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Spatial Heterogeneity and Controls of Ecosystem Metabolism in a Great Plains River Network

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Spatial Heterogeneity and Controls of Ecosystem Metabolism in a Great Plains River Network Spatial heterogeneity and controls of ecosystem metabolism in a Great Plains river network Walter K. Dodds, Sophie A. Higgs, Margaret J. Spangler, James Guinnip, Jeffrey D. Scott, Skyler C. Hedden, Bryan D. Frenette, et al. Hydrobiologia The International Journal of Aquatic Sciences ISSN 0018-8158 Volume 813 Number 1 Hydrobiologia (2018) 813:85-102 DOI 10.1007/s10750-018-3516-0 1 23 Your article is protected by copyright and all rights are held exclusively by Springer International Publishing AG, part of Springer Nature. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”. 1 23 Author's personal copy Hydrobiologia (2018) 813:85–102 https://doi.org/10.1007/s10750-018-3516-0 PRIMARY RESEARCH PAPER Spatial heterogeneity and controls of ecosystem metabolism in a Great Plains river network Walter K. Dodds . Sophie A. Higgs . Margaret J. Spangler . James Guinnip . Jeffrey D. Scott . Skyler C. Hedden . Bryan D. Frenette . Ryland Taylor . Anne E. Schechner . David J. Hoeinghaus . Michelle A. Evans-White Received: 17 June 2017 / Revised: 14 December 2017 / Accepted: 11 January 2018 / Published online: 20 January 2018 Ó Springer International Publishing AG, part of Springer Nature 2018 Abstract Gross primary production and ecosystem limitation, and was positively related to nutrients. respiration together define ecosystem metabolism and Smaller-scale main stem sampling revealed almost as help indicate the importance of internal and external much variance over a few kilometers as the larger carbon sources. Spatial variability of these processes is scale sampling. Local processes seemed to dominate poorly characterized in rivers. We measured metabo- dissolved oxygen dynamics, since diurnal dissolved lism in the Kansas River: (1) at 10 locations over 100 s oxygen patterns were better correlated with absolute of km in tributaries within the watershed and (2) over time than data corrected for travel times. A single- 20 km with detailed sampling in the main stem. station method compared against two-station metabo- Whole-river metabolism at the larger scale was lism methods indicated that local hotspots of metabo- decoupled from light, algal growth, and nutrient lism occur at scales less than 1 km and that single- station estimates average out this variance. The main stem data provide support to the idea that functional Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10750-018-3516-0) con- processing zones control characteristics used to esti- tains supplementary material, which is available to authorized mate system metabolism, but the nutrient effect at the users. whole watershed level indicates that transport from upstream can also be important. Handling editor: John M. Melack W. K. Dodds (&) Á S. A. Higgs Á J. Guinnip Á D. J. Hoeinghaus S. C. Hedden Á B. D. Frenette Á R. Taylor Á Department of Biological Sciences and the Advanced A. E. Schechner Environmental Research Institute, University of North Division of Biology, Kansas State University, Manhattan, Texas, Denton, TX 76203, USA KS 66506, USA e-mail: [email protected] M. A. Evans-White Department of Biological Sciences, University of M. J. Spangler Arkansas, Fayetteville, AR, USA Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO 80309, USA J. D. Scott Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, USA 123 Author's personal copy 86 Hydrobiologia (2018) 813:85–102 Keywords Scaling Á Respiration Á Gross primary Nutrient availability also acts as a driver of metabo- production Á Aeration Á Nutrients Á Light lism in rivers and such effects are well documented (e.g., Karr & Dudley, 1981; Wetzel, 2001; Richards et al., 2008). While the impact of abiotic factors on river metabolism has received substantial attention, Introduction few studies, of which we are aware, have empirically measured metabolism in large rivers at multiple points Ecosystem metabolism is a characteristic that controls within the network while paying explicit attention to food webs, biogeochemistry, and transport of organic scale of variance. materials and nutrients within and through rivers and Most investigators measure whole-system metabo- streams. This set of metabolic processes includes net lism at one or a few locations, with little understanding ecosystem production (NEP), the balance of gross of how representative these measures are of the entire primary production (GPP), and ecosystem respiration river (except see Williams et al., 2000; Reichert et al., (ER). Aquatic food webs rely on a continuous input of 2009; Demars et al., 2011; Hunt et al., 2012; Hondzo energy in the form of organic matter originating either et al., 2013; Houser et al., 2015; Siders et al., 2017). within (autochthonous) or outside of (allochthonous) When investigating drivers of metabolism, we expect the ecosystem (Wetzel, 2001). Thus, measures of specific parameters to differentially affect GPP and metabolism within an aquatic ecosystem are instru- ER and anticipate variation across scales. This vari- mental in understanding interactions among organ- ation in rivers has been represented mathematically isms and their environment (Thorp & Delong, 2002; (Reichert et al., 2009) but its assumptions have been Marcarelli et al., 2011) and act as important indicators questioned because results defying the laws of of trophic state (Dodds, 2006). Lotic waters are a biophysics of the system can emerge (Demars et al., unique system in which to study metabolism due to 2015), as previously found for other two-station their unidirectional flow, dynamic geomorphology, models (Demars et al., 2011). spatial heterogeneity, and high variability in physical, Lotic metabolism is sometimes measured at fine chemical, and biological characteristics (Wetzel, spatial scales or in controlled laboratory settings 2001). Some of these highly variable parameters (Oviatt et al., 1986; Findlay et al., 2003; Wiegner complicate the measurement and scaling of ecosystem et al., 2005), but these studies may not reflect whole rates, while others can inform models of metabolic ecosystem conditions and are therefore difficult to control over dissolved oxygen. accurately scale up to larger aquatic systems (Carpen- Several factors interact to determine spatial patterns ter, 1996). Studies that measure lotic metabolism of metabolic rates locally and across watersheds. across large spatial scales and incorporate heterogene- Light, temperature, and nutrient concentrations all can ity are lacking (Reichert et al., 2009), especially those directly influence GPP by stimulating photosynthetic with the goal of understanding processes that con- rates (Wetzel, 2001) while other factors, such as tribute to variation both within a river and across a grazing, sloughing, or scouring, can influence loss landscape (Hall et al., 2016). This heterogeneity could rates (Dodds & Whiles, 2010). In large rivers, light be important depending upon the extent to which availability can be spatially heterogeneous due to rivers and streams link to their upstream components factors such as canopy cover or the interaction (Siders et al., 2017). In fact, influential concepts in between depth and turbidity (Ochs et al., 2013). ER river ecosystem ecology variously recognize the can be influenced by carbon availability (GPP along- importance of upstream (i.e., River Continuum Con- side external inputs), as well as temperature and losses cept; Vannote et al., 1980) and lateral (i.e., Flood Pulse from grazing and scouring. Metabolic rates of aquatic Concept; Junk et al., 1989) linkages and modifications organisms generally increase with temperature (Allen thereof (i.e., Serial Discontinuity Concept; Ward & et al., 2005). Demars et al. (2011) found that NEP Stanford, 1995) for local metabolism. Alternatively, decreased exponentially with increasing temperatures local conditions can dominate ecosystem processes across a 20°C gradient due to ER increasing at a faster (i.e., Riverine Productivity Model; Thorp & Delong, rate than GPP. A better constrained study shows 1994), and this idea was more explicitly formalized as similar increases in ER and GPP (Demars et al., 2016). functional processing zones in rivers (i.e., Riverine 123 Author's personal copy Hydrobiologia (2018) 813:85–102 87 Ecosystem Synthesis; Thorp et al., 2006). These deployments of dissolved oxygen (O2), temperature, competing perspectives of river metabolism call into and light sensors along a 20-km stretch of the main question the specific applicability of river metabolism stem of the Kansas River coupled with multiple estimates. For example, if highly local processes measurements of physical habitat. dominate (e.g., Kupilas et al., 2017), but experimental methods measure metabolism on much greater spatial scales, incorrect inference is possible with respect to Materials and methods the interpretation of metabolic rate estimates (and vice versa). Methodology
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