Back to Search
Start Over
Scenario analyses of mariculture expansion in Southeastern China using a coupled cellular automata and agent-based model.
- Source :
- Resources, Conservation & Recycling; May2024, Vol. 204, pN.PAG-N.PAG, 1p
- Publication Year :
- 2024
-
Abstract
- • A coupled model is used to simulate mariculture expansion under various scenarios. • Growth rate of animal mariculture would surpass that of seaweed in all scenarios. • Animal mariculture expands by filling existing farms and exploring new areas. • Over 98 % of seaweed mariculture expansion occurs via density intensification. • Spatially-explicit models improve marine spatial planning and management. Mariculture growth constitutes a big challenge for sustainable development due to its role in food security and environmental protection. However, research on sea use and sea cover dynamics for marine ecosystems is largely missing. Here we take one of the biggest Chinese mariculture centers to generate projections of mariculture expansion using a coupled cellular automata and agent-based model. Specifically, we analyze the future dynamics under scenarios with different leading groups and development goals, and identify the main spatiotemporal patterns of expansion. Our results indicate that animal and seaweed mariculture would expand annually by 15.9–66.5 % and 0.1–2.6 %, respectively. Inertial-development and household-dominated scenarios favor animal production, whereas restricted-development and government-dominated scenarios promote seaweed farming. Our findings also show that animal and seaweed mariculture clusters follow distinctive densification, enlargement, and spread patterns. Our work demonstrates the potential of coupled systems approaches to understand human-ocean interactions and support sustainable marine planning and management. [Display omitted] [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09213449
- Volume :
- 204
- Database :
- Supplemental Index
- Journal :
- Resources, Conservation & Recycling
- Publication Type :
- Academic Journal
- Accession number :
- 176071947
- Full Text :
- https://doi.org/10.1016/j.resconrec.2024.107508