Large-scale predictions of salt-marsh carbon stock based on simple observations of plant community and soil type
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In: Biogeosciences, Vol. 16, No. 2, 25.01.2019, p. 425-436.
Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Large-scale predictions of salt-marsh carbon stock based on simple observations of plant community and soil type
AU - Ford, Hilary
AU - Garbutt, Angus
AU - Duggan-Edwards, Mollie
AU - Pages, Jordi F.
AU - Harvey, Rachel
AU - Ladd, Cai
AU - Skov, Martin W.
PY - 2019/1/25
Y1 - 2019/1/25
N2 - Carbon stored in coastal wetland ecosystems is of global relevance to climate regulation. Broadscale inventories of this “blue” carbon store are currently lacking and labour intensive. Sampling 23 salt marshes in the United Kingdom, we developed a Saltmarsh Carbon Stock Predictor (SCSP) with the capacity to predict up to 44 % of spatial variation in surface soil organic carbon (SOC) stock (0–10 cm) from simple observations of plant community and soil type. Classification of soils into two types (sandy or not-sandy) explained 32 % of variation in SOC stock. Plant community type (five vegetation classes) explained 37% of variation. Combined information on soil and plant community types explained 44 % of variation in SOC stock. GIS maps of sur- face SOC stock were produced for all salt marshes in Wales ( ∼ 4000 ha), using existing soil maps and governmental vegetation data and demonstrating the application of the SCSP for large-scale predictions of blue carbon stores and the use of plant community traits for predicting ecosystem services.
AB - Carbon stored in coastal wetland ecosystems is of global relevance to climate regulation. Broadscale inventories of this “blue” carbon store are currently lacking and labour intensive. Sampling 23 salt marshes in the United Kingdom, we developed a Saltmarsh Carbon Stock Predictor (SCSP) with the capacity to predict up to 44 % of spatial variation in surface soil organic carbon (SOC) stock (0–10 cm) from simple observations of plant community and soil type. Classification of soils into two types (sandy or not-sandy) explained 32 % of variation in SOC stock. Plant community type (five vegetation classes) explained 37% of variation. Combined information on soil and plant community types explained 44 % of variation in SOC stock. GIS maps of sur- face SOC stock were produced for all salt marshes in Wales ( ∼ 4000 ha), using existing soil maps and governmental vegetation data and demonstrating the application of the SCSP for large-scale predictions of blue carbon stores and the use of plant community traits for predicting ecosystem services.
U2 - 10.5194/bg-16-425-2019
DO - 10.5194/bg-16-425-2019
M3 - Article
VL - 16
SP - 425
EP - 436
JO - Biogeosciences
JF - Biogeosciences
SN - 1726-4170
IS - 2
ER -