SPECIES: A Spatial Evaluation of Climate Impact on the Envelope of Species

SPECIES: A Spatial Evaluation of Climate Impact on the Envelope of Species A model, A Spatial Evaluation of Climate Impact on the Envelope of Species (SPECIES), is presented which has been developed to evaluate the impacts of climate change on the bioclimatic envelope of plant species in Great Britain. SPECIES couples an artificial neural network with a climate–hydrological process model. The hybrid model has been successfully trained to estimate current species distributions using climate and soils data at the European scale before application at a finer resolution national scale. Using this multi-scale approach ensures encapsulation of the full extent of future climate scenarios within Great Britain without extrapolating outside of the model's training dataset. Application of the model to 32 plant species produced a mean Pearson correlation coefficient of 0.841 and a mean Kappa statistic of 0.772 between observed and simulated distributions. Simulations of four climate change scenarios revealed that changes to suitable climate space in Great Britain is highly species dependent and that distribution changes may be multidirectional and temporally non-linear. Analysis of the SPECIES results suggests that the neural network methodology can provide a feasible alternative to more classical spatial statistical techniques. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

SPECIES: A Spatial Evaluation of Climate Impact on the Envelope of Species

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Publisher
Elsevier
Copyright
Copyright © 2002 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
D.O.I.
10.1016/S0304-3800(02)00056-X
Publisher site
See Article on Publisher Site

Abstract

A model, A Spatial Evaluation of Climate Impact on the Envelope of Species (SPECIES), is presented which has been developed to evaluate the impacts of climate change on the bioclimatic envelope of plant species in Great Britain. SPECIES couples an artificial neural network with a climate–hydrological process model. The hybrid model has been successfully trained to estimate current species distributions using climate and soils data at the European scale before application at a finer resolution national scale. Using this multi-scale approach ensures encapsulation of the full extent of future climate scenarios within Great Britain without extrapolating outside of the model's training dataset. Application of the model to 32 plant species produced a mean Pearson correlation coefficient of 0.841 and a mean Kappa statistic of 0.772 between observed and simulated distributions. Simulations of four climate change scenarios revealed that changes to suitable climate space in Great Britain is highly species dependent and that distribution changes may be multidirectional and temporally non-linear. Analysis of the SPECIES results suggests that the neural network methodology can provide a feasible alternative to more classical spatial statistical techniques.

Journal

Ecological ModellingElsevier

Published: Sep 1, 2002

References

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