OSCILLATORY DYNAMICS AND SPATIAL SCALE: THE ROLE OF NOISE AND UNRESOLVED PATTERN

Predator-prey and other nonlinear ecological interactions often lead to os- cillatory dynamics in temporal systems and in spatial systems when the rates of movement are large, so that individuals are effectively well mixed and space becomes unimportant. When individuals are not well mixed, however, properties of fluctuations in population densities, and in particular their amplitudes, are known to vary with the spatial scale at which the system is observed. We investigate the relationship among dynamics at different spatial scales with an individual-based predator-prey model that is stochastic and nonlinear. Results elucidate the role of spatial pattern and individual variability in the dynamics of densities. We show that spatial patterns in this system reduce the per capita rates of predation and prey growth but preserve functional forms. The functional forms remain those one would expect in a well-mixed system in which individuals interact according to mean population densities, but with modified parameters. This similarity of the functional forms allows us to approximate accurately the long-term dynamics of the spatial system at large scales with a temporal predator-prey model with only two variables, a simple system of ordinary differential equations of the type ecologists have been using for a long time. This approximation provides an explanation for the stabilizing role of space, the decrease in the amplitude of fluctuations from the well-mixed to the limited-movement case. We also provide an explanation for the previously described aperiodic dynamics of densities at intermediate spatial scales. These irregular cycles result from the interplay of demographic noise with decaying oscillations, where the decay of the cycles is due to the spatial patterns. It is indeed possible to capture essential properties of these cycles, including their apparent sensitivity to initial conditions, with a model that follows individuals but parameterizes their spatial interactions in a simple way, using again the similarity of func- tional forms and the modified parameters. Thus, demographic noise appears essential at a spatial scale previously chosen for the high degree of determinism in the dynamics. Our results illustrate a semi-empirical approach to simplify and to scale spatial ecological systems that are oscillatory from individual or local-scale to large-scale dynamics.

[1]  R. May,et al.  Aggregation of Predators and Insect Parasites and its Effect on Stability , 1974 .

[2]  W. Gurney,et al.  A simple mechanism for population cycles , 1976, Nature.

[3]  L. Segel,et al.  Hypothesis for origin of planktonic patchiness , 1976, Nature.

[4]  M. Hassell The dynamics of arthropod predator-prey systems. , 1979, Monographs in population biology.

[5]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[6]  F. Takens Detecting strange attractors in turbulence , 1981 .

[7]  P. Crowley Dispersal and the Stability of Predator-Prey Interactions , 1981, The American Naturalist.

[8]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[9]  Andrew D. Taylor Metapopulations, Dispersal, and Predator‐Prey Dynamics: An Overview , 1990 .

[10]  K. Briggs An improved method for estimating Liapunov exponents of chaotic time series , 1990 .

[11]  Michael P. Hassell,et al.  Spatial structure and chaos in insect population dynamics , 1991, Nature.

[12]  William G. Wilson,et al.  Mobility versus density-limited predator-prey dynamics on different spatial scales , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[13]  Brown,et al.  Computing the Lyapunov spectrum of a dynamical system from an observed time series. , 1991, Physical review. A, Atomic, molecular, and optical physics.

[14]  S. Levin THE PROBLEM OF PATTERN AND SCALE IN ECOLOGY , 1992 .

[15]  Robert M. May,et al.  The spatial dynamics of host-parasitoid systems , 1992 .

[16]  S. Levin The problem of pattern and scale in ecology , 1992 .

[17]  W. Wilson,et al.  Dynamics of Age-Structured and Spatially Structured Predator-Prey Interactions: Individual-Based Models and Population-Level Formulations , 1993, The American Naturalist.

[18]  Comparing Terrestrial and Marine Ecological Systems , 1993 .

[19]  Hugh P. Possingham,et al.  Population Cycling in Space-Limited Organisms Subject to Density-Dependent Predation , 1994, The American Naturalist.

[20]  Simon A. Levin,et al.  Stochastic Spatial Models: A User's Guide to Ecological Applications , 1994 .

[21]  O P Judson,et al.  The rise of the individual-based model in ecology. , 1994, Trends in ecology & evolution.

[22]  H. B. Wilson,et al.  Using spatio-temporal chaos and intermediate-scale determinism to quantify spatially extended ecosystems , 1995, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[23]  M. Pascual Understanding nonlinear dynamics , 1996 .

[24]  I. Mezić,et al.  Characteristic length scales of spatial models in ecology via fluctuation analysis , 1997 .

[25]  S. Levin,et al.  Theories of Simplification and Scaling of Spatially Distributed Processes , 2011 .

[26]  B. Bolker,et al.  Using Moment Equations to Understand Stochastically Driven Spatial Pattern Formation in Ecological Systems , 1997, Theoretical population biology.

[27]  Peter Kareiva,et al.  Spatial ecology : the role of space in population dynamics and interspecific interactions , 1998 .

[28]  Discrete consumers, small scale resource heterogeneity, and population stability , 1998 .

[29]  S. Levin,et al.  FROM INDIVIDUALS TO POPULATION DENSITIES: SEARCHING FOR THE INTERMEDIATE SCALE OF NONTRIVIAL DETERMINISM , 1999 .

[30]  R. Nisbet,et al.  POPULATION DYNAMICS AND SPATIAL SCALE: EFFECTS OF SYSTEM SIZE ON POPULATION PERSISTENCE , 1999 .

[31]  V. Jansen,et al.  The role of space in reducing predator-prey cycles , 2000 .

[32]  R. Durrett,et al.  Lessons on pattern formation from planet WATOR. , 2000, Journal of theoretical biology.