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Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah
Article first published online: 13 JUL 2007
Journal of Applied Ecology
Volume 44, Issue 5, pages 1057–1067, October 2007
How to Cite
ZIMMERMANN, N. E., EDWARDS, T. C., MOISEN, G. G., FRESCINO, T. S. and BLACKARD, J. A. (2007), Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah. Journal of Applied Ecology, 44: 1057–1067. doi: 10.1111/j.1365-2664.2007.01348.x
- Issue published online: 23 JUL 2007
- Article first published online: 13 JUL 2007
- Received 5 August 2006; revised version accepted 25 April 2007Editor: Jack Lennon
- core-satellite species hypothesis;
- K-fold cross-validation;
- Landsat TM;
- partial regression;
- predictive habitat distribution models;
- species traits;
- variation partitioning
- 1Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits.
- 2We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics.
- 3More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species.
- 4Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species.
- 5Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change.