Habitat loss, fragmentation, and degradation primarily resulting from human activities pose a significant threat to global biodiversity. Consequently, efforts have been made to delineate marine systems into management zones, and in the last 20 years these systems have been treated as networks. Here, networks consist of a set of nodes (e.g., habitat patches, islands or populations) connected by edges or links (e.g., movement, dispersal probability). In network terms, tightly grouped nodes are considered communities that exhibit a higher likelihood of connecting to each other than with nodes from other communities. Applying these network-based community detection algorithms can help identify effective management and conservation units. In the marine environment, applications range from selecting marine protected areas and delineating fisheries zones to managing and monitoring marine invasive species. Despite these advantages, there is no consensus on the application of these algorithms, and a clear application to ecology has not been well-documented. This study evaluates the effectiveness of community detection algorithms through several marine connectivity case studies. This research demonstrates how one must appropriately select community detection algorithms to achieve a desirable conservation and management impact.