Coastal Carbon Research Coordination Network

Read the CCRCN Blue Carbon Inventory Report

The CCRCN collaborated with Pew Charitable Trusts to produce a report on the state of blue carbon data in the contiguous United States. In this first-ever inventory of our database, we assess the quantity, quality, and representativeness of soil core data on a state-by-state basis. Check out the national overview to find out how your state scores on our blue carbon data report card!

Participate in our Methane-Flux Chamber Synthesis

Have chamber-based methane flux data from a coastal wetland, old study, new, or forthcoming? Find out how to participate in a new community paper and synthesis dataset!

Mark your AGU 2019 Calendars for our annual Townhall on Thursday Night at 6:15 pm Moscone West - 2014, L3. Come learn about the network, get progress updates from our working groups and your input on how we use our next three years.

Take our Expert Elicitation Survey

Are you an expert on belowground plant traits and/or decay properties in tidal wetlands? Spend 15 to 30 minutes with our expert elicitation application and give us your best educated guess on several hard to measure traits. The survey is anonymous and will be used to inform our priors for a Bayesian data-model integration study.

Recording of Coastal Carbon Atlas Webinar Available

Missed our recent webinar on the Network and our Coastal Carbon Atlas? Thanks to host Restore America's Estuaries, a recording is available online. Also check out RAE's Blue Carbon webinar series for other upcoming discussions of new policy, research, and tools in the field.

Upcoming Webinar with Restore America's Estuaries

Learn more about the CCRCN and our Coastal Carbon Atlas by registering for the next Blue Carbon in Practice webinar on Wednesday, April 24th at 2:00pm EST, hosted by Restore America's Estuaries. We will discuss the mission of the Network, how to use the Atlas, and how you can get involved. Register for the webinar here.

Feature in SERC Shorelines Blog

The most recent addition of the Smithsonian's coastal newsletter featured a cover story on the CCRCN's data synthesis and modeling efforts related to carbon storage, and provides a clean overview of the importance of carbon (and especially methane) accounting. Check out the full story here.

Introducing the Coastal Carbon Atlas

The CCRCN is pleased to announce the public release of the Coastal Carbon Atlas as a feature of our Data Clearinghouse. Using this web map portal, you can visualize, query, and download directly from the CCRCN database, and provided with raw data is helpful resources and a catered bibliography. Click here to learn more about the CCRCN Data Clearinghouse.

Welcome to New Steering Committee Members, Thanks to Members Emeritus

We would like to welcome Dr. Steve Crooks, Dr. Patty Oikawa, and Dr. Ken Krauss to our Steering Committee, congratulations! We would also like to deeply thank Dr. Lisamarie Windham-Myers and Dr. Kevin Kroeger, who are rotating off of our steering committee, for their vital service in getting the CCRCN funded and for advising us during our first year. Read about the CCRCN administration here.

CCRCN Hosts Successful Inaugural Working Group

On December 8-9 2018, 17 members of the Soil Carbon working group convened at the Smithsonian Environmental Research Center, integrating biogeochemical, geospatial, botanical, and geomorphological expertise to accelerate modeling and mapping efforts. Learn more about the working group here.

Now Accepting Nominations for our Steering Committee

The CCRCN is seeking nominations for three new members on our steering committee. Committee members contribute towards shaping the goals and timelines of the CCRCN and participate in collaborative research. Review will begin on December 1, 2018. See here for more information.

CCRCN Town Hall at AGU Fall Meeting, December 13 2018

The CCRCN will be hosting another town hall at the American Geophysical Union Fall meeting, December 10-14 2018. The town hall will be held from 18:15-19:15 on Thursday, December 13 in in the Marriott Marquis.​ Learn more about the town hall here.

Soil Carbon Working Group Up and Running

The CCRCN's inaugural working group began collaborating in July. The group seeks to determine how much variation in carbon stocks and burial rates is attributable to scientific practices versus environmental covariates, and to develop map and model products. Learn more on the Soil Carbon Working Group page.

Learn Data Manipulation with the CCRCN R Coding Tutorials

We have just made available a suite of coding tutorials that utilize RStudio and tidyverse packages to teach data manipulation and visualization skills. These exercises, which are geared towards the introductory R user, enable you to explore the CCRCN Soil Carbon Data Release (version 1), but also will improve data skills related to your own projects.

Apply for a Working Group

The application portal for the CCRCN working groups is now open. Five working groups, each culminating with an in-person workshop, will take place over the next five years. The steering committee has decided to announce the titles and timing of the first two working groups, as well as suggestions for future working groups. If you are interested in participating in any of the working groups, please fill out an application. The survey will remain open indefinitely, however we will begin considering applicants for our working group 1 on Thursday, July 12th.

Apply Now

Soil Carbon Data Release

The dataset for a NASA CMS-funded and CCRCN-managed publication in Scientific Reports has now been made public via Smithsonian Libraries. Data from 1534 soil cores are available, including per-depth soil organic matter and carbon metrics, plant species identity, state of human impact, field and lab methodology, and core metadata. The paper, led by CCRCN manager James Holmquist, reveals that simple strategies are the most effective for mapping soil carbon, for now.

Public Comment Open on Three Project Principles Documents

In an effort to be responsive to the community, we would like to announce the beginning of a public comment period for 3 key documents: CCRCN Governance, Data Management Principles, and Controlled Vocabulary for our Tidal Soil Carbon Synthesis Products. Public comment will be open for two weeks starting Monday April 2 and ending Friday April 13th.

View Documents

First Data Release

We officially launch starting January 2018, but we are already hosting our first synthetic dataset on the Smithsonian's GitHub page: a literature review supporting the U.S. Coastal Wetland National Greenhouse Gas Inventory. The page includes summary tables, a report, and an open workflow including SAS code used to create the dataset.

Building a Collaborative Network for Coastal Carbon Cycle Synthesis

Tidal marshes, mangrove swamps and seagrass meadows are unique ecosystems found on coastlines worldwide. These wetlands support specialized plant, microbe and animal species that collectively form some of the Earth’s most productive ecosystems, influencing the ecology of estuaries and coastal oceans. Coastal wetlands are also under severe pressure from human activity which threatens to diminish the many benefits they provide to people and aquatic food webs. Among these benefits is the fact that they remove large amounts of the greenhouse gas carbon dioxide from the atmosphere and bury it in soils for centuries to millennia. Indeed, these ecosystems account for nearly 50% of the organic carbon buried in the oceans despite occupying less than 1% of ocean area. This surprising fact suggests an opportunity: that protecting, restoring and managing these ecosystems could help manage greenhouse gas concentrations in addition to the list of other ecological and social benefits they provide. The pace of research on this topic has accelerated and is now too rapid to be synthesized by individual investigators. The goal of this Research Coordination Network is to advance the synthesis of coastal wetland carbon cycle data.

The Coastal Carbon Research Coordination Network will accelerate scientific discovery, advance science-informed policy, and improve coastal ecosystem management by: (1) developing a community dedicated to coastal wetland carbon science for basic research, policy development, and management, (2) exploring the ecological links between coastal wetlands, estuaries, and the atmosphere, and (3) sharing data and analysis tools that support the diverse needs of scientists, policy makers and managers. Activity 1 is a repository for participant-contributed data, and a central portal for downloading data from repositories of interest to the coastal carbon community. Activity 2 is a Coastal Carbon website to attract participation of diverse users by providing a variety of resources that meet their needs. It will provide data analysis tools, a knowledge sharing resource, a video library of training modules in standard methods, a code library to support modeling, links to publications, and a webinar library. Activity 3 is outreach via a series of webinars and 'town hall' gatherings at professional meetings. Activity 4 is a series of workshops on scientific gaps in coastal carbon. Activity 5 is a web-based tool for modeling global warming potentials.

Current CCRCN Steering Committee

The Steering Committee oversees and advises the operations of the CCRCN, and guides the Network's overall mission. The Committee is composed of broad representation from academic institutions, federal agencies, and non-profit organizations, in order to provide a holistic understanding of the state of coastal carbon science.

CCRCN Personnel

Questions? Contact coastalcarbon@si.edu.

The Coastal Carbon Research Coordination Network is a consortium of biogeochemists, ecologists, pedologists, and coastal land managers with the goal of accelerating the pace of discovery in coastal wetland carbon science by providing our community with access to data, analysis tools, and synthesis opportunities. We have accomplished this goal by growing iteratively with community feedback, facilitating the sharing of open data and analysis products, offering training in data management and analytics, and leading topical working groups aimed at quantitatively reducing uncertainty in coastal greenhouse gas emissions and storage. We also curate a Data Clearinghouse that offers infrastructure and tools for accessing, visualizing, and summarizing data. Major achievements include:

Outreach Count
Mailing List Subscribers 200
Past and Present Steering Committee Members 9
Past and Present Working Group Participants 14

The CCRCN is overseen by three investigators and a rotating steering committee.

Call for CCRCN Steering Commitee Member Nominations

The Coastal Carbon Research Coordination Network Now Accepting Nominations for our Steering Committee

The Coastal Carbon Research Coordination Network (CCRCN) is seeking nominations for three new members on our steering committee. The CCRCN is an NSF-funded 5-year project hosted at Smithsonian Environmental Research Center with the goal of accelerating the pace of discovery in coastal carbon science. We do this by serving a diverse user base of scientists and practitioners with synthetic data, workshops, and open source modeling tools. Learn more about our project here.

Steering committee membership is a volunteer position and is essential to CCRCN governance. There are myriad benefits for participation. Steering committee members advise CCRCN administrators and personnel on critical programs and initiatives, direct and participate in collaborative research, and the CCRCN recognizes steering committee institutions as partner organizations. This is an opportunity to take a substantial leadership role within the coastal carbon community. Committee membership is a one-year tenure minimum with potential to extend or expand into other CCRCN leadership roles.

Duties for all steering committee members include:
• Attend a 1-hour monthly teleconference
• Attend an annual Town Hall at the American Geophysical Union Fall Meeting
• Contribute towards shaping the goals and timelines of CCRCN products
• Give regular constructive feedback to CCRCN personnel to improve project implementation and governance
• Network to identify potential new sources of funds or collaboration
• Vote on major decisions such as changes to the CCRCN governance structure

Steering committee members may also take on additional duties such as:
• Assist in the leadership and implementation of topical working groups
• Write grants or raise other funds to further CCRCN goals
• Speak on behalf of the CCRCN at meetings and conferences

We hope to address specific project needs with these new additions, but emphasize that we will take all nominees into consideration.

1. We will prioritize a nominee who can represent the U.S. Geological Survey, as our two representatives from that vital partner agency are currently stepping down.
2. We will prioritize a nominee willing to take a leadership role in planning and executing our Methane Working Group (“Improved process modeling and mapping of tidal wetland methane emissions”) starting in Summer 2019, with an in-person workshop in December 2019.
3. We will prioritize a candidate who can advise us on our product development on behalf of our stakeholder community. We strongly encourage students and early career scientists to apply, as well as women and people from underrepresented communities, so that our steering committee can better reflect the diversity of people within our growing field.

If you would like nominate yourself or a colleague please email CoastalCarbon@si.edu by December 1st. If nominating yourself please include a C.V. and a brief statement (no more than 300 words) indicating: 1. Your ability to execute the duties of an CCRCN steering committee member, 2. Any additional duties you would be willing to integrate into your CCRCN service, 3. How your participation would meet stated CCRCN needs, 4. Any ideas you have for pushing the CCRCN forward into 2019 and beyond.

The steering committee will meet to vote on nominations in December of 2019 and we will announce new members at our AGU Town Hall on Thursday December 13th.

Please email us at CoastalCarbon@si.edu if you have any inquiries regarding the priorities and initiatives of the CCRCN.

More about the CCRCN Steering Committee

The CCRCN is hosed at Smithsonian Environmental Research Center (SERC) staffed by two employees (James Holmquist, Manager; and David Klinges, Data Technician), supervised by two SERC principle investigators (J Patrick Megonigal, CCRCN Director, and Emmett Duffy), and advised remotely by five steering committee members (Lisamarie Windham-Myers, USGS; Kevin Kroeger, USGS; Jim Tang, MBL; Emily Pigeon, Conservation International; and Jorge Ramos, CI). Steering committee members serve for one year, but can be reappointed for one year at the discretion of the CCRCN Director. Candidates for the steering committee can nominate themselves or be nominated by their colleagues. The steering committee will vote on replacements, and members stepping down will help to choose their own replacement. Rotations are staggered so that no more than half of the rotating members on the Steering Committee are replaced in a given year. Learn more about CCRCN’s governance here and about current leadership here.

Coastal Carbon Working Groups

In order to quantitatively improve the state of the science through collaboration and training, one of our proposed activities is to use five topical working groups over the next five years to share data and expertise.

Apply for Working Groups

Interested in joining a future working group? Express your interest here. Although no formal applications are currently being received, entries here will be used to gauge interest for topics that deserve attention.

Methane Working Group

Time and Location: December 7 and 8, 2019, hosted at the NASA Ames Research Center, Moffett Field, California

Overarching goal of CH4 working group: Improve predictions of methane emissions from coastal wetlands. Specifically, we aim to compile all methane flux data from coastal habitats (not mangroves) in the CONUS to parameterize and validate a set of nested process-based CH4 models.

Research Questions

1. How well can we predict methane emissions from coastal wetlands? In other words, what are the main sources of error? What types of wetlands are the most difficult to model (along spectrum of salinity, disturbance/age, plant community etc.)
2. What data streams are needed to improve our predictions?
3. Can our process-based models predict interannual variability? If not, why? How does this influence predictions of CH4 budgets in the future?
4. What guidance can we give to the science and management communities based on these efforts?

The Coastal Carbon Research Coordination Network (CCRCN) is dedicated to providing a welcoming and supportive environment for all people, regardless of background or identity. However, we recognise that some groups in our community are subject to historical and ongoing discrimination, and may be vulnerable or disadvantaged. Membership in such a specific group can be on the basis of characteristics such as gender, sexual orientation, disability, physical appearance, body size, race, nationality, sex, colour, ethnic or social origin, pregnancy, citizenship, familial status, veteran status, genetic information, religion or belief, political or any other opinion, membership of a national minority, property, birth, age, or choice of text editor. We do not tolerate harassment of participants on the basis of these categories, or for any other reason.

Harassment is any form of behaviour intended to exclude, intimidate, or cause discomfort. Because we are a diverse community, we may have different ways of communicating and of understanding the intent behind actions. Therefore we have chosen to prohibit certain forms of behaviour in our community, regardless of intent. Prohibited harassing behaviour includes but is not limited to:

• Written or verbal comments which have the effect of excluding people on the basis of membership of a specific group listed above
• Causing someone to fear for their safety, such as through stalking, following, or intimidation
• The display of sexual or violent images
• Unwelcome sexual attention
• Nonconsensual or unwelcome physical contact
• Sustained disruption of talks, events or communications
• Incitement to violence, suicide, or self-harm
• Continuing to initiate interaction (including photography or recording) with someone after being asked to stop
• Publication of private communication without consent

Behaviour not explicitly mentioned above may still constitute harassment. The list above should not be taken as exhaustive but rather as a guide to make it easier to enrich all of us and the communities in which we participate. All CCRCN interactions should be professional regardless of location: harassment is prohibited whether it occurs on- or offline, and the same standards apply to both.

Enforcement of the Code of Conduct will be respectful and not include any harassing behaviors. Any changes to the meaning of this Code of Conduct must be approved by majority vote of the CCRCN Steering Committee. CCRCN working group and network members will be informed of these changes and any concerns that are raised will be discussed by the Steering Committee.

Thank you for helping make this a welcoming, friendly community for all.

This code of conduct is a modified version of that used by The Carpentries, which was a modified version of that used by PyCon, which in turn is forked from a template written by the Ada Initiative and hosted on the Geek Feminism Wiki. Contributors to this document: Adam Obeng, Aleksandra Pawlik, Bill Mills, Carol Willing, Erin Becker, Hilmar Lapp, Kara Woo, Karin Lagesen, Pauline Barmby, Sheila Miguez, Simon Waldman, Tracy Teal.

Members involved in CCRCN collaborations are entitled to their own intellectual property, and distinctions should be made clarifying what is individual property versus group property. Here are the principles to which the Soil Carbon working group adheres to:

• Anything developed as a group is collective intellectual property (IP) that members collectively decide what to do with (such as assigning a creative commons license or signing rights over to a publisher).
• Anything developed outside the group is the intellectual property of the individual who produced it or their institution (whichever is applicable).
• Any outside IP needs to be properly acknowledged and cited in CCRCN work.
• All members agree not to share unpublished work outside the group without explicit permission to do so.
• At the end of the project, working group members commit to making code and derivative data products that the group developed collectively open and available as part of data releases associated with papers and reports.
• The working group will publish products that only include data that is or will be publicly available (via repository) at the time of working group publication.

Participant Biographies

Dr. Ariane Arias-Ortiz

Dr. Ariane Arias Ortiz is an environmental and marine scientist interested in carbon biogeochemical cycling, ecosystems dynamics and the use of radionuclides and tracers in environmental records/materials to study present, past and future processes related to climate change. Her PhD research focused in understanding the capacity of Blue Carbon ecosystems in storing carbon at different time scales and the carbon stock losses and loss rates associated to the degradation of these habitats. Currently, as a NOAA Climate and Global Change post-doctoral fellow at the Berkeley Biometeorology Lab (UCB) and Biogeochemistry lab (UCSC), she studies the balance between carbon burial, carbon emissions and lateral export in different wetland types (tidal and tidally-restricted, intact and restored, saline and freshwater) as well as the processes that control soil organic matter decomposition and associated CO2 and CH4 emissions.​.

Dr. Scott Bridgham

Scott Bridgham is a professor and an ecosystem ecologist in the Institute of Ecology and Evolution at the University of Oregon. He has examined the biogeochemistry of methane dynamics in freshwater wetlands since the late 1980s, working primarily in peatlands. He was the lead of the first State of the Carbon Cycle Report (SOCCR) for North American wetlands and participated in the second report that was published in 2018. He currently is examining methane dynamics at a large warming and elevated atmospheric CO2 manipulative experiment (SPRUCE) in a northern Minnesota peatland. More recently he started examining trace gas emissions and soil carbon sequestration rates in two Oregon estuaries in restored, natural, and disturbed conditions along salinity gradients.

Dr. Etienne Fluet-Chouinard

Etienne is a Postdoctoral Research Fellow in the Department of Earth Systems Science. His research at Stanford University contributes to the Global Carbon Project methane budget by constraining the global distribution of wetland types using remote sensing and hydrological modeling, a key source of uncertainty in the global methane budget. Etienne’s background centers on the application of geospatial tools, remote sensing and modeling to the study of limnology and freshwater. His prior research has spanned a range of topics related to freshwater ecosystems at the global scale, ranging from anthropogenic stressors assessment, historical wetland drainage, wetland classification, conservation planning and inland fisheries underreporting.

Dr. Sara Knox

Dr. Sara Knox is an assistant professor in the Department of Geography at the University of British Columbia. Dr. Knox’s research focuses on measuring and modeling trace gas, water, and energy exchange in restored and natural wetlands to improve our understanding of the impacts of climate variability and human activities on wetland carbon and greenhouse gas dynamics. She investigates how wetland greenhouse gas fluxes respond to a changing climate and disturbances, and how we can modify land management practices for climate change mitigation and adaptation. She combines micrometeorological measurements with remote sensing and modelling to understand soil-plant-atmosphere interactions across a range of spatial and temporal scales. This research is done in collaboration with a broad group of researchers and institutions to help inform and advance climate policy. Visit her website here.

Dr. Gavin McNicol

Gavin McNicol is a postdoctoral fellow at Stanford University where he contributes to the Global Carbon Project’s FLUXNET-CH4 synthesis activity. His PhD from UC Berkeley focused on wetland biogeochemistry and ecosystem ecology in restored California Delta marshes where he downscaled ecosystem methane fluxes to wetland patches and used isotopic analyses to understand the provenance of wetland methane production. Gavin is broadly interested in improving understanding of methane biogeochemistry across microbial to global scales, including mitigation opportunities in human managed systems such as restored wetlands and the waste sector. In his current position he is compiling eddy covariance methane flux measurements across FLUXNET and leading an effort to produce a globally gridded product for freshwater wetland methane fluxes using remote sensing and machine learning techniques.

Dr. Brian Needelman

Dr. Brian Needelman is an Associate Professor of Soil Science at the University of Maryland in the Department of Environmental Science & Technology. He teaches and performs research in the fields of soil science, pedology, coastal wetlands, and coastal resiliency. His coastal wetland research focuses on management and restoration practices to increase tidal marsh sustainability. He also conducts research on greenhouse gas emissions and accounting in coastal wetland systems including carbon sequestration and methane emissions. His coastal resiliency research focuses on the integration of natural and social science approaches to better understand and increase the resilience of coastal socio-ecological systems.

Sarah Russell

Sarah Russell is completing an MSc in Geography at the University of British Columbia. She received a BS in Biological Sciences from Wellesley College in 2017. She is interested in land-atmosphere carbon dynamics and quantifying the terrestrial carbon sink. Her research at UBC involves modeling greenhouse gas fluxes from restored tidal wetlands in the Sacramento-San Joaquin River Delta.

Dr. Debjani Sihi

I am an environmental biogeochemist with a broad research interest in the role of microbial- and enzyme-mediated processes in soil organic matter decomposition and greenhouse gas emissions from natural and managed systems. I have taken leadership roles in various research projects at the interface of soil microbial ecology, evolutionary biology, and ecosystem ecology. My research work includes both empirical studies and process-based modeling. I use biogeochemical models to evaluate the fate of soil (and ecosystem) carbon (and nutrient) in the face of climate change in systems ranging from the tropics (El Yunque National Forest in Puerto Rico) and subtropics (Florida Everglades) to temperate (Harvard Forest, MA, USA) and boreal transition forests (Howland Forest, ME, USA).

Dr. Lisa-Marie Windham Myers

Dr. Lisamarie Windham-Myers is a wetland ecologist and lead scientist for the USGS-NRP program “Plant:Soil:Water Interactions in Wetland Ecosystems”. Broadly-trained in ecosystem ecology, her research focuses on plant physiology and its influence on carbon, nutrient, and trace-metal biogeochemistry. Her approaches span landscape-to-molecular scales as necessary to understand how human and stochastic alterations of wetland structure influence wetland function.  A San Francisco Bay native, her local research sites represent a wide range of salinity and management conditions, from rice agriculture to coastal and restored wetlands.  Lisa serves in several local, national and international science advisory efforts to evaluate wetland management and modeling approaches to quantify wetland carbon sequestration, greenhouse gas budgets and/or mercury methylation and export. Visit her website here

Dr. Patty Oikawa

Dr. Patty Oikawa is an assistant professor in the Department of Earth and Environmental Sciences at the California State University, East Bay. Patty is a biogeochemist who specializes in biosphere-atmosphere interactions. Her research investigates how land management practices influence greenhouse gas emissions and the role of land management in climate change mitigation. Patty employs field monitoring and manipulation techniques with a focus on micrometeorology. She also specializes in process-based biogeochemical modeling and model-data fusion approaches. She collaborates with regional to global-scale modeling projects and is actively incorporating models into carbon policies in California. Visit her website for more information.

Dr. James Holmquist

James Holmquist is an ecologist at the Smithsonian Environmental Research Center, specializing in global change and carbon cycling in wetlands. In 2015 he joined a NASA-funded project tracking U.S. coastal wetland greenhouse gas storage and emissions. He now manages the Coastal Carbon Research Coordination Network, and is the PI of a NASA Carbon Monitoring Systems project on Forecasting Coastal Carbon. James aims to improve the state of science and management using data synthesis, teamwork, and training.

Dr. Patrick Megonigal

Pat Megonigal is Senior Scientist and Associate Director of Research at the Smithsonian Environmental Research Center. Dr. Megonigal is an ecosystem ecologist with research interests in carbon and greenhouse gas cycling in wetlands and forests, particularly as they relate to global change. He is the Lead Investigator of the Smithsonian’s Global Change Research Wetland, a long-term research site dedicated to understanding the stability of tidal wetlands faced with accelerated sea level rise and biogeochemical interactions between wetlands and estuaries. Dr. Megonigal is a contributing author to the Coastal Blue Carbon Handbook and the VCS Methodology on Restoration of Tidal Wetlands and Seagrasses, and he is a member of the Scientific Working Group of the Blue Carbon Initiative.

Dr. Jim Tang

Jim Tang is interested in ecosystem biogeochemistry, soil-plant-atmosphere interactions, and global change ecology. His research focuses on the impacts of climate change and human activities on ecosystem processes and functions, and the feedback to the climate and Earth system. He uses observational, experimental, and modeling approaches to understand and simulate carbon, nitrogen, and water cycles within ecosystems and between the Earth surface and the atmosphere across various scales. His research improves our understanding of ecosystem services and informs sound environmental and climate policies.

Website Section
Coastal Carbon

Soil Carbon Working Group

The Coastal Carbon Research Coordination Network (CCRCN) is dedicated to providing a welcoming and supportive environment for all people, regardless of background or identity. However, we recognise that some groups in our community are subject to historical and ongoing discrimination, and may be vulnerable or disadvantaged. Membership in such a specific group can be on the basis of characteristics such as gender, sexual orientation, disability, physical appearance, body size, race, nationality, sex, colour, ethnic or social origin, pregnancy, citizenship, familial status, veteran status, genetic information, religion or belief, political or any other opinion, membership of a national minority, property, birth, age, or choice of text editor. We do not tolerate harassment of participants on the basis of these categories, or for any other reason.

Harassment is any form of behaviour intended to exclude, intimidate, or cause discomfort. Because we are a diverse community, we may have different ways of communicating and of understanding the intent behind actions. Therefore we have chosen to prohibit certain forms of behaviour in our community, regardless of intent. Prohibited harassing behaviour includes but is not limited to:

• Written or verbal comments which have the effect of excluding people on the basis of membership of a specific group listed above
• Causing someone to fear for their safety, such as through stalking, following, or intimidation
• The display of sexual or violent images
• Unwelcome sexual attention
• Nonconsensual or unwelcome physical contact
• Sustained disruption of talks, events or communications
• Incitement to violence, suicide, or self-harm
• Continuing to initiate interaction (including photography or recording) with someone after being asked to stop
• Publication of private communication without consent

Behaviour not explicitly mentioned above may still constitute harassment. The list above should not be taken as exhaustive but rather as a guide to make it easier to enrich all of us and the communities in which we participate. All CCRCN interactions should be professional regardless of location: harassment is prohibited whether it occurs on- or offline, and the same standards apply to both.

Enforcement of the Code of Conduct will be respectful and not include any harassing behaviors. Any changes to the meaning of this Code of Conduct must be approved by majority vote of the CCRCN Steering Committee. CCRCN working group and network members will be informed of these changes and any concerns that are raised will be discussed by the Steering Committee.

Thank you for helping make this a welcoming, friendly community for all.

This code of conduct is a modified version of that used by The Carpentries, which was a modified version of that used by PyCon, which in turn is forked from a template written by the Ada Initiative and hosted on the Geek Feminism Wiki. Contributors to this document: Adam Obeng, Aleksandra Pawlik, Bill Mills, Carol Willing, Erin Becker, Hilmar Lapp, Kara Woo, Karin Lagesen, Pauline Barmby, Sheila Miguez, Simon Waldman, Tracy Teal.

Members involved in CCRCN collaborations are entitled to their own intellectual property, and distinctions should be made clarifying what is individual property versus group property. Here are the principles to which the Soil Carbon working group adheres to:

• Anything developed as a group is collective intellectual property (IP) that members collectively decide what to do with (such as assigning a creative commons license or signing rights over to a publisher).
• Anything developed outside the group is the intellectual property of the individual who produced it or their institution (whichever is applicable).
• Any outside IP needs to be properly acknowledged and cited in CCRCN work.
• All members agree not to share unpublished work outside the group without explicit permission to do so.
• At the end of the project, working group members commit to making code and derivative data products that the group developed collectively open and available as part of data releases associated with papers and reports.
• The working group will publish products that only include data that is or will be publicly available (via repository) at the time of working group publication.

Participant Biographies

Dr. E. Fay Belshe

Dr. Fay Belshe is interested in how organic matter (OM) forms and decays within soils of coastal vegetated ecosystems, specifically seagrasses. Her work explores the mechanisms and controls governing OM persistence and residence times, with a focus on how living OM (plants, microbes), dead OM, and soil properties interact to determine the dynamic cycling of carbon under a constant and changing climate.

Dr. Brandon Boyd

Dr. Brandon Boyd’s research is focused on physical and biological processes in tidal marshes and how those marshes connect with estuary or coastal system. Dr. Boyd uses his expertise in radionuclide chronology and tracers to identify and quantify processes in those systems. His projects are often focused on determining how vertical accretion in restored wetlands may differ from their natural neighbors and designing marsh restorations with balance between storm resilience and ecologic benefit. To learn more about Dr. Boyd’s research, visit his lab website here.

Lauren Brown

Lauren completed her MA in Geography at UCLA and is finishing her PhD in the same program working with Dr. Glen MacDonald. While at UCLA she has collected sediment cores from over 10 marshes, spanning the California coast from Humboldt Bay to Tijuana River Estuary. She uses these sediment cores to investigate vulnerability to SLR, carbon storage, and long-term environmental change. Methods she has employed include radiometric dating, LOI, grain size analysis, EA, XRF, macro charcoal, and biological proxy data. Her final year of the PhD will be dedicated to improving her data management and analyses skills.

Dr. Samantha Chapman

Dr. Samantha Chapman has been a professor and scientist at Villanova University in Pennsylvania, USA since 2007. She is also currently the Anne Quinn Welsh Honors Faculty Fellow.  Sam received her Ph.D. from Northern Arizona University and did a postdoctoral fellowship at the Smithsonian Environmental Research Center. Dr. Chapman is an ecosystem ecologist who is interested in how climate change and altered biodiversity change the services that ecosystems provide. She has received grants from NASA, The U.S. National Science Foundation, The U.S. Forest Service and the U.S. Department of Agriculture to conduct her research. Sam and her team collaborate on projects ranging from learning how climate change and rising sea levels alter coastal ecosystems to assessing how nitrogen pollution impacts invasive plant species. For more information on research projects, see her lab group website here.

Dr. Ron Corstanje

Dr. Ron Corstanje is a professor of Data Sciences at Cranfield and Head of the Centre for Environmental and Agricultural Informatics. He specialises in the application of spatio-temporal models to understand the nature and behaviour of natural systems and processes. Ron is interested in the application of (spatial) modelling tools to understand the structure and function of environmental systems and processes. Environmental systems are complex, and this expresses itself as complex but determinable spatiotemporal patterns. The application of these approaches has led to significant advances in understanding on the spatial dynamics of ecosystem services, across systems including wetlands. Another key area in which these techniques have proven invaluable is in the area of resilience as applied to ecological systems, allowing significant insights into the nature and functioning of resilience. Both these areas have significant societal significance, in helping inform how to infer resilience in the natural and man-made systems on which we depend, but also in how to design and plan the environment to retain the benefits from the natural capital inherent in our greenspaces.

Dr. Meagan Gonneea

Dr. Meagan Gonneea is a research scientist at the US Geological Survey Woods Hole Coastal and Marine Science Center. She utilizes a suite of geochemical tools, including naturally occurring radioisotopes in the Uranium-Thorium decay series, to understand both the magnitude and rate of change within coastal ecosystems. In particular, Meagan is interested in how salt marshes have responded to more than a century of accelerating sea level rise, with a focus on their ability to store carbon and dynamically build elevation. She combines historical ecosystem information, gleaned from analysis of salt marsh peat, with modern environmental drivers to constrain future ecosystem responses.

Dr. Christopher Janousek

Dr. Janousek is a coastal wetlands ecologist and Assistant Professor (Senior Research) at Oregon State University. His academic training was completed at UC Santa Cruz (BA), UC San Diego (PhD), and UC Davis (post-doc). His main research interest is in coastal plant community ecology along the Pacific coast of the United States. His research also addresses coastal climate change, blue carbon, estuarine hydrology, and wetlands restoration. He is a wilderness and National Parks enthusiast and enjoys backpacking, tidepooling, kayaking, nature photography, and nature writing.

Dr. James Morris

Dr. James Morris is a Professor of Biological and Marine Sciences at the University of South Carolina and a Fellow of the Society of Wetland Scientists and of the American Association for the Advancement of Science.  Morris has served on numerous committees and panels, including those of the US National Science Foundation, the Irish National Science Foundation, the National Research Council of the National Academy of Sciences, and the IndoFlux committee of India. He also is a current member of the Conservation International/UNESCO Blue Carbon Working Group and NOAA’s NERR Science Collaborative Advisory Board. Dr. Morris has a long history of research at North Inlet, SC on the effects on coastal wetlands of changing sea-level. His discovery of a stabilizing feedback between marsh primary production, vertical marsh accretion, and sea-level rise has led to the development of the field of marsh biogeomorphology.   He is PI of an NSF LTREB project at North Inlet, SC, co-PI of the NSF Plum Island LTER project in Massachusetts, and is the developer of the Marsh Equilibrium Model (MEM) and the Cohort Theory Model (CTM). Visit his website here.

Dr. Gregory Noe

Dr. Greg Noe has been a Research Ecologist with the U.S. Geological Survey's Water Mission Area in Reston, VA since 2002. Greg studies mud. His research focuses on the interactive influences of geomorphology, hydrology, climate, and biology on nitrogen and phosphorus and carbon biogeochemistry and sediment transport in fluvial ecosystems, as well as plant community ecology and restoration ecology.  He has been studying the effect of sea level rise and salinization on tidal freshwater forested wetland ecosystem resilience and C cycling and sequestration in Virginia, Maryland, South Carolina, and Georgia.

Dr. André Rovai

Dr. Rovai is a Research Associate at the Department of Oceanography & Coastal Sciences at Louisiana State University. He is a marine ecologist who is interested in the potential role of coastal wetlands in mitigating the effects of greenhouse gases. Specifically, his research focuses on global controls on carbon storage and sequestration in coastal wetlands in response to geophysical and climatic drivers as well as climate change, with an emphasis on deltaic coastlines.

Dr. Jonathan Sanderman

Dr. Sanderman is a biogeochemist who specializes in understanding how soil carbon and nutrient cycles have been altered by land-use and climate change. He is particularly interested in understanding the carbon sink capacity of soils and coastal sediments and whether or not these sinks can be managed to mitigate climate change. Prior to joining the Center, Dr. Sanderman spent six years as a research scientist at the Australian Commonwealth Scientific and Industrial Research Organization. Dr. Sanderman holds a B.S. from Brown University and a Ph.D. from the University of California, Berkeley.

Dr. Amanda Spivak

Dr. Amanda Spivak focuses on developing an integrated understanding of ecological and biogeochemical processes in order to refine the role of coastal ecosystems in the global carbon cycle and predict the likelihood of recovery from human disturbances. She uses innovative geochemical tracer approaches, including stable isotopes and lipid biomarkers, in combination with landscape-scale experiments to quantify carbon pathways, transformations, and fate.

Dr. Katherine Todd-Brown

Dr. Kathe Todd-Brown is a computational biogeochemist. She specializes in soil carbon model analysis and theoretical development from the micro to global scales. She is currently a post-doctoral fellow at Wilfrid Laurier University and the data coordinator for the International Soil Carbon Network. Learn more about her work at her website here.

Megan Vahsen

Megan is interested in the potential for rapid evolution to drive ecosystem-level processes. She completed her M.S. in Ecology at Colorado State and is currently a Ph.D. student at Notre Dame working with Jason McLachlan to understand how the evolution of a marsh sedge (Schoenoplectus americanus) influences marsh accretion in the Chesapeake Bay. Megan uses field experiments, Bayesian modeling, and near-term ecological forecasting to address this question.

Dr. Patrick Megonigal

Pat Megonigal is Senior Scientist and Associate Director of Research at the Smithsonian Environmental Research Center. Dr. Megonigal is an ecosystem ecologist with research interests in carbon and greenhouse gas cycling in wetlands and forests, particularly as they relate to global change. He is the Lead Investigator of the Smithsonian’s Global Change Research Wetland, a long-term research site dedicated to understanding the stability of tidal wetlands faced with accelerated sea level rise and biogeochemical interactions between wetlands and estuaries. Dr. Megonigal is a contributing author to the Coastal Blue Carbon Handbook and the VCS Methodology on Restoration of Tidal Wetlands and Seagrasses, and he is a member of the Scientific Working Group of the Blue Carbon Initiative.

Dr. James Holmquist

James Holmquist is an ecologist at the Smithsonian Environmental Research Center, specializing in global change and carbon cycling in wetlands. In 2015 he joined a NASA-funded project tracking U.S. coastal wetland greenhouse gas storage and emissions. He manages the Coastal Carbon Research Coordination Network, and aims to improve the state of science and management using data synthesis, teamwork, and training.

Dr. Lisa-Marie Windham Myers

Dr. Lisamarie Windham-Myers is a wetland ecologist and lead scientist for the USGS-NRP program “Plant:Soil:Water Interactions in Wetland Ecosystems”. Broadly-trained in ecosystem ecology, her research focuses on plant physiology and its influence on carbon, nutrient, and trace-metal biogeochemistry. Her approaches span landscape-to-molecular scales as necessary to understand how human and stochastic alterations of wetland structure influence wetland function.  A San Francisco Bay native, her local research sites represent a wide range of salinity and management conditions, from rice agriculture to coastal and restored wetlands.  Lisa serves in several local, national and international science advisory efforts to evaluate wetland management and modeling approaches to quantify wetland carbon sequestration, greenhouse gas budgets and/or mercury methylation and export. Visit her website here

David Klinges

David is a Data Technician in the biogeochemistry lab at the Smithsonian Environmental Research Center, and assists in managing the Coastal Carbon Research Coordination Network. He specializes in ecological data curation and synthesis, as well as designing web interfaces and data-access tools. David possesses a B.A. in Biological Sciences from Dartmouth College. Visit his website here.

Soil Carbon Working Group

Improved measuring, reporting, modeling, and mapping of soil carbon burial rates and carbon stocks in coastal wetlands

Time and Location:

December 8 and 9, 2018, hosted at the Smithsonian Environmental Research Center, Edgewater, Maryland

Research Questions:

1. What is the potential for pairing Bayesian statistical methods with process-based modeling to map carbon stocks and sequestration rates?

2. How much variation in carbon stocks and burial rates is attributable to field and lab techniques, and how much to process uncertainty?

Final Products:

1. Paper on modeling and forecasting soil carbon sequestration rates

2. Papers on best practices for field, lab, and data management

3. Open-source R package(s) of marsh equilibrium models

4. Mapped products at the scale of the contiguous United States if applicable

Working Group Application

**APPLICATIONS FOR THE "IMPROVED PROCESS MODELING AND MAPPING OF TIDAL WETLANDS METHANE EMISSIONS" WORKING GROUP ARE CLOSED.**

A goal of the CCRCN is to quantitatively improve the state of the science. One of our proposed activities is to use five topical working groups over the next five years to share data and expertise. The steering committee has decided to announce the titles and timing of the first two working groups, and suggestions for future working groups. Our decisions have been made based on three insights:

1. The initial results of a sensitivity analysis of U.S. coastal wetland flux which quantitatively rank priorities for reducing uncertainty.
2. Feedback from you at our 2017 AGU Town Hall, our 2018 community priorities survey, and individual outreach with many of you.
3. A recognition that the CCRCN steering committee needs to offer enough detail and leadership so that the workshops have direction, but not at the expense of remaining flexible to the changing nature of research and community priorities over the next five years.

The five workshops are:

1. Improved measuring, reporting, modeling, and mapping of soil carbon burial rates and carbon stocks in coastal wetlands

Time and Location: December 8 and 9 (before AGU), hosted at the Smithsonian Environmental Research Center, Edgewater, Maryland

Research Questions: 1. How much variation in carbon stocks and burial rates is attributable to field and lab techniques, and how much to environmental covariates? 2. What is the potential for machine learning or process-based modeling to map carbon stocks and burial rates?

Final Products: 1. Paper on best practices for field, lab, and data management; 2. papers on modeling and mapping; 3. Mapped products at the scale of the contiguous United States if applicable.

2. Improved process modeling and mapping of tidal wetland methane emissions

Applications now being accepted!

Timing and Location: Workshop will be in December 2019, exact time and location TBD. Working group members will commit to remote collaboration in the months leading up to a two-day workshop, and are expected to contribute at the level of co-author to 1-2 culminating papers.

Potential Future Working Groups

Detecting Carbon Flux Associated with Wetland Loss and Restoration (Timing: 2020 or 2021)

CO2 Vertical Flux and Scaling from the Chamber, Eddy Flux, to the Globe  (Timing: 2020 or 2021)

Quantifying Uncertainty Reduced by CCRCN Products, Scaling Outside the US, Determining New Research Priorities (Timing: 2022)

Each participant will be expected to agree to a code of conduct, contribute at the level of a coauthor, participate in remote collaboration in the months leading up to a two day workshop, attend all of the workshop, and assist in revising analyses and reviewing paper drafts following the workshop. We strongly encourage students and early career scientists to apply, especially as participation in data synthesis may advance publication and career opportunities. To maximize the diversity and number of participants, applicants should not expect to be selected for more than two working groups over the next five years. Travel funding will be provided for in-person workshops. Unfortunately funding for non-U.S. based collaborators is very limited. If you can provide your own funding, please indicate this on the application as it may free up funds for international participants.

Personal Info
Working Group Preferences
Note: applications for Working Group 1 are now CLOSED. Display of topics is primarily to gauge interest. A future application will be provided for these later working groups. 1 is highest priority, 4 is lowest priority.
Experience
Travel

Principles and Governance

Version 1

3 July 2018

The goal of the Coastal Carbon Research Coordination Network (CCRCN) is to accelerate the pace of discovery in coastal wetland carbon science by providing our community with access to data, analysis tools, and synthesis opportunities. Our activities include bringing data libraries online, creating open source analysis and modeling tools, providing training and outreach opportunities, holding town halls, responding to community feedback, and hosting data synthesis workshops targeted at strategically reducing uncertainty in coastal carbon science issues. Our first focal activity is building a public online data library of soil carbon data.

The Coastal Carbon Research Coordination Network (herein Network) builds on work by the Blue Carbon Initiative, the NASA Blue Carbon Monitoring System, and the US Carbon Cycle Science Program. Our data management principles incorporate experience from these efforts, and best practices developed in collaboration with data management specialists across the Smithsonian and our partner institutions.

Contents

Core Principles

1. We are responsive to a global community of scientists and practitioners.
2. We focus on quantifiable improvements to the state of the science.
3. We adopt protocols, policies, and communication platforms that facilitate transparency, ease of adoption, program sustainability, and data stability.

Defined Roles and Responsibilities within the CCRCN

• Principle Investigators - Emmett Duffy, Patrick Megonigal, and James Holmquist are responsible for executing the project as proposed to NSF, reporting to NSF on project progress, and carrying out the fiduciary requirements of the grant.
• Director - The Director (Patrick Megonigal) is responsible for directing the activities of the Principal Investigators, and overall management of the Network.
• Manager - The Manager (currently James Holmquist) is hired by the Director with input from the steering committee. The manager is responsible for leading the daily activities of the Network, responding to stakeholders, and interacting with the Steering Committee.
• Steering Committee Members - Steering committee members are responsible for advising the Director and Manager on Network management, adherence to core principles, workshop topics, and evaluating steering committee nominees.
• CCRCN Personnel - Principal Investigators, the Steering Committee, and people with official Smithsonian affiliations who work on Network-related tasks. Personnel are responsible for implementing the activities of the Network.
• Collaborators / Collaborating Organizations - Collaborators include researchers who are not Network personnel, but are otherwise actively contributing to Network products in collaboration with Network personnel, or as part of data synthesis workshops. Collaborators are expected to participate at the level of co-author on synthesis products. Collaborating Organization responsibilities are similar to Collaborators except the organization and the Network have entered into a memorandum of understanding to formalize expectations with respect to the Network. Collaborating organizations are expected to explore financial support of activities that are not supported by the NSF funding that established the Network, and to explore opportunities to secure funding to support Network activities beyond the initial five years of NSF funding.
• Partners / Partner Organizations - Include anyone engaged in any Network activities, but more informally than Collaborators. These include remote consultations, town halls, Twitter, webinars, online surveys, and participating in public comment periods. The best way to be recognized as a partner is to sign up for regular Network email updates. Partner Organizations include any organizations that interact with the Network in a manner similar to Partners, but have entered into a memorandum of understanding with the Network that formalizes activities such as consultation or other non-monetary support for Network goals.
• Users - Anyone using data structures or synthesis products created by the Network. There is no obligation to involve Network personnel or collaborators in individual research efforts beyond the workshops funded by the Network NSF grant. Users are responsible for properly citing Network synthesis products, and properly citing original authors when datasets curated or synthesized by the Network are downloaded and reused. See the Data Use Policy.

Steering Committee Membership

The three Principal Investigators are permanent members of the Steering Committee. Five additional members will be chosen to assist the Network with existing or emerging needs as identified by the Steering Committee. In principle, the members should represent a range of stakeholder interests and technical expertise. Members serve for one year, but can be reappointed for one year at the discretion of the Director. Candidates for the steering committee can nominate themselves or be nominated by their colleagues. The steering committee will vote on replacements, and members stepping down will help to choose their own replacement. Rotations are staggered so that no more than half of the rotating members on the Steering Committee are replaced in a given year.

Data Synthesis Workshops

Data synthesis products led by the Network will be developed over the course of five workshops organized and led by Network personnel and collaborators. Potential collaborators can propose a workshop, or apply to participate in workshops. The Steering Committee will vote on topics and participants for each workshop, which will typically have 12-15 participants.

Coauthorship Policy

Those accepted to participate in any of the five synthesis workshops hosted by the Network will be expected to contribute before, during, and following the workshop, and will be granted co-authorship on publications resulting from the effort. We will follow the American Geophysical Union’s 2017 Scientific and Professional Ethics: Guideline B. Ethical Obligations of Authors/Contributors for determining co-authorship. Submitting data to the network alone will not merit co-authorship in data syntheses. If done according to the protocols established herein, it will result in citation.

Co-authorship policies in data sharing exercises often benefit established researchers from western industrialized nations at the expense of those from groups with less institutional power 1. We commit to adopting policies and technologies that facilitate engagement of students, people with indigenous knowledge, and researchers from low and middle income countries as attendees and co-authors in synthesis activities. This policy will be implemented within the limits of NSF CCRCN grant resources.

Data Use Policy

We refer to users as anyone using either data we curate, or synthesis products we create. Data that is curated, but not created, by the Network, should not be attributed to the Network. Users should cite all dataset DOIs and credit the datasets’ original authors. All synthesis products created by the Network and associated collaborators will be listed under a Creative Commons With Attribution license. The Network should be acknowledged and cited appropriately if users utilize any of the data structures, tools, or scripts developed by Network and associated collaborators. We will develop additional tools to assist users in generating lists of citations, but users will be ultimately responsible for correctly citing all data used.

1. Serwadda D, Ndebele P, Grabowski MK, Bajunirwe F, Wanyenze RK (2018). Open data sharing and the Global South: Who benefits?. Science, 359(6376), 642-643.https://doi.org/10.1126/science.aap8395

Coastal Carbon Data Clearinghouse

Data Management Plan (Version 1)

The CCRCN strives for transparency with methods of data archival and management. Here we overview data types and structure associated with the Network, and procedures for data storage and quality control.

Database Structure (Version 1)

An exhaustive description of the naming convention for attributes and variables recommended by the CCRCN.

Coastal Carbon Atlas

Use this web interface to visualize, query, and download data from the Coastal Carbon Clearinghouse.

Want to know how to use the Atlas? Check out this video tutorial:

Data Products

The Network recognizes three classes of data: (i) data that we curate, (ii) data that we ingest, and (iii) synthesis products we create. Data that we curate will be hosted on Smithsonian Institution (SI) servers, but the original data submitter and funding sources will be credited as the dataset’s creators. Data that we ingest will include both data we curate and data from any outside sources that meet basic availability, archiving, and metadata standards.

See below for a list of CCRCN data products.

Last Updated

Weston et al. (2022) Weston, Nathaniel B; Rodriguez, Elise; Donnelly, Brian; Solohin, Elena; Jezycki, Kristen; Demberger, Sandra; et al. (2022): Dataset: Recent Acceleration of Coastal Wetland Accretion Along the U.S. East Coast. Smithsonian Environmental Research Center. Dataset. https://doi.org/10.25573/serc.13043054 7 October 2022
Allen et al. (2022) Allen, Jenny R.; Cornwell, Jeffrey C; Baldwin, Andrew H. (2022): Dataset: Contributions of Organic and Mineral Matter to Vertical Accretion in Tidal Wetlands across a Chesapeake Bay Subestuary. Smithsonian Environmental Research Center. Dataset. https://doi.org/10.25573/serc.18130892 22 February 2022
Fell et al. (2021) Fell, Claire; Adgie, Therese; Chapman, Samantha (2021): Dataset: Carbon sequestration across Northeastern, Florida Coastal Wetlands. Smithsonian Environmental Research Center. Dataset. https://doi.org/10.25573/serc.15043920

3 December 2021

Carlin et al. (2021) Carlin, Joseph; Oikawa, Patty Y.; Arias-Ortiz, Ariane; Kanneg, Sadie; Duncan, Theresa; Beener, Katya (2021): Dataset: Sedimentary organic carbon measurements in a restored coastal wetland in San Francisco Bay, CA, USA. Smithsonian Environmental Research Center. Dataset. https://doi.org/10.25573/serc.16416684 1 November 2021
Arias-Ortiz et al. (2021) Arias-Ortiz, Ariane; Masqué, Pere; Paytan, Adina; Baldocchi, Dennis D. (2021): Dataset: Tidal and nontidal marsh restoration: a trade-off between carbon sequestration, methane emissions, and soil accretion. Smithsonian Environmental Research Center. Dataset. https://doi.org/10.25573/serc.15127743 1 November 2021
Ensign et al. (2021) Ensign, Scott H.; Noe, Gregory B.; Hupp, Cliff R.; Skalak, Katherine J. (2021): Dataset: Head of tide bottleneck of particulate material transport from watersheds to estuaries. Smithsonian Environmental Research Center. Dataset. https://doi.org/10.25573/serc.13483332 13 January 2021
Piazza et al. (2021) Piazza, S.C., Steyer, G.D., Cretini, K.F., Sasser, C.E., Visser, J.M., Holm, G.O., Sharp, L.A., Evers, E., and Meriwether, J.R., 2021, Geomorphic and ecological effects of Hurricanes Katrina and Rita on coastal Louisiana marsh communities: U.S. Geological Survey data release, https://doi.org/10.5066/P9D8WTQW 5 January 2021
St. Laurent et al. (2020) Laurent, Kari A. St.; Hribar, Daniel J.; Carlson, Annette J.; Crawford, Calyn M.; Siok, Drexel (2020): Dataset: Assessing coastal carbon variability in two Delaware tidal marshes. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.13315472.v1  4 December 2020
Keshta et al. (2020) Keshta, Amr E.; Yarwood, Stephanie A.; Baldwin, Andrew H. (2020): Dataset: Soil Redox and Hydropattern control Soil Carbon Stocks across different habitats in Tidal Freshwater Wetlands in a Sub-estuary of the Chesapeake Bay. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.13187549.v1  30 November 2020
Whigham et al. (2020) Whigham, Dennis; Holmquist, James R; Ogburn, Matthew B.; Goodison, Michael; McFarland, Liza; Megonigal, Patrick (2020): Dataset: 2015-2018 USA-MDA TMON Marsh Biomass Surveys. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.12636404.v1  14 October 2020
Kauffman et al.  (2020) Boone Kauffman, J.; Giovannoni, Leila R.; Kelly, James; Dunstan, Nicholas; Borde, Amy; Diefenderfer, Heida; et al. (2020): Dataset: Carbon stocks in seagrass meadows, emergent marshes, and forested tidal swamps of the Pacific Northwest. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.12640172.v2  11 August 2020
Sanborn and Coxson (2020) Sanborn, Paul; Coxson, Darwyn S. (2020): Dataset: Carbon data for intertidal soils and sediments, Skeena River estuary, British Columbia. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.12252005.v1  15 June 2020
Lagomasino et al. (2020) Lagomasino, David; Corbett, D. Reide; Walsh, J.P. (2020): Dataset: Influence of Wind-Driven Inundation and Coastal Geomorphology on Sedimentation in Two Microtidal Marshes, Pamlico River Estuary, NC. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.12043335.v1  27 March 2020
Nolte et al. (2020) Nolte, Stefanie (2020): Dataset: Does livestock grazing affect sediment deposition and accretion rates in salt marshes?. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.11958996.v1  20 March 2020
Buffington et al. (2020) Buffington, Kevin; Janousek, Christopher; Thorne, Karen; Dugger, Bruce (2020): Dataset: Carbon stocks and accretion rates for wetland sediment at Miner Slough, Sacramento-San Joaquin Delta, California. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.11968740.v1  19 March 2020
Messerschmidt et al. (2020) Messerschmidt, Tyler C.; Kirwan, Matthew L. (2020): Dataset: Soil properties and accretion rates of C3 and C4 marshes at the Global Change Research Wetland, Edgewater, Maryland. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.11914140.v1  3 March 2020
Vaughn et al. (2020) Vaughn, Derrick; Bianchi, Thomas; Shields, Michael; Kenney, William; Osborne, Todd (2020): Dataset: Increased Organic Carbon Burial in Northern Florida Mangrove-Salt Marsh Transition Zones. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.10552004  2 March 2020
Peck et al. (2019) Peck, Erin; Wheatcroft, Robert; Brophy, Laura (2019): Dataset: Controls on sediment accretion and blue carbon burial in tidal saline wetlands: Insights from the Oregon coast, U.S.A.. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.11317820.v2  24 January 2020
Breithaupt et al. (2020) Breithaupt, Joshua L.; Smoak, Joseph M.; Bianchi, Thomas S.; Vaughn, Derrick; Sanders, Christian J.; Radabaugh, Kara R.; et al. (2020): Dataset: Increasing rates of carbon burial in southwest Florida coastal wetlands. The Smithsonian Institution. Dataset. https://doi.org/10.25573/serc.9894266.v1  22 January 2020
Kemp et al. (2020) Kemp, Andrew C.; Sommerfield, Christopher K.; Vane, Christopher H.; P. Horton, Benjamin; Chenery, Simon; Anisfeld, Shimon; et al. (2020): Dataset: Use of lead isotopes for developing chronologies in recent salt-marsh sediments. figshare. Dataset. https://doi.org/10.25573/serc.11569419.v1 16 January 2020
McTigue et al. (2020) McTigue, Nathan; Davis, Jenny; Rodriguez, Antonio; McKee, Brent; Atencio, Anna; Currin, Carolyn (2020): Dataset: Carbon accumulation rates in a salt marsh over the past two millennia. figshare. Dataset. https://doi.org/10.25573/serc.11421063.v1 10 January 2020
Breithaupt et al. (2019) Breithaupt, Joshua L.; Smoak, Joseph M.; Sanders, Christian J.; Smith III, Thomas J. (2019): Dataset: Temporal variability of carbon and nutrient burial, sediment accretion, and mass accumulation over the past century in a carbonate platform mangrove forest of the Florida Everglades. figshare. Dataset. https://doi.org/10.25573/serc.11310926.v1 20 December 2019
Coastal Wetland Elevation and Carbon Flux Inventory with Uncertainty, USA, 2006-2011 (Associated Paper here) Holmquist, J.R., L. Windham-Myers, B. Bernal, K.B. Byrd, S. Crooks, M.E. Gonneea, N. Herold, S.H. Knox, K. Kroeger, J. Mccombs, P.J. Megonigal, L. Meng, J.T. Morris, A.E. Sutton-grier, T. Troxler, and D. Weller. 2019. Coastal Wetland Elevation and Carbon Flux Inventory with Uncertainty, USA, 2006-2011. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1650 17 December 2019
Thom (2019) Thom, Ronald M. (2019): Dataset: Accretion rates of low intertidal salt marshes in the Pacific Northwest. figshare. Dataset. https://doi.org/10.25573/data.10046189.v2 13 November 2019
Belshe et al. (2019) Belshe, E. Fay; Sanjuan, Jose; Leiva-Dueñas, Carmen; Piñeiro-Juncal, Nerea; Serrano, Oscar; S. Lavery, Paul; et al. (2019): Dataset: Modeling organic carbon accumulation rates and residence times in coastal vegetated ecosystems. figshare. Dataset. https://doi.org/10.25573/data.9856769.v1 01 November 2019
Abbott et al. (2019) Abbott, Katherine M; Quirk, Tracy; Delaune, Ronald D. (2019): Dataset: Factors influencing blue carbon accumulation across a 32‐year chronosequence of created coastal marshes. figshare. Dataset. https://doi.org/10.25573/data.10005215.v1 01 November 2019
Poppe and Rybczyk (2019) Poppe, Katrina L; Rybczyk, John M (2019): Dataset: Sediment carbon stocks and sequestration rates in the Pacific Northwest region of Washington, USA. figshare. Dataset. https://doi.org/10.25573/data.10005248.v1 24 October 2019
The Coastal Carbon Network - Below Ground Survey This is a web interface we designed with the goal of surveying best educated guesses on how key below ground plant traits and decay rates vary for tidal wetlands. The app uses a formal expert elicitation protocol and feedback will be pooled to inform the modeling effort of our soils working group. 21 October 2019
Boyd et al. (2019) Boyd, Brandon; Sommerfield, Christopher K.; Quirk, Tracy; Unger, Viktoria (2019): Dataset: Accretion and sediment accumulation in impounded and unimpounded marshes in the Delaware Estuary and Barnegat Bay. figshare. Dataset. https://doi.org/10.25573/data.9747065.v1 03 September 2019
Callaway et al. (2019) Callaway, John C.; Borgnis, Evyan L.; Turner, R. Eugene; Milan, Charles S. (2019): Dataset: Carbon sequestration and sediment accretion in San Francisco Bay tidal wetlands. figshare. Dataset. https://doi.org/10.25573/data.9693251.v1 03 September 2019
Doughty et al. (2019) Doughty, Cheryl; Langley, J. Adam; Walker, Wayne; Feller, Ilka C.; Schaub, Ronald; Chapman, Samantha (2019): Mangroves marching northward: the impacts of rising seas and temperatures on ecosystems at Kennedy Space Center. figshare. Dataset. https://doi.org/10.25573/data.9695918.v1 26 August 2019
The Coastal Carbon Atlas Use this web interface to visualize, query, and download data from the Coastal Carbon Clearinghouse. 22 February 2019
Tidal Wetland Soil Carbon Stocks for CONUS This dataset provides modeled estimates of soil carbon stocks for tidal wetland areas of the Conterminous United States (CONUS) for the period 2006-2010. 20 February 2019
Accuracy and Precision of Tidal Wetland Soil Carbon Mapping (associated paper here) Per-depth soil organic matter and carbon metrics, plant species identity, state of human impact, field and lab methodology, and metadata of 1534 soil cores 21 June 2018
Coastal National Greenhouse Gas Inventory: Report, Datasets, and Workflow Literature review, data, analysis, and report 9 December 2017

CCRCN-affiliated publications

Malhotra, A., Todd-Brown, K., Nave, L. E., Batjes, N. H., Holmquist, J. R., Hoyt, A. M., ... & Vindušková, O. (2019). The landscape of soil carbon data: emerging questions, synergies and databases. Progress in Physical Geography: Earth and Environment, 43(5), 707-719.

Rogers, K., Kelleway, J. J., Saintilan, N., Megonigal, J. P., Adams, J. B., Holmquist, J. R., ... & Woodroffe, C. D. (2019). Wetland carbon storage controlled by millennial-scale variation in relative sea-level rise.

Holmquist, J. R., Windham-Myers, L., Bernal, B., Byrd, K. B., Crooks, S., Gonneea, M. E., ... & Megonigal, J. P. (2018). Uncertainty in United States coastal wetland greenhouse gas inventorying. Environmental Research Letters, 13(11), 115005.

Holmquist, J. R., Windham-Myers, L., Bliss, N., Crooks, S., Morris, J. T., Megonigal, J. P., ... & Ferner, M. C. (2018). Accuracy and precision of tidal wetland soil carbon mapping in the conterminous United States. Scientific reports, 8(1), 9478.

Testimonials to Data Contribution

“The Coastal Carbon Research Coordination Network dataset has been invaluable in our recent research identifying global drivers of variability in coastal wetland carbon cycling. The Network’s dataset greatly complemented our own previous data collation efforts, filling important gaps in our record. The availability of a comprehensive and well-curated dataset allowed us to focus on the analysis and interpretation of data, deriving important new insights in global patterns of carbon storage.”

- Jeffrey Kelleway, Department of Environmental Sciences, Macquarie University

"The CCRCN database is a key cornerstone in accelerating the pace of discovery for coastal carbon cycling.  I recently downloaded version 1, and have begun analylzing it and intercomparing its features with other national and global sets on soil core characteristics. As it focuses only on soilcores from tidal wetland, it is the single largest and spatially explicit empirical dataset, globally, for populating carbon stock assessments or testing models across space and time. For coastal lands, it is an invaulable asset for scientists and managers alike.  The developers of the dataset and platform should be commended, as should the many community contributors who are fueling advances in science and practice by sharing their data."

"[The CCRCN administrators] are doing an amazing job at this organization and promoting inclusivity. I am floored by your intuition and abilities. You are the natural heirs to this community-building."

- Lisamarie Windham-Myers, U.S. Geological Survey

Data Management Plan

Contents

Types of Data

The Network recognizes three classes of data: (i) data that we curate, (ii) data that we ingest, and (iii) synthesis products we create. Data that we curate will be hosted on Smithsonian Institution (SI) servers, but the original data submitter and funding sources will be credited as the dataset’s creators. Data that we ingest will include both data we curate and data from any outside sources that meet basic availability, archiving, and metadata standards. These data will be pulled into intermediate files in a centralized database using R code. The workflow and files will be archived and publicly available on an SI-managed GitHub website. This document refers to soil depth profile data throughout, but it is our intention that these general structures and principles be applied to other types of data as the Network evolves.

Digital Object Identifiers

We encourage submitters to use best practices and to assign datasets a citable digital object identifier (DOI), which links to a repository containing downloadable data and associated metadata. We will prioritize ingesting such data into the synthesis. Data submitters can choose to forward DOIs issued outside of the Network for ingestion into the central data structure on the Network’s SI GitHub website. Some DOI-issuing repository services include Figshare and the Environmental Data Initiative. As a service to the community Network personnel can will be available to assist data submitters in archiving data according to outlined standards.

Submitters also have the option to host data on an SI server, and apply for a DOI through SI libraries, with the submitters credited as a dataset author and Network personel credited for their curatorial role. Landing pages with summaries of projects, sites, and cores will be viewable on Dspace, and the CCRCN website will advertise a link to the data release. While data will be digitally archived long-term in accordance with SI standards, we cannot guarantee new data will be accepted after Network funding ends.

While we recognize that there is no official definition for what constitutes a trusted repository, repositories associated with DOIs should in general have community recognition and trust in their long-term stability. For data curated by the Network we hope that SI’s reputation, DSpace’s status as an approved technology, and the SIL’s commitment to digital object curation, generate this level of community trust.

For data curated by the Network, we will use the Environmental Metadata Language standards. This includes an abstract, detailed submitter information, attribute definitions, and data types (e.g. character, factor, numeric, or dateTime). CCRCN personnel will use the R-based EML package in our workflow to create metadata for data that we curate. Code used to create EML will be documented, and archived in a Smithsonian GitHub repository.

Attribute Names

Attribute names (analogous to column names in a spreadsheet) should follow good management practices1. Attribute names should be self descriptive and machine readable. They should contain no spaces and must not begin with a number or special character; however, underscores (i.e. pothole_case) are acceptable. We will recommend and adopt controlled vocabulary for attribute naming. Any submitter defined attributes should follow the same naming principles and documentation.

Units for all attributes need to be defined and in some cases controlled. For some variables which typically have commonly reported units we will recommend submitters format using these controlled units. These include fraction_organic_matter (fraction), dry_bulk_density (g cm-3) and latitude and longitude (decimal degrees [world geographic survey 1984]). For attributes that are applicable to the synthesis, but typically have multiple common unit formats, we recommend an accompanying column defining these units. Uncommon data types, or data types not included in synthesis projects, simply need to have units defined in associated metadata.

Good data practices require consistently formatting no data values and categorical variables. We have adopted the R-based convention of representing no data values as NA for all variable types (never blanks). Categorical variables should have descriptive names stored as text, similar to attribute names. For example, one may code the categorical variable treatments as numeric values 0 and 1 standing in for experimental and control; however, best practices would dictate coding these as descriptive characters (experimental and control) rather than numbers.

For data we curate we will use controlled vocabulary units and variable types. For data we ingest, we will keep a file of corresponding controlled variables and aliases so that data not complying with controlled vocabulary can still be ingested. We will document transformations made to ingested data to standardize them with the data we curate in R code.

Proposed Level of Disaggregation

In general we believe that there should be community agreement on the finest level of data disaggregation archived for practical use and reuse. This fundamental unit should be the most detailed unit typically used and reported in the literature. For soils data we will stratify by site, by core, and by depth increment. For calculations such as loss-on-ignition and bulk density, data by depth increment will be the fundamental level of archiving. For age-depth information we will archive radiocarbon (14C) age ? sd of a sample for 14C dates, and counts per unit dry weight ? sd of a sample for 210Pb and 137Cs profiles.

Hierarchical Structure

We will ingest existing data and curate submitted data in a hierarchical framework. Information associated with submitters, projects, sites, cores, and depth profiles will all be hosted in separate tables related by index codes that are unique. A universal dataset index will be composed of the principal investigator’s family name, as well as the second author or ‘et al’ in the case of more than two authors, then the publication year. Sequential letters will be added to the end (a,b,c, etc.) in case of multiple publications per year (Example: Jane Doe, Lee Fakeman and Ben Mademup’s 2009 paper = Doe_et_al_2009).

Project metadata will have an abstract and information about coauthors, associated funding source, or set of funding sources, associated publications, and materials and methods. A project should be a discrete unit of research united by consistent personnel, funding sources, and/or materials and methods. A project can be associated with one or more sites.

Sites refer to discrete geographic or management units and are somewhat nebulous, project specific, and submitter defined. A site code should follow the same best practices for variable naming: not starting with a number, descriptive, brief, and meaningful to project documentation and design. Site metadata refer to data associated with the sites, such as location, notes on dominant vegetation types, salinity, and site condition. Although there are no standards for what constitutes a site, and different projects could have different names for the same site, this coding should be consistent within a project. A site can have one or more data sets, including one or more core, plot, or instrument locations.

Core/Plot/Instrument-Level Data refer to information specifically about the location of a soil core. This could include positional information such as latitude, longitude, and elevation. It could also include notes that are redundant but more detailed than site metadata, such as vegetation and salinity. Each core should have a unique code within a project. A core code should follow the same best practices for variable naming: not starting with a number, descriptive, brief, and meaningful to project documentation and design. For future syntheses this level of hierarchy could also house other types of data such as vegetation plots or instruments.

Depth Series Information: Soil cores have depth-series information which should include minimum and maximum depth increments, as well as measurements presented in their fundamental unit (explained above), with associated methods notes, and uncertainties. If replicate samples are taken from the same depth increment then these can be distinguisehd with a sample code. These can be submitter defined, but should conform to general principles for variable naming. In future syntheses time-series data could also be archived in this format with instrument replacing a core, and time replacing depth.

Data Storage

Tabular data will be stored in a flat text file, meaning that no data formatting will be included. We will default to using tab-delimited text files (.txt) for simplicity. Although comma separated values .csv are also common, these types of files can be corrupted if commas are ever used within a variable rather than to delineate variables. Submissions that are received in other formats, such Microsoft Excel files, will be edit-locked and archived as submission documents. However, a working version of this submission will be formatted according to Network standards in flat text files.

Tabular data will be stored in long-form tables, as opposed to wide-form tables. Each column should correspond to one variable, each row should correspond to one entry. Each column needs to have a single data type, and represent only one variable. Extra information such as units, notes, or operator code will not be encoded as an excel note, cell color, or be included in the same cell as a value.

Quality Control

Network personnel will perform a cursory visual check on all data we curate and relay any concerns to the data submitter during the curation process. We will also write scripts to check data type, to check that values for each attribute are in defined bounds if applicable (such as 0 to 1 for fractions), to check for completeness, and to ensure there are no conflicts or duplicates with previously archived data. For data that we curate, files will be edit-locked following QA:QC. Any updates or corrections will result in a new named version of the file, a change logged by Network personnel in a text file associated with the data, and a note of the change sent to the next update of the Network email list members.

Submitting Data

If you are interested in submitting data, please email CoastalCarbon@si.edu and CCRCN personnel will assist you in the process. We are working on building a webportal to automate a lot of this exchange, but until we do so this will remain a very friendly peer to peer handoff system. Data submissions can remain embargoed for a time specified by the submitter. In embargo cases a data release will be prepared and shared with the submitter via a private dropbox link, until the embargo period ends, the data release is made public, and the dataset is drawn into synthesis products.

1. Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, Teal TK (2017) Good enough practices in scientific computing. PLoS Comput Biol 13(6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510

Data Products

The Network recognizes three classes of data: (i) data that we curate, (ii) data that we ingest, and (iii) synthesis products we create. Data that we curate will be hosted on Smithsonian Institution (SI) servers, but the original data submitter and funding sources will be credited as the dataset’s creators. Data that we ingest will include both data we curate and data from any outside sources that meet basic availability, archiving, and metadata standards.

See below for a list of CCRCN data products.

Last Updated

Accuracy and Precision of Tidal Wetland Soil Carbon Mapping (associated paper here) Per-depth soil organic matter and carbon metrics, plant species identity, state of human impact, field and lab methodology, and metadata of 1534 soil cores 21 June 2018
Coastal National Greenhouse Gas Inventory: Report, Datasets, and Workflow Literature review, data, analysis, and report 9 December 2017

Testimonials to Data Contribution

“The Coastal Carbon Research Coordination Network dataset has been invaluable in our recent research identifying global drivers of variability in coastal wetland carbon cycling. The Network’s dataset greatly complemented our own previous data collation efforts, filling important gaps in our record. The availability of a comprehensive and well-curated dataset allowed us to focus on the analysis and interpretation of data, deriving important new insights in global patterns of carbon storage.”

- Jeffrey Kelleway, Department of Environmental Sciences, Macquarie University

"The CCRCN database is a key cornerstone in accelerating the pace of discovery for coastal carbon cycling.  I recently downloaded version 1, and have begun analylzing it and intercomparing its features with other national and global sets on soil core characteristics. As it focuses only on soilcores from tidal wetland, it is the single largest and spatially explicit empirical dataset, globally, for populating carbon stock assessments or testing models across space and time. For coastal lands, it is an invaulable asset for scientists and managers alike.  The developers of the dataset and platform should be commended, as should the many community contributors who are fueling advances in science and practice by sharing their data."

- Lisamarie Windham-Myers, U.S. Geological Survey

Website Section
Coastal Carbon

Database Structure

Overview

This page serves as guidance for the types and scope of data and metadata that will be archived as part of the Network’s developing tidal soil carbon synthesis. We propose the following data structure and standardized attribute names for metadata and data in order to make datasets machine-readable and interoperable. Each subheading lists a level of metadata or data hierarchy from study level metadata to site level to core level to depth series information. Each subheading also represents separate tables which can be joined by common attributes such as study_id, site_id, and core_id. We also include accompanying sets of recommended controlled vocabulary for key categorical variables (also known as factors). Some attributes have controlled units that we wish to keep uniform across datasets. Data that we curate will follow naming conventions outlined herein. Data that we ingest from outside sources will be converted to these conventions when being ingested into the central GitHub database using custom-built R scripts.

At a minimum a submission should have the following for inclusion in soil carbon synthesis products: study_id, author information, core_id, latitude and longitude information associated with either a core or the site, depth_min, depth_max, dry_bulk_density, organic_matter_fraction and/or carbon_fraction. The more auxiliary detail that you provide, the more widely your data can be used. Throughout the tables below mandatory attributes are shown in bold.

The depth series is the level at which carbon-relevant information is housed. This synthesis will not ingest core-level or site-level averages of variables like dry bulk density, fraction organic matter, or fraction carbon. These averages can be derived from the database, but are not immediately useful to our research questions unless those averages can be traced back to their original data.

There are many opportunities to express your data’s individuality. We refer throughout to ‘flags’ and ‘notes’. Flags refer to common methodological choices or data issues that can be coded using categorical variables. The idea behind flags is to allow users the option to query datasets based on methodology. Flags are very machine-readable but not very flexible from the standpoint of a submitter. Notes are available for almost all measured attributes and take the form of free-text allowing submitters to provide context, observations, or concerns about methods, sites, cores, or observations. These are more flexible from the perspective of a submitter but are less machine-readable.

Development Process to Date

This guidance is the culminations of three efforts:

1. A meeting of 47 experts in Menlo Park, CA in January 2016, hosted by the United States Carbon Cycle Science Program, in order to establish community priorities.

2. Experience with the initial curation of a dataset of ~1,500 public soil cores as part of the publication Holmquist et al., 2018 Accuracy and Precision of Tidal Wetland Soil Carbon Mapping in the Conterminous United States.

3. The results of 19 collaborators submitting commentary on an initial draft of these recommendations put up for public comment in April and May 2018.

Ongoing and Future Development

We acknowledge that this is a lot of information to process and do not want to imply >100 attributes are mandatory. They are not. While the entire entry template is available for download , we are also in the process of designing an application which will generate a custom submission template based on your answers to a questionnaire about your dataset.

Submitters can feel free to add other attributes to data submissions as long as the attributes and any associated categorical variables are defined with the submission. CCRCN personnel will accept and archive related soils data within reason, but will not be able to quality control data falling outside the outlined guidance. If attributes or variables are submitted often and there is community coordination behind their inclusion, they could be integrated into periodic updates to this guidance.

We anticipate that this guidance will evolve as we synthesize new datasets as part of five working groups. Part of each working group’s task will be to revisit this guidance and agree on new needed attribute names, definitions, variables, controlled vocabulary and units. Any further guidance based on the working group’s experience will be made available to the community via post-workshop reports and peer reviewed publications. Documentation on any changes to the data management plan and submission templates will be issued with version numbers. CCRCN produces will reference these documents and version numbers. We will avoid changing attribute or variable names, and will only do so if there is a compelling reason to. If in the future there ends up being more than one acceptable redundant attribute or variable name, names will be added to a database of synonyms and working synthesis products will be updated given the most current standards.

Study-level information is essential for formatting the Ecological Metadata Language, and is a great way for you to express your project’s history, context, and originality.

Study Information

Study Information
attribute name definition data type format, unit or codes
study_id Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year spearated by underscores. character
one_liner If this is data the CCRCN curates, the submitter should include a one line description of the study. character
study_code If this is data the CCRCN curates, the study will be assigned a 128-bit universal unique identifier. Submitters should only include this if it already exists for the data. Otherwise CCRCN personnel will generate this as part of the data ingestion process. character
study_start_date Study start date. Date YYYY-MM-DD
study_end_date Study end date. Date YYYY-MM-DD
title If this is data the CCRCN curates, the submitter should include a study title. If this is data the CCRCN is ingesting, this can be pulled from the metadata or source text. character
abstract If this is data the CCRCN curates, the submitter should include a one paragraph description of the study. If this is data the CCRCN is ingesting, this can be pulled from the metadata or source text. character

Keywords

Keywords are not necessary, but can help make your data more searchable in a database.

Keywords
attribute name definition data type format, unit or codes
study_id Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year spearated by underscores. character
key_words If this is data the CCRCN curates, the submitter should include five to fifteen descriptive words or phrases describing the study. If this is data the CCRCN is ingesting, this can be pulled from the metadata or source. Keywords help build some search functionality into the databases. character

Authors

For each dataset at least one corresponding author should be specified. Specifying author names will allow users (or you in the future) to query the dataset and see how many cores you’ve submitted.

Authors
attribute name definition data type format, unit or codes
study_id Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year separated by underscores. character
last_name Submitter’s family name. character
given_name Submitter’s first name, middle name, middle initial, or any other names. character
institution Submitter’s current institution. character
email Submitter’s current email address. character
phone Submitter’s current phone number. character
corresponding_author TRUE or FALSE indicating whether the author should be contacted as the corresponding author. factor TRUE = The author should be contacted with any further questions. FALSE = The author should not be contacted with any further questions.

Funding Sources

Your funders will love being acknowledged in a data release, and will appreciate being searchable in the database. One dataset can have multiple funding sources.

Funding
attribute name definition data type format, unit or codes
study_id Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year separated by underscores. character
funding_agency Agency name funding the research, spelled out, no acronyms. character
funding_id Code used by the agency to track the project funding. character
funding_notes Any other submitter-generated notes about the project funding. character

Associated Publications

One dataset can be affiliated with multiple publications. This allows an original work to be cited as a primary source, as well as any secondary or synthesis papers that added value to the dataset’s archival. Submitters can simply add a bibtex style citation, such as one copied over from Google Scholar, or they can fill out all of the relevant attributes for the data release. It’s all the same to us. Much of this guidance came from the Wikipedia page for BibTeX.

Associated Publications
attribute name definition data type format, unit or codes
study_id Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year separated by underscores. character
bibtex_citation Submitters can associate multiple BibTeX style citations with the dataset. They can also include the same information by filling out following attributes in tabular form if more convenient than BibTeX formatting. character
publication_type Code indicating the type of publication the study originates from. factor article = Journal article. book = Book. mastersthesis = Master’s Thesis. misc = Miscellaneous publications such as online datasets. phdthesis = PhD thesis or dissertation. techreport = Technical report. unpublished = Unpublished source.
author The names of the author separated by “and”. character
year The year of publication or, if unpublished, the year of creation. Date YYYY
title The title of the work. character
journal The journal or magazine the work was published in. character
volume The volume of a journal or multi-volume book. character
number The “(issue) number” of a journal, magazine, or tech-report, if applicable. (Most publications have a “volume”, but no “number” field.). character
pages Page numbers, separated either by commas or double-hyphens. character
url Permanent web address where the work can be located. character
doi Digital object identifier associated with the work. character
address Publisher’s address (usually just the city, but can be the full address for lesser-known publishers). character
annote An annotation for annotated bibliography styles (not typical). character
booktitle The title of the book, if only part of it is being cited. character
chapter The chapter number. character
crossref The key of the cross-referenced entry. character
edition The edition of a book, long form (such as “First” or “Second”). character
editor The name(s) of the editor(s). character
howpublished How it was published, if the publishing method is nonstandard. character
institution The institution that was involved in the publishing, but not necessarily the publisher. character
key A hidden field used for specifying or overriding the alphabetical order of entries (when the “author” and “editor” fields are missing). Note that this is very different from the key (mentioned just after this list) that is used to cite or cross-reference the entry. character
month The month of publication (or, if unpublished, the month of creation). Date MM
note Miscellaneous extra information. character
publisher The publisher’s name. character
school The school where the thesis was written. character
series The series of books the book was published in (e.g. “The Hardy Boys” or “Lecture Notes in Computer Science”). character
type The field overriding the default type of publication (e.g. “Research Note” for techreport, “{PhD} dissertation” for phdthesis, “Section” for inbook/incollection). character

Materials and Methods

For each study please fill out key data regarding materials and methods that are important to the soil carbon stocks meta-analysis. Some users may want to include or exclude certain methodologies, or see your commentary on the methods. Let’s make it easy for them.

Materials and Methods
attribute name definition data type format, unit or codes
study_id Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year separated by underscores. character
coring_method Code indicating what type of device was used to collect soil depth profiles. factor gouge auger = A half cylinder coring device in which the coring section is open, not sealed off by a fin. hargas corer = A large diameter (>10 cm) coring device consisting of a tube, piston, and a cutting head. mcauley corer = A half cylinder coring device with the coring section sealed off by a fin attached to a rotating pivot point. mccaffrey peat cutter = U-shaped blade that extracts a core by cutting down through peat. none specified = No coring device was specified. other shallow corer = Any other type of coring device typically taking cores shallower than 30 centimeters. piston corer = A device that extrudes core into tube upward with a plunger. push core = Any number of coring types involving driving a tube into the sediment to recover a core. pvc and hammer = PVC pipe was driven into the sediment with a hammer to recover a core. russian corer = A half cylinder coring device with the coring section sealed off by a fin attached to a rotating pivot point. vibracore = A technique involving collecting a core by sinking a continuous pipe into sediment attaching a source of vibration, then recovering using a winch and pulley. surface sample = A technique involving collecting a core shallower than ~5 cm using a circular metal cutter.
roots_flag Code indicating whether live roots were included or excluded from carbon assessments. factor roots and rhizomes included = Roots and rhizomes were included in dry bulk density and or organic matter and carbon measurements. roots and rhizomes separated = Roots and rhizomes were separated from soil before dry bulk density and or organic matter and carbon measurements.
sediment_sieved_flag Code indicating whether or not sediment was sieved prior to carbon measurements. factor sediment sieved = Sediment was sieved prior to analysis for organics. sediment not sieved = Sediment was not sieved prior to analysis for organics.
sediment_sieve_size If sediment was sieved, the size of sieve used. numeric millimeters
compaction_flag Code indicating how the authors qualified or quantified compaction of the core. factor compaction qualified = Compaction was at least qualified and noted by the authors. compaction quantified = Compaction was quantified and corrected for in core based measurements. corer limits compaction = Authors specified that the coring device’s design minimized compaction. no obvious compaction = Authors observed no obvious compaction. not specified = Compaction was not specified.
dry_bulk_density_temperature Temperature at which samples were dried to measure dry bulk density. This can include either samples that were freeze dried or oven dried. numeric celsius
dry_bulk_density_time Time over which samples were dried to measure dry bulk density. numeric hour
dry_bulk_density_sample_volume Sample volume used for bulk density measurements, if held constant. numeric cubicCentimeters
dry_bulk_density_sample_mass Sample mass used for bulk density measurements, if held constant. numeric grams
dry_bulk_density_flag Any notable codes regarding how the authors quantified dry bulk density. factor air dried to constant mass = Methodology specified that samples were air dried to a constant mass. modeled = Bulk density was not measured, but was modeled from loss on ignition and assumptions about the particle densities of organic and inorganic matter. freeze dried = Bulk density was measured on freeze dried samples. not specified = No additional details regarding bulk density methodology were provided. removed non structural water = Bulk density methodology did not specify drying temperature or length, only that non-strucural water was removed. time approximate = Bulk density time recorded herin is an approximate estimate. to constant mass = Bulk density methodology did not specify drying temperature or length, only that samples were dried to a constant mass.
loss_on_ignition_temperature Temperature at which samples were combusted to estimate fraction organic matter. numeric celsius
loss_on_ignition_time Time over which samples were combusted to estimate fraction organic matter. numeric hour
loss_on_ignition_sample_volume Sample volume used for loss on ignition, if held constant. numeric cubicCentimeters
loss_on_ignition_sample_mass Sample mass used for loss on ignition, if held constant. numeric grams
loss_on_ignition_flag Common codes regarding loss on ignition methodology. factor time approximate = Loss on ignition time recorded herein is an approximate estimate. not specified = No additional details regarding loss on ignition methodology or time were provided.
carbon_measured_or_modeled Code indicating whether fraction carbon was measured or estimated as a function of organic matter. factor measured = Fraction carbon was measured as opposed to modeled. modeled = Fraction carbon was modeled as opposed to measured.
carbonates_removed Whether or not carbonates were removed prior to calculating fraction organic carbon. factor FALSE = Carbonates were not removed before measuring organic carbon. TRUE = Carbonates were removed before measuring organic carbon.
carbonate_removal_method The method used to remove carbonates prior to measuring fraction carbon. factor direct acid treatment = Carbonates were removed using direct application of dilute acid. acid fumigation = Carbonates were removed by fumigating with concentrated acid. low carbonate soil = Organic carbon fraction was measured without removing carbonates assuming carbonate content of the soil type was minimal. carbonates not removed = Carbonates were not removed and low carbonate soil was not specified. none specified = Carbonate removal methodology was not specified.
fraction_carbon_method Code indicating the method for which fraction carbon was measured or modeled (Note: regression based models are permitted, but the use of the Bemmelen factor [0.58 gOC gOM-1] is discouraged). factor Craft regression = Used regression model from Craft et al., 1991, Estuaries, to predict fraction carbon as a function of fraction organic matter. EA = Each sample presented was measured using Elemental Analysis. Fourqurean regression = Used regression model from Fourqurean et al., 2012, Nature Geoscience, to predict fraction carbon as a function of fraction organic matter. Holmquist regression = Used regression model from Holmquist et al., 2018, Scientific Reports, to predict fraction carbon as a function of fraction organic matter. kjeldahl digestion = Each sample was measured kjeldahl digestion method. local regression = A regression model fit using a subset of measurements was used to predict fraction carbon as a function of fraction organic matter. not specified = No additional details were provided regarding fraction carbon methodologies. wet oxidation = Each sample was measured using a wet oxidation method.
fraction_carbon_type Code indicating whether fraction_carbon refers to organic or total carbon. factor organic carbon = Author specified that fraction carbon measurements were of organic carbon. total carbon = Author specified that fraction carbon measurements were of total carbon.
carbon_profile_notes Any other submitter defined notes describing methodologies for determining dry bulk density, organic matter fraction, and carbon fraction. character
cs137_counting_method Code indicating the method used for determining radiocesium activity. factor alpha = Alpha counting method used. gamma = Gamma counting method used.
pb210_counting_method Code indicating the method used for determining lead 210 activity. factor alpha = Alpha counting method used. gamma = Gamma counting method used.
excess_pb210_rate Code indicating the mass or accretion rate used in the excess_pb_210_model factor mass accumulation = Excess 210Pb modeled using mass accumulation rate. accretion = Excess 210Pb modeled using vertical accretion rate.
excess_pb210_model Code indicating the model used to estimate excess lead 210. factor CRS = Constant rate of supply model used. CIC = Constant initial concentration model used. CFCS = Constant flux constant sedimentation model used.
ra226_assumption Code indicating the assumption used to estimate the core’s background 226Ra levels. factor each sample = 226Ra was measured for each sample. total core = 226Ra was measured for the total core, at asymptote = asy
c14_counting_method Code indicating the method used for determining radiocarbon activity. factor AMS = Accelerator mass spectroscopy used. beta = Beta counting used.
dating_notes Any submitter defined notes elaborating on the process of dating the core not yet made clear by the coding. character
age_depth_model_reference Code indicating the reference or 0 year of the age depth model. factor YBP = Year zero is defined as years before present, 1960 CE. CE = Year zero is set according to Common Era and Before Common Era standards. core collection date = Year zero is set as the core’s collection year.
age_depth_model_notes Any submitter defined notes on how the age depth model was created. character

Site Level

Site information provides important context for your study. You should describe the site and how it fits into your broader study, provide geographic information (although this can be generated automatically from the cores as well), and add any relevant tags and notes regarding site vegetation and inundation. Vegetation and inundation can alternatively be incorporated into the core-level data, whatever makes the most sense for your study design.

Site Information
attribute name definition data type format, unit or codes
study_id Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year separated by underscores. character
site_id Site identification code unique to each study. character
site_description Site description including relevant study details and political geographic units. Some of these descriptions can be automated by the ingestion code. character
site_latitude_max Maximum latitude defining a bounding box for the site in decimal degree World Geodedic System of 1984 (WGS84). This can also be generated automatically by the ingestion code. numeric degree
site_latitude_min Minimum latitude defining a bounding box for the site in decimal degree WGS84. This can also be generated automatically by the ingestion code. numeric degree
site_longitude_max Maximum longitude defining a bounding box for the site in decimal degree WGS84. This can also be generated automatically by the ingestion code. numeric degree
site_longitude_min Minimum longitude defining a bounding box for the site in decimal degree WGS84. This can also be generated automatically by the ingestion code. numeric degree
site_boundaries As an alternative to submitting or automatically generating a bounding box, submitters can include a shapefile (.shp) or keyhole markup language (.kml) documenting the geographic boundaries of the site. This can be converted to and stored in well known text (WTK) format. character
salinity_class Code based on submitter field observation or measurement indicating average annual salinity (Note: Palustrine and freshwater should only include tidal wetlands, or wetlands that are potentially/formerly tidal but artificially freshened due to artificial tidal restrictions). factor estuarine C-CAP = 5-35 parts per thousand salinity (ppt) according to the coastal change analysis program. palustrine C-CAP = < 5 ppt according to the coastal change analysis program. estuarine = 0.5-35 ppt according to most other definitions. palustrine = < 0.5 ppt according to most other definitions. brine = >50 ppt. saline = 30-50 ppt. brackish = 0.5-30 ppt. fresh = <0.5 ppt. mixoeuhaline = 30-40 ppt. polyhaline = 18-30 ppt. mesohaline = 5-18 ppt. oligohaline = 0.5-5 ppt.
salinity_method Indicate whether salinity_class was determined using a field observation or a measurement. factor field observation = Salinity inferred by field observation such as vegetation. measurement = Salinity observed from local instrument.
salinity_notes Any relevant submitter generated notes on how salinity_class was determined. character
vegetation_class Code based on submitter field observations or measurement indicating dominant wetland vegetation type. factor emergent = Describes wetlands dominated by persistent emergent vascular plants. scrub shrub = Describes wetlands dominated by woody vegetation <= 5 meters in height. forested = Describes wetlands dominated by woody vegetation > 5 meters in height. forested to shrub = Dominated by forested to scrub/shrub biomass. forested to emergent = Dominated by forest and underlying marsh. seagrass = Describes tidal or subtidal communities dominated by rooted vascular plants.
vegetation_method Indicate whether vegetation_class was determined using a field observation or a measurement factor field observation = Vegetation inferred by field observation. measurement = Vegetation measured by counts or plots.
vegetation_notes Any relevant submitter generated notes on how vegetation_class was were determined character
inundation_class Code based on submitter field observation or measurement indicating how often the coring location is inundated factor high = Study-specific definition of an elevation relatively high in the tidal frame, typically defined by vegetation type. mid = Study-specific definition of an elevation in the relative middle of the tidal frame, typically defined by vegetation type. low = Study-specific definition of an elevation in relatively low in the tidal frame, typically defined by vegetation type. levee = Study-specific definition of a relatively high elevation zone built up on the edge of a river, creek, or channel. back = Study-specific definition of a relatively low elevation zone behind a levee.
inundation_method Indicate whether inundation_class was determined using a field observation or a measurement factor field observation = Inundation inferred by field observation such as vegetation. measurement = Inundation class assessed from elevation and nearby tide gauge or other similar method.
inundation_notes Any relevant submitter generated notes on how inundation was determined character

Core Level

Note that positional data can be assigned at the core level, or at the site level. However, it is important that this is specified, that site coordinates are not attributed as core coordinates, and that the method of measurement and precision is noted. Vegetation and inundation can alternatively be incorporated into the site-level data, whatever makes the most sense to your study design. In the future this level of hierarchy will be complemented by a ‘subsite level’ as this level of hierarchy can handle any sublocation information such as vegetation plot, and instrument location/description.

Core- (or Subsite-) Level Information
attribute name definition data type format, unit or codes
study_id

Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year separated by underscores.

factor NA
site_id Site identification code unique to each study. factor NA
core_id Core identification code unique to each site. factor NA
core_date Date of core collection. Date YYYY-MM-DD
core_notes Any other relevant submitter generated notes on how cores were collected. character
core_latitude Positional latitude of the core in decimal degree WGS84. numeric degree
core_longitude Positional longitude of the core in decimal degree WGS84. numeric degree
core_position_accuracy Accuracy of latitude and longitude measurement, if determined and recorded. numeric meter
core_position_method Code indicating how latitude and longitude were determined. factor

RTK = Real-time kinematic global position system (GPS). handheld = Conventional Commercially available hand-held GPS. other high resolution = Any other technique resulting in positional error < 1 meter. other moderate resolution = Any other technique resulting in positional error < 30 meters. other low resolution = Any other technique resulting in positional error > 30 meters.

core_position_notes Any relevant submitter generated notes on how latitude and longitude were determined. character
core_elevation Surface elevation of the core relative to defined datum. numeric meters
core_elevation_datum The datum relative to which the core elevation was measured against (For a complete list of datum names and aliases please refer to the ISO Geodedic Registry https://geodetic.isotc211.org/). factor

NAVD88 = A gravity-based geodetic datum, North American Vertical Datum of 1988. MSL = A tidal datum, Mean Sea Level as measured against a local tide gauge. MTL = A tidal datum, Mean Tidal Level as measured against a local tide gauge. MHW = A tidal datum, Mean High Water as measured against a local tide gauge. MHHW = A tidal datum, Mean Higher High Water as measured against a local tide gauge. MHHWS = A tidal datum, Mean Higher High Water for Spring Tides as measured against a local tide gauge. MLW = A tidal datum, Mean Low Water as measured against a local tide gauge. MLLW = A tidal datum, Mean Lower Low Water as measured against a local tide gauge.

core_elevation_accuracy Accuracy of elevation measurement, if determined and recorded numeric meters
core_elevation_method Code indicating how elevation was determined factor

RTK = Real-time kinematic GPS. other high resolution = Any other technique resulting in positional error < 5 cm of random error. LiDAR = Handheld GPS matched to lidar-based digital elevation model. DEM = Handheld GPS matched to another digital elevation model. other low resolution = Any other technique resulting in positional error > 5 cm of random error.

core_elevation_notes Any relevant submitter generated notes on how elevation was determined character
salinity_class Code based on submitter field observation or measurement indicating average annual salinity (Note: Palustrine and freshwater should only include tidal wetlands, or wetlands that are potentially/formerly tidal but artificially freshened due to artificial tidal restrictions). factor

estuarine C-CAP = 5-35 parts per thousand salinity (ppt) according to the coastal change analysis program. palustrine C-CAP = < 5 ppt according to the coastal change analysis program. estuarine = 0.5-35 ppt according to most other definitions. palustrine = < 0.5 ppt according to most other definitions. brine = >50 ppt. saline = 30-50 ppt. brackish = 0.5-30 ppt. fresh = <0.5 ppt. mixoeuhaline = 30-40 ppt. polyhaline = 18-30 ppt. mesohaline = 5-18 ppt. oligohaline = 0.5-5 ppt.

salinity_method Indicate whether salinity_class was determined using a field observation or a measurement factor

field observation = Salinity inferred by field observation such as vegetation. measurement = Salinity observed from local instrument.

salinity_notes Any relevant submitter generated notes on how salinity_class was determined character
vegetation_class Code based on submitter field observations or measurement indicating dominant wetland vegetation type. factor

emergent = Describes wetlands dominated by persistent emergent vascular plants. scrub shrub = Describes wetlands dominated by woody vegetation < 5 meters in height. forested = Describes wetlands dominated by woody vegetation > 5 meters in height. seagrass = Describes tidal or subtidal communities dominated by rooted vascular plants.

vegetation_method Indicate whether vegetation_class was determined using a field observation or a measurement factor

field observation = Vegetation inferred by field observation. measurement = Vegetation measured by counts or plots.

vegetation_notes Any relevant submitter generated notes on how vegetation_class and dominant_species were determined. character
inundation_class Code based on submitter field observation or measurement indicating how often the coring location is inundated. factor

high = Study-specific definition of an elevation relatively high in the

tidal frame, typically defined by vegetation type. mid = Study-specific definition of an elevation in the relative middle of the tidal frame, typically defined by vegetation type. low = Study-specific definition of an elevation in relatively low in the tidal frame, typically defined by vegetation type. levee = Study-specific definition of a relatively high elevation zone built up on the edge of a river, creek, or channel. back = Study-specific definition of a relatively low elevation zone behind a levee.

inundation_method Indicate whether inundation_class was determined using a field observation or a measurement factor field observation = Inundation inferred by field observation such as vegetation. measurement = Inundation class assesed from elevation and nearby tidegauge or other similar method.
inundation_notes Any relevant submitter generated notes on how elevation was determined character
core_length_flag Indicated whether or not the coring team believes they recovered a full sediment profile, down to bedrock, or other non-marsh interface. factor core depth limited by length of corer = The total depth of the core was limited by the length of the coring device. core depth represents deposit depth = Authors report that the depth of the core represents the depth of the wetland soil deposit. not specified = Authors did not specify whether or not the depth of the core represents the depth of the wetland soil deposit.

Soil Depth Series

This level of hierarchy contains the actual depth series information. At minimum a submission needs to specify minimum and maximum depth increments, dry bulk density, and either fraction organic matter or fraction carbon. Sample ID’s should be used in the case that there are multiple replicates of a measurements. There is plenty of room for recording raw data from various dating techniques as well as age depth models.

Soil Depth Series Information
attribute name definition data type format, unit or codes
study_id Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year separated by underscores. character
site_id Site identification code unique to each study character
core_id Core identification code unique to each site character
depth_min Minimum depth of a sampling increment. numeric centimeter
depth_max Maximum depth of a sampling increment. numeric centimeter
sample_id Sample identification unique to the core. This should be used in the case that there are relevant lab specific sample codes, or in the case that there are multiple replicate samples. character
dry_bulk_density Dry mass per unit volume of a soil sample. This does not include ash free bulk density. numeric gramsPerCubicCentimeter
fraction_organic_matter Mass of organic matter relative to sample dry mass. Ash free bulk density should not be used here but should be expressed as a loss on ignition fraction. numeric dimensionless
fraction_carbon Mass of carbon relative to sample dry mass. numeric dimensionless
compaction_fraction Fraction of the sample depth interval reduced due to compaction. numeric dimensionless
compaction_notes Any submitter generated notes on compaction. character
cs137_activity Radioactivity counts per unit dry weight for radiocesium (137Cs). numeric becquerelPerKilogram
cs137_activity_sd 1 standard deviation of uncertainty associated with cs137_activity. numeric becquerelPerKilogram
total_pb210_activity Total radioactivity counts per unit dry weight for excess lead 210 (210Pb). numeric becquerelPerKilogram
total_pb210_activity_sd 1 standard deviation of uncertainty associated with total_pb210_activity. numeric becquerelPerKilogram
ra226_activity Total radioactivity counts per unit dry weight for Radium 226 (226Ra) if measured as part of the 210Pb dating process. numeric becquerelPerKilogram
ra226_activity_sd 1 standard deviation of uncertainty associated with ra226_activity. numeric becquerelPerKilogram
excess_pb210_activity Excess radioactivity counts per unit dry weight for excess lead 210 (210Pb). numeric becquerelPerKilogram
excess_pb210_activity_sd 1 standard deviation of uncertainty associated with excess_pb210_activity. numeric becquerelPerKilogram
c14_age_sd Estimated uncertainty in c14_age. numeric radiocarbonYear
c14_material Description of the material selected for radiocarbon (14C) dating. character
c14_notes Any relevant submitter generated notes on 14C dating process. character
delta_c13 The isotopic signature of 13C. This is oftentimes measured along with c14_age and can be useful for analyzing carbon lability and provenance. numeric partsPerMillion
be7_activity Radioactivity counts per unit dry weight for 7Be. numeric becquerelPerKilogram
be7_activity_sd Estimated uncertainty in be_7_activity. numeric becquerelPerKilogram
am241_activity Radioactivity counts per unit dry weight for 241Am. numeric becquerelPerKilogram
am241_activity_sd Estimated uncertainty in am_241_activity. numeric becquerelPerKilogram
marker_date The age of any other dated depth horizon such as an artificial marker, pollen horizon, pollution horizon, etc. Date YYYY-MM-DD
marker_type Code indicating the type of marker. factor artificial horizon = Horizon was added to the surface artificially by using materials such as feldspar, glitter, or rare earth elements. pollen = Pollen analysis was used to tie horizon to the timing of vegetation change such as the arrival of invasives, or the beginning of local agriculture. pollution = Chemical analysis was used to tie the horizon to the timing of a pollution event. tsunami = Sediment analysis was used to tie the horizon to the timing of a tsunami event.
marker_notes Any other submitter generated notes about the origin of the marker. character
age Most likely, median, or mean age of the depth interval from submitter generated age depth model. numeric year
age_min Minimum age of the depth interval from submitter generated age depth model. numeric year
age_max Maximum age of the depth interval from submitter generated age depth model. numeric year
age_sd Standard deviation of age estimate from submitter generated age depth model. numeric year
depth_interval_notes Any other submitter generated notes specific to the depth interval. character

Multiple Special Conditions at the Level of the Site or Core

Because there may be multiple observations or conditions that are part of the study, such as species present, or degradation or restoration activities, that can affect a site or core, these are archived separately.

Dominant Species Present

You can record species codes associated with sites and/or cores. The CCRCN is species code system is derived from the USDA PLANTS Database, and for most taxa, the code consists of the first two letters of genus follow by the first two letters of the species (e.g., "Spartina alterniflora" = "SPAL").

Species Present at Site or Subsite
attribute name definition data type format, unit or codes
study_id Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year separated by underscores. character
site_id Site identification code unique to each study. character
core_id Core identification code unique to each site. character
species_code Code associated with a species or a vegetation assemblage. character

Anthropogenic Impacts Present

You can record various codes associated with degradation or restoration conditions at sites and/or cores.

Anthropogenic Impacts at Site or Subsite
attribute name definition data type format, unit or codes
study_id Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year separated by underscores. character
site_id Site identification code unique to each study. character
core_id Core identification code unique to each site. character
impact_class Code indicating any major anthropogenic impacts historically and currently affecting the coring location. factor tidally restricted = Tidal flow is muted or blocked by built structures. impounded = Water level is raised artificially by a tidal restriction, resulting in ponding of water on the wetland and or upland surface. managed impounded = Wetland is impounded seasonally, and other times natural or semi natural hydrology occurs. ditched = Tidal hydrology is altered because artificial ditches have been cut to promote tidal flooding and drainage. diked and drained = The wetland has been diked and drained, with or without flapper gates, pumping, or other means. farmed = Managed impoundment or drainage in which wetland has been converted to agricultural land. tidally restored = Tidal flow has been restored by removing an artificial obstruction. revegetated = Wetland vegetation has been reintroduced by replanting on unvegetated surfaces. invasive plants removed = Natural plant communities have been restored by the active removal of invasive plant species. invasive herbivores removed = Tidal wetland vegetation has been managed by the removal of invasive herbivores. sediment added = Elevation has been managed by artificially adding sediment to the site using techniques such as thin layering or sediment diversion. wetlands built = Constructed wetland using sediments such as dredge spoils or other sediment source.

Submitter Defined Attributes and Definitions

Part of the reason we control these attribute and variable names are so that the dataset does not become unmanageable, and we can deliver products that run cleanly and smoothly to you. However, we know that research is complicated, and not all of the data you want to include can be represented here. As long as it fits within this hierarchy, we allow you to submit user defined attributes.

Study Level Species Table

If species codes or common names are used anywhere in the study, there should be a separate table included defining all names using scientific names. The CCRCN is species code system is derived from the USDA PLANTS Database, and for most taxa, the code consists of the first two letters of genus follow by the first two letters of the species (e.g., "Spartina alterniflora" = "SPAL").

attribute name definition data type format, unit or codes
study_id Unique identifier for the study made up of the first author’s family name, as well as the second author’s family name or ‘et al.’ if more than three, then publication year separated by underscores. character
species_code Code associated with a species or a vegetation assemblage. character
genus Genus according to the most up to date classification. character
species Species according to the most up to date classification. character
sub_species Any nomenclature referring to subspecies special cases. character
hybrid Any nomenclature referring to special cases of hybridization. character
common_name Common name associated with the species, especially if it is referred to in any accompanying text. character
species_notes Any other submitter defined notes regarding the species. character

Other Attributes and Variables

Any submitter-defined attributes should be included in a separate table indicating the associated level of hierarchy, attribute name, data type (date, factor, character, or numeric). Attribute names should follow good naming practices: self-descriptive, don’t start with a number or special character, no spaces. Dates should be stored as a character string and should have an accompanying ‘string format’ indicating the position, number of digits and deliminators for the date time. For example June twenty-sixth two-thousand eighteen written as 2018-06-26 would be formatted as ‘YYYY-MM-DD’. Here is a handy dateTime reference. Numeric values should have their units defined. Factors (i.e. categorical variables) should be defined in a separate table.

level of hierarchy attribute name description data Type format, unit
ex. site level or core level (your column name here. [use good naming conventions]) (describe your attribute here.) Date, factor, character, or numeric (extra necessary info here)

Variable names, like attribute names, should be self-descriptive. Such as ‘experimental’ or ‘control’ as opposed to ‘1’ and ‘2’.

level of hierarchy attribute name categorical variable name description
ex. site level or core level (parent column name here) (your variable name here.) (describe your variable)

That’s It

You now know everything there is to know about soil carbon data management.

Methane Database Structure

Overview

This page serves as guidance for the types and scope of data and metadata that will be archived as part of the Coastal Carbon Network’s developing methane chamber data synthesis. We propose the following data structure and standardized attribute names for metadata and data in order to make datasets machine-readable and interoperable. Each subheading lists a level of metadata or data hierarchy from site level to chamber level to timeseries information. Each subheading also represents separate tables which can be joined by common attributes such as study_id, site_id, and chamber_id. We also include accompanying sets of recommended controlled vocabulary for key categorical variables (also known as factors). Some attributes have controlled units that we wish to keep uniform across datasets.

Throughout the table key attributes are bolded. Please strive to provide data for these attributes whenever possible.

Site Level

Site information provides important context for your study. You should describe the site and how it fits into your broader study, provide geographic information, and add any relevant tags and notes regarding site vegetation and inundation.

attribute_name definition unit, format, or codes
study_id Unique identifier for the study made up of the first author's family name, as well as the second author's or 'et al' if more than three, then publication year seperated by underscores.
site_id Site identification code unique to each study.
site_description Site description including relevant study details and political geographic units. Some of these descriptions can be automated by the ingestion code.
utc_offset Offset from UTC of site data hour
site_longitude Positional longitude of the site in decimal degree WGS84. degree
site_latitude Positional latitude of the site in decimal degree WGS84. degree
site_elevation Site elevation relative to defined datum. meter
site_elevation_datum The datum relative to which the site elevation was measured against (For a complete list of datum names and aliases please refer to the ISO Geodedic Registry).
site_elevation_MSL Elevation of mean sea level relative to a given datum meter
site_elevation_MSL_datum The datum relative to which the mean sea level was measured against (For a complete list of datum names and aliases please refer to the ISO Geodedic Registry).
site_elevation_MSL_station Station used to determine MSL free text
site_elevation_MSL_years Period used to determine MSL year
site_tidal_range Mean tidal range calculated as the difference between Mean High Water and Mean Low water meter
climate_classification Köppen climate classification Af; Am; Aw; Bsh; Bsk; Bwh; Bwk; Cfa; Cfb; Cfc; Csa; Csb; Cwa; Cwb; Cwc; Dfa; Dfb; Dfc; Dfd; Dsa; Dsb; Dsc; Dsd; Dwa; Dwb; Dwc; Dwd; EF; ET
land_cover_type_IGBP Vegetation type based on the IGBP definition Barren Sparse Vegetation; Croplands; Closed Shrublands; Cropland/Natural Vegetation Mosaics; Deciduous Broadleaf Forests; Deciduous Needleleaf Forests; Evergreen Broadleaf Forests; Evergreen Needleleaf Forests; Grasslands; Mixed Forests; Open Shrublands; Savannas; Snow and Ice; Urban and Built-Up Lands; Water Bodies; Permanent Wetlands; Woody Savannas
mean_annual_temp Climatological long-term Mean Annual average air Temperature (MAT) degreesCelsius
mean_annual_precipitation Climatological long-term Mean Annual average Precipitation (MAP) millimeter
salinity_class Code based on submitter field observation or measurement indicating average annual salinity (Note: freshwater should only include tidal wetlands, or wetlands that are potentially/formerly tidal but artificially freshened due to artificial tidal restrictions). fresh = <0.5 ppt.; oligohaline = 0.5-5 ppt.; mesohaline = 5-18 ppt.; polyhaline = 18-30 ppt.; mixoeuhaline = 30-40 ppt.; saline = 30-50 ppt.; brine = >50 ppt.
salinity_method Indicate whether salinity_class was determined using a field observation or a measurement. field observation = Salinity inferred by field observation such as vegetation.; measurement = Salinity observed from local instrument.
salinity_notes Any relevant submitter generated notes on how salinity_class was determined.
inundation_class Code based on submitter field observation or measurement indicating how often the site location is inundated. high = Study-Specific definition of an elevation relatively high in the tidal frame located above the Mean Highwater Mark (MHW), inundated infrequently either during high rainfall events, spring tides, or above normal high tides and typically defined by vegetation type (e.g., Spartina patens, Distichlis spicata, Salicornia sp., Juncus sp., and bulrush species).; mid = Study-specific definition of an elevation in the relative middle of the tidal frame, typically defined by vegetation type (e.g., Spartina patens).; low = Specific definition of an elevation relatively low in the tidal frame, typically defined by vegetation type (e.g., Spartina alterniflora).; levee = Study-specific definition of a relatively high elevation zone built up on the edge of a river, creek, or channel.; back = Study-specific definition of a relatively low elevation zone behind a levee.
inundation_method Indicate whether inundation_class was determined using a field observation or a measurement field observation = Inundation inferred by field observation such as vegetation.; measurement = Inundation class assessed from elevation and nearby tide gauge or other similar method.
inundation_time Fraction of time the site location is inundated daily fraction
inundation_notes Any relevant submitter generated notes on how inundation/elevation was determined
wetland_vegetation_class Code based on submitter field observations or measurement indicating dominant wetland vegetation type. mudflat = Describes unvegetated areas exposed and flooded by the tides.; emergent = Describes wetlands dominated by persistent emergent vascular plants.; scrub shrub = Describes wetlands dominated by woody vegetation less than or equal to 5 meters in height.; forested = Describes wetlands dominated by woody vegetation > 5 meters in height.; FO/SS = Dominated by forested to scrub/shrub biomass.; seagrass = Describes tidal or subtidal communities dominated by rooted vascular plants.
wetland_vegetation_method Indicate whether wetland_vegetation_class was determined using a field observation or a measurement field observation = Vegetation inferred by field observation.; measurement = Vegetation measured by counts or plots.
wetland_vegetation_notes Any relevant submitter generated notes on how vegetation_class was were determined
net_primary_productivity Annual Net Primary Productivity measured at the site level. Negative values to indicate net uptake gramsCarbonPerSquareMeterPerYear
ecosystem_respiration Annual ecosystem respiration measured at the site level gramsCarbonPerSquareMeterPerYear
impact_class Code indicating any major anthropogenic and/or natural impacts historically and currently affecting the site dates of measurement. undisturbed = No disturbance or management has occurred on the site.; tidally restricted = Tidal flow is muted or blocked by built structures.; impounded = Water level is raised artificially by a tidal restriction, resulting in ponding of water on the wetland and or upland surface.; managed impounded = Wetland is impounded seasonally, and other times natural or semi natural hydrology occurs.; ditched = Tidal hydrology is altered because artificial ditches have been cut to promote tidal flooding and drainage.; diked and drained = The wetland has been diked and drained, with or without flapper gates, pumping, or other means.; farmed = Managed impoundment or drainage in which wetland has been converted to agricultural land.; tidally restored = Tidal flow has been restored by removing an artificial obstruction.; submergence-salinization = due to sea level rise and salt water intrusion.; revegetated = Wetland vegetation has been reintroduced by replanting on unvegetated surfaces.; invasive plants removed = Natural plant communities have been restored by the active removal of invasive plant species.; invasive herbivores removed = Tidal wetland vegetation has been managed by the removal of invasive herbivores.; sediment added = Elevation has been managed by artificially adding sediment to the site using techniques such as thin layering or sediment diversion.; wetlands built = Constructed wetland using sediments such as dredge spoils or other sediment source.; fire = wildfire or managed burns.; land cover change = land use and land cover change.; invasion = woody or urban encroachment.; grazing = herbivory or browsing by mammals, managed or wild.; pests and disease = plant or soil damage from pests, insects, pathogens, blight, and other diseases.; storm or wind = Major storms including unusually high-precipitation and unusually high-wind events e.g. hurricane, hail, flooding from storm, etc. Temperature extreme + heat wave or freeze
ecosystem_age If historically disturbed, time since disturbance years
mean_dry_bulk_density Mean soil bulk density gramsPerCubicCentimeter
se_dry_bulk_density Standard error of the mean bulk density gramsPerCubicCentimeter
mean_soil_organic Mean Soil organic matter conent fraction
se_soil_organic Standard error of mean soil organic matter conent fraction
mean_soil_carbon Mean soil organic carbon conent fraction
se_soil_carbon Standard error of mean soil organic carbon conent fraction
mean_soil_total_nitrogen Mean soil total nitrogen conent fraction
se_soil_total_nitrogen Standard error of mean soil total nitrogen conent fraction
mean_soil_total_phosphorous Mean soil total phosphorous conent fraction
se_soil_total_phosphorous Standard error of mean soil total phosphorous conent fraction
mean_soil_CN_ratio Mean soil C:N dimensionless
se_soil_CN_ratio Standard error of mean soil C:N dimensionless
mean_soil_clay Mean fraction of clay (<4 microns) in soil (0-1) fraction
se_soil_clay Standard error of mean fraction of clay fraction
mean_soil_silt Mean fraction of silt (4 - 63 microns) in soil (0-1) fraction
se_soil_silt Standard error mean fraction of silt fraction
mean_soil_sand Mean fraction of sand (>63 microns) in soil (0-1) fraction
se_soil_sand Standard error of mean fraction of sand fraction

Chamber Level

attribute_name definition unit, format, or codes
study_id Unique identifier for the study made up of the first author's family name, as well as the second author's or 'et al' if more than three, then publication year seperated by underscores.
site_id Site identification code unique to each study.
chamber_id Chamber identification code unique to each site.
chamber_year Year of chamber installation YYYY
chamber_type Chamber type static; automated
collar_installation_date Date of chamber collar installation. YYYY-MM-DD
collar_installation_period Code indicating how far in advance collars were installed, if collar installation date is unknown. less than one week prior to sampling event; between one week and one month prior to sampling event; between one month and one year prior to sampling event; more than one year prior to sampling event
collar_insertion_depth Depth of collar insertion centimeter
chamber_volume Chamber volume. cubicMeter
chamber_area Collar area. squareMeter
chamber_opacity chamber opacity clear; opaque
mixing_fan_present Chamber mixing fan present and operating TRUE; FALSE
path_to_chamber Was there a boardwalk or other means to approach the chamber without compressing the soil? TRUE; FALSE
chamber_location_notes chamber location comment (hummock/hollow, wetland edge, ditch, etc) free text
chamber_notes Any other relevant submitter generated notes on chambers.
chamber_longitude Positional longitude of the chamber in decimal degree WGS84. degree
chamber_latitude Positional latitude of the chamber in decimal degree WGS84. degree
chamber_position_accuracy Accuracy of latitude and longitude measurement, if determined and recorded. meter
chamber_position_method Code indicating how latitude and longitude were determined. RTK; handheld; other high resolution; other moderate resolution; other low resolution
chamber_elevation Surface elevation of the chamber relative to defined datum. meter
chamber_elevation_datum The datum relative to which the chamber elevation was measured against (For a complete list of datum names and aliases please refer to the ISO Geodedic Registry).
chamber_elevation_accuracy Accuracy of elevation measurement, if determined and recorded meter
chamber_elevation_method Code indicating how elevation was determined RTK; other high resolution; LiDAR; DEM; other low resolution
chamber_elevation_MSL Surface elevation of the chamber relative to mean sea level meter
chamber_elevation_notes Any relevant submitter generated notes on how elevation was determined
salinity_class If salinity was determined at the site level, please leave it blank here. Code based on submitter field observation or measurement indicating average annual salinity (Note: freshwater should only include tidal wetlands, or wetlands that are potentially/formerly tidal but artificially freshened due to artificial tidal restrictions). fresh = <0.5 ppt.; oligohaline = 0.5-5 ppt.; mesohaline = 5-18 ppt.; polyhaline = 18-30 ppt.; mixoeuhaline = 30-40 ppt.; saline = 30-50 ppt.; brine = >50 ppt.
salinity_method Indicate whether salinity_class was determined using a field observation or a measurement field observation = Salinity inferred by field observation such as vegetation.; measurement = Salinity observed from local instrument.
salinity_notes Any relevant submitter generated notes on how salinity_class was determined
inundation_class If inundation was determined at the site level, please leave it blank here. Code based on submitter field observation or measurement indicating how often the chamber location is inundated. high = Study-Specific definition of an elevation relatively high in the tidal frame located above the Mean Highwater Mark (MHW), inundated infrequently either during high rainfall events, spring tides, or above normal high tides and typically defined by vegetation type (e.g., Spartina patens, Distichlis spicata, Salicornia sp., Juncus sp., and bulrush species).; mid = Study-specific definition of an elevation in the relative middle of the tidal frame, typically defined by vegetation type (e.g., Spartina patens).; low = Specific definition of an elevation relatively low in the tidal frame, typically defined by vegetation type (e.g., Spartina alterniflora).; levee = Study-specific definition of a relatively high elevation zone built up on the edge of a river, creek, or channel.; back = Study-specific definition of a relatively low elevation zone behind a levee.
inundation_method Indicate whether inundation_class was determined using a field observation or a measurement field observation = Inundation inferred by field observation such as vegetation.; measurement = Inundation class assessed from elevation and nearby tide gauge or other similar method.
inundation_time Fraction of time the chamber location is inundated daily fraction
inundation_notes Any relevant submitter generated notes on how inundation/elevation was determined
wetland_vegetation_class If wetland veg class was determined at the site level, please leave it blank here. Code based on submitter field observations or measurement indicating dominant wetland vegetation type. mudflat = Describes unvegetated areas exposed and flooded by the tides.; emergent = Describes wetlands dominated by persistent emergent vascular plants.; scrub shrub = Describes wetlands dominated by woody vegetation less than or equal to 5 meters in height.; forested = Describes wetlands dominated by woody vegetation > 5 meters in height.; FO/SS = Dominated by forested to scrub/shrub biomass.; seagrass = Describes tidal or subtidal communities dominated by rooted vascular plants.
wetland_vegetation_method Indicate whether vegetation_class was determined using a field observation or a measurement field observation = Vegetation inferred by field observation.; measurement = Vegetation measured by counts or plots.
wetland_vegetation_notes Any relevant submitter generated notes on how vegetation_class and dominant_species were determined.
field_or_manipulation_code Code to indicate whether this is a field study or an artificial growing manipulation like a marsh organ or mesocosm. field; marsh organ; mesocosm
chamber_treatment_code Code indicating whether there was an experimental treatment at the plot (as opposed to an unmanipulated control). control; treatment
chamber_treatment_notes Any relevant submitter generated notes on how chamber_treatment_code was determined.
chamber_species Species (Latin binomial) according to the most up to date classification. If multiple species per chamber, separate species name by semicolons
chamber_species_date Measurement date of species fraction cover, biomass, leaf area, LAI or height YYYY-MM-DD
fraction_cover Estimated ground area coverage of species or vegetation type in chamber (0-1). If multiple species per chamber, separate each species cover by semicolons following the same order as the chamber_species fraction
fraction_cover_method Method used to calculate fraction cover number; mass; area; abundance
clipping Was vegetation clipped before measurement period? TRUE; FALSE
biomass Live plant mass within chamber footprint. If multiple species per chamber, separate each species biomass by semicolons following the same order as the chamber_species gC/m2
specific_leaf_area Specific leaf area of plants in chamber squareCentimetersPerGram
leaf_area_index Leaf area index of plants in chamber m2/m2
species_CN_ratio carbon/nitrogen ratio in vegetation in chamber or representative of chamber dimensionless
height average plant height within chamber meter
species_notes Any other submitter defined notes regarding the species.
dry_bulk_density Soil bulk density gramsPerCubicCentimeter
soil_organic Soil organic matter conent fraction
soil_carbon Soil organic carbon conent fraction
soil_CN_ratio Soil C:N dimensionless

Chamber Time Series

This level of hierarchy contains the actual time series information. Sample ID’s should be used in the case that there are multiple replicates of a measurements.

attribute_name definition unit, format, or codes
study_id Unique identifier for the study made up of the first author's family name, as well as the second author's or 'et al' if more than three, then publication year seperated by underscores.
site_id Site identification code unique to each study.
chamber_id Chamber identification code unique to each site. Use chamber_id followed by a position and aggregation qualifiers H_V_R (horizontal_ vertical_replicate). For example two replicate chambers (Ch_1_1_1 and Ch_1_1_2), two different chambers in the same site (Ch_1_1_1 and Ch_2_1_1)
sample_id Unique identifier for samples if there are more than one replicate per sampling event.
sample_year Year of the sampling event. YYYY
sample_month Month of the sampling event. MM
sample_day Day of the sampling event. If DD was not recorded, use NA DD
chamber_eq_time Length of time the chamber equilibrated before measurements were taken. minutes
chamber_measurement_length Measurement length minutes
chamber_volume Chamber volume. cubicMeter
sample_start_time_hour Start of measurement period, local time, no DST. HH
sample_start_time_minutes Start of measurement period, local time, no DST. MM
sample_start_time_seconds Start of measurement period, local time, no DST. If seconds were not recorded use NA SS
sample_end_time_hour End of measurement period, local time, no DST HH
sample_end_time_minutes End of measurement period, local time, no DST MM
sample_end_time_seconds End of measurement period, local time, no DST. Use NA if seconds were not recorded SS
chamber_measurement_mode Measurement mode Close flow, open flow or point measurement
chamber_flow_rate Chamber flow rate L min-1
light_treatment Fraction (0-1) describing any light/dark treatment the sampling event was subjected too. Fraction ambient light (0 is dark)
ch4_analyzer model of analyzer(s) used for fluxes free text
ch4_flux Methane flux (CH4) inside chamber. Positive fluxes are net emissions to atmosphere. nanoMolPersquareMeterPerSecond
ch4_flux_model Specify model/method used to calculate flux over sampling period. linear; exponential ; summation of fluxes during specific time intervals; internal vs external gas concentration
ch4_flux_se The standard error of CH4 flux.
ch4_flux_n Typical number of sampling events within sampling period.
ch4_flux_r2 The R^2 value of the model used to calculate flux over sampling period. dimensionless
ch4_flux_pvalue_threshold p-value threshold of significance of the slope parameter
ch4_flux_notes Any user submitted notes about CH4 during the sampling period.
co2_flux Carbon dioxide (CO2) flux inside chamber. Positive fluxes are net emissions to atmosphere. microMolPersquareMeterPerSecond
co2_flux_model Specify model used to calculate flux over sampling period. linear; exponential ; summation of fluxes during specific time intervals; internal vs external gas concentration
co2_flux_se The standard error of CO2 flux.
co2_flux_n Number of sampling events within sampling period. It is valid to report a typical number for continuous measurements
co2_flux_r2 The R^2 value of the model used to calculate flux over sampling period. dimensionless
co2_flux_notes Any user submitted notes about CO2 during the sampling period.
n2o_flux The flux of N2O between the chamber and environment over the sampling period. nanoMolPersquareMeterPerSecond
n2o_flux_model Specify model used to calcuate flux over sampling period. linear; exponential ; summation of fluxes during specific time intervals; internal vs external gas concentration
n2o_flux_se The standard error of N2O flux.
n2o_flux_n Typical number of sampling events within sampling period.
n2o_flux_r2 The R^2 value of the model used to calculate flux over sampling period. dimensionless
n2o_flux_notes Any user submitted notes about N2O during the sampling period.
air_temperature_inside_chamber Air temperature inside chamber measured over the sampling time. degreesCelsius
air_temperature_outside_chamber Air temperature outside chamber measured over the sampling time. degreesCelsius
soil_temperature Soil temperature inside chamber measured over the sampling time. degreesCelsius
soil_temperature_depth Soil temperature probe depth. centimeter
soil_water_content Soil water content (volumetric) inside chamber, range 0-100 Percent
soil_water_content_depth Soil depth at which water content/maisture has been measured centimeter
par_inside_chamber Photosynthetically active raditation, incoming, inside the chamber micromolPhotonPerSquareMeterPerSecond
par_outside_chamber Photosynthetically active raditation, incoming, outside the chamber micromolPhotonPerSquareMeterPerSecond
conductivity Temperature corrected electrical conductivity (EC) milliSiemensPerMeter
salinity Salinity measured during the sampling interval ppt
conductivity_salinity_media Indicate whether conductivity and salinity where measured in surface, porewaters or bulk soil. surface; porewater; soil
conductivity_salinity_depth Conductivity and salinity probe depth. Negative values indicate depths below soil surface. centimeter
water_table_depth water table depth (negative values belowground surface) meter
sampling_interval_notes Any other relevant user submitted notes. free text

Porewater Level

attribute_name definition unit, format, or codes
study_id Unique identifier for the study made up of the first author's family name, as well as the second author's or 'et al' if more than three, then publication year seperated by underscores.
site_id Site identification code unique to each study.
chamber_id Chamber identification code unique to each site.
sample_id Unique identifier for samples if there are more than one replicate per sampling event.
porewater_method Method used to collect porewater samples sippers; peepers; piezometer; centrifugation; core
sample_year Year of the sampling event. YYYY
sample_month Month of the sampling event. MM
sample_day Day of sampling event. If day was not recorded please use NA DD
sample_start_time Start of measurement period, local time, no DST HH:mm:ss
sample_end_time End of measurement period, local time, no DST HH:mm:ss
porewater_depth Depth of the porewater draw centimeter
porewater_depth_min If porewaters integrate several centimeters, min (upper) depth of the porewater draw centimeter
porewater_depth_max If porewaters integrate several centimeters, maximum (bottom) depth of the porewater draw centimeter
porewater_temperature Temperature of porewaters at chamber or site location degreesCelcious
porewater_conductivity Porewater conductivity at chamber or site location milliSiemens
porewater_conductivity_depth Depth of the porewater draw centimeter
porewater_salinity Salinity measured in porewater at chamber or site location partsPerThousand
porewater_salinity_depth Porewater salinity probe depth centimeter
porewater_pH soil porewater pH numeric
porewater_O2_concentration Dissolved oxygen measured in porewater at sampling time. microMolar
porewater_SO4_concentration Sulfate measured in porewater at sampling time. milliMolar
porewater_H2S_concentration Sulfide measured in porewater at sampling time. microMolar
porewater_FeIII_concentration Ferric iron measured in porewater at sampling time. microMolar
porewater_FeII_concentration Ferrous iron measured in porewater at sampling time microMolar
porewater_NH4+ Ammonium measured in porewater at sampling time microMolar
porewater_NO3+NO2 Nitrate-Nitrite measured in porewater at sampling time microMolar
porewater_CH4_concentration Methane measured in porewater at sampling time. microMolar
porewater_DOC_concentration Dissolved organic carbon measured in porewater at sampling time microMolar
porewater_DIC_concentration Dissolved inorganic carbon measured in porewater at sampling time microMolar

Training and Resources

Coastal Wetlands Methodologies and Protocols

Provided below is a set of guides and documentation relevant to the field and laboratory methods of wetlands research. Have suggestions of other resources to include here? Email us at CoastalCarbon@si.edu.

Carbon Data Visualizations Across the Globe

From colleagues within the Smithsonian and beyond, here are a handful of interesting map products worth a visit.

One of the primary goals of the CCRCN is to provide training tools and tutorials for coastal carbon researchers. Here is a listing of the training products developed by the Network to date.

Collaborative Coding for Carbon Science

The CCRCN aspires to make coding a more enjoyable and collaborative process for all. We conform to tidyverse philosophies and grammar, which provides a unified methodology to approach R coding. Take a look at some of these tools and training developed by CCRCN managers as well as other developers from across the web.

Data Submission and Joining the Network

Submit Your Data to the CCRCN Data Clearinghouse

Why submit data to the CCRCN?

1. It will increase the likelihood that your data will be included in future syntheses and analyses. This means more citations, more collaborations, and more publicity of your work. We can also advertise your data to the community through the Coastal Carbon Atlas. This will also make your data directly accessible for projects and assessments at the state, region, and international scale, as we have detailed in our recent data inventory report.

2. The CCRCN is nested under the Smithsonian, a trusted institution with a legacy of long-term archival. The Smithsonian has a long history of archiving physical and digital content, as well as high visibility and recognition. Submitting data to the CCRCN will enable you to release your dataset as a distinct digital object identifier (DOI), which can be embargoed and versioned, and is stored under one of the Smithsonian Library's digital repositories (the default is Figshare).

3. Our data clearinghouse is targeted to serve the coastal carbon community and receptive to feedback. Because our focus is specialized, as opposed to a more general data repository, our vocabulary and structure is catered to those engaged in coastal carbon science, and we have the capability to quickly respond to new developments and recommendations through our version-controlled data structure.

4. We have full-time staff dedicated to assisting the data carpentry and archival process. Our staff are happy to assist the process of curating and publishing your data by assembling a data release that is complete with study metadata, affiliated licensing, and a reserved DOI.

5. You decide when and how the data are released to the public. By establishing an embargo on a data submission, we can ensure that the data are not made public before a certain date, or until you have published with them. Although our default data repository is the research-orientated Figshare, we are able to assist with data releases that will be deposited in other locations, such as a data bank designated by your funding sources.

Interested in contributing data to the CCRCN? If so, please email CoastalCarbon@si.edu and CCRCN personnel will assist you in the process. Data submissions can remain embargoed for a time specified by the submitter. In embargo cases a data release will be prepared and shared with the submitter via a private link. When a data release is made public, the dataset is drawn into synthesis products.

Please use our Data Submission templates, which can then be emailed to CoastalCarbon@si.edu.

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Internship Opportunity

SERC Fall Internship Opportunity: Mapping Restoration Opportunities for Managed Coastal Wetlands

Application Period Ends: August 30

Stipend: $550 / week for 10 weeks The Smithsonian Environmental Research Center (SERC) Biogeochemistry Lab is searching for an intern to assist in a coastal wetland restoration mapping project. We are looking for a candidate with experience and training in coding and data management and experience in contributing to environmental science research projects. Importantly we are seeking someone who learns quickly, loves to solve problems, has a passion for conservation science, and is a good collaborator. During their experience the successful candidate will have the opportunity to learn advanced coding, GIS and Remote Sensing skills, will contribute towards building open-source research products, will be introduced to an extensive network of collaborators and stakeholders, and will learn a lot about the science and policy surrounding our coastal wetlands. Candidates should have • Upper division class credit completed in Biology, Geography, or Environmental Science, or a related discipline. • Passed a course in quantitative statistics, or demonstrated equivalent experience. • The ability to code in R or Python, or a similar language. • Enough applied coding, GIS, or Remote Sensing experience to be able to design and troubleshoot multi-step workflows. • Some skills in data visualization. This internship will start in October 2019 and continue for 10 weeks. Women and members of underrepresented communities are strongly encouraged to apply. To apply, please send a curriculum vitae, a personal essay, and contact information for two references to James Holmquist [HolmquistJ@si.edu] and cc’ Dan Gustafson [GustafsonD@si.edu]. In your essay describe your interest in environmental studies, why you are interested in interning at the Smithsonian, and any relevant background experience. You may also use this section to describe your future career goals and how a SERC internship will help you. Essays should be no longer than 2 single-spaced pages. We look forward to hearing from you! Information about the Smithsonian Environmental Research Center: SERC is focused on understanding the causes and consequences of environmental change for marine, freshwater, and terrestrial ecosystems. The main campus is a 2,650-acre research site on the shores of the Chesapeake Bay in Edgewater Maryland. Facilities include the Global Change Research Wetland, the site of the known longest running ecological manipulation experiment. SERC is located about 10 miles south of Annapolis, MD. We recommend interns bring a vehicle for personal transportation, if possible. There is on-site dormitory space available for$105.00 per week. SERC does not supply board, but the dorms are equipped with full kitchens.

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