# Global dataset of soil organic carbon in tidal marshes

Tania Maxwell, André Rovai, Maria Fernanda Adame, Janine Adams, José Álvarez-Rogel, William Austin, Kim Beasy, Francesco Boscutti, Michael Böttcher, Tjeerd Bouma, et al.

► To cite this version:

Tania Maxwell, André Rovai, Maria Fernanda Adame, Janine Adams, José Álvarez-Rogel, et al. Global dataset of soil organic carbon in tidal marshes. Scientific Data, 2023, 10 (1), pp.797-10.1038/s41597-023-02633-x. hal-04366652

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# Global dataset of soil organic carbon in tidal marshes

Tania L. Maxwell et al. #

<figure><img src="image_2.png" /></figure>

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Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha⁻¹ in the top 30 cm and 231 ± 134 Mg SOC ha⁻¹ in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.

## Background & Summary

Tidal marshes are vegetated wetlands formed by herbaceous and woody vascular plants that are present on many of the world's depositional coastlines and are regularly inundated by tides¹. While tidal marshes naturally change in extent, anthropogenic pressures (sometimes operating over thousands of years²) have greatly accelerated this change in recent decades, degrading their condition globally. Tidal marshes have received considerable attention recently as blue carbon ecosystems, one of a group of ecosystems that have the capacity to capture and store large amounts of soil organic carbon (SOC) over hundreds to thousands of years³. Alongside mangroves and seagrasses, they accumulate organic carbon most effectively in their soils where decomposition is slow due to anoxic waterlogged conditions⁴,⁵. Precise and consistent global-scale information on tidal marsh extent, distribution change, or other ecosystem functions is lacking, highlighting a critical research gap given their potential value for climate change mitigation⁶,⁷.

Assessments of tidal marsh change have found that previous decades were characterised by extensive losses, with marshes disappearing at a rate of 1–2% per year⁸, leading to a total loss of 67% of tidal marshes over recent centuries⁹. In the period 2000 to 2019, one study estimated a global tidal marsh loss rate of 0.28% per year¹⁰, while another suggested that marshes have actually marginally increased globally in extent, including vegetation expansion onto existing tidal flats¹¹. A new 10 m resolution global map of tidal marsh extent estimates that the ecosystem occupies 52,880 km² (95% confidence intervals: 32,000 to 59,800 km²)¹², similar to previous estimates¹³. These ecosystems continue to be at risk due to direct anthropogenic impacts such as activities that lead to destruction, disturbance, or degradation, sea-level rise, and changes in climate¹⁴, which negatively impact their ability to retain their stored SOC or accumulate more SOC via carbon sequestration and sediment accretion¹⁵,¹⁶.

The quantification of organic carbon stocks in tidal marsh soils provides critical information to promote the protection, management, and restoration of these natural carbon sinks. Such information, and derived models, may support blue carbon assessments, and enable the incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies, including the Nationally Determined Contributions that form a core component of global climate actions. Previous global estimates have averaged values from a few select studies⁴,¹⁷, or relied on global datasets that are biased towards farmland soils¹⁰,¹⁸. There is a clear need for a centralised tidal marsh soil carbon dataset, and to this end the Coastal Carbon Research Coordination Network (CCRCN)¹⁹ has been collating and publishing core-level datasets. These data are mostly from the United States (U.S.) and have been used to model soil carbon of the Conterminous U.S. tidal marshes²⁰. Here, we expand on these efforts by collating site- and core-level tidal marsh SOC data distributed globally.

<footnote>*A full list of authors and their affiliations appears at the end of the paper.</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Workflow of the literature search, abstract screening, and dataset generation process for the MarSOC dataset.</figcaption></figure>

We collected data from 99 tidal marsh SOC peer-reviewed and unpublished studies and reformatted the data into a common structure using the R computing environment²¹. Studies were initially identified through a search of the peer-reviewed literature, and data were extracted directly from papers, from data repositories, or through personal communication from authors (Fig. 1). The tidal Marsh Soil Organic Carbon (MarSOC) database²² contains 17,454 data points, each with geographic coordinates, collection year, soil depth, and site information (country, site name). The database includes data from 29 countries with an extensive tidal marsh coverage, and over 40% of the data are soil samples deeper than 30 cm. Using these data and the data from the CCRCN¹⁹, we provide a first order estimate for a globally representative SOC stock value for tidal marshes to 30 cm depth of 79.2 ± 38.1 Mg C ha⁻¹ (median ± absolute deviation of the median; n = 26,349), and to 1 m depth of 231 ± 134 Mg C ha⁻¹ (n = 39,126). Because marshes can be shallower or deeper than this, region-specific studies should develop their own stock estimates. However, using this value we can estimate an average of 1.22 ± 0.20 Pg C stored in tidal marshes in the upper metre of soil globally.

Generally, carbon content is quantified using an elemental analyzer, but these analyses can incur high costs, particularly in countries where laboratories with this specialised equipment are not easily accessible. Therefore, many studies only record soil organic matter (SOM) content based on Loss On Ignition (LOI). Therefore, to
<figure><img src="image_4.png" /><figcaption>Fig. 2 Sample locations coloured by data type (core-level purple, review turquoise, site-level yellow).</figcaption></figure>

estimate soil organic carbon (SOC) content, a number of equations have been developed to calculate SOC from SOM. For example, Craft and collaborators measured both SOM and SOC from marshes in North Carolina, U.S., and developed an equation for this relationship<sup>23</sup>, which has been used extensively by researchers globally to predict SOC from SOM in wetlands. However, for mangroves, this relationship can change according to the coastal environmental setting<sup>24</sup>, and several studies have generated their own site-specific equations<sup>25-29</sup>. For marshes in the continental U.S., Holmquist and collaborators<sup>20</sup> developed their own equation using over 1,500 points from 6 studies. Ouyang and Lee<sup>30</sup> developed a global conversion equation, but they used only a subset of points from each of 11 studies in 4 countries (n = 344). Developing a more globally generalizable equation for tidal marshes is needed for large-scale analyses or as a starting point for new study sites. Within our database there are 17 studies with measurements of both SOM measured via LOI, and SOC measured via elemental analysis, allowing us to present this relationship. We therefore looked to include as many data points distributed globally using data from our database (n = 1,470) and the CCRCN database (n = 3,604), to create a universal conversion equation that spans the diversity of marsh soil types (e.g., minerogenic and organogenic settings) reported in the current literature.

The MarSOC dataset<sup>22</sup> described here can be used for new global or large-scale estimates of tidal marsh soil organic carbon, and also provide a foundation for additional data collection and collaboration to improve soil organic carbon in tidal marsh estimates, especially from underrepresented areas. The dataset is released for non-commercial use only and is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). All publications that use this database are encouraged to appropriately cite the data and this paper.

## Methods

**Literature search.** We compiled the MarSOC dataset from a systematic review of the literature. On 19 January 2022, we searched the title, abstract and keywords in both Scopus and the Web of Science (WoS) All Databases using a naive search string: (("soil C" OR "soil carbon" OR "soil inorganic carbon" OR "soil organic carbon") OR ("soil carbon sequestrat*" OR "soil carbon stabiliz*" OR "soil carbon stock*")) AND ("tidal marsh*" OR "salt marsh*" OR "saltmarsh*"). The search identified 259 studies from Scopus and 331 from the WoS (Fig. 1).

We used the `litsearchr` R package<sup>31</sup> to broaden our search terms using keyword co-occurrence networks<sup>31</sup>. All steps can be viewed in the published code with the dataset<sup>22</sup>. This resulted in our final search string: ("blue carbon" OR "carbon accumulation" OR "carbon cycle" OR "carbon dioxide" OR "carbon sequestration" OR "carbon stock*" OR "carbon stor*" OR "organic carbon" OR "organic matter" OR "soil carbon" OR "soil organic carbon" OR "soil organic matter" OR "soil respiration" OR "carbon content" OR "carbon dynamic*" OR "carbon pool*") AND ("coastal marsh*" OR "coastal salt marsh*" OR "salt marsh*" OR "tidal marsh*" OR "tidal salt marsh*" OR "marsh ecosystem*" OR "marsh soil*" OR "saltmarsh*").

On 28 January 2022, we searched both Scopus and the WoS All Databases using the final search string mentioned above within the University of Cambridge library account, which includes the following databases: Web of Science Core Collection, BIOSIS Previews, BIOSIS Citation Index, Current Contents Connect, MEDLINE, Zoological Record, Data Citation Index, KCI- Korean Journal Database, SciELO Citation Index, Russian Science Citation Index, and Derwent Innovations Index. This procedure aimed to ensure the inclusion of articles published in languages other than English. We retrieved 4,035 items from WoS and 2,428 from Scopus. We deduplicated the results, giving a total of 4,317 references (Fig. 1), which is tenfold higher than the original naive search.
<table><caption>Table 1. Description of variables contained in the dataset.</caption><thead><tr><th>Variable name</th><th>Units</th><th>Descriptor</th><th>Type</th></tr></thead><tbody><tr><td>Source</td><td></td><td>Study from which the data was extracted</td><td>Character</td></tr><tr><td>Original_source</td><td></td><td>If the source study was a review, the original study of the data</td><td>Character</td></tr><tr><td>Data_type</td><td></td><td>Core-level, site-level, or from a review</td><td>Character</td></tr><tr><td>Site</td><td></td><td>The name of the site where core(s) were taken</td><td>Character</td></tr><tr><td>Core</td><td></td><td>Core ID</td><td>Character</td></tr><tr><td>Plot</td><td></td><td>If site-level data, identifier of the site</td><td>Character</td></tr><tr><td>Site_name</td><td></td><td>Unique ID per plot or core</td><td>Character</td></tr><tr><td>Soil_type</td><td></td><td>Soil type (i.e., peat, sand, silt, mud) when specified</td><td>Character</td></tr><tr><td>Latitude</td><td>Decimal degrees</td><td>Geographic coordinate of sample location in WGS84 (N - S)</td><td>Numeric</td></tr><tr><td>Longitude</td><td>Decimal degrees</td><td>Geographic coordinate of sample location in WGS84 (E - W)</td><td>Numeric</td></tr><tr><td>accuracy_flag</td><td></td><td>Accuracy of geographic coordinate (direct from dataset, averaged, or estimated using Google Earth)</td><td>Character</td></tr><tr><td>Country</td><td></td><td>The name of the country where the soil cores were taken</td><td>Character</td></tr><tr><td>Admin_unit</td><td></td><td>Administrative unit below country level (Nation, State, Emirate)</td><td>Character</td></tr><tr><td>Year_collected</td><td></td><td>The year of the collection. If cores were taken over several years, the year the collection started</td><td>Integer</td></tr><tr><td>Year_collected_end</td><td></td><td>If cores were taken over several years, the last year collected</td><td>Integer</td></tr><tr><td>U_depth_m</td><td>Metres</td><td>Upper depth of soil core</td><td>Numeric</td></tr><tr><td>L_depth_m</td><td>Metres</td><td>Lower depth of soil core</td><td>Numeric</td></tr><tr><td>Method</td><td></td><td>Method used to measure organic carbon (%). Elemental analysis (EA), loss-on-ignition (LOI), Walkley Black</td><td>Character</td></tr><tr><td>Conv_factor</td><td></td><td>Conversion factor used to convert soil organic matter measured via LOI to organic carbon</td><td>Character</td></tr><tr><td>OC_perc</td><td>%</td><td>Soil organic carbon measurement</td><td>Numeric</td></tr><tr><td>BD_g_cm3</td><td>g cm<sup>-3</sup></td><td>Dry bulk density measurement</td><td>Numeric</td></tr><tr><td>SOM_perc</td><td>%</td><td>Soil organic matter measurement</td><td>Numeric</td></tr><tr><td>N_perc</td><td>%</td><td>Nitrogen (%), if measured alongside C in a CN analyser.</td><td>Numeric</td></tr><tr><td>Time_replicate</td><td></td><td>Time replicate for soil sampled more than once a year (summer, winter, month-specific)</td><td>Character</td></tr><tr><td>Treatment</td><td></td><td>Site-specific information (control, invaded, uninvaded, grazed, ungrazed, historic-breach, managed realignment, post-fire)</td><td>Character</td></tr><tr><td>n Cores</td><td></td><td>Number of cores in site-level or review measurements, when data available</td><td>Integer</td></tr><tr><td>SOM_perc_mean</td><td>%</td><td>Mean of soil organic matter measured (data not at core-level)</td><td>Numeric</td></tr><tr><td>SOM_perc_sd</td><td>%</td><td>Standard deviation of the mean of soil organic matter measured</td><td>Numeric</td></tr><tr><td>OC_perc_mean</td><td>%</td><td>Mean of soil organic carbon measured (data not at core-level)</td><td>Numeric</td></tr><tr><td>OC_perc_sd</td><td>%</td><td>Standard deviation of the mean of soil organic matter measured</td><td>Numeric</td></tr><tr><td>OC_perc_se</td><td>%</td><td>Standard error of the mean of soil organic matter measured</td><td>Numeric</td></tr><tr><td>BD_g_cm3_mean</td><td>g cm<sup>-3</sup></td><td>Mean of dry bulk density measured (data not at core-level)</td><td>Numeric</td></tr><tr><td>BD_g_cm3_sd</td><td>g cm<sup>-3</sup></td><td>Standard deviation of the mean of dry bulk density measured</td><td>Numeric</td></tr><tr><td>BD_g_cm3_se</td><td>g cm<sup>-3</sup></td><td>Standard error of the mean of dry bulk density measured</td><td>Numeric</td></tr><tr><td>OC_from_SOM_our_eq</td><td>%</td><td>Soil organic carbon estimated from soil organic matter using our equation (Fig. 4)</td><td>Numeric</td></tr><tr><td>OC_obst</td><td></td><td>Method of OC measurement: "Observed", "Estimated (study equation)" - OC from LOI with regional eq. (see Conv_factor column), "Estimated (our equation)" - OC from LOI with Eq. 3</td><td>Character</td></tr><tr><td>OC_perc_final</td><td>%</td><td>Coalesce of all columns of OC_perc (OC_perc, OC_perc_mean, and OC_from_SOM_our_eq)</td><td>Numeric</td></tr><tr><td>Notes</td><td></td><td>Varying sample-specific notes (i.e., flagged outliers)</td><td>Character</td></tr><tr><td>DOI</td><td></td><td>Source study DOI URL</td><td>Character</td></tr></tbody></table>

**Inclusion criteria.** The initial and retained articles, with inclusion criteria and additional labels, can be found on our sysrev projects, an open and online tool to screen and label abstracts. In the first sysrev project, we screened the title and abstracts of the 4,317 references to identify those that mentioned soil organic matter or organic carbon in tidal marsh studies. We excluded studies that did not meet these criteria, and separated these into SOC measured in mudflats or seagrasses, other C cycling variables measured in tidal marshes, or studies generally not in tidal marshes or without mention of SOC data. Included studies were labelled as reviews (studies with a general scope, studies with potentially large datasets), modelling (studies with raw data that was used for modelling purposes in that study), and raw data (studies that may contain raw data). Studies could have two tags, such as review studies that included raw data. All studies labelled as “reviews” were retained for full-text assessment, from which we were able to include 9 datasets from tables or the supplementary material. Some of the
<figure><img src="image_5.png" /><figcaption>Fig. 3 Distribution of data stored in this MarSOC database across all soil depths.</figcaption></figure>

studies labelled as “raw data” were easily identified as having extractable data (n = 23), such as published datasets (Fig. 1).

To reduce the number of studies requiring full-text screening, from the initial studies tagged “raw data” (n = 1,168), we focused on geographical locations from which we had few datasets (i.e., outside the U.S., U.K., China, and Australia). A second abstract screening with more specific labels was then conducted. We labelled abstracts to identify studies by continent, presence of SOC or SOM data, and inclusion of primary data. A total of 69 studies with primary data in data-poor regions were identified. From these, 21 datasets were extracted or provided by the lead authors on the corresponding papers (Fig. 1).

We searched the SEANOE, PANGAEA, CIFOR, and Marine Scotland Data repositories and found 5 additional studies that fit the inclusion criteria (Fig. 1). We also included data compiled previously for a separate project, which included 12 core-level and 10 site-level published studies. Correspondence with experts in the field led to the inclusion of 10 additional datasets from published studies and 2 from studies that are unpublished or in preparation (see Supplementary Information section I for corresponding sampling methodologies). Finally, data from 7 recent studies published beyond the search date of January 2022 were included. Datasets already held in the Coastal Carbon Research Coordination Network were not included, as our data compilation is intended to be complementary to that research database. The final extracted datasets were from 99 studies (Fig. 1).

**Data acquisition.** From the identified studies, when possible, we extracted data (SOM and/or SOC) from the publications' tables, figures, or supplementary information. When not available, we contacted authors and asked them to contribute their datasets. We downloaded published datasets in repositories from their respective online sources. In total, we extracted data (from tables, supplementary material) from 22 studies, received data via email from 33 studies, and included 22 published datasets from a variety of general (Dataverse, DRYAD, FigShare, Mendeley Data), subject-specific (SEANOE, PANGEA), and country-specific (Environmental Information Data Centre (EIDC), Marine Scotland Data, USGS) repositories. Finally, we appended data from 22 studies from a previous data compilation effort.

In total, we compiled data from 2,329 unique locations (Fig. 2). To be as comprehensive as possible, we included data recorded at the core-level (n = 72 studies<sup>25-28,32-97</sup>), site-level (n = 22<sup>7,98-118</sup>), and from reviews (n = 5<sup>119-123</sup>). This data identification is included in the *Data_type* column, while the unique ID for each core or plot sampled is reported in the *Site_name* column (Table 1).

For each data point (i.e., each row), the data include the upper and lower depth of the soil sample, with SOC percent and/or SOM percent (Fig. 3), alongside the method used to determine these values (elemental analyser, Loss-On-Ignition, etc.). Each data point in our dataset also includes geographical coordinates, with a corresponding accuracy flag. If available in the original datasets, dry bulk density (85% of the data) and nitrogen content (15% of the data) were also included. There is also information on the year the sample was collected and the site name and country where the sample was collected, with the name of any finer scale administrative unit if applicable.

Additionally, the data collated here provide the opportunity to calculate an updated and more globally representative average value for the soil organic carbon stock to 1 m depth in tidal marshes. To do so, our database was used with the data from the CCRCN<sup>19</sup> to maximise the number of points for this calculation (n = 38,945). Using the following equation (Eq. 1), we calculated soil organic carbon density for the subset of soil samples which recorded both a SOC content and measured dry bulk density value (Fig. 3d).

$$ \text{SOC density [g cm}^{-3}\text{]} = \text{dry bulk density [g cm}^{-3}\text{]} * (\text{SOC [\%]}/100) \quad (1) $$
<figure><img src="image_6.png" /><figcaption>Fig. 4 Data points with both soil organic matter and soil organic carbon values, used to calculate the conversion equation for SOM to SOC (solid black line, with prediction intervals in grey). Data extracted from the Coastal Carbon Research Coordination Network (CCRCN)¹⁹ are shown in circles, and values from this dataset are shown in triangles.</figcaption></figure>

We separated all SOC density samples according to their horizon midpoint into the following soil layer categories: 0–15, 15–30, 30–50, and 50–100 cm (Figure S1). Using all of the measured SOC density values within each of these soil layers (that is, depth interval bins), we calculated the median SOC density value for each layer, along with its absolute deviation. The median was chosen as opposed to the mean due to the skewness of the data (Fig. 3d). We then multiplied this value by the corresponding thickness of each layer, and by 100 to convert grams to megagrams and cubic centimetres to hectares, to get the median SOC stock for each layer (Eq. 2).

$$ \text{SOC stock[Mg ha}^{-1}\text{]} = \text{SOC density[gcm}^{-3}\text{]} * \text{Horizon thickness[cm]} * 100 \quad (2) $$

We then summed these estimated stocks of the four layers to get the estimated total stock to both 30 cm and to 1 m depth. The final estimated value of SOC stock to 30 cm was 79.2 ± 38.1 Mg ha⁻¹ (n = 26,239). With an additional 7,204 points located between 30 cm and 50 cm and 5,502 points between 50 and 100 cm, we calculated the stock to 1 m in tidal marsh soils as 231 ± 134 Mg ha⁻¹ (median ± median absolute deviation). By using SOC density values from each sample to estimate the density for their respective soil layer (i.e., 0–15, 15–30, 30–50, and 50–100 cm), all data points were used in the stock calculation without needing to extrapolate. To get a more refined estimate of global tidal marsh soil carbon storage, it is possible to multiply this stock value by the tidal marsh area estimate of 52,880 km² (95% CI: 32,000 to 59,800 km²) from the recent globally consistent extent map¹². This gives us a global estimate of tidal marsh soil carbon of around 1.22 ± 0.20 Pg C in the top metre of soil, which is lower than previous estimates¹⁷. However, we acknowledge that this is a general estimate, and that a study using machine learning and environmental predictors to estimate SOC at a finer scale would give a more appropriate and accurate spatial representation of SOC stocks across the world's coastal marshes. We also acknowledge that tidal marsh soils in different regions may be more shallow, or deeper than 1 m, so we recommend that regional studies develop their own carbon stock estimates.

**Global conversion factor.** To create our conversion factor between SOC and SOM, we identified 17 studies in which, both SOM and SOC were measured. While data from the CCRCN is not included in our final dataset, we did include all data with both SOM and SOC measurements from the CCRCN¹⁹ to create the conversion factor equation. Thus, we included 18 studies¹²⁴⁻¹²⁸ from the CCRCN¹²⁹⁻¹⁴¹ and 17 studies from our dataset to investigate the SOM to SOC relationship (Fig. 4). A further 10 studies, in which the authors developed their own conversion factor to convert SOM to SOC (Fig. 5), were selected for comparison.

To model SOC from SOM, we used the nls() and the lmer() functions in R to fit linear and quadratic models with an intercept fixed to 0, and included the study ID as a random effect. Based on Akaike's Information Criteria, testing model parsimony relative to explanatory power, the best fitting model was a quadratic function with study ID as a random effect (Eq. 3; Fig. 4, R² = 0.949, n = 5074).

$$ \text{SOC}[\%] = (0.000683 \pm 0.00563) * \text{SOM}[\%]^2 + (0.410 \pm 0) * \text{SOM}[\%] \quad (3) $$
This can be compared to 16 studies from our literature search that used a variety of conversion factors

<figure><img src="image_7.png" /><figcaption>**Fig. 5** Soil organic matter to soil organic carbon conversion relationships developed by different sources, along with the region, site, or species zone from which these were developed (equations detailed in Table S1). Our conversion equation is a solid *black line*, with prediction intervals in *grey*.</figcaption></figure>

(Table S1). We also fitted a quadratic model to each of the individual studies presented in Fig. 4, used to generate the general equation (Figure S2). We found that many of the study-specific quadratic equations were significantly different to the overall equation (Table S2), showing that there is high variability in the relationship between SOM and SOC between each study. While site-specific conversion equations will always be desirable, our general model captures a range of coastal tidal marsh types distributed across the climatic, oceanographic, and geomorphic gradients with applications to regional or larger-scale studies. Our equation lies amongst the other conversion equations (Fig. 5), and estimates less organic carbon from organic matter than the commonly used Craft²³ equation or the second equation presented in Blue Carbon Initiative handbook¹⁴², which used data from Maine. Our dataset can be used to analyse the uncertainty in how these different equations affect the calculation of a C stock for soils. For example, the uncertainty may be different for varying levels of soil organic matter, or for marshes with different coastal geomorphologies or soil type, which may influence the relationship between SOM and SOC²⁴,¹⁴³. It can also be used to estimate soil carbon stocks in tidal marshes for varying soil depths and using different methods, such as extrapolating cores to 1 m or confining the analysis to the topsoil. Finally, the data can serve as a basis for future work integrating other soil variables, such as soil total inorganic carbon, particulate organic and inorganic carbon, as well as isotope measurements.

## Data Records

The data and code used in the methods described above are archived in a Zenodo repository²². This is a static copy of the data peer-reviewed in 2023, which is a release from the dynamic Github repository https://github.com/Tania-Maxwell/MarSOC-Dataset. The data is currently being incorporated into the CCRCN Atlas. The repository is formatted in the following structure:

* `Maxwell_MarSOC_dataset.csv`: .csv file containing the final dataset. The data structure is described in the metadata file. It contains 17,454 records distributed amongst 29 countries.

* `Maxwell_MarSOC_dataset_metadata.csv`: .csv file containing the main data file metadata (equivalent to Table 1).

* `data_paper/`: folder containing the list of studies included in the dataset, as well as figures for this data paper (generated from the following R script: `reports/04_data_process/scripts/04_data-paper_data_clean.R`).

* `reports/01_litsearchr/`: folder containing .bib files with references from the original naive search, a .Rmd document describing the litsearchr analysis using nodes to go from the naive search to the final search string, and the .bib files from this final search, which were then imported into sysrev for abstract screening.

* `reports/02_sysrev/`: folder with .csv files exported from sysrev after abstract screening. These files contain the included studies with their various labels.

* `reports/03_data_format/`: folder containing all original data, associated scripts, and exported data.

* `reports/04_data_process/`: folder containing data processing scripts to bind and clean the exported data, as well as a script testing the different models for predicting soil organic carbon from organic matter and finalising the equation using all available data. A script testing and removing outliers is also included.
## Technical Validation

For consistency and to validate the inclusion criteria, the literature search and screening was conducted in a two-part process that included a repeated evaluation by different co-authors. All SOC and SOM values were extracted from numerical sources (tables, supplementary tables, or published datasets). The distribution of all quantitative variables was verified visually by two authors, and the following outliers were flagged: 1) SOC, SOM, and dry bulk density values greater than the sum of 2.2x the interquartile range plus the 95% percent quantile¹⁴⁴ of this dataset combined with the CCRCN dataset, 2) SOM values greater than 100, and 3) SOC values greater than SOM values, which may have been due to incomplete removal of water prior to LOI or due to incomplete removal of carbonates prior to SOC measurements. These values were removed from all calculations but remain in the dataset with an outlier flag in the “Notes” column of the dataset. In total, this represented less than 1% of data removal. These operations and the distribution of all variables (Fig. 3) can be found in the script 02_outliers.R.

## Usage Notes

This data descriptor manuscript and dataset was peer reviewed in 2023 based on a targeted search of the data available at the time. This compilation of 99 published and unpublished tidal marsh soil carbon datasets can be used to answer multiple research questions. First, the MarSOC dataset can be used to support large-scale models of soil carbon in tidal marshes and improve global estimates of carbon stored in these coastal ecosystems. Different drivers of soil carbon at the landscape-scale can be investigated, such as the influence of coastal geomorphology. In addition, our database serves as a baseline for targeted ecosystem design outcomes and restoration of degraded tidal marshes.

## Code availability

The code to format and process data was developed in R computing environment and is freely available in the Zenodo repository²². This is a static copy of the data peer-reviewed in 2023, which is a release from the dynamic Github repository https://github.com/Tania-Maxwell/MarSOC-Dataset. When using data from this dataset please cite this publication, along with the original sources. Both dataset and code are available under a Creative Commons License (CC-BY).

Received: 14 June 2023; Accepted: 11 October 2023;

Published online: 11 November 2023

## References

1. Adam, P. Saltmarshes in a time of change. *Environmental conservation* **29**, 39–61 (2002).

2. Allen, J. R. L. Morphodynamics of Holocene salt marshes: a review sketch from the Atlantic and Southern North Sea coasts of Europe. *Quaternary Science Reviews* **19**, 1155–1231 (2000).

3. Nellemann, C. & Corcoran, E. *Blue carbon: the role of healthy oceans in binding carbon: a rapid response assessment*. (UNEP/Earthprint, 2009).

4. Alongi, D. M. Carbon balance in salt marsh and mangrove ecosystems: A global synthesis. *Journal of Marine Science and Engineering* **8**, 767 (2020).

5. Kelleway, J. J., Saintilan, N., Macreadie, P. I. & Ralph, P. J. Sedimentary Factors are Key Predictors of Carbon Storage in SE Australian Saltmarshes. *Ecosystems* **19**, 865–880 (2016).

6. Hilmi, N. et al. The Role of Blue Carbon in Climate Change Mitigation and Carbon Stock Conservation. *Frontiers in Climate* **3** (2021).

7. Perera, N., Lokupitiya, E., Halwatura, D. & Udagedara, S. Quantification of blue carbon in tropical salt marshes and their role in climate change mitigation. *Science of The Total Environment* **820**, 153313 (2022).

8. Duarte, C. M., Dennison, W. C., Orth, R. J. W. & Carruthers, T. J. B. The Charisma of Coastal Ecosystems: Addressing the Imbalance. *Estuaries and Coasts: J CERF* **31**, 233–238 (2008).

9. Lotze, H. K. et al. Depletion, Degradation, and Recovery Potential of Estuaries and Coastal Seas. *Science* **312**, 1806–1809 (2006).

10. Campbell, A. D., Fatoyinbo, L., Goldberg, L. & Lagomasino, D. Global hotspots of salt marsh change and carbon emissions. *Nature* **612**, 701–706 (2022).

11. Murray, N. J. et al. High-resolution mapping of losses and gains of Earth’s tidal wetlands. *Science* **376**, 744–749 (2022).

12. Worthington, T. A. et al. The distribution of global tidal marshes from earth observation data. Preprint at https://doi.org/10.1101/2023.05.26.542433 (2023).

13. Mcowen, C. J. et al. A global map of saltmarshes. *Biodiversity Data Journal* **5**, e11764 (2017).

14. Saintilan, N. et al. Constraints on the adjustment of tidal marshes to accelerating sea level rise. *Science* **377**, 523–527 (2022).

15. Crosby, S. C. et al. Salt marsh persistence is threatened by predicted sea-level rise. *Estuarine, Coastal and Shelf Science* **181**, 93–99 (2016).

16. Hopkinson, C. S., Cai, W.-J. & Hu, X. Carbon sequestration in wetland dominated coastal systems—a global sink of rapidly diminishing magnitude. *Current Opinion in Environmental Sustainability* **4**, 186–194 (2012).

17. Duarte, C. M., Losada, I. J., Hendriks, I. E., Mazarrasa, I. & Marbà, N. The role of coastal plant communities for climate change mitigation and adaptation. *Nature Climate Change* **3**, 961–968 (2013).

18. Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. *PLOS ONE* **12**, e0169748 (2017).

19. Holmquist, J., Wolfe, J., Lonneman, M., Klinges, D. & Megonigal, J. P. Database: Coastal Carbon Network Data Library. *Smithsonian Environmental Research Center* https://doi.org/10.25573/serc.21565671 (2022).

20. Holmquist, J. R. et al. Accuracy and Precision of Tidal Wetland Soil Carbon Mapping in the Conterminous United States. *Sci Rep* **8**, 9478 (2018).

21. R Core Team. *R: A Language and Environment for Statistical Computing*. (R Foundation for Statistical Computing, 2022).

22. Maxwell, T. L. et al. Database: Tidal Marsh Soil Organic Carbon (MarSOC) Dataset. Zenodo https://doi.org/10.5281/zenodo.8414110 (2023).

23. Craft, C. B., Seneca, E. D. & Broome, S. W. Loss on Ignition and Kjeldahl Digestion for Estimating Organic-Carbon and Total Nitrogen in Estuarine Marsh Soils - Calibration with Dry Combustion. *Estuaries* **14**, 175–179 (1991).
24. Breithaupt, J. L. et al. An Improved Framework for Estimating Organic Carbon Content of Mangrove Soils Using loss-on-ignition and Coastal Environmental Setting. *Wetlands* **43**, 57 (2023).

25. Martins, M. et al. Carbon and Nitrogen Stocks and Burial Rates in Intertidal Vegetated Habitats of a Mesotidal Coastal Lagoon. *Ecosystems* **25**, 372–386 (2022).

26. de los Santos, C. B. et al. Sedimentary Organic Carbon and Nitrogen Sequestration Across a Vertical Gradient on a Temperate Wetland Seascape Including Salt Marshes, Seagrass Meadows and Rhizophytic Macroalgae Beds. *Ecosystems* **26**, 826–842 (2023).

27. Human, L. R. D., Els, J., Wasserman, J. & Adams, J. B. Blue carbon and nutrient stocks in salt marsh and seagrass from an urban African estuary. *Science of The Total Environment* **842**, 156955 (2022).

28. Ward, M. A. et al. Blue carbon stocks and exchanges along the California coast. *Biogeosciences* **18**, 4717–4732 (2021).

29. Smeaton, C. et al. Using citizen science to estimate surficial soil Blue Carbon stocks in Great British saltmarshes. *Frontiers in Marine Science* **9**, 959459 (2022).

30. Ouyang, X. & Lee, S. Y. Improved estimates on global carbon stock and carbon pools in tidal wetlands. *Nature Communications* **11**, 317 (2020).

31. Grames, E. M., Stillman, A. N., Tingley, M. W. & Elphick, C. S. An automated approach to identifying search terms for systematic reviews using keyword co-occurrence networks. *Methods in Ecology and Evolution* **10**, 1645–1654 (2019).

32. Adame, M. F. et al. Mangroves in arid regions: Ecology, threats, and opportunities. *Estuarine, Coastal and Shelf Science* **248**, 106796 (2021).

33. Adame, M. F. et al. Carbon and Nitrogen Sequestration of Melaleuca Floodplain Wetlands in Tropical Australia. *Ecosystems* **23**, 454–466 (2020).

34. Adame, M. F. et al. Carbon stocks and soil sequestration rates of tropical riverine wetlands. *Biogeosciences* **12**, 3805–3818 (2015).

35. Adame, M. F. et al. Carbon Stocks of Tropical Coastal Wetlands within the Karstic Landscape of the Mexican Caribbean. *PLOS ONE* **8**, e56569 (2013).

36. Anisfeld, S. C., Tobin, M. J. & Benoit, G. Sedimentation Rates in Flow-Restricted and Restored Salt Marshes in Long Island Sound. *Estuaries* **22**, 231 (1999).

37. Beasy, K. & Ellison, J. Comparison of Three Methods for the Quantification of Sediment Organic Carbon in Salt Marshes of the Rubicon Estuary, Tasmania, Australia. *International Journal of Biology* **5**, p1 (2013).

38. Bryant, J. C. & Chabreck, R. H. Effects of Impoundment on Vertical Accretion of Coastal Marsh. *Estuaries* **21**, 416 (1998).

39. Bulmer, R. H. et al. Blue Carbon Stocks and Cross-Habitat Subsidies. *Frontiers in Marine Science* **7**, 380 (2020).

40. Bunzel, D. et al. (Table A1) Organic carbon measurements for sediment sequences TB13-1, GeoHH-GIE, GeoHH-FK and GeoHH-KWK. *In supplement to: Bunzel, D et al. (2020): Integrated stratigraphy of foreland salt-marsh sediments of the south-eastern North Sea region. Newsletters on Stratigraphy*, 10.1127/nos/2020/0540. PANGAEA https://doi.pangaea.de/10.1594/PANGAEA.905218 (2019).

41. Burden, A., Garbutt, A., Hughes, S., Oakley, S. & Tempest, J. A. Soil biochemical measurements from salt marshes of different ages on the Essex coast, UK (2011). NERC *Environmental Information Data Centre* https://doi.org/10.5285/0b1faab4-3539-457f-9169-b0b1abd59bc2 (2018).

42. Burke, S. A., Manahan, J., Eichelmann, E. & Cott, G. M. Dublin's saltmarshes contain climate-relevant carbon pools. *Frontiers in Marine Science* **9**, 976457 (2022).

43. Cahoon, D. R., Lynch, J. C. & Powell, A. N. Marsh Vertical Accretion in a Southern California Estuary, USA. *Estuarine, Coastal and Shelf Science* **43**, 19–32 (1996).

44. Camacho, S., Moura, D., Connor, S., Boski, T. & Gomes, A. Geochemical characteristics of sediments along the margins of an atlantic-mediterranean estuary (the Guadiana, Southeast Portugal): spatial and seasonal variations. *RGCI* **14**, 129–148 (2014).

45. Chmura, G. L. & Hung, G. A. Controls on salt marsh accretion: A test in salt marshes of Eastern Canada. *Estuaries* **27**, 70–81 (2004).

46. Connor, R. F., Chmura, G. L. & Beecher, C. B. Carbon accumulation in bay of fundy salt marshes: Implications for restoration of reclaimed marshes. *Global Biogeochem. Cycles* **15**, 943–954 (2001).

47. Conrad, S. et al. Does Regional Development Influence Sedimentary Blue Carbon Stocks? A Case Study From Three Australian Estuaries. *Frontiers in Marine Science* **5**, 518 (2019).

48. Cott, G. M., Chapman, D. V. & Jansen, M. A. K. Salt Marshes on Substrate Enriched in Organic Matter: The Case of Ombrogenic Atlantic Salt Marshes. *Estuaries and Coasts* **36**, 595–609 (2013).

49. Craft, C. B., Seneca, E. D. & Broome, S. W. Vertical Accretion in Microtidal Regularly and Irregularly Flooded Estuarine Marshes. *Estuarine, Coastal and Shelf Science* **37**, 371–386 (1993).

50. Cuellar-Martinez, T., Ruiz-Fernández, A. C., Sanchez-Cabeza, J.-A., Pérez-Bernal, L.-H. & Sandoval-Gil, J. Relevance of carbon burial and storage in two contrasting blue carbon ecosystems of a north-east Pacific coastal lagoon. *Science of The Total Environment* **675**, 581–593 (2019).

51. Cuellar-Martinez, T. et al. Temporal records of organic carbon stocks and burial rates in Mexican blue carbon coastal ecosystems throughout the Anthropocene. *Global and Planetary Change* **192**, 103215 (2020).

52. de los Santos, C. B. et al. Vertical intertidal variation of organic matter stocks and patterns of sediment deposition in a mesotidal coastal wetland. *Estuarine, Coastal and Shelf Science* **272**, 107896 (2022).

53. Ewers Lewis, C. J., Carnell, P. E., Sanderman, J., Baldock, J. A. & Macreadie, P. I. Variability and Vulnerability of Coastal 'Blue Carbon' Stocks: A Case Study from Southeast Australia. *Ecosystems* **21**, 263–279 (2018).

54. Ford, H., Garbutt, A. & Skov, M. Coastal Biodiversity and Ecosystem Service Sustainability (CBESS) soil organic matter content from three soil depths on saltmarsh sites at Morecambe Bay and Essex. NERC *Environmental Information Data Centre* https://doi.org/10.5285/90457ba1-f291-4158-82dc-425d7cbb1ac5 (2016).

55. Gallagher, J. B., Prahalad, V. & Aalders, J. Inorganic and Black Carbon Hotspots Constrain Blue Carbon Mitigation Services Across Tropical Seagrass and Temperate Tidal Marshes. *Wetlands* **41**, 65 (2021).

56. González-Alcaraz, M. N., Aránega, B., Conesa, H. M., Delgado, M. J. & Álvarez-Rogel, J. Contribution of soil properties to the assessment of a seawater irrigation programme as a management strategy for abandoned solar saltworks. *CATENA* **126**, 189–200 (2015).

57. González-Alcaraz, M. N. et al. Storage of organic carbon, nitrogen and phosphorus in the soil–plant system of *Phragmites australis* stands from a eutrophicated Mediterranean salt marsh. *Geoderma* **185–186**, 61–72 (2012).

58. Gorham, C., Lavery, P., Kelleway, J. J., Salinas, C. & Serrano, O. Soil Carbon Stocks Vary Across Geomorphic Settings in Australian Temperate Tidal Marsh Ecosystems. *Ecosystems* **24**, 319–334 (2021).

59. Graversen, A. E. L., Banta, G. T., Masque, P. & Krause-Jensen, D. Carbon sequestration is not inhibited by livestock grazing in Danish salt marshes. *Limnology and Oceanography* **67**, S19–S35 (2022).

60. Grey, A. et al. Geochemical mapping of a blue carbon zone: Investigation of the influence of riverine input on tidal affected zones in Bull Island. *Regional Studies in Marine Science* **45**, 101834 (2021).

61. Gu, J., vanArdenne, L. & Chmura, G. Data for: Invasive *Phragmites* increases blue carbon stock and soil volume in a St. Lawrence estuary marsh. *Mendeley Data* https://doi.org/10.17632/2dg3spxsbh.2 (2020).

62. Guerra, R., Simoncelli, S. & Pasteris, A. Carbon accumulation and storage in a temperate coastal lagoon under the influence of recent climate change (Northwestern Adriatic Sea). *Regional Studies in Marine Science* **53**, 102439 (2022).
63. Hansen, K. et al. Factors influencing the organic carbon pools in tidal marsh soils of the Elbe estuary (Germany). *Journal of Soils and Sediments* **17**, 47–60 (2017).

64. Kauffman, J. B. et al. SWAMP Dataset-Mangrove soil carbon-marisma-2017. CIFOR https://doi.org/10.17528/CIFOR/DATA.00244 (2020).

65. Kohfeld, K. E., S.M. G.C. et al. (2022): Salt marsh soil carbon content, loss on ignition, dry bulk density, carbon stocks, lead-210 and carbon accumulation rates, for Clayoquot Sound, British Columbia, Canada. PANGAEA, 10.1594/PANGAEA.947825. PANGAEA https://doi.org/10.1594/PANGAEA.947824 (2022).

66. Kumar, M., Boski, T., González-Vila, F. J., Jiménez-Morillo, N. T. & González-Pérez, J. A. Characteristics of organic matter sources from Guadiana Estuary salt marsh sediments (SW Iberian Peninsula). *Continental Shelf Research* **197**, 104076 (2020).

67. Li, Y. et al. Plant biomass and soil organic carbon are main factors influencing dry-season ecosystem carbon rates in the coastal zone of the Yellow River Delta. *PLOS ONE* **14**, e0210768 (2019).

68. Markewich, H. W. et al. Detailed descriptions for sampling, sample preparation and analyses of cores from St. Bernard Parish, Louisiana. *Open-File Report* https://pubs.er.usgs.gov/publication/ofr98429 10.3133/ofr98429 (1998).

69. Mazarrasa, I. et al. Drivers of variability in Blue Carbon stocks and burial rates across European estuarine habitats. *Science of The Total Environment* **886**, 163957 (2023).

70. Miller, L. C., Smeaton, C., Yang, H. & Austin, W. E. N. Physical and geochemical properties of Scottish saltmarsh soils. *Marine Scotland* https://doi.org/10.7489/12422-1 (2022).

71. Nogueira, J. et al. Geochemistry of coastal wetland in the Northern Saharan environment through lacustrine sediment core THI. In: Nogueira, J. et al. (2020): Coastal wetland responses to a century of Climate Change in Northern Saharan Environment through lacustrine sediment core geochemistry. PANGAEA, 10.1594/PANGAEA.925346. PANGAEA https://doi.org/10.1594/ PANGAEA.925024 (2020).

72. Orson, R. A., Warren, R. S. & Niering, W. A. Interpreting Sea Level Rise and Rates of Vertical Marsh Accretion in a Southern New England Tidal Salt Marsh. *Estuarine, Coastal and Shelf Science* **47**, 419–429 (1998).

73. Patrick, W. H. & DeLaune, R. D. Subsidence. accretion. and sea level rise in south San Francisco Bay marshes. *Limnol. Oceanogr.* **35**, 1389–1395 (1990).

74. Pollmann, T., Böttcher, M. E. & Giani, L. Young soils of a temperate barrier island under the impact of formation and resetting by tides and wind. *CATENA* **202**, 105275 (2021).

75. Raw, J. et al. Salt marsh elevation and responses to future sea-level rise in the Knysna Estuary, South Africa. *African Journal of Aquatic Science* **45**, 49–64 (2020).

76. Roman, C. T., Peck, J. A., Allen, J. R., King, J. W. & Appleby, P. G. Accretion of a New England (USA) Salt Marsh in Response to Inlet Migration, Storms, and Sea-level Rise. *Estuarine, Coastal and Shelf Science* **45**, 717–727 (1997).

77. Ruranska, P., Ladd, C. J. T., Smeaton, C., Skov, M. W. & Austin, W. E. N. Dry bulk density, loss on ignition and organic carbon content of surficial soils from English and Welsh salt marshes 2019. NERC Environmental Information Data Centre https://doi. org/10.5285/e5554b83-910f-4030-8f4e-81967dc7047c (2022).

78. Ruranska, P. et al. Dry bulk density, loss on ignition and organic carbon content of surficial soils from Scottish salt marshes, 2018- 2019. NERC Environmental Information Data Centre https://doi.org/10.5285/81a1301f-e5e2-44f9-afe0-0ea5bb08010f (2020).

79. Russell, S. K., Gillanders, B. M., Detmar, S., Fotheringham, D. & Jones, A. R. Determining Environmental Drivers of Fine-Scale Variability in Blue Carbon Soil Stocks. *Estuaries and Coasts* (2023).

80. Rybczyk, J. M. & Cahoon, D. R. Estimating the potential for submergence for two wetlands in the Mississippi River Delta. *Estuaries* **25**, 985–998 (2002).

81. Sammul, M., Kauer, K. & Köster, T. Biomass accumulation during reed encroachment reduces efficiency of restoration of Baltic coastal grasslands. *Applied Vegetation Science* **15**, 219–230 (2012).

82. Santos, R. et al. Superficial sedimentary stocks and sources of carbon and nitrogen in coastal vegetated assemblages along a flow gradient. *Scientific Reports* **9**, 610 (2019).

83. Schile, L., Kauffman, J. B., Megonigal, J. P., Fourqurean, J. & Crooks, S. Abu Dhabi Blue Carbon project. Dryad https://doi. org/10.15146/R3K59Z (2016).

84. Serrano, O. et al. Australian vegetated coastal ecosystems as global hotspots for climate change mitigation. *Nature Communications* **10**, 4313 (2019).

85. Shamrikova, E. V., Deneva, S. V. & Kubik, O. S. Spatial Patterns of Carbon and Nitrogen in Soils of the Barents Sea Coastal Area (Khaypudyryskaya Bay). *Eurasian Soil Science* **52**, 507–517 (2019).

86. Siewert, M. B., Hugelius, G., Heim, B. & Faucherre, S. Landscape controls and vertical variability of soil organic carbon storage in permafrost-affected soils of the Lena River Delta. *CATENA* **147**, 725–741 (2016).

87. Smeaton, C. et al. Physical and geochemical properties of saltmarsh soils from wide diameter gouge cores in Essex, UK, collected in 2019. NERC Environmental Information Data Centre https://doi.org/10.5285/fa3f4087-528e-4c5d-90d8-6bb4675d6317 (2023).

88. Smeaton, C. et al. Physical and geochemical properties of saltmarsh soils from narrow diameter gouge cores in UK saltmarshes collected between 2018 and 2021. NERC Environmental Information Data Centre https://doi.org/10.5285/d301c5f5-77f5-41ba- 934e-a80e1293d4cd (2022).

89. Smeaton, C. et al. Physical and geochemical properties of saltmarsh soils from wide diameter gouge cores in UK saltmarshes collected between 2018 and 2021. NERC Environmental Information Data Centre https://doi.org/10.5285/279558cd-20fb-4f19- 8077-4400817a4482 (2022).

90. Smeaton, C., Rees-Hughes, L., Barlow, N. L. M. & Austin, W. E. N. Sedimentological and organic carbon data from the Kyle of Tongue saltmarsh, Scotland, 2018. NERC Environmental Information Data Centre https://doi.org/10.5285/b57ef444-54d4-47f9- 8cbf-3cfef1182b55 (2021).

91. Van de Broek, M. et al. 2018, GCB, Supplementary data. *Mendeley Data* https://doi.org/10.17632/2nnv9bw3hh.2 (2018).

92. Vitti, S., Pellegrini, E., Casolo, V., Trotta, G. & Boscutti, F. Contrasting responses of native and alien plant species to soil properties shed new light on the invasion of dune systems. *Journal of Plant Ecology* **13**, 667–675 (2020).

93. Ward, R. D. Carbon sequestration and storage in Norwegian Arctic coastal wetlands: Impacts of climate change. *Science of The Total Environment* **748**, 141343 (2020).

94. Wollenberg, J. T., Ollerhead, J. & Chmura, G. L. Rapid carbon accumulation following managed realignment on the Bay of Fundy. *PLOS ONE* **13**, e0193930 (2018).

95. Xia, S. et al. Storage, patterns and influencing factors for soil organic carbon in coastal wetlands of China. *Global Change Biology* **28**, 6065–6085 (2022).

96. Yando, E. S. et al. Salt marsh-mangrove ecotones: using structural gradients to investigate the effects of woody plant encroachment on plant-soil interactions and ecosystem carbon pools. *Journal of Ecology* **104**, 1020–1031 (2016).

97. Yuan, H.-W. et al. Sources and distribution of sedimentary organic matter along the Andong salt marsh, Hangzhou Bay. *Journal of Marine Systems* **174**, 78–88 (2017).

98. Cusack, M. et al. Organic carbon sequestration and storage in vegetated coastal habitats along the western coast of the Arabian Gulf. *Environmental Research Letters* **13**, 074007 (2018).
99. Day, J. W. et al. Vegetation death and rapid loss of surface elevation in two contrasting Mississippi delta salt marshes: The role of sedimentation, autocompression and sea-level rise. *Ecological Engineering* **37**, 229–240 (2011).

100. Ferronato, C. et al. Effect of waterlogging on soil biochemical properties and organic matter quality in different salt marsh systems. *Geoderma* **338**, 302–312 (2019).

101. Fuchs, M. et al. Soil carbon and nitrogen stocks in Arctic river deltas: New data for three Northwest Alaskan deltas. *5th European Conference on Permafrost* (2018).

102. Gailis, M., Kohfeld, K. E., Pellatt, M. G. & Carlson, D. Quantifying blue carbon for the largest salt marsh in southern British Columbia: implications for regional coastal management. *Coastal Engineering Journal* **63**, 275–309 (2021).

103. Gispert, M. et al. Appraising soil carbon storage potential under perennial and annual Chenopodiaceae in salt marsh of NE Spain. *Estuarine, Coastal and Shelf Science* **252**, 107240 (2021).

104. Gispert, M., Phang, C. & Carrasco-Barea, L. The role of soil as a carbon sink in coastal salt-marsh and agropastoral systems at La Pletera, NE Spain. *CATENA* **185**, 104331 (2020).

105. Hatton, R. S., DeLaune, R. D. & Patrick, W. H. Sedimentation, accretion, and subsidence in marshes of Barataria Basin, Louisiana: Marsh accretion, subsidence. *Limnology and Oceanography* **28**, 494–502 (1983).

106. Hayes, M. A. et al. Dynamics of sediment carbon stocks across intertidal wetland habitats of Moreton Bay, Australia. *Global Change Biology* **23**, 4222–4234 (2017).

107. Loomis, M. J. & Craft, C. B. Carbon Sequestration and Nutrient (Nitrogen, Phosphorus) Accumulation in River-Dominated Tidal Marshes, Georgia, USA. *Soil Science Society of America Journal* **74**, 1028–1036 (2010).

108. Macreadie, P. I. et al. Carbon sequestration by Australian tidal marshes. *Scientific Reports* **7**, 44071 (2017).

109. Morris, J. T. & Jensen, A. The carbon balance of grazed and non-grazed *Spartina anglica* saltmarshes at Skallingen, Denmark. *Journal of Ecology* **86**, 229–242 (1998).

110. Rathore, A. P., Chaudhary, D. R. & Jha, B. Biomass production, nutrient cycling, and carbon fixation by Salicornia brachiata Roxb: A promising halophyte for coastal saline soil rehabilitation. *International Journal of Phytoremediation* **18**, 801–811 (2016).

111. Sousa, A. I., Lillebo, A. I., Pardal, M. A. & Caçador, I. The influence of *Spartina maritima* on carbon retention capacity in salt marshes from warm-temperate estuaries. *Marine Pollution Bulletin* **61**, 215–223 (2010).

112. Sun, H. et al. Soil organic carbon stabilization mechanisms in a subtropical mangrove and salt marsh ecosystems. *Science of The Total Environment* **673**, 502–510 (2019).

113. Voltz, B. et al. A multiproxy study of intertidal surface sediments from two macrotidal estuarine systems (Canche, Authie) in northern France: Insights into environmental processes. *Continental Shelf Research* **230**, 104554 (2021).

114. Yang, R.-M. & Chen, L.-M. *Spartina alterniflora* invasion alters soil bulk density in coastal wetlands of China. *Land Degradation & Development* **32**, 1993–1999 (2021).

115. Ye, S. et al. Carbon Sequestration and Soil Accretion in Coastal Wetland Communities of the Yellow River Delta and Liaohe Delta, China. *Estuaries and Coasts* **38**, 1885–1897 (2015).

116. Yu, O. T. & Chmura, G. L. Soil carbon may be maintained under grazing in a St Lawrence Estuary tidal marsh. *Environmental Conservation* **36**, 312–320 (2009).

117. Yuan, J. et al. Data from: *Spartina alterniflora* invasion drastically increases methane production potential by shifting methanogenesis from hydrogenotrophic to methylotrophic pathway in a coastal marsh. *Dryad* https://doi.org/10.5061/DRYAD.6f60V3Q (2019).

118. Zubrzycki, S., Kutzbach, L., Grosse, G., Desyatkin, A. & Pfeiffer, E.-M. Organic carbon and total nitrogen stocks in soils of the Lena River Delta. *Biogeosciences* **10**, 3507–3524 (2013).

119. Fu, C. et al. Stocks and losses of soil organic carbon from Chinese vegetated coastal habitats. *Global Change Biology* **27**, 202–214 (2021).

120. Hatje, V. et al. Vegetated coastal ecosystems in the Southwestern Atlantic Ocean are an unexploited opportunity for climate change mitigation. *Communications Earth & Environment* **4**, 1–10 (2023).

121. He, Q. et al. Consumer regulation of the carbon cycle in coastal wetland ecosystems. *Philosophical Transactions of the Royal Society B: Biological Sciences* **375**, 20190451 (2020).

122. Hu, M., Sardans, J., Yang, X., Peñuelas, J. & Tong, C. Patterns and environmental drivers of greenhouse gas fluxes in the coastal wetlands of China: A systematic review and synthesis. *Environmental Research* **186**, 109576 (2020).

123. Wails, C. N. et al. Assessing changes to ecosystem structure and function following invasion by *Spartina alterniflora* and *Phragmites australis*: a meta-analysis. *Biological Invasions* **23**, 2695–2709 (2021).

124. Breithaupt, J. L. et al. Dataset: Increasing rates of carbon burial in southwest Florida coastal wetlands. https://doi.org/10.25573/serc.9894266.v1 (2020).

125. O'keefe Suttles, J. A., Eagle, M. J., Mann, A. C. & Kroeger, K. D. Collection, analysis, and age-dating of sediment cores from mangrove and salt marsh ecosystems in Tampa Bay, Florida, 2015. https://doi.org/10.5066/P9QB17H2 (2021).

126. Piazza, S. C. et al. Geomorphic and ecological effects of Hurricanes Katrina and Rita on coastal Louisiana marsh communities. i-126, https://doi.org/10.3133/ofr20111094 (2011).

127. Poppe, K. L. & Rybczyk, J. M. Dataset: Sediment carbon stocks and sequestration rates in the Pacific Northwest region of Washington, USA. 1095783 Bytes. https://doi.org/10.25573/DATA.10005248 (2019).

128. White, J. R., Sapkota, Y., Chambers, L. G., Cook, R. L. & Xue, Z. Biogeochemical properties of sediment cores from Barataria Basin, Louisiana, 2018 and 2019. https://doi.org/10.26008/1912/BCO-DMO.833824.1 (2020).

129. Abbott, K. M., Elsey-Quirk, T. & DeLaune, R. D. Factors influencing blue carbon accumulation across a 32-year chronosequence of created coastal marshes. *Ecosphere* **10**, e02828 (2019).

130. Arias-Ortiz, A. et al. Tidal and Nontidal Marsh Restoration: A Trade-Off Between Carbon Sequestration, Methane Emissions, and Soil Accretion. *JGR Biogeosciences* **126**, e2021JG006573 (2021).

131. Arriola, J. M. & Cable, J. E. Variations in carbon burial and sediment accretion along a tidal creek in a Florida salt marsh. *Limnology and Oceanography* **62**, 515–528 (2017).

132. Callaway, J. C., Borgnis, E. L., Turner, R. E. & Milan, C. S. Carbon Sequestration and Sediment Accretion in San Francisco Bay Tidal Wetlands. *Estuaries and Coasts* **35**, 1163–1181 (2012).

133. Carlin, J. et al. Dataset: Sedimentary organic carbon measurements in a restored coastal wetland in San Francisco Bay, CA, USA. *Smithsonian Environmental Research Center* https://doi.org/10.25573/SERC.16416684.V2 (2021).

134. Doughty, C. L. et al. Mangrove Range Expansion Rapidly Increases Coastal Wetland Carbon Storage. *Estuaries and Coasts* **39**, 385–396 (2016).

135. Hill, T. D. & Anisfeld, S. C. Coastal wetland response to sea level rise in Connecticut and New York. *Estuarine, Coastal and Shelf Science* **163**, 185–193 (2015).

136. McClellan, S. A., Elsey-Quirk, T., Laws, E. A. & DeLaune, R. D. Root-zone carbon and nitrogen pools across two chronosequences of coastal marshes formed using different restoration techniques: Dredge sediment versus river sediment diversion. *Ecological Engineering* **169**, 106326 (2021).

137. McTigue, N. et al. Sea Level Rise Explains Changing Carbon Accumulation Rates in a Salt Marsh Over the Past Two Millennia. *JGR Biogeosciences* **124**, 2945–2957 (2019).
138. Peck, E. K., Wheatcroft, R. A. & Brophy, L. S. Controls on Sediment Accretion and Blue Carbon Burial in Tidal Saline Wetlands: Insights From the Oregon Coast, USA. *JGR Biogeosciences* **125**, e2019JG005464 (2020).

139. Poppe, K. L. & Rybczyk, J. M. Tidal marsh restoration enhances sediment accretion and carbon accumulation in the Stillaguamish River estuary, Washington. *PLOS ONE* **16**, e0257244 (2021).

140. St. Laurent, K. A., Hribar, D. J., Carlson, A. J., Crawford, C. M. & Siok, D. Assessing coastal carbon variability in two Delaware tidal marshes. *J Coast Conserv* **24**, 65 (2020).

141. Unger, V., Elsey-Quirk, T., Sommerfield, C. & Velinsky, D. Stability of organic carbon accumulating in Spartina alterniflora-dominated salt marshes of the Mid-Atlantic U.S. *Estuarine, Coastal and Shelf Science* **182**, 179-189 (2016).

142. Howard, J., Hoyt, S., Isensee, K., Telszewski, M. & Pidgeon, E. *Coastal blue carbon: methods for assessing carbon stocks and emissions factors in mangroves, tidal salt marshes, and seagrasses*. https://gspace.cgiar.org/handle/10568/95127 (2014).

143. Pribyl, D. W. A critical review of the conventional SOC to SOM conversion factor. *Geoderma* **156**, 75–83 (2010).

144. Hoaglin, D. C. & Iglewicz, B. Fine-tuning some resistant rules for outlier labeling. *Journal of the American Statistical Association* **82**, 1147–1149 (1987).

## Acknowledgements

We would like to thank everyone who contributed to the collection of the soil cores and their lab analyses, without whom this dataset compilation would not be possible. The research was funded by The Nature Conservancy through the Bezos Earth Fund and other donor support. T.J.B., I.M., and J.M.N., were supported by the LIFE ADAPTA BLUES project Ref. LIFE18 CCA/ES/001160. A.S.R. and R.R.T. were supported by the U.S. Army Engineering, Research and Development Center (ACTIONS project, W912HZ2020070). C.B.d.I.S. and R.S. acknowledge Portuguese national funds from FCT - Foundation for Science and Technology through projects UIDB/04326/2020, UIDP/04326/2020, LA/P/0101/2020, and 2020.03825.CEECIND. O.S. was supported by I + D + i projects RYC2019-027073-I and PIE HOLOCENO 20213AT014 funded by MCIN/AEI/10.13039/501100011033 and FEDER. J.M.N. had the support of national funds through Fundação para a Ciência e Tecnologia, I.P. (FCT), under the projects UIDB/04292/2020, UIDP/04292/2020, granted to MARE, and LA/P/0069/2020, granted to the Associate Laboratory ARNET. J.A.R and M.N.G.A. were supported by the State Research Agency of Spain (AEI; CGL2007-64915), the Mancomunidad de los Canales del Taibilla (MCT), and the Science and Technology Agency of the Murcia Region (Seneca Foundation; 00593/PI/04 & 08739/PI/08). M.N.G.A. holds a Ramón y Cajal contract from the Spanish Ministry of Science and Innovation (RYC2020-029322-I). R.H.B. and C.J.L. were supported by New Zealand Ministry for Business, Innovation and Employment Contract #C01X2109. J.B.A. and J.L.R were supported by the South African Department of Science and Innovation (DSI)—National Research Foundation (NRF) Research Chair in Shallow Water Ecosystems (UID: 84375), and the Nelson Mandela University. L.G. and T.P. were supported by the German Research Foundation (DFG project number: GI 171/25-1). M.E.B.’s work on carbon in tidal areas is supported by the COOLSTYLE/CARBOSTORE project. W.E.N.A. and C.S. would like to acknowledge funding support from the Scottish Government and UK Natural Environment Research Council C-SIDE project (grant NE/R010846/1). J.F.P. acknowledges the financial support provided by the Welsh Government and Higher Education Funding Council for Wales through the Sêr Cymru National Research Network for Low Carbon, Energy and Environment; as well as the Spanish Ministry of Science and Innovation (project PID2020-113745RB-I00) and FEDER. C.T.C. acknowledges funding support from Xunta de Galicia (GRC project IN607A 2021-06). A.E.L.G, C.L.D and D.K.J. acknowledge funding from the Velux foundation (#28421, Blå Skove – Havets Skove som kulstofdræn). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

## Author contributions

T.L.M. conceptualization, data curation, methodology, writing-original draft. A.S.R data curation, validation, writing - review & editing. [M.F.A., J.B.A., J.Á., W.E.A., K.B., F.B., M.E.B., T.J.B., R.H.B., A.B., S.A.B., S.C., D.C., G.L.C., M.C., G.M.C., C.C., J.D., C.B.d.I.S., W.D., E.D.P., G.D., J.C.E., C.J.E.L., L.G., M.G., J.A.G., S.G., M.N.G., C.G., A.E.G., A.G., R..G., Q.H., J.R.H., A.R.J., J.A.J., B.P.K., K.K., D.K., A.L., P.S.La., E.A.L., C.L., P.S.Lo., C.E.L., C.J.L., P.I.M., I.M., J.P.M., J.M.N., J.N., M.J.O., J.F.P., N.P., T.P., J.L.R., M.R., A.C.R., S.K.R., J.M.R., M.Sa., C.Sa., R.S., O.S., M.Si., C.Sm., Z.S., C.T., R.R.T., M.V.d.B., S.V., L.V.A., B.V., C.W., R.D.W., M.W., J.W., R.Y., S.Z.] investigation, data curation, writing - review & editing. E.L. project administration, writing - review & editing. L.S.S. project administration, writing - review & editing. M.D.S. project administration, supervising, writing - review & editing. T.A.W. project administration, supervising, writing - review & editing.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41597-023-02633-x.

**Correspondence** and requests for materials should be addressed to T.L.M. or A.S.R.

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Tania L. Maxwell<fnref n="1" />,²✉, André S. Rovai³,⁴✉, Maria Fernanda Adame⁵, Janine B. Adams⁶, José Álvarez-Rogel⁷, William E. N. Austin⁸,⁹, Kim Beasy¹⁰, Francesco Boscutti¹¹, Michael E. Böttcher¹²,¹³,¹⁴, Tjeerd J. Bouma¹⁵,¹⁶,¹⁷, Richard H. Bulmer¹⁸, Annette Burden¹⁹, Shannon A. Burke²⁰, Saritta Camacho²¹, Doongar R. Chaudhary²², Gail L. Chmura²³, Margareth Copertino²⁴,²⁵, Grace M. Cott²⁰, Christopher Craft²⁶,²⁷, John Day³, Carmen B. de los Santos²⁸, Lionel Denis²⁹, Weixin Ding³⁰, Joanna C. Ellison³¹, Carolyn J. Ewers Lewis³², Luise Giani³³, Maria Gispert³⁴, Swanne Gontharet³⁵, José A. González-Pérez⁴, M. Nazaret González-Alcaraz⁷, Connor Gorham³⁷, Anna Elizabeth L. Graversen³⁸, Anthony Grey³⁹, Roberta Guerra⁴⁰, Qiang He⁴¹, James R. Holmquist⁴², Alice R. Jones⁴³,⁴⁴, José A. Juanes⁴⁵, Brian P. Kelleher³⁹, Karen E. Kohfeld⁴⁶,⁴⁷, Dorte Krause-Jensen³⁸, Anna Lafratta³⁷, Paul S. Lavery³⁷,⁴⁸, Edward A. Laws⁴⁹, Carmen Leiva-Dueñas³⁸, Pei Sun Loh⁵⁰, Catherine E. Lovelock⁵¹, Carolyn J. Lundquist⁵²,⁵³, Peter I. Macreadie⁵⁴, Inés Mazarrasa⁴⁵, J. Patrick Megonigal⁴², Joao M. Neto⁵⁵, Juliana Nogueira⁵⁶,⁵⁷, Michael J. Osland⁵⁸, Jordi F. Pagès⁴⁸, Nipuni Perera⁵⁹, Eva-Maria Pfeiffer⁶⁰, Thomas Pollmann³, Jacqueline L. Raw⁶, María Recio⁴⁵, Ana Carolina Ruiz-Fernández⁶¹, Sophie K. Russell⁴³,⁴⁴, John M. Rybczyk⁶², Marek Sammul⁶³, Christian Sanders⁶⁴, Rui Santos²⁸, Oscar Serrano³⁷,⁴⁸, Matthias Siewert⁶⁵, Craig Smeaton⁸, 0Zhaoliang Song⁶⁶, Carmen Trasar-Cepeda⁶⁷, Robert R. Twilley³, Marijn Van de Broek⁶⁸, Stefano Vitti¹¹,⁶⁹, Livia Vittori Antisari⁷⁰, Baptiste Voltz²⁹, Christy N. Wails⁷¹, Raymond D. Ward⁷²,⁷³, Melissa Ward⁷⁴,⁷⁵, Jaxine Wolfe⁴², Renmin Yang⁶⁶, Sebastian Zubrzycki⁷⁶, Emily Landis⁷⁷, Lindsey Smart⁷⁷,⁷⁸, Mark Spalding<fnref n="1" />,⁷⁹ & Thomas A. Worthington<fnref n="1" />

<footnote n="1">¹Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge, UK. ²Biodiversity and Natural Resources Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria. ³Department of Oceanography and Coastal Sciences, College of the Coast and Environment, Louisiana State University, Baton Rouge, LA, 70803, USA. ⁴US Army Engineer Research and Development Center, Vicksburg, MS, 39183, USA. ⁵Australian Rivers Institute, Centre for Marine and Coastal Research, Griffith University, Nathan, QLD, 4117, Australia. ⁶DSI-NRF Research Chair in Shallow Water Ecosystems, Institute for Coastal Marine Research, Nelson Mandela University, PO Box 77000, Gqeberha, 6031, South Africa. ⁷Department of Agricultural Engineering of the E.T.S.I.A. and Soil Ecology and Biotechnology Unit of the I.B.V., Technical University of Cartagena, 30203, Cartagena, Spain. ⁸School of Geography and Sustainable Development, University of St Andrews, KY16 9AL, St Andrews, UK. ⁹Scottish Association for Marine Science, Oban, Argyll, PA37 1QA, UK. ¹⁰College of Arts, Law and Education, University of Tasmania, Hobart, Tasmania, 7005, Australia. ¹¹Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, via delle Scienze 206, Udine, 33100, Italy. ¹²Geochemistry and Isotope Biogeochemistry Group, Department of Marine Geology, Leibniz Institute for Baltic Sea Research (IOW), Seestrasse 15, D-18119, Warnemünde, Germany. ¹³Marine Geochemistry, University of Greifswald, Friedrich-Ludwig-Jahn Str. 17a, D-17489, Greifswald, Germany. ¹⁴Interdisciplinary Faculty, University of Rostock, Albert-Einstein-Strase 21, D-18059, Rostock, Germany. ¹⁵Department of Estuarine and Delta Systems, Royal Netherlands Institute for Sea Research (NIOZ), 4401 NT, Yerseke, The Netherlands. ¹⁶Faculty of Geosciences, Department of Physical Geography, Utrecht University, 3508 TC, Utrecht, The Netherlands. ¹⁷Delta Academy Applied Research Centre, HZ University of Applied Sciences, Postbus 364, 4380 AJ, Vlissingen, The Netherlands. ¹⁸Tidal Research, Auckland, New Zealand. ¹⁹UKCEH Bangor, Bangor, UK. ²⁰School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, D04 V1W8, Dublin, Ireland. ²¹CIMA - Centro de Investigação Marinha e Ambiental, Faro, Portugal. ²²CSIR-CSMCRI, G. B. Marg, Bhavnagar, Gujarat, 364002, India. ²³McGill University Department of Geography, Montreal, Canada. ²⁴Institute of Oceanography - Federal University of Rio Grande, Rio Grande, Brazil. ²⁵Brazilian Network for Global Change Studies - Rede CLIMA, Rio Grande, Brazil. ²⁶O'Neill School of Public and Environmental Affairs, Indiana University, Bloomington, USA. ²⁷University of Georgia Marine Institute, Sapelo Island, Georgia, USA. ²⁸Centre of Marine Sciences of Algarve, University of Algarve, Faro, Portugal. ²⁹Univ. Littoral Côte d'Opale, CNRS, Univ. Lille, UMR 8187 - LOG - Laboratoire d'Océanologie et de Géosciences, 32, Avenue Foch, F-62930, Wimereux, France. ³⁰Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China. ³¹School of Geography, Planning Spatial Sciences, University of Tasmania, Launceston, Tasmania, 7250, Australia. ³²Department of Environmental Sciences, University of Virginia, 221 McCormick Road, Charlottesville, Virginia, 22903, USA. ³³Institute for Biology and Environmental Sciences, Carl von Ossietzky University of Oldenburg, Ammerländer</footnote>
Heerstrase 114-118, D-26129, Oldenburg, Germany. ³⁴Department of Chemical Engineering, Agriculture and Food Technology, Universitat de Girona, 17003, Girona, Spain. ³⁵LOCEAN UMR 7159 Sorbonne Université/CNRS/IRD/MNHN, 4 place Jussieu - boite 100, F-75252, Paris, France. ³⁶IRNAS-CSIC, Avda Reina Mercedes 10, 41012, Seville, Spain. ³⁷School of Sciences Centre for Marine Ecosystems Research, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA, 6027, Australia. ³⁸Department of Ecoscience, Aarhus University, 8000, Aarhus C, Denmark. ³⁹School of Chemical Science, Dublin City University, Dublin, Ireland. ⁴⁰Department of Physics and Astronomy (DIFA), Alma Mater Studiorum - Università di Bologna, Bologna, Italy. ⁴¹Fudan University, Shanghai, China. ⁴²Smithsonian Environmental Research Center, Edgewater, USA. ⁴³School of Biological Sciences, The University of Adelaide, Adelaide, Australia. ⁴⁴The Environment Institute, Adelaide, Australia. ⁴⁵IH Cantabria, Instituto de Hidráulica Ambiental de la Universidad de Cantabria, PCTCAN, 39011, Santander, Spain. ⁴⁶School of Resource and Environmental Management, Simon Fraser University, Burnaby, V5A 1S6, Canada. ⁴⁷School of Environmental Science, Simon Fraser University, Burnaby, V5A 1S6, Canada. ⁴⁸Centro de Estudios Avanzados de Blanes, Consejo Superior de Investigaciones Científicas (CEAB-CSIC), 17300, Blanes, Catalunya, Spain. ⁴⁹Department of Environmental Sciences, Louisiana State University, Baton Rouge, USA. ⁵⁰Zhejiang University, Hangzhou, China. ⁵¹The University of Queensland, St Lucia, Australia. ⁵²National Institute of Water and Atmospheric Research (NIWA), Hamilton, 3251, New Zealand. ⁵³School of Environment, University of Auckland, New Zealand, Auckland, 1142, New Zealand. ⁵⁴Deakin University, Centre for Marine Science, School of Life and Environmental Sciences, Burwood, Victoria, 3125, Australia. ⁵⁵MARE - Marine and Environmental Sciences Centre/ARNET - Aquatic Research Network, Department of Life Sciences, University of Coimbra, Coimbra, Portugal. ⁵⁶LARAMG - Radioecology and Climate Change Laboratory, Department of Biophysics and Biometry, Rio de Janeiro State University, Rua São Francisco Xavier 524, 20550-013, Rio de Janeiro, RJ, Brazil. ⁵⁷Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 165 00, Prague, Czech Republic. ⁵⁸U.S. Geological Survey, Wetland and Aquatic Research Center, Lafayette, Louisiana, 70506, USA. ⁵⁹Department of Zoology and Environment Sciences, University of Colombo, Colombo, 03, Sri Lanka. ⁶⁰Soil Science, Hamburg, Germany. ⁶¹Unidad Académica Mazatlán, Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Mexico City, Mexico. ⁶²Western Washington University, Bellingham, USA. ⁶³Elva Gymnasium, Puiestee 2, Elva, 61505, Estonia. ⁶⁴National Marine Science Centre, School of Environment, Science and Engineering, Southern Cross University, P.O. Box 157, Coffs Harbour, NSW, 2540, Australia. ⁶⁵Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden. ⁶⁶School of Earth System Science, Institute of Surface-Earth System Science, Tianjin University, Tianjin, China. ⁶⁷Departamento de Suelos, Biosistemas y Ecología Agroforestal, MBG sede Santiago (CSIC), Apartado 122, E-15780, Santiago de Compostela, Spain. ⁶⁸Department of Environmental Systems Science, ETH Zurich, 8092, Zürich, Switzerland. ⁶⁹Department of Life Sciences, University of Trieste, Via L. Giorgieri 10, 34127, Trieste, Italy. ⁷⁰Dipartimento di Scienze e Tecnologie Agro-alimentari, Viale G. Fanin, 40 - 40127, Bologna, Italy. ⁷¹Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, 24060, USA. ⁷²Centre For Aquatic Environments, University of Brighton, Moulsecoomb, Brighton, BN2 4GJ, UK. ⁷³Institute of Agriculture and Environmental Sciences, Estonia University of Life Sciences, Kreutzwaldi 5, EE-51014, Tartu, Estonia. ⁷⁴University of Oxford, Oxford, UK. ⁷⁵San Diego State University, San Diego, USA. ⁷⁶Center of Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany. ⁷⁷The Nature Conservancy, Arlington, VA, USA. ⁷⁸Center for Geospatial Analytics, College of Natural Resources, North Carolina State University, 2800 Faucette Drive, Raleigh, NC, 27695, USA. ⁷⁹The Nature Conservancy, Strada delle Tolfe, 14, Siena, 53100, Italy. e-mail: taniamaxwell7@gmail.com; Andre.S.Rovai@usace.army.mil