My forked version of Dan Carver's repository for creating species distribution models for target North American crop wild relatives. Small changes based on package updates since the repo was developed, and to input our specific 95 target taxa. You can find results at https://NorthAmericanFruitNutTreeCWR.github.io/. Original README below --

Crop Wild Relative of the USA

Code base for "Crop wild relatives of the United States require urgent conservation action"

Primary Authors : Colin Khoury, Dan Carver

maintainer: dan - [email protected]

Contributors : Harold Armando Achicanoy Estrella, Chrystiam Sossa, Maria Victoria Diaz

Code base for a reproducible maxnet modeling methodology written in R.

If you are interest in using this material for your own analysis, three folders are significant

dataPrep

• datePrep : Functions that are called by run_linear.R for individual species model runs.
• dataPrep/dataBaseTransform folder: a series of scripts that covert data from various sources into a common schema. The input datasets for these scripts are not provded in this repo. The final dataset used in the analysis for the paper is available on the Harvard Dataverse.

Modeling

• alternative : a script called by run_linear.R for species with not enough valid occurrence data to run the Maxnet modeling process.
• maxnet : scripts called by run_linear.R that conduct a variable section, model fitting, model project, and a series of model evaluations.

gapAnalysis

The gap analysis methodology can be applied to existing species distribution models. We recommend utilizing the GapAnalysis R package if you are applying these assessment methodology for your own work.

• Combinded: a script called by run_linear.R for generate final conservation metric
• exsitu: scripts called by run_linear.R forgenerating the three exsitu conservation metrics
• insitu: scripts called by run_linear.R for generating the three insitu conservation metrics
• redlist: a script called by run_linear.R for evaluting the EOO and AOO red list conservation metrics. Utilizes the function provided by redlisr package

run_lineal.R

Primary script for declaring all input variables and output locations