Weekly surface water samples from MICRO time series station at Newport Pier from 2012 to 2018

Website: https://www.bco-dmo.org/dataset/564351
Data Type: Other Field Results
Version: 2
Version Date: 2018-08-20

Project
» Biological Controls on the Ocean C:N:P ratios (Biological C:N:P ratios)

Programs
» Dimensions of Biodiversity (Dimensions of Biodiversity)
» Ocean Carbon and Biogeochemistry (OCB)
ContributorsAffiliationRole
Martiny, AdamUniversity of California-Irvine (UC Irvine)Principal Investigator, Contact
Larkin, Alyse A.University of California-Irvine (UC Irvine)Co-Principal Investigator
Gegg, Stephen R.Woods Hole Oceanographic Institution (WHOI BCO-DMO)BCO-DMO Data Manager
York, Amber D.Woods Hole Oceanographic Institution (WHOI BCO-DMO)BCO-DMO Data Manager


Coverage

Spatial Extent: Lat:33.6062 Lon:-117.9312
Temporal Extent: 2012-01-11 - 2018-12-26

Dataset Description

Weekly surface water samples from Newport Pier, CA collected between 11 January 2012 and 26 December 2018.

This project is a 4-year continuation of a time series with weekly surface water sampling from the Microbes in the Coastal Region of Orange County (MICRO) time series station at Newport Pier (33°36.37’N, 117°55.87’W). We measured dissolved nitrate and phosphate in the water, as well as particulate organic carbon, nitrogen and phosphorus.

These data were published in Fagan et al. (2019) and a pending publication, Larkin et al. (in press).


Methods & Sampling

Sampling and analytical procedures:

Surface water from the “Microbes in the Coastal Region of Orange County” (MICRO) time series station at Newport Pier (33 36.37’ N, -117 55.87’ W ) was collected weekly, in the morning, from 11 Jan 2012 to 26 December 2018. One liter polycarbonate bottles rinsed three times prior to sampling were filled for quantification of particulate organic matter and nutrient concentrations (three samples each from two bottles for a total of six replicates). Temperature salinity, and chlorophyll a were continuously monitored using an automated shore station mounted next to the sampling site (www.sccoos.org).

Triplicate 300 ml samples for POC/PON or POP from each bottle were filtered within an hour of collection through pre- combusted (500 C, 5 h) 25 mm GF/F filters (Whatman, MA). Each filter was rinsed with Milli-Q water before being fitted in order to remove potential P residues. The filtrate from the initial filtration was collected and used for macronutrient quantification. The filtrate was filtered through a 0.2 μm syringe filter into a 50 ml tube. Triplicates were collected for both macronutrient and stored at −20 C.

Nitrate and phosphate samples were collected in prewashed 50 mL Falcon tubes and filtered through a 0.2 μm syringe filter and stored at −20 C until further analysis. Soluble reactive phosphorus (SRP) concentrations were determined using the magnesium induced co-precipitation (MAGIC) protocol and calculated against a potassium monobasic phosphate standard. (Karl and Tien, 1992; Lomas et al., 2010). Nitrate samples were treated with a solution of ethylenediaminetetraacetate and passed through a column of copperized cadmium fillings (Knap et al., 1993). Measurements were conducted using the same standards and protocols throughout the time series.

After thawing, POC/PON filters were allowed to dry overnight at 65◦C before being packed into a 30 mm tin capsule (CE Elantech, Lakewood, New Jersey). Samples were then analyzed for C and N content on the same FlashEA 1112 nitrogen and carbon analyzer (Thermo Scientific, Waltham, Massachusetts), following the Sharp (1974) protocol. POC and PON concentrations were calibrated using known quantities of atropine.

For published methodologies, see Allison et al. (2012), Fagan et al. (2019) and Martiny et al., (2016).


Data Processing Description

Issue report:

SCCOOS salinity meter down between March - May 2016

Picoplankton columns are included in the dataset to maintain consistency with previously submitted data, but flow cytometry counts were not performed by our laboratory during the 2015-2018 time period.

BCO-DMO Data Manager Processing Notes:

Version 1 (2015-07-30):
 - Generated from original file: "BCODMO_Newport.xlsx" contributed by Celine Mouginot
 - Single column for Date inserted with combined Year,Month,Day formatted as YYYYMMDD
 - Parameter names edited to conform to BCO-DMO naming convention found at Choosing Parameter Name

Version 2 (2020-08-20): [data from 2015 to 2018 appended to version 1]
* Extracted data submitted in Excel file BCODMO-Data_MICRO_NewportPier_CA-2015-2018.xlsx to csv with values as formatted in Excel (some numeric values had more precision than the set formatting).
* Columns Month, Day and Year were removed from the 2015-2018 data.  They were not in v1 and are redundant with information in Date column.
* Added to data from version 1
* blank values in this dataset are displayed as "nd" for "no data."  nd is the default missing data identifier in the BCO-DMO system.
* converted Date format to ISO 8601 format YYYY-MM-DD


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Data Files

File
weekly_surface_water.csv
(Comma Separated Values (.csv), 256.83 KB)
MD5:4daa05688c3755d2e9b54c2b7090e678
Primary data file for dataset ID 564351

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Related Publications

Allison, S. D., Chao, Y., Farrara, J. D., Hatosy, S., & Martiny, A. C. (2012). Fine-Scale Temporal Variation in Marine Extracellular Enzymes of Coastal Southern California. Frontiers in Microbiology, 3. doi:10.3389/fmicb.2012.00301
Methods
Fagan, A. J., Moreno, A. R., & Martiny, A. C. (2019). Role of ENSO Conditions on Particulate Organic Matter Concentrations and Elemental Ratios in the Southern California Bight. Frontiers in Marine Science, 6. doi:10.3389/fmars.2019.00386
Results
Karl, D. M., & Tien, G. (1992). MAGIC: A sensitive and precise method for measuring dissolved phosphorus in aquatic environments. Limnology and Oceanography, 37(1), 105–116. doi:10.4319/lo.1992.37.1.0105
Methods
Knap, A., Michaels, R., Dow, R., Johnson, K., Gundersen, J., Sorensen, A., ... & Waterhouse, T. (1993). Bermuda Atlantic time-series study methods manual (Version 3). Bermuda Biological Station for Research, US JGOFS. https://www.researchgate.net/publication/245583966_Bermuda_Atlantic_Time-series_Study_Methods_Manual_Version_3
Methods
Larkin, A. A., Moreno, A. R., Fagan, A. J., Fowlds, A., Ruiz, A., & Martiny, A. C. (2020). Persistent El Niño driven shifts in marine cyanobacteria populations. PLOS ONE, 15(9), e0238405. https://doi.org/10.1371/journal.pone.0238405
Results
Lomas, M. W., Burke, A. L., Lomas, D. A., Bell, D. W., Shen, C., Dyhrman, S. T., & Ammerman, J. W. (2010). Sargasso Sea phosphorus biogeochemistry: an important role for dissolved organic phosphorus (DOP). Biogeosciences, 7(2), 695–710. doi:10.5194/bg-7-695-2010
Methods
Martiny, A. C., Talarmin, A., Mouginot, C., Lee, J. A., Huang, J. S., Gellene, A. G., & Caron, D. A. (2015). Biogeochemical interactions control a temporal succession in the elemental composition of marine communities. Limnology and Oceanography, 61(2), 531–542. doi:10.1002/lno.10233
Methods
Sharp, J. H. (1974). Improved analysis for “particulate” organic carbon and nitrogen from seawater1. Limnology and Oceanography, 19(6), 984–989. doi:10.4319/lo.1974.19.6.0984
Methods

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Parameters

ParameterDescriptionUnits
SiteSampling Site Id text
LatSampling Site Latitude (South is negative) decimal degrees
LonSampling Site Longitude (West is negative) decimal degrees
DaysCumulative sampling days since beginning of time series integer
DateDate in ISO 8601 format YYYY-MM-DD unitless
NO3_1Nitrate replicate 1 uM
NO3_2Nitrate replicate 2 uM
NO3_3Nitrate replicate 3 uM
NO3_4Nitrate replicate 4 uM
NO3_5Nitrate replicate 5 uM
NO3_6Nitrate replicate 6 uM
SRP_1Soluble reactive phosphorus replicate 1 uM
SRP_2Soluble reactive phosphorus replicate 2 uM
SRP_3Soluble reactive phosphorus replicate 3 uM
SRP_4Soluble reactive phosphorus replicate 4 uM
SRP_5Soluble reactive phosphorus replicate 5 uM
SRP_6Soluble reactive phosphorus replicate 6 uM
POP_1Particulate organic phosphorus replicate 1 uM
POP_2Particulate organic phosphorus replicate 2 uM
POP_3Particulate organic phosphorus replicate 3 uM
POP_4Particulate organic phosphorus replicate 4 uM
POP_5Particulate organic phosphorus replicate 5 uM
POP_6Particulate organic phosphorus replicate 6 uM
PON_1Particulate organic nitrogen replicate 1 uM
PON_2Particulate organic nitrogen replicate 2 uM
PON_3Particulate organic nitrogen replicate 3 uM
PON_4Particulate organic nitrogen replicate 4 uM
PON_5Particulate organic nitrogen replicate 5 uM
PON_6Particulate organic nitrogen replicate 6 uM
POC_1Particulate organic carbon replicate 1 uM
POC_2Particulate organic carbon replicate 2 uM
POC_3Particulate organic carbon replicate 3 uM
POC_4Particulate organic carbon replicate 4 uM
POC_5Particulate organic carbon replicate 5 uM
POC_6Particulate organic carbon replicate 6 uM
Chl_aChlorophyll a ug/L
TpTemperature Degs C
SalinitySalinity psu
picoeukPicoeukaryote concentration cells/ml
PNEPNE, Pico/nano-eukaryotic phytoplankton concentration cells/ml
ProProchlorococcus concentration cells/ml
SynSynechococcus concentration cells/ml


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Instruments

Dataset-specific Instrument Name
FlashEA 1112 Analyzer; Thermo Scientific, Waltham, Massachusetts
Generic Instrument Name
Elemental Analyzer
Generic Instrument Description
Instruments that quantify carbon, nitrogen and sometimes other elements by combusting the sample at very high temperature and assaying the resulting gaseous oxides. Usually used for samples including organic material.


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Deployments

MICRO_NewportPier_CA

Website
Platform
MICRO time series station at Newport Pier
Start Date
2012-01-11
End Date
2015-04-30


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Project Information

Biological Controls on the Ocean C:N:P ratios (Biological C:N:P ratios)

Coverage: western North Atlantic; 60N to 20N along 66W longitude; 20N to 15S in the tropical Pacific


One of the fundamental patterns of ocean biogeochemistry is the Redfield ratio, linking the stoichiometry of surface plankton with the chemistry of the deep ocean. There is no obvious mechanism for the globally consistent C:N:P ratio of 106:16:1 (Redfield ratio), especially as there is substantial elemental variation among plankton communities in different ocean regions. Thus, knowing how biodiversity regulates the elemental composition of the ocean is important for understanding the ocean and climate as a whole -- now and in the future.

The conceptual hypotheses for this study are as follows: 1. The C:N:P ratio of a cell is constrained by its broad taxonomic group, which determines, for example, whether it has an outer shell, its size, functional metabolism, membrane lipid composition. 2. Within a taxon, there is high genetic diversity. Some of this genetic diversity is potentially laterally transferred, or can be lost within taxa, and confers various functional abilities (organic phosphate assimilation, nitrate assimilation, photoheterotrophy, etc.). Functional diversity provides the cell with further flexibility, such as the ability to respond to varying nutrient supply rates/ratios, and affects a cell's C:N:P ratio within the range specified by the taxon. 3. Given these taxonomic and genetic constraints, a cell is physiologically plastic and modifies how it allocates cellular resources in response to nutrient supply rates/ratios in the environment. 4. The microbial diversity (taxonomic, genetic, and functional) of the surface ocean varies over time and space, driven by many factors in addition to nutrients. The sum of this mixture composes the ecosystem C:N:P, the ratio that Redfield described.

Based on this framework, the CoPIs will make field observations of taxon-specific stoichiometry and growth rates, genomic analyses, and conduct laboratory chemostat experiments to improve understanding of how ocean taxonomic, genetic, and functional biodiversity control the stoichiometry of the surface ocean plankton. Their analyses of these data would lead to a mechanistic understanding of variations in the Redfield ratio, both spatially and temporally.

This study will greatly expand knowledge of the genomic diversity among ocean microbes and how this diversity affects biogeochemistry. The stoichiometry of the ocean's microbes is a parameter that nearly every chemical or biological oceanographer uses, from converting measurements made in one element to another, to estimating regional and global nitrogen budgets. The research also has important implications for the global carbon budget and any changes that might result from climate change.

To understand mechanistically temporal and spatial variability of the plankton C:N:P ratio, biodiversity must be studied not only at the traditional taxonomic level, but at the genetic and functional levels which dictate organism response to their environment. Data will be integrated into a combined ocean ecological, evolutionary, and biogeochemical model, with flexible stoichiometry, including cellular biochemical allocations. Seeding a coupled physical-biological model of the oceans with multiple competing genotypes enables the exploration of ecological and evolutionary patterns of resource acquisition and C:N:P ratios. Developing a more mechanistic examination of the course of ecology and evolution, in which laboratory and field data define tradeoffs between different growth and nutrient acquisition strategies, would estabblish the framework of adaptive dynamics for determining "evolutionarily convergence". Finally, model outcomes will be evaluated against field data.

The field work planned for this project includes several cruises: BV46 (September/October 2011), BV48 (September 2012), a June 2013 cruise from Bermuda to the Labrador Sea, and a cruise from Hawaii to Tahiti (May 2014). Additionally, samples will be be acquired during cruises of opportunity.



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Program Information

Dimensions of Biodiversity (Dimensions of Biodiversity)


Coverage: global


(adapted from the NSF Synopsis of Program)
Dimensions of Biodiversity is a program solicitation from the NSF Directorate for Biological Sciences. FY 2010 was year one of the program.  [MORE from NSF]

The NSF Dimensions of Biodiversity program seeks to characterize biodiversity on Earth by using integrative, innovative approaches to fill rapidly the most substantial gaps in our understanding. The program will take a broad view of biodiversity, and in its initial phase will focus on the integration of genetic, taxonomic, and functional dimensions of biodiversity. Project investigators are encouraged to integrate these three dimensions to understand the interactions and feedbacks among them. While this focus complements several core NSF programs, it differs by requiring that multiple dimensions of biodiversity be addressed simultaneously, to understand the roles of biodiversity in critical ecological and evolutionary processes.


Ocean Carbon and Biogeochemistry (OCB)


Coverage: Global


The Ocean Carbon and Biogeochemistry (OCB) program focuses on the ocean's role as a component of the global Earth system, bringing together research in geochemistry, ocean physics, and ecology that inform on and advance our understanding of ocean biogeochemistry. The overall program goals are to promote, plan, and coordinate collaborative, multidisciplinary research opportunities within the U.S. research community and with international partners. Important OCB-related activities currently include: the Ocean Carbon and Climate Change (OCCC) and the North American Carbon Program (NACP); U.S. contributions to IMBER, SOLAS, CARBOOCEAN; and numerous U.S. single-investigator and medium-size research projects funded by U.S. federal agencies including NASA, NOAA, and NSF.

The scientific mission of OCB is to study the evolving role of the ocean in the global carbon cycle, in the face of environmental variability and change through studies of marine biogeochemical cycles and associated ecosystems.

The overarching OCB science themes include improved understanding and prediction of: 1) oceanic uptake and release of atmospheric CO2 and other greenhouse gases and 2) environmental sensitivities of biogeochemical cycles, marine ecosystems, and interactions between the two.

The OCB Research Priorities (updated January 2012) include: ocean acidification; terrestrial/coastal carbon fluxes and exchanges; climate sensitivities of and change in ecosystem structure and associated impacts on biogeochemical cycles; mesopelagic ecological and biogeochemical interactions; benthic-pelagic feedbacks on biogeochemical cycles; ocean carbon uptake and storage; and expanding low-oxygen conditions in the coastal and open oceans.



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Funding

Funding SourceAward
NSF Division of Ocean Sciences (NSF OCE)
NSF Division of Ocean Sciences (NSF OCE)

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