## 3 Results
### Stepwise Backward Regression
negative, if applicable), and what statistical significance level (\(p<0.001\) = *****, 0.001 = ***, 0.01 = **, 0.05 = *, or no * for no significance). In every model, the most important independent variable was MAP. MAP had a positive relationship with NDVI, meaning that higher precipitation resulted in higher NDVI values. MAP consistently had the highest level of significance (Table 1a,b, and Table 2a,b, \(<\)0.001) across each model. This is expected considering that savannas are water-limited systems. The second most important independent variable was mean population density. Mean population density had a negative relationship with NDVI. This means where there were higher human populations, there were lower NDVI values, likely due to human use of the landscape and potential subsequent degradation. Mean population density was significant across most of the models, but tended to be most significant in the buffer zones (Table 1a,b, and Table 2a,b, \(<\)0.001). This is due to higher densities of people in the buffer zones than in the parks. In parks, mean population density was significant at a relatively lower significance level and only in DJF 2000-2004 (0.05) and 2005-2008 (0.05), and in MAM 2000-2004 (0.01) and 2013-2016 (0.05). The relationship between population density and vegetation health is weaker in parks than in buffer zones. Over time, the level of significance has decreased, as parks have become better protected. Another important variable was whether or not a park was part of a transfrontier conservation area. Transfrontier areas had a positive relationship with NDVI values, so where there was a transfrontier conservation area, NDVI values were higher. This might be expected because areas of cooperation may lead to better management, with healthier vegetation. This variable had a high level of significance (Table 1a,b, and Table 2a,b, 0.001) across parks and buffers in DJF, except for parks in the earliest periods (2000-2004 and 2005-2008) when these transfrontier areas were not yet strongly established. Across parks and buffers in MAM, transfrontier areas had a mid level of significance (Table 1a,b, and Table 2a,b, 0.01), except for buffers in 2013-2016, which was a higher level of significance (Table 1a,b, and Table 2a,b, 0.001). This result is likely showing a strong significance due to the firm establishment of these transfrontier areas in this period, which helps to mitigate some human impact. This may be combined with the high level of biomass at the end of the rains then, when NDVI values are at a maximum. Elevation had a complicated relationship with NDVI. Mean elevation had a positive relationship with NDVI only in the buffer zones of DJF in 2000-2004 and 2013-2016 (0.05 level of significance), so with higher elevation there were higher NDVI values. This may be caused by humans not utilizing the landscape at higher elevations. Mean elevation had a negative relationship with NDVI only in parks of MAM in 2000-2004 (0.01) and 2013-2016 (0.05), so with higher elevation there were lower NDVI values. However, this may be due to a lack of vegetation at higher elevation in some parks. Overall, this was a much more mixed and weaker result. Area was not as significant as was hypothesized prior to the analysis. It was only significant in parks at the 0.05 level in DJF 2005-2008 (Table 1a,b, and Table 2a,b). Area had a negative relationship to NDVI, meaning for this one model, the larger the park the lower the NDVI values, which was unexpected. Land cover was significant in some cases. In DJF in buffer zones from 2000-2004, 2005-2008, and 2009-2012 land cover was significant at the 0.05 level. In MAM, land cover was significant (varying between 0.05, 0.01, and 0.001) across parks and buffers in all time periods. This suggests that land cover plays a more important role in NDVI values later in the wet season when the NDVI values themselves are much higher.