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Estimating and interpreting ecosystem metabolism remains an important challenge in stream ecology. Here, we propose a novel approach to model, estimate, and predict multiseasonal patterns of stream metabolism (gross primary production [GPP] and ecosystem respiration [ER]) at the reach scale leveraging on increasingly available long‐term, high‐frequency measurements of dissolved oxygen (DO). The model uses DO measurements to estimate the parameters of a simple ecosystem model describing the underlying dynamics of stream autotrophic and heterotrophic microbial biomass. The model has been applied to four reaches within the Ybbs river network, Austria. Even if microbial biomasses are not observed, that is, they are treated as latent variables, results show that by accounting for the temporal dynamics of biomass, the model reproduces variability in metabolic fluxes that is not explained by fluctuations of light, temperature, and resources. The model is particularly data‐demanding: to estimate the 11 parameters used in this formulation, it requires sufficiently long, for example, annual, time series, and significant scouring events. On the other hand, the approach has the potential to separate ER into its autotrophic and heterotrophic components, estimate a richer set of ecosystem carbon fluxes (i.e., carbon uptake, loss, and scouring), extrapolate metabolism estimates for periods when DO measurements are unavailable, and predict how ecosystem metabolism would respond to variations of the driving forces. The model is seen as a building block to develop tools to fully appreciate multiseasonal patterns of metabolic activity in river networks and to provide reliable estimations of carbon fluxes from land to ocean.
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pipelineâ idea, in which C is solely transported, to the
active ecosystemâ concept, in which both terrestrial derived and autochthonous C are processed and transported. Functioning, at ecosystem level, is encompassed by ``Ecosystem Metabolismâ which measures production (GPP) and respiration (ER) of organic matter within a stream reach. Proper estimation of GPP and ER is relevant as it bridges the terrestrial and aquatic C cycles through lateral carbon fluxes, and informs on the biogeochemical connectivity downstream and throughout fluvial networks. Whole-ecosystem metabolism has received considerable attention over the last decades. However, even though ecosystem metabolism and biomass dynamics are tightly coupled, these two components have often been studied separately. Moreover, despite the natural link between ecosystem-level processes in flowing waters, and the inherent heterogeneity of all metabolic drivers at the catchment scale, no study has attempted to upscale GPP and ER from different and separated river reaches to an entire river network, continuously, both in time and space.
In this perspective, this Thesis develops a multi-scale modeling framework for the investigation of riverine metabolism. In particular, the Thesis provides, firstly, a novel process based reach-scale model able to locally estimate metabolic fluxes based on measurements of dissolved oxygen (DO), by coupling DO dynamics with two non-observed state variables describing the temporal evolution of autotrophic and heterotrophic benthic biomass. The model integrates current knowledge on metabolic drivers and poses itself as a complementary tool to the widely employed single station approach. Furthermore, this Thesis illustrates the potential of machine learning in dealing with the upscale problem. Random forest has been used to extrapolate both in time and space reach-scale estimates of ecosystem metabolism to the scale of an entire stream network. This approach has been instrumental to establish annual regimes across an entire river network, to quantify the importance of metabolic drivers, to assess the relative contributions from small and large streams, and to disclose properties emerging from the network they form. Metabolic stability, allochthony, and scaling induced by the stream network have been investigated. Furthermore, the same algorithm has been applied to local observations of streamwater temperature and photosynthetic active radiation which made them available over the entire river network. This allowed us to couple the climatic regime with the reach-scale ecosystem model and integrate network structure, land cover, and the hydrological regime. We thus developed a process-based spatially-distributed framework able to simulate the ecological functioning of organic matter (and thus ecosystem metabolism) at network-scale, to investigate the variability with which ecosystems use available energy throughout the river network, and to directly assess the effect of climatic and hydrological change.
We leveraged on data availability in the Ybbs river network (Austria), to evaluate the models' capability in reproducing local patterns of GPP and ER. The case study confirmed the reliability of the model frameworks and provided empirical evidence for long-standing theory predicting shifts of ecosystem metabolism along the stream continuum.Tom Ian Battin, Enrico Bertuzzo, Pier Luigi Segatto