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Author's personal copy Marine Micropaleontology 70 (2009) 1–7

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Marine Micropaleontology j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / m a r m i c r o

Modelling the temperature dependent growth rates of planktic foraminifera Fabien Lombard a,⁎, Laurent Labeyrie a, Elisabeth Michel a, Howard J. Spero b, David W. Lea c a Laboratoire des Sciences du Climat et de l'Environnement/Institut Pierre-Simon Laplace, laboratoire Commissariat à l'Energie Atomique/Centre National de la Recherche Scientifique/ Université de Versailles Saint-Quentin, avenue de la Terrasse, F-91198 Gif-sur-Yvette CEDEX, France b Department of Geology, University of California Davis, Davis CA, USA c Dept. of Earth Science, University of California, Santa Barbara, CA 93106-9630, USA

a r t i c l e

i n f o

Article history: Received 24 April 2008 Received in revised form 17 September 2008 Accepted 19 September 2008 Keywords: Foraminifera Growth rates Temperature

a b s t r a c t The temperature influence on foraminifera growth rate was analysed using a mechanistic formulation that take into account enzyme inactivation at extreme temperatures. Growth rates are calculated using available published and unpublished laboratory culture experiments for eight species, including Neogloboquadrina pachyderma (sinistral and dextral forms), Neogloboquadrina dutertrei, Globigerina bulloides, Globigerinoides ruber, Globigerinoides sacculifer, Globigerinella siphonifera and Orbulina universa. Modeled growth formulas readily reproduce the observed growth patterns for all species. Similar growth patterns are observed for the species that have the same symbiotic algae G. ruber, G. sacculifer, and O. universa. However, different growth patterns are observed for herbivorous species (Neogloboquadrina genus) compared to carnivorous species with or without symbionts. Our growth estimates correspond well to in situ observations from both plankton tows and sediment traps. These estimates will help to improve the quantification of the effects of environmental parameters on foraminifera species distribution and abundance. © 2008 Elsevier B.V. All rights reserved.

1. Introduction Zooplanktonic foraminifera are widely distributed from polar to equatorial seas (Bé and Tolderlund, 1971). Foraminifera are protozoans that construct calcareous shells during their lifecycle. Once planktonic foraminifera have completed gametogenesis or died, empty shells sink rapidly through the water column to the seafloor. Despite their low abundance in the plankton, foraminifera are responsible for 32–80% of the global CaCO3 flux to the sediments (Schiebel, 2002). Fossilised tests collected from deep sea sediment cores are commonly used to reconstruct past ocean climate, notably as sea water temperature proxies (for example by their Mg:Ca ratio (Mashiotta et al., 1999; Elderfield and Ganssen, 2000) or analysis of the foraminiferal specific distribution (Imbrie and Kipp, 1971)). However, applications of such methods are limited by the lack of constraints on their growth environment, especially for temperature (season and depth). Foraminifera species distribution and fluxes depend on phytoplankton productivity (Ortiz et al., 1995; Watkins et al., 1996; Eguchi et al., 1999; Schiebel et al., 2001), season of occurrence (Žarić et al., 2006) and the depth of growth (Cleroux et al., 2007). Yet, most paleoceanographic reconstructions are based on indirect links or statistical relationships

⁎ Corresponding author. Tel.: +33 169 823 534; fax: +33 169 823 568. E-mail addresses: [email protected] (F. Lombard), [email protected] (L. Labeyrie), [email protected] (E. Michel), [email protected] (H.J. Spero), [email protected] (D.W. Lea). 0377-8398/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.marmicro.2008.09.004

between climatic indicators (from atlases or local measurements) and foraminifera assemblages observed in sediment core tops, plankton tows and sediment traps (Bé and Tolderlund, 1971). Core tops integrate decades to millennia of ocean hydrological variability. Sediment traps provide good temporal constraints, but neither core tops nor traps provide independent constraints on the foraminifera depth of growth. Plankton may be collected by nets with parallel records of hydrological parameters, but the method is time consuming, and only a small number of studies group a large number of plankton samples and the associated hydrological constraints. Even fewer studies give simultaneous information on food availability (e.g. Kuroyanagi and Kawahata, 2004). More precise knowledge on the conditions of foraminifera growth could be extracted from laboratory studies under controlled conditions (e.g. Caron et al., 1987; Hemleben et al., 1987; Bijma et al., 1990; Bijma et al., 1992; Spero and Lea, 1996). But this type of information is often collected as a by-product of geochemical studies. Sound interpretations may be obtained only within the framework of recently acquired knowledge on plankton biology (Kooijman, 2000). In that context, we propose an analytical formulation of the sensitivity of planktic foraminifera growth to temperature based on enzymatic thermodynamic properties for eight of the most studied species with available laboratory data. This is one of the first steps necessary to reach an understanding of the biological factors controlling the location, season and depth habitat of the foraminifera species proxies for paleoceanographic research.

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Table 1 Source and temperatures corresponding to the different growth observations of foraminifera Source

Temperature tested °C (specimens number)

G. sacculifer

Bijma et al. (1990) Hemleben et al. (1987)

11.6 (3) 19.5 (97; 59; 54; 36) 26.5 (82; 57; 60; 47) 23.5 (80; 46; 19; 24; 9; 49; 25; 27; 15) 19.5 (141) 29 (16; 11; 10; 6) 21 (21) 10 (1) 23.5 (42; 25; 31) 23.5 (45; 15; 65; 9; 34; 27; 38; 30) 26 (31; 30; 38; 22; 56; 55; 46) 23.3 (5) 11.6 (6) 23.5 (22; 28; 18) 19.5 (60) 15 (23) 11.6 (4) 23.5 (19) 21 (28) 13 (1) 6 (1) 16 (49; 30) 6 (1) 4 (1) 4.7 (1)

G. siphonifera

O. universa

G. ruber

N. dutertrei G. bulloides N. pachyderma (dex.) N. pachyderma (sin.)

Bijma et al. (1992) Caron et al. (1987a,b) Spero and Lea (1993) Spero and Lea (pers. comm.) Bijma et al. (1990) Bijma et al. (1992) Bijma et al. (1998) Faber et al. (1989) Spero and Lea (pers. comm.) Bijma et al. (1990) Bijma et al. (1992) Caron et al. (1987a,b) Spero and Lea (pers. comm.) Bijma et al. (1990) Bijma et al. (1992) Spero and Lea (pers. comm.) Bijma et al. (1990) Spero and Lea (pers. comm.) Spero and Lea (1996) Spero and Lea (pers. comm.) Spero and Lea (pers. comm.) Unpublished data

13.9 (14) 23.5 (80; 46; 56; 36) 29.5 (94; 42; 60; 36) 26.5 (82; 57; 13; 8) 22 (114)

15.6 (17)

23.3 (10) 11.7 (20) 26.5 (31; 24)

25.1 (10) 13.4 (33)

25.1 (1) 16 (2) 26.5 (19; 15) 22 (63) 16 (2) 13.5 (2) 26.5 (36) 23.3 (23) 14.4 (14) 9 (4) 22 (30; 57) 9.9 (5) 6 (11) 8 (1)

29.3 (9) 17 (8)

25 (111)

25 (62) 17 (18) 15.5 (6) 27.9 (13) 25.1 (29) 16 (13) 12.9 (13) 12.9 (42) 9 (80) 12 (1)

31 (21)

32.9 (1)

28 (112) 26.9 (7) 15.8 (22)

29.2 (46) 23.5 (92)

29.3 (16) 29.4 (17)

31 (1)

18 (10)

32.5 (1)

25.8 (16)

30.7 (20)

28 (32; 122) 21 (4) 27.8 (9)

22 (51) 30.6 (5)

23.3 (2) 32.9 (1)

26.9 (9) 17.9 (4) 16.2 (14)

29.2 (62) 28.6 (1) 19.2 (35)

29.3 (31) 31.5 (2)

33 (1)

14 (18) 12.9 (56)

16 (35) 16.2 (67)

17 (22) 19.2 (114)

22 (43) 23 (1)

25 (3)

32.4 (1)

27 (49)

25 (19)

29 (22)

28.3 (1)

29.2 (16)

F. Lombard et al. / Marine Micropaleontology 70 (2009) 1–7

Species

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Fig. 1. Growth rates (d− 1) of O. universa (A), G. sacculifer (B), G. siphonifera (C), G. ruber (D), N. dutertrei (E), G. bulloides (F), N. pachyderma dextral (G) and sinistral forms (H) in relation to experimental temperature (°C). Observations from laboratory cultures are represented by large dots for the mean population at one temperature or by small dots in the case of individual growth rates. Model results (line) and 95% confidence intervals of the model (dashed lines) are represented for each species. Observations based on plankton tow samples (Bé and Tolderlund, 1971) are shown as stepped filled bars, and the flux observed in sediment traps (Žarić et al., 2005) are shown in terms of overall distribution as a line and as optimal SST as an open bar.

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2. Material and methods Growth observations on foraminifera were collected from different sources (Table 1) including previously published papers (Caron et al., 1987a,b; Hemleben et al., 1987; Faber et al., 1989; Bijma et al., 1990, 1992; Spero and Lea, 1993, 1996; Bijma et al., 1998), unpublished data associated with published papers (Lea et al., 1996; Mashiotta et al., 1997; Bemis et al., 1998; Mashiotta et al., 1999; Russel et al., 2004; von Langen et al., 2005; Kimoto and Tsuchiya, 2006) or previously unpublished data from two of the authors (HJS and DWL). These data comprises eight different species including Neogloboquadrina pachyderma (sinistral and dextral forms), Neogloboquadrina dutertrei, Globigerina bulloides, Globigerinoides ruber, Globigerinoides sacculifer, Globigerinella siphonifera and Orbulina universa. Growth rate is generally quantified by the number of chambers precipitated within the observation period (as chamber d− 1 or µm d− 1), for each studied foraminifera. Such data may not be used directly as these units strongly depend on the mean size of the considered specimens but also on the fact that some foraminifera species have small and numerous chambers whereas other have few large chambers. Therefore it is necessary to reconsider the growth of the different foraminifera species with a biologically relevant growth rate. In our study, growth rate is quantified by the organic carbon weight increase for each studied foraminifera. Data selection is based on several criteria: 1) Morphology: we consider individuals that constructed at least two chambers, underwent gametogenesis, and did not show chamber resorption. These criteria are the same as those used in published studies (Caron et al., 1987a,b; Hemleben et al., 1987; Faber et al., 1989; Bijma et al., 1990, 1992; Spero and Lea, 1993, 1996; Bijma et al., 1998), thus keeping the data as homogeneous as possible. 2) Foraminiferal growth is strongly limited when animals are not fed (Bé et al., 1981) or not illuminated (symbiont-bearing species) (Caron et al., 1981). Specimens had to be maintained on a regular feeding schedule (fed daily, or every two or three days) and maintained under a dark/light cycle with sufficient light to exceed the compensation point (b50 μE m− 2 s− 1) at which symbiont photosynthesis exceeds respiration needs (Rink et al., 1998). 3) Extreme salinity concentrations affect foraminiferal development, but growth is stable or only slightly impacted in the salinity range 30–40 psu (Bijma et al., 1990; Hemleben et al., 1987). Therefore we considered only observations corresponding to the 30–40 psu salinity range. To be consistent in the analysis of published data, only mean initial size, final size of gametogenetic individuals and mean survival time (i.e., time elapsed between the first measurement and gametogenesis) are considered. The number of observations (foraminifera) corresponding to these mean values are tabulated simultaneously. That number will be used as a weighting factor in

the analysis. Individual growth rates, when recorded, are kept only as an indication of individual variability. However, observations obtained at the same temperature but with different protocols were considered as distinct data points, because different light intensity, salinity, feeding frequency or food composition could influence foraminiferal growth. Published studies do not generally consider experiments where all foraminifera died before reaching gametogenesis. Such experiments often correspond to extreme growth conditions which need to be taken in account. We use such data but only give them a small weight in the analysis (i.e., number of observations = 1, regardless of the number of individuals considered, which was usually 10–20). The organic carbon weight of specimens is calculated from the initial and final size using a conversion factor (0.089 pgC μm− 3; Michaels et al., 1995) and assuming a spherical volume of foraminifera. The growth rate (µ, d− 1) is calculated assuming an exponential growth of foraminifera, following the formulation: μ=

lnðWf =Wi Þ Δt

ð1Þ

where Wi and Wf are initial and final foraminifera organic carbon weights, respectively, in units of µgC ind− 1, and Δt (d) the time elapsed between the two measurements. The temperature influence on growth (µ) is generally considered with the convenient Q10 value that quantifies the growth rate increase for a 10 °C increase: T=10

ð2Þ

cðT Þ = c0 Q10

where µ0 (d− 1) is the growth rate at 0 °C. However, Q10 is only adapted for small temperature ranges, and it varies as a function to temperature. The Arrhenius relationship on the other hand is more stable over a larger temperature range (Koojiman, 2000). This relationship uses temperature on the Kelvin scale and has the following form:   T T cðT Þ = cðT1 Þexp A − A ð3Þ T1 T where µ(T1) (d− 1) is the growth rate for a chosen reference temperature T1 and TA (°K) is the Arrhenius temperature. This relationship is equivalent to the Q10 formulation with:   10dTA Q10 = exp TdT1

ð4Þ

The Arrhenius relationship, however, does not consider important physiological phenomena such as enzyme inactivation at low or high temperature that leads to a sharp decrease in the observed growth rates at both ends of the optimal temperature range (Kooijman, 2000). Sharpe and DeMichele (1977) proposed a mechanistic formulation for these rate reductions derived from the Arrhenius rate kinetics. This

Table 2 Parameters of the mechanistic model used to reproduce the growth rate of the different foraminifera species Parameters (±SD) Species

μ (T1)

TA

TL

TH

TAL

TAH

r2

O. universa G. sacculifer G. siphonifera G. ruber N. dutertrei G. bulloides N. pachyderma (dex.) N. pachyderma (sin.)

0.23 (±0.09) 0.30 (±0.01) 0.17 (±0.02) 0.24 (±0.11) 0.10 (±0.02) 0.25 (±0.02) 0.13 (±0.01) 0.37 (±0.05)

626 (±3851) 2155 (±686) 7105 (±1861) 1086 (± 5772) 6876 (±4805) 6482 (±1256) 6584 (± 1080) 6584a

289.8 (±3.4) 289.3 (±0.6) 284.9 (±0.3) 292.3 (±2.3) 280.1 (±1.6) 281.1 (±0.3) 277.8 (±0.3) –

304.0 (± 0.5) 304.4 (± 0.3) 301.8 (± 0.5) 303.4 (± 0.5) 298.8 (± 3.0) 298.5 (± 0.3) 293.7 (± 0.5) 279.8 (± 0.7)

30,953 (±13,119) 94,385 (±23,740) 349,991 (±283,836) 53,765 (±21,035) 210,746 (± 602,206) 295,374 (±266,947) 300,239 (±249,645) –

249,412 (±389,004) 171,209 (±48,514) 130,852 (±66,186) 165,490 (±102,487) 57,855 (±39,102) 159,618 (±44,756) 110,583 (±35,218) 59,491 (±15,098)

0.75 0.95 0.77 0.90 0.63 0.95 0.96 0.90

a

The same TA from dextral N. pachyderma is assumed for the sinistral form.

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Fig. 2. Comparison of the modelled growth rate (d− 1) of the different foraminifera species in relation to experimental temperature (°C). The color coding is: O. universa (green), G. sacculifer (blue), G. siphonifera (cyan), G. ruber (red), N. dutertrei (black), G. bulloides (pink), N. pachyderma dextral (dashed blue) and sinistral forms (dashed red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

formulation is based on several simplifying assumptions: the growth rate of an organism at a given temperature is assumed to be governed by a single rate-controlling enzyme which is reversibly inactivated at low and high temperatures; the total concentration of enzymes, in both active and inactive form, is assumed to remain constant and independent of temperature; and the growth rate is a function of the ratio of active enzymes to inactive ones. Using this formulation, growth rate µ is described as a function of temperature T (in Kelvin) (Kooijman, 2000):   cðT1 Þexp TTA1 − TTA     μ ðT Þ = 1 + exp TTAL − TTALL + exp TTAH − TTAH H

ð5Þ

where µ(T1) is the growth rate for an arbitrary chosen temperature T1 (20 °C or 293 K in this study) without considering enzymes inactivation and assuming only a normal increase of rate with temperature (Eq. (3)); TA is the Arrhenius temperature (Kooijman, 2000); TL and TH relate to the lower and upper temperature boundaries of the growth tolerance range and TAL and TAH are the Arrhenius temperatures for the decrease in growth rate respectively below and above these boundaries. All T (in K) are taken to be positive and in most organisms the growth pattern correspond to TAH N TAL N TA. The upper part of the relationship relates the classical increase of growth rate as a function of temperature whereas the lower part relates the enzymatic fraction that is in its active state. This is the analytical formula used in the present study. The parameters are adjusted by fitting Eq. (5) to the square root of the laboratory observed growth rates (Heitzer et al., 1991; Alber and Schaffner, 1992), taking into account the number of observations (Table 1) using a least square minimisation (Nelder–Mead simplex method). Uncertainties are estimated by the r2 (fraction of data variance explained by the model) and the 95% envelope defined by the t-test of significance. 3. Results Growth rates are reported as a function of temperature (Fig. 1). They follow approximately the same pattern for the different foraminifera species: a well-defined optimal temperature range with progressive growth rate increase, and no growth below or above that range. As a result, each species shows a maximum growth rate at a temperature close to the upper limit of the growth range. Within each species, the mean growth values derived from the different studies are

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relatively similar. However, variability among specimens is important and differences between individuals can reach one order of magnitude for the same temperature. Growth models have been calibrated over their whole temperature ranges for all species except for N. pachyderma sinistral. For this species, due to the scarcity of observations, only the maximal temperature decrease was fitted, assuming TA similar to the dextral form. The result of the growth model calibration for each species is showed on Fig. 1 and the corresponding parameters in Table 2. The model correctly reproduces the general growth pattern of all species, with r2 above 0.9 for five species (N. pachyderma (sin. and dex. forms), G. bulloides, G. rubber and G. sacculifer), and between 0.63 and 0.75 for the remaining three (G. siphonifera, O. universa and N. dutertrei); the lower portion of variance explained for these three could originate from discrepancies between the experimental protocols used in the different studies. The large deviations of the 95% confidence envelop observed for some of the species at the lower and upper limits of the optimum temperature range derive from an insufficient number of data points covering the growth rate decrease for these extreme conditions. Orbulina universa, G. sacculifer and G. ruber follow a similar growth pattern with an optimum growth between 20 and 29 °C and only a slight increase of the growth rate between these temperatures (TA comprised for the different species between 626 and 2155 K), a slow growth decrease at the lower temperature limit (TAL between 30,900 and 94,300 K) and minimum and maximum of the temperature growth limits respectively at 11 and 32 °C. Compared to these three species, the other species have a larger growth rate increase in function of temperature (TA between 6400 and 7100 K) and a sharper growth rate decrease at their lower temperature limit (TAL N 200,000 K). The optimal temperature ranges and maximal growth limits are around 20–29 °C and 11–32 °C respectively for G. siphonifera, 8–25 °C and 6–32 °C for N. dutertrei, 9–25 °C and 7–28 °C for G. bulloides and 6–20 °C and 4–23 °C for dextral N. pachyderma. Neogloboquadrina pachyderma sinistral presents an optimal growth below 5 °C and negligible growth at T N 12 °C. A comparison of the different species (Fig. 2) gives the highest growth rate for G. sacculifer at high temperatures (17–30 °C), G. bulloides at intermediate temperature (8–17 °C) and N. pachyderma s. under 7 °C. Orbulina universa, G. siphonifera and G. ruber have intermediate growth rates and dextral N. pachyderma and N. dutertrei have the lowest growth rates. 4. Discussion and conclusions To our knowledge, this study is the first attempt that quantifies the changes of foraminiferal growth rate with temperature, based on the increase of organic weight during laboratory cultures. Contrary to rates expressed in term of size increase per day or chamber formation per day, this technique is independent of the size of the organism and allows a comparison of growth rates between species that have different shell or chamber sizes. Our study shows that a simple mechanistic formulation based on enzyme activity and inactivation at extremes temperatures can efficiently represent the foraminifera growth pattern. This model has been fitted with success for the eight species studied and allows for characterization of their complete growth curves, including the upper and lower growth limits, except for N. pachyderma sinistral. Similar growth patterns have been observed for different species of bacteria, including marine endobiotic or thermophilic strains (Pomeroy and Wiebe, 2001; Ratkowsky et al., 2005; Miroshnichenko and Bonch-Osmolovskaya, 2006), macrophyte algae (Eggert and Wiencke, 2000), phytoplankton algae (Suzuki and Takahashi 1995; El-Sabaawi and Harrison, 2006), fresh water crustaceans (Yurista, 1999), and corals (Howe and Marshall, 2002). To our knowledge, however, this pattern has never been observed for oceanic zooplankton. It is important to note that the pattern we reconstruct for foraminifera growth rate is non symmetrical and significantly different from the Gaussian response of growth rate in

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function to temperature hypothesized by Fraile et al. (2008) based on sediment trap flux observations Žarić et al. (2005) In addition, and despite the fact that populations observed in sediment traps certainly depends on other parameters than just individual growth rate, the asymmetrical growth pattern we observe seems to correspond to the asymmetrical pattern in abundance observed in sediment traps in response to sea surface temperature (Žarić et al., 2005). The growth estimates used in our study derive from different sources and protocols with different feeding intervals, light illuminations or salinity. This may explain their large dispersion that leads to lower r2 of the model adjustment for some species (Fig. 1A–D). A large variability is also observed between individuals cultivated within apparently similar conditions (Fig. 1). At least part of that variability may correspond to different foraminifera responses to culture stress. Effectively, if some individuals recover rapidly from sampling and then grow relatively fast, others need more times to recover and produce only few shell chambers in culture before reaching the gametogenesis stage. For these individuals, growth rates may be slightly underestimated and not represent the in situ growth behaviour of these species. Despite this variability, these growth observations follow with a good confidence the general shape of the growth behaviour defined for each species. The temperature dependence derived from laboratory growth rates is qualitatively in good agreement with the in situ occurrence and abundance of species observed with planktons tows (Bé and Tolderlund, 1971) or in sediment traps (Žarić et al., 2005) if we take into account available mean annual or seasonal sea surface temperatures (Fig. 1). Even if the model fit is low for some species, (r2 between 0.63 and 0.95), the agreement between their growth pattern and in situ observations is good for the warm species O. universa, G. sacculifer, G. siphonifera, G. ruber and N. dutertrei. Their in situ temperature limits correspond to the laboratory determined growth, and their temperature range of maximum abundance correspond to the optimal growth temperature range in the model (Fig. 1A–E). This means that the parameter TL and TH correspond to the in situ observations. Discrepancies that do not appear to be attributable to the model (r2: 0.90–0.95) are seen for colder species, which we attribute to an insufficient knowledge of the precise conditions of growth in the oceans (in particular seasonality in temperature and depth habitat). For G. bulloides, our model estimation corresponds to the observations in sediment traps but individuals were observed in colder conditions in plankton nets (Fig. 1F). This could originate from a disparity between the mean annual sea surface temperature as used in Bé and Tolderlund (1971) and the spring to summer occurrence of G. bulloides under temperate conditions (Kuroyanagi et al., 2002; King and Howard, 2003; Bárcena et al., 2004; Marchant et al., 2004). But this can also originate from discrepancies between the diversity of genetic types observed in situ (Darling et al., 1999; Kucera and Darling, 2002), whereas all our observations on G. bulloides comes from the same location (California coast) and cannot represent the overall diversity. The oceanic distribution of N. pachyderma sinistral and dextral as defined from sediment traps (Žarić et al., 2005) is apparently larger than the growth limits modelled from foraminifera cultures (Fig. 1G–H). This could originate from their adaptation to a sub-surface habitat if sea surface temperature is too high and sufficient food is available (Kuroyanagi and Kawahata, 2004), although part of that discrepancy may also be derived from a higher culture stress near the upper temperature limits for these species. For N. pachyderma, Bé and Tolderlund (1971) give only information on the total abundance of this species with the indication that the dextral to sinistral shift occurs at an approximate temperature of 7.2 °C. This corresponds well to the shift in growth rate modelled between sinistral and dextral forms (7.8 °C Fig. 2). When comparing the species growth rates (Table 2, Fig. 2), different growth patterns are observed. For G. sacculifer, O. universa and G. ruber, the increase in growth rate with temperature appears relatively small. This is also the case for the growth rate increase below

the optimum temperature range when compared to the other species. Knowing that these three species have the same symbiotic algae species (Gast and Caron, 2001; Shaked and de Vargas, 2006), the temperature sensitivity of that algae could potentially drive this particular growth pattern. A clear distinction can also be made between carnivorous species that have a high growth rate and herbivorous species (N. dutertrei, N. pachyderma, Hemleben et al., 1989) that have a low growth rate. This difference could originate both from an effectively different growth rate linked to the energetic content of their prey or from an incorrect feeding of the herbivorous species in the laboratory cultures due to difficulties to provide them with the appropriate food. In addition, the species-specific temperature dependence of growth rates (Fig. 2) may explain at least in part the foraminifera species distribution in the oceanic environment. There is evidence that the predominance of one population over others could depend on specific growth rates (Eppley 1972; Goldman and Carpenter, 1974). Species that have a higher growth rate would be successful competitors and dominate the other species however other processes such as reproduction, mortality and predation rates had also to be considered. This may also be the case for foraminifera. Our model results show that, depending on water temperature range, N. pachyderma, G. bulloides and G. sacculifer should dominate the foraminifera population. N. pachyderma is effectively found to be the dominant species in polar areas, G. bulloides in temperate regions and, depending on the oligotrophy of the zone, G. ruber and G. sacculifer are dominant in tropical areas (Bé and Tolderlund, 1971). This model provides a new perspective on foraminiferal species distribution and abundance in the open ocean that could strengthen paleo-ecological interpretation of fossil data. However, for a fully integrated application, this model needs to be combined with other environmental parameters such as light and food availability in order to reproduce the natural seasonal and spatial variability of foraminiferal species abundance. Ideally, it would also be valuable to explore growth patterns in laboratory culture using different foraminiferal genotypes of common species to test the significance of genetic diversity on the growth pattern of cryptic species. Acknowledgments We greatly thank J.-C. Duplessy, E. Cortijo, G. Gorsky and the members of the Forclim Team for constructive discussions and their improvement of the manuscript, the French program ANR05BLAN0275-01 Forclim, the CEA and the CNRS for their base support to the LSCE. We are grateful to the staff of the Wrigley Marine Science Center for providing a world-class field station for the experiments that generated much of these data, and the many students, assistants and colleagues who participated in the foraminifera culturing program during the past decade. This research was supported by U.S. National Science Foundation (NSF) Grants 9416595, 0550703 (HJS) and 9415991, 9729327 (DWL). During the writing of this manuscript, HJS was supported by the NSF while he worked at the Foundation. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF. References Alber, S., Schaffner, D., 1992. Evaluation of data transformations used with the square root and schoolfield models for predicting bacterial-growth rate. Appl. Environ. Microbiol. 58, 3337–3342. Bárcena, M.A., et al., 2004. Planktonic response to main oceanographic changes in the Alboran Sea (Western Mediterranean) as documented in sediment traps and surface sediments. Mar. Micropaleontol. 53, 423–445. Bé, A.W.H., Tolderlund, D.S., 1971. 6. Distribution and ecology of living planktonic foraminifera in surface waters of the Atlantic and Indian Oceans. In: Funnell, B.M., Riedel, W.R. (Eds.), Micropaleontology of Oceans. Cambridge University Press, London, pp. 105–149.

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