Coastal salt marsh changes in China: Landscape pattern, driving factors, and carbon dynamics

• Mean center, standard deviational ellipse, and landscape indices of salt marsh species in China varied between 1985 and 2019. • Human disturbance dominated changes in salt marsh species distribution. • Salt marsh changes in China resulted in net emission of 68.1 Mt CO 2 during 1985–2019. • Highest CO 2 emission occurred as a result of Phragmites australis losses, and in Shandong Province. • CO 2 sequestration occurred mostly due to introduction and expansion of Spartina alterniflor a. Coastal salt marshes provide critical ecological services, including carbon sequestration. However, the landscape patterns, driving factors, and carbon dynamics associated with salt marsh losses and gains remain insufficiently explored, which is vital for effective restoration. This study conducted a comprehensive analysis of these aspects across China, with a focus on species-specific differences. Based on historical salt marsh data, landscape analysis was applied for identifying the spatiotemporal characteristics of changes. XGBoost algorithm was used for driving factor analysis. Carbon dynamics derived from salt marsh changes were estimated with statistical calculation. Our results indicated that the distribution patterns of salt marshes, as indicated by mean center, ellipse area, and landscape indices, varied significantly from 1985 to 2019, particularly between 2005 and 2010. Native species, such as Phragmites australis and Suaeda salsa , experienced significant losses with a 72 % reduction, while exotic species Spartina alterniflora showed substantial gains with a 680-fold. Human disturbances emerged as the primary driver of these changes, with mean temperature and precipitation influencing certain regions and years. Overall, salt marsh changes resulted in a net emission of 68.1 Mt CO 2 , with the highest emission in Shandong and linked to the loss of Phragmites australis . Conversely, carbon sequestration equivalent to 11.1 Mt CO 2 mainly resulted from the expansion of Spartina alterniflora , with Shanghai contributing the most. This species-specific and site-specific analysis of landscape patterns, drivers, and carbon dynamics in China could enhance our understanding of salt marsh changes and offer valuable insights for targeted restoration efforts at both local and national levels.

Authors

Institutions

Publication Details

Journal
Geography and sustainability
Published
2025-02-24
DOI
https://doi.org/10.1016/j.geosus.2025.100281
Citations
3
Primary Topic
Coastal wetland ecosystem dynamics
Type
article
Field-Weighted Citation Impact
5.15

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Coastal salt marsh changes in China: Landscape pattern, driving factors, and carbon dynamics

Dongfeng Xie, Jiaping Wu, Jiali Gu
3 citations
Geography and sustainability
Coastal wetland ecosystem dynamics
5.15
article

Coastal salt marsh changes in China: Landscape pattern, driving factors, and carbon dynamics

Dongfeng Xie, Jiaping Wu, Jiali Gu
article en
3 citations

Abstract

• Mean center, standard deviational ellipse, and landscape indices of salt marsh species in China varied between 1985 and 2019. • Human disturbance dominated changes in salt marsh species distribution. • Salt marsh changes in China resulted in net emission of 68.1 Mt CO 2 during 1985–2019. • Highest CO 2 emission occurred as a result of Phragmites australis losses, and in Shandong Province. • CO 2 sequestration occurred mostly due to introduction and expansion of Spartina alterniflor a. Coastal salt marshes provide critical ecological services, including carbon sequestration. However, the landscape patterns, driving factors, and carbon dynamics associated with salt marsh losses and gains remain insufficiently explored, which is vital for effective restoration. This study conducted a comprehensive analysis of these aspects across China, with a focus on species-specific differences. Based on historical salt marsh data, landscape analysis was applied for identifying the spatiotemporal characteristics of changes. XGBoost algorithm was used for driving factor analysis. Carbon dynamics derived from salt marsh changes were estimated with statistical calculation. Our results indicated that the distribution patterns of salt marshes, as indicated by mean center, ellipse area, and landscape indices, varied significantly from 1985 to 2019, particularly between 2005 and 2010. Native species, such as Phragmites australis and Suaeda salsa , experienced significant losses with a 72 % reduction, while exotic species Spartina alterniflora showed substantial gains with a 680-fold. Human disturbances emerged as the primary driver of these changes, with mean temperature and precipitation influencing certain regions and years. Overall, salt marsh changes resulted in a net emission of 68.1 Mt CO 2 , with the highest emission in Shandong and linked to the loss of Phragmites australis . Conversely, carbon sequestration equivalent to 11.1 Mt CO 2 mainly resulted from the expansion of Spartina alterniflora , with Shanghai contributing the most. This species-specific and site-specific analysis of landscape patterns, drivers, and carbon dynamics in China could enhance our understanding of salt marsh changes and offer valuable insights for targeted restoration efforts at both local and national levels.

Geography and sustainabilityVol. 6(4)
Zhejiang Ocean University (CN), Zhejiang Institute of Hydraulics & Estuary (CN), Zhejiang University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province
Life below water
Openalex Percentile: Top 6%
Coastal wetland ecosystem dynamics
5.15
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.