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

TY - JOUR

T1 - Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change

AU - Diniz-Filho, José Alexandre F.

AU - Bini, Luis Mauricio

AU - Rangel, Thiago Fernando

AU - Loyola, Rafael D.

AU - Hof, Christian

AU - Nogués-Bravo, David

AU - Araújo, Miguel B.

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

U2 - 10.1111/j.1600-0587.2009.06196.x

DO - 10.1111/j.1600-0587.2009.06196.x

M3 - Journal article

SN - 0906-7590

VL - 32

SP - 897

EP - 906

JO - Ecography

JF - Ecography

IS - 6

ER -