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| Concern over implications of climate change for
biodiversity has led to the use of bioclimatic models
to forecast the range shifts of species under future
climate-change scenarios. Recent studies have demonstrated
that projections by alternative models can be so
variable as to compromise their usefulness for guiding
policy decisions. Here, we advocate the use of multiple
models within an ensemble forecasting framework and
describe alternative approaches to the analysis of bioclimatic
ensembles, including bounding box, consensus
and probabilistic techniques. We argue that, although
improved accuracy can be delivered through the
traditional tasks of trying to build better models
with improved data, more robust forecasts can also
be achieved if ensemble forecasts are produced and
analysed appropriately. | |
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