Stream Ecosystems in a Changing Environment © 2016 Elsevier Inc

CHAPTER 6 Dissolved Organic Matter in Stream Ecosystems: Forms, Functions, and Fluxes of Watershed Tea

L.A. Kaplana, R.M. Coryb aStroud Water Research Center, Avondale, PA, United States bUniversity of Michigan, Ann Arbor, MI, United States

Every rill is a channel for the juices of the meadow. Last year's grasses and flower-stalks have been steeped in rain and snow, and now the brook flows with meadow tea—thoroughwort, mint, flagroot, and pennyroyal, all at one draught. Henry David Thoreau, March 8, 1840

Contents Introduction 242 DOM Sources 245 Autochthonous Inputs 245 Allochthonous Inputs 246 Molecular Characterization of DOM 253 Quantitative Geochemistry 253 Optical Methods 255 DOM Composition and Structure 258 DOM Chemogeography and Chemodiversity 260 DOM Transformations and Fates 262 Oxidative Reactivity of DOM 262 Bio-Reactivity of DOM 262 Conceptual Models of DOM Diagenesis and Substances 266 Pathways and Products of Photooxidation 269 Interactions Between Photochemistry and Biological Degradation 272 Rates of Photooxidation in Waters 275 DOM Contributions to Ecosystem Metabolism 280 DOM Uptake 280 Instream Hydrologic Forcing and DOM Export 281

DOM in the Anthropocene 281 Altering Ecosystems 282 Urbanization 283 Impacts of a Changing Environment 284 Summary of Impacts of the Anthropocene on DOM Sources and Processing 286 Future Research Challenges 287 Discussion Topics 289 Acknowledgments 289 References 289

INTRODUCTION In Thoreau’s journal, the American author and naturalist provided an early observation on the sources and complexity of what we have come to call dissolved organic matter (DOM). Here we borrow his evocative imagery of DOM as a cold-water extract of terrestrial organic matter, a watershed tea of biomolecules, and use it as the basis for our conceptual model of the watershed processes that define DOM forms and functions and control DOM fluxes in stream ecosystems (Fig. 1). We now know that the relevant watershed processes involving DOM encompass much more than terrestrial plant sources and their leachates. Nevertheless, we note that in connecting meadow tea to specific plant leachates that enter the stream, Thoreau

Fig. 1 Terrestrial and aquatic controls on DOM reactivity and oxidation. Flow paths from the terrestrial environment to the stream pass through different soil layers that influence DOM sources and their composition. DOM that is labile has shorter spiraling lengths than semi-labile DOM and the spiraling lengths of all DOM increase as streams and river get larger in a downstream direction. CO2 incorporated into plant biomass by photosynthesis is returned to the atmosphere through plant and soil respiration and photodegradation and bacterial respiration in lotic waters. Dissolved Organic Matter in Stream Ecosystems 243 arguably foreshadowed an early focus of stream ecologists on terrestrial plant sources of DOM (Höll, 1955; Cummins et al., 1972; Lock and Hynes, 1976) and the modern concept of watershed specificity in DOM composition (Jaffé et al., 2012). Through the inclusion of a longitudinal perspective depicting how the processing of DOM changes along a river network, our conceptual model also draws upon the theoretical concepts of the river continuum (Vannote et al., 1980), meta-ecosystems (Loreau et al., 2003), and material spiraling (Webster and Patten, 1979; Newbold et al., 1981) in order to place DOM dynamics within stream ecosystems into the broader spatial context of ecohydrology (Janauer, 2000) and ecosystem ecology. DOM constitutes the largest pool of organic matter in aquatic ecosystems (Schlesinger and Melack, 1981; Wetzel, 1992; Mulholland, 2003; Alvarez-Cobelas et al., 2012), and it is one of the most chemically complex mixtures on Earth (Sleighter and Hatcher, 2008). The diverse biogeochemical and ecological roles of DOM involve the modulation of processes that influence the fates of many natural and anthropogenic compounds (Prairie, 2008). DOM: controls geochemical reactions (Waples et al., 2005); regu- lates bacterial nutrient uptake and cycling (Meyer et al., 1988; Qualls, 2000; Bernhardt and Likens, 2002); provides C and energy for bacterial heterotrophs (del Giorgio and Williams, 2005; Wiegner et al., 2005) that shape the composition of microbial communities (Crump et al., 2003; Judd et al., 2006), alter patterns of microbial metabolism (Berggren and del Giorgio, 2015), and influence microbial biogeography (Findlay et al., 2008); alters the bioavailability of organic pollutants (Kukkonen and Oikari, 1991); interacts with drinking water disinfectants to produce disinfectant byproducts (Marhaba and Van, 2000; Jung et al., 2014; Li et al., 2014); chelates metals (McKnight and Bencala, 1990; Kuhn et al., 2015); influences both light penetration (Fee et al., 1996) and the quality of light within a water column (Kirk, 2011); and provides behavioral cues for planktonic aquatic larvae (Harder and Qian, 2000) and anadromous fishes (Hasler and Wisby, 1951; Scholz et al., 1976). The study of DOM has expanded enormously, achieving new insights regarding sources (Tipping et al., 2010; Inamdar et al., 2011; Lambert et al., 2011), composition (Kim et al., 2006), structure (Hedges et al., 2000), oxidative reactivity (Volk et al., 1997; Bertilsson and Tranvik, 1998; Cory et al., 2013, 2014; Sleighter et al., 2014), patterns within river networks (Mosher et al., 2015; Creed et al., 2015), global patterns of distribution (Jaffé et al., 2012), and its potential as a metric for both restoration (Stanley et al., 2012) and ecosystem function (Parr et al., 2016). Mechanistic understandings are emerging concerning source variability (Inamdar et al., 2011), the influences of 244 Stream Ecosystems in a Changing Environment

hydrology (Mei et al., 2012, 2014; Koch et al., 2013; McLaughlin and Kaplan, 2013; Pereira et al., 2014; Sawyer et al., 2014), geomorphology (Yamashita et al., 2010a), landscape-level phenomena (Petrone, 2010; Williams et al., 2010; Lu et al., 2013), and seasonal (Ågren et al., 2007; Miller and McKnight, 2010) and interannual variability (Holmes et al., 2012). This rapidly expanding understanding of DOM has benefited from the development and application of new tools that provide measures of the natural abundances of stable (Schiff et al., 1990; St-Jean, 2003) and radioactive (Bauer et al., 1992; Raymond and Bauer, 2001; Butman et al., 2015) isotopes, detailed molecular-level composition (Hertkorn et al., 2007; Hockaday et al., 2009), optical properties (Cory and McKnight, 2005), and the extensive and intensive temporal dynamics of solutes (Spencer et al., 2007; Downing et al., 2009; Jollymore et al., 2012; Wilson et al., 2013). New data processing and statistical methods have been developed for handling the massive data sets generated by some of these methods, including visualization graphics for complicated mass spectra (Kim et al., 2003), an automated compound identification algorithm (Kujawinski and Behn, 2006; Koch et al., 2007), parallel factor analysis (Stedmon and Bro, 2008), and two-dimensional correlation analysis (Abdulla et al., 2013). As more is learned about the connections between organic matter cycling in inland waters and the global carbon cycle (Cole et al., 2007; Battin et al., 2009; Aufdenkampe et al., 2011; Lauerwald et al., 2012), including the evasion of CO2 into the atmosphere (Richey et al., 2002; Butman and Raymond, 2011; Raymond et al., 2013; Kokic et al., 2014; Borges et al., 2015) and its burial, primarily in lakes (Tranvik et al., 2009), but also within large rivers and upon their flood plains (Aufdenkampe et al., 2011), research into the role of DOM as an energy resource in stream ecosystems has taken on a new urgency. It has become an intensive area of focus in aquatic sci- ences and has been recognized appropriately as a component of climate change (IPCC, 2014). This chapter presents our current understanding of DOM in lotic ecosystems with a particular emphasis on the reactivity of DOM constituents to microbial and photochemical oxidation. We begin with a review of sources and the hydrologic influences on terrestrial source loadings, quality, and fluxes. We explore the molecular complexity of DOM, present a conceptual model of DOM diagenesis and structure, and discuss how structure and composition influence biologically and photochemically driven oxidative processes. These topics are addressed from a broad ecosystem-level and watershed perspective, relating the cycling of C and N, and the flow of energy to stream ecosystem metabolism. This approach provides a context for discussing the roles of catchment morphology and how hydrology influences the spatial scales of transformation and export within Dissolved Organic Matter in Stream Ecosystems 245 stream and river networks. With this background as a foundation, we discuss the impact of the Anthropocene on DOM sources and dynamics, including changes in land cover, land use, and global climate. Lastly, we identify what we consider to be future challenges for extending knowledge of DOM in streams and rivers, and we provide a list of possible discussion topics.

DOM SOURCES Autochthonous Inputs There are streams and rivers in which aquatic primary productivity represents a major energy source, and presumably this translates into in-stream sources of DOM. Such ecosystems include springs (Odum, 1957); streams within deserts (Minshall, 1978; McKnight and Tate, 1997); streams in agri- culturally impacted landscapes (Griffiths et al., 2013), including some with tile-drained agricultural lands (Royer and David, 2005); lowland streams with macrophyte beds in the main channels (Madsen and Cedergreen, 2002; Riis et al., 2012); rivers with macrophytes in tidal bays (Nieder et al., 2004) or floodplain wetlands (Robertson et al., 1999); and rivers where water residence times are sufficiently long to permit the development of a true phytoplankton (Thorp and Delong, 1994; Thorp et al., 2002; Bianchi et al., 2004; Delong, 2010), especially when impoundments (Ward and Stanford, 1983; Dokulil, 2013) or tidal action (Bianchi et al., 2004) increase water residence times and reduce turbidity. Even for strongly heterotrophic streams with forested riparian zones, there can be seasonal periods when an open can- opy allows sufficient sunlight to support a high rate of primary production (Roberts et al., 2007). This enhanced primary production can lead to the release of autochthonous DOM from algal sources (Kaplan and Bott, 1982), which might be cycled within a benthic biofilm and never reach the water column (Kaplan and Newbold, 2003), or it might escape the biofilm and travel downstream prior to its utilization (Kaplan and Bott, 1982). In addition to photoautotrophs, chemolithotrophic nitrifying bacteria and archaea are ubiquitous, and while these organisms typically grow slowly (French et al., 2012), they use CO2 as their source of C, and through excretion or lysis, nitrification constitutes an autotrophic source of DOM (Glover, 1985). The role of autochthonous DOM sources has not received much attention, despite the potential of this relatively small pool of organic matter to turn over rapidly and contribute disproportionately to heterotrophy in many streams (Marcarelli et al., 2011; Cole, 2013; Ghosh and Leff, 2013). Perhaps this is because of the analytical difficulties in identifying DOM that is derived from in-stream or in-river primary productivity (Spencer et al., 2007; 246 Stream Ecosystems in a Changing Environment

Guillemette et al., 2013), measurements that often require a stable isotope tracer (Hotchkiss and Hall, 2015).

Allochthonous Inputs DOM derived from terrestrial plants and soil organic matter (SOM) typically dominates organic matter budgets of most streams and rivers (Thurman, 1985; Webster and Meyer, 1997). This includes some of the major drainages in the mid-Atlantic region of the United States (Hossler and Bauer, 2012). Roughly 1% of gross terrestrial primary productivity within a watershed enters streams, providing a major source of energy as dissolved organic carbon (DOC) and organic nutrients in many stream ecosystems (Gielen et al., 2011). Because terrestrial ecosystems heavily subsidize the energy budgets of stream ecosystems (Fisher and Likens, 1972; Webster and Meyer, 1997), soil formation factors, such as plant species (Strobel et al., 2001; Borken et al., 2011), SOM (Aitkenhead et al., 1999), soil mineral composition (Nelson et al., 1990, 1993), and soil retentive capabilities for organic molecules and water, are important factors in the quantity and quality of DOM entering streams. Streams directly intercept some terrestrial primary production as leaf litter (Meyer and Wallace, 1998), woody debris (Wallace et al., 2001), and throughfall (Levia et al., 2012), but the majority of terrestrial production entering streams enters soils as stemflow and leaf detritus falling onto the forest floor, or is produced within soils, such as root detritus, including exudates generated within forest soils and fine root turnover (Matamala et al., 2003). Outputs of carbon from the terrestrial environment are largely driven by soil microbial respiration within the surficial O and A soil horizons (Davidson et al., 2006), but more than half of SOM is located within subsurface soil horizons (Jobbagy and Jackson, 2000) and is dynamic on decadal time scales (Trumbore and Czimczik, 2008; Koarashi et al., 2012). SOM, which on a global scale has carbon stores that exceed the sum of carbon within the atmosphere and terrestrial vegetation (Pachauri et al., 2015; Harper and Tibbett, 2013), is the proximate source of most of the terrestrial organic carbon that is processed biotically and abiotically to varying degrees (Marín-Spiotta et al., 2014). It then enters stream ecosystems along a network of temporally and spatially variable flow paths (Trumbore et al., 1992; Fisher et al., 2004; Van Gaelen et al., 2014) (Fig. 1). Allochthonous sources also can include soil particles as well as solutes. When soils are eroded, SOM becomes more susceptible to microbial oxidation either in terrestrial (Berhe et al., 2012) or aquatic (Cole and Caraco, 2001) Dissolved Organic Matter in Stream Ecosystems 247

environments. Most streams and rivers are net sources of CO2 (Jones et al., 2003) and drainage networks display a general trend of diminishing area- normalized source inputs with increasing drainage area (Ågren et al., 2007) and increasing stream size (Battin et al., 2008). This indicates that the in- timate connection between small streams and the landscapes they drain influences organic matter inputs, and also suggests that the CO2 that is generated during below-ground decomposition of organic matter is carried by supersaturated groundwater to streams, where it contributes to the CO2 supersaturation of stream water and resulting high rates of evasion in headwater streams (Jones and Mulholland, 1998; Butman and Raymond, 2011; Wallin et al., 2013). In fact, a study of streams and rivers in the contiguous

United States found that terrestrially derived CO2 dissolved in groundwater dominates emissions in small streams, and the contributions of CO2 from aquatic metabolism increases with stream size (Hotchkiss et al., 2015). An analysis of respiration in the Hudson River, which combined estimates of gas, solute, and particulate fluxes, suggested that organic matter preserved in soils becomes activated and metabolized upon delivery to the aquatic environment (Cole and Caraco, 2001). Additional studies involving isotope natural abundances and modeling determined that the Hudson River food web was subsidized by millennial-aged organic carbon (Caraco et al., 14 2010), and measurements of Δ C-respiratory CO2 in Canadian lakes and streams showed that ancient terrestrial organic carbon supported contemporary respiration (McCallister and del Giorgio, 2012). These two studies have prompted the question of how organic matter stabilized in soils can become available for metabolism in aquatic ecosystems (Cole, 2013). An aquatic priming effect has been identified as one mechanism that could enhance the mineralization of terrestrial carbon (Guenet et al., 2014), though this is currently a controversial topic, with studies that have measured priming effects on DOM decomposition (De Haan, 1977; Hotchkiss et al., 2014) and others that have found no evidence of priming (Koehler et al., 2012; Franke et al., 2013; Bengtsson et al., 2014; Catalan et al., 2015; Kellerman et al., 2015). To more fully address the question of terrestrial preservation versus aquatic mineralization requires a consideration of mechanisms that allow organic matter in soils to persist. However, mechanisms associated with SOM stability and why some SOM is resistant to biological processing are also subjects of active debate (Kleber, 2010; Kleber et al., 2011; von Lützow and Kögel-Knabner, 2010). Natural organic compounds compete to adsorb minerals through a wide range of mechanisms (Jardine et al., 1989; Kaiser and Guggenberger, 2000; 248 Stream Ecosystems in a Changing Environment

Guo and Chorover, 2003). Sorption is partially reversible (Gu et al., 1996a,b; Butman et al., 2007), particularly in association with rising pH under reducing conditions (Grybos et al., 2009). Molecules can interlace, co-dissolve, and/or adsorb nearly irreversibly as a function of molecular size, functional group reactivity and diversity, and the concentrations of polyvalent metal ions (Chin et al., 1998; van de Weerd et al., 1999; Aufdenkampe et al., 2001; Sollins et al., 2006). Formation of these solid phase organo-mineral complexes in soils is a preferential process involving both selectivity of specific organic compounds (Scott and Rothstein, 2014) and mineral surfaces (Heckman et al., 2013). Thus, organo-mineral complexes, especially with iron and aluminum oxyhydroxides (von Lützow et al., 2006, 2008; Kögel- Knabner et al., 2008; Kalbitz et al., 2005; Masiello et al., 2004; Mikutta and Kaiser, 2011; Mikutta et al., 2014), affect the bio-reactivity of SOM by acting as a protective mechanism (Sollins et al., 2006, 2009) through the selective preservation of recalcitrant molecules and physical protection of biodegradable organic matter from microorganisms and enzymes within macro- and micro-aggregates (Ewing et al., 2006). Organo-mineral complexes form stable foundations for binding other molecules via cation bridging and hydrogen bonding (Wershaw, 1986; Sutton and Sposito, 2005; Kleber et al., 2007), which also can shield molecules from degradation (Knicker and Hatcher, 1997; Hsu and Hatcher, 2005). Specific surface area and organic loading of the surfaces are important mineral properties to consider when estimating stabilization (Feng et al., 2013, 2014), as is the presence of mineral surfaces that have been described as fresh or juvenile surfaces (Guggenberger and Kaiser, 2003). There is, however, direct evidence that sorption to particles is not spatially continuous, which would lead to a uniform organic coating, but rather proceeds at select, discrete sites (Vogel et al., 2014). Detailed chemical analyses of SOM have demonstrated that old (>600 years) and stable organic matter in soils is not uniformly recalcitrant, and recent soil carbon models describe bio-reactivity as only partly driven by molecular structure, with complex interactions among molecules, microorganisms, and abiotic variables also being key variables (Kleber et al., 2011). The idea that physicochemical and biological processes are more important than molecular structure in determining the persistence of SOM has led to the concept that the persistence of SOM is an ecosystem property (Schmidt et al., 2011). When erosion carries soil into aquatic environments, the disintegration of aggregates and the effects of the concomitantly higher pH values on organo-mineral complexes and tertiary structure (Battin et al., 2008), plus a reversal of sorptive preservation (Butman et al., 2007), likely Dissolved Organic Matter in Stream Ecosystems 249 play important roles in removing physical and chemical barriers to degradation (Rothman and Forney, 2007). Conversely, DOM not associated with mineral surfaces might undergo preservation reactions in sediments within a broad range of aquatic environments involving reactive iron phases, with the potential formation of nanometer-scale domains that are metastable over geological timescales (Lalonde et al., 2012).

Hydrologic Connections Soil structure exhibits a strong vertical component, including the exponential decline in the organic content of well-drained soils with depth (Jobbágy and Jackson, 2000), a concomitant decline in DOM concentrations (McDowell and Wood, 1984; Cronan and Aiken, 1985; Qualls and Haines, 1992b), and changes in DOM quality (Cory et al., 2004; Sanderman et al., 2008; Ohno et al., 2014). The changes in quality include a general decline in biodegradable DOM, though a slight reversal of that trend has been observed with depth within the B-horizon (Qualls and Haines, 1992a; Boyer and Groffman, 1996; Ohno et al., 2014). These gradients establish a close spatial/temporal link between the biogeochemical cycling of organic matter within soil horizons and the dynamics of paths that move water to stream under different hydrological settings (Hagedorn et al., 2000; Sanderman et al., 2009; Lambert et al., 2011, 2013; Bol et al., 2015). Under baseflow conditions, the movement of water and DOM molecules percolating through the vadose zone into the phreatic zone, and ultimately flowing as groundwater inputs to streams, provides opportunities for microbial processing of DOM (Battin et al., 2008). This results in chemically altered DOM that becomes more refractory during its transit to streams as a function of residence time (Fisher, 1977). Microorganisms metabolize DOM and excrete end products or use exoenzymes to cleave molecules and transport a moiety across cell membranes. Indeed, it has been suggested that the diversity of aquatic DOM is a direct consequence of microbial diversity, and that changes in molecular DOM composition might closely interrelate with changes in microbial community composition (Shabarova et al., 2014). Molecules transported under baseflow conditions contain some DOM that might be analogous in its intrinsic molecular properties of complexity and composition to the recalcitrant DOM molecules produced by the microbial carbon pump in deep oceans (Jiao et al., 2010) or to the persistent DOM in lakes (Kellerman et al., 2015). They also represent DOM moieties whose linkages are assumed to elude microbial enzymes (Blough and Del Vecchio, 2002). 250 Stream Ecosystems in a Changing Environment

The SOM template on a landscape scale strongly influences the DOM sources entering a stream along hydrologic flow paths (Merck et al., 2012). As water passes through soil horizons, abiotic adsorption dominates the initial removal of DOM from solution, but over time, microbial decomposition of the adsorbed DOM renews adsorption capacity (Qualls and Haines, 1992a). Thus, a combination of abiotic and biotic processes is responsible for the decline in organic content with the depth leading to minimum concentrations within the mineral soil horizons that remain relatively constant through the saprolite and bedrock formations (Kalbitz et al., 2000; Guggenberger and Kaiser, 2003). Detailed chemical investigations of changes in DOM quality with depth, including isotopic and spectropho- tometric analyses and cross polarization, magic angle 13C NMR reveal a decrease in the C:N ratio, specific UV adsorption, and the δ13C value, as well as decreases in Δ14C values that are consistent with a high degree of processing and selective adsorption. These patterns also are indicative of changes involving more than simple fractionation of surface organic matter, including exchange reactions that occur at depth (Sanderman et al., 2008; Kaiser and Kalbitz, 2012). The processes that remove DOM from water as it moves through soil horizons also are strongly dependent upon soil structure, with the abundance of clay minerals being a dominant factor (Nelson et al., 1990, 1993; Cleveland et al., 2004). The presence or absence of clay is largely responsible for the striking color differences in DOM between tea-colored blackwaters draining ecosystems in the sand-dominated Coastal Plain physiographic province along the eastern seaboard of North America (Meyer, 1990) and the ecosystems draining the more inland clay- rich Piedmont physiographic province (Kaplan et al., 1980). Hydrologic flow paths differ between baseflow and stormflow conditions and are influenced by antecedent moisture conditions (Biron et al., 1999) combined with catchment morphology (James and Roulet, 2009), the magnitude of the storm (Mulholland et al., 1990), and the season (Wilson et al., 2013). Storms generally increase DOM concentrations and exports in streams (Hinton et al., 1997). During a storm, there are temporal and spatial shifts in the depth of flow paths (Fig. 1), depending upon position during a storm hydrograph (McGlynn and McDonnell, 2003) and sub- sequent changes in the depth of the capillary fringe that spans the bottom of the vadose zone and the top of the phreatic zone (Daniels et al., 2008). DOM molecules are transported under storm flows when hydrologic conditions override the retentive and protective capabilities of soils; perhaps this is associated with water movement through macropores, overland flow, and other preferential flow paths that selectively mobilize and shunt DOM Dissolved Organic Matter in Stream Ecosystems 251 to the stream that contains freshly produced molecules that have largely or entirely escaped microbial processing (Hood et al., 2006; Fellman et al., 2009a,b; McLaughlin and Kaplan, 2013; Wilson et al., 2013). Runoff sources also change during storms, and a temporal sequence of sources beginning with throughfall early in a storm and shifting to shallow subsurface sources later in a storm have been observed within deep subsoil sources relegated to baseflow periods (Hagedorn et al., 2000; Inamdar et al., 2011, 2012). Because hydrologic flow paths integrate the spatial heterogeneity within drainage basins (Fisher et al., 2004), location within a watershed relative to stream channels or dry rills influences materials transported during storms and the temporal sequences associated with hydrologic connectivity in both small headwater streams (McGlynn and McDonnell, 2003) and large rivers (Tockner et al., 1999), with scaling adjustments because of increasing watershed size (Laudon et al., 2011). Various approaches, including hydrometric monitoring combined with chemical analyses (Dosskey and Bertsch, 1994; Sanderman et al., 2009; Sawyer et al., 2014), end-member mixing modeling (Morel et al., 2009; Inamdar et al., 2011), and physically based modeling (Seibert et al., 2009; Mei et al., 2012, 2014) have identified soils within stream riparian zones as a major source of DOM to streams. Other models have used seasonal and event- based hydrological connectivity (Birkel et al., 2014), strong linkages between soil carbon dynamics and hydrological processes (Xu et al., 2012), or the hydrological connections between DOM sources, storage, and watershed hydrology (Zhang et al., 2013) to simulate DOM dynamics and export in streams. Despite these advances, plus the recognition that storms dominate solute export, flow paths change during storms, and these processes determine the solute composition of stream water (Bonell, 1999; Butturini and Sabater, 2000), we have limited knowledge about the precise mechanisms connecting hydrology, the attendant water residence time in catchments (Soulsby et al., 2006), and stream water biogeochemistry. The development and deployment of in situ sensors that continuously measure hydraulic head, redox fluctuations, and DOC concentrations might help address this challenge (Sawyer et al., 2014; Jollymore et al., 2012).

The Impact of Landscapes and Topography The role of hydrology in delivering DOM to streams is influenced by the location of streams in the landscape and the landscape topography (Wolock et al., 1997; Ågren et al., 2007; James and Roulet, 2009), land cover (Laudon et al., 2011), and watershed soils (Strobel et al., 2001). Negative relation- ships between catchment size and either DOC concentrations, DOC flux 252 Stream Ecosystems in a Changing Environment per unit area, or both (Wolock et al., 1997; Ågren et al., 2007; Mattsson et al, 2005; Lauerwald et al., 2012) suggest that particular attributes of smaller basins such as subsurface contact time, slope, geomorphology of the channels, and the extensive intercalation of headwaters within the terrestrial landscape contribute to higher concentration or flux. The importance of local attributes diminishes at regional scales (Lottig et al., 2011). Other aspects of the landscape, such as the geology of a valley bottom and the surface morphology of hillslopes, valley width, bedrock profiles, and the depth of alluvial deposits, also impact stream-aquifer interactions (Ward et al., 2012, 2013a,b; Wondzell et al., 2010) and the residence time of water moving through soils to the stream (Lambert et al., 2015). Valley bottom morphology and flood plain slopes clearly influenced hydrologic gradients and the timing of solute inputs to a headwater stream during a large storm, as rapid dynamics of riparian zone chemistry were associated with the constrained floodplain on one bank of the stream and slower dynamics were associated with an unconstrained floodplain on the other stream bank (Sawyer et al., 2014). A near limitless source of DOM from riparian zone soils (Sanderman et al., 2009) in many ecosystems results from soil organic contents that are often three orders of magnitude higher than the organic content of the stream water. SOM also is replenished on an annual basis. In contrast, DOM source limitation has been observed in some snowmelt-dominated Alpine ecosystems that exhibit a flushing of DOM accumulated during the growing season, with a clear decline in concentrations as snowmelt progresses (Boyer et al., 1997). The important role of wetlands and other land covers characterized by poorly drained, permanently or transiently anaerobic soils, where organic matter accumulates at the surface, especially in the dissolved form, have been well studied and are known to contribute a disproportionate amount of DOM to streams. This includes the quantitative contributions to stream water DOM in watersheds containing swamps (Mulholland and Kuenzler, 1979; Mulholland, 1981; Dalva and Moore, 1991), wetlands (Dosskey and Bertsch, 1994; Kaplan et al., 2006), cryptic wetlands (Creed et al., 2003), or peatlands (Dillon and Molot, 1997; Dyson et al., 2011; Kortelainen et al., 2006; Mattsson et al., 2005), as well as wetland-associated impacts on DOM quality (Kothawala et al., 2014). Wetlands are dynamic landscapes with associated changes in DOM sources and processing (Yamashita et al., 2010b). Different land cover attributes, such as tree species, the presence of forest versus savan- nah, and soil type (Strobel et al., 2001; Borken et al., 2011; Lambert et al., 2015), also influence concentration, composition, and reactivity of DOM. Dissolved Organic Matter in Stream Ecosystems 253

MOLECULAR CHARACTERIZATION OF DOM Quantitative Geochemistry DOM has been operationally defined based on the organic matter that passes through a 0.45 μm pore size membrane, even though colloids, vi- ruses, and some bacteria are found in the filtrate (Ogura, 1970; Wotton, 1990). The exact pore size associated with DOM is a legacy of advances in membrane technology that have led to the production of filters with reproducible, absolute pore sizes and the adoption of Millipore HA membranes by drinking water microbiologists to capture microorganisms on the filter surface (Goetz et al., 1951; APHA, 2012). Aquatic ecologists began looking at the solute composition of filtrates as an alternative to su- pernatant fluids obtained by centrifugation (Birge and Juday, 1934). They used glass-fiber filters that could be rendered organic carbon-free through combustion to avoid contamination by organic carbon released from filter membranes. Glass-fiber filters are not true sieves with discrete pores, but rather are composed of overlapping fibers that remove particles along a tortuous path, producing a nominal pore size as a function of the numbers of fibers per unit area, or fiber density, and filter thickness. These filters became an integral part of “clean” techniques in the study of organic carbon and initially were selected for seawater analyses (Menzel and Vaccaro, 1964) and rapidly became the standard for both seawater (Strickland and Parsons, 1968; Sharp, 1973) and freshwater (Kaplan, 1992; Peter et al., 2012; Wetzel and Likens, 2000) analyses. Thus all analyses of DOM begin with a filtration step, sometimes through newer membrane materials that can be rinsed free of organic carbon, such as 0.6 μm polycarbonate filters (Zsolnay, 2003), or smaller pore-sized 0.2 μm nylon membranes, required for use with analytical chromatography columns (Gremm and Kaplan, 1997) in place of the precombusted 0.7 μm pore-sized glass-fiber filters. On average, most organic molecules are ~50% C by mass. The total carbon in the filtrate, quantified as DOC following oxidation by high temperature combustion, UV-promoted wet oxidation, or Pt-catalyzed wet oxidation, with quantification by a nondispersive IR detector or con- ductivity detection (Kaplan, 1992; Benner and Hedges, 1993; Aiken et al., 2002), provides a quantitative measure of DOM concentration. Analyses of unfiltered samples that contain particulate OM in addition to DOM with “total” organic carbon analyzers, a marketing-based misnomer, are problematic because of the numerous difficulties in handling samples that are suspensions rather than solutions (Aiken et al., 2002). 254 Stream Ecosystems in a Changing Environment

Organic nitrogen typically comprises less than 5% of the DOM pool (Newbold et al., 1995), yet it can play an important role in DOM dynamics. The fraction of nitrogen-containing moieties in the DOM pool is quantified as dissolved organic nitrogen (DON) (Merriam et al., 1996; Campbell et al., 2000). DON is the dominant form of N exported in most streams and rivers that are not subject to anthropogenic N-loading (Meybeck, 1982; Hedin et al., 1995; Hagedorn et al., 2000; Scott et al., 2007; McNamara et al., 2008). Strong correlations between DOC and DON concentrations have been reported for C-limited streams in Costa Rica (Newbold et al., 1995), a C-rich Arctic river (Mann et al., 2012), and a forested New England stream (Wilson et al., 2013). Coupled cycling of DOC and DON was found in an N-limited Appalachian Mountain stream (Brookshire et al., 2005). This, however, was not the case in many C-limited, forested streams in the Appalachian Mountains, which were impacted to varying degrees by N-loadings from atmospheric deposition (Lutz et al., 2011). The large variation in DON measured in that study led the investigators to suggest that the mechanisms controlling DON export might shift annually, season- ally, or in response to antecedent conditions (Lutz et al., 2011). A negative correlation of DOC and DON for streams within the Yukon River basin led to the suggestion that source water flow paths impacted DOM quality (O’Donnell et al., 2010). Others have found that DOC:DON is highly variable during high discharge (Martin and Harrison, 2011), and that DON:DOM is more variable than DOC:DOM (Wilson et al., 2013). In oligotrophic systems, DON in precipitation (Seitzinger and Sanders, 1999), some of it of bacterial origin (Yan et al., 2015) and some of anthropogenic origin (Altieri et al., 2009), can be an important N source. Much less is known about DON compared to DOC in streams, despite its being a potential indicator of DOM bioavailability (Wiegner et al., 2006), in part because of difficulties in accurately measuring DON when background inorganic N concentrations are high. Recent improvements in separation techniques should help address this issue (Graeber et al., 2012; Chon et al., 2013), and tracer additions of the stable isotope of nitrogen, 15N, have been used to quantify DON production in headwater streams (Johnson et al., 2013). Constituents within the DOM in streams and rivers that have been identified as individual molecules or their derivatives comprise a small fraction of the DOM, and include biomolecules such as carbohydrates (Sweet and Perdue, 1982; Gremm and Kaplan, 1997), amino acids (Lindroth and Mopper, 1979; Keil and Kirchman, 1991), organic acids (Peldszus et al., 1996, 1998), and lignin phenols (Hernes and Benner, 2003). In contrast, operationally defined humic substances, including humic, fulvic, and hydrophilic acids (Thurman Dissolved Organic Matter in Stream Ecosystems 255 and Malcolm, 1981; Aiken et al., 1992), can dominate the DOM pool. Based on quantitative analyses, we know that the carbon and nitrogen in stream and river DOM is composed of 50–75% humic substances, with approximately 20% of the DOM present as the sum of identifiable compounds that include carbohydrates, amino acids, and carboxylic acids (Thurman, 1985; Volk et al., 1997; Leenheer and Croué, 2003; Kaplan and Newbold, 2003).

Optical Methods While the above methods quantify the C, N, or individual compounds or fractions within the DOM pool, optical methods provide information about the properties of broad compound classes. For example, analyses of the light-absorbing chromophoric and light-emitting fluorescent fractions of DOM (CDOM and FDOM, respectively) are among the most widespread approaches to characterize DOM. Analyses of CDOM and FDOM provide information about the abundance, source and composition of compounds associated with the humic and fulvic acid fractions of DOM (McKnight et al., 2001), and also can provide information about nonhumic or labile DOM (Zepp et al., 2004; Fellman et al., 2009a,b; Cory and Kaplan, 2012; Sleighter et al., 2014). Not all DOM has these optical properties and the percentage that does cannot be quantified precisely, although it is known that FDOM is a subset of CDOM (Stubbins et al., 2014). The absorption spectrum of CDOM decreases approximately exponen- tially with increasing wavelength across the ultraviolet and visible portions of the spectrum. The absorbance increases with increasing concentration of CDOM, often quantified as the Naperian absorption coefficient at certain wavelengths (Hu et al., 2002): A a = l 2.303 (1) l l where A is the absorbance reading and l is the path length in meters. The spectral slope of the CDOM absorption spectrum describes the shape of the absorbance curve (S; Stedmon and Markager, 2001), and is independent of DOM concentration. Because the shape of the absorbance curve provides information about groups of likely interacting aromatic molecules across the UV-visible region (del Vecchio and Blough, 2004; Boyle et al., 2009; Fichot and Benner, 2012), spectral slopes are related to the average source, quality, and diagenesis of the DOM. For example, lower S values, corresponding to relatively higher absorbance at longer wavelengths, have been observed for terrestrially derived DOM sources, and higher values, corresponding to relatively higher absorbance at shorter wavelengths, have been observed in systems with greater autochthonous inputs (Stedmon and 256 Stream Ecosystems in a Changing Environment

Markager, 2001). In addition, photobleaching of DOM consistently results in an increase in spectral slope (Vähätalo and Wetzel, 2004; Helms et al., 2008). To minimize potential artifacts associated with the spectral slope calculation, the spectral slope ratio (SR), a dimensionless ratio of the slope of the shorter wavelength region (275–295 nm) divided by the slope of the longer wavelength region (350–400 nm), has been proposed (Helms et al., 2008). Additional advantages of the slope ratio are that it avoids use of spectral data near the detection limit of the instrument (eg, at long wavelengths), and it focuses on the wavelength ranges most commonly exhibiting large shifts as a function of DOM source, quality, and diagenesis (Helms et al., 2008). The slope ratio has been strongly, inversely correlated to the average molecular weight of the DOM (Helms et al., 2008), and, as such, might be a good proxy for relative differences in molecular weight of the DOM among samples or along a hydrologic flowpath. The specific UV absorbance at 254 nm normalized to DOC concentra- −1 −1 tion, SUVA254 (L mg C m ), is a widely used proxy for the aromatic content of DOM (Weishaar et al., 2003; Cory et al., 2007). SUVA254 is defined as the decadic UV absorbance coefficient at 254 nm in inverse meters (m−1) −1 divided by the DOC concentration measured in mg C L . SUVA 254 values for aquatic fulvic acids at neutral pH range from 1 to 6 L mg C−1 m−1, with the respective filtered whole waters often lower than the fulvic acid fraction (Cory et al., 2007). High nitrate and dissolved iron species can interfere with the SUVA measurement and interpretation (Weishaar et al., 2003). Analysis of FDOM signals observed in an excitation emission matrix (EEM) via parallel factor analysis (PARAFAC; Bro, 1997) is used to trace DOM and assess its dynamics in aquatic systems (Stedmon and Markager, 2005). This approach provides insights into different types of carbon within the DOM pool hypothesized to differ in bio-reactivity to bacteria. One type is carbon associated with humic-like substances derived from either terrigenous or microbial sources; the other is carbon associated with amino acid-like substances. Understanding the underlying variability captured in an EEM has been significantly advanced through PARAFAC, a statistical modeling approach that separates a dataset of EEMs into mathematically and chemically independent components (each representing a single fluorophore or a group of strongly co-varying fluorophores; Stedmon and Bro, 2008) multiplied by their excitation and emission spectra (representing either pure or combined spectra). PARAFAC analysis of an EEM dataset results in a reduction of complex, three-dimensional data into several two-dimensional spectra representing chemically independent components that describe the total EEM (Stedmon et al., 2003). This reduction ultimately allows for the Dissolved Organic Matter in Stream Ecosystems 257 identification of patterns in the dataset that otherwise would not be obtained by visual inspection of peak positions. A tutorial of the EEM/PARAFAC approach as applied to DOM provides a comprehensive review of this topic (Stedmon and Bro, 2008) and is complemented by a MATLAB-based tool- box for working with fluorescence data (Murphy et al., 2013). Recent studies have demonstrated that the different types of FDOM respond in contrasting ways to biogeochemical processes, namely photolysis or biodegradation (Cory et al., 2007; Lutz et al., 2012). Thus, the EEM/ PARAFAC approach increasingly is used to understand how different fractions of DOM change with hydrologic regime, season, or land cover in a watershed (Fellman et al., 2009a,b; Williams et al., 2010). For example, fluorescence from amino acid-like moieties has been correlated positively with biodegradable DOC (BDOC) concentrations and has been used increasingly as a proxy for the biodegradable DOM pool (Balcarczyk et al., 2009; Fellman et al., 2009b; Hood et al., 2009). Consistently, studies have shown that these different groups of fluorescing moieties have dissimilar lability profiles in streams, exhibiting variable uptake rates and temporal dynamics (Fellman et al., 2009a,b; Lutz et al., 2012). However, it also has been demonstrated, at least within an eastern deciduous forest headwater stream, that not all amino acid-like FDOM is labile, that there are lability differences between the categories of amino acid-like FDOM, and that a significant portion of the humic-like FDOM is biodegradable (Cory and Kaplan, 2012). Together, these studies demonstrate how different fractions of the DOM pool cycle in natural waters. Analyses of CDOM and FDOM capture the rapid dynamics of DOM in natural waters, and are useful proxies because they provide information about types of carbon based on correlations of CDOM or FDOM signals mainly with 13C NMR (McKnight et al., 1997; Weishaar et al., 2003; Cory and McKnight, 2005; Cory et al., 2007). 13C NMR quantifies the percentage distribution of the carbon compound class (aliphatic, carbohydrate, anomeric, aromatic, carboxyl, and aldehyde/ketone), assuming that the distribution of the isotopically heavier carbon atom is the same as the bulk carbon across all compound classes (Dria et al., 2002). Because structure is one key to reactivity, the functional group analysis is a critical component of DOM characterization as it provides information about the likely arrangement of the carbon, hydrogen, oxygen, nitrogen, and sulfur hetereoatoms. For example, aromatic and carboxyl fractions of Suwannee River fulvic acid were shown to preferentially sorb to a streambed, thus demonstrating how in-stream processes can control the composition of DOM exported downstream. This information also was used to imply the nature of the interactions controlling sorption of DOM to iron oxides in the stream (McKnight et al., 2002). 258 Stream Ecosystems in a Changing Environment

DOM Composition and Structure One of the best tools available to determine the composition of DOM is ultra-high resolution mass spectrometry. Recent advances in ultra-high resolution mass spectrometry (MS) based on Fourier Transform Ion Cyclotron Resonance (FT-ICR) have provided an unprecedented view into the molecular-level composition of DOM in streams and rivers (Kim et al., 2003; Sleighter and Hatcher, 2008). The mass spectrum obtained from FT- ICR analysis shows that the mass of an ion within the DOM pool on the x-axis and the height of the ion peak on the y-axis is proportional to the abundance of the ion within the DOM pool (Fig. 2). It should be noted, however, that not all molecules have equal ionization efficiencies (Sleighter and Hatcher, 2008). FT-ICR analysis reveals a complex heterogeneous mixture of thousands of individual molecules present in low concentrations (Kim et al., 2006), especially when complementary ionization methods are used (Hockaday et al., 2009) (Fig. 2). Zooming into less than 1 Da mass range, many peaks can be resolved out to a mass of five decimal places, allowing for calculation of unique molecular formulas for each of the many thousands of ion peaks present in DOM. However, FT-ICR mass spectrometry does not yet provide a quantitative view of DOM composition, given that factors other than concentration, such as ionization efficiency, influence the intensity of each ion detected in the mass spectrum, and that solid phase extraction typically used to concentrate DOM in freshwater samples recov- ers approximately 60% of the organic carbon (Dittmar et al., 2008). Ultra-high resolution mass spectrometry with FT-ICR analyses of DOM can produce complicated spectra that make either the visual presentation or the structural interpretation difficult. To address this challenge, a graphical display, the van Krevelen diagram, initially developed to assess kerogen and petroleum by plotting molecular H:C as a function of O:C (van Krevelen, 1950), was applied to aquatic DOM (Kim et al., 2003). This allows for the identification of possible reaction pathways and qualitative analyses of major compound classes, and can be extended to include peak or relative intensities on the z-axis (Kim et al., 2003). The 3D van Krevelen diagram facilitates the evaluation of struc- turally related compounds and exploration of the compositional differences among samples (Kim et al., 2003; Kujawinski et al., 2004; Jaffé et al., 2012). Molecular formulas provide information about composition but do not provide information about molecular structure. Each formula also could represent many structural isomers (Hertkorn et al., 2007; Sleighter and Hatcher, 2011), thereby complicating the challenge of understanding the geochemistry and biogeochemistry of natural DOM and its reactivity (Ball and Aluwihare,

2014). For example, a molecular formula of C17H14O9 corresponding to a mass Dissolved Organic Matter in Stream Ecosystems 259

Mass: 361.0565 C17H14O9 H/C = 0.82 O/C = 0.53

400 600 800 1000 361 361.1 361.2 361.3 Mass (Da) Mass (Da)

Three possible structures for C17H14O9:

Fig. 2 A typical mass spectrum of stream water from ultra-high resolution mass spectrometry using Fourier transform ion cyclotron resonance reveals the thousands of ions present in stream water. The mass of each ion is on the x-axis and the height of each ion peak on the y-axis represents the relative abundance of that peak in the DOM pool. Resolving each peak out to a mass of 5 decimal places allows for the determination of a unique molecular formula for each of the ions detected. Each formula can have many possible structural arrangements, as shown for an ion with mass 361.0565 and formula C17H14O9. Comparing these three of many possible structural arrangements of this ion, there are large differences in the arrangement of aromatic rings, double bonds, and oxygen-containing functional groups, such as alcohols, ketones, or carboxylic acids. For example, the structure on the left has no carboxyl functional groups, while the middle and right structures have one and two carboxyl groups, respectively. of 361.0565 Da and atomic H:C and O:C ratios of 0.82 and 0.53, respectively, has many potential arrangements of its atoms, such as the three different structures presented in Fig. 2. These are all plant-produced biomolecules with the same formula, but their different structures have variable reactivities. For example, a phenol might be less volatile, more soluble, and less oxidized than a ketone, so that small differences in structure influence reactivity. Thus, determining the molecular structure associated with a chemical formula and relating it to reactivity and degradation is the holy grail of DOM characterization. 260 Stream Ecosystems in a Changing Environment

Because structural analysis of complex mixtures is a contemporary analytical challenge that remains to be solved, the current best approach is to combine different techniques in order to constrain the likely compositions. In a study of DOM changes along a salinity gradient, FT-ICR mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy were combined, and a 2D cross-correlation analysis was used to identify the FT-ICR MS peaks that were correlated or anti-correlated to 13C NMR data corresponding to functional group distributions (Abdulla et al., 2013). Compounds associated with lignin/carboxyl-rich alicyclic molecules (CRAM; Hertkorn et al., 2006), tannins, or condensed aromatics decreased along the salinity gradient, while there was an increase in lipid-like compounds with low oxygen content. Relatively saturated aliphatic compounds showed no change along the salinity gradient. Another example of how these cross-comparison techniques can be used to constrain the compositional properties of DOM is provided in the sections about the relationship between DOM composition and bio-reactivity to bacteria.

DOM Chemogeography and Chemodiversity Given the diversity of organic compounds within a water sample, it is not surprising that extensive molecular-level chemical characterization of DOM in headwater streams has shown that DOM assemblages are unique in their overall composition for different streams (Frazier et al., 2003; Kim et al., 2006; Jaffé et al., 2012). This is consistent with a distinct geographical distribution or chemogeography (sensu Dunlop and Jefferies, 1985) for DOM. However, within these unique DOM assemblages, there are also remarkably similar characteristics among streams across broad climatic regions. For example, mass spectra reveal a common range and distribution of compound masses and H:C and O:C compositions (Kim et al., 2006; Jaffé et al., 2012), as well as similar patterns across bioregions for amino acid and functional group distributions (Jaffé et al., 2012). In addition, stream waters share five characteristic PARAFAC-defined peak regions (Jaffé et al., 2008) associated with different sources of organic matter and functional group distributions (Cory and McKnight, 2005). These results suggest that the similarities observed in stream water DOM composition might result from comparable diagenetic processes and SOM sources of DOM across vastly different regions (Jaffé et al., 2012). Molecular composition data for streams located in three climatic regions from North and Central America were consistent with these observations, showing that 70% of the 3286 individual chemical formulas were common to all Dissolved Organic Matter in Stream Ecosystems 261 biomes studied, 50% of all formulas were found in all streams, and the unique compounds within the DOM for a biome separated out within van Krevelen space (Mosher et al., 2015). Fewer studies have addressed the longitudinal changes in DOM within a river network. The River Continuum Concept proposed a theoretical framework in which the coupled physical structure and hydrologic processes of a river system form a template for, among other things, structuring the pattern of the “relative diversity of soluble organic compounds” across stream orders (Vannote et al., 1980). This prescient formulation, done without the benefit of the current understanding of the complexity of DOM chemistry, predates the meta-ecosystem concept (Loreau et al., 2003). It hypothesized that DOM diversity would peak in first-order streams, increasing rapidly after ground waters surfaced and extracted DOM from detrital litter, and then diversity would diminish sharply downstream as the streams grew larger and DOM was metabolized. Recently, this hypothesis was tested with ultra-high resolution mass spectrometry by measuring the DOM molecular composition across stream orders in forested headwater catchments. That test found that within a catchment: (1) formula diversity was highest in first-order streams, with 78–95% of all unique formulas restricted to first-order streams; (2) chemodiversity declined by 15–27% with increasing stream size, changing little beyond second-order streams; and (3) 63–71% of all formulas were common across all stream orders (Mosher et al., 2015). An analysis of a large DOM dataset from the National Water Information System of the US Geological Survey revealed the attenuation of DOM variability down a river network, leading to a perspective of river systems as chemostats (Creed et al., 2015). A shift from the dominance of aromatic DOM in headwaters to the dominance of aliphatic DOM downstream was observed, suggested that a combination of hydrological averaging of diverse terrestrial sources tended to reduce concentration variability and that the biogeochemical processing of DOM within the river network reduced DOM compositional variability (Creed et al., 2015). Collectively, the observed patterns of DOM diversity or variability in headwaters suggest that the unique DOM formulas (Mosher et al., 2015) or dominant aromatic DOM compounds (Creed et al., 2015) are of terrestrial origin and subjected to soil diagenetic processing as water moves from the vadose zone into the phreatic zone, which contains molecularly diverse groundwater DOM (Longnecker and Kujawinski, 2011). Most terrestrial DOC enters fluvial networks in the headwaters (Alexander et al., 2007). When groundwater surfaces and coalesces to form a stream, additional 262 Stream Ecosystems in a Changing Environment

DOM from benthic processes contributes to the stream water DOM pool (Kaplan et al., 1980), which subsequently is altered by selective adsorption to mineral surfaces and biotic and abiotic oxidation processes during downstream transport (Mosher et al., 2015; Creed et al., 2015).

DOM TRANSFORMATIONS AND FATES Oxidative Reactivity of DOM Historically, most of the complex molecules derived from terrestrial primary production have been considered to be both chemically and biologically refractory (Cummins et al., 1972). Recent studies have shown oxidative transformations of DOM are controlled largely by microorganisms and sunlight, and that microbiological (Kaplan et al., 2008; Ward et al., 2013b) and photolytic (Stubbins et al., 2010; Cory et al., 2014) alterations of terrestrially derived molecules play a major role in the fate of stream and river DOM; clearly these studies contradict conventional wisdom that DOM is refractory. Indeed, respiration of terrestrial organic carbon that enters freshwater ecosystems has been estimated to contribute from 1.2 Pg to 3.3 Pg of carbon dioxide to the atmosphere annually, with the potential for feedbacks on global climate change as bio-reactive DOM is metabolized to CO2, primarily by heterotrophic bacteria in streams and rivers (Richey et al., 2002; Sabine et al., 2004; Battin et al., 2009; Cole et al., 2007; Tranvik et al., 2009; Aufdenkampe et al., 2011). This underscores the substantial amount of metabolic energy that rivers transport to downstream ecosystems (Kaplan and Newbold, 1993; Mayer et al., 1998; Cole and Caraco, 2001; Battin et al., 2008), and high- lights the important role that inland waters play in terrestrial carbon cycling (Grace and Malhi, 2002; Aufdenkampe et al., 2011). However, while quantifying the bio- or photo-reactivity of DOM in streams and rivers is of critical importance to global change studies, a comprehensive understanding of DOM reactivity has been elusive. This elusiveness comes, in part, from the stunningly diverse assemblages of organic molecules within DOM in transport (Sleighter and Hatcher, 2008), the fact that approaches to characterizing DOM structure lag behind advances in understanding composition, and that even the chemical composition of the reactive fraction of DOM is largely unknown (Benner, 2003; Dittmar and Stubbins, 2014).

Bio-Reactivity of DOM Some of the earliest work on organic matter biodegradation in streams was associated with pollution of natural waters by sewage and the phe- nomenon of self-purification (Wuhrmann, 1964). In studies focused on Dissolved Organic Matter in Stream Ecosystems 263 pollution loading of streams, leaf-leachate was added to an experimental, re-circulating stream channel at levels that increased DOM concentrations approximately 10-fold (Cummins et al., 1972). The decline in DOC concentrations over time was modeled as the exponential decay of labile and resistant organic compounds, though it was recognized that the DOM was composed of a continuous array of molecules with different susceptibilities to biodegradation (Cummins et al., 1972; Wetzel and Manny, 1972). This concept has been described more recently as a biodegradability continuum and mathematically characterized by first-order kinetic models with several biodegradability pools (Boudreau and Ruddick, 1991), a beta model that expresses biodegradation through probability (Vähätalo et al., 2010), and a reactivity continuum model (Koehler et al., 2012). Current concepts of organic matter biological reactivity have been developed extensively for SOM (Trumbore, 1997) and marine DOM (Carlson, 2002; Hansell, 2013). Each discipline has generated a broad description of three bio-reactivity classes that are empirically derived and simplify what likely is a continuum of bio-reactivities. For soils, these classes typically are referred to as active, intermediate, and passive, while for marine waters the classes are labile, semilabile, and refractory (Nagata and Kirchman, 1996). These categories have been expanded based on ocean studies to include semi-refractory compounds with turnover times of decades, and ultra- refractory compounds with a turnover time that exceeds the circulation time of the deep ocean (Hansell, 2013). The temporal differences among these bio-reactivity categories, expressed as turnover times, span nine orders of magnitude for various constituents, listed as follows by the descriptors applied to soil/marine DOM: active (minutes to months)/labile (minutes to days); intermediate (years to centuries)/semilabile (months to years); and passive (millennia)/refractory (centuries to millennia). In lotic systems, where turnover times can be estimated from the ratio of uptake length (Newbold, 1992) to average stream water velocity, it is difficult to make comparisons to turnover times from either soils or the oceans. This is, in part, because the transit time for surface water from headwaters to the sea, even for the longest rivers on Earth, would be a few years, at most. There are also issues in the extrapolation from extended incubations in small bottles or recirculating chambers (Bott et al., 1997; Dodds and Brock, 1998) to a stream, where nearly all of the biological activity in streams is associated with the benthos (Tank et al., 2000; Sigee, 2005) and involves exchanges with the hyporheic zone (Findlay, 1995; Hall et al., 2009; Ward et al., 2013b). The dominance of microbial activity in benthic habitats versus the water column is certainly true for headwater streams, and is the reason 264 Stream Ecosystems in a Changing Environment why mass transfer velocities are used to assess the movement of energy and nutrients from the water column to the benthos (Tank et al., 2007). There are no estimates that isolate heterotrophic respiration from total ecosystem respiration, but for the benthos of an undisturbed, heavily shaded forested headwater stream from Peru, benthic respiration was 25 times greater than water column respiration (Bott and Newbold, 2013). Bacterial densities, while not an index of activity, are orders of magnitude higher for attached cells in the benthos versus cells suspended in stream water (Geesey et al., 1978; Lock, 1981; Kaplan and Bott, 1989). Extrapolating or scaling from streams to rivers within a network is problematic with unknown contributions from the benthos versus the water column. Rivers can develop an active bacterioplankton community and have greater sediment loads and larger floodplains than streams (Hall et al., 2013). However, there have been no comprehensive whole system analyses that quantify the distribution of heterotrophy between the water column and the benthos across a river network. When considering categories of DOM bio-reactivity in stream and river networks, we adopt the marine DOM terminology, though clearly these are relative terms. For example, turnover of a semilabile DOM pool measured in a headwater stream was more than one order of magnitude slower than the turnover for the most labile monomers and would travel downstream with an uptake length of more than 4 km before being metabolized, in contrast to monomer uptake lengths of approximately 200 m (Kaplan et al., 2008). In the context of solute uptake measurements in streams, there are logistical and analytical constraints in making whole-stream measurements over distances of greater than a few kilometers. Thus, it is not possible currently to make claims concerning the turnover times of the more recalcitrant components, other than that their degradation is slower than that of the semilabile molecules. Furthermore, we are unable to determine whether any truly refractory molecules (Carlson, 2002; Hansell, 2013) exist within the more recalcitrant components measured in streams. Significant strides have been made toward understanding sources, concentrations, and seasonal dynamics of biodegradable DOM (BDOM) in inland waters (Cleveland et al., 2004; Wickland et al., 2007; Fellman et al., 2008, 2009b; Wang et al., 2012). Bulk average characteristics associated with quantitative analyses of the total organic carbon pool (Mulholland, 1997), acidic functional group content (Sun et al., 1997), oxidation state (Vallino et al., 1996), fractionation with XAD resins (Aiken et al., 1992), elemental analysis (Hood et al., 2005), specific molecular components of DOM Dissolved Organic Matter in Stream Ecosystems 265

(Kirchman, 2003; Cheng and Kaplan, 2003; Hernes and Benner, 2003), and δ13C-DOC values (Maher and Eyre, 2011), plus qualitative measurements of optical properties with UV-visible (Weishaar et al., 2003; Helms et al., 2008; Spencer et al., 2009) or fluorescence spectroscopy (Cory et al., 2007; Hernes et al., 2009) have yielded useful information about the broad character of DOM and its bio-reactivity, but provide limited utility for ad- dressing molecular composition and structure or turnover rates of these different DOM pools. Most investigations that characterize DOM by these techniques correlate the initial composition of DOM with some measure of BDOM to generate information about the bio-reactivity of DOM fractions and their temporal or spatial variation in the environment (eg, Fellman et al., 2009a), but are unable to provide quantitative estimates of uptake rates or molecular-level dynamics. Organic molecules dissolved in stream water, in contrast to DOM within soils, are not protected or stabilized through interactions with surfaces, and thus a fundamental inference is that the bio-reactivity of stream water DOM is largely a function of its composition and structure (Mann et al., 2012). This inference is supported by studies of fresh waters across tropical, temperate, and boreal systems where DOM bio-reactivity has been related directly to DOM chemical composition. Bacterial consumption of DOM has been positively related to proxies for low-molecular weight or N-rich carbon DOM, such as amino acids and proteins (Hopkinson et al., 1998; Michaelson et al., 1998; Wickland et al., 2007, Balcarczyk et al., 2009; Fellman et al., 2009a,b; Hood et al., 2009; Cory and Kaplan, 2012; Guillemette and del Giorgio, 2011), and is inversely related to proxies for aromatic carbon associated with extensively processed SOM (Hopkinson et al., 1998; Cory and Kaplan, 2012; Mann et al., 2012). Studies also have provided indirect support for the relationship between the bio-reactivity of DOM and its chemical composition, by evaluating seasonal shifts in bacterial community composition or activity concomitant with inferred or measured shifts in DOM composition with the season (Crump et al., 2003; Judd et al., 2006; Holmes et al., 2008; Mann et al., 2012). Thus we know that the general attributes of some of the labile bio- reactive fractions of DOM that support a fraction of heterotrophic metabolism in streams include low-molecular weight molecules that are relatively rich in nitrogen-containing functional groups and less colored molecules that are more saturated with fewer aromatic rings or double bonds (Kaplan and Newbold, 2003). However, our current quantitative knowledge about the concentration and function of DOM presents us with a conundrum 266 Stream Ecosystems in a Changing Environment when considering bio-reactivity. Estimates of the uptake of stream DOM have showed that the mass transfer of DOC from the water column to benthic microbial heterotrophs accounts for approximately 70% of the community respiration (Kaplan et al., 2008). In contrast, the contribution of low-molecular weight compounds often assumed to constitute the bio- reactive DOM, including those that can be identified and quantified with compound specific approaches, supports only 20% of the respiratory demand (Kaplan and Newbold, 2003; Kirchman, 2003). These compounds include dissolved free and combined neutral sugars (Gremm and Kaplan, 1997), organic acids (Peldszus et al., 1998), and total dissolved amino acids (Lindroth and Mopper, 1979; Keil and Kirchman, 1991, 1993; Volk et al., 1997). Even when the acids and aldehydes produced photochemically from more recalcitrant DOM (eg, Goldstone et al., 2002) are included, the amount of DOM does not add up from a mass balance perspective to support the uptake rate and levels of DOM consumption by the microbial community in a real system. Therefore, as much as 80% of the support of bacterial respiration in stream ecosystems from biologically reactive DOM (from a mass perspective) is not the molecularly recognizable, low-molecular weight, N-rich category of compounds, but rather is an as-yet molecularly unidentified pool of labile and semilabile DOM. The fact that most of the DOM in streams and rivers is not susceptible to microbial oxidation over time scales that are equivalent to water residence times (Volk et al., 1997; Wiegner et al., 2005; Fellman et al., 2009c; Wickland et al., 2012) has made characterizing the biodegradable fraction that much more difficult.

Conceptual Models of DOM Diagenesis and Substances A growing body of evidence supports the idea that humic substances contribute to the pool of biodegradable DOM. For example, studies indicate that between 8% and 45% of humic-like FDOM and 40–100% of the amino acid-like FDOM are labile to bacterial biodegradation (Cory and Kaplan, 2012; Sleighter et al., 2014). These findings agree with prior studies with stream water-colonized plug-flow biofilm reactors (Kaplan and Newbold, 1995) in which humic substances were determined by XAD-8 analyses (Aiken et al., 1992). Humic substances, although largely refractory (73% not metabolized), accounted for 75% of the total stream water BDOM because of the large proportion of stream water DOC classified as humic substance-C. Combined amino acids, however, were more bio-reactive than humic substances (49% metabolized), but accounted for only 4.2% of the BDOM because of their low concentrations in stream water (Volk et al., Dissolved Organic Matter in Stream Ecosystems 267

1997). Further support for the role of humic materials as DOM that is biologically oxidized to CO2 is provided by a positive correlation between humic FDOM and % BDOM in Arctic rivers (Mann et al., 2012) and measurements of the extensive degradation of lignin and associated macromolecules in the Amazon River (Ward et al., 2013b). Collectively, these observations lead us a conceptual model of stream and river heterotrophy driven by oxidation of abundant but molecularly uncharacterized biodegradable humic DOM molecules. The general concept of the humic substances within the DOM pool also has undergone a paradigm shift. Humic DOM had been considered to be an assemblage of polymeric macromolecules formed through humification reactions, often involving oxidative polymerization of polyphenols (Larson and Hufnal, 1980) and polycondensation reactions (Swift, 1999; Jokic et al., 2004). More recently, detailed molecular-level investigations, including tandem MS measurements (MS/MS) performed on selected peaks with ultra-high resolution mass spectrometry (Stenson et al., 2003) and combined high-performance liquid chromatography (HPLC)and ultra-high resolution mass spectrometry (Liu et al., 2011) do not support a polymer synthesis model, but rather have led to a model of DOM as recognizable, but altered, biological molecules in various stages of decomposition that form dynamic associations stabilized by hydrophobic interactions and hydrogen bonds (Sutton and Sposito, 2005) as noncovalent binding forces between macromolecules, eg, supramolecular associations (Hockaday et al., 2009; Nebbioso and Piccolo, 2013). This suggests that the inorganic milieu within stream and river systems, meaning the concentration and speciation of major and trace cations and anions, or subphase characteristics, including ionic strength, pH, and the presence of bridging cations (Sjögren and Ulvenlund, 2004), can modify these supramolecular associations (Dogsa et al., 2014; Kloster et al., 2013), and alter their three-dimensional structure, and their relative susceptibility to oxidative reactivity. Temperature also can affect supramolecular isomerism (Han et al., 2014). We know little about the molecular nature of humic DOM fractions contributing to the labile and semilabile DOM pools. What we do know about those fractions comes from using ultra-high resolution mass spectroscopy to characterize the compounds preferentially removed or enriched after the DOM is incubated with bacteria (Kujawinski et al., 2004; Kim et al., 2006; Mesfioui et al., 2012; Sleighter et al., 2014). For example, previous work showed that microbial degradation by stream heterotrophs consumed some of the low-molecular weight compounds and oxygen-rich 268 Stream Ecosystems in a Changing Environment compounds, but generally modified the DOM pool to lower molecular weight, oxygen-poor molecules as indicated by the lower abundance of higher mass peaks (Fig. 3) and by the loss of peak intensity for compounds with O:C ratios between 0.3 and 0.5 (Fig. 4) (Kim et al., 2006). Other studies have shown that stream water bio-reactive DOM can be separated into two different groups: (1) H/C centered at 1.5 with O/C 0.1–0.5 or (2) low H/C of 0.5–1.0 spanning O/C 0.2–0.7 that are positively correlated with CDOM and FDOM, respectively (Sleighter et al., 2014). These findings overlap those describing ecosystem-specific DOM composition with Group 1 matching aquatic samples from a river and bogs and Group 2 matching those from forest soil solutions (Roth et al., 2014). These studies showed that recalcitrant DOM that was resistant to microbial degradation aligned tightly in the center of the van Krevelen space consistent with CRAM (H/C 1.0–1.5, O/C 0.25–0.6) and negatively correlated with CDOM and FDOM (Sleighter et al., 2014). The CRAM region of the van Krevelen diagram, however, represented the ubiquitous DOM found across four distinct ecosystems (Roth et al., 2014), as well as temperate and tropical headwater streams (Mosher et al., 2015). CRAM compounds, first identified as a major refractory component of marine DOM and considered likely to occur in freshwater and terrestrial environments (Hertkorn et al., 2006), include alicyclic terpenoids (Arakawa and Aluwihare, 2015), and, based on their proposed structures, are hypothesized to be more recalcitrant to bacterial degradation. These high-molecular weight, partially oxidized compounds have been observed ubiquitously in lakes (Lam et al., 2007), large rivers (Stubbins et al., 2010), and bays (Sleighter and Hatcher, 2008),

Rio Tempisquito White Clay Creek

(A) (C)

300 400 500 600 700 800 900 1000 300 400 500 600 700 800 900 1000 (B) m/z (D) m/z Fig. 3 Changes in molecular mass resulting from biodegradation showing a reduction in m/z in stream water from Rio Tempisquito (A) and White Clay Creek (C) as seen in effluents from bioreactors colonized by each stream, (B) and (D), respectively. Dissolved Organic Matter in Stream Ecosystems 269

Rio Tempisquito White Clay Creek

1.6

1.4

1.2

1.0

0.8

0.6

0.4 (A) (C) tio

H:C ra 1.6

1.4

1.2

1.0

0.8

0.6 (B) (D) 0.4 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 O:C ratio Fig. 4 Changes in the DOM pool of stream water from Rio Tempisquito (A) and White Clay Creek (C) with a shift to more oxygen-poor molecules as indicated by the loss of peak intensity for O:C compounds in the effluents from bioreactors colonized by each stream, (B) and (D), respectively. and represent a convergent evolution of DOM compositions over a range of terrestrial and aquatic ecosystems (Roth et al., 2014).

Pathways and Products of Photooxidation Photooxidation of DOM starts when sunlight exposure stimulates the light-absorbing chromophoric fraction of DOM (CDOM). Photo-excited CDOM interacts with water, dissolved oxygen, or iron (Fe) to produce reactive oxygen species (ROS) and organic radicals, which in turn can oxidize not only CDOM, but also moieties of uncolored DOM not accessible to direct photochemical degradation (Goldstone et al., 2002; Pullin et al., 2004; Scully et al., 2004; Cory et al., 2009; Cory et al., 2010a,b). A majority of the molecules comprising DOM (>50% of the C) are susceptible to 270 Stream Ecosystems in a Changing Environment photodegradation, given enough exposure to sunlight (Vähätalo and Wetzel, 2008; Stubbins et al., 2010; Rossel et al., 2013). ROS and organic radicals are expected to be quantitatively important in that process, especially considering that aromatic carbon or CDOM are estimated to account for ~15–80% of the total DOM pool (Blough et al., 1993; Cory et al., 2014). Pathways of photooxidation include the transfer of energy from

photo-excited CDOM to O2 to produce excited O2, known as singlet oxy- 1 gen ( O2) or superoxide (Fig. 5). Superoxide decomposes to form hydrogen peroxide (H2O2). In addition, a new pathway has been discovered for H2O2, 1 formed by the reaction of O2 with DOM (Cory et al., 2010a,b). Pathways for H2O2 formation are important because H2O2 is the key reactant in the Fenton reaction, where it oxidizes reduced (ie, ferrous) iron that is typically derived from terrestrially based redox reactions within soil or ground water, or water column photo-reduction to yield hydroxyl radical (•OH), a powerful, non-selective oxidant:

Light energy + initial DOM Singlet oxygen

Energy 2 1 + DOM O2 2. Indirect transfer to O Photoexcited Hydrogen 1 3 H2O2 DOM and DOM Electron peroxide transfer to O •− O2 Dismutation 2 + Fe2+, Fenton 1. Direct Superoxide chemistry

Hydroxyl Reactive oxygen •OH Breaking of radical chemical species (ROS) bonds

Altered DOM:

DOMLMW DOMox CO2 Low-molecular weight Partially oxidized acids and aldehydes “more labile” “more refractory” Fig. 5 Photochemical alteration of DOM by direct and indirect pathways.

· - Fe()II +®HO22 Fe()III ++OH OH (i) The photo-Fenton reaction might contribute up to 70% of the photo-produced •OH in laboratory-irradiated, high CDOM, high Fe coastal and riverine waters, based on concurrent analysis of Fe(II) and H2O2 (White Dissolved Organic Matter in Stream Ecosystems 271 et al., 2003). It has been demonstrated in laboratory experiments that exposure of lignin-derived DOM to •OH originating from Fenton-type photoreactions generate condensed aromatic and alicyclic aliphatic compounds typical of black carbon and CRAM (Chen et al., 2014; Waggoner et al., 2015). However, questions remain about the role of the Fenton reaction in the in situ oxidation of DOM in natural waters and the resulting products. Hydroxyl radical, likely produced by the photo-Fenton reaction in Arctic surface waters, has been estimated to account for ~5% of the total photochemical CO2 produced by the photomineralization of DOM (Page et al., 2014). These results are consistent with estimates suggested for the photomineralization of DOM by the photo-Fenton reaction in other natural waters (Southworth and Voelker, 2003). Multiple lines of evidence support the role of Fe in the photochemical processing of DOM, including reports of higher photobleaching and mineralization in Fe-rich, as compared to Fe-poor, fresh waters (Bertilsson and Tranvik, 2000). However, others have reported that the formation of photoinert complexes of Fe results in lower photobleaching (White et al., 2003) and lower photooxidation of DOM (Gao and Zepp, 1998). Oxidants other than •OH produced by the Fenton reaction include fer- ryl iron (Fe(IV)), which is a much more selective oxidant compared to •OH. At pH 7 in the presence of terrestrially derived DOM, formation of a probe compound indicative of •OH was found at only 26% of the expected yield if •OH were the only oxidant produced (Vermilyea and Voelker, 2009). Although the identity and reactivity of the Fenton oxidant(s) likely will depend on pH and Fe-DOM complexation, the evidence so far suggests that at a low pH (~5) or a high DOM concentration, production of •OH by Fenton reactions is likely. In contrast, at a circumneutral pH or at a low DOM concentration, the oxidation of Fe(II) might produce oxidants other than •OH. However, Fe might influence DOM photodegradation by other (non-Fenton) pathways, such as a ligand-to-metal charge transfer, where O2 oxidizes organic ligands with Fe(III) as a cyclic catalyst that is photo-reduced to Fe(II). Ligand charge transfers or photo-decarboxylation reactions of Fe are thought to contribute to the photo-production of CO2 from DOM (Miles and Brezonik, 1981; Xie et al., 2004). Small carboxyl-containing organic acids, such as citric acid or oxalic acid, can undergo photo-decarboxylation, which is catalyzed by iron

(Faust and Zepp, 1993) to produce CO2 and consume O2 in a 2:1 molar ratio.

This is consistent with the high ratio of photochemical CO2 produced per O2 consumed by DOM in high-Fe surface waters of the Arctic (Cory et al., 2014). Products of DOM photooxidation have been classified according to their expected lability to bacterial degradation and are based in large part 272 Stream Ecosystems in a Changing Environment on the analytical feasibility of identification and detection. These include:

(1) Inorganic C species, CO, and CO2, which are the most oxidized photoproducts, generated by a process referred to as photomineralization (Miller and Zepp, 1995); (2) Low-molecular weight acids and aldehydes, known to contribute to some of the observed enhanced lability of photolyzed DOM to bacteria (Wetzel et al., 1995; Bertilsson and Tranvik, 1998, 2000); (3) Partially oxidized, molecularly uncharacterized photoproducts that are hypothesized to contribute to the negative effects of sunlight on DOM lability (Cory et al., 2010a,b). How direct or indirect photochemical reactions involving ROS or organic radicals oxidize the DOM to form these three different classes of photoproducts, each with important roles in C cycling (Fig. 5), is poorly known, in part because of the difficulty of isolating a specific oxidant and describing its reactions with DOM. To address this knowledge gap, 18O was used as a tracer to isolate the oxidation of DOM by singlet oxygen, identifying 18O incorporation into DOM composition following photooxidation (Cory et al., 2010a,b). Partially oxidized photoproducts were expected to be the main product of 1 the oxidation of DOM by O2 because the rate of O2 incorporation into 1 DOM increased linearly with the concentration of O2 (Cory et al., 2009). This implied that the amount of DOM oxidized was related to the amount 1 of O2 present. Secondly, under conditions selective for oxidation of DOM 1 by O2 there was no detectable production of CO2 (eg, no detectable photomineralization). Photoproducts of the reaction were identified from two aquatic DOM samples considered as end-members along a spectrum of sources, ranging from terrestrially derived DOM from tracheophytes, ie, vascular plants, isolated from the Suwannee River, and authochthonous, microbially derived DOM isolated from Pony Lake, by tracing the 18O in the DOM composition using FT-ICR MS. For both DOM end-members, 18O-containing products were found that belong to a broad class of molecules known as CRAM (Fig. 6).

Interactions Between Photochemistry and Biological Degradation Sunlight exposure of DOM can either increase or decrease the rate of bacterial respiration (Tranvik and Bertilsson, 2001) and growth efficiency (Fasching and Battin, 2012; Fasching et al., 2014). Some studies have implicated ROS such as H2O2 in the short-term inhibition of bacterial carbon production from photo-exposed DOM (Kaiser and Sulzberger, 2004;

Pullin et al., 2004, Anesio et al., 2005; Judd et al., 2007) because H2O2 can be directly harmful to bacteria (González-Flecha and Demple, 1997; Lesser, Dissolved Organic Matter in Stream Ecosystems 273

2.0 1.8 1.6 Compounds 1.4 recalcitrant to bacteria in bioreactors 1.2 18 1.0 O-DOM oxidized, more recalcitrant

H:C ratio 0.8 0.6 0.4 0.2 0.0 0.00.2 0.40.6 0.81.0 1.2 O:C ratio Fig. 6 Overlap of DOM compounds that are produced by photo-oxidation (red) and those that are resistant to or the result of bio-degradation (blue).

2006). However, additions of H2O2 to aquatic DOM incubated with bacteria showed no effect on rates of bacterial respiration or growth as compared to controls with no H2O2 added (Cory et al., 2010a,b), and steady-state −9 −7 concentrations of ROS such as H2O2 are typically low (10 to 10 M; Burns et al., 2012). DOM or trace metals are much more likely to be sinks for photochemically produced ROS than bacteria (Blough and Zepp, 1995; Cory et al., 2009, Page et al., 2014). In addition, recent work has demonstrated that “dark” sources of H2O2 and other ROS can equal or exceed photochemical production rates in aquatic systems (Vermilyea et al., 2010, Page et al., 2012, 2013), thus demonstrating that ROS are ubiquitous in the water column and are not limited to sunlit surface waters. These recent findings imply that aquatic bacteria should have the capacity to cope with ROS produced in sunlit surface waters. In contrast, the evidence indicates that poorly characterized photoproducts account for the negative effects of photo-exposure on the bacterial consumption of DOM (Cory et al., 2010a,b; Amado et al., 2015). For example, the isotope tracer experiments, described previously, identified 1 DOM compounds that were partially oxidized by O2 and were expected 1 to be more recalcitrant to bacteria. Indeed, slowed bacterial growth on O2- oxidized DOM compared to unreacted controls provides additional evi- 1 dence that the oxidation of DOM by O2 results in recalcitrant molecules 1 such as CRAM. For the two aquatic end-members that reacted with O2, Suwannee River and Pony Lake, the response of bacteria as measured by their cell numbers was depressed for the first few days of the experiment, an 274 Stream Ecosystems in a Changing Environment

1 effect that was not observed in the dark controls. In addition, O2 has been implicated in the photochemical breakdown of free and combined amino acids (Boreen et al., 2008; Lundeen and McNeill, 2013; Amado et al., 2015). Given that free and combined amino acids comprise a fraction of the DOM pool labile to bacteria (Cory and Kaplan, 2012), it is hypothesized that the 1 negative response of bacteria to DOM oxidized by O2 is because of the degradation of these N-rich compounds (Cory et al., 2010a,b; Amado et al., 2015). Thus, taken together, these studies suggest that the photooxidation of 1 DOM, specifically by O2, might be one pathway to produce compounds identified as biologically recalcitrant. The implication is that in the environ- 1 ment, when opportunities for photooxidation of DOM by O2 are high, the net result might be DOM with a lowered lability to bacteria. In a separate study, FT-ICR MS analysis showed that the compounds most recalcitrant to bacteria, the compounds that remain after bacterial degradation of stream water DOM, occupied the same compositional space as the photooxidized DOM, or CRAM molecules (Sleighter et al., 2014, Fig. 6). One interpretation of this observation is that photooxidation with singlet oxygen produces some compounds that are not labile to bacteria. Alternatively, it perhaps indicates a similarity between oxidation end-points, where recalcitrant DOM is produced via separate mechanisms, biological oxidation, and photooxidation. However, because we do not know the structural arrangement of the atoms, it is not possible to classify the similarity of these groups beyond their elemental composition. The previous example is an illustration of coupled photodegredation and biological degradation, where photodegradation of DOM can reduce its lability to bacteria and inhibit conversion to CO2 (Tranvik and Bertilsson, 2001; Moran et al., 2000; Cory et al., 2013). Alternatively, photodegradation of DOM can enhance microbial degradation of fresh DOM with little prior light exposure (Wetzel et al., 1995; Obernosterer and Benner, 2004; Amado et al., 2006, 2007; Cory et al., 2013). There is also experimental evidence that photolysis can impact both biodegradability and bacterial growth efficiency of DOM sources in streams, sometimes in opposite directions (Fasching and Battin, 2012). Photolysis of terrestrially derived DOM sources of an alpine stream, including groundwater, soil water, and soil extracts, increased the concentrations of biodegradable DOC and increased bacterial growth efficiency; whereas photolysis of algal extracts reduced concentrations of biodegradable DOC but increased bacterial growth efficiency (Fasching and Battin, 2012). Recent evidence indicates that carbon released from thawing permafrost and thermokarst failures has a greater susceptibility to coupled Dissolved Organic Matter in Stream Ecosystems 275 photochemical and biological degradation compared to resident DOM in surface waters that drain from the annually thawed surface soils. (Cory et al., 2013; Mann et al., 2014). Photodegradation increased microbial respiration of DOM by >40% as compared to C held in the dark (Cory et al., 2013). While it is unclear exactly what controls this amplification effect, the increased bioavailability of DOM after photo-exposure often is attributed to the production of labile substrates such as low-molecular weight carbonyls and the release of bound nutrients (N and P) (Wetzel et al., 1995; Moran and Zepp, 1997; Bertilsson and Tranvik, 1998; Cotner and Heath, 1990). For example, the hydroxyl radical can oxidize DOM to form low- molecular weight acids and aldehydes, such as acetate or formaldehyde (Goldstone et al., 2002), that contribute to some of the positive effects of photodegradation on the biological lability of DOM (Wetzel et al., 1995; Bertilsson and Tranvik, 1998; Goldstone et al., 2002). Given that DOM constitutes the primary source and sink of the hydroxyl radical in Arctic surface waters (Page et al., 2014), it is possible that hydroxyl radicals contribute to the observed positive effects of sunlight on DOM lability to bacteria in these waters (Cory et al., 2013). The highly oxidized acids and aldehydes produced by this pathway are expected to be degraded rapidly by bacteria, and to increase the bacterial respiratory quotient, eg, the ratio of CO2 respired per O2 consumed (Berggren et al., 2012). Consistently, respiratory quotients in fresh waters were higher in waters expected to support high rates of DOM photodegradation (Dilly et al., 2011; Berggren et al., 2012), suggesting that oxidized photoproducts contribute to bacterial respiration of DOM to CO2. However, recent work found no effect of photoexposure on bacterial respiratory quotients (Cory et al., 2014), and low-molecular weight acids or aldehydes cannot account for the majority of the enhanced bacterial respiration or growth efficiency (Wetzel et al., 1995; Pullin et al., 2004). Thus the pathways and products responsible for the enhanced microbial degradation of DOM remain as open questions. In summary, two keys to understanding the contrasting effects of sunlight on the lability of DOM to bacteria rest in first characterizing the pathways and mechanisms involved in the production of photoproducts when DOM is exposed to sunlight and then assessing the biological lability of those compounds.

Rates of Photooxidation in Waters Water column rates of DOC photodegradation are the product of three wavelength-dependent spectra (Miller 1998; Vähätalo and Wetzel, 2004; Cory et al., 2014): (1) the apparent quantum yield of photodegradation 276 Stream Ecosystems in a Changing Environment

−1 −1 (Φλ, mol product mol photons absorbed nm ); (2) the photon flux absorbed over the depth z of the water column (Qa,λ, mol photons absorbed m−2 d−1 nm−1); and (3) the ratio of absorption by CDOM to the total absorption

(aCDOM,λ/atot,λ) (Fig. 7). Mathematically, rate of photomineralization can be expressed as a function of these spectra as follows:

100 Photon flux 80

–1 60 d –2

mmol photons 40 l m , z , s , 20 Photomineralization (CO ) =

d 2 lmax tot, 0 Q Qs,l,zaCDOM,lfl lmin 15 DOC absorbance 20 –1 ) d –1

10 –2 15 (m m l 2 10 5 CDOM, a

mol CO 5 µ

λ 0 0 PM 300 400 500 600 6 l (nm) –1 5

mol mol CO

2 4 AQY = 2 mol photons absorbed 3

phtotons 2 mmol CO

l 1 F 0 300 400 500 600 l (nm) Fig. 7 Three spectra that influence the photodegradation of DOM, including photon flux, DOC absorbance, and apparent quantum yield.

lmax --21 aCDOM,l (2) Photomineralizationm()ol Cm d = fllQa, dl ò a lmin tot,l

where λmin and λmax are the minimum and maximum wavelengths of light contributing to the photodegradation of DOC (often ~300–700 nm), atot,λ is the total absorption in the water column (CDOM, particles and water), and Qa,λ is the light absorbed by the water column: Dissolved Organic Matter in Stream Ecosystems 277

-K (3) QQmolphotons md--21=-1 e d ,lz ad,,ll()so ( ) where Qdso,l is the photon flux just below the water surface (accounting for reflection), and Kd,λ is the attenuation coefficient of the water column as a function of the local sun angle based on the latitude and time of day. Sunlight attenuation in the water column is controlled largely by the concentration of CDOM (Fee et al., 1996, 2004; Morris et al., 1995; Gareis et al., 2010). The greater the CDOM concentration, the higher the attenuation coefficient and the more rapidly that sunlight is attenuated in the water column. Given that DOM photodegradation is attributed mainly to higher energy UVB and UVA light (Vähätalo et al., 2000), and that CDOM most strongly absorbs in the UV, photochemical reactions in many fresh waters are limited to the upper <1 m of the water column. In “humic” fresh waters that have high concentrations of CDOM, UV light might penetrate <30– 50 cm (Gareis et al., 2010; Cory et al., 2014). Thus, deeper water columns protect a greater fraction of water and DOM from sunlight, keeping them in the dark, and allowing them to escape photooxidation. The apparent quantum yield term (Φλ) for DOM photodegradation is a measure of the lability of DOM to undergo photodegradation. Apparent quantum yields for complete photomineralization of aquatic DOM to CO2 range from <1 −1 to ~3 mmol CO2 mol photons absorbed by DOM (Vähätalo, 2009; White et al., 2010; Koehler et al., 2014; Cory et al., 2014). It has been suggested that the apparent quantum yield for photomineralization of DOM decreases with increasing light-exposure history (Andrews et al., 2000; Cory et al., 2014). That is, as the more easily mineralized moieties are degraded by sunlight, less and less CO2 is produced per photons absorbed by DOM. Studies are needed to compare the photolability of DOM from different waters, and the impacts of land use on DOM photodegradation. Simultaneous measurements of apparent quantum yields also are needed, as this is a critical, but often overlooked, component. Without consideration of apparent quantum yield attempts to compare the photolability of DOM from different waters would ignore the differences in the rate of light absorption by DOM in the different waters, even when samples are exposed to the same amount of sunlight for the same amount of time. This means that experimental exposures would not be normalized to photon flux, and therefore any conclusions about photolability would be con- founded by differences in the amount of light exposure and absorption, rather than providing a clear comparison of relative sensitivity to degradation by sunlight (Miller, 1998; Hu et al., 2002). Similarly, studies that 278 Stream Ecosystems in a Changing Environment attempt to compare the relative importance of photochemical oxidation versus dark bacterial oxidation in the water column must measure the wavelength-dependent spectra of DOM photochemical degradation (eg, rates of light absorption and the apparent quantum yield). Lastly, global extrapolations of photodegradation require a much larger database than is currently available on wavelength-dependent apparent quantum yield and the attenuation coefficient (Kd,λ) values that would quantify the actual light availability within a water column.

In situ Kd,λ values were collected from a large set of streams, lakes, and coastal ponds in the Alaskan Arctic, and these data were combined with ex- perimentally determined apparent quantum yield measurements of DOM photodegradation and incoming solar radiation. The conclusion was that photochemical processing of DOC is an important component of the

Arctic C budget, accounting for about one-third of the total CO2 released from surface waters (Cory et al., 2014). Specifically, up to 40% of the CO2 emissions from inland waters of the Arctic were because of direct photochemical mineralization of DOM (Cory et al., 2014). The higher relative importance of sunlight for DOM conversion to CO2 in the Arctic as compared to lower latitudes is likely because waters in the Arctic are shallow, with a mean depth ~1 m, so that sunlight penetrates most of the water column. This leaves the benthos, specifically heterotrophic bacteria and presumably archaea attached to sediments and in biofilms growing on rocks, as the habitats within streams and rivers where bacterial respiration might dominate the conversion of DOM to CO2 (Cory et al., 2014). Rates of bacterial respiration in the water column of Arctic streams and lakes fall toward the low end of the range reported for fresh waters, thereby elevating the relative importance of sunlight over bacteria in the water column (Cory et al., 2014). For example, direct photomineralization alone was, on average, nearly five-fold greater than water column dark bacterial respiration, and more than 10-fold greater in the water column of Imnavait Creek, a shallow headwater stream characterized by high CDOM. Water column rates of partial photooxidation of DOC exceeded water column rates of dark bacterial respiration by nearly 15-fold in a low-DOC, glacial-fed river (Cory et al., 2014). Water column rates of photostimulated bacterial respiration were similar to water column rates of dark bacterial respiration in streams. Scaling the water column measurements of DOC processing to the open-water period in water from small streams, larger rivers, and lakes cal- culated from: (1) mean water column bacterial respiration rates and mean Dissolved Organic Matter in Stream Ecosystems 279 apparent quantum yields for photo-reactions; (2) measured and modeled UV radiation in the atmosphere; and (3) underwater light absorption, revealed the overall dominance of photochemical processing in the water column of Arctic streams and that the fate of DOC varied consistently by water type (Cory et al., 2014). In the water columns of small streams,

DOC mainly was mineralized by sunlight to CO2, while in large rivers the main fate of DOC was a mixture of partial photooxidation and an equal or lower amount of photomineralization to CO2. This pattern was proposed to be a result of light-exposure history, where DOC exported from soils to headwater streams has little prior light exposure and is labile to complete photomineralization, but as light exposure increases farther downstream and in lakes with longer residence times, the lability of DOC to photomineralization decreases. It is unclear whether these findings for the high rates of photochemical degradation of DOM in the water column of inland waters apply outside the Arctic (Koehler et al., 2014; Tranvik, 2014) or how the water column photochemical degradation would compare to benthic metabolism of DOM. Others have reported higher experimental rates, measured in quartz tubes, of photooxidation of terrestrially derived DOM as compared to planktonic bacterial respiration rates of the same water (Amon and Benner, 1996; Amon and Meon, 2004). Thus, in any shallow and unshaded water where photoprocessing of DOM is confined to a thin boundary layer, it is likely that photochemical degradation of DOM plays an important role in freshwater C cycling. Even if photomineralization of DOM to CO2 were found to be a consistently smaller fraction of the

CO2 emitted from inland waters relative to bacterial respiration, as proposed in a study of more than 1000 Swedish lakes (Koehler et al., 2014), partial photooxidation, which can result in both the positive and negative effects on DOM lability to bacteria, might be a quantitatively important pathway for DOM degradation in inland waters (Cory et al., 2007,

2014). Thus to understand the fate of DOM in inland waters, as CO2 emitted into the atmosphere and DOM carried to the ocean, ongoing and future studies must quantify the apparent quantum yields for DOM photodegradation, light availability in the water columns of streams and lakes, and the residence time of DOM in sunlit waters. Similarly, attempts to compare photodegradation to biodegradation need to consider the relative roles of benthic organisms versus water column organisms in the metabolism of DOM, certainly in small streams (Sigee, 2005; Allan and Castillo, 2007), but also perhaps in larger systems. 280 Stream Ecosystems in a Changing Environment

DOM CONTRIBUTIONS TO ECOSYSTEM METABOLISM DOM Uptake Despite the increased awareness of organic matter metabolism in stream and river ecosystems (Cole et al., 2007), relatively few studies have addressed the question of DOM contributions to ecosystem metabolism. A major imped- iment continues to be the difficulty in measuring rates of DOM processing, not only at the reach-scale, but also, and especially, throughout a fuvial network. Measurements of DOC uptake and estimates that DOM provides 11–70% of the organic C supporting ecosystem respiration or bacterial C demand have been made with a variety of approaches. These include: • Transplant experiments where sediments were moved upstream to a zone of lower DOC concentration and the declines in biomass and ATP were followed and related to the reduction in DOC flux (Bott et al., 1984). • A mass-balance construction of a DOM budget (McDowell and Fisher, 1976). • Laboratory or whole-stream additions of leachates or model compounds (Kuserk et al., 1984; Hall and Meyer, 1998; Brookshire et al., 2005; Newbold et al., 2006; Johnson et al., 2009; Lutz et al., 2012). • Measurements of DOC uptake within hyporheic zones by sampling the water upstream and downstream ends of gravel bars or mesocosms designed to simulate the flow through gravel bars (Findlay et al., 1993; Sobczak and Findlay, 2002). • Following concentration changes in DOC and several DOC fractions as well as bacterial abundance and activity when stream water was perfused through sediments (Fischer et al., 2002). • Performing whole stream injections of 13C-DOC tracers from fresh extracts of 13C labeled trees to obtain mass transfer coefficients for labile and semilabile bioreactivity classes and combining these with laboratory bioreactor measurements of the uptake of 13C-DOC tracers and stream water DOC to obtain concentrations of the bioreactivity classes to allow for calculations of DOC flux (Kaplan et al., 2008). • An empirical DOC flux model (Lauerwald et al., 2012). • Spatially distributed sampling of DOC fractions combined with river network modeling (Wollheim et al., 2015). None of these measurements included a consideration of algal primary productivity and the associated excretion of DOM (Kaplan and Bott, 1982; Hotchkiss and Hall, 2015). Therefore, any contribution of autochthonous DOM either as a direct substrate or as a labile DOM source that could lead Dissolved Organic Matter in Stream Ecosystems 281 to priming (Guenet et al., 2010, 2014; Bianchi, 2011) was not included. The continuing challenge concerning accurate estimates of DOM uptake and contributions to ecosystem respiration involves measuring rates associated with natural DOM in stream water, estimating the rates at which DOM is simultaneously generated within streams and rivers, and placing these estimates within the context of a fluvial network through appropriate scaling and models.

Instream Hydrologic Forcing and DOM Export Hydrology influences ecosystem-level DOM dynamics not only by having an impact on how water gets to streams from the terrestrial environment, but also on instream processes. For example, uptake lengths of solutes are directly proportional to water column depth and velocity (Stream Solute Workshop, 1990; Newbold et al., 2006; Hall et al., 2013). During storms, the product of depth and velocity in small streams easily can increase 50-fold, meaning that, independent of biological processes, uptake lengths of biologically labile DOM could increase from a few hundred meters (Newbold et al., 2006; Kaplan et al., 2008) to kilometers, and uptake lengths for semilabile DOM could increase from a few kilometers (Kaplan et al., 2008) to several hundred kilometers, resulting in a translocation of DOM out of a stream reach with potential subsidy of downstream systems (McLaughlin and Kaplan, 2013; Wilson et al., 2013; Raymond et al., 2016). In addition, increased discharge associated with storms can scour the streambed, with different thresholds for sediment resuspension and bedload movement (Uehlinger, 2006), disturbing microbial communities and depressing ecosystem respiration by 19% (Uehlinger, 2006) or more (Uehlinger and Naegeli, 1998) to as much as 10-fold (Roberts et al., 2007). Increases in the DOM uptake lengths because of increased depth and velocity during storm flows would be extended by concomitant decreases in mass transfer coefficients associated with diminished biological demand of disturbed streambed communities. A major challenge in understanding the impacts of storms involves assessment of DOM uptake and ecosystem respiration integrated over the scale of an entire river network.

DOM IN THE ANTHROPOCENE When human activities change land use and land cover, the associated perturbations in the carbon cycle often increase the flux of DOM through river systems (Regnier et al., 2013); alter DOM sources, including the 282 Stream Ecosystems in a Changing Environment

mobilization of aged carbon (Butman et al., 2015); and modify DOM quality with potential downstream impacts on ecosystem services (Newcomer et al., 2012). The differences in DOM quantity, quality, and fluxes among largely undisturbed watersheds with low population densities, as compared to watersheds with anthropogenic perturbations, often are clear, both in terms of DOM quantity and lability (Kaplan et al., 2006) and the presence of pollutants (Aufdenkampe et al., 2006). Nevertheless, partitioning the contributions among myriad potential causes is challenging. Human- induced changes in land use, including soil disturbance leading to higher rates of soil erosion, soil compaction, increases in impervious surfaces, lim- ing, fertilization, point-source sewage discharges, dam construction, and water withdrawals, as well as the indirect effects of global climate change, can affect both the transfer of carbon and nutrients from terrestrial to aquatic environments (Regnier et al., 2013) and the rates of processing of DOM in river networks. Next, we review the ways in which human activities are altering DOM and speculate about the overall impacts on DOM in a changing environment, including changes in its susceptibility to photodegradation and bacterial respiration, which have the potential to alter the rates of CO2 evasion from stream ecosystems.

Altering Ecosystems Impacts in remote, unpopulated environments, such as by forest clear- cutting, long have been known to increase the mobilization of inorganic nitrogen and other ions (Bormann et al., 1968). More recent studies have extended these observations to include both increased DOC concentrations (Kreutzweiser et al., 2008; Laudon et al., 2009; Schelker et al., 2012, 2014) and runoff (Hornbeck et al., 1993; Schelker et al., 2013a,b). Intensive and extensive alterations of forests can have long-lasting effects, as seen in lower DOC concentrations or changes in DOM composition in streams 30 years (Yamashita et al., 2011) or 19 years (Burrows et al., 2013) after clear-cut tree harvests, and 50 years after conversion of a forest to a white pine plantation (Yamashita et al., 2011). When forests are converted to agricultural lands, impacts can include increased DOC and DON exports, as observed in headwater streams from northeastern Germany (Heinz et al., 2015), but that was not the case in south-central Ontario (Wilson and Xenopoulos, 2008, 2009). However, DOM quality was affected by agriculture in both regions, as well as in watersheds studied in southeastern Australia (Giling et al., 2014). Agricultural land use alters DOM composition, for example, increasing the amount of nonhumic DOM Dissolved Organic Matter in Stream Ecosystems 283

(Heinz et al., 2015; Wilson and Xenopoulos, 2008) with likely increases in the biologically available fraction of the DOM (Williams et al., 2010) and potentially the photoreactivity of the DOM (Lu et al., 2013). These changes should increase the relative proportion of labile DOM and generally lead to greater CO2 evasion (Fig. 1). A caveat about changing photoreactivity is that an unequivocal finding of a change in reactivity to photodegradation is not possible without correcting for changes in light absorption. Streams draining agricultural lands tend to be less retentive of organic carbon, a condition exacerbated when the land-use change includes channelization and subsurface tile drains (Griffiths et al., 2012). Because agriculture often increases nutrient levels in streams, the nutrient addition could lead to increased rates of processing of terrestrial C within the streams (Rosemond et al., 2015). When losses of wetlands (Wilson and Xenopoulos, 2008) or peatlands (Huotari et al., 2013) occur with the expansion of agriculture, the change in DOM composition can be accompanied by a decrease in DOM exports and an increase in bioavailability of the exported DOM (Hulatt et al., 2014). Little is known about the long-term trajectory of stream ecosystem recovery when prolonged agricultural disturbance is followed by reforestation, but the legacy of agricultural impacts can last for decades (Harding et al., 1998).

Urbanization Urbanization, typically quantified through levels of impervious surface area within a watershed (Harbott and Grace, 2005), has been placed into a theoretical framework involving modifications to the cycling of carbon and nitrogen, interactions of groundwater and surface water, and ecosystem metabolism (Kaushal et al., 2014a). A common theme associated with urban watersheds is the simplification of the DOM pool, with a decrease in humic-like DOM typically associated with terrestrial inputs, and an increase in bioavailablity, often with a shift toward more autochthonous DOM (Hosen et al., 2014; Parr et al., 2015; Newcomer et al., 2012), and a change in bacterial enzymatic activity patterns (Harbott and Grace, 2005). A high-density development in south-central Texas has been associated with higher DOC concentrations (Aitkenhead-Peterson et al., 2009), sometimes exceeding ex-urban concentrations by more than an order of magnitude. The landscape context for the example from Texas involves an arid environment where organic carbon is produced by landscaping and is leached by irrigation (Aitkenhead-Peterson et al., 2009), processes that would be less prevalent or missing from more mesic environments. 284 Stream Ecosystems in a Changing Environment

Organic loading associated with urbanization can be influenced by wastewater effluents (Sickman et al., 2007), or reversed when urban- associated structures, such as reservoirs are included, leading to a reduction in DOC concentration variability and diversity (Westerhoff and Anning, 2000). Urbanization also can lead to increased pollutant levels, particularly during storms, with storm waters carrying elevated levels of toxins (Crunkilton and DeVita, 1997) and nutrients (Li et al., 2007; Hathaway et al., 2012), while point-source discharges of wastewater often contain pharmaceuticals and personal care products (Rosi-Marshall and Royer, 2012; Rosi-Marshall et al., 2014) that could impact microbial communities within streambed biofilms (Rosi-Marshall et al., 2013). Urban ecosystem ecology is still a young scientific discipline, but one in which there has been an exponential increase in the number of studies since 1990 (Kaushal et al., 2014b). An overarching aspect of urbanization appears to be a decoupling of DOC concentration and composition from natural source areas, and the normal soil processes that change DOC temporally and spatially, resulting in stream ecosystems with altered physical, chemical, and biological structures (Edmonds and Grimm, 2011).

Impacts of a Changing Environment Climate change associated with human-derived increases in emissions of greenhouse gases has led to the unequivocal warming of the global climate. Global warming has diminished amounts of accumulated snow and ice, and is anticipated to increase the intensity of precipitation, especially in tropical and high-latitude areas, while there might be longer periods between rainfall in the mid-continental areas during summer, with a greater risk of drought (IPCC, 2014). The associated changes in temperature, precipitation, and hydrology regimes likely will impact inputs of terrestrial DOM into streams and rivers, not only in terms of quantity (Dawson et al., 2008), but also quality and reactivity (Tipping et al., 1999; Craine et al., 2010; Kothawala et al., 2014). Predicting future changes in these loadings is challenging, and is made all the more difficult because those changes not only might occur over time scales ranging from years to centuries (Solomon et al., 2015), but also might vary across geographic regions. Here, we identify observations that have been made for DOM across different geographical regions, discuss the mechanisms behind the phenomena, and relate them to some possible future scenarios. Increases in DOM concentrations in streams and lakes, a phenome- non identified as the browning of waters (Roulet and Moore, 2006), has been measured in many aquatic systems, including boreal and hemiboreal Dissolved Organic Matter in Stream Ecosystems 285 ecosystems dominated by taiga and mixed hardwood and conifer forests (Weyhenmeyer et al., 2014; Rasilo et al., 2015). While the drivers associated with the increased DOM concentrations are not fully understood (Weyhenmeyer et al., 2014) and the current synthesis suggests a range of mechanisms, including atmospheric chemistry, hydrology, soil processes, terrestrial vegetation, and climate (Solomon et al., 2015), there are convinc- ing arguments that lower sulfate deposition, associated with reduced sulfur emissions and a cyclical decline in sea-salt deposition, are the primary drivers (Evans et al., 2005, 2006; Monteith et al., 2007; Erlandsson et al., 2008). Under a scenario in which atmospheric chemistry is the primary driver for increased DOM within the browning effect, soils, and by extension, ecosystems, are in the process of recovery from acidification associated with industrial-age sulfur emissions (Ekstrom et al., 2011), rather than undergoing destabilization because of climate change (Evans et al., 2006, 2007). This would mean that in regions that have reduced emissions, the browning process could level off, but in regions with developing economies, soils might become acidified, depending, in part, on energy choices made, with an en- suing reduction in DOM mobilization. Soil characteristics influence DOM flux (Guggenberger and Kaiser, 2003) and play a role in the browning process in that most of the browning of waters has occurred in areas underlain by acid sensitive peat lands or thin, poorly buffered, glaciated soils. Well-buffered or unglaciated soils that are less acid-sensitive would be less susceptible to the processes and would have reduced DOM exports, and thus would be less likely to exhibit increased DOM export associated with reductions in acid deposition. Beyond atmospheric chemistry, climate change-associated increases in temperature that influence soil DOM availability and in precipitation that alter hydrologic regimes should increase DOM exports (Dawson et al., 2008; Dinsmore et al., 2013). Indeed, an analysis of temporal increases in DOC concentrations in temperate and boreal lakes in Canada showed a stronger relationship to climate parameters than sulfate (Couture et al., 2012). To the extent that a warmer, wetter climate prevails in boreal ecosystems, the transfer of terrestrial DOM to streams will increase, and given the extent of sources within the riparian zones of boreal catchments, the transfer has the potential to be long term (Ledesma et al., 2015), suggesting a continuation of this trend, or at least sustained levels of higher exports. The impact of climate change on Arctic tundra ecosystems underlain by permafrost is predicted to influence regional carbon balances (MacDougall et al., 2012), but changes to DOM biogeochemistry are difficult to predict with both increases and decreases observed. As the permafrost melts in 286 Stream Ecosystems in a Changing Environment

portions of central Siberia and Alaska, decreases in DOC exports have been documented for the Yukon River (Striegl et al., 2005), the upper Kuparuk River (McClelland et al., 2007), and streams in the Kulingdakan watershed (Prokushkin et al., 2007). This result is somewhat counterintuitive because the permafrost represents a vast soil organic carbon store. However, warming has affected the terrestrial flow paths for runoff, replacing shallow flow through organic-rich soil layers with infiltration and deeper flow paths through the mineral soils that adsorb DOM, and warming summer temperatures have increased the rates of soil respiration (Striegl et al., 2005). In contrast, in the Siberia Lowland, extensive peatlands provide deep, organic-rich layers, so that, while the permafrost melts, flows from the terrestrial to the aquatic environment still pass through soils that are DOM sources (Frey and Smith, 2005). However, even scenarios that involve an increased release of permafrost DOM from arctic soils to surface waters, while changing the composition of DOM (Abbott et al., 2014), might not lead to an increase in CO2 production (Ward and Cory, 2015). The interactions of processes associated with permafrost melting are complex and sensitive to the mode of permafrost degradation, land cover, soils, and topography, making predictions uncertain. Most scenarios, however, involve a transition from a system dominated by surface water to one dominated by groundwater runoff (Frey and McClelland, 2009). Another change affecting areas with glaciers is the retreat of glaciers and their replacement with forests, leading to an increase in DOM sources (Fellman et al., 2014). In mesic, temperate regions where climate change is predicted to increase the frequency of extreme storms, annual DOM exports likely would increase (Dhillon and Inamdar, 2013). This contrasts with regions where future climates are predicted to be drier, and drought and associated reduced flows, or even flow intermittency, should become more prevalent. The coupled biogeochemical and hydrological responses of watersheds to droughts involve a greater predominance in deeper groundwater inputs, as compared to more surficial inputs, with a concomitant reduction in DOM concentrations and biodegradability (Dahm et al., 2003). However, observations from Mediterranean streams affected by flow intermittency show an initial spike in DOC concentrations with the resumption of flow (Acuña et al., 2007), suggesting that episodic flood pulses could make these stream systems less retentive of organic matter inputs.

Summary of Impacts of the Anthropocene on DOM Sources and Processing This review of the ways in which human activities are altering DOM carries sufficient uncertainty as to make predictions highly speculative. Dissolved Organic Matter in Stream Ecosystems 287

Nevertheless, the overall impacts on DOM in a changing environment would suggest that a continuation of past and current land-use practices will lead to increased supplies of DOM and will include DOM that possesses higher susceptibility to photodegradation and bacterial respiration. Impacts associated with global climate change, both rising temperatures and altered hydrologic regimes, underscore a suite of complex interactions that require continued and expanded research efforts to fully understand. To the extent that either land-use changes or global climate change accentuate shallow flow paths for water moving from the terrestrial environment to the stream or bring more particulate organic matter into contact with runoff, the net result, placed into the context of our conceptual model (Fig. 1), would be a shift to more labile constituents within the DOM pool. On a global scale, these perturbations would be expected to increase the rates of CO2 evasion from stream ecosystems, which would exacer- bate climate change. On a regional scale, higher levels of DOM in waters used for drinking water supplies would provide heightened challenges to drinking water treatment plants from the perspective of limiting disinfectant byproduct production and pollutant removal. Of course, there is the potential that behaviors associated with climate change and land use could shift. For those impacts associated with land-use decisions, a growing environmental awareness could lead to policies that embrace the concepts Aldo Leopold (1949) presented in the final chapter of A Sand County Almanac, and adoption of a “conservation esthetic” that idealizes a world view in which humans are an integral part of the natural world. We would expand this idea to include the concept of an “ecosystem esthetic” in which human activities on the landscape facilitate ecosystem-level processes, including energy flow and nutrient cycling. Within an ecosystem esthetic, altered landscapes engineered to preserve natural processes, including the infiltration of storm waters, are valued and are perceived as having intrinsic natural beauty, and there is recognition of the beauty of the processes themselves. This would elevate ecosystem services as fundamental components of local land-use decisions and international efforts to control climate change.

FUTURE RESEARCH CHALLENGES The challenges that lie ahead in advancing knowledge of the forms, functions, and fluxes of DOM in streams and rivers will involve novel applications of existing methodologies, as well as technical advances in 288 Stream Ecosystems in a Changing Environment analytical organic geochemistry. Currently, the most powerful means of determining chemical composition of DOM, FT-ICR MS, is a qualitative measurement. Making this ultra-high resolution technique quantitative would allow a better idea of the mass balance of carbon and energy flow from an ecosystem perspective. Combining quantitative measures of composition with the determination of structure, currently achieved by constraining structures through the use of NMR to elucidate functional groups, would begin to provide greater insight into what makes a mol- ecule reactive to oxidation through biotic and abiotic processes, as well as insight into the interactions of photolysis on metabolism. Accurate predictions of the transformations and fates of DOM will require advances in these areas. Another intriguing approach to advancing the understanding of high-resolution temporal dynamics of DOM involves a combination of low-throughput, ultra-high sensitivity mass spectrometry with high-throughput, low-resolution optical techniques, specifically using statistical analyses to interpret the meaning of an EEM through correlations with FT-ICR MS measures of composition (Sleighter et al., 2014; Stubbins et al., 2014). A successful application of such a research front would make progress toward the ability to deploy simple optical sensors to understand the dynamics of DOM and changes in O2 through river networks. Our understanding of the interactions between terrestrial and aquatic environments would be advanced by the development of a mechanistic understanding of organomineral associations and how these complex interactions impact the availability of DOM to transport across ecosystem boundaries, as well as the susceptibility to oxidative processes. The dominant oxidative processes, microbial metabolism and photooxidation, appear to vary in their relative contributions to DOM processing across environments, along spatial scales and through time. The development of a theoretical formulation to explain these differences and concomitant advances in empirical approaches to test the theory would begin to provide a window into the processes that lead to the release of CO2 into the atmosphere from inland waters. Included in this challenge is parsing out the role of benthic versus water column processes when moving from small headwater streams to large river systems. Along these same lines, expanding analyses to capture DOM transformations and energy flow across whole river networks would move studies beyond the scale of a stream or river to reach an integrative measure of aquatic ecosystem impacts on the global carbon cycle. Dissolved Organic Matter in Stream Ecosystems 289

DISCUSSION TOPICS • What are impacts of climate change on DOM, and how do these vary across geographical regions? • How do autochthonous/allochthonous interactions vary in different biomes and over seasonal cycles? • What are the interactions between DOM lability and nutrient availability? • How do DOM quantity, quality, and dynamics change in human- dominated ecosystems? • What are the analytical chemistry and statistical challenges in relating high-resolution (FT-ICR MS) and low-resolution (EEMs) measurements? • How important are DOM-mineral interactions to the fate of DOM, ie, degradation versus preservation, in aquatic systems? • Develop a scheme to obtain DOM structure and composition though the use of FT-ICR and NMR and describe how these analyses could be made more quantitative? • CRAM appears to be ubiquitous in aquatic and terrestrial DOM. Is this an example of structure and composition controlling DOM oxidative reactivity? • How do DOM quantity and quality influence stream ecosystem function? • What is the role of the terrestrial ecosystem type in the balance of photooxidation and microbial degradation in stream waters?

ACKNOWLEDGMENTS LAK dedicates this chapter to the memory of W. B. Dixon Stroud, whose curiosity and generosity helped make 30 years of research about dissolved organic matter possible. Jamie Blaine pointed out the March 8, 1840, entry in Thoreau’s journal, and Thomas B. Parr provided critical comments that improved the chapter. During the writing of this chapter, NSF DEB 1120717 and EAR 1452039 helped support LAK and NSF EAR 1451372 helped support RMC.

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