In This Article Expand or collapse the "in this article" section Modeling and Data Analysis in Movement Ecology

  • Introduction
  • Movement Ecology Textbooks
  • Movement Ecology Concepts
  • Movement Data Collection and Processing
  • Circular Statistics
  • Random Walk Models of Movement
  • Movement Path Analysis and Metrics
  • Statistical Model Fitting of Movement Data
  • Foraging and Search
  • Home-Range
  • Habitat Space-Use and Resource Selection
  • Collective Movement
  • R Packages for Movement Ecology

Ecology Modeling and Data Analysis in Movement Ecology
Edward A. Codling, Joseph David Bailey, Luca Börger
  • LAST MODIFIED: 21 June 2024
  • DOI: 10.1093/obo/9780199830060-0254


Movement ecology is a rapidly emerging new research field that explores the underlying mechanisms of organism movement, from animals, humans, to plant and microorganisms, across a range of environmental contexts and spatiotemporal scales, from individual behavior to population-level processes. Research in this area draws on a diverse range of disciplines including animal behavior, ecology, biology, geography, physics, mathematics, statistics, and computer science, and utilizes a range of techniques and technologies, including telemetry, remote sensing, and bioacoustics. Animal movements are determined by a combination of both innate individual characteristics and environmental factors, such as food availability, predation risk, and habitat quality. Movements can range from small-scale foraging excursions within a home-range to seasonal migrations over thousands of kilometers, with characteristic patterns at different spatial scales. Animal movement may affect reproductive success, genetic diversity, and disease transmission within populations, and can play a role in wider ecosystem functioning via nutrient cycling or dispersal of seeds and pollen. The rapid growth of movement ecology since the early 2000s has arguably been driven by the increased functionality, availability, and durability of modern biologgers and sensors enabling detailed data sets to be collected from a wide range of species. In turn, this has necessitated the development of new mathematical, statistical, and computational methods for modeling and analyzing these rich, and increasingly very large, data sets. In its broadest sense, movement ecology encompasses a wide-ranging set of concepts, theories, and practical approaches that relate to animal movement. Rather than attempting to cover the entire field, this bibliography specifically focuses on recent developments in modeling and analysis of the most common type of animal movement data, namely (x,y) or (x,y,z) positional data, as collected directly or indirectly over time by spatial tracking systems such as GPS or radio telemetry. Data collected in other forms such as via mark-recapture studies is beyond our scope (although still an important approach within the field). The bibliography highlights the key Movement Ecology Textbooks in the field, explores key Movement Ecology Concepts, and explains methods for Movement Data Collection and Processing and dealing with Circular Statistics. The article gives an overview of Random Walk Models of Movement and how they relate to Movement Path Analysis and Metrics and Statistical Model Fitting of Movement Data in this context. It focuses on several key applications: Foraging and Search, Home-Range, Habitat Space-Use and Resource Selection, and Collective Movement, and finishes with a summary of recent R Packages for Movement Ecology.

Movement Ecology Textbooks

Okubo and Levin 2001 provides an updated second edition of the original classic work that broadly surveys how diffusion models (and the random walk framework that underlies them) can be applied to a range of classical ecological problems including animal movement, animal grouping and collective motion, and movements within a home range. Berg 1993 also gives a broad overview of random walk and diffusion models applied in various biological contexts and across a range of scales. Turchin 2015 gives a comprehensive and systematic guide to quantitative methods for analyzing and modeling animal movements in a range of contexts, with a primary focus on connecting the underlying mathematical models to observed movement data. Moorcroft and Lewis 2006 outlines how home range analysis can be extended through the use of correlated random work models for individual movement behavior, leading to mechanistic models that can give insights into the underlying ecological determinants of home range behavior rather than simply describing the observed patterns statistically. In the context of animal grouping and collective motion, Sumpter 2010 offers a synthesis of mathematical modeling, theory, and experimental work to investigate how animals move together, transfer information, make decisions, and synchronize their behavior, while Railsback and Grimm 2019 provides a general framework for agent-based modeling with several key examples relating to animal movement in individuals or groups. Viswanathan, et al. 2012 highlights how models from statistical physics can be used to explore random search behavior in the context of individual animal foraging. Lewis, et al. 2013 focuses on a broad range of movement and spatial ecology topics and the mathematical modeling approaches used to address them. New statistical tools such as hidden Markov models (HMMs) are becoming increasingly important to analyze animal movement data; Zucchini, et al. 2016 describes the underlying theory with examples that can be applied to movement data. Hooten, et al. 2017 gives a modern overview of statistical models and methods for analyzing animal telemetry data, including spatial point process models, discrete-time dynamic models, and continuous-time stochastic process models, with a wide range of applications.

  • Berg, Howard C. 1993. Random walks in biology. Princeton, NJ: Princeton Univ. Press.

    Classic exposition of random walk and diffusion models applied to the movement of particles, molecules, cells, and microorganisms. Although the book has a focus on cell motility at the microscopic scale, the general theory and method of application is highly relevant to a wide range of animal movement contexts.

  • Hooten, Mevin B., Devin S. Johnson, Brett T. McClintock, and Juan M. Morales. 2017. Animal movement: Statistical models for telemetry data. Boca Raton, FL: CRC Press.

    DOI: 10.1201/9781315117744

    Modern overview of statistical models and methods applied to animal telemetry data. Clear exposition of the links between discrete time and continuous time approaches for random walk movement models. Also covers spatial point process models applied to home range and resource selection analyses.

  • Lewis, Mark A., Philip K. Maini, and Sergei V. Petrovskii. 2013. Dispersal, individual movement and spatial ecology: A mathematical perspective. Heidelberg, Germany: Springer Berlin.

    DOI: 10.1007/978-3-642-35497-7

    Includes a range of case studies highlighting mathematical models and analysis approaches for analyzing movement across scales, from individuals to populations and ecosystems.

  • Moorcroft, Paul R., and Mark A. Lewis. 2006. Mechanistic home range analysis. Princeton, NJ: Princeton Univ. Press.

    Extensive mathematical framework and description of how the correlated random walk model can be used to provide additional insights into animal home range analysis.

  • Okubo, Akira, and Simon A. Levin. 2001. Diffusion and ecological problems: Modern perspectives. New York: Springer.

    DOI: 10.1007/978-1-4757-4978-6

    First published by Okubo in 1980 and expanded and updated by Levin in 2001, this seminal work is now a classic and forms the basis for the development of modern movement models and analysis approaches across a range of topics including home range and collective motion.

  • Railsback, Steven F., and Volker Grimm. 2019. Agent-based and individual-based modeling: A practical introduction. Princeton, NJ: Princeton Univ. Press.

    Focuses on the practical steps necessary to build, analyze, and interpret agent-based models. Based on the Netlogo language but concepts and approaches described are more general; includes several examples specifically relevant to animal movement.

  • Sumpter, David J. T. 2010. Collective animal behavior. Princeton, NJ: Princeton Univ. Press.

    DOI: 10.1515/9781400837106

    Classic overview of the theory, models, and empirical evidence behind collective behavior, including collective motion. Also covers collective decision-making and social organization.

  • Turchin, Peter. 2015. Quantitative analysis of movement: Measuring and modeling population redistribution in animals and plants. N.p.: Beresta Books.

    Classic work, first published in 1998, that was one of the first to explain how to link mathematical theory to empirical movement data in a systematic way. The practical steps and advice on collecting, managing, processing, and interpreting movement data are still highly relevant today.

  • Viswanathan, Gandhimohan M., Marcos G. E. da Luz, Ernesto P. Raposo, and H. Eugene Stanley. 2012. The physics of foraging: An introduction to random searches and biological encounters. Cambridge, UK: Cambridge Univ. Press.

    Extensive overview of the theory behind anomalous diffusion with specific models and applications for animal search and foraging.

  • Zucchini, Walter, Iain K. MacDonald, and Roland Langrock. 2016. Hidden Markov models for time series: An introduction using R. Boca Raton, FL: Chapman & Hall/CRC.

    Essential introduction to hidden Markov models (HMMs) that covers all necessary theory with practical illustrations and examples using R code. Although pages 227–242 specifically cover HMMs applied to animal movement, most of the background and examples covered earlier in the book are also directly relevant for any HMM analysis of movement data.

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