Drivers and hotspots of extinction risk in marine mammals

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.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...

Extinctions in ancient and modern seas

“…However, are these biological correlates of extinction comparable over time? Among contemporary marine mammals, greater body mass at weaning, fewer births per year, taxonomic group, small geographic range size, and small social group size are the major predictors of extinction risk globally [65]. In Canada, body size is the most important predictor of extinction risk for marine mammals and age at maturity for marine fishes [66].…”

Isotopic niches of fin whales from the Mediterranean Sea and the Celtic Sea (North Atlantic)

“…The International Whaling Commission recognizes seven management units in the Atlantic and one in the Mediterranean Sea, comprising three breeding populations (IWC, 2016(IWC, , 2007. Depending on the subpopulation they belong to, fin whales may face various anthropogenic threats, including ship strikes, entanglement in fishing gear, exposure to noise, chronic exposure to a variety of toxins and pollutants, and possible impacts of global climate change that include potential shifts in prey availability (Aguilar, 2009;Clapham et al, 1999;Davidson et al, 2012;Doney et al, 2012;Pinzone et al, 2015;Pompa et al, 2011). The connectivity between these subpopulations is still being debated.…”

Factors affecting the fishing impact on cartilaginous fishes in southeastern Spain (western Mediterranean Sea)

“…This study therefore proposes a new approach to analysing the impact of fishing, using in addition a novel methodological approach from the machine learning field. Regression trees allow for the recognition of patterns in data (Mendoza et al 2010, Davidson et al 2012. Here, these patterns are the combinations of technical, environmental and geographical factors that increase the fishing impact on cartilaginous fish in southeastern Spain.…”