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PREDATOR–PREY MODEL WITH AGE STRUCTURE

Published online by Cambridge University Press:  02 November 2017

J. PROMRAK
Affiliation:
Department of Mathematics, Faculty of Science, Mahidol University, Thailand email [email protected]
G. C. WAKE
Affiliation:
Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand email [email protected]
C. RATTANAKUL*
Affiliation:
Department of Mathematics, Faculty of Science, Mahidol University, Thailand Centre of Excellence in Mathematics, Commission on Higher Education, Thailand email [email protected]
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Abstract

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Mealybug is an important pest of cassava plant in Thailand and tropical countries, leading to severe damage of crop yield. One of the most successful controls of mealybug spread is using its natural enemies such as green lacewings, where the development of mathematical models forecasting mealybug population dynamics improves implementation of biological control. In this work, the Sharpe–Lotka–McKendrick equation is extended and combined with an integro-differential equation to study population dynamics of mealybugs (prey) and released green lacewings (predator). Here, an age-dependent formula is employed for mealybug population. The solutions and the stability of the system are considered. The steady age distributions and their bifurcation diagrams are presented. Finally, the threshold of the rate of released green lacewings for mealybug extermination is investigated.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© 2017 Australian Mathematical Society

References

Abia, L. M., Angulob, O. and Lpez-Marcosa, J. C., “Age-structured population models and their numerical solution”, Ecological Modelling 188 (2005) 112136;doi:10.1016/j.ecolmodel.2005.05.007.Google Scholar
Anita, S., Arnautu, V. and Capasso, V., An introduction to optimal control problems in life sciences and economics: From mathematical models to numerical simulation with MATLAB (Birkhauser, New York, 2011) 145146.Google Scholar
Arditi, R. and Berryman, A. A., “The biological control paradox”, Trends in Ecology and Evolution 6 (1991) 32; doi:10.1016/0169-5347(91)90148-Q.Google Scholar
Arditi, R. and Ginzburg, L. R., “Coupling in predator-prey dynamics: ratio-dependence”, J. Theoret. Biol. 139 (1989) 311326; doi:10.1016/S0022-5193(89)80211-5.CrossRefGoogle Scholar
Berryman, A. A., “The origins and evolution of predator-prey theory”, Ecology 73 (1992) 15301535; doi:10.2307/1940005.Google Scholar
Papacek, D. F., Broadley, R. H. and Thomas, M., The good bug book: Beneficial insects and mites commercially available in Australia for biological pest control (Australasian Biological Control, Department of Primary Industries, Queensland and Rural Industries Research and Development Corporation, Australia, 1995).Google Scholar
Bugs for Bugs Pty Ltd., “Lacewings”, 5 November 2014.http://www.bugsforbugs.com.au/lacewings-information/.Google Scholar
Busenberg, S. and Iannelli, M., “A class of nonlinear diffusion problems in age-dependent population dynamics”, Nonlinear Anal., Theory Math. Appl. 7 (1983) 501529;doi:10.1016/0362-546X(83)90041-X.Google Scholar
Calatayud, P. A. and , B. L., Cassava-mealybug interaction, IRD Éditions (Institut de recherche pour le dévelopment, Paris, 2006).Google Scholar
Chiu, C., “Nonlinear age-dependent models for prediction of population growth”, Math. Biosci. 99 (1990) 119133; doi:10.1016/0025-5564(90)90142-L.Google Scholar
Choeikamhaeng, P., Vinothai, A. and Sahaya, S., Utilization of green lacewing Plesiochrysa ramburi for the control of cassava mealybugs in field (Department of Agriculture Research Database, Thailand (in Thai), 2011) 2832. http://www.doa.go.th/research/attachment.php?aid=2083.Google Scholar
Cock, M. J. W., Day, R. K., Hinz, H. L., Pollard, K. M., Thomas, S. E., Williams, F. E., Witt, A. B. R. and Shaw, R. H., “The impacts of some classical biological control successes”, CAB Reviews 10 (2015) 158; doi:10.1079/PAVSNNR201510042.Google Scholar
Das, S. and Gupta, P. K., “A mathematical model on fractional Lotka–Volterra equations”, J. Theoret. Biol. 277 (2011) 16; doi:10.1016/j.jtbi.2011.01.034.Google Scholar
Das, S., Gupta, P. K. and Rajeev, “A fractional predator-prey model and its solution”, Int. J. Nonlinear Sci. Numer. Simul. 10 (2009) 873876; doi:10.1515/IJNSNS.2009.10.7.873.Google Scholar
Department of Agriculture, Thailand, “Technology in cassava production to solve mealybug problems”, 5 August 2014; http://agrimedia.agritech.doae.go.th/book/bookrice/RB%20043.pdf.Google Scholar
Gurtin, M. E. and MacCamy, R. C., “Nonlinear age-dependent population dynamics”, Arch. Rational Mech. Anal. 54 (1974) 281300; doi:10.1.1.176.2992 & rep=rep1 & type=pdf.Google Scholar
Gurtin, M. E. and MacCamy, R. C., “Product solutions and asymptotic behavior in age dependent population diffusion”, Math. Biosci. 62 (1982) 157167;doi:10.1016/0025-5564(82)90080-3.Google Scholar
Hoppensteadt, F., Mathematical theory of population demographics, genetics and epidemics, Volume 20 of CBMS-NSF Regional Conference Series in Applied Mathematics (Society for Industrial and Applied Mathematics, Philadelphia, 1975).Google Scholar
Iannellia, M. and Milnerb, F. A., “On the approximation of the Lotka–McKendrick equation with finite life-span”, J. Comput. Appl. Math. 136 (2001) 245254;doi:10.1016/S0377-0427(00)00616-6.Google Scholar
Keyfitz, B. L., “The McKendrick partial differential equation and its uses in epidemiology and population study”, Math. Comput. Modelling 26 (1998) 19;doi:10.1016/S0895-7177(97)00165-9.Google Scholar
Kumar, S., Kumar, A. and Odibat, Z. M., “A nonlinear fractional model to describe the population dynamics of two interacting species”, Math. Methods Appl. Sci. 40 (2017) 41344148; doi:10.1002/mma.4293.Google Scholar
Kunisch, K., Schappacher, W. and Webb, G. F., “Nonlinear age–dependent population dynamics with random diffusion”, Comput. Math. Appl. 11 (1985) 155173;doi:10.1016/0898-1221(85)90144-0.Google Scholar
Langlais, M., “A nonlinear problem in age dependent population diffusion”, SIAM J. Math. Anal. 16 (1985) 510529; doi:10.1137/0516037.Google Scholar
Li, X., “Variational iteration method for nonlinear age-structured population models’”, Comput. Math. Appl. 58 (2009) 21772181; doi:10.1016/j.camwa.2009.03.060.Google Scholar
Lotka, A. J., Elements of physical biology (Williams & Wilkins, Baltimore, MD, 1925) https://archive.org/details/elementsofphysic017171mbp.Google Scholar
McKendric, A. G. and Pai, M. K., “The rate of multiplication of micro-organisms: A mathematical study”, Proc. R. Soc. Edinburgh 31 (1911) 649653;doi:10.1017/S0370164600025426.Google Scholar
Norhayatiand Wake, G. C., “The solution and stability of a nonlinear age-structured population model’”, ANZIAM J. 45 (2003) 153165; doi:10.1017/S1446181100013237.Google Scholar
Pelovska, G. and Iannelli, M., “Numerical methods for the Lotka–McKendricks equation”, J. Comput. Appl. Math. 197 (2006) 534557; doi:10.1016/j.cam.2005.11.033.Google Scholar
Promrak, J., Wake, G. C. and Rattanakul, C., “Modified predator-prey model for mealybug population with biological control”, J. Math. System Sci. 6 (2016) 180193;doi:10.17265/2159-5291/2016.05.002.Google Scholar
Sharpe, F. R. and Lotka, A. J., “A problem in age-distribution”, Philos. Mag. 21 (1911) 435438;doi:10.1007/978-3-642-81046-6_13.Google Scholar
Skakauskas, V., “Product solutions and asymptotic behaviour of sex-age-dependent populations with random mating and females’ pregnancy’”, Math. Biosci. 153 (1998) 1340;doi:10.1016/S0025-5564(98)10032-9.Google Scholar
Suasa-ard, W., “Natural enemies of important insect pests of field crops and utilization as biological control agents in Thailand”, in: Proceedings of International Seminar on Enhancement of Functional Biodiversity Relevant to Sustainable Food Production in ASPAC, Tsukuba, Japan, November 9–11 (2010).http://www.naro.affrc.go.jp/archive/niaes/sinfo/sympo/h22/1109/paper_12.pdf.Google Scholar
Thierry, H., Sheeren, D., Marilleau, N., Corson, N., Amalric, M. and Monteil, C., “From the Lotka–Volterra model to a spatialised population-driven individual-based model”, Ecological Modelling 306 (2015) 287293; doi:10.1016/j.ecolmodel.2014.09.022.Google Scholar
Tusset, A. M., Piccirillo, V. and Balthazar, J. M., “A note on SDRE control applied in predator-prey model: biological control of spider mite Panonychus ulmi ”, J. Biol. Systems 24 (2016) 333344; doi:10.1142/S0218339016500170.Google Scholar
Volterra, V., “Variazioni e fluttuazioni del numero d’individui in specie animali conviventi”, Mem. R. Accad. Naz. dei Lincei. 2 (1926) 31113 http://mathematica.sns.it/media/volumi/429/volterra_5.pdf.Google Scholar
Yousefia, S. A., Behroozifarb, M. and Dehghanc, M., “Numerical solution of the nonlinear age-structured population models by using the operational matrices of Bernstein polynomials”, Appl. Math. Model. 36 (2012) 945963; doi:10.1016/j.apm.2011.07.041.Google Scholar