We compute bootstrap CIs by resampling transitions within the time series . Bootstraps that accommodate the dependence relationships that may be present in time series is an open - ended topic 72 , because generally there is no resampling scheme that logically guarantees resampled data to be equiprobable under the true generating process , in contrast to the bootstrap argument for independent samples . Nonetheless , in our case , the likelihood calculation sums over observations and expectations of change from one timestep to the next , and it is these transitions that are assumed to be independent across time periods and approximately independent between types . Unaccounted dependence between observations enters by either failures of the Markovian assumption in the true generating process , or by effects of competition between types induced by finite population size . We resample in a way that approximates independence and roughly preserves population size so that resampled transitions have similar frequencies to the originals .