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Background and Objectives: It is commonly assumed that allometric biomass models are species-specific and site-specific. However, the magnitude of species and site dependency in these models is not well-known. This study aims to investigate the variation in allometric models (i.e., aboveground biomass predicted by diameter at breast height and tree height) that has originated from the differences between tree species and between sites, thereby contributing to a better understanding of species and site-specificity issue in these models. Materials and Methods: The study is based on two large biomass datasets of 4921 and 5199 trees, from Eurasia and Canada. Using a nested ANOVA model on relative aboveground biomass residuals (with species and site as random effects), the proportion of variance explained by species or site was assessed by means of Variance Partition Coefficient (VPC). Results: The proportion of variance explained by species (VPCspecies = 42.56%, SE = 6.10% for Dataset 1 and VPCspecies = 47.54%, SE = 6.07% for Dataset 2) was larger than that explained by site (VPCsite = 20.08%, SE = 3.35% for Dataset 1 and VPCsite = 8.27%, SE = 1.38% for Dataset 2). The proportion of variance explained by site decreased by 24%–44% and the proportion of variance explained by species changed only slightly, when height is included in the allometric biomass models (i.e., models based on diameter at breast height alone, compared to models based on diameter at breast height and tree height). Conclusions: Allometric biomass models were more species-specific than they were site-specific. Therefore, the species (i.e., differences between species) seems to be a more important driver of variability in allometric models compared to site (i.e., differences between sites). Including height in allometric biomass models helped reduce the dependency of these models, on sites only.

China contains 119 million hectares of natural forest, much of which is secondary forest. An accurate estimation of the biomass of these forests is imperative because many studies conducted in northeast China have only used primary forest and this may have resulted in biased estimates. This study analyzed secondary forest in the area using information from a forest inventory to develop allometric models of the aboveground biomass (AGB). The parameter values of the diameter at breast height (DBH), tree height (H), and crown length (CL) were derived from a forest inventory of 2,733 trees in a 3.5 ha plot. The wood-specific gravity (WSG) was determined for 109 trees belonging to ten species. A partial sampling method was also used to determine the biomass of branches (including stem, bark and foliage) in 120 trees, which substantially easy the field works. The mean AGB was 110,729 kg ha–1. We developed four allometric models from the investigation and evaluated the utility of other 19 published ones for AGB in the ten tree species. Incorporation of full range of variables with WSG-DBH-H-CL, significantly improved the precision of the models. Some of models were chosen that best fitted each tree species with high precision (R2 = 0.939, SEE 0.167). At the latitude level, the estimated AGBof secondary forest was lower than that in mature primary forests, but higher than that in primary broadleaf forest and the average level in other types of forest likewise. 

There are numerous examples in the literature where researchers use multiple-regression models to predict ratio standards despite known dangers associated with such methodologies. The solution, to use allometric models, also appears to have been ignored, for example, when predicting cardiorespiratory fitness, using the ratio standard VO2max (ml·kg−1·min−1). Cross-sectional. This case-study compares a previously published multiple regression equation to predict VO2peak (ml·kg−1·min−1) that adopted additive predictors of body mass index (kg·m−2) and a 6-minute run/walk test, with an alternative multiplicative allometric model given by VO2peak (l·min−1) = Mk1 · HTk2 · 6WRTk3 · exp(a + b · age + c · age2) · ε, thought to provide a more interpretable model, as well as providing a superior quality-of-fit. The strong association between VO2peak (l·min−1) and body mass also identified the presence of heteroscedasticity, a characteristic in data that can be resolved using allometry. The allometric model explained over 90 % of the variance (R2 = 0.91) compared to less than 60 % (R2 = 0.58) reported by the original study. The allometric model's results also appear more interpretable, with a positive mass exponent similar to that previously reported in the literature M0.66 based on sound physiological grounds. The positive height exponent can also be explained given that taller children have greater lung function. Finally, the age quadratic identified that children's VO2peak peaks during puberty. This case study provides powerful evidence that allometric models are more interpretable and provide a superior fit compared with multiple regression models when predicting ratio standards. Note that a simple algebraic adjustment enables researchers to predict the ratio standard VO2peak (ml·kg−1·min−1) without further analyses.

Improved estimates of aboveground biomass (AGB) are required to improve our understanding of the productivity of mangrove forests to support the long-term conservation of these fragile ecosystems which are under threat from many natural and anthropogenic pressures. To understand how individual species affects biomass estimates in mangrove forests, five species-specific and four genus-specific allometric models were developed. Independent tree inventory data were collected from 140 sample plots to compare the AGB among the species-specific models and seven frequently used pan-tropical and Sundarbans-specific generic models. The effect of individual tree species was also evaluated using model parameters for wood densities (from individual trees to the whole Sundarbans) and tree heights (individual, plot average and plot top height). All nine developed models explained a high percentage of the variance in tree AGB ( R 2 = 0.97–0.99) with the diameter at breast height and total height (H). At the individual tree level, the generic allometric models overestimated AGB from 22% to 167% compared to the species-specific models. At the plot level, mean AGB varied from 111.36 Mg ha −1 to 299.48 Mg ha −1 , where AGB significantly differed in all generic models compared to the species-specific models ( p   < 0.05). Using measured species wood density (WD) in the allometric model showed 4.5%–9.7% less biomass than WD from published databases and other sources. When using plot top height and plot average height rather than measured individual tree height, the AGB was overestimated by 19.5% and underestimated by 8.3% ( p   < 0.05). The study demonstrates that species-specific allometric models and individual tree measurements benefit biomass estimation in mangrove forests. Tree level measurement from the inventory plots, if available, should be included in allometric models to improve the accuracy of forest biomass estimates, particularly when upscaling individual trees up to the ecosystem level.

Weller’s allometric model assumes that the allometric relationships of mean area occupied by a tree , i.e., the reciprocal of population density , , mean tree height , and mean aboveground mass density to mean aboveground mass hold. Using the model, the self-thinning line of overcrowded Kandelia obovata stands in Okinawa, Japan, was studied over 8 years. Mean tree height increased with increasing . The values of the allometric constant and the multiplying factor are 0.3857 and 2.157 m kg −θ , respectively. The allometric constant and the multiplying factor are −0.01673 and 2.685 m −3  kg 1−δ , respectively. The value was not significantly different from zero, showing that remains constant regardless of any increase in . The average of , i.e., biomass density , was 2.641 ± 0.022 kg m −3 , which was considerably higher than 1.3–1.5 kg m −3 of most terrestrial forests. The self-thinning exponent and the multiplying factor were estimated to be 1.585 and 16.18 kg m −2α , respectively. The estimators and are dependent on each other. Therefore, the observed value of cannot be used for the test of the hypothesis that the expectation of the estimator equals 1/3, i.e., , or 1/4, i.e., . The value was 0.6310, which is the same as the reciprocal of the self-thinning exponent of 1.585, and was not significantly different from 2/3 ( t  = 1.860, df  = 191, p  = 0.06429), i.e., . Thus the self-thinning exponent is not significantly different from 3/2 based on the simple geometric model. On the other hand, the self-thinning exponent was significantly different from 3/4 ( t  = 6.213, df  = 191, p  = 3.182 × 10 −9 ), i.e., . Therefore, the self-thinning exponent is significantly different from 4/3 based on the metabolic model.

Existing global models to predict standing biomass are based on trees characterized by a single principal stem, well developed in height. However, their use in open woodlands and shrublands, characterized by multistemmed species with substantial crown development, generates a high level of uncertainty in biomass estimates. This limitation led us to (a) develop global models of shrub individual aboveground biomass based on simple allometric variables, (b) to compare the fit of these models with existing global biomass models, and (c) to assess whether models fit change when bioclimatic variables are considered. Location: Global. Time period: Present. Major taxa studied: 118 species of shrubs. Methods: We compile a database of 3,243 individuals across 49 sites distributed worldwide. Including stem basal diameter, height and crown diameter as predictor variables, we built potential models and compared their fit using generalized least squares. We used mixed effects models to determine if bioclimatic variables improved the accuracy of biomass models. Results: Although the most important variable in terms of predictive capacity was stem basal diameter, crown diameter significantly improved the models? fit, followed by height. Four models were finally chosen, with the best model combining all these variables in the same equation [R 2 = 0.930, root mean square error (RMSE) = 0.476]. Selected models performed as well as established global biomass models. Including the individual bioform significantly improved the models? fit. Main conclusions: Stem basal diameter, crown diameter and height measures could be combined to provide robust aboveground biomass (AGB) estimates of individual shrub species. Our study supplements well-established models developed for trees, allowing more accurate biomass estimation of multistemmed woody individuals. We further provide tools for a methodological standardization of individual biomass quantification in these species. We expect these results contribute to improve the quality of biomass estimates across ecosystems, but also to generate methodological consensus on field biomass assessments in shrubs.

AIM: We developed a set of statistical models to improve spatial estimates of mangrove aboveground biomass (AGB) based on the environmental signature hypothesis (ESH). We hypothesized that higher tidal amplitudes, river discharge, temperature, direct rainfall and decreased potential evapotranspiration explain observed high mangrove AGB. LOCATION: Neotropics and a small portion of the Nearctic region. METHODS: A universal forest model based on site‐level forest structure statistics was validated to spatially interpolate estimates of mangrove biomass at different locations. Linear models were then used to predict mangrove AGB across the Neotropics. RESULTS: The universal forest site‐level model was effective in estimating mangrove AGB using pre‐existing mangrove forest structure inventories to validate the model. We confirmed our hypothesis that at continental scales higher tidal amplitudes contributed to high forest biomass associated with high temperature and rainfall, and low potential evapotranspiration. Our model explained 20% of the spatial variability in mangrove AGB, with values ranging from 16.6 to 627.0 t ha⁻¹ (mean, 88.7 t ha⁻¹). Our findings show that mangrove AGB has been overestimated by 25–50% in the Neotropics, underscoring a commensurate bias in current published global estimates using site‐level information. MAIN CONCLUSIONS: Our analysis show how the ESH significantly explains spatial variability in mangrove AGB at hemispheric scales. This finding is critical to improve and explain site‐level estimates of mangrove AGB that are currently used to determine the relative contribution of mangrove wetlands to global carbon budgets. Due to the lack of a conceptual framework explicitly linking environmental drivers and mangrove AGB values during model validation, previous works have significantly overestimated mangrove AGB; our novel approach improved these assessments. In addition, our framework can potentially be applied to other forest‐dominated ecosystems by allowing the retrieval of extensive databases at local levels to generate more robust statistical predictive models to estimate continental‐scale biomass values.

Forests and woodlands are critical terrestrial carbon stores. Tree aboveground biomass (AGB) can be estimated using allometric models and terrestrial laser scanning (TLS). However, internal tree stem damage from biotic decay is an unresolved source of error for both TLS and allometries, with implications for accurate carbon assessment. We destructively harvested 63 TLS‐scanned trees in an Australian savanna, quantified internal damage in each tree by sampling cross sections at multiple heights, and modelled the effect of damage on AGB estimation for individual trees and total estimated biomass. We tested the performance of TLS AGB modelling against five allometries, applying both database and field‐measured wood specific gravity. For TLS‐modelled and allometric AGB estimates, we tested if tree size and level of internal stem damage contributed to AGB deviations. Approximately half of the trees in the study sustained 1–10% damage by volume, which was most extensive in the base and main trunk, decreasing into the crown. On average, damaged trees had 5% internal stem damage (by volume, SD = 6.65%), with some as high as 30%. We found TLS‐derived quantitative structural models (TLS‐QSMs) using field‐measured wood specific gravity to be most accurate in estimating total biomass (R2 = 0.99, +0.59% bias). TLS‐QSMs tended to overpredict AGB of large, damaged trees, and AGB estimates from allometric models were largely unaffected by internal damage. For individual trees, all methods were effective for predicting field‐measured AGB (R2 > 0.84) and several ASMs performed well (± ~10% bias). In the absence of local wood specific gravity calibration, a pantropical ASM was most accurate. For systems where internal stem damage is low (<10% of tree volume), TLS can be used to estimate AGB with low levels of error, however more damaged wooded ecosystems (>10%) are likely to produce inflated biomass estimates if TLS is used without calibration for damage. Internal stem damage should be quantified in ASMs and incorporated into TLS‐modelled AGB calibration to avoid biomass overestimation and maintain high standards of precision in forest carbon accounting.

Light Detection and Ranging (LiDAR) provides three-dimensional information that can be used to extract tree parameter measurements such as height (H), canopy volume (CV), canopy diameter (CD), canopy area (CA), and tree stand density. LiDAR data does not directly give diameter at breast height (DBH), an important input into allometric equations to estimate biomass. The main objective of this study is to estimate tree DBH using existing allometric models. Specifically, it compares three global DBH pantropical models to calculate DBH and to estimate the aboveground biomass (AGB) of the Lake Broadwater Forest located in Southeast (SE) Queensland, Australia. LiDAR data collected in mid-2022 was used to test these models, with field validation data collected at the beginning of 2024. The three DBH estimation models—the Jucker model, Gonzalez-Benecke model 1, and Gonzalez-Benecke model 2—all used tree H, and the Jucker and Gonzalez-Benecke model 2 additionally used CD and CA, respectively. Model performance was assessed using five statistical metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), percentage bias (MBias), and the coefficient of determination (R2). The Jucker model was the best-performing model, followed by Gonzalez-Benecke model 2 and Gonzalez-Benecke model 1. The Jucker model had an RMSE of 8.7 cm, an MAE of −13.54 cm, an MAPE of 7%, an MBias of 13.73 cm, and an R2 of 0.9005. The Chave AGB model was used to estimate the AGB at the tree, plot, and per hectare levels using the Jucker model-calculated DBH and the field-measured DBH. AGB was used to estimate total biomass, dry weight, carbon (C), and carbon dioxide (CO2) sequestered per hectare. The Lake Broadwater Forest was estimated to have an AGB of 161.5 Mg/ha in 2022, a Total C of 65.6 Mg/ha, and a CO2 sequestered of 240.7 Mg/ha in 2022. These findings highlight the substantial carbon storage potential of the Lake Broadwater Forest, reinforcing the opportunity for landholders to participate in the carbon credit systems, which offer financial benefits and enable contributions to carbon mitigation programs, thereby helping to meet national and global carbon reduction targets.

Forest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees. However, the uncertainty in the model predictions is generally ignored with the result that the precision of the large area volume estimates is overestimated. The primary study objective was to estimate the effects of model residual variability and model parameter uncertainty on large area volume estimates and their uncertainties for a study area in northeastern Minnesota, USA. Monte Carlo simulation approaches were used because of the complexities associated with multiple sources of uncertainty and the nonlinear nature of the models. Two conclusions were important. First, for this study, the effects of uncertainty in model predictions on the large area volume estimates and their uncertainties were small when the models were calibrated using an average of 100 or more observations per species and when the average proportion of variance explained by the models was at least 0.95. Second, large area estimates and their uncertainties based on coniferous/deciduous and nonspecific models deviated very little from large area estimates based on species-specific models.