Effects of Surface Ocean Conditions on Deep Sea Calcite Dissolution Proxies in the Tropical Pacific

Transcription

1 Effects of Surface Ocean Conditions on Deep Sea Calcite Dissolution Proxies in the Tropical Pacific Figen Mekik Grand Valley State University Department of Geology Allendale, MI mekikf@gvsu.edu Lisa Raterink Wright State University Department of Earth and Environmental Sciences Dayton, OH ABSTRACT 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 % dissolved estimates when samples from the equatorial part of the region are excluded. Our results suggest 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. 1. Introduction 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 1

2 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 CO2, 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]. 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 Shackleton, 1992, Mekik et al., 2002]; (2) dissolution-induced 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 dissolutioninduced changes in foraminifer shells to the [CO 2-3 ] of bottom waters [e. g. Broecker and Clark, 2001a, 2001b; Dekens et al., 2002; Marchito 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 2-3 under-saturation of bottom waters and respiratory CaCO 3 dissolution within sediments driven by fluxes of organic carbon reaching the seabed [Emerson and Bender, 1981]. The ideal CaCO 3 dissolution proxy would be (1) time-efficient (short analysis time per sample); (2) based on species (and/or their fragments) which are easy to identify even by non-specialists, 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 0-100% 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 2-3 ], nutrient availability, productivity, and even in 2

3 areas where there is large variation to both organic carbon and calcite fluxes reaching the seabed. 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 multi-proxy 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 these research questions: (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 2-3 ]? Or does shell weight loss happen simultaneously with fragmentation under similar degrees of bottom water CO 2-3 under-saturation and organic carbon degradation in sediment pore waters? (2) How sensitive are SNSW and MFI to environmental influences within foraminifers calcification waters, such as temperature, [CO 2-3 ], 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 SNSW and MFI? 2. Background 2.1. Globorotalia menardii Fragmentation Index (MFI) The G. menardii fragmentation index (MFI) 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 = #holes + # >half + (# <half / 3) + (# keels / 5) (1) 3

4 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 CaCO3 dissolved (R 2 = 0.88) with the following calibration equation [Mekik et al., 2002]: % CaCO 3 Dissolved = (MFI * ) (MFI 2 * ) (2) 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 CO 2-3 (which is the [CO 2-3 ] of in situ waters less [CO 2-3 ] 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. All calibration samples experienced some bottom water CO 2-3 under-saturation ( CO ranges between to µmol/kg). Estimates of CO 3 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], 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 in Mekik et al. [2002]. 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 4

5 in samples from the OJP where surface ocean temperature variation is minimal but where there is a steep gradient to CO3 2- of bottom waters. Mg/Ca in P. obliquiloculata shells strongly correlates with MFI (R 2 = 0.94) and with MFI based % 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 paleo-thermometry. 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. 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 CO 2-3 is reached, and then fall to pieces randomly below this threshold [F. Mekik, unpublished data]; (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 (~ 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 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 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 5

6 Clark [2001a] relate shell mass loss to depth normalized bottom water [CO 2-3 ] which they define as: [CO 2-3 ]* = [CO 2-3 ] + 20 (4-z) (3) 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 / g per 1 mol/kg decrease in depth normalized [CO 2-3 ]. 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 The Eastern Equatorial Pacific Unlike surface waters above the Ontong Java Plateau, the eastern equatorial Pacific (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 Barner, 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 [Penington et al., 2006]. The EEP is generally a region of weak seasonality [Chavez and Toggweiler, 1995; Loubere, 1997; Loubere and Fariduddin, 1999] and highnitrate 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] due to iron and silica limitation [Dugdale et al., 1995; 2002; 6

7 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]. 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 co-vary (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 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 meters 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 50m 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 50m. G. menardii prefer 75m 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 13 C and 18 O from N. dutertrei shells in EEP core tops. For samples beyond Loubere s [2001] dataset (those from the OJP and some samples from the EEP), we used the average value of the environmental parameter of interest between 50 and 75m 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]. 7

8 We picked N. dutertrei shells from the m size range as described in Broecker and Clark [2001a, 2001b], and P. obliquiloculata s from the m 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. 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. We ascertain that our core tops are Holocene in several ways. First, Loubere [2001] provided 13 C and 18 O measurements from N. dutertrei shells in core tops from the EEP which are consistent with Holocene 13 C and 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] dataset, Mekik et al. [2007b] generated 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]. And lastly, 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 We followed methods outlined in Mekik et al. [2002] for generating MFI data, and used procedures described in Lohmann [1995] and Broecker and Clark [2001a] for 8

9 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 ultra-sonicator or other cleaning methods on foraminifers generally damages shells and distorts weight data. And 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, due to the low abundance of clean foraminifers in given size ranges in some samples. We aimed for 50 or more whole shells for weighing from each sediment sample. But in some samples, especially those from high dissolution areas, we had to base our mean weight on fewer foraminifers. Our dataset 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 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 due to lack of shell matter. 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 g. 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 9

10 foraminifers available. The reproducibility between two separately picked replicate mean weight measurements is high (R 2 = 0.97). In the following discussion represents the difference between two replicate mean weight measurements for the same species in the same sediment sample. Mean for P. obliquiloculata weight measurements is 3.74 g, and the standard deviation in for P. obliquiloculata weight is 3.02 g. The ratio of mean to average P. obliquiloculata mean weight from all samples (~65 g) is 5.8%. For N. dutertrei weight data, mean is 2.18 g, and its standard deviation is 2.02 g. The ratio for mean to average N. dutertrei mean weight from all samples (~37.5 g) is also 5.8%. 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 m 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 m size fraction weighs ~38 g. The uncertainty of the balance at +/- 5 g would substantially obscure the variation in our weight measurements of individual N. dutertrei shells. Data for [CO 2-3 ] at 50, 75 and 100 m water depth are from Archer [1996; and personal communication, 2001]. Other environmental data (temperature, [NO - 3 ] and AOU) at 50, 75 and 100 m are from NOAA s World Ocean Atlases [Locarnini et al., 2006 for temperature; Garcia et al., 2006a, 2006b for nutrients]. 4. Results 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 10

11 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 on-line material Calcite Dissolution Proxies on the Ontong Java Plateau First we compare MFI and SNSW in a subset of MFI s calibration samples in core tops from the OJP. These samples comprise 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. On OJP, MFI has a robust relationship with CO 2-3 (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. 2- dutertrei shell weight correlates very well with CO 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). 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 under-saturation as whole shell weight loss in P. obliquiloculatas (Figures 2a, 2b, 2d, and 2e). And Mg/Ca in P. obliquiloculata shells also has a weak relationship with P. obliquiloculata SNSW (Figure 2h). N. dutertrei shells are significantly less abundant and lighter in OJP samples (OJP average weight is 27 g) when compared to their counterparts in the EEP (EEP average weight is 39 g). Broecker and Clark [2001 a, b] also list lighter N. dutertrei weights [20-36 g] in their samples from the OJP within the same size fraction we used here. 11

12 Calcite Dissolution Proxies in the Eastern Equatorial Pacific Globorotalia menardii Fragmentation Index The fragmentation trend of G. menardii (MFI) has no clear mathematical relationship with any surface ocean parameter or CO 2-3 of bottom waters in the EEP (Figure 3). At the same temperature at 75 m water depth (~16 ºC) we see a wide range to MFI (0.4-1). Similarly, samples under a wide range of temperature (~15 22 ºC) yield more or less constant MFI values (~1) (Figure 3). We find similar patterns to that of MFI vs. temperature when we plot MFI against surface ocean [CO 2-3 ], [NO - 3 ] and AOU (Figure 3). The poor relationship between MFI and bottom water CO 2-3 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; Mekik et al., 2007b] Size Normalized Shell Weight P. obliquiloculata shell weight correlates well with MFI-based percent dissolved in the EEP (R 2 = 0.79) although it has no correlation with CO 2-3. This is unexpected because we were not able to find a good mathematical relationship between P. obliquiloculata shell weight and MFI-based % 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. 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 12

13 correlation between [CO 2-3 ] 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 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 CO 2-3 on the OJP (Figure 2b). This is different than the robust relationship between P. 2- obliquiloculata SNSW and bottom water CO 3 under-saturation [Broecker and Clark, 2001a]. The discrepancy lies in the different terms used to describe under-saturation 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 2- from GEOSECS. The CO 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 Archer s [1996] CO 2-3 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; Mekik et al., 2007b], and because MFI data are not available for the sample set used by Broecker and Clark [2001a] Post-Depostional Calcite Dissolution and Environmental Parameters in Foraminifer Habitat Waters 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 are from regions where bottom waters are under-saturated with respect to CO 2-3 ( CO 2-3 ranges between and µmol/kg among locations of our samples). Thus, we do not 13

14 have samples that experienced no dissolution. Even if we had very shallow samples (<1700 m), there is still the possibility of supra-lysoclinal CaCO3 dissolution in sediments of this region [e. g. de Villiers, 2005]. Second, all environmental parameters co-vary in the EEP (Table 1). Bijma et al. [1999] demonstrated that Orbulina universa shell weight in laboratory cultures is primarily influenced by [CO 2-3 ] of water in which the organism calcifies. The influence of [CO 2-3 ] on shell thickness and shell weight 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. 18 O and 13 C in Spero et al., 1997; and U/Ca in Russell et al., 2004]. Although our dataset does not allow us to rule out the potential effect of the other three environmental parameters on SNSW (because all surface ocean parameters co-vary 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. 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 µmol/kg) (Figure 4b). If calcite dissolution were the only influence on P. obliquiloculata shell weight in the EEP, we would expect the distribution of [CO 2-3 ] values in 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 ] of waters 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. 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 14

15 category (~15%) in figures 4c 4f is within MFI s error margin. Again, both dissolution and [CO3 2- ] 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). We performed multiple linear regression analysis to further explore the effect of post-depositional 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 50m are the independent variables. We used the resulting multiple linear regression equation (Figure 6) to estimate P. obliquiloculata SNSW from MFI-based 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 ] at 50 m water depth (Figure 6) Geographic Controls on Dissolution Proxies All environmental variables in the EEP co-vary (Table 1), but we believe the environmental factor most likely affecting N. dutertrei shell weight in our EEP samples is [CO 2-3 ] at DCM depths (Figure 5d) because of strong laboratory evidence supporting the effect of [CO 2-3 ] in ambient waters on shell weight [Bijma et al., 1999; Bijma et al., 2002] and core top work showing the influence of surface ocean [CO 2-3 ] on N. dutertrei SNSW [Naik and Naidu, 2007]. [CO 2-3 ] at the DCM seems to increase from south to north across the EEP with the exception of relatively low [CO 2-3 ] in the equatorial region. Within the EEP, areas beneath the North Equatorial Counter-Current and in the southwest beyond upwelling 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. 15

16 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 SNSW could reflect intraspecific ecophenotypic variation, multiple genotypes or both, particularly where the SNSW does not seem to be influenced by [CO3 2- ] (Figure 5d). We would need DNA data from our N. dutertrei specimens to discern 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. 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. 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. 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. 16

17 Other Complicating Factors 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 due to 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, meters 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 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 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 post-depositional shell dissolution and surface ocean parameters there. N. dutertrei SNSW, on the other hand, 17

18 shows a distinct latitudinal pattern in the EEP in keeping with regional high productivity 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. 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 CO 2-3 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 post-depositional 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. 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 2-3 ], cannot be ignored in down core applications. Our findings suggest that caution must be used when making paleoceanographic inferences from SNSW variations in the paleo-record. 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 paleo-calcite 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 18

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24 Richaud, M., P. Loubere, S. Pichat, and R. François, (2007), Influence of silica leakage on the flux ratio during the last 50,000 years in the Eastern Equatorial Pacific. Deep Sea Research II. doi: /j.dsr Rosenthal Y. and E. A. Boyle, (1993) Factors controlling the fluoride content of planktonic foraminifera: An evaluation of its paleoceanographic applicability, Geochim. Cosmochim. Acta, 57, Rosenthal, Y., E. A. Boyle and N. Slowey, (1997) Environmental controls on the incorporation of Mg, Sr, F and Cd into benthic foraminifera shells from Little Bahama Bank: Prospects for thermocline paleoceanography, Geochim. Cosmochim. Acta, 61, Rosenthal, Y., G. P. Lohmann, K. C. Lohmann and R. M. Sherrell, (2000). Incorporation and preservation of Mg in Globigerinoides sacculifer: Implications for reconstructing the temperature and 18 O/ 16 O of seawater. Paleoceanography, 15 (1), Rosenthal, Y., and G. P. Lohmann, (2002) Accurate estimation of sea surface temperatures using dissolution-corrected calibrations for Mg/Ca paleo-thermometry. Paleoceanography, 17(3), 1044, doi: /2001pa Russell, A. D., B. Honisch, H. J. Spero and D. W. Lea, (2004). Effects of seawater carbonate ion concentration and temperature on shell U, Mg, and Sr in cultures planktonic foraminifera. Geochimica et Cosmochimica Acta, 68 (21), Schiebel, R., Planktic foraminiferal sedimentation and the marine calcite budget. Global Biogeochemical Cycles, 16(4), 1065, doi: /2001gb001459, Schiebel, R., S. Barker, R. Lendt, and H. Thomas, (2007) Planktic foraminiferal dissolution in the twilight zone, Deep Sea Research II. doi: /j.dsr Schmidt, D.N., Renaud, S. and Bollmann, J Response of planktic foraminiferal size to late Quaternary climate change. Paleoceanography, 18(2) 1039, doi: /2002PA Schmidt, D.N., Renaud, S., Bollmann, J., Schiebel, R. and Thierstein, H.R., Size distribution of Holocene planktic foraminifer assemblages: biogeography, ecology and adaptation. Marine Micropaleontology, 50(3-4): Spero, H., J. Bijma, D. lea and B. Bemis, (1997). Effect of seawater carbonate concentration on foraminiferal carbon and oxygen isotopes. Nature, 390, Tans, P., I. Fing, and T. Takahashi, (1990). Observational constraints on the global atmospheric CO 2 budget, Science, 247, Thompson, P. R. and T. Saito, (1974) Pacific Pleistocene sediments: Planktonic foraminifera dissolution cycles and geochronology, Geology, 2, Toggweiler, J. R., D. Dixon, W. Broecker, (1991). The Peru upwelling and the ventilation of the south Pacific thermocline. Journal of Geophysical Research, 96, Wyrtki, K., (1981). An estimate of equatorial upwelling in the Pacific. Journal of Physical Oceanography, 11, Figure Captions: Figure 1. A. General locations for samples used herein. OJP: Ontong-Java Plateau; EEP: Eastern Equatorial Pacific. Dashed lines represent temperature contours for 75 m. water depthafter Locarnini et al. [2006]. B. Core top sample distribution in the eastern equatorial Pacific. MFI data is available from all samples. Gray diamonds show samples 24

25 from which N. dutertrei SNSW and black dots show samples from which P. obliquiloculata SNSW data were generated. Dashed lines represent seafloor bathymetry. Figure 2. All samples shown here are from OJP. A. MFI vs. CO B. P. obliquiloculata SNSW vs. CO Stars show SNSW data ( µm) from Broecker and Clark [2001a]. Diamonds show data from this study ( µm). C. N. dutertrei SNSW vs. CO Stars show SNSW data from Broecker and Clark [2001a; 2001b]. Diamonds show data from this study. Both are from the µm size fraction. D. MFI vs. model derived estimates of percent calcite dissolved. E. P. obliquiloculata SNSW vs. MFI-based % calcite dissolved estimates in MFI s calibration samples. F. N. dutertrei SNSW vs. MFI-based % calcite dissolved estimates. G. MFI vs. Mg/Ca in P. obliquiloculata shells. H. P. obliquiloculata SNSW vs. Mg/Ca in P. obliquiloculata. I. N. dutertrei SNSW vs. Mg/Ca in P. obliquiloculata. Figure 3. MFI vs. CO 3 2- and environmental parameters at 75 meters water depth in core top samples from the eastern equatorial Pacific. Gray line in the panel showing MFI vs. CO 3 2- represents the correlation line between these same parameters in core tops from the OJP (Figure 2a). Figure 4. P. obliquiloculata SNSW data vs. CO 3 2- and environmental parameters at 50 meters water depth in core top samples from the eastern equatorial Pacific. In panel B, color coding indicates [CO 3 2- ] at 50m. water depth in the EEP. In panels C-F, color coding indicates three dissolution brackets estimated with MFI. Figure 5. N. dutertrei SNSW data vs. CO 3 2- and environmental parameters 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 dots show samples from the region between the equator and 5 N. Figure 6. Multiple linear regression equation between Mg/Ca in P. obliquiloculata and MFI-based % dissolved and [CO 3 2- ] at 50m in samples from the EEP. Figure 7. Distribution of N. dutertrei SNSW in the eastern equatorial Pacific. NEqCC: North Equatorial Counter-Current; SEqC: South Equatorial Current, and CRD: Costa Rica Dome. Contours represent N. dutertrei shell weight in µg. The image is overlain on the chlorophyll-based surface ocean productivity map of Behrenfeld and Falkowski [1997]. 25

26 Figure 1 26

27 Figure 2 27

28 Figure 3 28

29 Figure 4 29

30 Figure 5 30

31 Figure 6 31

32 Figure 7 32