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Explicitly incorporating spatial dependence in predictive vegetation models in the form of explanatory variables: a Mojave Desert case study

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Abstract

Predictive vegetation modeling is defined as predicting the distribution of vegetation across a landscape based upon its relationship with environmental factors. These models generally ignore or attempt to remove spatial dependence in the data. When explicitly included in the model, spatial dependence can increase model accuracy. We develop presence/absence models for 11 vegetation alliances in the Mojave Desert with classification trees and generalized linear models, and use geostatistical interpolation to calculate spatial dependence terms used in the models. Results were mixed across models and methods, but in general, the spatial dependence terms more consistently increased model accuracy for widespread alliances. GLMs had higher accuracy in general.

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Acknowledgements

This research was supported by the San Diego State University Geography Department and the National Science Foundation (award #0451486). The authors gratefully acknowledge the comments and suggestions from the editor and three anonymous reviewers.

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Correspondence to Jennifer Miller.

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Miller, J., Franklin, J. Explicitly incorporating spatial dependence in predictive vegetation models in the form of explanatory variables: a Mojave Desert case study. J Geograph Syst 8, 411–435 (2006). https://doi.org/10.1007/s10109-006-0035-8

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  • DOI: https://doi.org/10.1007/s10109-006-0035-8

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