Alternation emerges as a multi-modal strategy for turbulent odor navigation

While looking for the source of a scent, rodents and dogs exhibit a complex alternating behavior: they spend most of the time moving while their noses explore the ground, but sometimes they pause to sniff in the air, rearing on their hind legs or raising their heads. This strategy has been observed in rodents exposed to airflow, suggesting that alternation has a primary role during plume-tracking. To understand where and when it is more convenient to sniff in the air, we combine fully resolved simulations of turbulent odor transport and Bellman optimization methods for decision-making under partial observability. We show that an agent trained to minimize search time in a realistic odor plume exhibits extensive alternation coupled with the characteristic cast-and-surge behavior observed in insects. Alternation is related to casting and occurs more frequently far downwind of the source, where the likelihood of detecting airborne cues is higher relative to ground cues. Casting and alternation emerge as complementary tools for efficient exploration with sparse odor cues. Our Partially Observable Markov Decision Process algorithm trained over simplified odor statistics performs robustly in different conditions, suggesting that evolution may have allowed mammals to develop tools to navigate optimally in chaotic environments. Finally, an analytical model based on marginal value theory captures the interplay between casting, surging, and alternation.