Regional movements of satellite-tagged whale sharks Rhincodon typus in the Gulf of Aden.

To gain insight into whale shark (Rhincodon typus) movement patterns in the Western Indian Ocean, we deployed eight pop-up satellite tags at an aggregation site in the Arta Bay region of the Gulf of Tadjoura, Djibouti in the winter months of 2012, 2016, and 2017. Tags revealed movements ranging from local-scale around the Djibouti aggregation site, regional movements along the coastline of Somaliland, movements north into the Red Sea, and a large-scale (>1,000 km) movement to the east coast of Somalia, outside of the Gulf of Aden. Vertical movement data revealed high occupation of the top ten meters of the water column, diel vertical movement patterns, and deep diving behavior. Long-distance movements recorded both here and in previous studies suggest that connectivity between the whale sharks tagged at the Djibouti aggregation and other documented aggregations in the region are likely within annual timeframes. In addition, wide-ranging movements through multiple nations, as well as the high use of surface waters recorded, likely exposes whale sharks in this region to several anthropogenic threats, including targeted and bycatch fisheries and ship-strikes. Area-based management approaches focusing on seasonal hotspots offer a way forward in the conservation of whale sharks in the Western Indian Ocean.

Predicting seasonal movements and distribution of the sperm whale using machine learning algorithms.

Implementation of effective conservation planning relies on a robust understanding of the spatiotemporal distribution of the target species. In the marine realm, this is even more challenging for species rarely seen at the sea surface due to their extreme diving behavior like the sperm whales. Our study aims at (a) investigating the seasonal movements, (b) predicting the potential distribution, and (c) assessing the diel vertical behavior of this species in the Mascarene Archipelago in the south-west Indian Ocean. Using 21 satellite tracks of sperm whales and eight environmental predictors, 14 supervised machine learning algorithms were tested and compared to predict the whales' potential distribution during the wet and dry season, separately. Fourteen of the whales remained in close proximity to Mauritius, while a migratory pattern was evidenced with a synchronized departure for eight females that headed towards Rodrigues Island. The best performing algorithm was the random forest, showing a strong affinity of the whales for sea surface height during the wet season and for bottom temperature during the dry season. A more dispersed distribution was predicted during the wet season, whereas a more restricted distribution to Mauritius and Reunion waters was found during the dry season, probably related to the breeding period. A diel pattern was observed in the diving behavior, likely following the vertical migration of squids. The results of our study fill a knowledge gap regarding seasonal movements and habitat affinities of this vulnerable species, for which a regional IUCN assessment is still missing in the Indian Ocean. Our findings also confirm the great potential of machine learning algorithms in conservation planning and provide highly reproductible tools to support dynamic ocean management.

Population structure of humpback whales from their breeding grounds in the South Atlantic and Indian Oceans.

Although humpback whales are among the best-studied of the large whales, population boundaries in the Southern Hemisphere (SH) have remained largely untested. We assess population structure of SH humpback whales using 1,527 samples collected from whales at fourteen sampling sites within the Southwestern and Southeastern Atlantic, the Southwestern Indian Ocean, and Northern Indian Ocean (Breeding Stocks A, B, C and X, respectively). Evaluation of mtDNA population structure and migration rates was carried out under different statistical frameworks. Using all genetic evidence, the results suggest significant degrees of population structure between all ocean basins, with the Southwestern and Northern Indian Ocean most differentiated from each other. Effective migration rates were highest between the Southeastern Atlantic and the Southwestern Indian Ocean, followed by rates within the Southeastern Atlantic, and the lowest between the Southwestern and Northern Indian Ocean. At finer scales, very low gene flow was detected between the two neighbouring sub-regions in the Southeastern Atlantic, compared to high gene flow for whales within the Southwestern Indian Ocean. Our genetic results support the current management designations proposed by the International Whaling Commission of Breeding Stocks A, B, C, and X as four strongly structured populations. The population structure patterns found in this study are likely to have been influenced by a combination of long-term maternally directed fidelity of migratory destinations, along with other ecological and oceanographic features in the region.