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. 2023 Nov 11;10(1):797.
doi: 10.1038/s41597-023-02633-x.

Global dataset of soil organic carbon in tidal marshes

Tania L Maxwell  1   2 André S Rovai  3   4 Maria Fernanda Adame  5 Janine B Adams  6 José Álvarez-Rogel  7 William E N Austin  8   9 Kim Beasy  10 Francesco Boscutti  11 Michael E Böttcher  12   13   14 Tjeerd J Bouma  15   16   17 Richard H Bulmer  18 Annette Burden  19 Shannon A Burke  20 Saritta Camacho  21 Doongar R Chaudhary  22 Gail L Chmura  23 Margareth Copertino  24   25 Grace M Cott  20 Christopher Craft  26   27 John Day  28 Carmen B de Los Santos  29 Lionel Denis  30 Weixin Ding  31 Joanna C Ellison  32 Carolyn J Ewers Lewis  33 Luise Giani  34 Maria Gispert  35 Swanne Gontharet  36 José A González-Pérez  37 M Nazaret González-Alcaraz  7 Connor Gorham  38 Anna Elizabeth L Graversen  39 Anthony Grey  40 Roberta Guerra  41 Qiang He  42 James R Holmquist  43 Alice R Jones  44   45 José A Juanes  46 Brian P Kelleher  40 Karen E Kohfeld  47   48 Dorte Krause-Jensen  39 Anna Lafratta  38 Paul S Lavery  38   49 Edward A Laws  50 Carmen Leiva-Dueñas  39 Pei Sun Loh  51 Catherine E Lovelock  52 Carolyn J Lundquist  53   54 Peter I Macreadie  55 Inés Mazarrasa  46 J Patrick Megonigal  43 Joao M Neto  56 Juliana Nogueira  57   58 Michael J Osland  59 Jordi F Pagès  49 Nipuni Perera  60 Eva-Maria Pfeiffer  61 Thomas Pollmann  34 Jacqueline L Raw  6 María Recio  46 Ana Carolina Ruiz-Fernández  62 Sophie K Russell  44   45 John M Rybczyk  63 Marek Sammul  64 Christian Sanders  65 Rui Santos  29 Oscar Serrano  38   49 Matthias Siewert  66 Craig Smeaton  8 Zhaoliang Song  67 Carmen Trasar-Cepeda  68 Robert R Twilley  28 Marijn Van de Broek  69 Stefano Vitti  11   70 Livia Vittori Antisari  71 Baptiste Voltz  30 Christy N Wails  72 Raymond D Ward  73   74 Melissa Ward  75   76 Jaxine Wolfe  43 Renmin Yang  67 Sebastian Zubrzycki  77 Emily Landis  78 Lindsey Smart  78   79 Mark Spalding  80   81 Thomas A Worthington  80
Affiliations

Global dataset of soil organic carbon in tidal marshes

Tania L Maxwell et al. Sci Data. .

Abstract

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Workflow of the literature search, abstract screening, and dataset generation process for the MarSOC dataset.
Fig. 2
Fig. 2
Sample locations coloured by data type (core-level purple, review turquoise, site-level yellow).
Fig. 3
Fig. 3
Distribution of data stored in this MarSOC database across all soil depths.
Fig. 4
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) are shown in circles, and values from this dataset are shown in triangles.
Fig. 5
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.

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