Jellyfish Use Novel Search Strategy
As computers become more human-like, researchers are finding evidence of computing-like behavior in the animal kingdom. A UK scientist discovered that certain species of jellyfish employ a supercomputing algorithm to locate food.
According to the new study, published in the Journal of the Royal Society Interface, the barrel jellyfish demonstrates movement patterns that are consistent with a type of supercomputing algorithm, called “fast simulated annealing,” described in the paper as “a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a large search space.”
In the mathematical world, this kind of algorithm is usually used in tandem with a powerful computer to find optimal solutions to complex problems in a relatively short time span. For the barrel jellyfish, the same strategy is used to locate the richest concentrations of plankton, its preferred food source. It also helps the species zero in on the olfactory trails emitted by more distant prey.
Such a sophisticated search strategy has never been observed before in nature, according to the study’s lead author Andy Reynolds, a scientist at Rothamsted Research, an agricultural research center in the UK. However, less complex mathematical patterns have been identified, the most common being the “Lévy walk” – named after French mathematician Paul Lévy, best known for his role in advancing probability theory.
Reynolds described the distinction in an interview with LiveScience. “A Lévy walk is random walk in which frequently occurring small steps are interspersed with more rarely occurring longer steps, which in turn are interspersed with even rarer, even longer steps and so on,” he said.
Species that rely on Lévy walks to find prey include sharks, penguins, honeybees, ants, turtles and even human hunter-gatherers.
Instead of using a consistent Lévy walk approach, barrel jellyfish also employ a bouncing technique to locate prey. These large jellies ride the currents to a new depth in search of food. If a meal is not located in the new location, the creature rides the currents back to its original location.
“In the presence of convective currents, it could become energetically favourable to search the water column by riding the convective currents,” Reynolds observes.
Another conclusion of the author is that the family of Lévy walkers is much larger than previously thought, extending to “spores, pollens, seeds and minute wingless arthropods that on warm days disperse passively within the atmospheric boundary layer.”
There is a reason why the jellyfish benefits from this optimized search algorithm, and that is because it requires a lot of plankton to become satiated.
Reynolds explains: “A Lévy search is highly effective in finding the next meal, when any meal will do. Fast simulated annealing, on the other hand, takes the forager to the best possible meal. This is what makes jellyfish special — they are very discerning diners, unlike bony fish, penguins, turtles and sharks, which are just looking for any meal.”