The Major Histocompatibility Complex (MHC) is a central element in the vertebrate immune system. While MHC genes are a common target of conservation genomic studies, it has been challenging to reliably amplify locus-specific alleles, which is especially problematic when studying endangered lineages, like some Harbour porpoise (Phocoena phocoena) populations and subspecies. Here, we manually annotated all MHC II genes in the Harbour porpoise genome, and genotyped every exon 2 in 94 individuals spanning six geographical regions, including the endangered Black Sea porpoise subspecies (Phocoena phocoena relicta) and the endangered Proper Baltic Sea population of the North Atlantic subspecies (P. p. phocoena). We performed gene-wise analyses of diversity and selection, and put the results into perspective with 24 available Harbour porpoise genomes. Furthermore, we characterized all MHC II genes in 19 available long-read cetacean and terrestrial outgroups genomes to study the MHC II evolution across the cetacean diversification. From the 10 MHC II loci annotated in the Harbour porpoise genome, two (DRB1 and DQB) exhibited inflated allelic diversity and signatures of positive selection. Interestingly, DRB genes followed different evolutionary trajectories in mysticetes and odontocetes. Our results have significant conservation implications since we identified reduced MHC II diversity in the endangered Black Sea subspecies, and provide a case study for reliable MHC II genotyping in other species. Further, our study demonstrates the need for long-read genomes to understand the genomic architecture of MHC and to accurately assess its diversity and evolution.

Implementation of effective conservation planning relies on a robust understanding of the spatio-temporal distribution of the target species. In the marine realm, this is even more challenging for cryptic species with extreme diving behaviour like the sperm whales. Our study aims at investigating the movements and predicting suitable habitat maps for this species in the Mascarene Archipelago in the South-West Indian Ocean. Using 21 satellite tracks of sperm whale and 8 environmental predictors, 14 supervised machine learning algorithms were tested and compared to predict the whales' 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 8 females that headed towards Rodrigues Island. The best performing algorithm was the random forest, showing a strong affinity 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. The results of our study fill a knowledge gap regarding seasonal movements and habitat affinities of this vulnerable species, for which IUCN regional assessments are still lacking in the Indian Ocean. Our findings also confirm the great potential of machine learning algorithms in conservation planning and provide concrete tools to support dynamic ocean management.