Sensing and decision-making in random search

While microscopic organisms can use gradient-based search to locate resources, this strategy can be poorly suited to the sensory signals avail-able to macroscopic organisms. We propose a framework that models search-decision making in cases where sensory signals are infrequent, sub-ject to large fluctuations, and contain little directional information. Our approach simultaneously models an organism’s intrinsic movement be-havior (e.g. Lévy walk) while allowing this behavior to be adjusted based on sensory data. We find that including even a simple model for signal response can dominate other features of random search and greatly im-prove search performance. In particular, we show that a lack of signal is not a lack of information. Searchers that receive no signal can quickly abandon target-poor regions. Such phenomena naturally give rise to the area-restricted search behavior exhibited by many searching organisms. Living organisms routinely locate resources in complex noisy environments