Trait-based vulnerability reveals hotspots of potential impact for a global marine invader.

Predation from the invasive Indo-Pacific lionfish is likely to amplify declines in marine fishes observed in multiple ocean basins. As the invasion intensifies and expands, there is an urgent need to identify species that are most at risk for extirpation-and possible extinction-from this added threat. To address this gap and inform conservation plans, we develop and apply a quantitative framework for classifying the relative vulnerability of fishes based on morphological and behavioural traits known to influence susceptibility to lionfish predation (e.g. body shape, water column position and aggregation behaviour), habitat overlap with lionfish, and degree of geographic range restriction. Applying the framework to fishes across the invaded Caribbean Sea and ahead of the invasion front in the southwestern Atlantic revealed the identity of at least 77 fishes with relatively small ranges that are likely to be most affected by lionfish predation. Trait-based vulnerability scores significantly predict the probability of fishes appearing within the diets of lionfish across the invaded region. Spatial richness analyses reveal hotspots of vulnerable species in the Bahamas, Belize and Curaçao. Crucially, our framework identifies 29 vulnerable fishes endemic to Brazil, which has not yet been colonized by lionfish. Of these, we suggest reefs around offshore island groups occupied by a dozen highly vulnerable and range-restricted species as priorities for intervention should lionfish spread to the region. Observations of the rate of lionfish spread across the invaded range suggest that an average of 5 years (with a median of nearly 2 years) elapses from first sighting to maximum observed densities. This lag may allow managers to mobilize plans to suppress lionfish ahead of an invasion front in priority locations. Our framework also provides a method for assessing the relative vulnerability of cryptobenthic and/or deep-reef fishes, for which population-monitoring data are limited.