Proposing an information criterion for individual-based models developed in a pattern-oriented modelling framework

Ecological Modelling

Volumn 220, Issue 17, 2009, Pages 1957-1967

  Piou, Cyril ^a,b,c   Berger, Uta ^c   Grimm, Volker ^d  

^a Ecologie Comportementale et Biologie des Populations de Poissons   (France)

^b UNIVERSITÉ DE PAU ET DES PAYS DE L'ADOUR   (France)

^c DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^d HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

Author keywords

Individual based modelling; Information theory; Kullback Leibler divergence; Markov Chain Monte Carlo; Metropolis sampling; Pattern oriented modelling

Indexed keywords

INDIVIDUAL-BASED MODELLING; KULLBACK-LEIBLER DIVERGENCE; MARKOV CHAIN MONTE CARLO; METROPOLIS SAMPLING; PATTERN-ORIENTED MODELLING;

MARKOV PROCESSES; MONTE CARLO METHODS; UNCERTAIN SYSTEMS;

INFORMATION THEORY;

COMPARATIVE STUDY; ECOLOGICAL MODELING; MODEL TEST;

EID: 67650074519     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2009.05.003     Document Type: Article

References (41)

1. Akaike H. Information theory and an extension of the maximum likelihood principle. In: Petrov B.N., and Csaki B.F. (Eds). Second International Symposium on Information Theory. Adademiai Kiado (1973) 267-281
2. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19 (1974) 716-723
3. Berger U., Wagner G., and Wolff W.F. Virtual biologists observe virtual grasshoppers: an assessment of different mobility parameters for the analysis of movement patterns. Ecological Modelling 115 (1999) 119-127
4. Berger U., and Hildenbrandt H. A new approach to spatially explicit modelling of forest dynamics: spacing, ageing and neighbourhood competition of mangrove trees. Ecological Modelling 132 (2000) 287-302
5. Berger U., Hildenbrandt H., and Grimm V. Age-related decline in forest productivity: modelling the role of neighbourhood competition. Journal of Ecology 92 (2004) 846-853
6. Berger U., Adams M., Grimm V., and Hildenbrandt H. Moldelling secondary succession of neotropical mangroves: causes and consequences of growth reduction in pioneer species. Perspectives in Plant Ecology, Evolution and Systematics 7 (2006) 243-252
7. Botkin D.B., Janak J.F., and Wallis J.R. Some ecological consequences of a computer model of forest growth. Journal of Ecology 60 (1972) 849-872
8. Bozdogan H. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52 (1987) 345-370
9. Burnham K.P., and Anderson D.R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed (2002), Springer Verlag
10. Clark J.S. Why environmental scientists are becoming Bayesians. Ecology Letters 8 (2005) 2-14
11. Ellison A. Bayesian inference in ecology. Ecology Letters 7 (2004) 509-520
12. Gelman A. Bayesian Data Analysis (2004), CRC Press
13. Grimm V., Frank K., Jeltsch F., Brandl R., Uchmanski J., and Wissel C. Pattern-oriented modelling in population ecology. Science of the Total Environment 183 (1996) 151-166
14. Grimm V., Wyszomirski T., Aikman D., and Uchmanski J. Individual-based modelling and ecological theory: synthesis of a workshop. Ecological Modelling 115 (1999) 275-282
15. Grimm V., and Berger U. Seeing the forest for the trees, and vice versa: pattern-oriented ecological modelling. In: Seuront L., and Strutton P.G. (Eds). Handbook of Scaling Methods in Aquatic Ecology: Measurement, Analysis, Simulation (2003), CRC Press, Boca Raton 411-428
16. Grimm V., Revilla E., Berger U., Jeltsch F., Mooij W.M., Railsback S.F., Thulke H.-H., Weiner J., Wiegand T., and DeAngelis D.L. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310 (2005) 987-991
17. Hastings W. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57 (1970) 97-109
18. Jachner S., van den Boogaart K.G., and Petzoldt T. Statistical methods for the qualitative assessment of dynamic models with time delay (R package qualV). Journal of Statistical Sofware 22 8 (2007) 1-31
19. Jeltsch F., Moloney K.A., Schurr F.M., Köchy M., and Schwager M. The state of plant population modelling in light of environmental change. Perspectives in Plant Ecology, Evolution and Systematics 9 (2008) 171-189
20. Johnson J.B., and Omland K.S. Model selection in ecology and evolution. Trends in Ecology and Evolution 19 (2004) 101-108
21. Kramer-Schadt S., Revilla E., Wiegand T., and Breitenmoser U. Fragmented landscapes, road mortality and patch connectivity: modelling influences on the dispersal of Eurasian lynx. Journal of Applied Ecology 41 (2004) 711-723
22. Kramer-Schadt S., Revilla E., Wiegand T., and Grimm V. Patterns for parameters in simulation models. Ecological Modelling 204 (2007) 553-556
23. Komuro R., Ford D.E., and Reynolds J.H. The use of multi-criteria assessment in developing a process model. Ecological Modelling 197 (2006) 320-330
24. MacKay D. Information Theory, Inference and Learning Algorithms (2003), Cambridge University Press
25. Marjoram P., Molitor J., Plagnol V., and Tavare S. Markov chain Monte Carlo without likelihoods. Proceedings of the National Academy of Science USA 100 (2003) 15324-15328
26. Menezes M., Berger U., and Worbes M. Annual growth rings and long-term growth patterns of mangrove trees from the Bragança peninsula, North Brazil. Wetlands and Saltmarshes 11 (2003) 233-242
27. Metropolis N., Rosenbluth A., Rosenbluth M., Teller A., and Teller E. Equation of state calculations by fast computing machines. Journal of Chemical Physics 21 (1953) 1087
28. Mooij W.M., and DeAngelis D.L. Uncertainty in spatially explicit animal dispersal models. Ecological Applications 13 (2003) 794-805
29. Platt J.R. Strong inference. Science 146 (1964) 347-353
30. Piou C., Berger U., Hildenbrandt H., Grimm V., Diele K., and D'Lima C. Simulating cryptic movements of a mangrove crab: recovery phenomena after small scale fishery. Ecological Modelling 205 (2007) 110-122
31. Piou C., Berger U., Hildenbrandt H., and Feller I.C. Testing the intermediate disturbance hypothesis in species-poor systems: a simulation experiment for mangrove forests. Journal of Vegetation Science 19 (2008) 417-424
32. R Development Core Team, 2008. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org.
33. Rossmanith E., Blaum N., Grimm V., and Jeltsch F. Pattern-oriented modelling for estimating unknown pre-breeding survival rates: the case of the Lesser Spotted Woodpecker (Picoides minor). Biological Conservation 135 (2007) 571-580
34. Samietz J., and Berger U. Evaluation of movement parameters in insects-bias and robustness with regard to resight numbers. Oecologia 110 (1997) 40-49
35. Silverman B. Density estimation for statistics and data analysis (1986), Chapman & Hall/CRC
36. Spiegelhalter, D., Best, N., Carlin, B., 1998. Bayesian deviance, the effective number of parameters, and the comparison of arbitrarily complex models. Available from http://eprints.kfupm.edu.sa/28000/1/28000.pdf.
37. Spiegelhalter D., Best N., Carlin B., and van der Linde A. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B: Statistical Methodology 64 (2002) 583-639
38. van der Linde A. DIC in variable selection. Statistica Neerlandica 59 (2005) 45-56
39. Wiegand T., Moloney K.A., and Milton S.J. Population dynamics, disturbance, and pattern evolution: identifying the fundamental scales of organization in a model ecosystem. American Naturalist 152 (1998) 321-337
40. Wiegand T., Jeltsch F., Hanski I., and Grimm V. Using pattern-oriented modeling for revealing hidden information: a key for reconciling ecological theory and application. Oikos 100 (2003) 209-222
41. Wiegand T., Revilla E., and Knauer F. Dealing with uncertainty in spatially explicit population models. Biodiversity and Conservation 13 (2004) 53-78