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This repository hosts an R package that is actively being developed forestimating biodiversity and the components of its change. The key innovations ofthis R package over other R packages that also carry out rarefaction (e.g.,vegan,iNext) is thatmobr is focused on 1) making empirical comparisons betweentreatments or gradients, and 2) our framework emphasizes how changes inbiodiversity are linked to changes in community structure: the SAD, totalabundance, 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 citemobr. 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 thatremotes is installed you can installmobr using the following R code:

remotes::install_github('MoBiodiv/mobr')

The packagevignetteprovides a useful walk-through the package tools, but below is some example codethat 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')

• Pleasereport any issues or bugs.
• License: MIT
• Get citation information formobr in R doingcitation(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.

• Gregor Seyer for providing a constructive review of our CRAN submission
• Kurt Hornik for helping us keep up with CRAN changes.