Optimal Movement Theory

Movement is rarely about distance alone — it is about energy. We are developing Optimal Movement Theory as a predictive framework in which the probability of any movement decision emerges from the expected energetic gain in the environment, weighed against the mechanical cost of locomotion and the animal’s internal state. By treating these as common, convertible currencies, OMT aims to explain not just how animals move, but why they choose one option over another — and to make those choices predictable across very different species and landscapes.

Projects

Navigating Dynamic Landscapes

Our work on the perception of energies asks how moving individuals detect and interpret forms of environmental energy that cannot be seen directly. We are especially interested in human pilots because, like soaring animals, they must negotiate dynamic airflows, locate usable uplift, and make rapid decisions under uncertainty. The difference is that humans can also share their knowledge with us directly. This gives us a rare opportunity to combine high-precision movement data with first-hand accounts of what individuals saw, sensed, expected and decided in flight.

Using paragliders as a model system, we combine high-tech, high-precision tracking with inertial sensors, video-based reconstruction, and measures of head and body orientation to examine how pilots move through shared airspace, what cues they attend to, and how they interpret the movements of others. By linking these reconstructions to subsequent movement decisions and flight performance, we can begin to quantify how social attention operates in real time and how information flows through groups moving in three-dimensional space.

Because paragliders face many of the same aerodynamic challenges as soaring birds, this system offers a fascinating route into questions that are otherwise very difficult to access directly in wild animals, and provides rare insight into how a bird may experience the energetic structure of the sky.

Energy Resource Landscapes

Our work on Optimal Movement Theory aims to build a general framework for understanding how animals make movement decisions when multiple forms of energy and uncertainty must be considered together. Rather than treating movement only through food search, habitat choice, or locomotion costs in isolation, this work asks how animals decide where and when to move when chemical, mechanical and environmental energies all shape the outcome. At its core is the idea that movement is a decision problem: individuals must act on incomplete information, weigh expected gains against costs, and update their behaviour as new information becomes available.

What began as a conceptual framework is now becoming an empirical research programme. Our current work uses simulations, agent-based models and real movement data to parameterise the theory, initially in systems where dependence on environmental energy is especially strong. Looking ahead, we will test how well these models predict movement decisions in natural systems, and extend the framework from individual decision-making to collective movement. The broader goal is to develop a predictive theory of movement that links behaviour, energetics and uncertainty across species and environments.