Metabolism of Streams and Rivers

CHAPTER 4

Metabolism of Streams and Rivers: Estimation, Controls, and Application Robert O. Hall, Jr.

Department of Zoology and Physiology, University of Wyoming, Laramie, WY, United States

Contents Introduction Approaches to Measuring Reach-Scale Metabolism Balance and Coupling of GPP and ER in Streams Primary Controls on Metabolism Metabolic Control of Element Cycling in Streams From Human Effects on Metabolism to Application Looking Ahead Discussion Questions References

151 154 160 163 167 168 171 172 173

INTRODUCTION Ecosystem metabolism encompasses the two central processes of the reduction of CO2 to organic carbon (C) via primary production and ­ ­oxidation back to CO2 via respiration. This concept represents a cornerstone for ecosystem ecology because it describes the total energetics of all autotrophic and heterotrophic organisms in an ecosystem. Research on metabolism and energy flow has a long history of ecosystem science in aquatic and terrestrial habitats (Gaarder and Gran, 1927; Lindeman, 1942;Woodwell and Whittaker, 1968; Whittaker and Likens, 1973); studies of streams and rivers have contributed prominently to this history (Odum, 1956; Fisher and Likens, 1973; Minshall, 1978). We can conceptualize metabolism as a group of interrelated fluxes that fix and mineralize organic carbon (C) (Woodwell and Whittaker, 1968). At the ecosystem level, gross primary production (GPP) is the total amount of C fixed by photosynthetic and chemosynthetic organisms. Ecosystem Stream Ecosystems in a Changing Environment http://dx.doi.org/10.1016/B978-0-12-405890-3.00004-X

© 2016 Elsevier Inc. All rights reserved.

151

152

Stream Ecosystems in a Changing Environment

respiration (ER) is the mineralization of organic C by all autotrophic and ­heterotrophic organisms in an ecosystem; in this chapter, I treat ER as a negative flux. The balance of these two processes is net ecosystem production (NEP) such that (1) NEP = GPP + ER NEP is positive when GPP > ER and negative when GPP < ER. Positive NEP means that an ecosystem must be accumulating or exporting organic C, negative NEP means that an ecosystem must be importing organic C (Fisher and Likens, 1973; Lovett et al., 2006).We can also consider net primary production (NPP), which is the change in plant biomass with time and is calculated as NPP = GPP + AR (2) where AR is respiration by autotrophic organisms. Unlike in terrestrial ecosystems dominated by vascular plants, AR is difficult to conceptualize and measure in aquatic ecosystems because autotrophs turn over their biomass rapidly and release much of GPP and dissolved organic carbon (DOC) (Baines and Pace, 1991; Hotchkiss and Hall, 2015), and because of the operational difficulty in separating autotrophs and heterotrophs. There have been two main approaches to measuring aquatic metabolism: (1) methods based on isolating a portion of the habitat into a benthic chamber or pelagic bottle and (2) free water methods that treat the ecosystem as a bottle, and integrate benthic and pelagic metabolism over (a portion of ) the ecosystem. The advantages and disadvantages of these disparate approaches are well known (Hall et al., 2007; Staehr et al., 2012). Much of the metabolism in streams occurs in the benthic or hyporheic zones, for which it is difficult to use chambers to calculate scaled-up estimates of ecosystem metabolism; hence, much of the recent research on metabolism has been via the free water method, and I will focus this chapter on studies using that technique. That said, much of the metabolism in rivers is planktonic, and to isolate the planktonic component requires the use of bottle methods (Lewis, 1988; Cole et al., 1992; Ochs et al., 2013; Reisinger et al., 2015). Unlike terrestrial habitats, it is difficult to estimate aquatic production as a change in net biomass because algal biomass turns over so rapidly and a fraction of GPP is released as DOM (Baines and Pace, 1991). Starting with H. T. Odum’s (1956) work, which was based on earlier studies of coral reefs (Sargent and Austin, 1949), there has been much research on the metabolism of streams, because of Odum’s elegant demonstration of how to estimate metabolism at a stream reach scale using diel oxygen



Metabolism of Streams and Rivers: Estimation, Controls, and Application

153

budgets. Oxygen is the usual currency of measuring metabolism in streams, relative to CO2, because its concentrations are low enough that metabolism can cause a large diel change in concentration (although, using oxygen only measures aerobic ER). Much data collected since Odum’s seminal work show that streams and rivers vary greatly in rates of metabolism (Hoellein et al., 2013). Streams can be highly heterotrophic (Fisher and Likens, 1973), with ER exceeding GPP, often by several orders of magnitude. On the other hand, some streams can be autotrophic, producing more C than they respire (Minshall, 1978), thus storing or exporting this NEP. Along a river continuum, this balance of GPP to ER may change in a predictable way, with high rates of GPP in midorder reaches with open canopy, yet lower in downstream reaches where suspended sediment may limit light penetration and thus, rates of GPP (Vannote et al., 1980). Following this historical backdrop, reach-scale metabolism studies have experienced recent growth primarily due to ease of measurement and computation (Fig.  1). Recording oxygen sensors are precise and getting less expensive. There are several means to estimate gas exchange rates, a key parameter in metabolism estimation. Recent developments in computation have enabled easier calculation of metabolism along with estimates of uncertainty on these rates. By installing sensors for extended periods, it is possible to get long time series of daily metabolism, which provide detailed insights into controls on metabolism that are not possible with occasional measures

Number of studies

100 80 60 40 20 0 1990

2000 Year

2010

Fig. 1  The number of studies of stream ecosystem metabolism has increased greatly during the past 25 years. Numbers of studies per year are based on a Google Scholar search for the following three terms: “Ecosystem metabolism,” (stream OR river), GPP. This search does not necessarily accurately capture the number of studies using diel oxygen changes to estimate metabolism in streams or rivers because it includes some from other ecosystems. Rather, this analysis represents the rate of change of studies on ecosystem metabolism.

154

Stream Ecosystems in a Changing Environment

(Uehlinger, 2000; Roberts et al., 2007; Izagirre et al., 2008). There is recent interest in using metabolism as a functional indicator of human degradation of streams and rivers (Young et al., 2008). Given this interest, my goal with this chapter is to integrate new methods and research into stream metabolism to describe how this area of research can advance the broader understanding of ecological processes in streams and rivers. I have three specific objectives: first, describe methods to estimate ecosystem metabolism in streams from field techniques to data analysis.This area is advancing quickly, so I will focus on recent advances in methods. Second is to describe controls on variation in stream metabolism. Third is to consider present and future applications of ecosystem metabolism as a technique to monitor and measure responses to human perturbations including climate change. Aquatic ecosystem metabolism has been the subject of several recent syntheses and reviews (Staehr et al., 2010a,b;Testa et al., 2013; Hoellein et al., 2013), which I will not duplicate here. Methods of lake metabolism were covered in depth by Staehr et al. (Staehr et al., 2010a). Staehr et al. (2012) covered history, concepts, and application of aquatic metabolism studies for all aquatic habitats, while Testa et al. (2013) reviewed estuarine metabolism studies. Hoellein et al. (2013) synthesized studies of freshwater and estuarine metabolism in the context of Odum (1956). Demars et al. (2015) addressed specific considerations in estimating stream and river metabolism, in particular the use of tracer gases and consideration of heterogeneity. Lastly, Tank et al. (2010) reviewed stream metabolism in a historical context as part of an overall review of stream organic matter dynamics. None of these reviews focused on metabolism of streams and rivers specifically, thus allowing this chapter to address more deeply the methods, controls, and applications of ecosystem metabolism in flowing water.

APPROACHES TO MEASURING REACH-SCALE METABOLISM Odum (1956) defined a means to estimate metabolism as a function of ­oxygen (O2, coded as O in equations) concentrations such that: dO (3) = GPP + ER + K ´ Odef dt where GPP increases O2, ER (a negative flux) lowers O2. Gas exchange between water and atmosphere is the product of a gas exchange rate K (1/d) and oxygen deficit Odef, which is the difference between O2 concentration in water at saturation for a given temperature and atmospheric pressure, and



Metabolism of Streams and Rivers: Estimation, Controls, and Application

155

measured O2 concentration in the water.This model is for a single station in a river, and therefore assumes longitudinal homogeneity along a river.There are several ways to implement this model, which I will discuss later. Given a model, the next step is to measure O2 concentration in water through time. Recording sondes have made this task easier than the sleepless nights in Odum’s time. Recently manufacturers have developed optical dissolved oxygen sensors, which exceed the performance, and have replaced the older Clark polarographic sensors. Optical sensors have higher precision and drift less than polarographic sensors.They do not form a boundary layer near the sensor, and thus have no requirement for stirring, allowing them to use less power. Low drift and power consumption means that sondes can be deployed for longer periods of time than before, allowing easier collection of time series. Some current time series exceed 9 years (Natalie Griffiths, Oak Ridge National Laboratory, personal communication). Air-water gas exchange (also called reaeration) is unknown and needed to estimate metabolism parameters. Prior to the mid-1990s, most estimates of metabolism relied on empirical equations of gas exchange versus stream physical attributes. The models are now quite robust and based on hundreds of measurements (Raymond et  al., 2012); however, the prediction error is high, which give uncertain estimates for gas exchange. In the 1990s, two studies showed how one could easily measure gas exchange for any particular metabolism study using the deliberate tracers of SF6 or propane (Wanninkhof et al., 1990; Marzolf et al., 1994). This technique allowed an empirical estimate of gas exchange for any one stream. The number of metabolism estimates in streams increased greatly following this technique. Gas exchange estimates are beneficial in the sense that one gets a value corresponding to near the day when they measure metabolism. The drawbacks are that the gas injection, sampling, and analysis are far more work than calibrating and installing the sondes.There is some uncertainty in scaling gas exchange across a range of Schmidt numbers (ratio of kinematic viscosity to molecular diffusivity) needed to convert K estimated from SF6 to K for O2 in turbulent streams dominated by bubble-mediated gas transfer (Asher and Wanninkhof, 1998). If gas exchange is really low it may be difficult to detect a decline in tracer over the study reach. Lastly, SF6 has 23,000 times the greenhouse forcing of CO2 and thus should be used sparingly. Floating chambers are another way to estimate gas exchange in rivers. These tend to be used in large rivers that have low turbulence but can be an effective method (Alin et al., 2011; Beaulieu et al., 2012). The premise is to measure change in gas concentrations in a dome floating down a river.

156

Stream Ecosystems in a Changing Environment

One can also estimate gas exchange rates from the oxygen time series data themselves. Two ways are the nighttime regression method (Hornberger and Kelly, 1975) and the less used delta method (Chapra and Di Toro, 1991). Both require a substantial amount of GPP and low gas exchange rates to estimate gas exchange accurately. However, if gas exchange is too low to measure via a gas tracer, then the nighttime regression method is likely to work well. Recent work has used inverse modeling approaches to solve for gas exchange along with GPP and ER (Holtgrieve et al., 2010; Grace et al., 2015; Hall et al., 2015b). This method also requires a low gas exchange rate and enough GPP to push dissolved O2 far enough from it nighttime equilibrium to be able to estimate K. Note that it is difficult to estimate K from the O2 data in streams with negligible GPP; any combination of K or ER can cause a given amount of under-saturation in stream water. It is also possible to measure gas exchange if some sort of a discontinuity in a river or stream pushes O2 (or any other gas) far from atmospheric equilibrium, which enables measuring the rate of change in O2 in a downstream direction (Hall et al., 2012). Unlike lakes, gas exchange rates in rivers can range from very low and lake-like to so high that measuring metabolism is difficult because gas exchange dominates O2 concentration, with only a small contribution from biological processes. High rates of gas exchange are troublesome because they can greatly increase uncertainty in metabolism estimates (McCutchan et al., 1998). There are a few ways to estimate metabolism parameters based on variations of Eq. (1). I will not exhaustively describe all, but rather provide a few examples. Variations include different ways to treat light (eg, Hanson et al., 2008) and ways of numerical integration.The first approach is a direct calculation: ö æ O - Oi -t Met i = ç i - K (Os - Oi -t ) ÷ z (4) t ø è where Meti is NEP at time i (g O2 m−2 d−1), O is O2 concentration (g m−3), t is time between measurements (d), Os is the saturation concentration of oxygen, and z is mean depth (m).We calculate ER as the mean of Meti during night scaled to 24 h, while GPP is the area under the hump-shaped curve during the day (Marzolf et al., 1994).This method has been used extensively. A more recently used approach is to invert Eq. (2) to solve for O (Van de Bogert et al., 2007). For example: GPP ´ PPFDi ER ´ t Oi = Oi -t + + + Kt (Os - Oi -t ) (5) z ´ åPPFD z



Metabolism of Streams and Rivers: Estimation, Controls, and Application

157

where PPFDi is photon flux density during time period i (μmol photons m−2 s−1). As presented, the model assumes whole-stream photosynthesis responds linearly to variation in light, though it is possible to model photosynthetic responses to light using a variety of approaches. For example, one can model light as a saturating function using a Jassby-Platt formulation (Jassby and Platt, 1976, Hanson et al., 2008; Holtgrieve et al., 2010). Using Eq. (5), one solves for the combination of parameters (GPP, ER, and sometimes K) that provides the best fit between modeled and measured O. This method is advantageous to the direct calculation because it allows estimating uncertainty in parameter estimates. In addition, it allows one, given high GPP and low K, to also solve for K, which is not possible with the direct calculation technique. There are several ways to estimate the best-fit metabolism parameters from a model given O2 data (Bolker et  al., 2013). One can use a myriad of nonlinear minimization routines to minimize an objective function, such as sum of squared deviations between model and data; however, this method does not lend itself to easy estimation of parameter uncertainty. Better to estimate the maximum likelihood, for which there are ways to calculate parameter uncertainty via approaches, such as the likelihood ratio tests (Hilborn and Mangel, 1997). Another is to implement a Bayesian solution that treats the parameters as probability distributions (Holtgrieve et al., 2010) (Fig. 2). Following Bayes rule, the posterior distribution is: P (q | D ) µ P ( D |q ) ´ P (q ) (6) where θ is a vector of parameters (eg, GPP, ER), and D is oxygen data. Parameter estimation is achieved by simulating the posterior distribution via Markov-chain Monte Carlo (MCMC) methods. In addition, Bayesian estimation allows prior information, which is especially useful if one has prior information about K from empirical formulae (Raymond et al., 2012) or via gas tracer experiments (Marzolf et al., 1994). I note that with any inverse modeling approach (especially if gas exchange is a parameter solved in the model fitting), there is a danger of an over-identified model, in which several combinations of parameter estimates produce an equally good fit of the model to the data. This phenomenon leads to large uncertainties in parameter estimates. Bayesian methods can help identify this problem in that MCMC runs will converge poorly or posterior parameter distributions will be very wide. If there is a long time series of data, large day-to-day variation in physical parameters (eg, gas exchange) may suggest high uncertainty in the estimates. As with all inverse modeling, it is best to use the simplest

158

Stream Ecosystems in a Changing Environment

Dissolved oxygen (mg/L)

10.5 10.0 9.5 9.0 8.5 8.0 00:00

12:00 Time

00:00

Fig. 2  Data and model for estimating metabolism in Spring Creek, Laramie, Wyoming on Oct. 27–29, 2014. Spring Creek is a channelized spring stream running through a city park. Gray points are oxygen data collected during two nights and one day. Line is a best fit model following Eq. (5). Parameter estimates were derived from a Bayesian solution of a state-space time series model incorporating process and observation error. Mean and 95% credible intervals (CI) were derived from simulating the posterior distribution in 1000 MCMC iterations using the program Stan (Stan Development Team, 2015). Gross primary production was 1.9 g O2 m−2 d−1 (95% CI = 1.6, 2.2), ecosystem respiration was −2.7 g O2 m−2 d−1 (95% CI = −3.1, −2.2) and gas exchange rate normalized to Schmidt number of 600 (K600) was 25.4 d−1 (95% CI = 21.0–29.6).

model that captures the processes of interest, and only add parameters if there is enough information in the data (Hanson et al., 2008). In many cases, it may be necessary to measure gas exchange if it is not possible to produce low-uncertainty estimates of gas exchange and metabolic parameters from inverse models. A one-station model is inappropriate in streams and rivers that have large longitudinal variation in physical or biological processes; in this case, a two-station method is needed. One-station techniques (Eqs. 3–5) measure a reach of stream that scales with the transport distance of oxygen; a proposed distance is about three times the transport distance of O2 (ie, 3V/K where V is mean water velocity) (Chapra and Di Toro, 1991), which corresponds to 95% O2 turnover in the reach. Spatial variability at the spatial scale of this distance will bias estimates of one-station metabolism. For example, a sonde placed in a tailwater below a dam may measure oxygen processes both in the tailwater and in the upstream lake. The solution to this problem is to place an additional sonde immediately below the dam and perform a



Metabolism of Streams and Rivers: Estimation, Controls, and Application

159

two-station calculation.Two-station methods follow a parcel of water along a reach on known travel time, τ. The mathematics of solving are similar to a one-station approach, but one substitutes the O2 value from the upstream station (Oi−τ) in place of Oi−t (Eq. 4) (Halbedel and Büttner, 2014, Hall et al., 2015b). Tributaries, waterfalls, point discharges of nutrients, and reach-scale experiments will all create large discontinuities in metabolism or gas exchange requiring a two-station method. Heterogenities at spatial scales much smaller than the turnover length of O2 in a river (eg, riffles and pools) are much less problematic than large discontinuities. The two-station method has its own assumptions, including constant travel time. Particularly pernicious is subdiel discharge variation associated with two-station metabolism estimates, which is often the case below dams used to generate hydropower or variation in flow due to diel variation in evapotranspiration. Such variation alters water transport time and requires an Eulerian water flow model coupled with O2 dynamics to create an O2 budget (R. A. Payn et al., Montana State University, unpublished data). Another consideration is smaller scale spatial heterogeneity, a wellknown problem for measuring metabolism in lakes (Van De Bogert et al., 2012) and streams (Reichert et  al., 2009; Demars et  al., 2011, Hondzo et  al., 2013). Smaller-scale spatial heterogeneity, such as pools, and spatial variation in gas exchange and production, can affect estimates of metabolism. Sonde placement may matter if a stream is not transversely mixed, because the water sampled does not represent the entire river (Villamizar et al., 2014). Another problem is measuring metabolism in gaining streams; input of groundwater can strongly bias estimates of respiration and should be corrected for if possible (Hall and Tank, 2005). Losing reaches are less problematic because the loss of water does not affect the concentration of O2. Practitioners should be aware of these possible biases in measuring metabolism. One novel approach to estimate metabolism at these smaller scales is by using the underwater eddy covariance method (Koopmans and Berg, 2015). This method measures vertical fluxes of O2 from a much smaller patch of benthos (eg, 10 m length) than does the open-channel methods and can address small-scale heterogeneity questions. Coupling these techniques, Koopmans and Berg (2015) showed that much of the perceived O2 demand in open-channel estimates was likely from low-O2 groundwater intrusion, further emphasizing the potential biasing effect of groundwater on estimates of ER. Isotopes of oxygen can be used to estimate stream ecosystem m ­ etabolism. 16 17 18 Oxygen has three stable isotopes: O, the very rare O, and O. Reaction

160

Stream Ecosystems in a Changing Environment

kinetics are slower for heavier oxygen isotopes, thus some reactions, such as aerobic respiration, will discriminate against the heavy 18O isotope, leaving the dissolved O2 pool with a higher 18O/16O ratio than if respiration were absent. In contrast, photosynthesis produces O2 with little to no discrimination against 18O, resulting in photosynthetic 18:16O2 with similar isotope values as H2O. This fractionation enables estimating aspects of metabolism that are not possible by measuring changes in bulk O2 mass. For example, if the fractionation factor of respiration (αR) is well known, then it is possible to estimate ER during the day using diel measurements of O2 and 18:16O2 (Tobias et al., 2007; Hotchkiss and Hall, 2014). The few studies that have examined daytime ER show that it can be much higher than nighttime ER. However, it turns out that the fractionation factor is not well known for all ecosystems and can be highly variable, which lead to estimates of daytime ER that are highly uncertain (Hotchkiss and Hall, 2014). That said, some authors assume a fractionation factor (or assume no fractionation, ie, αR = 1) and use the benefit of 18O as an additional dataset by which to estimate daily averaged GPP, ER, and K (Venkiteswaran et al., 2007; Holtgrieve et al., 2010). There is much further work to do to be able to reliably use 18O as an estimator of daytime ER in streams. The rare isotope of O2, 17O can also be used to estimate productivity in oceans (Luz and Barkan, 2000) but has not been used in streams to date. An emerging approach to measure ER is via chemical bioassays. A ­fluorochrome, resazurin, reduces to resorufin in the presence of cellular metabolism (Haggerty et al., 2009). The rate of conversion is proportional to that of aerobic respiration (González Pinzón et al., 2012). While more work than standard oxygen methods, this technique allows partitioning ER in transient storage zones within streams (Argerich et al., 2011).

BALANCE AND COUPLING OF GPP AND ER IN STREAMS Metabolism varies greatly among streams (Fig. 3), and streams tend to be more variable than other aquatic habitats (Hoellein et  al., 2013). Upper limits to stream GPP appear to be a bit lower than estuaries and lakes, with a 95% quantile at about 13 g O2 m−2 d−1 (Hoellein et al., 2013). High values >13 are found in streams with high light availability, high temperature, and high nutrient availability. Examples are a spring stream (Hall and Tank, 2005), desert stream (Mulholland et al., 2001), and a stream below a ­wastewater treatment plant (Gücker et  al., 2006). An upper bound for stream GPP appears to be 22 g O2 m−2 d−1 for a high light Pampean stream



Metabolism of Streams and Rivers: Estimation, Controls, and Application

161

50.0

ER g O2m–2d–1

20.0 10.0 5.0 2.0 1.0 0.5

0.05

0.20

1.00

5.00

20.00

GPP g O2m–2d–1

Fig.  3  Summary of gross primary production (GPP) and ecosystem respiration (ER) data from short-term studies in the literature. Data are from (Bernot et al., 2010) and (Marcarelli et al., 2011). Line is GPP = ER, and points above the line indicate heterotrophy on the day metabolism was measured. Axes are log scaled.

(Acuña et al., 2010) and a spring-fed river (Hall et al., 2015b). Controls on this upper limit are likely self-shading of algal mats (Davis et al., 2012). A key feature of stream metabolism is that ER can often greatly exceed GPP because many streams receive large inputs of terrestrial organic matter that can drive respiration to values much higher than can be supported by in situ production (Fisher and Likens, 1973; Marcarelli et  al., 2011). This finding is fairly well known and is because streams are tightly connected to the landscape, and often receive large inputs of organic matter to fuel heterotrophic respiration (Fisher and Likens, 1973; Hynes, 1975; Marcarelli et al., 2011). Combining data from many studies shows that streams can be highly heterotrophic (Fig. 3). Even if a stream has high rates of GPP, heterotrophy is common because such a stream will receive and mineralize C from the watershed, increasing ER relative to GPP. In addition, stream water dissolved O2 can reflect metabolism in the hyporheic zone; thus hyporheic metabolism may drive high rates of perceived heterotrophy in streams (Fellows et al., 2001). Few streams have large GPP relative to ER (Fig. 3). It is not possible for streams and rivers to have GPP ≫ ER, simply because the rate of ER will be high due to the respiration by autotrophs themselves, such that a minimum amount of ER is required to support a given GPP (Hall and Beaulieu, 2013). Given the degree to which streams are connected to the terrestrial landscape, it might be surprising that any streams would be autotrophic at all, yet autotrophy is possible in some times of the year. Many

162

Stream Ecosystems in a Changing Environment

of these examples could be that investigators focus their sampling in high light months when one day’s GPP may be higher than ER, such as spring in Walker Branch (Roberts et al., 2007). However, some flowing waters are not tightly connected to the landscape so that they receive little input of terrestrial organic matter. Springs are such an example, in that they can be large, wide ecosystems (with high rates of GPP) relative to their watershed area (Odum, 1957; Heffernan and Cohen, 2010). Rivers below dams may also be autotrophic because the dam severs organic matter transport from upstream watershed (Davis et al., 2012; Genzoli and Hall in press). Studies of continuous metabolism can show coupling of ER with GPP. In streams with high GPP, a substantial fraction of ER is from algae themselves and their associated heterotrophic bacteria (AR), thus GPP and ER covary. One can use this pattern to investigate the fraction of daily GPP that is respired by algae and their closely associated heterotrophs (Hall and Beaulieu, 2013). This approach estimates the slope of the upper bound of the GPP/ER relationship via quantile regression, which estimates the minimum amount of ER for a given GPP (Fig.  4). Data from several metabolism time series show a mean of about 45% of GPP is immediately respired. This value is higher than predicted by physiological responses, likely because of self-shading by algae and the fact that some of this respiration is from closely coupled heterotrophs (Hall and Beaulieu, 2013). 0 ER (g O2 m–2 d–1)

Slope = – 0.44 –5

–10

–15

0

2

4

6

8

10

GPP (g O2 m–2 d–1)

Fig.  4  Estimate of the fraction of daily gross primary production (GPP) immediately respired by algae and closely associated heterotrophs (ARf ). Points are daily values of GPP and ecosystem respiration (ER) from Walker Branch (Roberts et al., 2007). Line is a 90% quantile regression where the slope (0.44) represents ARf. Figure from Hall and Beaulieu (2013).



Metabolism of Streams and Rivers: Estimation, Controls, and Application

163

An assumption of this method is that GPP and heterotrophic respiration (HR, where ER = AR + HR) do not covary; in many streams, variation in HR will be from variation in respiration of allochthonous inputs. For example, warming temperature will increase HR, and these warming temperatures may covary with light, and thus GPP and autotrophic respiration. Indeed, this phenomenon may explain why ER is highly temperature-­ dependent in streams and rivers (Yvon-Durocher et  al., 2012). In many streams, ER and GPP do not covary at all; this pattern is usually when GPP is very low and ER is high and variable. Low GPP is often from riparian shading, water column turbidity, or hydrologic disturbance; shady, disturbed rivers will often have high and variable ER. A classic case of this phenomenon is a Mediterranean stream, which had light-limited GPP, and periodic drying caused by highly variable ER (Acuña et al., 2004). In the lower Mississippi River, ER was poorly correlated with GPP and GPP/ ER = 0.23, suggesting strong heterotrophic respiration contributing to metabolism in this river (Dodds et al., 2013).

PRIMARY CONTROLS ON METABOLISM Ecologists have gained considerable insight into the controls of metabolism by comparing rates among ecosystems, both within individual studies (Mulholland et al., 2001; Bernot et al., 2010), and in syntheses (Hoellein et  al., 2013). The approach compares rates among streams and rivers and then links with potential controlling variables via correlation or regression. Such a comparative approach has a long history in ecology and limnology (Birge and Juday, 1911; Cole et al., 1991). This comparison can be done in a single study (Mulholland et al., 2001) or via meta-analysis (Finlay, 2011). Having many streams provides sufficient statistical power to compare multiple controlling variables using statistics that investigate distal and proximal controls (Bernot et al., 2010). Typically, estimating temporal variation within a stream is sacrificed for spatial variation among streams. A limitation in deducing mechanism(s) causing variation in metabolism from spatially extensive studies is that temporal variation in metabolism can be huge in any one stream, and the antecedent conditions (eg, time since last flood) are usually not accounted for when interpreting a single or a few estimates of metabolism from a location. Temporal studies, thus, can examine controls on metabolism, such as light and nutrient availability in a single stream. In addition, if the temporal scale is fine enough, ideally daily, then it is possible to examine response and recovery to flood disturbance

Stream Ecosystems in a Changing Environment

10.0 2.0 0.5

ER g O2m–2d–1

50.0

164

0.05

0.20

0.05

0.20

1.00

5.00

20.00

5.00 0.50 0.05

ER g O2m–2d–1

50.00

(A)

(B)

1.00

5.00 20.00

GPP g O2m–2d–1

Fig. 5  (A) Daily estimates of GPP and ER from Walker Branch (hollow, black points) and Shepherd Creek (solid black points) have high variation in GPP and ER relative to single day estimates from many streams (red points, data replotted from Fig. 2). Note logarithmic scaled axes. Walker Branch data are from (Roberts et al., 2007) and Shepherd Creek are from (Beaulieu et  al., 2013). (B) Daily estimates from the Anorebieta River, Spain, have much higher variation than single day estimates from other rivers. Red points are daily data, and black points are data from on or near 1 May. (Data from (Izagirre et al. 2008)).

(Uehlinger, 2000; Beaulieu et al., 2013). Temporal variation in one stream’s metabolism can be as high as that from spatial comparisons (Fig. 5), thus there is strong potential to deduce controls on that variation. In fact, the huge temporal variation in metabolism within a stream may explain why choosing one day from a suite of streams, and then comparing controls on metabolism may not always work well. However, controls deduced from a single stream are not easily generalized to other streams. For example, resistance to same-size floods will vary strongly among streams. Light availability is a dominant control on rates of GPP, both when estimated among streams or within a stream.Variation in light can come from



Metabolism of Streams and Rivers: Estimation, Controls, and Application

165

shading, turbidity, or seasonal variation in solar angle. Among 11 streams in North America, light availability was the strongest covariate with GPP (Mulholland et al., 2001) and canopy cover was the dominant control of metabolism in a study across 70 streams (Bernot et  al., 2010). Temporal studies of metabolism also show light availability as the primary control on short- and long-term variation in GPP. Peaks in GPP often occur at high light times of year (Roberts et al., 2007). In a spring stream with little hydrologic control of variation in metabolism, solar insolation strongly predicted variation in GPP (Heffernan and Cohen, 2010). In the Colorado River Grand Canyon, variation in turbidity, coupled with seasonal variation in solar insolation, controlled most of the variation in GPP (Hall et  al., 2015a). Light was a strong predictor of temporal variation in GPP in tropical river (Hunt et  al., 2012), and in a temperate urban stream (Beaulieu et al., 2013). Highest rate of GPP in a Mediterranean stream was during times of year with little riparian shading (Acuña et al., 2004). Geomorphic properties can control metabolism by regulating solar insolation (Yard et al., 2005; Julian et al., 2008). Wider streams should have higher GPP due to less canopy shading, lower sediment mobility, and greater algal biomass, and in turn greater primary production. As watershed area increases streams widen with nearly threshold increases in GPP (Finlay et al., 2011). Temperature can also control rate of metabolism with the mechanism in part due to the direct control via temperature dependent kinetics of enzymes (Yvon-Durocher et  al., 2012), and other indirect effects, such as altering community structure. Effects of temperature from metabolism studies show strong positive relations with temperature and metabolism across a range of chemically similar, geothermally altered streams in Iceland (Demars et al., 2011; Rasmussen et al., 2011), though not in others in New Zealand, where variation in chemical and physical conditions likely overrode variation due to temperature (Hoellein et al., 2012). Temperature is difficult to tease apart from light in temporal studies of metabolism because they often covary (Huryn et al., 2014). In an arctic stream, light more strongly controlled rates of GPP relative to seasonal variation in temperature (Huryn et al., 2014). The effects of nutrients on the rate of ecosystem metabolism have been more difficult to clarify than that for light. Despite the well-known effects of nutrient loading on algal biomass and production in lakes (Schindler, 1974; Conley et al., 2009), spatial variation in nutrient concentrations and metabolism are often hard to pin down, in part because of narrow range of concentrations and the presence of other, stronger controls, such as light availability. Nutrient concentrations can be related to algal biomass, albeit

166

Stream Ecosystems in a Changing Environment

with much variability (Dodds et al., 2002). Across four regions of the United States, nutrients did not predict variation in GPP or ER, but nutrient concentrations were significantly related to GPP or ER within some regions (Frankforter et al., 2010). Nitrate concentration had only a small positive effect on variation GPP and ER in a study of 72 streams, despite that nitrate concentration varied >104-fold (Bernot et al., 2010). Higher stream water soluble reactive phosphorus concentration was related to higher GPP in tropical Cerrado streams (Gücker et al., 2009). It is easier to see nutrient effects in the context of a perturbation to a single stream (eg, fish farm effluent greatly increased GPP and ER in three Brazilian streams) (Rosa et al., 2012). Hydrologic variation can control much of the temporal variation in metabolism in streams due to floods scouring biofilms and reducing light availability to algae. Temporally intensive studies demonstrate these effects well and can show the recovery of metabolism. In a Swiss mountain river, GPP was highly sensitive to bed-moving spates, with an approximately 50% reduction following storms (Uehlinger, 2000). Recovery, on the other hand, was rapid (on the order of days) and correlated with temperature and light. ER is more resistant and less resilient than GPP to these spates. In the forested Walker Branch in Eastern Tennessee (USA), GPP following a storm was more suppressed than ER and recovered more slowly (Roberts et al., 2007). Nonetheless, GPP and ER recovered within a week, revealing high resilience to floods. Floods affected metabolism inconsistently in an urban stream, except for a large spring storm, which greatly reduced GPP and for which recovery took c.15 days (Beaulieu et al., 2013). Discharge was the primary control on temporal variation in GPP in a sub-Arctic River; GPP was highest at intermediate discharge and was low immediately following high flows (Benson et al., 2013). Increased discharge can lower GPP via light attenuation from increased DOC concentrations (Leggieri et al., 2013). Substrate composition in combination with discharge may determine overall rates of GPP. Streams with unstable sediments, such as sand, may always have low rates of production (Uehlinger et al., 2002; Atkinson et al., 2008). A model from a range of streams suggests that, in fact, GPP/ER can be predicted from a metric that combines mean depth and hydraulics as a function of bed movement (Hondzo et al., 2013). Fish can alter ecosystem metabolism via nutrient transformations, benthic feeding, and in extreme cases, geomorphic alteration (Taylor et al., 2006; Holtgrieve and Schindler, 2011; Levi et al., 2012). High-frequency (or at least repeated) estimates of metabolism, with and without fishes, show strong



Metabolism of Streams and Rivers: Estimation, Controls, and Application

167

effects that may be less easily seen with spatial comparisons. Salmon at high densities can geomorphically disturb a stream (Moore et  al., 2004), thus potentially lowering rates of GPP and ER. On the other hand, salmon can be large nutrient sources via excretion and decomposition. Unsurprisingly, the response of ecosystem metabolism is variable and site-specific. GPP may increase or decline, but ER consistently increases, likely from increased nutrient fueling sediment respiration (Holtgrieve and Schindler, 2011; Levi et  al., 2012). This finding contrasts somewhat with that for a sediment-­ feeding topical fish whose presence lowers both GPP and ER by feeding on algal-rich organic sediment deposited on stony surfaces (Taylor et al., 2006). These few studies reveal that fish can strongly regulate aspects of the C cycle in streams and are likely evident in any other stream where fish reach high densities via spawning migrations or when they feed on algae and sediment where they can achieve high biomass. Lastly, fish are not the only pulsed inputs of animals into streams; large fluxes of periodical cicadas can increase stream ER via their decomposition (Menninger et al., 2008). Hippos can import large quantities of terrestrial organic matter, which is likely to increase stream ER (Subalusky et al., 2015).

METABOLIC CONTROL OF ELEMENT CYCLING IN STREAMS Stream metabolic processes can control cycling of other elements beyond carbon. This effect can be direct, in the sense that assimilatory demand for nutrients is stoichiometrically linked to carbon fixation and heterotrophic assimilation. For example, metabolism is positively related to nutrient demand, measured by nutrient spiraling experiments and metabolism (Hall and Tank, 2003; Fellows et al., 2006). Dissimilatory N transformations may reflect rates of metabolism also, but for different reasons. In a study of 49 whole-stream denitrification estimates, ER, and not GPP, best predicted denitrification (Mulholland et al., 2009). The mechanism is not known but may be due to high ER indicating a suitable supply of organic substrate for denitrification, or high ER linked to anoxic zones where denitrification occurs (Mulholland et al., 2009). In addition to predicting rates of nutrient uptake measured experimentally, high frequency studies of metabolism can link rate of metabolism with concentration and export of nitrogen from watersheds. Net dissolved nitrogen removal (decline in load along a length of stream) was strongly related to temporal variation in GPP in Walker Branch, Tennessee (Roberts and Mulholland, 2007). Heffernan and Cohen (2010) coupled daily estimates

168

Stream Ecosystems in a Changing Environment

of metabolism with diel nitrate concentration to show that assimilatory uptake of nitrate was small in a Florida spring river. Denitrification was a much larger sink for nitrate, and variation in nitrate was driven by the previous day’s rate of GPP as a measure of production of labile organic matter to support denitrification (Heffernan and Cohen, 2010). GPP can cause a daily cycling of nitrate in streams, and interestingly, this cycling is expected to be most distinct when nitrogen is not limiting productivity (Appling and Heffernan, 2014). Although in streams highly polluted with N, we may not expect much influence of assimilatory demand on a large N pool. A similar pattern exists for dissolved phosphorus (P) in this river, in which daily increases in GPP lowers P because of increased assimilation and removal of P via coprecipitation of calcite (Cohen et al., 2013).

FROM HUMAN EFFECTS ON METABOLISM TO APPLICATION Humans can greatly alter stream metabolism through changing ­morphology, hydrology, and chemical and biological compositions in streams. We can often consider the suite of these specific effects in the context of human land use. Such effects of land use are often not easy to tease apart because land use represents several different and interacting controls on stream metabolism such as nutrients, light availability, and geomorphology. In addition, there is a hierarchy of controls. Proximal attributes, such as nutrient availability, directly control GPP and ER, while distal controls, such as fraction of watershed in agriculture, affect nutrient supply (Yates et al., 2012). In some cases, reach-specific factors (eg, local canopy cover) can override the effect of catchment land use (Young and Huryn, 1999). On the other hand, riparian condition may not mitigate upland disturbance. But patterns of human influence on metabolism can emerge. ER responded negatively to increasing catchment disturbance at a military base, despite relatively intact riparian zones, but there was no effect on GPP (Houser et al., 2009). In other cases, the effects of two controls can cancel each other out (Gücker et al., 2009; Hall et al., 2009), which may be why some studies fail to detect large effects of land uses, such as urbanization on stream metabolism (Meyer et al., 2005). GPP only weakly responded to land use across many New Zealand streams, with a stronger response from ER (Collier et al., 2013). In most studies examining the effect of land use on metabolism, altered light regimes are often a primary control (Bernot et al., 2010). Agricultural streams in the Southern Appalachians had higher GPP than forest streams with canopy cover as a primary control (McTammany et al., 2007).These effects of nearby land use



Metabolism of Streams and Rivers: Estimation, Controls, and Application

169

can also interact with stream morphology. Forested streams in southeastern Pennsylvania had lower rates of GPP than meadow streams on an areal basis, but on a linear basis, GPP was higher in forest streams because they were wider (Bott et al., 2006). To generalize among streams, the overall effect of human land use is to increase GPP. A meta-analysis of many streams showed that human-altered streams had higher GPP than reference streams (Finlay, 2011). Interestingly, this pattern was not shown in larger streams and rivers, suggesting that the mechanisms that increase GPP in small streams, increased light and nutrient availability, may not be as important in large rivers (Finlay, 2011). There has been much less generalization on the effect of human land use on time series of metabolism, although one might expect that increased variation in stream discharge in urban environments would increase variation in metabolism (Beaulieu et al., 2013). There is much interest in using measures of ecosystem function to infer ecological condition of streams (Young et al., 2008; Woodward et al., 2012); this interest dates back to the work of Streeter and Phelps (1925) who modeled biological oxygen demand in rivers. Metabolism is an appealing functional metric for assessing stream and river conditions because it represents a mechanism and a cause of why an ecosystem may be impaired (Palmer and Febria, 2012). A simple example is that governments often set dissolved O2 minima to prevent anoxia in streams. Metabolism and gas exchange jointly control oxygen concentration, and knowing controls on metabolism will allow understanding how to manage streams to prevent low-O2 events (Young et al., 2008). In addition to the straightforward link with water quality, metabolism is linked to other important aspects of ecosystem function, such as the base of food webs to support animal production. The application of ecosystem metabolism to assess stream conditions has so far been promising, not so much in terms of using absolute rates of GPP or ER, but rather evaluating relative changes in these processes. For example, ER tracked impairment across a land use gradient in New Zealand streams (Young and Collier, 2009), while GPP was less variable. GPP/ER showed a hump-shaped relationship, with low GPP/ER at low and high land use. This pattern matched that for litter decomposition across a nutrient gradient in Europe (Woodward et al., 2012), suggesting that responses of ecosystem function may be nonlinear. In some tropical streams in Brazil, ER increased as a function of dissolved N enrichment corresponding to urban land use (Silva-Junior et  al., 2014). Agricultural land use weakly stimulated GPP. It also appears that ER is more sensitive

170

Stream Ecosystems in a Changing Environment

to human perturbation than GPP; reasons for this finding may be related to organic pollution of streams (Gücker et al., 2006), or storm water discharge (Imberger et al., 2014). Metabolism can assess the efficacy of stream or river restoration. Restoration of the Kissimmee River, Florida reconnected a channelized reach to its previous meandering channel (Colangelo, 2007). This restoration increased both GPP and ER; these increases did not come at the expense of lower dissolved oxygen levels because increased velocity increased gas exchange rates. Reach-scale replanting of vegetation increased ER, likely due to organic matter inputs (Giling et al., 2013). The opposite is true; removing streamside vegetation from a prairie stream (a restoration in this context) lowered rates of ER (Riley and Dodds, 2012). In some cases, restoration does not affect stream metabolism (Sudduth et al., 2011); in these cases, it is possible that there are canceling effects of various changes to physical effects, or such high variability in seasonal estimates of metabolism that it is difficult to estimate a restoration effect, or, more simply, that the restoration had no effect on metabolism. A key point stressed by most authors is that assessing ecosystem metabolism works best when used in concert with other structural and functional indicators, ­although no one indicator will not work for all situations. Elosegi and Sabater (2013) assessed ways to monitor stream response to hydrogeomorphological alteration; they felt that ecosystem metabolism was highly relevant, worked well at reach scales but was not necessarily very sensitive to physical alteration because other factors (eg, nutrients, organic matter loading) also affect rates of GPP and ER. Long-term estimates of metabolism may represent a means to assess the responses of stream to climate change (Williamson et al., 2008; Marcarelli et al., 2010). Climate change should alter stream metabolism via increased temperature, potentially increasing rates of ER and GPP (Demars et  al., 2011; Perkins et al., 2011). Climate change will greatly alter the hydrology of streams, from changing the timing of runoff, for example, driving earlier runoff from snowmelt to rain (Barnett et  al., 2005). Earlier runoff in snowmelt-dominated streams may increase the productive capacity through lower flows during the high light time of year. On the other hand, summer flows might be much lower, especially below dams. Storms and drought frequency are likely to increase with corresponding changes to metabolism, given a tight linkage between GPP and ER and disturbance (Uehlinger, 2006). River basins with higher degrees of water development will ­experience more intense changes to the hydrologic regime (Palmer et al., 2008). Such changes in water use patterns, combined with climate



Metabolism of Streams and Rivers: Estimation, Controls, and Application

171

change, can dramatically affect metabolism. Marcarelli et  al. (2010) used a time series for metabolism from a western river to predict metabolism under different flow scenarios. They found little effect of temperature, but a much larger effect of changing flows; GPP increased greatly during low summer regulated flows. In addition to changes in flows and temperature, land use effects associated with climate change may greatly affect stream and river metabolism. For example, increased fire frequency in boreal forests may increase rates of GPP and ER (Betts and Jones, 2009). Given a complicated response of metabolism to land use (Bernot et  al., 2010), specific predictions are difficult because the effect may be site-specific. Nonetheless, the trajectory of metabolism with time may provide valuable information on stream ecosystem responses to short and long term environmental change. A key benefit of long-term, high-frequency data is that variation occurs over multiple temporal scales (Uehlinger, 2006; Roberts et al., 2007) such that daily-interannual variation can provide strong insight on controls of metabolism. Daily variation can show how streams respond to stormflow, annual variation shows seasonal effects, and interannual variation can show long-term trends in, for example, temperature or nutrient input (Uehlinger, 2006). A research frontier is to examine these multiple scales of variability; like solute export (Kirchner and Neal, 2013), it is possible that there is no characteristic scale of variability in metabolism.

LOOKING AHEAD Metabolism can play a strong role in addressing how streams respond to shortterm human impacts (eg, seasonal water management, restoration) to longterm response to climate change (Williamson et al., 2008). Recent advances in sensor technology have allowed sensors to collect long-term data with much less drift and problems, such that more continuous data sets are becoming available. I submit that these long-term, continuous data will provide a more sensitive response of metabolism to human alteration than will snapshot studies. Because variability within a stream can be higher than snapshot variability among streams, this variability provides ecological information. Stream metabolism can be a model process for testing disturbance response because the high-frequency data and large range of disturbances can enable strong tests of resistance and resilience of stream biota to floods. Recovery of streams from disturbances is much shorter than for terrestrial vegetation, allowing much more power to g­ eneralize their effects (Fisher, 1983). Also, continuous

172

Stream Ecosystems in a Changing Environment

data allow one to look at far more than simply the magnitude of differences, but the pattern of differences within streams, and do so in a mechanistic way (Solomon et al., 2013; Hall et al., 2015a). There will be difficulties with collecting and analyzing such data. One is the flood, so to speak, of the data themselves, but ecologists are developing tools to handle large data streams (Michener and Jones, 2012). Oxygen data should be comparatively easy compared to >1 Hz eddy covariance data or large genomic data sets. Second, there remains much research to examine ways of estimating daily metabolism using oxygen data. I presented one such model here, but in no way do I suggest that it is the only one. Models that consider light saturation effects, handle gas exchange differently, account for higher daytime ER, and address different forms of error (process versus observation error, Fig. 2) may all be necessary. Lastly, long time series of GPP and ER will require modeling approaches typical of time series data, which exhibit serial autocorrelation both with the data themselves and among predictors (Roley et  al., 2014). Analysis methods could be as simple as examining an annual shift in the peak of metabolism (Roberts et al., 2007), to more complicated time series approaches designed to measure increasing variability (Batt et al., 2013), or those based on coupling mechanistic and time series models (Hall et al., 2015a). There is a rich literature on time series modeling; these tools will enable limnologists to interpret the long time series of data that will be required to assess ecosystem response to climate change.

DISCUSSION QUESTIONS 1. In this chapter, one model is presented describing stream metabolism. How do other models describe stream metabolism? What are important terms to consider in developing a model for stream metabolism? 2. How do the controls on metabolism vary throughout a stream network? How does this variation in controls throughout a network affect the response of stream metabolism to disturbances such as flooding? 3. How does the competition between autotrophs and heterotrophs for nutrients affect stream metabolism? How are GPP and ER affected by the supply of labile organic carbon, and limiting nutrients, such as phosphorus and nitrogen? How will stream metabolism likely respond to changes in nutrient inputs? 4. How could you design an experiment to study the coupling between nutrient spiraling and stream metabolism?



Metabolism of Streams and Rivers: Estimation, Controls, and Application

173

5. How important are indirect effects of climate change, such as changing terrestrial vegetation for stream metabolism? If increased atmospheric CO2 produces leaf litter with high C:N, how will stream metabolism be affected? 6. Lotic ecosystems are the primary conduits linking continents to oceans and serve as the major vector for nutrients creating dead zones in coastal zones. Can we manage stream metabolism as an ecological service and increase nutrient retention in lotic ecosystems?

REFERENCES Acuña,V., Giorgi, A., Munoz, I., Uehlinger, U., Sabater, S., 2004. Flow extremes and benthic organic matter shape the metabolism of a headwater Mediterranean stream. Freshw. Biol. 49, 960–971. Acuña,V.,Vilches, C., Giorgi, A., 2010. As productive and slow as a stream can be-the metabolism of a Pampean stream. J. N. Am. Benthol. Soc. 30, 71–83. Alin, S., Maria de Fátima, F., Salimon, C., 2011. Physical controls on carbon dioxide transfer velocity and flux in low-gradient river systems and implications for regional carbon budgets. J. Geophys. Res. 116, G01009. http://dx.doi.org/10.1029/2010JG001398. Appling, A.P., Heffernan, J.B., 2014. Nutrient limitation and physiology mediate the finescale (de) coupling of biogeochemical cycles. Am. Nat. 184, 384–406. Argerich, A., Haggerty, R., Martí, E., Sabater, F., Zarnetske, J., 2011. Quantification of metabolically active transient storage (MATS) in two reaches with contrasting transient storage and ecosystem respiration. J. Geophys. Res. Biogeosci. 116, G03034. Asher, W., Wanninkhof, R., 1998. Transient tracers and air-sea gas transfer. J. Geophys. Res. 103, 15939–15958. Atkinson, B.L., Grace, M.R., Hart, B.T.,Vanderkruk, K.E., 2008. Sediment instability affects the rate and location of primary production and respiration in a sand-bed stream. J. N. Am. Benthol. Soc. 27, 581–592. Baines, S.B., Pace, M.L., 1991.The production of dissolved organic matter by phytoplankton and its importance to bacteria: Patterns across marine and freshwater systems. Limnol. Oceanogr. 36, 1078–1090. Barnett, T.P., Adam, J.C., Lettenmaier, D.P., 2005. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438, 303–309. Batt, R.D., Carpenter, S.R., Cole, J.J., Pace, M.L., Johnson, R.A., 2013. Changes in ecosystem resilience detected in automated measures of ecosystem metabolism during a wholelake manipulation. Proc. Natl. Acad. Sci. U. S. A. 110, 17398–17403. Beaulieu, J.J., Arango, C.P., Balz, D.A., Shuster, W.D., 2013. Continuous monitoring reveals multiple controls on ecosystem metabolism in a suburban stream. Freshw. Biol. 58, 918–937. Beaulieu, J.J., Shuster, W.D., Rebholz, J.A., 2012. Controls on gas transfer velocities in a large river. J. Geophys. Res. Biogeosci. 117, G02007. Benson, E.R., Wipfli, M.S., Clapcott, J.E., Hughes, N.F., 2013. Relationships between ecosystem metabolism, benthic macroinvertebrate densities, and environmental variables in a sub-arctic Alaskan river. Hydrobiologia 701, 189–207. Bernot, M.J., Sobota, D.J., Hall, R.O., Mulholland, P.J., Dodds, W.K., Webster, J.R., Tank, J.L., Ashkenas, L.R., Cooper, L.W., Dahm, C.N., Gregory, S.V., Grimm, N.B., Hamilton, S.K., Johnson, S.L., McDowell, W.H., Meyer, J.L., Peterson, B.J., Poole, G.C., Valett, H.M., Arango, C.P., Beaulieu, J.J., Burgin, A.J., Crenshaw, C., Helton, A.M., Johnson, L.T.,

174

Stream Ecosystems in a Changing Environment

Merriam, J., Niederlehner, B.R., O'Brien, J.M., Potter, J.D., Sheibley, R.W., Thomas, S.M., Wilson, K., 2010. Inter-regional comparison of land-use effects on stream metabolism. Freshw. Biol. 55, 1874–1890. Betts, E.F., Jones, J.B., 2009. Impact of wildfire on stream nutrient chemistry and ecosystem metabolism in boreal forest catchments of interior Alaska. Arct. Antarct. Alp. Res. 41, 407–417. Birge, E.A., Juday, C., 1911.The Inland Lakes of Wisconsin:The Dissolved Gases of the Water and Their Biological Significance. The state of Wisconsin. Bolker, B.M., Gardner, B., Maunder, M., Berg, C.W., Brooks, M., Comita, L., Crone, E., Cubaynes, S., Davies,T.,Valpine, P., 2013. Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS. Methods Ecol. Evol. 4, 501–512. Bott, T., Newbold, J., Arscott, D., 2006. Ecosystem metabolism in Piedmont streams: Reach geomorphology modulates the influence of riparian vegetation. Ecosystems 9, 398–421. Chapra, S.C., Di Toro, D.M., 1991. Delta method for estimating primary production, respiration, and reaeration in streams. J. Environ. Eng. 117, 640–655. Cohen, M.J., Kurz, M.J., Heffernan, J.B., Martin, J.B., 2013. Diel phosphorus variation and the stoichiometry of ecosystem metabolism in a large spring-fed river. Ecol. Monogr. 83, 155–176. Colangelo, D.J., 2007. Response of river metabolism to restoration of flow in the Kissimmee River, Florida, USA. Freshw. Biol. 52, 459–470. Cole, J.J., Lovett, G.M., Findlay, S., 1991. Comparative Analyses of Ecosystems: Patterns, Mechanisms, and Theories. Springer-Verlag, New York. Cole, J.J., Caraco, N.F., Peierls, B.L., 1992. Can phytoplankton maintain a positive carbon balance in a turbid, freshwater, tidal estuary? Limnol. Oceanogr. 37, 1608–1617. Collier, K.J., Clapcott, J.E., Hamer, M.P.,Young, R.G., 2013. Extent estimates and land cover relationships for functional indicators in non-wadeable rivers. Ecol. Indic. 34, 53–59. Conley, D.J., Paerl, H.W., Howarth, R.W., Boesch, D.F., Seitzinger, S.P., Havens, K.E., Lancelot, C., Likens, G.E., 2009. Controlling eutrophication: Nitrogen and phosphorus. Science 323, 1014–1015. Davis, C.J., Fritsen, C.H., Wirthlin, E.D., Memmott, J.C., 2012. High rates of primary productivity in a semi-arid tailwater: Implications for self‐regulated production. River Res. Appl. 28, 1820–1829. Demars, B.O., Russell Manson, J., Ólafsson, J.S., Gíslason, G.M., Gudmundsdottír, R., Woodward, G., Reiss, J., Pichler, D.E., Rasmussen, J.J., Friberg, N., 2011. Temperature and the metabolic balance of streams. Freshw. Biol. 56, 1106–1121. Demars, B.,Thompson, J., Manson, J.R., 2015. Stream metabolism and the open diel oxygen method: Principles, practice, and perspectives. Limnol. Oceanogr. Methods 13, 356–374. Dodds, W.K.,Veach, A.M., Ruffing, C.M., Larson, D.M., Fischer, J.L., Costigan, K.H., 2013. Abiotic controls and temporal variability of river metabolism: Multiyear analyses of Mississippi and Chattahoochee River data. Freshw. Sci. 32, 1073–1087. Dodds,W.K., Smith,V.H., Lohman, K., 2002. Nitrogen and phosphorus relationships to benthic algal biomass in temperate streams. Can. J. Fish. Aquat. Sci. 59, 865–874. Elosegi, A., Sabater, S., 2013. Effects of hydromorphological impacts on river ecosystem functioning: A review and suggestions for assessing ecological impacts. Hydrobiologia 712, 129–143. Fellows, C.S.,Valett, H.M., Dahm, C.N., Mulholland, P.J., Thomas, S.A., 2006. Coupling nutrient uptake and energy flow in headwater streams. Ecosystems 9, 788–804. Fellows, C.S., Valett, M.H., Dahm, C.N., 2001. Whole-stream metabolism in two montane streams: Contribution of the hyporheic zone. Limnol. Oceanogr. 46, 523–531. Finlay, J., 2011. Stream size and human influences on ecosystem production in river networks. Ecosphere 2, 87. Finlay, J.C., Hood, J.M., Limm, M.P., Power, M.E., Schade, J.D., Welter, J.R., 2011. Lightmediated thresholds in stream-water nutrient composition in a river network. Ecology 92, 140–150.



Metabolism of Streams and Rivers: Estimation, Controls, and Application

175

Fisher, S.G., 1983. Succession in Streams. In: Barnes, J.R., Minshall, G. (Eds.), Stream Ecology. Springer US, Boston, MA, pp. 7–27. Fisher, S., Likens, G., 1973. Energy flow in Bear Brook, New Hampshire: An integrative approach to stream ecosystem metabolism. Ecol. Monogr. 43, 421–439. Frankforter, J.D., Weyers, H.S., Bales, J.D., Moran, P.W., Calhoun, D.L., 2010. The relative influence of nutrients and habitat on stream metabolism in agricultural streams. Environ. Monit. Assess. 168, 461–479. Gaarder,T., Gran, H.H., 1927. Investigations of the production of plankton in the Oslo Fjord. Rapp. et Proc.-Verb. Cons. Internat. Explor. Mer 42, 1–48. Genzoli, L., Hall, R.O., In press. Shifts in Klamath River metabolism following reservoir cyanobacterial bloom. Freshw. Sci. Giling, D.P., Grace, M.R., Mac Nally, R., Thompson, R.M., 2013. The influence of native replanting on stream ecosystem metabolism in a degraded landscape: Can a little vegetation go a long way? Freshw. Biol. 58, 2601–2613. González Pinzón, R., Haggerty, R., Myrold, D.D., 2012. Measuring aerobic respiration in stream ecosystems using the resazurin‐resorufin system. J. Geophys. Res. Biogeosci. 117, G00N06. Grace, M.R., Giling, D.P., Hladyz, S., Caron,V., Thompson, R.M., Mac Nally, R., 2015. Fast processing of diel oxygen curves: Estimating stream metabolism with BASE (BAyesian Single‐station Estimation). Limnol. Oceanogr. Methods 13, 103–114. Gücker, B., Boëchat, I.G., Giani, A., 2009. Impacts of agricultural land use on ecosystem structure and whole‐stream metabolism of tropical Cerrado streams. Freshw. Biol. 54, 2069–2085. Gücker, B., Brauns, M., Pusch, M.T., 2006. Effects of wastewater treatment plant discharge on ecosystem structure and function of lowland streams. J. N. Am. Benthol. Soc. 25, 313–329. Haggerty, R., Martí, E., Argerich, A., von Schiller, D., Grimm, N.B., 2009. Resazurin as a “smart” tracer for quantifying metabolically active transient storage in stream ecosystems. J. Geophys. Res. Biogeosci. 114, G03014. Halbedel, S., Büttner, O., 2014. MeCa, a toolbox for the calculation of metabolism in heterogeneous streams. Methods Ecol. Evol. 5, 971–975. Hall, R.O., Beaulieu, J.J., 2013. Estimating autotrophic respiration in streams using daily metabolism data. Freshw. Sci. 32, 507–516. Hall, R.O., Tank, J.L., 2003. Ecosystem metabolism controls nitrogen uptake in streams in Grand Teton National Park, Wyoming. Limnol. Oceanogr. 48, 1120–1128. Hall, R.O.,Yackulic, C.B., Kennedy,T.A.,Yard, M.D., Rosi-Marshall, E.J.,Voichick, N., Behn, K.E., 2015a. Turbidity, light, temperature, and hydropeaking control primary productivity in the Colorado River, Grand Canyon. Limnol. Oceanogr. 60, 512–526. Hall, R.O., Tank, J.L., Sobota, D.J., Mulholland, P.J., O'Brien, J.M., Dodds, W.K., Webster, J.R.,Valett, H.M., Poole, G.C., Peterson, B.J., Meyer, J.L., McDowell,W.H., Johnson, S.L., Hamilton, S.K., Grimm, N.B., Gregory, S.V., Dahm, C.N., Cooper, L.W., Ashkenas, L.R., Thomas, S.M., Sheibly, R.W., Potter, J.D., Niederlehner, B.W., Johnson, L.T., Helton, A.M., Crenshaw, C.M., Burgin, A.J., Bernot, M.J., Beaulieu, J.J., Arango, C.P., 2009. Nitrate removal in stream ecosystems measured by 15N addition experiments: Total uptake. Limnol. Oceanogr. 54, 653–665. Hall, R.O., Tank, J.L., Baker, M.A., Rosi-Marshall, E.J., Hotchkiss, E.R., 2015b. Metabolism, gas exchange, and carbon spiraling in rivers. Ecosystems 19, 73–86. Hall, R.O., Thomas, S., Gaiser, E.E., 2007. Measuring freshwater primary production and respiration. In: Fahey, T.J., Knapp, A.K. (Eds.), Principles and Standards for Measuring Primary Production. Oxford University Press, Oxford, pp. 175–203. Hall, R.O., Kennedy, T.A., Rosi-Marshall, E.J., 2012. Air-water oxygen exchange in a large whitewater river. Limnol. Oceanogr. Fluids Environ. 2, 1–11. Hall, R., Tank, J., 2005. Correcting whole-stream estimates of metabolism for groundwater input. Limnol. Oceanogr. Methods 3, 222–229.

176

Stream Ecosystems in a Changing Environment

Hanson, P.C., Carpenter, S.R., Kimura, N., Wu, C., Cornelius, S.P., Kratz, T.K., 2008. Evaluation of metabolism models for free-water dissolved oxygen methods in lakes. Limnol. Oceanogr. Methods 6, 454–465. Heffernan, J.B., Cohen, M.J., 2010. Direct and indirect coupling of primary production and diel nitrate dynamics in a subtropical spring-fed river. Limnol. Oceanogr. 55, 677–688. Hilborn, R., Mangel, M., 1997. The Ecological Detective: Confronting Models |With Data. Princeton University Press, Princeton. Hoellein,T.J., Bruesewitz, D.A., Richardson, D.C., 2013. Revisiting Odum (1956): A synthesis of aquatic ecosystem metabolism. Limnol. Oceanogr. 58, 2089–2100. Hoellein, T.J., Bruesewitz, D.A., Hamilton, D.P., 2012. Are geothermal streams important sites of nutrient uptake in an agricultural and urbanising landscape (Rotorua, New Zealand)? Freshw. Biol. 57, 116–128. Holtgrieve, G.W., Schindler, D.E., 2011. Marine-derived nutrients, bioturbation, and ecosystem metabolism: Reconsidering the role of salmon in streams. Ecology 92, 373–385. Holtgrieve, G.W., Schindler, D.E., Branch, T.A., A'mar, Z.T., 2010. Simultaneous quantification of aquatic ecosystem metabolism and reaeration using a Bayesian statistical model of oxygen dynamics. Limnol. Oceanogr. 55, 1047–1063. Hondzo, M.,Voller,V.R., Morris, M., Foufoula-Georgiou, E., Finlay, J., Ganti,V., Power, M.E., 2013. Estimating and scaling stream ecosystem metabolism along channels with heterogeneous substrate. Ecohydrology 6, 679–688. Hornberger, G.M., Kelly, M.G., 1975. Atmospheric reaeration in a river using productivity analysis. J. Environ. Eng. Div. 101, 729–739. Hotchkiss, E.R., Hall, R.O., 2015. Whole‐stream 13C tracer addition reveals distinct fates of newly fixed carbon. Ecology 96, 403–416. Hotchkiss, E.R., Hall, R.O.J., 2014. High rates of daytime respiration in three streams: Use of δ18O-O2 and O2 to model diel ecosystem metabolism. Limnol. Oceanogr. 59, 798–810. Houser, J.N., Mulholland, P.J., Maloney, K.O., 2009. Catchment disturbance and stream ­metabolism: Patterns in ecosystem respiration and gross primary production along a gradient of upland soil and vegetation disturbance. J. N. Am. Benthol. Soc. 24, 538–552. Hunt, R.J., Jardine, T.D., Hamilton, S.K., Bunn, S.E., 2012. Temporal and spatial variation in ecosystem metabolism and food web carbon transfer in a wet‐dry tropical river. Freshw. Biol. 57, 435–450. Huryn, A.D., Benstead, J.P., Parker, S.M., 2014. Seasonal changes in light availability modify the temperature dependence of ecosystem metabolism in an arctic stream. Ecology 95, 2826–2839. Hynes, H., 1975. The stream and its valley. Verh. Int.Verein. Limnol. 19, 1–15. Imberger, S.J., Cook, P.L.M., Grace, M.R., Thompson, R.M., 2014. Tracing carbon sources in small urbanising streams: Catchment‐scale stormwater drainage overwhelms the effects of reach‐scale riparian vegetation. Freshw. Biol. 59, 168–186. Izagirre, O., Agirre, U., Bermejo, M., Pozo, J., Elosegi, A., 2008. Environmental controls of whole-stream metabolism identified from continuous monitoring of Basque streams. J. N. Am. Benthol. Soc. 27, 252–268. Jassby, A.D., Platt, T., 1976. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21, 540–547. Julian, J.P., Doyle, M.W., Stanley, E.H., 2008. Empirical modeling of light availability in rivers. J. Geophys. Res. Biogeosci. 113, G03022. Kirchner, J.W., Neal, C., 2013. Universal fractal scaling in stream chemistry and its implications for solute transport and water quality trend detection. Proc. Natl. Acad. Sci. U.S.A. 110, 12213–12218. Koopmans, D.J., Berg, P., 2015. Stream oxygen flux and metabolism determined with the open water and aquatic eddy covariance techniques. Limnol. Oceanogr. 60, 1344–1355.



Metabolism of Streams and Rivers: Estimation, Controls, and Application

177

Leggieri, L., Feijoó, C., Giorgi, A., Ferreiro, N., Acuña, V., 2013. Seasonal weather effects on hydrology drive the metabolism of non-forest lowland streams. Hydrobiologia 716, 1–12. Levi, P.S., Tank, J.L., Rüegg, J., Janetski, D.J., Tiegs, S.D., 2012. Whole-stream metabolism responds to spawning pacific salmon in their native and introduced ranges. Ecosystems 16, 269–283. Lewis, W.M., 1988. Primary production in the Orinoco river. Ecology 69, 679–692. Lindeman, R.L., 1942. The trophic-dynamic aspect of ecology. Ecology 23, 399–417. Lovett, G.M., Cole, J.J., Pace, M.L., 2006. Is net ecosystem production equal to ecosystem carbon accumulation? Ecosystems 9, 152–155. Luz, B., Barkan, E., 2000. Assessment of oceanic productivity with the triple-isotope composition of dissolved oxygen. Science 288, 2028–2031. Marcarelli, A.M., Baxter, C.V., Mineau, M.M., Hall, R.O., 2011. Quantity and quality: Unifying food web and ecosystem perspectives on the role of resource subsidies in freshwaters. Ecology 92, 1215–1225. Marcarelli, A.M., Van Kirk, R.W., Baxter, C.V., 2010. Predicting effects of hydrologic alteration and climate change on ecosystem metabolism in a western U.S. river. Ecol. Appl. 20, 2081–2088. Marzolf, E.R., Mulholland, P.J., Steinman, A.D., 1994. Improvements to the diurnal upstream-downstream dissolved oxygen change technique for determining whole­ stream metabolism in small streams. Can. J. Fish. Aquat. Sci. 51, 1591–1599. McCutchan Jr., J.H., Lewis Jr., W.M., Saunders III, J.F., 1998. Uncertainty in the estimation of stream metabolism from open-channel oxygen concentrations. J. N. Am. Benthol. Soc. 17, 155–164. McTammany, M.E., Benfield, E.F., Webster, J.R., 2007. Recovery of stream ecosystem metabolism from historical agriculture. J. N. Am. Benthol. Soc. 26, 532–545. Menninger, H.L., Palmer, M.A., Craig, L.S., Richardson, D.C., 2008. Periodical cicada detritus impacts stream ecosystem metabolism. Ecosystems 11, 1306–1317. Meyer, J.L., Paul, M.J., Taulbee, W.K., 2005. Stream ecosystem function in urbanizing landscapes. J. N. Am. Benthol. Soc. 34, 602–612. Michener, W.K., Jones, M.B., 2012. Ecoinformatics: Supporting ecology as a data-intensive science. Trends Ecol. Evol. 27, 85–93. Minshall, G., 1978. Autotrophy in stream ecosystems. Bioscience 28, 767–770. Moore, J.W., Schindler, D.E., Scheuerell, M.D., 2004. Disturbance of freshwater habitats by anadromous salmon in Alaska. Oecologia 139, 298–308. Mulholland, P.J., Fellows, C.S., Tank, J.L., Grimm, N.B., Webster, J.R., Hamilton, S.K., Martí, E., Ashkenas, L., Bowden,W.B., Dodds,W.K., McDowell,W.H., Paul, M.J., Peterson, B.J., 2001. Inter‐biome comparison of factors controlling stream metabolism. Freshw. Biol. 46, 1503–1517. Mulholland, P.J., Hall, R.O.J., Sobota, D.J., Dodds, W.K., Findlay, S., Grimm, N.B., Hamilton, S.K., McDowell, W.H., O'Brien, J.M., Tank, J.L., Ashkenas, L.R., Cooper, L.W., Dahm, C.N., Gregory, S.V., Johnson, S.L., Meyer, J.L., Peterson, B.J., Poole, G.C., Valett, H.M., Webster, J.R., Arango, C.P., Beaulieu, J.J., Bernot, M.J., Burgin, A.J., Crenshaw, C.M., Helton, A.M., Johnson, L.T., Niederlehner, B.R., Potter, J.D., Sheibly, R.W., Thomas, S.M., 2009. Nitrate removal in stream ecosystems measured by 15N addition experiments: 2. Denitrification. Limnol. Oceanogr. 54, 666–680. Ochs, C.A., Pongruktham, O., Zimba, P.V., 2013. Darkness at the break of noon: Phytoplankton production in the Lower Mississippi River. Limnol. Oceanogr. 58, 555–568. Odum, H.T., 1956. Primary production in flowing waters. Limnol. Oceanogr. 102–117. Odum, H.T., 1957. Trophic structure and productivity of Silver Springs, Florida. Ecol. Monogr. 27, 55–112. Palmer, M.A., Febria, C.M., 2012. Ecology. The heartbeat of ecosystems. Science 336, 1393–1394.

178

Stream Ecosystems in a Changing Environment

Palmer, M.A., Reidy Liermann, C.A., Nilsson, C., Flörke, M., Alcamo, J., Lake, P.S., Bond, N., 2008. Climate change and the world's river basins: Anticipating management options. Front. Ecol. Environ. 6, 81–89. Perkins, D.M., Yvon-Durocher, G., Demars, B.O.L., Reiss, J., Pichler, D.E., Friberg, N., Trimmer, M., Woodward, G., 2011. Consistent temperature dependence of respiration across ecosystems contrasting in thermal history. Glob. Chang. Biol. 18, 1300–1311. Rasmussen, J.J., Baattrup-Pedersen, A., Riis, T., Friberg, N., 2011. Stream ecosystem properties and processes along a temperature gradient. Aquat. Ecol. 45, 231–242. Raymond, P.A., Zappa, C.J., Butman, D., Bott, T.L., Potter, J., Mulholland, P., Laursen, A.E., McDowell, W.H., Newbold, J.D., 2012. Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers. Limnol. Oceanogr. Fluids Environ. 2, 41–53. Reichert, P., Uehlinger, U., Acuña, V., 2009. Estimating stream metabolism from oxygen concentrations: Effect of spatial heterogeneity. J. Geophys. Res. Biogeosci. 114, G03016. Reisinger, A.J., Tank, J.L., Rosi-Marshall, E.J., Hall, R.O., Baker, M.A., 2015. The varying role of water column nutrient uptake along river continua in contrasting landscapes. Biogeochemistry 125, 115–131. Riley, A.J., Dodds, W.K., 2012. The expansion of woody riparian vegetation, and subsequent stream restoration, influences the metabolism of prairie streams. Freshw. Biol. 57, 1138–1150. Roberts, B.J., Mulholland, P.J., 2007. In-stream biotic control on nutrient biogeochemistry in a forested stream, West Fork of Walker Branch. J. Geophys. Res. 112, G04002. Roberts, B.J., Mulholland, P.J., Hill, W.R., 2007. Multiple scales of temporal variability in ecosystem metabolism rates: Results from 2 years of continuous monitoring in a forested headwater stream. Ecosystems 10, 588–606. Roley, S.S., Tank, J.L., Griffiths, N.A., Hall, R.O., Davis, R.T., 2014. The influence of floodplain restoration on whole-stream metabolism in an agricultural stream: Insights from a 5-year continuous data set. Freshw. Sci. 33,1043–1059. Rosa, R.D.S., Aguiar, A.C.F., Boëchat, I.G., Gücker, B., 2012. Impacts of fish farm pollution on ecosystem structure and function of tropical headwater streams. Environ. Pollut. 174, 204–213. Sargent, M.C., Austin, T.S., 1949. Organic productivity of an atoll. Trans. Am. Geophys. Union 30, 245–249. Schindler, D.W., 1974. Eutrophication and recovery in experimental lakes: Implications for lake management. Science 184, 897–899. Silva-Junior, E.F., Moulton, T.P., Boëchat, I.G., Gücker, B., 2014. Leaf decomposition and ecosystem metabolism as functional indicators of land use impacts on tropical streams. Ecol. Indic. 36, 195–204. Solomon, C.T., Bruesewitz, D.A., Richardson, D.C., Rose, K.C., Van de Bogert, M.C., Hanson, P.C., Kratz, T.K., Larget, B., Adrian, R., Babin, B.L., Chiu, C.Y., Hamilton, D.P., Gaiser, E.E., Hendricks, S., Istvánovics,V., Laas, A., O'Donnell, D.M., Pace, M.L., Ryder, E., Staehr, P.A., Torgersen, T.,Vanni, M.J., Weathers, K.C., Zhu, G., 2013. Ecosystem respiration: Drivers of daily variability and background respiration in lakes around the globe. Limnol. Oceanogr. 58, 849–866. Staehr, P.A., Bade, D.,Van de Bogert, M.C., Koch, G.R.,Williamson, C., Hanson, P., Cole, J.J., Kratz, T., 2010a. Lake metabolism and the diel oxygen technique: State of the science. Limnol. Oceanogr. Methods 8, 628–644. Staehr, P.A., Testa, J.M., Kemp, W.M., Cole, J.J., Sand-Jensen, K., Smith, S.V., 2012. The metabolism of aquatic ecosystems: History, applications, and future challenges.Aquat. Sci. 74, 15–29. Staehr, P., Testa, J., Kemp, W., Cole, J., 2010b. The metabolism of aquatic ecosystems: History, applications, and future challenges. Aquat. Sci. 74, 159–169. Stan Development Team, 2015. RStan: The R interface to Stan, version 2.7.0. http:// mc-stan.org/rstan.html.



Metabolism of Streams and Rivers: Estimation, Controls, and Application

179

Streeter, H.W., Phelps, E.B., 1925. A study of the pollution and natural purification of the Ohio River III. US Public Health Service Public Health Bulletin. Subalusky, A.L., Dutton, C.L., Rosi-Marshall, E.J., Post, D.M., 2015.The hippopotamus conveyor belt:Vectors of carbon and nutrients from terrestrial grasslands to aquatic systems in sub‐Saharan Africa. Freshw. Biol. 60, 512–525. Sudduth, E.B., Hassett, B.A., Cada, P., Bernhardt, E.S., 2011. Testing the field of dreams hypothesis: Functional responses to urbanization and restoration in stream ecosystems. Ecol. Appl. 21, 1972–1988. Tank, J.L., Rosi-Marshall, E.J., Griffiths, N.A., Entrekin, S.A., Stephen, M.L., 2010. A review of allochthonous organic matter dynamics and metabolism in streams. J. N. Am. Benthol. Soc. 29, 118–146. Taylor, B.W., Flecker, A.S., Hall, R.O., 2006. Loss of a harvested fish species disrupts carbon flow in a diverse tropical river. Science 313, 833–836. Testa, J.M., Kemp, W.M., Hopkinson, C.S., Smith, S.V., 2013. Ecosystem metabolism. In: Day, J.W., Crump, B.C., Kemp, W.M., Yáñez-Arancibia, A. (Eds.), Estuarine Ecology. Wiley, Hoboken, NJ, pp. 381–415. Tobias, C.R., Böhlke, J.K., Harvey, J.W., 2007. The oxygen-18 isotope approach for measuring aquatic metabolism in high-productivity waters. Limnol. Oceanogr. 1439–1453. Uehlinger, U., 2000. Resistance and resilience of ecosystem metabolism in a flood‐prone river system. Freshw. Biol. 45, 319–332. Uehlinger, U., 2006. Annual cycle and inter‐annual variability of gross primary production and ecosystem respiration in a floodprone river during a 15‐year period. Freshw. Biol. 51, 938–950. Uehlinger, U., Naegeli, M., Fisher, S.G., 2002. A heterotrophic desert stream? The role of sediment stability. W. N. Am. Nat. 62, 466–473. Van De Bogert, M.C., Bade, D.L., Carpenter, S.R., Cole, J.J., Pace, M.L., Hanson, P.C., Langman, O.C., 2012. Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes. Limnol. Oceanogr. Methods 57, 1689–1700. Van de Bogert, M.C., Carpenter, S.R., Cole, J.J., Pace, M.L., 2007. Assessing pelagic and benthic metabolism using free water measurements. Limnol. Oceanogr. Methods 5, 145–155. Vannote, R., Minshall, G., Cummins, K., Sedell, J., Cushing, C., 1980. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137. Venkiteswaran, J.J., Wassenaar, L.I., Schiff, S.L., 2007. Dynamics of dissolved oxygen isotopic ratios: A transient model to quantify primary production, community respiration, and air-water exchange in aquatic ecosystems. Oecologia 153, 385–398. Villamizar, S.R., Pai, H., Butler, C.A., Harmon, T.C., 2014. Transverse spatiotemporal variability of lowland river properties and effects on metabolic rate estimates. Water Resour. Res. 50, 482–493. Wanninkhof, R., Mulholland, P.J., Elwood, J.W., 1990. Gas exchange rates for a first-­ order stream determined with deliberate and natural tracers. Water Resour. Res. 26, 1621–1630. Whittaker, R.H., Likens, G.E., 1973. Primary production: The biosphere and man. Hum. Ecol. 1, 357–369. Williamson, C.E., Dodds, W., Kratz, T.K., Palmer, M.A., 2008. Lakes and streams as sentinels of environmental change in terrestrial and atmospheric processes. Front. Ecol. Environ. 6, 247–254. Woodward, G., Gessner, M.O., Giller, P.S., Gulis, V., Hladyz, S., Lecerf, A., Malmqvist, B., McKie, B.G., Tiegs, S.D., Cariss, H., Dobson, M., Elosegi, A., Ferreira,V., Graça, M.A.S., Fleituch, T., Lacoursière, J.O., Nistorescu, M., Pozo, J., Risnoveanu, G., Schindler, M., Vadineanu, A.,Vought, L.B.-M., Chauvet, E., 2012. Continental-scale effects of nutrient pollution on stream ecosystem functioning. Science 336, 1438–1440.

180

Stream Ecosystems in a Changing Environment

Woodwell, G.M., Whittaker, R.H., 1968. Primary production in terrestrial ecosystems. Am. Zool. 8, 19–30. Yard, M.D., Bennett, G.E., Mietz, S.N., Coggins Jr., L.G., Stevens, L.E., Hueftle, S., Blinn, D.W., 2005. Influence of topographic complexity on solar insolation estimates for the Colorado River, Grand Canyon, AZ. Ecol. Model. 183, 157–172. Yates, A.G., Brua, R.B., Culp, J.M., Chambers, P.A., 2012. Multi‐scaled drivers of rural prairie stream metabolism along human activity gradients. Freshw. Biol. Young, R.G., Huryn, A.D., 1999. Effects of land use on stream metabolism and organic matter turnover. Ecol. Appl. 9, 1359–1376. Young, R.G., Collier, K.J., 2009. Contrasting responses to catchment modification among a range of functional and structural indicators of river ecosystem health. Freshw. Biol. 54, 2155–2170. Young, R.G., Matthaei, C.D., Townsend, C.R., 2008. Organic matter breakdown and ecosystem metabolism: Functional indicators for assessing river ecosystem health. J. N. Am. Benthol. Soc. 27, 605–625. Yvon-Durocher, G., Caffrey, J.M., Cescatti, A., Dossena, M., del Giorgio, P., Gasol, J.M., Montoya, J.M., Pumpanen, J., Staehr, P.A.,Trimmer, M.,Woodward, G., Allen, A.P., 2012. Reconciling the temperature dependence of respiration across timescales and ecosystem types. Nature 487, 472–476.