An Integrative Approach to Study How Driving Factors Control Biomass Carbon Density for Natural Mountain Forests, in China’s Loess Plateau
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Region
2.2. Data Collection and Preprocessing
2.2.1. Forest Inventory Data
2.2.2. Climate Data
2.3. Data Analyses
2.3.1. Statistical Analyses
2.3.2. Spatial Hot and Cold Spots Analyses
2.3.3. Multi-Group SEM
3. Results
3.1. Biomass Carbon Density
3.2. Spatial Hot Spots and Cold Spots Analyses for Biomass Carbon Densities
3.3. Influences of Various Factors on Biomass Carbon Density of Natural Mountain Forests
4. Discussion
4.1. Spatial Patterns of Biomass Carbon Density
4.2. The Relationships between Stand Factors and Biomass Carbon Densities
4.3. The Effects of Geographical Factors on Biomass Carbon Density
4.4. The Effects of Climatic Factors on Biomass Carbon Density
4.5. Integrative Framework of Biomass Carbon Density for the Mountainous Forests
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Forest Type | ELE (km) | LAT (°) | TEMP (°C) | PRCP (mm) | AGE (yr) | COV (%) | |
---|---|---|---|---|---|---|---|
Broad-leaved forests | Quercus liaotungensis | 1522.50 | 36.84 | 9.72 | 500.10 | 57 | 63 |
Broad-leaved mixed | 1358.79 | 36.30 | 10.80 | 528.91 | 43 | 61 | |
Betula platyphylla | 1924.57 | 38.45 | 6.87 | 474.03 | 45 | 59 | |
Quercus variabilis | 1048.27 | 35.34 | 12.43 | 584.30 | 43 | 70 | |
Quercus acutissima | 976.48 | 35.23 | 12.88 | 579.25 | 44 | 75 | |
Populus davidiana | 1545.72 | 37.84 | 8.780 | 463.77 | 29 | 54 | |
Others | 1247.84 | 36.2 | 10.55 | 543.05 | 30 | 58 | |
Mean | 1421.01 | 36.6 | 10.17 | 518.57 | 46 | 62 | |
Coniferous forests | Pinus tabuliformis | 1436.13 | 36.87 | 9.62 | 502.26 | 57 | 56 |
Coniferous mixed | 1506.84 | 36.94 | 9.73 | 495.41 | 50 | 58 | |
Platycladus orientalis | 970.90 | 36.07 | 11.20 | 520.21 | 50 | 50 | |
Pinus bungeana | 1223.65 | 36.34 | 10.85 | 496.86 | 50 | 68 | |
Picea wilsonii | 2311.46 | 38.75 | 5.67 | 469.67 | 70 | 69 | |
Larix principis-rupprechtii | 2230.15 | 38.77 | 5.94 | 469.18 | 56 | 52 | |
Mean | 1465.32 | 36.93 | 9.57 | 499.18 | 55 | 57 | |
Mean | 1438.42 | 1438.42 | 9.94 | 509.77 | 50 | 60 |
Forest Type | Arbor | Undergrowth | All | No. of Plots | |
---|---|---|---|---|---|
Broad-leaved forests | Quercus liaotungensis | 39.73 | 5.97 | 45.7 | 204 |
Broad-leaved mixed | 25 | 5.81 | 30.8 | 151 | |
Betula platyphylla | 32.39 | 5.95 | 38.33 | 37 | |
Quercus variabilis | 29.68 | 5.69 | 35.37 | 26 | |
Quercus acutissima | 27.96 | 6.33 | 34.29 | 21 | |
Populus davidiana | 18.66 | 6.35 | 25.01 | 18 | |
Others | 15.24 | 6.32 | 21.56 | 76 | |
Mean | 29.89 | 5.99 | 35.87 | 533 | |
Coniferous forests | Pinus tabuliformis | 27.79 | 6.41 | 34.21 | 176 |
Coniferous mixed | 32.64 | 7.91 | 40.55 | 93 | |
Platycladus orientalis | 10.16 | 6.75 | 16.9 | 30 | |
Pinus bungeana | 18.08 | 6.46 | 24.54 | 20 | |
Picea wilsonii | 95.93 | 9.84 | 105.77 | 13 | |
Larix principis-rupprechtii | 47.47 | 7.68 | 55.15 | 13 | |
Mean | 30.31 | 7.02 | 37.34 | 345 | |
Mean | 30.06 | 6.39 | 36.45 | 878 |
Free Parameter Whose between-Groups Was Constrained to Be Equal | MLχ2 | ΔMLχ2 | p-Value |
---|---|---|---|
none | 6.629 | ||
path from ELE to AGE | 16.179 | 3.974 | 0.002 |
path from AGE to BCD | 7.325 | 0.696 | 0.404 |
path from PRCP to COV | 8.548 | 1.922 | 0.166 |
path from LAT to PRCP | 15.436 | 9.041 | 0.003 |
path from COV to BCD | 8.089 | 1.461 | 0.227 |
path from TEMP to BCD | 17.457 | 0.026 | 0.001 |
path from PRCP to BCD | 6.629 | 0.002 | 0.986 |
path from LAT to AGE | 16.179 | 3.063 | 0.002 |
path from AGE to COV | 6.837 | 0.208 | 0.648 |
path from PRCP to TYPE | 14.913 | 2.620 | 0.004 |
path from ELE to TEMP | 17.457 | 7.559 | 0.001 |
Path from COV to TYPE | 17.457 | 3.046 | 0.001 |
path from TYPE to BCD | 16.179 | 3.281 | 0.002 |
path from ELE to TYPE | 38.211 | 31.582 | <0.001 |
path from TEMP to TYPE | 20.396 | 13.767 | <0.001 |
path from LAT to TEMP | 35.044 | 28.415 | <0.001 |
path from LAT to ELE | 24.932 | 18.303 | <0.001 |
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Sun, L.; Wang, Q.; Fan, X. An Integrative Approach to Study How Driving Factors Control Biomass Carbon Density for Natural Mountain Forests, in China’s Loess Plateau. Forests 2022, 13, 1114. https://doi.org/10.3390/f13071114
Sun L, Wang Q, Fan X. An Integrative Approach to Study How Driving Factors Control Biomass Carbon Density for Natural Mountain Forests, in China’s Loess Plateau. Forests. 2022; 13(7):1114. https://doi.org/10.3390/f13071114
Chicago/Turabian StyleSun, Lina, Qixiang Wang, and Xiaohui Fan. 2022. "An Integrative Approach to Study How Driving Factors Control Biomass Carbon Density for Natural Mountain Forests, in China’s Loess Plateau" Forests 13, no. 7: 1114. https://doi.org/10.3390/f13071114
APA StyleSun, L., Wang, Q., & Fan, X. (2022). An Integrative Approach to Study How Driving Factors Control Biomass Carbon Density for Natural Mountain Forests, in China’s Loess Plateau. Forests, 13(7), 1114. https://doi.org/10.3390/f13071114