## 4 Diversity, Landslide Dynamics, and Mountainscapes
### Plant Traits, Ecosystem Function, Montane Ecosystems, and Landsliding
Using a different approach in the eastern Andes of Peru, [PERSON] and collaborators [200] found that the CWMs for three leaf traits (LMA, non-structural carbohydrates, and percent water) were positively related to elevation, whereas a fourth one (chlorophyll) and FRic were negatively related. This work further showed that FRic was positively related to GPP and net primary productivity (NPP). The closest reference to functional traits retrieved from remotely sensed data in landslide studies makes use of the green-red vegetation (GRVI) and normalized vegetation (NDVI) indices (Files S1 and S2: Table S3) and tree height [201]. [PERSON] et al. [202] used GRVI in combination with back estimations of lateral apparent cohesion contributed by roots to understand slope resistance/susceptibility to intense rainfall events in China. In contrast, NDVI and tree height have been used to characterize vegetation recovery and its role in stabilizing sediment fluxes and capturing carbon, respectively. A common metric used in these studies is the Vegetation Recovery Ratio (VRR; Files S1 and S2: Table S3), representing the vegetation gained over time divided by the vegetation lost after a major landslide-triggering event [203; 204; 205; 206; 207; 208; 209; 210; 211; 212; 213; 214; 215; 216; 217; 218]. Here, vegetation gains and losses are estimated at pixel scales based on NDVI. Some interesting observations, as well as limitations, emerge from this work. First, the variability of NDVI within [210] and among studies (Files S1 and S2: Table S3) can be substantial. Second, the presence of active and inactive landslides, together with the variability of VRR in any given population, suggests that successional trajectories (Figure 1c) are highly variable, which may explain the within-study NDVI variability, e.g., [210]. Third, NDVI\({}_{0}\), VRR, and annual VRR varied among studies, most likely reflecting differences among sites and the time over which recovery was monitored. Fourth, VRR decreases with elevation and slope [206]. Fifth, rainfall-triggered landslides may recover faster than those triggered by earthquakes [213]. Finally, the limited use of ecological principles precludes a deeper understanding of ecosystem-geomorphic interactions. For example, NDVI is not modeled to characterize ecosystem development, nor is it related to variables such as aboveground biomass or tree height, two variables informative of important ecosystem processes. Similarly, the variability of NDVI at the scale of individual landslides may be used to characterize functional diversity; integrated over time and across landslides within a population, it may help understand the contribution of landslides to the diversity of montane ecosystems. In contrast to the aforementioned studies, [PERSON] and collaborators [201] focused on a landslide assemblage to examine changes in vegetation height and aboveground biomass in landslides of different ages along a broad elevation gradient in Peru's Kostipata Valley. These authors combined a 1 m-resolution top-of-canopy height (TCH) raster derived from high-resolution full waveform airborne LiDAR data [219] with mapped landslides. The latter were characterized by TCH's mean (TCH\({}_{\text{mean}}\)) and standard deviation (TCH\({}_{\text{sd}}\)), and TCH was subsequently used to estimate biomass at a 30 m resolution. Over a period of 25 years, TCH\({}_{\text{mean}}\) increased linearly with time; residual vegetation on landslides translated on average into larger TCH\({}_{\text{mean}}\) compared to landslides without residual vegetation. In contrast, in landslides without residual vegetation, the linear relationship between TCH\({}_{\text{sd}}\) and time exhibited a steeper slope than that of landslides with residual vegetation. Aboveground biomass increased linearly with time, and the rate of increase was larger at low elevations, followed by mid and high elevations.