Next Article in Journal
The Effect of Storage Conditions on the Strength Characteristic of Glulam Beams
Previous Article in Journal
Forest by-Product Valorization: Pilot-Scale Pinus radiata and Eucalyptus globulus Bark Mixture Extraction
Previous Article in Special Issue
Effects of Edaphic Factors at Different Depths on β-Diversity Patterns for Subtropical Plant Communities Based on MS-GDM in Southern China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 5
10.3390/f14050896
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Precipitation and Temperature Influence the Relationship between Stand Structural Characteristics and Aboveground Biomass of Forests—A Meta-Analysis

by
Yingdong Ma
1,
Anwar Eziz
2,
Ümüt Halik
1,*,
Abdulla Abliz
3 and
Alishir Kurban
2,*
1
College of Ecology and Environment, Ministry of Education Key Laboratory of Oasis Ecology, Xinjiang University, Ürümqi 830046, China
2
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi 830011, China
3
College of Tourism, Key Laboratory of Sustainable Development of Xinjiang′s Historical and Cultural Tourism, Xinjiang University, Ürümqi 830046, China
*
Authors to whom correspondence should be addressed.
Forests 2023, 14(5), 896; https://doi.org/10.3390/f14050896
Submission received: 30 March 2023 / Revised: 13 April 2023 / Accepted: 24 April 2023 / Published: 27 April 2023
(This article belongs to the Special Issue Maintenance of Forest Biodiversity)
Browse Figures
Versions Notes

Abstract

:
Forest aboveground biomass (AGB) is not simply affected by a single factor or a few factors, but also by the interaction between them in complex ways across multiple spatial scales. Understanding the joint effect of stand structural characteristics and climate factors on AGB on large scales is critical for accurate forest carbon storage prediction and sustainable management. Despite numerous attempts to clarify the relationships between stand structural characteristics (tree density/TD, diameter at breast height/DBH, basal area/BA), climate factors (mean annual temperature/MAT, mean annual precipitation/MAP), and AGB, they remain contentious on a large scale. Therefore, we explored the relationships between stand structural characteristics, climate factors, and AGB at a biome level by meta-analyzing datasets contained in 40 articles from 25 countries, and then answered the questions of how stand structural characteristics influence AGB at the biome level and whether the relationships are regulated by climate on a large scale. Through using regression analysis and the establishment of a structural equation model, the results showed that the influence of basal area on AGB at the biome level was more substantial than that of tree density and DBH, and the significant relationship between basal area and AGB was relatively stable regardless of biome variation, but the effects of tree density and DBH was non-negligible within the biome. Climatic factors (e.g., temperature and precipitation), should be considered. Our meta-analysis illustrated the complicated interactions between climate factors, stand structural characteristics, and the AGB of forests, highlighting the importance of climate effects on regulating stand structural characteristics and AGB relationships. We suggest that basal area be preferred and considered in forest sustainable management practice to optimize stand structure for increasing carbon storage potential, with close attention to local climate conditions. Overall, our meta-analysis will crucially aid forest management and conservation in the context of global environmental changes, and provide novel insights and a scientific reference to lead to future carbon storage research on large scales.

1. Introduction

Changes in carbon storage in terrestrial ecosystems have far-reaching implications for the carbon cycle of the global ecosystem and climate change [1,2]. Forest ecosystems play a vital role in the global carbon cycle. Furthermore, mitigation of the detrimental impact of global warming has great significance in ecosystem stability and function [3,4]. Thus, accurate forest biomass estimation is necessary and has been the main area of focus in climate change research, forest management, and sustainable development [5,6,7,8,9,10,11,12,13]. Forest degradation and deforestation caused by human activities and climate change pose a significant challenge to ecosystem function and sustainable development. An estimated 420 million ha of forest has been lost worldwide through deforestation since 1990, and the total carbon stock in forests decreased from 668 gigatonnes in 1990 to 662 gigatonnes in 2020 [14]. Thus, increasingly widespread concern about global climate change has led to an interest in reducing carbon emissions through quantifying carbon sequestration by forests [15]. Biomass is a valuable indicator in forest stand structure assessment and forest carbon stock estimation [16]. Aboveground biomass (AGB) and carbon storage are commonly derived from tree inventory data (diameter and height) [17,18]. In contrast, tree density, species diversity, and other forest structural attributes are interlinked with carbon storage in the forest ecosystem [19,20,21]. Along with variety, stand structure is equally critical in deciphering management implications [22].
It is widely accepted that current increases in forest biomass result partially from the positive effects of climate change and changes in forest management [23,24,25,26]. Additionally, numerous studies have suggested that mean annual temperature (MAT) and mean annual precipitation (MAP) are the main factors influencing forest biomass across broad geographic scales and climatic gradients, e.g., [27,28,29]. However, whether and to what extent climate influences forest biomass changes remain debatable [30]. For example, as carbon sinks, tropical forests are prone to carbon losses caused by variations in stand structure and species composition [31,32,33,34]. The positive effects of climate change on forest biomass may be offset by increasing climatic variability and extreme climate events, such as intense drought and extremely low temperatures [35]. Thus, temperature and precipitation can constrain AGB through seasonality and extended dry periods, such as by regulating the relationships between functional traits, including stand structure, and AGB at a community level through the length of the growing and the dry seasons [36,37,38]. Specifically, AGB is directly associated with stand structure as it is influenced by climate.
Previous studies have indicated that lower TD and BA affected forest biomass in tropical forests [31,39,40]. Furthermore, in subtropical or pantropical forests, community composition and stand structure (mainly as it relates to tree height) significantly impact AGB [41,42]. In temperate forests, TD, DBH, and BA tend to determine biomass allocation and AGB [43,44]. Future forest carbon sinks could be affected by large-scale changes in mortality and growth rate due to climate, stand structure, and their interactions [45,46]. In addition, these drivers, which are used to predict climate change’s effects on forest biomass, can also interact in complex ways across various spatial scales [47]. These studies on small scales contribute to our understanding of how climate change impacts forest dynamics and ecosystem services, which are crucial for managing forests and conserving biodiversity in the context of global environmental changes. However, the present relationships between the factors mentioned above (e.g., stand structural characteristics, climate factors, and aboveground biomass) remain contentious at a large scale. Therefore, an enhanced understanding of how stand structural characteristics and climate factors influence AGB in forests at large scales is essential for estimating future potentials in forest carbon storage. In particular, for sustainable forest management, the deeper comprehension is needed because the optimized stand structure will affect seedling regeneration in forests, and the regeneration will ultimately determine tree species composition of forest stands and, hence, the AGB. Here, the objective of our meta-analysis was to examine the relationships between stand structural characteristics and AGB, and further make it clear how climate (e.g., temperature, precipitation) affects this relationship. More specifically, we sought to answer the following scientific questions:
(1) What effect do stand structural characteristics have on AGB, and how do such trait effects compare?
(2) How do climate factors affect the relationships between stand structural characteristics and AGB?
We hypothesized that temperature and precipitation would affect the stand structural characteristics–AGB relationships. To test the hypothesis, we conducted a meta-analysis on the effect of MAT and MAP on the relationships between TD, DBH, BA, and AGB through a systematic literature review. We firstly evaluated the relationship variation between stand structural characteristics and AGB in each biome (e.g., boreal forest, temperate seasonal forest, tropical rain forest, and tropical seasonal forest). We then explored the joint influence of stand structural characteristics and climate factors on AGB across all biomes. Finally, we investigated the multiple relationships between stand structural characteristics, climate factors, biome, and AGB.

2. Materials and Methods

2.1. Literature Search and Data Compilation

A systematic literature search was conducted using the subject headings “forest structure” AND “stand structure” AND “aboveground biomass” AND “forest biomass” to search published articles in the Web of Science, Google Scholar, Open Access Library, and CNKI, while Google Scholar and the Open Access Library were used to download articles that are not accessible in the Web of Science. The articles that were shortlisted for analysis had to be published in English and exclusively focused on stand structure–AGB relationships. Hence, the relevant literature up to December 2022 were identified and filtered on the basis of the following strict criteria: (1) each experiment must cover one or more sample plots; (2) studies must present stand structural characteristics, including DBH, TD, and BA in each site; (3) studies must be based on stand-level field research, not greenhouse or growth chamber pot experiments; and (4) the forest stands must cover either managed or non-managed natural and secondary forest. Following the criteria for the literature selection, 40 articles from 25 countries (Supplementary Figure S1) were ultimately included in our meta-analysis. With regard to data compilation, most data were directly obtained from the original articles, whereas a small portion of data were extracted from figures and tables using GetData Graph Digitizer 2.2 (Free Software Foundation, Boston, MA, USA). Finally, 227 entries of DBH, BA, TD, and AGB data (Supplementary Table S1) were included in our datasets for analysis. Here, these values of stand structural characteristics and AGB refer to a stand level. To analyze the interactive effects of climate factors (MAT, MAP) on AGB, MAT and MAP data of the sampling points in our datasets were extracted directly from the 40 articles. Meanwhile, the sampling points from the literature were classified into four biomes (e.g., boreal forest, temperate seasonal forest, tropical rainforest, and tropical seasonal forest) based on the MAP and MAT of each point using Whittaker’s biome diagram [48].

2.2. Data Analysis

In the meta-analysis, we aimed to evaluate the relationship between stand structural characteristics and AGB within biomes and, further, the impact of climate on these relationships. To analyze the relationship of stand structural characteristics and AGB, the log transformation was first undertaken for each variable to ensure that they conformed to a normal distribution [49]. Then, the regression relationship between stand structural characteristics and AGB were described by the following allometric growth equation (Equation (1)):
lg y = α (lg x) + β
where y and x represent the AGB values and different structural characteristics in the stand, α is the allometric exponent (the slope of this equation), and β is the allometric constant (the intercept of this equation); the slope is compared against α = 1, representing the isometric relationship between two variables when the slope is exactly α = 1, and the allometric relationship when it does not (α < 1 or α > 1) [50,51,52]. Then, the approach of standard major axis regression analysis (SMA) was used to assess the parameters α and β, and the allometric relationship between structural characteristics and AGB was tested through a comparison to 1.0; if the slope α is significantly different from 1.0, this indicates an allometric relationship between stand structural characteristics and AGB; otherwise, they are isometric [49,53]. To explore the joint effects of MAP, MAT, and stand structural characteristics on AGB across all biomes, multiple linear regression analysis (MLR) was carried out, and the variance inflation factor (VIF > 10) was used to estimate the collinearity between variables in the analysis [54,55]; meanwhile, a Pearson correlation analysis was conducted to analyze the correlation between stand structural characteristics and climate factors.
Structural equation modeling (SEM) was used to assess the multiple effects of MAT, MAP, TD, DBH, BA, and the biome on AGB, and, furthermore, the relationships in the multivariate data and the complex relationships and causality between variables were identified. For the evaluation of the most effective SEM, several statistical parameters including the chi-square (χ2) test, goodness-of-fit index (GFI), comparative fit index (CFI), and standardized root mean square residual (SRMR) were used in the model. Here, we used the χ2 test to critically evaluate the model fit and selection of SEM. These indicators for a strong model fit to the data contained an insignificant (p > 0.05) χ2 test statistic, SRMR < 0.05, and GFI and CFI > 0.90, and the lowest AIC was selected as our final model among all acceptable models [19,56]. The SEM explained that a combination of factors, namely MAT, MAP, TD, DBH, BA, and the biome, would result in direct and indirect effects on AGB. All data processing, statistical analysis, and graphing were conducted using MS Excel, R 4.2.3 (https://www.r-project.org/, accessed on 20 March 2023), Amos 26.0 (https://www.ibm.com/spss, accessed on 24 December 2022), Origin 9.4 (OriginLab, Northampton, MA, USA), and ArcGIS 10.7.

3. Results and Analysis

3.1. Allometric Relationship of Stand Structural Characteristics and AGB

An SMA was performed to determine the allometric relationship between variables (Figure 1). The results showed that the allometric relationships were different across all biomes. Firstly, significant allometric associations of TD (α = −0.417; CI = −0.640, −0.272; p < 0.001) and DBH (α = 0.575, CI = 0.389, 0.851; p < 0.05), respectively, with AGB were observed, indicating that the growth rate in AGB exceeded TD and DBH in boreal forest (Figure 1a). In terms of temperate seasonal forest, the relationship of TD and AGB was significantly allometric (α = −0.76, CI = −0.908, −0.637; p < 0.05), whereas the relationships were not found for DBH nor BA (p > 0.05), indicating an isometric growth of DBH and BA, respectively, and AGB in this biome (Figure 1b). Moreover, significant allometric relationships of BA (α = 1.309, CI = 1.135, 1.510; p < 0.001) and DBH (α = 1.524, CI = 1.122, 2.069; p < 0.05), respectively, with AGB were observed in tropical rainforest (Figure 1c), whereas those of TD and AGB non-significant (p > 0.05). Meanwhile, significant allometric relationships of BA (α = 1.145, CI= 1.026, 1.279; p < 0.05) and TD (α = 0.608, CI = 0.471, 0.785; p < 0.001), respectively, with AGB were further found in tropical seasonal forest, whereas there was an isometric relationship between DBH and AGB here (p > 0.05) (Figure 1d).

3.2. Interrelation of Stand Structural Characteristics and Climate Factors

As shown in Table 1, correlation analysis indicated the significant correlation (p < 0.05) between stand structural characteristics and climate factors in boreal forest, which stated that the considerable influence of BA on AGB was related to climate factors. In addition, BA was negatively correlated with TD (r = −0.450, p < 0.05), whereas it was positively correlated with DBH (r = 0.743, p < 0.01), indicating that the increase in DBH rather than TD was suitable for BA in boreal forest. Significant correlations of TD and climate factors, such as MAT (r = 0.262, p < 0.01) and MAP (r = 0.231, p < 0.05), were observed in temperate seasonal forest; meanwhile, a negative correlation of TD with DBH (r = −0.205, p < 0.05) and positive correlation with BA (r = 0.246, p < 0.05) were also found, indicating that the relationship of TD and AGB was not simply affected by climate factors, but associated with DBH. This further revealed that increasing TD will promote BA in temperate seasonal forest. Moreover, MAP was positively correlated with BA (r = 0.499, p < 0.01) in tropical rainforest, indicating the reciprocal effect of MAP and BA on AGB. However, BA was not significantly correlated with any stand or climate factors in tropical seasonal forest (p > 0.05), whereas TD (p < 0.05) and DBH (p < 0.01), respectively, were associated with climate factors.

3.3. Joint Influences of Stand Structural Characteristics and Climate Factors on AGB

Multiple regression analysis indicated that AGB was jointly influenced by stand structural characteristics and climate factors (Figure 2). In terms of boreal forest, stand factors jointly influenced AGB, and a significant effect of BA on AGB was observed (r = 0.803, p < 0.05) (Figure 2a). However, BA (r = 0.647, p < 0.001) and TD (r = −0.387, p < 0.001) significantly affected AGB (r = 1.041, p < 0.05) in temperate seasonal forest, and the influence of BA was stronger than TD; meanwhile, a significant effect of MAT on AGB was found (r = −0.226, p < 0.01), showing a joint influence of a stand structural characteristic and climate factor on AGB (Figure 2b). Similarly, BA significantly affected AGB in tropical rainforest (r = 0.665, p < 0.001); meanwhile, a significant effect of DBH (r = 0.216, p < 0.05) and MAP (r = 0.226, p < 0.05) on AGB was observed here (Figure 2c). In addition, a significant effect of BA on AGB was found in tropical seasonal forest (r = 0.804, p < 0.001) (Figure 2d). According to the relative contribution of stand structural characteristics and climate factors to AGB across all biomes, the results revealed that TD, DBH, and BA explained 37.8% of the variation in AGB in boreal forest. Concerning the temperate seasonal forest, the contribution of stand structural characteristics and climate factors to AGB totaled 51%. A higher contribution of stand and climate factors was found in tropical rainforest (78.6%) and seasonal forest (64.9%), indicating a more substantial influence of stand structural characteristics and climate factors on AGB in tropical forest than in temperate and boreal forest (Figure 3).

3.4. The Establishment of SEM among Observed Variables

Our results (Figure 4) revealed that the final selected and best-fit SEM with the different paths for the interrelationships between climate factors, stand structural characteristics, and biome explained 43% of the variation in AGB. Meanwhile, the climate factors explained 3%, 7%, and 2% of the variation, respectively, in TD, DBH, and BA. An important role of climate factors was found when stand structural characteristics influenced AGB. However, at this point, the effects of climate factors were indirect. The SEM showed that BA had a significant impact on AGB (p < 0.001), which was promoted directly by MAT and MAP. TD had a non-significant impact on AGB, but MAP significantly and directly affected TD (p < 0.05). Meanwhile, DBH significantly affected AGB (p < 0.05), which was related to the direct effects of MAT (p < 0.001) and MAP (p < 0.01). Despite climate factors being associated with biome (p < 0.001), it was shown that the biome did not have a direct or indirect effect on stand structure. However, the biome significantly and directly affected AGB (p < 0.05). Furthermore, a significant influence of climate factors on AGB was observed in the SEM (p < 0.001), which was related to the biome (p < 0.001).

4. Discussion

4.1. Differences between Stand Structural Characteristics in Terms of Their Influence on AGB within Biomes

As the main characteristics of stand structure, TD, DBH, and BA strongly influenced AGB. However, owing to different abiotic and biotic conditions, the influence of stand structural characteristics on AGB differed across all biomes [57,58]. Alexander’s finding [43] showed that DBH and BA significantly influenced AGB in boreal forest, and the significance of BA on AGB was also found on a large scale in our study. However, Wang et al. [59] revealed that TD significantly affected AGB in boreal forest (e.g., Korean pine forests). Our findings further showed that the relationship between TD and AGB was allometric in boreal forest, which may be associated with regeneration after a fire and human disturbance [60,61]. Previous results revealed that AGB in Larix gmelinii stands remained constant at decreasing TD due to fire and nutrient limitations triggered by competition [62], indicating the important effects of self-thinning on AGB at the biome level [63]. The relationships between TD and AGB in competitive environments were mediated by tree crowns, since TD significantly influenced crown structure, including branches and leaves, by regulating crown growth in order to obtain plenty of sunshine [64,65,66]. However, tree death from repeated ground fires and slow recovery due to nutrient limitations changed self-thinning trajectories [67]. Particularly in even-aged stands that regenerated after fire, since the relationship between competition and habitat conditions are more complicated, the association of TD and AGB might be affected by insect outbreak or windthrow [68]. TD significantly affected AGB in temperate forest, consistent with the findings of Yang et al. [69]; meanwhile, an allometric relationship of TD and AGB was found within this biome. Most likely, community type and species diversity should be considered, such as in broadleaf and conifer forests or mixed forests [70], since trees’ biological traits (e.g., leaf area, thickness) may regulate different nutrient uptake and partitioning. Moreover, due to the resource complementarity effect, inter-species interactions drive trees to make efficient use of light, water, and soil nutrients [71,72].
Our study revealed that DBH significantly influenced AGB in tropical rainforest, likely because dominant tree species regulated the aboveground resource allocation via luxuriant branches, which have a positive feedback on carbon storage [73]. The relationship may also be explained by increased light capture and light use efficiencies in association with complex tree-sized structures [74,75], showing different responses of different-sized trees to light efficiency. Several authors have confirmed that large trees increased AGB in the natural forests [76,77,78], since large trees could obtain and utilize more nutrients through a well-developed root system and crown structure, and govern carbon sequestration [79]. However, the effects of small trees in a stand should not be ignored, since in forests with dense small DBH trees in larger numbers, and many sparse large trees, AGB was stable [80]. Our finding also showed a strong correlation between TD and AGB in boreal and temperate forests. Most likely, there is a competitive exclusion mechanism in forests; for example, large trees lead to species competition in different ways, and, hence, directly lead to a reduction in TD and indirectly to AGB [81]. A significant allometric relationship of TD and DBH, respectively, with AGB was found in tropical forests, where it has been confirmed that the relationships were related to site condition, tree age, tree species diversities, and origin of the forest [15,82,83,84,85].
BA significantly influenced AGB across all biomes in our results. Conversely, Alexander et al. [43] and Taylor et al. [86] concluded that a non-significant relationship occurred in the boreal forest of interior Alaska and central Canada, indicating the significance of the geographical environment. However, in southern Finland, the significant association between BA and AGB resulted from forest management [87], such as felling [88], which indicated that harvesting may alter the influence of BA on AGB regardless of stand age and species diversity. In this study, we showed a correlation between TD, DBH, and BA in boreal forest, which means that if felling reduced TD, fewer trees may affect DBH and BA, and, hence, AGB. It is perhaps for this reason that DBH and AGB were allometric. Similar management may also influence the relationship between BA and AGB in tropical forests [89,90]. Nevertheless, in unmanaged forests, BA in the upper trees significantly affected AGB; therefore, the vital role of dominant trees should be noted [91]. Furthermore, numerous authors have suggested that the relationship of BA and AGB in the tropics was involved in tree species, elevation, tree age, and soil properties [17,92,93,94], which may explain the potential reason for the allometry between them. In temperate forest, it was found that the significant influence of BA on AGB was consistent with previous studies [43,95]; however, the influence was not absolute due to dominant species. Even if the BA of different sizes of trees was a strong driver of AGB, overstory trees dominated the relationship and played a more critical role than the understory trees [96]. Moreover, owing to the dominant species, the effect of BA on AGB was stable with the change in altitude [97]. In our results, we did not find a significant correlation between BA and DBH in spite of the substantial impact of BA on AGB in temperate forest. However, other findings showed that BA in medium–small DBH trees had the most important effect on AGB, whereas BA in large trees slightly affected AGB, which may be related to tree species diversity [98].

4.2. Determinants of Co-Driving AGB among Climate, Stand Structural Characteristics, and Biome

Previous studies have proposed factors that govern forest AGB, highlighting several climatic variables, including MAT and MAP [28,99]. A consensus has yet to be reached on which climate variables are more important in terms of affecting the relationships on different spatial scales [86]. Our results revealed that both climate factors and stand structural characteristics jointly influenced AGB across all biomes, whereas there was a significant influence on AGB mainly in temperate and tropical rainforest. A possible explanation was that climate factors regulated stand structure in forests, and, hence, AGB. Specifically, temperature drives the trees’ utilization of light energy within the photosynthetic uptake processes to increase AGB [100]. In previous studies, the linear relationship between absorbed photosynthetic radiation and tree biomass has been confirmed for different tree species [101]. The temperature, which regulated the relationship of stand structure and AGB, could also have an adverse impact on AGB. For instance, the increasing temperature may lead to an increase in fire risk in the future [102], especially in boreal and temperate forests, since the risk of extreme fire events was expected to trigger great AGB loss [103]. Our results revealed that precipitation and BA together influenced AGB in tropical rainforest, and BA had a significant correlation with the precipitation, indicating that precipitation may influence BA and, thus, AGB. Similarly, the interrelations between stand structure characteristics and precipitation were also observed in tropical seasonal forest. Nevertheless, the climate factors and BA did not jointly affect AGB in this biome, which has been revealed in other studies due to influences of the topography [104,105,106], soil properties, management protocols, and species diversity [107,108,109,110]. Additionally, we found that the contribution of climate and stand factors to AGB in tropical and temperate forests was greater than that in boreal forest, but the potential ecological reasons remained unclear on a large scale.
Either a direct or indirect effect on AGB for climate, stand structure, and biome was further observed through the SEM. It was indicated that both temperature and precipitation had a direct effect on stand structure and AGB, which was not entirely consistent with the previous results showing the more important influence of temperature on AGB than other climatic variables [111]. This was likely associated with diverse biomes in our study on a large scale. Although our finding did not reveal any direct or indirect effects of biome on stand structure on a large scale, previous findings have confirmed that climate factors influenced stand structure due to biome on a local scale [112,113]. Most likely, the mutual effects of biome and climate factors reflected trees’ different growth strategies in adaptation to favorable climatic conditions [67,114,115]. This indicated that the effect of biome on the relationship between stand structure and AGB should be considered regardless of scales. Previous findings revealed that climate factors alone cannot explain the patterns of AGB on the regional scale [116], showing the interaction between temperature and precipitation in the influence on AGB, which was further confirmed in our findings. Moreover, we found that temperature and precipitation together had a direct effect on stand structural characteristics, especially on DBH and, thus, AGB; however, climate factors alone significantly and directly affected TD and BA and, hence, AGB, which means there were no fixed patterns in the effects of climate variables on the relationship between stand structure and AGB.

4.3. Study Limitations and Weaknesses

Our review highlights an important issue related to carbon storage across all biomes in recent years. Unfortunately, there were still potential research shortcomings and biases due to there being fewer datasets from limited studies with fewer biome types and fewer countries included. Although temperature and precipitation directly affected stand structural characteristics and, hence, AGB on a large scale, the common effects of climate factors and other stand structural characteristics such as tree height and crown breadth rather than only TD, DBH, and BA may affect AGB at the biome level, particularly in tropical and temperate forests. In the process of carrying out the meta-analysis, we found that fewer original studies directly resulted in smaller datasets, and, hence, affected the research results. For instance, our meta-analysis indicated that basal area had a more substantial influence on AGB than that of tree density and DBH in the boreal, temperate seasonal, tropical rain, and tropical seasonal forests; however, the relationships may be changed with variation in tree age, regional scale, and biodiversity, which were not fully revealed due to inadequate data on tree age in the original articles. Owing to the fewer original studies on the relationship between stand structural characteristics and AGB, including an incomplete data set or fewer parameters on stand structure (e.g., only one or two indicators used in characterizing the stand structure) in the literature, a comprehensive and detailed explanation of the complicated relation between climate, stand structure, and AGB could not be presented in the meta-analysis. Furthermore, this may give rise to several large and mostly unexplained relationships between stand structural characteristics and AGB in different aged stands, especially in forests of the underrepresented regions, or in undiscovered and complex biomes. In addition, an ecological mechanism of stand structural characteristics–AGB relationships could not be understood in depth due to the limited datasets from fewer original studies. In fact, our initial idea was to explore the relationships between stand structural characteristics of different sized trees and AGB in different temperature and precipitation conditions through a comparison of plantation forests and natural or secondary forests, which, however, was replaced due to the low number of original studies on the relationship between stand structural characteristics and AGB in plantation forests. However, it is also possible that several original articles were not found due to the limited databases. Nevertheless, we agree that temperature and precipitation will likely be a major determinant in the environmental components of regulating stand structural characteristics and AGB relationships, and large datasets are essential for detailed meta-analysis research on a large scale.

5. Conclusions

Understanding the relationships of stand structural characteristics and AGB on a large scale is vital to both predicting the rate and potential of forest carbon storage and guiding future multifunctional forest management in the face of global environmental changes. The effects of climate factors and stand structural characteristics on AGB were discussed based on a comparative analysis of the datasets used in our meta-analysis. The results showed that the important effects of stand structural characteristics and climate factors in increasing AGB should be considered for sustainable forest management. Different stand structural characteristics had different impacts on AGB within biomes; through comparison between stand structural characteristics, the significant influences of basal area on AGB were found to be more substantial than those of tree density and DBH in the boreal, temperate seasonal, tropical rain, and seasonal forest. Therefore, the main stand structural characteristic affecting AGB was undoubtedly basal area; however, the influence of tree density and DBH should not be ignored at the biome level. This study sheds light on the complicated interactions between climate factors, stand structural characteristics, and AGB in forests, and highlights that both temperature and precipitation affect the relationship between stand structural characteristics and AGB on a large scale, hence supporting our hypothesis. Therefore, to increase AGB and forest carbon storage, we suggest that basal area be preferred and considered in forest management practice to optimize stand structure for promoting AGB growth, with close attention to local climate conditions. Moreover, in terms of a meta-analysis on the potential for forest carbon storage on a large scale, future research should concentrate on clarifying the relationship between other stand structural characteristics and AGB in natural forests under hydrothermal conditions, and could also attempt an extended study of plantation forests using an adequate dataset.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14050896/s1, Figure S1: Sampling points from 25 countries; Table S1: Datasets from 25 countries.

Author Contributions

Y.M. designed and performed the research framework, collected and analyzed the data, and wrote and prepared the original draft. Ü.H. and A.K. designed and supervised the study, reviewed the manuscript, and approved the final draft. A.E. participated in data analysis and reviewed the manuscript draft. A.A. reviewed the manuscript with critical comments and language proofreading. All authors have read and agreed to the published version of the manuscript.

Funding

This research work was supported by the National Natural Science Foundation of China (Grant Nos.: 32260285, 32071655), the Third Xinjiang Scientific Expedition and Research Program (Grant No: 2022xjkk0301), and Enterprise research project funded by Tarim River Basin Authority (Grant No: TGJGLJJJG2021ZXFW0007).

Data Availability Statement

The data presented in this study are available upon reasonable request from the author team.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhou, L.; Liu, H.; Zhou, G.; Zhou, X.; Hong, Y.; Li, C.; Lu, C.; He, Y.; Shao, J.; Sun, X.; et al. Responses of biomass allocation to multi-factor global change: A global synthesis. Agric. Ecosyst. Environ. 2020, 304, 107115. [Google Scholar] [CrossRef]
  2. Tian, L.; Tao, Y.; Fu, W.X.; Li, T.; Ren, F.; Li, M.Y. Dynamic simulation of land use/cover change and assessment of forest ecosystem carbon storage under climate change Scenarios in Guangdong Province, China. Remote Sens. 2022, 14, 2330. [Google Scholar] [CrossRef]
  3. Payne, N.J.; Cameron, D.A.; Leblanc, J.D.; Morrison, I.K. Carbon storage and net primary productivity in Canadian boreal mixedwood stands. J. For. Res. 2019, 30, 1667–1678. [Google Scholar] [CrossRef]
  4. Li, X.; Du, H.; Mao, F.; Zhou, G.; Han, N.; Xu, X.; Liu, Y.; Zhu, D.; Zheng, J.; Dong, L.; et al. Assimilating spatiotemporal MODIS LAI data with a particle filter algorithm for improving carbon cycle simulations for bamboo forest ecosystems. Sci. Total Environ. 2019, 694, 133803. [Google Scholar] [CrossRef] [PubMed]
  5. De Castilho, C.V.; Magnusson, W.E.; De Araújo, R.N.O.; Luizão, F.J. Short-term temporal changes in tree live biomass in a central Amazonian forest Brazil. Biotropica 2010, 42, 95–103. [Google Scholar] [CrossRef]
  6. Furley, P. Tropical savannas: Biomass, plant ecology, and the role of fire and soil on vegetation. Prog. Phys. Geog. 2010, 34, 563–585. [Google Scholar] [CrossRef]
  7. Keith, H.; van Gorsel, E.; Jacobsen, K.L.; Cleugh, H.A. Dynamics of carbon exchange in a Eucalyptus forest in response to interacting disturbance factors. Agric. For. Meteorol. 2012, 153, 67–81. [Google Scholar] [CrossRef]
  8. Lu, D.; Chen, Q.; Wang, G.; Moran, E.; Batistella, M.; Zhang, M.; Laurin, G.V.; Saah, D. Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. Int. J. For. Res. 2012, 1, 436537. [Google Scholar] [CrossRef]
  9. Brahma, B.; Nath, A.J.; Deb, C.; Sileshi, G.W.; Sahoo, U.K.; Das, A.H. A critical review of forest biomass estimation equations in India. Tree For. People 2021, 5, 100098. [Google Scholar] [CrossRef]
  10. Fang, J.Y.; Guo, Z.; Hu, H.; Kato, T.; Muraoka, H.; Son, Y. Forest biomass carbon sinks in East Asia, with special reference to the relative contributions of forest expansion and forest growth. Glob. Chang. Biol. 2014, 20, 2019–2030. [Google Scholar] [CrossRef]
  11. McEwan, R.W.; Lin, Y.; Sun, I.; Hsieh, C.; Su, S.; Chang, L.; Song, G.M.; Wang, H.; Hwong, J.; Lin, K.; et al. Topographic and biotic regulation of aboveground carbon storage in subtropical broad-leaved forests of Taiwan. For. Ecol. Manag. 2011, 262, 1817–1825. [Google Scholar] [CrossRef]
  12. Pan, Y.; Birdsey, R.A.; Fang, J.; Houghton, R.; Kauppi, P.E.; Kurz, W.A.; Phillips, O.L.; Shvidenko, A.; Lewis, S.L.; Canadell, J.G.; et al. A large and persistent car bon sink in the world’s forests. Science 2011, 333, 988–993. [Google Scholar] [CrossRef] [PubMed]
  13. Ou, G.; Li, C.; Lv, Y.; Wei, A.; Xiong, H.; Xu, H.; Wang, G. Improving aboveground biomass estimation of Pinus densata forests in Yunnan using Landsat 8 Imagery by incorporating age dummy variable and method comparison. Remote Sens. 2019, 11, 738. [Google Scholar] [CrossRef]
  14. FAO. Global Forest Resources Assessment 2020: Main Report. Rome: FAO. 2020. Available online: https://www.fao.org/forest-resources-assessment/en/ (accessed on 20 September 2022).
  15. Azad, M.S.; Kamruzzaman, M.; Osawa, K. Quantification and understanding of above and belowground biomass in Medium Saline Zone of the Sundarbans, Bangladesh: The relationships with forest attributes. J. Sustain. For. 2019, 39, 331–345. [Google Scholar] [CrossRef]
  16. Soares, M.L.G.; Schaeffer-Novelli, Y. Aboveground biomass of mangrove species. I. Analysis of models. Estuar. Coast. Shelf Sci. 2005, 65, 1–18. [Google Scholar] [CrossRef]
  17. Gross, J.; Flores, E.E.; Schwendenmann, L. Stand structure and aboveground biomass of a Pelliciera rhizophorae mangrove forest, Gulf of Monitjo Ramsar Site, Pacific Coast, Panama. Wetlands 2014, 34, 55–65. [Google Scholar] [CrossRef]
  18. Clough, B.J.; Russell, M.B.; Domke, G.M.; Woodall, C.W. Quantifying allometric model uncertainty for plot-level live tree biomass stocks with a data-driven, hierarchical framework. For. Ecol. Manag. 2016, 372, 175–188. [Google Scholar] [CrossRef]
  19. Ali, A.; Yan, E.R.; Chen, H.Y.H.; Chang, S.X.; Zhao, Y.T.; Yang, X.D.; Xu, M.S. Stand structural diversity rather than species diversity enhances aboveground carbon storage in secondary subtropical forests in Eastern China. Biogeosciences 2016, 13, 4627–4635. [Google Scholar] [CrossRef]
  20. Wang, W.; Lei, X.; Ma, Z.; Kneeshaw, D.D.; Peng, C. Positive relationship between aboveground carbon stocks and structural diversity in spruce-dominated forest stands in New Brunswick, Canada. For. Sci. 2011, 57, 506–515. [Google Scholar] [CrossRef]
  21. Dănescu, A.; Albrecht, A.T.; Bauhus, J. Structural diversity promotes productivity of mixed, uneven-aged forests in southwestern Germany. Oecologia 2016, 182, 319–333. [Google Scholar] [CrossRef]
  22. Kaushal, S.; Baishya, R. Stand structure and species diversity regulate biomass carbon stock under major Central Himalayan forest types of India. Ecol. Process. 2021, 10, 14. [Google Scholar] [CrossRef]
  23. Huang, C.; Liang, Y.; He, H.S.; Wu, M.M.; Liu., B.; Ma, T.X. Sensitivity of aboveground biomass and species composition to climate change in boreal forests of Northeastern China. Ecol. Model. 2021, 445, 109472. [Google Scholar] [CrossRef]
  24. Hember, R.A.; Kurz, W.A.; Metsaranta, J.M.; Black, T.A.; Guy, R.D.; Coops, N.C. Accelerating regrowth of temperate-maritime forests due to environmental change. Glob. Chang. Biol. 2012, 18, 2026–2040. [Google Scholar] [CrossRef]
  25. Peng, J.; Dan, L.; Huang, M. Sensitivity of global and regional terrestrial carbon storage to the direct CO2 effect and climate change based on the CMIP5 model intercomparison. PLoS ONE 2014, 9, e95282. [Google Scholar] [CrossRef]
  26. Luyssaert, S.; Ciais, P.; Piao, S.L.; Schulze, E.D.; Jung, M.; Zaehle, S.; Schelhaas, M.J.; Reichstein, M.; Churkina, G.; Papale, D.; et al. The European carbon balance, Part 3: Forests. Glob. Chang. Biol. 2010, 16, 1429–1450. [Google Scholar] [CrossRef]
  27. Chen, X.; Luo, M.Y.; Larjavaara, M. Effects of climate and plant functional types on forest above-ground biomass accumulation. Carbon Balance Manag. 2023, 18, 305. [Google Scholar] [CrossRef]
  28. Sankaran, M.; Hanan, N.P.; Scholes, R.J.; Ratnam, J.; Augustine, D.J.; Cade, B.S.; Gignoux, J.; Higgins, S.I.; Le Roux, X.; Ludwig, F.; et al. Determinants of woody cover in African savannas. Nature 2005, 438, 846–849. [Google Scholar] [CrossRef] [PubMed]
  29. Zhang, T.; Ding, G.J.; Zhang, J.P.; Qi, Y.J. Stand, plot characteristics, and tree species diversity jointly dominate the recruitment biomass of subtropical forests. For. Ecol. Manag. 2023, 531, 120814. [Google Scholar] [CrossRef]
  30. McMahon, S.M.; Parker, G.G.; Miller, D.R. Evidence for a recent increase in forest growth. Proc. Natl. Acad. Sci. USA 2010, 107, 3611–3615. [Google Scholar] [CrossRef] [PubMed]
  31. Osuri, A.M.; Kumar, V.S.; Sankaran, M. Altered stand structure and tree allometry reduce carbon storage in evergreen forest fragments in India’s Western Ghats. For. Ecol. Manag. 2014, 329, 375–383. [Google Scholar] [CrossRef]
  32. Phillips, O.L.; Aragão, L.E.; Lewis, S.L. Drought sensitivity of the Amazon rainforest. Science 2009, 323, 1344–1347. [Google Scholar] [CrossRef]
  33. Calvo-Rodriguez, S.; Sánchez-Azofeifa, G.A.; Durán, S.M.; Espírito-Santo, M.M.D.; Nunes, Y.R.F. Dynamics of Carbon Accumulation in Tropical Dry Forests under Climate Change Extremes. Forests 2021, 12, 106. [Google Scholar] [CrossRef]
  34. Hiltner, U.; Huth, A.; Fischer, R. Importance of the forest state in estimating biomass losses from tropical forests: Combining dynamic forest models and remote sensing. Biogeosciences 2022, 19, 1891–1911. [Google Scholar] [CrossRef]
  35. Zhao, M.; Running, S.W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 2010, 329, 940–943. [Google Scholar] [CrossRef] [PubMed]
  36. Ali, A.; Lin, S.L.; He, J.K.; Kong, F.M.; Yu, J.H.; Jiang, H.S. Elucidating space, climate, edaphic and biodiversity effects on aboveground biomass in tropical forests. Land Degrad. Dev. 2019, 30, 918–927. [Google Scholar] [CrossRef]
  37. Michaletz, S.T.; Kerkhoff, A.J.; Enquist, B.J. Drivers of terrestrial plant production across broad geographical gradients. Glob. Ecol. Biogeogr. 2018, 27, 166–174. [Google Scholar] [CrossRef]
  38. Malhi, Y.; Wood, D.; Baker, T.R.; Wright, J.; Phillips, O.L.; Cochrane, T. The regional variation of aboveground live biomass in old-growth Amazonian forests. Glob. Chang. Biol. 2006, 12, 1107–1138. [Google Scholar] [CrossRef]
  39. Lee, L.; Lee, J.; Kim, S.J.; Roh, Y.J.; Salim, K.A.; Lee, W.K.; Son, Y.H. Forest structure and carbon dynamics of an intact lowland mixed Dipterocarp forest in Brunei Darussalam. J. For. Res. 2018, 29, 199–203. [Google Scholar] [CrossRef]
  40. Gao, L.S.; Zhang, X.L. Above-ground biomass estimation of plantation with complex forest stand structure using multiple features from airborne laser scanning point cloud data. Forests 2021, 12, 1713. [Google Scholar] [CrossRef]
  41. Feldpausch, T.R.; Lloyd, J.; Lewis, S.L.; Brienen, R.J.W.; Gloor, M.; Mendoza, A.M. Tree height integrated into pantropical forest biomass estimates. Biogeosciences 2012, 9, 3381–3403. [Google Scholar] [CrossRef]
  42. Wang, X.; Huang, X.; Wang, Y.; Yu, P.; Guo, J. Impacts of site conditions and stand structure on the biomass allocation of single trees in larch plantations of Liupan Mountains of Northwest China. Forests 2022, 13, 177. [Google Scholar] [CrossRef]
  43. Alexander, H.D.; Mack, M.C.; Goetz, S.; Beck, P.S.A.; Belshe, E.F. Implications of increased deciduous cover on stand structure and aboveground carbon pools of Alaskan boreal forests. Ecosphere 2012, 3, 45. [Google Scholar] [CrossRef]
  44. Mantgem, P.J.; Stephenson, N.L.; Byrne, J.C.; Daniels, L.D.; Franklin, J.F.; Fule, P.Z.; Harmon, M.E.; Larson, A.J.; Smith, J.M.; Taylor, A.H.; et al. Widespread increase of tree mortality rates in the western United States. Science 2009, 323, 521–524. [Google Scholar] [CrossRef]
  45. Ruiz-Benito, P.; Lines, E.R.; Go´mez-Aparicio, L.; Zavala, M.A.; Coomes, D.A. Patterns and drivers of tree mortality in Iberian forests: Climatic effects are modified by competition. PLoS ONE 2013, 8, e56843. [Google Scholar] [CrossRef]
  46. Ruiz-Benito, P.; Madrigal-Gonza´lez, J.; Ratcliffe, S.; Coomes, D.A.; Kandler, G.; Lehtonen, A.; Wirth, C.; Zavala, M.A. Stand structure and recent climate change constrain stand basal area change in European forests: A comparison across boreal, temperate, and mediterranean biomes. Ecosystems 2014, 17, 1439–1454. [Google Scholar] [CrossRef]
  47. Holdaway, R.J.; Easdale, T.A.; Carswell, F.E.; Richardson, S.J.; Peltzer, D.A.; Mason, N.W.H.; Brandon, A.M.; Coome, D.A. Nationally representative plot network reveals contrasting drivers of net biomass change in secondary and old-growth forests. Ecosystems 2017, 20, 944–959. [Google Scholar] [CrossRef]
  48. Westman, W.E.; Peet, R.K.; Robert, H. Whittaker (1920–1980): The man and his work. Vegetatio 1982, 48, 97–122. [Google Scholar] [CrossRef]
  49. Warton, D.I.; Wright, I.J.; Falster, D.S.; Westoby, M. Bivariate line-fitting methods for allometry. Biol. Rev. 2006, 81, 259–291. [Google Scholar] [CrossRef]
  50. Mankou, G.S.; Ligot, G.; Panzou, G.J.L.; Boyemba, F. Tropical tree allometry and crown allocation, and their relationship with species traits in central Africa. For. Ecol. Manag. 2021, 493, 119262. [Google Scholar] [CrossRef]
  51. Eziz, A.; Yan, Z.B.; Tian, D.; Han, W.X.; Tang, A.Y.; Fang, J.Y. Drought effect on plant biomass allocation: A meta-analysis. Ecol. Evol. 2017, 7, 11002–11010. [Google Scholar] [CrossRef]
  52. Li, J.L.; Wang, M.T.; Li, H.S.; Chen, X.P.; Sun, J.; Zhong, Q.L.; Cheng, D.L. Effects of Canopy Height on the Relationship Between Individual Leaf Mass and Leafing Intensity of 69 Broad Leaved Trees in Jiangxi Province. Scientia Silvae Sinicae 2021, 57, 62–71. [Google Scholar] [CrossRef]
  53. Niklas, K.J.; Cobb, E.D.; Niinemets, U.; Reich, P.B.; Sellin, A.; Shipley, B.; Wright, I.J. “Diminishing returns” in the scaling of functional leaf traits across and within species groups. Proc. Natl. Acad. Sci. USA 2007, 104, 8891–8896. [Google Scholar] [CrossRef] [PubMed]
  54. Gomez, R.S.; Perez, J.G.; Martin, M.D.L.; Garcia, C.G. Collinearity diagnostic applied in ridge estimation through the variance inflation factor. J. Appl. Stat. 2016, 43, 1831–1849. [Google Scholar] [CrossRef]
  55. Kim, J.H. Multicollinearity and misleading statistical results. Korean J. Anesthesiol. 2019, 72, 558–569. [Google Scholar] [CrossRef]
  56. Malaeb, Z.; Summers, J.; Pugesek, B. Using structural equation modeling to investigate relationships among ecological variables. Environ. Ecol. Stat. 2000, 7, 93–111. [Google Scholar] [CrossRef]
  57. Gandhi, D.S.; Sundarapandian, S. Large-scale carbon stock assessment of woody vegetation in tropical dry deciduous forest of Sathanur reserve forest, Eastern Ghats, India. Environ. Monit. Assess. 2017, 189, 187. [Google Scholar] [CrossRef]
  58. Becknell, J.M.; Kucek, L.K.; Powers, J.S. Aboveground biomass in mature and secondary seasonally dry tropical forests: A literature review and global synthesis. For. Ecol. Manag. 2012, 276, 88–95. [Google Scholar] [CrossRef]
  59. Wang, X.M.; Guo, Z.W.; Guo, X.; Wang, X.P. The relative importance of succession, stand age and stand factors on carbon allocation of Korean Pine Forests in the Northern Mt. Xiaoxing’anling, China. Forests 2020, 11, 512. [Google Scholar] [CrossRef]
  60. Kamara, M.; Said, S.M. Estimation of aboveground biomass, stand density, and biomass growth per year in the past using stand reconstruction technique in black spruce and Scotch pine in boreal forest. Polar Sci. 2022, 33, 100787. [Google Scholar] [CrossRef]
  61. Vygodskaya, N.N.; Schulze, E.D.; Tchebakova, N.M.; Karpachevskii, L.O.; Kozlov, D.; Sidorov, K.N.; Panfyorov, M.I.; Abrazko, M.A.; Shaposhnikov, E.S.; Solnzeva, O.N.; et al. Climatic control of stand thinning in unmanaged spruce forests of the southern taiga in European Russia. Tellus B 2002, 54, 443–461. [Google Scholar] [CrossRef]
  62. Schulze, E.D.; Schulze, W.; Kelliher, F.M.; Vygodskaya, N.N.; Ziegler, W.; Koba, K.I.; Koch, H.; Arneth, A.; Kusnetsova, W.A.; Sogachev, A.; et al. Aboveground biomass and nitrogen nutrition in a chronosequence of pristine Dahurian Larix stands in Eastern Siberia. Can. J. For. Res. 1995, 25, 943–960. [Google Scholar] [CrossRef]
  63. Mund, M.; Kummetz, E.; Hein, M.; Bauer, G.A.; Schulze, E.D. Growth and carbon stocks of a spruce forest chronosequence in central Europe. For. Ecol. Manag. 2002, 171, 275–296. [Google Scholar] [CrossRef]
  64. Ruiz-Benito, P.; Madrigal-González, J.; Young, S.; Mercatoris, P.; Cavin, L.; Huang, T.J.; Chen, J.C.; Jump, A.S. Climatic stress during stand development alters the sign and magnitude of age-related growth responses in a subtropical mountain pine. PLoS ONE 2015, 10, e0126581. [Google Scholar] [CrossRef] [PubMed]
  65. Sitcha, S.; Smithb, B.; Prenticec, I.C.; Arnethc, I.; Bondeaua, I.A.; Cramera, W.; Kapland, J.O.; Levisc, S.; Luchta, W.; Sykesb, M.T.; et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Chang. Biol. 2003, 9, 161–185. [Google Scholar] [CrossRef]
  66. Li, Q.C.; Liu, Z.L.; Jin, G.Z. Impacts of stand density on tree crown structure and biomass: A global meta-analysis. Agric. For. Meteorol. 2022, 326, 109181. [Google Scholar] [CrossRef]
  67. Wirth, C.; Schulze, E.D.; Luhker, B.; Grigoriev, S.; Siry, M.; Hardes, G.; Ziegler, W.; Backor, M.; Bauer, G.; Vygodskaya, N.N. Fire and site type effects on the long-term carbon balance in pristine Siberian Scots pine forest. Plant Soil. 2002, 242, 41–63. [Google Scholar] [CrossRef]
  68. Franc, A. Bimodality for plant sizes and spatial pattern in cohorts: The role of competition and site conditions. Theor. Popul. Biol. 2001, 60, 117–132. [Google Scholar] [CrossRef]
  69. Yang, B.Y.; Ali, A.; Xu, M.S.; Guan, M.S.; Li, Y.; Zhang, X.N.; He, X.M.; Yang, Y.D. Large plants enhance aboveground biomass in arid natural forest and plantation along differential abiotic and biotic conditions. Front. Plant Sci. 2022, 13, 999793. [Google Scholar] [CrossRef]
  70. Fernandez-Nunez, E.; Rigueiro-Rodriguez, A.; Mosquera-Losada, M.R. Silvopastoral systems established with Pinus radiata D. Don and Betula pubescens Ehrh.: Tree growth, understorey biomass and vascular plant biodiversity. Forestry 2014, 87, 512–524. [Google Scholar] [CrossRef]
  71. Morin, X.; Fahse, L.; Scherer-Lorenzen, M.; Bugmann, H. Tree species richness promotes productivity in temperate forests through strong complementarity between species. Ecol. Lett. 2011, 14, 1211–1219. [Google Scholar] [CrossRef]
  72. Forrester, D.I.; Ammer, C.; Annighöfer, P.J.; Barbeito, I.; Bielak, K.; BravoOviedo, A.; Coll, L.; del Río, M.; Drössler, L.; Heym, M.; et al. Effects of crown architecture and stand structure on light absorption in mixed and monospecific fagus sylvatica and pinus sylvestris forests along a productivity and climate gradient through Europe. J. Ecol. 2018, 106, 746–760. [Google Scholar] [CrossRef]
  73. Lutz, J.A.; Larson, A.J.; Swanson, M.E.; Freund, J.A.; Ben, B.L. Ecological importance of Large-diameter trees in a temperate mixed-conifer forest. PLoS ONE 2012, 7, e36131. [Google Scholar] [CrossRef]
  74. Yachi, S.; Loreau, M. Does complementary resource use enhance ecosystem functioning? A model of light competition in plant communities. Ecol. Lett. 2007, 10, 54–62. [Google Scholar] [CrossRef] [PubMed]
  75. Zhang, Y.; Chen, H.Y.H. Individual size inequality links forest diversity and above-ground biomass. J. Ecol. 2015, 103, 1245–1252. [Google Scholar] [CrossRef]
  76. Slik, J.F.; Paoli, G.; McGuire, K.; Amaral, I.; Barroso, J.; Bastian, M.; Blanc, L.; Bongers, F.; Boundja, P.; Clark, C.; et al. Large Trees drive forest aboveground biomass variation in moist lowland forests across the tropics. Glob. Ecol. Biogeogr. 2013, 22, 1261–1271. [Google Scholar] [CrossRef]
  77. Bastin, J.F.; Barbier, N.; Réjou-Méchain, M.; Fayolle, A.; Gourlet-Fleury, S.; Maniatis, D.; de Haulleville, T.; Baya, F.; Beeckman, H.; Beina, D.; et al. Seeing central African forests through their largest trees. Sci. Rep. 2015, 5, 13156. [Google Scholar] [CrossRef]
  78. Lutz, J.A.; Furniss, T.J.; Johnson, D.J.; Davies, S.J.; Allen, D.; Alonso, A.; Anderson-Teixeira, K.J.; Andrade, A.; Baltzer, J.; Becker, K.M.L.; et al. Global importance of large-diameter trees. Glob. Ecol. Biogeogr. 2018, 27, 849–864. [Google Scholar] [CrossRef]
  79. Mensah, S.; du Toit, B.; Seifert, T. Diversity–biomass relationship across forest layers: Implications for niche complementarity and selection effects. Oecologia 2018, 187, 783–795. [Google Scholar] [CrossRef]
  80. Boucher, Y.; Auger, I.; Arseneault, D.; Elzein, T.; Sirois, L. Long-term (1925–2015) forest structure reorganization in an actively managed temperate-boreal forest region of eastern North America. For. Ecol. Manag. 2021, 481, 118744. [Google Scholar] [CrossRef]
  81. Ouyang, S.; Xiang, W.H.; Wang, X.P.; Xiao, W.F.; Chen, L.; Li, S.D.; Sun, H.; Deng, X.W.; Forrester, D.I.; Zeng, L.X.; et al. Effects of stand age, richness and density on productivity in subtropical forests in China. J. Ecol. 2019, 107, 2266–2277. [Google Scholar] [CrossRef]
  82. Fotis, A.T.; Murphy, S.J.; Ricart, R.D.; Krishnadas, M.; Whitacre, J.; Wenze, J.W.; Queenborough, S.A.; Comita, L.A. Above-ground biomass is driven by mass-ratio effects and stand structural attributes in a temperate deciduous forest. J. Ecol. 2018, 106, 561–570. [Google Scholar] [CrossRef]
  83. Alves, L.F.; Vieira, S.A.; Scaranello, M.A.; Camargo, P.B.; Santos, F.A.M.; Joly, C.A.; Martinelli, L.A. Forest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest (Brazil). For. Ecol. Manag. 2010, 260, 679–691. [Google Scholar] [CrossRef]
  84. Zhang, J.W.; Fiddler, G.O.; Young, D.H.; Shestak, C.; Carlson, R. Allometry of tree biomass and carbon partitioning in ponderosa pine plantations grown under diverse conditions. For. Ecol. Manag. 2021, 497, 119526. [Google Scholar] [CrossRef]
  85. Cysneiros, V.C.; Souza, F.C.D.; Gaui, T.D.; Pelissari, A.L.; Orso, G.A.; Machado, S.D.A.; Carvalho, D.S.D.; Silveira-Filho, T.B. Integrating climate, soil and stand structure into allometric models: An approach of site-effects on tree allometry in Atlantic Forest. Ecol. Indic. 2021, 127, 107794. [Google Scholar] [CrossRef]
  86. Taylor, A.R.; Gao, B.L.; Chen, H.Y.H. The effect of species diversity on tree growth varies during forest succession in the boreal forest of central Canada. For. Ecol. Manag. 2020, 455, 117641. [Google Scholar] [CrossRef]
  87. Wittkea, S.; Yu, X.W.; Karjalainena, M.; Hyyppä, J.; Puttonen, E. Comparison of two-dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest. Int. J. Appl. Earth Obs. 2019, 76, 167–178. [Google Scholar] [CrossRef]
  88. Xu, W.R.; He, H.S.; Luo, X.; Tang, Z.Q.; Liu, K.; Cong, Y.; Gu, X.L.; Zong, S.W.; Du, H.B. Long-term effects of commercial harvest exclusion on forest structure and aboveground biomass in the Great Xing’an Mountains. China. Acta Ecol. Sin. 2018, 38, 1203–1215. [Google Scholar] [CrossRef]
  89. Chave, J.; Condit, R.; Lao, S.; Caspersen, J.P.; Foster, R.B.; Hubbell, S.P. Spatial and temporal variation of biomass in a tropical forest: Results from a large census plot in Panama. J. Ecol. 2003, 91, 240–252. [Google Scholar] [CrossRef]
  90. Urquiza-Haas, T.; Dolman, P.M.; Peres, C.A. Regional scale variation in forest structure and biomass in the Yucatan Peninsula, Mexico: Effects of forest disturbance. For. Ecol. Manag. 2007, 247, 80–90. [Google Scholar] [CrossRef]
  91. Kenin, L.; Elferts, D.; Baders, E.; Jansons, A. Carbon pools in a hemiboreal over-mature Norway Spruce stands. Forests 2018, 9, 435. [Google Scholar] [CrossRef]
  92. Azmana, M.S.; Sharma, S.; Shaharuddin, M.A.M.; Hamzah, M.L.; Adibah, S.N.; Zakaria, R.M.; MacKenzie, R.A. Stand structure, biomass and dynamics of naturally regenerated and restored mangroves in Malaysia. For. Ecol. Manag. 2021, 482, 118852. [Google Scholar] [CrossRef]
  93. Schedlbauer, J.S.; Finegan, B.; Kavanagh, K.L. Rain Forest Structure at Forest-Pasture Edges in Northeastern Costa Rica. Biotropica 2007, 39, 578–584. [Google Scholar] [CrossRef]
  94. Clark, D.B.; Clark, D.A. Landscape-scale variation in forest structure and biomass in a tropical rain forest. For. Ecol. Manag. 2000, 137, 185–198. [Google Scholar] [CrossRef]
  95. Burrow, W.H.; Henry, B.K.; Back, P.V.; Hoffmann, M.B.; Tait, L.J. Growth and carbon stock change in eucalypt woodlands in northeast Australia: Ecological and greenhouse sink implications. Clim. Chang. Biol. 2002, 8, 769–784. [Google Scholar] [CrossRef]
  96. Larsary, M.K.; Pourbabaei, H.; Sanaei, A.; Salehi, A.; Yousefpour, R.; Ali, A. Tree-size dimension inequality shapes aboveground carbon stock across temperate forest strata along environmental gradients. For. Ecol. Manag. 2021, 496, 119482. [Google Scholar] [CrossRef]
  97. Aiba, S.; Hillb, D.A.; Agetsuma, N. Comparison between old-growth stands and secondary stands regenerating after clear-felling in warm-temperate forests of Yakushima, southern Japan. For. Ecol. Manag. 2001, 140, 163–175. [Google Scholar] [CrossRef]
  98. Mensah, S.; Noulekoun, F.; Ago, E.E. Aboveground tree carbon stocks in West African semi-arid ecosystems: Dominance patterns, size class allocation and structural drivers. Glob. Ecol. Conserv. 2020, 24, e01331. [Google Scholar] [CrossRef]
  99. Raich, J.W.; Russell, A.E.; Kitayama, K.; Parton, W.J.; Vitousek, P.M. Temperature influences carbon accumulation in moist tropical forests. Ecology 2006, 87, 76–87. [Google Scholar] [CrossRef] [PubMed]
  100. Cerny, J.; Pokorny, R.; Vejpustkova, M.; Sramek, V.; Bednar, P. Air temperature is the main driving factor of radiation use efficiency and carbon storage of mature Norway spruce stands under global climate change. Int. J. Biometeorol. 2020, 64, 1599–1611. [Google Scholar] [CrossRef]
  101. Larcher, W. Physiological Plant Ecology: Ecophysiology and Stress Physiology of Functional Groups; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2003. [Google Scholar]
  102. Silva, P.S.; Bastos, A.; Libonati, R.; Rodrigues, J.A.; DaCamara, C.C. Impacts of the 1.5 °C global warming target on future burned area in the brazilian cerrado. For. Ecol. Manag. 2019, 446, 193–203. [Google Scholar] [CrossRef]
  103. Silva, P.; Bastos, A.; DaCamara, C.C.; Libonati, R. Future projections of fire occurrence in brazil using EC-earth climate model. Rev. Bras. Meteorol. 2016, 31, 288–297. [Google Scholar] [CrossRef]
  104. Blundo, C.; Malizia, A.; Malizia, L.R.; Lichstein, J.W. Forest biomass stocks and dynamics across the subtropical Andes. Biotropica 2021, 53, 170–178. [Google Scholar] [CrossRef]
  105. Chi, C.H.; McEwan, R.W.; Chang, C.T.; Zheng, C.Y.; Yang, Z.J.; Chiang, J.M.; Lin, T.C. Typhoon disturbance mediates elevational patterns of forest structure, but not species diversity, in humid monsoon Asia. Ecosystems 2015, 18, 1410–1423. [Google Scholar] [CrossRef]
  106. Xiong, X.Y.; Zhu, J.L.; Li, S.; Fan, F.; Cai, Q.; Ma, S.H.; Su, H.J.; Ji, C.J.; Tang, Z.Y.; Fang, J.Y. Aboveground biomass and its biotic and abiotic modulators of a main food bamboo of the giant panda in a subalpine spruce-fir forest in southwestern China. J. Plant Ecol. 2022, 15, 1–12. [Google Scholar] [CrossRef]
  107. Yang, X.Q.; Blagodatsky, S.; Liu, F.; Beckschäferf, P.; Xu, J.C.; Cadisch, G. Rubber tree allometry, biomass partitioning and carbon stocks in mountainous landscapes of sub-tropical China. For. Ecol. Manag. 2017, 404, 84–99. [Google Scholar] [CrossRef]
  108. Paoli, G.D.; Curran, L.M.; Slik, J.W.F. Soil nutrients affect spatial patterns of aboveground biomass and emergent tree density in southwestern Borneo. Oecologia 2008, 155, 287–299. [Google Scholar] [CrossRef]
  109. Gao, Y.; Skutsch, M.; Rodríguez, D.L.J.; Solórzano, J.V. Identifying variables to discriminate between conserved and degraded forest and to quantify the differences in Biomass. Forests 2020, 11, 1020. [Google Scholar] [CrossRef]
  110. Bennett, A.C.; Penman, T.D.; Arndt, S.K.; Roxburgh, S.H.; Bennett, L.T. Climate more important than soils for predicting forest biomass at the continental scale. Ecography 2020, 43, 1692–1705. [Google Scholar] [CrossRef]
  111. Huang, X.; Huang, C.; Teng, M.; Zhou, Z.; Wang, P. Net primary productivity of Pinus massoniana dependence on climate, soil and forest characteristics. Forests 2020, 11, 404. [Google Scholar] [CrossRef]
  112. Coomes, D.A.; Flores, O.; Holdaway, R.; Jucker, T.; Lines, E.R.; Vanderwel, M.C. Wood production response to climate change will depend critically on forest composition and structure. Glob. Chang. Biol. 2014, 20, 3632–3645. [Google Scholar] [CrossRef]
  113. Stegen, J.C.; Swenson, N.G.; Enquist, B.J.; White, E.P.; Phillips, O.L.; Jorgensen, P.M.; Weiser, M.D.; Mendoza, A.M.; Vargas, P.N. Variation in above-ground forest biomass across broad climatic gradients. Glob. Ecol. Biogeogr. 2011, 20, 744–754. [Google Scholar] [CrossRef]
  114. Chu, C.J.; Bartlett, M.; Wang, Y.S.; He, F.L.; Weiner, J.; Chave, J. Does climate directly influence NPP globally? Glob. Chang. Biol. 2016, 22, 12–24. [Google Scholar] [CrossRef]
  115. Lohbeck, M.; Bongers, F.; Martinez-Ramos, M.; Poorter, L. The importance of biodiversity and dominance for multiple ecosystem functions in a human-modified tropical landscape. Ecology 2016, 97, 2772–2779. [Google Scholar] [CrossRef]
  116. Sichone, P.; De Cauwer, V.; Chissungui, A.V.; Goncalves, F.M.P.; Finckh, M.; Revermann, R. Patterns of above-ground biomass and its environmental drivers: An analysis based on plot-based surveys in the dry tropical forests and woodlands of southern Africa. Biodivers. Ecol. 2018, 6, 309–316. [Google Scholar] [CrossRef]
Forests 14 00896 g001 550
Figure 1. Allometric relationships of stand structural characteristics and AGB in boreal forest (a), temperate seasonal forest (b), tropical rainforest (c), and tropical seasonal forest (d). The diamond’s position relative to the box indicates the slope, and the two short horizontal lines denote the confidence interval (CI). DBH: diameter at breast height; TD: tree density; BA: basal area.
Figure 1. Allometric relationships of stand structural characteristics and AGB in boreal forest (a), temperate seasonal forest (b), tropical rainforest (c), and tropical seasonal forest (d). The diamond’s position relative to the box indicates the slope, and the two short horizontal lines denote the confidence interval (CI). DBH: diameter at breast height; TD: tree density; BA: basal area.
Forests 14 00896 g001
Forests 14 00896 g002 550
Figure 2. Multiple regression relationships of stand structural characteristics, climate factors, and AGB in boreal forest (a), temperate seasonal forest (b), tropical rainforest (c), and tropical seasonal forest (d). The significant results are indicated with an asterisk (* p < 0.05; ** p < 0.01; *** p < 0.001). MAP: mean annual precipitation; MAT: mean annual temperature; DBH: diameter at breast height; TD: tree density; BA: basal area.
Figure 2. Multiple regression relationships of stand structural characteristics, climate factors, and AGB in boreal forest (a), temperate seasonal forest (b), tropical rainforest (c), and tropical seasonal forest (d). The significant results are indicated with an asterisk (* p < 0.05; ** p < 0.01; *** p < 0.001). MAP: mean annual precipitation; MAT: mean annual temperature; DBH: diameter at breast height; TD: tree density; BA: basal area.
Forests 14 00896 g002
Forests 14 00896 g003 550
Figure 3. Total contribution of stand structural characteristics and climate factors to AGB.
Figure 3. Total contribution of stand structural characteristics and climate factors to AGB.
Forests 14 00896 g003
Forests 14 00896 g004 550
Figure 4. The final best-fit structural equation model (SEM) for evaluating multiple influences of climate factors (MAT, MAP), stand structural characteristics (tree density, DBH, basal area), and biome on AGB. “−” are negative relationships while “+” are positive ones, and the dotted line indicates insignificant relationships (* p < 0.05; ** p < 0.01; *** p < 0.001). R2 indicates the total variation in a dependent variable that is explained by the combined independent variables. MAP: mean annual precipitation; MAT: mean annual temperature.
Figure 4. The final best-fit structural equation model (SEM) for evaluating multiple influences of climate factors (MAT, MAP), stand structural characteristics (tree density, DBH, basal area), and biome on AGB. “−” are negative relationships while “+” are positive ones, and the dotted line indicates insignificant relationships (* p < 0.05; ** p < 0.01; *** p < 0.001). R2 indicates the total variation in a dependent variable that is explained by the combined independent variables. MAP: mean annual precipitation; MAT: mean annual temperature.
Forests 14 00896 g004
Table
Table 1. Relationships of stand structural characteristics and climate factors.
Table 1. Relationships of stand structural characteristics and climate factors.
BiomeVariablesMATMAPTDDBH
Boreal
forest		MAP0.951 **
TD−0.473 *−0.599 **
DBH0.781 **0.841 **−0.594 **
BA0.816 **0.803 **−0.450 *0.743 **
Temperate
seasonal forest		MAP0.263 **
TD0.262 **0.231 *
DBH0.12−0.12−0.205 *
BA0.060.150.246 *0.13
Tropical rainforestMAP0.09
TD0.786 **−0.08
DBH−0.320.24−0.31
BA−0.120.449 **−0.070.10
Tropical
seasonal forest		MAP−0.302 *
TD0.03−0.311 *
DBH−0.691 **0.559 **−0.25
BA−0.24−0.100.07−0.07
* indicates a significant correlation at the 0.05 level, and ** represents significance at the 0.01 level. MAP: mean annual precipitation; MAT: mean annual temperature; DBH: diameter at breast height; TD: tree density; BA: basal area.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ma, Y.; Eziz, A.; Halik, Ü.; Abliz, A.; Kurban, A. Precipitation and Temperature Influence the Relationship between Stand Structural Characteristics and Aboveground Biomass of Forests—A Meta-Analysis. Forests 2023, 14, 896. https://doi.org/10.3390/f14050896

AMA Style

Ma Y, Eziz A, Halik Ü, Abliz A, Kurban A. Precipitation and Temperature Influence the Relationship between Stand Structural Characteristics and Aboveground Biomass of Forests—A Meta-Analysis. Forests. 2023; 14(5):896. https://doi.org/10.3390/f14050896

Chicago/Turabian Style

Ma, Yingdong, Anwar Eziz, Ümüt Halik, Abdulla Abliz, and Alishir Kurban. 2023. "Precipitation and Temperature Influence the Relationship between Stand Structural Characteristics and Aboveground Biomass of Forests—A Meta-Analysis" Forests 14, no. 5: 896. https://doi.org/10.3390/f14050896

APA Style

Ma, Y., Eziz, A., Halik, Ü., Abliz, A., & Kurban, A. (2023). Precipitation and Temperature Influence the Relationship between Stand Structural Characteristics and Aboveground Biomass of Forests—A Meta-Analysis. Forests, 14(5), 896. https://doi.org/10.3390/f14050896

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop