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Simone Pigolotti

Publications and source records attributed to Simone Pigolotti.

5 recordsLinked to original sources

Dynamical evolution of ecosystems.

The assembly of an ecosystem such as a tropical forest depends crucially on the species interaction network, and the deduction of its rules is a formidably complex problem. In spite of this, many recent studies using Hubbell's neutral theory of biodiversity and biogeography have demonstrated that the resulting emergent macroscopic behaviour of the ecosystem at or near a stationary state shows a surprising simplicity reminiscent of many physical systems. Indeed the symmetry postulate, that the effective birth and death rates are species-independent within a single trophic level, allows one to make analytical predictions for various static distributions such as the relative species abundance, beta-diversity and the species-area relationship. In contrast, there have only been a few studies of the dynamics and stability of tropical rain forests. Here we consider the dynamical behaviour of a community, and benchmark it against the exact predictions of a neutral model near or at stationarity. In addition to providing a description of the relative species abundance, our analysis leads to a quantitative understanding of the species turnover distribution and extinction times, and a measure of the temporal scales of neutral evolution. Our model gives a very good description of the large quantity of data collected in Barro Colorado Island in Panama in the period 1990-2000 with just three ecologically relevant parameters and predicts the dynamics of extinction of the existing species.

Animals↗

Species lifetime distribution for simple models of ecologies.

Interpretation of empirical results based on a taxa's lifetime distribution shows apparently conflicting results. Species' lifetime is reported to be exponentially distributed, whereas higher-order taxa, such as families or genera, follow a broader distribution, compatible with power-law decay. We show that both forms of evidence are consistent with a simple evolutionary model that does not require specific assumptions on species interaction. The model provides a zero-order description of the dynamics of ecological communities, and its species lifetime distribution can be computed exactly. Different behaviors are found as follows: an initial t(-3/2) power law, emerging from a random walk type of dynamics, which crosses over to a steeper t(-2) branching process-like regime and finally is cut off by an exponential decay that becomes weaker and weaker as the total population increases. Sampling effects also can be taken into account and shown to be relevant. If species in the fossil record were sampled according to the Fisher log-series distribution, lifetime should be distributed according to a t(-1) power law. Such variability of behaviors in a simple model, combined with the scarcity of data available, casts serious doubt on the possibility of validating theories of evolution on the basis of species lifetime data.

Animals↗

Properties making a chaotic system a good pseudo random number generator.

We discuss the properties making a deterministic algorithm suitable to generate a pseudo random sequence of numbers: high value of Kolmogorov-Sinai entropy, high dimensionality of the parent dynamical system, and very large period of the generated sequence. We propose the multidimensional Anosov symplectic (cat) map as a pseudo random number generator. We show what chaotic features of this map are useful for generating pseudo random numbers and investigate numerically which of them survive in the discrete state version of the map. Testing and comparisons with other generators are performed.

Journal Article↗

Stochastic model for the species abundance problem in an ecological community.

We propose a model based on coupled multiplicative stochastic processes to understand the dynamics of competing species in an ecosystem. This process can be conveniently described by a Fokker-Planck equation. We provide an analytical expression for the marginalized stationary distribution. Our solution is found in excellent agreement with numerical simulations and compares rather well with observational data from tropical forests.

Animals↗

Coarse-grained probabilistic automata mimicking chaotic systems.

Discretization of phase space usually nullifies chaos in dynamical systems. We show that if randomness is associated with discretization dynamical chaos may survive and be indistinguishable from that of the original chaotic system, when an entropic, coarse-grained analysis is performed. Relevance of this phenomenon to the problem of quantum chaos is discussed.

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