Forest Growth Research Articles

Water yield forecast in Shandong Province based on InVEST model

A better understanding of the effects of land use change and climate change on water yield is highly important for water resource planning and sustainable management. Land use changes in Shandong Province were analyzed over recent years. The PLUS Model was applied to forecast the land use layout for 2032, with adjustments made based on actual conditions and utilizing various CMIP6 scenarios for precipitation and actual evapotranspiration. The 2032 Water Yield was quantified using the InVEST Model at both grid and administrative scales, and its temporal-spatial characteristics and spatial correlation were analyzed. The findings revealed several key points: first, a decrease of 16.92% in arable land area, along with diminishing trends in grassland and unused land, and an increase of 33.92% in built-up land area, accompanied by growth in forest and water area. Second, the water yield decreases from southeast to northwest in Shandong Province, with significant inter-annual variations. Third, future predictions suggest that the annual water yield generally increases during flat water years, with offshore areas exhibiting greater water yield depth compared to inland areas. Rizhao is projected to have the highest water yield depth, while Binzhou and Dongying are expected to have the lowest depths, with a spatially positive correlation between them.

Quantifying forest resource change on the Korean Peninsula using satellite imagery and forest growth models

Quantifying forest resource change on the Korean Peninsula using satellite imagery and forest growth models

Long-Term Effects of Mistletoe Removal on Radial Growth of Semi-Arid Aleppo Pine Forests

Long-Term Effects of Mistletoe Removal on Radial Growth of Semi-Arid Aleppo Pine Forests

10-year progress on Forest Carbon Research in Canada

In 2012 <i>A Blueprint for Forest Carbon Science in Canada:2012–2020</i> was published to guide policy and research and to support Canada’s obligations for climate change mitigation and adaptation, sustainable forest management, and international reporting. Over the past decade, the body of scientific research focused on forest carbon dynamics in Canada has significantly evolved, providing crucial insights into the intricate interplay between various forest ecosystems and the global carbon cycle. This comprehensive review synthesizes key findings from this period, highlighting improvements to Canada’s estimates of current forest greenhouse gas emissions and removals, the effects of global changes on Canada’s future forest carbon, and forest contributions to mitigate climate change. Collaborating with the forest carbon science community, we identified 426 peer-reviewed published articles on landscape-scale forest carbon research for Canada from 2012 to 2021. The review emphasizes anthropogenic influences and natural disturbances in contemporary GHG emissions and removals for managed forests in the national GHG inventory, highlights the existing dichotomy between management and research models, and stresses the need for integrating disturbance and climate effects for comprehensive GHG estimates. Despite significant progress in estimating and tracking forest carbon using modelled, remotely sensed and ground-based observations, challenges remain in reducing uncertainties, particularly regarding climate impacts on forest growth, decay, and disturbances. Furthermore, the review showcases recent advancements in climate change mitigation strategies and the use of a systems approach that includes forest ecosystem emissions and removals, wood product emissions and substitution benefits of avoided fossil emissions. Through comprehensive scenario analyses, the review underscores regional variations in assessing GHG reductions and notes a shift towards a more holistic approach that considers environmental, economic, and social values. By providing a nuanced understanding of the complexities underlying forest carbon dynamics in Canada, this review sets the stage for future research and policy initiatives aimed at fostering sustainable forest management practices and mitigating the impact of climate change on these vital ecosystems.

Developing the Additive Systems of Stand Basal Area Model for Broad-Leaved Mixed Forests.

Stand basal area (SBA) is an important variable in the prediction of forest growth and harvest yield. However, achieving the additivity of SBA models for multiple tree species in the complex structure of broad-leaved mixed forests is an urgent scientific issue in the study of accurately predicting the SBA of mixed forests. This study used data from 58 sample plots (30 m × 30 m) for Populus davidiana × Betula platyphylla broad-leaved mixed forests to construct the SBA basic model based on nonlinear least squares regression (NLS). Adjustment in proportion (AP) and nonlinear seemingly unrelated regression (NSUR) were used to construct a multi-species additive basal area prediction model. The results identified the Richards model (M6) and Korf model (M1) as optimal for predicting the SBA of P. davidiana and B. platyphylla, respectively. The SBA models incorporate site quality, stand density index, and age at 1.3 m above ground level, which improves the prediction accuracy of basal area. Compared to AP, NSUR is an effective method for addressing the additivity of basal area in multi-species mixed forests. The results of this study can provide a scientific basis for optimizing stand structure and accurately predicting SBA in multi-species mixed forests.

Climate Change-Induced Growth Decline in Planted Forests of Quercus variabilis Blume near Beijing, China

A progressive decline in tree growth may be induced by global warming, affecting tree health and eventually resulting in death, particularly for trees growing in rocky mountainous regions where seasonal droughts have become more pronounced. However, tree growth dynamics in areas experiencing pronounced climate change have received little attention. In this study, a total of 100 (10 m × 10 m) grid plots were investigated in planted forests of Chinese cork oak (Quercus variabilis Blume), which were established in the 1960s and 1970s in a rocky mountainous area near Beijing, northern China. Furthermore, the radial growth of Q. variabilis (a total of 843 trees sampled from the dominant [D], co-dominant [CD], and suppressed [S] crown classes) was analyzed using retrospective dendrochronology and generalized additive models. The effects of meteorological changes between 1962 and 2020 on radial growth across the three crown classes were examined using correlation analysis. The results indicated that the growth of Q. variabilis initially increased and then decreased after 2010 at the regional level, and these trends varied according to crown class. The radial growth of the D trees responded more positively to an increase in temperature and drought severity index (<0 for dry and >0 for wet conditions) compared with that of the CD and S trees. The growth of the D and CD trees continuously increased under higher temperatures during the rainy seasons (June to September); however, the increases were higher for D than for CD trees. In contrast, the radial growth of S trees declined. We confirmed the historical effects of rising temperatures on tree growth and health, which are linked to water availability. Our data suggested that Q. variabilis trees will be considerably affected by intensified droughts. This study furthers our knowledge regarding the impact of climate change on tree and forest growth and provides management strategies for afforestation projects in rocky mountainous areas that are facing climate change.

Modelling the growth of secondary cowberry pine forest of different densities in Murmansk region

Modelling the growth of secondary cowberry pine forest of different densities in Murmansk region

Multi-objective optimization of forest ecosystem services under uncertainty

Forest ecosystems deliver multiple services crucial for human wellbeing and welfare, with complex relationships among forest ecosystem services (FESs). Implementing sustainable forest management (SFM) policy requires insights into trade-offs, conflicts and synergies among key FESs, necessitating decision-support tools for multiple objectives. However, uncertainty in key parameters adds complexity. We formulated a tri-objective optimization problem concentrating on three vital ecosystem services in Nordic boreal forests: timber supply, climate change mitigation, and biodiversity conservation. Furthermore, future timber price uncertainty was integrated into the optimization model. Utilizing the TreeSim individual-tree growth and yield simulator, we simulated forest growth and yields, carbon stocks, and biodiversity values. Pareto-optimal solutions were found for four distinct management strategies using the epsilon-constrain method to solve the optimization problem. These management strategies differed in spatial restrictions on harvest location and timing, including considerations like harvest adjacency, green-up and maximum harvest opening area (MOA). The analysis revealed trade-offs among all three objectives, with variations influenced by management intensity. Furthermore, within the Pareto solutions for each strategy, a compromise solution was identified based on the knee-point method. The study demonstrated nuanced impacts of green-up constraints related to harvest practices, showing minor effects on harvesting, carbon sequestration, and biodiversity conservation when MOA was restricted to 35 hectares or less. However, more substantial impacts were observed with a MOA limited to 60 hectares or in a strategy without spatial restrictions. Our results underscore the importance of considering multiple objectives in forest management and policy under uncertain wood prices, preventing undesired effects from a singular or deterministic approach. This work provides insights into trade-offs and synergies, contributing to strategic planning and policy design.

Mixed forests provide higher levels of growth and yield than monocultures across a broad range of conditions

Compared with single-species forests, mixed forests tend to provide more forest ecosystem functions and services. However, there is a lack of detailed understanding of the basic mechanisms of the impact of species diversity on tree growth and forest productivity. The main drivers of this relationship are also still under discussion. Here, we synthesized information from 93 studies to quantify the increase in tree size, crown size and stand productivity in mixed stands, and conducted a meta-analysis on the response of tree growth and forest productivity to species mixing. Tree size, crown size and stand productivity increased by 7.4 % (2.4–12.5 %), 12.6 % (5.6–20.1 %), and 33.0 % (18.7–49.0 %), respectively, in mixed stands compared with single-species stands. Our results show that when all observations are included, the mean effect sizes of tree size, crown size, and stand productivity were greater than when only the equal stand density observations were included. Stand age and stand density were the most important moderators affecting tree size in mixed forests. Stand age, shade tolerance and soil acidity were the most important moderators influencing the effect sizes of crown size, and shade tolerance was the most important moderator influencing the effect sizes of stand productivity. We found that these positive mixing effects were also modulated by tree functional traits, stand characteristics, and climatic conditions. For different growth variables, the moderators that affect their growth in the mixed forests were different. Therefore, in the afforestation practices for differing management objectives, foresters should give priority to the factors that have the most important impact on them to achieve their goals. Our results have implications for linking biodiversity-ecosystem functioning relationships and forest management practices and may aid the development of more appropriate afforestation strategies.

Different climate conditions drive variations in gross primary productivity and woody biomass accumulation in a temperate and a boreal conifer forest in Canada

Understanding the complex relationships between climate, forest carbon (C) uptake and biomass growth has become a research priority, crucial for assessing the climate change impacts on forest C sequestration. Such associations are expected to vary across biomes, due to different climate constraints on tree physiology. However, our understanding of the seasonal dynamics of long-term C sequestration and how climate influences them in different forest biomes is still limited.We investigated a boreal Pinus banksiana forest (Old Jack Pine, OJP) and a temperate Pinus strobus plantation (Turkey Point, TP39) in Canada. We aimed to assess the link between C uptake and above-ground woody biomass growth, and the effects of climate inter-annual variability on them. We used daily records of climate and gross primary productivity (GPP, period 1999–2019 at OJP and 2003–2018 at TP39), and investigated xylem biomass proxies at cell (cell wall area) and tree-ring level (ring wall area) from 1970 to 2019 at OJP, and from 1970 to 2018 at TP39.In both forests, the direct link between C sink and C source was revealed by the common inter-annual variability of GPP and xylem biomass. GPP and xylem biomass were mostly influenced by spring and late summer temperature at OJP. However, at both sites, summer drought negatively influenced GPP, and biomass growth especially in recent decades. Analysis of dry and cold years evidenced short-term negative effects of low temperature on GPP and xylem biomass in OJP, and of drought in TP39.Long-term intra-annual analyses are crucial to assess the variable effects of climate on forest C cycle. Warm spring and autumn can benefit both GPP and biomass growth, but even in the boreal forest, summer drought has negative impacts. Increasing dry spells, especially in the temperate site, could reduce future forest capacity to uptake C and fix it in wood biomass.

The Growth-Climate Relationships of Three Dominant Subalpine Conifers on the Baima Snow Mountain in the Southeastern Tibetan Plateau.

The impact of climates on the radial growth of muti-species remains insufficiently understood in the climate-sensitive southeastern Tibetan Plateau, and this hampers an effective assessment of forest growth under the background of global warming. Here, we studied the growth-climate relationships of three major species (Abies georgei, Larix potaninii, and Picea likiangensis) on the Baima Snow Mountain (BSM) by using dendrochronology methods. We constructed basal area increment (BAI) residual chronologies based on the dated ring-width measurements and correlated the chronologies with four climate factors. We also calculated the contributions of each climate factor to species growth. We found that photothermal conditions played a more important role than moisture in modulating radial growth, and P. likiangensi presented the strongest sensitivity to climate change among the three species. The growing season (June and July) temperature positively affected the radial growth of three species. Winter (previous December and current January) SD negatively impacted the tree growth of A. georgei and P. likiangensis. Significant correlations between growth and precipitation were detected only in A. georgei (January and May). Warming since the beginning of the 1950s promoted the growth of A. georgei and P. likiangensis, while the same effect on L. potaninii growth was found in the recent 50 years.

Environmental Response of Tree Species Distribution in Northeast China with the Joint Species Distribution Model

The composition, distribution, and growth of native natural forests are important references for the restoration, structural adjustment, and close-to-nature transformation of artificial forests. The joint species distribution model is a powerful tool for analyzing community structure and interspecific relationships. It has been widely used in biogeography, community ecology, and animal ecology, but it has not been extended to natural forest conservation and restoration in China. Therefore, based on the 9th National Forest Inventory data in Jilin Province, combined with environmental factors and functional traits of tree species, this study adopted the joint species distribution model—including a model with all variables (model FULL), a model with environmental factors (model ENV), and a model with spatial factors (model SPACE)—to examine the distribution of multiple tree species. The results show that, in models FULL and ENV, the environmental factors explaining the model variation were ranked as follows, climate > site > soil. The explanatory power was as follows: model FULL (AUC = 0.8325, Tjur R2 = 0.2326) > model ENV (AUC = 0.7664, Tjur R2 = 0.1454) > model SPACE (AUC = 0.7297, Tjur R2 = 0.1346). Tree species niches in model ENV were similar to those in model FULL. Compared to predictive power, we found that the information transmitted by environmental and spatial predictors overlaps, so the choice between model FULL and ENV should be based on the purpose of the model, rather than the difference in predictive ability. Both models can be used to study the adaptive distribution of multiple tree species in northeast China.

Модель формирования березово-еловых насаждений в условиях южной тайги Русской равнины

The results of 30 years or research on the formation and growth of birch forests from the subcanopy spruce population in the conditions of the Russian Plain southern taiga are presented. The studies have been carried out on 19 permanent sample plots (PSPs). They were laid in regionally representative sorrel-blueberry birch forests aged 15–115 years with high density and productivity. The birch plantations on the PSPs belong to the same natural age series. The majority of spruce trees appeared under the canopy of birch forests up to 40 years old. As the birch trees aged, the age structure of spruce trees changed from even-aged to conditionally even-aged. Based on the study of the dynamics of the number of trees, the vertical structure of stands, the growing stock and the total stem wood stock, the age stages of development of the spruce population in the birch forests up to 120 years old have been determined. They are regeneration, young growth, polewood and maturation. The end of the maturation stage occurs at the birch tree age of 120 years. At the same time, the average spruce tree age in the first and second layers is 90 years. The duration of the stages varies from 10 years (the young growth stage) to 40 years (the regeneration and maturation stages). To determine the time of onset and end of other age stages of the subcanopy spruce population, the studies in birch forests older than 120 years are needed. The work performed has made it possible to propose a model of the joint formation of birch forests and the subcanopy spruce population, in which the main taxation characteristics are given for all elements of the stand: age, number of trees, the sum of the cross-section of the stems, the stock of the growing and dead wood, as well as total productivity. According to the model, by the age of a birch of 120 years, the total productivity of modal plantations will be about 950 m3/ha and the growing stock of the stand will be about 490 m3/ha. The share of spruce wood will be 28 % or about 150 m3/ha. After the birch forest decay, a low-density spruce forest of the 3rd quality class can form in its place.

Biodiversity and Soil Properties of Saranda Sal Forest: Implications for Conservation and Management

Aims: This study is significant as it elucidates the relationship between floristic composition and soil nutrient availability of India's Saranda Sal (Shorea robusta Gaertn.) forest. Study Design: Vegetation sampling was done following a grid of 5 km x 5 km, and soil sampling was done in each sampling plot using a Z-pattern. Place and Duration of Study: The study was conducted in Saranda Forest of West Singhbhum district, Jharkhand, Eastern India, during 2021-2022 Methodology: Sampling has been designed as grid methods (5 km X 5 km) following a forest survey of India, and vegetation sampling was done by quadrat method. The soil samples were collected from three depths (i.e., surface: 0–30 cm, sub-surface: 30–60 cm and inner: 60-90 cm from each selected site. ANOVAs were used to compare the chemical properties of soil samples from various forests. Pearson correlation analyses to examine the effects of climatic variables on chemical properties of the soil of selected Sal forest as well as their relation with plant diversity. Results: A comprehensive analysis of 5432 vascular plants from 65 species and 34 families was conducted across the 17 sites. Fabaceae is the most dominant family with 07 species. The study also examined soil chemical parameters and micronutrients in different sites and established their relations with vegetation dynamics. Notably, tree density showed a significant positive correlation with soil pH (r=0.59, p<0.05), but a significant negative correlation with Shannon diversity (H') (r=-0.53, p<0.05). Similarly, there was a significant positive correlation of Organic carbon with copper (r=0.59, p<0.05) and iron (r=0.61, p<0.00); however, there was a significant negative correlation with Available Phosphorus (r=-0.52, p<0.05). Conclusion: Research findings underscore the importance of soil nutrients in promoting forest health and growth. Importantly, they can guide the formulation of practical and effective soil-forest management strategies for S. robusta and its associated forests, directly benefiting the forestry and environmental science community.

Nitrogen and potassium limit fine root growth in a humid Afrotropical forest

Nutrient limitations play a key regulatory role in plant growth, thereby affecting ecosystem productivity and carbon uptake. Experimental observations identifying the most limiting nutrients are lacking, particularly in Afrotropical forests. We conducted an ecosystem-scale, full factorial nitrogen (N)-phosphorus (P)-potassium (K) addition experiment consisting 32 40 × 40 m plots (eight treatments × four replicates) in Uganda to investigate which (if any) nutrient limits fine root growth. After two years of observations, added N rapidly decreased fine root biomass by up to 36% in the first and second years of the experiment. Added K decreased fine root biomass by 27% and fine root production by 30% in the second year. These rapid reductions in fine root growth highlight a scaled-back carbon investment in the costly maintenance of large fine root network as N and K limitations become alleviated. No fine root growth response to P addition was observed. Fine root turnover rate was not significantly affected by nutrient additions but tended to be higher in N added than non-N added treatments. These results suggest that N and K availability may restrict the ecosystem’s capacity for CO2 assimilation, with implications for ecosystem productivity and resilience to climate change.

Patterns of acorn selection in Peromyscus mice and possible implications in a changing climate

Abstract Rodents play a key role in forest regeneration by dispersing seeds and interspecific differences in seed preferences could have important consequences for forest growth. The White-footed Mouse (Peromyscus leucopus) and Deer Mouse (Peromyscus maniculatus gracilis) are 2 closely related species that can exploit Red Oak (Quercus rubra) acorns but may display different preference levels due to the high tannin concentration present in these acorns. Here we investigated how White-footed Mice and Deer Mice differ in their ability to exploit Red Oak acorns. We conducted a cafeteria-style experiment where we offered mice (n = 61) acorns of Red Oak and Bur Oak (Q. macrocarpa; control seed with lower tannin levels) and video-recorded their preference and the acorn fate (consumed or cached). Using mixed-effects models, we found that both mouse species selected and consumed Bur Oak acorns at a high rate; however, White-footed Mice were 6.67 times more likely to select and consume Red Oak acorns than Deer Mice. Furthermore, White-footed Mice tended to cache Red Oak acorns near the surface where there is a higher chance of germination, while they usually consumed Bur Oak acorns. Our results suggest that Red Oaks have a better strategy for avoiding predation than Bur Oaks (i.e., high tannin levels leading to caching), and the strong interaction between the White-footed Mouse and Red Oak might be an important mechanism behind the current range expansion of both species in response to climate change.

Treegraph: tree architecture from terrestrial laser scanning point clouds

AbstractAccurate quantification of tree architecture is critical to interpreting the growth, health and functioning of trees and forests. Terrestrial laser scanning (TLS) offers millimetre‐level point cloud data, but current approaches to 3D tree reconstruction from TLS point clouds primarily focus on retrieving total volume at tree scale for aboveground biomass (AGB) estimation. Few methods have been designed specifically to provide tree architectural properties, including branch‐level morphology and topology, rather than AGB; derived topological traits have tended to be a compromise, and of secondary importance to volume. We present Treegraph, a new approach explicitly designed to retrieve the architectural traits of trees at multiple scales, from the whole tree scale down to individual branches and internodes, using TLS data with limited assumptions about tree form. It provides morphological traits such as branch length and diameter, alongside topological traits including parent–daughter connections of branches and internodes, furcation (branching) number and branch order. We compare Treegraph‐derived morphological and topological traits with manual measurements of branches from eight destructively harvested trees, yielding RMSE values of 0.60 m (5.96%) for branch length, 2.99 cm (33.45%) for branch diameter, 0.46 (19.38%) for furcation number and 0.08 m (33.16%) for internode length, respectively. In a broader application to 603 trees from tropical, temperate and urban forests, we demonstrate that the derived morphological and topological traits support testing of structure‐related metabolic scaling theories. Testing branches over 10 cm in diameter across 18 657 branching nodes shows that Treegraph‐derived branch‐level scaling exponents deviate from WBE predictions, exhibiting area‐preserving behaviour while displaying asymmetry in length and diameter of daughter branches. Available as open‐source Python software, Treegraph provides fine‐level branching network information, promoting improved insights into tree structure and function. This data‐driven approach reduces the need for empirical heuristic parameters, which has the potential for advancing large‐scale ecological studies on tree architecture.

Increased habitat availability as revealed by LiDAR contributes to the tropicalization of a subtropical butterfly community

AbstractSpecies redistribution through climate change remains a global problem. However, factors such as habitat availability can complicate the attribution of species distribution shifts. We used habitat metrics derived from repeated airborne LiDAR surveys in 2010 to 2020 to examine the underlying causes for the establishment of new butterfly species in Hong Kong. For six species newly arrived since 2000, we built species distribution models using the Random Forest algorithm based on LiDAR data from 2020 to characterize species' preferred habitats across the region. Through hindcasting, we applied the model to LiDAR data from 2010 to observe any changes in the availability of preferred habitat. We found widespread vertical forest growth across Hong Kong and increased probability of occurrence based on increased habitat availability for all six species. The underlying habitat drivers, however, varied significantly across species; two species (Lethe chandica, Notocrypta paralysos) were associated with closed forest while two other species (Prosotas dubiosa, Prosotas nora) were associated with urbanicity. Our results highlight how changes in habitat can occur concurrently with climatic change and together drive the redistribution of biodiversity. Particularly for vertically complex tropical forests, airborne LiDAR data can be leveraged to observe changes in habitat complexity and how these relate to shifts in species distributions.

Modelling Diameter at Breast Height Distribution for Eight Commercial Species in Natural-Origin Mixed Forests of Ontario, Canada

Diameter at breast height (DBH) is a unique attribute used to characterize forest growth and development for forest management planning and to understand forest ecology. Forest managers require an array of DBHs of forest stands, which can be reconstructed using selected probability distribution functions (PDFs). However, there is a lack of practices that fit PDFs of sub-dominating species grown in natural mixed forests. This study aimed to fit PDFs and develop predictive models for PDF parameters, so that the predicted distribution would represent dynamic forest structures and compositions in mixed forest stands. We fitted three of the simplest forms of PDFs, log-normal, gamma, and Weibull, for the DBH of eight tree species, namely balsam fir (Abies balsamea [L.] Mill.), eastern white pine (Pinus strobus L.), paper birch (Betula papyrifera Marshall), red maple (Acer rubrum L.), red pine (Pinus resinosa Aiton), trembling aspen (Populus tremuloides Michx), and white spruce (Picea glauca [Moench] Voss), all grown in natural-origin mixed forests in Ontario province, Canada. We estimated the parameters of the PDFs as a function of DBH mean and standard deviation for these species. Our results showed that log-normal fit the best among the three PDFs. We demonstrated that the predictive model could estimate the recovered parameters unbiasedly for all species, which can be used to reconstruct the DBH distributions of these tree species. In addition to prediction, the cross-validated R2 for the DBH mean ranged between 0.76 for red maple and 0.92 for red pine. However, the R2 for the regression of the standard deviation ranged between 0.00 for red pine and 0.69 for sugar maple, although it produced unbiased predictions and a small mean absolute bias. As these mean and standard deviations are regressed with dynamic covariates (such as stem density and stand basal area), in addition to climate and static geographic variables, the predicted DBH distribution can reflect change over time in response to management or any type of disturbance in the regime of the given geography. The predictive model-based DBH distributions can be applied to the design of appropriate silviculture systems for forest management planning.

Stepwise Calibration of Age‐Dependent Biomass in the Integrated Biosphere Simulator (IBIS) Model

AbstractMany land surface models (LSMs) assume a steady‐state assumption (SS) for forest growth, leading to an overestimation of biomass in young forests. Parameters inversion under SS will potentially result in biased carbon fluxes and stocks in a transient simulation. Incorporating age‐dependent biomass into LSMs can simulate real disequilibrium states, enabling the model to simulate forest growth from planting to its current age, and improving the biased post‐calibration parameters. In this study, we developed a stepwise optimization framework that first calibrates “fast” light‐controlled CO2 fluxes (gross primary productivity, GPP), then leaf area index (LAI), and finally “slow” growth‐controlled biomass using the Global LAnd Surface Satellite (GLASS) GPP and LAI products, and age‐dependent biomass curves for the 25 forests. To reduce the computation time, we used a machine learning‐based model to surrogate the complex integrated biosphere simulator LSM during calibration. Our calibrated model led to an error reduction in GPP, LAI, and biomass by 28.5%, 35.3% and 74.6%, respectively. When compared with net biome productivity (NBP) using no‐age‐calibrated parameters, our age‐calibrated parameters increased NBP by an average of 50 gC m−2 yr−1 across all forests, especially in the boreal needleleaf evergreen forests, the NBP increased by 118 gC m−2 yr−1 on average, increasing the estimate of the carbon sink in young forests. Our work highlights the importance of including forest age in LSMs, and provides a novel framework for better calibrating LSMs using constraints from multiple satellite products at a global scale.