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.International Union for the Conservation of Nature Red List | threatened and endangered species | life history | random forest models O ceans occupy 71% of the earth's surface and harbor much of its biodiversity. Despite the vast expanse of the oceans, no area remains unaffected by humans (1). Human activities are polluting, warming, and acidifying the oceans, melting sea ice, overharvesting fisheries, and altering entire food webs (1-4). Fisheries bycatch causes deaths of more than 650,000 marine mammals each year (5). Overfishing has depleted food supplies by reducing fish populations by 50-90%, and industrial-scale krill harvesting will likely further deplete food resources (6-8). In addition, polar oceans are warming at rates twice as fast as the global average (3); this has already altered whale migrations, reduced benthic prey populations, and caused declines in seals and polar bears (Ursus maritimus) whose lifestyles are dependent on sea ice (9). The International Union for the Conservation of Nature (IUCN) Red List currently classifies 25% (32 of 128 species) of marine mammals as threatened with extinction. Examination of the threats on the basis of the Red List shows that nearly half of all species are threatened by two or more human impacts, with pollution being the most pervasive, followed by fishing, invasive species, develop...

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Spatially associated patterns are often found in geographical phenomena, since nearby entities are often more related than distant ones. Such spatial association also changes over time; hence, the temporal aspect of spatial association needs to be examined using both spatiality and temporality. This paper describes a method of modeling the temporal signatures of spatial association, and thus of grouping similar changes. We employed a Moran scatterplot to assess the local characteristics of a spatial association and then extended it to a time-series Moran scatterplot quadrant signature (MSQS) to capture spatiotemporal changes in regions categorically. We used sequence comparison and data grouping techniques to classify similar regions in terms of the time-series MSQS. We tested the feasibility of the proposed method using a case study of a twenty-four-month (June 2004–May 2006) housing price index for sixty-nine administrative units in the Seoul Metropolitan Area, South Korea.

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