Simulating datasets of species abundances with varying degrees of turnover in R I am working with a data set containing the abundance of 100+ species at 50+ locations over 20+ time-points. I want to simulate equivalent data sets with varying degrees of community turnover i.e. abundant species staying abundant/rare staying rare --> abundant becoming rare and vice versa while maintaining the species abundance distribution in the original data set. I have seen mentions of resampling simulation techniques using bootstrapping where the desired difference between "template" and simulated data sets can be specified in terms of the proportion of all species that were present in both assemblages.

I am wondering if that is the best way forward to get the desired simulated data sets and if anyone could point me towards a tutorial on how to do this in R/give me some tips themselves.

Thanks!
Without any example of your data or distributions it is a hard question to answer. Without further info I think there are two approaches:


resample your data using stratified sampling. The R sample package can be of good help here.
generate a multivariate distribution, as far as I know there are no packages that will directly do this for you. Depending on the distributions of your data, creating the right multivariate distribution which includes the right correlations may be very hard. In some cases you may even need to use copulas (this is especially necessary if you are interested in tail events, correlation will not be sufficient to create dependence of these events). Note that this topic can be a separate PhD thesis on itself.


I hope this helps.