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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.