Resilient spatio-temporal patterns in plant-soil systems

In hard environmental conditions, vegetation self-organizes forming heterogeneous landscapes that increase ecosystem resilience. So far, this has been studied in vegetation with scale-dependent feedbacks leading to stationary patterns. In this paper, we study plant species that accumulate toxins in the soil, generating complex spatiotemporal landscapes, as in Posidonia oceanica. This plant organizes in traveling patterns where the toxin–plant interaction is globally reduced, allowing the ecosystem to survive under adverse conditions. The patterns follow a predictable sequence as mortality increases, matching aerial images of P. oceanica. Our results can predict regime shifts due to climate change and apply to a whole family of ecosystems with plant–soil interactions.

Figure: A-D Patterns found in Posidonia oceanica meadows. E-H Simulations of our model for different parameters and initial conditions.