Microorganisms are key drivers of biogeochemical cycles in natural environments. Microbially mediated biogeochemical reactions are influenced by both biotic and abiotic factors, including microbe-microbe interactions, enzyme kinetics, and chemical traits. To address the impact of chemical substrates on biogeochemical reactions, this chapter provides guidance on substrate-explicit thermodynamic modeling (SXTM) (also known as lambda modeling). SXTM enables automatic formulation of stoichiometric and kinetic models of biogeochemistry from the chemical formulas of organic matter (OM). This approach is particularly useful for formulating biogeochemical reaction models from ultra-high-resolution mass spectrometry data that identify thousands of compounds with distinct molecular formulas. Regardless of the complexity of OM data, SXTM requires only two parameters, maximum growth rate (μmax) and harvest volume (Vh), thereby avoiding the issue of overparameterization caused when one attempts to include a large set of chemical compounds. While the original formulation has been demonstrated with a focus on aerobic respiration, recent work has extended its scope to various other forms of electron acceptors. Here, we provide a tutorial on formulating biogeochemical reaction models using SXTM, with river corridor OM data collected by the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium as an example. The software packages for implementing SXTM are available in two languages, Python and R, and are referred to as LambdaPy and LambdaR, respectively. These tools will significantly facilitate modeling of complex OM pools in microbially driven biogeochemical cycling and will help improve our understanding of the interplay among microbes, enzymes, and OM when integrated with complementary approaches, including microbial- and enzyme-explicit modeling.

Abstract. Distributed data generation, or data collected from multiple sources and locations using standardized approaches and involving coordination among investigators, has emerged as a powerful approach to meet contemporary demands for scalable environmental knowledge. However, practitioners often lack guidance on best practices for distributed data generation, and a framework classifying its modalities is missing. To address these gaps, we developed a conceptual framework organizing distributed data generation along two axes: participant-based (ranging from highly formalized to highly flexible) and method-based (from experimental to observational). This framework provides common vocabulary across modalities and describes how different approaches affect data generation logistics and outcomes. We propose operational best practices across three critical pillars: outreach, operations, and output (i.e., publications, data), leveraging lessons learned from over 35 existing distributed data projects. Lastly, we explore how emerging artificial intelligence (AI) capabilities may help address longstanding challenges in distributed data generation, including in coordination, adaptive sampling, and cross-project data integration. This perspective provides strategies and identifies opportunities to advance distributed data generation for addressing pressing biogeochemical, environmental, and societal challenges. We underscore the transformative potential of distributed data generation for modern, broad-scale environmental research, and provide guidance on how to realize that potential.

Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial growth and metabolism. Despite promising demonstrations in the past, computational inefficiency has been pointed out as a critical issue to overcome because it requires repeated application of linear programming (LP) to obtain flux balance analysis (FBA) solutions in every time step and spatial grid. To address this challenge, we propose a new simulation method where we train and validate artificial neural networks (ANNs) using randomly sampled FBA solutions and incorporate the resulting surrogate FBA model (represented as algebraic equations) into RTMs as source/sink terms. We demonstrate the efficiency of our method via a case study of Shewanella oneidensis MR-1. During aerobic growth on lactate, S. oneidensis produces metabolic byproducts (such as pyruvate and acetate), which are subsequently consumed as alternative carbon sources when the preferred nutrients are depleted. To effectively simulate these complex dynamics, we used a cybernetic approach that models metabolic switches as the outcome of dynamic competition among multiple growth options. In both zero-dimensional batch and one-dimensional column configurations, the ANN-based surrogate models achieved substantial reduction of computational time by several orders of magnitude compared to the original LP-based FBA models. Moreover, the ANN models produced robust solutions without any special measures to prevent numerical instability. These developments significantly promote our ability to utilize genome-scale networks in complex, multi-physics, and multi-dimensional ecosystem modeling.

Continuous measurement and monitoring of surface water coverage in non-perennial streams are essential for understanding the exchange fluxes between surface and subsurface waters under both inundated and noninundated conditions. In this study, a wildlife camera photo-based framework was developed to monitor small stream water inundation, depth, discharge, and velocity. Two advanced machine learning models, YOLOv8 and Mask2Former, were utilized to efficiently analyze images captured by wildlife cameras. The accuracy of the framework was validated against on-site depth measurements at six sites in the Yakima River Basin, along with the gage height, discharge, and velocity data from four USGS sites. This approach facilitates long-term, continuous monitoring and quantification of river intermittency and water availability with high precision and low cost, thereby advancing river ecosystem research and management.

Structure and dynamics of turbulent open channel flow over permeable and impermeable sediment beds are investigated using pore-resolved, direct numerical simulations. Time-space double-averaged statistics are computed in four configurations: (i) permeable bed with randomly packed sediment grains, (ii) an impermeable wall with full layer of roughness elements matching the top layer of the sediment bed, (iii) an impermeable wall with half layer of roughness elements, and (iv) a smooth wall. It is observed that the mean velocity, Reynolds stresses, and form-induced pressure–velocity correlations representing ejection and sweep fluxes are similar in magnitude for the permeable-bed and impermeable full-layer cases. The wall-blocking effect present in the impermeable half layer results in higher streamwise and lower wall-normal stresses compared to the permeable bed. Bed roughness increases Reynolds shear stress, whereas permeability has minimal influence. However, bed permeability significantly influences form-induced shear stress. Pressure fluctuations and volume-averaged bed-normal distribution of the drag force peak in the top layer of the bed. These findings suggest that reach-scale transport in the hyporheic zone will be better captured by providing boundary conditions based on stream flow simulations that incorporate the roughness effect of the top layer of the bed.

Shifting phosphorus (P) dynamics after wildfires can have cascading impacts from terrestrial to aquatic environments. However, it is unclear whether shifts in P composition or P concentration are responsible for changes in P dynamics post-fire. We used laboratory leaching experiments of Douglas fir forest and sagebrush shrubland chars to examine how the potential mobility of P compounds is influenced by different burn severities. Burning produced a 6.9- and 29-fold increase in particulate P mobilization but a 3.8- and 30.5-fold decrease in aqueous P released for Douglas fir forest and sagebrush shrubland, respectively. The mechanisms driving particulate- and dissolved-phase P compound mobilization were contrasting. Phosphorus compound mobilization in the particulate phase was controlled by solid char total P concentrations, while the aqueous phase was driven by solubility changes of molecular species. Nuclear magnetic resonance (NMR) and X-ray absorption near-edge structure (XANES) on the solid chars indicated that organic orthophosphate monoester and diester species were thermally mineralized to inorganic P moieties with burning in both vegetation types, which decreases P solubility. This coincided with the production of calcium- and magnesium-bound inorganic P compounds. With increasing burn severity there were systematic shifts in P concentration and composition - higher-severity chars mobilized P compounds in the particulate phase, although the magnitude of change was vegetation-specific. Our results indicate a post-fire transformation to both the composition of the solid charred material and how P compounds are mobilized, which may influence its environmental cycling and fate.

The structure, function, and dynamics of Earth's terrestrial ecosystems are profoundly influenced by how often (frequency) and how long (duration) they are inundated with water. A diverse array of natural and human-engineered systems experience temporally variable inundation whereby they fluctuate between inundated and non-inundated states. Variable inundation spans extreme events to predictable sub-daily cycles. Variably inundated ecosystems (VIEs) include hillslopes, non-perennial streams, wetlands, floodplains, temporary ponds, tidal systems, storm-impacted coastal zones, and human-engineered systems. VIEs are diverse in terms of inundation regimes, water chemistry and flow velocity, soil and sediment properties, vegetation, and many other properties. The spatial and temporal scales of variable inundation are vast, ranging from sub-meter to whole landscapes and from sub-hourly to multi-decadal. The broad range of system types and scales makes it challenging to predict the hydrology, biogeochemistry, ecology, and physical evolution of VIEs. Despite all experiencing the loss and gain of an overlying water column, VIEs are rarely considered together in conceptual, theoretical, modeling, or measurement frameworks and approaches. Studying VIEs together has the potential to generate mechanistic understanding that is transferable across a much broader range of environmental conditions, relative to knowledge generated by studying any one VIE type. We postulate that enhanced transferability will be important for predicting changes in VIE function in response to global change. Here we aim to catalyze cross-VIE science that studies drivers and impacts of variable inundation across Earth's VIEs. To this end, we complement expert mini-reviews of eight major VIE systems with overviews of VIE-relevant methods and challenges associated with scale. We conclude with perspectives on how cross-VIE science can derive transferable understanding via unifying conceptual models in which the impacts of variable inundation are studied across multi-dimensional environmental space.

The transport of motile bacteria in porous media is highly relevant to many fields, ranging from ecology to human health. Still, critical gaps remain in our understanding of the impacts of hydrodynamics and pore structure on bacterial transport. Here, we present direct visualizations of three species of motile bacteria under variable flow rates and porosities. We find that at higher flow rates, motility is less critical to the transport of bacteria, as motion is controlled by hydrodynamic advection, making it difficult for bacteria to move across streamlines. We show that this lack of motion across streamlines results in increased velocity autocorrelation and bacterial spreading in the direction of flow. Furthermore, we find that transport of bacteria with different motility types are impacted by flow rates to different extents. At low flow rates, the transport of bacteria with pili-mediated twitching motility is strongly controlled by advection, whereas bacteria with flagella still display active motility. At higher flow rates, we show that bacteria with peritrichous flagella maintain their motility characteristics to a greater degree than bacteria with pili or monotrichous flagella. We also examine experimental net speeds of bacteria in relation to the simulated flow fields and find that the interactions between hydrodynamics, motility, and porous media geometry lead to oversampling of medium-velocity regions of a pore network by all three species. The study presents new perspectives on how different types of motile bacteria are transported and dispersed in porous media aided by strength of differentially advecting fluid.

Understanding aquatic ecosystem metabolism involves the study of two key processes: carbon fixation via primary production and organic C mineralization as total ecosystem respiration (ERtot). In streams and rivers, ERtot includes respiration in the water column (ERwc) and in the sediments (ERsed). While literature surveys suggest that ERsed is often a dominant contributor to ERtot, recent studies indicate that the relative influence of sediment-associated processes versus water column processes can fluctuate along the river continuum. Still, a comprehensive understanding of the factors contributing to these shifts within basins and across stream orders is needed. Here, we contribute to this need by measuring ERwc and aqueous chemistry across 47 sites in the Yakima River basin, Washington, USA. We find that ERwc rates vary throughout the basin during baseflow conditions, ranging from 0 to −7.38 g O2 m−3 d−1, and encompass the entire range of ERwc rates from previous work. Additionally, by comparing to ERtot estimates for rivers across the contiguous United States, we suggest that the contribution of ERwc rates to reach-scale ERtot rates across the Yakima River basin is likely highly variable, but we do not test this directly. We observe that ERwc is locally controlled by temperature, dissolved organic carbon, total dissolved nitrogen, and total suspended solids, which explains 49 % of ERwc variability across the basin using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Our findings highlight the potential relevance of water column processes in aquatic ecosystem metabolism across the entire stream network and that these influences are likely not predictable simply by knowing the position in the stream network. Our results are generally congruent with previous work in terms of locally influential variables, suggesting that the observed variability and suite of associated environmental factors influencing ERwc are potentially transferable across basins.

Soil salinization, exacerbated by climate change, poses a global threat to coastal ecosystems and soil function. Salinity affects soil carbon cycling by directly impacting microbial activity and indirectly altering soil physicochemical properties. Current models inadequately represent these complexities, relying on linear reduction functions that overlook specific physicochemical changes induced by salinity. AquaMEND addresses this gap by integrating microbial-explicit carbon decomposition modeling with advanced geochemical processes. Through the incorporation of equilibrium chemistry via PHREEQC, AquaMEND accurately predicts soil chemistry responses to salinization and enables detailed simulations on how salinity impacts microbial processes. To represent microbial responses to salinity, we developed salt-sensitive and slat-resistant response functions, with microbial activity inhibited by 50% at 4 ppt and 55 ppt, respectively. While the choice of salinity response functions influences model outcomes, simulations revealed that respiration responses to salinization varied depend on the underlying microbial mechanisms. Increased microbial mortality and impaired extracellular enzyme activity led to decreased respiration, while reduced carbon use efficiency could enhance respiration unless substrate uptake was also inhibited by high salinity. These microbial processes interact in a coordinated manner with multiple abiotic factors, collectively determining both the direction and magnitude of soil carbon responses. These findings highlight the need for novel experiments to disentangle the complex interactions governing microbial and geochemical responses to salinity. AquaMEND's capability to model such interactions offers a versatile tool for studying and predicting the effects of soil salinization on belowground carbon cycling.

Predicting heterogeneous and non-linear processes remains a fundamental challenge in Earth sciences. Here, we present an artificial intelligence (AI)-guided framework that iteratively combines predictive modeling with targeted field sampling to rapidly improve environmental predictions. We demonstrate our workflow by predicting oxygen consumption, a key process of stream metabolism, across the contiguous United States (CONUS). Our approach consisted of 18 iterative loops of measurements and models, combining distributed participatory field sampling, lab analysis, automated machine learning (ML) predictions, and error and distinctiveness analyses to autonomously guide the next sampling at optimal site locations. Through our approach, we increased the predictive power of sediment oxygen consumption across CONUS by over fifteenfold between the first and last iteration. Relative to our last sampling iteration, our first sampling missed sites with high rates and underestimated median oxygen consumption rates by 68%. In addition to identifying areas of high oxygen consumption rates, iterations enabled refinement of laboratory and data handling methods, and engagement with a broad community of field researchers. We conclude that AI-guided iterative loops between targeted sampling and predictive modeling are a powerful and efficient approach for improving predictions of heterogeneous environmental processes. ### Competing Interest Statement The authors have declared no competing interest. Office of Biological and Environmental Research, https://ror.org/0114b2m14, This research was supported by the U.S. Department of Energy (DOE), Office of Biological and Environmental Research (BER), Environmental System Science (ESS) Program as part of the River Corridor Science Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL). PNNL is operated by Battelle Memorial Institute for the DOE under Contract No. DE-AC05-76RL01830.

AbstractRiver sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi‐scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature‐rich (i.e., 100+ possible input variables) data set. Here, we present results from a two‐tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud‐based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger‐scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger‐scale features to generate data‐driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

Quantifying hydrologic exchange fluxes (HEFs) at the stream-groundwater interface and their residence time distributions (RTDs) in the subsurface are important for managing the water quality and ecosystem health in dynamic river corridors. However, field measurements and directly simulating high-spatial resolution HEFs and RTDs can be time-consuming, particularly at watershed-scale. Recent research has proposed the potential application of using hydromorphic units (HUs), which cluster the bathymetry and surface water hydrodynamics attributes, to aid in RTD estimation. However, more pioneering studies have demonstrated that underground structure and large-scale river channel geomorphology are the primary factors controlling the HEFs and RTDs. To address this contradiction, this work evaluates the HEFs and resulting HU-RTD relationships for two 10-km long river sections along the Columbia River, leveraging a one-way coupled three-dimensional transient surface-subsurface water transport modeling framework. Applying such a framework at the two river sections with similar HUs allows for quantitative comparisons of HEFs and RTDs using both statistical tests and machine learning classification models. The comparison reveals that the similarity and transferability of the HU-RTD relationship is very low for the two investigated river sections. This suggests that, creating a general algorithm to estimate RTDs in large-scale river reaches based solely on surface water hydrodynamics and shortdistance bathymetry topography data may be nearly impossible.

Hydrologic exchange processes are critical for ecosystem services along river corridors. Meandering contributes to this exchange by driving channel water, solutes, and energy through the surrounding alluvium, a process called sinuosity-driven hyporheic exchange. This exchange is embedded within and modulated by the regional groundwater flow (RGF), which compresses the hyporheic zone and potentially diminishes its overall impact. Quantifying the role of sinuosity-driven hyporheic exchange at the reach-to-watershed scale requires a mechanistic understanding of the interplay between drivers (meander planform) and modulators (RGF) and its implications for biogeochemical transformations. Here, we use a 2D, vertically integrated numerical model for flow, transport, and reaction to analyze sinuosity-driven hyporheic exchange systematically. Using this model, we propose a dimensionless framework to explore the role of meander planform and RGF in hydrodynamics and how they constrain nitrogen cycling. Our results highlight the importance of meander topology for water flow and age. We demonstrate how the meander neck induces a shielding effect that protects the hyporheic zone against RGF, imposing a physical constraint on biogeochemical transformations. Furthermore, we explore the conditions when a meander acts as a net nitrogen source or sink. This transition in the net biogeochemical potential is described by a handful of dimensionless physical and biogeochemical parameters that can be measured or constrained from literature and remote sensing. This work provides a new physically based model that quantifies sinuosity-driven hyporheic exchange and biogeochemical reactions, a critical step toward their representation in water quality models and the design and assessment of river restoration strategies. Meandering causes pressure gradients that induce water flow from the channel to the alluvial aquifer and back to the channel. This circulation process is known as sinuosity-driven hyporheic exchange, and it has traditionally been associated with ubiquitous and favorable impacts on ecosystem services. However, its presence and biogeochemical implications can vary across river networks and even result in detrimental conditions. Here, we conducted a systematic modeling study to understand the hydrodynamics of sinuosity-driven hyporheic exchange and its implications for nitrogen transformations. Our results show that the compressing effect of RGF can significantly reduce or vanish the hyporheic zone. Yet, narrow meander necks, characteristic of high-sinuosity channels, shield the hyporheic zone even under extreme regional gradients. This shielding effect has been previously ignored and highlights the persistent nature of the exchange and its resilience against external modulators. We also use our model to propose and evaluate a framework based on measurable physical and biogeochemical parameters to identify the conditions leading to a meander acting as a net source or sink of nitrogen. These mechanistic insights can guide the design and evaluation of river restoration strategies and provide a critical foundation for its representation in water quality models. We assess the role of hydrodynamic drivers and modulators in the hyporheic exchange and the biogeochemical potential of meandering rivers The meander neck in high-sinuosity channels shields the effect of regional groundwater fluxes, resulting in persistent hyporheic zones Hyporheic denitrification potential decreases with increasing sinuosity, and dissolved and particulate organic carbon availability limits it

Streambed grain sizes control river hydro-biogeochemical (HBGC) processes and functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo-driven, artificial intelligence (AI)-enabled, and theory-based workflow for extracting the quantities, distributions, and uncertainties of streambed grain sizes from photos. Specifically, we first trained You Only Look Once, an object detection AI, using 11,977 grain labels from 36 photos collected from nine different stream environments. We demonstrated its accuracy with a coefficient of determination of 0.98, a Nash-Sutcliffe efficiency of 0.98, and a mean absolute relative error of 6.65% in predicting the median grain size of 20 ground-truth photos representing nine typical stream environments. The AI is then used to extract the grain size distributions and determine their characteristic grain sizes, including the 10th, 50th, 60th, and 84th percentiles, for 1,999 photos taken at 66 sites within a watershed in the Northwest US. The results indicate that the 10th, median, 60th, and 84th percentiles of the grain sizes follow log-normal distributions, with most likely values of 2.49, 6.62, 7.68, and 10.78 cm, respectively. The average uncertainties associated with these values are 9.70%, 7.33%, 9.27%, and 11.11%, respectively. These data allow for the computation of the quantities, distributions, and uncertainties of streambed HBGC parameters, including Manning's coefficient, Darcy-Weisbach friction factor, top layer interstitial velocity magnitude, and nitrate uptake velocity. Additionally, major sources of uncertainty in grain sizes and their impact on HBGC parameters are examined.

Abstract. Aerobic respiration of organic matter is a key metabolic process influencing carbon (C) biogeochemistry in aquatic ecosystems. Anthropogenic and environmental perturbations to stream ecosystem metabolism can have deleterious effects on downstream water quality. Various environmental features of rivers also influence stream metabolism, including physical (e.g., discharge, light, flow regimes) and chemical factors (nutrients, organic matter) and watershed characteristics (e.g., stream size or drainage area, land use). The relative proportion of surface water contact with benthic sediments has been considered the primary driver of ecosystem processes, including ecosystem respiration (ER). While aquatic ecosystem respiration occurs in the water column (ERwc) and in benthic sediments—including surficial and subsurface sediments (ERsed)—ERsed has long been assumed to be the primary contributor to whole-river ecosystem respiration (ERtot). Recent studies show, however, that somewhere along the river continuum (e.g., 5th–9th order), rivers transition from being dominated by benthic processes to being dominated by water column processes. Yet few metabolism studies have parsed contributions from the water column (ERwc) to ERtot, making it difficult to evaluate the relative magnitude and importance of ERwc across the river continuum and across biomes. In this study, we used the Yakima River basin, Washington, USA, to increase our understanding of basin-scale variation in ERwc. We collected ERwc data and water chemistry samples in triplicate at 47 sites in the Yakima River basin distributed across Strahler stream orders 2–7 and different hydrological and biophysical settings during summer baseflow conditions in 2021. We found that observed ERwc rates were consistently slow throughout the basin during baseflow conditions, ranging from −0.11–0.03 mg O2 L⁻1 d⁻1, and were generally at the very slow end of the range of published ERwc literature values. When compared to reach-scale ERtot rates predicted for rivers across the conterminous United States (CONUS), the very slow ERwc rates we observed throughout the Yakima River basin indicate that ERwc is likely a small component of ERtot in this basin. Despite these slow rates, ERwc nonetheless shows spatial variation across the Yakima River basin that was well explained by watershed characteristics and water chemistry. Multiple linear regression model results show that nitrate (NO3-N), dissolved organic carbon (DOC), and temperature together explained 41.5 % of the spatial variation in ERwc. Supporting the findings of other studies, we found that ERwc increased linearly with increasing NO3-N, increasing DOC, and increasing temperature. We hypothesize that low concentrations of nutrients, DOC, and low temperatures in the water column, coupled with low TSS concentrations, likely contribute to the slow ERwc rates observed throughout the Yakima River basin. Because ERtot measurements integrate contributions from water column respiration and sediment-associated respiration (ERsed), estimating ERtot in cold, clear, low nutrient rivers like those in the Yakima River basin with very slow ERwc will essentially measure contributions from ERsed.

The increasing availability of high-resolution characterization of natural organic matter (OM) data has shifted the paradigm of lumped descriptions of OM components and potential microbial activities. Our recent development of a substrate-explicit thermodynamic model uniquely enables incorporating complex OM pools to formulate biogeochemical reaction models based on their elemental compositions. While this previous work facilitates prediction of aerobic respiration of complex OM, it is equally imperative to consider the role of non-oxygenic electron acceptors in regulating OM turnover and the fate of carbon. In this study, we significantly expand our previous model by flexibly incorporating both detailed OM chemistry and electron acceptors other than oxygen. Our modeling analysis has revealed substantial variations in the energy status of OM molecules across different soils, which drive the co-occurrence of different electronaccepting processes. We demonstrated the effectiveness of the proposed model using a consistency check with experimental data. Through systematic evaluation of the impact of diverse chemical inputs (both electron donors and acceptors) on OM decomposition, the new model also revealed how key microbial growth parameters such as carbon use efficiency (CUE) and reaction rates vary across different electron-accepting processes. Our model provides a unified framework integrating thermodynamic and kinetic constraints on microbial metabolic activities. It complements traditional kinetic models, which are often designed solely to capture mass fluxes. We conclude that thermodynamic modeling emerges as a powerful tool for describing the mechanisms underlying the interplay between microbial growth and OM chemistry and cycling across different electron acceptors, enhancing our ability to project complex ecosystem behaviors in dynamic environments.