Variations in biomass-carbon of forest can substantially impact the prediction of global carbon dynamics. The allometric models currently used to estimate forest biomass face limitations, as model ...parameters can only be used for the specific species of confirmed sites. Here, we collected allometric models LnW = a + b*Ln(D) (n = 817) and LnW = a + b*Ln(D
H) (n = 612) worldwide and selected eight variables (e.g., mean annual temperature (MAT), mean annual precipitation (MAP), altitude, aspect, slope, soil organic carbon (SOC), clay, and soil type) to predict parameters a and b using Random Forest. LnW = a + b*Ln(D), drove mainly by climate factors, showed the parameter a range from - 5.16 to - 0.90 VaR explained (model evaluation index): 66.21%, whereas parameter b ranges from 1.84 to 2.68 (VaR explained: 49.96%). Another model LnW = a + b*Ln(D
H), drove mainly by terrain factors, showed the parameter a range from - 5.45 to - 1.89 (VaR explained: 69.04%) and parameter b ranges from 0.43 to 1.93 (VaR explained: 69.53%). Furthermore, we captured actual biomass data of 249 sample trees at six sites for predicted parameters validation, showing the R
(0.87) for LnW = a + b*Ln(D); R
(0.93) for LnW = a + b*Ln(D
H), indicating a better result from LnW = a + b*Ln(D
H). Consequently, our results present four global maps of allometric model parameters distribution at 0.5° resolution and provides a framework for the assessment of forest biomass by validation.
With the development of national-scale forest biomass monitoring work, accurate estimation of forest biomass on a large scale is becoming an important research topic in forestry. In this study, the ...stem wood, branches, stem bark, needles, roots and total biomass models for larch were developed at the regional level, using a general allometric equation, a dummy variable model, a mixed effects model, and a Bayesian hierarchical model, to select the most effective method for predicting large-scale forest biomass. Results showed total biomass of trees with the same diameter gradually decreased from southern to northern regions in China, except in the Hebei province. We found that the stem wood, branch, stem bark, needle, root, and total biomass model relationships were statistically significant (p-values < 0.01) for the general allometric equation, linear mixed model, dummy variable model, and Bayesian hierarchical model, but the linear mixed, dummy variable, and Bayesian hierarchical models showed better performance than the general allometric equation. An F-test also showed significant differences between the models. The R2 average values of the linear mixed model, dummy variable model, and Bayesian hierarchical model were higher than those of the general allometric equation by 0.007, 0.018, 0.015, 0.004, 0.09, and 0.117 for the total tree, root, stem wood, stem bark, branch, and needle models respectively. However, there were no significant differences between the linear mixed model, dummy variable model, and Bayesian hierarchical model. When the number of categories was increased, the linear mixed model and Bayesian hierarchical model were more flexible and applicable than the dummy variable model for the construction of regional biomass models.
Soil nutrients play critical roles in regulating and improving the sustainable development of economic forests. Consequently, an elucidation of the spatial patterns and drivers of soil nutrients in ...these forests is fundamental to their management. For this study, we collected 314 composite soils at a 0–30 cm depth from a typical hickory plantation in Lin’an, Zhejiang Province, China. We determined the concentrations of macronutrients (i.e., soil organic carbon, available potassium, available phosphorus, available sulfur, and hydrolyzed nitrogen) and micronutrients (i.e., soil available boron, iron, manganese, zinc, and copper) of the soils. We employed random forest analysis to quantify the relative importance of factors affecting soil nutrients to predict the concentrations, which could then be extrapolated to the entire hickory region. Random forest models explained 43–80% of the variations in soil nutrient concentrations. The mean annual temperature, mean annual precipitation, and altitude were key predictors of soil macronutrient and micronutrient concentrations. Moreover, slope and parent material were important predictors of soil nutrients concentrations. Distinct spatial patterns of soil nutrient concentrations were driven by climate, parent material, and topography. Our study highlights the various environmental controls over soil macronutrient and micronutrient concentrations, which have significant implications for the management of soil nutrients in hickory plantations.
Land use change (LUC) alters the global carbon (C) stock, but our estimation of the alteration remains uncertain and is a major impediment to predicting the global C cycle. The uncertainty is partly ...due to the limited number and geographical bias of observations, and limited exploration of its predictors. Here we generated a comprehensive global database of 5,980 observations from 790 articles. The number of sites evaluated is at least seven times larger than in previous meta‐analyses. Our constrained estimates of different LUC's effects on soil organic C (SOC) and their variations across global climates reveal underestimation/overestimation in previous estimates. Converting forests and grasslands to croplands reduced SOC by 24.5% ± 1.53% (−11.03 ± 1.06 Mg ha−1) and 22.7% ± 1.22% (−8.09 ± 0.67 Mg ha−1), while 28.0% ± 1.56% (4.46 ± 0.42 Mg ha−1) and 33.5% ± 1.68% (5.8 ± 0.38 Mg ha−1) increases, respectively, were obtained in the reverse processes. Converting forests to grasslands decreased SOC by 2.1% ± 1.22% (−1.13 ± 0.44 Mg ha−1), while the reverse process increased SOC by 18.6% ± 1.73% (3.31 ± 0.51 Mg ha−1). Modeled relative importance of 10 drivers of LUC's impact on SOC revealed that higher initial SOC (iSOC) does not solely determine SOC loss in SOC‐negative LUC scenarios as previously proposed. Across four decades, reconverting croplands to forests and grasslands recovered only 49.5% (6.1 ± 0.51 Mg ha−1) and 75.3% (7.0 ± 0.38 Mg ha−1) of the iSOC, respectively, indicating the need for protecting C‐rich ecosystems. Our global data set advances information on LUC's effect on SOC and can be valuable to constrain Earth system models to reliably estimate global SOC stocks and plan climate change mitigation strategies.
Plain Language Summary
Land use change (LUC) could increase or decrease the global soil organic carbon (SOC) stock and affect carbon cycling and climate change, but estimating its effect size is the most uncertain aspect of the global carbon cycle. Available estimates vary among studies, most of which often use few observations that do not cover most global regions. To provide more accurate estimates of different LUC types' effects on SOC, we compiled a comprehensive database that contains 5,980 observations across all global regions; and revealed that previous studies have underestimated/overestimated these effects. We modeled the predictors of SOC change and found that previous conclusions that higher initial SOC is the main reason for higher organic carbon loss under negative LUC scenarios were inexact. We show that land use practices aimed at restoring SOC could only recover part of the amount lost during similar time frames; hence, it is important to protect carbon‐rich ecosystems. Our study provides robust estimates of LUC's effect on SOC and provides data sets that can reliably assess and model the global carbon cycle and plan strategies for controlling global climate change.
Key Points
Comprehensive global database advances robust estimates of land use change's (LUCs) effect on soil organic carbon (SOC) vital for Earth system modeling
Higher initial SOC does not solely control rapid SOC loss in carbon‐negative LUC as often proposed
Despite SOC buildup in the past four decades, the amount lost cannot be regained by restoration during similar time frames
Understanding the drivers of variations in fine root lifespan is key to informing nutrient cycling and productivity in terrestrial ecosystems. However, the general patterns and determinants of forest ...fine root lifespan at the global scale are still limited. We compiled a dataset of 421 fine root lifespan observations from 76 tree species globally to assess phylogenetic signals among species, explored relationships between fine root lifespan and biotic and abiotic factors, and quantified the relative importance of phylogeny, root system structure and functions, climatic and edaphic factors in driving global fine root lifespan variations. Overall, fine root lifespan showed a clear phylogenetic signal, with gymnosperms having a longer fine root lifespan than angiosperms. Fine root lifespan was longer for evergreens than deciduous trees. Ectomycorrhizal (ECM) plants had an extended fine root lifespan than arbuscular mycorrhizal (AM) plants. Among different climatic zones, fine root lifespan was the longest in the boreal zone, while it did not vary between the temperate and tropical zone. Fine root lifespan increased with soil depth and root order. Furthermore, the analysis of relative importance indicated that phylogeny was the strongest driver influencing the variation in forest fine root lifespan, followed by soil clay content, root order, mean annual temperature, and soil depth, while other environmental factors and root traits exerted weaker effects. Our results suggest that the global pattern of fine root lifespan in forests is shaped by the interplay of phylogeny, root traits and environmental factors. These findings necessitate accurate representations of tree evolutionary history in earth system models to predict fine root longevity and its responses to global changes.
The effect of evolutionary history on wood density variation may play an important role in shaping variation in wood density, but this has largely not been tested. Using a comprehensive global ...dataset including 27,297 measurements of wood density from 2621 tree species worldwide, we test the hypothesis that the legacy of evolutionary history plays an important role in driving the variation of wood density among tree species. We assessed phylogenetic signal in different taxonomic (e.g., angiosperms and gymnosperms) and ecological (e.g., tropical, temperate, and boreal) groups of tree species, explored the biogeographical and phylogenetic patterns of wood density, and quantified the relative importance of current environmental factors (e.g., climatic and soil variables) and evolutionary history (i.e., phylogenetic relatedness among species and lineages) in driving global wood density variation. We found that wood density displayed a significant phylogenetic signal. Wood density differed among different biomes and climatic zones, with higher mean values of wood density in relatively drier regions (highest in subtropical desert). Our study revealed that at a global scale, for angiosperms and gymnosperms combined, phylogeny and species (representing the variance explained by taxonomy and not direct explained by long-term evolution process) explained 84.3% and 7.7% of total wood density variation, respectively, whereas current environment explained 2.7% of total wood density variation when phylogeny and species were taken into account. When angiosperms and gymnosperms were considered separately, the three proportions of explained variation are, respectively, 84.2%, 7.5% and 6.7% for angiosperms, and 45.7%, 21.3% and 18.6% for gymnosperms. Our study shows that evolutionary history outpaced current environmental factors in shaping global variation in wood density.
•Wood density shows a significant phylogenetic signal.•Phylogeny and species together explain the vast majority of total wood density variation at a global scale.•Evolutionary history outpaces current environmental factors in explaining global variation in wood density.
Soil resident bacterial communities are involved in myriad key processes that facilitate ecosystem functionality. However, our understanding of their diversity and compositional dynamics following ...ecological restoration, and the main factors that influence them, remains inadequate. We employed a chronosequence (0–1, 5–6, 11–12, 20–24, and 28–34 years since restoration) to examine the dynamic changes in soil bacterial diversity and composition, as well as the essential factors that affected them since the cessation of anthropogenic disturbances (e.g., recurring fuelwood collection and domestic animal grazing), and used old-growth forests as a reference in the subtropical forests of Eastern China. We found that soil bacterial diversity increased with time since restoration, and community compositions shifted toward being similar to those of old-growth forests over time. However, the recovery process was prolonged since the significant difference in soil bacterial diversity between degraded and restored forests did not occur until after 24 years since restoration. Multivariate analysis using multiple-response permutation procedures indicated the soil bacterial communities were compositionally distinct between degraded, restored, and old-growth forests. An analysis of indicator species revealed that forests at the early stage of recovery times supported Rokubacteria and Actinobacteria, while old-growth forests were distinguished by Chlamydiae. Soil carbon, microbial biomass carbon, soil water content, and microbial biomass nitrogen recovered over time and became increasingly akin to those of old-growth forest soils. Soil carbon, soil water content, and soil pH could explain 84.5% of the variations in bacterial community dynamics following restoration. Overall, this study revealed a prolonged recovery process of the community structures of soil bacteria (e.g., diversity, composition, and phylum abundance) following restoration, which was coupled with changes in soil properties in subtropical forests of China.
•Soil bacterial diversity increased following restoration.•But a significant increase occurred only 30 years since the restoration.•Bacterial composition shifted from Rokubacteria and Actinobacteria to Chlamydiae.•Soil properties can explain bacterial community dynamics following restoration.
Introduction
Soil organic carbon (SOC) accumulation changed with forest succession and hence impacted the SOC storage. However, the variation and underlying mechanisms about SOC during tropical ...forest succession are not fully understood.
Methods
Soil samples at four depths (0–10 cm, 10–20 cm, 20–40 cm and 40–60 cm), litter, and roots of 0–10 cm and 10–20 cm were collected from three forest succession stages (plantation forest, secondary forest, and old– growth forest) in the Jianfengling (JFL) National Nature Reserve in Hainan Island, China. The SOC, soil enzyme activities, physiochemical properties, the biomass of litter and roots were analyzed.
Results
Results showed that forest succession significantly increased SOC at 0–10 cm and 10–20 cm depth (from 23.00 g/kg to 33.70 g/kg and from 14.46 g/kg to 22.55 g/kg, respectively) but not at a deeper depth (20–60 cm). SOC content of the three forest succession stages decreased with increasing soil depth and bulk density (BD). With forest succession from plantation to secondary and old–growth forest, the soil pH at 0–10 cm and 10–20 cm depth decreased from 5.08 to 4.10 and from 5.52 to 4.64, respectively. Structural equation model (SEM) results showed that the SOC at depths of 0–20 cm increased with total root biomass but decreased with increasing soil pH value. The direct positive effect of soil TP on SOC was greater than the indirect negative effect of decomposition of SOC by soil acid phosphatase (AP).
Discussion
To sum up, the study highlighted there was soil P– limited in tropical forests of JFL, and the increase in TP and total root biomass inputs were main factors favoring SOC sequestration during the tropical forest succession. In addition, soil acidification is of great importance for SOC accumulation in tropical forests for forest succession in the future. Therefore, forest succession improved SOC accumulation, TP and roots contributed to soil C sequestration.
Estimation of soil loss using the Revised Universal Soil Loss Equation (RUSLE) has long been an active research topic, but its application in a large area is a challenge due to data availability and ...quality. In this study, the RUSLE model was used to evaluate soil erosion risk based
on soil samples, a soil type map, digital elevation model (DEM) data, and Landsat Thematic Mapper (TM) images. Multiple regression analysis was used to identify major factors influencing soil erosion risks. A regression model based on DEM-derived slope gradient and TM-derived fractional soil
and vegetation images was developed to map soil erosion risk distribution in a forest ecosystem in Zhejiang, China. The developed method has the potential to quickly examine spatial distribution of soil erosion risks. This study provides a new insight for evaluating soil erosion risks in forest
ecosystems with the integration of remote sensing and GIS.
Accurate biomass estimations are important for assessing and monitoring forest carbon storage. Bayesian theory has been widely applied to tree biomass models. Recently, a hierarchical Bayesian ...approach has received increasing attention for improving biomass models. In this study, tree biomass data were obtained by sampling 310 trees from 209 permanent sample plots from larch plantations in six regions across China. Non-hierarchical and hierarchical Bayesian approaches were used to model allometric biomass equations. We found that the total, root, stem wood, stem bark, branch and foliage biomass model relationships were statistically significant (p-values < 0.001) for both the non-hierarchical and hierarchical Bayesian approaches, but the hierarchical Bayesian approach increased the goodness-of-fit statistics over the non-hierarchical Bayesian approach. The R2 values of the hierarchical approach were higher than those of the non-hierarchical approach by 0.008, 0.018, 0.020, 0.003, 0.088 and 0.116 for the total tree, root, stem wood, stem bark, branch and foliage models, respectively. The hierarchical Bayesian approach significantly improved the accuracy of the biomass model (except for the stem bark) and can reflect regional differences by using random parameters to improve the regional scale model accuracy.
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