Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change

• Published in: Ecography (Copenhagen) Vol. 32; no. 6; pp. 897 - 906
• Main Authors: Diniz-Filho, José Alexandre F, Mauricio Bini, Luis, Fernando Rangel, Thiago, Loyola, Rafael D, Hof, Christian, Nogués-Bravo, David, Araújo, Miguel B
• Format: Journal Article
• Language: English
• Published: Oxford, UK Oxford, UK : Blackwell Publishing Ltd 01.12.2009; Blackwell Publishing Ltd; Blackwell Publishing; Blackwell
• Subjects: Analytical forecasting; Animal and plant ecology; Animal, plant and microbial ecology; Biological and medical sciences; Climate change; Climate models; Climatology. Bioclimatology. Climate change; Earth, ocean, space; Ecological modeling; Exact sciences and technology; External geophysics; Forecasting models; Fundamental and applied biological sciences. Psychology; General aspects; Meteorology; Modeling; Principal components analysis; Species; Statistical variance; America; Partition; Interaction; Emission; Modeling; Forecasting; Variance; Dynamical climatology; Climate change; Vertebrata; Maps; Geographic distribution; Ecological niche; Turnover; Alternative method; Models; Aves; Distribution range
• ISSN: 0906-7590; 1600-0587
• DOI: 10.1111/j.1600-0587.2009.06196.x

Forecasts of species range shifts under climate change are fraught with uncertainties and ensemble forecasting may provide a framework to deal with such uncertainties. Here, a novel approach to partition the variance among modeled attributes, such as richness or turnover, and map sources of uncertainty in ensembles of forecasts is presented. We model the distributions of 3837 New World birds and project them into 2080. We then quantify and map the relative contribution of different sources of uncertainty from alternative methods for niche modeling, general circulation models (AOGCM), and emission scenarios. The greatest source of uncertainty in forecasts of species range shifts arises from using alternative methods for niche modeling, followed by AOGCM, and their interaction. Our results concur with previous studies that discovered that projections from alternative models can be extremely varied, but we provide a new analytical framework to examine uncertainties in models by quantifying their importance and mapping their patterns.