- Project description
- Package description
- Installing the package
- biosurvey functions and vignettes
- Workflow description
This repository is for the project “Biological Survey Planning Considering Hutchinson’s Duality” developed during the program GSoC 2020.
Student: Claudia Nuñez-Penichet
GSoC Mentors: Narayani Barve, Vijay Barve, Tomer Gueta
Complete list of authors: Claudia Nunez-Penichet, Marlon E. Cobos, A. Townsend Peterson, Jorge Soberon, Narayani Barve, Vijay Barve, Tomer Gueta
Motivation:
Given the increasing intensity of threats to biodiversity in the world, one of the challenges in biodiversity conservation is to complete inventories of existing species at distinct scales. Species distributions depend on the relationships between accessible areas, environmental conditions, and biotic interactions. As planning a survey system only aims to register species in a region, biodiversity interaction can be overlooked in this case. However, the relationship between environmental conditions and the geographic configuration of an area is of crucial importance when trying to identify key sites for biodiversity surveys. Among the diverse packages in R for selecting survey sites, such considerations are not implemented and are limited to a random selection of sampling sites or analyses that allow detecting potential sampling sites based on the environmental similarity between sampled and unsampled areas. Given the need for more solutions, the biosurvey package aimed for considering the relationship between environmental and geographic conditions in a region when designing survey systems that allow sampling of most of its biodiversity.
At the moment we have completed the three main modules of the package. We have made modifications to the original list of products, which have helped us to improve the package functionality. The package is fully functional and almost ready for submission to CRAN.
All commits made can be seen at the complete list of commits.
Following you can find a brief description of this R package, as well as general descriptions of how to use it.
The biosurvey R package implements multiple tools to allow users to select sampling sites increasing efficiency of biodiversity survey systems by considering the relationship of environmental and geographic conditions in a region. Three main modules are included: 1) Data preparation; 2) Selection of sets of sites for biodiversity sampling; and, 3) Tools for testing efficiency of distinct sets of sampling sites. Data are prepared ways that avoid the need for more data in posterior analyses, and allow concentrating in critical methodological decisions to select sampling sites. Various algorithms for selecting sampling sites are available, and options for considering pre-selected sites (known to be important for biodiversity monitoring) are included. Visualization is a critical component in this set of tools and most of the results obtained can be plotted to help to understand their implications. The options for selecting sampling sites included here differ from other implementations in that they consider the environmental and geographic structure of a region to suggest sampling sites that could increase the efficiency of efforts dedicated to monitoring biodiversity.
biosurvey is in a GitHub repository and can be installed and/or loaded using the code below (make sure to have Internet connection). If you have any problem during installation, restart R session, close other RStudio sessions you may have open, and try again. If during the installation you are asked to update packages, do so if you don’t need a specific version of one or more of the packages to be installed. If any of the packages gives an error when updating, please install it alone using install.packages(), then try re-installing biosurvey again.
# Installing and loading packages
if(!require(remotes)){
install.packages("remotes")
}
# To install the package use
remotes::install_github("claununez/biosurvey")
# To install the package and its vignettes use
remotes::install_github("claununez/biosurvey", build_vignettes = TRUE)
# Load biosurvey
library(biosurvey)
To check all functions in the package use:
help(biosurvey)
If the package was installed with its vignettes you can see all options with:
vignette(package = "biosurvey")
To check each vignette you can use:
# For a guide on how to prepare data for analysis
vignette("biosurvey_preparing_data")
# For a guide on how to select sampling sites
vignette("biosurvey_selecting_sites")
# For a guide on how to select sampling sites when some sites have been preselected
vignette("biosurvey_selection_with_preselected_sites")
# For a guide on how to use the testing module
vignette("biosurvey_testing_module")
To use biosurvey efficiently the first thing to do is to prepare an
object containing all information to be used in following analyses. This
can be done using the function preapare_master_matrix. After that
recommend intermediate steps are: exploring the data using the function
explore_data_EG and creating blocks of points in environmental space
using make_blocks. Then, distinct functions can be used to select
sampling sites:
random_selection.- Random selection of sites to be sampled in a survey.uniformG_selection.- Selection of sites to be sampled in a survey, with the goal of maximizing uniformity of points in geographic space.uniformE_selection.- Selection of sites to be sampled in a survey, with the goal of maximizing uniformity of points in environmental space.EG_seletion.- Selection of sites to be sampled in a survey, with the goal of maximizing uniformity of points in environment, but considering geographic patterns of data.
All functions mentioned above have the option to include user
preselected sites which will be inserted as part of the selection,
trying to maintaining the properties of each algorithm. See also how
your selected sites look like with the function plot_sites_EG.
After selection of sampling sites and if enough data are available, functions from the testing module can be used to explore which of the sets of sites selected could be better to monitor biodiversity more efficiently. Explore the following functions to explore your data and how well your selected sites perform in representing the exiting biodiversity:
prepare_base_PAM.- Prepares a presence-absence matrix (PAM) in which all sites of interest (rows) will have a value for presence or absence of a species of interest (columns).PAM_indices.- Calculates a set of biodiversity indices using values contained in a presence-absence matrix.plot_PAM_geo.- Plot of PAM indices in geography.subset_PAM.- Subsets of a base_PAM object according to survey sites contained in a master_selection object.selected_sites_SAC.- Creates species accumulation curves for each set of selected sites contained in elements of PAM_subset.plot_SAC.- Creates species accumulation curve plots for selected sites.compare_SAC.- Creates comparative plots of two species accumulation curves from information contained in lists obtained with the functionselected_sites_SAC.