Tackling Uncertainties of Species Distribution Model Projections with Package mopa

M Iturbide,J.M Gutiérrez,J Bedia

doi:10.32614/rj-2018-019

Abstract

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 Remy Petit and Francois Ehrenmann for providing the distribution of Oak phylogenies.

Highlights

Species Distribution Models (SDMs) are statistical tools used for the generation of probabilistic predictions of the presence of biological entities in the geographical space (Guisan and Zimmermann, 2000; Elith and et al, 2006)
The impacts of climate change on the biological systems are of current concern worldwide, and future SDMs have become a key tool for the vulnerability and impact assessment community
The utilities in package mopa can help in the SDM production chain since the early stage to the ultimate phase in which a final set of models is retained for ensemble generation and map production

Summary

Introduction

Species Distribution Models (SDMs) are statistical tools used for the generation of probabilistic predictions of the presence of biological entities in the geographical space (Guisan and Zimmermann, 2000; Elith and et al, 2006). TS is based on imposing restrictions to both the environmental range and the spatial extent of the background from which pseudo-absences are sampled This method has been shown to outperform other common approaches in terms of resulting SDM robustness (Iturbide et al, 2015). It has been noted that the background extent from which pseudo-absences are sampled is an important factor affecting model performance, and its transferability and biological meaning Van der Wal and Shoo (2009) With this regard, Iturbide et al (2015) propose a selection criterion based on the response of model performance as a function of distance radius, that is generalizable to different SDM characteristics and spatial scales. For maximum user flexibility, a matrix containing the observed and predicted probability values for each calibration point is returned, allowing other types of user-tailored model performance assessments

Background radius

Summary