SSP
Simulated Sampling Procedure for Community Ecology
The Simulation-based Sampling Protocol (SSP) is an R package designed to estimate sampling effort in studies of ecological communities. It is based on the concept of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon, 2015,
SSP development version
Estimation of sampling effort in community ecology with SSP
Edlin Guerra-Castro, Juan Carlos Cajas, Juan Jose Cruz-Motta, Nuno Simoes and Maite Mascaro
SSP is an R package design to estimate sampling effort in studies of ecological communities based on the definition of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon 2015) and simulation of community data and resampling (Guerra-Castro et al., 2021).
SSP includes seven functions: assempar for extrapolation of
assemblage parameters using pilot data; simdata for simulation of
several data sets based on extrapolated parameters; datquality for
evaluation of plausibility of simulated data; sampsd for repeated
estimations of MultSE for different sampling designs in simulated data
sets; summary_ssp for summarizing the behavior of MultSE for each
sampling design across all simulated data sets, ioptimum for
identification of the optimal sampling effort, and plot_ssp to plot
sampling effort vs MultSE.
R Packages needed
- Required: vegan, sampling, stats, ggplot2. These are installed automatically.
- Suggested: devtools, knitr, and rmarkdown to build SSP from github. All these must be installed by you.
Installation:
The SSP package is available on CRAN but can be downloaded from github using the following commands:
## Packages needed to build SSP and vignettes
install.packages(pkgs = c('devtools', 'knitr', 'rmarkdown'))
library(devtools)
library(knitr)
library(rmarkdown)
## install the latest version of SSP from github
install_github('edlinguerra/SSP', build_vignettes = TRUE)
library(SSP)
For examples about how to use SSP, see help('SSP') after
instalation.