Foraging flights.

First, he tracked basking sharks—filterfeeding leviathans that look like supersized great whites—in the coastal waters near Great Britain, and then Atlantic cod, leatherback turtles, Magellanic penguins, and bigeye tuna. He’s wrangled and tagged ocean sunfish, blue sharks, and shortfin mako sharks (and claims success by the fact that he still has all his fingers). Only after David Sims had exhausted many other species did he find his ultimate prey: the albatross. Sims, a senior research fellow at the Marine Biological Association of the United Kingdom, Plymouth, and a marine ecology professor at the University of Southampton, United Kingdom, has tracked many a species during his career, and it’s all to answer a fundamental ecology question: What’s the best way for an animal to search for small and irregularly distributed pockets of food when it has limited information, and the whole ocean is open to it? After investigating the feeding patterns of all these species, Sims has found that the optimal search often traces a mathematical fractal called the Lévy flight, which is characterized by long segments followed by shorter hops in random directions. Sims proposes the pattern has evolved as a naturally selected strategy that gives animals an edge in the search for sparse prey. This strategy may also guide Homo sapiens: More recent studies suggest that the fractal also fits some human behaviors, like bidding in online auctions. Moving beyond behavior, it shows up in models of dripping faucets, variations in a healthy heartbeat, the movement of light in certain materials, and astrophysical phenomena as well. The pattern isn’t just a mathematical curiosity that helps animals find food, it’s an optimization strategy. From engineering to data analysis to financial decisions, optimization offers a way to make better forecasts when confronted with random data. That’s appealing to business analysts who have a different prey in mind: consumers. Business entrepreneurs have been contacting Sims, eager to turn data on people’s decisions into profitable predictions of future behavior. “It does rather suggest that there might be a deeper mechanism underlying the behavior we’re seeing [in animals],” Sims says.