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R scripts that help to perform common analyses in niche modeling exercises

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ENM manuals: R tools
================
Marlon E. Cobos

- <a href="#citation" id="toc-citation">Citation</a>
- <a href="#description" id="toc-description">Description</a>
- <a href="#enm-manual-sections" id="toc-enm-manual-sections">ENM manual
 sections</a>
 - <a href="#getting-data" id="toc-getting-data">Getting data</a>
 - <a href="#data-cleaning-and-more" id="toc-data-cleaning-and-more">Data
 cleaning and more</a>
 - <a href="#delimitation-of-areas-for-model-calibration-m"
 id="toc-delimitation-of-areas-for-model-calibration-m">Delimitation of
 areas for model calibration (M)</a>
 - <a href="#variables-processing" id="toc-variables-processing">Variables
 processing</a>
 - <a href="#ecological-niche-modeling-with-maxent-and-kuenm"
 id="toc-ecological-niche-modeling-with-maxent-and-kuenm">Ecological
 niche modeling with Maxent and kuenm</a>
 - <a href="#post-modeling-analyses"
 id="toc-post-modeling-analyses">Post-modeling analyses</a>
- <a href="#references" id="toc-references">References</a>

## Citation

Simões, M., Romero-Alvarez, D., Nuñez-Penichet, C., Jiménez, L. & Cobos,
M.E. (2020) General theory and good practices in ecological niche
modeling: A basic guide. Biodiversity Informatics 15, 67–68.
<https://doi.org/10.17161/bi.v15i2.13376>

## Description

This repository was created to store R scripts that help to perform
common procedures in Ecological Niche Modeling exercises. The scripts
are organized according to the main activities that need to be done when
creating ecological niche models (ENMs).

This repository accompanies a series of manuals published in the journal
<a href="https://journals.ku.edu/jbi" target="_blank">Biodiversity
Informatics</a>. These open access publications are available following
their specific links
<a href="https://journals.ku.edu/jbi/article/view/7600" target="_blank">Data
cleaning manual</a> and ENM/SDM manual (in process).

## ENM manual sections

### Getting data

Currently only one script is available in
<a href="https://github.com/marlonecobos/ENM_manuals/tree/master/Getting_data" target="_blank">this
section</a>. This script allows to download and retain georeferenced
records from the GBIF data base.

- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Getting_data/GBIF_data.R" target="_blank">Getting
 GBIF data for one or multiple species</a>

### Data cleaning and more

Scripts in this section help to clean multiple types of errors that can
be found in occurrence data sets. These errors decrease the quality of
results obtained when creating ENMs and can lead to misleading
conclusions; hence, data cleaning processes need to be done carefully
and consciously.

All available scripts for performing data cleaning (cleaning of species
occurrences) can be seen
<a href="https://github.com/marlonecobos/ENM_manuals/tree/master/Data_cleaning" target="_blank">here</a>.

List of scripts in this section:

- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Data_cleaning/Occurrences_initial_corrections.R" target="_blank">Initial
 corrections to occurrence data</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Data_cleaning/Out_continents_or_M.R" target="_blank">Correction
 of occurrences outside of continents and/or calibration areas</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Data_cleaning/Environmental_outlier_detection.R" target="_blank">Environmental
 outlier detection</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Data_cleaning/Thinning_split_train-test.R" target="_blank">Thinning
 and splitting training and testing data</a>

### Delimitation of areas for model calibration (M)

In this section scripts that allow users to create distinct hypotheses
of **M** (areas for model calibration) are stored. See all scripts
<a href="https://github.com/marlonecobos/ENM_manuals/tree/master/M_hypotheses" target="_blank">here</a>.

List of scripts in this section:

- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/M_hypotheses/Construction_of_simple_Ms.R" target="_blank">Simple
 hypotheses of M</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/M_hypotheses/M_from_polygon_intersection.R" target="_blank">M
 based on intersections of multiple hypotheses</a>

### Variables processing

To see all available scripts for processing environmental variables
(predictors) that can be used in ENMs go to the following
<a href="https://github.com/marlonecobos/ENM_manuals/tree/master/Variables_processing" target="_blank">link</a>.

List of scripts in this section:

- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Variables_processing/Masking_variables_with_M.R" target="_blank">Masking
 variables to areas for model calibration</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Variables_processing/Variables_correlation_evaluation.R" target="_blank">Variables
 correlation analysis</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Variables_processing/PCA_raster_and_projections.R" target="_blank">PCA
 with raster layers and projections</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Variables_processing/Variable_sets_from_all_combinations.R" target="_blank">Preparing
 variable sets from all their combinations</a>

### Ecological niche modeling with Maxent and kuenm

Scripts in this section help to automate critical steps of ecological
niche modeling. All available scripts for performing the analyses can be
seen
<a href="https://github.com/marlonecobos/ENM_manuals/tree/master/ENM_process" target="_blank">here</a>.

List of scripts in this section:

- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/ENM_process/Evaluation_metrics.R" target="_blank">Evaluation
 metrics (pROC, OR, AICc; Examples)</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/ENM_process/Model_calibration.R" target="_blank">Model
 calibration</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/ENM_process/Final_models.R" target="_blank">Final
 model creation and projections</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/ENM_process/Final_model_evaluation.R" target="_blank">Final
 model evaluation (optional step)</a>

### Post-modeling analyses

In this section the scripts listed below help to perform important
analyses that are not explicit part of ecological niche modeling
exercises. However, currently, this analyses are some of the common
(good) practices to be done in order to summarize results and make
interpretations more straightforward.

All available scripts from this section can be seen
<a href="https://github.com/marlonecobos/ENM_manuals/tree/master/Post_modeling" target="_blank">here</a>.

List of scripts in this section (under construction):

- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Post_modeling/Model_statistics.R" target="_blank">Summarizing
 model outputs (descriptive statistics)</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Post_modeling/Model_variability.R" target="_blank">Layers
 of model variance coming from distinct sources</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Post_modeling/Model_variance_partitioning.R" target="_blank">Hierarchichal
 partitioning of model variability</a>
- <a href="https://github.com/marlonecobos/ENM_manuals/blob/master/Post_modeling/MOP.R" target="_blank">Anlysis
 of extrapolation risks (MOP)</a>

## References

Important references to be considered are listed below. These references
describe theoretical and methodological bases for many of the analyses
proposed above.

- Anderson, R.P., Gonzalez, I., 2011. Species-specific tuning
 increases robustness to sampling bias in models of species
 distributions: An implementation with Maxent. Ecol. Modell. 222,
 2796–2811. <https://doi.org/10.1016/j.ecolmodel.2011.04.011>
- Anderson, R.P., Lew, D., Peterson, A.T., 2003. Evaluating predictive
 models of species’ distributions: Criteria for selecting optimal
 models. Ecol. Modell. 162, 211–232.
 <https://doi.org/10.1016/S0304-3800(02)00349-6>
- Barve, N., Barve, V., Jiménez-Valverde, A., Lira-Noriega, A., Maher,
 S.P., Peterson, A.T., Soberón, J., Villalobos, F., 2011. The crucial
 role of the accessible area in ecological niche modeling and species
 distribution modeling. Ecol. Modell. 222, 1810–1819.
 <https://doi.org/10.1016/j.ecolmodel.2011.02.011>
- Cobos, M.E., Osorio-Olvera, L., Peterson, A.T., 2019a. Assessment
 and representation of variability in ecological niche model
 predictions. bioRxiv. <https://doi.org/10.1101/603100>
- Cobos, M.E., Peterson, A.T., Barve, N., Osorio-Olvera, L., 2019b.
 kuenm: An R package for detailed development of ecological niche
 models using Maxent. PeerJ 7, e6281.
 <https://doi.org/10.7717/peerj.6281>
- Cobos, M.E., Peterson, A.T., Osorio-Olvera, L., Jiménez-García, D.,
 2019c. An exhaustive analysis of heuristic methods for variable
 selection in ecological niche modeling and species distribution
 modeling. Ecol. Inform. 53, 100983.
 <https://doi.org/10.1016/j.ecoinf.2019.100983>
- Muscarella, R., Galante, P.J., Soley-Guardia, M., Boria, R.A., Kass,
 J.M., Uriarte, M., Anderson, R.P., 2014. ENMeval: An R package for
 conducting spatially independent evaluations and estimating optimal
 model complexity for Maxent ecological niche models. Methods Ecol.
 Evol. 5, 1198–1205. <https://doi.org/10.1111/2041-210X.12261>
- Owens, H.L., Campbell, L.P., Dornak, L.L., Saupe, E.E., Barve, N.,
 Soberón, J., Ingenloff, K., Lira-Noriega, A., Hensz, C.M., Myers,
 C.E., Peterson, A.T., 2013. Constraints on interpretation of
 ecological niche models by limited environmental ranges on
 calibration areas. Ecol. Modell. 263, 10–18.
 <https://doi.org/10.1016/j.ecolmodel.2013.04.011>
- Peterson, A.T., Cobos, M.E., Jiménez‐García, D., 2018. Major
 challenges for correlational ecological niche model projections to
 future climate conditions. Ann. N. Y. Acad. Sci. 1429, 66–77.
 <https://doi.org/10.1111/nyas.13873>
- Peterson, A.T., Papeş, M., Soberón, J., 2008. Rethinking receiver
 operating characteristic analysis applications in ecological niche
 modeling. Ecol. Modell. 213, 63–72.
 <https://doi.org/10.1016/j.ecolmodel.2007.11.008>
- Peterson, A.T., Soberón, J., 2012. Species distribution modeling and
 ecological niche modeling: Getting the concepts right. Natureza &
 Conservação 10, 1–6. <https://doi.org/10.4322/natcon.2012.019>
- Peterson, A.T., Soberón, J., Pearson, R.G., Anderson, R.P.,
 Martínez-Meyer, E., Nakamura, M., Araújo, M.B., 2011. Ecological
 Niches and Geographic Distributions. Princeton University Press,
 Princeton.
- Peterson, A.T., Soberón, J., Sánchez-Cordero, V., 1999. Conservatism
 of ecological niches in evolutionary time. Science 285, 1265–1267.
 <https://doi.org/10.1126/science.285.5431.1265>
- Saupe, E.E., Barve, V., Myers, C.E., Soberón, J., Barve, N., Hensz,
 C.M., Peterson, A.T., Owens, H.L., Lira-Noriega, A., 2012. Variation
 in niche and distribution model performance: The need for a priori
 assessment of key causal factors. Ecol. Modell. 237–238, 11–22.
 <https://doi.org/10.1016/j.ecolmodel.2012.04.001>
- Soberón, J., 2007. Grinnellian and Eltonian niches and geographic
 distributions of species. Ecology Letters 10, 1115–1123.
 <https://doi.org/10.1111/j.1461-0248.2007.01107.x>
- Soberón, J., Peterson, A.T., 2019. What is the shape of the
 fundamental Grinnellian niche? Theor Ecol.
 <https://doi.org/10.1007/s12080-019-0432-5>
- Soberón, J., Peterson, A.T., 2005. Interpretation of models of
 fundamental ecological niches and species’ distributional areas.
 Biodiversity Informatics 2, 1–10.
 <https://doi.org/10.17161/bi.v2i0.4>
- Warren, D.L., Seifert, S.N., 2011. Ecological niche modeling in
 Maxent: The importance of model complexity and the performance of
 model selection criteria. Ecol. Appl. 21, 335–342.
 <https://doi.org/10.1890/10-1171.1>

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