Richards-like two species population dynamics model

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.

[1]  Charles W. Fowler,et al.  Density Dependence as Related to Life History Strategy , 1981 .

[2]  S S Cross,et al.  FRACTALS IN PATHOLOGY , 1997, The Journal of pathology.

[3]  Eugene V Koonin,et al.  Mathematical modeling of tumor therapy with oncolytic viruses: Regimes with complete tumor elimination within the framework of deterministic models , 2006, Biology Direct.

[4]  A. Tsoularis,et al.  Analysis of logistic growth models. , 2002, Mathematical biosciences.

[5]  C. Bauch,et al.  An antibiotic protocol to minimize emergence of drug-resistant tuberculosis , 2014 .

[6]  Alexandre Souto Martinez,et al.  PRISONER'S DILEMMA IN ONE-DIMENSIONAL CELLULAR AUTOMATA: VISUALIZATION OF EVOLUTIONARY PATTERNS , 2007, 0708.3520.

[7]  A. Martinez,et al.  Generalized exponential function and discrete growth models , 2008, 0803.3089.

[8]  Jim Hone,et al.  Population growth rate and its determinants: an overview. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[9]  R D Holt,et al.  Dynamical mechanism for coexistence of dispersing species. , 2001, Journal of theoretical biology.

[10]  D. Helbing,et al.  Growth, innovation, scaling, and the pace of life in cities , 2007, Proceedings of the National Academy of Sciences.

[11]  Leah Edelstein-Keshet,et al.  Mathematical models in biology , 2005, Classics in applied mathematics.

[12]  C. Thompson The Statistical Mechanics of Phase Transitions , 1978 .

[13]  F. Ribeiro A Non-Phenomenological Model to Explain Population Growth Behaviors , 2014, 1402.3676.

[14]  D. Wodarz,et al.  Viruses as antitumor weapons: defining conditions for tumor remission. , 2001, Cancer research.

[15]  D L S McElwain,et al.  A history of the study of solid tumour growth: The contribution of mathematical modelling , 2004, Bulletin of mathematical biology.

[16]  Connections Between von Foerster Coalition Growth Model and Tsallis q-Exponential , 2009 .

[17]  C. Tsallis What are the Numbers that Experiments Provide , 1994 .

[18]  A. Hastings Transients: the key to long-term ecological understanding? , 2004, Trends in ecology & evolution.

[19]  F. Grabowski,et al.  Towards possible q-generalizations of the Malthus and Verhulst growth models , 2008 .

[20]  Marcelo Alves Pereira,et al.  Pavlovian Prisoner's Dilemma-Analytical results, the quasi-regular phase and spatio-temporal patterns. , 2010, Journal of theoretical biology.

[21]  C. Tsallis Possible generalization of Boltzmann-Gibbs statistics , 1988 .

[22]  M. Tokeshi,et al.  Habitat complexity in aquatic systems: fractals and beyond , 2011, Hydrobiologia.

[23]  James H. Brown,et al.  A general model for ontogenetic growth , 2001, Nature.

[24]  Benjamin Gompertz,et al.  On the Nature of the Function Expressive of the Law of Human Mortality , 1815 .

[25]  Hüseyin Koçak,et al.  Intermittent transition between order and chaos in an insect pest population , 1995 .

[26]  James D. Murray Mathematical Biology: I. An Introduction , 2007 .

[27]  A Hastings,et al.  Intermittency and transient chaos from simple frequency-dependent selection , 1995, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[28]  Erik Matthysen,et al.  Demographic Characteristics and Population Dynamical Patterns of Solitary Birds , 2002, Science.

[29]  V. K,et al.  Dynamic Complexities in Host–Parasitoid Interaction , 1999 .

[30]  B. Cabella,et al.  Data collapse, scaling functions, and analytical solutions of generalized growth models. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Arithmetical and geometrical means of generalized logarithmic and exponential functions: Generalized sum and product operators , 2007, 0709.0018.

[32]  C. A. Condat,et al.  Vector growth universalities , 2011 .

[33]  Alexandre Souto Martinez,et al.  EXPLORATION OF THE PARAMETER SPACE IN AN AGENT-BASED MODEL OF TUBERCULOSIS SPREAD: EMERGENCE OF DRUG , 2012 .

[34]  Alexandre Souto Martinez,et al.  Generalized Allee effect model , 2014, Theory in Biosciences.

[35]  J. Bogaert,et al.  The Fractal Dimension as a Measure of the Quality of Habitats , 2004, Acta biotheoretica.

[36]  Alexandre Souto Martinez,et al.  Continuous growth models in terms of generalized logarithm and exponential functions , 2008, 0803.2635.

[37]  Benjamin Gompertz,et al.  XXIV. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. In a letter to Francis Baily, Esq. F. R. S. &c , 1825, Philosophical Transactions of the Royal Society of London.

[38]  Andrew Adamatzky,et al.  Three-valued logic gates in reaction–diffusion excitable media , 2005 .

[39]  Lai Unpredictability of the asymptotic attractors in phase-coupled oscillators. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[40]  Ney Lemke,et al.  A mean-field theory of cellular growth , 2002 .

[41]  M A Nowak,et al.  Antigenic diversity thresholds and the development of AIDS. , 1991, Science.

[42]  Pier Paolo Delsanto,et al.  Does tumor growth follow a "universal law"? , 2003, Journal of theoretical biology.

[43]  M A Savageau,et al.  Growth of complex systems can be related to the properties of their underlying determinants. , 1979, Proceedings of the National Academy of Sciences of the United States of America.

[44]  A. d’Onofrio Fractal growth of tumors and other cellular populations : linking the mechanistic to the phenomenological modeling and vice versa , 2014 .

[45]  Marco Idiart,et al.  ERRATUM: A mean-field theory of cellular growth , 2002 .

[46]  Winslow,et al.  Geometric properties of the chaotic saddle responsible for supertransients in spatiotemporal chaotic systems. , 1995, Physical review letters.

[47]  F Kozusko,et al.  A unified model of sigmoid tumour growth based on cell proliferation and quiescence , 2007, Cell proliferation.

[48]  Ying-Cheng Lai,et al.  Persistence of supertransients of spatiotemporal chaotic dynamical systems in noisy environment , 1995 .

[49]  Immanuel M. Bomze,et al.  Lotka-Volterra equation and replicator dynamics: new issues in classification , 1995, Biological Cybernetics.

[50]  Alexandre Souto Martinez,et al.  An agent-based computational model of the spread of tuberculosis , 2011 .

[51]  Harvey Gould,et al.  An introduction to computer simulation methods , 1988 .

[52]  Mark Pagel,et al.  On the Regulation of Populations of Mammals, Birds, Fish, and Insects , 2005, Science.

[53]  F. J. Richards A Flexible Growth Function for Empirical Use , 1959 .

[54]  Heinz von Foerster,et al.  Doomsday: Friday, 13 November, A.D. 2026 , 1960 .

[55]  Natalia L. Komarova,et al.  Computational Biology of Cancer: Lecture Notes and Mathematical Modeling , 2005 .

[56]  Alexandre Souto Martinez,et al.  Effective carrying capacity and analytical solution of a particular case of the Richards-like two-sp , 2011, 1111.2796.