Limnol. Oceanogr., 52(5), 2007, 1978–1990
2007, by the American Society of Limnology and Oceanography, Inc.
E
Discontinuities in stream nutrient uptake below lakes in mountain drainage networks
Christopher D. Arp1 and Michelle A. Baker
Department of Biology and The Ecology Center, Utah State University, Logan, Utah 84322
Abstract
In many watersheds, lakes and streams are hydrologically linked in spatial patterns that influence material
transport and retention. We hypothesized that lakes affect stream nutrient cycling via modifications to stream
hydrogeomorphology, source-waters, and biological communities. We tested this hypothesis in a lake district of
{3
the Sawtooth Mountains, Idaho. Uptake of NO {
was compared among 25 reaches representing the
3 and PO 4
following landscape positions: lake inlets and outlets, reaches .1-km downstream from lakes, and reference
reaches with no nearby lakes. We quantified landscape-scale hydrographic and reach-scale hydrogeomorphic,
source-water, and biological variables to characterize these landscape positions and analyze relationships to
nutrient uptake. Nitrate uptake was undetectable at most lake outlets, whereas PO {3
4 uptake was higher at outlets
as compared to reference and lake inlet reaches. Patterns in nutrient demand farther downstream were similar to
lake outlets with a gradual shift toward reference-reach functionality. Nitrate uptake was most correlated to
sediment mobility and channel morphology, whereas PO {3
uptake was most correlated to source-water
4
characteristics. The best integrated predictor of these patterns in nutrient demand was % contributing area (the
{3
proportion of watershed area not routing through a lake). We estimate that NO {
3 and PO 4 demand returned to
50% of pre-lake conditions within 1–4-km downstream of a small headwater lake and resetting of nutrient
demand was slower downstream of a larger lake set lower in a watershed. Full resetting of these nutrient cycling
processes was not reached within 20-km downstream, indicating that lakes can alter stream ecosystem functioning
at large spatial scales throughout mountain watersheds.
Fluvial nutrient cycling is an increasingly important
research area because of concerns about nutrient loading
and eutrophication of freshwater and coastal ecosystems
(Alexander et al. 2000). Research on fluvial nutrient cycling
suggests that streams function importantly in retention and
transformation, but this functionality varies with discharge
(Peterson et al. 2001; Doyle 2005), channel morphology
and disturbance regimes (Valett et al. 2002; Bernhardt et al.
2003; Doyle et al. 2003), upstream source-waters (Alexander et al. 2000; Wollheim et al. 2001; Cross et al. 2005), and
biological productivity (Peterson et al. 2001; Hall and Tank
2003; Wollheim et al. 2006).
Broad goals of fluvial nutrient cycling research have
been to understand landscape-scale patterns that can be
1 To whom correspondence should be addressed (carp@usgs.
gov). Present address: U.S. Geological Survey, Alaska Science
Center, Anchorage, Alaska 99508-4650.
Acknowledgments
We thank A. Myers, A. Chartier, L. Symms, K. Nydick, B.
Brandewie, P. Brown, J. Rothlisberger, G. de Guzman, and K.
Goodman who helped with field and laboratory work. W.
Wurtsbaugh, K. Nydick, B. Koch, R. Hall, A. Marcarelli, M.
Gooseff, and J. Schmidt provided important logistical, technical,
and/or intellectual support. B. Koch, R. Hall, M. Valett, A.
Marcarelli, J. Stark, J. MacMahon, A. Huryn and two
anonymous reviewers provided helpful comments on earlier drafts
of this manuscript. We thank L. Dean and the Sawtooth National
Recreation Area and K. Grover-Wier and the Boise National
Forest for allowing us access to study areas where we did this
work.
This research was funded by the National Science Foundation
(DEB 01-32983), and C.A.’s graduate education was partially
supported by a Subsurface Science Fellowship from the Inland
Northwest Research Alliance and a STAR fellowship from the US
Environmental Protection Agency.
used to model and predict nutrient transport in large
watersheds and to identify locations of high nutrient
loading and retention in order to better focus watershed
management efforts (Alexander et al. 2000; Peterson et al.
2001). An example of a landscape-scale pattern in the
fluvial nutrient cycle is the inverse relationship observed
between stream size and inorganic N uptake (Peterson et al.
2001; Wollheim et al. 2001). Empirical research and
modeling studies both suggest that low-order, headwater
streams play a disproportionately large role in watershed
nutrient retention and transformation, whereas larger
middle- and high-order streams may function primarily as
transport systems (Alexander et al. 2000; Peterson et al.
2001). The mechanistic explanation for this landscape-scale
pattern is that small streams have higher proportions of
benthic substrate area relative to water flux and nutrient
supply, and these substrates provide the habitat for algae
and bacteria that remove nutrients from the water column
(Peterson et al. 2001; Wollheim et al. 2001). However, more
recent research suggests that relationships between stream
size and nutrient removal can vary with channel hydraulic
and geomorphic conditions, upstream nutrient inputs, and
biological activity, so that in certain drainage networks,
large rivers function importantly in nutrient removal
(Wollheim et al. 2006).
Long-standing concepts from fluvial geomorphology
(i.e., hydraulic geometry; Leopold and Maddock 1953)
and aquatic ecology (i.e., the river continuum concept;
Vannote et al. 1980) predict continuous downstream
changes in hydraulics, geomorphology, stream metabolism,
and biological communities. These downstream patterns
relate to nutrient retention, as demonstrated in the before
mentioned stream biogeochemical research (e.g., Alexander
et al. 2000; Peterson et al. 2001; Wollheim et al. 2001;
Wollheim et al. 2006). Incorporating such ideas with reach-
1978
Lakes affect stream nutrient uptake
1979
Fig. 2. Longitudinal profiles of the three watersheds where
multiple reaches were studied.
retention for some distance downstream in lotic ecosystems.
Accordingly, our goal was to understand if and to what
extent lakes affect downstream biogeochemical continua
and to use this understanding to improve concepts of
fluvial nutrient cycling in stream networks. We hypothesized that lakes would effect stream nutrient uptake
because of associated downstream discontinuities in channel geomorphology, upstream source-waters, and benthic
biomass, and that these effects on nutrient uptake would
gradually be reset downstream. Our work was conducted in
the Sawtooth Mountains Lake District (SMLD) in Idaho,
a mountain headwater region with many lakes.
Methods
Fig. 1. A drainage network map of the Sawtooth Mountain
Lake District showing the location of study reaches and the map
extent in Idaho, USA.
based studies of nutrient uptake may identify patterns of
stream biogeochemical continua of use for network models
of nutrient cycling.
A complementary set of ideas to fluvial continuum
concepts is the serial discontinuity concept ([SDC] sensu
Ward and Stanford 1983), which makes predictions of how
human-created dams and reservoirs modify or reset the
river continuum, and the process domain concept ([PDC]
sensu Montgomery 1999), which emphasizes the role of
localized controls on stream habitat as related to sediment
sources. Recent work has integrated the SDC and PDC by
showing that because natural lakes function as sediment
sinks they create abrupt transitions downstream in channel
geometry and sediment mobility (Arp et al. 2007). A larger
body of work on ecological characteristics of streams below
lakes also has demonstrated an abrupt transition in sourcewater characteristics (Magnuson and Kratz 2000) and
biological processes (Robinson and Minshall 1990; Vadeboncoeur 1994; Marcarelli and Wurtsbaugh 2006). Thus, it
might be expected that fluvial discontinuities, such as lakes
set in drainage networks, will affect nutrient cycling and
Study sites—We analyzed 25 stream reaches located in
eight watersheds of the SMLD (Fig. 1). These study
reaches were selected according to their position relative
to lakes: (1) reference, located in lakeless watersheds or
.1 km upstream of lakes; (2) lake inlets, located directly
above lakes; (3) lake outlets, located directly below lakes;
and (4) below-lake reaches, located .1-km downstream of
a lake. In three of these watersheds, we studied 5–6 reaches
spaced longitudinally along each stream (Fig. 2), and in the
other five watersheds, study reaches were located as paired
inlets and outlets with reference reaches located at similar
landscape positions (i.e., elevation, drainage area, and
valley form) in adjacent watersheds (Fig. 1; Table 1).
Reach lengths varied from 130 m to 840 m and were
selected according to consistent reach-scale geomorphology
(Frissell et al. 1986), as well as optimum length for stream
nutrient spiraling and hydraulic experiments (Stream
Solute Workshop 1990).
The SMLD is composed primarily of granites and
granodiorites with some metasedimentary rock. Most
valley bottoms upstream from lakes are composed of
mixed coarse- and fine-grained Pleistocene and Holocene
sediments, and downstream from lakes are mostly moraines and glacial outwash (Kiilsgaard et al. 2003). Five of
27.1
29.1
11.1
16.4
21.4
25.2
3.0
9.0
7.7
15.0
27.4
9.7
2.0
14.4
25.8
27.8
40.7
11.7
6.3
16.8
31.3
31.1
46.2
11.9
13.8
Reference
Beaver
Elk
Fir-1
Fir-2
Stanley Lake-1
Stanley Lake-2
Warm Springs-1
Warm Springs-2
Lake inlet
Pettit Lake-N
Pettit Lake-S
Stanley Lake-3
Warm Springs-3
Yellowbelly-1
Yellowbelly-3
Yellowbelly-5
Lake outlet
Pettit Lake
Stanley Lake-4
Warm Springs-4
Yellowbelly-2
Yellowbelly-4
Yellowbelly-6
Below lake
Hell Roaring
Stanley Lake-5
Warm Springs-5
Warm Springs—6
33.9
12.1
1.4
14.9
0.2
0.1
0.1
0.1
0.0
0.8
75.8
75.8
91.2
100.0
32.2
30.8
28.2
100.0
100.0
100.0
100.0
89.9
91.4
100.0
100.0
CA
(%)
2.3
1.4
1.6
1.5
7.1
1.6
1.7
4.4
2.8
3.9
5.4
2.8
,0.1
0
1.3
2.2
1.8
0
0
0
,0.1
,0.1
0
0
0
LA
(%)
0.26
1.50
0.49
0.14
0.73
0.14
0.10
4.18
1.00
0.41
0.21
0.91
0.10
0.25
0.84
5.06
0.13
1.53
0.11
0.51
0.57
0.93
0.33
1.37
0.65
S
(%)
13
84
19
10
45
29
16
205
98
45
15
28
5
11
83
92
9
42
8
20
18
40
10
24
10
D50
(mm)
1.03
0.96
1.01
0.43
0.68
0.34
0.37
0.37
0.67
0.39
0.74
1.86
1.24
1.16
0.33
1.79
0.83
1.85
0.74
0.94
2.05
1.21
2.16
2.10
4.27
to : tcr
21.5
19.1
19.0
18.5
19.1
21.4
20.0
18.0
19.0
21.0
10.8
13.3
15.1
10.5
16.0
16.0
17.0
10.8
12.5
14.2
14.7
12.4
14.9
9.5
10.0
36.0
19.0
—
77.7
115.0
24.0
78.4
—
—
48.0
47.2
59.9
36.4
27.1
—
—
60.7
17.0
18.3
80.0
84.4
28.8
37.0
42.0
29.2
Temp
TDN
(Cu) (mg L21)
3.0
3.5
—
7.7
3.0
2.3
2.3
—
—
2.6
1.1
2.0
2.4
4.8
—
—
2.2
4.0
3.0
3.0
5.0
3.2
2.0
1.0
0.8
TNP
(mg L21)
—
—
—
—
76.0
60.7
97.1
64.4
58.4
54.2
96.0
84.9
37.2
23.2
25.6
77.7
58.9
—
—
—
—
38.0
22.0
—
—
TN
(mg L21)
—
—
—
—
1.7
8.3
18.5
3.3
13.9
7.9
7.6
4.9
3.4
14.1
1.5
3.2
14.3
—
—
—
—
15.0
3.3
—
—
1.0
1.2
—
1.5
1.2
1.3
1.5
—
—
1.5
0.9
0.6
0.5
0.7
—
—
0.5
1.0
0.8
1.1
1.1
1.2
0.6
0.7
0.7
TP
DOC
(mg L21) (mg L21)
0.23
2.03
—
1.70
1.41
0.48
4.20
—
—
1.20
0.13
0.39
0.15
0.70
—
—
0.20
1.53
0.22
0.40
1.20
0.06
0.13
0.10
0.87
Epilithon
(mg cm-2)
4.9
8.4
—
5.0
1.9
2.4
8.0
—
—
—
12.7
—
2.5
0.7
—
—
—
9.6
35.2
18.1
4.5
—
2.1
—
2.2
FBOM
(mg cm22)
* DA, drainage area; CA, % contributing area; LA, upstream lake area; S, stream gradient; D50, median bed sediment size; Temp, water temperature; TDN, total dissolved nitrogen; TDP; total
dissolved phosphorous; TN, total nitrogen; TP, total phosphorus; and DOC, dissolved organic carbon; and FBOM, fine benthic organic matter.
DA
(km2)
Landscape-scale and reach-scale variables for study reaches.
Reach Type
Site (Creeks)
Table 1.
1980
Arp and Baker
Lakes affect stream nutrient uptake
Table 2.
1981
Summary of primary stream solute injection variables and measurements.
Nitrate
Phosphate
Reach Type Site
(Creek)
Q
(L s21)
w
(m)
h
(m)
AS : A
a
(s21)
qLin
(L s21 m21)
[NO3-N]
(mg L21)
Sw
(m)
EF
[PO4-P]
(mg L21)
Sw
(m)
EF
Reference
Beaver
Elk
Fir-1
Fir-2
Stanley Lake-1
Stanley Lake-2
Warm Springs-1
Warm Springs-2
334
303
189
250
445
580
156
177
5.9
6.4
3.8
4.9
7.6
6.8
2.3
4.4
0.18
0.22
0.27
0.24
0.16
0.27
0.24
0.33
0.21
0.52
0.15
0.05
0.12
0.21
1.40
0.12
0.00018
0.00018
0.00018
0.00018
0.00018
0.00013
0.00012
0.00018
0.01
0.00
0.03
0.21
0.19
0.39
0.05
0.15
2.0
3.7
28.2
51.7
12.3
8.1
8.8
5.7
7,692
2,778
1,337
390
5,882
2,128
1,282
541
8.8
4.0
1.5
1.2
3.1
5.3
2.7
4.2
3.3
2.9
3.0
3.8
2.6
bd
bd
bd
1,408
1,282
3,460
877
2,079
2,217
—
—
3.2
3.7
3.3
2.6
4.6
5.0
—
—
Lake inlet
Pettit Lake-N
Pettit Lake-S
Stanley Lake-3
Warm Springs-3
Yellow Belly-1
Yellow Belly-3
Yellow Belly-5
69
178
670
232
78
409
701
4.9
4.0
12.0
2.8
4.8
6.1
8.2
0.18
0.20
0.20
0.32
0.21
0.24
0.38
0.15
0.17
0.33
0.64
0.17
0.03
0.19
0.00009
0.00037
0.00068
0.00007
0.00028
0.01400
0.00078
0.06
0.20
0.43
0.11
0.13
0.67
0.11
25.3
17.4
13.3
5.1
7.5
21.0
24.8
1,563
1,370
‘
1,754
455
345
1,785
5.2
5.3
3.0
3.6
4.2
4.0
2.4
bd
1.8
1.5
bd
bd
bd
bd
637
1,224
0
600
286
244
2,439
5.0
5.0
6.0
3.5
2.8
2.9
8.0
Lake Outlet
Pettit Lake
Stanley Lake-4
Warm Springs
Yellow Belly-2
Yellow Belly-4
Yellow Belly-6
410
830
167
254
619
760
7.9
12.8
9.7
3.7
6.7
15.4
0.17
0.41
0.25
0.23
0.43
0.16
0.15
0.39
0.17
0.13
0.14
0.18
0.00031
0.00092
0.00012
0.00055
0.00038
0.00016
0.88
3.03
0.02
0.11
1.00
1.20
11.2
1.5
1.2
13.0
15.5
7.1
7,143
‘
‘
‘
‘
‘
2.7
7.5
2.2
3.2
4.1
3.5
2.0
1.3
bd
bd
bd
bd
348
535
307
253
1,664
990
10.0
13.0
3.1
3.6
2.1
6.0
Below lake
Hell Roaring
Stanley Lake-5
Warm Springs-5
Warm Springs-6
610
704
238
276
7.4
11.4
4.2
3.6
0.26
0.15
0.23
0.36
0.09
0.12
0.66
0.34
0.00023
0.00021
0.00029
0.00010
0.07
0.77
0.04
0.08
17.1
4.3
1.7
4.1
2,778
‘
‘
2,681
1.9
4.8
3.1
2.9
2.0
2.8
bd
1.7
781
848
—
—
3.5
5.0
—
—
* DQ is stream discharge, w is mean stream width, h is mean stream depth, As : A is the relative storage zone area, a is the storage zone exchange
coefficient, qLin is groundwater influx, Sw is the uptake length, and EF is the enrichment factor above background; bd is below analytical detection limits.
the lakes we studied were formed by moraine dams,
whereas the other two, Farley and Toxiway Lakes in the
Yellowbelly Creek watershed, are paternoster lakes set in
glacially scoured bedrock (Fig. 2). The highest peaks in the
Sawtooth Mountains are .3,000 m and the Salmon River
at Stanley, Idaho is ,2,000 m. All study reaches occur
between 2,050 m and 2,150 m elevation, have gravel to
cobble beds, and have ,2% channel gradient, except the
four higher elevation reaches that are steeper with bedrock
or colluvial channels (Tables 1 and 2).
The SMLD has a snow-dominated precipitation regime,
averaging 90 cm with .60% as snow. Streamflow is
dominated by spring snowmelt peakflows, and stable
baseflow conditions are usually reached by early fall.
Bankfull discharges (QB) for most third to sixth order
streams of the SMLD have 1.5–2-yr recurrence intervals
(Emmett 1975). Most surface waters throughout the
SMLD have low ionic strength, usually ,70 mS cm21
electrical conductance, circaneutral pH, and low alkalinity
and nutrient concentrations (Emmett 1975). Periphyton
production is typically co-limited by nitrogen and phosphorus (Marcarelli 2006).
Forests in the SMLD are dominated by lodgepole pine
(Pinus contorta), and most riparian plant communities are
composed of willows (Salix spp.), birch (Betula spp.), alder
(Alnus incana), sedges (Carex spp.), and various grasses and
herbs. Forest proximity to our study-reach channels
averaged 26 m and ranged from 1 m to 150 m, varying
more with elevation than landscape position relative to
lakes (Arp et al. 2007). Most watersheds we studied have
some alpine terrain with treeline near 2,800-m elevation. All
watersheds we worked in are relatively pristine and are
official or de facto wilderness with minor recreational
land use and low atmospheric nitrogen deposition,
,100 kg km22 yr21 (NADP 2001).
Conservative tracer and nutrient uptake experiments—All
tracer experiments were conducted in late July and early
August of 2003 and 2004 during summer baseflow
conditions at the mid-portion of sunny days. For inlet–
outlet pairs, nutrient injections were conducted simultaneously above and below the lake. The day before each
experiment, we measured the reach length using a tape held
along the channel thalweg and marked 8–10 sampling
1982
Arp and Baker
stations spaced longitudinally at geometric intervals starting 25–50 m below the injection station to ensure full tracer
mixing. Just before nutrient injections, background water
samples were collected at each station. Discharge was
measured using an electronic velocity meter and wading
rod at the reach top and bottom to use for hydraulic
modeling of conservative tracers, calculating uptake
parameters, and estimating lateral water inflow (qL-IN)
and outflow (qL-OUT). We installed a continuously recording submersible fluorometer at the bottom of the reach
to measure rhodamine WT (RWT). Nutrients (NO {
3 as
as K2PO4 salts) and a water tracer
NaNO3 and PO {3
4
(RWT) were mixed with stream water in a 20-liter plastic
carboy and pumped into the stream at a constant rate with
a metering pump. Although co-injection of nutrients can
pose analytical problems, our plateau concentrations were
targeted so as to not alter nitrogen : phosphorus (N : P)
ratios or exceed saturating concentrations (Davis and
Minshall 1999; Mulholland et al. 2002). We conservatively
targeted pump rates to elevate nutrient concentrations to
23 ambient, by estimating discharge and ambient NO {
3
and PO {3
4 concentrations from earlier samples. In practice,
several injections were .53 ambient concentration; however, these differences in enrichment did not appear to
{3
correlate with uptake rates for either NO {
3 or PO 4
(Table 2). We collected samples of stock solution and
measured pump rates three times during the experiment to
verify solute loads. RWT concentration was monitored at
the end of the reach until a steady plateau was reached,
typically at ,43 the nominal travel time. When plateau
concentration was reached, we collected grab samples and
measured RWT concentrations using a hand-held fluorometer at each station. Duplicate samples were collected
for ,10% of stations.
An inverse modeling approach was used to fit the RWT
breakthrough curve data using the one-dimensional transport with inflow and storage model with parameter
estimation (Runkel 1998). The primary hydraulic parameters of interest from these experiments were qL-IN, qL-OUT
and storage-zone area (AS), and exchange coefficient (a).
We estimated qL-IN and qL-OUT by a combination of
longitudinal dilution of RWT concentration at downstream
stations as compared to discharge measurements at the
reach head and tail.
{3
were
Nutrient uptake parameters for NO {
3 and PO 4
calculated from longitudinal decline in concentration
downstream to estimate uptake length (SW) after correcting
for dilution by qL-IN and background concentrations:
SW ~ {1=kw
ð1Þ
where kw is the slope from the least-squares linear
regression of downstream reach distance versus the
natural-log of background-corrected nutrient concentrations (Stream Solute Workshop 1990). Based on kw, uptake
velocity (vf) was calculated as
vf ~ u | h | k w
ð2Þ
where u is mean stream velocity, and h is mean stream
depth. The aerial uptake rate (U) was calculated as
U ~ vf | C 0
ð3Þ
where C0 is the background nutrient concentration (some
were undetectable,
background concentrations for PO {3
4
so we used half the detection limit concentration) (Stream
Solute Workshop 1990).
Reach hydrogeomophic, source-water, and biological
characterization—The channel form of each study reach
was measured by topographic surveying of channel
longitudinal profiles and cross-sections using a engineer’s
level, leveling rod, and plastic tape. Streambed pebble
counts (n 5 100) were used to estimate reach sediment size
distributions (i.e., D50 is the median particle size).
Channel gradient (S) and bankfull depth (DB) were
measured from longitudinal plots of the channel bed, water
surface, and floodplain (bankfull elevation) fit separately
with a least-squares line, where DB is the average elevation
distance between the bed and bank (Emmett 1975). Crosssection surveys were used to measure channel area (AB) and
hydraulic radius (RB). Bed sediment mobility was predicted
as the ratio of boundary shear stress (to) and critical shear
stress (tcr):
to ~ rf gRB S
ð4Þ
tcr ~ t g rs { rf D50
ð5Þ
where t* is the critical dimensionless shear stress assumed to
be 0.045 (Knighton 1998; Lorang and Hauer 2003), g is
gravity, rs is the particle density using 2,650 kg m23, and rf is
the fluid density of water. Values of to : tcr . 1 indicate that
D50 sediments are mobile at bankfull discharge (QB), whereas
values of to : tcr , 1 indicate that D50 sediments are immobile
at QB. Site drainage areas and other hydrographic metrics
were measured from 30-m digital elevation models (DEMs).
Stream-water grab samples were collected and filtered
(0.7-mm Whatman GF/F) into acid-washed 120-mL high
density polyethylene (HDPE) bottles and kept at ambient
stream temperature until transported to our field station
and frozen (typically within 3–6 h, but always ,2 d).
Unfiltered samples were also collected at a majority of the
sites and frozen before processing for total nitrogen (TN)
and total phosphorus (TP).
Dominant benthic biomass compartments were quantified by sampling standing stocks as ash-free dry mass
(AFDM) per unit area. Epilithon was measured by
collecting and scraping 3–5, D50–D84-sized sedimentparticles at five locations along each reach. The scraped
slurry was measured volumetrically and collected on a 0.7mm filter and frozen. Fine benthic organic matter (FBOM)
was collected per unit area using a 20-cm-diameter stove
pipe set into bed sediments at five locations along each
reach. Sediments were agitated to 1–2-cm depth, and the
slurry was collected, measured volumetrically, collected on
a filter, and frozen. Epilithon and FBOM samples were air
dried to a constant weight, and AFDM was measured by
mass loss on ignition at 450uC for 4 h.
Lakes affect stream nutrient uptake
1983
Analytical chemistry—Nitrate (NO {
3 -N) and phosphate
(PO 4 -P) were measured using a DIONEX-500 ion chromatograph with concentrator and AS14 analytical and
guard column set. Detection limits were ca. 0.5 mg L21 for
NO 3 -N and 2 mg L21 PO4-P. Total dissolved nitrogen
(TDN), total dissolved phosphorus (TDP), TN, and TP
were measured using persulfate oxidation (Valderama
1981) followed by NO3-N and PO4-P quantification using
second derivative spectroscopy for NO3-N (Crumpton et
al. 1992) and colorimetry for PO4-P (Motomizu et al. 1983).
Detection limits were 15 mg L21 and 51 mg L21 for these
measures. Dissolved organic carbon (DOC) was measured
using wet persulfate oxidation on an OI 700 TOC analyzer
with detection limit of 50 mg L21 (Menzel and Vacarro
1964).
Statistical analysis and modeling—Study reaches were
placed into four categories by landscape position relative to
lakes: lake inlets (n 5 7), lake outlets (n 5 6), .1-km below
lakes (n 5 4), and reference reaches (n 5 8). All reach
variables and nutrient uptake parameters were compared
by landscape position using a one-way analysis of variance
(ANOVA) and Tukey-Kramer test with significance at a 5
0.05. We also compared these reach variables and nutrient
uptake parameters longitudinally downstream in three
individual watersheds where we studied 5–6 sequential
reaches. Relationships among reach variables and between
reach variables and nutrient uptake parameters were
evaluated using Pearson’s correlation analysis with significance at a 5 0.05. To cast our results in a broader context,
we also developed multiple linear regression models based
on continuous landscape variables to estimate changes in vf
downstream in watersheds with and without lakes, and
with lakes of differing sizes and watershed positions. All
variables were evaluated using residual plots to assess
normality and equal variance and, if necessary, transformed, typically with natural-log or square-root transformations.
Results
Patterns in nutrient uptake—Uptake lengths (Sw) varied
{3
among lake-associated
considerably for NO {
3 and PO 4
landscape positions as did stream discharge at the time of
nutrient injections (Table 2). However uptake length (Sw)
2
was unrelated to discharge for both Sw-NO {
3 (r , 0.01)
2 5 0.03). The S -NO { in these streams
(r
and Sw-PO {3
w
3
4
ranged from 350 m to .7,000 m, but eight of these reaches
(flat or non-significant
had infinitely long SW-NO {
3
longitudinal declines in concentration) indicating no deranged from 250 m to
tectable uptake. The Sw-PO {3
4
.3,000 m, with only one reach having undetectable PO {3
4
uptake (Table 1).
Nitrate uptake velocity showed strong differences among
landscape positions (r2 5 0.41, p , 0.01) with significantly
higher mean rates of 2.8 mm min21 (60.9 SE) at reference
reaches and 3.1 mm min21 (61.5 SE) at lake inlets as
compared to 0.07 mm min21 (60.07 SE) at lake outlets
and 0.9 mm min21 (60.5 SE) at reaches .1-km below
lakes (Fig. 3A). In fact, all but one lake-outlet reach had no
Fig. 3. (A) Mean nutrient velocity and (B) aerial uptake rate
of study reaches categorized by landscape position (bars are one
standard error, r2 and p values are from a one-way analysis of
variance, and different letters indicate significant differences at a 5
0.05 from a Tukey-Kramer test).
detectable NO {
3 uptake, and this one, Pettit Lake Creek,
was still very low: 0.4 mm min21. Patterns of the areal
uptake rate (U) among landscape positions were similar to
vf (r2 5 0.43, p , 0.01) (Fig. 3B).
Phosphate uptake velocity also showed strong differences among landscape positions (r2 5 0.31, p 5 0.08), but
nearly opposite patterns as observed for NO {
3 . Average vf was highest at lake outlets, 7.0 mm min21 (62.1
PO {3
4
SE), and lowest at reference reaches, 2.2 mm min21 (60.4
SE) (Fig. 3A). At reaches .1-km downstream from lakes,
was also high, 6.1 mm min21 (61.0
the average vf -PO {3
4
among landscape positions
SE). Comparison of U-PO {3
4
showed slightly greater mean differences (r2 5 0.32, p 5
0.065) because of variation in PO 4 -P concentration among
reaches, such that U was slightly more related to landscape
position than vf (Fig. 3A,B).
1984
Arp and Baker
Fig. 4. Patterns of uptake velocity (vf) in the three watersheds with multiple study reaches.
These patterns in nutrient uptake among lake-associated
landscape positions of stream reaches could be more closely
observed longitudinally at the three individual watersheds
where we did nutrient uptake experiments farther upstream
and downstream from lakes (Fig. 2). At Warm Springs
Creek with one small lake (0.28 km2 surface area), vf -NO {
3
declined slightly approaching the lake, whereas at the lake
dropped to below detection limits. In
outlet vf -NO {
3
increased to 3.0 mm min21 and incontrast, vf -PO {3
4
creased farther downstream to 7.7 mm min21 (Fig. 4A). At
Stanley Lake Creek with a single larger lake (0.65 km2),
a similar pattern was observed with the exception of the
{3
uptake
lake inlet, which had no detectable NO {
3 or PO 4
(Fig. 4B). Yellowbelly Creek has three large lakes spaced
across a 15-m distance that vary by ,400-m in elevation
(Fig. 2). In this watershed, our results showed a similar
pattern at the first lake to that observed in the other
watersheds with single lakes—no NO {
3 uptake and high
uptake at the lake outlet. However, at the next two
PO {3
4
remained low at lake outlets,
lakes downstream, vf -PO {3
4
whereas the pattern for vf -NO {
3 of higher rates at inlets
and very low rates at outlets still occurred at the lower lakes
(Fig. 4C).
Stream- reach characteristics by landscape position—A
number of consistent and significant differences ( p , 0.05)
were noted in hydrogeomorphic, source-water, and biological variables among landscape positions. Stream
channels at lake outlets had significantly coarser sediment
and were wider and shallower than lake-inlet and reference
reaches (Fig. 5A). Our estimates of excess shear stress
(to : tcr) at QB indicated that on average, inlet and reference
reaches had mobile D50 bed sediment (to : tcr . 1), whereas
reaches below lakes consistently had immobile sediments
(to : tcr , 1, Fig. 5B). Stream discharge during our
experiments was statistically similar among landscape
positions, averaging 390 L s21 and ranging from 70 L s21
to 830 L s21 (Table 1). However, groundwater influx (qLIN) was significantly higher at lake outlets, 1.04 6 0.45 L
s21 m21 , as compared to other landscape positions, 0.19 6
0.05 L s21 m21 (Fig. 5B); although at one lake outlet reach,
Warm Springs Creek, groundwater influx was very low,
0.02 L s21 m21, during the injection experiment.
Source-waters for each reach landscape-position were
best distinguished by DOC (r2 5 0.68, p , 0.001) and
temperature (r2 5 0.75, p , 0.001) with higher values of
both consistently occurring at lake outlets and below-lake
reaches as compared to lake inlet and reference reaches
(Fig. 5C,D). Nitrate concentrations ranged from 1.7 mg N
concentrations were
L21 to 52 mg N L21, whereas PO {3
4
,4 mg L21 (as P) in all reaches and often undetectable
(Table 1). TN, TP, TDN, and TDP also varied considerably among streams and watersheds, but no consistent
patterns were noted with respect to landscape position
associated with lakes (Table 1). Although mean molar N : P
ratios did not differ significantly among landscape positions, five of seven inlet reaches were more likely P limited
(N : P . 16), and four of six outlets were more likely N
limited (N : P , 16). Stream bed epilithon biomass was
significantly lower at lake-inlets and reference reaches as
compared to lake outlets, while FBOM standing stocks
showed no clear patterns related to landscape position
(Fig. 5E).
Percent contributing area (%CA), the proportion of the
watershed area not routing through a lake, was the best
variable describing reach landscape-position relative to
lakes (r2 5 0.86, p , 0.001) (Fig. 5F). A comparison of
%CA, a landscape-scale variable, to reach-scale physical,
chemical, and biological variables showed many significant
correlations, including a positive correlation to to : tcr (r 5
+0.62, p , 0.001) and negative correlations to the channel
width : depth (W : D) ratio (r 5 20.69, p , 0.001), qL-IN (r
5 20.47, p , 0.01), DOC (r 5 20.63, p , 0.001), water
temperature (r 5 20.88, p , 0.001), and epilithon biomass
(r 5 20.54, p , 0.01) (Table 3). Drainage area (DA) was
similar among landscape positions (Fig. 5F), ranging from
2 km2 to 46 km2, but was significantly correlated to W : D
(r 5 +0.53, p , 0.01), qL-IN (r 5 +0.58, p , 0.001), and
temperature (r 5 +0.45, p , 0.05) (Table 3).
Relationships to nutrient uptake—Uptake velocity (vf) of
{3
were not correlated among our study
NO {
3 and PO 4
reaches (r 5 +0.14), and similarly, uptake rates (U) of these
nutrient forms were not correlated (r 5 +0.11) (Table 3),
indicating that these processes may have been controlled by
{3
independent factors. Neither NO {
3 nor PO 4 uptake were
Lakes affect stream nutrient uptake
1985
Fig. 5. Mean landscape position characteristics of study reaches categorized by landscape position (bars are one standard error, r2
and p values are from a one-way analysis of variance, and different letters indicate significant differences at a 5 0.05 from a TukeyKramer test).
+0.20 20.63*
+0.45* 20.88*
20.13 20.20
+0.15
20.37
20.27
+0.01
+0.19
Source–water variables
DOC
Temp
N/P
Biological variable
Epilithon
Nutrient uptake parameters
vf–NO3
U–NO3
vf–PO4
U–PO4
+0.20
+0.47* 20.35
+0.48* 20.21
20.65
+0.44*
20.39
+0.25
20.54*
.29
+0.44*
+0.68*
+0.53* 20.69* +0.55*
20.20
+0.62* 20.42*
+0.58* 20.47* +0.29
20.12
+0.32 20.23
+0.04
20.63*
Hydrogeomorphic variables
W:D
to : tc
qL–in
Ts
20.63*
20.21
% LA
20.21
+0.04
% CA
Landscape variables
DA
% CA
% LA
DA
20.55*
20.52*
+0.32
+0.20
+0.35
+0.52*
+0.75*
+0.25
20.46*
+0.64*
20.17
+0.53*
20.69*
+0.55*
W:D
qL-in
Ts
DOC
Temp
N/P
+0.12
+0.25
+0.06
+0.04
20.23
+0.61* +0.42
+0.14
+0.59* +0.01
+0.59*
+0.09
+0.01 +0.09
+0.75*
20.61*
+0.52*
20.33
20.29 20.44* 20.44* +0.04
20.29 20.40* 20.41* +0.07
20.16 +0.57* +0.52* +0.33
20.09 +0.53* +0.36 +0.29
20.05
+0.33 20.06
+0.52* 20.33
+0.04 20.23
+0.64* 20.17 +0.52
20.24 20.06 20.52*
20.32 +0.33
20.32
20.06
+0.57* 20.29
+0.46* 20.34
20.15
+0.29
+0.04
+0.17
20.16
20.52*
20.61*
+0.06
20.24
20.06
20.46*
20.20
+0.58* 20.12 +0.20 +0.45* 20.13
+0.62* 20.47* +0.32 20.63* 20.88* 20.20
20.42* +0.29 20.23
.29 +0.44* +0.68*
to : tc
20.33
20.42*
+0.34
+0.28
+0.61*
+0.42
+0.14
+0.35
20.16
+0.12
20.05
+0.15
20.54*
+0.20
20.52*
+0.46*
20.34
20.29
+0.32
20.15
+0.29
20.16
+0.20
+0.04
+0.17
20.09
20.42* +0.34
+0.28
+0.93* +0.14 +0.18
+0.93*
+0.02 +0.11
+0.14 +0.02
+0.86*
+0.18 +0.11 +0.86*
20.33
20.44* 20.40* +0.57* +0.53*
20.44* 20.41* +0.52* +0.36
+0.04 +0.07 +0.33 +0.29
20.55*
+0.57*
20.29
20.29
20.37 20.27 +0.01 +0.19
+0.47* +0.48* 20.65* 20.39
20.35 20.21 +0.44* +0.25
Epi-lithon vf-NO3 U-NO3 vf-PO4 U-PO4
Table 3. Correlation matrix of reach landscape. (DA is drainage area, % CA is contributing area, % LA is upstream lake area), hydrogeomorphic (W : D is the channel
width to depth ratio, to : tc is the shear stress ratio, qL-in is groundwater influx, Ts is the storage zone residence time), source-water (DOC is dissolved organic carbon, N/P is
the total dissolved nitrogen to phosphorous ratio), and biological variables and nutrient uptake parameters (vf is the uptake velocity and U is the areal uptake rate) (values are
Pearson’s correlation coefficients and * indicates p , 0.05).
1986
Arp and Baker
Lakes affect stream nutrient uptake
correlated to TDN : TDP (Table 3) or TN : TP for a more
limited set of study reaches (lake inlets and outlets only).
For sites with TN : TP . 16, U-PO4 was positively
correlated with TN : TP (r 5 +0.59, p , 0.05).
Variation in vf -NO {
3 was most strongly correlated to the
hydrogeomorphic variables to : tcr (r 5 +0.57, p , 0.01) and
was most
W : D (r 5 20.55, p , 0.01), while vf -PO {3
4
strongly correlated with the source-water variables temperature (r 5 +0.52, p , 0.01) and DOC (r 5 +0.57, p , 0.01).
These same source-water variables were also correlated to
vf -NO {
3 , but in the opposite direction and to a lesser, yet
still significant, degree (Table 3). The hydrogeomorphic
variables to : tcr and W : D were not significantly correlated
uptake. The biological variable epilithon had
with PO {3
4
a weak negative correlation to NO {
3 uptake and a weak
uptake
(Table 3).
positive correlation to PO {3
4
Landscape-scale variables describing the position of
stream reaches relative to lakes, specifically %CA, were
shown to integrate relationships among many reach
hydrogeomorphic and source-water variables (Table 3)
and were also significantly correlated to nutrient uptake:
{3
(r 5 20.65, p
vf -NO {
3 (r 5 +0.47, p , 0.05) and vf -PO 4
, 0.001). Percent lake area upstream (%LA) was also
positively correlated to vf -PO {3
4 (r 5 +0.44, p , 0.01). The
main descriptor of watershed position and general stream
size, DA, was not related to any metrics of nutrient uptake
(Table 3).
Because %CA was shown to be the best representation
of landscape position (r2 5 0.86, Fig. 5F), we used this
metric coupled with DA to predict changes in vf -NO {
3
downstream through a lake (vf -NO {
3 5 2.72–0.12 3 DA 2
0.013 3 %CA; r2 5 0.32, p , 0.05). A similar model was
by also adding %LA (vf -PO {3
5
developed for PO {3
4
4
2.56–0.08 3 DA 2 0.013 3 %CA 2 0.07 3 %LA; r2 5
0.44, p , 0.01). We used these landscape-scale predictor
models to estimate the extent to which lakes modify vf in
two watersheds: (A) with a small headwater lake and (B)
with a larger lake located farther downstream (Fig. 6). In
both watershed configurations, these models predicted that
vf -PO {3
4 abruptly increased below lakes by ,400% and vf NO {
3 more slightly decreased below lakes by ,200%
compared to watersheds without lakes. Further comparisons showed that in watershed A, vf -NO {
3 was reset by
50% within 1.3-km downstream of the lake and 75%
in watershed A,
within 5.5-km downstream. For vf -PO {3
4
resetting occurred by 50% within 3.8-km downstream and
75% within 10.5-km downstream (Fig. 6). In watershed B,
with a larger lake positioned lower in the watershed, 50%
reset was not reached for vf for either nutrient within the
limits of the empirical data used in developing these
models—approximately 16 km of mainstem stream distance or an approximate 50 km2 drainage area.
Discussion
Nutrient uptake in a mountain lake district—Streams in
the SMLD varied widely in nutrient uptake parameters,
and much of this variation was explained by landscape
position relative to lakes. We observed large shifts in
{3
in streams affected by lakes
demand for NO {
3 and PO 4
1987
Fig. 6. A conceptual model showing downstream patterns in
uptake velocity for (A) a 0.1-km2 lake with a 5-km2 watershed and
(B) a 1.0-km2 lake with a 15-km2 watershed. This model is based
on multiple regression models of data from this study using
drainage area, % contributing area, and lake area (vf-PO {3
4 only)
as predictors of uptake velocity.
compared to those unimpacted by lakes, and these
differences persisted well below lakes in mountain drainage
networks. Overall, our results support the hypothesis that
sharp and contrasting shifts in demand for NO {
3 and
PO {3
4 measured below lakes and much farther downstream
(.1 km) are controlled by a combination of stable benthic
habitat and lake-derived source-waters and how these
habitats and resources structure benthic biological communities and their nutrient requirements.
Our results were not consistent with predictions from
ecological stoichiometry (Cross et al. 2005) in that, based
on molar N : P ratios, lake outlets were more likely to be N
limited, but exhibited little demand for NO {
3 whether
measured as vf or U. Similarly, inlet reaches were more
likely to be P limited, yet had moderate to high demand for
{3
both NO {
when measured as vf, and high
3 and PO 4
{
demand for NO 3 and low demand for PO {3
when
4
measured as U. This suggests that water column stoichiometry alone may not be sufficient for understanding
controls on nutrient uptake in streams.
Deviations from stoichiometric theory might indicate
that dissimilatory pathways (e.g., denitrification and
dissimilatory nitrate reduction to ammonia) are important
for nitrate removal from the water column (Burgin and
Hamilton 2007). However, we note here that NO {
3 only
1988
Arp and Baker
represents a portion of inorganic N demand, and measurements from one lake outlet in the SMLD showed moderate
uptake rates for NH z
4 (Koch 2005). Nitrogen demand in
outlets also may be met via other sources such as upwelling
groundwater or mineralization of lake-derived organic
matter (Valett et al. 1994). Biofilm stoichiometry was not
quantified in our study and certainly may be related to
water column uptake given that benthic biofilms represent
a consortium of homeostatic algae and non-homeostatic
bacteria and fungi (Cross et al. 2005).
Other studies have not directly addressed the role of
lakes in modifying nutrient spiraling processes, but Hall et
of 11.2 mm min21 at a lake
al. (2002) reported a vf-PO {3
4
outlet: the second highest rate measured from a set of 13
reaches during summer experiments. Hall and Tank (2003)
21 also at a lake
reported a low vf-NO {
3 of 0.9 mm min
outlet with the other 10 streams in this study averaging
2 mm min21. These results provide additional evidence
that lakes may play a similar role in affecting stream NO {
3
and PO {3
processing in regions beyond our study area.
4
Toward a network approach to stream nutrient cycling—
Where a stream reach is positioned within the landscape,
particularly in the mountains, provides much information
about its current geomorphology and the processes that
created and maintain it (Montgomery and Buffington 1997;
Montgomery 1999). That variation in nutrient uptake in
our study was strongly related to landscape position
relative to lakes and associated changes in continuous
fluvial processes (i.e., water and sediment transport
downstream), points to a need to study stream biogeochemistry in a network context.
Recent efforts to scale nutrient uptake from reaches to
networks assume constant vf (Wollheim et al. 2006). While
such an approach represents a logical first step given
published relationships between discharge and Sw (Peterson
et al. 2001), our results showed that vf varies within
mountain drainages, and this variation is best explained by
proximity to lakes—a local hydrogeomorphic control (Arp
et al. 2007). Landscape features such as lake abundance,
tributary junctions, and bedrock type likely strongly
influence stream nutrient cycling such that generalized
patterns related to stream size begin to fall apart in
a network context. Considering where such local hydrologic and geologic controls occur in the landscape and how
these locations create discontinuities to water and sediment
transport and associated stream habitat and biological
communities (e.g., dense persistent epilithic algae on stable
bed sediments) may be an important step toward better
predicting nutrient transport and retention in complex
watersheds.
A network view suggests that the effect of lakes on
streams is best considered in terms of disruptions to fluvial
water and sediment flux downstream. At a lake outlet, all
water delivered to a stream comes from the lake and all
mineral sediment from higher in the watershed will be
trapped in the lake, causing a dramatic change in benthic
resources and habitat (Ward and Stanford 1983, Arp et al.
2007). Our results showed a strong correlation between
NO {
3 uptake and sediment size and mobility, suggesting
the need to consider this potential mechanism further,
particularly in separating the relative roles of channel form
and hydraulics from sediment residence time to understand
how these influence benthic communities and nutrient
cycling.
Another fundamental aspect of lakes in drainage networks is water storage and how this water is modified
physically, chemically, and biologically before export
downstream. Depending on hydraulic residence time (i.e.,
lake volume : inflow), mixing dynamics, and productivity,
the type of water flowing out of a lake may be transformed
substantially compared to upstream source-waters (Hillbricht-Ilkowska 1999; Essington and Carpenter 2000; Kling
et al. 2000). The lake outlet water in our study reaches was
notably different from other streams in the SMLD, having
higher temperature and DOC during summer baseflow.
Our results showed a strong correlation between PO {3
4
uptake and source-water characteristics, suggesting the
potential importance of both temperature and dissolved
and particulate organic matter in controlling P uptake. Yet,
these source-water variables strongly co-varied, so we were
unable to resolve their relative importance in this study.
Farther downstream from a lake, groundwater accrual,
hillslope sediment-sources, and tributaries will add new
water and sediment to the stream, both gradually and
episodically resetting the system to more alluvial, groundwater-fed conditions as is characteristic of most lowgradient mountain streams. This downstream resetting
can generally be quantified by increases in contributing
area (the watershed area below a lake), which follows the
SDC (Ward and Stanford 1983), but considers the role of
watershed contributions rather than just stream distance in
resetting the ecosystem. Although, within any given
watershed other local controls (i.e., PDC, Montgomery
1999), such as downstream sediment sources (e.g., tributaries and hillslope connections) and sinks (e.g., other lakes),
should more exactly control how rapidly stream ecosystem
processes become reset relative to above lake conditions
(Arp et al. 2007).
Integrating water and sediment cycles with stream nutrient
cycling—We contend that there is a strong interaction
among the fluvial water, sediment, and nutrient cycles.
Although this may seem obvious, we think this relationship
has not been fully considered by many stream ecologists
and aquatic biogeochemists. The relationship between the
water and sediment cycles has been long understood and
continues to be an important research topic (e.g., Gilbert
1880; Syvitski et al. 2005). The relationship between the
water and nutrient cycles has been progressively emerging
during the last several decades (e.g., Bencala 1993; Doyle
2005). However, the link between the fluvial sediment cycle
and nutrient cycles has largely been ignored (but see Doyle
et al. 2003, 2005), even though sediments create the
principle habitat for benthic microbial communities that
remove, transform, and retain the majority of nutrients
from the water column (Peterson et al. 2001).
The transition from viewing streams as pipes to
considering streams as complex ecosystems that function
in nutrient transport, transformation, and retention has
Lakes affect stream nutrient uptake
been a critical conceptual shift in riverine science and
management (Bencala 1993; Bernhardt et al. 2003).
Drainage networks also transport, transform, and retain
water and sediment, and these processes set the fundamental structure of streams and channels. Nutrient cycling is
strongly coupled to these processes and structure. These
processes work along a river continuum, but also are
continually disrupted or modified by local controls, such as
lakes. We believe that stream ecology could be advanced
greatly by more careful consideration of these basic stream
processes at landscape-scales that incorporate both continuous and local controls on stream ecosystems.
Our analysis of streams in a mountain lake district
provides a somewhat extreme example of this important
interaction among the fluvial water, sediment, and nutrient
cycles because of the strong modifications to these fluxes
downstream from lakes. Yet, lakes and other fluvial
discontinuities are common features in many landscapes
(Meybeck 1995; Winter 2001), and presently the effects of
dams and river regulation have fundamentally altered large
proportions of many drainage networks worldwide (Graf
1999; Nilsson et al. 2005). The prevalence of natural and
artificial lakes in many landscapes suggests the need to
include understanding of such fluvial discontinuities in
concepts of the fluvial nutrient cycling and predictive
models of nutrient transport in complex river networks.
References
ALEXANDER, R. B., R. A. SMITH, AND G. E. SCHWARZ. 2000. Effect
of stream channel size on the delivery of nitrogen to the Gulf
of Mexico. Nature 403: 758–761.
ARP, C. D., J. C. SCHMIDT, M. A. BAKER, AND A. K. MYERS. 2007.
Stream geomorphology in a mountain lake district: Hydraulic
geometry, sediment sources and sinks, and downstream lake
effects. Earth Surface Processes and Landforms 32: 525–543.
BENCALA, K. E. 1993. A perspective on stream-catchment
connections. J. North American Benthological Society 12:
44–47.
BERNHARDT, E. S., G. E. LIKENS, D. C. BUSO, AND C. T. DRISCOLL.
2003. In-stream uptake dampens effects of major forest
disturbance on watershed nitrogen export. Proc. Natl. Acad.
Sci. USA 100: 10304–10308.
BURGIN, A. J., AND S. K. HAMILTON. 2007. Have we overemphasized the role of denitrification in aquatic ecosystems?
A review of nitrate removal pathways. Front. Ecol. Environ.
5: 89–96.
CROSS, W. F., J. P. BENSTEAD, P. C. FROST, AND S. A. THOMAS.
2005. Ecological stoichiometry in freshwater benthic systems:
Recent progress and perspectives. Freshw. Biol. 50:
1895–1912.
CRUMPTON, W. G., T. M. ISENHART, AND P. D. MITCHELL. 1992.
Nitrate and organic N analyses with second-derivative
spectroscopy. Limnol. Oceanogr. 37: 907–913.
DAVIS, J. C., AND G. W. MINSHALL. 1999. Nitrogen and
phosphorus uptake in two Idaho (USA) headwater wilderness
streams. Oecologia 119: 247–255.
DOYLE, M. W. 2005. Incorporating hydrologic variability
into nutrient spiraling. J. Geophys. Res. DOI:10.1029/
2005JG000015. 110.
———, E. H. STANLEY, AND J. M. HARBOR. 2003. Hydrogeomorphic controls on phosphorus retention in streams. Water
Resources Research. DOI:10.1029/2003/WR002038. 39.
1989
EMMETT, W. W. 1975. The channels and waters of the Upper
Salmon River Area. Idaho: U.S. Geological Survey.
ESSINGTON, T. E., AND S. R. CARPENTER. 2000. Nutrient cycling in
lakes and streams: Insights from a comparative analysis.
Ecosystems 3: 131–143.
FRISSELL, C. A., W. J. LISS, C. E. WARREN, AND M. D. HURLEY.
1986. A hierarchical framework for stream habitat classification: Viewing streams in a watershed context. Environ.
Manag. 10: 199–214.
GILBERT, G. K. 1880. Geology of the Henry Mountains. US
Geolographical and Geological Survey.
GRAF, W. L. 1999. Dam nation: A geographic census of American
dams and their large-scale hydrological impacts. Water
Resour. Res. 35: 1305–1311.
HALL, R. O., E. S. BERNHARDT, AND G. E. LIKENS. 2002. Relating
nutrient uptake with transient storage in forested mountain
streams. Limnol. Oceanogr. 47: 255–265.
———, AND J. L. TANK. 2003. Ecosystem metabolism controls
nitrogen uptake in streams in Grand Teton National Park,
Wyoming. Limnol. Oceanogr. 48: 1120–1128.
HILLBRICHT-ILKOWSKA, A. 1999. Shallow lakes in lowland river
systems: Role in transport and transformations of nutrients
and in biological diversity. Hydrobiologia 408/409: 349–358.
LORANG, M. S., AND F. R. HAUER. 2003. Flow competence and
streambed stability and evaluation of techniques and application. J. North American Benthological Society 22: 475–491.
KIILSGAARD, T. H., L. R. STANFORD, AND R. S. LEWIS. 2003.
Preliminary geologic map of the northeast part of the
Deadwood River 30 3 60 minute quadrangle. Idaho: Idaho
Geologic Survey.
KLING, G. W., G. W. KIPPHUT, M. M. MILLER, AND W. J. O’BRIEN.
2000. Integration of lakes and streams in a landscape
perspective: The importance of material processing on spatial
patterns and temporal coherence. Freshw. Biol. 43: 477–497.
KNIGHTON, D. 1998. Fluvial forms and processes: A new
perspective. Oxford Univ. Press.
KOCH, B. J. 2005. Invertebrate-mediated nitrogen cycling in three
connected aquatic ecosystems. MS thesis, Univ. of Wyoming.
LEOPOLD, L. B., AND MADDOCK T. 1953. The hydraulic geometry
of stream channels and some physiographic implications. US
Geological Survey Professional Paper 252.
MAGNUSON, J. J., AND T. K. KRATZ. 2000. Lakes in the landscape:
Approaches to regional limnology. Verh. Internat. Verein.
Limnol. 27: 74–87.
MARCARELLI, A. M. 2006. Cyanobacterial nitrogen fixation in
subalpine, oligotrophic watersheds: Spatial and temporal
variations. Ph.D. dissertation, Utah State Univ.
———, AND W. A. WURTSBAUGH. 2006. Temperature and nutrient
supply interact to control nitrogen fixation in oligotrophic
streams: An experimental examination. Limnol. Oceanogr.
51: 2278–2289.
MENZEL, D. W., AND R. F. VACARRO. 1964. The measurement of
dissolved and particulate organic carbon in seawater. Limnol.
Oceanogr. 9: 138–142.
MEYBECK, M. 1995. Global distribution of lakes, p. 1–35. In A.
Lerman and J. Gat [eds.], Physics and chemistry of lakes.
Springer-Verlag.
MONTGOMERY, D. R. 1999. Process domains and the river
continuum. J. Am. Water Resour. Assoc. 35: 397–410.
———, AND J. M. BUFFINGTON. 1997. Channel-reach morphology
in mountain drainage basins. Geological Society of America
Bulletin 109: 596–611.
MOTOMIZU, S., T. WAKIMOTO, AND K. TOEI. 1983. Spectrophotometric determination of phosphate in river waters with
molybdate and malachite green. Analyst 108: 361–367.
1990
Arp and Baker
MULHOLLAND, P. J., AND oTHERS. 2002. Can uptake length in
streams be determined by nutrient addition experiments?
Results from an interbiome comparison study. J. North
American Benthological Society 21: 544–560.
NADP. 2001. Annual summary National Atmospheric Deposition Program Data Report 2000–01. Illinois State Water
Survey.
NILSSON, C., C. A. REIDY, M. DYNESIUS, AND C. REVENGA. 2005.
Fragmentation and flow regulation of the world’s large river
systems. Science 308: 405–408.
PETERSON , B. J., AND oTHERS . 2001. Control of nitrogen
export from watersheds by headwater streams. Science 292:
86–90.
ROBINSON, C. T., AND G. W. MINSHALL. 1990. Longitudinal
development of macroinvertebrate communities below oligotrophic lake outlets. Great Basin Naturalist 50: 303–311.
RUNKEL, R. L. 1998. One-dimensional transport with inflow and
storage: A solute transport model for streams and rivers. U.S.
Geological Survey.
STREAM SOLUTE WORKSHOP. 1990. Concepts and methods for
assessing solute dynamics in stream ecosystems. J. North
American Benthological Society 9: 95–119.
SYVITSKI, J. P. M., C. J. VOROSMARTY, A. J. KETTNER, AND P.
GREEN. 2005. Impact of humans on the flux of terrestrial
sediment to the global coastal ocean. Science 308: 376–380.
VADEBONCOEUR, Y. 1994. Longitudinal dynamics of seston
concentration and composition in a lake outlet stream. J.
North American Benthological Society 13: 181–189.
VALETT, H. M., C. L. CRENSHAW, AND P. F. WAGNER. 2002. Stream
nutrient uptake, forest succession, and biogeochemical theory.
Ecology 83: 2888–2901.
———, S. G. FISHER, N. B. GRIMM, AND P. CAMILL. 1994. Vertical
hydrologic exchange and ecological stability of a desert
stream ecosystem. Ecology 75: 548–560.
VALDERAMA, J. C. 1981. The simultaneous analysis of total
nitrogen and total phosphorus in natural waters. Mar. Chem.
10: 109–122.
VANNOTE, R. L., G. W. MINSHALL, K. W. CUMMINS, J. R. SEDELL,
AND C. E. CUSHING. 1980. The river continuum concept. Can.
J. Fish Aquat. Sci. 37: 130–137.
WARD, J. V., AND J. A. STANFORD. 1983. The serial discontinuity
concept of lotic ecosystems, p. 29–42. In T. D. Fontaine and
S. M. Bartell [eds.], Dynamics of lotic ecosystems. Ann Arbor
Science, The Butterworth Group, Kent, England.
WINTER, T. C. 2001. The concept of hydrologic landscapes. J. Am.
Water Resour. Assoc. 37: 335–349.
WOLLHEIM, W. M., C. J. VORÖSMARTY, B. J. PETERSON, S. P.
SEITZINGER, AND C. S. HOPKINSON. 2006. Relationship between
river size and nutrient removal. Geophysical Research Letters
33: 1–4.
———, AND oTHERS . 2001. Influences of stream size on
ammonium and suspended particulate nitrogen processing.
Limnol. Oceanogr. 46: 1–13.
Received 5 January 2006
Accepted: 17 April 2007
Amended: 14 May 2007