Comparing marine primary production estimates through different methods and development of conversion equations

Introduction

Plankton photosynthesis, responsible for about half of the primary production in the biosphere (Field et al., 1998), is a fundamental process at the global and the ecosystem scale. At the global scale, phytoplankton primary production affects oxygen and CO₂ fluxes, constraining gas exchange with the atmosphere and thus the gaseous composition of the atmosphere. Furthermore, phytoplankton primary production is the main source of organic matter fueling marine food webs (Duarte et al., 1999). The measurement of phytoplankton primary production is so a fundamental property of the ocean ecosystem, receiving considerable effort that has resulted in several million estimates available to-date (del Giorgio and Williams, 2005).

Photosynthetic rates of marine phytoplankton were first measured using the oxygen evolution method in phytoplankton communities in the Oslo Fjord by Gaarder and Gran (1927). This method originally suffered from poor resolution, being unable to resolve the low primary production rates in the less productive regions of the ocean (Truesdale et al., 1955; Mortimer, 1956; Richards and Corwin, 1956). These limitations were resolved with the development of high-precision Winkler analyses using automatic titrators and end-point detection of the Winkler reaction (Carpenter, 1965; Carrit and Carpenter, 1966), which allow low primary production rates to be resolved. However, the ¹⁴C method was developed before improved oxygen-based techniques were available (Steeman Nielsen, 1952) and rapidly became the standard for the oceanographic community, used to calibrate remote sensing algorithms, despite recurrent caveats as to what exactly the ¹⁴C incorporation method measures (Dring and Jewson, 1982; Behrenfeld and Falkowski, 1997a; Banse, 2002). Since then, other approaches have been derived, such as the use of tracer methods based on stable isotope additions, such as ¹³C (Slawyk et al., 1977) and ¹⁸O (Bender et al., 1987), as well as a suite of incubation-free techniques, including the use of active fluorescence, FRRF (fast repetition rate fluorometry) (Kolber and Falkowski, 1993), the non-intrusive bio-optical (OPT) method (Loisel and Morel, 1998; Claustre et al., 1999), the triple oxygen isotopes method (Luz and Barkan, 2000) or the analysis of oxygen records from buoys and gliders (Nicholson et al., 2008). These methods differ, however, in assumptions and the particular process through which primary production is represented and thus yield different results when applied to any one community (Marra, 2002). Indeed, resolving estimates derived from different methods can be a challenging exercise and the differences in estimates among various methods continue to fuel discussion (Quay et al., 2010; Marra, 2012). Awareness of the fundamental differences in the specific component of primary production addressed by the different methods to assess phytoplankton primary production has generated a demand to derive conversion equations allowing rates derived with different methods to be compared and eventually integrated. An approach to do so is the development of equations using available concurrent, paired measurements of primary production using two or more techniques (Marra, 2002).

Whereas the majority of published studies evaluate the primary production of planktonic community using a single method, a number of reports using two or more techniques have now become available. Different approaches to measure the volumetric primary production have been compared in a series of papers examining the rates delivered by different methods for specific locations, cruises or sampling events (Williams et al., 1996; Marra, 2002; Corno et al., 2005; Juranek and Quay, 2005; Gazeau et al., 2007; González et al., 2008; Robinson et al., 2009). Most of these comparisons revealed differences among methods, varying in magnitude, attributable to differences in the specific components of primary production addressed by each method as well as to their inherent assumptions. However, these comparisons have been limited in scope to date and cannot be used with confidence to allow interconversions across estimates derived with methods. A set of different equations allowing comparisons among primary production estimates derived with different methods would allow the estimation of, for instance, gross primary production (GPP) from satellite-based primary production estimates, which are currently calibrated against the particulate ¹⁴C primary production method. This difficulty is not minor, as current estimates of primary production in the global ocean do not necessarily represent either gross or net primary production and cannot be readily reconciled with estimates of terrestrial primary production into estimates of global photosynthesis, introducing uncertainty into our understanding of global carbon and oxygen budgets.

Here we compare estimates of volumetric rates of marine phytoplankton primary production derived concurrently using different methods and describe the scaling among different metrics of phytoplanktonic primary production, providing equations that allow conversion of estimates among different methods. We do so based on a compilation of data on volumetric estimates of primary production rates concurrently estimated with at least two different methods, thereby allowing the comparison among the derived rates. We first provide a summary of the major assumptions, strengths and weaknesses for the various methods, and then compare concurrent estimates derived from different methods to derive conversion equations.

Materials and Methods

We searched the published literature for volumetric estimates of primary production of natural marine plankton communities produced using at least two different methods applied concurrently. The O₂:Ar method evaluates the net primary production while in this study, we only use GPP of plankton communities. Although the ¹⁴C method estimates values of primary production closer to net primary production than GPP, we considered it essential to include those data to our study due to its widespread use by the scientific community. Most in situ methods (i.e., non-intrusive bio-optical (OPT) method, the triple oxygen isotopes method, and estimates derived from the analysis of oxygen records obtained by gliders surveys or buoys) yield estimates of phytoplanktonic primary production integrating across the mixed layer and variable time scales and were thus not included here as they have only occasionally paired with estimates derived from other methods, therefore precluding quantitative comparisons among estimates derived from these and other methods. The FRRF method is the only in situ method that allows for volumetric estimates to be derived and has been paired with other methods, thereby allowing comparisons and is therefore included in the evaluation presented here.

In this paper, we present and compare the following methods: the oxygen evolution in dark and light incubations, the FRRF method and methods based in tracer additions (¹⁸O, ¹⁴C, and ¹³C).

An Overview of Methods to Measure Marine Phytoplanktonic Primary Production

During photosynthesis, carbohydrates are synthesized and O₂ produced from CO₂ and H₂O, respectively, using solar radiation as the energy source. Approaches to measure primary production include in vitro methods based on either oxygen production or inorganic carbon incorporation and methods based on the analysis of oxygen and gas field in situ. In vitro oxygen-based estimates of primary production involve the evaluation of the release of oxygen during photosynthesis from change in the bulk O₂ concentration, corrected for respiratory O₂ consumption of communities enclosed in bottles (i.e., dark-light method), or that of ¹⁸O₂ when ¹⁸O-labeled H₂O was added (i.e., ¹⁸O method). In vitro carbon-based estimates of primary production use tracer additions as ¹⁴C or ¹³C labeled bicarbonate followed by the measurement of the assimilation of the tracer onto organic carbon (particulate or total) following incubation of communities enclosed in bottles for a variable time in the light (i.e., ¹⁴C and ¹³C methods). In situ methods involve the evaluation of oxygen fields retrieved from time series of oxygen derived from moored sensors (Dickey, 1991) or sensors mounted in gliders (Nicholson et al., 2008), corrected for atmospheric exchange, or the synoptic evaluation of triple oxygen isotopes (¹⁶O₂, ¹⁷O₂, ¹⁸O₂) to extract GPP from the anomalies relative to the values expected from atmospheric equilibrium.

The in vitro methods are prone to errors due to the confinement which may affect the organisms involved (e.g., excluding zooplankton or enhancing trophic interactions within the bottle) or the environmental conditions (e.g., temperature, light and nutrient fields), which are not affected by the in situ methods (Maske and Garcia-Mendoza, 1994; Karl et al., 1998; Robinson and Williams, 2005; Duarte et al., 2013; Williams et al., 2013). For instance, modifications of the light field (Kirk, 1994) are observed as the incubations have been conducted with borosilicate bottles, a material that excludes UVB radiation. Indeed, recent analyses comparing estimates derived using quartz and glass bottles have shown that estimates of net community production are affected by the removal of the ambient UVB radiation when glass or plastic materials that filter out UV-B radiation are used, as has been the case for most analyses (Godoy et al., 2012; Agustí et al., 2014; Regaudie-de-Gioux et al., 2014). Furthermore, it is rare for in vitro incubations to be carried out at the correct corresponding environmental temperature. Considering that temperature is a fundamental parameter for respiration rates, the oxygen-based primary production may be over- or underestimated if the incubation temperatures differs from that in situ. Moreover, incubation periods for in vitro methods range from a few hours (0.5–6 h) for ¹⁴C or ¹³C methods to 24 h for the dark-light method. The different incubation time may bias the estimation of PP and the comparison between the different in vitro methods. In contrast, in situ methods are very sensitive to assumptions regarding mixing within the mixed layer and exchanges of oxygen with the atmosphere and the waters below the mixed layer (Duarte et al., 2013).

In this study, we compared estimates of volumetric primary production rates and considered therefore only estimates derived from the dark-light method, the FRRF method and methods based in tracer additions (¹⁸O, ¹⁴C, and ¹³C).

The ¹⁴C-labeled method (Steeman Nielsen, 1952) consists of measuring the photosynthetic incorporation of ¹⁴C labeled inorganic C, added as a NaH¹⁴CO₃ solution, into particulate and total pools of organic carbon. Whereas the bulk of the measurements available (>90%) report the ¹⁴C incorporated into particles retained in filters, as originally proposed Steeman Nielsen (1952), the technique also allows the measurement of total organic carbon (TOC) production [i.e., ¹⁴C incorporated into the dissolved organic carbon (DOC), and the particulate organic carbon (POC)], from measurements of the ¹⁴C activity in the water sample following removal of the NaH¹⁴CO₃by purging the sample after acidification. The use of the ¹⁴C-labeled method to resolve total organic carbon (TOC) production requires use of high NaH¹⁴CO₃activity in the sample as to yield a sufficient signal in the water tested. This method has the advantage of allowing to differentiate photosynthetic carbon retained into particulate and dissolved (as total - particulate production) fractions, and allows precise estimates to be derived over short time intervals (Table 1). Furthermore, the high sensitivity of the ¹⁴C-labeled method allows the determination of the photosynthesis production in unproductive oceans (Ichimura et al., 1962). However, it is also subject to bottle effects and the underestimation of primary production as it does not include any organic carbon produced that has been respired by the plankton community during the incubation (Table 1). Furthermore, the question of whether the ¹⁴C method measures net or gross photosynthesis has been addressed several time in the literature (Ryther, 1954, 1956a; Steemann Nielsen and Al Kholy's, 1956; Peterson, 1980; Dring and Jewson, 1982). The incubation period and nutrient availability may bias the PP estimation. Indeed, Rodhe (1958) and Vollenweider (1969) compared the summation of a series of short 4 h incubations to one long ¹⁴C incubation. They reported that the sum of five 4 h incubation exceeded the results of a 20 h incubation by 9–35% depending on depth. They both recommended using short incubations. As described by Morán and Estrada (2002) and other authors (Steeman Nielsen, 1952; Marra, 2002; Halsey et al., 2010; Lasternas and Agustí, 2013) a short incubation period should be selected when measuring DOC produced to match a compromise between the times needed to obtain a significant signal in the PP phase, but at the same time, minimize the loss of ¹⁴C-labeled DOC due to assimilation by heterotrophic prokaryotes. Short-time incubations are recommended to optimize the measurements and minimize the contribution of trophic-related processes to DOC production. Health concerns about the use of ¹⁴C, international regulation and the potential of contamination on ships may be a real issue for the ongoing use of this method. Indeed, radioisotope legislation in some countries restricts (e.g., Spain) or prohibits altogether (e.g., Japan) use of ¹⁴C on research vessels.

TABLE 1

Table 1. Summary of the main characteristics, advantages, and limitations of different approaches measuring volumetric phytoplanktonic primary production.

The ¹³C-method has also been used to evaluate particulate organic carbon production (Slawyk et al., 1977). This method is similar to the ¹⁴C-labeled method except that bicarbonate is labeled with ¹³C rather than ¹⁴C. The main advantages compared to the ¹⁴C-labeled method are that the ¹³C-method allows primary production incorporated by different components of the food web to be resolved using compound-specific analyses (Boschker and Middelburg, 2002), and that ¹³C is a natural stable carbon isotope, which does not involve any risks to the operator.

The dark-light (or bulk oxygen evolution) method has been used to assess primary production for nearly one century (Gaarder and Gran, 1927). The dark-light method consists of the evaluation of changes in oxygen concentration using high-precision Winkler method, allowing 0.1% precision in oxygen determinations (Carpenter, 1965; Carrit and Carpenter, 1966), following the incubation, typically for 24 h, of natural plankton communities enclosed in clear and dark bottles. Primary production is calculated as the sum of the rate of change in oxygen concentration in clear bottles, the net community production, and that in dark bottles, the dark respiration. This estimate, which is calculated rather than derived directly, is used as a metric of GPP, defined as the total photosynthetic oxygen production prior to any losses, but relies on the assumption that respiration in the dark does not differ from that in the light (Table 1). Indeed, Grande et al. (1989) showed that natural populations show equal respiration rates during day and night. Furthermore, Marra and Barber (2004) assumed that virtually all CO₂ respired during the day is re-fixed during the photosynthesis and concluded that twice the dark loss of carbon equals the 24 h rate of phytoplankton respiration. However, there is evidence that respiratory losses are often enhanced in the light (Harris and Lott, 1973), so that the dark-light method is likely to underestimate GPP.

The ¹⁸O method measures the GPP using the stable isotope ¹⁸O as a tracer of molecular oxygen production through photosynthesis (Bender et al., 1987). The sample water is enriched in ¹⁸O derived by the photosynthetic release of ¹⁸O from added H₂¹⁸O (Table 1), and thus provides an estimate of GPP free of assumptions on the effect of light on respiration, but still subject to the potential bottle effects indicated above (Table 1). The error in GPP estimates derived from this method is considered lower than 2% (Bender et al., 1999). One advantage of this method is that considering the daily turnover time of phytoplankton, recycling of labeled O₂ will be weak (2%) during 24 h in comparison with that of labeled PO¹⁴C, which can be very large (Bender et al., 1999). A second advantage of the ¹⁸O method is that it can measure directly the gross photosynthetic O₂ production in comparison with the dark-light or ¹⁴C method that measure the NCP and CR or TOC and POC production, respectively. Lastly, there are no health hazards associated with using ¹⁸O because it is a stable isotope. One disadvantage of this method is that gross oxygen production determined by the ¹⁸O method evaluates total oxygen production and it is unclear if this is directly linked to carbon assimilation (Bender et al., 1999; Robinson et al., 2009). Indeed, four different metabolic pathways mainly involve oxygen consumption in the light, respiration through the cytochrome oxidase pathway, respiration by the alternative oxidase pathway, photorespiration and the Mehler reaction (Robinson et al., 2009). Although the GPP measured by the ¹⁸O method could be corrected by a factor of 20% for the Mehler reaction and photorespiration, it is expected that the GPP would be overestimated by a factor up to 20–50% (Laws et al., 2000; Hendricks et al., 2004) when it is converted into carbon units using a photosynthetic quotient. The ¹⁸O method is superior in assessing GPP to all other in vitro methods (Marra, 2002), but it does account for a small fraction of all in vitro measurements available.

The FRRF method measures phytoplanktonic production from active chlorophyll fluorescence (Kolber and Falkowski, 1993). This method evaluates the instantaneous depth and time dependent value of primary production. Indeed, it resolves primary production at spatial (<1 m) and temporal (~1 s) resolutions that cannot be achieved by in vitro approaches (Robinson et al., 2009). The FRRF method has the potential to quantify rapid changes in productivity and make instantaneous measurements of certain physiological parameters (Sakshaug et al., 1997). It also provides a better signal-to-noise ratio and allows more robust measurements in oligotrophic ecosystems. However, assumptions and uncertainties have been reported in the FRRF method (Suggett et al., 2001, 2004; Moore et al., 2003). The maximum light utilization efficiency (∝*) can be overestimated by the FRRF method due to the decoupling of the electron transport rate (ETR) by the cyclic electron flow around the photosystem II (PSII) (Falkowski et al., 1986; Prášil et al., 1996), photorespiration (Raven and Johnston, 1991) and the Mehler reaction (Kana, 1992). Furthermore, Suggett et al. (2001, 2004) reported that uncertainties remain in the estimation of the photosynthetic unit size of the photosystem II (PSU_RCII), in the assumption of equal distribution of excitation energy between RCI and RCII within the wavelength of the FRRF light source, in the evaluation of the fraction of photochemically active RCIIs from 1.8/(F_v/F_a), in the measurements of the absorption of light by photosynthetic pigments, and in the use of the assumed values of the ratio of PSII reaction centers to the chlorophyll a concentration (n_PSII) for prokaryotes and eukaryotes. The FRRF method may be prone to errors when the measured sample contains Colored Dissolved Organic Matter (CDOM) that can affect the spectral absorption and so the accurate in situ measurement of active fluorescence. In addition, the quantification of primary production in terms of carbon involves significant uncertainties as the use of appropriate controls remains a challenge and the method is prone to a number of sources of bias (Laney, 2003).

Comparing Across Primary Production Estimates

Our search of the literature yielded 19 studies measuring primary production concurrently using ¹⁴C and dark-light methods (Table 2) including 188 different stations and 692 individual primary production estimates. Twelve studies measured primary production concurrently using ¹⁴C and ¹⁸O methods including 65 different stations and 367 individual production estimates (Table 2). Six studies reported estimates derived using both the ¹⁸O and dark-light method including 45 stations and 232 individual rate estimates. Two reports determined the primary production using FRRF method with ¹⁴C (4 stations and 70 individual estimates) and only one with dark-light and ¹⁸O methods concurrently including 3 stations and 15 individual production estimates (Table 2). Finally, only one report presented primary production measured concurrently by ¹⁴C and ¹³C methods including 198 primary production estimates (Table 2).

TABLE 2

Table 2. Incubation period, references, description of the location, number of stations and of estimates of the different studies analyzed here measuring GPP rate by two different methods.

The compiled ¹⁴C primary production data (¹⁴C-TOC, ¹⁴C-POC, or ¹⁴C-DOC) have been estimated according to different cited reports from 3 to 24 h (from dawn to dawn) of incubation (Table 2). In general, the phytoplankton community receives 12 h of sunlight per day. When the primary production has been measured after 2–4 h of incubation, we estimated the hourly rate and then scaled to 12 h of light. We considered that the primary production estimated after 12 or 24 h of incubation received the same amount of light (12 h) and so, are comparable.

The relationship between paired primary production estimates derived using different methods (x and y) were described by fitting power equations of the type,

${\text{PP}}_{\text{y}}={\text{aPP}}_{\text{x}}^{\text{b}}$

using reduced major axis (RMA) regression analysis on log-transformed data where log transformation was found necessary to address the problem of heteroscadicity affecting the untransformed relationships between variables.

The predictive power of the relationship between paired primary production was tested by evaluating the errors derived from bootstrap analyses. Bootstrapping analysis is a statistical approach for assigning measures of accuracy to sample estimates. In this study, we selected randomly 90% of a paired-method dataset. From those 90% selected, we estimate the relationship between paired primary production estimates. Using the conversion equations derived, we predict the primary production of one type of method from values obtained using another method for the 10% left of the paired-method dataset. Error (as absolute error, mean ± SE prediction error; and relative error, as the mean ± SE percent error) were derived from 10 bootstrapping iterations of the conversion equations obtained.

Results

The geographic distribution of the different stations which the primary production has been evaluated by at least two distinct methods (Figure 1, Table 2) shows that the studies are scattered across the ocean. Indeed, the studies where primary production was evaluated by the ¹⁴C and dark-light methods, the ¹⁴C and ¹⁸O methods or ¹⁸O and dark-light methods took place across contrasting oceanic regions such as low productive oceans (Pacific Ocean and the Mediterranean Sea), high productive oceans (Southern and Arctic Ocean) and the Atlantic Ocean, thereby spanning a broad range of communities and environmental conditions. However, the study using ¹³C and ¹⁴C methods concurrently was confined to individual oceanic region (North Western Atlantic Ocean), thereby limiting the ability to develop conversion equations.

FIGURE 1

Figure 1. Geographic distribution of the stations where the primary production has been evaluated by two different methods at least.

The ratios between pairs of metrics ranged greatly, typically >10-fold, and up to 200-fold (Figures 2, 3). The ratios presented here are orders of magnitude greater than the ratio observed in laboratory cultures studies (Halsey et al., 2010) and in field studies (Bender et al., 1999; Marra, 2002). This broad range indicates that (a) the relation between the different components resolved by primary production methods and their sources of error are highly variable (Table 1), and (b) the use of simple ratios as conversion factors between two different methods can lead to very large errors. Some of these differences are expected, as the processes resolved by the methods differ. For instance, GPP-DO, GPP-¹⁸O, GPP-O₂FRRF, and ¹⁴C-TOC intend to measure GPP whereas ¹⁴C-POC and ¹³C-POC attempt to resolve specific components of primary production. The comparison between the in vitro metrics indicated that the GPP measured by the ¹⁸O method (GPP-¹⁸O) tends to produce the highest estimates of primary production, followed by the GPP measured by the dark-light method (GPP-DO), the TOC measured by the ¹⁴C method (¹⁴C-TOC), the POC measured by the ¹³C and ¹⁴C methods respectively (¹³C-POC and ¹⁴C-POC) (i.e. GPP-¹⁸O > GPP-DO > ¹⁴C-TOC > ¹³C-POC > ¹⁴C-POC, Figure 2, Table 3).

FIGURE 2

Figure 2. Box plot showing the variation of the primary production ratios measured by two different methods concurrently. The boxes encompass the lower (25%) and upper (75%) quartiles, and the central line shows the median ratio, and the whiskers go down to the smallest value and up to the largest.

FIGURE 3

Figure 3. Relationships between the log-log transformed primary production rates measured by different methods concurrently. The bold solid lines represent the linear regressions, the thin solid lines represent the 1:1 lines and the thin dotted lines represent the 1:10, 1:100, 1:0.1, 1:0.01, 1:0.001. Details of linear regressions parameters in Table 4.

TABLE 3

Table 3. Mean (±SE), median, range (minimum–maximum), and number of observations (n) of the primary production ratio between different methods used concurrently, and the probability P, statistics t, and degrees of freedom df of the t-test testing if primary production rates measured by two methods concurrently are significantly different (*represents the significant difference).

The only in situ technique reported used to measure GPP reported here, the FRRF method (GPP-O₂FRRF) yielded estimates significantly higher than ¹⁴C-POC estimates (t-test, t = 6.7, df = 69, P < 0.0001; Figure 2, Table 3). In contrast, GPP-O₂FRRF was 2-times lower than GPP-DO (t-test, t = 3.7, df = 14, P = 0.0022; Figure 2, Table 3) and 7-times lower than GPP-¹⁸O (t-test, t = 6, df = 14, P < 0.0001; Figure 2, Table 3). Comparisons among in situ and in vitro methods were limited to a subset of all possible metrics. Indeed, we found two studies reporting the concurrent use of FRRF and ¹⁴C methods (Corno et al., 2005; Robinson et al., 2009), and only one study reporting the concurrent use of FRRF, ¹⁸O and dark-light to measure GPP (Robinson et al., 2009).

The power equation between estimates of PP derived from different methods allows for ratios between methods shifting with PP (i.e., slope ≠ 1, Figure 3, Table 4). Slopes <1 imply that the ratio of y/x (i.e., a PP_y/PP_x) declines with increasing x (PP_x), and slopes >1 implies that the ratio of y/x increases with increasing x (Table 4). All of these pairwise relationships between methods were significant (P < 0.05) and strong (R² > 0.35). The error associated with the prediction of primary production in one type of method from values obtained by another method improved the predictive power of those relationships (Table 5).

TABLE 4

Table 4. Principal component RMA regression equations of the form PP_y = aPP^b_x showing the relationship between the log primary production estimates derived in parallel using different methods, along with the corresponding adjusted coefficient of determination (Adj-R²), the associated probability (P), the probability P of the F-test testing if the slope is different from 1 (* represents the significant difference of the slope from 1), and the y/x (i.e., aPP^b_x/PP_x) ratio for the minimum and the maximum values of x.

TABLE 5

Table 5. Error (absolute, as mean ± SE prediction error; and relative, as the mean ± SE% error) associated with the prediction of primary production in one type of method from values obtained using another method.

The power slopes between the pairs of metrics were statistically different from 1 for most relationships, indicating that the ratios between these metrics changed systematically as primary production increased (Figure 3, Table 4). The departure from 1 indicates that the ratios between estimates derived from various methods change with x, and indeed the ratios predicted from the fitted regression equations varied greatly (Table 4). For instance, the ratio of y/x varied greatly when the minimum or maximum value of x is used (x_min or x_max) ranging about 10-fold across the range of PP_x estimates (Table 4). In contrast, the greater departure from a power slope of 1, indicative of a uniform ratio between the metrics, was observed between GPP-DO and ¹⁴C-TOC (Table 4).

Discussion

The comparison of estimates of marine phytoplankton primary production derived from different methods provided above reveals very large differences between methods. Some of these differences are expected, as the processes resolved by the methods differ (GPP measurement or specific components of PP measurement) or the methodology itself may be different (i.e., incubation time, temperature, light, or nutrient availability restricted by the incubation process). Indeed, GPP-¹⁸O provided the highest estimates of primary production and the ¹⁴C-POC provided the smallest ones, as expected, consistent with previous reports. Indeed, Grande et al. (1989) showed that the ¹⁸O content of the dissolved oxygen pool increased with photosynthesis and is 2 to 3 times larger than the pool of POC labeled by the ¹⁴C. Juranek and Quay (2005) observed that the GPP-¹⁸O rates were 1.5–2 times higher than ¹⁴C-POC rates. Furthermore, we observed here that GPP-O₂FRRF estimates were much lower than the GPP-¹⁸O. These results support the observation by Robinson et al. (2009) that the main source of uncertainty in the calculation of GPP-O₂FRRF is the use of fixed values for n_PSII (the photosynthetic unit size of PSII), which future applications of this method should aim at resolving for natural plankton communities. The ¹³C method was expected to yield estimates of particulate primary production similar to those of the ¹⁴C method, as the only difference is that ¹³C is a stable isotope. However, our results indicated that the ¹³C method yields estimates of particulate primary production higher than those of the ¹⁴C method (Figure 2, Table 3). Indeed, Mousseau et al. (1995) observed that 70% of PP determined by the ¹³C method was higher than PP determined by ¹⁴C method. Although they were unable to give a complete explanation of the differences between the two methods, they related it to biological and/or environmental conditions (biomass and/or irradiance).

The ¹⁸O method should be considered as the best approach to resolve GPP with the greatest precision (here, median 9% error for GPP-¹⁸O). Although this method is also robust against changes in respiration over the natural light/dark cycle, it seems to overestimate the gross organic carbon production as it measures all oxygen production without taking into account if it is directly linked to carbon fixation (Laws et al., 2000). Although a correction of 20% for the Mehler reaction and the photorespiration (Laws et al., 2000; Hendricks et al., 2004) could be applied to the GPP-¹⁸O, a constant correction factor may not be adequate. Indeed, Steeman Nielsen (1975) argued that photorespiration is correlated with the internal O₂ concentration, which is highest at high photosynthetic rates. In this study, GPP-¹⁸O rates were higher than ¹⁴C-POC rates, the GPP-DO rates and the GPP-O₂FRRF measured concurrently (Tables 3, 4, Figures 2, 3). These discrepancies may be explained by the changes in respiration over the natural day–night cycle. Indeed, Grande et al. (1989) indicated that the difference between ¹⁴C-POC and GPP-¹⁸O rates was due to respiratory ¹⁴C losses by both autotrophs and heterotrophs. Furthermore, although some studies reported a lack of differences between light and dark respiration (Marra and Barber, 2004; González et al., 2008), other reports found that light respiration may be higher than dark respiration leading to an underestimation of GPP measured by the dark-light method (Bender et al., 1987; Dickson and Orchardo, 2001; Dickson et al., 2001; Pringault et al., 2007). Moreover, we observed in this study that the difference between production estimates derived using GPP-DO and GPP-¹⁸O increases with increasing primary production, as expected. The discrepancy observed here between GPP-¹⁸O and GPP-DO rates suggests that the assumption that dark and light respiration is similar maybe questioned. Our results tend to reject the assumption embedded in the dark-light method that dark respiration is equal to light respiration (Table 1), as there is a wealth of indications that respiratory processes are enhanced in the light (Harris and Lott, 1973). In contrast to GPP, NCP estimates should not be influenced by this effect, and the dark-light method should still yield reliable estimates of NCP (Duarte et al., 2013).

Estimates derived using ¹⁴C-POC are often indicated to provide a metric close to net primary production, NPP, which is defined as the production available to support phytoplankton growth (i.e., after accounting for losses due to respiration and excretion; Ryther and Vaccaro, 1954; Antia et al., 1963; Eppley and Sharp, 1975). However, ¹⁴C-POC represents strictly the production recovered in particulate form after the incubation time, includes the effects of trophic interactions within the incubated sample and does not account for DOC release nor respiratory losses by the community (Bender et al., 1987). Indeed, ¹⁴C-POC probably underestimates NPP, as heterotrophic respiration of PP consumed by microzooplankton grazers also affects ¹⁴C-POC. The measurement of ¹⁴C incorporation into total organic matter partially overcomes this problem by accounting for the ¹⁴C recovered in the DOC pool, which can be substantial (González et al., 2008). Yet, this estimate of primary production falls short of accounting for respiratory losses, both by autotrophs and heterotrophs (Bender et al., 1987). Oxygen-based estimates are believed to derive estimates closely approaching GPP (González et al., 2008). Moreover, Ryther (1956b) concluded that respired CO₂ could be reutilized into photosynthetic pathways, whereas the O₂ released is not similarly consumed in respiration. For that reason, O₂ based-methods provide estimates of GPP while ¹⁴C-POC provides estimates closer to NPP, relative to that measured by O₂ (Marra, 2002). Where ¹⁴C needs be used, ¹⁴C-TOC, which better approximates GPP, should be measured in parallel to ¹⁴C-POC measurements. In this study, ¹⁴C-TOC was significantly lower than GPP-DO (Table 3). These differences may be explained by several factors. First, ¹⁴C incubation time varied in this study from 2 to 12 h and may result in a ¹⁴C-TOC closer to NPP (<4 h of incubation) or to GPP (incubations up to 6 h). Furthermore, some uncertainties regarding the magnitude of extracellular release of newly fixed carbon may explain these differences (González et al., 2008).

We argue that none of the methods tested here resolve NPP with confidence in natural communities. We suggest that the use of NPP to assess primary production by phytoplankton communities should be replaced with a measure of GPP or NCP. Unfortunately, the global primary production of the ocean, a property of interest for multiple applications, has been derived from remote-sensing ocean color calibrated with ¹⁴C-POC (Behrenfeld and Falkowski, 1997b). As indicated above it is unclear what these values actually measure and what is, therefore, represented by these global primary production estimates. An option may be to convert these estimates into GPP. However, Peterson (1980) reported that the ¹⁴C method underestimated GPP rates by about a factor of 2–100. At the ocean time series HOT, GPP-¹⁸O was reported to be 2.4 at surface to 1.1-fold at 100 m greater than ¹⁴C-POC, which was interpreted to represent NPP (Nicholson et al., 2012). These observations confirm the results presented here that use of a single conversion factor to estimate GPP from ¹⁴C-POC yields biased estimates, as the ratio is highly variable (Table 4). Instead, the conversion equations reported here should be applied to the individual estimates prior to integration at the basin or global scale. Furthermore, several authors assessed the uncertainty in NPP models derived from remote sensing by comparison with in situ ¹⁴C uptake and observed a significant systematic bias (Westberry et al., 2008; Friedrichs et al., 2009; Saba et al., 2010, 2011). We were able to evaluate here the integrated PP using the vertically generalized production model (VGPM, Behrenfeld and Falkowski, 1997a) from remote sensing (irradiance and temperature) and in situ data (chlorophyll a) for each oceanographic station of our dataset where in situ integrated ¹⁴C-POC rates were available (data not shown here). As expected, we observed that the integrated PP_Predicted was very weakly related to the integrated PP_Observed (Figure 4, R² = 0.28, P = 0.03). Milutinović and Bertino (2011) reported that P^b opt (maximum PP per unit of chlorophyll) contributes the most to the random uncertainty in VGPM NPP. Previous results that suggest that remotely sensed estimates of primary production are not precise and until new models are derived and tested widely, the estimates of NPP derived from remote sensing should be considered as approximations only.

FIGURE 4

Figure 4. Relationship between the log-log transformed integrated PP_Predicted and PP_Observed rates (mg C m⁻² d⁻¹) for oceanographic stations where integrated ¹⁴C-POC was available. The dotted line represents the 1:1 lines and the solid line represents the linear regression with equation: Log PP_Predicted = 0.20 (±0.08) log PP_Observed + 5.46 (±0.50), R² = 0.28, P = 0.02775, n = 17.

Following a comparison of various methods, Marra (2002) concluded that there is probably no single method able to provide an absolute estimate of primary production in the ocean and that various methods should be combined in any research programme. However, the scientific community predominantly uses a single method, typically the ¹⁴C-POC method, possibly the method with highest uncertainties as to the process actually measured, for the assessment of primary production, and data on primary production estimates derived concurrently with various methods are limited. Moreover, many factors, such as irradiance, temperature, nutrient concentrations, plankton community structure and others may affect the variability in the ratios between estimates derived from different methods. Unfortunately, few studies publish information related to the environmental conditions or community structure so their impact on the ratios between techniques could not be assessed here.

Our study shows that the ¹⁸O method provides the most accurate measure of GPP with the fewest assumptions required during estimate of the rate. The remaining flaw may be the presence of bottle effects, which can be partially mitigated using quartz bottles to avoid modifications to the light field (Godoy et al., 2012) and simulating correctly the in situ temperature. While the scientific community embrace a new standard, the regression equations presented here (Table 4) provide the best available approach to convert data across methods, and, therefore, integrate and synthesize available and future data derived using different methods.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This is a contribution to the “Malaspina 2010” CONSOLIDER project funded by the Spanish Ministry of Science and Innovation, the Metaoceans Marie Curie Early Stage Research Network (019678-2), funded by the Framework Program 6 of the EU and the Marine and Coastal Carbon Biogeochemistry Flagship Cluster, funded by the CSIRO. We thank J. Arístegui, N. Cassar, M. Cottrell, D. Kirchman, X. Morán, D. Nicholson, N. González, V. S. S. Sarma, P. Serret, E. Fernández, L. Mousseau, K. Westwood, P. J. le B Williams, and D. M. Karl for providing published data. Aurore Regaudie-de-Gioux was supported by the METAOCEANS project.

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