Aquat Sci (2011) 73:419–436
DOI 10.1007/s00027-011-0190-y
Aquatic Sciences
RESEARCH ARTICLE
Phytoplankton abundance and contributions to suspended
particulate matter in the Ohio, Upper Mississippi and Missouri
Rivers
Paul A. Bukaveckas • Amy MacDonald • Anthony Aufdenkampe •
John H. Chick • John E. Havel • Richard Schultz • Ted R. Angradi
David W. Bolgrien • Terri M. Jicha • Debra Taylor
•
Received: 20 September 2010 / Accepted: 10 February 2011 / Published online: 5 March 2011
Ó Springer Basel AG 2011
Abstract Main channel habitats of the Ohio, Missouri,
and Upper Mississippi Rivers were surveyed during the
summers of 2004, 2005 and 2006 using a probability-based
sampling design to characterize inter-annual and inter-river
variation in suspended chlorophyll a (CHLa) and related
variables. Large (fivefold) differences in CHLa were
observed with highest concentrations in the Upper Mississippi (32.3 ± 1.8 lg L-1), intermediate values in the
Missouri (19.7 ± 1.1 lg L-1) and lowest concentrations in
the Ohio (6.8 ± 0.5 lg L-1). Inter-annual variation was
small in comparison to inter-river differences suggesting
that basin-specific factors exert greater control over
P. A. Bukaveckas (&) A. MacDonald
Department of Biology, Center for Environmental Studies,
Virginia Commonwealth University, 1000 West Cary Street,
Richmond, VA 23284, USA
e-mail: [email protected]
river-wide CHLa than regional-scale processes influencing
climate and discharge. The rivers were characterized by
variable but generally low light conditions as indicated by
depth-averaged underwater irradiance \4 E m-2 day-1
and high ratios of channel depth to euphotic depth ([3).
Despite poor light conditions, regression analyses revealed
that TP was the best single predictor of CHLa (R2 = 0.40),
though models incorporating both light and TP performed
better (R2 = 0.60). Light and nutrient conditions varied
widely within rivers and were inversely related, suggesting
that riverine phytoplankton may experience shifts in
resource limitation during transport. Inferred grazing and
sedimentation losses were large yet CHLa concentrations
did not decline downriver indicating that growth and loss
processes were closely coupled. The contribution by algae
to suspended particulate organic matter in these rivers
(mean = 41%) was similar to that of lakes (39%) but lower
relative to reservoirs (61%).
A. Aufdenkampe
Stroud Water Research Center, 970 Spencer Road,
Avondale, PA 19311, USA
Keywords Ecosystems Rivers Phytoplankton Chlorophyll Suspended particulate matter
J. H. Chick
Illinois Natural History Survey, National Great Rivers Research
and Education Center, East Alton, IL 62012, USA
Introduction
J. E. Havel
Department of Biology, Missouri State University,
901 South National Avenue, Springfield, MO 65897, USA
R. Schultz
Department of Biology, University of Louisville,
Louisville, KY 40292, USA
T. R. Angradi D. W. Bolgrien T. M. Jicha D. Taylor
Mid-Continent Ecology Division, United States Environmental
Protection Agency, 6201 Congdon Boulevard,
Duluth, MN 55804, USA
Aquatic food webs include diverse assemblages of benthic
and pelagic consumers that feed on suspended particulate
matter. The quantity and composition of suspended particulate matter is determined by contributions from allochthonous and autochthonous sources and by differences in their
biochemical composition. Terrestrial sources of particulate
matter derive primarily from soils and partially decomposed
plants. They are characterized by the presence of structural
compounds (e.g., cellulose, lignin) of low nutritional value
(Jassby et al. 1993; Sobczak et al. 2005; Canuel 2001;
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420
McCallister et al. 2006) and often by low organic matter
content. In contrast, autochthonous sources (particularly
algae) are enriched in mineral nutrients (N, P) and important
biochemicals (e.g., fatty acids, proteins) whose concentrations vary with species composition and growth conditions
(Elser et al. 2002; Jones and Flynn 2005; Brett et al. 2006;
Malzahn et al. 2007). Ultimately, the quantity and composition of suspended particulate matter is determined by
relative rates of aquatic and terrestrial production, the
delivery of terrestrial matter to aquatic environments and
rates of diagenesis occurring within aquatic and terrestrial realms. Through these mechanisms, the quantity and
quality of food resources is coupled to hydrologic and
biogeochemical processes within the waterbody and its
catchment.
A primary focus in the assessment of human impacts on
aquatic ecosystems has been the question: How do nutrient
inputs affect food resources for consumers? In lakes and
estuaries, it is well established that inputs of nitrogen and
phosphorus stimulate phytoplankton production and alter
community composition, thereby affecting the quantity and
quality of suspended particulate matter (Boesch 2002;
Verity 2002; Smith 2003). However, it is also recognized
that sensitivity to nutrient inputs can vary greatly within
and among aquatic systems (Cloern 2001). Large rivers are
comparatively under-studied with respect to eutrophication
effects but are known to vary in their sensitivity to nutrient
inputs (Smith 2003). Like lakes, grazing by benthic and
pelagic consumers may be important to constraining phytoplankton abundance in rivers (Gosselain et al. 1998a;
Caraco et al. 2006; Strayer et al. 2008). However, a river’s
response to eutrophication differs markedly from that of a
lake due to physical factors, namely turbidity effects on
light availability and short water residence times (Søballe
and Kimmel 1987; Sellers and Bukaveckas 2003; Koch
et al. 2004; Kennedy and Whalen 2008). These factors
constrain phytoplankton production and thereby reduce the
efficiency with which light and nutrients are converted to
biomass.
Underwater irradiance is dependent on incident solar
radiation, the depth of the mixed layer and light attenuation
within the water column. Incident solar radiation varies in
response to cloud cover and photoperiod (Fisher et al.
2003). In free-flowing rivers, mixing forces are sufficient to
preclude stratification such that mixing depth is determined
by the depth of the channel (Bormans and Webster 1999).
Light attenuation is principally governed by non-algal
particulate matter, although light absorption by dissolved
organic compounds is important in some rivers (Julian
et al. 2008). The delivery of particulate matter is dictated
by fluvial transport and therefore variation in light attenuation typically follows seasonal and storm-related changes
in discharge. Discharge also determines the transit time of
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P. A. Bukaveckas et al.
water and its dissolved and particulate constituents along
the river course. The effects of transit time on phytoplankton abundance may be viewed from a Lagrangian
perspective that considers processes regulating growth and
loss within a parcel of water moving along the longitudinal
dimension of the river (Doyle and Ensign 2009). Under
favorable conditions (growth exceeding loss), slower water
velocities result in greater biomass accrual per unit of
transit time or distance (Lucas et al. 2009). Higher water
velocities limit opportunities for phytoplankton to utilize
light and nutrients, thereby reducing resource utilization efficiency (Søballe and Kimmel 1987; Lewis 1988;
Reynolds and Glaister 1993).
During transit, phytoplankton experience variable
nutrient conditions arising from tributary and point source
inputs and variable light conditions associated with changes in channel depth, water clarity and incident solar
radiation. Potential shifts between light and nutrient limitation of growth rates and between growth and grazer
control of biomass accrual complicate assessments of factors controlling phytoplankton abundance in rivers.
Phytoplankton typically exhibit saturating growth responses to increasing light and nutrients such that saturation
thresholds may be used to infer limiting factors (Hill et al.
2009). A number of recent studies have reported light
limitation thresholds in the range of 3–5 E m-2 day-1
below which phytoplankton growth rates are wholly or in
part constrained by light availability (Koch et al. 2004;
Oliver and Merrick 2006; Whalen and Benson 2007).
Corresponding values for nutrient thresholds are based on
half-saturation coefficients for N (DIN = 15–300 lg L-1)
and P (PO4 = \5–25 lg L-1) uptake (Hamilton and
Schladow 1997). Nutrient thresholds are low in comparison
to typical concentrations for rivers in human-dominated
catchments suggesting a prevalence of light-limiting conditions (Reynolds and Descy 1996; Salmaso and Braioni
2008).
Few spatially extensive studies of light and nutrient
conditions in rivers have been undertaken and comparative
studies of large rivers are especially rare (Bergfeld et al.
2009; Hadwen et al. 2009). Furthermore, while the effects
of individual factors affecting growth and loss have been
widely studied, there are few examples for which the relative importance of and interactions among the factors are
well understood (Strayer et al. 2008). We conducted spatially extensive surveys of main channel habitats in the
Ohio, Upper Mississippi and Missouri Rivers to characterize inter-annual and inter-river variation in CHLa. We
relate variation in CHLa to factors influencing phytoplankton abundance including light and nutrient
availability, tributary inputs, and losses due to grazing and
sedimentation. Lastly, we examine phytoplankton contributions to particulate organic matter in rivers relative to
Phytoplankton abundance and contributions to suspended particulate matter
other freshwaters (lakes and reservoirs) where the trophic
significance of phytoplankton is widely accepted.
Methods
Study sites
The Ohio, Upper Mississippi and Missouri Rivers comprise
73% of the watershed of the Mississippi River, which is
among the ten largest rivers in the world (Fig. 1). The
Missouri River has the largest basin and drains an area
greater than the Ohio and Upper Mississippi Rivers combined. However, much of the Missouri River basin lies in
the semi-arid Great Plains and therefore water yield and
discharge are comparatively low (Table 1). The river
basins span a range of climatic and geologic conditions and
have been variably affected by agriculture, urbanization
and water regulation. Agriculture is widespread in all three
catchments but particularly in the Upper Mississippi River
where 70% of the land is cultivated for row crops (mostly
corn and soy beans). The intensity of agriculture is similar
in the northern portion of the Ohio River basin (Wabash
River drainage), whereas areas in the south (Tennessee and
Cumberland River drainages) are predominantly forested.
The basin as a whole has comparable areas devoted to
Fig. 1 Map of the Ohio, Upper Mississippi and Missouri Rivers
depicting study reaches for the EPA EMAP Great Rivers Project. For
the Missouri River, the study area included two segments in the inter-
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natural (47%) and agricultural (48%) land use. The Missouri River basin has less agricultural activity (33%) and
much of this is low intensity grazing. Major population
centers are concentrated in the Ohio and Upper Mississippi
River basins (mean density = 50 km-2) with lower densities occurring in the Missouri River basin (\10 km-2).
Further information on land use and relationships to
nutrient load is presented in Hill et al. (2010).
The rivers differ in their channel form and in the extent
to which hydrology is altered by artificial structures. The
Ohio is naturally constricted along much of its course with
somewhat greater floodplain habitat in the lower third. The
Upper Mississippi has extensive floodplain habitat,
including backwater lakes, along its upper course, whereas
shoreline levees restrict floodplain connectivity in the
lower course (Sparks et al. 1998). Both rivers are regulated
by low dams (\10m) that are spaced at intervals of ca.
100 km to maintain a minimum navigable channel depth of
3 m. The cumulative storage capacity of these navigation
dams is small in comparison to average daily discharge
(e.g., 12 days for the Ohio River; Bukaveckas et al. 2005).
In contrast, the Missouri River is regulated by six large
impoundments with a cumulative storage volume equivalent to 536 days of the annual mean discharge (Galat et al.
2005). In addition, dams are present on a number of tributaries in all three basins.
reservoir zone (Ft. Peck and Garrison Reaches) but the reservoirs
themselves were not sampled as part of this study
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Table 1 River and basin
characteristics of the Ohio,
Upper Mississippi and Missouri
Rivers (from Benke and
Cushing 2005)
P. A. Bukaveckas et al.
Ohio
Upper Mississippi
Missouri
Basin Area (km2)
529,000
489,510
1,371,017
Precipitation (cm year-1)
104
96
50
Annual mean discharge (m3 s-1)
8,733
3,576
1,956
Length (km)
1,575
2,320
3,768
Hydrology and geomorphology
Land use
Agriculture
48%
70%
33%
Natural
47%
25%
42%
Population density (# km-2)
49
54
8
20 (low)
26 (low)
6 (high)
Water regulation
Mainstem dams (height)
Sampling design
Data were collected as part of the Environmental Protection Agency’s Environmental Monitoring and Assessment
Program for Great River Ecosystems (EMAP-GRE). The
sampling design was similar to previous EMAP efforts
for lakes, streams and wetlands in that it utilized a surveybased, probabilistic approach (Bolgrien et al. 2005;
Angradi et al. 2009). EMAP design algorithms were
applied to GIS-based representations of the Ohio River
(from Pittsburgh to its confluence with the Mississippi), the
Upper Mississippi River (from Minneapolis to its confluence with the Ohio River) and the Missouri River (from
Fort Peck Dam, Montana to its confluence with the Mississippi River). The algorithms allow for randomized
selection of study sites and the derivation of inclusion
probabilities for each site selected (Schweiger et al. 2005).
Selection criteria were used to define the target population and to bolster sample size for under-represented
sub-populations of special interest. For EMAP-GRE, two
criteria were used: (1) large impoundments on the Missouri
River were excluded a priori to limit the sample population
to flowing waters, and (2) sites selected in 2006 were
screened to increase representation of potential reference
sites. Exclusion criteria reduced the length of the Missouri
River study reach by 25% (ca. 1,000 km) as five of the six
large impoundments were located within the study area
(Fig. 1). Selection criteria to bolster sampling of reference
sites included a variety of metrics such as distance to dams,
major urban centers and point source discharges. Site visits
were performed during the late summer, base-flow period
(July–September) over three successive years (2004–2006).
When sampling adjacent locations, sites were visited in an
upstream direction to avoid re-sampling the same parcel
of water. The total number of sites visited over 3 years
was 141 (Ohio), 165 (Mississippi) and 216 (Missouri).
Sampling effort was similar among the three rivers when
weighted according to length of the river course with
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average sampling intervals of 10 km (Mississippi), 14 km
(Ohio) and 16 km (Missouri).
Sample collection and analysis
At each site, samples were collected along a lateral (crosssectional) transect that included the thalweg (deepest point
of the channel) and points situated halfway between the
thalweg and each bank. A composite sample (4L) was
obtained by pooling water pumped from three depths (near
surface, midpoint, near bottom) at each of the three sampling locations (Angradi 2006). The composite sample was
used to determine chlorophyll a (CHLa), total suspended
solids (TSS), nutrients (dissolved and particulate fractions
of N and P) and particulate organic carbon (POC). Samples
for all analyses of suspended materials, including CHLa,
were sub-sampled from a churn sample splitter to ensure
equal concentrations of particles (Lane et al. 2003). Sample
filtrations and turbidity measurements (as NTU) were
performed in the field or immediately upon return to the
laboratory. Filtrations were performed at low vacuum
pressure (\7 psi). Samples for TSS were filtered onto a
pair of stacked pre-weighed membrane filters (Millipore
HAWP; nominal pore-size of 0.45 lm). After drying filters
12–18 h at 60°C to constant mass, TSS concentrations
were quantified as the change in the mass of the top filter
(sediment mass) minus the change in mass of the bottom
filter (solute mass and balance drift) divided by the filtered
water volume. The average difference for field duplicates
of TSS was 3.8%. Samples for determination of CHLa and
POC were collected on 47 mm glass fiber filters (Millipore
AP40; nominal pore-size of 0.7 lm). Filters for CHLa
analysis were stored frozen until processing within 3 weeks
of collection at the University of Louisville Environmental
Analyses Lab. Chlorophyll was extracted in 90% buffered
acetone, and concentrations were determined by fluorescence (Turner Designs 10-AU) with acid correction
following EPA standard method 445.0 (Arar and Collins
Phytoplankton abundance and contributions to suspended particulate matter
1997). The fluorometer was calibrated annually with a
primary standard and checked against a secondary standard
during each use. For CHLa, the average difference for field
duplicates was 13% (N = 55). For POC, filters were subsampled by cutting out disks with a core-borer. Cutouts
were wetted with nanopure water and fumed with hydrochloric acid vapors for 18 h to remove carbonates, then
dried and analyzed for carbon using a Costech ECS 4010
Elemental Analyzer (Aufdenkampe et al. 2001; Richardson
et al. 2009). POC concentrations were calculated from the
mass of C analyzed, the fraction filter analyzed, and the
volume of water filtered. The average difference for field
duplicates of POC was 6.1%. Samples for nutrient analysis
were shipped to a central facility (USGS Upper Midwest
Environmental Sciences Center) and analyzed using standard methods for automated analyses of colorimetric
reactions (APHA 1998; Søballe and Fischer 2004).
Light climate
Secchi depth was measured at each sampling location and
attenuation coefficients (kd) were derived from Secchi depth
using a generalized model based on paired measurements
from diverse aquatic systems (kd = 1.7/Secchi; Idso and
Gilbert 1974). For the Ohio River, our inferred attenuation
coefficients (mean = 1.75 ± 0.07 m-1) were very similar
to direct measurements from the McAlpine and Smithland
Pools (mean = 1.77 ± 0.27 m-1; N = 47) obtained in
May–October 1998–2002 (Sellers and Bukaveckas 2003;
Bukaveckas unpubl. data). For the Mississippi River,
our inferred attenuation coefficients (mean = 3.58 ±
0.19 m-1) were also very similar to directly measured
values from the Lock & Dam 8 and 9 Pools (mean =
3.51 ± 0.08 and 3.61 ± 0.11 m-1; N = 301) obtained in
May–October 1990–2006 (Giblin et al. 2010; J. Sullivan WI
DNR, unpublished data).
To characterize light conditions at each of the sampling
locations, we used a standard formula for estimating the
average irradiance within the water column (Iwc):
Iwc ¼ Is =ðkd Zwc Þ
where Is is incident solar radiation (as photosynthetically
active radiation, or PAR), kd is the light attenuation coefficient, and Zwc is the depth of the water column (Fisher
et al. 2003). We substituted the average cross-sectional
depth (Zx-sec) for Zwc to derive an estimate of the average
irradiance representing a vertically and laterally mixed
river channel (hereafter, Ix-avg). The average cross-sectional
depth of the main channel was determined from depths
recorded at the three sampling locations. We also tested the
utility of using upstream bathymetry to depict antecedent
light conditions by substituting the site-specific cross-sectional depth with the average of cross-sectional depths for
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sites located within 1–3 days of transit time above the
sampling location. For incident solar radiation (Is), we used
an average value of 33.9 E m-2 day-1 (equivalent to an
average instantaneous value of 673 lE m-2 s-1 over a
14 h photoperiod). This value was obtained by averaging
daily solar radiation measurements during the period of
sample collection (July–September 2004–2006) from two
locations. One site was located near the Mississippi River
at La Crosse, Wisconsin (ca. river km 1,075). The other site
(Hancock Biological Station, Murray, Kentucky) was
located 1,070 km to the south near the confluence of the
three rivers. Solar radiation measurements at the La Crosse
site were converted to PAR assuming a constant 2.05 E/KJ
of radiation (Aber and Freuder 2000).
Water velocity
We estimated water velocity at each sampling location by
dividing discharge by cross-sectional channel area. The
cross-sectional area of the channel was calculated as the
product of measured depth (see above) and width. Discharge
data were obtained from mainstem (N = 30) and tributary
(N = 58) gauging stations. Discharge at a given site was
interpolated from the values recorded on the date of sample
collection at upstream and downstream gauging stations.
The average distance between sampling locations and the
nearest gauging station was 60 km (Mississippi River),
56 km (Missouri River) and 82 km (Ohio River). Contributions from tributaries were taken into account where
present. We derived an average transit time for each of the
three rivers based on the distance between the upper- and
lower-most sampling locations and the average water
velocity among all sites. The transit time estimate for the
Missouri River includes only the lower (unimpounded)
segment.
Grazing
Sampling methods for benthic (dreissenid mussels) and
pelagic (zooplankton) grazers are described in detail elsewhere (Grigorovich et al. 2008; Havel et al. 2009) and
summarized here. Micro- and macro-zooplankton samples
were collected by pumping water from three depths at each
of three cross-channel locations (as for CHLa, TSS and
POC). Composite water samples of 18 and 180 L were
passed through nets of 20 and 63 lm, respectively. Samples
were preserved in buffered formalin. Rotifers and nauplii
were identified and enumerated from microzooplankton
samples using a Sedgwick-Rafter cell at 1009 magnification. Cladocerans and copepods (adults and copepodites)
were identified and enumerated from macrozooplankton
samples using a Bogorov tray at 259 magnification. Rotifers were identified to genus, cladocerans to species and
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adult copepods to order (cyclopoid, calanoid, or harpacticoid). Filtration rates were estimated from measured
densities at each of the sampling locations and previously
published taxon-specific grazing rates derived from laboratory and field feeding experiments (see Bukaveckas and
Shaw 1998 and references therein).
Dreissenid densities at each sampling location are average values from paired snag and kick samples collected
from a known area of woody debris and littoral substrate.
Densities were higher on snags relative to littoral benthos
but snag habitats were rare by comparison (Grigorovich
et al. 2008). Therefore, average values likely over-estimate
actual densities. To derive filtration rates, we used previously published values for Hudson River zebra mussels
(0.115 L ind-1 h-1; Roditi et al. 1996). The average concentration of suspended particulate matter in the Hudson
River (10 mg L-1) was similar to that of the Ohio
(13 mg L-1) but lower than that of the Mississippi
(38 mg L-1). Filtration rates have generally been found to
decrease with increasing concentrations of suspension,
suggesting that our values likely over-estimate benthic filtration rates for the Mississippi. Filtration rates and channel
depth were used to estimate the proportion of water filtered
daily. Extrapolation of lab-based measurements to in situ
filtration rates is complicated by uncertainty regarding
re-filtration near the benthic boundary layer. Our daily values
assume a well-mixed river channel and no re-filtration.
Influence of tributaries and retention zones
A synoptic survey of selected tributaries was conducted in
summer 2006. Samples were collected from 17 tributaries
at paired locations 1 km above their confluence with the
mainstem. To assess tributary contributions, we calculated
tributary and mainstem loads as the product of concentrations and discharge. The sum of loads was divided by the
sum of discharge to derive the predicted increase in
mainstem CHLa concentrations below the confluence.
Hydrologic retention zones proximal to each of the sampling locations were identified from satellite images. The
number of retention features was determined within an area
spanning the width of the river valley and extending 10 km
above each sampling location. Retention features included
connected backwaters, floodplain lakes and wetlands, side
channels and in-river structures (e.g., wingdams). The
number of retention features at each site was included as an
explanatory variable in CHLA regression models (see
Statistics below).
Algal contributions to POC
The CHLa content of algae varies widely and therefore
estimates of their contributions to POC are sensitive to the
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P. A. Bukaveckas et al.
means by which the C:CHLa ratio is derived (Cloern et al.
1995; Putland and Iverson 2007). We derived values of
C:CHLa for each of the three rivers from the slope of the
regression relating log POC to log CHLa (Banse 1977).
Ratios derived by this approach have been shown to be in
good agreement with biovolume estimates for marine
phytoplankton (Chang et al. 2003), though their applicability to riverine environments is untested. Estimates of
algal C derived from C:CHLa ratios and measured CHLa
concentrations were divided by POC to estimate the proportion of organic matter contributed by suspended algae.
We compared algal contributions to POC in rivers with
other freshwaters by compiling similar data for selected
North American lakes and reservoirs. The comparison
dataset included regional reservoirs in Kentucky (Yurista
et al. 2001) and Ohio (Knoll et al. 2003) and lakes in
Michigan (Pace and Cole 2000; Carpenter et al. 2001) and
Manitoba (Elser et al. 1998). Lake and reservoir data were
restricted to summer only values to correspond with the
period when the river samples were collected.
Statistical analyses
A total of 522 sites were sampled for CHLa and related
parameters; 433 sites were unique locations. Approximately 15% of the sites were re-visited in the same year or
subsequent years. Re-visits occurring within the same year
were spaced at monthly intervals and therefore we assumed
these to be independent samples for statistical analyses.
Inter-river and inter-annual differences in CHLa and related variables was tested using a two-way ANOVA (SAS
GLM) with year and river as predictor variables. Resource
utilization efficiencies for light and phosphorus (RUEI,
RUEP; respectively) were derived from ratios of CHLa to
Ix-avg and TP. Univariate and multivariate regression
models were tested to determine the proportion of interand intra- river variation in CHLA and RUEs that could be
explained by light (Ix-avg), nutrients, velocity and retention
zones.
Results
Inter-river and inter-annual variation
During the 3-year study, the average discharge for the
3-month sampling period ranged from 1,200 to
2,200 m3 s-1 in the Missouri, 3,200 to 6,400 m3 s-1 in the
Ohio, and 2,700 to 5,600 m3 s-1 in the Upper Mississippi
(Fig. 2). For all three rivers, highest discharge was in 2004
(12–45% above the 20-year mean) and lowest discharge
was in 2005 (7–33% below 20-year mean). Averaged over
the 3-year study, discharge of the Ohio and Missouri Rivers
Phytoplankton abundance and contributions to suspended particulate matter
was similar to their 20-year means (?8%, -5%; respectively), whereas the discharge of the Upper Mississippi was
22% below average. Cross-sectional average water velocity
was greatest in the Missouri (1.2–1.4 m s-1) and lower
in the Upper Mississippi (0.3–0.6 m s-1) and Ohio
(0.3–0.7 m s-1). Transit times from upper to lower sampling locations were 34 days for the Mississippi and
42 days for the Ohio based on average water velocity
during the 3-year study. Transit time in the lower segment
of the Missouri (below impoundments) was 13 days.
Annual mean CHLa was highest in the Upper Mississippi
(27–36 lg L-1) and lowest in the Ohio (3–9 lg L-1).
Inter-annual variation was small (CV = 13–48%) in
comparison to the fivefold differences in CHLa among
rivers. The Upper Mississippi also exhibited the highest
concentrations of TP (173–187 lg L-1) and DIN
(987–1,416 lg L-1). TP was similar in the Missouri
(137–186 lg L-1) but DIN was lower (258–528 lg L-1).
TP was lowest in the Ohio (37–76 lg L-1) which was
intermediate with respect to DIN (745–958 lg L-1). Interannual variations in nutrient concentrations were small
(CV \ 30%) in comparison to the threefold differences
among rivers. The Ohio had the lowest TSS and turbidity
and greatest Secchi depths. The Missouri had the lowest
water clarity according to all three measures. Inter-annual
variation in TSS was small (CV = 25–42%) in comparison
to the tenfold differences among rivers. Inferred estimates
of light attenuation corresponded to average euphotic
depths (Z1%) of 3.2 m (Ohio), 1.6 m (Mississippi) and
1.1 m (Missouri). Differences in water clarity were largely
offset by variation in channel depth with the Ohio having
the deepest channel (mean = 8.8 m) and the Missouri
being the shallowest (mean = 3.9 m; Table 2). As a result,
all three rivers had similar average light conditions (Ix-avg =
2.4–2.8 E m-2 day-1) and ratios of euphotic depth to
channel depth (3.2–3.4). Principal components analysis was
used to depict inter-river and inter-annual differences based
on the composite suite of variables and revealed greater
differences among rivers than years (Fig. 2).
Site- and date-specific variation
Site-specific estimates of average underwater irradiance
(Ix-avg) varied widely within all three rivers (Fig. 3).
Greatest variation occurred in the Missouri where the
average for the upper 10%-tile of sites (10.5 E m-2 day-1)
was 30-fold higher than the lowest 10%-tile (0.36 E m-2
day-1). Corresponding values for the Mississippi (5.1 and
0.56 E m-2 day-1) and Ohio (6.6 and 0.88 E m-2 day-1)
differed by ten- and sevenfold, respectively. For all three
rivers, the majority of sites was characterized by low light
conditions (80% \4 E m-2 day-1). Inorganic nutrient
concentrations also varied widely with the Missouri again
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exhibiting the greatest range of variation (SRP =
2–114 lg L-1; DIN = 20–1,840 lg L-1). SRP concentrations in the Mississippi and Ohio also varied by more
than tenfold (10–180 and 2–52 lg L-1; respectively),
whereas variation in DIN was smaller in these rivers
(145–3,950 and 450–1,300 lg L-1; respectively). Light
and nutrient availability were inversely related in all three
rivers. In the Missouri, this relationship was due to longitudinal gradients with the lower segment characterized by
high CHLa (mean = 32 lg L-1), high TP ([200 lg L-1)
and low light (Ix-avg \ 2 E m-2 day-1), and the upper,
inter-reservoir zone, characterized by low CHLa and TP
(mean = 3 and 33 lg L-1; respectively) and high crosssectional average irradiance (mean = 5.62 E m-2 day-1).
The Ohio exhibited a modest rise in TP (ca. 50 lg L-1)
and CHLa (ca. 10 lg L-1) but no consistent longitudinal
trends in light availability. There were no significant differences in CHLa among the upper (constricted) and
lower (floodplain) segments of the Ohio. In the Upper
Mississippi, light availability declined and TP increased
downriver (no consistent trend in CHLa). There were no
significant differences between the upper (high floodplain connectivity) and lower (levied) segments of the
Mississippi. Seasonal patterns were apparent only in the
Mississippi River in 2005 when CHLa was elevated riverwide during mid-July to mid-August. During this period,
average CHLa was threefold higher (mean = 48.7 lg L-1)
compared to early and late summer (means = 16.4 and
15.8 lg L-1, respectively).
Tributaries
All three rivers gained appreciable discharge along their
course due to inputs from tributaries. Discharge at sampling locations near the top of the study reaches averaged
13% (Ohio), 14% (Mississippi) and 31% (Missouri) of
discharge at sites located near the bottom of the study
reaches. CHLa concentrations of tributaries were as variable as mainstem sites (ca. 1–100 lg L-1; Table 3). For 9
of the 17 tributaries, CHLa concentrations exceeded that of
the mainstem. However, the predicted rise in CHLa below
the confluence was small in all cases (\3 lg L-1). The
proportional increase due to tributary contributions was
occasionally large (e.g., Big Sioux, Muskingum, Wabash
and Cumberland [20%) due to low mainstem concentrations in the Ohio and upper Missouri. The Big Sioux enters
the Missouri at river km 1,184, which is below the last
impoundment and upstream of the zone of elevated CHLa
(river km 1,200–500; Fig. 3). The Wabash and Cumberland Rivers also delivered large quantities of CHLa (59 and
32% increase of Ohio River load, respectively) but had
little influence on river-average values as their confluence
occurs in the lower fifth of the river course.
123
426
P. A. Bukaveckas et al.
2000
40
MO
30
1500
DIN (µg L-1)
CHLa (µg L-1)
MS
OH
20
1000
500
10
0
0.0
0
1.0
2.0
3.0
4.0
0
5.0
50
100
200
250
TP (µg L )
POC (mg L )
8.0
5.0
6.0
4.0
Depth 1% (m)
Ix-avg (E m-2 d-1)
150
-1
-1
4.0
2.0
3.0
2.0
1.0
0.0
0
50
100
150
0.0
200
0
2
4
TSS (mg L-1)
6
8
10
Depthx-sec (m)
1.6
1.5
2005
1.2
PCA Axis 2
-1
Velocity (m s )
1.0
0.8
0.4
2005
0.5
2004
2004
2006
0.0
2006
-0.5
2004
-1.0
2006
-1.5
0.0
0
2000
4000
6000
3
8000
-1
Discharge (m s )
-2.0
2005
-2
-1
0
1
2
PCA Axis 1
Fig. 2
Inter-river and inter-annual variation in chlorophyll
a (CHLa), particulate organic C (POC), dissolved inorganic N
(DIN), Total P (TP), cross-sectional average irradiance (Ix-avg), total
suspended solids (TSS), euphotic depth (Depth1%), cross-sectional
average depth (Depthx-sec), cross-sectional average velocity and
discharge for the Ohio (OH), Missouri (MO) and Upper Mississippi
(MS) Rivers during 2004–2006 (mean ± SE). Arrows denote
20-years mean discharge for months corresponding to the sampling
period (left to right MO, MS and OH). Variables for ordination
analysis (PCA) also included temperature and dissolved oxygen (not
shown below)
CHLa models
predictor of CHLa accounting for 46% of the variation in the
pooled dataset (p \ 0.001; Fig. 4). Model performance
improved by normalizing CHLa relative to Ix-avg with TP
accounting for 60% of the variation in light utilization
efficiency (RUEI; p \ 0.001; Fig. 4). Among individual
rivers, the proportion of variation in CHLa explained by
univariate and multivariate models was highly variable. The
Missouri River exhibited the broadest range of concentrations and regression models for this site accounted for a
larger proportion of variation in CHLa (80%) compared to
the Mississippi (14%) and Ohio (25%). For the Missouri,
Cross-sectional average irradiance was not a significant
predictor of CHLa in the pooled dataset (Fig. 4). Among
individual rivers, Ix-avg had a weak positive relationship with
CHLa in the Ohio (R2 = 0.10; p \ 0.0001) and a strong
negative relationship with CHLa in the Missouri
(R2 = 0.50; p \ 0.0001). The negative relationship was due
to high light availability and low CHLa in the shallow, clear,
inter-reservoir zone and low light availability and high
CHLa in the turbid, lower river. TP was found to be the best
123
Phytoplankton abundance and contributions to suspended particulate matter
427
Table 2 Mean values for metrics used to characterize light, nutrient and water velocity conditions in the Ohio, Upper Mississippi and Missouri
Rivers
Ohio
Upper Mississippi
Mean ± SE
Missouri
River
Year
p
R2
R9Y
CHLa (lg L-1)
6.8 ± 0.5
32.3 ± 1.8
19.7 ± 1.1
\0.0001
0.003
ns
0.27
Turbidity (NTU)
12 ± 1
26 ± 3
66 ± 7
\0.0001
0.003
0.013
0.14
0.45
120 ± 5
61 ± 2
42 ± 3
\0.0001
\0.0001
\0.0001
TSS (mg L-1)
13 ± 1
38 ± 3
125 ± 10
\0.0001
ns
0.022
0.23
Depth (m)
8.8 ± 0.2
5.2 ± 0.1
3.9 ± 0.1
\0.0001
ns
ns
0.55
l1% ðmÞ
lxsec E m2 d1
3.2 ± 0.1
2.8 ± 0.1
1.6 ± 0.1
2.4 ± 0.1
1.1 ± 0.1
2.6 ± 0.2
\0.0001
ns
\0.0001
0.026
\0.0001
\0.0001
0.45
0.06
TP (lg L-1)
53 ± 2
182 ± 4
171 ± 11
\0.0001
ns
0.047
0.22
SRP (lg L-1)
15 ± 1
82 ± 4
37 ± 3
\0.0001
\0.0001
\0.0001
0.39
977 ± 58
\0.0001
0.0007
ns
0.32
Secchi (cm)
TN (lg L-1)
-1
1,152 ± 21
2,101 ± 77
DIN (lg L )
842 ± 20
1,269 ± 77
444 ± 40
\0.0001
0.019
0.024
0.23
Temperature (C°)
27.9 ± 0.2
24.7 ± 0.3
23.8 ± 0.3
\0.0001
\0.0001
0.013
0.34
Velocity (m s-1)
0.44 ± 0.03
0.46 ± 0.04
1.27 ± 0.04
\0.0001
0.0002
0.044
0.42
Depth and Ix-avg are average cross-sectional values (irradiance as PAR). Statistical results are for 2-way ANOVAs (main effects = River, Year;
interaction = ‘R 9 Y’); R2 is the proportion of variation explained by the model ‘ns’ denotes p [ 0.05
Upper Mississippi
60
30
0
30
3.0
15
-1
9.0
-2
-1
9.0
-2
6.0
30
0
Ix-avg (E m d )
Ix-avg (E m d )
9.0
45
12.0
12.0
-2
6.0
3.0
6.0
3.0
0.0
0.0
250
250
200
200
200
150
100
50
0
-1
-1
-1
SRP (µg L )
0.0
250
SRP (µg L )
-1
60
0
12.0
Ix-avg (E m d )
-1
-1
90
60
CHLa (µg L )
CHLa (µg L )
-1
CHLa (µg L )
90
120
SRP (µg L )
Ohio
Missouri
150
150
100
50
0
300
600
Distance (km)
900
0
150
100
50
0
500
1000
1500
2000 2500
Distance (km)
Fig. 3 Longitudinal variation in chlorophyll a (CHLa; note difference in scales), cross-sectional average irradiance (Ix-avg) and SRP in
the Upper Mississippi, Missouri and Ohio Rivers based on surveys
performed in July–September of 2004–2006 (0 = mouth of river).
3000
0
0
400
800
1200
1600
Distance (km)
Solid lines are moving averages representing ca. 1 day of transit
time. Dashed lines are suggested limitation thresholds for light
(4 E m-2 day-1) and SRP (25 lg L-1) based on prior studies
123
428
P. A. Bukaveckas et al.
Table 3 Contributions of water and CHLa from 17 tributaries of the Upper Mississippi, Missouri and Ohio Rivers
River
Confluence
Tributary
Mississippi
Missouri
Ohio
Rkm
Discharge (m3 s-1)
CHLa (lg L-1)
Tributary
River
Tributary
D CHLa
%
River
DesMoines
581
130
1,241
27.8
45.1
-1.6
-4
Illinois
351
208
1,468
5.0
35.2
-3.8
-11
Niobrara
1,358
16
680
6.5
1.7
0.1
7
James
1,288
2
722
23.2
3.7
0.1
1
Big Sioux
1,184
21
735
100.2
6.8
2.6
39
Platte
957
109
803
41.2
38.4
0.3
1
Kansas
Grand
591
402
63
37
1,104
1,066
17.8
10.8
37.6
31.6
-1.1
-0.7
-3
-2
Osage
209
113
1,259
1.1
19.6
-1.5
-8
Gasconade
168
61
1,486
3.5
15.7
-0.5
-3
Muskingum
1,295
74
937
22.1
5.1
1.2
25
Kanawha
1,084
92
859
2.9
2.4
0.05
2
Kentucky
701
40
782
8.5
7.8
0.03
0.4
Green
317
54
1,165
3.1
5.1
-0.1
-2
Wabash
217
124
1,251
28.9
3.8
2.3
59
Cumberland
98
236
2,647
18.1
3.6
1.2
32
Tennessee
79
503
2,647
2.3
3.6
-0.2
-6
D CHLa is the predicted change in mainstem concentrations below the point of confluence based on tributary and mainstem discharge
(% = proportional change). Discharge values correspond to dates when samples were collected
both TN (R2 = 0.61) and TP (R2 = 0.53) were significant
predictors of variation in CHLa. TN was not a significant
predictor of CHLa in the Ohio or Mississippi and including
TN with TP yielded only small improvements in model
performance for individual rivers and the pooled dataset
(1–9% additional variation explained). Substituting inorganic (DIN, SRP) for total fractions generally did not
improve model performance except that SRP was a better
predictor of CHLa than TP for the Mississippi (16% of
variation explained). For the pooled dataset, water velocity
and the density of retention zones were also significant
model components (p \ 0.001) but together accounted for
only an additional 5% of the variation in CHLa. The density
of retention features was significantly different among rivers
(one-way ANOVA, p \ 0.001) with the Mississippi exhibiting the highest average density (mean = 13.1 ±
0.9 site-1), followed by the Missouri (5.7 ± 0.5 site-1) and
Ohio (1.6 ± 0.2 site-1).
Grazing
Total daily filtration (benthic ? pelagic) was highest in
the Mississippi (52% day-1) and lower in the Ohio
(27% day-1) and Missouri (17% day-1; Table 4). Filtration rates were found to be significantly different among
rivers but not among years (ANOVA, F7,477 = 15.96,
p \ 0.0001). Microzooplankton were the dominant grazers
123
in all three rivers accounting for [80% of total (benthic
plus pelagic) daily filtration. Microzooplankton were
principally rotifers which accounted for 96% (Missouri),
72% (Mississippi) and 61% (Ohio) of microzooplankton
filtration. Macrozooplankton communities were dominated
by Bosmina, daphnids and calanoid and cyclopoid copepods. Highest densities were observed in the Ohio
(8.9 ind L-1) with lower densities occurring in the Mississippi (4.4 ind L-1) and Missouri (0.6 ind L-1). Average
filtration rates for macrozooplankton ranged from
0.6% day-1 (Missouri) to 5.2% day-1 (Ohio). For all three
rivers, total zooplankton filtration rates (macro ? micro)
were positively correlated with CHLa, although the proportion of variance explained was low (14–17%).
Dreissenids (principally zebra mussels; Dreissena polymorpha) were found to be widely distributed in the Ohio
(54% of sites) and Upper Mississippi Rivers (71% of sites)
but were nearly absent from the Missouri River. Densities
were typically less than 100 ind m-2 and were similar in
the two rivers (Mississippi = 46 ± 31 ind m-2, Ohio =
66 ± 20 ind m-2). In the Ohio, densities increased
downriver (p = 0.004), though longitudinal distance
explained only 6% of the variation. There was no longitudinal trend in Dreissenid densities in the Mississippi.
Dreissenid filtration rates averaged 2.9% (Mississippi)
and 2.6% (Ohio) of the water column per day across all
sites.
Phytoplankton abundance and contributions to suspended particulate matter
2.5
MS
-1
logCHLa (µg L )
2.0
MO
OH
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.0
-0.5
0.0
0.5
1.0
-2
1.5
2.0
-1
log Ix-sec (E m d )
2.5
-1
log CHLa (µg L )
2.0
1.5
1.0
0.5
0.0
R2 = 0.46
p < 0.0001
-0.5
-1.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
429
contributions to POC were inferred from the intercept of
the CHLa–POC regression and were higher in the Mississippi (2.0 ± 0.2 mg L-1) and Missouri (1.9 ± 0.4
mg L-1) and lower in the Ohio (0.4 ± 0.05 mg L-1). The
three rivers exhibited a similar range of CHLa and POC to
the comparative dataset for lakes and reservoirs (Fig. 5).
The predictive power of the regression models was also
similar among systems (R2 = 0.47–0.69), although models
derived for some site-specific datasets (e.g., Ohio reservoirs, ELA lakes, UNDERC lakes) had higher correlation
coefficients (R2 = 0.78–0.81). Analyses of covariance
revealed that System (lakes, reservoirs, rivers) explained
31% of the variation in model parameters with Site (e.g.,
Mississippi River, Kentucky reservoirs) accounting for an
additional 16% of variation. C:CHLa ratios for rivers
(50 ± 6 lg lg-1) were intermediate of values obtained for
lakes (32 ± 3 lg lg-1) and reservoirs (93 ± 5 lg lg-1).
Estimated contributions of background (non-algal) C were
higher in rivers (1.4 ± 0.2 mg L-1) than in lakes
(0.8 ± 0.1 mg L-1) and reservoirs (0.3 ± 0.1 mg L-1).
Algal contributions to POC were significantly different
among systems (F2,754 = 37.7; p \ 0.0001) and were
highest in reservoirs (61%) with rivers and lakes exhibiting
lower and similar values (41 and 39%, respectively).
-1
log TP (µg L )
2.5
Discussion
2.0
1.5
log RUEI
1.0
0.5
0.0
-0.5
-1.0
R2 = 0.60
p < 0.0001
-1.5
-2.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
log TP (µg L-1)
Fig. 4 Relationships among CHLa, light (cross-sectional average
irradiance; Ix-avg) and nutrients (TP) for the Upper Mississippi (MS),
Missouri (MO) and Ohio (OH) Rivers. The resource utilization
efficiency for light (RUEI) is the ratio of CHLa to Ix-avg
Algal C and POC
CHLa–POC relationships were used to derive C:CHLa
ratios and yielded R2 values ranging from 0.34 to 0.64
(Table 5). C:CHLa values for the Mississippi and Ohio
were similar (22 ± 5 and 26 ± 4 lg lg-1; respectively) as
were estimated contributions of algal C to POC (Mississippi = 28%; Ohio = 38%). For the Missouri, the C:CHLa
ratio was higher (70 ± 14 lg lg-1) and the estimated
algal contribution to POC was 52%. Non-algal C
Our survey revealed large (fivefold) differences in summer
average CHLa within main channel habitats of the Ohio,
Missouri and Upper Mississippi Rivers. Inter-annual variation in summer average CHLa was comparatively small
despite higher river discharge in 2004 and use of modified
site selection procedures in 2006. These findings suggest
that basin-and channel- specific factors exert greater control over river-wide CHLa concentrations than regionalscale processes influencing summer climate and discharge.
In all three rivers, estimates of underwater irradiance were
low but comparable to values reported for estuaries (e.g.,
3–4 E m-2 day-1 in upper Chesapeake Bay; Adolf et al.
2006). Our prior work with phytoplankton from the Ohio
River and its tributaries showed that at irradiances of
2 E m-2 day-1 phytoplankton experienced light but not
nutrient limitation (Koch et al. 2004). At 4 E m-2 day-1,
light limitation was prevalent but a small proportion of
communities (\20%) also responded to nutrient additions.
At 6 E m-2 day-1, nutrient limitation was more prevalent
than light limitation. Based on these thresholds, we estimate that the proportion of sites experiencing light but not
nutrient limitation (\2 E m-2 day-1) ranged from 35%
(Mississippi and Ohio) to 65% (Missouri), the proportion
of sites where co-limitation could occur (2–6 E m-2
day-1) ranged from 25% (Missouri) to 60% (Mississippi
123
430
P. A. Bukaveckas et al.
Table 4 Abundances and daily filtration rates of benthic and pelagic grazers in the Mississippi (MS), Missouri (MO) and Ohio (OH) Rivers
River
Dreissenids
Macrozooplankton
Microzooplankton
Total
N
Density
(#/m2)
Filtration
(%/days)
N
Density
(#/L)
Filtration
(%/days)
N
Density
(#/L)
Filtration
(%/days)
Filtration
(%/days)
MS
171
46
2.9
169
4.4
3.9
169
923
45
52
MO
203
0
0.0
212
0.6
0.6
212
413
16
17
OH
147
66
2.6
108
8.9
5.2
108
404
22
27
Benthic values are for dreissenids only, macrozooplankton include cladocerans and copepods, microzooplankton are rotifers and nauplii.
N denotes number of samples collected. Ohio zooplankton data are for 2005 and 2006 only; other rivers are for 2004–2006
Table 5 Regression models relating POC to CHLa (lg:lg) for selected rivers, reservoirs and lakes
N
Slope
SE
Intercept
SE
R2
CHLa (lg L-1)
POC (mg L-1)
Algal C (%)
Rivers
Mississippi
109
21.8
5.2
1,986
217
0.34
34.6
2.85
28
Missouri
154
70.1
14.2
1,910
387
0.64
21.2
3.39
52
97
25.5
4.3
390
47
0.41
8.5
0.60
38
360
50.0
6.1
1,352
181
0.58
21.8
2.44
41
165
38.4
3.1
266
34
0.37
8.7
0.60
57
51
102.5
8.9
656
194
0.78
16.5
2.35
71
216
92.7
5.2
32
74
0.47
10.6
1.01
61
79
64.2
4.3
784
123
0.81
17.4
1.90
45
102
181
22.5
32.4
1.5
2.6
659
822
58
90
0.80
0.69
24.0
21.2
1.20
1.51
34
39
Ohio
All
Reservoirs
Kentucky
Ohio
All
Lakes
ELA
UNDERC
All
The slope of the regression is the C:CHLa ratio; the intercept is the background (non-algal) C concentration (lg L-1). R2 values are for log–log
regressions; p \ 0.0001 for all models. CHLa and POC are mean values of the input datasets to the model. Algal C% is the proportional
contribution to POC
and Ohio) and the proportion of sites where nutrient limitation was likely ([6 E m-2 day-1) ranged from \5%
(Mississippi and Ohio) to 10% (Missouri).
Despite the potential importance of light limitation, the
cross-sectional average irradiance was not found to be a
useful predictor of site-specific CHLa. Characterizing the
underwater light environment of rivers in a manner that is
relevant to interpreting variation in phytoplankton abundance is a challenging endeavor. Of the three variables
needed to estimate underwater irradiance (depth, light
attenuation and incident solar radiation), light attenuation
is the most straightforward to quantify as it may be measured directly or inferred from water clarity metrics such as
Secchi depth, turbidity and TSS. In our study, the three
metrics were themselves strongly related (R2 = 0.74–0.90;
p \ 0.001) and our inferred kd values showed good
agreement with prior direct measurements of attenuation in
these rivers (see ‘‘Methods’’). Incorporating spatial–temporal variation of incident solar radiation was problematic
due to the sparse distribution of PAR monitoring stations
(Aber and Freuder 2000). Lacking site-specific data for our
123
500? sampling locations, we used a regionally representative average value for the period of study. Therefore, our
estimates of cross-sectional average irradiance represent
expected values for typical solar conditions which may
limit their utility for predicting CHLa given localized and
day-to-day variations in cloud cover. Without site-specific
measurements, we are unable to assess effects arising from
inter-site variation in incident PAR but, by substituting
date-specific measurements for the summer-average value,
we were able to simulate the effects of daily variation in
cloud cover on Ix-avg. The estimates based on daily versus
summer-average incident PAR were well-correlated
(R2 = 0.62; p \ 0.001), indicating that depth and water
clarity accounted for the majority of variation in Ix-avg.
Substituting the date-specific measurements of underwater
irradiance did not improve CHLa regressions. The daily
PAR values yielded a mean Ix-avg that was similar to the
estimate based on summer-average PAR (2.91 ± 0.15 and
2.53 ± 0.10 E m-2 day-1; respectively). Overall, this
analysis does not suggest that CHLa-irradiance relationships for these rivers can be improved by incorporating
Phytoplankton abundance and contributions to suspended particulate matter
100.0
Rivers
POC (mg L-1)
MS
10.0
MO
OH
1.0
0.1
100.0
Lakes
POC (mg L-1)
ELA
10.0
UNDERC
1.0
0.1
100.0
Reservoirs
POC (mg L-1)
KY
10.0
OH
1.0
0.1
0.1
1
10
100
-1
CHLa (µg L )
Fig. 5 Relationships between CHLa and POC observed in rivers (top
panel), lakes (middle panel) and reservoirs (lower panel). River data
are from this study: Ohio (OH), Upper Mississippi (MS) and Missouri
(MO). Lake data are from Canada (Experimental Lakes Area, ELA)
and Michigan (University of Notre Dame Environmental Research
Center, UNDERC). Reservoir data are from Kentucky (Hancock
Biological Station, KY) and Ohio (OH)
greater spatial and temporal resolution in incident light
measurements.
The most difficult challenge to estimating underwater
irradiance is the incorporation of spatial–temporal variation
in depth. We used the average cross-sectional depth at a
sampling location to characterize local irradiance, but this
does not take into account depth and therefore light conditions experienced by phytoplankton during transit to the
collection point. We tested the utility of using upstream
bathymetry to depict antecedent light conditions by
substituting the average depth derived over distances representing 1–3 days of transit time. These did not improve
light-CHLa relationships. Spatial–temporal averaging of
431
depth conditions resulted in uniformly low irradiance
because sites with high euphotic depth relative to channel
depth were rare (with the exception of the inter-reservoir
zone of the Missouri; Fig. 4). These analyses suggest that
variations in light availability due to changes in channel
morphometry occur at a fine scale relative to expected
phytoplankton generation times. Periodic exposure to high
light intensities may temporarily release phytoplankton
from light limitation despite overall low light availability
in these rivers (Mitrovic et al. 2003; Wagner et al. 2006;
Lavaud et al. 2007).
Based on previously reported thresholds for P limitation
(5–25 lg L-1; Hamilton and Schladow 1997), the proportion of sites potentially experiencing P limitation was
3–11% (Mississippi), 36–52% (Missouri) and 41–79%
(Ohio). The proportion of sites falling below the previously reported limitation threshold for DIN (150 lg L-1;
Hamilton and Schladow 1997) was lower (5 and 1%,
Mississippi and Ohio, respectively) except in the Missouri
(45%). TP was found to be the best predictor of CHLa in
the pooled dataset and for two of the three rivers (excluding
the Missouri where TN was the best predictor). However,
CHLa–TP regressions yielded low explanatory power
(R2 = 0.46) and were thus more similar to those derived
for benthic CHLa in streams than for suspended CHLa in
lakes and reservoirs (Dodds et al. 2002). Furthermore, our
CHLa–TP relationship remained linear and did not show
signs of saturation even at high TP concentrations occurring in the Mississippi. In systems where phytoplankton
account for a large fraction of particulate P, self-correlation
may arise because the independent variable (TP) includes P
from the dependent variable (phytoplankton). To assess
the potential influence of this effect, we estimated the
proportion of TP contributed by phytoplankton using
river-specific C:CHLa ratios and assuming Redfield stoichiometry. Phytoplankton accounted for a relatively small
proportion of TP (mean = 21 ± 3%) which is consistent
with prior work showing that P complexes with a variety of
suspended materials (Sutula et al. 2004; James and Larson
2008). In addition, the significant positive correlation
between CHLa and SRP (R2 = 0.26, p \ 0.0001) suggests
that the relationship between CHLa and P was not solely
due to self-correlation.
Although correlations should be viewed cautiously, our
data are suggestive of a causal relationship between TP and
CHLa in these rivers despite low light conditions. When
CHLa was normalized relative to local light conditions
(RUEI), the predictive power of TP improved (R2 = 0.60).
In rivers, co-limitation may arise as phytoplankton shift
between light- and nutrient-limiting conditions during
downstream transport. We observed inverse relationships
between light and nutrient availability in all three rivers
indicating that phytoplankton experience conditions of
123
432
high-light/low-nutrients
and
low-light/high-nutrients.
These inverse relationships occur in the absence of consistent spatial or temporal gradients in light and nutrient
availability (except in the Missouri) suggesting that shifts
in resource limitation occur over short spatial–temporal
scales (e.g., kms, days). Photosynthesis and respiration in
lakes varies over similar time scales due to changes in
incident solar radiation and mixing depth (Staehr and SandJensen 2007). In rivers, changes in resource availability
may be in response to localized nutrient inputs from tributaries and point sources, discharge-driven changes in light
attenuation, daily variation in incident solar radiation and
spatial variation in channel morphometry. Luxury uptake
of nutrients may enable rapid growth when light conditions
are favorable. The combined effects of light and nutrient
limitation on phytoplankton are poorly understood, in part
because responses to nutrient enrichment are often inferred
from bioassay-type incubations performed at near-surface
irradiances. Results from these experiments likely reflect
potential nutrient limitation (under optimal light conditions) rather than realized rates of nutrient utilization at
average in situ irradiance. More recent studies have taken
into account light attenuation, water column mixing and
incident solar radiation to measure nutrient utilization
under representative light conditions (Koch et al. 2004;
Oliver and Merrick 2006; Whalen and Benson 2007).
Water velocity was found to be a significant predictor of
CHLa but accounted for a small percentage of variation.
Prior studies have suggested that water velocities exceeding 0.5 m s-1 preclude phytoplankton development due to
short transit times that limit opportunities for converting
nutrients into new biomass (Gosselain et al. 1998b;
Ameziane et al. 2003). In the Mississippi, highest utilization efficiencies were associated with water velocities less
than 0.5 m s-1 and low utilization was observed when
velocities exceeded 1 m s-1. The negative relationship
between water velocity and resource utilization suggests
that conditions for phytoplankton growth were generally
favorable (Lucas et al. 2009). Recent work on a tributary of
the Mississippi (Minnesota River) has similarly shown that
longer transit times coincide with periods of increasing
CHLa and decreasing SRP (James and Larson 2008).
Despite favorable conditions, longitudinal increases in
CHLa were not observed in the Mississippi (see also
Houser et al. 2010), suggesting close coupling of growth
and loss rates.
Though our analysis of inter- and intra-river variation in
CHLa has emphasized main channel processes, CHLa
inputs from tributaries and lateral areas may also influence
river concentrations. Tributary CHLa concentrations often
exceeded those of the mainstem, particularly for the
Ohio and upper Missouri Rivers, but low tributary discharge resulted in small proportional contributions. The
123
P. A. Bukaveckas et al.
importance of tributary inputs may be in providing inocula
of phytoplankton which, if mainstem conditions are
favorable, allow for higher CHLa concentrations to be
attained over a shorter time and distance. For example,
the Big Sioux River joins the Missouri below the interreservoir zone where mainstem CHLa concentrations
were low (\10 lg L-1). Although the immediate effect
on the CHLa load of the Missouri was small, subsequent
production in the main channel may contribute to the
rapid rise in CHLa observed downriver. Lateral inputs
are diffuse and more difficult to quantify. Our sampling
was restricted to the main channel thus limiting our
abilities to assess the importance of retention features
within the river and in floodplain areas. The number of
retention features within 10 km upriver was a significant
but weak predictor of CHLa at a given sampling location. Wahl et al. (2008) reported a similar finding with
respect to backwater areas as sources of zooplankton to
the Illinois River.
Inferred estimates of filtration rates by dreissenids and
zooplankton suggest that microzooplankton (principally
rotifers) were the dominant grazers in these rivers. This
finding is consistent with prior studies showing that smallbodied zooplankton are favored in riverine environments
(Ferrari et al. 1989; Lair 2006; Wahl et al. 2008), but our
results suggest that their relative contribution to community filtration rates may be under-appreciated. Caution must
be used when deriving in situ grazing rates using per capita
values from other systems. Our prior work has shown
that grazing rates estimated in this fashion were wellcorrelated (R2 = 0. 84) with measured grazing rates in lakes
(Bukaveckas and Shaw 1998), though this approach may
over-estimate grazing rates in rivers due to interference by
non-algal particulate matter. Inferred grazing rates for the
three rivers largely reflect the high abundance of rotifers
obtained from microzooplankton (20 lm) samples. The
prevalence of rotifers and other small-bodied zooplankton
in rivers has been attributed to faster growth rates that
allow populations to attain higher density during transport
along the river course (Lair 2006; Havel et al. 2009).
Recent work also suggests that rotifers may be less sensitive
to detrimental effects associated with turbulence (Sluss
et al. 2008). Dreissenid mussels were common in two of the
three rivers (Ohio and Upper Mississippi) but were not
abundant and therefore inferred filtration rates were low
(\5% day-1). By comparison, dreissenid grazing rates of
5–119% day-1 have been reported for the Hudson River
(Roditi et al. 1996) where densities can be an order of
magnitude higher (1,000’s ind m-2). Our consideration of
grazing rates did not include other benthic consumers (e.g.,
unionids, benthic insects) or large pelagic filter-feeders
(e.g., gizzard shad, paddlefish), but despite their omission,
we estimate that grazers remove 17–52% of particulate
Phytoplankton abundance and contributions to suspended particulate matter
matter daily. Zooplankton filtration rates (macro ? micro)
were positively correlated with CHLa as has been observed
in other systems (Basu and Pick 1997). Positive associations with zooplankton abundance suggest that phytoplankton contributions to food resources in rivers are
important. Our prior work suggests that zooplankton in the
Ohio River are often food limited, exhibiting higher individual and population growth rates in response to elevated
CHLa (Guelda et al. 2005; Acharya et al. 2005, 2006).
These findings are consistent with results from stable isotope studies showing that autochthonous sources are
important to a wide range of riverine consumers (Delong
and Thorp 2006; Delong 2010).
Sedimentation also contributes to phytoplankton losses,
though analysis of its importance in rivers is lacking. Loss
rates due to sedimentation were estimated using previously
published values for phytoplankton sinking rates (mean =
1.6 ± 0.4 m day-1; Wetzel 2001). The sinking rate along
with depth and water velocity was used to calculate the
average distance that phytoplankton travel before settling
from the water column (Richardson et al. 2009). This value
was converted to percent loss per day for comparison with
grazing losses. Average CHLa loss rates due to sedimentation ranged from 18% day-1 (Ohio) to 41% day-1
(Missouri) and were comparable to average grazing rates
(17–52% day-1). Inter-river differences followed differences in depth with the shallowest river (Missouri)
exhibiting the highest sedimentation losses and the deepest
river (Ohio) the lowest. Combined loss rates (grazing
and sedimentation) were highest in the Mississippi
(83% day-1) followed by the Missouri (58% day-1) and
the Ohio (45% day-1). CHLa loss rates correspond to
turnover times of 2 days in the Ohio, 1.7 days in the
Missouri and 1.2 days in the Mississippi. These values
should be interpreted cautiously particularly with respect to
sedimentation losses as they do not take into account
benthic boundary layer effects on particle settling and
re-suspension. To assess whether our loss estimates were
realistic, we converted daily mass loss rates from CHLa to
C (using the previously-derived C:CHLa ratios) and estimated the rate of production required to balance losses
(i.e., to maintain uniform CHLa concentrations as observed
in the Ohio and Mississippi). For the Ohio, our estimate
of 81 mg C m-3 day-1 is similar to previously published
values (mean = 53 mg C m-3 day-1; Sellers and Bukaveckas 2003; Koch et al. 2004). For the Upper Mississippi,
our estimate of 577 mg C m-3 day-1 is also similar to
direct measurements of production (mean = 673 mg C m-3
day-1; J. Houser, pers. comm.). Our values show good
agreement with direct measurements of phytoplankton
production and suggest that our combined grazing and
sedimentation losses are reasonable. These estimates are
based on a number of untested assumptions and serve
433
principally to show that loss and turnover rates are high and
that both grazing and sedimentation are important.
Positive relationships between CHLa and POC in all
three rivers suggest that phytoplankton were important
contributors to the organic fraction of riverine particulate
matter. The strength of these relationships was generally
similar to those obtained from lake and reservoir datasets.
Algal contributions to POC were similar among rivers
(41%) and lakes (39%), though lower than those observed
in regional reservoirs (61%). It should be emphasized that
our estimates (inclusive of lake and reservoir data) are
based on summer samples and therefore represent annual
maxima. By comparison, Duan and Bianchi (2006) reported an annual average value of 12% for the lower
Mississippi and Acharya et al. (2006) estimated that less
than 2% of POC was algal-derived during periods of elevated discharge in the Ohio River. Overall, our findings
support other recent work suggesting that autochthonous
sources are a larger component of POC in these rivers than
previously thought (Duan and Bianchi 2006; Bianchi et al.
2007; Mayer et al. 2008). Estimation of algal contributions
to POC is sensitive to assumptions regarding C:CHLa
ratios. The range of values reported here for rivers, lakes
and reservoirs (20–100 lg lg-1) is within the lower range
of previously published estimates for oceanic, coastal
and estuarine environments (5–300 lg lg-1; Putland and
Iverson 2007). Our values were somewhat higher than
those reported for the turbid Schelde estuary (mean = 15,
range = 1–70; Lionard et al. 2008). However, comparison values from other turbid-advective systems suggest
a somewhat higher upper range: 140–165 lg lg-1 for
the Skidaway River Estuary (Verity 2002) and 23–
345 lg lg-1 for Apalachicola Bay (Putland and Iverson
2007).
Lastly, we estimated the output of algal C from the three
rivers to the Lower Mississippi based on the average discharge, CHLa and C:CHLa for each of the three rivers. The
average daily mass flux of algal C was lowest from the
Ohio (73 tonnes day-1), with higher and similar fluxes
from the Missouri (203 tonnes day-1) and Upper Mississippi (233 tonnes day-1). The Missouri yielded a
disproportionately large fraction of the algal C load (40%)
relative to its discharge (16%), whereas the opposite was
true of the Ohio (discharge = 46%, algal C = 14%). To
compare export losses with internal transformations, we
converted volumetric rates of grazing and sedimentation
losses to whole-river values. Estimated losses were
653 tonnes C day-1 (Ohio), 4,750 tonnes C day-1 (Missouri) and 10,800 tonnes C day-1 (Mississippi). For all
three rivers, export losses were small (\10%) in comparison with losses due to grazing and sedimentation,
suggesting that internal processes determine the fate of
algal C in these rivers.
123
434
A survey of the Ohio, Upper Mississippi and Missouri
Rivers revealed consistent and large differences in CHLa
among rivers located in adjacent watersheds and in similar
physiographic settings. The mechanisms that account for
inter-river differences in main channel CHLa are unclear as
comparisons to threshold values suggest a predominance of
light-limiting conditions, whereas correlation analysis
attributed greater explanatory power to nutrient concentrations. Nutrient concentrations and cross-sectional
average irradiance varied widely ([tenfold) within each of
the rivers and were inversely related suggesting that phytoplankton experience dynamic conditions of resource
limitation during transport. Growth and loss processes
appear to be closely coupled as inferred grazing and sedimentation losses were large, yet CHLa concentrations did
not decline downriver. Phytoplankton contributions to
particulate organic matter in these rivers were comparable
to those of lakes and reservoirs within the region.
Acknowledgments We are grateful to the EMAP GRE field crews
for their dedication in the collection of these data. PAB is also
grateful to the organizer, Jeff Houser, and participants of the 2010
meeting of the Mississippi River Research Consortium for their many
positive comments on this study. PAB was supported as a Fulbright
Scholar at the Klaipeda University Coastal Research and Planning
Institute during completion of this manuscript.
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