Monte Carlo modeling of the effect of extreme events on the extinction dynamics of faunal species with 2-stage life cycles

Our paper computationally explores the extinction dynamics of an animal species effected by a sudden spike in mortality due to an extreme event. In our study, the animal species has a 2-stage life cycle and is endowed with a high survival probability under normal circumstances. Our proposed approach does not involve any restraining assumptions concerning environmental variables or predator–prey relationships. Rather it is based on the simple premise that if observed on an year-to-year basis, the population size will be noted to either have gone up or come down as compared to last year. The conceptualization is borrowed from the theory of asset pricing in stochastic finance. The survival probability (λ) is set at unity i.e. the model assumes that all young members of the population mature into adults capable of reproduction. As we bias our model heavily in favor of survival, the chance of the population size increasing over time is much higher than it suffering a decline, if no extreme events occur. One of the critical parameters in our simulation model is the shock size i.e. the number of immediate mortalities that may be caused by an extreme event. We run our model for two pre-selected fecundity levels denoted as “high” and “low”. Under each of the two fecundity levels one hundred independent simulation runs are conducted over a time period of ten stages (i.e. five generations) and the relevant descriptive statistics are reported for the terminal (i.e. the fifth) generation. Shock sizes are varied until at least one scenario of total extinction is observed in the simulation output. Any extinctions occurring in t0 are treated as “trivial cases” and not counted. Our results indicate that an extreme event with a minimum shock size exceeding 2 / 3 the size of the pristine population can potentially drive animal species with 2-stage life cycles to extinction for both “low” and “high” fecundity levels.