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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (2): 266-281    DOI: 10.1007/s40333-024-0005-3
Research article     
Effects of landscape fragmentation of plantation forests on carbon storage in the Loess Plateau, China
LEI Hangyu, DUAN Dantong, CHEN Yi, GUO Huifeng, LI Jiangtao, LI Xiang*()
College of Landscape Architecture and Art, Northwest A&F University, Yangling 712100, China
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Abstract  

Tree plantation and forest restoration are the major strategies for enhancing terrestrial carbon sequestration and mitigating climate change. The Grain for Green Project in China has positively impacted global carbon sequestration and the trend towards fragmentation of plantation forests. Limited studies have been conducted on changes in plantation biomass and stand structure caused by fragmentation, and the effect of fragmentation on the carbon storage of plantation forests remains unclear. This study evaluated the differences between carbon storage and stand structure in black locust forests in fragmented and continuous landscape in the Ansai District, China and discussed the effects of ecological significance of four landscape indices on carbon storage and tree density. We used structural equation modelling to explore the direct and indirect effects of fragmentation, edge, abiotic factors, and stand structure on above-ground carbon storage. Diameter at breast height (DBH) in fragmented forests was 53.3% thicker, tree density was 40.9% lower, and carbon storage was 49.8% higher than those in continuous forests; for all given DBH>10 cm, the trees in fragmented forests were shorter than those in continuous forests. The patch area had a negative impact on carbon storage, i.e., the higher the degree of fragmentation, the lower the density of the tree; and fragmentation and distance to edge (DTE) directly increased canopy coverage. However, canopy coverage directly decreased carbon storage, and fragmentation directly increased carbon storage and tree density. In non-commercial forests, fragmentation reduces the carbon storage potential of plantation, and the influence of patch area, edge, and patchy connection on plantation should be considered when follow-up trees are planted and for the plantation management. Thus, expanding the area of plantation patches, repairing the edges of complex-shaped patches, enhancing the connectivity of similar patches, and applying nutrients to plantation forests at regular intervals are recommended in fragmented areas of the Loess Plateau.

Key words： landscape fragmentation      plantation      carbon storage      tree allometry      tree density      structural equation modelling     
Received: 16 October 2023      Published: 29 February 2024
Corresponding Authors: *LI Xiang (E-mail: lx@nwafu.edu.cn)
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LEI Hangyu, DUAN Dantong, CHEN Yi, GUO Huifeng, LI Jiangtao, LI Xiang. Effects of landscape fragmentation of plantation forests on carbon storage in the Loess Plateau, China. Journal of Arid Land, 2024, 16(2): 266-281.

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http://jal.xjegi.com/10.1007/s40333-024-0005-3     OR     http://jal.xjegi.com/Y2024/V16/I2/266

Fig. 1 Sampling locations in fragmented and continuous forests of the study area
Component Allometric equation R2
Leaf ln(y)= -3.65+1.38ln(x) 0.70
Branch ln(y)= -3.38+2.38ln(x) 0.91
Bark ln(y)= -3.95+2.23ln(x) 0.96
Stem without bark ln(y)= -2.58+2.30ln(x) 0.96
Table 1 Allometric equations for estimating each component biomass of black locusts in the Loess Plateau
Fig. 2 Conceptual diagram illustrating axes of fragmentation and variables associated with fragmentation included in analyses. Green squares represent forest pixels, and adjacent pixels represent a patch. The red outline indicates a focal patch for isolation measures.
Fig. 3 Conceptual model describing how fragmentation and environmental attributes relate to forest structure and how all of these attributes relate to carbon storage. To test the conceptual model, we used patch area, edge, and proximity index with patch density as proxies for forest fragmentation, topographic moisture index (abiotic factor), distance to edge, canopy coverage, and tree density (structure), and above-ground carbon storage (carbon).
Fig. 4 Forest structure under fragmented and continuous forests. (a), DBH (diameter at breast height); (b), stand height; (c), above-ground carbon; (d), tree density; (e), canopy coverage. Bars are standard errors, and vertical line represents 95% confidence interval. ***, P<0.001 level.
t-test DBH (cm) Stand height (m) Carbon storage
(t/hm2)		 Tree density
(trees/0.01 hm2)		 Canopy
coverage (%)
df 42 42 42 42 42
P value <0.010*** 0.140 <0.010*** <0.010*** <0.010***
Fragmented forests 19.89 10.02 52.60 7.68 0.55
Continuous forests 12.97 10.59 35.10 13.00 0.77
Difference 6.93 -0.58 174.90 -5.32 -0.23
SE 0.57 0.39 33.98 0.37 0.02
Table 2 t-tests for DBH, stand height, above-ground carbon storage, tree density, and canopy coverage for two types of forests
Fig. 5 Relationship between stand height and DBH (diameter at breast height) under fragmented and continuous forests. Shaded area represents the 95% confidence interval of the regression.
Fig. 6 Relationships between four types of fragmentation indices and carbon storage. (a), AREA (patch area); (b), SHAPE (edge); (c), PROX (isolation); (d), PD (patch density). Shaded area represents the 95% confidence interval of the regression.
Fig. 7 Relationships between four types of fragmentation indices and tree density. (a), AREA (patch area); (b), SHAPE (edge); (c), PROX (isolation); (d), PD (patch density). Shaded area represents the 95% confidence interval of the regression.
Fig. 8 Structural equation model for combined effects of fragmentation, edge effect, abiotic factor, and stand structure on above-ground carbon in plantation forests on the Loess Plateau. Solid lines indicate positive impacts, dashed lines indicate negative impacts, and grey lines indicate coefficients that were not significant at P>0.050 level. DTE, distance to edge; TWI, topographic wetness index.
Standardised Coefficient SE z
value		 P
value		 95% confidence interval R2
value
Structural
TD 0.430
Fragmented 0.48 0.16 2.93 0.000 0.16-0.80
DTE 0.17 0.17 1.00 0.320 -0.17-0.51
TWI 0.19 0.11 1.74 0.080 -0.02-0.41
_cons 2.99 0.48 6.19 0.000 2.04-3.94
TCC 0.520
Fragmented 0.42 0.15 2.77 0.010 0.12-0.72
DTE 0.32 0.16 2.07 0.040 0.02-0.63
TWI 0.16 0.10 1.55 0.120 -0.04-0.36
_cons 4.45 0.57 7.82 0.000 3.33-5.56
C 0.360
TD -0.25 0.23 -1.09 0.270 -0.69-0.20
TCC -0.66 0.21 -3.07 0.000 -1.08- -0.24
Fragmented 0.48 0.20 2.45 0.010 0.10-0.87
DTE -0.15 0.19 -0.77 0.440 -0.53-0.23
TWI 0.17 0.12 1.39 0.160 -0.07-0.42
_cons 7.51 0.75 10.02 0.000 6.04-8.97
var(e.TD) 0.57 0.10 0.41-0.81
var(e.TCC) 0.48 0.09 0.33-0.69
var(e.C) 0.64 0.11 0.45-0.91
LR test of model vs. saturated: chi2(1)=27.36, prob>chi2=0.000
Table S1 Parameter statistics for structural equation modelling
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