# Evolution of the South Pacific's Iron Cycle Over the Cenozoic

Logan Tegler, Tristan Horner, Sune Nielsen, Andy Heard, Katherine Squires, Silke Severmann, Bernhard Peucker-Ehrenbrink, Jerzy Blusztajn, Ann Dunlea

► To cite this version:

Logan Tegler, Tristan Horner, Sune Nielsen, Andy Heard, Katherine Squires, et al. Evolution of the South Pacific's Iron Cycle Over the Cenozoic. Paleoceanography and Paleoclimatology, 2025, 40 (7), pp.e2025PA005149. 10.1029/2025PA005149. hal-05344320

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Distributed under a Creative Commons Attribution 4.0 International License
# Paleoceanography and Paleoclimatology®

## RESEARCH ARTICLE

10.1029/2025PA005149

**Key Points:**

* We combine Os and Fe isotope chemostratigraphy, geochemical analysis, and statistical modeling to identify major sources of Fe to seawater

* South Pacific and Southern Ocean Fe sources shifted from hydrothermal to dust dominance over the past 93 million years

* Despite low dust deposition in the modern South Pacific, results indicate that dust is currently the primary source of Fe in the region

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

L. A. Tegler and A. G. Dunlea,
tegler@hawaii.edu;
adunlea@whoi.edu

**Citation:**

Tegler, L. A., Horner, T. J., Nielsen, S. G.,
Heard, A. W., Squires, K. R., Severmann,
S., et al. (2025). Evolution of the South
Pacific's iron cycle over the Cenozoic.
Paleoceanography and Paleoclimatology,
40, e2025PA005149. https://doi.org/10.
1029/2025PA005149

Received 6 MAR 2025
Accepted 6 JUN 2025

**Author Contributions:**

**Conceptualization:** Logan A. Tegler,
Tristan J. Horner, Sune G. Nielsen,
Bernhard Peucker-Ehrenbrink,
Jerzy Blusztajn, Ann G. Dunlea

**Data curation:** Logan A. Tegler,
Bernhard Peucker-Ehrenbrink, Ann
G. Dunlea

**Formal analysis:** Logan A. Tegler, Tristan
J. Horner, Sune G. Nielsen, Andy
W. Heard, Silke Severmann,
Bernhard Peucker-Ehrenbrink,
Jerzy Blusztajn, Ann G. Dunlea

**Funding acquisition:** Logan A. Tegler,
Tristan J. Horner, Sune G. Nielsen,

## Evolution of the South Pacific's Iron Cycle Over the Cenozoic

<figure><img src="image_2.png" /></figure>

Logan A. Tegler¹,²,³,⁴,⁵ ID, Tristan J. Horner¹,² ID, Sune G. Nielsen¹,⁶,⁷ ID, Andy W. Heard¹,⁷ ID,
Katherine R. Squires²,³,⁵, Silke Severmann⁸, Bernhard Peucker-Ehrenbrink² ID,
Jerzy Blusztajn²,⁷ ID, and Ann G. Dunlea² ID

¹NIRVANA Laboratories, Woods Hole Oceanographic Institution, Woods Hole, MA, USA, ²Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA, USA, ³Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA, ⁴Now at Department of Oceanography, School of Ocean and Earth Science and Technology, University of Hawai'i at Mānoa, Honolulu, HI, USA, ⁵MIT-WHOI Joint Program in Oceanography, Woods Hole, MA, USA, ⁶Now at CRPG, CNRS, Université de Lorraine, Vandoeuvre lès Nancy, France, ⁷Department of Geology and Geophysics, Woods Hole Oceanographic Institution, Woods Hole, MA, USA, ⁸Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, USA

**Abstract** Iron (Fe) availability impacts marine primary productivity, potentially influencing the efficiency of the biological carbon pump. Stable Fe isotope analysis has emerged as a tool to understand how Fe is sourced and cycled in the water column; however its application to sediment records is complicated by overlapping isotope signatures of different sources and uncertainties in establishing chronologies. To overcome these challenges, we integrate Fe and osmium isotope measurements with multi-element geochemical analysis and statistical modeling. We apply this approach to reconstruct the history of Fe delivery to the South Pacific from three pelagic clay sequences spanning 93 million years. Our analysis reveals five principal Fe sources—dust, distal background, two distinct hydrothermal inputs, and a magnesium-rich volcanic ash. Initially, hydrothermal inputs dominated Fe deposition, but as the sites migrated away from their respective mid-ocean ridges, other sources became prominent. Notably, from 66 to 40 million years ago (Ma), distal background Fe was the primary source before a shift to increasing dust dominance around 30 Ma. This transition implies that Fe in South Pacific seawater has been dust-dominated since ≈30 Ma, despite extremely low dust deposition rates today. We speculate that the shift to episodic and low Fe fluxes in the South Pacific and Southern Ocean over the Cenozoic helped shape an ecological niche that favored phytoplankton that adapted to these conditions, such as diatoms. Our analysis highlights how Fe delivery to the ocean is driven by large-scale tectonic and climatic shifts, while also influencing climate through its integral role in marine phytoplankton and Earth's biogeochemical cycles.

**Plain Language Summary** Iron (Fe) in the ocean is important for marine life and affects carbon removal from the atmosphere. We studied three deep-sea sediment cores far from the continents to track where the Fe came from over the past 93 million years. By analyzing the sediments' chemistry and Fe isotopes, we identified five main Fe sources to the South Pacific: dust, distal background, two types of hydrothermal inputs, and volcanic ash. Initially, hydrothermal fluids were the main source, but dust slowly came to dominance around 30 Ma. Today, the South Pacific Ocean gets more dust than it has in the past 93 million years. This study helps frame iron's role in Earth's climate history.

## 1. Introduction

Iron (Fe) is an essential micronutrient that exerts control over the productivity of many marine ecosystems. In most high-nutrient, low-chlorophyll regions, Fe limits biological productivity and thereby carbon fixation (Browning & Moore, 2023; Moore et al., 2013). Thus, discerning the sources and cycling of Fe over geological timescales is integral for understanding the relationship between Fe and Earth's climate. The prevailing paradigm posits that dust was the predominant source of Fe to the Southern Ocean over at least the last 4 million years (Martínez-Garcia et al., 2011; Wengler et al., 2019). Recent studies show that hydrothermal venting and ema-nations from sediment pore waters along continental margins can be important sources of Fe, especially in regions that are far from significant dust deposition, such as the South Pacific Ocean (Tagliabue et al., 2010). Indeed, the presence of Fe of hydrothermal origin has been observed several thousands of kilometers away from known vent

© 2025. The Author(s).
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properly cited.
**Bernhard Peucker-Ehrenbrink, Ann G. Dunlea**

**Investigation:** Logan A. Tegler, Tristan J. Horner, Sune G. Nielsen, Andy W. Heard, Bernhard Peucker-Ehrenbrink, Jerzy Blusztajn, Ann G. Dunlea

**Methodology:** Logan A. Tegler, Tristan J. Horner, Sune G. Nielsen, Andy W. Heard, Katherine R. Squires, Silke Severmann, Bernhard Peucker-Ehrenbrink, Jerzy Blusztajn, Ann G. Dunlea

**Project administration:** Tristan

J. Horner, Sune G. Nielsen, Ann G. Dunlea

**Resources:** Logan A. Tegler, Tristan

J. Horner, Sune G. Nielsen, Bernhard Peucker-Ehrenbrink, Jerzy Blusztajn, Ann G. Dunlea

**Software:** Logan A. Tegler, Tristan

J. Horner, Sune G. Nielsen, Andy W. Heard, Bernhard Peucker-Ehrenbrink, Jerzy Blusztajn, Ann G. Dunlea

**Supervision:** Tristan J. Horner, Sune G. Nielsen, Bernhard Peucker-Ehrenbrink, Ann G. Dunlea

**Validation:** Logan A. Tegler, Tristan

J. Horner, Sune G. Nielsen, Andy W. Heard, Bernhard Peucker-Ehrenbrink, Jerzy Blusztajn, Ann G. Dunlea

**Visualization:** Logan A. Tegler, Tristan

J. Horner, Sune G. Nielsen, Bernhard Peucker-Ehrenbrink, Ann G. Dunlea

**Writing – original draft:** Logan

A. Tegler, Tristan J. Horner, Sune G. Nielsen, Ann G. Dunlea

**Writing – review & editing:** Logan

A. Tegler, Tristan J. Horner, Sune G. Nielsen, Andy W. Heard, Katherine R. Squires, Bernhard Peucker-Ehrenbrink, Jerzy Blusztajn, Ann G. Dunlea

sites (Fitzsimmons et al., 2017; Resing et al., 2015). Given the important role of Fe in marine ecosystems and the interplay of multiple Fe sources in shaping oceanic Fe budgets, there is strong interest in identifying and quantifying the various sources of Fe, both in the present and throughout Earth's history.

Stable Fe isotope analysis has emerged as a powerful tool to trace the sources and cycling of Fe (Dauphas et al., 2017; Fitzsimmons & Conway, 2022). Interpretation of Fe isotope data relies on the premise that each major Fe source to the oceans—dust deposition, hydrothermal inputs, and continental margin sediments (reducing and oxic)—has a distinct and characteristic Fe isotope signature. This approach faces two main challenges. First, studies have shown that Fe isotopes in seawater are affected by fractionation during internal cycling in the water column. Processes such as oxide- and sulfide-precipitation, ligand binding, and biological uptake can modify the initial Fe isotope signature (Lough et al., 2017; Marsay et al., 2018; Severmann et al., 2004) and make tracing the Fe measured back to its source complicated. Second, the Fe isotope compositions of certain Fe sources, such as hydrothermal venting or non-reductive margin inputs, can overlap. This similarity renders it challenging to apportion Fe sources using Fe isotope compositions alone if multiple sources are mixing. Modern studies address this challenge by focusing on specific oceanographic domains and considering a limited number of Fe sources within each region (e.g., Conway & John, 2014). In contrast, paleoceanographic studies typically interpret Fe isotope records within their broader oceanographic context (e.g., Chu et al., 2006; Levasseur et al., 2004). For instance, despite the isotopic overlap of some sources, Fe isotope data can still differentiate between surface-derived Fe (e.g., dust) and deep-ocean sources (e.g., margin sediments and hydrothermal inputs; e.g., Horner et al., 2015). Given these limitations, a key objective in the field is to move beyond semi-quantitative Fe source apportionment and develop space- and time-resolved approaches that can quantitatively assess the relative contributions of different Fe sources over geological time. Achieving this requires sedimentary archives that offer broad spatiotemporal coverage, faithfully preserve coeval seawater Fe isotope chemistry, and allow for the construction of reliable chronologies—criteria well met by pelagic clays (Dunlea et al., 2021).

Pelagic clays serve as a robust archive of trace element and Fe isotope chemistry over million-year timescales (e.g., Dunlea, Murray, Sauvage, Spivack, et al., 2015; Dunlea et al., 2021). Based on comparisons between the Fe isotope composition of a hydrogenetic nodule (Marcus et al., 2015), particles in a nearby benthic nepheloid layer, and the Fe-weighted average Fe isotopic composition of the overlying water column, it was shown that these deep-sea sediments effectively record the isotopic signature of dissolved Fe in the overlying water column (Dunlea et al., 2021; Marsay et al., 2018). Furthermore, due to their fully oxic porewaters extending to oceanic basement, these sediments experience minimal Fe redistribution after deposition (e.g., D'Hondt et al., 2011). Although the limited preservation of microfossils complicates conventional biostratigraphic dating of pelagic clays, their hydrogenous fraction can be reliably dated using osmium (Os) isotope stratigraphy. This method leverages the fact that Os is scavenged from seawater by Fe and Mn oxides (Klemm et al., 2005; Nielsen et al., 2009; Pegram et al., 1992; Peucker-Ehrenbrink et al., 1995). By comparing pelagic clay-derived Os isotope records to established reference curves (e.g., Peucker-Ehrenbrink & Ravizza, 2012, 2020) this approach enables age estimation and uncertainty assessment through wiggle-match dating, offering several advantages over conventional cobalt-based age models.

This study aims to reconstruct the evolution of Fe sources and cycling in the high-latitude South Pacific over the last 93 Myr. To achieve this, we analyzed sediment from three pelagic clay sites drilled during IODP Expedition 329, focusing on the hydrogenous fraction for Fe isotopes, Os isotopes, and trace metal concentrations. Building on the work of Dunlea et al. (2021), we develop new age models for these cores using Os isotope chemostatigraphy, apply a multivariate statistical approach to disentangle multiple Fe sources in these pelagic clay records, and interpret how these sources evolved as the core sites migrated from the Southern Ocean into the South Pacific over the Cenozoic and late Cretaceous. These findings not only offer new insights into the long-term evolution of Fe sources in the ocean, but also provide a framework for investigating Fe isotope records at other pelagic clay sites.

# 2. Materials and Methods

## 2.1. Sample Description

Marine sediment samples were collected from three sites during IODP Expedition 329 (Sites U1366, U1369, U1370) in the South Pacific Gyre (Figure 1). The sediment cores consist of metalliferous pelagic clays with homogeneous grain sizes ($\varphi \approx 7-9.5$; Dubois et al., 2014), and are primarily composed of smectite, zeolites, mica-
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Location and backtrack paths of Sites U1366, U1370, and U1369 at (a) 0 Ma, (b) 13 Ma, (c) 34 Ma, and (d) 65 Ma. Maps were generated in GPlates (Gurnis et al., 2012; Seton et al., 2012; modified from Dunlea, Murray, Sauvage, Spivack, et al., 2015). Backtracks and land areas are plotted against a latitude and longitude reference frame. The sites are downwind of Australia, the largest source of dust to the Southern Hemisphere (Kok et al., 2021).</figcaption></figure>

group minerals, and red-brown to yellow-brown semi-opaque Fe- and Mn-
oxide minerals (D'Hondt et al., 2011; Yang et al., 2016). At each site,
porewaters are oxygenated down to the basement, with consistently low
dissolved Fe concentrations (<10 µM), indicating minimal post-depositional
Fe remobilization (D'Hondt et al., 2011). Previous studies have constrained
the bulk sediment provenance and established a cobalt-based age model
(Dunlea, Murray, Sauvage, Pockalny, et al., 2015).

## 2.2. Sample Preparation

Several grams of sediment from each site were freeze-dried, hand-powdered, and homogenized using an agate mortar and pestle. For Fe and trace metal analysis, approximately 200 mg of sediment was leached in high-density polyethylene vials with 5 mL of reagent-grade 1 M HCl at 20°C for 24 hr on a shaker table. This procedure selectively dissolves hydrogenous Fe and Mn (oxy)hydroxides while minimizing the dissolution of detrital silicates (Dunlea et al., 2021). The leachate was then separated by centrifugation (1,500 g, 10 min) and aliquots were taken for Fe isotope and trace metal analyses. Due to occasional low Os yields from 1 M HCl leaches, an alternative method was used for Os isotope analysis. In this approach, ~200 mg of powdered sediment was reacted with 2 mL of reagent-grade concentrated HNO₃ in sealed perfluoroalkoxy alkane vials at 90°C for 17 hr. After cooling, the sample was diluted with 8 mL of 18.2 MΩ cm deionized water, and analyzed immediately. Tests on in-house standards and international reference materials confirmed that both leaching methods yielded indistinguishable Os isotope values (see Section 2.3.2).

## 2.3. Analyses

### 2.3.1. Element Concentrations

Major, minor and trace element concentrations were measured using a
Thermo iCAP-Qc inductively coupled plasma mass spectrometer (ICP-MS)
at the Woods Hole Oceanographic Institution (WHOI) Plasma Facility (Data
Set S1, https://doi.org/10.17632/cgvdf4gv3g.1). To prepare for elemental
analysis, 100 µL of leachate was diluted to 2 mL with 2% nitric acid and
100 µL of an indium solution. The ion beam intensities were then corrected
for drift and ionization efficiency using indium as the internal standard. A
suite of six multi-element standards were prepared to cover the range of
element concentrations in the samples. The standards were used to construct
calibration curves (r² > 0.999) that convert ion counts per second of the
samples to concentrations (results in µg per g of unleached sediment).
Reproducibility was monitored with two in-house pelagic clay standards that
were analyzed in every analytical session.

### 2.3.2. Osmium Isotopic Compositions

Osmium isotopes were measured using a Thermo Finnigan Neptune multi-collector ICP-MS at the WHOI Plasma Facility. Samples were sparged with 1.2 L min⁻¹ of Ar gas to introduce volatilized OsO₄ into the instrument (Hassler et al., 2000; Sen & Peucker-Ehrenbrink, 2014). Ion beams corresponding to *m/z* 185 (Re), 187 (Os, Re), 188 (Os), 189 (Os), 190 (Os, Pt), 192 (Os, Pt), 194 (Pt), and 196 (Pt) were measured dynamically in four cycles with 30 repeats. Procedural blanks were negligible. Long term precision and accuracy were assessed via measurement of in-house standards and international reference materials, respectively. The in-house South Pacific Gyre sediment had a ¹⁸⁷Os/¹⁸⁸Os = 0.61 ± 0.07 (2 SD, *n* = 17), while a North Atlantic
core-top sample yielded 1.03 ± 0.10 (2 SD, n = 4), implying a typical precision for pelagic clay samples of ±11% (2 RSD). The long-term mean ¹⁸⁷Os/¹⁸⁸Os values for NOD-A-1 and NOD-P-1 were 1.01 ± 0.04 (2 SD, n = 10) and 0.95 ± 0.04 (2 SD, n = 10). These values fall within the community-accepted ranges of 0.97 ± 0.02 for NOD-A-1 and 0.94 ± 0.02 for NOD-P-1 (Jochum et al., 2005), confirming the accuracy of the sparging method.

### 2.3.3. Iron Isotopic Compositions

Splits of each leachate were oxidized using a mixture of 1 mL HNO₃ and 500 µL of H₂O₂, dried, and reconstituted in 6 M HCl prior to ion-exchange chromatography, which used pre-cleaned AG-1X8 resin (200–400 mesh; Craddock & Dauphas, 2011; Dauphas et al., 2009). Purified samples were analyzed for their Fe isotope compositions via a Thermo Finnigan multi-collector ICP-MS at WHOI, employing sample-standard bracketing with IRMM-524A, which has the same Fe isotope composition as IRMM-014 (Craddock & Dauphas, 2011). Iron isotope compositions are reported relative to IRMM-524A using the delta notation (i.e., δ⁵⁶Fe; Coplen, 2011):

$$ \delta^{56}\text{Fe} (\text{\textperthousand}) = \left( \frac{\delta^{56/54}\text{Fe}_{\text{sample}}}{\delta^{56/54}\text{Fe}_{\text{IRMM-524A}}} - 1 \right) $$

Additional splits were measured via multi-collector ICP-MS at Rutgers University using external normalization to Cu. Repeated analysis of the same samples at both institutions yielded Fe isotope data that were identical to within ±0.05–0.06‰ (2 SD). To further assess accuracy, the in-house North Atlantic pelagic clay standard was leached and processed alongside sample unknowns, yielding δ⁵⁶Fe = −0.25 ± 0.05‰ (2 SD, n = 7). Additionally, the geo-standard IF-G (bulk digestion), yielded δ⁵⁶Fe = +0.64 ± 0.06‰ (2 SD, n = 7), consistent with published values (+0.64 ± 0.01‰; Craddock & Dauphas, 2011). Collectively, these data confirm the accuracy of the methods and indicate a long-term 2 SD external precision for Fe isotope analysis of pelagic clays of approximately ±0.06‰. More information can be found in the Supporting Information S1.

### 2.4. Q-Mode Factor Analysis

Q-mode Factor Analysis (QFA) is a statistical technique that reduces multi-element data into a few independent factors that capture most of the variance (Leinen & Pisias, 1984; Pisias et al., 2013). Factor scores represent how strongly each element covaries with other elements within a given factor, while factor loadings indicate the relative importance of each factor in a sample. Here, QFA was applied to the element concentrations in the leachate to examine covariation in the hydrogenous fraction of the sediment. We did a range pretreatment before the QFA and a VARIMAX rotation post-QFA. In Dunlea, Murray, Sauvage, Spivack, et al. (2015), an additional mixing model (constrained least squares multiple linear regression; CLS) was constructed to determine end-member mass fractions of the bulk sediment. Unlike bulk-sediment mixing models, the hydrogenous leach fraction cannot be used to determine end-member mass fractions. Instead, QFA factor loadings are interpreted as proxies for the relative importance of each factor within each sample. A factor loading approaching 1 can be interpreted as that factor being dominant in a given sample.

Thousands of iterations of QFA were conducted, varying the elements included (e.g., Al, P, Li, V, Cr, Co, Ni, Cu, Zn, Sr, Mo, Ba, Ce, Nd, Sm, Gd, Dy, Er, Yb, Th, U, and Tl) and testing different subsets of the data (e.g., individual sites vs. collective sites) to ensure that the final factors reported here were robustly reproduced and not anomalies. The final QFA analyses includes 9 element concentrations (Mg, Ca, Ti, V, Mn, Fe, Y, La, and Pb) of samples from three sites in the South Pacific Gyre (Sites U1366, U1369, U1370; n = 135; Figure 2).

### 2.5. Os Age Model Construction

A previous study developed an age model for these sites using the Co constant-flux chronometer (Dunlea, Murray, Sauvage, Pockalny, et al., 2015), though Dunlea et al. (2021) later recognized that Os isotope stratigraphy could further refine the age estimates. In this study, Os isotopes were used to establish ages for the samples from each site through a three-step probabilistic approach, which was partially inspired by a method previously applied to determine age-depth relationships in Fe–Mn crusts (Josso et al., 2019). First, marine ¹⁸⁷Os/¹⁸⁸Os reference data (Peucker-Ehrenbrink & Ravizza, 2020) were aligned to the GTS 2020 timescale and smoothed using a locally weighted regression (LOESS in MATLAB). This smoothing was applied to account for the ~1 m Myr⁻¹ sedimentation rates at these sites, where the combined effects of sampling resolution and
<figure><img src="image_4.png" /><figcaption>**Figure 2.** VARIMAX-rotated factor scores (x-axis) for each element (y-axis) used in the Q-mode factor analysis. Q-mode factor analysis of element concentrations in the hydrogenous fraction of the sediment produced five factors that explain 97.9% of the variability in the data (46.2%, 22.2%, 17.3%, 9.1%, and 3.1%, respectively).</figcaption></figure>

bioturbation likely obscured more rapid Os isotope variations. Second, a MATLAB-based algorithm, Sedimentary Osmium Ages Generated by Enforcing Stratigraphy (SOsAGES) was developed to randomly select subsets of samples, identify all possible ages where each ¹⁸⁷Os/¹⁸⁸Os ratio intersected the smoothed reference curve within uncertainty, and construct age-depth models that obeyed the law of superposition. By repeating this process iteratively, SOsAGES generated probabilistic age ranges for each sample. Finally, the resulting sample age distributions (arithmetic mean, 16th percentile, and 84th percentile) were input into rBacon (Blaauw & Christen, 2011), which applies Bayesian age-depth modeling with Markov chain Monte Carlo simulations to estimate sediment accumulation rates and construct age models. Details for the Os age model constructions can be found in the Supporting Information S1 (Text S2, Figures S1, and S2).

For older intervals at Site U1366, where Os isotope data were unavailable (Text S2 in Supporting Information S1), a linear sedimentation rate was extrapolated from the deepest Os-dated horizon (~72 Ma) to the basement (~93 Ma). Of the 157 total samples, seven were excluded as outliers due to anomalous radiogenic or unradiogenic values (Figure 3). The final age uncertainties across the three sites were calculated as ±2.1 Myr (mean), ±2.3 Myr (median), and ±4.2 Myr (maximum).

### 3. Results

The Fe concentrations and isotopic compositions exhibit significant downcore variation at all three sites. Together, the samples have an δ⁵⁶Fe range between -0.40 and +0.18‰ (Figures 4d-4f). The Fe concentration of the leach ranges between 0.12 and 2.26 wt. % (Figures 4g-4i). The Fe concentrations over Site U1366 range from 0.12 wt. % to 2.07 wt. %, Site U1370 has Fe concentrations between 0.19 and 2.26 wt. %, and Site U1369 has the most consistent Fe concentrations, ranging from 0.46 to 0.86 wt. %. Iron isotopes are also variable at each site. The δ⁵⁶Fe values at Site U1366 range from -0.40 to +0.11‰ and Site U1370 has δ⁵⁶Fe values between -0.26 and +0.18‰. Site U1369 has the narrowest range of δ⁵⁶Fe values, from -0.24 to -0.02‰.

On a site-by-site basis, we can observe trends in the downcore profiles of Fe isotopes. At Site U1366, four periods are defined by changes in δ⁵⁶Fe. From 40 to 0 Ma δ⁵⁶Fe values fall close to -0.1‰, from 60 to 40 Ma, the δ⁵⁶Fe values become more negative to around -0.2‰, then from 65 to 55 Ma δ⁵⁶Fe reaches the most positive values at around +0.1‰, and from between 90 and 70 Ma the δ⁵⁶Fe show a range of values downcore. Site U1370 shows variability as well: from 30 to 0 Ma, a δ⁵⁶Fe value of -0.1‰ dominates. Then, the δ⁵⁶Fe value becomes more positive from 50 to 30 Ma (e.g., up to 0‰). The δ⁵⁶Fe values become more negative at the oldest part of the record, on the order of -0.2‰. Finally, Site U1369 gives a range of δ⁵⁶Fe values between -0.25 and +0.01‰ but does not show the smooth transitions that were observed in the other two sites.

The QFA results (Figures 2 and 4) produced five factors that explain 97.9% of the variability amongst the samples from the three sites. Factor 1 explains 46% of the variability and shows that Ti, Fe, and Pb covary strongly. The second factor, which explains 22% of the variability, shows covariance between Ca, Y, and La. The third factor
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Depth (meters below seafloor) at Sites U1366, U1369, and U1370 versus (a–c) $^{187}$Os/$^{188}$Os measured values, (d–f) estimated ages in Ma from the SOsAGES model, (g–i) final age estimates generated using rBacon applied to the data and uncertainties from SOsAGES. The “x” markers denote samples that were flagged as outliers and removed from additional processing. See Data Sets S4 and S5, https://doi.org/10.17632/cgvdf4gv3g.1.</figcaption></figure>

explains 17% of the variability and Mn, V and Fe covary most strongly. Factor 4 (9% variability explained) has high factor scores only for Mg, while factor five (3% variability explained) shows covariation between Ti, Mn, and Fe.
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Ages (y axis; Ma) from ¹⁸⁷Os/¹⁸⁸Os chemostratigraphy plotted against (a–c) QFA factor loadings (colors correspond to the 5 factors in Figure 2), (d–f) δ⁵⁶Fe measurements for each site, and (g–i) concentration of leachable Fe (wt. %) from each site.</figcaption></figure>

For all three cores, the ¹⁸⁷Os/¹⁸⁸Os value tends to decrease with depth toward less radiogenic values (Figure 3). All ¹⁸⁷Os/¹⁸⁸Os values are between 0.20 and 0.99 (Figure 3), as would be expected for the general evolution of ¹⁸⁷Os/¹⁸⁸Os in seawater. The age of samples at Site U1366 ranged from 93 to 0 Ma, Site U1369 sample ages ranged from 30 to 4 Ma, and Site U1370 sample ages ranged from 49 to 1 Ma (Figure 3). The results of the Bayesian age–depth modeling are plotted for comparison against the previously published Co-based ages and Sr
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Cobalt age model (Dunlea, Murray, Sauvage, Pockalny, et al., 2015) plotted against the Os age model (this study) for (a) Site U1366, (b) Site U1369, and (c) Site U1370. The gray dashed line represents a 1:1 relationship. Cobalt age model (triangles) and Os age model (circles, stars and squares) plotted against depth (mbsf) for sites (d) Site U1366, (e) Site U1369, and (f) Site U1370.</figcaption></figure>

isotope based ages (Figure 5; Figure S3 in Supporting Information S1; Dunlea, Murray, Sauvage, Pockalny, et al., 2015; Tanaka et al., 2024).

# 4. Discussion

## 4.1. Evaluating the Os-Based Chronologies

We compare our age models against a Co-based age model that was previously used to date these sites. Cobalt-based age models have been used infrequently to date pelagic sediments that are not amenable to other dating techniques (Dalai & Ravizza, 2006; Dunlea, Murray, Sauvage, Pockalny, et al., 2015; Kadko, 1985; Kyte et al., 1993; Zhou & Kyte, 1992). Cobalt-based methods assume that the flux of non-detrital Co to the seafloor is constant. While this assumption may be valid in some regions over certain timescales, the boundaries of where it
is an effective approach are not well defined. If it is assumed that the accumulation rate of non-detrital Co is constant, the mass accumulation rate of the sediment is inversely proportional to the non-detrital Co concentration. A single mass accumulation rate is applied to a depth interval to calculate the age of each sample (Dunlea, Murray, Sauvage, Pockalny, et al., 2015). The inherent assumption is that there is continuous sediment deposition in between samples, which prevents detection of hiatuses or periods of slow deposition in between samples. Given the uncertainty surrounding the Co-based ages, it is important to have an independent technique to check the previously published age models.

Instead of determining instantaneous sedimentation rates, the ¹⁸⁷Os/¹⁸⁸Os records directly determine ages by aligning them with reference Os isotope values for specific times in the past. In this sense, the Co-based and the Os isotope approaches are complementary. However, the ¹⁸⁷Os/¹⁸⁸Os records presented in this study have different types of uncertainty. For example, the presence of micrometeorites and leached terrigenous material can impact the Os isotope measurements, shifting to lower or higher values, respectively. Slow accumulation rates and bioturbation can smooth abrupt transitions in the Os isotope record or blur periods of non-deposition in the sediment. Additionally, gaps in the reference curve for Os isotope over the Cenozoic limit the current use of the technique, though it could be improved with additional measurements.

Regardless of the differences between approaches and their unique uncertainties, the ages derived from the Co-based and Os isotope techniques agree well at Site U1366, particularly between 0 and 13 m below seafloor (mbsf; Figures 5a and 5d). They begin to diverge slightly in samples deeper than 13 mbsf, but still agree within uncertainty. In contrast, Sites U1369 and U1370 show less agreement between the two approaches. At both sites, the Os isotope method predicts older ages than the Co-based method for intervals between 0 and 9 mbsf (Site U1369; Figure 5e) and 0 and 47 mbsf (Site U1370; Figure 5f), whereas for samples deeper in the cores, the pattern reverses, with the Os isotope method predicting younger ages at both sites (Figures 5b and 5c).

We suggest that the discrepancies between the two techniques is due to hiatuses or periods of slow deposition that were not captured by the Co-based technique. At Site U1369, the discrepancy between the two techniques could be explained by a ≈28 Myr hiatus or slow sedimentation rate from 58 Ma (age of the basalt; Gradstein et al., 2004) to 30 Ma. That is, either no sediment was deposited for the first ≈28 Myrs of the site's history or sediment was eroded or winnowed. A similar trend is observed at Site U1370, implying very slow or no deposition for ≈16 Myrs after the oldest age boundary.

We consider the possibility of such a long hiatus for a few reasons. First, a large area of the modern seafloor in the South Pacific is suggested to be bare or have extremely thin sediment cover (<7 m), despite having quite old basement rock (94–85 Ma; Rea et al., 2006). The extremely slow accumulation rates result from low biological productivity, shallow carbonate compensation depth, minimal terrestrial input, and minimal hydrothermal deposition. Second, as low as dust fluxes are in the South Pacific Gyre today, they were even lower in the past (e.g., Dunlea, Murray, Sauvage, Spivack, et al., 2015; Zhou and Kyte, 1992). Thus, the region of very thin and no sediment today was likely even more spatially expansive in the past. At Site U1370, a Mn-encrusted hardground was recovered at ~52 mbsf, suggesting extremely slow sedimentation rates or a hiatus sometime after the Cretaceous-Paleogene (K–Pg) boundary. Third, with extraordinarily low fluxes of sediment accumulating on the seafloor, sediment will winnow or erode from the peaks in the local bathymetry over longer periods of time, making hiatuses on bathymetric highs more common. Thus, our Os isotope age model implies that the Co-based model missed some significant hiatuses early in the late Cretaceous. Additionally, due to the smoothing of the Os isotope record in sediments with such slow sedimentation rates, further undetected hiatuses may still exist within the stratigraphy (e.g., Tanaka et al., 2024).

## 4.2. Identification of Principal Fe Sources

Many sources and internal processes within the ocean potentially affect the δ⁵⁶Fe values in our samples. In this section, we integrate Fe isotope measurements with statistical analysis of multi-element sediment geochemistry to identify and interpret five distinct source components contributing to the Fe budget of the sediment leaches.

### 4.2.1. Factor 1: Hydrogenous Dust

Factor 1 denotes a covariance in the leaches between Ti, Fe, and Pb, which are elements commonly associated with an aluminosilicate-like dust. We estimate the isotope composition of this and other factors by comparing
<figure><img src="image_8.png" /><figcaption>**Figure 6.** The δ⁵⁶Fe of each sample (y axis) plotted against the VARIMAX factor loadings. As the relative importance of an individual factor increases, the samples converge on or trend toward a δ⁵⁶Fe value that is suggestive of the isotope composition of the factor (black triangle). The isotope value in the title shows the average of the samples that were >0.8 before normalization (a–d) and the extrapolated value for (e).</figcaption></figure>

δ⁵⁶Fe values with QFA factor loadings for each sample and extrapolating to 1 (i.e., a sediment composition entirely dominated by this factor; Figure 6). Samples where Factor 1 is the dominant component (factor loading >0.9) have an average δ⁵⁶Fe of -0.17 ± 0.09‰, which could be interpreted as representing Fe from dust.

A dust component may come from two scenarios. In the first scenario, our leach unintentionally dissolves some detrital material. Dust has a δ⁵⁶Fe ≈ +0.1 ± 0.1‰ (Beard, Johnson, Damm, & Poulson, 2003; Beard, Johnson, Skulan, et al., 2003; Waeles et al., 2007). Our estimated δ⁵⁶Fe for Factor 1 does not overlap with this value, suggesting this scenario cannot explain Factor 1.

In the second scenario, Fe that dissolves from dust into seawater becomes stabilized by organic ligands. This stabilization process induces isotopic fractionation, with the heavier Fe isotopes preferentially bound by the ligand. As a result, dissolved Fe in seawater becomes isotopically heavier by ≈+0.6 ± 0.1‰ (Conway & John, 2014; Dideriksen et al., 2008). When Fe-oxyhydroxides precipitate Fe from this heavier pool of ligand-stabilized Fe, they preferentially incorporate the light Fe isotopes (e.g., ΔFe-oxide-Seawater ≈ -0.8 ± 0.1‰; Horner et al., 2015). The net effect of these processes leaves dust-derived Fe-oxyhydroxide particles deposited in pelagic clays with an isotope composition of δ⁵⁶Fe ≈ -0.1 ± 0.1‰. This isotopic value is the same within uncertainty as the δ⁵⁶Fe estimated from the loadings of Factor 1, supporting the second scenario that the Fe derived from dust was previously dissolved in seawater. Our conclusions are further supported by the Fe:Ti ratios of the leachate that were strongly elevated above the bulk ratios at all three sites (Figure 7), suggesting that far more non-detrital Fe was dissolved in the leach than detrital Fe.

### 4.2.2. Factor 2: Distal Background Fe

The second factor is defined by strong covariance between Ca, Y, and La. In pelagic clay lithologies that have no calcium carbonate microfossils, these elements are typically associated with bioapatite and are expected to be concentrated in sediments with slow sedimentation rates.

The samples that have high loadings for Factor 2 also have low Fe concentrations and trend toward heavy Fe isotope compositions in the leach (Figure 6b). Factor 2 is the most important factor in only a few samples. The average isotopic composition of the samples that have a Factor 2 loading >0.9 is δ⁵⁶Fe = +0.09 ± 0.04‰. These heavy Fe isotopic compositions cannot be easily explained by Fe in reducing sediment (δ⁵⁶Fe ≈ -3 to -4‰; Severmann et al., 2006, 2010) or dust (≈-0.2 ± 0.1‰; Section 4.2.1), both of which are generally isotopically lighter. Hydrothermal input is also unlikely, as Fe concentrations in the sediment are extremely low. Instead, low concentrations of dissolved Fe in seawater with an isotope composition of approximately +0.5‰ have been observed in the open ocean, far from any known source (Fitzsimmons et al., 2017; Marsay et al., 2018). These signatures have been interpreted as ligand-bound background Fe (Marsay et al., 2018). If this Fe source is a background ligand-bound Fe that precipitated as an Fe-oxyhydroxide in a mechanism similar to the dust factor (Horner et al., 2015), its seawater isotope composition would be expected to approach +0.9‰.

Alternatively, the heavy isotopic values could result from non-reductive sediment dissolution (δ⁵⁶Fe ≈ +0.2 ± 0.2‰; Homoky et al., 2013), which is thought to primarily consist of suspended organo-mineral
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Depth (mbsf) versus the ratio of Fe (wt. %) to Ti (wt. %) in the leach (red, blue and green shapes and solid lines) and the bulk (black shapes) at (a) Site U1366, (b) Site U1370, and (c) Site U1369. The leach has an elevated Fe:Ti relative to the bulk samples.</figcaption></figure>

Fe colloids generated in oxic ocean sediments (Homoky et al., 2021). If the Fe originated from settled colloidal particles, their expected Fe isotope composition would be +0.1 ± 0.1‰ (Homoky et al., 2021). Further research is needed to determine if this signature represents a permanent “rusty source” of colloidal background Fe to the seafloor or whether it is from recalcitrant ligand-bound Fe that has undergone prolonged processing in the water column (Marsay et al., 2018). Regardless, the δ⁵⁶Fe of distal background Fe in seawater is the only isotopic signature that overlaps with the δ⁵⁶Fe estimated for Factor 2. As such, we interpret Factor 2 as representing Fe that is deposited far from any major oceanic Fe source.

### 4.2.3. Factor 3: Hydrothermal 1

Factor 3 is characterized by a strong covariance between Mn, Fe and V, with lower factor scores for Mg and Pb. Hydrothermal particles typically exhibit strong covariances between Fe, Mn, and V (Feely et al., 1998; German et al., 1991; Trefry & Metz, 1989; Wu et al., 2022).

The samples that have high Factor 3 loadings converge on a δ⁵⁶Fe value that is isotopically light at -0.18‰ (Figure 6c). The one sample that has a Factor 3 loading of >0.9 has a δ⁵⁶Fe value of -0.22‰. The values are identical within uncertainty when we take an average of the 9 samples that have Factor 3 loadings >0.8 (δ⁵⁶Fe = -0.19 ± 0.07‰; Figure 6c).

Hydrothermal end-member fluids typically fall between δ⁵⁶Fe ≈ -0.6‰ to -0.2‰ (Beard, Johnson, Damm, & Poulson, 2003; Marsay et al., 2018; Rouxel et al., 2016; Severmann et al., 2006). Although there are still many questions surrounding the range of ligand-leachable Fe isotope composition of hydrothermal plumes (Fitzsimons et al., 2017; Wang et al., 2022), we posit that the particles that are incorporated into the sediment were formed either in the vent or from the dissolved Fe that precipitated as Fe-oxides during advection. Thus, the covariance of Fe, Mn, and V as well as the δ⁵⁶Fe of Factor 3 makes it likely that Factor 3 represents a hydrothermal source of Fe in our samples.
#### 4.2.4. Factor 4: Mg-Rich Volcanic Ash

Factor 4 has high VARIMAX factor scores for Mg and low to moderate scores for Mn and Ti. Mg and Ti can be derived from volcanic ashes or altered volcanic ashes (Vogel et al., 2017). At Sites U1366, U1369, and U1370, discrete ash layers were observed and the occurrence of dispersed altered ash was inferred (Dunlea, Murray, Sauvage, Spivack, et al., 2015; Dunlea et al., 2017). Thus, we suggest that this component represents a source of volcanic ash to the sediment.

Estimating the Fe isotope composition of this factor is challenging, as Factor 4 loadings never exceed 0.67 in any samples. The three samples with the highest Factor 4 loadings have $\delta^{56}\text{Fe} \approx -0.16\‰$, which we adopt as the factor composition, given the inability to reliably extrapolate to 1.

Volcanic eruptions contribute substantial trace metal inputs, including Fe (Zhang et al., 2024), though the Fe isotope composition of volcanic material remains poorly characterized. One study suggested that fresh tephra that had not experienced oxic diagenesis had an $\delta^{56}\text{Fe} = +0.02\‰$, whereas buried tephra that had experienced diagenesis had $\delta^{56}\text{Fe}$ values as low as $-0.26\‰$ (Longman et al., 2023), implying that tephra dissolution preferentially liberates isotopically heavy Fe. Similar to dust, we speculate that the Fe may be dissolved from tephra, become ligand-bound, and then precipitate as an Fe-oxide. However, since fresh tephra has a lower starting isotope composition than dust, we might expect the tephra value to be slightly lower, perhaps between $\approx-0.25$ and $-0.15\‰$, consistent with our estimated value for Factor 4.

#### 4.2.5. Factor 5: Hydrothermal 2

Factor 5 shows a strong covariation between Fe, Mn, and Ti. While this factor is never the most important factor (i.e., factor loadings are always <0.53), there are intervals where this component is likely influencing the Fe isotopic composition. To estimate the $\delta^{56}\text{Fe}$ of Factor 5, we regressed a line to the plot of Factor 5 loadings and $\delta^{56}\text{Fe}$ (Figure 6e). Extrapolating to a factor loading of 1 indicates a $\delta^{56}\text{Fe}$ of $-0.5\‰$. One of the known sources with such low $\delta^{56}\text{Fe}$ is hydrothermally derived Fe. The hydrothermal source could be similar to the Kama'e-huakanaloa vent site, where the vent fluid has little interaction with sulfides and $\delta^{56}\text{Fe}$ values can reach below $-1\‰$ (Rouxel et al., 2018). Similarly, at the East Pacific Rise, Rouxel et al. (2016) demonstrated that Fe isotope fractionation during the formation and settling of Fe-sulfides results in $\delta^{56}\text{Fe}$ ranging from $-0.7$ to $-0.9\‰$. If our sediments were influenced by a similar vent with a lighter isotope signature than Factor 3 (hydrothermal 1), it may suggest that multiple hydrothermal sources contributed to these sites.

### 4.3. Evolution of Fe Sources Over 90 Ma

#### 4.3.1. Late Cretaceous (93–66 Ma)

Site U1366 is the only site in this study that existed during the late Cretaceous. Between 93 Ma and 66 Ma, Site U1366 was located between 53 and 43°S (Figure 1). The factor loadings and isotope composition of sediment from this section suggest that the site was dominated by Factor 3 (hydrothermal 1). The prevalence of hydrothermal Fe is consistent with the site's geographical location: it is situated between four mid-ocean spreading ridges, including the Osbourne Trough, which was an active spreading ridge at the time (Figure 1). At 66 Ma, the hydrothermal importance decreases significantly. This decrease in hydrothermal Fe can be attributed to the site moving away from the ocean ridges. However, it is also possible that the sudden decline in hydrothermal Fe may be due to the extinction of the Osbourne Trough, which became inactive around 66 Ma (Billen & Stock, 2000; Figure 1).

In addition to the dominant hydrothermal Fe source, from 80 to 70 Ma on there are moderately important contributions of Fe from volcanic ash, which broadly align with widespread volcanism in the Pacific that began in the early Cretaceous and lasted for $\approx40$ Myr (Schlanger et al., 1981; Straub & Schmincke, 1998). The volcanism that occurred during that time included the Nauru Basin and Marshall Islands, Central Pacific Basin, Mid-Pacific Mountains, Line Islands, and more (Schlanger et al., 1981). However, given that the Pacific is encircled by many active, explosive, and ash-emitting volcanoes (the Ring of Fire), it is possible that increased volcanism could have originated from anywhere in the Southern Hemisphere. The associated subaerial volcanic activity may have produced large amounts of ash that was transported to the sites. Additionally or alternatively, the proximity to multiple spreading ridges and submarine volcanoes may have caused Site U1366 to receive volcanic ash from submarine eruptions. Figure 4 shows that the relative importance of the volcanic ash is lower than the
hydrothermal signal, suggesting that the volcanic contribution may be diluted by the Fe being supplied by hy-
drothermal sources.

4.3.2. Early Cenozoic (66 Ma–34 Ma)

Both Sites U1366 and U1370 date back to the early Cenozoic (66–34 Ma), with estimated paleolatitudes of 43 to 35°S and 58 to 50°S, respectively (Figure 1). During this period, the Osbourne Trough vent system had recently gone extinct, and both sites were continuing to migrate away from mid-ocean ridges. Additionally, from the start of the Cenozoic to the end of the Eocene, oceanic heat flux declined by approximately 20%, accompanied by an even greater reduction in global seafloor production rates (Becker et al., 2009). Given these factors, it is unsurprising that Factor 3 (hydrothermal 1) recedes to relatively low importance. The sites are far from land, which limits the amount of dust that can reach the sediments. Exacerbating the low dust fluxes from geographical distance, the Earth was experiencing greenhouse conditions (Martin, 2006). Humidity and weak prevailing winds would have contributed to the low Fe fluxes experienced in the South Pacific during this interval. Thus, without significant sources of Fe, the primary Fe supply came from Fe that was able to persist in the water column over long distances and timescales: likely from either ligand-bound Fe or colloidal Fe (Homoky et al., 2021; Marsay et al., 2018). This persistent Fe source is likely ubiquitous throughout the modern and past oceans. However, the signal is only concentrated when it is undiluted by Fe fluxes from other sources, as it is during this Fe-starved period.

Around 50-40 Ma, the background Fe signal begins to recede as the hydrothermal factors show a period of importance that coincides with the opening of the Drake Passage (41 Ma; Scher & Martin, 2006), indicating a change in the hydrothermal supply of elements during the tectonic realignments, and subsequent changes in ocean properties and dynamics, associated with the formation of the Antarctic circumpolar circulation (34–33 Ma; Barker et al., 2007; Toumoulin et al., 2020).

4.3.3. Late Cenozoic (34 Ma–0 Ma)

Dust becomes an important source to all three sites in the Oligocene, and since 30 Ma it has been the dominant source of Fe to the region. Although the sites are still far from the continents, between 50 and 41.5°S (Site U1366), 50 to 41.5°S (Site U1370), and 48 and 39°S (Site U1369; Figure 1), the uptick in Fe fluxes from dust could be due to two primary factors. First, the Eocene-Oligocene transition (34.44–33.65 Ma; Katz et al., 2008; Hutchinson et al., 2021) reshaped the climate by shifting the Earth from greenhouse conditions to ice-house conditions. During this time, the strength of the westerly winds likely increased as Antarctica glaciated and the Equator-to-pole thermal gradient steepened (Janecek & Rea, 1983). The enhanced strength of westerly winds would have elevated the amount of dust supplied to the region. Second, studies have indicated that the aridity of Australia increased during the early-to-mid Miocene (23 Ma to 11 Ma; Groeneveld et al., 2017; Martin, 2006), thereby becoming a larger source of dust that could be delivered to the South Pacific. Additional changes in the westerly winds co-occurring with changes in meridional temperature gradients also occurred at ~3 Ma (Li et al., 2015), and this could potentially have driven some of the variability observed in the late Cenozoic. Our data cannot differentiate if wind strength or aridification of Australia led to a higher prevalence of dissolved Fe derived from dust in the region of our sites. While it is possible that some dust could have arrived from South America (Longman et al., 2022; Struve et al., 2022), the majority of dust supplied to this region is expected to be derived from Australia (Dunlea, Murray, Sauvage, Spivack, et al., 2015; Kok et al., 2021).

Mahowald et al. (2005) estimated present day Fe delivery from dust to this region as approximately 0.02 µg m⁻³ yr⁻¹, which is orders of magnitude lower than in the Northern Hemisphere. The supply of Fe from dust in the South Pacific was likely even lower in the Oligocene than in the modern day. Despite the overall dust supply to the region being exceedingly low, our data indicate that dust is still the most important source of Fe during this period.

Although dust is the dominant source of Fe at this time, there are also episodes of increased volcanism from the Oligocene to the present, which is unsurprising given that the sites as bounded by the “Ring of Fire.” For example, New Zealand and the Tonga-Kermadec subduction zone that are both upwind of Sites U1369 and U1370 during this interval. Site U1369 shows elevated volcanic input at ~20 Ma. At Site U1370, volcanic ash became relatively more important than hydrothermal and background sources from 40 to 10 Ma (Figures 4a–4c). Increasing volcanism from the Coromandel and Taupo Volcanic Zones of New Zealand in the mid-to-late Miocene may
account for some of the observed ash inputs (Carter et al., 2004), although it cannot account for the long period of ash input at Site U1370. Volcanic ash may be explained by either subaerial or submarine volcanic sources, but our approach cannot distinguish the two. Similar to dust, there may be increased subaerial volcanic input because of increased wind strength and intensity. A more quantitative assessment of ash deposition across the Cenozoic will require the identification and geochemical characterization of specific ash sources, which should be considered a priority for future work in this area.

Over the past 10 Myrs, all three sites exhibit an increase in hydrothermal Fe sources. This uptick could result from increased hydrothermal activity, changing ocean currents, new hydrothermal activity emanating from the East Pacific Rise or western Pacific vent systems, or a lessening of another Fe source. Interestingly, Site U1366 data imply that the hydrothermal Fe source is less significant there compared to Sites U1369 and U1370. This disparity may indicate a hydrothermal source supplying Fe to the more southern Sites U1366 and U1370, or alternatively, increased dust deposition near Site U1366 that effectively diluted the hydrothermal contributions.

## 4.4. Impacts of Variable Fe Sources on Ecosystems

Records from Sites U1366, U1370, and U1369 reveal a dynamic iron cycle in the South Pacific and Southern Ocean since the Late Cretaceous. Beyond the transition from hydrothermal to dust dominance—coinciding with declining oceanic heat flow and the shift into dustier icehouse conditions—multiple volcanic episodes and an increase in hydrothermal Fe sources since the Late Miocene highlight additional complexity. While much of this variability likely reflects a passive response to tectonic and climatic processes, Fe could also play a potential role in climate regulation through its influence on marine phytoplankton, key drivers of the marine carbon cycle. In this section, we speculate on how Fe sources not only respond to, but also could shape large-scale biogeochemical processes.

Iron is a limiting nutrient in the modern Southern Ocean, but it is unknown whether it limited primary productivity in the past. If Fe limited productivity in the past, the five sources of Fe identified in this study would not have fertilized the surface ocean equally. The depth at which Fe enters seawater is important for determining the likelihood that it ever reaches sea-surface ecosystems. For example, dust from continents and volcanic ash emitted from subaerial eruptions are directly deposited in the surface ocean. Thus, these sources can supply Fe to the euphotic zone before traversing the water column and being deposited in the sediment. If a submarine volcano erupts pumice that floats on the sea surface, it could also be a source of Fe to the surface ocean. In contrast, Fe derived from submarine volcanic ash erupted in the deep ocean or Fe from hydrothermal vents must be upwelled to reach the surface ocean. Much of the Fe from underwater hydrothermal vents may be removed from seawater to sediments before ever reaching photosynthetic organisms (e.g., Roshan et al., 2020).

In the Cretaceous, hydrothermal vents were the dominant source of Fe and dust input was relatively low in the water column overlying Site U1366. If the hydrothermal Fe was upwelled and largely bioavailable, it may have eased potential Fe limitation. However, in the early Cenozoic, the hydrothermal supply at Site U1366 decreased and only a persistent background source of Fe remained. The region around Site U1366 would have been extremely Fe-starved, beyond what is observed in the central oligotrophic Pacific ocean today at Sites U1366, U1369, and U1370. Around 30 Ma when dust supply began to increase, there was a fundamental change in the Fe delivery to sea surface ecosystems. Instead of upwelling being required to sustain a consistent Fe supply to the surface ocean, dust was episodically delivered directly to the surface ocean.

Throughout the Cenozoic, Sites U1366, U1369, and U1370 have been southwest of and downwind of Australia. Thus, the increase in dust at these sites indicates that dust fluxes from Australia to the Southern Ocean likely increased at the same time. Coincident with these changes in Fe sources during the Cenozoic, diatoms replaced radiolarians as the dominant siliceous organism preserved in marine sediment worldwide (Renaudie, 2016). Changes in temperature, ocean circulation, and Si availability likely played a role in this shift of marine eco-systems during the Cenozoic. However, we speculate that changes in the Fe cycle may also have given diatoms an ecological advantage, as they are able to adapt to the low and sporadic Fe supply from dust. Diatoms have a large storage vacuole and utilize ferritin, allowing them to store excess Fe during periods of scarcity (Falkowski et al., 2004; Marchetti et al., 2009; Sunda & Huntsman, 1995). These adaptations facilitate survival in Fe-limited environments by enabling luxury uptake, dynamic regulation of Fe transporters, substitution of Fe for other metals, and adjustments to intracellular Fe quotas (Armbrust, 2009; Marchetti et al., 2006, 2009; Peers &
Price, 2006; Strzepek & Harrison, 2004; Sunda et al., 1991). Future research should examine how changes in the Fe cycle may have shaped ecological niches and influenced the rise of diatoms in the Southern Ocean.

**5. Summary and Future Directions**

We analyzed an operationally-defined hydrogenous fraction of pelagic clays from three sites in the South Pacific. In addition to refining age models for these sites, we also measured their Fe isotopic compositions; determined concentrations of a suite of major, minor, and trace elements in the hydrogenous fraction of the sediments; and, performed statistical modeling to identify covariation patterns amongst the element concentrations. We interpret the δ⁵⁶Fe data and factors produced by Q-mode factor analysis as reflecting five distinct sources of Fe to the Southern Ocean and South Pacific over the last 93 Myr: dust, a distal background source, two sources of hydrothermal material, and volcanic ash. Early in the sites' depositional histories, from the Late Cretaceous to the Eocene (66–34 Ma), hydrothermal Fe sources dominate, whereas dust sources became progressively more important since the Oligocene. However, volcanic ash, background Fe, and hydrothermal sources exhibit substantial downcore variations, demonstrating a dynamic evolution of Fe sources to the Southern Ocean and South Pacific over the Cenozoic. The changes to the Fe cycle may have impacted the delivery of micronutrients to the Southern Ocean and helped create an ecological niche favorable to the expansion of diatoms during the Cenozoic. Understanding the history of the Fe cycle and its impact on microbial communities is important for predicting how marine ecosystems may respond to future perturbations in Fe supply. Future work integrating records like ours with additional tracers of hydrothermal deposition, such as Pb isotopes, would provide a more comprehensive picture of hydrothermal metal fluxes over time. Such reconstructions would allow for independent assessments of hydrothermal variability, free from the uncertainties associated with using ocean crust production rates as a proxy for past hydrothermal activity. Additionally, expanding records like this across a broader spatial scale—particularly in the Southern Ocean, where the shift to diatom-dominated ecosystems was most pronounced—would offer deeper insight into the long-term interplay between Fe supply, ocean biogeochemistry, and ecosystem evolution.

**Conflict of Interest**

The authors declare no conflicts of interest relevant to this study.

**Data Availability Statement**

Supplementary data can be found in Data Set S1 (multi-element, ¹⁸⁷Os/¹⁸⁸Os, and δ⁵⁶Fe data), Data Set S2 (¹⁸⁷Os/¹⁸⁸Os seawater reference data), Data Set S3 (Os curve smoothed for sedimentation rate and bioturbation), Data Set S4 (rbacon and SOsAGES output), Data Set S5 (final Os-derived age values), Data Set S6 (factor loadings), and Data Set S7, https://doi.org/10.17632/cgvdf4gv3g.1 (Fe MAR). Texts S1–S3 can be found in the Supporting Information S1. The data produced in this study are available at Mendeley Data, an open-source online data repository (Tegler, 2024). See references for links to data.

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**Acknowledgments**

This research used samples and data provided by the Integrated Ocean Drilling Program (IODP). We are grateful for the scientists aboard IODP Expedition 329 that collected these samples and the Gulf Coast Repository that archived and provided additional samples. Funding for this research was provided by the U.S. National Science Foundation: OCE-1850807 (A.G. D., T. J. H., and B.P.-E.), OCE-2048604 (T.J.H.), and Graduate Research Fellowship 1122374 (L.A.T.). We extend our gratitude to three anonymous reviewers for their insightful comments, as well as to Ursula Röhl for editorial oversight. We appreciate their efforts and recognize that our study has been improved by their input. Open Access funding enabled and organized by MIT Hybrid 2025.
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