Refining predictions of climate change impacts on plant species distribution through the use of local statistics

Abstract Bioclimate envelope models are often used to predict changes in species distribution arising from changes in climate. These models are typically based on observed correlations between current species distribution and climate data. One limitation of this basic approach is that the relationship modelled is assumed to be constant in space; the analysis is global with the relationship assumed to be spatially stationary. Here, it is shown that by using a local regression analysis, which allows the relationship under study to vary in space, rather than conventional global regression analysis it is possible to increase the accuracy of bioclimate envelope modelling. This is demonstrated for the distribution of Spotted Meddick in Great Britain using data relating to three time periods, including predictions for the 2080s based on two climate change scenarios. Species distribution and climate data were available for two of the time periods studied and this allowed comparison of bioclimate envelope model outputs derived using the local and global regression analyses. For both time periods, the area under the receiver operating characteristics curve derived from the analysis based on local statistics was significantly higher than that from the conventional global analysis; the curve comparisons were also undertaken with an approach that recognised the dependent nature of the data sets compared. Marked differences in the future distribution of the species predicted from the local and global based analyses were evident and highlight a need for further consideration of local issues in modelling ecological variables.

[1]  T. Dawson,et al.  Predicted response of the lichen epiphyte lecanora populicola to climate change scenarios in a clean-air region of northern Britain , 2007 .

[2]  Giles M. Foody,et al.  Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna , 2004 .

[3]  M. Araújo,et al.  Reducing uncertainty in projections of extinction risk from climate change , 2005 .

[4]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[5]  Constantine A Gatsonis,et al.  Hierarchical models for ROC curve summary measures: Design and analysis of multi‐reader, multi‐modality studies of medical tests , 2008, Statistics in medicine.

[6]  Mark W. Schwartz,et al.  How fast and far might tree species migrate in the eastern United States due to climate change , 2004 .

[7]  S. Suárez‐Seoane,et al.  Non‐stationarity and local approaches to modelling the distributions of wildlife , 2007 .

[8]  T. Dawson,et al.  Bioclimate envelope models: what they detect and what they hide — response to Hampe (2004) , 2004 .

[9]  J. Fleiss,et al.  Statistical methods for rates and proportions , 1973 .

[10]  Tongli Wang,et al.  Potential effects of climate change on ecosystem and tree species distribution in British Columbia. , 2006, Ecology.

[11]  M. Luoto,et al.  Uncertainty of bioclimate envelope models based on the geographical distribution of species , 2005 .

[12]  A. Hampe.  Bioclimate envelope models: what they detect and what they hide , 2004 .

[13]  T. Dawson,et al.  SPECIES: A Spatial Evaluation of Climate Impact on the Envelope of Species , 2002 .

[14]  J. Travis,et al.  Modelling species' range shifts in a changing climate: the impacts of biotic interactions, dispersal distance and the rate of climate change. , 2007, Journal of theoretical biology.

[15]  B. Meatyard.  New Atlas of the British and Irish Flora: By C.D. Preston, D.A. Pearman and T.D. Dines. Published by Oxford University Press, 2002. ISBN 0 19 851067 5 (Hardback with CD). 912 pages. Price £99.95 , 2003 .

[16]  M. Luoto,et al.  The role of land cover in bioclimatic models depends on spatial resolution , 2006 .

[17]  M. Sykes,et al.  Methods and uncertainties in bioclimatic envelope modelling under climate change , 2006 .

[18]  M. Austin.  Species distribution models and ecological theory: A critical assessment and some possible new approaches , 2007 .

[19]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[20]  L. Zhang,et al.  Local Modeling of Tree Growth by Geographically Weighted Regression , 2004 .

[21]  C. Dormann.  Effects of incorporating spatial autocorrelation into the analysis of species distribution data , 2007 .

[22]  T. Dawson,et al.  Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? , 2003 .

[23]  M. Luoto,et al.  Does seasonal fine‐tuning of climatic variables improve the performance of bioclimatic envelope models for migratory birds? , 2006 .

[24]  H. Resit Akçakaya,et al.  Use and misuse of the IUCN Red List Criteria in projecting climate change impacts on biodiversity , 2006 .

[25]  David R. B. Stockwell,et al.  Forecasting the Effects of Global Warming on Biodiversity , 2007 .

[26]  A. Toledano.  Three methods for analysing correlated ROC curves: a comparison in real data sets from multi‐reader, multi‐case studies with a factorial design , 2003, Statistics in medicine.

[27]  T. Dawson,et al.  Model‐based uncertainty in species range prediction , 2006 .

[28]  R. S. D. Dios,et al.  Predictive modelling of tree species distributions on the Iberian Peninsula during the Last Glacial Maximum and Mid-Holocene , 2007 .

[29]  Omri Allouche,et al.  A comparative evaluation of presence‐only methods for modelling species distribution , 2007 .