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SSP (simulation-based sampling protocol) is an R package that uses simulation 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 do not improve the precision beyond a threshold value (e.g. 2.5 %). The performance of SSP was validated using real data, and in all examples the simulated data mimicked well the real data, allowing 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 range 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, since it offers new possibilities for complex sampling designs, as it has been advised for multi-scale studies in ecology.