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Transforming the Science of Behavioral Ecology
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Transforming the Science of Behavioral Ecology

Understanding how the behavior of species changes over time is vital to how we might protect and preserve key species. This is critical for those species that are vulnerable to changes in the environment, such as the response of global ecosystems to climate change and human activity. In particular, understanding the movement and spatial dynamics of individuals, between individuals and their environment, and spatial locations and patterns that are important for species survival, is vital. However, established techniques for studying the movement and behavior of individual animals are typically limited, inefficient, time consuming, and expensive.

The two most commonly used approaches to observing species are far from perfect. First, is a scientist spending years observing migratory behavior, for example, which tells us only when animals depart and when they return (if at all), but nothing about what they do when, where, and why they migrate. Second, are tracking technologies, which are expensive, inflexible, inappropriate (size, range, functionality, weight), and labor intensive, limiting both the applicability and scale of ecological and behavioral studies. In addition, even when data are collected, few if any computational and software tools exist to analyze the data easily and accurately or to enable the development and testing of predictive models that use the data. As a consequence, we understand remarkably little about the behavior of most key species, and less still about how their behavior is or will change as the environmental changes.

Now, Robin Freeman, a zoologist in the Computational Ecology and Environmental Sciences group at Microsoft Research, has developed an open, reconfigurable, flexible, wirelessly-enabled, and low-cost tracking technology, and set of software tools that address almost all of these problems. These technologies are enabling Robin; collaborators Tim Guilford, Ben Dean, and Holly Kirk at the University of Oxford; and now, other researchers worldwide, to undertake previously impossible scientific studies collecting novel types of data and employing new kinds of analyses. These are initially focused on the migratory and foraging behavior of pelagic seabirds. The new platform is an open design with open software, so researchers can choose to modify the existing designs as their projects require, and Microsoft Research has provided a number of solutions for most common tracking problems.

Furthermore, the group’s software tools for data analysis, modeling, and visualization enable Robin and colleagues to analyze these new data to form a fundamentally better understanding of the movement and behavior of individuals, groups, and populations of important species, including the Manx Shearwater, Hutton Shearwater, Black Petrel, Puffins, and Guillemots. This project exemplifies how targeted research can help us understand social behavior and spatial dynamics at different levels. With the rapid growth of personal devices that include sensors, such as smartphones, there are potential applications for the techniques used here to investigate how people interact in society.

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Primary Researchers

Tim Guilford

Tim Guilford, Ph.D., leads the Oxford Navigation Group in Animal Behaviour at the Department of Zoology, Oxford University. His research explores the mechanisms and processes of animal navigation and movement, principally in avian systems. He has pioneered a number of techniques for understanding avian navigation, leading to new insights and understanding. Recently, he has developed a variety of methods for investigating highly pelagic seabirds, including endangered and vulnerable species.

Robin Freeman

Robin Freeman, Ph.D., is a researcher in Computational Ecology at Microsoft Research Cambridge and a research fellow in CoMPLEX (the Centre for Mathematics and Physics in the Life Sciences and Experimental Biology) at University College London. He is also research associate with the Animal Behaviour group at Oxford University. His research focuses on topics at the interface between behavior, ecology, and computation. He is particularly interested in the application of computational techniques for analysing animal behavior: from systems to autonomously record the behavior of animals in the wild, to the development and application of machine-learning techniques to analyse these data.