About

Jody Niebuhr Tracey is an Adjunct Assistant Professor at Teachers College, Columbia University. She is affiliated with the Organization & Leadership and Social-Organizational Psychology departments. Her office is located at Teachers College, Columbia University, 525 West 120th Street, New York, NY 10027. She offers office hours by appointment. The profile indicates her involvement in academic and research activities within her affiliated departments, but does not provide specific details about her research focus, background, or key contributions.

Research topics

• Ecology
• Chemistry
• Environmental science
• Biology
• Environmental chemistry
• Geology
• Oceanography

Selected publications

• Substrate Effect on the Contribution of Ammonium and Urea to Marine Nitrification and Nitrous Oxide Production

  Environmental Microbiology · 2025-10-01 · 1 citations

  articleOpen access

  ABSTRACT Nitrification (microbial oxidation of ammonia to nitrite and nitrate) controls nitrogen speciation and is the main source of nitrous oxide (N 2 O) in the ocean. It was recently shown that the most abundant marine ammonia oxidizers, the ammonia‐oxidising archaea (AOA), are also capable of oxidising urea, providing a previously ignored source of nitrite. Here, we show that the relative magnitude of urea and ammonia oxidation rates, and the relative rates of N 2 O production from the two substrates, is correlated with the ratio of the substrate concentrations. By examining all reported measurements of urea and ammonium concentrations and the paired urea and ammonia oxidation rates, we show that this relationship likely holds across the global ocean. Examination of newly acquired and previously published metagenomic data shows that the fraction of AOA with the genetic capability for urea oxidation increases with the urea:ammonium ratio, rather than depending on the urea or ammonium concentration alone. These results corroborate the correlation between substrate ratios and oxidation rate ratios, and extend it to N 2 O production. This may help explain the distribution of nitrification rates and N 2 O production in the ocean.

  PublisherOA PDFDOI

• Nitrogen and phosphorus differentially control marine biomass production and stoichiometry

  Nature Communications · 2025-07-01 · 6 citations

  articleOpen access

  Globally averaged, surface particulate nitrogen and phosphorus approximate the 16:1, N:P “Redfield ratio.” In observations, N:P ratios vary latitudinally at ranges attributable to both phytoplankton community composition and physiological acclimation, but their relative contributions to the N:P ratio remain unclear. Here, results from a 29-day mesocosm experiment highlight how inorganic nitrogen and/or phosphorus nutrient supply can affect the bulk particle stoichiometry of a North Pacific Subtropical Gyre plankton community. Nitrogen additions, with and without phosphorus, increase total productivity and diatom abundance, whereas treatments with just phosphorus additions remain similar to the no-nutrient addition control. Continual nitrogen supply without phosphorus results in higher particulate N:P ratios than expected based on the phytoplankton community present. Several P-stress markers identified in those treatments highlight the importance of acclimation in extending particulate N:P ratios beyond the Redfield ratio. Phytoplankton’s ability to maintain growth under P-stress conditions has implications for global carbon cycling. Mesocosm experiments revealed that both phytoplankton community composition and cellular acclimation influence marine particulate C:N:P ratios, with community shifts more sensitive to nitrogen supply and acclimation to the nutrient N:P supply ratio

  PublisherOA PDFDOI

• Tried and true vs. shiny and new: Method switching in long‐term aquatic datasets

  Limnology and Oceanography Letters · 2025-01-21

  articleOpen accessSenior author

  Long-term datasets in aquatic science are important for detecting temporal changes, generating hypotheses regarding ecological phenomena, and understanding the effects of stressors on ecosystems. With rapid technological advances over recent decades, long-term data collection methodologies are continually refined, updated, and often completely switched. However, there is a shortage of discourse regarding the best practices in switching methods for long-term data collection in aquatic ecosystems. In this paper, we discuss factors that contribute to the successes and failures of method switches in long-term aquatic datasets. We present three case studies that demonstrate successful method switching and then outline best practices for maintaining data integrity during these transitions. Our goal is to initiate discussion among current and future managers of long-term aquatic monitoring programs to help guide decisions regarding method switching. Long-term datasets are foundational resources in aquatic research, vital for establishing baselines and detecting shifts in aquatic biodiversity, water quality, and ecosystem function. For example, the Hawaii Ocean Time Series (HOTS), which has sampled biogeochemical data at Station Aloha in the North Pacific Subtropical Gyre since 1988, played a crucial role in documenting temporal variability in ocean carbon inventories and fluxes and provided the first evidence for a multi-decade decline in marine pH associated with climate change (Dore et al. 2009). Research from U.S. National Science Foundation Long Term Ecological Research sites has advanced understanding of ecosystem dynamics, including the long-term effects of invasive species on lakes (e.g., Walsh et al. 2016) and the influence of disturbances on watershed biogeochemical processes (e.g., Miniat et al. 2021). Finally, another NSF initiative, the Continuous Plankton Recorder surveys, are some of the longest-running aquatic long-term datasets, with one survey collecting data continuously since 1931 (www.cprsurvey.org). These surveys have demonstrated how climate change is affecting plankton communities. The insights gained from such long-term datasets are only as robust as the data that have been collected. It is, therefore, a priority for those managing long-term datasets to ensure data quality. Advances in technology or sampling methods often leave researchers with a dilemma: switch to the newer method (i.e., “emerging” method) and take advantage of novel technologies, or continue with the older, existing method (i.e., “established” method) and maintain continuity in sampling protocol. Long-term dataset managers may choose to adopt emerging methods for many reasons: the emerging method could be faster, more efficient and/or more cost-effective, it might offer real-time data collection, or it could reveal previously unattainable or undetectable information. As a group of early career researchers, many of the authors of this essay have been in the position of taking responsibility for managing long-term aquatic datasets and have seen first-hand the importance of mindful data stewardship. Researchers commonly acknowledge the challenges associated with method switching in long-term monitoring programs. However, these discussions often occur informally between small groups of colleagues, not among the wider scientific community. As such, the literature lacks first-hand examples of how to proceed with adopting new methods. Here, our goal is to initiate broader discussion among current and future managers of long-term datasets in the aquatic sciences to help guide decisions about method switching. To achieve this, we discuss indicators of method-switching successes and failures. Then, we outline three case studies of method-switching successes in long-term datasets and suggest a set of best practices. We acknowledge that certain emerging methods produce data resembling those of the established methods but improve efficiency, speed, or cost-effectiveness, whereas other emerging methods generate entirely new data types. While the decision to begin collecting novel data types is worthy of discussion, we focus on the former. A successful method switch in long-term data collection depends on two factors: (1) achieving the pre-established goals of the method switch and (2) ensuring that the data collected from both methods are comparable, thereby maintaining the dataset continuity. Thus, it is important for researchers to establish clear goals for a method switch and to follow well-defined best practices throughout the method switch to ensure continuity (see Section Best practices for method switching of this paper for best practices). As new technological advances enable the collection of data at increasingly finer resolutions, switching to methods that are faster, more efficient, or more cost-effective can be appealing to researchers managing long-term datasets. Researchers may have many reasons to switch methods. For example, the increased availability of remote sensors and autonomous vehicles provides researchers with significantly more real-time data than manual sampling methods, while reducing researcher time and increasing data throughput (Latifi et al. 2023). Furthermore, the rise of AI and machine learning has increased the amount of data that can be processed and information that can be obtained from a dataset (e.g., Fuchs et al. 2022; Kraft et al. 2022). In addition, emerging technologies can enable the collection of previously unattainable or undetectable data, for example, lowering detection limits (e.g., Leskinen et al. 2012) or using eDNA to monitor rare, cryptic, or invasive species (e.g., Barata et al. 2021). The long-term, collaborative nature of these datasets means that collection and management will be carried out by multiple generations of students, post docs, faculty, and government/agency scientists. The dynamic nature of such research teams means that establishing clear goals from inception and following best practices during the transition will aid in maintaining the integrity of long-term datasets during method switches. Accordingly, method switching failures in long-term datasets usually occur when (1) the pre-established goal(s) are not met and/or (2) the data collected from the established and emerging method are not comparable, resulting in a discontinuous dataset. While not meeting a pre-established goal is often straightforward (e.g., financial or labor cost was not reduced, the detection limit was not lowered, etc.), discontinuous datasets will compromise one's ability to capture ecological insights but can occur for a variety of reasons. For example, what was measured previously and what the new method captures may be representative of the same ecological process but are not the same measurement (e.g., algal chlorophyll a vs. total cell biovolume; Ramaraj et al. 2013). Furthermore, as emerging technologies increase sample throughput through automation, the scale of data collection may change dramatically. This can make statistical comparison between the established and emerging methods challenging (Cutter 2013). Finally, switching to a method that lowers the limits of quantification or detection can sometimes be straightforward to account for. However, in other cases, this may complicate comparisons between old and new methods. While the collectors of such data may appreciate and understand these changes, long-term datasets often serve a variety of different end-users, making the ability to capture ecological insights increasingly difficult. Due to the numerous challenges associated with method switches (Fig. 1), it can be difficult to define a method switch as a success or a failure; rather, outcomes exist on a continuum. For example, while a method switch might be considered a “success” within its own long-term data collection program, it may pose challenges for other researchers aiming for methodological consistency between studies. Switching to more advanced technology might make it more difficult for other labs to replicate methodologies, reducing global access to and comparability among datasets. Furthermore, researchers may be motivated to repeatedly switch methods to capture the “best” data when a field is just establishing long-term datasets. Chasing “the best,” unfortunately, can lead to delays in establishing datasets that would benefit policy and regulation. A prime example of this is micro- and nanoplastics pollution research, which suffers from a lack of continuous datasets despite a decade of widespread interest in the topic (Lusher and Primpke 2023). Given these nuanced challenges, there are often many reasons to avoid method switching altogether. To highlight method-switching successes in long-term datasets, we present case studies that fall into three common categories of method switching: (1) manual-to-manual, (2) automated-to-automated switching, and (3) manual-to-automated. Here, “manual” refers to methods where the majority of the method, analysis, and interpretation is carried out by a person (e.g., measuring Secchi disk depth or cell counting with light microscopy). Conversely, “automated” refers to methods where most of the method, analysis, and interpretation is carried out by a machine or an automated process (e.g., satellite imaging or flow cytometry). Long-term datasets characterizing fish age are essential for assessing and managing fish populations, studying life histories and responses to environmental change, and ensuring sustainable fisheries (e.g., Fergusson et al. 2018). The conventional method for fish aging is to collect fish otoliths, which feature incremental growth patterns—similar to tree rings (Campana 1999). Fish age can be determined by counting annual growth rings (Campana 1999), but environmental stressors and physiological factors can obscure these growth patterns, making visual aging challenging (Heimbrand et al. 2020). However, emerging methods, such as chemical aging based on otolith elements (e.g., magnesium, zinc, and phosphorus), can enhance precision: Heimbrand et al. (2020) found higher overall precision and percentage agreement among humans analyzing otolith images of Baltic Cod with chemical vs. visual makers. These findings demonstrate method switching success because (1) the researcher's goal of increasing precision in age estimate was fulfilled and (2) the data from both methods are comparable (Fig. 2A). Aerial imaging surveys offer crucial data for long-term monitoring of the distribution and abundance of aquatic organisms. For example, the Chesapeake Bay Program (CBP) has employed aerial surveys to map the abundance and distribution of submerged aquatic vegetation in the Chesapeake Bay and its tributaries since 1984 (Orth et al. 2022). Although the CBP originally used a panchromatic camera for its surveys, in 2014, it introduced a digital mapping camera to incorporate emerging technology. By 2016, CBP had completely phased out the film. This case study demonstrates a method switching success because (1) it fulfilled the researchers' goals of eliminating a data processing step, increasing picture resolution, and increasing spatial accuracy (Orth et al. 2022) and (2) data from the film (established) and digital (emerging) methods are comparable (Fig. 2B), allowing for a continuous dataset. Understanding phytoplankton community dynamics is important for assessing ecosystem health, addressing climate change impacts, protecting water quality, and guiding management efforts. Traditionally, researchers have assessed phytoplankton community composition using light microscopy, which involves time-consuming sample preparation and visual identification. Recognizing the labor intensity of this approach, the benefits of the recent development of automated observing technologies are clear (Muller-Karger et al. 2018). Imaging flow cytometry is a commonly explored technique as an alternative to manual cell counting (Owen et al. 2022). This automated method combines the high-event-rate capability of flow cytometry with the benefits of single-cell image capture, generating tens of thousands of phytoplankton images per hour. Together with machine learning, flow cytometry can enable near-real-time monitoring of in situ phytoplankton communities by automatically classifying images (Fuchs et al. 2022). This case study demonstrates a successful method switch because it fulfilled the researchers' goal of reducing person-hours. Although there are discrepancies between data collected using the manual counting (established) and the flow cytometry/machine learning (emerging) methods (Fig. 2C), the researchers have implemented a multiyear overlap period of methodologies, which allows end data users to implement their own calibration methods depending on the application (e.g., Fischer et al. 2020). Long-term aquatic datasets provide invaluable insights. However, maintaining their integrity amidst evolving methodologies poses challenges. This raises two considerations for dataset managers: whether to adopt emerging methodologies or maintain established techniques and how to ensure data integrity during a method transition. While the decision to switch methods is case-specific, our paper addresses the critical need for structured discussions on such switches and the development of standardized guidelines for transparent data reporting. With the aquatic sciences trending toward increasingly collaborative, interdisciplinary research that employs automated data collection methods and Big Data (Durden et al. 2017), dataset managers must deliberate on adapting their data collection methods to ensure continuous and effective monitoring of Earth's ecosystems. We extend a sincere thank you to the Eco-DAS program coordinator, Dr. Paul Kemp. We also thank the University of Hawai'i at Manoa as well as the 15 Eco-DAS mentors and speakers who supported this work by providing a venue for our initial discussions and aided us in focusing the manuscript's scope. Finally, we thank the Association for the Sciences of Limnology and Oceanography (ASLO) and the National Science Foundation (Award #OCE-1925796) for generously supporting Eco-DAS XV.

  PublisherOA PDFDOI

• A tale of two nutrients: how nitrogen and phosphorus differentially control marine biomass production and stoichiometry

  Research Square · 2024-05-06

  preprintOpen access
  PublisherOA PDFDOI

• Nutrient management offsets the effect of deoxygenation and warming on nitrous oxide emissions in a large US estuary

  Science Advances · 2024-12-20 · 7 citations

  articleOpen access

  Many estuaries experience eutrophication, deoxygenation and warming, with potential impacts on greenhouse gas emissions. However, the response of N 2 O production to these changes is poorly constrained. Here we applied nitrogen isotope tracer incubations to measure N 2 O production under experimentally manipulated changes in oxygen and temperature in the Chesapeake Bay—the largest estuary in the United States. N 2 O production more than doubled from nitrification and increased exponentially from denitrification when O 2 was decreased from >20 to <5 micromolar. Raising temperature from 15° to 35°C increased N 2 O production 2- to 10-fold. Developing a biogeochemical model by incorporating these responses, N 2 O emissions from the Chesapeake Bay were estimated to decrease from 157 to 140 Mg N year −1 from 1986 to 2016 and further to 124 Mg N year −1 in 2050. Although deoxygenation and warming stimulate N 2 O production, the modeled decrease in N 2 O emissions, attributed to decreased nutrient inputs, indicates the importance of nutrient management in curbing greenhouse gas emissions, potentially mitigating climate change.

  PublisherDOI

• All about nitrite: exploring nitrite sources and sinks in the eastern tropical North Pacific oxygen minimum zone

  Biogeosciences · 2023-06-30 · 16 citations

  articleOpen access1st authorCorresponding

  Abstract. Oxygen minimum zones (OMZs), due to their large volumes of perennially deoxygenated waters, are critical regions for understanding how the interplay between anaerobic and aerobic nitrogen (N) cycling microbial pathways affects the marine N budget. Here, we present a suite of measurements of the most significant OMZ N cycling rates, which all involve nitrite (NO2-) as a product, reactant, or intermediate, in the eastern tropical North Pacific (ETNP) OMZ. These measurements and comparisons to data from previously published OMZ cruises present additional evidence that NO3- reduction is the predominant OMZ N flux, followed by NO2- oxidation back to NO3-. The combined rates of both of these N recycling processes were observed to be much greater (up to nearly 200 times) than the combined rates of the N loss processes of anammox and denitrification, especially in waters near the anoxic–oxic interface. We also show that NO2- oxidation can occur when O2 is maintained near 1 nM by a continuous-purge system, NO2- oxidation and O2 measurements that further strengthen the case for truly anaerobic NO2- oxidation. We also evaluate the possibility that NO2- dismutation provides the oxidative power for anaerobic NO2- oxidation. The partitioning of N loss between anammox and denitrification differed widely from stoichiometric predictions of at most 29 % anammox; in fact, N loss rates at many depths were entirely due to anammox. Our new NO3- reduction, NO2- oxidation, dismutation, and N loss data shed light on many open questions in OMZ N cycling research, especially the possibility of truly anaerobic NO2- oxidation.

  PublisherOA PDFDOI

• All about Nitrite: Exploring Nitrite Sources and Sinks in the Eastern Tropical North Pacific Oxygen Minimum Zone

  Zenodo (CERN European Organization for Nuclear Research) · 2023-05-10

  articleOpen access1st authorCorresponding

  This is the raw data for the Biogeosciences paper titled "All about Nitrite: Exploring Nitrite Sources and Sinks in the Eastern Tropical North Pacific Oxygen Minimum Zone."

  PublisherDOI

• Comment on egusphere-2022-1437

  2023-03-16

  peer-reviewOpen access1st authorCorresponding

  <strong class="journal-contentHeaderColor">Abstract.</strong> Oxygen minimum zones (OMZs), due to their large volumes of perennially deoxygenated waters, are critical regions for understanding how the interplay between anaerobic and aerobic nitrogen (N) cycling microbial pathways affects the marine N budget. Here we present a suite of measurements of the most significant OMZ N cycling rates, which all involve nitrite (NO₂^&ndash;) as a product, reactant, or intermediate, in the Eastern Tropical North Pacific (ETNP) OMZ. These measurements and comparisons to data from previously published OMZ cruises present additional evidence that NO₃^&ndash; reduction is the predominant OMZ N flux, followed by NO₂^&ndash; oxidation back to NO₃^&ndash;. The combined rates of both of these N recycling processes were observed to be much greater (up to nearly 200x) than the combined rates of the N loss processes of anammox and denitrification, especially in waters near the anoxic / oxic interface. We also show that NO₂^&ndash; oxidation can occur in functionally anoxic incubations, measurements that further strengthen the case for truly anaerobic NO₂^&ndash; oxidation. We also evaluate the possibility that NO₂^&ndash; dismutation provides the oxidative power for anaerobic NO₂^&ndash; oxidation. Although almost all treatments returned little evidence for dismutation (as based on product inhibition, substrate stimulation, and stoichiometric hypotheses), results from one treatment under conditions closest to in situ NO₂^&ndash; values may support the occurrence of NO₂^&ndash; dismutation. The partitioning of N loss between anammox and denitrification differed widely from stoichiometric predictions of at most 29 % anammox; in fact, N loss rates at many depths consisted entirely of anammox. Through investigating the magnitudes of NO₃^&ndash; reduction and NO₂^&ndash; oxidation, testing for anaerobic NO₂^&ndash; oxidation, examining the possibility of NO₂^&ndash; dismutation, and further documenting the balance of N loss processes, these new data shed light on many open questions in OMZ N cycling research.

  PublisherDOI

• Nitrous Oxide Consumption in Oxygenated and Anoxic Estuarine Waters

  Geophysical Research Letters · 2022 · 21 citations

  • Environmental chemistry
  • Environmental science
  • Chemistry

  Abstract Estuaries emit a large but highly uncertain amount of Nitrous oxide (N 2 O) into the atmosphere. To better understand N 2 O cycling processes in estuaries, we provide the first direct observations of N 2 O consumption in the seasonally anoxic Chesapeake Bay, the largest estuary in the United States. N 2 O consumption rates in anoxic waters reached up to 3.3 nmol L −1 d −1 but were generally undetectable in oxygenated waters. However, N 2 O consumption rates were substantially enhanced when the oxygen concentration was experimentally decreased in initially oxygenated samples, indicating the potential of N 2 O consumption in oxygenated environments, for example, surface waters. These potential N 2 O consumption rates followed Michaelis‐Menten kinetics as a function of increasing N 2 O substrate concentration. N 2 O‐consuming microbes that predominantly contained the clade II nitrous oxide reductase gene were detected throughout the water column. These new observations of environmental controls on N 2 O consumption will benefit the modeling of N 2 O cycling and help to constrain the estuarine N 2 O flux.

  PublisherOA PDFDOI

• Nitrous oxide production in the Chesapeake Bay

  Zenodo (CERN European Organization for Nuclear Research) · 2022-07-08

  articleOpen access

  This dataset is associated with Tang, W., Tracey, J. C., Carroll, J., Wallace, E., Lee, J. A., Nathan, L., ... & Ward, B. B. (2022). Nitrous oxide production in the Chesapeake Bay. Limnology and Oceanography, 67(9), 2101-2116. https://doi.org/10.1002/lno.12191 This dataset contains: 1. Depth profiles of nitrous oxide production rates from ammonium, urea, nitrite and nitrate in the Chesapeake Bay; nirS, amoA gene abundance; ammonium, urea, nitrite and nitrate concentration. 2. Nitrous oxide production rates from ammonium, nitrite and nitrate under manipulated oxygen concentration in the Chesapeake Bay.

  PublisherDOI

Frequent coauthors

• Xin Sun

  Carnegie Institution for Science

  21 shared

• Bess B. Ward

  Princeton University

  16 shared

• Claudia Frey

  University of Basel

  14 shared

• Katherine L. DuRussel

  14 shared

• Elizabeth Wallace

  Princeton University

  12 shared

• Tyler Tamasi

  8 shared

• Andrew R. Babbin

  Massachusetts Institute of Technology

  8 shared

• Sergey Oleynik

  Princeton University

  8 shared

Education

• Ph.D, Geosciences

  Princeton University

  2022

• BS, Biological Sciences

  Fordham University - Rose Hill Campus

  2015