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1 doi:./nature97 G. ruber G. sacculifer G. ruber Ren et al. 9 G. sacculifer Ren et al. 9 FB-δ N ( vs. air) 7 Supplementary Figure. FB-δ N of single planktonic species G. ruber (dark green) and G. sacculifer (dark blue) from this study including the FB-δ N from Ren et al. 9 (same two species, G.ruber light green, G. sacculifer light blue). The earlier data from Ren et al. 9, measured on the same core over the last ky shows very good agreement with our data over this period (the Ren et al. 9 samples are from - cm thick depth intervals, whereas our samples are from cm thick intervals). Error bars represent standard deviation based on oxidation replicates.

2 (a) N ( vs. air) MIS N GR/GS N bulk sediment (b) (c) % G.ruber and % fragments N-content bulk (umol/g) N content GR N content GS % G.ruber % fragments Supplementary Figure. (a) FB-δ N (black) and bulk sediment δ N (red) from ODP Site 999, (b) bulk sediment N content (orange) and foraminifera N content of G. ruber (green) and G. sacculifer (blue), and (c) preservation data from Schmidt et al. : % G. ruber (pink) and % fragments (violet). Neither FB-N content nor the preservation data show precession dominated cyclicity over the last ky, and the N content of the foraminifera is very stable.

3 - (d) Dry Zr/Al. - humidity index Wet. (c) (b) O benthic ( ) (a) Supplementary Figure. Age model and reproducibility of Zirconium-to-Aluminium (Zr/Al) record from ODP Site. Benthic δ O from ODP Site (b) is matched to the same data as contained in the LR benthic stack (a); black arrows depict tie-points. The good correspondence between Zr/Al (c) with the grain-size derived humidity index from neighboring core GeoB79- (d) is used to refine the δ O-based age model (green triangles depict additional tie-points). Grey arrows are tie-points from comparison between XRF Ca counts in A/B and %CaCO in radiocarbon-dated C (not shown),9. For the Zr/Al record, different colors indicate data from different holes at Site showing good reproducibility; the grey line is uncorrected Zr/Al data from C, which is offset from the other records due to higher water content leading to lower Al counts. The C ratios have been corrected (dark red line) by regression between Zr/Al from C and B for the section of overlap.

4 Zr/Al. - yr - )..... N ( vs. air) erage N GR/GS. (a) MIS... th flux Dust flux (g m - kxr - ) Dust Flux - ka - ) - ka - ) Supplementary Figure. (a) Dust-flux record of ODP Site 9 (dark), Th-flux record from the eastern equatorial Pacific (light green) and terrigenous (%) from ODP (dashed grey). (b) The dust record of ODP Site (dark green) and MAR of magnetic minerals 7 (calculated from magnetic susceptibility, dark violet) and iron (violet) of ODP Site 999. (c) FB-δ N from Site 999 (black).

5 e N GR/GS NHP dn N ( vs. air) N ( vs. air) Bulk dn N ( vs. air) 9 California margin 7 Arabian Sea RC7- dn N ( vs. air) 9 7 Mexican margin 9 7 Peru margin dn TR MIS ODP 999A Equatorial Pacific N ( vs. air) N ( vs. air) N ( vs. air) Supplementary Figure. FB-δ N from ODP Site 999 (top, black circles) in comparison with δ N records communicating with major denitrification zones: equatorial Pacific, California margin, Arabian Sea 9,, Mexican margin 9 and Peru margin (dark grey, left y- axis; light grey and black right y- axis).

6 Supplementary Figure. Cross-spectral analysis of FB-δ N (average) of ODP Site 999 (black) and FB-δ N of the single species G.ruber (green) and G.sacculifer (blue) versus δ O of G.ruber,. Coherence and phase estimates were computed using the Arand software package, and cross-spectral power density was computed in MATLAB. All series were detrended and linearly interpolated to ky before analysis.

7 dn SST 999 (a) (b) (c) (d) (e) (f) N ( vs. air) ky filter N ( vs. air) ky filter N ( vs. air) ky filter SST 999A dn - dn - dn O SW-IVF -. dn SST cold RC-7 dn dn - - dn Supplementary Figure 7. FB-δ N versus SST at ODP 999 (pink ), versus δ O SW-IVF (orange ) and the SST of the equatorial Atlantic upwelling region (blue 7 ). In each case, the precessional component of the records is shown in the lower panels. The precessional component (filters of data for precession) is computed using a Gaussian in the software Analyseries. - SST ( C) ky filter O SW-IVF ky filter SST ( C) ky filter SST SST 999 SST SST 999 SST 999 SST

8 a) G. Ruber O ODP Site 999 ka Global Spectral Power b) Mg/Ca SST ODP Site 999 Global Spectral Power c) OSW-IVF ODP Site 999 ka ka.... Global Spectral Power d) FB- N ODP Site 999 ka ka Global Spectral Power e) SSTc RC-7 ka ka Global Spectral Power f) % F. Profunda RC-7 Global Spectral Power ka Supplementary Figure. Wavelet power spectrum of the different time-series discussed in the text. (a) ODP Site 999 δ O measured in the planktonic foraminifera G. ruber,,. (b) ODP Site 999A Mg/Ca sea surface temperature (SST) reconstuction (c) ODP Site 999 δ O

9 SW-IVF. (d) ODP Site 999 average FB-δ N (this study). (e) Estimated SST based on a planktonic foraminifera transfer function at core RC-7 7. (f) Percentage of F. profunda in the marine sediment core RC-7 7. The local wavelet power spectrum (left panels) and the global wavelet spectral power (right panels) have been computed using the methods proposed by Torrence and Compo 99, using a morlet wavelet. In the left panels, the thick black contour encloses regions of greater than 9% confidence for a red noise process with a lag- coefficient of.7, and the black dashed line indicates the cone of influence where edge effects might become important. In the right panels, the red dashed line indicates the 9% confidence level for the global wavelet power spectrum. The different records have been interpolated to an evenly spaced time series of ky prior to the spectral analysis of the data, which is similar to their average sampling resolution. 9

10 a) G. Ruber O / FB- N ODP Site b) SST cold RC-7 / FB- N ODP Site c) Mg/Ca SST / FB- N ODP Site d) O SW-IVF / FB- N ODP Site Supplementary Figure 9. Cross-wavelet coherence and phase of ODP Site 999 FB-δ N with G. ruber δ O at Site 999 (a) and with equatorial Atlantic upwelling as reconstructed with faunal SST in core RC-7 7 (b), computed using the method proposed by Grinsted et al.

11 . Squared wavelet coherence between two time series identifies regions of significant (9% confidence) coherence and its relative phase in time-frequency space. The 9% confidence level against red noise is calculated using the Monte Carlo method and is shown as a thick contour that encloses the significant sections. The light shading represents the cone of influence (COI), which ensures that the edge effects are negligible beyond this point. Black arrows indicate the relative phase relationship between the two time series, with inphase pointing right, anti-phase pointing left, FB-δ N leading a given climate variable by 9º pointing down, and FB-δ N lagging by 9º pointing up. The different records have been interpolated to an evenly spaced time series of ky prior to the spectral analysis of the data, which is similar to their average sampling resolution.

12 ... % 9% Spectral Power.... 9% 99% Supplementary Figure. Spectral power of the FB-δ N record generated using the Lomb- Scargle Fourier transform for unevenly spaced data 7-9 in combination with a Welch- Overlapped-Segment-Averaging procedure, for consistent spectral estimates, using the software REDFIT. This method allows us to test if the precessional peaks in the spectrum of the FB-δ N record are significant against the red-noise background using the original unevenly-spaced time series and therefore avoiding the potential biases introduced during the interpolation. The results of this analysis unambiguously show that the precession cycle is significant against red noise at the 9% confidence level, which is in good agreement with the results obtained after linear interpolation of the time series using the wavelet method (Supp. Fig. d).

13 Melosira V- Melosira V-9 Melosira/g (x ^) Melosira/g (x^) Supplementary Figure. Melosira data from core V- (red, S, 9 W, 7 m) and V-9 (orange, N, W, 9 m). See Supplementary Discussion on dust/iron.

14 Zr/Al.... verage N GR/GS N ( vs. air) Melosira (# /g x ) ODP Zr/Al combined th flux.... MIS Dust Flux terrigenous (%) ODP Supplementary Figure. Melosira data (red) from core ODP, Zr/Al data from ODP (green), terrigenous % of ODP (grey), and FB-δ N (black) data from ODP 999. See Supplementary Discussion on dust/iron.

15 Supplementary Discussion on Dust/Iron In the main text, we distinguish the FB-δ N changes over the full glacial cycle (i.e. the basic difference of MIS - and from MIS and ) from those occurring repeatedly in response to the precession cycle. Therefore, our discussion of dust flux here is also separated according to this distinction. With regard to the former, all dust records (Supp. Fig. ) agree that glacial stages were characterized by higher dust fluxes, with one Melosira (wind-blown lake diatom) record from the equatorial Atlantic, core V-, being the only exception that we have noted (Supp. Fig. ). Due to discrepancies between different published Melosira records and between a Melosira record and an offshore terrigenous flux record from the same core,, (Supp. Fig. and ), we would argue that this proxy is probably not the most reliable proxy for dust export on the large scale (see more discussion of this below). Thus, with regard to the major glacial/interglacial transitions, given that N fixation is low when dust is high, it is clear that the reconstructed N fixation changes are not due to the dust flux changes. The relationship between dust and N fixation over the precession cycle is bound to be less certain. The N fixation record is remarkable for its phasing, with maxima during NH summer insolation minima; this rules out many possible environmental factors. However, it does not immediately rule out the possibility of North African dust flux playing a role. It would not be surprising if dust flux from North Africa to the Atlantic increased during NH summer insolation minima, as these times would have been associated with a more southward ITCZ (Inter Tropical Convergence Zone) and thus possibly a weaker North African monsoon and greater aridity. Thus, even if North African aridity and N fixation have nothing to do with one another mechanistically, they might have the same phasing in the precession cycle. The expectation of high aridity during NH summer insolation minima is fit roughly by Melosira data in cores near the equator,. However, Melosira records are variable core to core, and the more northern core site near Site yields a different picture (Supp. Fig. ). We suspect that Melosira abundance in the equatorial Atlantic cores is at least partly driven by stronger and more south-biased trade winds during NH summer insolation minima, the same change that generates the equatorial Atlantic upwelling changes which we have argued is the main driver of the precession cycle in N fixation. Melosira also speaks to desiccated lake beds in specific, not aeolian dust transport in general.

16 Other indicators are not supportive of the hypothesis that continental aridity changes led to maximal dust fluxes from Africa during NH summer insolation minima. The Site Zr/Al record shown in the manuscript appears out of phase with N fixation by a quarter of a precession cycle. From near the equator, the Site terrigenous flux record of demenocal et al. (99) shows very little precession (Supp. Fig. ), but what might be attributed to precession is out of phase with the N fixation record, in a similar sense as the Site Zr/Al record. Moreover, the Fe and magnetic mineral MAR from Caribbean site 999 shows only minor precessional fluctuations, which match remarkably well the Site Zr/Al record (see Fig. in the main manuscript and Supp. Fig. ). All together, while dust flux reconstruction is not yet adequate to preclude a role for dust changes in the precessional cycle of N fixation, the following seems clear: The preponderance of data fails to support the proposal that the precessional cycle in N fixation was caused by dust flux changes, and the argument for a role for dust is further weakened by the obvious anticorrelation between dust flux and N fixation over the full glacial cycle.

17 Supplementary References Winckler, G., Anderson, R.F., Fleisher, M.Q., McGee, D., Mahowald, N. Covariant Glacial-Interglacial Dust Fluxes in the Equatorial Pacific and Antarctica. Science, 9-9 (). 7 Sigurdsson, H., Leckie, R. M., Acton, G.D. & Draper, G. Proc. ODP, Sci. Results, (997). Liu, Z., Altabet, M.A., Herbert, T.D. Plio-Pleistocene denitrification in the eastern tropical North Pacific: Intensification at. Ma. Geochem. Geophys. Geosys. 9, Q (). 9 Altabet, M.A., Higginson, M.J., Murray, D.W. The effect of millennial-scale changes in Arabian Sea denitrification on atmospheric CO. Nature, 9- (). Clemens, S.C., Prell, W.L. A, year summer-monsoon multi-proxy stack from the Owen Ridge, Northern Arabian Sea. Marine Geology, - (). Robinson, R.S., Mix, A. & Martinez, P. Southern Ocean control on the extent of denitrification in the southeast Pacific over the last 7ka. Quaternary Science Reviews, -, (7). ARAND Time-Series Analysis Software (Providence RI., ). Paillard, E., Labeyrie, L. & Yiou, P. Macintosh program performs time-series analysis. Eos Trans 77 (99). Schmidt, D.W., Spero, H.J. Meridional shifts in the marine ITCZ and the tropical hydrologic cycle over the last three glacial cycles. Paleoceanography, PA (). Torrence, C., Compo, G.P. A practical guide to wavelet analysis. Bulletin of the American Metrological Society 79, -7 (99). Grinsted, A., Moore, J.C., Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear processes in geophysics (). 7 Lomb, N.R. LEAST-SQUARES FREQUENCY-ANALYSIS OF UNEQUALLY SPACED DATA. Astrophys. Space Sci. 9, 7- (97). Scargle, J.D. STUDIES IN ASTRONOMICAL TIME-SERIES ANALYSIS.. STATISTICAL ASPECTS OF SPECTRAL-ANALYSIS OF UNEVENLY SPACED DATA. Astrophys. J., - (9). 9 Scargle, J.D. An introduction to chaotic and random time series analysis. Int. J. Imaging Syst. Technol., - (99). Welch, P.D. USE OF FAST FOURIER TRANSFORM FOR ESTIMATION OF POWER SPECTRA - A METHOD BASED ON TIME AVERAGING OVER SHORT MODIFIED PERIODOGRAMS. IEEE Transactions on Audio and Electroacoustics AU, (97). Percival, D.B. & Walden, A.T. Spectral analysis for physical applications, multitaper and conventional univariate techniques. (Cambridge University Press, 99). Schulz, M. and Mudelsee, M. REDFIT: estimating red-noise spectra directly from unevenly spaced paleoclimatic time series. Comput. Geosci., - (). Pokras, E.M. & Mix, A.C. Eolian evidence for spatial variability of late Quaternary climates in tropical Africa. Quaternary Research, 7-9 (9). 7