# Community Abundance of Resprouting in Woody Plants Reflects Fire Return Time, Intensity, and Type
## 2 Methods
### Analysis of Relationships between the Incidence of Resprouters and Fire Properties  
We first investigated the relationship between the FRI and the incidence of resprouting. The FRI was log-transformed before the analysis to reduce skewness. We used box plots to display the patterns in different categories of FRI and tested the significance of the mean and median differences between the FRI categories using the pairwise t-test and the non-parametric Kruskal-Wallis test. We compared the combined data from Australia and Europe and the data from each continent separately using the four different estimates of the FRI. The incidence of resprouting could be affected by other aspects of the fire regime, such as the fire intensity, the speed of fire spread, and whether an event was a ground fire or crown fire [7; 29; 30]. The radiative power of fire has been used as a measure of fire intensity [31], but it represents the intensity at the flaming front and is thus difficult to use at the plot level. Here, we used GPP as a surrogate for intensity based on the assumption that high-intensity fires occur in regions with large amounts of fuel [3; 28]. The abundance of herbaceous vegetation is a primary control of whether events are crown fires or ground fires and also determines the speed of fire spread [6]. We used grass cover as a surrogate index for herbaceous vegetation. The GPP data were derived from the P-model [32; 33; 34], an optimality-based light-use efficiency model that simulates GPP as a function of the atmospheric pressure, the atmospheric CO\({}_{2}\) concentration, the air temperature, the vapor pressure deficit (VPD), the incident photosynthetic photon flux density (PPFD), the fraction of incident PPFD absorbed by vegetation (fAPAR), the soil moisture (\(\theta\)), and the C\({}_{3}\)/C\({}_{4}\) vegetation fraction. The model was run using the atmospheric pressure calculated using elevation data from WFDEI [35], climate data obtained from CRU TS4.04 [36], fAPAR from GIMMs 3 g [37], and the C\({}_{3}\)/C\({}_{4}\) vegetation fraction from [38]. The P-model has been shown to produce reliable estimates of GPP compared to site-based flux tower measurements [32; 33; 34] while requiring the calibration of only two parameters. Thus, it provides more transparent estimates than other GPP products. Simulated monthly GPP values at a 0.5\({}^{\circ}\) resolution over the period from 2001 to 2016 were summed to derive the annual total GPP. Since the year-to-year variation in GPP was small, we used the mean annual total GPP over the 16-year interval. We converted the data to 0.25\({}^{\circ}\) via bilinear interpolation for consistency with the FRI estimated using the remotely sensed products. The grass cover was obtained from ESACCI LC data [39]. The raw data had a 300 m spatial resolution on an annual basis from 2001 to 2016. We used the fractional grass cover to calculate the mean grass cover over these years and then converted this to 0.25\({}^{\circ}\) via bilinear interpolation. The year-to-year variations over the 16-year interval were also examined for grass cover. GPP and grass cover data were extracted for each of the sPlotOpen plots according to their geographic locations. We investigated the relationships between the FRI, GPP, and grass cover using a binomial generalised linear model (GLM). GLMs are suitable for analyses when the response variables have highly non-Gaussian error distributions and allow quantification of the independent effects of multiple predictors, even when they are partially correlated with one another [40; 41]. We applied log-transformations to GPP and the FRI to reduce skewness. We calculated the variance inflation factor (VIF) for each variable in the GLM to ensure that the regression coefficients were not inflated due to multicollinearity [42]. We used partial residual plots, which showed the effect of each predictor when the others were held constant [43], to understand the relationship between each predictor and the proportion of resprouters.