Ecosystems https://doi.org/10.1007/s10021-018-0311-8 Ó 2018 Springer Science+Business Media, LLC, part of Springer Nature Estimating Ecosystem Metabolism to Entire River Networks Tamara Rodrı´guez-Castillo,* Edurne Este´vez, Alexia Marı´a Gonza´lez-Ferreras, and Jose´ Barquı´n Environmental Hydraulics Institute, Universidad de Cantabria, Avda. Isabel Torres, 15, Parque Cientı´fico y Tecnolo´ gico de Cantabria, 39011 Santander, Spain ABSTRACT River ecosystem metabolism (REM) is a promising discharges and to different human activities (de- cost-effective measure of ecosystem functioning, as forestation or sewage outflows) that disrupt this it integrates many different ecosystem processes pattern. GPP was better explained by a combina- and is affected by both rapid (primary productivity) tion of ecosystem size, nitrate concentration and and slow (organic matter decomposition) energy amount of benthic chlorophyll a, whereas ER was channels of the riverine food web. We estimated better explained by spatial patterns of GPP plus REM in 41 river reaches in Deva-Cares catchment minimum water temperatures. The presented (northern Spain) during the summer period. We methodological approach improves REM predic- used oxygen mass-balance techniques in which tions for river networks compared to currently used primary production and ecosystem respiration were methods and provides a good framework to orien- calculated from oxygen concentration daily curves. tate spatial measures for river functioning restora- Then, we used recently developed spatial statistical tion and for global change mitigation. To reduce methods for river networks based on covariance uncertainty and model errors, a higher density of structures to model REM to all river reaches within sampling points should be used and especially in the river network. From the observed data and the the smaller tributaries. modeled values, we show how REM spatial pat- terns are constrained by different river reach Key words: spatial modeling; river ecosystem characteristics along the river network. In general, metabolism; primary production; ecosystem respi- the autotrophy increases downstream, although ration; ecosystem functioning; river network; vir- there are some reaches associated to groundwater tual watershed; SSN model. INTRODUCTION Ecosystem metabolism represents a cornerstone for ecosystem ecology as it includes the total interre- lated fluxes that fix (primary production) and Received 6 July 2018; accepted 25 September 2018 Electronic supplementary material: The online version of this article mineralize (respiration) organic carbon (C) of all (https://doi.org/10.1007/s10021-018-0311-8) contains supplementary autotrophic and heterotrophic organisms in an material, which is available to authorized users. ecosystem (Hall 2016). Therefore, river ecosystem Author contributions: TR-C designed the study, performed the field surveys, analyzed the data, contributed new methods and wrote the metabolism (REM) that fluctuate along river net- paper. EE participated in the design of the study and field surveys, and works is a promising cost-effective measure of helped draft the paper. AMG-F participated in the field surveys and data ecosystem functioning, as it integrates many dif- analysis, and helped draft the paper. JB designed and coordinated the study, performed the field surveys and helped draft the paper. ferent ecosystem processes and is affected by both *Corresponding author; e-mail: [email protected] T. Rodrı´guez-Castillo and others rapid (primary productivity) and slow (organic 2016a, b) and it will assist on prioritizing river matter decomposition) energy channels of the reaches for ecosystem functioning restoration riverine food web. (Palmer and Febria 2012). REM is mainly represented by two metabolic In recent years, there has been a great develop- processes, gross primary production (GPP) and ment and improvement of spatial statistical models ecosystem respiration (ER). GPP is the total C fixed (Ver Hoef and others 2006; Ver Hoef and Peterson by photosynthetic and chemosynthetic aquatic 2010; Isaak and others 2014; O’Donnell and others organisms, and ER is the organic C mineralization 2014). Spatial statistical models resemble tradi- by all autotrophic and heterotrophic aquatic tional linear models, but allow spatial autocorrela- organisms. The balance of these two processes, tion in the random errors (Peterson and Ver Hoef considering ER as a negative flux, is net daily me- 2010). Local deviations from the mean are modeled tabolism (NDM). NDM is positive when the using the covariance between neighboring sites, ecosystem is accumulating or exporting organic C which represents the strength of spatial autocor- and negative when it is importing organic C (Lovett relation between two sites given their separation and others 2006). Most studies estimating REM distance (that is, Euclidean distance). These type of have dealt with river reach scales focusing on an- models have been frequently applied to terrestrial nual estimates (for example, Uehlinger 2006; ecosystems, but in river ecosystems river reaches Dodds and others 2013) or changes along distur- are hierarchically structured in a network, with bance gradients (for example, Collier and others nested catchments and river reaches connected by 2013; Aristi and others 2014; Rodrı´guez-Castillo flow (Campbell Grant and others 2007). and others 2017). However, no studies have at- Thus, Euclidean distances might be insufficient tempted to upscale REM estimates from different to characterize the spatial dependency of river and separated river reaches to a whole continuous reaches, being necessary to include covariance river network, with the exception of Saunders and matrices based on hydrologic distances (that is, others (2018), who only estimated GPP for a river distance along the river network between two network using a multiple linear regression model. locations). This takes into account the flow-con- Achieving this will allow improving our knowledge nected and flow-unconnected relationships, about the spatial complexity of biogeochemical depending on whether or not two points in a river patterns and processes in freshwater ecosystems network share flow (Peterson and Ver Hoef 2010; (McGuire and others 2014). Ver Hoef and Peterson 2010). The use of spatial In addition, producing estimates of REM to entire stream network (SSN) models based on both Eu- river networks will be very important from both a clidean (2D scale) and hydrologic (stream network theoretical and an applied point of view. Theoret- scale) distances can represent the spatial configu- ically, REM has long been hypothesized to be het- ration, longitudinal connectivity, discharge, or flow erotrophic (ER > GPP) in headwaters, changing direction characteristics of a river network (Cressie toward autotrophy as we move downstream (mid- and others 2006; Ver Hoef and others 2006), order reaches) and reverting the pattern in large increasing accuracy and validity of statistical rivers (Vannote and others 1980). Current empiri- inferences. SSN models have been successfully ap- cal studies based on river reach estimates of REM plied to model different river ecosystem compo- demonstrate this (McTammany and others 2003; nents such as water temperature (for example, Hall and others 2016), although there are others Isaak and others 2014; Ashley Steel and others that have found different patterns (Meyer and 2016), water chemistry variables (for example, Ver Edwards 1990; Young and Huryn 1996). In this Hoef and Peterson 2010; Scown and others 2017) regard, characterizing REM spatial patterns at a and fish populations (for example, Isaak and others whole river network scale would be very valuable 2017). However, none has yet tried to model river to better understand the drivers determining spatial functioning rates, as ecosystem metabolism, using patterns of river ecosystem functioning (see Thorp this type of models. and others 2006; McCluney and others 2014). In this study, we aim to (1) explore GPP and ER Moreover, this will also allow estimating the het- spatial patterns and the main factors controlling erotrophic–autotrophic balance of a whole river these ecosystem processes at a river network scale; network integrating REM rates and areas of all the and to (2) model GPP and ER rates for all reaches of different river reaches. From an applied point of the river network, obtaining the metabolic balance view, these advances will help improving carbon for the whole river network. Our expectations are circulation models and understanding the effects of that GPP and ER will increase downstream fol- global change on river functioning (Val and others lowing the expected increase in their most likely River Network Metabolism control factors: light reaching the streambed and The ‘‘Picos de Europa’’ National Park conditions the chlorophyll a concentration in the benthic zone for catchment geomorphology, with deep and narrow GPP; and GPP, river ecosystem biomass and water valleys describing sharp curves (that is, boxed temperature for ER (Young and others 2008; Ber- meanders). Sandstones and shales predominate in not and others 2010; Beaulieu and others 2013). the upper-middle area of the Deva River. In the rest We believe that the overall river network metabolic of the catchment, limestone and karst formations balance will be heterotrophic, as the selected river dominate. The average altitude is about 1100 m. catchment has a very well developed forest that The climate is quite variable within the