Marsh Carbon Storage in the National Estuarine Research ......CEC. 2015. Marsh Carbon Storage in the National Estuarine Research Reserves, USA: A Comparison of Methodologies and Coastal

Commission for Environmental Cooperation

Marsh Carbon Storage in the National Estuarine Research Reserves, USA

A Comparison of Methodologies and Coastal Regions

Prepared for the CEC by Kristin Wilson, Wells National Estuarine Research Reserve &

Erik Smith, North Inlet-Winyah Bay National Estuarine Research Reserve

Version 1 March 31, 2015

Please cite as:

CEC. 2015. Marsh Carbon Storage in the National Estuarine Research Reserves, USA: A Comparison of Methodologies and Coastal Regions.

Montreal, Canada: Commission for Environmental Cooperation. 67 pp.

This report was prepared by Kristin Wilson and Erik Smith for the Secretariat of the Commission for Environmental Cooperation. The information contained herein is the responsibility of the author and does not necessarily reflect the views of the CEC, or the governments of Canada, Mexico or the United States of America.

About the author(s):

Kristin Wilson is the Research Coordinator at the Wells National Estuarine Research Reserve in Wells, ME. Erik Smith is the Research Coordinator at the North Inlet-Winyah Bay National Estuarine Research Reserve in Georgetown, SC.

Reproduction of this document in whole or in part and in any form for educational or non-profit purposes may be made without special permission from the CEC Secretariat, provided acknowledgment of the source is made. The CEC would appreciate receiving a copy of any publication or material that uses this document as a source.

Except where otherwise noted, this work is protected under a Creative Commons Attribution Noncommercial-No Derivative Works License.

© Commission for Environmental Cooperation, 2015

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Marsh Carbon Storage in the National Estuarine Research Reserves, USA - Version 1

Commission for Environmental Cooperation iii

Table of Contents

List of Abbreviations and Acronyms .............................................................................. vi

Abstract ........................................................................................................................... viii

Executive Summary ....................................................................................................... viii

Acknowledgments ............................................................................................................. x

1 Introduction .................................................................................................................. 1

1.1 Blue Carbon in Coastal Ecosystems ....................................................................... 1

1.2 Measuring Sediment Organic Carbon .................................................................... 2

1.3 The National Estuarine Research Reserve System ................................................. 3

2 Methods ......................................................................................................................... 3

2.1 Study Locations ...................................................................................................... 3

2.1.1 Wells NERR, Maine...................................................................................... 5

2.1.2 Delaware NERR, Delaware ......................................................................... 7

2.1.3 North Inlet-Winyah Bay NERR, South Carolina .......................................... 8

2.1.4 Guana Tolomato Matanzas NERR, Florida ............................................... 10

2.1.6 Grand Bay NERR, Mississippi ................................................................... 11

2.1.7 San Francisco NERR, California ............................................................... 12

2.1.8 Lake Superior NERR, Wisconsin ............................................................... 13

2.1.9 Old Woman Creek NERR, Ohio ................................................................. 14

2.2 Field Methods & Laboratory Techniques ............................................................ 15

2.3 Data Analyses ....................................................................................................... 18

2.3.1 Statistical Analyses .................................................................................... 18

2.3.2 Data Workshop .......................................................................................... 19

3 Study Results & Discussion ....................................................................................... 20

3.1 A Comparison of Methodologies: Percent LOI vs. Percent Carbon Content ....... 20

3.2 Variability in Sediment Properties ....................................................................... 23

4 Conclusions & Future Work ..................................................................................... 27

4.1 Key Findings .......................................................................................................... 27

4.2 Future Studies ........................................................................................................ 28

5 Appendices .................................................................................................................. 29

Spatial Variability in Carbon Storage Within and Across Marshes of the National

Estuarine Research Reserve System (NERRS), USA: A Comparison of

Methodologies and Coastal Regions ......................................................................... 60

6 Works Cited ................................................................................................................ 65


Commission for Environmental Cooperation iv

List of Tables

Table 1. Characteristics of the specific marsh sites sampled in this study .............. 4

Table 2. Site, general elevational zone (low, high), dominant vegetation type, and

mean visual percent cover plus or minus the standard deviation of the

top three plant species composing each dominant vegetation category ... 4

Table 3. Results of the model comparison showing Model 2 to be the best fit

model, where y = percent organic carbon and x = percent organic matter

by LOI ..................................................................................................... 21

Table 4. Individual regression lines for each site, where y = percent organic

carbon and x = percent organic matter by LOI ....................................... 21

List of Figures

Figure 1. The National Estuarine Research Reserve System includes 28 sites

across the United States of America. Sites in red indicate those included

in this study ............................................................................................... 1

Figure 2. Distributions of percent organic carbon (% C) and bulk density (BD) of

salt marsh sediments integrated over 0-10 cm and 20-30 cm along a

single transect in the North Inlet, SC, estuary extending from the upland

edge of the marsh platform to the creekbank (a). Relationships between

percent organic carbon (elemental analysis) and percent organic matter

(LOI) for data collected in three regions of the salt marsh platform (b) .. 2

Figure 3. The Wells NERR located in Wells, ME showing landscape views (left)

and representative examples of dominant vegetation types sampled in

this study (right) ........................................................................................ 7

Figure 4. The Delaware NERR located in Dover, DE showing landscape views

(left) and representative examples of dominant vegetation types sampled

in this study (right) .................................................................................... 8

Figure 5. The North Inlet-Winyah Bay NERR located in Georgetown, SC

showing landscape views (left) and representative examples of dominant

vegetation types sampled in this study (right) .......................................... 9

Figure 6. The GTM NERR located in, FL showing landscape views (left) and

representative examples of dominant vegetation types sampled in this

study (right)............................................................................................. 10

Figure 7. The Grand Bay NERR located in, MS showing landscape views (left)

and representative examples of dominant vegetation types sampled in

this study (right) ...................................................................................... 11

Figure 8. The San Francisco NERR located in Wells, CA showing landscape

views (left) and representative examples of dominant vegetation types

sampled in this study (right) ................................................................... 12


Commission for Environmental Cooperation v

Figure 9. The Lake Superior NERR located in Superior, WI showing landscape

views (left) and representative examples of dominant vegetation types

sampled in this study (right) ................................................................... 13

Figure 10. The Old Woman Creek NERR located in Wells, OH showing

landscape views (left) and representative examples of dominant

vegetation types sampled in this study (right) ........................................ 15

Figure 11. The modified coring device used in this study. Close-up of the core

head showing the cutting crown to reduce sediment compaction (a). We

manufactured a removable key with handle (b) to assist in core

collection. Assembled cores before shipping (c) .................................... 17

Figure 12. Faces of the fieldwork: Kristin Wilson, Wells NERR, Maine (a), Erik

Smith North Inlet-Winyah Bay NERR, South Carolina (b), Shon

Schooler, Lake Superior NERR, Wisconsin (c), and Jason Lynn, Guana

Tolomato Matanzas NERR, Florida (d) .................................................. 17

Figure 13. Core locations (yellow dots) for each study site; also available online

at http://bit.ly/1utPbJ8. Photographs are from ArcOnline. ..................... 18

Figure 14. Workshop participants ponder preliminary results (a). From left to

right, Amelie Jensen (Research Technician, Wells NERR), Tim Dubay

(Research Technician, Wells NERR), Kristin Wilson (Research

Coordinator, Wells NERR), Nikki Dix (Research Coordinator, GTM

NERR), Mark Woodrey (Research Coordinator, Grand Bay NERR),

Lyndie Hice-Dunton (Research Coordinator, Delaware NERR), and

Erik Smith (North Inlet-Winyah Bay NERR). Taking a break from data

analyses, Research Coordinators snowshoe to the salt marsh to observe

Maine’s frozen carbon stores (b). From left to right, Nikki Dix, Mark

Woodrey, Lyndie Hice-Dunton, Erik Smith, Kristin Wilson, and Kristi

Arend (Research Coordinator, Old Woman Creek NERR) .................... 19

Figure 15. Sediment organic carbon content (%) versus organic matter content by

LOI (%) ................................................................................................... 22

Figure 16. Relationship between sediment dry bulk density and sediment organic

carbon content (%) showing two distinct relationships as reveled by

break point analysis ................................................................................ 23

Figure 17. Distribution of organic carbon content (upper panel), bulk density

(middle panel) and organic carbon density (lower panel) within and

across eight marsh sites of the NERRS. Median values denoted by

horizontal line, boxes contain the 25th and 75th quartiles, wiskers denote

the 10th and 90th percentiles, and outliers are denoted by points. Sites

with the same letter are not significantly different from one another (p >

0.05). State abbreviations indicate site location ..................................... 24

Figure 18. Mean sediment carbon density by dominant vegetation type, zone, and

site. Bars that share the same letter are not statistically significant from

one another .............................................................................................. 26


Commission for Environmental Cooperation vi

List of Abbreviations and Acronyms

AIC Akaike Information Criterion

ANOVA Analysis of Variance

BD Bulk Density

C Carbon

°C degrees Celsius

CA California

CEC Commission for Environmental Cooperation

cm centimeter

DE Delaware

FL Florida

g gram

GTM Guana Tolomato Matanzas

GPS Global Positioning System

H Hydrogen

HSD Honestly Significant Difference

ID Identification

km kilometer

LOI Loss On Ignition

m meter

MA Massachusetts

ME Maine

MS Mississippi

N Nitrogen

NC North Carolina

NERR National Estuarine Research Reserve

NERRS National Estuarine Research Reserve System

NOAA National Oceanic and Atmospheric Administration

OH Ohio

OWC Old Woman Creek

% Percent

SC South Carolina

SWMP System Wide Monitoring Program

TIDES Training for the Integration of Decisions and Ecosystems Science


Commission for Environmental Cooperation vii

USA United States of America

WI Wisconsin


Commission for Environmental Cooperation viii

Abstract

‘Blue Carbon’ refers to the organic carbon stored in coastal habitats, such as tidal marshes. The

largest store of organic carbon in coastal habitats is in the sediment. A full accounting of global

carbon storage in coastal marshes has been limited by a lack of data on how sediment organic carbon

varies spatially, both within and across different marsh types. This is further complicated by some

uncertainty in the way carbon content is often estimated and especially in how transferable this

estimation approach is across broadly different marsh types. Therefore, this study directly measured

sediment organic carbon, as well as assessed the common approach for indirectly estimating sediment

organic carbon, in eight National Estuarine Research Reserves across the United States. These eight

sites represented a range of marsh types, from freshwater wetlands to high salinity salt marshes. Mean

sediment organic carbon, as a % of total sediment mass, showed significant spatial variability within

most, but not all, marshes, as well as among most of the different marsh types. Overall, sediment

organic carbon content ranged from 0.1 to 32.2 % and was inversely related to sediment density, but

not vegetation type. While a single predictive relationship could be used with some confidence to

estimate organic carbon across all marshes, there were distinctly different relationships evident when

considering spatial variability at finer scales. This study emphasizes the importance of considering

spatial variability in carbon content within coastal marshes and offers an improved means of

estimating organic carbon from common loss on ignition measurements.

Executive Summary

This research fills critical gaps in understanding spatial variability in soil organic carbon density in

the upper 20 cm across a range of sites of the National Estuarine Research Reserves System (NERRS)

of Maine, Delaware, South Carolina, Florida, Mississippi, California, Wisconsin, and Ohio. The sites

selected for this study represent a gradient from freshwater (WI, OH), to brackish (DE, CA), to high

salinity (ME, SC, FL, MS) marshes that vary in their geomorphic context and dominant vegetative

communities. Including these ranges was purposeful, to improve estimates of sediment carbon density

in the full extent of marsh types and to better understand the potential for loss on ignition

methodology to be extrapolated across marsh habitats throughout North America.

Twenty, 7.62 cm diameter by 20 cm long cores were collected randomly within the four dominant

vegetation types at each of the eight sites. Cores were split and processed for paired samples of

percent sediment organic matter through loss on ignition and percent organic carbon content through

elemental analysis over homogenized 5 cm depth intervals. Using percent carbon content and dry

bulk density measurements, sediment carbon density was calculated and compared across sites, marsh

zones, and vegetation types.

Results show that sediment organic matter by loss on ignition and sediment organic carbon content

(%) are highly correlated, that the global relationship differs from that of other published studies (e.g.,

Craft et al. 1991; Callaway et al. 2012), and that individual sites significantly contribute to variation

in this global relationship. Across broad spatial scales, a single curve adequately captures the vast

majority of the variability in sediment organic carbon explained by LOI. Study results show that at

finer scales in some regions, variability in sediment properties may dictate the use of site-specific

calibration curves, however.

Results of a break point analysis revealed a shift in the relationship between organic carbon content

and bulk density at an organic carbon content of 2.04 %. Above this value, the relationship was

highly significant and had a slope very similar to those observed in previous studies. Below this

value, the relationship was not statistically significant. Interestingly, the samples with organic carbon

content below 2.04 % were almost exclusively from the southeastern United States (South Carolina,


Commission for Environmental Cooperation ix

Florida, and Mississippi), and were largely confined to the mid-marsh halophyte and/or short-form

Spartina alterniflora vegetation zone.

Mean sediment organic carbon density in the upper 20 cm ranged from 0.001 to 0.061 g C cm-3, with

a grand mean of 0.030 ± 0.011 g C cm-3, and differed significantly by site. Sediments from Maine

and California contained significantly more organic carbon per cubic centimeter than the other sites

sampled. Mean sediment organic carbon density also differed significantly by high and low marsh

zones in South Carolina, Mississippi, and Florida, though the pattern of this difference was not

uniform. In Mississippi, the low marsh zone had significantly greater organic carbon density than the

high marsh zone, while in South Carolina and California, the opposite pattern was observed. Finally,

mean sediment organic carbon density differed significantly by vegetation at half the sites sampled

(Maine, Mississippi, California, and South Carolina). These results reveal that there is considerable

spatial variation in sediment organic carbon density in the upper 20 cm at the marsh scale.

This study greatly increases the number of sites across the United States for which sediment carbon

density measurements exist and improves our understanding of methodologies that use loss on

ignition as a proxy for sediment organic carbon. Our results suggest that region-specific calibration

curves that relate sediment organic matter to sediment carbon content are needed in some regions and

that there are significant differences in sediment organic carbon density in the upper 20 cm by site,

zone, and vegetation type. Importantly, the significant within and across marsh differences in

sediment carbon density have implications for how marsh-scale carbon budgets are calculated and

incorporated into blue carbon policies.

Future work should further expand the number of locations for which we have sediment carbon

density measurements and explore the degree to which additional regional calibration curves are

needed. Additional studies should also explore changes in sediment carbon density with depth by

collecting longer cores to improve calculations of carbon budgets at the marsh scale. The 28 Reserves

of the National Estuarine Research Reserve System are excellent potential partners to expand blue

carbon work in protected wetlands of the United States that encompass a range of marsh types,

management regimes, and natural and anthropogenic stressors.


Commission for Environmental Cooperation x

Acknowledgments

The authors wish to thank the Commission for Environmental Coordination for funding this work.

They also wish to thank the Research Coordinators from the six other National Estuarine Research

Reserves (NERR) who contributed to the project, including: Kristin Arend (Old Woman Creek

NERR, Ohio), Nikki Dix (Guana Tolomato Matanzas (GTM) NERR, Florida), Lyndie Hice-Dunton

(Delaware NERR, Delaware), Matt Ferner (San Francisco Bay NERR, California), Shon Schooler

(Lake Superior NERR, Wisconsin), and Mark Woodrey (Grand Bay NERR, Mississippi).

The Wells NERR, Maine thanks John Speight for his help in core construction design and fabrication;

summer research student intern Dana Cohen-Kaplan for his assistance fabricating cores and testing

protocol designs; Beverly Johnson of Bates College for project advice; University of New Hampshire

TIDES graduate student volunteer, Sydney Nick for her help with field work; Wells NERR staff

Jacob Aman and Jeremy Miller for assistance in the field; Wells NERR staff Sue Bickford for her

help with fieldwork and GIS components; and Wells NERR research interns Tim Dubay and Amelie

Jensen for their assistance in the field, organization of project photographs, assistance with figure and

table creation, and the many overtime, holiday, and off-hours they spent in the lab processing project

cores for loss on ignition and preparation for CHN analyses to meet tight project timelines. Partial

funding for this work was provided through NOAA and the George and Eleanor Ford Research Fund.

The North Inlet-Winyah Bay NERR, South Carolina thanks Tracy Buck for assistance with field

sampling and processing of vegetation biomass samples and Susan Denham for conducting all CHN

analysis and especially for her extraordinary effort at ensuring all analyses were completed on a very

compressed time-frame.

The Old Woman Creek NERR, Ohio thanks the following people for their assistance with field work:

University of New Hampshire TIDES intern Rebecca Jacobson; OWC volunteers Russ Clause and

Natalie Moore; OWC staff Jen Bucheit, Bill Pifer, Cheryl Wolf-Cragin, and Kim Johnston.

The GTM NERR, Florida thanks Jason Lynn and Pamela Marcum for collecting samples in the field

and sorting biomass. Todd Osborne, University of Florida Whitney Laboratory, provided drying

ovens and the analytical balance, plus performed the final weights for biomass estimates.

The Delaware NERR, Delaware thanks Delaware Coastal Programs and NERR staff Drexel Siok,

Kenny Smith, Christina Whiteman, and Amanda Santoni for the assistance with field work and

Kenny Smith and volunteer Lihoshimar Gonzalez for their assistance processing biomass samples.

The San Francisco Bay NERR, California thanks Anna Deck for field planning, data collection, and

shipping of cores; Sarah Ferner and Lara Martin for assistance in the field; Mike Vasey for

transporting biomass samples; Rebecca Crowe for drying and weighing biomass samples, and Tom

Parker for providing laboratory access. Partial funding support was provided by a grant under the

Federal Coastal Zone Management Act, administered by the National Oceanic and Atmospheric

Administration’s Office of Ocean and Coastal Resource Management and awarded to San Francisco

State University.

The Lake Superior NERR, Wisconsin thanks Ralph Garono, Michael Krick, and Tracey Ledder for

their assistance with sample collection and lab processing.

The Grand Bay NERR, Mississippi would like to thank Ron Cole for invaluable assistance in the field

and laboratory. Logistical and partial funding support was provided to Mark Woodrey by the

Mississippi Agricultural and Forestry Experiment Station and the Grand Bay National Estuarine

Research Reserve through NOAA grant #NA13NOS4200072 and Mississippi Department of Marine

Resources Grant DMR #651 – 717.


Commission for Environmental Cooperation 1

1 Introduction

1.1 Blue Carbon in Coastal Ecosystems

Coastal ecosystems are known to contain substantial amounts of blue carbon (Pendleton et al. 2012)

and have value as locations that sequester and store carbon over the long-term (Beaumont et al.

2014), but an understanding of global carbon storage in marshes is limited by uncertainties in

conversion factors that use loss on ignition as a proxy for organic carbon and by the sparse number

and inadequate spatial coverage of studies to date (Commission for Environmental Cooperation

(CEC) 2013). This lack of data undermines accurate quantification of blue carbon stores, the role of

these stores in the global carbon cycle and potential value to carbon markets, and predictions of how

these stores may respond to changing environmental conditions and anthropogenic stressors

(Pendleton et al. 2012; McLeod et al. 2011; Crooks et al. 2010; Craft et al. 2009).

Therefore, to address these uncertainties in coastal blue carbon stores, the goals of this study were to:

1. Compare direct measures of sediment organic carbon determined by elemental analysis and

organic matter concentration measured by loss on ignition in a subset of eight National

Estuarine Research Reserves across the United States (Figure 1) that encompass a range of

salinity, vegetation, and geomorphic conditions, to assess the viability of the loss on ignition

methodology as a means of reliably estimating sediment organic carbon content.

2. Quantify sediment organic carbon density at those eight sites thus increasing the number of

locations across the United States for which marsh carbon density measures exist, thus filling

critical gaps in current carbon storage estimates across a range of marsh types.

Figure 1. The National Estuarine Research Reserve System includes 28 sites across the United States of America. Sites in red indicate those included in this study



1.2 Measuring Sediment Organic Carbon

Studies of sediment organic carbon content usually measure it one of two ways: (1) directly, using an

elemental analyzer (e.g., Callaway et al. 2012) or (2) indirectly, using loss on ignition (LOI) and an

assumed conversion factor (Pribyl 2010) or previously reported empirical relationships between

organic carbon content and LOI (e.g., Craft et al. 1991). The second option has been used widely in

coastal blue carbon studies (e.g. Chmura 2011; Macreadie et al. 2013; Coverdale et al. 2014), as the

LOI methodology is less time and cost intensive. Further, the availability of a general empirical

relationship between the two measures provides a means of generating organic carbon values even

more broadly in regions where cost or access to analytical capabilities limit direct collection of

organic carbon measurements, but where additional measures of wetland carbon are desired (CEC

2013). Use of the LOI method as a proxy for organic carbon assumes, however, that the relationship

between carbon concentration within soil organic matter is relatively constant (Callaway et al. 2012)

such that an empirical relationship derived in one type of environment or region (e.g., that of Craft

[1991] for North Carolina salt marshes) will be broadly applicable to other regions or habitats.

The nature and generality of relationships between organic carbon measured directly by elemental

analysis and organic matter measured by LOI has been debated for decades in the terrestrial soil

science community (Chatterjee et al. 2009; Szava-Kovats 2009), but only recently gained much

attention with respect to coastal habitats (e.g., Howard et al. 2014). Recent work in salt marshes, sea

grass beds, and mangroves suggests that this relationship between organic carbon and LOI varies by

region and habitat (Callaway et al. 2012; Hansen et al. 2013). Further work shows that sediment

carbon concentrations vary spatially and with depth down-core and that these differences can be

explained by species-specific differences in vegetation and geomorphic context (Howard et al. 2014;

Lavery et al. 2013; Saintilan et al. 2013; Bernal & Mitsch 2013). Preliminary data collected at the

North Inlet–Winyah Bay NERR reveal significant spatial variability in the soil percent organic carbon

content and bulk density over relatively short distances along the marsh platform as well as with

sample depth (Figure 2a). Furthermore, observed relationships between percent soil organic carbon

and percent soil organic matter derived from the more traditional LOI measurements show variability

among adjacent marsh vegetation zones (Fig. 2b). These results suggest that the scientific community

may be oversimplifying carbon dynamics in blue carbon habitats.

Figure 2. Distributions of percent organic carbon (% C) and bulk density (BD) of salt marsh sediments integrated over 0-10 cm and 20-30 cm along a single transect in the North Inlet, SC, estuary extending from the upland edge of the marsh platform to the creek bank (a). Relationships



between percent organic carbon (elemental analysis) and percent organic matter (LOI) for data collected in three regions of the salt marsh platform (b)

1.3 The National Estuarine Research Reserve System

The National Estuarine Research Reserve System (NERRS) is a network of 28 National Oceanic and

Atmospheric Administration (NOAA)-supported, protected sites throughout the United States that are

dedicated to protecting and restoring coastal ecosystems through integrated research, stewardship,

education, and community partnerships (Figure 1).

This work builds on an existing, long-term monitoring program within the NERRS known as the

System Wide Monitoring Program (SWMP) that “measures short-term variability and long-term

changes in the water quality, biological systems, and land-use/land-cover characteristics of estuaries

and estuarine ecosystems for the purposes of informing effective coastal zone management” (NERRS

2011). Recently, the NERRS has expanded SWMP to include standardized measurements of short-

and long-term variations in marsh vegetation and sediment dynamics in response to changing climate

(NERRS 2011). SWMP is an excellent, existing platform that could readily include more

measurements of blue carbon storage and sequestration, thereby greatly expanding our understanding

of processes governing blue carbon in diverse, protected settings across the United States.

2 Methods

2.1 Study Locations

The NERRS sites selected for this study represent a gradient from freshwater (WI, OH), to brackish

(DE, CA), to high salinity (ME, SC, FL, MS) marshes that vary in their geomorphic context and

dominant vegetative communities (Tables 1, 2; Appendix 1). Including these ranges is critical to

improving estimates of sediment carbon density in the full extent of marsh types and the potential for

LOI methodology to be extrapolated across marsh habitats throughout North America.



Table 1. Characteristics of the specific marsh sites sampled in this study

State Site Latitude, Longitude Mean Annual Air

Temperature ± SD

Mean Annual

Salinity ± SD

Mean

Tidal

Range

Mean

Water

Depth

Maine Webhannet Marsh 43.311858, -70.574431 9.1 ± 9.7 30.5 ± 1.8 2.6 -

Delaware St. Jones River 39.084152, -75.437877 13.3 ± 10.1 11.0 ± 7.0 1.5 -

South Carolina Crabhaul Marsh 33.347256, -79.194680 18.0 ± 8.2 33.3 ± 3.7 1.4 -

Florida Hat Island 29.977153, -81.322631 20.7 ± 6.7 29.8 ± 6.8 1.6 -

Jason’s Creek 29.822866, -81.284898 20.7 ± 6.7 34.7 ± 2.5 1.7 -

Moses Creek 29.771384, -81.287675 20.7 ± 6.7 34.8 ± 2.2 1.4 -

Pellicer Creek 29.660294, -81.246602 20.7 ± 6.7 17.4 ± 9.8 0.6 -

Pine Island 30.086837, -81.367022 20.7 ± 6.7 29.8 ± 6.8 1.6 -

Washington Oaks 29.629838, -81.215771 20.7 ± 6.7 17.4 ± 9.8 1.7 -

Mississippi Middle Bayou 30.398732, -88.414874 19.6 ± 7.8 16.5 ± 8.2 0.6 -

Middle Bay 30.374761, -88.411634 19.6 ± 7.8 22.2 ± 5.8 0.6 -

North Rigolets 30.361677, -88.415282 19.6 ± 7.8 22.2 ± 5.8 0.6 -

California Rush Ranch 38.201369, -122.028248 14.7 ± 6.3 4.1 ± 2.7 2.3 -

Wisconsin Pokegama Bay 46.672662, -92.136929 2.9 ± 13.2 0.2 ± 0.1 - 1.4

Ohio Old Woman Creek 41.377557, -82.513726 10.6 ± 10.6 0.2 ± 0.1 - 0.4

Note: Mean annual air temperature is in degrees Celcius, mean annual salinity in practical salinity units, mean

tidal range and mean water depth in meters.

Table 2. Site, general elevational zone (low, high), dominant vegetation type, and mean visual percent cover plus or minus the standard deviation of the top three plant species composing each dominant vegetation category

Site Zone Dominant Vegetation Type Mean Visual Percent Cover ± SD Maine Low Spartina alterniflora, tall S. alterniflora tall (92 ± 8), bare ground (6 ± 7), Ascophyllum

nodosum (1 ± 2) (Suaeda linearis, Spartina patens)

Spartina alterniflora, short S. alterniflora short (98 ± 4), S. patens (1 ± 2), Plantago maritima (< 1 ± 1) (Salicornia europaea)

High Forb panne Plantago maritima (26 ± 22), Triglochin maritimum (23 ± 15), Glaux maritima (14 ± 31) (Agalinis maritima, S. alterniflora short, Limonium nashii, Distichlis spicata, S. patens)

Spartina patens S. patens (98 ± 2), bare ground (1 ± 2), S. europaea (< 1 ± 1), S. alterniflora (< 1 ± 1) (G. maritima)

Delaware Low Spartina alterniflora, tall S. alterniflora tall (72 ± 9), wrack (13 ± 14), bare ground (13 ± 9) (other)

Spartina alterniflora, short S. alterniflora short (73 ± 12), wrack (18 ± 9), bare ground (7 ± 3) (other)

High Spartina patens S. patens (86 ± 8), D. spicata (7 ± 8), bare ground (4 ± 2) (S. alterniflora short , Iva frutescens, wrack)

Spartina cynosuroides S. cynosuroides (49 ± 9), bare ground (26 ± 7), S. alterniflora tall (13 ± 13) (other, wrack, S. alterniflora short)

South Carolina

Low Spartina alterniflora, tall S. alterniflora tall (52 ± 4), bare ground (48 ± 4) Spartina alterniflora, short S. alterniflora short (90 ± 6), bare ground (10 ± 6)

High Salicornia depressa/mixed S. depressa (72 ± 35), Limonium carolinianum (10 ± 20), S. alterniflora (10 ± 15) (D. spicata)

Juncus roemerianus J. roemerianus (85 ± 13), Borrichia frutescens (3 ± 4), bare ground (3 ± 4)



(L. carolinianum, D. spicata, S. depressa) Florida Low Spartina alterniflora, tall bare ground (63 ± 21), S. alterniflora tall (35 ± 22), Batis

maritima (2 ± 4) Spartina alterniflora, short S. alterniflora short (52 ± 19), bare ground (45 ± 18), Sarcocornia

perennis (1 ± 2) (wrack)

High Juncus roemerianus J. roemerianus (48 ± 23), bare ground (40 ± 16), B. maritima (8 ± 12) (S. alterniflora tall, wrack)

Batis maritima bare ground (39 ± 16), B. maritima (50 ± 32), J. roemerianus (8 ± 18) (S. perennis, S. alterniflora short, wrack, D. spicata)

Mississippi Low Spartina alterniflora S. alterniflora (80 ± 4), bare ground (20 ± 4) Juncus roemerianus J. roemerianus (95 ± 0), bare ground (5 ± 0)

High Spartina patens/ Spartina spartinae

S. patens (34 ± 19), bare ground (28 ± 20), S. spartinae (16 ± 25) (J. roemerianus, Salicornia virginica, Panicum virgatum, B. maritima, A. maritima, Solidago sempervirens)

Salicornia virginica, Batis maritima

bare ground (69 ± 17), S. virginica (31 ± 17)

California Low Schoenoplectus acutus/californicus

S. acutus/californicus (49 ± 33), wrack (32 ± 39), Typha sp. (14 ± 26), bare ground (14 ± 21) (Lepidium latifolium, other)

Schoenoplectus americanus S. americanus (53 ± 29), wrack (52 ± 35), L. latifolium (3 ± 7) (other)

High Distichlis spicata/Salicornia pacifica

S. pacifica (93 ± 6), wrack (12 ± 22), Cuscuta pacifica (8 ± 12) (D. spicata, Frankenia salina, Jaumea carnosa)

Distichlis spicata/Juncus balticus

Juncus ariticus sp. balticus (95 ± 5), D. spicata (7 ± 10), bare ground (3 ± 5), Atriplex prostrata (3 ± 3) (F. salina)

Wisconsin Low Sagittaria rigida S. rigida (93 ± 4), bare ground (4 ± 9), Scirpus validus (1 ± 1) (Zizania palustris, S. eurycarpum, Potamogeton pectinatus)

Sparganium eurycarpum S. eurycarpum (94 ± 8), Typha sp. (3 ± 7), Z. palustris (<1 ± 1) (S. rigida, Ceratophyllum demersum)

High Typha sp. Typha sp. (82 ± 19), S. eurycarpum (12 ± 17), Potentilla palustris (2 ± 5), Equisetum fluviatile (2 ± 5) (Rumex orbiculatus, Lythrum salicaria, Carex lacustris)

Carex lacustris C. lacustris (86 ± 26), Carex stricta (8 ± 18), L. salicaria (5 ± 9) (Typha sp., Onoclea sensibilis, Lycopus uniflorus, E. fluviatile)

Ohio Low Sparganium eurycarpum open water (56 ± 7), S. eurycarpum (36 ± 11), Polygonum hydropiperoides (4 ± 9) (Nymphaea odorata, Sagittaria latifolia)

Typha angustifolia open water (57 ± 21), T. angustifolia (22 ± 8), N. odorata (14 ± 19) (Leersia oryzoides, Nelumbo lutea, S. latifolia)

Leersia oryzoides open water (56 ± 27), L. oryzoides (40 ± 27), P. hydropiperoides (1 ± 2), S. latifolia (1 ± 2), T. angustifolia (1 ± 2) (Scirpus sp., Phragmites australis)

High Phragmites australis open water (87 ± 12), P. australis (12 ± 13), S. eurycarpum (1 ± 2) (S. latifolia, T. angustifolia)

Note: When there was a tie among the top three species in the plot, all species in the tie are listed. Additional

species present in the plot are listed below in parentheses. Means were calculated as the average of five plots for

each dominant vegetation type.

2.1.1 Wells NERR, Maine

The Webhannet Estuary (Figure 3) is part of the Wells National Estuarine Research Reserve (NERR)

and the Rachel Carson National Wildlife Refuge in Wells, ME and lies within the Arcuate



Embayments coastal compartment of Maine’s coast (Kelley 1987). This high salinity estuary is well-

mixed, owing to its large tidal range and low river discharge (~1 m3/sec; Dionne et al. 2006,

Fitzgerald et al. 1989). Pope’s Creek, Depot Brook, Blacksmith Brook, and the Webhannet River

introduce little minerogenic sediment to the estuary; most “new” sediment to the system is the result

of re-worked glacigenic deposits exposed sub- or intertidally (Byrne & Ziegler 1977; Shipp 1989;

Kelley et al. 1995). The tidal marsh platform is underlain by thick deposits of peat (> 5 m in places;

Kelley et al. 1995) and incised by numerous tidal channels, creeks and salt marsh pools (Wilson et al.

2009, 2010). Tidal exchange with the ocean is restricted to a single inlet at Wells Harbor and marshes

are flooded twice-daily by semidiurnal tides. Vegetation is dominated by high-marsh meadow (mostly

Spartina patens), intermixed with forb pannes (mostly Triglochin maritimum and Plantago

maritima). Spartina alterniflora dominates at lower elevations and along the banks of tidal creeks

(Jacobson & Jacobson 1989). Sampling for this study was confined to the area between Drakes Island

Road and Mile Road.



Figure 3. The Wells NERR located in Wells, ME showing landscape views (left) and representative examples of dominant vegetation types sampled in this study (right)

2.1.2 Delaware NERR, Delaware

The St. Jones Reserve, located in Dover, DE, is one of two components of the Delaware National

Estuarine Research Reserve that are sub-estuaries of the Delaware River/Bay Estuary. This reserve is

distributed along 8.8 km of predominantly brackish tidal river situated at the lower end of the St.

Jones River watershed, with the river discharging into mid-Delaware Bay. Salt marsh habitats in the

St. Jones Reserve are dominated by saltmarsh cordgrass (Spartina alterniflora) and salt hay (Spartina

patens), buffered by wooded fringe, farmlands, and meadows.



Figure 4. The Delaware NERR located in Dover, DE showing landscape views (left) and representative examples of dominant vegetation types sampled in this study (right)

2.1.3 North Inlet-Winyah Bay NERR, South Carolina

The North Inlet Estuary is a small, bar-built estuary, approximately 3,200 ha in size with a watershed

of approximately 3,800 ha. In general, freshwater input to the estuary is relatively minor, and comes

largely via precipitation-driven groundwater inflow from the surrounding watershed. Most of this

watershed is forested and < 2% of the watershed is developed. The estuarine basin is dominated by

inter-tidal salt marsh comprised mainly of salt marsh cordgrass (Spartina alterniflora), although the

fringing back-barrier and upland border marshes demonstrate the typical salt marsh plant species

zonation patterns found in the Southeast United States. These marshes are flooded and drained twice

daily by semidiurnal tides through several large sub-tidal creeks interconnected by numerous smaller

inter-tidal creeks. Exchange with the ocean is restricted to a single inlet, with some limited exchange

of water with the adjacent Winyah Bay estuary to the south (Buzzelli et al. 2004).



Sampling for the present study was confined to the Crabhaul Creek sub-basin of North Inlet, which is

the most western of the individual tidal creek/marsh sub-basins of North Inlet. The marsh platform of

Crabhaul Creek boarders the upland forest and displays distinct vegetation zones from creek bank to

upland edge typical of marshes of the Southeastern United States. It is one of the few sub-basins in

North Inlet to have a broad high marsh zone, including both a mixed meadow composed of species

such as Spartina patens, Distichlis spicata, Borrichia frutescens, and Limonium carolinianum, as well

as a hyper-saline zone of salt panne and dense growth of Salicornia depressa. Stratigraphy and

radiocarbon dating indicates that Crabhaul Creek was forested about 2,600 years before present and

that the marsh platform continues to actively expand south and west, transgressing forest habitat on

its western, upland edge (Gardner & Porter 2001).

Figure 5. The North Inlet-Winyah Bay NERR located in Georgetown, SC showing landscape views (left) and representative examples of dominant vegetation types sampled in this study (right)



2.1.4 Guana Tolomato Matanzas NERR, Florida

The GTM NERR is comprised of approximately 300 km2 of relatively undeveloped coastal and

estuarine habitat, 47 km2 of which is salt marsh, in northeast Florida in the transition zone between

subtropical and temperate climates. The Reserve, which is split into two main components north and

south of the city of St. Augustine, forms a narrow bar-bounded, tidally-dominated estuarine

ecosystem that spans approximately 56 km north to south. Similar to other southeastern USA Atlantic

coast estuaries, the GTM NERR consists of a mosaic of multiple interconnected estuarine habitats

that include the vegetated marsh edge and surface, marsh pools, tidal creeks, and open water habitats.

Sediment cores were collected from six sites spread throughout the Reserve in tributaries of the

Tolomato and Matanzas Rivers.

Figure 6. The GTM NERR located in, FL showing landscape views (left) and representative examples of dominant vegetation types sampled in this study (right)



2.1.6 Grand Bay NERR, Mississippi

The Grand Bay Estuary, located in Jackson County immediately adjacent to the Mississippi-Alabama

state line, is the largest estuary in Mississippi encompassing about 7,304 hectares (18,049 acres). This

National Estuarine Research Reserve is a pristine, intact coastal plain salt marsh system which

includes tidal and non-tidal wetlands (Mississippi Department of Marine Resources 1998). Because it

is a strongly marine-influenced and micro-tidal estuary (< 1 m tide range), it is dominated primarily

by Juncus roemerianus with scattered smaller patches of Spartina alterniflora, driven primarily by an

irregular flooding regime (Wieland 2007).

Figure 7. The Grand Bay NERR located in, MS showing landscape views (left) and representative examples of dominant vegetation types sampled in this study (right)



2.1.7 San Francisco NERR, California

Rush Ranch, one of two components of the San Francisco Bay NERR, includes approximately 1,050

acres of high-elevation brackish marsh innervated with tidal sloughs and creeks and bordered by

undeveloped uplands. Although Rush Ranch is mostly surrounded by a mosaic of managed wetlands,

the area sampled for this study is subject to tidal inundation and receives limited seasonal inputs of

local run-off. An important aspect of the region is the lack of any major local freshwater influence

other than upstream flows coming through the Sacramento-San Joaquin River Delta.

Figure 8. The San Francisco NERR located in Wells, CA showing landscape views (left) and representative examples of dominant vegetation types sampled in this study (right)



2.1.8 Lake Superior NERR, Wisconsin

Sediment samples were taken in the upper Pokegama River embayment, a tributary of the St. Louis

River that flows into Lake Superior in northwestern Wisconsin. The Pokegama River flows through a

highly erodible clay plain that was created from glacio-lacustrine deposits following the last

glaciation (approx. 10,000 years BP). The embayment developed following the drowning of the St.

Louis River mouth which was caused by differential isostatic rebound; greater crustal rise in the east

resulted in a rise in the lake outlet at Sault Ste. Marie, Michigan effectively “drowning” river mouths

in western Lake Superior. The bay is relatively young, created approximately 1,000 BP when the

Sault outlet began to govern the water level of Lake Superior. Pokegama Bay exhibits estuarine

properties due to the tides and seiches of Lake Superior. Water levels fluctuate with a daily mean

fluctuation of 12.6 cm at the mouth and a river-lake transition zone where river and lake waters mix

that extends > 20 km upriver from the mouth. The primary habitat in Pokegama Bay is emergent

freshwater marsh with dominant vegetation including; Carex lacustris, Typha spp. (Typha latifolia,

Typha angustifolia, and Typha x glauca), Sparganium eurycarpum, and Sagittaria rigida.

Figure 9. The Lake Superior NERR located in Superior, WI showing landscape views (left) and representative examples of dominant vegetation types sampled in this study (right)



2.1.9 Old Woman Creek NERR, Ohio

Old Woman Creek National Estuarine Research Reserve and State Nature Preserve (OWC) is located

on the southern coast of Lake Erie, 5 km east of the city of Huron, OH, USA. The OWC preserve

includes 60 hectares of freshwater estuarine wetlands, extending 2.1 km south of the Lake Erie

shoreline (Herdendorf et al. 2004). The estuary drains a 69 km2 watershed, which is dominated by

agricultural land use (Herdendorf et al. 2004). The headwaters of Old Woman Creek originate at

about 270 m above sea level on a till plain surface and descend 96 m to Lake Erie, flowing through

rolling till plain, high Berea escarpment, and lake plain (Herdendorf et al. 2004). The OWC NERR

includes a 60 hectare freshwater barred drowned river-mouth wetland (Albert et al. 2005). A barrier

beach at the mouth of the estuary closes the inlet of the estuary to Lake Erie for approximately 40%

of the year, preventing direct surface water exchange. The mean depth of the estuary is 0.4 m, with

channel depths up to at least 3.5 m in constricted areas (i.e., under road and rail bridges; Herdendorf

et al. 2004). Depth increases gradually when the inlet is closed and decreases quickly and drastically

in response to a precipitation-induced breaching of the barrier beach. Water residence time when the

inlet is unbarred is less than one day. The majority of wetland habitat is located in the main basin of

the estuary, which contains 82% of the total estuary area. Dominant submerged and emergent

vegetation includes coontail (Ceratophyllum demersum), pondweed (Potamogeton spp.), American

lotus (Nelumbo lutea), rice cutgrass (Leersia oryzoides), swamp smartweed (Polygonum

hydropiperoides), broadfruit bur-reed (Sparganium eurycarum), narrowleaf cattail (Typha

angustifolia), and common reed (Phragmites australis). Exposed mudflats are common during late

fall and early spring, when the inlet is open and water levels are low. The dominant vegetative

community (submerged versus emergent) can vary substantially among years in response to Lake Erie

water levels (Trexel-Kroll 2002).



Figure 10. The Old Woman Creek NERR located in Wells, OH showing landscape views (left) and representative examples of dominant vegetation types sampled in this study (right)

2.2 Field Methods & Laboratory Techniques

A modified 7.62 cm diameter coring device was used to sample the upper 20 cm of sediment (Figures

11, 12). At each site, 20 cores were collected using a random, stratified sampling scheme with the

four dominant vegetation types at each site as strata (Figure 13, Table 2). Each core location was

photographed, dominant vegetation type recorded, visual percent cover estimated for a 1 m2 plot

(Appendix 2), aboveground biomass clipped for a centralized 0.25 m2 plot, and a hand-held GPS

point collected. Biomass was bagged and sorted in the lab into live and dead factions, dried at 60˚C to

a constant weight, then weighed. Cores were collected from the center of the clipped plot, capped,

kept upright and cool in the field, then were frozen and shipped in coolers upright to the Wells NERR

for processing. Core compaction was estimated in the field by measuring the elevation difference of

the sediment surface inside and outside of the core barrel. Any core compaction was noted in the field

and corrected for during sectioning (Appendix 3). If compaction was greater than 5 cm a new core



was collected; compaction for all cores averaged 0.6 cm. All cores arrived frozen at the Wells NERR

with stratigraphy intact (evidenced by photographs pre- and post-shipping). Cores were thawed, split,

photographed (Appendix 4), and sectioned at 5 cm (or corrected) intervals (Appendix 3). The lowest

section (15-20 cm) of one Lake Superior core (PO-WI-01-2014) was not retrieved, leaving the total

number of sediment samples at n = 639. Each core section was dried at 60˚C for 72 hours or until a

constant weight was achieved (after Howard et al. 2014). Dry weight and wet volume were used to

calculate sediment dry bulk density, where:

Dry bulk density (g/cm3) = dry weight (g) / wet volume (cm3)

Any rocks, shells, or other large items were removed from the dried samples, which were then ground

to a fine powder in a SPEX 8000M Mixer/Mill to homogenize sediment over the sample interval.

Samples were split into replicates for separate LOI (Wells NERR) and carbon analyses (North Inlet-

Winyah Bay NERR). Percent organic matter by loss on ignition (Ball 1964; Craft et al. 1991) was

calculated by mass loss after samples were combusted at 450˚C for 8 hours, where,

Percent organic matter = (dry mass before combustion – dry mass after combustion)/

dry mass before combustion *100

Direct determinations of sediment carbon content were conducted on subsamples from above LOI

analysis using a Costech ECS 4010 Elemental Analyzer calibrated with atropine standards (Costech

#031042; 70.56% C, 4.84% N). Total carbon content (%) was determined by analyzing approximately

5 – 15 mg of the samples that had been dried (60 °C), ground and homogenized for LOI analysis. To

correct for possible bias associated with inorganic carbon content (%) of sediments (e.g., carbonates),

a separate subsample of approximately 25 – 40 mg (corresponding to the dried LOI samples that were

subsequently combusted at 450 °C for 8h) was analyzed to determine carbon content of the sample

ash, which by definition is taken to be all inorganic carbon. To calculate the organic carbon content

(%) of sediment samples, the inorganic carbon content, scaled by the ratio of ash weight to dry

weight, was subtracted from the total carbon content (Howard et al. 2014).

Analytical performance of the elemental analyzer was verified for each run of the instrument by

analyzing atropine check standards, Buffalo River sediment standards (National Institute of Standards

and Technology certified reference material, RM #8704; 3.351% C) and North Inlet marsh sediment

internal standards produced at the Baruch Marine Field Laboratory. Atropine check standards as well

as a method blank were run every 15 samples, while a sample of either the Buffalo River sediment or

the North Inlet marsh sediment was run every 10 samples. Across all instrument runs, percent

recovery of carbon content for Buffalo River Sediment standard reference material averaged 100.81%

(n=106), with a range of 97.9 to 103.6 % and a coefficient of variation (CV) of 1.1%. For 32 sediment

samples randomly selected for duplicate analysis, mean CV for % organic carbon determinations was

5.1%.

From the percent organic carbon values, we calculated sediment carbon density, using the following

formula:

Sediment carbon density (gC/cm3) = dry bulk density (g/cm3)*(percent organic carbon/100)



Figure 11. The modified coring device used in this study. Close-up of the core head showing the cutting crown to reduce sediment compaction (a). We manufactured a removable key with handle (b) to assist in core collection. Assembled cores before shipping (c)

Figure 12. Faces of the fieldwork: Kristin Wilson, Wells NERR, Maine (a), Erik Smith North Inlet-Winyah Bay NERR, South Carolina (b), Shon Schooler, Lake Superior NERR, Wisconsin (c), and Jason Lynn, Guana Tolomato Matanzas NERR, Florida (d)



Figure 13. Core locations (yellow dots) for each study site; also available online at http://bit.ly/1utPbJ8. Photographs are from ArcOnline.

2.3 Data Analyses

2.3.1 Statistical Analyses

For every site, visual percent cover estimates were averaged across plots for each dominant

vegetation type and standard deviations calculated to create the values listed in Table 2. Dominant

vegetation types were binned by zone (low or high) based on known elevations at each site. Down

core values for percent organic matter, percent organic carbon, dry bulk density, and carbon density

were averaged to calculate mean values and standard deviations per core. To explore the relationship

between percent sediment organic matter and percent organic carbon content, polynomial and linear

regressions with and without the intercepts forced through zero, were run on the global data set.

Using the AICcmodavg package in RStudio (RStudio, Boston, MA) weighted Akaike Information

Criterion (AIC) values and an evidence ratio were calculated to compare and select the best of the

four models; this model was then compared to that of Craft et al. 1991, the most widely-cited

calibration curve. To determine if site significantly influenced the best fit model, the restricted log

http://bit.ly/1utPbJ8



likelihoods of the model and a mixed model that specified site as a random effect, were compared

(RStudio, RStudio, Boston, MA). In RStudio (RStudio, Boston, MA), a breakpoint analysis was

conducted on log-transformed data to examine potential regional differences in the relationship

between sediment dry bulk density and organic carbon content (%). To explore within and across site

variability in sediment properties (organic carbon content (%), dry bulk density, and organic carbon

density) and to meet assumptions of normality, dry bulk density values were log transformed and

percent organic carbon data cube root transformed and an Analysis of Variance (ANOVA) in JMP

software (SAS Institute, Cary, NC) run, to compare means across sites. Where ANOVA results were

significant (p < 0.05), a Tukey’s Honestly Significant Difference (HSD) post-hoc test was used to

determine differences among means across sites. Statistical results are presented on the untransformed

data. For salt and brackish water sites, to explore across site differences in sediment carbon density by

zone, a 2-way ANOVA with site and zone as factors was run in JMP (SAS Institute, Cary, NC). Least

squares linear and non-linear regression analysis was used to determine whether mean sediment

carbon density in the upper 20 cm could be predicted by total aboveground biomass. An ANOVA and

Tukey’s HSD post-hoc test when applicable, were used to understand within site differences in mean

organic sediment carbon density by dominant vegetation type.

2.3.2 Data Workshop

From February 23-27, 2015, six Research Coordinators from participating NERRs traveled to Maine

for a workshop to discuss project progress, preliminary results, and plans for writing a manuscript

from this work (Figure 14). Unfortunately, individuals from the Lake Superior NERR and San

Francisco Bay were unable to attend; however, co-PIs Wilson and Smith updated these reserves

following the workshop.

Figure 14. Workshop participants ponder preliminary results (a). From left to right, Amelie Jensen (Research Technician, Wells NERR), Tim Dubay (Research Technician, Wells NERR), Kristin Wilson (Research Coordinator, Wells NERR), Nikki Dix (Research Coordinator, GTM NERR), Mark Woodrey (Research Coordinator, Grand Bay NERR), Lyndie Hice-Dunton (Research Coordinator, Delaware NERR), and Erik Smith (North Inlet-Winyah Bay NERR). Taking a break from data analyses, Research Coordinators snowshoe to the salt marsh to observe Maine’s frozen carbon



stores (b). From left to right, Nikki Dix, Mark Woodrey, Lyndie Hice-Dunton, Erik Smith, Kristin Wilson, and Kristi Arend (Research Coordinator, Old Woman Creek NERR)

3 Study Results & Discussion

3.1 A Comparison of Methodologies: Percent LOI vs. Percent Carbon Content

Across all sediment samples (n=639) organic carbon and organic matter contents ranged from 0.03 to

43.81 % and 1.44. to 79.01 %, respectively. These ranges are similar to those observed previously in

marsh habitats (e.g., Craft 1991; Callaway et al. 2012) as well as in upland forest soils (e.g., De Vos

et al. 2007). As has been observed in all previous studies comparing organic carbon content by

elemental analysis and organic matter content by LOI, the two measures were strongly correlated

across all samples in the present study (Figure 15). While both linear and polynomial forms of

regressions were highly significant, a comparison of models based on the weighted AIC value and the

evidence ratio (2.33), reveals that a polynomial regression with an intercept represents the best model

fit to the data (Table 3; Figure 15). This result is similar to that of Craft et al. (1991) in suggesting a

nonlinear relationship is the better fit between organic carbon and LOI measurement, although the

Craft model specifically excludes an intercept term and the data in the present study do not exhibit

nearly the same degree of curvature in this relationship as those measured by Craft et al. (1991). It

should be noted, however, that in the difference between the linear and polynomial fit, in terms of

explanatory power (as judged by R2 values) is quite small, less than a third of one percent, and over

most of the data distribution the regression lines are essentially indistinguishable from one another.

While the use of LOI and a single predictive model could account for better than 97 % of the

variability in organic carbon concentration across all sites and vegetation types combined, there were

some significant departures from this single broad relationship when data from individual marsh sites

were considered separately (Table 4). Results from the model comparison reveal that site contributes

significantly to the variance in the global relationship (df = 4, p <0.0001). These results thus suggest

that the choice and appropriateness of calibration curves to predict organic carbon from LOI

measurements may be scale dependent and use of regional calibration curves may be judicious in

some instances and given adequate resources, results that agree with the recommendations of Howard

et al. 2014. The most extreme example of this in the present data set is the differences in the nature of

the relationships observed between upper and lower marsh zones in South Carolina. Here, the slope of

the linear fit (to be consistent with previous studies in this location) between organic carbon content

and LOI for the lower marsh zone was almost half of that derived for the upper marsh zone (0.279 vs.

0.544, for lower and upper zones, respectively). The use of a single predictive relationship in this

marsh would thus lead to large errors within each zone. Interestingly, the slope of the relationship



found for the low marsh in the present study is quite similar to the slope obtained (0.225) in the one

earlier study conducted in the lower marsh of this same estuary (Morris and Whiting 1986). It is

likely that the unusually low slope found by both studies for this lower marsh is due to the high clay

content of these sediments, which has been shown to affect the results of the LOI methodology (De

Vos et al. 2005; Chatterjee et al. 2009). Indeed, it has been proposed previously for terrestrial soils

that the exact form of the relationship between organic carbon and LOI depends heavily on the

performance of the LOI method in different soil types rather than the inherent nature of carbon to

organic matter ratios (De Vos et al. 2005, Szava-Kovats 2009).

Table 3. Results of the model comparison showing Model 2 to be the best fit model, where y = percent organic carbon and x = percent organic matter by LOI

Model Equation R2 AIC Delta AIC AIC Weighted

1 y = -0.8798 + 0.4842x 0.9739 1947.748 79.33 0.0

2 y = -0.1931 + 0.4043x + 0.0015x2 0.9770 1868.420 0.00 0.7

3 y = 0.4539x 0.9865 2078.480 210.06 0.0

4 y = 0.3891x + 0.0017x2 0.9903 1870.115 1.70 0.3

Craft et al. 1991 y = 0.40x + 0.0025x2 0.990 ~ ~ ~

Table 4. Individual regression lines for each site, where y = percent organic carbon and x = percent organic matter by LOI

Site Polynomial Equation R2

Global y = 0.0015x2 + 0.4043x - 0.1930 0.9771

ME y = 0.0014x2 + 0.3770x + 1.5778 0.9804

DE y = 0.0012x2 + 0.4426x - 0.9266 0.9901

SC y = -0.0296x2 + 0.8983x - 1.2207 0.7520

FL y = 0.0055x2 + 0.2663x + 0.1453 0.9346

MS y = -0.0001x2 + 0.4670x - 0.6158 0.9874

CA y = 0.00008x2 + 0.5323x - 3.3027 0.9942

WI y = 0.0080x2 + 0.1911x + 1.1727 0.7941

OH y = 0.0009x2 + 0.4689x - 0.3390 0.9870



Figure 15. Sediment organic carbon content (%) versus organic matter content by LOI (%)

Over the entire dataset there was a significant inverse relationship between sediment organic carbon

content and the dry bulk density of that sediment, as has been seen previously (e.g., Callaway et al.

2012). In contrast to previous studies, however, the portion of the data that had the very lowest

organic carbon content clearly deviated from the relationship observed for the remainder of the

dataset (Figure 16). Results of a break point analysis of the dry bulk density versus organic carbon

content revealed two distinct relationships (Figure 16). Analysis indicated that this shift in the

relationship between organic carbon content and bulk density occurred at an organic carbon content

of 2.04 %. Above this value, the relationship observed in this study was highly significant and had a

slope very similar to those observed in previous studies (e.g., Callaway et al. 2012, Hansen and

Nestlerode 2014). Below this value of 2.04 % organic carbon content, the relationship was not

statistically significant and organic carbon content continued to decline while bulk density values

remained essentially constant. Interestingly, the samples with organic carbon content below 2.04 %

were almost exclusively from the southeastern United States (South Carolina, Florida, and

Mississippi), and were largely confined to the mid-marsh halophyte and/or short-form Spartina

alterniflora vegetation zone, which is typically dominated by very sandy sediments.



Figure 16. Relationship between sediment dry bulk density and sediment organic carbon content (%) showing two distinct relationships as reveled by break point analysis

3.2 Variability in Sediment Properties

Across all eight NERR sites, sediment organic carbon density ranged from 0.001 to 0.061 g C cm-3,

with a grand mean of 0.030 ± 0.011 g C cm-3. This mean value is similar to the mean (0.039) reported

by Chmura et al. (2003) for salt marsh sediments in their global synthesis of organic carbon storage in

tidal saline wetlands and may be skewed lower because only marsh sites are included in this analysis.

There were significant differences among sites for mean organic carbon content (F7,152 = 24.975, p <

.0001), mean dry bulk density (F7,152 = 20.923, p < .0001), and mean organic carbon density (F7,152 =

12.475, p < .0001; Figure 17). Since organic carbon density is the product of the percentage of

organic carbon and the bulk density of the sediments, and sites with greater percent organic carbon

content generally had lower dry bulk density values, variability in organic carbon density was muted

relative to among-site differences in raw organic carbon content. Within-site ranges in sediment



carbon density tended to be more variable for southeastern United States sites (Mississippi, Florida,

and South Carolina) and California than other northeast or freshwater sites that had smaller ranges.

Organic carbon content and density both generally increased with increasing latitude, a trend similar

to that observed by Chmura et al. (2003). As such, the marshes of Maine had significantly more

organic carbon per cm3 of sediments than every other site except California and Delaware. Organic

carbon content and density did not appear to have any relationship to site salinity, however, a finding

similar to what has been reported for fresh to saline wetlands along the Gulf of Mexico (Hansen and

Nestlerode 2014). The two freshwater wetlands both had mean organic carbon densities that were

almost exactly in the middle of the range observed across the saline marshes (Figure 17).

Figure 17. Distribution of organic carbon content (upper panel), bulk density (middle panel) and organic carbon density (lower panel) within and across eight marsh sites of the NERRS. Median values denoted by horizontal line, boxes contain the 25th and 75th quartiles, whiskers denote the



10th and 90th percentiles, and outliers are denoted by points. Sites with the same letter are not significantly different from one another (p > 0.05). State abbreviations indicate site location



In addition to significant variability in the means of carbon content, bulk density and carbon density

among sites, there were also clear differences in the degree of variability in these measures within

sites, as well. Previous studies have reported significant differences in organic carbon density as a

function of marsh elevation. For example, in tidal marshes of San Francisco Bay, mid and high

marsh zones had significantly more organic carbon content than did the low marsh zone, although the

reverse pattern held true for bulk density (Callaway et al. 2012). For the eight marshes sampled in this

study, significant spatial variability by zone was observed at some sites but not others (F11,108 =

12.069, p < .0001). Sites with significant differences (p < 0.05 for all) in organic carbon density

between high and low marsh zones were South Carolina, Mississippi, and California. The pattern in

these differences was not consistent across those sites, however. In Mississippi, the low marsh zone

had significantly greater organic carbon density than the high marsh zone, while in South Carolina

and California, the opposite pattern was observed.

When considering variability by vegetation communities rather than elevation zone, mean sediment

organic carbon density was significantly different among vegetation types for half the sites sampled

(Maine, Mississippi, California, and South Carolina; Figure 18). In Maine, Spartina alterniflora tall

form, had significantly less carbon per cm3 than forb pannes which were similar to Spartina

alterniflora short form and Spartina patens values. In Mississippi, high marsh plant communities like

those dominated by S. patens/S. spartinae and Salicornia virginica/Batis maritima had lower

sediment organic carbon density values than lower zones. In California, the Schoenoplectus

acutus/californicus community type had significantly lower sediment carbon density than other

dominant vegetation types which were all the same. In South Carolina, Juncus roemerianus had

significantly greater sediment organic carbon density compared to other vegetative zones. These

results reveal that there is considerable spatial variation in sediment carbon density in the upper 20

cm at the marsh scale, at least for some sites (Maine, Mississippi, California, and South Carolina).

Differences in sediment organic carbon density among different vegetation communities could not be

explained by the standing biomass of those plant communities, however, as there were not significant

relationships between any measures of sediment organic carbon density and total aboveground

biomass, either within or among sites. Combined, these findings have implications for calculations of

marsh-scale carbon budgets.



Figure 18. Mean sediment carbon density by dominant vegetation type, zone, and site. Bars that share the same letter are not statistically significant from one another

4 Conclusions & Future Work

4.1 Key Findings

This study greatly increases the number of sites across the United States for which sediment carbon

density measurements exist and improves understanding of methodologies that use loss on ignition as

a proxy for sediment organic carbon.



To address uncertainties in coastal blue carbon stores, the original goals of this study were to:

1. Compare direct measures of sediment organic carbon determined by elemental analysis and

organic matter concentration measured by loss on ignition in a subset of eight National

Estuarine Research Reserves across the United States that encompass a range of salinity,

vegetation, and geomorphic conditions, to assess the viability of the loss on ignition

methodology as a means of reliably estimating sediment organic carbon content.

o Key Finding: Results show that sediment organic matter by loss on ignition (LOI)

and sediment organic carbon content (%) are highly correlated, that the global

relationship differs from other published studies (e.g., Craft et al. 1991; Callaway

et al. 2012), and that individual sites significantly contribute to the variation

observed in this global relationship.

o Key Implications: Across broad scales, a single curve adequately captures the

vast majority of the variability in sediment organic carbon explained by LOI. In

some regions, however, fine scale variability in sediment properties may dictate

the use of site-specific calibration curves.

2. Quantify sediment organic carbon density at the eight NERRS sites thus increasing the

number of locations across the United States for which marsh carbon density measures exist,

thus filling critical gaps in current carbon storage estimates across a range of marsh types.

o Key Findings:

Mean sediment organic carbon density in the upper 20 cm ranged from

0.001 to 0.061 g C cm-3, with a grand mean of 0.030 ± 0.011 g C cm-3,

and this differed significantly by site. Sediments from Maine and

California contained significantly more organic carbon per cubic

centimeter than the other sites sampled.

Mean sediment organic carbon density also differed significantly by high

and low marsh zones in South Carolina, Mississippi, and Florida, though

the pattern of this difference was not uniform. In Mississippi, the low

marsh zone had significantly greater organic carbon density than the high

marsh zone, while in South Carolina and California, the opposite pattern

was observed.

Mean sediment organic carbon density differed significantly by

vegetation type at half of the sites sampled (Maine, Mississippi,

California, and South Carolina). These results reveal that there is

considerable within marsh spatial variation in sediment organic carbon

density in the upper 20 cm.

o Key Implications: Significant differences in sediment carbon density exist by site,

zone, and vegetation type for some marshes. This spatial variation has important

consequences for how marsh-scale carbon budgets are calculated and

incorporated into blue carbon policies.

4.2 Future Studies

Future work should further expand the number of locations for which sediment carbon density

measurements exist and explore the degree to which additional regional calibration curves are needed.

Additional studies should also explore changes in sediment carbon density with depth by collecting

longer cores to improve calculations of carbon budgets at the marsh scale. The 28 Reserves of the

National Estuarine Research Reserve System are excellent potential partners to expand blue carbon

work in protected wetlands of the United States that encompass a range of marsh types, management

regimes, and natural and anthropogenic stressors.



5 Appendices

Appendix 1. Photographs of each plot used to calculate the visual percent cover estimates organized by dominant vegetation zone. Labels below each photograph refer to the core ID









Appendix 2. Visual percent cover estimates by site and plot







Appendix 3. Bulk density, percent organic matter, percent organic carbon, and sediment carbon density down-core (top 20 cm for homogenized 5 cm intervals)

State Core ID Sample Interval Dry Bulk Density

% Organic Carbon

% Organic Matter

Sediment Carbon Density Dominant Vegetation

ME WH-ME-01-2014 0-5 0.20 22.86 46.90 0.04 Spartina alterniflora short








ME WH-ME-03-2014 0-5 0.13 20.57 45.53 0.03 Spartina patens








ME WH-ME-05-2014 0-4.75 0.25 16.91 36.57 0.04 Forb panne

ME WH-ME-05-2014 4.75-9.5 0.25 15.88 34.26 0.04 Forb panne


ME WH-ME-05-2014 14.25-19 0.31 16.93 32.51 0.05 Forb panne

ME WH-ME-06-2014 0-4.75 0.28 18.78 39.23 0.05 Spartina patens

ME WH-ME-06-2014 4.75-9.5 0.19 21.85 47.39 0.04 Spartina patens

ME WH-ME-06-2014 9.5-14.25 0.18 20.15 41.11 0.04 Spartina patens

ME WH-ME-06-2014 14.25-19 0.15 21.66 44.41 0.03 Spartina patens

ME WH-ME-07-2014 0-5 0.57 7.74 16.84 0.04 Spartina alterniflora tall


























ME WH-ME-13-2014 0-5 0.20 28.00 58.03 0.06 Forb panne




ME WH-ME-14-2014 0-4.75 0.31 13.92 26.30 0.04 Spartina alterniflora tall

ME WH-ME-14-2014 4.75-9.5 0.21 20.44 43.03 0.04 Spartina alterniflora tall

ME WH-ME-14-2014 9.5-14.25 0.24 19.36 41.04 0.05 Spartina alterniflora tall

ME WH-ME-14-2014 14.25-19 0.28 15.81 34.76 0.04 Spartina alterniflora tall





















ME WH-ME-20-2014 0-4.75 0.27 20.95 43.74 0.06 Forb panne



ME WH-ME-20-2014 14.25-19 0.67 4.68 10.35 0.03 Forb panne

DE SJ-DE-01-2014 0-4.625 0.30 10.59 24.56 0.03 Spartina alterniflora short

DE SJ-DE-01-2014 4.625-9.25 0.37 11.02 25.06 0.04 Spartina alterniflora short





DE SJ-DE-02-2014 0-4.125 0.22 19.48 41.83 0.04 Spartina patens

DE SJ-DE-02-2014 4.125-8.25 0.22 16.67 35.78 0.04 Spartina patens



DE SJ-DE-03-2014 0-4.25 0.26 13.31 28.94 0.04 Spartina alterniflora short



DE SJ-DE-03-2014 12.75-17 0.32 11.14 25.52 0.04 Spartina alterniflora short

DE SJ-DE-04-2014 0-5 0.33 9.99 23.43 0.03 Spartina alterniflora short




DE SJ-DE-05-2014 0-5 0.43 7.92 17.52 0.03 Spartina alterniflora tall




DE SJ-DE-06-2014 0-5 0.88 4.96 12.96 0.04 Spartina cynosuroides




DE SJ-DE-07-2014 0-4.5 0.73 4.85 12.65 0.04 Spartina cynosuroides

DE SJ-DE-07-2014 4.5-9 0.49 4.98 12.70 0.02 Spartina cynosuroides



DE SJ-DE-08-2014 0-4.375 0.77 5.03 12.76 0.04 Spartina alterniflora tall

DE SJ-DE-08-2014 4.375-8.75 0.60 4.61 11.57 0.03 Spartina alterniflora tall
























DE SJ-DE-13-2014 12.75-17 0.21 14.76 33.66 0.03 Spartina alterniflora tall












DE SJ-DE-16-2014 12.75-17 0.45 6.52 15.93 0.03 Spartina alterniflora tall

DE SJ-DE-17-2014 0-5 0.27 11.91 27.16 0.03 Spartina patens





DE SJ-DE-18-2014 3.75-7.5 0.49 5.18 13.55 0.03 Spartina cynosuroides











FL MO-FL-01-2014 0-4.625 0.63 3.88 11.45 0.02 Spartina alterniflora tall

FL MO-FL-01-2014 4.625-9.25 0.66 2.77 8.29 0.02 Spartina alterniflora tall



FL MO-FL-02-2014 0-4.75 0.59 4.33 12.81 0.03 Spartina alterniflora short

FL MO-FL-02-2014 4.75-9.5 0.53 5.44 15.17 0.03 Spartina alterniflora short

FL MO-FL-02-2014 9.5-14.25 0.43 6.39 17.79 0.03 Spartina alterniflora short

FL MO-FL-02-2014 14.25-19 0.42 6.48 18.75 0.03 Spartina alterniflora short



FL MO-FL-03-2014 0-5 0.75 2.83 8.11 0.02 Batis maritima




FL MO-FL-04-2014 0-5 0.31 9.81 26.40 0.03 Juncus roemerianus




FL PI-FL-01-2014 0-5 0.31 12.67 28.78 0.04 Spartina alterniflora short




FL PI-FL-02-2014 0-4.875 0.42 8.83 20.02 0.04 Juncus roemerianus

FL PI-FL-02-2014 4.875-9.75 0.50 8.23 16.62 0.04 Juncus roemerianus



FL PI-FL-03-2014 0-5 0.36 8.75 21.94 0.03 Spartina alterniflora tall




FL PI-FL-04-2014 0-4.625 1.13 2.89 6.64 0.03 Batis maritima

FL PI-FL-04-2014 4.625-9.25 1.21 1.57 3.97 0.02 Batis maritima



FL HI-FL-01-2014 0-5 1.21 1.31 3.44 0.02 Batis maritima




FL HI-FL-02-2014 0-5 0.46 5.33 13.76 0.02 Spartina alterniflora short




FL HI-FL-03-2014 0-5 1.18 1.09 2.97 0.01 Juncus roemerianus




FL HI-FL-04-2014 0-4.75 0.60 3.22 9.84 0.02 Spartina alterniflora tall

FL HI-FL-04-2014 4.75-9.5 0.50 3.58 14.08 0.02 Spartina alterniflora tall

FL HI-FL-04-2014 9.5-14.25 0.54 2.86 10.11 0.02 Spartina alterniflora tall

FL HI-FL-04-2014 14.25-19 0.52 3.33 12.09 0.02 Spartina alterniflora tall

FL JC-FL-01-2014 0-4.75 1.23 0.64 2.31 0.01 Juncus roemerianus

FL JC-FL-01-2014 4.75-9.5 1.24 0.55 2.36 0.01 Juncus roemerianus

FL JC-FL-01-2014 9.5-14.25 1.34 0.81 2.39 0.01 Juncus roemerianus



FL JC-FL-01-2014 14.25-19 1.43 0.43 2.39 0.01 Juncus roemerianus

FL JC-FL-02-2014 0-5 0.47 5.84 16.16 0.03 Batis maritima




FL JC-FL-03-2014 0-4.925 0.28 8.48 23.20 0.02 Spartina alterniflora short

FL JC-FL-03-2014 4.925-9.85 0.30 9.30 25.39 0.03 Spartina alterniflora short



FL JC-FL-04-2014 0-5 0.38 5.60 18.67 0.02 Spartina alterniflora tall




FL PC-FL-02-2014 0-5 0.42 7.08 18.99 0.03 Spartina alterniflora short




FL PC-FL-03-2014 0-5 0.35 9.44 23.78 0.03 Juncus roemerianus




FL WO-FL-01-2014 0-5 0.44 5.54 15.38 0.02 Batis maritima




FL WO-FL-02-2014 0-5 1.04 0.95 3.29 0.01 Spartina alterniflora tall




OH OW-OH-01-2014 0-5 0.19 17.69 34.55 0.03 Phragmites australis






















OH OW-OH-06-2014 0-5 0.22 8.94 19.54 0.02 Typha angustifolia
















OH OW-OH-10-2014 0-4.8 0.53 4.01 9.35 0.02 Typha angustifolia

OH OW-OH-10-2014 4.8-9.6 0.68 4.20 7.58 0.03 Typha angustifolia



OH OW-OH-11-2014 0-5 0.28 9.53 21.23 0.03 Sparganium eurycarpum








OH OW-OH-13-2014 0-4.3 0.38 7.06 15.16 0.03 Sparganium eurycarpum

OH OW-OH-13-2014 4.3-8.6 0.54 4.80 11.02 0.03 Sparganium eurycarpum



OH OW-OH-14-2014 0-4.375 0.50 3.48 8.47 0.02 Sparganium eurycarpum










OH OW-OH-16-2014 0-5 0.27 9.03 18.89 0.02 Leersia oryzoides




















SC CM-SC-01-2014 0-4.75 0.60 3.60 12.79 0.02 Spartina alterniflora tall

SC CM-SC-01-2014 4.75-9.5 0.53 3.44 10.96 0.02 Spartina alterniflora tall

SC CM-SC-01-2014 9.5-14.25 0.70 2.81 9.72 0.02 Spartina alterniflora tall

SC CM-SC-01-2014 14.25-19 0.55 3.36 12.31 0.02 Spartina alterniflora tall

SC CM-SC-02-2014 0-5 0.31 4.78 17.62 0.01 Spartina alterniflora tall


















SC CM-SC-06-2014 0-5 1.17 1.98 5.76 0.02 Spartina alterniflora short




















SC CM-SC-11-2014 0-5 0.75 6.56 10.75 0.05 Salicornia depressa/mixed





















SC CM-SC-15-2014 15-19.5 1.66 0.39 1.77 0.01 Salicornia depressa/mixed

SC CM-SC-16-2014 0-5 0.90 4.28 7.49 0.04 Juncus roemerianus




















WI PO-WI-01-2014 0-3.75 0.38 4.02 10.26 0.02 Carex lacustris

WI PO-WI-01-2014 3.75-7.5 0.73 3.77 10.30 0.03 Carex lacustris


WI PO-WI-01-2014 11.25-15 0.49 3.21 9.33 0.02 Carex lacustris

WI PO-WI-02-2014 0-4.5 0.41 5.14 14.03 0.02 Typha sp.

WI PO-WI-02-2014 4.5-9 0.49 5.46 14.03 0.03 Typha sp.

WI PO-WI-02-2014 9-13.5 0.53 4.83 13.11 0.03 Typha sp.

WI PO-WI-02-2014 13.5-17 0.64 5.94 17.15 0.04 Typha sp.

WI PO-WI-03-2014 0-5 0.58 5.21 11.97 0.03 Carex lacustris




WI PO-WI-04-2014 0-4.5 0.24 11.71 23.58 0.03 Typha sp.

WI PO-WI-04-2014 4.5-9 0.34 13.03 26.52 0.04 Typha sp.

WI PO-WI-04-2014 9-13.5 0.50 4.99 12.39 0.03 Typha sp.

WI PO-WI-04-2014 13.5-18 0.52 3.26 8.59 0.02 Typha sp.







WI PO-WI-06-2014 0-4.25 0.40 7.71 17.58 0.03 Typha sp.

WI PO-WI-06-2014 4.25-8.5 0.58 6.51 13.58 0.04 Typha sp.

WI PO-WI-06-2014 8.5-12.75 0.76 4.23 10.05 0.03 Typha sp.

WI PO-WI-06-2014 12.75-17 0.75 4.07 9.43 0.03 Typha sp.





WI PO-WI-08-2014 0-5 0.80 3.02 8.72 0.02 Typha sp.

WI PO-WI-08-2014 5-10 0.68 3.70 9.53 0.03 Typha sp.

WI PO-WI-08-2014 10-15 0.59 3.20 9.56 0.02 Typha sp.

WI PO-WI-08-2014 15-20 0.76 2.63 7.57 0.02 Typha sp.





WI PO-WI-10-2014 0-5 1.37 0.63 1.52 0.01 Typha sp.

WI PO-WI-10-2014 5-10 1.28 5.10 4.35 0.07 Typha sp.

WI PO-WI-10-2014 10-15 1.06 2.23 9.96 0.02 Typha sp.

WI PO-WI-11-2014 0-4.5 0.42 8.36 22.10 0.03 Sparganium eurycarpum

WI PO-WI-11-2014 4.5-9 0.52 5.90 14.20 0.03 Sparganium eurycarpum



WI PO-WI-12-2014 0-5 0.41 4.65 11.48 0.02 Sagittaria rigida





WI PO-WI-13-2014 4.25-8.5 0.58 4.94 14.27 0.03 Sparganium eurycarpum

































MS MB-MS-01-2014 0-5 1.52 0.03 1.52 0.00 Salt panne




















MS MB-MS-06-2014 0-5 0.73 4.43 10.33 0.03 Spartina patens-spartinae






















MS MY-MS-11-2014 0-5 0.45 5.37 13.55 0.02 Juncus roemerianus




















MS NR-MS-16-2014 0-5 0.27 7.98 19.63 0.02 Spartina alterniflora tall






















CA RR-CA-01-2014 0-5 0.27 13.13 30.90 0.04 Schoenoplectus americanus




CA RR-CA-02-2014 0-4.875 0.20 17.40 39.44 0.03 Schoenoplectus americanus

CA RR-CA-02-2014 4.875-9.75 0.32 12.95 30.49 0.04 Schoenoplectus americanus



CA RR-CA-03-2014 0-5 0.19 30.29 64.38 0.06 Distichlis spicata / Juncus balticus




CA RR-CA-04-2014 0-5 0.24 19.53 43.93 0.05 Distichlis spicata / Salicornia pacifica















CA RR-CA-07-2014 4.5-9 0.36 9.07 23.26 0.03 Schoenoplectus americanus


CA RR-CA-07-2014 13.5-18 0.52 9.19 23.51 0.05 Schoenoplectus americanus





CA RR-CA-09-2014 0-5 0.51 4.15 13.55 0.02 Schoenoplectus acutus/californicus




















































Appendix 4. Photographs of core splits for each site organized by dominant vegetation type and labelled with the core ID

















Appendix 5. Completed project synopsis

Spatial Variability in Carbon Storage Within and Across Marshes of the National Estuarine Research Reserve System (NERRS), USA: A Comparison of Methodologies and Coastal Regions

Last Modified (30 March 2015)

*Note, this information may be shared only after this work has been published in the

peer-reviewed literature.

Principal Investigators (Name, Affiliation)

Kristin Wilson, Wells National Estuarine Research Reserve Erik Smith, North Inlet-Winyah Bay National Estuarine Research Reserve

Project description (“What” from JPAC poster)

This research fills critical gaps in understanding the spatial variability of carbon storage within and across the National Estuarine Research Reserves System (NERRS) of Maine, Delaware, South Carolina, Florida, Mississippi, California, Wisconsin, and Ohio. This study quantified the percent soil organic matter, percent organic carbon content, and variability in sediment carbon density in the upper 20 cm of the soil across a range of marsh types that differ in geomorphic setting, dominant vegetation, and salinity. Results assist with the prediction of carbon stocks in salt marshes experiencing changing environmental conditions and anthropogenic stressors and where access to the more expensive analytical technology and expertise is unavailable or cost-prohibitive.

Key results (and context within which they were achieved. E.g. “the first…”) that CEC can share May include graph or table

Results show that sediment organic matter by loss on ignition and sediment organic carbon content (%) are highly correlated, that the global relationship differs from other published studies (e.g., Craft et al. 1991; Callaway et al. 2012), and that individual sites significantly contribute to variation in this global relationship (figure below). Study results reveal that across broad spatial scales, a single curve adequately captures the vast majority of the variability in sediment organic carbon explained by LOI. At finer spatial scales and in some regions, variability in sediment properties may dictate the use of site-specific



calibration curves.

Sediment organic carbon content (%) versus organic matter content by LOI (%). Results of a break point analysis revealed a shift in the

relationship between organic carbon content and dry bulk density at an organic carbon content of 2.04 %. Above this value, the relationship was highly significant and had a slope very similar to those observed in previous studies. Below this value, the relationship was not statistically significant. Interestingly, the samples with organic carbon content below 2.04 % were almost exclusively from the southeastern United States (South Carolina, Florida, and Mississippi), and were largely confined to the mid-marsh halophyte and/or short-form Spartina alterniflora vegetation zone.

Mean sediment organic carbon density in the upper 20 cm ranged from 0.001 to 0.061 g C cm-3, with a grand mean of 0.030 ± 0.011 g C cm-3, and mean densities differed significantly by site. Sediments from Maine and California contained significantly more organic carbon per cubic centimeter than the other sites sampled (figure below).



Distribution of organic carbon density within and across eight marsh sites of the NERRS. Median values denoted by horizontal line, boxes contain the 25th and 75th quartiles, whiskers denote the 10th and 90th percentiles, and outliers are denoted by points. Sites with the same letter are not significantly different from one another (p > 0.05). State abbreviations indicate site location.

Mean sediment organic carbon density differed significantly by high and low marsh zones in South Carolina, Mississippi, and Florida, though the pattern of this difference was not consistent across sites. In Mississippi, the low marsh zone had significantly greater organic carbon density than the high marsh zone, while in South Carolina and California, the opposite pattern was observed.

Mean sediment organic carbon density differed significantly by vegetation at half the sites sampled (Maine, Mississippi, California, and South Carolina). These results reveal that there is considerable spatial variation in sediment organic carbon density in the upper 20 cm at the marsh scale.

Science/Policy Implications

Results suggest that region-specific calibration curves that relate sediment organic matter to sediment carbon content, are needed in some regions.

Results reveal significant spatial variation in sediment organic carbon density in the upper 20 cm by site, zone, and vegetation type in some marshes. These findings have implications for how marsh-scale carbon budgets are calculated and incorporated into blue carbon policies.



Challenges/ Lessons Learned

The timeframe for completing this project was tight, especially because it took ~2 months of the 12-month project period to get funds in hand.

The data workshop was critical to advance analyses of the data. More lead time to prep for this event and more time for post-workshop follow-up, would have benefitted data syntheses.

Gaps/Future Research Needs

Explore changes in sediment carbon density with depth (>20 cm) by collecting longer cores to improve calculations of carbon budgets at the marsh scale.

Continue to explore spatial variability in carbon density measurements and link these to high-resolution habitat maps of marshes.

Top opportunities for collaboration across North America

The 28 NERRs are excellent platforms and partners to expand blue carbon work in protected wetlands of the United States that encompass a range of marsh types, management strategies, and natural and anthropogenic stressors.

Recommendations

Expand the number of locations for which sediment carbon density measurements exist and explore the degree to which additional regional calibration curves are needed.

Publications (citations and links)

None yet. We are currently preparing a peer-reviewed manuscript to PLOS ONE with anticipated submission in May 2015.

Photos (and photo captions)

Co-PIs Kristin Wilson (left) of the Wells National Estuarine Research Reserve and Erik Smith (right) of the North Inlet-Winyah Bay National Estuarine Research Reserve collect cores for the project.



Cores were collected from the eight National Estuarine Research Reserves included in this photo compilation. Top row (Maine, Delaware, Florida, South Carolina). Bottom row (Mississippi, California, Wisconsin, Ohio). The National Estuarine Research Reserve System a network of 28 National Oceanic and Atmospheric Administration (NOAA)-supported, protected sites throughout the United States that are dedicated to protecting and restoring coastal ecosystems through integrated research, stewardship, education, and community partnerships.

In the upper 20 cm, cores from Maine marshes contained significantly more carbon per cubic centimeter than all other sites except California. Compared to Maine, cores from the southeastern United States had significantly lower sediment carbon densities, but were similar to one another. Superficially, these differences can be observed in the above photo collage.



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Documents

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