The R Journal: article published in 2018, volume 10:1

Tackling Uncertainties of Species Distribution Model Projections with Package mopa PDF download
M. Iturbide, J. Bedia and J.M. Gutiérrez , The R Journal (2018) 10:1, pages 122-139.

Abstract 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.

Received: 2017-05-29; online 2018-05-21, supplementary material, (2.2 KiB)
CRAN packages: mopa, sdm, biomod2, dismo, SDMTools, raster, sp, e1071, stats, ranger, earth, tree, rpart, caret
CRAN Task Views implied by cited CRAN packages: MachineLearning, Multivariate, Environmetrics, Spatial, SpatioTemporal, Survival, Cluster, Distributions, HighPerformanceComputing, Psychometrics

CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

  author = {M. Iturbide and J. Bedia and J.M. Gutiérrez},
  title = {{Tackling Uncertainties of Species Distribution Model
          Projections with Package mopa}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-019},
  url = {},
  pages = {122--139},
  volume = {10},
  number = {1}