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.

Automated summary

SSP is an R package that uses ecological data simulations and MultSE to evaluate sampling efforts for hypothesis testing, identifying the optimal sampling effort. Its performance was validated using real data, and it can estimate sample size in various situations, making it a valuable tool for multi-scale studies in ecology.