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

Authors

  • José Alexandre F. Diniz-Filho,

  • Luis Mauricio Bini,

  • Thiago Fernando Rangel,

  • Rafael D. Loyola,

  • Christian Hof,

  • David Nogués-Bravo,

  • Miguel B. Araújo


J. A. F. Diniz-Filho (diniz@icb.ufg.br), L. M. Bini and R. D. Loyola, Depto de Ecologia, ICB, Univ. Federal de Goiás (UFG), Cx.P. 131, 74001-970 Goiânia, GO, Brasil. – T. F. Rangel, Dept of Ecology and Evolution, Univ. of Connecticut, Storrs, CT 06269-3043, USA. – C. Hof and D. Nogués-Bravo, Depto de Biodiversidad y Biología Evolutiva, Museo Nacional de Ciencias Naturales, CSIC, ES-28006 Madrid, Spain and Center for Macroecology, Evolution and Climate, Dept of Biology, Univ. of Copenhagen, DK-2100 Copenhagen, Denmark. – M. B. Araújo, Depto de Biodiversidad y Biología Evolutiva, Museo Nacional de Ciencias Naturales, CSIC, ES-28006 Madrid, Spain and Cátedra Rui Nabeiro – Biodiversidade, CIBIO, Univ. de Évora, Largo dos Colegiais, PT-7000 Évora, Portugal.

Abstract

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.

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