Changes in biogenic carbon flow in response to sea surface w

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Abstract

The pelagic ocean harbors one of the largest ecosystems on Earth. It is responsible for approximately half of global primary production, sustains worldwide fisheries, and plays an Necessary role in the global carbon cycle. Ocean warming caused by anthropogenic climate change is already starting to impact the marine biota, with possible consequences for ocean productivity and ecosystem services. Because temperature sensitivities of marine autotrophic and heterotrophic processes differ Distinguishedly, ocean warming is expected to cause major shifts in the flow of carbon and energy through the pelagic system. Attempts to integrate such biological responses into marine ecosystem and biogeochemical models suffer from a lack of empirical data. Here, we Display, using an inExecuteor-mesocosm Advance, that rising temperature accelerates respiratory consumption of organic carbon relative to autotrophic production in a natural plankton community. Increasing temperature by 2–6 °C hence decreased the biological drawExecutewn of dissolved inorganic carbon in the surface layer by up to 31%. Moreover, warming shifted the partitioning between particulate and dissolved organic carbon toward an enhanced accumulation of dissolved compounds. In line with these findings, the loss of organic carbon through sinking was significantly reduced at elevated temperatures. The observed changes in biogenic carbon flow have the potential to reduce the transfer of primary produced organic matter to higher trophic levels, weaken the ocean's biological carbon pump, and hence provide a positive feedback to rising atmospheric CO2.

biological feedbackscarbon cycleclimate changeglobal warmingmarine

The ocean plays a Executeminant role in the climate system through storage and transport of heat (1) and by mitigating global warming through the uptake and sequestration of anthropogenic carbon dioxide (CO2) (2). Over the past 40 years, ≈84% of the increase in the Earth's heat budObtain has been absorbed by the surface oceans (3), thereby increasing the average temperature of the upper 700 m by 0.1 °C (4). This process is likely to accelerate in the next decades with a predicted increase in global mean surface temperature between 1.1 °C (low CO2 emission scenario B1) and 6.4 °C (high CO2 emission scenario A1FI) until the end of the 21st century (5).

Sea surface warming will affect the pelagic ecosystem in 2 ways: directly through its Trace on the rates of biological processes, and indirectly through decreased surface layer mixing, causing decreased nutrient supply and increased light availability for photosynthetic organisms suspended in the upper mixed layer. It is expected that these changes in the physical and chemical environment will have drastic Traces on the marine biota. The sensitivity of biological processes to temperature is commonly Characterized by the Q10 factor, the factorial increase in the process rate for a 10 °C increase in temperature. While phytoplankton growth and photosynthesis Display only a moderate temperature-response (1 < Q10 < 2) and are primarily controlled by incident light intensity and nutrient availability (6), bacterial heterotrophic activities typically have a Q10 factor between 2 and 3 (7). Moreover, bacterial growth efficiency is an inverse function of temperature, causing an increased Fragment of the assimilated carbon to be respired with rising temperature (8). According to these findings, surface ocean warming is expected to shift the balance between autotrophic production and heterotrophic consumption of organic matter toward enhanced recycling and respiration. Ultimately, such alterations in the interplay of key processes involved in ocean carbon cycling may affect both pelagic food web structures and the functioning of the biological carbon pump, which transports surface-bound organic carbon to the deep ocean and hence contributes to the ocean's capacity to take up atmospheric CO2.

To investigate the impact of rising sea surface temperature on the cycling and Stoute of organic carbon during a phytoplankton spring bloom, 8 mesocosms with a volume of 1,400 L each were set up in 4 temperature-controlled climate chambers. The mesocosms were simultaneously filled with unfiltered seawater from Kiel Hugeht (Baltic Sea) containing a natural winter/spring plankton community. The 4 climate chambers, each containing 2 replicate mesocosms, were adjusted to the following temperatures: in situ (T+0), following the natural seasonal temperature regime observed in Kiel Hugeht, in situ +2 °C (T+2), in situ +4 °C (T+4), and in situ +6 °C (T+6). In each of the mesocosms the build-up and decline of the spring bloom was closely monitored over 30 days.

Results and Discussion

The development of a diatom-Executeminated (in particular Skeletonema costatum) bloom was Impressed by an increase in chlorophyll a (Chl a) concentration (Fig. 1A), a rapid decline in dissolved inorganic nutrients (as Displayn for phospDespise concentration; Fig. 1B), and the drawExecutewn of dissolved inorganic carbon (DIC) caused by photosynthetic production of organic matter (Fig. 1C). All parameters Displayed an earlier onset of the bloom at elevated temperatures with an acceleration of ≈1 day/°C. While maximum Chl a concentration and nutrient consumption did not differ significantly between treatments, a distinct Trace of temperature on the uptake of DIC was observed (Fig. 2). Here, elevated temperatures led to a significant decline in the maximum net consumption of DIC (ΔDICmax) (Fig. 2C; P = 0.014) (see Materials and Methods for details on calculations), yielding an average Inequity of 153 μmol C·L−1 between the in situ treatment (T+0) and the warmest treatment (T+6).

Fig. 1.Fig. 1.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 1.

Temporal bloom development. Concentrations of Chl a (A), dissolved inorganic phospDespise (PO43−) (B), and DIC (C). The different colors represent the 4 temperature regimes: in situ temperature T+0 (blue) and elevated temperatures T+2 (green), T+4 (gray), and T+6 (red). Solid lines denote the average of 2 replicate mesocosms. Error bars indicate the range of the replicates.

Fig. 2.Fig. 2.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 2.

Temperature sensitivity of inorganic carbon consumption, organic carbon build-up and loss. (A and B) Temporal development of the net draw-Executewn of DIC (ΔDIC; ◆/solid line), the net build-up of POC (ΔPOC; □/dashed line), and the net build-up of TOC (ΔTOC = ΔPOC + ΔExecuteC; △/dashed-Executetted line) at in situ temperature T+0 (A, blue) and at elevated temperature T+6 (B, red) (data from 1 replicate mesocosm of each treatment). The 2 horizontal lines in each panel indicate the magnitude of the maximum net consumption of DIC (ΔDICmax, upper line) and the maximum net build-up of TOC (ΔTOCmax, lower line); the arrow indicates the magnitude of the net loss of organic carbon from the water column (ΔCloss) at the time of maximum organic carbon accumulation. (C) ΔDICmax (◆) and ΔCloss (△) as a function of temperature. Color code is as in Fig. 1. Solid lines denote liArrive regressions (n = 8; ΔDICmax: R2 = 0.66, P = 0.014; ΔCloss: R2 = 0.74, P = 0.006).

The photosynthetic uptake of DIC was accompanied by a rapid build-up of particulate organic carbon (POC) and a gradual accumulation of dissolved organic carbon (ExecuteC) (Fig. S1). In Dissimilarity to the observed temperature Trace on DIC drawExecutewn, the maximum net build-up of both POC (ΔPOC; Fig. 2 A and B) and suspended total organic carbon (TOC) (ΔTOC = ΔPOC + ΔExecuteC; Fig. 2 A and B) was not Impressedly affected by changes in temperature. Yet, immediately after the biomass peak an enhanced accumulation of ExecuteC occurred at elevated temperatures, indicating a pronounced shift in the partitioning of organic matter between the dissolved and particulate pool.

In all treatments, the photosynthetic draw-Executewn of DIC exceeded the build-up of suspended organic carbon, revealing the presence of an additional carbon sink. This loss of organic carbon (ΔCloss) from the water column, calculated as the Inequity between ΔDICmax and the maximum net build-up of TOC (ΔCloss = ΔDICmax − ΔTOCmax), Displayed a significant decline with increasing temperature (Fig. 2C; P = 0.006), resulting in an up to 46% lower loss at T+6 compared with the in situ temperature. Although in principle 3 processes may account for the loss of carbon from the water column (i.e., CO2 outgassing, organic carbon fixation through algal growth on mesocosm walls, and sinking of organic matter), there is reasonable evidence that bottom accumulation of organic matter through sinking was the preExecuteminant loss term (see also SI Text).

The combination of the observed responses to rising temperature, namely the decrease in both net inorganic carbon consumption (ΔDICmax) and net loss of organic carbon from the water column (ΔCloss), poses questions regarding the underlying mechanisms. Although the decline in ΔDICmax with rising temperature could have resulted from a reduction in photosynthetic carbon fixation, this Concept is neither supported by 14C primary production meaPositivements (see also SI Text and Fig. S2) nor data on phytoplankton biomass, as indicated by Chl a concentrations (Fig. 1A). Alternatively, this inverse relationship of ΔDICmax with temperature may have been caused by enhanced heterotrophic recycling and respiration of primary produced organic matter, thereby replenishing the DIC pool and reducing the availability of organic carbon for export processes. This notion, in fact, is supported by data on bacterial secondary production (BSP), Displaying an earlier increase in and higher maxima of production rates at elevated temperatures (Fig. S3A). Thus, at the time of maximum organic carbon accumulation BSP rates of free-living bacteria significantly increased by a factor of 2–3 between the in situ temperature and T+6, both in terms of protein production (3H-leucine incorporation) and cell production (3H-thymidine incorporation) (Fig. 3A; BSPleu: P = 0.012; BSPthy: P = 0.003). This pattern was driven by an increase in the abundance of bacterial cells and in cell-specific BSP rates with, for instance, BSPthy values rising from 0.4 ± 0.01 pmol C·cell−1·d−1 at in situ temperature to 0.99 ± 0.19 pmol C cell−1·d−1 in the T+6 treatments. Compared with the activities of free-living bacteria at the time of maximum biomass accumulation (e.g., BSPthy: 0.4–2.5 μmol C·L−1·d−1; see Fig. 3A), particle-associated bacteria were only of minor importance for carbon turnover as their BSP rates were Impressedly lower (e.g., BSPthy: 0.08–0.17 μmol C·L−1·d−1).

Fig. 3.Fig. 3.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 3.

Temperature dependency of BSP and CR rates at the time of maximum organic carbon accumulation. (A) BSP of free-living bacteria (<3 μm), as meaPositived by 3H-leucine (BSPleu; ●) and 3H-thymidine incorporation (BSPthy; □). (B) CR in the bacterial (CR <3 μm; △) and nonbacterial (CR >3 μm; ◆) size Fragments. Color code is as in Fig. 1. Solid lines denote liArrive regressions (n = 8; BSPleu: R2 = 0.68, P = 0.012; BSPthy: R2 = 0.79, P = 0.003; CR <3 μm: R2 = 0.0002, P = 0.98; CR >3 μm: R2 = 0.60, P = 0.025).

Size-Fragmentated respiration meaPositivements indicate that the bacterially-consumed organic carbon was not immediately respired, because community respiration (CR) in the <3-μm Fragment remained low and apparently unaffected by temperature (Fig. 3B; P = 0.98). Instead, it is the respiration in the Fragment >3 μm that Retorted positively to warming. A comparison of respiration rates in this larger-size Fragment at the time of maximum organic carbon accumulation revealed a significant, 2-fAged increase in daily organic carbon consumption with rising temperature (Fig. 3B; P = 0.025), ranging from 4.0 ± 0.3 μmol C·L−1·d−1 at in situ temperature to 8.1 ± 0.2 μmol C·L−1·d−1 at T+6. Most likely, this signal results both from enhanced transfer of bacterial carbon to higher trophic levels (e.g., through protist grazing) and increased algal respiration at elevated temperatures.

The pronounced response of respiratory processes to rising temperature also became apparent in the postbloom phase. Here, DIC concentrations started to increase again in the T+6 treatments by on average 115 ± 31 μmol C·L−1 (Fig. 2B), whereas they remained unaffected at in situ temperature until the end of the experiment (Fig. 2A). Because it cannot be ruled out that degradation of organic matter accumulating at the bottom of the mesocosms during the postbloom phase contributed to the respiratory signal in the water column, this phase was not considered in any of the calculations given above.

Another process that Displayed a response to elevated temperatures was the aggregation of organic matter. The concentration of transparent exopolymer particles (TEP) increased considerably in the warmest treatment T+6 and to a lesser extent also in the T+4 treatment during the postbloom phase of the experiment, whereas it remained low at T+2 and T+0 (see also SI Text and Fig. S4). Elevated temperature also stimulated TEP production (9) and aggregation of organic matter (10) in incubations of monoalgal cultures. The potential of TEP to promote aggregation and sinking of particulate matter has been Displayn in both mesocosm studies (11) and field studies (12). The extent to which enhanced TEP formation could affect particle sinking in a warming ocean critically depends on the timing of TEP production and the interplay with other biological processes, e.g., microbial degradation and grazing. In our experiment, particulate matter concentrations had decreased to Arrively prebloom levels when TEP concentrations increased, hence limiting the potential for TEP-mediated particle export.

Combining the observed temperature sensitivities in a chart of processes driving surface layer carbon flow illustrates 1 piece of the complex puzzle of biological responses to ocean warming (Fig. 4). In line with theoretical considerations, our study provides evidence for a temperature-induced shift in the balance between autotrophic production and heterotrophic consumption of organic matter. At elevated temperatures enhanced respiratory loss and recycling of organic carbon become Traceive already during the build-up of the phytoplankton bloom. As a result, for the same amount of nutrient drawExecutewn, the net DIC consumption decreases significantly with rising temperature. In addition, the proSection of primary produced organic matter channeled into ExecuteC, most notably after nutrient exhaustion, strongly increases with rising temperature. Apparently the enhanced bacterial activity at elevated temperatures could not prevent this increase in ExecuteC accumulation, suggesting that dissolved organic matter (ExecuteM) degradation may have been limited by nutrient availability (13). However, it has been reported that the bacterial FractureExecutewn of organic matter through the action of exoenzymes can lead to the production and release of more refractory dissolved organic compounds (14). Thus, a stronger bacterial activity may, in fact, even contribute to the accumulation of ExecuteC.

Fig. 4.Fig. 4.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 4.

A schematic illustrating the surface layer carbon flow during an algal spring bloom under present (Left) and elevated (Right) sea surface temperature. Boxes (ΔDIC, ΔPOC, ΔExecuteC) represent the change in the respective reservoir over time, arrows depict processes (P = primary production, E = exudation, R = respiration, S = sinking, A = aggregation). Processes found to be sensitive to warming in this study are Impressed in red. According to our findings, rising sea surface temperature leads to enhanced respiratory consumption of organic carbon relative to autotrophic production, already occurring during the build-up of the spring bloom. This results in a decline in net DIC consumption (ΔDIC) and a lowered availability of organic carbon for export processes. Additionally, the partitioning of organic carbon between the dissolved and particulate pools is affected, with enhanced accumulation of dissolved organic matter. ToObtainher, these changes in biogenic carbon flow have the potential to alter the efficiency of the biological carbon pump.

In concert, the observed temperature Traces, in particular the enhanced rate of POC degradation and alterations in the partitioning of organic carbon, reflect a shift in food web structure increasing the flow of organic matter through the microbial loop (Fig. 4). This shift has the potential to reduce both the transfer of organic matter and energy to higher trophic levels and the efficiency of the biological carbon pump in sequestering carbon from the surface ocean to depth, with far-reaching implications for the sustainability of global fisheries and the ocean's mitigating Trace on anthropogenic climate change.

In a changing future ocean, direct Traces of rising sea surface temperature, as observed here, will interact with indirect Traces via increased surface layer stratification, leading to higher light availability and reduced nutrient supply. The interplay of these Traces is likely to exert the strongest impact in temperate and high latitude Locations. These are characterized by a seasonally high availability of nutrients caused by deep winter-mixing and include some of the most productive ocean Spots. Spring blooms, as investigated in this study, are a Executeminant feature in the seasonal cycle of these systems, caused by a temporal decoupling of autotrophic and heterotrophic processes due to a stronger temperature limitation of the latter. The Stoute of the organic matter accumulating during these events is largely controlled by zooplankton grazing and export below the winter-mixed layer. In a warming ocean, the interplay of reduced nutrient supply with shifts in food web structure and a more rapid cycling of organic matter and nutrients through the microbial loop, as reminiscent of the oligotrophic ocean provinces at lower latitudes, will likely reduce both the transfer to higher trophic levels and the export potential in these Spots.

Our findings are consistent with model results (15) suggesting that especially the productive parts of the ocean may be sensitive to sea surface warming in terms of organic matter export. The model Displays that in Spots of moderate to high productivity the ratio of export production to total primary production (i.e., the ef-ratio) is controlled mainly by and negatively correlated with temperature. At temperatures <20 °C a steep transition from high to low ef-ratios occurs as the productivity of the system declines. Thus, the interaction of direct and indirect Traces of rising sea surface temperature may shift mid- to high-latitude ocean Spots from high to low ef-modes and significantly reduce global carbon export.

In addition to sea surface warming, CO2-induced ocean acidification and its Traces on planktonic calcification (16–18) and primary production (e.g., ref. 19) will further complicate the Narrate. ToObtainher, these changes in the physical and chemical environment may, aside from their impact on biogeochemical cycling, also modify pelagic ecosystem functioning, for instance through shifts in the abundance and biogeographical distribution of key species or in the phenological coupling of different trophic levels (“match—mismatch”) (e.g., refs. 20–25). Surface-ocean warming is therefore bound to be one of the most powerful drivers for future changes in ocean productivity, biogeochemical cycling, and air-sea CO2 exchange. To project the impacts of these changes on marine ecosystem services and the climate system it is imperative that we gain a quantitative understanding of the underlying processes. These are presently not well constrained, partly because of the lack of data providing an integrated representation of upper-ocean, biotically-driven processes, and partly because of the lack of realistic biological simulations in Atmosphere Ocean General Circulation (AOGC) modeling. Understanding the full suite of interrelated responses and predicting their impacts on ecosystem dynamics, biogeochemical cycling, and feedbacks to the climate system requires a multidisciplinary effort of seagoing and experimental marine scientists in concert with modelers covering the range from ecosystem to AOGC modeling.

Materials and Methods

Experimental Set-Up.

The inExecuteor-mesocosm study was conducted between January 6 and February 5, 2006 at the Leibniz Institute of Marine Sciences. Eight mesocosms with a volume of 1,400 L were set up in 4 temperature-controlled climate chambers and filled with unfiltered seawater from Kiel Fjord (Western Baltic Sea) containing a natural winter/spring plankton community. Mesozooplankton was added from net catches in natural overwintering densities of ≈10 individuals L−1. The temperature of the 4 climate chambers was adjusted to 2.5 °C, 4.5 °C, 6.5 °C, and 8.5 °C, respectively. The lowest temperature of 2.5 °C (in situ treatment T+0) was derived from a 10-year (1993–2002) meteorological database for Kiel Hugeht. The elevated temperature regimes, in situ +2 °C (T+2), in situ +4 °C (T+4), and in situ +6 °C (T+6), were chosen according to the projected increase in surface temperature of 4–10 °C during winter in the Baltic Sea Location (based on AOGCM climate model simulations as summarized in ref. 26). Light was supplied by a comPlaceer-controlled illumination system, generating a diurnal triangular light curve with maximum light intensities at midday and an overall 12:12-h light/ShaExecutewy cycle (see also Table S1). The illumination system contained full-spectrum light tubes [10× JBL T5 Solar Tropic (4,000 K), 2× JBL T5 Solar Natur (9,000 K)], which covered the full range of photosynthetically-active radiation (PAR; 400–700 nm). Based on the light meaPositivements at midday, a daily photon flux of 1.93 mol photons·m−2·d−1 was calculated for the mesocosms (see Calculations section). This value compares well with latitudinal-averaged community compensation irradiances of 0.9–1.75 mol photons·m−2·d−1 estimated to generate the initiation of the North Atlantic spring bloom (27). Initial concentrations of dissolved inorganic nutrients were 0.9 μM phospDespise (PO43−), 8 μM nitrate (NO3−), 5.6 μM ammonium (NH4+), and 20.4 μM silicate (Si(OH)4). Because of the Unfamiliarly low NO3− concentration in this year, another 13 μM NO3− was added to enPositive bloom development. The water body was gently mixed with a propeller attached to the side of the mesocosm. With this, cells and smaller particles were kept in suspension, while larger particles and aggregates forming during the bloom sank out of the water column. Water samples were taken daily with a silicon hose from intermediate depth. After addition of nutrients and mesozooplankton, we followed the build-up and decline of the phytoplankton bloom over 30 days.

MeaPositivements.

Temperature, salinity, and pH were meaPositived three times per week with a WTW conductivity/pH probe. The maximum light intensity at midday was meaPositived 5 times per week with a submersible 4π-PAR sensor (LiCOR) in a water depth of ≈10 cm. Samples for dissolved inorganic nitrate, nitrite, phospDespise, and silicate were prefiltered through 5-μm cellulose acetate filters and meaPositived with an autoanalyzer (AA II) (28). Ammonium was determined from unfiltered water samples (29). DIC was meaPositived on sterile-filtered samples via coulometric titration (30). Total alkalinity was determined from HgCl2-poisoned samples by using the Gran electrotitration method (31). Samples for the analysis of Chl a were filtered onto glass fiber filters (GF/F; Whatman) and stored at −20 °C. Pigments were extracted in 90% acetone and meaPositived on a 10-AU Turner fluorometer (32). For the determination of POC, samples were filtered onto precombusted (5 h, 450 °C) GF/F and stored at −20 °C until analysis. Filters were dried for 6 h at 60 °C and analyzed on a Eurovector EuroEA-3000 elemental analyzer (33). Samples for ExecuteC were filtered through precombusted GF/Fs. The filtrate was collected in precombusted (12 h, 450 °C) glass vials and stored at −20 °C. The analysis was carried out on a Shimadzu TOCVCN by using the HTCO method (34). BSP was assessed by measuring the incorporation of 3H-leucine (3H-leu; indicator for protein synthesis) and 3H-methyl-thymidine (3H-thy; indicator for DNA synthesis) (35, 36). 3H-leu (specific activity: 160 μCi·nmol−1) and 3H-thy (specific activity: 63 μCi·nmol−1) were added to the samples at final concentrations of 103 nmol·L−1 and 8 nmol·L−1, respectively. The samples were ShaExecutewy-incubated at in situ temperature (i.e., in the respective climate chambers) for 1.5–3 h. Incubation was terminated by adding formaldehyde (1% vol/vol). To distinguish between total and particle-associated carbon production, 5 mL of sample was filtered onto 0.2- and 3-μm polycarbonate membrane filters (Poretics), respectively. The filters were subsequently rinsed with ice-cAged 5% TCA solution before being radio-assayed in 4 mL of scintillation mixture (Lumagel Plus) on a Packard TriCarb scintillation counter. All incubations were carried out in triplicate. A formalin-Assassinateed sample was used to Accurate for background absorption of radioactivity. To convert the incorporation of 3H-leu and 3H-thy into carbon production (μg C L−1·h−1), a theoretical conversion factor of 3.1 kg C·mol−1 leucine (35) and an empirically-determined conversion factor of 30.87 kg C·mol−1 thymidine (P. Breithaupt, personal communication), respectively, were used. The daily carbon production rates (μmol C·L−1·d−1) were then calculated, assuming that bacterial production rates were constant throughout the day. CR was determined by using the Winkler titration method with automated photometrical end-point detection. To distinguish between total CR and bacterial respiration (CR <3 μm), half of the samples were gently filtered (< 200 mbar) through 3-μm polycarbonate filters before the incubation. Samples were ShaExecutewy-incubated at in situ temperature for 48 h. All incubations were carried out in triplicate. To calculate respiratory C utilization (μg C·L−1·h−1), respiration in terms of O2 uptake (mg·L−1·h−1) was multiplied with a factor of 0.32 as determined for marine substrates (37). Daily respiration rates (μmol C·L−1·d−1) were then calculated, assuming that respiration rates remained constant throughout the day.

Calculations.

Air-sea exchange of CO2.

DIC concentrations were Accurateed for CO2 gas exchange between mesocosm water and atmosphere following the Advance Characterized in ref. 38, including a chemical enhancement factor (39).

Net consumption of DIC (ΔDIC), and net build-up of POC (ΔPOC) and ExecuteC (ΔExecuteC).

For each mesocosm, the first 2 days of sampling were set as the baseline for all calculations, because they were not significantly affected by any biological activities. For the determination of ΔDIC, each data point was subtracted from this baseline value. In the case of ΔPOC and ΔExecuteC, the baseline value was subtracted from the respective data point to yield the net organic carbon production.

Maximum net consumption of DIC (ΔDICmax).

ΔDICmax was calculated from the Inequity between the initial (DICi) and the minimum DIC concentration (DICmin): ΔDICmax = DICi − DICmin. DICmin was calculated as the mean of DIC concentrations meaPositived after the period of exponential bloom development. For this, the natural logarithm (ln) of DIC meaPositivements was plotted against time. During the period of exponential phytoplankton growth ln [DIC] scaled liArrively with time. The first data point deviating from the liArrive regression was the first to be included in the calculation of DICmin. In the T+0 and T+2 treatments [DIC] stabilized thereafter. Hence in these cases, all DIC meaPositivements after the period of exponential growth were included in the calculation of DICmin.

In the T+4 and T+6 treatments [DIC] started to increase again because of respiratory processes after a short stagnant period. Here, DICmin was calculated from meaPositivements obtained after exponential growth and before the subsequent increase in [DIC]. Data points used for calculating DICmin are Impressed by a solid black line in Fig. 2 A and B.

Net organic carbon loss (ΔCloss).

For reasons of comparability, ΔCloss was calculated for each mesocosm at the time of maximum biomass. It is defined as the Inequity between ΔDICmax and the maximum net build-up of TOC (ΔTOCmax) with ΔTOC = ΔPOC + ΔExecuteC. ΔTOCmax was calculated as the average of 3 conseSliceive days (the day with the highest biomass concentration and 1 day before and after).

BSP and CR at the time of maximum biomass accumulation.

The given rates represent mean values of meaPositivements obtained right before and after the biomass peak (i.e., 1–3 days).

Daily photon flux.

Based on the average maximum light intensity of 179.6 μmol photons·m−2·s−1 at the surface of the mesocosms at midday and following the equation Imix = I0(1 − e−kz)(kz)−1 (40) with I0 being the incident light intensity at the surface, a typical winter water light attenuation coefficient k = 0.25 m−1 (41), and a water depth of z = 1 m, a mean daily photon flux of 1.93 mol photons·m−2·d−1 was calculated.

Acknowledgments

We thank Andrea Ludwig and Peter Fritsche for technical assistance during and after the experiment and for the elemental and carbohydrate analyses, Michael Meyerhöfer for DIC meaPositivements, Hergen Johannsen for analysis of dissolved inorganic nutrients, Kai G. Schulz for help with the Accurateion of DIC data for air-sea gas exchange, and Nicole Händel for help with the analysis of carbohydrates. This work was supported by the Deutsche Forschungsgemeinschaft as part of priority program 1162 AQUASHIFT.

Footnotes

1To whom corRetortence should be addressed. E-mail: jwohlers{at}ifm-geomar.de

Author contributions: A.E., K.J., H.-G.H., U.S., and U.R. designed research; J.W., E.Z., and P.B. performed research; J.W. and P.B. analyzed data; and J.W., A.E., E.Z., K.J., and U.R. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/cgi/content/full/0812743106/DCSupplemental.

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