ICES mar. Sei. Symp., 197: 159-171. 1993

Estimation of primary production by the simulated in situ method

Steven E. Lohrenz

Lohrenz, S. E. 1993. Estimation of primary production by the simulated in situ method. - ICES mar. Sei. Symp. ,197: 159-171.

The simulation of in situ conditions for primary production incubations may seem a clear objective. However, the inherent assumptions of the method, that subsurface in situ conditions are fully understood and can be duplicated in a surface incubator, are inevitably invalid. It is not surprising then that differences exist between in situ and simulated in situ (SIS) incubation measurements. The objective of this paper is to evaluate the SIS incubation method and discuss its utility. I consider evidence for potential factors causing errors in SIS estimates of actual in situ primary production. These factors may be grouped as (1) irradiance effects, (2) temperature effects, (3) sampling handling effects including, for example, sample collection, processing, and incubation duration, and (4) methods of data analysis by which a final production number is calculated. I argue that proper control of these factors can minimize, although not necessarily eliminate, differences between SIS incubation measurements and actual in situ primary production. Evidence is cited to show that residual differences are comparable to the limits of precision of in vitro primary production estimates. It is concluded that the SIS method, because of its capabilities for extended spatial coverage and logistical control over incubation conditions, remains a valuable tool for the assessment of primary production and microplankton rate processes.

Steven E. Lohrenz: Center fo r Marine Science, University o f Southern Mississippi, Stennis Space Center, M S 39529, U SA.

“The precise determination of in situ daily production beneath a unit of sea surface is extremely difficult and has probably never been achieved.”

J. D. H. Strickland, 1965

Introduction

While most oceanographers today would agree with the first part of Strickland’s statement, the issue of whether we have yet achieved precise determination of in situ daily production is more controversial. This paper criti­cally examines one approach, the simulated in situ method. In early applications of in vitro techniques to measure primary production in aquatic environments (e.g., Gaarder and Gran, 1927), the use of in situ incubation methods was preferred. However, conven­tional in situ incubation methods were time-consuming and, hence, expensive for oceanographic research con­ducted with large ships. An alternative method was the “imitated” or “simulated" in situ technique for measuring primary production. The simplest version of this method involved collecting surface samples and

suspending incubation bottles in a water bath on the deck of a ship (Riley, 1939). Subsequently, the approach was modified to include collection of samples from several depths and imitation of light conditions with neutral density (Steemann Nielsen, 1958; R ytheref a / . , 1966) and color filters (Jitts, 1963; Laws et al., 1990).

The simulated in situ (SIS) method offers several practical advantages over conventional in situ (IS) incu­bation methods. The lack of recovery and deployment of an IS array makes ship operations less complicated. SIS incubations do not require remaining on or returning to a given location, thus allowing for increased spatial resolution. In addition, SIS incubations provide greater flexibility and more logistical control over incubation conditions (e.g., increased temporal resolution). A dis­advantage of the SIS method is that in situ conditions can only be approximated. Despite this, the SIS method is widely used. For example, at least 75% of productivity data currently archived with the US National Oceano­graphic Data Center were obtained using SIS methods (W. Balch, pers. comm.).

Several studies have compared SIS and IS estimates of

160 S. E. Lohrenz IC E S m ar . Sei . S y m p . , 197 ( 1993)

primary production (Table 1). The majority of SIS primary production estimates at discrete depths fall within 100% (factor of 2) of IS values. Agreement is better between SIS and IS estimates of water column- integrated primary production. Generally, errors in SIS measurements of in situ primary production can be attributed to the following: (1) irradiance effects, (2) temperature effects, (3) sample handling (sample collection/processing, incubation duration), and (4) methods of data analysis and computation. The objec­tive of this paper is critically to examine evidence that these factors contribute to errors in SIS (and IS) esti­mates of primary production. Based on this analysis, recommendations for SIS incubation procedures were made. Finally, the precision and accuracy of SIS incu­bation estimates of in situ primary production were considered. It was concluded that the SIS method, because of its ability to provide extended spatial cover­age and flexibility in sample treatment and incubation conditions, remains an important tool for the measure­ment of primary production and microplankton rate processes.

Evaluation of factors contributing to variations in IS and SIS estimates of primary production

Irradiance effects

Variations in total irradiance

Control of irradiance in SIS incubations is routinely accomplished using filters (neutral density or spectral) to adjust irradiance (solar or artificial) to a desired level. Determination of variations in irradiance with depth can be accomplished by direct measurement (e.g. Jerlov, 1976; J it tseJa /. , 1976). Alternatively, irradiance may be derived indirectly using estimates of vertical attenuation of surface irradiance through the water column. For the latter approach, a common method (e.g. Parsons e/«/.,1984) has been to determine an average irradiance attenuation coefficient ( K \ m ^ 1) for the euphotic zone. The coefficient, K ', can be estimated from subsurface irradiance data or from empirical relationships based on Secchi depth (Isdo and Gilbert, 1974; cf. Preisendorfer, 1986; Megard and Berman, 1989) or other optical properties. Errors in estimating subsurface irradiance may result from the assumption of a uniform K' with depth. Depth-dependent variations in K(z) occur be­cause attenuation of irradiance by sea water varies with wavelength and is affected by composition of particulate and dissolved substances (e.g., Tailing, 1957; Jerlov, 1976; Kirk, 1983; Siegel and Dickey, 1987). Largest depth-related variations in K(z) generally occur near the surface (Fig. 1). Errors in calculating I(z) from a uniform

K (z) ( m 1)0.0 0.1 0.2 0 .3 0 . 4 0 .5

20

40

60

a

0 TCF

20

25

30

Figure 1. Vertical profiles of attenuation coefficients (K(z)) in the northern Gulf of Mexico during October 1990 estimated by direct measurement (solid line) using a Li-Cor LI-192SA underwater quantum sensor with LI-190SA surface reference sensor (both 2j i sensors). The average value of K(z) expected from a Secchi depth determination is shown for comparison (dashed line), (a) Offshore station, 28°31.15'N, 89°2 1 .9 1 'W .

(b) Coastal station, 28°46.71'N, 89°30.55'W.

K' occur in both surface and deep waters for oligotro- phic conditions and primarily in deeper water for more turbid conditions (Fig. 2). More precise approximation of K(z) can be accomplished by modeling it as a linear sum of absorption and scattering properties associated with pure water, particulate matter, and dissolved materials (e.g.. Smith and Baker, 1978; Austin and Petzold, 1984; Prieur and Sathyendranath, 1981; Sath- yendranath and Platt, 1988; Lohrenz era/., 1992).

Temporal variations in irradiance contribute to vari­ations in estimates of production over a wide range of scales (e.g., Marra, 1980; Falkowski, 1984). For many types of variations, simulating in situ irradiance con­ditions in an incubator is essentially impossible. An example is wave focusing and reflection (Jerlov, 1976; Walsh and Legendre, 1983; Queguiner and Legendre, 1986). In addition, both IS and SIS incubations have been criticized because their “static” nature fails to account for photosynthetic responses to fluctuating ir­radiance associated with vertical motions (e.g., Harris

IC E S m ar . Sei. S y m p . . 197(1 993) Primary production by the simulated in situ method 161

Table 1. Absolute differences between SIS and IS measured primary production expressed as a percentage of the IS value. All measurements were determined by the l4C method unless otherwise indicated. Means are given, along with standard deviations (s.d.) and ranges, to provide an indication of dispersion (n = number of observations).

Discrete depths Water column

Reference Mean (s.d.) Range n Mean (s.d.) Range n

Berge (1958) 7 (7) 0-18 6 _ _ _

Jitts ( 1963“) 42(41) 0-200 35 18(15) 0-40 6Doty et al. (1962) 27 ( 11 ) 8-63 19 - - -(in Jitts, (1963)) Doty et al. (1965) _ _ _ 27 (3) 23-35 5Head, 1976ah - - - 27(21) 0-78 23Jitts etal. (1976) 55 (53) 0-230 32 - - -Boalch et al. (1978) 20 - - - - -Brown (1982e)

0 2 method 49(41) 0-153 36 28(13) 9-44 9l4C method 46 (49) 2-260 30 36(25) 13-85 7

Grande et al. ( 1989e) 0 2 method 43(18) 30-56 2I4C 39 (34) 2-70 3 - - -

Gieskes et al. (1990°) 32 (42) 4-250 39 - - -Chiswell et al. (1990) 38 (27) 2-100 31 26 (26) 2-62 4Winn et al. (1992) 53 (34) 1-180 39 20 (9) 10-28 5Lohrenz et al. (1992)

12 h incubations 52 (43) 9-170 28 27(14) 17-42 31 h incubations 30 (5) 24-33 3 - - -

Barber, unpublished 20 (27) 2-67 5 - - -

“Statistics based on digitization of plotted data contained in reference.bBecause of the large number of closely spaced data, digitization of data for discrete depths was not attempted. ^'Negative rates were omitted.

I r r a d i a n c e

( m o l q u a n t a m h )4 5 - 1 0 0

% E r r o r

-50 0 50 100

20

40

60

so

20

25

30

Figure 2. Offshore (A) and coastal (B) vertical profiles of irradiance based on measured (solid line) and depth-invariant (dashed line) estimates of K(z) as in Figure 1. Also shown are the errors in estimated irradiance resulting from the assumption of a uniform K(z) with depth for the offshore (C) and coastal (D) stations. Locations as in Figure 1.

and Lott, 1973; Harris and Piccinin, 1977; Harris, 1978, 1980, 1984; Marra, 1978, 1980). Under some conditions a significant negative bias may exist for static incubation estimates because of increased photoinhibition with increasing exposure time (see under “Incubation dur­ation and non-linearity in rates”).

Alternatively, photoinhibition may occur naturally as, for example, during diel stratification events (Vin­cent et al., 1984). Internal wave activity has also been considered as a source of variation in in situ irradiance (Haury etal., 1983; Fahnenstiel et al., 1988). Some of the problems of irradiance fluctuations can be addressed through models which incorporate information about photosynthesis-irradiance (P-I) properties and suscep­tibility to photoinhibition with descriptions of optical and physical regimes (e.g., Gallegos and Platt, 1985). Data for constructing P-I relationships have generally been obtained by incubating samples in artificial light gradients (e.g., Fee, 1973a, b; Jitts etal., 1976; Harrison et al., 1985). Recent work has considered time depen­dence of P-I parameters (Cullen and Lewis, 1988; Pahl- Wostl, 1990). It is also possible to derive "composite” P - I parameters from pooled water column measurements of available irradiance and primary production deter-

162 S. E. Lohrenz I C E S m ar . Sei. S y m p . . 197 ( 1993)

mined by IS or SIS techniques (e .g ., Menzel and Ryther, 1961; Herman and Platt, 1986; Bower et al., 1987; Lohrenz et al., 1990, 1992). A potential limitation of composite P-I curves is that the derived P-I parameters are not representative of any one phytoplankton popu­lation or photoadaptive state (cf. Lewis et al., 1984; Cullen, 1990). Even so, the utility of the composite P-I approach has been demonstrated in coastal (Yoder and Bishop. 1985) and oceanic (Lohrenz et al., 1992) waters.

In addition to considerations of temporal variations in in situ irradiance, it is also important to recognize potential irradiance artifacts associated with SIS and IS incubation procedures. The anisotropic nature of solar irradiance, the reflective nature of many incubator tank materials, and the potential for shading by ship structure may result in temporal and spatial variations in the percentage of available surface irradiance within an incubator. As illustrated in Figure 3, this problem can be evaluated by routinely monitoring the SIS irradiance field. It is also important to avoid even brief pre­incubation exposure of IS and SIS incubation bottles to full sunlight, as this may significantly depress rates of 14C-fixation (e.g., Doty et al., 1965). Post-incubation exposure should also be avoided as this may cause significant overestimates of productivity in deeper samples.

Variations in spectral quality

Effects of spectral quality of irradiance on photosyn­thesis have long been recognized (Emerson and Lewis, 1943) as well as potential implications of this for SIS primary production estimates (Steemann Nielsen, 1958). Spectral quality of irradiance varies with depth and water type (Jerlov, 1976). Despite this, many inves­tigators have used neutral density screening (e.g., McAllister and Strickland, 1961) to adjust solar irradi­ance in SIS incubations. Negative bias in SIS incubation measurements using neutral density irradiance attenua­tion has generally been estimated at 10-60% for rates at discrete depths and 10-30% for estimates of water column-integrated primary production (Table 2).

Various materials have been used to simulate submar­ine spectral conditions (cf. Jitts, 1963; Kiefer and Strick­land, 1970; Jitts etal., 1976; Gieskes etal., 1990; Laws et al., 1990). The optimal approach seems to be a combi­nation of neutral density and spectral filters (e.g., Fig. 2 in Lohrenz et al., 1992). An experiment to determine the sensitivity of primary production estimates to spectral conditions was conducted using different combinations of blue (Acrylite 625-5) and neutral density filters (Loh­renz, unpublished). P-I characteristics of natural popu­lations were determined under solar irradiance attenu­ated by the Acrylite 625-5/neutral density (A/ND) combination and under solar irradiance attenuated

120

00

80

60 Blue (

4 0Blue + N e u t r .

20

00 .0 0 .2 0 .4 0 .6 0 .8 1.0

Cos(Solar Zen i th Angle)

Figure 3. Variations in percent of available surface irradiance (photosynthetically active radiation, 400-700 nm) in an incu­bator in relation to solar zenith angle. Irradiance was measured using a 4jt sensor (Biospherical Instruments QSL-100). Error bars indicate one standard deviation (single samples available for some data) and are indicative of spatial variation within the incubator. Variability is also apparent with solar zenith angle, which varies both seasonally and throughout the photoperiod. The horizontal dotted lines indicate the transmission properties of the attenuating materials, including clear polycarbonate (Acrylite 011-9), blue polycarbonate (Acrylite 625-5), and neutral density fiberglass screening.

using only neutral density (ND) filters. Samples were collected from both coastal and offshore stations in the northern Gulf of Mexico. Chlorophyll maximum samples from either coastal or offshore waters exhibited a significantly higher a B under A/ND spectral conditions (Table 3). Values of a B were only slightly higher for offshore surface populations. No significant effects of spectral quality were evident for estimates of PELx- Estimates of water column-integrated primary pro­duction based on the P-I relationships in Table 3 were 19% (coastal) to 28% (offshore) higher for the A/ND spectral conditions than for ND conditions. These differences were consistent with findings of previous studies reporting negative bias attributable to neutral density irradiance attenuation (Table 2).

Another consideration regarding spectral conditions of both IS and SIS incubations is exposure to ultraviolet radiation (cf. Jitts e ta/., 1976; Smith et al., 1980; Smith and Baker. 1980; Worrest et al., 1980). Suppression of radiocarbon estimates of primary productivity due to ultraviolet radiation of wavelengths 280-320 nm (UV-B) and 320-400 nm (UV-A) has been demonstrated (e.g., Smith et al., 1980; Maske, 1984; Buhlmann etal., 1987). Because UV radiation of wavelengths less than 345 nm is absorbed by conventional polycarbonate, SIS incu­bations using this material may overestimate rates. Analysis of the results of Smith et al. (1980) indicated that the exclusion of UV led to a 40% mean enhance­ment of rates in the upper attenuation length of the

IC E S m ar . Sei. S y m p . , 197(1993) Primary production by the simulated in situ method 163

Table 2. Estimated negative bias attributable to inappropriate spectral conditions in SIS primary production measurements using neutral density light attenuation (%).

Reference

Underestimation relative to in situ:

Discrete depths Water column

Jitts (1963) 0-60 _

Kiefer and Strickland (1970) 66 -

Shimura and Ichimura (1973) 10-30 _

Morel (1978) 45 -

Harrison et al. (1985) 40-60 10-15Lewis et al. (1985a) 38 -

Lewis et al. (1985b) - 30Herman and Platt (1986) 10-30 20Prezelin et al. (1989) - 6-13Laws et al. (1990) 50 -

Prezelin and Glover (1991) - 8-14Schofield el al. ( 1991 ) 30-85 -

This paper 0-54 19-28

water column in the oligotrophic Pacific Ocean. Enhancement was negligible at greater depths. More precise estimates of effects of U V exposure can be made using models that employ UV dose-response relation­ships (e.g., Smith and Baker, 1980; Smith et a l., 1980; Cullen and Lesser, 1991; Smith et a l ., 1992) in the context of the physical regime.

Temperature effects

Simulation of in situ temperatures is, in principle, less complicated than irradiance because in situ tempera­tures can be easily measured and incubation tempera­tures can be controlled precisely. An exception would be in ecosystems having significant temporal and spatial variations (e.g., diel variation in stratification and sur­face warming). Correction for errors in incubation tem­peratures may be possible through the evaluation of

photosynthesis-temperature responses. Tailing (1957) expressed the view that usually only light-saturated rates of photosynthesis were temperature-dependent (how­ever, cf. Tilzer et a l., 1986). Li (1985) examined natural phytoplankton assemblages over a wide latitudinal range and found that temperature responses of light- saturated photosynthetic rates were characterized by an optimum range and declined at either suboptimal or supraoptimal temperatures. Based on these results, small differences (i.e., 2-3°C) between ambient and incubation temperatures would have a minimal effect on rates provided that ambient temperatures were near the optimum. However, away from the optimum, rates would be expected to be much more sensitive to tem­perature changes above or below ambient. A correspon­dence between optimum and ambient temperatures has been observed only at higher temperatures (>16-18°C) (Aruga, 1965; Li, 1985). Given that surface tempera-

Table 3. A comparison of photosynthesis-irradiancc parameters obtained under different spectral regimes in the northern Gulf of Mexico, October 1990 (n = number of samples, standard errors in parentheses).

Spectraltype

Depth(m)

Localtime(H) n PLx a B ß B

OFFSHORE:blue 10 9.4 10 2.9 (0.2) 9.1 (1.4) 0.04 (0.02)

neut. dens. 10 9.4 10 2.7 (0.4) 6.5 (1.1) 0.04 (0.03)blue 72 9.4 10 4.5 (0.5) 47.7 (6.1) 0.67 (0.11)

neut. dens. 72 9.4 10 5.8 (0.7) 28.2 (1.8) 0.80 (0.12)

COASTAL:blue 5 14.7 10 7.0 (0.9) 13.2 (4.5) 0.0 (0.0)

neut. dens. 5 14.7 10 6.6 (0.9) 9.2 (2.8) 0.0 (0.0)blue 15 14.7 10 6.1 (0.2) 19.0 (2.0) 0.0 (0.0)

neut. dens. 15 14.7 10 5.9 (0.5) 11.0 (1.8) 0.0 (0.0)

Units: P%;,x: gC gChl 1 h a B and ß n: gC gChl 1 (mol quanta m 2)

164 S. E. Lohrenz IC E S m ar . Sei. S y m p . , 1 97(1993)

tures in many productive areas in the ocean are lower than this, it would seem that temperature control should be a critical consideration in SIS incubations.

A common approach to regulation of temperature for SIS methods is to circulate surface sea water through decktop incubators. Inappropriate incubation tempera­tures may result if there is a large vertical gradient from surface to depth (e.g., Lohrenz et al., 1992) or large horizontal gradients encountered during the incu­bations. In addition, temperature of surface water sup­plied by ships’ seawater systems may be poorly regulated (e.g., Marra et al., 1988). A more precise method is to control incubator temperatures using thermostatic baths (e.g., Lohrenz et al., 1992).

Sample handling/incubation duration

Sample handling

There is extensive literature discussing various aspects of sample handling for in vitro (both IS and SIS) primary production measurements. Specific issues include, for example, toxicity effects (Carpenter and Lively, 1980; Fitzwater et al., 1982; Colijn et al., 1983; although cf. Marra and Heinemann, 1984), bottle size or type (Gieskesefa/., 1979; Sakamoto eta l. , 1984), selection of sample depths (Doty etal., 1965; Richardson, 1991), l4C solutions (Bresta eta l., 1987), filtration/sample fixation procedures (Schindler et al., 1972; Lean and Burnison, 1979; Gachter et al., 1984; Goldman and Dennett,1985), and radioassay techniques (Doty et al., 1965; Richardson, 1991). Variations in any procedural step can introduce variability into measurements. For example, average coefficients of variation attributed to processing of identical subsamples by different labora­tories were shown to be on the order of 20% (Doty etal., 1965; Richardson, 1991).

Another issue of some concern is the extent to which the physiology of phytoplankton populations may be altered during sample collection prior to incubations (e.g., Taylor et al., 1982). One approach to avoid these problems is to perform all aspects of sampling and incubation in situ. Results from such in situ approaches have been compared to simultaneous SIS incubations that used conventional sample collection methods (Fig. 4). These results support the view that conventional sampling operations do not introduce serious bias, given proper treatment of samples as discussed in the refer­ences cited above.

Incubation duration and non-linearity in rates

The choice of incubation duration represents a compro­mise between (1) incubating long enough to obtain a measurable response integrated over appropriate time scales, and (2) incubating short enough to minimize

effects of confinement. Incubations of up to 24 h are routinely used for estimating daily production, with the understanding that such measurement periods integrate over diel periodicity in rates and that dark respiratory losses are reflected by a decrease in 14C-particulate carbon (Eppley et al., 1973; Eppley and Sharp, 1975). However, long incubations increase the likelihood that population characteristics may be altered by confine­ment in bottles (e.g., Venrick et al., 1977; Goldman et al., 1981). For example, summation of a series of short incubations has been shown to produce an estimate higher than obtained with a single long-term incubation (Rodhe, 1958; Vollenweider and Nauwerck, 1961; Goldman and Dennett, 1984). An alternative is to conduct short incubations and linearly extrapolate rates over the photoperiod. However, this approach gives rise to other uncertainties.

P r o d u c t i o n ( m gC m h )

0 .0 0 .2 0 .4 0 .6 0 .8 1.00

20

40• • o

60 • in s i t u o d e c k

• o80

12 16840

20

25

Figure 4. Comparison of simulated in situ (SIS) measurements with measurements of primary production in which sample collection, innoculation with 14C, and incubation were all performed in situ. Samples for SIS measurements were col­lected using Niskin bottles on a Rosette. (A) SIS measurements in relation to in situ measurements determined using an auton­omous sampler incubation device as described in Taylor and Doherty (1990) and Lohrenz et al. (1992). (B) SIS measure­ments (methods in Huntsman and Barber. 1977) compared with in situ measurements executed by scuba divers (R. Barber, unpublished).

IC E S m ar . Sei. S y m p . . 197 (1993) Primary production by the simulated in situ method 165

Compartmentalization of cellular carbon pools may cause short incubations with l4C-inorganic carbon to reflect something between gross and net photosynthetic rates (cf. Hobson et a i , 1976; Dring and Jewson, 1982). Another problem is that extrapolation of short-term rates is complicated by diel periodicity (Doty and Oguri, 1957; Shimada, 1958; Ryther et al., 1961; Lorenzen. 1963; Fee, 1975; Harding et al., 1982a, b). Photosynthe­tic maxima have been reported to occur at various times of day (Harding et al., 1982a). Consequently, there is no a priori best time of day for making short-term incu­bation measurements. Finally, there is no way to esti­mate night respiratory losses directly from short 14C incubations (e.g., Eppley and Sharp, 1975).

Despite the problems associated with both long- and short-term incubations, daily primary production esti­mated from photoperiod extrapolation of a single mid­day short-term incubation can agree well (within 50% on

25

20

15

10

5

00 1 2 3

0 1 2 3

4 00

0 20 40 60

S h o r t T e r m P r o d u c t i o n— 3 — 1

(mgC m h )

Figure 5. Relationship between rates estimated with single mid-day short-term (2-6 h) versus 24 h simulated in situ incu­bations in various locations and times of the year. Short-term rates were expressed on an hourly basis and 24 h rates were expressed on a daily basis. Methods of incubation as described in Lohrenz et al. (1988, 1990). The dotted lines indicate the predicted relationship if hourly rates were extrapolated over the photoperiod. (A) Western Mediterranean, May 1986, 13 h photoperiod. The average difference between hourly extrapo­lated and 24 h incubation estimates expressed as a percentage of the 24 h value was 42% (range 0.3-98, n=20); (B) western Mediterranean, November 1987,6 h photoperiod. The average difference was 20% (0.4-57, n=30), (C) northwest Atlantic, October 1988, 8 h photoperiod. The average difference was 25% (0.7-180, n=51); (D) Gulf of Mexico, April 1990, 12 h photoperiod. The average difference was 45% (9-110. n=8).

average) with 24 h incubation estimates (Fig. 5). The results in Figure 5 illustrate how a combination of incubation strategies can be used to determine sensi­tivity of production estimates to incubation duration effects.

Data analysis and computation o f integral primary production

Use of different computational approaches can contrib­ute to variation in both IS and SIS measurements (e.g., Richardson, 1991). The simplest computational method is to integrate discrete estimates over the depth of the euphotic zone. However, simple integration is sensitive to the number and position of samples in a vertically heterogeneous water column. Furthermore, results may be strongly influenced by measurement errors in portions of the water column where production is high. As an alternative to simple integration, various numeri­cal approaches have been used (e.g., Table 4 and cf. Balch et al., 1992). Comparisons between P-I modeled and IS incubation estimates of primary production re­veal differences similar in magnitude to those observed between SIS and IS incubation estimates (cf. Table 4 and Table 1). However, the predictive capabilities of P-I methods allow for increased temporal and spatial resol­ution. “Composite” P-I parameters may be derived from water-column measurements of available irradi­ance and primary production determined by IS or SIS techniques (see under “Variations in total irradiance”). This approach reduces the possibility of bias due to a single measurement and allows for adjustment for errors in simulating in situ irradiance (e.g., Lohrenz el al., 1992).

Conclusions

R ecom m endations for SIS incubations

(1) Irradiance (incubator and in situ) should be measured precisely and temporal variations considered (even for IS incubations, irradiance conditions may be altered from those experienced by unconfined popu­lations). Determination of P-I relationships in the con­text of the physical regime may help in determining sensitivity of rates to variations in irradiance and allow for corrections to errors in simulating in situ irradiance. Simulation of spectral quality is recommended, particu­larly for accuracy in determination of rates at depth.

(2) Temperatures should be precisely controlled, par­ticularly where differences between ambient and opti­mal temperatures can be expected. Simulation of in situ temperatures should be less difficult than irradiance, except where temperature structure is spatially and/

166 S. E. Lohrenz IC E S m ar . Sei. S y m p . . 197 ( 1993)

Tabic 4. Absolute differences (expressed as a percentage of the IS value) between IS measured primary production and estimates using light gradient incubations and/or P-l models. All measurements were determined by the l4C method unless otherwise indicated. Means are given, along with standard deviations (s.d.) and ranges, to provide an indication of dispersion (n = number of observations).

Discrete depths Water column

Reference Mean (s.d.) Range n Mean (s.d.) Range n

Ryther and Yentsch - _ _ 35 (36) 5-140 11(1957) ( 0 2 method) Ryther and Yentsch _ _ _ 110(24) 0-550 17(1958a) Tailing (1966) 28 (28) 6-100 8 _ _ _

(O? method) F e e (1973bü) _ _ _ 12(9) 2-26 6Gargas and Nielsen (1976) - - - 19(14) 2-40 6Jitts el al. (1976) 60 (68) 0-280 32 - - -Fee (1978b) 25 (44) 0-250 50 - - -Colijn etal. (1983) 29 (29) 0-180 79 26(21) 8-56 9Flik and deKloet 48 (30) 4-120 24 - - -(1983) (in Colijn etal. (1983))Cote and Piatt (1984) 34 (43) 0-280 68Bower et al. (1987) - - - 36(16) 8-73 20Fahnenstiel and Scavia - - - 44 (23) 18-86 9(1987)Prezelin and Glover _ _ 22(17) 1—40 4(1991)Lohrenz etal. (1992) - - - 10(6) 4-15 3

“Statistics based on digitization of plotted data contained in reference. C om parison based on corrected model values as described by the author.

or temporally complex (e.g., diel stratification). Rela­tively small errors in incubation temperature may be correctable through evaluation of photosynthesis- temperature relationships.

(3) Sample handling/processing and computational methods should be standardized within a study and the sensitivity of estimates to treatment effects should be assessed. The SIS method provides flexibility for eva­luating the sensitivity of in vitro rate estimates to vari­ations in sample treatment and environmental con­ditions.

Precision and accuracy o f the SIS method

There have been few studies evaluating the precision of primary production estimates. Richardson (1991) reported coefficients of variation ranging from 25 to 40% for estimates by independent laboratories using their own incubation methods on a pooled water sample. Doty et al. (1965) determined a mean coefficient of variation of 27% for estimates of water column- integrated primary production by different laboratories using SIS methods. This level of variation is comparable to the magnitude of differences observed between SIS and IS incubation estimates (Table 1). Therefore, the

reported differences between SIS and IS methods (Table 1) may be explained largely on the basis of the precision of the methods. The individual investigator must decide if this level of precision is adequate to resolve differences in rate processes within a given study. It is important to recognize that the differences in Table 1 would not include errors due to aliasing that may occur with discrete sampling of spatially and temporally heterogeneous patterns (cf. Platt, this volume). The capability of the SIS method for more extended spatial coverage and temporal resolution than conventional IS measurements can be useful in reducing aliasing errors.

The evaluation of the accuracy of the SIS method is difficult because it requires comparison to some absolute reference of in situ primary production, and error-free techniques to assess such a reference value do not exist. The issue of accuracy of primary production methods has been reviewed by several authors (Eppley, 1980; Harris, 1980, 1984; Peterson, 1980; Smith, 1982; Platter al., 1989). At best, independent methods for estimating primary production can be compared to determine if there is systematic bias. A comparison between SIS and IS incubation estimates of water column-integrated pri­mary production from various studies revealed no con­sistent bias (Fig. 6A). In contrast, P-I models most often yielded estimates of primary production that were

IC E S m ar . Sei. S y m p . . 197 (1993) Primary production by the simulated in situ method 167

100

80

60

40

20

VII-

0 10 20 30 40 50o

10080

60

40

20

V I I I- 2 0- 4 0

- 6 0

- 8 0

- 1 0 0

I I I

IV

0 10 20 30 40 50 60 70 80

O b s e r v a t i o n

Figure 6. Differences between IS and (A) SIS or (B) P-l modeled estimates of water column-integrated daily primary production for selected studies. Differences are expressed as a percentage of the IS value. Roman numerals correspond to data from the following citations: (A) I, Jitts (1963); 11, Head (1976); III, Brown (1982) ( 0 2 method); IV, Brown (1982) ( l4C method); V, Chiswell etal. (1990); VI, Winn etal. (1992); VII, Lohrenz et al. (1992). (B) I, Ryther and Yentsch (1957); II, Ryther and Yentsch (1958); III, Fee (1973b); IV, Gargas and Nielsen (1976); V. Colijn etal. (1983); VI, Bower et al. (1987); VII, Fahnenstiel and Scavia (1987); VIII, Prezelin and Glover (1991); IX. Lohrenz et al. (1992).

higher than those determined by IS incubations (Fig. 6B). Explanations include the fact that IS (and SIS) incubations were generally longer than those used to determine P-I parameters and, therefore, more suscep­tible to sample confinement effects.

This leads to the question of the accuracy of in vitro

(both SIS and IS) methods. Evidence from a variety of studies suggests that in vitro estimates, including both 14C and oxygen methods, have not vastly underesti­mated in situ primary production. This view has been supported by studies comparing in vitro measurements with observed changes in algal cultures (Eppley and Sloan, 1965; Ryther and Menzel, 1965; Peterson, 1978; Lohrenz and Taylor, 1987), ecosystem enclosures (McAllister et a l.. 1961; Antia et a l ., 1963; Davies and

Williams, 1984), and unconfined waters (Ryther et a l.,

1971; Bower e ta l . , 1987; Fahnenstiel and Carrick, 1988; Williams and Purdie, 1991; Daneri, in press). While there do not appear to be large discrepancies, there is a trend of slight negative bias (ca. 30-60%) in the in vitro

measurements compared with determinations in uncon­fined waters (Bower et a l., 1987; Fahnenstiel and Car­rick, 1988; Williams and Purdie, 1991). Conclusions about the significance of a negative bias of 30-60% must remain tentative, since this is comparable to the pre­cision of the in vitro measurements. Further investi­gation of the precision and accuracy of both in vitro and in situ methods is needed.

SIS measurements o f microplankton rate processes

The question of the relationship of photosynthetic pro­duction to carbon cycling in natural populations remains elusive. Attempts to use in vitro rate estimates in con­structing carbon budgets for natural populations have been complicated by the fact that net in situ changes may be small relative to production or loss rates (e.g., Jassby and Goldman, 1974; Reynolds et a l ., 1985). A clearer understanding of the role of the non-algal microbial community in carbon cycling is required to resolve these issues. To address these issues, some research requires the use of SIS methods. Consequently, as long as in vitro

techniques are used for studying microplankton pro­cesses, the SIS technique will continue to be an import­ant tool.

Acknowledgements

I gratefully acknowledge the comments of G. L. Fahn­enstiel and G. A. Knauer on earlier versions of this paper. R. Barber and W. Balch graciously provided data. This research was supported by the office of Naval Research (N00014-88-K-0155), the National Science Foundation (OCE-8801089) through a subcontract with the Bermuda Biological Station for Research, Inc., and by the Coastal Ocean Program Office of the National Oceanic and Atmospheric Administration (NA90AA- D-SG688, Project No. R/LR-25 to Mississippi-Alabama Sea Grant). The Naval Research Laboratory at Stennis Space Center, Mississippi provided facilities support.

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