The metabolic regimes of flowing waters

Abstract

The processes and biomass that characterize any ecosystem are fundamentally constrained by the total amount of energy that is either fixed within or delivered across its boundaries. Ultimately, ecosystems may be understood and classified by their rates of total and net productivity and by the seasonal patterns of photosynthesis and respiration. Such understanding is well developed for terrestrial and lentic ecosystems but our understanding of ecosystem phenology has lagged well behind for rivers. The proliferation of reliable and inexpensive sensors for monitoring dissolved oxygen and carbon dioxide is underpinning a revolution in our understanding of the ecosystem energetics of rivers. Here, we synthesize our current understanding of the drivers and constraints on river metabolism, and set out a research agenda aimed at characterizing, classifying and modeling the current and future metabolic regimes of flowing waters.

Full text

The metabolic regimes of flowing waters
E. S. Bernhardt ,
1
* J. B. Heffernan ,
2
N. B. Grimm ,
3
E. H. Stanley ,
4
J. W. Harvey ,
5
M. Arroita,
6,7
A. P. Appling,
8
M. J. Cohen,
9
W. H. McDowell ,
10
R. O. Hall, Jr.,
7,a
J. S. Read,
11
B. J. Roberts,
12
E. G. Stets,
13
C. B. Yackulic
14
1
Department of Biology, Duke University, Durham, North Carolina
2
Nicholas School of the Environment, Duke University, Durham, North Carolina
3
Arizona State University, Tempe, Arizona
4
Center for Limnology, University of Wisconsin, Madison, Wisconsin
5
National Research Program, U.S. Geological Survey, Reston, Virginia
6
Department of Plant Biology and Ecology, University of the Basque Country, Bilbao, Spain
7
Department of Zoology and Physiology, University of Wyoming, Laramie, Wyoming
8
US Geological Survey, Office of Water Information, Tucson, Arizona
9
School of Forest Resources and Conservation, University of Florida, Gainesville, Florida
10
Department of Natural Resources and the Environment, University of New Hampshire, Durham, New Hampshire
11
U.S. Geological Survey, Office of Water Information, Middleton, Wisconsin
12
Louisiana Universities Marine Consortium, Chauvin, Louisiana
13
U.S. Geological Survey, National Research Program, Boulder, Colorado
14
U.S. Geological Survey, Southwest Biological Science Center, Flagstaff, Arizona
Abstract
The processes and biomass that characterize any ecosystem are fundamentally constrained by the total
amount of energy that is either fixed within or delivered across its boundaries. Ultimately, ecosystems may
be understood and classified by their rates of total and net productivity and by the seasonal patterns of pho-
tosynthesis and respiration. Such understanding is well developed for terrestrial and lentic ecosystems but
our understanding of ecosystem phenology has lagged well behind for rivers. The proliferation of reliable
and inexpensive sensors for monitoring dissolved oxygen and carbon dioxide is underpinning a revolution
in our understanding of the ecosystem energetics of rivers. Here, we synthesize our current understanding of
the drivers and constraints on river metabolism, and set out a research agenda aimed at characterizing, classi-
fying and modeling the current and future metabolic regimes of flowing waters.
The fuel that powers almost all of Earth’s ecosystems is
created by organisms capable of the alchemy of photosyn-
thesis, in which solar energy, water, and carbon dioxide are
converted into reduced carbon compounds that are then
used to sustain life. We measure this conversion of solar
energy into organic energy as the gross primary productivity
(GPP) of ecosystems. The collective dissipation of this
organic energy through organismal metabolism (of both
autotrophs and heterotrophs) is measured as ecosystem res-
piration (ER). Together, GPP and ER are the fundamental
metabolic rates of ecosystems that constrain the energy sup-
ply and energy dissipation through food chains, and the bal-
ance of these two fluxes, measured as net ecosystem
production (NEP), determines whether carbon accumulates
or is depleted within an ecosystem. Terrestrial ecosystems
often have predictable annual cycles, with both GPP and
NEP typically peaking during warmer and wetter months of
the year. In many well-studied lakes productivity peaks
when warming temperatures, lengthening days, and high
nutrient concentrations occur in concert. The life cycles of
many consumers are likely synchronized to these seasonal
oscillations such that periods of peak energetic demand by
consumers coincide with or follow the peak productivity of
their preferred plant or prey (e.g., Lampert et al. 1986; Berger
et al. 2010). As a result, ecosystem respiration tends to
*Correspondence: emily.bernhardt@duke.edu
a
Present address: Flathead Lake Biological Station, University of Montana,
Polson, Montana
This is an open access article under the terms of the Creative Commons
Attribution-NonCommercial License, which permits use, distribution and
reproduction in any medium, provided the original work is properly
cited and is not used for commercial purposes.
1
LIMNOLOGY
and
OCEANOGRAPHY Limnol. Oceanogr. 00, 2017, 00–00
V
C2017 The Authors Limnology and Oceanography published by Wiley Periodicals, Inc.
on behalf of Association for the Sciences of Limnology and Oceanography
doi: 10.1002/lno.10726

covary with GPP. This phenology of the ecosystem, or its
seasonal timing of carbon, water and energy exchange (sensu
Noormets 2009) is both a cause and a consequence of the
phenology of all component organisms.
The same “cause and consequence of organism
phenology” argument cannot be made for river ecosystems
for three reasons. First, in many rivers seasonal variation in
light is uncorrelated with seasonal variation in temperature,
because of the reduction in light supply due to canopy inter-
ception, sediment loads or colored organic matter. Second,
in many rivers intense and frequent high flow events regu-
larly reduce the biomass of autotrophs (algae, mosses, and
macrophytes) through scouring or burial while stream drying
can strand and desiccate autotrophs on the channel bed.
Finally, most rivers receive energetic subsidies in the form of
detritus and dissolved organic matter from their surrounding
watersheds. These allochthonous inputs can match or exceed
in situ GPP and thus decouple the seasonal and annual pat-
terns of GPP and ER. For each of these reasons we expect a
reduced coherence between climate drivers and river ecosys-
tem productivity and respiration.
Of course, the relative importance of terrestrial shading
and terrestrial organic matter inputs varies with river size
(Vannote et al. 1980). Just as the importance of terrestrial
inputs of nutrients and organic matter to lake food webs
diminishes as the ratio of lake size to watershed size
increases (Tanentzap et al. 2017), we expect that the relative
importance of canopy shading, allochthonous inputs, and
hydrologic disturbance should decline between headwater
streams and large rivers. This gradient in river size impacts
the expected ecosystem phenology, with well-lit and less fre-
quently disturbed large rivers having regular summer pro-
ductivity peaks while shaded headwaters with frequent
flooding are likely to have productivity peaks that are mis-
matched to the terrestrial growing season (Fig. 1). These pre-
dicted longitudinal patterns can be obscured for rivers with
high sediment or colored organic matter inputs. Because the
temporal signals of GPP and ER are so diverse across streams
Fig. 1. Temporal trends in dissolved oxygen (DO shown as % of atmospheric saturation) for four contrasting U.S. rivers over a 4-yr period. High diel
variation in dissolved oxygen is a proxy for high ecosystem GPP. The top trace is from the Menominee River in northern, Wisconsin, a large river with
clear summer peaks and winter lows in stream metabolism. Dissolved oxygen traces for the more southern Five Mile Creek in Alabama and San Anoto-
nio River in Texas show little seasonality, with the smaller Creek having sustained high diel variation in DO and the more urban river having many
alternating periods of high and low diel DO variation. The bottom trace is from Fanno Creek, a heavily shaded and frequently flooded small stream in
western Oregon. Data from each of these four streams appears again (along with axes labels) in Figs. 3, 5. Here, we remove axes scores to focus on
the differences in seasonality of a common signal across streams. The scale of both axes is the same for all four series. [Color figure can be viewed at
wileyonlinelibrary.com]
Bernhardt et al.Metabolic regimes
2

and years, and so often asynchronous with terrestrial pro-
ductivity and climate drivers, we propose that the term meta-
bolic regimes is more appropriate than phenology to describe
these patterns. Here, we define a metabolic regime as the
characteristic temporal pattern of ecosystem GPP and ER
observed for a river.
Despite the frequent mismatches in the timing of peak
energy supply, thermal optima, and disturbance river ecosys-
tems support a tremendous diversity of species (Strayer and
Dudgeon 2010) and can convert enormous quantities of
organic matter and inorganic nutrients into CO
2
,CH
4
,N
2
,
and N
2
O gases (Cole et al. 2007; Mulholland et al. 2008; Bat-
tin et al. 2009; Raymond et al. 2013; Stanley et al. 2016).
The capacity of rivers to support these critical functions
depends, fundamentally, on ecosystem metabolism, defined
as “the production and destruction of organic matter, and the
associated fluxes of nutrients, through the gross photosynthetic
and respiratory activity of organisms” (Odum 1956). Although
H. T. Odum developed the general concept and made the
first measurements of whole ecosystem metabolism in a river
over 60 yr ago (New Hope Creek, North Carolina, Odum
1956), stream ecologists have made remarkably little progress
in building our empirical understanding of stream metabo-
lism (McDowell 2015).
It is perhaps not surprising, given the extremely dynamic
nature of river metabolism, that we have uncovered no uni-
versal or even regional predictors of river productivity.
Unlike terrestrial ecosystems, where rainfall and temperature
explain much of the global variation in primary production
(Whittaker 1962; Leith 1975; Field et al. 1998), or lentic eco-
systems, in which nutrient loading and organic matter are
primary drivers of productivity (Schindler 1978; Lewis et al.
2011), attempts to uncover these same patterns in streams
have proven elusive (Lamberti and Steinman 1997; Bernot
et al. 2010). Ironically, there is a rich history of conceptual
models predicting spatial patterns of metabolism in streams
and rivers beginning with the River Continuum Concept
(RCC; Fig. 2) (Vannote et al. 1980). Tests of this and other
models have been limited and often equivocal (e.g., Bott
et al. 1985; Minshall et al. 1992; McTammany et al. 2003). A
literature synthesis showed that watershed area (and thus
river size) predicted the magnitude of annual GPP in rela-
tively undisturbed watersheds, but had little predictive
power for GPP in even moderately developed watersheds, or
for ER across all rivers (Finlay 2011). Other syntheses at daily
rather than annual timescales have found even less predict-
ability in metabolic rates (Mulholland et al. 2001; Bernot
et al. 2010; Hoellein et al. 2013).
We attribute our limited success in uncovering patterns or
building predictive models of river ecosystem metabolism to
the challenging combination of technological constraints
and the dynamic physical environment characteristic of
many rivers. In theory, the measurement of river metabolism
is straightforward. An investigator simply measures changes
in the concentration of dissolved oxygen (DO) throughout a
diel (24-h) cycle, using increases during daylight hours and
overnight declines to calculate rates of GPP, ER, and NEP. In
Odum’s initial metabolism estimates (Odum 1956, 1957)
these changes were documented by collecting samples at 2–
4 h intervals throughout a day followed by manual analysis
of DO concentration via titration. Sampling around the
clock by this method is labor intensive, and the duration of
early studies was thus limited to a handful of days during
the year. Later efforts were enabled by instantaneous meas-
urements of DO with first generation environmental sensors.
Because these sensors were expensive and required frequent
calibration, researchers tended to deploy them for very lim-
ited periods of time. As a result, most reported rates of river
ecosystem metabolism were derived from a small number of
measurement days over a year (e.g., 2–12 d; synthesized by
Lamberti and Steinman 1997; Finlay 2011). These brief sam-
pling regimes are biased toward optimum field conditions
for sensor deployment (e.g., sunny days with low flows).
They are thus unlikely to provide accurate estimates of mean
or annual metabolic rates, and are almost certainly insuffi-
cient for capturing the impacts of hydrologic disturbance or
pulsed resource supply.
Fortunately, the proliferation of more rugged and less
expensive environmental sensors is now allowing river scien-
tists to overcome the traditional logistical challenges of mea-
suring metabolism. Looking forward, we should now be able
to address fundamental questions about how river ecosys-
tems function, such as: What controls the variation in
the magnitude and timing of productivity within and
among rivers? How are these controls changing in
response to climate or land use change? How will
resulting changes in river ecosystem productivity con-
strain their capacity to support freshwater biodiver-
sity, food production, and the maintenance of water
quality? In this paper, we hope to help shape and catalyze
this research frontier by summarizing our current under-
standing of river metabolism; proposing a series of new and
fundamental research questions; and providing a series of
examples demonstrating exciting new applications of contin-
uous metabolism datasets.
What do we know so far?
How we measure and model river metabolism
Stream ecosystem metabolism (in units of oxygen, g O
2
m
22
d
21
) is estimated from the daily variation in the produc-
tion and consumption of oxygen by stream organisms fol-
lowing a simple model:
dDO
dt 5GPP1ER
z1KDOsat2DOðÞ
where dDO
dt is the change in dissolved oxygen concentration
through time, GPP is the rate of photosynthetic O
2
production,
Bernhardt et al.Metabolic regimes
3

ER is the rate of oxygen consumption through both autotro-
phic and heterotrophic respiration and KDOsat2DO
ðÞ
is the net
exchange of oxygen between the water column and the overly-
ing air and is governed by a per unit time gas exchange rate K.
Mean river depth (z) converts from volumetric to areal rates. By
convention, any process that lowers DO concentration in the
water takes on a negative value so that ER is always a negative
number. Whenever autotrophs are present, GPP will increase
with light. Typically, GPP results in peak DO during the day
(Fig. 3A), except when high rates of gas exchange or flow varia-
tion obscure this pattern. Variation in the shape of this oxygen
curve reflects variation in rates of GPP, ER, and Kthus, with suf-
ficient rates of GPP, it is possible to use curve fitting routines
to estimate all three parameters from the oxygen data
(Holtgrieve et al. 2010; Hall 2016) available for each day within
a time series.
A first wave of sensor-enabled studies using this model-
ing approach clearly documented that light availability and
Fig. 2. A conceptual model depicting differences in how climate, light and hydrologic regimes vary along the river continuum and between three
terrestrial biomes. The climate diagrams across the top show average monthly precipitation in blue bars with daily air temperatures shown as blue
(minimum) and red (maximum) lines. [Color figure can be viewed at wileyonlinelibrary.com]
Bernhardt et al.Metabolic regimes
4

flooding are dominant and countervailing drivers of river
ecosystem productivity (Uehlinger and Naegeli 1998;
Houser et al. 2005; Roberts et al. 2007; Izagirre et al. 2008;
Beaulieu et al. 2013; Hope et al. 2014; Huryn et al. 2014;
Roley et al. 2014). In contrast to lakes there is thus far lim-
ited evidence that nutrient availability controls metabolic
rates in rivers (Hoellein et al. 2013; Solomon et al. 2013). In
contrast, variation in stream metabolic rates can affect
nutrient dynamics both within and across rivers (Hall and
Tank 2003; Roberts and Mulholland 2007; Heffernan and
Cohen 2010; Lupon et al. 2016). It thus seems likely that
widespread nutrient pollution of rivers resulting from land
use change is having less of a direct effect on river metabo-
lism than are the often-accompanying reductions in ripar-
ian canopy shading and increases in the frequency and
severity of floods or droughts. Indeed, these alterations to
river light and flow disturbance regimes are likely changing
the potential for rivers to perform the critical ecosystem ser-
vice of nutrient assimilation and retention (Fellows et al.
2006; Mulholland et al. 2008). Those alterations that reduce
annual GPP and ER or which shift the timing of peak activ-
ity away from peak nutrient loading are likely to exacerbate
nutrient pollution by reducing the potential for instream
processing.
Fig. 3. Diel and Storm Recovery Patterns in Rivers. Figures in row A are conceptual models representing the typical variation in O
2
(as % saturation)
at diel (A1, A2) and over storm-recovery trajectories (A3). Figures in row B show three contrasting diel curves selected from each of the four rivers
shown in Fig. 1. In row C, we show 60 d of estimated rates of gross primary productivity (in green, derived from the O
2
data in Fig. 1) alongside the
river hydrograph (gray shading). Each 60-d period encompasses at least one major flood. The color and date of the triangles shown at the top of pan-
els in row C correspond to the dates for the diel O
2
curves shown in row B. [Color figure can be viewed at wileyonlinelibrary.com]
Bernhardt et al.Metabolic regimes
5

The light and thermal regimes of rivers
For terrestrial ecosystems, light and thermal regimes are
controlled by latitude, topography and precipitation pat-
terns, and thus two very basic descriptors of climate, mean
annual temperature and mean annual precipitation, can
explain much of the variation in GPP across terrestrial bio-
mes (Whittaker 1962). Unfortunately, the light and thermal
regimes of streams are less easily linked to these widely avail-
able environmental data. The light that penetrates from the
atmosphere to river surfaces is strongly affected by both
channel orientation with respect to landscape features (e.g.,
canyon walls or banks) and shading by terrestrial vegetation
(Vannote et al. 1980; Hill and Dimick 2002; Julian et al.
2008). For small channels, the phenology of terrestrial vege-
tation can lead to rapid transitions in the light regime dur-
ing the leaf out and litterfall periods that are particularly
pronounced in temperate streams (Hill and Dimick 2002).
Once light reaches the river surface there is a predictable
attenuation with water depth that can be exacerbated by the
reflectivity of suspended materials and light absorption by
dissolved organic matter (Davies-Colley and Smith 2001;
Julian et al. 2008). Light attenuates exponentially with water
depth, such that benthic light availability is highest during
baseflow periods and lowest during high flows. Moreover,
light attenuation tends to increase in the downstream direc-
tion as larger rivers are deeper than their headwaters (Julian
et al. 2008). For all these reasons, light availability varies sub-
stantially among sites as well as over time, well beyond the
latitudinal variation in solar radiation that is the primary
driver of variation in light regime among terrestrial ecosys-
tems. Because of these light filtering mechanisms, periods of
high light availability may not be matched to periods of
high temperature or high incident solar radiation. Indeed,
for many small, forested streams of the temperate zone, the
highest light availability occurs in early spring before the for-
est canopy leafs out and late autumn after litterfall (Roberts
et al. 2007).
Like the light regime, the thermal regime of rivers can
deviate substantially from the temperature of the overlying
atmosphere. The temperature of a river at any given time is
determined by the cumulative fluxes of energy into and out
of the water, including groundwater, evaporation, and sun-
light (Poole and Berman 2001). In general, river tempera-
tures will fluctuate seasonally in concert with air
temperature, but the average temperature and the magnitude
of diel and seasonal swings in temperature will be consider-
ably muted in rivers relative to the overlying atmosphere.
For rivers with significant rates of water and heat exchange
with groundwater, or rivers below dams that receive most of
their flow from reservoir depths there can be little to no cor-
relation between air and water temperatures over time (Poole
and Berman 2001; Olden and Naiman 2009). The resulting
mismatches between temperature and light generate a much
more diverse combination of light and thermal regimes in
river ecosystems relative to their terrestrial or lentic counter-
parts (e.g., Huryn et al. 2014). Ecosystem respiration within
rivers appears to have stronger temperature sensitivity than
GPP, leading some to suggest that rivers are poised to
become increasingly large sources of CO
2
to the atmosphere
in a warming climate (Demars et al. 2011a,b).
Because terrestrial vegetation both reduces light and
enhances organic matter loading to rivers, we are unlikely to
see strong coherence between the productivity of rivers and
the productivity of their surrounding terrestrial biome.
Indeed, regions of high terrestrial primary productivity (e.g.,
tropical rain forests) are often associated with very low light
availability for small receiving streams (Mulholland et al.
2008), while low productivity terrestrial ecosystems (e.g.,
deserts, tundra) can be drained by some of the most produc-
tive streams in the world (Grimm 1987). Terrestrial vegeta-
tion exerts less influence on the light regimes of larger rivers
in which the attenuation of light by water depth and turbid-
ity become dominant constraints (Vannote et al. 1980). Sim-
ilarly, as rivers increase in size, their thermal regimes
become increasingly insensitive to the stature and status of
surrounding terrestrial vegetation. Thus, the position of any
river segment within its river network is likely to be as
important a determinant of its metabolic regime as the ter-
restrial biome which it drains. The distance along the river
continuum at which terrestrial influence wanes is likely to
vary considerably among different terrestrial biomes (Fig. 2).
For example, riparian canopy cover is typically absent from
the headwaters of desert, grassland, and alpine streams and
seasonal light regimes are considerably different for small
streams draining temperate deciduous vs. tropical or ever-
green forests despite their similar channel widths. These
interactions between geomorphology, vegetation, and chan-
nel size will drive wide variation in both the annual magni-
tude and the timing of primary production and ecosystem
respiration.
Disturbance regimes in river ecosystems
Though much of what we have learned about annual pat-
terns of river metabolism has come from studies of spring
fed or regulated streams (Odum 1957; Roberts et al. 2007;
Heffernan and Cohen 2010), a minority of rivers have such
predictable flows. The potential for rivers to support high
rates of GPP is maximized in slow flowing, clear water
streams with stable flow and high light availability where
productivity can rival that of temperate forests (Odum 1957;
Heffernan and Cohen 2010; Acu~
na et al. 2011). In contrast,
rivers with frequent bed-moving events typically have signif-
icantly lower productivity, even under similarly high light
regimes (Uehlinger and Naegeli 1998; Uehlinger 2006). A
rapidly growing body of literature documents the overriding
importance of high-flow or desiccation-initiated disturbances
of benthic primary producers in structuring the magnitude
and timing of river ecosystem productivity (Acu~
na et al.
Bernhardt et al.Metabolic regimes
6

2004, 2005; Atkinson et al. 2008; O’Connor et al. 2012;
Beaulieu et al. 2013).
Floods can regulate spatial and temporal patterns of many
ecological processes in rivers including metabolism (Fig. 3).
The return interval for events that lead to reductions in liv-
ing biomass or organic matter stocks in rivers is much
shorter than for almost any other ecosystem type (Grimm
et al. 2003). In the most extreme cases forests may be har-
vested on decadal time scales and grasslands may burn annu-
ally, but some rivers experience dozens of biomass-reducing
disturbances per year. A large flood can have multiple effects
on primary producers and the macro- and microconsumers
that live on or within riverbed sediments. Floods that are
sufficiently powerful to mobilize bed sediments may lead to
burial, scour or export of benthic producers and organic mat-
ter (Grimm and Fisher 1989; Young and Huryn 1996; Ueh-
linger and Naegeli 1998; Biggs et al. 1999; Uehlinger 2000;
Uehlinger et al. 2002; Atkinson et al. 2008). It is literally true
that “the rolling stone gathers no moss
1
,” as mobile substrates
are unable to accumulate large and stable amounts of ben-
thic biomass. Even in the absence of physical disturbance of
channel sediments, the deeper and more turbid waters typi-
cal of floods decrease light availability to the channel bed
(Hall et al. 2015). Research to date in small streams suggests
that recovery times for both GPP and ER are expected to be
longer for large floods that exceed the critical threshold for
bed disturbance compared with small floods that mainly
generate turbidity events (Cronin et al. 2007; O’Connor
et al. 2012). It is likely, yet untested, that the mechanism
through which floods impact river productivity may shift in
larger rivers where a greater proportion of GPP may be per-
formed by algae within the water column (Oliver and Mer-
rick 2006). In these larger rivers, we may expect the light
attenuation caused by flood-associated pulses of suspended
sediment that raise turbidity to become the dominant distur-
bance impact rather than bed disturbance.
Low- and no-flow drought conditions are also strong con-
straints on river ecosystem productivity. The most conspicu-
ous effect is obviously the reduction of total habitat area
associated with drying (Stanley et al. 1997). Within the
shrinking wetted channel area, responses to diminishing
flow can be highly variable. In one case, progression of dry-
ing coupled with high light conditions were associated with
growth of floating algae and extremely high rates of produc-
tivity in increasingly isolated pools (Acu~
na et al. 2005).
Under such conditions, dissolved organic carbon can become
highly concentrated. As streambed sediments are desiccated,
primary production ceases in surface sediments and reestab-
lishment may require an extended period of recovery after
rewetting in some ecosystems (e.g., desert streams, Stanley
et al. 1997) or activate immediately in others (Antarctic
streams, McKnight et al. 2007). There has been limited study
to date of the ecosystem respiration rates of dry riverbeds,
but one might expect enhanced mineralization of buried
organic matter as anoxic areas of the sediments are oxygen-
ated during drydown as well as pulses of microbial activity
following rewetting of desiccated sediments (Merbt et al.
2016), analogous to the pulse of mineralization often
observed when rain falls on dry soils (Fierer and Schimel
2002).
Nutrient impacts on stream metabolic regimes
When light and disturbance are not limiting, nutrient
supply may limit the magnitude or influence the phenology
of river metabolism (Hill et al. 2009). Common anthropo-
genic impacts on rivers such as the removal of riparian vege-
tation and flow regulation should exacerbate the sensitivity
of metabolism to nutrient loading by reducing light limita-
tion and disturbance frequency as constraints on river eco-
system productivity. There are plentiful examples of nutrient
limitation within individual rivers (Grimm and Fisher 1986;
Francoeur 2001; Tank and Dodds 2003). Experiments to
measure the impact of nutrient loading on river metabolism
have also documented substantial increases in GPP for a river
in Alaska fertilized with phosphorus (Peterson et al. 1985)
and enhanced rates of ER under N 1P enrichment in heavily
shaded, low nutrient streams in North Carolina (Kominoski
et al. 2017). Outside of these experimental studies in well
protected watersheds, we have had limited success in uncov-
ering consistent relationships between nutrient supply and
algal biomass across rivers (Francoeur et al. 1999; Dodds and
Smith 2016). Since nutrient loading from farm or road run-
off and wastewaters is often coincident with changes in
organic matter and sediment loading and changes in distur-
bance regimes, it is perhaps not surprising that differences in
these ultimate constraints (light and disturbance) override
our ability to detect the additional effects of nutrient supply.
Linking river “climate” with river metabolism
Though we know that light, temperature and hydrologic
disturbance (a river’s “climate”) are primary determinants of
metabolic activity in rivers, and that nutrients may acceler-
ate the metabolic response to each of these drivers, we have
only limited information with which to predict how the dis-
tinct temporal dynamics of each driver interact to determine
river metabolic regimes. Because of extensive historic invest-
ments in flow monitoring, we have large amounts of data
available to understand fine scale variation in river flows
(Poff et al. 1997, 2006) and thermal regimes (Olden and
Naiman 2009; Maheu et al. 2016), but we have far less infor-
mation about river light regimes (Hill 1996). We lack empiri-
cal measurements of light availability to the river surface for
all but the largest rivers and a few well studied streams (Hill
and Dimick 2002; Hill et al. 2011). We have even less infor-
mation about the light regimes experienced by the primary
1
First recorded in 1508 by Erasmus in his collection of Latin prov-
erbs, Adagia.
Bernhardt et al.Metabolic regimes
7

producers attached to bed sediments or transported in river
flow, and thus we have limited empirical data and few mod-
els with which to estimate light availability to river auto-
trophs (Ochs et al. 2013). Better information on this aspect
of the “climate” of rivers is sorely needed to generate mecha-
nistic models that can predict the metabolic capacity of river
ecosystems.
Such model development is essential if we want to better
understand how river ecosystems are being altered by three
major trajectories of change: rising global temperatures; land
use change; and flow regulation (Table 1). Climate change is
leading to rising temperatures and altered hydrographs for
rivers globally (Oki and Kanae 2006; Grimm et al. 2013).
Land use change is increasing sediment loads, nutrient
inputs and flood and drought frequency for at least some
portion of the channel network in every major river basin
(Vorosmarty and Sahagian 2000; Vorosmarty et al. 2000;
Allan 2004). The widespread construction of dams (Lehner
et al. 2011; Zarfl et al. 2014) has fundamentally altered the
flow regimes of many rivers (sensu Poff et al. 1997). The
implications for river ecosystem energetics are difficult to
predict, in part because the direct impacts of these drivers
on river metabolic rates can be antagonistic (higher
nutrients vs. higher disturbance frequency) and because
these direct impacts may be mitigated or enhanced by the
response of riparian vegetation to climate change. Periods of
high light, high nutrients, and stable flow—the “windows of
opportunity” for high metabolic rates within stream ecosys-
tems—seem likely to shift in their timing, duration and mag-
nitude because of these competing drivers of change (e.g.,
Ulseth et al. 2017).
Allochthonous carbon and river metabolism
Further complicating efforts to measure and model river
metabolism is that the energetic basis of many river food
webs is not limited to in situ productivity. Streams with
dense canopies of riparian vegetation, rivers carrying high
particulate loads, and blackwater rivers all can support
diverse and rich food webs and high rates of ecosystem respi-
ration that are dominantly or exclusively supported by car-
bon derived from upslope and upstream ecosystems (Fisher
and Likens 1973; Meyer et al. 1997; Wallace et al. 1997;
Moore et al. 2004). At the other end of the spectrum, rivers
with high water clarity and no canopy shading still receive
subsidies to the autochthonous-based food web in the form
of particulate detritus and dissolved organic matter from the
surrounding watershed. These fixed carbon subsidies are
often delivered to rivers in pulses. These may be regular and
predictable (e.g., annual litterfall or snowmelt fluxes of dis-
solved organic matter); frequent but unpredictable (e.g.,
inputs of large amounts of DOM during floods; Boyer et al.
1997; Raymond and Saiers 2010); or very infrequent cata-
strophic events (e.g., hurricanes, ice storms, fires) that may
Table 1. Changes in global temperatures and land cover will interact with widespread flow regulation to alter the light, thermal
and flow regimes that ultimately shape the metabolic regimes of rivers. Direct effects of these global trends on the drivers of metabo-
lism appear in black boxes while indirect effects appear as regular text.
Bernhardt et al.Metabolic regimes
8

deliver large quantities of terrestrial organic matter to river
ecosystems (Bernhardt et al. 2003; Earl and Blinn 2003;
Dahm et al. 2015). Floods deliver, bury and remove the
organic matter stored within riverbeds. The extent to which
rivers store or transmit this supply of fixed OM downstream
will determine the degree to which ER is tied to temporal
variation in river productivity. In those river reaches where
large standing stocks of organic matter accumulate, rates of
ER may be decoupled from GPP, and the stored OM can sup-
port significant biological activity in rivers where primary
productivity is negligible (reviewed in Webster and Meyer
1997). Differences between rivers in their capacity to store
OM introduces considerable variation in the extent to which
autochthonous and allochthonous energy sources support
river food webs, as well as the coherence between seasonal
and disturbance recovery patterns of GPP and ER.
Annual patterns of river metabolism
As we amass a new understanding of the annual patterns
of metabolism in rivers, we anticipate that there are likely to
be discrete classes of river ecosystems that share a character-
istic rhythm in the foundational metabolic processes of GPP
and ER (e.g., Fig. 4). These “metabolic regimes” may be char-
acterized by differences in: mean rates and their variability
and skewness; the temporal structure of metabolism such as
the autocorrelation and periodicity of ER and GP; the dura-
tion and timing of metabolic peaks and troughs; the post-
disturbance rates of recovery; and by the variation in these
characteristics among years and decades. These patterns are
likely to be linked to the overlapping temporal patterns of
the many factors that potentially control metabolism, and
thus provide diagnostic information to determine the pri-
mary constraints on metabolism and to predict the likely
energetic consequences of climate change, land use change
or flow management for river ecosystems. Particularly for
river ecosystems that have only short periods of peak metab-
olism, small shifts in the timing or magnitude of resource
supply or of the peak flows, droughts or other events that
constrain or reset metabolic rates may have disproportion-
ately large impacts on river productivity at daily, seasonal,
and annual time scales.
A global effort to measure, model and synthesize metabo-
lism across many river ecosystems will reveal controls on the
rates, timing and sensitivity of freshwater metabolism. Dif-
ferences in metabolic regimes among rivers, and changes in
these regimes over time, are likely to have wide-ranging eco-
logical consequences. Metabolic regimes incorporate not
only the amount of energy available to fuel secondary pro-
duction, but also the temporal pattern of energy availability
that determines which consumers are phenologically best
suited to capitalize on metabolic products. Understanding
ecosystem phenology and what it reveals about coupling
between energetics and element cycling will open new
opportunities to apply our emerging understanding of meta-
bolic regimes as diagnostic tools for river management.
Three frontier opportunities suggest themselves as particu-
larly exciting avenues for progress:
FRONTIER #1 – linking metabolic regimes and
organismal phenology
Ecosystem rates of GPP and ER are maximized at times
when and places where multiple potentially limiting resour-
ces are abundant and the biomass of primary producers is at
its peak. Variation in resource supply and biomass removal
thus drive considerable temporal variation in energy produc-
tion across systems (Fig. 5). The study of the match or mis-
match between an organism’s life history and organic matter
supply has a rich history in stream ecology, where many
consumers are dependent upon relatively discrete and pre-
dictable periods of either autochthonous production (e.g.,
spring algal blooms) or allochthonous organic matter supply
(e.g., autumn litter fall). Disturbances that occur within
these short periods of peak metabolic activity may have dis-
proportionate effects on annual rates of ecosystem metabo-
lism as well as on the productivity and identity of secondary
consumers (Wootton et al. 1996; Power et al. 2008; Spon-
seller et al. 2010). In some rivers, changes in the identity of
secondary consumers associated with intermediate levels of
disturbance can lead to increased production of tertiary and
higher trophic levels even as primary and secondary con-
sumption decline (Power et al. 1996; Cross et al. 2011).
Fig. 4. A conceptual model of four possible and contrasting patterns of annual productivity that could arise for river ecosystems within the same lati-
tude and terrestrial biome. The annual pattern of solar radiation (above any vegetation) is shown in yellow while GPP is shown in green. Maximum
potential GPP is always constrained by incident light. Disturbance and the interception of light by canopy, DOC or sediment each reduce the capacity
for river autotrophs to levels of productivity well below that predicted from light availability alone. [Color figure can be viewed at wileyonlinelibrary.
com]
Bernhardt et al.Metabolic regimes
9

However, more frequent disturbances have been shown and
are predicted to favor small bodied, fast growing consumers
(Gray 1981; Fisher and Gray 1983). Increased mortality and
longer recovery times of larger predatory animals are pro-
posed explanations for this trend. It is also possible, and
likely, that reductions in the total energy available to con-
sumers because of the removal of autotrophs and organic
matter are in part responsible for these negative effects on
higher trophic levels (as per predictions of the trophic pyra-
mid, Elton 1927).
We suggest that simultaneous measurement and model-
ing of river ecosystem energetics and food web properties
will be needed to predict the likely consequences of climate
and land use change (Table 1) on river biodiversity and food
web structure. Sabo et al. (2010) documented a negative rela-
tionship between flow variability (estimated from 16 to 20 yr
of continuously measured flow data) and food chain length
(FCL) for 20 U.S. rivers. In the same study, the authors con-
cluded that there was no relationship between FCL and rates
of gross primary production (Sabo et al. 2010). Since the GPP
estimates used in the Sabo et al. (2010) synthesis were
derived from only 1 to 20 d of midsummer (low flow) meas-
urements they are likely highly inaccurate estimates of
annual ecosystem productivity. As our measurements of river
metabolism grow more similar in temporal resolution and
longevity to our hydrologic records we predict we will dis-
cover strong, synergistic interactions between flow and meta-
bolic regimes in constraining biodiversity and food web
structure. There is mounting evidence that frequent high
flows significantly constrain river GPP (Uehlinger 2006;
Beaulieu et al. 2013, Fig. 3), and we suggest that the altera-
tions in animal assemblages often attributed to hydrologic
alteration or disturbance (Poff and Ward 1989; Sabo et al.
2009; Poff and Zimmerman 2010) may be due in part to the
indirect effects of these flow alterations on ecosystem metab-
olism. If more comprehensive data support this prediction, it
will only reinforce the importance of maintaining and
restoring natural flow regimes to sustain both the distur-
bance and the energetic regimes that structure river food
webs.
Widespread measurement of river ecosystem metabolism
offers a new opportunity to confront this rich conceptual
understanding with large volumes of empirical data describ-
ing the seasonal patterns of energy production and dissipa-
tion in rivers (Fig. 5). Coupling ecosystem metabolism time
series with contemporaneous measurements of benthic sec-
ondary production or food web characteristics is likely to
lead to new insights and re-examination of current assump-
tions. For example, studies of the impacts of altered flow
regimes on the species composition of insect and fish com-
munities often focus on how the life history of declining or
extirpated taxa depended on natural flow regimes (Lytle and
Poff 2004; Lytle et al. 2008). There has been far less consider-
ation of how the direct impacts of flow modification on
Fig. 5. Cumulative annual metabolism graphs for the four rivers from
Fig. 1. GPP is in green, ER is in brown. Units on the yaxis are in g O
2
m
22
.
Note that the southern river, Five Mile Creek has relatively uniform GPP
while the northern Menominee River has high accumulation only during
the summer months. The San Antonio River in Texas has alternating peri-
ods of high and very low productivity, while Fanno Creek has low GPP
year-round. Rates of ER exceed GPP annually and for most daily time steps
in all four streams. [Color figure can be viewed at wileyonlinelibrary.com]
Bernhardt et al.Metabolic regimes
10

higher trophic levels may be comfounded by the indirect
effects of flow on metabolism, the energetic base of these
food webs. A cursory look at newly available data from just a
few rivers in the United States (e.g., Fig. 3) demonstrates
that high flows consistently reduce the biomass and cumula-
tive GPP of river autotrophs. It is not just the “green,” or
algal supported food web that is likely to be affected. Many
river biota consume detritus delivered from the surrounding
terrestrial ecosystem, with the growth and diversity of shred-
ders and detritivores strongly tied to the standing stocks of
leaf litter in small streams (Wallace et al. 1997, 1999). In soil
and sediment food webs, heterotrophs rely on slow turnover
of massive standing stocks of organic carbon—but in streams
and rivers this “brown food web” is far more temporally
dynamic due to events that can export, bury or fragment
benthic organic matter standing stocks (Wallace et al. 1995).
Thus, shifts in the timing of OM supply (via changes in ter-
restrial phenology) and removal or burial (via changes in
storm magnitudes or timing) may have important implica-
tions for the wide variety of shredders and collectors that
rely on these allochthonous carbon sources. The lost produc-
tion that results from increasingly frequent or severe flows
will certainly outlast the impact of the physical disturbance
and, in some systems, may substantially alter the commu-
nity of secondary consumers, with additional effects at
higher trophic levels. Shifts in peak and total annual produc-
tivity resulting from global change drivers (Table 1) are likely
to have important implications for the biomass and diversity
of aquatic consumers that can be supported within a river,
while shifts in the timing of peak productivity (Table 1) may
favor new types of animals at the expense of historically
dominant taxa.
FRONTIER #2 - coupling energetics and element
cycling
In rivers, indeed in all ecosystems, cycling of nutrients
and other elements can both depend on and influence meta-
bolic patterns, and these reciprocal interactions operate over
a wide range of time scales, within days, across seasons and
through recovery from antecedent disturbances (Kirchner
et al. 2004; Appling and Heffernan 2014). There is a rich his-
tory of stream ecological research that has documented
strong links between metabolic phenology and the timing
and magnitude of nutrient retention (Meyer 1980; Mulhol-
land et al. 1985; Meyer et al. 1998; Roberts and Mulholland
2007). The emerging transition of stream metabolism studies
from daily rates to annual regimes is part of a much wider
effort to document and draw inference from hydrochemical
patterns across the full span of relevant time scales, from
days and storm events to seasons and decades (Kirchner
et al. 2004). These efforts are greatly enabled by new tech-
nologies and approaches that can measure a rapidly
expanding portfolio of constituents continuously over long
time periods (Porter et al. 2012; Pellerin et al. 2016).
Diel oscillations in a wide range of solutes are common in
rivers and are linked to metabolism by both direct and indi-
rect mechanisms (Nimick et al. 2011; Hensley and Cohen
2016). By quantifying variation in diel oscillations over time,
recent empirical studies have demonstrated close coupling
between instantaneous rates of metabolism and the assimila-
tion of dissolved nutrients (e.g., Heffernan and Cohen 2010;
Rode et al. 2016). While links between metabolism and
nutrient cycling are also observed across rivers (e.g., Hall and
Tank 2003), high-frequency continuous monitoring can illu-
minate relationships between metabolic processes and
longer-term changes in material export. For example, in a
Tennessee stream, daily GPP can drive variation in both diel
NO2
3oscillations and total uptake (Roberts and Mulholland
2007) (Fig. 6); similarly, interannual variation in springtime
GPP due to storm frequency and timing accounts for much
of interannual variation in watershed nutrient export (Lutz
et al. 2012) (Fig. 6).
Joint high-frequency measurements of oxygen and other
solutes can also illuminate the cascade of interactions
between metabolism and other biogeochemical processes. In
a spring-fed Florida River, variation in metabolism drives diel
and seasonal variation in NO2
3uptake, and also directly
affects day-to-day variation in denitrification, which
accounts for the majority of riverine NO2
3removal (Hef-
fernan and Cohen 2010). In the same system, metabolism
affects diel variation in PO32
4concentrations, both directly
through assimilatory uptake and indirectly via effects of pH
on calcite saturation (de Montety et al. 2011; Cohen et al.
2013).
Riverine metabolism is obviously not the only potential
cause of fine-scale variation in river chemistry; indeed, many
such patterns reflect hydrologic and anthropogenic processes
occurring in hillslopes, riparian zones, and upstream within
river networks (Kirchner et al. 2001; Harvey 2016). For exam-
ple, daily variation in evapotranspiration rates of catchment
vegetation can drive diel variation in streamflow and thus
the relative contribution of groundwater and shallow subsur-
face flowpaths, a mechanism that can drive diel variation in
stream solute concentrations even in the absence of high
rates of instream nutrient assimilation. Assimilation and
transformation processes well upstream of a sampling loca-
tion can result in temporal variation in solute concentra-
tions which are inconsistent in their timing or magnitude
with local control by in-stream processes (Pellerin et al.
2009, 2014). Relatively few studies have attempted to disen-
tangle hydrologic and biotic drivers of diel nutrient variation
using direct measurements of both processes (but see Aubert
and Breuer 2016; Lupon et al. 2016).
Although nutrients do not appear to drive major differ-
ences in metabolism across rivers, it is likely that nutrients
do limit metabolism in rivers where nutrient supply is low,
Bernhardt et al.Metabolic regimes
11

light is abundant and disturbance is infrequent. It has
been suggested that continuous daily estimates of nutrient
concentrations could provide strong direct evidence for
nutrient dependence of metabolism (Hall 2016). At still
finer scales of diel variation, theoretical models predict
that the magnitude of diel variation, relative to autotro-
phic demand, may depend on whether nutrients are avail-
able in sufficient supply (Appling and Heffernan 2014).
High-resolution sensors are particularly valuable for testing
such links between organismal physiology and ecosystem
processes.
Taking full advantage of the growing capabilities of in
situ sensors will require ongoing methodological and theo-
retical advances (Pellerin et al. 2016; Rode et al. 2016).
Within rivers, the physical dynamics of transport are likely
to affect covariation of solutes in ways that may both mask
and influence biogeochemical processes. As one example,
the footprint of processes that involve dissolved gases is
likely to differ from those that lack gas phase reactants and
products (Hensley and Cohen 2016). Similar challenges may
arise in linking and disentangling processes that act at very
different rates or in opposition (e.g., nitrification vs.
Fig. 6. Data collected from Walker Branch watershed in Oak Ridge Tennessee (Roberts and Mulholland 2007; Roberts et al. 2007; Lutz et al. 2012).
Upper panels during periods of the year with high GPP, diel increases in dissolved oxygen are linked to diel declines in stream nitrate concentrations
while nitrate concentrations are constant (and elevated) during summer months when the forest canopy is closed. Middle panels: Seasonal variation
in GPP explains the low stream nitrate concentrations during spring and fall. Lower panels: Years with larger spring algal blooms (e.g., 2005) have
greater total annual instream nitrogen uptake than years in which spring storms reduce the longevity and peak productivity of spring algae (e.g.,
2006). [Color figure can be viewed at wileyonlinelibrary.com]
Bernhardt et al.Metabolic regimes
12

denitrification) within different habitat compartments (Hel-
ton et al. 2012; Gomez-Velez and Harvey 2014). Experimen-
tal approaches that take advantage of sensor capabilities may
help provide estimates of whole system and compartment-
specific biogeochemical processes.
One clear need is the further development of integrative,
ecosystem-level models that link metabolic, biogeochemical,
and hydrologic processes within rivers. Widely used chemi-
cal loading models (i.e., Sparrow, HYDRA) couple dynamic
hydrology with static assumptions about element processing
within rivers. At present, river science lacks the portfolio of
theoretical and modeling approaches akin to those devel-
oped by terrestrial biogeochemists and ecologists (e.g., CEN-
TURY, Biome-BGC, P-NET) that allow for dynamic, non-
equilibrium modeling of ecosystem and biogeochemical pro-
cesses. Because rivers provide opportunities to observe meta-
bolic and biogeochemical processes over comparable time
scales, novel river ecosystem models offer the potential to
develop and test more general theory of the interactions
among element cycles and energy flow in ecological systems.
Frontier #3 - river metabolism for diagnosis and
management
Basal metabolism constrains the “work” that ecosystems
can do and we can measure this process for an entire ecosys-
tem just as we do for an organism through monitoring its
exchange of O
2
or CO
2
with the atmosphere. Many govern-
ment agencies monitor dissolved oxygen in freshwater eco-
systems to avoid or regulate low oxygen events. At no
additional costs, these same data can be repurposed to pro-
vide real time, continuous measures of ecosystem function.
To take advantage of this new information, we need new
diagnostic tools that are easy to explain and interpret. We
propose the metabolic fingerprint as one such diagnostic
tool and framework for hypothesis testing (Fig. 7). The meta-
bolic fingerprint can be represented as the entire distribution
of daily estimates of GPP and ER that are observed for a
river, or the summary of those data into kernel density plots
that allow easy visualization of both peak and median meta-
bolic rates as well as variance in their ratio (Fig. 7). In Fig. 7,
we show an explanatory cartoon of the fingerprint graph
alongside the “fingerprints” of the four rivers presented pre-
viously in Figs. 1, 3. The highly productive Menominee River
in Wisconsin occupies a much larger volume of metabolic
space than does the very frequently flooded Fanno Creek in
Oregon. We hypothesize that higher light and nutrient sup-
plies will expand both the total area and the maximal rates
represented by a river’s fingerprint, while hydrologic distur-
bance and sediment loading will constrain the metabolic fin-
gerprint to values near the origins of both the GPP and ER
axes. We suspect that as we gain annual metabolism data
from an increasing number of rivers we will discover recog-
nizable clusters of river metabolic regimes. As we develop
our mechanistic understanding of how river metabolic
regimes are established, maintained and disrupted, we will
become better able to determine how river metabolic
regimes are being altered by pollution, flow regulation or cli-
mate change as well as how effective mitigation, restoration,
and preservation efforts are at returning rivers toward more
natural metabolic regimes.
In addition to comparing fingerprints across streams,
these same visualization tools can be used to compare across
Fig. 7. The metabolic fingerprint is a diagnostic tool for comparing annual patterns of metabolism across rivers or across years for the same river.
Panel A shows the metabolic fingerprint for 2014 GPP and ER estimates for each of the four streams shown in Fig. 1. The “fingerprint” is represented
as a kernel density plot of all daily estimates of GPP and ER rates observed within each stream. The area of a river’s fingerprint (its variation along
both the 1 : 1 line and ER axis) should be compressed by hydrologic disturbances and expanded by the supply of either solar energy of fixed carbon.
Bernhardt et al.Metabolic regimes
13

years or among sites within the same river to examine the
effects of infrastructure or restoration efforts, land use
change, or hydrologic extremes. For example, the metabolic
fingerprint of the Oria River in the northern Spain changed
in response to the implementation of a wastewater treatment
plant (WWTP) (Fig. 8). The Oria River drains a basin of
882 km
2
with 126,000 inhabitants in which industries had
previously diverted their wastewater directly to the river.
Historic water quality data collected by the monitoring sta-
tion located in the upper basin (333 km
2
) revealed that
untreated sewage directly coming from houses and industries
carried large amounts of both inorganic and organic pollu-
tants, elevating ammonium in the river to concentrations as
highs as 2.5 mgL
21
. High loads of organic matter resulted in
very high rates of stream ER that were not accompanied by
similarly high rates of GPP. Although hyperoxic conditions
(saturation 150%) were common during the day due to the
high rates of GPP, even higher ER rates derived from organic
pollution frequently resulted in hypoxia at night (satu-
ration <40%). In 2003, a WWTP was put into operation
approximately 8 km upstream from the water quality moni-
toring station, and now treats the domestic sewage of 60,000
inhabitants as well as industrial sewage. The amount of inor-
ganic and organic pollutants entering the river subsequently
declined, with average ammonium concentrations falling to
0.18 mgL
21
. This improvement in water quality resulted in
reduced rates of both GPP and ER, significantly changing the
distribution of metabolic rates—the “metabolic finger-
print”—of the Oria River (Fig. 8). Daily amplitudes of dis-
solved oxygen greatly decreased due to the reduced
metabolic rates with oxygen saturation now consistently
between 80% and 120%, resulting in greatly improved con-
ditions for river biota. This case study shows just one exam-
ple of how the metabolic “fingerprint” can serve as both a
sensitive detector and a clear outreach tool to describe eco-
system functional responses to intervention.
Concluding remarks
Syntheses of daily metabolism measurements from across
different rivers have revealed enormous variability and had
limited success in explaining this variance. We suggest that
estimating GPP and ER for one or a few days provides little
information about the energetics of most rivers where fre-
quent disturbance and highly variable light regimes lead to
high temporal variation in GPP and ER rates. By examining
the patterns of metabolism over entire years, we can observe
how the extrinsic controls of light, heat, allochthonous
inputs, and disturbance together shape metabolism, and we
can begin to understand and predict how these drivers have
changed and are likely to change because of widespread flow
regulation, climate change, land use, and eutrophication.
Land use and climate change are altering the light, ther-
mal and disturbance regimes of rivers at both local and
Fig. 8. Two photos of the Oria River and its metabolic fingerprint before (black) and after (green) the construction of a wastewater treatment plant.
Photo credits to M. Arroita and A. Elosegi. [Color figure can be viewed at wileyonlinelibrary.com]
Bernhardt et al.Metabolic regimes
14

global scales. Climate change has already shifted the timing,
magnitude and frequency of flooding events in rivers
throughout the world (Milly et al. 2002, 2005; Oki and
Kanae 2006). Even in the absence of directional climate
change, hydropower and water supply dams are leading to
major shifts in the hydrologic regimes of most the world’s
rivers (Poff and Zimmerman 2010; V€
or€
osmarty et al. 2010);
while the frequency and magnitude of peak flows in smaller
streams have been increased by urbanization’s spreading
influence (Paul and Meyer 2001; Walsh et al. 2005). All
forms of land use change tend to generate increased turbid-
ity events and accelerated bed and bank erosion (Leopold
et al. 1964). Approaches to manage the stressors and protect
ecosystem services of rivers are in their infancy, and we sug-
gest that effective river conservation and management
requires that we devote as much attention to the metabolic
regimes of rivers as we have already invested in understand-
ing river flow regimes.
Fortunately, we now have the technical and logistical
capacity to measure dissolved gas and solute fluxes and
model ecosystem metabolism at the same highly resolved
time steps at which streamflow has historically been mea-
sured. If we can match these technological innovations with
equally innovative theoretical breakthroughs we will enable
river ecosystem science to link energetics and element cycles
at the time scales at which: organisms assimilate elements
(seconds to hours to days); disturbance initiates succession
(weeks to months); and terrestrial and aquatic element cycles
are linked (from hours to seasons). The emergent research
will generate novel and sophisticated understanding of the
multi-scale controls of stream metabolism, facilitating the
transition from short-term, reach-scale studies toward annu-
alized, scalable models of freshwater ecosystem energetics. In
turn, these models will enable sophisticated predictions
about how river food webs, biodiversity, and nutrient proc-
essing capacity are likely to change under realistic scenarios
of climate and land use change.
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Acknowledgments
We wish to thank fellow river metabolism enthusiasts Tom Battin,
Joanna Blaszczak, Natalie Griffiths, Ashley Helton, Brian McGlynn, Eliza-
beth Sudduth, Phil Savoy, and Amber Ulseth for the many productive
conversations on these topics that have helped refine the ideas pre-
sented here. The ideas discussed here have resulted from collaborations
among the authors that have been enabled by working group funds
through the USGS Powell Center Synthesis Center and the NSF Macro-
systems Program (Grant # EF 1442439). Individual participants have
been supported by postdoctoral funding from the Basque Government
(to M. Arroita) and a Humboldt Foundation Sabbatical Fellowship (to E.
Bernhardt).
Conflict of Interest
None declared.
Submitted 18 May 2017
Revised 29 August 2017
Accepted 14 September 2017
Associate editor: Ryan Sponseller
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