ACHEMS 2019
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SPLTRAK Abstract Submission
Interpretation of the Spatial-Temporal Structure of Turbulent Chemical Plumes for Odor Tracking
Brenden Michaelis1, Yuriy Bobkov2, Jose Principe2, Barry Ache2, Il Park3, Tom Matthews4, Matthew Reidenbach1
1University of Virginia, Charlottesville, VA, United States
2University of Florida, Gainsville, FL, United States
3SUNY Stony Brook, Stony Brook, NY, United States
4Florida Fish and Wildlife, Marathon, FL, United States

Animals often use their sense of smell to locate food, identify mates and predators, and find suitable living habitats. Odor molecules are often dispersed from their source by turbulent wind or water currents. In both terrestrial and aquatic environments, the instantaneous temporal and spatial distribution of odors is complex, and odor plumes are often composed of filaments of chemicals at high concentrations that are adjacent to fluid with little or no odor. Navigation in turbulent chemical plumes has typically been considered a spatial information problem where individuals aim to path towards higher concentration. Concentration information alone is too irregular in turbulent plumes, particularly in water, to explain search speed and accuracy of many animals that undergo search. Recent discoveries of bursting olfactory neurons in the spiny lobster, Panulirus argus, suggest a mechanism for accurately sampling the temporal structure of chemical signals. Lobster pathing behavior in small scale flume experiments is compared to the time series of encountered odor concentration at the antennules, as measured by planar induced fluorescence. Reactions to the intermittent signal are quantified by measuring changes in search speed and trajectory in response to the encountered plume. We observe that decisions in a lobster’s search are better explained by a combination of concentration and temporal cues than concentration cues alone. We believe that considering the temporal element of chemical cues, such as intermittency encoding, is necessary to provide plume information on time scales relevant for informing efficient search behavior.