ABSTRACT Black pine plantations play a vital role in forest productivity, carbon sequestration, and ecological restoration in Türkiye, particularly across the Central Anatolia region. This study was conducted in Akdağ National Park, a protected area in Western-Central Anatolia. Thinning treatments with four intensities – unthinned (control), light (15% basal area reduction), moderate (25%), and heavy (35%) – were applied with three replications. Tree measurements (height and DBH) as well as soil and forest floor samples were collected after thinning in 2018 and again in 2023. Tree carbon stocks were estimated using biomass equations. Forest floor characteristics (mass, C, N, P, K) and soil properties (bulk density, pH, organic matter, nutrients) were analyzed. Forest floor mass was significantly higher in light thinning (3.34 kg ha−1), while nutrient contents did not differ significantly. Thinning intensity significantly affected ecosystem carbon stocks, which were highest in the control (285.8 t ha−1), followed by light (264.3 t ha−1), moderate (218.0 t ha−1), and heavy thinning (199.8 t ha−1). Increased thinning intensity reduces carbon storage. Light and moderate thinning more effectively maintain ecosystem carbon stocks than heavy thinning, indicating their suitability for carbon-focused forest management.

Urban trees sequester and store carbon through photosynthesis, while their planting and maintenance processes consume energy and materials, resulting in the release of carbon back into the atmosphere. Therefore, a comprehensive and scientifically rigorous assessment of the net carbon sequestration capacity of landscape trees must account for both carbon uptake and emissions. In this study, three types of urban green spaces-parks, roadsides, and residential areas-were selected as research sites. Based on the life cycle assessment, biomass equation method and emission factor method, the life cycle carbon sequestration and carbon emissions of the main tree species in Shihezi City were calculated. The genotype main effect plus genotype-by-environment interaction (GGE) biplot and Pearson correlation analyses were employed to elucidate species-environment interactions across the three green space types and to identify management strategies for maximizing net carbon sequestration. The main findings were as follows: (1) The mean carbon storage per tree in parks (366.78 kgC tree-1) was significantly higher than that in roadsides (305.59 kgC tree-1) and residential green spaces (236.39 kgC tree-1), with Ulmus pumila exhibiting the highest per-tree carbon storage (595.39 kgC tree-1). (2) The order of carbon emissions in the whole life cycle is: park (461.15 kgC tree-1)> roadside (395.48 kgC tree-1)> residential area (283.65 kgC tree-1). Irrigation is the main emission source, accounting for more than 50% of the total carbon emissions in the whole life cycle. (3) Under current management practices, Ulmus pumila consistently maintained a positive life cycle carbon budget across all green space types, whereas some other large tree species showed positive carbon budgets only in specific green space types and had negative carbon budget in others. All small tree species exerted negative effects on the carbon sequestration function of urban green spaces across all three types of green space. Based on the findings of this study, strategies for low-carbon urban green space construction were proposed through enhancing carbon sequestration and reducing maintenance-related emissions.

Abstract Dry afromontane forests are important for climate change mitigation in Ethiopia, yet a systematic synthesis of their biomass and carbon storage capacity is lacking. This review aims to (1) quantify the biomass and carbon stocks in these forests, (2) map their geographic distribution, and (3) identify the key biophysical and anthropogenic factors driving carbon stock variation. Following the PRISMA guidelines, we systematically reviewed 72 relevant studies (2000–2025) identified from Web of Science, Scopus, PubMed, and Google Scholar. The results showed that aboveground biomass (AGB) ranged from 35.1 ± 16.6 t ha⁻¹ in Desa Forest to 720.7 ± 503 t ha⁻¹ in Banja Forest, with belowground biomass (BGB) following a similar pattern and generally representing 18–22 % of AGB. Soil organic carbon (0–30 cm depth) also varies substantially from 58 ± 7.6 t ha⁻¹ in Gara Muktar to 277.6 ± 11.6 t ha⁻¹ in Egdu Forest. Forests such as Banja, Gedo, Egdu, Ades, and Zafenigus show particularly high AGB, highlighting the capacity of well-conserved high forests to store roughly 215–425 t ha⁻¹, depending on site conditions. Factors contributing to this variation include measurement errors, the choice of allometric equations for biomass and carbon estimation, species composition and community structure, and topographic factors such as altitude and slope. Additionally, human disturbances play a significant role. Future research focuses on integrating advanced remote sensing technologies, particularly LiDAR, and applying climatic and biogeochemical models (e.g., CO 2 Flux, BIOME-BGC) to simulate future biomass and carbon dynamics.

This study evaluated airborne laser scanning (ALS) as a large-scale tool for forest carbon quantification by comparing ALS-derived estimates with traditional field sampling across multiple forest strata. Above-ground biomass was estimated using two different, commonly used equations, while below-ground biomass was derived from peer-reviewed root-to-shoot ratios. ALS and field estimates differed across forest strata and carbon pools: ALS detected higher live tree carbon in harvested areas—capturing residual trees often missed in traditional cruises—but underestimated dead wood carbon, relative to field-based methods. Consistent differences were also observed between biomass equations, with Woodall estimates being 12.8% and 16.7% lower than Jenkins estimates for ALS and field methods, respectively. The study further incorporated soil organic carbon (SOC) and carbon dating data, providing additional insight into subsurface carbon stocks and the temporal dynamics of forest carbon pools. Overall, ALS proved to be an efficient, repeatable, and scalable method for carbon assessment, offering clear advantages in monitoring carbon flux over time when integrated with forest management protocols. Although further research is needed to refine biomass equations and explore emerging technologies such as Geiger Mode LiDAR, ALS has strong potential to enhance forest carbon crediting processes and support climate change mitigation goals.

Accurate estimation of forest carbon stocks is essential for climate change mitigation, particularly in peatland ecosystems known for their high soil organic carbon content. However, biomass equations currently used in Germany, such as the "regular" biomass equation of the National Forest Inventory integrated in the TapeS R package, are primarily calibrated for mineral soil sites and may misestimate biomass in peatland forests. This study evaluates the applicability of existing biomass equations for Alnus glutinosa and Betula pubescens in forested peatlands across Germany by comparing estimates of the biomass equation of the National Forest Inventory with a set of alternative allometric functions, including peatland-specific equations. Using data from 65 forests at peatland and 1266 forests at mineral soil sites, we assessed tree growth patterns, aboveground biomass, and carbon stocks. Results indicate significant differences in growth dynamics between peatland and mineral sites, with trees at peatland sites exhibiting lower heights and biomass at a given diameter. Despite this, stand level carbon estimates by the standard biomass equation of the National Forest Inventory aligned closely with the mean of all equations for both species and did not show a consistent bias, although it overestimated individual tree biomass for Betula pubescens. Notably, peatland-specific functions show high variability and no clear advantage over the biomass equation of the National Forest Inventory. We conclude that while the equation of the National Forest Inventory currently provides robust estimates for the carbon stock of peatland forests in Germany on stand level for Betula pubescens and Alnus glutinosa, future recalibration may be needed as restoration efforts and climate change alter site conditions. For local-scale applications, especially in intact or rewetted peatlands, site-adapted equations are recommended to account for the high spatial heterogeneity and complex growth dynamics of these ecosystems.

Abstract. Mangroves are highly productive ecosystems and are considered the largest potential sinks of atmospheric carbon. However, variability among species and communities significantly influences total carbon stock. This study evaluates the species-wise above-ground biomass (AGB) and carbon stock potential of mangrove species in Maharashtra, India, using primary field data from 44 geotagged plots. The available biomass equations were used to estimate the AGB and carbon at the plot level. Total carbon content was determined by multiplying per hectare AGB with the mangrove area for the 2018–19. Comparative assessment was carried out for the biomass and carbon between different mangrove species, class (dense and open) in the northern (Palghar, Thane, Mumbai, Raigarh districts) and southern zones of Maharashtra (Ratnagiri and Sindhudurg districts). Out of 18 recorded species, Avicennia marina contributes the highest carbon stock per hectare (particularly in the dense plots of the northern zones) of more than 1500 t/ha, followed by A. officinalis and Sonneratia apetala. The lowest biomass was observed in Sonneratia alba (within open plots in the southern region) with values falling below 100 t/ha. The top 8 carbon-storing species together accounted for more than 90% of total estimated carbon. Species contribution of the different mangrove species to the average carbon stock in the northern zone was in the order of. Avicennia marina > Avicennia officinalis > Sonneratia apetala, and in the southern zone it was broadly Avicennia officinalis > Lumnitzera racemosa > Excoecaria agallocha. The biomass recorded was specific for each mangrove species, which may be attributed to adaptabilities of these species to environmental variables. The accumulation of biomass is primarily influenced by species, wood density, age of tree, climate, management regime, proximity to water channel, and nutrient sediment that supplement mangrove productivity (Kairo et al., 2008; Fatoyinbo et al., 2008). This study confirms significant inter-species variability in carbon sequestration potential among mangrove species in Maharashtra. Species such as Avicennia marina and A. officinalis should be prioritized in restoration projects for maximizing carbon benefits.

Seedlings and saplings are vital elements of understory vegetation, the accurate biomass estimation of which is important for quantifying carbon storage within forest ecosystems. With data of 620 seedlings and saplings individuals from six species-Acer mono, Populus davidiana, Ulmus laciniata, Fraxinus mandschurica, Quercus mongolica, and Syringa amurensis-across 101 broadleaf mixed forest plots in Maoershan Mountain, we developed power-function biomass models utilizing basal diameter, plant height, and crown area as independent variables and identify the optimal models as the base models. We further assessed the error structure of each base model through likelihood analysis, and established a biomass equation system for the six species using seemingly unrelated regression (SUR). The results showed that the univariate model utilizing only basal diameter was the most effective for F. mandschurica. For S. amurensis, the ternary model that encompassed basal diameter, plant height, and crown area was superior. For the other species, the binary biomass models that included basal diameter and plant height yielded the best results. The adjusted coefficients of determination (Ra2) varied from 0.716 to 0.990, while the root mean square errors (RMSE) ranged from 0.060 to 6.403, with all model parameters showing significance. The error structure for both component and total biomass across the species was found to be multiplicative (ΔAICc>2). Consequently, linear biomass models following logarithmic transformation were employed to develop the SUR biomass models for the six species. These models had high Ra2 values (0.713-0.987) and low RMSE values (0.062-7.408), suggesting they were appropriate for accurately estimating the biomass of seedlings and saplings in the understory.

Straw incorporation, as a widely recommended agronomic practice, has been continuously enhancing global crop production and soil–water conservation. However, the absence of a direct predictive capability for the long-term residual biomass of incorporated straw, based on management practices, constrains an accurate assessment of its effectiveness for soil conservation. To address these knowledge gaps, this study conducted systematic 4-year in situ monitoring of decomposition pits with varying incorporation amounts (A6 with 6 kg ha−1, A8 with 8 kg ha−1, A10 with 10 kg ha−1, A12 with 12 kg ha−1, and A14 with 14 kg ha−1) and burial depths (D1 with 0–10 cm, D2 with 10–20 cm, D3 with 20–30 cm, D4 with 30–40 cm, D5 with 40–50 cm) to analyze long-term decomposition dynamics. Furthermore, time-dependent equations for post-incorporation residual biomass were developed based on management variables (incorporation amount and burial depth) to enhance the accuracy of soil loss prediction. The results showed that the higher incorporation amounts accelerated decomposition, with the residual straw ratios (RSRs) reduced by 27.4–62.2% compared to lower amounts at equivalent burial depths. Decomposition slowed with depth, and the RSR increased significantly with greater burial depth, rising at rates of 0.2–1.2% cm−1 (p < 0.05). The RSR decreased significantly with longer incorporation duration at rates of 6.9–18.6% a−1 (p < 0.05), with deeper soil layers exhibiting greater decline rates than shallower depths. The relationship between RSR and landfill amount (m), burial depth (d), and landfill years (a) is represented as follows: RSR = 101.62 a−1 m−0.54 d0.45 (R2 = 0.76). Based on this equation, the soil loss ratios (SLRs) under continuous straw incorporation for 4 years were estimated, and the results suggest that constant straw incorporation exerts cumulative effects, progressively reducing the SLR. This study provides the theoretical foundation for promoting and managing straw incorporation practices.

Large-scale tree planting programs that store carbon provided by wood and non-wood products are being promoted to mitigate climate change. Assessing the biomass pool of plantations is thus an essential task in forest ecology. This study investigated biomass allocation and allometric equations for above- and belowground components along an age-sequence of Pinus tabuliformis plantations (8, 18, 32, and 46 years old) in northern Hebei Province, China. The biomass of each tree component (root, stem, branch, foliage) was quantified by destructive harvesting. Allometric equations and biomass conversion and expansion factors (BCEFs) were subsequently developed for each tree component. The mean above- and belowground biomass was 5.86, 20.05, 41.26, and 135.28 kg tree−1 and 1.73, 3.42, 11.39, and 27.30 kg tree−1 in the 8-, 18-, 32-, and 46-year-old stands, respectively. The proportion of stem biomass to total tree biomass increased from 28.7% for the 8-year-old stand to 55.8% for 46-year-old stand. In contrast, the contributions of foliage and branch decreased along the chronosequence. The root contribution to total tree biomass also showed a declining trend with stand age. Allometric models based on diameter at breast height showed a good fit (p < 0.001) and incorporating stand age as an additional variable improved the fit of allometric equations (higher R2 and lower ACI) for branch, aboveground, root, and total tree biomass. BCEFs decreased for all tree components as stand age increased. These findings indicate that changes in tree biomass allocation and allometry across stand development must be considered to improve estimates of plantation biomass and carbon stocks at regional and national scales.

BackgroundThe advancement of carbon sequestration projects holds significant potential to deliver mutually beneficial outcomes for both the environment and the economy. In this context, biomass models have been extensively developed to estimate the aboveground biomass of woody plants — such as trees and shrubs — using dendrometric characteristics, like diameter and height. The datasets presented in this study compile dendrometric traits from multiple tree and shrub species, supporting the construction of robust biomass models. As a result, tree and shrub biomass can serve as integral indicators for evaluating the effectiveness of carbon sequestration projects, incorporating key factors such as diameter, height and plantation density. By establishing reliable biomass estimation models, it becomes possible to enhance the monitoring and verification of carbon storage, thereby providing a scientific basis for the planning, management and policy-making of carbon sink initiatives. This approach contributes significantly to ecological restoration and climate change mitigation efforts.New informationThis study presents two dendrometric datasets of individual trees and shrub bushes from carbon sequestration projects in north-western China, covering sites in Xining and Haidong (Qinghai Province), Tianshui (Gansu Province) and Aba (Sichuan Province). Specifically, the tree dataset comprises measurements of canopy breadth (in two perpendicular directions), height, diameter at breast height (DBH) and base perimeter for 2084 individuals across 25 species. The shrub dataset includes crown diameter (in two perpendicular directions), height and basal perimeter for 998 bushes across 36 species. These dendrometric traits serve as key parameters in biomass estimation equations. Furthermore, as the diameter and height of trees and shrubs significantly influence understorey plant diversity — primarily through their effects on stand density, species interactions and community composition — these datasets are valuable for advancing biomass modelling and assessing plant diversity outcomes under conservation management.

Teak (Tectona grandis L.f.) is a leading tropical plantation species valued for high-quality timber and carbon (C) storage. This study assessed stand growth across ages and sites, quantified biomass and C by tree component and stand, and developed DBH-based allometric equations for biomass and C estimation. Six stand ages (5, 6, 9, 11, 14, and 17 years) were assessed in three municipalities of Nayarit, Mexico. Dendrometric inventories in permanent plots and destructive sampling of 35 trees provided calibration data for leaves, branches, stem, and roots. C concentration was determined with an elemental analyzer, and nonlinear regression models were adjusted and validated. Stand biomass and C increased with age, peaking at ages 11–14 (>130 Mg ha−1; >60 Mg C ha−1), with lower values at age 17. San Blas and Rosamorada accumulated significantly more than Tuxpan, reflecting site quality. C concentration was stable across sites and ages, with stem and roots consistently ranging between 48% and 50%, and leaves and branches averaging 45%–46%. Allometric equations were most accurate for stem and total biomass/C (R2 = 0.73–0.79), while foliage showed higher variability. On average, 60%–70% of biomass was allocated to the stem and 15%–20% to roots. Indicators were stable, with an aboveground-to-belowground ratio (A:B) ≈ 4.9 and a biomass expansion factor (BEF) ≈ 1.5. The current annual increment (CAI) presented two main peaks: ~20 Mg ha−1 yr−1 at ages 5–6 and ~11 Mg ha−1 yr−1 at ages 9–11, followed by a decline after age 14. Teak in western Mexico reaches peak productivity at ages 6–11, with belowground biomass essential for accurate C accounting.

Abstract. Ha NT, Bao TQ, Tuan NT, Rodríguez-Hernández DI, Dung NT, Ngoan TT. 2025. Destructive sampling-based allometric equations for biomass and carbon estimation in Acacia hybrid plantations in Southeastern Vietnam. Nusantara Bioscience 16: 203-217. This study developed accurate allometric equations for estimating aboveground and belowground biomass, as well as carbon stocks, for Acacia hybrid (Acacia mangium × Acacia auriculiformis) plantations in Southeastern, Vietnam. A dataset of 45 destructively sampled trees with varying ages and diameter classes was used to validate the models. The fresh biomass of the four tree components (stem, branches, leaves, and roots) was measured for a total of 180 samples. Samples were oven-dried at 105°C for stems and branches, and 80°C for leaves, to determine their biomass. Linear and non-linear equations were employed to model both individual tree and stand-level dry biomass (AGB: aboveground biomass, BGB: belowground biomass, TGBG: total biomass), and carbon stocks (AGC: aboveground carbon, BGC: belowground carbon, TGC: total carbon). Diameter at breast height (DBH), tree height (H), stand density (SD), and stand age (A) were included as predictor variables. The best-fitting models were selected based on coefficients of determination (R²), sum of squared errors (SEE), mean absolute error (MAE), sum of squared residuals (SSR), correction factors (CF), mean absolute percentage error (MAPE), and root mean square error (RMSE), with R² values greater than 0.895 and RMSE values less than 0.363. The results revealed strong relationships between aboveground and belowground biomass, and logarithmic functions of DBH and tree height were found to be good predictors for all biomass components. The key equations are: ln(AGB) = -3.03805 + 0.586847*ln(DBH*H) + 1.58329*ln(DBH); ln(BGB) = -0.597955 + 0.485409*ln(DBH)2; ln(TGB) = -2.65453 + 2.11674*ln(DBH) + 0.57522*ln(H). Among the variables, DBH was found to be particularly effective in estimating BGB. At the stand level, total biomass (TSB) has a significant correlation with stand density, mean diameter, and stand height, as shown in the following equation: Ln(TSB) = -9.85561 + 1.09128*ln(SD) + 1.96789*ln(Ds) + 0.608831*ln(Hs). These models provide foresters with valuable tools for estimating biomass and carbon accumulation in Acacia hybrid plantations. The total carbon stock of the Acacia hybrid population in the study area ranged from 29.0 tons/ha to 313.3 tons/ha. This information can support carbon accounting efforts and contribute to Vietnam's initiatives for carbon reduction and climate change mitigation.

Teluk Kimi District, a coastal location in Nabire Regency with a mangrove forest, has degraded due to many destructive activities. The study seeks fundamental data on species diversity (H', richness, E) and carbon stock potential. Basic random sampling is used. Square plots are used for sampling, with a 10 m × 10 m plot for tree observation, a 5 m × 5 m plot for saplings, and a 2 m × 2 m plot for seedlings, litter, and soil. Carbon stocks are assessed using allometric equations for biomass and destructive sampling for understorey, litter, necromass, and soil. The Taman Wisata Alam (TWA) Nabire species richness was 1.62 and 18. Pantai Pelangi has a 2.11 H’ value and 17 richness. Muara Air Mandidi has a 1.15 H' value and 8 richness. TWA Nabire and Pantai Pelangi have moderate to high biodiversity and balanced species richness. Monodominance is shown in Muara Air Mandidi’s low species richness and H' value. Pantai Pelangi has a community structure with an evenness above 0.6. TWA Nabire’s uniformity approaches ecological stability. Muara Air Mandidi is moderately uneven and dominated by Rhizophora mucronata. TWA Nabire, Pantai Pelangi, and Muara Air Mandidi have carbon stocks of 592.46, 383.47, and 331.67 tC/ha. The moderate carbon stock in TWA Nabire (>500 tC/ha) is suitable for blue carbon conservation. Pantai Pelangi and Muara Air Mandidi are low-class coastal transitions and degraded ecosystems. The second implication concerns the eventual location of community-based rehabilitation.

China has implemented large-scale mangrove restoration and afforestation initiatives in recent years. However, there has been a paucity of research on the growth of mangrove seedlings in a composite stress environment and the allometric growth equation of mangrove seedlings. To enhance juvenile mangrove survival rates and develop precise carbon sequestration models, this study examines biomass accumulation patterns and allometric equation development under diverse environmental and biological conditions. A manipulative field experiment employed a three-factor full factorial design using seedlings from eight mangrove species. The experimental design incorporated three variables: salinity, flooding (environmental stressors), and aboveground interspecific competition (a biological factor). Following a two-year growth period, measurements of surviving seedlings’ basal diameter, plant height, and above- and belowground biomass were collected to assess growth responses and construct allometric models. Results indicated that high salinity reduced total mangrove biomass, whereas prolonged flooding increased tree height. Interspecific competition favored fast-growing species (e.g., Sonneratia caseolaris) while suppressing slow-growing counterparts (e.g., Avicennia marina). Synergistic effects between salinity and flooding influenced biomass and basal diameter, whereas salinity–flooding and salinity–competition interactions demonstrated antagonistic effects on tree height. High salinity, prolonged flooding, and competition elevated the proportion of aboveground biomass allocation. The results suggest that salinity stress and flooding stress were major growth-limiting factors for juvenile mangroves. Slow-growing species are not suitable to be mixed with fast-growing species in mangrove afforestation projects. Allometric models fitting for juvenile mangroves growing under different environmental factors were also developed. This study deepens our understanding of the growth of mangrove seedlings under composite stress conditions, provides effective tools for assessing the carbon sink potential of mangrove seedlings, and provides scientific guidance for future mangrove restoration projects.

This study presents a comprehensive methodology for estimating potential biomass and carbon accumulation in European afforestation activities expected over a 40-year timespan, developed for the Life Terra project (LIFE19 CCM/NL/001200). We synthesized data from allometric equations, Yield tables, National Forest Inventories, and National Greenhouse Gas Inventory Reports across four European biogeographic regions: Alpine, Atlantic, Continental, and Mediterranean. While Life Terra encompasses six planting categories (ecological restoration, timber plantations, agroforestry/food forests, gardens, green infrastructure, and others), our analysis focused primarily on timber plantations due to data availability and reliability constraints. The study showed significant regional variations in planting density and growth patterns. Initial planting densities in timber plantations varied substantially across biogeographic regions (1,869–7,702 trees/ha), following exponential decline patterns over time. By year 40, individual tree biomass estimates ranged from 0.08 to 0.20 t/tree across regions and species types (conifers and broadleaves), with survival rates varying between 22.0 and 49.7%. This translated to stand-level biomass estimates of 54.7–232.6 t/ha at age 40 years. Our biomass estimates generally aligned with country-specific literature and IPCC default values, though showing considerable variation across sites, highlighting the importance of local conditions in tree growth and stand dynamics. The study provides a robust framework for assessing carbon sequestration potential in European afforestation projects, while acknowledging key uncertainties related to survival/mortality rates and climate change impacts. This methodology remains open to refinement through additional biomass equations and revised Yield tables. The future field validation studies should also include non-timber plantation categories that are not covered here.

The complexity of forest ecosystems leads to differences in the distribution patterns of different vegetation types along elevation gradients. This study aimed to explore the characteristics of AGB variations along elevation gradients for different forest types and tree species components in the Qinling–Daba Mountains. Based on 329 field vegetation survey plots, including four sampling transects and four representative mountains, individual tree AGB was calculated using allometric biomass equations. Further, generalized additive models (GAMs) were used to investigate the relationships between AGB and elevation for four forest types (broadleaf forests, coniferous forests, mixed coniferousbroadleaf forests, and shrublands) and three AGB components (total AGB (tAGB), broadleaf species AGB (bAGB), and coniferous species AGB (cAGB)) across eight vegetation survey regions. The results showed that the AGB of different forest types is significantly related to elevation (p < 0.05), with broadleaf forest AGB showing a unimodal pattern with elevation, coniferous forest and mixed forest AGB increasing with elevation, and shrubland AGB exhibiting a noticeable rise at higher elevations. The AGB components across different vegetation survey regions also showed significant relationships with elevation (p < 0.05), with broadleaf species AGB displaying a monotonically increasing trend in regions with a small elevation range and exhibiting a unimodal or bimodal distribution in regions with a large elevation range, while coniferous species AGB generally increased with elevation. Although elevation significantly influenced forest AGB, the variation in R2 values indicated that elevation is not the sole determinant of AGB variation. This study improves the understanding of spatial patterns of forest biomass along elevation gradients.

Allometric aboveground biomass equations to quantify carbon content of Southeast Australian saltmarsh vegetation

Abstract. In the context of global change, it is essential to quantify and monitor the carbon stored in forests. Allometric equations are mathematical models that predict the biomass of a tree from dendrometrical characteristics that are easier to measure, such as tree diameter, height, or wood density. Various model forms have been proposed for allometric equations. Moreover, the model choice has a critical influence on the estimate of the biomass of a forest. So far, model selection for allometric equations has been performed based on the tree-level predictive performance of the models. However, allometric equations are used to estimate the biomass of plots rather than individual trees. The distribution of trees sampled for establishing allometric equations often differs from the forest structure. Moreover, at the plot level, the residual individual errors for different trees can cancel off. Therefore, we expect the plot-level predictive performance of a model to differ from its tree-level performance. Using a dataset giving the observed biomass of 844 trees in central Africa and a null model for the size distribution of trees in the forest, we simulated forest plots between 0.1 and 50 ha in area. Then, using a Monte Carlo approach, we calculated the mean sum of squared errors (MSS) of the differences between observed and predicted plot biomass. We showed that MSS could be well approximated by a three-term formula, where the first term corresponded to bias, the second one corresponded to the tree residual error, and the third one corresponded to the uncertainty on model coefficients. For small plots (≤ 0.1 ha), the plot-level predictive performance was dominated by the tree residual error term. Model selection based on plot-level predictive performance was then consistent with that based on tree-level performance. For large plots, this term vanished. Model selection based on plot-level performance could then differ from that based on tree-level performance. In the case of large plots, chains of models that combined a general equation to predict biomass and local equations to predict some of the predictors of the biomass equation could provide a good trade-off between the bias in and the uncertainty on model coefficients. We recommend using plot-level rather than tree-level predictive performance to select allometric equations. The three-term formula that we developed provides an easy way to assess the effect of plot size on model selection and to balance the respective contributions of bias, tree residual error, and the uncertainty on model coefficients.

Validated allometric models for volume, biomass, carbon, and nutrient estimation in forest ecosystems of Bangladesh: A step toward sustainable forest management and climate resilience

Comparing statistical and deep learning approaches for simultaneous prediction of stand-level above- and belowground biomass in tropical forests