Model-based uncertainty in species’ range prediction
Pearson, Richard G., Thuiller, Wilfried, Araújo, Miguel B., Martinez-Meyer, Enrique, Brotons, Lluís, McClean, Colin, Miles, Lera, Segurado, Pedro, Dawson, Terence P. and Lees, David C. (2006) Model-based uncertainty in species’ range prediction. Journal of Biogeography, 33, (10), 1704-1711. (doi:10.1111/j.1365-2699.2006.01460.x).
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Aim: Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy-guiding applications.
Location: The Western Cape of South Africa.
Methods: We applied nine of the most widely used modelling techniques to model potential distributions under current and predicted future climate for four species (including two subspecies) of Proteaceae. Each model was built using an identical set of five input variables and distribution data for 3996 sampled sites. We compare model predictions by testing agreement between observed and simulated distributions for the present day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis).
Results: Our analyses show significant differences between predictions from different models, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence-only approaches) and assumptions made by each algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges.
Main conclusions: We highlight an important source of uncertainty in assessments of the impacts of climate change on biodiversity and emphasize that model predictions should be interpreted in policy-guiding applications along with a full appreciation of uncertainty.
|Digital Object Identifier (DOI):||doi:10.1111/j.1365-2699.2006.01460.x|
|Keywords:||bioclimate envelope modelling, biodiversity, Cape Flora, climate change, conservation biogeography, distribution modelling, environmental niche modelling, Proteaceae, South Africa, species biodiversity|
|Subjects:||G Geography. Anthropology. Recreation > G Geography (General)
G Geography. Anthropology. Recreation > GE Environmental Sciences
G Geography. Anthropology. Recreation > GB Physical geography
|Divisions:||University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis
|Date Deposited:||14 Aug 2008|
|Last Modified:||31 Mar 2016 12:39|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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