publication . Article . 2016

Sensitivity analysis of Repast computational ecology models with R/Repast

Antonio Prestes García; Alfonso Rodríguez-Patón;
Open Access English
  • Published: 21 Nov 2016 Journal: Ecology and Evolution, volume 6, issue 24, pages 8,811-8,831 (eissn: 2045-7758, Copyright policy)
  • Publisher: John Wiley and Sons Inc.
  • Country: Spain
Abstract
Abstract Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual‐based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom‐up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based ...
Subjects
free text keywords: Original Research, computational ecology, individual‐based modeling, Repast, sensitivity analysis, systems biology, Informática, Informática, Ecology, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation, Matemáticas, Individual-Based Modeling, Sensitivity analysis, Repast, Computational Ecology, Systems Biology, Matemáticas, Rotation formalisms in three dimensions, Computational ecology, Management science, Artificial intelligence, business.industry, business, Single model, Modeling and simulation, Global sensitivity analysis, R package, Computer science, Machine learning, computer.software_genre, computer, Ecological systems theory
Related Organizations
Communities
  • EC Post-Grant Open Access Pilot
Funded by
EC| PLASWIRES
Project
PLASWIRES
Engineering multicellular biocircuits: programming cell-cell communication using plasmids as wires
  • Funder: European Commission (EC)
  • Project Code: 612146
  • Funding stream: FP7 | SP1 | ICT
56 references, page 1 of 4

Arutyunov, D., & Frost, L. S. (2013). F conjugation: Back to the beginning. Plasmid, 70(1), 18–32.23632276 [PubMed]

Beck, J. V., & Arnold, K. J. (1977). Parameter estimation in engineering and science. Wiley series in probability and mathematical statistics. New York, NY: Wiley.

Berec, L. (2002). Techniques of spatially explicit individual‐based models: Construction, simulation, and mean‐field analysis. Ecological Modelling, 150(1–2), 55–81.

Bergstrom, C. T., Lipsitch, M., & Levin, B. R. (2000). Natural selection, infectious transfer and the existence conditions for bacterial plasmids. Genetics, 155(4), 1505–1519.10924453 [OpenAIRE] [PubMed]

Bettonvil, B., & Kleijnen, J. P. C. (1996). Searching for important factors in simulation models with many factors: Sequential bifurcation. European Journal of Operational Research, 96, 180–194.

Bonabeau, E. (2002). Agent‐based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the USA, 99(Suppl. 3), 7280–7287.12011407 [OpenAIRE] [PubMed]

Box, G. E. P., & Draper, N. R. (1987). Empirical model‐building and response surfaces (Wiley series in probability and statistics, 1st ed.). New York, NY, USA: Wiley.

Campolongo, F., Cariboni, J., & Saltelli, A. (2007). An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software, 22, 1509–1518.

Chen, I., Christie, P. J., & Dubnau, D. (2005). The ins and outs of DNA transfer in bacteria. Science, 310(5753), 1456–1460.16322448 [OpenAIRE] [PubMed]

Crawley, M. J. (2007). The R book (1st ed.). The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, United Kingdom: Wiley.

Dieckmann, U., Law, R., & Metz, J. A. J. (2000). The geometry of ecological interactions (Vol. 1). Cambridge University Press: New York.

Emrich, Š., Suslov, S., & Judex, F. (2007). Fully agent based modellings of epidemic spread using Anylogic. Proceeding EUROSIM 2007, Ljubljana, Slovenia, 2007, pp. 1–7.

Evans, M. R., Grimm, V., Johst, K., Knuuttila, T., Langhe, R., Lessells, C. M., … Benton, T. G. (2013). Do simple models lead to generality in ecology? Trends in Ecology & Evolution, 28(10), 578–583.23827437 [PubMed]

Ferrer, J., Prats, C., & López, D. (2008). Individual‐based modelling: An essential tool for microbiology. Journal of Biological Physics, 34(1–2), 19–37.19669490 [OpenAIRE] [PubMed]

Grimm, V., & Railsback, S. F. (2005). Individual‐based modeling and ecology (Princeton series in theoretical and computational biology). Princeton, NJ: Princeton University Press.

56 references, page 1 of 4
Abstract
Abstract Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual‐based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom‐up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based ...
Subjects
free text keywords: Original Research, computational ecology, individual‐based modeling, Repast, sensitivity analysis, systems biology, Informática, Informática, Ecology, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation, Matemáticas, Individual-Based Modeling, Sensitivity analysis, Repast, Computational Ecology, Systems Biology, Matemáticas, Rotation formalisms in three dimensions, Computational ecology, Management science, Artificial intelligence, business.industry, business, Single model, Modeling and simulation, Global sensitivity analysis, R package, Computer science, Machine learning, computer.software_genre, computer, Ecological systems theory
Related Organizations
Communities
  • EC Post-Grant Open Access Pilot
Funded by
EC| PLASWIRES
Project
PLASWIRES
Engineering multicellular biocircuits: programming cell-cell communication using plasmids as wires
  • Funder: European Commission (EC)
  • Project Code: 612146
  • Funding stream: FP7 | SP1 | ICT
56 references, page 1 of 4

Arutyunov, D., & Frost, L. S. (2013). F conjugation: Back to the beginning. Plasmid, 70(1), 18–32.23632276 [PubMed]

Beck, J. V., & Arnold, K. J. (1977). Parameter estimation in engineering and science. Wiley series in probability and mathematical statistics. New York, NY: Wiley.

Berec, L. (2002). Techniques of spatially explicit individual‐based models: Construction, simulation, and mean‐field analysis. Ecological Modelling, 150(1–2), 55–81.

Bergstrom, C. T., Lipsitch, M., & Levin, B. R. (2000). Natural selection, infectious transfer and the existence conditions for bacterial plasmids. Genetics, 155(4), 1505–1519.10924453 [OpenAIRE] [PubMed]

Bettonvil, B., & Kleijnen, J. P. C. (1996). Searching for important factors in simulation models with many factors: Sequential bifurcation. European Journal of Operational Research, 96, 180–194.

Bonabeau, E. (2002). Agent‐based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the USA, 99(Suppl. 3), 7280–7287.12011407 [OpenAIRE] [PubMed]

Box, G. E. P., & Draper, N. R. (1987). Empirical model‐building and response surfaces (Wiley series in probability and statistics, 1st ed.). New York, NY, USA: Wiley.

Campolongo, F., Cariboni, J., & Saltelli, A. (2007). An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software, 22, 1509–1518.

Chen, I., Christie, P. J., & Dubnau, D. (2005). The ins and outs of DNA transfer in bacteria. Science, 310(5753), 1456–1460.16322448 [OpenAIRE] [PubMed]

Crawley, M. J. (2007). The R book (1st ed.). The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, United Kingdom: Wiley.

Dieckmann, U., Law, R., & Metz, J. A. J. (2000). The geometry of ecological interactions (Vol. 1). Cambridge University Press: New York.

Emrich, Š., Suslov, S., & Judex, F. (2007). Fully agent based modellings of epidemic spread using Anylogic. Proceeding EUROSIM 2007, Ljubljana, Slovenia, 2007, pp. 1–7.

Evans, M. R., Grimm, V., Johst, K., Knuuttila, T., Langhe, R., Lessells, C. M., … Benton, T. G. (2013). Do simple models lead to generality in ecology? Trends in Ecology & Evolution, 28(10), 578–583.23827437 [PubMed]

Ferrer, J., Prats, C., & López, D. (2008). Individual‐based modelling: An essential tool for microbiology. Journal of Biological Physics, 34(1–2), 19–37.19669490 [OpenAIRE] [PubMed]

Grimm, V., & Railsback, S. F. (2005). Individual‐based modeling and ecology (Princeton series in theoretical and computational biology). Princeton, NJ: Princeton University Press.

56 references, page 1 of 4
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