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.
Fuente: The R Journal Vol. 10/1, July 2018
Editorial: R Foundation for Statistical Computing
Fecha de publicación: 01/07/2018
Nº de páginas: 18
Tipo de publicación: Artículo de Revista