Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Related Publications
• Parameters
• Instruments
• Project Information
• Program Information
• Funding

Time-series dataset is supported by 3 separate NSF projects (see project and funding resources). 

Water was sampled at ~10:30am local from a dock using a 5 L niskin bottle centered at 1 m with a bottle length of 0.7 m.  Subsamples were processed immediately following water collection.

DIC: DIC was measured on mercuric chloride poisoned samples by acidification and subsequent quantification of released CO₂ using a CO₂ detector (Li-Cor 7000). DIC samples were collected following recommended procedures {Dickson et al., 2007} and measurements were calibrated against Certified Reference Materials provided by Dr. A. G. Dickson at Scripps Institution of Oceanography (SIO), University of California, San Diego (UCSD) (Dickson et al., 2007)

Chlorophyll:  Methods described in Johnson et al. 2010: Chlorophyll concentrations were measured by filtering 25  mL of seawater sample onto a 0.22 µm pore size polycarbonate filter using gentle vacuum (<100 mm Hg) and extracting in 100% MeOH at -20°C in the dark for >24 h following (Holm-Hansen and Riemann, 1978).  Fluorescence was measured using a Turner Designs 10-AU fluorometer following (Welschmeyer, 1994) that was calibrated against a standard chlorophyll solution (Ritchie, 2006).

Secchi Depth: Secchi depth was measured in duplicate using a 20 cm disk with four alternating white and black quadrants by lowering the disk until no longer visible and recording the depth.

Salinity: Salinity was measured using a calibrated handheld digital refractometer (Atago PAL-06S), using a refractometer (Vista A366ATC), YSI Pro30, YSI ProODO, YSI ProSolo or using a Guideline Portasal 8410A all according to manufacturer’s instructions and calibrated against known reference materials.

Turbidity: Turbidity reported in Nephelometric Turbidity Units [NTU]  was measured in duplicate on discrete samples using a calibrated handheld turbidimeter (Orion AQ4500) according to manufacturer’s instructions.

Temperature: Temperature was measured in duplicate using NIST traceable thermocouples (VWR#23609-232) from bottle water or from in situ probes YSI Pro30, YSI ProODO or YSI ProSolo according to manufacturer’s instructions.

pH: pH was measured spectrophotometrically (Clayton and Byrne, 1993) in triplicate at standard temperature (25 degrees C) immediately following collection. pH samples were collected following recommended procedures (Dickson et al., 2007).

Bacteria, Synechococcus, picocyanobacteria, pico-photosynthetic eukaryotes: Bacterioplankton (“bacteria”, DNA containing, non-red fluorescing populations), Synechococcus (small, red and orange fluorescing populations),  “picocyanobacteria” (small, red fluorescing populations; includes Prochlorococcus and ‘green’ Synechococcus), and “picoeukaryotes” (DNA containing, red and orange fluorescing populations) were measured flow cytometrically as previously described (Johnson et al., 2010) using a BD FACSCalibur, or using Hoechst 34580 or Sybr Green I DNA stains using an Attune NxT with 405 nm excitation and 440±25, 512±13, 603±24, 710±25 nm emission and 488 nm excitation and 530±15, 574±13, 695±20, 780±30 nm emission as previously described (Selph 2021).

Inorganic Nutrients (NH4, NO3, SiOH4): Water was filtered through a 0.22 um Sterivex cartridge filter, Millipore #SVGPL10RC using a peristaltic pump input line at 1 m for later nutrient analysis (NO3, NH4, SiOH4). Water was sampled in duplicate into HCl-cleaned HDPE bottles (VWR#414004-110) and stored at -80 degrees C until later analysis using an Astoria-Pacific A2 autoanalyzer (NO3 and SiOH4), following the manufacturer’s recommended protocols by running each replicate sample in duplicate. NH4 was measured in triplicate following Holmes et al. 1999 using a Turner 10-AU fluorometer. For some time points inorganic nutrients were processed by the Scripps Institute of Oceanography STS/ODF chemistry laboratory

Certified reference materials were used to verify protocols (Inorganic Ventures: QCP-NT, QCP-NUT-1, CGSI1-1). The detection limits were: NO2 = 0.05 uM, NO3 = 0.1 uM, PO4 = 0.05 uM, SiOH4 = 0.2 uM. Values measured below these limits are reported as zero.

Production and Respiration: Production and respiration quantified using Winkler oxygen (Labasque et al., 2004) were measured using the light/dark bottle technique with 24 h incubations at ambient temperature in a sinusoidal incubator (Sanyo MLR-351H) with ~1000 µmol quanta m^-2 sec^-1 peak PAR. 

Means, where replicates were available, were calculated and reported with standard deviations.  All variable means represent n=2, except pH and DIC where n=3.

blank = missing data

From the project website:
Carbon dioxide is rising at ~3% per year in the atmosphere and oceans leading to increases in dissolved inorganic carbon and a reduction in pH. This trend is expected to continue for the foreseeable future and ocean pH is predicted to decrease substantially making the ocean more acidic, potentially affecting the marine ecosystem. However, coastal estuaries are highly dynamic systems that often experience dramatic changes in environmental variables over short periods of times. In this study, the investigators are measuring key variables of the marine carbon system along with other potential forcing variables and characteristics of the ecosystem that may be affected by these pH changes. The goal of this project is to determine the time-scales and magnitude of natural variability that will be superimposed on any long term trends in ocean chemistry.

Other PICO-related projects in BCO-DMO:
Ocean Acidification: microbes as sentinels of adaptive responses to multiple stressors: contrasting estuarine and open ocean environments

Collaborative Research: BoCP-Design: A multidomain microbial consortium to interrogate organic matter decomposition in a changing ocean

NSF2026: EAGER: Identifying microbes’ population-level environmental responses using Bayesian modeling

NSF Award Abstract:
Despite low standing biomass, extensive carbon processing occurs in the oceans, largely by diverse microbial consumers. Until recently, bacteria were considered the main degraders of organic matter, while non-bacterial consumers’ role in carbon cycling was largely been ignored. However, eukaryotes such as fungi exhibit distinct metabolic capacities and responses to environmental variables, suggesting global change may alter the balance of microbial activities in the oceans and potentially alter the fate of marine carbon. Here, researchers integrate field data with modeling and laboratory experiments with representative cultures to identify microbes’ functional roles in marine organic matter degradation and to determine their response to changing environmental conditions. This project will open new windows into the diversity of microbial metabolisms and how these dynamics will shift with global change driven increases in temperature and other environmental factors. Additionally, this projects builds a new scientific research team and expands scientific training at levels ranging from K-5 teachers, to undergraduate and PhD students.

This project will leverage a decade-long, coastal microbial time series, the Piver’s Island Coastal Observatory (PICO), to examine how diverse heterotrophic microbial communities (bacteria, phytoplankton, fungi and Labyrinthulomycetes protists) metabolize carbon compounds under different thermal regimes. This project will develop a model microbial consortium that has the potential to transform perception of carbon cycling in coastal systems by integrating functional, organismal-interaction and environment-dependent responses into a modeling framework. Empirical Dynamic Modeling will identify drivers of the observed dynamics, differentiate causation from correlation, infer effects of possibly unobserved variables (e.g. predation), and quantify interactions between organisms. This data will further be used to develop a culturable model consortium whose members metabolize distinct components of phytoplankton-derived organic matter. To test both model predictions and how well the consortium represents complex microbiomes, both the model consortium and a “wild” coastal seawater microbiome will be assayed for changes in function (phytoplankton DOM degradation) as temperatures increase (+4 °C). These experiments will compare outcomes for individual isolates, the consortium, and a wild coastal microbiome in composition/abundance, gene expression and degradation of specific compounds. Finally, experimental results will be used to parameterize and refine an Ensemble Sparse Identification of Nonlinear Dynamics model that can predict the fate and transformation of carbon in marine systems under varying climate scenarios. While this research leverages existing expertise in marine microbiomes, this model consortium approach can be applied to diverse systems to answer questions about environmental filtering, organismal interactions and functional diversity critical to predicting ecosystem-level responses to environmental change.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This project is closely related to Pivers Island Coastal Observatory: https://www.bco-dmo.org/project/2281

NSF Award Abstract:
With support from the Directorate for Geosciences and the NSF 2026 Fund Program in the Office of Integrated Activities, Professors Dana Hunt, Mark Borsuk, and James Clark at Duke University conduct research that provides new insights into the factors that shape microbial productivity and function in the oceans as well as how this change during extreme events such as hurricanes. The driver of this research comes from the fact that marine microbes provide essential ecosystem services, including primary production (photosynthesis) and organic matter turnover, that sustains all marine organisms. That said, it still remains unclear as to what extent microbiomes are shaped by environmental factors, such as temperature and primary productivity, that can be altered by season, disturbances, global change, and other factors. This research combines long-term observations at a coastal site at Beaufort Island, North Carolina and uses these data to capture annual changes in microbiomes and their environments using high frequency measurements that were taken before and after hurricanes Florence (2018) and Dorian (2019). Examining the impact of hurricanes on marine biomes is important because hurricanes are multi-factor disturbances that introduce both foreign freshwater and terrestrial microbes into a stable system while altering salinity, nutrients, and organic matter in the coastal ocean. This work combines information from field observations and modeling to develop new approaches that will allow the differentiation of factors that often co-occur in field samples, such as warmer temperatures and higher primary production that occur during the summer months in the coastal Atlantic Ocean. By integrating multiple aspects of microbiome research, this work deepens current understanding of the coastal ocean microbiome system and its functionality. It also develops new testable hypothesis to guide future research. Broader impacts of the work include advanced training for undergraduate, graduate, and postdoctoral students, as well as translating research results into products for K-12 students and the public. Additional impacts include the production of detailed user manuals and training materials for software developed in the course of the project to facilitate the use of research results for future microbiome research and undergraduate education.

This research leverages an established decade-long microbial time-series, the Piver’s Island Coastal Observatory (PICO, Beaufort Inlet, NC USA) to improve the modeling of microbial populations and their relationship to changing environments. With 10 years of weekly (or more frequent) microbial community SSU rRNA gene sequence datasets, coupled with the suite of sample, in-situ, and environmental parameters, the PICO dataset is one of the most complete, long-term datasets for coastal ocean microbiomes. The work carried out uses the application of Bayesian modeling to the PICO time series to improve understanding and predictions of microbiome responses to ocean conditions. Bayesian models are well suited to microbial systems because they have the ability to handle sparse datasets, capture non-linear responses to environmental changes, and include impacts of disturbances. This research integrates microbiome applications and the Bayesian model gjamTime. This combination has the potential to transform microbial ecology by leveraging advances in multivariate time-series methods that accommodate the dependence among individual taxa and their environment over time. One goal of the project is to test model predictions using time-series data from natural disturbances (i.e., hurricanes) at the Beaufort Inlet site and explore various key environmental parameters such as temperature (+3 °C) and primary production as key environmental parameters. Similar work will be done more broadly for the ocean. Impacts of the research extend beyond the targeted coastal dataset as, if successful, the approach can be applied to other diverse study systems such as soil and human microbiomes. It can also be used to address questions about environmental filtering, disturbance and stochasticity, each of which is critical to understanding the factors and processes that govern microbial responses to environmental change.

This project responds to the NSF2026 Idea Machine winning entries of "Global Microbiome in a Changing Planet" and "Imagine a Life with Clean Oceans"

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This project is closely related to Pivers Island Coastal Observatory: https://www.bco-dmo.org/project/2281

The biodiversity found in nature is essential for healthy ecosystems and human well-being. However, the disruption and decline of Earth’s biodiversity is currently occurring at an unprecedented rate. The resulting shifts in biodiversity dynamics — including changes in the scope and structure of biodiversity — are increasingly significant but not well understood. Shifting biodiversity dynamics (i.e., shifts in scope, structure, and interactions of biodiversity) in turn influence functional biodiversity, which includes the roles of traits, organisms, species, communities, and ecosystem processes in natural systems. Changes in biodiversity dynamics and functional biodiversity are components of future planetary resilience under environmental change, including climate change. The connection between functional biodiversity and biodiversity dynamics on a changing planet is the main focus of the Biodiversity on a Changing Planet (BoCP) program. The program encourages proposals that integrate ecological and evolutionary approaches in the context of the continual gain, loss, and reorganization of biodiversity on a changing planet. To advance a comprehensive understanding of functional biodiversity requires a highly integrative approach – including consideration of spatial and temporal dimensions from the organismal to the ecosystem level, and from recent to deep timescales.

The BoCP program is a cross-directorate and international program led by NSF that invites submission of interdisciplinary proposals addressing grand challenges in biodiversity science within the context of unprecedented environmental change, including climate change. Successful BoCP proposals will test novel hypotheses about functional biodiversity and its connections to shifting biodiversity on a changing planet, with respect to both how environmental change affects taxonomic and functional biodiversity, as well as how the resulting functional biodiversity across lineages feeds back on the environment. Proposals that seek to improve predictive capability about functional biodiversity across temporal and spatial scales by considering the linkages between past, present, and future biological, climatic, and geological processes are also encouraged. While this focus complements several core programs at NSF, it differs by requiring an integrative approach to understanding functional biodiversity as it relates to shifting biodiversity under changing environmental conditions.

The program supports both US-only collaborative proposals and proposals with international partnerships with the National Natural Science Foundation of China (NSFC), the São Paulo Research Foundation (FAPESP) of Brazil, and the National Research Foundation (NRF) of South Africa. International collaborative proposals are to be submitted jointly, with the US PIs submitting to NSF and the collaborating Chinese, Brazilian, or South African PIs submitting to their appropriate national funding agencies. These agreements do not preclude other international collaborations (see solicitation for additional details).