, Volume 22, Issue 4, pp 892–911 | Cite as

Estimating Ecosystem Metabolism to Entire River Networks

  • Tamara Rodríguez-CastilloEmail author
  • Edurne Estévez
  • Alexia María González-Ferreras
  • José Barquín


River ecosystem metabolism (REM) is a promising cost-effective measure of ecosystem functioning, as it integrates many different ecosystem processes and is affected by both rapid (primary productivity) and slow (organic matter decomposition) energy channels of the riverine food web. We estimated REM in 41 river reaches in Deva-Cares catchment (northern Spain) during the summer period. We used oxygen mass-balance techniques in which primary production and ecosystem respiration were calculated from oxygen concentration daily curves. Then, we used recently developed spatial statistical methods for river networks based on covariance structures to model REM to all river reaches within the river network. From the observed data and the modeled values, we show how REM spatial patterns are constrained by different river reach characteristics along the river network. In general, the autotrophy increases downstream, although there are some reaches associated to groundwater discharges and to different human activities (deforestation or sewage outflows) that disrupt this pattern. GPP was better explained by a combination of ecosystem size, nitrate concentration and amount of benthic chlorophyll a, whereas ER was better explained by spatial patterns of GPP plus minimum water temperatures. The presented methodological approach improves REM predictions for river networks compared to currently used methods and provides a good framework to orientate spatial measures for river functioning restoration and for global change mitigation. To reduce uncertainty and model errors, a higher density of sampling points should be used and especially in the smaller tributaries.


spatial modeling river ecosystem metabolism primary production ecosystem respiration ecosystem functioning river network virtual watershed SSN model 



This study was partly funded by the Spanish Ministry of Economy and Competitiveness (MINECO) as part of the project HYDRA (REF: BIA2015-71197). José Barquín was supported by a Ramón y Cajal grant (Ref: RYC-2011-08313) of the Ministry of Economy and Science of the Total Environment Competitiveness. We are also grateful to the University of Cantabria for the funding to Tamara Rodríguez-Castillo through a Postgraduate Grant.

Supplementary material

10021_2018_311_MOESM1_ESM.docx (3 mb)
Supplementary material 1 (DOCX 3067 kb)


  1. Adrados L, Alonso V, Bahamonde JR, Farias P, Fernández González LP, Gutiérrez Claverol M, Heredia Carballo N, Jiménez Sánchez M, Meléndez Asensio M, Merino Tomé O, Villa Otero E. 2010. Parque Nacional de los Picos de Europa : guía geológica. 2nd ed. (Adrados Ed., editor.). Instituto Geológico y Minero de España
  2. Álvarez-Cabria M, Barquín J, Peñas FJ. 2016. Modelling the spatial and seasonal variability of water quality for entire river networks: relationships with natural and anthropogenic factors. Sci Total Environ 545:152–62.CrossRefPubMedGoogle Scholar
  3. Álvarez-Martínez JM, Jiménez-Alfaro B, Barquín J, Ondiviela B, Recio M, Silió-Calzada A, Juanes JA. 2018. Modelling the area of occupancy of habitat types with remote sensing. Isaac N, editor. Methods Ecol Evol 9:580–93. Scholar
  4. APHA, AWWA, WEF. 1999. Standard Methods for the Examination of Water and Wastewater 20th edition.Google Scholar
  5. Aristi I, Arroita M, Larrañaga A, Ponsatí L, Sabater S, von Schiller D, Elosegi A, Acuña V. 2014. Flow regulation by dams affects ecosystem metabolism in Mediterranean rivers. Freshw Biol 59:1816–29. Scholar
  6. Ashley Steel E, Sowder C, Peterson EE. 2016. Spatial and temporal variation of water temperature regimes on the snoqualmie river network. JAWRA J Am Water Resour Assoc 52:769–87. Scholar
  7. Barquín J, Benda LE, Villa F, Brown LE, Bonada N, Vieites DR, Battin TJ, Olden JD, Hughes SJ, Gray C, Woodward G. 2015. Coupling virtual watersheds with ecosystem services assessment: a 21st century platform to support river research and management. Wiley Interdiscip Rev Water 2:609–21. Scholar
  8. Battin TJ, Kaplan LA, Findlay S, Hopkinson CS, Marti E, Packman AI, Newbold JD, Sabater F. 2008. Biophysical controls on organic carbon fluxes in fluvial networks. Nat Geosci 1:95–100. Scholar
  9. Beaulieu JJ, Arango CP, Balz DA, Shuster WD. WD. 2013. Continuous monitoring reveals multiple controls on ecosystem metabolism in a suburban stream. Freshw Biol 58:918–37. Scholar
  10. Benda L, Miller D, Andras K, Bigelow P, Reeves G, Michael D. 2007. NetMap: a new tool in support of watershed science and resource management. For Sci 53:206–19.
  11. Benda L, Miller D, Barquin J, McCleary R, Cai T, Ji Y. 2016. Building virtual watersheds: a global opportunity to strengthen resource management and conservation. Environ Manag 57:722–39.
  12. Bernot MJ, Sobota DJ, Hall RO, Mulholland PJ, Dodds WK, Webster JR, Tank JL, Ashkenas LR, Cooper LW, Dahm CN, Gregory SV, Grimm NB, Hamilton SK, Johnson SL, McDowell WH, Meyer JL, Peterson B, Poole GC, Maurice Valett HM, Arango C, Beaulieu JJ, Burgin AJ, Crenshaw C, Helton AM, Johnson L, Merriam J, Niederlehner BR, O’Brien JM, Potter JD, Sheibley RW, Thomas SM, Wilson K. 2010. Inter-regional comparison of land-use effects on stream metabolism. Freshw Biol 55:1874–90.CrossRefGoogle Scholar
  13. Bott TL. 2007. Primary productivity and community respiration. In: Methods in stream ecology. pp 663–90.Google Scholar
  14. Bott TL, Montgomery DS, Newbold JD, Arscott DB, Dow CL, Aufdenkampe AK, Jackson JK, Kaplan LA. 2006. Ecosystem metabolism in streams of the Catskill Mountains (Delaware and Hudson River watersheds) and Lower Hudson Valley. J North Am Benthol Soc 25:1018–44. Scholar
  15. Campbell Grant EH, Lowe WH, Fagan WF. 2007. Living in the branches: population dynamics and ecological processes in dendritic networks. Ecol Lett 10:165–75.
  16. Collier KJ, Clapcott JE, Duggan IC, Hamilton DP, Hamer M, Young RG. 2013. Spatial variation of structural and functional indicators in a large New Zealand river. River Res Appl 29:1277–90.CrossRefGoogle Scholar
  17. Cressie N, Frey J, Harch B, Smith M. 2006. Spatial prediction on a river network. J Agric Biol Environ Stat 11:127–50. Scholar
  18. Detenbeck NE, Morrison AC, Abele RW, Kopp DA. 2016. Spatial statistical network models for stream and river temperature in New England, USA. Water Resour Res 52:6018–40. Scholar
  19. Dodds WK, Veach AM, Ruffing CM, Larson DM, Fischer JL, Costigan KH. 2013. Abiotic controls and temporal variability of river metabolism: multiyear analyses of Mississippi and Chattahoochee River data. Freshw Sci 32:1073–87. Scholar
  20. Dodov B, Foufoula-Georgiou E. 2004. Generalized hydraulic geometry: insights based on fluvial instability analysis and a physical model. Water Resour Res 40:1–15.Google Scholar
  21. Escoffier N, Bensoussan N, Vilmin L, Flipo N, Rocher V, David A, Métivier F, Groleau A. 2016. Estimating ecosystem metabolism from continuous multi-sensor measurements in the Seine River. Environ Sci Pollut Res . Scholar
  22. Finlay JC. 2011. Stream size and human influences on ecosystem production in river networks. Ecosphere 2:1–21. Scholar
  23. Fisher SG, Likens GE. 1973. Energy flow in Bear Brook, New Hampshire: an integrative approach to stream ecosystem metabolism. Ecol Monogr 43:421–39. Scholar
  24. Frazer G, Canham C, Lertzman K. 1999. Gap Light Analyzer (GLA), Version 2.0: Imaging software to extract canopy structure and gap light transmission indices from true-colour fisheye photographs, users manual and program documentation. Program.Google Scholar
  25. Garreta V, Monestiez P, Ver Hoef JM. 2009. Spatial modelling and prediction on river networks: up model, down model or hybrid? Environmetrics 21:439–56. Scholar
  26. González-Ferreras AM, Barquín J, Peñas FJ. 2016. Integration of habitat models to predict fish distributions in several watersheds of Northern Spain. J Appl Ichthyol 32:204–16. Scholar
  27. Graham AA, McCaughan DJ, McKee FS. 1988. Measurement of surface area of stones. Hydrobiologia 157:85–7.CrossRefGoogle Scholar
  28. Griffiths NA, Tank JL, Royer TV, Roley SS, Rosi-marshall EJ, Whiles MR, Beaulieu JJ, Johnson LT. 2013. Agricultural land use alters the seasonality and magnitude of stream metabolism. Limnol Oceanogr 58:1513–29.CrossRefGoogle Scholar
  29. Hall RO. 2016. Metabolism of streams and rivers: estimation, controls and application. In: Stream ecosystems in a changing environment. pp 151–80.Google Scholar
  30. Hall RO, Tank JL, Baker MA, Rosi-Marshall EJ, Hotchkiss ER. 2016. Metabolism, gas exchange, and carbon spiraling in rivers. Ecosystems 19:73–86. Scholar
  31. Hauer FR, Lamberti GA. 2007. Methods in stream ecology. 2nd edn. Cambridge: Academic Press.Google Scholar
  32. Isaak DJ, Ver Hoef JM, Peterson EE, Horan DL, Nagel DE. 2017. Scalable population estimates using spatial-stream-network (SSN) models, fish density surveys, and national geospatial database frameworks for streams. Can J Fish Aquat Sci 74:147–56. Scholar
  33. Isaak DJ, Peterson EE, Ver Hoef JM, Wenger SJ, Falke JA, Torgersen CE, Sowder C, Steel EA, Fortin M-J, Jordan CE, Ruesch AS, Som N, Monestiez P. 2014. Applications of spatial statistical network models to stream data. Wiley Interdiscip Rev Water 1:277–94. Scholar
  34. Lovett GM, Cole JJ, Pace ML. 2006. Is net ecosystem production equal to ecosystem carbon accumulation? Ecosystems 9:152–5. Scholar
  35. Marcarelli AM, Baxter CV, Mineau MM, Hall RO. 2011. Quantity and quality: unifying food web and ecosystem perspectives on the role of resource subsidies in freshwaters. Ecology 92:1215–25.CrossRefPubMedGoogle Scholar
  36. Marsha A, Steel EA, Fullerton AH, Sowder C. 2018. Monitoring riverine thermal regimes on stream networks: insights into spatial sampling designs from the Snoqualmie River, WA. Ecol Indic 84:11–26.
  37. McCluney KE, Poff NL, Palmer MA, Thorp JH, Poole GC, Williams BS, Williams MR, Baron JS. 2014. Riverine macrosystems ecology: sensitivity, resistance, and resilience of whole river basins with human alterations. Front Ecol Environ 12:48–58. Scholar
  38. McGuire KJ, Torgersen CE, Likens GE, Buso DC, Lowe WH, Bailey SW. 2014. Network analysis reveals multiscale controls on streamwater chemistry. Proc Natl Acad Sci 111:7030–5. Scholar
  39. McTammany ME, Webster JR, Benfield EF, Neatrour M a. 2003. Longitudinal patterns of metabolism in a southern Appalachian river. J North Am Benthol Soc 22:359–70.
  40. Melching CS, Flores HE. 1999. Reaeration equations derived from U.S. Geological Survey Database. J Environ Eng 125:407–14.
  41. Meyer JL, Edwards RT. 1990. Ecosystem metabolism and turnover of organic carbon along a blackwater river continuum. Ecology 71:668–77.CrossRefGoogle Scholar
  42. Money E, Carter GP, Serre ML. 2009. Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey. Water Res 43:1948–58.
  43. Neill AJ, Tetzlaff D, Strachan NJC, Hough RL, Avery LM, Watson H, Soulsby C. 2018. Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment. Sci Total Environ 612:840–52.
  44. O’Donnell D, Rushworth A, Bowman AW, Marian Scott E, Hallard M. 2014. Flexible regression models over river networks. J R Stat Soc Ser C Appl Stat 63:47–63.CrossRefPubMedGoogle Scholar
  45. Palmer MA, Febria CM. 2012. The Heartbeat of Ecosystems. Science 80:336.
  46. Peñas FJ, Barquín J, Snelder TH, Booker DJ, Álvarez C. 2014. The influence of methodological procedures on hydrological classification performance. Hydrol Earth Syst Sci 18:3393–409.
  47. Peterson EE, Ver Hoef JM. 2010. A mixed-model moving-average approach to geostatistical modeling in stream networks. Ecology 91:644–51. Scholar
  48. Peterson EE, Ver Hoef JM. 2014. STARS : An ArcGIS toolset used to calculate the spatial information needed to fit spatial statistical models to stream network data. J Stat Softw 56:1–17.
  49. Rivas-Martínez S, Penas A, Díaz TE. 2004. Bioclimatic & Biogeographic Maps of Europe.
  50. Rodríguez-Castillo T, Barquín J, Álvarez-Cabria M, Peñas FJ, Álvarez C. 2017. Effects of sewage effluents and seasonal changes on the metabolism of three Atlantic rivers. Sci Total Environ 599–600:1108–18.
  51. Rodriguez-Iturbe I, Rinaldo A. 1997. Fractal river basins: chance and self-organization.Google Scholar
  52. Rodriguez-Iturbe I, Rinaldo A, Rigon R, Bras RL, Ijjasz-Vasquez E, Marani A. 1992. Fractal structures as least energy patterns: the case of river networks. Geophys Res Lett 19:889–92.
  53. Saunders WC, Bouwes N, McHugh P, Jordan CE. 2018. A network model for primary production highlights linkages between salmonid populations and autochthonous resources. Ecosphere 9:e02131. Scholar
  54. Scown MW, McManus MG, Carson JH, Nietch CT. 2017. Improving predictive models of in-stream phosphorus concentration based on nationally-available spatial data coverages. JAWRA J Am Water Resour Assoc 53:944–60. Scholar
  55. Sinsabaugh RL, Repert D, Weiland T, Golladay SW, Linkins AE. 1991. Exoenzyme accumulation in epilithic biofilms. Hydrobiologia 222:29–37.CrossRefGoogle Scholar
  56. Smith RM, Kaushal SS. 2015. Carbon cycle of an urban watershed: exports, sources, and metabolism. Biogeochemistry 126:173–95.CrossRefGoogle Scholar
  57. Steinman AD, Lamberti G a., Leavitt PR. 2007. Biomass and pigments of benthic algae. In: Methods in stream ecology. pp 357–79.Google Scholar
  58. Thorp JH, Thoms MC, Delong MD. 2006. The riverine ecosystem synthesis: biocomplexity in river networks across space and time. River Res Appl 22:123–47.CrossRefGoogle Scholar
  59. Thyssen N, Erlandsen M, Jeppesen E, Holm TF. 1983. Modelling the reaeration capacity of low-land streams dominated by submerged macrophytes. Dev Environ Modell 5:861–7.Google Scholar
  60. Uehlinger U. 2006. Annual cycle and inter-annual variability of gross primary production and ecosystem respiration in a floodprone river during a 15-year period. Freshw Biol 51:938–50. Scholar
  61. US-Geological-Survey. 2011. Office of Water Quality Technical Memorandum 2011.03 - Subject: Change to Solubility Equations for Oxygen in Water.Google Scholar
  62. Val J, Chinarro D, Pino MR, Navarro E. 2016a. Global change impacts on river ecosystems: a high-resolution watershed study of Ebro river metabolism. Sci Total Environ 569:774–83.CrossRefPubMedGoogle Scholar
  63. Val J, Pino R, Navarro E, Chinarro D. 2016b. Addressing the local aspects of global change impacts on stream metabolism using frequency analysis tools. Sci Total Environ 569–570:798–814.
  64. Vannote RL, Minshall GW, Cummins KW, Sedell JR, Cushing CE. 1980. The river continuum concept. Can J Fish Aquat Sci.Google Scholar
  65. Ver Hoef JM, Peterson E, Theobald D. 2006. Spatial statistical models that use flow and stream distance. Environ Ecol Stat 13:449–64. Scholar
  66. Ver Hoef JM, Peterson EE. 2010. A moving average approach for spatial statistical models of stream networks. J Am Stat Assoc 105:6–18.CrossRefGoogle Scholar
  67. Ver Hoef JM, Peterson EE, Clifford D, Shah R. 2014. SSN : an R package for spatial statistical modeling on stream networks. J Stat Softw 56:1–45.
  68. Xu J, Yin W, Ai L, Xin X, Shi Z. 2016. Spatiotemporal patterns of non-point source nitrogen loss in an agricultural catchment. Water Sci Eng 9:125–33.CrossRefGoogle Scholar
  69. Young RG, Huryn AD. 1996. Interannual variation in discharge controls ecosystem metabolism along a grassland river continuum. Can J Fish Aquat Sci 53:2199–211.
  70. Young RG, Matthaei CD, Townsend CR. 2008. Organic matter breakdown and ecosystem metabolism: functional indicators for assessing river ecosystem health. J North Am Benthol Soc 27:605–25. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Environmental Hydraulics InstituteUniversidad de CantabriaSantanderSpain

Personalised recommendations