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    • Acknowledgements

    T.C. is supported by CNRS-INSU. Funding from LEFE-IMAGO CNRS INSU project SeaSalt is acknowledged. T.C. was partly supported by the ‘Laboratoire d’Excellence’ LabexMER (ANR-10-LABX-19) and co-funded by a grant from the French government under the program ‘Investissements d’Avenir’, and by a grant from the Regional Council of Brittany (SAD programme). J.A.C. acknowledges funding from the ERC project ‘STEEPClim’. E.S. and L.D. acknowledge funding through the DFG Research Center/Cluster of Excellence ‘The Ocean in the Earth System’ at MARUM – Center for Environmental Sciences. A.S. acknowledges funding through the LaScArBx, a programme supported by the Agence Nationale de la Recherche (ANR-10-LABX-52). C.G.-C. was supported by CREST (grant number JPMJCR12A3; P.I. SLS) funded by the Japan Science and Technology (JST). Core MD96-2048 was collected during the MOZAPHARE cruise of the RV Marion Dufresne, supported by the French agencies Ministère de l’Education Nationale de la Recherche et de la Technologie, Centre National de la Recherche Scientifique (CNRS) and Institut Paul Emile Victor (IPEV).

    Reviewer information

    Nature thanks C. O’Brien, M. Petraglia, K. Uno and the other anonymous reviewer(s) for their contribution to the peer review of this work.

    Author information

    Affiliations

    1. EPOC, UMR 5805, CNRS, University of Bordeaux, Pessac, France

      • Thibaut Caley
      • , Thomas Extier
      • , Bruno Malaizé
      • , Linda Rossignol
      • , Frédérique Eynaud
      • , Philippe Martinez
      • , Karine Charlier
      • , Mélanie Wary
      • , Pierre-Yves Gourves
      • , Isabelle Billy
      •  & Jacques Giraudeau

    2. Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France

      • Thomas Extier
      •  & Didier M. Roche

    3. GFZ – German Research Center for Geosciences, Section 5.1 Geomorphology, Organic Surface Geochemistry Laboratory, Potsdam, Germany

      • James A. Collins

    4. Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany

      • James A. Collins

    5. MARUM – Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany

      • Enno Schefuß
      •  & Lydie Dupont

    6. PACEA, UMR 5199, CNRS, University of Bordeaux, Pessac, France

      • Antoine Souron

    7. Department of Geography, Durham University, Durham, UK

      • Erin L. McClymont

    8. Department of Biogeochemistry (JAMSTEC), Yokosuka, Japan

      • Francisco J. Jimenez-Espejo

    9. Research and Development Center for Global Change, (JAMSTEC), Yokohama, Japan

      • Carmen García-Comas

    10. Ecology Group, University of Vic – Central University of Catalonia, Barcelona, Spain

      • Carmen García-Comas

    11. Vrije Universiteit Amsterdam, Faculty of Science, Cluster Earth and Climate, Amsterdam, The Netherlands

      • Didier M. Roche

    12. Unité Géosciences Marines, Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER), Plouzané, France

      • Stephan J. Jorry

    Contributions

    T.C. designed the study. T.C., T.E., M.W. and P.-Y.G. performed the Mg/Ca measurements. L.R. analysed the foraminifera assemblages, and T.C. and F.E. analysed the results and performed the transfer function. T.C., T.E., B.M. and K.C. performed the δ18O analyses on foraminifera. T.C., J.G., P.M. and I.B. performed the XRF measurements and F.J.J.-E. and C.G.-C. conducted the statistical analyses on XRF. J.A.C. and E.S. performed plant-wax δD and δ13C analyses. L.D. performed the pollen analysis. A.S. and T.C. produced the synthesis on the ecology and environments of South African hominins and conducted the comparisons to the marine record. T.C. and D.M.R. performed and analysed the iLOVECLIM model results. T.C. analysed the results and all authors participated in the interpretation. T.C. wrote the manuscript with contributions from all authors.

    Competing interests

    The authors declare no competing interests.

    Corresponding author

    Correspondence to Thibaut Caley.

    Extended data figures and tables

    1. Extended Data Fig. 1 ln(Fe/Ca) as a proxy for Limpopo runoff.

      Calcium and iron both have complex and multiple origins in marine sediments. Iron can be related to redox variations, detrital and fluvial input, among others, and calcium can be related to the biogenic fraction (foraminifera or nannofossils) and detrital input. To properly interpret the ln(Fe/Ca) ratio at our study location, we applied principal components analysis120. a, PC1 describes 66% of the total variance for the entire site MD96-2048. The negative loadings for PC1 are calcium and strontium, and all other elements (aluminium, silicon, potassium, titanium, iron and zirconium) have positive loadings. Calcium and strontium are associated with biogenic carbonate and are mainly related to presence of foraminifera. Element matrix correlation shows a strong positive linear correlation (R > 0.70) between iron and typically detrital elements, such as aluminium, silicon, titanium and potassium. Calcium shows negative correlation with iron (R = −0.5). b, ln(Fe/Ca) shows a strong correlation with PC1 (R = 0.94) and a strong relationship with Limpopo runoff proxies (Extended Data Fig. 3). Iron and titanium elements are related to terrigenous and siliciclastic components (heavy minerals and oxides) and the variation in carbonate content (calcium) is mainly due to dilution by terrigenous sediment. ln(Fe/Ca) is therefore a proxy of Limpopo runoff, consistent with previous studies in riverine basins throughout the African continent10,121,122,123,124. To confirm a weak influence of sea-level changes on the Fe/Ca record, we compared our ln(Fe/Ca) record with a previous reconstruction of the deep-water δ18O component for relative sea level125, (b, bottom). Both records are plotted against the LR04 chronology. Visual inspection and statistical testing do not support a dominant effect of sea-level changes on the ln(Fe/Ca) record (R = 0.05). PC3, which describes 11% of the total variance for the entire site MD96-2048, is closely related to sea-level changes. The negative loadings for PC3 are mainly strontium and, to a lesser degree, potassium and titanium, and the main positive loadings are zirconium and, to a lesser degree, silicon.

    2. Extended Data Fig. 2 Control on the δD composition of precipitation in the Limpopo catchment.

      a, b, Seasonal δD composition of precipitation (a) and amount of precipitation at Pretoria station126 (b), in comparison to the results of the iLOVECLIM model at the corresponding latitude and longitude48,49. All data are centred on their annual average. Depleted δD values are indicative of increasing amounts of rainfall127. c, Results of the transient simulation with the isotope-enabled numerical climate model iLOVECLIM for the δD composition of precipitation and precipitation in the Limpopo catchment (about −27.5° S to −22° S and 30° E to 36° E), for the past 150 kyr (Methods)51. Black curves show the results after filtering with a low-pass filter. The δD composition of precipitation and precipitation amount in the Limpopo catchment are negatively correlated (R = −0.63, [Math Processing Error]
    1. ) for the past 150 kyr. Maxima of precipitation are phased with maxima in austral summer insolation at 30° S and lead to more-depleted δDprecipitation (amount effect).

    2. Extended Data Fig. 3 Relationship between Limpopo runoff, local Southern Hemisphere insolation and the C31 n-alkane δ13C record for the past 800 kyr.

      a, Comparison between the ln(Fe/Ca) XRF signal and austral summer local insolation at 30° S31. b, Comparison between the ln(Fe/Ca) XRF signal and the brGDGT concentration in the sediment15. brGDGTs are commonly found in soil and can be attributed to Limpopo River runoff15. c, Comparison between the ln(Fe/Ca) XRF signal and the C31 n-alkane δ13C record16. An increased amount of Limpopo River discharge is associated with more C4 plant input and an increase in austral summer insolation at 30° S. d, Comparison between inverted ln(Fe/Ca) XRF signal and the accumulation rate (AR) of CaCO3 as a measure of biogenic carbonate. The ln(Fe/Ca) XRF record is not primarily controlled by dilution due to biological productivity (R = 0.1). A previous study of the past 0.8 Myr of core MD96-2048 interpreted shifts towards more-depleted δ13Cwax as potentially reflecting more-humid conditions16. However, the anti-correlation between δ13Cwax and δDwax values (Extended Data Fig. 4) in our study indicates that enriched δ13Cwax values are associated with more-humid conditions. Because C4 plants in the Limpopo catchment are dominant in the interior (Fig. 1), we propose that more-enriched δ13Cwax values indicate a higher relative contribution from sources located farther upstream (more C4 plants) during times of high runoff, compared to only downstream sources (more C3 plants) during times of low discharge. In addition, humid conditions would have favoured the extension of sedge-rich vegetation (Cyperaceae, of which 20–60% are C4 plants in this region128) in riverine swamps and floodplains along the river course, explaining the detected increase in Cyperaceae pollen at times of increased fluvial discharge (Fig. 2). Studies of sediments from the adjacent Zambezi catchment similarly suggest the extension of swampy sedge-rich vegetation—including C4-Cyperaceae—when river discharge was high, and infer that more C4 plant waxes are exported to the ocean when the flooding of floodplains occurs during rainfall maxima10,129.

    3. Extended Data Fig. 4 Relation between the δ13C C31 n-alkanes record and the δD C31 n-alkanes record.

      a, Correlation between the record of δ13C C31 n-alkanes and the record of δD C31 n-alkanes, with or without vegetation and ice-volume correction (vc-ivf) over the past 2.14 Myr (n = 19 samples). An anti-correlation exists between the δ13C and the δD signals of the C31 n-alkanes. The C31 n-alkane is used because it is the most abundant homologue in the samples. b, Raw δ13Cwax, δDwax data and δDwax adjusted for ice-volume and vegetation changes from core MD96-2048. Mean analytical uncertainties are indicated. Top, δ13Cwax of the C31 homologue (data from a previous study16 in light green, and data from this study in dark green). Middle, δDwax of the C31 homologue. Bottom, δDwax of the C31 homologue adjusted for ice-volume changes (ivf) using a seawater δ18O curve125 and converting to δD assuming an increase of 7.2‰ at the Last Glacial Maximum. We use 7.2‰ because measurements of sediment pore water δ18O and δD suggest that the glacial ocean δD increase has a mean value of 7.2‰130. We also adjusted the δDwax record for vegetation changes (vc) using published fractionation factors (−123‰ ± 31‰ for C3 trees, −139‰ ± 27‰ for C4 grasses131) and the δ13Cwax signal following a previously published procedure132. End-member δ13Cwax values used for C3 and C4 vegetation were −36‰ and −21.5‰, respectively133. The error ranges for the vegetation fractionation factors are very large131. They derive from the compilation of a global dataset from individual plants, which is not comparable to an ecosystem fractionation in a specific catchment (such in the Limpopo) that will fractionate with a much smaller uncertainty. However, as we do not know the exact fractionation factor in the Limpopo catchment and regard the uncertainties from the global compilation as unrealistic for a specific ecosystem we refrained from propagating this uncertainty into the vegetation corrections. The vegetation and ice-volume-adjusted δDwax record is very similar to the unadjusted record, highlighting the fact that the adjustments have a minor effect.

    4. Extended Data Fig. 5 Statistical analyses for the ln(Fe/Ca) XRF record and PC1 SST record.

      a, Spectral power for ln(Fe/Ca) by wavelet analysis realized with a previously published MatLab package134. The thick contour designates the 5% significance level against red noise. Dashed black lines indicate the variability at the precession, obliquity and eccentricity periods. b, Spectral analysis of ln(Fe/Ca) with REDFIT135. The red line shows the false-alarm level at the 95% confidence interval. Spectral peaks exceeding the false-alarm level can be considered significant135. c, Blackman–Tukey cross correlation between ln(Fe/Ca) XRF and eccentricity–tilt–precession (ETP) realized with the Analyseries software37 for the past 2.14 Myr. ETP is constructed by normalizing and stacking eccentricity, tilt (obliquity) and negative precession to evaluate coherence and phase (timing) relative to orbital extremes136. The red curve shows the spectral power for ln(Fe/Ca) record. The black curve shows the spectral power for ETP. The coherency, which varies between 0 and 1, is represented by the grey curve and gives the interval within which the spectrum is significant. In our case, the non-zero coherency is higher than 0.55 and is significant at the 95% confidence interval (grey line). There are significant spectral peaks for eccentricity and precession but not for obliquity. The ln(Fe/Ca) XRF record and ETP are in phase at the 400-kyr period, the eccentricity leads by 16 kyr the ln(Fe/Ca) record at the 100-kyr period and the ln(Fe/Ca) record is in anti-phase with negative precession (in-phase with positive precession) at the 19- and 23-kyr periods. The three statistical analyses are consistent and indicate significant variability at the 400-, 100-, 23- and 19-kyr periods and insignificant variability at the 41-kyr period. d, Comparison between the precessional component of the ln(Fe/Ca) record (Gaussian filter frequency 1/23,000; bandwidth: 5 ×10−6) obtained with the Analyseries software37 and the precession index. Maxima of the ln(Fe/Ca) precession component are in phase with precession index maxima. The precession cycles in the ln(Fe/Ca) record appear particularly strong between about 0.9 and 0.6 Ma. e–g, The same statistical analyses as in a–c, respectively, but for the PC1 SST record. In e, dashed white lines indicate the variability at the precession, obliquity and eccentricity periods. The three statistical analyses indicate significant variability at the 100- and 41-kyr periods but not significant power for the 400-kyr and 23-kyr (precession) periods.

    5. Extended Data Fig. 6 Reconstruction using SST proxies for core MD96-2048 for the past 2.14 Myr.

      a, Reconstruction of SST using two different methods: Mg/Ca reconstruction based on previous15 and new data (Mg/Ca ratios were converted into temperature values by applying a previously established equation40) and foraminifera transfer function reconstruction using the modern analogue technique. Error bars represent the error on the calibrations40 (Extended Data Fig. 7). b, Empirical orthogonal function analysis47 of the two SST records for the past 2.14 Myr. PC1 contains 74% of the total variance for the past 2.14 Myr. Correlation between SST proxies and PC1 for the past 2.14 Myr is R = 0.71.

    6. Extended Data Fig. 7 Foraminifera transfer function used for core MD96-2048.

      a, Location of the modern database, composed of 367 core tops from the south Indian Ocean45 with present-day SST from the World Ocean Atlas (WOA) 200929. b, Test for the modern database45 yielding to a precision of 0.8 °C for the annual SST reconstructions. Modern hydrological parameters were obtained from the WOA (1998) database using a previously developed tool (http://www.geo.uni-bremen.de/geomod/Sonst/Staff/csn/woasample.html).
    7. Extended Data Table 1 Fossil finds, their location and associated ages
      Full size table
    8. Extended Data Table 2 δ13C enamel of hominin and contemporaneous herbivores and associated statistical parameters for different sites in the Limpopo catchment
      Full size table

    Source Data

    1. Source Data Figure 2

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