@article{10902/15016,
year = {2018},
month = {7},
url = {http://hdl.handle.net/10902/15016},
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 projections 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 provides 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.},
organization = {We acknowledge the ENSEMBLES project (GOCE-CT-2003-505539), supported by the European Commission’s 6th Framework Program for providing publicly the RCM simulations and observational data used in this study. We are also grateful to Rémy Petit and François Ehrenmann for providing the distribution of Oak phylogenies.},
publisher = {R Foundation for Statistical Computing},
publisher = {The R Journal Vol. 10/1, July 2018},
title = {Tackling Uncertainties of Species 
Distribution Model Projections with 
Package mopa},
author = {Iturbide Martínez de Albéniz, Maialen and Gutiérrez Llorente, José Manuel and Bedia Jiménez, Joaquín},
}