Fitting dynamic models to animal movement data: The importance of probes for model selection, a reply to Franz and Caillaud

American Naturalist

Volumn 175, Issue 6, 2010, Pages 762-764

  Dalziel, Benjamin D ^a   Morales, Juan M ^b   Fryxell, John M ^c  

^a Cornell University ^*  (United States)

^b Instituto de Investigaciones en Biodiversidad y Medioambiente Bariloche   (Argentina)

^c UNIVERSITY OF GUELPH   (Canada)

Author keywords

Animal movement; Artificial neural networks; Model fitting; Model selection; Probes

Indexed keywords

ANIMAL; ANIMAL BEHAVIOR; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; LOCOMOTION; NOTE;

ANIMALS; BEHAVIOR, ANIMAL; LOCOMOTION; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER);

ANIMALIA;

EID: 77952028735     PISSN: 00030147     EISSN: None     Source Type: Journal    
DOI: 10.1086/652521     Document Type: Note

References (14)

1. Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19: 716-723.
2. Anders, U., and O. Korn. 1999. Model selection in neural networks. Neural Networks 12: 309-323.
3. Dalziel, B. D., J. M. Morales, and J. M. Fryxell. 2008. Fitting probability distributions to animal movement trajectories: using artificial neural networks to link distance, resources, and memory. American Naturalist 172: 248-258.
4. Franz, M., and D. Caillaud. 2010. Fitting artificial neural network models to animal movement data: some necessary precautions. American Naturalist 175: 759-761.
5. Gelman, A., J. B. Carlin, and H. S. Stern. 2004. Bayesian data analysis. CRC, Boca Raton, FL.
6. Grimm, I., E. Revilla, U. Berger, F. Jeltsch, W. M. Mooij, S. F. Railsback, H. H. Thulke, J. Weiner, T. Wiegand, and D. J. DeAngelis. 2005. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310: 987-991.
7. Kendall, B. E., C. J. Briggs, W. M. Murdoch, P. Turchin, S. P. Ellner, E. McCauley, R. M. Nisbet, and S. N. Wood. 1999. Why do populations cycle? a synthesis of statistical and mechanistic modeling approaches. Ecology 80: 1789-1805.
8. Moody, J. 1994. Prediction risk and architecture selection for neural networks. In J. H. Friedman and H. Wechsler, eds. Statistics to neural networks: theory and patterns recognition applications. Springer, New York.
9. Morales, J. M., D. T. Haydon, J. Frair, K. E. Holsinger, and J. M Fryxell. 2004. Extracting more out of relocation data: building movement models as mixtures of random walks. Ecology 85: 2436-2445.
10. Morales, J. M., D. Fortin, J. L. Frair, and E. H. Merrill. 2005. Adaptive models for large herbivore movement in heterogeneous landscapes. Landscape Ecology 20: 301-316.
11. Murata, N., S. Yoshizawa, and S. Amari. 1994. Network information criterion-determining the number of hidden units for an artificial neural network. IEEE Transactions on Neural Networks 5: 865-872.
12. Patterson, T. A., L. Thomas, C. Wilcox, O. Ovaskainen, and J. Matthiopoulos. 2008. State-space models of individual animal movement. Trends in Ecology & Evolution 23: 87-94.
13. Schick, R. S., S. R. Loarie, F. Colchero, B. D. Best, A. Boustany, D. A. Conde, P. N. Halpin, L. N. Joppa, C. M. McClellan, and J. S. Clark. 2008. Understanding movement data and movement processes: current and emerging directions. Ecology Letters 11: 1338-1350.
14. Turchin, P. 1998. Quantitative analysis of movement: measuring and modeling population redistribution in animals and plants. Sinauer, Sunderland, MA.