Turing patterns in a networked vegetation model.

A vegetation model composed of water and plants was proposed by introducing a weighted graph Laplacian operator into the reaction-diffusion dynamics. We showed the global existence and uniqueness of the solution via monotone iterative sequence. The parameter space of Turing patterns for plant behavior is obtained based on the analysis of the eigenvalues of the Laplacian of weighted graph, while the amplitude equation determining the stability of Turing patterns is obtained by weakly nonlinear analysis.

Turing Pattern Formation in a Semiarid Vegetation Model with Fractional-in-Space Diffusion.

A fractional power of the Laplacian is introduced to a reaction-diffusion system to describe water's anomalous diffusion in a semiarid vegetation model. Our linear stability analysis shows that the wavenumber of Turing pattern increases with the superdiffusive exponent. A weakly nonlinear analysis yields a system of amplitude equations, and the analysis of these amplitude equations shows that the spatial patterns are asymptotic stable due to the supercritical Turing bifurcation.

Turing pattern dynamics and adaptive discretization for a super-diffusive Lotka-Volterra model.

In this paper we analyze the effects of introducing the fractional-in-space operator into a Lotka-Volterra competitive model describing population super-diffusion. First, we study how cross super-diffusion influences the formation of spatial patterns: a linear stability analysis is carried out, showing that cross super-diffusion triggers Turing instabilities, whereas classical (self) super-diffusion does not. In addition we perform a weakly nonlinear analysis yielding a system of amplitude equations, whose study shows the stability of Turing steady states.

Turing pattern dynamics in an activator-inhibitor system with superdiffusion.

The fractional operator is introduced to an activator-inhibitor system to describe species anomalous superdiffusion. The effects of the superdiffusive exponent on pattern formation and pattern selection are studied. Our linear stability analysis shows that the wave number of the Turing pattern increases with the superdiffusive exponent.

Delay-driven irregular spatiotemporal patterns in a plankton system.

An inhomogeneous distribution of species density over physical space is a widely observed scenario in plankton systems. Understanding the mechanisms resulting in these spatial patterns is a central topic in plankton ecology. In this paper we explore the impact of time delay on spatiotemporal patterns in a prey-predator plankton system.

Traveling wave governs the stability of spatial pattern in a model of allelopathic competition interactions.

Inhomogenous distribution of populations across physical space is a widely observed scenario in nature and has been studied extensively. Mechanisms accounting for these observations are such as diffusion-driven instability and mechanochemical approach. While conditions have been derived from a variety of models in biological, physical, and chemical systems to trigger the emergence of spatial patterns, it remains poorly understood whether the spatial pattern possesses asymptotical stability.

Delay-driven spatial patterns in a plankton allelopathic system.

Spatial patterns have received considerable attention in the physical, biological, and social sciences. Generally speaking, time delay is a prevailing phenomenon in aquatic environments, since the production of allelopathic substance by competitive species is not instantaneous, but mediated by some time lag required for maturity of species. A natural question is how delay affects the spatial patterns.