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Article

Relative Humidity, Soil Phosphorus, and Stand Structure Diversity Determine Aboveground Biomass along the Elevation Gradient in Various Forest Ecosystems of Pakistan

by
Shahab Ali
1,
Shujaul Mulk Khan
1,2,*,
Zeeshan Ahmad
1,
Abdullah Abdullah
1,
Naeemullah Kazi
3,
Ismat Nawaz
4,
Khalid F. Almutairi
5,
Graciela Dolores Avila-Quezada
6 and
Elsayed Fathi Abd_Allah
5
1
Department of Plant Sciences, Quaid-i-Azam University Islamabad, Islamabad 45320, Pakistan
2
Pakistan Academy of Science, Islamabad 45320, Pakistan
3
Sindh Wildlife Department, Karachi 76090, Pakistan
4
Department of Biosciences, Islamabad Campus, COMSATS University Islamabad, Islamabad 45550, Pakistan
5
Plant Production Department, College of Food and Agricultural Sciences, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia
6
Facultad de Ciencias Agrotecnológicas, Universidad Autónoma de Chihuahua, Chihuahua 31350, Mexico
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(9), 7523; https://doi.org/10.3390/su15097523
Submission received: 25 February 2023 / Revised: 23 April 2023 / Accepted: 25 April 2023 / Published: 4 May 2023
(This article belongs to the Special Issue Sustainable Forest Management and Environmental Sustainability: Challenges and Management Implications)
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Abstract

:
The direct effects of relative humidity and soil on aboveground biomass (AGB) versus the indirect effects mediated by stand structural diversity remain unclear in forest ecosystems across large-scale elevation gradients. Forest inventory data containing 15,260 individual trees and 104 tree species from 200 forest plots were collected. The result shows that the relative humidity, elevation, and Coefficient of Variation of Diameter at breast height (CVD) significantly influence AGB in the Tropical Thorn Forest (TTF). Regarding elevation, CVD was positive and significant, and relative humidity and SR negatively impacted AGB in sub-tropical broad-leaved forests (STBLF). In moist temperate mixed forests (MTMF), soil phosphorus and CVD have a significant positive impact, while relative humidity, elevation, and SR negatively influence AGB. Elevation and CVD have positive, while SR and soil phosphorus have a negative and insignificant effect on AGB in Dry Temperate Conifer Forests (DTCF). Soil phosphorus and relative humidity positively affected AGB (β = 0.021), while elevation, CVD, and SR negatively affect AGB in dry temperate, pure pine forests (DTPPF). Relative humidity and soil phosphorus have a positive direct effect on AGB in multi-species forests. The current study suggests that AGB primarily depends on relative humidity, soil phosphorus, and elevation in different forest types.

1. Introduction

The influence of species richness (SR) and stand structure diversity (SSD) on aboveground biomass (AGB) has been widely studied and found to be significant. Species richness and SSD contribute to overall ecosystem productivity and health and directly impact AGB. High species richness and stand structure diversity are important for maintaining long-term ecosystem sustainability and resilience [1,2]. SR is considered a part of stand structure complexity (SSC), but variations in tree diameter and height, combined or alone, are primarily categorized as SSC in the forest ecosystem [3]. The ecological tools for linking the various biotic, i.e., biodiversity, stand structure, and abiotic, i.e., relative humidity and soil factors concerning aboveground biomass, have been comparatively well evaluated [2,4,5,6,7,8]. Direct and indirect effects on AGB, SR, and SSC across regional scale ecological gradients persist less contested in natural forest ecosystems [2,4,5].
Nevertheless, ecological theories and regional-level experimental studies indicate that climatic conditions mainly determine AGB [9,10,11,12]. Especially in the natural forest ecosystem, higher SR and AGB have usually experienced in regions with high precipitation and climatic water availability [13,14]. Diversity might greatly enhance AGB production due to the enabling effect in severe ecological conditions instead of a productive environment due to the sturdy antagonistic effect in inferior species forest ecosystems [15]. Furthermore, the fertility soil hypothesis assumes the plant can grow competently under high nutrient availability, leading to significant competition [16]. Nevertheless, most tropical forest species are distributed on deficient nutrients and wet soil [17,18]. In addition, it is well-assumed in the natural forest ecosystem that conservative and acquisitive species are situated on the opposite excesses of nutrient-axis soil, e.g., nutrient-deficient and rich soil, respectively [19,20,21,22]. However, in this condition, it is reasonable to assume that SR enhances AGB and demographic processes, i.e., growth and enlistment underneath suitable climatic conditions instead of under excellent fertile soil conditions in a natural tropical forest ecosystem [2,13,23]. Through assistance mechanisms and niche distinction multi-layered stand structure, theories have been suggested to enhance light capturing and its utilization within and amongst the constituent species in a community [24]. However, SR and species diversity within a forest stand or community recognize frequent ecological mechanisms and might increase or decrease AGB directly, i.e., niche complementarity, mass ratio and competitive elimination effects [13,25,26,27]. Furthermore, SR or species diversity and SSC endorse one another to achieve maximum AGB or storage of carbon over the effect of niche complementarity in a natural forest ecosystem [26,28,29]. Nevertheless, the direct independent predictors of AGB might be SSC, and species diversity withstands structure as a better predictor [30,31,32]. However, the multivariate association among SR, SSC, and AGB remains debated. This paper documents the direct and indirect effects of relative humidity and soils on SR, SSC, and AGB in different natural forest types along regional-scale elevation gradients.

Hypothesized Model

The key objective of the current study was to define the combined effect of relative humidity, soil via SR, and coefficient of diameter variations, such as breast height (CVD) on AGB, across different forest types and species mixtures along regional-scale elevation gradients across Pakistan (Figure 1). The forest inventory dataset comprised 15,260 individuals belonging to 104 tree species from 200 forest plots in this study. These plots were classified into five local forest types and two groups of single- and multiple-species stand forests based on species composition. We used structural equation modeling, a powerful integrative approach to forest inventory data [33]. We hypothesized that climatic factors (relative humidity), soil (phosphorus) stand structure complexity, and regional-scale elevation gradients have a robust positive effect on AGB in multi-species forest types compared to single-species stand forest types. Secondly, we hypothesized that the relationship among CVD, SR, relative humidity, and soil directly depends on the forest’s stand type, while the multi-species forest leads to higher AGB through higher relative humidity water availability and higher soil fertility factors and size of the individual trees (Figure 1).

2. Materials and Methods

2.1. Study Area

The current study was conducted across five different forest types in Pakistan. (1) Tropical thorn forest (2) Sub-tropical broad-leaved forest (3) Dry temperate conifer forest (4) Moist temperate mix forest (5) Dry temperate, pure pines forest (Figure 2). The tropical thorn forest ecosystem of the Kirthar is located in the mountainous range of southwestern Sindh. The area has been categorized as a protected category II by the international union for the conservation of nature (IUCN). It covers the rocky mountainous ranges from north to south, separated by flat valleys [34]. The sub-tropical broad-leaved forest of Margalla Hills National Park is located at the western extremities of the foothills of the lesser Himalayas, covering an area of 126.05 km2 [35]. The dry temperate conifer forest lies in Dir upper, with approximately 2000 masl. The monthly mean minimum and maximum temperatures have been recorded as 11.22 °C and −2.39 °C [36]. The moist temperate mixed forest is located in Murree and Ayubia in the temperate region of the sub-Himalayan Mountains. The mountains of Murree and Ayubia reach an altitude of about 5000 to 8200 feet [37]. The dry temperate, pure pine forest located in the Suleiman Range District Shirani covers an area of 260 km2 and contains the world’s largest Chilghoza forest (Pinus gerardiana). It is an extension of the Hindu Kush mountain at the junction of the three provincial borders of Balochistan, Khyber Pakhtunkhwa, and Punjab [38] (Figure 2). The detailed description, i.e., forest types, location, and latitude and longitude climatic condition, stand type and main or dominant species are given in Table 1.

2.2. Forest Inventory

We randomly sampled 40 (20 × 20 m) plots in each forest from March 2020 to September 2020. Plots were established at one km or 20 m altitudinal variation. All three plant individuals with a ≥1 cm diameter at breast height (DBH) were noted within each plot. The tree DBH was recorded through measuring tape. The recorded tree plants (≥1 cm DBH) present in the plots were identified by taxonomists and confirmed at http://www.efloras.org/flora_page.aspx?flora_id=5, (accessed on 12 October 2020) http://www.theplantlist.org/tpl1.1/search, (accessed on 12 October 2020). GPS (geographical positioning system) and elevation values were also noted for each plot on the x-axis.

2.3. Statistical Analysis Predictor and Predicted Variable

Our hypothetical model (Figure 1) comprises six variables: elevation, mean relative humidity, soil phosphorus, SR, CVD, and AGB. Climatic data (relative humidity) were noted for each plot using a digital humidity meter [2]. The current study used soil phosphorus (P) as a soil factor. Soil phosphorus was determined by following the protocol of [39,40]. We used the Shannon’s species diversity index because the index deals with species evenness and SR of the community, i.e., plots, and predicts better AGB in the natural forest [26], and, similarly, for quantifying the coefficient variation of DBH for each plot [41]. Pantropical allometric equations were used (AGB = 0.0673 (ρ dbh × h) 20.976) for tropical forests, and each tree’s AGB estimation was based on tree height, diameter at breast height (dbh), and species wood density [42].
For Pines Forest, we used the below equation;
Crown Biomass
WC = 0.1377 (D)1.4873 × (L)0.4052
WS = 0.0600 (D)0.7934 × 0.7934 (L)1.8005
Total Biomass = WC + WS
where WC = woody crown, WS = woody stem.

2.4. Statistical Analysis

The first question to answer is whether the fixed factors, which include relative humidity, elevation, soils, CVD, and SR, or the random factors that affect, i.e., types of forests single-species, species mixture, and climatic factors, explain higher variability in aboveground biomass. Piecewise structure equation modeling (PSEM) deals with the random and fixed effects in a single equation [33]. This study used two piecewise structural equation models to test the primary conceptual model as a random effect of species mixture and forest type. The above hypothetical model described: (1) The effect of soil, relative humidity, CVD, and SR on AGB along a large regional scale elevation gradient; (2) AGB is affected by climatic factors, soil, and DBH; (3) Elevation, SR, and soil effect in different types of forest and mixture species forest on the AGB. We determined the Pearson correlation and removed the insignificant relationship in the equation to obtain a good fit model. We used random effect PSEM for species mixture and excluded the model’s fixed effect to avoid the problem of model circularity. To evaluate the model fit of PSEM, we used Fisher statistics. Moreover, we applied the directional separation test in the CFA in the case of poor fit or over the fitted model; it included or excluded any significant or insignificant path, though in our study, the case was not a strong one.
Therefore, the second question is the relationship between relative humidity, soil, SR, CVD, and AGB along different regional-scale elevation gradients in different forest types. We divided the collective dataset into five local forest types; for each conceptual model, we used linear structural equation modeling (SEM) techniques (Figure 1). To investigate the 3rd question, to determine whether the relationship between relative humidity, soil, SR, CVD, AGB, and elevation fluctuates along with single-species or multi-species forest types, we again divided the data into two groups, single-species and multi-species forests, then applied linear SEM. Four fit indices, i.e., we determined the Acai information criteria (AIC), comparative fit index (CFI), the goodness of fit index (GFI), and root mean square residual (SRMR) via linear SEM model fit. The goodness of fit model is based on CFI > 0.90, GFI > 0.90, and SRMR < 0.08 [43]. We applied the same models for all seven equations. Here, we mainly focused on the relationship amongst the variables, not only the goodness of fit models. The direct effect was identified among dependent and independent variables through a direct path. The multiplication of dependent and independent measured the models’ indirect effect and mediator variables. For the current analysis, R codes previously developed by [44] were used.
We calculated the simple linear regression for each conceptual model among pooled and binary variables (forest type and species mixture). We converted all the variables into continuous form to estimate linear SEM analysis values through a natural logarithm. We transformed them to standardize and satisfy linearity and normality assumptions. We estimated the Pearson correlation coefficient of all the independent variables and took only those variables in the model with a significant relationship. We also checked the multicollinearity among the variables through the VIF test; there was no such issue of multicollinearity in our case.

3. Results

In the sub-tropical thorn forest (TTF), we found the impact of soil phosphorus had a negative and significant on AGB (β = −0.849), and relative humidity had an insignificant positive effect on AGB (β = 1.352). In contrast, elevation had a significant positive effect on AGB (β = 3.126). The effect of a stand structure, i.e., coefficient of variation of DBH (CVD), was recorded as positive and insignificant (β = 0.087), and species richness (SR) had a negative and insignificant effect on AGB (β = −0.823) (Figure 3A & Table S1).
In the sub-tropical broad-leaved forest (STBLF), the impact of soil phosphorus had a positive and significant effect on AGB (β = 0.048), and relative humidity had an insignificant negative effect on AGB (β = −0.001), while elevation had a significant positive impact on AGB (β = 0.025). Conversely, relative humidity and elevation had an indirect (through CVD & soil phosphorus) positive effect on AGB. The effect of CVD was recorded as positive and significant (β = 0.046), and SR had a negative and insignificant effect on AGB (β = −0.004) (Figure 3B & Table S2).
In the moist temperate mixed forest (MTMF), the impact of soil phosphorus had a positive and significant effect on AGB (β = 0.484), and relative humidity had an insignificant negative effect on AGB (β = −0.229), while elevation had a significant negative effect on AGB (β = −0.161). Conversely, relative humidity and elevation had an indirect (through CVD & soil phosphorus) positive effect on AGB. The effect of CVD was recorded as positive (β = 0.062), and SR had a negative and insignificant effect on AGB (β = −0.046) (Figure 3C & Table S3).
In the dry temperate conifer forest (DTCF), the impact of soil phosphorus had a negative and significant effect on AGB (β = −0.849), and relative humidity had a positive effect on AGB (β = 1.352). Similarly, elevation had a significant positive effect on AGB (β = 3.126). Conversely, relative humidity had a positive indirect effect on AGB through soil phosphorus and CVD, while elevation had a negative indirect effect on AGB through soil phosphorus, CVD, and SR. The effect of CVD was recorded as positive (β = 0.087), and SR had a negative and insignificant effect on AGB (β = −0.823) (Figure 3D & Table S4).
In the dry temperate, pure pine forest (TTPPF), the impact of soil phosphorus had a positive effect on AGB (β = 0.023), and relative humidity had a significant negative effect on AGB (β = 0.021), while elevation had an insignificant negative effect on AGB (β = −0.045). The effect of CVD was recorded as positive and insignificant (β = 0.017), and SR had a negative and insignificant effect on AGB (β = −0.001) (Figure 3E & Table S4). The detail of the descriptive analysis is given in Supplementary Table S5.
We compared direct (dark green bar) and indirect (maroon bar) effects derived from structural equation models of elevation, relative humidity, soil, CVD, and SR on AGB and regional forest types. We took standardized coefficient plus and minus standard error. Abbreviations: elevation (Elev), soil phosphorus (P), relative humidity (mrh), species richness (SR), and coefficient of variation of DBH (CVD) (Figure 4).
Results of the linear structural equation model of single tree forests showed that elevation, soil phosphorous, and CVD had a positive effect on AGB, i.e., (β = 0.255), (β = 0.084), (β = 0.046), while relative humidity had a negative effect on AGB, i.e., (β = −0.001). At the same time, relative humidity had a positive indirect effect on AGB through CVD, and soil, i.e., (β = 0.69), and (β = 0.073), respectively (Figure 5A & Table S7).
In multiple tree species forest, we saw that elevation, soil phosphorous, and relative humidity had a positive effect on AGB, i.e., (β = 0.255), (β = 0.302), (β = 0.04), while CVD had a negative effect on AGB, i.e., (β = −0.010). Relative humidity and elevation had a positive indirect effect on AGB through soil phosphorous and CVD, i.e., (β = 0.21), (β = 0.169) (Figure 5B & Table S8).
The result showed that there are strong relationships among all variables. The two red values represented negative and significant relations between elevation, and relative humidity. In comparison, other variables showed strong positive and significant relationships among the variables (Figure 6).
We compared direct (dark green bars) and indirect (maroon bar) effects derived from structural equation models of elevation, relative humidity, soil, and CVD on AGB along regional-scale elevation gradients of single and multi-species forest types in Pakistan. We took a standardized coefficient (Figure 6). Abbreviations: Elev, elevation; P, soil phosphorus; much, relative humidity; SR, SR, and CVD.
Overall, we tested our proposed hypothesis and found our hypothesis acceptable because soil factors, climatic factors, and structural complexity along regional-scale elevation gradients have a strong positive effect on AGB in multi-species forest types compared to single-species forest types. Similarly, the relationship among CVD, SR, relative humidity, and soil P is directly dependent on the stand type of the forest. Still, the multi-species forest leads to higher AGB through higher relative humidity water availability, higher soil fertility factors, and the size of the individual trees (Figure 7).

4. Discussion

The current study examines soil and relative humidity’s direct and indirect effect on AGB via SR and stand structure complexity along regional-scale elevation gradients across tropical, subtropical, and temperate forests. The current study shows that relative humidity affects AGB indirectly along regional-scale elevation gradients. Moreover, soil affects AGB directly and indirectly via stand structure complexity. In addition, stand structure complexity directly increase AGB along regional-scale elevation gradients.

4.1. Effect of Relative Humidity and Soil on AGB in Individual Forest Type

In SEM, the AGB is essentially and significantly affected by soil (phosphorus) rather than relative humidity. In the present study, the direct effect of relative humidity and soil is also supported by other researchers’ findings that relative humidity and soil predominantly regulate AGB on a large scale [14]. The indirect findings of the current study are also supported by [2,45]. AGB variation is mostly affected by relative humidity and soil along large-scale ecological gradients. In addition, the insignificant direct effect of relative humidity on AGB in our SEM is strongly supported by the previous study, i.e., that AGB is insignificantly directly affected by climatic water availability [46,47]. We also found that elevation has an insignificant effect on AGB in overall and single forest types. Nevertheless, the present study also highlighted that when elevation increases, AGB decreases; this might be because the soil fertility decreases with an increase in elevation, and wind pressure also increases; hence decline occurs in AGB. The present study is critically supported by the findings of other researchers accordingly. We found the opposite relation of AGB with elevation gradients [48,49]. In the cited literature, soil fertility becomes a more limiting factor than plant needs at higher elevations, decreasing forest productivity and biomass accumulation [50,51]. Commonly decreasing soil nutrients has been recommended to decrease tree height and biomass production with elevation [52,53].
We investigated the effect of relative humidity and soil along the elevation gradient in tropical thorn forests, subtropical broad-leaved forests, moist-temperate mix forests, dry-temperate coniferous forests, and dry-temperate pine forests. We found that relative humidity had an insignificant effect on AGB while indirectly, through stand structure complexity, positive and significant effects were noted on AGB in individual forest types. Yet, soil’s positive and direct effect on AGB was noted, along with a significant indirect effect on AGB through stand structural complexity, though the SR had an insignificant direct impact on aboveground biomass. The previous literature supported these results, i.e., the elevation is strongly correlated and harms AGB in China’s tropical and temperate forests, where the soil fertility and climatic variables vary [54]. Our study found that AGB was strongly correlated with soil in all forest types and had a positive and significant effect on AGB directly and indirectly. The finding of our study aligns with the results of the Guyanese tropical rainforest, where AGB and productivity strongly correlated with a high concentration of phosphorus [55,56]. The current study found that overall elevation has a considerable positive effect on AGB and production in the tropical thorn forest, followed by the subtropical broad-leaved forest, dry-temperate pine forest, dry-temperate coniferous forest, and multi-species forests.
However, elevation has an insignificant and adverse impact in moist temperate mixed and single-species forests, while, in our study, the forest type and species mixture in the SEM indicate a positive and insignificant effect on elevation. Our result is supported by other researchers’ findings that elevation, directly and indirectly, affects aboveground biomass, even in the sub-tropical forest with small elevation gradients affected by AGB [54]. It is mostly due to temperature and rainfall varying with elevation changes [57,58]. Nevertheless, elevation has an insignificant positive effect in the moist-temperate mix forest; similarly, in the single-species forest type, insignificant and negative effects were observed. The findings of our study were supported by the findings of other researchers stating that an increase in elevation leads to a decline in soil fertility. Hence, plants need more fertile soil for growth, and, as a result, decreases occur in AGB [48,50,51,59,60,61].

4.2. Direct and Indirect Effects in Single and Multi-Species Forest Type

Our study of the SEM shows that elevation and relative humidity have negative and insignificant effects on AGB in the single-species forest. The negative relationship between AGB and elevation may refer to a statement from previous studies that an increase in altitudinal gradients leads to a lower soil water content and lower temperature. As a result, AGB and production decrease [62]. In addition, the areas at higher altitudes have a shorter growing season, leading to lower AGB [45,63]. In contrast, the positive effect of the altitudinal gradient in the tropical-thorn forest, and the sub-tropical forest, in which the AGB strongly and positively correlates with altitudinal gradients, increases AGB with an increase in altitude up to a limit. The other studies also found that biomass varies with elevation in lower altitudes from 1000 to 1500 m [64,65,66]. In comparison, it shows an insignificant positive effect on AGB through a stand’s structural complexity and soil. The low-land forest-type soil is significantly correlated with large trees [67]. Soil positively correlates with stand structure complexity and insignificantly affects AGB in the single-species forest types. The positive relationship between soil and stand structure complexity is due to the interaction among the plants in a community that covers all the available spaces [68,69,70,71]. On the other hand, the soil is positively and significantly correlated with aboveground biomass in multi-species forest types.

5. Conclusions

We concluded that the single-species forest relative humidity and elevation have a negative and insignificant effect on AGB. Moreover, stand structure diversity indirectly affects AGB, while soil has a positive impact through stand structural complexity. We also concluded that the multi-species forest, relative humidity, and soil phosphorus positively affect AGB. Soil phosphorus affects AGB directly and indirectly through stand structural diversity. Our study suggested that relative humidity, elevation, and soil primarily influence the AGB. CVD played an essential role in AGB, while SR did not significantly affect AGB productivity in different forest types.
This study suggests that aboveground biomass is mainly determined by stand structural complexity, followed by positive indirect effects on climatic water availability and soil fertility via stand structural complexity. Species diversity has a nonsignificant direct impact on aboveground biomass but a significant positive indirect effect via stand structural diversity. This study also suggests high species diversity is essential for conserving biodiversity and maintaining stand structural complexity for high forest functioning.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su15097523/s1, Table S1. Summary relationship of the linear SEM for founding the relationship amongst AGB, CVD, soil (P), SR, mrh and elevation in sub- tropical thorn forest of Pakistan. The significant relationship is indicated by bold (p < 0.05). Table S2. The summary relationship of the linear SEM for founding the relationship amongst AGB, CVD, soil (P), SR, mrh and elevation in subtropical broad leaved forest of Pakistan. The significant relationship indicate by bold. Which p < 0.05. Table S3. The summary relationship of the linear SEM for founding the relationship amongst AGB, CVD, soil (P), SR, mrh and elevation in moist temperate mix forest of Pakistan. The significant relationship indicates by bold. Which p < 0.05. Table S4. The summary relationship of the linear SEM for founding the relationship amongst AGB, CVD, soil (P), SR, mrh and elevation in dry temperate conifer forest of Pakistan. The significant relationship indicates by bold. Which p < 0.05. Table S5. The summary relationship of the linear SEM for founding the relationship amongst AGB, CVD, soil (P), SR, mrh and elevation in dry pure pines forest of Pakistan. The significant relationship indicates by bold. Which p < 0.05. Table S6. Descriptive statistics of the explanatory variables. Table S7. The summary relationship of the linear SEM for founding the relationship amongst AGB, CVD, soil (P), SR, mrh and elevation in single species forest of Pakistan. The significant relationship indicates by bold. Which p < 0.05. Table S8. The summary relationship of the linear SEM for founding the relationship amongst AGB, CVD, soil (P), SR, mrh and elevation in Multi species forest of Pakistan. The significant relationship indicates by bold. Which p < 0.05.

Author Contributions

S.A.: Investigation, Methodology, Software, Writing—original draft. S.M.K.: Supervision, Conceptualization, project administration, Writing—review & editing. Z.A.: Formal analyses, Methodology, Writing—review & editing, A.A.: review & editing. N.K.: Methodology, Facilitation in fieldwork, I.N.: Methodology, Writing—original draft, K.F.A.: Funding acquisition, Writing—review & editing, G.D.A.-Q.: Funding acquisition, Writing—review & editing, E.F.A.: Funding acquisition, Writing—review & editing. All authors have read and agreed to the published version of the manuscript.

Funding

The authors would like to extend their sincere appreciation to the Researchers Supporting Project Number (RSPD2023R561), King Saud University, Riyadh, Saudi Arabia. The authors are also thankful to WWF Pakistan for providing financial assistance under the project “Linking Forest Diversity, Structure and Functions along the Climate and Soil Conditions across Pakistan”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be available upon special request from the first and correspondence authors.

Acknowledgments

This paper is a part of PhD study conducted by the first author Shahab Ali at Plant Ecology and Conservation Lab., Quaid-i-Azam University Islamabad, Pakistan. The authors would like to extend their sincere appreciation to the Researchers Supporting Project Number (RSPD2023R561), King Saud University, Riyadh, Saudi Arabia. The authors are also thankful to the WWF Pakistan for providing financial assistance for the field work through their approved project titled “Linking Forest Diversity, Structure and Functions along the Climate and Soil Conditions across Pakistan”. The authors also thank Shahfahad Ali Shah, Department of Economics, Quaid-i-Azam University of Islamabad, Pakistan for providing assistance in the statistical analyses and data interpretation.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Liang, J.; Crowther, T.W.; Picard, N.; Wiser, S.; Zhou, M.; Alberti, G.; Schulze, E.-D.; McGuire, A.D.; Bozzato, F.; Pretzsch, H. Positive biodiversity-productivity relationship predominant in global forests. Science 2016, 354, aaf8957. [Google Scholar] [CrossRef] [PubMed]
  2. Poorter, L.; van der Sande, M.T.; Arets, E.J.; Ascarrunz, N.; Enquist, B.J.; Finegan, B.; Licona, J.C.; Martínez-Ramos, M.; Mazzei, L.; Meave, J.A. Biodiversity and climate determine the functioning of Neotropical forests. Glob. Ecol. Ecol. Ecol. Biogeogr. 2017, 26, 1423–1434. [Google Scholar] [CrossRef]
  3. 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]
  4. ur Rahman, A.; Khan, S.M.; Ahmad, Z.; Alamri, S.; Hashem, M.; Ilyas, M.; Aksoy, A.; Dülgeroğlu, C.; Khan, G.; Ali, S. -Impact of multiple environmental factors on species abundance in various forest layers using an integrative modeling approach. Glob. Ecol. Conserv. 2021, 29, e01712. [Google Scholar] [CrossRef]
  5. Ahmad, Z.; Khan, S.M.; Abd_Allah, E.F.; Alqarawi, A.A.; Hashem, A. Weed species composition and distribution pattern in the maize crop under the influence of edaphic factors and farming practices: A case study from Mardan, Pakistan. Saudi J. Biol. Sci. 2016, 23, 741–748. [Google Scholar] [CrossRef]
  6. Li, W.; Shi, Y.; Zhu, D.; Wang, W.; Liu, H.; Li, J.; Shi, N.; Ma, L.; Fu, S. Fine root biomass and morphology in a temperate forest are influenced more by the nitrogen treatment approach than the rate. Ecol. Indic. 2021, 130, 108031. [Google Scholar] [CrossRef]
  7. Yan, Y.; Jarvie, S.; Liu, Q.; Zhang, Q. Effects of fragmentation on grassland plant diversity depend on the habitat specialization of species. Biol. Conserv. 2022, 275, 109773. [Google Scholar] [CrossRef]
  8. Li, J.; Zhao, Y.; Zhang, A.; Song, B.; Hill, R.L. Effect of grazing exclusion on nitrous oxide emissions during freeze-thaw cycles in a typical steppe of Inner Mongolia. Agric. Ecosyst. Environ. 2021, 307, 107217. [Google Scholar] [CrossRef]
  9. Currie, D.J.; Mittelbach, G.G.; Cornell, H.V.; Field, R.; Guégan, J.F.; Hawkins, B.A.; Kaufman, D.M.; Kerr, J.T.; Oberdorff, T.; O’Brien, E. Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecol. Lett. 2004, 7, 1121–1134. [Google Scholar] [CrossRef]
  10. Huxman, T.E.; Smith, M.D.; Fay, P.A.; Knapp, A.K.; Shaw, M.R.; Loik, M.E.; Smith, S.D.; Tissue, D.T.; Zak, J.C.; Weltzin, J.F. Convergence across biomes to a common rain-use efficiency. Nature 2004, 429, 651–654. [Google Scholar] [CrossRef]
  11. O’Brien, E.M. Biological relativity to water–energy dynamics. J. Biogeogr. 2006, 33, 1868–1888. [Google Scholar] [CrossRef]
  12. Gillman, L.N.; Wright, S.D. Species richness and evolutionary speed: The influence of temperature, water and area. J. Biogeogr. 2014, 41, 39–51. [Google Scholar] [CrossRef]
  13. Poorter, L.; van der Sande, M.T.; Thompson, J.; Arets, E.J.; Alarcón, A.; Álvarez-Sánchez, J.; Ascarrunz, N.; Balvanera, P.; Barajas-Guzmán, G.; Boit, A. Diversity enhances carbon storage in tropical forests. Glob. Ecol. Biogeogr. 2015, 24, 1314–1328. [Google Scholar] [CrossRef]
  14. Ali, A.; Yan, E.-R. Consequences of phylogenetic conservativeness and functional trait similarity on aboveground biomass vary across subtropical forest strata. For. Ecol. Manag. 2018, 429, 28–35. [Google Scholar] [CrossRef]
  15. Paquette, A.; Messier, C. The effect of biodiversity on tree productivity: From temperate to boreal forests. Glob. Ecol. Biogeogr. 2011, 20, 170–180. [Google Scholar] [CrossRef]
  16. Ali, A.; Sanaei, A.; Li, M.; Nalivan, O.A.; Pour, M.J.; Valipour, A.; Karami, J.; Aminpour, M.; Kaboli, H.; Askari, Y. Big-trees–Energy mechanism underlies forest diversity and aboveground biomass. For. Ecol. Manag. 2020, 461, 117968. [Google Scholar] [CrossRef]
  17. Anwar, S.; Khan, S.M.; Ahmad, Z.; Ullah, Z.; Iqbal, M. Floristic composition and ecological gradient analyses of the Liakot Forests in the Kalam region of District Swat, Pakistan. J. For. Res. 2019, 30, 1407–1416. [Google Scholar] [CrossRef]
  18. Li, J.; Charles, L.S.; Yang, Z.; Du, G.; Fu, S. Differential mechanisms drive species loss under artificial shade and fertilization in the alpine meadow of the Tibetan Plateau. Front. Plant Sci. 2022, 13, 63. [Google Scholar] [CrossRef]
  19. Ahmad, Z.; Khan, S.M.; Afza, R.; Ullah, A.; Zeb, S.A.; Issayeva, K.S.; Bekzatqyzy, I.S. Angiosperms distribution under the influence of microclimatic factors across a polluted ecosystem. J. Hazard. Mater. Adv. 2023, 9, 100223. [Google Scholar] [CrossRef]
  20. Ali, A.; Yan, E.-R. Functional identity of overstorey tree height and understorey conservative traits drive aboveground biomass in a subtropical forest. Ecol. Indic. 2017, 83, 158–168. [Google Scholar] [CrossRef]
  21. Yang, Y.; Li, T.; Pokharel, P.; Liu, L.; Qiao, J.; Wang, Y.; An, S.; Chang, S.X. Global effects on soil respiration and its temperature sensitivity depend on nitrogen addition rate. Soil Biol. Biochem. 2022, 174, 108814. [Google Scholar] [CrossRef]
  22. Yang, Y.; Dou, Y.; Wang, B.; Xue, Z.; Wang, Y.; An, S.; Chang, S.X. Deciphering factors driving soil microbial life-history strategies in restored grasslands. iMeta 2023, 2, e66. [Google Scholar] [CrossRef]
  23. Toledo, M.; Peña-Claros, M.; Bongers, F.; Alarcón, A.; Balcázar, J.; Chuviña, J.; Leaño, C.; Licona, J.C.; Poorter, L. Distribution patterns of tropical woody species in response to climatic and edaphic gradients. J. Ecol. 2012, 100, 253–263. [Google Scholar] [CrossRef]
  24. 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]
  25. Szwagrzyk, J.; Gazda, A. Above-ground standing biomass and tree species diversity in natural stands of Central Europe. J. Veg. Sci. 2007, 18, 555–562. [Google Scholar] [CrossRef]
  26. Ali, A.; Yan, E.-R.; Chen, H.Y.; 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]
  27. Ali, A.; Yan, E.-R. The forest strata-dependent relationship between biodiversity and aboveground biomass within a subtropical forest. For. Ecol. Manag. 2017, 401, 125–134. [Google Scholar] [CrossRef]
  28. Zhang, Y.; Chen, H.Y. Individual size inequality links forest diversity and above-ground biomass. J. Ecol. 2015, 103, 1245–1252. [Google Scholar] [CrossRef]
  29. Zhao, L.; Du, M.; Du, W.; Guo, J.; Liao, Z.; Kang, X.; Liu, Q. Evaluation of the Carbon Sink Capacity of the Proposed Kunlun Mountain National Park. Int. J. Environ. Res. Public Health 2022, 19, 9887. [Google Scholar] [CrossRef]
  30. 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]
  31. Ali, A.; Mattsson, E. Individual tree size inequality enhances aboveground biomass in homegarden agroforestry systems in the dry zone of Sri Lanka. Sci. Total Environ. 2017, 575, 6–11. [Google Scholar] [CrossRef]
  32. Yuan, Z.; Wang, S.; Ali, A.; Gazol, A.; Ruiz-Benito, P.; Wang, X.; Lin, F.; Ye, J.; Hao, Z.; Loreau, M. Aboveground carbon storage is driven by functional trait composition and stand structural attributes rather than biodiversity in temperate mixed forests recovering from disturbances. Ann. For. Sci. 2018, 75, 67. [Google Scholar] [CrossRef]
  33. Lefcheck, J.S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 2016, 7, 573–579. [Google Scholar] [CrossRef]
  34. Enright, N.; Miller, B.; Akhter, R. Desert vegetation and vegetation-environment relationships in Kirthar National Park, Sindh, Pakistan. J. Arid. Environ. 2005, 61, 397–418. [Google Scholar] [CrossRef]
  35. Masud, R. Master Plan for Margalla Hills National Park, Islamabad, Pakistan 1979 to 1984; National Council for Conservation of Wildlife: Islamabad, Pakistan, 1977; Available online: https://portals.iucn.org/library/node/19127 (accessed on 24 February 2023).
  36. Hazrat, A.; Shah, J.; Nisar, M. Medicinal plants of Sheringal Valley, Dir Upper, KPK, Pakistan. FUUAST J. Biol. 2011, 1, 131–133. [Google Scholar]
  37. Khan, A.N. Impact of landslide hazards on housing and related socio-economic characteristics in Murree (Pakistan). Pak. Econ. Soc. Rev. 2001, 1, 57–74. [Google Scholar]
  38. Urooj, R. Status, distribution and dynamics of Chilgoza Pine (Pinus gerardiana Wall) Forest in Suleiman Mountain Range, Pakistan. Middle East J. Bus. 2019, 14, 17–19. [Google Scholar] [CrossRef]
  39. Khan, W.; Khan, S.M.; Ahmad, H.; Ahmad, Z.; Page, S. Vegetation mapping and multivariate approach to indicator species of a forest ecosystem: A case study from the Thandiani sub Forests Division (TsFD) in the Western Himalayas. Ecol. Indic. 2016, 71, 336–351. [Google Scholar] [CrossRef]
  40. Ahmad, Z.; Khan, S.M.; Ali, M.I.; Fatima, N.; Ali, S. Pollution indicandum and marble waste polluted ecosystem; role of selected indicator plants in phytoremediation and determination of pollution zones. J. Clean. Prod. 2019, 236, 117709. [Google Scholar] [CrossRef]
  41. Staudhammer, C.L.; LeMay, V.M. Introduction and evaluation of possible indices of stand structural diversity. Can. J. For. Res. 2001, 31, 1105–1115. [Google Scholar] [CrossRef]
  42. Chave, J.; Réjou-Méchain, M.; Búrquez, A.; Chidumayo, E.; Colgan, M.S.; Delitti, W.B.; Duque, A.; Eid, T.; Fearnside, P.M.; Goodman, R.C. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 2014, 20, 3177–3190. [Google Scholar] [CrossRef] [PubMed]
  43. Hoyle, R.H. Handbook of Structural Equation Modeling; Guilford Press: New York, NY, USA, 2012. [Google Scholar]
  44. Ullah, F.; Gilani, H.; Sanaei, A.; Hussain, K.; Ali, A. Stand structure determines aboveground biomass across temperate forest types and species mixture along a local-scale elevational gradient. For. Ecol. Manag. 2021, 486, 118984. [Google Scholar] [CrossRef]
  45. 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]
  46. Ali, A. Forest stand structure and functioning: Current knowledge and future challenges. Ecol. Indic. 2019, 98, 665–677. [Google Scholar] [CrossRef]
  47. Michaletz, S.T.; Cheng, D.; Kerkhoff, A.J.; Enquist, B.J. Convergence of terrestrial plant production across global climate gradients. Nature 2014, 512, 39–43. [Google Scholar] [CrossRef]
  48. Leuschner, C.; Moser, G.; Bertsch, C.; Röderstein, M.; Hertel, D. Large altitudinal increase in tree root/shoot ratio in tropical mountain forests of Ecuador. Basic Appl. Ecol. 2007, 8, 219–230. [Google Scholar] [CrossRef]
  49. Moser, G.; Hertel, D.; Leuschner, C. Altitudinal change in LAI and stand leaf biomass in tropical montane forests: A transect study in Ecuador and a pan-tropical meta-analysis. Ecosystems 2007, 10, 924–935. [Google Scholar] [CrossRef]
  50. Raich, J.W.; Russell, A.E.; Vitousek, P.M. Primary productivity and ecosystem development along an elevational gradient on Mauna Loa, Hawai ‘i. Ecology 1997, 78, 707–721. [Google Scholar]
  51. Kitayama, K.; Aiba, S.I. Ecosystem structure and productivity of tropical rain forests along altitudinal gradients with contrasting soil phosphorus pools on Mount Kinabalu, Borneo. J. Ecol. 2002, 90, 37–51. [Google Scholar] [CrossRef]
  52. Grubb, P. Control of forest growth and distribution on wet tropical mountains: With special reference to mineral nutrition. Annu. Rev. Ecol. Syst. 1977, 8, 83–107. [Google Scholar] [CrossRef]
  53. Tanner, E.; Vitousek, P.M.; Cuevas, E. Experimental investigation of nutrient limitation of forest growth on wet tropical mountains. Ecology 1998, 79, 10–22. [Google Scholar] [CrossRef]
  54. Ding, Y.; Zang, R. Determinants of aboveground biomass in forests across three climatic zones in China. For. Ecol. Manag. 2021, 482, 118805. [Google Scholar] [CrossRef]
  55. van der Sande, M.T.; Arets, E.J.; Peña-Claros, M.; Hoosbeek, M.R.; Cáceres-Siani, Y.; van der Hout, P.; Poorter, L. Soil fertility and species traits, but not diversity, drive productivity and biomass stocks in a Guyanese tropical rainforest. Funct. Ecol. 2018, 32, 461–474. [Google Scholar] [CrossRef]
  56. Cleveland, C.C.; Townsend, A.R.; Taylor, P.; Alvarez-Clare, S.; Bustamante, M.M.; Chuyong, G.; Dobrowski, S.Z.; Grierson, P.; Harms, K.E.; Houlton, B.Z. Relationships among net primary productivity, nutrients and climate in tropical rain forest: A pan-tropical analysis. Ecol. Lett. 2011, 14, 939–947. [Google Scholar] [CrossRef] [PubMed]
  57. Culmsee, H.; Leuschner, C.; Moser, G.; Pitopang, R. Forest aboveground biomass along an elevational transect in Sulawesi, Indonesia, and the role of Fagaceae in tropical montane rain forests. J. Biogeogr. 2010, 37, 960–974. [Google Scholar] [CrossRef]
  58. Sundqvist, M.K.; Sanders, N.J.; Wardle, D.A. Community and ecosystem responses to elevational gradients: Processes, mechanisms, and insights for global change. Annu. Rev. Ecol. Evol. Syst. 2013, 44, 261–280. [Google Scholar] [CrossRef]
  59. Waide, R.B.; Zimmerman, J.K.; Scatena, F. Controls of primary productivity: Lessons from the Luquillo Mountains in Puerto Rico. Ecology 1998, 79, 31–37. [Google Scholar] [CrossRef]
  60. Xu, R.; Wang, Y.N.; Sun, Y.; Wang, H.; Gao, Y.; Li, S.; Guo, L.; Gao, L. External sodium acetate improved Cr (VI) stabilization in a Cr-spiked soil during chemical-microbial reduction processes: Insights into Cr (VI) reduction performance, microbial community and metabolic functions. Ecotoxicol. Environ. Saf. 2023, 251, 114566. [Google Scholar] [CrossRef]
  61. Iqbal, M.; Khan, S.M.; Ahmad, Z.; Hussain, M.; Shah, S.N.; Kamran, S.; Manan, F.; Haq, Z.U.; Ullah, S. Vegetation classification of the margalla foothills, islamabad under the influence of edaphic factors and anthropogenic activities using modern ecological tools. Pak. J. Bot. 2021, 53, 1831–1843. [Google Scholar] [CrossRef]
  62. Galicia, L.; López-Blanco, J.; Zarco-Arista, A.; Filips, V.; Garcıa-Oliva, F. The relationship between solar radiation interception and soil water content in a tropical deciduous forest in Mexico. Catena 1999, 36, 153–164. [Google Scholar] [CrossRef]
  63. Chu, C.; Lutz, J.A.; Král, K.; Vrška, T.; Yin, X.; Myers, J.A.; Abiem, I.; Alonso, A.; Bourg, N.; Burslem, D.F. Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees. Ecol. Lett. 2019, 22, 245–255. [Google Scholar] [CrossRef] [PubMed]
  64. Alves, L.F.; Vieira, S.A.; Scaranello, M.A.; Camargo, P.B.; Santos, F.A.; 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]
  65. Grubb, P.J. The maintenance of species-richness in plant communities: The importance of the regeneration niche. Biol. Rev. 1977, 52, 107–145. [Google Scholar] [CrossRef]
  66. Bruijnzeel, L.; Veneklaas, E.J. Climatic conditions and tropical montane forest productivity: The fog has not lifted yet. Ecology 1998, 79, 3–9. [Google Scholar] [CrossRef]
  67. Paoli, G.D.; Curran, L.M.; Slik, J. Soil nutrients affect spatial patterns of aboveground biomass and emergent tree density in southwestern Borneo. Oecologia 2008, 155, 287–299. [Google Scholar] [CrossRef] [PubMed]
  68. Forrester, D.I. Linking forest growth with stand structure: Tree size inequality, tree growth or resource partitioning and the asymmetry of competition. For. Ecol. Manag. 2019, 447, 139–157. [Google Scholar] [CrossRef]
  69. Mumshad, M.; Ahmad, I.; Khan, S.M.; Rehman, K.; Islam, M.; Sakhi, S.; Khan, S.U.; Afridi, S.G.; Shams, S.; Azam, S.; et al. Phyto-ecological studies and distribution pattern of plant species and communities of Dhirkot, Azad Jammu and Kashmir, Pakistan. PLoS ONE 2021, 16, e0257493. [Google Scholar] [CrossRef]
  70. Anwar, S.; Khan, S.M.; Ahmad, Z.; Ullah, Z.; Afza, R.; Abbas, Z.; Abdullah, A.; Hussain, M. Plant diversity and communities pattern with special emphasis on the indicator species of a dry temperate forest: A case study from Liakot area of the Hindu Kush mountains, Pakistan. Trop. Ecol. 2023, 64, 37–52. [Google Scholar] [CrossRef]
  71. Ali, S.; Khan, S.M.; Siddiq, Z.; Ahmad, Z.; Ahmad, K.S.; Abdullah, A.; Hashem, A.; Al-Arjani, A.B.; Abd_Allah, E.F. Carbon sequestration potential of reserve forests present in the protected Margalla Hills National Park. J. King Saud Univ. Sci. 2022, 34, 101978. [Google Scholar] [CrossRef]
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Figure 1. A theoretical model for evaluating the hypothesized association among elevation, Soil, relative humidity, and SR on AGB in a regional forest of Pakistan.
Figure 1. A theoretical model for evaluating the hypothesized association among elevation, Soil, relative humidity, and SR on AGB in a regional forest of Pakistan.
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Figure 2. GIS-generated map of the studied forests across Pakistan.
Figure 2. GIS-generated map of the studied forests across Pakistan.
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Figure 3. The relation among elevation, relative humidity, soil, DBH, and aboveground biomass in (A) Sub-tropical thorn forest, (B) Sub-tropical broad-leaved forest, (C) Moist temperate mixed forest, (D) Dry temperate conifer forest, and (E) Dry temperate, pure pine forest. Note: The black and orange bold lines represented positive and negative significant impacts, respectively (p < 0.05). In contrast, the dashed black and orange line shows the positive and negative insignificant impact amongst the predictors (p > 0.05). We used a standardized regression coefficient for each path. The model fits indices for tropical thorn, i.e., tropical thorn forest. CFI; 0.84; GFI; 0.90; SRMR; 0.105; AIC; 379.131. Sub-tropical broad-leaved forest. CFI; 0.981 GFI; 0.936 SRMR; 0.094 AIC; 119.305. Moist temperate mix forest, CFI; 0.913 GFI; 0.978 SRMR; 0.092 AIC; 222.596. Dry temperate conifer forest, CFI; 0.981 GFI; 0.936 SRMR; 0.094 AIC; 119.305. Dry temperate, pure, CFI; 0.840 GFI; 0.900 SRMR; 0.105 AIC; 379.131.
Figure 3. The relation among elevation, relative humidity, soil, DBH, and aboveground biomass in (A) Sub-tropical thorn forest, (B) Sub-tropical broad-leaved forest, (C) Moist temperate mixed forest, (D) Dry temperate conifer forest, and (E) Dry temperate, pure pine forest. Note: The black and orange bold lines represented positive and negative significant impacts, respectively (p < 0.05). In contrast, the dashed black and orange line shows the positive and negative insignificant impact amongst the predictors (p > 0.05). We used a standardized regression coefficient for each path. The model fits indices for tropical thorn, i.e., tropical thorn forest. CFI; 0.84; GFI; 0.90; SRMR; 0.105; AIC; 379.131. Sub-tropical broad-leaved forest. CFI; 0.981 GFI; 0.936 SRMR; 0.094 AIC; 119.305. Moist temperate mix forest, CFI; 0.913 GFI; 0.978 SRMR; 0.092 AIC; 222.596. Dry temperate conifer forest, CFI; 0.981 GFI; 0.936 SRMR; 0.094 AIC; 119.305. Dry temperate, pure, CFI; 0.840 GFI; 0.900 SRMR; 0.105 AIC; 379.131.
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Figure 4. The direct and indirect effect of the independent variable on aboveground biomass.
Figure 4. The direct and indirect effect of the independent variable on aboveground biomass.
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Figure 5. (A) Link among elevation, relative humidity, soil, CVD, and aboveground biomass in single-species forests, (B) The link among elevation, relative humidity, soil, DBH, and aboveground biomass in a multi-species forest of Pakistan. Note: The black and orange bold lines represent positive and negative significant impact, respectively (p < 0.05), while the dashed black and orange lines show positive and negatively insignificant impact among the predictors (p > 0.05). The model fit indices for single-species forest type CFI; 0.805 GFI; 0.921 SRMR; 0.087 AIC; 618.877 and model fit indices for the multi-species forest type CFI; 0.921 GFI; 0.965; SRMR; 0.071 AIC; 4792.035.
Figure 5. (A) Link among elevation, relative humidity, soil, CVD, and aboveground biomass in single-species forests, (B) The link among elevation, relative humidity, soil, DBH, and aboveground biomass in a multi-species forest of Pakistan. Note: The black and orange bold lines represent positive and negative significant impact, respectively (p < 0.05), while the dashed black and orange lines show positive and negatively insignificant impact among the predictors (p > 0.05). The model fit indices for single-species forest type CFI; 0.805 GFI; 0.921 SRMR; 0.087 AIC; 618.877 and model fit indices for the multi-species forest type CFI; 0.921 GFI; 0.965; SRMR; 0.071 AIC; 4792.035.
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Figure 6. The direct and indirect effect of an independent variable on aboveground biomass.
Figure 6. The direct and indirect effect of an independent variable on aboveground biomass.
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Figure 7. Pearson correlation among explanatory variables. Abbreviations: Elev, elevation; P, soil phosphorus; mrh, relative humidity; SR, Species Richness, and CVD, Coefficient Variation of DBH.
Figure 7. Pearson correlation among explanatory variables. Abbreviations: Elev, elevation; P, soil phosphorus; mrh, relative humidity; SR, Species Richness, and CVD, Coefficient Variation of DBH.
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Table
Table 1. Forest type, elevation, stand type, mean annual temperature, mean annual precipitation, and main or dominant species of the studied forest types.
Table 1. Forest type, elevation, stand type, mean annual temperature, mean annual precipitation, and main or dominant species of the studied forest types.
S/NoForest TypeStand TypeCoordinatesElevation
m.a.s.l 		Mean Annual Temperature (°C)Mean Annual Precipitation (mm) Dominant Species
1Sub-tropical thorn
Forest		Conserved natural forest670 10| to 670 55| E longitudes to 250 13| to 260 12| N latitude56–30233.8245.3Prosopis glandulosa Torr., Prosopis juliflora (Sw.) DC.,
Acacia modesta Wall., Ziziphus nummularia (Burm.f.) Wight & Arn.,
Salvadora oleoides Decne.,
Combretum molle R.Br. ex G.Don
2Sub-tropical broad-leaved forestConserved natural forest33,040′–33,044′ N longitude to 33,055′–73,020′ E latitude555–111727.81572.1Acacia modesta Wall., Cassia fistula L.,
Justicia adhatoda L., Carissa spinarum L.,
Mallotus philippensis (Lam.) Müll.Arg., Dodonaea viscosa (L.) Jacq., Bauhinia variegata L., Albizia lebbeck (L.) Benth., Celtis australis L.
3Moist temperate mix forestOld age mix forest33°52′ to 33°59′ N and 73°24′ to 73°31′ E1249–289217.81596.1Pinus wallichiana A.B.Jacks., Diospyros virginiana L.,
Aesculus indica (Wall. ex Cambess.) Hook., Populus alba L., Cedrus deodara (Roxb. ex D.Don) G.Don, Castanea dentata (Marshall) Borkh., Abies pindrow (Royle ex D.Don) Royle, Quercus dilatata A.Kern.
4Dry temperate conifer forestOld age conifer forest350–280 N latitude to 720–200 E longitude1040–256623.41371.8Pinus roxburghii Sarg., Abies pindreow (Royle ex D.Don) Royle,
Pinus wallichiana A.B.Jacks., Picea smithiana (Wall.) Boiss.,
Cedrus deodara (Roxb. ex D.Don) G.Don
5Dry temperate pure Pinus gerardiana forestOld age pure Pinus gerardiana forest310–36 N latitude and 690–59 E longitude1841–228225.9299.0Pinus gerardiana Wall. ex D.Don
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Ali, S.; Khan, S.M.; Ahmad, Z.; Abdullah, A.; Kazi, N.; Nawaz, I.; Almutairi, K.F.; Avila-Quezada, G.D.; Abd_Allah, E.F. Relative Humidity, Soil Phosphorus, and Stand Structure Diversity Determine Aboveground Biomass along the Elevation Gradient in Various Forest Ecosystems of Pakistan. Sustainability 2023, 15, 7523. https://doi.org/10.3390/su15097523

AMA Style

Ali S, Khan SM, Ahmad Z, Abdullah A, Kazi N, Nawaz I, Almutairi KF, Avila-Quezada GD, Abd_Allah EF. Relative Humidity, Soil Phosphorus, and Stand Structure Diversity Determine Aboveground Biomass along the Elevation Gradient in Various Forest Ecosystems of Pakistan. Sustainability. 2023; 15(9):7523. https://doi.org/10.3390/su15097523

Chicago/Turabian Style

Ali, Shahab, Shujaul Mulk Khan, Zeeshan Ahmad, Abdullah Abdullah, Naeemullah Kazi, Ismat Nawaz, Khalid F. Almutairi, Graciela Dolores Avila-Quezada, and Elsayed Fathi Abd_Allah. 2023. "Relative Humidity, Soil Phosphorus, and Stand Structure Diversity Determine Aboveground Biomass along the Elevation Gradient in Various Forest Ecosystems of Pakistan" Sustainability 15, no. 9: 7523. https://doi.org/10.3390/su15097523

APA Style

Ali, S., Khan, S. M., Ahmad, Z., Abdullah, A., Kazi, N., Nawaz, I., Almutairi, K. F., Avila-Quezada, G. D., & Abd_Allah, E. F. (2023). Relative Humidity, Soil Phosphorus, and Stand Structure Diversity Determine Aboveground Biomass along the Elevation Gradient in Various Forest Ecosystems of Pakistan. Sustainability, 15(9), 7523. https://doi.org/10.3390/su15097523

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