Skip to main content
Log in

Nonlinear time-series modeling of vole population fluctuations

  • Special Feature 1
  • Published:
Researches on Population Ecology

Abstract

A central goal of population ecology is to understand and predict fluctuations in population numbers. Until recently, much of the debate focused on the issue of population regulation by density-dependent factors. In this paper, I describe an approach to nonlinear modeling of time-series data that is designed to go beyond this question by investigating the possibility of complex population dynamics, characterized by lags in regulation and periodic or chaotic oscillations. The questions motivating this approach are: what are relative contributions of endogenousvs. exogenous components of dynamics? Is the irregular component in fluctuations entirely due to exogenous noise, or do nonlinearities contribute to it, too? I describe the philosophy and the technical details of the nonlinear modeling approach, and then apply it to a collection of time-series data on vole population fluctuations in northern Europe. The results suggest that population dynamics of European voles undergo a latitudinal shift from stability to chaos. Dynamics in northern Fennoscandia are characterized by positive Lyapunov exponent estimates, and a high degree of short-term (one year ahead) predictability, suggesting a strong endogenous component. In more southerly populations estimated Lyapunov exponents are negative, and there is no one-step ahead predictability, suggesting that fluctuations are driven by exogenous factors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Andrewartha, H. G. and L. C. Birch (1954)The distribution and abundance of animals. University of Chicago Press, Chicago.

    Google Scholar 

  • Argoul, F., A. Arneodo, P. Richetti, J. C. Roux and H. L. Swinnery (1987) Chemical chaos: from hints to confirmation.Accounts of Chemical Research 20: 436–442.

    Article  CAS  Google Scholar 

  • Ayala, F. J., M. E. Gilpin and J. G. Ehrenfeld (1973) Competition between species: theoretical models and experimental tests.Theoretical Population Biology 4: 331–356.

    Article  PubMed  CAS  Google Scholar 

  • Berryman, A. A. (1978) Population cycles of the douglas-fir tussock moth (Lepidoptera: Lymantriidae): the time-delay hypothesis.Canadian Entomologist 110: 513–518.

    Google Scholar 

  • Berryman, A. A. (1986) On the dynamics of blackheaded budworm populations.Canadian Entomologist 118: 775–779.

    Article  Google Scholar 

  • Berryman, A. A. (1991) Chaos in ecology and resource management: what causes it and how to avoid it. pp. 23–38.In J. A. Logan and F. Hain (eds.)Chaos and insect ecology. Virginia Experiment Station Information Series 91-3. Blacksburg, VA.

  • Berryman, A. A. and J. A. Millstein (1990)Population analysis system. POPSYS series 1, single species analysis. Ecological Systems Analysis, Pullman, WA.

    Google Scholar 

  • Box, G. E. P. and D. R. Cox (1964) An analysis of transformations.Journal of Royal Statistical Society B26: 211–252.

    Google Scholar 

  • Box, G. E. P. and N. R. Draper (1987)Empirical model-building and response surfaces. John Wiley & Sons, New York.

    Google Scholar 

  • Bulmer, M. G. (1974) A statistical analysis of the 10-year cycle in Canada.Journal of Animal Ecology 43: 701–718.

    Article  Google Scholar 

  • Casdagli, M. (1989) Nonlinear prediction of chaotic time series.Physica D 35: 335–356.

    Article  Google Scholar 

  • Casdagli, M., S. Eubank, J. D. Farmer and J. Gibson (1991) State space reconstruction in the presence of noise.Physica D 51: 52–98.

    Article  Google Scholar 

  • Costantino, R. F., J. M. Cushing, B. Dennis and R. A. Desharnais (1995) Experimentally induced transitions in the dynamics behaviour of insect populations.Nature 375: 227–230.

    Article  CAS  Google Scholar 

  • Ellner, S. (1991) Detecting low-dimensional chaos in population dynamics data: a critical review.In F. Hain and J. Logan (eds.)Proceedings of the symposium “Does chaos exist in ecological systems” XIX IUFRO World Congress.

  • Ellner, S., A. R. Gallant, D. McCaffrey and D. Nychka (1991) Convergence rate and data requirements for Jacobian-based estimates of Lyapunov exponents from data.Physics Letters A 153: 357–363.

    Article  Google Scholar 

  • Ellner, S., D. W. Nychka and A. R. Gallant (1992)LENNS, a program to estimate the dominant Lyapunov exponent of noisy nonlinear systems from time series data. North Carolina State University, Raleigh, NC.

    Google Scholar 

  • Ellner, S. and P. Turchin (1995) Chaos in a noisy world: new methods and evidence from time series modeling.American Naturalist 145: 343–375.

    Article  Google Scholar 

  • Ellner, S., B. Bailey, G. Bobashev, A. R. Gallant, B. Grenfell and D. W. Nychka (1996) Noise and determinism in measles epidemic dynamics: estimates from nonlinear forecasting. (in press)

  • Finerty, J. P. (1980)The population ecology of cycles in small mammals. Yale University Press, New Haven.

    Google Scholar 

  • Godfray, H. C. J. and S. P. Blythe (1990) Complex dynamics in multispecies communities.Philosophical Transactions of the Royal Society London B 330: 221–233.

    Google Scholar 

  • Hanski, I., L. Hansson and H. Henttonen (1991) Specialist predators, generalist predators, and the microtine rodent cycle.Journal of Animal Ecology 60: 353–367.

    Article  Google Scholar 

  • Hanski, I., H. Henttonen and L. Hansson (1994) Temporal variability and geographic patterns in the population density of microtine rodents: a reply to Xia and Boonstra.American Naturalist 144: 329–342.

    Article  Google Scholar 

  • Hanski, I. and E. Korpimaki (1995) Microtine rodent dynamics in northern Europe: parameterized models for the predator-prey interaction.Ecology 76: 840–850.

    Article  Google Scholar 

  • Hanski, I., P. Turchin, E. Korpimaki and H. Henttonen (1993) Population oscillations of boreal rodents: regulation by mustelid predators leads to chaos.Nature 364: 232–235.

    Article  PubMed  CAS  Google Scholar 

  • Hansson, L. and H. Henttonen (1985) Gradients in density variations of small rodents: the importance of latitude and snow cover.Oecologia 67: 394–402.

    Article  Google Scholar 

  • Hansson, L. and H. Henttonen (1988) Rodent dynamics as a community processes.Trends in Ecology and Evolution 3: 195–200.

    Article  Google Scholar 

  • Hassell, M. P., J. H. Lawton and R. M. May (1976) Patterns of dynamical behavior in single species populations.Journal of Animal Ecology 45: 471–486.

    Article  Google Scholar 

  • Hastings, A., C. L. Hom, S. Ellner, P. Turchin and H. C. J. Godfray (1993) Chaos in ecology; is mother nature a strange attractor?Annual Review of Ecology and Systematics 24: 1–33.

    Google Scholar 

  • Henttonen, H., T. Oksanen, A. Jortikka and V. Haukisalmi (1987) How much do weasels shape microtine cycles in the northern Fennoscandian taiga?Oikos 50: 353–365.

    Google Scholar 

  • Hörnfeldt, B. (1994) Delayed density dependence as a determinant of vole cycles.Ecology 75: 791–806.

    Article  Google Scholar 

  • Ivankina, E. V. (1987)Numerical dynamics and population structure of bank vole near Moscow. Ph. D. Thesis. Moscow University, Moscow.

    Google Scholar 

  • Ivanter, E. V. (1981) Dinamika chislennosti. pp. 245–267.In N. V. Bashenina (ed.)Evropeyskaya ryzhaya polevka. Nauka, Moscow. (in Russian)

    Google Scholar 

  • Korpimäki, E. (1994) Rapid or delayed tracking of multi-annual vole cycles by avian predators?Journal of Animal Ecology 63: 619–628.

    Article  Google Scholar 

  • Korpimäki, E. and K. Norrdahl (1991) Numerical and functional responses of kestrels, short-eared owls, and long-eared owls to vole densities.Ecology,72: 814–826.

    Article  Google Scholar 

  • Koshkina, T. V. (1966) On the periodical changes in the numbers of voles (as exemplified by the Kola Peninsula).Bulletin of the Moscow Society of Naturalists, Biological Section 71: 14–26. (in Russian).

    Google Scholar 

  • Laine, K. and H. Henttonen (1983) The role of plant production in microtine cycles in northern Fennoscandia.Oikos 40: 407–415.

    Google Scholar 

  • Leigh, E. (1968) The ecological role of Volterra's equations. pp. 1–61.In M. Gerstenhaber (ed.)Some mathematical problems in biology. American Mathematical Society, Providence.

    Google Scholar 

  • Lewontin, R. C. (1966) On the measurement of relative variability.Systematic Zoology 57: 15: 141–142.

    Article  Google Scholar 

  • Lindström, E. R., H. Andrén, P. Angelstam, G. Cederlund, B. Hörnfeldt, L. Jäderberg, P. A. Lemnell, B. Martinsson, K. Sköld and J. E. Swenson (1994) Disease reveals the predator: sarcoptic mange, red fox predation, and prey populations.Ecology 75: 1042–1049.

    Article  Google Scholar 

  • Logan, J. and J. C. Allen (1992) Nonlinear dynamics and chaos in insect populations.Annual Review of Entomology 37: 455–477.

    Article  Google Scholar 

  • May, R. M. (1974) Biological populations with nonoverlapping populations: stable points, stable cycles, and chaos.Science 186: 645–647.

    Article  PubMed  CAS  Google Scholar 

  • May, R. M. (1976) Simple mathematical models with very complicated dynamics.Nature 261: 459–467.

    Article  PubMed  CAS  Google Scholar 

  • May, R. M. (1987) Chaos and the dynamics of biological populations.Proceedings of Royal Society, London A 413: 27–44.

    Article  Google Scholar 

  • Millstein, J. A. and P. Turchin (1994)RAMAS/time, ecological time series modeling and forecasting. Applied Biomathematics, Setauket, New York.

    Google Scholar 

  • Moran, P. A. P. (1953a) The statistical analysis of the Canadian Lynx cycle. I. Structure and prediction.Australian Journal of Zoology 1: 163–173.

    Article  Google Scholar 

  • Moran, P. A. P. (1953b) The statistical analysis of the Canadian Lynx cycle. II. Synchronization and meteorology.Australian Journal of Zoology 1: 291–298.

    Article  Google Scholar 

  • Morris, W. F. (1990) Problems in detecting chaotic behavior in natural populations by fitting simple discrete models.Ecology 71: 1849–1862.

    Article  Google Scholar 

  • Mueller, L. D. and F. J. Ayala (1981) Dynamics of single-species population growth: stability of chaos?Ecology 62: 1148–1154.

    Article  Google Scholar 

  • Nicholson, A. J. (1954) An outline of the dynamics of animal populations.Australian Journal of Zoology 2: 9–65.

    Article  Google Scholar 

  • Nychka, D., S. Ellner, D. McCaffrey and A. R. Gallant (1992) Finding chaos in noisy systems.Journal of Royal Statistical Society B 54: 399–426.

    Google Scholar 

  • Olsen, L. F. and W. M. Schaffer (1990) Chaos versus noisy periodicity: alternative hypotheses for childhood epidemics.Science 249: 499–504.

    Article  PubMed  CAS  Google Scholar 

  • Perry, J. N., I. P. Woiwod and I. Hanski (1993) Using responsesurface methodology to detect chaos in ecological time series.Oikos 68: 329–339.

    Google Scholar 

  • Pollard, E., K. H. Lakhani and P. Rothery (1987) The detection of density-dependence from a series of annual censuses.Ecology 68: 2046–2055.

    Article  Google Scholar 

  • Pucek, Z., W. Jedrzejewski, B. Jedrzejewska and M. Pucek (1993) Rodent population dynamics in a primeveal deciduous forest (Bialowieza National Park) in relation to weather, seed crop, and predation.Acta Theriologica 38: 199–232.

    Google Scholar 

  • Rand, D. A. and H. B. Wilson (1991) Chaotic stochasticity: a ubiquitous source of unpredictability in epidemics.Proceedings of the Royal Society London, Series B 246: 179–184.

    CAS  Google Scholar 

  • Royama, T. (1977) Population persistence and density dependence.Ecological Monographs 47: 1–35.

    Article  Google Scholar 

  • Royama, T. (1981) Fundamental concepts and methodology for the analysis of animal population dynamics, with particular reference to univoltine species.Ecological Monographs 51: 473–493.

    Article  Google Scholar 

  • Royama, T. (1992)Analytical population dynamics. Chapman & Hall, London.

    Google Scholar 

  • Ruppert, D. (1989) Fitting mathematical models to data: a review of recent developments.In C. Castillo-Chavez, S. A. Levin and C. A. Shoemaker (eds.)Mathematical approaches to problems in resource management and epidemiology. Lecture Notes in Biomathematics 81: 274–284.

  • Schaffer, W. M. (1985) Order and chaos in ecological systems.Ecology 66: 93–106.

    Article  Google Scholar 

  • Schaffer, W. M. and M. Kot (1985a) Nearly one-dimensional dynamics in an epidemic.Journal of Theoretical Biology 1112: 403–427.

    Article  Google Scholar 

  • Schaffer, W. M. and M. Kot (1985b) Do strange attractors govern ecological systems?Bioscience 35: 342–350.

    Article  Google Scholar 

  • Schaffer, W. M. and M. Kot (1986) Chaos in ecological systems: the coals that Newcastle forgot.Trends in Ecology and Evolution 1: 58–63.

    Article  Google Scholar 

  • Sokal, R. R. and F. J. Rohlf (1981)Biometry. Freeman, New York.

    Google Scholar 

  • Southern, H. N. (1979) The stability and instability of small mammal populations. pp. 103–134.In D. M. Stoddard (ed.)Ecology of small mammals. Chapman & Hall, London.

    Google Scholar 

  • Stenseth, N. C. and E. Framstad (1980) Reproductive effort and optimal reproductive rates in small rodents.Oikos 34: 23–34.

    Google Scholar 

  • Sugihara, G. and R. M. May (1990) Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series.Nature 344: 734–741.

    Article  PubMed  CAS  Google Scholar 

  • Turchin, P. (1990) Rarity of density dependence or population regulation with lags?Nature 344: 660–663.

    Article  Google Scholar 

  • Turchin, P. (1991a) Reconstructing endogenous dynamics of a laboratoryDrosophila population.Journal of Animal Ecology 60: 1091–1098.

    Article  Google Scholar 

  • Turchin, P. (1991b) Nonlinear modeling of time series data: limit cycles and chaos in forest insects, voles, and epidemics. pp. 39–62.In J. A. Logan and F. P. Hain (eds.)Chaos and insect ecology. Virginia Experiment Station Information Series 91-3. Blacksburg, VA.

  • Turchin, P. (1993) Chaos and stability in rodent population dynamics: evidence from nonlinear time-series analysis.Oikos 68: 167–172.

    Google Scholar 

  • Turchin, P. (1995) Population regulation: old arguments and a new synthesis. pp. 19–40.In N. Cappuccino and P. Price (eds.)Population dynamics. Academic Press, New York.

    Google Scholar 

  • Turchin, P. and I. Hanski (1997) An empirically-based model for the latitudinal gradient in vole population dynamics.American Naturalist (in press)

  • Turchin, P. and J. A. Millstein (1994) Theoretical background. pp. 1–50.In J. A. Millstein and P. Turchin.RAMAS/time, ecological time series modeling and forecasting. Applied Biomathematics, Setauket, New York.

    Google Scholar 

  • Turchin, P. and A. Taylor (1992) Complex dynamics in ecological time series.Ecology 73: 289–305.

    Article  Google Scholar 

  • Wolf, A., J. B. Swift, H. L. Swinney and J. A. Vastano (1985) Determining Lyapunov exponents from a time series.Physica D 16: 285–317.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Turchin, P. Nonlinear time-series modeling of vole population fluctuations. Res Popul Ecol 38, 121–132 (1996). https://doi.org/10.1007/BF02515720

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02515720

Key words

Navigation