Drivers, impacts, and feedbacks of woody/grass dynamics and grazing quality in the central Kalahari

Authors: Alex Marden*, University of Texas - Austin, Thoralf Meyer, University of Texas - Austin, Thomas Christiansen, University of Texas - Austin
Topics: Biogeography, Coupled Human and Natural Systems, Remote Sensing
Keywords: Keywords: semi-arid savanna; grazing quality; woody plant encroachment; Kalahari; socioecological interactions
Session Type: Paper
Day: 4/11/2018
Start / End Time: 1:20 PM / 3:00 PM
Room: Iberville, Marriott, River Tower Elevators, 4th Floor
Presentation File: No File Uploaded

Climate, grazing, soil and fire have all been identified as important drivers of woody encroachment in semi-arid savanna ecosystems. The central Kalahari encompasses vast semi-arid savannas that contain sizable populations of livestock and some of the earth’s largest remaining terrestrial wildlife habitats, both of which are vulnerable to changes in grazing quality. A study area was chosen at the intersection of the Central Kalahari Game Reserve and the Central District where the country’s largest wildlife protection area meets the district with the largest cattle population. Species-level woody vegetation data were collected at 13, 100 x 2 meter belt transects, and species level grass, grass biomass, and ground cover was collected in 1 square meter plots across the transects. Post burn assessments were conducted for two of the sites following a fire event. Species level data for woody and grass species as well as pre-and post-burn measurements provided detailed grazing quality measurements that reveal a high range of grazing quality and quantity throughout the study area. To explore the relationships, feedbacks and influence of drivers on the diverse grazing conditions, remotely sensed fire history time series and EVI, NDVI, and SAVI time series were produced across multiple scales and sensors to measure fire, productivity and seasonality. Grazing stocking numbers were used as measurements of grazing intensity. Linear regression analyses were performed using these in situ and remotely sensed data to measure the relationships of the drivers on grazing quality and test the effects of scale on these analyses.

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