## 4 Discussions  
### Analysis of GPP Spatiotemporal Dynamics  
In the GKM, different seasons displayed different patterns of spatial distribution and we considered three reasons for these phenomena according to our analysis. Understory vegetation, such as dwarf shrubs, mosses, and reindeer lichens, played an important role. Studies found that the productivity of understory vegetation is probably comparable with that of the trees ([PERSON], 2016), and this is especially obvious in spring when understory vegetation grew more lushly, while most of the trees were just starting to sprout new leaves, providing a bright environment in which understory vegetation can acquire more sunshine. The northern GKM had the largest AG values (Figure 2a) in spring, which can be attributed to understory vegetation. We found much evidence demonstrating that understory vegetation in the northern GKM was much lusher than that in other parts of the GKM. On one hand, a majority of bright coniferous forest, which is usually sparser than mixed broadleaf-conifer forest or \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dominant Factor** & **GSL** & **FPAR** & **Temperature** & **Sunshine Duration** \\ \hline Zonal mean coefficients & 0.399134 & 0.453251 & 0.448733 & 0.518619 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Zonal mean value of the coefficient for each dominant factor. Figure 10: Spatial distribution of (**a**) dominant and (**b**) subdominant factors for AGG in the GKM from 1982 to 2015 (Non-significant means that there is no significantly effective factor for AGG among these five driving factors according to the \(F\)-test and \(t\)-test. A significant \(F\)-test was under \(p<0.05\), and a significant \(t\)-test was under the absolute value of \(t\geq 1.96\)). broadleaf forest, mainly concentrated in the northern GKM (Figure 11 shows the contrast of spring AG in the bright coniferous zone and other areas), so understory vegetation flourished here [65]. On the other hand, fire is also a primary determinant for understory vegetation [64; 66], so the disastrous fire that occurred in the northern GKM in 1987 may have contributed to the luxu variance of the understory vegetation in that region. Altitude was another crucial factor for the spatial distribution of AG in our study area. GPP in areas lying along the Greater Khingan Mountain Ridges (GKMR) was always smaller than that in other parts (Figure 12 shows the variation of AGG with elevation, ratio = \(-0.18\) g C/m\({}^{2}\) m\({}^{-1}\), \(p<0.05\)); this was especially obvious in summer (as shown in Figure 3b). As we all know, temperature decreases with the increase in elevation, so the GKMR is a low-temperature zone with low GPP values by nature. In summer, vegetation in the high-temperature zone grew rapidly, while growth remained slow along the GKMR, so the gap in productivity increased further. Land cover also influenced the spatial distribution of AG. When combining with land cover data of the GKM (Figure 1c), we found cropland, instead of forest, being widely distributed in the southern GKM (Figure 11 shows the contrast of spring AG in the cropland zone and other areas). Figure 11: Contrast of AG in specific land cover and other areas in the GKM. Figure 12: Variation of AGG with elevation. Because of human intervention, cropland can grow well in a relatively low temperature and thus the southern GKM had the largest GPP values across the GKM in autumn when the temperature has greatly decreased. Apart from spatial distribution, reasons for the temporal variation of AG were also analyzed. Annual variation in GPP displayed an obviously unimodal seasonal cycle because _Larix gmelnii_, a kind of deciduous coniferous forest, is the dominant species in the GKM. Due to the low temperature and frozen soil before the 100 th day (mid-April) and after the 270 th day (early-October), photosynthesis is weak and thus GPP is mainly produced from mid-May to mid-September, so there is a shorter growing period than in other regions.