A new look at habitat suitability curves through FDA

• A scientific research by Jeremie Boudreault, André St-Hilaire, Normand Bergeron and Fateh Chebana
• This project is part of my master studies at *Institut national de la recherche scientifique.
• All scripts and data are made freely available here under the
• Any questions should be sent to firstname.lastname@inrs.ca

The research A new look at habitat suitability curves through functional data analysis was published in Ecological Modelling in 2022.

Data

Data consists of a field survey that have been conducted during summer 2017 on the Sainte-Marguerite River (SMR) and Petite-Cascapedia River (PCR) :

• data/field-data/* : contains the two raw xlsx files filled after each day of field work.
• data/gps/* : locations of the studied sites for the two rivers.

R scripts

Scripts are all from Jeremie Boudreault. They make the use of the R package FDboost for fitting functional regression models (FRM) :

• s01_clean_field_data.R : clean the field data spreadsheets and produced cleaned datasets.
• s02_compute_salmon_numbers.R: convert measured salmon fork-lengths to number of fry and parr.
• s03_select_study_case.R: select optimal data subset for this project.
• s04_map_studied_sites.R: map sites studied in this project.
• s05_generate_fd_curves.R: generate (functional) curves of availability and selection/preference.
• s06_prepare_data_modelling.R: prepare data prior to fit function regression models (FRM).
• s07_fit_function_models.R: fit FRMs to curves using a parallelized k-fold cross-validation.
• s08_define_optimal_stopping.R: choose optimal early stopping parameter.
• s09_compute_coef_bootstrap.R: compute standard-error of FRM coefficients using bootstrap.
• s10_plot_frm_coefficients.R: plots intercept and coefficients surfaces from fitted FRM.
• s11_build_classical_models.R: build classical local and regional models of HSC.
• s12_assess_models_performance.R: calculate some goodness-of-fit metrics for the fitted models.

Enjoy ! ✌🏻