Environmental and Ecological Statistics

, Volume 18, Issue 3, pp 393–410 | Cite as

Modeling and inference of animal movement using artificial neural networks

  • Jeff A. Tracey
  • Jun Zhu
  • Kevin R. Crooks


Movement of animals in relation to objects in their environment is important in many areas of ecology and wildlife conservation. Tools for analysis of movement data, however, still remain rather limited. In previous work, we developed nonlinear regression models for movement in relation to a single landscape feature. Here we greatly expand these previous models by using artificial neural networks. The new models add substantial flexibility and capabilities, including the ability to incorporate multiple factors and covariates. We devise a likelihood-based model fitting procedure that utilizes genetic algorithms and demonstrate the approach with movement data for red diamond rattlesnakes. The proposed methodology can be useful for global positioning system tracking data that are becoming more common in studies of animal movement behavior.


Circular Statistics Genetic algorithm Movement path Semi-parametric model Telemetry data von Mises distribution 


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  1. Batschelet E (1981) Circular statistics in biology. Academic, New YorkGoogle Scholar
  2. Bishop CM (2005) Neural networks for pattern recognition. Oxford University Press, New YorkGoogle Scholar
  3. Brillinger DR, Preisler HK, Ager AA, Kie JG (2004) An exploratory data analysis (EDA) of the paths of moving animals. J Stat Plan Inference 122: 43–63CrossRefGoogle Scholar
  4. Burnham KP, Anderson D.R. (2002) Model selection and multimodel inference, 2nd edn. Springer, New YorkGoogle Scholar
  5. Christ A, Ver Hoef JM, Zimmerman DL (2008) An animal movement model incorporating home range and habitat selection. Environ Ecol Stat 15: 27–38CrossRefGoogle Scholar
  6. Dalziel BD, Morales JM, Fryxell JM (2008) Fitting probability distributions to animal movement trajectories: using artificial neural networks to link distance, resources, and memory. Am Nat 172: 248–258PubMedCrossRefGoogle Scholar
  7. Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, New YorkGoogle Scholar
  8. Enquist M, Ghirlanda S (2005) Neural networks and animal behavior. Princeton Univesity Press, PrincetonGoogle Scholar
  9. Environmental Systems Reseach Institute Inc: (1999) ArcView 3.2 with spatial analyst extension. Redlands, CAGoogle Scholar
  10. Forester JD, Im HK, Rathouz PJ (2009) Accounting for animal movement in estimation of resource selection functions: sampling and data analysis. Ecology 90: 3554–3565PubMedCrossRefGoogle Scholar
  11. Hassoun MH (1995) Fundamentals of artificial neural network. MIT Press, CambridgeGoogle Scholar
  12. Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, CambridgeGoogle Scholar
  13. Hooten MB, Johnson DS, Lowry JH (2010) Agent-based inference for animal movement and selection. J Agric Biol Environ Stat (to appear)Google Scholar
  14. Jander R (1975) Ecological aspects of spatial orientation. Annu Rev Ecol Syst 6: 171–188CrossRefGoogle Scholar
  15. Johnson DS, Thomas DL, Ver Hoef JM, Christ A (2008) A general framework for the analysis of animal resource selection from telemetry data. Biometrics 64: 968–976PubMedCrossRefGoogle Scholar
  16. Kareiva PM, Shigesada N (1983) Analyzing insect movement as a correlated random walk. Oecologia 56: 234–238CrossRefGoogle Scholar
  17. Krebs JR, Davies NB (1993) An introduction to behavioral ecology, 3rd edn. Blackwell Science Ltd, OxfordGoogle Scholar
  18. Lima SL, Zollner PA (1996) Towards a behavioral ecology of ecological landscapes. Trends Ecol Evol 11: 131–135PubMedCrossRefGoogle Scholar
  19. Mardia KV, Jupp PE (2000) Directional statistics. Wiley, New YorkGoogle Scholar
  20. Mitchell M (1996) An introduction to genetic algorithms. MIT Press, CambridgeGoogle Scholar
  21. Morales JM, Fortin D, Frair JL, Merrill EH (2005) Adaptive models for large herbivore movements in heterogeneous landscapes. Landsc Ecol 20: 301–316CrossRefGoogle Scholar
  22. Patterson TA, Thomas L, Wilcox C, Ovaskainen O, Matthiopoulos J (2008) State-based models of individual animal movement. Trends Ecol Evol 23: 87–94PubMedCrossRefGoogle Scholar
  23. R Development Core Team (2008) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. ISBN 3-900051-07-0Google Scholar
  24. Reed RD, Marks RJI (1999) Neural smithing. MIT Press, CambridgeGoogle Scholar
  25. Strand E, Huse G, Giske J (2002) Artificial evolution of life history and behavior. Am Nat 159: 624–644PubMedCrossRefGoogle Scholar
  26. Tracey JA (2000) Movement of red diamond rattlesnakes (Crotalus ruber) in heterogeneous landscapes in coastal southern california. Master’s thesis, University of California, San DiegoGoogle Scholar
  27. Tracey JA (2006) Individual-based modeling as a tool for conserving connectivity. In: Crooks KR, Sanjayan M (eds) Connectivity conservation. Cambridge University Press, CambridgeGoogle Scholar
  28. Tracey JA, Zhu J, Crooks K (2005) A set of nonlinear regression models for animal movement in response to a single landscape feature. J Agric Biol Environ Stat 10: 1–18CrossRefGoogle Scholar
  29. Turchin P. (1998) Quantitative analysis of movement. Sinauer Associates, SunderlandGoogle Scholar
  30. Van Vuren D (1998) Mammilian dispersal and reserve design. In: Caro T (eds) Behavioral ecology and conservation biology. New York, OxfordGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Fish, Wildlife, and Conservation BiologyColorado State UniversityFort CollinsUSA
  2. 2.SigmaLogistic Consulting, Inc.San DiegoUSA
  3. 3.Department of StatisticsColorado State UniversityFort CollinsUSA
  4. 4.Department of StatisticsUniversity of Wisconsin-MadisonMadisonUSA

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