Jacob Zwart

University of Notre Dame | ND · Department of Biological Sciences

Predicting ecosystem function from environmental conditions is a central goal of ecosystem ecology. However, many traditional ecosystem models are tailored for specific regions or ecosystem types, requiring several regional models to predict the same function. Alternatively, trait-based approaches have been effectively used to predict community str...

Over the last several decades, many lakes globally have increased in dissolved organic carbon (DOC), calling into question how lake functions may respond to increasing DOC. Unfortunately, our basis for making predictions is limited to spatial surveys, modeling, and laboratory experiments, which may not accurately capture important whole-ecosystem p...

The frequency and magnitude of extreme events are expected to increase in the future, yet little is known about effects of such events on ecosystem structure and function. We examined how extreme precipitation events affect exports of terrestrial dissolved organic carbon (t-DOC) from watersheds to lakes as well as in-lake heterotrophy in three nort...

The observed pattern of lake browning, or increased terrestrial dissolved organic carbon (DOC) concentration, across the northern hemisphere has amplified the importance of understanding how consumer productivity varies with DOC concentration. Results from comparative studies suggest these increased DOC concentrations may reduce crustacean zooplank...

Metabolism is a fundamental process in ecosystems that crosses multiple scales of organization from individual organisms to whole ecosystems. To improve sharing and reuse of published metabolism models, we developed LakeM-etabolizer, an R package for estimating lake metabolism from in situ time series of dissolved oxygen, water temperature, and, op...

Deep learning (DL) models are increasingly used to make accurate hindcasts of management‐relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real‐time observations, where the difference between model predictions and observations today is used to adjust the mod...

This chapter provides an overview of machine learning models and their applications to the science of inland waters. Such models serve a wide range of purposes for science and management: predicting water quality, quantity, or ecological dynamics across space, time, or hypothetical scenarios; vetting and distilling raw data for further modeling or...

Deep learning (DL) models can accurately predict many hydrologic variables including streamflow and water temperature; however, these models have typically predicted hydrologic variables independently. This study explored the benefits of modeling two interdependent variables, daily average streamflow and daily average stream water temperature, toge...

Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low‐nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing clima...

Near‐term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast re...

The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of...

Historically, estimates of pelagic primary production in lake ecosystems were made by measuring the uptake of carbon‐14 (14C)‐labeled inorganic carbon in samples incubated under laboratory or in situ conditions. However, incubation approaches are increasingly being replaced by methods that analyze diel changes in high‐frequency in situ data such as...

Following the 2020 “Virtual Summit: Incorporating Data Science and Open Science in Aquatic Research” (DSOS; Meyer and Zwart 2020), a grassroots group of scientists convened the 2nd Virtual DSOS Summit on 22–23 July 2021. DSOS combined forces with the Aquatic Ecosystem MOdeling Network - Junior (AEMON-J; https://github.com/aemon-j) to host a 4-d “Ha...

Accurate prediction of water temperature in streams is critical for monitoring and understanding biogeochemical and ecological processes in streams. Stream temperature is affected by weather patterns (such as solar radiation) and water flowing through the stream network. Additionally, stream temperature can be substantially affected by water releas...

Near-term forecasts of environmental outcomes can inform real-time decision making. Data assimilation modeling techniques can be used for forecasts to leverage real-time data streams, where the difference between model predictions and observations can be used to adjust the model to make better predictions tomorrow. In this use case, we developed a...

Inland lakes are socially and ecologically important components of many regional landscapes. Exploring lake responses to plausible future climate scenarios can provide important information needed to inform stakeholders of likely effects of hydrologic changes on these waterbodies in coming decades. To assess potential climate effects on lake hydrol...

Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate...

Aim: The aim of this article is to provide an overview of what contributes to lake metabolism, a brief overview of methods for estimating lake metabolism, and drivers of metabolism variability within and across lakes. Main concepts covered: In this article, we describe the key drivers of within and across lake variability in metabolism including la...

Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficie...

This paper proposes a physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent graph network model to capture the interactions among multiple segments in the river network. Then we present a p...

Physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitations due to simplified representations of the physical processes being modeled or challenges in selecting appropriate parameters. While-state-of-the-art machine learning mo...

The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Gui...

Lakes support globally important food webs through algal productivity and contribute significantly to the global carbon cycle. However, predictions of how broad‐scale lake carbon flux and productivity may respond to future climate are extremely limited. Here, we used an integrated modeling framework to project changes in lake‐specific and regional...

River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to...

River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to...

River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to...

River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to...

Lakes are biogeochemical hotspots on the landscape, contributing significantly to the global carbon cycle despite their small areal coverage. Observations and models of lake carbon pools and fluxes are rarely explicitly combined through data assimilation despite successful use of this technique in other fields. Data assimilation adds value to both...

Light and nutrient availability are key physiological constraints for primary production. Widespread environmental changes are causing variability in loads of terrestrial dissolved organic carbon (DOC) and nutrients from watersheds to lakes, contributing to simultaneous changes in both light and nutrient supply. Experimental evidence highlights the...

Inland lakes constitute an important global freshwater resource and are often defining features of local and regional landscapes. While coupled surface water (SW) and groundwater (GW) models are increasingly available, there is a clear need for spatially explicit yet computationally parsimonious modeling frameworks to explore the impacts of climate...

Lakes are areas of intense biogeochemical processing in the landscape, contributing significantly to the global carbon cycle despite their small areal coverage. However, current large-scale estimates of lake biogeochemical fluxes are all generated by multiplying a mean observed areal rate by regional or global lake surface area, which ignores impor...

Formal integration of models and data to test hypotheses about the processes controlling carbon dynamics in lakes is rare, despite the importance of lakes in the carbon cycle. We built a suite of models (n=102) representing different hypotheses about lake carbon processing, fit these models to data from a north-temperate lake using data assimilatio...

Negative relationships between dissolved organic carbon (DOC) concentration and fish productivity have been reported from correlative studies across lakes, but to date there have not been experimental tests of these relationships. We increased the DOC concentration in a lake by 3.4 mg·L⁻¹, using a before–after control–impact design, to quantify the...

Current efforts to scale lake processes rely heavily on empirical observations and do not consider inter-lake heterogeneity that is likely to regulate terrestrial dissolved organic carbon (tDOC) decomposition in lakes. We created a simple, analytical model of tDOC decomposition in lakes that highlights the role of lake size and catchment hydrologic...

Examining patterns and processes along the aquatic continuum from headwaters to ocean can benefit from a landscape ecology approach, where hydrologic and ecological processes depend on landscape position. We conducted a study of freshwater wetland ponds subject to similar climatic conditions but distributed along a 20-km trajectory from glacial hea...

Dissolved organic carbon (DOC) in lakes reduces light penetration and limits fish production in low nutrient lakes, reportedly via reduced primary and secondary production. Alternatively, DOC and light reductions could influence fish by altering their visual feeding. Previous studies report mixed effects of DOC on feeding rates of zooplanktivorous...

Many lakes exhibit seasonal stratification, during which they develop strong thermal and chemical gradients. An expansion of depth-integrated monitoring programs has provided insight into the importance of organic carbon processing that occurs below the upper mixed layer. However, the chemical and physical drivers of metabolism and metabolic coupli...

Ecosystem metabolism and the contribution of carbon dioxide from lakes to the atmosphere can be estimated from free-water gas measurements through the use of mass balance models, which rely on a gas transfer coefficient (k) to model gas exchange with the atmosphere. Theoretical and empirically based models of k range in complexity from wind-driven...

Metabolism is a fundamental process in ecosystems that crosses multiple scales of organization from individual organisms to whole ecosystems. To improve sharing and reuse of published metabolism models, we developed LakeMetabolizer, an R package for estimating lake metabolism from in situ time series of dissolved oxygen, water temperature, and, opt...

Ecosystem metabolism and the contribution of carbon dioxide from lakes to the atmosphere can be estimated from free-water gas measurements through the use of mass balance models, which rely on a gas transfer coefficient (k) to model gas exchange with the atmosphere. Theoretical and empirically based models of k range in complexity from wind-driven...

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data...

Background/Question/Methods Non-resource mediated effects of dissolved organic carbon concentration (DOC) have the potential to influence predator prey interactions by changing light levels and fish feeding efficiency. We manipulated dissolved organic carbon concentration (range = 3 - 25 mg/L) in yellow perch Perca flavescens zooplanktivory trial...