## 3 Results
### Impact of Forest Fragmentation on Amazon Biomass and Productivity at Landscape Level
Figure 5: The frequency distribution for the mean aboveground biomass (**a**) and net primary productivity (**b**) within a 10 \(\times\) 10 km\({}^{2}\) tile, divided according to fragmentation landscape classes (low-fragmented, moderate-fragmented and highly fragmented). The frequency distribution of the aboveground biomass (Figure 5a) shows a clear difference in the distributions for the three fragmentation landscape types with the highest values in low-fragmented landscapes. Low-fragmented areas show a mean of 238 Mg odm ha\({}^{-1}\), moderate-fragmented areas have a mean of 210 Mg odm ha\({}^{-1}\) and highly fragmented areas have a mean of 171 Mg odm ha\({}^{-1}\). For low-fragmented areas a lower standard deviation is observed than in highly fragmented areas. The CV for highly fragmented areas (24%) is 1.5 times greater than the CV for low-fragmented areas (16%). For net primary productivity (Figure 5b), the mean values for all fragmentation types are quite similar (Table 2). Highly fragmented areas have a mean value of 5.2 Mg C ha\({}^{-1}\) a\({}^{-1}\), moderate-fragmented areas a mean value of 4.9 Mg C ha\({}^{-1}\) a\({}^{-1}\) and low-fragmented areas a mean value of 4.6 Mg C ha\({}^{-1}\) a\({}^{-1}\). The mean value therefore increases with the degree of fragmentation. Figure 6: A Scatterplot showing the amount of forest edge area in a forested landscape against the aboveground biomass of forest core area in this landscape. The proportion was calculated by dividing the area of edge forest by the total forest area in a landscape. One pixel corresponds to a 100 km\({}^{2}\) tile in the Amazon. Note that the vertical lines at 0%, 50% and 100% result from the landscape pixels with a sparse amount of forest, and thus a low number of lidar measurements (<20 GEDI shots per 100 km\({}^{2}\)). Typically, a 100 km\({}^{2}\) landscape pixel has about 2000 lidar measurements. The vertical lines at 0% and 100% correspond to landscape pixels in which there are only core areas (0% fraction of forest edge area) or only edge areas (100% fraction of forest edge area). Figure 7: Scatterplot showing the relationship between the aboveground biomass and net primary productivity. Each point represents a 10 \(\times\) 10 km\({}^{2}\) section in which the GEDI shots and the associated forest attributes were averaged and assigned to the fragmentation landscape types (low-fragmented, moderate-fragmented and highly fragmented) based on their edge proportion. For gross primary productivity, we observe slightly higher values in low-fragmented landscapes (Figure S7a and Table 2): 24 Mg C ha\({}^{-1}\) a\({}^{-1}\) for low-fragmented areas and 22 Mg C ha\({}^{-1}\) a\({}^{-1}\) for highly fragmented areas. The mean values of the leaf area index are 4.6 for low-fragmented areas, 4.3 for moderate-fragmented areas and 3.8 for highly fragmented areas (Table 2, Figure S7b). The CV also increases for the areas with higher fragmentation. We also analyzed how the biomass of core forest is influenced by fragmentation (of the landscape). The behavior of the biomass in the core area was then investigated with the percentage of edge per 100 km\({}^{2}\) tile (Figure 6). For landscapes with higher fragmentation, lower biomasses dominate, whereas for landscapes with a low edge proportion, core forests show high biomass. For edge percentages of 90-100%, the biomass values are between 80 and 180 Mg odm ha\({}^{-1}\), whereas the biomass for edge percentages of 0-10% is mainly between 130 and 350 Mg odm ha\({}^{-1}\). From this, we derive that for high biomass values core forests show a dependency on fragmentation (see also Section 4).