Theory in Biosciences

, Volume 133, Issue 3–4, pp 135–143 | Cite as

Richards-like two species population dynamics model

  • Fabiano Ribeiro
  • Brenno Caetano Troca Cabella
  • Alexandre Souto Martinez
Original Paper

Abstract

The two-species population dynamics model is the simplest paradigm of inter- and intra-species interaction. Here, we present a generalized Lotka–Volterra model with intraspecific competition, which retrieves as particular cases, some well-known models. The generalization parameter is related to the species habitat dimensionality and their interaction range. Contrary to standard models, the species coupling parameters are general, not restricted to non-negative values. Therefore, they may represent different ecological regimes, which are derived from the asymptotic solution stability analysis and are represented in a phase diagram. In this diagram, we have identified a forbidden region in the mutualism regime, and a survival/extinction transition with dependence on initial conditions for the competition regime. Also, we shed light on two types of predation and competition: weak, if there are species coexistence, or strong, if at least one species is extinguished.

Keywords

Complex systems Population dynamics (ecology) Pattern formation ecological  

Notes

Acknowledgments

The authors acknowledges support from CNPq (305738/2010-0, 476722/2010-1 and 127151/2012-5) and CAPES.

References

  1. Araujo RP, McElwain DLS (2004) A history of the study of solid tumour growth: the contribution of mathematical modelling. Bull Math Biol 66:1039–1091PubMedCrossRefGoogle Scholar
  2. Arruda TJ, González RS, Terçariol CAS, Martinez AS (2008) Arithmetical and geometrical means of generalized logarithmic and exponential functions: generalized sum and product operators. Phys Lett A 372:2578–2582CrossRefGoogle Scholar
  3. Barberis L, Condat C, Roman P (2011) Vector growth universalities. Chaos Solitons Fractals 44:1100–1105CrossRefGoogle Scholar
  4. Bettencourt LMA, Lobo J, Helbing D, Kuhnert C, West GB (2007) Growth, innovation, scaling, and the pace of life in cities. Proc Nat Acad Sc 104:7301–7306CrossRefGoogle Scholar
  5. Bomze I (1995) Lotka–Volterra equation and replicator dynamics: new issues in classification. Biol Cybern 72:447–453CrossRefGoogle Scholar
  6. Cabella BCT, Martinez AS, Ribeiro F (2011) Data collapse, scaling functions, and analytical solutions of generalized growth models. Phys Rev E 83:061902CrossRefGoogle Scholar
  7. Cabella BCT, Ribeiro F, Martinez AS (2012) Effective carrying capacity and analytical solution of a particular case of the Richards-like two-species population dynamics model. Phys A 391:1281–1286CrossRefGoogle Scholar
  8. Cavalieri LF, Kocak H (1995) Intermittent transition between order and chaos in an insect pest population. J Theor Biol 175:231–234CrossRefGoogle Scholar
  9. Cross SS (1997) Fractals in Pathology. J Pathol 182:1–8PubMedCrossRefGoogle Scholar
  10. dOnofrio A (2009) Fractal growth of tumors and other cellular populations: linking the mechanistic to the phenomenological modeling and vice versa. Chaos Solitons Fractals 41:875–880Google Scholar
  11. dos Santos LS, Cabella BCT, Martinez AS (2014) Generalized Allee effect model. Theory Biosci doi: 10.1007/s12064-014-0199-6 PubMedGoogle Scholar
  12. Edelstein-Keshet L (ed) (2005) Mathematical models in Biology. SIAM, PhiladelphiaGoogle Scholar
  13. Espíndola AL, Bauch C, Cabella BCT, Martinez AS (2011) An agent-based computational model of the spread of tuberculosis. J Stat Mech 2011:P5003CrossRefGoogle Scholar
  14. Espíndola AL, Girardi D, Penna TJP, Bauch C, Martinez AS, Cabella BCT (2012) Exploration of the parameter space in an agent-based model of tuberculosis spread: emergence of drug resistance in developing vs developed countries. Int J Mod Phy C 23:12500461–12500469CrossRefGoogle Scholar
  15. Espíndola AL, Girardi D, Penna TJP, Bauch C, Cabella BCT, Martinez AS (2014) An antibiotic protocol to minimize emergence of drug-resistant tuberculosis. Phys A 400:80–92CrossRefGoogle Scholar
  16. Fowler CW (1981) Density dependence as related to life history strategy. Ecology 62:602–610CrossRefGoogle Scholar
  17. Gavrilets S, Hastings A (1995) Intermittency and transient chaos from simple frequency- dependen selection. Proc R Soc B Biol Sci 261:233–238CrossRefGoogle Scholar
  18. Gompertz B (1825) On the nature of the function expressive of the law of human mortality, and on the new mode of determining the value of life contingencies. Phil Trans Royal Soc Lond A 115:153Google Scholar
  19. Gould H, Tobochnik J, Christian W (2006) An introduction to computer simulation methods. Addison-WesleyGoogle Scholar
  20. Guiot C, Degiorgis PG, Delsanto PP, Gabriele P, Deisboeck TS (2003) Does tumor growth follow a “universal law”? J Theoretical Biol 225:147–151PubMedCrossRefGoogle Scholar
  21. Harrison M (2001) Dynamical mechanism for coexistence of dispersing species. J Theoretical Biol 213:53–72PubMedCrossRefGoogle Scholar
  22. Hastings A (2004) Transients: the key to long-term ecological understanding? Trends Ecol Evol 19(1):39–45PubMedCrossRefGoogle Scholar
  23. Imre AR, Bogaert J (2004) The fractal dimension as a measure of the quality of habitats. Acta Biotheor 52(1):41–56PubMedCrossRefGoogle Scholar
  24. Kaitala V (1999) Dynamic complexities in host-parasitoid interaction. J Theor Biol 197:331–341PubMedCrossRefGoogle Scholar
  25. Kozusko F, Bourdeau M (2007) A unified model of sigmoid tumour growth based on cell proliferation and quiescence. Cell Prolif 40:824–834PubMedCrossRefGoogle Scholar
  26. Lai Y (1995a) Persistence of supertransients of spatiotemporal chaotic dynamical-systems in noisy environment. Phy Lett A 200:418–422CrossRefGoogle Scholar
  27. Lai Y (1995b) Unpredictability of the asymptotic attractors in phasecoupled oscillators. Phys Rev E 51:2902–2908CrossRefGoogle Scholar
  28. Lai Y, Winslow R (1995) Geometric-properties of the chaotic saddle responsible for supertransients in spatiotemporal chaotic systems. Phys Rev Lett 74:5208–5211PubMedCrossRefGoogle Scholar
  29. Martinez AS, González RS, Espíndola AL (2009) Generalized exponential function and discrete growth models. Phys A 388:2922–2930CrossRefGoogle Scholar
  30. Martinez AS, González RS, Terçariol CAS (2008) Continuous growth models in terms of generalized logarithm and exponential functions. Phys A 387:5679–5687CrossRefGoogle Scholar
  31. Mombach JCM, Lemke N, Bodmann BEJ, Idiart MAP (2002a) A mean-field theory of cellular growth. Europhy Lett 59:923–928CrossRefGoogle Scholar
  32. Mombach JCM, Lemke N, Bodmann BEJ, Idiart MAP (2002b) A mean-field theory of cellular growth. Europhy Lett 60:489–489CrossRefGoogle Scholar
  33. Murray JD (2002) Mathematical biology I: an introduction. Springer, New YorkGoogle Scholar
  34. Motoike IN, Adamatzky A (2005) Three-valued logic gates in reaction-diffusion excitable media. Chaos Solitons Fractals 24:107–114CrossRefGoogle Scholar
  35. Novozhilov AS, Berezovskaya FS, Koonin EV, Karev GP (2006) Mathematical modeling of tumor therapy with oncolytic viruses: regimes with complete tumor elimination within the framework of deterministic models. Biol Dir 1:6CrossRefGoogle Scholar
  36. Nowak MA, Anderson RM, McLean AR, Wolfs TF, Goudsmit J, May RM (1991) Antigenic diversity thresholds and the development of aids. Science 254:963–969PubMedCrossRefGoogle Scholar
  37. Pereira MA, Martinez AS (2010) Pavlovian prisoner’s dilemma analytical results, the quasi-regular phase and spatio-temporal patterns. J Theretical Biol 265:346–358CrossRefGoogle Scholar
  38. Pereira MA, Martinez AS, Espíndola AL (2008) Prisoner’s dilemma in one-dimensional cellular automata: visualization of evolutionary patterns. Int J Mod Phy C 19:187–201CrossRefGoogle Scholar
  39. Ribeiro F (2014) A non-phenomenological model to explain population growth behaviors. http://arxiv.org/abs/1402.3676. Accessed 8 Aug 2014
  40. Richards FJ (1959) A flexible growth functions for empirical use. J Exp Bot 10:290–300CrossRefGoogle Scholar
  41. Saether BE, Engen Matthysen SE (2002) Demographic characteristics and population dynamical patterns of solitary birds. Science 295:2070–2073Google Scholar
  42. Savageau MA (1979) Growth of complex systems can be related to the properties of their underlying determinants. Proc Natl Acad Sci USA 76(11):5413–5417PubMedCentralPubMedCrossRefGoogle Scholar
  43. Silby RM, Barker D, Denham MC, Hone J, Pagel M (2005) On the regulation of populations of mammals, birds, fish, and insects. Science 309:607–610CrossRefGoogle Scholar
  44. Sibly RM, Hone J (2002) Population growth rate and its determinants: an overview. Philos Trans R Soc Lond Ser B 357:1153–1170CrossRefGoogle Scholar
  45. Strzalka D (2009) Connections between von Foerster coalition growth model and Tsallis \(q\)-exponential. Acta Physica Polonica B 40:41–47Google Scholar
  46. Strzalka D, Grabowski F (2008) Towards possible \(q\)-generalizations of the Malthus and Verhulst growth models. Phys A 387:2511–2518CrossRefGoogle Scholar
  47. Tokeshi M, Arakaki S (2012) Habitat complexity in aquatic systems: fractals and beyond. Hydrobiologia 685:2747CrossRefGoogle Scholar
  48. Tsallis C (1988) Possible generalization of Boltzmann-Gibbs statistics. J Stat Phy 52:479–487CrossRefGoogle Scholar
  49. Tsallis C (1994) What are the numbers experiments provide? Química Nova 17:468–471Google Scholar
  50. Tsoularis A, Wallace J (2002) Analysis of logistic growth models. Math Biosci 179:21–55PubMedCrossRefGoogle Scholar
  51. von Foerster H, Mora PM, Amiot LW (1960) Doomsday: friday, 13 November, A.D. 2026. Science 132(3436):1291–1295CrossRefGoogle Scholar
  52. West GB, Brown JH, Enquist BJ (2001) A general model for ontogenetic growth. Nature 413:628–631PubMedCrossRefGoogle Scholar
  53. Wodarz D (2001) Viruses as antitumor weapons: defining conditions for tumor remission. Cancer Res 61(8):3501–3507PubMedGoogle Scholar
  54. Wodarz D, Komarova N (2005) Computational biology of cancer: lecture notes and mathematical modeling. Scientific Publishing Company, SingaporeCrossRefGoogle Scholar
  55. Yeomans JM (1992) Statistical mechanics of phase transitions. Oxford Science Publications.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Fabiano Ribeiro
    • 1
  • Brenno Caetano Troca Cabella
    • 2
  • Alexandre Souto Martinez
    • 3
    • 4
  1. 1.Departamento de Ciências Exatas (DEX)Universidade Federal de Lavras (UFLA)LavrasBrazil
  2. 2.Sapra AssessoriaSão CarlosBrazil
  3. 3.Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP)Universidade de São Paulo (USP)Ribeirão PretoBrazil
  4. 4.Instituto Nacional de Ciência e Tecnologia em Sistemas Complexos (INCT-SC)Rio de Janeiro Brazil

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