Managing the fuzzy boundaries and partitions of marine ecological systems using network theory

Management and monitoring of populations in complex habitat mosaics is challenging, requiring effective zonation and bio regionalization strategies. In recent years, marine systems have been partitioned in multiple ways, such as marine protected zones and fishery stocks to enhance conservation and resource management. Viewing these systems as complex ecological networks of connected areas, habitat patches, or sub-populations (nodes) connected by the movement of organisms (edges) helps improve management. Network theory identifies communities or clusters of tightly connected sub-populations, revealing ecologically meaningful structures. Applying network-based community detection algorithms can uncover these ecological units, enhancing marine seascape management. However, there is no consensus on the best methods for identifying ecologically meaningful communities. This study evaluates several community detection algorithms in ecology and demonstrates their effectiveness using two marine case studies: a larval dispersal network and a ship traffic network. We show where algorithms agree in detecting communities and highlight the importance of aligning the nature of the algorithm, connectivity data, and management goals. We also suggest that disagreements between algorithms may indicate areas where management boundaries should be flexible or fluid to better reflect the system's true nature. This study proposes an improved approach to partitioning for optimal conservation and management outcomes.