Treffer: An evolutionary game theory for event-driven ecological population dynamics.
Title:
An evolutionary game theory for event-driven ecological population dynamics.
Authors:
Araujo G; Faculty of Science and Engineering, Department of Biosciences, Swansea University, Singleton Park, Swansea, SA2 8PP, UK. gui.dav.araujo@gmail.com.
Source:
Theory in biosciences = Theorie in den Biowissenschaften [Theory Biosci] 2025 Feb; Vol. 144 (1), pp. 95-105. Date of Electronic Publication: 2025 Jan 17.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Urban & Fischer Country of Publication: Germany NLM ID: 9708216 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1611-7530 (Electronic) Linking ISSN: 14317613 NLM ISO Abbreviation: Theory Biosci Subsets: MEDLINE
Imprint Name(s):
Publication: Jena : Urban & Fischer
Original Publication: Jena : Gustav Fischer, c1997-
Original Publication: Jena : Gustav Fischer, c1997-
MeSH Terms:
References:
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Araujo G, Moura RR (2022) Individual specialization and generalization in predator-prey dynamics: The determinant role of predation efficiency and prey reproductive rates. J Theor Biol 537:111026. (PMID: 10.1016/j.jtbi.2022.11102635063412)
Araujo G, Moura RR (2023) Beyond classical theories: an integrative mathematical model of mating dynamics and parental care. J Evol Biol 36:1411–1427. (PMID: 10.1111/jeb.1421037691454)
Argasinski K (2006) Dynamic multipopulation and density dependent evolutionary games related to replicator dynamics. a metasimplex concept. Math Biosci 202:88–114. (PMID: 10.1016/j.mbs.2006.04.00716797041)
Argasinski K, Broom M (2013) Ecological theatre and the evolutionary game: how environmental and demographic factors determine payoffs in evolutionary games. J Math Biol 67:935–962. (PMID: 10.1007/s00285-012-0573-222936541)
Argasinski K, Broom M (2018a) Evolutionary stability under limited population growth: eco-evolutionary feedbacks and replicator dynamics. Ecol Complex 34:198–212. (PMID: 10.1016/j.ecocom.2017.04.002)
Argasinski K, Broom M (2018b) Interaction rates, vital rates, background fitness and replicator dynamics: how to embed evolutionary game structure into realistic population dynamics. Theory Biosci 137:33–50. (PMID: 10.1007/s12064-017-0257-y29159683)
Argasinski K, Broom M (2021) Towards a replicator dynamics model of age structured populations. J Math Biol 82:1–39. (PMID: 10.1007/s00285-021-01592-4)
Auger P, de la Parra RB, Morand S, Sanchez E (2002) A predator-prey model with predators using hawk and dove tactics. Math Biosci 177:185–200. (PMID: 10.1016/S0025-5564(01)00112-211965255)
Bertram J, Masel J (2019) Density-dependent selection and the limits of relative fitness. Theor Popul Biol 129:81–92. (PMID: 10.1016/j.tpb.2018.11.00630664884)
Brown JS (2016) Why Darwin would have loved evolutionary game theory. Proc R Soc B: Biol Sci 283:20160847. (PMID: 10.1098/rspb.2016.0847)
Cressman R, Křivan V (2022) Using chemical reaction network theory to show stability of distributional dynamics in game theory. J Dyn Games 9:351–371. (PMID: 10.3934/jdg.2021030)
Dieckmann U, Law R (1996) The dynamical theory of coevolution: a derivation from stochastic ecological processes. J Math Biol 34:579–612. (PMID: 10.1007/BF024097518691086)
Doebeli M, Ispolatov Y, Simon B (2017) Towards a mechanistic foundation of evolutionary theory. Elife 6:e23804. (PMID: 10.7554/eLife.2380428198700)
Fearnhead P, Giagos V, Sherlock C (2014) Inference for reaction networks using the linear noise approximation. Biometrics 70:457–466. (PMID: 10.1111/biom.1215224467590)
Feinberg M (2019) Foundations of chemical reaction network theory. In: Applied Mathematical Sciences, vol 202. Springer, New York.
Fromhage L, Jennions MD (2016) Coevolution of parental investment and sexually selected traits drives sex-role divergence. Nat Commun 7:12517. (PMID: 10.1038/ncomms1251727535478)
Galanthay TE, Křivan V, Cressman R, Revilla TA (2023) Evolution of aggression in consumer-resource models. Dyn Games Appl 13:1049–1065. (PMID: 10.1007/s13235-023-00496-w)
Geritz SA, Kisdi É (2012) Mathematical ecology: Why mechanistic models? J Math Biol 65:1411–1415. https://doi.org/10.1007/s00285-011-0496-3. (PMID: 10.1007/s00285-011-0496-322159789)
Gillespie DT (1976) A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys 22:403–434. (PMID: 10.1016/0021-9991(76)90041-3)
Gillespie DT (2007) Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 58:35–55. (PMID: 10.1146/annurev.physchem.58.032806.10463717037977)
Golightly A, Wilkinson DJ (2006) Bayesian sequential inference for stochastic kinetic biochemical network models. J Comput Biol 13:838–851. (PMID: 10.1089/cmb.2006.13.83816706729)
Hofbauer J, Sigmund K (1998) Evolutionary games and population dynamics. Cambridge University Press, Cambridge. (PMID: 10.1017/CBO9781139173179)
Van Kampen NG (1992) Stochastic processes in physics and chemistry, vol 1. North-Holland Personal Library.
Kokko H, Jennions MD (2008) Parental investment, sexual selection and sex ratios. J Evolut Biol 21:919–948. (PMID: 10.1111/j.1420-9101.2008.01540.x)
Komorowski M, Finkenstädt B, Harper CV, Rand DA (2009) Bayesian inference of biochemical kinetic parameters using the linear noise approximation. BMC Bioinform 10:1–10. (PMID: 10.1186/1471-2105-10-343)
Křivan V, Galanthay TE, Cressman R (2018) Beyond replicator dynamics: from frequency to density dependent models of evolutionary games. J Theor Biol 455:232–248. (PMID: 10.1016/j.jtbi.2018.07.00329990466)
Lacroix JJ (2021) Dichotomy between heterotypic and homotypic interactions by a common chemical law. Phys Chem Chem Phys 23:17761–17765. (PMID: 10.1039/D1CP02171K34241615)
Martinez-Garcia R, Fleming CH, Seppelt R, Fagan WF, Calabrese JM (2020) How range residency and long-range perception change encounter rates. J Theor Biol 498:110267. (PMID: 10.1016/j.jtbi.2020.11026732275984)
Maynard Smith J, Price GR (1973) The logic of animal conflict. Nature 246:15–18. (PMID: 10.1038/246015a0)
Novak S, Chatterjee K, Nowak MA (2013) Density games. J Theor Biol 334:26–34. (PMID: 10.1016/j.jtbi.2013.05.02923770399)
Schnoerr D, Sanguinetti G, Grima R (2017) Approximation and inference methods for stochastic biochemical kinetics-a tutorial review. J Phys A: Math Theor 50:093001. (PMID: 10.1088/1751-8121/aa54d9)
Tilman AR, Plotkin JB, Akçay E (2020) Evolutionary games with environmental feedbacks. Nat Commun 11:915. (PMID: 10.1038/s41467-020-14531-632060275)
Araujo G, Moura RR (2022) Individual specialization and generalization in predator-prey dynamics: The determinant role of predation efficiency and prey reproductive rates. J Theor Biol 537:111026. (PMID: 10.1016/j.jtbi.2022.11102635063412)
Araujo G, Moura RR (2023) Beyond classical theories: an integrative mathematical model of mating dynamics and parental care. J Evol Biol 36:1411–1427. (PMID: 10.1111/jeb.1421037691454)
Argasinski K (2006) Dynamic multipopulation and density dependent evolutionary games related to replicator dynamics. a metasimplex concept. Math Biosci 202:88–114. (PMID: 10.1016/j.mbs.2006.04.00716797041)
Argasinski K, Broom M (2013) Ecological theatre and the evolutionary game: how environmental and demographic factors determine payoffs in evolutionary games. J Math Biol 67:935–962. (PMID: 10.1007/s00285-012-0573-222936541)
Argasinski K, Broom M (2018a) Evolutionary stability under limited population growth: eco-evolutionary feedbacks and replicator dynamics. Ecol Complex 34:198–212. (PMID: 10.1016/j.ecocom.2017.04.002)
Argasinski K, Broom M (2018b) Interaction rates, vital rates, background fitness and replicator dynamics: how to embed evolutionary game structure into realistic population dynamics. Theory Biosci 137:33–50. (PMID: 10.1007/s12064-017-0257-y29159683)
Argasinski K, Broom M (2021) Towards a replicator dynamics model of age structured populations. J Math Biol 82:1–39. (PMID: 10.1007/s00285-021-01592-4)
Auger P, de la Parra RB, Morand S, Sanchez E (2002) A predator-prey model with predators using hawk and dove tactics. Math Biosci 177:185–200. (PMID: 10.1016/S0025-5564(01)00112-211965255)
Bertram J, Masel J (2019) Density-dependent selection and the limits of relative fitness. Theor Popul Biol 129:81–92. (PMID: 10.1016/j.tpb.2018.11.00630664884)
Brown JS (2016) Why Darwin would have loved evolutionary game theory. Proc R Soc B: Biol Sci 283:20160847. (PMID: 10.1098/rspb.2016.0847)
Cressman R, Křivan V (2022) Using chemical reaction network theory to show stability of distributional dynamics in game theory. J Dyn Games 9:351–371. (PMID: 10.3934/jdg.2021030)
Dieckmann U, Law R (1996) The dynamical theory of coevolution: a derivation from stochastic ecological processes. J Math Biol 34:579–612. (PMID: 10.1007/BF024097518691086)
Doebeli M, Ispolatov Y, Simon B (2017) Towards a mechanistic foundation of evolutionary theory. Elife 6:e23804. (PMID: 10.7554/eLife.2380428198700)
Fearnhead P, Giagos V, Sherlock C (2014) Inference for reaction networks using the linear noise approximation. Biometrics 70:457–466. (PMID: 10.1111/biom.1215224467590)
Feinberg M (2019) Foundations of chemical reaction network theory. In: Applied Mathematical Sciences, vol 202. Springer, New York.
Fromhage L, Jennions MD (2016) Coevolution of parental investment and sexually selected traits drives sex-role divergence. Nat Commun 7:12517. (PMID: 10.1038/ncomms1251727535478)
Galanthay TE, Křivan V, Cressman R, Revilla TA (2023) Evolution of aggression in consumer-resource models. Dyn Games Appl 13:1049–1065. (PMID: 10.1007/s13235-023-00496-w)
Geritz SA, Kisdi É (2012) Mathematical ecology: Why mechanistic models? J Math Biol 65:1411–1415. https://doi.org/10.1007/s00285-011-0496-3. (PMID: 10.1007/s00285-011-0496-322159789)
Gillespie DT (1976) A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys 22:403–434. (PMID: 10.1016/0021-9991(76)90041-3)
Gillespie DT (2007) Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 58:35–55. (PMID: 10.1146/annurev.physchem.58.032806.10463717037977)
Golightly A, Wilkinson DJ (2006) Bayesian sequential inference for stochastic kinetic biochemical network models. J Comput Biol 13:838–851. (PMID: 10.1089/cmb.2006.13.83816706729)
Hofbauer J, Sigmund K (1998) Evolutionary games and population dynamics. Cambridge University Press, Cambridge. (PMID: 10.1017/CBO9781139173179)
Van Kampen NG (1992) Stochastic processes in physics and chemistry, vol 1. North-Holland Personal Library.
Kokko H, Jennions MD (2008) Parental investment, sexual selection and sex ratios. J Evolut Biol 21:919–948. (PMID: 10.1111/j.1420-9101.2008.01540.x)
Komorowski M, Finkenstädt B, Harper CV, Rand DA (2009) Bayesian inference of biochemical kinetic parameters using the linear noise approximation. BMC Bioinform 10:1–10. (PMID: 10.1186/1471-2105-10-343)
Křivan V, Galanthay TE, Cressman R (2018) Beyond replicator dynamics: from frequency to density dependent models of evolutionary games. J Theor Biol 455:232–248. (PMID: 10.1016/j.jtbi.2018.07.00329990466)
Lacroix JJ (2021) Dichotomy between heterotypic and homotypic interactions by a common chemical law. Phys Chem Chem Phys 23:17761–17765. (PMID: 10.1039/D1CP02171K34241615)
Martinez-Garcia R, Fleming CH, Seppelt R, Fagan WF, Calabrese JM (2020) How range residency and long-range perception change encounter rates. J Theor Biol 498:110267. (PMID: 10.1016/j.jtbi.2020.11026732275984)
Maynard Smith J, Price GR (1973) The logic of animal conflict. Nature 246:15–18. (PMID: 10.1038/246015a0)
Novak S, Chatterjee K, Nowak MA (2013) Density games. J Theor Biol 334:26–34. (PMID: 10.1016/j.jtbi.2013.05.02923770399)
Schnoerr D, Sanguinetti G, Grima R (2017) Approximation and inference methods for stochastic biochemical kinetics-a tutorial review. J Phys A: Math Theor 50:093001. (PMID: 10.1088/1751-8121/aa54d9)
Tilman AR, Plotkin JB, Akçay E (2020) Evolutionary games with environmental feedbacks. Nat Commun 11:915. (PMID: 10.1038/s41467-020-14531-632060275)
Grant Information:
140327/2016-9 Conselho Nacional de Desenvolvimento Científico e Tecnológico; RPG-2022-114 Leverhulme Trust
Contributed Indexing:
Keywords: Behavioral ecology; Eco-evolution; Markov jump process; Mass-action law; Reaction networks
Entry Date(s):
Date Created: 20250117 Date Completed: 20250501 Latest Revision: 20250501
Update Code:
20250502
DOI:
10.1007/s12064-024-00433-4
PMID:
39820947
Database:
MEDLINE
Weitere Informationen
Despite being a powerful tool to model ecological interactions, traditional evolutionary game theory can still be largely improved in the context of population dynamics. One of the current challenges is to devise a cohesive theoretical framework for ecological games with density-dependent (or concentration-dependent) evolution, especially one defined by individual-level events. In this work, I use the notation of reaction networks as a foundation to propose a framework and show that classic two-strategy games are a particular case of the theory. The framework exhibits a strong versatility and provides a standardized language for model design, and I demonstrate its use through a simple example of mating dynamics and parental care. In addition, reaction networks provide a natural connection between stochastic and deterministic dynamics and therefore are suitable to model noise effects on small populations, also allowing the use of stochastic simulation algorithms such as Gillespie's with game models. The methods I present can help to bring evolutionary game theory to new reaches in ecology, facilitate the process of model design, and put different models on a common ground.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)