1 Vegetation attributes derived from species and plant functional types (PFTs) directly or indirectly drive the carbon (C) cycle in peatlands. However, anthropogenic-based disturbances may alter petland soil-plant interactions and their ability to sequester carbon. Likewise, it is unclear how the soil-plant linkages among different soil C decomposition-based pools and plant attributes vary under disturbance conditions. 2 We aimed to assess how anthropogenic disturbances affect the relationships between aboveground vegetation attributes and belowground C pools in a peatland located in Northern Patagonia, Chile. We further evaluated if attributes derived from PFTs are better suited to predict soil C pools than attributes derived from species. We used structural equation modeling and regression analyses to explore these differences. 3 We found that undisturbed peatland has more soil-plant significant relationships between soil C pools and vegetation attributes, yielding higher predictive accuracies than disturbed areas. The species-based attributes yielded consistently better results predicting soil C pools than PFT-based attributes. However, PFT-based information showed significant interactions with the highly-decomposed C pools in the undisturbed peatland. Likewise, plant height and diversity were only significant with C pools in the undisturbed peatland. 4 We observed that water-logged plant communities have different soil-plant interactions than dryer communities. These differences were observed in both areas but were higher in the disturbed peatland, making it impossible to find meaningful soil-plant relationships across vegetation types and taxa. 5 Our results highlight the importance of accounting for disturbance or management when linking vegetation attributes to soil C pools in peatlands. This implies that up-to-date extensive monitoring of peatland disturbances is needed to accurately monitor soil C attributes at the regional level using vegetation as proxies. We also need to aggregate species into specific plant functional types that hold these soil-plant interactions across landscapes, regions, and disturbances to generalize the soil-plant relationships accurately.

Many remote sensing-based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four remote sensing based ET models: Priestley & Taylor Jet Propulsion Laboratory (PT-JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman-Monteith Mu model (PM-MOD), and Penman-Monteith Nagler model (PM-VI). ET was predicted satisfactorily by all four models, with correlations consistently higher (R²>0.6) for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of PBIAS (-10