## 3 Results  
### Stepwise Backward Regression  
Results of the stepwise backward regression are presented below in Table 1a,b, and Table 2a,b. Each of the models, regardless of season, time period, and park or buffer, had over 60% of the variation explained by the independent variables. This indicates that the models are successful in explaining the majority of spatial variations in NDVI values. However, parks in each season and period had higher R\({}^{2}\) values than buffers. There was also a greater range in park R\({}^{2}\) values relative to buffers. For parks, between 65% and 69% of the NDVI variation was explained by these models across seasons and time periods. For buffers, between 61% and 62% of the NDVI variation was explained by these models across seasons and time periods. The model varied slightly in the number of variables in the best model, but across all models, country (X1) was never used as an important variable. The intercepts in these models were very low (hovering around zero) due to the small range in which NDVI is measured. The Akaike Information Criterion (AIC) was included to demonstrate exactly how much these models differed, but within each of the season, time periods, and parks or buffers, the lowest AIC was used to select the best model. In comparison across parks and buffers, the parks had lower AIC values, also supporting that the park models performed better overall. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area/Time** & **R\({}^{2}\)** & **Best Model** & **Intercept** & **Akaike’s Criterion** \\ \hline
**Parks DJF 2000–2004** & 0.68 & y–X2 + X4 + X5 + X7 & \(2.346\times 10^{-1}\) & \(-331.36\) \\ \hline
**Buffers DJF 2000–2004** & 0.62 & y–X2 + X4 + X5 + X6 + X7 & \(-4.158\times 10^{-2}\) & \(-276.89\) \\ \hline
**Parks DJF 2005–2008** & 0.67 & y–X3 + X4 + X5 + X7 & \(1.813\times 10^{-1}\) & \(-326.71\) \\ \hline
**Buffers DJF 2005–2008** & 0.62 & y–X2 + X4 + X5 + X6 + X7 & \(-3.596\times 10^{-2}\) & \(-275.83\) \\ \hline
**Parks DJF 2009–2012** & 0.66 & y–X4 + X5 + X7 & \(1.656\times 10^{-1}\) & \(-325.82\) \\ \hline
**Buffers DJF 2009–2012** & 0.61 & y–X2 + X4 + X5 + X6 + X7 & \(-2.330\times 10^{-2}\) & \(-272.69\) \\ \hline
**Parks DJF 2013–2016** & 0.65 & y–X2 + X3 + X4 + X5 + X7 & \(2.502\times 10^{-1}\) & \(-317.62\) \\ \hline
**Buffers DJF 2013–2016** & 0.62 & y–X2 + X4 + X5 + X6 + X7 & \(-3.360\times 10^{-2}\) & \(-277.28\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Stepwise backward regression results including the best model, intercept, and Akaike Criterion for parks and buffers at each of the time periods 2000–2004, 2005–2008, 2009–2012, 2013–2016 in (**a**) December, January and (**b**) February, March, April, and May. The independent variables in these regressions were: country (X1), land cover (X2), area (X3), mean annual precipitation (X4), human population (X5), elevation (X6), and transfrontier (X7 and n = 31 of the 79 parks). A variable results table of the stepwise backward regression are below in Table 1a,b, and Table 2a.b. This shows each of the variables, when they were significant, how they were significant (positive or negative). \begin{table}  
\end{table} 
Table 2: Stepwise backward regression results for parks and buffers in (**a**) December, January, February and (**b**) March, April, and May at each of the time periods 2000–2004, 2005–2008, 2009–2012, 2013–2016 including each model independent variable: country (X1), land cover (X2), area (X3), mean annual precipitation (X4), human population (X5), elevation (X6), and transfrontier (X7 and n = 31 of the 79 parks), positive or negative relationship between the dependent and independent variable (if applicable), and the level of significance of the relationship between the dependent and independent variable. Variable significance is designated by asterisks. More asterisks indicate higher significance: 0 = **** 0.001 = **** 0.01 = **** 0.05 = * 1 = none.