Spatial and temporal variation in phytoplankton community structure in a southeastern U.S. reservoir determined by HPLC and light microscopy

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Spatial and temporal variation in phytoplankton community structure in a southeastern U.S. reservoir determined by HPLC and light microscopy
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  PRIMARY RESEARCH PAPER Spatial and temporal variation in phytoplankton communitystructure in a southeastern U.S. reservoir determinedby HPLC and light microscopy Engela Sthapit   Clifford A. Ochs   Paul V. Zimba Received: 20 May 2007/Revised: 24 October 2007/Accepted: 6 November 2007/Published online: 7 December 2007   Springer Science+Business Media B.V. 2007 Abstract  Spatial and temporal variation in phyto-plankton community structure within a large flood-control reservoir (Sardis Reservoir, MS, USA) wasinvestigated in relation to variation in physicochem-ical properties, location within the reservoir, hydraulicresidence time (HRT), nutrient concentrations, tem-perature, and light conditions over a 14-month period.During periods of short HRT, phytoplankton commu-nities throughout the reservoir were homogeneous inbiomass, composition, and production. With a gradualincrease in HRT from spring to summer, spatiallyheterogeneous phytoplankton communities developedalong the longitudinal axis of the reservoir. Duringthis period of longer HRT, diatoms and chlorophyteswere a larger proportion of total phytoplanktonbiomass at shallow and more turbid locations nearthe head of the reservoir, whereas cyanobacteria werea larger proportion of the community at deeper andless turbid locations closer to the outflow. Seasonalsuccession of the phytoplankton community wasrepresented by high abundance of diatoms in spring,increasing biomass of cyanobacteria through summer,and a secondary bloom of diatoms in fall. Species of  Cyclotella ,  Asterionella ,  Nitzschia , and  Ankistrodes-mus  were among the first colonizers in the earlygrowing season, closely followed by  Aulacoseira ,whereas species of   Staurastrum  and  Tetraedron appeared later in the spring. Species of   Synedra , Crucigenia ,  Selenastrum ,  Scenedesmus , and  Mer-ismopedia  occurred throughout the sampling period.As the diatoms started to decrease during mid-spring,cryptophytes increased, prior to dominance of speciesof   Pseudanabaena  in summer. Reservoir managementof HRT, in combination with spatial variation inreservoir morphology and seasonal variation in tem-perature and riverine nutrient inputs, createsseasonally variable yet distinct spatial patterns inphytoplankton community biomass, composition, andproduction. Keywords  Phytoplankton    Plankton   Reservoir    Lakes    HPLC Introduction Reservoirs combine features of rivers and lakes and,in fact, have been described as ‘‘river-lake hybrids’’ Handling editor: L. Naselli-FloresE. Sthapit ( & )    C. A. OchsDepartment of Biology, University of Mississippi,214 Shoemaker Hall, University, MS 38677, USAe-mail: engelasthapit@gmail.comC. A. Ochse-mail: byochs@olemiss.eduP. V. ZimbaCatfish Genetics Research Unit, Agricultural ResearchService, United States Department of Agriculture,141 Experimental Station Road, Stoneville, MS 38776,USAe-mail: paul.zimba@ars.usda.gov  1 3 Hydrobiologia (2008) 600:215–228DOI 10.1007/s10750-007-9234-7  (Kimmel et al., 1990). The degree to which areservoir resembles a river or a lake depends onhow and for what purpose the reservoir is managed,the time of year, reservoir morphology, and locationwithin the reservoir. Reservoirs managed for drinkingwater supply or for hydroelectric power, where waterinputs and removal are in balance, may vary little ona seasonal basis in surface area, volume, depth, andhydraulic residence time (HRT), while still experi-encing horizontal advective transport (Ford, 1990). Incontrast, reservoirs managed for flood-control canvary dramatically on a seasonal basis in rates of waterremoval, surface area, volume, depth, and HRT. Suchreservoirs are more like rivers at times of the yearwhen water is rapidly being withdrawn, but becomemore hydrologically stable and lake-like at times of the year when the reservoir is filled (Ochs & Rhew,1997).At times of the year when HRT is at a maximum,reservoirs typically develop a longitudinal gradient inriverine to lacustrine conditions, and an associatedgradient in physical and chemical properties (Kimmelet al., 1990). In general, as one moves from the moreriverine upper to the more lacustrine lower portion of areservoir, it becomes deeper and broader, and the waterbecomes more clear and nutrient depleted due toreduction in current, development of thermal stratifi-cation, and sinking of suspended sediments (Bettoliet al., 1985; Ramberg, 1987; Soballe & Kimmel, 1987; Bailey-Watts et al., 1990; Kimmel et al., 1990; Zohary et al., 1996). Adding to reservoir spatial complexity,along the length of the reservoir, tributaries of differingwater quality can lead to localized conditions distinctfrom the main channel, and further contribute to spatialheterogeneity (Kimmel et al., 1990).Where there is a gradient in physicochemicalconditions, a gradient in phytoplankton communitycharacteristics can be expected. For example, duringsummer, with a decline in nutrient availability fromthe more turbulent, riverine to more stratified,lacustrine regions of a reservoir, there is commonlya decline in phytoplankton biomass and biomassproduction (McCullough, 1978; Knowlton & Jones,1995; Ochs & Rhew, 1997; Schallenberg & Burns, 1997; Buckaveckas & Crain, 2002). Compared to natural lakes, there has been muchless research investigating the extent to which areservoir phytoplankton community varies, eitherspatially or with time, in taxonomic composition(Nogueira, 2000). The purpose of this study was todetermine the degree of spatial, as well as seasonalvariation in phytoplankton taxonomic composition,biomass, and productivity within a large flood-controlreservoir, Sardis Reservoir, Mississippi. We hypoth-esized that spatial variation in phytoplanktoncommunity composition occurs when there is agradient in riverine to lacustrine conditions and inthe associated physicochemical conditions within areservoir. Samples were taken over the course of 14 months at six spatially distributed stations alongthe longitudinal axis and major tributary embaymentareas of Sardis Reservoir. Phytoplankton communitycharacteristics were examined in relation to seasonaland spatial variation in nutrient availability, temper-ature, light conditions, and hydraulic retention time.The patterns we detected in phytoplankton commu-nity properties may be applicable to other flood-control reservoirs in the United States, as mostreservoirs are located at similar latitudes, between33  N and 42  N, where large natural lakes areuncommon (Kalff, 2002). Methods Study siteSardis Reservoir was constructed after the greatMississippi River flood of 1927 with the purpose of controlling local flooding and movement of waterinto the Mississippi River. It was constructed in 1940by damming the Little Tallahatchie River whichdrains the Little Tallahatchie watershed (LTW), anarea of about 3,900 km 2 . (Fig. 1).The LTW consists of about 45% forest, 34%cropland or pasture, 11% urban/residential or miscel-laneous uses, and 10% open water/reservoir (USDA-NRCS, 2007). There are three major sub-watershedsdrained by creeks entering Sardis Reservoir from thesoutheast. The Clear Creek sub-watershed is 122 km 2 .inareaandconsistsof60%forest,25%agriculture,3%urban/residential, and 12% open water. The TobyTubby sub-watershed is 150 km 2 . in area and consistsof45%forest,10%agriculture,35%urban/residential,and 10% open water. The Hurricane Creek sub-watershed is 85 km 2 . in area and consists of 50%forest, 36% agriculture, 8% urban/residential, and 6%open water (unpublished aerial photos taken in 2005, 216 Hydrobiologia (2008) 600:215–228  1 3  personal communication with H. Patterson, USDA,Oxford, MS).Flood control operation and seasonally variableamounts of runoff from tributary streams result inlarge seasonal changes in water level and flow rates(Aumen et al., 1992). By releasing water in latesummer and through the winter, the reservoir isdrawn down to a minimum surface area of  \ 50 km 2 in preparation for the annual rise of the MississippiRiver with northern snow melt and rain during spring.Starting in mid-spring, water release is minimizedand the reservoir is allowed to fill to its maximumsurface area of over 120 km 2 . (Ochs & Rhew, 1997).SamplingThe reservoir was sampled from March 2004 to April2005. There were a total of 15 sampling dates, withone to two site visits per month. Samples were nottaken between December and February, when waterretention time is short ( \ 100 days), and spatialvariation in phytoplankton community propertieswas not detected previously (Ochs & Rhew, 1997).Samples were collected at three stations (stations 1, 3,6) along the longitudinal axis of the reservoir andthree stations (stations 2, 4, 5) within major tributary(Clear Creek, Toby Tubby Creek, and HurricaneCreek) embayments (Fig. 1). Station 1 is the down-lake region, nearest to the dam and outflow, station 6is the up-lake region, near the mouth of the river, andstation 3 is the mid-lake region, at the middle of thereservoir. Samples were not collected at station 5 inSeptember–November, 2004 as it was too shallowand thus inaccessible from the reservoir.At all stations, water samples were collected at0.5-m depth in the mixed layer as three replicates in 2-lHDPE Nalgene bottles. Although depths of mixingvaried between sampling locations (see ‘‘Results’’), the0.5-mdepthwasconsideredrepresentativeofthemixedlayer for each sampling site. Samples were kept cooland darkuntil processing, within2–4 h after collection.Physical and chemical propertiesTemperature and oxygen profiles were measured inthe field using an YSI Model 57 oxygen meter. Lightextinction profiles were obtained using a Licor LI-1000 radiometer with a spherical quantum sensor anddeck-mounted reference cell. Water transparency wasmeasured as turbidity in the laboratory with a HachModel 2100A turbidimeter. Mixing depth was esti-mated by profiles of oxygen and temperature. Fig. 1  Sardis Reservoir inthe Little Tallahatchiewatershed, northeasternMississippi. The lake areaillustrates the conservationpool water level (71.9 mabove mean sea level).Numbers in the mapindicate six samplinglocations :  Station 1: Down-lake; Station 2: Clear Creek embayment; Station 3: Mid-lake; Station 4: Toby TubbyCreek embayment; Station5: Hurricane Creek embayment; Station 6:Up-lakeHydrobiologia (2008) 600:215–228 217  1 3  Total dissolved nitrogen (TDN) and total dissolvedphosphorus (TDP) were measured with an Astoriaauto-analyzer in water filtered through Whatman GF/ F filters. The filtrates were digested with alkalinepersulfate prior to analysis (Charles & Kryskalla,2003).Hydraulic water residence time, in days, of thereservoir was calculated by dividing daily measure-ments of volume (in km 3 ) by daily measurements of discharge (in km 3 day - 1 ). Lake volume was obtainedfrom a hypsographic curve for Sardis Reservoir. Lakeelevation and discharge measurements are made dailyat the dam by the U.S. Army Corps of Engineers,Vicksburg District (USACE, 2005).The saturating intensity of light for phytoplanktonphotosynthesis in Sardis Reservoir was experimen-tally determined, using photosynthesis-irradiancecurves, as about 400  l mol PAR m - 2 s - 1 (data notshown). The depth to which this saturating light leveloccurs was determined for stations 1 and 6 from lightextinction coefficients determined on all samplingdates. For these calculations, we used 1,200  l molPAR m - 2 s - 1 (I o  in the equation for light extinction,Kalff, 2002) as the maximum light intensity enteringthe water on a typical sunny day.Phytoplankton primary productionLight-saturated volumetric primary production wasmeasured in the laboratory for all sites at all datesby the fixation rate of radio-labeled carbon dioxide.About 24 ml of water samples were inoculated with25  l l of NaH 14 CO 3  (20  l Ci ml - 1 ) in closed glassserum vials and incubated for 2–3 h at in situtemperature and 550–560  l mol PAR m - 2 s - 1 irra-diance. Following incubation, the samples werefiltered through GN-6 0.45- l m Metricel membranefilters. The filters were placed in 7-ml scintillationvials and acidified with 0.1 ml of 1N HCl to removeunincorporated dissolved inorganic carbon (DIC).After 24 h, ethyl glycol monoethyl ether was addedto dissolve the filters, a few hours after whichscintillation fluid was added and radioactivitymeasured in a Beckman scintillation counter. TotalDIC assimilated per time was calculated from theratio of radio-labeled carbon assimilated to avail-able DIC (Ochs & Rhew, 1997; Wetzel & Likens,2000).Phytoplankton biomass and compositionby pigment analysisHigh performance liquid chromatography (HPLC)was used to identify the presence and relativeimportance of major taxonomic groups of phyto-plankton from their diagnostic pigments (Jeffreyet al., 1997; Pickney et al., 2001; Pearl et al., 2003). We measured chlorophyll  a  (Chl  a ) biomassand five division-level pigments [chlorophyll  b  (Chl  b ),fucoxanthin, zeaxanthin, peridinin, and alloxan-thin]. Chl  b  was used as a diagnostic pigmentfor estimation of chlorophyte biomass, fucoxanthinfor bacillariophyte (diatom) biomass, zeaxanthin forcyanobacterial biomass, peridinin for dinoflagellatebiomass, and alloxanthin for cryptophyte biomass(Jeffrey et al., 1997). Although zeaxanthin doesoccur at low concentrations in some chlorophytes,the proportion of zeaxanthin to Chl  a  is higher incyanobacteria (Schlu¨ter et al., 2006). Although Chl  b is also a major pigment in euglenoids, they were notabundant in this reservoir.Phytoplankton pigment extraction and determi-nation were conducted using a slight modification of the method of Jeffrey et al. (1997). Water samples(200–400 ml) were filtered through Whatman GF/Ffilters under low vacuum pressure ( \ 380 mm Hg),and filters stored in an ultra-cold freezer ( - 70  C)until pigment extraction. For extraction, filterswere cut into small pieces and soaked in 90%acetone for 2 h at 4  C. The filter-acetone mixturewas sonicated under low light and in an ice-bathfor 30–60 s using a Fisher Sonic 60 Dismembrator.The filter residue with acetone was centrifuged at2,000 rpm for 2 min, and the supernatant filteredthrough a 0.4- l m Millex PTFE filter prior to pigmentseparation by HPLC.The HPLC system consisted of a photodiode arraydetector (Dionex PDA 100), pump (Dionex P580),and reversed-phase silica based column (AlletechAllsphere ODS-2 5  l ). We used a 1 ml min - 1 flowrate, column temperature of 30  C, and 3-solventgradient system (Jeffrey et al., 1997). The pigmentswere identified by retention time and absorptionspectrum, and their concentrations analyzed byDionex Chromeleon software. The HPLC was cali-brated with pigment standards obtained from theInternational Agency for  14 C Determination (DHIWater and Environment), Hørsholm, Denmark. 218 Hydrobiologia (2008) 600:215–228  1 3  Sample preservation, microscopic counts, andestimation of biomass from countsHPLC can only reliably classify phytoplanktoncommunities to the lowest commonality level of thepigments analyzed, in this case to division level(Havens et al., 1999), while microscopy can detectchanges in composition at the level of genus orspecies (Roy et al., 1996). For more detailed exam-ination of the phytoplankton communities of stations1 and 6, we also quantified phytoplankton communitycomposition by microscopy.Water samples preserved in 1% Lugol’s solutionwere concentrated by gravity sedimentation. Phyto-plankton counts were made with an Olympus IMT-2inverted microscope at 600 9  magnification onrandom non-overlapping fields until at least 100 unitsof the most abundant species larger than about 2  l m(nanoplankton) in diameter were counted (APHA,1995). Based on a Poisson distribution, for 100 unitscounted and 95% confidence interval, the countingerror is  ± 20% (APHA, 1995).Identification of nanoplankton was made at thegenus level.The average biovolume of each identified phyto-plankton genus was calculated from its geometricalshape (Wetzel & Likens, 2000) and average dimen-sions. Genus-specific biomass was calculated usingthe equations of Menden-Deuer & Lessard (2000):Genus-specific biomass per liter was obtainedfrom the product of cell abundance and cell biomass.Data analysisCorrelation analysis was used to quantify degrees of association between pigment concentrations and thebiomass of each phytoplankton group. Factorialanalysis of variance (ANOVA) was used to determinethe relationships of station and date to environmental(TDN, TDP, turbidity) and biological responsevariables (productivity, biomass, P:B ratio, andpigment concentrations). For the ANOVA, we com-bined data by season with four sampling dates inMarch–May 2004 considered spring, five samplingdates in June–August 2004 considered summer, andfour sampling dates in September–November 2004considered fall. Most of the tests met the assumptionsof ANOVA for normality (Kolmogorov-SmirnovTest) and equal variance (Levene’s Test). In a fewcases, where the data were not normally distributedand/or variance was unequal, we performed ANOVAafter log 10  transformation of the data. The results forANOVA reported here are for the untransformedvalues.For each significant ANOVA, a Tukey’s post hoctest was conducted to determine which station meanswere significantly different. ANOVA and Tukey’stest used the GLM procedure in STATISTICA(StatSoft, Inc.). Results Physical and chemical conditionsThe deepest location in the Sardis Reservoir wasnearest to the dam (station 1) and the shallowestlocation near the mouth of the river (station 6)(Table 1). The average depth at station 1 variedbetween 12 m in spring and fall, and 16 m insummer. As indicated by the mixing depths, thereservoir was well mixed in spring and fall, whereasit was stratified at deeper stations in summer. Atstation 1, during summer the water column wastypically mixed to 5-m depth. The average temper-ature at 0.5 m depth was fairly similar throughout thereservoir in a given season, ranging between 29  C insummer and 20  C in fall.Hydraulic residence time was mostly \ 100 daysthrough winter and early spring 2004, increasedrapidly starting in April to about 1,200 days in June,For diatoms  :  logpgC l  1 ¼ 0 : 541 þ  0 : 811    log cell  l m  3 l  1    For other protists  :  logpgC l  1 ¼ 0 : 665 þ  0 : 939    log cell  l m  3 l  1    Hydrobiologia (2008) 600:215–228 219  1 3
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