Localization of odors is essential to animal survival, and thus animals are adept at odor-navigation. In natural conditions animals encounter odor sources in which odor is carried by air flow varying in complexity. We sought to identify potential minimalist strategies that can effectively be used for odor-based navigation and asses their performance in an increasingly chaotic environment. To do so, we compared mouse, model, and Arduino-based robot odor-localization behavior in a standardized odor landscape. Mouse performance remains robust in the presence of increased complexity, showing a shift in strategy towards faster movement with increased environmental complexity. Implementing simple binaral and temporal models of tropotaxis and klinotaxis, an model and Arduino robot, in the same environment as the mice, are equally successful in locating the odor source within a plume of low complexity. However, performance of these algorithms significantly drops when the chaotic nature of the plume is increased. Additionally, both algorithm-driven systems show more successful performance when using a strictly binaral model at a larger sensor separation distance and more successful performance when using a temporal and binaral model when using a smaller sensor separation distance. This suggests that with an increasingly chaotic odor environment, mice rely on complex strategies that allow for robust odor localization that cannot be resolved by minimal algorithms that display robust performance at low levels of complexity. Thus, highlighting that an animal's ability to modulate behavior with environmental complexity is beneficial for odor localization. A promising body of work has been devoted to designing robots and algorithms that address the strategies used by animals during odor-based navigation. One method to do so is by designing models that account for complex navigational tactics implemented by a particular species. How do these models directly compare to animal behavior in the same environment? We addressed this question by comparing odor-localization performance of minimal spatial and temporal algorithms and in a robot to the strategies and performance of mice in the same odor environment. Through implementing this unique comparison, we revealed that mouse behavior remains robust with an increase in odor plume complexity, whereas simple algorithm behavior (although high-performing at low plume complexity) does not.