Title: Early warning signals in plant disease outbreaks,
Abstract: Summary
1. Infectious disease outbreaks in plants threaten ecosystems, agricultural
crops and food trade. Currently, several fungal diseases are affecting forests
worldwide, posing a major risk to tree species, habitats and consequently
ecosystem decay. Prediction and control of disease spread are difficult, mainly
due to the complexity of the interaction between individual components
involved.
2. In this work, we introduce a lattice-based epidemic model coupled with a
stochastic process that mimics, in a very simplified way, the interaction
between the hosts and pathogen. We studied the disease spread by measuring the
propagation velocity of the pathogen on the susceptible hosts. Quantitative
results indicate the occurrence of a critical transition between two stable
phases: local confinement and an extended epiphytotic outbreak that depends on
the density of the susceptible individuals.
3. Quantitative predictions of epiphytotics are performed using the framework
early-warning indicators for impending regime shifts, widely applied on
dynamical systems. These signals forecast successfully the outcome of the
critical shift between the two stable phases before the system enters the
epiphytotic regime.
4. Synthesis: Our study demonstrates that early-warning indicators could be
useful for the prediction of forest disease epidemics through mathematical and
computational models suited to more specific pathogen-host-environmental
interactions.