Work

Evaluating bio-carbon sequestration in Beibu Gulf marine ranching areas via EwE modelling and LMDI analysis

Year: 2025
Type: article
Abstract: Marine ranching, which are artificially constructed nearshore ecosystems, increase carbon sequestration efficiency through bioremediation techniques such as stock enhancement and artificial reef deployment. Marine ranching enhances carbon sequestration but lacks robust evaluation methods. This article first develops a transferable framework for evaluating carbon sequestration in marine ranching by integrating ecosystem carbon budgets, dynamic carbon cycling, and the effects of species enhancement and fisheries management, and then applies this framework to the Beibu Gulf to demonstrate its generalisability and to identify the primary drivers. Based on field survey data, we developed Ecopath carbon mass models for a marine ranch and a nearby control area and fitted Ecosim models to simulate 12 stock enhancement-fishing scenarios combined with logarithmic mean Divisia index (LMDI) analysis. The results revealed that current artificial reef deployment increased the carbon flux and total carbon sequestration in the marine ranching area by 2.52 and 3.43 times compared with those in the control area, respectively. The "Mytilus + fishing" scenario (releasing Mytilus while maintaining current fishing) was simulated to achieve a high peak carbon sequestration efficiency of 2.21 times that of the baseline scenario. LMDI analysis revealed that expansion of the biomass scale compared to the community structure was the primary driver of carbon sequestration in all scenarios (>90.99% contribution). Given the growing need for fisheries management to incorporate carbon sequestration considerations, particularly in marine ranching and other coastal ecosystems, we conclude by discussing the strengths and potential limitations of this framework and suggest directions for future development.
Language: English
Subfield: Oceanography
Open Access status: hybrid
Estimated APC paid: $3,410
OpenAlex ID: W4416449440
FWCI: 0.493
Cites: 81
Cited by: 1
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