Abstract
Coastal wetlands play a crucial role in the global carbon (C) cycle; however, they have been affected by the invasive plant Spartina alterniflora. The impacts of S. alterniflora invasions on soil organic carbon (SOC) source and composition in wetlands are poorly understood compared to SOC stocks. This hinders the accurate estimation of blue C budgets owing to the long-term effects of S. alterniflora invasions. Here, a space-for-time substitution method was applied to investigate SOC accumulation in invasive 5-, 10-, 15-, 18-, and 21-year-old S. alterniflora communities. This was compared with bare flat in the topsoil (0–10 cm) and subsoil (30–60 cm) of coastal wetlands. Biomarkers (lignin phenols and amino sugars) and Fourier transform infrared spectroscopy were used to describe the accumulation patterns of plant- and microbial-derived C across varying S. alterniflora invasion times, as well as associated stabilization mechanisms. Plant-derived C dominated the SOC content compared to microbial-derived C during S. alterniflora invasions, while microbial-derived C played a more critical role in SOC stability than that of plant-derived C. At both soil depths, microbial-derived C gradually increased with increasing invasion time, whereas plant-derived C initially increased and then decreased, consistent with changes in SOC content. The variation in inundation conditions, influenced by tides, emerged as a significant factor affecting the accumulation of plant- and microbial-derived C, as well as SOC stability. Decreased inundation frequency and increased oxygen exposure, driven by tides, may lead to a rapid rise in SOC mineralization and the enrichment of microbial-derived C at the expense of plant-derived C. Consequently, this process improved SOC stability. These findings hold substantial implications for potential C loss resulting from the long-term impacts of S. alterniflora invasions. Furthermore, they may help predict the long-term effects of S. alterniflora invasions on blue C budgets within the context of global change scenarios.
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