How can modelling tools inform environmental and conservation policies?

  • Authors

    • Mohd Hafiz Mohd
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.28.22610
  • Among the important environmental and ecological problems are to determine the distributions of species (e.g. endangered, native and invasive species) across geographical regions and to understand the determinant of species range limits (i.e. the boundaries of the locations in which a species is found). Various studies highlight that abiotic environments (e.g. temperature, climate) and biotic interactions (e.g. competition) can influence species distributions. To investigate this problem, two mathematical models for predicting species distributions have been employed. Such models generally take the form of deterministic systems such as partial-differential equations, in which they aim to understand the interactions between species at the population scale. Thinking of interacting species as finite groups of agents, rather than continuous densities, may alter the structure of the modelling frameworks. This problem can be studied using stochastic individual-based models (IBM). These two models are used to examine the outcomes of species interactions and to understand how these species are distributed in spatially changing environments. As such, comparing and contrasting the observations between the IBM and deterministic models may offer important insights in predicting species range limits and help us to develop robust predictions of species potential distributions in nature.

  • References

    1. [1] Allen, L.J., An introduction to stochastic processes with applications to biology2003: Pearson Education Upper Saddle River, NJ.

      [2] DeAngelis, D.L. and Y. Matsinos, Individual-based population models: linking behavioral and physiological information at the individual level to population dynamics. Ecol. Austral., 1995. 6: p. 23-31.

      [3] Nisbet, R.M. and W.S.C. Gurney, Modelling fluctuating populations1982: Wiley.

      [4] Renshaw, E., Modelling Biological Populations in Space and Time1993: Cambridge University Press.

      [5] Wilson, W.G., Resolving discrepancies between deterministic population models and individual-based simulations. The American Naturalist, 1998. 151(2): p. 116-134.

      [6] Law, R., D.J. Murrell, and U. Dieckmann, Population Growth in Space and Time: Spatial Logistic Equations. Ecology, 2003. 84(1): p. 252-262.

      [7] Wilson, W., E. McCauley, and A. De Roos, Effect of dimensionality on Lotka-Volterra predator-prey dynamics: Individual based simulation results. Bulletin of Mathematical Biology, 1995. 57(4): p. 507-526.

      [8] DeAngelis, D.L. and W.M. Mooij, Individual-Based Modeling of Ecological and Evolutionary Processes. Annual Review of Ecology, Evolution, and Systematics, 2005. 36(1): p. 147-168.

      [9] Michael Weisberg and Kenneth Reisman, The Robust Volterra Principle. Philosophy of Science, 2008. 75(1): p. 106-131.

      [10] Johnson, A.R., et al., Animal movements and population dynamics in heterogeneous landscapes. Landscape Ecology, 1992. 7(1): p. 63-75.

      [11] Faugeras, B. and O. Maury, Modeling fish population movements: From an individual-based representation to an advection–diffusion equation. Journal of Theoretical Biology, 2007. 247(4): p. 837-848.

      [12] Goss-Custard, J.D., et al., Carrying capacity in overwintering migratory birds. Biological Conservation, 2002. 105(1): p. 27-41.

      [13] Pettifor, R.A., et al., Spatially Explicit, Individual-Based, Behavioural Models of the Annual Cycle of Two Migratory Goose Populations. Journal of Applied Ecology, 2000. 37: p. 103-135.

      [14] Jongejans, E. and P. Schippers, Modeling Seed Dispersal by Wind in Herbaceous Species. Oikos, 1999. 87(2): p. 362-372.

      [15] Tam, T.-w. and P.O. Ang, Object-oriented simulation of coral competition in a coral reef community. Ecological Modelling, 2012. 245(0): p. 111-120.

      [16] Smith, M., Using massively-parallel supercomputers to model stochastic spatial predator-prey systems. Ecological Modelling, 1991. 58(1–4): p. 347-367.

      [17] Case, T.J., et al., The community context of species' borders: ecological and evolutionary perspectives. Oikos, 2005. 108(1): p. 28-46.

      [18] Mohd, M.H., et al., Effects of biotic interactions and dispersal on the presence-absence of multiple species. Chaos, Solitons & Fractals, 2017. 99: p. 185-194.

      [19] Mohd, M.H.B. Modelling biotic interactions, dispersal effects and the stability of multi-species community compositions. in AIP Conference Proceedings. 2018. AIP Publishing.

      [20] Roughgarden, J., Theory of population genetics and evolutionary ecology: an introduction. 1979.

      [21] Nes, Egbert H.v. and Marten Scheffer, Slow Recovery from Perturbations as a Generic Indicator of a Nearby Catastrophic Shift. The American Naturalist, 2007. 169(6): p. 738-747.

      [22] Colwell, R.K. and D.C. Lees, The mid-domain effect: geometric constraints on the geography of species richness. Trends in Ecology & Evolution, 2000. 15(2): p. 70-76.

      [23] Colwell, R.K., C. Rahbek, and N.J. Gotelli, The mid-domain effect and species richness patterns: what have we learned so far? The American Naturalist, 2004. 163(3): p. E1-E23.

      [24] McCain, C.M., The midâ€domain effect applied to elevational gradients: species richness of small mammals in Costa Rica. Journal of Biogeography, 2004. 31(1): p. 19-31.

      [25] McCain, C.M., Elevational gradients in diversity of small mammals. Ecology, 2005. 86(2): p. 366-372.

      [26] Morin, P.J., Community ecology2011: John Wiley & Sons.

      [27] Shurin, J.B., et al., Alternative stable states and regional community structure. Journal of Theoretical Biology, 2004. 227(3): p. 359-368.

      [28] Gotelli, N.J., A primer of ecology1995: Sinauer Associates Incorporated.

      [29] Mittelbach, G.G., Community Ecology2012: Sinauer Associates, Incorporated.

      [30] Burnett, T., Effects of initial densities and periods of infestation on the growth-forms of a host and parasite population. Canadian Journal of Zoology, 1960. 38(6): p. 1063-1077.

      [31] Jones, W.A., S. Greenberg, and B. Legaspi, The effect of varying Bemisia argentifolii and Eretmocerus mundus ratios on parasitism. BioControl, 1999. 44(1): p. 13-28.

      [32] Tang, S., G. Tang, and R.A. Cheke, Optimum timing for integrated pest management: modelling rates of pesticide application and natural enemy releases. Journal of Theoretical Biology, 2010. 264(2): p. 623-638.

  • Downloads

  • How to Cite

    Mohd, M. H. (2018). How can modelling tools inform environmental and conservation policies?. International Journal of Engineering & Technology, 7(4.28), 333-337. https://doi.org/10.14419/ijet.v7i4.28.22610