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Journal ArticleDOI

Can noise induce chaos

TLDR
In this article, it was shown that the sensitivity of a stochastic Lyapunov exponent (SLE) to the initial conditions of a deterministic model is not a sign of chaos.
Abstract
An important component of the mathematical definition of chaos is sensitivity to initial conditions. Sensitivity to initial conditions is usually measured in a deterministic model by the dominant Lyapunov exponent (LE), with chaos indicated by a positive LE. The sensitivity measure has been extended to stochastic models; however, it is possible for the stochastic Lyapunov exponent (SLE) to be positive when the LE of the underlying deterministic model is negative, and vice versa. This occurs because the LE is a long-term average over the deterministic attractor while the SLE is the long-term average over the stationary probability distribution. The property of sensitivity to initial conditions, uniquely associated with chaotic dynamics in deterministic systems, is widespread in stochastic systems because of time spent near repelling invariant sets (such as unstable equilibria and unstable cycles). Such sensitivity is due to a mechanism fundamentally different from deterministic chaos. Positive SLE's should therefore not be viewed as a hallmark of chaos. We develop examples of ecological population models in which contradictory LE and SLE values lead to confusion about whether or not the population fluctuations are primarily the result of chaotic dynamics. We suggest that “chaos” should retain its deterministic definition in light of the origins and spirit of the topic in ecology. While a stochastic system cannot then strictly be chaotic, chaotic dynamics can be revealed in stochastic systems through the strong influence of underlying deterministic chaotic invariant sets.

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Landscape connectivity and animal behavior: functional grain as a key determinant for dispersal

TL;DR: In this article, the authors argue that dispersal behavior changes with landscape configuration stressing the evolutionary dimension that has often been ignored in landscape ecology, and that the functional grain of resource patches in the landscape is a crucial factor shaping individual movements, and therefore influencing landscape connectivity.
Journal ArticleDOI

Linking climate change to lemming cycles

TL;DR: It is shown that winter weather and snow conditions, together with density dependence in the net population growth rate, account for the observed population dynamics of the rodent community dominated by lemmings in an alpine Norwegian core habitat between 1970 and 1997, and predict the observed absence of rodent peak years after 1994.
Journal ArticleDOI

Demography in an increasingly variable world.

TL;DR: This work discusses how understanding the demographic consequences of environmental variation will have applications for anticipating changes in populations resulting from anthropogenic activities that affect the variance in vital rates and highlights new tools for anticipating the magnitude and temporal patterning of environmental variability.
Journal ArticleDOI

Pollinator dispersal in an agricultural matrix: opposing responses of wild bees and hoverflies to landscape structure and distance from main habitat

TL;DR: The data show that taxa of the pollinator guild may perceive landscapes quite differently, and hoverflies may play an important role in maintaining pollination services in agricultural landscapes unsuitable for bee species.
References
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Journal ArticleDOI

Ergodic theory of chaos and strange attractors

TL;DR: A review of the main mathematical ideas and their concrete implementation in analyzing experiments can be found in this paper, where the main subjects are the theory of dimensions (number of excited degrees of freedom), entropy (production of information), and characteristic exponents (describing sensitivity to initial conditions).
Book

Handbook of Stochastic Methods: For Physics, Chemistry and the Natural Sciences

TL;DR: The Handbook of Stochastic Methods as mentioned in this paper covers the foundations of Markov systems, stochastic differential equations, Fokker-Planck equations, approximation methods, chemical master equations, and quatum-mechanical Markov processes.
Book

A second course in stochastic processes

TL;DR: A First Course Algebraic methods in Markov Chains Ratio Theorems of Transition Probabilities and Applications Sums of Independent Random Variables as a Markov Chain Order Statistics, Poisson Processes, and Applications Continuous Time Markov chains Diffusion Processes Compounding Stochastic Processes Fluctuation Theory of Partial Sum of Independent Identically Distributed Random Variable Queueing Processes Miscellaneous Problems Index as discussed by the authors.
Book

Non-linear time series. A dynamical system approach

Howell Tong
TL;DR: Non-linear least-squares prediction based on non-linear models and case studies and an introduction to dynamical systems.
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