Effects of surface ocean conditions on deep-sea calcite dissolution proxies in the tropical Pacific

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1 PALEOCEANOGRAPHY, VOL. 23,, doi: /2007pa001433, 2008 Effects of surface ocean conditions on deep-sea calcite dissolution proxies in the tropical Pacific Figen Mekik 1 and Lisa Raterink 2 Received 14 February 2007; revised 23 October 2007; accepted 8 November 2007; published 26 March [1] Finding the ideal deep-sea CaCO 3 dissolution proxy is essential for quantifying the role of the marine carbonate system in regulating atmospheric pco 2 over millennia. We explore the potential of using the Globorotalia menardii fragmentation index (MFI) and size-normalized foraminifer shell weight (SNSW) as complementary indicators of deep-sea CaCO 3 dissolution. MFI has strong correlations with bottom water [CO 3 2 ], modeled estimates of percent CaCO 3 dissolved, and Mg/Ca in Pulleniatina obliquiloculata in core top samples along a depth transect on the Ontong Java Plateau (OJP) where surface ocean temperature variation is minimal. SNSW of P. obliquiloculata and Neogloboquadrina dutertrei have weak correlations with MFI-based percent dissolved, Mg/Ca in P. obliquiloculata shells and bottom water [CO 3 2 ] on the OJP. In core top samples from the eastern equatorial Pacific (EEP), SNSW of P. obliquiloculata has moderate to strong correlations with both MFI-based percent CaCO 3 dissolved estimates and surface ocean environmental parameters. SNSW of N. dutertrei shells shows a latitudinal distribution in the EEP and a moderately strong correlation with MFI-based percent dissolved estimates when samples from the equatorial part of the region are excluded. Our results suggest that there may potentially be multiple genotypes of N. dutertrei in the EEP which may be reflected in their shell weight. MFI-based percent CaCO 3 dissolved estimates have no quantifiable relationship with any surface ocean environmental parameter in the EEP. Thus MFI acts as a reliable quantitative CaCO 3 dissolution proxy insensitive to environmental biases within calcification waters of foraminifers. Citation: Mekik, F., and L. Raterink (2008), Effects of surface ocean conditions on deep-sea calcite dissolution proxies in the tropical Pacific, Paleoceanography, 23,, doi: /2007pa Introduction [2] Accurately quantifying deep marine CaCO 3 dissolution has been a challenging oceanographic problem for many decades [e.g., Arrhenius, 1952; Berger, 1973; Broecker, 1982; Archer and Maier-Reimer, 1994; Mekik and François, 2006]. Calcite preservation is a major component of the marine carbonate system (others include the influx of ions into the ocean as weathering products from land, the air-sea exchange of CO 2, the marine biological pump, and the rain ratio, which is the ratio of organic carbon to calcite flux at the seabed); and developing a reliable CaCO 3 preservation proxy is important because the dissolution of carbonates in deep-sea sediments is an integral part of the global carbon cycle in regulating atmospheric pco 2 over thousands of years [Broecker, 1971; Archer and Maier-Reimer, 1994; Archer et al., 2000]. [3] Most CaCO 3 dissolution indicators are based, at least in part, on the preservation state of foraminifer shells. Some of the ways this preservation state has been defined are (1) the ratio of the number of foraminifer test fragments for a given species to the number of whole shells from that species [e.g., Peterson and Prell, 1985a, 1985b; Le and 1 Department of Geology, Grand Valley State University, Allendale, Michigan, USA. 2 Department of Earth and Environmental Sciences, Wright State University, Dayton, Ohio, USA. Copyright 2008 by the American Geophysical Union /08/2007PA Shackleton, 1992; Mekik et al., 2002]; (2) dissolutioninduced loss in size-normalized whole foraminifer shell weight [Lohmann, 1995; Broecker and Clark, 2001a, 2001b]; and (3) changes in the Mg/Ca ratio of foraminifer shells through dissolution [Brown and Elderfield, 1996; Rosenthal et al., 2000; Dekens et al., 2002; Rosenthal and Lohmann, 2002; Mekik and François, 2006; Mekik et al., 2007a]. Most proxies anchor dissolution-induced changes in foraminifer shells to the [CO 3 2 ] of bottom waters [e.g., Broecker and Clark, 2001a, 2001b; Dekens et al., 2002; Marchitto et al., 2005]. Instead, Mekik et al. [2002] related the fragmentation trend of Globorotalia menardii shells to model derived estimates of percent CaCO 3 dissolved in deep-sea sediments. Their percent CaCO 3 dissolved estimates take into account both the CO 3 2 undersaturation of bottom waters and respiratory CaCO 3 dissolution within sediments driven by fluxes of organic carbon reaching the seabed [Emerson and Bender, 1981]. [4] The ideal CaCO 3 dissolution proxy would be (1) timeefficient (short analysis time per sample); (2) based on species (and/or their fragments) which are easy to identify even by nonspecialists, if dependent on biogenic components; (3) without biological/ecological bias, or at least have a bias that is quantifiable and accurately predictable; (4) sensitive to a wide range of dissolution (ideally from 0 to 100% calcite dissolved); (5) calibrated against an independent and quantitative estimate of percent CaCO 3 dissolved; and (6) reliably applicable in areas with strong gradients to surface ocean conditions like temperature, [CO 3 2 ], nutrient 1of15

2 availability, productivity, and even in areas where there is large variation to both organic carbon and calcite fluxes reaching the seabed. [5] We assume uniformity among foraminifer shells when developing CaCO 3 dissolution proxies, yet no two foraminifers are exactly alike because vital processes may affect shell composition, thickness and therefore weight. The best means for quantifying deep-sea CaCO 3 dissolution may be using a multiproxy approach within the same sediment samples [e.g., Mekik and François, 2006; Naik and Naidu, 2007; Ni et al., 2007]. We will focus on two dissolution proxies, size normalized whole foraminifer shell weight (SNSW) [Broecker and Clark, 2001a; 2001b, 2003] and the G. menardii fragmentation index (MFI) [Mekik et al., 2002], with some independent corroboration from foraminifer Mg/Ca. We undertake the following research questions: [6] 1. Intuitively, loss in foraminifer shell weight would precede fragmentation as dissolution progresses. Is there such a sequential relationship between SNSW and MFI with increasing CaCO 3 dissolution in the sediments and decreasing bottom water [CO 3 2 ]? Or does shell weight loss happen simultaneously with fragmentation under similar degrees of bottom water CO 3 2 undersaturation and organic carbon degradation in sediment pore waters? [7] 2. How sensitive are SNSW and MFI to environmental influences within foraminifers calcification waters, such as temperature, [CO 3 2 ], nutrient availability and apparent oxygen utilization (AOU)? Mg/Ca in foraminifer shells is predominantly governed by calcification temperature [e.g., Nürnberg, 1995; Elderfield and Ganssen, 2000; Lea et al., 2000; Anand et al., 2003]; is this also true of SNSWand MFI? 2. Background 2.1. Globorotalia menardii Fragmentation Index [8] The G. menardii fragmentation index is the ratio of the number of damaged G. menardii specimens (D) to the number of whole plus damaged specimens of this species within a sediment aliquot. Damaged specimens are grouped into categories as whole specimens with small holes (holes), pieces greater than half intact (>half), pieces less than half intact (<half) and keels: D ¼ number with holes þ number > half þ ðnumber < half=3þ þ ðnumber keels=5þ ð1þ [9] Mekik et al. [2002] based MFI on Ku and Oba s [1978] laboratory experiments, which showed that dissolution damage in G. menardii shells is quantifiable. The available MFI transfer function relates the fragmentation trend of G. menardii shells in core tops of deep Pacific sediments to model-derived estimates of percent CaCO 3 dissolved (R 2 = 0.88) with the following calibration equation [Mekik et al., 2002]: percent CaCO 3 dissolved ¼ 5:111 þ ðmfi*160:491þ MFI 2 *79:636 ð2þ [10] By percent CaCO 3 dissolved, we mean the fraction of the vertical calcite flux that has been lost to dissolution in any one spot on the sea bottom. Mekik et al. [2002] used the biogeochemical model Muds [Archer et al., 2002] to calculate the percent CaCO 3 dissolved for sample locations along two depth transects in the Pacific Ocean: on the Ontong Java Plateau (OJP), and on the East Pacific Rise outside of the equatorial upwelling region ( m depth). These values were then used to calibrate MFI. Both bottom water DCO 3 2 (which is the [CO 3 2 ] of in situ waters less [CO 3 2 ] at saturation) and organic carbon fluxes reaching the sediments were included both in the model [Archer et al., 2002] and in calculations of percent CaCO 3 dissolved [Mekik et al., 2002]. This is because CaCO 3 dissolution on the seafloor is in part driven by organic carbon degradation in the top meter of sediment. [11] All calibration samples experienced some bottom water CO 3 2 undersaturation (DCO 3 2 ranges between 0.32 and mmol/kg). Estimates of DCO 3 2 for each sample location in MFI s calibration sample set are from Archer s [1996, personal communication, 2001] global gridded database. Organic carbon flux estimates used to calibrate MFI are from (1) satellite-based surface ocean productivity estimates from Behrenfeld and Falkowski [1997]; (2) surface ocean productivity compilations of Berger et al. [1987] and Berger [1989] and the attenuation of organic carbon with water depth using Berger et al. s [1987] equation; and (3) Jahnke s [1996] global gridded database for benthic oxygen fluxes. Details regarding the equation, calibration and modeling of MFI are discussed by Mekik et al. [2002]. [12] Mekik and François [2006] provided independent corroboration for MFI as a dissolution proxy using Mg/Ca and Mg/Sr in shells of P. obliquiloculata and G. menardii in samples from the OJP where surface ocean temperature variation is minimal but where there is a steep gradient to DCO 3 2 of bottom waters. Mg/Ca in P. obliquiloculata shells strongly correlates with MFI (R 2 = 0.94) and with MFI-based percent dissolved (R 2 = 0.84) on the OJP [Mekik and François, 2006] where Mg/Ca decreases with increasing MFI-based percent calcite dissolved. Subsequently, Mekik et al. [2007a] used MFI for dissolution correction of Mg/Ca paleothermometry. Mekik et al. [2002, 2007b] expanded MFI s applicability to core top samples in the eastern equatorial Pacific (EEP) where both surface ocean productivity and the rain ratio reaching the seabed are highly variable. Also, Loubere et al. [2004] and Richaud et al. [2007] applied MFI in down core work for estimating CaCO 3 fluxes to the deep sea. [13] In summary, MFI is unique among available dissolution proxies because (1) G. menardiis provide a quantifiable fragmentation trend with increasing dissolution where other species tend to stay intact until a threshold value of DCO 3 2 is reached, and then fall to pieces randomly below this threshold (F. Mekik, unpublished data, 2000); (2) it is the only dissolution proxy anchored against model-derived estimates of percent CaCO 3 dissolved per sample location [Mekik et al., 2002]; (3) it is efficient (20 30 minutes per sample); (4) it uses a species whose fragments are easy to identify; (5) it works at least in one region (EEP) where the 2of15

3 surface ocean has a strong productivity gradient [Mekik et al., 2002, 2007a, 2007b]; and (6) as explained above, there is some independent corroboration for MFI as a dissolution proxy from Mg/Ca and Mg/Sr in multiple species of planktonic foraminifers [Mekik and François, 2006]. However, neither the relationship between MFI and SNSW, nor SNSW s application in an upwelling region like the EEP has previously been explored. That is our goal herein Size-Normalized Foraminifer Shell Weight [14] The size-normalized foraminifer shell weight (SNSW) method is founded on the assumption that foraminifer test weight loss within a specified size range is driven solely by dissolution of foraminifer shells in sediments [Lohmann, 1995; Broecker and Clark, 2001a, 2001b]. This has been well established for several species of planktonic foraminifers including Neogloboquadrina dutertrei, Pulleniatina obliquiloculata and Globigerinoides ruber [e.g., Broecker and Clark, 2001a, 2001b, 2003]. Broecker and Clark [2001a] relate shell mass loss to depth normalized bottom water [CO 2 3 ] which they define as CO 2 3 * ¼ CO 2 3 þ 20ð4 zþ ð3þ where [CO 2 3 ]* represents depth normalized [CO 2 3 ] and z is water depth in kilometers. [CO 2 3 ] values in their work are extrapolated from GEOSECS data. They report an average size normalized foraminifer weight loss slope of 0.30 ± 0.05 mg per 1 mmol/kg decrease in depth normalized [CO 2 3 ]. [15] It seems that SNSW and MFI may potentially serve to expand and complement one another since, intuitively, foraminifer shell mass loss should precede fragmentation. We explore this issue as well as potential environmental effects on each proxy Eastern Equatorial Pacific [16] Unlike surface waters above the Ontong Java Plateau, the EEP is an expansive region of both coastal and equatorial upwelling with high pco 2 in its surface waters [Tans et al., 1990]. The South Equatorial Current (SEqC) is driven by trade winds and marks the northern branch of the South Pacific subtropical gyre [Pennington et al., 2006] where it feeds a major open ocean upwelling system in the EEP. The SEqC seems to originate from the SW Antarctic Pacific [Toggweiler et al., 1991; Kessler, 2006]. The EEP cold tongue results from the divergence of flow along the equator and generally spans between 3 N and 3 S though it is not usually symmetrical about the equator [Wyrtki, 1981; Fiedler and Talley, 2006]. This cold upwelling process brings macronutrients to the euphotic zone [Chavez and Barber, 1987] and the deep chlorophyll maximum is shallow in this region of the EEP [Fiedler and Talley, 2006; Kessler, 2006] where phytoplankton in the equatorial undercurrent display only weak seasonality [Pennington et al., 2006]. The EEP is generally a region of weak seasonality [Chavez and Toggweiler, 1995; Loubere, 1998; Loubere and Fariduddin, 1999] and high-nitrate low-chlorophyll concentration [Behrenfeld and Kolber, 1999; Pennington et al., 2006]. This means that upwelled nutrients are never fully utilized by the plankton [Chavez and Barber, 1991] because of iron and silica limitation [Dugdale et al., 1995, 2002; Dugdale and Wilkerson, 1998]. The Costa Rica Dome is an oceanic upwelling center in the EEP along the coasts of Nicaragua and Costa Rica where the thermocline approaches very near the sea surface [Fiedler and Talley, 2006]. [17] Because the EEP is a major upwelling zone, all surface ocean parameters we consider herein have steep gradients across the region. This makes the area an ideal study site for the effects of environmental factors on sedimentary CaCO 3 dissolution proxies using tropical planktonic foraminifers. However, precisely because it is an upwelling zone, all surface ocean parameters in this region tend to covary (Table 1) which makes distinguishing the effect of one parameter (e.g., temperature) from another (e.g., [NO 3 ]) challenging. 3. Methods 3.1. Samples [18] We used whole tests from P. obliquiloculata and N. dutertrei for SNSW because those two species are most commonly used in dissolution work [e.g., Broecker and Clark, 2001a, 2001b; Dekens et al., 2002; Mekik and François, 2006; Naik and Naidu, 2007]. Because there is a steep gradient to temperature between 50 and 150 m water depth in the EEP, it is important to identify more precise depth habitats for each of our species. All of our species are thermocline dwellers [Bé, 1960; Hilbrecht, 1996; Anand et al., 2003; Farmer et al., 2007]; however, P. obliquiloculata prefer living at 50 m water depth [Farmer et al., 2007] and Mekik et al. [2007a] found the best relationship between Mg/Ca in shells of this species and water temperatures at 50 m. G. menardii prefer 75 m water depth [Farmer et al., 2007] while N. dutertrei are known to live in the deep chlorophyll maximum (DCM) [Fairbanks et al., 1982; Fairbanks and Wiebe, 1980; Loubere, 2001]. We used Loubere s [2001] habitat depth estimates for N. dutertreis based on isotope equilibrium depths calculated using a combination of d 13 C and d 18 O from N. dutertrei shells in EEP core tops. For samples beyond Loubere s [2001] data set (those from the OJP and some samples from the EEP), we used the average value of the environmental parameter of interest between 50 and 75 m because this is the mean value of habitat depth in Loubere s [2001] work and this depth is also in keeping with independent DCM depth estimates for the EEP [Fiedler and Talley, 2006; Kessler, 2006; Pennington et al., 2006]. [19] We picked N. dutertrei shells from the mm size range as described by Broecker and Clark [2001a, 2001b], and P. obliquiloculata s from the mm size range because smaller P. obliquiloculata shells are not abundant in our samples from the EEP. Many studies have illustrated that using larger foraminifers improves analytical accuracy because larger foraminifer size minimizes ontogenetic effects [Kroon and Darling, 1995] and provides more consistent results among samples [Oppo and Fairbanks, 1989]. Even in Mg/Ca work on planktonic foraminifer shells, Elderfield et al. [2002] established that geochemical data from larger foraminifers yield results which are more consistent with temperatures in foraminifer habitat waters. 3of15

4 Table 1. Correlations Between Variables in the EEP a R 2 Water Depth, m DCO 3 2 Calcite Dissolved, % Temperature Nitrate AOU CO m 75 m DCM 50 m 75 m DCM 50 m 75 m 100 m 50 m 75 m 100 m Water depth DCO Percent dissolved T 50 m T 75 m T DCM Nitrate 50 m Nitrate 75 m Nitrate DCM AOU 50 m AOU 75 m AOU DCM CO m CO m CO 2 3 DCM a T is temperature; DCM stands for deep chlorophyll maximum. Furthermore, J. Bijma s (personal communication, 2006) unpublished SNSW data from Globigerinoides sacculifer tests also supports the assertion that analytical accuracy improves with increasing foraminifer size. [20] We ascertain that our core tops are Holocene in several ways. First, Loubere [2001] provided d 13 C and d 18 O measurements from N. dutertrei shells in core tops from the EEP which are consistent with Holocene d 13 C and d 18 O values. Some of the core tops we are using herein overlap with a subset of Loubere s [2001] samples, and the geographic distribution of his core tops is broad enough to allow for a regional assessment of age distribution in the EEP. In addition to Loubere s [2001] data set, Mekik et al. [2007b] generated d 18 O data from foraminifers in samples from very deep core tops where chemical erosion may have obliterated Holocene sediments and they excluded samples with values inconsistent with those for the Holocene. We also excluded those samples herein. Second, Mekik et al. s [2007b] EEP rain ratio maps generated from a subset of samples used herein fit well with chlorophyll-based estimates of Recent surface ocean productivity in the EEP [after Behrenfeld and Falkowski, 1997]. Last, Mekik et al. s [2007a] Mg/Ca data from planktonic foraminifers from the same core tops as those used herein show consistent patterns with Recent sea surface temperatures in the habitat waters of each species Sample Preparation [21] We followed methods outlined by Mekik et al. [2002] for generating MFI data, and used procedures described by Lohmann [1995] and Broecker and Clark [2001a] for SNSW measurements with three modifications in order to improve data quality. First, foraminifers were picked individually within given size ranges instead of trapping foraminifers between two sieves; and all the picking was done by the same person (F. Mekik). This ensures that size measurements for each foraminifer are made on the shortest diameter and improves the consistency of foraminifer size within each range. Second, a wet picking technique was used because wet foraminifers are more transparent. This facilitates picking the cleanest specimens because using an ultrasonicator or other cleaning methods on foraminifers generally damages shells and distorts weight data. Third, we weighed two separately picked, size-normalized populations for each species from each sample in order to compare replicate measurements of species-specific mean weight from each sediment sample. Replicate mean weight measurements for all samples are not available, however, because of the low abundance of clean foraminifers in given size ranges in some samples. [22] We aimed for 50 or more whole shells for weighing from each sediment sample. However, in some samples, especially those from high-dissolution areas, we had to base our mean weight on fewer foraminifers. Our data set for MFI (Figure 1) is larger than that for SNSW because fragments of G. menardii are far more abundant than clean whole foraminifer shells in tight size ranges Analytical Method [23] Mekik et al. [2002] estimated MFI s error margin at 10 15%. It is customary to count 300 or more G. menardii whole shells and fragments per sample to obtain statistically robust results. Counts in some high-dissolution samples fell below 300 because of lack of shell matter. [24] There are three main sources of uncertainty in SNSW measurements: (1) error margin of the balance; (2) reproducibility of mean weight measurements; and (3) variation in shell weight within a given size range. We used a Mettler microgram scale at Grand Valley State University whose error margin is ±5 mg. To test for reproducibility of mean weight measurements, we made replicate weight measurements from a separately picked, second population of foraminifers from the same species and in the same size fractions from each sediment sample where we had a sufficient number of foraminifers available. The reproducibility between two separately picked replicate mean weight measurements is high (R 2 = 0.97). In the following discussion 8 represents the difference between two replicate mean weight measurements for the same species in the same sediment sample. Mean 8 for P. obliquiloculata weight 4of15

5 Figure 1. (a) General locations for samples used herein are shown as open and shaded diamonds. OJP is Ontong Java Plateau; EEP is eastern equatorial Pacific. Dashed lines represent temperature contours for 75 m water depth after Locarnini et al. [2006]. (b) Core top sample distribution in the eastern equatorial Pacific. MFI data are available from all samples. Shaded diamonds show samples from which N. dutertrei SNSW were generated, and dots show samples from which P. obliquiloculata SNSW data were generated. Dashed lines represent seafloor bathymetry. measurements is 3.74 mg, and the standard deviation in 8 for P. obliquiloculata weight is 3.02 mg. The ratio of mean 8 to average P. obliquiloculata mean weight from all samples (65 mg) is 5.8%. For N. dutertrei weight data, mean 8 is 2.18 mg, and its standard deviation is 2.02 mg. The ratio for mean 8 to average N. dutertrei mean weight from all samples (37.5 mg) is also 5.8%. [25] Following the work of others [Rosenthal et al., 2000; Barker et al., 2004], we examined single-foraminifer weights within three size ranges for P. obliquiloculata whole shells in a sample from the OJP, ERDC 89. The variation coefficient for each size range is listed as a percentage (Table 2) and it is the ratio of the standard deviation for all weight measurements within the size range to the mean weight of foraminifers in that size range. Note that the variation coefficient for P. obliquiloculata weight in the mm size range is significantly higher than that for the two larger and wider size ranges. These results confirm our choice for using larger P. obliquiloculata specimens because weight variation within a size range seems to be lower among larger foraminifers. We did not generate single-foraminifer weight data for N. dutetrei because an average N. dutertrei shell in the mm size fraction weighs 38 mg. The uncertainty of the balance at ±5 mg would substantially obscure the variation in our weight measurements of individual N. dutertrei shells. [26] Data for [CO 3 2 ] at 50, 75 and 100 m water depth are from Archer [1996, personal communication, 2001]. Other environmental data (temperature, [NO 3 ] and AOU) at 50, 75 and 100 m are from NOAA s World Ocean Atlases (see Table 2. Variation Coefficients in Single P. obliquiloculata Shell Weights in Sample ERDC 89 on the Ontong Java Plateau mm mm mm Mean weight, mg Standard deviation Variation coefficient, % of15

6 Locarnini et al. [2006] for temperature and Garcia et al. [2006a, 2006b] for nutrients). 4. Results [27] Our sediment samples include core tops along (1) a depth transect on the OJP where surface ocean parameters are mostly invariable allowing us to isolate the dissolution signal in our proxies; and, for comparison, (2) a large group of core top samples (more than 100) from the EEP ( m depth) where surface ocean parameters are strongly variable (Figure 1). All samples are from gravity cores. Listings for all data used herein are available as auxiliary material Calcite Dissolution Proxies on the Ontong Java Plateau [28] First we compare MFI and SNSW in a subset of MFI s calibration samples in core tops from the OJP. These samples compose a depth transect ( m) beneath surface waters with fairly similar environmental parameters. This allows us to isolate the CaCO 3 dissolution signal in our proxies. 2 [29] On OJP, MFI has a robust relationship with DCO 3 (R 2 = 0.92, Figure 2a), model-derived percent CaCO 3 dissolved (R 2 = 0.88, Figure 2d), and Mg/Ca from P. obliquiloculata shells (R 2 = 0.94, Figure 2g). However, neither P. obliquiloculata nor N. dutertrei shell weight correlates very well with DCO 2 3 (Figures 2b and 2c), MFI-based percent CaCO 3 dissolved (Figures 2e and 2f) or Mg/Ca from P. obliquiloculata shells (Figures 2h and 2i). [30] Though P. obliquiloculata shell weight drops with increasing dissolution in OJP samples (Figures 2b and 2e), we do not see a complementing relationship between MFI and SNSW there. Instead, increasing fragmentation in G. menardii shells appears to be happening under the same conditions of bottom water CO 2 3 undersaturation as whole shell weight loss in P. obliquiloculatas (Figures 2a, 2b, 2d, and 2e). In addition, Mg/Ca in P. obliquiloculata shells also has a weak relationship with P. obliquiloculata SNSW (Figure 2h). [31] N. dutertrei shells are significantly less abundant and lighter in OJP samples (OJP average weight is 27 mg) when compared to their counterparts in the EEP (EEP average weight is 39 mg). Broecker and Clark [2001a, 2001b] also list lighter N. dutertrei weights (20 36 mg) in their samples from the OJP within the same size fraction we used here Calcite Dissolution Proxies in the Eastern Equatorial Pacific Globorotalia menardii Fragmentation Index [32] The fragmentation trend of G. menardii (MFI) has no clear mathematical relationship with any surface ocean parameter or DCO 2 3 of bottom waters in the EEP (Figure 3). At the same temperature at 75 m water depth (16 C) we see a wide range to MFI (0.4 1). Similarly, samples under a wide range of temperature (15 22 C) yield more or less constant MFI values (1) (Figure 3). We find similar 1 Auxiliary materials are available at ftp://ftp.agu.org/apend/pa/ 2007pa patterns to that of MFI versus temperature when we plot MFI against surface ocean [CO 3 2 ], [NO 3 ] and AOU (Figure 3). The poor relationship between MFI and bottom water DCO 3 2 in the EEP is not unexpected [Mekik et al., 2002]. Unlike on the OJP where there is little variation in seabed organic carbon flux, respiration of carbon in sediment pore waters drives additional CaCO 3 dissolution within sediments in the EEP [Mekik et al., 2002, 2007b] Size-Normalized Shell Weight [33] P. obliquiloculata shell weight correlates well with MFI-based percent dissolved in the EEP (R 2 = 0.79) although it has no correlation with DCO 3 2. This is unexpected because we were not able to find a good mathematical relationship between P. obliquiloculata shell weight and MFI-based percent dissolved or Mg/Ca in P. obliquiloculata shells in samples from our depth transect on the OJP where temperature and other surface ocean parameters are mostly unchanging (Figures 2b, 2e, and 2h). It appears that in the EEP, P. obliquiloculata shell weight is influenced by both respiratory CaCO 3 dissolution within the sediments and, to a lesser extent, environmental parameters in the surface ocean. Also, P. obliquiloculata SNSW has somewhat variable but weaker relationships with all four environmental parameters at 50 m water depth (R 2 = ) (Figure 4). We are using exponential relationships because they provide the best fit with our data. [34] N. dutertrei SNSW in core tops from the EEP (Figure 5) show no significant correlation with any dissolution or environmental parameter. However, we see a moderately strong correlation between MFI-based percent CaCO 3 dissolved and N. dutertrei SNSW (Figure 5b) if equatorial samples are removed. We also observe a correlation between [CO 3 2 ] and/or [NO 3 ] at the DCM and N. dutertrei SNSW, again if equatorial samples are removed (Figures 5d and 5e). Furthermore, N. dutertrei shell weight in equatorial samples seem to be heavier than what the trend seen in other samples indicates (Figures 5c 5f); and N. dutertrei weights from just north of the equator are heavier than those from immediately south of the equator. 5. Discussion 5.1. Comparison of Dissolution Proxies on the Ontong Java Plateau [35] Both our SNSW data using P. obliquiloculata shells and those of Broecker and Clark [2001a] using smaller individuals of the same species have weak correlations with DCO 2 3 on the OJP (Figure 2b). This is different than the robust relationship between P. obliquiloculata SNSW and bottom water CO 2 3 undersaturation [Broecker and Clark, 2001a]. [36] The discrepancy lies in the different terms used to describe undersaturation of CO 2 3 in bottom waters. Broecker and Clark [2001a] use [CO 2 3 ]*, which they define as a depth normalized bottom water [CO 2 3 ] indicator based on [CO 2 3 ] values extrapolated from GEOSECS. The DCO 2 3 values given by Archer [1996] are also derived from GEOSECS, but these are based on the empirical relationship between [CO 2 3 ] and temperature, salinity, O 2 and nutrients, and CaCO 3 solubility formulations. We use 6of15

7 Figure 2. All samples shown here are from OJP. (a) MFI versus DCO 2 3.(b)P. obliquiloculata SNSW versus DCO 2 3. Stars show SNSW data ( mm) from Broecker and Clark [2001a]. Diamonds show data from this study ( mm). (c) N. dutertrei SNSW versus DCO 2 3. Stars show SNSW data from Broecker and Clark [2001a, 2001b]. Diamonds show data from this study. Both are from the mm size fraction. (d) MFI versus model-derived estimates of percent calcite dissolved. (e) P. obliquiloculata SNSW versus MFI-based percent calcite dissolved estimates in MFI s calibration samples. (f) N. dutertrei SNSW versus MFI-based percent calcite dissolved estimates. (g) MFI versus Mg/Ca in P. obliquiloculata shells. (h) P. obliquiloculata SNSW versus Mg/Ca in P. obliquiloculata. (i) N. dutertrei SNSW versus Mg/Ca in P. obliquiloculata. Archer s [1996] DCO 3 2 data herein for comparing MFI with SNSW in order to maintain consistency with former work on MFI [Mekik et al., 2002; Mekik and François, 2006; Mekik et al., 2007a, 2007b], and because MFI data are not available for the sample set used by Broecker and Clark [2001a] Postdepostional Calcite Dissolution and Environmental Parameters in Foraminifer Habitat Waters [37] It is difficult to isolate the influence of a single specific surface ocean parameter on CaCO 3 dissolution proxies in the EEP for two reasons. First, all our core tops 7of15

8 Figure 3. MFI versus DCO 2 3 and environmental parameters at 75 m water depth in core top samples from the eastern equatorial Pacific. Shaded line in first plot showing MFI versus DCO 2 3 represents the correlation line between these same parameters in core tops from the OJP (Figure 2a). are from regions where bottom waters are undersaturated with respect to CO 3 2 (DCO 3 2 ranges between 0.42 and 41.8 mmol/kg among locations of our samples). Thus we do not have samples that experienced no dissolution. Even if we had very shallow samples (<1700 m), there is still the possibility of supralysoclinal CaCO 3 dissolution in sediments of this region [e.g., de Villiers, 2005]. Second, all environmental parameters covary in the EEP (Table 1). [38] Bijma et al. [1999] demonstrated that Orbulina universa shell weight in laboratory cultures is primarily influenced by [CO 3 2 ] of water in which the organism calcifies. The influence of [CO 3 2 ] on shell thickness and shell weight 8of15

9 Figure 4. P. obliquiloculata SNSW data versus (a) DCO 2 3 and environmental parameters (b) MFI percent dissolved calcite, (c) water temperature, (d) carbonate ion and (e) nitrate concentrations, and (f) apparent oxygen utilization at 50 m water depth in core top samples from the eastern equatorial Pacific. In Figure 4b, color coding indicates [CO 2 3 ] at 50 m water depth in the EEP. In Figures 4c 4f, color coding indicates three dissolution brackets estimated with MFI. 9of15

10 Figure 5. N. dutertrei SNSW data versus (a) DCO 2 3 and environmental parameters (b) MFI-based percent calcite dissolved, (c) water temperature, (d) carbonate ion and (e) nitrate concentrations, and (f) apparent oxygen utilization at the deep chlorophyll maximum (N. dutertrei habitat depths from Loubere [2001]) in core top samples from the eastern equatorial Pacific. Yellow triangles show samples falling in the region between the equator and 5 S, and yellow circles show samples from the region between the equator and 5 N. 10 of 15

11 should not be restricted to O. universa and Globigerinoides sacculifer [Bijma et al., 2002]. Naik and Naidu [2007] provided evidence in core tops from the western tropical Indian Ocean supporting the strong effect of [CO 2 3 ] in calcification waters on SNSW of both N. dutertrei and P. obliquiloculata while Barker and Elderfield [2002] demonstrated the same effect in down core work. Even studies on shell chemistry, not just SNSW, show that the [CO 2 3 ] of habitat waters may bias results (e.g., d 18 O and d 13 C[Spero et al., 1997] and U/Ca [Russell et al., 2004]). Although our data set does not allow us to rule out the potential effect of the other three environmental parameters on SNSW (because all surface ocean parameters covary in this region), previous studies point to [CO 2 3 ] in calcification waters as the most influential environmental parameter on shell weight. So, we will focus our discussion around the effect of [CO 2 3 ] in habitat waters on SNSW in the EEP. [39] In order to examine the effect of [CO 2 3 ] of habitat waters on the dissolution trend seen in P. obliquiloculata shell weight, we grouped our samples into three ranges of [CO 2 3 ] at 50 m water depth (<175, , and >220 mmol/kg) (Figure 4b). If calcite dissolution were the only influence on P. obliquiloculata shellweightin the EEP, we would expect the distribution of [CO 2 3 ]valuesin Figure 4b to be overlapping and random. Instead, SNSW of P. obliquiloculata (Figure 4) seems to respond both to postdepositional CaCO 3 dissolution in the sediments (R 2 = 0.79; estimated with MFI) and, most likely, [CO 2 3 ]ofwaters at 50 m in the EEP. Our results are supported by Naik and Naidu s [2007] findings of the strong influence of [CO 2 3 ]in calcification waters on P. obliquiloculata SNSW. [40] Likewise, we grouped samples into three categories based on the extent of dissolution each experienced (<45%, 45 60%, and >60%). The width of each dissolution category (15%) in Figures 4c 4f is within MFI s error margin. Again, both dissolution and [CO 2 3 ] in ambient waters appear to influence P. obliquiloculata shell weight (Figure 4d), but there is also a systematic decrease in the sensitivity of P. obliquiloculata SNSW to [CO 2 3 ] in samples which have experienced high dissolution (green dots). [41] We performed multiple linear regression analysis to further explore the effect of postdepositional calcite dissolution and [CO 2 3 ] at 50 m water depth on P. obliquiloculata SNSW. In our analysis P. obliquiloculata SNSW is the dependent variable and MFI-based percent CaCO 3 dissolved and [CO 2 3 ] at 50 m are the independent variables. We used the resulting multiple linear regression equation (Figure 6) to estimate P. obliquiloculata SNSW from MFIbased percent CaCO 3 dissolved and [CO 2 3 ] at 50 m. water depth as input parameters for each sample. We find a high correlation between measured P. obliquiloculata weights and those calculated with the regression equation (Figure 6). This suggests that 83% of the variation in P. obliquiloculata SNSW in our samples may be explained by the effects of both postdepositional CaCO 3 dissolution and [CO 2 3 ]at50m water depth (Figure 6) Geographic Controls on Dissolution Proxies [42] All environmental variables in the EEP covary (Table 1), but we believe the environmental factor most Figure 6. Multiple linear regression equation between Mg/Ca in P. obliquiloculata and MFI-based percent calcite dissolved and [CO 2 3 ] at 50 m in samples from the EEP. likely affecting N. dutertrei shell weight in our EEP samples is [CO 3 2 ] at DCM depths (Figure 5d) because of strong laboratory evidence supporting the effect of [CO 3 2 ] in ambient waters on shell weight [Bijma et al., 1999, 2002] and core top work showing the influence of surface ocean [CO 3 2 ]onn. dutertrei SNSW [Naik and Naidu, 2007]. [CO 3 2 ] at the DCM seems to increase from south to north across the EEP with the exception of relatively low [CO 3 2 ] in the equatorial region. [43] Within the EEP, areas beneath the North Equatorial Counter Current and in the southwest beyond the upwelling zone contain the heaviest N. dutertrei shells within our given size ranges (Figure 7). Both of these areas have very low chlorophyll concentrations (low productivity) in surface waters. By contrast, lighter N. dutertrei shells are found beneath upwelling regions on the equator by the South Equatorial Current (Figure 7) and at the Costa Rica Dome [Fiedler and Talley, 2006], where productivity is high. [44] The geographic pattern of SNSW for N. dutertrei populations in the EEP may indicate the presence of cryptic species of N. dutertrei, which may prefer specific environmental factors [Darling et al., 1996; Huber et al., 1997; Darling et al., 1999; Kucera and Darling, 2002; Darling et al., 2003]. Cryptic species are distinct genotypes within a morphospecies that can be difficult to identify with morphological features alone. The distribution of N. dutertrei 11 of 15

12 Figure 7. Distribution of N. dutertrei SNSW in the eastern equatorial Pacific. Abbreviations are NEqCC, North Equatorial Counter Current; SEqC, South Equatorial Current; and CRD, Costa Rica Dome. Contours represent N. dutertrei shell weight in mg. The image is overlain on the chlorophyll-based surface ocean productivity map of Behrenfeld and Falkowski [1997]. SNSW could reflect intraspecific ecophenotypic variation, multiple genotypes or both, particularly where the SNSW does not seem to be influenced by [CO 3 2 ] (Figure 5d). We would need DNA data from our N. dutertrei specimens to discern among these possibilities, but this data is not available. We note, though, that Kucera and Darling [2002], using DNA data, describe three distinct genotypes for N. dutertrei but only one for P. obliquioculata and one for G. menardii. This finding may explain the regional distribution of N. dutertrei shell weight as evidence of multiple populations of the N. dutertrei morphotype in the EEP distinguished by their shell thickness. [45] Schmidt et al. [2003, 2004] reported that planktonic foraminifer size relates to latitude. They attribute this change to surface water temperature, because higher temperatures promote growth in foraminifers. Schmidt et al. [2004] also conclude that planktonic foraminifer assemblages tend to be smaller in upwelling regions. Although they do not present SNSW data, it is possible that the SNSW distribution of N. dutertrei across the EEP (Figure 7) reflects variations in temperature and upwelling. [46] Furthermore, the SNSW of N. dutertrei also varies between the OJP and EEP, with much lighter specimens on the OJP. Broecker and Clark [2001a] note that N. dutertrei shells from the Atlantic Ocean are heavier than those from the Indian and Pacific Oceans. Thus there appears to be multiple populations of the N. dutertrei morphotype both in the EEP and across the equatorial Pacific between the EEP and OJP. [47] One last geographic control on dissolution proxies stems from the absence of G. menardiis in Atlantic sediments from the Last Glacial Maximum (LGM). This limits MFI s applicability in down core work in Atlantic cores Other Complicating Factors [48] A factor often ignored in studies using foraminifer SNSW is that significant shell loss may occur as foraminifer tests settle though the water column; however, this has not been well established for many foraminifer species because of the scarcity of sediment trap data. Schiebel [2002] estimated that only 25% of initially produced foraminifer shell material settles to the bottom. In a more recent study, Schiebel et al. [2007] illustrated that Globigerina bulloides and Globigerinita glutinata lose an average of 19% of their original shell weight while settling through the twilight zone, m water depth. They detected no average test weight loss below the twilight zone, and report that foraminifer shells may even gain weight there. On the other hand, most foraminifer tests experiencing dissolution through the water column likely belong to juveniles because most foraminifers in sediments are gametogenic and probably sank to the seabed rapidly after death (J. Bijma, personal communication, 2006). Thus dissolution of adult tests during sinking seems unlikely. Moreover, fragmentation of G. menardii shells in the water column has not been reported. G. menardiis are known to be somewhat resistant to dissolution even in sediments [Berger, 1968, 1970; Thompson and Saito, 1974] and are used often to estimate 12 of 15

13 deep-sea CaCO 3 dissolution [Oba, 1969; Ku and Oba, 1978; Peterson and Prell, 1985a, 1985b; Mekik et al., 2002; Mekik and François, 2006]. 6. Conclusions [49] We explored the significance of environmental influences on two deep-sea CaCO 3 dissolution proxies in a large number of core top samples from the tropical Pacific: sizenormalized foraminifer shell weight and the G. menardii fragmentation index. We find that SNSW and MFI do not complement each other and, instead, trace dissolution concurrently in core tops from both the OJP and the EEP. The dissolution signal in P. obliquiloculata SNSW is weak in samples from the OJP where surface ocean parameters are mostly constant. Conversely, SNSW of P. obliquiloculata shells in samples from the EEP carries a strong dissolution signal. Though we cannot isolate which environmental parameter is affecting P. obliquiloculata shell weight in the EEP with our data, we are able to show that P. obliquiloculata SNSW responds to both postdepositional shell dissolution and surface ocean parameters there. N. dutertrei SNSW, on the other hand, shows a distinct latitudinal pattern in the EEP in keeping with regional highproductivity zones and current systems suggesting that there may be intraspecific ecophenotypic variations in this morphotype in the EEP which may be reflected in its SNSW. [50] Our study is not the first to find an environmental influence of foraminifers calcification waters on their SNSW. This is the first study, however, where environmental effects on SNSW are documented with core top sediment samples in the EEP where calcite dissolution is driven by both bottom water DCO 3 2 and organic carbon degradation in sediment pore waters. This is important for paleoceanographic work because calibration equations from core top sediment samples are often used for down core applications and because culture experiments and sediment trap data are few. Furthermore, it is not possible to reliably study postdepositional shell dissolution in laboratory work and with sediment traps because the effect of organic carbon degradation in sedimentary pore waters on calcite dissolution is difficult to mimic in a laboratory setting. [51] Although our results cannot offer a clear mechanistic explanation for the variation in shell weight in the EEP and further work with laboratory cultures is required to accomplish this, the correlations we demonstrate between SNSW and both MFI-based percent dissolved values and surface ocean parameters, particularly [CO 3 2 ], cannot be ignored in down core applications. Our findings suggest that caution must be used when making paleoceanographic inferences from SNSW variations in the paleorecord. [52] Finally, we show that MFI-based percent CaCO 3 dissolved estimates are mostly insensitive to surface ocean environmental parameters in G. menardii s calcification waters in the EEP. Mekik and François [2006] showed linear decreases in Mg/Ca and Mg/Sr in P. obliquiloculata and G. menardii shells with increasing dissolution estimated using MFI. Mekik et al. [2002, 2007a, 2007b] demonstrated MFI s applicability outside its calibration area in core top samples from the EEP upwelling region. Loubere et al. [2004] and Richaud et al. [2007] showed MFI s applicability in down core work in reconstructing paleocalcite fluxes. With all of these qualities, MFI seems to approach our definition of the ideal CaCO 3 dissolution proxy described in the introduction of this paper with three caveats: (1) a biological/ecological bias in MFI remains to be explored; (2) MFI s range of percent dissolved is still limited to 25 76%; and (3) G. menardiis are absent in Atlantic sediments from the LGM. Thus the ideal CaCO 3 dissolution proxy is still elusive. [53] Acknowledgments. This manuscript benefited substantially from many fruitful discussions with Paul Loubere and Roger François. Constructive and thoughtful comments by Jerry Dickens and three anonymous reviewers much improved our manuscript. We gratefully acknowledge the curators and repositories that provided sediment samples and help in selecting cores for this work (June Padman, Oregon State University; Larry Peterson, RSMAS; Rusty Lotti-Bond, Lamont-Doherty Earth Observatory; Warren Smith, Scripps Institution of Oceanography; and curators at the University of Hawaii). Thanks also go to the National Science Foundation for the support it provides to those repositories. This study was supported in full by grant OCE from the National Science Foundation. References Anand, P., H. Elderfield, and M. H. 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