Allometric Models Research Articles

Tree allometry and stand structure in dryland forests relics of northern Côte d’Ivoire

The contribution of dryland forests in West Africa to carbon stocks remains poorly documented due to the lack of appropriate allometric models and inventory data. In this study, we gathered such data for dryland forest relics of northern Côte d’Ivoire. Inventory data were used to determine and sample the dominant species, and to characterize the stand structure. Destructive biomass data were used to develop local mixed-species allometric models for estimating tree aboveground biomass (AGB) and test the validity of existing allometric models, and to characterize the stand structure (including carbon stock). A total of 118 tree species belonging to 102 genera and 36 families were inventoried in 41 plots of 25 × 25 m (625 m² each, totalling 2.56 ha) sampled in three sites in the Korhogo department. Among them, seven predominant species, including one exotic, were selected for destructive sampling in one site, the Lataha Forest Research Station. Destructive biomass data for 34 trees belonging to these dominant species and with diameter at breast height between 6 and 41 cm were used to fit allometric equations specific to this forest type and to test the predictions of existing equations earlier developed for the global tropics and for African dry forests. Six equations integrating three predictors, stem diameter (D), total tree height (H) and wood specific gravity (ρ) were developed and the models with the three predictors showed the best performance. Pantropical models significantly over-estimated tree AGB by 20 % while models developed for African dry forests provided reliable estimates, suggesting a specific allometry of trees in dryland forest relics that needs to be further explored.. With our best AGB model, a quantity of 141.26 ± 92.72 tDM.ha−1 was estimated confirming that these dryland forest relics are important carbon stocks for this region where open vegetation dominates. This study constitutes an important contribution for the carbon accounting programs related to the implementation of REDD+ initiatives in Côte d’Ivoire.

INDIVIDUAL TREE AGB ESTIMATION BASED ON FRACTAL PARAMETERS AND TREE VOLUME

Abstract. Forest is an important component of ecosystem. To estimate forest above-ground biomass (AGB) accurately, this paper proposed an individual tree AGB estimation method based on fractal geometry and individual tree volume. In this study, fractal parameters, such as fractal dimension and intercept were first calculated. And then, a fast tree volume estimation method based on point clouds voxelization was proposed. By combining fractal parameters, tree volume and specific wood density together, an individual tree AGB estimation method was developed. The datasets of three different tree species with harvest referenced AGB values were used for evaluating the performance of the developed model. Experimental results showed that the coefficient of determination (R2) of the developed model was 0.853. Compared with other four traditional allometric models, the proposed model performs the best no matter which accuracy indicator was adopted.

Modelling above-ground biomass estimation using combination of vegetation height and forest canopy density at secondary peat swamp forest of Kalimantan

Rapid estimation of carbon stock, particularly carbon from Above-Ground Biomass (AGB) in a high-potential carbon forest, is required to assess potential carbon emission projects. Satellite imagery has been widely used as the main data source to rapidly estimate AGB. Canopy Height (CH) data from NASA’s Global Ecosystem Dynamics Investigation (GEDI) and Forest Canopy Density (FCD) data from satellite Landsat 8 imagery are widely used to estimate AGB. An allometric model with multiple linear regression equations has been used to estimate the AGB value using both parameters of the FCD value and the CH value. 43 values of calculated AGB by non-destructive field survey located in a peat swamp forest area use as a training dataset to develop an allometric model. The CH and FCD indices performed several tests, and the test result met the requirements for developing allometric equations. The coefficient correlation (r) of CH and AGB from 43 sample plots is 0.546, indicating a moderate correlation. The coefficient correlation (r) for FCD and AGB is 0.267 which indicates a weak correlation. The validation test was carried out to assess AGB from the developed allometric model and AGB of field survey with a non-destructive method. The validation result of the allometric model has the value of coefficient correlation (r), RMSE, and R2, respectively 0.569; 22.235; and 0.324. In terms of allometric model validation, another forest with typical conditions, particularly basal area, the model’s AGB result, and the validated area have RMSE 46.506 and r value 0.664.

Método não destrutivo para estimar a área foliar do crisântemo

ABSTRACT Chrysanthemum (Dendranthema grandiflora) is the second most produced and commercialized ornamental plant in the world. Measuring leaf area through non-destructive methods is fundamental for studies on its growth and production. The estimation of leaf area by linear dimensions of the leaves can be a strategy for this purpose. The objective of this study was to find allometric equations to estimate the leaf area of chrysanthemum. The linear, linear without intercept, quadratic, cubic, power, and exponential regression models were used for the analysis. The choice of equations was based on the highest coefficients of determination. The non-destructive method using allometric models has accuracy for estimating the leaf area (LA) of chrysanthemum from the product between leaf length (L) and leaf width (W). The LA of chrysanthemum can be estimated using the equation ŷ = 0.6611*LW0.9490 (L - leaf length; W - leaf width). This equation will allow researchers and producers to determine leaf area non-destructively.

Biomass estimation models for Acacia saligna trees in restored landscapes

Acacia saligna, originating from Australia, is a naturalized multipurpose tree species widely grown to restore degraded lands of Africa. The contribution of A. saligna in biomass restoration can be quantified using a precise estimation of tree biomass carbon. This study developed species-specific allometric models and evaluated the spatial variation of tree biomass across restored areas in exclosures and open grazing landscapes. These models could play a considerable role in the monitoring of carbon dynamics across A. saligna planation dominated areas. We harvested, excavated, and weighed twenty-one sample trees representing different size classes to develop allometric models for the estimation of aboveground (AGB), belowground (BGB) and total tree (TB) biomass. The average dry-to-fresh mass ratio and the root-to-shoot ratio was 0.47 (±0.13) and 0.28 (±0.14), respectively. Tree biomass significantly correlated with diameter at breast height (r = 0.93; P < 0.001), diameter at stump height (r = 0.88, P < 0.001) and tree height (r = 0.56, P < 0.05). Our best biomass estimation models explained 86%, 82% and 87% of variations in AGB, BGB, and TB, respectively. Models using DSH and DSH & H explained 70%–78% of the variation in AGB, BGB, and TB. Estimated C-stock showed a significant relationship with stem density (R 2 = 0.91, P < 0.01). Estimated TB varied between 1.5–18 Mg ha−1 on grazed land and exclosures. Estimated C-stocks in the exclosure exceeded the estimated C-stock in the open grazing land by ∼60%. This implies that with proper management practices and enrichment planting A. saligna significantly contributes to increasing carbon accumulation on degraded landscapes, playing a key role in climate change mitigation efforts while improving land productivity.

Identification of key somatic features that are common and the ones that differ between swim strokes through allometric modeling.

The aim of this study was to explore which key somatic features are common to four swim strokes and medley, and specifically to identify which characteristics benefit only specific strokes. The sample was composed of 130 swimmers (95 males aged 19.5 ± 2.9 years and 35 females aged 18.4 ± 2.8 years). A set of anthropometric variables was used to predict swimming speed in the four swimming strokes and medley. A multiplicative model with allometric body size components was used to identify the demographic and anthropometric predictors of swimming speed. Trunk height and waist circumference were the only variables significantly different among swimming strokes (p < 0.05). Associations between swimming speed and arm length were similar in breaststroke and medley, and in freestyle, backstroke and butterfly (R2 = 60.9%). The model retained as swimming speed predictors the age2, upper body circumference, hand breadth, waist circumference, and subscapular skinfold thickness (these last two had negative associations). All these predictors were common to all four swim strokes and medley. Arm length was also retained as a significant predictor, but this one varied significantly between the four different swim strokes and medley. These findings highlight the importance of having a "V-shape" trunk, longer upper limbs, and large hands as predictors of swimming performance.

Allometric determinations in the early development of Schoenoplectus californicus to monitor nutrient uptake in constructed wetlands

Studies of Schoenoplectus californicus focus on the development of aboveground biomass estimations. However, below-ground assessments are important for net primary productivity monitoring in wetlands. This study aims to monitor nutrient assimilation by developing allometric models for above and below-ground biomass estimation of S. californicus in constructed wetlands. The plants we evaluated were grown in vitro, allowing us to control the experimental conditions. The results show that shoot length and apex diameter are significant parameters for building our models. Two non-destructive allometric equations were established to predict above and below-ground biomass and the models were highly significant (R2 = 0.79 and 0.49, p < 0.001). During active growth, both the aboveground and below-ground components showed to be essential for removing nutrients from the water. Both non-destructive equations allowed the monitoring of biomass accumulation in constructed wetlands for seven months, demonstrating that this low-cost method can be used to evaluate the performance of wetlands for wastewater.

Estimation of carbon storage of tropical mangroves in the North Maluku Islands region

The mangroves possess a remarkable capacity to absorb carbon dioxide (CO2), surpassing tropical rainforests by fourfold. This underscores the crucial role played by mangroves in global efforts to combat climate change. This study aimed to assess the biomass, carbon content, and CO2 absorption capabilities of mangrove ecosystems. Data collection followed the line transect method, utilizing the Mangrove Watch guide, which involved recording the diameter of mangroves at breast height (DBH), typically around 1.3 meters above the ground, as well as identifying the mangrove species and quantifying the number of stands for each tree species. The analysis of mangrove biomass, carbon content, and CO2 absorption capacity was conducted through an allometric modeling approach. The findings revealed the presence of seven distinct mangrove tree species at the study site, namely Rhizophora mucronata, R. apiculata, R. stylosa, Sonneratia alba, Bruguiera gymnorrhiza, Xylocarpus granatum, and Ceriops decandra. The mangrove density in this area was recorded at 1406 trees per hectare, with a corresponding biomass of 630,508 tons per hectare, a carbon content of 246,035 tons of carbon per hectare, and an impressive CO2 absorption capacity of 902,127 tons of CO2 per hectare. Notably, the mangrove vegetation in this research site exhibited a substantial capacity for CO2 absorption and biomass storage, surpassing many other locations in different regions. These findings underscore the significant role that island mangrove ecosystems can play in mitigating the effects of climate change.

Study on the spatial and temporal correlation and allometric growth mechanism between population aging and carbon emissions in China.

Population aging and carbon emissions are critical issues for China's development. As an enormous complex system, the population and the carbon emission development process have non-negligible differences in time, space, and speed. Therefore, this paper first demonstrates the spatial and temporal correlation between population aging and carbon emissions from 1995 to 2020, then uses the allometric growth analysis model to make a cross-sectional temporal comparison and a vertical spatial comparison of the relationship and development rate of the two, and finally uses the ridge regression model to determine the forces and interaction mechanisms of the factors influencing the relationship between population aging and carbon emissions at allometric rates. The results show that (1) China has a long-term positive temporal correlation effect relationship between population aging and carbon emissions from 1995 to 2020, and the overall correlation is high. The spatial correlation intensity between population aging and carbon emissions varies significantly across Chinese provinces, with a general spatial distribution trend of high in the south, low in the north, and prominent in the center. (2) China's population aging and carbon emissions mainly show a negative allometric growth type of relationship, i.e., a strong trend of population aging expansion and a strengthening trend of carbon emission system shrinking. The number of provinces with negative allometric growth is gradually increasing, mainly in North, East, Central, and Southwest China. (3) From 1995-2010 period to the 2011-2020 period, the influence of the factors of the population, production, and economic dimensions on the population aging index and the carbon emission allometric scalar index gradually weakened, and the influence of the consumption and technology dimensions increased significantly. The factors on the population and consumption side of the dimension mainly contribute to the expansion of carbon emissions and drive positive allometric growth. The production side, the economic structure, and technology dimension factors drive negative allometric growth. The paper fully explores the bidirectional correlation, differential development trend, and interaction mechanism between the two systems of population and carbon emissions and effectively compensates for the lack of research content in terms of elemental correlation, spatial and temporal connection, and speed synergy.

Allometric Aboveground Biomass Models for Northern Delhi Ridge Forest

Bio energy obtained through forest biomass is a promising renewable energy option that provides a more environmentally sustainable alternative to fossil resources by reducing the net flux of greenhouse gases to the atmosphere. India, a developing nation is also in the process of building forest biomass models to study the potential of forests in short and long - term carbon mitigation. Earlier, traditional and destructive sampling were the popular methods for the aforesaid purpose. Recently, non - destructive methods have been adopted applying different allometric equations mostly developed outside India. Thus, there was a need to develop non- destructive allometric aboveground (AGB) models complementing the Indian climate. Also, there was a scarcity of domestic/local AGB models for arid, semi- arid, northern tropical thorn forest of India. The objectives of this study were to develop site-specific and mixed- species allometric models to predict AGB, at the Northern Delhi Ridge Forest (NDRF) using nonlinear mathematical functions. The model has a wide scope and can be applied to adjoining areas as well having similar climatic conditions. Three allometric combinations were tried to fit the aboveground biomass data obtained from the ridge forest. A three- parametric Richard’s model (with predictor variable x=D2H) was best fitted to AGB (R2adj= 0.9463) values for larger trees. Meanwhile, for juvenile plants, a three- parametric Richard function was best fitted (R2adj= 0.9733) when (x = DH) was used as a predictor variable. Cross- validation of model parameters exhibited statistical stability.

Non-trophic interactions amplify kelp harvest-induced biomass oscillations and biomass changes in a kelp forest ecological network model

Kelp forests are important marine ecosystems providing habitat for numerous species. Despite over 50 yr of mechanical harvesting in the Northeast Atlantic, the indirect impacts of kelp harvesting and associated habitat loss on faunal species within kelp forests remain poorly understood. We investigated the consequences of kelp harvesting by developing an allometric trophic network model for a subtidal Northeast Atlantic kelp forest (dominated by Laminaria hyperborea). Additionally, we designed a novel mechanistic model to explore the non-trophic interactions between kelp and age-class 0 Atlantic cod Gadus morhua and between kelp and European lobster Homarus gammarus, specifically focusing on the increased survival benefits provided by the kelp habitat. Simulations were conducted over a 50 yr period, incorporating harvesting cycles of 2, 5, and 9 yr, as well as low and high harvesting intensities. Our findings reveal the complex dynamics resulting from kelp harvesting. The recovery of kelp biomass was observed with 5 and 9 yr harvesting cycles, whereas a decline was observed with a 2 yr cycle. Furthermore, the non-trophic interactions facilitated a higher pre-harvest biomass for both European lobster and Atlantic cod compared to scenarios without this interaction. These results highlight the multitrophic effects of kelp harvesting and emphasize that the recovery of kelp-associated species may not necessarily align with kelp recovery, depending on harvesting intensity and recovery periods. Importantly, our study contributes to a better understanding of the ecological consequences of kelp harvesting and underscores the need for sustainable management practices to mitigate habitat loss in kelp ecosystems.

Modelling the role of marine protected area in biodiversity conservation

This study explored the anthropogenic stressors of a coral habitat and predicted the role of a proposed MPA around coral habitat on biodiversity conservation. The study was conducted in the Saint Martin's Island which is located in the south east coastal area of Bangladesh. Data on anthropogenic stressors were collected through intensive stakeholder survey and the role of MPA in biodiversity conservation was explored by utilizing Allometric Trophic Network model. This study identified about 16 anthropogenic stressors for the coral reef ecosystem. Increasing fishing pressure significantly reduced the species biomass while reduced fishing pressure increased the biomass of species. Biomass of the species in the system with no fishing area increased significantly (3–6%) while compared with the species biomass of an area without fishing restriction. Our simulations found that imposing fishing restriction in an exploited system significantly improve the species standing stock. Our model outputs suggest that declaration of MPA is beneficial for conservation of biodiversity. However, for sustainable implementation of conservation initiatives a proper management framework is necessary integrating diverse stakeholders.

Remote-Sensed Tree Crown Diameter as a Predictor of Stem Diameter and Above-Ground Biomass in Betula pendula Roth and Populus tremuloides Michx. × Populus tremula L. Plantations

Striving for climate neutrality and wider implementation of climate change mitigation measures including tree introduction in agricultural land, request for approaches and general allometric models for estimating carbon (C) stock in tree above-ground biomass (AGB) based on relatively easily obtainable remote sensing data is increasing. Here, we present estimates of individual trees’ crown diameters (CDs) for Betula pendula Roth (B. pendula) and Populus tremuloides Michx. × Populus tremula L. (P. tremuloides × P. tremula) in 11-year-old tree plantations (tree height ranged up to 12.8 and 18.1 m, respectively) in the hemiboreal region of Europe (in Latvia). Individual trees’ CDs were measured using a drone orthophoto map. Afterwards, linear equations were developed to predict individual trees’ stem diameters at breast height (DBHs) and, consequently, tree AGB (which was then converted to C stock) from remote-sensed tree CD data. RMSEs of the prediction models of tree stem DBH were in the ranges of 1.87–2.12 cm for B. pendula and 2.50–3.12 cm for P. tremuloides × P. tremula. This demonstrated approach is applicable to carry out, for instance, a self-assessment and approximate C stock in the AGB of selected tree species by land owners, managers, and other implementers of climate change mitigation measures.

Biomass and carbon stocks in mangrove-afforested areas, central coastal areas of Bangladesh

Mangroves are the remarkably productive ecosystems of the world which losses its natural existence to a great portion and presently emphasis is given on its plantation for future conservation. Thus, present study focused on the development of biomass models for the Sonneratia caseolaris (L.) and Sonneratia apetala (Buch-Ham), and assessment of carbon stock for the plantation of central coastal areas of Bangladesh. Semi-destructive approach was applied for estimating the biomass of trees. The best-fit TAGB models for S. apetala and S. caseolaris plantations were Ln (TAGB) = -1.89 + 2.10 * Ln(D) + 0.23*Ln(H), and Ln (TAGB) = -3.09 + 2.31*Ln(D) + 0.35*Ln(H), respectively. Carbon stocks in TAGB and below-ground were estimated for a young mangrove plantation (<20 years) with Sonneratia apetala and Sonneratia caseolaris; and for an older plantation (>40 years) with Sonneratia apetala separately. The mean ecosystem carbon stock (above- and below-ground) of young and mature were estimated to be 350.53 ± 14.23 t C/ha and 483.64 ± 16.35 t C/ha, respectively which varied significant (p < 0.05) between the sites. The current study recommended assessing the biomass of studied species using the site and species-specific allometric models for the correct carbon accounting.

Allometric equations and carbon stocks assessment for Bambusa vulgaris Schrad. ex J.C.Wendl. in the bimodal rainfall forest of Cameroon

Over the last two decades, bamboo has received increasing attention owing to its socio-economic and environmental importance. Environmentally, bamboo plays an important role in carbon sequestration, thus enhancing climate change mitigation. In Cameroon, knowledge about the importance of Bambusa vulgaris Schrad. ex J.C.Wendl. to climate change mitigation is deficient, despite the fact that it is the most abundant bamboo species in Cameroon's Bimodal rainforest agroecological zone (Agroecological zone 5 - AEZ5). This study was initiated to develop allometric equations and estimate carbon stocks of B. vulgaris in Cameroon's AEZ5. The destructive, clump-based method was used for bamboo biomass data collection on 40 clumps and 86 culms. Regression analyses were performed to obtain allometric models for B. vulgaris biomass prediction which were used for B. vulgaris carbon stocks estimation in AEZ5. The best allometric model for culms was obtained when all predictive variables including age, diameter and height were considered into the model. For clump, the best model was obtained when the number of culms per clump, girth and average diameter were considered in the model. Model quality adjustment was better for clump aboveground biomass (AGB) compared to those of culm AGB. The model of B. vulgaris of the evergreen rainfall forest was validated with a bias of 45.5 %. Bamboo aboveground biomass proportions were 77 %, 15 % and 8 %, respectively for culms, branches and leaves. The mean density and carbon stocks of B. vulgaris were estimated at 2,0679 culms.ha−1, 257 clumps.ha−1, and 61.65 tC ha−1. B. vulgaris has a veritable carbon sequestration capacity which policymakers should consider in climate change mitigation strategies like those linked to payments for ecosystem services, voluntary carbon stocks market, Bonn Challenge, AFR100 initiative, and the Paris agreement ratified by the government of Cameroon.

Aerial Imaging-Based Fuel Information Acquisition for Wildfire Research in Northeastern South Korea

Tree detection and fuel amount and distribution estimation are crucial for the investigation and risk assessment of wildfires. The demand for risk assessment is increasing due to the escalating severity of wildfires. A quick and cost-effective method is required to mitigate foreseeable disasters. In this study, a method for tree detection and fuel amount and distribution prediction using aerial images was proposed for a low-cost and efficient acquisition of fuel information. Three-dimensional (3D) fuel information (height) from light detection and ranging (LiDAR) was matched to two-dimensional (2D) fuel information (crown width) from aerial photographs to establish a statistical prediction model in northeastern South Korea. Quantile regression for 0.05, 0.5, and 0.95 quantiles was performed. Subsequently, an allometric tree model was used to predict the diameter at the breast height. The performance of the prediction model was validated using physically measured data by laser distance meter triangulation and direct measurement from a field survey. The predicted quantile, 0.5, was adequately matched to the measured quantile, 0.5, and most of the measured values lied within the predicted quantiles, 0.05 and 0.95. Therefore, in the developed prediction model, only 2D images were required to predict a few of the 3D fuel details. The proposed method can significantly reduce the cost and duration of data acquisition for the investigation and risk assessment of wildfires.

Assessing the Allometric Scaling of Vectorized Branch Lengths of Trees with Terrestrial Laser Scanning and Quantitative Structure Modeling: A Case Study in Guyana

Allometric scaling is closely related to the morphology, function and behavior of trees, which are of great significance to the study of ecology. However, most of the traditional allometric scaling studies used the scalar attributes of trees, without considering the 3D vector mode of tree growth. In order to investigate the allometric relationships between branch lengths in 3D vector mode, in this study, an accurate and detailed quantitative structure model was used to reconstruct tree architectures from 3D point cloud data collected by terrestrial laser scanning and extract the structural parameters of each branch (length, branching level and zenith angle). The standardized major axis was used to establish and analyze the scalar and vector allometric relationships between branch lengths. Our results show that at the same branching level and using the same allometric model, the scaling exponents between the lengths of branches and the lengths of cumulative child (descendant) branches (no matter whether the lengths are in scalar or vector form) are similar among trees, and there is no significant difference between the scaling exponents of most trees. And the scaling exponents between the lengths of the horizontal components of branches and the cumulative lengths of the horizontal components of the child (descendant) branches are much larger than those between the lengths of the vertical components of branches and the cumulative lengths of vertical components of the child (descendant) branches. At different branching levels, the scaling exponents between the lengths of branches and the cumulative lengths of descendant branches tend to decrease with the increase in the branching level. The allometric models in terms of the cumulative lengths of horizontal components of the child (descendant) branches and the allometric models in terms of the cumulative lengths of child (descendant) branches have similar model accuracy and scaling exponents. The study results of allometric relationships between tree branch lengths in 3D mode are of great importance for understanding the crown morphology and the branching rule, which is helpful to further understand the growth strategies and adaptation mechanisms of trees and explain the growth and development mechanisms of trees from a physiological perspective.

Modelling branch surface area of Picea abies [L.] Karst

ABSTRACT Allometric models based on limited sub-samples are widely used for predicting forest-scale information. Here, we develop allometric models for the branch surface area of the widespread conifer species Picea abies [L.] Karst. Branch surface area is a proxy for the capacity of tree branches to intercept and store water and air pollutants. Based on “probability proportional to size” sampling, we measured the surface area for 285 branches and then calculated the branch surface area of 30 trees (and their 3298 branches). We developed allometric models to estimate the total surface area of branches, as well as their number and diameters, for trees across a range of diameters (DBH), heights, and crown ratios (CR). We show that DBH and CR play significant roles in branch characteristics. The branch surface area was linearly related to the stand basal area. Reducing stand density will proportionally reduce interception capacity. The approach outlined here may help stimulate further studies (more species, regions, and management practices) required to optimize stand density for ecosystem services related to crown characteristics, such as hydrology, forage quality, and quantity or capacity for air pollutants.

Multi-scale biomass estimation of an alpine desert shrub based on relative cover: A case study of Asterothamnus centraliasiaticus in the Qaidam Desert on the Qinghai–Tibet Plateau

Biomass estimates and the allocation characteristics of shrubs are essential indicators for studying the structure, function, and material and energy flows of alpine desert ecosystems. However, conventional destructive biomass estimation methods are unsuitable for large-scale application in alpine desert areas that are characterized by sparse vegetation and comprise extremely fragile ecosystems. In this study, we propose a multi-scale method for estimating the biomass of alpine desert shrubs based on relative cover and verified its accuracy, feasibility, and efficiency using data obtained from 100 individual plants and 27 quadrat plots of Asterothamnus centraliasiaticus, an important species growing in the Qaidam Desert on the Qinghai–Tibet Plateau. Having gained this information, we developed models to estimate the new branch (NBB), aboveground (AGB), and belowground (BGB) biomasses of A. centraliasiaticus at individual, quadrat, and regional scales. The modelling results revealed that an allometric relationship model (power law function) could be used to accurately represent the synergistic relationship between plant morphological variables and biomass increases at all assessed scales. Moreover, we established that at the individual scale, estimation models (NBB = 0.3015 × C1.0610 and AGB = 1.0046 × C1.4239) incorporating canopy area (C) as a parameter can better reflect the cumulative characteristics of the new branch and aboveground biomasses of A. centraliasiaticus, respectively, whereas canopy volume (CH, resembles a cylinder) is the most appropriate parameter for the model (BGB = 0.3262 × CH0.8822) used to estimate belowground biomass. In addition, at the sample scale, the total canopy cover (TC) of A. centraliasiaticus in the sample was established to be an appropriate parameter for estimating total biomass (TNBB = 0.0042 × TC1.4647, TAGB = 0.0033 × TC1.6308, and TBGB = 0.0110 × TC1.2554). Furthermore, at the regional scale, we found that incorporating relative canopy cover (RC) in models was effective for estimating the biomass density of A. centraliasiaticus (DNBB = 0.0012 × RC1.4647, DAGB = 0.0014 × RC1.6308, and DBGB = 0.0020 × RC1.2554) and can be combined with regional area estimates to determine the total biomass of A. centraliasiaticus at any spatial scale. On the basis of our findings, we established that the biomass estimation model for alpine desert shrubs, incorporating the parameter relative cover, can effectively solve the longstanding problem of scale conversion and facilitates the rapid and accurate estimation of the biomass components of alpine desert shrubs. In future studies, biomass could be efficiently estimated by combining models based on the relative cover data of shrub communities in relevant areas obtained using aerial photography and remote sensing. The results of this study have important implications for estimating the biomass of alpine desert shrubs, and will accordingly make a valuable contribution to the management of alpine desert ecosystems.

New aboveground biomass equations by components for small black spruce in peatland ecosystems of Western Canada

Black spruce is the most common tree species in Canada, dominating treed peatlands where they are usually stunted. We used 495 destructively sampled trees from 56 plots to develop allometric models of aboveground biomass by components (stem, branches, and needles) for small (&lt;5 m tall) black spruce from peatlands in the Taiga Plains and Boreal Plains Ecozones of Western Canada, for which there were no specific models available of biomass by components. We used leave-one-plot-out cross-validation to assess transferability and compare our models with existing national and ecozone-specific equations. Our models predicted total tree biomass with better accuracy and less biased estimates than the national model (relative RMSE: 30% versus 35% national; relative bias: +1% versus –12% national). Similar results were obtained in other external datasets. Existing ecozone equations performed worse than either our models or the national ones. When we applied the models at the plot level to predict aboveground biomass density (Mg·ha−1), our models outperformed the national model again (relative RMSE: 15.9% versus 18.6% national, relative bias: +3.5% versus –13.6% national). These results indicate that at least for peatlands of Western Canada, our models provide better aboveground biomass estimates for small black spruce trees than existing models.