Spatial patterns of biomass carbon density
The results of spatial hot and cold spots analyses showed that the areas in which the plots with high biomass carbon density distributed continuously in space were identified as hot spots areas (Fig 4). These hot spots were widely distributed in the mountainous areas with higher elevation (Fig S4), in most of which natural reserves were established, for instance, Luyashan National Natural Reserve in Luya Mountain, Pangquangou National Natural Reserve in Guandi Mountain, Lingkongshan National Natural Reserve in Taiyue Mountain. In these reserves, the forests were less disturbed by human activities and would be relatively older. Meanwhile, the coniferous forests in hot spots areas consisted of Picea wilsonii forests, Larix principis-rupprechtii forests, coniferous mixed forests, and Pinus tabulaeformis forests, and deciduous broad-leaved forests consisted of Quercus liaotungensis forests, Betula platyphylla forests, and deciduous broad-leaved mixed forests. Compared with the total average biomass carbon density of natural forests, these forests had higher mean biomass carbon densities (Table 2). This promoted the formation of hot spots in these areas. Meanwhile, continuous distribution of the natural coniferous (deciduous broad-leaved) forests also helped to form hot spots areas in these areas (Fig 1b). In addition, the composition of tree species in hot spots areas was also relatively simple, and every tree species was widely distributed and concentrated in groups (Fig S1). This also reduced the inhomogeneity of spatial distribution of biomass carbon density in space due to the differences among forest types in these areas (Figs 5 and 6), which was unfavorable for the information of the hot spots.
In contrast to the hot spots, the cold spots for biomass carbon density of natural mountain forests were mainly detected in or around the Coal Mining areas. Shanxi province being abundant in coal resources is one of the best important providers of coal in China ( Shanxi Geological Exploration Bureau, 2018.). For instance, cold spots at the 1% significance level (CS99s) in the southernmost tip of the Lüliang Mountains are located in Xiangning colliery, which is part of Hedong coalfield (Shi, 2017; Shanxi Fenwei Energy Development Consulting Co., Ltd, 2007). Another CS99s region detected in the south of Taihang Mountains lied to the east of Lingchuan colliery or in the Jincheng colliery, which belongs to Qinshui coalfield (Shi, 2017; Shanxi Fenwei Energy Development Consulting Co., Ltd, 2007). Higher impacts of long-term mining activities would lead to low forest biomass carbon densities and the formation of cold spots in these areas.
The Zhongtiao Mountain, known as “Shanxi natural botanical garden”(Zhang and Wu, 2015), is a key region for the woody flora of North China, in which many rare and endangered plants were distributed. In our study, the entire Mountain was identified as an area of random distribution for the biomass carbon densities of natural mountain forests (Fig 4). The most likely explanation for this is that rich and varied plant species are randomly distributed in space (Fig 1b). On the other hand, it’s well known that the Mountain is one of the birthplaces of Chinese civilization, and the emperors of Yao, Shun, Yu, and Tang had been here many times(Zhang and Wu, 2015). With the continuous expansion of agricultural production and the increasing population density around the Mountain, human activities had a more and more profound impact on natural forest vegetation in the course of history. Meanwhile, in ancient times the area was a major center for copper mining (Chenw. et al., 1996) and is today one of the operational bases of Zhongtiao Mountain Non-ferrous Metals Group Co., Ltd, one of China’s largest metal processing companies. Therefore, long-term mining and processing of mineral resources had also been producing a negative effect on natural forests in the mountains. Besides, the continuous development and utilization of Tourism Resources in the Mountain could also be a reason for the random distribution of biomass carbon densities in Zhongtiao Mountain. In a word, frequent human disturbance should also be a decisive factor which cannot be neglected.
The relationships between stand factors and biomass carbon density
The results of SEM analysis showed that AGE and COV were important predictors in modulating the BCD for mountainous coniferous and deciduous broad-leaved forests. Especially for the deciduous broad-leaved forests, the effect of AGE or COV was stronger than that of the other factors (Fig 6). The findings were in agreement with Xu et al. (L. Xu et al., 2018) who found that canopy density and forest age were the dominant determinants of vegetation carbon density in subtropical forest ecosystems in Zhejiang Province, China. The strong positive effect of AGE on BCD, possibly because the stand age controls the duration of forest carbon accumulation (Pregitzer and Euskirchen, 2004) and biomass carbon density increases with stand development (Cheng et al., 2014; Fonseca et al., 2011). While owing to nutrient limitation, stomatal constraint and decline in photosynthesis during the stand development, stand net primary productivity (NPP) declines along with the increase of tree age (Gower, 2003; Mcdowell and Harireche, 2002; Tang et al., 2014). Consequently, with the increasing stand age, the biomass carbon accumulation reaches a steady state when the carbon sequestration rate of forests approaches zero (Goulden et al., 2011). We found that equilibrium points were about 95yr and 85yr for mountainous broad-leaved and coniferous forests in our study area, respectively (Fig 3b). Meanwhile, TYPE was another stand factor in modulating the BCD for the mountainous forests. The effect of TYPE on BCD was greater for the coniferous forests than the deciduous broad-leaved forests (Fig 6). The result could be due to the differences of averaged BCDs of different forest types between the coniferous and broad-leaved forests. The further result showed that the difference of mean BCDs of different forest types for the coniferous forests was weaker significantly larger than that for the broad-leaved forests (Table 2) (t=0.973, P=0.058).
The effects of geographical factors on biomass carbon density
Compared with other geographical factors (longitude, slope aspect, and slope position), the introduction of ELE and LAT into our model could be mainly determined by the special geographical environment conditions of mountainous forests in our study. Deciduous broad-leaved forests and coniferous forests ranged from 520m to 2309m and from 483m to 2560m in elevation, respectively. Elevation gradient is associated with changes in temperature and precipitation, and forest type (Sanaei et al., 2018). By regulating moisture and soil water availability (Fisk et al., 1998), elevation can affect forest canopy, stem density, and stand basal area, and therefore affect aboveground biomass (L. Xu et al., 2018; M. Xu et al., 2018). Therefore, the larger elevation gradient of the natural forests could probably lead to the significant effects of ELE on BCD in the mountainous terrain of the Shanxi plateau. Similarly, based on the SEM, Xu et al. (L. Xu et al., 2018) also reported that altitude was the most important abiotic driving factor of vegetation carbon stocks in Zhejiang Province, China. Furthermore, a positive effect of ELE on BCD was detected for natural coniferous and deciduous broad-leaved forests in our study (Fig 6). Similar results were also reported by Liu et al. (Liu and Nan, 2018), who found a positive linear relationship between vegetation carbon stock and altitude across three forests (Larix principis-rupprechtii (LP) forest, Picea meyerii (PM) forest, and Pinus tabulaeformis (PT) forest) on Loess Plateau in an altitudinal range of 1200-2700m. In addition, a similar relationship between biomass carbon density and elevation was reported by Sumeet Gairola et al. (Gairola et al., 2011) in moist temperate forests of the Garhwal Himalaya. As for LAT, it also had a significant effect on BCD in our study due to that the distribution of natural forests also had a larger latitude span. Deciduous broad-leaved forests and coniferous forests ranged from 34.79° to 39.89° and from 34.97° to 39.75° in elevation, respectively. The larger latitude span would lead to great changes in the environmental conditions along latitude gradients from the north to the south, including the light, heat, and moisture, and would affect plant growth and therefore influence the biomass carbon accumulation in the natural forest from the north to the south. It is noteworthy that in the areas of natural forests distribution in our study, the higher the latitude, the higher the elevation (r=0.708, P<0.001). So the positive effects of ELE on BCD enhanced the positive effects of LAT (Fig 6).
The effects of climatic factors on biomass carbon density
The total effects of climatic factors (TEMP and PRCP) were lower than those of geographical factors (ELE and LAT) and biotic factors (AGE, COV, and TYPE) in temperate mountainous forests in Shanxi. According to the SEM, we found a negative effect of TEMP on BCD for the natural coniferous forests, indicating that the lower the temperature, the higher the BCD of the forest. It could be the result of natural selection of the coniferous tree species for the site conditions, and the spatial distribution of these tree species in our study. For example, the Picea and Larix principis-rupprechtii forests with the higher mean biomass carbon densities were generally distributed in the regions with lower temperatures, but Platycladus orientalis forests with the lowest mean biomass carbon density were generally distributed in warmer areas (Table 1). A similar result was also found by Fehse et al. (Fehse et al., 2002), who demonstrated that the forests under favorable site conditions at high altitudes with low temperature were not inferior in biomass accumulation and productivity compared with the forests at low altitude with high temperature. Meanwhile, the smaller leaf surface area of coniferous trees which makes the water and heat not easy to lose is beneficial to cold resistance. And the lower temperature for mountainous coniferous forests in our study not only could not be lower to limit the photosynthetic carbon sequestration rate of these coniferous trees but also might decrease the amount of carbon released by respiration because the respiration generally required a higher temperature than photosynthesis. Consequently, lower temperatures for coniferous forests probably increased the biomass carbon accumulation. Besides, for the temperate deciduous broad-leaved forests, which were the zonal vegetation in our study area, TEMP had no significant effect on BCD. Similarly, Liu et al. (Liu et al., 2014) also found a nonlinear relationship exists between the above-ground biomass carbon density and mean annual temperature in the temperate mountain system for mature forests on the global scale.
Integrative framework of biomass carbon density for the mountainous forests
Numerous studies on the influencing factors of forest carbon storage in mountainous areas were only concentrated on the effects of elevation on the forest carbon storage (Chang et al., 2015; Gairola et al., 2011; Liu and Nan, 2018; Seedre et al., 2015; Zhu et al., 2010), and a very small number of studies focused on the interaction between different influencing factors, and on the indirect effects of these factors on forest carbon storage. In our study, an integrative framework of biomass carbon density for the mountainous forests was proposed, which combined the three kinds of driving factors, including biotic (AGE, COV, and TYPE), climatic (TEMP and PRCP), and geographical factors (ELE and LAT), and then the direct and indirect effects of these factors on biomass carbon density were derived from it. For example, we found that geographical factors had affected biomass carbon density indirectly in our study (Fig S2). In detail, ELE had affected BCD through the direct effects of ELE on TYPE, AGE, and TEMP (Fig S2). Meanwhile, we also found a positive relationship between ELE and AGE (Fig 5). This was probably because the forests at higher elevations had usually experienced little disturbance, and could grow steadily and presented larger average stand age until the mature stage. Moreover, ELE had effects on the distribution of the coniferous forest types for the coniferous forests. Noticeably, for coniferous forests, the Picea and Larix principis-rupprechtii forests were generally distributed at a higher elevation than Pinus tabuliformis and Platycladus orientalis forests across the entire region (Table 1), and the BCDs for the former two was generally greater than for the latter two (Table 2). In short, the indirect effects were helpful for us to understand why elevation was an important driving factor of biomass carbon density for mountain forests and how geographical factors affect biomass carbon density in our study.
Meanwhile, multi-group SEM as an integrative approach also was used to compare differences between groups in our study. It could be revealed whether the effects of these factors were consistent between the coniferous forests and broad-leaved forests. For instance, the effect of AGE (COV) on BCD for the coniferous forests was equal to that for deciduous broad-leaved forests, but the effects of climatic and geographical factors varied greatly between them. For the deciduous broad-leaved forests, the positive connection between ELE and AGE was the main reason for the positive effects of ELE on BCD. But for the coniferous forests, ELE had effects on TEMP and TYPE besides the positive effect on AGE (Fig 5). Therefore, the total effect of ELE on BCD was obviously different between the deciduous broad-leaved and coniferous forests (Fig 6). Furthermore, if we did not use the multi-group SEM, the model we proposed in our study, the qualitative structure of which was correct, would be incorrectly rejected because the coniferous forests and deciduous broad-leaved forests differ in the numerical strength of these causal relationships.