Figure 2. Phase-contrast (A) and corresponding fluorescence (B) micrographs of Astrammina pseudopodia that were first labeled with fluorescein-

tagged goat IgG, and then washed free of unbound IgG and fixed. (Arrows denote sites of newly secreted adhesive. Bar = 20 micrometers.) (C)High-voltage electron micrograph of a fixed and critical-point dried Astrammina pseudopod, showing the extrusion of fibrillar "adhesive matrix"material (fm) from a secretory vesicle (sv). (Bar = 0.25 micrometers.)

with the elastic bioadhesive fibrils representing continuoustension elements and the mineral grains serving as discontin-uous, noncompressible struts (Fuller 1961).

The 1994 field program participants included Samuel S.Bowser(7 Octoberto 12December1994),DouglasCoons (7October to 17 December 1994), Lawrence A. Haywood (7October to 16December 1994),RoyK.Kinoshita (7 October to16 December 1994), Neal W. Pollock (1 September to 9December 1994), and Robert W. Sanders (7 October to 12December 1994). We are indebted to the Antarctic SupportAssociates personnel, who helped establish our camp and dive

'tes at Explorers Cove, as well as the pilots and crewofVXE-6..this work was supported by National Science Foundationgrant OPP 92-20146;John H. Hayden was supported in part byan ROAsupplement to OPP 92-20146.

References

Bowser, 5.5., S.P.Alexander, w.L. Stockton, and T.E. DeLaca. 1992.Extracellular matrix augments mechanical properties of

pseudopodia in the carnivorous foraminiferan Astrammina rara:Role in prey capture. Journal of Eukaryotic Microbiology, 39,724-732.

Bowser, 5.5., and I.M. Bernhard. 1993. Structure, bioadhesive distribu-

tion and elastic properties of the agglutinated test of Astrammtnarara (Protozoa, Foraminiferida). Journal of EukaryoticMicrobiology, 40,121-131.

Bowser, 5.5., and T.E. DeLaca. 1985. Rapid intracellular motility anddynamic membrane events in an antarctic foraminifer. Cell BiologyInternational Reports, 9, 901-910.

DeLaca, T.E. 1986. The morphology and ecology of Astrammina rara.Journal of Foraminiferal Research, 16,216-223.

DeLaca, T.E., D.M. Karl, and I.H. Lipps. 1981. Direct use of dissolvedorganic carbon by agglutinated benthic foraminifera. Nature, 289,287-289.

DeLaca, T.E.,I.H. Lipps, and RR Hessler. 1980.The morphology andecology of a new large agglutinated antarctic foraminifer(Textulariina: Notodendrodiidae Nov.). Journal of the LinneanSociety London, Zoology. 69, 205-224.

Fuller, RB. 1961. Tensegrity. Portfolio Artnews Annual, 4, 112-127.Gooday, A.I., LA. Levin, P.Linke, and T. Heeger. 1992. The role of ben-

thic foraminifera in deep-sea food webs and carbon cycling. In G.T.Rowe and V. Pariente (Eds.). Deep-sea food chains and the globalcarbon cycle. Netherlands: Kluwer Academic Publishers.

Statistical analyses of environmental predictors forphytoplankton photosynthetic parameters and productivity

in an antarctic time series databaseMARKA. MOUNE and BARBARAB. PREZEUN, Department of Biological Sciences, University of California, Santa Barbara.

Santa Barbara, California 93106

OSCARSCHOFIELD,Institute of Marine and Coastal Studies, Rutgers University, New Brunswick, New jersey 08903-0231

Prom 3 December 1991 to 27 February 1992, 249 discretewater samples were collected at the Palmer Long-Term

~~ological Research (LTER)program's station B (Waters andllith 1992). For each discrete sample, physical, biological,

and chemical measurements were made including incidentirradiance, in situ irradiance, temperature, density, and

nitrate, phosphate, silicate, particulate organic carbon, andparticulate organic nitrogen levels. In addition, 15 distinctalgal pigments were determined by high-performance liquidchromatography (HPLC),and the algal pigments were classi-fied into groups based on their functionality (Le.,photoprotec-tive carotenoids and photosynthetic carotenoids). In addition,

ANTARCTIC JOURNAL - REVIEW19951a"

"

photosynthesis-irradiance (P-I) relationships were deter-mined for each sample from which the photosynthetic para-meters Pmax(photosynthetic capacity) and alpha (the light-limited photosynthetic efficiency) were derived. Furtherdetails of sample collection and analyses are described else-where (Prezelin et al. 1992;Moline et al. in press).

The dynamics of the physical, chemical, and biologicalcomponents of the system were higWy coupled at station Bover the 1991-1992 season (Moline et al. in press). In earlyDecember 1991, freshwater input from melting fast ice andnearby coastal glaciers and low wind speeds permitted thewater column to stratify. This enhanced stability allowed alarge bloom [approximately 30 milligrams of cWorophyll-a percubic meter (mg cW-a m3)] to develop, a size that depletedmacronutrients to detection levels (Moline and Prezelin 1994).This bloom accounted for 75percent of the integrated produc-tivity over the season (Moline and Prezelin in preparation). Aperiod of high wind advected the water mass out of the area,and for the following 2 months, the water column was wellmixed. The phytoplankton community varied over the seasonwith diatoms, chrysophytes, cryptophytes, or prymnesio-phytes dominating at any given time. This study examines thepredictive capability of the environmental variables measuredover this dynamic period at station B in determining the tem-poral variability in the P-I parameters.

Stepwise forward and backward multiple linear regressionanalyses were used with the above variables to generate algo-rithms to predict the P-I parameters. The statistical(enter/remove) constraint for the analyses was p<0.015.Thisapproach was similar to that used by Schofield et al. (1993)within the Southern California Bight. Once significant vari-ables and their coefficients were determined, the algorithmswere verified using nonparametric randomization regressiontechniques. Results of these analyses are presented in thetable. A majority of the variance in both alpha and Pmaxwereexplained by density and the concentration phosphate and

Results of multiple linear regression analyses fromPalmer LTER station B (p-value for regression is<0.00001in all cases)

Photosynthetic Significant R2Category of parameter independentvariables variables

aphotoprotective carotenoidsbPhotosynthetic carotenoids

A. 75 T Physical+ Nutrients ,Pmax-/("1.(N03-n 1:1 ,,'

AJpha-/{"1.(po.3-n """"'". ,,''-

1

. .. ~"""'015. "~ . ~'.- .--g0

B. e;.75

55

35

R2 = 0.650

..Physical + Nutrients + Chill ., ,

Pmax-/("1.IN03-I.Chlal ,,'

Alpha-/1"1.(PO.3-].Chlal """"

, , ,... '

'- ,-,.'.'

-.i:=55'"E

035OJ

.§.~15'S:

g 0 L;

C. ~ 75 I Physical + Nutrients + HPLCa. Pmax - 11"1.[N03-I.PPCI

Alpha- Ilat. IPO.3-1.PSCI

"

R2 = 0.899

.,'

55 ..35 .

R2 = 0.900

0 ~0 15 30 45 60

Productivity (mg C m-3 h-l) - (Measured)

Figure 1. Comparison of in situ primary productivity calculated frompredicted Pmaxand alpha and measured Pmaxand alpha. Variablesused to predict Pmaxand alpha are included in the figure. R2values areindicators of how well the predictor variables ~ere for Pmaxand alpha.The closeness of the regression line (solid) to the 1:1 line (dashed) indi-cates how well the regression calculated the variable coefficients. (mgC m-3 h-1 denotes milligrams of carbon per cubic meter per hour.)

75

nitrate, respectively.The relationship improved with the addi-tion of the biomass indicator, cWorophyll-a. Fullpigmentationinformation provided only a slightly stronger relationship,suggesting that over the season, despite large variations innutrient concentration, water column stability, and commu-nity composition, the capacity of light harvesting by phyto-plankton changed little. The Pmaxand alpha values predictedfrom these regressions were then used to estimate the in situproductivity (Ps) using the following relationship (Platt andGallegos 1980),

P(z, t) = P max' (QpPAR(Z, t) )Ik(z, t)

where Ik is equal to Pmaxialpha and Qpar is the in situ light field[400-700 nanometers (nm)]. Once the in situ productivity

based on the predicted Pmax and alpha variables had been esti-mated, the productivity estimated from the measuredP-I para-meters was determined using the same approach. The pre-dicted productivity and measured productivity from station Bwere then compared (figure 1). Sixty-fivepercent of the vari-ance in productivity could be explained by the P-I parameterspredicted from density and the nutrient concentrations (figure1A). The relationship, however, was biased toward the higher

ANTARCTIC JOURNAL - REVIEW1995.."'~

Physical + Pmax crt, NO3 . 0.65nutrient Alpha crt, PO4 0.59

Physical + Pmax crt, NO3' chl-a 0.84nutrient + Alpha crt, PO4' chl-a 0.83chl-a

Physical + Pmax crt, NO3,PPca 0.86nutrient + Alpha crt, PO4' PSCb 0.85HPLC

B, is shown in figure 2B.Eventhough the regressions overes-timated productivity by ap-proximately 10 percent overthe season, the main featuresof production could be de-tected and the relationship wassignificant.

Results from this studyshow that for 1991-1992, thephysical- and nutrient-basedregression model describedthe majority of the spring/summer variation in produc-tivity; however, the predictivelinkages were strongly depen-dent on the occurrence of abloom in stratified, nutrient-depleted waters. For periods ofwater column instability, thephysical- and nutrient-basedregression model was not ade-quate to predict variability inprimary productivity, unlessknowledge of phytoplanktonpigmentation was incorpo-rated into the regression.Lastly, primary productivity atstation E could be significantlypredicted using the outcome ofthe regression analyses fromstation B. This suggests thedynamics of these antarcticcoastal stations are closelycoupled and exhibit similarprocesses controlling primaryproduction.

Special thanks go to K.Seydell,K.Scheppe, P.Handley,T. Newberger, and B. Frankfor assistance in the field.

This work was supported byNational Science Foundation

grant OPP 90-11927 awarded to B.B.Prezelin. This is PalmerLTERcontribution 90.

40 50

1992

10

9

8

7

6

5

4

40 50

1992

Figure 2. Contours of (A) the 1991-1992 in situ productivity measured at station E and (8) the productivitypredicted from the regressions derived for station B in figure 1C. Closed circles indicate the discrete sam-pling points. (m denotes meter.)

productivity values; it predicted the bloom accurately (75per-cent of the seasonal productivity) but performed poorly whenpredicting the periods of low productivity. As before, whenchlorophyll-awas added as a predictorvariablefor Pmaxandalpha, the relationship greatly improved (figure IB). The addi-tion of algal pigmentation in the regressions shifted the rela-tionship to nearly 1:1 and was a better predictor than chloro-phyll-aforthe periods oflowproductivity (figure Ie).

The regressions used in figure 1C, derived from station B,were then used to predict in situ productivity at station E, 3kilometers from station Bwithin the same LTERnearshore grid(Waters and Smith 1992).The measured. production is shownwith depth over the 3-month sampling period in figure 2A Thecalculated production, based on the regressions from station

References

Moline, M.A., and B.B. Prezelin. 1994. Palmer ITER: Impact of a largediatom bloom on macronutrient distribution in Arthur Harborduring austral summer 1991-1992. Antarctic Journal of the U.S.,29(5),217-219.

Moline, M.A.,and B.B.Prezelin. In preparation. High resolution timeseries data for in situ carbon fixation at the Palmer ITERsite and itsimplications for modeling primary production in the southernocean. PolarBiology.

Moline, M.A., RB. Prezelin, O. Schofield, and RC. Smith. In press.Temporal dynamics of coastal antarctic phytoplankton:Environmental driving forces through a 1991-1992 summer

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164

A. 0

20-E

-40..c::-c.Q) 60Q

80

100335 345 355 365 10 20 30

1991 Julian Date

B0

20-E

-40..c::-c.Q) 60Q

80

100335 345 355 365 10 20 30

1991 Julian Date

diatom bloom on the nutrient and light regime. In B. Battaglia, J.Valencia, and D.w.H. Walton (Eds.), Antarctic Communities.Cambridge: Cambridge University Press.

Platt, T., and c.L. Gallegos. 1980. Modeling primary productivity. InP.G. Alkowski (Ed.), Primary production in the sea. New York:Plenum Press.

Prezelin, B.B.,M.A.Moline, K. Seydel, and K. Scheppe. 1992. PalmerLTER:Temporal variability in HPLC pigmentation and inorganicnutrient distribution in surface waters adjacent to Palmer Station,

December 1991-February 1992.Antarcticjournal of the U.S.,27(5),245-248.

Schofield, 0., B.B.Prezelin, RR Bidigare,and RC. Smith. 1993.In situphotosynthetic quantum yield. Correspondence to hydrographicand optical variabilitywithin the Southern California Bight. MarineEcologyProgressSeries,93, 25-37.

Waters, K,J.,and RC. Smith. 1992.Palmer LTER:A sampling grid forthe Palmer LTERprogram. Antarctic journal of the U.S., 27(5),236-238.

Excluding connectivity leads to inaccurate estimates of thePSII absorption cross section of an antarctic macrophyte

using "pump and probe" fluorometryBERNDM.A. KROON,Amsterdam Research Institute for Substances in Ecosystems (ARISE), University of Amsterdam,

The Netherlands

BARBARAB. PREzEUN,Department of Biological Sciences, University of California at Santa Barbara,SantaBarbara, California 93106

,-.

During Icecolors '93, we used the Pulse-Amplitude-Modulated (PAM)fluorescence technique to locate the

target site at photosystem II (PSII)for solar ultraviolet-B (UV-B) radiation in antarctic ice-algae (Kroon, Schofield, andPrezelin in preparation). Furthermore, we began to explorehow PAMand its ability to simulate "pump and probe" fluores-cence (PPF)might be applied to derive photobiologically rele-vant parameters in other groups of antarctic algae. Here, wereport an experiment designed to evaluate the accuracy toderive the absorption cross section of a macrophyte brownalga isolated from Arthur Harbor, Antarctica, using the PAMtosimulate the PPE

Theoretically, photosynthetic rates are determined by justthree parameters: incident light, light absorption, and thephotochemical quantum yield. The latter two are biologicalparameters and nonlinearly related to many ecosystem vari-ables. The recent development of PAM and PPF techniquesallows us to quantitatively determine light absorption andquantum yield, albeit by different principles and with differentunderlying assumptions to interpret data. The critical differ-ence between PPF and PAM lies in the derivation of opticalcross sections for photosynthesis and, thus, the bio-opticalderived estimate of in situ photosynthesis. The PPF analysisassumes that variable fluorescence is a direct measure for theamount of closed PSIIreaction centers, and consequently, theabsorption cross section has a fixed value and is independenton the amount of closed PSII. Under this assumption, thecross section can be derived by a target function describing theincrease in variable fluorescence with increasing light energy.If,however, antennae pigments of different PSII'sshare energy(connectivity; see Hipkins 1978), it is then possible forabsorbed photons to be trapped by an open reaction centerthat belongs to a PSII that is different from the PSII whoseantennae absorbed the photons. Consequently, fluorescencevaries less than linearly with the amount of closed PSII. The

absorption cross section will be variable, depending on theamount of closed PSII. The PAManalysis allows connectivity,and its quantification is possible. Hence, PAM-derived crosssections will differ from those derived by the PPF method. Weadapted the PAMmethod to simulate the PPF method accu-rately and assessed the impact of connectivity in the dataanalyses to quantify the absorption cross section in a low-lightadapted antarctic macrophyte. '

We collected an antarctic macrophyte from 40 metersdepth near Arthur Harbor, Antarctica, on 1 September 1993,maintaining it in the laboratory under low light and incubat-ing it for 1.5 hours in darkness prior to the fluorescence mea-surements. The average of nine fluorescence decay curves at20-second (s) intervals were monitored at 25-microsecond res-olution during 20 milliseconds after a single turnover flash of5-microsecond duration and of variable intensity. Decay pro-files were deconvoluted into fast and slow kinetic compo-nents. Complete methodology for simulating PPF with a PAMfluorometer is presented in Kroon (1994).The fast componentamplitude varied nonlinearly with increasing flash intensity asa result ofPSII closure, but the fraction ofPSlIs with fast kinet-ics was constant (figure 1). It was impossible to fit the relationbetween variable fluorescence and flash intensity with a targetfunction having a single value for the cross section (figure 2),in contrast to common PPF analysis procedures (Kolber andFalkowski 1993). Our data revealed the cross section of thelow-light adapted kelp varied 27-fold from 0.0003, whenalmost all PSIIwere open, to 0.008,when all PSII'swere closed(figure 2).Wewere not surprised to observe that a dependencyof the cross section on the fraction of open PSII existed inantarctic kelp, though the extent of the variation was dramaticwith significant implications for future fluorescence probingof aquatic photosynthesis in polar environments. The excitedstate of an absorbed photon (exciton) has a lifetime of severalpicoseconds. Assume the exciton can visit N pigment mole-

",,",

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