Experimental support of the scaling rule for demographic stochasticity.

A scaling rule of ecological theory, accepted but lacking experimental confirmation, is that the magnitude of fluctuations in population densities due to demographic stochasticity scales inversely with the square root of population numbers. This supposition is based on analyses of models exhibiting exponential growth or stable equilibria. Using two quantitative measures, we extend the scaling rule to situations in which population densities fluctuate due to nonlinear deterministic dynamics. These measures are applied to populations of the flour beetle Tribolium castaneum that display chaotic dynamics in both 20-g and 60-g habitats. Populations cultured in the larger habitat exhibit a clarification of the deterministic dynamics, which follows the inverse square root rule. Lattice effects, a deterministic phenomenon caused by the discrete nature of individuals, can cause deviations from the scaling rule when population numbers are small. The scaling rule is robust to the probability distribution used to model demographic variation among individuals.

[1]  D. Kendall Stochastic Processes and Population Growth , 1949 .

[2]  P. H. Leslie A STOCHASTIC MODEL FOR STUDYING THE PROPERTIES OF CERTAIN BIOLOGICAL SYSTEMS BY NUMERICAL METHODS , 1958 .

[3]  Kenneth E. F. Watt,et al.  Ecology and resource management: a quantitative approach , 1968 .

[4]  Samuel Karlin,et al.  On Branching Processes with Random Environments: I: Extinction Probabilities , 1971 .

[5]  John Maynard Smith Models in ecology , 1974 .

[6]  R. May,et al.  Stability and Complexity in Model Ecosystems , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  M. Shaffer Minimum Population Sizes for Species Conservation , 1981 .

[8]  R. Costantino,et al.  The Approach to Equilibrium and the Steady-State Probability Distribution of Adult Numbers in Tribolium brevicornis , 1982, The American Naturalist.

[9]  M. Hassell,et al.  Variability in the abundance of animal and plant species , 1982, Nature.

[10]  William Gurney,et al.  Modelling fluctuating populations , 1982 .

[11]  Robert A. Desharnais,et al.  Population Dynamics and the Tribolium Model: Genetics and Demography , 1991, Monographs on Theoretical and Applied Genetics.

[12]  R. Lande Risks of Population Extinction from Demographic and Environmental Stochasticity and Random Catastrophes , 1993, The American Naturalist.

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

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

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

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

[17]  Costantino,et al.  Moving toward an unstable equilibrium: saddle nodes in population systems , 1998 .

[18]  Øyvind Bakke,et al.  Demographic and Environmental Stochasticity Concepts and Definitions , 1998 .

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

[20]  Consistency and fluctuation theorems for discrete time structured population models having demographic stochasticity , 2000, Journal of mathematical biology.

[21]  S. Engen,et al.  Population dynamical consequences of climate change for a small temperate songbird. , 2000, Science.

[22]  Do Le Paul Minh,et al.  Applied Probability Models , 2000 .

[23]  James H. Matis,et al.  Stochastic Population Models , 2000 .

[24]  R F Costantino,et al.  Lattice Effects Observed in Chaotic Dynamics of Experimental Populations , 2001, Science.

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

[26]  O. Bjørnstad,et al.  DYNAMICS OF MEASLES EPIDEMICS: SCALING NOISE, DETERMINISM, AND PREDICTABILITY WITH THE TSIR MODEL , 2002 .

[27]  O. Bjørnstad,et al.  Dynamics of measles epidemics: Estimating scaling of transmission rates using a time series sir model , 2002 .

[28]  Jim M Cushing,et al.  Chaos in Ecology: Experimental Nonlinear Dynamics , 2002 .

[29]  Brian Dennis,et al.  Explaining and predicting patterns in stochastic population systems , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[30]  James S. Clark,et al.  UNCERTAINTY AND VARIABILITY IN DEMOGRAPHY AND POPULATION GROWTH: A HIERARCHICAL APPROACH , 2003 .

[31]  Paul Beier,et al.  Population viability analysis. , 2016 .

[32]  R. Costantino,et al.  Anatomy of a chaotic attractor: Subtle model-predicted patterns revealed in population data , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[33]  John Sabo,et al.  Morris, W. F., and D. F. Doak. 2003. Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis. Sinauer Associates, Sunderland, Massachusetts, USA , 2003 .

[34]  D. Doak,et al.  Book Review: Quantitative Conservation biology: Theory and Practice of Population Viability analysis , 2004, Landscape Ecology.

[35]  R. Peterson,et al.  The influence of prey consumption and demographic stochasticity on population growth rate of Isle Royale wolves Canis lupus , 2004 .

[36]  Alun L Lloyd,et al.  Estimating variability in models for recurrent epidemics: assessing the use of moment closure techniques. , 2004, Theoretical population biology.

[37]  A. Hastings Transients: the key to long-term ecological understanding? , 2004, Trends in ecology & evolution.

[38]  R. Costantino,et al.  Species competition: uncertainty on a double invariant loop , 2005 .

[39]  Sturdy cycles in the chaotic Tribolium castaneum data series. , 2005, Theoretical population biology.

[40]  Jim M Cushing,et al.  Nonlinear Stochastic Population Dynamics: The Flour Beetle Tribolium as an Effective Tool of Discovery , 2005 .

[41]  John M. Drake,et al.  Population Viability Analysis , 2019, Encyclopedia of Theoretical Ecology.