Species asynchrony stabilizes productivity over 20 years in Northeast China

Jia, Bo; He, Jingyuan; Wang, Xinjie

doi:10.1002/ece3.9991

Cited by 1 publication

(1 citation statement)

References 82 publications

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“…Piecewise structural equation modelling (pSEM) extends traditional SEM to handle non-normal distributions and hierarchical structures, which we employed to further investigate the relationships between geographical factors and the carbon sink potential. We utilized the following five indices [39,48,49]: degrees of freedom (DF), p-value, standardized root mean square residual (SRMR), root mean square error of approximation (RMSEA), and the goodness-of-fit index (GFI). DF reflects complexity, with lower values typically indicating a more parsimonious model.…”

Section: Influencing Factor Analysis Methodsmentioning

confidence: 99%

The Estimation of Forest Carbon Sink Potential and Influencing Factors in Huangshan National Forest Park in China

Huang,

Wang,

Zhang

2024

Sustainability

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In this study, the biomass expansion factor (BEF) method was combined with the tree growth function in order to obtain a more accurate growth function of tree species through the fitting of different growth functions to tree growth, and to determine the characteristics of the forest carbon stock as well as the carbon sink potential of Huangshan National Forest Park (HNFP) in China. The carbon sink potential of each tree species and the integrated influencing factors, such as the stand and soil, were directly represented by structural equation modelling (SEM) to clarify the size and path of each influencing factor against the carbon sink potential. The results showed the following: (1) the logistic growth function fitting results for the seven major tree species in HNFP were better than those from the Richard–Chapman growth function, and the R2 was greater than 0.90. (2) In 2014, the total carbon stock of the forest in HNFP was approximately 9.59 × 105 t, and the pattern of carbon density, in general, was higher in the central region and the northeastern region and lower in the northern and southern regions, while the distribution of carbon density was lower in the northern and southern regions. The carbon density pattern generally showed a higher distribution in the central and northeastern regions and a lower distribution in the northern and southern regions; most of the high-carbon-density areas were distributed in blocks, while the low-carbon-density areas were distributed sporadically. (3) The total carbon sink of the forest in HNFP was 8.26 × 103 t in 2014–2015, and due to the large age structure of the regional tree species, the carbon sinks of each tree species and the total carbon sink of HNFP showed a projected downward trend from 2014 to 2060. (4) For different tree species, the influencing factors on carbon sink potential are not the same, and the main influence factors involve slope position, slope, altitude, soil thickness, etc. This study identified the carbon stock and carbon sink values of the forest in HNFP, and the factors affecting the carbon sink potential obtained by SEM can provide a basis for the selection of new afforestation sites in the region as well as new ideas and methods to achieve peak carbon and carbon neutrality both regionally and nationally in the future.

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Section: Influencing Factor Analysis Methodsmentioning

confidence: 99%