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diveRpine

diveRsification of pine plantations in Mediterranean mountains

diveRpine is an interactive R tool which simulates the way in which species diversity in pine plantations varies depending on the landscape configuration, the internal structure of the plantation (e.g. past land uses, tree density), and the composition of the dispersion vectors (e.g. birds, mammals).


The aim of the application is to develop a decision-support tool that simulates the dynamics of forest ecological processes.
The application projects the most likely ecological succession in each stand based on the ecological context, and visualizes the relative importance of the different ecological mechanisms involved in the process. The resulting simulations help the manager to identify forest stands that most need intervention (active restoration), compared to other stands where intervention is unneeded (passive restoration), depending on the ecosystem services that the manager wishes to maximize. It also allows the user to visualize some of the complex ecological processes that underlie the diversification of pine plantations in Mediterranean mountain areas.
This tool uses information published in scientific journals in a synthetic and straightforward way, enabling the manager to visualize different scenarios and perform simulations based on solid field data interpreted in scientific contexts.

Installation

diveRpine was built in R, an open source programming language using the Shiny package, a web application framework for R.

You can download the development version of diveRpine from GitHub with:

# install.packages("devtools")
devtools::install_github("ajpelu/diveRpine")

How to run diveRpine

The recommended option to use the application is to download the package and run it locally. You can also use a running example here.

Locally

  1. The use of Rstudio is recommended

  2. Download and install the diveRpine pkg using:

# install.packages("devtools")
devtools::install_github("ajpelu/diveRpine")
  1. Run the app:

All required code can be found in this github repository.