SSP (version 1.1.0)

ioptimum: Identification of the Optimal Sampling Effort

Description

Estimates the sampling effort at which the improvement in precision (MultSE) per additional sampling unit becomes sub-optimal or redundant, based on predefined cut-off thresholds.

Usage

ioptimum(xx, multi.site = TRUE, c1 = 10, c2 = 5, c3 = 2.5)

Value

A vector or matrix indicating the sampling sizes corresponding to each cut-off point.

Arguments

xx

A data frame generated by summary_ssp.

multi.site

Logical. Indicates whether multiple sites were simulated.

c1

First cut threshold. Default is 10% improvement over the highest MultSE.

c2

Second cut threshold. Default is 5% improvement over the highest MultSE.

c3

Third cut threshold. Default is 2.5% improvement over the highest MultSE.

Details

Sampling efforts between the minimum (e.g. 2 samples) and c1 represent the necessary effort to achieve acceptable precision. Efforts between c1 and c2 reflect sub-optimal gains, and those between c2 and c3 are considered optimal. Beyond c3, any additional effort results in marginal improvements in MultSE and may be considered redundant. This classification helps support cost-benefit decisions in ecological survey design (see Underwood, 1990). If c3 is not reached within the simulated range, the maximum available effort is returned with a warning.

References

Underwood, A. J. (1990). Experiments in ecology and management: Their logics, functions and interpretations. Australian Journal of Ecology, 15, 365–389.

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: tools:::Rd_expr_doi("10.1111/ecog.05284")

See Also

plot_ssp, summary_ssp, sampsd

Examples

## Single site example
data(micromollusk)
par.mic <- assempar(data = micromollusk, type = "P/A", Sest.method = "average")
sim.mic <- simdata(par.mic, cases = 2, N = 20, 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)
opt.mic <- ioptimum(xx = summ.mic, multi.site = FALSE)

## See the full multi-site workflow in the vignette