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Many species of bats rely on echoes to forage and navigate in densely vegetated environments. Foliage echoes in some cases can help bats gather information about the environment, whereas in others may generate clutter that can mask prey echoes during foraging. It is therefore important to study foliage echoes and their role in bat’s sensory ecology. In our prior work, a foliage echo simulator has been developed; simulated echoes has been compared with field recordings using a biomimetic sonar head. In this work, we improve the existing simulator by allowing more flexible experimental setups and enabling a closer match with the experiments. Specifically, we add additional features into the simulator including separate directivity patterns for emitter and receiver, the ability to place emitter and receiver at distinct locations, and multiple options to orient the foliage to mimic natural conditions like strong wind. To study how accurately the simulator can replicate the real echo-generating process, we compare simulated echoes with experimental echoes measured by ensonifying a single leaf across four different species of trees. We further extend the prior work on estimating foliage parameters to estimating a map of the environment. 

Synopsis The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists. 

Social animals exhibit collective behavior whereby they negotiate to reach an agreement, such as the coordination of group motion. Bats are unique among most social animals, since they use active sensory echolocation by emitting ultrasonic waves and sensing echoes to navigate. Bats’ use of active sensing may result in acoustic interference from peers, driving different behavior when they fly together rather than alone. The present study explores quantitative methods that can be used to understand whether bats flying in pairs move independently of each other or interact. The study used field data from bats in flight and is based on the assumption that interactions between two bats are evidenced in their flight patterns. To quantify pairwise interaction, we defined the strength of coupling using model-free methods from dynamical systems and information theory. We used a control condition to eliminate similarities in flight path due to environmental geometry. Our research question is whether these data-driven methods identify directed coupling between bats from their flight paths and, if so, whether the results are consistent between methods. Results demonstrate evidence of information exchange between flying bat pairs, and, in particular, we find significant evidence of rear-to-front coupling in bats’ turning behavior when they fly in the absence of obstacles.