SAVANNA LAB
The whole world is a savanna; forests and grasslands are just special cases
The whole world is a savanna; forests and grasslands are just special cases
We study how drylands plant communities function and interact with climate, soils, fire, and herbivory. Namely, what controls woody plant populations in temperate and tropical savannas? Which processes (relating to climate, soils, fire, invertebrate and vertebrate herbivores) affect plant populations, community structure, and ecosystem function at meter to landscape scales? We answer these questions by observing the underlying shifts in structure and plot-scale dynamics of plant communities in response to climate, fire, and herbivory. Currently, one of our special projects is researching the role of termite mounds in landscape-scale hydrology and function. Our research takes place in both our greenhouses and in field sites located in the Jornada long-term ecological research (JRN-LTER) site, Eastern, Western, and Southern Africa, and occasionally other places around the world.
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Lab members involved : Julius Anchang, Wenjie Ji, Qiuyan Yu, Wade Ross, Caroline Toth, Robert Wojcikiewicz, Brianna Lind, Lara Prihodko, Niall Hanan
Our remote sensing techniques include the use of field-based UAVs, and both airborne and satellite remote sensing. We use a variety of optical, microwave, and lidar approaches at many scales ranging from cm-scale UAV retrievals of canopy structure at intensive field sites, to km-scale analysis at regional and continental scales. We use remote sensing to analyze tree-grass (woody-herbaceous) systems, with particular emphasis on the partitioning of woody and herbaceous vegetation components (e.g. woody and herbaceous leaf area index), and the estimation of woody canopy cover (e.g. height and biomass).
Related publications: links
Lab members involved: Julius Anchang, Wenjie Ji, Qiuyan Yu, Kaboro Samasse, Wade Ross, Robert Wojcikiewicz, Brianna Lind, Lara Prihodko, Niall Hanan
Our research includes analysis of carbon stocks (biomass) and fluxes in dryland and savanna vegetation, including short-term (daily and seasonal) and longer-term (annual and decadal) changes associated with physiology, demographics, land use and land cover change, shrub encroachment, and woody population dynamics. These themes are approached using a combination of greenhouse and field experiments, remote sensing, and modeling. Our water-related research includes investigation of the role of rainfall, biotic, and edaphic process in controlling evapotranspiration and energy balance, vegetation dynamics, and surface water for livestock, wildlife, and pastoral communities.
Related publications: links
Lab members involved: Qiuyan Yu, Wade Ross, Lara Prihodko, Niall Hanan
Our modeling activities include the use and analysis of low dimensional ecological models describing vegetation community dynamics. These vegetation dynamics are impacted by climate, plant competition, fire, and herbivory and can be analyzed using 2-dimensional dynamic vegetation (tree-grass) models and even more complex 2- and 3-dimensional land surface-atmosphere exchange models. Our modeling approaches inform our understanding of the ecology and function of dryland and savanna systems.
Related publications: links
Lab members involved: Wenjie Ji, Qiuyan Yu, Wade Ross, Brianna Lind, Lara Prihodko, Niall Hanan
Shrub encroachment has been identified as a common phenomenon in not only the southwestern US drylands, but also globally. However, the rates and underlying causes, whether related to climate change, land use and disturbance, or other processes are not always clear (or the same). We are investigating both the rates and causes of shrub encroachment at the Jornada Basin LTER site and beyond, using a combination of both field and greenhouse experiments and regional-global scale observations via meta-analysis and remote sensing to better understand where and why shrub encroachment occurs.
Related publications: links
Lab members involved: Wenjie Ji, Caroline Toth, Robert Wojcikiewicz, Niall Hanan
Field experiments, remote sensing, geospatial analysis, and modeling generate large and complex datasets. We use high performance and cloud-based computing resources to process and analyze the large data associated with large-area and high-resolution remote sensing by using machine learning, simulation, and statistical modeling. We are particularly interested in interpretable machine learning focused on accurate prediction with enhanced understanding of underlying processes and ecological insight.
Related publications: links
Lab members involved: Julius Anchang, Wenjie Ji, Qiuyan Yu, Wade Ross, Lara Prihodko, Niall Hanan