Software license
The web-based application is entirely based on Shiny, an open source R package (R 4.0.2, R Core Team 2021). The packages and versions used for building the application are shiny (1.6.0), shinydashboard (0.7.1), shinyjs (2.0.0), shinyBS (0.61.1), rmarkdown (2.13) and knitr (1.34). The code is available at https://github.com/facuxpalacio/stepFD/tree/main/R.
stepFD workflow
stepFD is intended to aid researchers and students in performing functional diversity (FD) analyses from the inception of the main ecological question to the proper reporting of data, metadata and code. The layout is based on the eight steps proposed in Palacio et al. (2021), and works mainly as a checklist of what to report in an FD study. Steps 1 and 2 relate to the question of interest and the sampling design, respectively. Steps 3-4 relate to community and trait data. Steps 5-7 deal with data exploration, FD computation and model fit and assessment. Step 8 has information on what to report to fully reproduce a study. Although this step comes at last, we emphasize that this should not be the last thing that is done, and should be part of the research paradigm from the conception of the study.
The application can be used by accessing the following link: https://facuxpalacio.shinyapps.io/stepFD. There is no need to install R, RStudio or Shiny. The application layout is a form with text inputs and checkboxes. Many of the fields provide more information to the user when hovering the mouse cursor over the inputs. Once all the fields have been filled out, the user has the option of printing a final report. The full checklist can also be downloaded (as a .csv or .doc file) to be filled out offline. The file created can be included as a supplementary material of scientific publications, so that the methods provides sufficient detail to be carried out by other researchers.
References
Palacio, F.X., Callaghan, C.T., Cardoso, P., Hudgins, E.J., Jarzyna, M.A., Ottaviani, G., Riva, F., Graco-Roza, C., Shirey, V., & Mammola, S. (2021). A protocol for reproducible functional diversity analyses. EcoEvoRxiv https://doi.org/10.32942/osf.io/yt9sb
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org