| Abstract | The world’s oceans are undergoing profound changes as a result
of human activities. However, the consequences of escalating human
impacts on marine mammal biodiversity remain poorly understood.
The International Union for the Conservation of Nature
(IUCN) identifies 25% of marine mammals as at risk of extinction,
but the conservation status of nearly 40% of marine mammals
remains unknown due to insufficient data. Predictive models of
extinction risk are crucial to informing present and future conservation
needs, yet such models have not been developed for
marine mammals. In this paper, we: (i) used powerful machinelearning
and spatial-modeling approaches to understand the intrinsic
and extrinsic drivers of marine mammal extinction risk; (ii)
used this information to predict risk across all marine mammals,
including IUCN “Data Deficient” species; and (iii) conducted a spatially
explicit assessment of these results to understand how risk is
distributed across the world’s oceans. Rate of offspring production
was the most important predictor of risk. Additional predictors
included taxonomic group, small geographic range area, and small
social group size. Although the interaction of both intrinsic and
extrinsic variables was important in predicting risk, overall, intrinsic
traits were more important than extrinsic variables. In addition
to the 32 species already on the IUCN Red List, our model identified
15 more species, suggesting that 37% of all marine mammals
are at risk of extinction. Most at-risk species occur in coastal areas
and in productive regions of the high seas. We identify 13 global
hotspots of risk and show how they overlap with human impacts
and Marine Protected Areas.
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