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Overview

Species distribution modeling has become a standard tool in several research areas such as ecology, conservation biology, biogeography, paleobiogeography, and epidemiology. Species distribution modeling is an area of active research in both theoretical and methodological aspects. One of the most exciting features of flexsdm is its high manipulation and parametrization capacity based on different functions and arguments. These attributes enable users to define a complete or partial modeling workflow specific for a modeling situation (e.g., number of variables, number of records, different algorithms, algorithms tuning, ensemble methods).

Structure of flexsdm

The functions of flexsdm package are organized into three major modeling steps

1. Pre-modeling functions

Set tools that prepare modeling input data (e.g., species occurrences thinning, sample pseudo-absences or background points, delimitation of calibration area).

calib_area() Delimit calibration area for constructing species distribution models
correct_colinvar() Collinearity reduction on predictors
env_outliers() Integration of outliers detection methods in the environmental space
part_random() Data partitioning for training and testing models
part_sblock() Spatial block cross validation
part_sband() Spatial band cross validation
part_senv() Environmental cross-validation
plot_res() Plot different resolutions to be used in part_sblock
get_block() Transform a spatial partition layer to the same spatial properties of environmental variables
sample_background() Sample background points
sample_pseudoabs() Sampel pseudo-absence
sdm_directory() Create directories for saving the outputs of the flexsdm
sdm_extract() Extract environmental data based on x and y coordinates
occfilt_env() Perform environmental filtering on species occurrences
occfilt_geo() Perform geographical filtering on species occurrences
occfilt_select() Select filtered occurrences when it was tested with different filtering values

2. Modeling functions

It includes functions related to modeling construction and validation. Several of them can be grouped into fit_*, tune_*, and esm_* family functions. fit_* construct and validate models with default hyper-parameter values. tune_* construct and validate models searching for the best hyper-parameter values combination. esm_ construct and validate Ensemble of Small Models.

Model evaluation

sdm_eval() Calculate different model performance metrics

`fit_*` functions family

fit_gam() Fit and validate Generalized Additive Models
fit_gau() Fit and validate Gaussian Process models
fit_gbm() Fit and validate Generalized Boosted Regression models
fit_glm() Fit and validate Generalized Linear Models
fit_max() Fit and validate Maximum Entropy models
fit_net() Fit and validate Neural Networks models
fit_raf() Fit and validate Random Forest models
fit_svm() Fit and validate Support Vector Machine models

`tune_*` functions family

tune_gbm() Fit and validate Generalized Boosted Regression models with exploration of hyper-parameters
tune_max() Fit and validate Maximum Entropy models with exploration of hyper-parameters
tune_net() Fit and validate Neural Networks models with exploration of hyper-parameters
tune_raf() Fit and validate Random Forest models with exploration of hyper-parameters
tune_svm() Fit and validate Support Vector Machine models with exploration of hyper-parameters

Model ensemble

fit_ensemble() Fit and validate ensemble models with different ensemble methods

`esm_*` functions family

esm_gam() Fit and validate Generalized Additive Models with Ensemble of Small Model approach
esm_gau() Fit and validate Gaussian Process models Models with Ensemble of Small Model approach
esm_gbm() Fit and validate Generalized Boosted Regression models with Ensemble of Small Model approach
esm_glm() Fit and validate Generalized Linear Models with Ensemble of Small Model approach
esm_max() Fit and validate Maximum Entropy models with Ensemble of Small Model approach
esm_net() Fit and validate Neural Networks models with Ensemble of Small Model approach
esm_svm() Fit and validate Support Vector Machine models with Ensemble of Small Model approach

3. Post-modeling functions

Tools related to models’ geographical predictions, evaluation, and correction.

sdm_predict() Spatial predictions of individual and ensemble model
sdm_summarize() Merge model performance tables
interp() Raster interpolation between two time periods
extra_eval() Measure model extrapolation
extra_truncate() Constraint suitability values under a given extrapolation value
msdm_priori() Create spatial predictor variables to reduce overprediction of species distribution models
msdm_posteriori() Methods to correct overprediction of species distribution models based on occurrences and suitability patterns.

4. Graphical model exploration

Useful tools to visually explore models’ geographical and environemtal predictions, model extrapolation, and partial depnendece plot.

p_pdp() Create partial dependence plot(s) to explore the marginal effect of predictors on suitability
p_bpdp() Create partial dependence surface plot(s) to explore the bivariate marginal effect of predictors on suitability
p_extra() Graphical exploration of extrapolation or suitability pattern in the environmental and geographical space
data_pdp() Calculate data to construct partial dependence plots
data_bpdp() Calculate data to construct partial dependence surface plots

Installation

You can install the development version of flexsdm from github

:warning: NOTE: The version 1.4-22 of terra package is causing errors when trying to instal flexsdm. Please, first install a version ≥ 1.5-12 of terra package available on CRAN or development version of terra and then flexsdm.

# install.packages("remotes")

# For Windows and Mac OS operating systems
remotes::install_github("sjevelazco/flexsdm")

# For Linux operating system
remotes::install_github("sjevelazco/flexsdm@HEAD")

Package website

See the package website (https://sjevelazco.github.io/flexsdm/) for functions explanation and vignettes.

Package citation

Velazco, S.J.E., Rose, M.B., Andrade, A.F.A., Minoli, I., Franklin, J. (2022). flexsdm: An R package for supporting a comprehensive and flexible species distribution modelling workflow. Methods in Ecology and Evolution, 13(8) 1661–1669. https://doi.org/10.1111/2041-210X.13874

Test the package and give us your feedback here or send an e-mail to sjevelazco@gmail.com{.email}.