*The author would like to thank her advisor, Janet Franklin, and other committee members—Mike Goodchild, Joel Michaelsen, and Serge Rey—for their comments on an earlier version. The author gratefully acknowledges the Geography Department at San Diego State University for support during this research. Finally, the author thanks the editor and reviewers for helpful comments that improved this article.
Incorporating Spatial Dependence in Predictive Vegetation Models: Residual Interpolation Methods*
Article first published online: 9 MAY 2005
The Professional Geographer
Volume 57, Issue 2, pages 169–184, May 2005
How to Cite
Miller, J. (2005), Incorporating Spatial Dependence in Predictive Vegetation Models: Residual Interpolation Methods. The Professional Geographer, 57: 169–184. doi: 10.1111/j.0033-0124.2005.00470.x
- Issue published online: 9 MAY 2005
- Article first published online: 9 MAY 2005
- Initial submission, May 2004; revised submission, October 2004; final acceptance, October 2004
- vegetation models;
- spatial dependence;
Predictive vegetation modeling can be used statistically to relate the distribution of vegetation across a landscape as a function of important environmental variables. Often these models are developed without considering the spatial pattern that is inherent in biogeographical data, resulting from either biotic processes or missing or misspecified environmental variables. Including spatial dependence explicitly in a predictive model can be an efficient way to improve model accuracy with the available data. In this study, model residuals were interpolated and added to model predictions, and the resulting prediction accuracies were assessed. Adding kriged residuals improved model accuracy more often than adding simulated residuals, although some alliances showed no improvement or worse accuracy when residuals were added. In general, the prediction accuracies that were not increased by adding kriged residuals were either rare in the sample or had high nonspatial model accuracy. Regression interpolation methods can be an important addition to current tools used in predictive vegetation models as they allow observations that are predicted well by environmental variables to be left alone, while adjusting over- and underpredicted observations based on local factors.