FROM INDIVIDUALS TO POPULATION DENSITIES: SEARCHING FOR THE INTERMEDIATE SCALE OF NONTRIVIAL DETERMINISM

The degree of stochasticity or determinism in the dynamics of ecological systems varies with sampling scale. We propose the application of a determinism test from nonlinear data analysis to describe this variation and to identify a characteristic length scale at which to average spatiotemporal systems. Specifically, we investigate the spatial scale at which to aggregate individuals into densities in a system that combines demographic noise with local density-dependent interactions. The proposed approach is applied to the dynamics of a spatial and individual-based predator-prey model. The selected spatial scale is smaller than the one obtained by a previously proposed method whose similarities and differences we discuss. Two models, the simplest candidates for approximating the dynamics of densities at the selected scale, are examined: a predator-prey system of differential equations that ignores the local nature of the dynamics, and an extension of it that adds demographic noise. These approximations perform poorly, failing to capture broad statistical features of the predator and prey fluctuations. These findings indicate that spatial factors are nonnegligible at the selected intermediate scale of aggregation. Thus, predator-prey systems and other oscillatory ecological systems may display a dynamic regime at an intermediate scale of aggregation in which local interactions are still important. We discuss the type of model needed to approximate population densities in this dynamic regime.

[1]  G. F.,et al.  From individuals to aggregations: the interplay between behavior and physics. , 1999, Journal of theoretical biology.

[2]  A. R. Gallant,et al.  Noise and Nonlinearity in Measles Epidemics: Combining Mechanistic and Statistical Approaches to Population Modeling , 1998, The American Naturalist.

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

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

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

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

[7]  Simon A. Levin,et al.  Biologically generated spatial pattern and the coexistence of competing species , 1997 .

[8]  Jonathan A. Sherratt,et al.  Oscillations and chaos behind predator–prey invasion: mathematical artifact or ecological reality? , 1997 .

[9]  W. Wilson Lotka's game in predator-prey theory: linking populations to individuals. , 1996, Theoretical population biology.

[10]  R. Durrett,et al.  From individuals to epidemics. , 1996, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[11]  Mercedes Pascual,et al.  Detecting nonlinear dynamics in spatio-temporal systems, examples from ecological models , 1996 .

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

[13]  Stephen P. Ellner,et al.  Chaos in a Noisy World: New Methods and Evidence from Time-Series Analysis , 1995, The American Naturalist.

[14]  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.

[15]  Monica G. Turner,et al.  Exploring Aggregation in Space and Time , 1995 .

[16]  T. Powell Physical and Biological Scales of Variability in Lakes, Estuaries, and the Coastal Ocean , 1995 .

[17]  Douglas H. Deutschman,et al.  Details That Matter: The Spatial Distribution of Individual Trees Maintains Forest Ecosystem Function , 1995 .

[18]  M. Hay,et al.  Species as 'noise' in community ecology: do seaweeds block our view of the kelp forest? , 1994, Trends in ecology & evolution.

[19]  George Sugihara,et al.  Nonlinear forecasting for the classification of natural time series , 1994, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.

[20]  H. Tong,et al.  On prediction and chaos in stochastic systems , 1994, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.

[21]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[22]  R. Armstrong,et al.  Grazing limitation and nutrient limitation in marine ecosystems: Steady state solutions of an ecosystem model with multiple food chains , 1994 .

[23]  R. Steneck,et al.  A functional group approach to the structure of algal-dominated communities , 1994 .

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

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

[26]  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.

[27]  Mercedes Pascual,et al.  Diffusion-induced chaos in a spatial predator–prey system , 1993, Proceedings of the Royal Society of London. Series B: Biological Sciences.

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

[29]  Anthony W King,et al.  Aggregating Fine-Scale Ecological Knowledge to Model Coarser-Scale Attributes of Ecosystems. , 1992, Ecological applications : a publication of the Ecological Society of America.

[30]  D. DeAngelis,et al.  Individual-Based Models and Approaches in Ecology , 1992 .

[31]  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.

[32]  A. King Translating models across scales in the landscape , 1991 .

[33]  G Sugihara,et al.  Distinguishing error from chaos in ecological time series. , 1990, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[34]  George Sugihara,et al.  Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series , 1990, Nature.

[35]  Mark Kot,et al.  Multidimensional trees, range searching, and a correlation dimension algorithm of reduced complexity , 1989 .

[36]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[37]  A. N. Sharkovskiĭ Dynamic systems and turbulence , 1989 .

[38]  D. DeAngelis,et al.  New Computer Models Unify Ecological TheoryComputer simulations show that many ecological patterns can be explained by interactions among individual organisms , 1988 .

[39]  M. Kot,et al.  Changing criteria for imposing order , 1988 .

[40]  S. Levin Pattern, Scale, and Variability: An Ecological Perspective , 1988 .

[41]  Farmer,et al.  Predicting chaotic time series. , 1987, Physical review letters.

[42]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[43]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[44]  W. Gurney,et al.  Modelling fluctuating populations , 1982 .

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

[46]  Shang‐keng Ma Modern Theory of Critical Phenomena , 1976 .