Interface Focus. 2012 Apr 06;2(2):241-54. doi: 10.1098/rsfs.2011.0083. Epub 2012 Jan 05.
Computational ecology as an emerging science.
Interface focus
Sergei Petrovskii, Natalia Petrovskaya
Affiliations
Affiliations
- Department of Mathematics, University of Leicester, Leicester LE1 7RH, UK.
PMID: 23565336
PMCID: PMC3293204 DOI: 10.1098/rsfs.2011.0083
Abstract
It has long been recognized that numerical modelling and computer simulations can be used as a powerful research tool to understand, and sometimes to predict, the tendencies and peculiarities in the dynamics of populations and ecosystems. It has been, however, much less appreciated that the context of modelling and simulations in ecology is essentially different from those that normally exist in other natural sciences. In our paper, we review the computational challenges arising in modern ecology in the spirit of computational mathematics, i.e. with our main focus on the choice and use of adequate numerical methods. Somewhat paradoxically, the complexity of ecological problems does not always require the use of complex computational methods. This paradox, however, can be easily resolved if we recall that application of sophisticated computational methods usually requires clear and unambiguous mathematical problem statement as well as clearly defined benchmark information for model validation. At the same time, many ecological problems still do not have mathematically accurate and unambiguous description, and available field data are often very noisy, and hence it can be hard to understand how the results of computations should be interpreted from the ecological viewpoint. In this scientific context, computational ecology has to deal with a new paradigm: conventional issues of numerical modelling such as convergence and stability become less important than the qualitative analysis that can be provided with the help of computational techniques. We discuss this paradigm by considering computational challenges arising in several specific ecological applications.
Keywords: computational ecology; conceptual modelling; predictive modelling
References
- Science. 1971 Jan 29;171(3969):385-7 - PubMed
- PLoS Comput Biol. 2005 Jul;1(2):101-5 - PubMed
- Science. 1975 Sep 26;189(4208):1088-90 - PubMed
- Oecologia. 1998 Aug;116(1-2):103-112 - PubMed
- Bull Math Biol. 2003 May;65(3):425-46 - PubMed
- Oecologia. 2010 Jul;163(3):759-73 - PubMed
- J Theor Biol. 2010 Jul 7;265(1):45-54 - PubMed
- Evolution. 2011 Jun;65(6):1739-51 - PubMed
- Am Nat. 1993 Sep;142(3):412-42 - PubMed
- Annu Rev Entomol. 1998;43:243-70 - PubMed
- Ecol Lett. 2005 May;8(5):513-23 - PubMed
- Adv Biophys. 1986;22:1-94 - PubMed
- Science. 2011 Jun 24;332(6037):1551-3 - PubMed
- Ecology. 2011 Feb;92(2):289-95 - PubMed
- Nature. 2008 Apr 24;452(7190):987-90 - PubMed
- Theor Popul Biol. 1998 Apr;53(2):108-130 - PubMed
- Proc R Soc Lond B Biol Sci. 1989 Nov 22;238(1291):113-25 - PubMed
- Proc Natl Acad Sci U S A. 1995 Mar 28;92(7):2524-8 - PubMed
- Biometrika. 1951 Jun;38(1-2):196-218 - PubMed
- J R Soc Interface. 2012 Mar 7;9(68):420-35 - PubMed
- J Theor Biol. 2011 Aug 21;283(1):82-91 - PubMed
- J Theor Biol. 2004 Apr 7;227(3):349-58 - PubMed
- Bull Math Biol. 2007 Apr;69(3):931-56 - PubMed
- Ecology. 2008 Feb;89(2):542-54 - PubMed
- Science. 2008 Jun 13;320(5882):1444-9 - PubMed
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