summary_ssp: Summary of MultSE for Each Sampling Effort in Simulated Data...

In SSP: Simulated Sampling Procedure for Community Ecology

Summary of MultSE for Each Sampling Effort in Simulated Data Sets

Description

Computes the average MultSE (pseudo-multivariate standard error) for each sampling effort across simulated datasets, and estimates associated variation and rate of change.

Usage

summary_ssp(results, multi.site)

Arguments

results	A matrix generated by sampsd containing MultSE values for each simulation and sampling configuration.
multi.site	Logical. Indicates whether multiple sites were simulated.

Details

For each sampling effort in each simulated data set, the average MultSE is computed (Anderson & Santana-Garcon, 2015). The function then calculates the overall mean and associated lower and upper quantiles of these averages. To evaluate how precision improves with effort, the average MultSE values are relativized to the maximum (typically at the lowest effort), and a numerical forward finite difference derivative is calculated to approximate the rate of change.

This output is used to support the identification of optimal and redundant sampling efforts based on precision gain.

Value

A data frame summarizing MultSE for each sampling effort, including the mean, quantiles, relativized values, and estimated derivative.

Note

This data frame can be used to plot MultSE versus sampling effort and to apply cutoff rules using ioptimum.

References

Anderson, M. J., & Santana-Garcon, J. (2015). Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecology Letters, 18(1), 66–73.

Guerra-Castro, E.J., Cajas, J.C., Simões, N., Cruz-Motta, J.J., & Mascaró, M. (2021). SSP: an R package to estimate sampling effort in studies of ecological communities. Ecography 44(4), 561-573. doi: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/ecog.05284")}

See Also

sampsd, ioptimum

Examples

## Single site example
data(micromollusk)
par.mic <- assempar(data = micromollusk, type = "P/A", Sest.method = "average")
sim.mic <- simdata(par.mic, cases = 3, N = 10, sites = 1)
sam.mic <- sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A",
                  method = "jaccard", n = 10, m = 1, k = 3)
summ.mic <- summary_ssp(results = sam.mic, multi.site = FALSE)

## Multiple site example
data(sponges)
par.spo <- assempar(data = sponges, type = "counts", Sest.method = "average")
sim.spo <- simdata(par.spo, cases = 3, N = 20, sites = 3)
sam.spo <- sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root",
                  method = "bray", n = 10, m = 3, k = 3)
summ.spo <- summary_ssp(results = sam.spo, multi.site = TRUE)

SSP documentation built on June 8, 2025, 11:41 a.m.