Predicting how many animals will be where: How to build, calibrate and evaluate individual-based models

Ecological Modelling

Volumn 326, Issue , 2016, Pages 113-123

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

^a UNIVERSITY OF READING   (United Kingdom)

Author keywords

Approximate bayesian computation; Energy budget; Individual based models; Model selection; Parameter estimation; Population dynamics

Indexed keywords

AGRICULTURAL CHEMICALS; ANIMALS; BAYESIAN NETWORKS; BUDGET CONTROL; CALIBRATION; COMPUTATION THEORY; ECOLOGY; PARAMETER ESTIMATION; PHYSIOLOGY; POPULATION DYNAMICS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; UNCERTAINTY ANALYSIS;

APPROXIMATE BAYESIAN; ENERGY BUDGETS; EVOLUTIONARY PERSPECTIVE; INDIVIDUAL BASED MODEL; LIFE HISTORY THEORY; MAJOR CONSTRUCTION PROJECT; MODEL SELECTION; STATISTICAL TECHNIQUES;

PHYSIOLOGICAL MODELS;

BAYESIAN ANALYSIS; BEHAVIORAL ECOLOGY; COMPLEXITY; ENERGY BUDGET; ESTIMATION METHOD; EVOLUTIONARY BIOLOGY; GROWTH RATE; INDIVIDUAL-BASED MODEL; LANDSCAPE; LIFE HISTORY THEORY; POPULATION DYNAMICS; PREDICTION; SEXUAL MATURITY;

ANIMALIA;

EID: 84961121966     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2015.08.012     Document Type: Article

References (75)

1. Beaumont M.A. Approximate Bayesian computation in evolution and ecology. Annu. Rev. Ecol., Evol. Syst. 2010, 41:379-406.
2. Beaumont M.A., Zhang W., Balding D.J. Approximate Bayesian computation in population genetics. Genetics 2002, 162:2025-2035.
3. Begon M., Townsend C.R., Harper J.L. Ecology: From Individuals to Ecosystems 2006, Blackwell Publishing, Malden, MA. fourth ed.
4. Bertorelle G., Benazzo A., Mona S. ABC as a flexible framework to estimate demography over space and time: some cons, many pros. Mol. Ecol. 2010, 19:2609-2625.
5. Blum M.G.B., François O. Non-linear regression models for approximate Bayesian computation. Stat. Comput. 2010, 20:63-73.
6. 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.
7. Brown J.H., Sibly R.M. The metabolic theory of ecology and its central equation. Metabolic Ecology: A Scaling Approach 2012, 21-33. Wiley-Blackwell, Oxford. R.M. Sibly, J.H. Brown, A. Kodric-Brown (Eds.).
8. Clauss M., Schwarm A., Ortmann S., Streich W.J., Hummel J. A case of non-scaling in mammalian physiology? Body size, digestive capacity, food intake, and ingesta passage in mammalian herbivores. Comp. Biochem. Physiol., A: Mol. Integr. Physiol. 2007, 148:249-265.
9. Csilléry K., Blum M.G.B., Gaggiotti O.E., François O. Approximate Bayesian computation (ABC) in practice. Trends Ecol. Evol. 2010, 25:410-418.
10. Csillery K., François O., Blum M.G.B. abc: An R package for approximate Bayesian computation (ABC). Methods Ecol. Evol. 2012, 3:475-479.
11. Dalkvist T., Sibly R.M., Topping C.J. How predation and landscape fragmentation affect vole population dynamics. PLoS ONE 2011, 6:e22834.
12. Davies N.B., Krebs J.R., West S.A. An Introduction to Behavioural Ecology 2012, Wiley-Blackwell, Oxford, UK. fourth ed.
13. DeAngelis D.L., Mooij W.M. Individual-based modeling of ecological and evolutionary processes. Annu. Rev. Ecol., Evol. Syst. 2005, 36:147-168.
14. Evans M.R., Bithell M., Cornell S.J., Dall S.R.X., Diaz S., Emmott S., Ernande B., Grimm V., Hodgson D.J., Lewis S.L., Mace G.M., Morecroft M., Moustakas A., Murphy E., Newbold T., Norris K.J., Petchey O., Smith M., Travis J.M.J., Benton T.G. Predictive systems ecology. Proc. R. Soc. B: Biol. Sci. 2013, 280:20131452.
15. Galic N., Forbes V. Ecological models in ecotoxicology and ecological risk assessment: an introduction to the special section. Environ. Toxicol. Chem. 2014, 33:1446-1448.
16. Glazier D.S. Beyond the '3/4-power law': variation in the intra- and interspecific scaling of metabolic rate in animals. Biol. Rev. 2005, 80:611-662.
17. Glazier D.S. Resource allocation patterns. Resource Allocation Theory Applied to Farm Animal Production 2008, 22-43. CABI Publishing, Wallingford, UK. W.M. Rauw (Ed.).
18. Gosler A.G., Greenwood J.J.D., Perrins C. Predation risk and the cost of being fat. Nature 1995, 377:621-623.
19. Grimm V., Railsback S.F. Individual-based Modeling and Ecology 2005, Princeton University Press, Princeton, NJ.
20. Grimm V., Railsback S.F. Pattern-oriented modelling: a 'multi-scope' for predictive systems ecology. Philos. Trans. R. Soc. B: Biol. Sci. 2012, 367:298-310.
21. 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.
22. 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.
23. 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.
24. Hartig F., Dislich C., Wiegand T., Huth A. Technical note: approximate Bayesian parameterization of a process-based tropical forest model. Biogeosciences 2014, 11:1261-1272.
25. Hartman K.J., Kitchell J.F. Bioenergetics modeling: progress since the 1992 symposium. Trans. Am. Fish. Soc. 2008, 137:216-223.
26. Hendriks A.J. Allometric scaling of rate, age and density parameters in ecological models. Oikos 1999, 86:293-310.
27. Holling C.S. The components of predation as revealed by a study of small-mammal predation of the European pine sawfly. Can. Entomol. 1959, 91:293-320.
28. Hou C. Increasing energetic cost of biosynthesis during growth makes refeeding deleterious. Am. Natur. 2014, 184:233-247.
29. 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. Modell. 2014, 280:5-17.
30. Karasov W.H., Martinez del Rio C. Physiological Ecology 2007, Princeton University Press, Princeton, NJ and Oxford, UK.
31. Kaspari M. Stoiciometry. Metabolic Ecology: A Scaling Approach 2012, 14-47. Wiley-Blackwell, Oxford, UK. R.M. Sibly, J.H. Brown, A. Kodric-Brown (Eds.).
32. Kass R.E., Raftery A.E. Bayes factors. J. Am. Stat. Assoc. 1995, 90:773-795.
33. Kerkhoff A.J. Modeling metazoan growth and ontogeny. Metabolic Ecology: A Scaling Approach 2012, Wiley-Blackwell, Oxford, UK. R.M. Sibly, J.H. Brown, A. Kodric-Brown (Eds.).
34. Kooijman S.A.L.M. Dynamic Energy Budget Theory 2010, Cambridge University Press, Cambridge, UK. third ed.
35. Krebs C.J. Ecology 2009, Benjamin Cummings, San Francisco, CA. sixth ed.
36. Kulakowska K.A., Kulakowski T.M., Inglis I.R., Smith G.C., Haynes P.J., Prosser P., Thorbek P., Sibly R.M. Using an individual-based model to select among alternative foraging strategies of woodpigeons: Data support a memory-based model with a flocking mechanism. Ecol. Modell. 2014, 280:89-101.
37. Lind J., Jakobsson S., Kullberg C. Impaired predator evasion in the life history of birds: behavioral and physiological adaptations to reduced flight ability. Current Ornithology 2010, vol. 17:1-30. Springer, New York.
38. Lindstedt S.L., Schaeffer P.J. Use of allometry in predicting anatomical and physiological parameters of mammals. Lab. Anim. 2002, 36:1-19.
39. Liu C., Sibly R.M., Grimm V., Thorbek P. Linking pesticide exposure and spatial dynamics: an individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes. Ecol. Modell. 2013, 248:92-102.
40. Marjoram P., Molitor J., Plagnol V., Tavaré S. Markov chain Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. U.S.A. 2003, 100:15238-15324.
41. Martin B.T., Jager T., Nisbet R.M., Preuss T.G., Grimm V. Predicting population dynamics from the properties of individuals: a cross-level test of dynamic energy budget theory. Am. Natur. 2013, 181:506-519.
42. Martin B.T., Zimmer E.I., Grimm V., Jager T. Dynamic energy budget theory meets individual-based modelling: a generic and accessible implementation. Methods Ecol. Evol. 2012, 3:445-449.
43. 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. Am. Natur. 2011, 177:E136-E152.
44. Monroy F., Aira M., Domínguez J., Velando A. Seasonal population dynamics of Eisenia fetida (Savigny, 1826) (Oligochaeta, Lumbricidae) in the field. Comptes Rendus Biol. 2006, 329:912-915.
45. Moses M.E., Hou C., Woodruff W.H., West G.B., Nekola J.C., Zuo W., Brown J.H. Revisiting a model of ontogenetic growth: estimating model parameters from theory and data. Am. Natur. 2008, 171:632-645.
46. Nabe-Nielsen J., Sibly R.M., Forchhammer M.C., Forbes V.E., Topping C.J. The efects of landscape modifications on the long-term persistence of animal populations. PLoS ONE 2010, 5:e8932.
47. Nabe-Nielsen J., Sibly R.M., Tougaard J., Teilmann J., Sveegaard S. Effects of noise and by-catch on a Danish harbour porpoise population. Ecol. Modell. 2014, 272:242-251.
48. Peters R.H. The Ecological Implications of Body Size 1983, Cambridge University Press, Cambridge, UK.
49. Petit O., Gautrais J., Leca J.B., Theraulaz G., Deneubourg J.-L. Collective decision making in white-faced capuchin monkeys. Proc. R. Soc. B: Biol. Sci. 2009, 1672:3495-3503.
50. Pond C.M. Morphological aspects and ecological and mechanical consequences of fat deposition in wild vertebrates. Ann. Rev. Ecol. Syst. 1978, 9:519-570.
51. Prangle D., Blum M.G.B., Popovic G., Sisson S.A. Diagnostic tools for approximate Bayesian computation using the coverage property. Aust. N. Z. J. Stat. 2013, 56:309-329.
52. Price C.A., Weitz J.S., Savage V.M., Stegen J., Clarke A., Coomes D.A., Dodds P.S., Etienne R.S., Kerkhoff A.J., McCulloh K., Niklas K.J., Olff H., Swenson N.G. Testing the metabolic theory of ecology. Ecol. Lett. 2012, 15:1465-1474.
53. 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.
54. Reinecke A.J., Viljoen S.A. The influence of feeding patterns on growth and reproduction of the vermicomposting earthworm Eisenia fetida (Oligochaeta). Biol. Fertil. Soils 1990, 10:184-187.
55. Ricklefs R.E., Miller G.L. Ecology 2000, W.H. Freeman and Co., New York, NY. fourth ed.
56. Sibly R.M. Life history theory. Encyclopedia of Evolution 2002, 623-627. Oxford University Press, Oxford. M. Pagel (Ed.).
57. Sibly R.M., Calow P. Physiological Ecology of Animals 1986, Blackwell Scientific Publications, Oxford, UK.
58. Sibly R.M., Grimm V., Martin B.T., Johnston A.S.A., Kulakowska 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.
59. Sih A., Cote J., Evans M., Fogarty S., Pruitt J. Ecological implications of behavioural syndromes. Ecol. Lett. 2012, 15:278-289.
60. Simpson S.J., Sibly R.M., Lee K.P., Behmer S.T., Raubenheimer D. Optimal foraging when regulating intake of multiple nutrients. Anim. Behav. 2004, 68:1299-1311.
61. Sinclair A.R.E. Population regulation in animals. Ecological Concepts 1989, 197-241. Blackwell Scientific, Oxford. J.M. Cherrett (Ed.).
62. Sisson S.A., Fan Y., Tanaka M.A. Sequential Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. U.S.A. 2007, 104:1760-1765.
63. Stearns S.C. The Evolution of Life Histories 1992, Oxford University Press, Oxford, UK.
64. Stillman R.A., Goss-Custard J.D. Individual-based ecology of coastal birds. Biol. Rev. 2010, 85:413-434.
65. Stillman R.A., Railsback S.F., Giske J., Berger U., Grimm V. Making predictions in a changing world: the benefits of individual-based ecology. BioScience 2015, 65:140-150.
66. Tavaré S., Balding D.J., Griffiths R.C., Donnelly P. Inferring coalescence times from DNA sequence data. Genetics 1997, 145:505-518.
67. 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.
68. 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. Simul. 2014, 17:11.
69. 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. Interf. 2009, 6:187-202.
70. 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.
71. van der Vaart E., Beaumont M.A., Johnston A., Sibly R.M. Calibration and evaluation of individual-based models using approximate Bayesian computation. Ecol. Modell. 2015, 312:182-190.
72. van der Vaart E., Johnston A., Sibly R.M. Linking Levels-Runs. Figshare 2015, 1494757. doi: http://dx.doi.org/10.6084/m9.figshare.
73. van der Vaart E., Johnston A.S.A., Sibly R.M. Linking Levels-Code. Figshare 2015, 1494754. doi: http://dx.doi.org/10.6084/m9.figshare.
74. Wilensky U. NetLogo 1999, Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL, 〈http://ccl.northwestern.edu/netlogo/〉.
75. Witter M.S., Cuthill I.C. The ecological costs of avian fat storage. Philos. Trans. R. Soc. London Ser. B: Biol. Sci. 1993, 340:73-92.