Biography:

One of their most recent publications is Temporally variable dispersal and demography can accelerate the spread of invading species. Which was published in journal Theoretical Population Biology.

More information about Stephen P. Ellner research including statistics on their citations can be found on their Copernicus Academic profile page.

Stephen P. Ellner's Articles: (2)

Temporally variable dispersal and demography can accelerate the spread of invading species

AbstractWe analyze how temporal variability in local demography and dispersal combine to affect the rate of spread of an invading species. Our model combines state-structured local demography (specified by an integral or matrix projection model) with general dispersal distributions that may depend on the state of the individual or its parent. It allows very general patterns of stationary temporal variation in both local demography and in the frequency and distribution of dispersal distances. We show that expressions for the asymptotic spread rate and its sensitivity to parameters, which have been derived previously for less general models, continue to hold. Using these results we show that random temporal variability in dispersal can accelerate population spread. Demographic variability can further accelerate spread if it is positively correlated with dispersal variability, for example if high-fecundity years are also years in which juveniles tend to settle further away from their parents. A simple model for the growth and spread of patches of an invasive plant (perennial pepperweed, Lepidium latifolium) illustrates these effects and shows that they can have substantial impacts on the predicted speed of an invasion wave. Temporal variability in dispersal has received very little attention in both the theoretical and empirical literature on invasive species spread. Our results suggest that this needs to change.

Inferring mechanism from time-series data: Delay-differential equations

AbstractWhen there is qualitative information about the underlying processes and structure of a dynamical system, it may be possible to infer very accurate quantitative information about these processes using only an output time series from the system. We illustrate how this can be accomplished for time series data from a delay-differential equation with a single fixed delay. Our approach exploits modern techniques for non-parametric function estimation, is robust to fairly high levels of dynamic noise and measurement error, and can be extended straightforwardly to more general delay-differential systems and multivariate systems.

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