flexsdm

Useful tools for constructing species distribution models

Science Score: 49.0%

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Keywords

ecological-niche-modelling ensemble-modelling model-fit-for-purpose model-tuning spatial-ecology spatially-structured-validation species-distribution-modelling

Last synced: 7 months ago · JSON representation

Repository

Useful tools for constructing species distribution models

Basic Info

Host: GitHub
Owner: sjevelazco
Language: R
Default Branch: main
Homepage: https://sjevelazco.github.io/flexsdm/
Size: 109 MB

Statistics

Stars: 52
Watchers: 3
Forks: 6
Open Issues: 6
Releases: 5

Topics

ecological-niche-modelling ensemble-modelling model-fit-for-purpose model-tuning spatial-ecology spatially-structured-validation species-distribution-modelling

Created about 5 years ago · Last pushed 7 months ago

Metadata Files

Readme Changelog

README.html














README.knit



























































flexsdm

 
    


flexsdm - email list
Dear flexsdm user, if you are interested in receiving email
notifications about modifications made to the package (e.g., new
functions, arguments, or vignettes), please fill out this
form so we can keep you updated on what is new in flexsdm.
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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.

Owner

Name: Santiago J.E. Velazco
Login: sjevelazco
Kind: user
Company: Instituto de Biología Subtropical, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Misiones, Puerto Iguazú, Misiones, Argentina

Twitter: Santiag43066556
Repositories: 4
Profile: https://github.com/sjevelazco

A forest engineer interested in science

GitHub Events

Total

Issues event: 12
Watch event: 2
Delete event: 1
Issue comment event: 9
Push event: 74
Pull request event: 69
Fork event: 2
Create event: 14

Last Year

Issues event: 12
Watch event: 2
Delete event: 1
Issue comment event: 9
Push event: 74
Pull request event: 69
Fork event: 2
Create event: 14

Committers

Last synced: 8 months ago

All Time

Total Commits: 954
Total Committers: 6
Avg Commits per committer: 159.0
Development Distribution Score (DDS): 0.332

Past Year

Commits: 63
Committers: 3
Avg Commits per committer: 21.0
Development Distribution Score (DDS): 0.429

Top Committers

Name	Email	Commits
Santiago Velazco	s**o@g**m	637
sjevelazco	s**c@g**m	173
Brooke Rose	5****8	117
Janet Franklin	j**t@s**n	12
Andre Felipe Alves de Andrade	a**e@g**m	9
iminoli-dev	m**p@g**m	6

Committer Domains (Top 20 + Academic)

serges-imac.lan: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time

Total issues: 41
Total pull requests: 168
Average time to close issues: 5 months
Average time to close pull requests: about 1 hour
Total issue authors: 28
Total pull request authors: 4
Average comments per issue: 1.32
Average comments per pull request: 0.01
Merged pull requests: 160
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 10
Pull requests: 65
Average time to close issues: 22 days
Average time to close pull requests: 7 minutes
Issue authors: 8
Pull request authors: 2
Average comments per issue: 0.4
Average comments per pull request: 0.0
Merged pull requests: 59
Bot issues: 0
Bot pull requests: 0

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Issue Authors

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JoelMet (1)
iminoli-dev (1)
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Pull Request Authors

sjevelazco (144)
mrose048 (21)
drjanetfranklin (2)
wardfont (1)

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Dependencies

DESCRIPTION cran

R >= 3.5.0 depends
Rlof * imports
doParallel * imports
dplyr * imports
foreach * imports
gbm * imports
grDevices * imports
kernlab * imports
maxnet * imports
methods * imports
mgcv * imports
nnet * imports
randomForest * imports
spThin * imports
terra >= 1.5 imports
utils * imports
covr * suggests
knitr * suggests
rgeos * suggests
rmarkdown * suggests
testthat >= 3.0.0 suggests

.github/workflows/R-CMD-check.yaml actions

actions/cache v2 composite
actions/checkout v2 composite
actions/upload-artifact v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite

.github/workflows/test-coverage.yaml actions

actions/cache v1 composite
actions/checkout v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite