Modeling the coupled dynamics of stream metabolism and microbial biomass

Tom Ian Battin, Enrico Bertuzzo, Pier Luigi Segatto
2020
Journal paper

Abstract

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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https://infoscience.epfl.ch/record/274398?ln=en

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Tom Ian Battin, Enrico Bertuzzo, Pier Luigi Segatto
2020
Journal paper

Abstract

Official source

https://infoscience.epfl.ch/record/274398?ln=en

About this result

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Related publications

Modeling of Stream Metabolism from Reach-scale to the Scale of Entire River Networks

Pier Luigi Segatto

Streams are key components of the global carbon (C) cycle. The river network conceptualization moved from the 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.

EPFL2021

Stream ecosystem metabolism integrates production and respiration of organic matter and plays a fundamental role in the global carbon (C) cycle. Several studies have identified distal and proximal physical controls, for example, land use and transient storage, or the effects of water chemistry, that is, organic matter and nutrient availability, on stream metabolism. In parallel, research on organic matter quality has identified conspicuous gradients of chemical composition, yet mostly without demonstrating any functional implications. 2. We hypothesise that organic matter holds a key position in a more comprehensive causal framework of stream ecosystem metabolism, and that a concurrent study can improve mechanistic understanding. Specifically, we here postulate that dissolved organic matter (DOM) quality, that is, its chemical composition, acts as a control of ecosystem respiration (ER) as much as it is a result of gross primary production (GPP). As such, DOM quality likely forms a central link between land use and stream metabolism, besides known physical controls including transient storage and light availability. 3. To examine these hypotheses, we studied 33 streams in north-eastern Austria, a region with diverse land use ranging from semi-natural, forested areas to agricultural areas and settlements. We analysed DOM composition by absorbance and fluorescence spectroscopy, including modelling excitation–emission matrices with parallel factor analysis. We then opposed these data to GPP and ER estimated by fitting a metabolism model to single-station diurnal oxygen records. 4. Structural equation modelling revealed land use as a control on light conditions, DOM composition and concentration and nutrient concentrations, which together ultimately shaped GPP and ER. In particular, humified, coloured and aromatic DOM of predominantly terrestrial origin was prevalent in coniferous forest catchments and increased stream ER. Agricultural and urban areas enriched streams with phosphorous and nitrogen, which increased ER and GPP. Besides nutrients, GPP seemed to be weakly correlated with light availability and – in contrast to our hypothesis – left only a weak imprint on DOM composition. 5. Land-use change is rated as the most pervasive human influence on natural ecosystems and our results highlight its impact on aquatic GPP and ER in streams. To understand the role of inland waters in the global C cycle will require mechanistic understanding of ecosystem metabolism, which notably includes organic matter quality as a hitherto underappreciated key player.

Wiley-Blackwell2017

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Streams and rivers form dense networks that drain the terrestrial landscape and are relevant for biodiversity dynamics, ecosystem functioning, and transport and transformation of carbon. Yet, resolving in both space and time gross primary production (GPP), ecosystem respiration (ER) and net ecosystem production (NEP) at the scale of entire stream networks has been elusive so far. Here, combining Random Forest (RF) with time series of sensor data in 12 reach sites, we predicted annual regimes of GPP, ER, and NEP in 292 individual stream reaches and disclosed properties emerging from the network they form. We further predicted available light and thermal regimes for the entire network and expanded the library of stream metabolism predictors. We found that the annual network-scale metabolism was heterotrophic yet with a clear peak of autotrophy in spring. In agreement with the River Continuum Concept, small headwaters and larger downstream reaches contributed 16% and 60%, respectively, to the annual network-scale GPP. Our results suggest that ER rather than GPP drives the metabolic stability at the network scale, which is likely attributable to the buffering function of the streambed for ER, while GPP is more susceptible to flow-induced disturbance and fluctuations in light availability. Furthermore, we found large terrestrial subsidies fueling ER, pointing to an unexpectedly high network-scale level of heterotrophy, otherwise masked by simply considering reach-scale NEP estimations. Our machine learning approach sheds new light on the spatiotemporal dynamics of ecosystem metabolism at the network scale, which is a prerequisite to integrate aquatic and terrestrial carbon cycling at relevant scales.

2021

Modeling of Stream Metabolism from Reach-scale to the Scale of Entire River Networks

Pier Luigi Segatto

EPFL2021

Stream ecosystem metabolism integrates production and respiration of organic matter and plays a fundamental role in the global carbon (C) cycle. Several studies have identified distal and proximal physical controls, for example, land use and transient storage, or the effects of water chemistry, that is, organic matter and nutrient availability, on stream metabolism. In parallel, research on organic matter quality has identified conspicuous gradients of chemical composition, yet mostly without demonstrating any functional implications. 2. We hypothesise that organic matter holds a key position in a more comprehensive causal framework of stream ecosystem metabolism, and that a concurrent study can improve mechanistic understanding. Specifically, we here postulate that dissolved organic matter (DOM) quality, that is, its chemical composition, acts as a control of ecosystem respiration (ER) as much as it is a result of gross primary production (GPP). As such, DOM quality likely forms a central link between land use and stream metabolism, besides known physical controls including transient storage and light availability. 3. To examine these hypotheses, we studied 33 streams in north-eastern Austria, a region with diverse land use ranging from semi-natural, forested areas to agricultural areas and settlements. We analysed DOM composition by absorbance and fluorescence spectroscopy, including modelling excitation–emission matrices with parallel factor analysis. We then opposed these data to GPP and ER estimated by fitting a metabolism model to single-station diurnal oxygen records. 4. Structural equation modelling revealed land use as a control on light conditions, DOM composition and concentration and nutrient concentrations, which together ultimately shaped GPP and ER. In particular, humified, coloured and aromatic DOM of predominantly terrestrial origin was prevalent in coniferous forest catchments and increased stream ER. Agricultural and urban areas enriched streams with phosphorous and nitrogen, which increased ER and GPP. Besides nutrients, GPP seemed to be weakly correlated with light availability and – in contrast to our hypothesis – left only a weak imprint on DOM composition. 5. Land-use change is rated as the most pervasive human influence on natural ecosystems and our results highlight its impact on aquatic GPP and ER in streams. To understand the role of inland waters in the global C cycle will require mechanistic understanding of ecosystem metabolism, which notably includes organic matter quality as a hitherto underappreciated key player.

Wiley-Blackwell2017