The Fast-Greedy algorithm reveals hourly fluctuations and associated risks of shark communities in a South Pacific city
Unprovoked shark bites are increasing globally. Regional hotspots like Nouméa show rising incidents involving bull sharks (Carcharhinus leucas) and tiger sharks (Galeocerdo cuvier), leading to the culling of these protected species. Identifying high-risk areas and times is key to balancing human saf...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002729 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Unprovoked shark bites are increasing globally. Regional hotspots like Nouméa show rising incidents involving bull sharks (Carcharhinus leucas) and tiger sharks (Galeocerdo cuvier), leading to the culling of these protected species. Identifying high-risk areas and times is key to balancing human safety and shark conservation. Here, we collected five years of acoustic telemetry data for both shark species in the lagoon of Nouméa. The data were categorized by species, divided into 24 hourly subsets, and modeled as bipartite graphs. The Fast-Greedy algorithm was applied to identify distinct communities of sharks and stations. Normalized mutual information was used to cluster communities and detect spatiotemporal patterns. The study revealed up to 9 hourly communities for bull sharks and 21 for tiger sharks, each grouping into 3 clusters. Several high-risk areas and times were identified. Bull sharks formed schools, and a cluster was observed in the harbor between 6:00 and 13:00, increasing bite risk on nearby beaches in the morning. Tiger sharks were more solitary and were present day and night at most stations except those in relatively turbid areas. Both species showed fission–fusion dynamics, with communities merging at dusk, indicating increased movement and a higher risk during this low-light period. A key innovation of our modeling framework was its ability to handle temporal variability in community detection algorithms applied to bipartite networks. The model identified key overlap periods of shark–human activity, highlighting the need for real-time monitoring, safety measures, and public awareness to reduce bite risk and promote coexistence. |
|---|---|
| ISSN: | 1574-9541 |