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Description

Biodiversity Index Calculation and Bootstrap Confidence Interval Estimation.

Provides tools for the calculation of common biodiversity indices from count data. Additionally, it incorporates bootstrapping techniques to generate multiple samples, facilitating the estimation of confidence intervals around these indices. Furthermore, the package allows for the exploration of how variation in these indices changes with differing numbers of sites, making it a useful tool with which to begin an ecological analysis. Methods are based on the following references: Chao et al. (2014) <doi:10.1890/13-0133.1>, Chao and Colwell (2022) <doi:10.1002/9781119902911.ch2>, Hsieh, Ma,` and Chao (2016) <doi:10.1111/2041-210X.12613>.

biosampleR

The goal of biosampleR is to provide a simple set of functions to generate common biodiversity measures from count data, along with confidence intervals around these measures using bootstrapping. The package also provides functions to assess the effect of sampling effort on the precision of these measures.

Installation

You can install the development version of biosampleR from GitHub with:

# install.packages("devtools")
devtools::install_github("csim063/biosampleR")

Or you can install the stable version of biosampleR from CRAN with (the package is not yet on CRAN):

install.packages("biosampleR")

Example

The the functions in the package may be used in a single workflow as follows:

library(biosampleR)

# Import count data
df <- BCI #Using the BCI dataset from the vegan package as an example

# Calculate biodiversity measures with confidence intervals
# (both per site and overall for all sites)
stats <- get_sample_stats(df)

# Generate subsamples of a data frame with a number of sites between a minimum
# and maximum value.
ss <- generate_subsamples(df,
                          min_sites = 1,
                          max_sites = 5,
                          step = 1,
                          reps = 2)

# Calculate change in variance of biodiversity measures with increasing sampling effort
data  <- unlist(ss, recursive = FALSE)
data <- do.call(rbind, data)

calc_delta_var(data,
              col_name = "richness",
            site_name = "num_sites",
          rep_name = "rep",
        visualize = TRUE)

Code of Conduct

Please note that the spectre package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

To see how to contribute to this project, please see the Contributing guidelines.

Metadata

Version

1.0.4

License

Unknown

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