Abstract. In coastal waters of several locations globally, whale sharks (Rhincodon typus) form seasonal aggregations, most of which largely comprise juvenile males of 4-8 m length. Evaluation of the period that individuals stay within these size-and age-specific groupings will clarify our understanding of the transition between life-stages in this species and how this might affect their long-term conservation. Long-term photo-identification studies in Seychelles and Djibouti provided data to evaluate this.

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.

Marine megafauna are typically highly mobile species with migration pathways that can cross the boundaries of multiple national jurisdictions (Block et al., 2011;Sequeira et al., 2018). These international movements may expose migrating animals to a number of anthropogenic threats, including fluctuating levels of fishing pressure and shipping activity, that together with the varying extent of legal protection encountered through movements and the conservative life histories of many megafauna species, can have large-scale impacts on populations (Hays et al., 2019). In addition, such movements complicate conservation and management efforts through the need for coordinated efforts among many nations and international organizations (Lascelles et al., 2014). A crucial first step in identifying the threats faced by marine megafauna and in mitigating their potential impacts on populations is to describe the distribution and movement patterns of these vulnerable species (Hammerschlag et al., 2011;Hays et al., 2016).Whale sharks (Rhincodon typus) are large (max. TL 20 m) elasmobranchs that live in warm temperate-tropical waters (Rowat &

Background An aggregation of juvenile whale sharks were first reported off Arta, Djibouti, in 2003 and formally investigated in 2006. Standardised monitoring started in 2009 to establish the demographics of this aggregation and how it relates to the broader Red Sea and Indian Ocean whale shark population. Approach Photo-identification images have been collected from 2003 to present. Satellite relayed archival tags have been deployed to show longer term movements and tissue samples have been collected for DNA. Plankton and environmental information are routinely recorded. For the last two years digital Photo-IDs were collected by volunteers at the local dive operation to enable a longer annual study period to help establish seasonality and sighting trends. Results During 2003–2015, a total of 503 individual sharks were identified by photo-identification from more than 6300 in-water encounters; a maximum of 181 individuals were recorded in a single year. Overall 85% of sharks identified were males and mean body total length ranged from 3.5–4.3 m among years, with no significant difference between sexes. Sharks which were sighted in more than one year had a mean period of inter-annual residency of 4 years (maximum 11 years, n=3). Using data from 2003–2015, mark and recapture models estimated a gross population of 660–777 with 53–78% of individuals being re-sighted in any one year. Satellite tag data showed tagged individuals left the immediate area and travelled into the Red Sea and Northern Indian Ocean; however, 2 of the 3 PAT tagged sharks were seen off Djibouti in subsequent years. Diving depth data showed all sharks made short duration dives to depths greater than 400 m (maximum 832 m) but that all spent at least 45% of the time within 10 m of the surface and an average of 73% of the time shallower than 40 m. Comparison with plankton and environmental data showed that sharks were associated primarily with high plankton concentrations and swimming crab (possibly Charybdis erythrodactyla) spawning events. Conclusions The Arta area off Djibouti is host to a regular and significant aggregation of whale sharks. The average size of the sharks is smaller than those found in other Indian Ocean coastal aggregations suggesting that this may be an intermediate or kindergarten group from which the sharks will leave as they grow to join other juvenile aggregations. Satellite tracking and photo ID support the movement of sharks into Red Sea aggregations.

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