# Community Abundance of Resprouting in Woody Plants Reflects Fire Return Time, Intensity, and Type
## 3 Results
### Relationships between the Incidence of Respruoting, GPP, and Grass Cover  
The three predictor variables used in the GLM were not significantly correlated, with pairwise correlation coefficients that were always <0.2 (Figure S3). The GLM analysis showed that GPP had a significant positive relationship with the proportion of resprouters (z-value = 9.73), while the FRI and grass cover had significant negative relationships with the proportion of resprouters, with z-values of \(-\)9.43 and \(-\)7.37, respectively (Table 2). The partial residual plots (Figures 3 and S4) also illustrate these relationships. The VIFs of the FRI, GPP, and grass cover were all around 1.00, indicating that they each made an independent  
Figure 2: Changes in the incidence of resprouting woody plants (P1(resprouters)) as a function of the fire return interval (FRI). The FRI was derived from MODIS MCD64 CMQ. Individual plot values are shown as blue dots, the red dots show the means, the black lines show the medians, and the boxes show the interquartile ranges of the proportions of resprouters. The significance of the mean and median differences in different FRI categories are indicated by different letters in red (mean) and black (median). The number of observations in each category is indicated in brackets. contribution to the GLM model, confirming the partial correlation results. The year-to-year changes in both GPP and grass cover (Figure S5) were small; thus, using annual mean values of GPP and grass cover in the GLM did not impact the robustness of the results. The overall performance of the GLM was modest (Cragg-Uhler pseudo-R\({}^{2}\) < 0.10), but the relationships between GPP, the FRI, and grass cover and the proportion of resprouters were the same in Australia and Europe (Table S2) and were also robust when different sources of information were used for the calculation of the FRI (Table S3). The classification of resprouting ability was largely based on responses to fire (92% of all species with information), but we also included responses to other disturbances when they were known. Model performance and the relationship of each predictor to the proportion of resprouters were unaffected by whether resprouting ability was based only on the response to fire (Table S4). The same relationships were also seen when the proportion of resprouters was estimated relative to the total cover of all woody species (P\({}_{2}\) resprouter; Table S5), although the overall model performance, as measured using the Cragg-Uhler pseudo-R\({}^{2}\), which decreased from 0.05 to 0.01, was worse. Thus, it would seem that the lack of information about some species is not a reflection of a bias in the recording of resprouters compared to non-resprouters. Despite the fact that the individual predictors were significant and that the relationships make intuitive sense, the overall R\({}^{2}\) of the model was low (Cragg-Uhler pseudo-R\({}^{2}\) < 0.10). Analyses of subsets of the plot data that were classified according to the proportion of species for which information on resprouting ability was available (Table S6) showed that model performance improved as the relative known proportion increased. This suggests that the low R\({}^{2}\) of the final model was partly a reflection of the lack of information about the resprouting abilities of many species. The wide scatter in the predictor variables (Figure 3) could also contribute to the poor model performance. The relatively short period covered by all the observations was a major cause of uncertainty, particularly in the case of the estimation of the \begin{table}
\begin{tabular}{c c c c c} \hline  & **Estimate Coefficient** & **Standard Error** & **z-Value** & **VIF** \\ \hline (Intercept) & 1.58 & 0.19 & 8.39 *** & \\ Fire return & \(-\)0.14 & 0.02 & \(-\)9.43 *** & 1.01 \\ interval & 0.45 & 0.05 & 9.73 *** & 1.02 \\ GPP & 0.45 & 0.05 & 9.73 *** & 1.02 \\ Grass cover & \(-\)1.70 & 0.23 & \(-\)7.37 *** & 1.03 \\ \hline \end{tabular}
\end{table} 
Table 2: Summary statistics from the final GLM.