Conference Information
Stochastic growth models and sigmoidal dynamics are essential tools for
describing patterns that frequently arise in natural systems. They are
widely used in biology and ecology to represent mechanisms such as
population development, disease spread, and adaptive responses to
environmental fluctuations. In this work, we investigate a lognormal
diffusion process subject to random catastrophic events, modeled as
sudden jumps that reset the system to a new random state.
The novelty of the model lies in the assumption that the
post-catastrophe restart level follows a binomial distribution. As a
consequence, after each catastrophic event the population does not
simply revert to its initial size, but instead to a random level
reflecting the survival probability of individual members. We apply this
framework to the dynamics of a wolf population exposed to external
disturbances. The findings show that the model can realistically
reproduce complex population trajectories, displaying periods of gradual
growth interspersed with sudden declines triggered by unpredictable
external shocks.