SSP: An R package to estimate sampling effort in studies of ecological communities

SSP (simulation-based sampling protocol) is an R package that uses
simulations of ecological data and dissimilarity-based multivariate
standard error (MultSE) as an estimator of precision to evaluate the
adequacy of different sampling efforts for studies that will test
hypothesis using permutational multivariate analysis of variance. The
procedure consists in simulating several extensive data matrixes that
mimic some of the relevant ecological features of the community of
interest using a pilot data set. For each simulated data, several sampling
efforts are repeatedly executed and MultSE calculated. The mean value,
0.025 and 0.975 quantiles of MultSE for each sampling effort across all
simulated data are then estimated and standardized regarding the lowest
sampling effort. The optimal sampling effort is identified as that in
which the increase in sampling effort does not improve the highest MultSE
beyond a threshold value (e.g. 2.5 %). The performance of SSP was
validated using real data. In all three cases, the simulated data mimicked
the real data and allowed to evaluate the relationship MultSE – n beyond
the sampling size of the pilot studies. SSP can be used to estimate sample
size in a wide variety of situations, ranging from simple (e.g. single
site) to more complex (e.g. several sites for different habitats)
experimental designs. The latter constitutes an important advantage in the
context of multi-scale studies in ecology. An online version of SSP is
available for users without an R background.