ecocbo (version 1.0.0)

ecocbo-package: ecocbo: Calculating Optimum Sampling Effort in Community Ecology

Description

A system for calculating the optimal sampling effort, based on the ideas of "Ecological cost-benefit optimization" as developed by A. Underwood (1997, ISBN 0 521 55696 1). Data is obtained from simulated ecological communities, and the optimization follows the following procedure of two functions (1) scompvar() calculates the variation components necessary for (2) sim_cbo() to calculate the optimal combination of number of sites and samples depending on either an economical budget or on a desired statistical accuracy. Additionally, (3) sim_beta() estimates statistical power and type 2 error by using Permutational Multivariate Analysis of Variance, and (4) plot_power() represents the results of the previous function.

Author

The ecocbo development team is Edlin Guerra-Castro and Arturo Sanchez-Porras.

Details

The functions in ecocbo package can be used to identify the optimal number of sites and samples that must be considered in a community ecology study by using simulated data. Together with SSP package, ecocbo proposes a novel approach to the determination of he appropriate sampling effort in community ecology studies.

ecocbo is composed by five functions: prep_data gives the appropriate format to the data so that it can be used by the other functions in the package. scompvar calculates the components of variation for the analized dataset, and finally, sim_cbo determines an estimate of the number of sites and samples to consider to optimize the cost-benefit for an ecological sampling study. For getting more information on the data, sim_beta calculates statistical power for different sampling efforts and plot_power plots those results to help the user define the a combination of sampling effort and power to move on.

ecocbo is being developed at Github(https://github.com/arturoSP/ecocbo), where up-to-date versions can be found.

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Underwood, A. J., & Chapman, M. G. (2003). Power, precaution, Type II error and sampling design in assessment of environmental impacts. Journal of Experimental Marine Biology and Ecology, 296(1), 49-70.

Anderson, M. J. (2014). Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online, 1-15.

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.

Examples

# \donttest{
# Load and adjust data.
data(epiDat)

simResults <- prep_data(data = epiDat, type = "counts", Sest.method = "average",
                        cases = 5, N = 100, M = 10,
                        n = 5, m = 6, k = 20,
                        transformation = "none", method = "bray",
                        dummy = TRUE, useParallel = FALSE,
                        model = "single.factor")
# }
simResults

# Computing components of variation
compVar <- scompvar(data = simResults)
compVar


# Determination of statistical power
epiBetaR <- sim_beta(simResults, alpha = 0.05)
epiBetaR

# Cost-benefit optimization
cboResult <- sim_cbo(epiBetaR, cn = 75)
cboResult

# Visualization of statistical power
plot_power(data = epiBetaR, method = "power")