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.

中文翻译：

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SSP：R包，用于评估生态社区研究中的抽样工作

SSP（基于模拟的采样协议）是一个R程序包，它使用生态数据模拟和基于差异的多元标准误差（MultSE）作为精确度的评估器，以评估不同采样工作的适用性，这些研究将使用置换多元变量检验假设方差分析。该过程包括使用试点数据集模拟几个广泛的数据矩阵，这些矩阵模拟感兴趣社区的一些相关生态特征。对于每个模拟数据，将重复执行几次采样工作并计算MultSE。MultSE的平均值，0.025和0.975分位数然后，针对所有模拟数据中的每个采样工作量，针对最低的采样工作量进行估算和标准化。最佳采样工作被确定为其中采样工作的增加并未使最高MultSE超出阈值（例如2.5％）的情况。使用实际数据验证了SSP的性能。在所有这三种情况下，模拟数据都模仿了真实数据，并允许评估MultSE – n的关系，超出了初步研究的抽样规模。SSP从简单的（例如单个站点）到更复杂的（例如用于不同栖息地的多个站点）实验设计，可以用于估计各种情况下的样本量。在生态学的多尺度研究的背景下，后者构成了重要的优势。没有R背景的用户可以使用SSP的在线版本。