Interannual variability in fire extent has been identified as one of the most important factors behind variations in atmospheric carbon dioxide and global aerosols (Patra et al., 2005; Schultz et al., 2008). Recently, continental-scale modeling exercises have aimed to characterize this interannual variability by relating it to variability in environmental drivers of fire (Williams et al., 2007; Lehstenet al., 2009). A variety of human, environmental and climatic factors influence the amount of burning in southern African savanna and grassland ecosystems (Phillips, 1930; Trollope, 1984; Van Wilgen. & Scholes, 1997; Archibald et al., 2009).
Similarly, large fires associated with periods of extreme fire are responsible for a disproportionate share of the total burnt area in Australian savannas (Yates et al., 2008), and high fire hazard indices result in more fires in the Brazilian Cerrado (Hoffmann et al. al., 2002). Satellite data: The collection 5 MODIS global burnt area product (MODB45) (Royet al., submitted) was used as a regional representation of the extent of the fire. This burnt area product was developed in southern Africa and has been extensively tested in the region (Roy et al., 2008; Roy & Boschetti, 2009).
RESULTS
Correlations with dry season length were all positive, but rarely significant by themselves. There also appeared to be a weak relationship with last year's burned area, indicating that the covariance structure of the mixed effects model had to take this into account. The linear mixed effect analysis gave no indication that the slope of the precipitation response was different between the 10 parks studied (question 1).
The most likely formulation of the model had annual % area burned as a function of accumulated precipitation with an average area burned that varied between parks (variable intercept) but a constant response (slope) across all parks (Table 3 A). Including the length of the dry season as a predictor improved the precipitation model (Question 2), but there was no indication that the number of days with high fire danger should be included in a predictive model for burned area. A model with seasonality and accumulated precipitation had a weighting of 0.7 compared to 0.3 with accumulated precipitation alone (Table 3 B), but none of the models with FDI had BIC weighting > 0.005.
The LME therefore did not support the inclusion of a higher-level rainfall effect, but a closer examination of the accumulated rainfall - burned area regression did indicate that average annual rainfall influences the strength of the relationship. The model developed from the field data implies that areas in southern Africa that have high variability in rainfall and the length of the dry season should be associated with more variable fire regimes. It appears that the climatic influence on burned area that was so evident in the field data has been significantly reduced over a large part of the southern African region (Question 4).
When run on grid cells with the same environmental characteristics as the field data, the null model (no climate driver) was the most likely model 74 percent of the time, although seasonality was shown to be significant in 28% of the model runs. Accumulated precipitation was never a significant factor in any of the models considered, which is a notable departure from the results of the protected areas analysis.
DISCUSSION
In a mixed effects analysis on the regional data, the seasonal response coefficient was 0.13, which is only slightly lower than the 0.23 for the field data analysis, while the coefficient for accumulated rainfall was close to zero (compared to 0.69 for the field data ). - see table 4 and equation 2). High numbers of domesticated grazers across much of the region, along with a more fragmented landscape where fires cannot spread, could explain this. Outside of protected areas, fires are much smaller (Archibald et al., submitted) and it is the number of fires, rather than the size of individual fires, that determines how much of the landscape burns.
Thus, it appears that one of the main effects humans have on fire regimes is to reduce the size of large wildfires, and consequently reduce the impact that climate variables - and rainfall in particular - have on fire regimes in the region. The regional data did provide evidence that accumulated rainfall, seasonality and FDI days were associated with variation in burned area between years, and that the relative importance of these three climatic factors changes as one moves from arid to wetter parts of the region. These results should be interpreted with an understanding of the limitations of the data used.
Eight years of burned area records are sufficient to describe fire regimes in systems with fire return periods of 2–3 years, but for drier parts of southern Africa, where fire return periods are more in the order of 5–10 years, the eight years. satellite data product may not provide reliable indications of the true variability in the system. However, it is reassuring that results from a reduced 8-year subset of the long-term fire data were not significantly different from results using the entire time series (Appendix S3), indicating that 8 years of. Such burning programs are already being implemented in Kruger National Park - where rangers are given monthly burning targets based on the rainfall of the previous two growing seasons (Van Wilgen et al., 2008).
This analysis shows that not only the size of the fire, but also the variability between years has been substantially reduced compared to what would be expected based on the climate in large parts of the region. The current understanding is that climate change will increase the incidence of extreme fires in many parts of the world (IPCC2007).
ACKNOWLEDGMENTS
Parks are ordered in terms of increasing rainfall: Etosha National Park = ENP; The four regions of the Kruger National Park = north, northwest, central and south; Pilanesberg Game Reserve = PGR; Hwange National Park = HWG; Mkuze Game Reserve = MGR;. The region for which the field data is representative is marked in black: it characterizes the seasonal range and extent of high Fire Danger Index (FDI) days quite well, but does not give a good representation of the higher rainfall savannas. Parks are presented in order of increasing average annual rainfall: the explanatory power of the relationship (r2) appears to decrease as rainfall increases.
A mixed-effects analysis (Table 3) shows no difference in the slope of the relationship between parks. The parks are ranked in terms of increasing rainfall: Etosha National Park = ENP; The four regions of Kruger National Park = north, farnorth, central and south; Pilanesberg reserve game. A mixed-effects analysis (Table 3) shows no difference in the slope of the relationship between parks.
Only grid cells within the environmental boundaries of the field data (see Fig. 3) are included. ENP = Etosha National Park; north, northwest, central and south = the four regions of the Kruger National Park; PGR = Pilanesberg Game Reserve; HWG = Hwange National Park; MGR = Mkuze Game Reserve; umf, hlu = the two regions of Hluhluwe iMfolozi Park. Spatial autocorrelation is tested by including the value of the nearest grid cell as a factor in the models.
Two different LME models were run for low and high rainfall regions to test whether the importance of the input variables changes with the rainfall gradient. In low-rainfall regions, the null model was consistently preferred over the other models, but the model with seasonality (the extent of the dry season) was equally significant 46% of the time. In regions with high rainfall, the model incorporating seasonality performed best, but because this region is quite small, the spatial correlation term was significant 32% of the time.
BIC = Bayes Information Criterion, Rain = Accumulated Precipitation, Season = Extent of Dry Period (Precipitation Concentration), FDI = Number of Days with High FDI, Nearest = Annual Burned Area of Nearest Grid.
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SUPPLEMENTARY MATERIAL
