ALBERT

Description: Vegetation structure in water-limited systems is to a large degree controlled by ecohydrological processes, including mean annual precipitation (MAP) modulated by the characteristics of precipitation and geomorphology that collectively determine how rainfall is distributed vertically into soils or horizontally in the landscape. We anticipate that woody canopy cover, crown density, crown size, and the spatial distribution of woody plants in the landscape, will vary across environmental gradients. Exploring these trends can extend our knowledge of how semi-arid vegetation structure is constrained by rainfall regime, soil type, topography, and disturbance processes such as fire. However, a lack of data on woody vegetation structure across African savannas has so far prevented a thorough analysis of their relationships with abiotic factors. Using high spatial resolution imagery, a flexible classification framework, and a crown delineation method, we extracted woody vegetation properties from 876 sites spread over African savannas. At each site, we estimated woody cover, mean crown size, crown density, and the degree of aggregation among woody plants. This enables us to elucidate the effects of rainfall regimes (MAP and seasonality), soil texture, slope, and fire frequency on woody vegetation properties. We estimate trends in mean crown size across the African savanna rainfall gradient and show that previously documented increases in woody vegetation cover with rainfall is more consistently a result of increasing crown size than increasing density of woody plants. We also find a unimodal relationship between mean crown size and sand content suggesting that maximal savanna tree-sizes do not occur in either coarse sands or heavy clays. When examining the occurrence of periodic vegetation patterns (PVPs), we find that the same factors that contribute to the formation of PVPs also correlate with higher levels of woody plant aggregation elsewhere in savannas and that rainfall seasonality plays a key role for the underlying processes.

Description: Vegetation structure in water-limited systems is to a large degree controlled by ecohydrological processes, including mean annual precipitation (MAP) modulated by the characteristics of precipitation and geomorphology that collectively determine how rainfall is distributed vertically into soils or horizontally in the landscape. We anticipate that woody canopy cover, crown density, crown size, and the level of spatial aggregation among woody plants in the landscape will vary across environmental gradients. A high level of woody plant aggregation is most distinct in periodic vegetation patterns (PVPs), which emerge as a result of ecohydrological processes such as runoff generation and increased infiltration close to plants. Similar, albeit weaker, forces may influence the spatial distribution of woody plants elsewhere in savannas. Exploring these trends can extend our knowledge of how semi-arid vegetation structure is constrained by rainfall regime, soil type, topography, and disturbance processes such as fire. Using high-spatial-resolution imagery, a flexible classification framework, and a crown delineation method, we extracted woody vegetation properties from 876 sites spread over African savannas. At each site, we estimated woody cover, mean crown size, crown density, and the degree of aggregation among woody plants. This enabled us to elucidate the effects of rainfall regimes (MAP and seasonality), soil texture, slope, and fire frequency on woody vegetation properties. We found that previously documented increases in woody cover with rainfall is more consistently a result of increasing crown size than increasing density of woody plants. Along a gradient of mean annual precipitation from the driest (

Description: Black-tailed prairie dogs (Cynomys ludovicianus) have been described as a keystone species and are important for grassland conservation, yet many concerns exist over the impact of prairie dogs on plant biomass production and consequently livestock production. The ability to map plant communities in pastures colonized by prairie dogs can provide land managers with an opportunity to optimize rangeland production while balancing conservation goals. The aim of this study was to test the ability of random forest (RF) models to classify five plant communities located on and off prairie dog towns in mixed-grass prairie landscapes of north central South Dakota, assess the stability of RF models among different years, and determine the utility of utilizing remote sensing techniques to identify prairie dog colony extent. During 2015 and 2016, Pleiades satellites were tasked to image the study site for a total of five monthly collections each summer (June–October). Training polygons were mapped in 2016 for the five plant communities and used to train RF models. Both the 2015 and 2016 RF models had low (1 %) out-of-bag error rates. However, comparisons between the predicted plant community maps using the 2015 imagery and one created with the 2016 imagery indicate over 32.9 % of pixels changed plant community class between 2015 and 2016. The results show that while RF models may predict with a high degree of accuracy, overlap of plant communities and interannual differences in rainfall may cause instability in fitted models. A final RF model combining both 2015 and 2016 data yielded the lowest error rates and was also highly accurate in determining prairie dog colony boundaries.