============
============
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')mobr in R doing
citation(package = 'mobr')