README.md
In MoBiodiv/mobr: Measurement of Biodiversity

mobr

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Measurement of Biodiversity in R

This repository hosts an R package that is actively being developed for estimating biodiversity and the components of its change. The key innovations of this R package over other R packages that also carry out rarefaction (e.g., vegan, iNext) is that mobr is focused on 1) making empirical comparisons between treatments or gradients, and 2) our framework emphasizes how changes in biodiversity are linked to changes in community structure: the SAD, total abundance, and spatial aggregation.

The concepts and methods behind this R package are described in three publications.

McGlinn, D.J., S.A. Blowes, M. Dornelas, T. Engel, I.S. Martins, H. Shimadzu, N.J. Gotelli, A. Magurran, B.J. McGill, and J.M. Chase. accepted. Disentangling non-random structure from random placement when estimating β-diversity through space or time. Ecosphere. https://doi.org/10.1101/2023.09.19.558467

McGlinn, D.J. X. Xiao, F. May, N.J Gotelli, T. Engel, S.A Blowes, T.M. Knight, O. Purschke, J.M Chase, and B.J. McGill. 2019. MoB (Measurement of Biodiversity): a method to separate the scale-dependent effects of species abundance distribution, density, and aggregation on diversity change. Methods in Ecology and Evolution. 10:258–269. https://doi.org/10.1111/2041-210X.13102

McGlinn, D.J. T. Engel, S.A. Blowes, N.J. Gotelli, T.M. Knight, B.J. McGill, N. Sanders, and J.M. Chase. 2020. A multiscale framework for disentangling the roles of evenness, density, and aggregation on diversity gradients. Ecology. https://doi.org/10.1002/ecy.3233

Chase, J.M., B. McGill, D.J. McGlinn, F. May, S.A. Blowes, X. Xiao, T. Knight. 2018. Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities. Ecology Letters. 21: 1737–1751. https://doi.org/10.1111/ele.13151

Please cite mobr. Run the following to get the appropriate citation for the version you're using:

citation(package = "mobr")

install.packages('mobr')

Or, install the Github version

install.packages('remotes')

Now that remotes is installed you can install mobr using the following R code:

remotes::install_github('MoBiodiv/mobr')

The package vignette provides a useful walk-through the package tools, but below is some example code that uses the two key analyses and related graphics.

library(mobr)
library(dplyr)

data(tank_comm)
data(tank_plot_attr)
indices <- c('N', 'S', 'S_n', 'S_C', 'S_PIE')
tank_div <- tibble(tank_comm) %>% 
  group_by(group = tank_plot_attr$group) %>% 
  group_modify(~ calc_comm_div(.x, index = indices, effort = 5,
                               extrapolate = TRUE))
plot(tank_div)
tank_mob_in <- make_mob_in(tank_comm, tank_plot_attr, coord_names = c('x', 'y'))
tank_deltaS <- get_delta_stats(tank_mob_in, 'group', ref_level='low',
                             type='discrete', log_scale=TRUE, n_perm = 5)
plot(tank_deltaS, 'b1')

Please report any issues or bugs.
License: MIT
Get citation information for mobr in R doing citation(package = 'mobr')
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.