Achandroth/ENM_manuals

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

ENM manuals: R tools

Marlon E. Cobos

• Citation
• Description
• ENM manual sections
  • Getting data
  • Data cleaning and more
  • Delimitation of areas for model calibration (M)
  • Variables processing
  • Ecological niche modeling with Maxent and kuenm
  • Post-modeling analyses
• References

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 Biodiversity Informatics. These open access publications are available following their specific links Data cleaning manual and ENM/SDM manual (in process).

ENM manual sections

Getting data

Currently only one script is available in this section. This script allows to download and retain georeferenced records from the GBIF data base.

• Getting GBIF data for one or multiple species

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 here.

List of scripts in this section:

• Initial corrections to occurrence data
• Correction of occurrences outside of continents and/or calibration areas
• Environmental outlier detection
• Thinning and splitting training and testing data

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 here.

List of scripts in this section:

• Simple hypotheses of M
• M based on intersections of multiple hypotheses

Variables processing

To see all available scripts for processing environmental variables (predictors) that can be used in ENMs go to the following link.

List of scripts in this section:

• Masking variables to areas for model calibration
• Variables correlation analysis
• PCA with raster layers and projections
• Preparing variable sets from all their combinations

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 here.

List of scripts in this section:

• Evaluation metrics (pROC, OR, AICc; Examples)
• Model calibration
• Final model creation and projections
• Final model evaluation (optional step)

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 here.

List of scripts in this section (under construction):

• Summarizing model outputs (descriptive statistics)
• Layers of model variance coming from distinct sources
• Hierarchichal partitioning of model variability
• Anlysis of extrapolation risks (MOP)

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