Jacob Zwart, PhD
Dr. Jacob Zwart is a senior research data scientist and aquatic biogeochemist for the USGS Water Resources Mission Area.
Jacob is a senior research data scientist and aquatic biogeochemist focused on several interdisciplinary research objectives: 1) Improving our understanding of aquatic biogeochemical processes and their responses to future global changes. 2) Advancing artificial intelligence (AI) methods for environmental science, including knowledge-guided machine learning (KGML), equitable machine learning for water predictions, and responsible AI. 3) Innovating on deep learning forecasting methods including assimilating real-time observations into KGML models and characterizing deep learning forecast uncertainty.
He was awarded the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2025 for his environmental forecast research, the highest honor bestowed by the U.S. government on outstanding scientists and engineers early in their careers. Jacob has received four outstanding publication awards including three as the lead author, an outstanding dissertation award from University of Notre Dame Biology Department, and he is an author of three book chapters in the fields of aquatic ecology, machine learning, and knowledge-guided machine learning.
Jacob also serves as a Peer Support Worker at USGS promoting awareness and education on topics and USGS policies for antiharassment, discrimination, biases, and scientific integrity, as well as providing peer-to-peer support for USGS employees.
Professional Experience
2025 – present: Senior Research Data Scientist, U.S. Geological Survey
2022 – 2025: Senior Data Scientist, U.S. Geological Survey
2021 – 2022: Data Scientist, U.S. Geological Survey
2019 – 2021: Mendenhall Postdoctoral Fellow, U.S. Geological Survey
2017 – 2019: National Science Foundation Earth Sciences Postdoctoral Fellow, U.S. Geological Survey
2014 – 2017: National Science Foundation Graduate Research Fellow, University of Notre Dame
2012 – 2014: Research and Teaching Assistant, University of Notre Dame
Education and Certifications
Ph.D., Biological Sciences, 2017, University of Notre Dame
- Hydrologic Regulation of Lake Carbon Cycling in Both Time and Space. Advisor: Dr. Stuart Jones
B.S., Biology, Calvin College, 2012
Honors and Awards
Presidential Early Career Award in Science and Engineering (PECASE), the highest honor bestowed by the U.S. government on outstanding scientists and engineers early in their careers, 2025
American Water Resources Association’s William R. "Randy" Boggess Award 2024 for best paper published in the Journal of the American Water Resources Association during the previous year.
Ecological Society of America Ecological Forecasting Outstanding Publication Award 2023 - annual award in recognition of an outstanding scholarly publication published within the last three years.
Best Applied Data Science Paper Award in the SIAM International Conference on Data Mining 2021 – awarded annually for outstanding publication in Society for Industrial and Applied Mathematics (SIAM)
U.S. Geological Survey Mendenhall Postdoctoral Fellowship, 2019 – 2021
National Science Foundation Earth Sciences Postdoctoral Fellowship, 2017 – 2019
Best dissertation award for the University of Notre Dame Biology Department, 2017
University of Notre Dame Linked Experimental Ecosystem Facility Research Grant, 2017
Exceptional Promise in Graduate Research Award, Ecological Society of America Aquatic Ecology Section – awarded to scientists in recognition of an outstanding paper resulting from research done as a g
National Science Foundation Graduate Research Fellowship, 2014 – 2017
University of Notre Dame Center for Aquatic Conservation Graduate Fellow, 2014
University of Notre Dame Environmental Research Center Graduate Research Fellowship, 2013 – 2015
University of Notre Dame Environmental Research Center Graduate Mentoring Fellowship, 2012
Science and Products
Filter Total Items: 37
Measurement and variability of lake metabolism
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 lake morphometry, nutrients, light...
Authors
Jacob Aaron Zwart, Ludmila S Brighenti
Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?
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 factors affecting riverine water...
Authors
Charuleka Varadharajan, Alison Paige Appling, Bhavna Arora, Danielle Christianson, Valerie Hendrix, Vipin Kumar, Aranildo R. Lima, Juliane Mueller, Samantha K. Oliver, Mohammed Ombadi, Talita Perciano, Jeffrey Michael Sadler, Helen Weierbach, Jared D. Willard, Zexuan Xu, Jacob Aaron Zwart
Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density
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 results. Uncertainty partitioning...
Authors
Mary Lofton, Jennifer A. Brentrup, Whitney S. Beck, Jacob Aaron Zwart, Ruchi Bhattacharya, Ludmila S Brighenti, Sarah H. Burnett, Ian M. McCullough, Bethel Steele, Cayelan C. Carey, Kathryn L. Cottingham, Michael C. Dietze, Holly A. Ewing, Kathleen C. Weathers, Shannon L. LaDeau
Multi-task deep learning of daily streamflow and water temperature
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, together using multi-task DL. A multi-task...
Authors
Jeffrey Michael Sadler, Alison Paige Appling, Jordan S. Read, Samantha K. Oliver, Xiaowei Jia, Jacob Aaron Zwart, Vipin Kumar
Machine learning for understanding inland water quantity, quality, and ecology
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 analysis; generating and exploring...
Authors
Alison Paige Appling, Samantha K. Oliver, Jordan S. Read, Jeffrey Michael Sadler, Jacob Aaron Zwart
Estimating pelagic primary production in lakes: Comparison of 14C incubation and free-water O2 approaches
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 free-water dissolved oxygen (O2). While...
Authors
Noah R. Lottig, Joseph Phillips, Ryan D. Batt, Facundo Scordo, Tanner J. Williamson, Stephen R. Carpenter, Sudeep Chandra, Paul R. Hanson, Christopher T. Solomon, Michael J. Vanni, Jacob Aaron Zwart
Physics-guided machine learning from simulation data: An application in modeling lake and river systems
This paper proposes a new physics-guided machine learning approach that incorporates the scientific knowledge in physics-based models into machine learning models. Physics-based models are widely used to study dynamical systems in a variety of scientific and engineering problems. Although they are built based on general physical laws that govern the relations from input to output...
Authors
Xiaowei Jia, Yiqun Xie, Sheng Li, Shengyu Chen, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison Paige Appling, Samantha K. Oliver, Jordan S. Read
The AEMON-J “Hacking Limnology” workshop series & virtual summit: Incorporating data science and open science in aquatic research
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 “Hacking Limnology” Workshop Series prior...
Authors
Michael Frederick Meyer, Robert Ladwig, Jorrit Mesman, Isabella Oleksy, Carolina C. Barbosa, Kaelin M. Cawley, Alli N. Cramer, Johannes Feldbauer, Patricia Q. Tran, Jacob Aaron Zwart, Gregario A. Lopez Moreira, Muhammed Shikhani, Deviyani Gurung, Robert T. Hensley, Elena Matta, Ryan P. McClure, Thomas Petzoldt, Nuria Sanchez Lopez, Karline Soetaert, Mridul K. Thomas, Simon Nemer Topp, Xiao Yang
Physics-guided recurrent graph model for predicting flow and temperature in river networks
This paper proposes a physics-guided machine learning approach that combines 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 transfer knowledge from physics-based models to guide the...
Authors
Xiaowei Jia, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison Paige Appling, Samantha K. Oliver, Steven L. Markstrom, Jared D. Willard, Shaoming Xu, Michael Steinbach, Jordan S. Read, Vipin Kumar
Projected changes of regional lake hydrologic characteristics in response to 21st century climate change
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 hydrology, we combined a previously published...
Authors
Zachary J. Hanson, Jacob Aaron Zwart, Stuart E. Jones, Alan F. Hamlet, Diogo Bolster
Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles
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 on aquatic ecosystems and assisting in...
Authors
Xiaowei Jia, Jared D. Willard, Anuj Karpatne, Jordan S. Read, Jacob Aaron Zwart, Michael Steinbach, Vipin Kumar
Graph-based reinforcement learning for active learning in real time: An application in modeling river networks
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 efficiently. This problem differs from...
Authors
Xiaowei Jia, Beiyu Lin, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison Paige Appling, Samantha K. Oliver, Jordan S. Read
Non-USGS Publications**
Farrell, K.J., A.N. Cramer, K.L. Hondula, S.K. Thompson, J.A. Zwart. 2019. Support of early-career researchers supports the future of ASLO. Limnology and Oceanography Bulletin 28: 34-34
Tiegs, S., et al. (148 co-authors), Zwart, J.A. 2019. Global patterns and drivers of ecosystem functioning in rivers and riparian zones. Science Advances 5: eaav0486
Hanson, Z.J., J.A. Zwart, *sJ. Vanderwall, C.T. Solomon, S.E. Jones, A.F. Hamlet, D. Bolster. 2018. Integrated, regional-scale hydrologic modeling of inland lakes. Journal of American Water Resources Association 54: 1302-1324.
Zwart, J.A., Z.J. Hanson, J. Vanderwall, D. Bolster, A.F. Hamlet, S.E. Jones. 2018. Spatially-explicit, regional-scale simulation of lake carbon cycling. Global Biogeochemical Cycles 32: 1276-1293.
Hararuk, O., J.A. Zwart, S.E. Jones, Y.T. Prairie, C.T. Solomon. 2018. Model-data fusion to test hypothesized drivers of lake carbon cycling reveals importance of physical controls. Journal of Geophysical Research – Biogeosciences 123: 1130-1142.
Kelly, P.T., C.T. Solomon, J.A. Zwart, S.E. Jones. 2018. A framework for understanding variation in pelagic gross primary production of lake ecosystems. Ecosystems 21: 1364-1376
Koizumi, S., N. Craig, J.A. Zwart, P.T. Kelly, J.P. Ziegler, B.C. Weidel, S.E. Jones, C.T. Solomon. 2018. Whole-lake experimental increase in DOC leads to a zero-sum change in fish productivity. Canadian Journal of Fisheries and Aquatic Sciences 75: 1859-1867
Jones, S.E., J.A. Zwart, P.T. Kelly, C.T. Solomon. 2018. Hydrologic context constrains lake heterotrophy and terrestrial carbon fate. Limnology and Oceanography Letters 3: 256-264; DOI: 10.1002/lol2.10054
Vizza, C., J.A. Zwart, S.E. Jones, S.D. Tiegs, G.A. Lamberti. 2017. Landscape patterns shape wetland pond ecosystem function from glacial headwaters to ocean. Limnology and Oceanography 62: S207-S221. DOI: 10.1002/lno.10575
Wilkinson, G.M., C. Gaynus, T. Moore, S. Rosengard, H. Schiebel, J.A. Zwart. 2017. Innovations and solutions for ASLO student travel grants. Limnology and Oceanography Bulletin. DOI: 10.1002/lob.10168
Giling, D.P., P.A. Staehr, H.P. Grossart, M.R. Andersen, B. Boehrer, C. Escot, F. Evrendilek, L. Gómez-Gener, M. Honti, I.D. Jones, N. Karakaya, A. Laas, E. Moreno-Ostos, K. Rinke, U. Scharfenberger, S.R. Schmidt, M. Weber, R.I. Woolway, J.A. Zwart, B. Obrador. 2017. Delving Deeper: Metabolic processes in the metalimnion of stratified lakes. Limnology and Oceanography 62: 1288-1306.
Weidel, B.C., Baglini, K., Jones, S.E., Kelly, P.T., Solomon, C.T., and Zwart, J.A. 2017. Differences in light climate due to dissolved organic carbon concentration drive species-specific changes in fish zooplanktivory. Inland Waters 7: 210-217.
Zwart, J.A., S.D. Sebestyen, C.T. Solomon, and S.E. Jones. 2017. The influence of hydrologic residence time on lake carbon cycling dynamics following extreme precipitation events. Ecosystems 20: 1000-1014.
Read, E., V. Patil, S. Oliver, A. Hetherington, J. Brentrup, J.A. Zwart, K. Winters, J. Corman, E. Nodine, R.I. Woolway, H. Dugan, A. Jaimes, A. Santoso, G. Hong, P. Hanson, L. Winslow, K. Weathers. 2015. The importance of lake-specific characteristics for water quality across the continental United States. Ecological Applications 25(4):943-955.
Zwart, J.A., C.T. Solomon, and S.E. Jones. 2015. Phytoplankton traits predict ecosystem function in a global set of lakes. Ecology 96(8): 2257-2264.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Filter Total Items: 37
Measurement and variability of lake metabolism
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 lake morphometry, nutrients, light...
Authors
Jacob Aaron Zwart, Ludmila S Brighenti
Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?
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 factors affecting riverine water...
Authors
Charuleka Varadharajan, Alison Paige Appling, Bhavna Arora, Danielle Christianson, Valerie Hendrix, Vipin Kumar, Aranildo R. Lima, Juliane Mueller, Samantha K. Oliver, Mohammed Ombadi, Talita Perciano, Jeffrey Michael Sadler, Helen Weierbach, Jared D. Willard, Zexuan Xu, Jacob Aaron Zwart
Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density
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 results. Uncertainty partitioning...
Authors
Mary Lofton, Jennifer A. Brentrup, Whitney S. Beck, Jacob Aaron Zwart, Ruchi Bhattacharya, Ludmila S Brighenti, Sarah H. Burnett, Ian M. McCullough, Bethel Steele, Cayelan C. Carey, Kathryn L. Cottingham, Michael C. Dietze, Holly A. Ewing, Kathleen C. Weathers, Shannon L. LaDeau
Multi-task deep learning of daily streamflow and water temperature
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, together using multi-task DL. A multi-task...
Authors
Jeffrey Michael Sadler, Alison Paige Appling, Jordan S. Read, Samantha K. Oliver, Xiaowei Jia, Jacob Aaron Zwart, Vipin Kumar
Machine learning for understanding inland water quantity, quality, and ecology
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 analysis; generating and exploring...
Authors
Alison Paige Appling, Samantha K. Oliver, Jordan S. Read, Jeffrey Michael Sadler, Jacob Aaron Zwart
Estimating pelagic primary production in lakes: Comparison of 14C incubation and free-water O2 approaches
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 free-water dissolved oxygen (O2). While...
Authors
Noah R. Lottig, Joseph Phillips, Ryan D. Batt, Facundo Scordo, Tanner J. Williamson, Stephen R. Carpenter, Sudeep Chandra, Paul R. Hanson, Christopher T. Solomon, Michael J. Vanni, Jacob Aaron Zwart
Physics-guided machine learning from simulation data: An application in modeling lake and river systems
This paper proposes a new physics-guided machine learning approach that incorporates the scientific knowledge in physics-based models into machine learning models. Physics-based models are widely used to study dynamical systems in a variety of scientific and engineering problems. Although they are built based on general physical laws that govern the relations from input to output...
Authors
Xiaowei Jia, Yiqun Xie, Sheng Li, Shengyu Chen, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison Paige Appling, Samantha K. Oliver, Jordan S. Read
The AEMON-J “Hacking Limnology” workshop series & virtual summit: Incorporating data science and open science in aquatic research
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 “Hacking Limnology” Workshop Series prior...
Authors
Michael Frederick Meyer, Robert Ladwig, Jorrit Mesman, Isabella Oleksy, Carolina C. Barbosa, Kaelin M. Cawley, Alli N. Cramer, Johannes Feldbauer, Patricia Q. Tran, Jacob Aaron Zwart, Gregario A. Lopez Moreira, Muhammed Shikhani, Deviyani Gurung, Robert T. Hensley, Elena Matta, Ryan P. McClure, Thomas Petzoldt, Nuria Sanchez Lopez, Karline Soetaert, Mridul K. Thomas, Simon Nemer Topp, Xiao Yang
Physics-guided recurrent graph model for predicting flow and temperature in river networks
This paper proposes a physics-guided machine learning approach that combines 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 transfer knowledge from physics-based models to guide the...
Authors
Xiaowei Jia, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison Paige Appling, Samantha K. Oliver, Steven L. Markstrom, Jared D. Willard, Shaoming Xu, Michael Steinbach, Jordan S. Read, Vipin Kumar
Projected changes of regional lake hydrologic characteristics in response to 21st century climate change
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 hydrology, we combined a previously published...
Authors
Zachary J. Hanson, Jacob Aaron Zwart, Stuart E. Jones, Alan F. Hamlet, Diogo Bolster
Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles
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 on aquatic ecosystems and assisting in...
Authors
Xiaowei Jia, Jared D. Willard, Anuj Karpatne, Jordan S. Read, Jacob Aaron Zwart, Michael Steinbach, Vipin Kumar
Graph-based reinforcement learning for active learning in real time: An application in modeling river networks
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 efficiently. This problem differs from...
Authors
Xiaowei Jia, Beiyu Lin, Jacob Aaron Zwart, Jeffrey Michael Sadler, Alison Paige Appling, Samantha K. Oliver, Jordan S. Read
Non-USGS Publications**
Farrell, K.J., A.N. Cramer, K.L. Hondula, S.K. Thompson, J.A. Zwart. 2019. Support of early-career researchers supports the future of ASLO. Limnology and Oceanography Bulletin 28: 34-34
Tiegs, S., et al. (148 co-authors), Zwart, J.A. 2019. Global patterns and drivers of ecosystem functioning in rivers and riparian zones. Science Advances 5: eaav0486
Hanson, Z.J., J.A. Zwart, *sJ. Vanderwall, C.T. Solomon, S.E. Jones, A.F. Hamlet, D. Bolster. 2018. Integrated, regional-scale hydrologic modeling of inland lakes. Journal of American Water Resources Association 54: 1302-1324.
Zwart, J.A., Z.J. Hanson, J. Vanderwall, D. Bolster, A.F. Hamlet, S.E. Jones. 2018. Spatially-explicit, regional-scale simulation of lake carbon cycling. Global Biogeochemical Cycles 32: 1276-1293.
Hararuk, O., J.A. Zwart, S.E. Jones, Y.T. Prairie, C.T. Solomon. 2018. Model-data fusion to test hypothesized drivers of lake carbon cycling reveals importance of physical controls. Journal of Geophysical Research – Biogeosciences 123: 1130-1142.
Kelly, P.T., C.T. Solomon, J.A. Zwart, S.E. Jones. 2018. A framework for understanding variation in pelagic gross primary production of lake ecosystems. Ecosystems 21: 1364-1376
Koizumi, S., N. Craig, J.A. Zwart, P.T. Kelly, J.P. Ziegler, B.C. Weidel, S.E. Jones, C.T. Solomon. 2018. Whole-lake experimental increase in DOC leads to a zero-sum change in fish productivity. Canadian Journal of Fisheries and Aquatic Sciences 75: 1859-1867
Jones, S.E., J.A. Zwart, P.T. Kelly, C.T. Solomon. 2018. Hydrologic context constrains lake heterotrophy and terrestrial carbon fate. Limnology and Oceanography Letters 3: 256-264; DOI: 10.1002/lol2.10054
Vizza, C., J.A. Zwart, S.E. Jones, S.D. Tiegs, G.A. Lamberti. 2017. Landscape patterns shape wetland pond ecosystem function from glacial headwaters to ocean. Limnology and Oceanography 62: S207-S221. DOI: 10.1002/lno.10575
Wilkinson, G.M., C. Gaynus, T. Moore, S. Rosengard, H. Schiebel, J.A. Zwart. 2017. Innovations and solutions for ASLO student travel grants. Limnology and Oceanography Bulletin. DOI: 10.1002/lob.10168
Giling, D.P., P.A. Staehr, H.P. Grossart, M.R. Andersen, B. Boehrer, C. Escot, F. Evrendilek, L. Gómez-Gener, M. Honti, I.D. Jones, N. Karakaya, A. Laas, E. Moreno-Ostos, K. Rinke, U. Scharfenberger, S.R. Schmidt, M. Weber, R.I. Woolway, J.A. Zwart, B. Obrador. 2017. Delving Deeper: Metabolic processes in the metalimnion of stratified lakes. Limnology and Oceanography 62: 1288-1306.
Weidel, B.C., Baglini, K., Jones, S.E., Kelly, P.T., Solomon, C.T., and Zwart, J.A. 2017. Differences in light climate due to dissolved organic carbon concentration drive species-specific changes in fish zooplanktivory. Inland Waters 7: 210-217.
Zwart, J.A., S.D. Sebestyen, C.T. Solomon, and S.E. Jones. 2017. The influence of hydrologic residence time on lake carbon cycling dynamics following extreme precipitation events. Ecosystems 20: 1000-1014.
Read, E., V. Patil, S. Oliver, A. Hetherington, J. Brentrup, J.A. Zwart, K. Winters, J. Corman, E. Nodine, R.I. Woolway, H. Dugan, A. Jaimes, A. Santoso, G. Hong, P. Hanson, L. Winslow, K. Weathers. 2015. The importance of lake-specific characteristics for water quality across the continental United States. Ecological Applications 25(4):943-955.
Zwart, J.A., C.T. Solomon, and S.E. Jones. 2015. Phytoplankton traits predict ecosystem function in a global set of lakes. Ecology 96(8): 2257-2264.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.