Estimating Ecosystem Metabolism to Entire River Networks

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.