Woody Cover and Hominin Environments in the Past 6 Million Years

ARTICLE doi:10.1038/nature10306

Woody cover and hominin environments in the past 6 million years

Thure E. Cerling1, Jonathan G. Wynn2, Samuel A. Andanje3, Michael I. Bird4, David Kimutai Korir3, Naomi E. Levin5, William Mace1, Anthony N. Macharia1, Jay Quade6 & Christopher H. Remien1

The role of African savannahs in the evolution of early hominins has been debated for nearly a century. Resolution of this issue has been hindered by difficulty in quantifying the fraction of woody cover in the fossil record. Here we show that the fraction of woody cover in tropical ecosystems can be quantified using stable carbon isotopes in soils. Furthermore, we use fossil soils from hominin sites in the Awash and Omo-Turkana basins in eastern Africa to reconstruct the fraction of woody cover since the Late Miocene epoch (about 7 million years ago). 13C/12C ratio data from 1,300 palaeosols at or adjacent to hominin sites dating to at least 6 million years ago show that woody cover was predominantly less than 40% at most sites. These data point to the prevalence of open environments at the majority of hominin fossil sites in eastern Africa over the past 6 million years.

13 There is long-standing debate as to the importance of woody versus their discrimination against CO2 during photosynthesis, the stable herbaceous cover in the evolution of humans over the past 6 million carbon isotopic composition of soils can be used as a direct indicator years (Myr)1–5. Despite uncertainty as to the nature of the last common of the fraction of woody cover in tropical ecosystems17–19. ancestor (LCA) that we share with modern chimpanzees6, it is widely We present a method using stable carbon isotopes to quantify the recognized that the LCA inhabited wooded environments, and that fraction of woody cover in tropical ecosystems using new data from hominin habitats became less wooded after this divergence some eastern Africa that builds on earlier observations: the fraction of C4 5–8 Myr ago2,3,5,6. Woody plants provide shade, shelter and food biomass is related to the fraction of woody cover17,18,20. We then apply resources, and as such could have an important role in the evolution this relationship to well-dated fossil sites in the Awash and Omo- ofterrestrialmammals, includinghumans.For example, shade provided Turkana basins in Ethiopia and Kenya, which contain sedimentary by this cover may have influenced thermoregulatory and endurance archives that are critical to understanding hominin evolution in the running adaptations, or nesting and hunting behaviours of early Pliocene and Pleistocene epochs. The in-depth analysis of the rela- hominins7–11. tionship between stable carbon isotopes and woody cover in modern In broader ecosystem-scale terms, the role of ‘savannah’ ecosystems soils permits the reconstruction of the woody cover, or shade, avail- in hominin evolution remains a subject of debate1–5, although it is able to hominins in eastern Africa during the past 6 Myr. widely recognized that savannahs may have influenced a variety of hominin adaptations such as bipedalism and dietary adaptations to Calibrating a ‘palaeo-shade’ proxy novel foods2,3,6–13. Consideration of the role of savannahs in human We report results of woody cover measurements and the 13C/12C ratio evolution began in 192514 with the introduction of what is often (d13C) values of surface horizons from tropical soils for 76 locations in described as the ‘savannah hypothesis’ and continues today in efforts Kenya, Ethiopia, Malaysia, Botswana20, Zambia20, Australia17 and to reconcile the fossil record of human origins with diverse palaeo- Brazil21 (Supplementary Tables 1 and 2). Sites were selected because environmental proxies1–3,5,15. However, an imprecise and often overly of their undisturbed nature (that is, from National Parks or Reserves) simplistic application of the definition of savannahs hinders progress or because of minimal agricultural disturbance. ‘Gap’ samples are from in the debate over the timing and nature of their role in human soils that are not directly under woody cover and ‘canopy’ samples are evolution. To move past this persistent problem, we develop a rela- from soils directly beneath woody cover. The d13C values of the gap tionship between the modern carbon isotope ratio in soils and the and the canopy samples are well correlated for the entire data set amount of woody cover in tropical environments and show that (Fig. 1; see also Supplementary Information). In closed settings with this can be used as a calibration for estimating woody cover of past woody cover of approximately .80%, both the canopy and gap areas 13 17 environments. By using this relationship we can focus on the degree to show d C values characteristic of predominantly C3 vegetation . which habitats were wooded, thereby circumventing any need to Likewise, open settings, with woody cover approximately ,20%, show 13 apply a functional definition of savannah to past environments where d C values characteristic of predominantly C4 vegetation, both in the only structure can be inferred. canopy and gap samples. Thus, d13C values in these soils reflect the Stable carbon isotopes in palaeosols are a key means of reconstruct- amount of woody cover on the timescales of carbon turnover (,10 yr 22 13 ing ancient environments, particularly those in the tropics in the past in the tropics ), and C4-rich ecosystems have a different soil d C 6 Myr or longer. Woody plants, almost all of which use the C3 pho- signature than C3-rich ecosystems whether under a tree canopy or tosynthetic pathway, would have provided mammals with shade and not. We invert the relationship in Fig. 2 to reconstruct the fraction of 16 13 shelter from the direct sun . Tropical grasses, on the other hand, use woody cover (fwc)fromd C values of organic matter and soil carbon- 16 the C4 photosynthetic pathway . Because these two pathways differ in ate in fossil soils (see methods in Supplementary Information):

1University of Utah, Salt Lake City, Utah 84112, USA. 2University of South Florida, Tampa, Florida 33620, USA. 3Kenya Wildlife Service, PO Box 40241-00100 Nairobi, Kenya. 4James Cook University, PO Box 6811, Cairns QLD 4870, Queensland, Australia. 5Johns Hopkins University, Baltimore, Maryland 21218, USA. 6University of Arizona, Tucson, Arizona 85721, USA.

–10 20 Meru grassland

–15 Number of days 0 20 30 40 50 60 –20 C canopy 13 δ Meru 25 woodland –25

1:1 line Number of days –30 0 –30 –25 –20 –15 –10 20 30 40 50 60 δ13C gap Figure 1 | Correlation of d13C between gap and canopy samples for 76 Meru 50 forest tropical soils used in this study. Best-fit line is using the major axis regression 13 13 2 where d Ccanopy 5 0.793d Cgap 26.4; r 5 0.89. 2 f ~ sin {1:06688{0:08538 d13C Number of days wc om 0

This relationship is not a simple linear mixing line between C3 plants 20 30 40 50 60 (ca. -24 to -35%)andC4 plants (approximately 211% to 214%; Daily high surface ground temperature (°C) ref. 16) because C3 herbaceous plants may occur in both open and Figure 3 | Surface soil temperatures from soil temperature profiles. closed areas, and canopy areas may contain both C3 trees and both Calculated maximum daily soil-surface temperatures for a 12-month interval mixed C3–C4 understory. for forest, woodland and grassland sites in the Meru National Park region, Shade provided by woody cover also affects soil temperatures and Kenya (see Supplementary Tables 1 and 2). the proportion of C3 and C4 biomass in the herbaceous understory. Figure 3 shows maximum daily soil surface temperature for three sites oxygenase activity relative to carboxylase activity. Humidity and soil in Meru National Park as calculated from sub-surface soil-temperature moisture are also higher in shaded areas. Thus in the areas with higher loggers for the period June 2009 through to May 2010. Soil ground shade cover due to a woody canopy, C3 photosynthesis may be surface temperatures varied between 25 uCand35uC in riparian forest, favoured even in the gap areas. Conversely, C4 photosynthesis in open between 28 uC and 48 uC in woodland, and between 30 uCand60uCin areas is favoured by lower daily-integrated shade where there is higher grassland. Over a 1-year period, 130 days had daily maximum ground temperature, higher light intensity, with lower relative humidity and surface temperatures exceeding 45 uC in the grassland (unshaded) soil moisture. Taken together, the abundance of C3 biomass in gaps in environment. C4 photosynthesis is an adaption to both high leaf C3-dominated ecosystems, and the abundance of C4 biomass under the temperature and low atmospheric CO2 (ref. 16); C4 photosynthesis canopy in C4-dominated portions of the landscapes, is related to the at higher leaf temperatures is favoured over C3 photosynthesis light intensity and the surface ground temperature, and their effects on because of increasing photorespiration in C3 plants related to increased photorespiration, humidity and soil moisture.

–10 A structural classification for palaeo-vegetation It is useful to define formal terminology of vegetation structural categories like ‘forest’, ‘woodland’, ‘grassland’ before proceeding with our reconstruction of ancient vegetation. Because of the strong rela- –15 13 tionship between d Candfwc (Fig. 2), we adopt a vegetation classification system that is based primarily on woody cover (the United Nations Educational, Scientific and Cultural Organization (UNESCO) clas- –20 sification of African vegetation23). The principal vegetation types are: C (SOM)

13 (1) forest: a continuous stand of trees at least 10-m tall with interlock- δ ing crowns. (2) Woodland/bushland/thicket/shrubland: where wood- –25 land is an open stand of trees at least 8-m tall with woody cover .40% and a field layer dominated by grasses; bushland is an open stand of bushes usually between 3- and 8-m tall with woody cover .40%; –30 thicket is a closed stand of bushes and climbers usually between 3- 0.00 0.50 1.00 and 8-m tall; and shrubland is an open or closed stand of shrubs up to Fraction woody cover 2-m tall. (3) Wooded grassland: land covered with grasses and other herbs, with woody cover between 10% and 40%. (4) Grassland: land Figure 2 | Woody cover and soil d13C for 76 tropical soils used in this study. 13 covered with grasses and other herbs, with woody cover ,10%. (5) Canopy-weighted d C values from Supplementary Table 1 have been corrected for the Suess effect (to 1,750)43,44, assuming a residence time for Desert: arid landscapes with a sparse cover dominated by sandy, stony carbon in soils to be 10 years22. Ordinary linear regression was carried out on or rocky substrate. arcsine square-root-transformedpffiffiffiffiffiffi values of fractional woody cover Because this classification does not define a boundary between forest 45 13 (arcsinpffiffiffiffiffiffifwc ) ; the dashed line is the OLR function d C 529.02 and woodland in terms of woody cover, we will consider that ‘forest’ has 2 arcsin fwc 214.49 (r 5 0.77). SOM, soil organic matter. .80% woody cover based on the requirement for ‘interlocking crown

52 | NATURE | VOL 476 | 4 AUGUST 2011 ©2011 Macmillan Publishers Limited. All rights reserved ARTICLE RESEARCH canopies’. Our reconstruction method cannot distinguish between hominin sites in eastern Africa over the past ,6 Myr. More than 70% functionally distinct categories such as woodland, bushland and shrub- of these palaeosols reflect woody cover ,40%. Less than 1% of the land, which are defined by the height of woody vegetation. palaeosols are associated with woody cover .70%; therefore, ‘closed’ The term savannah suffers from colloquial misuse, and for that forests (.80% woody cover) represent a very small fraction of the reason is not recognized in the UNESCO classification. Still, a modern environment represented by these palaeosols. ecological definition of the term savannah is comprehensive and Strata from the Awash Valley and Omo-Turkana Basin span the includes structural, functional and evolutionary aspects24. Because period from 7.4 Myr ago to the present, and thus bracket the diver- our focus is on reconstructing the physiognomic structure of gence of the LCA of humans and chimpanzees. Much of the hominin palaeo-vegetation, we use a purely structural definition of savannah: fossil record derives from these two basins, which are well dated, and ‘‘mixed tree–grass systems characterized by a discontinuous tree contain abundant palaeosols (Fig. 5 and Supplementary Tables 3–6). canopy in a conspicuous grass layer’’ (from ref. 24). This, and other We applied our method to long sequences in these two basins to common usage of the term1–3,11,19,25,26, would include at least ‘wooded reconstruct changes in the woody cover over this period (Fig. 6 and grasslands’ and ‘grasslands’ in the UNESCO structural categories Supplementary Fig. 1). The record in both basins begins with a period described above, although woody cover varies significantly within marked by relatively sparse woody cover in the Late Miocene to early the savannahs (from about 5–80%; refs 24, 27). Rainfall is widely Pliocene. At Aramis, where Ardipithecus ramidus was found, isotopic recognized as the primary determinant of woody cover along with compositions are characteristic of grasslands and wooded grass- tolerance to fire, herbivory and soil fertility18,24,27,28. However, variation lands31, whereas more wooded conditions were present during in these primary determinants, such as between regions or continents roughly the same interval for Ar. ramidus-bearing sites at Gona. 13 or soil types, does not obscure the relationship between fwc and d C Thus, there is strong evidence for open habitats in the Late Miocene values of soil organic matter as shown in Fig. 2. to early Pliocene in the Middle Awash region (approximately 5.7–4.4 Myr ago), as well as at Lothagam (7.4–5.7 Myr ago) in the Application to the fossil record Omo-Turkana Basin. These generally open habitats are directly asso- Palaeosols have previously been used to quantify the fraction of C4 ciated with the earliest purported members of the Hominini, and this biomass in hominin environments25,26,29–31, but the relationship between fact should be considered in debate regarding potential mechanisms d13C and woody cover (Fig. 2) provides a means to estimate the fraction for novel adaptations of this clade, which include bipedalism, the of woody cover in past environments. Figure 4 shows a summary of reconstructed woody cover for .1,300 palaeosols associated with 14°

δ13C (palaeosol carbonate) –15 –10 –5 0 12° Gulf of Aden

99.9 East African palaeosols associated with hominin sites 10° 99 (n = 1,380) Awash Valley 95 Ethiopia 90 80 70 8° 50 Percentile

30 Omo R. 20 10 6° 5 Woodland/ Shungura 1 Wooded Grass- Forest bushland/ Nachukui grassland land Koobi Fora 0.1 shrubland 4° L. Turkana

Gona Hadar Lothagam 200 Dikika 2° N Kanapoi Middle Awash Kenya

0° Meru NP 100 Indian Ocean 2° S Number of palaeosols

4° Modern soil/woody cover/ 0 temperature sites 1.00 0.50 0.0 Palaeosol/hominin sites Fraction woody canopy cover 6° 34° E 36° 38° 40° 42° 44° 46° Figure 4 | Estimated fraction of woody cover based on >1,300 published analyses of palaeosols from eastern African hominin sites from 6 Myr ago to Figure 5 | Map with modern soil sites and hominin-fossil bearing localities present25,26,29–31,46–50. Vegetation classification is from ref. 23. Top: cumulative in the East African Rift System overlain on GTOPO30 digital elevation frequency of palaeosol values related to fraction of woody cover. Bottom: model. Red circles, modern soil sites; yellow triangles, hominin-fossil-bearing histogram of palaeosol values related to fraction of woody cover. localities.

Awash Valley Omo-Turkana Busin

Woodland/bushland Wooded Grass- Woodland/bushland Wooded Grass- Forest Forest /shrubland grassland land /shrubland grassland land Aduma (n = 4, n = 3) Encephalized n c om Carbonate 0 c = 17 0 Koobi Fora/Nachukui fm., E./W. Turkana (n ) 0.16 T Megadont Shungura fm. Omo Valley (n ) n O c = 22

n n Homo n n 0.64 c = 29, om = 2 O = 2, T = 9 0.83 n n Bouri fm. ( c = 4, om = 4) Daka 1 1 n = 35 ‘Nariokotome boy’ Pleistocene c

n n O = 5, T = 92 1.38 n n 1.61 1.63 O = 5, T = 147

Busidima formation n n 1.87 O = 17, T = 94 ‘1470’ n 2 c = 84 Acheulean 2

Gona stone tools n n 2.33 O = 10, T = 43 Paranthropus n 2.58 c = 73 2.7 Oldowan ‘Lucy’ n = 6, n = 39 2.85 2.94 n O T 3 c = 124 3 ‘Selam’ 3.24 n = 10 c 3.42 n n = 43 3.44 c = 18 T

Hadar fm. 3.60 r) Pliocene 3.6 n

= 33 (Koobi Fora, Nachukui, Shungura fms Omo group

T y

n 3.9 e (M Australopithecus T = 14 g Age (Myr) 4 n = 18 4 A 4.1 c 4.2 4.2 n n Aramis ( c = 35, om = 50) 4.3

4.6 Gona (n = 25) c ‘Ardi’ n T = 4

5 Sagantole fm. Ardipithecus 5

0.1 Western Margin, M. Awash (n = 19, n = 32) c om Prob. 5.5 0 5.7 5.7

Overall median 6 Probability density 6 LCA 0.1

Adu-Asa fm. Prob. n Individual medians T = 11 n 0 Carbonate ( c) Miocene

Lower Awash Valley (Hadar, Dikika, Gona) Nawata formation Middle Awash sites (as indicated) n +/– Organic carbon ( om) 7 7 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 Fraction of woody cover (areal basis) Fraction of woody cover (areal basis) 7.4 Figure 6 | Composite record of palaeosol stable isotopic composition from Middle Awash region (Aduma, Daka, Aramis and Western Margin). Because the Awash Valley, Ethiopia (left) and Omo-Turkana Basin, Kenya and the Shungura formation of the Omo River Valley preserves different Ethiopia (right). A hominin phylogram is shown at the centre (adapted from environments from the remainder of the basin36, data from this region are ref. 32). Stable isotope data are presented as normalized probability density shown with probability density functions distinct from the remainder of the functions of predicted woody cover determined for palaeosols in a series of record (Koobi Fora and Nachukui formations, East and West Turkana). The temporal bins defined for each basin. Temporal bins are divided based on number of analyses from the Omo Valley (nO) and the Lower Turkana Basin marker tephra in each sedimentary basin (see Supplementary Tables 3 and 4). (nT) are indicated. Hominin species ranges are spread according to their age The number of pedogenic carbonate analyses (nc) and of organic matter distribution (vertical axis) and roughly corresponding to their anatomical 32 analyses (nom) are indicated for each temporal bin; the median value of woody features (horizontal axis) . Major archaeological innovations of early stone tool cover for all data from each temporal bin is shown with a narrow white bar. development are also indicated: Oldowan technology, first stone tools, 2.6 Myr Data from the Awash Valley include relatively continuous records from ago; Acheulean technology, 1.7 Myr ago. co-adjacent research areas at Hadar, Gona and Dikika, along with data from the advent of megadontia, and diminution of the canine premolar honing lake system, surrounded by generally mesic environments33. A Middle complex5,6,32. Pliocene peak of woody vegetation in the Awash Valley occurs later The relatively open conditions of the Late Miocene to early than that in the Omo-Turkana Basin, but is also associated with a Pliocene are followed by generally increasing woody cover in the period marked by rapid sedimentation in a tectonically controlled lake Middle Pliocene (after about 3.6 Myr ago; Fig. 6). Most palaeosols that favoured more wooded settings34. Thus, the Middle Pliocene of during this time interval in these basins suggest ,40–60% woody both basins is largely characterized by environments that are more cover and limited areas of more open environments. This period of wooded than those in either the Late Miocene (up to about 5.3 Myr increased woody cover in the Turkana Basin is in part marked by the ago) or the Pleistocene (,1.8 Myr onwards)—a trend consistent with onset of rapid basin sedimentation in a tectonically driven, basin-wide global biome reconstruction and model-data comparisons that show

54 | NATURE | VOL 476 | 4 AUGUST 2011 ©2011 Macmillan Publishers Limited. All rights reserved ARTICLE RESEARCH expansion of woody vegetation across Africa during the middle throughout this basin during the past 4 Myr. Open areas can have Pliocene35. These generally more wooded conditions are coincident daily high surface ground temperatures—up to 25 uC higher than in with the earliest clear evidence of bipedalism, and the earliest widely nearby well-shaded riparian forests or woodlands (Fig. 3). This is accepted member of the Hominini, as well as more efficient bipedalism because in shaded areas, incident solar radiation is partially balanced and megadonty of the genus Australopithecus6,32. by latent and sensible heat transfer from the surface area of trees, The interval straddling the transition from the Pliocene to whereas in open areas of dry soil, sensible heat flux to the soil pre- Pleistocene (,3.6–1.4 Myr ago) shows the return of open environ- dominates. Early hominins would have been affected by this uneven ments such as wooded grasslands and grasslands in these two basins distribution of heat, and this may have influenced physiological and (Fig. 6). The extent of open grasslands peaks during the Pleistocene behavioural adaptations that occurred since divergence from the (,1.8–0.01 Myr ago) and represents the culmination of a longer-term LCA2,7–13. trend towards diminishing woodlands that started in the Late Our observations of the environment of some of the earliest hominins Miocene. These developments, however, were not synchronous across do not contradict the longstanding hypothesis that savannahs in Africa the Omo-Turkana system: more wooded conditions persisted longer may have had a role in the development of bipedal locomotion, or other in the Omo Valley (into the Late Pliocene, up to about 1.8 Myr ago) key defining characteristics of hominins post-dating the LCA. If either than in the lower Turkana Basin. The presence of a large, perennial species of Ardipithecus (Ar. ramidus or Ar. kadabba) is validated as the river draining the highlands of the Omo River basin probably ‘‘long-sought potential root species for the Hominidae’’42 then the soil favoured the more wooded environment apparent in these data36. carbonate data now make it clear that both species were surrounded by Thus, in the environment of the generally open conditions of hot more open environments than Australopithecus,whichwasmoreeffi- and dry wooded grasslands in the Lower Turkana Basin, areas closer ciently bipedal and occurred in more wooded environments of both the to the axial river system had more interconnected and dense woody Omo-Turkana Basin and the Awash Valley (Fig. 6). Thus, the combined cover, nourished by more abundant groundwater and surface water. results from two of the most significant hominid-bearing regions in After 1.9 Myr ago, environments with .50% woody cover nearly eastern Africa leave the savannah hypothesis as a viable scenario for disappear from the stratigraphic record in the basin, including areas explaining the context of earliest bipedalism, as well as potentially later previously characterized by more wooded conditions, such as the evolutionary innovations within the hominin clade. Omo River valley. Woody cover in the Awash Valley also decreases after about 2.9 Myr ago, which is coincident with a shift from lacustrine METHODS SUMMARY to fluvial deposition36. This overall trend in both basins may be attri- Soils in Kenya and Ethiopia were selected because of their undisturbed nature butable to coeval global and regional climate change4,15,37–40, although a (National Parks or Reserves) or because of minimal agricultural disturbance. Soil pronounced reorganization of fault blocks within the Ethiopian Rift samples (0–5 cm depth) were collected following a protocol17,20 whereby multiple System, and shift from lacustrine to fluvial deposition in Hadar, Gona soils from canopy and gap areas are collected in each site over an area comprising and Dikika areas34, may also have contributed to the decrease in the several hectares. Canopy cover was estimated using several methods as described woody cover after ,2.9 Myr ago. A lack of abundant woody cover in the Supplementary Information. persists throughout the Awash Valley during the Pleistocene, with Stable carbon isotopes were measured on the organic fraction in modern soils remaining after removal of carbonates on an isotope ratio mass spectrometer , several important sites marked by environments with 10% woody operating in continuous-flow mode after combustion at 1,600 uC in an elemental cover. This expansion of grasslands across the Pliocene–Pleistocene analyser. Results are reported in the standard per mil (%) notation: 13 transition has been linked to global climate change and major develop- d C 5 (Rsample/Rstandard 2 1) 3 1000 using the isotope standard Vienna Pee ments in the hominin clade3,4,15,40, such as the more obligate bipedalism Dee Belemnite. Overall d13C values of the sites were estimated using the of the genus Homo, increase in encephalization, and reduction in tooth canopy-weighted values for gap and canopy samples20. d13C values of modern and gut proportions32. soils were corrected for the change in the d13C of the atmosphere43,44,assuminga 10-year residence time for carbon in the surface horizon of tropical soils22. Palaeosol Discussion carbonates were analysed on CO2 produced by reaction with H3PO4 at 90 uC. The several sections of the Supplementary Methods provide additional details Palaeosol stable carbon isotope data from the Omo-Turkana and of the methods used. Awash River basins provide a strong basis for reconstructing the ecological context of hominin evolution, reinforcing debate over mechan- Received 27 January; accepted 15 June 2011. isms of co-evolutionary ecology. The evolution of early hominins within African savannahs has been a core principle of the savannah 1. Jolly, C. J. 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A latitudinal gradient in carbon turnover times in paleosol studies in a framework of Ethiopian climate change. J. Hum. Evol. 55, forest soils. Nature 381, 143–146 (1996). 532–550 (2008). 23. White, F. The Vegetation of Africa Vol. 20 (United Nations Scientific and Cultural 47. Levin, N. E., Quade, J., Simpson, S. W., Semaw, S. & Rogers, M. J. Isotopic evidence Organization, 1983). for Plio-Pleistocene environmental change at Gona, Ethiopia. Earth Planet. Sci. Lett. 24. Ratnam, J. et al. When is a ‘forest’ a savanna, and when does it matter. Glob. Ecol. 219, 93–110 (2004). 48. Quinn, R. L., Lepre, C. J., Wright, J. D. & Feibel, C. S. Paleogeographic variations of Biogeogr. doi:10.1111/j.1466-8238.2010.00634.x (2011). 13 25. Cerling, T. E. Development of grasslands and savannas in East Africa during the pedogenic carbonate d C values from Koobi Fora, Kenya: implications for floral Neogene. Palaeogeogr. Palaeoclimatol. Palaeoecol. 97, 241–247 (1992). compositions of Plio-Pleistocene hominin environments. J. Hum. Evol. 53, 26. Wynn, J. G. Influence of Plio-Pleistocene aridification on human evolution from 560–573 (2007). paleosols of the Turkana Basin, Kenya. Am. J. Phys. Anthropol. 123, 106–118 49. White, T. D. et al. Asa Issie, Aramis, and the origin of Australopithecus. Nature 440, (2004). 883–889 (2006). 50. Wynn, J. G. Paleosols, stable carbon isotopes and paleoenvironmental 27. Sankaran, M. et al. Determinants of woody cover in African savannas. Nature 438, interpretation of Kanapoi, Northern Kenya. J. Hum. Evol. 39, 411–432 (2000). 846–849 (2005). 28. Good, S. P. & Caylor, K. K. Climatological determinants of woody cover in Africa. Supplementary Information is linked to the online version of the paper at Proc. Natl Acad. Sci. USA 108, 4902–4907 (2011). www.nature.com/nature. 29. Plummer, T. W. et al. Oldest evidence of toolmaking hominins in a grassland- dominated ecosystem. PLoS ONE 4, e7199 (2009). Acknowledgements We thank the governments of Kenya and Ethiopia for permission 30. Sikes, N. E. & Ashley, G. M. Stable isotopes of pedogenic carbonates as indicators of to conduct this research, and F. H. Brown for discussions. The authors thank Kenya paleoecology in the Plio-Pleistocene (upper Bed I), western margin of the Olduvai Wildlife Service and members of the Dikika and Gona Research Projects for support in Basin, Tanzania. J. Hum. Evol. 53, 574–594 (2007). the field, and Z. Bedaso for aid with analyses. This research was supported by funding 31. WoldeGabriel, G. et al. The geological, isotopic, botanical, invertebrate and lower from the LSB Leakey Foundation and NSF grants BCS 0621542, EAR-0617010, vertebrate surroundings of Ardipithecus ramidus. Science 326, 65 (2009). EAR-0937819 and BCS-0321893. 32. Wood, B. A. & Lonergan, N. The hominin fossil record: taxa, grades and clades. Author Contributions S.A.A., M.I.B., T.E.C., D.K.K., N.E.L., W.M., J.Q. and C.H.R. designed J. Anat. 212, 354–376 (2008). the modern soil surveys. T.E.C., M.I.B., A.N.M., W.M. and J.G.W. evaluated the amount of 33. Feibel, C. S., Harris, J. M. & Brown, F. H. in Koobi Fora Research Project Vol. 3 woody cover. C.H.R. and W.M. analysed the soil temperature data. M.I.B., N.E.L., A.N.M. (ed. Harris, J. M. Harris) 321–346 (Clarendon, 1991). and J.G.W. analysed modern soils. N.E.L., J.Q. and J.G.W. contributed new palaeosol 34. Quade, J. & Wynn, J. G. in Geological Society of America Special Publications Vol. 446 data. T.E.C. and J.G.W. wrote the paper with input from all authors. (Geological Society of America, 2008). 35. Salzmann, U., Haywood, A. M., Lunt, D. J., Valdes, P. J. & Hill, D. J. A new global biome Author Information Reprints and permissions information is available at reconstruction and data-model comparison for the Middle Pliocene. Glob. Ecol. www.nature.com/reprints. The authors declare no competing financial interests. Biogeogr. 17, 432–447 (2008). Readers are welcome to comment on the online version of this article at 36. Levin, N. E., Brown, F. H., Behrensmeyer, A. K., Bobe, R. & Cerling, T. E. Paleosol www.nature.com/nature. Correspondence and requests for materials should be carbonates from the Omo Group: Isotopic records of local and regional addressed to T.E.C. ([email protected]).

Supplementary Figure 1. Composite record of paleosol stable isotopes from the Awash Valley, Ethiopia and Omo‐Turkana Basin, Kenya and Ethiopia. Stable isotope data are presented as normalized probability density functions (PDFs) of predicted

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woody cover determined for paleosols in a series of temporal bins defined for each basin. Temporal bins are divided based on marker tephra in each sedimentary basin (see Supplementary Information for details). Data from the Awash Valley include a relatively continuous record from adjacent research areas at Hadar, Gona and Dikika; data from other, more distant sites in the Middle Awash (Aduma, Daka, Aramis and Western Margin) are inserted here, and shown with overlapping PDFs. The number of pedogenic carbonate analyses (nc) and of organic matter analyses (nm) are indicated for each temporal bin. Data from the Omo‐Turkana Basin also form a relatively continuous sequence. Because the Shungura Fm. of the Omo River Valley preserves different environments from the remainder of the basin1, data from this region are shown with PDFs distinct from the remainder of the record (Koobi Fora and Nachukui Fms., East and West Turkana). The number of analyses from the Omo Valley (nO) and the Lower Turkana Basin (nT) are indicated.

best-fit line prediction interval w f () arcsin 0.2 0.4 0.6 0.8 1.0 1.2 1.4

-25 -20 -15 13 Com

Supplementary Figure 2. Regression analysis used to compute a predicted woody cover value for a new value of carbon isotopic composition. Data are from Supplementary Information Table 1; the best‐fit line and 95.5% prediction interval are described in the detailed methods.

DETAILED METHODS

Modern Soil Organic Carbon & Woody Cover Data Collection

Sites were selected because of their undisturbed nature (i.e., from National Parks or Reserves) or because of minimal agricultural disturbance. Soil samples (0 –

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5 cm depth) were collected following a protocol2,3 whereby multiple soils from “canopy” and “gap” areas are collected in each site over an area comprising several hectares. "Canopy" samples were collected at half‐crown‐ distance from local trees, while "gap" samples were collected from localities approximately equidistant from the canopy edges of trees. The purpose of the "canopy" and "gap" samples was to encompass the likely maximum and minimum carbon contents and δ13C values at each sample locality, and to stratify the data set such that δ13C values can be scaled up to a representative δ13C value for each site.

Ethiopian and Kenyan sites were represented by a single ca. 1 to 5 hectare sample area. This data set was supplemented with data from tropical soils2‐4 collected using the same protocols but where multiple localities were aggregated into single values; these represented total areas of 5 to 15 ha. Taken together, over 3000 individual soils were collected from the 76 sites. Summarized data are reported in Supplementary Tables 1 and 2.

Canopy cover was estimated using: visual field estimate along 100‐m transects; high‐resolution (0.6 m) aerial photography using areal proportions; high resolution (0.6 m) aerial photography using multiple linear transects normalized to a grayscale; “fish‐eye” photography (Kenyan sites only). For a few sites high resolution photography was not available. Total woody cover was taken to be the average value of all methods used in a particular locality. Because canopy values vary between the limits 0.0 and 1.0 the dependent variable in a regression of canopy cover to δ13C is not normally distributed. Linear regression was carried out on arcsin square root transformed fractions as is commonly done in studies of fractional woody cover5.

Soil Temperature Data Collection

Data‐logging soil temperature sensors were emplaced at 5, 15, and 25 cm depths in sites in Kenya. Daily soil temperature envelopes were calculated from daily high and low temperatures at each soil depth. Daily soil temperature envelopes were calculated6,7 from daily high and low temperatures at each soil depth.

Stratigraphic and hominin record

The sediments of the Omo‐Turkana Basin are very well dated8,9, have produced abundant fossil hominins dating from the Late Miocene to Holocene, and contain abundant paleosols in a relatively continuous sedimentary succession. Like the Omo‐Turkana Basin, the sediments of the Awash Valley have produced abundant hominin fossils (e.g., refs. 10‐19), are well dated, and contain paleosols that are common in the stratigraphic succession 20‐23. All paleosols from these two basins are placed in temporal bins based on recognizable stratigraphic horizons in both sedimentary basins (Supplementary Tables 3 and 4). Our hominin taxonomy follows

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that of ref. 24, which summarizes numerous data from these basins (e.g., refs. 10‐19,25‐ 31).

Vegetation terminology

We adopt the definitions used in vegetation classification by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) system of classification for African vegetation32 which is based on the areal fraction of woody canopy cover. In short: “forests” and “woodlands/bushlands/shrublands” have more than 40% woody canopy cover; “wooded grassland” have between 40‐10 percent woody cover; and “grasslands” have less than 10% woody cover. The boundary between forests and woodlands in the UNESCO classification is based on stratification of the canopy (forests have multi‐tiered canopies, unlike woodlands, which have a grassy understory). Because neither our woody cover data, nor the predictions of woody cover from paleosols provide information that reflects canopy stratification, we approximate the division between forests and woodlands at 80% woody cover. Because cubic packing of circles dictates the closest arrangement of approximately equal tree canopies with a fractional area of π/4 (= ~78.5%), we use 80% as an approximation of this boundary, above which the canopies of equally sized circular trees would begin to overlap in a multi‐tiered configuration typical of forests.

.Tropical savannas are considered to be represented by a discontinuous tree cover with an understory of C4 grasses and other herbaceous plants. Various classification schemes describe “savannas” with an upper boundary of woody cover ranges from approximately 20% to 80%33

Stable Isotope Measurements

Stable isotope results are reported in the standard per mil (‰) notation:

13 δ C = (Rsample / Rstandard – 1) * 1000

using the isotopic reference scale VPDB.

For the modern soils, stable carbon isotopes were measured on the organic fraction remaining after removal of carbonates on an isotope ratio mass spectrometer operating in continuous‐flow mode following combustion at ~1600°C in an elemental analyzer. Overall δ13C values of the modern sites were estimated using the canopy‐weighted values for “gap” and “canopy” samples2,3. δ13C values of modern soils were corrected for the change in the δ13C of the atmosphere34,35, assuming a 10‐year residence time for carbon in the surface horizon of tropical soils36.

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For the paleosols, δ13C was measured on soil carbonate collected from > 30 cm depth within paleosols, following reaction of carbonate with 100% phosphoric acid. Because soil carbonate is 13C‐enriched with respect to the soil organic matter from which it derives by an amount equal to the sum of equilibrium and kinetic fractionation factors, we considered the offset between the isotopic composition of soil organic carbon and carbonate isotope enrichment factor, ⎡ ⎤ 13 12 37 εcarbonate−organicC = ⎣(Rcarbonate / RorganicC )−1⎦×1000, where R is the isotopic ratio, C/ C) . We approximated this offset by subtracting 14‰ from the δ13C values for carbonate to convert to the equivalent isotopic composition of organic carbon, following the carbonate‐organic matter offset used by [ref 20]. In addition to new data from the Gona and Dikika areas (see Supplementary Information), we compiled similarly collected stable isotopic data on soil carbonate and organic matter from all Late Miocene to Pleistocene paleontological sites reported from the Awash Valley and from the Omo‐Turkana Basin. New data from the Gona and Dikika Research Project areas presented in Supplementary Tables 5 and 6. Other similarly collected data reported from the Awash Valley are from the Awash Valley (Aduma23, Daka22, Gona1,38, Hadar39, Dikika40, Asa Issie11, Aramis20, Western Margin of Middle Awash21) and from the Turkana Basin (Koobi Fora41, West Turkana42, Kanapoi43, Lothagam44 and the Shungura Formation1).

Woody cover reconstruction

We used the data in Supplementary Information Table 1 and R statistical software (GNU project) to convert measurements of carbon isotopic composition to predictions of the fraction of woody cover. The regression and prediction interval were computed by ordinary least squares regression using the functions “lm” and “predict”. The predicted variable ( ) was used as the dependent variable, arcsin( fwc ) 13 and the predictor (δ Com ) as the independent variable, with all error assumed to be associated with the predicted variable. fwc is the fraction of woody 13 cover; δ Com is the isotopic composition of organic matter expressed in δ‐notation with respect to the VPDB isotopic standard, and corrected for the 13C Suess effect as described above.

The predicted woody cover for a given new paleosol carbon isotopic composition according to this regression is (Supplementary Figure 2):

2 f = sin ⎡−1.06688 − 0.08538 δ 13C ⎤ wc {}⎣ ()om ⎦

The 95.45% prediction interval is approximately linear over the range of δ13C values (Supplementary Figure 2). The mean range of the prediction interval, between upper and lower limits of fwc is 0.811 (but the range varies from 0.800 to 0.821). We use this mean prediction range as an estimate of the standard deviation of a new predicted fwc value, i.e., +/‐ 0.406, 2σ ; +/‐ 0.203, 1σ. Note that this method

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is appropriate for the prediction of a new value of y for a given new x value, but differs from the confidence interval determined using Model II regression, which is appropriate to estimate the parameters of a functional relationship (Legendre, P., 2008; Model II Regression User’s Guide, R online Edition, ref. 45).

This predictive relationship was used to convert each paleosol δ13C value to a normal probability distribution of the fraction of woody cover. Matlab® was then used to produce the predicted probability distribution of woody cover for paleosol δ13C values from each temporal bin. The total probability distribution function (PDF) for temporal intervals for a group of paleosol samples within the interval was computed by summing these individual PDFs and normalizing to unity.

The code used to create a total PDF for nc carbonate analyses and nom organic carbon analyses is included here:

function f = gauss_distribution(x, mu, s)

% make a composite probability density function from carbon isotope data

clear global; clf reset;

% enter the name of the site

Sitename = 'test';

% set up some constants % difference between pedogenic carbonate d13C and organic C d13C

pedoCoffset = 14;

% 1s standard deviation of arcsin transformed relationship

s = 0.4056/2;

% read in the d13C data from pedogenic carbonate % csv file read should have EoF indicator

Sitecarb=csvread(strcat(Sitename,'carb.csv')); Siteorg=csvread(strcat(Sitename,'org.csv')); d13Ccarb=Sitecarb(:,1); d13Corg=Siteorg(:,1);

% convert pedogenic carbonate d13C values to equivalent for organic C

d13Ccarbconv=d13Ccarb-pedoCoffset;

% determine the number of analyses

numanalscarb = nnz(d13Ccarbconv); numanalsorg = nnz(d13Corg);

% set up empty vectors

x = 0:0.01:1; for n = 1:length(x); pdfCompositecarb(n) = 0; end;

% check for values beyond limits of arcsin transformation, and replace

for n = 1:numanalscarb

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if d13Ccarbconv(n) > -12.496 d13Ccarbconv(n) = -12.496; elseif d13Ccarbconv(n) < -30.893 d13Ccarbconv(n) = -30.893; end; end;

for n = 1:numanalsorg

if d13Corg(n) > -12.496 d13Corg(n) = -12.496; elseif d13Corg(n) < -30.893 d13Corg(n) = -30.893; end; end;

% convert d13C values to fraction of woody vegetation using arcsin

CCcarb=power(sin(-1.06688-0.08538*d13Ccarbconv),2); CCorg=power(sin(-1.06688-0.08538*d13Corg),2);

% start a loop for each carbonate d13C analysis

for n = 1:numanalscarb

% for each prediction of woody cover, calculate normal distribution

mucarb = CCcarb(n);

p1=-((x - mucarb)/s) .^ 2; p2=2*s*s; p3=sqrt(2*pi*s*s); pdfNormalcarb = 1/p3*(exp(p1/p2));

% add this normal distribution to a composite probability distribution

pdfCompositecarb = pdfCompositecarb + pdfNormalcarb;

end;

x = 0:0.01:1; for n = 1:length(x); pdfCompositeorg(n) = 0; end;

% repeat loop above for organic C d13C analyses

for n = 1:numanalsorg

muorg = CCorg(n);

p1=-((x - muorg)/s) .^ 2; p2=2*s*s; p3=sqrt(2*pi*s*s);

pdfNormalorg = 1/p3*(exp(p1/p2));

pdfCompositeorg = pdfCompositeorg + pdfNormalorg;

end;

%normalize to 1 unit area

totcarb = sum(pdfCompositecarb); totorg = sum(pdfCompositeorg); pdfCompositecarb = pdfCompositecarb/totcarb; pdfCompositeorg = pdfCompositeorg/totorg; pdfCompositecarb = pdfCompositecarb * numanalscarb/(numanalscarb+numanalsorg); pdfCompositeorg = pdfCompositeorg * numanalsorg/(numanalscarb+numanalsorg); pdfTotal = (pdfCompositeorg + pdfCompositecarb);

% make a figure and plot the data

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figure(1);

area(x, pdfTotal, 'LineWidth',1,'FaceColor',[1 0.75 1]); hold on; area(x, pdfCompositecarb, 'LineWidth',1,'FaceColor',[0 0 1]); hold off; xlim([0 1]); ylim([0 0.1]); set(gca,'XDir','reverse') h = legend('Organic','Carbonate');

Each temporal bin produced a probability distribution function (PDF), and cumulative distribution function (CDF). These are replicated in detail in Supplementary Figure 1. The scale for probability is constant for each panel, and shown in the lowermost panel. Each PDF is normalized to a total area of 1 (for combined carbonate and organic matter, if present). See legend of Figure 6 in the main text for explanation of symbols.

Supplementary Table 1 (following pages). Locations, soil δ13C, and estimated fraction woody cover for soils from Kenya and Ethiopia. Latitude and longitude are given for representative vegetation types surveyed and soils sampled. Australian sites are distributed over a large (>10 km2) whereas Kenyan, Ethiopian and Brazilian sites are over smaller regions (up to several ha). Measured δ13C values are given, as well as corrected for the 13C Suess effect using the data as described in the text of the supplementary information.

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Supplementary Table 1

13 13 13 13 Site locality Country vegetation type MAT MAP fWC δ C δ C δ C δ C °C mm open canopy mean pre-Ind 1 2 3 4 5 6 7 8 9 10 11

Forest BRZ1 flona 67 km Brazil dense evergreen forest 26 1909 0.99 -27.8 -27.8 -26.3 BRZ2 ZF2 km 34 Brazil dense evergreen forest 27 2285 0.99 -29.0 -29.0 -27.5 BRZ3 ZF2 km 25 Brazil dense evergreen forest 27 2285 0.99 -29.3 -29.3 -27.8 KEN Kennedy State For. QLD Australia rainforest 16.5 1027 0.99 -27.6 -27.7 -27.7 -26.2 SOKC Sokoke NR Kenya Cynometra forest 25.3 1229 0.97 -26.6 -26.6 -25.1 SOKM Sokoke NR Kenya mixed forest 25.3 1229 0.96 -27.5 -27.7 -27.7 -26.2 BRZ4 Ji-Parana Brazil open evergreen forest 26.6 2040 0.95 -28.1 -28.1 -26.6 KAKF Kakamega NR Kenya Guineo-Congolian forest 20.8 1934 0.93 -25.5 -25.5 -24.0 FRB Fraser Island, QLD Australia rainforest 15.7 1566 0.90 -27.8 -28.0 -28.0 -26.5 HIN Hinchinbrook Isl., QLD Australia rainforest 19.1 2198 0.90 -27.6 -27.6 -27.6 -26.1 SAR Sarawak Malaysia rainforest 26.8 3325 0.90 -29.4 -29.4 -29.4 -27.9 CHI Chilli Beach, QLD Australia rainforest 23.8 1849 0.87 -28.0 -28.1 -28.1 -26.6 MAC Maclean, NSW Australia eucalypt open forest 13.6 1416 0.81 -27.2 -27.3 -27.3 -25.8 SHMF Shimba Hill NR Kenya coastal forest 26.8 1211 0.81 -27.7 -27.7 -26.2 NBIF Nairobi NP Kenya dry forest 18.8 909 0.80 -23.4 -24.2 -24.0 -22.5 FRA Fraser Island, QLD Australia eucalypt open forest 16.6 1405 0.75 -27.3 -27.3 -27.3 -25.8 MOR Moreton Island, QLD Australia eucalypt open forest 15.1 1597 0.75 -28.1 -28.1 -28.1 -26.6 NAKF Nakuru NP Kenya forest 16.9 909 0.75 -21.8 -22.1 -22.0 -20.5 BAM Cyp, QLD Australia eucalypt open forest 24.3 1518 0.68 -27.1 -26.9 -27.0 -25.5

Riparian Forest / Woodland TRPR Tana River NR Kenya riparian forest 27.5 475 0.86 -26.4 -28.1 -27.9 -26.4 SAMF Samburu NR Kenya riparian forest 23.5 648 0.84 -27.1 -27.6 -27.5 -26.0 AWSF Awash Ethiopia riparian forest 30.3 140 0.77 -22.9 -23.5 -23.4 -21.9 MERF Meru NP Kenya riparian forest 28.5 321 0.70 -25.2 -26.0 -25.8 -24.3

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Supplementary Table 1 (cont)

MZMS Mzima Springs Kenya riparian woodland 22.6 625 0.70 -21.1 -24.9 -23.7 -22.2 ILTF Ileret Kenya riparian woodland 29.2 178 0.61 -23.6 -25.8 -24.9 -23.4 TSVE Tsavo East NP Kenya riparian woodland 24.9 549 0.40 -18.0 -23.5 -20.2 -18.7

Woodland SOKB Sokoke NR Kenya Brachystegia woodland 25.3 1229 0.79 -25.2 -26.2 -26.0 -24.5 BUN Bundaberg, QLD Australia eucalypt woodland 16 1013 0.64 -24.8 -25.1 -25.0 -23.5 KIR Kirama Station, QLD Australia eucalypt open woodland 16.3 1192 0.64 -24.1 -25.3 -24.9 -23.4 BLE Blencoe Falls, QLD Australia eucalypt open woodland 16.9 909 0.61 -22.3 -23.4 -23.0 -21.5 MUS Musgrave, PLD Australia eucalypt woodland 23 1165 0.53 -24.7 -25.6 -25.2 -23.7 DAR Mary River NP, NT Australia eucalypt woodland 24 1329 0.52 -24.8 -25.7 -25.3 -23.8 MERB Meru NP Kenya acacia woodland 28.5 321 0.52 -20.2 -25.4 -22.9 -21.4 PIL Pilliga, NSW Australia pine open woodland 10 608 0.48 -25.0 -25.5 -25.3 -23.8 COE Coen, QLD Australia eucalypt woodland 22.2 1151 0.47 -23.9 -25.6 -24.7 -23.2

Shrubland / bushland ILTB Ileret Kenya dwarf acacia shrubland 29.2 178 0.60 -21.7 -22.6 -22.2 -20.7 NAKB Nakuru NP Kenya acacia – euphorbia bushland 16.9 909 0.47 -14.7 -17.4 -16.0 -14.5 HAP Happy Valley Sta., QLD Australia shrubland 15.5 355 0.46 -20.2 -22.4 -21.2 -19.7 NEH New Haven Downs, QLD Australia shrubland 15.5 354 0.46 -20.1 -21.8 -20.9 -19.4 MON Mongu Zambia dry deciduous woodland 23.2 920 0.45 -25.4 -24.7 -25.1 -23.6 HAN Hann River, QLD Australia eucalyptus shrubland 22.9 1021 0.44 -23.4 -24.7 -23.9 -22.4 TURK Turkwell Kenya dwarf acacia shrubland 29.2 178 0.41 -23.7 -25.4 -24.4 -22.9 LAS Ayres Rock, NT Australia shrubland 11.7 194 0.35 -20.0 -23.3 -21.2 -19.7 NCH N. Chobe Botswana dry deciduous forest 22 575 0.34 -22.0 -22.7 -22.2 -20.7 KAK Kakadu, NT Australia eucalypt woodland 21.4 1293 0.32 -25.3 -25.9 -25.5 -24.0 LAR Lark Dinosaur Qry, QLD Australia shrubland 15.2 341 0.28 -16.4 -19.0 -17.1 -15.6 TOP Top Springs, NT Australia mixed shrubland 19.3 487 0.22 -20.4 -22.9 -20.9 -19.4 TEN Wauhope, NT Australia mixed shrubland 16.5 282 0.18 -19.0 -21.0 -19.4 -17.9

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Supplementary Table 1 (cont)

Wooded grassland TSVW Tsavo West NP Kenya Acacia bushland 22.6 625 0.38 -18.7 -21.5 -19.2 -18.3 MAU Maun Botswana Mopane tree/bush savanna 21.7 410 0.36 -20.0 -22.2 -20.8 -19.3 DER Derby, WA Australia eucalypt shrubland 23.1 586 0.33 -23.2 -24.8 -23.8 -22.3 KAT Katherine, NT Australia eucalypt open woodland 21 832 0.33 -21.9 -23.4 -22.4 -20.9 SCH S Chobe Botswana Mopane tree/bush savanna 22 525 0.23 -20.1 -21.6 -20.5 -19.0 Deleted: TSVW Tsavo SAMG Samburu NR Kenya wooded grassland 23.5 648 0.22 -19.9 -22.9 -20.6 -19.1 West NP Kenya Acacia OKA Okavango Botswana Mopane tree/bush savanna 21.7 410 0.19 -21.8 -24.2 -22.3 -20.8 bushland 22.6 625 0.38 -18.7 -21.5 -19.2 -18.3¶ SEK Sekoma Botswana south Kalahari bush savanna20.1 330 0.17 -17.2 -20.7 -17.8 -16.3 FIT Fitzroy Crossing, WA Australia tussock grassland shrubs 20.9 498 0.15 -19.9 -24.1 -20.5 -19.0 AWSB Awash Ethiopia edaphic grassland 30.3 140 0.13 -18.2 -21.5 -18.6 -17.1 INN Cameron Corner, SA Australia grassland/shrub/forbland 12.3 142 0.10 -20.4 -22.7 -20.6 -19.1

Grassland ANA Anna Plains, WA Australia acacia shrubland 20 386 0.08 -18.5 -20.9 -18.6 -17.1 DIK Dikbos Botswana south Kalahari bush savanna20 300 0.08 -17.0 -19.5 -17.2 -15.7 MERG Meru NP Kenya grassland savanna 28.5 321 0.08 -15.9 -18.6 -16.2 -14.7 COR Cordillo Downs, SA Australia grassland with eucaplyt 13.3 166 0.07 -18.1 -20.5 -18.2 -16.7 KAKO Kakamega NR Kenya grassy glade 20.8 1934 0.05 -14.7 -21.3 -15.0 -13.5 NAP Nappa Merrie Hstd, QLD Australia shrub/forbland 12.6 174 0.05 -18.7 -20.8 -18.8 -17.3 ARR Arrabury Sta., QLD Australia grassland/shrub/forbland 13 170 0.03 -20.5 -21.7 -20.5 -19.0 BIR Birdsville, QLD Australia tussock grassland/shrubland 13.9 133 0.03 -20.2 -23.2 -20.3 -18.8 DIA Diamantina River, QLD Australia bare tussock grassland 13.9 142 0.02 -21.6 -23.1 -21.6 -20.1 NAKG Nakuru NP Kenya grassland savanna 16.9 909 0.02 -14.0 -13.6 -14.0 -12.5 NAKL Nakuru NP Kenya alkali grassland 16.9 909 0.01 -14.9 -14.9 -13.4 NBIG Nairobi NP Kenya grassland savanna 18.8 909 0.02 -14.6 -18.2 -14.7 -13.2 SHMG Shimba Hill NR Kenya grassland 26.8 1211 0.01 -18.1 -18.1 -16.6 WEL Welverdein Botswana arid shrub savanna 20 225 0.02 -18.5 -21.8 -18.6 -17.1 CRK Cork Station, QLD Australia pasture savanna grassland 15.7 332 0.01 -13.9 -16.3 -13.9 -12.4 ILTG Ileret Kenya alkali grassland 29.2 178 0.01 -17.4 -18.5 -17.4 -15.9 GRO Grove Station, QLD Australia pasture savanna grassland 16.2 362 0.00 -15.5 -15.5 -14.0

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Supplementary Table 1 (cont)

Key to column headings

1. Site abbreviation 2. Locality 3. Country 4. Vegetation type 5. Mean annual temperature 6. Mean annual precipitation 7. Fraction of woody cover (average value from Supplementary Table 2). 8. δ13C from open sites using method of Bird et al2 9. δ13C from canopy sites using method of Bird et al. 2 10. weighted average value for ecosystem 11. corrected for Suess34,35 effect assuming 10 year residence time for carbon in soils36.

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Supplementary Table 2 (following pages). Full data from Kenyan and Ethiopian sites including locations, soil δ13C, and estimated fraction woody cover by several methods for soils from Kenya and Ethiopia.

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Supplementary Table 2

sample colloquial vegetation description UNESCO abbrev. location Country date latitude longitude elev. (m) vegetation class classification 1 2 3 4 5 6 7 8 9 10 11

ANA Anna Plains, WA Australia May 2001 19.727 S 121.689 E 29 acacia shrubland grassland

August ARR Arrabury Station, QLD Australia 2002 27.103 S 141.016 E 97 hummock grassland/shrub/forbland grassland

February AWSB Awash Ethiopia 2001 11.069 N 40.536 E 516 edaphic grassland wooded grassland

February AWSF Awash Ethiopia 2001 11.067 N 40.539 E 515 riparian forest (riparian) forest

October BAM Cyp, QLD Australia 2001 11.120 S 142.311 E 33 eucalypt open forest forest

September BIR Birdsville, QLD Australia 2000 25.897 S 138.881 E 65 tussock grassland with low shrubs grassland

August BLE Blencoe Falls, QLD Australia 2002 18.206 S 145.529 E 593 eucalypt open forest woodland

October BRZ1 flona 67 km Brazil 2003 2.860 S 54.920 W 200 dense evergreen forest forest

BRZ2 ZF2 km 34 Brazil May 2003 2.583 S 60.100 W 140 dense evergreen forest forest

September BRZ3 ZF2 km 25 Brazil 2003 2.609 S 60.209 W 130 dense evergreen forest forest

BRZ4 Ji-Parana Brazil May 2001 10.080 S 61.920 W 190 open evergreen forest forest

January BUN Bundaberg, QLD Australia 2001 25.251 S 152.586 E 57 eucalypt woodland woodland

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Supplementary Table 2 (cont)

October CHI Chilli Beach, QLD Australia 2001 12.631 S 143.423 E 40 rainforest forest

COE Coen, QLD Australia May 2000 14.507 S 143.418 E 270 eucalypt woodland woodland

August COR Cordillo Downs, SA Australia 2002 26.952 S 140.966 E 73 grassland with eucaplyt grassland

August CRK Cork Station, QLD Australia 2002 22.885 S 142.371 E 189 pasture savanna grassland grassland

September DAR Mary River NP, NT Australia 2000 12.727 S 131.811 E 32 eucalypt open forest woodland

DER Derby, WA Australia May 2001 17.355 S 123.735 E 22 eucalypt shrubland/tussock grassland wooded grassland

Diamantina River, August DIA QLD Australia 2002 25.976 S 139.360 E 40 bare tussock grassland grassland

DIK Dikbos Botswana April 1999 26.700 S 21.100 E 905 south Kalahari bush savanna grassland

FIT Fitzroy Crossing, WA Australia May 2001 18.139 S 125.293 E 116 tussock grassland with eucalypt shrub wooded grassland

January FRA Fraser Island, QLD Australia 2001 25.227 S 153.232 E 116 eucalypt open forest forest

FRB Fraser Island, QLD Australia March 2001 25.484 S 153.083 E 131 rainforest forest

August GRO Grove Station, QLD Australia 2002 22.316 S 142.619 E 168 pasture savanna grassland grassland

HAN Hann River, QLD Australia May 2000 15.254 S 143.954 E 50 eucalyptus melaleuca shrubland shrubland

Happy Valley Station, August HAP QLD Australia 2002 22.745 S 142.628 E 248 shrubland shrubland

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Supplementary Table 2 (cont)

Hinchinbrook Island, August HIN QLD Australia 2002 18.450 S 146.327 E 16 rainforest forest

ILTB Ileret Kenya May 2010 4.287 N 36.260 E 435 dwarf acacia commiphora shrubland shrubland

ILTF Ileret Kenya May 2010 4.317 N 36.261 E 391 riparian woodland (riparian) woodland

ILTG Ileret Kenya May 2010 4.277 N 36.221 E 364 alkali grassland grassland

September INN Cameron Corner, SA Australia 2000 28.999 S 140.694 E 95 hummock grassland/shrub/forbland wooded grassland

Septermber KAK Kakadu, NT Australia 2000 13.513 S 132.263 E 240 eucalypt woodland woodland

KAKF Kakamega NR Kenya May 2010 0.356 N 34.861 E 1628 Guineo-Congolian forest forest

KAKO Kakamega NR Kenya May 2010 0.348 N 34.869 E 1571 grassy glade grassland

September KAT Katherine, NT Australia 2000 14.892 S 131.945 E 180 eucalypt open woodland wooded grassland

Kennedy State August KEN Forest, QLD Australia 2002 18.207 S 145.764 E 609 rainforest forest

August KIR Kirama Station, QLD Australia 2002 18.107 S 145.632 E 676 eucalypt open forest woodland

Lark Dinosaur Quarry, August LAR QLD Australia 2002 23.034 S 142.438 E 252 shrubland shrubland

October LAS Ayres Rock, NT Australia 2000 25.156 S 132.184 E 484 shrubland shrubland

January MAC Maclean, NSW Australia 2000 29.781 S 153.273 E 60 eucalypt open forest forest

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Supplementary Table 2 (cont)

August MAU Maun Botswana 1999 19.900 S 23.600 E 944 Mopane tree and bush savanna wooded grassland

MERB Meru NP Kenya May 2010 0.070 S 38.413 E 342 acacia woodland woodland

MERF Meru NP Kenya May 2010 0.072 S 38.419 E 330 riparian forest (riparian) forest

MERG Meru NP Kenya May 2010 0.180 N 38.227 E 590 grassland savanna grassland

MON Mongu Zambia April 2000 15.300 S 23.100 E 1000 dry deciduous forest woodland

February MOR Moreton Island, QLD Australia 1999 27.149 S 153.399 E 172 eucalypt open forest forest

MUS Musgrave, PLD Australia May 2000 13.712 S 143.101 E 120 eucalypt woodland woodland

Tsavo West NP. MZMS Mzima Springs Kenya May 2010 2.987 S 38.022 E 690 riparian woodland (riparian) woodland

NAKB Nakuru NP Kenya May 2010 0.466 S 36.103 E 1798 acacia- euphorbia bushland bushland

NAKF Nakuru NP Kenya May 2010 0.418 S 36.125 E 1783 forest forest

NAKG Nakuru NP Kenya May 2010 0.417 S 36.126 E 1779 grassland savanna grassland

NAKL Nakuru NP Kenya May 2010 0.358 S 36.059 E 1799 alkali grassland grassland

Nappa Merrie August NAP Homestead, QLD Australia 2002 27.894 S 141.443 E 122 shrub/forbland grassland

NBIF Nairobi NP Kenya May 2010 1.348 S 36.767 E 1792 dry forest forest

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Supplementary Table 2 (cont)

NBIG Nairobi NP Kenya May 2010 1.352 S 36.796 E 1689 grassland savanna grassland

NCH N. Chobe Botswana May 1999 18.200 S 25.400 E 1073 dry deciduous forest shrubland

New Haven Downs, August NEH QLD Australia 2002 22.992 S 142.809 E 235 shrubland shrubland

August OKA Okavango Botswana 1999 19.400 S 23.200 E 956 Mopane tree and bush savanna wooded grassland

PIL Pilliga, NSW Australia May 2000 30.577 S 149.544 E 253 pine open forest woodland

SAMF Samburu NR Kenya May 2010 0.567 N 37.528 E 872 riparian forest (riparian) forest

SAMG Samburu NR Kenya May 2010 0.582 N 37.537 E 884 wooded grassland wooded grassland

SAR Sarawak Malaysia April 2002 4.000 N 113.800 E 200 rainforest forest

August SCH S Chobe Botswana 1999 19.400 S 23.200 E 956 Mopane tree and bush savanna wooded grassland

August SEK Sekoma Botswana 1999 24.300 S 23.700 E 1097 south Kalahari bush savanna wooded grassland

SHMF Shimba Hill NR Kenya May 2010 4.235 S 39.418 E 405 coastal forest forest

SHMG Shimba Hill NR Kenya May 2010 4.234 S 39.419 E 395 grassland grassland

SOKB Sokoke NR Kenya May 2010 3.322 S 39.925 E 30 Brachystegia woodland woodland

SOKC Sokoke NR Kenya May 2010 3.321 S 39.887 E 60 Cynometra forest forest

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Supplementary Table 2 (cont)

SOKM Sokoke NR Kenya May 2010 3.322 S 39.932 E 26 mixed forest forest

September TEN Wauhope, NT Australia 2000 20.794 S 134.210 E 365 mixed shrubland shrubland

September TOP Top Springs, NT Australia 2000 17.188 S 132.354 E 252 mixed shrubland shrubland

Tana River Primate TRPR Reserve Kenya May 2010 1.877 S 40.140 E 44 riparian forest (riparian) forest

TSVE Tsavo East NP Kenya May 2010 3.362 S 38.645 E 505 riparian woodland (riparian) woodland

TSVW Tsavo West NP Kenya May 2010 2.747 S 38.129 E 884 Acacia bushland wooded grassland

TURK Turkwell Kenya May 2010 3.140 N 35.868 E 449 dwarf acacia shrubland shrubland

WEL Welverdein Botswana April 1999 26.700 S 21.000 E 902 arid shrub savanna grassland

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Supplementary Table 2 (cont, second panel)

Fraction of woody cover

Field FE ar. tr. %WC d13C d13C gap d13C d13C canopy abbrev. est. ±1s areal ±1s trans. ±1s fisheye ave. ±1s gap sd canopy sd Reference 1 12 13 14 15 16 17 18 19 20 21 22 23 24 24

ANA 0.09 0.03 0.03 0.03 0.12 0.04 0.08 0.05 -18.45 0.20 -20.85 0.20 Ref. 5

ARR 0.05 0.01 0.00 0.01 0.03 0.04 0.03 0.02 -20.48 0.11 -21.67 0.21 Ref. 5

AWSB 0.09 0.17 0.11 0.13 0.06 -18.19 1.13 -21.47 4.09 Ref. 27

AWSF 0.69 0.86 0.05 0.77 0.12 -22.94 2.14 -23.55 1.29 Ref. 27

BAM 0.60 0.16 0.81 0.22 0.62 0.14 0.68 0.12 -27.08 0.20 -26.94 0.20 Ref. 5

BIR 0.05 0.00 0.01 0.01 0.03 0.01 0.03 0.02 -20.23 0.19 -23.18 0.22 Ref. 5

BLE 0.60 0.15 0.74 0.15 0.50 0.15 0.61 0.12 -22.32 0.11 -23.39 0.39 Ref. 5

BRZ1 0.99 0.99 -27.84 0.43 Ref. 18

BRZ2 0.99 0.99 -28.97 0.77 Ref. 18

BRZ3 0.99 0.99 -29.30 0.41 Ref. 18

BRZ4 0.95 0.95 -28.09 0.30 Ref. 18

BUN 0.49 0.09 0.85 0.06 0.58 0.08 0.64 0.19 -24.84 1.86 -25.13 1.52 Ref. 5

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Supplementary Table 2 (cont, second panel)

CHI 0.77 0.04 0.92 0.05 0.93 0.02 0.87 0.09 -27.97 0.20 -28.11 0.20 Ref. 5

COE 0.48 0.03 0.50 0.12 0.43 0.11 0.47 0.04 -23.89 0.34 -25.63 0.33 Ref. 5

COR 0.05 0.01 0.08 0.03 0.08 0.05 0.07 0.02 -18.06 0.52 -20.52 0.08 Ref. 5

CRK 0.01 0.00 0.00 0.00 0.01 0.01 0.01 0.00 -13.90 0.13 -16.25 0.23 Ref. 5

DAR 0.51 0.07 0.71 0.15 0.33 0.08 0.52 0.19 -24.81 0.30 -25.73 0.22 Ref. 5

DER 0.32 0.06 0.40 0.14 0.28 0.15 0.33 0.06 -23.24 0.20 -24.79 0.20 Ref. 5

DIA 0.02 0.00 0.03 0.02 0.02 0.01 0.02 0.01 -21.58 0.04 -23.12 0.03 Ref. 1

DIK 0.08 0.00 0.08 0.06 0.08 -16.99 -19.47 Ref. 17

FIT 0.20 0.02 0.17 0.09 0.09 0.04 0.15 0.06 -19.90 0.20 -24.08 0.10 Ref. 5

FRA 0.56 0.04 0.85 0.11 0.84 0.12 0.75 0.16 -27.34 0.43 -27.33 0.24 Ref. 5

FRB 0.90 0.90 -27.80 0.48 -27.99 Ref. 5

GRO 0.00 0.01 0.01 0.01 0.00 0.01 0.00 0.01 -15.54 0.59 Ref. 5

HAN 0.38 0.08 0.65 0.23 0.30 0.12 0.44 0.18 -23.35 1.19 -24.69 1.15 Ref. 5

HAP 0.60 0.11 0.53 0.11 0.26 0.05 0.46 0.18 -20.16 -22.42 0.23 Ref. 5

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Supplementary Table 2 (cont, second panel)

HIN 0.90 0.02 0.90 -27.58 0.04 -27.60 0.01 Ref. 5

ILTB 0.55 0.59 0.65 0.09 0.60 0.05 -21.67 1.13 -22.62 0.75 this study

ILTF 0.60 0.60 0.68 0.11 0.56 0.61 0.05 -23.62 0.62 -25.80 1.22 this study

ILTG 0.01 0.00 0.02 0.01 0.00 0.01 0.00 -17.35 0.78 -18.47 2.01 this study

INN 0.16 0.04 0.06 0.02 0.09 0.05 0.10 0.05 -20.37 0.52 -22.74 0.58 Ref. 5

KAK 0.44 0.05 0.33 0.19 0.19 0.08 0.32 0.13 -25.34 0.45 -25.86 0.13 Ref. 5

KAKF 0.93 0.85 0.96 0.04 0.97 0.93 0.05 -25.55 1.22 this study

KAKO 0.00 0.00 0.06 0.10 0.07 0.03 0.05 0.04 -14.69 0.65 -21.33 0.16 this study

KAT 0.36 0.07 0.42 0.18 0.21 0.07 0.33 0.11 -21.86 1.41 -23.42 0.94 Ref. 5

KEN 0.99 0.02 0.99 -27.62 0.02 -27.74 0.09 Ref. 5

KIR 0.60 0.18 0.67 0.18 0.66 0.19 0.64 0.04 -24.09 0.12 -25.34 0.05 Ref. 5

LAR 0.20 0.06 0.31 0.06 0.34 0.12 0.28 0.07 -16.36 0.30 -18.99 0.22 Ref. 5

LAS 0.40 0.00 0.35 0.16 0.30 0.04 0.35 0.05 -20.00 1.01 -23.33 0.46 Ref. 5

MAC 0.60 0.12 0.89 0.10 0.95 0.05 0.81 0.18 -27.22 0.23 -27.28 0.24 Ref. 5

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Supplementary Table 2 (cont, second panel)

MAU 0.30 0.05 0.41 0.07 0.36 0.10 0.36 -20.04 1.26 -22.17 0.38 Ref. 17

MERB 0.63 0.69 0.14 0.24 0.52 0.25 -20.19 0.57 -25.44 0.57 this study

MERF 0.57 0.84 0.11 0.68 0.70 0.14 -25.22 0.13 -26.00 1.21 this study

MERG 0.00 0.00 0.10 0.12 0.03 0.02 0.08 0.05 -15.94 1.29 -18.61 1.87 this study

MON 0.45 0.45 -25.40 -24.70 Ref 17

MOR 0.58 0.07 0.83 0.06 0.85 0.09 0.75 0.15 -28.07 0.13 -28.13 0.13 Ref. 5

MUS 0.40 0.55 0.08 0.64 0.08 0.53 0.12 -24.71 0.44 -25.57 0.35 Ref. 5

MZMS 0.71 0.67 0.69 0.09 0.71 0.70 0.02 -21.10 3.89 -24.86 1.04 this study

NAKB 0.44 0.51 0.36 0.08 0.59 0.47 0.10 -14.69 2.02 -17.39 1.26 this study

NAKF 0.69 0.89 0.64 0.08 0.77 0.75 0.11 -21.84 1.79 -22.11 1.88 this study

NAKG 0.03 0.00 0.01 0.02 0.02 0.02 0.02 0.01 -14.03 1.35 -13.64 this study

NAKL 0.00 0.00 0.03 0.01 0.01 0.00 0.02 0.01 -14.88 1.13 this study

NAP 0.05 0.01 0.05 0.02 0.05 0.03 0.05 0.00 -18.66 0.11 -20.81 Ref. 5

NBIF 0.74 0.88 0.77 0.11 0.83 0.80 0.06 -23.44 1.35 -24.15 1.49 this study

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Supplementary Table 2 (cont, second panel)

NBIG 0.02 0.00 0.02 0.03 0.03 0.02 0.00 -14.61 0.52 -18.15 1.95 this study

NCH 0.30 0.05 0.34 0.02 0.37 0.08 0.34 -21.96 -22.67 Ref. 17

NEH 0.45 0.07 0.45 0.07 0.47 0.15 0.46 0.01 -20.13 0.13 -21.80 0.42 Ref. 5

OKA 0.25 0.05 0.20 0.11 0.13 0.13 0.19 -21.80 -24.20 Ref. 17

PIL 0.41 0.05 0.67 0.05 0.36 0.03 0.48 0.17 -25.04 0.22 -25.48 0.32 Ref. 5

SAMF 0.83 0.68 0.94 0.04 0.89 0.84 0.11 -27.10 0.30 -27.58 0.30 this study

SAMG 0.20 0.22 0.24 0.13 0.21 0.22 0.02 -19.88 1.54 -22.94 1.60 this study

SAR 0.90 0.90 -29.40 -29.42 this study

SCH 0.16 0.10 0.26 0.17 0.26 0.16 0.23 -20.14 1.48 -21.61 0.86 Ref. 17

SEK 0.19 0.05 0.17 0.14 0.16 0.14 0.17 -17.15 0.84 -20.73 1.32 Ref. 17

SHMF 0.80 0.74 0.03 0.88 0.81 0.07 -27.70 0.45 this study

SHMG 0.00 0.00 0.02 0.03 0.03 0.01 0.02 0.01 -18.13 1.57 this study

SOKB 0.70 0.81 0.82 0.03 0.82 0.79 0.06 -25.19 1.06 -26.18 0.84 this study

SOKC 0.96 0.97 0.99 0.96 0.97 0.01 -26.62 0.84 this study

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Supplementary Table 2 (cont, second panel)

this SOKM 0.95 0.98 0.99 0.92 0.96 0.03 -27.46 1.17 -27.70 0.75 study

TEN 0.15 0.00 0.21 0.13 0.19 0.04 0.18 0.03 -18.99 0.54 -21.00 0.53 Ref. 5

TOP 0.38 0.13 0.13 0.08 0.15 0.05 0.22 0.14 -20.35 0.34 -22.90 0.53 Ref. 5

this TRPR 0.79 0.73 0.99 0.91 0.86 0.12 -26.41 2.44 -28.12 0.25 study

this TSVE 0.40 0.39 0.18 0.40 0.01 -17.99 0.93 -23.49 2.77 study

this TSVW 0.40 0.35 0.41 0.09 0.37 0.38 0.03 -18.72 1.14 -21.45 0.88 study

this TURK 0.41 0.05 0.41 -23.74 1.28 -25.42 1.31 study

WEL 0.01 0.03 0.03 0.03 0.02 -18.55 -21.83 Ref. 17

Key to column headings

1 Abbreviation 2 Locality description 3 Country 4 Month and year of collection 5 representative latitude (in most cases multiple sites were sampled) 6 latitude (N or S) 7 representative longitude (in most cases multiple sites were sampled) 8 longitude (E or W) 9 representive elevation (msl) 10 field description of vegetation 10a J.A. Carnahan classification: ref: Australian Soil and Land Information Group (AUSLIG) 1990,

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Atlas of Australian Resources. Volume 6 Vegetation,` AUSMAP, Department of Administrative Services, Canberra. 10b http://www.anra.gov.au/topics/vegetation/pubs/native_vegetation/nat_veg_contents.html 11 UNESCO classification 12 Fraction woody cover: field estimate based on transects 13 Fraction woody cover: field estimate uncertaintiy Fraction woody cover: calculated using the fraction of areal coverage of "woody" vegetation - 14 gray scale or multi-spectral data 15 Fraction woody cover: areal estimate uncertainty Fraction woody cover: calculated using transects of woody cover using photography using gray 16 scale 17 Fraction woody cover: transect estimate uncertainty 18 Fraction woody cover: using fish-eye photography assuming crown canopy cover 19 Fraction woody cover: average of all methods 20 Fraction woody cover: uncertainty of (19) 21 d13C of "gap" samples using Bird et al (2004) method 22 uncertainty in d13C of "gap" samples (1 sd) 23 d13C of "canopy" samples using Bird et al (2004) method 24 uncertainty in d13C of "canopy" samples (1 sd) 25 reference

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Supplementary Table 3. Stratigraphic markers used to define temporal bins from the Omo‐Turkana Basin8,9,27,28,46.

Stratigraphic Bin Lower division Upper division Age (Myr ago) Nawata Formation Lower Marker Purple Marker 7.44‐5.7 (above Lower Marker) Apak Member of the Purple Marker Lothagam Basalt 5.7‐4.3 Nachukui Formation Kanapoi strata Basalt at base Kanapoi Tuff 4.2 – 3.9 of section Basal‐A Base of Tuff A ~4 – 3.6 Lonyumun Mb. (=Lokochot) of Nachukui Fm. A‐B Tuff A Tuff B (=Tulu 3.6 – 3.44 (=Lokochot) Bor) B‐C Tuff B (=Tulu Tuff C (=Hasuma) 3.44 – 2.85 Bor) C‐F Tuff C Tuff F 2.85 – 2.33 (=Hasuma) (=Kalochoro) F‐H2 Tuff F Tuff H2 (=KBS) 2.33 – 1.87 (=Kalochoro) H2‐J4 Tuff H2 (=KBS) Tuff J4 1.87 – 1.61 (=Morutot) J4‐L Tuff J4 Tuff L (=Chari) 1.61 – 1.38 (=Morutot) L‐present Tuff L (=Chari) modern 1.38 – modern

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Supplementary Table 4. Stratigraphic markers used to define temporal bins from the Awash Valley11,20‐23,47.

Stratigraphic Bin Lower division Upper division Age (Myr ago) Adu‐Asa Formation Base of Base of 5.77‐5.54 (Middle Awash) formation formation Sagantole Formation at Segala Noumou As Duma Fault 4.6 – 4.2 Gona Fault Aramis site 4.4 Asa Issie site 4.2 – 4.1 Basal Member of the Base of Hadar Sidi Hakoma Tuff 3.8 – 3.42 Hadar Formation Fm. (SHT) Sidi Hakoma Member Sidi Hakoma Tuff Triple Tuff 4 (TT‐ 3.42 – 3.24 (SHT) 4) Denen Dora and Kada Triple Tuff 4 (TT‐ Busidima 3.24 – 2.92 Hadar Members 4) Unconformity Surface (BUS) BUS – Busidima Gauss/Matuyama 2.7 – 2.58 Gauss/Matuyama Unconformity transition (G/M) transition, G/M, Surface (BUS G/M – Boolihinan Tuff, Gauss/Matuyama Boolihinan Tuff 2.58 – 1.63 transition (G/M) Boolihinan Tuff – Boolihinan Tuff Dahuli Tuff 1.63 – 0.83 Dahuli Tuff, Daka site, Bouri ~1.0 Formation, Dahuli Tuff – Bironita Dahuli Tuff Bironita Tuff 0.83 – 0.64 Tuff, Bironita Tuff – WAVT, Bironita Tuff Waidedo Vitric 0.64 – 0.15 Tuff (WAVT) WAVT‐present, Waidedo Vitric present 0.15 – 0 Tuff (WAVT)

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Supplementary Table 5 (following pages). Stable isotope data from Gona (data not previously published)

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Supplementary Table 5 13 18 Sample ID Type Formation d CVPDB dOVPDB n Area Marker Meters +/-

(+ for distance to lower strat, - for mean (‰) 1s mean (‰) 1s distance to upper strat) sub- GON05-406.1 nodule mod./modern -6.5 -1.4 Busidima surface/modern sub- GON05-418 gravel pendant mod./modern -1.1 -5.2 Badu Dora surface/modern GON07-13 nodule Busidima -5.8 -4.7 Odele ASASH07-30 4.0 GON07-30 nodule Busidima -2.7 -3.2 Odele ASASH07-30 3.5 GON07-31 nodule Busidima -3.7 -3.3 Odele ASASH07-30 1.5 GON07-37 nodule Busidima -1.1 -5.7 Odele ASASH07-30 45.0 below WAVT GONJQ-135 nodule Busidima -0.9 -5.7 Busidima Dahuli Tuff 11.4 GONJQ-136 nodule Busidima -5.2 -2.9 Busidima Dahuli Tuff 18.4 GONJQ-137 nodule Busidima -5.7 -5.0 Busidima Dahuli Tuff 25.4 GONJQ-203 nodule Busidima -5.8 -6.5 Asbole Bironita Tuff 6 GONJQ-204 nodule Busidima -0.4 -6.1 Asbole Bironita Tuff 8.5 GONJQ-212 nodule Busidima -5.4 -5.0 Asbole Gawis Tuff 3.5 GONJQ-213 nodule Busidima -3.7 -6.7 Asbole Bironita 14 GONJQ-214 nodule Busidima -5.8 -5.8 Asbole Bironita 14 GONJQ-232 nodule Busidima -5.4 -3.9 Busidima Boolihinan Tuff 33.25 GONJQ-268 nodule Busidima -4.5 -3.5 Asbole Gawis Tuff 3 GONJQ-269 nodule Busidima -5.5 -4.3 Asbole Gawis Tuff 14 GONJQ-282 nodule Busidima -1.6 -4.1 Yaalu Gawis Tuff 1 GONJQ-283 nodule Busidima -4.9 -4.4 Yaalu Gawis Tuff 1 GONJQ-284 nodule Busidima -3.7 1.8 -2.5 2.0 2 Yaalu Gawis Tuff 1 GONJQ-285 nodule Busidima -2.7 -6.7 Yaalu Gawis Tuff 6.5 GONJQ-286 nodule Busidima -2.5 -6.7 Yaalu Gawis Tuff 10.5 GONJQ-288 nodule Busidima -1.7 0.6 -4.5 0.8 2 Yaalu Gawis Tuff 28 GONJQ-308 nodule Busidima -2.8 -2.4 Yaalu Talata Tuff 2.5 GONJQ-309 nodule Busidima -1.7 -6.2 Asbole Gawis Tuff -3 below Bironita GONJQ-202 nodule Busidima -7.0 -4.8 Asbole Bironita Tuff -1

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Supplementary Table 5 (cont) below Dahuli GON07-12 nodule Busidima -4.3 -3.9 Busidima Boolihinan Tuff 2.5 GONJQ-067 platey vertic fill Busidima -5.6 -4.8 Ounda Gona GONASH-16 3 GONJQ-068 nodule Busidima -5.0 -5.5 Ounda Gona GONASH-16 3 GONJQ-069 nodule Busidima -4.8 -5.0 Ounda Gona GONASH-16 3 GONJQ-071 nodule Busidima -3.4 -5.5 Ounda Gona Fialu Tuff 8 GONJQ-072 nodule Busidima -3.6 -4.8 Ounda Gona Fialu Tuff -12 GONJQ-108 nodule Busidima -2.0 -6.2 Busidima Boolihinan Tuff 1.5 GONJQ-153 nodule Busidima -1.1 -7.9 Dana Aoule Olduvai top 5.5 GONJQ-156 nodule Busidima -6.5 -5.4 Dana Aoule Olduvai top 5.35 GONJQ-157 nodule Busidima -6.0 -5.2 Dana Aoule Olduvai top 9.5 GONJQ-158 nodule Busidima -5.8 -3.8 Dana Aoule Olduvai top 12 GONJQ-230 nodule Busidima -5.4 -6.2 Busidima Boolihinan Tuff 1.25 GONJQ-231 nodule Busidima -3.5 -4.7 Busidima Boolihinan Tuff 11.75 GONJQ-255 nodule Busidima -3.2 -5.4 Ounda Gona Fialu Tuff -2 GONJQ-257 nodule Busidima -6.2 -4.7 Ounda Gona Fialu Tuff -11 GONJQ-265 nodule Busidima -2.4 -6.1 Busidima Dahuli Tuff -17 GONJQ-266 nodule Busidima -3.3 -4.2 Busidima Dahuli Tuff -10 below Boolihinan GONJQ-014 platey vertic fill Busidima -5.2 -4.7 Kada Gona AST-2.75 4 GONJQ-064 nodule Busidima -4.2 -6.3 Ounda Gona GONASH-16 -4 GONJQ-085 nodule Busidima -3.3 -7.7 Ounda Gona G35 7.5 GONJQ-098 platey vertic fill Busidima -5.1 -6.3 Kada Gona AST-2.75 4 GONJQ-104 rhizolith Busidima -5.8 -4.0 Busidima Boolihinan Tuff -21 GONJQ-113 rhizolith Busidima -5.1 -4.1 Busidima Boolihinan Tuff -0.5 GONJQ-124 nodule Busidima -4.4 -6.5 Kada Gona AST-2.75 -1 GONJQ-143 nodule Busidima -6.0 -4.1 Dana Aoule GONASH-39 28 GONJQ-155 rhizolith Busidima -6.6 -5.1 Dana Aoule Olduvai top 3 GONJQ-160 nodule Busidima -3.8 -4.1 Ounda Gona GONASH-11/12 5 GONJQ-185 nodule Busidima -6.5 -6.0 Kada Gona AST-2.75 -4 GONJQ-186 nodule Busidima -5.1 -6.5 Kada Gona AST-2.75 0 GONJQ-187 nodule Busidima -5.6 -6.2 Kada Gona AST-2.75 0 GONJQ-225 nodule Busidima -5.6 -3.9 Busidima Boolihinan Tuff -0.3

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Supplementary Table 5 (cont) GONJQ-228 nodule Busidima -5.4 -5.3 Busidima Boolihinan Tuff -1 GONJQ-229 nodule Busidima -5.3 -5.6 Busidima Boolihinan Tuff -2 GONJQ-254 nodule Busidima -4.4 -5.2 Ounda Gona GONASH-16 -3 GONJQ-256 nodule Busidima -6.2 -5.0 Ounda Gona Fialu Tuff -28 below Gauss/Matuyama GONJQ-016 nodule Busidima -4.9 -6.7 Kada Gona AST-2.75 -21.5 GONJQ-048 nodule Busidima -4.0 -6.9 Kada Gona AST-2.75 -0.5 GONJQ-053 nodule Busidima -6.4 -6.5 Ounda Gona DD3 Sand 45 GONJQ-054 nodule Busidima -6.9 -8.3 Ounda Gona DD3 Sand 38.5 GONJQ-055 nodule Busidima -5.8 -7.7 Ounda Gona DD3 Sand 41 GONJQ-057 nodule Busidima -5.8 -6.4 Ounda Gona GONASH-14 -19 GONJQ-058 nodule Busidima -5.7 -4.9 Ounda Gona GONASH-14 -8 GONJQ-122 nodule Busidima -5.0 0.9 -6.4 0.8 4 Kada Gona AST 2.75 7 GONJQ-125 rhizolith Busidima -5.4 -6.4 Kada Gona AST 2.75 8.5 GONJQ-140 nodule Busidima -6.4 -7.3 Dana Aoule GONASH-39 -5.5 GONJQ-141 nodule Busidima -7.6 -3.3 Dana Aoule GONASH-39 1 GONJQ-142 nodule Busidima -5.5 -6.0 Dana Aoule GONASH-39 8.5 GONJQ-152 nodule Busidima -5.7 -6.2 Ounda Gona GONASH-11/12 -9 GONJQ-159 nodule Busidima -5.5 -6.5 Ounda Gona GONASH-14 -1 GONJQ-167 rhizolith Busidima -4.9 -5.7 Ounda Gona GONASH-14 -9 GONJQ-170 nodule Busidima -5.9 -6.4 Ounda Gona GONASH-14 -21 GONJQ-171 nodule Busidima -3.8 -5.6 Ounda Gona GONASH-14 -1 GONJQ-172 nodule Busidima -5.9 -5.3 Ounda Gona GONASH-14 -13 GONJQ-173 nodule Busidima -5.6 -5.5 Ounda Gona GONASH-14 -7.75 GONJQ-174 nodule Busidima -5.2 -5.4 Ounda Gona GONASH-14 -6 GONJQ-175 nodule Busidima -5.6 -5.3 Ounda Gona GONASH-14 -5.15 GONJQ-176 nodule Busidima -5.6 -5.6 Ounda Gona GONASH-14 -4.5 GONJQ-177 nodule Busidima -5.7 -5.9 Ounda Gona GONASH-14 -4 GONJQ-178 nodule Busidima -5.5 -5.1 Ounda Gona GONASH-14 -20.5 GONJQ-179 nodule Busidima -6.2 -4.7 Ounda Gona GONASH-14 -11 GONJQ-188 nodule Busidima -4.3 -6.7 Kada Gona AST-2.75 -4 GONJQ-211 rhizolith Busidima -5.8 -5.8 Kada Gona AST-2.75 -6

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Supplementary Table 5 (cont) below Unconformity GONJQ-018 nodule Hadar -7.2 -9.1 Kada Gona KHT 23.5 GONJQ-020 nodule Hadar -7.2 -6.6 Kada Gona KHT -1 GONJQ-022 nodule Hadar -12.5 -4.0 Kada Gona DD3 5 GONJQ-028 nodule Hadar -7.4 -4.2 Kada Gona SHT 92 GONJQ-037 nodule Hadar -6.4 -4.2 Kada Gona SHT 53 GONJQ-046 rhizolith Hadar -5.1 -6.5 Kada Gona AST-2.75 -9.5 GONJQ-138 rhizolith Hadar -7.0 -7.3 Dana Aoule GONASH-39 -22.5 GONJQ-147 platey vertic fill Hadar -5.1 -5.3 Ounda Gona GONASH-11/12 -30 GONJQ-148 nodule Hadar -6.0 -5.8 Ounda Gona GONASH-11/12 -32 GONJQ-150 nodule Hadar -6.2 -6.4 Ounda Gona GONASH-11/12 -32 GONJQ-151 nodule Hadar -5.3 -6.6 Ounda Gona GONASH-11/12 -32 Sagantole Formation GONJQ-130 nodule Sagantole -3.3 3.2 -8.2 1.3 2 GWM Red Beds GONJQ-252 nodule Sagantole -8.7 0.5 -14.5 1.4 6 WM GONJQ-289 nodule Sagantole -8.6 -10.8 GWM-67 GONNL-015 nodule Sagantole -8.1 1.0 -13.8 0.8 2 GWM Red Beds GONNL-045 nodule Sagantole -5.0 0.3 -6.4 0.4 3 GWM South

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Supplementary Table 6. Stable isotope data from Dikika (data not previously published)

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Supplementary Table 6 Sample ID pedogenic CaCO3 organic matter meters 13 18 13 18 13 Year Sample Sub δ CVPDB δOVPDB δCVPDB δOVPDB n δ CVPDB %C Formation Age bin Area Marker +/- mean (‰) mean (‰) 1σ 1σ n (‰)

E06 7024 A -1.8 -6.2 Busidima M/S/M Halalalee Bed Korina Tuff 13 E06 7024 B -1.0 -5.2 Busidima M/S/M Halalalee Bed Korina Tuff 13 E06 7025 -0.9 -4.7 Busidima M/S/M Halalalee Bed Korina Tuff 13 WAVT (0.16 Myr)

ZKE06 7038 A-K -7.8 -3.1 0.40 0.89 11 Busidima UB Nogus Kaberie Bironita Tuff 5 E02- 1128 -5.5 -4.6 Busidima UB Nogus Kaberie Bironita Tuff 0 E02- 1144 -4.5 -3.6 Busidima UB Elamuita Bironita Tuff 2 Bironita Tuff (0.64 Myr)

E02- 1125 -6.9 -4.0 Busidima DB Nogus Kaberie Bironita Tuff -1 E02- 1119 -6.0 -2.5 Busidima DB Hohoye Bironita Tuff -1 E02- 1124 -7.3 -3.9 0.01 0.00 2 Busidima DB Nogus Kaberie Bironita Tuff -3 E02- 1145 -4.0 -3.3 0.71 1.41 2 Busidima DB Bairetele Bironita Tuff -3 E02- 1142 -6.4 -4.1 Busidima DB Elamuita Bironita Tuff -3 E02- 1143 -5.0 -4.7 Busidima DB Elamuita Bironita Tuff -3 E02- 1141 -6.7 -4.7 Busidima DB Elamuita Bironita Tuff -5 E02- 1140 -6.6 -3.1 Busidima DB Elamuita Bironita Tuff -7 E02- 1123 -7.2 -4.0 Busidima DB Nogus Kaberie Bironita Tuff -5 E03- 3063 -6.6 -4.7 Busidima DB Urbolo Bironita Tuff -4 E02- 1139 a-b -6.8 -4.9 Busidima DB Elamuita Bironita Tuff -8 E02- 1138 b -7.1 -4.7 Busidima DB Elamuita Bironita Tuff -8 E02- 1138 c -6.8 -4.5 Busidima DB Elamuita Bironita Tuff -8 E02- 1148 -6.2 -4.4 Busidima DB Bairetele Bironita Tuff -7 E02- 1122 -7.6 -4.4 Busidima DB Nogus Kaberie Bironita Tuff -6 E06 7014 -7.6 -1.9 Busidima DB Andidi E06-7017 6 E02- 1118 -5.4 -3.8 Busidima DB Hohoye Bironita Tuff -8 E02- 1117 -5.6 -5.2 Busidima DB Hohoye Bironita Tuff -10 E02- 1121 e -7.0 -3.5 Busidima DB Nogus Kaberie Bironita Tuff -8 E02- 1150 -5.4 -4.4 0.74 0.12 2 Busidima DB Bairetele Bironita Tuff -12 E02- 1120 -3.7 -3.8 0.75 0.45 2 Busidima DB Hohoye Bironita Tuff -12 E02- 1146 -5.9 -4.7 0.04 0.03 2 Busidima DB Bairetele Bironita Tuff -13

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Supplementary Table 6 (cont) E02- 1134 c -6.6 -4.5 0.70 0.35 5 Busidima DB Bairetele Bironita Tuff -13 E02- 1134 -17.0 0.05 Busidima DB Bairetele Bironita Tuff -13 E02- 1134 -17.3 0.05 Busidima DB Bairetele Bironita Tuff -13 E03- 3061 A -5.5 -3.6 Busidima DB Urbolo Bironita Tuff 2 E03- 3061 B -6.0 -4.3 Busidima DB Urbolo Bironita Tuff 2 E02- 1147 A -4.2 -4.9 Busidima DB Bairetele Bironita Tuff 13 E02- 1147 B -6.8 -5.2 Busidima DB Bairetele Bironita Tuff 13 Approximate position of Dahuli Tuff (0.83 Myr)

E02- 1149 -6.5 -4.1 Busidima UMB Bairetele Bironita Tuff -30 E02- 1116 -7.1 -8.0 Busidima UMB Hohoye Bironita Tuff -27 E02- 1115 -7.1 -4.5 Busidima UMB Hohoye Bironita Tuff -30 E02- 1113 e -6.5 -4.3 Busidima UMB Hohoye Bironita Tuff -37 E02- 1112 -5.8 -4.4 Busidima UMB Hohoye Bironita Tuff -39 Approximate position of Boolihinan Tuff (1.6 Myr)

ZBE10 9000 -3.0 -6.7 Busidima MB* (or UMB) Inaalale Inaalale Tuff ~3 ZBE10 9001 -3.7 -7.4 Busidima MB* (or UMB) Inaalale Inaalale Tuff ~3 ZBE10 9002 -5.1 -5.2 Busidima MB* (or UMB) Inaalale Inaalale Tuff ~3 E10 9101 -5.3 -6.2 Busidima MB* (or UMB) Inaalale Inaalale Tuff 12 E10 9103 -5.5 -4.4 Busidima MB* (or UMB) Inaalale Inaalale Tuff 3 E10 9104 -7.4 -5.6 Busidima MB* (or UMB) Inaalale Inaalale Tuff 4 E10 9105 -5.8 -5.1 Busidima MB* (or UMB) Inaalale Inaalale Tuff 7 E10 9106 -5.2 -5.9 Busidima MB* (or UMB) Inaalale Inaalale Tuff 5 Busidima Unconformity Surface (2.94-2.7 Myr)

E02- 1080 -6.6 -5.2 Hadar DD/KH Gango Akidora TT-4 32 E03- 3040 a -4.5 -3.9 Hadar DD/KH Gango Akidora TT-4 32 E03- 3040 b -7.0 -2.9 Hadar DD/KH Gango Akidora TT-4 32 E03- 3040 c -6.7 -4.2 Hadar DD/KH Gango Akidora TT-4 32 E03- 3040 d -6.0 -6.0 Hadar DD/KH Gango Akidora TT-4 32 E03- 3040 e -6.8 -4.5 Hadar DD/KH Gango Akidora TT-4 32 E03- 3040 f -6.3 -5.1 Hadar DD/KH Gango Akidora TT-4 32 E03- 3040 g -5.8 -4.3 Hadar DD/KH Gango Akidora TT-4 32 E03- 3040 h -6.4 -6.2 Hadar DD/KH Gango Akidora TT-4 32 E03- 3040 I -7.7 -6.9 Hadar DD/KH Gango Akidora TT-4 32 E03- 3040 j -7.4 -6.9 Hadar DD/KH Gango Akidora TT-4 32

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Supplementary Table 6 (cont) E02- 1079 -7.5 -6.3 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 A -6.2 -5.4 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 B -6.3 -7.1 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 C -7.2 -4.9 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 D -8.6 -6.4 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 E -6.9 -5.2 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 F -7.4 -5.9 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 G -7.9 -6.8 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 H -9.5 -4.3 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 I -7.6 -8.1 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 J -9.0 -5.8 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 K -7.4 -5.3 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 L -6.7 -4.5 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 M -7.9 -6.8 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 N -7.7 -4.9 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 O -7.6 -4.2 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 P -5.9 -3.9 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 R -6.0 -5.1 Hadar DD/KH Gango Akidora TT-4 26 E03- 2027 S -4.6 -0.3 Hadar DD/KH Gango Akidora TT-4 26 E02- 1078 -7.8 -7.1 Hadar DD/KH Gango Akidora TT-4 18 E02- 1077 -9.5 -4.3 Hadar DD/KH Gango Akidora TT-4 16 TT-4 (3.24 Myr)

E02- 1072 -6.1 -7.2 SH Gango Akidora TT-4 -5 E03- 3039 a -6.6 -4.6 SH Gango Akidora TT-4 -5 E03- 3039 b -7.0 -3.7 SH Gango Akidora TT-4 -5 E03- 3039 c -7.4 -2.3 SH Gango Akidora TT-4 -5 E03- 3039 d -3.4 -3.1 SH Gango Akidora TT-4 -5 E03- 3039 e -5.8 -3.0 SH Gango Akidora TT-4 -5 E03- 3039 g -7.0 -5.3 SH Gango Akidora TT-4 -5 E03- 3039 h -6.0 -1.6 SH Gango Akidora TT-4 -5 E03- 3039 I -8.2 -4.9 SH Gango Akidora TT-4 -5 E03- 3039 j -9.1 -5.1 SH Gango Akidora TT-4 -5 E02- 1070 -7.7 -7.6 SH Gango Akidora TT-4 -10 E02- 1045 a-b -7.7 -4.9 SH Meshele SHT 61 E02- 1037 -5.1 -4.0 SH Meshele SHT 55

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Supplementary Table 6 (cont) E02- 1060 -5.3 -7.9 SH Gango Akidora TT-4 -21 E02- 1059 -3.1 -8.4 SH Gango Akidora TT-4 -22 E02- 1039 b -6.1 -4.4 SH Meshele SHT 43 E02- 1039 e -6.7 -4.8 SH Meshele SHT 43 E02- 1039 g -6.8 -3.5 SH Meshele SHT 42 E02- 1039 g -7.0 -4.9 SH Meshele SHT 42 E02- 1062 -8.4 -7.9 SH Gango Akidora TT-4 -28 E02- 1057 -6.6 -7.3 SH Gango Akidora TT-4 -32 E02- 1058 -7.8 -9.4 SH Gango Akidora TT-4 -34 E02- 1009 -8.8 -5.7 SH Andedo SHT 34 E02- 1036 -7.4 -8.2 SH Simbil Dere SHT 33 E02- 1035 -7.6 -7.0 SH Simbil Dere SHT 32 E02- 1016 -8.1 -8.1 SH Meshele SHT 31 E03- 2019 -7.5 -7.9 0.81 0.88 11 SH Andedo SHT 30 E03- 2022 A -9.1 -6.1 0.22 1.12 9 SH Andedo SHT 30 E03- 2029 -8.3 -5.8 1.32 1.05 18 SH Andedo SHT 30 E03- 3022 a -5.8 -3.9 SH Andedo SHT 43 E03- 3022 b -9.2 -5.3 SH Andedo SHT 43 E03- 3022 c -5.9 -5.8 SH Andedo SHT 43 E03- 3022 d -7.3 -4.9 SH Andedo SHT 43 E03- 3022 e -8.3 -4.6 SH Andedo SHT 43 E03- 3022 f -4.8 -9.1 SH Andedo SHT 43 E03- 3022 g -4.4 -9.0 SH Andedo SHT 43 E03- 3022 h -7.4 -6.5 SH Andedo SHT 43 E03- 2017 A -8.9 -6.2 SH Andedo SHT 30 E03- 2017 B -8.7 -5.9 SH Andedo SHT 30 E03- 2017 C -5.9 -4.7 SH Andedo SHT 30 E03- 2017 C -8.2 -5.6 SH Andedo SHT 30 E03- 2017 C -10.0 -5.2 SH Andedo SHT 30 E03- 2017 D -6.4 -4.5 SH Andedo SHT 30 E03- 2017 E -8.5 -6.0 SH Andedo SHT 30 E03- 2017 F -8.3 -6.1 SH Andedo SHT 30 E03- 2017 G -8.4 -6.2 SH Andedo SHT 30 E03- 2017 H -7.3 -6.1 SH Andedo SHT 30 E03- 2017 I -8.3 -5.2 SH Andedo SHT 30 E03- 2017 J -8.1 -7.0 SH Andedo SHT 30

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Supplementary Table 6 (cont) E03- 2017 K -8.6 -5.2 SH Andedo SHT 30 E03- 2017 K -8.6 -6.6 SH Andedo SHT 30 E03- 2017 M -8.5 -6.2 SH Andedo SHT 30 E03- 2017 N -8.3 -5.4 SH Andedo SHT 30 E03- 2017 O -8.3 -6.0 SH Andedo SHT 30 E03- 2017 P -8.6 -6.4 SH Andedo SHT 30 E03- 2017 Q -8.3 -7.6 SH Andedo SHT 30 E03- 2017 R -8.3 -5.5 SH Andedo SHT 30 E03- 2017 S -8.9 -4.8 SH Andedo SHT 30 E02- 1087 -7.4 -8.1 SH Meshele SHT 28 E02- 1008 -7.3 -8.3 SH Meshele SHT 27 E02- 1015 -8.9 -6.4 SH Meshele SHT 23 E02- 1014 -9.5 -4.3 SH Meshele SHT 22 E02- 1086 -9.7 -4.6 SH Meshele SHT 22 E02- 1086 -8.7 -3.6 SH Meshele SHT 22 E03- 2009 -10.3 -5.2 0.65 0.67 8 SH Andedo SHT 21 E03- 2010 A -9.3 -5.3 SH Andedo SHT 21 E03- 2010 B -10.9 -10.4 SH Andedo SHT 21 E03- 2010 C -8.4 -6.3 SH Andedo SHT 21 E03- 2010 D -10.8 -4.0 SH Andedo SHT 21 E03- 2010 E -8.9 -3.7 SH Andedo SHT 21 E03- 2010 G -10.1 -5.3 SH Andedo SHT 21 E03- 2010 H -8.0 -7.4 SH Andedo SHT 21 E03- 2010 I -9.6 -5.5 SH Andedo SHT 21 E03- 2010 J -11.5 -8.3 SH Andedo SHT 21 E03- 2010 K -10.0 -6.8 SH Andedo SHT 21 E03- 2010 M -10.3 -4.3 SH Andedo SHT 21 E03- 2010 N -10.5 -6.1 SH Andedo SHT 21 E03- 2010 O -9.9 -7.9 SH Andedo SHT 21 E03- 2010 P -9.2 -5.1 SH Andedo SHT 21 E03- 2010 R -7.7 -5.1 SH Andedo SHT 21 E03- 2010 S -7.5 -7.9 SH Andedo SHT 21 E03- 2010 T -9.5 -4.9 SH Andedo SHT 21 E03- 2012 A -9.3 -6.1 SH Andedo SHT 21 E03- 2012 B -9.7 -5.2 SH Andedo SHT 21 E03- 2012 C -10.3 -5.5 SH Andedo SHT 21

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Supplementary Table 6 (cont) E03- 2012 D -9.4 -8.7 SH Andedo SHT 21 E03- 2012 E -8.2 -8.6 SH Andedo SHT 21 E03- 2012 F -9.7 -5.3 SH Andedo SHT 21 E03- 2012 G -10.3 -7.9 SH Andedo SHT 21 E03- 2012 H -10.3 -5.4 SH Andedo SHT 21 E03- 2012 I -9.0 -4.9 SH Andedo SHT 21 E03- 2012 J -8.4 -5.7 SH Andedo SHT 21 E03- 2012 K -10.3 -6.1 SH Andedo SHT 21 E03- 2012 L -10.0 -5.8 SH Andedo SHT 21 E03- 2012 M -11.5 -6.6 SH Andedo SHT 21 E03- 2012 N -10.3 -4.1 SH Andedo SHT 21 E03- 2012 O -9.8 -5.4 SH Andedo SHT 21 E03- 2012 P -8.3 -5.7 SH Andedo SHT 21 E02- 1006 -9.3 -6.7 SH Meshele SHT 20 E02- 1085 -3.5 -2.5 SH Meshele SHT 19 E02- 1054 -9.0 -6.6 SH Gango Akidora TT-4 -10 E02- 1007 -7.7 -8.9 SH Meshele SHT 16 E03- 3030 -7.2 -6.2 SH Arbosh Iere SHT 14 E03- 3029 -3.7 -5.5 SH Arbosh Iere SHT 3 SHT (3.42 Myr) E02- 1091 -8.9 -6.7 BM Ilanlei SHT -1 E02- 1066 -7.4 -4.6 BM Gango Akidora SHT -3 E02- 1068 -6.8 -7.4 BM Gango Akidora SHT -4 E02- 1065 -8.2 -2.7 BM Gango Akidora SHT -5 E03- 3023 -7.7 -4.0 BM Arbosh Iere SHT -4 E03- 3016 -4.1 -2.7 BM Arbosh SHT -10 E02- 1025 -7.5 0.5 BM Meshele SHT -11 E02- 1099 -9.8 -6.2 BM Shibelei SHT -37 E10- 9109 -7.5 -6.8 BM Wudud Garabe SHT -50 E10- 9110 -5.3 -6.3 BM Wudud Garabe SHT -50

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attempts a model calculation of spin-fluctua- Paul Michael Grant is at W2AGZ 3. Hubbard, J. Proc. R. Soc. Lond. A 276, 238–257 tion-mediated pairing, provides an encourag- Technologies and is an emeritus research staff (1963). 4. Eliashberg, G. M. Sov. Phys. JETP 11, 696–702 ing start. In this context, it may be pertinent to member at the IBM Almaden Research Center, (1960). quote the response to David Mermin’s obser- San Jose, California 95120, USA. 5. McMillan, W. L. Phys. Rev. 167, 331–344 vation8, when questioning the physics faculty e-mail: [email protected] (1968). while a graduate student at Harvard, about the 6. Grant, P. M. J. Phys. Conf. Ser. 129, 012042 1. Jin, K., Butch, N. P., Kirshenbaum, K., Paglione, J. & (2008). weirdness of that first quantum conundrum — Greene, R. L. Nature 476, 73–79 (2011). 7. Le Tacon, M. et al. Nature Phys. http://dx.doi. entanglement. He was advised to simply “shut 2. He, R.-U. et al. Science 331, 1579–1583 org/10.1038/nphys2041 (2011). up and calculate”. ■ (2011). 8. Mermin, N. D. Phys. Today 57(5), 10–11 (2004).

ANTHROPOLOGY suite of other characteristics that make us human. However, in recent years, further site dis- coveries have challenged this classical view. Shades of the savannah For instance, the idea that Miocene Africa was home to unbroken forest has begun to Whether African savannahs had an impact on the evolution of our early fragment, with evidence4 of true desert in ancestors has been a matter of debate. A study of carbon isotopes from ancient the Sahara. Meanwhile, suggestions of more soils provides fresh clues. See Article p.51 wooded conditions during the Plio-Pleisto- cene clouded the other side of the transforma- tion2. At the same time, semantic arguments CRAIG S. FEIBEL to a growing understanding of past climates. about the meaning of the term savannah — But the component of the savannah setting that encompassing almost everything between e are creatures of the African is most difficult to reconstruct forms the very desert and forest in Africa — have crippled savannah. Through some six mil- core of the savannah concept — the vegeta- much of the discussion. lion years of evolution, members tion that clothed the ancient landscape. In this Cerling and colleagues’ work1 enlists iso- Wof our branch of upright, bipedal primates context, the rarity of plant fossils and debates topic data to tackle this problem. The authors — the hominins — have adapted to the pre- surrounding the terminology used to describe deftly sidestep ambiguities about the exact vailing conditions of savannah life. Writing the diverse manifestations of the mix of habi- meaning and implications of the term savan- on page 51 of this issue, Cerling et al.1 add a tats within a local landscape, known as the nah by focusing on a discrete, quantifiable new dimension to our understanding of what savannah mosaic, have clouded our under- characteristic of the savannah mosaic — the early savannahs looked like, and how they standing of the conditions and changes that percentage of woody cover. Using a data set changed through time, ultimately shaping our shaped our evolutionary path2,3. of some 1,300 samples from ancient soils, evolutionary course. An underlying tenet to most discussions of the team tracked the woody component, and Although there is a rich trove of fossils human origins is the savannah hypothesis. In hence the percentage of trees and available recording the evolution of the hominins, its broadest manifestation, it is the idea that shade, through some six million years of savan- as well as abundant stone artefacts that environmental change transformed forests nah history. They drew the samples from two document their cultural awakenings, the envir of the Miocene Epoch — some 23 million of the richest fossil regions of East Africa — the onment that shaped them over time has not to 5 million years ago — into the savannahs Awash and Omo-Turkana basins (Fig. 1). been preserved. Sediments reflect important of Plio-Pleistocene times (the past 5 mil- The results show less than 40% of woody details of depositional settings, and proxy evi- lion years), and that this shift led to upright cover for most of the study period; thus rela- dence from lakes and oceans has contributed bipedalism, increased brain mass and the tively open habitats were common from the T. CERLING T. K. GARRETT/NATIONAL GEOGRAPHIC STOCK GEOGRAPHIC K. GARRETT/NATIONAL

Figure 1 | Sampling sites. In their efforts to identify the importance of a savannah habitat in human evolution, Cerling et al.1 collected soil samples from the Awash (left) and Omo-Turkana (right) basins in eastern Africa.

4 AUGUST 2011 | VOL 476 | NATURE | 39 © 2011 Macmillan Publishers Limited. All rights reserved RESEARCH NEWS & VIEWS early ancestry of the hominins. The details of soils lack these carbonate nodules, reflecting way to address these issues. Nonetheless, variation through time are equally striking. wetter times or moister areas in the land- Cerling and co-workers’ data1 go a long way to Whereas more open habitats prevailed for scape. How well this proxy reflects the overall reconstructing a fundamental control on the the creatures that some regard as the earliest temporal and spatial variability in ancient characteristics and variation of the ancient bipeds, woodier habitats expanded throughout vegetation is unclear. habitats available to our ancestors. ■ the early range of Australopithecus — a more Placing our ancient ancestors within par- broadly accepted early biped. ticular habitats on these landscapes is another Craig S. Feibel is in the Department of Earth Intriguing questions remain. The material wild card. Most of the fossils we have were and Planetary Sciences, Rutgers University, Cerling et al. used for most of their analyses is found where an organism died, or where its Piscataway, New Jersey 08854-8066, USA. soil carbonate — minerals precipitated within bones were transported to, and not necessarily e-mail: [email protected] the soil and which integrated the isotopic where it lived. Studies focusing on spatial vari- 1. Cerling, T. E. et al. Nature 476, 51–56 (2011). signal of carbon dioxide found there. These ability across landscapes, and intensive collect- 2. White, T. D. et al. Science 326, 75–86 (2009). nodules form primarily where evaporation ing protocols linking fossil assemblages and 3. Cerling, T. E. et al. Science 328, 1105 (2010). greatly exceeds precipitation. But many ancient communities to specific habitats, are under 4. Schuster, M. et al. Science 311, 821 (2006).

NEUROSCIENCE respond to similar temperatures. Trigemi- nal nerves also receive sensory input from the head and face in bats and snakes. But in bats, they innervate detectors not in Heat-thirsty bats pits1 but on the upper lip and modified noseleaf 3 (Fig. 1). Vampire bats sense infrared radiation to locate places where blood flows close to How do these differences affect the animals’ their prey’s skin. At a molecular level, this ability is underpinned by the intricate hunting behaviour? Pit vipers often prey on redesign of an ion channel on facial nerves. See Letter p.88 small mammals and can better detect hot spots at night. A good example of the importance of thermal cues to these snakes is the defensive M. BROCK FENTON TRPV1 activation threshold is lowered behaviour of California ground squirrels4. to about 30 °C through alternative splic- When confronted by a pit viper such as a he ability to sense heat or cold is vital: ing of its gene transcripts, which results Pacific rattlesnake, ground squirrels flag their it allows animals to detect, and so avoid, in truncation of the channel’s carboxy- tails to distract the predator. This display has a debilitating or fatal temperatures. But terminal domain. The truncated version thermal component that is missing when the Tthere is more to thermosensation than keep- of the protein is not expressed in the bats’ predator is a gopher snake — a species that ing one’s cool (or heat). On page 88 of this issue, dorsal root ganglia, which innervate the lacks thermosensation. Like pit vipers, some Gracheva and colleagues1 report on modifica- spinal cord. Instead, it occurs only in species of python and boa also use infrared tions to the facial nerves that allow the common their facial (trigeminal) nerves, where ion sensors in facial pits to detect warm-blooded vampire bat (Desmodus rotundus) to detect channels in both vipers and bats usually prey and to guide their strikes even in the infrared thermal radiation associ- absence of visual cues5. ated with hot spots — areas where Vampire bats can detect a heat blood flows close to the skin of source from about 20 cm (ref. 3), the bats’ prey — so that they can and probably use this proximal efficiently access the blood they eat. cue to find hot spots on their prey M. B. FENTON This paper complements — often areas that are not covered earlier work2 on infrared sensors with fur or feathers. Repeated in snakes, including some boas, attacks on the same cattle sug- pythons and pit vipers, which gest that, together, the vampire’s also use radiation in this range to memory and the prey’s breathing locate food. The endings of soma- sounds6 provide distal cues that tosensory trigeminal nerves in the allow the bats to locate a sleep- pits on the pit viper’s face detect ing target7. As yet, there are no infrared radiation. Specifically, pit data to suggest defences against vipers detect infrared radiation thermoperception by vampire through TRPA1 — a cell-surface bats similar to those of California ion channel that is usually heat ground squirrels against vipers. insensitive — on these nerves. The However, it is important to note heat sensors of pit vipers are more that the bats take only about two sensitive than those of pythons tablespoonfuls of blood (25 ml), or boas, indicating independent and so, unlike those of pit vipers, evolution of thermosensation. vampire attacks are not fatal. Gracheva et al.1 show that Nonetheless, there is evidence vampire bats sense infrared that some prey develop antibod- radiation through a different ies to the anticoagulants in the ion channel, TRPV1, which is vampire’s saliva8, directly affect- used by other mammals to ing the bats’ feeding time. detect noxious heat (> 43 °C). Figure 1 | Vampire bat. Infrared sensors are located on the bat’s upper lip In vampire bats, the organiza- In vampire bats, however, the and modified noseleaf. tion of the Trpv1 gene seems to