Supporting Online Material for

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1 Supporting Online Material for The Global Extent and Determinants of Savanna and Forest as Alternative Biome States A. Carla Staver,* Sally Archibald, Simon A. Levin *To whom correspondence should be addressed. Published 14 October 2011, Science 334, 230 (2011) DOI: /science This PDF file includes: Materials and Methods Figs. S1 and S2 Table S1 References

2 METHODS We analyzed patterns of tree cover with relation to climate (mean annual rainfall and dry season length) and fire frequency from satellite data and with relation to soils from global datasets. Tree cover and fire frequency data were available from MODIS satellite reflectance data at a 500m resolution. Mean annual rainfall and dry season length were derived from the Tropical Rainfall Measuring Mission best-estimate precipitation product (TRMM 3B43), which has a resolution of 0.25 degrees (approx. 28km x 28km at the equator). Tree cover: Percentage tree cover was calculated from the 500m MOD44B Collection 3 product (35). This product gives percent canopy cover at a scale and resolution appropriate for discriminating regional patterns (8,36). Root mean square error of the entire global dataset is estimated at 9.1% but decreases substantially when data are averaged over slightly larger scales (35). The product was only calibrated against trees above 5m tall and may underestimate shrubby species. Fire frequency: We used the monthly MCD45A1 burnt area product to derive an estimate of fire frequency (37). MCD45A1 uses change detection procedures based on a bi-directional reflectance distribution function (BRDF) to identify burn scars in the landscape and identifies burnt areas in Africa with high accuracy (R 2 =0.75) (37). For this analysis, monthly data layers from 2000 to 2010 were combined to calculate total number of times individual pixels burned over the time period. Pixels where more than 70% of the observations were invalid (85 out of 120 observations invalid) were discarded. 1

3 Rainfall and seasonality: The TRMM combines satellite-data, rain gauge data, and precipitation models to produce a best estimate of global precipitation at hourly to annual scales. A validation over West Africa demonstrated a root mean square error of 0.7mm per day and a zero bias (38). In this analysis, monthly precipitation rates were summed to produce annual rainfall for each year ( ) and then averaged to estimate mean annual rainfall (MAR); dry season length was determined by calculating the number of months in which 30% of total annual precipitation fell for 1998 through 2010 and then averaging across years. Soils: Soils data were derived from the Food and Agriculture Organization s Harmonized World Soil Database (FAO HWSD), which provides a global best estimate of soil physical and chemical properties from a synthesis of the Soil Map of the World, Soils and Terrain Programme regional databases, and the World Inventory of Soil Emission Potential (WISE) database. Over the spatial extent of this study, physical and chemical soil properties from the FAO HSWD covary; for this reason, we have chosen topsoil (0-30cm) sand fraction as an easily interpretable soil physical property proxy. The centroid of each 0.25 degree TRMM pixel was used to create a regular grid of sampling points over sub-saharan Africa, Australia and South East Asia, and South America. Fire and tree data were re-projected to an Albers equal area projection and degraded to yield data at a 25x25km resolution. The FAO percent sand values were rasterised to a 1km resolution, and then similarly averaged to the same 0.25 degree grid as the fire and tree data. Because fire and tree cover vary at local scales, whereas climatic variables like rainfall vary at regional to global scales, the scale of analysis could be 2

4 expected to impact findings. However, a sensitivity analysis in Africa (6) using scales from 500m to 250km showed that patterns are scale insensitive, so we restricted this analysis to an approximately 25km scale. Data were excluded to restrict our analysis to areas with a minimum of human impact and where neither elevation nor cold is likely to play a role in determining tree cover. At high elevation, temperature is likely to play a significant role in determining tree cover. Given the focus herein on tropical and sub-tropical savannas and forests, we have excluded these areas. Impacted sites were identified using the Global Land Cover 2000 product (GLC2000) (36) and were excluded where more than 10% of a 25x25km grid square had experienced intensive human impacts (codes: and 22) or were bare or flooded (codes: 15 and 19-21). Grid squares with any area with elevation greater than 1200m or less than 0m were also excluded using the elevation layer from the FAO HWSD. We also excluded sites with winter rainfall, because winter rainfall, which is generally spatially restricted, tends to result in Mediterranean shrubland type biomes, possibly because C4 (and even C3) grasses are less favored. We have excluded these because they appear to behave as phenomenologically distinct systems. These have been defined as having a ratio of temperature ( o C) during the driest month to temperature during the wettest month of less than 1.25, a likely conservative definition. Temperature data were taken from the WorldClim dataset (37). After removing both invalid fire data and areas of high impact, high elevation or winter rainfall, we were left with a total of 18,978 data points for Africa, 7,467 valid data points for Australia and South East Asia, and 3,577 valid data points for South America. Data were then sub-sampled after they were extracted for all analyses to minimize biases 3

5 introduced from non-uniform rainfall distributions and to standardize sample sizes across continents as much as possible. From each continent, we randomly extracted 100 points from every 100mm range of rainfall (i.e. 100 points with rainfall from 0mm-100mm, 100 points with MAR from 100mm-200mm, etc.), yielding a total of 2,064 data points for Africa, 1,611 valid data points for Australia and South East Asia, and 2,230 valid data points for South America. Table S1. Results of generalized additive models fitting tree cover to rainfall (MAR), fire presence, dry season length (DSL) and soil sand content. N=5905 for the entire table. Statistics Added factor improved fit? Model R 2 GCV Factor F or t df p F df p tree cover ~MAR MAR <0.001 fire pres./abs tree cover 5887, MAR fire < <0.001 ~MAR fire 8.5 MAR no fire <0.001 fire pres./abs tree cover MAR fire < , ~MAR fire MAR no fire < < DSL DSL fire <0.001 DSL no fire tree cover ~MAR fire DSL sand tree cover ~MAR fire DSL sand continent fire pres./abs MAR fire <0.001 MAR no fire <0.001 DSL fire <0.001 DSL no fire sand fire <0.001 sand no fire <0.001 fire pres./abs continent MAR fire Af <0.001 MAR no fire Af <0.001 MAR fire Au <0.001 MAR no fire Au <0.001 MAR fire SA <0.001 MAR no fire SA <0.001 DSL fire Af <0.001 DSL no fire Af DSL fire Au <0.001 DSL no fire Au DSL fire SA <0.001 DSL no fire SA sand fire Af <0.001 sand no fire Af <0.001 sand fire Au <0.001 sand no fire Au sand fire SA <0.001 sand no fire SA , , 62.9 <0.001 <

6 Figure S1. Rainfall and dry season length frequency distributions of savannas (< 55% tree cover; grey bars) and forests ( 55% tree cover; hashed black bars). Figure S2. Tree cover v. sand fraction within the climate envelope in which both savanna and forest are possible (1000mm < MAR < 2500mm; 7 months < dry season length), colored by rainfall (darkest colors = highest rainfall). 5

7 References 1. A. C. Staver, S. Archibald, S. Levin, Tree cover in sub-saharan Africa: Rainfall and fire constrain forest and savanna as alternative stable states. Ecology 92, 1063 (2011). 2. C. E. Lehmann, S. A. Archibald, W. A. Hoffmann, W. J. Bond, Deciphering the distribution of the savanna biome. New Phytol. 191, 197 (2011). 3. M. Sankaran et al., Determinants of woody cover in African savannas. Nature 438, 846 (2005). 4. G. Bucini, N. Hanan, A continental-scale analysis of tree cover in African savannas. Glob. Ecol. Biogeogr. 16, 593 (2007). 5. M. Sankaran, J. Ratnam, N. Hanan, Woody cover in African savannas: The role of resources, fire and herbivory. Global Ecol. Biogeogr. 17, 236 (2008). 6. M. Swaine, W. Hawthorne, T. Orgle, The effects of fire exclusion on savanna vegetation at Kpong, Ghana. Biotropica 24, 166 (1992). 7. A. Moreira, Effects of fire protection on savanna structure in central Brazil. J. Biogeogr. 27, 1021 (2000). 8. W. Bond, What limits trees in C 4 grasslands and savannas? Annu. Rev. Ecol. Syst. 39, 641 (2008). 9. S. Archibald, D. Roy, B. van Wilgen, R. Scholes, What limits fire? An examination of drivers of burnt area in Southern Africa. Glob. Change Biol. 15, 613 (2009). 10. S. Pueyo et al., Testing for criticality in ecosystem dynamics: The case of Amazonian rainforest and savanna fire. Ecol. Lett. 13, 793 (2010). 11. R. Williams, G. Duff, D. Bowman, G. Cook, Variation in the composition and structure of tropical savannas as a function of rainfall and soil texture along a large-scale climatic gradient in the Northern Territory, Australia. J. Biogeogr. 23, 747 (1996). 12. P. G. Cruz Ruggiero, M. A. Batalha, V. R. Pivello, S. T. Meirelles, Soil-vegetation relationships in cerrado (Brazilian savanna) and semideciduous forest, southeastern Brazil. Plant Ecol. 160, 1 (2002). 13. S. P. Good, K. K. Caylor, Climatological determinants of woody cover in Africa. Proc. Natl. Acad. Sci. U.S.A. 108, 4902 (2011). 14. E. Veenendaal, O. Kolle, J. Lloyd, Seasonal variation in energy fluxes and carbon dioxide exchange for a broad-leaved semi-arid savanna (Mopane woodland) in southern Africa. Glob. Change Biol. 10, 318 (2004). 15. D. Cahoon Jr., B. Stocks, J. Levine, W. Cofer III, K. O Neill, Seasonal distribution of African savanna fires. Nature 359, 812 (1992). 16. Materials and methods, as well as other supporting material, is available on Science Online.

8 17. H. Prins, H. van der Jeugd, Herbivore population crashes and woodland structure in East Africa. J. Ecol. 81, 305 (1993). 18. A. C. Staver, W. J. Bond, W. D. Stock, S. J. Van Rensburg, M. S. Waldram, Browsing and fire interact to suppress tree density in an African savanna. Ecol. Appl. 19, 1909 (2009). 19. S. Archibald, W. Bond, W. Stock, D. Fairbanks, Shaping the landscape: Fire-grazer interactions in an African savanna. Ecol. Appl. 15, 96 (2005). 20. M. A. Cochrane et al., Positive feedbacks in the fire dynamic of closed canopy tropical forests. Science 284, 1832 (1999). 21. J. Balch et al., Negative fire feedback in a transitional forest of southeastern Amazonia. Glob. Change Biol. 14, 2276 (2008). 22. J. Keeley, P. Rundel, Fire and the Miocene expansion of C 4 grasslands. Ecol. Lett. 8, 683 (2005). 23. T. Desjardins, A. C. Filho, A. Mariotti, C. Girardin, A. Chauvel, Changes in the forest-savanna boundary in Brazilian Amazonia during the Holocene revealed by stable isotope ratios of soil organic carbon. Oecologia 108, 749 (1996). 24. K. J. Willis, L. Gillson, T. M. Brncic, How virgin is virgin rainforest? Science 304, 402 (2004). 25. F. E. Mayle, R. P. Langstroth, R. A. Fisher, P. Meir, Long-term forest-savannah dynamics in the Bolivian Amazon: Implications for conservation. Philos. Trans. R. Soc. London Ser. B 362, 291 (2007). 26. D. Goetze, B. Horsch, S. Porembski, Dynamics of forest-savanna mosaics in northeastern Ivory Coast from 1954 to J. Biogeogr. 33, 653 (2006). 27. E. Mitchard, S. Saatchi, F. Gerard, S. Lewis, P. Meir, Measuring woody encroachment along a forest-savanna boundary in central Africa. Earth Interact. 13, 1 (2009). 28. K. K. Kumar, B. Rajagopalan, M. A. Cane, On the weakening relationship between the Indian monsoon and ENSO. Science 284, 2156 (1999). 29. O. L. Phillips et al., Drought sensitivity of the Amazon rainforest. Science 323, 1344 (2009). 30. Y. Malhi et al., Exploring the likelihood and mechanism of a climate-change induced dieback of the Amazon rainforest. Proc. Natl. Acad. Sci. U.S.A. 106, (2009). 31. G. P. Asner, A. Alencar, Drought impacts on the Amazon forest: The remote sensing perspective. New Phytol. 187, 569 (2010). 32. M. Hansen et al., Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS vegetation continuous fields algorithm. Earth Interact. 7, 1 (2003). 33. L. Miles et al., A global overview of the conservation status of tropical dry forests. J. Biogeogr. 33, 491 (2006).

9 34. D. P. Roy, L. Boschetti, C. O. Justice, J. Ju, The collection 5 MODIS burned area product global evaluation by comparison with the MODIS active fire product. Remote Sens. Environ. 112, 3690 (2008). 35. S. Nicholson et al., Validation of TRMM and other rainfall estimates with a highdensity gauge dataset for West Africa. Part 1: Validation of GPCC rainfall product and pre-trmm satellite and blended products. J. Appl. Meteorol. 42, 1337 (2003). 36. E. Bartholomé, A. Belward, GLC2000: A new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26, 1959 (2005). 37. R. Hijmans, S. Cameron, J. Parra, P. Jones, A. Jarvis, Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965 (2005).