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Journal of Environmental Accounting and Management
António Mendes Lopes (editor), Jiazhong Zhang(editor)
António Mendes Lopes (editor)

University of Porto, Portugal


Jiazhong Zhang (editor)

School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China

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A Simulation Approach to Understanding The Effect of Mimicry on Prey’s Flourishing When Predators Decline Due to Environmental Disturbance

Journal of Environmental Accounting and Management 6(3) (2018) 235--247 | DOI:10.5890/JEAM.2018.09.005

Hongchun Qu; Kaidi Zou; Dandan Zhong; Li Yin; Xiaoming Tang

College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

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Ecological interactions and their consequences to system evolution in the context of environmental disturbance are of special concern in ecological conservation since the environmental conditions have been changing so quickly in the past decades. Understanding how these interactions, particularly the indirect ones such as mimicry, could change prey variabilities in facing of predator loss is an interesting question. In this research, we incorporated Batesian mimicry into a three-species predator prey system to investigate the role of mimicry on regulating prey abundance when the system is suffering predator loss in various patterns. The Netlogo mimicry model was adopted to run the simulation experiments. We found that the timing of predator loss interacting with mimicry can induce significant difference in mimic prey’s abundance if partial predators were removed from the system. However, the variations of frequency of predator loss did not change the mimic prey’s abundance in all conditions. Our findings suggested that indirect interactions can change the final species composition on the long term evolutionary scale if environmental disturbances occur in the particular time window. This is the first report that addresses the question of how indirect interactions such as mimicry affects species bundance when environmental disturbance occurred. We expect that this finding could shed the light on conditions under which species and ecological balance can be better managed when environmental disturbances are inevitable to come.


Funding was received from the National Natural Science Foundation of China (61871061) and Chongqing Research Program of Basic Research and Frontier Technology (cstc2017jcyjAX0453, cstc2015jcyjA40007).


  1. [1]  Bates, H.W. (1861), Contributions to an insect fauna of the Amazon valley, Lepidoptera: Heliconidae, Transactions of the Linnean Society, 23(3), 495-566.
  2. [2]  Belvisi, S. and Venturino, E. (2013), An ecoepidemic model with diseased predators and prey group defense, Simulation Modelling Practice and Theory, 34, 144-155.
  3. [3]  Berger, J., Stacey, P.B., Bellis, L., and Johnson, M.P. (2001), A mammalian predator-prey imbalance: Grizzly bear and wolf extinction affect avian neotropical migrants, Ecological Applications, 11(4), 947-960.
  4. [4]  Deangelis, D.L. and Mooij, W.M. (2005), Individual-Based Modeling of Ecological and Evolutionary Processes, Annual Review of Ecology, Evolution, and Systematics, 36(36), 147-168.
  5. [5]  Devaurs, D. and Gras, R. (2010), Species abundance patterns in an ecosystem simulation studied through Fisher's logseries, Simulation Modelling Practice and Theory, 18, 100-123.
  6. [6]  Dunn, A.M. (2010), How parasites affect interactions between competitors and predators, Ecology Letters, 9(11), 1253- 1271.
  7. [7]  Dunne, J.A. andWilliams, R.J. (2009), Cascading extinctions and community collapse in model food webs, Philosophical Transactions Biological Sciences, 364(1524), 1711-1723.
  8. [8]  Fraser, C. (2011), The Crucial Role of Predators: A New Perspective on Ecology Yale Environment 360 (New Haven, CT, 15 September 2011), Retrieved from
  9. [9]  Graham, J.H., Stearns, B.P., and Stearns, S.C. (2000), Watching, from the Edge of Extinction, Yale University Press.
  10. [10]  Johnstone, R.A. (2002), The evolution of inaccurate mimics, Nature, 418(6897), 524-526.
  11. [11]  Kokko, H., Mappes, J., and Lindström, L. (2010), Alternative prey can change model-mimic dynamics between parasitism and mutualism, Ecology Letters, 6(12), 1068-1076.
  12. [12]  Lang, J.M. and Benbow, M.E. (2013), Species Interactions and Competition, Nature Education Knowledge, 4(4), 8.
  13. [13]  Llibre, J. and Valls, C. (2007), Global analytic first integrals for the real planar Lotka-Volterra system, Journal of Mathematical Physics 48(3), 1854-1867.
  14. [14]  Maynard-Smith, J. and Harper, D. (2004), Animal Signals, Oxford University Press.
  15. [15]  McDonald, T., Justin, J., and Kingsley, W.D. (2016), National standards for the practice of ecological restoration in Australia, Restoration Ecology, 24(S1), S4-S32.
  16. [16]  Ponge, J.F. (2013), Disturbances, organisms and ecosystems: a global change perspective, Ecology and Evolution, 3(4), 1113-1124.
  17. [17]  Qu, H., Seifan, T., Tielbörger, K., and Seifan, M. (2013), A spatially explicit agent-based simulation platform for investigating effects of shared pollination service on ecological communities, Simulation Modelling Practice and Theory, 37(3), 107-124.
  18. [18]  Rowland, S.M. (2009), Extinction, and the evolving view of earth history in the late eighteenth and early nineteenth centuries, Geological Society of America Memoirs, 203(16), 225-246.
  19. [19]  Sahney, S. and Benton, M.J. (2008), Recovery from the most profound mass extinction of all time, Proceedings of the Royal Society of London, Series B: Biological Sciences, 275(1636), 759-765.
  20. [20]  Seno, H. and Kohno, T (2012), A mathematical model of population dynamics for Batesian mimicry system, Journal of Biological Dynamics, 6(2), 1034-1051.
  21. [21]  Steneck, R.S., Leland, A., McNaught, D.C., and Vavrinec. J. (2013), Ecosystem flips, locks, and feedbacks: the lasting effects of fisheries on Maine's kelp forest ecosystem, Bulletin of Marine Science, 89(1), 31-55.
  22. [22]  Stephen, H.R., Shea, K., and Wilson, J.B. (2004), The Intermediate Disturbance Hypothesis: patch dynamics and mechanisms of species coexistence, Ecology, 85(2), 359-371.
  23. [23]  Wickler, W. (1968),Mimicry in plants and animals, Journal of Animal Ecology, 6(1), 86-89.
  24. [24]  Wilensky, U. (1997), NetLogo Mimicry model, Center for Connected Learning and Computer-BasedModeling, Northwestern University, Evanston, IL. Retrieved from
  25. [25]  Wilensky, U. (1997), NetLogo, Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL, Retrieved from
  26. [26]  Yorke, J.A. and Anderson, W.N. (1973), Predator-Prey Patterns (Volterra-Lotka equations), Proceedings of the National Academy of Sciences of the United States of America, 70(7), 2069-2071.
  27. [27]  Zdilla, K.M. (2010), Trophic Cascades: Predators, Prey and the Changing Dynamics of Nature, In J. Terborgh & J. A. Estes(Ed.),Washington DC: Island Press.
  28. [28]  Zeckhauser, R. (2017), Human hunters and nonhuman predators: Fundamental differences, Proceedings of the National Academy of Sciences of the United States of America 114(1), 13-14.