Species Distribution Models (SDMs) constitute an important tool to assist decision-making in environmental conservation and planning in the context of climate change. Nevertheless, SDM pro jections are affected by a wide range of uncertainty factors (related to training data, climate projections and SDM techniques), which limit their potential value and credibility. The new package mopa pro vides tools for designing comprehensive multi-factor SDM ensemble experiments, combining multiple sources of uncertainty (e.g. baseline climate, pseudo-absence realizations, SDM techniques, future projections) and allowing to assess their contribution to the overall spread of the ensemble projection. In addition, mopa is seamlessly integrated with the climate4R bundle and allows straightforward retrieval and post-processing of state-of-the-art climate datasets (including observations and climate change projections), thus facilitating the proper analysis of key uncertainty factors related to climate data.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-019.zip
mopa, sdm, biomod2, dismo, SDMTools, raster, sp, e1071, stats, ranger, earth, tree, rpart, caret
MachineLearning, Multivariate, Environmetrics, Spatial, SpatioTemporal, Survival, Cluster, Distributions, HighPerformanceComputing, Psychometrics
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For attribution, please cite this work as
Iturbide, et al., "The R Journal: Tackling Uncertainties of Species Distribution Model Projections with Package mopa", The R Journal, 2018
BibTeX citation
@article{RJ-2018-019,
author = {Iturbide, M. and Bedia, J. and Gutiérrez, J.M.},
title = {The R Journal: Tackling Uncertainties of Species Distribution Model Projections with Package mopa},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-019},
doi = {10.32614/RJ-2018-019},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {122-139}
}