Pastor Rollan Ane, Berlow Eric L, Williams Rich, Treml Eric A
School of Life and Environmental Sciences, Deakin University, Geelong, VIC, Australia.
Vibrant Data Labs, 2151 1/2 Stuart St, Berkeley, San Francisco, CA, 94705, USA.
Sci Rep. 2025 Jul 16;15(1):25803. doi: 10.1038/s41598-025-09601-y.
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.
对复杂栖息地镶嵌体中的种群进行管理和监测具有挑战性，需要有效的分区和生物区域化策略。近年来，海洋系统已通过多种方式进行划分，如海洋保护区和渔业种群，以加强保护和资源管理。将这些系统视为由生物移动（边）连接的相连区域、栖息地斑块或亚种群（节点）组成的复杂生态网络，有助于改善管理。网络理论识别紧密相连的亚种群群落或集群，揭示具有生态意义的结构。应用基于网络的群落检测算法可以发现这些生态单元，加强海洋景观管理。然而，对于识别具有生态意义的群落的最佳方法尚无共识。本研究评估了生态学中的几种群落检测算法，并通过两个海洋案例研究证明了它们的有效性：一个幼体扩散网络和一个船舶交通网络。我们展示了算法在检测群落方面的一致性，并强调了使算法性质、连通性数据和管理目标相匹配的重要性。我们还表明，算法之间的分歧可能表明管理边界应灵活或可变的区域，以更好地反映系统的真实性质。本研究提出了一种改进的分区方法，以实现最佳的保护和管理效果。
