently generated a lower R2 and a higher RMSE. We found that the proposed stand-level biomass climate-based model type 4 (M-4) performed better than the other models and slightly better than in previous studies of climate-sensitive models. This study provided an additional and beneficial method of analyzing climate effects on stand-level biomass estimation.

Estimation of grass biomass consumed by rabbits housed in movable paddocks

Biomass allowance is a key feature in pasture-based rabbit production systems. It conditions not only the stock density (rabbits/m²) and/or the number of grazing days, it also influences the grazing behaviour of animals. When herbage restriction occurs, pelleted feed and/or cereal intake goes up. Inadequate pasture management may also impair the biomass quantity and quality if overgrazing occurs. To avoid the undesirable effects of overgrazing and better manage pellet and cereal intake, information on both biomass availability and rabbits’ grazing capacity are needed. Here, we present an adaptation of the rising plate meter method (developed for biomass intake measures for ruminants) for use in rabbit. To this end, we designed an experiment where two groups of 12 rabbits each were kept in two different fields: under an apple orchard (AO) or on fallow land (FL). We followed the animals for 5 consecutive weeks (from 45 to 80 d old). Rabbits lived in 25 m² movable paddocks, and every week a new paddock location (called paddock-spot) was made available for them. At each new paddock-spot, we measured the herbage height inside the paddocks and performed samplings of the available biomass (i.e. herbage cut after herbage height measurement) outside the paddocks. From this data we estimated the available biomass inside each paddock-spot by fitting linear regression equations of biomass to herbage height. Overall, rabbits in the AO and FL had access to 1328±65.7 and 1386±58.6 kg of dry matter (DM) per ha, respectively. In every field and paddock-spot, the biomass available was lower than the rabbits’ grazing capacity; overgrazing was the rule. Roughly, and under a restricted herbage allowance, rabbits in the AO ingested 45.2 g DM/d and rabbits in the FL 43.4 g DM/d. In the last week (64 to 80 d old), the biomass intake of rabbits in the AO and AL represented 26.4 and 23.5% of the total DM intake, respectively. These values, however, does not represent the real grazing capacity of growing rabbits. In this study, we provide some advice on the sampling method to obtain reliable biomass estimations and we mention two methods for handling influential observations in linear regression.

Forest carbon stock and biomass estimation in West Central India using two allometric models

The west central Indian forest ecosystems strongly contribute to livelihood of local human communities as well as climate change mitigation. However, due to the lack of accurate biomass equations and predictive tools, the contribution of these forests towards carbon stock and sequestration remains poorly recognized. At present due to some developmental and mining activities these forests face huge anthropogenic pressure.Therefore, the objective of the present study was to estimate the biomass and carbon stock of forest tree species of west central India to provide baseline information. It will also be useful for measuring the changes after the intervention as well as for future management and conservation.The non-destructive allometric equation approach was adopted to estimate the biomass, carbon stock and carbon dioxide equivalent from total 28 plots of 0.1 ha at seven sites of dry deciduous forests of west central India. In the present study, the results of biomass and carbon stock were calculated by employing two different allometric models.The model with tree diameter and wood specific gravity gave higher estimations of total plant biomass (123.39 – 65.86 Mg ha−1) and carbon stock (58.61—31.28 Mg ha−1) as compared with model having only tree diameter (108.84 – 55.91 Mg ha−1) and (51.70 -26.55 Mg ha−1), respectively.We conclude that the allometric model with tree diameter and wood specific gravity was found best suited for these forests. The results also showed the positive relation between tree diversity, basal area and aboveground biomass and carbon stock.

Allometric models for aboveground biomass estimation of small trees and shrubs in African savanna ecosystems

Quantification of plant biomass and carbon in ecosystems is critical for establishing climate change mitigation potential. For large trees in various ecosystems, allometric models for estimating biomass have been developed but few biomass equations exist for small trees and shrubby vegetation. Allometric above-ground biomass (AGB) models are needed for small trees and shrubs in order to improve the quantification of biomass, particularly for savanna ecosystems, where small trees and shrubs comprise a significant portion of the biomass. In this study we have developed species-specific and multi-species allometric models for biomass estimation of small tree species and shrubs in the savanna ecosystem of Lake Mburo National Park in South Western Uganda. For our models we selected 27 small tree species (N = 403 individuals) and 12 shrub species (N = 177) common in savanna ecosystems for destructive sampling. We developed species-specific and multi-species allometric AGB models to provide estimates of AGB using specific biometric variables recorded for the small trees (i.e. species, DBH, height and crown area), and shrubs (species, height and crown area). We found that crown area was the best single predictor of species-specific AGB for small trees and for species-specific and multi-species models for shrubs. Species-specific models had the best predictive capacity of AGB compared to multi-species biomass models for small trees and shrubs. Multiple-variable models had the best predictive capacity of AGB in both species-specific and multi-species modeling compared to single-variable models. Based on these findings we conclude that the evaluation of carbon stocks of tropical savanna ecosystems should use multi-variable species-specific models for AGB estimation at the individual level, and multi-species models for AGB at the ecosystem level.

Allometric equations for the estimation of above- and below-ground biomass for Larix sibirica Ledeb. in Northern Mongolia

The accurate estimation of tree above-ground (AGB) and below-ground (BGB) biomass components and their root/shoot ratio play key roles in stand and country-level forest biomass and carbon stock estimation. Nevertheless, site-specific and appropriate biomass equations and root/shoot ratio are hardly available for natural larch (Larix sibirica Ledeb.) forests in Mongolia. The present study aimed (1) to develop allometric equations to estimate the above- and below-ground biomass of L. sibirica trees, and (2) to estimate the root/shoot ratio applicable for estimating the root biomass based on above-ground biomass of natural larch forests in northern Mongolia. A total of 40 trees with DBH ranging from 6.8 to 40.8 cm were sampled for tree biomass analyses. For each biomass component, we calculated the proportion of biomass allocated to different components, and also tested four allometric equations based on diameter at breast height (DBH) and height (H) as independent variables. Our results, based on measurements of oven-dried biomass, revealed that stem biomass on average accounted for 44.5% and followed by branch (28.6%) and root (19.9%) biomass, respectively. Stem and branch biomass proportions were gradually increased with increasing DBH, while a contrary trend was observed for needles. The root/shoot ratio averaged 0.25. A comparison of the allocation of root biomass by diameter fractions showed an ever-growing trend of coarse roots with an increase in stem diameter, which often exceeded more than 50% of the total root biomass. However, biomass equations, which include both DBH and H were more precise than equations that are solely based only on DBH. Consequently, among the proposed allometric regression models for estimating the AGB and BGB, the equation y = aD b H c was selected as the best-fitted equation for estimating each biomass component in Siberian larch forests. These allometric equations are available to be used for the estimation of natural larch forest biomass and carbon stocks in the Khentii Mountains of Mongolia, where extreme continental climate conditions dominate.

Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.

Research on the Establishment of Carbon Inversion Model in Engebei Ecological Demonstration Area of the Kubuqi Desert Based on Remote Sensing Data

Studying the changes of vegetation coverage and carbon storage in terrestrial ecosystem will provide important information for ecosystem optimization. The Kubuqi Desert is a typical ecological fragile area, and its ecological construction is not only to build an ecological barrier to protect northern China, but also to be a case of similar regions in the world. Engebei ecological demonstration area, located in the northern margin of the central Kubuqi Desert, was selected as the study area. The objective is to establish a carbon inversion model through the LASSO regression model in this area which can promote its ecological development. Taking Landsat, Sentinel-2A remote sensing data and field sample data as data sources. The vegetation coverage was calculated through the remote sensing data. The carbon reserves in plots were calculated through the biomass equation. Then, the appropriate carbon storage estimation model was found out, which was established by the LASSO regression model and the Sentinel-2A remote sensing images. Finally, the carbon reserves over the years were calculated by this estimation model. Research shows that: first, the overall vegetation coverage of the study area was not high, which was around 27% – 37%; second, the proportion of lower (Ⅰ) level vegetation coverage in the study area decreased overall, and the proportion of higher (Ⅴ) level vegetation coverage generally stabilized; third, carbon reserves remain roughly above 71752.64t, up by 20,209.61t in 2021 compared to 2020, and up by 14,867.02t in 2021 compared to 1991.

Alfalfa Biomass Estimation Using Crop Surface Modeling and NDVI

<b>Highlights</b> <list list-type=bullet><list-item> A UAV with NDVI and RGB cameras captured aerial images of alfalfa plots before and after harvest. </list-item><list-item> Structure from Motion (SfM) and crop surface models were used to determine change in alfalfa crop height. </list-item><list-item> An equation to predict dry matter fraction (DMF) as a function of NDVI was developed. </list-item><list-item> Dry alfalfa yield models using DMF and change in crop height were developed. </list-item></list> <b>Abstract.</b> Alfalfa is an important forage crop grown for hay, silage, and pasture production. Accurate yield estimations before harvest or grazing are crucial for producers to optimize forage utilization. The goal of this research was to develop an aboveground dry biomass prediction function for alfalfa using remote sensing from an unmanned aerial vehicle (UAV). A DJI Mavic Pro equipped with RGB and NDVI cameras captured aerial images. High-resolution orthomosaics and digital surface models were obtained using Structure from Motion (SfM) with Agisoft Metashape. An equation to estimate wet biomass was developed using three variables: change in canopy height (∆H) from digital elevation and crop surface models, and NDVI based canopy density index (CDI) data. The dry biomass yield was estimated as the product of a wet biomass prediction function and a correlation equation to estimate the dry matter fraction (DMF). The best correlation equation for wet biomass (BMwet) only required SfM methods to measure ΔH. The linear regression equation for BMwet had an R2 of 0.963 and the mean coefficient of variation (CV) was ±23%. The best prediction equation for DMF was a quadratic equation with an R2 of 0.642 with a mean CV of ±11%. It was also determined that the DMF varied significantly by season of harvest. As a result, the dry biomass could be estimated using the DMF equation or seasonal mean DMF values. Comparison of the observed dry biomass measurements with the empirically determined biomass prediction function that used the NDVI data to estimate the DMF showed excellent agreement. The model underpredicted the dry biomass by 1.1% with a standard error of the estimate of 236 kg DM/ha (CV = ±26%). The model predictions using the seasonal mean DMF values were not significantly different.

Above-ground biomass allocation and potential carbon sink of black pine – a case study from southern Poland

Biomass allocation is a key factor for understanding the forest carbon balance and reflects plants’ ecological strategies in different environmental conditions. Allocation patterns and biomass models outside of the native range of black pine have not been analyzed in the context of the observed climate changes. The study's goals were to develop biomass equations for mature black pine from southern Poland and assess biomass and carbon allocation patterns and the potential of trees of different social statuses for carbon sequestration. A total of 129 felled black pine trees were measured, among which 14 were destructively sampled to determine biomass and carbon content in tree components. The developed set of biomass equations provided allocation patterns and accumulation of trees of different social statuses. Biomass and carbon allocation patterns were different but related to tree social status. The introduction of diameter at crown base significantly improved the accuracy of the developed models. The analyzed trees allocated relatively more in stem than in crown in comparison with that observed in other studies. Biomass and carbon allocation patterns of the analyzed black pines differ from those of the native range. They should be considered in biomass modeling with factors influencing social status structure.

A dataset of woody plant biomass models for subtropical mixed evergreen and deciduous broad-leaved forests

Tree biomass equations are the most commonly used method to estimate tree and forest biomass on various spatial and temporal scales because of their minor damage, ease of use, and high relative accuracy. The systematic compilation of models for the biomass of mixed evergreen and deciduous broad-leaved forest (a unique and essential class of typical vegetation types in the subtropical zone of China) has not been reported so far. In this paper, we compiled a species list through an inventory of 28.9 hm2 of mixed evergreen deciduous broad-leaved forests in southwestern Hubei Province with fixed detection sample plots, produced a species list, and used it to retrieve, collect and establish a model dataset of woody plant biomass in subtropical mixed evergreen and deciduous broad-leaved forests. The species list contains a total of 665 biomass models in 167 groups as well as the information corresponding to each mode, such as the plant species name, Latin name, plant life type, plant components calculated by the model, model independent variables, measurement units and ranges of independent variables, model correlation coefficient or coefficient of determination, model sample size, climate zone, and soil type. is also recorded. By establishing this dataset, this paper not only provides essential information for in-depth research on the productivity and carbon sink of this specific vegetation but also serve as a scientific basis for the management of this type of forest, the conservation of biodiversity, and the evaluation of forest ecological benefits.

Allometric Volume and Biomass Equations for Nepalese Tree Species

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Genç Kızılçam Biyokütle Denklemleri; Kozağacı Orman İşletme Şefliği Örneği

Ormanların karbon depolama kapasitelerine ilişkin hesaplamalar tüm dünyada giderek daha önemli hale gelmiştir. Karbon bildirimlerinde uluslararası kurumlar, orman ekosistemlerinde depolanan karbon miktarlarının envantere dayalı olarak, tam ve kesin olarak belirlenmesini arzu etmektedir. Tüm ülkenin ormanlık alanlarındaki karbon stokunun belirlenmesi için, her ağaç türünün farklı habitatlarda biriktirdiği biyokütle miktarının ve bağladıkları karbon miktarının yöresel olarak belirlenmesi gerekmektedir. Bu çalışmada genç kızılçamlar yöresel olarak dikkate alınarak, Mersin Kozağacı Orman İşletme Şefliği içerisindeki saf kızılçam meşcerelerine ait toprak üstü ve toprak altı biyokütle denklemleri geliştirilmiştir. Çalışmada ayrıca kızılçam için odun yoğunluk oranı ve ibre kuru ağırlık oranı belirlenmiştir.

Time delays and pollution in an open‐access fishery

AbstractWe analyze the impacts of pollution on fishery sector using a dynamical system approach. The proposed model presupposes that the economic development causes emissions that either remediate or accumulate in the oceans. The model possesses a block structure where the solutions of the rate equations for the pollutant and the economic activity act as an input for the biomass and effort equation. We also account for distributed delay effects in both the pollution level and the economic activity level in our modeling framework. The weight functions in the delay terms are expressed in terms of exponentially decaying functions, which in turn enable us to convert the modeling framework to a higher‐order autonomous dynamical system by means of a linear chain trick. When both the typical delay time for the economic activity and the typical delay time for the pollution level are much smaller than the biomass time scale, the governing system is analyzed by means of the theory for singularly perturbed dynamical systems. Contrary to what is found for population dynamical systems with absolute delays, we readily find that the impact of the distributed time lags is negligible in the long‐run dynamics in this time‐scale separation regime. Recommendations for Resource Managers Resource managers should be aware of the dynamical interrelations between fisheries and pollution, also those that are time delayed. Time lags between biological, economic, and environmental variables are affecting the time paths of these variables. In the long run, the impact from distributed time lags have a negligible influence on the long‐run dynamical evolution. Neither the past history of economic activities nor the past history of pollution growth can affect the fishery dynamics in the long run. More empirical and theoretical research are needed to increase our understanding of the interrelation between fisheries and pollution.

Woody Species Diversity and Biomass Carbon Sequestration in Private Residential Green Infrastructure of Dilla Town, Southern Ethiopia

Urban forests have an important role in biodiversity conservation, environmental improvement, and ecosystem services including climate change mitigation enhancement. The objectives of the current study were to: assess plant types and management strategies of the owners; woody species’ composition, structure, and diversity; and estimate aboveground biomass of trees and associated carbon stock in private residential green infrastructure (PRGI) at Dilla town. This study was conducted at three kebeles, the lowest administrative unit in Ethiopia. Ninety-four households were randomly selected from a proportional sample size for each kebele. A complete inventory of woody species was done after measuring the area occupied by plants at each household. At plot level, the aboveground biomass of sampled trees was calculated by using an allometric biomass equation developed for agroforestry species. Diversity was described by using different indices The free software EstimateS 9.1.0 was used to generate data for the construction of sample-based rarefaction curves and SPSS version 20 for descriptive statistics. Based on plant types and arrangement, the households manage their PRGI in 15 categories on area size, ranging from 10 m2 to 1229 m2, with an average holding size of 207.5 m2. A total of 66 plant species belonging to 45 families were identified. Overall, a total of 1220 stem ha−1 contributed to an aboveground carbon stock of 64.35 ton ha−1 of which 50.4% is from fruit trees and the rest from timber trees. The results suggest that PRGIs can serve as reservoirs of non-native and native plant species, including five native tree species currently facing conservation concerns.

Length–biomass equations to allow rapid assessment of semi‐aquatic bug biomass in tropical streams

AbstractLength–biomass equations are relatively easy and cost‐effective for deriving insect biomass. However, the exact relationship can vary between taxa and geographical regions. Semi‐aquatic bugs are abundant and are indicators of freshwater quality, but there are no studies investigating the effect of habitat disturbance on their biomass, although it is useful in assessing ecological processes. We identified the best‐fit length–biomass models to predict the biomass of semi‐aquatic bugs (Hemiptera, Gerromorpha) collected from streams in Sabah, Malaysia. We used 259 juvenile and adult semi‐aquatic bugs to compare a range of plausible length–biomass functions, and to assess whether relationships differed across the following families and body forms: (1) Cylindrostethinae, Gerrinae, and Ptilomerinae, which are subfamilies within Gerridae consisting of small‐to‐large bugs that have long and slender bodies, (2) Halobatinae, a subfamily within Gerridae, consisting of small‐to‐medium bugs with wide heads and thoraxes as well as short abdomens, and (3) Veliidae, which are small bugs with stout bodies. Estimation used five fitting functions – linear regression, polynomial regression order 2, 3, and 4, and power regression – on the following groupings: three body forms combined; each body form with life stages (juvenile and adult) combined; and each body form with life stages separated. Power regressions were the best fit in predicting the biomass of semi‐aquatic bugs across life stages and body forms, and the predictive power of models was higher when the biomass of different body forms was calculated separately (specifically for Halobatinae and Veliidae). Splitting by life stages did not always result in additional improvement. The equations from this study expand the scope of possible future ecological research on semi‐aquatic bugs, particularly in Southeast Asia, by allowing more studies to consider biomass‐related questions.

Growth characteristics of Cunninghamia lanceolata in China

Chinese fir (Cunninghamia lanceolata) is one of southern China's most important native tree species, which has experienced noticeable climate-induced changes. Published papers (1978–2020) on tree growth of Chinese fir forests in China were collected and critically reviewed. After that, a comprehensive growth data set was developed from 482 sites, which are distributed between 102.19° and 130.07°E in longitude, between 21.87° and 37.24°N in latitude and between 5 and 2260 m in altitude. The dataset consists of 2265 entries, including mean DBH (cm), mean H (m), volume (m3), biomass (dry weight) (kg) (stem (over bark) biomass, branches biomass, leaves biomass, bark biomass, aboveground biomass, roots biomass, total trees biomass) and related information, i.e. geographical location (Country, province, study site, longitude, latitude, altitude, slope, and aspect), climate (mean annual precipitation-MAP and mean annual temperature-MAT), stand description (origin, age, canopy density and stand density), and sample regime (plot size, number and investigation year). Our results showed that (1) the best prediction of height was obtained using nonlinear composite model Height = 1.3 + 34.23*(1 - {text{e}}^{{left( { - 0.01025*{text{DBH}}^{1.347} } right)}} ), (R2 = 0.8715, p < 0.05), (2) the equation Volume = DBH2/(387.8 + 19,190/Height) (R2 = 0.9833, p < 0.05) was observed to be the most suitable model for volume estimation. Meanwhile, when the measurements of the variables are difficult to carry out, the volume model Volume = 0.03957 − 0.01215*DBH + 0.00118*DBH2 (R2 = 0.9573, p < 0.05) is determined from DBH only has a practical advantage, (3) the regression equations of component biomass against DBH explained more significant than 86% variability in almost all biomass data of woody tissues, which were ranked as total trees (97.25%) > aboveground (96.55%) > stems (with bark) (96.17%) > barks (88.95%) > roots (86.71%), and explained greater than 64% variability in branch biomass. The foliage biomass equation was the poorest among biomass components (R2 = 0.6122). The estimation equations derived in this study are particularly suitable for the Chinese fir forests in China. This dataset can provide a theoretical basis for predicting and assessing the potential of carbon sequestration and afforestation activities of Chinese fir forests on a national scale.

Allometry, Biomass and Productivity in Pinus massoniana Forests of China: An Updated Review of Published Data

Field measurements of forest biomass are labor-consuming and usually destructive, but such direct datasets are the foundation for further developing indirect estimation such as biomass equation and biomass expansion factors that were used to scale biomass estimation or carbon storage from site or local surveys up to regional or national level. Pinus massoniana is widely distributed throughout 17 provinces of China and has a high economic value. This study is an updated review of the allometric equations, biomass allocation data and productivity estimations in Pinus massoniana forests of China published in articles during the period from 1982 to 2019. Different allometric equations of Pinus massoniana showed site-specific dependences. The aboveground and total tree biomass were expressed as power equations of mean age for Pinus massoniana forests. The mean value of aboveground tree biomass, total tree biomass and ecosystem biomass across all ages for Masson pine forests was 109.81, 127.65 and 138.76 Mg/ha, respectively. The mean proportion of stem, branch and foliage to aboveground tree biomass for Pinus massoniana was 77.1%, 15.9% and 7%, respectively. The tree root biomass continuously increased with the aboveground tree biomass and a power relationship was found between the tree root biomass and aboveground tree biomass for Pinus massoniana forests. The tree net primary productivity increased with aboveground tree biomass for Pinus massoniana forests, and the relationship was expressed as a linear equation. The mean tree, litter and ecosystem net primary productivity was 7.04, 4.88 and 8.30 Mg/ha/year, respectively. These findings provide key parameters for the biomass estimation and carbon accounting studies of widely planted Pinus massoniana forests in China.