Chaos and population control of insect outbreaks

We used small perturbations in adult numbers to control large fluctuations in the chaotic demographic dynamics of laboratory populations of the flour beetle Tribolium castaneum. A nonlinear mathematical model was used to identify a sensitive region of phase space where the addition of a few adult insects would result in a dampening of the life stage fluctuations. Three experimental treatments were applied: one in which perturbations were made whenever the populations were inside the sensitive region (“in-box treatment”), another where perturbations were made whenever the populations were outside the sensitive region (“out-box treatment”), and an unperturbed control. The in-box treatment caused a stabilization of insect densities at numbers well below the peak values exhibited by the out-box and control populations. This study demonstrates how small perturbations can be used to influence the chaotic dynamics of an ecological system.

[1]  Ying-Cheng Lai,et al.  Controlling transient chaos to prevent species extinction , 1999 .

[2]  Ditto,et al.  Experimental control of chaos. , 1990, Physical review letters.

[3]  Jim M Cushing,et al.  ESTIMATING CHAOS AND COMPLEX DYNAMICS IN AN INSECT POPULATION , 2001 .

[4]  E. Hunt Stabilizing high-period orbits in a chaotic system: The diode resonator. , 1991 .

[5]  Brian Dennis,et al.  Chaotic Dynamics in an Insect Population , 1997, Science.

[6]  W. Ditto,et al.  Controlling chaos in the brain , 1994, Nature.

[7]  Celso Grebogi,et al.  Using small perturbations to control chaos , 1993, Nature.

[8]  Bradford A. Hawkins,et al.  Theoretical Approaches to Biological Control , 2008 .

[9]  Ying-Cheng Lai,et al.  Controlling chaos , 1994 .

[10]  Jim M Cushing,et al.  An interdisciplinary approach to understanding nonlinear ecological dynamics , 1996 .

[11]  Singer,et al.  Controlling a chaotic system. , 1991, Physical review letters.

[12]  Jim M Cushing,et al.  Transitions in population dynamics: Equilibria to periodic cycles to aperiodic cycles , 1997 .

[13]  M. Doebeli The evolutionary advantage of controlled chaos , 1993, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[14]  Michael B. Bonsall,et al.  Chaos in Real Data. The Analysis of Non‐Linear Dynamics from Short Ecological Time Series , 2001 .

[15]  A Garfinkel,et al.  Controlling cardiac chaos. , 1992, Science.

[16]  Valery Petrov,et al.  Controlling chaos in the Belousov—Zhabotinsky reaction , 1993, Nature.

[17]  R. Costantino,et al.  NONLINEAR DEMOGRAPHIC DYNAMICS: MATHEMATICAL MODELS, STATISTICAL METHODS, AND BIOLOGICAL EXPERIMENTS' , 1995 .

[18]  B. Nevitt,et al.  Coping With Chaos , 1991, Proceedings of the 1991 International Symposium on Technology and Society - ISTAS `91.

[19]  Ricard V. Solé,et al.  Controlling chaos in ecology: From deterministic to individual-based models , 1999, Bulletin of mathematical biology.

[20]  B. Kendall,et al.  WHY DO POPULATIONS CYCLE? A SYNTHESIS OF STATISTICAL AND MECHANISTIC MODELING APPROACHES , 1999 .

[21]  R. Costantino,et al.  Experimentally induced transitions in the dynamic behaviour of insect populations , 1995, Nature.

[22]  Robert H. Smith,et al.  Chaos in Real Data , 2000, Population and Community Biology Series.

[23]  Jim M Cushing,et al.  A chaotic attractor in ecology: theory and experimental data , 2001 .

[24]  Tomasz Kapitaniak Controlling chaos through feedback , 1996 .

[25]  Ian P. Woiwod,et al.  Estimating Chaos in an Insect Population , 1997 .

[26]  Stephen P. Ellner,et al.  Living on the edge of chaos: population dynamics of fennoscandian voles , 2000 .

[27]  R F Costantino,et al.  Nonlinear population dynamics: models, experiments and data. , 1998, Journal of theoretical biology.

[28]  Douglas W. Nychka,et al.  Chaos with Confidence: Asymptotics and Applications of Local Lyapunov Exponents , 1997 .

[29]  Roy,et al.  Tracking unstable steady states: Extending the stability regime of a multimode laser system. , 1992, Physical review letters.