README.md

In SSP: Simulated Sampling Procedure for Community Ecology

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), simulation of data and resampling (Guerra-Castro et al., 2020).

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_sd 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 PACKAGE NEEDED IN SSP

• 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.

HOW TO RUN SSP:

The SSP package will be 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.

SSP documentation built on March 31, 2020, 5:10 p.m.