scari is an R package and research compendium that documents a multiscale species distribution modeling (SDM) workflow to forecast establishment and impact risk of a species invasion as it shifts with climate change.
We developed this workflow to quantify the shifting risk of future establishment of the invasive species Lycorma delicatula (spotted lanternfly or SLF) in important viticulture regions worldwide. The R function create_risk_report produces risk maps, range shift estimates, risk plots and other outputs at the scale of countries or smaller geopolitical units.
The package scari is a research compendium for:
Owens, S. M. (2024). Multi-scale Modeling of the Spotted Lanternfly Lycorma delicatula (Hemiptera: Fulgoridae) Reveals Displaced Risk to Viticulture and Regional Range Expansion Due to Climate Change [M.S., Temple University]. In ProQuest Dissertations and Theses (3099643448). https://www.proquest.com/dissertations-theses/multi-scale-modeling-spotted-lanternfly-em/docview/3099643448/se-2?accountid=130527
This package should be first be downloaded and installed from GitHub by running the following code:
require(devtools)
# install.packages("devtools") # if devtools is not installed yet
devtools::install_github("ieco-lab/scari")
library(scari)
The dependency packages should then be installed for the package to run properly:
Here are the main packages that scari depends on:
install.packages(c('cli', 'common', 'CoordinateCleaner', 'devtools', 'dismo', 'dplyr', 'dsmextra', 'ENMTools', 'formattable', 'GeoThinneR', 'gginnards', 'ggplot2', 'gitcreds' 'grid', 'here', 'humboldt', 'kableExtra', 'kgc', 'knitr', 'lydemapr', 'patchwork', 'pkgdown', 'plotROC', 'pROC', 'raster', 'rasterVis', 'readr', 'renv', 'rgbif', 'rJava', 'rmarkdown', 'rnaturalearth', 'rnaturalearthhires', 'scales', 'SDMtune', 'sf', 'sp', 'spThin', 'stats', 'stringr', 'terra', 'tibble', 'tidygeocoder', 'tidyr', 'tidyverse', 'usethis', 'utils', 'viridis', 'webshot', 'webshot2'))
# we also suggest installing the following packages:
install.packages(c("blockCV", "knitr", "rmarkdown"))
# install specific version of ggnewscale
library(remotes)
remotes::install_version("ggnewscale", version = "0.4.10")
This project is organized into general sections of our modeling pipeline: Our vignettes follow this general order:
- 010: Initialize
scari: initialization ofrenvpackage for dependencies - 020-030: Retrieve and tidy input data for MaxEnt
- 040-090: SDM modeling pipeline: train global and 3 regional-scale models
- 100-110: Ensemble Regional-scale SDMs
- 120-142: Quantify SLF risk and model fit
- 150: Quantify risk to specific viticultural regions using our function create_risk_report()
Before diving into this project and our modeling workflow, an end user should:
- read the companion paper, which outlines the conceptual underpinnings for this project
- install the package
renv, which ensures that R package versions are consistent for running this package (this craetes a projct-specific R package library, so it should not affect your main library) - run the first vignette, 010_initialize_renv, which initializes
renvand lists our package's dependencies. - See "Get Started" for help in using our package to: 4.1 Produce localized reports on SLF risk to viticulture, and 4.2 Recreate our analysis for another invasive species of interest
This package was developed and its vignettes were rendered on a Dell Precision desktop PC with the following characteristics:
- Core: intel Xeon CPU, 3.60 GHz
- RAM memory: 64 GB
- Operating System: Windows 10 Enterprise, version 22H2
- R version: 4.4.2
Gallien, L., Douzet, R., Pratte, S., Zimmermann, N. E., & Thuiller, W. (2012). Invasive species distribution models – how violating the equilibrium assumption can create new insights. Global Ecology and Biogeography, 21(11), 1126–1136. https://doi.org/10.1111/j.1466-8238.2012.00768.x
Huron, N. A., Behm, J. E., & Helmus, M. R. (2022). Paninvasion severity assessment of a U.S. grape pest to disrupt the global wine market. Communications Biology, 5(1), 655. https://doi.org/10.1038/s42003-022-03580-w
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026