Calibration and evaluation of individual-based models using Approximate Bayesian Computation

Ecological Modelling

Volumn 312, Issue , 2015, Pages 182-190

  van der Vaart, Elske ^a   Beaumont, Mark A ^b   Johnston, Alice S A ^a   Sibly, Richard M ^a  

^a UNIVERSITY OF READING   (United Kingdom)

^b UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

Approximate Bayesian Computation; Individual based models; Model selection; Parameter estimation; Population dynamics

Indexed keywords

BAYESIAN NETWORKS; BUDGET CONTROL; ECOLOGY; POPULATION DYNAMICS;

APPROXIMATE BAYESIAN; COMPONENT MODULES; CREDIBLE INTERVAL; INDIVIDUAL BASED MODEL; MODEL SELECTION; POSTERIOR DISTRIBUTIONS; PRIOR DISTRIBUTION; STATISTICAL FRAMEWORK;

PARAMETER ESTIMATION;

ACCURACY ASSESSMENT; BAYESIAN ANALYSIS; BODY MASS; CALIBRATION; CORRELATION; EARTHWORM; ENERGY BUDGET; MOVEMENT; NUMERICAL MODEL; OPTIMIZATION; SIMULATION; UNCERTAINTY ANALYSIS;

EID: 84930960067     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2015.05.020     Document Type: Article

References (48)

1. Augusiak J., van den Brink P.J., Grimm V. Merging validation and evaluation of ecological models to 'evaludation': a review of terminology and a practical approach. Ecol. Model. 2014, 280:117-128.
2. Beaumont M.A. Approximate Bayesian computation in evolution and ecology. Annu. Rev. Ecol. Evol. Syst. 2010, 41:379-406.
3. Beaumont M.A., Zhang W., Balding D.J. Approximate Bayesian computation in population genetics. Genetics 2002, 162:2025-2035.
4. Blum M.G.B., François O. Non-linear regression models for approximate Bayesian computation. Stat. Comput. 2010, 20:63-73.
5. Blum M.G.B., Nunes M.A., Prangle D., Sisson S.A. A comparative review of dimension reduction methods in approximate Bayesian computation. Stat. Sci. 2013, 28:189-208.
6. Buhl J., Deneubourg J.-L., Grimal A., Theraulaz G. Self-organized digging activity in ant colonies. Behav. Ecol. Sociobiol. 2005, 58:9-17.
7. Csilléry K., Blum M.G.B., Gaggiotti O.E., François O. Approximate Bayesian computation (ABC) in practice. Trends Ecol. Evol. 2010, 25:410-418.
8. Csillery K., François O., Blum M.G.B. abc: an R package for approximate Bayesian computation (ABC). Meth. Ecol. Evol. 2012, 3:475-479.
9. DeAngelis D.L., Mooij W.M. Individual-based modeling of ecological and evolutionary processes. Annu. Rev. Ecol. Evol. Syst. 2005, 36:147-168.
10. Gelman A., Carlin J.B., Stern H.S., Dunson D.B., Vehtari A., Rubin D.B. Bayesian Data Analysis 2003, Chapman & Hall, London.
11. Grimm V., Railsback S.F. Individual-Based Modeling and Ecology 2005, Princeton University Press, Princeton, NJ.
12. Grimm V., Railsback S.F. Pattern-oriented modelling: a 'multi-scope' for predictive systems ecology. Phil. Trans. R. Soc. B 2012, 367:298-310.
13. Grimm V., Revilla E., Berger U., Jeltsch F., Mooij W.M., Railsback S.F., Thulke H.-H., Weiner J., Wiegand T., DeAngelis D.L. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 2005, 310:987-991.
14. Gunadi B., Blount C., Edwards C.A. The growth and fecundity of Eisenia fetida (Savigny) in cattle solids pre-composted for different periods. Pedobiologia 2002, 46:15-23.
15. Gunadi B., Edwards C.A. The effects of multiple applications of different organic wastes on the growth, fecundity and survival of Eisenia fetida (Savigny) (Lumbricidae). Pedobiologia 2003, 47:321-329.
16. Hartig F., Calabrese J.M., Reineking B., Wiegand T., Huth A. Statistical inference for stochastic simulation models-theory and application. Ecol. Lett. 2011, 14:816-827.
17. Hartman K.J., Kitchell J.F. Bioenergetics modeling: progress since the 1992 symposium. Trans. Am. Fish. Soc. 2008, 137:216-223.
18. Hemelrijk C.K., Puga-Gonzalez I. An individual-oriented model on the emergence of support in fights, its reciprocation and exchange. PLoS One 2012, 7:e37271.
19. Hildenbrandt H., Carere C., Hemelrijk C.K. Self-organized aerial displays of thousands of starlings: a model. Behav. Ecol. 2010, 21:1349-1359.
20. Jakoby O., Grimm V., Frank K. Pattern-oriented parametrization of general models for ecological application: towards realistic evaluations of management approaches. Ecol. Model. 2014, 275:78-88.
21. Johnston A.S.A., Hodson M.E., Thorbek P., Alvarez T., Sibly R.M. An energy budget agent-based model of earthworm populations and its application to study the effects of pesticides. Ecol. Model. 2014, 280:5-17.
22. Keith J.M., Spring D. Agent-based Bayesian approach to monitoring the progress of invasive species eradication programs. Proc. Natl. Acad. Sci. 2013, 110:13428-13433.
23. Martínez I., Wiegand T., Camarero J.J., Batllori E., Gutiérrez E. Disentangling the formation of contrasting tree-line physiognomies combining model selection and Bayesian parameterization for simulation models. The American Naturalist 2011, 177:E136-E152.
24. Marjoram P., Molitor J., Plagnol V., Tavaré S. Markov chain Monte Carlo without likelihoods. Proc. Natl Acad. Sci. USA 2003, 100. 15324-15238.
25. May F., Giladi I., Ristow M., Ziv Y., Jeltsch F. Metacommunity, mainland-island system or island communities? Assessing the regional dynamics of plant communities in a fragmented landscape. Ecography 2013, 36:1-12.
26. Prangle D., Blum M.G.B., Popovic G., Sisson S.A. Diagnostic tools for approximate Bayesian computation using the coverage property. Aust. NZ J. Stat. 2013, 56:309-329.
27. Pritchard J.K., Seielstad M.T., Perez-Lezaun A., Feldman M.W. Population growth of human Y chromosomes: A study of Y chromosome microsatellites. Mol. Biol. Evol. 1999, 16:1791-1798.
28. R Core Team, 2014. R: A Language and Environment for Statistical Computing, http://www.R-project.org, 3.1.1 ed. The R Foundation for Statistical Computing, Vienna, Austria.
29. Rasmussen R., Hamilton G. An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata. Environ. Model. Softw. 2012, 29:1-10.
30. Ratmann O., Andrieu C., Wiuf C., Richardson D.M. Model criticism based on likelihood-free inference, with an application to protein network evolution. Proc. Natl. Acad. Sci. 2009, 106:10576-10581.
31. Reinecke A.J., Viljoen S.A. The influence of feeding patterns on growth and reproduction of the vermicomposting earthworm Eisenia fetida (Oligochaeta). Biol. Fert. Soils 1990, 10:184-187.
32. Robert C.P., Cornuet J.-M., Marin J.-M., Pillai N.S. Lack of confidence in approximate Bayesian computation model choice. Proc. Natl Acad. Sci. 2011, 108:15112-15117.
33. Schmolke A., Thorbek P., Chapman P., Grimm V. Ecological models and pesticide risk assessment: current modeling practice. Environ. Toxicol. Chem. 2010, 29:1006-1012.
34. Scranton K., Knape J., de Valpine P. An approximate Bayesian computation approach to parameter estimation in a stochastic stage-structured population model. Ecology 2014, 9:1418-1428.
35. Sibly R.M., Grimm V., Martin B.T., Johnston A.S.A., Kułakowska K., Topping C.J., Calow P., Nabe-Nielsen J., Thorbek P., DeAngelis D.L. Representing the acquisition and use of energy by individuals in agent-based models of animal populations. Methods Ecol. Evol. 2013, 4:151-161.
36. Sisson S.A., Fan Y., Tanaka M.A. Sequential Monte Carlo without likelihoods. Proceedings of the National Academy of Sciences of the United States of America 2007, 104:1760-1765.
37. Sottoriva A., Tavaré S. Integrating approximate Bayesian computation with complex agent-based models for cancer research. COMPSTAT 2010-Proceedings in Computational Statistics 2010, 57-66. Physiva Verlag, Heidelberg, Germany. G. Saporta, Y. Lechevallier (Eds.).
38. Stillman R.A., Goss-Custard J.D. Individual-based ecology of coastal birds. Biol. Rev. 2010, 85:413-434.
39. Tavaré S., Balding D.J., Griffiths R.C., Donnelly P. Inferring coalescence times from DNA sequence data. Genetics 1997, 145:505-518.
40. Thiele J.C., Kurth W., Grimm V. RNetLogo: an R package for running and exploring individual-based models implemented in NetLogo. Methods Ecol. Evol. 2012, 3:480-483.
41. Thiele J.C., Kurth W., Grimm V. Facilitating parameter estimation and sensitivity analysis of agent-based models: a cookbook using NetLogo and R. J. Artif. Soc. Soc. Sim. 2014, 17:11.
42. Toni T., Welch D., Strelkowa N., Ipsen A., Stumpf M.P.H. Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J. R. Soc. Interface 2009, 6:187-202.
43. Topping C.J., Dalkvist T., Grimm V. Post-hoc pattern-oriented testing and tuning of an existing large model: lessons from the field vole. PLoS One 2012, 7:e45872.
44. van der Vaart, E., Beaumont, M.A., Johnston, A.S.A., Sibly, R.M. (2015a): ABC for IBMs - Earthworm Runs & Results of Checks. figshare. http://dx.doi.org/10.6084/m9.figshare.1412720.
45. van der Vaart, E., Beaumont, M.A., Johnston, A.S.A., Sibly, R.M. (2015b): ABC for IBMs - Earthworm Models & ABC Code. figshare. http://dx.doi.org/10.6084/m9.figshare.1420526.
46. van der Vaart, E., Johnston, A.S.A., Sibly, R.M., submitted. Predicting how many animals will be where: A recipe for building, calibrating and evaluating individual-based models. Ecological Modelling.
47. Wilensky U. NetLogo 1999, Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL, http://ccl.northwestern.edu/netlogo/.
48. Wilkinson R.D. Approximate Bayesian Computation (ABC) gives exact results under the assumption of model error. Stat. Appl. Genet. Mol. Biol. 2013, 12:129-141.